ffa-supplement.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('pandas version : ', u'0.20.1')\n",
      "('xarray version : ', '0.9.6')\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": 7,
   "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 1052500 1055000 ...\n",
      "  * time         (time) datetime64[ns] 1989-10-01 1989-10-02 1989-10-03 ...\n",
      "Data variables:\n",
      "    nse          (hcdn, time) float32 0.532613 1.11103 1.35535 1.28482 ...\n",
      "    kge          (hcdn, time) float32 0.923137 1.49746 1.74754 1.68964 ...\n",
      "    kge_alpha    (hcdn, time) float32 0.994104 1.35776 1.50395 1.45667 ...\n",
      "    kge_2alpha   (hcdn, time) float32 0.861706 1.42786 1.67888 1.62626 ...\n",
      "    kge_5alpha   (hcdn, time) float32 0.948378 1.52275 1.77234 1.71452 ...\n",
      "    kge_10alpha  (hcdn, time) float32 0.942386 1.5129 1.76088 1.70395 ...\n",
      "    sig1         (hcdn, time) float32 1.48747 1.70963 1.76399 1.69169 ...\n",
      "    obs          (hcdn, time) float32 0.620306 0.553696 0.711895 0.807647 ...\n",
      "\n",
      "mHM flow data\n",
      "-------------\n",
      "<xarray.Dataset>\n",
      "Dimensions:     (hcdn: 491, time: 6940)\n",
      "Coordinates:\n",
      "  * hcdn        (hcdn) int64 1022500 1031500 1047000 1052500 1055000 1057000 ...\n",
      "  * time        (time) datetime64[ns] 1989-10-01 1989-10-02 1989-10-03 ...\n",
      "Data variables:\n",
      "    nse         (hcdn, time) float32 5.87352 7.34512 9.51631 8.70196 7.76989 ...\n",
      "    kge_2alpha  (hcdn, time) float32 6.48915 8.53673 11.692 10.4205 9.03782 ...\n",
      "    kge         (hcdn, time) float32 6.82398 8.66997 12.0485 10.7206 9.27146 ...\n",
      "    sig1        (hcdn, time) float32 5.04871 5.95959 7.23878 6.731 6.19706 ...\n",
      "    kge_5alpha  (hcdn, time) float32 7.30043 8.81332 11.4428 10.4816 9.38536 ...\n",
      "    obs         (hcdn, time) float32 4.2192 3.7661 4.8422 5.4935 5.0121 ...\n",
      "\n",
      "vic runoff data\n",
      "-------------\n",
      "<xarray.Dataset>\n",
      "Dimensions:               (hcdn: 491, time: 6940)\n",
      "Coordinates:\n",
      "  * hcdn                  (hcdn) int64 1022500 1031500 1047000 1052500 ...\n",
      "  * time                  (time) datetime64[ns] 1989-10-01 1989-10-02 ...\n",
      "Data variables:\n",
      "    nse_dir_flow          (hcdn, time) float32 0.0 3.774 0.37 0.236 0.0 ...\n",
      "    nse_baseflow          (hcdn, time) float32 0.343 0.349 0.365 0.383 0.398 ...\n",
      "    kge_dir_flow          (hcdn, time) float32 0.0 3.935 0.379 0.231 0.0 ...\n",
      "    kge_baseflow          (hcdn, time) float32 0.64 0.634 0.769 0.836 0.828 ...\n",
      "    kge_alpha_dir_flow    (hcdn, time) float32 0.0 2.386 0.241 0.12 0.0 ...\n",
      "    kge_alpha_baseflow    (hcdn, time) float32 0.879 0.838 0.845 0.925 0.995 ...\n",
      "    kge_2alpha_dir_flow   (hcdn, time) float32 0.0 3.85 0.369 0.223 0.0 ...\n",
      "    kge_2alpha_baseflow   (hcdn, time) float32 0.594 0.595 0.743 0.797 0.775 ...\n",
      "    kge_5alpha_dir_flow   (hcdn, time) float32 0.0 3.949 0.38 0.235 0.0 ...\n",
      "    kge_5alpha_baseflow   (hcdn, time) float32 0.659 0.651 0.789 0.859 0.853 ...\n",
      "    kge_10alpha_dir_flow  (hcdn, time) float32 0.0 3.93 0.379 0.237 0.0 ...\n",
      "    kge_10alpha_baseflow  (hcdn, time) float32 0.651 0.643 0.782 0.853 0.846 ...\n",
      "    sig1_dir_flow         (hcdn, time) float32 0.0 1.964 0.198 0.101 0.0 ...\n",
      "    sig1_baseflow         (hcdn, time) float32 1.205 1.087 1.094 1.243 1.342 ...\n",
      "\n",
      "mHM runoff data\n",
      "-------------\n",
      "<xarray.Dataset>\n",
      "Dimensions:              (hcdn: 491, time: 6940)\n",
      "Coordinates:\n",
      "  * hcdn                 (hcdn) int64 1022500 1031500 1047000 1052500 ...\n",
      "  * time                 (time) datetime64[ns] 1989-10-01 1989-10-02 ...\n",
      "Data variables:\n",
      "    nse_dir_flow         (hcdn, time) float32 0.876 1.175 1.433 1.297 1.158 ...\n",
      "    nse_baseflow         (hcdn, time) float32 0.192 0.193 0.195 0.198 0.2 ...\n",
      "    kge_2alpha_dir_flow  (hcdn, time) float32 0.966 1.381 1.759 1.549 1.343 ...\n",
      "    kge_2alpha_baseflow  (hcdn, time) float32 0.154 0.155 0.158 0.161 0.163 ...\n",
      "    kge_dir_flow         (hcdn, time) float32 1.016 1.398 1.813 1.595 1.379 ...\n",
      "    kge_baseflow         (hcdn, time) float32 0.186 0.186 0.19 0.195 0.197 ...\n",
      "    sig1_dir_flow        (hcdn, time) float32 0.758 0.934 1.091 1.008 0.928 ...\n",
      "    sig1_baseflow        (hcdn, time) float32 0.426 0.426 0.426 0.426 0.426 ...\n",
      "    kge_5alpha_dir_flow  (hcdn, time) float32 1.091 1.4 1.724 1.565 1.401 ...\n",
      "    kge_5alpha_baseflow  (hcdn, time) float32 0.325 0.327 0.337 0.346 0.352 ...\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','kge_10alpha','sig1']\n",
    "# List of models used\n",
    "model_list = ['vic']\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": 8,
   "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": 10,
   "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 model_list:\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": 11,
   "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 model_list:\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": 12,
   "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": 13,
   "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": 14,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'mhm'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-14-bf6bfd945a5e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     46\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     47\u001b[0m \u001b[0;31m# convert numpy arrays to DataFrames\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 48\u001b[0;31m \u001b[0mdf_nse_mhm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnse_all\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'mhm'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdf_hcdn\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'id'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     49\u001b[0m \u001b[0mdf_kge_mhm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkge_all\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'mhm'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdf_hcdn\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'id'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m \u001b[0mdf_alpha_mhm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0malpha_all\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'mhm'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdf_hcdn\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'id'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'mhm'"
     ]
    }
   ],
   "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": 15,
   "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 - kge_10alpha: 1.65e-57\n",
      "nse - sig1: 2.38e-114\n",
      "kge - kge_2alpha: 1.28e-06\n",
      "kge - kge_5alpha: 1.38e-13\n",
      "kge - kge_10alpha: 1.29e-15\n",
      "kge - sig1: 1.33e-62\n",
      "kge_2alpha - kge_5alpha: 2.07e-02\n",
      "kge_2alpha - kge_10alpha: 2.40e-03\n",
      "kge_2alpha - sig1: 2.88e-55\n",
      "kge_5alpha - kge_10alpha: 5.37e-01\n",
      "kge_5alpha - sig1: 6.22e-46\n",
      "kge_10alpha - sig1: 2.50e-44\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": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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xzJo1q3oXZGdDdDSMHAmAVgv//ANjxkCmJBGk0TCpnrd6Uj9MpTStVFYVaaY6\nngKBpdm2bRujR4/GNj4eMjKgEg/9336DU6dkZ/xrydLruTs8nEy9nqX29g2WGrE2SJJEcWQx2ke0\nnLnzDO4PutM/tH+DGlEQhrRZ8M8//yBJEoMHD67eBUeOgIMD9O0LyM4I9vbwwAMQbzajBPq2aFFv\n/TWVmMjckInPmz44+NXv9rFA0Fwwm83s3buX8ePHy/u2Pj7Qo0eFdffuhbvugt69r3/tkchI1EYj\nx/r2xbuJCC+UJJQQPT2aIN8gTvU6hWSSGBA+AP/v/bFr1zBZqa6maXyKgipJTEykU6dOuLpWU3T5\n0CE5mMzamosX4ZtvYNYscHeHH/R6HvH0xKYeB1jykmTMejNeU73q7R4CQXNjz549ZGRkcM8998C5\nc1DJxFmvl1OkjR9//WvnS0o4XFDAj9270+FajcBGhiRJlCSWkPVzFmfGnqHgSAHt32rPwOiBOG53\npEVA/U32a4o4I20GhIWF0bNnz+pV1uth0yZYuBCQH1q2lLNB/JyVxRmTiVXt2tVbX3P+yCFleQrd\n1nbD2u3m6PcKBM2BDz/8kCeffJIOHTrA+fMVLzeRj2mKimDSpPLlwYWFvBAbS0c7OwLrccfJEqj/\nVRP5UCRGtRGVswq3u93ovKIzdj7y6rO2seb1hViRNgMOHz7MiBEjqld57145j9K0aZhM8Ouvshyg\ntTWc0GgYrFIxrCZi9zXAnGkmZnoMraa2os0My8asCQTNmbCwMA4ePMgLl91vExJkZ6MKWLMGxo6V\n841eJk2n477wcNrY2PBX796N2i9BkiQSXk3Adawrg+IGMUI9gp5be5YZ0caIMKRNnKysLM6dO8fI\nS45DN+TECTl+tGVLUlNBrZaTd4OcwNunnrZ0tbFaSl4qwc7Xjm7fCkF6gaAmrFixgrvuuotBgwZB\nXBzEx8vj+BrMZjhwAK6OZtqSlUW3kyfpYm/P1p496VrPYW11QZIkEuclUny2mM4fdcahi0OTkAwV\nhrSJ8++//+Lg4ECfPn1uXLm0VF6CDhkCQGQkWFmBr6+ch/CYRsNdVpbf7ZfMEuH3hkMLCNgZgMqh\nacasCQQNgU6n448//mDChAnySnL/fmjbtkJDunGjrKs7fLj83Gg282ZCAuM9PDjUt2+jTo2mOaXh\nqMdRUj9KpevXXbFr33hXoNcizkibODt37mTMmDFYVydf6KJFssv8m28CcPCgPOCsreGTxFQGOjkR\nKEkW72Penjx053U4bHHA1lvkFxUIakJGRgYFBQWMGzdOLkhIgJ49r0virdXKPg+zZ8sOvQA/ZGaS\nodezskuXRh3mokvREfNUDDZcFvSTAAAgAElEQVStbOgX1K/JefM33umJ4IaUlpbyyy+/MHHixBtX\nliT46SfZiF4SYTh0CPz84GJpKeszMpjXvr3Fz04kSSL5/WRaT2+NqqNYiQoENSUnJwcAL69LXu4H\nD0K/ftfV+/ZbeZ781lvy8xSdjudjY3m9fXs8bRqncpixwEjs7FhO9jiJykFF4IHAJmdEQRjSJk14\neDharbZ6SbzPn4fkZLjnHgAKC+Xj0ocegpVpaXS0s2N8PaTPyv41G02QhvZvtrd42wLBrUBcXBzd\nunXDxsZGXnaGhMhBotcQEyOrk3l7y8//Ex9PdwcHllTilNQYiPm/GLK3ZtP16670Pd633tOd1RfC\nkDZhgoKCcHZ2rl786LZt0KkTBAQAEBwMCgWMGCHxzcWLvOrjg9LCq1HNSQ0xT8bQbk47HLo1vVmm\nQNAYiIqKYsDl89CYGDCZ5K2ka9i5s2yezLGCArbn5PB118apHCZJEnl788j5PQe/1X60ntYapVXT\nNUdNt+cC1q1bx+zZs6tX+exZOYD70rnKn3/KvgqnjPkUGI3cWV0xhxpwftF5HHs50vH9jhZvWyC4\nVYiOjr5iSFevhj594JpY7/37ITW1zI+Qd5KSeNjDg+H1FMpWV1KWpRB+dzguo1zwmND0E8kLQ9pE\nKSws5MyZM9xVwRZPhURGyg4Klzh+XDakv2Znc5erKx0trHJSklSCep8an9d9hJeuQFBLiouLSU5O\nvpJ789w5WcvzqlWm2QzPPw/PPisfnUYXF/NPfj6v+TTOZBCFoYUkv5dMxw860md/H5TWTd8MNf13\ncIsSFBSEUqmU48puhMkEUVFlhvTIETh2DGbMgOMaDUPrIdNL+jfpOPRwwGNi059tCgQNRWxsLJIk\n0aNHD1mV7NSpK8vOS2zaBImJslO+QgFrMzLws7dnSCNbjZp0Ji7+7yJht4XhNNiJtrPbNnSXLIYw\npE2Uo0eP0q9fPxyqE1x98qTspHAp6ffatXKeQmX3QkKLiphs4Uwvklki76883O93b9LnHgJBQxMa\nGoqrqyvu7u7yalSvh8ur00t88om8GvX2hnitlk9SU3mqdesG6nHFSGaJ4L7BxM+Nx3uWN4H7ArFy\nbj7Rl83nndxiHD16lOGXo65vxKefwrBh0LYtej3s2wevvAKLz59niLMzPRwdLdq3jPUZlMSX4D3T\n26LtCgS3GqtXr76yrRseLoeueV1J9hAaCmfOyMYUYK9ajbeNDW+1b1xe8snvJaON0zIkcUiTElqo\nLmK50AQxGo0cP368evq6aWnwyy9yll/kSW1aGowdb+TP3FwWW9g1XpIkUlek0nZ222Y5YASCm4Va\nrSYkJITHH39cLjh3Tk6bdtX56H//C6NGwe23y89/y8nhXje3RuWpm7kpk+QPkun2Xbdm+50gDGkT\nJD4+nuLiYoYNG3bjytHR8sC7pMV76BA4O0OJRwkSMNDJyaJ9KzpThDZKi/fzYjUqENSFAwcO0KJF\nC7p1u6RNnZ4uSwNeIjcX9uyRDalKBWeLivhHrWaShY9q6oL2nJZzz52j3Zx2tJ7euLabLYkwpE2Q\n8PBwOnbsSOvqnIN8+63snntJ2WTTJnj4Ycgx6rFRKHCxsLZuwcEC7DrbYd+xcec6FAgaO8nJyXTo\n0EEWYgB5Rep9ZYK6YAEUF8MTT8jPN2VlMcDJibvd3Bqgt9djOmPiVOApWo5oScdlHRvVKtnSCEPa\nBImJiWHgJcehKikulgNG33gDkDXrT52CyZMhQ6/Hy8bG4v/cmhMaWg5vXN6CAkFTJDg4GH9/f/lJ\neLjsbj9lCgB5ebB9u3xi06WLXGVvXh73urs3UG/Lo96vRvuyFo9xHvTe3bvZOx0273fXTImNjSUw\nMPDGFf/5Rw59uRRrevYsGAzyAvVIQQGdLRw7KkkS6n/UOA207HaxQHCrodPp2L59Ow8//LBc8Oef\nEBhYlvFl+3a5eOZM+TFWqyWkqKhehFVqgllvJu2LNM7ceQar263osblHs16JXkYY0iaGTqcjLi7u\nitJJVaxbBxMmwKV4stOn5VzAbu4Sv+fk8LiFz1L0mXoMmQZcbnO5cWWBQFApl3W0x44dKxckJ0PX\nrmWvBwXJmZscHORUafeGh9O/RQsGW9jnoaakfJRC4uuJtH25LXYL7FComr8RBWFImxxr167Fxsbm\nxkIM2dnwxx+y6sIlTp+WJ7TntFpy60EWUL1XjaqlCofuQldXIKgLMTExtGnTRtbRNhph925ZKxvQ\n6WRd3aFD5bpHNRrO63Rs69ULqwbON6o5qqHNc23wW+mHwurWMKJQjThSnU7Hnj17CA0NpaCgAFdX\nV/r27cudd96JvYW3BgVVo1arefvtt3niiSdwcbnBqu+PP8DVtVyWiKAg2TFhV14enezs8LWzrCv6\nxe8u4jXVC6WNmJ/VJ2JMNn+OHj1adj5qk50tC+k+9hgAX34p66s895xc96fMTEa7uNDewuO5phSG\nFaL+W03bl5uPYlF1qdKQbty4kZCQEAYPHsy4ceNwd3ensLCQ6Oho5s+fT58+fXjyySdvVl9vebZs\n2YKdnR0zrlplVkheHixeDKNHy37xyMelUVGyotEyjYbRLi4WPbsw5BooOFxAp+WdLNam4HrEmLw1\n2Lp1K++++y4AVrm5cmGXLkgSrF8vKxk5O4POZGJLdjafXfY4aiCSlyaT9E4SHg964HZP4/AavplU\naUj79+9/JRj4KgICApg8eTIxMTH11jFBeSRJ4tNPP+Xxxx/H2tq66spbt8pT1lWryoqCg+UMan36\nQNQpLf9nYQmxnN9yUNopceovHI3qEzEmmz9paWnk5+cz5JKmrnVODjg5gaMj52Jkp8GtW+W6u/Py\nKDWbeagecglXB8kkEf9KPOlfp9N9fXe8pnndEs5F11KlIe3evTsA3377Lc888wwKhYKioiI++ugj\nlixZUvb61YSEhLBlyxYA5s+fj7OzM3///TdbtmzB3d2dGTNmcPr0aRISErC2tuaty+ncBVUSGhpK\nXFwcM2bMoLS0tOrKJ07IUidXSYkdPy5L7RrNZhJLSuhk4S3AC19eoPXTrVHaim3d+kSMyebPsWPH\ncHR0LPODsMnIKEubtnOnHO5y+c8cpNEwvGVLnCwcD14dTFoTUY9FUXCogMB9gbiMunWdDKv1radQ\nKJgxYwbbt2/nqaeeKpspVcTWrVtZsmQJjzzyCLt27QJkDzQPDw/s7e1p3749p0+fZuHChTg5OZGY\nmGiZd9LMOXr0KH5+fvTq1avqiqmp8NtvVzTDAI0G9u6FsWMhvLgYrdnMEAtmfDHpTBRHFt+SWzoN\nhRiTzZcjR44wdOhQlJcch2wyM8HXF4CwsCvJXyRJYkduLn4NdC6e8HoCBUcLCNgZcEsbUaimIZ00\naRJ2dnZ89tlnjBgxgnsup2GvAJPJhK2tLZ6enmRnZwMwduxYFi9ezJgxY9i2bRtWl2ZPrVq1Kqsj\nqJrjx49X+WVZxsaNcrjL9OllRRs2yG7y48fDfrWajnZ2eF1WS7EA2b9ko7RVirCXm4gYk82X1NTU\nK7KAgN3582WqC0eOlIWSEqTREKPV8moD5B01G8zkbM+hw6IOtBwmBFiqtR8wbdo0Zs+ezZdffsn6\n9et57LHHyraKrsXe3h69Xk92djYel/btIyIi8Pf3x9nZGZPJVDbTysrKupLZ4AYEBwdXq15taOxt\nG41G9uzZwwsvvFDWXmXtdtu4kZL+/UmJji4rW726O/fdV0hExAU2FBczysqqyn7VpM9SoUTxvGKU\ngUrOxJ25Yf3G/lk3RNu1QYzJxtWuJduOiIjAx8eH4OBglEVFBJ48SdKYMaQcDCU5uQ9OTuc4fbqI\n/9Nqud3KivyoKOpy55r026w2U/p5KeYIM+Y8Mxk9MsgMzrRI2zWlUY1JqQrMZrMkSZKkVqvLlZ8/\nf77c61dz9uxZ6c0335Tmzp0rbd++XYqLi5P27NkjzZs3T3rrrbek/Px8aevWrdKiRYukZcuWVXX7\nMk6fPl2terWhKbS9YsUKyd7eXrp48WLl7ZaUSNLMmZIEkhQWVlZsMkmSvb0k7dghScVGo2Tz77/S\n5sxMi/U5/o14KcgvSNKr9Tes2xQ+68bethiTja9dS7atVqslhUIhHTx4UC44eFAe01lZ0t69kmRl\nJUlarSSFFRZKHDggRRUV1el+Nel3zq4c6aDDQSnIL0hK+SRF0oRqLNZ2TWlsbVe5Il2yZAk9evRg\nzJgx5cqdnJz46aefiI6OZsml9FyX6dWrF8uXLy9X1qVLlysKHcjbUoLq8+WXX/LWW29VLVL/1FNy\n0Pa2bbKU2CUSE6GkBPz9YX1GBkqFgnstJGpt0pnI/TOXVlNaYe1yA09igUUQY7J5s3fv3nKORvz5\nJ0UBAbTw9OT0aejdG+zt4UCqnHfU38K5hCvDrDeTsjQF56HO9NreCysnkcr6aqr8NF5//XVOnTrF\nokWLSEpKwsrKCpVKRdu2bXnooYd47FKAsKD+OH78OAkJCVWegZGSAlu2yCIMDzxQ7qUTJ8DNDTp3\nhk9PpPJy27Y4W8DDT5IkIh+JpDStlDZPt6lze4LqIcZk82bfvn0MGTIEu8viCv/8g2bwYFoAcXFX\nvHX35+cz7iYK1Mc8FYM2TkvfQ32FEa2AKj+RmTNn8sMPPzBq1CjWr1/PU089dZO6JbjM4cOHCQgI\nqDrby65dcnT2/fdf91JQkOzld7QgnwSdjictFD9acKiAvN159DvRDzvf5pmstzEixmTzxWAwsGHD\nBlauXHml8Px5dA8/jCTB0aPyxlOKTsd+tZoNlzPD1COSSSJudhxZP2URuD8Qh65C/rMiqjSkkiSV\n/f7PP/+IQdsAREZGEhAQUHkFSYLVq+GFF+QE3tcQFATjxsl6nL0dHelpoa2gjB8ycOrvhPOAisNo\nzGYzRqPxunK9Xm+R+1dEY25bqVSWecbWBTEmmy+///47Op2OcePGyQWFhZCbS6m3NydPQmysrBK4\nOy8Pd2trJtSzCIM+R0/E+Ag0JzT4b/LH9Y6GzSzTmKky/OVWVKhoTOh0Ovbv3191ppfvv5cT/lYg\nC5eXB6GhcNttEKfV4u9gmdmkJEnk/J5D2xcr1tSUJAmNRlPuSx+gc+fOFrl/RTT2tvV6PYWFhXVu\nR4zJ5ktERAT9+vWjTZtLRyVJSQDovb354QcYNQrwKmF5SgojWrZEWc//C0nzkzBkGxgYMRCvx7xu\nfMEtTJVT5OTkZJYtW4YkSWW/X0aon9Q/Bw8eJCMjg6effrriCjExsujm7NnQo8d1L2/fDi1ayOmW\n3ggv5kELnanEvxKPucRMy5EVx48ZjUbs7OywtbUtV24wGLCxYPxqU2rbxsYGrVaL0Wis08pUjMnm\nS3BwMEMvp3QBSEgAR0dKWrixeTN8/DF8ceECdkol316VUq0+UO9Xk/VTFp0/7Yxj95vj0NSUqXJE\nf/zxx2W/33nnnfXeGUF5fv31VwIDA3GuSIXIZIL77oP+/eHDDyu8/t9/5SrW1nLiX//27evcJ22s\nlvQv0+mxuQf2nSpWVJEkqSwuUXAFlUqF2WyuUxtiTDZPJEkiODj4yrYuQEgI9O1L+kVb8vLgzjvh\ny6x8HmvVihb1KAmo/ldN5ORIXMe60nq6ZTW5mytV/jVumPNSUG/s2rWL//3vf/z0008VV0hPl7d+\n/voLKlgtGQyyD9Ly5ZCm01FgMtHdAlu75989j0M3BzwmNoxIdlPGEtuyYkw2T1JTU7l48SK33Xbb\nlcLISOjdm8xMG1QqsPEwEJxYxOd+fvXWD8ksET0tGrd73ei2phtKazEhrg7iU2qkvPPOOzz77LNM\nnjy54gonT8pLzUsanNeSliafkY4ZA+d1OgDaX7PVWlO08VqyNsvbPeKsTiCwHKGhoTg4OOB32UhK\nkpz3sEMHUlNt8fWF4BINVgoF/Vq0qJc+mI1mEl5NwJBjoOP7HVE5qOrlPs0RERDUCCkoKCAkJITP\nP/+88kqXY0YrMY5//AFWVuDjA9szCvF3cKhzhoiM9Rk49nLE9c6aee/l5ORQVFSEVqvFoRar4hYt\nWpRJ213LqlWryMvLw9bWlpKSErRaLS4uLjg6OvLSSy/x8ccfYzQaKSwsZN68ebi6Cs9DQeMjLCyM\ngIAAVJfyBxMdLTsRPvAAKR/Y0bmLxGsJCTzo7o6dqn4MXPyceDI3ZNLtu27YdxAJ4muCMKSNkL//\n/hulUknPnj0rr7RtG3z2WYUvSZK8pTt7trxoPZyfz7A6Znsx6Uxk/piJ93PeNVqN6nQ6fH190Wq1\ntb63g4MDubm5V4LUr2Hs2LH07t2bWbNmYWVlxbBhw+jZsydHjhwhPj6e7t27U1xczJkzZxg9enSt\n+yEQ1Bdnzpwpn9np11/BxQW6dyc4WMvwpwvZp9Wys6pQuDpQcLSA9C/TCfgzAPf7b57QQ3NBGNJG\nyNq1a5kwYQIuLhVnU3E6eRKKi2HkyApfz8uDixflBDCSJHGooIAv6niuUnC4AH26Hu8XvWt0nZ2d\nHcnJyXVekVZmREEWZQf5vb7++uukp6ezcOFCZs6cSUBAAHPmzOHUqVO4WUgaUSCwJAaDgd27d7N2\n7dorhbt3w3PPcSFdQUyMIx2HRDPY3snieYQBDLkGIidH4vGwhzCitUQY0kZGfn4+e/bs4Y8//qi0\njsf27fDQQ1CJcUxJkR99fSFTr0dtNBJQRyGG/AP5OPZ2rJWmroeHBx4eHhQXF+NYz9qgn3/+Od7e\n3vTq1YsRI0awc+dOFi9eTG5uLkuXLq3XewsEteHs2bPodDruuOMOucBkgogImDuXnTvBtYOWv4yZ\nfNXG8iEvhn0GgpYHYe1uTbdvut34AkGFCEPayAgJCcFkMl0ZVNdSVITTqVPy3m0lhIZCq1byztBf\neUWooE7Jf80GM2mfp9HupXa1bqO+eOmllwAoLi7mhx9+uO71q8NFBILGyMGDB2nfvj1eXpdED86d\nk1WNhgzh0DzodlcOQcATXpYVRTCoDZSuKqXNtDZ0/rgzKnvhXFRbhNduI+PDDz/k9ttvv07MoIwX\nX0QhSTB1aqVt7N4Nd98tKwZ+d/Ei4z086uSgYMwzYi420+rxVrVuQyAQXI9er+eLL74on30nLg7s\n7KBtWy5ehNKuhXSxt8fBgk5Gklki7LYwFDYKOizqIIxoHRGGtBERHx/P3r17WbFiRcUVTp+GH37g\n/KJF4ORUaTvBwbKaEcAJjYb76qhopI3VgoJKBRgEAkHt2Lt3L+np6bz++utXCk+ehAEDKNYqOHgQ\nVJ2KGFJHZ8FrKThcQHFEMXaf2GHjUT+KYLcSwpA2Inbv3o2Pjw99+vSpuMJnn8GYMRRU4mQE8q5Q\nUpJsSC+UlnJBr2dQFUa3OujT9Vi5WIm4MoHAwoSEhNC9e/cr27ogz4QDAjh6FCQrM3FuOovGjmZv\ny+bsuLO0HNkSVQcxpi2BMKSNBEmSWLp0KePHj684vCQ6Ws45OmtWle1s2QI9e0KvXrAvLw9Pa2t6\n1NHBJ3NDJo69hd6mQGBpjh49Wt4fQpJkQzpsGFu2wPDJJRQgMcnT0yL3M+QZiJ0Zi/s4dwL+qJ9Q\nmlsRYUgbCefPnycjI6PMeeY6Tp6Eli1h4sRK2zCZ4H//g8cfl59vzMpikJMTqjqoEElmifyD+Y3S\n0QhkQYawsLCG7oZAUCuioqLKx49GR0NODsZBw/j9d/B5UI2nQoF3HVXJLpP0ThJKeyV+q/2wail8\nTS2F+CQbCZGRkdjZ2dGlS5eKK4SHQ58+FeYcvcxnn0FqKkybBkazmWMFBWyqICtMTShJLMFUaKJF\n3/qRJbMEZrOZ5cuX4+fnx+nTp3F3dycoKIjdu3fzxRdfkJ+fT0FBAbNnz8a3EklFgeBmo9FoSEtL\nw//qBN2HDkH79hy92Am1Ghy6ltC9QGmRlGkZGzJI/yadHht71CqMTVA5wpA2EmJiYujatWvFWVOK\nimDTJnj55Uqvz8uDN96A776TZQF/y86lxGxmRMuKU51Vl6yNWVi5WWHXoXJBhBuSkwNFRSi0WqiN\ncH6LFlBFEuP333+fe++9l4iICN5//32cnZ0ZP348CQkJHDlyhAEDBmA0Gjl58qQwpIJGQ0xMDEB5\nQ/rbb3D77YSHQ9eucIESfCyQSSnj+wxi/i+GDos74DnZMtvEgisIQ9pIuGxIK+TLL+XHKgzp2bPy\n42OPyY9LkpN5tk0b3K1rP/MsvVBK2qo02r/ZHoWyljNinU5WhtBqqXXuGQcHyM2VQwIqYPr06fzy\nyy+0bNmy7HxZqVRiNptp3749r732GjExMeguifcLBI2B6OhovL29aXl5snvxIuzbB7/+ysEN4N9D\n4oxWy0N1NKS6ZB3nnjuHz2s+dFjQoe4dF1yHMKSNhOPHjzNjxozrXygthW++kZ2MqnAaOngQOnUC\ne3soNZtJLCnhVR+fOvUp8tFIHPwc8J5ZM1nActjZQXIy1EEikBYtKjWiAL6+vjz77LOsW7eORYsW\n4eHhgVarxc/PD2dnZxYuXEh2djaLFi2q/fsQCCxMWFgY3bpdpSa0eTN4e8O4cZx8GZ5ZomW7Tkfv\nOqQ/lEwS0U9EY+9nT6elnSzQa0FFCEPaCEhLSyMqKoqR14a1FBfLnkOFhfD885Vev3YtLF4Ml5PF\nHC8ooMhk4t46aMtqTmrQHNUwMGogVs51/Dfx8AAPD6Ti4ionA7XhamUjSZI4dOgQCoWCxy4tzRcs\nWGDR+wkEluDMmTOsXLmSTz755ErhDz/AtGkUlahITYX9HZPwt3egey1XpOZSM/H/iafgcAEDowei\nUInUh/WFMKSNgC1btuDr61s+aXNREYwaBfHx8nZPq4pVhXQ6mDMHFi6EF16Qy+JKSvC2tcWtDtu6\nOTtycBrghKN/0wl7GTVqFKNGjWrobggEN2THjh306dOHuXPnygVFRRAWBp99xrlzclGclYYPfDqi\nTE+v1T3SVqWRtSWLHj/3wLF70xnHTRER/tII2Lx5M1OmTCkfP/rhh7IRDQmBqw3sNRw4AHo9vPoq\nXJ64ntNq6VmHlZ9JayJ7azZu94hsKQJBfRAbG0vA1SnRIiLkxz59iI4Gzw5GLhr0+NdyW1cyS1z8\n5iLt5rSj1SNC2rO+EYa0gYmNjeX06dNMmTLlSmFCAqxaBR99BJ07V3qtyQRLlsgqRpeFTyRJ4rhG\ng08t487MF8wcb3scQ56B1v/XulZtCASCqjlz5gx+V2dvCg2Fjh2RnFty8iS43ZuLs0pF71oqGqn3\nqSlJKqHNM20s1GNBVYit3Qbm7bffZsiQIQQGBl4pnDNHTpH29NNVXrtnDwQFQWzslbIMvZ5jGg1L\nOnasVX8MfxqwcrWi75G+2HpbJghcIBBcIT8/n7Nnz7JmzZorhSEh6AIH88BdsuPgkJ/z8XB0rJVQ\nfUlSCZGPRuI50RPbtmIM3wzEirQB+ffff9m2bRurV68uv6175ox84GlV+TxHkuCtt2DGjPJpSX/K\nyqKFSsVttYgfNWlN6DfpaTennTCiAkE9sXnzZhQKRXlFo9BQfrV6lFOn5FC27Db5PFwLWUCz3kzs\n87HYeNngv9H/xhcILILFV6QhISFs2bIFgPnz5+Ps7MyOHTsICgqisLCQBQsWsHv3bkJDQ7G3t2fu\n3Lm0qsSRprmzfPlyHn30Ufr163elMCoK0tJg6NAqr921Sx5wv/xypcwsSezIyWGypyfWtfD0S/s8\nDVTgNd2yeQ8FDYsYk40Hs9nMsmXLmD17Ni0ub9uaTBAZyb/u/bjjDsCnmHMZJdzp6lqjtiVJInRE\nKIWnC+m9uzdKG7FOullY3JBu3bqVJUuWEB4ezq5du5gyZQqOjo4sXbqUffv2ERISQlRUFC4uLjg6\nOuJRhWJNc6a0tJTjx4/z5WWxhcts3QoBAeBf+WyyoEDF1KnyovXq1eiylBROFRbyZWXCDlVQGFZI\n8nvJ2M60tbh82CVhI7RahcWFjVatWsXIkSPLnzfVgNLSUhYuXIitrS35+fksWbIEFxeXcnVOnz7N\nuXPneO6558qVp6WlsWbNGpYsWVKre98sxJhsPBw4cICUlJTyadOOHYPSUg4leDPzRfhbrcbbxoaA\nGp6PFhwqoPBUIQPODKBF78Yr6dkcsbghNZlM2Nra4unpSVBQEABjxowhLy+Pffv2sWTJEtq0aUNg\nYCBbt27l8OHD1QpZCA4OtnRXG7Tt3377DZPJRKtWrcrV8f/pJ/JHjeJiFX0KCnLFbDby5JNnuFwt\nw2zm3eJi3rC1pSQmhpq8I0mS0D6lRTlYifVj1hb5PDp37ozBYLgkbOSAVquAWmobOThIpKRoK9Rk\n0Ov1aLVali9fjq+vL6Ghobi6unLq1Cm2bdvGmjVrKCgooKCggOeee4727duXuz4zM5MHHniAfv36\n8eOPP3Lq1CnS0tJISEggOzube++9F1dXV/R6PStWrCA7OxsfHx+sra25/fbbCQ4OZsGCBVy8eJGl\nS5eya9euctfeddddZfcyGAwkJCTU6jOoC2JM1n+71W175cqVjBw5kqysLLKysgBo99VXpHadQOw5\nK9w8o3g1IZsHrMuPwxu1bQwxUjKrBKvRVpwznKMmXwAN/Zk0xrZrisUNqb29PXq9nuzs7LKZbWpq\nKmvWrGHBggXY2dkRGRlJnz59cHZ2xmg0Vqvd/v37W7qrgPzHuNltS5LExIkT+c9//lPui5Z//4XY\nWBw2bMC7ij69/XYeY8daMXjwlToTIyLoLkm8P3BgjQWuk5cnkxSZROB3gcQr4uv8eej1egBsbGxw\ndCwTNqq1slGLFgrc3SsO57GxseGTTz7h3nvvJTY2lmXLlpVp7WZkZHDixAkGDBgAQERERHldU6BT\np0506tSJ8PBwEhMTeeaZZ4iMjKRnz56EhYVx4sQJ7rnnHmxsbJAkiUceeYQBAwYwZ84c7rrrLrp2\n7cr777/P6tWrSU1NpWLmH/YAACAASURBVF+/fuWunTBhQrnPJSAgABubK4mUb8aXgRiT9dtuTdqO\njo5m9uzZV+qq1XDgAGv7/Yy/Erzva01+eBafDBiAx6X/kxu1XZpRSvhT4XhN9cL/+5qdizaGz6Qx\ntl1TLG5IJ0+ezMKFCzEYDIwcOZL4+Hjeffdd3N3dWbJkCZMmTUKSJBYtWoQkSbek8kxOTg6pqalM\nmjTpSmF6OkyeDJMmwcCBlV772Wewf78r27ZdKdOaTBxQq/nCz6/GRvTCVxdIXpJMh8UdaDmkZY1m\nstXlkrARxcWSpYWNgLpr7W7evJmMjAyWLl2KlZUVn376KdOnTycgIIDExMRydSVJAsBoNKJUKnG6\nlDTd2toak8nEqlWrKr22oRBjsnGQkZFBfHx8eQWzdetApSLUbgiDh0i8FBfH/e7uZUb0RqR/l07c\ni3E49nCk03IhAdhQWNyQ9urVi+XLl5cr27hxY7nngwcPtvRtmxQ//fQTrq6uV3Q2CwvhjjugQwe4\n5rO6GpMJPvkEpk3LZNy4KzGen6WlUWI284C7e436UZpRStysOHwX+tJ+XvsbX9BIqYvW7smTJ1m9\nejWDBw9m3rx5TJ06FTc3N44fP45er6eoqKhc/R9//JG9e/cyYsQIVBWEJlR1bUMhxmTj4PDhwzg5\nOZUPdfv+e5g+nUP/s+LNj0pZX1LC91X4R1xGkiTSPksj4dUE/Fb54T3TW0gANiAijrQB2Lx5M88/\n/zx2lw/91qyBrCw4ehSqkPVbv162uY88ko1CIRvSpJISFp0/zyo/P1xqKAmYfyAfhY0C33d8UVo1\nTQ+/umrtDho0iP9n787joqreB45/hmHfVEAEUVFxF/etLHfLrUxLTc00l3Irf6Vpln7d0zJTK9PS\nLLfKfU/cK8FdFhEFRdmRfR+YYbb7+wNFETBRZAY47168jHvv3HnmDmeeufee8xxvb+8Cywr0or63\n727duvHDDz8wbtw4Wrdunb/ufkej+x2RREISinPp0iU6der04AuYVguBgfhP/YWEBLjcMIrGJla0\nfYJORglbErgz/Q6Nf2mM63hRdMHQRCItY3FxcVy+fPlBT8/gYJg/Hz7+GP7jjPLChbwTV1dXdf4y\nn4wMbOVyPnAteWOKXRNLjVE1ym0SfdR/1dqNjY3lwIEDBZZ16dKlYKm2x7iftAXhaVy9erXg2NF7\nQ5L2X61H27bgQzKza9V57NA1XbaOuF/juP3xbVzGuogkaiREIi1DkiTx8ccf4+npSdeuXfMWzpiR\nV+Nv4cLHPvbmTdi2DTZseLAsW6djRXQ0fRwcChZ0eAJZvllknsvEY3nxJQgrGjc3N6ZMmWLoMIRK\nKiAggDFjxjxYsGIFTJjA6UAnWgxSsDk3lxft7Yt9vPKOkittriCTy6g9vba4J2pERCItQ/v27WPn\nzp3s3bs3r+fm7t1w/Dh4e8NjSoFJErz9dl6+feedvDr2ANsTE7mtVLL34W+5Tyj5YDLWTayp8lLJ\nKyAJglAyCQkJJCUlPeg1fvs2BARwcOw+zm4Emy+jaGZpTYtieuNJOombE24it5HT6U4n5NYlLx0o\nPD8ikZahM2fO8MorrzB48OC8KgUTJ8Lcuf9ZxcjHJ69q4K1bcP/EMzQnh+m3bzOtVi08rKxKFIc+\nV0/yvmSqDy15CTJBEEpu586dODs707x587wFBw9CgwYcvOrO669DThUNA2wdMS3isq6kkgj9KJSM\nsxm0820nkqgRqhg3x8qJwMDA/DGN/PwzmJvD7NmPfYwkwTffQOvWBasYbY6Pp66lJV8+RXH6iMUR\nZF/LxvG1kvXyFQSh5HJzc1mzZg1vvfUWFvdnZbpwAdq3JzxChq5nPCfT0oq9rKtapCL5QDItDrfA\ntoWoWGSMxBlpGYqOjubtt9+G3FzYvh1GjKDIcj33SFLeDC+HDsGpUw+Wp+j1rL17lxm1a5d43Kik\nk0g7lobbNDfsOxZ/P6Y0JavVKHQ6cnJzsX6KGsC2cnmx4+qetUQgwKRJk/LLAk6ZMqVQ9aPyXiJQ\nMKxDhw5x69YtfHx88hbk5MCpU0RM/57TS7SYzA1hoqsrg4oozaiKVKH9W0vznc1xeFXMD2ysRCIt\nIxEREYSHh9PQwyPvRmdMTF5P3cdYvTqvL9LLL8PDRTx2azTYyeXMqFWrxHHE/RJH9vVsmu1sVuLH\nPg2VTof7hQvk6PVPvQ9rExNSXnoJy2LuI+v1er766isaNmzIlStXcHR05MKFC3h5ebFmzRrS09PJ\nyMjgww8/xN3dvcBj1Wo1UVFRODs7Y2tri5ubG7///juhoaEkJiby+uuv51dj+uGHH0hISMDd3R0z\nMzN69+6Nv78/CxcuJDY2lpUrV3LgwIECj+3Xr99Tv26h/AsNDWXkyJHMmjWL6vdnc9m2DXJyWHbr\nLZxHJ5AlN2GFh0ehDoM5N3O4+upVTNxNxNUjIycSaRlZunQpDg4O9PD1zbs/cvky1Cm+CMK0aXlz\ney9enHcb9WHeWi2ja9cuNrEUJ/NyJrem3qL+svpY1SvZfdWnZSmXE/nCC3lnpEol1iW8nwt5Z6SP\ne61LliyhX79+BAUFsWTJkvwSgXfu3MHHx4f27duj1Wq5dOlSoUSq0+lYtmwZrVq1Ytu2bRw5cgRP\nT08aNmxIQEAAZ86coW/fvvnbDxo0KL9EYLdu3WjQoAHz58/nxx9/JCQkpNBjRSKt3NavX4+bmxtf\nf/31g4Xr1nF1xFdsOALS9lBW1PPA9pEpE9UJaoLeCsLMyQzTpaYVZohaRSXenTJw9OhRNmzYwM6Z\nM5HNmQNLlsDD1U0eceMGrFkDS5fmzTn6sJ2JiYTq9Qwr4VyFuhwdMStjsPG0oc7Msq1i5GRuTl0r\nK9wtLKhrZVXin/8qlzZmzBjOnDlDTk5OsSUChw8fXuTl35SUFGJiYgCoWrUqGo2GlStXolAoaNGi\nRX5JwPv+q0Tg4x4rVD5eXl68++67Dxbk5EBAAL9mvEmVZSE4mpoyqWbNQo+LXReLLkuH5z5PTJzE\nx7SxE2ekZWDz5s183qYN3efPh5Ej4dNPi91Wr4fXX4d+/WDmzIKjYiRJYmpoKIPNzEo0xVKWbxbX\nh11Hn6On0fqST7Fm7J6lRGCNGjU4c+YMgYGBKBQK5s6di7e3d4UqESgYxo4dO7h+/Tq7H540+NAh\nsLLicBVT0uul4+3ZGptH/o6Ud5TEb4rHZYwLlnUsIamMAxdKTCTS5+zatWtc276dLZaWMGECfP/9\ngzEsRVi9Ou/26Zkz8MjVHibcvEmyRsOoElZ+DxkbgpmTGa2Ot8K0SsV6y5+1RKCZmVnBy27AqlWr\nCvwuSgQKT+PIkSMMHTqUJk2aPFh4/jyHm04jbPAd3jB34eVH5r4FCB4VjImFCW4fuZVhtMKzqFif\nqkZGpVIxaeJEfrCzw6xVK1i16rFJ9OLFvM5Fv/8Obo+0oRydjt8TEljdoAG1EhKeOIbMy5lkX8um\n7YW2FS6JPkqUCBSMhb+/P7t27eL7779/sDAujuj1XkzscRwTq0i2dGxQ6HGKIAWZFzJpe7Et5tWf\nbAYYwfAq9iergR04cACrgAB66HSwfHnhU8yHKBQwejT06ZN39fdh2TodQ69fx8nMjPEuLtx8wkQq\nSRK3PriFQ18H7DraPctLqRBEiUChLGRnZzN8+HB69+7N+PHjH6z49lsm8BNpbyfTx94R+0c+D5L2\nJHFr6i0c33DEroNor+WJuIv9nNy+fZv4b7/luJkZ9O792OpFx45B8+Z5yfSPPwqukySJsSEhBCgU\n/N26daHefY+TuCMRRaACj28Ld60XBOH5+Oqrr1AoFPz6668P2l1cHIpvf+JU3TYo62SxqEntAo+J\n3xbP9SHXcRnjQrPtzUR7LWdEIn0O1ElJ3G3Xji16PRYTJ+bV1C2Gn1/esNLOnfNGxDg8NOZakiS+\njY5mV1ISaxs2pOG98YxPQqfSEbEgApfRLtg0ew6zaQuCUKSNGzcyZ84cnB4usLByJTMtf8ByWDzd\n7KvS/l4Vo6T9SVxsdJGQMSG4fuCKx9ceyC1FCcDyRlzafQ5u9OxJh8xMvOfPp0sRPUXvS06GHj3y\nLudu2VJwKlKtXs+goCC8UlPZ1KQJg0o43CV6eTTadC0eKyrP7C6CYGjHjx8nKSmJnj17Plh4+TKs\nWMFxtxTMX7pJb4e8qc8SticQ/E4wLmNcaHG4BdaNnvyLsmBcxBlpaQoLQztkCK2Dgjj78cdYv/56\nsZvevAlvvglWVrB5c+H5vI+lpfFXaipX27dnjItLicLICsgicmkk9ZbUw8yxZJN9l2e7d+/m9u3b\nRa7Lysrio48+IiAgoIyjEiqTP/74gwEDBuT11NXr8+7bTJ3K3Y6DCO+RRa6plkm5jgQNDiJ4VDD1\nl9Wnya9NRBIt58QZaWlJSIAePUg1M+NTMzPWLl7MzZs3i9186FCwt4eTJ/OS6aP2JCXxmqMjnk84\nXlSv1ZN6NJXk/ckk7UzC6XUno5n0V52sRqfQkZuTi4l1yb+7yW3lmDsV3YPx+PHj+Pj4kJGRgUKh\noEGDBhw+fBgfHx9sbGyIjo5m/fr1rFu3DsvH1DUWhGchSRLbtm1j165d/PTTT3mXm7p2zZuy6a23\nGOOwEuntUH6u2Zjwt0LQq/W0ONQCh76ifm5FIBJpaZAkpBEjSFIoaJiayodffIFtMQlQq4Wvv4Zr\n1/IqGN2fnvBhYUole5OSWFb/ySbuzbmdw/U3r5N9LZuqvarSaF0jqg+pbhQdFnQqHRfcL6DPefpa\nuybWJryU8lKR947i4+MxNTWlT58++N2bqPXw4cP89NNPJCYm8vm90lCzZs3ihx9+eOoYBOFxVq9e\nzfbt21m8eDEjR46E+fPzkmhkJDsDanDyxnV6RNnRYHwkWo1E2wttsahpYeiwhVIiEmkpiP7sM2r/\n/TevyOWsWL+e999/v8jtfvklr1pRbm5e/dyHx2nfl6nV8m5wME2srfmgiNJhj1JFqfBt74ttK1s6\n3e6ElUfZ1NB9UnJLOS9EvoBOoUOZo8TKuuTxyW3lxXbAaNu2LV27dsXLywuze9fHNRpN3uNKWItY\nEEoqLi6OgwcP8vvvv/Pbb7/x3ujR8NNP8NVXsGkTISpXpt24xGen1fQ9qof6lrTxbiOSaAUjEukz\nOH36NMGLFzP1n3840rgxXqdPU7OY5BcXB598AuPGwRdfQI0ahbdJ02h47do1YnNz8WnTBvkTnFEm\nH0hGl6Gj5bGWRtvbz9zJHJxAn63HyqZ0E31UVBQnTpzA3Nycmzdv0qdPH/r378+8efOQyWRYPUWR\nfEH4L8HBwcybN4/du3fj6OjI0KFDGd29O7z9NuzdCzNnsrN3Hzas9mb71xJmtc1puKMB1Ycax5Ui\noXSJRPoU0tLS2LlzJwcmT+awJHH1rbfot2tXsQ3k5Mm8IS4NGsCiRVClSuFtzmZkMCgoiGqmphxo\n0YJaT3A/L/VEKren3cZ9nrvRJtHnrX///vTv35/s7Gxs7pVODAgIwNrampycHEaNGpW/rahMJDyr\nhIQEPvvsM7Zu3Uq3bt04c/QoLyUnk7V2LSYNGqBr1gblD0fwsmzI0ek3mL1dIqOfEwN2NsHUVnzc\nVlTinS2h2NhYWrZsSdf0dPabmGAyeTKtHnPvLSQkr1LRoEF5dXQfHQoakJXFX6mpfBsdTXs7Ow61\naIHFf0x+rTmm4dria6QdS6PG6BrUnVe3FF5ZxfHee+8ZOoQiSZIkzkbKMUmSGDp0KGlpaQRNm0bT\nkBAYMgRkMjTdu6Pde5yLn1qjmaqiilkUPe3l2MypS89FtcT7XsGJRPoYkiSRnZ1NZmYmUVFR/DBn\nDrW9vdlhZ0cvSUK2YEHhyULvyciAn392Zfv2vLGiP/wAFg/dFtFJEtsSEhgXEkITa2s+qVWLj2vV\nemwSVSeriV4ejepbFfZv29Po50bUeLeG0TVSmUyGTqczdBhGR6fTYf4fU8IJxken07Fv3768Cd8v\nXeLq1Kk4r1qFNPVDMl6YyF2TZvgdTsLhHR2p9rnM+tEU53AP/vrYBTc342qbwvMhEuk9WVlZ+Pr6\ncvToUc6cOUNwcDCZmZno9Xm9TT2BA3I5tSwtMXn1VWQDBsBDlw2zsyE0NK8n7o4d4O0NFhaOfPst\nDHlXyx1tLklKDckaDb5ZWWxPTCROrebT2rVZVr8+Jo8kQ51KhypMhTpBTW5sLgmbE0g7lYZFHQss\nl1nSbFazsjw8JWJqakp2djYymaxAktdoNKjV6ufynMa8b0mS0Ol06HQ6TEtQ4lEwnLi4ODZt2oS3\ntzc+3j60U5gzq8brLNN9xO1VdThV7W9sfpNhn60nrN5dQhrBzRGWRDSrwifV3Pl0svXj5qcQKphK\n3aqzs7M5efIkEydOxM/PD5lMRrt27RjQpw9L+vWjoZ8fdklJWEdHYx4Vhb5zZ0z27QNHRyBvvPW/\n/+YlzR9/hMREsLCUqN9aw7Q1udyudY2frM2ZdEHB/cEf1UxNaWBlxaSaNRnj4kKNIs5Qcm7ncK3/\nNZShSpCBmZMZVXtWpfWZ1th3ssc/0L8Mj1LJyWQy7O3t0Wq1BZbfuXPniWdaKSlj3rdMJsPc3Fwk\nUSOjVqsJDw8nNDSU0NBQbt++TWhoKKrrKuzv2tOGJnxq/j6fqT9Bwgx5SjZX3K0428qEdAcJubkV\nJnWdqOdanUbaGJa/V8RYNqFSKPWW7efnx44dOwCYM2cO9vb2HD9+HG9vb/R6PQsXLmTHjh3cuXMH\nMzOz/HF+z4tCocDPz49ly5aRmJhIbkYGtdPSMM3OxiI3FydTU2a0bMmrL75I1cxM5NHRsGwZ6HTQ\nvTsRjfvwT9O2hFTtwHU7K+JWqknSxpGk0ZCsU6O11WBXW4P1BjWuDhpSdGqCJYmFgJskY6JTTb71\n8KCJtTWOZmaYFXPpVq/Wo03TknoilZB3Q6jStQqtTrXC3NUcE9PyV4DKxMSkyMuYz/PSZnnd9/Nm\nbG2yLGk0GiIiIggNDeXvv/9m06ZN+YkzMjISnU6Hk4UT3V2608a6DT0V3XG8Wx0zy3RsVbe47hzJ\nno6duOFmSWhTG3QeOXRQOHPi9YZUMXvw8enrm2PAVykYWqkn0p07d7Jo0SICAwM5cuQIw4cPx8vL\ni1WrVrF//37Onz/PlStXWLVqFWvWrCEsLIz6T1h4oFgxMRAYSG5EBKnR0WQnJ5MWG8uxyyGkyqsi\nM7XiparVcbTxRG6Vi9ZKTo6pJTnWdiitbfGzq8o5FzeC8SBBsiXDygSFjYwcSx25VmpkDhpMnG+h\ns9bikATVFOa4aUxpLTOlro0pbpamOGgsqZYgp5okx0Evp7rMjLjQcOpFW6JXKlGnZhGVqkGbqkWT\ndu/fe79r07ToFA/uKdacVJOGaxsa3b1PoXwySJt8TnLjc9Gr9EhaKe9Hk/evVqkl9tZtsm6FkXDz\nNlFXr5ObnIY2MxtzyQQbmRntLG1wsHLEVt4Cc+klTOyrkK2sjU5VDSlKj8JWQaK9gr2vxvFvi9rE\nu7mja5OBQ5I5npbW9Kluxcvu1rzmXA1LMUZZeEipJ1KdToeFhQXVq1fnwoULeU9y75JW9erVSUhI\nyP/d2dmZpKSkZ2u0CQlQrx7I5aSamJCsVJIDqCws+N/e42D9mIo6WhloZch0MmQ6E0zRYiXPxF5n\nTi3McJKb08DJnuY1zXGxMOeFGDPCe1wF1Pd+CpOZyVCayYg2k6E30RNmFYaJlQlmDmaYOphiVs0M\n8xrm2DS1yfvdwQzTaqYP/t/BFHNnc5FEhVJT5m3yOUk9kUrgq4H/sVUNquJENTogQ4dMpkcmlzAx\nkZB0Osw0esxMNZjaaklzlLOqczVCmsAdDxM0uqqYKB2xUprjKrdiQBUrhrjV4d3ujmXy+oTyq9QT\nqZWVFWq1mqSkpPxphO5XmLm/zOTe5c3ExETatWv3RPv19fUtfuW9D4f7TAFb4EqJo79PokCyTMz7\nJxmwu/L0E+5q7/1XLCUQe+/nPzz2eDwjse+y3ffzZpA2+YyK3LfDs7W/+/TktWwboOg+9wC5eT+Z\n6fj6RjzRfsvr35/Y97OTSZIkleYOg4KC2LZtGxqNhi5duuDp6UlMTAwnT55EkiQWLVrE3r17uXHj\nBhYWFsyePbs0n14QhEeINikIz1epJ1JBEARBqEzKX3dQQRAEQTAiIpEKgiAIwjMQiVQQBEEQnoFI\npIIgCILwDIymZtnzrL5S1L4PHjzIhQsXyMrKYu7cuXh5eeHv74+VlRXTp0/H2dn5qfd98uRJduzY\ngaOjI2PHjuXKlSulFveqVatISEjgxo0bzJgxg/Dw8KeKG+D8+fMcPnyYL7/8EqBUq908uu/SOt5F\n7bu0jndR+y6N433+/Hn27duHSqViypQpNGnSpFxUFhJt8sn2LdqkaJNGc0Z6v/rKkCFDOHLkCABe\nXl4sXryYDh065FdfmTdvHnZ2doSFhT3Tvm1sbFi6dCkDBw7Ez8+PGzduULVqVRwcHPLH2j3tvgMD\nA3FycsLKyoo6deqUatyffPIJs2fPpmPHjnTr1u2p446MjOTGjRvk5ubmLyut413UvkvreBe179I6\n3kXtuzSOt1Kp5Ouvv2bSpEn4+PgApXesnyfRJp9s36JNijZpNIn04eorSUlJQMHqK0lJSYWqrzzL\nvnv16kVqaionTpygR48ejBw5kgULFuDu7o63t/cz7fvVV19l4cKF9OrVi71795Zq3ADbtm1jxIgR\nAE8dt7u7O+PHjy+wrLSOd1H7Lq3jXdS+S+t4F7VvePbj3bNnT5RKJVu3bmXw4MFA6R3r50m0ySfb\nN4g2WdnbpNEk0pJWXynJN6ai9h0dHc2qVauYO3culpaWXL9+vdhZS0q676CgIORyOfb29uh0ulKN\nG/ImF/fw8AB46riLUlrHuyildbyLUlrHuzjPerzT0tL48ssvmTZtGo73Zg56nse6tIg2+WT7BtEm\nH1XZ2qR8wYIFC57ytZSq6tWr89133xEYGIiHhwempqY4Ozvzyy+/EBsby5gxY8jIyGD37t3o9XoG\nDhz4TPueO3cuJiYm+Pj4UKVKFVJSUti7dy8xMTG8++67+Qf1afatUqn49ddfuX79OuPHj0epVJZa\n3Obm5vj5+dG1a1cg7xLK08R93/Hjx8nJySnV413UvkvreBe179I63kXtuzSO9/z580lOTsbf3/+5\nHevnQbTJJ9u3aJOF913Z2qSobCQIgiAIz8BoLu0KgiAIQnkkEqkgCIIgPAORSAVBEAThGYhEKgiC\nIAjPQCTSSurixYv5lUIeFRMTw5QpU8o4IkGonPbu3cumTZseu01ubi6HDx8um4CEEhOJVBAEwcgl\nJSXlV1ISjI/R1NoVnq+0tDTmzJlDTk4O2dnZDB8+HMg7+5w1axbm5uakp6czd+5cXFxciI6OZsKE\nCaSkpDB79mw6derEokWLiIiIIDU1lY8//pju3bsb9kUJQgVx+vRpTp06hbm5OUuWLGH58uUkJydj\nZ2fH8uXL2bJlC76+vuzfv59q1aqxceNG1Go19evXZ+nSpYYOv9ITZ6SVRHR0NKNHj2bTpk0MGDCA\nnJyc/HUpKSmsX7+eNWvWsGbNGgA0Gg1r167NL96cnp5O06ZN+fXXX1m2bBl79uwx1EsRhArHzc2N\nrVu3MmjQIAYOHEi7du3YunUr/fv3Z+vWrYwePZp27doxaNAgoqOj+fnnn/nzzz8JDg5GoVAYOvxK\nT5yRVhIODg5s3ryZffv2kZycTJcuXfLXtWzZEnNzc2rVqkV6ejoA9evXx9zcHAcHB1QqFdbW1gQH\nB/Ppp59iamqKTqcz1EsRhAqndevWADRv3pzWrVuzd+9ejh07hkajwdPTs8C21apVY/bs2djY2JCR\nkYFerzdEyMJDRCKtJDZv3kyvXr3o378/ixcvLrAuNDQUvV5PXFwc9vb2RT7+zJkz6HQ6VqxYwbFj\nx9i/f39ZhC0IlUJISAiQV+6uVatW1KpVi0GDBhEYGEhSUhIymYz7Rei++eYbTp48iUqlol+/foji\ndIYnEmkl0a1bNxYvXsy2bduoUqUKgYGB+d+C1Wo148ePR6FQMH/+/CIf36JFC3744QeGDx+Oi4sL\nGRkZZRm+IFRoMTExjB49GktLSxYtWsTSpUvZvXs3Op2O5cuX4+DgwN27d9m5cyddu3blrbfewtbW\nlho1apCcnEyVKlUM/RIqNVFrt5KLiYlh6dKlrF271tChCIIglEuis5EgCIIgPANxRioIgiAIz0Cc\nkQqCIAjCMxCJVBAEQRCegUikgiAIgvAMRCIVBEEQhGcgEqkgCIIgPAORSAVBEAThGZSLyka+vr6G\nDkEQnpt27doZOoQSE21SqMhK2ibLRSIF4/qw8fX1Nap4QMT0JJ4lHr1az92f75K8N5l073SkGub8\n7QK+XdV4vwS6FD0ut+N5r0okQ4cMpkmTJk8cU3lliPfWkH9Thnruyv6adTk6VOEq4jbGEbMqBpmF\nDMvalmiqaUgzTyOyaSQbvdeT5vE9kTOBbBOqBGipHxxONZmGLvUkJk4ehKur6xM/d0mVm0QqCIaS\nczuHsFlhJO9Lxu1DNyz+z4122mC02SYMv3aL3Hc/pn/fPuzZswcTE3G3RBCehSZFg/aClvigeDIv\nZxK3IQ5JLWFiZYLr165MODiBoOtBpN9Op6WbG1YxH3Oz8V6UM2IYEa1iRjNnXMe6ULNmzzKLWSRS\nQXgMSZLwf8kfXbaOlkdbsr1aGlNzrmOSZMoHK7axNXwb48e+x3fffSeSqCA8g4yzGUR/G03K4RQk\nvUS4azjmbuY0/qUx1XpXY9vhbYxaPIrY2FjWLF3O5Tt6jlZvQ1w3Myx0kYxTSqwf1Qe5TFbmsYtE\nKgjFUCequTnhJppEDZ3COnHaNocP/WNwOGuKy3ejSOzZlnWz1jFs2DDMzMwMHa4glFsJ2xMIficY\nxwGOeB7wJNwhtUll7AAAIABJREFUnPad2uevP336NBM+mMDo0aMZPXYivXVqcLbGNduMzyyrsfDl\nOlgY8IusSKSCUITUY6kEjwlGZiKj1d+tOKXRMuifW5hcq8Y3OxawoakrO3ftQi6XGzpUQSi3lHeU\nJO5MJPb7WFzHudJofSNkMhkRvhEPtlEqmT59Oj17Tub6jeX09oqBF5O53b45Hi1sDBf8Q8S1KEF4\niKSXiF0bS2DfQJwGOtHOtx23mpkxMNwfs3iJgL9msDL+FO+++65IooLwlCSdxI2RN7jU9BLxm+Nx\nGeeCx0oPZI9cllWpVEyePJmQkBDsak7A79Or2HSJYlWNGkaTREGckQpCAXc+vUPsj7F4rPSg9ie1\nAZjyuz+yTEtCv5vOx61qkJ3qwPvvv2/gSAWh/Eral0Tin4k0/qUxLuNcCiVQgICAAPr164dSqWTu\nzl3MM8+iUaiCKxN6YmtlZYCoiycSqSAAOaE5xKyKIW5DHE02NaHGOzUAuJOUjJ+rijd2pHF3zVz2\n9u+Pl5eXuCcqCE8hNz6X2DWx3F13l5pTa+I6vvghKfPmzaNFx440WLaMpfGJSEdr8Gdja6NLoiAS\nqSCgTlYT2DcQZNBgdQOcRzoD4JeVRa9L/nDbiaVftODDL4bSvn17+vTpY+CIBaF80Sl1xKyOIeqr\nKOTWclzHuVLn8zpFbhsfH8+5c+fw8fGh0dat7IyIxuzn2vS9lknrOx3LOPInIxKpUOklbEtAl62j\nQ1AHzJ3MAcjV63ntwhWyrzgx7VpVVl77jLNnz3L8+HEDRysI5Yukk7gz4w53193FfZ47NSfWxKKm\nRZHbKhQK3N3d0ev1NJs0iUtW1tSa7ohdWm3+DLHBACNbnohIpEKllrA9gYgFEdScWDM/iQK85x9K\ngsKUbjvMaDHdh/ff38jvv/9Ot27dDBitIJQ/Md/FEL8lniZbm+AyyuWx2/7vf//D1NSUDw4dYrWJ\nCTU22VDlbg0OXrTDwviu6OYTiVSolHLjc7n1wS1SDqVQc2pN6i6sm78uUa1mZ3I8rhutOLRZRvcp\n3/PRRx8xcuRIg8UrCOWNTqkjaGAQaSfTaPB9g/9MoufPn2f16tV4HT/OKLmcAdHmHNvcmlN/p1Ov\nXhkF/ZREIhUqHUkvETo5FOUdJS0Ot8Chv0OBXoNjroSgT7Tgl9xTLN2v4MqVK2zZssWAEQtC+aJV\naAmfG07mhUzaX22PbUvbYrfNyclhxIgRHD58mFGjRpHTujUZQUFkTs9lkMtFmnd/uQwjfzoikQqV\nTuiHoSTvT8bzgCeOAxwLrEtQqzmuSKXxJjNiB9qz9OMv2LhxI02bNjVQtIJQvsRvjufOrDsANFzb\n8LFJFGD16tX4+PiwZ88eXnvtNWZHRND6VizeSaO44KMti5CfmUikQqWiDFcS/1s8zXY1w2mgU4F1\nGr2ecVdDkeIs+cI+hq/Xr+Xzzz9n3LhxBopWEMqX1GOphIwNofas2tSdVxe59eOLlly+fJlFixYx\nd+5cBg0aBMCZqLtEXm5As/pKOr1kxDdGHyIqGwmVhnqfGr8X/LB/0R6nN5wKrd+SkMDxjGRqfmvL\ny8MgODiY1157zQCRCkL5I+kkwr4Iw3m4M/WX1f/PJCpJEpMnT6Zbt2588cUXAKSrtVzR6zENrsoJ\n7/KRREGckQqVxN31d8n9Mpdan9Si/rL6mJgV/A6ZodUyO+QO2j9rs5ADvLtmN507d+bFF180UMSC\nUL5EfxuNwk9Bsz+bFVmp6L7U1FQ2bdrEjh07CAgIICAgIH/mpIHHryEpLPi2+3Vq1nyyOX2NgUik\nQoUX9XUUYbPDMB9jjse3het5SpLE/PBwVKlaOu5MpfGWupx/6zyRkZGP/UAQBCGv/SQfSCbs8zDq\nLqiLdSPrx27fu3dv4uPjef311/ntt99o1qwZAH+lpOBjk07DBVVotLZ2WYReakQiFSo0xTUF4f8L\nx2OVB4ldEotMjDsSE/kuNha+b86s8TeYv2YNrVq1onbt8tWYBaGs6LV6kvckE/6/cFQRKiSNRPUh\n1YutVgR5s7gsWLAAf39/Ll++TPv2D6ZJU2i1TLl1C8eD9nRXKkFuWRYvo9SIRCpUWJKUV1GlSpcq\n1JpWi0T/xELbhObkMOVWKBb7nfhAdQfPyS6cbnKaa9euGSBiQTB+2gwtAT0CUAQqsGtrR/M9zbFq\naIV1Y+tir+BER0czcOBA4uPjOXjwYIEkCrAjKYmELA22P9dl3uYIEsrihZQikUiFCivrUhZpJ9Jo\nd6UdMpPCDVySJKaEhmKfLKFeV5uv72hZ8+evuLq60rx5cwNELAjGS9JLKAIV3P74NnqlnhejX8TC\ntehSf49aunQpKSkpXLlyBTc3twLrFFot8+5EoD5Ug6/r/kGtoTNI8PV9Hi/huRGJVKiQJJ1EyPgQ\nHF93xLZt0ePYbimVnExLw+TL5sxofJ7lGy+yYMECli9fLu6NCsJDFFcVBA0KQhWhwsbThpbHWj5x\nEg0NDWXz5s1s3LixUBIFWB4dTWa2Cvmm2oy+Mri0Qy8TIpEKFVL81nhU4SpanWxVbFL8Jy0dkxwZ\nIzJC6bPGhFf6LeS3337j3XffLeNoBcF4Sfq8L6XWza1pfaY1lrWf/P5lXFwcbdq0oUOHDgwbNqzQ\n+mydjh+iYzD92ZkP6lzErHGP0gy9zIhEKlQ4OpWOiHkR1J5eGwuXor81+2dlMfXmLfQ73Pnfl4l0\nf28Mo0eP5r333ivbYAXByMVvjicnOAfP/Z5Y1ipZJ6CAgAA0Gg2nTp1CLi84rlSr1zP55i0yNDrs\nvFxYHuxcmmGXKVGQQahwQqeEolfqqf1p8b1u51yNQHetCvPOn+dU0hXUajXff/99GUYpCMZPn6wn\n7PMw6nxep8RJVJIk9u/fT4MGDTA1LXzOtu7uXXbHxGE6uwmHFt7Cpm710gq7zIkzUqFCST+TTvxv\n8TTf3RzTKkX/eXulpOClTaHJFnMm721J3fbtmTZtGvb29mUcrSAYt9zvcpGby6k9o2RDwdRqNYMH\nD+bIkSMcPHiwyG1W3oxCtt+Rgeosus4q34VPRCIVKgxJLxH6USjO7zjj9GbhEoAAPmkZDAq4Dn+5\nsML+MCcDXLC0tOSrr74q42gFwbjF/RqH1ktLgy0NkFs9vtzfo9atW8eJEyfw8vKib9++hdafS88g\nQq7mhdPZbDzfq7RCNhiRSIUKI+10GtnXs2l5pGWxHYzm+ceivliN73/4mT6RM2nWvTu9e/fOL1Em\nCMK9fgaLIjAbbEaNUTVK9FhJkti+fTvDhg0rMolKksRH3oFwuyqb+56lSrVXSitsgxGJVKgw0k+n\nY9/JHgu34rvlX9dk4Rhox0cjUrielkZoaCi7d+8uwygFwfjFfBtDbmQuthttSzwU7OjRo1y4cIHV\nq1cXuf5iZiZ+djp6/ZpOo8AppRGuwYmv4UKFkXYqjao9qha7XilJJJop6XjTH955h6lTp9K5c2c8\nPT3LMEpBMG6qaBXh88Kpu6AusqolH0+9Y8cO+vfvT6dOnQqty9JqGX0lCPlleya/ZgsVZLx2qZ+R\n+vn5sWPHDgDmzJmDvb09q1atIiEhgRs3bjBjxgzCw8Px9/fHysqK6dOn4+xcfrs9C8ZBm6FF4aeg\n/tL6xW5zXqsFtQmDkgPQ9/6KC2+8wZ49eyr8ZV3RJoUnJUkSUV9FYdPcBvd57qT4pZT48f/88w8z\nZ84scv2SyEjuaLS0XSvj1cD2RW5THpX6J8jOnTtZtGgRQ4YM4ciRIwB88sknzJ49m44dO9KtWzdu\n3LhB1apVcXBwwMmp6E4hglASGWczkCQJ23ZFVzEC+CVWBhcd6NzNkeXffENubi6NGjUqwygNQ7RJ\n4Unk3MrBr5Mfd9fdpd6Sek9V3ev69etERkbSv3//Qut+i4tjeXQ0Zsvr8eu4SOzsSiNq41DqiVSn\n02FhYUH16tVJSkrKX75t2zZGjBgBwMiRI1mwYAHu7u54e3uXdghCJaNJ0XBn5h2chztjVtWsyG0C\nsrIIraqk3+EAGn/al/nz5/P111/TsGHDMo627Ik2KTyOJEncnHSTS40vIekkOt7siNPAp/sydfLk\nSVxcXKhbt26B5V4pKUy4eRPz7W5MObOfFjNeLYXIjUepX9q1srJCrVaTlJRU4JttbGwsHh4eQN63\nltatW2Nvb49Wq32i/foaWRFjY4sHKm9Myi+U6HP1yMbIinw+lSQxXJEDZx0Zl7yL3cFy1Go1np6e\nRnnMSltFa5OGfM8q4mvWHNSg+lWF1bdW0AmCM4PhoacryXOvWrWKV155BT8/v/xl6ZLE6IwcpH+d\nqPezjg/7nMU3+KX/3Fd5apulnkiHDRvGvHnz0Gg0dOnShdu3b+Pi4oKNjU3+NpIksWDBAiRJYu7c\nuU+033bt2pV2qE/N19fXqOKByhtT8oFkgk4E0fqf1lTtWnRHo+VRUSRkRCBfV5dX33Zktrc3LVu2\npF+/fgYvTl8WHxYVqU0a8u/cUM/9vJ5X0kkkbEsgZHEIru+70nh642d67piYGKKiopg4cWKBx8wL\nD0cZq6HpSh1Bq08jm7b9PzsZGfp9LqlST6Senp5FDm5/uHGOGjWqtJ9WqIQknUTY52G4fehWbBJV\naLUsj4hGv92N/8WvwK7Pi/w1aRIzZ840eBItK6JNCo9K+SuF0Gmh5MbmUm9JPep8VvyE3E/qo48+\non79+nTs2LHA8pPxGWQfq84q20XIJm2oMD11HybGkQrlVtKeJFSRKup8UfyHwIGUFDJVelx22DCl\n21n+tepBVFQUvXv3LsNIBcF4ZJzN4Npr13Cd6Eq9RfUwdzZ/5n3q9XpOnDjBb7/9hpnZg34KkSoV\nF1TpVL1ozyv/zAGLJ5t6rbwRiVQot1KPp+LQx6HYGV4AtsUlIPNxYlnWLOKGvcnQoUN54403aNy4\n8GUsQajoVDEqbr5/E+cRzjT+qfTawLFjx1Cr1XTu3LnA8k0xMVgmwIC0aGRNupXa8xmbij2ATqiw\nJEki9Wgq1XpVK3abaJWK42mp2J6vyhvmXhxJTycnJ4dffvml0lzWFYT74n6L42K9i8jt5DRY1aBU\n933u3Dk6duxYYOLuQIWCJRExKP9syNRFtUr1+YyNSKRCuZR+Oh11vBrH1xyL3WZWSDimd60ZkXEL\n+5dbEnTjBp07dxbjJIVKJ90nnVuTb1F3QV3anmuLeY1nv5x7n1qtZvfu3XTrVvCMc8LNm1hdtWBm\nyGVeHOVRas9njEQiFcqluxvuUq13NSzdi54j8UpmJrtTE7E85sYiuzWomzXjwIED9OzZs4wjFQTD\nUkWqCBoYhMMrDtT5og4yeeldjdFqtQwcOJC4uDjGjRuXvzxQoeByVhZZ61rQ892KfTYKIpEK5ZAk\nSaR6pVLjnaJnpdDq9Xxw6xY17lajb6YtDmcP4WtpiV6v5+OPPy7jaAXBcFTRKq69dg0bTxua72te\nqrc0JElixowZnD59mpMnT+aPSQbYEh+PXZgJnilpdJtRcUoBFkckUqHcUYYq0WXqsGlhU+T63xMT\nuZGZQ9KsxnRPPgzNm3NQLqdBgwZYWVmVcbSCYBiJOxK57HkZub2c5rubY2Jauh/3p0+f5scff+TQ\noUO0b18wWV6+m0KWryvr/hdHZWhyIpEK5U70ymhsWtlg26rourp7olJR/+3EFyPVTLo4lvQPPmD9\nhg3isq5QKUiSRMwPMdwYcQOnwU60/rd1qQxxedT27dvp168fffr0KbBco9dzJUdJlXAdnf+vQ6k/\nrzESiVQoV5QRSuI3xeP+uXuRl6nSNBpOpaZRPduG/3U+hczCHC8bG7RaLcOGDTNAxIJQdjTpGoIG\nB3Fn+h0a/tiQppualvqZ6H3+/v688MILhZZvT0wkxxwGJcdgUor3Y42ZSKRCuRK3IQ4LVwuqD6te\n5PqFl+PJyZYxv50rJqdOQOvWBAUH06ZNG8zNS/9buSAYC61CS+ArgShvKml7oS1uk93++0FPSa1W\nExQURKtWrQqt+yTwFvxRm4n9dc/t+Y2NSKRCuSFJEol/JuI60bXIs9EktZqf0qNocNOVKYM0sHs3\nvP02Z8+eLbLBC0JFocvW4dvWl9zYXJrva45du+c7R9nhw4cxMTHhxRdfLLA8S6kkxVzPS4F3efF/\nlad6mEikQrmRfS0bVbgK57eLnnR6VOBNctPkLGtdGxYvhpwcsoYNw9/fv1DFFUGoKNJ90vHr7Ic2\nXUv7wPbYNCm6E15pOnbsGN27d8fRseA47ivePqCDd95tDnL5c4/DWIhEKpQbcb/EYdPSBqt6hbsB\n/hQby3FFCg6/NWZwyyRYsQK+/ppf/vgDS0tL+vbta4CIBeH5StydSECXAKw8rGj9T2vMnZ7/7YuU\nlBQ2b97MyJEjC607GhoLt215qZvDc4/DmIhau0K5kBubS+wPsTTf3bzQOpVOxyd37tDCx4MXPaoh\nP/oLODrCpEns6dqV9957jypVqhggakF4fpR3lNwcf5M6s+tQf1n9MnnO7OxsRo0ahaurK8OHDy+4\nTqdjt31VZFfsaPxemYRjNMQZqVAuKMOVADgMKPxN95/0dNR6PWnbXGnUCDhxAnr1QpGTw8WLF8VM\nL0KFowxTEtg3EPuO9tRbUq/MnvfHH3/k9OnT7Nq1C1PTgudhP92+TZy9DS0uOlXUSV6KJRKpYPQk\nSeLu2rtYN7VGbikvtG5BRAR97JyIuWlKjxbJ8Ndf8Prr/PLLL9ja2vLSSy8ZKHJBKH2Kqwr8X/LH\nsq4lzfc2L9WSf4+j1+s5fvw4w4cPL1SAQS9J7IiJweaUDW90f74dnYyRuLQrGL249XEk70umzdk2\nhdb9npDA1exs5oQ240p18PTbAg4OMGgQJ4cOZfTo0VhbWxsgakF4PoJHB2P/oj3NtjfDxLzszoW8\nvLzw8fHh7Nmzhdb9m57OZbkcDrai56bKN8xMnJEKRk2XrSNiUQS1Pq6FXduC33RPpqbybkgIE1xd\nObTeilGjwPzoQejbF6ysCAgIoGXLlgaKXBBKlypSRfTKaLIDs6m7sG6ZJlGA48eP06VLF9q1a1dg\n+T9paYwOCKD+5VSsEyx5ZHWlIBKpYNRiVseABHU+r1NguSRJrIiOZpCTE+PVHly6BO+1DYR//4WJ\nEwkLCyM2NrbQODdBKG80JzVcqH+BC3UvEPN9DLU+qYWN5/Mf4vKwkJAQNm3axBtvvFFg+Z8JCfS4\nepU254KIWDCAVd/Jsat8V3bFpV3BeGnSNcT+GIvreFdM7Qv+qQbn5HAsLY2LbdryzRQTmjWDlle3\nQuvW0K4dx9atw83NjaZNmxooekF4dlqFltyvc3F+zZnmu5pj29bWIJPSjxkzhpdffpkpU6bkLzuW\nmsqU0FDev36Xf+YPoFljUx6aSa1SEYlUMFrpf+dN3l3709qF1oUplVjIZGT52fHHH3DqhB4+Ow33\nCmjv2LGDAQMGGORDRxBKg16tJ3RyKJJKotG6RshtDFPgICgoiEuXLnH69GlMTPIuYnqlpND/2jWG\nqizY+3EvGjYwY/M+OaaVNKOIS7uC0coOzMb+RXtMqxRunZezsmhha8uXi2V07gw9nQLBzw/GjSM2\nNpZ///2XIUOGGCBqQSgdkUsjSfFKwfpHa4MlUYAVK1bQpk0bunXrlr9sWVQUA0yqEPa6FS2qJnLm\nmkPe0LNKSiRSwWjl3MzBqmHRkxkeSE6mA9X4+2/44gsgMhJsbaFRI9avX4+joyPdu3cv03gFobRk\nXs4k6ssoGqxsgLyl4ZJoeno6f/31F2PHjs0/G10SEYFPRgbHJ9RDj5xfT9TBzMxgIRoFkUgFoyTp\nJbKuZGHlUTiRxqhUXM3Oxu123gwwPXsC165B7dpkZ2ezevVqFi9ejFllb91CuaTL0XHt9Ws4DXKi\nxrs1DBrLjz/+iKmpKRMnTgTgUHIyCyIimHi9NlVCNVyatZt6rUXVsEp6RVswdtk3slGGKqk+pPB0\naYdTUrA1kfPNhzaMHQtWVkBQELz0Et7e3qhUKsaMGVP2QQtCKYhaFoVepafxL40Neo8/JyeHw4cP\n8/vvv2Nubo5Cq2VZVBRvOFTnyBeOjHA4jOm8LwwWnzH5z0SqUqk4duwY/v7+ZGRkUK1aNdq0aUPv\n3r2xsir6spsgPKvMc5lY1LHApmnBbv4pGg2rYmKol1qNdCsTfvrp3oroaOjTh9DQUOrXr1+hizCI\nNllxZV7JJHJJJM33NS+yb0BZunjxIjqdjj59+iBJEr2vXiUqN5c+oY05kGXJ/A/vUOlqARbjse/U\n77//jp+fH506deL111/H0dGRrKwsgoODmTNnDq1bt2b06NFlFatQiSTvT6Zq96qFlv9faChZOh1m\naz0YOhTMzYGsLPD3hylT+HffPjp06FD2AZcR0SYrLm2WljvT71C1e1WqDyp64vqyIkkSc+fO5eWX\nX8be3p7jqalczMriaI0OvDHRmo/kP+LYQxQ7ue+xibRdu3a88847hZa3aNGCYcOGERIS8twCEyov\nXbaO1OOptDxSsKFq9XqOpqbSP9qD7f9YseeHeysOHgS9HnXv3hybNIlff/217IMuI6JNVkySXiKw\nXyCaBA0t/mph6HBYsWIFfn5+bN++HYVWy5tBQbzh6Ej8JRuszbV8mzUNXrxr6DCNxmMTaZMmTQDY\nsGEDEyZMQCaToVAoWL58OYsWLcpf/zA/Pz927NgBwJw5c7C3t+fkyZPs2LEDR0dHxo4dy5UrV7hz\n5w5mZmZ8/vnnz+FlCeVZln8W6MH+BfsCy89lZpKi1ZLtXY3Bg6Fx43srrl6Frl3xj4hAoVDQq1ev\nsg+6jIg2WfHoc/VEfhmJwl9Bx5sdsaxladB4JEni+++/58svv6RWrVpsjI/HRCZjU5MmvP0pDHC/\nholVe3BxMWicxuSJeu3KZDLGjh3Lvn37eO+993jhhReK3Xbnzp0sWrSIIUOGcOTIEQACAwNxcnLC\nysqKOnXqcOXKFebNm4ednR1hYWGl80qECiN5XzK2bW0LVDOSJInPwsJ4zcGRv7dbUmCe7oAA8PTk\n3LlzNGnSBAeHij+psGiTFUPYF2F4V/Em5vsYGm9obPAkCrBr1y5iYmLo378/kiTxS1wck2rWhGwz\n/vlHYqRmy72u8sJ9T5RIhw4diqWlJatXr+bll1+mb4FPsYJ0Oh0WFhZUr16dpKQkAF599VUWLlxI\nr1692Lt3b/48ds7OzvnbCMJ9yttKqnQu2KX+7/R0LmRmMlpVn7Q06Nfv3gpJgsuXoX17vL296dy5\nc9kHbACiTZZ/GecyiPoqioZrGvJi5IvUGGnYoS4AUVFRTJgwgZkzZ9KkSROu6nQEZWfzvqsra9aA\nhamenjfXwuDBhg7VqDxRt7BRo0bx4YcfsnbtWjZt2sSIESPyLxU9ysrKCrVaTVJSEk5OTkBeiamm\nTZtib2+PTqfLH9ibmJhYaCaB4vj6+j7RdmXF2OKBihNT9q1szOqakembmb/si5wcusjlnNmahoeH\nCbGxN4iNhaqnT1M/IwN/a2uOHDnCokWLHvucxniMnkZlbpOGfA9L47n1SXrU69VoDmow7WtKXJs4\n4m7HPffn/S8xMTFMmzaN+vXrM2TIEC5fucIejYYGJiZEX7jFihWefNhwDxqaEmRqCs85pvLUVmWS\nJEnFrZQkCZlMRnp6OlWrPuhBGRkZibu7e/76hwUFBbFt2zY0Gg1dunTB09OTsLAw/v77b2QyGZ99\n9hnHjx/nxo0bWFhYMHv27P8M0tfX94kbd1kwtnig4sQk6SXOOp6l0U+NcH7bOW+ZJGHv48OWJk3Y\nOLY61avDb7/de8D06eDvz96PPuKtt94iPj6eGjWK/mZfEY5RZW+ThnwPS+O5tVlaLjW9hNxaTp3P\n6lBjTA1MTB9/YbCsXvOHH37IuXPn+Oeff7C3t2dpZCRLw8PZ0aIF3t84susPNdeiq2H93TKYNu25\nxlLe3ufHnpEuWrSIZs2aFeq8YWdnx59//klwcDCLFi0qsM7T05OvvvqqwLIGDRrw6quv5v8+dOjQ\nEgUpVB65Mblo07XYtHowfjRUqUSh06G9Zctff+XNlAaASgWbN8PMmezcuZMBAwYUm0QrCtEmy7fI\nJZHoMnS0D2iPuZPxTIB97NgxfvvtNxYvXoy9vT0ZWi2LIiL4zNKSvlUd+WArfFjjANb29WDqVEOH\na3Qem0hnzpzJ5cuXWbBgAeHh4ZiamiKXy3Fzc+PNN99kxIgRZRWnUEmkHk3F1MEUq3oPCgsEKBRU\nkcu5cSpv0uCuXe+tOH8eMjKQpk7Fv317xo8fb5igy5Bok+WTpJdIO5VG9LfRNP65sVElUaVSyfTp\n03nttdeYdu9Mc1diIhpJoqepKfv3Q1KinvfvTob9G0FuuNq/xuqxiXTSpEls2bKFbt26sWnTJt57\n770yCkuorOJ+jaP6m9UxsXhwuetyVhatbG3Zt1fGgAEPbezlBfXqcS08nFu3bjFo0KCyD7iMiTZZ\n/qgT1QS/E0zayTRcxrngMs64ho18+umnZGZmsnLlSkxNTbmcmcnU0FA+q1MH84R0lizQMkG/EadP\nRsPAgYYO1yg99uL8w7dPT58+/dyDESo3VYyKrEtZBT5oLmdm8l1MDM1TnQgMhGHD7q3Q6fIu606e\nzJEjR2jYsCENGzY0TOBlSLTJ8kWr0BLYL5DMy5m0OdeGJhubGNUcuVqtll27djF37lzc3Nz4NS6O\nLv7+DK1enU+r1mPSpEbERar5RP49fPklGFHsxuSxidSY3nCh4kvalYRlXcsChRjmR0TQq1o1NHvc\nGDAAWrW6t+L6dUhMhGHDuH37Nu3atasUf6+V4TVWFJIkETwqGFWkig5XO1DlReOaJUWlUvHmm2+S\nnZ3N4MGDWR0dzfibN1lUrx6bGjXl7bdlBAXZsCfrFRrOHHRvdgihKI+9tBsZGcmyZcuQJCn//+8T\n1U+E0paiJwdcAAAgAElEQVRyOAWHfg75ySJbp+Of9HQ2N27Kh3tMmD//oY19fKBePahVi4sXLzIs\n/1S1YhNtsnzQKXUEDQ4i/e90Wp1ohaW74QstPEyv19OzZ08iIiI4ffo02ipVmHfpEgvr1mVWnTp8\n/jmcOiXxV485vBQQAkt8DB2yUXtsIv3mm2/y/793797PPRih8krcnUjGmQzqL62fv+zf9HRUej3V\nY6qSmAgFboH+/Te8/DIajYagoCDWrFlT9kEbgGiTxk+Xo8O/qz/K20o6BHXAuqHxzUTk4+PDxYsX\nCQsLw75mTbr4+9PM2pov6tQhNxfWrpX4sdsu+pxdCX/9JS7p/ofHJtKOHTuWVRxCJZdxJgOH/g7Y\nd3pwWfd0Whrdq1bl8DYzmjSBmjXvrUhIgH37YP9+QkNDAahVq5YBoi57ok0aJ026hpzgHDIvZhK9\nIhqZXEZ73/ZFTkxvDPbu3UuLFi1wd3dnw927JGk0eLdpg6mJCQuXgLmUy9B/PyTs22U0qMC1q0uL\nmNhbMDi9Wk/SniSqDy04ddTFrCw6WFZh40ZYuPChFUeO5M2D2Ls3wX/9hb29PR4eHmUbtCCQdx80\nekU0EfMi0Kv0WNSyoOakmtSaVqtArWhjcvr0adasWcPatWsBuKVU0tzGhmpmZpw7B0uXSvwhG4/T\n1LeJzB9rJjyOcb7TQqWivKNEfVeN21Q3AHSSxPKoKM5lZPBafD2ysyF/iKhGA0uWwOTJYGmJl5dX\nhZ5/VDBekiQRPDKYxO2JeKzwwHWiK6a2xv2RmpOTw6RJkxg4cCATJkxAodWyLjaWRfXqgSSxc2EI\nHfXpvD0SWLECgoIMHXK58ERF6wXheVKFq5BZyPIvg51ITWV+RARf1quH2rcKnp5gc7/Q0YULEB4O\nM2cCcOjQId544w0DRS5URnqNnpjvYwjoGkDijkTaXm5L7Rm1jT6JAvz5558kJiayYcMGTExMOJqa\nilKv531XVzInzWLTcVc+6B0OW7fmXfURnojxv/NChZcdlI2luyUyk7wODWcyMuhkb89sd3ea/g6v\nvfbQxv/8A82aQY0aXLhwgcTERHr06GGQuIXKJ+HPBKKWRZF9PZuaE2tS78t62Le3/+8HGoHc3FxW\nrlzJoEGDcHR0BGBFdDTDnZ2xy/j/9u47vKmyfeD4N6NJ2rRN94BCoWwoW0X8gSJQUXlBReRVmSq4\nUBQHgoCjitsXQQVFZRSRqSAqleECgQK2QIGWUaB003Sn2ck5vz8CfQXxFW1pWng+13WulPQ0584h\nT+4znud+Kpm4oAWhkWruXn+vOMX6m0QiFbxKdsvkf5hP5Mj/1sj9rrSUwaGhfP89HD4MSUlnV5Zh\n/nyYPBmAr7/+mv/7v/8jPj7eC5ELVxLJLuH4ysHhdw4TNS6KTl92apC9cf+XF198kdLSUt566y0A\n1hmN7DKZ+LhdO068sYpPGc+K98Rw0X9CJFLBq8p/LMeebydmsqfXbYHdTrrZzKft2nHPTZ4hLz16\nnFl5714oLIS77wY8Xfh79+7tpciFK4Hb6qZoYRFFSUXY0+3ETomlRWKLRlcYIzs7m9mzZ7NgwQIi\nIiLItdl44MgRnmjalK5+fjz0vh9dmpRw+7+bejvURkmcwAtedfrz04TcHIIm3FPEe3N5OXqlkqDi\nAI4fh2nTflcj+/PP4aqroFkzjEYjqamp9OnTx3vBC5et0uRSdnfYzTa/bWQ9lYVfez/0X+hp+UrL\nRpdEf/75Z9q0aUOLFi0YOXIklS4XozIzaa7T8U6rVlR8/QsLq+5k2kwfMVz0HxKJVPAaWZKp+KmC\nkJtDap77trSUO0LCGXmvglatoHv3M79wOmHFChg5EoCFCxcSGRnJrbfe6oXIhcuRLMuU/1jOkYeP\ncOBfB/Dr6EeXjV24ruA6OizpgLJ54/u6tNvt3HffffTr148DBw7gVCi4KjWVXLudLzt1Qu1y8fyY\nPLQqFzeNivB2uI2WuLQreE31vmrsuXZCbw2teW53VRXtU2M5dAh27wYfnzO/WLwYrFY4M03Y119/\nzfDhw/GpWUEQaifn9RxOTj9JcEIwbee3JXJMJCpd454ybOHChZhMJlavXo1Go+FLo5ECu52C667D\noFYjv/0OOyyDePZZGX9/b0fbeIlEKnhN1a4qtLFafOM8vRv2mUzk2O2UfxbIu+9Cp06/W3npUhgz\nBiIjKSwsZOfOnbz77rveCVy47JR+V8rJ6Sdp9Z9WNJvczNvh1AmXy8VXX33FgAEDCAoKwi3LJBUV\n0ddgwKBWw7p1TJ6i5rC6A8NGilRQG43vWoVwWZAcEkWLigi+MRgAi9vNk1lZdHEE4zzsz7///buV\nq6o8p6c33QTApk2bCAgIEOXyhFqTXBLGtUYyR2fS5JEmxDx5+ZSafP/999mxYwdjx45FlmVuSU9n\nfWkpTzdrxpEjcOM9UcxTPsbsuWo6d/Z2tI2bOAwRvKJqZxWm30x0WuM57Xz2+HGOWa2Ez+3GqFEQ\nHPy7lT/4AEJD4UyR9hUrVnDnnXeiUjXuy26Cd8huGcsxC5YMC3mz86hKqSLq/ihavd2q0XUk+jNm\ns5nPPvuMRx55hFtvvZWvjEY2l5fzfZcuJISEcMe/qymwBbNi7mkG3y966taWSKSCV5h+M+Hb1hdd\ncx2by8qYX1DAx/quPPidH18c+v2KJkhMhLlzQavF4XCwa9cu5s6d67XYhcbJbXaTNzeP00tPY8m0\noPRTEnJLCFelX4W+g/6vX6ARef311zEajUycOJFih4P7Dh/m6ZgYBoWEkJcH63/w40vdS9z+2Bdw\neRw7eJVIpEK9c1W7yJ+XX9Nbd3FREcPCwtjxbjDdunkKF9U4ehTsdrjrLgA2b96MxWJhyJAhXohc\naKwcpx0cvP0g5oNmmjzchI4rOqLvrL9szkB/z+l0smTJEh5//HFatGjBPRkZBKvVvNqyJQDTp8t0\n8T3G0FFBYnq0OiISqVDvSr8pxVnipMXLLVhnNLKiuJjpckdeWeyZZvQchw9DWFjNtd49e/Zw1VVX\nYTAY6jtsoZGx5dnInpntuYx7yIK2mZaeqT3xa9u4KhL9XXPmzMFisXD7yJE8cOQIK41Gtnbrhk6l\nYvFiSEpS8Iv2cZRPvOftUC8bIpEK9c60x0TgtYFowjR8kl7Ibf4RfHBTBMOGQb9+5628cCH8rnpR\nWloa7du3r9d4hcajNLmU4pXFVKdVY84w49/Nn9B/hdJkQhPChoWhDri8v/KMRiNvvPEGj73wAmNK\nS6l0uVjTqRN9g4JYswYmjJdIVLzM9XPuPO/Sj1Abl/enSmhwZLdMyboSoidEk2+3s6GsjP5bOxIS\n4qm3cA67HbZtg/XrASgrK+Pbb79l9erV9R+40OCZM8wcuPUAYXeEETkmkoDuARj6GFBqr5zBCWvW\nrEFu3Zq5PXrQAkjp0YNwjQabDZ6d7ORJ38+Y+e98ePAlL0d6eRGJVKhXxjVGHIUOou+P5idzNWoU\nbH8rjAXzfld84azdu8Hl8pQFxDMhsUql4pZbbqn/wIUG7fSy02QnZhN4XSDxX12ZkxhIssySn37C\nOXMm1wQE8H2XLqiVnoOI58fm48hX8Ow1G2D2MnFvtI5dOYdqQoNQuLCQyDGRaCI1HDKbCTb5ITuV\n3HHHBVZOTvbUCAwLA2D16tUMGTIEP7/L+x6XcPHsRXb2DdhH5qhMggcE0/GLK/NyZY7NRoutW9n1\n6KP46/Us79ixJokem5vMJ6sNTG23logfV0BAgJejvfyIRCrUG8khUbm9kpBBnt66P5RWUHlYR2Li\nBdq2JHnmTzsz00tJSQlr1qxh2LBh9Ry10BDJsszJmSfZGb0Te56d7ju703ZeW3SxOm+HVu/2VFXR\nOy0NY0EBg5KSyO3bl3CNBo4epXLsJIY80ZK2EZWM3jIOxEHoJSEu7Qr1pmxTGZJNIqhfEOtLSthQ\nUYrP8u6M33yBlfftg/z8miL1K1euJCgoiOHDh9dv0EKDlD83n5y3c2g9pzVhw8LQxVx5CRQg02xm\nZGYmrVQqCiZMYOLy5Z5CJXY7PPMMU38ewSlNG1I2qggSdRcumTpPpGlpaaxcuRKA6dOnExgYyPr1\n60lJScFkMjFjxgySk5PZu3cvvr6+PPXUU0REiFkHrgSl60sJGRSCT4gPz+46TtsDTekVbyA09LwV\n3W54/nno1g2aNMHpdLJu3TpuuOEGdLor8wuzNi6nNilLMvnz88l6Oov2C9sTNSbK2yF5zSNHj/JR\nQQHXBgbSetUqDvv5MWjQIDIzYeMDX7Nw9+sclDry/fcKunb1drSXtzq/tLtq1SoSExMZPnw4GzZs\nAECv1/Paa68xdOhQ0tLSyMjIICgoiJCQEMLO3P8SLn/lW8oJGRTCyuJijlqtnHivCX37XmDFH3+E\nLVtquvFOmzaNlJQURo8eXb8BXyYuhzbpLHdyatYpzEPMHHv0GHGz4q7YJOqUJFYXF7OwsJA3g4OJ\nfvVVFr35JvPnz8do1NCli8w7u/pwUx8r+/crzpaoFi6hOj8jdbvdaLVawsPDSUlJAWDAgAGUlZWx\nefNmEhMTiY6OpmvXrqxatYpt27Zxww03/OXrpqam1nWotdLQ4oGGHZNUIWE7aaMgqIB5aW6U+0O4\nq1c1Xboc5vywW736Kopevciqrsa1axdLly5lwoQJNG/evNbvsSHuo0utMbdJ2SbjPuzG/pYd6aiE\nzx0++Nzlg7GtEWOq8ZJv/3ze+vyc3W6OJDHVauWUJNGjtJTnb7mFrh06kJSURExMC0aPLidObyHN\n0JejiYtxOFL/0L7+6bbrW2Nqq3WeSH19fXE4HBiNxpoj29zcXBYsWMCMGTPQ6XQcOnSIbt26ERgY\niMvluqjX7dmzZ12H+o+lpqY2qHig4ceU81YOOUE5hN0Wz7Y9e+ie24bPPw9DqYw8949On4atW+G7\n7+jZsye//PILxcXFPPfcc0RGRl5gK/8snoaiPr4sGmObtBy1cGLaCco2eO6rB/QKoFN2Jw6VHPLa\n/6G3Pj9nt3vCauXhjAxUOh2Z8fEMueYa7hs5kgULFuBwKHjwQdizR+bHjjPR9x5K9+uvr7Nt1zdv\nttV/0ibrPJGOGDGCF154AafTSd++fcnKyuLFF18kNDSUxMRE7rrrLmRZ5qWXXkKWZWbMmFHXIQgN\njLPUyckZJ2nxVhy9U9ORDwUyb0woygvdWEhNBa0WEhIA+Pnnn+nUqVOtk+iVrDG1SVmSOfXaKXJe\ny8G3lS8dV3TEcL0Bn+Azg4xLvBaaV9116BBrjEY66/X80LUr2WlpHDp0iHXr1qFQKHj8YSfJa11s\n0t/L1bvXwYz13g75ilLniTQ+Pp433njjnOeWLVt2zr979epV15sVGrCCTwpQG9RsG6ijsNjG9Vu7\n0+uJPxkQPncu9OlTU51h8+bNDDwzfZrwzzSGNmnLtZH3Xh6nPz+Nq9xF6zmtiR4fjdLnyh6hZ3W7\nmWOzsdZkIrlzZwaFhJCVlcWtt97KnXfeSatWrdj88QmWLmnCAp/H6T2+JUzOgWaXx+TkjYUY/iJc\nUrIsU/FjBfQPZFzOEQLSoljzkfbCK2dmwsaN8PPPABw7dozt27fz5ptv1l/AQr2SZZkTz50g9+1c\ntLFaYmfGEnZbGLpmond2od3Ok1lZrHI6md+mDTeHhuJwOBg8eDDdunVj+fLlFOe7mDDRh0GhqYw8\n+i4Ei8kcvEEkUuGSynkzh4pfKnh3ugY535etw9sQHv4nKy9dCgYDnOnosmbNGlq1asW1115bfwEL\nl5wly0LhJ4WYdpswZ5hxlbuIXxdP6JBQFEpRuq7Y4eC9vDxez8mhja8vH/v68mBTzyDQpKQkcnJy\nWLt2FxPGq/hutYUoqYpFuzuhFEnUa0QiFS4ZqVwie2Y2uVNC+Ob6UgYv70a3CaoLr/zdd/D66/DB\nBwDY7Xa2bNlCz549PQPMhctC0edFHHvkGLo4HaG3hhJ1fxRB/YLEGegZp2w2rklNxSXLLGzXjjFR\nUexLSwNAkiRefvllRo16mptvDsLPUsLrjhkMmz+Q4JadvBz5lU0kUuGScNvcWCdacfrC+H6l8G5b\n3pvte+GVZRlWroQbb4SJEwFPB5mMjAw2btxYj1ELl0p1ejVlyWVkv5RN00lNaTmrJUr1lX3/83z/\nyc3lmePHidPp2NWzJ6HnzeKwY8ce8vI6sOeHp6nMr2arT19afjoVxt3lpYiFs0QiFS4J4xoj0nGJ\n2Qv98NkcQj9nE1q3/pOV33/fU3zhzHRpFRUVfPPNN2zcuJEuXbrUX9BCnavYWoFxtZH8D/LRxmhp\n8VILmj3TDIVKXMI9yyVJTMrK4uOCApLat+feyEiUv5udRZZhw4YCbrstBgUbaFm1k5Vt36Xl999D\nixbeC1yoIRKpUOesJ62cePYE5jvUbGhuIXZFO9as+ZOV7XaYNQuefhpuvhmAd955h8jISPr06VN/\nQQu1Zs22Ur6lHGuWFesxK5YjFiyHLBj6GGj2XDPiZsWJBHqeHZWVvJmTQ3JZGYvbt2dU1LnVmkwm\nGD06msOHmxCgPcwBnwHEKkthmUiiDYlIpEKdO/boMdThPjw52g6/BTP7wUD8/S+woizDo4+C1QpP\nPQVAZWUl8+bN44UXXsDX908uBQsNgtvsJv+DfCxHLNjz7FT8VIEmSoNfez98W/tiuM5AUP8gArqL\nabt+r9Tp5IjFwqayMl4+dYrrDQZSevSgx3lTINlscN11pzl82MzQoVNZE5iPzy4JMotA9BtoUEQi\nFeqU7Jap2lPFsid8OAnckt6eO565wFmILMOcObBwIXzxBWe78i5ZsgSlUsn48ePrN3Dhb9sRvQPZ\nKRN2Rxj+3fyJeSKGkFtDUIhJoy/osNnMiIwMDpjNADTVaBgdGUlShw4XXP/uu3dx8GAsQ4a8z7pJ\nt6FISPBU/RJJtMERiVSoU5tmHcFtdbG6mYzP1K48NfcCY0ZtNhg2DDZv9vTSveceZFlmy5YtvPPO\nOzz44IP4X/AUVmhI2n3ajpBBIagN4mvkr6wsLmbCkSPcEBTEovbtaefri7/6z/eb0Whi/frmDBhw\niBdfHIvi/vvhlls8xUqEBke0AKFOOCWJXeklqF8uIulmPUMyO/PwokP06nVevUxJ8nwZpKZ65hzt\n2pWioiKGDBnCvn37GDhwIE8++aR33oTwt0SMaJhTrTUU+6urWVFczLqSErKsVl6IjWV6bOw5HYnO\nd/p0CePHHyA5ORpoxgevtCdyyhhIT4clS+oveOFvEYlUqBVZlnn02DGWnypkyosyTUNUmHx6sPR5\nFVlZ563scHjuhaamwrffQteupKen07t3byIiIsjKyiI2NtYr70MQassty3yQn8/PFRUcs1g4ZLHQ\nzd+fMZGR3BUeTms/v//597/88gv9+q0C3ufqqw8w7aFK2o/si7OqyjM8rFu3+nkjwt8mEqnwj5lc\nLp46fpzkPYW8/owv0UYnzqmd+PpVFRc86P7wQ1i8GL78EgYPJjMzk969e9O1a1c2btxIQIDolCI0\nLhlmM79UVJBhsfB9WRlZVisPRkdzfXQ0Xf396R8cfFGv43bD5MnLUKv/w1OTZd68ywFP/RsMBg4u\nWkT3i5jWTvAekUiFv0WWZVJNJpbkFbN5VwG3rpP55Fsl1UEq2n3fmQ4Jf3LUbTbD9OnwxBMwbBj5\n+fkkJCTQr18/vv32W9FBRWhUnJLE27m5TD95kjidjk56PcPDw+kfFERCSMhFv47LBfPnu3n+eRvV\n1QuICqpi5jfXwtu/wcCBsGgRUmXlJXwnQl0QiVS4KKdsNjaXlvPukQIOK00M+1DLvDUSBX56zPfF\ncOe8SJSa/1Gp5rffPGNGp04lJSWFZ555BkmSWLlypUiiQqOyvbKScYcPc9rh4PMOHRj5D6f4W7vW\nc6cjP9+Ny/k5K6J/ZEThKhQ33w3fr4Gztzka0QTXVyqRSIW/tKG0lMEHDqCwK2FHKPftjmb0pqPE\nvtGKG6fE/HUilGVPLd2ePbHrdAwbNoy4uDg2btwoeucKjYZLlnny2DHm5OczMiKCOW3a/KGM3/9i\nt8OuXXDkiGcUyxdfyFxzTSbZ2Xew+56eXL1mLezdC127cuF7I0JDJRKp8AdVVbBlC3yeYyTdr5Tj\nbYvgm2iGHm/DnEkW8j7eT8ioSOKeu4g5D91ueP55ePttmD2badOmUVZWRnp6OmFhYZf+zQjCP3D2\nFkaGxcIxq5WjFgtbzWbMViuL2rVjbFTU37qScuoU3HQTHD0KISEVuN3ZwNfsT32NrSNu5+oVK+Cd\nd0SHokZKJFKhhluWGf2pkRU7zdC7FLlbNU3yg7n5YEumWcEvN42TA6ox9DHQ5oM2f/l6PkYj9O8P\ne/bARx+xRKfjvaeeYtmyZSKJCg1Wcmkpz588yb7qaqI1Gtr4+tLG15d7NRoe79aNFhdZcau83HNH\nIzXVU046JsbJ9dffx7593zB53Djuy82l+XegWLUKJk+uqe4lND4ikV7h7JJEdpWdzUctzCo+SVGM\nhZbDArg61o9/R8VyfZWCzImZSDaJoMea0vGLjvi1u0CHIqcTMjI8E3Onp0N+Ph3T0iAuDuMvvzBl\n3jySkpJ47bXXuOeee+r/jQrCBciyTLHTyXGrleNWK4ctFt7KzWVcVBRrO3U6J2mmpqb+aRK1WCA3\n13PmuXMnrFghc/iwArVaJi7OSnT0Qfbu/RdRUTp2vPcenZ5+GoKC4L33YPBgaHYRV3eEBksk0itQ\nhdNJSqWJdUerWGTNwaGSAFDbdLxw+hqe7S9R9GkBVTtyOZhSRfT4aFq+3hJNmMZzqbao6NzlyBFY\nsADKyrA1bUpJx45UhYRwpE8fkgMDWXTddcTFxfHrr7/Su3dvL7974UpyyGym0G6nwuWi0u2mwuWi\n0G7nuM3GcauVE1YrZsnz+Y/SaGil0/FmXBxP/UViKyuDn3/2nHHu3Albt8pIkgKlUiI6uhi7fRXw\nBS7XPo4etaNSqZgyZQqJd9yBeuJE6NIFNmyAvxhbKjQOIpFeQQ5WV7Mku4QPc/OxapxQpCP0eDTD\n5Biee0BDXF8lp5ef5rfOhwm8OpDgm4Jp8XILgvsFoPh8KSxaBDt2eKoTAbKPD66wMLIdDr4ODORD\ni4Xs/Hy0JSUEBgai1WoJDw9nxowZzJw5E6VSzD8p1A+nJPHqqVMknjqFCjCo1RjUaoLUaiJ8fGjl\n60tfg4FWvr600ulo6euL/iJq2B49KvPBB6V89lkQkiQTFZWPv/8R9PqPMZl2oddbiAqMZmz37twe\n0RtDdWd8y8pQFxejWL7cM3n99ddDUpJIopcRkUgvc7sqq5ifXUSGycJeRyVyrh/B2RG807wlt92k\npulIz3pFS4rYPjkLV7mL8BHhdFzREQV4ekfcPBzS0nAPHsyWbt34JSeH1IIC9mRlUV5YSJcuXRgx\nYgQvNm1Kv379aHFmeqfU1FR69uz5Z6EJQp2QZJlCh4Nsm43NZWWkVVezx2Si0uViSfv2jI6M/MdD\nrMxm2L4dfvqpjGXLAsjNVQCnUSjep2nTpTRpEk1MTAzvxWu5YYcadakTMjM9S3y855Jt06bQubPn\nsWdP6NGjbneA4HUikV4GrFbIPKwjq8jJnioTW9xGsuRqrP52pGAH7DfAQQOaE02Z2jeMaZMlJKOd\n4s/ySNtUhjXLivO0k+YP+xOjXIfmZBp0PAk5OWCxYO3cmRkDBvDJN9/gcrm4/fbb6TN0KA+0a0e7\ndu2Ij49HJWakEC4xWZYpkSR2VFZy0mbjhNXKCZuNXyoqOGmzAdDJz4/+wcH8KzSU4eHhhFxgeIrb\n7aaqqorKykoqKyupqKigvLySTZuCOH7cl4oKBVajhL1Kzcmyjkiykghy6KBOY1b0FAaHHCdYpURh\nwnOg+dtvnvKXL78M3bt7ZjKKifEswhVBJNJGyOmE9d9J/HDQzo8V5RxpUgxtTaDfDnowHAmhlzqM\nDlVqOpWq6BWsI7i/G2UrM6cSt5Hy8pl7ov7QpNMxmgbtx499BHy0HZo1I69XLzLbtuVkq1ZsLyvj\n85QUeup0zJ07l0GDBhEdHe3lPSBcrmRZxi5JWCSJg2YzqSYTRWfONr8rLfXcz9y7lyiNhpY6HVGS\nRNeCAu4uLcXfZEKyWLDZbByxWplps2E7sxQXF5OTk0NRURFVVc3Q0Y8ADGgxoMaASxFPoXwNPZXb\niJDLaSKXYKCCeM07/F/gDgxNg1BGRBDSrh2EdYfAwP8uAQGeM89Onby9+wQvEYm0gaquhg1bnRwr\nd3DSYiNTVUm2y0qpwo7dYIdQO/QBnV3NbepIuuDgth4diDrqxlVYjGnTUcpSVUgOJVVAtcqFWm0l\nRJ9NM/2XaEqOoKkuQ+XoBHcNhpb3c0g5njlbt/LZkiV0796dpk2b0vKaa/jx9de5QdT6FP6GKpeL\n9OpqTjudmN1uzG431WcezZKE0eEg3+Gg0G6n2u3GIklYzjzKv3udOKWSQLsdjdlM/7w8KvfsQVlc\nTOHJkxzIz2dndTVarZasNm3Q6XTodDq0Wh0qVSgKRQSyHIvsDCbM6k9TfwP5IbFsqeqJgSJachID\nlRjUFsL8s7m/30/0vEoB0dHQo9cfCiOkpqYSIm5VCBcgEqkXyLKn6EFhoafSya6jDnZpjJxW2KhW\nurD6OHFEWKClBfwAtwJDYQDRkp6rgvW0C9ZydayO7tFaWmo0nH7lFRRLvsRWcTXHKkfhSwGByoPE\n6rPQRmZh91dg1ftg9vXFqNVyRKMnO+AGsrRaymw2qtPSyFu/ngMHDjBw4EDWrl3L0KFDvb2bhEbE\n6naTZbVyzGpljdHIyuJiZCBIrUavUuGvUqFXKtGrVOhVKsJ8fLgqIIDo0FACVSr8VCp8JIkNX31F\n+p49HExNxVJUxAmjEb1eT0xMDP5NmxLg60uX7t2JGTKE0NDmbPy+J4cOhVNdBUWlUGVSYDIrkeX/\nJj1WblYAAA2TSURBVMAAqgjHSIS6jCa+ZSR3nsKgmxUonnkagoPhb1QnEoQLEYm0HjgliQq7m5SD\nLp5fVM3hKiuuYDuE21FH23ElmFC5FLQo1hJtdxHqtBNjtjMmPZfW1aX4FxdjLy/HXlmJvcqE8ZQF\nuRSqLT6cdBpw0JEi/oMTK6mKT/lFs479vjoUOh0ahQatS4vGqkHjdqN1OvH398ffbkevVhMcHEyz\nZs3o06cPS5YsoZuorCJcpG579mByuzG53RidTgACVSo66fWs79yZGwwGdIDZbKaoqIjk5GR27dqF\n2Wym8MzQE+vvluLiYnx9fRk1ahTPzJxJx44diYmJocQYwNF0O8d2FHNw9V727ohmVXUUJ5zN8MPC\nZF4ngmICqfIsGjshnZsS3kxHWKweXe/ungLwoa3ORJ7gtX0mXJ5EIv0H0tP1nDrl6eRTbZUodNkp\nkK0USdXka02cCK3CrnITVCYRmyehAlRuz9JED90lO6HlVsJzTURUlRNSZCT+eDY6hwuHxg+nWoPD\nrcDqlMiSwIUPEs2Q6IKKaFQ0AcCudFERYMevWwCagUqun9KfQbrbeN67u0e4QgzRaHBVV+OsrERZ\nWoqcm8vW9espKCpibHU1ZrMZ25lOQACxsbH079+fqKgofH19/7vodAS7XGhLnfjY2mIr0VC6wMLX\nx7aTXhzFSutQNChphZk4jS8duzm5rXUOHTqconu8i8DIgaDXg7+/5zEoCLRaL+4Z4UpT54k0LS2N\nlStXAjB9+nQCAwPZtGkT27ZtQ5IkXn75ZVauXMnx48fx8fFh2rRpdR1C7bndYDKByURlQTULlxey\n1ajhmEKHTdLio5HxC0hBo3eg0UroHKC1gr8R+uZLjDsm0aQCQiwqJIWMpHSjUEiAC5VkQ5LduHHh\nkmVcBGBDzw6a4VTKoFCiUqlQ+aoIDQ/FL9APnb8OfbQe/xh/tNFaggYEoWuhQ6VX1XTrT01NRa0T\nx0XCH12qNvlqt6cBNb5aPcGGMIICQ4iKGEmPG9qhUelQq7SolRpUSg0+Ki3+egPmajhwUENhsRpT\nhZtqoxWTTY1J9seBFgUSoaoKQrQWQoIkQtsqWTfkN/51q4QqNobUQqsYUiU0OHX+zbtq1SoSExNJ\nT09nw4YN3H333SQnJzN79mzWrVvHzp07+e2335g9ezYffPABJ06cIC4u7m9vp9rlIstq5YTJQXa5\nE7vNgbOyCofVgcPmxGKxYjVbcTpdOJ1uXE4XTreM2y3hcku4JNmzyOCUFbhkJS6FEidKdDY1UZU6\nAh0+6NwqgsxKbs508thxJyqpuiYGp9KJU+HAhRWnZMWhsGNSmijwKSJde5p8QzEnA06i1qjx8fEh\nICCAvn370qRJE8LCwggLizrzGEZoaChacRQtXAKXqk2q+B41LpR2F+ZiF/ZiF6VZLo7scKHm/KUa\nNRXosNGRDLqTQ4DaRkDnFgTc0AP/NtGEtA2jUy9//ANCgN/P6fm7YSSFhXW+fwShtuo8kbrd7pqK\nNikpKZ6NqD2bCQ8P5/Tp0zX/joiIwGg0/qNEmpCeTkpVFUqXAqlUAy4FuBUgKUBSgtsfFP6gUnh6\n3qlA6ZbBDQpJRiHJ4PY8KtxuFJIbhVsCt5svPlagt8NpP4lKdTXVcjb5/kbKhulo0rMJhlgDA/81\nEI1Wg4+Pj5hPU2jQLlWbdG36CdRqUKn++HjOc37n/a6d52e9HjSaS/fGBaGe1Hki9fX1xeFwYDQa\na2b4ODtY/+xzZ0vFFRcXX/RlmtTzJrf9ADzjtwCC/+qv5fP+rTiznHXebhjheQgFIAj447jJI0eP\n/NVG6935+6ghaGgxNbR46sMla5MhIRf+hdvtWRyO2gd/oe168f/QW9sW77lhU8iyfH6WqZWDBw/y\n+eef43Q66du3L/Hx8eTl5bFlyxZkWSYxMZGvvvqKjIwMtFotU6dOrcvNC4JwHtEmBeHSqvNEKgiC\nIAhXEjEdhyAIgiDUgkikgiAIglALIpEKgiAIQi2IRCoIgiAItdDgSuFcTBWWs2PevBnT+vXrSUlJ\nwWQyMWPGDCIjI70aD8Dx48eZNWsWCxcurLdY/ldMu3fvJjk5GbfbzeOPP054eLjXY/rpp5/YsWMH\nJSUlTJ06tV7/387auXMn3377LbNmzQLw+uf7Ypw6dYq5c+eiUql4/PHHadasGeB5L2vXrsVms/Ho\no4/Svn37Ot2ut74PvNnmvdW+vdmGvd1Wa9smG9wZ6dkqLMOHD2fDhg0AJCcn88orr3D11Vezc+fO\nBhGTXq/ntddeY+jQoaSlpXk9HofDwapVqwgO/stBtfUW09q1a/H398fX15fQ0NAGEVNqaio5OTlU\nVlZ6ZV+dOnWKjIwM7HZ7zXPe/nxfjDVr1vDkk0/y5JNPsmrVqprnrVYrb775Jg8//DC//vprnW/X\nW98H3mzz3mrf3mzD3myrddEmG1wi/X0VFqPRCJxbheXsc96OacCAAZSVlbF582ZuvPFGr8ezYMEC\n7rvvPq9VWbpQTPn5+Tz++OO0bt2abdu2NYiY+vfvz/z580lISGDfvn31HlNsbCwPPPDAOc95+/P9\nZxYvXsxDDz3EQw89xMKFC4mIiPhDjP3798dqtbJ06VLuuOOOOo/BW98H3mzz3mrf3mzD3myrddEm\nG1wivZgqLA0hptzcXGbPns2MGTPQ6XRejcdqtbJ//37mzZvHgQMHvHJWc6F9FBkZiUajwWAwIElS\ng4hp8eLFKJVKQkJCqKioqPeYLsTbn+8/M27cOD7++GM+/vhjHn74YYxG4x9iLC8vZ9asWUyaNOmS\nnLF46/vAm23eW+3bm224obXVv/sZU7300ksvXeKY/pbw8HDmzJlDeno6rVq1Qq1WExERwaeffkp+\nfj5jx46tKWfmzZhmzJiBUqnk119/xWAwEBMT89cvdIni8fX1Zdy4cdx4443s37//D0dX3ohJrVYT\nFRXFggULyM/P59577633e38XikmhUPDFF19w6tQpxo4d67X7kZs2bcJisTSIz/fFiImJYc6cOWzf\nvp1HHnmE/fv3k5+fzyeffEJJSQl79+5FkiRat25dp9v11veBN9u8t9q3N9twQ2irtWmTorKRIAiC\nINRCwzv0FQRBEIRGRCRSQRAEQagFkUgFQRAEoRZEIhUEQRCEWhCJtJH77bffGDt2LKNHj2b06NEc\nP3681q85evRoqqqqLvi7vLw8UlJSAE8FEkEQ/rmvvvqKxYsXA2CxWLjnnntITk5m+/btjBo1ipEj\nRzJy5Eh+/PHHmvUTEhJq2vvo0aPJzMz04jsQoAGWCBQuXmlpKW+99RaffPIJBoOBw4cPM2XKFL78\n8stLts3du3dTVVXFtddeW1NOSxCE2nE6nUyaNIm77rqL+Ph4nn32WebPn09wcDBms5mHH36Ya665\nBoCRI0cybtw47wYsnEMk0kbsxx9/JCEhAYPBAED79u1ZuHAhy5YtY+3atSgUCh566CEGDhzIiBEj\nCAgIoE+fPiQnJ9f83LJlSz766CNkWWbMmDEMHjy45vUPHDjAu+++i8vlQq/XM2/ePJKSkjCbzfTu\n3ZspU6bw9ddfM3v2bFJSUlAoFEyZMoUePXpw++23Exsby4kTJ5gwYQJDhw711m4ShAZNlmWmTJlC\nnz59GDZsGPPnz+fuu++uKYun1+tJSkryWtUy4a+JRNqIlZaW/mFQuNvtZv369axYsQKHw8GIESO4\n8cYbKSkp4cMPPyQ8PJylS5fy4YcfEhoayp133sny5ctRqVSMHj2aAQMG1LzWiRMnePPNN4mMjGTi\nxIlkZWUxZswYqqqqaNeuHQAZGRlkZWWxcuVKiouLmThxIqtXryYvL49ly5ZhNpt57LHHRCIVhD/x\n2Wef0bx585pSdIWFhfTu3RuAzZs3k5SURFVVFc8++ywAy5Yt44cffqj5+6VLl9Z/0MI5RCJtxCIj\nI8nPzz/nuTVr1tRUBlGr1URGRlJWVoZer6+ZueHsz6WlpRQUFDBhwgQATCbTOXUlIyIieO211/Dz\n8yMnJ+eCJcKys7Pp2rVrzfoOhwOAJk2aoNfr0ev15xSDFgThXP379+eFF15gxIgR7N27l4iICE6f\nPg1AQkICCQkJvP/++9hsNkBc2m2IRGejRuyGG27ghx9+qOkYlJ6ezooVKzhx4gRutxuLxUJhYSFB\nQUHnlLg6+3NQUBCxsbEsWrSIpKQkhgwZQkRERM16b7zxBjNnziQxMRG1Wo0sy3+4vNS8eXPS09MB\nKC4urqlRKS5DCcLFiYuLQ61Wk5iYyPTp00lISGDx4sVUVlYCnhl2Dhw44OUohf9FnJE2YiEhIUya\nNImHHnoIlUqFQqHg008/ZefOndxzzz24XC6ee+45fHx8Lvj3KpWK8ePHM2rUKBwOBwMHDkSr1db8\n/uabb2bs2LEEBgbi7++P0WikdevWTJ06lR49egAQHx9PXFwcd999Ny6Xi8TExHp574JwuYmPj6df\nv36sXr2aJ554gkmTJiFJEmazmaFDh9KvXz/Wr1//h0u7DzzwAP369fNe4IKotSsIgiAItSEu7QqC\nIAhCLYhEKgiCIAi1IBKpIAiCINSCSKSCIAiCUAsikQqCIAhCLYhEKgiCIAi1IBKpIAiCINSCSKSC\nIAiCUAv/D3pQwoS0q4o8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114477748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 4 panel CDFs (alpha, beta, correlation and KGE)\n",
    "model = 'vic'\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": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Tmt1+3333NZud5oiY9IlIdbhdNDXV1l32NBqNTXfj69Onj7BWPiB4wx0iIiKSD5M+EamO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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1144b3668>"
      ]
     },
     "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": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SAFquWEfkebj2vgOUbhW1ZntOZ+8ORtpmNptRU1MjxUz8RJ6FSd8BBoMB0dHR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\nJn0iIiKN+H9wtRzpjqDFxwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1144fb2b0>"
      ]
     },
     "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": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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iYryFqL+f6CBt6jx9ko+i3ftarRYDBgwAAPTp08d48wPAH/7wB2zatAkODg6w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\ngkGfiIhIEgz6REREkmDQJyIiksT/Ays7OmEShbAWAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113402f98>"
      ]
     },
     "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": 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5aGho4PDDD5+wZ3vsfwq7wuaubpJJqLBnMT9aV5w0Biq1hJEilonzXqaRmmAV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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": 16,
   "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": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Dhlw2a+S///2vFi5cqJCQECUkJGjHjh2SpBYtWmjkyJFavXq1Vq9erWbNmqlmzZpatGiR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\nAGBiBAEAAEyMIAAAgIkRBAAAMLH/B1Xj18+TY2eEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x121af7320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Construct boxplots for metrics group-by periods\n",
    "model = 'mhm'\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",
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