ffa.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('pandas version : ', u'0.23.4')\n",
      "('xarray version : ', u'0.11.0')\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import LinearSegmentedColormap\n",
    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
    "import math\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import xarray as xr\n",
    "import seaborn as sns\n",
    "import scipy.stats as stat\n",
    "\n",
    "print(\"pandas version : \", pd.__version__)\n",
    "print(\"xarray version : \", xr.__version__)\n",
    "\n",
    "sns.set_style(\"whitegrid\")\n",
    "plt.rcParams[\"font.family\"] = 'sans-serif'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Objective function analysis\n",
    "VIC parameters were calibrated with NSE, scaled KGEs and annual peak Q error, at each of 490 HCDN basins independently. SCE was used with 20,000 maximum trials.\n",
    "\n",
    "note:\n",
    "vic streamflow is mm/day\n",
    "mhm streamflow is m3/sec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VIC flow data\n",
      "-------------\n",
      "<xarray.Dataset>\n",
      "Dimensions:      (hcdn: 491, time: 6940)\n",
      "Coordinates:\n",
      "  * hcdn         (hcdn) int64 1022500 1031500 1047000 ... 14362250 14400000\n",
      "  * time         (time) datetime64[ns] 1989-10-01 1989-10-02 ... 2008-09-30\n",
      "Data variables:\n",
      "    nse          (hcdn, time) float32 ...\n",
      "    kge          (hcdn, time) float32 ...\n",
      "    kge_alpha    (hcdn, time) float32 ...\n",
      "    kge_2alpha   (hcdn, time) float32 ...\n",
      "    kge_5alpha   (hcdn, time) float32 ...\n",
      "    kge_10alpha  (hcdn, time) float32 ...\n",
      "    sig1         (hcdn, time) float32 ...\n",
      "    obs          (hcdn, time) float32 ...\n",
      "\n",
      "mHM flow data\n",
      "-------------\n",
      "<xarray.Dataset>\n",
      "Dimensions:     (hcdn: 491, time: 6940)\n",
      "Coordinates:\n",
      "  * hcdn        (hcdn) int64 1022500 1031500 1047000 ... 14362250 14400000\n",
      "  * time        (time) datetime64[ns] 1989-10-01 1989-10-02 ... 2008-09-30\n",
      "Data variables:\n",
      "    nse         (hcdn, time) float32 ...\n",
      "    kge_2alpha  (hcdn, time) float32 ...\n",
      "    kge         (hcdn, time) float32 ...\n",
      "    sig1        (hcdn, time) float32 ...\n",
      "    kge_5alpha  (hcdn, time) float32 ...\n",
      "    obs         (hcdn, time) float32 ...\n",
      "\n",
      "vic runoff data\n",
      "-------------\n",
      "<xarray.Dataset>\n",
      "Dimensions:               (hcdn: 491, time: 6940)\n",
      "Coordinates:\n",
      "  * hcdn                  (hcdn) int64 1022500 1031500 ... 14362250 14400000\n",
      "  * time                  (time) datetime64[ns] 1989-10-01 ... 2008-09-30\n",
      "Data variables:\n",
      "    nse_dir_flow          (hcdn, time) float32 ...\n",
      "    nse_baseflow          (hcdn, time) float32 ...\n",
      "    kge_dir_flow          (hcdn, time) float32 ...\n",
      "    kge_baseflow          (hcdn, time) float32 ...\n",
      "    kge_alpha_dir_flow    (hcdn, time) float32 ...\n",
      "    kge_alpha_baseflow    (hcdn, time) float32 ...\n",
      "    kge_2alpha_dir_flow   (hcdn, time) float32 ...\n",
      "    kge_2alpha_baseflow   (hcdn, time) float32 ...\n",
      "    kge_5alpha_dir_flow   (hcdn, time) float32 ...\n",
      "    kge_5alpha_baseflow   (hcdn, time) float32 ...\n",
      "    kge_10alpha_dir_flow  (hcdn, time) float32 ...\n",
      "    kge_10alpha_baseflow  (hcdn, time) float32 ...\n",
      "    sig1_dir_flow         (hcdn, time) float32 ...\n",
      "    sig1_baseflow         (hcdn, time) float32 ...\n",
      "\n",
      "mHM runoff data\n",
      "-------------\n",
      "<xarray.Dataset>\n",
      "Dimensions:              (hcdn: 491, time: 6940)\n",
      "Coordinates:\n",
      "  * hcdn                 (hcdn) int64 1022500 1031500 ... 14362250 14400000\n",
      "  * time                 (time) datetime64[ns] 1989-10-01 ... 2008-09-30\n",
      "Data variables:\n",
      "    nse_dir_flow         (hcdn, time) float32 ...\n",
      "    nse_baseflow         (hcdn, time) float32 ...\n",
      "    kge_2alpha_dir_flow  (hcdn, time) float32 ...\n",
      "    kge_2alpha_baseflow  (hcdn, time) float32 ...\n",
      "    kge_dir_flow         (hcdn, time) float32 ...\n",
      "    kge_baseflow         (hcdn, time) float32 ...\n",
      "    sig1_dir_flow        (hcdn, time) float32 ...\n",
      "    sig1_baseflow        (hcdn, time) float32 ...\n",
      "    kge_5alpha_dir_flow  (hcdn, time) float32 ...\n",
      "    kge_5alpha_baseflow  (hcdn, time) float32 ...\n"
     ]
    }
   ],
   "source": [
    "# Getting Data: \n",
    "# 1. simulated streamflow from vic and mhm\n",
    "# 2. runoff output from vic and mhm\n",
    "# 3. Basin list file\n",
    "\n",
    "data_path = './data'\n",
    "\n",
    "# List of objective functions used\n",
    "obj_fun_list = ['nse','kge','kge_2alpha','kge_5alpha','sig1']\n",
    "# List of models used\n",
    "model_list = ['vic', 'mhm']\n",
    "\n",
    "# Read model simulated streamflow at daily steps\n",
    "ds_vic_flow_all = xr.open_dataset('%s/results_hcdn_flow_vic_491.nc'%data_path)\n",
    "ds_mhm_flow_all = xr.open_dataset('%s/results_hcdn_flow_mhm_491.nc'%data_path)\n",
    "\n",
    "# Read model runoff output i.e., baseflow and surface flow at daily steps\n",
    "ds_vic_ro_all = xr.open_dataset('%s/results_hcdn_vic_output_491.nc'%data_path)\n",
    "ds_mhm_ro_all = xr.open_dataset('%s/results_hcdn_mhm_output_491.nc'%data_path)\n",
    "\n",
    "# Read basin attributes \n",
    "df_hcdn = pd.read_csv('%s/hcdn.calib.conus.491.list'%data_path, delim_whitespace=True,\n",
    "                   header=None, names=['id', 'lat', 'lon', 'area'])    #area is sq-meters\n",
    "\n",
    "nbasin = len(df_hcdn['id'])\n",
    "ndata = len(obj_fun_list)\n",
    "\n",
    "# Print data variables\n",
    "print('VIC flow data')\n",
    "print('-------------')\n",
    "print(ds_vic_flow_all)\n",
    "print('\\nmHM flow data')\n",
    "print('-------------')\n",
    "print(ds_mhm_flow_all)\n",
    "print('\\nvic runoff data')\n",
    "print('-------------')\n",
    "print(ds_vic_ro_all)\n",
    "print('\\nmHM runoff data')\n",
    "print('-------------')\n",
    "print(ds_mhm_ro_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Personal functions used here.\n",
    "\n",
    "def nse(qsim, qobs):\n",
    "    return 1-np.sum((qsim-qobs)**2)/np.sum((qobs-np.mean(qobs))**2)\n",
    "\n",
    "    \n",
    "def corr(qsim, qobs):\n",
    "    return np.corrcoef(qsim, qobs)[0, 1]\n",
    "    \n",
    "    \n",
    "def alpha(qsim, qobs):\n",
    "    return math.sqrt(np.sum((qsim-np.mean(qsim))**2)/len(qsim))/math.sqrt(np.sum((qobs-np.mean(qobs))**2)/len(qobs))\n",
    "    \n",
    "\n",
    "def beta(qsim, qobs):\n",
    "    return np.mean(qsim)/np.mean(qobs)\n",
    "\n",
    "\n",
    "def kge(qsim, qobs):\n",
    "    return 1-math.sqrt((1-corr(qsim, qobs))**2 + (alpha(qsim, qobs)-1)**2 + (beta(qsim, qobs)-1)**2)\n",
    "\n",
    "\n",
    "def pbias(qsim, qobs):\n",
    "    return np.sum((qsim-qobs))/np.sum(qobs)\n",
    "\n",
    "\n",
    "def bfi(qin, a=0.95):   #baseflow index\n",
    "    qbs = np.zeros(len(qin))\n",
    "    for t in np.arange(1,len(qin)):\n",
    "        qbs[t] = a*qbs[t-1] + (1-a)*(qin[t] + qin[t-1])/2\n",
    "        if qbs[t] >= qin[t]:\n",
    "            qbs[t] = qin[t]\n",
    "    return np.sum(qbs)/np.sum(qin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Calibration period\n",
    "ds_vic_flow_cal = ds_vic_flow_all.sel(time=slice('1999-10-01', '2008-09-30'))\n",
    "ds_mhm_flow_cal = ds_mhm_flow_all.sel(time=slice('1999-10-01', '2008-09-30'))\n",
    "\n",
    "#Initialize dictionary\n",
    "nse_cal = {}\n",
    "alpha_cal = {}\n",
    "beta_cal = {} \n",
    "corr_cal = {} \n",
    "kge_cal = {} \n",
    "pbiasFHV_cal = {}\n",
    "pbiasQpeak_cal = {} \n",
    "\n",
    "prob_cal=np.arange(1,float(len(ds_vic_flow_cal['time'])+1))/(1+len(ds_vic_flow_cal['time'])) #probability\n",
    "for d in range(len(prob_cal)):\n",
    "    idx50=d\n",
    "    if prob_cal[d] > 0.5: break\n",
    "for d in range(len(prob_cal)):\n",
    "    idx30=d\n",
    "    if prob_cal[d] > 0.3: break\n",
    "for d in range(len(prob_cal)):\n",
    "    idx80=d\n",
    "    if prob_cal[d] > 0.8: break\n",
    "\n",
    "for model in ['vic','mhm']:\n",
    "    nse_array = np.zeros((nbasin, ndata))\n",
    "    alpha_array = np.zeros((nbasin, ndata))\n",
    "    beta_array = np.zeros((nbasin, ndata))\n",
    "    corr_array = np.zeros((nbasin, ndata))\n",
    "    kge_array = np.zeros((nbasin, ndata))\n",
    "    pbiasFHV_array = np.zeros((nbasin, ndata))\n",
    "    pbiasQpeak_array = np.zeros((nbasin, ndata))     \n",
    "    r=0\n",
    "    for hid in df_hcdn['id']:\n",
    "        for c, obj in enumerate(obj_fun_list): \n",
    "            if model == 'vic':\n",
    "                sr_sim = ds_vic_flow_cal[obj].sel(hcdn=hid).to_pandas() \n",
    "            elif model =='mhm':\n",
    "                sr_sim = ds_mhm_flow_cal[obj].sel(hcdn=hid).to_pandas()\n",
    "                \n",
    "            sr_sim.where(sr_sim>0.0, 1.0e-7, inplace=True)\n",
    "            sr_sim_sort = sr_sim.sort_values()\n",
    "            sr_sim_ann=sr_sim.resample('A-SEP').max()\n",
    "            if c == 0:\n",
    "                if model == 'vic':\n",
    "                    sr_obs = ds_vic_flow_cal['obs'].sel(hcdn=hid).to_pandas()\n",
    "                elif model =='mhm':\n",
    "                    sr_obs = ds_mhm_flow_cal['obs'].sel(hcdn=hid).to_pandas()\n",
    "                sr_obs.where(sr_obs>0.0, 1.0e-7, inplace=True)\n",
    "                sr_obs_sort = sr_obs.sort_values()\n",
    "                sr_obs_ann=sr_obs.resample('A-SEP').max()\n",
    "            \n",
    "            nse_array[r,c] = nse(sr_sim, sr_obs)\n",
    "            alpha_array[r,c] = alpha(sr_sim, sr_obs)\n",
    "            beta_array[r,c] = beta(sr_sim, sr_obs)\n",
    "            corr_array[r,c] = corr(sr_sim, sr_obs)\n",
    "            kge_array[r,c] = kge(sr_sim, sr_obs)\n",
    "            pbiasQpeak_array[r,c] = pbias(sr_sim_ann, sr_obs_ann)\n",
    "            pbiasFHV_array[r,c] = pbias(sr_sim_sort[idx80:], sr_obs_sort[idx80:])\n",
    "               \n",
    "        r += 1\n",
    "        \n",
    "    nse_cal[model] = nse_array\n",
    "    alpha_cal[model] = alpha_array\n",
    "    beta_cal[model] = beta_array\n",
    "    corr_cal[model] = corr_array\n",
    "    kge_cal[model] = kge_array\n",
    "    pbiasQpeak_cal[model] = pbiasQpeak_array\n",
    "    pbiasFHV_cal[model] = pbiasFHV_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Validation period\n",
    "ds_vic_flow_val = ds_vic_flow_all.sel(time=slice('1989-10-01', '1999-09-30'))\n",
    "ds_mhm_flow_val = ds_mhm_flow_all.sel(time=slice('1989-10-01', '1999-09-30'))\n",
    "\n",
    "#Initialize dictionary\n",
    "nse_val = {}\n",
    "alpha_val = {}\n",
    "beta_val = {} \n",
    "corr_val = {} \n",
    "kge_val = {} \n",
    "pbiasFHV_val = {}\n",
    "pbiasQpeak_val = {} \n",
    "\n",
    "prob_val=np.arange(1,float(len(ds_vic_flow_val['time'])+1))/(1+len(ds_vic_flow_val['time'])) #probability\n",
    "for d in range(len(prob_val)):\n",
    "    idx50=d\n",
    "    if prob_val[d] > 0.5: break\n",
    "for d in range(len(prob_val)):\n",
    "    idx30=d\n",
    "    if prob_val[d] > 0.3: break\n",
    "for d in range(len(prob_val)):\n",
    "    idx80=d\n",
    "    if prob_val[d] > 0.8: break\n",
    "\n",
    "for model in ['vic','mhm']:\n",
    "    nse_array = np.zeros((nbasin, ndata))\n",
    "    alpha_array = np.zeros((nbasin, ndata))\n",
    "    beta_array = np.zeros((nbasin, ndata))\n",
    "    corr_array = np.zeros((nbasin, ndata))\n",
    "    kge_array = np.zeros((nbasin, ndata))\n",
    "    pbiasFHV_array = np.zeros((nbasin, ndata))\n",
    "    pbiasQpeak_array = np.zeros((nbasin, ndata))     \n",
    "    r=0\n",
    "    for hid in df_hcdn['id']:\n",
    "        for c, obj in enumerate(obj_fun_list): \n",
    "            if model == 'vic':\n",
    "                sr_sim = ds_vic_flow_val[obj].sel(hcdn=hid).to_pandas() \n",
    "            elif model =='mhm':\n",
    "                sr_sim = ds_mhm_flow_val[obj].sel(hcdn=hid).to_pandas()\n",
    "                \n",
    "            sr_sim.where(sr_sim>0.0, 1.0e-7, inplace=True)\n",
    "            sr_sim_sort = sr_sim.sort_values()\n",
    "            sr_sim_ann=sr_sim.resample('A-SEP').max()\n",
    "            if c == 0:\n",
    "                if model == 'vic':\n",
    "                    sr_obs = ds_vic_flow_val['obs'].sel(hcdn=hid).to_pandas()\n",
    "                elif model =='mhm':\n",
    "                    sr_obs = ds_mhm_flow_val['obs'].sel(hcdn=hid).to_pandas()\n",
    "                sr_obs.where(sr_obs>0.0, 1.0e-7, inplace=True)\n",
    "                sr_obs_sort = sr_obs.sort_values()\n",
    "                sr_obs_ann=sr_obs.resample('A-SEP').max()\n",
    "            \n",
    "            nse_array[r,c] = nse(sr_sim, sr_obs)\n",
    "            alpha_array[r,c] = alpha(sr_sim, sr_obs)\n",
    "            beta_array[r,c] = beta(sr_sim, sr_obs)\n",
    "            corr_array[r,c] = corr(sr_sim, sr_obs)\n",
    "            kge_array[r,c] = kge(sr_sim, sr_obs)\n",
    "            pbiasQpeak_array[r,c] = pbias(sr_sim_ann, sr_obs_ann)\n",
    "            pbiasFHV_array[r,c] = pbias(sr_sim_sort[idx80:], sr_obs_sort[idx80:])\n",
    "               \n",
    "        r += 1\n",
    "        \n",
    "    nse_val[model] = nse_array\n",
    "    alpha_val[model] = alpha_array\n",
    "    beta_val[model] = beta_array\n",
    "    corr_val[model] = corr_array\n",
    "    kge_val[model] = kge_array\n",
    "    pbiasQpeak_val[model] = pbiasQpeak_array\n",
    "    pbiasFHV_val[model] = pbiasFHV_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Annual flood Frequency analysis based on the entire time serise\n",
    "\n",
    "yrs = pd.date_range('1989-10-01', '2008-09-30', freq='A-SEP')\n",
    "prob_ann = np.arange(1,float(len(yrs)+1))/(1+float(len(yrs))) #probability\n",
    "for d in range(len(prob_ann)):\n",
    "    idx20=d\n",
    "    if prob_ann[d] >= 0.95: break\n",
    "for d in range(len(prob_ann)):\n",
    "    idx10=d\n",
    "    if prob_ann[d] >= 0.90: break\n",
    "for d in range(len(prob_ann)):\n",
    "    idx5=d\n",
    "    if prob_ann[d] >= 0.80: break\n",
    "\n",
    "pbiasQ20 = {}\n",
    "pbiasQ10 = {}\n",
    "pbiasQ5 = {}\n",
    "for model in model_list:        \n",
    "    pbiasQ20_array = np.zeros((nbasin,ndata))\n",
    "    pbiasQ10_array = np.zeros((nbasin,ndata))\n",
    "    pbiasQ5_array = np.zeros((nbasin,ndata))\n",
    "    r=0\n",
    "    for hid in df_hcdn['id']:\n",
    "        for c, obj in enumerate(obj_fun_list):\n",
    "            if model == 'vic':\n",
    "                sr_sim = ds_vic_flow_all[obj].sel(hcdn=hid).to_pandas()\n",
    "            elif model == 'mhm':    \n",
    "                sr_sim = ds_mhm_flow_all[obj].sel(hcdn=hid).to_pandas()\n",
    "            sr_sim.where(sr_sim>0.0, 1.0e-7, inplace=True)\n",
    "            sr_sim_ann = sr_sim.resample('A-SEP').max()\n",
    "            sr_sim_ann_sort = sr_sim_ann.sort_values()\n",
    "            if c == 0:\n",
    "                if model == 'vic':\n",
    "                    sr_obs = ds_vic_flow_all['obs'].sel(hcdn=hid).to_pandas()\n",
    "                elif model == 'mhm':\n",
    "                    sr_obs = ds_mhm_flow_all['obs'].sel(hcdn=hid).to_pandas()\n",
    "                sr_obs.where(sr_obs>0.0, 1.0e-7, inplace=True)\n",
    "                sr_obs_ann=sr_obs.resample('A-SEP').max()\n",
    "                sr_obs_ann_sort = sr_obs_ann.sort_values()\n",
    "            \n",
    "            pbiasQ20_array[r,c] = pbias(sr_sim_ann_sort[idx20], sr_obs_ann_sort[idx20])\n",
    "            pbiasQ10_array[r,c] = pbias(sr_sim_ann_sort[idx10], sr_obs_ann_sort[idx10])\n",
    "            pbiasQ5_array[r,c] = pbias(sr_sim_ann_sort[idx5], sr_obs_ann_sort[idx5])\n",
    "        r += 1\n",
    "\n",
    "    pbiasQ20[model] = pbiasQ20_array\n",
    "    pbiasQ10[model] = pbiasQ10_array\n",
    "    pbiasQ5[model] = pbiasQ5_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Annual maximum peak flow stats\n",
    "yrs = pd.date_range('1989-10-01', '2008-09-30', freq='A-SEP')\n",
    "\n",
    "qpeak_alpha = {}\n",
    "qpeak_beta = {}\n",
    "qpeak_corr = {}\n",
    "for model in model_list:        \n",
    "    qpeak_alpha_array = np.zeros((nbasin,ndata))\n",
    "    qpeak_beta_array = np.zeros((nbasin,ndata))\n",
    "    qpeak_corr_array = np.zeros((nbasin,ndata))\n",
    "    r=0\n",
    "    for hid in df_hcdn['id']:\n",
    "        for c, obj in enumerate(obj_fun_list):\n",
    "            if model == 'vic':\n",
    "                sr_sim = ds_vic_flow_all[obj].sel(hcdn=hid).to_pandas()\n",
    "            elif model == 'mhm':    \n",
    "                sr_sim = ds_mhm_flow_all[obj].sel(hcdn=hid).to_pandas()\n",
    "            sr_sim.where(sr_sim>0.0, 1.0e-7, inplace=True)\n",
    "            sr_sim_ann = sr_sim.resample('A-SEP').max()\n",
    "            if c == 0:\n",
    "                if model == 'vic':\n",
    "                    sr_obs = ds_vic_flow_all['obs'].sel(hcdn=hid).to_pandas()\n",
    "                elif model == 'mhm':\n",
    "                    sr_obs = ds_mhm_flow_all['obs'].sel(hcdn=hid).to_pandas()\n",
    "                sr_obs.where(sr_obs>0.0, 1.0e-7, inplace=True)\n",
    "                sr_obs_ann=sr_obs.resample('A-SEP').max()\n",
    "            \n",
    "            qpeak_alpha_array[r,c] = alpha(sr_sim_ann, sr_obs_ann)\n",
    "            qpeak_beta_array[r,c] = beta(sr_sim_ann, sr_obs_ann)\n",
    "            qpeak_corr_array[r,c] = corr(sr_sim_ann, sr_obs_ann)\n",
    "        r += 1\n",
    "\n",
    "    qpeak_alpha[model] = qpeak_alpha_array\n",
    "    qpeak_beta[model] = qpeak_beta_array\n",
    "    qpeak_corr[model] = qpeak_corr_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VIC\n",
      "NSE\n",
      "      nse       kge      kge_2alpha      kge_5alpha       sig1      \n",
      "      cal  val  cal  val        cal  val        cal  val   cal   val\n",
      "0.25 0.49 0.46 0.42 0.41       0.32 0.33       0.26 0.28 -0.54 -0.14\n",
      "0.50 0.59 0.55 0.52 0.52       0.46 0.48       0.41 0.43 -0.02  0.20\n",
      "0.75 0.69 0.64 0.64 0.62       0.62 0.60       0.60 0.59  0.29  0.38\n",
      "\n",
      "KGE\n",
      "      nse       kge      kge_2alpha      kge_5alpha       sig1      \n",
      "      cal  val  cal  val        cal  val        cal  val   cal   val\n",
      "0.25 0.50 0.45 0.53 0.49       0.48 0.48       0.40 0.44 -0.29 -0.00\n",
      "0.50 0.63 0.59 0.69 0.64       0.67 0.63       0.66 0.61  0.21  0.34\n",
      "0.75 0.73 0.69 0.80 0.74       0.79 0.74       0.78 0.73  0.52  0.57\n",
      "\n",
      "alpha\n",
      "      nse       kge      kge_2alpha      kge_5alpha      sig1     \n",
      "      cal  val  cal  val        cal  val        cal  val  cal  val\n",
      "0.25 0.65 0.60 0.72 0.66       0.90 0.76       0.98 0.82 1.13 0.99\n",
      "0.50 0.74 0.72 0.92 0.83       0.98 0.89       0.99 0.92 1.29 1.15\n",
      "0.75 0.84 0.82 0.99 0.97       1.00 1.01       1.00 1.03 1.47 1.34\n",
      "\n",
      "beta\n",
      "      nse       kge      kge_2alpha      kge_5alpha      sig1     \n",
      "      cal  val  cal  val        cal  val        cal  val  cal  val\n",
      "0.25 0.93 0.87 1.00 0.93       1.00 0.94       1.00 0.97 1.14 1.12\n",
      "0.50 1.01 0.97 1.06 1.01       1.10 1.04       1.12 1.08 1.56 1.42\n",
      "0.75 1.12 1.07 1.14 1.09       1.29 1.20       1.40 1.27 2.13 1.85\n",
      "\n",
      "corr\n",
      "      nse       kge      kge_2alpha      kge_5alpha      sig1     \n",
      "      cal  val  cal  val        cal  val        cal  val  cal  val\n",
      "0.25 0.71 0.71 0.69 0.69       0.64 0.65       0.63 0.64 0.60 0.63\n",
      "0.50 0.78 0.76 0.75 0.75       0.72 0.72       0.71 0.71 0.67 0.68\n",
      "0.75 0.83 0.82 0.81 0.81       0.81 0.80       0.80 0.80 0.74 0.75\n",
      "-------------------\n",
      "\n",
      "mHM\n",
      "NSE\n",
      "      nse       kge      kge_2alpha      kge_5alpha       sig1     \n",
      "      cal  val  cal  val        cal  val        cal  val   cal  val\n",
      "0.25 0.65 0.58 0.58 0.53       0.55 0.51       0.53 0.49 -0.01 0.00\n",
      "0.50 0.72 0.67 0.67 0.63       0.66 0.61       0.64 0.60  0.27 0.32\n",
      "0.75 0.78 0.73 0.74 0.71       0.73 0.70       0.72 0.69  0.44 0.51\n",
      "\n",
      "KGE\n",
      "      nse       kge      kge_2alpha      kge_5alpha      sig1     \n",
      "      cal  val  cal  val        cal  val        cal  val  cal  val\n",
      "0.25 0.66 0.58 0.76 0.66       0.75 0.65       0.74 0.64 0.27 0.33\n",
      "0.50 0.74 0.69 0.82 0.74       0.82 0.74       0.81 0.74 0.51 0.51\n",
      "0.75 0.81 0.76 0.87 0.80       0.86 0.80       0.86 0.80 0.63 0.64\n",
      "\n",
      "alpha\n",
      "      nse       kge      kge_2alpha      kge_5alpha      sig1     \n",
      "      cal  val  cal  val        cal  val        cal  val  cal  val\n",
      "0.25 0.77 0.71 0.96 0.84       0.98 0.85       0.99 0.86 0.96 0.86\n",
      "0.50 0.83 0.80 0.99 0.94       0.99 0.95       1.00 0.96 1.09 1.04\n",
      "0.75 0.87 0.88 1.00 1.03       1.00 1.04       1.00 1.04 1.20 1.19\n",
      "\n",
      "beta\n",
      "      nse       kge      kge_2alpha      kge_5alpha      sig1     \n",
      "      cal  val  cal  val        cal  val        cal  val  cal  val\n",
      "0.25 0.89 0.82 0.99 0.87       0.99 0.86       0.99 0.86 0.78 0.78\n",
      "0.50 0.97 0.92 1.00 0.93       1.00 0.93       1.00 0.93 1.04 0.99\n",
      "0.75 1.04 1.01 1.01 1.01       1.01 1.01       1.01 1.01 1.37 1.25\n",
      "\n",
      "corr\n",
      "      nse       kge      kge_2alpha      kge_5alpha      sig1     \n",
      "      cal  val  cal  val        cal  val        cal  val  cal  val\n",
      "0.25 0.82 0.79 0.78 0.77       0.78 0.76       0.76 0.75 0.58 0.62\n",
      "0.50 0.85 0.83 0.83 0.82       0.83 0.81       0.82 0.81 0.70 0.71\n",
      "0.75 0.89 0.87 0.87 0.85       0.87 0.85       0.86 0.85 0.77 0.78\n",
      "-------------------\n"
     ]
    }
   ],
   "source": [
    "# Compute quartiles and median for metrics for each objective function and period\n",
    "# Multi-level header\n",
    "iterables = [obj_fun_list, ['cal', 'val']]\n",
    "index = pd.MultiIndex.from_product(iterables)\n",
    "\n",
    "#Initialize dictionary\n",
    "nse_all = {}\n",
    "alpha_all = {}\n",
    "beta_all = {} \n",
    "corr_all = {} \n",
    "kge_all = {} \n",
    "pbiasFHV_all = {}\n",
    "pbiasQpeak_all = {} \n",
    "\n",
    "for model in model_list:\n",
    "    # Combine cal and val arrays\n",
    "    nse_all_array = np.zeros((nbasin,ndata*2))\n",
    "    kge_all_array = np.zeros((nbasin,ndata*2))\n",
    "    alpha_all_array = np.zeros((nbasin,ndata*2))\n",
    "    beta_all_array = np.zeros((nbasin,ndata*2))\n",
    "    corr_all_array = np.zeros((nbasin,ndata*2))\n",
    "    for idx, _ in enumerate(obj_fun_list):\n",
    "        nse_all_array[:,idx*2] = nse_cal[model][:,idx]\n",
    "        nse_all_array[:,idx*2+1] = nse_val[model][:,idx]\n",
    "        kge_all_array[:,idx*2] = kge_cal[model][:,idx]\n",
    "        kge_all_array[:,idx*2+1] = kge_val[model][:,idx]    \n",
    "        alpha_all_array[:,idx*2] = alpha_cal[model][:,idx]\n",
    "        alpha_all_array[:,idx*2+1] = alpha_val[model][:,idx]    \n",
    "        beta_all_array[:,idx*2] = beta_cal[model][:,idx]\n",
    "        beta_all_array[:,idx*2+1] = beta_val[model][:,idx]    \n",
    "        corr_all_array[:,idx*2] = corr_cal[model][:,idx]\n",
    "        corr_all_array[:,idx*2+1] = corr_val[model][:,idx]\n",
    "        \n",
    "    nse_all[model] = nse_all_array\n",
    "    kge_all[model] = kge_all_array\n",
    "    alpha_all[model] = alpha_all_array\n",
    "    beta_all[model] = beta_all_array\n",
    "    corr_all[model] = corr_all_array\n",
    "    \n",
    "# convert numpy arrays to DataFrames\n",
    "df_nse_vic = pd.DataFrame(nse_all['vic'], index=df_hcdn['id'], columns=index)\n",
    "df_kge_vic = pd.DataFrame(kge_all['vic'], index=df_hcdn['id'], columns=index)\n",
    "df_alpha_vic = pd.DataFrame(alpha_all['vic'], index=df_hcdn['id'], columns=index)\n",
    "df_beta_vic = pd.DataFrame(beta_all['vic'], index=df_hcdn['id'], columns=index)\n",
    "df_corr_vic = pd.DataFrame(corr_all['vic'], index=df_hcdn['id'], columns=index)\n",
    "\n",
    "# convert numpy arrays to DataFrames\n",
    "df_nse_mhm = pd.DataFrame(nse_all['mhm'], index=df_hcdn['id'], columns=index)\n",
    "df_kge_mhm = pd.DataFrame(kge_all['mhm'], index=df_hcdn['id'], columns=index)\n",
    "df_alpha_mhm = pd.DataFrame(alpha_all['mhm'], index=df_hcdn['id'], columns=index)\n",
    "df_beta_mhm = pd.DataFrame(beta_all['mhm'], index=df_hcdn['id'], columns=index)\n",
    "df_corr_mhm = pd.DataFrame(corr_all['mhm'], index=df_hcdn['id'], columns=index)\n",
    "\n",
    "# Print quartiles\n",
    "pd.options.display.float_format = '{:,.2f}'.format\n",
    "print('VIC')\n",
    "print('NSE')\n",
    "print(df_nse_vic.quantile([.25, .5, .75]))\n",
    "print('\\nKGE')\n",
    "print(df_kge_vic.quantile([.25, .5, .75]))\n",
    "print('\\nalpha')\n",
    "print(df_alpha_vic.quantile([.25, .5, .75]))\n",
    "print('\\nbeta')\n",
    "print(df_beta_vic.quantile([.25, .5, .75]))\n",
    "print('\\ncorr')\n",
    "print(df_corr_vic.quantile([.25, .5, .75]))\n",
    "print('-------------------')\n",
    "print('\\nmHM')\n",
    "print('NSE')\n",
    "print(df_nse_mhm.quantile([.25, .5, .75]))\n",
    "print('\\nKGE')\n",
    "print(df_kge_mhm.quantile([.25, .5, .75]))\n",
    "print('\\nalpha')\n",
    "print(df_alpha_mhm.quantile([.25, .5, .75]))\n",
    "print('\\nbeta')\n",
    "print(df_beta_mhm.quantile([.25, .5, .75]))\n",
    "print('\\ncorr')\n",
    "print(df_corr_mhm.quantile([.25, .5, .75]))\n",
    "print('-------------------')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### K-S two distribution test\n",
    "Statistically test metric distributions based on two different calibrations are drawn from the same distribution. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alpha\n",
      "nse - kge: 2.69e-17\n",
      "nse - kge_2alpha: 2.57e-39\n",
      "nse - kge_5alpha: 1.32e-56\n",
      "nse - sig1: 2.38e-114\n",
      "kge - kge_2alpha: 1.28e-06\n",
      "kge - kge_5alpha: 1.38e-13\n",
      "kge - sig1: 1.33e-62\n",
      "kge_2alpha - kge_5alpha: 2.07e-02\n",
      "kge_2alpha - sig1: 2.88e-55\n",
      "kge_5alpha - sig1: 6.22e-46\n"
     ]
    }
   ],
   "source": [
    "print('alpha')\n",
    "for idx1, obj1 in enumerate(obj_fun_list):\n",
    "    df1 = df_alpha_vic.iloc[:,df_alpha_vic.columns.get_level_values(1)=='val'][obj1]\n",
    "    for idx2, obj2 in enumerate(obj_fun_list[idx1+1:]):\n",
    "        df2 = df_alpha_vic.iloc[:,df_alpha_vic.columns.get_level_values(1)=='val'][obj2]\n",
    "        \n",
    "        _, pval = stat.ks_2samp(df1['val'], df2['val'])\n",
    "        print('%s - %s: %.2e'%(obj1, obj2, pval))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAdIAAAFnCAYAAAASZ8jwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nkAqCIAhCG/w/ivKHrYl/X7AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116e0c890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 4 panel CDFs (alpha, beta, correlation and KGE)\n",
    "model = 'mhm'\n",
    "\n",
    "fig = plt.figure(figsize=(6.5, 5.0))\n",
    "fig.suptitle('%s' % model, fontsize=14, fontweight='bold')\n",
    "fig.subplots_adjust(left=0.095,right=0.975,bottom=0.095,top=0.935,wspace=0.25,hspace=0.25)\n",
    "\n",
    "ax1 = plt.subplot2grid((2, 2), (0, 0))\n",
    "ax2 = plt.subplot2grid((2, 2), (0, 1))\n",
    "ax3 = plt.subplot2grid((2, 2), (1, 0))\n",
    "ax4 = plt.subplot2grid((2, 2), (1, 1))\n",
    "\n",
    "ax1.tick_params(labelsize=7)\n",
    "ax2.tick_params(labelsize=7)\n",
    "ax3.tick_params(labelsize=7)\n",
    "ax4.tick_params(labelsize=7)\n",
    "\n",
    "clr=['k','r','b','c','m','g']\n",
    "lstyle=['-','-','-','-','-','-','-']\n",
    "\n",
    "#panel a) alpha\n",
    "for c in range(ndata):\n",
    "    xdata = alpha_val[model][:,c]\n",
    "    xdata_sort = np.sort(xdata)\n",
    "    prob_metric = np.arange(1,float(len(xdata)+1))/(1+len(xdata))\n",
    "    maxval = 1.1*np.max(xdata)\n",
    "    minval = 0.9*np.min(xdata)\n",
    "    ax1.plot(xdata_sort,prob_metric,c=clr[c],ls=lstyle[c],label=obj_fun_list[c],linewidth=1.25)\n",
    "lgd = ax1.legend(frameon=True,loc=\"lower right\", handlelength=2, prop={'size':8})\n",
    "lgd.get_frame().set_facecolor('white')\n",
    "ax1.set_xlim(0.0, 2.0) #ratio\n",
    "ax1.set_ylim(0, 1)\n",
    "ax1.set_yticks(np.arange(0, 1, 0.25))\n",
    "ax1.set_xlabel('alpha', fontSize=9)\n",
    "ax1.set_ylabel('F(x)', fontSize=9)\n",
    "\n",
    "#panel b) beta\n",
    "for c in range(ndata):\n",
    "    xdata = beta_val[model][:,c]\n",
    "    xdata_sort = np.sort(xdata)\n",
    "    prob_metric = np.arange(1,float(len(xdata)+1))/(1+len(xdata))\n",
    "    maxval = 1.1*np.max(xdata)\n",
    "    minval = 0.9*np.min(xdata)\n",
    "    ax2.plot(xdata_sort,prob_metric,c=clr[c],ls=lstyle[c],label=obj_fun_list[c],linewidth=1.25)\n",
    "ax2.set_xlim(0.0, 2.0) #ratio\n",
    "ax2.set_ylim(0, 1)\n",
    "ax2.set_yticks(np.arange(0, 1, 0.25))\n",
    "ax2.set_xlabel('beta', fontSize=9)\n",
    "ax2.set_ylabel('F(x)', fontSize=9)\n",
    "\n",
    "#panel c) correlation\n",
    "for c in range(ndata):\n",
    "    xdata = corr_val[model][:,c]\n",
    "    xdata_sort = np.sort(xdata)\n",
    "    prob_metric = np.arange(1,float(len(xdata)+1))/(1+len(xdata))\n",
    "    maxval = 1.1*np.max(xdata)\n",
    "    minval = 0.9*np.min(xdata)\n",
    "    ax3.plot(xdata_sort,prob_metric,c=clr[c],ls=lstyle[c],label=obj_fun_list[c],linewidth=1.25)\n",
    "ax3.set_xlim(0.0, 1.0) # corr\n",
    "ax3.set_ylim(0, 1)\n",
    "ax3.set_yticks(np.arange(0, 1, 0.25))\n",
    "ax3.set_xlabel('Correlation', fontSize=9)\n",
    "ax3.set_ylabel('F(x)', fontSize=9)\n",
    "\n",
    "#panel d) KGE\n",
    "for c in range(ndata):\n",
    "    xdata = kge_val[model][:,c]\n",
    "    xdata_sort = np.sort(xdata)\n",
    "    prob_metric = np.arange(1,float(len(xdata)+1))/(1+len(xdata))\n",
    "    maxval = 1.1*np.max(xdata)\n",
    "    minval = 0.9*np.min(xdata)\n",
    "    ax4.plot(xdata_sort,prob_metric,c=clr[c],ls=lstyle[c],label=obj_fun_list[c],linewidth=1.25)\n",
    "ax4.set_xlim(-0.2, 1.0)\n",
    "ax4.set_ylim(0, 1)\n",
    "ax4.set_yticks(np.arange(0, 1, 0.25))\n",
    "ax4.set_xlabel('KGE', fontSize=9)\n",
    "ax4.set_ylabel('F(x)', fontSize=9)\n",
    "plt.savefig('../../Paper5_ffa/supple_figure/kge.cdf.%s.pdf'%(model),\n",
    "            format='pdf', \n",
    "            dpi=300,\n",
    "            bbox_extra_artists=(lgd,))\n",
    "            #bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/naokimizukami/anaconda3/lib/python3.6/site-packages/seaborn/categorical.py:482: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
      "  box_data = remove_na(group_data[hue_mask])\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAf0AAAFNCAYAAAAKBrb9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c1a4b9e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Construct boxplots for annual peak bias group-by periods\n",
    "model = 'mhm'\n",
    "df_pbiasQpeak_cal = pd.DataFrame(pbiasQpeak_cal[model], index=df_hcdn['id'], columns=obj_fun_list)\n",
    "df_pbiasQpeak_val = pd.DataFrame(pbiasQpeak_val[model], index=df_hcdn['id'], columns=obj_fun_list)\n",
    "df_pbiasQpeak_cal['Period'] = ['cal' for _ in df_hcdn['id']]\n",
    "df_pbiasQpeak_val['Period'] = ['val' for _ in df_hcdn['id']]\n",
    "df_pbiasQpeak = pd.concat([df_pbiasQpeak_cal, df_pbiasQpeak_val], axis=0)\n",
    "\n",
    "dd_pbiasQpeak=pd.melt(df_pbiasQpeak, id_vars=['Period'], value_vars=obj_fun_list, var_name='OF')\n",
    "\n",
    "plt.figure(figsize=(6.5,5))\n",
    "sns.set_style(\"whitegrid\")\n",
    "ax11 = sns.boxplot(x='Period', y='value', data=dd_pbiasQpeak, hue='OF', linewidth=1.0)\n",
    "handles, labels = ax11.get_legend_handles_labels()\n",
    "lgd = plt.legend(bbox_to_anchor=(1.01, 1), loc=2, borderaxespad=0.)\n",
    "ax11.set_ylim([-1.0,0.8])\n",
    "ax11.set_title('%s' % model, fontsize=14, fontweight='bold')\n",
    "plt.savefig('../../Paper5_ffa/supple_figure/pbiasQpeak.boxplot.%s.pdf'%(model),\n",
    "            format='pdf', \n",
    "            dpi=300,\n",
    "            bbox_extra_artists=(lgd,), \n",
    "            bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.75, 1.0)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY0AAAEtCAYAAAD0uzw/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c1cd0a198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Construct scatter plot for annual peak bias group-by periods\n",
    "model = 'mhm'\n",
    "df_pbiasQpeak_cal = pd.DataFrame(pbiasQpeak_cal[model], index=df_hcdn['id'], columns=obj_fun_list)\n",
    "df_pbiasQpeak_val = pd.DataFrame(pbiasQpeak_val[model], index=df_hcdn['id'], columns=obj_fun_list)\n",
    "df_pbiasQpeak_cal['Period'] = ['cal' for _ in df_hcdn['id']]\n",
    "df_pbiasQpeak_val['Period'] = ['val' for _ in df_hcdn['id']]\n",
    "df_pbiasQpeak = pd.concat([df_pbiasQpeak_cal, df_pbiasQpeak_val], axis=0)\n",
    "\n",
    "plt.figure(figsize=(6.5,5))\n",
    "fig.suptitle('%s' % model, fontsize=14, fontweight='bold')\n",
    "fig.subplots_adjust(left=0.095,right=0.975,bottom=0.095,top=0.935,wspace=0.25,hspace=0.25)\n",
    "\n",
    "ax1 = plt.subplot2grid((1, 1), (0, 0))\n",
    "\n",
    "ax1.tick_params(labelsize=7)\n",
    "\n",
    "sns.set_style(\"whitegrid\")\n",
    "ax1.plot(df_pbiasQpeak_val['nse'], df_pbiasQpeak_val['kge'],'o')\n",
    "ax1.plot(np.arange(-0.75,1,0.01), np.arange(-0.75,1,0.01),'-k')\n",
    "ax1.set_ylim([-0.75,1.0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/naokimizukami/anaconda3/lib/python3.6/site-packages/seaborn/categorical.py:482: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
      "  box_data = remove_na(group_data[hue_mask])\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAf0AAAFNCAYAAAAKBrb9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAIABJREFUeJzt3Xl8U2W6B/Bf0o2ulEJZFKrSucWl\n6UDrQOej7SzAWEEsCqUQqYgIsnPBQZYriKhlkxllHWiZsngppYgUvDOO4igoUpdIJfWieIFSUChL\ny9J9Se4fTM805UDbJOc9Sc7v+9fTnOS8b0LIc973vIvOarVaQURERB5Pr3YFiIiISAwmfSIiIo1g\n0iciItIIJn0iIiKNYNInIiLSCCZ9IiIijWDSJ1LZ6tWr0atXL2zcuFHtqhCRh2PSJyIi0ggmfSIi\nIo1g0idysrNnz6JXr16YPHky1qxZg379+uGhhx7C3r17cfDgQQwcOBC9e/fG3LlzUVtbK72uvLwc\nM2fORExMDJKSkvDFF19Ix3r16oXk5GTk5OTg4Ycfxq9+9Sts2rQJZrMZjz/+OGJiYjBx4kRcu3ZN\njbdMRG5Cx2V4iZzr7Nmz6N+/P/z8/ODv74/u3bujsLAQvr6+8PLygsFggNlsRlVVFV599VWUlJRg\nzZo18PHxQffu3WG1WlFUVIQePXpg//79AG4kfT8/P3h7eyMqKgpHjhyBTqdDYGAgoqKicPLkSVy5\ncgUTJ07EzJkzVf4EiMhVsaVPpJD6+nrs3LkTu3btQkREBGprazFu3Dhs27YNEyZMAAAcP35cen6f\nPn3w97//He+++y6CgoJw5swZlJaWSsdramqwbt067NixA/Hx8bBarRg4cCCys7Mxf/78m85HRNQc\nkz6RQrp27Yq77roLOp0OnTt3BgA8+OCDACD93bR7/1e/+hV0Oh0CAgLQsWNHAEBVVZV0XKfToW/f\nvgCALl26tHg+IqLmmPSJFOLr6yvFev2N/2r+/v42fzfVrl27m57f9O6bj4+P9HhrzkdE1Bx/KYiI\niDSCSZ+IiEgjmPSJiIg0glP2iIiINIItfSIiIo1g0iciItIIoUnfYrFg4cKFSE1NRVpaGk6fPm1z\nfNOmTXjyyScxbNgwfPjhhyKrRkRE5PG8RRa2f/9+1NbWIicnBwUFBVi6dCnWr18PALh27Rq2bduG\nDz74AFVVVRg6dCgGDhwosnpEREQeTWhL32QyISEhAQDQu3dvFBYWSsf8/f1xxx13oKqqClVVVdDp\ndCKrRkRE5PGEtvTLy8sRFBQk/e3l5YX6+np4e9+oRrdu3TB48GA0NDTg+eefb9U5TSaTInUlIiLn\niouLU7sKmic06QcFBaGiokL622KxSAn/4MGDuHDhAj766CMAwLhx4xAbG4uYmJgWz8svEjnLvHnz\nAABLlixRuSbaYTabpQ2D0tPTYTAYVK4RKYENNNcgNOnHxsbi448/xqBBg1BQUICoqCjpWPv27dGu\nXTv4+vpCp9MhODiYe4OTYqZMmYLi4uJbHh8yZIjs4xEREVi7dq1S1dKk7du328S84HIus9kMALyY\nIgCCk/7AgQNx6NAhjBw5ElarFenp6cjKykJERAT69++Pzz//HCNGjIBer0dsbCweeughkdUjDbld\n4h4yZAj27dsnsDZEysnIyAAArFq1SuWakCsQmvT1ej0WL15s81hkZKQUT58+HdOnTxdZJSJSmdFo\nlLr3jUajyrXxLGazGadOnZJiV23tr92QhXMXrjjtfN06h2LK82Oddj5PIjTpExE1ZzAYEB0dLcXk\nPI2t/MbYVVv75y5cwcnrnZ14xgtOPJdnYdInItWxha+MkpIS2ZiA3bt348CBA6iurkZxcTHGjx+P\nqqoq7NmzR7rFPGfOHJw7dw4LFixATU0N/Pz88Oqrr6Jbt25qV99uTPpEpDq28JXRuXNnFBUVSTHZ\nKi8vx6ZNm1BUVISJEyciMDAQCxYsQO/evbF9+3bU19dj2bJlSEtLw29+8xscPnwYb7zxBlauXKl2\n1e3GtfeJiDzUhAkTZGO64d577wVwY42Y2tpaLFmyBDt27MDo0aPx888/w2q14vjx49iwYQPS0tKw\ndu1alJaWqlxrx7ClT0REmtR85dedO3filVdegZ+fH8aNG4cjR46gZ8+eePbZZxEbG4sTJ07gq6++\nUqm2zsGkT0TkobgGQtv06tULw4cPR4cOHdClSxf88pe/xJw5c7Bo0SLU1NSguroa//Vf/6V2NR3C\npE9ERKrq1jkUzhxxf+N8t/fkk09KsZ+fH/75z38CAFJSUmye16NHD2zatMlpdVMbk76DuNoVEbkq\nd1kDgXPqxWHSd1Bj9xm7zYjI1XANBGqOSd8BZrNZ2h7YlVe7IiLtio+PV7sK5EI4Zc8BzQfJEBG5\nmvz8fOTn56tdDXIRTPpERB6qsTeysLBQGn9E2sak74CmA2NceZAMEWkTeyOpOd7TdwAHyRAROW7d\nX/+Cc2XO2xugW4cumPzsxNs+Z/fu3Th58iT++Mc/Oq1cd8Ck7yC28InIVbnLlL1zZSU4E+bE5W3d\ne6VcRTHpO4gtfCJyVeyNbFlpaSkmT56MiRMnYvfu3bhw4QK6deuGr776Cp999hl++OEHvPbaawCA\n0NBQpKenIzg4WOVa24/39InIhtls5qAvD2I0Gl26la+my5cvY9KkSZg3bx5Onz6N7t27Y8eOHZg6\ndSouX74MAFiwYAFefvllbNu2DYmJicjMzFS51o5hS5+IbHDBKc/CFv6tffrppwgPD4fFYsGJEyeQ\nmJgIAIiMjERYWBgA4MSJE3jllVcAAHV1dbjnnntUq68zMOkTkYQLTpGWDB06FEOHDsWMGTMwYsQI\nHDlyBAMGDEBxcTHKysoAAPfccw+WLVuGO+64AyaTCRcvXlS51o5h0iciCXdlUxb36nA9v/jFL/D4\n44/jxx9/RHl5OZ566inccccd8PPzAwAsWrQIc+bMQUNDAwDg9ddfV7O6DmPSJyIShLdO5HXr0MWp\nI+67dejS4nOa7rL3/PPP45tvvkFlZSUefvhhFBUV4ciRIwCA6OhobNu2zXmVUxmTPhFJ3GWKlzvi\nrZNba2lOvQg9evTArFmzsGbNGtTX12PhwoVqV0kRTPpEJNHSFC/RXe28deLawsPDPapFfytM+kRk\nQystfNFd7RUVFbIxkUhM+kRkw9Nb+IA6Xe1Wq1U2dpYpU6aguLi4za+LiIjA2rVrnV4fck1M+kSk\nOWp0tQcFBcnGznK7xD1kyBDs27fP6WWS+xG6Ip/FYsHChQuRmpqKtLQ0nD592ub4gQMHMGLECIwY\nMQKLFi1S5GqYiFyPFlYB5K6c5AqEtvT379+P2tpa5OTkoKCgAEuXLsX69esBAOXl5VixYgW2bt2K\nsLAwZGRkoKysTFoViYg8l+j762rMUtDSIMm22rJuHa6dO++084V064oxkyff9jlK7LL3888/Y/78\n+WhoaIDVasXixYvRs2dPu8qfO3cuBg0aJK0S6CxCk77JZEJCQgIAoHfv3tI9NQA4cuQIoqKisGzZ\nMpw5cwYpKSlM+EQaoMb9dbUSMFv48q6dO4/I02ecdr4TTjtT27z11lsYPXo0BgwYgE8//RR/+tOf\nsGbNGpVqI09o0i8vL7e5l+Xl5YX6+np4e3ujrKwMX3zxBfbs2YOAgAA89dRT6N27d6vWOTaZTEpW\nmzSI3ylxsrKypHjDhg0YO3askHLj4uIAqPNvrZUy3YEzd9mbM2eOdKyhoUFa1e/tt9/GBx98gPr6\negQHB2P16tXSa86ePYsZM2YgPDwcJSUlSExMxMyZMwEAOTk5yMzMRHl5ORYtWoSYmBisXLkShYWF\nqKioQGRkZJt7x4Qm/aCgIJupKhaLBd7eN6oQGhoKg8GA8PBwAMCDDz6IY8eOtSrpN/7nJXIWfqfE\n2bVrlxQHBwcL++y19m+s9vt1xYuOxl325s+fj4KCAnTv3h2rVq3CiRMn8NhjjwG4scteeno6fvGL\nXyA3NxeZmZlSUm6usXf65MmTWLZsGdauXQuLxYIrV65g8+bN0Ov1GDdu3E3jV3766Sds2rQJwcHB\nMBqN+O677wAADzzwACZPnozdu3dj9+7d6NmzJ0JCQpCVlQWLxYLBgwejpKQEXbq0vAJhI6FJPzY2\nFh9//DEGDRqEgoICREVFSceio6Nx/PhxlJaWIiQkBN9++y1GjBghsnpEpAKuAkhqUWKXvfz8fLzy\nyitYvny5dD/fx8cHs2bNQkBAAM6fP4/6+nqb19x7770IDQ0FAMTExODUqVMAbiR9AOjUqROqq6vh\n5+eH0tJS6VyVlZWoq6tr03sWmvQHDhyIQ4cOYeTIkbBarUhPT0dWVhYiIiLQv39/vPDCC3juuecA\nAElJSTYXBUTkmTjAjdTi7F328vPz8frrryMzMxN33nknAOD777/H/v37kZubi6qqKjz55JM3zUw7\nceIEqqqq4Ovri6NHj2LYsGH47LPPoNPpbJ538OBBnDt3Dm+++SZKS0vx4YcftnmWm9Ckr9frsXjx\nYpvHIiMjpXjw4MEYPHiwyCoRkQvQSgufu+y5Hmfuspeeno66ujrMnTsXwI0Lhnnz5sHf3x9PPvkk\nfH19ER4ejgsXLti8zsfHBzNmzMClS5eQlJSEe++9V/b8MTExWLduHUaMGAFfX1/06NEDFy5cQI8e\nPVr9frk4DxGpTitJkLvsyQvp1tWpI+5DunVt8TlK7LK3d+9e2ce3bt16y9ecPXsWnTp1wsaNG20e\nX7p0qRQnJiZKtx7eeeedVtXlVpj0iYgE4C57t9bSnHoRWrvLXm1tLcaNG3fT4/fcc89NPdmuiEmf\niEgA7rLn2lq7y56vr69Td+Pr3r07du7c6bTztUToMrxERK5CC0v/EjXHpE+twh9I8jTbt2+3aX0r\njWvvkytg0qdWEf0DSaSkxvvrhYWFvJglTWHSpxbxB1JbtNCr0/z+uqeWSdQcB/JRizgASVs4rUwZ\nTZcgbxoTkLFhMy5dvOK083UKD8X455+57XOU2GXvypUreOSRR6SF5QYMGIAxY8bIPnf16tXo1KkT\nRo0aJXs8LS0NixYtslnLxhmY9MllcSET8bQyrUyNpX+brpzW1lXUPN2li1dgrbrTief7yWnnaov/\n/d//xWOPPYYFCxaoUn5rMOlTi9RaG50tTvG00qujxtK/TXcYbRqTupy5y15hYSG+++47jB49GmFh\nYXjppZfQuXPn2+6M98UXX+Avf/kL9Ho9Ll68iNTUVDz11FMAgLVr1+LSpUuoqqrCn/70J9xxxx1Y\nuHAhzp8/j7KyMiQmJuI///M/2/R+eU+fWtT4AxkdHS3sB5LjCEhpRqNR6EUsR++7nsZd9ubNm4fT\np0+je/fu2LFjB6ZOnYrLly8DuLHL3ssvv4xt27YhMTERmZmZtzxfz549MX36dLz99tsYMGAAXnvt\nNZSXl0s74+3YsQMFBQUoKSmxeV1JSQnWr1+PnTt3YvPmzVLZv/nNb7B161YkJibi/fffx7lz59C7\nd29s2rQJ2dnZyM7ObvN7ZkufWiU+Pl5oeVppcboaLe14J/rWBTcWcj3O3mUvPj4e/v7+AG5sMLdq\n1apW7YzXp08f+Pr6AgD+4z/+A8XFxQAgfV86deqES5cuITQ0FGazGfn5+QgKCkJtbW2b3zOTPrVK\nfn4+ACA5OVnlmpCSmJiU5ekXUu7G2bvsvfTSS/jDH/6AQYMG4fDhw3jggQdatTPesWPH0NDQgNra\nWvzf//0f7rrrLtnz7969G8HBwVi8eDFOnz6NnTt3wmq13rQb3+0w6VOL1BjcpaUWp6tR4/PWyqBN\nT39/7siZu+y98MILmD9/PrKzs+Hv74/XXnsNOp1Odme8purr6zF+/HhcuXIFkyZNknoZmvv1r3+N\nWbNmwWQywd/fH3fddRcuXLiALl26tPr9MulTi9ToameLUz1qfN4ctKltncJDnTrivlN4aIvPUWKX\nvR49esg+V25nvLi4OAA3BvJFRkbiz3/+s83xpudpOq1v3759rarLrTDpk8tiC18btDJNkG6tpTn1\nInCXPaJ/UaurnT/+2sBBm+QK1Nplr1+/fujXr5/TztcSJn1qEbvaSUlNB0bdbpAUETmOSZ9ahV3t\n2iF6UN3Vq1dlY6VpZfAgUVNcnIdaxWAw8MdRI0TvqKjX62VjpXHnSNIiJn0ikqixEqIaK9VxxUfS\nKnbvE5FEjUF1ycnJUrmiFn/i4EHXsm3LRly/6rzxHMHtw5E2ZkKbXnPs2DF89NFHmDp16m2f9+23\n3+KNN95w6mA+kZj0iUh1HDOibdevXkR0z7qWn9hKhSfbfgFx33334b777rvtczIyMrB3715pqV13\nxO59IpKotSlMcnKy0CWe1XqfZrOZtxNcxKlTpzBy5EiMHj0aY8aMwd69ezFz5kwAQG5uLoYOHYox\nY8bgueeew+7duwEAERERWL16tZrVdhhb+kQk0cr0TLXe58aNGwHA7ROHJ/j888/xwAMPYO7cufj6\n669x4sQJADe22s3MzMSePXvg6+uLp59+WnrNI488grNnz6pVZadg0iciG1rpahf9Ps1mM4qKiqTY\nky+q3MHw4cORkZGB5557DsHBwXjooYcAAMXFxYiMjJS68Pv06aNmNZ1OaPe+xWLBwoULkZqairS0\nNJw+fVr2Oc8995xd+wSTctgtqR1amZ4p+n02tvKbx6SOjz76CHFxcdiyZQuSkpKQkZEB4EYX/smT\nJ1FdXQ2LxYKjR4+qXFPnEtrS379/P2pra5GTk4OCggIsXboU69evt3nOm2++KXSBDmodbohC5Jhz\n587JxqSO6OhozJ49G6tXr4Zer0daWhqOHj2KsLAwjB8/HkajEaGhoaipqYG3t+d0igt9JyaTCQkJ\nCQCA3r17S5tsNHr//feh0+mQmJgosloO0cKqXtwQhchxTfc8b8v+51oQ3D7crhH3tztfSyIiIpCT\nk3PT4/X19bhw4YI0eO+pp55Ct27dpOPdu3fHzp07nVZX0YQm/fLycgQFBUl/e3l5ob6+Ht7e3jh+\n/Djee+89rFq1CmvXrm3TeU0mk7Or2mpZWVkAgLFjx6pWB6U1vkcA2LBhg0e/10ZqfqfIM7Vv3x7V\n1dVSLPo75srf6bbOqVeSt7c3qqqq8MQTT8DHxwcxMTF48MEH1a6W0whN+kFBQaioqJD+tlgsUrfJ\nnj17UFJSgjFjxuCnn36Cj48P7rzzzla1+hv3JRbNbDZL4xJ8fX09tgW8a9cuKQ4ODlbt8xZJC++R\nxJoxY4a0W+WMGTOE/16o/Z125YuO5mbNmoVZs2apXQ1FCB3IFxsbi4MHDwIACgoKEBUVJR178cUX\nkZubi23btuGJJ57AM8884/Ld/M1X9fJUas1pzsvLQ15enrDyiJRkMBjQtWtXdO3a1WMbCOT6hLb0\nBw4ciEOHDmHkyJGwWq1IT09HVlYWIiIi0L9/f5FVoTZQa06z6KVZiZTmziu5kWcQmvT1ej0WL15s\n81hkZORNz5s2bZqoKjnEaDRK3XWePrdZ9PvLy8tDZWWlFDPxk7OJHoRrNptx6tQpKWZrn9TAZXgd\n0NgCjo6O9vj/wKLnNGvl1gmpR/TWuvxOkyvwnMmHKvH0Fn4jLUxNJO3gNFTSKrb0HaSV1ctEt4rU\nGjxI6hC94qMarW5+p8kVMOm7IdE/kI2tosLCQmHl9uzZUzYmzyT6olINBoMBfn5+8PPz00RDgVwT\nk74b0sK9SN7/1A41LirVaHWbzWbU1NSgpqaG+1iQapj03YwaP5BESlLjAk+NQbjccIdcAZO+m9HK\nvUje/ySlRUREICIiQlh5Fy5ckI2JRGLSpxZpaWoiiafWBd4nn3yCTz75RFh5ISEhsjGRSEz6bkat\nH0ij0Si0PN7TJyU1Lv5UWVkpbKnndu3aycZEIjHpuxm2usnTaGWgaNMdRpvGRCIx6bsh0a1u4MbA\nI5GDj3hPXzvKy8tlY0/D7zS5AiZ9NyR6QSCz2YyioiIUFRVxxgA5XeMe881jJf32t7+VjZVkMBjQ\nrl07tGvXjr10pBomfWqRGlONOL1JO65duyYbK6np3u6i9nk3m82orq5GdXU1L55JNUz6bkj0inxq\nTDU6d+6cbEyep0uXLrKxktT4TmdkZMjGRCIx6buhjIwMoT8aakw1slgssjF5nvHjx8vGnqakpEQ2\nJhKJSd/NNO7JferUKWGtfTWmGnl5ecnGpDzRPUkGgwE6nQ46nU7YvW41ehc6d+4sGxOJxKTvZtTo\nIlRjqlHXrl1lY1Ke6L0d8vLyYLVaYbVahc2Znz59umyspAkTJsjGRCJ5q10Bahs1ugiNRiPmz58v\nxSJMmDBBKtOeH8ixz4zBpculdpc/ZMiQNr+mU8cwZG3eYneZrkCNfebffvttmzg5OVnxMtVgMBhw\n9913SzGRGpj03UxwcDAqKyulmORdulyK2c/HCS1zxQYxo8CV1HzRmiVLliheZm1trWysJDXeJ8AW\nPqmPSd/N6HQ62VhJavxANp+yt3r1asXLJHWEh4dLvVbh4eEq18Y5pkyZguLi4ja/LiIiAmvXrlWg\nRkQ3MOm7GTXmNF+6dEk2VtL58+dlY1KWGrdyZsyYIZU5Y8YMp5+/pQRcWFgoezvHkQR8u9cNGTIE\n+/bts+u8RI5i0nczISEhUve+qOlzZWVlsrGSGhoaZGNSlsFggF6vl2JnaykBNyb/5pRIwPPmzUNh\nYSETMGkKk76b8ff3l409DefpqyMvL0/6vPPy8pw+qO5WCdhsNmP+/PlCE3DTXg0ireCUPTcTGBgo\nGyupQ4cOsrGSmPTVodaWxmqMZucIetIiJn03Ex8fLxsrqVOnTrKxktQYsEhE5OmY9N1M0+5PUV2h\nalxosKWvDjV2nyMicYQmfYvFgoULFyI1NRVpaWk4ffq0zfHNmzcjJSUFKSkpWLNmjciq2U30kqUX\nL16UjZWUn58vG5PnaTrIzp4pZ0Tk2oQm/f3796O2thY5OTl44YUXsHTpUunYmTNnsHfvXuzYsQM5\nOTn47LPP8P3334usnl1EL1nq6+srG3uagIAA2ZiIiOwnNOmbTCYkJCQAAHr37i0t9wncWF89MzMT\nXl5e0Ov1qK+vh5+fn8jqtVnjkqWFhYXCWvu///3vZWMlqdG9zyl76mg6N1/UPH0iEkfolL3y8nKb\nDVu8vLxQX18Pb29v+Pj4ICwsDFarFcuXL8f999+Pe+65p1XnNZnUWf40KytLijds2ICxY8cqXuZ3\n331nE4t47++8845N3L17d8XLbE6tf+O2cpd63sqpU6ek+IcffhC2LG4jNT4/lklaIjTpBwUFoaKi\nQvrbYrHA2/vfVaipqcH8+fMRGBiIl19+udXnjYsTu8Z6o127dklxcHCwkHq88cYbUnz58mUhZTZf\nnMfZZba0YEtNTQ0WLVp00+OuuGSpWt9FZ2n6nTaZTBg+fLjQ8tX4/FimGLzocA1Ck35sbCw+/vhj\nDBo0CAUFBYiKipKOWa1WTJ48Gf369XObTSni4+OlWxSiur3Ly8tlY3d2q8SdmpqKyspKrphG9C+O\n7B6p1Z0jyZbQpD9w4EAcOnQII0eOhNVqRXp6OrKyshAREQGLxYIvv/wStbW1+PTTTwEAs2bNQp8+\nfURWsU2aj2r31C1B1WI0GpGZmal2NTRFjbX3qfVE7x7pCTtHki2hSV+v12Px4sU2j0VGRkqxyKlv\n5PqSk5OZ9AUzGAzSAFquWEfkebj2vgOUbhW1ZntOZ+8ORtpmNptRU1MjxUz8RJ6FSd8BBoMB0dHR\nUuxscom7cWMSAEhPT3d6ubzQ0LaMjAybeNWqVSrWhoicjUnfQaLvexoMBnTt2hXnz58XdqEB/DvR\nc1CdZzt79qxsTESegWvvO8hgMAjvAp0+fbrQ8gCgX79+wssk8err62VjIvIMTPpuSI37rC+99JLw\nMkm8pqtguvqKmETUdkz6RCQZPXq0bExEnoH39Ik0qqVBm5mZmbJTJjlok8h9Mek7qHFtAU5tIndz\nq8Sdl5eHzMxMDtok8kDs3neQ6K11iZTGlSWJPBeTvgPU2FqXiIjIXkz6Dmjawmdrn4iIXB2TvgM8\nccc7IiLyXEz6Drh69apsTERE5IqY9B3ApE9ERO6EU/Yc4OPjI+1I5uPjo3JtqDnuBU5EZItJvxVa\ns/NcTU0Nd59zMbOfjxNaHi8yiMjVMem3wu2S9vDhw1FTU8OFTG5jzJhnUFp62e7Xy11MERFR2zHp\nOygtLU12qVL6t9LSy/h1zNNCyzx8dKvQ8oiI3EGLSf/q1atYsWIFiouLsWrVKixbtgxz585F+/bt\nRdTP5SUnJ9ud9J9+5mmUXS6zu2x7WsDeej3qLRahZRIRkWtoMekvWLAADz30EI4ePYqAgAB07twZ\ns2fPxsaNG0XUz6OVXS5Dxyd7Ci3z8u6TmNShk9Ay15ddEloeac8zTz+Ny2X2XUDbcyHbsUMHWHV6\nu29b2VNmWFhHu8oiaqrFpH/27FmkpqYiOzsbvr6+mDlzJh5//HERdSMiN+RID5Y9ybBDxw4oKysT\nejHbeCEr8rYVb1mRM7SY9L28vHD9+nXodDoAQFFREfR6Tu8nInmie7Au7z4prCxXwFki5IgWk/60\nadOQlpaGc+fOYfLkySgoKEB6erqIupEHYSuFyDlETkXlBYbnaTHpJyYmIjo6GkePHkVDQwMWL16M\nTp3E3hMm51LjHjtH7xMRqa/FpL9mzRqbv48dOwYAmDp1qjI10hg1uiY5kI+ISJvaNE+/rq4On376\nKX75y18qVR/NUWP0PhERaVOLSb95i37KlCl49tln7S7QYrFg0aJF+OGHH+Dr64vXXnsNd911l3R8\n586d2LFjB7y9vTFp0iT87ne/s7ssIiIi+rc2r8hXUVGBn3/+2e4C9+/fj9raWuTk5KCgoABLly7F\n+vXrAQAXL17Etm3b8M4776CmpgZGoxEPPfQQfH197S6PiIiIbmgx6f/+97+XputZrVZcvXoV48aN\ns7tAk8mEhIQEAEDv3r1RWFgoHTt69Cj69OkDX19f+Pr6IiIiAt9//z1iYmLsLo+IiIhuaDHpb9u2\nTYp1Oh1CQkIQFBRkd4Hl5eXnLnlQAAAWpUlEQVQ2r/fy8kJ9fT28vb1RXl6O4OBg6VhgYCDKy8tb\nPKfJpP60EleoQ2t4QfzAOi+9l2ZG07vL96A1POm9KEUL32t+DzzLLZP+nj17bvvCoUOH2lVgUFAQ\nKioqpL8tFgu8vb1lj1VUVNhcBNxKXJzYLVSdVYcOHTsIH1jXoWMHbN1s3w/VkCFDhO8maG+Zau0R\n4ArfRWdxt/fi6VNR1brAcNb3gBcPruGWSf+LL7647QvtTfqxsbH4+OOPMWjQIBQUFCAqKko6FhMT\ngzfffBM1NTWora3FiRMnbI57GnuTL6BOAiZyZWosw0vkbm6Z9JcsWXLLF1VXV9td4MCBA3Ho0CGM\nHDkSVqsV6enpyMrKQkREBPr374+0tDQYjUZYrVbMnDkTfn5+dpdFROrg1FAi19TiPf1//vOfePPN\nN1FZWQmr1QqLxYLq6mocPnzYrgL1ej0WL15s81hkZKQUjxgxAiNGjLDr3ETkGrj2PpFrajHpL1my\nBK+++iqysrIwceJE7N+/H1VVVSLqJtTTTz+DsjL7tskE7NwdrENHbN262e4yiYiI2qLFpB8cHIz4\n+Hh88803uH79OmbPno1BgwaJqJtQZWWXEXzfSLFlHtshtDwiItK2FpN+u3btcOrUKURGRuLLL79E\nfHw86urqRNSNiBzkSA+Wvb1XROS6Wkz6M2fOxJtvvokVK1Zg48aNyMnJwfDhw0XUjYgcJLoHi71X\nRK6tVff0a2trsXnzZqxZswYBAQFo3769iLoRERGRE7WY9Hfv3o3Tp0/jvffew4QJExAaGork5GSP\nbO1fZyuFiIg8WKs23LnrrrswduxYREREICsrCxs3bvTIpC96IB8vMoiISKQWk/6HH36Iffv24dtv\nv8Xvfvc7vPTSS4iNjRVRNyIilxUW1lHo0rhhYR1RWmr/tGIioBVJf+/evUhOTsbKlSvh4+Mjok5E\n5MZE7ynRoWMH6C1il8bt2KEDNm/ZbNdrHVlCW609JchztJj0V69eLaIe5OLMZrPaVWiTTh3DsGKD\n2A0+OnUME1qeq9LChk5E7qpV9/TJtaiRgDMyMoSX6YiszVvsfi0TARF5Kr3aFaC22759u9DyzGYz\nTp06JcUiyyUiIudhS9/NmM1mFBYWSrHBYHDq+adMmYLi4uJbHp8/f77s4xEREVi7dq1T6yL64sZT\ncZYIETVi0v+XDh06Cl9NrKUlS9VIwHKvS01NRWVlJQAgICAAOTk5dp27LZS+uNESkVNReYFB5NqY\n9P/Fkd3ulLoHLJeAp0+fLnW133PPPVi1apXTy20uJCRESvohISGKlwfYtvK3b9+OJUuWCCmXyJWJ\nHqDKwameh0nfzVy5ckU2dmct9WgUFhbKTlVS4pYCkSuzd4AqB6dSIyZ9N3P16lXZWEnXrl2TjZ1F\nLnGbzWbp9kV6ejq794mInICj992Mr6+vbKykzp07y8ZKMhgMuPvuu3H33Xcz4RMROQmTvpsZPXq0\nbKykLl26yMZKu3r1qrDeDCIiLWDSpxZ99dVXsrGSzGYzysrKUFZWxvn6REROwqTvZt5++23Z2NO8\n9dZbsjEREdmPSd/NVFdXy8ZKSkpKko2VdPHiRdmYiIjsx6RPLXr44YdlYyV5eXnJxkREZD8mfTej\n0+lkYyU1XyhHhLCwMNmYiIjsx3n6bubRRx/F3/72Nyn2VOHh4SgpKZFiso/o5aVbWlqaiNTFpO9m\nJk2aJCX9SZMmCSnTaDRKC+UYjUYhZcbHx0tr78fHxwsp0xPZu7w0V3Aj8kxCu/erq6sxbdo0GI1G\njB8/HqWlpTc9Z9myZUhNTcWwYcOwc+dOkdVzC3l5ebKxp8nPz5eNiYjIfkKTfnZ2NqKiorB9+3YM\nHToU69atszmen5+P4uJi5OTkIDs7GxkZGVycpRk17q+rUSYRETmf0KRvMpmQkJAAAEhMTMThw4dt\njvfp0wfp6enS3w0NDfD25h2IphoaGmRjT9P0NoKoWwpERJ5OsYyam5uLLVtsd4Tq2LEjgoODAQCB\ngYG4fv26zXE/Pz/4+fmhrq4Oc+fORWpqKgIDA1ssy2QSt9Wk2nXw8fFBTU2NFIsoNy4uTrq/HhcX\nJ+y9+vn5AQBqa2uF/xu7wndKbWp8BizTs8ok16NY0k9JSUFKSorNY1OnTkVFRQUAoKKiQnZv9qtX\nr2L69Ono27cvnn/++VaVFRcX53iF7dR4X11UHerr621iEeU2TfTDhw9XvDzgxjK8jRc3vr6+wjfd\nUfM75SrU+AxYpmeV2RQvOlyD0O792NhYHDhwAABw8ODBm76E1dXVeOaZZzBs2DBMmTJFZNXs9te/\n/lVoeVarVTZWmtFoFNrNznEERETOJ/SG+ahRozBnzhyMGjUKPj4+WLlyJQBg+fLlSEpKwjfffIMz\nZ84gNzcXubm5AG7spd6jRw+R1bzJlClTUFxcfNvnDBkyRPbxiIgI2f3i7dWtWzcUFRVJsSjc3paI\nyP0JTfr+/v5YtWrVTY+/+OKLAICYmBg888wzIqvUKrdK2snJybBYLAAAvV4vZArd/fffLyX9+++/\nX/Hy1KLG2gBERJ6Oy/A6oDHhN4+V9Mknn8jGnsZgMCA6OhrR0dHsZSAichLOhyOXxRY+EZFzsaXv\nAH9/f9lYSVqav24wGNjKJyJyIiZ9B7Rr1042VlJycjICAgIQEBCA5ORkIWUSkXOYzWa1q0Aax+59\nBzRdIljkcsGe3sInEkGNBLxx40bhZRI1xaTvAC8vL2kAn5eXl7By2cIncpzo9R/MZrM088ZsNvPW\nFamCSd8BYWFh0p7vYWFhKteGiFrLbDZLS0srkYBbWtujcTpqc85e14OoOSZ9B6gxkI/InbWUDJVY\n5EqNBCz3utTUVFRWVgIAAgICkJOTY9e5iRzBpO+A6upq2ZiI5N0qiU6bNg0AsHr1aiFlTps2Tepq\nv/vuuxUptzk/Pz8p6TduJkUkGkfvO+DatWuyMRG1XuO97qKiImGD63Q6nWyspKqqKtmYSCQmfQd0\n6dJFNiai1ms6ol3U6HY1EnBDQ4NsTCQSk74Dxo8fLxsTUetduHBBNlbSpUuXZGMiT8ek7wCDwQCd\nTgedTsfpN0R2UqPHrL6+XjYm8nRM+g7Iy8uD1WqF1WoVssMekSfq37+/bOxprFarbEwkEpO+A5ou\n7iF6oQ8iT5Gfny8bK6npuhqi1thQY1dOouaY9B3AgTlE7ikkJEQ2VlLnzp1lYyKRmPQd0KFDB9mY\niFpPjZ0jg4KCZGMlTZ8+XTYmEolJ3wFckY/IcQaDQdo5UtSAWDUuNAwGA7p27YquXbty4C+phivy\nOYADc8idqbEkrhyz2SytVCdqIxqDwYDo6GgpFoUtfFIbk74D1OgiJHIWV9nYpfmA2CVLlggpNz4+\nXkg5TbGFT2pj974D1OgiJCLnyM/PFzZbgMhVMOk7QI17kURKM5vNwtbAB2xb3KJa341b6xYWFgp9\nr0RqY9J3QOO9yMrKSv5wkMfIyMhARkaGsPLUmKfPNTZIq5j0HcAfDvI0ZrMZp06dwqlTp4RdyJaX\nl8vGROR8TPpEJGnawhfV2ldjm1uOxyGtYtJ3AH84yNOcO3dONlZSYGCgbKwkjschrRKa9KurqzFt\n2jQYjUaMHz8epaWlss+rqqpCcnIyDh48KLJ6bdY41zc6Opo/HOQR6urqZGMlqTWQj+NxSIuEJv3s\n7GxERUVh+/btGDp0KNatWyf7vMWLFwvr5nOU0WhkK588hhr7SXAgH5E4QpO+yWRCQkICACAxMRGH\nDx++6TmbNm1Cnz59cO+994qsmt0MBgNb+URuhoMHSasUW5EvNzcXW7ZssXmsY8eOCA4OBnDj3t31\n69dtjh8+fBinT5/G4sWL8c0337S6LJPJ5HiFiZrgd+oGEZ9DXFwcCgsLpVhEmVVVVTaxFv69tfAe\nqWWKJf2UlBSkpKTYPDZ16lRUVFQAACoqKm7a0nLXrl346aefkJaWhpMnT+K7775DeHg47rvvvtuW\nFRcX59zKk+Zp9Ts1aNAg/O1vf5NiEZ9DXFwcDhw4AAAYPny44uUBQHh4OEpKSqRYC//ear9HXnS4\nBqHd+7GxsdJ/7oMHD970JVy5ciV27NiBbdu2ISEhAbNnz24x4ROR80yaNEk2VppOpxM6joczb0ir\nhG64M2rUKMyZMwejRo2Cj48PVq5cCQBYvnw5kpKSEBMTI7I6RCRj0KBBQstrXBCoMfbkXfaI1CY0\n6fv7+2PVqlU3Pf7iiy/e9NjSpUtFVMlhjdN9+MNBnkJkCx9Qb5c9tvBJi7i1roMaf7BE/VAReZrG\ncT7NY6XxQp20iCvyOYA7dRE5zmq1ysZE5HxM+g7gAh9EjgsKCpKNicj5mPQdwAU+iBzHkfRE4vCe\nvgPU2B2MSGl5eXkAgOTkZCHlaWkkPQf+ktqY9B2gxu5gREprvFUlKukD2mnhc+AvqY3d+w5gtyR5\nmry8PGn3ucYWvwha2MOCA3/JFTDpO4Bb65Kn4eBU5fCzJVfA7n0HsYVPRETugi19B2mhW5K0g7es\nlMPPllwBkz4RSZKTk6HX66HX64UO5NMC3g4kV8DufSKSmM1mWCwWKWZyci628EltbOkTkYSDzZTF\n24GkNrb0SZOmTJmC4uLiWx4fMmSI7OMRERFYu3atUtUiIlIUkz5pEhO3PKPRiPnz50sxEXkWJn0i\nkmhpSVwiLWLSJyIb8fHxaleB7MBbVtQaTPpEZCM/Px+A2LX3yXFM3NQaHL1PRBKuD0/k2Zj0iUjC\nKXtEno1Jn4jIg5nNZvbakIRJn4gkXB/e82zfvp29NiThQD4iknDKnmdpHKPRGPPflNjSJyIbRqOR\nrXwPwTEa1Bxb+kRkg61BIs/Flj4RkYfiGA1qTmhLv7q6GrNnz8bly5cRGBiIZcuWISwszOY5u3fv\nRnZ2NhoaGtC/f39MmTJFZBWJiDwGx2hQc0Jb+tnZ2YiKisL27dsxdOhQrFu3zuZ4cXExsrOzsW3b\nNuzatQt1dXWoq6sTWUUiIo/CMRrUlNCkbzKZkJCQAABITEzE4cOHbY5//vnniI6Oxpw5czB69GjE\nxsbCx8dHZBWJiDyKwWBgK58kinXv5+bmYsuWLTaPdezYEcHBwQCAwMBAXL9+3eZ4WVkZvv76a2Rn\nZ6OmpgajRo3Crl27EBISctuyTCaTcytPRETkgRRL+ikpKUhJSbF5bOrUqaioqAAAVFRU3JTMQ0ND\n0bdvXwQFBSEoKAiRkZEoKipCTEzMbcuKi4tzbuWJiMip2DhzDUK792NjY3HgwAEAwMGDB29K1rGx\nsfjyyy9RU1ODyspKnDhxAhERESKrSERE5LGEjt4fNWoU5syZg1GjRsHHxwcrV64EACxfvhxJSUmI\niYnBsGHDMGrUKFitVkyePBmhoaEiq0hEROSxdFar1ap2JRxhMpnYvU/kRI2bs3DwFzkTf6tdAxfn\nISIb3KCFyHMx6RORpHGDlsLCQm7HSuSBmPSJSMINWog8G5M+ERGRRjDpE5GEG7QQeTZurUtEEm7Q\nQuTZmPSJyIYaLXxOEyQSg0mfiGyokXgbBw0uWbJEeNlEWsJ7+kSkKk4TJBKHSZ+IVKXWNEGz2cyL\nDNIcJn0i0iSuPEhaxKRPRKpSY5ogbymQVjHpE5GqGqcJRkdHCxtEyJUHSas4ep+IVKeVhYA4NZHU\nxpY+EanOYDAITYRqrTzIcQSkNrb0iUhz1Fh5sHEcQWPM1j6pgS19ItIko9EovJUvFxOJxJY+EWkS\nW9qkRWzpExEJwB0MyRWwpU9EJAB3MCRXwKRPRCQIW/ikNiZ9IiJB2MIntfGePhERkUYw6RMREWkE\nkz4REZFGMOkTERFphNCBfNXV1Zg9ezYuX76MwMBALFu2DGFhYTbPWbJkCUwmE/R6PebMmYO4uDiR\nVSQiIvJYQlv62dnZiIqKwvbt2zF06FCsW7fO5vj333+PI0eOIDc3F8uXL8frr78usnpEREQeTWjS\nN5lMSEhIAAAkJibi8OHDNsc7d+6Mdu3aoba2FuXl5fD25oxCIiIiZ1Esq+bm5mLLli02j3Xs2BHB\nwcEAgMDAQFy/ft22Mt7e0Ov1ePTRR3H9+nW8+uqrrSrLZDI5p9JEREQeTLGkn5KSgpSUFJvHpk6d\nioqKCgBARUUFQkJCbI7v2bMHnTp1wqZNm1BRUQGj0Yg+ffqgS5cutyyH9/yJiIhaR2j3fmxsLA4c\nOAAAOHjw4E0JOyQkBAEBAfDy8kJgYCB8fX2liwQiIiJyjM5qtVpFFVZVVYU5c+bg4sWL8PHxwcqV\nKxEeHo7ly5cjKSkJDzzwAF555RUcP34cDQ0NGDRoEMaOHSuqekRERB5NaNInIiIi9XBxHiIiIo1g\n0iciItIIJn2yW1paGk6cOKF2NYicht9p8nRM+kRERBrBJe/oJtXV1Zg3bx5+/vln1NXVYe7cufjv\n//5vXL9+HWVlZUhJSYHRaFS7mkStNnXqVDz99NPo27cvjh49ihUrViAsLIzfadIcJn26yY4dO3Dn\nnXfiz3/+M44fP45Dhw5h8ODB+MMf/oCSkhKkpaXxB5LcSkpKCt5991307dsX7777Lvr164eoqCh+\np0lzmPTpJidPnkRiYiIAICoqCu3bt8fKlSvxwQcfICgoCPX19SrXkKhtEhISsGLFCly5cgVff/01\nMjMz+Z0mTeI9fbpJZGQkzGYzAODMmTN49dVX0bt3b7zxxhtISkoCl3Ygd6PX65GUlIRFixZhwIAB\n+Otf/8rvNGkSW/p0k5EjR2L+/PkYPXo0Ghoa0L9/f2zduhX79u1DaGgovLy8UFtbq3Y1idpk2LBh\nGDBgAP7xj3/g7NmzWLRoEb/TpDlckY+IiEgj2L1PRESkEUz6REREGsGkT0REpBFM+kRERBrBpE9E\nRKQRTPpECjh79iyio6ORnJyMoUOHYvDgwRg7dizOnz/f6nMkJye3qcy0tDR88cUXba0qEWkIkz6R\nQjp37oy8vDzs2bMH//M//4NevXph+fLlrX59Xl6egrUjIi1i0icSpF+/fvjxxx9x9OhRjBo1Ck88\n8QSeffZZnDlzBsCNlvrUqVPxyCOP4NixY+jVqxcAoKqqCi+88AIee+wxDBkyBHv27AEA1NbWYvbs\n2Xj00Ufx3HPPoaysTLX3RkTugSvyEQlQV1eHf/zjH4iOjsZLL72Ev/zlL7jjjjvw6aefYsGCBdi8\neTMAoFevXlizZo3Na1evXo0OHTrgvffeQ2lpKVJSUnDvvffi0KFDAIC///3vKCoqwuOPPy76bRGR\nm2HSJ1LIhQsXpPvytbW1iImJwbBhw/D+++9j0qRJ0vPKy8ulOCYm5qbz5OfnIz09HQAQFhaG/v37\n48svv8SXX36J1NRUAMDdd9+NPn36KPl2iMgDMOkTKaTxnn5T33//Pbp37y493tDQgEuXLknH27Vr\nd9N5mq+UbbVa0dDQAJ1OZ3PM25v/nYno9nhPn0ignj174urVq/j6668BAO+88w7++Mc/3vY18fHx\n2LVrFwCgtLQUH330Efr27Ytf//rX2LdvHywWC3766Sd88803itefiNwbmwZEAvn6+uKtt97C66+/\njpqaGgQFBWHZsmW3fc2UKVOwaNEiDBkyBA0NDZg4cSIeeOABREVF4ccff8Sjjz6KO++8E1FRUYLe\nBRG5K+6yR0REpBHs3iciItIIJn0iIiKNYNInIiLSCCZ9IiIijWDSJyIi0ggmfSIiIo1g0iciItII\nJn0iIiKN+H9wtRzpjqDFxwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c19e09f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Construct boxplots for metrics group-by periods\n",
    "model = 'mhm'\n",
    "df_pbiasFHV_cal = pd.DataFrame(pbiasFHV_cal[model], index=df_hcdn['id'], columns=obj_fun_list)\n",
    "df_pbiasFHV_val = pd.DataFrame(pbiasFHV_val[model], index=df_hcdn['id'], columns=obj_fun_list)\n",
    "df_pbiasFHV_cal['Period'] = ['cal' for _ in df_hcdn['id']]\n",
    "df_pbiasFHV_val['Period'] = ['val' for _ in df_hcdn['id']]\n",
    "df_pbiasFHV = pd.concat([df_pbiasFHV_cal, df_pbiasFHV_val], axis=0)\n",
    "\n",
    "dd=pd.melt(df_pbiasFHV, id_vars=['Period'], value_vars=obj_fun_list, var_name='OF')\n",
    "\n",
    "plt.figure(figsize=(6.5,5))\n",
    "sns.set_style(\"whitegrid\")\n",
    "ax21 = sns.boxplot(x='Period', y='value', data=dd, hue='OF', linewidth=1.0)\n",
    "handles, labels = ax21.get_legend_handles_labels()\n",
    "lgd = plt.legend(bbox_to_anchor=(1.01, 1), loc=2, borderaxespad=0.)\n",
    "ax21.set_ylim([-0.8,0.8])\n",
    "ax21.set_title('%s' % model, fontsize=14, fontweight='bold')\n",
    "plt.savefig('../../Paper5_ffa/supple_figure/pbiasFHV.boxplot.%s.pdf' % (model),\n",
    "            format='pdf', \n",
    "            dpi=300, \n",
    "            bbox_extra_artists=(lgd,), \n",
    "            bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Flood frequency bias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/naokimizukami/anaconda3/lib/python3.6/site-packages/seaborn/categorical.py:482: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
      "  box_data = remove_na(group_data[hue_mask])\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAf0AAAFNCAYAAAAKBrb9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngkGfiIhIEgz6REREkmDQJyIiksT/Ays7OmEShbAWAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1015d28d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Construct boxplots for flood magnitude bias at three return periods - gropu by return periods\n",
    "model = 'vic'\n",
    "df_pbiasQ20 = pd.DataFrame(pbiasQ20[model], index=df_hcdn['id'], columns=obj_fun_list)\n",
    "df_pbiasQ10 = pd.DataFrame(pbiasQ10[model], index=df_hcdn['id'], columns=obj_fun_list)\n",
    "df_pbiasQ5 = pd.DataFrame(pbiasQ5[model], index=df_hcdn['id'], columns=obj_fun_list)\n",
    "df_pbiasQ20['return period'] = ['Q20yr' for _ in df_hcdn['id']]\n",
    "df_pbiasQ10['return period'] = ['Q10yr' for _ in df_hcdn['id']]\n",
    "df_pbiasQ5['return period'] = ['Q5yr' for _ in df_hcdn['id']]\n",
    "\n",
    "df_pbiasQ = pd.concat([df_pbiasQ5, df_pbiasQ10, df_pbiasQ20], axis=0)\n",
    "\n",
    "dd_Q = pd.melt(df_pbiasQ, id_vars=['return period'], value_vars=obj_fun_list, var_name='OF')\n",
    "\n",
    "plt.figure(figsize=(6.5,5))\n",
    "sns.set_style(\"whitegrid\")\n",
    "ax31 = sns.boxplot(x='return period', y='value', data=dd_Q, hue='OF', linewidth=1.0)\n",
    "handles, labels = ax31.get_legend_handles_labels()\n",
    "lgd = plt.legend(bbox_to_anchor=(1.01, 1), loc=2, borderaxespad=0.)\n",
    "ax31.set_ylim([-0.8,0.8])\n",
    "ax31.set_title('%s' % model, fontsize=14, fontweight='bold')\n",
    "plt.savefig('../../Paper5_ffa/supple_figure/pbiasQ.boxplot.%s.pdf' % (model),\n",
    "            format='pdf', \n",
    "            dpi=300, \n",
    "            bbox_extra_artists=(lgd,), \n",
    "            bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Why calibration with annual peak flow bias still underestimate substantially?\n",
    "It is probably because we use bias of annual peak flow for objective function, and year-year variabilty and timing of annual peak series are not considered, so potentially more extream peak flow is likely to be underestimated.\n",
    "\n",
    "This is to illustrate this"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "correlation with %bias Q20yr\n",
      "      alpha beta\n",
      "vic - 0.90  0.62\n",
      "mhm - 0.93  0.69\n",
      "\n",
      "correlation with %bias Q5yr\n",
      "      alpha beta\n",
      "vic - 0.59  0.79\n",
      "mhm - 0.59  0.83\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAdkAAAG5CAYAAADCs05YAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIYQQLiJNVgghhHARabJCCCGEi/wHB/4qMp6qDT0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c49eef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ------------------------------------\n",
    "r_alpha_q20_vic = corr(qpeak_alpha['vic'][:,-1],pbiasQ20['vic'][:,-1])\n",
    "r_beta_q20_vic  = corr(qpeak_beta['vic'][:,-1],pbiasQ20['vic'][:,-1])\n",
    "\n",
    "r_alpha_q20_mhm = corr(qpeak_alpha['mhm'][:,-1],pbiasQ20['mhm'][:,-1])\n",
    "r_beta_q20_mhm  = corr(qpeak_beta['mhm'][:,-1],pbiasQ20['mhm'][:,-1])\n",
    "\n",
    "r_alpha_q5_vic = corr(qpeak_alpha['vic'][:,-1],pbiasQ5['vic'][:,-1])\n",
    "r_beta_q5_vic  = corr(qpeak_beta['vic'][:,-1], pbiasQ5['vic'][:,-1])\n",
    "\n",
    "r_alpha_q5_mhm = corr(qpeak_alpha['mhm'][:,-1],pbiasQ5['mhm'][:,-1])\n",
    "r_beta_q5_mhm  = corr(qpeak_beta['mhm'][:,-1], pbiasQ5['mhm'][:,-1])\n",
    "\n",
    "print('correlation with %bias Q20yr')\n",
    "print('      alpha beta')\n",
    "print('vic - %.2f  %.2f' % (r_alpha_q20_vic,r_beta_q20_vic))\n",
    "print('mhm - %.2f  %.2f' % (r_alpha_q20_mhm,r_beta_q20_mhm))\n",
    "print('\\ncorrelation with %bias Q5yr')\n",
    "print('      alpha beta')\n",
    "print('vic - %.2f  %.2f' % (r_alpha_q5_vic, r_beta_q5_vic))\n",
    "print('mhm - %.2f  %.2f' % (r_alpha_q5_mhm,r_beta_q5_mhm))\n",
    "\n",
    "# ------------------------------------\n",
    "fig = plt.figure(figsize=(6.5, 6.5))\n",
    "fig.subplots_adjust(left=0.095,right=0.975,bottom=0.095,top=0.935,wspace=0.25,hspace=0.35)\n",
    "\n",
    "ax41 = plt.subplot2grid((2, 2), (0, 0))\n",
    "ax42 = plt.subplot2grid((2, 2), (0, 1))\n",
    "ax43 = plt.subplot2grid((2, 2), (1, 0))\n",
    "ax44 = plt.subplot2grid((2, 2), (1, 1))\n",
    "\n",
    "ax41.plot(qpeak_alpha['vic'][:,-1],pbiasQ20['vic'][:,-1],'o',alpha=0.5, label='VIC')\n",
    "ax41.plot(qpeak_alpha['mhm'][:,-1],pbiasQ20['mhm'][:,-1],'o',alpha=0.5, label='mHM')\n",
    "ax41.legend()\n",
    "ax41.set_xlim([0.0,2.0])\n",
    "ax41.set_ylim([-1.0,1.2])\n",
    "ax41.set_xlabel(r'$\\alpha$ (Annual peakQ)')\n",
    "ax41.set_ylabel('%bias Q20yr')\n",
    "ax41.text(0.1,1.0,'VIC: %.2f'%r_alpha_q20_vic, bbox={'facecolor':'w','edgecolor':'w'})\n",
    "ax41.text(0.1,0.8,'mHM: %.2f'%r_alpha_q20_mhm, bbox={'facecolor':'w','edgecolor':'w'})\n",
    "lgd = ax41.legend(frameon=True,loc=\"lower right\", handlelength=2, prop={'size':9})\n",
    "lgd.get_frame().set_facecolor('white')\n",
    "\n",
    "ax42.plot(qpeak_beta['vic'][:,-1],pbiasQ20['vic'][:,-1],'o',alpha=0.5)\n",
    "ax42.plot(qpeak_beta['mhm'][:,-1],pbiasQ20['mhm'][:,-1],'o',alpha=0.5)\n",
    "ax42.set_xlim([0.0,2.0])\n",
    "ax42.set_ylim([-1.0,1.2])\n",
    "ax42.set_xlabel(r'$\\beta$ (Annual peakQ)')\n",
    "ax42.text(0.1,1.0,'VIC: %.2f'%r_beta_q20_vic, bbox={'facecolor':'w','edgecolor':'w'})\n",
    "ax42.text(0.1,0.8,'mHM: %.2f'%r_beta_q20_mhm, bbox={'facecolor':'w','edgecolor':'w'})\n",
    "\n",
    "ax43.plot(qpeak_alpha['vic'][:,-1],pbiasQ5['vic'][:,-1],'o',alpha=0.5)\n",
    "ax43.plot(qpeak_alpha['mhm'][:,-1],pbiasQ5['mhm'][:,-1],'o',alpha=0.5)\n",
    "ax43.set_xlim([0.0,2.0])\n",
    "ax43.set_ylim([-1.0,1.2])\n",
    "ax43.set_xlabel(r'$\\alpha$ (Annual peakQ)')\n",
    "ax43.set_ylabel('%bias Q5yr')\n",
    "ax43.text(0.1,1.0,'VIC: %.2f'%r_alpha_q5_vic, bbox={'facecolor':'w','edgecolor':'w'})\n",
    "ax43.text(0.1,0.8,'mHM: %.2f'%r_alpha_q5_mhm, bbox={'facecolor':'w','edgecolor':'w'})\n",
    "\n",
    "ax44.plot(qpeak_beta['vic'][:,-1],pbiasQ5['vic'][:,-1],'o',alpha=0.5)\n",
    "ax44.plot(qpeak_beta['mhm'][:,-1],pbiasQ5['mhm'][:,-1],'o',alpha=0.5)\n",
    "ax44.set_xlim([0.0,2.0])\n",
    "ax44.set_ylim([-1.0,1.2])\n",
    "ax44.set_xlabel(r'$\\beta$ (Annual peakQ)')\n",
    "ax44.text(0.1,1.0,'VIC: %.2f'%r_beta_q5_vic, bbox={'facecolor':'w','edgecolor':'w'})\n",
    "ax44.text(0.1,0.8,'mHM: %.2f'%r_beta_q5_mhm, bbox={'facecolor':'w','edgecolor':'w'})\n",
    "\n",
    "plt.savefig('../../Paper5_ffa/supple_figure/scatter_plot_peakQ_vs_annualQ_stat.pdf',\n",
    "            format='pdf', \n",
    "            dpi=300,\n",
    "            bbox_extra_artists=(lgd,), \n",
    "            bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Plot time series for each basin\n",
    "\n",
    "4 plots\n",
    "- Annual maximum flow for each year from 1991-2008\n",
    "- 1991-2008 mean annual cycle at daily step\n",
    "- 1991-2008 monthly mean series\n",
    "- 1991-2008 annual mean series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clr=['red','blue','green','black']\n",
    "obj_fun_ts_plot = ['nse','kge','sig1','obs']\n",
    "linestyle = ['-','-','-','--']\n",
    "lgnd = [True,False]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# annual maximum series\n",
    "for idx, hid in enumerate(df_hcdn['id']):\n",
    "    \n",
    "    fig = plt.figure(figsize=(6.5, 5.5))\n",
    "    fig.subplots_adjust(left=0.095,right=0.975,bottom=0.095,top=0.935,wspace=0.25,hspace=0.45)\n",
    "\n",
    "    ax51 = [plt.subplot2grid((2, 1), (0, 0)),\n",
    "            plt.subplot2grid((2, 1), (1, 0))]\n",
    "\n",
    "    ax1.tick_params(labelsize=7)\n",
    "    ax2.tick_params(labelsize=7)\n",
    "    \n",
    "    cms2mmd = 60.0*60.0*24.0*1000.0/df_hcdn.loc[df_hcdn['id']==hid]['area'][idx]\n",
    "    for jdx, model in enumerate(model_list):\n",
    "        if model == 'vic':\n",
    "            sr_sim = ds_vic_flow_all.sel(hcdn=hid).to_dataframe()[obj_fun_ts_plot] \n",
    "        elif model =='mhm':\n",
    "            sr_sim = ds_mhm_flow_all.sel(hcdn=hid).to_dataframe()[obj_fun_ts_plot]\n",
    "            sr_sim=sr_sim*cms2mmd\n",
    "        sr_sim.where(sr_sim>0.0, 1.0e-7, inplace=True)  \n",
    "        sr_sim_ann=sr_sim.resample('A-SEP').max()   \n",
    "        \n",
    "        ax51[jdx].axvline(x=sr_sim_ann.index[9],color=[0.5,0.5,0.5],linewidth=1.25)\n",
    "        sr_sim_ann.plot(ax=ax51[jdx],\n",
    "                        color=clr,\n",
    "                        style=linestyle,\n",
    "                        linewidth=1.25,\n",
    "                        legend=lgnd[jdx],\n",
    "                        title = '%s: Annual maximum'%model)\n",
    "        \n",
    "        ax51[jdx].set_xlabel(\"\")\n",
    "        ax51[jdx].set_ylabel(\"mm/day\")\n",
    "        ax51[jdx].xaxis.set_tick_params(labelsize=8)\n",
    "        ax51[jdx].yaxis.set_tick_params(labelsize=8)\n",
    "        ax51[jdx].grid(color=[0.7,0.7,0.7], linestyle='-', linewidth=0.5)\n",
    "        \n",
    "    plt.savefig('../../Paper5_ffa/supple_figure/per_basin/annual_max_series_%d.pdf'%hid,\n",
    "            format='pdf', \n",
    "            dpi=200, \n",
    "            bbox_extra_artists=(lgd,), \n",
    "            bbox_inches='tight')\n",
    "    plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Annual cycle\n",
    "for idx, hid in enumerate(df_hcdn['id']):\n",
    "    \n",
    "    fig = plt.figure(figsize=(7.5, 5.5))\n",
    "    fig.subplots_adjust(left=0.095,right=0.975,bottom=0.095,top=0.935,wspace=0.25,hspace=0.45)\n",
    "\n",
    "    ax52 = [plt.subplot2grid((2, 1), (0, 0)),\n",
    "           plt.subplot2grid((2, 1), (1, 0))]\n",
    "\n",
    "    ax1.tick_params(labelsize=7)\n",
    "    ax2.tick_params(labelsize=7)\n",
    "    \n",
    "    cms2mmd = 60.0*60.0*24.0*1000.0/df_hcdn.loc[df_hcdn['id']==hid]['area'][idx]\n",
    "    for jdx, model in enumerate(model_list):\n",
    "        if model == 'vic':\n",
    "            sr_sim = ds_vic_flow_all.sel(hcdn=hid).to_dataframe()[obj_fun_ts_plot] \n",
    "        elif model =='mhm':\n",
    "            sr_sim = ds_mhm_flow_all.sel(hcdn=hid).to_dataframe()[obj_fun_ts_plot]\n",
    "            sr_sim=sr_sim*cms2mmd\n",
    "        sr_sim.where(sr_sim>0.0, 1.0e-7, inplace=True)\n",
    "        sr_sim_climatology_mean = sr_sim.groupby(sr_sim.index.dayofyear).mean()  \n",
    "        \n",
    "        sr_sim_climatology_mean.plot(ax=ax52[jdx],\n",
    "                                     color=clr,\n",
    "                                     style=linestyle,\n",
    "                                     linewidth=1.25,\n",
    "                                     legend=lgnd[jdx],\n",
    "                                     title='Annual cycle')\n",
    "        \n",
    "        ax52[jdx].set_xlabel(\"\")\n",
    "        ax52[jdx].set_ylabel(\"mm/day\")\n",
    "        ax52[jdx].xaxis.set_tick_params(labelsize=8)\n",
    "        ax52[jdx].yaxis.set_tick_params(labelsize=8)\n",
    "        ax52[jdx].grid(color=[0.7,0.7,0.7], linestyle='-', linewidth=0.5)\n",
    "        \n",
    "    plt.savefig('../../Paper5_ffa/supple_figure/per_basin/annual_cycle_%d.pdf'%hid,\n",
    "            format='pdf', \n",
    "            dpi=200, \n",
    "            bbox_extra_artists=(lgd,), \n",
    "            bbox_inches='tight')\n",
    "    plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Monthly mean series\n",
    "for idx, hid in enumerate(df_hcdn['id']):\n",
    "    \n",
    "    fig = plt.figure(figsize=(7.5, 5.5))\n",
    "    fig.subplots_adjust(left=0.095,right=0.975,bottom=0.095,top=0.935,wspace=0.25,hspace=0.45)\n",
    "\n",
    "    ax53 = [plt.subplot2grid((2, 1), (0, 0)),\n",
    "            plt.subplot2grid((2, 1), (1, 0))]\n",
    "\n",
    "    ax1.tick_params(labelsize=7)\n",
    "    ax2.tick_params(labelsize=7)\n",
    "    \n",
    "    cms2mmd = 60.0*60.0*24.0*1000.0/df_hcdn.loc[df_hcdn['id']==hid]['area'][idx]\n",
    "    for jdx, model in enumerate(model_list):\n",
    "        if model == 'vic':\n",
    "            sr_sim = ds_vic_flow_all.sel(hcdn=hid).to_dataframe()[obj_fun_ts_plot] \n",
    "        elif model =='mhm':\n",
    "            sr_sim = ds_mhm_flow_all.sel(hcdn=hid).to_dataframe()[obj_fun_ts_plot]\n",
    "            sr_sim=sr_sim*cms2mmd\n",
    "        sr_sim.where(sr_sim>0.0, 1.0e-7, inplace=True)\n",
    "        \n",
    "        sr_sim_mon=sr_sim.resample('M').mean()   \n",
    "\n",
    "        ax53[jdx].axvline(x=sr_sim_mon.index[119],color=[0.5,0.5,0.5],linewidth=1.25)\n",
    "        sr_sim_mon.plot(ax=ax53[jdx],\n",
    "                        color=clr,\n",
    "                        style=linestyle,\n",
    "                        linewidth=1.25,\n",
    "                        legend=lgnd[jdx],\n",
    "                        title = '%s: Monthly'%model)\n",
    "        \n",
    "        ax53[jdx].set_xlabel(\"\")\n",
    "        ax53[jdx].set_ylabel(\"mm/day\")\n",
    "        ax53[jdx].xaxis.set_tick_params(labelsize=8)\n",
    "        ax53[jdx].yaxis.set_tick_params(labelsize=8)\n",
    "        ax53[jdx].grid(color=[0.7,0.7,0.7], linestyle='-', linewidth=0.5)\n",
    "  \n",
    "    plt.savefig('../../Paper5_ffa/supple_figure/per_basin/Monlthly_mean_series_%d.pdf'%hid,\n",
    "            format='pdf', \n",
    "            dpi=200, \n",
    "            bbox_extra_artists=(lgd,), \n",
    "            bbox_inches='tight')\n",
    "    plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Annual mean series\n",
    "for idx, hid in enumerate(df_hcdn['id']):\n",
    "    \n",
    "    fig = plt.figure(figsize=(7.5, 5.5))\n",
    "    fig.subplots_adjust(left=0.095,right=0.975,bottom=0.095,top=0.935,wspace=0.25,hspace=0.45)\n",
    "\n",
    "    ax53 = [plt.subplot2grid((2, 1), (0, 0)),\n",
    "            plt.subplot2grid((2, 1), (1, 0))]\n",
    "\n",
    "    ax1.tick_params(labelsize=7)\n",
    "    ax2.tick_params(labelsize=7)\n",
    "    \n",
    "    cms2mmd = 60.0*60.0*24.0*1000.0/df_hcdn.loc[df_hcdn['id']==hid]['area'][idx]\n",
    "    for jdx, model in enumerate(model_list):\n",
    "        if model == 'vic':\n",
    "            sr_sim = ds_vic_flow_all.sel(hcdn=hid).to_dataframe()[obj_fun_ts_plot] \n",
    "        elif model =='mhm':\n",
    "            sr_sim = ds_mhm_flow_all.sel(hcdn=hid).to_dataframe()[obj_fun_ts_plot]\n",
    "            sr_sim=sr_sim*cms2mmd\n",
    "        sr_sim.where(sr_sim>0.0, 1.0e-7, inplace=True)\n",
    "        \n",
    "        sr_sim_ann=sr_sim.resample('A-SEP').mean()   \n",
    "\n",
    "        ax53[jdx].axvline(x=sr_sim_ann.index[9],color=[0.5,0.5,0.5],linewidth=1.25)\n",
    "        sr_sim_ann.plot(ax=ax53[jdx],\n",
    "                        color=clr,\n",
    "                        style=linestyle,\n",
    "                        linewidth=1.25,\n",
    "                        legend=lgnd[jdx],\n",
    "                        title = '%s: Annual mean'%model)\n",
    "        \n",
    "        ax53[jdx].set_xlabel(\"\")\n",
    "        ax53[jdx].set_ylabel(\"mm/day\")\n",
    "        ax53[jdx].xaxis.set_tick_params(labelsize=8)\n",
    "        ax53[jdx].yaxis.set_tick_params(labelsize=8)\n",
    "        ax53[jdx].grid(color=[0.7,0.7,0.7], linestyle='-', linewidth=0.5)\n",
    "  \n",
    "    plt.savefig('../../Paper5_ffa/supple_figure/per_basin/Annual_mean_series_%d.pdf'%hid,\n",
    "            format='pdf', \n",
    "            dpi=200, \n",
    "            bbox_extra_artists=(lgd,), \n",
    "            bbox_inches='tight')\n",
    "    plt.close()\n",
    "    break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## working in progress next cell, may be removed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# daily flow and annual maximum series during validation periods\n",
    "for idx, hid in enumerate(df_hcdn['id']):\n",
    "    \n",
    "    fig = plt.figure(figsize=(7.5, 5.5))\n",
    "    fig.subplots_adjust(left=0.095,right=0.975,bottom=0.095,top=0.935,wspace=0.25,hspace=0.45)\n",
    "\n",
    "    ax51 = [plt.subplot2grid((2, 2), (0, 0)),   # VIC daily\n",
    "            plt.subplot2grid((2, 2), (0, 1))]   # mHM daily\n",
    "    ax52 = [plt.subplot2grid((2, 2), (1, 0)),   # VIC annual maxima\n",
    "            plt.subplot2grid((2, 2), (1, 1))]   # mHM annual maxima\n",
    "    \n",
    "    ax1.tick_params(labelsize=7)\n",
    "    ax2.tick_params(labelsize=7)\n",
    "    \n",
    "    cms2mmd = 60.0*60.0*24.0*1000.0/df_hcdn.loc[df_hcdn['id']==hid]['area'][idx]\n",
    "    for jdx, model in enumerate(model_list):\n",
    "        if model == 'vic':\n",
    "            sr_sim = ds_vic_flow_val.sel(hcdn=hid).to_dataframe()[obj_fun_ts_plot] \n",
    "        elif model =='mhm':\n",
    "            sr_sim = ds_mhm_flow_val.sel(hcdn=hid).to_dataframe()[obj_fun_ts_plot]\n",
    "            sr_sim=sr_sim*cms2mmd\n",
    "        sr_sim.where(sr_sim>0.0, 1.0e-7, inplace=True)  \n",
    "        sr_sim_ann=sr_sim.resample('A-SEP').max()   \n",
    "        \n",
    "        sr_sim.plot(ax=ax51[jdx],\n",
    "                    color=clr,\n",
    "                    style=linestyle,\n",
    "                    linewidth=1.25,\n",
    "                    logy=True,\n",
    "                    legend=lgnd[jdx],\n",
    "                    title = '%s: Daily'%model)\n",
    "        sr_sim_ann.plot(ax=ax52[jdx],\n",
    "                        color=clr,\n",
    "                        style=linestyle,\n",
    "                        linewidth=1.25,\n",
    "                        legend=lgnd[jdx],\n",
    "                        title = '%s: Annual maximum'%model)\n",
    "        \n",
    "        ax51[jdx].set_xlabel(\"\")\n",
    "        ax51[jdx].set_ylabel(\"mm/day\")\n",
    "        ax51[jdx].xaxis.set_tick_params(labelsize=8)\n",
    "        ax51[jdx].yaxis.set_tick_params(labelsize=8)\n",
    "        ax51[jdx].grid(color=[0.7,0.7,0.7], linestyle='-', linewidth=0.5)\n",
    "        \n",
    "    plt.savefig('../../Paper5_ffa/supple_figure/per_basin/Time_series_%d.pdf'%hid,\n",
    "            format='pdf', \n",
    "            dpi=200, \n",
    "            bbox_extra_artists=(lgd,), \n",
    "            bbox_inches='tight')\n",
    "    plt.close()\n",
    "    break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Low flow analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pbiasLQ10_val = {}\n",
    "\n",
    "prob_val=np.arange(1,float(len(ds_vic_flow_val['time'])+1))/(1+len(ds_vic_flow_val['time'])) #probability\n",
    "for d in range(len(prob_val)):\n",
    "    idx90=d\n",
    "    if prob_val[d] > 0.9: break\n",
    "\n",
    "for model in ['vic','mhm']:\n",
    "    pbiasLQ10_array = np.zeros((nbasin, ndata))\n",
    "    r=0\n",
    "    for hid in df_hcdn['id']:\n",
    "        for c, obj in enumerate(obj_fun_list): \n",
    "            if model == 'vic':\n",
    "                sr_sim = ds_vic_flow_val[obj].sel(hcdn=hid).to_pandas() \n",
    "            elif model =='mhm':\n",
    "                sr_sim = ds_mhm_flow_val[obj].sel(hcdn=hid).to_pandas()\n",
    "                \n",
    "            sr_sim.where(sr_sim>0.0, 1.0e-9, inplace=True)\n",
    "            sr_sim_desc = sr_sim.sort_values(ascending=False)\n",
    "\n",
    "            if c == 0:\n",
    "                if model == 'vic':\n",
    "                    sr_obs = ds_vic_flow_val['obs'].sel(hcdn=hid).to_pandas()\n",
    "                elif model =='mhm':\n",
    "                    sr_obs = ds_mhm_flow_val['obs'].sel(hcdn=hid).to_pandas()\n",
    "                sr_obs.where(sr_obs>0.0, 1.0e-9, inplace=True)\n",
    "                sr_obs_desc = sr_obs.sort_values(ascending=False)\n",
    "            \n",
    "            pbiasLQ10_array[r,c] = pbias(sr_sim_desc[idx90:], sr_obs_desc[idx90:])\n",
    "               \n",
    "        r += 1\n",
    "        \n",
    "    pbiasLQ10_val[model] = pbiasLQ10_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pbiasLQ10_cal = {}\n",
    "\n",
    "prob_val=np.arange(1,float(len(ds_vic_flow_cal['time'])+1))/(1+len(ds_vic_flow_cal['time'])) #probability\n",
    "for d in range(len(prob_cal)):\n",
    "    idx90=d\n",
    "    if prob_cal[d] > 0.9: break\n",
    "\n",
    "for model in ['vic','mhm']:\n",
    "    pbiasLQ10_array = np.zeros((nbasin, ndata))\n",
    "    r=0\n",
    "    for hid in df_hcdn['id']:\n",
    "        for c, obj in enumerate(obj_fun_list): \n",
    "            if model == 'vic':\n",
    "                sr_sim = ds_vic_flow_cal[obj].sel(hcdn=hid).to_pandas() \n",
    "            elif model =='mhm':\n",
    "                sr_sim = ds_mhm_flow_cal[obj].sel(hcdn=hid).to_pandas()\n",
    "                \n",
    "            sr_sim.where(sr_sim>0.0, 1.0e-9, inplace=True)\n",
    "            sr_sim_desc = sr_sim.sort_values(ascending=False)\n",
    "\n",
    "            if c == 0:\n",
    "                if model == 'vic':\n",
    "                    sr_obs = ds_vic_flow_cal['obs'].sel(hcdn=hid).to_pandas()\n",
    "                elif model =='mhm':\n",
    "                    sr_obs = ds_mhm_flow_cal['obs'].sel(hcdn=hid).to_pandas()\n",
    "                sr_obs.where(sr_obs>0.0, 1.0e-9, inplace=True)\n",
    "                sr_obs_desc = sr_obs.sort_values(ascending=False)\n",
    "            \n",
    "            pbiasLQ10_array[r,c] = pbias(sr_sim_desc[idx90:], sr_obs_desc[idx90:])\n",
    "               \n",
    "        r += 1\n",
    "        \n",
    "    pbiasLQ10_cal[model] = pbiasLQ10_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/naokimizukami/anaconda3/lib/python3.6/site-packages/seaborn/categorical.py:482: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
      "  box_data = remove_na(group_data[hue_mask])\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgIAAAFNCAYAAAByowfoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nsDCCAAAAFkYQAADAwv4PrOe6m7dRgZMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c1c55ac50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Construct boxplots for metrics group-by periods\n",
    "model = 'vic'\n",
    "df_pbiasLQ10_cal = pd.DataFrame(pbiasLQ10_cal[model], index=df_hcdn['id'], columns=obj_fun_list)\n",
    "df_pbiasLQ10_val = pd.DataFrame(pbiasLQ10_val[model], index=df_hcdn['id'], columns=obj_fun_list)\n",
    "df_pbiasLQ10_cal['Period'] = ['cal' for _ in df_hcdn['id']]\n",
    "df_pbiasLQ10_val['Period'] = ['val' for _ in df_hcdn['id']]\n",
    "df_pbiasLQ10 = pd.concat([df_pbiasLQ10_cal, df_pbiasLQ10_val], axis=0)\n",
    "\n",
    "dd=pd.melt(df_pbiasLQ10, id_vars=['Period'], value_vars=obj_fun_list, var_name='OF')\n",
    "\n",
    "plt.figure(figsize=(6.5,5))\n",
    "sns.set_style(\"whitegrid\")\n",
    "ax21 = sns.boxplot(x='Period', y='value', data=dd, hue='OF', linewidth=1.0)\n",
    "handles, labels = ax21.get_legend_handles_labels()\n",
    "lgd = plt.legend(bbox_to_anchor=(1.01, 1), loc=2, borderaxespad=0.)\n",
    "ax21.set_ylim([-1.0,1.0])\n",
    "ax21.set_title('%s' % model, fontsize=14, fontweight='bold')\n",
    "plt.savefig('../../Paper5_ffa/supple_figure/pbiasLQ10.boxplot.%s.pdf' % (model),\n",
    "            format='pdf', \n",
    "            dpi=300, \n",
    "            bbox_extra_artists=(lgd,), \n",
    "            bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Flow component analysis\n",
    "How objective functions affect model flow components - fast (direct flow) and slow (baseflow) runoff."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Compute mean ratio of direct runoff to total runoff for each basin and each objective function\n",
    "ds_vic_ro_cal = ds_vic_ro_all.sel(time=slice('1999-10-01', '2008-09-30'))\n",
    "ds_mhm_ro_cal = ds_mhm_ro_all.sel(time=slice('1999-10-01', '2008-09-30'))\n",
    "ds_vic_ro_annual_cal = ds_vic_ro_cal.resample('A', dim='time', how='sum')\n",
    "ds_mhm_ro_annual_cal = ds_mhm_ro_cal.resample('A', dim='time', how='sum')\n",
    "    \n",
    "#Initialize dictionary\n",
    "ratio_cal = {}\n",
    "\n",
    "for model in ['vic','mhm']:\n",
    "    ratio_array = np.zeros((nbasin, ndata))\n",
    "\n",
    "    for r, hid in enumerate(df_hcdn['id']):\n",
    "        for c, obj in enumerate(obj_fun_list):\n",
    "            direflow_name = '%s_dir_flow'%obj\n",
    "            baseflow_name = '%s_baseflow'%obj\n",
    "            if model == 'vic':\n",
    "                sr_dire_ro = ds_vic_ro_annual_cal[direflow_name].sel(hcdn=hid).to_pandas()\n",
    "                sr_base_ro = ds_vic_ro_annual_cal[baseflow_name].sel(hcdn=hid).to_pandas() \n",
    "            elif model =='mhm':\n",
    "                sr_dire_ro = ds_mhm_ro_annual_cal[direflow_name].sel(hcdn=hid).to_pandas()\n",
    "                sr_base_ro = ds_mhm_ro_annual_cal[baseflow_name].sel(hcdn=hid).to_pandas()\n",
    "            var_temp = sr_dire_ro/(sr_dire_ro+sr_base_ro)\n",
    "            ratio_array[r,c] = var_temp.mean(skipna=True)\n",
    "        \n",
    "    ratio_cal[model] = ratio_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Compute mean ratio of direct runoff to total runoff for each basin and each objective function\n",
    "ds_vic_ro_val = ds_vic_ro_all.sel(time=slice('1989-10-01', '1999-09-30'))\n",
    "ds_mhm_ro_val = ds_mhm_ro_all.sel(time=slice('1989-10-01', '1999-09-30'))\n",
    "ds_vic_ro_annual_val = ds_vic_ro_val.resample('A', dim='time', how='sum')\n",
    "ds_mhm_ro_annual_val = ds_mhm_ro_val.resample('A', dim='time', how='sum')\n",
    "    \n",
    "#Initialize dictionary\n",
    "ratio_val = {}\n",
    "\n",
    "for model in ['vic','mhm']:\n",
    "    ratio_array = np.zeros((nbasin, ndata))\n",
    "\n",
    "    for r, hid in enumerate(df_hcdn['id']):\n",
    "        for c, obj in enumerate(obj_fun_list):\n",
    "            direflow_name = '%s_dir_flow'%obj\n",
    "            baseflow_name = '%s_baseflow'%obj\n",
    "            if model == 'vic':\n",
    "                sr_dire_ro = ds_vic_ro_annual_val[direflow_name].sel(hcdn=hid).to_pandas()\n",
    "                sr_base_ro = ds_vic_ro_annual_val[baseflow_name].sel(hcdn=hid).to_pandas() \n",
    "            elif model =='mhm':\n",
    "                sr_dire_ro = ds_mhm_ro_annual_val[direflow_name].sel(hcdn=hid).to_pandas()\n",
    "                sr_base_ro = ds_mhm_ro_annual_val[baseflow_name].sel(hcdn=hid).to_pandas()\n",
    "            var_temp = sr_dire_ro/(sr_dire_ro+sr_base_ro)\n",
    "            ratio_array[r,c] = var_temp.mean(skipna=True)\n",
    "        \n",
    "    ratio_val[model] = ratio_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Construct boxplots for metrics group-by periods\n",
    "df_ratio_vic = pd.DataFrame(ratio_val['vic'], index=df_hcdn['id'], columns=obj_fun_list)\n",
    "df_ratio_mhm = pd.DataFrame(ratio_val['mhm'], index=df_hcdn['id'], columns=obj_fun_list)\n",
    "df_ratio_vic['Model'] = ['vic' for _ in df_hcdn['id']]\n",
    "df_ratio_mhm['Model'] = ['mhm' for _ in df_hcdn['id']]\n",
    "df_ratio = pd.concat([df_ratio_vic, df_ratio_mhm], axis=0)\n",
    "\n",
    "dd=pd.melt(df_ratio, id_vars=['Model'], value_vars=obj_fun_list, var_name='OF')\n",
    "\n",
    "plt.figure(figsize=(6.5,5))\n",
    "ax51 = sns.boxplot(x='Model', y='value', data=dd, hue='OF', linewidth=1.0)\n",
    "handles, labels = ax51.get_legend_handles_labels()\n",
    "lgd = plt.legend(bbox_to_anchor=(1.01, 1), loc=2, borderaxespad=0.)\n",
    "ax51.set_ylim([0.0,1.1])\n",
    "ax51.set_title('ratio of direct flow to total flow - validation period', fontsize=14, fontweight='bold')\n",
    "plt.savefig('../../Paper5_ffa/supple_figure/flow_ratio.boxplot.pdf',\n",
    "            format='pdf', \n",
    "            dpi=300, \n",
    "            bbox_extra_artists=(lgd,), \n",
    "            bbox_inches='tight')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}