notebooks/NERSC-plot_flux_from_massBalance.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generate flux bar plot from Mass Balance file (.dat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-25T18:50:38.911859Z",
     "start_time": "2018-06-25T18:50:38.905625Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'3.6.1 |Anaconda custom (64-bit)| (default, May 11 2017, 13:09:58) \\n[GCC 4.4.7 20120313 (Red Hat 4.4.7-1)]'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sys\n",
    "sys.version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-16T21:39:50.219617Z",
     "start_time": "2018-07-16T21:39:50.212622Z"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "import matplotlib.cm as cm\n",
    "from matplotlib.colors import LinearSegmentedColormap\n",
    "\n",
    "# import seaborn as sns\n",
    "# plt.style.use('default')\n",
    "\n",
    "import h5py as h5\n",
    "import glob\n",
    "import matplotlib.gridspec as gridspec\n",
    "import datetime\n",
    "from datetime import timedelta\n",
    "from scipy import interpolate, stats\n",
    "import os\n",
    "import re\n",
    "from natsort import natsorted, ns, natsort_keygen\n",
    "import pandas as pd\n",
    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
    "from shapely.geometry.polygon import Polygon\n",
    "from scipy.stats.kde import gaussian_kde\n",
    "import pickle\n",
    "from textwrap import wrap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-13T20:42:01.162820Z",
     "start_time": "2018-07-13T20:42:01.135948Z"
    }
   },
   "outputs": [],
   "source": [
    "def batch_time_to_delta(origin, x, time_format):\n",
    "    y = []\n",
    "    for ix in x:\n",
    "        temp_y = abs(datetime.strptime(\n",
    "            ix, time_format) - origin).total_seconds()\n",
    "        y.append(temp_y)\n",
    "    y = np.asarray(y)\n",
    "    return(y)\n",
    "\n",
    "def batch_delta_to_time(origin, x, time_format, delta_format):\n",
    "    y = []\n",
    "    for ix in x:\n",
    "#         ix = np.asscalar(ix)\n",
    "        if delta_format == \"hours\":\n",
    "            temp_y = origin + timedelta(hours=ix)\n",
    "        elif delta_format == \"days\":\n",
    "            temp_y = origin + timedelta(days=ix)\n",
    "        elif delta_format == \"minutes\":\n",
    "            temp_y = origin + timedelta(minutes=ix)\n",
    "        elif delta_format == \"weeks\":\n",
    "            temp_y = origin + timedelta(weeks=ix)\n",
    "        elif delta_format == \"seconds\":\n",
    "            temp_y = origin + timedelta(seconds=ix)\n",
    "        elif delta_format == \"microseconds\":\n",
    "            temp_y = origin + timedelta(microseconds=ix)\n",
    "        elif delta_format == \"milliseconds\":\n",
    "            temp_y = origin + timedelta(milliseconds=ix)\n",
    "        else:\n",
    "            print(\"Sorry, this naive program only solve single time unit\")\n",
    "        y.append(temp_y.strftime(time_format))\n",
    "    y = np.asarray(y)\n",
    "#     y = [datetime.strptime(x, \"%Y-%m-%d %H:%M:%S\") for x in y]\n",
    "    return(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def natural_sort(l): \n",
    "    convert = lambda text: int(text) if text.isdigit() else text.lower() \n",
    "    alphanum_key = lambda key: [ convert(c) for c in re.split('([0-9]+)', key) ] \n",
    "    return sorted(l, key = alphanum_key)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## I/O files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-16T20:17:36.565230Z",
     "start_time": "2018-07-16T20:17:36.546004Z"
    }
   },
   "outputs": [],
   "source": [
    "# inputs\n",
    "data_dir = \"/global/project/projectdirs/m1800/pin/Reach_scale_model/data/\"\n",
    "fname_usgs_gage = data_dir + \"USGS_flow_gh_12472800.csv\"\n",
    "fname_mass1_coord = data_dir + \"MASS1/coordinates.csv\"\n",
    "fname_river_middle = data_dir + \"river_middle.csv\"\n",
    "fname_river_north = data_dir + \"river_north.csv\"\n",
    "fname_river_south = data_dir + \"river_south.csv\"\n",
    "fname_river_geo = data_dir + \"river_geometry_manual.csv\"\n",
    "\n",
    "case_name =  \"HFR_model_100x100x2_cyclic/\"\n",
    "\n",
    "model_dir = \"/global/cscratch1/sd/pshuai/\" + case_name\n",
    "fname_model_origin = model_dir + \"model_origin.txt\"\n",
    "fname_material_h5 = model_dir + \"HFR_material_river.h5\"\n",
    "fname_pflotran_h5 = model_dir + \"pflotran_100x100x2_cyclic_2011_2015.h5\"\n",
    "fname_mass_dat = model_dir + \"pflotran_100x100x2_cyclic-mas.dat\"\n",
    "\n",
    "model_dir_2 = \"/global/cscratch1/sd/pshuai/\" + \"HFR_model_100x100x2_new_iniH/\"\n",
    "fname_mass_dat_2 = model_dir_2 + \"pflotran_100x100x2_6h_bc_new_iniH-mas.dat\"\n",
    "\n",
    "fname_river_bc = \"/global/project/projectdirs/m1800/pin/Reach_scale_model/Inputs/river_bc/bc_6h_smooth_032807/\"\n",
    "fname_river_bc_1w = \"/global/project/projectdirs/m1800/pin/Reach_scale_model/Inputs/river_bc/bc_1w_smooth_032807/\"\n",
    "# outputs\n",
    "out_dir = \"/global/project/projectdirs/m1800/pin/Reach_scale_model/Outputs/\" + case_name\n",
    "# out_dir_2 = \"/global/project/projectdirs/m1800/pin/Reach_scale_model/Outputs/HFR_model_200x200x2_6h_bc/\"\n",
    "\n",
    "fig_dir = \"/global/project/projectdirs/m1800/pin/Reach_scale_model/figures/\"\n",
    "\n",
    "fig_river_stage = \"/global/project/projectdirs/m1800/pin/Reach_scale_model/Outputs/river_stage/\"\n",
    "# fig_river_stage_6h = out_dir_2 + \"river_stage/stage_6h.png\"\n",
    "# fig_river_stage_1w = out_dir + \"river_stage/stage_1w.png\"\n",
    "fig_finger_flux = out_dir + \"mass_balance/\"\n",
    "fig_net_exchange_bar = out_dir + \"mass_balance/net_exchange.png\"\n",
    "fname_net_exchange_txt = out_dir + \"mass_balance/net_exchange.txt\"\n",
    "fig_flux_snapshot = out_dir + \"mass_balance/\"\n",
    "fig_block_middle_flux_snapshot = out_dir + \"flux_block_middle/\"\n",
    "fig_block_north_flux_snapshot = out_dir + \"flux_block_north/\"\n",
    "fig_block_south_flux_snapshot = out_dir + \"flux_block_south/\"\n",
    "\n",
    "result_dir = \"/global/project/projectdirs/m1800/pin/Reach_scale_model/results/\" + case_name\n",
    "fname_flux_array= \"/global/project/projectdirs/m1800/pin/Reach_scale_model/results/HFR_model_100x100x2_1w_bc/finger_flux_array.pk\"\n",
    "\n",
    "# fig_finger_flux_6h = out_dir_2 + \"finger_flux/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-13T20:43:00.623960Z",
     "start_time": "2018-07-13T20:43:00.611410Z"
    }
   },
   "outputs": [],
   "source": [
    "date_origin = datetime.datetime.strptime(\"2007-03-28 12:00:00\", \"%Y-%m-%d %H:%M:%S\")\n",
    "# model_origin = np.genfromtxt(\n",
    "#     fname_model_origin, delimiter=\" \", skip_header=1)\n",
    "\n",
    "model_origin = [551600, 104500]\n",
    "material_file = h5.File(fname_material_h5, \"r\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "create sub blocks along river reach"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-13T20:43:57.289825Z",
     "start_time": "2018-07-13T20:43:57.267281Z"
    }
   },
   "outputs": [],
   "source": [
    "# middle block\n",
    "block_middle_coord = np.genfromtxt(fname_river_middle, delimiter=\",\", skip_header=1)\n",
    "block_middle_x = [(np.min(block_middle_coord[:, 1]) - model_origin[0]) / 1000,\n",
    "           (np.max(block_middle_coord[:, 1]) - model_origin[0]) / 1000]\n",
    "\n",
    "block_middle_y = [(np.min(block_middle_coord[:, 2]) - model_origin[1]) / 1000,\n",
    "           (np.max(block_middle_coord[:, 2]) - model_origin[1]) / 1000]\n",
    "# north block\n",
    "block_north_coord = np.genfromtxt(fname_river_north, delimiter=\",\", skip_header=1)\n",
    "block_north_x = [(np.min(block_north_coord[:, 1]) - model_origin[0]) / 1000,\n",
    "           (np.max(block_north_coord[:, 1]) - model_origin[0]) / 1000]\n",
    "\n",
    "block_north_y = [(np.min(block_north_coord[:, 2]) - model_origin[1]) / 1000,\n",
    "           (np.max(block_north_coord[:, 2]) - model_origin[1]) / 1000]\n",
    "# south block\n",
    "block_south_coord = np.genfromtxt(fname_river_south, delimiter=\",\", skip_header=1)\n",
    "block_south_x = [(np.min(block_south_coord[:, 1]) - model_origin[0]) / 1000,\n",
    "           (np.max(block_south_coord[:, 1]) - model_origin[0]) / 1000]\n",
    "\n",
    "block_south_y = [(np.min(block_south_coord[:, 2]) - model_origin[1]) / 1000,\n",
    "           (np.max(block_south_coord[:, 2]) - model_origin[1]) / 1000]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## import USGS gage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-03T18:52:10.555817Z",
     "start_time": "2018-07-03T18:52:10.468442Z"
    }
   },
   "outputs": [],
   "source": [
    "USGS_gage = pd.read_csv(fname_usgs_gage, parse_dates= ['Date'])\n",
    "\n",
    "# USGS_gage = USGS_gage[(USGS_gage['Date'] >= '2006-1-1') \n",
    "#                                 & (USGS_gage['Date'] <= '2018-1-1')]\n",
    "\n",
    "USGS_gage.rename(columns={'Discharge (ft3/s)': 'Discharge'}, inplace=True)\n",
    "\n",
    "USGS_gage['Discharge'] = USGS_gage['Discharge']*(0.3048**3) # convert cubic feet/sec to m3/s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-03T18:52:11.626496Z",
     "start_time": "2018-07-03T18:52:11.519793Z"
    }
   },
   "outputs": [
    {
     "data": {
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EQSQGoZGT4gTn/HfG2DwA6wAUAvgDwPsAygCYwxi7F8BeAH0Cx29ijM0BsDlw/IOcc2GL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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b789fad8588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "fig, ax1 = plt.subplots(1, 1) \n",
    "\n",
    "USGS_gage.plot(x='Date', y= 'Discharge', ax=ax1, color = 'k', legend = None)\n",
    "\n",
    "ax1.plot(('1/1/2011', '1/1/2016'), (3409, 3409), '--b')\n",
    "\n",
    "ax1.set_xlim(['1/1/2011', '1/1/2016'])\n",
    "ax1.set_ylim([0, 1e4])\n",
    "ax1.set_ylabel('Discharge ($m^3/s$)')\n",
    "ax1.set_xlabel('Year')\n",
    "ax1.tick_params('y', colors='k')\n",
    "\n",
    "ax1.legend( ['Dam', 'NEXSS'], loc = 'best', frameon = False)\n",
    "\n",
    "fig.set_size_inches(8, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-03T18:52:11.626496Z",
     "start_time": "2018-07-03T18:52:11.519793Z"
    }
   },
   "outputs": [],
   "source": [
    "fname = out_dir + 'Dam_discharge.png'\n",
    "fig.savefig(fname, dpi=300)\n",
    "plt.close(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-03T18:52:15.453431Z",
     "start_time": "2018-07-03T18:52:15.448112Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    4384.000000\n",
       "mean     3389.719001\n",
       "std      1474.424043\n",
       "min      1052.760000\n",
       "25%      2300.082500\n",
       "50%      3056.400000\n",
       "75%      4075.200000\n",
       "max      9876.700000\n",
       "Name: Discharge, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "USGS_gage['Discharge'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-25T18:50:58.210500Z",
     "start_time": "2018-06-25T18:50:57.836157Z"
    }
   },
   "outputs": [],
   "source": [
    "discharge_file = open(fname_usgs_gage, \"r\")\n",
    "discharge_data = discharge_file.readlines()\n",
    "# print(discharge_data)\n",
    "\n",
    "discharge_data = [x.replace('\"', \"\") for x in discharge_data]\n",
    "discharge_data = [x.split(\",\") for x in discharge_data[1:]]\n",
    "discharge_data = [list(filter(None, x)) for x in discharge_data]\n",
    "discharge_data = np.asarray(discharge_data)\n",
    "discharge_time = [datetime.strptime(x, \"%Y-%m-%d\")\n",
    "                  for x in discharge_data[:, 3]]\n",
    "discharge_value = discharge_data[:, 4]  # .astype(float)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## model dimensions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-13T20:44:15.163648Z",
     "start_time": "2018-07-13T20:44:11.442311Z"
    }
   },
   "outputs": [],
   "source": [
    "# read model dimensions\n",
    "all_h5 = glob.glob(fname_pflotran_h5)\n",
    "all_h5 = np.sort(all_h5)\n",
    "\n",
    "input_h5 = h5.File(all_h5[0], \"r\")\n",
    "x_grids = list(input_h5[\"Coordinates\"]['X [m]'])\n",
    "y_grids = list(input_h5[\"Coordinates\"]['Y [m]'])\n",
    "z_grids = list(input_h5[\"Coordinates\"]['Z [m]'])\n",
    "input_h5.close()\n",
    "\n",
    "# x_grids = np.arange(0, 60001, 100)\n",
    "# y_grids = np.arange(0, 60001, 100)\n",
    "# z_grids = np.arange(0, 201, 5)\n",
    "\n",
    "dx = np.diff(x_grids)\n",
    "dy = np.diff(y_grids)\n",
    "dz = np.diff(z_grids)\n",
    "\n",
    "nx = len(dx)\n",
    "ny = len(dy)\n",
    "nz = len(dz)\n",
    "x = x_grids[0] + np.cumsum(dx) - 0.5 * dx[0]\n",
    "y = y_grids[0] + np.cumsum(dy) - 0.5 * dy[0]\n",
    "z = z_grids[0] + np.cumsum(dz) - 0.5 * dz[0]\n",
    "\n",
    "grids = np.asarray([(x, y, z) for z in range(nz)\n",
    "                    for y in range(ny) for x in range(nx)])\n",
    "\n",
    "west_area = dy[0] * dz[0]\n",
    "east_area = dy[0] * dz[0]\n",
    "south_area = dx[0] * dz[0]\n",
    "north_area = dx[0] * dz[0]\n",
    "top_area = dx[0] * dy[0]\n",
    "bottom_area = dx[0] * dy[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**read river section information**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-13T20:44:25.612133Z",
     "start_time": "2018-07-13T20:44:24.223137Z"
    }
   },
   "outputs": [],
   "source": [
    "mass1_sections = [s for s, s in enumerate(\n",
    "    list(material_file[\"Regions\"].keys())) if \"Mass1\" in s]\n",
    "group_order = np.argsort(np.asarray(\n",
    "    [x[6:] for x in mass1_sections]).astype(float))\n",
    "mass1_sections = [mass1_sections[i] for i in group_order]\n",
    "nsection = len(mass1_sections)\n",
    "section_area = []\n",
    "for isection in mass1_sections:\n",
    "    faces = list(material_file[\"Regions\"][isection]['Face Ids'])\n",
    "    iarea = faces.count(1) * west_area + faces.count(2) * east_area + \\\n",
    "        faces.count(3) * south_area + faces.count(4) * north_area + \\\n",
    "        faces.count(5) * bottom_area + faces.count(6) * bottom_area\n",
    "    section_area.append(iarea)\n",
    "section_area = np.asarray(section_area)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**read mass1 coordinates**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-13T20:44:30.249931Z",
     "start_time": "2018-07-13T20:44:30.238180Z"
    }
   },
   "outputs": [],
   "source": [
    "\n",
    "section_coord = np.genfromtxt(\n",
    "    fname_mass1_coord, delimiter=\",\", skip_header=1)\n",
    "\n",
    "section_coord[:, 1] = section_coord[:, 1] - model_origin[0]\n",
    "section_coord[:, 2] = section_coord[:, 2] - model_origin[1]\n",
    "# subtract last mass1 location to get length for each segment\n",
    "mass1_length = section_coord[:, 4] - section_coord[-1, 4]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-13T20:44:59.286173Z",
     "start_time": "2018-07-13T20:44:59.279794Z"
    }
   },
   "outputs": [],
   "source": [
    "# add three lines to contour indicating mass1 location\n",
    "line1 = section_coord[0, 1:3] / 1000\n",
    "line2 = section_coord[int(len(section_coord[:, 1]) / 2), 1:3] / 1000\n",
    "line3 = section_coord[-1, 1:3] / 1000\n",
    "line1_x = [line1[0]] * 2\n",
    "line1_y = [line1[1] - 5, line1[1] + 5]\n",
    "line2_x = [line2[0] - 5, line2[0] + 5]\n",
    "line2_y = [line2[1]] * 2\n",
    "line3_x = [line3[0] - 5, line3[0] + 5]\n",
    "line3_y = [line3[1]] * 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**read mass balance data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-13T20:45:11.074582Z",
     "start_time": "2018-07-13T20:45:01.529631Z"
    }
   },
   "outputs": [],
   "source": [
    "mass_file = open(fname_mass_dat, \"r\")\n",
    "mass_data = mass_file.readlines()\n",
    "mass_header = mass_data[0].replace('\"', '').split(\",\")\n",
    "mass_header = list(filter(None, mass_header))\n",
    "mass_data = [x.split(\" \") for x in mass_data[1:]]\n",
    "mass_data = [list(filter(None, x)) for x in mass_data]\n",
    "mass_data = np.asarray(mass_data).astype(float)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**find columns of desired mass1 colmns**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-13T20:45:19.441762Z",
     "start_time": "2018-07-13T20:45:19.438312Z"
    }
   },
   "outputs": [],
   "source": [
    "\n",
    "# e.g River_40 Water Mass [kg]\n",
    "mass_index = [i for i, s in enumerate(mass_header) if (\n",
    "    \"River\" in s and \"Water\" in s and \"kg]\" in s)]\n",
    "# e.g River_40 Water Mass [kg/h]\n",
    "flux_index = [i for i, s in enumerate(mass_header) if (\n",
    "    \"River\" in s and \"Water\" in s and \"kg/h\" in s)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-13T20:45:23.204435Z",
     "start_time": "2018-07-13T20:45:23.158813Z"
    }
   },
   "outputs": [],
   "source": [
    "# get total river mass/flux across the river bed for each time step (6h)\n",
    "total_mass = np.sum(mass_data[:, mass_index], axis=1).flatten()\n",
    "total_flux = np.sum(mass_data[:, flux_index], axis=1).flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-13T20:45:24.812033Z",
     "start_time": "2018-07-13T20:45:24.808499Z"
    }
   },
   "outputs": [],
   "source": [
    "# shift mass when restart happened, new starting mass would shift to zero\n",
    "diff_total_mass = abs(np.diff(total_mass))\n",
    "restart_index = np.arange(len(diff_total_mass))[diff_total_mass > 2e10]  # select 2e10 as a threshold\n",
    "\n",
    "# add mass at restart check point to all after time steps\n",
    "for i_restart_index in restart_index:\n",
    "    total_mass[(i_restart_index + 1):len(total_mass)] = total_mass[(\n",
    "        i_restart_index + 1):len(total_mass)] + total_mass[restart_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5303])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "restart_index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# plot river stage\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mass1 points (40, 186, 332) are used as upstream, middle and downstream river stage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-13T22:10:31.578695Z",
     "start_time": "2018-07-13T22:10:31.576151Z"
    }
   },
   "outputs": [],
   "source": [
    "start_time = datetime.datetime.strptime(\"2007-03-28 12:00:00\", \"%Y-%m-%d %H:%M:%S\")\n",
    "\n",
    "time_ticks = [\n",
    "    \"2011-01-01 00:00:00\",\n",
    "    \"2012-01-01 00:00:00\",\n",
    "    \"2013-01-01 00:00:00\",\n",
    "    \"2014-01-01 00:00:00\",\n",
    "    \"2015-01-01 00:00:00\",\n",
    "    \"2016-01-01 00:00:00\"]\n",
    "time_ticks = [datetime.datetime.strptime(x, \"%Y-%m-%d %H:%M:%S\") for x in time_ticks]\n",
    "\n",
    "selected_ticks = [\n",
    "    \"2012-02-25 00:00:00\",\n",
    "    \"2012-04-30 00:00:00\",\n",
    "    \"2012-07-04 00:00:00\",\n",
    "    \"2012-08-18 00:00:00\",\n",
    "    \"2012-10-17 00:00:00\"\n",
    "    ]\n",
    "selected_ticks = [datetime.datetime.strptime(x, \"%Y-%m-%d %H:%M:%S\") for x in selected_ticks]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## import river stage from generated mass1 datum files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-13T20:46:56.224363Z",
     "start_time": "2018-07-13T20:46:56.204034Z"
    }
   },
   "outputs": [],
   "source": [
    "datum_files = glob.glob(fname_river_bc + \"Datum*.txt\")\n",
    "\n",
    "datum_files = natural_sort(datum_files)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-13T20:46:56.224363Z",
     "start_time": "2018-07-13T20:46:56.204034Z"
    }
   },
   "outputs": [],
   "source": [
    "# int(re.findall(r'\\d+', datum_files[0])[-1])\n",
    "# datum_files = natsorted(datum_files) # sort files naturally, i.e. 1, 2, 11, 22, ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-13T20:50:08.791499Z",
     "start_time": "2018-07-13T20:50:07.665298Z"
    }
   },
   "outputs": [],
   "source": [
    "river_north_datum = pd.read_table(datum_files[0], sep=' ', header=None, names=['time', 'x', 'y', 'wl'])\n",
    "river_middle_datum = pd.read_table(datum_files[(len(datum_files)-1)//2], sep=' ', header=None, names=['time', 'x', 'y', 'wl'])\n",
    "river_south_datum = pd.read_table(datum_files[-1], sep=' ', header=None, names=['time', 'x', 'y', 'wl'])\n",
    "\n",
    "river_north_datum['time'] = batch_delta_to_time(date_origin, river_north_datum['time'], \"%Y-%m-%d %H:%M:%S\", \"seconds\")\n",
    "river_middle_datum['time'] = batch_delta_to_time(date_origin, river_middle_datum['time'], \"%Y-%m-%d %H:%M:%S\", \"seconds\")\n",
    "river_south_datum['time'] = batch_delta_to_time(date_origin, river_south_datum['time'], \"%Y-%m-%d %H:%M:%S\", \"seconds\")\n",
    "# datum_time = [datetime.strptime(x, \"%Y-%m-%d %H:%M:%S\") for x in datum_time]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-13T21:00:23.746349Z",
     "start_time": "2018-07-13T21:00:16.388358Z"
    }
   },
   "outputs": [],
   "source": [
    "river_north_datum['time'] = river_north_datum['time'].apply(pd.to_datetime)\n",
    "river_middle_datum['time'] = river_middle_datum['time'].apply(pd.to_datetime)\n",
    "river_south_datum['time'] = river_south_datum['time'].apply(pd.to_datetime)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## plot 6h-smooth river stage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** river stage from 2011-2016 **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-13T21:46:50.785423Z",
     "start_time": "2018-07-13T21:46:49.603696Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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soMS2/fvvvwMoeW9ffvll1PeIhnA4HNd7hsNhfPvtt7aCniI+OPF2uY0xdpAx\n9h8AnQEMKF1+8RQi6kBE6wGsBTBMEEZcYxQ0ojE4NYZWNxuMvFYbGq93xRVXYMeOHTh06BCKiopM\njc7KCoyxiPwt9913n+f3CYVCSRNnZcaMGRg5cqRtuY8//lin8YsVcdnl0ksvxS233IJ3330X3333\nnU7AOP/887Vto+YjIyND814ytu1XXnlF27YbfNOZoqIiaW4WrzAKrmJfdsUVV2D37t1lwh4iHA47\nzlSu8B8nBqcL+TZjbBtjbI24zysYYz8yxpoDaAfgX2Z5Y4hoKBEtI6Jle/bskV6LR/bjBlTRGJyK\nkRqB+M2EzYQZtV5+nJNO0pscPffcc57fIxQK4YQTTvD8utFw+PBhR94AX3zxhXTNP1r4oMU1iaFQ\nCEQUYUMgCsQyzceaNWsAWLfhe+4pE4mypRQUFHgqfPzxxx+638bnbuzLCgsLky7+R6zwpfA+ffpo\n+3j7/d///peoaikETEdkIqpARFUAVCOiykRUpfRfPQCx5Se3gDG2ESW5ZFqYHH+9NLtu2+rVq0uv\nUb58eQDATz/9BCBy2WXy5Mm2IdONwoqZzYfX6nCzDjovLy9ua/2JRPRSkhGvSJl8lmQkEZkxA4EA\nJk6cGPf78kGqfPny2jMnIgSDQdOYEzLNx2+//aadq4iksLAwqqXbM844Q/pMa9eujapVqwKQT5pk\neZJycnLQqVMnbd+FF17ouj7JRP369QHooyT37t1btcEkwkodcAtKbDyalv7P/30M4BWL81xDRKdz\nA1Miqlt6z23RXo9/XHytORQKYceOHZrF+GeffYaff/7Z8hrBYFCXUtxMy8JDSHuFmYZm3759eOSR\nRzy7T7Ji583CGIuL27SZ8NGwYUPf723EbMnQmGTMa0H4vvvuw+bNm9GsWTPt+lz4MHO9JSKd5uPQ\noUNYsGCBdswOxlhM9g2pSEFBQVRZrI22bSJ8f1FRUYTxvOwc49K0lWtvsiPaI4mI3/TYsWPjWSWF\nBNNenDH2EmPsdADDGWP1GWOnl/5rxRizFT6IaAZKDFObEFEuEQ0ioh5ElAugE4C5RDS/tPh5AFYT\n0SoAHwK4jTG2N9o/in9covABwFUQHasPW8Rvmw+FnlhC5bu9T7LEhjETPipXrqxtc88BL59NpUqV\nUKtWLe2aW7ZswbZt27B3715MmiRPaG3UfIjbToWPV199NcaapxZmWlU7nPRPMlsOo+YjHA4npL37\nZUS9cuXvSaQ/AAAgAElEQVRK02O8DZo5ECjih5PWtouITgQAInqEiD4gIttINoyx6xhjpzLGMhlj\ntRljbzLGPizdLs8YO5kx1qW07NuMseaMsdaMsTaMsY9i+aN4o+YNjX9sbtz7nBomBQIBT/3jrTqA\naDupdCLRmg+/MOswp02bhjfeeEN6TNQwcO2el4hLXDt37sSyZcvwr3/9C0CkXQHHKHyI8XKcCh9l\njRdffNH0mNXzsOqfuAGvTEMlM0ANh8NYtGhRwgx/3bz3Tz/91PTYU089hfPOO8/0mlbPzJj5V+Ev\nTnrXRxljfxPReQAuBfAmgNf8rVZsGF3JZOnY7QSLjIwMRx+E0SsmVpTmwxqnmo9BgwbF5T5eYRaZ\n8o8//rDMZMrxwyBaFD7MBPdp06bpfhsNTkUhxfg8+XKO8Z6K41h5oYTDYaxbt056rGPHjujUqZM0\nPpBM88GFyoULPfcl8Jwrr7zS9Bj/OwBI7fqs4iWNHz++TLa//fv32xfyASfCBx+5rwTwOmNsLgD/\n/MI8gDeggoIC1K1bVxM+GjduDADYvXs3+vXrZ3kNM1X3oEGDdGuoXms+jMgGwLL4gXDq16+Pa6+9\n1rZcrFqLZFl2cWoL8PDDD3t+byfGvddff73ut1HzIWK81pgxY6T3VBzH7FmuXLkSRITmzZubnnvT\nTTehqKgoom0Yn/H27dsTEngrmnfNPadkGIOkyb4dO5diM41eIgmFQhH2XV4RDoc14+R446R3/ZOI\nJgLoC+BTIirv8LyE0bp1awDH1/X4B7pp0yYAJamp7TATPpYvX45Ro0bpynkpfBg7aFnnn4gwyMlC\n/fr1MWTIENtyXoRSTgYt1FdffSXdP3nyZADHM51yd2MvB28r4WPnzp3S/UbNh4jxWsFgUNfx5efn\nl0nhY8CAAabHPvnkE91v/p6NBvEyMjMzcfDgQSxevFi336j5EN9Xsj9/YwRo0ZXW6In2ww8/RJwv\na5u5ublan+qkb/ELo5HvFVdcAaAkR1KsmlwzLr/cGIQ8fjgRIvoAmA+gC2PsIIAqAGLL3JVg+Dqo\nVXQ9cdbL0z2//vrrEbNhv4PWuF23TDfE9PaymbIZsQof8XLpdYMoiPDOyDhwxcv128xV3ejtYnWt\nYDCIBg0aaL83bdqkecYAyT8QekWNGjVMjxljUvCAesXFxbZtvFy5cti2bVvEoGbsP0RtQDz7Fi/e\n73vvvYcVK1bgjz/+cPS9yrSZp512GiZMmACgZGI6Z86cmOsVDUb35nnz5gEAJk2a5FuYBTMvznjg\nJMJpHmPsA8bYr6W/dzLGFtidlyisjJEA4NJLL9W2//GPf5iWEz9sHgDolltu0TXekSNH+p6QqawL\nH3yQ/b//+z9XrsaxCh/xtPkw64SNs7C+fftKy4ntYf78+dIyXtbLCit7KuPzDAQCun1Lly7VBoHv\nvvtO01g6sXlJZazaWUFBgXS/k5QRmZmZjtqwuHT2zjvvmN4zUf3Ovffeq00AZfTq1QtffPFFhGAh\n9svcaLtSpUoR57du3Rp33303gJLYUFdffbUX1XaE0+CBe/dG7fyZtCT18kk0vPXWW5bHFy5cGNGp\nyoQHWfIlQN9RjBkzJiHher26n+gPX1hYqAuTnSzw91CuXDlXwoAX9hrxEj527dol3W/m5SLyzTff\noHPnztpvO+HbDUbtj9MonGbCuJ3wUaFCBW1t+7zzzsPGjRu17XRly5YteOaZZwDI7RFk3irXXXed\nI5ukcuXKobi42NbLT1yG3rdvnzSjNFAySO/bt8/yWtFiJei+8847OHbsWMR+Hrdp27Zt+Prrry0j\nuVaoUBIwW6aVq1ixYlR1joabbtJnJrHLUM01Hn5pAROZKyxthA+eLIg3Mit4xEVOnTp1Ij58seGK\nM4xly5YBON6Ily5dGncbjGjzMLzzzju632KciEGDBmnh6BOFWeAqwP3apJN2YIXVx7527Vr06NEj\npusDwAMPPKDZITnBTBjyKzmXUfiwej8ioVAII0aMiNgvW3Yx/k5U9uZt27YlRMMiPlMxyumnn36K\nMWPGaMdr1qypHZs+fbqjZcFKlSph//79Ec/Z7jyzyc2uXbs87eucDqhmSwM8KR5QMum00nzwZyBr\nX8Z0DV7xyy+/YObMmSgoKMCvv/4KAJgyZYrlObm5ubrfPIp3Omq7LYUPIgoSkdziLckYPnw4AGcz\nXvGD79ChA3bs2BHhbvTiiy8iMzMTgPxjPXLkiLYdb1fbaD+W/v37AzhumCU+B7OogPHko48iw7tw\ng0TjjMGOWL0/OnfujEsuuUR6rEOHDtK6umXx4sWuZpKJsIGIRvuzfv16dO/ePWK/8ds0uqlv3LjR\nkRraD7755htXcYC8Qnwm4vvdtWsXtm7dqi2BGN9D8+bN8Z///Mfy2llZWThy5IhrLeC4ceNMj3lt\n0BwtK1eu1MWQkcEFjaysLC0ysUz48DqH04wZM/Dtt99i3bp1mDVrFjZs2KAzjH366adt62wkHe2f\nLFslYywEIExE/oiGPvDZZ5+5Kr906VIA0Naahw0bpvnOc+FDhrgG6bXmw8oAzQtiCRH+/PPPmy4T\nxIrMpiHahFs5OTkxRcq84IILTNX9XAXMtWCx8ssvvzgql2wGsFbIOlGZJ1dWVpb2+4knnojQSqY7\n4jMpKCiImOWL2hBRM5OTk2MaG4YTDAbxww8/uLZ/4stdRg4dOuTpICgGV7vzzjstyxrv26ZNGzzw\nwAO6fcZ+mLvaHj16VOvzZIab3GvI7nlaMXfuXM0mbeHChTj//PMjEjByZFpBjrF8OmcbdiISHwGw\nlojeJKKX+T+/KxYtZi6AdvCGu3btWq2BWn20Z599trbtpeajQoUKmotVMjJu3Diceuqpvt+nY8eO\nMV/j1ltv9aAm5rRr186T61x88cWOyhGRbUJEr3jmmWdi0obJvh2Z8CF2tumoWrZDfCYffPABBgwY\ngA0bNmDWrFkASpaEAaBatWo455xzXF07EAjgnXfeca35MJtMFRYWeip8iLYlopeTDCf3tZoE8mcg\nE3J4W3UbQXrOnDnaWLFv3z5tGYi/04KCAksNKY+tUhbbPeBM+PgAwKMAFkGfYC6tkDVcK+FDtD72\nUvNx0kkn4b///a9n1zNDFmI5mRDr53XYcDc89dRTcbuXXQcbCoXitjTw3HPP6ZYWY4Frr+yEj0ST\niLqI97zllluQlZWFdevWaV5LH374IQBEJfDzAdet5sPKhihR7ytW4cPqGcjipTjRODz00ENanJ3i\n4mJd5mdA36cyxiKCoPEJ1gcffKDbf88990TcK5m+E69w4mo7FcAsAD8wxqbyf/5XLb5w+4fvv/9e\n2+fUCjrZBm43FBQU4ODBg57YMHiJ+LHFQ9Nihpl7ayKIt7tdLB2e2Nnzzt1JAL2yhnHALF++vLYt\nGid26NDB9bW58JEMkXpjxSvNB3B82YMbd8qEj0cffdRRnYgIbdu2xaBBg3DgwAG0bt1au5exzqKb\n78yZM7Vto6AjS4C6d+9eLFmyxLZOseBXLBEzbFslEV0NYBWAz0p/tyaiT6zPSj1kvu1WNh8iqRxx\ntHr16jqvFxnz58+PkM79pGHDhkkj6derV8/X64t/ZyKyKFvdR6yPLMDbWWedZXp++/bttW0e5Mpo\nyQ/o/+Yrr7wSjRo1iqq+Vrzxxhv46aefPL+uF0ydGjmPM77jL7/8UhdV2SnRaj44fELywAMPaH1c\nMms+OnXqpG3/+eefumOi8HHBBRcAKAkuBsi9rpyEM+fuzsuXlywE5OfnY/Xq1ZpNmFhn4zu97rrr\ntG2xHM+VZJxo7NixI6o2YIXxb7QbB7zGiUg8CkB7AAcBgDG2CkB9H+vkOaNGjbK1aBb9yHljsAtf\nzImX5sOqs3cL/xutgvdwli9fHtfO++mnn04a4SOeOBFE4tXWjMKHbCZoZZfDO9uGDRtq9lGi8aR4\njxNOOAGvv/46mjVrhhYtWnhRfR1fffWVY6PeeGP0fJg+fXrEQOXUJsgIv060mg/uTv7CCy9o7S4e\n3i7vv/9+RL/k9L48zg3XINWvXzJUiQKGeK01a9bo+vmTTjoJOTk5WLFihTS2iIhZoDeuyXA7sQBK\nAlcCx11s/SQnJ8f3e1jhpFUWMcYOGfal1DqDk8yzTZo00ba5ZOxU+IiX5sNpfZzidBYdDocRDAbj\nGuwpmYSPG264IWJftMamxtm/LJHVsGHDULt2ben58Xouf/31F958882I/b179wYAtGzZEsOGDbO9\nzrPPPqttGwdBLnyEw2FkZmbqynLS2do/XriN82EkFArF9XscOXIktm3bptvn9P7G+D4yAUz8+y+6\n6CJdv8q1Gd99911EHYyYCbQ8DYLTOjstJ8tVk8o4ET7WE1E/AEEiakRE4wF8b3dSPLEb/AOBABhj\nmuU4EOnOKrN5GD9+vO29mzVrFrfZqPiRvPjii1I1tlPcdCbiB+lnJ8QHtmQzROQJCsX2E21sgNNO\nO003oIoW//xvnjFjhqmRrZvnsnDhwgj1sxtk31V2drbteTNmzABQknfm//7v/7T9MuGD38cspk6v\nXr0AJJcwyjFqcryCB0z0Ctns3G37jbddmzHLrtP3z/9Wo/Gn2K7EdliuXDndBECMHOvEzV/2jXCP\nS9Hd3IvlUr/aW6JwInzcCaA5gAIA0wEcAhBpjpsgFi9erHXmxoBNPLst13yIcR94giaOzJjHSt3Z\nvXt3LciPn5qPBg0aaDkuxAY8ffr0qN2KOXYfBP/gR44cqX2QTjIC8zwJQIm63mnHxd8XFz6s/OHj\nCf/bRSv0WIz4XnjhBW176NChEcet3ovYCXNhTUTMV/TSSy9p69Fecfnll+O9996zLHPttdcCsI/m\nCJT8PaFQyNQjhre3yZMn4/3334+ixv7ZyXiVitwYNNDrGa5M+Bg/fjy6dOliey53H42XdjcvLw/B\nYBBDhgzRPElq1aoFxpgjLZhb4SMQCOiS99mFrV+2bJkuFo1V3/bVV1+hbdu2AKyFJ6eCldMIw6mC\nkx60KWPsYcZYu9J/jzDGkkIEO3ToEC644AKtUfLgYEDJuiHXXPD8K2Kjkn2Q3K1NBvex5x1vvXr1\n0LlzZwwePNj3WQFfmxPrHw6HfQ9Ffckll2gCnZPBlhslvvzy8TAw48aNs3w+oqGv0ajtmmuucV9p\nH5ANXrEIH2adiJNOiDGGU089FUQkzWMkLo35kXeob9++2ntxq42Qqf8ZY5YDCw94tWrVqrgHIGOM\nWaa7l8VB4c+7uLjYceh8HtNDBrcBiAVZX1ezZk1H2o+6devqfvtt81GlShWtvv/5z3/AGMOff/4J\nxpij98/PtYoCLd7X+Gz4EjMgX/J78803dYEsRbsQo10c94aJZ3yeaIlFQxotTnrQ54loIxGNJSLv\nrcFiYPPmzQCOd+bGQeLcc88FcHzZReb+J/LDDz9EfGycb775BhUqVNDUw4FAAPXr18fll1/u+6yA\nLxGJA96KFStcpZg34qQT2bhxoybgOBlsZR2l3RKKaFjFO24iQrly5ZJG0pf97bHOpufPn29qUW+n\n+eDPVDaocBsKfh2zDKWxUqdOHVf5c7p16xaxj/8dBw4c0OJabN26VVeGW/078T6Ilu3bt0s1RIwx\nvP32265cHLmBY25uLi677DJH58SagTna6xsFU9HN1wj/ho0aY68pKCjQvrennnpKazdcSLXD6F4s\n+5YYY2jatKmunHiMPy9ZJGdjFnPR6Nroqjpjxgxt7LEiEUuKxonrP//5z7jXwUmcj4sBXAxgD4CJ\nRLSWiGxzmxPRZCLaTUTrhH29iWg9EYWJqK2wvzMRLS+99nIikifVMEHWMYmJl3gDOPPMM7Xjsg8y\nFAph+/btGDhwYMSxjIwMNGrUSLsmnxE5MWaNBjG41emnnw4g8kOKdWBxMoA+9NBDjsua3cPq4xKt\n2sVy5cuX923gdAvvoKpUqaLti0X4YIzhrrvuishNwf9+q2t/+eWXll4MY8aMQc+ePbXj1157LX78\n8UcsXLgw6vrKmD17ts5I24527dpFxGtp3749Zs+ejUqVKml/u1mUSSeJ1KywaoN9+vTB+PHjI0Kb\nh0IhBAIBy8iiXLtx7NgxFBQUaEsF4XDYcX3jLXyMGTMG7du3dxVokJf1KqvtypUrTY+J7ZoLo4WF\nhY76WSexZMLhMH7++WcA+uR0/BifmF500UUR5xqFD7f1kZGIUA2nnHKK7jfXvsVTEHKkO2aM7WKM\nvQxgGEpifjzm4LQpAIypSNcB6ImSaKkiewFczRhrCWAAgLed1IvDIzGKL1p8iLwxiw88GAxqft4c\n3rjN1sn5NevWrYtWrVpp1/aj8Tz44IN45ZVXcPbZZ+tmsrL6RIOT1OtEpEVbNQ50xo7ALA23leRv\njPgnkkzCB3/u2dnZaNCggW/3cfI+f/vtN/z555/o379/RHvgQfE+/vhj7NmzRzu+aNEiTJkyxVX2\nWzu1vFtB4JFHHokIlJWRkYFq1aoBsDfu83OJked3Ao6r2ouLi01dKUW4AJaVlaXTCjgRlvj7NtMq\nyga/aDD+De3atUPVqlUj2puVJsurQYm3wTZt2pheU9Qg8GfYtGlTRw4AdpmTu3btKr0v17CFw2Fd\nviEjGRkZWn/vJPAbX3axQjbZ9ZOxY8dGJFLlxNOw2EmQsTOIaBQRrQXAPV3kfoACjLFFAPYb9m1k\njEX4JzHGVjLGuHn/egAnEJG5DtCAbNnFzMCI069fP/z4449o3bq1FsNAzO8io6CgAJmZmbpr+6X5\nAErWLTMzM3H++ecD8DZC5K+//urqfOMz5NoYzvXXX6/7zT9mIjJ9PosXL9a2eUyBbt26gYiQkZGR\nNG6W4t9ulek4VviatvHaMtuXt99+O6KcOHMvKirSnh8RYfny5a68KHbs2BFX4e/xxx+3PC6Gr/aC\nffv2Ye7cuRH7a9asiXXr1qFz58669X8niAKSneEicFzQMbuHV9lWza5/6aWX6n5fddVVpteYPHmy\nJ3URDaLNMHtuXHs3aNCgiGMnn3yy9ByjV6OZHRQ3Hg6Hw5g9e7Zp3YLBoCtBOBmj+HLvPRniRHrn\nzp147bXXfKuHE83HZJQEGOvCGLuIMfYaY8zPvNe9AKxgjDnu+ewag6wxn3TSSZoamA98dhqMzz//\nHOeee67uhfil+QCOq2779OmDKVOm4LbbbtMd9yNsslkUPeOsw6iuNGbC5GpNK82HGEHW6Or87LPP\najY7iUbsQNzYObilWbNmABAxKzHrwPj+YcOG4YorrtBpDwKBAD7++GPtNxec3WDspL2OMyNi91zf\nffddvPHGG1FdW6Zh27RpE8aOHavbxxjDnj17UFRUhAMHDuDBBx909Y2JzyscDmPLli2W5e2Ej6ys\nLE/6FrPrd+vWTWfj9s4775he49577wUQH7W8+MzFZVn+fLn7tYgYc0YUoozLjVbLwDzgm1XwLT7Z\n3LFjR4Q7sAynwscnnyRH0HCxDefm5nomdMpwYvPRiTH2b0Ez4RtE1BzA0wBusSgzlIiWEZGWz1xm\nmGin+ZAxYcIES+vyunXromLFirj88uOrSX5qPhhjWt0HDBgQ4Rrn9O9auXIlpk+f7qjs6NGjtW3x\nGdpZQxsHTN5pHjt2LGL2weGdiFGQAYDmzZvrciEkEtFdj3dm4rPZu3evK4NIqw78m2++cV2/Tp06\nSQ3neDslIuTn5+uEkWhw4mYdLU40DL/88gt+++23CNsMO+bMmeOoHDd65YPQK6+8EpXw8ffff9sK\nDeFwWNN0iX+7OIhmZ2fHtNzEl5nNnu3JJ5+MF198UfMMScZZOrehAY47GNi1FVFzIb6/33//3VIT\n+8ADD0ScY4TbfBjfS5s2baTlnbaf7t27OyrntzbS2G6XLVtmUjJ2TJ8MEc0q/X8tEa0R/q0lojVe\nV4SIagP4EMCNjDHTKQNj7HXGWFvGmGawWlRUFKF2cyN8MMZ00q4Yd98OPzUf3OCNc+KJJ0aUcSL4\nrFu3LsLGw82atBVr1qxBv379IpIpiUsmZgMz3y/OvpIxCZb4nGTJBocPH27pLukG2ZKf3Xs68cQT\nNUHtkktKbLV52HJ+vlvPIZlqNtHhmIESwUCMy+CWDRs2aBEoAXkb//rrryPSD9iF2havtXbtWkuh\n4a+//sKePXt0cYiAkjg33FsmIyMDFSpUiEn4qFy5Mlq2bGmqscrKykKPHj1ca/O8yvO0e7dcgW7X\n70QrJJ122mmOzhWFG2P/amZwamaPYmXXFg08T5JfnHHGGRH7uBbba6x6eh4p6ioAVwv/+G/PIKJK\nAOYCGMEY+87t+UVFRcjJydE1LK7CBmAZJpcPwuJHHggEdBoAK/y0Tbj00ktx++23mx4nogijWbNy\nHNGYywsjvlatWmHGjBkR+QyiFcimTZtmufacSGrWrGnqwrd06VJPgkP99ddfrs+55JJLtARl/Pkb\nXW6d8vPPP2szzK5du7quS7Q4tW+w856yY9GiRZg5c6b2TGQeO7Ln5WQJUMx/YtUncI2EcdllxYoV\nWgTZCy+8EBUrVozZ3dyJ0azRvdkO2bJHNMiS6gGxLe1Ec67RDkWcABn7Vy58GNuImZaWO0N4xZ9/\n/qkJxH369PH02oA83cPDDz/s+X0AC+GDMbaz9P/t4j8AfwCwTfJBRDMALAHQhIhyiWgQEfUgolwA\nnQDMJaL5pcXvANAQwGNEtKr0n1xXL6GoqCjiA+N+3IB1xMDevXujRo0aus5i/vz5qFWrlqN7Z2dn\nazMYr6lTp44WIY8jZm7Mzs42DcNthH+UXGVt9ZEWFxejqKhIK8MTg1nBJfxYMy9mZ2f77noYDTVq\n1ECnTp2kAxMRYd68eY48iDhmAajMDC95yHIZRKR1mHwA7NGjR1Qd8VtvvaVpcdz8PbESDAbRuXNn\n23KxLg3ceuutmjE0Ywy//vqro3s4CW3Nn/eCBQts1eNGo3W+j2sppk2bhm+//RavvPKK7X2t2LBh\ng+335MawNR42H37eo1KlSqhQoYIWhReAzgV84cKFOuHD2L8+/vjjUuEjXn3W/fffr9mH2EUajgXR\nOP2DDz5AYWGho3HADVbLLhWJ6F9E9AoRXUYl3AngNwC2Ihdj7DrG2KmMsUzGWG3G2JuMsQ9Lt8sz\nxk5mjHUpLfs4YyybMdZa+OfYqLWwsBDBYNC0YwoEAroYDWKGzocffhi1atXSRcTbu3ev42iK2dnZ\n+Pzzz51WNWa+//54Wh1ZwjM7+DOy+sCfe+45NGrUSJuFuzE05JH8ohkkksW7RYZVDA63MSi87lxl\nLuY//fSTrs5O78nLJmL9f8GCBdq2MQ4Bd8n1gsLCQhCRzsVWxOwd28EFvxUrVti2ZZnwAZR4cuTk\n5CAYDDpa6nFCIoJYWSET+ESslpJXrVqlCQf//ve/LQ01ZXZqkydPxmWXXabLJiy61l5yySURgsSE\nCRO07aKiogit7rPPPouGDRtK65CMdjR2/Pbbbxg+fLhuX0FBgWkMnmixWnZ5G0ATAGsBDAbwFYBr\nAPwfY8yZdUycKCoqwsaNG01nJ0SkRavr37+/VEpt3bq1TrXqJKlQouDR6Nw0bDdli4qKdIZeTqy6\nOVYdh2iIJ+sQZSq/ZGfLli0RSx5mjBs3zlE5I9G+Z36fQ4cOuQoOlQyDldH2h2sreMhtt4jt0olN\ngV1GUxn8uuFw2JURutgfBYNBZGZmIhAIOEriZ8eNN97oqR2VF4PpvHnzLI9bvZ9AIKDVoWbNmrji\niisAlCxvXHPNNWjZsqVWtmbNmhHn80lqnTp1cOWVVwKI1PwYM+DecccduuPPPPMMwuGwZtBds2ZN\nU81HIBCIainVDCeTR44x4qpTWrRoobuXcdsrrFplfcbYQMbYRADXAWiGEnfbVZ7XIka47YLZ2mUg\nENDcDFu0aGEa9198wMksfHCfdDedsNOyAwcOdJw9USaUWHW61113ndbxyMLYJ8OgZ4bZx8fV63l5\neZg2bZrlNdasKbHTdvt3clWxk7qJ1+bvwk0n5Ec+GLc0aNDANsS8W9wEciKiiKB5sm/C2Edww7xP\nP/3UldGkceDidhpeeHtNnTpVtwRth53LZyxtg/cXYuJJGVbPTlxiFAN43XzzzdixY0dUrvBG4UN8\nH2Z1CYfDaNy4sVYPMwKBgC6zc6zYtSvx/VSuXDmme3CB38l9o8FKn65ZIzLGQkSUy5IkoZwRo7Gj\nEVGSvf/++6VljOf6GdMgVox1LSgosMzLsG7dOuTm5gI4/qF88cUXpuWdGouKeQ04ZkaORqMlruWo\nXr26IzuURJKTk4O77rrLssyePXt8qf+5556LCRMmWM5ezTq/aOrjJBdFPBD/pmiNus00ENF4U2zb\ntg3Hjh1DhQoVNAN1ozGoGBnZzSAdCAQiPCyCwSDatm0bV7sbALj6amtfgmhcwTm8D7LDTvjgz0pc\nHmzWrJnruvFzjfFvZN+a0XA3FAppYwS/jtiXGe/hFXxZVNZHFxcXo3HjxpYmA05s8vjzF13r/egT\nrDQfrYjocOm/vwGcybeJSB5LO0Fw2wezB2R0u5U1LrEhJ0twKzNEq3pAnl1T5OWXX45w0fr666+l\nZcXYIrHUzci4ceNshZpkGPRkZGdnRwhPRvWnk7q7UZlyFi9eHGHPZBT6zDq4M844AzVq1HDVAY4Z\nM0YX2CkRGJ+PMZouV4P//fffljE8XnvtNWlna/f8zdp/7969tUzCotZD5vrrti2LHT13sR89erRp\ntOV0hkd0liFqPvhvjtsJIz/X6F4qW0IR+7WRI0ciHA5HCC1Lly5Fo0aNIu7hx5LFxIkTI/YxxkyX\nrn///XdMnjw5IhikDFk/5Yc21MrbJcgYq1j670TGWIawHRnsIAngD4gn1uI4GUz79u2rdXIdO3ZM\nakMho/Bh1dGtW7fOleEaK43yGC1WAoaYipojPudEq/ujIVb7AzucGD+aLbucdtpp6Nu3r+v6JTqn\nTtr9z50AACAASURBVDgc1qnCzdbTc3NzTTWZQElsDrO2b6cql1FQUCBdvnnwwQcj9tm9Y+N3Isbw\nEWfZfP091eAq++LiYtffyNtvW6f2sspY6wZ+vuj5YnbdUCikswMSQ+/z8vXq1UPt2raZR2KC981i\nuyYiXXZwI9u2bcOUKVMwaNAgR89MViauwkcqwh+QcSbiRPgYNmwY6tSpgyNHjiTM2t8tToQP0QAL\nsJ99m7kfOiWWRupn8jCvqVOnDoDjWX/d4PWHbCZ8cC0WT4LolER7HYXDYZ1mwUz4+Ouvv0wTGgIl\nM1SZMOy1hk0WRt3uHlZCupPYHMkOHwxHjBiBKVOmeHZdUfMR63vk342Tvj4UCmkB4PhvrmlJhiCJ\ne/fulT6PTz75BIsXL5ZqSsyQjStK+LDBybKLFfPnz3eVfCvR8L/X7eACWNsJxLKu67aRiu/s/fff\nj/q+8YbHBrBKQmUkmmUXN9cFoOvoufAhGi6uWmVvL56IFN8ixcXFOhW6LCfNHXfcgZEjR1rGucnL\nyzP9W6z+Ri8mHnbfgWgEbBQ03Ca0S2aOHj2qGet68VyJSIvB5IU3kBl79uzRpaQIhUIRAfwyMzPR\ntGlT3TKosa36MZFljEn7ENm9unfvjrvvvttVpNV4CR/Ja1UZBVlZWVp4aRGn0iifbd19992WBpyJ\nxjiIif7se/fuRVFRkS5wjohdoqBYB0a3A1ey2nlEi1Vnw//Wp556ytG1nHZaYjnRvZAnJhRx8n4S\nrfmoX7++7Vr+hAkTHKWcly3rMcZw0003mZ5jlseIvz8rDV3nzp3x+eef27br5s2bAwBeeOGFiNQQ\nqaB5daOdMQYtjAUiQu3atXVt25jzyilW40K1atV0S39GI06u+TAOym6TN0aLU+ED0NsTvfvuu7bX\n5suuxkSJXpNWmo9y5cpFZH4FnHfivDHWqVPHNEVzMmA1g/7vf/+LF154ATt27MB330VGqn/iiScA\n+GdfIeZxkaW+TgdhY/HixabHrrjiCscB6uywWhoTMfPHX7NmDUKhkM6Gx8nzjzWkd6zMnj1bF/bc\nzJBQHPwuvPBC2+vypbKVK1dazgTNbF74Eo/My4vDJy1Ov6/LLrtMOtGJt/DB3fedYsxCbUY4HMYL\nL7yg9TtWuAnzLj4fmS2Z02vI+iiO6LZ71lln6c7jmg/jeza6Xx87dsxXTaebY24RJypK+JAgqpll\nMz3AueYj2WcbHLt6MsawZMkSbQmJB9MxlpFhlVbbCStXrtS2zbQsr7/+urbNk5X5marea847zzy7\nwPbt2y2D3XmBU+Fj+vTpWLhwIYYMGeLq+olcdtm6dWtEfAsxAqWI+F2bJdwSnxXXaOzevVsLOugG\nPoO0ElxEewTZdwckZ1s3Po8OHTrEfM0FCxbg9ddfR1FREYqKimwT0l122WW21/SyjyYiS62Jmfb7\n66+/xqFDh6SaD6M2aPv27Z5FquU4nZTEimhPxbN5e0nKCx/ih2w243SqHpQJKWZhcxOJleZDZkQl\nc530WwNhFaiMeyiMHj1a63B4OvNUwcq+iAcT8wtjOH8z4UOmhrXrvLt16+ZbriIn1KtXD0DJWjUP\n4mSMMGlk8ODBpse8bOd8OcrJzDMcDkv7kw4dOiTlJMeoXfIiSSIfdPk7EG0o7Bg2bJh0v5fPzi6m\njdm48c0332DDhg1S4SNejBw5MmIfYwwFBQWmWcTdcu+992rbN954oyfXFEl54UNsICNHjpQ2TqcN\ntkKFChEGhGYJwJIB44ezc+dObVtUncuWCfwQPsy8LkRCoVCEh8KECRPitlbqFVbCx3XXXaf9FjVB\nXj1zswi9/P7i/YwDoJ3BaaNGjZLC2PGqq66KSDlvhP+tfibYEmfjTgYacWIg63eWLl2aMI+IRMED\nY7lBdH/1K8x3ixYtLF1UiQht2rSRHsvLy5Muu/CkjH7CGJNO7ubOnQsAUbnXm93HT1L+KzB2TLEs\nu8gCxyTjLMVM81GzZk3s378fQEkoczcBsLzgkUcesS3To0cPbZtb9TPGUk74MBuIjO2lTZs2vj9/\nK82Hse3/9NNP2nZubq7UYM4qdkYisBM+rIj12f/yyy/aNl+O2rx5MwB5sjtx2cWs3/FaDZ8o9u7d\nqwtweOzYMc80AeLSpl/fz2OPPYaLL77Y9DhjzNTeqKCgIGGaD7N236dPSb5XLoQkOykvfDgRLJwK\nHzyyYKog+yiffPJJbb9Xho9OETtjpx0GnxUls3eRDDedDre98asTtZoZGn+LQl6rVq00YZXDDebc\nqMj9gte9a9euUu2Gk281lmfesmVL3fMzesFY2SiY2Z/xY17Uzy+c5ASZPXs27rvvPt1Mf/DgwXj/\n/fejjpArPgszgTOenliyySgnFApJNR/xxk3CSKfEayKYOiOtCXaaDyLCLbfc4uhaqRLcxy5eBDf0\n9Nv2gMM9g2KJVlqxYlIGzTXFLOupaKzJO8rdu3cDiH2geeaZZ6T73QgfYu4OmTqcl48l0JxX8ARk\ngUAA11xzjW15UaPghdavX79+lsetrn306FHfhSO/cJKFderUqRFti0czjTYhnixYG6BfZoynMXQ4\nHDbVfJgJH6NHj45H1TSiMZy2I15tMuWFD+MHbvwgatWqpRmx2ZEqwX3shI9oUylHy9lnnw1Ab7Tm\nRPjgqmke0TKZ3ZuNzJgxQ7pfzJ1g9Ipw+1Eb3R+dBFWyEz4GDBigbUebsC1eNGjQQLq0weHPk/cB\nPMfTwYMHccEFF8R8/2jycvDyAwYMcHRugwYNoqqbn8Qy8+UBuIz7YkFM6hhPYc1K+CguLpYKH/FY\npk9GU4BoSHnhQ8yJYIbTl5Uqyy48+I3Y8O2yrvoJF9hEda2TGUo4HNYM84LBYMIja3pNrJ2E8XlE\nk5fB+FucKVWoUCHheVyskGlmRO8znqqcf7M8Mm9RUZEmBMY6WJlNRmrXrq25iZvhpC8xcyNORYxt\nLRrNq1kb5wHl4rnMYfRYqlOnDqZOnQqgxKA/UcJHupD8I60NdssubjqfwsLCiCAxycidd94JALoo\njVaBr/yGf6Dih+qkkxDLJPssPBaiXQKQLYmIURedIBsAb7/9dgAlcQyMVvNW4crjjUz4ENvMiBEj\nABz/G3kyM34uEFsKePHaRgYNGmQaRdhYh3TG+H7E34899hgAd89B9rxFISDemg+xPueee6423hQU\nFCTM5uOrr76K+z39IOWFD7tlFzehiouKilLC60L2gSZy7Zg/X7fCB9c0McZSTvNhZyArrv1GK3zI\nnqFbf3vxnQwdOhQA8OqrrwI4rvkQ7zNp0iRX1/cTmfAh/ubLi7LvgQsi0Ro/csyEDytvFuO5R44c\niakOieKtt96yPE5E2LRpkzYYWqUVePLJJx3dk4jwr3/9S/s9efJkXR8ez37u4osv1oJY1q9fX5dM\nzsrb5aGHHsLTTz/tWT2MQQ2//fZbbdssHkoqkPLCh5Non06Fj1WrVqWE8JFsRKv5ePXVVzWbj4yM\njJQSPuyiVI4aNUrruKLpMCtVqiTVfBj3ia6gMsR30r17d92xChUq4I8//kC7du1c1y8eOI0PIf6N\nEyZM0BmKxjpYWQkYsiUZ8X6833HiPZKMcBsaM/766y8sXrwY06ZNsyznJlx/cXGxLhZNZmYmBg8e\nrHmMxVPTUL58eZxyyikAgFtuuQXFxcU6zUdGRoZpoMdmzZp5Vg+j7Zc4nomCSKrhm/BBRJOJaDcR\nrRP29Sai9UQUJqK2wv6qRPQVER0holdc3sfy986dO111QE4svRV6uKeK2FH379/f9ry///5by5MQ\nDAZTatnFzBBN5L777tP9dtMOy5UrFxHZUzYY8yigZojfg1jnP//8E5s2bcK6desiXEj9iGYYDXaa\nD45ox3LHHXfgiy++8KwOZsLHjBkzpMc2bNigbU+fPh0HDx5MqXbthnnz5unex8yZMz3RTMybN0/3\n+/TTT0erVq0wYcIEXeLEeMInR06WXaxC63uB38t58Wqvfmo+pgC43LBvHYCeAIyJGPIBPApguNub\nGIUF2YsRA+HY0bZtW/tCZRhucCUiW3ZxCl92STXNR+PGjfGPf/zDUdloOuTMzMwIVXU0sz5xZiQK\nH6tWrcLBgwexf/9+y1ggiUT2LVtFd/UD3qbr16+v2//7779L2/vPP/+s+x2NQW+yCH9u4JF8vQrt\nLeO2227TNBHx5tZbb9UJH4cOHbI0OPVSQBg7dqxumTddbIl8Ez4YY4sA7Dfs28gYi9ATM8aOMsa+\nRYkQ4piqVatq0QbNyMnJsTUM4wSDwah91BPN6tWrfb0+V/1ZDUzRCh9c85GMeXTMYIw5Nk6OxubD\nrKyV66kd4jIB78AKCwsjUhQAwGuvvRb1fbzE+BzeeOONuN6ft2mjVwq3U7LD7D1a2QzJBPxkhTGG\nyZMna2HIvZ5AJMNAO2vWLJxwwgmYM2cO5syZg6ZNm+Kzzz6Lm8HpGWec4dqhwMsAk0YNrleknM0H\nEQ0lomVEtCw7O1v7uHlgLWNjNUvwJINnrVREYiXRx5Jjo27dujjllFNARLoYGcmOm5gwvINyI3zI\nlnWGDh2KsWPHOr6G1TV5XfLz86UCpZNlJb+JJVUCxyubD1lmYCd1kQ1OVatWxRNPPOEoTECyY3y+\nt956q22ZVKN3794ASrRfffv21ZJgyoSP22+/3fPBOiMjA2eeeab224lHmlkQxGjw6/2lnPDBGHud\nMdaWMda2evXq2uyTDwTGDstN7I506Az8gj9Dq5mIEzsPI3fccYdm2JYMsxynuDFkjubjlQ3+gUDA\ndRC8Ro0a6c431kkmfCTzYOGFx5BTevXqhY4dOwKINC6VebtMnDgxwiZBdv+CggLk5+djz549Udct\nWXDyPpy+M76k5qe9RCzk5OSgatWqmrt7ZmZmhKanUqVKqFKliuf3FvsaY/BCGU6XhEVkhueTJk3y\nTbuTcsKHER5oyGxmnipRS5ORO++8U8ssyp9r+/btI8rxY6kWIj0WnLhaimXd4lWb5e8PkM/UReGD\nZ+M977zz0LRpU0/un2hicbVt2LAhHnzwQQCRz06mUT3zzDMjBiPx3fMl4iNHjmDChAkpl8/Ib66/\n/noAJa6qAHTZoZMBPpaIBvbJLKi7ZcmSJRH7rr/+eqX5MMPO2NHNsotCT6NGjSLsFayeZX5+vm6m\nnc44XfMHSkKuu12S8mrZQ3xf/Ftp3ry5ZggpCh8zZ84EUBK8zhhbIFVxY2wug79jJ5oPWawaMd/M\n999/r20nw7KWFzjR/jmdOfNryWyTkgGuRedCY40aNVzZuMRiLJ2osO3cG9EP/HS1nQFgCYAmRJRL\nRIOIqAcR5QLoBGAuEc0Xym8D8AKAgaXlHTlKG4WPWGw+FHpatWqlRYi0Mprs1KmTdixZPCX8xk27\nWrVqlS77pxO8ynMj1pG7pDZq1EhL1CbGLlBEwp8ff0Z81it7/7IovZdccom2LRqtpkuf5GRW7HSA\nNj6TZNMqPPDAA9qy2po1a1CpUiUsW7bMtLyYRwkAXnzxRQD2AQoThUz4yMzMxPjx4wHA8m+NBj+9\nXa5jjJ3KGMtkjNVmjL3JGPuwdLs8Y+xkxlgXoXw9xlgVxlhOaZkNVtfn8BmE2bKLm7X5VOLcc8/1\n/R6BQECT1q2Ej0GDBgGILhFXquJm2SUag1OvYlWINghc7S/Ourn74M6dOz25n5dkZmZGpK33e0DK\nzc3V/eZCB39mouecsa3LNB/iso/43FNR4JMtgzh5H07jRhjbYJcuXXDGGWc4q1wcGDhwoBYwrmXL\nlgCOJ9WUwYMMinTt2tVVMsGHH34YQGI0HyeddJKundoFVnRLyovfNWrUAGA+k2jevHlaDojxcDkU\nn5sT1ZsSPszLiv87IdbBib+Hp556StvHjSdF7RQXPhIVvMmKrKysqDyoYsHolm/UfIjt24mrtSiM\niO3FywiY8aB79+7STMFLly61Pdep2v79998HcPw7GThwIM466ywXtUxu+N/lpo98/PHHXZ/jBaNH\nj44qsaUbUl744JhpPtauXZuI6qQF4mBpN4ByFWNZET6s0m0bSYT6mGtOMjIytBgtPBaDTPORKvj9\nLGXaDPF/kZtvvlnb5vGBjPUTO3B+7cceewz333+/NxWOI9F+24lIvqZwR48ePXS/Dx8+HPHevF4q\nTHvhI10Hw3gPaHZLB1OmTEGDBg1MEx3x9U5OqnvGuDE4jWbZJVa4oAEcN7rka81ip5Kuob+jxdhf\nWGk+xM549+7dEdcaOnSoVPPRuHFjqRYhmeFRiKPBbRtLF3sYM7i2PpkwLhHl5OREGGt7PUlJm7ec\nrkKGGfHwKuHPdNu2bWCMIScnx3IArVWrFm644YaIzqN37966IDmAdwaViYI/DyeEQiEQEWbPnu1z\nrUr46KOPdL+5x4XMJToUCuG7777TfiebkV+iMWo+jP3MWWedhTPOOAOZmZlatF5OuXLlUFRUpP2O\nJQ1BouDBGxljqF69elTXcNumjKHs0wkiisrrJV5tZujQoWjcuLHmRABYa/9iIXW+AhvMOod0xS9X\nPZ7bRuwA6tati3A4jJ49e9oOuEQU0UhnzZqls/oHSrK+fv31195UOgEwxrQU9XbEW+3cvXt33Yx7\nxIgROtfZ0047DUBJNEZR8AAQkWQu2cnOzvb1+kbNhxEiwttvv61tiwM0D0YllgVSy8023gJTZmZm\nTCkEkhlZpmorRo8erW37Pa6JNmIrVqxAly5d8OabbwIAioqKEA6HPRcK00b44EZ0qTSrSEb69u0L\nQO6yPGHChIjETsZohFlZWTqholatWrrj/fr1Q7169dCvXz9ceOGFHtY8viS7zUeVKlXQoUMHAMCT\nTz6p5Ya49tprtXdidAUEkPRRN43P8oknnvD1frxf4cJHVlaWzjg3GAxq2g0iQlZWFubMmQPA3CC1\nRYsWflbZc3r06IFp06bFJfJougoewPFlTz8jI8dK5cqVNYGe2zT55UiQNiM1f7FK+IgN0SLb6O3C\nf1900UXafuMAFggEcM4552i/J0yYoG23aNECTzzxhKd5BxKFn+HVTz/99GiqpIOI8MMPP0TsnzFj\nBnr16oU5c+agRYsW2LVrF3799Vfs2bMHR48exccffxzzvf2EMaZ57QD+zQiHDy9JsC1OaoYPH47F\nixdj+/btmktujRo1sHfvXgAlGsLx48dL67RgwQIwxvDEE08klfuoEypWrIicnBzfDJMHDx4MoKRt\niur+dKNq1apo0qSJbX/w73//O041Ok4iVgxSR/9nQdeuXbVY9qmUlj0ZufTSSwFENsb8/HxNjfzV\nV19h4sSJyMvLs3QZ3LRpk842Zc2aNdi7dy9effVVH2oeX9wIH4WFha6WXrZs2RJttRyRnZ2tzWJP\nOOGElLO/adGihVSw8pJnn31W254+fTqaNm2qm/lz7dGMGTO07yI7Oxv/+Mc/NM2H+M67dCkJafTz\nzz/7Wm+vicfse9KkSXjjjTfQo0cPdO/e3ff7JYqOHTuiY8eOEZ4lRqJxzU9F0kL4+PTTT7XtWHI5\nKI6vUxsbfseOHXVapVtuucXyOqKmhMPXxGWZL1MNN3E+AHd2H2XFbilZMdrBWOUYkSWjvOqqq7Bw\n4UIUFRWhZcuWyM/PR9u2bbXAVKmGn+2xcePGAEqWr4LBYNJG/4w36dBH2pEWwgdn48aNaNKkSaKr\nkRYYZ/aypENWpPsAKhOugJLMpjLBTGnkvIGIpPlqvERcNowWo4G1Qs7EiRMBmH9P6cJdd92lbQ8Y\nMCDCI+2SSy7BBx98gEqVKuHCCy/EwYMH017zkVYGEk2bNk3rBhxv0r3xx4LZsouZB4wKtOQNbjVO\nitho0qSJLpXDSy+95On1uU1NuqbB4IjLeDwTu4iYyuKss86KcNtOR9RXrNDBOwAleFhj1VnyMNEi\nTjUfZSUrcCx4OUg9+uijnl0rHTnnnHM0g1DAu2c/adIk7fpeXzsZcRKKXyRewkc8PJjMUMJHmjBw\n4MCYzufJ4TivvfZayuWfiCdWM3CZC+6CBQsAAL/88kvEsT179uD8888HoLy1rGjfvn2E0Bft8+rY\nsSPy8/MxZswYr6qncMHgwYMxceJE3btMZ+HDDuOEJV7Cx5w5c3TZluOJ6unSBD5jvvrqq12dxztv\nnpuC/3/ZZZdp24pIrNaoZQMi13zIMkNWq1YtJaNfJgq3A9aMGTMAHE+CxhjDkiVLNONGK4NShR4v\nNaJOg/SlM3369AGAiKinwWDQd+FDTDqZCMFP9XRphtvoiePHj8czzzwDIgJjLOXcLhOFOAPfs2eP\n5koJWAsQfJbB131/++03AMfXhHv16uVLfdOFaAIe8fgU7dq1kx7nkRwV/iOGmy/L8PhI7777rvQ4\nEfkmfPAgkIle4lXCR5pgllhPhEcU5QNlMBgEEaFLly66Qa9BgwY+1jQ9EIWPQCCAzz77TDtmJXzw\nCI5vvfUWgOMBxdq3b48aNWrgjjvu8KvKaYGdYSL3nhCxE1aMYdAV7jj11FMdl02lDMpeMH/+fOmE\nsFatWhg7dqzpedEsu9j12/v37wdQEgzvqaeeQrdu3Vxd32uU8JEmOFHbDxkyBMBxNfTgwYNBRDjz\nzDN1USN//fVXH2uaPpgJfE6WTgKBQMQSTLq7G8aKrI23bt1aV6ZmzZq6tiyeZ8XLL7/sQQ3TH9my\nS9WqVU3L83fBsyyXtfZ98cUXmx6zehZcEy3y0ksvoU6dOqbnmMVI4Us6lStXBgDUq1cPDz74YMJz\nDCnhI8Uxzjruvvtu07LXX389gOONEJB3JmWtg4gWxhgqVqwYtfBhfPbKjdQeo+ajXr16EceNgyE3\n5rXizjvv9KR+6U7Pnj0j9lnN0Hl6hfbt21te1+54KmNmJ8P3cy2o8ZgsSGM02gq7uDV9+/ZNiCCi\nero0gTdUNx9xgwYNXKlMFcfhMxNZJ+FU+DAm0VKaD2ucaPdk7yOdk5XFG54RWaR8+fIYNWqUbt/m\nzZsBHF9m4UkOzfjxxx+9qWCSkZGRoeX+ERHb6A033BBx3Mu4J3ZLXZMnT05IZFnfhA8imkxEu4lo\nnbCvNxGtJ6IwEbU1lP8XEW0mol+IqEvkFRUyuBZDtgRQsWJFy3Pvv/9+acAbhT1iPJRohI/y5ctr\nHbTsuopIxKSHImL03Vg67ZUrV0ZfuTKEMUx8OByOmJHXrVsXwHED+GuvvTY+lUsyiMiV1+CTTz4J\noCT792233eb6XjKSVZvqZ62mALjcsG8dgJ4AFok7iagZgGsBNC8951UiKluWSVGSkZFh6gGwfft2\nAProegrvsNJ8OMnjIXO7Vdhj7Ew7dOig6+Bl6monGO1HFHKMM/WioiItUimHvyMeXEu19UhkyzEj\nRowAUGKQWr9+fcfXevrpp03jMpU54YMxtgjAfsO+jYyxyChLQHcAMxljBYyxrQA2A0jfRUCPaNOm\nDVq1aqXbJ67d8Q+/f//+ca1XWcBu2cXot6+InREjRqBhw4a6582ff69evfDPf/7T1IZJaZT8o7i4\nOEL44M9bfQdyvG6PtWvX1vp+oza7zAkfLqkF4A/hd27pPoUFy5cv14yVxGUX7lL7/+3df4wUdxnH\n8fdzwPHDIyDteamHZ638sGAi9i7WBm0bSCMiRNBgoI1pUAN/QGIRSExJU0mhGlMNCgmGxD9ook2s\n2FhpUiKk1RCLAg2a3lE4MARpQFACXiFgDh7/2Jl1bm9vOY7vzuzOfV7J5Pb2dmefeW5u7pn5fuf7\n1QGgem7V7JLshFd6FaSjo0+LYx8a1n5gixYtorm5uV/xAYWcT5s2bcBml0uXLgGV78yQwVm1alWf\n78td+Yh/B6XPS/U1NTX1+V7FRyBmtsLMDpnZoQsXLmQdTk2KB5GBQturxjEIb+rUqTQ2Npa9QyVZ\nfJS7q+XgwYNl13nkyJE+dyJJecm+THFu46JjoOIjfs+5c+fSCTLHSofjLi0+kncXlftd7N27t3rB\n1YnHH3+8OLrpUMRz48SSx5jt27cDsHbtWgAWLFgw5M+pplopPt4Dkt2oJ0fP9ePuO9y9w907mpub\nUwmuHp05c4axY8dy8uTJrEPJpT179tDa2trnDpV4gLC4+Ghra+Phhx8u27G0nLa2tpo9S6kla9as\nKV5iTh50KxUfsazHNsiTeD6S0uIj2cyb3J83bdoEwNy5c1OKsHbdf//9TJ8+fUjv3b17d5/J/mJm\nxpYtW2hqamLKlCmsXLkSKIymOmXKlDuKtxpq5Uj3KrDUzEab2ceAqcBfMo6pbpw9e7bfP63W1kKr\nVXybVeml/m3btqUTXE6Va3bZunUr8P/io729ndWrV3Pt2rXM4swjM+u3v5cWIVJd7s748eOBQvER\nF3UTJkxg/vz5xdclf08bNmxIN8icSs5Eu3Hjxj4/i8d5unHjRp9bbI8fPw6Uv1U6K9W81fYl4C1g\nupmdMbNvmtliMzsDPAS8ZmZ7ANy9E/gV0AW8Dqxy98HNQS60tLTccpCk0kv9pe22MjTl/tHFxUe5\nUQrlzplZcVj6cs0ukq6LFy8yatQoDhw4wLhx4/r099CVvOpat24d0L+v2M2bN2loaCiOMBv/Tk6d\nOpVqfJVU7Rqkuw80VeQrA7x+M7C5WvHknTp21Y5k8SHhjR49mqNHj9LQ0FDM8dKlS2ltbaWrqwsz\nY968eX3m25Hwkv/wxowZw4MPPthvziMVH+HMmjWr38Sfyf43yeNNfOUjOQYO1FbnUzWADjPl2gpl\n6J5++ul+IwgmZ6TUmXh1xAfaeK6Lxx57DIDOzs7ia9rb2zl8+HD6wQ0TM2fOLD6OR8h8/vnni2Ou\nxGffEkbpNAG7du0qPi49zvT09NT8JH4qPoaZ0l7ScmeeffbZfs9dvXq13x0Bkp54VMn169ezf//+\nrMPJrcmTJ3Pfffcxe/bs4nPLly8vPo6Lj3IzDcudS86z09DQ0OcusMuXL6v4EBlurly5wsSJJZWt\nqQAABT1JREFUE2lrayvOK3Ls2LEh926Xwbt+/TqNjY28+OKLjBgxgjlz5mQdUq6dOHFiwObFuAlm\nxYoVKUc1/Cxbtqzfrbu9vb0ZRTM4Kj5yZOfOnVmHIMAjjzxCe3s7kyZNAgr9cUpnX5XqWLJkCQsX\nLqz5s768qNSvqbu7W80uKVizZk2//jWLFy8u3vFYq6ye26Q7Ojr80KFDWYchMii9vb0aZyKg06dP\nF/t8iEj1mdlhdx94iObboLJUJCUqPMJS4SFSv1R8iIiISKpUfIiIiEiqVHyIiIhIqlR8iIiISKpU\nfIiIiEiqVHyIiIhIqlR8iIiISKpUfIiIiEiqVHyIiIhIqlR8iIiISKrqem4XM+sBjmUdxzB2N/Cv\nrIMYppT77Cj32VL+szPd3ceHWFG9TzZxLNQkN3L7zOyQ8p8N5T47yn22lP/smFmwmVzV7CIiIiKp\nUvEhIiIiqar34mNH1gEMc8p/dpT77Cj32VL+sxMs93Xd4VRERETqT71f+RAREZE6U1PFh5l9xMze\nMLMuM+s0s29Hz08ys9+bWXf09YPR83dFr3/fzLaVrGuzmf3DzN7PYlvqUaj8m9k4M3vNzN6N1vOD\nrLapXgTe9183s79G6/mZmY3IYpvqRcjcJ9b5qpm9k+Z21KvA+/6bZnbMzI5Ey4ey2KZ6ETj3jWa2\nw8yOR8f+r1b67JoqPoBeYK27zwA+C6wysxnAd4F97j4V2Bd9D3ANeAZYV2ZdvwM+U/2QcyVk/l9w\n908AnwZmm9kXqx59fQuZ+6+5+6eATwLNwJJqB1/nQuYeM/sKoJOewQuaf+AJd58VLeerHHu9C5n7\nDcB5d58GzAD+UOmDa6r4cPez7v529LgHOAq0Al8GdkYv2wksil5zxd33U0hI6boOuPvZVALPiVD5\nd/er7v5G9Pi/wNvA5FQ2ok4F3vf/Ez0cCTQC6thVQcjcm1kT8B1gUwqh50LI/MvtCZz7bwDfj153\n090rDgRXU8VHkpndS+Gs+c9AS6KQOAe0ZBTWsBEq/2Y2EVhIoXqWQQiRezPbA5wHeoBfh48ynwLk\n/jngR8DVasSXd4GOOzujJpdnzMzCR5lPd5L76DgP8JyZvW1mL5tZxffUZPERnT3sAp5KnMUB4IXb\nc3QmV0Wh8m9mI4GXgJ+6+9+DB5pDoXLv7l8A7gFGA3NCx5lHd5p7M5sFfNzdX6lelPkVaN9/wt1n\nAp+Plq8HDzSHAuR+JIWr239y9weAt4AXKr2h5ooPMxtFIQm/cPffRE//08zuiX5+D4UzOqmCwPnf\nAXS7+5bwkeZP6H3f3a8Bv6VwCVUqCJT7h4AOMzsF7Aemmdmb1Yk4X0Lt++7+XvS1B/gl6vd3S4Fy\n/28KV/vi978MPFDpDTVVfESXyH4OHHX3Hyd+9CrwZPT4SQoHVAksZP7NbBMwAXgqdJx5FCr3ZtaU\nOGiMBL4EvBs+4vwIlXt33+7uH3b3e4HPAcfd/dHwEedLwH1/pJndHT0eBSwAdMdRBQH3fadwk8ej\n0VNzga6KH+7uNbNQ+IN14G/AkWiZD9xFoc9AN7AXmJR4zyngIoXe5WeAGdHzP4y+vxl9/V7W21fr\nS6j8U7j85hQ6L8Xr+VbW21fLS8DctwAHo/W8A2wFRma9fbW8hDzuJH5+L/BO1ttWD0vAff8DwOFo\nPZ3AT4ARWW9fLS+B/+d+FPhjtK59QFulz9YIpyIiIpKqmmp2ERERkfxT8SEiIiKpUvEhIiIiqVLx\nISIiIqlS8SEiIiKpUvEhIiIiqVLxISIiIqlS8SEiIiKp+h+0CXm74OksaAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b1652c31588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots(1, 1)\n",
    "\n",
    "# river_north_datum.plot(x='time', y='wl',color= 'black', linewidth = 0.5, ax=ax1, legend=None)\n",
    "river_middle_datum.plot(x='time', y='wl',color= 'black',linewidth = 0.5, ax=ax1, legend=None)\n",
    "# river_south_datum.plot(x='time', y='wl',color= 'black', linewidth = 0.5, ax=ax1, legend=None)\n",
    "\n",
    "ax1.set_ylabel('River stage (m)')\n",
    "ax1.set_xlabel('')\n",
    "# ax1.set_title('River stage from upstream to downstream')\n",
    "# ax1.set_aspect(\"equal\", \"datalim\")\n",
    "ax1.xaxis.set_major_locator(mdates.YearLocator())\n",
    "# ax1.xaxis.set_major_locator(mdates.MonthLocator())\n",
    "ax1.xaxis.set_major_formatter(mdates.DateFormatter('%Y'))\n",
    "# ax1.xaxis.set_major_formatter(mdates.DateFormatter('%b-%d'))\n",
    "ax1.xaxis.set_tick_params('labelbottom')\n",
    "\n",
    "ax1.set_xlim([time_ticks[0], time_ticks[-1]])\n",
    "plt.setp( ax1.xaxis.get_majorticklabels(), rotation=0, ha = 'center' )\n",
    "\n",
    "# ax1.legend(handles=legend_elements, loc='best')\n",
    "fig.tight_layout()\n",
    "fig.set_size_inches(8, 3.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-13T21:48:07.502829Z",
     "start_time": "2018-07-13T21:48:07.320116Z"
    }
   },
   "outputs": [],
   "source": [
    "fig.savefig(fig_river_stage + 'river_stage_m_2011_2016.jpg', dpi=300)\n",
    "plt.close(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**plot river stage for individual year**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-13T22:19:59.606899Z",
     "start_time": "2018-07-13T22:19:58.431168Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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44APdfI/Hg/fff7+HRpcau3btwtChQ3t6GP2aWMuFzGSJFR7BYBAWiwWTJk1C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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2ad24a1ade48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots(1, 1)\n",
    "\n",
    "# river_north_datum.plot(x='time', y='wl',color= 'black', linewidth = 0.5, ax=ax1, legend=None)\n",
    "river_middle_datum.plot(x='time', y='wl',color= 'black',linewidth = 0.5, ax=ax1, legend=None)\n",
    "# river_south_datum.plot(x='time', y='wl',color= 'black', linewidth = 0.5, ax=ax1, legend=None)\n",
    "\n",
    "## add line for selected time\n",
    "for itime in selected_ticks:\n",
    "    x = [itime, itime]\n",
    "    y = [0, 200]\n",
    "    ax1.plot(x, y, 'r-')\n",
    "\n",
    "ax1.set_ylabel('River stage (m)')\n",
    "ax1.set_xlabel('Year 2011')\n",
    "# ax1.set_title('River stage from upstream to downstream')\n",
    "# ax1.set_aspect(\"equal\", \"datalim\")\n",
    "# ax1.xaxis.set_major_locator(mdates.YearLocator())\n",
    "ax1.xaxis.set_major_locator(mdates.MonthLocator())\n",
    "# ax1.xaxis.set_major_formatter(mdates.DateFormatter('%Y'))\n",
    "ax1.xaxis.set_major_formatter(mdates.DateFormatter('%b-%d'))\n",
    "ax1.xaxis.set_tick_params('labelbottom')\n",
    "\n",
    "ax1.set_ylim([109, 117])\n",
    "ax1.set_xlim([time_ticks[0], time_ticks[1]])\n",
    "plt.setp( ax1.xaxis.get_majorticklabels(), rotation=0, ha = 'center' )\n",
    "\n",
    "# ax1.legend(handles=legend_elements, loc='best')\n",
    "fig.tight_layout()\n",
    "fig.set_size_inches(8, 3.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-13T22:20:08.981313Z",
     "start_time": "2018-07-13T22:20:08.887750Z"
    }
   },
   "outputs": [],
   "source": [
    "fig.savefig(fig_river_stage+ 'river_stage_2011.jpg', dpi=300)\n",
    "plt.close(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## plot 5d-smoothed river stage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-13T23:28:14.087010Z",
     "start_time": "2018-07-13T23:28:14.062652Z"
    }
   },
   "outputs": [],
   "source": [
    "datum_files = glob.glob(fname_river_bc_1w + \"Datum*.txt\")\n",
    "\n",
    "# int(re.findall(r'\\d+', datum_files[0])[-1])\n",
    "datum_files = natsorted(datum_files) # sort files naturally, i.e. 1, 2, 11, 22, ...\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-13T23:28:15.957644Z",
     "start_time": "2018-07-13T23:28:14.812424Z"
    }
   },
   "outputs": [],
   "source": [
    "river_north_datum_s = pd.read_table(datum_files[0], sep=' ', header=None, names=['time', 'x', 'y', 'wl'])\n",
    "river_middle_datum_s = pd.read_table(datum_files[(len(datum_files)-1)//2], sep=' ', header=None, names=['time', 'x', 'y', 'wl'])\n",
    "river_south_datum_s = pd.read_table(datum_files[-1], sep=' ', header=None, names=['time', 'x', 'y', 'wl'])\n",
    "\n",
    "river_north_datum_s.time = batch_delta_to_time(date_origin, river_north_datum_s.time, \"%Y-%m-%d %H:%M:%S\", \"seconds\")\n",
    "river_middle_datum_s.time = batch_delta_to_time(date_origin, river_middle_datum_s.time, \"%Y-%m-%d %H:%M:%S\", \"seconds\")\n",
    "river_south_datum_s.time = batch_delta_to_time(date_origin, river_south_datum_s.time, \"%Y-%m-%d %H:%M:%S\", \"seconds\")\n",
    "# datum_time = [datetime.strptime(x, \"%Y-%m-%d %H:%M:%S\") for x in datum_time]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-13T23:28:23.985170Z",
     "start_time": "2018-07-13T23:28:16.582321Z"
    }
   },
   "outputs": [],
   "source": [
    "river_north_datum_s['time'] = river_north_datum_s['time'].apply(pd.to_datetime)\n",
    "river_middle_datum_s['time'] = river_middle_datum_s['time'].apply(pd.to_datetime)\n",
    "river_south_datum_s['time'] = river_south_datum_s['time'].apply(pd.to_datetime)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-13T23:28:25.270246Z",
     "start_time": "2018-07-13T23:28:24.043372Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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6zYkTJ5CXl+c/oxiGCVpceT4AAESUAuAvAPIstxdCOL69+W39lU5WfeLmdYvd2RSM+Co+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\niEikVHyIiIhIpFR8iIiISKT+H8W1wXuMgwQXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b16655edc88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots(1, 1)\n",
    "\n",
    "# river_north_datum_s.plot(x='time', y='wl',color= 'black', linewidth = 0.5, ax=ax1, legend=None)\n",
    "river_middle_datum_s.plot(x='time', y='wl',color= 'black',linewidth = 0.5, ax=ax1, legend=None)\n",
    "# river_south_datum_s.plot(x='time', y='wl',color= 'black', linewidth = 0.5, ax=ax1, legend=None)\n",
    "\n",
    "ax1.set_ylabel('River stage (m)')\n",
    "ax1.set_xlabel('')\n",
    "# ax1.set_title('River stage from upstream to downstream')\n",
    "# ax1.set_aspect(\"equal\", \"datalim\")\n",
    "ax1.xaxis.set_major_locator(mdates.YearLocator())\n",
    "# ax1.xaxis.set_major_locator(mdates.MonthLocator())\n",
    "ax1.xaxis.set_major_formatter(mdates.DateFormatter('%Y'))\n",
    "# ax1.xaxis.set_major_formatter(mdates.DateFormatter('%b-%d'))\n",
    "ax1.xaxis.set_tick_params('labelbottom')\n",
    "\n",
    "ax1.set_xlim([time_ticks[0], time_ticks[-1]])\n",
    "plt.setp( ax1.xaxis.get_majorticklabels(), rotation=0, ha = 'center' )\n",
    "\n",
    "# ax1.legend(handles=legend_elements, loc='best')\n",
    "fig.tight_layout()\n",
    "fig.set_size_inches(8, 3.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-13T23:28:30.510523Z",
     "start_time": "2018-07-13T23:28:30.428740Z"
    }
   },
   "outputs": [],
   "source": [
    "fig.savefig(fig_river_stage+'river_stage_m_2011_2016_s.jpg', dpi=300)\n",
    "plt.close(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# plot flux heat map"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## plot finger flux - first case"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-13T23:30:49.655292Z",
     "start_time": "2018-07-13T23:30:49.554220Z"
    }
   },
   "outputs": [],
   "source": [
    "# plot fingerprint plots\n",
    "simu_time = mass_data[:, 0]\n",
    "real_time = batch_delta_to_time(\n",
    "    date_origin, simu_time, \"%Y-%m-%d %H:%M:%S\", \"hours\")\n",
    "real_time = [datetime.datetime.strptime(x, \"%Y-%m-%d %H:%M:%S\") for x in real_time]\n",
    "plot_time = [\n",
    "    \"2011-01-01 00:00:00\",\n",
    "    \"2016-01-01 00:00:00\"]\n",
    "plot_time = [datetime.datetime.strptime(x, \"%Y-%m-%d %H:%M:%S\") for x in plot_time]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-13T23:31:01.826953Z",
     "start_time": "2018-07-13T23:31:01.824125Z"
    }
   },
   "outputs": [],
   "source": [
    "time_ticks = [\n",
    "    \"2011-01-01 00:00:00\",\n",
    "    \"2012-01-01 00:00:00\",\n",
    "    \"2013-01-01 00:00:00\",\n",
    "    \"2014-01-01 00:00:00\",\n",
    "    \"2015-01-01 00:00:00\",\n",
    "    \"2016-01-01 00:00:00\"]\n",
    "time_ticks = [datetime.datetime.strptime(x, \"%Y-%m-%d %H:%M:%S\") for x in time_ticks]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-13T23:31:04.698482Z",
     "start_time": "2018-07-13T23:31:04.613937Z"
    }
   },
   "outputs": [],
   "source": [
    "flux_array = mass_data[:, flux_index]\n",
    "flux_array = np.asarray(flux_array) / 1000 * 24\n",
    "for itime in range(len(real_time)):\n",
    "    flux_array[itime, :] = flux_array[itime, :] / section_area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-16T18:50:28.860828Z",
     "start_time": "2018-07-16T18:50:14.106633Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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RBZ4jR47g3//+t2X3doW3g/lDAFYA6EZEFwFUAWBdrmE/4CoRkrzOPXv2bABa\nEqOYmBins2YrZ0GuXqyiCAa8k5g6dapIRuSIQYMG4auvvrIsqqMj+LpimTJlfPo9zpAj/RUFOayw\nnMtB5s033zQNiGO2NusLuGAwa9Ys3Vp3zZo17VwVAW2W37FjR/H5rrvuwtSpU+3avCwYmAkdXFvl\n7e+8ePEinnnmGa+u9RVGd+cxY8botkeNGoW+fft6dW9vBvQvvvgCs2bNwrvvvouff/4ZgHW5XMzI\nysqyi8rpqn/isTtatmxpZyAuw118Z8+erXPbBgrzRbz66qsuyygv6eTm5gqhXBYMjHZGRs0q1w4n\nJyebaiddaXk9gS8lM8aQlpamW/r1Nd5GPrxORN8R0UHb9ikiWmlt0bxH7qgcqciN66W8w+PIsxke\n/GjEiBGoXLmySPcrw63JrRQM5BfZzAhv2bJlOv9zd3DmpmZ2LncTfP31190y8pEt7N01kpNDHQP2\nEQj9iewWVhSqVKmCDz8sdNTh7rIyjjoz2SLaFdx33Rvk2XxYWJjISVGrVi3T1NetWrXCtGnT8M9/\n/lOsFZstCcj2A2YaEa6Nu3Tpki4RkNV4MqBa4QoqLzcZVeByYiRPMVuedCd896lTpzBv3jwx2PnK\nhZGIcPr0aSxevFgYeteoUQN/+9vfnD4D3s4LCgqcBs+SPTc+/vhj3TH+vprZAJ08eVJn+CnbRxER\nBg4cCAAi0J07buCutHlmnkdW4E6acyvxu/qfMfY5Y+wMY2y3tK8vY2wPY6yAMZYo7e/CGEtmjO2y\n/b/Lne/YuXOneICylJWeni5mhGXKlNGp6s3UVDwTGrdUjYyMREhIiFClyksKX331FWbNmmWpwZMs\nGJjFnP/mm28wduxYt+/nacfQqFEjoe5jjAnVqLN7d+7cWezr0KGDw/Pff/998dm4fi+vyQYKdy3w\nnTFt2jRkZGQgJyfHI1cxvr4aHR1tGv9etmlxR43qCKOa/+DBg0JwNOvQo6KicMstt+Cee+5BXFwc\nAHPBIDQ0FAUFBXj44YeFrY4scHEX1LS0NK9TiTsbcC5cuGDq496mTRtcvnzZ9FruVcDJysoSkwNX\nCa24dsjRjC4mJsap/YorzCYbXOjs1q2bw+u4TQt/N32VLv5///uf0IaMHz8eR48exalTp1CxYkWn\nsSv4pM2V26acht442eATvNOnT9s912HDhmHt2rVCe/Xll1+KY7J3Fs/9QERBY/ci9/2XLl3CHXfc\n4dfYNYF3tpfSAAAgAElEQVSwC/gCQHfDvt0AegNYb9h/DsB9RNQCwBAAznOWSnCjHTlzXuXKlYWK\nNDw8HAUFBWJ99J577rG7hzFbGe80K1WqhEuXLomZz+LFixEWFob777/f0oZVrlw54SfLJWp5/S09\nPd0rQSQnJ8cuJr8ZR48eNe3sjC+gp7+ZiHTBc4IxWBMR2WmRvGHw4MFu5xXg8HXKqKgo0w7zjTfe\n0Fmr79ixAwMGDMD58+fduj/vdGTBQO6cV65cKYy1XFGnTh07DdqDDz6I119/Xad1u3Llit21V69e\n9erZuwokxl3NjMZbv/32G9q3b+/QaIyrhlu1aoWLFy9i9OjRAFynwOaTkPLly5ser1u3rksjQ2cY\n368PPvhA+PcbQ6ebwfsOs+Uhd+Gze25wyuERYOXnywfvyZMnO/Xm4QN2Xl6eU41BbGys+Gz0QOB1\nfuDAAbuQ4VxoNcaRcIQrDZO7QdaKyqFDh0zjPAS1xoAxFsoY8zplGhGtB3DesG8fEdlZeBDRn0TE\nHVb3AIi0GTq6hBsMyp1faGioqNzQ0FAwxjBo0CBxfM2aNXj00UdFMhJ50OWaBt6RlS1bVnSePB6A\n1S5YERERuqhXgwcPLlKsgQ0bNoCIcOrUKVMLecA+GAs3eJEl2Hbt2umWaOREO+7ApfWCggK0b99e\nZ8/hriGcrykoKMDgwYOLdI/Dhw+bDnxmLp9mBq379+/XPe8HH3xQ1J2s6s/KysKCBQucGsXKcC2Q\nrBXJz88XnXOlSpXc7oTi4uLsNDzR0dGiYzO6osqcOnXK66Baxk58586d+Pzzz3X7Ro0aheXLl6NJ\nkya6masja3iuifnjjz9w7NgxnUGuMw8grg0y0zLVrVsXly5dKlI4aaNtQNu2bREVFYXY2FgRPMrZ\nktKTTz7p9Xdz+Dv6xhtv6PYTEYhIVz88Euavv/4qIgOa9RFci0REDgWDxMREu98fGhoqtJGyPZLx\nGYWHhyMnJ0fYMrjClWCQnJzs1n2KSuPGjd2KEurLiIgeCwZElA+ggDHmWlS1lgcBbCMi0zBXjLHh\njLGtjLGtQKFgYExRyjvqkJAQhISEiJeZMYaOHTuibNmyQj0/ceJEITgYDeIiIiKwZs0aXaPlkcp8\nxezZs4Uhkac8/vjj2Lx5M4jIafwD2TpYRnbp3LdvH3Jzc5Gfn4+DBw+aronzejc7xmcdM2fOxG+/\n/aYbPD2dXfsKIkKPHj3s9i9cuNAt105nvPrqq3ZCB1fNy5QtW1YM0MOGDUP37t1NPVU8FUa5sCyv\nl65fv75Ig5cjnAkGvG3s2bMHK1e6b6I0adIkMXMkIly7dg379++3yyXx2WefoUePHjh48CC6du0K\nQHvv3THCM7bDWbNm2Z3DhexFixY5vM+JEydw+fJlrxNQOYsQ+NNPP4kwvPHx8W6F5PU2SqCjNsbr\nUj4u22vwiQbXvshwY+Y//vhDLBfIkf5WrlyJkJAQuwE/JCQEq1evRp06dRAeHu7QYDgiIgK5ubmY\nMWOGy98HuBYM5GVSZ3gaV8FdLly4oNN2ffnll06DRBUFb3UTVwHsYoz9hzH2Af+zsmAyjLHmAN6C\nlqvBFCKaSUSJRJQIaJ1q9erV7ToBOYJcSEiIeGHl7HzyQNWoUSOEh4cLQzFj45HPjYyMtCzsqMz0\n6dN1ZTYipyp1xH/+8x+3vssdj4ALFy6AiFC/fn1dyFJ5PZzXu1l9cD/9f/zjH2It2FdBQrzl0Ucf\ntYtimZ+fj9deew0vvfSS2y+/2UwzPDxc2K+4gj/vsWPHolevXrpjPBW3J0s506ZNQ0hICJo2bar7\nfX//+999sqTjzqxm8+bNpq5fjpCXTGbPno0OHTqIsnfp0sX0Gj5wbd++HQkJCS6/w9hvmA2MTZo0\nwe7du+3cyLjLa9++fXHbbbfh3LlzXtft6NGjHQ5YCQkJSEhIMDUUNYMxZnlkVl42b6JMcsGgUqVK\non5atWoljvNnaay7999/H2FhYahUqRIqVKgg2n9SUpLOFis7Oxs5OTluLyVY5bUxdepUXL58GUuX\nLrXUrZYnIpN/T/Xq1UFEOi8oK/BWMPgOwGvQbALkZEqWwxirDeB7AIOJ6LCr8zm5ubkIDw+3e6n4\nGn25cuXQokUL8aLwtbq3334bjz32mK4x5ubmurXezBjzSXAao2vWzp07xbpn9erVLU3HW65cOXTo\n0MFlB1JQUCAGJo4sJPCXzKxTM5u18BldsGAmSF25cgVHjx7FjBkznFony7/ZLECNmW+/o6yL/NwG\nDRrYLRVwwzNPNAbPP/88cnNzkZSUJKyyfUmPHj0cZsHj75gnXhhGnnvuORHHftGiRQ4DAjkbuMza\nqDE2g5lAzgVhY3u+5ZZb0L17d1StWtUtWx5npKWluYx+eOedd4Ix5tKOgTFWJJdjbjQqY6YxMMKF\nUUATuBMSEjBw4EA7zdu8efPsrv3oo4/QvHlzzJw5U+zj0U23b9+OmJgY4Qmwd+9eTJkyRWwvXrwY\nf/31l+5Zzps3z2GsEqviPEyePBmpqanYuXOnMGy0imXLlqFKlSq6fadPn7bbV1S8dVecDWAhgN+I\naDb/s7RkABhjlQEsB/AyEXkUkzYrKwt//fWXXcIZ3hk1atQI8+fPx7hx4xAfHy80B1FRUShTpgxa\nt26Nu+66S6hX+UzaleTfokULT4rpFS1atBBeFI7S9TrC1Xl9+vTBhg0bXM6IjVbcjr6Hq3q7d9fs\nTdu3b2+n/gpG40Mz1qxZI2YnfDAyQxYazH6bmVambt26iI+P98h6nXdkcqfszHOEw4VmfxASEuJQ\n1cvLP2rUKI86ZV6nsmusK+T01UbM3gmeGIrHMHG0FOCo7fbu3Ru5ublFXp5JSkoSBsiO4HUn20uZ\n4e2MmLcpsyyQRIQ33njDLpGWTNu2bUU9TJ8+HW3atEF8fDwOHz6sm1xwt0GZxMREVKhQQee9wu0V\n+DPh7T87Oxs///wzjh8/Ls4dN24cevfujUceeUR8h6OIqrKnlBWYtQ05E+no0aPt7B9c9buZmZnI\nysrSadh8kRPEK8GAMXYfgO0AfrZtxzPGlrh57XwAmwE0YYydZIwNY4z1YoydBNAGwHLGGNcrPwPg\nBgCvM8a22/7csr7jQYhcpVIOCQkxleoKCgqQm5srZmy8Mw+WXOfGJRB3BgTAdefgyLLaiKugSDyg\nT7Vq1TBixAih1di4caPdDCtY6pRjFiIb0JfTW1sPwFwweOGFF/Dnn3+aahNcIS8luLOG7E/BAHDv\n+f73v/8FABw/ftypBqxfv37ifnJ+C0+FyyVLlmD48OEA9H7yXODlAXfWrVvn0X35zJbbGzHGTD2e\n3KVcuXJo3ry5y/MYY+jcubPbWUg9wZjzQCY/P9+lZwRjTPRXPMFWmTJlsHPnToeaMmc4ytnBg0zJ\nmuL77rsPeXl5dtoULijIuBNa2RPGjh0rPBm4B5kco2Xv3r3C+PbUqVOYOnWq22HgZSPQoBEMAIwH\n0ArARQAgou0AHGf8kSCiAURUg4jCiag2Ef2HiL63fS5DRHFE1M127iQiKk9E8dKf49i9JhQUFKBa\ntWoeJxyaO3cu4uLicPnyZZQpUwYRERFBNbPlLxpX0TmSNLOysnSJhbyNX+4p8lqf7D9shrtrgP7C\nkepbNkBzVyVrtv5vTKMLFGad86SNEREeeOAB3Xe447KYl5fnV8HA3d+Un5+PTz/9FO+8847p8StX\nrmDhwoWWCJIHDx40FW4dBTpy9F4Yy8JntrIhsq/iB/iC9957D+vWrcOFCxfsojeasWXLFpdLUowx\nIXwZJzTe4Ora0NBQscyWkJBg2t6nTZtmd50vJih8Ylq9enVdngxAc3985513sG3bNtx6660YM2aM\n2/Uih2n2RURLbwWDXCK6ZNgXlMnV8/Pz8fXXX5uqwZzRuHFjfPvtt/jggw/w9NNP+7UjdQdZAgf0\nkdC41wCgDQKyUOQqS6BVg7QnAkZR0qb6E1lL8N1337nURjnC2JbktODu1JucWc7oIvvss8+6vD4r\nK8snHgiO4Maz3LXOEXXq1MGbb74p3MKMHj587ZivRctxEHg2VHfhKl13fdMdPRduG2OMbBkWFua3\nYDnygObOwOLMHmDbtm04ceIErl69igULFiAjI8PpPd2ZcIWEhKB///4YO3YsQkNDUb16dbdda90h\nMzNTt3ywd+9eETqZx1kICwvT5TEw6899rbk0avPOnj2LNWvW4J577hHLq+64KQL69hhMGoM9jLGH\nAYQyxhozxqYDsM4CzkLcCXPpDMYY3n33Xa/djXyFUTCQueeee/DXX3+hS5cudo3dlVuY0eXLGxYs\nWICnn37abr+vwoX6krS0NIfRJb01+DEOyrKfuTsvOV/uSUlJQXh4uM710Z3Zw5UrV/wq6DZv3hyN\nGzfWLXeZGcrxderc3FwQkcuYHf/85z/Fu+2pAS5X+fMok46YN28eypUrp/NmAuwHV6M7o6sgTFbS\nvXt3kS7Ynb7OmUEcL/O4ceOQlpaGVq1auUws5spFmzGGuLg4TJo0CSEhIRg7dqypPYE7GIPOAVpb\nkpdAH374YfF527ZtpktnZoKxMQ6GFchtwPhu8mPeuD/L992+fbuXpXOMt4LBswCaA8gGMA/AJQCj\nnF7hR+Twq2PGjPE6YlSlSpV0M4Fq1aq5pV7zB3KgJpmLFy+CMYa8vDysWrUKBQUFHs1s3XWjMyKv\njQ0YMMCuU8zJyRGz2WHDhqFPnz646aab7GKfBxs1atRwOLvOzs4W7eP8+fNu17NRQJKfIe88nBlz\nyYSHh+tmIu5kh/S3YABoM/SQkBAhmDpb8iAiNG7c2C4xkdmg562bFrflcGXTkZ2djQ4dOtjZhdSo\nUcNtexxfc8cddwiN4TPPPKOL9uot8vKfHILbDC7MPvXUU6bHjc/N24lajRo1HGqdjF4knKVLl5oG\nmPJ1+zdLmcyXEmTXeCvwNjmXM7wVDJoS0Vgiut32N46IrHfg9xL5oXuSNMhIjRo17KxhucFSoHGk\nMYiOjsbRo0dF+FNj5+or7r77bqfHs7Ozhd1BXl4eGGMYO3ZskULFBgN8lvHSSy85zQ3hDFlw5Z2s\nM1Wr3KE4WxJ4+eWXTTUQDz/8cMCWxrhvuqOOHNAEJ2deBN4iq5L5LJfP1hx5E40YMcI0KNK5c+ec\nuln6U2Mg0717d9SpU8cuDbRMamoqXnjhBV35JkyYgJycHK8GK75k4ij5mVWhhJ0ZBzrrRzIzMxEW\nFqYbB3ytAebfZZZEjMPrP9hsrADvBYN3GWP7GGNvMMZutrREFmDUEHgrGOTk5HhlJe4PnC0lABDR\nvlyt61qFOwM8N5DMyckBESE8PBxRUVFuxXsPJHv27HF4TE5d7G3IVPkZ8oQ9zgZ8uUN3FpDqo48+\nshu8uK1NMNjMuDNw8hj8Zj7unuLMDsGRS+W1a9ewfv16jwfMQAkGbdq0cWns+NZbb2Hq1Km68o0f\nPx7PP/885s+f7/F38nwqjtqsVfXg7J1wphXOyspCRESEqS2GO1kqi8L+/ftNf//69euDzhtLxts4\nBh0BdARwFsCntuyHzh3b/Yhx7dfbpYRgFgz4b5JtApwFCbLS2EfGmDLZHXjSFB6f34pkRb5Ejplv\nxIr2IQsGXEhyJszKg5RZ2+aGdRUrVrRbv2zTpg2A4BAMzGjWrJlum6unBw4c6DOvoMaNG2PXrl2m\nx9auXYurV68WG8EgJCREl3vE2XnG38SXpIzhul1pHXksAlkL9Oqrr4olDV9YzbsiOjpaeJ1kZmY6\nbO++WhqW7WnM2sH58+cDUi/u4nW6JiI6TUQfAHgKWkyD111c4jeMsyhvO5STJ08GrWAAaC4wchIa\nZ1HWfGWFHh4ergst7Q6nTp0Smc9CQ0MtTTzlC5xpQ7KyskQKXI6nA4LcaZnVJR/Mze4vq4x50Cuu\n0q1YsSIOHDgg1JmHDx8W4Xv96ZXgCTzBE2fevHnC3sLMxcwK+vbt63Dwk9eKeawFdwiUYMDp1KmT\nw2O8XK+88opOcDQrrzvxUfgzk1PX9+7dW7RDX1jNmyFHZvzb3/4mDEuvXbtmt5TAsSriquwSDgAP\nPPCA+GyWsC4kJMRtD4RA4G2Ao5sYY+MZY7sAcI+E2i4u8xtWGbsAwTuzAjx74XyVsrNevXooKCjw\nKHHIxo0bReazkJCQoFapAa7rzugi6MlMYNiwYbpBmmtSZIwW97JaXZ6lyevkR48eRUpKCmbNmiXi\nAsj39TaZTiDg2U6NAphVLFy4UPeM4+PjxWf+LB999FE89thjbrfVQAsGzrQGW7ZsQX5+PmbNmoVr\n164JG4ElS9yKUecQWaBNSEjAypUrMXfuXJEgyde0b98egBZJ8/r16yhXrhwAzY7E0RhgVUAoo42V\n/OzNvECCKSaOGd6OFp9DC27UjYjuJKJPPA085E+8fQi33Xab5TGorcQ40zbLec9x18rdjHbt2gEw\nXzbgGhVPsyKWL18eV69eRc2aNXHbbbd5XTZ/EBoaKjodd/BkQJg4caKuQ2WM4c4773R6DU+9DOi1\nGXI75zO91NRUXXRAHjOhcePGbpfR17gb7c0qeBhxzqFDh3TPQDai48LUxYsXTYU2R/Yx9957L+bO\nnWtVkb2C54VxxMWLF3HjjTeaZtssCtxbgzGGgQMHOgwY5gvef/99vPfee2IZODY2FgsXLjQ915Gx\npDf06tXLoUbJzA3yueees+R7XYXB9hZvbQzaENE0IrIP4RYE8I6QJxrydrbsr/zb3uKvGQlfmjGb\nCfO6NuakcMUrr7yCgQMH4rbbbsPEiROLXkgfUlBQ4NFSiSfPxWzZS44a6ag8HEfLHLwMV69eRUhI\nCPLy8nDu3Dmxhs9nU8FA69atff4drlTGRuGMIz9LYzApQIt0+NNPP9ndLywszCfhiT3BUfwNmaIk\nVTKjW7duAV1+HTlyJIDCsN8//PCDw3NlAbuoRtoRERG6QdpVACJHYdc9oVGjRnbxNazCoxGTMbbQ\n9n8XY2yn9LeLMWZ9WkEv4apZ/j/Y1TbBSu3atfHKK6+ITtNswON1e99993l075o1ayI2NrbohfQD\nBQUFbq/Jv/322x4JBt50orJg4Ehg4UItj62wfPly9O3bF2XKlPFYiCsJcG8PwLwdGwUDvj5tdA3N\nzc3VJf5ZunSpSBBWWpGDRM2ZM8dny5aekJOTg/DwcMTFxQHQloR58iWOPC4UdYyoWrWqx/fwVBgx\nCppjxoxxqiUuCp4+Qa7/6AHgPumPbwcVvIGWVMHAVeyAosLDjDoLyMGTeXD3w5KIJ4LBiy++6JGN\ngTczd9mS2qxzq1u3rhgIGWPIz8/Htm3bkJaWhjJlyqBRo0Yef6eVGJdK/PF+ygM/Fwx4PH1jGUJC\nQsQsT/ZIkfMfcIK5zXubYMpT5GXKsmXLejxJ8AVr1qzBLbfcgoYNG+L69et4+umn8dJLL5meu337\ndl0djR492qMQ+r169QLgeT17OqgbtVXDhg3DRx99BADYudPaeblHggERnbL9Py7/AfgLQDtLS1ZE\nKleu7DBhxzfffBOIIlmOFb7dzjBK/mYzrXr16qFjx45gjAVNJDirKSgo8GgW5InGwJtAK/Hx8cIK\nOiwszC4FtqyF4GWZOHGiyGoXaFwtlVgJN9yUBTteJ3JdyGGpK1eubKr+LVOmDLKyskQH3aJFi6CJ\nhOqM999/3zREuS+IioryyHvDV8TExCA0NBSMMURGRiIsLMyhEGdmi8ENXt3hu+++A+C5YOBOwjNO\nmzZtRLvjdm/ye261gOrpUkJFxtgrjLEPGWNdmcazAI4AeMjSkhURIhKdgbFT79OnTyCKVCy54447\n8Pe//x2A4wFvzZo1SEpKwoMPPmh6PNBrrVbgyW9YsGCBD0uiCWM8Nn5YWJhQSfJ2Lndqubm5OkE4\nGCNN+tJWhg/wZoKB3JG/8MILADQD2/fff9/UhbZs2bLIzs4W9xw0aJDORS/YaNWqFYCiRX91l969\ne/v0/r7GkV2Jt/ewml9//RVvvPEGGGOmXh4BFQwAzAHQBMAuAI8D+AVAHwAPENH9lpasiFy6dMmS\nFJ+lDWPimm7duuHOO+/ErbfeiqlTp9r51HPq1KmDrl276kJIFxQUgIh0VuC//fabbo22OMAYswv6\n4gwzYzQzhgwZ4lV5WrRogdGjR6Njx46oU6cO7r33XsyePRtdunTBTTfdJGaxH3/8sV1o4WAUDHwJ\nX9YxW0qQNQZ89rV161aEhoaioKAAq1at0t2rTJkyyM7OFu59/rS294a6desC0AQEq/z1HbFo0SKf\n3t+f3HzzzWCMufWu+DO53osvvojz589jy5YtIiBXfn4+8vPzrV8eJCK3/wDskj6HAjgDoKwn9/DH\nX0JCAlWrVo327t1LAGjXrl1UUgFg6d/kyZPFZ866deuoXbt2RES0du1aAkBRUVE0c+ZMu/JMnDjR\n7vqffvqJ9uzZQwCooKDA95ViMV26dKGff/7Z7Tq866673DpvyZIlRS6bsT4LCgooNzeX/vzzT8rM\nzKS0tDTau3cvpaenU25ubtDVPwB6/PHHadiwYaJe2rdvb1l7XrNmDQGgy5cvExHR119/TQMGDKDF\nixfT9evXKScnhzIyMoiIaOTIkaJce/bsoby8PAJA4eHhunsmJCTQ5MmTA1JfniK/h1b3FStXriQA\n9NRTTwXwF1pDgwYNxO86cOAA/fTTTxQREeGyDipUqKC7j9V1bOxLnQFgK1k0hnoqGGxzth0sfwkJ\nCZSXlyce1MmTJ92q2OKILxqgsTEeOnSIFi5c6FZ5srOzRd2XFLp27WonGMTGxlJ0dLRpHXbq1Mll\nPTdq1CjQPysoAEDDhg2j1157TdRNx44dLWvP165do0OHDnldvqtXr9K2bdtoxowZ9P3339OiRYvo\n9OnTlJ+fb2Et+A5fCQZffPGF3f2LM/Xq1bPr926//Xa7312uXDnxef/+/VSuXDndfUqKYOBpXNSW\njDEeQ5MBiLRtM1vhg2Yx2ZmLncIzGjVq5LaqKphDSHuLWRS7cuXK4dixY6bLVO4YKj7yyCOWla+4\nExISoqszd9SzVapUcct4q1y5ckVSs5YvXx633nqrVzlBgoHp06eLz//617/cim3gDt4ugwUrfMnp\n4MGDYt/vv/8OxhiefPJJEdb42rVr6NOnD/r164c6der4Ldyzv/HUKyGUiCra/qKIKEz6HDRCQWml\nKKFHjdHgeMY0hSYYGF0QnQmcO3fuNA1JK0eH7NGjh3UFLMZMmzYNZcuW1QkD7ggGVatW1W03a9YM\nR44c0QWtUQDPPPOM+GyFR8qVK1ewePHiIt8n2ODGxWZ9qLE9EmnGnGYBr6wkOjo6YImWAh+JQmEZ\nCQkJADyLb9CjRw+8+uqrGDhwoC4kqhq4CpE1BmvWrHEZ0CY9PR2jRo0CUJgtcO3atUhMTBRGWr6K\nWFbc4BoXufN1R+NCREhJSRGf9+zZgwYNGqBp06aYPXt20KfyDgSO6jUpKcnltTt27AAAVKhQAT17\n9rS0XMGAs7gFiYmJOHr0qAjIJgsGvtAY9O3bF4Dmzhgow3klGJQguMvKihUr7I7x6GSxsbH45JNP\nAGhqxurVq+ONN97AyJEjRWrm/fv3+6nExQP55Wzbti1mzJjh8pr+/fsDgKhr3vFwty4ekU2hdbSy\nYDBgwADdcU9Thvfs2RNTp061pGwliaIIS3KCrpKIowRdFStWRK9evVChQgXRDrlgYDZoF2WJMCsr\nC+Hh4Zg7d27Alyj8Lhgwxj5njJ1hjO2W9vVljO1hjBUwxhIN57/CGDvEGNvPGOtmf0cFhwsGZpHH\n+vbti9atW6N8+fIi4lvz5s2RkJCAkJAQXSO/8cYb/VPgYgLXGPCAKe7YrfDZWZkyZdCxY0dfF7HY\nI89m77+/0PN53rx5duradu3aOZ1JVa5cGY899liR49+XNB599FHT/c2bN7fbxwXZe+65B4Bjl++S\nMolgjGHZsmWm+wFt6Yq7CIaGhgpB1hgIqV69errlGyNGTaHsEsmXJiIiInTvg1XJrTwhEBqDLwAY\ndbG7AfQGsF7eyRhrBqA/gOa2az5mjPnPcbSYwRtxhQoVAOjVszy617Fjx8S+jh074qmnnvJfAYsp\n3MaAJ1PiMwbAcYY2frx169ZYs2aN38pa3AgNDbUzPixfvjyWL18OQJvl8rgBHHejUPIMkwoNxpgY\n6GXkwa1OnToACicZH3zwgdN7lqRJBI8m6ggeJOvLL78Uk68TJ07YnedMaDUekzUDvG8x4qnGzAr8\nLhgQ0XoA5w379hGRmeh5P4AFRJRNREcBHALQyg/FLDbs27cPGzduBFDY6F555RUkJiYiMTERN954\nI+6++240bdpUHA+0mqq4wbUEXCAgIjE4ycZBPD01v6ZDhw6m9zMGkSrNPPnkk3jrrbfskhjxAezu\nu+9GXl6enSr73//+N2rWrGk62+UEQzKfYGPYsGF2++TBiAfH4oKBHOY8mDJy+ouIiAhTjyTetjwN\ncEREIgcNADuhN1gI9jenFrQ8DJyTtn12MMaGM8a2Msa2nj171i+FCwaaNm2Ktm3bIjo6Wgz8zZs3\nx7Jly/Dcc89h6tSpWLhwoRjUAp1Apzjy2muvibDQjDFUqFBBRJLLzs4WlvA8BC0/b926dab3S01N\n9XGJiw9hYWEoW7asbiZrTAyVm5sr3GDDwsIQFRWFXr16ISoqSqh3Fe7BQ2k74rnntDx5oaGhIt8A\nXzcPhEo70Bw5csQuK6Mrypcvj7FjxwrjWJn+/fsjKioKTzzxBEaOHImKFSvi999/x80332xVkS0h\n2AUDtyGimUSUSESJxSWdr5Wkp6fjoYcK01XExcVhwIABuPfee1GhQgU0a9YMkydPFp2uvKSgcM7t\nt9+uSxFdrVo1YYCYmZkpOg45eYwzdaK7mRpLE40bNxYhho11R0Qiv8mLL76IqKgocUyFO/eMypUr\ngy/I0dIAACAASURBVIjQuXNnsa9u3brCiwbQwqCXKVNGJA/78ssvAQCRkZF+L2+g4cuynlCuXDlM\nmjRJt8z4wAMPANDSskdHRyMuLg4xMTFo1qwZmjZtKrIjzpkzx5qCF5FgFwxSAdSRtmvb9ikMhIeH\no0uXLg6PR0dHi5wHZcuWVYZZXmBWv5mZmaLDlDtOX/o3l0QYY3Y2Abt3C/tkkUZaFgoU3tO6dWvx\nuW7dunjvvfeEx8yQIUMQHh6us6VRuMewYcN0wZ8GDx6MgQMH2iWZunr1KjIzM/Hrr78iKipK1POg\nQYP8Wl5HBPvUZQmAeYyxqQBqAmgM4PfAFql4s3DhwkAXodiycuVKu31ZWVmIjIxEtWrVdBnOgnXt\nMJgxDkJG+4EpU6bg0UcfxZkzZ/xZrBLJa6+9hlq1aukMD3mMjYKCArEUpuJBeIYxsdbs2bMxePBg\nEBEmTZok9k+bNg3ly5fHm2++6ecSugfzd8hgxth8AHcCqAogHUASNGPE6QBiAVwEsJ2IutnOHwvg\nMQB5AEYRkcvUdYmJibR161aflD/Y+f3333Vr3Qrfcv78eVSpUgX5+fnYtm0bDh06hPPnz6Nv377K\nyNALhg8fjpkzZ+r28TpW+Ifs7Gxcv37d47V1hTnJycmIi4vTuSquXr0a9evXt9TmizGWTESJrs90\n414lMZdAaRYMFAqFQlH6sFIwCHYbA4VCoVAoFH5ECQYKhUKhUCgESjBQKBQKhUIhUIKBQqFQKBQK\ngRIMFAqFQqFQCEqkVwJj7AqAkpH2K3ipCqD0xUj1L6qOfY+qY/+g6tn3NCEiSyKABXuAI2/Zb5Xb\nhsIcxthWVce+RdWx71F17B9UPfsexphlPvpqKUGhUCgUCoVACQYKhUKhUCgEJVUwmOn6FEURUXXs\ne1Qd+x5Vx/5B1bPvsayOS6TxoUKhUCgUCu8oqRoDhUKhUCgUXqAEA4VCoVAoFAIlGCgUCoVCoRAo\nwUChUCgUCoVACQYKhUKhUCgESjBQKBQKhUIhUIKBQqFQKBQKgRIMFAqFQqFQCEpkEqWqMYwizhdu\nn2qegBp7khEJILoZkLYXqNkYSDuoHa8IoEJLAAeA3EwgvIq2P+08EJJQDdUPnMHpG6uhTPIZZAKo\nGQmkZwIRgLZdBUAdID+MIT2ZkJ1QD+f/qopqdU7jIiqjAY7iAqIRkZyKfAA1mwH4C8i9Apy1lbFm\nFe37ACDSdl8AqBkFpF3RynjZZzXmmFAA+S7O8aZsNSOh/dAYAKdtvzGhHC4nX9eOR0HLx1YROBdW\nBTnJ53XPzNH3Rtr+Z8IxNRsAaUcL6zaQxMLW3nL1ZakZB+SmA+G8vUYBCAeQCaQ5+HFyuwG0Ok7L\nhGj3CAe2sXjcdmI7DjZohCyURQ2cQmjyeWRCe9ZxtYD0VCDOVkcVE8qhTPJ1hMcBaemF3xMOoEKV\nwvJkJ9RDJDJRkHxGPBf+/WZU1H6y0+dkNTVbAmk79O+aXB5XbTgSQHQVIKtBBMoeyBHvZYVQAOUA\nVLW1K+l3V0mIQGhyDq4lVETlo5eR34DhLGJR/fIZXDgIVID2jK9GlkMY8lB2b464Vr7PqSYJaF5h\nF8pm5mjtwUndeopVfUtNW1sFtHZ9FkDNUAA3a/XOz8F+IC2/8BgOAKitvdsVAVSIg/Y+nLdtG977\nmg2Aq0e1MkckaH0D/86r0NrmNQBxcUB6OlAFUj/bEsAR7aS081oZ0lx1cCYEqj+Wkd/3UAAngXNE\nFGvFvUtk5MPEeEb37SjcHr+fML4JQ1MA/bYDE+KBpBXAhG7a8ccB1DoDoBOANABDtf0T3gVq0iN4\n4uE5+Pe8UYhg03AFQFIMMCED6AxgC4DRYwC8BFyIjcQHLBOHaSbmjH4Co6b+G9/jAYzFZGxAe9zL\nhiMFwEiKRPQLmUh9F5hlK2PSAGDC/MLyiP2dgAmrte9a5aP6ckZTACkuzvGmbEktAFQD8CKAKcDU\n1UBrSsAqlqwd7wTtOXQDPot9BGlsDpLOABOq6e8xYZd9Wdrang8nCkAt6XfwZ580RnvGgSQpBtrv\nTCt8/oBWNnwBYLWtvQ6wHdht/5ud3XtChvYM+60AEAfUb7kPx265CffsXIQziEMffCvadRSA0W8B\nX78E9JsDTHgE6EwJaHtLMvCitg0U1me/TgC2a9+xhRahAzYgm00T7TfJ8BxkOtv++7NN8/Zj9tzl\nd84RUQBGdwIurIpE9C2ZmLBLq9t2AGqNARCv1RF/ZwGgH9VD06rHselcAto+nIwL8yLxLfrgiZVz\nsKmb1laxGtjUMgExOIemnY+L972t1CeMX0fY2CERbc8ma7/BSd16ilV9S5KtbwUKy5fUAlobrlZ4\nDjrpj6ETgHe0d7IzgLZjoLUrXg9zCtseACTNAVIf0Z5XTdL6higAo2OArzOA2rbfk2Rry4D07p8B\n8Jz2ecJ87+sxCkCA5xR2ffN4INmqDJZBs5TAGGvCGNsu/V1mjI1ijFVhjP2PMXbQ9j/a1b3yw5hu\nO7L6BfuTVhZ+rNUCwJ+2jZrQBixbQ26GveI/bwhTjQ1pu3Z99A5NfjuHGKA2EAdtirUB7XEOMagN\n7WGmoxpQDdgo3+OMoTw2Um0dTCCEAkB7yVxR39ubnyn83xpA27PJ4tCm1dAGxv8W1uOF2Ej99dW0\n+mwq7doCAF31p43uBPST6lSu66BHFgLOQBNcveEMgHSgF34A0oAmOIBm2Ktr11cArS1L/2NwTpvV\nSQLZFWgd0terTd4FAF/b/rvqcOt78zsCSC3b/+gdmbr6OMY/mDybOJwBatr6kTNA9MpMtMd6rW0D\n2JQB2Jq3hu2+bWPMy8DfAauEAsBHz6Gm9HmF9i8KEL+1KVBYh2kQ7a0+gNR3tbYFACf5cUj32F64\nzfvn1tJ31pe+2q7/SrOdV9N4QCETNIIBEe0nongiigeQAOA6gO8BvAxgNRE1hiZfvuzqXqF5ei1I\ns4p78TiAVEA0Ulkw4NupvBM+AzF4xOAcAOBG7Ben2kmKZ2z3s13fARsQ+fgFxCADHbABzbAXVZGB\nVdA61KY7jgNT9NLeptXSZ2kwcDWL8TUnfXTf1F3QXlLbYHcSKBTOoAlCU1cDmAK0xwYAWqequ8dq\nrQ7lerwC2D9bAF/LA+wo274AawsAFAqiRtKA1AwAP9q2bW1ykxcDQm3b/XBG60hTMzRhqxn2oucO\nQ2XtttWnbXfTHceB3dB1xpwUFL4LXNAA3JtJ1QdQy8Hg53NMBvBjnlyfrt/cAmjP0KSOuBARvTJT\ne8dXAk1XHsem+VobXwUAX2hCsahr2AQu6dHU65CCtmeT7d6BoMJEwJFpbfufmmHri4HCdi7V3dco\nfKfltijuIZ3LJxP1bd9ZG1K7OiPdQy5jmvYXBZgKCE3td9kRaG0B4FqTWxSCRjAw0AnAYSI6DuB+\nALNt+2cDeMCn3yxpCwCbxC/9d3qdROZpTbGxF83Mzy8mEqs7jW+j61NMr+GD3KYMc80En6Gl88qN\n8+KLzLDVvTvaEJ/DhSMTijpwptrq9yRs37FdEwyOFe22lnAMheXzO7vtd21x47JUoHDSID0zu0HC\nTNCD85m5URtWy3CcC12+4JjP7gwhhEXB9vtt9SZ+X7rxgiJwRnuOol2lufdcjQRFvxBgglUw6A+A\nr7jGEdEp2+fTcDA8MMaGM8a2Msa2nj1lf3wjCiVWI3yGXivGNnOfC6Hqi96RidT5hcsEMidh6xSk\nmS8AxCBDLF80w17EIEP/Yq/QawUAx6rD4oA3kmsKCmdLqyANXoZzNmUATc8e13aYCFNRZjc3nhdv\n0M7Y6t5X2hBPmMBnS4bfnjpfrx1IXW3fZlzBtU0pgNaeV2oasFXQ2mgMMgo1aBK6Ot0FhwOdkRi4\nP9KvQgC0YTaNlJmNhjszwCuwaZ5+LNRAiXa1HYXtThY8dtn6FPn5njG0vZVA9Fn7/kV+/lWR4f0y\nkgssW6aUZvZ8CXRCBoA0re5mAWK2n8LP2W4TTGz1sxF6ocj4fq+ynSsmI7ZnOgvA1F3S9wCYOt/k\nuW63lXOldmyqSVsI1LJtMBF0ggFjLAJATwDfGI+RZilpai1JRDOJKJGIEmMr6Y+dQwxSYXsZTQYX\nLlWmZhSuaYmXcpetEZpItrxT2GRr/LxxZyAGmaejkYEYnEOMWCMXbLdvfPLsqVQ1TKmjMBuAjGuM\nMg41FcbzDdviebsunf8wCAYboReWjtm2vRVmvs7QBAsuZO21WRjYkSYJu4Cp0GJGBoqBZGuBbQlX\nbdsJw2cg2m+qPNikFT6zY9Juue19zfsP6VohONvwROgKGNJ75sh+CmmFxzYCwHZ9m06Bvm6uGK+3\n3UPUvxPNjamwt13rr3n/HgxLAsFI0AkGAO4GsI2I+GiazhirAQC2/x6/3s2wF63hPxVROuKA04Wd\nbzritH1OCNh6a5Bgtq4XBf0zS4mtpzteG/YqVwBATcNMw9Biahn+B5x4aAZ+buCtMCMEY1vnLQby\neBcX8s7exdLXOZsOojRgugRSDaKOHL3L9aXPcturDbi3tGil2t3H6NqpQeBXqvrgJxgFgwEoXEYA\ngCUAhtg+DwGw2OUdwvWbTXAAbVsAbVsAsLko8o64KYB+ABAP1OqkuR4hXlLtV7Ptk8Z1Puh0tn1u\nG6Ndo3sBTmqCwfED5qYsUTAMXjX1x0oNtt/dLwbArdpnXmO1oF9iOYAmukvrQ3s2dvVVzfGyEWzX\nyP8DTjzsBuh2KGyLgFYP7WBrq17QD7Y6ttU3H8gvdDV4esTb6p+Xx+Cl4wyH9jTBQgvXp7iiKbR+\norPxQDwAm7ud7lnWtD3HFjZvo3jts9z26kufuVBhbNNC62iVnY0vkPow3ftn63N5u2prew68fffj\n/Se0+quFwt+v817gxEsTCTeXueRr28YU9ivuGBqWRoIqwBFjrDyALgCelHb/G8BCxtgwAMcBPOTq\nPlmREQBy9DvnaP82xSYASAbeB5IArUHWBC4MikR0i0zU4muqZwA8AlzoGolab2UipWU9JHXSfIxH\nj7HdsxrQ9gxEpwBofrqDbTOnc4gBtgIbbtTcFcXLMhQYvVu7fsJqmy/7ICBpirYmN9rEt5b7GneG\n1pF8jeBRg3niC/w4pBlVTQAvAp0fAbAaWBLbFUljVmr1Pxea22E1IKVlPQDHsRfNkNRipebzn6F1\n0IgHRq80rBsPBdrGA/W510FX7f6cWp207VotoHcHDABJLYCUQfUQhzNIWpkp4gLUegta/XQD8C6A\nadIs8xFHdyuE+3VvyrB1gkMBVNOM3JIGZOIeaO3zW/RBZ8zROuuu2r37rbB9bzzEM+BCG4fHi0C8\ntla81KYVqwN9u+S+1tzvm/9PagHgZn3sBl/SGYXtyBj/oim0Ga6j94mX+XHY2s5QoG01APO1WAPY\nDVx40RafYM4coAUQFa8NjhcGRaIWtP6j6ZzjuNBSE8RqvZWJpjYf+1pjbMaHsUB010w0na/Nqk/C\nsGRheAbG31cf2nKFp0uRzmI4PA4P+ppHoLVV2GIRvKu1kU0tE5DUKVlrS48CiAeSVqKw37S186Qv\noMU1AYAzWqyJfm9pxx6Pt72vttgn/boCmAukdK2HpJjj2r3P6GO+JLUotDsQPAohwCRNAdDVPqaF\nHIfCE5zFg3AW94Bf5+wc+Zh4h+Q4JTHAeAtXm4JKY0BE14gohoguSfsyiKgTETUmos5EdN7ZPQAg\nC2V123FIR0rLekhpWU/MalJi6+n8WdMdiJ7cKCgDVQv9443aAVuHeyE2EjijdbiRPTTjw8geF8SS\nQlKM9jBTutbTXgB+v67QXpCuNgnWRK1Y3/b/JDy3IjbOPqzSSHSGY4mba1Met/3vDO231+oE7fdK\nv/EkgAstI7Vnw1Wyg2znxRdqCvaimbaP11s1iMFL95tsM7NanYBaA7ROO0meLQ61/X9RK58jvK2n\npiicUbpzjwNogg1oD8CgpvZ0NiR9Z2sA6Aq05fVta2N70QyoqWnRqiIDG9BezOBMv882GBmt5tvG\nQDwf7dJ07MeNOhU5H3CBwk5NdHxOvDHkOnvcsE9ub7yOk2K0zvxx22cZfn7bFrZ32HCsM2wzVul7\njG16tO14rQEQQbcw1DbzlfoCuf1egd4i/gCaADUl2454LbZGP5tAJvc/tVGoDeOkIw4psfXsY3nY\n4N/ljQ1KrTGFGkz+vgI27cgYLQ5IUovC40ZtiYglYui3tkATTEW9wNaOJK3HhdhI0X8KbBo00Q5s\n903dBa3NGLQmqRkQXjf8ewHNpsY40PLvA6C1wSK8Y0ac1f3oAfb7eFttO0D7zOvZrF+VNYWtYXsG\ntiVTWeNiFSUy8mHtxDhatbWwof2AXgAg1vnfO/sKno99U+cpwNdHZSOfPvgW36IP4pCOvWiGGGTg\nibNz8O/YUXbGQPz6ZtiLb9FH0xYYGIvJAIBv0QfNsBdxSEfPsyuxKTYBe9EMzbAXbc8m40KsFhlR\nDHxxfLaj79za7kjWW43bXhg+K4k+m4kLsZGi0+GGZ8a1eu6KmY5qyEBVUS9ciOLbcucllyMDMbo4\nD8bjZvXESUccmmEvNqA99qIZ2mMDMhCjq9+9aIbXMRHP4X08iU/Fb09pWQ8H0AQxyNDqwjbT/azl\nI4hDOmKQgQzE4Bd0RB98K3yel8R2Rc+zK7EktivaYwOi52ozdSGU2H5mSldtJh89N1MTNlZAm3EA\nwJ+aNil6ZabdTO5CrCbktD2bjE2xCSIWBq/r6B3aDDIDVZGBGGGH0hG/CA+Wb9FH1NeT+FRsA8AT\nO+ZoWi7DIJGOaojDGfEczeparlNuHNsLPyAO6Uj/P3nvHyT3kR32fb72XepWDmnNzc5OaXE2KMsC\nx1vEza7A+FweLusY4gYxo0C8GBZMeiGBEXG4uMomBVILnJy6Da5KviNM8ED9k8PxKoS0EBk4cEjR\nNi2MgMIFy01Chyvu3rL2FowjceTblWZ2J2sfpRtJd8k3f3S/7tc93/mx4IJyole1tTPf6W9/+/v6\n9evX7ydFh3v9XPntWG02oEVt2pHx/erS54wtvOiFPf3+LYYdXlrkOVirGdwfwNnQBe/yTqWNOquF\n3cF/wfEYKx0mjDybtBgmz6ajD8Hzw7zirvkxGTzt4WZXutXtBI+CF/lNxgPwMK8AhvfkabHCWNBe\nkkvpe8Hk7BAeoOdK5iFPi2MXZ9maGnLzrMeXt7MqUFqq+xfQPgqKZl8rVNnDTYo03XwJ/kVgjfEj\nY89ttK0W1rSdZI48m8xxv2tzmUM8wPWAvmKcSp+6jaZJie5qkQ/wr3Eua0f4lNCAaWve5zzH3fOF\ndg5x2eeHKMJrZcsXbCTaVnnIHBDX7eelNvPlfYyxQm6pzVbZz0Xl4oIzWQkfljnUfFTWaZEmc0y6\nd9O4kbGXNuq8Vqi63zTO5D6An0pqO5b58D8oU8JOwff5aMC0GhSdja5Fnq3CkA3WyrvfYufAPC1H\nYOI8KFK7fNbRBnJ/npYTCpr22sp3zaIfvtNMrNh387RoFIqOSa8wxkrBbsbPrgTCijCW+H2Abdsd\nZcHIf72QDE5G1OfOznttOFnXBb9ZeNYbI3hhRLdrkedLfBHwjGqlPBbM30p5DMq4PvRmMcckLfJc\nLviFtVIwba7zAMenvgZTXpgR3Mh75qhnZktrMEKuaBmvctLLbbShEG7cmoHnRusBfuVd/f+RABcy\nZgcZWi7pP0sg0H1n/Sb0F4/jPMedQNwiT6va37nwK2WTPUozur4x+KOe4Q4Khkbz7n8M8SYu/9/l\n7oC2smi3Y311aavnJ+4z3rBlPPI/plE93rnCpOvvAa4H/TYo8sLUkWATGWMlmD8tGACebkfpGuGj\n87TEOVvc/Ee01WAECs3gmryr5q9ZtKVB8069JjROXHgtRGMoBvQV49Tjfbjj+X6MI+Qm6m69NjA8\nOV/e9MJSYZNioemuC98ynxVeDmRkaA3etb+KIqYVCn7uY2H0dkWr7LhgkCTJCFDBkGEbE9X7Vpqm\n/89OP+uDgiDVCAHF4Fo38AmPGq59zJhEIBABYeTORubvtzqpHUyr4X6ARnZMdBZ0MJDbCPGYs5j5\npj0ZaKYg1yFc9BrvcqLo1XevcemTRefvlkl2S3fbIBQY1v13ERDMeMP+jfCRd2PIor8sQSp+fhaj\nyaLJbtCkyN28656rmc8KY2ySz9R+xc+TNvGmak65/enM0WwXz/tuicaKNDucUruBbCItMkKIdxD0\nIUSDfNf4lLnqh2Pdl6HZfF/66AlKkJW5z220yRc23ed4g9M06w9NIf11E6pi0Ju8nPzleqemsrPP\n8GQ9OL1njc0IDfXOxl37GJzmBGLNhf6cpSXo9j63PN/bhB0TDJIkeQCTrvjjmLQTTeBjmEyFP5Yk\nyWXgbJqmt70o1RBtpxoTOLhRY6swxByT5JbaHCpfdql25TSepUoUQpLfc/vbHLp62Z0qRXUlUuMe\nbrraCCOKCJuRZCuqMs1EYkFBxlM5s8DY9IpXaY/iE9O8TuhBDkFq4Rxtc+K1QgNAZWLBtIlpbNSq\namt1KNZZLe8OTnyi1vWStWcKosLUKmAZv94YYqlfFv2xpVleKB/hEJdpMMIc97t2mjHI/Rp3MU7j\na+ATTUmfugaGb2tVdxfrQN04pC61DY7jlLfrkCu2DZUrfAtOK0sL0IhwPerblibqUAjxlqu1nRq9\nSINJbtBimNxSm8nynB+r7S9HGwp+w9Qqyk5BJMS7PtEf52tu3rR6fo7JgBGNseLWVYMRb8bZWGCl\nMOaYvKRaXi1bU8zTbeOYJifWBoxVrcpXEoOt4xxBS0t1R6u5ovmcK7bNf1tPrkSdYtXg7OCEz6yz\nVbBzJmuh4FXGYNaUVreLCUzTaayO1/iVtgIan9o0KPeLaUzoTzResTpd41nT8MGNGquF3UH7g7Ua\ne6o3KZ2pexOMrG8xEVzBmxgb+DwLwhJH7D3rMFZYcdqa0rrfIHPrhu6c0PY2FKumg9yZtje7qdok\nGneTzDHHJM/zhMO/oWvT52uFquOrY6w43Mn7H7xYs465DUdPeTbdnJWW6rxWrlqxqrvwKeurwYjj\n+cKr3T7xtqWtIoyVV8izaUyvb9s1/CImoqFo1nW+avhirtZmy9KhO5jFNUOLRksY8AnsXEn/cqBY\nB0RDKfA2tKorSmjobu7aKdhJjcFDwLE0TX83/iFJko8AP4mJOPhnO/jMrtDtNNWgGNh+NHh1bKiq\nEsk8tqOH9+Y72gyrk9SIZZi6T2EWWaYCz6R6nLb2qv/CHKBvTPRWYYjcRLZWocUwTNTd51hy1T4L\n0kZUfQJZDLQvRGOWk53pr9ixkWvbY9xGmLTeDEXAEfvnHm52jDsz7fUohrInMExVO+qNEjJjPH6K\nBeNL0IHrom/nxxbOcTd1o1wXtWfcTpsT9HvLZ8FTfKLvlu5btDcCseDa7XumJsz6ROTW22xVh7z2\nZ289yAEAyqaLvUedXuWzMztMKJzLe/Sh/yycZ13zvw1TpJm5Hro/I7utCGRa0NV2/CyItZT6mgOh\nRQHZ+PVhYQKnKeiwf5dXQhyq3/IFK7jGPEOeoSA2NRjxPdYqjJjDSg/I0+pwDNSHrxjkNy3whiaO\nrDVhhMEVxszmHPWHPVBtFYbMZh/5EokJwr1PTHf2u9BnaSJ859XCbkoT5jBWtAJ+ozDiM73ae3NF\n7wCv9wXtK7MdLekgsGOCQZqmv9Djtx8Ar+7Us/rBD6LXWmGMRqGIPvnK9X72L7nuEG+l4xbDHfe3\nyAcqplg9GPcvG1MsiHgVsxVurDd5Ze+Cc1obm1oxzFOrshUhxuCk/j5MMzzddzLKfjayWxIKMA5Q\ncn/WvWLuiTedTt+QzZ6LRDNs/T94V8vwxIaaG2n764K/hl+0Gt+98OMYTGSWEO3IWHXFMS7te5Av\nbwaCI4UBancE79zdBi8gG7Wm6Sy1a9ZzM22q2g/iWS+kr5SHwzmyG5oIDTENyxw41bU1zeRG665d\nrNbOUXcChb7ejS4Mw+1t1utH97LZiyOx4E58NKC3IKXbxb9rX6cAJNRanDyr3m+Iqnf0BOsoG5kY\n9Ttpnhg6iyqcFfzcb00POQ2igDgPy2YLRpgR+pb+GhR5t+AdC+XdYtwx4R1c58v7XFunTSx7TZDQ\na78NMsazdgrUY3TCh9DjtMHfamE3pQyhZr5qnWnXa+4+TcMthtlS+JNrq4VOgV6302PoPEAU+zrM\n3ircDh+DHwZ+BhNx4/pP0/Qf7vSzekHWQhamJxBv6hCqWLMW7Nb0UKYwETs7aW2BBiFe7Skt9+ux\nlWqG+HITXhLdKg85InBSahT6k9toZzOxQtPZu4XZyme9uKWfLNBmAjPmYfdO2oYmv4u5RbftdooS\nPAgjmGQuwI1oEIRxiJpSvktEh3lXz6i16UH6MrgfDrQSgm/bONjY5IQeqAiLfg70SUvev8EIudF6\ngGttR8+ao35MTWhrkrmO+7sJCjFdxn2Jc1ssnPrneOcxXRa7UfDzK+tKtDLCaMVxy/Tfg3lF+12s\nkZLTunaYXS3sDvrUJ8FVofNCb42bvl/UzdqPJdY6xriMBSftqJnlUyN/2rwVj12ilcyYWtZRejjQ\nJtI0tVu2qqHWSB9K5L2FJ8QHg6z30p+zhAPtnGvMXU13PW9dqrMgy0ya5QAa0784ene7r5dGRoRP\nL3z1NjX0A8FfL4HcveNop2bVjL0zWiLroKKFM++HEGu4bq854XZEJbwO/G8Yi+GfmsOhhIWJykwv\n6lKtzrvVu4PrAvp7HAoVOo5kO/7IAs4SCroxaf1Mp0HQ9uwizikogCuYdlXzPzdqTqPFQjNQxbqT\n1EXze+5A3fko5JrKq/4hKE41XWhOjjpb5aYP5ZnA2Nqmm1Z69nY+J1xcgcaUIeRSrc5q1bRZLXc/\nmcU+CC3yznFPO0aZk3K2Y1OelrNfN6qh967WNMhGp4W7MVaCIljisyHqPa7gT/oydVegNGrs8h2W\ntQAAIABJREFU36WlUI2Yw9sTS9TNHI0TFJlxp46qH5O8W2VpwURZWIiZUe5im9xI3YUEajqXTdTh\nJIKs6BvtJBk8J76uNB3FQqepThixp592Jt3q9Sc0nFtWQleBwJdFnOLMNU+fNDwOGoz40LxZgqRj\nsrnmaVFaqlMsh34YInzkNtqMFbxKPVdrQ9Xgs7RUp1UeDuZJQl73cLMjZFILtoLnTLwD+UI3TcJm\nsA7cs21orflNC+LaIdcIvrkzbc9LrIaBvQSn3lZhuOPU7PK3FAwtVWoLXutwBZ+Hogr5qggKDRfC\n1yp7m/igGTFjB9wsE0pWe32ffg+jard+Q0v1DhOyaOZKG3V4AmMyXIQ9z940tCQ8Eo/D3Kj5PHbV\n+nyNQKlZhwPGJMAzpp/c3ja5p+ouh6+m261y05rEvF+P+Ek4urP+BywCF6H4Lc/T5+2cHOKyDZ0c\n5uBGRq35DwC3QzD4WJqmJ25Dv9sHS0/6RCoL5zoPuM1DbyL6ZBkzxvMc5zjnexK6/q0ZMeE4lEZv\nVhrytOCxcFM3kmV06l6vh2ppuzG608E6Tu3smKkfXJjow8IgEQ2ZbQLnmQgGcACP50fCNs3tRWdq\nENAqd/ku8fVyj3jWi6YhfkagflRhRqIyFMk+sIfG7yIJV3pVvouKyGghwmzk2fb/rJNcN8httJ2a\nXWtqYoh9L85znJWCd8jS8FqhGlzTuTS0YBWPebWw22pthgM6jyHTdJdFK1bYFWFD6Lwj2U3Dt++q\n+e9Ci5k03VTCUZdxaf4Qaxe1h7nWvMhnofFuh5MWebcxB4eKk7jcAVn+UPrZcYnoYOwQRNBkaQq7\n0pII0gNYtGKTabf3lTYuJFmZt7RjsW7fC8w92QJsAOvqf9Y61jgcpfc7d8O1opOY1jo0Fhq3Me8A\nKyje3oiy2yEYzCZJcgz4F8Afy8VBMhZaM8Q3MJUMUuC/Am5isnLehUn699Npmm7166vBCA21l+iQ\nGCa8qrkXxImA4Nbywcfag03ypoyqAm2+AO2c5m2yHYvrACEIsxQY9Qt+qzBE7oAhyNXybkqj9Q5G\nIBtiEWN2kHetFBf8mKblUWaxzZf30aDoNo3idNMtxtWq2VxXq52qL83ostSM8dwMEqajTRu6/WYG\nI9GnjTFWAntgpmPUXsIIEIF1zDyIx7F97GrVJ+QRT26qlinY5Eji2R9DfLLpMDuok3BWe/OuIUPX\nwkAvLVn43DBfhxZMs1TBAjqpUNZzpD5DMMeSQAp/UpQTcSDsWhD86YROQmdjVe9LFKpt86xWTVKs\n2DY7X9jXoSFsTPnnrVYNfev3eq1cde8bQxaf6IWzGEe97jVC62DhbLkjdR81UMTocWO2J34bev1b\nAVbwN181+Fkt7IYpo1kEX47eF4sboVHOtt33gu3io3s/4ZzL2unmcC6/5R4yp38OmPksjdb9When\nzYZPatZgxGhe3zYpzc1Yh+Fo3Tsox+m+11XyuaVOLZ0zEwvdlb0GV3Im6LZz3M9Y2eDYHJx2Tmtw\nO1Ii/wnwT4D/FViwf28NeO/zwG+kaVrCpKv5NiYE8lqapj+OyXh/ql8nH+EHQHaijp6e01FbAc3A\ntkPsxtLcsGf9wZ8H4tHbJxmGJN0pqv8jKj0zka1Le37b6/oZXRNz2OfETl5ZJwnpM9RwdCZ7icO1\nNGQljgrtheHcZPXVS+jrfoK1TERFDADe6VBwbPG9VRjyOJXrCrSwEcxl0T8rVn9mja2Xw188J93m\nJhYIYrwNArEJph94Fa8PDRSnLufcNYJXcdv0wg1GwvcrEtCzXDcRMSMddBbju5/mwo+3M2HUoBD3\nLREw+rc4GsQ/tz8+Q62MH1uWYC3hf45uFM2yV32P/JOCvhWv0HiVde38FkZDgVyPN87VEWpTwjEP\nSo/9DnNxoi/Nh+Lw6qA/wcuowZ9L3axoz61bizdpU9qoU6Rp5ls5KLv7IufwBiOZ+O32XV/Xv93O\nXBy3Q2PwFPBX0zTdlq4jSZK/CNyPzWSfpumfAH+SJMlPAZ+2zX4F+CZwsldfP+AjDoHvcnfHxtMr\nM1U38CaHkcwTrYZuBN5rYcSgT2dZzo6A0wKsFna7rH/6XpFIW5hoiT2Fm1DwJxy9iLS9NXRiajm1\nuuAx64QUhwtpE82tJmLpd4qI/3drf791ooszR+proZknXJiuAuGzvl2+bMKFJqfmXEiXBkN3GRtS\noUmjMBKEGnU6WGU7LbnxFHwKVm1Tjun8gyZDifHazfFLC9yDRN7IeMHjVrzqJXlMi2HnRIr4p9gs\nc4L7eIy9kmhlaTXiHA36mlZbi1apn7ZQq7tDgaWTPnvxkKxTcoz73k55in7LoSd8li+Jey+Lb6Fn\ncSzNUuGHAlq29q+XcDUIrcamtEGSpGVdN/jKjnaSd6Ds00G7dmXvbJ2zOV5aDIPNXKrbOJiwZh7B\ns+LBewo3/TothJo1zTOlvcNllF2y19zvFNwOjcG/Ab53C/f9KLABvJgkydtJknzDVlsspmn6e7bN\n79OZlgeAJEk+lyTJW0mSvPWHG+0O4ui1EW/n5NTNK1WHl+0EZJ3OPjijD0/x8oxem0s3hh6PqZv9\nsNe49YYTn6C2eyqNF86t4CrEhdiFO08bAll0E0v1ul854WY9rxd0OB9m2MO3q3K9FYjpJevZmm4H\nHZN+n15asiwNlLnH05F2Ls0afywQZL1D1rUPgt/t8JdB6FbjVt51kFC9LOh1Ou02rl5rvNdBRkO2\nE3K2cNmtvUA3v5xBaFA/K67pobWfWosophzdpq92l5C/9orSCnEpz8jG5+0QCuD2aAz+EFhMkuQ6\noY9Bv3DFjwA/AfyDNE3fTJLkeSKzQZqmaZIkmVWf0jT9OvB1MEWUIDzZhgTTxxkFz0RMez8p/ZKR\n6P60b4GkntVteoX3DAI6FG67ED8jLixj+g+9hLczNn1/r98ED7FnflZ7rZbsptLOGp84L/UaT7dN\noD9jGTxvQzcmEDP5GGKai31ftpue9XZALxNOHGsu7QdlarE2ajvQT7u33XbdIGs9dftNP1O32a5p\nJ6sf6asfnmKV9HaFoCztSbx+41oS/Z4Rb+pBiGYX0FqKXuOO10h8IPLXhzP9I7ppFDsOU0oj00+A\n1u+Q9blXO/ntVmmmH9wOweBVbi2Z0XeA76Rp+qb9fhkjGDSSJPmRNE1/L0mSH2EgP9jexHS7Y0AF\n4syH/aBfWE4MWbaprIxuvb7Hv/VTyWaNSf/+QZjrByXyWz3VxeaO2PQhp4ZBkwr10p70W8xZGpDO\nPrqfNuJ+Pizotml3e18vbGcLt1lMNEv93Ivesk5ZvU6yvRj07cKn0Fo3HH3wNRHyiNgptduaj8eR\n9bnX773mfbs8olf7fn3FpqGs3/rRmjahxPfKgaPXPN4K6HF/GNrAGG6HYPBOmqZBfskkSX6y301p\nmv5+kiT/NkmSu9M0vQk8CKzYv58FvmL///oHHeCgG1mWKqqb+luDZpLdEh0NEmMuY5X+umsYekiw\nUV967P1U/L3G1Q0GPwVuX02rVY2x6eGDOuJ0Y356sWc5tJnP2YJTt3DY7ZyWs6AbTd7Kvb1gu2Mc\nRLPUeerpL+TIfVn47LdRZcFOnLS2o6Lebrt+avt+gqdALwG/Hw1l8YesNTLouvsgm1uv9S3r81bn\no5sZphsv7sUnpE2vd+h2+NjuuLPGtpNwOwSDF5Ik+Zk0Td8BSJLkEeBJTPhiP/gHwK8lSfIfAb8N\nPIbxg/inSZL8HKYE1k/fyqD0pqK922Mm0c32JRM0yY3AIScmyiyVdb+IhCyIF3i3BR9L//KO/SCr\nza06CfaCWLDpRsCDOHfdLohpQJexFe9y8R2ITw+DnNoHxaku9KRxJXUd9FgHFS76MYys5+0E6DkX\nfPYTbLvRb6xSvhUa7Scsx0JNtv9If/zE3u7aVHYrIPUqQlt4yLd0Bk95rg7H1G0HFSr7jVdnbewG\nEiIuiY/6mSZjHhDzbM2fuh0SNOj3ju/Nmvd47AL9Dh69tBHdrvU6BGb1301YvF3ahNshGBwCLidJ\n8igwiUmPXO19i4E0TReBezN+enA7A/g+H+24phlpLHFrdVq8yet2QJD3O/5NvmdN1uYHOKV0Iwrz\nJJstS4rLbLRd8pM47aak0s2KM9fP0J76O6Ua6x+F8cEIfFCtQS/hR8agEyGZ/O83AJORcL5scDvG\nCnNMuup3cdXJBkUmuRHgWp4bb/QCOpFLTHNZzGAQQW5Q/5CdYjDxOhIcym8Qjluq24ldV6ruCW2/\ny90BjuX/a4WqE9xigXIQ27SAXuNaSOrXthv0O8XH0G8Ou20g+n+cuEvflxVRENvPL3OIQ1wGjD9L\nVgVNXXVTP7PXmovnvdv7xO3j94w/Z71r1qaapfrv1q/gRKfNL9LoWThPnqHHLRVR5b64aqloHYs0\nHa5FgJM2WQLyTmi5tgM7LhikafrbSZL8XYyfwe8C1TRNO92obyN8lO9nLkZZhFmbSBaBdzulZ7XV\n1+IJbGESGu20HcoziOEgPCZL1dig2LFQs1SFWQswi3l2W6jZoWnb23SyYrz7MdB+4aP945972/Rb\n5G0BK8+MGxRZLZiEOZKhToN4wHdjjIOq2wf9vZ9WZrtwKxqkQR39BHRY5wpjUOjcSOYL+4zAXRh2\n/7sJ+Ro+iBr+g0Iv/iOf+0E/U2WWtjKGrPnL2oBj3uCjcUJ6jU/aMc/oh8Nu79SLv3aLOpDfBuE3\n/TRAQfEmC/34pA41FIjDaPXBQAsdJtqgv7niw9AOZMGOCQZJkixjshUKfBz488CbSZKQpuknd+pZ\ntwrdJMpuCNcLT04nk9zgVT7bc3Heqnqu23hFUi/abSYeWzcVbAyxl3CvZ273t7jddolY8DbIqexW\noJ9GITYlQCezyDLbZG3++pndxiLP7MfYIWR+WsiJ+79Vs0CWGr3XmojbZo1Fq477qVpjZii/y7Vu\nAm2vTV7G+mEyU1GZa01brOK/lT67mVb06VJDN38M+a2fsKDbdMPxdrQyRRrBXMS4kd9i7Y/QkPwW\n+xNoXPfT9A4yxu2s4SzTdLf2WQJG1rVBBZoPA3ZSY9DXwfDDgtiU0G9C5Hs/b/wWeV/ycxub8nYn\nN1bHiqQZj6ebEBKrnbIW5HaY5nYW2K0y4l4MaJAFeyt2wKznQ6dqthvN3KpqPgv3gwqT/TbDW4Ht\naHj6tdWb0a1qleJ2vU57t/vkvx3oxeRv1cegG+3rE2jWRtjrpNmLH2XhOwvX2/EHGsS23q9N1vNE\naxePMwv6/bYdWh0EtsPzP6iG9XbATgoGrTRN/6BXgyRJ/uN+bXYCPsr3ObVxzuX9Ps9xxlhhDzd5\nlc9yaumcy1ktxXWyFpeWYIs0OLV0Dp6C+av73KLUJyO5R77L/Ye4zByTPMwrtuKgyaClHXNie/6p\n2jlTWesA0IDXqlUOLtWcjduVwH0Rk17zdUx1MAgLyMjQGvgqf1K5S35ftvc0YWtqyFQNA16YOsIY\nK1SWzLO2yr6mu9jTVxhjjklXhz22tUuVNwHBp47N1w5q4gch9uQWw1zmEMc5z5f4outLbPzauejg\nRg3W4YXyEQCO1WZhorM40rvc7arfyXwfvGjzjEuRoxFcfvjcxbbBy6Ntk5ezafF9ElNN7aR9kVHc\nmCsbC2ZuHlO4xs7BIib5N/BC4YjDgfgyAE4rlafFsY1ZXigcccLNwaWao19ts9TlWqFTjanx3SLv\naO9grcZ8dR95NpnjfrexH+Iy5znu6PnUxjnTwRWTH17nvrjMIcCUcZ5kjlyt7eoSHNyoMV/Y1zG2\nBkWOnZn1+HgEQ59HcDQpuekdHUuhrmXM2lj3eHcVPgXfNgtdZWOB1wpVVhjjOOedb0g3OtVltAXE\nd0dwqbVKgs9DXEb8ImK8SzlqodlJbiC+Qa8VqlznATuPjcAJtbK04ExYMg/HPjkLU+a9t6ZU5kh8\nRkh5P6ll4HD3IoZ+xzFpgEc9jiSjpi7DLjUkpMYHE/aHddWfLfim+ewc9zvtqry7xoXBs9cGCA+Y\nY5LjfA0wFRFlLWf5kkj7mAcLHrXPjtjyBf/yXDB+LqUz9p0PGP4xyQ1Tir2Gocc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QAAAg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Kn3sP76PaeuhrXnOQ1ZnWve5y1dZVv6VxCmzAm6i0NxmCyuiAfxUxUNGhlvd/vkl1A2zHmQ7l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\ngoKCgoKCgojCGBQUFBQUFBRE/H9MNEci8R02WwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b005451fda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "# plot finger flux\n",
    "# fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True)\n",
    "fig = plt.figure()\n",
    "\n",
    "gs = gridspec.GridSpec(2, 1, height_ratios=[2,3])\n",
    "gs.update(hspace = 0.1) # adjust vertical spacing b/w subplots\n",
    "ax1 = plt.subplot(gs[0])\n",
    "ax2 = plt.subplot(gs[1], sharex=ax1)\n",
    "\n",
    "# river_north_datum_s.plot(x='time', y='wl',color= 'black', linewidth = 0.5, ax=ax1, legend=None)\n",
    "river_middle_datum.plot(x='time', y='wl',color= 'black',linewidth = 0.5, ax=ax1, legend=None)\n",
    "# river_south_datum_s.plot(x='time', y='wl',color= 'black', linewidth = 0.5, ax=ax1, legend=None)\n",
    "# ax1.set_yticks(np.arange(0, 76, 10))\n",
    "ax1.set_ylabel('River stage (m)')\n",
    "ax1.set_xlabel('')\n",
    "# ax1.set_title('River stage from upstream to downstream')\n",
    "\n",
    "# plt.xticks(time_ticks, rotation=0, horizontalalignment = \"center\")\n",
    "# ax1.set_xticks(time_ticks)\n",
    "\n",
    "# ax1.set_xlim([time_ticks[0], time_ticks[-1]])\n",
    "\n",
    "\n",
    "cf1 = ax2.contourf(real_time,\n",
    "                   0.5 * (mass1_length[1:] + mass1_length[:-1]),\n",
    "                   -np.transpose(flux_array),\n",
    "                   cmap=plt.cm.jet,\n",
    "                   levels=np.arange(-0.1, 0.105, 0.005),\n",
    "                   extend=\"both\",\n",
    "                   )\n",
    "ylabel = '\\n'.join(wrap(\"Distance to downstream end (km)\", 20))\n",
    "ax2.set_ylabel(ylabel)\n",
    "# ax2.set_title(\"Exchange flux of weekly boundary\")\n",
    "ax2.set_xticks(time_ticks)\n",
    "ax2.set_yticks(np.arange(0, 76, 10))\n",
    "ax2.set_ylim([0, 73])\n",
    "ax2.set_xlim([time_ticks[0], time_ticks[-1]])\n",
    "\n",
    "divider = make_axes_locatable(ax2)\n",
    "cax = divider.new_vertical(size=\"5%\", pad=0.4, pack_start=True)\n",
    "fig.add_axes(cax)\n",
    "cb1 = fig.colorbar(cf1, cax=cax, orientation=\"horizontal\")\n",
    "\n",
    "# cb1 = plt.colorbar(cf1, extend=\"both\", orientation = \"horizontal\", pad = 0.2)\n",
    "cb1.ax.set_xlabel(\"Exchange flux (m/d)\", labelpad=0.3)\n",
    "\n",
    "ax1.minorticks_off()\n",
    "# fig.suptitle(\"Finger flux plot for weekly boundary\", fontsize = 12)\n",
    "# fig.tight_layout()\n",
    "fig.set_size_inches(8, 6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-16T18:55:19.849269Z",
     "start_time": "2018-07-16T18:55:15.671540Z"
    }
   },
   "outputs": [],
   "source": [
    "fig.savefig(fig_finger_flux + 'finger_flux.jpg', dpi=300, transparent=False)\n",
    "plt.close(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## plot finger flux- 2nd case"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-16T20:18:51.367332Z",
     "start_time": "2018-07-16T20:18:44.721677Z"
    }
   },
   "outputs": [],
   "source": [
    "# read mass balance data\n",
    "mass_file_2 = open(fname_mass_dat_2, \"r\")\n",
    "mass_data_2 = mass_file_2.readlines()\n",
    "mass_header = mass_data_2[0].replace('\"', '').split(\",\")\n",
    "mass_header = list(filter(None, mass_header))\n",
    "mass_data_2 = [x.split(\" \") for x in mass_data_2[1:]]\n",
    "mass_data_2 = [list(filter(None, x)) for x in mass_data_2]\n",
    "mass_data_2 = np.asarray(mass_data_2).astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-16T20:19:04.405915Z",
     "start_time": "2018-07-16T20:19:04.402202Z"
    }
   },
   "outputs": [],
   "source": [
    "# find columns of desired mass1 colmns\n",
    "# e.g River_40 Water Mass [kg]\n",
    "mass_index = [i for i, s in enumerate(mass_header) if (\n",
    "    \"River\" in s and \"Water\" in s and \"kg]\" in s)]\n",
    "# e.g River_40 Water Mass [kg/h]\n",
    "flux_index = [i for i, s in enumerate(mass_header) if (\n",
    "    \"River\" in s and \"Water\" in s and \"kg/h\" in s)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-16T20:19:09.679714Z",
     "start_time": "2018-07-16T20:19:09.577331Z"
    }
   },
   "outputs": [],
   "source": [
    "# plot fingerprint plots\n",
    "simu_time = mass_data_2[:, 0]\n",
    "real_time_2 = batch_delta_to_time(\n",
    "    date_origin, simu_time, \"%Y-%m-%d %H:%M:%S\", \"hours\")\n",
    "real_time_2 = [datetime.datetime.strptime(x, \"%Y-%m-%d %H:%M:%S\") for x in real_time_2]\n",
    "plot_time = [\n",
    "    \"2011-01-01 00:00:00\",\n",
    "    \"2016-01-01 00:00:00\"]\n",
    "plot_time = [datetime.datetime.strptime(x, \"%Y-%m-%d %H:%M:%S\") for x in plot_time]\n",
    "time_ticks = [\n",
    "    \"2011-01-01 00:00:00\",\n",
    "    \"2012-01-01 00:00:00\",\n",
    "    \"2013-01-01 00:00:00\",\n",
    "    \"2014-01-01 00:00:00\",\n",
    "    \"2015-01-01 00:00:00\",\n",
    "    \"2016-01-01 00:00:00\"]\n",
    "time_ticks = [datetime.datetime.strptime(x, \"%Y-%m-%d %H:%M:%S\") for x in time_ticks]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-16T20:19:19.550771Z",
     "start_time": "2018-07-16T20:19:19.492865Z"
    }
   },
   "outputs": [],
   "source": [
    "flux_array_2 = mass_data_2[:, flux_index]\n",
    "flux_array_2 = np.asarray(flux_array_2) / 1000 * 24 # convert to m/d\n",
    "for itime in range(len(real_time_2)):\n",
    "    flux_array_2[itime, :] = flux_array_2[itime, :] / section_area"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**plot finger flux with river stage**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-16T20:48:36.782232Z",
     "start_time": "2018-07-16T20:47:11.282079Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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uY8eOoV27diobDsawYcN0IzKWdNlRRBR4jh07hn//+9+mXdsVng7mTwBYCaAT\npfQygKoAzMs17ANcJUIS17lnzZoFQEliFBUV5XTWbOYsyNWLVRLBgHUSU6ZM4cmIHNG/f3989913\npkV1dARbVwwLC/PqfZwhRvorCWJYYTGXg8g777yjGxBHb23WGzDBYObMmaq17ri4ODtXRUCZ5bdt\n25Z/fvDBBzFlyhS7Ni8KBnpCB9NWefo7L1++jBdffNGjc72F1t151KhRqu8jRoxAr169PLq2JwP6\nN998g5kzZ+LDDz/Er7/+CsC8XC565Obm2kXldNU/sdgdd911l52BuAhz8Z01a5bKbRsozhfxxhtv\nuCyjuKRTUFDAhXJRMNDaGWk1q0w7nJaWpquddKXldQe2lEwIQWZmpmrp19t4GvnwJqX0R0rpYdv3\nM5TSVeYWzXPEjsqRily7Xso6PIY4m2HBj4YNG4YqVarwdL8izJrcTMFAfJH1jPCWLVum8j83gjM3\nNb1jmZvgW2+9ZcjIR7SwN2okJ4Y6BuwjEPoS0S2sJFStWhWfflrsqMPcZUUcdWaiRbQrmO+6J4iz\n+eDgYJ6TombNmrqpr5s3b46pU6fin//8J18r1lsSEO0H9DQiTBt35coVVSIgs3FnQDXDFVRcbtKq\nwMXESO6itzxpJHz3mTNnMHfuXD7YecuFkVKKs2fPYvHixdzQu0aNGvi///s/p8+AtXOr1eo0eJbo\nufH555+r9rH3Vc8G6PTp0yrDT9E+ilKKfv36AQAPdGfEDdyVNk/P88gMjKQ5NxOfq/8JIV8TQs4R\nQvYK23oRQvYRQqyEkGRhewdCSBohZI/t/4NG7rF7927+AEUpKysri88Iw8LCVKp6PTUVy4TGLFXD\nw8MRFBTEVaniksJ3332HmTNnmmrwJAoGejHnv//+e4wdO9bw9dztGBo2bMjVfYQQrhp1du327dvz\nbW3atHF4/Mcff8w/a9fvxTVZf2HUAt8ZU6dORXZ2NvLz891yFWPrq5GRkbrx70WbFiNqVEdo1fyH\nDx/mgqNehx4REYE777wTXbp0QWxsLAB9wcBiscBqteLJJ5/ktjqiwMVcUDMzMz1OJe5swLl06ZKu\nj3vLli1x9epV3XOZVwEjNzeXTw5cJbRi2iFHM7qoqCin9iuu0JtsMKGzU6dODs9jNi3s3fRWuvj/\n/e9/XBsyfvx4HD9+HGfOnEGlSpWcxq5gkzZXbptiGnrtZINN8M6ePWv3XIcMGYJ169Zx7dW3337L\n94neWSwTJvngAAAgAElEQVT3A6U0YOxexL7/ypUruP/++30au8YfdgHfAOis2bYXQA8AGzTbLwB4\nhFLaDMBAAM5zlgowox0xc16VKlW4ijQkJARWq5Wvj3bp0sXuGtpsZazTrFy5Mq5cucJnPosXL0Zw\ncDAeffRRUxtW+fLluZ8sk6jF9besrCyPBJH8/Hy7mPx6HD9+XLez076A7v5mSqkqeE4gBmuilNpp\nkTxhwIABhvMKMNg6ZUREhG6H+fbbb6us1Xft2oW+ffvi4sWLhq7POh1RMBA751WrVnFjLVfUrl3b\nToP2+OOP46233lJp3a5du2Z37vXr1z169q4CiTFXM63x1h9//IGUlBSHRmNMNdy8eXNcvnwZI0eO\nBOA6BTabhFSoUEF3f506dVwaGTpD+3598skn3L9fGzpdD9Z36C0PGYXN7pnBKYNFgBWfLxu8J0+e\n7NSbhw3YhYWFTjUG0dHR/LPWA4HV+aFDh+xChjOhVRtHwhGuNExGg6yVlCNHjujGeQhojQEhxEII\n8ThlGqV0A4CLmm0HKKV2Fh6U0h2UUuawug9AuM3Q0SXMYFDs/CwWC69ci8UCQgj69+/P969duxaD\nBg3iyUjEQZdpGlhHVq5cOd55sngAZrtghYaGqqJeDRgwoESxBjZu3AhKKc6cOaNrIQ/YB2NhBi+i\nBNu6dWvVEo2YaMcITFq3Wq1ISUlR2XMYNYTzNlarFQMGDCjRNY4ePao78Om5fOoZtB48eFD1vB9/\n/HFed6KqPzc3F/Pnz3dqFCvCtECiVqSoqIh3zpUrVzbcCcXGxtppeCIjI3nHpnVFFTlz5ozHQbW0\nnfju3bvx9ddfq7aNGDECy5cvR5MmTVQzV0fW8EwT89dff+HEiRMqg1xnHkBMG6SnZapTpw6uXLlS\nonDSWtuAVq1aISIiAtHR0Tx4lLMlpeeee87jezPYO/r222+rtlNKQSlV1Q+LhPn777/zyIB6fQTT\nIlFKHQoGycnJdr/fYrFwbaRoj6R9RiEhIcjPz+e2DK5wJRikpaUZuk5JadSokaEood6MiOi2YEAp\nLQJgJYS4FlXN5XEA2ymlumGuCCFDCSHbCCHbgGLBQJuilHXUQUFBCAoK4i8zIQRt27ZFuXLluHp+\n4sSJXHDQGsSFhoZi7dq1qkbLIpV5i1mzZnFDInd55plnsGXLFlBKncY/EK2DRUSXzgMHDqCgoABF\nRUU4fPiw7po4q3e9fWzWMWPGDPzxxx+qwdPd2bW3oJSia9eudtsXLlxoyLXTGW+88Yad0MFU8yLl\nypXjA/SQIUPQuXNnXU8Vd4VRJiyL66UbNmwo0eDlCGeCAWsb+/btw6pVxk2UJk2axGeOlFLcuHED\nBw8etMsl8dVXX6Fr1644fPgwOnbsCEB5740Y4Wnb4cyZM+2OYUL2okWLHF7n1KlTuHr1qscJqJxF\nCPzll194GN7ExERDIXk9jRLoqI2xuhT3i/YabKLBtC8izJj5r7/+4ssFYqS/VatWISgoyG7ADwoK\nwpo1a1C7dm2EhIQ4NBgODQ1FQUEBpk+f7vL3Aa4FA3GZ1BnuxlUwyqVLl1Tarm+//dZpkKiS4Klu\n4jqAPYSQ/xBCPmF/ZhZMhBDSFMC7UHI16EIpnUEpTaaUJgNKp1q9enW7TkCMIBcUFMRfWDE7nzhQ\nNWzYECEhIdxQTNt4xGPDw8NNCzsqMm3aNFWZtYipSh3xn//8x9C9jHgEXLp0CZRS1KtXTxWyVFwP\nZ/WuVx/MT/8f//gHXwv2VpAQTxk0aJBdFMuioiK8+eabGD16tOGXX2+mGRISwu1XXMGe99ixY9G9\ne3fVPpaK252lnKlTpyIoKAjx8fGq33ffffd5ZUnHyKxmy5Ytuq5fjhCXTGbNmoU2bdrwsnfo0EH3\nHDZw7dy5E0lJSS7voe039AbGJk2aYO/evXZuZMzltVevXrjnnntw4cIFj+t25MiRDgespKQkJCUl\n6RqK6kEIMT0yKyubJ1EmmWBQuXJlXj/Nmzfn+9mz1Nbdxx9/jODgYFSuXBkVK1bk7T81NVVli5WX\nl4f8/HzDSwlmeW1MmTIFV69exdKlS011q2WJyMTfU716dVBKVV5QZuCpYPAjgDeh2ASIyZRMhxBS\nC8BPAAZQSo+6Op5RUFCAkJAQu5eKrdGXL18ezZo14y8KW6t7//33MXjwYFVjLCgoMLTeTAjxSnAa\nrWvW7t27+bpn9erVTU3HW758ebRp08ZlB2K1WvnAxBCFBPaS6XVqerMWNqMLFPQEqWvXruH48eOY\nPn26U+tk8TfrBajR8+13lHWRHVu/fn27pQJmeOaOxuCVV15BQUEBUlNTuVW2N+natavDLHjsHXPH\nC0PLyy+/zOPYL1q0yGFAIGcDl14b1cZm0BPImSCsbc933nknOnfujGrVqhmy5XFGZmamy+iHDzzw\nAAghLu0YCCElcjlmRqMiehoDLUwYBRSBOykpCf369bPTvM2dO9fu3M8++wxNmzbFjBkz+DYW3XTn\nzp2IioringD79+/He++9x78vXrwYf//9t+pZzp0712GsErPiPEyePBkZGRnYvXs3N2w0i2XLlqFq\n1aqqbWfPnrXbVlI8dVecBWAhgD8opbPYn6klA0AIqQJgOYDXKaVuxaTNzc3F33//bZdwhnVGDRs2\nxLx58zBu3DgkJiZyzUFERATCwsLQokULPPjgg1y9ymbSriT/Zs2auVNMj2jWrBn3onCUrtcRro7r\n2bMnNm7c6HJGrLXidnQfpurt3FmxN01JSbFTfwWi8aEea9eu5bMTNhjpIQoNer9NTytTp04dJCYm\numW9zjoysVN25jnCYEKzLwgKCnKo6mXlHzFihFudMqtT0TXWFWL6ai167wRLDMVimDhaCnDUdnv0\n6IGCgoISL8+kpqZyA2RHsLoT7aX08HRGzNqUXhZISinefvttu0RaIq1ateL1MG3aNLRs2RKJiYk4\nevSoanLB3AZFkpOTUbFiRZX3CrNXYM+Etf+8vDz8+uuvOHnyJD923Lhx6NGjB5566il+D0cRVUVP\nKTPQaxtiJtKRI0fa2T+46ndzcnKQm5ur0rB5IyeIR4IBIeQRADsB/Gr7nkgIWWLw3HkAtgBoQgg5\nTQgZQgjpTgg5DaAlgOWEEKZXfhHAbQDeIoTstP0Zsr5jQYhcpVIOCgrSleqsVisKCgr4jI115oGS\n61y7BGJkQABcdw6OLKu1uAqKxAL6xMTEYNiwYVyrsWnTJrsZVqDUKUMvRDagLqenth6AvmDw6quv\nYseOHbraBFeISwlG1pB9KRgAxp7vf//7XwDAyZMnnWrAevfuza8n5rdwV7hcsmQJhg4dCkDtJ88E\nXhZwZ/369W5dl81smb0RIUTX48ko5cuXR9OmTV0eRwhB+/btDWchdQdtzgORoqIil54RhBDeX7EE\nW2FhYdi9e7dDTZkzHOXsYEGmRE3xI488gsLCQjttChMURIyEVnaHsWPHck8G5kEmxmjZv38/N749\nc+YMpkyZYjgMvGgEGjCCAYDxAJoDuAwAlNKdABxn/BGglPallNaglIZQSmtRSv9DKf3J9jmMUhpL\nKe1kO3YSpbQCpTRR+HMcu1cHq9WKmJgYtxMOzZkzB7Gxsbh69SrCwsIQGhoaUDNb9qIxFZ0jSTM3\nN1eVWMjT+OXuIq71if7DehhdA/QVjlTfogGaUZWs3vq/No0uUJx1zp02RinFY489prqHEZfFwsJC\nnwoGRn9TUVERvvzyS3zwwQe6+69du4aFCxeaIkgePnxYV7h1FOjI0XuhLQub2YqGyN6KH+ANPvro\nI6xfvx6XLl2yi96ox9atW10uSRFCuPClndB4gqtzLRYLX2ZLSkrSbe9Tp061O88bExQ2Ma1evboq\nTwaguD9+8MEH2L59O+6++26MGjXKcL2IYZq9EdHSU8GggFJ6RbMtIJOrFxUVYcGCBbpqMGc0atQI\nP/zwAz755BO88MILPu1IjSBK4IA6EhrzGgCUQUAUilxlCTRrkHZHwChJ2lRfImoJfvzxR5faKEdo\n25KYFtxIvYmZ5bQusi+99JLL83Nzc73igeAIZjzLXOscUbt2bbzzzjvcLUzr4cPWjtlatBgHgWVD\nNQpT6Rr1TXf0XJhtjDayZXBwsM+C5YgDmpGBxZk9wPbt23Hq1Clcv34d8+fPR3Z2ttNrGplwBQUF\noU+fPhg7diwsFguqV69u2LXWCDk5Oarlg/379/PQySzOQnBwsCqPgV5/7m3NpVabd/78eaxduxZd\nunThy6tG3BQBdXsMJI3BPkLIkwAshJBGhJBpAMyzgDMRI2EunUEIwYcffuixu5G30AoGIl26dMHf\nf/+NDh062DV2V25hWpcvT5g/fz5eeOEFu+3eChfqTTIzMx1Gl/TU4Ec7KIt+5kZecrbck56ejpCQ\nEJXro5HZw7Vr13wq6DZt2hSNGjVSLXfpGcqxdeqCggJQSl3G7PjnP//J3213DXCZyp9FmXTE3Llz\nUb58eZU3E2A/uGrdGV0FYTKTzp0783TBRvo6ZwZxrMzjxo1DZmYmmjdv7jKxmCsXbUIIYmNjMWnS\nJAQFBWHs2LG69gRG0AadA5S2JC6BPvnkk/zz9u3bdZfO9ARjbRwMMxDbgPbdZPs8cX8Wr7tz504P\nS+cYTwWDlwA0BZAHYC6AKwBGOD3Dh4jhV0eNGuVxxKjKlSurZgIxMTGG1Gu+QAzUJHL58mUQQlBY\nWIjVq1fDarW6NbM16kanRVwb69u3r12nmJ+fz2ezQ4YMQc+ePXH77bfbxT4PNGrUqOFwdp2Xl8fb\nx8WLFw3Xs1ZAEp8h6zycGXOJhISEqGYiRrJD+lowAJQZelBQEBdMnS15UErRqFEju8REeoOep25a\nzJbDlU1HXl4e2rRpY2cXUqNGDcP2ON7m/vvv5xrDF198URXt1VPE5T8xBLceTJh9/vnndfdrn5un\nE7UaNWo41DppvUgYS5cu1Q0w5e32r5cymS0liK7xZuBpci5neCoYxFNKx1JK77X9jaOUmu/A7yHi\nQ3cnaZCWGjVq2FnDMoMlf+NIYxAZGYnjx4/z8KfaztVbPPTQQ0735+XlcbuDwsJCEEIwduzYEoWK\nDQTYLGP06NFOc0M4QxRcWSfrTNUqdijOlgRef/11XQ3Ek08+6belMeab7qgjBxTByZkXgaeIqmQ2\ny2WzNUfeRMOGDdMNinThwgWnbpa+1BiIdO7cGbVr17ZLAy2SkZGBV199VVW+CRMmID8/36PBii2Z\nOEp+ZlYoYWfGgc76kZycHAQHB6vGAW9rgNm99JKIMVj9B5qNFeC5YPAhIeQAIeRtQsgdppbIBLQa\nAk8Fg/z8fI+sxH2Bs6UEADzal6t1XbMwMsAzA8n8/HxQShESEoKIiAhD8d79yb59+xzuE1MXexoy\nVXyGLGGPswFf7NCdBaT67LPP7AYvZmsTCDYzRgZOFoNfz8fdXZzZIThyqbxx4wY2bNjg9oDpL8Gg\nZcuWLo0d3333XUyZMkVVvvHjx+OVV17BvHnz3L4ny6fiqM2aVQ/O3glnWuHc3FyEhobq2mIYyVJZ\nEg4ePKj7+zds2BBw3lginsYxaAugLYDzAL60ZT907tjuQ7Rrv54uJQSyYMB+k2gT4CxIkJnGPiLa\nlMlGYElTWHx+M5IVeRMxZr4WM9qHKBgwIcmZMCsOUnptmxnWVapUyW79smXLlgACQzDQIyEhQfWd\nqaf79evnNa+gRo0aYc+ePbr71q1bh+vXr5cawSAoKEiVe8TZcdrfxJaktOG6XWkdWSwCUQv0xhtv\n8CUNb1jNuyIyMpJ7neTk5Dhs795aGhbtafTawcWLF/1SL0bxOF0TpfQspfQTAM9DiWnwlotTfIZ2\nFuVph3L69OmAFQwAxQVGTELjLMqat6zQQ0JCVKGljXDmzBme+cxisZiaeMobONOG5Obm8hS4DHcH\nBLHT0qtLNpjrXV9UGbOgV0ylW6lSJRw6dIirM48ePcrD9/rSK8EdWIInxty5c7m9hZ6LmRn06tXL\n4eAnrhWzWAtG8JdgwGjXrp3DfaxcY8aMUQmOeuU1Eh+FPTMxdX2PHj14O/SG1bweYmTG//u//+OG\npTdu3LBbSmCYFXFVdAkHgMcee4x/1ktYFxQUZNgDwR94GuDodkLIeELIHgDMI6GWi9N8hlnGLkDg\nzqwA9144b6XsrFu3LqxWq1uJQzZt2sQznwUFBQW0Sg1wXXdaF0F3ZgJDhgxRDdJMkyKitbgX1eri\nLE1cJz9+/DjS09Mxc+ZMHhdAvK6nyXT8Act2qhXAzGLhwoWqZ5yYmMg/s2c5aNAgDB482HBb9bdg\n4ExrsHXrVhQVFWHmzJm4ceMGtxFYssRQjDqHiAJtUlISVq1ahTlz5vAESd4mJSUFgBJJ8+bNmyhf\nvjwAxY7E0RhgVkAorY2V+Oz1vEACKSaOHp6OFl9DCW7UiVL6AKX0C3cDD/kSTx/CPffcY3oMajPR\nzrT1ct4zjFq569G6dWsA+ssGTKPiblbEChUq4Pr164iLi8M999zjcdl8gcVi4Z2OEdwZECZOnKjq\nUAkheOCBB5yew1IvA2pthtjO2UwvIyNDFR2QxUxo1KiR4TJ6G6PR3syChRFnHDlyRPUMRCM6Jkxd\nvnxZV2hzZB/z8MMPY86cOWYV2SNYXhhHXL58GY0bN9bNtlkSmLcGIQT9+vVzGDDMG3z88cf46KOP\n+DJwdHQ0Fi5cqHusI2NJT+jevbtDjZKeG+TLL79syn1dhcH2FE9tDFpSSqdSSu1DuAUArCNkiYY8\nnS37Kv+2p/hqRsKWZvRmwqyutTkpXDFmzBj069cP99xzDyZOnFjyQnoRq9Xq1lKJO89Fb9lLjBrp\nqDwMR8scrAzXr19HUFAQCgsLceHCBb6Gz2ZTgUCLFi28fg9XKmOtcMYQn6U2mBSgRDr85Zdf7K4X\nHBzslfDE7uAo/oZISZIq6dGpUye/Lr8OHz4cQHHY759//tnhsaKAXVIj7dDQUNUg7SoAkaOw6+7Q\nsGFDu/gaZuHWiEkIWWj7v4cQslv420MIMT+toIcw1Sz7H+hqm0ClVq1aGDNmDO809QY8VrePPPKI\nW9eOi4tDdHR0yQvpA6xWq+E1+ffff98twcCTTlQUDBwJLEyoZbEVli9fjl69eiEsLMxtIa4swLw9\nAP12rBUM2Pq01jW0oKBAlfhn6dKlPEHYrYoYJGr27NleW7Z0h/z8fISEhCA2NhaAsiTMki8xxHGh\npGNEtWrV3L6Gu8KIVtAcNWqUUy1xSXD3CTL9R1cAjwh/7HtAwRpoWRUMXMUOKCkszKizgBwsmQdz\nPyyLuCMYvPbaa27ZGHgycxctqfU6tzp16vCBkBCCoqIibN++HZmZmQgLC0PDhg3dvqeZaJdKfPF+\nigM/EwxYPH1tGYKCgvgsT/RIEfMfMAK5zXuaYMpdxGXKcuXKuT1J8AZr167FnXfeiQYNGuDmzZt4\n4YUXMHr0aN1jd+7cqaqjkSNHuhVCv3v37gDcr2d3B3WttmrIkCH47LPPAAC7d5s7L3dLMKCUnrH9\nPyn+AfgbQGtTS1ZCqlSp4jBhx/fff++PIpmOGb7dztBK/nozrbp166Jt27YghARMJDizsVqtbs2C\n3NEYeBJoJTExkVtBBwcH26XAFrUQrCwTJ07kWe38jaulEjNhhpuiYMfqRKwLMSx1lSpVdNW/YWFh\nyM3N5R10s2bNAiYSqjM+/vhj3RDl3iAiIsIt7w1vERUVBYvFAkIIwsPDERwc7FCI07PFYAavRvjx\nxx8BuC8YGEl4xmjZsiVvd8zuTXzPzRZQ3V1KqEQIGUMI+ZQQ0pEovATgGIAnTC1ZCaGU8s5A26n3\n7NnTH0Uqldx///247777ADge8NauXYvU1FQ8/vjjuvv9vdZqBu78hvnz53uxJIowxmLjBwcHc5Uk\na+dip1ZQUKAShAMx0qQ3bWXYAK8nGIgd+auvvgpAMbD9+OOPdV1oy5Urh7y8PH7N/v37q1z0Ao3m\nzZsDKFn0V6P06NHDq9f3No7sSjy9htn8/vvvePvtt0EI0fXy8KtgAGA2gCYA9gB4BsBvAHoCeIxS\n+qipJSshV65cMSXF562GNnFNp06d8MADD+Duu+/GlClT7HzqGbVr10bHjh1VIaStVisopSor8D/+\n+EO1RlsaIITYBX1xhp4xmh4DBw70qDzNmjXDyJEj0bZtW9SuXRsPP/wwZs2ahQ4dOuD222/ns9jP\nP//cLrRwIAoG3oQt6+gtJYgaAzb72rZtGywWC6xWK1avXq26VlhYGPLy8rh7ny+t7T2hTp06ABQB\nwSx/fUcsWrTIq9f3JXfccQcIIYbeFV8m13vttddw8eJFbN26lQfkKioqQlFRkfnLg5RSw38A9gif\nLQDOASjnzjV88ZeUlERjYmLo/v37KQC6Z88eWlYBYOrf5MmT+WfG+vXraevWrSmllK5bt44CoBER\nEXTGjBl25Zk4caLd+b/88gvdt28fBUCtVqv3K8VkOnToQH/99VfDdfjggw8aOm7JkiUlLpu2Pq1W\nKy0oKKA7duygOTk5NDMzk+7fv59mZWXRgoKCgKt/APSZZ56hQ4YM4fWSkpJiWnteu3YtBUCvXr1K\nKaV0wYIFtG/fvnTx4sX05s2bND8/n2ZnZ1NKKR0+fDgv1759+2hhYSEFQENCQlTXTEpKopMnT/ZL\nfbmL+B6a3VesWrWKAqDPP/+8H3+hOdSvX5//rkOHDtFffvmFhoaGuqyDihUrqq5jdh1r+1JnANhG\nTRpD3RUMtjv7Hih/SUlJtLCwkD+o06dPG6rY0og3GqC2MR45coQuXLjQUHny8vJ43ZcVOnbsaCcY\nREdH08jISN06bNeunct6btiwob9/VkAAgA4ZMoS++eabvG7atm1rWnu+ceMGPXLkiMflu379Ot2+\nfTudPn06/emnn+iiRYvo2bNnaVFRkYm14D28JRh88803dtcvzdStW9eu37v33nvtfnf58uX554MH\nD9Ly5currlNWBAN346LeRQhhMTQJgHDbd2IrfMAsJjtzsZO4R8OGDQ2rqgI5hLSn6EWxK1++PE6c\nOKG7TGXEUPGpp54yrXylnaCgIFWdGVHPVq1a1ZDxVvny5UukZq1QoQLuvvtuj3KCBALTpk3jn//1\nr38Zim1gBE+XwQIVtuR0+PBhvu3PP/8EIQTPPfccD2t848YN9OzZE71790bt2rV9Fu7Z17jrlWCh\nlFay/UVQSoOFzwEjFNyqlCT0qDYaHMuYJlEEA60LojOBc/fu3bohacXokF27djWvgKWYqVOnoly5\nciphwIhgUK1aNdX3hIQEHDt2TBW0RgK8+OKL/LMZHinXrl3D4sWLS3ydQIMZF+v1odr2SKlizKkX\n8MpMIiMj/ZZoyf+RKCSmkZSUBMC9+AZdu3bFG2+8gX79+qlCosqBqxhRY7B27VqXAW2ysrIwYsQI\nAMXZAtetW4fk5GRupOWtiGWlDaZxETtfIxoXSinS09P553379qF+/fqIj4/HrFmzAj6Vtz9wVK+p\nqakuz921axcAoGLFiujWrZup5QoEnMUtSE5OxvHjx3lANlEw8IbGoFevXgAUd0Z/Gc5LwaAMwVxW\nVq5cabePRSeLjo7GF198AUBRM1avXh1vv/02hg8fzlMzHzx40EclLh2IL2erVq0wffp0l+f06dMH\nAHhds46HuXWxiGwSpaMVBYO+ffuq9rubMrxbt26YMmWKKWUrS5REWBITdJVFHCXoqlSpErp3746K\nFSvydsgEA71BuyRLhLm5uQgJCcGcOXP8vkThc8GAEPI1IeQcIWSvsK0XIWQfIcRKCEnWHD+GEHKE\nEHKQENLJ/ooSBhMM9CKP9erVCy1atECFChV4xLemTZsiKSkJQUFBqkbeuHFj3xS4lMA0BixgihG7\nFTY7CwsLQ9u2bb1dxFKPOJt99NFiz+e5c+faqWtbt27tdCZVpUoVDB48uMTx78sagwYN0t3etGlT\nu21MkO3SpQsAxy7fZWUSQQjBsmXLdLcDytIVcxG0WCxckNUGQqpbt65q+UaLVlMoukSypYnQ0FDV\n+2BWcit38IfG4BsAWl3sXgA9AGwQNxJCEgD0AdDUds7nhBDfOY6WMlgjrlixIgC1epZF9zpx4gTf\n1rZtWzz//PO+K2AphdkYsGRKbMYAOM7Qxva3aNECa9eu9VlZSxsWi8XO+LBChQpYvnw5AGWWy+IG\nMIxGoWQZJiUKhBA+0IuIg1vt2rUBFE8yPvnkE6fXLEuTCBZN1BEsSNa3337LJ1+nTp2yO86Z0Krd\nJ2oGWN+ixV2NmRn4XDCglG4AcFGz7QClVE/0fBTAfEppHqX0OIAjAJr7oJilhgMHDmDTpk0Aihvd\nmDFjkJycjOTkZDRu3BgPPfQQ4uPj+X5/q6lKG0xLwAQCSikfnETjIJaemp3Tpk0b3etpg0jdyjz3\n3HN499137ZIYsQHsoYceQmFhoZ0q+9///jfi4uJ0Z7uMQEjmE2gMGTLEbps4GLHgWEwwEMOcB1JG\nTl8RGhqq65HE2pa7AY4opTwHDQA7oTdQCPQ3pyaUPAyM07ZtdhBChhJCthFCtp0/f94nhQsE4uPj\n0apVK0RGRvKBv2nTpli2bBlefvllTJkyBQsXLuSDmr8T6JRG3nzzTR4WmhCCihUr8khyeXl53BKe\nhaBlx61fv173ehkZGV4ucekhODgY5cqVU81ktYmhCgoKuBtscHAwIiIi0L17d0RERHD1rsQYLJS2\nI15+WcmTZ7FYeL4Btm7uD5W2vzl27JhdVkZXVKhQAWPHjuXGsSJ9+vRBREQEnn32WQwfPhyVKlXC\nn3/+iTvuuMOsIptCoAsGhqGUzqCUJlNKk0tLOl8zycrKwhNPFKeriI2NRd++ffHwww+jYsWKSEhI\nwOTJk3mnKy4pSJxz7733qlJEx8TEcAPEnJwc3nGIyWOcqRONZmq8lWjUqBEPMaytO0opz2/y2muv\nITxSVpkAACAASURBVCIigu+T4c7do0qVKqCUon379nxbnTp1uBcNoIRBDwsL48nDvv32WwBAeHi4\nz8vrb9iyrDuUL18ekyZNUi0zPvbYYwCUtOyRkZGIjY1FVFQUEhISEB8fz7Mjzp4925yCl5BAFwwy\nANQWvteybZNoCAkJQYcOHRzuj4yM5DkPypUrJw2zPECvfnNycniHKXac3vRvLosQQuxsAvbu5fbJ\nPI20KBRIPKdFixb8c506dfDRRx9xj5mBAwciJCREZUsjMcaQIUNUwZ8GDBiAfv362SWZun79OnJy\ncvD7778jIiKC13P//v19Wl5HBPrUZQmAuYSQKQDiADQC8Kd/i1S6Wbhwob+LUGpZtWqV3bbc3FyE\nh4cjJiZGleEsUNcOAxntIKS1H3jvvfcwaNAgnDt3zpfFKpO8+eabqFmzpsrwkMXYsFqtfClMxoNw\nD21irVmzZmHAgAGglGLSpEl8+9SpU1GhQgW88847Pi6hMYivQwYTQuYBeABANQBZAFKhGCNOAxAN\n4DKAnZTSTrbjxwIYDKAQwAhKqcvUdcnJyXTbtm1eKX+g8+eff6rWuiXe5eLFi6hatSqKioqwfft2\nHDlyBBcvXkSvXr2kkaEHDB06FDNmzFBtY3Us8Q15eXm4efOm22vrEn3S0tIQGxurclVcs2YN6tWr\nZ6rNFyEkjVKa7PpIA9cqi7kEbmXBQCKRSCS3HmYKBoFuYyCRSCQSicSHSMFAIpFIJBIJRwoGEolE\nIpFIOFIwkEgkEolEwpGCgUQikUgkEk6Z9EoghFwDUDbSfgUu1QDcejFSfYusY+8j69g3yHr2Pk0o\npaZEAAv0AEeectAstw2JPoSQbbKOvYusY+8j69g3yHr2PoQQ03z05VKCRCKRSCQSjhQMJBKJRCKR\ncMqqYDDD9SGSEiLr2PvIOvY+so59g6xn72NaHZdJ40OJRCKRSCSeUVY1BhKJRCKRSDxACgYSiUQi\nkUg4UjCQSCQSiUTCkYKBRCKRSCQSjhQMJBKJRCKRcKRgIJFIJBKJhCMFA4lEIpFIJBwpGEgkEolE\nIuGUySRK1SoTGnq1+Ht2UlNEpe1DNICQqkDmRaB8UiXcTCs+KO4u4PouoKIFQBMgc7+yvWpSKCxp\n+QhKIshJo7gKIM4CZBUBFQBUZLmsGgBFwQRZaRRFSTVx7mJ1xFQ9i+uoiGicRyGCYU07p9yrPoAr\nSjn4/WsCmRmOf1OcBcgsAsIB5JS4hsylEoCrLo9SEw6gUhKB5SJFwXHgPNTPJC7CduFC4HItZbv2\nmUVDOU/EAiDWVleOYHUdFwtkZpX8t5SEuAjg+jXls3jfoqSaiEMmLGepUtZwpXDa8jqD1U84gMgE\nAAVAWmgSkk6l4XDjhshFOUTjPMLSzvE2FVcVyLoIxN4FZO5S2n+5/flAVXX7rASgYlUAOUBmDhCU\nFINgFCI/7SKMUEkpjs/asgVAbCMg87D+fr22pHuNCCC3cSjK7cznbSzOAlxOrIQqZ6/yZ5Vp+2FV\nk0JR7ng+LtevhCoXrwKVgLPBMah+9RyyDgNVAQQlERQgBCEogOUiReZx2zMTrnMlqQnq4zjKXc13\n+Bs8Jc7F+2L4OgnF/WZcBJB5TXnOuUlVebuwAGC3qgSg4l0ADgGor5wbVxVACICbyvkAEKd5bqyv\nvgrlPbGkZaje23AA+QBiY4FLWUBFFD/buLsAHFM+s+uXVlT9ZVUg7SIuUEqjzbh2mYx8mNyY0EeE\nhvTulYsYXbkqngFQcxQw4UPgbtoRO8gqfkzqOSAjBqjZDMBsYEKisr03rYv4aieBA8CUGOAagGcA\nLADQAkCrdrYLzAMuRYfjE5KDo3QGZs99FiOe/DcOojHaYCOyEIsqZIxyr50AVgITRheXMfXd4u/x\nANI1vyk1CpiQrb/P3zwDYKab57QH0OocgB0AngSmZAMtaBJWkzQAQGo7AB0BnAO++uApZJLZ9s+s\nGTBhj/q68QB6twMmrFHf6zSK6y11NjDhqeL/Jf0tJSG1HbB5DVBPc98wOgLP4UtEvpeDCaNt9ZGo\ntF2jtAewGrY6WalsI/Uo6H0EXS4swjnEoid+QCiZCtZHpvYFFswDep8DJsQA7WkSWj2ZBjwNTOhU\nfO14AL2bAchU2uVl+g5ikYU8MtVw2WArny+IADBypfo3iBh57hEARrYDLq0OR2S1HEzILj43jXZE\nt/dW8WfF2t9wGo7IV3Ow5IOO6DZnFdAJ+Cr6KTw7ZzYWPAW0BnCN1kU2qiEKFxC/6iQmdFKuWVO4\nzlK6CR/jZbQ6n4YJMSWsDA9+uxFSdxb3m6l9gQnzlGuvoMr7C6j7r9QoAAcAtAPwgfJsUkfZdu4s\n/u3a95T11TOhtLsqZAz/DREAatrukToKWGB7X/g9dwJ4V/k8YZ4JP9qPtNf0l2QN0szKYBkwSwmE\nkCaEkJ3C31VCyAhCSFVCyP8IIYdt/yNdXixE/TWmkv00KwUb1Rt2sIMBZCkNDABicQ6IA7ASvPNc\nAOVzPQA4V3x+5C5h/lMLiIVy3/1IwEE0BqC8GIgTzmPsLP7YWucnLbB1QoEmFHhKPQDILP5eE0AC\n9vPvm9cAmANgZ3E92hGj1Gd7YVM6ADxtq2cbrUYBtcTzMjX/BTYZKbzJ1ANQM0q9LQUblfYktAu9\n8rq6LmD77eeUv6TGm5GRDTTBISRgP6KQDdXEaa/tv+19iMIFpb3Gqq+dDmDBHkWg84R6QvkCAW39\nO0P1ngM4ASAK2fbvNIDI8zlAjK2/WQFgJZCCDcA3QAaU9ha/66R+eUogAES4PkR9L7OJK752CjY4\nPibT9ifUXcaHwAJBsBfbfQRQ3FcDaIvf+H0ApR/h73qM8tlOEavznEojUbhQ/GWn4+M8IWAEA0rp\nQUppIqU0EUASgJsAfgLwOoA1lNJGANbYvrtFNWQjHsqADtuEM3JV8csdYdu+CeAdaG/bPvZiY0Xx\n9VhHegIAMoGMNbbr2mZlCdiPum3SkYD9vANugkMAbAPXDqXxi2QI0usCnd9QVgQCVYeVBeAckGEb\nXMRnshqKNmDBmmIhrjEOqq6VsUapF7tZ5wpNfWWqj8mwaWY2j4YdPq/nczYtVZx6cwL2A7NRPFDb\n2qW78Pq2dcAJ2I8TUIStBOxHT/ygOj5jj60ObB1N/K6TymeNZgZQjmPvQgL22wvbTqgZ5aUByQGu\ntMabDQg4NQHlGWRB9by2wtZJ63XOtkEvclWO0kesAOJXncSCNUqZ0gFgNtDqfJpS16uKyyP2CQnY\nj1bn03gfY6isvsaB/M4En/bQmfRk2eqe9a87lf5P9R7a6iQeipYWxUpDVZuLgCII1GMbbM9D9eyF\nMrojPAUi8eeLBcoMDwV0RwSMYKChHYCjlNKTAB4FMMu2fRaAxzy5oChFijDVEz9GK6UbmKU56+Cy\nYaz382UnaTbuzLLZS7oJUF7cTOVzLYdnAFklmTo5oZ5XruoeGToDLiAIpAwPqmATlPo+DXDBQtTK\nmI3Rtg4oHZnZnZlLnAhWWw2cng4Uz26FfsGp0OGGTYhWONTtEwy2AyfmSvbHevs5xNlv8sagfA3K\nczzBNuw19lxLLUIbPGHypQNVMOgDgMnLsZTSM7bPZ2Gn1FQghAwlhGwjhGw7r3kZY5CFrbBpAdiL\nZeskuNQOpQPdvAbATmHNbaVGrSVwGq47uChkIwrZanX4Th2tgPDy+EXaLwGezLLTAeA9AHOUz1sB\n3Y47HWrJWA+7TiZOs03TMW3S/PcnMwFs3qMjIOyAagDKWGPTTLlBuvj/G+UvCtlYjeJ2KWppVLBn\n4UBw0RKLLEWdbpCZ8K0tBwCn6lajdmhTbLNb7RJKLM4V9y3ifXZCmeGKbfucZuBmM2CmVrchajGi\nkG2ncneGO3Z1pj0HYSaPb5R/E4TfsFq41zUo7V51jgu4djCz+N1lyzrseVxDsXZwwh6delhle5fm\nuVdHAYmgPTLbVifgvBIIIaEAugEYo91HKaWEEF1rSUrpDNjyUSfXsj/mGhSpqqaTF4sPcJoXOx1w\nOGjVgiIg9NZoFs5djUVWpVhkIwqxyFLPpnbqNErh/LKybOCKBdlALeGFdjgbstVNNqoBKBYSTkA4\nV4Nqm+bZpGv++5utsM3qRWzaFCZ0utKquGJKtiJwxiILmVDsXvQ4wT6wOjO4dpmlL68HFiasw16D\n60FFNVE4pwh9NTVtUDw/Y48wGbD1M74yyjQV4TcuyNbfLrIVQKtMQaMF6PeN2r53r/Du2uZbhgf5\nzMCYEJiCyXYFIoGoMXgIwHZKKZtiZxFCagCA7b/bK61NcAgRsKmOvaOVVpGFWOScjcRGpGA/Euw7\nTb1foKNuK+uIs6YIwIEuqBi9wSwCOhqDGM02TX0H2tqiymDKCXbCg5uI9c0F1btdnORG51MqhANv\nIhjcOdrHENsgP95VH5AFn/RfphNgfVtJBOyAwotGlIEoGPRF8TICACwBMND2eSCAxS6vEKr+moD9\nGBmluP/A5k6DZppzEgXjmERhe5zNwl3Yxl7q9gBaRdnOiYPdS3sBUbhg64Bddpql8YUvIb1RvM4/\nMgq8A4nXP9xuDbselEHVbuklUbNNU7dsn6P7+JreUYLbKyMOyu+wtdPWtuM8vgeUOhbV/dmIwubo\nJNVxraI07T1G+HNCNqIcaiECBhPesQgo/Yi27exHguJeC6j7D/YMY2zbbf1EC/FkceAszf2A8DtE\nI8P06Lr8sygQtbCdo+pzE5W6VQnv2jq5Q6j/WPvrOiVR6LNLO4muD/GUgFpKIIRUANABwHPC5n8D\nWEgIGQJFj/yEq+sUVSXAcc1qwlwAdyuxBjA6B5vvSkJquzTe6C71D0erzBylcTcDUjNtfq5P2WwT\nRtv8dNcAI5mvbQyAONtAY/OPTl0FDLANYKyj3IgUXEBUcWfQEXjGtl48EzZ/3qdt188ujlkgwnzS\nU6MUtXAgrY/pldfZsRnZtlnSVACZQPxoAHOBJdEdkTpqlVKvc8CfTfpddQGcxH4k4Fvb+TOhdNDM\ne0SMW5DesS56tzuJKWtsAkcXqETN3rb4B73dKLe3SGX10Ezx157yofJsL/UPVwwQEwF0AmrOth2/\nwrj/9TO2/zVt7QtQBvDUvkAXWxvdiBTEI00ROjoqx/VeBWAQgNFQ6i4TdpqFeNgElURlzXa2sF1c\noolAcVtV+bD3Vf77ypc8NQrK79G5XzwUbYqrd4rFFsDTQO8YZUmBCW5f2QSD1L5pwGggIlEZ+C71\nD0dkTA7SO9ZFfOxJIE7pg1qNysHqD20DXH9lWxZiEJ94EpinbK8FzZKCA+0Om6Cw9yLVJkAafid1\n4oEw3Iqb8hQQ/6FyfM13gWdsXj8r0AapUbN5G5zwoe35JwIYBNS0tfHUbwC8BvROhBIfY54tfsdr\nSn/JBazRQO84AKuUvuEZnETNvsDmeUp9Oe0rB6G4z9bEMPEX7sam4e/UaAC2NtS7LzDexHcpoDQG\nlNIblNIoSukVYVs2pbQdpbQRpbQ9pdRlaLUchNtvvFuRXNlgrZ19ZiFGX+VlW9C4FB2unhHoSPaX\nopX7XkAU6jYuftT7kYD9SEA8lMZ66bVw1HwXqNnX1jF0hKLBSBTiHGioZ/ufYYJQYNZMub2Ta7WH\n0oDbC9+fAYCOts5V8xsvdQxXno1N2ELH4r9DaAJAqVd+PlA8C0tUzxgOoQmQaBMaOgLp/euqy/ma\n5r/JxKO4XhzNZLT1dumucCBG/WwvRYfbL68YUMuy2A4129nqitXlUzbNVZziwlsN2diAFCVQEaDS\nElyKDlfKbnOlZG2bwYQCdg6L1SGqaUWhANB0fk7cL8U609bTMzqf20MZ3J5B8aAYIdQDoPwGbd2x\n/b2jirVI7Dy9e9bsC2Vw66T853UbI2hMbPdhRnDMoyYb1ZB+V93iPihRuW7vKOV6+5Fgs6FRaA21\n10w2opAeXdfuOQA2o2lBCMjINuZ+yelf/NvZewsUDziAUr+A/jvP6yxOeMbnFHuVE7ay87rX04jE\nCO0rFkqddhHagU0gytgDuzbD6+xcsRDFPBE26/SVl6LD/aKVSY3S2WarU17HNk1UapT6OQDqOu8N\ndeyWkmgSHVEmIx/WSo6lq7cVv0A/ozuAYnX+R+fH4JXod7jrViyysB8JXM2ajSjul70RKQDA9ydg\nv67KlAkaCdiPH9CTLyGITMFIZKMafkBPtMVvquvtRwL3Vb4UrURL4wNfLJs1g0dIy0Y1tNqVpliN\nx0A1gFy6S/ntkedzVB1J5HnFgjc9ui5icY53WrHC2yZuU37zBb6flTELMapOLBtRNiv3C8K24v16\n9aTcK5bXGbPHSMFGfj2lHFn4DW3xFibiZXyM5/AljxCX3rEuv0+rXTYf70Tg3x1HIAUbeT39gJ54\nDtO5d8OS6I7otmsVltzVEd3Or1LO24lioQTK50sdbfX4Xo4yGKwE1wwhS9kfuSpH+X++2Lr/UrQi\n5LQ6n6aq61icU47LBDbflYQoXMAhNEEWYvlvTsEGxOIcvhSUZq+fn4qvoounN8+en83vI8Lu4Vj4\nLb4Pa9cXEIXn8aXqebBzn8OX+AE9EYssZCEWz66aDcQq7SsLMVxgA4Af0BMA8O35oTwADRP2WLtg\nbTcW5/j9u61apdR9J/C2nN6xLq+r9Oi6iD9/Unknzudgc3QSf2/ZtbVtTbwf+71s4H4MP6nqSKyn\nxjioupZ4bfasxHpinkZZiLUN7FG8/T6GnwAofU8Usvn92Dns3We/g5UhBRvRalea6h3eHJ3En0MU\nsvHsnNlAJ+U9ZuXT+w2tzqcpz0IULrNs3wUhaUl0Rx4PgL3jjI1I4dcU64Mdw8rHjmXv3Ua04cew\n/k5838U2+v/kvX+U3Nd12Pf5JvKJ1i2ZjGe/O/WCCRgnWYy3XM0iUKOcjJZHDJlBw7qIlKJGxS5s\nMBYENW1CmWQAyO7RFsqxTSIEBOqfGqJPCXth4iBBKplJGGNCHLpcbU+Zao1dLc9mgaYOJ+WuPbM7\n3UZMNImt9ts/3rvv3ffmOz8ALGTVvefs2ZnvfH+873333Xd/X5kffVxncsmY9DzK73q8E9wK+JqM\n2eDd8LCLnHC85TDXjJVmqxHQrKxRhy+x1GziijLtVNR5456nSpaP3EfWqKxN/Vn+C940rgWKbFPe\nagQuP5nrJiUmWaNNkSYlPpvM71rlw+8rV8Juwe/xAwHTimEnHbHJWoYom5Tcn17sTcYcMcvvQNci\n1wxV+3Bbcv63zfk//+DPAoYZy7OF6QrBr6X23i81g3uuMenO7VkJsA80GaNgI/rbjFKi5RaOrhPg\nGc2ow5FmPpqJ+XuX3O+DxxFuPkAQoCm41d/bFPkSXwRwG0qx1g6et1aZhIp/hpzXpMQtJvh5fpZi\n6gW/tYoRxpppiZnZt2HWv7MsTvlcoOE1TmGoMgXyOsJIxq0AlgqdeXwCkLYo0AkWt3zWuNY4EHrt\nxmXveRuEd2GyAG/xmKMtDYJzmYN2Lbwuhm2KvJB+nmKtHdxvYO0EwavdtIQ++4ERTj3e8kAYpjnf\n/L/NvgBHebE/ssb0ffW9BGSDyhfARoP7x/fSwoL+vUmJhcqMG0Mx9Ruk8KBXZkMbuMZ1XAQsFgLu\nFnrhOqZN4VPCO2L8x3iQawDHi+V8jRPhRPG1sjnq5+tz/LjD+dDnrqdQ3t9w7pxmCqXUb9xOWZJj\nqVWa0m3TZANPV5O1NQpbHXNN2k3DolTF9K0VMr13tSlCSsD/9W+9lIB7hV13JSRJMpYkyaeSJPmv\nkyT5a0mS/LkkSX5fXRa9Av/ivOthcrFlkepz4416jUlalFzw4diDzaAsc6vHeO70ublgHxNIvREU\n2aaw1Qk0/Lxzev927475PELepuje0wslRSdIiWVFNqr4u9zzFhOOibYpDsR3L+tGALqMssZrk8Ba\noH/Tx+8Wn/2EQM1c9P1jJtoPBDealvUc3GLCzUGv8RZp9wyyNfS63ff9A2jm40rjcli83m/ohWON\nvxg0PevNdlsd15tj3nrIownPH6LNqBf5xHQMAU8QHBvlpWU/e1wbpWmMeyk+pmlOcKGFCk3H2tKi\n/8efB8EwSpXmB8HaHgJ60alYwEq0umhW47gffC+zfnbNlZAkyWOYcsU/hC/P8mFgAvhTwDXgXJZl\n97153cMfLWa//k3jodGmGjCbdnVliVcqRzm+Mm9MQisdXq8Yc5psMrIlifQmknj5bIPFk8YMrM1q\n2pT4DC93uRJalDjBRbehiWlMWww0gZdoOtNY4XLH+MkvN7wp+1WMJvAG3jfcz49XItRym9F/C9pE\nJu4LbQITs66AmJQFX/rcXhBrbCWaHFqp80rlqKurrk3sco5294gJTYPgUzRgOXeBGYdv83xvtpTj\nej6rZ5cMLg9iKPksJh6hrnC9SWD+DvAsNeDFDKnNuS8Cx7ybQptCCysd56JYYMaNq1xvsFgzNFfe\nagTWCaDLXJmHe3k3rXkILf4MPxdqRnbNxKm2h7nmxrCe7nXzXt1a4qfTX3B4Pb11AW4qs+wzwMsK\nb3h3RPlyw+PyunmnndkRX3gph0YdHQtu46A8hR+JiNcWoDwcabOz/iwQm6jDaw0dFmlTXVlybqLY\nehNrzZoe80zoAKcvX2B9di8X+Zw79uX6F8J3F/qyeHJuiLrttTGN4cZjeB+9cj+uV/bm9msQHDoT\n+s3IvYa5905tpIsP3GZfYBbXJv3CZeOaez01QVsT3OI2+zi0VWcxPeDwc6heZ71m6OzQSr3LnVpe\naTie0WtO5dnyu4xL/gu/CuhN3hlCPonFt3YlyE53EL/moZvfWvwFEM/hfoVrAXvNYu2Aexe9lrW1\n5OPJ0velK+FJ4HiWZf8y/iFJkg8BP4bJOPj7u/jMnhATivbxMB5JjuP+HA1tRgOpXSBmGtqsqe8x\nahf+NkXGaAb+IGEAIhULw5bPA6VImz0B9r9mngNMhyIM5QVkOZfDeDeDbDPKThriVXCkhZo8H58+\n35+nzIzROLSmoM2FGm+yeQromBHx17cZDQQAwMUy6HHHDMxBCRfnwTQe5xKPoH2Q+OjyUtqisGJ8\njIX9SuuwDYmEHjXdiKunl1AlBZ6E8fdihNr1ozcf+a/puR+dadNuHm7yLAe5YIMXRYOS8bcZhamG\nx6XFc5MxCvsNLuS6OJ5C47brN9XMuc0ok6wF5tnCVodm1JzW4Kq/9SHPTSPv7GhpvPe5WvvX9BjT\ncQx6fvpqvLHbYD/hRiebkxIeZKw7FW8612BM4nZDj+nYPrMfHcYKD9A3+C8WmvLumwc67il2lfW6\n/jb73D4h9AY2XiBteY0/h08Cfm3r/hWaB0eCe2F/J9j419O9lPc33P+ddMS5JkBZHOw9Q6tgzFd3\n15Wwa4JBlmV/q89v3wW+vlvPGgTf5UOOWJuMOc1HtMRiapC6XjHSKGm0SRGa8QL/3cFQ+9LQyzyu\nfx/kj/dmNDXxU56JLqZGGynNWsLVDV0iQtQQELn+nwPCjPMYZa9NS869G9NiiabTHnotftmk4s3K\nXOOZTyzI9MJzKNwUQ9+s3aB20hGa6Rjl1YYXAsB93klHKJQ6XRaUvnCKLhOuBO61K1761zEt1N72\ntJZ6DWhY6GXShrADqPzXsSyxbz3vuXn0JpfspCPwkl2H6agbv9uoNV7VxqatIKTdAWWFccPIdY68\nY/I0AkFErl1jkglu5Y6338Y8jKtJ1u1iesDhUJ4p98+L45Df4jglER6YCvlMcL1o/DWPAz3eYrpN\nedNueGojjxUhM8ceR9pyEoDyme/M+uBIARHAJ7gVjFksdi7mpOYVJG05XEwPhIrYfp+RtFg5EJwL\nZr20KTrrYj+hSfPsOAhS8wxNT4I3TUtyLICDmODA1AZBQz5PSFt2r/G0vJOaWK/1tDtYUjLh+rkz\n8oJvdwN2PfgwSZI/BvwEJtvG3T/Lsr+5288aFrSmGBNXv+Agrb03KTHBLUegeZuTvt8obWJ3gma2\nekzCCPTx8krDa6PCLLWmBUbD1G4AS8DaJKyJUjSpNSaZTP0mFPvCdOBM7MfOM6lq/2b8WxzNrPEs\njFIHYsnv4taR88TaIkxXrAN6g6tuLcEmNCulrt80lGhabaHp7heYUmPNa8rilk6XplSg0VPTKow3\nAly6uI9myHQFD7FpMA+MtePtoQWwPFO2/hzjUf8mVhU55npWbEI79fOqN4FJ1gLBp1fmQAw76QiF\nzY4di854CQNl9fndgWbmuvXUM9Y49iLETRj9HWcr9HLL6KyEOEBONrvYKqNBeIvGfUzrDsZxxx3Y\nHhrijumlKbuW2Rr6WEflHnKtCHDmnuF6zutfEse29AtUFRxqoSg+dz3d28Vn84KX4zGIYpWnMPQT\nAHfSEbeOe8UK9II8KzJ4/hnTtFak8gI7dQZP3v3vhzCg4X5kJbwB/C8Y7+v/cx/uPxA+xHeRYA+J\nmtUEUa43WKjlB3PoyYlTE4VBmc/50r9AXrpiLDxAuHiCwJpVDIMVM1IqhK2YrJiwbHGSwrjZeMo0\nvL+r1vJuA5uWVz265CvSQOizfsmaDVc6FJqN0CdZa1E426HwdMNF3FdXlni9UnMpPwUa7NTMAivU\nbWGXlQbrFUPM2u8oDDRm3m2KxmKR+uMS6e/PyzFrax+fBdG8Q1dHKZi/Im1/nfhiwT9vFQrNjm+I\nUzN4LI8b4c0JFeM4d4DMTVm+S2lhi+vqfiPEUCEwc4vvdK3i6a680mCh4t+pXG844WUn9W6hxXQ0\nYERxFHcvjUpwG2uuMc71ht/LlOmeaYMy9Rzq97ldse983dy30PLMt4rXumQdT6ZrNn0RR2uTNRub\nISmjK53A97tTG3ExMRI3Ua5bd00tTGsUepV7go35qJg1X15psFgJBeFDW3VeT2tMcIu3eCx4Ry3Y\n5prTURtO5NaQNV5k2+A8+t2l1hLypPgZ5bpt46zX9xhwUNElcDvdh6Qhx4Kp2yiFh2w2DL/RtnEK\nOgAAIABJREFU6b2z5jxZy+WVBu3KGkW2HQ/ttaFr3ATrMRhDPt3G/Ddu+603YKED/TyXGr5i3AGF\n6Y5fpxKbkVcSvGXP3cQ40N+A0muWz1peXB5vmDiwg1AYb7CT+lRlrKt2PbW8o+KFUOG97pXr5q/0\nLe/aWExFaGg6uryTlufDwP0QDD6cZdmz9+G+dwWGObW7tHxt7oNuIpPNSRZlm6LLge3nz9G/tSgx\nRtOnLUYaTsyIXYoSbWeq0xp4LHWy2Qg1AtkYe7kJNnt8VpthT+k4x3KdK4VHXeTsS+Req3EVWwya\naWhmFFeDzJnXZLy0LcFJAsKsRSOL8a8tOBJUBbj8+YA5C8SZHi3ClEUduBSfK93xcpT9bn9o/+8O\np6n/XEy3c/3nMei4gwVmHI3HrrHX01rwXB38pdeT0KxkMKxX9nZp3/K8eBMAQppRtSRcbMFmGDcg\neOyKPWiGvw8CbREQundxEKk3lWvhpt/6j03mWngS+gsCP9OQ5kV4E0GxTTFIwXXC7CmCID3/+iUb\nV2OPCw7kf8wXLH1KGm+etuzM6DFf2cTMVUTjYTyEBCKXArzlpYIafLU9TRJaenVQZiyE3mm0fk/+\nLUW3dJCm/g261659/1y+2SJcp32ga93m0a/gesC9dgPuh2AwnyTJceAfAv9ODg5TsdC6IX4JeATI\ngL8G3MJ0KX4YU0jrx7Ms2+l3HxdjEOWROsY0qHGMhS5/012ApC1q2KboAhNj8MQe+lS1mdAtMonk\nlqjjJl1MoLCltKiD5pz1yl7KLzcC5ifgGG3qA+Sktekak1SfXnIBduB9f+vpXkq1sFBS09YGWK91\nB8bpYjH5gUb5tRI0nrqFudC8Jox0u4fGFoPbJIjMpBJ1/AaurKuDzSiK3tzIZJHY6H3NLAtbRjtZ\nT/dC6rMv9DvsVEK6jQWeLvrd7+dN/sfm+9h6oKGXRivWFs+QvaUnzy2h30E/p+veeeZxoeUWrqiR\n05ZlU1Lfd2ZHHH1KoatizW7ElZbbUCUmR/AsxZMgFISEfn1MwDZUvOCgg/Vk/YmwmlczpVffiH6m\n8NiqpZ+ljxmhtVeMkp/znVnr/qqpE/RmpQKWdfGzglgcx73bca1mi5ulY1CB8sGG17ax6926w4qV\n7dz3vxOl6m4gjhfQkBdX4gqRjS85a8rOS5YnHrTC0DQ+W0BnGeljT/v1XL6ueMdRHN2KtcBljKx0\nKKUtF+QtsQblrYZZ75Z+JZZM8CogQd/CG4wwVb87xOXA/RAMfhf4O8DPYjZ37P8fGeLal4Ffz7Ls\nsG2//IPAzwA3six7IUmS05iUyFP9bvIhvguEfrTA/6XMc72kzjh18E6JNm/jz1sYg1wSeeDuoaNl\nVfqRk/K1P1HFGMg5wlh15HdeNUStKehzBCT+Iq/gTvxZINb2B+Eh3rw0Hvtter2g23fpfbU7cWES\naX5jMwoAb8YfVwJBKfyfG5S3SdDAq9v/7K0w8i7lrUauluDmIidTJAa9ueic8Dz/Zi8YdM5oMA/d\nFQQllc2Y9u3uNIa3EoipGx/T0WSMQqkRxCAIfruzMHpV2xwmeLD7nEHpiRriPHsxzcuxfrR9p370\nOLMnjGeI3G9RVVQ72PA/QwTYEa4RaDgeo92rPniu6QQy+S5geEWp693yMwm613kvHhoLwz7WYJQ4\nXsTMiXGd7KQjFKZyYgq0JVB/L6mYI4CbUKq1Ap4b/C6g+LEEz8agi6JJvRn/Pnnr+v7EGtwPweA5\n4E9nWXZHlUeSJPmjwKNYnSzLst8FfjdJkr8CfMKe9svAbzBAMACvaedJ82GqzmBtctBCjUEIvJdw\noP/3Aq11aQ1NM4KddARmewQSpZ4hS2CWVOrypspRhyfZXPRv7v1Tbz6GfA1Jpybl4ShmftrkereQ\nt6nl3fNRG0Sns0y6q4j59+uqJma1I4mubzNKsWJoa2bW+JKdtmpBV9gLFvq4uZe4QrrfpTtIMxYw\nRCDQY9bPyotl0TAs3uP79FoHeZqu/hy+Z8jIdmom8rrJGKVZI5AJ7ki9OV3jXL7L2pBnxb7oPLoQ\nmo+tHia41QdSintEzutVCr0XxGXDh79u8NzEY4+vk3W4k47QrPlS3HlCPSjrhsV3Ox3t+k3jWu5t\nnjsWzEE/vqbnYJj3jt0NvYSG+L5xEJ/BV/d8BN8rvgS8KFGuzLGyprQZZTJd8xbpFGc1EX4MFm8i\nzDslVNW2SD2fjl2d4dpvdR2L5/pe+Wge3I+KhP8c+M5dXPcngS3g1SRJbiZJ8ku222Ipy7Lftuf8\nDt3yLwBJknw2SZJvJknyzX+19XsBQ+63mQ9jYu4XYXy/QIill8l2OAYSSs86TiHW6PP6H2gc+ud2\nWwvkfP1fbyaDFn2/DawXA8yLCcm7z91YZPLuF2obXqvPE8ryNMzwfce6jhfdt8HR073Gq/8POu9u\noK1G2euegrdYI+z33MJWx+GsX3GmmD7zQJ7bSzjKG3+v48OOH+58vu6Fmes1N2iMYvmLhctBVpbw\n3GLXZtTv3H5rEcL1E2cx9IJ+v2n+HPMV+T5Meq9s3E3GAsFnJx3pSj8M6FVZAnpVg4x5aWz10aDn\nN75fP4FwN+F+WAz+DbCcJMlbhDEGg9IVPwT8WeBvZFn2TpIkL2PcBg6yLMuSJMkt1Zhl2VeBr4Jp\nogS9F3acw54HeiLjzShPO4mvhfzMhGFgEBPy4+hOk7ub5+h0pH5juVMiHMZ8KtrNIKFHFrmcq6/p\nNxfxdb2gFzMbdiMdhsHeZp9L84vHp8+JrS6xphr7Uu9EM72TDWy3mI4E3emA37txn3lT8t2ZT8Vl\nlQeD1vQw99b/u+rd93hmfM7dKB76PjpmRTc9yx9zvqA1DM3HNBluwj5AOOadbYrWIjQ4a2tYWomV\nkzyQgMb4WXmWO2dhdRp+WPxMtH7BwWLqrXS6HsqdCJq9BHsZXy/edL8U1fshGHyduytm9D7wfpZl\n79jv1zCCQTNJkh/Osuy3kyT5YYaKN+5PTMMylniSdL+CYSCvloG+t0yqLiiTB73eRXxn+vuwFbGG\nYQQ6an/QuO6VsWrNPk/L7xXJrCFfYxocHzJo7OL3C1PDenvK+mnvwzDdfgzFHxvt+5xBMIwl5W6t\nLXnQ2yrkg4QH1WbIs0rpMQ7jOslzI/U6736D3vhiBj+s8jEIYktgnh/+TmAQ3uS7vm+cTXCnuL0X\nvtIPhl2n8Wcd6xAL8TpY/F5A88J7vdfdwP0QDN7NsmxJH0iS5McGXZRl2e8kSfJ/JEmyL8uyW8Dj\nwJr9+0ngBfv/1+51gMMvqpDANVPpR6yaWHoJB3l+2X5SYS/psNsF0LvaXy+G2i9tM37XfmO8kwWf\nZ37u94x+/ste83En4+nnstFVNP2Yw405ZhAxrcQLvd8mNogRDiOoDbo2z/9/N/fTkBccFtNtt2m/\n292XZ/7Xvw0SBu7WddUP7tSaNMwzBs11b4VgsLbdT5nIu0ceneZZSuOxDbtp3+/N7W7XhJ4DvS7j\n4lX6u5wXnwP9ra/6vFg47TVvw9LqbuP3fggGryRJ8hNZlr0LkCTJp4HPY9IXB8HfAH7VZiT8FvA0\nJg7i7yZJ8lNAA/jxuxmUFO7QG4zkxopPNJYEdZVCMUMOMt3kbZC9UhP7QSxxy/NjZjJI+r8TLaoX\n7Kb21IuAdXDXbmiqdyqk6I19hgW3+HXBFy0MxJvgMJuEDmjToGkqr/KhRHHnjfVOhIF+MCiVbNC1\n8Ti0MCtVK2MBIS8NUv8u0IuJ7gbkmfD7mWgH4UhbAfW97kZI1fxGXx/PVclyMKHXXjTa71n9PudB\nXsXMGMR9pM9vYmotLPBo1/VxgGceP44FWu0e6sc38q4d1soZK0bxNXlBxMO6P4aB7kDpwRbFe4X7\nIRgcBq4lSfIUMIMpj1zrf4mBLMuWgbzuUI/f66B0IJ9m5v1MRvHnfpM5yOx+L9BLcpdgtThP3vVT\nsCk6sqGZErdhh7FeKZT6ObsJvZjVILztplk7D/TzdXSwlCAGXOc8MExsgRnXEa665av1iWVB/I06\nul2Ox7Sonx0zntgne6c0lse4BjGbO4E8jT4Pn7GW2aTkOtyJP7yw1eGV9CjSUFtwLLUNBNfSkyAv\nW2CQhUtDngAwTBDcMC66fsficd4J5Gn7cjzGRfwueRXyrnHY0biYwuNuhAs8GuBaC/Hx2OSZeixh\n74RHc3HSr/dML34hCt0w7sL4Wi2wSWRBTC/y7nluZBHE9LgPc83+FsZ/6XiPNqPuOqFxec4Mbwc1\nXvSY44y2+2mF2XXBIMuy30qS5L/AxBn8S6CWZVlnt58zCPKQpq0Deb8Ngl4aX971vZjxvULs4zL/\nlekq9QJPnLoWM+heuIi107xnx++Wd+69wJ0w90Eg2sbd3k/wuF7xhWVE0JQKdCKM5QWs3qm2O8ic\n+L0yL96NMHYnpuUi7SCtU6d4yXfBMfggL131b5DFJm9DuRvryDDX9Hv3QVrnsPcZdizyHP0f8gsv\nCa+Qz+b/aNe1WpHSWvduaLD6ne8ktiJPGct7fj8LUJF2l0A+7JqL6S9M7wxTuHWvHvncXS56tOez\n70TovFfYNcEgSZJVfEEjgB8C/jDwTpIkZFn2kd161rAQm6IEtItAYBiGkedeiK8ZZDa8E0Yr49PS\npW7PrMcu9+7nf+3ZpW0XoJfmNSzhfi+l4UHj6GUmzXPbiCVgWF93v822F/PSzE8LObs1h8OugzhY\nVq8hPRZtNh7GfaKtKQIap3kR8HJdfM9hTf29fovX8v0QIIYBwV2vQNy8zXMYuuhVi6Efb9Cbv3ar\nym+98KaP689xMKL8FmvfUutEfouf1Sv26k54SEzTMQzrEtBWklgBzePHeb/lWXx+vyDJstzsvzu/\nUZL0rR+cZVl+qaf7AA99tJT9N998yn3XErrePO9Eir+T3/v5ueLfY4LMe8YgRj0IYin/brWm+w29\ncBwLPL2gH/7z5rrX83oJlDHEAmKv3/LuP6zGd78isgfR3G6dlyfAxsxdfosZeh5uv59pF4Zv+DOM\nBUsrAPGGOawAk7dpxxDjfjctf8NCHo++0+vuB9wprQ3DO/rR9L3Q9heSC0tZluW54u8YdtOV0M6y\n7F/3OyFJkn9/0Dm7Bae3Lji/5Avp55E+4V/nU5xeueBqTIs/RzSxeAHK98Ncc722FysHnCkoNlPn\nVfR7jLdYY5JP8jXK9Yar177ATCAFa6I6vXLB1I5/GrgJr9SOcnxlntcrNYq0nU+bFzEle9/AdPrS\nGV8tfPndJr5jmNT7JjoXW1/ddglbnzU4ku6Br1dqzLAQNK9ZY5IFZsy72VasujgIhMVFxMcW5xVL\nwJ/EQUg3tCZjXORznOAiX+KLTLJGiaabSy3xH9qqwya8UDHzLb5p3RxmJx1hgRlmWAjGcGjF1hlf\nxZRAnfZ97stnG+ycHKHwVMfgGIvvl4Fn7H9bW16qo1W3lkx3NTt/jiRWzbU7rxkcXeOwG4P41ZuM\n8XU+5TTv4/V5XqkdDc7R+BQcx/UNdAxJnh9emOrp+gUWawfcfMo6EBwJPZ9eueC6dC7OmvOFbuU9\nHuMtDtXr0PL9DMpbDV5Pa7bBj/GvyvnHz86bEu/nMDVTp029+kK9Y0rPVvznoJukWhvSAEvm1/Wt\naJlxVreWeCU9SpsiJ7joujBqHMq4ckuC42N3wOTE52maMyxQZNv54zXeH+MtxGRtzn0bqar3elpz\nDb9KNN1amOCW4xfaqnL8I/Omwt7TYUyLrvYqlq/C5U7IAy4ZHDONw+nraS2I4xBwvQRWltw87NRG\ngvbhLPtxSJO5Gd7m63yKT/I1F08g7wRmrb1eqQXWABnvAjOc4BcBUwr8ldTTfUzDcn4ezxUaFv4s\nXV1l7mRMrvviZdMfYuek72Xg3k/6eEjJbjk2jlnjLeCUpc0nOub7S/a8Fr5ngu0kyrQZ43ptL+Wz\nDdZP7qV8ueEa50klRTdnwGLtAHGKtOC4Xyba3cJuVj78tSRJziVJ8qitWAhAkiQ/kiTJTyVJch34\nj3fxeT2hwwhs2sW9mSOR34GAOcy18Tm3mHCbzi0m3HFZdFJda8CDXd91Wt212MH+tunPcTW9W9F3\nIfCW/b+pztHHpNnHqr+2RMtcb9877kIXgLpHYauDacXact/lGt+LPMwGMcfyq4ZBiMuYcRdpd7Vd\n1tXKfMtT+30lGr9ct4nZqFrqObaTn8PpJvAu3V0U4y6M0rK1hcHpqr9W4yOGrqJVOZU78nAsQWP9\nil4Jc32bmQCfeXUZwiJKRYOj692/FdnmFhP+fsvmT8bHpgjere7nCF0KvevjGiL60/Ttjm2qeWoR\ntLqWDVw6BWr8a8Ex7jDozuvqIhia8+/VBNymGM2HXRN1L/Q5H3+03mXMubU1ZE1oHjCoEsym751Q\nZNvhS+aUTQwtK95R2Oo4QVPGk7tGcz7L+3eNPwfnMcT8oxfk0p4eo8WNm2+NL6FNzUObGIFf8YRg\nPWu+ij8n4Luo//p6mS+F43htm+DQUs/3ulfYNYtBlmWPJ0nyJHACqCZJUgC+i+mO+I+An8yy7Hd2\n63n9QJooaXiLx1hjkreZ4XTpAhf5XGCSE+lSg/afXuMwxcp2oOnLbxBuWr/x7cfo/MMCSw9VTdmm\np8yxyXSNYq3NRU4E/rMibSf5Om3QMrXFygGq40u8xWPMVJSWm2J61Esfe9tnPW6N6rp5qX7365W9\npguYPlevYdXkpwusZuzr9RcdgWqQzpRtRqmy5DRpHeSjfdXXOOy0B4BPpl7bWGOSwlaH3/gjj7Hv\nwdu0KXJoq04xbTsN4DDXzGIqmU0PPLP+JF+jmZoe9AvMcI3DTFRuOS3tMNeMMHAU3zDpIC4a+dBm\n3eNIcPaI1WanO0H3Q0nDqrLkNdyxThcjEKFFa4Ja6tfBosen5oOgpiLb3R0gFSyo9xftKPaF7uM2\nYHB1onaRr/OpgB5vMcHp+gXWauYe2xQNTY4DU7jzZay/8e3H6PxOgV+dmDXntHDdBycqt1hgJng/\nt5GO45vC2Va+a0xSPWi01AVmmKktBNcU023Kq6YLnXSwbDPKZMVo/OWxhtGGhSmP55vrdc8Jj3cv\nmAqjXkwPUN1ccptzjEttNemVWilzIXxCNFZSz5u2KfIoC1S3lnxn1xpc5ARvM+PSnhfbUK2ZdRxb\nhSZZ4y0ec+uoylJ3EfkxY2XRGQbSmp4U10+lmNqgvNo1g9eDBtdlGmbTmsJYzsa1lbCE7rQo+BKa\nbFJyfEzTzy0mkPisr/MpAE5ULoYCEeE8tjEtmvPcgjpOSFuC43ggZ/3Z9OtpPd1LuWS/L2M68/Rq\nXLgMnMMGJo9SbS15urPWRWlDXsAKHPvl2Y9Srs1zm32UDzacRWqytmYUlz4CXFxTxbzf92l3xSzL\n3sCQyu8r/Fs+bJnBGFRaznQsBLFTGXHEJsd6VSi8xYRbkLfZxwKP9tQOWpboPvHgW6w9Nemu+8e3\n/yr8Drz1qNmIrn37MJ948C3A1Di4xQRjNN0YirRdt7k1JllLJ7nFBNc4HDGabQrTDWNSve6J03U4\nS30b2erYklnITRtEN93wDEP6j4+p9qF245HuduZziZ1KyFC8X9DgWo7raN9i6tvexotcBzJtB5uk\nN4+N2d8FZ3phB8KcNT0L3uOAqsl0zTHgBR4N5hb8AtYpnm2KHJqumw1+1phldyqm098CMxx6uh6k\nM7nNbxyYtpvcfuv2EYaxjEtREqYYj1k2kFHarrW1wG32UUrDdLKmao6VxwTleqExYdRijpW5yFsH\nLvhx2mwoTcbcM+T8Tzz4Fq0H7bwcNH8ScR0z48CPOoY3a08DT5uxyqanBSRZr5OswcGGKzEdC1fl\n/Q0/Dnyb4jZFM48l0yBH0+QMC665FUA7HXWbyxqTTFbWuuZGv4dWDDSNy9zGeNXjFYE4PqfNKK/X\n9vl3tlD9tJmHOO0vvmebYtiB1ZqwedqvUblOBAutDMlxWQ87FVP5cz3FCF+WxiUj5xYTwYYs869x\nAwT0JniM8QY4ZWGbfOFZv3deYGNebJJeazLGGRace0VooFRpUVjtwEkr3E8rBUx3Wa0ZfuBKYD9i\nzlmf3etcsO63irHsiSDbpgj77byNh0rCZGXNdG696WlBwDe6KwZrcTfhftQx+P6AH4V2u8EDwHbm\nCW6NSb6SdNjOQr9ry05ISxHRr5z9LD938meYZI1R2eTsItfnjSmJ9BYTxuDz7RI8CK1vl9g7sQ4T\nnnlMPrjmnjVKO7iXMJv12b3OhFii6c4Rwm5T5GpiGeA5eAKobjbMhl8HLsFGG6ovLrF4Cs4De4B1\nYK64xJke1re5xxuwjP091Ern5uc5cxTmHl8ygsS34OFkibVskvJHGpxfhWeLUP1nxr9+5hTMLS9x\nZhrm5huGUVuLA5vwSsX4fU9vXeDJ9O87fIBhHqIBtTApa6O0XYwBr0L7pJlTyR1er+x1Grvka7/N\njLMgCJNp2c1GnlddWYKjuJgF8WcKYzlzCj5zqkOhaN/p8Q5nbsDcspHQjx+bdya/mZdMjMT5MfgA\n+MzRJc7k4fn5OlyCL7/2BX6i9lXWmGSMJl9e+QIchYvfOuFopPqRJZ75li8UcyCp8x2gPNWgfKMB\nP4qpFPIalJ9o8Mk3v2ZzohcCbRbMRn6Ya1z8trn/2INN3uIxR1fBOtjvN/5tirxeq3UJBPr8tW9P\nsvDgDM20FGhp2t8L8H6yxHZWZB+3OXPKIsRuWnPPQOdKnfKLUD5o6c/GZZi+qwbPZh7qMA0b52DP\ncxi6fwTOXIEHgI8B1ceXWLwBp5cv8ELl83wlMQx+7sWGEX7HbbxEssRXgLnn6k6AO3PFrKvjy/Oc\nn4bD2TVn+XiMtzj0kTqL3zpA9fISvzh7Iogr+JWVz8I4XEsP06LkhFvBl/ATOX/MrvEmJXbSEWeR\nKdFkgRm28d1ad14b4RqH3fXa+ijxTJOscegjdc6s5tBeC6rTS94yWIFDZ+tMnLxF+SPG1169sGRw\nfgpjSaPB1VXYwAi5V/X9WOLqKdiX3eb42XmunjK4OnS5Dgfho+k3nIVKYidk/IB79zVrM5WYA/ku\n+JKYF100adsKE9sU+ZWtz/p4nlVYm53k9MoFI8iPmk3+Sye/6Obg0Gidme0FvpN0OIOhmQ/oUKbO\nVwKM5bj8zpl/DwAfnOoYulmGMzcsTs42OHMKysCR63U2DkJhCq6uwsdpUH6xweTJNTbG4Phz82yc\ng0PP1Q0Nb9LFn+feWII6XG3DkeuGx8+8tMBa0uFwdo3C5Q5f6B7lXcMfSMHgR/gth9gHgK/wjAs+\nmWGBTeBzXHQkBQRanyzIjVPwlZP+WpGGtRQaR8ILE1x70AodD3qzlgSKaIZ5aKXOYuWADbq5SOFs\nx2yg1ip2ovaLGD+SMZebRWNcGpp434eu2IL3gD3WHPUBRigAIzD0hGVjqswFsQW1YHEVqnWz6c1t\nXeD8qnnGmTbMXQfO2nNfVNeOYQjfWiiOj8+7c372pZ93QpAEV4omMska1ZUlqqufNfd5F86swtxB\nG6Bp71mYblA6eY3j9Xl4Hqq1JU6fuuBNdHbDX2PSBfQdr8/DU0ANTr98wQWvVfcbc255pcEZ4JeA\nJwQvyi2wcQP2PEkYX0HDMc5f6oXnS9YkfBZerj3jhdTn4PwqfJEvOTo8vwonuOhwI/d8YhWqr8L5\nNnzsClSPNVi8AdV6g1LtmjFHWiGMirmmyDbllQYnKhcBExQn9A1RgaVn4HOvXeTQSp2ZygLV+pIz\n62sN1pmyH5zk0OW6CWC93KE4axBWXVni0HjdaT9ngPM863Abw7rgWDY1HTsjflo1D78EzNUt07XX\nfGBPWbxh18Y8nB6/4J+3jKGbaahOKeGtTuDbfgeoWtqWtSeBrVdX4Uh9CS7Br87OOiGsTdEEUo7B\nidcuBiZupxGyRnVriZ10BB3APMMChac6nH75AmC0Vdn45ZzC5Q7HD877QFcbHLjGpJujwuVOrlDg\n8DmGC1Se4Ba0CAIPHX7tPRYjvAawbObsy1tfYOOUVT4u102922Nw4qWLTnhfY5Ly5QY/M/tzQaOp\nJr4iotBWkW0e4y1nManWlzhcu+ZcNiLEC1/lmXDspw9egBeh8GSHM234zCk4cdLPx5k2zD3fcXxU\n3m2d4UGuuXou+qEVfn4P2BPx5cJKhzpwJIqlyOO/G1eMMPYBOMWvMNbhTaB6trObXgTg+0wwSJLk\nPcy7/9/Ad7Ms+2iSJD+EwcnDGJz+eJZlO/3u84f/RZiCeeislcRoUK0ZJnDo+XrAZMq1eT+ZduM6\ngyXwS8BYwwWcHTpW99H/+ppxYBkOYTQZpx1bP9zGOSgXzRhkEzda9hLV2hKLV+BNoGy1qA3gWavB\nHz82D5fw10bBWQ+BX/CY51Xtx+oUsGqY3AfAniLQY/NfbBtEl+leIItX7IdNcw7P2++PRwzjkn3v\nNgZnAjLuMbh6xS6Id62QwZJxd8h71e27vgvlRxpsXOneZK9Oh2MsX4EjZzte2l6FB87Bs1PG77fY\nhmpxier0ktH4H5/n/A079itmc/lO2y7WIpSnG2zcyMfTA5j32KMtm+OEGQiIFtIN59vm+Ds34Nkn\nOkaD2/Qax6HnPY2dAZ49a4WoZUMjYDe8ZXOfN4Hq8/5YodTxAUxjfiMvnDVBZF8+ZfWLeYNralZT\nnF5yNH3mCsy16kbYmFoy81Q02tOhY4oT2XV0aKxu5vXzHYPrS0a73LgBe6b85gLGshHj9gHMfZ6I\nkaUCtOT/A+rnJzDv+BBmPsptQxcPA9/Aro1LhGumbgTkPS1cQCXAxqphMu/b73vUcwsrHQrjDeOH\nv27WJ5eM8FF9XrmLxpXmOD0fjl9HttehMN7heG3ejW3jBnwF+MwV8y4FOh7X9pzzN+AIxmKEXRt7\nih3HQ6pTHa72Ego0ToX3bTXMmG4SZjWN+f/vAx/HvHMePT8A8LhfoxtHrTB9Do5PzzvxcSN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yciEJj1qTXbXNy/aO8zBqWTZgNbSE3RM1rQnjX8c6c2EvRu0EKWXof+vX3ZdKleKngVuhWBXm+Q\nRdqu+63wr8XKAa9gvQqMN1iYnXHXlM82aJ8cpXp2yTi+awZX1YNLLtC5cMwGdl7CZ+5IULsN7i5L\nxdkSLjDYleUmbG6leXKRNtJvRsdWCJ5dIGoLMyYb17Fe8fNQXmlAJYp92QXoGUSYJMl/lSTJKrAv\nSZJvqb9/AXyrzz3/HuZ1/lvgb6m/7xn8QFchA5mEcHPWMMi8HRNx3u+9tN64Xnhg4rrp/VKTrN1R\nDvwfJOiH/0ALr3e6WkbHXQpdR8R6x9Urj0EsLRJR/v8XyDOL94PfD7/6/1ehF1/Qv+XxiWHgfpiL\nv5/gbt/vDzpefr+gn8XgNeAfA7+AKSok8MGAYkXfzbLsv9+Nwd0LSEqh17CV+dTmccf+Wu33K9L2\nKUMWevmn5Pr4WHydaEwuGpw1U9qy1WFy1vZOv4SJnq357oTgTZJeoxs1Vb+mCaLr4+h/B/t9FKzO\nyHBZDhacKyH1GRXSaew2+yidDM2wrhlILTT/gjEL32YfM7WFwK+pmSQQ+NtFW439oLfZx6Gtukl5\nk/Srk1ZiXgVmTQR4myKH6nWDR4BpaFZKztIgWSO8iKHw6xht4SVcl8Ty/oZrslMebxh6kZRESeGy\nuCpv5gseLuJY5iTFN0WZ9hHT4sMUnyaY+YzjYwK6koYv1iIi2S9tRinOep9vL6apXQbajRWDzKOM\nS/Cr763dIG1Mk6IFZny3PguBln/Mf9zzaXya7CpdGRl5Y5J7xW4q1+wn9amYkukiY9E9SOT9wfCL\noNNiCpOzyuRe6cantpqI1urwuF/e2/vpw/codo1B49M1eErX3H0E1mfDroplawLX99lJR4yFME7R\nUxlKvVq/x9pnnjZaOGrpfhyHY9B8qwj7fbMurd371sH+nfJcLnmxWLF/X2KATJMnr41Lcy2xxlaf\n9uWn3bNtyurOScsD7dp2PNGmYO6kI6Y7rax9SW1WvMBlkRzrmIwp+b7VcfiLLV1ilcsDnXnBpll7\npN4SKHw3brC2W9BTMMiy7F9hagoMDByM4B8kSfLXMcGH/07db9jKh/cMuu2y8e00XSc7wKWiaJOd\nOTfytW1CKQ39V1wHZkNBQAe8yUSPWibRL/ioyRiFJxswZVPNphpw0i8uMaX6qPjQdO6qgIkrYBOf\nvieQqnNTvCtByrxu2vMl1dD6C8Vk7HqTl8yY5Tjgxqb9jzLmItvObyZ+yDyhSXCh50Af72J2Nevn\nfxK/cRMxFkmNs++YOwfT9rlTwEvmGpcSdtMIB+101Oc6S1EgleetF7qzWqTelyj4cL/dxNFesbbt\nzO1CW+Wthp/LisLFG1CaVRu3raeg50Kb3fMYuQ5Q064xjesYBG+32YfrQpcDWqiL7zvBLV+D/zoU\npgyjK2Le36FQXm/Tp6kJ6PcMxxzGlWiXktCuwa/HiZyjA3PbFN01efUeBK+9XAHmPLPWnXBnaa+Y\ntnvSfS+IAz7zNnDNa7huTOATtVvGEibxMGocMcTrON60RMg3z8px4ejaG6nns1poM3E4ht7kPSSu\npGDXl978hc8J3YUBedtuzDIPQncSc5FHJ05wvInZZFO1rjahmG6HFkfhoTfV903jamQKz3Olzoyt\n8+DSzzWO7RwIDWrcarz3WrfOBdn076LPEyuzax29i3A/eiX8pP2v3QcZ8CP34Vm58GH+rV8Y4zCZ\nrtlgJesTbJno6UK943KCJytrjvHpAD4d0AXAG0aTEKKVfvBybnmlQaniG6JoDZgXzQKePLnmfHG8\nYf7KpxrGr4XyT1n/daHeMY1RVtQmDmGureTIygZvz3FxBE0f+FZY6eQX5EFtZLIgpNhGywaG2QVW\nwC9Gt7gsE3I+dxugxyYul1sgDvSS5jQzLDg/pLaWTHDLWFSsLxBgYvaWK2aj/fxcxzVbYhM+efJr\npkdAyyz646fm2TiF9yXaYUkAnNTVnzy5Bm/Ycq7CDKSmwabHo4tNQAlmdg5E4td4FLpxQprFhxYc\nZmYXrG+3DXXPJMVNIvRRoOPynCfTtcC3r7URvQnqoELx31a3TNMooffb7HPrQc4PM0+8di3CoT4n\nKEwVF8tq4X3gulyw2sTce1XCeJjCeMMx7VKt5foX+EC4VoAXaR9eTA2NuTWkfOyiBBQwFiU3Bhts\nWkzNdcVKGEDng8E8nsXnK+tQNkXJNtAaa2y1EVzKJjrBLbeJ6nGFwbANZ2Usl0yMSaGpBK4816Qt\nBFW42TECf2W7G482xmin0grnUvLpVUnjyYqhu4n0VhDgKLE0gkvXZ8KWW64etI3KthqspZMU6ka4\nce9pBcrJdM0F9RVKJi6nhIldmqytwXUbG4Vff8KfXOCvpa+Z9G2Phybuvu59Wvjv4GkzKmHMmDrn\nDY8LOSa81tGEBhsDMVkxcUaCP5ljJ6iouCQd+xLU1bF4323YdcEgy7I/udv3vFMYoeMDvaYxxHbd\ntHHdSVss3rCMto6b0JnKgouQLpQafkLs5El06uIVmHjtlp/sVUuUEhBy3W9SRsJTVoM68IiVWKUW\nuRTOWMUTmlRna9kiHJcswev65S1cE5o9dgN00mxcHlOIWApoqII7QFD5sECn+zc7rvLBRlAZTIoa\nlfc3XJAMqI1yWTErbEDYuL+v4EkzE+lsCLjoXRftuywNl8z15aMNvxB1gNWyweti21Rnq9Eyx961\nuLhpO/KpzozOKtCyDZ7ehcJ0h6tXbLe4S3jmIPcCL6jExabkPL3h6evsNYWmFU5tkOeGNM+pmeM7\nlZbpAidVNG+a89wcy3PGrTa+6plkWBTFZzF4jcVsb9WtJcdc3YZTMTEaxVrbCQ26+lxhv1lLEiDl\nGJecs2zwx7LF0ZjtCaELGi3bzpA37JyKcCvWmZbd5C6pd1G07NbEtKfbwljH07qqMleatWO1Qore\nQNz6W7U4FLDHylOGH1THfXEk6M76IQXmMVYsu06Er0zWurMjZOMTgQx8HE2hZMd1ybzHxMlbds5G\nYd7ixVbtu3rDlkWW82t4WteCl2x8mheglJIxNWcax6sWr9o6qaoBFqY6ng9cV/itG026WNl2x8tP\nN4w70M6n4HZydg3OWn4hIeivWlydvOWrnk5bYc8GCU/Ubvl3hoD3BUXX7DllnVkga1/4sAZdKXbT\n0q5WohQfdtcKzbWi+8TFpcaBNzCFvywPYNlaHbRRSuZKfgOPZ1VhVAc67hYMLHAEkCTJXuDPZFn2\nZpIkI8CHsiz7oMe5Pwg8C/yJLMs+myTJnwH2ZVn2D3dz4P3go/9hkv2nSoGcu44hjCeBKTgzDXPL\nsDitavDfwBCfILxuW/u+iJmgo8B12wde7iflMR/BlTRePArtrMZbPMZhrjkf29vM8LHkP+MB4Mls\nL+WPNFhctd0UMZ3ApLviE/hOiNLlcK5oGgH16m72vYC5KYJWrlLP++N0dz58gHD8D2Bq2H8cuzno\nKmKXgVlYP7mX8lONsH58yVZRe6oBTxr8gsHTnDi5RLhSAs6ZVT+Guetw/qDH29ynbefAou9yqMcs\n8Jmc99ptmCtiaGfZMHgp9Tn3OK6vwZmDMPec/eFSd692ARm/xvV72F4FU3h812HnTeOP/Tqf4vTZ\nC75csQg5EnV9Chedz1PqYTX8hnMJdrZthbpRs3lebZvnXyWfXp8gbBp2r6Df/YOc43NTwKxZv3J8\nD2ZDfRg/z09ghEm9zmSsc0K3jxBqkWMYfB0EfhS4AGcsnc4tAy/C+mt7g0wZFxuzaa6TsseFyx1f\nFhtDp2VM4zWB82Om18b5tulRIHiM331YyOukKiDv3u/eZWwn2Bfh6inLrx43HSbnpjC89UftyRY3\nDwDPCo3XzTlnxuyafhnjV7ebfFWt3zK2DPMmnL8Cz14360OPU+DZom9hLDiaEx7fgjPndneN3y3+\nBfqtCek4+g7+vapTptX4EeviSNrfw5LISZIcx/Qg+CHgT2HW0i/SO4XwVUzTpL9gv29gMhW+Z4JB\nF1yiZ9c46agXdDnTm4xIlXFsljLnOmlw00zcn1anifm2ZU2FjnA2o25wrbCLnZwnC1a6A/5+wvnV\n8Pt69F9DPP4P7Od1gLbpMvkQRlO8uqoaIClzexfeN6OOYvY86VInTEDaTX+gzvugx3X6eLyo77dQ\nAGZej4gFSP+gNRJw9Bh3j9TwgfrvcG3hgVWDoyMtY3URL7aYsBfbpjufe7Y8U/7GzDnvYDajqnQ7\nXDbH5X4b7VAY6MUod1Mo0M+Jnyffz6/Cs63weIyjXuOSY2fa8EAbPrZqhImrwJEbquHRpsWjvti6\nkAIXkMCmOkcfE55iYV2fa8d+1dLuOznveqfQj6/Iu/e793reZ/uui6umfLLjs5vqfto9F11HU631\nVjc/ide04EEfyxWgN8P/7+WccrdwrwpbvzWhf9Nt5dcxtH3HnW4HwDAlkZeBPwe8k2XZfntsNcuy\nqR7nfzPLso8mSXJTnb/Sq+nS/YA//dE/mv3zT3zbbdg7LxkpfYFHAXgymWc++zynVy64a16v1Jx5\nzzXtuITRmMbh9bRGkTbVjyyx+C2VH7uJqwc+icks+OnZX8gd15frX4ASvFD5PCe4aMyPz/hnsImJ\nltdmaOznk3T13D5zhVwQ6fJh+39P0SxM6U1ffVw1GLLv4OAYXhiyZlhnHXkJnxUAPq9Xxqjvo02/\nregaOV7CmcMWawe4xmG+yJe6orXf4jG+yJf4El/kMNcM7l/FN8WS+Abb5GVnVjWzGYdX0qMcX5l3\n+f6LtQNUn19i56URE3tw1m7QVvI+0zY4rD4HV891M06nhRbNuaIxgbKKCI4/jbdoYBjl+8AREatP\nhk1hOItrwiJ09XCy5JsoWU3qTVRnRsGpaLIyJzpmTY7ZGhmvV2o0KbHGJCf4RVOHYr/Hp8TN6Mqb\nh87W/VxO+3uxCi/Mfp4mJUPjYooWi4KYWq017syq1QxbRquEUFuas1a8xbah1as3fNlVoWWwGQ11\nfA8GLdCIRn8S1zhsp2YbDglomtQ+ZS2QyXmCV6FbDTaT4ut8ihNcNJaT5w1/WHzJ8IoX0s+7PP01\nJl2FSImlEcviJ/lamEL7nHnm66/V3OMOPVH3lp1x2Dhn6eAYgdtlY9UIt2IFeQivVX9MHas+p+6h\n4z3G8c2ChCfF+BEcYy1+Z61Z+2nMGp2GV2p2/WFrC5xdMvdWRfJfSY9y/Ow8PK147WUT87N+ci/l\n5xveOvuyveg6LM4eoPrUUjiH2p2nxzlmLDdg/f7PeZydv2EFXmtZ4qxvLy+a+UN0a+mLbbvG7fo6\nf8rwBlmbG23lPgNvCdXuHHH96EZtAuLyuKC+y+9TBgfS2yWZ3j2LwTCCwTtZln1MNvokST4E/GaW\nZR/pcf7/jLEmLGZZ9meTJPlTwJUsy/7cbgx4GJBeCQJxipWPpM+vwS6QV0GuV1W5vGIv8TV5deH7\nPV+u7QVSZSxO2xqUvpJXb14+94O89KHcdLoe19zJuAaNp18N+Pg+EokvuM8bkw7Mi3ETV3KM3y2u\ntKav7TfGPJzE49IV5OL3GSaHe1BtiHhcvc6R8/LmUmcj5KXmxaDHnlcmPA+P8fc7zV/Po9P4Hfqd\n12vda9Bji+es13j7vUuvSphxufF+6zceR79x63sMyxM05K2lfumww45BIG8O9HV5vKkXv4rHOYim\n9O+9zhUe3+8d8o7fyzrW8D3tlQD8T0mS/AwwkiTJXwL+OvAP+pw/B/w68MeTJPlVjGXt2DCDSZLk\nDwPfBDayLPuxJEl+CGOxexgjvP14lmU7w9zr+Na8+/xKejQguuNn5/npk7/AJ/maY/TXOBwQUYkm\nMyxwjcOAn/RDZ+u8cvKoIw75L5P7Sb7Gz/OzAK76YZE22xT5JF+jRIsv8UVHjLLpiLAgJYdd1LZo\nPqfwbWXNi5jWwlegOmXiFaqP22C8Y4SSpUjQormdw0vLROce9M9xnQpt4NDi7AFTXUzl7uoKbdrH\nr7vX6a5nLs/Y5uHqSnzyWeeGyzmP8RbXOOyitk2HwEeDxXhoq+7mW1elkypqJq63QHcAACAASURB\nVLLc1B0/vjLP65Wai8gX7YYWbBy1LVTPAS9aX+ZzDRbPQfXTKijsNdg4aDUGCSp8xN7nabw1SNrV\ngm+Vfc7M4WLtgJt7Kb8MXqMHOH52nldOHnV0eahe99qb5ilRTrUvCe4zCOJKhoBbB5I9o7MM9IZ0\neutCQBuudOxWg59IvwpgLDr1pTCKW6CF0XLmjRWvsNWBHzVaVfVFTKzJpsGrBJByDhPf84jCoUSQ\nnzL36qrBKlkgyzZHfavDYnqABWYcX9DpkG694WsouBRLeltR9EYVl9o+zDUALnICKa4Wby6xEKqV\nB/m9enYJpuGna94KefzsfKjBi4XvaXJ5BE8SukYhsOTtvDZiqqhOq/vZIGXdNt21BlYpdIJjedcT\nXARM/QKp6nqNwy7DZYEZTm9dcPUuNF5OcJFrHHbHTm9dCCwxMgdi+Z3hbS7yOU5wER1UK6CrLJZt\n8O4r6VH3+/GteRZTa/19Btd+fadmLY5ita0RVj68hLdELQNHVYzIaMdYCSQmSHirDnBGzZ3w6zrd\nPF6Cjeuw/pJPYRceKplDcc2X3YBhBIPTwE9hDLEnMKytp/s1y7J/kiTJbwJ/HkiAZ7Isy6/i0A3P\nYDrVP6iefSPLsheSJDltv8dsoAt+jx8w1Rcskbe/VVRpQCU4a1PRIhACbWPKJRe2OrRTqU9g85EV\n7cl5GtqM8jYzNF4rg5XdWhOLLN2u8rMTP4/JQy26a4Uhd03si4SmOwiZrWWcbwJvWt//m9Ysy7n+\n+HnCmqK54Y858/hzPihnz3WzOBYtxouz20agsAylMNah8HIjHK/8ZsulFsZtdLItKVx40jLgWZ/m\nlVeH/tBWndfTGiWavM0MX976An8zfRlJnSvXG9yu+YyPCXzqIrPh++bm3+tpExP+MaBuiPszN6zf\nTnyZlyyuryhcPWUXgnLpzEm8wCl7nyt+sejgpLnHzYY4mfnOjSVaQR0DB8qFVKTtI/1lQ4gYjMP9\nQZsRMt5gZ9a2RmaSw1xzAq/UT9C19wWOb83zQvp5i66SK/fKdDi+nXSEt60p/Fe2PhtEtTsYw/vO\nL0HzpTEKmw3nL507632nD+hA0edswOuqDyxzQYE2WPcz50xw5eKqCSbW8Thz48ZVVJ1fYq0yaTiM\nZC9YKEw3nPm8fKzhhRK7eRamDQ2XX7Jlb20GkqTvLaYHnFLhWiPb35ppKVfQMqm5bzvcCv5E6HWC\nieU3WhvfOAV7ZL2JCT0Wwmxsw/krNrZiOQy0BR+AeWS6Y4Jx3wUuG5/1EWBP3RRfu3rOBxh/BuOS\nlCDiM22YWzZCRfFk26WnFmfbcN3wjNh6x038BonX9HXfBW/B6V3qNxYE4noExcq2nydLQwFvkHTq\nm8Yt+8AVE9hXuNGBpzwdPXHOu7kkULN8w7tm5vBZLs7/r3jwE6gAV8Vz56wrbe5d616z/FNiuT6G\nD0Kfm274TDX5fBIKZztUp5e6ivHdKwwjGIwA/0OWZa+A0+pHgO/0usD2N/hHdzKQJEkeAv4T4Ocw\nWQ0AfwX4hP38y8BvMIRg8AP8nvHv2kArX4vANPcwwVJrAWGJpC5QpG0LYCjp3dZd990LvcCgCxx9\njotce+qwO+8EF7k2cRgp0nE4vRY0I9LPd3Xel22KjJwgfj2ddnSXkBfk4hiG1SiuYhlKXfl+txps\n3PABO9UigXa2ccMzDfD+78VVeHjVfrfjN6mjI0hxjzjPm5u4nOZP8XW4Dp+a/TqHuWaY7zIc2l+3\nBWS2XTojmNQnvdkVnug4Cb9Uabl5cQVUlm2MwbIZK5h33CO1BwiD/gRXV/Ms5lY7EGFAS9CaKQuT\nntvquHfQtSACE2hLF9TaNoLHqsKxaC7g09RkLJbZFKZMHnkpbdpcaHP/w1xz9RR8QygrBF8Pa3Y4\nTX3M51NLLvXn0otGeBAr17uEm5Wi2Y12N1NfzMEv+JTc+Dh4uvwloGwDsd6JNj/esALDKqa+SF3F\nKIgsbtfVxirskYDKG4a+N9qmhsXiKlTtRlyy/T9kQzbNtMRu2DK4s3thKfXdQCe45YTUSdZUkx3f\nUXEGmzYt63wZqJnjcs5V4FkRwnU8heC+5H+TIL/FGC+CX/C0sxkG5MqaWMfH0Ag9r2MCiAHDM1pQ\nOtl0adfFWVsgzuLDF+cpGnpUdSGkhomhQ1UpctPUkJCGZJ5ODR4KWx1Hz6iibQCs2nmSAm32t0CR\nk6BQ+/4fYGMGmqFwqfmlDrh2sUc6OyUHBgbaSmCx5Z+C/+A6sTSqOS8/3XC4320YRjC4gRF6/rX9\nPoIZzl/oecXdwQUM+35AHStlWfbb9vPv0B320xMkDbH6OFxjMth0JOgmNsFos+kak1THl7o1+VPh\nufqaNsXAvBO7JsQ9UKy17THfYMiZvSQFb8ymmJ1TwY0Vr3FIMZ4HroRaqHMTTNuAIltyVqfm9E3R\nGTP3OX8DQ4wKdtIR9kx1jLAyhtGUX8WZ2L5hpeg9Nud44xzseQ3eF3P7MYIZFA22urVEOxWNyPrx\nW76ARzs1TOMEv2gEgBdtumELqrUlo+3LmI5B9ewSG6egfL3hJWnLPAsHbYGQlt+cNkTUPAbVaaha\nLXynNkLhRofymHEtzLWsZG81pSNi/n7JP2N9dq8Z/5Ull5J13m5umjE/+6JJnZNubiWaplHWSZPH\nHlQQPBb6IctHG+yJzekt+/moT0WUEsmAM7/KPAaCxrgvXQve3bBx1BSRKj9vGkkJftlvTO5i1lxI\nPc3zvMXHmAkaE2gzymTN0G+1uMTrzNiNwQTpOeHzRbxGbAMc56w5laM4F9jiURM0BzjGfPWcCeqU\nQLJ3MHUh3gc46HH4HipgzAarfifpGEHBCj7VT5tr3wP2vATVs7B48oB3f9V8k6DyVoOF1LTFdfO2\n37srfDDtaIh3azaWza/JGIXLHePKehET2PzmiCsZLtaIQ2otLVYOmEJd1nIhUKxYYfmUuc/DnppM\n6uu0Lxu/zhhz0w0X5DcHUDNCAdMwJxkX1jL2MIZ/uI3xGMYKRMkFy91mH+Vp7465zT5X5nlndsTa\nbxWNY95Fd2jcqYw4un09rbky2y6Txr7v66kxP0xUlKVl1bozqBseYAMoA3/+MizMznBo3GhDn8EI\nXdWSF4QGpdU+Ye/DlLgTjOVAUqIfwKRUnj8Iz07Z7BjLk9df2ssTSQOOWavXczY7yypZXam+Ing8\nieHNkkZ8ClfzYbegZxMlBR/OskyEAuznH9zNQSRJ8mNAK8uypV7nZCZKsmekZJIkn02S5JtJknzz\n21u/y0bbalXLdDUwWscXd4mDwII6/Zuh8LCTjkDTH9PR8zrOwNdK9C6CJiXHKMz5Y4HFwAkgOhsB\nXOlbLaC4z+/maAEiEG8a6dNJmHhN/z36gJVKP7D3j6XRjVW8lqL/b1qNDf9dnp+bDrUZao0af74/\n/ZjbOIP3i6PPW4TjbNlnC+hsjihdCcy5G+A2BbmXzM+6vYdo6c56oHFgwfWOIEyh65one43MbdC9\ncNnTp9BhQK9RFUWX8qU0IzOWkGbke2HLCB5ivl5P9wa4l+d8w75PoIlZQcsw+bEga6FN0eCo5HGR\nN5bFdp+Aq5alT/t+O+mIx7O896aiMwXCzD/Ap/x+g5D+NtphGpzAN1BWC/vsb6DSAdW5mj9gx9HF\nSzYNnuNAZMlK0NBm1POCMbVux/01Zv5GPU5j03GEC3sh2Pe6Gp9rx+fM7/20Tntvwdt7OadstHHW\nAFknsRtEPrtWwoqmNf+Vd+3VSyLmq/p/kGIbf89ZV4DjD05bX/XrVaeD5oFLeW9GrgwbcxSve7Hg\nGJoZzcepXQMfRMd41481j/fsJgxjMfg3SZL82SzLfhMgSZIDEBfkDyFJkgLwxwnbLv9mn0uqwKEk\nSZ4EPgw8mCTJZaCZJMkPZ1n220mS/DB90JBl2VeBrwI88tE/kn3jX8KRIjANn+Mih7bqrm/2TXCu\nAd1TXKcPTbIGL8LMawsc5ppfzJd8lTcd9Sq/SzDhPm4HZqu4uYqDZVM2dyZd8IRVI6ymh+9T4Eqj\nbpq0GSmG9Ky1FuxURkz52FU4Mo4rzDTXMrioXjL3ft9K/0eKvnDSx8D54Obq5vnahF5Y6bAG7DkW\njp9jwGWjSW+0bcBSvcNDVwBbfIRjVgOXkrTzJv7gOPNwE2Zqb7vmIJOsBY1fSjThICYValmZl5/E\nL/qT+BS65+0mIYFWxzD3k6pj4MzchXrH0QklfHCRWBREEh83710+Zywic5csbqfNO0rJWHFZHcCa\nqmsmXgEiK8200VB4FU4/fSFMT1uGEycvug1o8RTMnFzw9PYUzmWxU7HNmo7Z8dcINmxdVx1MwNZO\nOuJM2oJvrZEKHHocFgXP48AprwV7n7onkEnW2POcHVOz48zHhZWOqVY3byw854Fnt+bh1bAozbPP\nmWdUL/ngxCZjJlZiyo7BxhQ/exn/zmNmDp61tDgHvuqhSpU8zjwU7bo4hg+GtFAVOrBr5aEbJi1t\npzZCYdML567h1TTOVH1664IPZEtxaYaPvfaWC0iUSqiaF3Q1PdtvU1lrOF4xw9uuNXKbUTMfAE3r\n1rQWAKkcirz/ZYNbjeO5KVy6nzz7Nvson2qYe1v/9U7NNA7aqVl+Mm+KsEmZ8Te1G+2oiUc4vXUB\nLhm633O5bqw6Y6bqowQGF1PjoqqOLRn/voUFHvW4syA0eZt9Ju7ibIPqwSVer9Sce2YSf28dQMy4\nDUZeNbRx9YrBqy/zvQ11fCAvxgK4eApvgcRo8KK5P6D+H8Gs54fs/88ctBZegXkTy1V9zuDy2U+b\nNTonAYhWjjliCy5V5/HphzVrtbyhrBUSk3HZnncSVxVTN2zbLRhGMPg88PeSJNnEBBP+B6h6NDEk\nSfK3Mcvuf8dr+BnwF3tdk2XZF4Av2Os/ATyfZdlskiR/B9N74QX7/9eGGC8f7vwu61gfWcsSwzOm\n9Gd5vMFNbJ/2F30J1ZnXlG9vygRtbVyBmdfepnDZ9ui+acxpk6+t+brhYg6UQJfLHXZmW4HrQoKO\nxC85cVKVVLZEKKVIz7StWUlLgy2oHl3ydfkB3vC+PwmS4zoUrnfCAk1NzPfICiGV3bRvV54lyovU\nPgDLWFat1iC+T9HgrG++OgW0cc1x3gGq0oykRFCS1uVGY8ZabjVgVtUVv+7r3M9UFlwXRKkWqXF3\npg1zl3Ga3nkr8S+es35hEbDsZlE+aMqyFjCBaWfa8MQNU2GNZVUSearj3AyLq1D9f9l74yA5j+uw\n8/dFYnybBIzHszNjzdIGLr6A4w3Ws2vKJftGYMwAHiRMCqZ8PG3gLBKoTIpO3VUsgyqASlUMwZc6\niwhBU44vMSU7hAxENHxMKPEc3mFCHC2CyBXPWmOhZVYL5OrOkyJWmtkdrY+MPbEl57s/ul/3657v\nm5ldLGypLq9qama+r7/+ul+/fv36vdfv7TOMIICXTfsLXTMutUv21IL1rwDDKAfwrHcrz+E0RRcv\n2yhyF/o0ykZV/QvAKck13zH+CfM9E6K1sN73uLRMMSshjoCcHnm4/oLz5C60+jSsSr/S9LHxb122\nC88539bCsnFslLDV0HaxPDqUjflo1tiDayhHvlk7Hi27E3rOjGcASxgHxypurtQ+1PZOjyLYiRkF\nvMNph9DBUTs+VnHOZ1d7NnSw0szJnBJ/gkDDZHF8ted9UpyjqNj1LY1NnzCbiUZz0cVeOPzJFjxn\nBDsJNe3G4ZrNz9BU/OQ5699g51iBvnE2a8pJCZ8bQTKznr5snOHmy30XfloCTwm43ecaTtgtdM1Y\n7j9xBZ7DOLFZfBXOmPY4xz3Bt9YIaCibsZCIl1NLZp42WnC40nIhjRsnFo3AMAO1EyqU78JrcAQa\nZxeD01AF2iac9Rnbty7GPABwHvY/9ZpxKNVjqk8CvGy+VzEbikZr0fXjas8IoVdlo2HH/aLq3++o\nLoq/1ypW6O95jcJFcLythsFFo4hxBr7QD+Ms2HY2qouhAKt9FLrmeSeAKc2si5jZVcHFdlhzMNKU\nkKbpb2H6+neBnwS+d5jKH/gg8D1pmv5wmqYP2E+uUDACPgH8SJIk/w6z8fzEuA8+gnWCa1rmKcfJ\nYpWbVslGyA0kQMj3cOjEf8u2uLnhVI92RyopQqkSMjB5r4QKtTbQTLXYm2ZQprC7/mWMY9VJPDNe\nw0mVlA1jBvNbiHwPfrFriI3V+gxMHTD3ahhvXcq2bBWza6lgjpE1jS8HTdt2gBn7jjl7T+CQ/eyz\n3zMMelTLO+ynQzkTTzRNm3bJ79nBvrly8qlaW6BoF8qmfw3ZkVYtE4mYX2MGaPm+iNbi4vMYgeWs\nxe8ZDDOydlkwv13obWx7qxZX0i9Fh86HQ9OjorEp7LNNu+sTWinjNAYEj/r/m6WJAJcdyt5ZLYKp\nA/Z+Ez9OOQ5W8o6pI4RpfpVgJmrTg7b/UtcUxv5K1+JQGKUV/C6eNf4qnDGfi2fxTnYX8PQjx8mq\nuJDCbtxtmxoH8EdKmxgarBrNgGu7xWfjgK+7ofokpsBAy6T721WhvyP8BFAhOJomY9k4gJlf0bzY\nLE348YZgzFblv3pNo2jmrYQurwmOWjiaFcH31kkMr1jDBPeR/xDS82y4WLqxtDva+QMWj9KOMt7E\nZWl8wGavheSOwlWWeUTAOjwHMBt9AB40O/Zd2HYpaFiac2M7a3iGOE8fDIszP2N4reBRNAgCwndu\ngc+hsobz1bp1EucsePWsuXf1LAbP5+zD1q/D0aiA8OVjGJq19Ova3mRHYdwkSj+Awdm7ge9PkoQ0\nTX81p+ybwLezTRkmTdPfxJw+kNMNeaGXc+GPJhKmHk/d7mGzNGGOy3UIF3dL6E4lCJ6pWYbRYxIO\ntZ2N8y2shCY78RjWfCa7zMA+Fc9YCrQHFoBTM4Rx6EUK7oTlbi2bSXj6sveglShyu84O2raEiN/o\nwfGW9SrGTHKJA3+xB/OzGFX1CYwzXhO3s9tsTtA4EKmsjlmcHlP4tFqC+SPqmixsVf/cZmmCTqns\nNC2gys2gfDImjRbHOoE1ZNdy1ODq+Kz93cGoYh/HJxoq4yOlYfDZwRyVk7zr82JmkA9GUzK/7HcG\nEmdfjjH+MnCqY/GuhIhXeuaI4tQROcplxuUg3mnxHdsOsO0WaOHbMofZMa33jSBRwe18523UQGbt\njk7OVM+Zb3HAcmYycCc0JM+7jm0A/micOxlRMmPUYxI+ZHElWfvszny1bjLj0VH+BA+2A02Sptl5\na1pplPHqUMkPsc9H8mycU6c2fioMpS2WC8ljcnrZmnWiHdmty3aMniSw1wcMtILfXVoh0/22+HVa\niBOoUwQ2TbHOcTDX92mky/Ycu/CeQ31TXuFptWQ87MVcslKymSqbK2YnL4LqOdxmQp5tlBc9fl/2\nxw6dtmRWfV+A+SbwppkzpyOBF+DUhT6/gDn+2Wj5I3e1y0a9LkcZJWJlsJOV91bxMQ7s71MX8LEV\nxBwlgvw+fB+141zFbpxKXQr2GCTY8TiB58+aj4sgIHPEgqZ/l5dhzrZH2irjfNTgSjZlQlNPLxvh\n6nTP9n8GVnumH/N4H5bjlrZPW2dwOWKL4HXGmhLV9cbjhve+pRK1XX0e9th5MKVXvgxhUNYo4I//\nuGKSJOcxORKWgD+yl1MgTzD4OeBakiRvAn8gF9M0PXx7Td0iCIEu2RMGs1Ztc8gS5iU8kmXygxce\n7AIV2F3PmQGctyl8qeDq1OeO5Zk4QIyoSYv1nglB28Udq5QAGFeXzeQUpirHAxtNfBaxESKXFgpE\nINDXRHWmnbKu9ryT3dWeVcHLLsHiprBuJ6o+rSATdc232Zkaqhg8C8PWKl5Rg5d8mufaettLzlVv\nFpleWAlT92K9xUWltoZXKbfCPtzqwdRJzPPncOrpq89D41jfBImyTK6h6l8F3xZwOHdHxeRdEdSw\nwsSbYZk3gLc0M23Z43FCb7N4dWK0uQyOrOpdlOBYfstYWG/lQqXvj801TQc6lM1iVFK0/aSv0p3d\nr/ZdinGX9ljoEtPe2rI1Fazh7cWyO3pehYSdwQuIkbo0CwbMLnmgcVy1c8eqXAecT4UfxKYCCLVy\nktlUcKvVv4LfkjeLFapWoLUbB8D7PcSwZEwwmyUbNMjOk9qhtsveWrjeD9p763mYmoVes+jjcVhz\nwWkrhILV5EXmQtcuyzMCnGTAHnnO4v/9qk45xitZS3fhT340LqlK9HuFr4pGyi7M75NyysxZW2+7\naxJaXvpeqPR9fWW8UFCOzGZSn/Arm3pb4no4UFoaHTb51rJPNnarZ4+uxhA5LN5CaSxz4C39nAbb\npnuw/OZNMwYyTvdcVloJ6eYa3nxlhZtCq/8nEsfgvcB0Ok4aRgOfwbCaZeA/bbdhtwPv+kZqVLwY\npBc/u+HtsB2L6JdtnACRxCKPblGBVha67nywHNdxzEJ2RpogFdPRNkXwzLL41IZjsq8Aby2bo0G3\nrLNJw9ZxtefVbg1Z7CwzvAguWIZe4GVXKiBOMgLvkH0uV65dPWl+v2EDo8jOC8zu7erzPvvc8Tcx\nkr91SHK7vCXv0CjSds06cgGB939hpu+FNDmH3bW4kt3zAm7BOd3zk6UhDNQuDDJmtyzeZFdzqoNR\nQS9byfyat39qHAfOPoR9FzPMOyj8njNfB7EqPeUEedpG5JZF7h0i7/hlu6MVYUAJgQCNYwSnPZzA\n2vVj8P7Ldlex5M+c37rsj6hqB9bCXN8eU+zCk1B5qut8DC4+r8ZGFs4ZM65uF2z7K6msXba/SCsg\ntPDLqHPuy/D+ZSvgYo4THl8mEDCftnNrF95uKypbifUvQXYkyIwEgjndg4N2HN9YDoVgl+3vvMGH\n5L44qOMUHPHvP14MfWtWMY67Vy/bMcEKVGsY76tn/PixZoUTS29UofChvvcNagEfsjh9Ga/WrxoB\nQ049OR+KrhVEWzZ4kN39nl72am5xjBO+IX4St5Z9AKLa2cGTQY5m7eL0CPaIZEtlPrS0c0r5yGje\nInNFzBFTsxYnGLy4cZ7xdYlwdeusTWMsWps1L9gJfn4ZG/SnSnhCSnh2S33LhkRs8Uu4rKV0rd9Q\nC7OYduznDIGDquzoHzmpdvdRn09n4AFM+7Rvgob3Y3jjADwIPG9MR29cNu3+HXKS01m8Sv6Qq5cN\n/7p12WqnzmW/e7swznHFNzEOh+PC76dp+gtpmr6apukX5LPN9u0ciMSoVFBBcgtRQ5UJVFLOBqtB\n7N9qZ7BZmjBqRfu/R9HVoU0J5liPP8ZVw9vtp8R+FdkW3Tt1PxjMqLUr+pY+7mJ82JNTd+Z9bUfU\nUFVOetWc91dzfmfU52yO2gYfl9N40eMKbvxcm9RCtod8/AzUE0PV13FbUA3ft0dfH4EbvevIw4ez\nX2dAYb0fPis2dvte91wlalvGu3pMut+78Pi+B9O3PVntj2BKfYutd4+qQ+rTZYeBy1gZvXeP1KGd\nRNX9e+xnl31WyouqOw9cOctLNksTwbvzxiHAszIH6HnmfAwUBDiwPIRy2K8Bh1micZT2aTyIdk/a\nslMg75hhgAfskfda2CXlYy2a+GaI/1GGlm0oyPM5z42c9zmQhees+hy/yeCbe8Z8156s53cS0jQd\n+gFeBTYx+7qX5DOk/NMYc8IPAd8vn1Hv2cnPvff9mTQ9QJoeIU0vkb6e3pd+LZ1I0yXSL6e7049D\nmp4nTbvm/9fSifT19L70y+nu9PX0Pvc7vYS79rV0Ik0fJ00fJ/1aOjHwzOfTZvr5tJl+LZ1IP5Ue\nTX8u/Uj6enpf+qn0qPufLpnnX0/vM7+XTBvSJVNn8PtJ0rcw7U8v2WuXTPmvpROm/UdMPz4Opr+X\nSNMnbd/Ok6Yz9tqMLXvElHsd88zrmHsft99n5X2P2/oOmN9vQZoWce1Pj9j/l2yZ6LrDlX3/x6Ve\n2790ybRTcC99kn6nXdUfO37u+nl7/fzg81LelTti8SHtO6Dwc0TVtRT1w/Zb6vs17H/B9ZMKt1Lu\nSfWxuH/dln0Lg1v5Fnz8GoP90DQitJU+yQBdSntiupRr8efL6W7zrMX/59Omf+8lT3/uHRaHQTsu\nKdqStlrcC/1rmg4+mrYfx73j4wov0o+PY5+RsRGc6vfP2OuXPG2/LvNA6H1G1SttlntCA9K+A6rO\n8+q9R+xzat47/DzuyztcLll6te39fNoc4CduDKVPXRz+NK+SjzzjcCz9eTzCT9ePh4yf0N3HIZy3\nile4ubPkcZmeV2Xtu37NjtPr0Udo9svpbj82XRz/lfYLz5Q+a94qPPH19D5PP9Gz+hkpJzzXjUlX\n9W1J0Zwda3n29fQ+j79Ltq9P4nlE0c7PGY8/4ZtnNS4PKDxZ3iHri/BVh5PzlpfG9FzE85Gi57cf\n1/xC85cl/7638LgGvrhTa+g4poSPb1HWEGvHD2r5gyHHFe8IiMRbMao/iRzYY5KDmHPRq6Xdzqkn\nM4BQxR+n6VCmcNTYwbICbwjIvTgTWJGeiVJmoymu1ne790pI3Cule110r8OHWkwtwWpzt32XjxwH\nxvZbWDKqRdnVAN6Wpz2Uy+raLOy5rFSJAk28HV/KW/Xf1Azey1sgy/4G4e7CmmYGduQZz7qIjocW\nzXnwE4su7kGPojnuVrrPRfKrrbdd3AM5+yz22xWmDX7o+xgD9P35YXGUQiXNOds2x6SszVjvzOaL\n/bBf4g8Qg1apN00eereLs6pEiX5m6jXfQoPF0oZxSDvfd9c1vgboUo+vvFuPqQKdfEVDhzKdJgEe\nxflLYlpITASXHhozFtMlT4+S+EaOLVISJ1zlo2OvNWYXbd/azlt86nkG/VcExHlQ21kVXgTcqRpF\n71OQ7d0dPy/0nqfJytudubExY9UR06G2hdv7m82uK7fZ9LkWdIApwPEqwEew1Hg86Z0aV0u7qVXb\nQSCwaVbM9TV1HFDaqp3YNL3G/h7ik6HgHowGZg/Kb0S0WlJvhgbCJzOTaubv/gAAIABJREFUvodB\ny+TU1mrdzMXN0gSUfN/lWTlSK/NA5q6vK+qvhQ5lCrNt5yjqYNiYNq3poWz6ums50pbO4sOPa21u\nkyAfgvOzsN9OeyDX5H2RxlPix8yjToEM0x7m8eNtwsi0y9+KcM97K+nFL37XQDQyWawf4kUXajMv\npapOxRmn7xyWySov1WicMXAUjJMKVFKqSlCluC9x+tMwF4RPISxOkhJMRLdBg65X/sfvAx9Sdy83\nXIjoOJPaVlLnZo1R1v+4fN7/rLqHgeBZgqqYYDX5uM2qXxi7MHkTsCYM8arfpxlnVvrmLDrbCl7j\nMcvDRx6Ox60vC3QbJQumxongWL83qx3D3qnxLjSdlw5Zp+3Nmpt5eNV16Xs9fMbPODS6blt8Le5f\nVp+y7ueNuc7WKnkwsurRaeCHtUviOMSmFJ2xU6c3F/xoHOTNlWFzadi1rHtZKY2zcBQndxpWpw58\nJwHEhO/GeBJ+58KED+mrfrf8lveZsn5uZIFkma3Q4UeT1o6lXR4pGCRJ8oPAPwa+F/jTwLuA30vT\n9O4hz/x14C9hohgCkKbpz+5Eg8eBynvvSX/6i/9tcC1mrHkL9zCIF7ase/G7ttz2EW2IIWsBHEfw\n+GaEvDHISlk7Tj1ZY54Fo+qLmUXMQL6V8Q3D+x/j8HbqyoP/v+AyLpcncOTNgWH3/zP8Z/hY8syO\nCQbjmBJ+EfibwP+MOaHwt0ElH4ggSZJfwuRSeADj3Pkw8H/edku3AHfxdbfjFolNTyaR6DRkTbaY\nUQXZ/yIYNlmlDsmQ9ioPuGdkdygT/zGepXC971Syn+MDufXGWoVBKXf4bjZv8bzd3WSexiKrbXF7\n8u7v5wov8DAVOgZHZ/psnpjAB5PqOknepLQ15aUOCZ8KBClwp1nh8IWWcRRr+hzxug/jCA55EONM\nB73KinORd03/d+nDo/q3uqiO6tdjPMuzPBZkoxxGjwBPXH+Gl+rNARzmaXG8tqrnyur7o7QxGoaV\nz9J2xc/mvXccoSXGZd57dDnZUcfXJMTvamn3AL5FCxBrVHQ7twpZfdX34vfE/ZDxi+sZOK6t6tmO\nALhdOgc/Z4b1R/oRb/C0FiRLOxdvFkdtDPNoO+u6vFu0lLr/t4OPUTBWgKM0Tf+vJEnelabpHwHP\nJUlyDRvCOAP+6zRNvy9Jki+laXo6SZKzwP+6Uw0eFx69YIKqHy63+ETzIzzEi26SvZIs8m3pfh7j\nWZfdT/LOP8SL9JikcX0RHveZ6uTZRw+e5xOvfMS9Jyb+h3iRZ/lJrrCfDbUorTDNr7Y+DF1YWZhG\n0iw3WovsbXp10ErSN8fBin1+v9fmicefgZPw6dLRIGdDkQ12JW1zpOdxjM3qk8C10BYstnIXQ71i\nA9NI6OEqLqwzl2yeg/W+879orC+6ACSrJ8xzIrRoU4QLELWmskGqxTdOuCNqNjGFXOH+YGJIfHgw\nE62xvsiV0n4e4kUKk30TPKjV97nJ34TCvja1k21YhltH4dHueVZLu7nC/Wbxt2fpV5rT5v+CiZV+\n2gYZOnWg78OtHjOZEmtn2pw+aTNXSpAVe/589cRuahfabC5MBPilajK+Hb7eMn4lNl03HVxEyr2f\nNWGxr9bvc2P6MC+40K+bX5rgWR4D4MHkPItp0zGuftJiGptd8KQf8w5lauttl20uVm/n0awwTc0c\nexRNW07g1NG1j7Z5YvaZIDueFnQ2KHJrFg5fapkESDZ7nz6zT9VmRlwCzpvjg9MsmljxNnaGPAc2\nQ16r5WhaclKAoWMRCEXNK/QGOAG7sN7npZIRVh69ft49q9XAh1stmLN+LCrHgxvL9RafLh0NcCkm\nSY2DJ9af4dOlozz6feY9n/hSyHsERCgqsuFoX+aFhPitNds8UXmGzfoEP8vPuEWicWbRZeHcbE5Q\n+Kid609NmDETXwmJlXGA0M/oaNAUPl03vEX6MM2KOxv/UqnpTAi19bYPGiRRBGeh+FTP9ePRC+fh\nkMXZR1vsf+oKz/KYyaOA4WOPXjhvwhQ3d7s5vsI0D/EiN7nX0eZjPAvAszzGQ7xI7cfb8CB8YuEj\nFOmxn9f4HB/gidYzZt49iTmiiBfyhc80zixC1T87zYq5dggX4+TwiZb3tXgZn5PC/m9UF818O4LJ\nYvskPl/LIUPPtDD+WMdUbhgdG0f4b6vtfFAkUJjk2tD+TYUL5uhm8bM+xbnQqM7kmyWA3Q6MIxj8\nfpIkfxpYSpLkDPAVhh9z7KvnJFzGe26vmVuDr3OXOaMqQVWaJgb/a/X93MtNvh14gYd54sIzJgfC\nc7BywkyI2vU2VE189qcvw/H1PgXadEpG2jx9Gb4Nv5Pcy41gx19rtbnR3MvizYZpzFfh/vttYBJ7\nxtbYPq3deckEn5FJLzEC5Hz9qXOmD5WFjs1hvuGCKZ22/T191nyf6ppY+/OP911Ur0LTnkM/Z8/A\nFqH2TNuci60C+3x8had7cLxqGEvtqMXDc+YsOMD8rAlAVHiwT6Hchjmf18CdU25B4USfxswiXDLx\n4wtn+hQOtanUvQNcodV3zpS1622ov8b3v32Nf333j1jNQDdg3KyZBCwukJTgM/5exp0HnlpT+dtV\nSNYiPTcO2unq6mUVowETw0L6LlElDx41OHy6B8cPtXn6KBxf6pswvdh4APvMws/j0Hh+0TAQG29B\nzj+fOmlSGU9/ySxiRTYofLTvztmfutBnemGFX+Ix/oBn+HsS3AYjx60CnLVBmdagsGRwzCWTi8Nk\n7PM2dfFHMEKX0aYALN5s8PreUPsYpA/WYONGFGb61pE3TCJWll2kpbfCjHVUtLHxJYDMK0DjSUN3\nEtVwz1Efo2AeH+Rn72dvcPEQzB/pW+HPBr45YGPtL2GEwzlDq40PLfpU4OdsDhJg71M3zBx9HBMG\n+5in39XSbhPtswmNTy76ePQ21sDep27Ac1A5YXaGIkTVrre5We+4hfNvvv1rPPHcM0yfWHERBqv0\nqF1v87GJn+ev7f2XbuH9AJ+zuO46fFfo+LgRKhhTodOn11S7wgsmfTgtEx/h9Fkbe2C27+KQ7ELF\ncxDafd7zlFs9M0f2AJW0Q+GgEd4aJw29XrXR+vZ+6Qa1CzaJ1XmV0+Wyr/vUyUW4BleaGL67BPuf\nusLps3DqaJ/X6vt54jkjGEyfWDFppWeg9lTbBQb6WX6GJ64/A3X4JR7jfq74PA02h8rTz5vYKcWF\nnuPVr9X388SZZ0xQouf98c0CfYrNHoXrfRrVRa6etM7WC7IL33C5Fzjno5Te6vn4DxI8SmfoPNWy\nERCP2ngSNqbI/JKJhfEOhnZOWT7IBR9T4mrP5oh4qm1o2jpw1o4ZOp5+ZQWOQOFY3znYujgcx9pu\n/Wh8yNBozfLn6RODCdBuF8YRDI5iBIH/HvhpTNbEHxtS/jeSJPl24B8Bv405kfDLQ8oDkCTJfwG8\nhll33w28kKbpqSRJvgMTx2MPhpY/mKbp5qj6JJjNrst2AejApJUU18Ds5s9hBuCCCSAyzYpZ5GzE\nsXfA/J8hyPqlpf3a9bbzIC7Sg3PQbVbgq7bwW+Zd3bcrPm78CdyuRCb5/rrJriiLvQQPOd0zE3nv\nwg2TWKjTHvSetvD0ZdPmW2d9qONHLvvfYIL+nFryQWH2LJvB2dWz/T1ng2aUMVLwBRVoo4UPJqI9\nvc/hE5D0YP4cLuNi4VDfhVQu0Acb4Y2u2dFN11dg2RB5/8sFGt+7yEppmsJ633jpy8p9CRMs6lqY\n8dEt+Gs2fGkXaPqIbIVO3wf6scF3KgsdaEHxxEYQGEQHN7p4GeYv9AcCw+jASZKeVRZzsMxiGaY/\n2zWC5RpOKHtDV3TJ0Oi8FfYqdKGlcL1kxrxrBU4jFBphUvJSvQJm54IPfMUZE9lxs9S1C7dnGBU6\nTsUr2ix+A6aPew/4DhX2csOYu1oYDYmFp2046PmqGcu99RtuFyPz43Vg3gaTmj8PlC3O7EK5S06+\ntEKmIL8l4qTkoz/+yTYX8Uy3Zud17bIN330Ot0t0ga5sZMCLl31++9rJNjdLHS5eNt71DXXKobLQ\nNYmpnocp2QFK8JiiFZItjwClbr9kopgKjfaXCnDBMGqhpQods7h9N3T3Vui+XWHy7p5bnAots4AF\nINoVCd41CxtNL4SdXoZTl2yQILuAvGPHXt77DoOgeYqGg/RMIibM2AquX1mGU9fbLuLq1bPQOMLg\niRwbia/S7JggRagQ88vQrVccfyiyYfjNsokgWpHTGXXgSah9sk23ZLfSVotUXOjBk3auLSuz4HlT\n98XL5oTPL2PDkc8A52w0TsvPJZCcbMpq623X1jhokQTmylq0JAKkfDue8WaI89M9OHXGB30Ti8Er\nVmjQodQPnrUCW6tveEYZd+Ih4L0STExO0MzieGxHBdLbCRgnwNFDaZr+xzRN307T9HSapseBv5FX\nOE3T/yFN099N0/RfALuBWpqm/2CM9/wB8FfSNK1juvxXrePjE8DlNE3/Ikb2fWJURXfxdYfQdzCe\nz3SNqlbUjNOs+IV6DWeXlmu/I5UtAcuhjXqaFRrri2ane8mo/8zzr/kwrcDE7CbcY3ZT/a8W3MBW\n6FBbb1O43jcx/5etql+HFtWwZEOGSnuFSCIQwtR55IOc8rpP9rpEeHtH3Xsd+45lFQrV3rvaU22Q\ndtjrOqwyS7i+3Vr2eCy0+j7s6yUVBW7N4usSDs+y6BTpudDWwdiIUGBDmL4j16Q/to0rTLtY8bxp\nvauXTP0uhXMEq6quXHhZlVXwBmaRFcFSogUGzHpJRTTU/VG/a+ttHwbX0qAzAal3vYHtW8uOzzWD\n570Yc8W0UzYa81Xhet8d0uUtMwaFVt++7zVq6+YZut5MVKFrhE5p5zKOhp2kou67chGdCg6Ghj22\nNOgEc/VcEBVO6FGH417y9BaUuWYWhVWLL1qY8VsyuHoHS/dL/vO6tNPScGN90c114R+N9UVn2th9\n/yq3ltWiiNdOTcxumufuXqFstYO19XYQQh3BmUT56/r5Nmk1lDI33DxQ/GYkveaA1Cn4Cuj0kk0Y\n1rV4ezMSzCHgR69g8S9teRlHS4b3GSHqHfsu0QpMs2L6cg3K0k87DhU6rp+rtr3CQ6ZZYRVPTxex\nPOtNm6l1ybdNaEciV76Sg7N4Pmt4I/oeBhd72QJaLFi5ugT3byqeqe6xZPsm/NdGuOQSoSZ1B2Ac\nweDvZFw7llc4SZI/kyTJP0iS5NNpmv4BUE6SJFeQEEgN/Af79y77SYEfxYRZxn4/NEabXUaxXUgS\nnvD+vdz052/jM+qzYcYteXbA2WNYZLrvhPLdHSZmN5mkx8R3bvqsWQJjnD3dpcvp87JDol69HxuP\nHBsxLi5gn30f/iy94ItZG3HOvkNnBXT/pR9yhtdG7GoUbXtnced/QUU502e77bMu218Vpu9egbKZ\nuJslYzN39jYZIz02Tdx4TR2wYWLt/4Oqnx0qsE/ZWlV9eVHOXNa4YWDv74ouvw9DcxLSt1FUUfTU\nszUII+nNqjKz2BDGVnKYyc4y+T77mZohzOIoeI7KV+hA1TDfMh14b0Y0vjVbrmlwJ2NxELsDt+Pr\nxk75Wsn9eXl39H4J5duYGRKRs+qjHsZ0Ls/U7L09+j2WZiXqn0QRbRR9G2vYuSHZ6yxN7kLRva3v\nHnxshKkiDg9gecBsmK1ymhWmZjLwWTW8YJoVb27BmjDK0fE/e4Ze2rfH9kGgQyXs1w5Gv9tFRn0S\nh0OiMO7LmDPqLL/LFRLzLDsvO5TZhZqrCnf63R0qgzEC7HMuHkfZ8GQdk+V9RFliLV3sQvE42668\nKLO1wUsO5u33AE+N47yQHwkxBuHVrq/7/Hx2YPvTENxqHjubH1Fzu5ArGCRJciRJkv8F+C+TJHlJ\nfX4T+NqQOp/D7P5/yP6/BfzDcRqTJMm7kiQR2f9fp2n6BlBJ0/QrtshXyUl+nCTJh5Mk+WKSJF/s\nr/8e8zMm/vfxopUQoyAxFTpmMHVwDvDMYp8lEFnU8JJ1luevu2ZpdvfeVSbpUb6749W2TeBBQ/Sr\npd1sliYyw2UexDPQeVXnKJBnpmasLwHm+/0YXEj9PKhSDdtFdF4t6i7tc8W0eRdqwVeLsU71K2mA\njxfVvSYGf01Vnw6IIoudJWy3G0KCnkx6W7cNSLTZnDA2R9sPnVJZUj9zyKo8HzTvucFeI8racj0m\njRMi08HCVbOfXQxnum5BUwKWrkOYlFvcrDBzXI/1rLUx2vcLHo6rcehQ9nRVHTw/XsP0uWH9GhzO\nK75eYRh7ueFMYJulCe7lJvdyk90/vuoYrculINA082WFaW5yL40DIW1owa1Mh0l6Jk9DU5Ure5wJ\nPYJpr8ZHICTYxUenvT1oywhjnre0OyX0JsFlBAdNK/TK/6rpy/yMp3tHh1XTliDduaYnOw/A7HhF\nHS1jJMGFJum5tNdCSz2KMAP3c4UiPYd3oW/mPE8SvsAsjraFP+hNybyeY5pGZyPBKQeyBDKXWr3q\n8e7wb3Gk8SnzxPGFWTUuTUNLj9h7givJEnm86MdAhKMiPSek3ctN37B9vu+7UJsCO3YVOo7+H0GN\nWTNMSX68GAYU2yxNuLYKbzxo8aA3SzVCOpSMh5JbRAs5upzwEKHbIPX1PvXb9inAuaJhV6fmdeJI\nWrF1VXLSet8GDPMx+DcYR8NJnCUTMNqOLw157nvSNJ1PkuQIQJqmv58kSTJOY+yph1nro/BikiT7\novtpkiSZgRfSNP0U8CmAPe8tpnzw95hSPC5TonoQs3CV5ahemYJElXvQMCEdLavIBgeJVH/xeMzi\ndgWyK+ti7IoSE9KwFZu6Ve1aqNjkJbbOhrUvAqHKyy6ukkzG7cIOmKiGkgd83iY5mrLPnDpnn52x\nhL2AyUdupdHja4SEVzV4OK6Tl8jODMI0p7NRGWGmsgOyZd0piTmvbhUBoUgPKl7dqBdCGYcr7Ofw\nh1rML+GiGoLNwGbfv1raTe1Y23myT9Izv1W2Qdfefd5uLZP91jLOg/sgRuUouD5lmaeUOWjxLln9\nNJN0eekVAz9lbddY/G+WJrwGqmnScB9vEWRLO1X0/dwsTXDQ+vc2jqh+2HF1v4eACXhjEHG/cmoU\nL36wancXddMGYbKLo4yl8y+w/jtFel6XqObeI4QLeO15XBpeSRF+3CaG2QOOhhpL5l2nipiF2pqN\nTr1pcBcIn/JcBe9NfgLnde7m/0JUFlVW6LaC18qJIKs81F1wsDmPT8GZ4Gjeeu1fpQdVv7gJ3sXf\nQ/OlCh1uci81SfOepZEEP59mMSYnfK4MJ2xZ+r247J3cqKpvgbKJdz+vEm811lTGVxHs5fuQqXte\n6uoSZvZbMPV0KFN7sg2HbJ+d1nXD+60I/uV5SxNOS6bbOWscggW/m6UJ51gqAuGU2kk7ENo4huPz\nQVsPmaRTj5y189W+69QFVc+s5cX2XY9cNs+eavn7fMjgrHFOZRRtGhp2uFzC8YdTwtutT1RDNLQi\nnFpoPG7qlHXK4due/ioc6+941EMYIhikadoG2kmSHAT6aZr+pyRJ9mLocDnvOeAPkySZwJgBSJLk\ne1Dpl8eBNE1/N0mSV4G/CnSSJHlPmqZfSZLkPYxMOgzv5hvmhx3Y2nrbhc8Ew6hepue1AU6bMOnT\nn4pntAUzkSdpHGizSrjAefXiJBxqm50DuG8Nm6UJfyIBjGTbVMdTjvU9AciAi1AgRG8JTdJ1Spa4\nIGSsEKNMYC1YLKt6YhubtdNmhoPNOgWgvwXW1DNr6v6y91TPIuYgpG4EspitME2x1KNxbNGNaY9J\npus2BDI+/GyHsk0v3DNCnw6ZKq940GYKXMItGlOKKTRmlOOcas9UkTCdtHzbzzQr3rNccKRV3bZO\n42Rp6UQYpK3XCI+dSJVcplFUR01DJBmwNv/aXNuPb72LRL/TWgFzuqYLFRzuC9W2e7+p1nJwOdqF\nb7vULwufO6JVxt1zJhwrVL5fspSWjbblLft3j1QsOsOy7ZMIW5qmNZ1Km0QwU/PWtdU6bzrQNCt4\nE7qtRPetn0Oh1Tf3LC4hFGIrdKwGYBJmPa2JSUiEgSzQC9ZqaTeVkuVD+/pBObf7FqiqzJgivCgH\nNWce0d/y2/536dRFMNK4xv7vqG+pX3jINTztapoU3lMi4LHMth29afygzDCO7qUNTV+n0GVhru2F\nsQoDvEz7esQsJeYHmeaRMt6HY5//7coqXuz+zxJsSN2zEPZn1pfP7OdSVEaD7qfw8BI7CuOcSngN\n2J8kSQHj8vBbmM3038opfwr434DvSpLkn2McP4+NekmSJCXg61YomAB+BHPQ6yWMn8Mn7Pfnx2hz\nyByuGe9XIaiLWCYvc7EDNO2RMXt2mY6pwxGX5K+39epz+5WSqbvIBnS856uA+71mBY6S2m0t4d9p\ny7jFVPrQ9e1xxDjrPWIlxfH8svUKt973AVhHpj0948l867Kyk3XVZxYzAUQzIM5QwnSF6PVk0It/\nrNlAtVs/Z6FS6gYOeIX1Pp0SweIFEsBIQq5uBI5M8pwTeDLqqdB1OCw2N4JvcVBz6a6ttzJV73wp\nDklXe2B99phfsqcfNL6toClj63YKCpcXezAvOFuDYmnD427J47tY2jALiW2XnGS5aMlpXuNVGJQ+\nMWKdBCl7ei2WNoJTBA53Hftd6g4wGhcISxyfZMGu4OmyGY2DLNraAdEyutex/V9SXt1L3hHW0W8V\n7zym+6YFMk0mMgZ67kv5cjh/A4YugobeTcflFF4lnoKbzxavHSrGybG5EbRB4zsOYCOCoQhf06w4\n3rBZmvAnLVB8RBYNRa9g6VfT4pI92bEUOgz+DspWreev7qvUIYIAqoz0TZ6bxfa753BdoRvOexEg\nSxuunkLVzFGHn06IK9dPebcaxwqKTiN8BNqRZVUXgkf7vOKrOqW9ozvs7t8KBbeWYUrKthSeBIdL\nUep0mfuCZ/mdtWFYCt/r+iufDgN0X6h6wbxS1xPh9mEcwSCx5oCfAP5JmqZnrB/AYEFjMljFHGf8\nQSABfipN03EOWb4H+EySJO/C+D78epqmv5Ekyf8B/Lp9fxv44KiKvsG7fYKLJQI1YOBjAIO7rqyd\nsoUKXTfBnMaASGNAmxd5iPbNGrv3rtJ9u0L57g7dtyv8PB9zxGwSzXSNOqwa1qcJR7zmp4RQNNO3\nIIuWHK+5tayI0xLvrZ73fp3q2nP+lhj1yYNGSy2MTdWOni3fJRQONFPVTAM80bcIVbMGmbkQCwU9\nio5hdKgYdWu5HeB9s9SlsGYnSjOsxwVg6uLKu4Qr0ibRuHTtYrUMjX1+NysgOJRTD+9g8P061lkN\n3IkLvWA6j/meOrnxJgPBZsaBIH10F7Mj0cxRGKMWGiIV8kB0topc9xoDCSrlIsYt4cdS6rI4vcJ+\nynSMinfJxhqw6s+rPZt4R2hJPSfg8AKOXqdsuVs9+6ymM017EM5VzWzxbe41J+HNdogfvRDKwjCr\n+il163seiS6AktMYVCL6ckUrA7HztfZA+xk43rDeN4tQXF7U0LZ/MY3GIDELQAliskkomn44nGgc\nC6yp70gT6I42P6jaN2qNis0ZCgKTr9JquP9ZEDs6yjtm1O88sG19C8P39mAEAeHzFzGxE24t298t\nG4fD8s6ne8rUShjzoNFVdEz42+EyT+sqIBsJERA0/z2kri3kPL9NGEswSJLkhzAagp+w196VVdD6\nALycpukM8K+20pA0Tb9EaK2S6z3gwFbq+gbvDm3lMz6DFxh75kuYt2m1XY9JVq20Wml2KdB36mph\npFOPm4F3Wc9KGwTZGeeMCaH7nZt0367Q/2qBybtX6FLxUqwFyfolv8EGaxG1WNdOXOmLtaUJnJqx\ni7j6Zg045s83yy5/as0S8CxB5j/Kfqcc2Luqpi9ChFNZKi1t1xZGNav+a7WsXFcOd2DGpTEn2fZ8\nxrRY3S0Clcaz9kHoUKbQzT6y4wIcacYyq9SaXct8LVN0TnKzxsxw+rLB9dPL3kHr6jJwzJxBnnrc\n7oCl34KXYxHOZo1w9chlnD11sz7h+rFZ7zrBUF/nmOmfRIlrHGmH3tradqzHac5fF496TeOCm83S\nBJ2SF7JWBc9ZKkplEnD1Wxro2kWtQNvZWCkrerLfx8EIRB1ja73aM/bdR2ygLY5ZBlrG2IAFr9JH\nTWOazkTzpLVv4ktzyPapGeFKvruq7lgT0cQsxkdNGaHRwlzbmatkwTfjVnQ60h5Fw2OCc6ljwpqd\n082c8NXizyCCkPAJwcGS9VdpWn7Q9Quac7wuW/NPt+/xqFXkc4QnXKR+yzOmypi+ViyPPAos23nd\nXHRmUjEbiLlV8BjQ41yYnZZD+LE85NsjWVUlfozLoCp+RlVMNFDJlnqNgOdINtPG0UV33Tl5d/H0\n1/VxUab2eVyest+Olo95e3+g7VqwAq2lsynhs0cJ5630U/qoaRmYv8AAHwHlZzEr68fOHVkcRzD4\nCCb88Ytpmv7bJEn+AvDqkPK/nSTJD6Rp+ls70sLtgrLbDKTbVA4sMOjRqW1Y8ts5J862gwUpBvGu\nL99tRrZ7t9kplO/uwDrBgDvbbmnCmyK0574cCZzFi0x6oyeLeNOqtpr4XWq8k9K7RnHQEiLUjFQW\nNnluRtUTCwbyrPRJ3iv1Quh8SMZY4MfBe16HaVrliJ1mjtppz6lYbfvEqStwClMqUfHvcPgu4+2A\nZSudN33bTy0BC3D8Ao52GhYfzgtaJr0IBdJvYawdX/9UGYdXs1gX3W+scBDQZDPCyYORr0Ql+p5T\neLVMV2fWi6Ok5Xo0V6NcETL+8fhCcKJkQDisRs811bVjdrGa9U6yTsuCZbjLfe98pfuqBCIXqhe8\n8C3vnlV0l0ej8kwkuAKB/VzmKmBSU0fgcKm2OJpfxCeahI51WGoHgotm9JwIRHHb9bZK8CR9jsw5\nofaoTGHG0NRm3YecpmIdeWn71OVZGlblLxDcn/NOxZrGsniA4AI20sGAAAAgAElEQVRULoWqx89m\naYLCXN/5aGU9J+/WQoI8pzdkzpQjdDPT9zhdw/tZ6D4qWgKMMCTDb52nC5W2c+gWoVabNwX3m/UJ\nE3itQjAWuo+Bf0SXcGytQO42lnPZWqrbgZGCQZqmXwC+oP7/38DfG/LI+4C/lSRJG/g9jDkhTdP0\n+26zrVuDGf9zgPFV/XWd63xUMooek1BuD1zTzwVHzPCBScBqJ5pd9ZyRegOVuDg/HlJOiGVF7LGp\nQxbypvotntWqr9qxarOeM8nL4bObpQmjnhf0aaEkYxI6qGTcr2YLYjHO9QKobbHaedSpW+M6Ztqq\nTDlgyAOM2gp9epfu8C2aD6EhJSQETFV2xZqR2L7LJBfG6hi4Zuh5MOwehJqPivpWz2nBKIZYsNX/\n9XhonANucZZdoE5gFQh1Mg4z4eOaJt1Co2nY3tNjJYuWqOw109SCj6jfwZjjpNwAfWbgK6DzqqJZ\n60ch/7NwOmyTEEO26SCsr0PF4bJTgprVIAYJd46RoVuN+icQ05vQuSqn+Y4TTi3uxEwn1wUCnOaY\nY7NOgoU4GOSdGjS+tbOg4EKX1+MvbZB0065NkUkhaJ/aSLqNmYC+rjdI8Wapqj6aT2gebNtVkHDy\nVa+1lj4EtFwxQssAHUudQ0yytwO5gkGSJM+kafoRG8tg4IhgmqaHcx49tFONux1YrXumFpyHxyRp\niRPM6P8iufdK4XMdKhSbG8G1vHS+WRnEbtpTzMCArTFuT7HUo9jcQCeJKdY3AkY0fWIlSCwjzNOo\nLtVEB+fwqOsS3Djntwh6TELdO+uEi7HPY7C3fiP7WXU/K2+54FRnNMwaC8FdjMu4nOAnazyulu5z\nv1eYBqX+pY7DB0DtUNsxpUq9S6Fsk9XMeAGnMNM3yU9oB8l8dN/FzHS1ZMfMOrDW5kzClGJpY6Af\nN7mXYqkX4GG1udu1VWgwC7RpK8b1sIxvEqc/C+9SR4+im1MxQ9djI+UkL4YWvOXZ6fqKG4OAhu2C\nrwWSHpOs1sO+6T7q9zphUk55WJNIse7xGfMFB3V1DJGQHvTc0+Oi/QLkt4yn0JvkVojLaYjHRien\nqixoujLPShIroYNgXpfUvI7mrXN4ZjBYk9Yo9Zh0ApfcC/BLz+FL3iVzV/od1lcMEqLpzJAaNzGO\n5L0O3yWPf8ejKbr6NN3LHNJ9ER8l/T6hD2eWsO+dXvD/RcAQ3lqb86fcJPlRjyK90iTTC56ezQIv\neAzHxpjrJh3NeRyG39K2K+wPT9UIbuuTCt+L7BQM0xict99PbbHOf5imaeBSlSTJebblZrUzEC/C\n2vM1Tl+rCScrBWYMMZOQclry12WFmIelzJTjjPoolGbQUkakaJHmhdnKM7pfRTZs5jwdO9+f1ICQ\nyKXve7kRtMMTcij5C0yz4ibqXm4ERD5OTvmsfPUxjiR5lRa+soJOaU/vuJyus8iGyxwHoT2+xySN\nuUWHY7eDqdsFse61P7pO0/dQ6yHv8eOyoX73gvYM017phUDGKKaRLEEgj9ayhIIeJsNlrN7O2iHr\n1LPSFl021kho84lWmUqgoFigkfcLnm5yb6bgrd/hcd5z9eg+xO+QzKUah+EYhhsCqVvbxAXvsWYg\nizazQPo5GOgrpF3BnxZk4j4JuJMmFgTH0jaNm6z5Fi/CMW8CuML9KvjboNCm35F1TXAW8wRNx6NS\nGWt+q8dc8zGpMx4PmYeS4EqclYUfyJyNNSByDF54XpEN95yMm6ZT/T+eq1kbTAlIJrkk5HkRHHUC\ns235sAyBYXEMFu33F+xRQtI0XR+jzr+k/9hTBvfllL0j8G6+EcSOvhIlQOISVBY6AwSiQeKS692U\nxDe/WTKDoAdUCw/xIi6/ZTC13VYzUvBMQafrlB2WpC8WqF03KYYlIIre5WjwhN61moWuq78xt+hV\nbMtQXPD2d7eQ2XPxxaYsoGZhusm9geAAuKQw8myvFO4YBXS/ZdIK/vQ1watksZTftettVtUuT/dR\nMwLwE17aIYKF9FNyXhQO9Z0vQKFi6q/QNbHIrXpcPPZZA+S+Bc0UBU9xGmBJ6lJcMFn3evXJAYYm\neANDY0JzQm+F6ybj56YVBuN3XWH/AKOIaVRwqQPuSDn5XVtvc6UUtsuplSMQWjfMMsSL1l4Jvk2/\nN1wmxELZ2FyL9Y2BxUUWI6190KmjNQ3o3bFWJQsDNmnDuwOLvCT00sKz9PcK+x0uNJgF0uNN41HG\nXAsOenGVcjEP0guTaP4kFbbUL/QhfdPjr7UEtXVL12XvM0CHAY1TvKhq4V+3U+NF58eoND2vFPz2\nKLrx0AtXvGHS9CYbM8GPXuBlzopGUPNSLXhrcEeb8Yu70yaBa5/0Z7q54mNVXPKZOQFzrHy2b0wA\nz5lEZVyyvNce1RR+XCibzLFCf5rWQu2Z51Mxv3Opv1t9aBFoZX3fN4L/OwlJmmYGEjQ3k+TjmKyK\nfwrjK/AN4B+nafqzGWU/Bvx9YAL4fbkM/CHwqTRNP7ajLR8C97y3kv6TL3rjj0ikQlD7uRKos/Ig\nzlufd02D3kVoSV8mSrzTiQc0noh6tx1LlcJEtAo7ZqpZO+pYktVtydqtDdNuDDhMRfekT3kSv25L\nXl2SPljwas56h3Xq54SR6rrz+i9MSAsqsVbk8HrLqBDtDkHwLIJRllo7C58Ce7kRqFZjepC+yfWY\n5varaIXDNAujzAcCeeMXt18Wu6x647HR5eL/uj/7ec0xT6Hl2Gw0TDsQvyemIT1vZA7Gz2W1Ma+O\nPIjpNxYM8mhb0768V9NqTAtSd1Z9cfuy6Nq319NsvHDn9T0Lv/q9Gr9+PCsDO9o8fpLHG7Pmry6f\np9HNaqume2mf3jRkaflEQ5U15+WaFl7iekbxg1G41loCjW/ps9DHx5JnFtM0fW9e/VuBYT4GxzGn\nuH4gTdP/x177C8A/TZLkp9M0/XldPk3TnwN+LkmSn/vjFALyILbZ6+tZi9A4Ey3vmoa8RV8Tecx8\nsswQMTPK00LEqtc8NXJ8bZwysapxVH9H3R+m+o1V2VlqRQgZeFZ98bUsfGT1R9v640VA2xCz1PV5\n+BmGtywTlPzXWqcsk9Q44yLl8pjRqIUuC7TAtZVyeYzd/PZmGBHWxxmvGLLmR9b1PBrP+6/bMKrf\ncflh1/LmvlyLVc5Zbcm6F+/GNcR8MOt61nO678PmcPzsuHM067nYpJFlAhjVpqy+ZN2LcRnjRpeL\n53yW4Kbrifs1DOL+aJwO46NboctxYVh2xaPAEREKwJ1IWAD+9pDnfiNJkj8LkCTJQpIkTydJsntI\n+T92yFO7ZEncWffHFRi8oit7Eo+CUYSU5cOwk3A76qm4z8P6r1WrAjKpttKnOzFBsuq/XTwPez42\nK0D+jn4cu+I4ONF0rX9nCVXbwfFOqzljGGeBGPbcTpXTINSbBzGu9XfWO4fd0+r0uA1bhaxFZ9RC\nNGpuZ/0eBduZY8P6mzUew3zGsq5vZaO4lc2UxvEwWh732k7AMOfDu7IiFqZpup4kyV1DnvunQD1J\nkjrwOCYg368Cf/m2WnobMM4uJ+sZ/b0TAzBM7a4hVicNb+dG9D/bCW8c1Wje+7JUWMPel3U9r9xW\n8SptyNo9xLtUfW8U3ocxLVF9B17rGQt43MY8HOsyeW0ZR3iI+xWPa94OK742DsMel3bHAemftgHn\ntSNLvZpV37B3yXfWgjpOXePygnHobBQMw7P0XTvTZkFMA1qVPQpGzdthAutWBYRx5/4onGjz0HY0\nYXH7YojfnVc2NgPpdmv6HZc3jnq/njM7LSAMEwz+cJv3vmEjIP4o8Itpmv6KDWc8FJIk+S6MAFHB\nHI/8VJqmn0yS5Dsw0Sj3YCJ7fjBN081R9cWevZrZNtYXeaH0sLuv78lEEntRTGSySMSDLt9it7od\nyHs+tlWJk9LVHAc/Ae23kKW6FMjSQMSOLllt3IrkvV3Yijo5Br0Q6WvaqSsPKnQotIwzkXnHZMTk\nNkYurvq9YoPU7dHjWqET0BgwEEq3sb6Y6fk87qKk/WDGgdvV2sTtkoWtSM+f0ijlxxPJW4yGCbh5\nZfIEszz77bD3ZDFlGQsZ55j3xDCuVnCYxkD3V/M9cQzOir0wbAHTdcb40MKJCMq6jBZatC+JpnXd\n3lELmuCuSM/VreeLbns8D/Q9PR5SVjv+xU6e2udA/zf+Bt5BW+5LGzWfHMYjYxrNA8Fz1hhJPTvN\na2G4KaGeJMnbGZ93GAxdouEd64i4APyrJEn+FDBMwyDwDeDxNE2nMXkW/rskSaaBJ4DLaZr+ReCy\n/X97IAk96GUyHe/Mkh/0Ww9sFkMe164rDolZ6sBRAx5Ex/oWha2qmUVQ0WOVJYELXuPdVeyoJOVg\nUF14p00ToyDLYUvgm3Xs8xZL+S7S+6Zo+05pPwSyHOCG8Y+dfncsrN5p882dgNtt87DN0U6+53be\nv5N0dyfHOFcwSNP0XWma3p3x2ZWm6bCFfh6TZvkn0jT9Kibnyz8a1ZA0Tb+Spulv29/vAF/GREn9\nUeAztthngIfG6dgwqTqGrEU+X0Wer7qP7VjjDNwoZ564TbIoxoxA/84SNDRzjp8fB2LP+1E21K3C\nMNV8FuQ5DuWVywJxcBrmyFWkZzKZSXZCHU45anvW/0F162TmdbkmC6e+P7CQDksKQ/7YaJrI0hbt\nxJhm27831Pdo5qnpd9h8knvx907AsLqyhFHdPtPunREKRo2JxquYZvS7s46WjtIuxvxhJ/BaDGZa\nOLaaJmOepcdWCzxZz2f1ZRwwWo+sI7iTwe9hEUMNL8kI5U42vYzL58yT+dE174S2AMbLlbAlsMLA\n0+r/v8eYCMaGJEn2YIJ+vgFU0jT9ir31VcjGRJIkHwY+DPDt370LyPfCdYlOMu7pBSdL3Zp3NE0/\n76eqv9fF25ZEjSrqpzwnPV0ufgdE8eF3AIYJKcMI+Xbsz1oVnrdAx2WEKeSpg3XEtDw7qGgH8hbK\nAGayw7tuF/ImuvRntbQ76Fscmjgv1vzttSnf+11gGI6kXLb2LGSYOonTTuD1TjHHrYDQpuBsmKkm\naz7lzb0gB0VufaE6W9c3GBNivF31KG3ZsLka36/QccftNJ7iZ2J6k2vaNp/nQ2S+8xfQ+Ai3qSu7\nfNyGeLz0UWZp01acDfOujXo+yxH4TmkNdlwwSJLkx4AnMdGiE3yuhLvHfP7PAf8C+Eiapm+bTM4G\nrO9CZuCFNE0/BXwKTByDYd7UJkpVtkOiJjyZlLpc3oTXDHEjqrerhA39njxbb95uL4YVppkuragy\ng5M+Fkb0+7MgtqdmCU15z90ujEPkMW6zYJTQp8vkLYi63GbdhkeOdoFZWptRsfP1OAyjUQ0mm1w+\n/d4Jk8d2bZfaL0VgmMCZm8BpG6Bpdqs0nCd8j3pOgx6TvPTKWwW9UdG7Zf0/5iuaLmIfmDhENYyv\nTc1qV3x/2HzLwo2GUQty/FzegpnlExJvEmLhLY9n5OFYl8sSaHYK/qSE3mE+BtuFM8DhNE3/vDI9\njCsU3IURCv55mqb/0l7uJEnyHnv/PYzO9r1jkDcoo3bHk9ECV6ZDGR+pC7JtkqPem93GwfwFGmLJ\n/k/abn67MKlwmAfD7o+j2Ri+Mx4M8yv/x02oc6eZSOwrcbtwO/geBbG61nxna4+ydpN58M2gRciD\nYfja6R1gNk8Ir90OHu8knreKC033We3ajgPoVmhuHNiOwPknATuuMQA6aZp+easPJUY18CvAl9M0\nfVrdegn4O8An7Pfnx6lvO3bnYapn/T+rnFwTM8J2YTsq+UFb2HC/hTwva4GsXcqwkxJ5EzhWtw3T\n0mx1gmxnRzsKt6NNJrF3t96ZhTtkjeO4rfp/3uKXt4OR/9v1cs9653br2Up9cZ15atFRi9YwbUBe\n2/LoLr6vy8W7cl1+FNwOs79d9XBMd1vVKuXxNv2d1c5hKv7tQpaGdZiWdzt4k3q3smDHdDwMx9vj\n54N+OqN4+k7DnRAMvpgkyUXgcxgnRACUBiAPGpigSstJkkj28L+PEQh+3R55bAMf3Pkm7yxM0mOD\n4oDmQIcLjR0esyak1ip8s0qWcHsTcyuw1frjY1waj8Ocx+TZ7eI8i9F8s0AW0xkGsYYri0ntlKPa\ndhbF2xmnrS6a45a7HRwMcw7dLtyOD9C3OmyHpu6k7f5bBe6EYHA3JldCU11LgaGCQZqmr2P8EbLg\nwFYa8HXuGohjsB3mcTvSmKi8NZHpc7ix7WrYxB22W2+sL3K1NEnj+qKzhTfWF02BNZN+2iQ28amZ\nV5h2Z+EliYg4gemY9Z/jA85eHOcP0AuEFmyyFg59ZFDKbgW34yzYeeeEszyee4w+S613SYXrfa7U\n7+Xw9RZUDa4KrT6rzd0Od+KDIGF99/Oay62g8duhTK3V5qVmkyK9zJwdo1Touj9Z2gnd/mF1bAfy\ndmyjnFPz/vuEWLuDbHUmPsd9jvbknLnkrZBvqSPbdt7lWR5z5+ll3ONslNIeodMOlSDWwjDbdx7t\nZJ2lHwa3I1zFGj5Nu5IhEzyuhU9op0XJHyO5K8z1smubJEUTnEs+C/GTytK2ZOHjdmAUDev7WU7d\nw7Q/+3nN/f4cH3DxA2RO65gQtfU2L5XM8raXG3yOD7hnH+aFILV14Xqfq/X7XF3CN6TeR9fP81Kp\nGdCzb59Pnf0CDw/QlM5HsdOagzshGPzdNE3/4x2od2y4i68HiWZixrSf1wYcWbIIN05eM+yYX14s\nA3064QUedtchexcg75CFBcI0wPr5vdyAJ2H6qRV4EgrH+hQqJusXAF2o0Tb3Zvvm2N2lPo1DiyZ7\nGH0Ks22TOYw+VPH3ym2mmyuGmUg2tTm/wImQIQuiy1JmQed71wsjGIYjjEUHFYkdmjTD0wlZivQG\nkuEIznpM8gIPB8wxNlXIf6l3mhUOr7eCcQhwft5mNzsPlKFQ7cM5qFlcF2Yt3pehuGD6UrvehvNQ\nO2rLVA2zKNCGM7D/S1corPdZKU0PTOo4MEzM5OIMfJppybff4RuBTrRUwkxe5YGhu+0sAWM/V5Ck\nMfE9nURJmGpWuRhq1w191k4qfC2be42ZRSfQ0rGZQJ+zGe0ks515AcyFGf8A6ML0wgqN64tQxy58\nV0x91VBY62GEa6oEwpyk0RVBRQu4ImDE+UqyjtHl4VoLLfKMfk4ElDCJ1muZgk0WP6m12txsmsRx\nhQt9M9eX+xRmTBZEgUKrz97mDZchkLLJMFqxZQotwz9qM23oQM3ygsL1vsvq+jk+4Oah/L4diOk+\nTxjLMvvESdcEtCAuz02zQq1laalistk21hcNPXVh/8IV0/8lHL88fKgFVeCSoTHhS4ULfXecuDDb\nhxY0qovm2jLwsuUbHdjbvAGXTP2OrgU6QMX+X4b9C6HAZua5Eeg6lLnC/dvGcxbcCcHgzSRJOsAV\n+3k9TdP/9w68ZygMPy40uS0JdlxCD73VB3cCo84lb6ltJ01fG59cdBJnpd51DK1DmdonBxfmWtXv\ndN0kl1SvC55h9Jg0aXSb3UA4KdbjLI6T7hhabCdeLQ0eF9IgttD4zLpmBHHY05iBxvVp0Ls//f6x\n8Xwy/N4sTVCY6bNa3+0YZ4eyy5neoUKxvkGtanZYlXroL1uY6Q+cKNkqrDBNseQzv2mnyDw7b5at\nWOM1S0CB4TZOXXbcnaFeJDfrExROGiGzUO0H+HL0ptNrn7ApyOthCvIekwPphIWRrtZ3u/avMJ1B\nu4Y2TLnwSGXH0m6xtDGAyxgvw2IuCI6yNgXx9WExLrayM6zQYbM5YbUIZQqH2nAIFzVT93+1udsI\ncnXDP+KNyN5mdhTFTh1Hg/LOcZ1w47bGfYV8X4+s5+R/vIPO24hpH4zNZnjyaLW0m0pTze1ml8Kc\nFRLn+p7XLoRze3NhIuC9lWbXzdVKvUvhUD/QulQWjOZg/4krLux62MYuhWof7dxsON+kE/TvhJ/B\nnYhj8F8lSfLdwH7grwP/U5Ikv5um6eyIR3cMvs5dAYKFAIQZNdYXuVIanXZ5LzcGJtCoOOVSblzI\nImzDHAzBCYH5BcDXXbve9jupNaiUvECgBQG989HXzPsUYa9BrzTprms1bmG9D6XuAHPQAX90XeOe\nETbPeo2B4COLCetdfhYetWpUgw5LGzsETmO0IqyF8QGEMRbZMLtTu1MCKFT6DkeCV8HLXm64/myW\nJlybBDeF9T7RkA8sqDHdyQ5MQMxAoyBetASPMV7GMWN57c3kAH7lfp7NPq5fhxovtPpQwe7iCUwz\nAiIUmNCw2adwzOKRnVZYt1/TaXy+v0I3uKZpvUJ3IB10jB9NmzEd5zlLanOQxleWOl6PQZZAEpsw\n9nKDwvU+03WjXXI7WfpMl3S69iISJjlfwPTXNQ4L631ulrxmSLRKmq5in6pRIH3VvFZrDfNAC7Ke\nL3nNWQyyuRDtW4E2rBntoNeSAiVjAgAozPVDbRWwt3TD4V7wXKBPp2TwY4RKy5vUJknMMIKjUDui\n2rsGlPz1uNy3hGCQJMk9GEfC/UAd+LfA6zv9nmFwF1+nx3dmEgPIYjs67XLW4rYVXwUxIfzm2w8A\n8PDdLwT3hTBjdZkQKtcMIXZKOdqKqu9Pgb6Z6KVuNvNU1wf6Ze8Vqu2B+6OeH3UcapSDVxZjHKxv\nsA5tDxY8xsKg1CnjkOVjYFhQF5atOQUMXktdL6WXvY+G4BwYiu/BhcmWKXWdYKGfG7bbjuvSC6eO\n5a7t7DG+s/AruB+Xacs7stTioSo8jCEfmyUCWp4L35HVtyycGsFtMPBM2KbB50YtfLov+rq0aRid\n6raMqz2JaXvUqaitQI9JqLYdnQp9x4LXuO/SuHNCU8lvBrwwtHWNgYZRsRPy2zfoE+Kv5eeTCfph\nF+7NktcQdChTmDNCg9MWlnZnCpjCi0VwXy3tDvAWCq/jnXoRvhy/Kxb0dxLuhCnh3wO/BfyPaZr+\n5B2ofyxwNiKAZmhaKHy0D0/5slmq/wodGq1FaJrBkt3Vw7zAszw2oOKSAc1yBvnhu1/NjHxYwSfM\nkcngHAcFrhmb1God5/zjJuVPYfwGjhkptWYJqDCjbFV4O2yh2zZhp8QeK/Osg7ORNw55x8VNa5IQ\nW1tBds0ySeoTwU7EQbVNDLEpQ8N+rvAqDwTX9ASXEwWS1+DR9fNwCfYu3HCJUPTu7gFeZS83nJZF\nxkubIESDsJcbbjfg+NEyFMp9p4nho1DYZ8sovVdN/DNm2ya6RhlvF6xGOFyz9XfN9en6CoVWn15z\nMJdD7UKb/QtXnD200VpkpekTJhXO9M1YNu37pN0VNT45ONfqSM1YspzHjP2y4nBv7mUzV7nfWF90\nDoSiQYkDQxVafa/2tzTMLM5/w9OvEsgERDBbyzCZKZ+cwAHM7tqucL/xI4ABH4Pa9XZw3dF9RbWh\nPmgC0E65eh4/un4e1mClHiZRis0N2u9Dru3nivNpEDxq7Y5xOL7PjYXgV+oRk2Ct1Ta+MA+a+So0\nWqj2KZTVHG1a7WN1MJqqXgDd+K334TlL98D+pmnvldJ+j9+6p4VXeSA4kRUKc9nHRQW/gtdpVlwy\nMQnwFePOjy0hfwLHw1jwuG9cXzQmvwtteBk3txuzi2BdjgqzFlcti7uO8RuoLVucVo0vTLFunDN5\nTj1X8XTsoIoROuwmw/lsrYfChmv3GnAJGh/y64Ke14LjnRYQ7oRgMAe8H/jxJEmeAP4d8IU0TX/l\nDrwrFzZLE9A0CBY1mZM4FXPPUmM7mBuu+s4ajKxr3YgpxMQfgF5kZT5WGJicFbqmHxcwRC3l1zAL\nlCxUWXWvEQaWlt8zijFUvSRdmLNMsoonZqvaWi3tpqJ2DZohazVsngrYqA2HR78TtaKzS5cmKJRl\nXLWWwLxHaxE0CDPRJqHaulnc6RKeo9H4WQP2YZhDFY9f1DXBreC9qvCxRqBpcGXn/AJtXmnpbyZC\nQLSrZpYwzNcc/h1Ki6SdESEUCrIgy4YtGgVRE+t6YuhQGS/Ese7PLAaHs/hxUORQWO87Zzi6BLh2\nuC2ZsddM1rep7LQ/4BeLeNcs18X+W6DNZnMiuB+DbDZiwa5H0e0cY9BjPUxbk4dHbRIx9QzGe3PP\nzmFwOoPH35r6WFxWml1vJkMJsZXQPOnwUzK2cnm+Q5lOKcST4GAr2oNRpoeRUTJzNKhgTQADGxj7\nPUPIA4Q+Ff8VwZWK/S7b55ZNPU7LUO17IUzXoXhGAWtO7Nh2gaNjjWNXdjYU0LQJSOh1p80JOx75\nME3T65hkR88B/zvwl4Gf2en3jILCet/sBJWnsuwVOROq8LKIsEgPnhv0Eh4nN4FRppr6u1QG8ibE\n7TTvswvcMoZorAcrSzgvbda8Xbuw3oczcHUZQ3Bv2jJVQqKsqN/gFy3dZb1TVpKq8y24hGcq1/Dv\nx2gxCut9CtfNh2u4CVhbb7s6BsMJbwwwtxvsdfbFGJ/+ucEwxBqkLu37sDFMXb6GwXHLfpYweF/G\nSetP98y9qz17X3+EV4nAIMzDgmPSwmyFAcm1ACfWLnvet79Dxe1CHHwUOIenjUu27msMeObnCQWi\n1dJe2oPmh0FGXFjvk5W9L7SRmx2R2/1Y2nB085yhjdq6OaFx67LqDwRCz2ZpImTIRPcy2hovqtLm\nHkVDo2s4mtRtFJoP2m3pXGsthI/EQr0ICnLElUvhyZpRqnDvh+D7Iu0MnrVj7HiBxW+RDfe7QtfQ\nzQUMfZzD8xMRsCwv6FA2+BWhv4r736Ec4DPgCUJ3hD40giuNM62lGxfipEm16+1gzg/4D1j6Fz6k\n8cNzBONRoWO0sevmNAwtzHxsRfiS0wTCI+R315YTIQtPQ4FAIQJETL8V83H+YEqoEXBaq1bE9/H4\n1ifBdhLuhI/BF4FvA/4N5lTC/Wma3pnW58Cf4z+YbA1LwCwUmxs8ev08K3UzcU/bhfYxngXMgL5a\nf8Cpq8CoOy+ehPlDi9CBXtMyzZ+C6c96FaLO0w1G7fus3aUOpXYAACAASURBVHlO0jNs5m1DjNN3\nh8fQpjHHDBtHF/3xlHO2E114etmkl5yfsZLjy1B4sO8WpNOWNzf0TiBPU9AlJFqZCA/iGXJLPWuP\nNrKEFzqewjPwKmbXcCmj3vN2kpyHwlGz4yvM9KGuiNmZeV6jcKbPAyde5QZ73U5e+wCIyl+O2BXW\n+0ZFOhepfW27phdW7IQx6r5uveIW3cPrLVZK0049z5Nw9Xl4BTho6eItYL4FNOHW8/AOHtd7LsOU\nrJ+zFjey81CaAhHmCp2+x70wl1mLnyfh8MmWU1E31o3p4uJZmC+3KZ4wL336JLzvhD9ZIfR7agk4\nAbdOwtQB016WMM589M3OLnI6hcFjuGAcHIWG93KDK9xv1Mt1X0aOYtUOtQNVPOBMQXKszR2LK5vF\nX9PVxcswb2nudA92Ae+7DA3ZuQlusXiy4xpr0wr0nW9IoarMY0oGrC0Z1lP7UJuHSi8avvCgrfta\n35Svd+Fxgz+3m6vgj6ceMrQ8cLqk1We6uWLxd4UXeJhHr5/nav0+OAC3evDAwqtOBa5BFjXhN/q+\nOBVPN+0x5HKfygnVqY9C7al2SFdvQu1824SIW4PCCcO/VoHaSbiFSXs7VcRrxuwuVo5/MovXDlqo\niNpbNApzhm45Bxd7MF+E2pcN7qebK3DE1H+42oKj0HhqkReaD9M4Y1ThvRPF4Gjw1dJ9gD9KumIP\nvBbpGVNIpU2vXjRmgqNQa7aZfmrFmQKu1PdTO2PokY/gd/ZdKHT7jp6unjXfD5x41R/B/iiwz8z/\nt4D5Mm4D8AZwvAgXnzfq79cxfBgMT5jCPNc4AJyBxjOLcAYuLsP8ETXQWlso9Csav7J3uuWa4Wes\nWZ8C2Uj8uGlPo2mFLHVk9HClBcvmZMROCwh3wpTw19I0Xb8D9W4NFPH3mIRlw+QqdFw4xqzdv2ak\n8wdwgVfiM9v+rLIREIZJwuW7s3erRXqunZtNu8AdwzHR48IQy5hgOodswJxyGypmwq8CrBkmNNXF\nEJwwDIEldV0EB7n/sionJgbZLc/iiVqereIJW8wWiuk7CXsGb7KxC2Zg951TC9YhlK+AP0GitTo3\nudf5CABwUmUdLHXpNMvuSOU0K5mpiYv0Ag9fAB6Exiw0WhjcCx5mgTnDAHgeThWN5mCqaPpyywpt\nescwwATEdq7NDMDVy9DomLKb9Qmc46RlyPMHzD2xof4ghs5k8ahhcpkL7UwdwY+VNlnkQHbirm5w\nFrpHkdW6wa/Q9uqC92/JUxGvNs0zjlbX24HpzvXvQYOX2vOGhhtFBmlWaEtrwGROiAARm960NkbR\nqlNrz7Y93VqVcWG979vYsdev2TKahmOoyCPWv6Fkni+yAU0zH8UynOdodoX9A7xD8N5jkkZzEeYi\nR7Pzpozbvf8UXijdZz8YPF+9bBcvzRPeJKRNLdhfU78FN6Lpqnq83OpZGpwlNJs01bVyf8AMtpcb\noX9TiUzoUXQmsg4V8+6m+ThcCK2L9uOE+t0FDuHMcg276XnJPrJZmqBwwggOjSNmsRf+1qjCHit8\nizAwn8FPGmKiaeLMNfMz+I1UWeEN/BjoOWr5QDA+wkNt397oQUMLvLrf9prhhTsnHNwJweAPkyR5\nGhyX+QLws6NiGSRJ8s+AvwF00zTdZ699B3AR2AP8DvDBNE03t9Sa2dAxEAze3SJhkasXHScwRMwg\nT6Udg9yT92mv+MznsuQGq8obBvPSnGMwJYsZhDboGVWPMNtD9n+XkGnrZ0R9q4UBsX2LGkx76l+z\n99dwZ6ULnX6mTbfHJI21RTol83+zbo773cvNoX4bEjxHJlDsY5BngxTnxbi+Cp1QSMqBGqb/x5Uf\nwtQ+/ztYPIQRykSWRUZBA3t90DwcQHyUUGh0D1ZAeRCDcxlHy0gl8BTgbL/alKDpUAcCy4LY81mE\nglFHEgMthbJnBxqtqqLhJp6pCz7z5oCoZcW3AkuL2qYvtNoxvx1tVHFzInA+LLcdDkVw1er1Qqc/\n2PdqSHMDuLS3jPNjmCo89pzPu+673MkULobSblctXtLMpvotQlPd9M/1XZ2+WS3tNjtZWciVqXKP\n7WOHMoVK2y/WkfkyM3V0R5UbB1Q/Ha3FQr7QD8CMbfta29NGOeLbCg9TVkPIGvCmnV/HCE0vVjCZ\nkk3ERzFzUG+uRFBQdOa+RWAR2tQ+ETN9558RbFjL8D4pa+eQ+HUV1vtWaBzhe7ENuBOCwT/DyEwf\ntP+PYiw8PzbiuXPALwK/qq49AVxO0/QT1pHxCXy4maGw2ZygUOk76duY0azH8BH4hGZklkCLbHiG\n1jRqs5vc64hvmhWYFQYtR7e6toqQgQM8xIuAt+c2ri8yXTcq4Yd40agMKxjbV7NMYc0EINGBisCo\nm8S5xbW3CnwWv0ArTccAodStTcp65HYoBx7dAhJ8Qy+4lXrXBfPpMcl0PVJDW1VzsbkR3O9hgqL0\nmLRHgMKFZLM+4U4N3ORe9nPF4VUviDoIiYveZscTrFliDVbqfuFz0dvsQjRdt5EN14Dz8HD9BThn\nvKkBNk9MmL7LLuy6729jdpF5MIygMhjrQLQUAu74YMmnYRYP/ErTO21dLd3nIqYFeGlOGBXoIW9j\nnT8AV9WxxCmr4l5t2jZWF/2xKCcI5DsjyTu1/bawbs629yg67Y3g3weNyXZeDE82hELDZmnCmL/A\nMExhjLLgKxoWnMa4pBn6kwh9SuAhsEF26qH/SWzqK9Jjc2EiU7ipnPCakCIbrJQmg/PnxaYJJqV3\n90EkxFI3FI6O4fiKcRwLo6Zm/c7cNKjFU+aFzB2waudjeA3Kg3ihaRbPK6xqWhwqNZ5vcq+fqxCY\nj3oU6UW4mF5YYUqEsEO4wEhgAvwIfTVOLDpTgYx3j8ng5I7QmzuiXVKbNCtQOrwe9XQS+BeVfYAi\nTRsAV+tmnA+faMGMj3ehgxbJeyQ4kQjym011IkbhrvCUDYf+VNt8z1jh40Tf0bJriw6m1SRoW3Ai\no65OBZV6UDJla2fbNJZxp1B6pUmnQS2WwlNu3+wag+9J0/S/Uf9Pq6RIuZCm6WtJkuyJLv8o8MP2\n92eA32RMwWAsEDVOBozjZKhhnPOoeWVjyIowJm3q5KjeYFAg0GfcdVSvrNgE8j9+Nsvr2ZcZ3MHk\nZSAMy2xlqxDWHRyzGgZKE7DlozwdeS77aF4WxItm7rOOQRZ3VNLPOoevAzkJbCfiZwxZpxdiEBoO\n5pGQkp53GVqBrKiE+QGzhl/f6pl6WTiyrm2ZbreI6kyNwBCo0A3NAFlQjb4ZdEiV3/FxVd0u/VvP\nw7FOoYwAHTxqnDmXh6M4QNV2YLM04eKMAAH+BG+FuYgHKTNpIAApGEXHWSBxKMYru7OnEu6EYNBP\nkuT9NikSSZI0IOPcznhQSdP0K/b3VxmifEqS5MPAhwG+47v/LCt8r5NiO1SUpFyBk/Z7Ljxup8NO\nyk5Ehz6dZgW6YaAcHbkKTBRC+f05PhDuCIWASn7QtX1OdqwC+vx7kQ0X3RDIVSHmCRVZC3xWOR3e\n1MVvqI9W1w8LJpMF4g0vBB3npBAti1azrjDt8jfIsy+Vmk5yBjM2LmlJs2uPqxal8a4PcgabOW9H\ndTkdulF+B60etRALaXonsBWQ/lXouPCpjUNGA3CTe03bj0VxBw6FzCJOvjIMsrzks8wJcjokXgzl\nXL2APj0h/djLjSCSnAN5zFbpaD9SCW8Xl1mgF9thYdJ1ONo4zLSpJyuQlxfAnK9AxYa6deF0B1lW\nLKjFJxbk/R0qnmZLftwOt1rsbd7IjKIZgNJYAMGx4lGQJ/w5vMy1B8yDcZTYzYUJN4c3F9QxRm3q\nwhxXlqOusVZGgtHJgisxHSp0vDNopE7PMnWJpkjX7TROJR8DpkOZTt3zS4c7takSza3404i2brq5\nEmzAdgL0+qR5s2lLGBlzJ+GOJFECPpMkyZ/HZEv8Gt6ta9uQpmmaJEk65P6ngE8B3PPeSgpDpM8x\nJXlPDFuTxl6zi94GRboRkWvQC0/WmXwdFKdC15x7FWYg9v+W9XzHerVi7MpZxCmmAwlpGp9zh/wQ\noln1ZfsDhJH34gBQWaBjSfj3eRPCDfYijngCWt2t3z3AFEpdx3QKlb71OfER4Qq0/YmGChTW+u7s\nd4G+d3gTT+dO3/kPVJrWY7s+iJthkdZYIxBmRoKSxXoU3WKbxeg1/vMjKY4+Ohe8T4GExhaI54hR\nd3fDEwIiHIiNVb/aOlpJkinRYoXH0vJDHecJEcOEi6x7OoSu1vpIDBTT19BHYGejFObENun6++59\nwg+EbqVc1mbhGk5AFijQHuBFg/PW/I9jCsRaDb3bz1uosmgtbwMT9NUdhcxn2Hl0HAfsGqmdW/Nh\njAWE1p3mS2ILRHMg3NR0B8IgSxs8jvMjlGaBbCazjgiPE6J/O3AnciUsAfUkSe62/9++jeo6SZK8\nJ03TryRJ8h5Gumvlg6Re1kgUSU9OFQw6VIWv61F0kRAhJEq5tlmaoPt2hf5XC7AXum9X3KkEHRDF\n2eG5wUpJkjRHTFipkSQsp3MGAu+5+i0Msgv2uJkcYCoiXG38f+2de5DdR3XnP2clGwtjm2HmzrVH\ndmyTQh4mqGZkEyCRRUHsyIYFDAnEZDHhEcKmavMg2YSQYhMgQAUIoZYsKV4hiDww3vAI3sRBwlqI\nhYJ5yJKQPcgGvJpEGrhXuhHYxGMbQ+8f3af7/PremflJc0fzUH+rbt17f7/+9a/79Ov06fNgMDJd\n1iY6d05lvcRVojiO+0AmuivfxdNpNj6enIyootn2mfR7ONzr3yagC1ZqkiwxhtKOvUnP/mlxMrts\na/Xhx0JVCa5Fs8v/Rr6g6IR7zOSlymhRDGuVCY1CX2TMViBm8+NwcPziuAuG7rlitoXM0rmSZjjd\nj9et5EVpq0qExu1xNL1cABZy9GQXY5XItGhW5kE9L9c5sVL3IAlo0YwOfizjr/nnelFzIVcgr/tc\nnXz7LS2YCyfqE+JE0TfGQER+e5brADjn3nUS2d4MvAx4W/j+dJ2H1vJIUmaZhnvGW9XG3+b9EiTF\nwdSYFWdGxk453ttH1EbPg52oEhePgnXnJ+OJIToco/vsspe72Mr/4NwCgjRgb/oG4DXBxlV3DaFc\no9dMBa9aU115DUzM+Chr08RJecC4Tdb6eg9cSbkv5jdNJVxrPhjiziTThbALV1XLvRnt6ntpYaug\nsduLYTUugvoBON5Yx8d5oZF8JAmI1qU53go7qVay0Q40HGDGO4WBGDb1TTvh6p3B1jn4xtAdhpXY\nDDSzULb7Z5JLZG2D8B4NbKMBaCp12g6DN5i+sp14LKYOkMC/t3re6X8fnFMPpdk1qahdd6cxFO/r\n5242xHRVN+OpXjmzoCGn46JlfRMERa+BjcH3gDqXwd+LrmSjVnxy12v7noZO9u9v9+yHxxvt6DJX\nFx2dF/Se0lz7xsFxU9eQtyJfuHJJl3ek5ZUQ47gZT2anFjpv2IBSPaG+Rbaaa9syaZbSbuNMDLk+\nMBJ+32LSqkTB0j/QdGDHDAeDcpwq4DIyFZXe8pC/PjS7l54NjqfdsEo7OgzGPmWv+7pXnRTNqlcR\npB2Q2mdwPDk9sk5+tJ/Y8aftG+sDlXGkx2Kxvw57fxf6btqGTnuDv4xN3ufDwMbEdI1tVbf27Rie\nefSaKTP2p2IbRZfI4bo9qrD9seIaeS+VdSh5lFVl7YUzNjn6KTE4J3xfBvwkyWT0ucCX53tYRG7E\nKxoOichh4A14huB/i8gv46n7C7PnUEXFP31A5H7vnN2dcUWb2TABURTUtunSYh816aeBS2HmOwNx\nmrK7AY1aqGXJxVIVH9nWu54qGRllo90d75hns514Fblo0YrElS7N7LtdpV2zUT3OiB6+zILQUycC\noptahRUPV6we8A6Lmo1WpKuNaZDo1jIMVidOwJW22QsDzRma4y2TxzFioKSor9Gp6IpUJliCj4J2\n9fqtOR3tfbMjrijbtTKa6DMKc6SgZewwlI4K9DhlX7Zz6+UjwSiX2fPQPGiLisOta2ilzWDjWJxs\nwE/m6pc+HmGocyvj6nng6AztRjMxcGoRohvG/ChBTRbVH4HmqzQyaDbaaQyrkx09TgvMgnXmZGk0\ngBfpqmg3Oscy9yo0bXVLCa07YM3DuiVOSrq+f6ppYqWvUTWHjjolUR6W+rZNGzciw8TrLZrJD4HS\nDvw1M4ajfwJNmyspatsobduZwmimmK20jaJ20+918Y0bI0g0aKQx7tvTmg0f6xrD9r+WN86rpn3y\n8aD9pJdDr4hW1QQ4zr32uEtNa5Vm1keLpauB9o2BozOpf7dMevN+IOnVQNxAaR6+bu2udSBKNUlH\nxd63y+xHMgtB3xgD59ybAETkNuBy59z94f8bgX+s8fwvznLrqhMty1k8mHZnbRjc6t1pdoJJ2+4D\nobPuCLuTlt89RimDcng7YOy1VQ6YbTD2zmRa45XU2unsbTvMPH8AvgozTx7wKpNPb9G+r8noQz5f\nXQTVrC5ypeC5fHWM8Q7vVOd6YL36lN9K3EXoYnVTcDZSYRDMuTj4/7t3Bi+JFjpJh9+RE24nz3Jd\nzmSuwQ9Cq70LaWIaNpIGnbxNcJqDjdT5O40h2OvbCIiOYvS7g9fTUM9yioH9M9E8LUoDtvn3DY53\nojRGJ+roke8Wb3rGLWFXvt3X78iNwT4Z+AvgDds8HW/K+MfdB4L9NrDeukbOJ4Am3YvdLUTnJwP7\nPW3VvHMQX8bNm/b4IC2vSLstdqQFZgN3px22tfUxE7x6mezlhrrbeqIT3feOjiTadxqTbD66h/c3\nDIPydlIfVBwg7mh9oLBjsC30tTv9vSM7A72s4xely77gQc/WxfjhGBiZqdAx9rk2aYxPhOtW6h0m\nZ/Um19R4CXvTvcrZu+60m2Es6jiEKJ4f3eTbxO5Yu0TH202/avt5RcNoWylY9JmyY4YNW5O+zCAp\niNLxxjpPx7Cv0F33mw7AG97hrx0Jx4mHCE58tG8M+9+7D8DmW+hmCtrEuWSgOeOlEGZ3rHPn2A0m\nZLOhZ/ScOpF0bZrjrUi3sa2TsN33711sifQdvKFjAlZBpzFUiVtSkTDc4qUSg1s7cR5Sxl/HS3Or\nf+cAqZ9oH6mEmw7v9yaD1Y1EdIeuvgpUgqj9bgQ/dpVR2kF1EzZsJLlK6+GQtzLJygTr2Nnur6nS\nY0UXxG5o2+Z9TdMG+HDPA/tDm80hJTwZ9D1WAr74D5v/DzOHNcFiYC2PpD86YbeSXfhhiBYGuphF\nbne6+pz6p1a72iOdJN7RnZbeV4kC4BmCr/rP1D2jXufA5FsR/yjHqp/QGd7V8e54D0HVP3++88SL\nuYEqZ6tcb3jnrYSJZAdpx2F/9zIqtfTQvFs9vvNJOaOjheWIlWbWeVHcwXKMDdzNMK3gC967mG7S\nggPankHMuRe/6Jo6JL/ixiEKdtfRjrS8CU9vZQR2d/z1g1nZlc6HQn5HOqQ22deDNpZpsO1hdilx\nl96uptNokkc6qe82aXOkE95rYftEK/Xb3Le65qOfMSYrEqFKrIntRDfhg3TgzkSXSE/TvsdIQam+\npPfbgVaYZwyOdLy73lh+m6cdD5a+Sjs7HlpZuoxR03gJtgyVXRkmn5ymhjG24zbNDcciXS0NffpO\n3E1XP8eiVLJJmzEmGQq7WWXUBvbPcDjQaAjP7EYpV6DNIfzncA/adiHrX7FuyvxbuuZj2EojchoF\nWAlcjDGRSXEq8174b+fX1D+PRfpXPJm2uqW3sUx2t27LmEmN7dGIvXfkANVxZGkFc9PXzo/tHtct\nXZXxyObWyvrTC1YKYfvZImAxGIO/Ar4sIm8M0oIvkSIAnFrYRm77nZAV/dtGi+Iq6Bafa4c+ECa5\ncM4aRW7TVESyV2zY7X04Xgg82esbrDv/uGEKgua2dhDbmcNkt3unZwogSAbuDIvWnVRFr4SJFX/t\niHKomi5b9G8K6TTNbuMIqEsElovD8s5v09uJHVMfM5F3dfzpKnOlmsg6MTZpM7p/isu4J56rR3H1\ntmqQJrb5uuzeSfWMdzor352hHHea8uzztL6fxAjcSqBNDxzSH23vQ/3ITqpMlpHSVBaqfX5hPWIW\nV50U41GHYRyUJofQI6hjXRNtz092Lw9kZUW2m4/uSe12IGN63+H79eajexhjkpsOBOZS62s+W9jF\nUJCY3RQY2ps6Pt/DZIxUtnivt/XJGVuzWB3ZaZ5V+iozaCfffXQvbhhGkOw92VFZhY4580FiSPO8\nm7TSBN9OaQeOzkTGV48QYhCpfb6/b2FXYo41iM923x8P4dth9OhUlBrcFJjYL+HpewTSmLfj2dYV\nKuMg0uuW0FaWwQp1jnNcO8vbtFXFbDIfc9PVubZJ2zMiB0hHA3uTNMSP6VZqq2lDz5BHXNh13s7n\nHluubKHVsVYR+4c0X9BE+Tiy6LVw235q+6GlpZUstqm+W+dg7Yut7H6vtmxV159+MwmLYZXwVhH5\nJ4hh217hnNs71zOLgiZdvs7VX/qVTHE/x6ouNKEaqKWJ9zluHVYMT3mxXhDpRBM46/xiYophWp4R\nON/rGgyf648ScicjA82pajhPiD7vD6ekXI0vy/XTpEA5AEFE+1SCeHsiiLf12KGH86Y4CYdztEvI\n0jXN/WZKFzFh0pD9Vgz3uD5iNKVtGUKe3oyyk54zk5IybdaEURFDQzPD5kG/8HSovmuQTuoL2r5P\nMmWYINLSYvMgHO5UpQZXAus3hnwm4MKdKX4CT6KqMW6dy4TPZv0f0lXOQzMtfr23eRAOhgeON9ax\nfnAmlduI3Xu+29BIUbH8UJH5BCnsdtO7/33gQFKaajZazOD74vrcze6Eb5vh0E7XD4bjgZDuyp0p\n3fo2aVxN+LyuPGBop7oHhHppfxg2wauUVrrADZtrFiavaAWhDrKGs/6Y007bSHVy5ujTSuPYz0x+\nFY+lOcwiYyU5cTGZ8PTevBEmjRIt+H6oOISJnaHKiuGzeZDU7+0RjtImfC7UMht6V+ZGpcMISf8g\npKk4BRrJnsnGQKUNmmkebDEcjxsjQj+pWAcNJ5PVgZEQw0bLms2llfbN+kYlT6VBJ6ORuVehWU6b\nUJc4JttUfZ9ktK70EaWnpW3ez/R9dm6m1/qzvD0f4py7A7hjMfKug0dYCyMPx0bqMFgh9vqNYbI3\nxI627nbhmsgm7iaVYDXamcHYV4+EHe650KYJ53pu+Ni5gxyk6sCo0hGyReH6wRR5Lg5u7ZyhY12N\nZyCiP3Q7MCboGtjnhEhhGuiE6RCVz06sdgBp+bLztK40+j1NV+dVRGco6m0xTJaRQSK0U8YsDDSD\nmPHZ/nIUHYYyxUliwkebW48PXGPfsfnongrjdbyxjoGJmfgNwE5P6/X43ddTQ9ord4YIdeH6+o2k\nRWzCtI3S39Zf6TaNdwNsF7/Y74YTHYan0nNNY+5l6tqknc4pdfKZY8Gy6Bn8aKPJy/TpDkOMXjXl\ngwCFNrneMkC2PzZ9uwzR8de3huiUYWJfr11eFygdQ4H5Xm/ez7Apk6VjmxSvw06sWhZIm4G2+U+q\nV8Ud76YqU2kn9Lho2HEZ8rL0O24co0URufavkZmKU57c02WcawItKu0SdDaYCGP7SekZn27Kj1uI\nfb7ynKWbXrdzgWUkA/3ihkefGza0MIpvcTFS5q7SZwZj29kxFt+l6NFfFZXxMDKVYjGMhLFhgzIZ\nxjSW3bRZpX2DRMO2g8472i8327JZJtX+NpsC2//jXGn7pKUl4fpGk+eIoedEdaGvwL6D1Hd6MlN9\nwqIwBkuNR1jrzWy2qiOfpg+Fijdh2fzXe+gwxMGt1UG9iy0VxzNjr52MHvpaNL0yy40zxv/BYFSW\niZ7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CgoKCglOHlXCUUFBQUFBQUHCKUBiDgoKCgoKCgogVwRiIyEUi8jkRmRSRu0TkN8P1\nx4nIZ0XkG+F7IFwfDOm/LyLvyfJ6q4j8m4h8fynqslzRLxqLyKNF5B9F5GDIZ3731acJ+tyPPyMi\n+0M+7wseQk979JPGJs+bRaT4RTHoc1/+vIjcLSL7wufk3MeuMvSZxmeKyAdE5J4wN//8XO9eEYwB\n8Ajw351zY8DTgP8mImPA64CdzrknADvDf4AHgT8AfqdHXv8H702xoIp+0vidzrlRvOf5zSLyrEUv\n/cpAP2n8C865cbwzrwbwosUu/ApBP2mMiPwcUDYR3egrnYGXOOcmwmd+t6CnB/pJ49cDbefcBmAM\n+Oe5XrwiGAPn3Ledc3eE3/cDXwfW410jfyQk+wjw/JDmP5xzX8ATKs/rdufct09JwVcQ+kVj59wD\nzrnPhd8PA3fgfU+c9uhzP74v/FwLnAkULWL6S2MReQzw28BbTkHRVxT6SeeC3ugzjV8J/HFI9yPn\n3JxeKFcEY2AhIpfgd6JfAppmkf8O3bEJC04C/aKxiDwWeC6eqy0w6AeNRWQ7PvrC/cDH+1/KlY0+\n0PjNwJ8CDyxG+VYL+jRffCQcI/yBiEj/S7mysRAah3kY4M0icoeI/J2IzPnMimIMAgf/CeA1ZscE\ngPN2l2XXtED0i8Yisha4Efgz59y9fS/oCka/aOycuwa4AHgU8DP9LudKxkJpLCITwI875z61eKVc\n+ehTX36Jc+4ngC3h89K+F3QFow80XouX2v6Lc+5y4IvAO+d6YMUwBiJyBp44f+uc+2S43BKRC8L9\nC6BGyLqCWdFnGn8A+IZz7n/2v6QrF/3ux865B4FPcyIRR1c5+kTjnwKeLCKHgC8AG0Tk84tT4pWJ\nfvVl59yR8H0/8FGKDlhEn2jcwUu99Pm/Ay6f64EVwRgE0dKHgK87595lbt0MvCz8fhl+giw4CfST\nxiLyFuA8fDDjgoB+0VhEHmMmhrXAfwYO9r/EKw/9orFz7r3OuRHn3CXAlcA9zrln9L/EKxN97Mtr\nRWQo/D4DeA4lMi7Q177s8Er3zwiXroJ54qM755b9Bz8wHfA1YF/4PBsYxJ9ffwO4FXiceeYQ8O94\njeLDwFi4/o7w/0fh+41LXb/l8OkXjfEiK4dXlNF8XrXU9VsOnz7SuImPIfI1/CT6v4C1S12/5fDp\n51xh7l8C3LnUdVtOnz725bOBPSGfu4B3A2uWun7L4dPnde9i4LaQ107gx+Z6d3GJXFBQUFBQUBCx\nIo4SCgoKCgoKCk4NCmNQUFBQUFBQEFEYg4KCgoKCgoKIwhgUFBQUFBQURBTGoKCgoKCgoCCiMAYF\nBQUFBQUFEYUxKCgoKCgoKIgojEFBQUFBQUFBRGEMCgoKCgoKCiIKY1BQUFBQUFAQURiDgoKCgoKC\ngoi1S12A5YgQse7xwBPD59ylLVFBQUFBwSrCj/ABjybx0ROPL21xqjitgyiJyJnAE+BFd8JR/Off\nw90BoAFcBKyrkdvQCby5WTPdYM10jXrJ6lQD4LH9fS0Aj6uZbqBmurrkrvveuvnVLN+ZA/fXzBAe\ne/Z3a6U7l/vq5Ue9OeY86pXxnJrvHaj53nNrvhfq16UuberTsF6bnPPdh2uli9PKfDiR5aFeEaFT\nM933+pxf3fL1ux7AsZpdrFU3vz6n+w98tXXVeRA/PTfwU9Et8Ew803DULcEifVpIDETk0cBl8HN3\npKb4LjCCX3w7+MX6SfiZf415+pyab1l/AiW6pM95XlgvWd3WPrtmurp8C8D5fU5Xs8pLld+a8+vP\n8OvOrTc9nVdzZmzUnO6GauY3WDNds+Z76+bn81wzfyJgEKmZ7gc131tvZRk4WisZTNdMV3elAmj3\n+d118zuzZrp6TQc/rJmuPj/JkZrpDtVMd7jP7+1VlQdIq9NT4XPH8AzExSI08EzDZ2ArPqT9kcVk\nGFYdYyAil8J19yYS34+f9YfC74uATfhtcVGxKCgoKChYejwauDh8LB7ESyKOAj8NO3RVu0iEITzD\n8Fl4DnBLv5iFVccYAJfDfjwZz8KrCgxDJOHZUHN3UVBQUFBQsJQ4C7+1PR9/IqVb3mmC6AAYg3+Y\n9Ot5XfnLnFh1jIFz7hOElV9EhqDzRHjObXAQ2IUXzjwKonBGP+dQGIaCgoKCgqXED0gSgqPh93G8\nfHsQv1p9Hn4Or4Nwb9u5eudjJ4BVxxhYOOeO4bmByoovIufBv47CdbfDt4Db8cKZtST1D2UYzqYc\nORQUFBQU9BMPUWUAjgL34VchZQB2wXPxDMCUc64v0oA6OK2tEnKIyNnAZfCCPanJvl/z6boaOSeS\n9oz+5leXv+nza08oz7qs6hKRsG755Iz6Y/iMNfUY/rU8sqzTnVFTsa9ufieSdqnqsuaRmvNn3T3d\niez96pKxpuFE7fz6XZd+1wP4Qc3h91Dd/GqmOxFSn0nagm6Ha/EMwOGlsELIURiDGhCRVzvnPrDU\n5egHVktdVks9oNRlOWK11ANKXZYrlnNdioy8Hl691AXoI1ZLXVZLPaDUZTlitdQDSl2WK5ZtXQpj\nUFBQUFBQUBBRGIOCgoKCgoKCiMIY1MOyPAc6SayWuqyWekCpy3LEaqkHlLosVyzbuhTlw4KCgoKC\ngoKIIjEoKCgoKCgoiDhtGQMReZyIfFZEvhG+e8bNE5G/FJG2iNxZ93kR+X0R+aaI3C0i1yyjulwb\nyvRNEXmduX6TiOwLn0Misi9cv0REZsy9962AurxRRI6YMj/b3Ftp7fInInJQRL4mIp8SkceG66ek\nXWYrl7kvIvJn4f7XROTyGnWqRZPlUhcRuUhEPicikyJyl4j8pnlm1r623OoR7h0SkQOhrF8111da\nm1xmaL5PRO4TkdeEe6e8TWrWZVREvigiD4nI79R5dqnaBQDn3Gn5Ad4BvC78fh3w9lnSPR24HLiz\nzvPAGD5Yw6OAS/GuFdcsdV3wsc6+hQ8ecWYo41iPdH8K/GH4fUle7+XQLnPVBXgj8Ds9nllx7YKP\npLY2/H676WOL3i51+gvwbOCf8J5FnwZ8qUadao27ZVSXC4DLw+9zgHvm62vLsR7h3iFg6GT66XKr\nS5bPd4CLl6JNTqAuw8BPAm+15VtuY0U/p63EALgO+Ej4/RHg+b0SOeduo3c09dmevw74mHPuIefc\n/wO+CTylX4WeBXXq8hTgm865e51zDwMfC89FiIgAvwDcuIhlnQ99qcss+a6odnHO7XDOqV+426kf\nHLofqEPj64C/ch63A48VkQvmebbWuOszTrouzrlvO+fuAHDO3Y+PW3MiMdb7iYW0yVxYUW2SpbkK\n+JZzbmrxizwr5q2Lc67tnPsK3U4Ul9tYAU7jowSg6Zz7dvj9HaDZp+fXA/9m0h1m8SeSOnWpU64t\nQMs59w1z7dIgkvtnEdnStxLPjn7U5deD6PEvjfhtJbcLwCvxuyfFYrdLnXLNlmauZxc67k4GC6lL\nhIhcgo/Z/iVzuVdfWywstB4OuFVE9oiIda6zYtsEeDHdG5lT2SawsLlluY0VYJUHURKRW/HRKnO8\n3v5xzjkROWnzjIU+XwenqC6/SHWQfRv4MedcR0SuAP5eRH7COXffSeYPLHpd3gu8GT8Jvhl/NPLK\nkylnHZyKdhGR1+Pdq/9tuLQo7XKqcSrGTb8gIo8BPgG8xtD5lPa1PuBK59wRERkGPisiB4NENGKF\ntcmZwPOA3zeXV1qb1MKpbpdVzRg4566e7Z6ItFRUGMRT7RPMfrbnjwAXmXQXhmsLQh/qMme5RGQt\nPpTnFeadDxHijDjn9ojIt4ANwFdZABazLs65lsnrg8A/zPfMQnAK2uXlwHOAq1w4bFysdjmRcs2T\n5ow5nl3ouDsZLKQuiMgZeKbgb51zn9QEc/S1xcKC6uGc0++2iHwKL8a+jRXYJgHPAu6w7bAEbQIL\nm1vmenYp2gU4vY8SbgZeFn6/DPh0n56/GXixiDxKRC4FngB8eYFlPdmyWHwFeIKIXBo47ReH5xRX\nAwedc4f1gog0RGRN+P14fF3uXYTyWyyoLtkZ5AsAtSZZce0iItcCrwWe55x7QB84Re0yX38h/P8l\n8Xga8L0g+pzr2YWOu5PBSdcl6N18CPi6c+5d9oE5+tpiYSH1OFtEzgnlPhuv2GrHxoppE3M/l3Au\nRZtAvbqczLNL0S4eJ6KpuJo++JDXO4FvALcCjwvXR4BbTLob8aLbH+DPf355rufDvdfjNU3vBp61\njOrybLxW9beA12d5bAN+Nbv288BdwD7gDuC5y70uwF8DB4Cv4QfWBSu1XfAKkv8W6L8PeN+pbJde\n5QJ+VfsJXlv8z8P9A8CTa9Rp1nGzyG1xUnUBrsSLpb9m2uHZ8/W1ZViPx+M13veHvrNi2yTcOxvo\nAOdleZ7yNqlZl/Px68d9wHfD73OX41hxzhXPhwUFBQUFBQUJp/NRQkFBQUFBQUGGwhgUFBQUFBQU\nRBTGoKBgGUBEfihVN69dblVr5PFGydytLhVE5EUi8nXx7oSfISJ90Q4XkXXifTesOcHnLhCRHT2u\nbxORF4bfHxORJ/SjnAUFKxmr2lyxoGAFYcY5N7HUhegjfhn4FefcF0TkGX3M95XAJ51zPzzB564F\nts+T5r14K5BfOZmCFRSsFhSJQUHBMoWInCc+uMpl4f+NIvIr4fe1InKHiOwXkZ3msTER+byI3Csi\nv2Hy+nvxHu/uEuP1TkS+LyJvDfncLiLNcP3Hw/8DIvIWEfm+eeZ3ReQr4r3LvalHuf8Qr83/IRH5\nk+xeRaohIneKDwr1kyG/s4Jp3V0i8qQeZHkJwWwrSCL+WUQ+Her7NhF5iYh8OZT7x81z1wL/FEzf\n3hPoeiveh71iF3C1eJ8eBQWnLQpjUFCwPLAuO0q43jn3PeDXgG0i8mJgwDn3QRFpAB8Eft45Nw68\nyOQzClyDd17zBvHOeQBe6Zy7Angy8BsiMhiunw3cHvK5jbRbfjfwbufcRrxpFQAishXvN+EpwARw\nhYg83VbEOfdHeGdLL3HO/W6dyjvvR/5m4C344DF/45zLI5qeCTzeOXfIXB7Hm4U9EXgpsME59xTg\nL4BfD8+tAS5zzk3ibdsvwwfV+iXgp00ZfoQ3ER2vU+aCgtWKwhkXFCwP9DxKcM59VkRehLfn1gXr\nacBtzgeDwjlng3z9owueEUWkjfevfhjPDLwgpLkIv7h3gIdJ3uH2AD8bfv8UKWjLR4F3ht9bw2dv\n+P+YkFfFte5J4o/wDl8eBH6jx/0hvA24xVdccHoj3gOk6hEcAJ4Zfj+VFN/g6cCN4ShiWkT+b5Zf\nG+9nYs8C6lFQsKJRGIOCgmUMEflP+N3wA8AAZvc+Cx4yv38IrA1n/FcDP+Wce0BEPg+cFdL8wCVn\nJj9k/jlBgD92zr2/diWqeISqpPIs83sQz2icEa7/R/bsTJYeqvX9kfn/I1JdngV8pmb5zgrvKSg4\nbVGOEgoKljd+Cx/q978AHw5HA7cDTxfv2hkRedw8eZwHHA9MwShe4jAfbsd7WATvplWxHXil+KBC\niMh68UF56uIQcHl49nLgUnPv/cAf4INFvT1/0Dl3HFgjIjlzMB+uwnuOAy/ZuF5E1gT3uc/M0m7g\n1LjRLShYtigSg4KC5YF1IrLP/P8M8GHgVcBTnHP3i8htwP9wzr0hKBB+MkgU2qQjgF74DPCrIvJ1\nvDvo22uU5zXA34iP7PgZ4HsAzrkdIvJE4IsiAvB94AbqB3j5BN7//V148f49ACLyS3jpxUeDTsC/\niMjPOOdyUf8OvGLjrdRA0Md40Dl3f7j0KeBngEngX4EvmrRN/JHOd2rWpaBgVaK4RC4oKOiCiDwa\nv0i6oPj4i86565ZBuS4Hfss599Ka6W8ALnTOva1G2t8C7nPOfWiBxSwoWNEoEoOCgoJeuAJ4j3ix\nwHdZJjHtnXN3BKdJa+r4MnDO/c0JZP9dfBCegoLTGkViUFBQUFBQUBBRlA8LCgoKCgoKIgpjUFBQ\nUFBQUBBRGIOCgoKCgoKCiMIYFBQUFBQUFEQUxqCgoKCgoKAg4v8DvmmDxR7sMZEAAAAASUVORK5C\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b166b806b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "# plot finger flux\n",
    "# fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True)\n",
    "fig = plt.figure()\n",
    "gs = gridspec.GridSpec(2, 1, height_ratios=[2,3])\n",
    "gs.update(hspace = 0.1) # adjust vertical spacing b/w subplots\n",
    "ax1 = plt.subplot(gs[0])\n",
    "ax2 = plt.subplot(gs[1], sharex=ax1)\n",
    "\n",
    "# river_north_datum.plot(x='time', y='wl',color= 'black', linewidth = 0.5, ax=ax1, legend=None)\n",
    "river_middle_datum.plot(x='time', y='wl',color= 'black',linewidth = 0.5, ax=ax1, legend=None)\n",
    "# river_south_datum.plot(x='time', y='wl',color= 'black', linewidth = 0.5, ax=ax1, legend=None)\n",
    "\n",
    "ax1.set_ylabel('River stage (m)')\n",
    "ax1.set_xlabel('')\n",
    "# ax1.set_title('River stage from upstream to downstream')\n",
    "\n",
    "# plt.xticks(time_ticks, rotation=0, horizontalalignment = \"center\")\n",
    "\n",
    "# ax1.set_xticks(time_ticks)\n",
    "# ax1.set_xlim([time_ticks[0], time_ticks[-1]])\n",
    "# ax1.xaxis.set_major_locator(mdates.YearLocator())\n",
    "# ax1.xaxis.set_major_formatter(mdates.DateFormatter('%Y'))\n",
    "# ax1.xaxis.set_tick_params('labelbottom')\n",
    "\n",
    "\n",
    "cf1 = ax2.contourf(real_time_2,\n",
    "                   0.5 * (mass1_length[1:] + mass1_length[:-1]),\n",
    "                   -np.transpose(flux_array_2),\n",
    "                   cmap=plt.cm.jet,\n",
    "                   levels=np.arange(-0.1, 0.105, 0.005),\n",
    "                   extend=\"both\",\n",
    "                   )\n",
    "# ax2.set_ylabel(\"\")\n",
    "ylabel = '\\n'.join(wrap(\"Distance to downstream end (km)\", 20))\n",
    "ax2.set_ylabel(ylabel)\n",
    "# ax2.set_title(\"Exchange flux of weekly boundary\")\n",
    "ax2.set_xticks(time_ticks)\n",
    "ax2.set_yticks(np.arange(0, 76, 10))\n",
    "ax2.set_ylim([0, 73])\n",
    "ax2.set_xlim([time_ticks[0], time_ticks[-1]])\n",
    "\n",
    "ax2.xaxis.set_major_locator(mdates.YearLocator())\n",
    "ax2.xaxis.set_major_formatter(mdates.DateFormatter('%Y'))\n",
    "ax2.xaxis.set_tick_params('labelbottom')\n",
    "\n",
    "divider = make_axes_locatable(ax2)\n",
    "cax = divider.new_vertical(size=\"5%\", pad=0.4, pack_start=True)\n",
    "fig.add_axes(cax)\n",
    "cb1 = fig.colorbar(cf1, cax=cax, orientation=\"horizontal\")\n",
    "\n",
    "# cb1 = plt.colorbar(cf1, extend=\"both\", orientation = \"horizontal\", pad = 0.2)\n",
    "cb1.ax.set_xlabel(\"Exchange flux (m/d)\", labelpad=0.3)\n",
    "\n",
    "# fig.suptitle(\"Finger flux plot for hourly boundary\", fontsize = 12)\n",
    "ax1.minorticks_off()\n",
    "# fig.tight_layout()\n",
    "fig.set_size_inches(8, 6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-16T20:49:27.722478Z",
     "start_time": "2018-07-16T20:49:02.523469Z"
    }
   },
   "outputs": [],
   "source": [
    "fig.savefig(fig_finger_flux + 'finger_flux_6h.jpg', dpi=300)\n",
    "plt.close(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## plot net flux difference between 6h and 1w bc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-16T20:56:12.026842Z",
     "start_time": "2018-07-16T20:56:12.024684Z"
    }
   },
   "outputs": [],
   "source": [
    "# flux_array_1w = flux_array[::20] # 1-w bc simulation has 6h output freq. and 6-h bc has 120 h output freq.\n",
    "flux_array_6h = flux_array_2\n",
    "flux_array_1w = flux_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# with open(fname_flux_array, \"wb\") as f:\n",
    "#     pickle.dump((flux_array_6h, flux_array_1w), f)\n",
    "\n",
    "with open(fname_flux_array, \"rb\") as f:\n",
    "    flux_array_6h, flux_array_1w = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.savetxt(result_dir + \"flux_array_6h.csv\", flux_array_6h, delimiter=\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.savetxt(result_dir + \"flux_array_1w.csv\", flux_array_1w, delimiter=\",\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### use abs flux"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-23T18:53:58.741454Z",
     "start_time": "2018-05-23T18:53:58.736903Z"
    }
   },
   "outputs": [],
   "source": [
    "flux_array_diff = (np.abs(flux_array_6h) - np.abs(flux_array_1w))/np.abs(flux_array_1w)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-29T17:09:58.128296Z",
     "start_time": "2018-05-29T17:09:52.782449Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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YofoHWOOY+ddff4kO76uvvhLObaY6kPSEr53x119/qR6O0NBQGjFiBM2cOZN+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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b493bcbe978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True)\n",
    "\n",
    "river_north_datum.plot(x='time', y='wl',color= 'black', linewidth = 0.5, ax=ax1, legend=None)\n",
    "river_middle_datum.plot(x='time', y='wl',color= 'black',linewidth = 0.5, ax=ax1, legend=None)\n",
    "river_south_datum.plot(x='time', y='wl',color= 'black', linewidth = 0.5, ax=ax1, legend=None)\n",
    "\n",
    "ax1.set_ylabel('Water Level (m)')\n",
    "ax1.set_xlabel('')\n",
    "\n",
    "# gs = gridspec.GridSpec(1, 1)\n",
    "# fig = plt.figure()\n",
    "# ax1 = fig.add_subplot(gs[0, 0])\n",
    "cf1 = ax2.contourf(real_time_2,\n",
    "                   0.5 * (mass1_length[1:] + mass1_length[:-1]),\n",
    "                   np.transpose(flux_array_diff),\n",
    "                   cmap=plt.cm.jet,\n",
    "                   levels=np.arange(-100, 100, 10),\n",
    "                   extend=\"both\",\n",
    "                   vmin = -100, vmax = 100,\n",
    "                   )\n",
    "\n",
    "# ax2.imshow(np.transpose(flux_array_diff_num), cmap=cm.gist_rainbow)\n",
    "# ax2.set_ylabel(\"\")\n",
    "ax2.set_ylabel(\"Distance (km)\", wrap = True)\n",
    "ax2.set_xticks(time_ticks)\n",
    "ax2.set_ylim([0, 73])\n",
    "ax2.set_xlim([time_ticks[0], time_ticks[-1]])\n",
    "\n",
    "\n",
    "divider = make_axes_locatable(ax2)\n",
    "cax = divider.new_vertical(size=\"5%\", pad=0.4, pack_start=True)\n",
    "fig.add_axes(cax)\n",
    "cb1 = fig.colorbar(cf1, cax=cax, orientation=\"horizontal\")\n",
    "\n",
    "# cb1 = plt.colorbar(cf1, extend=\"both\", orientation = \"horizontal\", pad = 0.2)\n",
    "cb1.ax.set_xlabel(\"Abs exchange flux diff (%)\", labelpad=0.3)\n",
    "\n",
    "# fig.suptitle(\"Finger flux plot for hourly boundary\", fontsize = 12)\n",
    "ax1.minorticks_off()\n",
    "fig.tight_layout()\n",
    "fig.set_size_inches(8, 6)\n",
    "\n",
    "\n",
    "\n",
    "# gs = gridspec.GridSpec(1, 1)\n",
    "# fig = plt.figure()\n",
    "# ax1 = fig.add_subplot(gs[0, 0])\n",
    "# cf1 = ax1.contourf(real_time_2,\n",
    "#                    0.5 * (mass1_length[1:] + mass1_length[:-1]),\n",
    "#                    np.transpose(flux_array_diff),\n",
    "#                    cmap=plt.cm.jet,\n",
    "#                    levels=np.arange(-100, 100, 10),\n",
    "#                    extend=\"both\",\n",
    "#                    vmin = -100, vmax = 100,\n",
    "#                    )\n",
    "# ax1.set_ylabel(\"\")\n",
    "# ax1.set_ylabel(\"Distance to Downstream Location (km)\")\n",
    "# ax1.set_xticks(time_ticks)\n",
    "# ax1.set_ylim([0, 73])\n",
    "# ax1.set_xlim([time_ticks[0], time_ticks[-1]])\n",
    "# cb1 = plt.colorbar(cf1, extend=\"both\", extendrect = True)\n",
    "# cb1.ax.set_ylabel(\"Net exchange flux difference (%)\", rotation=270, labelpad=20)\n",
    "\n",
    "# fig.tight_layout()\n",
    "# fig.set_size_inches(10, 3.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-29T17:10:35.440127Z",
     "start_time": "2018-05-29T17:10:33.192418Z"
    }
   },
   "outputs": [],
   "source": [
    "fname = out_dir + 'mass_balance/finger_flux_abs_diff.jpg'\n",
    "fig.savefig(fname, dpi=300)\n",
    "plt.close(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### use original flux"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-23T23:05:36.183195Z",
     "start_time": "2018-05-23T23:05:36.179197Z"
    },
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "flux_array_diff_sign = (flux_array_6h - flux_array_1w)/flux_array_1w*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-23T23:07:39.704933Z",
     "start_time": "2018-05-23T23:07:36.945818Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x252 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gs = gridspec.GridSpec(1, 1)\n",
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(gs[0, 0])\n",
    "cf1 = ax1.contourf(real_time_2,\n",
    "                   0.5 * (mass1_length[1:] + mass1_length[:-1]),\n",
    "                   np.transpose(flux_array_diff_sign),\n",
    "                   cmap=plt.cm.jet,\n",
    "                   levels=np.arange(-500, 500, 100),\n",
    "                   extend=\"both\",\n",
    "                   )\n",
    "ax1.set_ylabel(\"\")\n",
    "ax1.set_ylabel(\"Distance to Downstream Location (km)\")\n",
    "ax1.set_xticks(time_ticks)\n",
    "ax1.set_ylim([0, 73])\n",
    "ax1.set_xlim([time_ticks[0], time_ticks[-1]])\n",
    "cb1 = plt.colorbar(cf1, extend=\"both\")\n",
    "cb1.ax.set_ylabel(\"Net exchange flux difference (%)\", rotation=270, labelpad=20)\n",
    "fig.tight_layout()\n",
    "fig.set_size_inches(10, 3.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-23T23:09:28.192962Z",
     "start_time": "2018-05-23T23:09:26.510220Z"
    },
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "fig.savefig(fig_finger_flux_diff, dpi=300)\n",
    "plt.close(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### use 0, 1 represent flux"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-16T20:58:33.054263Z",
     "start_time": "2018-07-16T20:58:32.097126Z"
    }
   },
   "outputs": [],
   "source": [
    "flux_ind_6h = [0 if iflux <0 else 1 for iflux in flux_array_6h.flatten()] # 0--gaining; 1--losing\n",
    "flux_ind_1w = [0 if iflux <0 else 1 for iflux in flux_array_1w.flatten()] # 0--gaining; 1--losing\n",
    "flux_ind_6h = np.asarray(flux_ind_6h).reshape(flux_array_6h.shape[0], flux_array_6h.shape[1])\n",
    "flux_ind_1w = np.asarray(flux_ind_1w).reshape(flux_array_1w.shape[0], flux_array_1w.shape[1])    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1--switch to gaining; -1 -- switch to losing; 0 -- does not change\n",
    "flux_array_diff_num =  flux_ind_6h - flux_ind_1w \n",
    "\n",
    "flux_array_diff_num = flux_array_diff_num.astype('float')\n",
    "\n",
    "flux_array_diff_num[flux_array_diff_num == 0] =  np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-16T22:11:43.136873Z",
     "start_time": "2018-07-16T22:11:35.703443Z"
    },
    "run_control": {
     "marked": false
    }
   },
   "outputs": [
    {
     "data": {
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T/ghnOnXqhIKCAgBoVuGgy5m9e/di+PDhvO6ERqNBbGwst4dLIxrMZjMvM67V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\n9TjnnNJBw9dddx2uvfba1L/wmc/zPFMCV84HgmEAlI6P3bZtG0444QQ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7PmlUz0P8UsHA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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b58ad7aac18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "# fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True)\n",
    "fig = plt.figure()\n",
    "gs = gridspec.GridSpec(2, 1, height_ratios=[1,1])\n",
    "gs.update(hspace = 0.1) # adjust vertical spacing b/w subplots\n",
    "ax1 = plt.subplot(gs[0])\n",
    "ax2 = plt.subplot(gs[1], sharex=ax1)\n",
    "\n",
    "# river_north_datum.plot(x='time', y='wl',color= 'black', linewidth = 0.5, ax=ax1, legend=None)\n",
    "river_middle_datum.plot(x='time', y='wl',color= 'grey',linewidth = 0.5, ax=ax1, legend=None, label = \"base case\")\n",
    "# river_south_datum.plot(x='time', y='wl',color= 'black', linewidth = 0.5, ax=ax1, legend=None)\n",
    "\n",
    "river_middle_datum_s.plot(x='time', y='wl',color= 'black',linewidth = 0.5, ax=ax1, legend=None, label = \"without dam operation\")\n",
    "\n",
    "ax1.set_ylabel('River stage (m)')\n",
    "ax1.set_xlabel('')\n",
    "ax1.minorticks_off()\n",
    "ax1.legend(loc = \"upper right\", frameon = False)\n",
    "\n",
    "colors = [(0, 0, 1), (1, 0, 0)]\n",
    "cm = LinearSegmentedColormap.from_list(\"mylist\", colors, N=2)\n",
    "\n",
    "cf1 = ax2.contourf(real_time_2 ,\n",
    "                   0.5 * (mass1_length[1:] + mass1_length[:-1]),\n",
    "                   -np.transpose(flux_array_diff_num),\n",
    "                   cmap=cm\n",
    "#                    colors = ('blue', 'gray', 'red'),\n",
    "#                    levels=np.arange(-1, 1, 1),\n",
    "#                    extend=\"both\"\n",
    "                   )\n",
    "\n",
    "# ax2.imshow(np.transpose(flux_array_diff_num), cmap=cm.gist_rainbow)\n",
    "# ax2.set_ylabel(\"\")\n",
    "\n",
    "ylabel = '\\n'.join(wrap(\"Distance to downstream end (km)\", 20))\n",
    "ax2.set_ylabel(ylabel)\n",
    "ax2.set_xticks(time_ticks)\n",
    "ax2.set_ylim([0, 73])\n",
    "ax2.set_xlim([time_ticks[0], time_ticks[-1]])\n",
    "# cb1 = plt.colorbar(cf1, extend=\"both\")\n",
    "# cb1.ax.set_ylabel(\"Net exchange flux difference\", rotation=270, labelpad=20)\n",
    "# fig.tight_layout()\n",
    "fig.set_size_inches(8, 6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-16T22:13:07.778805Z",
     "start_time": "2018-07-16T22:13:05.570765Z"
    }
   },
   "outputs": [],
   "source": [
    "fig.savefig(fig_finger_flux + 'finger_flux_01.png', transparent = True, dpi=300)\n",
    "plt.close(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## plot histgram with PDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-29T18:33:05.636015Z",
     "start_time": "2018-05-29T18:32:41.895897Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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G6RVyioqKNG/ePM2cOZPA5Cavvvqq2rZtW+Ml1wGgPqhJHyOZP5i76KKLCEwN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\nk0ZllaaqIpF1e1F+XTQH8Ai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UF8d+kSTtPlZ1lUmSdlmWIj/6s1RaWtutAgD4KlfnNBXn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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2ad8bb4cfbe0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# n_bins = 50\n",
    "# bins = [-0.35,-0.25,-0.15,-0.05,0.05,0.15,0.25,0.35]\n",
    "\n",
    "fig, axs = plt.subplots(1, 2, sharey=True, tight_layout=False)\n",
    "\n",
    "# We can set the number of bins with the `bins` kwarg\n",
    "# axs[0].hist(-flux_array_6h, bins=bins, facecolor = 'black')\n",
    "# axs[1].hist(-flux_array_1w, bins=bins, facecolor = 'black')\n",
    "\n",
    "axs[0].hist(-flux_array_6h.flatten(), np.arange(-0.7, 0.5, 0.02), facecolor = 'b', normed = True)\n",
    "axs[1].hist(-flux_array_1w.flatten(), np.arange(-0.7, 0.5, 0.02), facecolor = 'g', normed = True)\n",
    "\n",
    "lnspc = np.linspace(-0.7, 0.7, len(flux_array_6h.flatten()))\n",
    "\n",
    "# choose distribution funcction\n",
    "# params = stats.exponpow.fit(-flux_array_6h.flatten(), floc = 0)\n",
    "# pdf = stats.exponpow.pdf(lnspc, *params)\n",
    "\n",
    "params = stats.norm.fit(-flux_array_6h.flatten())\n",
    "pdf = stats.norm.pdf(lnspc, *params)\n",
    "\n",
    "params2 = stats.norm.fit(-flux_array_1w.flatten())\n",
    "pdf2 = stats.norm.pdf(lnspc, *params2)\n",
    "# fit PDF\n",
    "axs[0].plot(lnspc, pdf)\n",
    "axs[1].plot(lnspc, pdf2)\n",
    "# plt.yscale('log')\n",
    "\n",
    "axs[0].set_title('(a) with daily fluctuations')\n",
    "axs[1].set_title('(b) without daily fluctuations')\n",
    "axs[0].set_xlabel('Exchange flux (m/d)')\n",
    "axs[1].set_xlabel('Exchange flux (m/d)')\n",
    "axs[0].set_ylabel('Probability')\n",
    "axs[0].set_xlim([-0.7, 0.7])\n",
    "axs[1].set_xlim([-0.7, 0.7])\n",
    "\n",
    "\n",
    "fig.set_size_inches(10, 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2ad8bb729048>]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JLhBgIq3AONMYxAvYRcwCzN69e/Xmm29q5cqVYR87YcIEbdiwocd9nE6nnE5n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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2ad8bafd2c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# a is your data array\n",
    "a = flux_array_6h\n",
    "hist, bins = np.histogram(a, bins=np.arange(-0.7, 0.7, 0.02), normed=False, density= True)\n",
    "bin_centers = (bins[1:]+bins[:-1])*0.5\n",
    "plt.plot(bin_centers, hist)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### combo histogram with 1 to 1 plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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GHXfc0a7Aa8G58wRteFbVecC8rKyse52uxXRv6XHpyJONPsAeSgf2O1WOMa0V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\nboQRI5zRaNM9hO3IszVJMcaY9hORocDzwBk4ya3wRZLb8oTRJlR10pe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GQFQULF/ulKn7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\nIAiCIAhK8k8fHwCwANHX5gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b3ebdd606a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (axs, axs2) = plt.subplots(1, 2)\n",
    "\n",
    "def colorcode(xx, yy):\n",
    "    color = []\n",
    "    for x, y in zip(xx, yy):\n",
    "        if x < 0 and y > 0 :\n",
    "            icolor = 'red'\n",
    "#             red_ct += 1\n",
    "        elif x > 0 and y < 0 :\n",
    "            icolor = 'blue'\n",
    "#             blue_ct += 1\n",
    "        else:\n",
    "            icolor = 'gray'\n",
    "        color.append(icolor)\n",
    "    return color   \n",
    "\n",
    "# We can set the number of bins with the `bins` kwarg\n",
    "# axs[0].hist(-flux_array_6h, bins=bins, facecolor = 'black')\n",
    "# axs[1].hist(-flux_array_1w, bins=bins, facecolor = 'black')\n",
    "\n",
    "axs.hist(-flux_array_6h.flatten(), np.arange(-0.7, 0.7, 0.02), facecolor = 'none', edgecolor = 'b', histtype='step', log=True, label='base case')\n",
    "axs.hist(-flux_array_1w.flatten(), np.arange(-0.7, 0.7, 0.02), facecolor = 'none', edgecolor = 'g', histtype='step', log=True, label='without dam operation')\n",
    "\n",
    "lnspc = np.linspace(-0.7, 0.7, len(flux_array_6h.flatten()))\n",
    "\n",
    "m, s = stats.norm.fit(-flux_array_6h.flatten())\n",
    "\n",
    "# axs.yscale('log')\n",
    "axs.set_xlabel('Exchange flux (m/d)', fontsize=12)\n",
    "axs.set_ylabel('Counts', fontsize=12)\n",
    "axs.set_xlim([-0.7, 0.7])\n",
    "axs.set_ylim([0, 1e7])\n",
    "\n",
    "axs.legend(\"upper left\", frameon=False)\n",
    "\n",
    "axs.tick_params(axis = 'both', which = 'major', labelsize = 10)\n",
    "\n",
    "xx = -flux_array_1w.flatten()\n",
    "yy = -flux_array_6h.flatten()\n",
    "\n",
    "axs2.scatter(xx, yy, marker='o', edgecolors = colorcode(xx, yy), facecolors = 'none')\n",
    "\n",
    "axs2.plot((-1, 1), (0, 0), 'k--')\n",
    "axs2.plot((0, 0), (-1, 1), 'k--')\n",
    "axs2.plot((-1,1), (-1,1), 'k-')\n",
    "# ax.set_title('(a) with daily fluctuations')\n",
    "\n",
    "axs2.set_xlabel('Exchange flux without dam operation (m/d)', fontsize=12)\n",
    "axs2.set_ylabel('Exchange flux under base case (m/d)', fontsize=12)\n",
    "axs2.set_xlim([-0.7, 0.7])\n",
    "axs2.set_ylim([-0.7, 0.7])\n",
    "axs2.set_aspect('equal')  \n",
    "\n",
    "axs2.tick_params(axis = 'both', which = 'major', labelsize = 10)\n",
    "\n",
    "fig.set_size_inches(12, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-29T18:33:42.662572Z",
     "start_time": "2018-05-29T18:33:40.440019Z"
    },
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "fig.savefig(fig_finger_flux + 'hist1d_combo.png', dpi=300, transparent = True)\n",
    "plt.close(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**calculate % of flow reversal**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "red_ct, blue_ct = 0, 0\n",
    "for x, y in zip(xx, yy):\n",
    "    if x < 0 and y > 0 :\n",
    "#         icolor = 'red'\n",
    "        red_ct += 1\n",
    "    elif x > 0 and y < 0 :\n",
    "#         icolor = 'blue'\n",
    "        blue_ct += 1\n",
    "    else:\n",
    "        icolor = 'gray'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "200124 322180 0.09231207796275394 0.148613386090824\n"
     ]
    }
   ],
   "source": [
    "print(red_ct, blue_ct, red_ct/len(xx), blue_ct/len(xx))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## plot PDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2af5bbe2c748>]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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BD5jZTuD9wWORM8Z0oC8caLiOrlokccvMtYK739HgqRsjrkWkZ0xM5gFYPjxLD11XLZKY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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2af5bbc36748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# this create the kernel, given an array it will estimate the probability over that values\n",
    "kde = gaussian_kde( -flux_array_6h.flatten() )\n",
    "# these are the values over wich your kernel will be evaluated\n",
    "dist_space = np.linspace( np.nanmin(-flux_array_6h), np.nanmax(-flux_array_6h), 100 )\n",
    "# plot the results\n",
    "plt.plot( dist_space, kde(dist_space) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## count max,min of flux for each year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "flux_2011 = flux_array_6h[98:1558 , :]\n",
    "flux_2015 = flux_array_6h[5939:7400 , :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1 to 1 plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ff54DBw4ghODnP/85b7zxBmazma1bt2K1WjEYDHR3dwe6ZK8NFKJuGo2G5ORk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\nXgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2af512f78dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1)\n",
    "\n",
    "ax.plot(-flux_array_1w.flatten(), -flux_array_6h.flatten(), 'ko', mfc = 'None', alpha = 0.5)\n",
    "\n",
    "ax.plot((-1,1), (-1,1), 'r-')\n",
    "# ax.set_title('(a) with daily fluctuations')\n",
    "\n",
    "ax.set_xlabel('Exchange flux in WR (m/d)', fontsize=12)\n",
    "ax.set_ylabel('Exchange flux in DR (m/d)', fontsize=12)\n",
    "ax.set_xlim([-0.7, 0.7])\n",
    "ax.set_ylim([-0.7, 0.7])\n",
    "ax.set_aspect('equal')   \n",
    "ax.tick_params(axis = 'both', which = 'major', labelsize = 10)\n",
    "\n",
    "# fig.tight_layout()\n",
    "fig.set_size_inches(6, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "fname = fig_finger_flux + 'flux_one2one.png'\n",
    "fig.savefig(fname, dpi=300, transparent = True)\n",
    "plt.close(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# plot net exchange bar plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-02T23:23:03.489735Z",
     "start_time": "2018-05-02T23:23:03.478427Z"
    },
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "# calculate net mass\n",
    "n_segment = len(time_ticks) - 1\n",
    "sum_mass = np.array([0.] * n_segment)\n",
    "all_mass = np.array([0.] * n_segment)\n",
    "for i_segment in range(n_segment):\n",
    "    select_index = []\n",
    "    for i_index in range(len(real_time)):\n",
    "        if (real_time[i_index] >= time_ticks[i_segment] and\n",
    "                real_time[i_index] < time_ticks[i_segment + 1]):\n",
    "            select_index.append(i_index)\n",
    "    sum_mass[i_segment] = total_mass[select_index[-1]] - \\\n",
    "        total_mass[select_index[0] - 1]\n",
    "    time_inverval = real_time[select_index[-1]] - real_time[select_index[0] - 1]\n",
    "    time_scale = 365.25 * 24 * 3600 / time_inverval.total_seconds()\n",
    "    sum_mass[i_segment] = sum_mass[i_segment] * time_scale / 1000\n",
    "\n",
    "abs_mass = np.array([0.] * n_segment)\n",
    "out_mass = np.array([0.] * n_segment)\n",
    "in_mass = np.array([0.] * n_segment)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-02T23:23:12.101088Z",
     "start_time": "2018-05-02T23:23:12.073656Z"
    },
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "# calculate dam discharge\n",
    "discharge_flow = np.array([0.] * n_segment)\n",
    "for i_segment in range(n_segment):\n",
    "    select_index = []\n",
    "    for i_index in range(len(discharge_time)):\n",
    "        if (discharge_time[i_index] >= time_ticks[i_segment] and\n",
    "                discharge_time[i_index] < time_ticks[i_segment + 1]):\n",
    "            select_index.append(i_index)\n",
    "    sum_discharge = sum(np.asarray([discharge_value[i]\n",
    "                                    for i in select_index]).astype(float))\n",
    "    sum_discharge = sum_discharge * 3600 * 24 * (0.3048**3)\n",
    "    # print(\"{:.5E}\".format(sum_discharge))\n",
    "    discharge_flow[i_segment] = sum_discharge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-02T23:23:30.303292Z",
     "start_time": "2018-05-02T23:23:30.104109Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x216 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## plot bar plots for net gaining volumn\n",
    "# plot dam discharge\n",
    "start_year = 2011\n",
    "fig_name = fig_net_exchange_bar\n",
    "gs = gridspec.GridSpec(1, 2)\n",
    "fig = plt.figure()\n",
    "ax0 = fig.add_subplot(gs[0, 0])\n",
    "ax0.bar(start_year + np.arange(n_segment), discharge_flow, color=\"black\")\n",
    "ax0.set_ylim([0, 1.5e11])\n",
    "ax0.set_xlabel('Time (year)')\n",
    "ax0.set_ylabel('River Discharge ($m^3$/year)')\n",
    "ax0.set_title(\"Priest Rapids Dam Discharge\", y=1.05)\n",
    "\n",
    "# plot net exchange\n",
    "ax1 = fig.add_subplot(gs[0, 1])\n",
    "ax1.bar(start_year + np.arange(n_segment), -sum_mass, color=\"blue\")\n",
    "#ax1.set_ylim([0, 1e8])\n",
    "ax1.set_xlabel('Time (year)')\n",
    "ax1.set_ylabel('Exchange flux ($m^3$/year)')\n",
    "ax1.set_title(\"River Net Exchange Volume\", y=1.05)\n",
    "fig.tight_layout()\n",
    "fig.subplots_adjust(left=0.2,\n",
    "                    right=0.95,\n",
    "                    bottom=0.08,\n",
    "                    top=0.85,\n",
    "                    wspace=0.30,\n",
    "                    hspace=0.38\n",
    "                    )\n",
    "fig.set_size_inches(7, 3)\n",
    "# fig.savefig(fig_name, dpi=600, transparent=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-02T23:23:15.556214Z",
     "start_time": "2018-05-02T23:23:14.398986Z"
    },
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "fig.savefig(fig_name, dpi=600)\n",
    "plt.close(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-02T23:45:11.134814Z",
     "start_time": "2018-05-02T23:45:11.131980Z"
    },
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "# a = -sum_mass\n",
    "np.savetxt(fname_net_exchange_txt, -sum_mass, fmt = '%.2e')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## import river geometry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "river_geo = pd.read_csv(fname_river_geo)\n",
    "\n",
    "river_geo['x'] = (river_geo['x'] - model_origin[0])/1000\n",
    "river_geo['y'] = (river_geo['y'] - model_origin[1])/1000\n",
    "\n",
    "polygon = Polygon(river_geo.loc[:, [\"x\", \"y\"]].values)\n",
    "river_x,river_y = polygon.exterior.xy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# plot flux exchange across riverbed snapshots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-03T21:01:37.344955Z",
     "start_time": "2018-05-03T21:01:36.638604Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2011-01-01 00:00:00\n",
      "2011-01-06 00:00:00\n",
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      "2015-01-05 00:00:00\n",
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      "2015-08-03 00:00:00\n",
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      "2015-09-02 00:00:00\n",
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      "2015-09-12 00:00:00\n",
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      "2015-10-12 00:00:00\n",
      "2015-10-17 00:00:00\n",
      "2015-10-22 00:00:00\n",
      "2015-10-27 00:00:00\n",
      "2015-11-01 00:00:00\n",
      "2015-11-06 00:00:00\n",
      "2015-11-11 00:00:00\n",
      "2015-11-16 00:00:00\n",
      "2015-11-21 00:00:00\n",
      "2015-11-26 00:00:00\n",
      "2015-12-01 00:00:00\n",
      "2015-12-06 00:00:00\n",
      "2015-12-11 00:00:00\n",
      "2015-12-16 00:00:00\n",
      "2015-12-21 00:00:00\n",
      "2015-12-26 00:00:00\n",
      "2015-12-31 00:00:00\n"
     ]
    }
   ],
   "source": [
    "## plot exchange flux accross riverbed snapshots\n",
    "for itime in np.arange(98, 7399, 20):\n",
    "    print(real_time[itime])\n",
    "    yx_flux = np.asarray([np.nan] * (ny * nx)).reshape(ny, nx)\n",
    "    for isection in range(len(mass1_sections)):\n",
    "        # need minus 1 as python index started with 0\n",
    "        cell_ids = list(material_file[\"Regions\"]\n",
    "                        [mass1_sections[isection]]['Cell Ids'])\n",
    "        cell_ids = (np.asarray(cell_ids) - 1).astype(int)\n",
    "        xy_cell_index = [grids[i, 0:2] for i in cell_ids]\n",
    "        xy_cell_index = np.unique(xy_cell_index, axis=0)\n",
    "        for iindex in range(len(xy_cell_index)):\n",
    "            yx_flux[xy_cell_index[iindex][1],\n",
    "                    xy_cell_index[iindex][0]] = flux_array[itime, isection]\n",
    "    \n",
    "    gs = gridspec.GridSpec(1, 1)\n",
    "    fig = plt.figure()\n",
    "    ax1 = fig.add_subplot(gs[0, 0])\n",
    "    ax1.plot(line1_x, line1_y, \"black\", alpha =0.7)\n",
    "    ax1.plot(line2_x, line2_y, \"black\", alpha =0.7)\n",
    "    ax1.plot(line3_x, line3_y, \"black\", alpha =0.7)\n",
    "    cf1 = ax1.imshow(-yx_flux,\n",
    "                     cmap=plt.cm.jet,\n",
    "                     origin=\"lower\",\n",
    "#                      aspect = 'equal',\n",
    "#                      levels = np.arange(-0.1, 0.1, 0.01),\n",
    "                     vmin=-0.1,\n",
    "                     vmax=0.1,\n",
    "                     extent=[(x[0] - 0.5 * dx[0]) / 1000,\n",
    "                             (x[-1] + 0.5 * dx[0]) / 1000,\n",
    "                             (y[0] - 0.5 * dy[0]) / 1000,\n",
    "                             (y[-1] + 0.5 * dy[0]) / 1000]\n",
    "                     )\n",
    "    # plot river geometry\n",
    "    ax1.plot(river_x, river_y, color='black', alpha=0.7, linewidth=1, solid_capstyle='round', zorder=2)    \n",
    "    \n",
    "    ax1.set_xlabel(\"X (km)\")\n",
    "    ax1.set_ylabel(\"Y (km)\")\n",
    "#     ax1.set_aspect(\"equal\", \"datalim\")\n",
    "    ax1.set_xlim([np.min(x_grids) / 1000, np.max(x_grids) / 1000])\n",
    "    ax1.set_ylim([np.min(x_grids) / 1000, np.max(x_grids) / 1000])\n",
    "    cb1 = plt.colorbar(cf1, extend=\"both\")\n",
    "    cb1.ax.set_ylabel(\"Exchange flux (m/d)\", rotation=270, labelpad=20)\n",
    "    fig.tight_layout()\n",
    "    fig.set_size_inches(6, 5)   \n",
    "\n",
    "    fig_name = fig_flux_snapshot + str(real_time[itime]) + \".png\"\n",
    "    fig.savefig(fig_name, dpi=300)\n",
    "    plt.close(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## plot flux exchange across riverbed snapshots for _north block_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-03T21:26:37.947888Z",
     "start_time": "2018-05-03T21:26:37.357334Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2010-12-07 00:00:00\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 468x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for itime in range(len(real_time[0:1])):\n",
    "    print(real_time[itime])\n",
    "    yx_flux = np.asarray([np.nan] * (ny * nx)).reshape(ny, nx)\n",
    "    for isection in range(len(mass1_sections)):\n",
    "        # need minus 1 as python index started with 0\n",
    "        cell_ids = list(material_file[\"Regions\"]\n",
    "                        [mass1_sections[isection]]['Cell Ids'])\n",
    "        cell_ids = (np.asarray(cell_ids) - 1).astype(int)\n",
    "        xy_cell_index = [grids[i, 0:2] for i in cell_ids]\n",
    "        xy_cell_index = np.unique(xy_cell_index, axis=0)\n",
    "        for iindex in range(len(xy_cell_index)):\n",
    "            yx_flux[xy_cell_index[iindex][1],\n",
    "                    xy_cell_index[iindex][0]] = flux_array[itime, isection]\n",
    "    fig_name = fig_block_north_flux_snapshot + str(real_time[itime]) + \".png\"\n",
    "    gs = gridspec.GridSpec(1, 1)\n",
    "    fig = plt.figure()\n",
    "    ax1 = fig.add_subplot(gs[0, 0])\n",
    "    cf1 = ax1.imshow(-yx_flux,\n",
    "                     cmap=plt.cm.jet,\n",
    "                     origin=\"lower\",\n",
    "                     vmin=-0.1,\n",
    "                     vmax=0.1,\n",
    "                     extent=[(x[0] - 0.5 * dx[0]) / 1000,\n",
    "                             (x[-1] + 0.5 * dx[0]) / 1000,\n",
    "                             (y[0] - 0.5 * dy[0]) / 1000,\n",
    "                             (y[-1] + 0.5 * dy[0]) / 1000]\n",
    "                     )\n",
    "    ax1.axis(\"off\")\n",
    "    ax1.set_aspect(\"equal\", \"datalim\")\n",
    "    ax1.set_xlim(block_north_x)\n",
    "    ax1.set_ylim(block_north_y)\n",
    "    # ax1.set_xlabel(\"Easting (km)\")\n",
    "    # ax1.set_ylabel(\"Northing (km)\")\n",
    "    fig.tight_layout()\n",
    "    fig.set_size_inches(6.5, 5.5)\n",
    "#     fig.savefig(fig_name, dpi=600)\n",
    "#     plt.close(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-03T21:26:49.687564Z",
     "start_time": "2018-05-03T21:26:47.662779Z"
    },
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "    fig.savefig(fig_name, dpi=600)\n",
    "    plt.close(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## plot flux exchange across riverbed snapshots for _middle block_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-03T21:28:23.927209Z",
     "start_time": "2018-05-03T21:28:23.318785Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2010-12-07 00:00:00\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAcgAAAF0CAYAAACnqankAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAACjtJREFUeJzt3U+IXWcZx/FnnGtKC0MFia6EBqSIIC7cuawLoRtLKUhAcSkUu6gOMyRMCGOGlJSxWQwI0m4EoQhFIkjAjUvduBFBSqGk4K4iUqYEohmuK4Xg7536Ts6Ze+69n8/yzM3Nyfz78sBz3mzM5/MCAB71qUXfAABMkUACQCCQABAIJAAEAgkAgUACQCCQABAIJAAEAgkAwWyRf/l+7Q5yjM/D2ozXb9TNjSHeH4D1Y4IEgGChE2RLayLsda2uxgnVZAnAJzFBAkAgkAAQCCQABAIJAIFAAkCwMZ8P8ijioFrbp2Oz3QrAf5ggASAQSAAIBBIAAoEEgEAgASCY5BZry1DbrbM66Xr99bpluxVgzZggASAQSAAIBBIAAoEEgEAgASCYLfoGxtS7rfqwNke6EwCWjQkSAAKBBIBAIAEgEEgACAQSAAKBBIBgqQ4rb9mv3a5/RO/jHK3HRRxiDrC6TJAAEAgkAAQCCQCBQAJAIJAAEKzEFmvLtboa/3G9h5gf11bX6w9rz3YrwJIzQQJAIJAAEAgkAAQCCQCBQAJAsNJbrC2ts1t7t1VPOs90raq6XVdsuAIsARMkAAQCCQCBQAJAIJAAEAgkAASzRd/AIoy9rfrwDNutAEyLCRIAAoEEgEAgASAQSAAIBBIAgrU8i7Xl1Xqt65Nxlm3Vrfo4Xr9ZP3ZGK8CEmCABIBBIAAgEEgACgQSAQCABILDF+n94pV7v+iS1NlWr+s91vVXXbbcCLIAJEgACgQSAQCABIBBIAAgEEgCC2aJvYBkc1U7XJulu7XevBj9V93v/CAAjMkECQCCQABAIJAAEAgkAgUACQOAs1nO2X7tdn/Cv1J/j9RfrrjNaAUZkggSAQCABIBBIAAgEEgACgQSAwBbrRPyqnu/6QjyoC/H65bpjuxVgACZIAAgEEgACgQSAQCABIBBIAAhssU7c2/VC/AJt1XHX+xzXVrxu6xUgM0ECQCCQABAIJAAEAgkAgUACQGCLdUn9pr4Rv3CtbdWWz9eH8fpz9XvbrcBaM0ECQCCQABAIJAAEAgkAgUACQGCLdcW0zm5tbau2PHfvD/kDl+a2W4G1YIIEgEAgASAQSAAIBBIAAoEEgMAW65r4XX09fqFb26r7l3a63n+zTuL1vTq09QosJRMkAAQCCQCBQAJAIJAAEAgkAAS2WNfdvY34DdDaYm1tq/ay3QpMnQkSAAKBBIBAIAEgEEgACAQSAAJbrHQ5qO2FfMPYegXOmwkSAAKBBIBAIAEgEEgACAQSAAJbrAzCdiuwakyQABAIJAAEAgkAgUACQCCQABAIJAAEHvNgIYZ6LGT3o5/E659+eu7xD+CxmCABIBBIAAgEEgACgQSAQCABILDFyqS0tltb26ott57+UfNj36+fxesX69jmK/BfJkgACAQSAAKBBIBAIAEgEEgACGyxshT+9dFG/EZtbau2NlVPc/Hex/kDl5zrCuvIBAkAgUACQCCQABAIJAAEAgkAgS1WVtLfaqv5jd3cVm3Yv7QTr2/Vcbz+w/qprVdYASZIAAgEEgACgQSAQCABIBBIAAhmi74BGMPFOj5lkzSf69raVu31bj3TtRq+WSdd7/+X+nLX61v+WF+L12/UTVu4UCZIAIgEEgACgQSAQCABIBBIAAicxQqf4I16Of6QPF93u95natuqLS/VO/H6V+s9262sFRMkAAQCCQCBQAJAIJAAEAgkAAS2WOGMWmeujr2tOtRWakvrfi7XHVusrBUTJAAEAgkAgUACQCCQABAIJAAEs0XfACyrL9UHcavz1/XNQVbDh9pW7d2Sfb++2PV6WFUmSAAIBBIAAoEEgEAgASAQSAAIbLHCwL5Vv+06s/RaXY1br0OdodpiWxVOZ4IEgEAgASAQSAAIBBIAAoEEgGBjPh/k2EjgjP5Uz3b9EI69rXpSm12vv163urZ2YVmYIAEgEEgACAQSAAKBBIBAIAEgsMUKE/V2vdD1wzn2tuqTdb/r9Tt1ZLuVpWaCBIBAIAEgEEgACAQSAAKBBIBgtugbALLLdSdugR7UdtxuHXsrteWf9cQg7wNTY4IEgEAgASAQSAAIBBIAAoEEgMBZrLAi9ms3/jD3bquOvZW6V4fOaGUpmCABIBBIAAgEEgACgQSAQCABIHAWK6yIqW2rPlMfxOu/qJeaq/PfqXdsuDIZJkgACAQSAAKBBIBAIAEgEEgACJzFCivuoLa7fshb26dD+bA+1/zYP+oz8fqNumm7lXNnggSAQCABIBBIAAgEEgACgQSAwBYrrLjTzj7tcdr2aXK/norXH9SF5p+Z1UnX37HZeP1eHdp65bGZIAEgEEgACAQSAAKBBIBAIAEgEEgACDzmAWvqjXq564e/9dhGS+txjt5HOU7TesyjxeMf9DBBAkAgkAAQCCQABAIJAIFAAkAwW/QNANOyCtuqMAQTJAAEAgkAgUACQCCQABAIJAAEzmIFHnFQ2/GXwjJtq57UZrz+ZN2P13fqyBmt/A8TJAAEAgkAgUACQCCQABAIJAAEzmIFHrFXh3Gjc792J7fy3tpWhSGYIAEgEEgACAQSAAKBBIBAIAEgcBYrMIrW1utQ561W9Z+52uIsVhITJAAEAgkAgUACQCCQABAIJAAEtliBc3VQ292/dHrPXG1tsZ50Hj99pW7bbl1jJkgACAQSAAKBBIBAIAEgEEgACPpWugAe014dNjdDezdce89cbendkmU9mCABIBBIAAgEEgACgQSAQCABIHAWK7C0Xq9X4i+w1pmrvduqF+pBvL5TR85oXQMmSAAIBBIAAoEEgEAgASAQSAAIbLECa6N11mtrW7WX7dbVYoIEgEAgASAQSAAIBBIAAoEEgMAWK7D2Wme6DsV263IyQQJAIJAAEAgkAAQCCQCBQAJAYIsVoGGo7daTmsXrV+q27dYJM0ECQCCQABAIJAAEAgkAgUACQJBXqwBoam2ltmzWw5HuhDGZIAEgEEgACAQSAAKBBIBAIAEgcBYrQKfX6tX4i3OobdWdOnJG6wSYIAEgEEgACAQSAAKBBIBAIAEgcBYrQKfWtmrvGa1MmwkSAAKBBIBAIAEgEEgACAQSAAJnsQIMpHVG61YdD/L+P6i3nNF6jkyQABAIJAAEAgkAgUACQCCQABAIJAAETtYFGEjv4xxP1IOR7oQhmCABIBBIAAgEEgACgQSAQCABIHBYOcDI3qzvdv2i/Wz9PV5/se46rPwcmSABIBBIAAgEEgACgQSAQCABIHAWK8DIWlupTJsJEgACgQSAQCABIBBIAAgEEgACW6wAI/trfWHRt8AZmCABIBBIAAgEEgACgQSAQCABINiYz7v+o2sABvLz+vYgv4C/V7/cGOJ9eJQJEgACEyQABCZIAAgEEgACgQSAQCABIBBIAAgEEgACgQSAQCABIBBIAAgEEgACgQSAQCABIBBIAAgEEgACgQSAQCABIBBIAAgEEgACgQSAQCABIBBIAAgEEgACgQSA4N+tiJSYlhZY8wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 468x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for itime in range(len(real_time[0:1])):\n",
    "    print(real_time[itime])\n",
    "    yx_flux = np.asarray([np.nan] * (ny * nx)).reshape(ny, nx)\n",
    "    for isection in range(len(mass1_sections)):\n",
    "        # need minus 1 as python index started with 0\n",
    "        cell_ids = list(material_file[\"Regions\"]\n",
    "                        [mass1_sections[isection]]['Cell Ids'])\n",
    "        cell_ids = (np.asarray(cell_ids) - 1).astype(int)\n",
    "        xy_cell_index = [grids[i, 0:2] for i in cell_ids]\n",
    "        xy_cell_index = np.unique(xy_cell_index, axis=0)\n",
    "        for iindex in range(len(xy_cell_index)):\n",
    "            yx_flux[xy_cell_index[iindex][1],\n",
    "                    xy_cell_index[iindex][0]] = flux_array[itime, isection]\n",
    "    fig_name = fig_block_middle_flux_snapshot + str(real_time[itime]) + \".png\"\n",
    "    gs = gridspec.GridSpec(1, 1)\n",
    "    fig = plt.figure()\n",
    "    ax1 = fig.add_subplot(gs[0, 0])\n",
    "    cf1 = ax1.imshow(-yx_flux,\n",
    "                     cmap=plt.cm.jet,\n",
    "                     origin=\"lower\",\n",
    "                     vmin=-0.1,\n",
    "                     vmax=0.1,\n",
    "                     extent=[(x[0] - 0.5 * dx[0]) / 1000,\n",
    "                             (x[-1] + 0.5 * dx[0]) / 1000,\n",
    "                             (y[0] - 0.5 * dy[0]) / 1000,\n",
    "                             (y[-1] + 0.5 * dy[0]) / 1000]\n",
    "                     )\n",
    "    ax1.axis(\"off\")\n",
    "    ax1.set_aspect(\"equal\", \"datalim\")\n",
    "    ax1.set_xlim(block_middle_x)\n",
    "    ax1.set_ylim(block_middle_y)\n",
    "    # ax1.set_xlabel(\"Easting (km)\")\n",
    "    # ax1.set_ylabel(\"Northing (km)\")\n",
    "    fig.tight_layout()\n",
    "    fig.set_size_inches(6.5, 5.5)\n",
    "#     fig.savefig(fig_name, dpi=600)\n",
    "#     plt.close(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-03T21:28:37.444881Z",
     "start_time": "2018-05-03T21:28:35.423422Z"
    },
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "    fig.savefig(fig_name, dpi=600)\n",
    "    plt.close(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## plot flux exchange across riverbed snapshots for _south block_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-03T21:29:46.087199Z",
     "start_time": "2018-05-03T21:29:45.489477Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2010-12-07 00:00:00\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 468x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for itime in range(len(real_time[0:1])):\n",
    "    print(real_time[itime])\n",
    "    yx_flux = np.asarray([np.nan] * (ny * nx)).reshape(ny, nx)\n",
    "    for isection in range(len(mass1_sections)):\n",
    "        # need minus 1 as python index started with 0\n",
    "        cell_ids = list(material_file[\"Regions\"]\n",
    "                        [mass1_sections[isection]]['Cell Ids'])\n",
    "        cell_ids = (np.asarray(cell_ids) - 1).astype(int)\n",
    "        xy_cell_index = [grids[i, 0:2] for i in cell_ids]\n",
    "        xy_cell_index = np.unique(xy_cell_index, axis=0)\n",
    "        for iindex in range(len(xy_cell_index)):\n",
    "            yx_flux[xy_cell_index[iindex][1],\n",
    "                    xy_cell_index[iindex][0]] = flux_array[itime, isection]\n",
    "    fig_name = fig_block_south_flux_snapshot + str(real_time[itime]) + \".png\"\n",
    "    gs = gridspec.GridSpec(1, 1)\n",
    "    fig = plt.figure()\n",
    "    ax1 = fig.add_subplot(gs[0, 0])\n",
    "    cf1 = ax1.imshow(-yx_flux,\n",
    "                     cmap=plt.cm.jet,\n",
    "                     origin=\"lower\",\n",
    "                     vmin=-0.1,\n",
    "                     vmax=0.1,\n",
    "                     extent=[(x[0] - 0.5 * dx[0]) / 1000,\n",
    "                             (x[-1] + 0.5 * dx[0]) / 1000,\n",
    "                             (y[0] - 0.5 * dy[0]) / 1000,\n",
    "                             (y[-1] + 0.5 * dy[0]) / 1000]\n",
    "                     )\n",
    "    ax1.axis(\"off\")\n",
    "    ax1.set_aspect(\"equal\", \"datalim\")\n",
    "    ax1.set_xlim(block_south_x)\n",
    "    ax1.set_ylim(block_south_y)\n",
    "    # ax1.set_xlabel(\"Easting (km)\")\n",
    "    # ax1.set_ylabel(\"Northing (km)\")\n",
    "    fig.tight_layout()\n",
    "    fig.set_size_inches(6.5, 5.5)\n",
    "#     fig.savefig(fig_name, dpi=600)\n",
    "#     plt.close(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-03T21:29:51.087454Z",
     "start_time": "2018-05-03T21:29:48.939490Z"
    },
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "    fig.savefig(fig_name, dpi=600)\n",
    "    plt.close(fig)"
   ]
  }
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