notebooks/learning/gwf_advtidal.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Contents\n",
    "\n",
    "- 1. Load and explore example\n",
    "- 2. Check and run simulation\n",
    "- 3. Plot results\n",
    "- 4. Breakdown simulation level packages\n",
    "\t- 4(a). Temporal Discretization (TDIS) Package\n",
    "\t- 4(b). Iterative Model Solution (IMS)\n",
    "- 5. Breakdown model level packages\n",
    "\t- 5(a). Structured Discretization (DIS)\n",
    "\t- 5(b). Node Property Flow (NPF) Package\n",
    "\t- 5(c). Initial Conditions (IC) Package\n",
    "\t- 5(d). Storage (STO) Package\n",
    "\t- 5(e). General-Head Boundary (GHB) Package\n",
    "\t\t- 5(e)(i). Time Series Package\n",
    "\t\t- 5(e)(ii). Observation Package\n",
    "\t- 5(f). Well (WEL) Package\n",
    "\t- 5(g). River (RIV) Package\n",
    "\t- 5(h). Recharge (RCH) Package\n",
    "\t\t- 5(h)(i). Zone 1\n",
    "\t\t- 5(h)(ii). Zone 2\n",
    "\t\t- 5(h)(iii). Zone 3\n",
    "\t- 5(i). Evapotranspiration (EVT) Package\n",
    "\t- 5(j). Output Control (OC)\n",
    "\t- 5(k). Observation (OBS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "import flopy as fp\n",
    "from matplotlib.patches import Patch\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Load and explore example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading simulation...\n",
      "  loading simulation name file...\n",
      "  loading tdis package...\n",
      "  loading model gwf6...\n",
      "    loading package dis...\n",
      "    loading package npf...\n",
      "    loading package ic...\n",
      "    loading package sto...\n",
      "    loading package ghb...\n",
      "    loading package wel...\n",
      "    loading package riv...\n",
      "    loading package rch...\n",
      "    loading package rch...\n",
      "    loading package rch...\n",
      "    loading package evt...\n",
      "    loading package oc...\n",
      "    loading package obs...\n",
      "  loading solution package ex-gwf-advtidal...\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "sim_name = ex-gwf-advtidal\n",
       "sim_path = C:\\Users\\buckl\\Documents\\Software\\mf6.4.1\\examples\\ex-gwf-advtidal\n",
       "exe_name = C:\\Users\\buckl\\Documents\\Software\\mf6.4.1\\bin\\mf6.exe\n",
       "\n",
       "###################\n",
       "Package mfsim.nam\n",
       "###################\n",
       "\n",
       "package_name = mfsim.nam\n",
       "filename = mfsim.nam\n",
       "package_type = nam\n",
       "model_or_simulation_package = simulation\n",
       "simulation_name = ex-gwf-advtidal\n",
       "\n",
       "\n",
       "###################\n",
       "Package ex-gwf-advtidal.tdis\n",
       "###################\n",
       "\n",
       "package_name = ex-gwf-advtidal.tdis\n",
       "filename = ex-gwf-advtidal.tdis\n",
       "package_type = tdis\n",
       "model_or_simulation_package = simulation\n",
       "simulation_name = ex-gwf-advtidal\n",
       "\n",
       "\n",
       "###################\n",
       "Package ex-gwf-advtidal\n",
       "###################\n",
       "\n",
       "package_name = ex-gwf-advtidal\n",
       "filename = ex-gwf-advtidal.ims\n",
       "package_type = ims\n",
       "model_or_simulation_package = simulation\n",
       "simulation_name = ex-gwf-advtidal\n",
       "\n",
       "\n",
       "@@@@@@@@@@@@@@@@@@@@\n",
       "Model ex-gwf-advtidal\n",
       "@@@@@@@@@@@@@@@@@@@@\n",
       "\n",
       "name = ex-gwf-advtidal\n",
       "model_type = gwf6\n",
       "version = mf6\n",
       "model_relative_path = \n",
       "\n",
       "###################\n",
       "Package dis\n",
       "###################\n",
       "\n",
       "package_name = dis\n",
       "filename = ex-gwf-advtidal.dis\n",
       "package_type = dis\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-advtidal\n",
       "\n",
       "\n",
       "###################\n",
       "Package npf\n",
       "###################\n",
       "\n",
       "package_name = npf\n",
       "filename = ex-gwf-advtidal.npf\n",
       "package_type = npf\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-advtidal\n",
       "\n",
       "\n",
       "###################\n",
       "Package ic\n",
       "###################\n",
       "\n",
       "package_name = ic\n",
       "filename = ex-gwf-advtidal.ic\n",
       "package_type = ic\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-advtidal\n",
       "\n",
       "\n",
       "###################\n",
       "Package sto\n",
       "###################\n",
       "\n",
       "package_name = sto\n",
       "filename = ex-gwf-advtidal.sto\n",
       "package_type = sto\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-advtidal\n",
       "\n",
       "\n",
       "###################\n",
       "Package ghb_ts\n",
       "###################\n",
       "\n",
       "package_name = ghb_ts\n",
       "filename = ex-gwf-advtidal.ghb.ts\n",
       "package_type = ts\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-advtidal\n",
       "parent_file = ghb-tidal\n",
       "\n",
       "\n",
       "###################\n",
       "Package ghb_obs\n",
       "###################\n",
       "\n",
       "package_name = ghb_obs\n",
       "filename = ex-gwf-advtidal.ghb.obs\n",
       "package_type = obs\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-advtidal\n",
       "parent_file = ghb-tidal\n",
       "\n",
       "\n",
       "###################\n",
       "Package ghb-tidal\n",
       "###################\n",
       "\n",
       "package_name = ghb-tidal\n",
       "filename = ex-gwf-advtidal.ghb\n",
       "package_type = ghb\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-advtidal\n",
       "\n",
       "\n",
       "###################\n",
       "Package wel_ts\n",
       "###################\n",
       "\n",
       "package_name = wel_ts\n",
       "filename = ex-gwf-advtidal.wel.ts\n",
       "package_type = ts\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-advtidal\n",
       "parent_file = wel\n",
       "\n",
       "\n",
       "###################\n",
       "Package wel\n",
       "###################\n",
       "\n",
       "package_name = wel\n",
       "filename = ex-gwf-advtidal.wel\n",
       "package_type = wel\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-advtidal\n",
       "\n",
       "\n",
       "###################\n",
       "Package riv_ts\n",
       "###################\n",
       "\n",
       "package_name = riv_ts\n",
       "filename = ex-gwf-advtidal.riv.ts\n",
       "package_type = ts\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-advtidal\n",
       "parent_file = riv\n",
       "\n",
       "\n",
       "###################\n",
       "Package riv\n",
       "###################\n",
       "\n",
       "package_name = riv\n",
       "filename = ex-gwf-advtidal.riv\n",
       "package_type = riv\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-advtidal\n",
       "\n",
       "\n",
       "###################\n",
       "Package rch_ts\n",
       "###################\n",
       "\n",
       "package_name = rch_ts\n",
       "filename = ex-gwf-advtidal.rch1.ts\n",
       "package_type = ts\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-advtidal\n",
       "parent_file = rch-zone_1\n",
       "\n",
       "\n",
       "###################\n",
       "Package rch-zone_1\n",
       "###################\n",
       "\n",
       "package_name = rch-zone_1\n",
       "filename = ex-gwf-advtidal.rch1\n",
       "package_type = rch\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-advtidal\n",
       "\n",
       "\n",
       "###################\n",
       "Package rch_1_ts\n",
       "###################\n",
       "\n",
       "package_name = rch_1_ts\n",
       "filename = ex-gwf-advtidal.rch2.ts\n",
       "package_type = ts\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-advtidal\n",
       "parent_file = rch-zone_2\n",
       "\n",
       "\n",
       "###################\n",
       "Package rch-zone_2\n",
       "###################\n",
       "\n",
       "package_name = rch-zone_2\n",
       "filename = ex-gwf-advtidal.rch2\n",
       "package_type = rch\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-advtidal\n",
       "\n",
       "\n",
       "###################\n",
       "Package rch_2_ts\n",
       "###################\n",
       "\n",
       "package_name = rch_2_ts\n",
       "filename = ex-gwf-advtidal.rch3.ts\n",
       "package_type = ts\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-advtidal\n",
       "parent_file = rch-zone_3\n",
       "\n",
       "\n",
       "###################\n",
       "Package rch-zone_3\n",
       "###################\n",
       "\n",
       "package_name = rch-zone_3\n",
       "filename = ex-gwf-advtidal.rch3\n",
       "package_type = rch\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-advtidal\n",
       "\n",
       "\n",
       "###################\n",
       "Package evt\n",
       "###################\n",
       "\n",
       "package_name = evt\n",
       "filename = ex-gwf-advtidal.evt\n",
       "package_type = evt\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-advtidal\n",
       "\n",
       "\n",
       "###################\n",
       "Package oc\n",
       "###################\n",
       "\n",
       "package_name = oc\n",
       "filename = ex-gwf-advtidal.oc\n",
       "package_type = oc\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-advtidal\n",
       "\n",
       "\n",
       "###################\n",
       "Package obs_1\n",
       "###################\n",
       "\n",
       "package_name = obs_1\n",
       "filename = ex-gwf-advtidal.obs\n",
       "package_type = obs\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-advtidal\n",
       "\n",
       "\n"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "workspace = Path(\"C:\\\\Users\\\\buckl\\\\Documents\\\\Software\\\\mf6.4.1\\\\examples\\\\ex-gwf-advtidal\")\n",
    "sim_name = \"ex-gwf-advtidal\"\n",
    "sim = fp.mf6.MFSimulation.load(\n",
    "    sim_name=sim_name,\n",
    "    version=\"mf6\",\n",
    "    exe_name=\"C:\\\\Users\\\\buckl\\\\Documents\\\\Software\\\\mf6.4.1\\\\bin\\\\mf6.exe\",\n",
    "    sim_ws=workspace\n",
    ")\n",
    "sim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ex-gwf-advtidal']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.model_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name = ex-gwf-advtidal\n",
       "model_type = gwf6\n",
       "version = mf6\n",
       "model_relative_path = \n",
       "\n",
       "###################\n",
       "Package dis\n",
       "###################\n",
       "\n",
       "package_name = dis\n",
       "filename = ex-gwf-advtidal.dis\n",
       "package_type = dis\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-advtidal\n",
       "\n",
       "\n",
       "###################\n",
       "Package npf\n",
       "###################\n",
       "\n",
       "package_name = npf\n",
       "filename = ex-gwf-advtidal.npf\n",
       "package_type = npf\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-advtidal\n",
       "\n",
       "\n",
       "###################\n",
       "Package ic\n",
       "###################\n",
       "\n",
       "package_name = ic\n",
       "filename = ex-gwf-advtidal.ic\n",
       "package_type = ic\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-advtidal\n",
       "\n",
       "\n",
       "###################\n",
       "Package sto\n",
       "###################\n",
       "\n",
       "package_name = sto\n",
       "filename = ex-gwf-advtidal.sto\n",
       "package_type = sto\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-advtidal\n",
       "\n",
       "\n",
       "###################\n",
       "Package ghb_ts\n",
       "###################\n",
       "\n",
       "package_name = ghb_ts\n",
       "filename = ex-gwf-advtidal.ghb.ts\n",
       "package_type = ts\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-advtidal\n",
       "parent_file = ghb-tidal\n",
       "\n",
       "\n",
       "###################\n",
       "Package ghb_obs\n",
       "###################\n",
       "\n",
       "package_name = ghb_obs\n",
       "filename = ex-gwf-advtidal.ghb.obs\n",
       "package_type = obs\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-advtidal\n",
       "parent_file = ghb-tidal\n",
       "\n",
       "\n",
       "###################\n",
       "Package ghb-tidal\n",
       "###################\n",
       "\n",
       "package_name = ghb-tidal\n",
       "filename = ex-gwf-advtidal.ghb\n",
       "package_type = ghb\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-advtidal\n",
       "\n",
       "\n",
       "###################\n",
       "Package wel_ts\n",
       "###################\n",
       "\n",
       "package_name = wel_ts\n",
       "filename = ex-gwf-advtidal.wel.ts\n",
       "package_type = ts\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-advtidal\n",
       "parent_file = wel\n",
       "\n",
       "\n",
       "###################\n",
       "Package wel\n",
       "###################\n",
       "\n",
       "package_name = wel\n",
       "filename = ex-gwf-advtidal.wel\n",
       "package_type = wel\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-advtidal\n",
       "\n",
       "\n",
       "###################\n",
       "Package riv_ts\n",
       "###################\n",
       "\n",
       "package_name = riv_ts\n",
       "filename = ex-gwf-advtidal.riv.ts\n",
       "package_type = ts\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-advtidal\n",
       "parent_file = riv\n",
       "\n",
       "\n",
       "###################\n",
       "Package riv\n",
       "###################\n",
       "\n",
       "package_name = riv\n",
       "filename = ex-gwf-advtidal.riv\n",
       "package_type = riv\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-advtidal\n",
       "\n",
       "\n",
       "###################\n",
       "Package rch_ts\n",
       "###################\n",
       "\n",
       "package_name = rch_ts\n",
       "filename = ex-gwf-advtidal.rch1.ts\n",
       "package_type = ts\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-advtidal\n",
       "parent_file = rch-zone_1\n",
       "\n",
       "\n",
       "###################\n",
       "Package rch-zone_1\n",
       "###################\n",
       "\n",
       "package_name = rch-zone_1\n",
       "filename = ex-gwf-advtidal.rch1\n",
       "package_type = rch\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-advtidal\n",
       "\n",
       "\n",
       "###################\n",
       "Package rch_1_ts\n",
       "###################\n",
       "\n",
       "package_name = rch_1_ts\n",
       "filename = ex-gwf-advtidal.rch2.ts\n",
       "package_type = ts\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-advtidal\n",
       "parent_file = rch-zone_2\n",
       "\n",
       "\n",
       "###################\n",
       "Package rch-zone_2\n",
       "###################\n",
       "\n",
       "package_name = rch-zone_2\n",
       "filename = ex-gwf-advtidal.rch2\n",
       "package_type = rch\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-advtidal\n",
       "\n",
       "\n",
       "###################\n",
       "Package rch_2_ts\n",
       "###################\n",
       "\n",
       "package_name = rch_2_ts\n",
       "filename = ex-gwf-advtidal.rch3.ts\n",
       "package_type = ts\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-advtidal\n",
       "parent_file = rch-zone_3\n",
       "\n",
       "\n",
       "###################\n",
       "Package rch-zone_3\n",
       "###################\n",
       "\n",
       "package_name = rch-zone_3\n",
       "filename = ex-gwf-advtidal.rch3\n",
       "package_type = rch\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-advtidal\n",
       "\n",
       "\n",
       "###################\n",
       "Package evt\n",
       "###################\n",
       "\n",
       "package_name = evt\n",
       "filename = ex-gwf-advtidal.evt\n",
       "package_type = evt\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-advtidal\n",
       "\n",
       "\n",
       "###################\n",
       "Package oc\n",
       "###################\n",
       "\n",
       "package_name = oc\n",
       "filename = ex-gwf-advtidal.oc\n",
       "package_type = oc\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-advtidal\n",
       "\n",
       "\n",
       "###################\n",
       "Package obs_1\n",
       "###################\n",
       "\n",
       "package_name = obs_1\n",
       "filename = ex-gwf-advtidal.obs\n",
       "package_type = obs\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-advtidal\n",
       "\n"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = sim.get_model(model_name='ex-gwf-advtidal')\n",
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[package_name = ex-gwf-advtidal.tdis\n",
       " filename = ex-gwf-advtidal.tdis\n",
       " package_type = tdis\n",
       " model_or_simulation_package = simulation\n",
       " simulation_name = ex-gwf-advtidal\n",
       " \n",
       " Block options\n",
       " --------------------\n",
       " time_units\n",
       " {internal}\n",
       " ('days')\n",
       " \n",
       " \n",
       " Block dimensions\n",
       " --------------------\n",
       " nper\n",
       " {internal}\n",
       " (4)\n",
       " \n",
       " \n",
       " Block perioddata\n",
       " --------------------\n",
       " perioddata\n",
       " {internal}\n",
       " (rec.array([( 1.,   1, 1.), (10., 120, 1.), (10., 120, 1.), (10., 120, 1.)],\n",
       "           dtype=[('perlen', '<f8'), ('nstp', '<i4'), ('tsmult', '<f8')]))\n",
       " \n",
       " ]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# what is the temporal discretization?\n",
    "sim.package_type_dict['tdis']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First stress period is steady-state; second, third, and fourth stress periods are transient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "package_name = dis\n",
       "filename = ex-gwf-advtidal.dis\n",
       "package_type = dis\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-advtidal\n",
       "\n",
       "Block options\n",
       "--------------------\n",
       "length_units\n",
       "{internal}\n",
       "('meters')\n",
       "\n",
       "\n",
       "Block dimensions\n",
       "--------------------\n",
       "nlay\n",
       "{internal}\n",
       "(3)\n",
       "\n",
       "nrow\n",
       "{internal}\n",
       "(15)\n",
       "\n",
       "ncol\n",
       "{internal}\n",
       "(10)\n",
       "\n",
       "\n",
       "Block griddata\n",
       "--------------------\n",
       "delr\n",
       "{constant 500.0}\n",
       "\n",
       "delc\n",
       "{constant 500.0}\n",
       "\n",
       "top\n",
       "{constant 50.0}\n",
       "\n",
       "botm\n",
       "Layer_1{constant 5.0}\n",
       "Layer_2{constant -10.0}\n",
       "Layer_3{constant -100.0}\n",
       "\n"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# what is the spatial discretization?\n",
    "model.get_package('dis')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First layer is an unconfined aquifer that is 45 m thick.\n",
    "\n",
    "Second layer is a 15 m thick confining unit.\n",
    "\n",
    "Third layer is a 90 m thick confined aquifer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['dis',\n",
       " 'npf',\n",
       " 'ic',\n",
       " 'sto',\n",
       " 'ghb_ts',\n",
       " 'ghb_obs',\n",
       " 'ghb-tidal',\n",
       " 'wel_ts',\n",
       " 'wel',\n",
       " 'riv_ts',\n",
       " 'riv',\n",
       " 'rch_ts',\n",
       " 'rch-zone_1',\n",
       " 'rch_1_ts',\n",
       " 'rch-zone_2',\n",
       " 'rch_2_ts',\n",
       " 'rch-zone_3',\n",
       " 'evt',\n",
       " 'oc',\n",
       " 'obs_1']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# what packages does the model have?\n",
    "model.package_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x23558f04050>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(4, 6))\n",
    "ax.set_title('Recharge Zones', fontweight='bold')\n",
    "mapview = fp.plot.PlotMapView(model=model, ax=ax)\n",
    "mapview.plot_ibound()\n",
    "mapview.plot_grid()\n",
    "\n",
    "mapview.plot_bc(package=model.get_package('rch-zone_1'), color='tab:red', alpha=0.4)\n",
    "mapview.plot_bc(package=model.get_package('rch-zone_2'), color='tab:blue', alpha=0.4)\n",
    "mapview.plot_bc(package=model.get_package('rch-zone_3'), color='tab:green', alpha=0.4)\n",
    "ax.legend(handles=[\n",
    "    Patch(color='tab:red', alpha=0.4, label='Zone 1'),\n",
    "    Patch(color='tab:blue', alpha=0.4, label='Zone 2'),\n",
    "    Patch(color='tab:green', alpha=0.4, label='Zone 3')\n",
    "], loc='lower left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x23558ff8250>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(4, 6))\n",
    "ax.set_title('Map View of Layer 1', fontweight='bold')\n",
    "mapview = fp.plot.PlotMapView(model=model, ax=ax, layer=0)\n",
    "mapview.plot_ibound()\n",
    "mapview.plot_grid()\n",
    "\n",
    "mapview.plot_bc('WEL', kper=3)\n",
    "mapview.plot_bc('RIV')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x2355c03d690>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(4, 6))\n",
    "ax.set_title('Map View of Layer 2', fontweight='bold')\n",
    "mapview = fp.plot.PlotMapView(model=model, ax=ax, layer=1)\n",
    "mapview.plot_ibound()\n",
    "mapview.plot_grid()\n",
    "\n",
    "mapview.plot_bc('GHB')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x23558fcdc50>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(4, 6))\n",
    "ax.set_title('Map View of Layer 3', fontweight='bold')\n",
    "mapview = fp.plot.PlotMapView(model=model, ax=ax, layer=2)\n",
    "mapview.plot_ibound()\n",
    "mapview.plot_grid()\n",
    "\n",
    "mapview.plot_bc('GHB')\n",
    "mapview.plot_bc('WEL', kper=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Check and run simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checking model \"ex-gwf-advtidal\"...\n",
      "\n",
      "ex-gwf-advtidal MODEL DATA VALIDATION SUMMARY:\n",
      "  No errors or warnings encountered.\n",
      "\n",
      "  Checks that passed:\n",
      "    npf package: zero or negative horizontal hydraulic conductivity values\n",
      "    npf package: vertical hydraulic conductivity values below checker threshold of 1e-11\n",
      "    npf package: vertical hydraulic conductivity values above checker threshold of 100000.0\n",
      "    npf package: horizontal hydraulic conductivity values below checker threshold of 1e-11\n",
      "    npf package: horizontal hydraulic conductivity values above checker threshold of 100000.0\n",
      "    sto package: zero or negative specific storage values\n",
      "    sto package: specific storage values below checker threshold of 1e-06\n",
      "    sto package: specific storage values above checker threshold of 0.01\n",
      "    sto package: zero or negative specific yield values\n",
      "    sto package: specific yield values below checker threshold of 0.01\n",
      "    sto package: specific yield values above checker threshold of 0.5\n",
      "    ghb-tidal package: BC indices valid\n",
      "    ghb-tidal package: not a number (Nan) entries\n",
      "    ghb-tidal package: BC in inactive cells\n",
      "    wel package: BC indices valid\n",
      "    wel package: not a number (Nan) entries\n",
      "    wel package: BC in inactive cells\n",
      "    riv package: BC indices valid\n",
      "    riv package: not a number (Nan) entries\n",
      "    riv package: BC in inactive cells\n",
      "    rch-zone_1 package: BC indices valid\n",
      "    rch-zone_1 package: not a number (Nan) entries\n",
      "    rch-zone_1 package: BC in inactive cells\n",
      "    rch-zone_2 package: BC indices valid\n",
      "    rch-zone_2 package: not a number (Nan) entries\n",
      "    rch-zone_2 package: BC in inactive cells\n",
      "    rch-zone_3 package: BC indices valid\n",
      "    rch-zone_3 package: not a number (Nan) entries\n",
      "    rch-zone_3 package: BC in inactive cells\n",
      "    evt package: BC indices valid\n",
      "    evt package: not a number (Nan) entries\n",
      "    evt package: BC in inactive cells\n",
      "\n",
      "Checking for missing simulation packages...\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<flopy.utils.check.mf6check at 0x2355c0d9210>]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.check()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FloPy is using the following executable to run the model: ..\\..\\bin\\mf6.exe\n",
      "                                   MODFLOW 6\n",
      "                U.S. GEOLOGICAL SURVEY MODULAR HYDROLOGIC MODEL\n",
      "                        VERSION 6.4.1 Release 12/09/2022\n",
      "\n",
      "   MODFLOW 6 compiled Dec 09 2022 18:18:37 with Intel(R) Fortran Intel(R) 64\n",
      "   Compiler Classic for applications running on Intel(R) 64, Version 2021.7.0\n",
      "                             Build 20220726_000000\n",
      "\n",
      "This software has been approved for release by the U.S. Geological \n",
      "Survey (USGS). Although the software has been subjected to rigorous \n",
      "review, the USGS reserves the right to update the software as needed \n",
      "pursuant to further analysis and review. No warranty, expressed or \n",
      "implied, is made by the USGS or the U.S. Government as to the \n",
      "functionality of the software and related material nor shall the \n",
      "fact of release constitute any such warranty. Furthermore, the \n",
      "software is released on condition that neither the USGS nor the U.S. \n",
      "Government shall be held liable for any damages resulting from its \n",
      "authorized or unauthorized use. Also refer to the USGS Water \n",
      "Resources Software User Rights Notice for complete use, copyright, \n",
      "and distribution information.\n",
      "\n",
      " \n",
      " Run start date and time (yyyy/mm/dd hh:mm:ss): 2024/01/15 13:18:14\n",
      " \n",
      " Writing simulation list file: mfsim.lst\n",
      " Using Simulation name file: mfsim.nam\n",
      " \n",
      "    Solving:  Stress period:     1    Time step:     1\n",
      "    Solving:  Stress period:     2    Time step:     1\n",
      "    Solving:  Stress period:     2    Time step:     2\n",
      "    Solving:  Stress period:     2    Time step:     3\n",
      "    Solving:  Stress period:     2    Time step:     4\n",
      "    Solving:  Stress period:     2    Time step:     5\n",
      "    Solving:  Stress period:     2    Time step:     6\n",
      "    Solving:  Stress period:     2    Time step:     7\n",
      "    Solving:  Stress period:     2    Time step:     8\n",
      "    Solving:  Stress period:     2    Time step:     9\n",
      "    Solving:  Stress period:     2    Time step:    10\n",
      "    Solving:  Stress period:     2    Time step:    11\n",
      "    Solving:  Stress period:     2    Time step:    12\n",
      "    Solving:  Stress period:     2    Time step:    13\n",
      "    Solving:  Stress period:     2    Time step:    14\n",
      "    Solving:  Stress period:     2    Time step:    15\n",
      "    Solving:  Stress period:     2    Time step:    16\n",
      "    Solving:  Stress period:     2    Time step:    17\n",
      "    Solving:  Stress period:     2    Time step:    18\n",
      "    Solving:  Stress period:     2    Time step:    19\n",
      "    Solving:  Stress period:     2    Time step:    20\n",
      "    Solving:  Stress period:     2    Time step:    21\n",
      "    Solving:  Stress period:     2    Time step:    22\n",
      "    Solving:  Stress period:     2    Time step:    23\n",
      "    Solving:  Stress period:     2    Time step:    24\n",
      "    Solving:  Stress period:     2    Time step:    25\n",
      "    Solving:  Stress period:     2    Time step:    26\n",
      "    Solving:  Stress period:     2    Time step:    27\n",
      "    Solving:  Stress period:     2    Time step:    28\n",
      "    Solving:  Stress period:     2    Time step:    29\n",
      "    Solving:  Stress period:     2    Time step:    30\n",
      "    Solving:  Stress period:     2    Time step:    31\n",
      "    Solving:  Stress period:     2    Time step:    32\n",
      "    Solving:  Stress period:     2    Time step:    33\n",
      "    Solving:  Stress period:     2    Time step:    34\n",
      "    Solving:  Stress period:     2    Time step:    35\n",
      "    Solving:  Stress period:     2    Time step:    36\n",
      "    Solving:  Stress period:     2    Time step:    37\n",
      "    Solving:  Stress period:     2    Time step:    38\n",
      "    Solving:  Stress period:     2    Time step:    39\n",
      "    Solving:  Stress period:     2    Time step:    40\n",
      "    Solving:  Stress period:     2    Time step:    41\n",
      "    Solving:  Stress period:     2    Time step:    42\n",
      "    Solving:  Stress period:     2    Time step:    43\n",
      "    Solving:  Stress period:     2    Time step:    44\n",
      "    Solving:  Stress period:     2    Time step:    45\n",
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      "    Solving:  Stress period:     2    Time step:    84\n",
      "    Solving:  Stress period:     2    Time step:    85\n",
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      "    Solving:  Stress period:     2    Time step:    87\n",
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      "    Solving:  Stress period:     2    Time step:    89\n",
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      "    Solving:  Stress period:     2    Time step:    91\n",
      "    Solving:  Stress period:     2    Time step:    92\n",
      "    Solving:  Stress period:     2    Time step:    93\n",
      "    Solving:  Stress period:     2    Time step:    94\n",
      "    Solving:  Stress period:     2    Time step:    95\n",
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      "    Solving:  Stress period:     2    Time step:    98\n",
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      "    Solving:  Stress period:     2    Time step:   101\n",
      "    Solving:  Stress period:     2    Time step:   102\n",
      "    Solving:  Stress period:     2    Time step:   103\n",
      "    Solving:  Stress period:     2    Time step:   104\n",
      "    Solving:  Stress period:     2    Time step:   105\n",
      "    Solving:  Stress period:     2    Time step:   106\n",
      "    Solving:  Stress period:     2    Time step:   107\n",
      "    Solving:  Stress period:     2    Time step:   108\n",
      "    Solving:  Stress period:     2    Time step:   109\n",
      "    Solving:  Stress period:     2    Time step:   110\n",
      "    Solving:  Stress period:     2    Time step:   111\n",
      "    Solving:  Stress period:     2    Time step:   112\n",
      "    Solving:  Stress period:     2    Time step:   113\n",
      "    Solving:  Stress period:     2    Time step:   114\n",
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      "    Solving:  Stress period:     2    Time step:   116\n",
      "    Solving:  Stress period:     2    Time step:   117\n",
      "    Solving:  Stress period:     2    Time step:   118\n",
      "    Solving:  Stress period:     2    Time step:   119\n",
      "    Solving:  Stress period:     2    Time step:   120\n",
      "    Solving:  Stress period:     3    Time step:     1\n",
      "    Solving:  Stress period:     3    Time step:     2\n",
      "    Solving:  Stress period:     3    Time step:     3\n",
      "    Solving:  Stress period:     3    Time step:     4\n",
      "    Solving:  Stress period:     3    Time step:     5\n",
      "    Solving:  Stress period:     3    Time step:     6\n",
      "    Solving:  Stress period:     3    Time step:     7\n",
      "    Solving:  Stress period:     3    Time step:     8\n",
      "    Solving:  Stress period:     3    Time step:     9\n",
      "    Solving:  Stress period:     3    Time step:    10\n",
      "    Solving:  Stress period:     3    Time step:    11\n",
      "    Solving:  Stress period:     3    Time step:    12\n",
      "    Solving:  Stress period:     3    Time step:    13\n",
      "    Solving:  Stress period:     3    Time step:    14\n",
      "    Solving:  Stress period:     3    Time step:    15\n",
      "    Solving:  Stress period:     3    Time step:    16\n",
      "    Solving:  Stress period:     3    Time step:    17\n",
      "    Solving:  Stress period:     3    Time step:    18\n",
      "    Solving:  Stress period:     3    Time step:    19\n",
      "    Solving:  Stress period:     3    Time step:    20\n",
      "    Solving:  Stress period:     3    Time step:    21\n",
      "    Solving:  Stress period:     3    Time step:    22\n",
      "    Solving:  Stress period:     3    Time step:    23\n",
      "    Solving:  Stress period:     3    Time step:    24\n",
      "    Solving:  Stress period:     3    Time step:    25\n",
      "    Solving:  Stress period:     3    Time step:    26\n",
      "    Solving:  Stress period:     3    Time step:    27\n",
      "    Solving:  Stress period:     3    Time step:    28\n",
      "    Solving:  Stress period:     3    Time step:    29\n",
      "    Solving:  Stress period:     3    Time step:    30\n",
      "    Solving:  Stress period:     3    Time step:    31\n",
      "    Solving:  Stress period:     3    Time step:    32\n",
      "    Solving:  Stress period:     3    Time step:    33\n",
      "    Solving:  Stress period:     3    Time step:    34\n",
      "    Solving:  Stress period:     3    Time step:    35\n",
      "    Solving:  Stress period:     3    Time step:    36\n",
      "    Solving:  Stress period:     3    Time step:    37\n",
      "    Solving:  Stress period:     3    Time step:    38\n",
      "    Solving:  Stress period:     3    Time step:    39\n",
      "    Solving:  Stress period:     3    Time step:    40\n",
      "    Solving:  Stress period:     3    Time step:    41\n",
      "    Solving:  Stress period:     3    Time step:    42\n",
      "    Solving:  Stress period:     3    Time step:    43\n",
      "    Solving:  Stress period:     3    Time step:    44\n",
      "    Solving:  Stress period:     3    Time step:    45\n",
      "    Solving:  Stress period:     3    Time step:    46\n",
      "    Solving:  Stress period:     3    Time step:    47\n",
      "    Solving:  Stress period:     3    Time step:    48\n",
      "    Solving:  Stress period:     3    Time step:    49\n",
      "    Solving:  Stress period:     3    Time step:    50\n",
      "    Solving:  Stress period:     3    Time step:    51\n",
      "    Solving:  Stress period:     3    Time step:    52\n",
      "    Solving:  Stress period:     3    Time step:    53\n",
      "    Solving:  Stress period:     3    Time step:    54\n",
      "    Solving:  Stress period:     3    Time step:    55\n",
      "    Solving:  Stress period:     3    Time step:    56\n",
      "    Solving:  Stress period:     3    Time step:    57\n",
      "    Solving:  Stress period:     3    Time step:    58\n",
      "    Solving:  Stress period:     3    Time step:    59\n",
      "    Solving:  Stress period:     3    Time step:    60\n",
      "    Solving:  Stress period:     3    Time step:    61\n",
      "    Solving:  Stress period:     3    Time step:    62\n",
      "    Solving:  Stress period:     3    Time step:    63\n",
      "    Solving:  Stress period:     3    Time step:    64\n",
      "    Solving:  Stress period:     3    Time step:    65\n",
      "    Solving:  Stress period:     3    Time step:    66\n",
      "    Solving:  Stress period:     3    Time step:    67\n",
      "    Solving:  Stress period:     3    Time step:    68\n",
      "    Solving:  Stress period:     3    Time step:    69\n",
      "    Solving:  Stress period:     3    Time step:    70\n",
      "    Solving:  Stress period:     3    Time step:    71\n",
      "    Solving:  Stress period:     3    Time step:    72\n",
      "    Solving:  Stress period:     3    Time step:    73\n",
      "    Solving:  Stress period:     3    Time step:    74\n",
      "    Solving:  Stress period:     3    Time step:    75\n",
      "    Solving:  Stress period:     3    Time step:    76\n",
      "    Solving:  Stress period:     3    Time step:    77\n",
      "    Solving:  Stress period:     3    Time step:    78\n",
      "    Solving:  Stress period:     3    Time step:    79\n",
      "    Solving:  Stress period:     3    Time step:    80\n",
      "    Solving:  Stress period:     3    Time step:    81\n",
      "    Solving:  Stress period:     3    Time step:    82\n",
      "    Solving:  Stress period:     3    Time step:    83\n",
      "    Solving:  Stress period:     3    Time step:    84\n",
      "    Solving:  Stress period:     3    Time step:    85\n",
      "    Solving:  Stress period:     3    Time step:    86\n",
      "    Solving:  Stress period:     3    Time step:    87\n",
      "    Solving:  Stress period:     3    Time step:    88\n",
      "    Solving:  Stress period:     3    Time step:    89\n",
      "    Solving:  Stress period:     3    Time step:    90\n",
      "    Solving:  Stress period:     3    Time step:    91\n",
      "    Solving:  Stress period:     3    Time step:    92\n",
      "    Solving:  Stress period:     3    Time step:    93\n",
      "    Solving:  Stress period:     3    Time step:    94\n",
      "    Solving:  Stress period:     3    Time step:    95\n",
      "    Solving:  Stress period:     3    Time step:    96\n",
      "    Solving:  Stress period:     3    Time step:    97\n",
      "    Solving:  Stress period:     3    Time step:    98\n",
      "    Solving:  Stress period:     3    Time step:    99\n",
      "    Solving:  Stress period:     3    Time step:   100\n",
      "    Solving:  Stress period:     3    Time step:   101\n",
      "    Solving:  Stress period:     3    Time step:   102\n",
      "    Solving:  Stress period:     3    Time step:   103\n",
      "    Solving:  Stress period:     3    Time step:   104\n",
      "    Solving:  Stress period:     3    Time step:   105\n",
      "    Solving:  Stress period:     3    Time step:   106\n",
      "    Solving:  Stress period:     3    Time step:   107\n",
      "    Solving:  Stress period:     3    Time step:   108\n",
      "    Solving:  Stress period:     3    Time step:   109\n",
      "    Solving:  Stress period:     3    Time step:   110\n",
      "    Solving:  Stress period:     3    Time step:   111\n",
      "    Solving:  Stress period:     3    Time step:   112\n",
      "    Solving:  Stress period:     3    Time step:   113\n",
      "    Solving:  Stress period:     3    Time step:   114\n",
      "    Solving:  Stress period:     3    Time step:   115\n",
      "    Solving:  Stress period:     3    Time step:   116\n",
      "    Solving:  Stress period:     3    Time step:   117\n",
      "    Solving:  Stress period:     3    Time step:   118\n",
      "    Solving:  Stress period:     3    Time step:   119\n",
      "    Solving:  Stress period:     3    Time step:   120\n",
      "    Solving:  Stress period:     4    Time step:     1\n",
      "    Solving:  Stress period:     4    Time step:     2\n",
      "    Solving:  Stress period:     4    Time step:     3\n",
      "    Solving:  Stress period:     4    Time step:     4\n",
      "    Solving:  Stress period:     4    Time step:     5\n",
      "    Solving:  Stress period:     4    Time step:     6\n",
      "    Solving:  Stress period:     4    Time step:     7\n",
      "    Solving:  Stress period:     4    Time step:     8\n",
      "    Solving:  Stress period:     4    Time step:     9\n",
      "    Solving:  Stress period:     4    Time step:    10\n",
      "    Solving:  Stress period:     4    Time step:    11\n",
      "    Solving:  Stress period:     4    Time step:    12\n",
      "    Solving:  Stress period:     4    Time step:    13\n",
      "    Solving:  Stress period:     4    Time step:    14\n",
      "    Solving:  Stress period:     4    Time step:    15\n",
      "    Solving:  Stress period:     4    Time step:    16\n",
      "    Solving:  Stress period:     4    Time step:    17\n",
      "    Solving:  Stress period:     4    Time step:    18\n",
      "    Solving:  Stress period:     4    Time step:    19\n",
      "    Solving:  Stress period:     4    Time step:    20\n",
      "    Solving:  Stress period:     4    Time step:    21\n",
      "    Solving:  Stress period:     4    Time step:    22\n",
      "    Solving:  Stress period:     4    Time step:    23\n",
      "    Solving:  Stress period:     4    Time step:    24\n",
      "    Solving:  Stress period:     4    Time step:    25\n",
      "    Solving:  Stress period:     4    Time step:    26\n",
      "    Solving:  Stress period:     4    Time step:    27\n",
      "    Solving:  Stress period:     4    Time step:    28\n",
      "    Solving:  Stress period:     4    Time step:    29\n",
      "    Solving:  Stress period:     4    Time step:    30\n",
      "    Solving:  Stress period:     4    Time step:    31\n",
      "    Solving:  Stress period:     4    Time step:    32\n",
      "    Solving:  Stress period:     4    Time step:    33\n",
      "    Solving:  Stress period:     4    Time step:    34\n",
      "    Solving:  Stress period:     4    Time step:    35\n",
      "    Solving:  Stress period:     4    Time step:    36\n",
      "    Solving:  Stress period:     4    Time step:    37\n",
      "    Solving:  Stress period:     4    Time step:    38\n",
      "    Solving:  Stress period:     4    Time step:    39\n",
      "    Solving:  Stress period:     4    Time step:    40\n",
      "    Solving:  Stress period:     4    Time step:    41\n",
      "    Solving:  Stress period:     4    Time step:    42\n",
      "    Solving:  Stress period:     4    Time step:    43\n",
      "    Solving:  Stress period:     4    Time step:    44\n",
      "    Solving:  Stress period:     4    Time step:    45\n",
      "    Solving:  Stress period:     4    Time step:    46\n",
      "    Solving:  Stress period:     4    Time step:    47\n",
      "    Solving:  Stress period:     4    Time step:    48\n",
      "    Solving:  Stress period:     4    Time step:    49\n",
      "    Solving:  Stress period:     4    Time step:    50\n",
      "    Solving:  Stress period:     4    Time step:    51\n",
      "    Solving:  Stress period:     4    Time step:    52\n",
      "    Solving:  Stress period:     4    Time step:    53\n",
      "    Solving:  Stress period:     4    Time step:    54\n",
      "    Solving:  Stress period:     4    Time step:    55\n",
      "    Solving:  Stress period:     4    Time step:    56\n",
      "    Solving:  Stress period:     4    Time step:    57\n",
      "    Solving:  Stress period:     4    Time step:    58\n",
      "    Solving:  Stress period:     4    Time step:    59\n",
      "    Solving:  Stress period:     4    Time step:    60\n",
      "    Solving:  Stress period:     4    Time step:    61\n",
      "    Solving:  Stress period:     4    Time step:    62\n",
      "    Solving:  Stress period:     4    Time step:    63\n",
      "    Solving:  Stress period:     4    Time step:    64\n",
      "    Solving:  Stress period:     4    Time step:    65\n",
      "    Solving:  Stress period:     4    Time step:    66\n",
      "    Solving:  Stress period:     4    Time step:    67\n",
      "    Solving:  Stress period:     4    Time step:    68\n",
      "    Solving:  Stress period:     4    Time step:    69\n",
      "    Solving:  Stress period:     4    Time step:    70\n",
      "    Solving:  Stress period:     4    Time step:    71\n",
      "    Solving:  Stress period:     4    Time step:    72\n",
      "    Solving:  Stress period:     4    Time step:    73\n",
      "    Solving:  Stress period:     4    Time step:    74\n",
      "    Solving:  Stress period:     4    Time step:    75\n",
      "    Solving:  Stress period:     4    Time step:    76\n",
      "    Solving:  Stress period:     4    Time step:    77\n",
      "    Solving:  Stress period:     4    Time step:    78\n",
      "    Solving:  Stress period:     4    Time step:    79\n",
      "    Solving:  Stress period:     4    Time step:    80\n",
      "    Solving:  Stress period:     4    Time step:    81\n",
      "    Solving:  Stress period:     4    Time step:    82\n",
      "    Solving:  Stress period:     4    Time step:    83\n",
      "    Solving:  Stress period:     4    Time step:    84\n",
      "    Solving:  Stress period:     4    Time step:    85\n",
      "    Solving:  Stress period:     4    Time step:    86\n",
      "    Solving:  Stress period:     4    Time step:    87\n",
      "    Solving:  Stress period:     4    Time step:    88\n",
      "    Solving:  Stress period:     4    Time step:    89\n",
      "    Solving:  Stress period:     4    Time step:    90\n",
      "    Solving:  Stress period:     4    Time step:    91\n",
      "    Solving:  Stress period:     4    Time step:    92\n",
      "    Solving:  Stress period:     4    Time step:    93\n",
      "    Solving:  Stress period:     4    Time step:    94\n",
      "    Solving:  Stress period:     4    Time step:    95\n",
      "    Solving:  Stress period:     4    Time step:    96\n",
      "    Solving:  Stress period:     4    Time step:    97\n",
      "    Solving:  Stress period:     4    Time step:    98\n",
      "    Solving:  Stress period:     4    Time step:    99\n",
      "    Solving:  Stress period:     4    Time step:   100\n",
      "    Solving:  Stress period:     4    Time step:   101\n",
      "    Solving:  Stress period:     4    Time step:   102\n",
      "    Solving:  Stress period:     4    Time step:   103\n",
      "    Solving:  Stress period:     4    Time step:   104\n",
      "    Solving:  Stress period:     4    Time step:   105\n",
      "    Solving:  Stress period:     4    Time step:   106\n",
      "    Solving:  Stress period:     4    Time step:   107\n",
      "    Solving:  Stress period:     4    Time step:   108\n",
      "    Solving:  Stress period:     4    Time step:   109\n",
      "    Solving:  Stress period:     4    Time step:   110\n",
      "    Solving:  Stress period:     4    Time step:   111\n",
      "    Solving:  Stress period:     4    Time step:   112\n",
      "    Solving:  Stress period:     4    Time step:   113\n",
      "    Solving:  Stress period:     4    Time step:   114\n",
      "    Solving:  Stress period:     4    Time step:   115\n",
      "    Solving:  Stress period:     4    Time step:   116\n",
      "    Solving:  Stress period:     4    Time step:   117\n",
      "    Solving:  Stress period:     4    Time step:   118\n",
      "    Solving:  Stress period:     4    Time step:   119\n",
      "    Solving:  Stress period:     4    Time step:   120\n",
      " \n",
      " Run end date and time (yyyy/mm/dd hh:mm:ss): 2024/01/15 13:18:15\n",
      " Elapsed run time:  1.296 Seconds\n",
      " \n",
      " Normal termination of simulation.\n"
     ]
    }
   ],
   "source": [
    "success, buff = sim.run_simulation()\n",
    "assert success, \"MODFLOW did not terminate normally!\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Plot results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "361"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hds = fp.utils.binaryfile.HeadFile(Path(workspace, \"ex-gwf-advtidal.hds\"))\n",
    "times = hds.get_times()\n",
    "len(times)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2355c1007d0>]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Ee9//B33GHpd77Rw4cEAQBEFYvnx5ufOzZ8/W3VNQUCDY2NgILVu2fGw7hliWcO9r/u9//3vfNTc3NyE0NLTan7eymcj3Hp6enuWSwURMizikWUmGDx/OoUOHGDZsGD4+PlhYWGBnZ0erVq34/PPP2bdvX7VKbsnlcv755x++++472rVrh52dHVZWVoSEhDBx4kRdOxcXF6Kionj33XcJDQ3F0tISW1tb2rZty8KFC1m1apVZ1/T09/fn1KlTvPXWW4SFhWFlZYW9vT1hYWGMGzeOTZs26ZIiFAoFO3fuZPTo0Tg4OODg4MDw4cPZt2/fY9srO5z59NNPP7BN2aSO0mHNkSNH8vvvv9OoUSMUCgWNGjVi1apVDBky5IF9vPTSS7z77rt4eHhgbW1Nz549OXjwIMHBwY/t66NYvnw5v/76K926dcPR0RGFQoG/vz+9evXiu+++45VXXtGLnbLzeHK5nDZt2gB3I71Syg5/7tmzhzt37hilukqrVq04ffo0kydPpn79+igUCpycnAgPD2fy5Mns3r1b13bGjBl8+umnBAQEYGNjQ/fu3dmzZw9eXl739VvZz9srr7zCiBEjCA4Oxs7ODgsLC+rVq8fo0aP5999/9ZIIJqIfJIKgp0wFEZE6xOzZs/noo48AWLp06QOLHtdFpk6dyoIFC4iKiqJ9+/amdkdEpBxihCciIqI3mjdvzscff1zpJSMiIsZATFoRERHRG5MmTTK1CyIiFSJGeCIiIiIidQJxDk9EREREpE4gRngiIiIiInUCUfBEREREROoEtTZpRaPRkJycjL29vVmvRxMRERF5XARBIC8vDx8fH6TS6scrhYWFKJVKPXh2PwqFolprkg1BrRW85OTkctXcRURERGoL165dw9fXt1p9FBYW4usZREZuip68Ko+Xlxfx8fFmJXq1VvBKdyz49rnL2MhdH9FafwgSJXFe2srsgbdeRSoojGZbI1GS4K617XlnGlKMaBslqTY/AGBv9QoSifFsC4KSvMIFAKiCJ4NUbjTbaFTIY7U1TeN7T0CQGe+flERdTNCuJQBIJwxHoofdNx4XobgYzZK1ADSf1g2ZQmY022qlmjM/7Adg4lP1sLAw3sxMcbGGxX9rC06/WnjLiP/CQAn8T+LA3LlzK9yRpVL9KZVk5KawZc41bK0cqu9gGfILcxnwjh9KpVIUPGNQOoxpI3fFTu5mNLsayd0/sK3czeiCp7OtdjO64JXatrZ2M7rgqdDaltq5gtSIX0MaJYqS1y13dEGwMJ7YSopVuvdc5uKMRG4824JKhbrEtr2bAzKF8b5K1Mpi3et2cbJFYUTBUxZrdLbdBIXRBc9Sot1uSZ/TNDbWDtha61fwBDOdRRKTVkRERERE6gS1NsITEREREXk0gkT/EZkY4YmIiIiIiJgQMcITERERqcPUpQhPFDwRERGRuowU9L4zqpmOHZqpWyIiIiIiIvpFjPBERERE6jB1aUhTjPBEREREROoEYoQnIiIiUpeRlBz67tMMESM8EREREZE6gRjhiYiIiNRhxDk8ERERERGRWoYoeCI1nqRrx7lw4QIZGRmmdqXSqJVF3Di4jfgtqxEEwSQ+JB05zuk1G8hLSTWYjcI7hZzYHsXO5Vv02u+Zi4ms2PgvSTfSq91X/p0idh48x8o/D1Wrn1t3Cth4NY4tsYnV9skYCFLDHOaImbolIvL4HPh3PuvWrePq1aumdqXSaFRK9r02nMPvT0JdWGASH7bM+pA/Jr7GjVNnDWYjPzuP9/u/zrwJn6JSqvTW73/+u4aX3/mZg8euVLuvWxm5PPPS10x7fynFxeoq97MtLokhG7czJ+pktX0S0S+i4InUeKRS7V5sGo3GxJ5UHgsbO90+dsrcLJP4YO/lCUDuTcNFeC7ebljaWKHRaEhLvKm3foMDvQCITaj+Jqb+9VyxspSjVBWTcP1Wlftp5ekOwOm0DDQmitorjUTPh5kiCp5IjUci0X6MTTUkWB0kEgmWDs4AFOWYRvAcvLWCl3fTMDtfg/Z1+oRod+i+EX1Nb/2GBGh9j0movlhLpVIaBGkF9Gpc1UU51NUJKwsZt5UqYrJyqu2XoSlNWtH3YY6IgidS45FKtRFSTYzwABSOWsEzVYRn5+kBQF5qmkHtlArezZjreuuzfongReshwgMIDfYB4HJs1QXPQiqlubsrACdTqh4piugfUfBEajw1eUgTQGE2EZ7hhjQBfEL8AT1HeIGlEV6KXiL8sPpawbsSl1ytflp5aYc1T6ZWP5nG0AgSwSCHOSIKnkiNp1TwauKQJoClowsAShMJXukcntEivFj9RXgB9dyRyaTcKVByMy272v2FBnsD1RvSBGjp4QbAqRogeHUJUfBEajwSSc2O8CxLhjSLanHSCmCQOTyFwoJAX200FR1f/WHNhvW1gnclNrlaP6DuRni3zP6HmLgsQUSkBiGr6XN4JUOaypxMk9gvHdLMT7uFurjYYHZ8GvgBkJpwk2KV/uyUHdasLg2CvJBKJeTkFZByq+oJJ+FuLlhIpWQWFpGUe7vafonoB1HwRGo8kho+pFmatGKqOTwbN1ekMhmCIJB/y3BDcC7eblhaW6JRq/W6NCGkZGmCPhJXLBVygvy0STzVGda0tJDRxE37dzX3YU0xS1NEpAZR05NWSoc0TTWHJ5VKsStZO2bIxBWpVIp3iDbK0+ewZgPdWjz9+N6wvra/K7HVTFzxvDusKWIeiIInUuOp6YKnG9I00RweGD9xJVmPSxNCStbO6WNIEyBUT5maLT1rRuKKGOFVQGBgIBKJ5L5j6tSpABQWFjJ16lRcXV2xs7Nj6NChpKaW/9WVlJTEgAEDsLGxwcPDg7feeovie+YN9u3bR6tWrbC0tCQkJIRly5ZV71WK1Gqkkpo9pGlp4iFNMGLiSnCJ4Ok1wtP6HnftVrVKgpVSuhbvSlz1BLRVieCZ/dIEfVdZMeNqK5USvGPHjnHz5k3dsXPnTgCGDRsGwIwZM/j7779Zt24d+/fvJzk5mSFDhujuV6vVDBgwAKVSyeHDh1m+fDnLli3jgw8+0LWJj49nwIAB9OjRg9OnTzN9+nReeukltm/fro/XK1ILkYgRXrUxRrUVuJu4khyrP8Hz8XTGylJOcbGaRD0UkQ4LvpupWR2ae7ghAZJv55Oaf6fafolUn0rth+fu7l7u+Zw5cwgODqZbt27k5OSwZMkSVq1aRc+ePQFYunQpjRo1Iioqig4dOrBjxw4uXrzIrl278PT0pEWLFnzyySfMmjWL2bNno1AoWLRoEUFBQXz99dcANGrUiH///Ze5c+fSr1+/Sr9AQaJEI1FW+r6qUtaWMe3eZxsj2y5jTxCMa1ta8mtSo9GARn+FiR+LMvYkxVWzbWXnAGgjvMr0UbatoKre67Zz10YjeTdTHquvsm3UysePqrwCtGKSHH0dtbJqmZpl7amKNWAhpX6AJxevXudS7E386rk/5O5HE+SvHSJNTs0iPTsfBzvr8vZKeNSnXKGQ08DFiauZ2RxNTadfff9q+WWof1WGWEZgrssSqrwBrFKpZMWKFcycOROJRMKJEydQqVT07t1b1yYsLAx/f38iIyPp0KEDkZGRNG3aFE9PT12bfv36MWXKFC5cuEDLli2JjIws10dpm+nTpz/Un6KiIoqKinTPc3NzAYjz+h4rK6uqvsxqEe/5tUnsAqTYfmMy23mF84xqT6WJArRDmoro74xquyzB2xdV6b7CwkIA1IV3CPjzG+RyeaX70CxaVSXbpdheiAEgJ/IE6u+XV+reU9/seey2OTnaVP+UuBsc+WwbMpmsUrbuZcFG7VygILMH4Jc/L3Ep1alafQLY2dlx+/ZtPll8Cl9f3we2+dra84Hny6Lw8YXMbOZlFHCyyaPbP5KSz4pI1aiyDv/5559kZ2fz/PPPA5CSkoJCocDJyalcO09PT1JSUnRtyopd6fXSaw9rk5ubS0FBxdunfPHFFzg6OuoOPz+/qr40kRqGRKIN8WrqkKalpaXuNTzsM25I7O21gnH7tmHXjNnb22NhYYFGo9GJnz5wddXWrtTXnohubtqINz29ekOk3t7aiLb0+80cqUtJK1WO8JYsWUL//v3x8fHRpz9V5t1332XmzJm657m5ufj5+eGf8Sq2Cjej+aFBSaK7NrLzz3gDKQqj2k5y1dr2KJhpdNtp1tqo0t5qOhKJ8WxbK+YCO9FoNCgbvAbSykdIVUaj0kWV8b2nIFhUzbbi2x8oys4guvUQnIMbP9Y9kmIVQbsWAiBMHA1ViAxLsTt3HlatIk9djPDK84++QaVCsnglAI3f6ItU8fhfJd5/rODaxTjsewcT3i+i0r5qlMVc/HoHAC+MDEYul+Joncy///6LlaKASWMbVLrPe4mPDiEhIQFfL3W5/lQqDUtXxwLwhs1tFI/4Yt8T5MQJGytaW0t417Z6PyaUAnxWWOWvbBGqKHiJiYns2rWLDRs26M55eXmhVCrJzs4uF+Wlpqbi5eWla3P06NFyfZVmcZZtc29mZ2pqKg4ODlhbW1MRlpaWWFpa3ndeisKoX/yibZBIFEYVPJlMO2QtCIJW7KSmed2ChbzKgmfp4ExRdgZF+XlV60Mur5bg2ZcM2+WnZ6CRSJBaPP5Xg1RhgawSgufTMIBrF+NIS7xRqfsehFwuRS6X0rBBPQDiE1KQy6s/gRTaUNtfbNzNCvtTSHik4PUN8Sf9o6m6CN4cMUREZq4RXpU+GUuXLsXDw4MBAwbozrVu3Rq5XM7u3bt1565cuUJSUhIREdpfcREREZw7d460tLtrfXbu3ImDgwONGzfWtSnbR2mb0j5ERO5FKtV+jGvqkCaUqbZiokxNWzdXJDIZgkZj0GorAF4h+l+aEFyyWPxGcgYFhdVP72gYohW86GpmakqlErMWu7pGpQVPo9GwdOlSxo8fj0WZX4GOjo5MmDCBmTNnsnfvXk6cOMELL7xAREQEHTp0AKBv3740btyYsWPHcubMGbZv387777/P1KlTddHZ5MmTiYuL4+233+by5cssWLCAtWvXMmPGDD29ZJHaRk0vHg3oNoE1VbUVSdlqKykGXovXQJutmBKjP8FzcbbHydEW0EZ51aVsxKisYjZpjUFch1cxu3btIikpiRdffPG+a3PnzmXgwIEMHTqUrl274uXlVW7YUyaTsXnzZmQyGREREYwZM4Zx48bx8ccf69oEBQWxZcsWdu7cSfPmzfn666/5+eefq7QkQaRuUNO3BwJQOGi3CDJVhAdgX5IsZmjB8yopL3ZTj4InkUgILtnpILaaW/sAeHk6Y2dnjVqt0YuAmjMaiWEOc6TSA+h9+/at8IvFysqK+fPnM3/+/ArvDwgIYOvWrQ+10b17d06dOlVZ10TqKDV9x3MoU0/THMqLGVjwSutppsbdQF1cjKwS84UPIzjImxOnYvQieBKJhAbBPpw6E0t0bDKhDR+8NEEfqNRq1BoBK7mYkGJozHR5oIjI41PTa2nC3U1gTVlezK5E8G4buJ6mq68ncksF6uJibiXpL3qqX1JTM1YP++IBNAjRZqBfjbmhl/4ehLJYzc9HzvHtwRMGs/FIpAY6zBAzdUtE5PGR1PBammAe5cWMFeFJpVK8Smpq3oxO0lu/9YP0N6QJ0KCkpmZ0NQVPoxHKfTY1GoFitYZitQaFhQwXGytWnLzEpVT9rCEUqRhR8ERqPLUjwistIG2aTWChjOAZuJ4m3J3HS4nV364JpZmacfH6EbzSxJXomOplan617yg7rybqnkulEixkUixk2q9fW4Wci6kZ/H0xtlp2qoq48FxEpAZROodXGyI8kw5pemo3PjX0kCaAR0lNzVt63Ai2NMLLyMwjO/s2Tk521eqv7NIEjUajW/5SWbZfSSA6PYu+oYFoNAL7Yq+x9XIcO64mcCk1E0sLGY08XHC3rXidsYh+EAVPpMZTG9bhWZrDkKa3NkIy9JAmgHup4OlxDs/O1govT2dSUrOIS0ihVYuQavUX4O+BXC7jzp0ikm9m4luvahWbXmgbzotrtxGVeJPLaVqBC/NwoWuQL7N6tKOxpyte9rY4WJqoYIJEQJDo98eivvvTF6LgidR4pLVgHZ6pF54D2JdEePm30tEUF1eq2kplcTdAhAcQXN+blNQsYuNuVlvw5HILggK8uBpzg+jY5CoLXr/QQDSCQIcAbz7sE0GYhyveDrY4WCmwNOB7LHI/4hyeSI2nNgxplkZ4xXduo1YZd3ulUmyMWG3FYIJXmriir0zNknm8q9FVT1zxtLfFRi6ne7Afw1uE0czHHXc7m/vETqMx0edXXHguIlJzqA1JK3I7RygpQaXMzTaJD1KZzGjVVkoFL+tmOiql/vYwLE1c0VemZsMQ/WRqDmoSwrXsvAdeyykoIjUvH6nUNCohYICkFZO8kkcjxtMiNZ7asPBcKpNh6ehCUXYGBekpWLt6mMQPBx9v8pJTyIiJw6dlc4PZcXR3xsrWmsL8ApLORRPc+vF2iHgUISVLCY6duIparUEmq95v+rCSBecHDl1Ara7652t2344IJTJw6/YdNl2IZfuVeI5fTyW3UEl7fy/Cvdz4qF8ncQG6AREjPJEaj1xuA4Cqmrt+mxrHoFAAsmMumMyHgAht3du4fQcMakcikdCiZGugyPW7H9H68enSKRxnJztuJGewa0/1qzX17d0aZyc74hNS+HvrkSr308DdmYbuLuyOTqTHwjW8+udutl9NICErh9e7tKJvaCA7oxOZsn4n6fl3qu13ZSjd8Vzfhzlipm6JiDw+CoVW8JRK08x96QvnBk0ByIo+ZzIfQnp3ByBm9z6Dz4l2HNYHgENrd+rNlrWVgtEjugPwy287q92fna0Vk158AoB58/+qlp+x6dm8sGYboR4u/Pn8Mxx4ZSTfPNWDnVcT6RcayL4pIzidfIsN56Kr7bfIgxEFT6TGY6nQVsmv6RGec8NwADKvnjeZD37t2yK3teF2ahqp5y8a1FabgV1QWFmSEnuduFOX9dbvuNG9kUgk7Nl3hjg9FH5+6fl+2NpaceFiIrv2Vj1q3HwpllB3F356ti99QwNp7uPB9K6t8Xe257uDJ3GwsqRPwwA2XTDyAnRDLDoXk1ZERAxDaYSnUqkQavA8XtkIz1QZpxaWlgR17ghoozxDYm1nQ+sBnQE4vG6X3voNCvSkZ3ft/OPyFdXv18nJjhfHaqPR7xZUPcrTCAL5ShWu9ywwt1XIkZWsJQ1ycSTjTkH1HBapEFHwRGo88hLBA1Apa+6XhVNwYyRSKUVZ6RSkm25LmuBePQCI3bPP4LY6Ddf/sCagE6hVa/bpZUPYyROfxMpSzolTMSQkJFSpj/5hQVzLzmP92auk5eVz6kYqs7cf4ucj53i2WUMAugf7sWBI72r7WxnqUmkxUfBEajwKeRnBKzLuhL8+sbCyxiGgAQBZV00/j5cUdYyivNsGtdV6QBcU1lakxl0n7uQlvfXbq0cL/P3cyc7JZ+Omw9Xuz8PdiedGan8IHDx4sEp9hHm48p8+HXh+zT88uWQDMzftY/OlOL57pifdgrW1RUPdXWhZz7Pa/oo8GFHwRGo8UqkMi5JFvKqifBN7Uz3MIXHFJSgQl/qBaFQqEg5WXywehpWttW5Y89Da6ieZlCKTSRk/WhspLf1VP/1Oe3kgFhYy4uLiuH69akWvJ3VoztHXxjCxfTN6NwjgqwHdmNi+me66KdbiiRGeiEgNQy6XA6AqqrlDmnA3cSXLhIkrcHdYM2b3XoPbKh3WPLxul16HNZ8b0Z2+vVrx5utD9NKvn687QwZ1Aqoe5QE08nTl5YjmvNe7Az0b+KOwkFXbt+pgjoI3Z84cJBIJ06dP150rLCxk6tSpuLq6Ymdnx9ChQ0lNrVyBBFHwRGoFCoW28K4Y4emHkF7dAYjZtdfgCTStn+yMpY0VqfE3iD2hv2FNN1cHVi59i359WiOR6CfkmDb5KQCuXLnC+Zu3qtWXIAg1uhyeoTh27Bg//vgjzZo1K3d+xowZ/P3336xbt479+/eTnJzMkCFDKtW3KHgitYLSCE9Zg+fwAJwbagUvNzGa4kLTRauBnTsiUyjITrpGZmycQW1phzW7AHBo7Q6D2qouIfV9aNxYWxXmv3uPVqsviUSiNyGuFma04/nt27cZPXo0ixcvxtnZWXc+JyeHJUuW8M0339CzZ09at27N0qVLOXz4MFFRUZV6qSIiNZ7aEuFZu3lh6eSKoNGQE6e/aKeyKGxtCOjYHjD88gQw3LCmIejSRSvOa09fJibddLtb1ARyc3PLHUVFRQ9tP3XqVAYMGEDv3uUzVU+cOIFKpSp3PiwsDH9/fyIjIx/bH1HwRGoFd+fwanaEJ5FIdFFepgkzNQGCe3YHIGbXPoPbav1kJyxtrEhLSCbmuGEXvJfl4uUk3n5vCYeiHt+mt7c3DRo0QCMIfFnNKM8cMOQcnp+fH46Ojrrjiy++qNCP1atXc/LkyQe2SUlJQaFQ4OTkVO68p6cnKSmPv4RHFDyRWoEuwius2REegHODksSVaNMmrpTO4yUcOkxxYaFBbVnaWNP2qa6AcYY1S6NImVSKRCrl0zmrK3V/aZS3/PgFrlewC4IIXLt2jZycHN3x7rvvVtju9ddfZ+XKlVhZWRnMn1pflluDEg3Gq7FY1pYx7ZqTbUEwrm1BUN6dwyvMA40R7WvuljOTFOuntJlLsHaOKPvq2Yf2We6aAcqquYfUx97bi7ybKSQePExw9y4PtKdRFuvFXofBPfl3zQ4Or9vFmE+nVTi/VdaeSlW5yjpqtQaptHTuTKB+kA+vTRnEgCEfsGnzEfr3a/vQ+0vt+fv707m+L//GXefLfcf4elDPSvlRFZQGGuk1xDKC0v4cHBxwcHB4ZPsTJ06QlpZGq1atdOfUajUHDhzghx9+YPv27SiVSrKzs8tFeampqXh5eT22X7Ve8JJcvzfoL4aH2/7aJHYB0qy/MZntvMJ5RrdZKnhC8i4UVwwbjVRE0K6FeunHJi2FQ0D2xRME/fPtYyU2SBav1Ivtcn0CIV7enLqZQuzcBYRcfHCNx4tf6yciUyi1P1xuJd5k+7TF+Pr6PvKepasfXndSEASOHz/OrVu3aNasWbk+S7eTkkql+Ac0ZO6CXVxLc3psf4M69eDfuN9YFHUW64he2NraPva9Vcc0n21D06tXL86dKz+E/8ILLxAWFsasWbPw8/NDLpeze/duhg4dCmgzZZOSkoiIiHhsO7Ve8ETqBqVDmjV9xwQANzc3pFIpRUVFZGdnl8tWMzYhISGcOnWKmJgY+vXrZ1BbCoWC0NBQzp8/z4ULFx5L8B6FRCLh9OnT3Lhxg+vXryOTyWjVqhWhoaHY2Nyt0JOeno6npyeCIDx25mT9+vXx8fEhOTmZqKgoevXqVW1/TYJE0B767rMS2NvbEx4eXu6cra0trq6uuvMTJkxg5syZuLi44ODgwKuvvkpERAQdOnR4bDu1XvC+BfQz4PJ4yIG3Sx57FMxEisJotjUodZGdh2omEiPaFlCSJtfatreajkRiRNuCErlcG2UU2YRTHPym0WyjUWIR/x0AN7pNQZDJ9dKt0+o/yYw5z0WPtgR0efKBbSRqFfX2a6PK22PHg1w/tsvimZODZMMGbt26RXK//jjUq6e9oFJh99tyALymD0Kq0M9XSZdQJ86Pmk5c+k183h76wDYaZTEp8/4CYMCUVljIH75w27WRhP+88jWL/v6Yrev28ueKHZy9dJLQpvVp17U5p6MuEhMTw5QPR9DjyXYP7atYpWbLwpMAvNnegSb0Y/hHSzlz4hjrZzyB4z2FofWJUi3w2QH9R3iGHNLUJ3PnzkUqlTJ06FCKioro168fCxYsqFQftV7wVBhX8MoiRWFUwSuLxMi2y86kSCQKowoelElaURaC1DTvuSCTI1jox7ZLg2ZkxpwnI/4S/j2eefQNcrlBBM/KzQ2fVq25cewoCf8epNmYsfe1kSos9CZ4TQd2Y+Jv/6X5wB6P1aeFXIaF4uGC131Ae9RqDVfOxzH1/bFMfGsEuzcdJmrfKZZ8swYvXw/+u/xd+jzTuVK+KmQShnYKp0mgFwVFKq6nZuLeoPpRqYiWffv2lXtuZWXF/PnzmT9/fpX7rPWCJ1I3qC0Lz0txCSlZmhBj2qUJAIE9enDj2FHi9+59oODpE4W1Fe1HDdR7vyMnDmT5d3/wxNCuKCwV9B/Wnf7Dule7X6lUypbPJlLPzRELmWlLhFWVmhLh6QNxWYJIraC2LDwvxbVkaUKGiZcmAAT10GYgJh48gLqGbrL7wvRhXDwVw/UKNoTVVGMfxQBPlxordnUNUfBEagW1ZeF5KaURXt6NOJT5pl3n5dmsGdYurijz8rh54oTJ/NCo1eSmZVTpXntHW75d/YFuNwKNRsOFU9Fci79JTmYeUundr0J9V3ox98ox5lg82lCIQ5oitYK7EV7tEDwrJzds3Ly5k36TrLgLeDZ9/Ew0fSORSgns1o1LGzcQv3cPvpXIitMXRfl3mD/0VRQ21gyfU7WkpO5P3vU7Kz2Xb977mYSYG4Q2DcLG1prWncIJDgsgtGl9HF3s9eJ3WlYe//fLFjo2DuLF/u310qdI1REjPJFaQW2L8ABcSnZOyDTxzgkAgSXDmgl7Db9d0L0IgoClrQ2DP5mOjZM9y17+T7X7dHK1x97Rlvqhfvxv+f8R3CiAa/E3mf/Zb8z9YAmTn3mP/Lzqf5byC5XUc3Pk8993sXrvyWr3ZwgEqWEOc8RM3RIRqRy1LWkFzCxxpXs3AFLPnSU/vXrb4jwupUOBpevigto2ZdiXb5GRmMzFi9WrtymTyRg0pg9nj13G2taKKe+O5u05L/P1b+9x8VQ0h3efZPuGA9X2P8jblQ/G9OPdUb14bf5GMnJqxxxzTUUUPJFaQW1LWgFwCTGfxBVbdw88m2r3J0vct9+gtorytT9a7l0AXqxSYe/uQmCb8CrvOA7aklVqtZoeAyLw9HFj3ZKtRO09xciur9Iz5DnkCjmzvnyZlhFNqmyjWK0uI9gwplcbGtRzY92B01Xu01CIc3giIjWM2jik6Vq6GWzcBTRqNVITZwIG9uhB6rmzxO/dQ+NBg/Tev0at5stuY8i8nkKjXhH4NQulQec2eIcFYWlrg4VcjkatJu7oWTo0bVllO7KS9/FOfiEePq588dZC5HIZXfq1Y/I7o2nSqgHObk5YVGIncrVaQ1JaFu5OdthZW5bL2pRKpVjI4EZ6jkEXplcdA1RawTwTdUTBE6kV6EqL1aIIz8E3BJnCiuKCfPJuxOHo38Ck/gT16MmR774lYd8+hGqk8VeEVCYjqF0z4o6cwaO+H4d/+4utXy7GwdMVG0d7XAN8uLDzMDaO9oSGhlbZjkpVzNShHxC19xRevu4gCLz95WSefaE/arVaJ4iP3V+xGu/hH5KZd4f2Yf4ANA7wokmgFx4lArh4SyQ5+YW0CK5XZb9Fqo8oeCK1gtIIT6NWoS5WIbPQf9URYyO1sMC5fmPSL58kM+acyQXPu3VrFPb2FGRmkHruHPrJYyzPiK/f4eAvf+AVVp8Pj2/gVvx10uOuEX/sHEmnL9Fv5gu0GNCD4vXHgKql/MvlFjRuGUKfZzrTpW9bfv56Dak30gEqLXYAcgsZs8f147X5G/lo/BNcvX6Lo5eT2HMqmpjkdAqVKup7u/LXxxNoFOAJaJdFlF0KYUrq0sJzUfBEagWlER5o5/FkFk6mc0aPuDRoRvrlk2REnyOo5xCT+iKTywno3IXof7YSv38fIXb63x1AIpEw4n+zWDntY5r274p7kC/uQb406nW3Ir5GWUwyx1CpVNzOzcfZ3bHSdqb9ZzwymRSJRMIbn72EtU31dlSZ9kwXVu4+wS/bjrL6/XHlrqVm5eHpXP7nwe0CJQ62ptnFpS5jHj8xRESqiUwm0yU5qJS1aR5Pm7iSGWP6xBXQzuMBxO3eZbAF1V1fGo6tqxNrZmp3vhYEQXdo1GoAjh07xvfff8/roz5m3oe/VNqGhcXdz0t1xa6UrZ9PYsuRiyz6+xCgTVwB8HS2RxAEklKz+OWfI7R8+X/0mbWQ5z77jb2no/ViuzrUpaQVUfBEagUSiUS372HB7UwTe6M/3Bu3ASD5+D4KMlNN7A3U79UbiUzGzVOnOHr0qMHsvLL2WyQyKYV5+UgkEt2hUatRFhQSFRVF06ZNad+tBUf3n+GLNytXNd8QONvb8OP0Ybw2fyMpmbm6xBVVsZrdJ68y/qtVTPluHV2a1mdghyY42lox6IMlpGWJO6YbC3FIU6TW4ODgQEFBAbkZ1/HwC3/0DTUAt0atcW/SllsXjnF+9XzavvKxSf2x9/Gh6/v/Yf9Hs9m2bRseHh74GMCOT+MQhnw6Ayt77bDp1QPH2PjBt1hYKvBvHoaDgwMRERGMntWDPoM7M67PG4ye8gz+wdXzRlmkZP2ybQx/aUCV5vOe69UauYWMvDtFeLloz/1z9BITv1lDgKcLl5a8Q30fN137+JRMvvh9F3NfGVwtv6tDXZrDEyM8kVqDo6N2Licn45qJPdEfEomEFs9rd1i8tOEnCnOqVktSn7R5eTKNBw9BEATWrl1LekLV18Q9DFtn7d/z8t4jzH1yIvbuLnQY/TQZSTdJSEjQCVJQqB+h4UGcP3Gl2jY/nDqPz99YwBtjPqPgTtX2nhvWrQUNfN0B2H8mhqEfLWVw56YcnT+D+j5uKFXFumLVao0GFwdj7JQuAqLgidQiSgUvNyPJxJ7oF7+OT+DSoCmqO7e5uG6hqd1BIpHQ58sv8fHxoaCggAUjXtctFjcEyRdjcK/vy0u/fkWncc8w6df/EhoaysqVK0m9kc6mlTs5FXURd2/Xatvq2q8dcoWc3X8f5qUB75B5K7ta/a07cIYeLUJYNH04AEXKYhRyC6RSKUcvJ3LoQjxezobId318xDk8EZEaiJOTEwA56bVL8CQSCS3Ga6O8C+sWobydY2KPQG5lzYgRI7C1teX6uav88uL/GSyJJfvmLdwCfVFYW1Fcsj1ReHg4RUVFzJowh/++u5hxrw6hdafqD2P3H9adxX9/gYOzHWePXWZMrxkkRFc+ghUEgSJlMQkpmbRu4AdoF6dblmxqu2LXcYZ/spyhXZoxcUDEw7oyOKLgiYjUQHRDmum1Z0izlMDug3AKDEWZl83F9T+Z2h1A+36PGDECmdyCE+u3s+WLHw1ip8Pop7h68DixUaexKFlvefHiRdzd3Zm36gO2nv2Fl94Yobd1ba07hfPbrrnUC/TiWtxNxvSawanIC5XqQyKRYKmwwMHGiivX01CqipHJpGTm5jP1uz/4as0e+rQK5d1RvYGqrScUqTyi4InUGu4OadY+wZNIpTQvifLOr/4eVYF5VJTx9/fnubnvAfDXh99x+m/976bg0yiYTs8PYdXrn/Lz+LdZ8uI7ZGVl0aJFC5xcHHBxd8LJ1UGvNuuH+rFi91zCWzckJzOPlwa+U6Vi0j+8OoSjl5PoOuMH+r/7I0FjPuXYlWuM6N6Sd0b1onGAF3B/3VCjIjHQYYaIgidSa9AJXuZ1NBq1ib3RP/V7DcW+Xn2KcjK5/Gfl154Zii4vPkuPKaMQBIGfx71F8qVYvdsYNff/6PLis2QkJCMIAo0aNcLf31/vdsri5unMkq1f0X1AB5RFKt4c9zm/fr++UtGYi4Mt/3w+iRefaIeboy3/nfQUv7w5kreG9yC4TLamiHEQBU+k1mBnZ4dUZoGgUZOXlWxqd/SO1MKCFuO1m5+eWzWP4qICE3t0lxHfvEvDLm0ozMtn/pCp3MnO1buN7i+P5K29vzL2hw/p2rWrbt2lIbGxtWLeqv/w3OSnAZj74S9s3bpVl2X5ODSt78OkgR357Z0xTBrYkfAgbxRy81kRJkgFgxzmiCh4IrUGqVSKg4svALm1cB4PILjfSGw9/SjISOXqlpWmdkeHhVzOlLXf4uLvTWp0Ij+NflNXFUWfSKVSLG1tKrwedzlJ7/NhMpmMd/47hbfmTEIikXDs2DFWr15NfmFRpfsS5+pMiyh4IrUKB1et4OXUsqUJpcjkCpqNmQHAmVXfUlxcbGKP7mLv7sK09T+gsLbiws5DxBw+ZVT729YfYGiHKfz45Sq99y2RSBg3bQhfLX0XCwsLrl69Sp+3FpCSWblI1qRzdRUgGOgwRyoteDdu3GDMmDG4urpibW1N06ZNOX78uO66IAh88MEHeHt7Y21tTe/evYmOLl8vLjMzk9GjR+Pg4ICTkxMTJkzg9u3b5dqcPXuWLl26YGVlhZ+fH1999VUVX6JIXcLRVZsCXhsTV0ppOHAcNm5e5Kfd4MyZM6Z2pxz+LRszYfmXzNi6mIZd2jyyvT4jnlspGRQXq5n/6W+smP+n3votS++nOzF+/Hisra05cfUaEa99y6VE05d8E3k8KiV4WVlZdOrUCblczj///MPFixf5+uuvcXZ21rX56quv+O6771i0aBFHjhzB1taWfv36UVh4t2rB6NGjuXDhAjt37mTz5s0cOHCASZMm6a7n5ubSt29fAgICOHHiBP/973+ZPXs2P/1kHunYIuaLLsKrpUOaABaWVjR9bjoA//77L2oDDB1Wh9ZD+tK4d8dHtsu7lcm+H1ejqsLQ4IMYO3Uwr7w3FoAvZy1i46/b9dLvvfj5+fHSSy8R7ONGQkomHV//lv1nYgxiyyhIDXSYIZVy68svv8TPz4+lS5fSrl07goKC6Nu3L8HBwYD219q8efN4//33GTRoEM2aNePXX38lOTmZP//8E4BLly6xbds2fv75Z9q3b0/nzp35/vvvWb16NcnJ2kSDlStXolQq+eWXX2jSpAkjR47ktdde45tvvtHvqxepdTi6aTP3atvi83sJHfQCVo6uZGVlcf68eeykUFlSrsRzZd9R5j45UW99Tn7nOca9qt1Gafa0b9m2vvJLCR4HV1dXDsx7jYjGgWTfLqDvO4tYtfuEQWyJ6I9KCd6mTZto06YNw4YNw8PDg5YtW7J48WLd9fj4eFJSUujdu7funKOjI+3btycyMhKAyMhInJycaNPm7nBH7969kUqlHDlyRNema9eu5fY469evH1euXCErK6tSL1BugqMUDUqjH6UIRrYrlLUtKI1+6D5vLtp1TbkZiaBRGucoQaJWISlWGuVQyOU0HT4ZgIMHD6IpLASVyrhH6edcWVylo37bZjz7+RtoitXMH/pqpe4tpVilplh591CrNEyf/SKDx/ZDo9Hw7oQv2bslqlybah2qu9G0g50t/8yZzODOzVCq1Iz+YgWfrtxJUbEGpVowyGEI6tIcXqVyY+Pi4li4cCEzZ87k//7v/zh27BivvfYaCoWC8ePHk5KSAoCnp2e5+zw9PXXXUlJS8PDwKO+EhQUuLi7l2gQFBd3XR+m1skOopRQVFVFUdHdoJDdXO5n8OmCqbRbTrE0XkabJTWc7r3CeyWy7FOwDICctBovY/xnVdr39xq1z6eaq5pyVFenp6Vx7712aNGliVPulpMz7q9L35OfnY2urLZr8ZJvOrFixgv0T59CgQeV2dd+y8OQDzzcNak90+A3Onz/PzNGfMmbMGAIDAyvt58P4+qh2W5+mPZ8hRW1LZGQk/1m6lY1nUhgwoGq7LZgEKQj6HoKsDUOaGo2GVq1a8fnnn9OyZUsmTZrExIkTWbRokaH8e2y++OILHB0ddYefn5+pXRIxAQ4O2oobRUVF5eaNayNWVla0b98egAMHDtSYlPfMzEyWLFlCaqo22cPFRbuPjlKpfNhtlUIqlTJ48GAaNmxIcXExq1at4vp1w+zqIJVK6devH/3790cikXDy5El+//33cj/ARcyDSkV43t7eNG7cuNy5Ro0asX79egC8vLTDSampqXh7e+vapKam0qJFC12btLS0cn0UFxeTmZmpu9/Ly0v3j6FsH2Vt3Mu7777LzJkzdc9zc3Px8/PD8840bNXGq2igQUmKrTa6+gpQPby5XpEDb5c8dmUGEhQPa65XBJRkMBcAW5vpSCRGtC0oyb8zDwDL+m9hbfsjBfmZ5FgPxsrXwFGPRgk3vgMgve00kMkfcYMeUatof+cOkZGRpKamcsglmIAeTxjFtKRYhe8m7XSG6sWxIK/4dRdkZ3PtyFEa9usLgD0QmJPFstW/0/eTj4jds498lQr7l55H1awpaqUSmeIhnx+VCvkvvwHgN+MppIqKv8benPkUcwdP49K+o/y+fi2zdvyCX3jlosiyaJTFXJv7NwA9X+2AhfxuFNeXTnTf2oEPXvicmJgY/tiylv+t+xR3b/18/xSr1Gz97z699FUWoeQ/ffdpjlQqwuvUqRNXrpTfc+rq1asEBAQAEBQUhJeXF7t379Zdz83N5ciRI0REaCuCR0REkJ2dzYkTdyd49+zZg0aj0f1ajYiI4MCBA6jKzBPs3LmT0NDQBw5nAlhaWuLg4FDu0L5AhdGPUlQmOEqRoEAiMeJR5nUb1W7JoUOqwNG1JHEl6yZIFYY/SpHJQaYw4iHHxsaGdu3aAXD653loZBYIFnKjHDrk8goPjUTCjg8+Iunocd05QSaj0xvTsfXw4Ob5C6THxNDn809wCq7PhU2bOfjt92Qn33xov3f/3BYPPSwdbJn+1w8Ed2hOflYuXw98mbTEG4+872FHKRZyGRaK8kePZ7qwaMc3uHg4E302lkm9XicxOum+dlU65DVkiNSMqZTgzZgxg6ioKD7/XPsLZtWqVfz0009MnToV0C6qnD59Op9++imbNm3i3LlzjBs3Dh8fH5555hlAGxE+8cQTTJw4kaNHj3Lo0CGmTZvGyJEj8fHR7lb83HPPoVAomDBhAhcuXGDNmjV8++235SI4EZGKKF2LV5s2gn0YHTp0QGZlTfq5kyQf1n/x5uogtbCg2agRnFn5O7G79wDaQtgSiZT8tFs0enogL+7eTvNRI7B2ciL/1i1uXbrM6pGjuZOZqRcfrOxseWPzQvyaNSQnNYOv+k4g49pNvfT9IJq0bcSS/T8Q0NCP1OtpvNTjNY7uefBcozkgSA1zmCOVcqtt27Zs3LiR33//nfDwcD755BPmzZvH6NGjdW3efvttXn31VSZNmkTbtm25ffs227ZtK1f3buXKlYSFhdGrVy+efPJJOnfuXG6NnaOjIzt27CA+Pp7WrVvzxhtv8MEHH5RbqyciUhG6CK+WVlu5Fzs7O8KeHQfA6YX/Nbu5vKCuXej8xnS2TH+TM7+vIXr7Tv79ei6uDUNwCa6PRCJBKKlN2W7yJAYtWoBXs6asGTVWbz7YOjvy1rbFeDYIICPpJl/1fYnMG4ZbMF4vyJuf931Py87NyM/N5/WnZ7H5N8OsCxR5fCqtwwMHDuTcuXMUFhZy6dIlJk4sv4ZGIpHw8ccfk5KSQmFhIbt27aJhw4bl2ri4uLBq1Sry8vLIycnhl19+wc7OrlybZs2acfDgQQoLC7l+/TqzZs2qwssTqYs4upREeJl1I8IDaPr8VKRyBakno0g5ftjU7txH+1cm0+7liVz88y/WjhlP2qVLBPfsgXXJFIWkzF52Clsbes7+D8VFRSQc+FdvPjh6ujFrx8+4+nuTcjWBj9qPIPboWb31f589Fwe+3/IVfYf1QF2s5uOJX/LzZ7+a3Q+SurQswUwDTxGRqlM6pJldRyI8AFsPLxoOHQPAmUXGXY7xuHSYNoXBixcx6d+9DF+xnM5vzCgndGXJT7vFnVvpyBT6TQJy9ffh3T3LqNckhOybt/ii+3giV23Wq42yKCwVfLz8Pca/OQqAnz5ZxqeT/2d2oldXMJ89KkRE9ISTqzaJqq7M4ZXSbMLrXPnjV5Ij95F25hgezdua2qX7sHJwwMLSkrg9+0i/ehVrFxfsvb2QW1lj6WCPjasLyafPEL1tB4JGjY2rq959cA/y5T//rmTR2Fmc3ryPRWNncf1CDEM/eU1vu6aXRSqVMvXTiXgHePHV698S0NDPrIpIG2LOzVzn8ETBE6l1lEZ4eVk3UKuLkcnqxsfcrp4/IU+PJHrDCs4s+h99Fq4xtUsPRCqTsf+LL0m7dJnALp3JvX4DjUaNRqVCrSrG0t4euY01w1f+hmuDEIP4YO1gx+sbvuOP/3zHli9/ZvOcxSRfjGXSr3Owtrc1iM0hE5+iWUQTgpsEPbqxiEGoG98EInUKWwdPpDI5GrWKvOxknFwNuzO2OdHspenE/LmKa/t3kHHxLK6Nm5napfuQWlgwdNkSfunVjyZDB9Ni9ChUBQUgCNxOTUNuY4Olgz1ya2s0ajVSA1UskcpkDP98BvUah7B00gec3LSHT7uMYfqfP+AeWM8gNkPC6xukX5HHw0wDTxGRqiOVSu8mrtSxYU3HwGCC+muLJ5/+0Tzn8gBc6gcx6McFbJ3xJpe3bEVubY3cxgbnoEDsPD2QW1sjaDQGE7uydBrzFO/sWYajpyvXz13lo/YjuHKw7hSCFiQG2PFcYp5zlKLgidRK7i5NSDSxJ8an+ctvAHAn9SbFReZbXq1B3970nfMZG16YyM3T9+/rV1FCiyEI6dCcD4+sIaBVY/LSs/iyz4vsX7LeaPZFjIMoeCK1krq2+LwsziFhDFp/gIG/78DC8v7S6ao7+WaTJdjmxedp9cJ44g8cNLUruPp5897+X2n7bD/UqmJ+mfQBK2d8gdqMdpU3BOKyBBGRGo5O8OrQWryyuDZq+sBMQEEQiN64km0vPsPltcuM79gDeOLLz+n42jRTuwGApY01U1d/zeDZ2upRO75bwTcDp5CfnWtiz0T0gSh4IrUSRxftkGZ2et0b0nwYQnExbuGt8O/Zn3NLvuPPIV3JvHrB1G49lMR/D2uTWoyERCLhmf+8wrQ136CwtuL8zsO81+wZ/nj/W25crME7m1eAIDHMYY6IgidSK/H0DQcgKfowKpX5zmMZG6lcjkfzNjQZO5nBf/6Lc8PGnF82H7Uet+bRJ3kpqax6dgTfN2/Nno8/IzvJeBF722f78f7BFbj6e5N1I5W/v/iJ91sPZeHChfz777+kXEt7dCciZoUoeCK1Ep+gNji6+qMszCPm7DZTu2M2lBU2C2sb6vcfQtyW9dy5lWJCryomOyEBOy9PCjKziPzuB+a3bs/a5ycQExODpqT+piEJaNmIORc388rvX9PyqR7I5Bakpqaya9cuhjQZzaRer7N+8SayM3IM7ouhECM8EZEajlQqpUnbZwE4f2Stib0xHzIunub3bo24un4FhVkZXPnjV5zqN0Rh7wiAKj+P/JQbpF84bVpHS/Dr0J6px6N49tdfCOrWFQSB6O07WbFiBfPnz2fn/JXcyckzqA8KayvaD3+C6X/+wLyEPTz11FMEBgYikUg4fegcX746j/4BzzJzyP+xfc1uCvKNN/yqFySCYQ4zRFx4LlJrCW8/nMPbvuHqma0oi/JRWBqmgkZNwq1pa5q++CpRn8/i6obfQBBo+9YnWDo4cnHFT8Rt/QNV/m3UKhVyWzs6zZ6LW5MWJvVZamFB6JP9CX2yP+nR0Zz4+RfO/raKjIwMVr35Fes//J6OY56m9yuj8K3G5q6Pg52LI61bt6Z169Y0H96AvX8dYPua3Vw5HcO/W6P4d2sUVjZWdHuqE0+M6kX7Xm2wkItfs+aC+JcQqbV4B7TExSOEzLQYrpzeTNP2I0ztkkm4k3YTK2c3pHI5UpmM8PGvYGFtQ8KOTfT67jfkNrZc+G0Rpxd8Rctp7xDYdxAaZRHnly9gx8vD6PLZfPy69TX1ywDArUED+n36MX08vDl79iyn4i6RfCmOvT+uYe+Pawjr1pber4yi5aCeWDxkF3Z94OnrwZgZIxgzYwTxlxPZsWYP21bv4kb8Tbav2c32NbtxdHWg99Du9BvRi2YRTQxSq7O6GGIIUhzSFBExMhKJhCbt6vawpkatJvKzWaSdOaZ7DmDn7UtuQgzFhQWknTnG2cXzsPPxoyA9jesHdmBXz58O/zeHdm9/inuz1qZ8CQ/E0tKStm3b8umJDbyzeylthvRBKpNxef8xfhgxkzfq9+WvTxeSnXLLKP4EhQXw8ocvsOHiCn45MJ8RU4fg4uFMTkYu63/axKRerzM4bDTz319M/GUxc9hUiIInUqsJbz8cgJhz2ym8U3MTC6qKRqXEytmVfW9N5MKvCynMvEVOQizx2/7E2t0LZU4WV9YsozDzFgoHRyysbbi4cjHbJz2LKv829Z8cipWz/ncs0BcSiYRG3dvx6rp5fB23g6ffexkHD1eyk9PY8OEPzAzszcLRbxF9+JRRFttLJBLC2zXija+nsTluLd/9/SUDxvTD1t6Gm0mpLP/f7+z90/SL7MtSl5JWxCFNkVqNp2847vUac+vGRS6f/IsWnceZ2iWjYmFlTafZc4nf/hcn5n1CzF+rKcrLAY2Glq/+HwpHZ2I2r6XFK7MIHz8Fua09/j36s//tSaSejMK3S29Tv4THxsXXi6Efv8bT703m+Pod7FrwOzGRp4lavZWo1VvxbxFG71eeo8OoJ7G0sTa4PxYWMjr0aUuHPm2Z9f10Dv0TxbbVu+g3vKfBbYs8GFHwRGo94e2GsXfjR5w/uq7OCV4pQf0GEdRvENcO7ERh74C1mycOfoHEbFqNnbcvgX2fRm5rD4Bzg0YUZNwiO/ZKjRK8UuSWCiKeG0jEcwNJOHmR3Qt+J/L3LSSdvswvkz5gzaz/0Xn8M7R6uichEc2xUCgM7pOVtSW9hnSj15BuBrdVaSQlh777NEPEIU2RWk+TdsMAiLu4m/y8dBN7Y1r8uvbBs2V7HPwCAbBydqMoJwupxd3fvjcO78XGw0u3VKEmE9iqMRN+/oR5SXsY8dWbuAf5kp+Vy/Z5v/JFz+eZ6t6JuYOmsmvBKlKiE82mxqiIYRAjPJFaj5tXQ7wDWnIz8RSXTvxJm+4vmdols8GxfkMcAoK5fnAXNh7e3ElN5vyy+Vi5uOHerBWgrb9Zti5n2umjxG/dgNqiCH//mrHXoJ2rE0++8QJPTB/H2W3/ErV6K+d3HibvVianN+/j9OZ9ALgF1qNp306E9+lIo57tsXVyMK3jxqAORXii4InUCZq0G8bNxFOcP7JWFLwy2Nfzp9lL04n8+E2ubliBMicbW+96tJj8Fs4NGgPcV4Q6dvM6Lq36mQuAjY0NDWOuEvrUQAK7dsbC0tIEr+LxkcpktBjQjRYDuqHRaLh25grndx7i/M7DXP33JOkJN9j701r2/rQWqUxG/fZNCe/TiaZ9OhLUNtxcv8erhUQiINHzQnF996cvRMETqRM0afcsu9b9HwlX9pOXfRN7J29Tu2Q2BPZ5isA+T5GwazP2Pv44BATp5vMehH/PJ1Hl5XJ95ybu3LnD6VWrOb1qNQo7O0L69CZ0QH+Ce/XE0t7OiK+i8kilUgJaNiKgZSMGvP0SRfl3uLz/OOd2aAXw5uU4Yg6fJubwaf78aD42Tg407t4Or2IrQkJCTO2+SBWo9YKnQYkG4xXGLWvLsMte76esPQGlUTelEsq8bkEwbiHicvY0D7bt7OKDb3B7rsce4eKxtbTvNUU/xsvaU6v00+fjUsaepLj6toO697v75CH9+bbrjF+r9ni3CCUhIYGLEoEr23dwOzWNixv/5OLGP5FZWhLUtTNhT/anQZ/e2Li6VNs/Haq7vmmU+turTi5X0LR3R5r27ghARtJNzu+O5MKuSC7ujSI/K5fjf+7StV//9zra92pD+15taNmlOTZ2hs38LFapDdJvHRrRRCLU0lna3NxcHB0deeedd7Cyun8TTJG6R1RUFNu2bcPPz48JEyaY2p1ahUaj4caNG1y+fJlLly6RmZmpuyaRSAgICKBRo0aEhYXh6FjzkmE0Gg3JycnExsYSGxvLtWvXyiW4SKVS/Pz8CAkJITg4GC8vL4NUVSksLGTOnDnk5OTg4FC9+cXS78jvLqZgba/fucqCvFxea+ylFz/1Sa2P8ERESmncuDHbtm3j2rVrZGdn4+TkZGqXag2lX/h+fn707t2btLQ0nfilpKSQkJBAQkIC//zzDz4+PoSFhREWFoa7u/sDN6o1N6RSKb6+vvj6+tKtWzcKCwuJj4/XCWBWVhaJiYkkJiaye/dubGxsqF+/PsHBwQQHB5vVl/69SKTaQ999miO1XvACbr2KrcLNaPY0KEnw/BoA79szkWL4NT5lbd+0+waArwBjDrDJgbdLHpvSttTjdSTSB7/nzl4Q1PA48VcPcCnJnc5h06ttW9Ao0aR9C0BRw9dAasSBbI0Ky6vfAZDVahrIjGhbrcL55A/AA/7eEgl4emqPbt2wyMpCc+kSwuXLCElJJCcnk5yczJ49e8DODklAAJLAQKQBAfAYAmgOnzUrKyv+btQIVaNGAFhkZqKJiUGIjUWIj+fOnTucP3+e8+fPa2/08EAaHIwkOFj7eqtQ51MOvKa3V1I3qfWCJ0VhVNExF9sqjPtFYC62JVJFhYIH0LTtcG4knkRVrHxouyohlYO++3xcZHKQmelnzdkZOnbUHrdvw5UrcOkSxMfD7dsIFy4gXLiABsDGBgICtEdgIHh4wEOGBs3mc+7iAu3aaQ+1Gq5fh9hY7XHjBqSloUlLg8hI6N5de5gJEgQkep7w13d/+qLWC56ISFladBhLy4hxyBWGLy0l8gDs7KB1a+2hUmnFICEBEhPh2jW4c0crhpcuadtbWYG/v1b8AgLAywtkMlO+gkcjk90V7Z49ta8pLu6uAAYHm9rDOosoeCJ1CoWljaldEClFLtcKWWCg9nlxMSQna8UvIUErgIWFcPWq9gBQKCj29+ffwEACAgIQfHzMXwBtbCA8XHuYYY6gRKI99N2nOSIKnoiIiHlgYaGN5vz9oUsX7dDgzZt3BTApCYqKEGJi2BUTo71HLgc/v7tDoPXqafsxV8xQCcxB8BYuXMjChQtJSEgAoEmTJnzwwQf0798fgO7du7N///5y97z88sssWrSoUnbM+JMhIiJSp5HJwNdXe3TqBBoNpKYiTUigYUlGZEFBgXa4MC6u/D2lQ6C+vmCE4tAi1cPX15c5c+bQoEEDBEFg+fLlDBo0iFOnTtGkSRMAJk6cyMcff6y7x8am8qM1ouCJ1Hk0Gg2gTT3XaDRmuSu1CNrkFW9vZN7ejIyIQKPR8PmtWxSXRoCJiZCfr/1/YuLde+rVuyuAfn5g5uXPjI4ZrDx/6qmnyj3/7LPPWLhwIVFRUTrBs7GxwcvLq1puiYInUucpK3Clj+8tmCxifkilUiSlyx/atdPOj6Wn3x0CTUyEvDztXOC1a3DwoHaszcdHO2zq7g5ubuDqqp1nE//eeic3N7fcc0tLSywf8YNDrVazbt068vPziYiI0J1fuXIlK1aswMvLi6eeeor//Oc/lY7yRMETqZMU3snh8rktXI87gqW1IwX5mRQXF+Hg5ENBfhY2dm70evoDU7spUhkkEq2IubtDmzZaAczKuit+CQmQk6PNDL1xo/y9VlZ3xa/0/66u2uUGVVgzV5Mw5Byen59fufMffvghs2fPfuA9586dIyIigsLCQuzs7Ni4cSONG2sLmD/33HMEBATg4+PD2bNnmTVrFleuXGHDhg2V8ksUPJE6SVZGAhuWTqBxq8G4eARjbeOMVCbDxa0+/6x7m8xbMdjYuRDRc5qpXRWpKhKJVrBcXKCVdqsjsrO14nf9OmRkaI+cHG026PXr2uNenJx0Iqh2dSXOzQ1XV1cEBwcxKnwE165dK1dl5mHRXWhoKKdPnyYnJ4c//viD8ePHs3//fho3bsykSZN07Zo2bYq3tze9evUiNjaW4Eos8xAFT6RO4lkvnDZdJpBw9SAjJ60CoOBONgf++RI7Bw+CQrvi7dfCtE6K6B8nJ+3RvPndc0olZGZqh0NLRbD0cVGRViSzsyEmBg3wa+l9cvndSPDe6LAGzRMacuG5g4PDY5dVUygUul0oWrduzbFjx/j222/58ccf72vbvn17AGJiYkTBExF5FFKpjN6DPmLp3H6sX/oinfrMYPv6dyjIzyK0+UCatxuJi3t9U7spYgwUCu2C9nsTIgRBWxmmjAhKMjJwTk8nKysLQaWClBTtcS92dg8eInVyMv91g2aCRqOhqKjogddOnz4NgLd35bb5EgVPpE6i0WiwsXNlyPNLWPRFR+KvHsQ3sA2tOo6nSeuhSKUy7tzOID/vFvaO3ljZ1LwK/yLVRCIBe3vtUbI43gJtPUu1Ws0XWVkUPygqzM/XCuXt29p5w7JIpTBw4N0hVjPAHNbhvfvuu/Tv3x9/f3/y8vJYtWoV+/btY/v27cTGxrJq1SqefPJJXF1dOXv2LDNmzKBr1640a9asUnZEwROpk0ilUrIzkjh24CfsHb3Jy75Biw5zCWs+kIy0GI4d+JkT//6CwsoeO3t3wtsMo0u/N0zttoiZIJPJkLi5aaO3eykouF8E09O1w6bFxWBra3yHH4I5CF5aWhrjxo3j5s2bODo60qxZM7Zv306fPn24du0au3btYt68eeTn5+Pn58fQoUN5//33K+2XKHgidZLCglzWLRmHRlNM++5TuHp+GzGXdiEIGnZsfJ/8vFv0Gfwp9o7e3MnP4O9Vr+Ll24wGTfqY2nURc8fa+u6C+bJoNJCbq70uUo4lS5ZUeM3Pz+++KitVRRQ8kTqJlbUDXZ94m6LCPJq1G0GXfm9w9fx2/vjlefzqt2fK/0WVq7uZFHuY01G/EdK4t7g+T6RqSKXaOTyzQyg59N2n+SEKnkidJbTZk7rHMRd3s3L+YDr0nEr/Yf8FoFhVhIVcm213OycVN6+GotiJiNRgxBpKIiJAYsy/1AtsQ7+hc4DyYnfx1F9EX9iOh08TU7ooImIYJHfn8fR16L1UmZ4QBU+kzqPRqMnKSMCzXjhSqQxBEHRid2jnXP5Z9xbte7xC607Pm9ZRkZqLIEBMDKxYoV3TJ2ISxCFNkTqPVCrDzaMhp6NWkp2RhL2TDzmZSezY+D7pKZdp1Pwp2nWfbGo3RWoixcVw/jwcPgxpadpzUVHwxBOm9asMEgyQpanf7vSGKHgiIkD3Ae8Sc2kXy+Y9iVQqIz31KgEhnWjT5SWatBqCvWP1qrSL1DEKCuDECThyRFvAGrSVWVq1gpIqISLGRxQ8EZESxk77k+vxx7iecBxv32Z4+TbF3slHTFQReXyysrQR3MmToFJpz9nZQYcO0Lq1WS5JMId1eMZCFDwRkRIsrewJbtST4EY9H9ou+sIOrscfo9uT75jt0I2IkblxQztsefGidr4OwMMDOnaE8HDz3oVdXJYgIlK3qWg/vMI7Ofzxywvk56URe3kPz77wM3Ym8E/E9AgaDVy9CpGRdzecBahfXyt0wcHmG+rUUUTBExF5ABUNY1rZOPLEs1/y96pXSbh6gPmfdmDQU30JCwszsocipkKlUnHmzBmKIyO1ZcNAu6i8aVOIiLi/CLWZIw5pioiIVEjLiDH4B3dg7eIx3Eg8werVq2nbti3d6r+C3EphavdEDEV+Pupjx5h79Ch37tzRnrO01G422749POY2OCKmo1Lr8GbPno1EIil3lP1lW1hYyNSpU3F1dcXOzo6hQ4eSmpparo+kpCQGDBiAjY0NHh4evPXWWxQXF5drs2/fPlq1aoWlpSUhISEsW7as6q9QRMQAuHqEMHHWATr1eR2AY8eOsfz9HqRfv2Riz0T0Tno6/P03zJ2LZt8+7ty5g6OjI9J+/WDmTOjTp0aLnUQiGOQwRyod4TVp0oRdu3bd7aDMZOyMGTPYsmUL69atw9HRkWnTpjFkyBAOHToEaLfUGDBgAF5eXhw+fJibN28ybtw45HI5n3/+OQDx8fEMGDCAyZMns3LlSnbv3s1LL72Et7c3/fr1q+7rFRHRGxYWCp4Y+jlBnhls3LiRW0kXWPpOBL2f/x8tek0QsztrMoIASUnaRJQrV3SnJT4+DO3YkUaNGjFHJkNjQhdFKk+lBc/CwgKvB4xR5+TksGTJElatWkXPntost6VLl9KoUSOioqLo0KEDO3bs4OLFi+zatQtPT09atGjBJ598wqxZs5g9ezYKhYJFixYRFBTE119/DUCjRo34999/mTt3bpUET4MSDcpK31dVytoypt177cmNarm8PVPaFjTGfc8FjZKQkBCmTJnC+h0niT+7h20/TSX+9A76T/oeaztnwxnXqO4+VqsqbmcIytirTZ81Qa1GuHQJTWQkwo0buvOShg2RduyIIiCA8JIfMqZ83frEEJXAzPWnXqUFLzo6Gh8fH6ysrIiIiOCLL77A39+fEydOoFKp6N27t65tWFgY/v7+REZG0qFDByIjI2natCmenp66Nv369WPKlClcuHCBli1bEhkZWa6P0jbTp09/qF9FRUXldsfNzc0FINH9e6ysrCr7MvXCTbtvTGIX4G2TWTatbU3atyaxa2dnx9hnOhPlZcGuXbu4cvQvbl7ey9ChQwkICDC4feeTPxjcRkXUhs9aUVERp06dIioqiuyS0l8ymYwWLVrQoUMH3N3dDWa7MhSawGZtolKC1759e5YtW0ZoaCg3b97ko48+okuXLpw/f56UlBQUCgVO92x/4enpSUpKCgApKSnlxK70eum1h7XJzc2loKAA6woWbn7xxRd89NFHlXk5IiJ6RSqV0rFjRwICAli/fj2ZmZksW7aMbt260bVrV6RSsXStuZGbm8vRo0c5fvw4hYVaObGxsaFt27a0bdsWO7vav+hEzNKsgP79++seN2vWjPbt2xMQEMDatWsrFCJj8e677zJz5kzd89zcXPz8/Kif8iq28gfsSmwgNBIlsT7a4diAW28gxXhZexqUJLprbXsUzTS67TRLbUTrJJuBRGI824KgJFs9F4CvAGMO7sm5+0tfZ7tePYSXX0aydSvCmTPs27eP/fHxyIYMQeLoaFjbRqKs7dSIVxBkxhvgk6hVeEYu0Np+9iWwqLztjCsXObN0EdFbNqApqYjiGFCf5i9MpuGgYcitbcgH8u+9sViF5x8/AyCbPAqJ3HivW1CpYN5SvfcrDmk+Jk5OTjRs2JCYmBj69OmDUqkkOzu7XJSXmpqqm/Pz8vLi6NGj5foozeIs2+bezM7U1FQcHBweKqqWlpZYWlred14iKJAKpkkVl6IwquiYi22JRGFUwSuLCuN+8Vdo29ISBg/WLkLesgUhMZHihQth0CBo1Miwto2MIJMjyEy0HMNCjlAJwVMrlfz78SwurV6uO+fVpgPNJ0wjsNcTSEqi8IpyDMt+kUvkcqMKnkj1qdYYy+3bt4mNjcXb25vWrVsjl8vZvXu37vqVK1dISkoiIiICgIiICM6dO0daadVwYOfOnTg4ONC4cWNdm7J9lLYp7UNEpEbRvDm8/DL4+EBhIaxZA5s3362zKGI0CjIz2Dx+iFbsJBLq9x/E4D928MzqrQT1eVIndnUOiWCYwwyp1F/4zTffZP/+/SQkJHD48GEGDx6MTCZj1KhRODo6MmHCBGbOnMnevXs5ceIEL7zwAhEREXTo0AGAvn370rhxY8aOHcuZM2fYvn0777//PlOnTtVFZ5MnTyYuLo63336by5cvs2DBAtauXcuMGTP0/+pFRIyBqyu8+KK23BTA8eOwePHd7WJEDE5m9GU2Du3DzWOHkdva8eTPq+n7/VI8W7QxtWsiRqRSQ5rXr19n1KhRZGRk4O7uTufOnYmKitJlMM2dOxepVMrQoUMpKiqiX79+LFiwQHe/TCZj8+bNTJkyhYiICGxtbRk/fjwff/yxrk1QUBBbtmxhxowZfPvtt/j6+vLzzz+La/BEajYWFtC3r3aIc+NGrdj99BP066et1GGus/y1gKT9u9j1+gSUt/Ow9wug/4+rcGmo/2HlmoqYtFIBq1evfuh1Kysr5s+fz/z58ytsExAQwNatWx/aT/fu3Tl16lRlXBMRqRmEhMCUKVrRi42FLVu0O2G3aQP+/tq5PxG9kh0XjfJ2Ht5tI+g7/1esXVxN7ZKIiRBraYqIGBs7Oxg9Wrtv2q5d2koeV65oCxD7+EBQkPbw89NuGipyHxXtZgGgVqlAo0FW8uOh6fOTsXJyIXjAYGQKsdbpvYhZmiIiIoZFKtXO6QUFwdGjEB8P2dlw/br2OHgQZDLw9b0rgPXqmfm+asZhz1ev4HY7kb59+z7weuLuf7h5PIqO732mq/nbcPAII3spYo6I/3pEREyJt7d2qQJod8tOSNCKX3w85OVp91lLTIR9+7Ri5++vE0DB21srinWMlHORRPTtpnuuLioi6cBuAnr2QyqTYetdj7htm2j/1gdYWJqmylKNwhBZlWaapSkKnoiIueDsrD1attQWL87MvCt+8fFw5w7ExWkPoFihYGVAAEFBQQiBgdp92OpAan1BTka5NblFeTlsf2UsL5yMx9LeAaegEFS381ArlaLgiZRDFDwREXNEItEuZ3B11Sa0CALcunVX/BISoLCQ6OhooqOjtfdYWUFgoDYCDAwEDw/zTZerBlYOzuTn362BoszNQSqTUZSViaW9A0U52UjlCjRK4xYSr6lIJdpD332aI6LgiYjUBCQSrYB5eGg3G9VosEhNpWd8PPHx8UQnJmoXtl++rD0AbGzuil9QkFY8a4EAejVpz5kzZ3Bzc0OtVHJpza/Y+wZwZsl8Gg4ewbnlP+LRorWYoCJyH6LgiYjURKRSJN7edPT2pmPHjnyqVlN88+bdCDApSTsEeuGC9gCwty8vgM4G3LrIgLQe8xZbXuvFihUrsDgYRc61BPrNX07Ul7PZ8sKzKOwd6PnfhSjsa+6mrCKGQRQ8EZEytL5xg0bp6RVe3xQaSm7JdlPNb96kZVoazUqujQaKy7Td2qABmTY2ADRJTaVlyY4gD2JHcDBpJZX5Q2/dom1ycoVt9wQFkVyyw7ZzRgZ+168zptS2g4O2nFl4OGRlcUClIunmTbh+nYC8PLqcPQtnz2o7srYmzdeXXc88g8bW9iHvinnh5NeA559/nvPnz5MZGEa3Od/hFBjMoN83k3n1Erbe9bAUxe6xkSAgqbB6aNX7NEdEwROp01grlRSUGfoadf48b0RGVtg+bOpUneANuXSJDw4c0F0bfE/b1pMm6QRv4NWrzLmnRmxZuj7/vE7w+sTF8f0//1TY9onRo3WCF5CYyKBNm+6zXcrQ4cNJeuIJUKnocOAAvx08ePdiQQFER/Pbr7/y/Msvo6lBCS+urq506tSJmIh+ZFw6T+rJoyjsHbCv5y8mqohUiCh4InWWejk57F2+nO/bteP7knqvV1xd2RYcXOE9+WXEMcbFhe3BwZS2jqV8lf3cMlVT4p2dH9pvVpmswyRHx4e2TS8RUYA8BwdigoPvs11Kqq2ttoxZTAwp0dFsK3NNAvQExqamItm4kfGDB5u96AmCgAQ4d+4ce/fuJfOzz3TXJFIptl4+BPV5kuYTpmHnXc90jtYgxNJiIiK1HN+cHPYuW0ZIVhbTo6JY0qoVdxQKFrdpw+I2j1dQ+LcWLVjdogXvlTz/jIq36FkbHs7a8PDH6ndTWBibwsIeq21sSAixISHlbSuV2nm86GjYsAFycgDYX3Lg4gINGkCDBgwuKGDNxo345uZiWVxcLto1RyQSCfGHtnJ4xw4aNmxIp69/RFlYxJmfv8ejeWv8uvQk6quP0BSraf/G++I8nkg5RMETqXP4ZWezd/lygrOyiHNyovvzz3PHzL/oH4WQkaEVuOho7ZIFtfruRZlMm6hSInK43q0luRHoa2fH0Xr1zF7sSrmybQXh4eH069eP1IiuCBZy3Bo3ZcfU8YQMHMLAZX/w97jBpJ45gV/nHqZ21+wRIzwRkVqKf3Y2e5cto352NrHOzvQYP55rZTYsrjGoVGgSEvgnJobo6GiKMzPLX3d0hIYNtQIXGAgPEbN9QUHlng+9cIE/w8JQm2kVF1VRAdZO5TeDtnJ2QRAEcq8l4NGsFRKJhIJb4vZLIuURBU+kzuCfnc2+ZcsIys4mxtmZHs8/z3VHR1O79fhkZd2N4uLjURcXc6T0mlQKAQF3ozg3tyr9zP6/Awf4bM8e1jRpwughQ8xS9LzDO5Dw71qCgoKQFhagkSo5t+xHpBYWOAaUzH1KpAiCxrSO1hDELE0RkVrIgKtXCcrOJtrFhR7jx3PD3MWuuFi7nq5U5O5dLmFvT6sGDWjQoAHr69enWA9bC5319EQplTLiwgUkgsDooUMpNjPRaz5sKoeO/smKFStwvxSD6k4+GVcv0W7me7iHNyf3ehIBPfriFNzQ1K6KmBmi4InUGRa2awfAX2FhurR+syMnR7s/XnS0tmZm2fJYEol2y6CSKM7C05OnS6I4fU2ZbA4NZciIEaxfu5bhFy8iFQRGPfusWYme3NqO4cOHExsby3m5PTIbW7p8/DVujZsC4ODrT7uZ7z2iF5G6iCh4IrUa/+xsMq2tuV0S/ZSKntmg0ZSP4tLumXeytb07TFm/PpRZvmCovIAtoaEMHjGCjWvW8OylS0jXrWPks8+iMqOtiaRSKQ0aNMBh5BQEi/v3DLx3v7wrG34nK+YqbWf8HzJxj8FyiEkrIiK1gKDMTPYtW0aSoyP9x4zRiZ7ZoNHAunVw6VL5876+d0XORDsg/NOwIc+MHMnG1asZcvkyq//4g6EjRpjdN5kgCA/cDLbs84KMWxz88C2KC+5w89hhes/7Gft6fsZ2VcQMMO9VpiIiVaR+Zib7ly3DPzcXtzt3sDXHyvk7d2rFTiaDpk1hyBB46y146SXo1k27+7kJF4Jva9CAZ0aOpMDCgp3BwWYndoBug9eHYe3qTs//LkBh70DqqWOse6or8Tu3GMlD80e747mg58M8ESM8kVpHcEYGe5cvxy83l0tubvQYP55Ue3tTu1WeY8egtITZM89oBc8M2d6gAQ1efdX8E3weQf0nnsatSXN2TZ9A2pmTbJ8yFq82HWg6bhJBfQciNaPhWhHDIUZ4IrWKkIwM9i1bhl9uLhfd3Oj+/PPmJ3YxMbB1q/Zxjx5mK3allBU7lzt3+O/27SiKix9yh3ni4BfAoN+30nziq0gtLEg5HsXO115kZfcWnFo0j8KszEd3UgspncPT92GOiIInUmtwSU9n57Jl+OblccHdnR5lCjKbDampsHatdkPX5s2ha1dTe/T4CAKbfv+dNyMjWbNmDbIaKHoyhYKIWR8xev8ZWk97EysXN/JTkjnyv4/5rXM4+/7vdTIuXzC1myIGotbH8YJEiUZivPmbsrY0GHfeqKw9U9oWBOPaLrVnoVajUKu54O5O3/HjybKzw9D5ePIKHj8IIS+P4lWrQKlEEhCA7KmnHjn/pC/bekEi4eMePdi4ahUDoqNpsGYNa4cPR6KuqIKogdwoa69YVaX5IjtXN9pNe4vWk14jZutfnP11MekXz3J57W9cXvsbPu060nTsRAJ79kNadklG8V3bgsq4r9tw9vS/8PzBpcxNj0QQBPP0rJrk5ubi6OjIO++8g5WVuF1IXcE9LY18GxvumFlkp1QqWbZsGcnJybi6ujJhwgRsyux6UJMIjIvjuVWrkBcXEx0SwpoRI1DX8FR/QRC4du0aR44c4eLFi5R+LTo6OtKuXTtatWqFtbX1I3oxPIWFhcyZM4ecnBwcqrmWtPQ78o/0OGwd9Dvsn5+bx7Nu9fXipz4RhzRFajRut27hn5Cge37Lw8PsxE6j0bBhwwaSk5Oxtrbmueeeq7FiB5BQvz4rR49GKZfTICaGkatXIzNytKNvJBIJ/v7+DBs2jOnTp9O5c2esra3Jyclh586dfPPNN/z999+k3btOUqRGUeuHNOunvIqt3M1o9jQSJbE+XwMQmPoGUoxXgV6DkgRPrW2PgplGt51m/Q0AX1HxNjn6pFFaGtuXL8deqWT1uHFc9/Mzmu1S5MDbJY+Vwa+B9P73fPeq97h8+TIyCwXPvvUX9qEd9TPgrFGiiP0OgKwW00BmvCgrq4UKJBKeW7mSkNhY/LZsYeIzzxjFdtn33CB/b0dH6N0boVs3ZOfOoT5yBFVqKidOnODEiRPUr1+f9u3b82eDBhQbcdmIHHjNAP1K0H8RAzPNWan9gicRFEgF02x7IkVhVNExF9sqDC86jdLS2LF8OZ75+Zzx9CSjZMsbY9iuEKniPsE7tftnjmz5FoABL/+EX6PuhrEtk4PMuH/vxMBAVo4eTcTmzXzSpYtJ3neD/r3lcmjVClq2hMREOHIELl8mLi6OuLg4cHaGdu2018VpkxpBrRc8kdpHk9RU9ixfjsedO5zy8qL/uHFMMcMhwrizO9n2y6sAdBn6H8I7P2dij/RPUmAgr0yZQmHZxA5BMN+89KogkWi3WAoMxCI7m3ZHj3Ly5EkKs7Jg+3bYuxdatNCKn5vxRpP0RV3aLUGcwxOpUZQVu5NeXvQaN45MMxS7tGvn2ThvFIJGTXjn5+g89H1Tu2Qwym4h9OTVq2xZuRJrc6xsowckTk707duXmTNnIhs4ENzdtQW+jx6FH36AFSu0NVE14tZE5ogY4YnUGOpnZrJ3+XLc79zhhLc3fcaOJcvGxjgp+ZXgdnYq674aTFFBLn5hnXly0o/VWn5QU7AtKmLZn3/ifucOm1et4qnnnqvxO8lXhEKhQNqmDerWrSE+XjvceeWKtqhATIx2V/l27bSRn7nVcL2XOjSJJ0Z4IjWGaw4OHPLz47i3N73HjSPLDCM7VdEd/vjfEHLSE3H2CmHozHVYyM38C09P5FtaMmjkSHIVCnomJLBl5Upsammkp0Mi0e5iMWoUvPYadOigFbiMDPjnH/jmG9i2De7dkV7EJIiCJ1JjUFlYMHzYMHqNH0+2GayJuhdBo2HTghdIjj2GtZ0Lw9/+Ext7V1O7ZVQi/f3pO3YsOZaWdE9MZOvKldgWFZnaLePg4gJPPAEzZ8KTT2qjvKIiiIqC776DVasgNlY7x2lGSAx0mCOi4ImYNc1v3uSLnTt1XxIqCwtyzTQjbt/a2Vw5uhGpTM7Qmetw9a6bO24f8fPTiV63uiZ6oI3w2rWDqVNhzBjtNk8AV6/Cb79pE13MCP3vlGCIyi36QRQ8EbOlZXIye5Yv551Dh5hRurOAmXLixAki/9auQxzw8o/4N+piYo9My1FfX/qMHUu2pSVdk5KYeuyYqV0yPlIphITA6NEwbZpWBBUKaNzY1J7VWcSkFRGzpGVyMrt+/RWXwkKi6tVjSatWpnapQuLi4tiyRbu/Wuch79G0yxgTe2QeHPP1pc+4cYw/fZr/dexoandMi5ubdpizVy+t6JkR4o7nIiImpFWJ2DkXFnLY15cnxowhz0yHMYW0NNasWYNGo6FJx+F0efYDU7tkVhyvV4/j9erpnks1GqxVKvLNPXPRUNTV120miEOaImZFmxs3dGJ3yM/PrMWO27cpXrWKoqIi/Pz8GDBxQZ1YflBVpBoNyzduZMdvv2FfWGhqd0RKEJNWRERMgH1hIf+sWIFzYSH/mrvYqVSwejVkZ+Ps7MzIkSOxUJipr2ZCYHY2T0ZH0/H6dbavWIGDKHoiRkYc0hQxG/KsrHhlwAAmHz/OoFGjuG2uwz+CAH/+Cdevg5UVo0ePxtbW1sg7ENY84lxc6DV+PLt+/ZWI69fZt2wZ+wMCAEixs+PLLncTfWYdPIjX7dsP7CfH2hq6d9c9n3n4MH45OQ9sm69Q8H6vXrrnU48cIaSCNXFKmYxZffvqnk88fpzGt26VayMF2pU8/qx/f93550+donlKygP7BZjVpw9KC+3X7aizZ2l340aFbd/v2VM35Dv0wgU6JyXpbAcZ4N9EXSotJgqeiOkpU3txXXg465o0Md9Zb4DTp+HCBZBKkY0YgVsNrJ9oKk57e9Nr3Dh2//orLVNSaFkiEhfc3csJ3tizZ2lyj9iUkuDoyPIygjfy/HnaJic/sO0tG5tygvfsxYt0T0x8YNt8ubyc4A26coUB0dEVv5gygvdkdDTDLl6ssOl7vXrpfhD1jY3l+TNnKmz7WdeuOsHrkZBQLsM13tm5Yn9EHokoeCImpf21a8zfupVBI0dyw9FRe9Kcxe727bvrqHr2RBoUZFp/aiBnvL1p/9JLjD53DoVaDUCarW25Nj+1bo1Hfv4D78+zsqJs2YGlLVqwIzj4gW3v3LMx7YpmzTjk7//Atqp7tvpZ26QJp728yp2TAZ0ecO+GRo246lpxkYGyfW9u2JAbD9kUtazP24ODyS4Z1pcBDRUK2L27wnurgpilKSJiBDpcu8b2337DQank4717mWCk/dSqxbZtUFgIXl4QEWFqb2ossa6ufFwmSruX7zp0qPCaHHivzPOF7dpV1PQ+lrRu/dhtf23R4pG2S1ndtOn9JzUayM2F9HQ4dkz7//R01vfpw/omTR7Lh7/Dwvg7LExn+43CQr0LHgglh777fHwWLlzIwoULSSjZzLlJkyZ88MEH9C+JogsLC3njjTdYvXo1RUVF9OvXjwULFuDp6VkpO6LgiZiEjklJbFuxAnulkj2Bgbz65JOmdunRREfD+fPan69PPw1lt8QRqbuoVNpambdu6URNdxQX398+NRV8fY3vpxnj6+vLnDlzaNCgAYIgsHz5cgYNGsSpU6do0qQJM2bMYMuWLaxbtw5HR0emTZvGkCFDOHToUKXsiIInYnQ6JSbyz8qV2CuV7A4K4qlRoygws8W491FUBJs3ax936AA+Pqb1R8T45OejSU/nRHo66enpFJeKWlZWxfdIpdqamm5ud4+SRB1zwRw2S3jqqafKPf/ss89YuHAhUVFR+Pr6smTJElatWkXPnj0BWLp0KY0aNSIqKooODxkNuBdR8ESMSufERP5ZsQI7lYpdQUE8XRPEDrSbfObkgKNjuQxBkVqGRgPZ2fdHaunpcOcOauDvB91nZVVe1NzctHvlOTnV6ZGA3Nzccs8tLS2xfESmqVqtZt26deTn5xMREcGJEydQqVT07t1b1yYsLAx/f38iIyNFwRMxUwSBb7Zvx06lYmf9+jw9ahSFcnPbze4B3Lih3e8MYOBAsVpGbUCp1G7hc6+oZWQ8eBiyFEdHgt3ccHNz45i7O5pScbO1Nd9MjUdgyAjPz8+v3PkPP/yQ2bNnP/Cec+fOERERQWFhIXZ2dmzcuJHGjRtz+vRpFAoFTk5O5dp7enqS8pClIA9CFDwR4yGR8PSoUXywfz8z+/WrGWKnVsOmTdqlE02b3q18L1KzyM+HxERISNAeaWkVt5XJtMOQ7u7lIzZXV+QKBWNLmp0ExH3NH861a9dwKJOR+rDoLjQ0lNOnT5OTk8Mff/zB+PHj2b9/v179EQVPxOC45eeTXpJ2nmJvzysDB5rYo0oQGalNMrC2hn79TO2NyOOSl3dX4BITtQkl92Jtfb+oublphyGldakIleGyNB0cHMoJ3sNQKBSEhIQA0Lp1a44dO8a3337LiBEjUCqVZGdnl4vyUlNT8bpn2cijEAVPxKD0iIvjr9WreWXAAFY0b25qdypHZibs26d93Lcv2NmZ1B2Rh5Cbe1fcEhK0Q5P34uGhTRgJDAR/f7C3N7KTIpVBo9FQVFRE69atkcvl7N69m6FDhwJw5coVkpKSiKjk0iBR8EQMRs+4ODavWoV1cTHDLlxgRbNmNWeeQxDg77+18zlBQfCANVkipiM7O5vExESKS4coH5Qp6eVVXuDuWdwuokWK/osqV7a/d999l/79++Pv709eXh6rVq1i3759bN++HUdHRyZMmMDMmTNxcXHBwcGBV199lYiIiEolrFTFr3LMmTMHiUTC9OnTdecKCwuZOnUqrq6u2NnZMXToUFJTU8vdl5SUxIABA7CxscHDw4O33nqL4nsmivft20erVq2wtLQkJCSEZcuWVcdVESPTKzZWJ3abGzRg2PDhNUfsAM6cgfh4sLDQJqrUJN9rG4KgjbZPnYKNG1HNm8e8efPYuHEjwqlTWrGTSMDbW1sMYORImDULJk/Wlv9q1EgUu4chEQxzVIK0tDTGjRtHaGgovXr14tixY2zfvp0+ffoAMHfuXAYOHMjQoUPp2rUrXl5ebNiwodIvtcoR3rFjx/jxxx9p1qxZufOPWiCoVqsZMGAAXl5eHD58mJs3bzJu3Djkcjmff/45APHx8QwYMIDJkyezcuVKdu/ezUsvvYS3tzf9xHkUs6d3TAzrV6/GuriYvxs25Nnhw3WFc2sE+fl3y4d166ZNYBAxHqUCV3aI8p70dolEgo+PDzcDAtCURnDmurOGyCNZsmTJQ69bWVkxf/585s+fXy07VfoWun37NqNHj2bx4sV8+umnuvM5OTmPXCC4Y8cOLl68yK5du/D09KRFixZ88sknzJo1i9mzZ6NQKFi0aBFBQUF8/fXXADRq1Ih///2XuXPnioJn5gTHxDDr99+xUqv5KzSU4cOG1SyxA235sIIC8PSEur5TtzEQBO2SgLICd+9OCVIp1KsHAQHIAgN5288PS0tLPqMGZEqWCnhsrPbo3VubLGMmmMPCc2NRpW+iqVOnMmDAAHr37l1O8B5ngWBkZCRNmzYtVwOtX79+TJkyhQsXLtCyZUsiIyPL9VHapuzQ6eMiSJRoJMbbuKWsLY2RN4wpa89UtgMTErBSq9kUGspzw4YhWFhg6MUH8goeVwVNTAzqc+cAkD31FNJHLBouZ09j5A2CytpTq4xru4y9qrznQkYGmthYhIQEhMREbVRdFpkMSb16SAIDkQQEIPHzQ1JSoEAOlCa3G3thy+N+1oSCAoT4eITYWDSxsdrF7CVIAwORVUHwasAiHrOn0oK3evVqTp48ybEyW1aUkpKS8sgFgikpKfcV/Cx9/qg2ubm5FBQUYG1tzb0UFRVRVFSke166wj/O63usTDTUkeD5tUnsAqRZf2MSu7t79SLd1ZVzTZvytgkiu7erca9SqWTB5s1kA+3bt6d/JesdKmK/q4b16uF8+geT2X6c97y4uJikpCSuXr1KdHQ0GfdkUVpYWODr60tAQACBgYH4+voif4x1mtX5e1eXsrbVajXJycnExMQQGxvLjRs3EIS781hSqRR/f3+Cg4MJCwmhqvGdIbbMFSO8Crh27Rqvv/46O3fuNJmIVMQXX3zBRx99ZGo36iS+165x08sLtVwOEglnWrY0tUtVYu/evWRnZ+Po6KgbkhepOrdv3yY6Opro6GhiYmJQKu9GpKUCEBQURGBgIPXq1cOihg19Z2VlERsbS2xsLHFxceV+cAO4uroSEhJCcHAwAQEBjyypJWJ4KvUJO3HiBGlpabRq1Up3Tq1Wc+DAAX744Qe2b9/+yAWCXl5eHD16tFy/pVmcZdvcm9mZmpqKg4PDA6M70Ka1zpw5U/c8NzcXPz8/Am+9iq3ceBt0aiRK4ksiu3o5M5FivDqRGpTccNRGdl8BxhjkevLKFd5Zu5Z9QUEcGzkStYUF9lbTkUiM97oFQUle4Tztk3qvgbTytpMTTxMV9TEAA8YvwTLkice7UaOEG9rILr3tNJAZceBJrcLtmDayu/70RAQL49mWFKvw3bQYANWLY0EuRxAEUs6dJ2bXHmJ27Sb59Bnt/FUJNq6uhPTuSUjvngR17YJVmQXJApX4vKpUyH/5DYB6M55GqjCOUBbk3ubS7iiOzv2d2NhYMu/ZOd3e2Z423VvStmcb2vZsjZd/5baueRRqlZptX+3Va58g7nheIb169eJcyfxGKS+88AJhYWHMmjULPz+/Ry4QjIiI4LPPPiMtLQ0PDw8Adu7ciYODA40bN9a12bp1azk7O3fufOgiw4qKkkoFBVLBNMWJpSiMKnhlUWF4wRt45Qpr16xBodGQp1AglKTuSyQKowpeOaSKSgueWl3MpmXTEAQNTdoNo2HLp6tmWyYHmWlet2AhN6rglVJUVET07r3E7N5DzK493L6ntqFXs6aE9OlNSL8++LRojkTPFUykCguDCZ5GrSbhxAXO7zzMhZ2HiI08g7rM8imZhYymHZrQrncb2vVuQ1irhsjqcKHomkClPin29vaEh4eXO2dra4urq6vu/KMWCPbt25fGjRszduxYvvrqK1JSUnj//feZOnWqTrAmT57MDz/8wNtvv82LL77Inj17WLt2LVu2bNHHaxbRA09fvsy6tWtRaDSsadKEF4YMYVYN/cceuW0uKUmnsbJxov9zppn7rEnkXkvg+t5/2LfuNxISElCX7FoOILe1IahrF0L69iGkdy/svStX+snUZCQlc36HVuAu7o4kP6v8cgiPYH8CXDwJDg7m+a9H4ej2eGWzzBvTbwBrLPT+02ju3LlIpVKGDh1abmfaUmQyGZs3b2bKlClERERga2vL+PHj+fjjj3VtgoKC2LJlCzNmzODbb7/F19eXn3/+WVySYCYMunSJtevWodBo+D08nLGDBz8ym9Fcib+8n90bPgCg74ivsHPU7zBUbUCjUpF6+ijX92/n2r4dZMddKXfdyd+PkH59CenTi4COEViY2fz+wyi8nc+V/cc5v/MQF3Ye5ubluHLXrR3tadyzA036dKRJn464+Xpz7Uvtgmdbh9qxmF1MWqkE+0prDZbwOAsEAwIC7huyvJfu3btz6tSp6ronomcGXbrEunXrkGs0rAoPZ9zgwahlMr2XJjIGORnXWLfgOQSNmqYdRtGyy3hTu2Q2FGZlcP3gLq7t386NQ3tQ5uborklkMjxbtKOpix0NGzbE8b23dUsGagJpsUkcXbeNCzsPE33oJGrV3WFKiVRKcIfmNOnTkfA+nQhqG46sTDKNRvmQrYNEzJ6alRYlYnJS7ewosLBgbWgo4595BnUNjexUqkLWzB/BnbxbePk356nnFyKpw+XDBEEg6+oFru3bzrUDO7h15jiC5u6SbktHZ3y79sGvW1/qdeqFla0tfhu0IzeqGvC+FatUnPprD/t+WsvF3ZHlrrkF1iO8byfC+3amUY922DjVhmHKx0ci0X/lPHP9SIiCJ1Ipovz8aD9xIlddXdHU4C1U/lkxneT441jZOjNi2loUljamdsnoaFQqbhzeoxW5/TvIT7lR7rpzaBP8uvbFr3s/3Ju1KT9sXWzkhe5VJD3hBvt/XsfBpRvISUkHtGXJmvTpRMune9CkT0c8gv3r9I+duoQoeCKPZMjFi8Q7OXHKxweAy2ZUFqkqHN/3MycP/AISCc++/BvO7kGmdsnoFGSms3vac6SdvltAQmZphU+Hrvh164dv1z7Y+fg9pAfzRV1czJnN+9j301rO7zikWwDu6OVGlxeG0O2lYbgF1jOqT0WFSiK3H2HX2j288ukkfIK8jWr/4eh/WUKdSVoRqV0MP3+elevXk2tpSdtJk4hzcTG1S9XiWuwRtq54HYBeQz4mpGlfE3tkfLLjrrJzygjyriWgsHeg/oBn8evWF+92XbCwrrmRbkZSMgeWrOfAL+vJTr67o3mT3h3pPmk4LZ7ugcVjVG/RFyqlimO7T7Bz3R4ObDrEnbw7AIS2bMjYN0cZzQ+Ru4iCJ1IhI86dY8WGDVgIAptCQ0m4p2RcTeN2TiprfxiBRq2iUetn6DzAlIWpTMPNY4fY/eoYlLnZ2PkG0HfRWpzqNzS1W1VGo1ZzdvO/7PtpLWf/OaCbd7R3d6Hz84Pp9tKzeIYEGM0ftVrNyf2n2bVuL3s3HiAvK093zdPPg97DetCxf+X2cDM0YpamSJ1n1Nmz/LZxIzJB4JcWLZj49NM1es5OXaxi7YJR5GUn4+YdyjMTltS5eZuYTWv49/1X0RSrcG/elt4/rMTatWYOT+fm5nLy5EnO/ryIzOt3F7uH9WhP94nDafVML+SWxskc1Wg0nIu8wM61e9i7cT+ZqXc3o3X1cqHnkG70Ht6T8PaNkdbgf0O1AVHwRO7jubNn+bVE7Ja0bMnEp55CqOH/UHesmUXS1X9RWNkz8tU/sLS2N7VLRkMQBE4v+IpT8+cAENh3EF3nLMTC6sFl+swVjVpN3N59nFr6K9E7durm5uxcneg0/hm6TxyOV8NAo/giCAKXTlxm758H2P3HXtKu39Jdc3BxoMfgrvQZ3pMWXZqZffUVsbSYSJ2lb0yMTuwWt2rFywMH1nixO3N4JUd2aWtODpm4FDfvUBN7ZDzUSiWHPnydmL9WA9B0wmu0mfGh3kt8GZLbqWmcWfU7p35bSU7SNd15f39/+s2eQtsRTyC3MnxhZkEQuHbuKrt37+b8+fNkfXQ3krN1sKXboM70frYHbXu1xkIufrWaI+JfRaQcB/392R8QQIyLC5Nrgdil3bjA38tfAaDrU+8S1qqKdTJrIGqlkh0vD+PmkQNIZDIi/vM/woY/b2q3HhtVQQHb3n6X8+vWoympYWnl6EjTYUNpq7DCw8MDv1EDjFI8+szW/ayd9T+SL8bqzlnZWNF5QAS9h/WkQ792WFrVnMX3dRVR8ETKUaBQMGD0aIpkshovdgBHdi2gWFlAcJM+dH/mA1O7Y1Qu/f4zN48cwMLGjp7zluHbuZepXXpsigsL+eP5CcTt1u4OUK9tG1qNH0ujQU8ht7BA/uMvRvFDo9Hw18fz2fTJQgAsFHJC6gcTHh7OpB/GY+9sZxQ/DImYtCJSpxh3+jTBmZl82KMHSCQUGjF125BoNBqunPobgIgnpiOVmvdcij4pzMrg9MKvAGj/zmc1S+yKilj/wkTidu9FbmPNsN+WEdSt690GKuMser+dmc1PY2dxbttBAHpMHsmQ2a+SsWgnANa2NWsOVEQUvDrP86dOseSvv5ACR+vVY0to7ZnfuhF/jNs5KVhaOxAY1s3U7hiVU/PnoMzNwSU0nAaDR5vancdGrVSyYcLLxOzchYW1FcNX/kZgl05G9yPp9CV+ePZ1bsVfR25lyfiFs+k0bhAaZTEZj769RiEmrYjUCV48eZLFmzYhBX5o25YtDWvueqwHcfnkXwA0aNYfC4u6M7+SFXOZy2uWAtD+nc9rzE4WapWKjRMnE71tOxZWVgxfsdwkYnfo179YPmU2qsIi3IN8mfbHt/i3aGR0P0T0jyh4dZSXTpxg8d/a4b7v2rXj9f79zbfiaxW5fHITQJ1KVAE49t//IKjV+Pd8Eu/2XUztzmOhVqn4c9IUrmz5B5mlJcN+W1p+GNMIFCuV/D7zS/Ys/B2Apk90YdJvX2Ln4mRUP0QMhyh4dZCJx4/z0+bNAHzbvj3Tn3ii1ondrZuXyUi5isxCQUjTurOP4vWDu7h+cBdSCzlt3/z40TeYAZriYjZNeZXLf29BplDw7LIl1O/R3ag+ZN1IZf6IGcRGngbg6f9MYdAHU8WF4rUMUfDqGKG3brGoROzmtW/PjFoodnA3ugtq1BMr67qx3YumuJijX/0HgEajJ+IYGGxijx6NRq1m09TXuPjnX0jlcob8spiQPsZNsLm8/xgLR84kNy0DGycHJi6fQ4uB3Y3qgykR5/BEai1X3N2ZMnAgDTMyeLNv31opdlA3hzOvrFtGduxlLJ1caDH5LVO780g0ajWbX53OhfUbkVpYMGTJjzR8wnjFvAVBYMe85ayd9TUatRq/ZqFM++NbPIL9DWbzVnI67j5uBuu/KtSlZQlivF5HkBff3an5pzZteLNfv1ordrlZydyIOwpAaIsBJvbGOBTl5nDy+y8AaDntHSwdnUzr0CMQNBq2TH+Dc2v/QCKTMXjxQkKf7G80+4W381n03JusfvMrNGo1HZ4byHuHVhlU7Hat28OzjUbzz8odBrMh8nBEwasDvHL0KEcXL8Y1P9/UrhiFK6e1Q7a+we2xdzKnfccMx+mfvqYoOxOn+qGEDX/B1O48FEEQ2P7Oe5z9fQ0SqZRnflxA2FMDjWY/NSaRTzuO4ujaf5BZWDD6u/eY9OuXWNoYZl2dIAgs+XQ5/xnzCcpCJQc2/aurA2oOSATBIIc5Ig5p1nKmHTnC9//8A8Bz587xfQfz2prEEOiGM1vWjeHMjIwMLq5aAkC7WZ8itTDvf9ZR3y/gxC/LQCLh6QXf0/gZ4/2dbl6J58ue48lJScfJ251X1sylQadWBrNXWFDE55O+YufaPQCMen0YU794uc7t1GEumPe/DJFq8VpUFN9u2wbAF50783379ib2yPAU3skh/pK2HFVdmb/buXMnmmIV9Tr3wrdLb1O781AubvyLPR9/CkCfT2YT/uwQo9lOuZrAV72eJyclHd+mDXnjn8U4eRtue6SMlExmDXufC0cvIbOQ8fb3M3j6RfMbYq9Lc3ii4NVSXo2M5Ovt2wH4rEsX3u/Zs9bO2ZUl+twONGoVbt6hdWJXhPj4eC5fvoxEJqPd25+a2p2Hci3qCJumvgZA20kTaDd5ktFsp0Qn8GWv58m+eYt64Q14a+cvOLi7GMxe9NkY3hryHqnX0rB3tueL1R/RuntLg9kTeTxEwauFdIiMpF+J2H3apQv/qSNiB3D5lHb+ri4MZ2rUaraVRPBhz47DOSTMxB5VTEZ0DOvGvoBaqaThk0/Q+5OPjGY7NSaRr3q9QHZyGvWahPC2gcXu4N+H+HD8pxTkF+LfwI//bfwcvwa+BrNXXcRlCbUIjUSJRqI0qj3dY4xnt9SeoqiI9lFRAHzWtSsf9+iB3AhiV7bctPD/7Z13eBTl9se/s33TG2kkQOi9tyAivaqgoHApYsMLBpXyE8XKtYF4EUQRvBbAAipIUUCQGkBDC4TeCQTS+6Zvmff3x+xMspSQZGd2Z3ffz/PsY7Ize847Zpkz57ynEMdeN6/PbDbj8mnO0LfsNBxgHbCOqjosjmlqzHNhx/fIzMyEVqtFlxdmgTE7Tr+Nrvs0cy7JycHP4yagLD8fkZ06YtTnn0HBsgDL1k15FX2s0VzNiUDWtZtYOPhZ5KdmIrJVE/zftq/hE+B/38/di6qfs5gsNscIIViz5Fd89e43IISga7/OeG/12/AN9IXZaLldVK25XR+l9ri9wbte73PodDqn6E71/9QJWrVYPXkyWl64AHNsLN50gmdXVL7E4ToBLrxnLC+Gj48PIpV7gZvxDtUfcvQLh+mqqKjA4W+XAgAeeughND3wm8N03476ux/uecxoNGLd6tUoSE1FQEAAxg8YCK8f1oqmO3Xx7/c8lpeXh1WrVsFgMCAkJATjRzyGwpXxKBRJ976lCcLPZrMZW7ZsQVJSEgCga9euGNZ7GI58d1IkbdJB9/AoLkdgbi7yg4MBAAVBQTjUq5eTV+R4Lly4AABo2bKl27eEOnDgAEpKShAUFITu3bs7ezl3hWVZbNiwAampqdDpdJg4cSJ8fBwzPy4/Px+rV68WjN3kyZMl011SUoJffvkFKSkpYBgGQ4cORffu3Wkmpgxxe4MXaZgO7wrHdTZgYRQ8u4UAHBFkmnPgAKbu3YvxY8einXW8TwiZCQaOmxBAYEQOsxgA4OU7AwzjQN3EiOLCT3Hx4kUAQNOBb8LYwkHZiqwJmsucp3Wj/zQQpfSzBIvSU/DPR1yR+aBBg6BSqVAyaTLgyDmGJhO8f1gNAKg/Y+Rdp47/POdjXLhwASqNGq/8sQLNH+giimrWaEbqEm4SxohpnaFS206DSLuZhecf/hKFhYVo2LQ+vv59AeqFi7NnZzZZsHX5cQDA7O6+uHorE4+9+x1S0nPh56XDj29MwpBu0kxWMFoIPtxfLoFkYn2JLVN+uL3BU0ADhQNv/FUxQXqD98b+/fhgD1fj0yozE7AaPAYahxq8qjCMxqEGDwBSU1NRXFwMrd4PDdsNBBSOv3aiVIM4YAxR4or3wRorEN75QbRsaU1UUasda/CqoNCo7jB4Oz//ATu/+BEA8PzK+WjZT5qSGJVaCZWm0uCl38zCC4++jvSbWWjYtD6++3MhQiOCJdG978RFTPjwexhKyxETHoQtH05B64bhkuiSEk8Kabp33MfNeTM+Hh9ajd2b/fvj4z6OHaciJ/hwZpOOg6F049l3WScTcH3XbwDDoNtLH8gybHZ88y6sncV5oGM+nIUeYx1Te5ZxKxvPDJuD1BuZaNAkEt9u+1gyY3f48GGMfPtrGErL0bttDA5/MdMljZ2n4fYenrvy9r59eG/fPgDA3AEDsODBB+Gc53t5wBu85t0c16LK0RCWxZElrwMAmj3yFIKbtwNu7HXyqmy5duQUvpr4KggheGjKkxg+53mH6M1ItRq76xmIbhyBb7d9jDAJmjSbTGZs3boVR48eBQBMHtwNX814Etq7hHRdBSlagdHWYhTReHfvXsyL5zIQXxs4EAt793byipxLVuZF5ObmQqFQoEmHQc5ejmRc2/Ercs8lQuXlg05T33H2cu4g69pNLBk5DcaycrQf1geTPn/bIR5oZloOnh02B7eS0xEVE4Hvti1EeH3xO6gYCooxa8IHOHo0CQDw4XMPY+64/rL0sil3hxo8V4MQNCjkEqtfHTQI/33gAScvyPmcPcMVmzdu3BhaL/ecfWcqK8HxZZyRaz/5/6APDgPMjq13rI7ivAIsfuTfKMrOQ4NOrTBt7adQOqCnZ1ZaDl4Y9QZuXktH/Ubh+G7bxwiPEt/YpVxNQ9yYd3D98i2o1Wo8/vjj+L8ne7iFsfOkPTxq8FwNhsGURx/FujZtsL1ZM2evRhacOfMHAFQmcLghZ3/6DKXZafCOaIDW/5ru7OXYYCqvwOePT0fGxWQERUdgxuYV0Pl4S663qKgIL4x6AzeupKJ+wzB8t+1jRESHiq7n6P6TmDnxAxTmFSG8fj2MfPhxRER4xhQOd4MmrbgChOCJM2egtHCdFliFgho7K4bCdKTcsM6+a+GevTNLstJw9oclAIAuce9BqXVOI4W7wbIsvvv327h0MBF6Px/M/GMFAiPFNzq3U1RUhFWrVuHGlVRERIfi220fI7JBmOh6NqzejhcefQOFeUVo17UFftj1qdsZO0ailxyhBk/uEIIP9uzBr+vX4/uNGwGZbgY7i7NnOe8uKioKvr6+Tl6NNJxYPg/m8lLUa98TjQaOdvZybNizZw+OrOPmyk1ftxRRbZtLrrMwMwerV69Gbm4uwuvXw3d/LkR9kTMkLRYL/vvG13g3bgnMZguGjXkI3/25ECFh0vXgpEgPNXhyhhB8tHs33jxwAABwpH59j2kCXVPOnOYKkN01nJlzLhFXt60BAHSf+bGs9oyOHTuGgwcPAgCe/t/7aD0gVnKdhZk5+O/w55GTkwM/Pz98/cd8RDUS19iVFJXilXHvYfVSrl3bi29MxMcrX4dOr7VLbrnRhNe+/gP5RaViLFNkiMgveUL38OQKIViwaxde+/tvAMDLQ4d6xPDW2lBebsCVy1wdojsaPEIIji7myhAaDx2HkNbidCoRg2t792Dr1q0AgEfffBG9nxoluU5DVi4+GfwM0s5fha+vL55++mlENRI3vJiWkonpT7yLy2evQ6vT4IMVszF0zEN2y83KL8Kod79DwrnrOHk1DX/Of0E2Dy90WgLFuRCCj3fuxJx//gEATB82DMs8YHhrbblwfjssFhNCQ1sgJMRx7eMcxY09G5F1KgFKrR6dX3TcOJ37kXn6NH6fNhWEEHTs2BGPvjFVcp2G7DwsHPwMUs9eQUBEKCaNGYugIHHDi0mHz+GVce8hL7sAIWGBWPrLPLTrav++8OlraXjk7W9wIzMfAT56vPpkP9kYO0+DhjRlyIe7dwvGLm74cGrs7gEfzmzT9hEnr0R8LBXlSPz8bQBA20kz4B1W38kr4jDcuoXfJk2AqbQUMTExePjhhyW/eRfl5OOTwc8g9cxlBETUw5wd3yE4WNwOKlt+2YPnhr+GvOwCtGzfBGvjl4pi7LYeOoteryzFjcx8NKtfD4c+n4EBnaXf56wNNGmF4lTiGzVCmUqFF4cPx5cy7YTvbMxmIy6c/xMA0Lad+3VXubD+fyhOvwF9vQi0mTjD2csBwA2c3fD0UyjJzERIixYYO3YsVBLX2rEsi08ffgG3Tl/ijN2u1Qhr2lBUHRu/34G5zy2EscKE/g/3wuq//itKLd/Gg6fw6DvforisAv06NsWhz19BCwnKJig1h4Y0ZchfTZui6csvI83PPYuoxSAn5wrKyw3Q6fwQFd0VZcX7nL0kUbm+eyMAoP0zc6DWS1/TVhNSjxxB9tmz0Pr5YfTqH6Dbvk1ynRf2Hsb1Y2eg9/PBnJ2rENEips7DW++GyWjCF+9/DwCYGPcYXp0/RZTRUiazBbNXbAbLEkwe3A1fzxoLtUp5/w86AYZwL7FlyhHq4ckBQvDu3r1onpMjvEWNXfXk5V4DAAQHN3G72XfG4kLknk8EAET1GuLk1VRycQs3bLXZsOHwi4x0iM6D33OGv+f4hxHRsrHo8rf+uhdZ6bkIjQjGrPefFe27tHL7YSRn5CEs0BdfvjxGtsbO03CvO4UrQgiWbN+OefHx2L16NbwrKpy9IpcgN8dq8ELEvwk6m8ykf0BYFr5RjeET0cDZywHANa6+ZM3KbP6wY0LIZYZiJG7YCQDo/dRjossnhGD1Z1zpwYQXR0GtEaf9eoXRjA9+4tY9918D4KWT+/QOsUsS5FuaQEOazoQQLP3zT7x05AhYAO/264cSrX21Pp5Cbm4yACAoOMbJKxGfjGNcY/DwLvIZ95R69ChKMjOh9fNDwwcds66j67fDWFaOiJaNEdOtnejyD+48hivnb8DLR48xzwwTTe7X2xJwM7sA9UP88e+He4kml2I/1OA5C0LwxbZtiDt6FCyA5x99FCs7d3b2qlyG3NyrAIDgYPfz8NKtBi+iq/31X2JxaQvX0abJ4CFQabWASerRxsDB1ZsAAA88NUqSTNBVS9YDAMY8Mwx+AT6iyCyrMOKjtbsAAG+OHwSdSF6jtIhfh0c9PIoAw7L4Yts2vHjsGFgAz40ciVWdOjl7WS4F7+G5m8ErL8hB/uXTAOTj4RGWxaVtXDizxSOOKQHJvHIDl/9OBKNQoNeER0WXf/bEZRzZfxJKpQITXxwlmtzlf/yD9FwDGoYF4rlhrlFO5ElJK9TgOYHZCQmCsXtm1Ch837Gjs5fkUrAsi7w89zR4GYlcG7mAJq25EUAyIO14IorS0qDx8UGjPo7xOv/+fhMAoO2gBxBYX/z/D6s/47y7oWMeEm3CQnFZBeZbvbt3Jg6BRk1vr3KD/kWcwDedO2PMuXNY1q0bfqDGrtYUGdJhNpVDoVAiILABANbZSxKNjERrOLOLnMKZ3LzBJoOHQKWTflIDy7L4+weuqcADk0eJLj/1Rgb+2sg9WDz9yhjR5H6+8QByCkvQtH4InhrcVTS5UkPn4VHEhxCh8XOBXo9ezz0H1s3S6R1FrrUkITCwIZRKFQiRzyBUe+H378Jlsn9HCBEMXgsHZWde2HsYeTfToff3RedHB4gu/4cvNsJiYdGzXye0bN9EFJmFxWX4ZN1eAMC7k4ZApaRlCHKE3nEdAMOy+Pr33/FKQoLwHjV2dcddMzTLcjNhuHEZjEKB8M69nb0cAEBG0gkYUm9B7e2NRn37OUSnUHs3bjjUOnGzlgvzi7Dh+x0AgKdfEW/U0pIN8cgvKkWrBmH4Vz/XSj7jm0eL/ZIj1MOTGIZl8c3vv+PZpCSYGQbbmjXDZTdsdOxIKjM0xXk6lwv64DCM+f0Cci+ehMY3wNnLAQBc/MOanTloENR6veT6qtbePSBB7d2677ahrKQczdo0Qq8B4kyfyDOU4NP1nGc+76khUCrpw6xcqdVfZvny5Wjfvj38/Pzg5+eH2NhY/Pnnn8Lx8vJyxMXFITg4GD4+Phg9ejQyMzNtZKSkpGDEiBHw8vJCaGgoXn31VZjNtq2C9u3bh86dO0Or1aJp06ZYtWpV3a/QiShYFt9ajZ2FYTDx8cepsROBPCFD0708PADwDotCgz4jnL0MAHw4kzN4zUc4Jjvz6G87hNq7xt3biyrbZDRhzXJub/DpV8aIVuqwaP0+GErL0b5xJMb06SCKTEfCECLJS47UyuBFRUVhwYIFSExMxLFjx9C/f3+MHDkSZ8+eBQDMnDkTf/zxB9atW4f4+HikpaXh8ccfFz5vsVgwYsQIGI1G/PPPP1i9ejVWrVqFd955RzgnOTkZI0aMQL9+/ZCUlIQZM2bg+eefx44dO0S6ZMfAsCy+3rwZz1g9u/GjR+OXduIXz3oiuTmchxfkZhmaciPz5EkU3rwJtV6Pxv37O0Tn36u5cOYDk8Svvdu2bh+yM/IQGhGMYSLMuAOA7IJifLZhPwDgvclD3a7NnbtRq5DmI7fV4Hz44YdYvnw5Dh06hKioKHz77bdYs2YN+lv/caxcuRKtWrXCoUOH0LNnT/z11184d+4cdu3ahbCwMHTs2BHvv/8+XnvtNcybNw8ajQYrVqxATEwMFi1aBABo1aoVDh48iMWLF2PIkNr3FWRhBAvHJTWwMIJhWYzctAkdTp2CmWEwacwYbGzTBlKXoFaVTxx4zbfrkzqJpLIGLxqEGG31sdIXRNtQRR9jcaxuG30SFIJf/J3zhhr3HwC1Wm2ro8rPYjVzzryagksHudq7nk8Ov6fcqu+bTZYaySaECIXm4154BAwUMBtr9tmqVNVntBDM/3k3SsqN6NI8GkN7tIHRIp1nI6VsT6HOe3gWiwXr1q1DSUkJYmNjkZiYCJPJhIEDBwrntGzZEg0aNEBCQgJ69uyJhIQEtGvXDmFhlXU1Q4YMwbRp03D27Fl06tQJCQkJNjL4c2bMmFHteioqKlBRpQ+lwWAAAKT5fQGdA1Kpq9L84mV0OHUKLMNgw5gxaNmmDd506AqAHGaxgzVWUlq0RDLZFRUVKC7OAgDoNL+jxPCXzXHN5aWS6b4fDfcsd5pu7x9WiyqPEILLP68FALT30sP7u2/ueW7qks2i6Nyzh5te3zgmBsU/HURxDT6zdfnxGsm+cuUKrpy/AY1GA315GDYvPWrHSjnm7U7D55sOAgBaduuDBYeK7JbpDORQljB//nxs2LABFy5cgF6vR69evfDxxx+jRYvKmYR9+/ZFfHy8zef+/e9/Y8WKFTXWU2v/+/Tp0/Dx8YFWq8XUqVOxceNGtG7dGhkZGdBoNAgICLA5PywsDBkZGQCAjIwMG2PHH+ePVXeOwWBAWVnZPdc1f/58+Pv7C6/o6OjaXppoXGrRArsGDsT6J57A+TZtnLYOdyQ/Px8AoNfrHf4g40lkZGQgPz8fKpUKzZo1k1wfy7I4efIkAKCjBLWp/1gHKnfu3Bl6kZJvDhw4ALPZjKioKDRt2lQUmZ5KfHw84uLicOjQIezcuRMmkwmDBw9GSUmJzXlTpkxBenq68Fq4cGGt9NTaw2vRogWSkpJQWFiI9evXY/LkyXdYXWcwd+5czJo1S/jdYDAgOjoanwEQb3rWvVFaLNCZzTBqtZgD4O/evRFeMguRJY7rlM7CiAzvTwEA9SyzwMBxugmMyFZyunV+M8Aw0uguTfkdwAoE12sNvf8cTjcxotywBABQ3PplQOHA/oWsCT7nOK/y+vB/g6jqrpsQUqt9K8ZsQqNtX3G//Hs8GLV4133ug08AAM2HDYR21pQ7jhOTCfhqDQCg7eyBUGjsS/g+vfcYCt8rhJe/D8Z8+xK0+ns/zLBGM84s4jqaPPdkDNSq6p/bz56/gXnzrkGhYLD0oyfRwI7hriYzi29/TUZhYSGSErkRTl8P7Yn+eum9OyMBPiwX/9+VFEkmtZW3fft2m99XrVqF0NBQJCYmok+fyhZ7Xl5eCA8Pr/O6av0t1Wg0wtNMly5dcPToUXz22WcYO3YsjEYjCgoKbLy8zMxMYYHh4eE4cuSIjTw+i7PqObdndmZmZsLPz6/aJzOtVgvtXSYNmCC9wVNaLPh+wwZEGQx4dOJEwLoOBTRQONDoVIWBxqEGz0Y3o5HM4OXl3gTAlSTcVYdCDSidc91Epb6vwSMsi6JbyfCNigFzlwSHut52GLVaNINHCMHZ37nhrm1GPXJPufxaFRoVlHYavFuXrkOlVuHBcYPh5V/zRs5qlQJqdfUG76tV3M105PAeaBIjTpuy/fv3w2ix4KHGURjSNBoS9LZ2C/itJZ573advp7CwEAAQFBRk8/5PP/2EH3/8EeHh4XjkkUfw9ttvw8vLq8brsTuliGVZVFRUoEuXLlCr1di9e7dw7OLFi0hJSUFsbCwAIDY2FqdPn0ZWVpZwzs6dO+Hn54fWrVsL51SVwZ/Dy5AbKosFa377DePOnkX31FR0Tktz9pLcGr7LiqsWnaf9swfxsycj7wIXvivNTMPNvdtw6n+f4NK672BIuerkFQKZZ84j79p1qHRaNB8ifqeTu/HwS+OwMn07xr79vKhyU9NzseEPruFD3BRxyj3y8/Nx4sQJAMD7g3tJMsnBkUhZeB4dHW2z1TR//vz7rodlWcyYMQMPPPAA2rZtK7w/fvx4/Pjjj9i7dy/mzp2LH374ARMnTqzVtdbqsWzu3LkYNmwYGjRogKKiIqxZswb79u3Djh074O/vj+eeew6zZs1CUFAQ/Pz88NJLLyE2NhY9e/YEAAwePBitW7fGpEmTsHDhQmRkZOCtt95CXFycYPWnTp2KL774AnPmzMGzzz6LPXv24Ndff8VW6/BJOcEbuyfOnYNRocDosWOxPyYGDzp7YW5MnjD41TWLzpO3rUNQi3YIbt0JOWcSkfTFh0g/vA/BbbjuHOmH9qHL7A/gE+m8wa9nN3P/1poN7Autrzhjc2qCX3CA6DK/Xv0XzGYLenVviU7txSljiY+PB8uyGNisIR6MiRJFprty8+ZN+Pn5Cb/XxLuLi4vDmTNncPDgQZv3X3jhBeHndu3aISIiAgMGDMDVq1fRpEnN7ge1MnhZWVl46qmnkJ6eDn9/f7Rv3x47duzAoEGDAACLFy+GQqHA6NGjUVFRgSFDhuDLL78UPq9UKrFlyxZMmzYNsbGx8Pb2xuTJk/Hee+8J58TExGDr1q2YOXMmPvvsM0RFReGbb76pU0mClKgsFqxdvx5jzp9HhVKJ0U8+ia0tWkheeuDpuLqHl3f+JFpNehEAcOLz9+EVVh+jd5yFSu+F3HNJOPTBTCR98QEe+PArp3gOhBCc3cT1zmwzyjG9M6WiqLgMq9dwmZ9ieXdXk9OF5Jp5g91juKuU44H4JiU1Zfr06diyZQv279+PqKjqHyZ69ODGL125ckUag/ftt99We1yn02HZsmVYtmzZPc9p2LAhtm3bVq2cvn37CiEDOaKyWPDz+vUYbTV2j40diz+bN3f2stwelrUgP+86ANf08FizGcHtuqA8lwvpF924io4vvgmv0AgAQESPh9D7gxVIeG8Gim8lwzfa8YX1WecvIufyVSg1GjQfMvD+H5AxP/26D4aiUjRtHIHB/TuKInPRFxtBCEHz5s3RLTpCFJkU7kHrpZdewsaNG7Fv3z7ExNz/gTYpKQkAEBFR878D7aVZB6ILC/HgjRsoVyrx2Lhx2O6AtG0KUFiYCovFBKVSDX//+s5eTq1RqFSIjO2HowvnIqhlB9R/cDByzh5HvQ7dhHPUPn4oSUtxirEDIHh3TQc8BJ2fr1PWIAZmswXLv+XaHsY9P1yUDigXLt3Cxi2HAAD9+jmmkbanEBcXhzVr1mDz5s3w9fUVytT8/f2h1+tx9epVrFmzBsOHD0dwcDBOnTqFmTNnok+fPmjfvuYt6KjBqwPJQUHoP3kyIouKsJPW3zgMvqVYYFAjKBSuOX4lZtgY5F86g3/mTQdrMoI1m6H28kZQy/Yw3LiC6zs2ol6H7k5b39nNfHamPPp51pXf/zyCW2m5CAn2w5OPizN54uMlv4EQglatWlm9CsN9P+MKyKEsYflyrmlD3759bd5fuXIlnn76aWg0GuzatQtLlixBSUkJoqOjMXr0aLz11lu10kMNXg1Rm81onZ2Nk1b3+WxYGM6GyWMitadQ2TTatXtodn5lHoJadcCNHRuRffIo/nl3OtTevlB7+yK0Uw90mfW+U9aVdeESsi9cglKtRouhg0SXn3k9Ddk30qH39UaTzi1Fl89DCMEXX3OJN88/NQg6rf1lKqfPXcfvfx4BwzB33JQp9kPuYyCjo6NFqfemBq8GaMxmrPv1VwxITsbwCROwv1EjZy/JI8njE1ZCXNPgFaffRHbSYSjUGjQa/Bii+w5HWW4WTEWFqCjIA6NSI7RTT6elufPZmU3694E+wF9U2Qkb92L9RytxNfE8AsKDMXvNB2jevS1Szl1D9o10tO7dEQFhwaLo+ufwBZw8nQydVo1nJ4pjuBcs/g0AMGpEzzs6Qbk6Usyvo/PwXBSN2YzffvkFD1++jDKVChpL7RvOUsQhN8d1MzSzTx7ByRUfI/3wPoAQtBj7PNpPfR3ZSYeRc+Y46j84CJGd+9xXjpQI2ZkjxQ9nrnr1M/QZPxRvbPovNi36EQkb9uK3Batx63wyAsKCkPTXIUz8KE6U0oRlVu/uX088hOAg+/chj5+8iu27jkOhYDB7+mPYdajUbpkU50BnWVSD1mTCBquxK1Wp8PD48dhVw/RXivjwHp4rDn49/e1i6AJD8OS+Kxj2/U4YblzF32+8gMRFbyPvwikc+s8ruLblF6etL/vSFWSduwiFSoWWwweLKvvGmSsoLSzGmNefRnD9UDz26lPY9sWvaNKlJV7f8AkenTkee3/Yhl3f2t+E+uKVVOzYcwIMw2Das8NEWD2wYDE3ZeHJx3qjaWP7MjNf27YfCTfk1ZyCkeglR6jBuwdakwkbf/kFI6oYuz2NXTOU5i7kCkXnrufhZR3/B60mToXWLxAh7bog/9IZeEc2wCO//Y1BX21CUMv2SNn9O0ylNZkRID7nrMkqjfv2hj4wQFTZyUmXEBYTibJizjO6mngeQfVDMfq1yWjapRX6/GsoJrw/DSd3HbmPpPuzwpqZOWxQFzSJqXvPRZ7Dxy5id/wpqFRKvPry4/f/QDXsunwDC+OPoe9X65BVTL1EZ0BDmndBazJh088/Y+jVqyhVqTBiwgTsq0FdCEU6ykoLUFqaBwAIDHKtv0XB1QtQeflAH8zt/ZhKilCWk4lWE6ZB68/1Cmzz9MtImPcyzKUlUHs5rrsJz9nNXDizrQTF5s26t4GpwoRty9YhJCoUe1ZvQWSzaGj0OlgsFiiVShTnG+7aW7Q2ZGUX4pcNXHeO6VOGi7F0fPQp592NH9MHjRqEwmRi6ySHZQle/5Nb29Se7RHqU/P+j1IjZeG53KAe3l1gGQZlajVK1GoMp8ZOFuTlcRma3j71oNO5Vn1YRUEe6j8wABZjufB7h6mvw69RZUmLuawUxmID9CGOT4jIv56CjNPnoFAq0XKE+B2N6jdviNjR/bHls7XYsPB7PDRhGIrzDLh6/AKUSiUuHzuHxG1/o/eT9hW6//DLXlQYTejSsQm6d7G/EcSxE1dwMOEcNBoVZk8fZZesbReTkZiaCR+NGm/272H32ih1g3p4d8GkUmHsmDE2ZQgU51JcxHUn8fOLdPJKak+9jj3g16gpVHruqd6nfkO0n/qaTTbmrf07ENSy5gW0YpKWdAoAENGhLbyCAiXRMe6dKRj3zhRkXLuF8MZRuHXhOt7q+28079kWmddSEdOxOXqPtW/vMOHIBU7X6D6iZLr+c/g8AGBI/06Iqh9il6y/Ll0HAEzu0lpW3h0AgBDuJbZMGUINnhW90YhnT5zAsu7dAYaBSaWixk5GlJUVAAD0XgFOXUddUCiV0AeH2rzHMIww/+7GX5uQmfg32k35P6esr8XwwYj7ZyeM1QxYFovwxlx/xCmf/R+a92iLs/GJaNOnM0bOmgCdd90HsxJCcPIMFwUQq0m0IK+D/UlSB69ziSoPNXbeYOp7QcsSPAy90Yg/1q7FgORkNCoowKsya1RNAcpK+UnnAc5diIjwXkhEz77Qh0YguFUHp6xDpdEgrE0rSWSXFZfiwNodAMOgXd8uiGhaecN/aPxQPDR+qCh6bqXmIC+/GGq1Eq1biGNUkk5zBq9jO/u2NAzlFTiZng0AeKCR60Uo3AmPN3heRiP+WLMG/a9fR5FGg42tpPmHT7GPsjLe4EkTcnMmGr8AhHZ0v32d1Es38OObX+LkriNQKBQwVRjx1McvYUTck8I5FosFrIWFWmPfnJETVuPUqnk0tFr7Z5YUFJbgegoXRu/Q1j6DdyglHSwhaBzkj0g/xyck3Q8pyghoWYIM8TIascVq7AwaDYZMnIh/GjhvDhnl3rhySNNT2frFr6goKcNH8f/Dj7m7MXru09i2bB1uXbwutJJKvXAd21f8ZrcuPvzYoW0ju2VVldeoQSgC/L3tknXweioAoHcj12t47m54rMHzMhqx9aef0I83dpMmIYEaO9lSVloAAPDycn0Pryw3C4StW3q7K3FkczwGv/A4GrVvBpZlMfq1yQiJDsOWz34Wwrm/LViFkzsP263rpNXD62Bn+PEOeXZ6d0Dl/l1vmYYz+ebRYr/kiGcaPEKwee1a9L1xA4VaLQZPmoRD0fLbTKZU4k4hzT8ej8Xa2CgUXLvo7KVIRn5GDvS+XgiJ5sosFAoFlCoVxr83FQd/3YkriVwG5Ol9xzHwuZF26aqasCKGgQKqeIx2GlCTxYJDKekAqIcnBzzT4DEM/telC3L0egyeNAmHqbGTPULSiot7eGW52SjPz4G5vBQ+Ec753rEse9/u9PZiqjAhMCIEFxNO2bzfokdb9BozAKte/QzpV28hPy0bPUY+ZJcuPmFFpVKidUtx/p+ePHMdgP0G9ERaFspMZgR76dAyNEiElYkPX3gu9kuOeGzSyrq2bbGjaVMYdDpnL4VSA0qte3g6vbhd/B1N4VXOs/GNaiTU5TkaMYah3o/QhhF4e+tnqCjliu1ZawhXoVDg0RnjsXjSO1j0rzfQvGdbu2vmkqzGqVXzKFFGARUUliD5RiYA+/cEDyZz4cwHGtV32hQMSiUe4+H5lpfjh99+Q1RhofAeNXauA+/hebl4SDP/CmfwApq2dor+0rx8nP7td1zdewDGUq7ujpVoP1GtUcMngOuKo1AooFAoYLFYENWyEbo93BtXjp3HgGcetVuPmPttQGU4s2F0PQQG2JdVWZmwIs/9Ow4i0Ut+eISH51teju0//ohet26hRW4uuk+ZAtCnLZeiXMjSdG2Dx3t4AU0dX/6See4C9n28BDf+OQxTeTmCYhrh0SULUL9zB1hMJijVahjS0qHUauEdLE34TankJtWPnvsMGrZrinb9utotU6z9NkGeUH9nXwE7IcQlMjRpL003wreiAjusxi5Pp8O/H36YGjsXg2VZlJdznrmrGzzBw2vieIP399IVMFdUYPT/luLFAzvgHxWJjS/OQuGtNCjVXO3axrj/w8HFyyRfi1qjRuzj/eET6GeXnKoJK/YWiPOIZUAv5xQgu6QMOpUSneuH3v8DFMlxe4O3/uefEXvrFnL1egyYPBknIuUcWqDcjfLyQiHJwpU7rRBCUHDFeR7e5Z170StuCpr0exCBjRpg/Jpv4R0SjJ3zPhLOybt2HVHdOjt8bXXlVloucvOKZJmwwnt33aPDoVXJOJgmRUmCTMsSZPxXEIeu6enI1esx9KmncDYiAvb3YKieqvJZGCXWZktVfcTBuqvqI0Rc3aUlXMcLtVoPpZK5Q77N76xJVN33pYo+xly97tKsdJiKCsEolfCPanjf8+9H1c8TU/WyjCWlUKrV8AkOsjn30cXzsfqx8Tj23Q/oPHEsDKlpiOrU/r7yqh5njeb7rnXToh9x6+J1DHj6EbTqZV8Ltar6TpziZiS2bBYFpUJV5/E9PFUTVlq3aHCHPJO58nfjfe7p+5M5gxfbqP59z60JYsjwdNze4JXqdNg0eTIeDg+H+JO+qifD+1MHa6wkW+k83eWGJaLKK8jmMt10OhXKChdWe67PuaWi6q4NjbZ9Ve3xK1euAACCAwPRdPvX4ir/ak21aQIlBQUIUGtwad4nCI6NFd4PBPBgx844+P4n0CWcAFgWvr/tqFXKwZlFu+57zp5vNuHWrVsIKNLAFJ9eC+nV88P6JACAWheEFWuu2i3v2jXOgAYEBOCXbZkAMu957qKK6sOxfyRz13krshnml9sXuq2kXCQ5ldDm0W7E2vHjYQi3f/IxxXmUl3P/yHUunlWblcV5qqGhjt/P8fHxQa9evaCyhtZYlhXKEzp06ICUlBT8+uuvqF9f/OQKs9mM9HTu5i+2/LQ07mEoUqStCrHkFRcXIy+PG1gcFRVl97oo4uD2Bk/PvI+QTPtmWdUGFkZcD1sEAGiQOxsK2F8XVBvdKcGc7lDTLDAO1E1gRJaa8yq1ATPAMOLptig3Avge3r7NoAucc6duYkRFwRIAgKHdS4BS6sB11cWZ4Hf6cwBA2sgpIKp7676exHVW0fUdgdTRL9qtmjGbELmZ8xR1L44Fo67+n3MnAIaMLGjrBUNhzZbkGfrEANwYMQGNRw6B/pUJ99VNTGaUf/kLAKDHjIeg1Cjvee6l4xdh+cAC30BfPLxgjN31aBajBYeXxHN7ormcB/bquPbo2tZ+o3fqnwIAwNgBLTB75J3yjGYWS7dmAABm56dDcw9PZtMVLvGlXUgQ3ivPB8rz7V6bEQw+lKAsx5OyNN3e4CmgcajRkYtuxsG6q+50MIxGVINXVlYEANB7Bd1frlINKJ3z/5yo1NUavPyrnMELbN6m2vPqAqNWgalm4kB+yi1se3sBClMz0KhnFwx6cyYIIbiwYy/KCg3oOOYRvHzgd25GXy0nFyg1ymoN3mWroW/ZrTVUWvFuOQzD4Mi6d3H60k10btUAGpX9OXgnz98AAHRvF3NfeRqQexq8Q2mcR/tgZPg9z6E4Hrc3eBTXxx1m4RGWrTR4zRyfobn5/+ahJDcfDXt0wbEff0N4m5bYu2g5FErupn5l7994fOmH0PuLtddUyfkjZwEALbqJf91hIf6IDhfH6ykwlOLqTW5uXafW9jWSP5jGeZ69I8PsXhdFPKjBo8ged+ijWZx2E+bSEijUGvg1EGcid03JungFKUeSELd3A4JjGqBZvweweuwU9I57Ft2eehKpSWewcebbuPjXPnR8wv7OJ7dz4ShXitGym3O6y9SUE1bvLqZ+CIL8695hpcRkwvGsHABA70j55w9IMd1ArtMSqMGjyB5hFp4Le3j5l631d42bQeHgmqyUo0kIbFAfwTGc12IxmeAVFIi+s6bBK9Af9Zo1RuaFKzi1cZvoBq+4sBg3L3CGpGV3eRu8xLPXAQCdWje0S87hjCxYCEEDXx9E+8pv4KsnQw0eRfaUuUFbMb7DijPCmXk3biK4cUOYysqh1uuQc/U6GvboDK9Af7AWCxRKpWTz+S4nXgQhBOGNIhAYKu+/3/FznGHubKfBc7VwJvXwKBQZ4Q4hzfzLFwAAgU1bOlx3eOsW0Pn5wlxRAbVeh+6Tx6LL+NEAIGRr3ko8iUa9uomu+8LRcwCAlhLs34nNCdEMHpfF6QrhTE+DGjyK7HGHpBVnenjtHxsOi9kMpTWUqvPjJhgQQsAwDM5u3YnCtEw0699bdN28wWsh8v4dv3axsE1YqbvBM7MsEtK5ektXMXiM9SW2TDni9r00Ka5PZUgzwKnrqCusxYKCq5cAAIFO6KEJQDB2VeENRkiTGAx6cwaiOrUTXW9lwkrdr7u8tBzG8gqbobUMw+DatWu4cuPenVBqA5+w0qh+CILtGAl0KicPxSYT/DUatAl23YiEu0I9PIrsqfTwXPMGUpSSDIuxAkqdHr5R9oXLpCCsZVOEtWwqutzs1GzkpGZDoVCgWecWdZbzw/srwSgYhDUIQ2iDcPj6+8JkMmH79u3oWN+M1k0i7F6rePt3XDjzgcgwKFxkKgtXeC72Hp6o4kSDGjyKrCGEuPwsvDxrhmZgkxZgHDBtXC5cPMZdd6O2jaH31tdZzrrFa8FaWHj7+6CksBgKpQJ6nR4lJSX4bcdR5OYXoX5oACaN7AVtLYvmefgMTbp/595Qg0eRNcaKYrCsBYDrGryCK9aEFSfs3zmTC0fsT1ixWCyYMv9FpFy4jinzX4RWr0XKuRvY9uFGbNmyBYQAa/5IQEauAc8/8VCd9YiRsEIIcbkMTQGZemRi4zmPmxSXpNQazlQqNVCr6+4lOBPBw3NChubt7PxoCZY++CiOr90oua6L/P6dHfV3SqUST8wch9DoMLzSZyqyb2WhcbsmaNiwITQaDf767lVc3L4AhUe/rLMOsRJWkg1FSC8phUapQLewenWWQ5EO6uFRZE1ZGV+SECBqVp4jKXBihubtXE9IxK3jp2AsLZVUD8uyQkjTng4rfDbmpLeeQWBYEF4fPgvv/vwhcnNz4e3tjQqjCUpGDaWy7s/uYiWs8OHMrqH1oJPzwNfboHV4FIpMKC8tAOC6CSus2YyC69wcPDl4eKknzwAA6kuQkWmj58otlBhKoNVr0ah1ozrL4R9yLBYLHp4yElq9Ft+98xUuHjqPiIgIaDX2GTsAOHnhJgCgYyv7+mcespYjPOBi4UxPKkugBo8ia4zGEgCARuPt5JXUjbK8bBCzGYxCAa8wcWa21ZXyomKU5nIec72mMZLqyrzOTQuIbFL/riURtYEQAqVSCUIIBk0cCoVCgZST19G0qTiZpWlZ3P+TxlH2hSFTiooBAM0D/O1eE0UaqMGjyBqTiRv+qta45v5dWTaXxKAPDr1jBp2jKbzFGSGdv69QfC4V2ancnlhIffuH3fJeHv/fPo/3Q8GhbHh5edktGwAycw0AgNBg+yZFpJVwYeJIb3HW5TAI4V5iy5Qh1OBRZI3JyN1EXDVhpTSL29fR13N+mKvgFjfNOyBK/Knmt5N9iwvv1bPTazKbzDj21xHkpuegWafmaN6lJRQKhc3UeJPJDPV9ht9WR5ZIBi/davAiXM3geRDU4FFkjclUBsCFDZ7Vw/OSk8GLlj60mmP18OrZ4eGVl5Zj/eKf8eNHq+Ht5wVDXhEGTxqKWV++BpZloVAokJNfhLVbEjD7maF11sN7eGF2GDwzyyKzlPuuupqHpyAECpE9MrHliQUtS6DIGsHgaVzrJsJTafCcX4hccJP38OzvTHI/eA8vxA4P71zCGexaswOvrXwTv6VvxcLti3Fy/wl889ZyKKwF/AlJV7Dg6612rVUMDy+ztAwEgJJhUM/LNR/OPAFq8CiyxuVDmtlcSFNWHl6U9B5etgge3umDJxHdoiH6PTkQFrMZnfp1wfTFM3Fw036cPn0aAHDxWgbaNouqsw6LhUVOAZdsYo+Hx4czw729XKalmCdCDR5F1rh6SLMsx5q0IgeDx3t40dLv4eWIsIeXm56DkMgQAFzza5Zl0XNELwyaOBT79++HwWDA5RsZaN2k7gY8J78ILMvV+oXYUYPHJ6xEUO9O1lCDR5E1lSFN17yRyCqkecsxIc2ykjIU5RcBAEKi6u7hhUaHIeN6OnLTcwBACGOOe3UiAgICsGfPHhw+edWudmD8/l1IoA9Uqrpn0fIeXqSP65XP8IXnYr/kCE1aocgaoSzBRT08uYQ0WZZFYSpXliB10gqfsKL30cPbr+4GYOSLj6N5l5bw9rf1vJRKJYYNG4ZffvkFWVnZ6N6+cZ11CPt3QXaWJBS7aEkC+GkJ4suUI9TgUWSNK+/hEUJQlsOF9pxt8Eqyc2GuMIJhGPhL3Mk/55Z1/y4q1K52cL6Bfug+tOddjwUFBWHUqFEovJWEJtF1D5uKkaEJAOmltCTBFaAGjyJrXDlLs6IgD6zJBADQhzjX4PHhTN/wUCjVdRuhU1OyU60ZmvWlbaAcERGBRVO7QKOq+85MVp5IRefFXEcg1/TwPKeXZq2+KfPnz0e3bt3g6+uL0NBQjBo1ChcvXrQ5p7y8HHFxcQgODoaPjw9Gjx6NzEzbqcQpKSkYMWIEvLy8EBoaildffRVms9nmnH379qFz587QarVo2rQpVq1aVbcrpLg0rpy0wocztQFBUGo0Tl1LwS3HhDOBypKE0GjnJ+rcD/E8PO57Sj08eVMrgxcfH4+4uDgcOnQIO3fuhMlkwuDBg1FSUiKcM3PmTPzxxx9Yt24d4uPjkZaWhscff1w4brFYMGLECBiNRvzzzz9YvXo1Vq1ahXfeeUc4Jzk5GSNGjEC/fv2QlJSEGTNm4Pnnn8eOHTtEuGSKK+HKIU1ZFZ3fTAXg2KJzqT08MRCry4pre3jSvORIrUKa27dvt/l91apVCA0NRWJiIvr06YPCwkJ8++23WLNmDfr37w8AWLlyJVq1aoVDhw6hZ8+e+Ouvv3Du3Dns2rULYWFh6NixI95//3289tprmDdvHjQaDVasWIGYmBgsWrQIANCqVSscPHgQixcvxpAhQ2p1gSyMYGGs1WfsoaouR+q9XR+BEawDdZOquol4120ycQZPpdbcU67N+xaTaLprRBV9jNlWd1kGZ2S8QkLvOCYGVWUSk7maM4H8G7cAAP4R4SBG+9dSVZ/FaLE5lpXCGfrg8JA7jtlDQXYBFsctREhECDpHdgDDMDCa7fuWZ+RwBi840LdGsqqeY7TOBDCzLLLKuOSqEG9v4X2xkUquJ2HXHl5hYSEAbgMZABITE2EymTBw4EDhnJYtW6JBgwZISEhAz549kZCQgHbt2iEsrPKpd8iQIZg2bRrOnj2LTp06ISEhwUYGf86MGTPuuZaKigpUVFQIvxsM3Bf5Rr3PodPp7LnMOpMSvMgpegEgS/2p03RXFCwRT1ZZMgCAVGxBef6l+57vd/pz0XTXlsjNX9v8fu3AAQBASGkB6v9W9wGlNaH8y1+qPZ637zAAwPtKKso++0lU3YeXxNv8fv34NQBAdkIa/sndI5qetLQ0JGw5CB8fH3T5v44AgKVbM+ySefoqV/Jw4JIJGZvTavXZRYFceYfBYABrndv3TWRjoXxCEsrLJRBKIP7Ic3m6eHX+y7AsixkzZuCBBx5A27ZtAQAZGRnQaDQICAiwOTcsLAwZGRnCOVWNHX+cP1bdOQaDAWVlZXddz/z58+Hv7y+8oqOj63ppFBlhsiZ9qCVOtJCC4mKug4ePT90LmsWCfzj195d+dA3/sOnnZ1+Y8Hak+P/Jb8d4e9e9fKLquiQ1dhS7qbOHFxcXhzNnzuDgwYNirqfOzJ07F7NmzRJ+NxgMiI6ORpPUl+CjDnHYOljGiMvRnGfXKHM2FHBcsgILI66HcbpDK2Y5XHeWlvMqtQEzwDDi6LawKwHkwDvoGegCu9/1HEKMgleZ33E6oHSgcbSYEJj0BQAgc/QUEFWl7uxDx7n19RmGjLEviK6aMZsQ9hvnVfpOfwJMNRMDDMuXAQAipz0Jv05t7dZNTGYUfbEOAND7ld5QariibWO5EaXzuDD0sDeHwc/O+raqFK0sAtYADdtXFprPHlgPGlXdQn2EEPy9vQHSsgvwxqMxiAi5/8OA0UywaBe3Rzk77QY0hOB8fgEMTRrDR63C3NTrdVpLTTAyDD4MEn8/mGEJGFbkLE2R5YlFnQze9OnTsWXLFuzfvx9RUZV97MLDw2E0GlFQUGDj5WVmZiI8PFw458iRIzby+CzOqufcntmZmZkJPz8/6PV3T17QarXQarV3vK8gGiiIczLkFNA41OjIRTfDaEQzeHzhuUbjVzOZSjWgdM51E5XaxuCV8jV44ZE270sBo1aB0dxdh7miAkWZ3E06qHGDe55XV5QapWDw8m9xw1S1ei0CwgLsqsO7nfxsTjbfbgwANCrGrrKEv754uZafqNzD0xACDSHoEOCPtf0e5N6UaTp+dXjSxPNafVMIIZg+fTo2btyIPXv2ICbGdmpyly5doFarsXv3buG9ixcvIiUlBbGxsQCA2NhYnD59GllZWcI5O3fuhJ+fH1q3bi2cU1UGfw4vg+I5uHSWZpY8sjQL0rh1qHVaeIcESaorK7Wyh6aYxg4ActNzAQBB4dJeA8V9qZWHFxcXhzVr1mDz5s3w9fUV9tz8/f2h1+vh7++P5557DrNmzUJQUBD8/Pzw0ksvITY2Fj17ch0TBg8ejNatW2PSpElYuHAhMjIy8NZbbyEuLk7w0KZOnYovvvgCc+bMwbPPPos9e/bg119/xdat9o0Bobgerlp4TgipLEsIdW4fzapjgcQ2QreTzXdZkaAkITeDM3jB4cFAgejiPRZaeH4Pli9fjsLCQvTt2xcRERHC65dfKjPEFi9ejIcffhijR49Gnz59EB4ejg0bNgjHlUoltmzZAqVSidjYWEycOBFPPfUU3nvvPeGcmJgYbN26FTt37kSHDh2waNEifPPNN7UuSaC4NhaLCSzLpb+7modnKi6CuYzzTp3t4eU7sujcWoMXasdYoHvBe3jBEcGiy6Z4BrXy8EgNrLZOp8OyZcuwbNmye57TsGFDbNu2rVo5ffv2xYkTJ2qzPIqbYTJWZuS6msHjvTu1ty/UXs7toM97eIEOGfzK99EU38PLsU5NCA4PRtb5rPucTakpntQ8mubQUmQLH84EAJXaObWUdaUynOn8Liv5whw86T08YQ9P5JAmy7LIy8gDYA1pUih1gDaPpsiWqn00pd57EpvSLHmMBQIqG0cHOqSPpjQeniHXAIuZ69oSGBpYJxksy9I6ubtBiPjZpTLdw6MGjyJbXLtxtDwyNAEg/6Z1Dy/KcXt4Ynt4/P6df4g/1HUsq1AoFCCEIDk1B+VGE3QaNQJ8vRDk73pDWyl1gxo8imzhSxJULmjwSngPz8kZmkBlSFNqD89sMgthx1A7Jp3fDT5DMySi7k0kFv24E3/+cxblFSaolArotGqEBvrigQ5NMGZAZwQH3L+Di9lsgdlshkrF3ToJITiVlw8TyyJUr0MDGXTVqTWe01mM7uFR5Avv4WlcrCQBkI+HV1ZoQEUR1/oqQOKkldz0XBBCoNao4V+DriW1ISfNmrBShwxNlmUx76s/8OlPuxATGYxBPVqhX9cWaNUoHLmFJXjjy83oP20x0rIL7itr+sdrcePGDQBARmkpXjiYgH8fTMDzB/7B5Pi/8cPlq7CwjmzbLgJ8SFPsVy0Qa/Tc/aAeHkW2uEVI08lJK7x35xUUAK3Eo2uybvGDX0NE3yuzqcGrJdn5xfhy/X58MWcsnhzU9Y7jmbkGjJ37NV797Df89MFz95RTXmHCut2JeOrpjmBZFh+dOIU9ael4uU0rhOh0OJaTg+cP/AMvlQqjYxreUw7lTvjRc926dYPZbMYbb7yBwYMH49y5c0Kf05kzZ2Lr1q1Yt24d/P39MX36dDz++OP4+++/a6yHGjyKbOHLElwtQxOo2mXFuSFNR4UzgUqDJ0nRuR01eNn5RWBZVjB25RUm8DlQSoUCYcF+mD1xEKYtWFOtnDxDCRgwCAwMRHFxMTYn38C2IQPQKYRb04SmjdHC3x8LTp52LYMng6QVMUbP1QQa0qTIFtcOaXJ7eN7ObitmLTp3SIYmX3Qu8v4dUMXDq4PB02nVCA/2x+ufbwTLstBp1dBquJdKpYShuAxHzl5H/XoB1cqpMJoRHR6IzMxMaDQa+GrUCNbZ9u9t6OONtNLSWq/RXTEYDDavqiPcqqO2o+dqCvXwKLKFN3iu5uFZjEYYDdw/WH2I+Df/2lCYam3/V196T5PfZ6va3Fks8jO5xtFBYbXvo9k0OhRznhqMN5dvxvELKejRNgYNI4IQ6OsFo8mCrX+fxj+nruGDaY9WK6dBeBCeGNgFX27cjAEDBqB3eBg+OXUWr3VoC2+VCqklpVh7NRnd6jluOosoSJi0cvuYtnfffRfz5s2r9qN1HT1XE6jBo8gWi4WbZK5S3TkFQ84YiwzCzxo/6efPVUdpfgEAwDuobrVrtaG4gEuOEXMkkCC70DpzrgaZlHdj/NBu8PfRYf3uE9h68DTyi0pRVmGCxcKiZaNwLJszDsMeaAtiHeR6N5RKBZ4d+QD+TMrCgQMHoCgowK2SEsSnZ6KRrzcuFRqgUyrx/UO963yd7sbNmzdt5iLebaLN7Ug5eo4aPIpsYS3c8Felk8b91BVjMWfw1N4+UCiVTl1Lab7V0wwQ3wjdTonBOkzVT/y6ttJia19Sn7qFt1UqJUb27YiRfTsiM9eAwuIyKBQMosMCoa1S13e/Bgfhwf4YMGAALly4gAZXLiG3rAypJaUoMZsxuVlTPNeiKULvMcJMtki4h+fn51erQcD2jJ6rCdTgUWSL2cx5eEpHDnQVAWNxEQBA7ePr5JUAZQWc8fUKlN7T5D28unph1VFWxIW37TWmRpMZYcF+CAuuvAkTQsCyBErl/VMaWJaFVqtFhw4dMDfEH5rbDEWFxQKWEChcrDOQsyGE4KWXXsLGjRuxb9++akfPjR49GsCdo+dqAjV4FNnChzRdzsOzhjQ1PtJ7Vfej0sOT3uCVFHIeno+/+AaP9x71vnXznn7Yeggr/0iAhWUxuEcrTBvzEIL8vYV2Y5v2nUBM/RB0btmgWjn3K7f44MQptA70x7+aNK7TOj0VMUbP1QSapUmRLRY+pKlyLYNnsnp4Gjl4eIWO8/CKCrnrFjukaawwwmzixkR5+dY+pLlhzwnMX7UdIQHe6NwiGiv/SMCcpRusYU3uFvifr7fi0Olr1cq5kZ6LC9czYDAYUFJSgoKKCpSYTCg3W2BmWZhYFptv3ERGaVm1cmQHIQAr8quWIVIxRs/VBOrhUWRLpYfnYiFN3sPzdb6Hx4c09f7SG1/BwxM5pFlaVJnm7+XrBVhq9/lvNh3EsF5t8d8Zo8EwDMYO7opn/vM93v3qDyyY/hh0WjXKKowID67+oeCtL3/H1r9Pgyg00Gg0SFAwCNSoEaTVIkSnRYBGgysGg2u2F3MyYo2eux/U4FFkS2XSCjV4dYEQ4rCQJiFE2MPzFrkZc6mBM3g6Lx2USiUsltpZvMs3s/DC4w+CYRhYLCx6tmuMnz54Fo+/ugIh/j546/nhKCqtQGhQ9Q8Fp6+mon+3lqjQR6KkpATNM9ORW1aG5KJiHMvJRbHJhHKLBVESd7Sh1B1q8CiyxXWTVvg9POeGNI0lpWDN1lCgxCFNY3ll2NE3QNzr5j28uoQzASDA1wuG4nIAXGmB2WxB55YN8PWbEzH+re8QFRaAkrIK1LuPZxoTGYwmUfXgFcN1bJmbev2OpBXdyh/go6a3VblC9/AossXiqmUJMvHweO9OoVJBI7HXUVTA7d8pFArofcRNyxcMnl/druGhzs3w0/YjKK/gvk8qlRIWC4tBPVtj8awn8OpnG1BSZryvh/fUiJ6IrBcAk8kEQgjMLCu8LCyLlOJieKlUqKdzrUYJou/f8S8ZQh9FKLLFVZNW5JKlWVbAGTyvQH/JB+jy+3fe/t6i6yop4mTXtQbvg2kjUVBUCp22MlKgVHKz8SYO7wGzxYKF3/8FP+/qDfWovh1RWmHGf3dxbc5UCgVUVTy8SC8vHHx4mOvV4XkQ1OBRZIuQtKJwtZCmPOrwSvmEFQcUnUu1fwfYH9LUadUI194Z0uUN89OP9MLTj/S6rxyGYaBW3buRgEqhQOvAgDqt0bl4zkA8GtKkyBYLv4fnah5esTxCmmXWkKaXI2rwrHVyvhJkg/JJK3UNaVIoPNTDo8gWV93Dk0sdXqk1pKl3RA2edQ9PUg+vjiFNyn1grS+xZcoQtzd4LGMEyxgdqk/4GY7Te7s+Z+omRBzdFguXWadQMtXKtDlmNZIOo4o+xsz9bDRwHp7Wy0t4TwqqyibWDMmqlOUWAAD0fr4gRnHXUVWfxWhBUR5n8Lx8vWAx1rJQ7j7w+4N6bz0sRouNfKOZoC5310dnLkNhcSm+enMSWjaqeS9GTh/H7vQM/O/MeXStF4xXO7av9Rpqi1GyfVjPCWm6vcG7Wv9z6JyUNXU9bJFT9AJAlvZTp+muKFgiihxj2QUAAFu+C+X5uTX6TGDSF6Lorgthv30NADBnpgIAIg79hfCbZx2iu+iLdXe8l7+HmwStupEJw+K1kuk++NlBnP77NACg5FYJ4j+JF1X+xT0XAQC553PvkL1oV3adZB44mYzS0lKsiM9G2IW67ewsZ9T44/oNnNNoYa7fqE4yak15uWP0uClub/AorgvLck/uSidPHKgt/JDLmoxCkZJy683REQ98vC4prlmK/58mE+fxajR1D5cbjUa7ZcgCKcoIaFmCc2h+4yX4qB03kJFljLgQw3l2jdNnQ0Ec94+BZYy4FsHpDi2bBQUcqBtGZOk5r1IdNAMMY79uVrELwDVoAx6HJvjxe55HiBGmvCUAgPzO0wFHFqpbTAg8znmVWWOeA1EoUfGf/wAAise+ALaehANgzSaErv8WAOD/8hgwtxU8W84nAkeAoKGxCHh5nKiqicmMwqXrAQAPvRyLk2kngANAq37N0f//xJ0Hd+BMPHAcaDu0FfrH9YbFaEH8Um7K9ey+wdAoaxfqY1kW8+ZxBu/VAeGoF1jzvVajhWDRPi7a0D0zEzsAdC8vx9xrV2u1hrpgZBh8GCH95Hp3xu0NnoJoHGp0ZKMbGocavKowjEYUg2cx84XCXjWXp1QDzkpyUalhKi8TGudqgoIAtWOML6NWgdHY6iqzNnP2Cgm645iYKDVKoVbON8gXSo24HnlpCZe04hPofYdsjZKBRlU7g1dcWrmfGeirq/Xnecqt308/leqOjisUeULLEiiyhbVwiRGu1FqMr8FTqFRQap3bcaMyS9MBw1+FxtFSZGly0wfEytIsLuNCpAzDQK+t+3er2Jq4Q1uJuQ70L0WRLa44LcFYZC1J8PWTvLvJ/SjLt44GckAdXlGhdfirn/iTAviyBG+R6vBKyrjvlbdeY9ffqMTap9RH5Trfz7vCQoI9PHHFiQX18CiyRTB4LlR4bpRJDR5Q6eE5YhaeVKOBAKDEzk4rt8N7eD56+5Jgiq0hTerhuQ7U4FFkiysWnsulcTQ3GoifheeA1mK8h+cCheeCh6ez73slhDRV1OC5CvQvRZEtFhccD1QhE4NnKq+AxZo27+WAPbxivnm0yNPOAQlCmuWch+dtp4fnPiFNzylLoB4eRbZU7uG5jocnl7ZifB9NhVIJra+0E7gtFktlL02RQ5pmkxnGcqvhFiukWSpSSNNEQ5quBv1LUWSLK44HqvTwZNJHM0D65Bk+ixKQYNq51bsDxDN4JeWVSSt2ybF6eN6uHtL0nM5i1MOjyBdXDGnKZQ9P2L9zxGgg6/6dRqeBRivuw0mJdVKCRqeBSiRPivfwvHVihTRd3OB5EPQvRZEtFtYFk1ZkFtJ0SIamNZwpacKKSN4dULmH5+NlZ9KKu+zhESI0SxBVpgyhHh5FlhBCXHIenmw8vALHzcLjh7/6+EtQg1csbsIKUDVLs+4eHiGE7uG5IPQvRZElLFs5foaGNGtPab7juqwUS+jh8SFNMWfhiVGHZzKZhG0qlw9pUg+PQnEulipz5mhIs/aUFTiuywof0vSWwsOTIqRZZn/SCj8pAQC8XN3geRD0L0WRJWwVg6dQuM7X1FRiTeDwlrYU4H6UG7h16PylN7y8F+YtolHiKSvlxg7pffSiySytsJY52FF4zo8X0imVUDi5hZzdeFCWpuvcSSgehcVSGdJUKF3na2q2zm5TOmnoMI+xlCsV0HiJZyjuqctqQLR21rXdVXY5P3NOvLC20dohRWOHZ2axcFPXtQo3CJLRkCaF4lwIsQg/M4zrfE0tVoOncvKkBJN1IKvaztT7mlBh3RPT2Nmq626YrMZUbcdUg9sxmrjvlkZd9zFG/HBijTsYPA/CdR6dKR4FPxpIoVA6fepAbbBUcIZGqXHutHMTb4Qc4eGV8x6eFAaPCx2KavCs5QRqVd0NHu/hqd3B4FEPj0JxLqzVw2MU4g4TlRrBw9NJb2iqgw9pqvXSe5pGq1ESu+jcVrZ4Bq/CyBk8rR3lBLzBox6ea0E9PIosqfTwXOsraq7gDI1S62wPzxrSlGBf7XaEfTZJQpom0WUbzdb9N40IBk/pJgZPng6Z6LjJX4vibvAensJFPTynGzxhD096D6/C2rlEkqQVIx/SFM/gievhudb309NxrcdnisdAWN7guc5XlDWbwVr3h5yetGJN53fkHp6USStiZmlWWLM0xfDw1ArX2V++J3QPj0JxLnynFVfy8CzGCuFnp3t4QkjTAXt4fNKKhCFNMZNWqIfnubjO4zPFo2CtHh7DuM4NhQ9nAoDSyR6esdxxe3gVvIcnRUhTiqQVETw8typLYK0vsWXKEDf4a1HcESGk6VJF55yRUajVUCida6idEdLUSpClKYmHZ+2SIkbhuVuUJXgQtf5r7d+/H4888ggiIyPBMAw2bdpkc5wQgnfeeQcRERHQ6/UYOHAgLl++bHNOXl4eJkyYAD8/PwQEBOC5555DcXGxzTmnTp3Cgw8+CJ1Oh+joaCxcuLD2V0dxWSx8SNMFPTxnhzOBKiFNBySt8J1WpNjDM1aIn7TCF56LkqXpDgaP38MT+yVDav3XKikpQYcOHbBs2bK7Hl+4cCGWLl2KFStW4PDhw/D29saQIUNQbg2xAMCECRNw9uxZ7Ny5E1u2bMH+/fvxwgsvCMcNBgMGDx6Mhg0bIjExEZ988gnmzZuH//3vf3W4RIorwnt4rlSHx3t4Ko1zw5mEEMfW4ZXxWZoSeHjWJs20Dk9CiEQvGVLrv/iwYcMwbNiwux4jhGDJkiV46623MHLkSADA999/j7CwMGzatAnjxo3D+fPnsX37dhw9ehRdu3YFAHz++ecYPnw4/vvf/yIyMhI//fQTjEYjvvvuO2g0GrRp0wZJSUn49NNPbQwjxX2pTFpxnZCmXDw8c0VlJ3+NlyPKEsSvleMRQpqyzdJ0A4PnQYh6N0lOTkZGRgYGDhwovOfv748ePXogISEB48aNQ0JCAgICAgRjBwADBw6EQqHA4cOH8dhjjyEhIQF9+vSBRlP5D2jIkCH4+OOPkZ+fj8DAwBqviWWMYBnj/U8Uiaq6HKn3Dt1wsO4q+gixX7fFwhkPhUJxX3k2x6tMWXAIVfRZyrgxOUqNFjA5YB3mSh3EVNls21hYJPysUipBjOKvxUaftQ5PpVTBYrTc6yN1wmgd5aNUKgXZVXUYLbV3JXiDxyiUMJpr93leH2/wVAoFjA5qfSeZHg8qSxDV4GVkZAAAwsLCbN4PCwsTjmVkZCA0NNR2ESoVgoKCbM6JiYm5QwZ/7G4Gr6KiAhVVsuQMBm4e2KWGn0PnpM711yIWOUUvAGTpP3WablPeErtlGAuuAQAYkg9jbs33bwOPf2G37rris3MTAEBXXoLQn1c4VHfh0vXCz/x3n2EYFC39TXLdxXmcoU/65Swy4nNFlZ1zIw8AcH7rZbBX7vSmFu2rnT6WZWGxcCmEyxMM8PIy3+cT95YDABf8/TG/cZM6yagTVbaGKLXHbfzx+fPnw9/fX3hFR0c7e0kUO+BvKK7UONrMF507eSAoP6tNrXbMpHhenxTXLXhSIsnm/0YA5zXWFX5d9siQDR6UtCLqNzQ8PBwAkJmZiYiICOH9zMxMdOzYUTgnKyvL5nNmsxl5eXnC58PDw5GZmWlzDv87f87tzJ07F7NmzRJ+NxgMiI6ORovkl+CjDrHvwmoByxhxvgnn2TVOnw0Fcdy0bpYxCl5laNksKOBA3TAKXqU6aAYYxj7dyowdAH6EUhMBTfCcas8lxCh4lfmdpwNKx9zoAQAWk+BV5nXrA6xdCxIZjezxU6XXbTah3q/fAgCCXh4DxronVXHmIvD559D6+yL4/8ZJopoYzcizepWsteiq74uxCIsOq+5jtebbH78BMoHu4zuhY+/2AACLyYJ9SxMAALMfCoJGWfOHooKiUnxk/fmNgaHQ1DJxxWghWBSfJxi8bsXFmHvlSq1k1BUjw+DD+vUdostdEdXgxcTEIDw8HLt37xYMnMFgwOHDhzFt2jQAQGxsLAoKCpCYmIguXboAAPbs2QOWZdGjRw/hnDfffBMmk0l4St25cydatGhxz/07rVYL7V2SBRRE41CjIxvd0DjU4FWFYTR2GzxCuJuYQqGunSylGlA657otVu9BqdMDDvKueBiNSjB4JuventpLJ7wnFYQQIbHEy88LKo24Hg/vPep9tHeVrVEy0KhqbvCqzln01qnqHEHgDZ5eoYBGpt5MjfGgiee1DmkWFxcjKSkJSUlJALhElaSkJKSkpIBhGMyYMQMffPABfv/9d5w+fRpPPfUUIiMjMWrUKABAq1atMHToUEyZMgVHjhzB33//jenTp2PcuHGIjIwEAIwfPx4ajQbPPfcczp49i19++QWfffaZjQdHcW9Y1vWaR5tlkqVpdGANXtUQoaRZmiLV4QklCZq6GzugSpamC4XcKXXw8I4dO4Z+/foJv/NGaPLkyVi1ahXmzJmDkpISvPDCCygoKEDv3r2xfft2m8SRn376CdOnT8eAAQOgUCgwevRoLF26VDju7++Pv/76C3FxcejSpQtCQkLwzjvv0JIED4K4oMHjh786exZe5fBXxxo8KXppCq3FRCpLEEoS7KjBA9ytDo9mad6Tvn37glRzMQzD4L333sN77713z3OCgoKwZs2aavW0b98eBw4cqO3yKG6CK9bhma3No1XO9vAcWHTOGzyFQgGlHRPE74XYrcXEKDoHqvTSdIekFZZwL7FlyhA3eDyhuCOsC3ZasVhTxp3dONqRkxL4PTatXiNJRi0/Hkgsg8cPf61tssrt0JCma+I6j88Uj6JyAKzrfEXlsodXOfxV+nXwHp4U+3cWi0WomdOItodnNdBihTTdwcOjSSsUinNhLS44D69CLiFNx01KkNLg8eFMMeWL0VYMcLM9PA/CdR6fKR4Fnz7uUtMSjNaQptOTVhwX0uQNnlYCb9JY1eDJbA/PrQyeByWtuMFfi+KOsC45D08eHh5v8KQYyHo7jvDwGIYRLSFGbA+PNo92LVznbkLxKPiQJk1aqT1CHZ4DQ5rSlCRUJqyIlRAjVlmCW008px4eheJcWBcMacomaYX38BxYeK6VwJvkPTwxZ+Hxw181avu+V24V0vQgqIdHkSVC0ooLhTSFwnOZGDy1A0KafFmCWFmUVTEaxZ92LnbhuVuEND0oS9N17iYUj4JPWmFcyMOzGHkPz8khTTfL0pRk2jnN0qyEFp5TKM6FEG6PRMG4zlfUYvV2lA5uHH07ZqtnpBRxSvi9EDwdCZpUm03ij1syWQvP1XbWz/F7eCpaeO5SUA+PIksIPw/PhZJWWL7Nlsq5Bq/S8Er/z5u/8UvRVoy1Fp0rlOI99Fj49dopk2+vqHQXgydPh0x0XOfxmeJR8B6eKw2AJVZvh3Fy9w3Wug6FAwbRSmnweO9REoNnZyiSN3gKF/p+UqiHR5EpxPrIybhQSFMwNM42ePxcPgcYPMHTkdDDs9cbq4rFYl2vwj5DxRt6tzB4tCyBQnEuvIcHuM4NhfC1gw4wNNXBmh3v4YnphfFYzBJ6eDSk6ZFQD48iSyr38FznmUwwNE7ed+QNhRRe1+0IIU0JvFrBwxPxOkQPadq9IhlAPTwKxbm4YkiTH1rrfA+PT55x9T083nsU0eBZ6B6eJ0M9PIosEZJWXCikWVks72QPz8QbPEd6eOI/mPB7oqLu4VnrwxR27uG5VUjTgwrPXefxmeJZWG8orhTSJGaZZGnySSuuXpYgwf4gzdK8C3zhudivWrJ//3488sgjiIyMBMMw2LRpk83xp59+GgzD2LyGDh1aKx2uczeheBSuWJYgl3ZolXt4rm3wLFLU4QkhTZqlKTdKSkrQoUMHLFu27J7nDB06FOnp6cJr7dq1tdJBQ5oUWeKaWZpy2cPjszSl9zQFT0fKpBURZbN8KJJmaVYik5DmsGHDMGzYsGrP0Wq1CA8Pr+OiqIdHkSmVISPX+YrKpg7P4h5JK8L/TxHD2qInrdi9IvfGYDDYvCqsE0Xqyr59+xAaGooWLVpg2rRpyM3NrdXn6d+LIksED8+FnqD5vTPn7+E5vixB5SohTVacwnO32sPjyxLEfgGIjo6Gv7+/8Jo/f36dlzl06FB8//332L17Nz7++GPEx8dj2LBhQkeemuD2IU2WMYJljA7Vd7efHa4bDtZdRR8h9uuunJZwf3k2xy0mu3XXiir6+LIEBSGAyQHrMFfRbZ0CwC3J6uGBsXlfTHi5vMFjGAZmY81vPDXBVGG+q2yLqfJno6V2sTOj9WEAjAJGc+3jbrw+3uBZFAoYHWT0HKVHTG7evAk/Pz/hd60do7PGjRsn/NyuXTu0b98eTZo0wb59+zBgwIAayXB7g3cx5nPoHDAI825ci1jkFL0AkKX/1Gm6TXlL7JZhLj0MAGDLj8CYu7DGnws8/oXduusKMXKGN+SPtfD393eo7ryl64WfTQVFAADDjzuRu+u0pHp5g3ft7xTsVhwUVfbZxIsAgLzrBdi96O6yF8Xn1UrmsZtlAIB/bpRj/p7ahcOqwhu8rxs2REhISJ3l1JrycvFlEkhQeM79x8/Pz8bgiUnjxo0REhKCK1eu1Njg0ZAmRZYQsf8BOgAhc8/JpRSOXIeUuoSwoYiyq3qkcpBDqTu3bt1Cbm4uIiIiavwZt/fwWiS/BB+1457AWMaI8004z65J2mwoiPiDMavTfTWS0x1aNgsKOFA3jIJXqQ6aAYaxT7dClwXgMFTevaAJnlPtuYQYBa8yt8t0QOnA8TwWE4ITvwAhRLhB5z/xDMqDHPCdM5tQ79dvAQBBL48Bw8+kW7wIKAOCpzyC4GYxkqgmRjPylq4XbvytBjTFoLjeouoo+joXW7YA4S3qYdDsStkWkwV7liYAAGY/FASNsuZG5/oRDY4D6NfUG6/1D671mowWgkXxecLfOu7mTcTk59daTl0wMgw+rF9ffMEyydIsLi7GlStXhN+Tk5ORlJSEoKAgBAUF4T//+Q9Gjx6N8PBwXL16FXPmzEHTpk0xZMiQGutwe4OnIBqHGh3Z6IbGoQavKgyjsdvggXA3MQWjrp0spRpQOv66+Rs/ADA6PeDgIbCMRiUYPKEOT6+tNIISwd/41RoVVBppkmRUauU9ZWuUDDSqmhs8vmWdRqWs1efukGO9bh0h0LhgNMIGmUw8P3bsGPr16yf8PmvWLADA5MmTsXz5cpw6dQqrV69GQUEBIiMjMXjwYLz//vu12hd0e4NHcU1cLUuzqsFzekjT7XppSlCWUAuv8G64VZamTOjbt2+1Wxk7duywWwc1eBRZ4mrNo208PJkUnrt8azEpmkeL3FrMPQrP6bQECsWpEBdLCqj6ZOrM1mKEkCoTzx1XhyfFPDwpm0fTXpqeCfXwKDLFdT08R4QS77kOc2XdnSN7aUpSeC5F82gRQppVE5TcwuDJJGnFEbjG3YTicVQ2j3aNr6hNSNOJe3h8wgrg4PFAUoY0xWwtJkJIs6o37xYhTQ+CengUWeJq8/CE0J7T9+8qPTxHrKWyebQUIU0JmkeLENK0CV/bvSIZQPfwKBTnwt9UXGUenlCI7OTRQGwVD8/Vk1bMEvQEFTw8Owy0jcGjHp5LQT08iixx1ZCmsxtHW6p6eA5YS+XEcwmnJUgx8dwOQ+V2IU2Z1OE5AmrwKLLE1ebhVWYrymNSgkKpdEiGq7QGz35v7HbEGA/kdh4eTVqhUJyLENJ0EQ9P2Mty8h6eMCnBAQkrgAsWnrP2Z2naZOS6g8HzIKiHR5EllSFN17ihCCFNhbM9PMd1WQE8s/Dc7UKaNGmFQnEyLubhVWZpOnsPz3FdVgDPLDx3uyxND4J6eBRZ4uUTgoCghtDqpJmlJTYKhQK+UdHwCgl18joYBDeKgs7XxyH6fH19odQx0HnVfbDnPWUH+iK8QRh8A3xFkxkW6IvosED42LnegIAA+BqN7hPSlKdDJjrU4FFkyaNPLMGjTyxx9jJqTFhYGJ7ac9ThUxJup17TRvjPpX0O0zd58mQMmt1bkkkJL7zzDF545xlRZa7/6Hm7ZXh5eWHGjBmYe+UKGJmG7ih3hxo8CoVC8WQ8qCyBhqApFAqF4hFQD49CoVA8GQ/K0qQGj0KhUDwZWnhOoVAoFIp7QT08CoVC8WQ8KKRJPTwKhUKheATUw6NQKBRPhrW+xJYpQ6iHR6FQKBSPgHp4FAqF4snQPTx5sGzZMjRq1Ag6nQ49evTAkSNHnL0kCoVCobgosjV4v/zyC2bNmoV3330Xx48fR4cOHTBkyBBkZWU5e2kUCoXiPvAentgvGSLbkOann36KKVOm4JlnuOaxK1aswNatW/Hdd9/h9ddfr7EcljGCZYxSLfOu+u72s8N1w8G6q+gjxLG6bfRZTA7VbaPP7GDdVfQRo9mhqqvqs5gsDtVdVZ/R4tgba1V9RgdPSpBMnwcVnjOEyM8UG41GeHl5Yf369Rg1apTw/uTJk1FQUIDNmzff8ZmKigpUVFQIvxcWFqJBgwaYOXMmtFrxR5dQKBSKo6moqMDixYtRUFAAf39/u2QZDAb4+/vj5oIF8NPpRFqhVXZ5OaJffx2FhYXw85PPiC9Zeng5OTmwWCwICwuzeT8sLAwXLly462fmz5+P//znP3e8v3jxYknWSKFQKM4iNzfXboOn0WgQHh6O6FpEzGpDeHg4NBqNJLLriiwNXl2YO3cuZs2aJfxeUFCAhg0bIiUlxe4vhithMBgQHR2NmzdvyurJSmroddPr9gT4yFVQUJDdsnQ6HZKTk2E0SrMFodFooBPZc7QXWRq8kJAQKJVKZGZm2ryfmZmJ8PDwu35Gq9XeNXTp7+/vUf8gePz8/Oh1exD0uj0LhUKcfEOdTic7oyQlsszS1Gg06NKlC3bv3i28x7Isdu/ejdjYWCeujEKhUCiuiiw9PACYNWsWJk+ejK5du6J79+5YsmQJSkpKhKxNCoVCoVBqg2wN3tixY5GdnY133nkHGRkZ6NixI7Zv335HIsu90Gq1ePfddz0uQ5NeN71uT4Bet2ddt1jIsiyBQqFQKBSxkeUeHoVCoVAoYkMNHoVCoVA8AmrwKBQKheIRUINHoVAoFI/ALQ2eq48V2r9/Px555BFERkaCYRhs2rTJ5jghBO+88w4iIiKg1+sxcOBAXL582eacvLw8TJgwAX5+fggICMBzzz2H4uJim3NOnTqFBx98EDqdDtHR0Vi4cKHUl3ZP5s+fj27dusHX1xehoaEYNWoULl68aHNOeXk54uLiEBwcDB8fH4wePfqO5gQpKSkYMWIEvLy8EBoaildffRVms21j5X379qFz587QarVo2rQpVq1aJfXl3ZPly5ejffv2QgF1bGws/vzzT+G4O17z3ViwYAEYhsGMGTOE99zx2ufNmweGYWxeLVu2FI674zXLCuJm/Pzzz0Sj0ZDvvvuOnD17lkyZMoUEBASQzMxMZy+txmzbto28+eabZMOGDQQA2bhxo83xBQsWEH9/f7Jp0yZy8uRJ8uijj5KYmBhSVlYmnDN06FDSoUMHcujQIXLgwAHStGlT8q9//Us4XlhYSMLCwsiECRPImTNnyNq1a4lerydfffWVoy7ThiFDhpCVK1eSM2fOkKSkJDJ8+HDSoEEDUlxcLJwzdepUEh0dTXbv3k2OHTtGevbsSXr16iUcN5vNpG3btmTgwIHkxIkTZNu2bSQkJITMnTtXOOfatWvEy8uLzJo1i5w7d458/vnnRKlUku3btzv0enl+//13snXrVnLp0iVy8eJF8sYbbxC1Wk3OnDlDCHHPa76dI0eOkEaNGpH27duTV155RXjfHa/93XffJW3atCHp6enCKzs7WzjujtcsJ9zO4HXv3p3ExcUJv1ssFhIZGUnmz5/vxFXVndsNHsuyJDw8nHzyySfCewUFBUSr1ZK1a9cSQgg5d+4cAUCOHj0qnPPnn38ShmFIamoqIYSQL7/8kgQGBpKKigrhnNdee420aNFC4iuqGVlZWQQAiY+PJ4Rw16hWq8m6deuEc86fP08AkISEBEII96CgUChIRkaGcM7y5cuJn5+fcJ1z5swhbdq0sdE1duxYMmTIEKkvqcYEBgaSb775xiOuuaioiDRr1ozs3LmTPPTQQ4LBc9drf/fdd0mHDh3uesxdr1lOuFVI02g0IjExEQMHDhTeUygUGDhwIBISEpy4MvFITk5GRkaGzTX6+/ujR48ewjUmJCQgICAAXbt2Fc4ZOHAgFAoFDh8+LJzTp08fm27mQ4YMwcWLF5Gfn++gq7k3hYWFACA0yU1MTITJZLK57pYtW6JBgwY2192uXTub5gRDhgyBwWDA2bNnhXOqyuDPkcP3w2Kx4Oeff0ZJSQliY2M94prj4uIwYsSIO9bnztd++fJlREZGonHjxpgwYQJSUlIAuPc1ywW3MnjVjRXKyMhw0qrEhb+O6q4xIyMDoaGhNsdVKhWCgoJszrmbjKo6nAXLspgxYwYeeOABtG3bVliTRqNBQECAzbm3X/f9rule5xgMBpSVlUlxOffl9OnT8PHxgVarxdSpU7Fx40a0bt3ara8ZAH7++WccP34c8+fPv+OYu157jx49sGrVKmzfvh3Lly9HcnIyHnzwQRQVFbntNcsJ2bYWo3gucXFxOHPmDA4ePOjspTiEFi1aICkpCYWFhVi/fj0mT56M+Ph4Zy9LUm7evIlXXnkFO3fu9Khu/cOGDRN+bt++PXr06IGGDRvi119/hV6vd+LKPAO38vDqMlbI1eCvo7prDA8PR1ZWls1xs9mMvLw8m3PuJqOqDmcwffp0bNmyBXv37kVUVJTwfnh4OIxGIwoKCmzOv/2673dN9zrHz8/PaTccjUaDpk2bokuXLpg/fz46dOiAzz77zK2vOTExEVlZWejcuTNUKhVUKhXi4+OxdOlSqFQqhIWFue21VyUgIADNmzfHlStX3PrvLRfcyuB5wlihmJgYhIeH21yjwWDA4cOHhWuMjY1FQUEBEhMThXP27NkDlmXRo0cP4Zz9+/fDZDIJ5+zcuRMtWrRAYGCgg66mEkIIpk+fjo0bN2LPnj2IiYmxOd6lSxeo1Wqb67548SJSUlJsrvv06dM2xn7nzp3w8/ND69athXOqyuDPkdP3g2VZVFRUuPU1DxgwAKdPn0ZSUpLw6tq1KyZMmCD87K7XXpXi4mJcvXoVERERbv33lg3OzpoRm59//plotVqyatUqcu7cOfLCCy+QgIAAm6wmuVNUVEROnDhBTpw4QQCQTz/9lJw4cYLcuHGDEMKVJQQEBJDNmzeTU6dOkZEjR961LKFTp07k8OHD5ODBg6RZs2Y2ZQkFBQUkLCyMTJo0iZw5c4b8/PPPxMvLy2llCdOmTSP+/v5k3759NinbpaWlwjlTp04lDRo0IHv27CHHjh0jsbGxJDY2VjjOp2wPHjyYJCUlke3bt5N69erdNWX71VdfJefPnyfLli1zasr266+/TuLj40lycjI5deoUef311wnDMOSvv/4ihLjnNd+LqlmahLjntc+ePZvs27ePJCcnk7///psMHDiQhISEkKysLEKIe16znHA7g0cIIZ9//jlp0KAB0Wg0pHv37uTQoUPOXlKt2Lt3LwFwx2vy5MmEEK404e233yZhYWFEq9WSAQMGkIsXL9rIyM3NJf/617+Ij48P8fPzI8888wwpKiqyOefkyZOkd+/eRKvVkvr165MFCxY46hLv4G7XC4CsXLlSOKesrIy8+OKLJDAwkHh5eZHHHnuMpKen28i5fv06GTZsGNHr9SQkJITMnj2bmEwmm3P27t1LOnbsSDQaDWncuLGNDkfz7LPPkoYNGxKNRkPq1atHBgwYIBg7Qtzzmu/F7QbPHa997NixJCIigmg0GlK/fn0yduxYcuXKFeG4O16znKDjgSgUCoXiEbjVHh6FQqFQKPeCGjwKhUKheATU4FEoFArFI6AGj0KhUCgeATV4FAqFQvEIqMGjUCgUikdADR6FQqFQPAJq8CgUCoXiEVCDR6FQKBSPgBo8CoVCoXgE1OBRKBQKxSOgBo9CoVAoHsH/Axkv1ePAnomxAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 500x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(5, 6))\n",
    "ax.set_title('Unconfined Aquifer w/ Heads', fontweight='bold')\n",
    "mapview = fp.plot.PlotMapView(model=model, ax=ax, layer=0)\n",
    "mapview.plot_ibound()\n",
    "mapview.plot_grid()\n",
    "\n",
    "hds_steady = hds.get_data(totim=times[0])\n",
    "hds_fill = mapview.plot_array(hds_steady, cmap='rainbow_r', alpha=0.4)\n",
    "plt.colorbar(hds_fill)\n",
    "hds_cntr = mapview.contour_array(hds_steady, colors='black')\n",
    "plt.clabel(hds_cntr, fmt='%.0f')\n",
    "\n",
    "mapview.plot_bc('RIV')\n",
    "ax.plot(\n",
    "    [0, 2250, 3250, 3750, 5000],\n",
    "    [2500, 4750, 4750, 4250, 4250],\n",
    "    'r--'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<a list of 5 text.Text objects>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.set_title(\"Cross-Section along River\", fontweight='bold')\n",
    "xsect = fp.plot.PlotCrossSection(model=model, line={'line': [\n",
    "    (0, 2250),\n",
    "    (2250, 4750),\n",
    "    (3250, 4750),\n",
    "    (3750, 4250),\n",
    "    (5000, 4250)\n",
    "]})\n",
    "xsect.plot_ibound()\n",
    "xsect.plot_grid()\n",
    "\n",
    "hds_fill = xsect.plot_array(hds_steady, head=hds_steady, cmap='rainbow_r', alpha=0.4)\n",
    "plt.colorbar(hds_fill)\n",
    "hds_cnt = xsect.contour_array(hds_steady, head=hds_steady, colors=\"black\")\n",
    "plt.clabel(hds_cnt, fmt='%.0f')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<a list of 8 text.Text objects>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(5, 6))\n",
    "ax.set_title('Confined Aquifer w/ Heads', fontweight='bold')\n",
    "mapview = fp.plot.PlotMapView(model=model, ax=ax, layer=2)\n",
    "mapview.plot_ibound()\n",
    "mapview.plot_grid()\n",
    "\n",
    "hds_fill = mapview.plot_array(hds_steady, cmap='rainbow_r', alpha=0.4)\n",
    "plt.colorbar(hds_fill)\n",
    "hds_cntr = mapview.contour_array(hds_steady, colors='black')\n",
    "plt.clabel(hds_cntr, fmt='%.0f')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<a list of 11 text.Text objects>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(5, 6))\n",
    "ax.set_title('Confining Unit w/ Heads', fontweight='bold')\n",
    "mapview = fp.plot.PlotMapView(model=model, ax=ax, layer=1)\n",
    "mapview.plot_ibound()\n",
    "mapview.plot_grid()\n",
    "\n",
    "hds_fill = mapview.plot_array(hds_steady, cmap='rainbow_r', alpha=0.4)\n",
    "plt.colorbar(hds_fill)\n",
    "hds_cntr = mapview.contour_array(hds_steady, colors='black')\n",
    "plt.clabel(hds_cntr, fmt='%.0f')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Breakdown simulation level packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['nam', 'tdis', 'ims'])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.package_type_dict.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4(a). [Temporal Discretization (TDIS) Package](https://flopy.readthedocs.io/en/latest/source/flopy.mf6.modflow.mftdis.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "package_name = ex-gwf-advtidal.tdis\n",
       "filename = ex-gwf-advtidal.tdis\n",
       "package_type = tdis\n",
       "model_or_simulation_package = simulation\n",
       "simulation_name = ex-gwf-advtidal\n",
       "\n",
       "Block options\n",
       "--------------------\n",
       "time_units\n",
       "{internal}\n",
       "('days')\n",
       "\n",
       "\n",
       "Block dimensions\n",
       "--------------------\n",
       "nper\n",
       "{internal}\n",
       "(4)\n",
       "\n",
       "\n",
       "Block perioddata\n",
       "--------------------\n",
       "perioddata\n",
       "{internal}\n",
       "(rec.array([( 1.,   1, 1.), (10., 120, 1.), (10., 120, 1.), (10., 120, 1.)],\n",
       "          dtype=[('perlen', '<f8'), ('nstp', '<i4'), ('tsmult', '<f8')]))\n",
       "\n"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.get_package('tdis')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Reverse-engineer class summary!\n",
    "\n",
    "First stress period is steady-state, the rest are transient.\n",
    "\n",
    "```python\n",
    "time_units = \"days\"\n",
    "nper = 4\n",
    "# list of (perlen, nstp, tsmult)\n",
    "perioddata = [\n",
    "    # first stress period: steady state\n",
    "    # 1 day, 1 time step\n",
    "    (1.0, 1, 1.0),\n",
    "    # second stress period: transient\n",
    "    # 10 days, 120 time steps\n",
    "    (10.0, 120, 1.0),\n",
    "    # third stress period: transient\n",
    "    (10.0, 120, 1.0),\n",
    "    # fourth stress period: transient\n",
    "    (10.0, 120, 1.0)\n",
    "]\n",
    "filename = \"ex-gwf-advtidal.tdis\"\n",
    "pname = \"ex-gwf-advtidal.tdis\"\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4(b). [Iterative Model Solution (IMS)](https://flopy.readthedocs.io/en/latest/source/flopy.mf6.modflow.mfims.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "package_name = ex-gwf-advtidal\n",
       "filename = ex-gwf-advtidal.ims\n",
       "package_type = ims\n",
       "model_or_simulation_package = simulation\n",
       "simulation_name = ex-gwf-advtidal\n",
       "\n",
       "Block nonlinear\n",
       "--------------------\n",
       "outer_dvclose\n",
       "{internal}\n",
       "(1e-09)\n",
       "\n",
       "outer_maximum\n",
       "{internal}\n",
       "(50)\n",
       "\n",
       "\n",
       "Block linear\n",
       "--------------------\n",
       "inner_maximum\n",
       "{internal}\n",
       "(100)\n",
       "\n",
       "inner_dvclose\n",
       "{internal}\n",
       "(1e-09)\n",
       "\n",
       "rcloserecord\n",
       "{internal}\n",
       "(rec.array([('inner_rclose', 1.e-06, 'strict')],\n",
       "          dtype=[('inner_rclose_label', 'O'), ('inner_rclose', '<f8'), ('rclose_option', 'O')]))\n",
       "\n"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.get_package('ims')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Reverse-engineer class summary!\n",
    "\n",
    "```python\n",
    "outer_dvclose = 1e-9\n",
    "outer_maximum = 50\n",
    "inner_maximum = 100\n",
    "inner_dvclose = 1e-9\n",
    "rcloserecord = [1e-6, \"strict\"]\n",
    "filename = \"ex-gwf-advtidal.ims\"\n",
    "pname = \"ex-gwf-advtidal\"\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Breakdown model level packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['dis',\n",
       " 'npf',\n",
       " 'ic',\n",
       " 'sto',\n",
       " 'ghb_ts',\n",
       " 'ghb_obs',\n",
       " 'ghb-tidal',\n",
       " 'wel_ts',\n",
       " 'wel',\n",
       " 'riv_ts',\n",
       " 'riv',\n",
       " 'rch_ts',\n",
       " 'rch-zone_1',\n",
       " 'rch_1_ts',\n",
       " 'rch-zone_2',\n",
       " 'rch_2_ts',\n",
       " 'rch-zone_3',\n",
       " 'evt',\n",
       " 'oc',\n",
       " 'obs_1']"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.package_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5(a). [Structured Discretization (DIS)](https://flopy.readthedocs.io/en/latest/source/flopy.mf6.modflow.mfgwfdis.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "package_name = dis\n",
       "filename = ex-gwf-advtidal.dis\n",
       "package_type = dis\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-advtidal\n",
       "\n",
       "Block options\n",
       "--------------------\n",
       "length_units\n",
       "{internal}\n",
       "('meters')\n",
       "\n",
       "\n",
       "Block dimensions\n",
       "--------------------\n",
       "nlay\n",
       "{internal}\n",
       "(3)\n",
       "\n",
       "nrow\n",
       "{internal}\n",
       "(15)\n",
       "\n",
       "ncol\n",
       "{internal}\n",
       "(10)\n",
       "\n",
       "\n",
       "Block griddata\n",
       "--------------------\n",
       "delr\n",
       "{constant 500.0}\n",
       "\n",
       "delc\n",
       "{constant 500.0}\n",
       "\n",
       "top\n",
       "{constant 50.0}\n",
       "\n",
       "botm\n",
       "Layer_1{constant 5.0}\n",
       "Layer_2{constant -10.0}\n",
       "Layer_3{constant -100.0}\n",
       "\n"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.package_dict['dis']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Reverse-engineer class summary!\n",
    "\n",
    "```python\n",
    "length_units = \"meters\"\n",
    "nlay = 3\n",
    "nrow = 15\n",
    "ncol = 10\n",
    "delr = 500.0\n",
    "delc = 500.0\n",
    "top = 50.0\n",
    "botm = [\n",
    "    # bottom of unconfined aquifer\n",
    "    5.0,\n",
    "    # bottom of confining unit\n",
    "    -10.0,\n",
    "    # bottom of confined aquifer\n",
    "    -100.0\n",
    "]\n",
    "filename = \"ex-gwf-advtidal.dis\"\n",
    "pname = \"dis\"\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5(b). [Node Property Flow (NPF) Package](https://flopy.readthedocs.io/en/latest/source/flopy.mf6.modflow.mfgwfnpf.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "package_name = npf\n",
       "filename = ex-gwf-advtidal.npf\n",
       "package_type = npf\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-advtidal\n",
       "\n",
       "Block options\n",
       "--------------------\n",
       "cvoptions\n",
       "{internal}\n",
       "([(True, 'DEWATERED')])\n",
       "\n",
       "perched\n",
       "{internal}\n",
       "(True)\n",
       "\n",
       "save_specific_discharge\n",
       "{internal}\n",
       "(True)\n",
       "\n",
       "\n",
       "Block griddata\n",
       "--------------------\n",
       "icelltype\n",
       "Layer_1{constant 1}\n",
       "Layer_2{constant 0}\n",
       "Layer_3{constant 0}\n",
       "\n",
       "k\n",
       "Layer_1{constant 5.0}\n",
       "Layer_2{constant 0.1}\n",
       "Layer_3{constant 4.0}\n",
       "\n",
       "k33\n",
       "Layer_1{constant 0.5}\n",
       "Layer_2{constant 0.005}\n",
       "Layer_3{constant 0.1}\n",
       "\n"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.package_dict['npf']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Reverse-engineer class summary!\n",
    "\n",
    "```python\n",
    "cvoptions = [(True, \"DEWATERED\")]\n",
    "perched = True\n",
    "save_specific_discharge = True\n",
    "icelltype = [\n",
    "    # unconfined aquifer is unconfined/convertible\n",
    "    1,\n",
    "    # confining unit is confined\n",
    "    0,\n",
    "    # confined aquifer is confined\n",
    "    0\n",
    "]\n",
    "# (horizontal) hydraulic conductivity\n",
    "k = [\n",
    "    # unconfined aquifer\n",
    "    5.0,\n",
    "    # confining unit\n",
    "    0.1,\n",
    "    # confined aqufier\n",
    "    4.0\n",
    "]\n",
    "# (vertical) hydraulic conductivity\n",
    "k33 = [\n",
    "    # unconfined aquifer\n",
    "    0.5,\n",
    "    # confining unit\n",
    "    0.005,\n",
    "    # confined aquifer\n",
    "    0.1\n",
    "]\n",
    "filename = \"ex-gwf-advtidal.npf\"\n",
    "pname = \"npf\"\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5(c). [Initial Conditions (IC) Package](https://flopy.readthedocs.io/en/latest/source/flopy.mf6.modflow.mfgwfic.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "package_name = ic\n",
       "filename = ex-gwf-advtidal.ic\n",
       "package_type = ic\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-advtidal\n",
       "\n",
       "Block griddata\n",
       "--------------------\n",
       "strt\n",
       "{constant 50.0}\n",
       "\n"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.package_dict['ic']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Reverse-engineer class summary!\n",
    "\n",
    "```python\n",
    "# starting head\n",
    "strt = 50.0\n",
    "filename = \"ex-gwf-advtidal.ic\"\n",
    "pname = \"ic\"\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5(d). [Storage (STO) Package](https://flopy.readthedocs.io/en/latest/source/flopy.mf6.modflow.mfgwfsto.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "flopy.mf6.modflow.mfgwfsto.ModflowGwfsto"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.package_dict['sto'].__class__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "package_name = sto\n",
       "filename = ex-gwf-advtidal.sto\n",
       "package_type = sto\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-advtidal\n",
       "\n",
       "Block griddata\n",
       "--------------------\n",
       "iconvert\n",
       "{constant 1}\n",
       "\n",
       "ss\n",
       "{constant 1e-06}\n",
       "\n",
       "sy\n",
       "{constant 0.2}\n",
       "\n",
       "\n",
       "Block period\n",
       "--------------------\n",
       "steady-state\n",
       "{internal}\n",
       "(True)\n",
       "\n",
       "transient\n",
       "{internal}\n",
       "(True)\n",
       "\n"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.package_dict['sto']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Reverse-engineer class summary!\n",
    "\n",
    "```python\n",
    "# flag for each cell that specifies whether or not a cell is convertible for the storage calculation\n",
    "# iconvert > 0: confined storage is used when head is above cell top and a mixed formulation of unconfined and confined storage is used when head is below cell top\n",
    "iconvert = 1\n",
    "# specific storage\n",
    "ss = 1e-6\n",
    "# specific yield\n",
    "sy = 0.2\n",
    "# indicates stress period IPER is steady-state\n",
    "steady_state = True\n",
    "# indicates stress period IPER is transient\n",
    "transient = True\n",
    "filename = \"ex-gwf-advtidal.sto\"\n",
    "pname = \"sto\"\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5(e). [General-Head Boundary (GHB) Package](https://flopy.readthedocs.io/en/latest/source/flopy.mf6.modflow.mfgwfghb.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "flopy.mf6.modflow.mfgwfghb.ModflowGwfghb"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.package_dict['ghb-tidal'].__class__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "package_name = ghb-tidal\n",
       "filename = ex-gwf-advtidal.ghb\n",
       "package_type = ghb\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-advtidal\n",
       "\n",
       "Block options\n",
       "--------------------\n",
       "boundnames\n",
       "{internal}\n",
       "(True)\n",
       "\n",
       "ts_filerecord\n",
       "{internal}\n",
       "(rec.array([('ex-gwf-advtidal.ghb.ts',)],\n",
       "          dtype=[('ts6_filename', 'O')]))\n",
       "\n",
       "obs_filerecord\n",
       "{internal}\n",
       "(rec.array([('ex-gwf-advtidal.ghb.obs',)],\n",
       "          dtype=[('obs6_filename', 'O')]))\n",
       "\n",
       "\n",
       "Block dimensions\n",
       "--------------------\n",
       "maxbound\n",
       "{internal}\n",
       "(30)\n",
       "\n",
       "\n",
       "Block period\n",
       "--------------------\n",
       "stress_period_data\n",
       "{0: <flopy.mf6.data.mfdataplist.PandasListStorage object at 0x0000023558985B90>}\n"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.package_dict['ghb-tidal']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rec.array([((1, 0, 9), 'tides',   15., 'estuary-l2'),\n",
       "           ((1, 1, 9), 'tides',   15., 'estuary-l2'),\n",
       "           ((1, 2, 9), 'tides',   15., 'estuary-l2'),\n",
       "           ((1, 3, 9), 'tides',   15., 'estuary-l2'),\n",
       "           ((1, 4, 9), 'tides',   15., 'estuary-l2'),\n",
       "           ((1, 5, 9), 'tides',   15., 'estuary-l2'),\n",
       "           ((1, 6, 9), 'tides',   15., 'estuary-l2'),\n",
       "           ((1, 7, 9), 'tides',   15., 'estuary-l2'),\n",
       "           ((1, 8, 9), 'tides',   15., 'estuary-l2'),\n",
       "           ((1, 9, 9), 'tides',   15., 'estuary-l2'),\n",
       "           ((1, 10, 9), 'tides',   15., 'estuary-l2'),\n",
       "           ((1, 11, 9), 'tides',   15., 'estuary-l2'),\n",
       "           ((1, 12, 9), 'tides',   15., 'estuary-l2'),\n",
       "           ((1, 13, 9), 'tides',   15., 'estuary-l2'),\n",
       "           ((1, 14, 9), 'tides',   15., 'estuary-l2'),\n",
       "           ((2, 0, 9), 'tides', 1500., 'estuary-l3'),\n",
       "           ((2, 1, 9), 'tides', 1500., 'estuary-l3'),\n",
       "           ((2, 2, 9), 'tides', 1500., 'estuary-l3'),\n",
       "           ((2, 3, 9), 'tides', 1500., 'estuary-l3'),\n",
       "           ((2, 4, 9), 'tides', 1500., 'estuary-l3'),\n",
       "           ((2, 5, 9), 'tides', 1500., 'estuary-l3'),\n",
       "           ((2, 6, 9), 'tides', 1500., 'estuary-l3'),\n",
       "           ((2, 7, 9), 'tides', 1500., 'estuary-l3'),\n",
       "           ((2, 8, 9), 'tides', 1500., 'estuary-l3'),\n",
       "           ((2, 9, 9), 'tides', 1500., 'estuary-l3'),\n",
       "           ((2, 10, 9), 'tides', 1500., 'estuary-l3'),\n",
       "           ((2, 11, 9), 'tides', 1500., 'estuary-l3'),\n",
       "           ((2, 12, 9), 'tides', 1500., 'estuary-l3'),\n",
       "           ((2, 13, 9), 'tides', 1500., 'estuary-l3'),\n",
       "           ((2, 14, 9), 'tides', 1500., 'estuary-l3')],\n",
       "          dtype=[('cellid', 'O'), ('bhead', 'O'), ('cond', '<f8'), ('boundname', 'O')])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.package_dict['ghb-tidal'].stress_period_data.get_data(key=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Class summary for ghb-tidal\n",
    "\n",
    "```python\n",
    "# indicates boundary names may be provided with the list of general-head boundary cells\n",
    "boundnames = True\n",
    "# will create a ModflowUtlts package\n",
    "timeseries = {\n",
    "    \"time_series_namerecord\": [\"tides\"], # check this\n",
    "    \"interpolation_methodrecord\": [\"linear\"] # check this\n",
    "    # list of (ts_time, ts_array)\n",
    "    # ts_time (double): time relative to the start of the simulation\n",
    "    # ts_array (double): floating-point values\n",
    "    \"timeseries\": [\n",
    "        ( 0.   , -2.5), ( 0.05 , -2.1), ( 0.206,  2.1), ( 0.256,  2.5),\n",
    "        ( 0.306,  2. ), ( 0.462, -2.4), ( 0.512, -2.8), ( 0.562, -2.4),\n",
    "        ( 0.718,  2.4), ( 0.768,  2.8), ( 0.818,  2.4), ( 0.974, -2.1),\n",
    "        ( 1.024, -2.5), ( 1.074, -2.1), ( 1.23 ,  2.1), ( 1.28 ,  2.5),\n",
    "        ( 1.33 ,  2. ), ( 1.486, -2.4), ( 1.536, -2.8), ( 1.586, -2.4),\n",
    "        ( 1.742,  2.4), ( 1.792,  2.8), ( 1.842,  2.4), ( 1.998, -2.1),\n",
    "        ( 2.048, -2.5), ( 2.098, -2.1), ( 2.254,  2.1), ( 2.304,  2.5),\n",
    "        ( 2.354,  2. ), ( 2.51 , -2.4), ( 2.56 , -2.8), ( 2.61 , -2.4),\n",
    "        ( 2.766,  2.4), ( 2.816,  2.8), ( 2.866,  2.4), ( 3.022, -2.1),\n",
    "        ( 3.072, -2.5), ( 3.122, -2.1), ( 3.278,  2.1), ( 3.328,  2.5),\n",
    "        ( 3.378,  2. ), ( 3.534, -2.4), ( 3.584, -2.8), ( 3.634, -2.4),\n",
    "        ( 3.79 ,  2.4), ( 3.84 ,  2.8), ( 3.89 ,  2.4), ( 4.046, -2.1),\n",
    "        ( 4.096, -2.5), ( 4.146, -2.1), ( 4.302,  2.1), ( 4.352,  2.5),\n",
    "        ( 4.402,  2. ), ( 4.558, -2.4), ( 4.608, -2.8), ( 4.658, -2.4),\n",
    "        ( 4.814,  2.4), ( 4.864,  2.8), ( 4.914,  2.4), ( 5.07 , -2.1),\n",
    "        ( 5.12 , -2.5), ( 5.17 , -2.1), ( 5.326,  2.1), ( 5.376,  2.5),\n",
    "        ( 5.426,  2. ), ( 5.582, -2.4), ( 5.632, -2.8), ( 5.682, -2.4),\n",
    "        ( 5.838,  2.4), ( 5.888,  2.8), ( 5.938,  2.4), ( 6.094, -2.1),\n",
    "        ( 6.144, -2.5), ( 6.194, -2.1), ( 6.35 ,  2.1), ( 6.4  ,  2.5),\n",
    "        ( 6.45 ,  2. ), ( 6.606, -2.4), ( 6.656, -2.8), ( 6.706, -2.4),\n",
    "        ( 6.862,  2.4), ( 6.912,  2.8), ( 6.962,  2.4), ( 7.118, -2.1),\n",
    "        ( 7.168, -2.5), ( 7.218, -2.1), ( 7.374,  2.1), ( 7.424,  2.5),\n",
    "        ( 7.474,  2. ), ( 7.63 , -2.4), ( 7.68 , -2.8), ( 7.73 , -2.4),\n",
    "        ( 7.886,  2.4), ( 7.936,  2.8), ( 7.986,  2.4), ( 8.142, -2.1),\n",
    "        ( 8.192, -2.5), ( 8.242, -2.1), ( 8.398,  2.1), ( 8.448,  2.5),\n",
    "        ( 8.498,  2. ), ( 8.654, -2.4), ( 8.704, -2.8), ( 8.754, -2.4),\n",
    "        ( 8.91 ,  2.4), ( 8.96 ,  2.8), ( 9.01 ,  2.4), ( 9.166, -2.1),\n",
    "        ( 9.216, -2.5), ( 9.266, -2.1), ( 9.422,  2.1), ( 9.472,  2.5),\n",
    "        ( 9.522,  2. ), ( 9.678, -2.4), ( 9.728, -2.8), ( 9.778, -2.4),\n",
    "        ( 9.934,  2.4), ( 9.984,  2.8), (10.034,  2.4), (10.19 , -2.1),\n",
    "        (10.24 , -2.5), (10.29 , -2.1), (10.446,  2.1), (10.496,  2.5),\n",
    "        (10.546,  2. ), (10.702, -2.4), (10.752, -2.8), (10.802, -2.4),\n",
    "        (10.958,  2.4), (11.008,  2.8), (11.058,  2.4), (11.214, -2.1),\n",
    "        (11.264, -2.5), (11.314, -2.1), (11.47 ,  2.1), (11.52 ,  2.5),\n",
    "        (11.57 ,  2. ), (11.726, -2.4), (11.776, -2.8), (11.826, -2.4),\n",
    "        (11.982,  2.4), (12.032,  2.8), (12.082,  2.4), (12.238, -2.1),\n",
    "        (12.288, -2.5), (12.338, -2.1), (12.494,  2.1), (12.544,  2.5),\n",
    "        (12.594,  2. ), (12.75 , -2.4), (12.8  ,  2.8), (12.85 , -2.4),\n",
    "        (13.006,  2.4), (13.056,  2.8), (13.106,  2.4), (13.262, -2.1),\n",
    "        (13.312, -2.5), (13.362, -2.1), (13.518,  2.1), (13.568,  2.5),\n",
    "        (13.618,  2. ), (13.774, -2.4), (13.824, -2.8), (13.874, -2.4),\n",
    "        (14.03 ,  2.4), (14.08 ,  2.8), (14.13 ,  2.4), (14.286, -2.1),\n",
    "        (14.336, -2.5), (14.386, -2.1), (14.542,  2.1), (14.592,  2.5),\n",
    "        (14.642,  2. ), (14.798, -2.4), (14.848, -2.8), (14.898, -2.4),\n",
    "        (15.054,  2.4), (15.104,  2.8), (15.154,  2.4), (15.31 , -2.1),\n",
    "        (15.36 , -2.5), (15.41 , -2.1), (15.566,  2.1), (15.616,  2.5),\n",
    "        (15.666,  2. ), (15.822, -2.4), (15.872, -2.8), (15.922, -2.4),\n",
    "        (16.078,  2.4), (16.128,  2.8), (16.178,  2.4), (16.334, -2.1),\n",
    "        (16.384, -2.5), (16.434, -2.1), (16.59 ,  2.1), (16.64 ,  2.5),\n",
    "        (16.69 ,  2. ), (16.846, -2.4), (16.896, -2.8), (16.946, -2.4),\n",
    "        (17.102,  2.4), (17.152,  2.8), (17.202,  2.4), (17.358, -2.1),\n",
    "        (17.408, -2.5), (17.458, -2.1), (17.614,  2.1), (17.664,  2.5),\n",
    "        (17.714,  2. ), (17.87 , -2.4), (17.92 , -2.8), (17.97 , -2.4),\n",
    "        (18.126,  2.4), (18.176,  2.8), (18.226,  2.4), (18.382, -2.1),\n",
    "        (18.432, -2.5), (18.482, -2.1), (18.638,  2.1), (18.688,  2.5),\n",
    "        (18.738,  2. ), (18.894, -2.4), (18.944, -2.8), (18.994, -2.4),\n",
    "        (19.15 ,  2.4), (19.2  ,  2.8), (19.25 ,  2.4), (19.406, -2.1),\n",
    "        (19.456, -2.5), (19.506, -2.1), (19.662,  2.1), (19.712,  2.5),\n",
    "        (19.762,  2. ), (19.918, -2.4), (19.968, -2.8), (20.018, -2.4),\n",
    "        (20.174,  2.4), (20.224,  2.8), (20.274,  2.4), (20.43 , -2.1),\n",
    "        (20.48 , -2.5), (20.53 , -2.1), (20.686,  2.1), (20.736,  2.5),\n",
    "        (20.786,  2. ), (20.942, -2.4), (20.992, -2.8), (21.042, -2.4),\n",
    "        (21.198,  2.4), (21.248,  2.8), (21.298,  2.4), (21.454, -2.1),\n",
    "        (21.504, -2.5), (21.554, -2.1), (21.71 ,  2.1), (21.76 ,  2.5),\n",
    "        (21.81 ,  2. ), (21.966, -2.4), (22.016, -2.8), (22.066, -2.4),\n",
    "        (22.222,  2.4), (22.272,  2.8), (22.322,  2.4), (22.478, -2.1),\n",
    "        (22.528, -2.5), (22.578, -2.1), (22.734,  2.1), (22.784,  2.5),\n",
    "        (22.834,  2. ), (22.99 , -2.4), (23.04 , -2.8), (23.09 , -2.4),\n",
    "        (23.246,  2.4), (23.296,  2.8), (23.346,  2.4), (23.502, -2.1),\n",
    "        (23.552, -2.5), (23.602, -2.1), (23.758,  2.1), (23.808,  2.5),\n",
    "        (23.858,  2. ), (24.014, -2.4), (24.064, -2.8), (24.114, -2.4),\n",
    "        (24.27 ,  2.4), (24.32 ,  2.8), (24.37 ,  2.4), (24.526, -2.1),\n",
    "        (24.576, -2.5), (24.626, -2.1), (24.782,  2.1), (24.832,  2.5),\n",
    "        (24.882,  2. ), (25.038, -2.4), (25.088, -2.8), (25.138, -2.4),\n",
    "        (25.294,  2.4), (25.344,  2.8), (25.394,  2.4), (25.55 , -2.1),\n",
    "        (25.6  ,  2.5), (25.65 , -2.1), (25.806,  2.1), (25.856,  2.5),\n",
    "        (25.906,  2. ), (26.062, -2.4), (26.112, -2.8), (26.162, -2.4),\n",
    "        (26.318,  2.4), (26.368,  2.8), (26.418,  2.4), (26.574, -2.1),\n",
    "        (26.624, -2.5), (26.674, -2.1), (26.83 ,  2.1), (26.88 ,  2.5),\n",
    "        (26.93 ,  2. ), (27.086, -2.4), (27.136, -2.8), (27.186, -2.4),\n",
    "        (27.342,  2.4), (27.392,  2.8), (27.442,  2.4), (27.598, -2.1),\n",
    "        (27.648, -2.5), (27.698, -2.1), (27.854,  2.1), (27.904,  2.5),\n",
    "        (27.954,  2. ), (28.11 , -2.4), (28.16 , -2.8), (28.21 , -2.4),\n",
    "        (28.366,  2.4), (28.416,  2.8), (28.466,  2.4), (28.622, -2.1),\n",
    "        (28.672, -2.5), (28.722, -2.1), (28.878,  2.1), (28.928,  2.5),\n",
    "        (28.978,  2. ), (29.134, -2.4), (29.184, -2.8), (29.234, -2.4),\n",
    "        (29.39 ,  2.4), (29.44 ,  2.8), (29.49 ,  2.4), (29.646, -2.1),\n",
    "        (29.696, -2.5), (29.746, -2.1), (29.902,  2.1), (29.952,  2.5),\n",
    "        (30.002,  2. ), (30.158, -2.4), (30.208, -2.8), (30.258, -2.4),\n",
    "        (30.414,  2.4), (30.464,  2.8), (30.514,  2.4), (30.67 , -2.1),\n",
    "        (30.72 , -2.5), (30.77 , -2.1), (30.926,  2.1), (30.976,  2.5),\n",
    "        (31.026,  2. )\n",
    "    ],\n",
    "    \"filename\": \"ex-gwf-advtidal.ghb.ts\"\n",
    "}\n",
    "# will create a ModflowUtlobs package\n",
    "observations = {\n",
    "    # list of (obsname, obstype, id, id2)\n",
    "    # obsname is column name in output file\n",
    "    # id: can be cellid or boundary name\n",
    "    \"continuous\": [\n",
    "        ('ghb_2_6_10', 'ghb', (1, 5, 9), None),\n",
    "        ('ghb_3_6_10', 'ghb', (2, 5, 9), None),\n",
    "        ('estuary2', 'ghb', 'estuary-l2', None),\n",
    "        ('estuary3', 'ghb', 'estuary-l3', None)\n",
    "    ],\n",
    "    \"filename\": \"ex-gwf-advtidal.ghb.obs\"\n",
    "}\n",
    "# the maximum number of general-head boundary cells that will be specified for use during any stress period\n",
    "maxbound = 30\n",
    "# list of (cellid, bhead, cond, boundname)\n",
    "stress_period_data = [\n",
    "    ((1, 0, 9), 'tides',   15., 'estuary-l2'),\n",
    "    ((1, 1, 9), 'tides',   15., 'estuary-l2'),\n",
    "    ((1, 2, 9), 'tides',   15., 'estuary-l2'),\n",
    "    ((1, 3, 9), 'tides',   15., 'estuary-l2'),\n",
    "    ((1, 4, 9), 'tides',   15., 'estuary-l2'),\n",
    "    ((1, 5, 9), 'tides',   15., 'estuary-l2'),\n",
    "    ((1, 6, 9), 'tides',   15., 'estuary-l2'),\n",
    "    ((1, 7, 9), 'tides',   15., 'estuary-l2'),\n",
    "    ((1, 8, 9), 'tides',   15., 'estuary-l2'),\n",
    "    ((1, 9, 9), 'tides',   15., 'estuary-l2'),\n",
    "    ((1, 10, 9), 'tides',   15., 'estuary-l2'),\n",
    "    ((1, 11, 9), 'tides',   15., 'estuary-l2'),\n",
    "    ((1, 12, 9), 'tides',   15., 'estuary-l2'),\n",
    "    ((1, 13, 9), 'tides',   15., 'estuary-l2'),\n",
    "    ((1, 14, 9), 'tides',   15., 'estuary-l2'),\n",
    "    ((2, 0, 9), 'tides', 1500., 'estuary-l3'),\n",
    "    ((2, 1, 9), 'tides', 1500., 'estuary-l3'),\n",
    "    ((2, 2, 9), 'tides', 1500., 'estuary-l3'),\n",
    "    ((2, 3, 9), 'tides', 1500., 'estuary-l3'),\n",
    "    ((2, 4, 9), 'tides', 1500., 'estuary-l3'),\n",
    "    ((2, 5, 9), 'tides', 1500., 'estuary-l3'),\n",
    "    ((2, 6, 9), 'tides', 1500., 'estuary-l3'),\n",
    "    ((2, 7, 9), 'tides', 1500., 'estuary-l3'),\n",
    "    ((2, 8, 9), 'tides', 1500., 'estuary-l3'),\n",
    "    ((2, 9, 9), 'tides', 1500., 'estuary-l3'),\n",
    "    ((2, 10, 9), 'tides', 1500., 'estuary-l3'),\n",
    "    ((2, 11, 9), 'tides', 1500., 'estuary-l3'),\n",
    "    ((2, 12, 9), 'tides', 1500., 'estuary-l3'),\n",
    "    ((2, 13, 9), 'tides', 1500., 'estuary-l3'),\n",
    "    ((2, 14, 9), 'tides', 1500., 'estuary-l3')\n",
    "]\n",
    "filename = \"ex-gwf-advtidal.ghb\"\n",
    "pname = \"ghb-tidal\"\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5(e)(i). [Time Series Package](https://flopy.readthedocs.io/en/latest/source/flopy.mf6.modflow.mfutlts.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "flopy.mf6.modflow.mfutlts.ModflowUtlts"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.package_dict['ghb_ts'].__class__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "package_name = ghb_ts\n",
       "filename = ex-gwf-advtidal.ghb.ts\n",
       "package_type = ts\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-advtidal\n",
       "parent_file = ghb-tidal\n",
       "\n",
       "Block attributes\n",
       "--------------------\n",
       "time_series_namerecord\n",
       "{internal}\n",
       "(rec.array([('tides',)],\n",
       "          dtype=[('time_series_names', 'O')]))\n",
       "\n",
       "interpolation_methodrecord\n",
       "{internal}\n",
       "(rec.array([('linear',)],\n",
       "          dtype=[('interpolation_method', 'O')]))\n",
       "\n",
       "\n",
       "Block timeseries\n",
       "--------------------\n",
       "timeseries\n",
       "{internal}\n",
       "(rec.array([( 0.   , -2.5), ( 0.05 , -2.1), ( 0.206,  2.1), ( 0.256,  2.5),\n",
       "           ( 0.306,  2. ), ( 0.462, -2.4), ( 0.512, -2.8), ( 0.562, -2.4),\n",
       "           ( 0.718,  2.4), ( 0.768,  2.8), ( 0.818,  2.4), ( 0.974, -2.1),\n",
       "           ( 1.024, -2.5), ( 1.074, -2.1), ( 1.23 ,  2.1), ( 1.28 ,  2.5),\n",
       "           ( 1.33 ,  2. ), ( 1.486, -2.4), ( 1.536, -2.8), ( 1.586, -2.4),\n",
       "           ( 1.742,  2.4), ( 1.792,  2.8), ( 1.842,  2.4), ( 1.998, -2.1),\n",
       "           ( 2.048, -2.5), ( 2.098, -2.1), ( 2.254,  2.1), ( 2.304,  2.5),\n",
       "           ( 2.354,  2. ), ( 2.51 , -2.4), ( 2.56 , -2.8), ( 2.61 , -2.4),\n",
       "           ( 2.766,  2.4), ( 2.816,  2.8), ( 2.866,  2.4), ( 3.022, -2.1),\n",
       "           ( 3.072, -2.5), ( 3.122, -2.1), ( 3.278,  2.1), ( 3.328,  2.5),\n",
       "           ( 3.378,  2. ), ( 3.534, -2.4), ( 3.584, -2.8), ( 3.634, -2.4),\n",
       "           ( 3.79 ,  2.4), ( 3.84 ,  2.8), ( 3.89 ,  2.4), ( 4.046, -2.1),\n",
       "           ( 4.096, -2.5), ( 4.146, -2.1), ( 4.302,  2.1), ( 4.352,  2.5),\n",
       "           ( 4.402,  2. ), ( 4.558, -2.4), ( 4.608, -2.8), ( 4.658, -2.4),\n",
       "           ( 4.814,  2.4), ( 4.864,  2.8), ( 4.914,  2.4), ( 5.07 , -2.1),\n",
       "           ( 5.12 , -2.5), ( 5.17 , -2.1), ( 5.326,  2.1), ( 5.376,  2.5),\n",
       "           ( 5.426,  2. ), ( 5.582, -2.4), ( 5.632, -2.8), ( 5.682, -2.4),\n",
       "           ( 5.838,  2.4), ( 5.888,  2.8), ( 5.938,  2.4), ( 6.094, -2.1),\n",
       "           ( 6.144, -2.5), ( 6.194, -2.1), ( 6.35 ,  2.1), ( 6.4  ,  2.5),\n",
       "           ( 6.45 ,  2. ), ( 6.606, -2.4), ( 6.656, -2.8), ( 6.706, -2.4),\n",
       "           ( 6.862,  2.4), ( 6.912,  2.8), ( 6.962,  2.4), ( 7.118, -2.1),\n",
       "           ( 7.168, -2.5), ( 7.218, -2.1), ( 7.374,  2.1), ( 7.424,  2.5),\n",
       "           ( 7.474,  2. ), ( 7.63 , -2.4), ( 7.68 , -2.8), ( 7.73 , -2.4),\n",
       "           ( 7.886,  2.4), ( 7.936,  2.8), ( 7.986,  2.4), ( 8.142, -2.1),\n",
       "           ( 8.192, -2.5), ( 8.242, -2.1), ( 8.398,  2.1), ( 8.448,  2.5),\n",
       "           ( 8.498,  2. ), ( 8.654, -2.4), ( 8.704, -2.8), ( 8.754, -2.4),\n",
       "           ( 8.91 ,  2.4), ( 8.96 ,  2.8), ( 9.01 ,  2.4), ( 9.166, -2.1),\n",
       "           ( 9.216, -2.5), ( 9.266, -2.1), ( 9.422,  2.1), ( 9.472,  2.5),\n",
       "           ( 9.522,  2. ), ( 9.678, -2.4), ( 9.728, -2.8), ( 9.778, -2.4),\n",
       "           ( 9.934,  2.4), ( 9.984,  2.8), (10.034,  2.4), (10.19 , -2.1),\n",
       "           (10.24 , -2.5), (10.29 , -2.1), (10.446,  2.1), (10.496,  2.5),\n",
       "           (10.546,  2. ), (10.702, -2.4), (10.752, -2.8), (10.802, -2.4),\n",
       "           (10.958,  2.4), (11.008,  2.8), (11.058,  2.4), (11.214, -2.1),\n",
       "           (11.264, -2.5), (11.314, -2.1), (11.47 ,  2.1), (11.52 ,  2.5),\n",
       "           (11.57 ,  2. ), (11.726, -2.4), (11.776, -2.8), (11.826, -2.4),\n",
       "           (11.982,  2.4), (12.032,  2.8), (12.082,  2.4), (12.238, -2.1),\n",
       "           (12.288, -2.5), (12.338, -2.1), (12.494,  2.1), (12.544,  2.5),\n",
       "           (12.594,  2. ), (12.75 , -2.4), (12.8  ,  2.8), (12.85 , -2.4),\n",
       "           (13.006,  2.4), (13.056,  2.8), (13.106,  2.4), (13.262, -2.1),\n",
       "           (13.312, -2.5), (13.362, -2.1), (13.518,  2.1), (13.568,  2.5),\n",
       "           (13.618,  2. ), (13.774, -2.4), (13.824, -2.8), (13.874, -2.4),\n",
       "           (14.03 ,  2.4), (14.08 ,  2.8), (14.13 ,  2.4), (14.286, -2.1),\n",
       "           (14.336, -2.5), (14.386, -2.1), (14.542,  2.1), (14.592,  2.5),\n",
       "           (14.642,  2. ), (14.798, -2.4), (14.848, -2.8), (14.898, -2.4),\n",
       "           (15.054,  2.4), (15.104,  2.8), (15.154,  2.4), (15.31 , -2.1),\n",
       "           (15.36 , -2.5), (15.41 , -2.1), (15.566,  2.1), (15.616,  2.5),\n",
       "           (15.666,  2. ), (15.822, -2.4), (15.872, -2.8), (15.922, -2.4),\n",
       "           (16.078,  2.4), (16.128,  2.8), (16.178,  2.4), (16.334, -2.1),\n",
       "           (16.384, -2.5), (16.434, -2.1), (16.59 ,  2.1), (16.64 ,  2.5),\n",
       "           (16.69 ,  2. ), (16.846, -2.4), (16.896, -2.8), (16.946, -2.4),\n",
       "           (17.102,  2.4), (17.152,  2.8), (17.202,  2.4), (17.358, -2.1),\n",
       "           (17.408, -2.5), (17.458, -2.1), (17.614,  2.1), (17.664,  2.5),\n",
       "           (17.714,  2. ), (17.87 , -2.4), (17.92 , -2.8), (17.97 , -2.4),\n",
       "           (18.126,  2.4), (18.176,  2.8), (18.226,  2.4), (18.382, -2.1),\n",
       "           (18.432, -2.5), (18.482, -2.1), (18.638,  2.1), (18.688,  2.5),\n",
       "           (18.738,  2. ), (18.894, -2.4), (18.944, -2.8), (18.994, -2.4),\n",
       "           (19.15 ,  2.4), (19.2  ,  2.8), (19.25 ,  2.4), (19.406, -2.1),\n",
       "           (19.456, -2.5), (19.506, -2.1), (19.662,  2.1), (19.712,  2.5),\n",
       "           (19.762,  2. ), (19.918, -2.4), (19.968, -2.8), (20.018, -2.4),\n",
       "           (20.174,  2.4), (20.224,  2.8), (20.274,  2.4), (20.43 , -2.1),\n",
       "           (20.48 , -2.5), (20.53 , -2.1), (20.686,  2.1), (20.736,  2.5),\n",
       "           (20.786,  2. ), (20.942, -2.4), (20.992, -2.8), (21.042, -2.4),\n",
       "           (21.198,  2.4), (21.248,  2.8), (21.298,  2.4), (21.454, -2.1),\n",
       "           (21.504, -2.5), (21.554, -2.1), (21.71 ,  2.1), (21.76 ,  2.5),\n",
       "           (21.81 ,  2. ), (21.966, -2.4), (22.016, -2.8), (22.066, -2.4),\n",
       "           (22.222,  2.4), (22.272,  2.8), (22.322,  2.4), (22.478, -2.1),\n",
       "           (22.528, -2.5), (22.578, -2.1), (22.734,  2.1), (22.784,  2.5),\n",
       "           (22.834,  2. ), (22.99 , -2.4), (23.04 , -2.8), (23.09 , -2.4),\n",
       "           (23.246,  2.4), (23.296,  2.8), (23.346,  2.4), (23.502, -2.1),\n",
       "           (23.552, -2.5), (23.602, -2.1), (23.758,  2.1), (23.808,  2.5),\n",
       "           (23.858,  2. ), (24.014, -2.4), (24.064, -2.8), (24.114, -2.4),\n",
       "           (24.27 ,  2.4), (24.32 ,  2.8), (24.37 ,  2.4), (24.526, -2.1),\n",
       "           (24.576, -2.5), (24.626, -2.1), (24.782,  2.1), (24.832,  2.5),\n",
       "           (24.882,  2. ), (25.038, -2.4), (25.088, -2.8), (25.138, -2.4),\n",
       "           (25.294,  2.4), (25.344,  2.8), (25.394,  2.4), (25.55 , -2.1),\n",
       "           (25.6  ,  2.5), (25.65 , -2.1), (25.806,  2.1), (25.856,  2.5),\n",
       "           (25.906,  2. ), (26.062, -2.4), (26.112, -2.8), (26.162, -2.4),\n",
       "           (26.318,  2.4), (26.368,  2.8), (26.418,  2.4), (26.574, -2.1),\n",
       "           (26.624, -2.5), (26.674, -2.1), (26.83 ,  2.1), (26.88 ,  2.5),\n",
       "           (26.93 ,  2. ), (27.086, -2.4), (27.136, -2.8), (27.186, -2.4),\n",
       "           (27.342,  2.4), (27.392,  2.8), (27.442,  2.4), (27.598, -2.1),\n",
       "           (27.648, -2.5), (27.698, -2.1), (27.854,  2.1), (27.904,  2.5),\n",
       "           (27.954,  2. ), (28.11 , -2.4), (28.16 , -2.8), (28.21 , -2.4),\n",
       "           (28.366,  2.4), (28.416,  2.8), (28.466,  2.4), (28.622, -2.1),\n",
       "           (28.672, -2.5), (28.722, -2.1), (28.878,  2.1), (28.928,  2.5),\n",
       "           (28.978,  2. ), (29.134, -2.4), (29.184, -2.8), (29.234, -2.4),\n",
       "           (29.39 ,  2.4), (29.44 ,  2.8), (29.49 ,  2.4), (29.646, -2.1),\n",
       "           (29.696, -2.5), (29.746, -2.1), (29.902,  2.1), (29.952,  2.5),\n",
       "           (30.002,  2. ), (30.158, -2.4), (30.208, -2.8), (30.258, -2.4),\n",
       "           (30.414,  2.4), (30.464,  2.8), (30.514,  2.4), (30.67 , -2.1),\n",
       "           (30.72 , -2.5), (30.77 , -2.1), (30.926,  2.1), (30.976,  2.5),\n",
       "           (31.026,  2. )],\n",
       "          dtype=[('ts_time', '<f8'), ('ts_array', '<f8')]))\n",
       "\n"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.package_dict['ghb_ts']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5(e)(ii). [Observation Package](https://flopy.readthedocs.io/en/latest/source/flopy.mf6.modflow.mfutlobs.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "flopy.mf6.modflow.mfutlobs.ModflowUtlobs"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.package_dict['ghb_obs'].__class__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "package_name = ghb_obs\n",
       "filename = ex-gwf-advtidal.ghb.obs\n",
       "package_type = obs\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-advtidal\n",
       "parent_file = ghb-tidal\n",
       "\n",
       "Block continuous\n",
       "--------------------\n",
       "continuous\n",
       "{internal}\n",
       "(rec.array([('ghb_2_6_10', 'ghb', (1, 5, 9), None),\n",
       "           ('ghb_3_6_10', 'ghb', (2, 5, 9), None),\n",
       "           ('estuary2', 'ghb', 'estuary-l2', None),\n",
       "           ('estuary3', 'ghb', 'estuary-l3', None)],\n",
       "          dtype=[('obsname', 'O'), ('obstype', 'O'), ('id', 'O'), ('id2', 'O')]))\n",
       "\n"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.package_dict['ghb_obs']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5(f). [Well (WEL) Package](https://flopy.readthedocs.io/en/latest/source/flopy.mf6.modflow.mfgwfwel.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "package_name = wel\n",
       "filename = ex-gwf-advtidal.wel\n",
       "package_type = wel\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-advtidal\n",
       "\n",
       "Block options\n",
       "--------------------\n",
       "boundnames\n",
       "{internal}\n",
       "(True)\n",
       "\n",
       "ts_filerecord\n",
       "{internal}\n",
       "(rec.array([('ex-gwf-advtidal.wel.ts',)],\n",
       "          dtype=[('ts6_filename', 'O')]))\n",
       "\n",
       "\n",
       "Block dimensions\n",
       "--------------------\n",
       "maxbound\n",
       "{internal}\n",
       "(5)\n",
       "\n",
       "\n",
       "Block period\n",
       "--------------------\n",
       "stress_period_data\n",
       "{1: <flopy.mf6.data.mfdataplist.PandasListStorage object at 0x0000023558E3FD50>, 2: <flopy.mf6.data.mfdataplist.PandasListStorage object at 0x0000023558E42B10>, 3: <flopy.mf6.data.mfdataplist.PandasListStorage object at 0x0000023558E43210>}\n"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.package_dict['wel']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rec.array([((0, 11, 2), '-50.00000000', nan),\n",
       "           ((2, 4, 7), 'well_1_rate', 'well_1'),\n",
       "           ((2, 3, 2), 'well_2_rate', 'well_2')],\n",
       "          dtype=[('cellid', 'O'), ('q', 'O'), ('boundname', 'O')])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# second stress period\n",
    "model.package_dict['wel'].stress_period_data.get_data(key=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rec.array([((2, 3, 2), 'well_2_rate', 'well_2'),\n",
       "           ((2, 4, 7), 'well_1_rate', 'well_1')],\n",
       "          dtype=[('cellid', 'O'), ('q', 'O'), ('boundname', 'O')])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# third stress period\n",
    "model.package_dict['wel'].stress_period_data.get_data(key=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rec.array([((2, 4, 7), 'well_1_rate', 'well_1'),\n",
       "           ((2, 3, 2), 'well_2_rate', 'well_2'),\n",
       "           ((0, 11, 2), '-10.00000000', nan),\n",
       "           ((0, 2, 4), '-20.00000000', nan),\n",
       "           ((0, 13, 5), '-40.00000000', nan)],\n",
       "          dtype=[('cellid', 'O'), ('q', 'O'), ('boundname', 'O')])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fourth stress period\n",
    "model.package_dict['wel'].stress_period_data.get_data(key=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "package_name = wel_ts\n",
       "filename = ex-gwf-advtidal.wel.ts\n",
       "package_type = ts\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-advtidal\n",
       "parent_file = wel\n",
       "\n",
       "Block attributes\n",
       "--------------------\n",
       "time_series_namerecord\n",
       "{internal}\n",
       "(rec.array([('well_1_rate', 'well_2_rate', 'well_6_rate')],\n",
       "          dtype=[('time_series_names_0', 'O'), ('time_series_names_1', 'O'), ('time_series_names_2', 'O')]))\n",
       "\n",
       "interpolation_methodrecord\n",
       "{internal}\n",
       "(rec.array([('stepwise', 'stepwise', 'stepwise')],\n",
       "          dtype=[('interpolation_method_0', 'O'), ('interpolation_method_1', 'O'), ('interpolation_method_2', 'O')]))\n",
       "\n",
       "\n",
       "Block timeseries\n",
       "--------------------\n",
       "timeseries\n",
       "{internal}\n",
       "(rec.array([( 0.,     0.,    0.,    0.), ( 1.,  -200.,    0., -100.),\n",
       "           (11., -1800., -500., -200.), (21.,  -200., -400., -300.),\n",
       "           (31.,     0., -600., -400.)],\n",
       "          dtype=[('ts_time', '<f8'), ('ts_array_0', '<f8'), ('ts_array_1', '<f8'), ('ts_array_2', '<f8')]))\n",
       "\n"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.package_dict['wel_ts']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Reverse-engineer class summary!\n",
    "\n",
    "```python\n",
    "boundnames = True\n",
    "timeseries = {\n",
    "    \"time_series_namerecord\": [\"well_1_rate\", \"well_2_rate\", \"well_6_rate\"],\n",
    "    \"interpolation_methodrecord\": [\"stepwise\", \"stepwise\", \"stepwise\"],\n",
    "    # list of (ts_time, well_1_rate, well_2_rate, well_6_rate)\n",
    "    \"timeseries\": [\n",
    "        # beginning of simulation\n",
    "        ( 0.,     0.,    0.,    0.),\n",
    "        # end of first stress period\n",
    "        ( 1.,  -200.,    0., -100.),\n",
    "        # end of second stress period\n",
    "        (11., -1800., -500., -200.),\n",
    "        # end of third stress period\n",
    "        (21.,  -200., -400., -300.),\n",
    "        # end of fourth stress period\n",
    "        (31.,     0., -600., -400.)\n",
    "    ],\n",
    "    \"filename\": \"ex-gwf-advtidal.wel.ts\"\n",
    "}\n",
    "maxbound = 5\n",
    "# list of (cellid, q, boundname)\n",
    "# cellid: (layer, row, column)\n",
    "# q: volumetric well rate or time-series name\n",
    "# boundname: name of the well cell\n",
    "stress_period_data = {\n",
    "    # second stress period\n",
    "    1: [\n",
    "        ((0, 11, 2), '-50.00000000', None),\n",
    "        ((2, 4, 7), 'well_1_rate', 'well_1'),\n",
    "        ((2, 3, 2), 'well_2_rate', 'well_2')\n",
    "    ],\n",
    "    # third stress period\n",
    "    2: [\n",
    "        ((2, 4, 7), 'well_1_rate', 'well_1'),\n",
    "        ((2, 3, 2), 'well_2_rate', 'well_2'),\n",
    "        ((0, 11, 2), '-10.00000000', None),\n",
    "        ((0, 2, 4), '-20.00000000', None),\n",
    "        ((0, 13, 5), '-40.00000000', None)\n",
    "    ],\n",
    "    # fourth stress period\n",
    "    3: [\n",
    "        ((2, 4, 7), 'well_1_rate', 'well_1'),\n",
    "        ((2, 3, 2), 'well_2_rate', 'well_2'),\n",
    "        ((0, 11, 2), '-10.00000000', None),\n",
    "        ((0, 2, 4), '-20.00000000', None),\n",
    "        ((0, 13, 5), '-40.00000000', None)\n",
    "    ]\n",
    "}\n",
    "filename = \"ex-gwf-advtidal.wel\"\n",
    "pname = \"wel\"\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5(g). [River (RIV) Package](https://flopy.readthedocs.io/en/latest/source/flopy.mf6.modflow.mfgwfriv.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "package_name = riv\n",
       "filename = ex-gwf-advtidal.riv\n",
       "package_type = riv\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-advtidal\n",
       "\n",
       "Block options\n",
       "--------------------\n",
       "boundnames\n",
       "{internal}\n",
       "(True)\n",
       "\n",
       "ts_filerecord\n",
       "{internal}\n",
       "(rec.array([('ex-gwf-advtidal.riv.ts',)],\n",
       "          dtype=[('ts6_filename', 'O')]))\n",
       "\n",
       "\n",
       "Block dimensions\n",
       "--------------------\n",
       "maxbound\n",
       "{internal}\n",
       "(20)\n",
       "\n",
       "\n",
       "Block period\n",
       "--------------------\n",
       "stress_period_data\n",
       "{0: <flopy.mf6.data.mfdataplist.PandasListStorage object at 0x0000023558E29890>}\n"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.package_dict['riv']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rec.array([((0, 2, 0), 'river_stage_1', 1001., 35.9, nan),\n",
       "           ((0, 3, 1), 'river_stage_1', 1002., 35.8, nan),\n",
       "           ((0, 4, 2), 'river_stage_1', 1003., 35.7, nan),\n",
       "           ((0, 4, 3), 'river_stage_1', 1004., 35.6, nan),\n",
       "           ((0, 5, 4), 'river_stage_1', 1005., 35.5, nan),\n",
       "           ((0, 5, 5), 'river_stage_1', 1006., 35.4, 'riv1_c6'),\n",
       "           ((0, 5, 6), 'river_stage_1', 1007., 35.3, 'riv1_c7'),\n",
       "           ((0, 4, 7), 'river_stage_1', 1008., 35.2, nan),\n",
       "           ((0, 4, 8), 'river_stage_1', 1009., 35.1, nan),\n",
       "           ((0, 4, 9), 'river_stage_1', 1010., 35. , nan),\n",
       "           ((0, 9, 0), 'river_stage_2', 1001., 36.9, 'riv2_upper'),\n",
       "           ((0, 8, 1), 'river_stage_2', 1002., 36.8, 'riv2_upper'),\n",
       "           ((0, 7, 2), 'river_stage_2', 1003., 36.7, 'riv2_upper'),\n",
       "           ((0, 6, 3), 'river_stage_2', 1004., 36.6, nan),\n",
       "           ((0, 6, 4), 'river_stage_2', 1005., 36.5, nan),\n",
       "           ((0, 5, 5), 'river_stage_2', 1006., 36.4, 'riv2_c6'),\n",
       "           ((0, 5, 6), 'river_stage_2', 1007., 36.3, 'riv2_c7'),\n",
       "           ((0, 6, 7), 'river_stage_2', 1008., 36.2, nan),\n",
       "           ((0, 6, 8), 'river_stage_2', 1009., 36.1, nan),\n",
       "           ((0, 6, 9), 'river_stage_2', 1010., 36. , nan)],\n",
       "          dtype=[('cellid', 'O'), ('stage', 'O'), ('cond', '<f8'), ('rbot', '<f8'), ('boundname', 'O')])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.package_dict['riv'].stress_period_data.get_data(key=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "package_name = riv_ts\n",
       "filename = ex-gwf-advtidal.riv.ts\n",
       "package_type = ts\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-advtidal\n",
       "parent_file = riv\n",
       "\n",
       "Block attributes\n",
       "--------------------\n",
       "time_series_namerecord\n",
       "{internal}\n",
       "(rec.array([('river_stage_1', 'river_stage_2')],\n",
       "          dtype=[('time_series_names_0', 'O'), ('time_series_names_1', 'O')]))\n",
       "\n",
       "interpolation_methodrecord\n",
       "{internal}\n",
       "(rec.array([('linear', 'stepwise')],\n",
       "          dtype=[('interpolation_method_0', 'O'), ('interpolation_method_1', 'O')]))\n",
       "\n",
       "\n",
       "Block timeseries\n",
       "--------------------\n",
       "timeseries\n",
       "{internal}\n",
       "(rec.array([( 0., 40., 41. ), ( 1., 41., 41.5), ( 2., 43., 42. ),\n",
       "           ( 3., 45., 42.8), ( 4., 44., 43. ), ( 6., 43., 43.1),\n",
       "           ( 9., 42., 42.4), (11., 41., 41.5), (31., 40., 41. )],\n",
       "          dtype=[('ts_time', '<f8'), ('ts_array_0', '<f8'), ('ts_array_1', '<f8')]))\n",
       "\n"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.package_dict['riv_ts']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Reverse-engineer class summary!\n",
    "\n",
    "```python\n",
    "boundnames = True\n",
    "timeseries = {\n",
    "    \"time_series_namerecord\": [\"river_stage_1\", \"river_stage_2\"],\n",
    "    \"interpolation_methodrecord\": [\"linear\", \"stepwise\"],\n",
    "    # list of (ts_time, river_stage_1, river_stage_2)\n",
    "    \"timeseries\": [\n",
    "        ( 0., 40., 41. ), ( 1., 41., 41.5), ( 2., 43., 42. ),\n",
    "        ( 3., 45., 42.8), ( 4., 44., 43. ), ( 6., 43., 43.1),\n",
    "        ( 9., 42., 42.4), (11., 41., 41.5), (31., 40., 41. )\n",
    "    ],\n",
    "    \"filename\": \"ex-gwf-advtidal.riv.ts\"\n",
    "}\n",
    "maxbound = 20\n",
    "# list of (cellid, stage, cond, rbot, boundname)\n",
    "# cellid: (layer, row, column)\n",
    "# stage: time-series name\n",
    "# boundname: name of the river cell\n",
    "stress_period_data = [\n",
    "    ((0, 2, 0), 'river_stage_1', 1001., 35.9, None),\n",
    "    ((0, 3, 1), 'river_stage_1', 1002., 35.8, None),\n",
    "    ((0, 4, 2), 'river_stage_1', 1003., 35.7, None),\n",
    "    ((0, 4, 3), 'river_stage_1', 1004., 35.6, None),\n",
    "    ((0, 5, 4), 'river_stage_1', 1005., 35.5, None),\n",
    "    ((0, 5, 5), 'river_stage_1', 1006., 35.4, 'riv1_c6'),\n",
    "    ((0, 5, 6), 'river_stage_1', 1007., 35.3, 'riv1_c7'),\n",
    "    ((0, 4, 7), 'river_stage_1', 1008., 35.2, None),\n",
    "    ((0, 4, 8), 'river_stage_1', 1009., 35.1, None),\n",
    "    ((0, 4, 9), 'river_stage_1', 1010., 35. , None),\n",
    "    ((0, 9, 0), 'river_stage_2', 1001., 36.9, 'riv2_upper'),\n",
    "    ((0, 8, 1), 'river_stage_2', 1002., 36.8, 'riv2_upper'),\n",
    "    ((0, 7, 2), 'river_stage_2', 1003., 36.7, 'riv2_upper'),\n",
    "    ((0, 6, 3), 'river_stage_2', 1004., 36.6, None),\n",
    "    ((0, 6, 4), 'river_stage_2', 1005., 36.5, None),\n",
    "    ((0, 5, 5), 'river_stage_2', 1006., 36.4, 'riv2_c6'),\n",
    "    ((0, 5, 6), 'river_stage_2', 1007., 36.3, 'riv2_c7'),\n",
    "    ((0, 6, 7), 'river_stage_2', 1008., 36.2, None),\n",
    "    ((0, 6, 8), 'river_stage_2', 1009., 36.1, None),\n",
    "    ((0, 6, 9), 'river_stage_2', 1010., 36. , None)\n",
    "]\n",
    "filename = \"ex-gwf-advtidal.riv\"\n",
    "pname = \"riv\"\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5(h). [Recharge (RCH) Package](https://flopy.readthedocs.io/en/latest/source/flopy.mf6.modflow.mfgwfrch.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5(h)(i). Zone 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Reverse-engineer class summaries!\n",
    "\n",
    "```python\n",
    "fixed_cell = True\n",
    "auxiliary = [\"auxiliary\", \"multiplier\"] # check this\n",
    "auxmultname = \"multiplier\"\n",
    "boundnames = True\n",
    "print_input = True\n",
    "print_flows = True\n",
    "save_flows = True\n",
    "timeseries = {\n",
    "    \"time_series_namerecord\": [\"rch_1\"],\n",
    "    \"interpolation_namerecord\": [\"stepwise\"],\n",
    "    \"timeseries\": [\n",
    "        # beginning of simulation\n",
    "        ( 0., 0.0015),\n",
    "        # end of first stress period\n",
    "        ( 1., 0.001 ),\n",
    "        # end of second stress period\n",
    "        (11., 0.0015),\n",
    "        # end of third stress period\n",
    "        (21., 0.0025),\n",
    "        # end of fourth stress period\n",
    "        (31., 0.0015)\n",
    "    ],\n",
    "    \"filename\": \"ex-gwf-advtidal.rch1.ts\",\n",
    "    \"pname\": \"rch_ts\"\n",
    "}\n",
    "maxbound = 84\n",
    "# list of (cellid, recharge, multiplier, boundname)\n",
    "stress_period_data = [\n",
    "    ((0, 0, 0), 'rch_1', 1. , nan), ((0, 0, 1), 'rch_1', 1. , nan),\n",
    "    ((0, 0, 2), 'rch_1', 0.5, nan), ((0, 1, 0), 'rch_1', 1. , nan),\n",
    "    ((0, 1, 1), 'rch_1', 1. , nan), ((0, 1, 2), 'rch_1', 1. , nan),\n",
    "    ((0, 1, 3), 'rch_1', 0.5, nan), ((0, 2, 0), 'rch_1', 1. , nan),\n",
    "    ((0, 2, 1), 'rch_1', 1. , nan), ((0, 2, 2), 'rch_1', 1. , nan),\n",
    "    ((0, 2, 3), 'rch_1', 1. , nan), ((0, 2, 4), 'rch_1', 0.5, nan),\n",
    "    ((0, 3, 0), 'rch_1', 1. , nan), ((0, 3, 1), 'rch_1', 1. , nan),\n",
    "    ((0, 3, 2), 'rch_1', 1. , nan), ((0, 3, 3), 'rch_1', 1. , nan),\n",
    "    ((0, 3, 4), 'rch_1', 1. , nan), ((0, 3, 5), 'rch_1', 0.5, nan),\n",
    "    ((0, 4, 0), 'rch_1', 1. , nan), ((0, 4, 1), 'rch_1', 1. , nan),\n",
    "    ((0, 4, 2), 'rch_1', 1. , nan), ((0, 4, 3), 'rch_1', 1. , nan),\n",
    "    ((0, 4, 4), 'rch_1', 1. , nan), ((0, 4, 5), 'rch_1', 1. , nan),\n",
    "    ((0, 5, 0), 'rch_1', 1. , nan), ((0, 5, 1), 'rch_1', 1. , nan),\n",
    "    ((0, 5, 2), 'rch_1', 1. , nan), ((0, 5, 3), 'rch_1', 1. , nan),\n",
    "    ((0, 5, 4), 'rch_1', 1. , nan), ((0, 5, 5), 'rch_1', 1. , nan),\n",
    "    ((0, 6, 0), 'rch_1', 1. , nan), ((0, 6, 1), 'rch_1', 1. , nan),\n",
    "    ((0, 6, 2), 'rch_1', 1. , nan), ((0, 6, 3), 'rch_1', 1. , nan),\n",
    "    ((0, 6, 4), 'rch_1', 1. , nan), ((0, 6, 5), 'rch_1', 1. , nan),\n",
    "    ((0, 7, 0), 'rch_1', 1. , nan), ((0, 7, 1), 'rch_1', 1. , nan),\n",
    "    ((0, 7, 2), 'rch_1', 1. , nan), ((0, 7, 3), 'rch_1', 1. , nan),\n",
    "    ((0, 7, 4), 'rch_1', 1. , nan), ((0, 7, 5), 'rch_1', 1. , nan),\n",
    "    ((0, 8, 0), 'rch_1', 1. , nan), ((0, 8, 1), 'rch_1', 1. , nan),\n",
    "    ((0, 8, 2), 'rch_1', 1. , nan), ((0, 8, 3), 'rch_1', 1. , nan),\n",
    "    ((0, 8, 4), 'rch_1', 1. , nan), ((0, 8, 5), 'rch_1', 1. , nan),\n",
    "    ((0, 9, 0), 'rch_1', 1. , nan), ((0, 9, 1), 'rch_1', 1. , nan),\n",
    "    ((0, 9, 2), 'rch_1', 1. , nan), ((0, 9, 3), 'rch_1', 1. , nan),\n",
    "    ((0, 9, 4), 'rch_1', 1. , nan), ((0, 9, 5), 'rch_1', 1. , nan),\n",
    "    ((0, 10, 0), 'rch_1', 1. , nan), ((0, 10, 1), 'rch_1', 1. , nan),\n",
    "    ((0, 10, 2), 'rch_1', 1. , nan), ((0, 10, 3), 'rch_1', 1. , nan),\n",
    "    ((0, 10, 4), 'rch_1', 1. , nan), ((0, 10, 5), 'rch_1', 1. , nan),\n",
    "    ((0, 11, 0), 'rch_1', 1. , nan), ((0, 11, 1), 'rch_1', 1. , nan),\n",
    "    ((0, 11, 2), 'rch_1', 1. , nan), ((0, 11, 3), 'rch_1', 1. , nan),\n",
    "    ((0, 11, 4), 'rch_1', 1. , nan), ((0, 11, 5), 'rch_1', 1. , nan),\n",
    "    ((0, 12, 0), 'rch_1', 1. , nan), ((0, 12, 1), 'rch_1', 1. , nan),\n",
    "    ((0, 12, 2), 'rch_1', 1. , nan), ((0, 12, 3), 'rch_1', 1. , nan),\n",
    "    ((0, 12, 4), 'rch_1', 1. , nan), ((0, 12, 5), 'rch_1', 1. , nan),\n",
    "    ((0, 13, 0), 'rch_1', 1. , nan), ((0, 13, 1), 'rch_1', 1. , nan),\n",
    "    ((0, 13, 2), 'rch_1', 1. , nan), ((0, 13, 3), 'rch_1', 1. , nan),\n",
    "    ((0, 13, 4), 'rch_1', 1. , nan), ((0, 13, 5), 'rch_1', 1. , nan),\n",
    "    ((0, 14, 0), 'rch_1', 1. , nan), ((0, 14, 1), 'rch_1', 1. , nan),\n",
    "    ((0, 14, 2), 'rch_1', 1. , nan), ((0, 14, 3), 'rch_1', 1. , nan),\n",
    "    ((0, 14, 4), 'rch_1', 1. , nan), ((0, 14, 5), 'rch_1', 1. , nan)\n",
    "]\n",
    "filename = \"ex-gwf-advtidal.rch1\"\n",
    "pname = \"rch-zone_1\"\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "flopy.mf6.modflow.mfgwfrch.ModflowGwfrch"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.package_dict['rch-zone_1'].__class__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "package_name = rch-zone_1\n",
       "filename = ex-gwf-advtidal.rch1\n",
       "package_type = rch\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-advtidal\n",
       "\n",
       "Block options\n",
       "--------------------\n",
       "fixed_cell\n",
       "{internal}\n",
       "(True)\n",
       "\n",
       "auxiliary\n",
       "{internal}\n",
       "(rec.array([('auxiliary', 'multiplier')],\n",
       "          dtype=[('auxiliary_0', 'O'), ('auxiliary_1', 'O')]))\n",
       "\n",
       "auxmultname\n",
       "{internal}\n",
       "('multiplier')\n",
       "\n",
       "boundnames\n",
       "{internal}\n",
       "(True)\n",
       "\n",
       "print_input\n",
       "{internal}\n",
       "(True)\n",
       "\n",
       "print_flows\n",
       "{internal}\n",
       "(True)\n",
       "\n",
       "save_flows\n",
       "{internal}\n",
       "(True)\n",
       "\n",
       "ts_filerecord\n",
       "{internal}\n",
       "(rec.array([('ex-gwf-advtidal.rch1.ts',)],\n",
       "          dtype=[('ts6_filename', 'O')]))\n",
       "\n",
       "\n",
       "Block dimensions\n",
       "--------------------\n",
       "maxbound\n",
       "{internal}\n",
       "(84)\n",
       "\n",
       "\n",
       "Block period\n",
       "--------------------\n",
       "stress_period_data\n",
       "{0: <flopy.mf6.data.mfdataplist.PandasListStorage object at 0x0000023558E6E190>}\n"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.package_dict['rch-zone_1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rec.array([((0, 0, 0), 'rch_1', 1. , nan), ((0, 0, 1), 'rch_1', 1. , nan),\n",
       "           ((0, 0, 2), 'rch_1', 0.5, nan), ((0, 1, 0), 'rch_1', 1. , nan),\n",
       "           ((0, 1, 1), 'rch_1', 1. , nan), ((0, 1, 2), 'rch_1', 1. , nan),\n",
       "           ((0, 1, 3), 'rch_1', 0.5, nan), ((0, 2, 0), 'rch_1', 1. , nan),\n",
       "           ((0, 2, 1), 'rch_1', 1. , nan), ((0, 2, 2), 'rch_1', 1. , nan),\n",
       "           ((0, 2, 3), 'rch_1', 1. , nan), ((0, 2, 4), 'rch_1', 0.5, nan),\n",
       "           ((0, 3, 0), 'rch_1', 1. , nan), ((0, 3, 1), 'rch_1', 1. , nan),\n",
       "           ((0, 3, 2), 'rch_1', 1. , nan), ((0, 3, 3), 'rch_1', 1. , nan),\n",
       "           ((0, 3, 4), 'rch_1', 1. , nan), ((0, 3, 5), 'rch_1', 0.5, nan),\n",
       "           ((0, 4, 0), 'rch_1', 1. , nan), ((0, 4, 1), 'rch_1', 1. , nan),\n",
       "           ((0, 4, 2), 'rch_1', 1. , nan), ((0, 4, 3), 'rch_1', 1. , nan),\n",
       "           ((0, 4, 4), 'rch_1', 1. , nan), ((0, 4, 5), 'rch_1', 1. , nan),\n",
       "           ((0, 5, 0), 'rch_1', 1. , nan), ((0, 5, 1), 'rch_1', 1. , nan),\n",
       "           ((0, 5, 2), 'rch_1', 1. , nan), ((0, 5, 3), 'rch_1', 1. , nan),\n",
       "           ((0, 5, 4), 'rch_1', 1. , nan), ((0, 5, 5), 'rch_1', 1. , nan),\n",
       "           ((0, 6, 0), 'rch_1', 1. , nan), ((0, 6, 1), 'rch_1', 1. , nan),\n",
       "           ((0, 6, 2), 'rch_1', 1. , nan), ((0, 6, 3), 'rch_1', 1. , nan),\n",
       "           ((0, 6, 4), 'rch_1', 1. , nan), ((0, 6, 5), 'rch_1', 1. , nan),\n",
       "           ((0, 7, 0), 'rch_1', 1. , nan), ((0, 7, 1), 'rch_1', 1. , nan),\n",
       "           ((0, 7, 2), 'rch_1', 1. , nan), ((0, 7, 3), 'rch_1', 1. , nan),\n",
       "           ((0, 7, 4), 'rch_1', 1. , nan), ((0, 7, 5), 'rch_1', 1. , nan),\n",
       "           ((0, 8, 0), 'rch_1', 1. , nan), ((0, 8, 1), 'rch_1', 1. , nan),\n",
       "           ((0, 8, 2), 'rch_1', 1. , nan), ((0, 8, 3), 'rch_1', 1. , nan),\n",
       "           ((0, 8, 4), 'rch_1', 1. , nan), ((0, 8, 5), 'rch_1', 1. , nan),\n",
       "           ((0, 9, 0), 'rch_1', 1. , nan), ((0, 9, 1), 'rch_1', 1. , nan),\n",
       "           ((0, 9, 2), 'rch_1', 1. , nan), ((0, 9, 3), 'rch_1', 1. , nan),\n",
       "           ((0, 9, 4), 'rch_1', 1. , nan), ((0, 9, 5), 'rch_1', 1. , nan),\n",
       "           ((0, 10, 0), 'rch_1', 1. , nan),\n",
       "           ((0, 10, 1), 'rch_1', 1. , nan),\n",
       "           ((0, 10, 2), 'rch_1', 1. , nan),\n",
       "           ((0, 10, 3), 'rch_1', 1. , nan),\n",
       "           ((0, 10, 4), 'rch_1', 1. , nan),\n",
       "           ((0, 10, 5), 'rch_1', 1. , nan),\n",
       "           ((0, 11, 0), 'rch_1', 1. , nan),\n",
       "           ((0, 11, 1), 'rch_1', 1. , nan),\n",
       "           ((0, 11, 2), 'rch_1', 1. , nan),\n",
       "           ((0, 11, 3), 'rch_1', 1. , nan),\n",
       "           ((0, 11, 4), 'rch_1', 1. , nan),\n",
       "           ((0, 11, 5), 'rch_1', 1. , nan),\n",
       "           ((0, 12, 0), 'rch_1', 1. , nan),\n",
       "           ((0, 12, 1), 'rch_1', 1. , nan),\n",
       "           ((0, 12, 2), 'rch_1', 1. , nan),\n",
       "           ((0, 12, 3), 'rch_1', 1. , nan),\n",
       "           ((0, 12, 4), 'rch_1', 1. , nan),\n",
       "           ((0, 12, 5), 'rch_1', 1. , nan),\n",
       "           ((0, 13, 0), 'rch_1', 1. , nan),\n",
       "           ((0, 13, 1), 'rch_1', 1. , nan),\n",
       "           ((0, 13, 2), 'rch_1', 1. , nan),\n",
       "           ((0, 13, 3), 'rch_1', 1. , nan),\n",
       "           ((0, 13, 4), 'rch_1', 1. , nan),\n",
       "           ((0, 13, 5), 'rch_1', 1. , nan),\n",
       "           ((0, 14, 0), 'rch_1', 1. , nan),\n",
       "           ((0, 14, 1), 'rch_1', 1. , nan),\n",
       "           ((0, 14, 2), 'rch_1', 1. , nan),\n",
       "           ((0, 14, 3), 'rch_1', 1. , nan),\n",
       "           ((0, 14, 4), 'rch_1', 1. , nan),\n",
       "           ((0, 14, 5), 'rch_1', 1. , nan)],\n",
       "          dtype=[('cellid', 'O'), ('recharge', 'O'), ('multiplier', '<f8'), ('boundname', '<f8')])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.package_dict['rch-zone_1'].stress_period_data.get_data(key=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "package_name = rch_ts\n",
       "filename = ex-gwf-advtidal.rch1.ts\n",
       "package_type = ts\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-advtidal\n",
       "parent_file = rch-zone_1\n",
       "\n",
       "Block attributes\n",
       "--------------------\n",
       "time_series_namerecord\n",
       "{internal}\n",
       "(rec.array([('rch_1',)],\n",
       "          dtype=[('time_series_names', 'O')]))\n",
       "\n",
       "interpolation_methodrecord\n",
       "{internal}\n",
       "(rec.array([('stepwise',)],\n",
       "          dtype=[('interpolation_method', 'O')]))\n",
       "\n",
       "\n",
       "Block timeseries\n",
       "--------------------\n",
       "timeseries\n",
       "{internal}\n",
       "(rec.array([( 0., 0.0015), ( 1., 0.001 ), (11., 0.0015), (21., 0.0025),\n",
       "           (31., 0.0015)],\n",
       "          dtype=[('ts_time', '<f8'), ('ts_array', '<f8')]))\n",
       "\n"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.package_dict['rch_ts']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5(h)(ii). Zone 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Reverse-engineer class summaries!\n",
    "\n",
    "```python\n",
    "fixed_cell = True\n",
    "auxiliary = [\"auxiliary\", \"multiplier\"] # check this\n",
    "auxmultname = \"multiplier\"\n",
    "boundnames = True\n",
    "print_input = True\n",
    "print_flows = True\n",
    "save_flows = True\n",
    "timeseries = {\n",
    "    \"time_series_namerecord\": [\"rch_2\"],\n",
    "    \"interpolation_methodrecord\": [\"stepwise\"],\n",
    "    \"timeseries\": [\n",
    "        # beginning of simulation\n",
    "        ( 0., 0.0016),\n",
    "        # end of first stress period\n",
    "        ( 1., 0.0018),\n",
    "        # end of second stress period\n",
    "        (11., 0.0019),\n",
    "        # end of third stress period\n",
    "        (21., 0.0016),\n",
    "        # end of fourth stress period\n",
    "        (31., 0.0018)\n",
    "    ],\n",
    "    \"filename\": \"ex-gwf-advtidal.rch2.ts\",\n",
    "    \"pname\": \"rch_1_ts\"\n",
    "}\n",
    "maxbound = 20\n",
    "# list of (cellid, recharge, multiplier, boundname)\n",
    "stress_period_data = [\n",
    "    ((0, 0, 2), 'rch_2', 0.5, nan), ((0, 0, 3), 'rch_2', 1. , nan),\n",
    "    ((0, 0, 4), 'rch_2', 1. , nan), ((0, 0, 5), 'rch_2', 1. , nan),\n",
    "    ((0, 0, 6), 'rch_2', 1. , nan), ((0, 0, 7), 'rch_2', 1. , nan),\n",
    "    ((0, 0, 8), 'rch_2', 1. , nan), ((0, 0, 9), 'rch_2', 0.5, nan),\n",
    "    ((0, 1, 3), 'rch_2', 0.5, nan), ((0, 1, 4), 'rch_2', 1. , nan),\n",
    "    ((0, 1, 5), 'rch_2', 1. , nan), ((0, 1, 6), 'rch_2', 1. , nan),\n",
    "    ((0, 1, 7), 'rch_2', 1. , nan), ((0, 1, 8), 'rch_2', 0.5, nan),\n",
    "    ((0, 2, 4), 'rch_2', 0.5, nan), ((0, 2, 5), 'rch_2', 1. , nan),\n",
    "    ((0, 2, 6), 'rch_2', 1. , nan), ((0, 2, 7), 'rch_2', 0.5, nan),\n",
    "    ((0, 3, 5), 'rch_2', 0.5, nan), ((0, 3, 6), 'rch_2', 0.5, nan)\n",
    "]\n",
    "filename = \"ex-gwf-advtidal.rch2\"\n",
    "pname = \"rch-zone_2\"\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "package_name = rch-zone_2\n",
       "filename = ex-gwf-advtidal.rch2\n",
       "package_type = rch\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-advtidal\n",
       "\n",
       "Block options\n",
       "--------------------\n",
       "fixed_cell\n",
       "{internal}\n",
       "(True)\n",
       "\n",
       "auxiliary\n",
       "{internal}\n",
       "(rec.array([('auxiliary', 'multiplier')],\n",
       "          dtype=[('auxiliary_0', 'O'), ('auxiliary_1', 'O')]))\n",
       "\n",
       "auxmultname\n",
       "{internal}\n",
       "('multiplier')\n",
       "\n",
       "boundnames\n",
       "{internal}\n",
       "(True)\n",
       "\n",
       "print_input\n",
       "{internal}\n",
       "(True)\n",
       "\n",
       "print_flows\n",
       "{internal}\n",
       "(True)\n",
       "\n",
       "save_flows\n",
       "{internal}\n",
       "(True)\n",
       "\n",
       "ts_filerecord\n",
       "{internal}\n",
       "(rec.array([('ex-gwf-advtidal.rch2.ts',)],\n",
       "          dtype=[('ts6_filename', 'O')]))\n",
       "\n",
       "\n",
       "Block dimensions\n",
       "--------------------\n",
       "maxbound\n",
       "{internal}\n",
       "(20)\n",
       "\n",
       "\n",
       "Block period\n",
       "--------------------\n",
       "stress_period_data\n",
       "{0: <flopy.mf6.data.mfdataplist.PandasListStorage object at 0x0000023558E7EAD0>}\n"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.package_dict['rch-zone_2']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rec.array([((0, 0, 2), 'rch_2', 0.5, nan), ((0, 0, 3), 'rch_2', 1. , nan),\n",
       "           ((0, 0, 4), 'rch_2', 1. , nan), ((0, 0, 5), 'rch_2', 1. , nan),\n",
       "           ((0, 0, 6), 'rch_2', 1. , nan), ((0, 0, 7), 'rch_2', 1. , nan),\n",
       "           ((0, 0, 8), 'rch_2', 1. , nan), ((0, 0, 9), 'rch_2', 0.5, nan),\n",
       "           ((0, 1, 3), 'rch_2', 0.5, nan), ((0, 1, 4), 'rch_2', 1. , nan),\n",
       "           ((0, 1, 5), 'rch_2', 1. , nan), ((0, 1, 6), 'rch_2', 1. , nan),\n",
       "           ((0, 1, 7), 'rch_2', 1. , nan), ((0, 1, 8), 'rch_2', 0.5, nan),\n",
       "           ((0, 2, 4), 'rch_2', 0.5, nan), ((0, 2, 5), 'rch_2', 1. , nan),\n",
       "           ((0, 2, 6), 'rch_2', 1. , nan), ((0, 2, 7), 'rch_2', 0.5, nan),\n",
       "           ((0, 3, 5), 'rch_2', 0.5, nan), ((0, 3, 6), 'rch_2', 0.5, nan)],\n",
       "          dtype=[('cellid', 'O'), ('recharge', 'O'), ('multiplier', '<f8'), ('boundname', '<f8')])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.package_dict['rch-zone_2'].stress_period_data.get_data(key=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "package_name = rch_1_ts\n",
       "filename = ex-gwf-advtidal.rch2.ts\n",
       "package_type = ts\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-advtidal\n",
       "parent_file = rch-zone_2\n",
       "\n",
       "Block attributes\n",
       "--------------------\n",
       "time_series_namerecord\n",
       "{internal}\n",
       "(rec.array([('rch_2',)],\n",
       "          dtype=[('time_series_names', 'O')]))\n",
       "\n",
       "interpolation_methodrecord\n",
       "{internal}\n",
       "(rec.array([('stepwise',)],\n",
       "          dtype=[('interpolation_method', 'O')]))\n",
       "\n",
       "\n",
       "Block timeseries\n",
       "--------------------\n",
       "timeseries\n",
       "{internal}\n",
       "(rec.array([( 0., 0.0016), ( 1., 0.0018), (11., 0.0019), (21., 0.0016),\n",
       "           (31., 0.0018)],\n",
       "          dtype=[('ts_time', '<f8'), ('ts_array', '<f8')]))\n",
       "\n"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.package_dict['rch_1_ts']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5(h)(iii). Zone 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Reverse-engineer class summaries!\n",
    "\n",
    "```python\n",
    "fixed_cell = True\n",
    "auxiliary = [\"auxiliary\", \"multiplier\"] # check this\n",
    "auxmultname = \"multiplier\"\n",
    "boundnames = True\n",
    "print_input = True\n",
    "print_flows = True\n",
    "save_flows = True\n",
    "timeseries = {\n",
    "    \"time_series_namerecord\": [\"rch_3\"],\n",
    "    \"interpolation_methodrecord\": [\"stepwise\"],\n",
    "    \"timeseries\": [\n",
    "        # beginning of simulation\n",
    "        ( 0., 0.0017),\n",
    "        # end of first stress period\n",
    "        ( 1., 0.002 ),\n",
    "        # end of second stress period\n",
    "        (11., 0.0017),\n",
    "        # end of third stress period\n",
    "        (21., 0.0018),\n",
    "        # end of fourth stress period\n",
    "        (31., 0.002 )\n",
    "    ],\n",
    "    \"filename\": \"ex-gwf-advtidal.rch3.ts\",\n",
    "    \"pname\": \"rch_2_ts\"\n",
    "}\n",
    "maxbound = 54\n",
    "# list of (cellid, recharge, multiplier, boundname)\n",
    "stress_period_data = [\n",
    "    ((0, 0, 9), 'rch_3', 0.5, nan), ((0, 1, 8), 'rch_3', 0.5, nan),\n",
    "    ((0, 1, 9), 'rch_3', 1. , nan), ((0, 2, 7), 'rch_3', 0.5, nan),\n",
    "    ((0, 2, 8), 'rch_3', 1. , nan), ((0, 2, 9), 'rch_3', 1. , nan),\n",
    "    ((0, 3, 6), 'rch_3', 0.5, nan), ((0, 3, 7), 'rch_3', 1. , nan),\n",
    "    ((0, 3, 8), 'rch_3', 1. , nan), ((0, 3, 9), 'rch_3', 1. , nan),\n",
    "    ((0, 4, 6), 'rch_3', 1. , nan), ((0, 4, 7), 'rch_3', 1. , nan),\n",
    "    ((0, 4, 8), 'rch_3', 1. , nan), ((0, 4, 9), 'rch_3', 1. , nan),\n",
    "    ((0, 5, 6), 'rch_3', 1. , nan), ((0, 5, 7), 'rch_3', 1. , nan),\n",
    "    ((0, 5, 8), 'rch_3', 1. , nan), ((0, 5, 9), 'rch_3', 1. , nan),\n",
    "    ((0, 6, 6), 'rch_3', 1. , nan), ((0, 6, 7), 'rch_3', 1. , nan),\n",
    "    ((0, 6, 8), 'rch_3', 1. , nan), ((0, 6, 9), 'rch_3', 1. , nan),\n",
    "    ((0, 7, 6), 'rch_3', 1. , nan), ((0, 7, 7), 'rch_3', 1. , nan),\n",
    "    ((0, 7, 8), 'rch_3', 1. , nan), ((0, 7, 9), 'rch_3', 1. , nan),\n",
    "    ((0, 8, 6), 'rch_3', 1. , nan), ((0, 8, 7), 'rch_3', 1. , nan),\n",
    "    ((0, 8, 8), 'rch_3', 1. , nan), ((0, 8, 9), 'rch_3', 1. , nan),\n",
    "    ((0, 9, 6), 'rch_3', 1. , nan), ((0, 9, 7), 'rch_3', 1. , nan),\n",
    "    ((0, 9, 8), 'rch_3', 1. , nan), ((0, 9, 9), 'rch_3', 1. , nan),\n",
    "    ((0, 10, 6), 'rch_3', 1. , nan), ((0, 10, 7), 'rch_3', 1. , nan),\n",
    "    ((0, 10, 8), 'rch_3', 1. , nan), ((0, 10, 9), 'rch_3', 1. , nan),\n",
    "    ((0, 11, 6), 'rch_3', 1. , nan), ((0, 11, 7), 'rch_3', 1. , nan),\n",
    "    ((0, 11, 8), 'rch_3', 1. , nan), ((0, 11, 9), 'rch_3', 1. , nan),\n",
    "    ((0, 12, 6), 'rch_3', 1. , nan), ((0, 12, 7), 'rch_3', 1. , nan),\n",
    "    ((0, 12, 8), 'rch_3', 1. , nan), ((0, 12, 9), 'rch_3', 1. , nan),\n",
    "    ((0, 13, 6), 'rch_3', 1. , nan), ((0, 13, 7), 'rch_3', 1. , nan),\n",
    "    ((0, 13, 8), 'rch_3', 1. , nan), ((0, 13, 9), 'rch_3', 1. , nan),\n",
    "    ((0, 14, 6), 'rch_3', 1. , nan), ((0, 14, 7), 'rch_3', 1. , nan),\n",
    "    ((0, 14, 8), 'rch_3', 1. , nan), ((0, 14, 9), 'rch_3', 1. , nan)\n",
    "]\n",
    "filename = \"ex-gwf-advtidal.rch3\"\n",
    "pname = \"rch-zone_3\"\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "package_name = rch-zone_3\n",
       "filename = ex-gwf-advtidal.rch3\n",
       "package_type = rch\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-advtidal\n",
       "\n",
       "Block options\n",
       "--------------------\n",
       "fixed_cell\n",
       "{internal}\n",
       "(True)\n",
       "\n",
       "auxiliary\n",
       "{internal}\n",
       "(rec.array([('auxiliary', 'multiplier')],\n",
       "          dtype=[('auxiliary_0', 'O'), ('auxiliary_1', 'O')]))\n",
       "\n",
       "auxmultname\n",
       "{internal}\n",
       "('multiplier')\n",
       "\n",
       "boundnames\n",
       "{internal}\n",
       "(True)\n",
       "\n",
       "print_input\n",
       "{internal}\n",
       "(True)\n",
       "\n",
       "print_flows\n",
       "{internal}\n",
       "(True)\n",
       "\n",
       "save_flows\n",
       "{internal}\n",
       "(True)\n",
       "\n",
       "ts_filerecord\n",
       "{internal}\n",
       "(rec.array([('ex-gwf-advtidal.rch3.ts',)],\n",
       "          dtype=[('ts6_filename', 'O')]))\n",
       "\n",
       "\n",
       "Block dimensions\n",
       "--------------------\n",
       "maxbound\n",
       "{internal}\n",
       "(54)\n",
       "\n",
       "\n",
       "Block period\n",
       "--------------------\n",
       "stress_period_data\n",
       "{0: <flopy.mf6.data.mfdataplist.PandasListStorage object at 0x0000023558E89B90>}\n"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.package_dict['rch-zone_3']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rec.array([((0, 0, 9), 'rch_3', 0.5, nan), ((0, 1, 8), 'rch_3', 0.5, nan),\n",
       "           ((0, 1, 9), 'rch_3', 1. , nan), ((0, 2, 7), 'rch_3', 0.5, nan),\n",
       "           ((0, 2, 8), 'rch_3', 1. , nan), ((0, 2, 9), 'rch_3', 1. , nan),\n",
       "           ((0, 3, 6), 'rch_3', 0.5, nan), ((0, 3, 7), 'rch_3', 1. , nan),\n",
       "           ((0, 3, 8), 'rch_3', 1. , nan), ((0, 3, 9), 'rch_3', 1. , nan),\n",
       "           ((0, 4, 6), 'rch_3', 1. , nan), ((0, 4, 7), 'rch_3', 1. , nan),\n",
       "           ((0, 4, 8), 'rch_3', 1. , nan), ((0, 4, 9), 'rch_3', 1. , nan),\n",
       "           ((0, 5, 6), 'rch_3', 1. , nan), ((0, 5, 7), 'rch_3', 1. , nan),\n",
       "           ((0, 5, 8), 'rch_3', 1. , nan), ((0, 5, 9), 'rch_3', 1. , nan),\n",
       "           ((0, 6, 6), 'rch_3', 1. , nan), ((0, 6, 7), 'rch_3', 1. , nan),\n",
       "           ((0, 6, 8), 'rch_3', 1. , nan), ((0, 6, 9), 'rch_3', 1. , nan),\n",
       "           ((0, 7, 6), 'rch_3', 1. , nan), ((0, 7, 7), 'rch_3', 1. , nan),\n",
       "           ((0, 7, 8), 'rch_3', 1. , nan), ((0, 7, 9), 'rch_3', 1. , nan),\n",
       "           ((0, 8, 6), 'rch_3', 1. , nan), ((0, 8, 7), 'rch_3', 1. , nan),\n",
       "           ((0, 8, 8), 'rch_3', 1. , nan), ((0, 8, 9), 'rch_3', 1. , nan),\n",
       "           ((0, 9, 6), 'rch_3', 1. , nan), ((0, 9, 7), 'rch_3', 1. , nan),\n",
       "           ((0, 9, 8), 'rch_3', 1. , nan), ((0, 9, 9), 'rch_3', 1. , nan),\n",
       "           ((0, 10, 6), 'rch_3', 1. , nan),\n",
       "           ((0, 10, 7), 'rch_3', 1. , nan),\n",
       "           ((0, 10, 8), 'rch_3', 1. , nan),\n",
       "           ((0, 10, 9), 'rch_3', 1. , nan),\n",
       "           ((0, 11, 6), 'rch_3', 1. , nan),\n",
       "           ((0, 11, 7), 'rch_3', 1. , nan),\n",
       "           ((0, 11, 8), 'rch_3', 1. , nan),\n",
       "           ((0, 11, 9), 'rch_3', 1. , nan),\n",
       "           ((0, 12, 6), 'rch_3', 1. , nan),\n",
       "           ((0, 12, 7), 'rch_3', 1. , nan),\n",
       "           ((0, 12, 8), 'rch_3', 1. , nan),\n",
       "           ((0, 12, 9), 'rch_3', 1. , nan),\n",
       "           ((0, 13, 6), 'rch_3', 1. , nan),\n",
       "           ((0, 13, 7), 'rch_3', 1. , nan),\n",
       "           ((0, 13, 8), 'rch_3', 1. , nan),\n",
       "           ((0, 13, 9), 'rch_3', 1. , nan),\n",
       "           ((0, 14, 6), 'rch_3', 1. , nan),\n",
       "           ((0, 14, 7), 'rch_3', 1. , nan),\n",
       "           ((0, 14, 8), 'rch_3', 1. , nan),\n",
       "           ((0, 14, 9), 'rch_3', 1. , nan)],\n",
       "          dtype=[('cellid', 'O'), ('recharge', 'O'), ('multiplier', '<f8'), ('boundname', '<f8')])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.package_dict['rch-zone_3'].stress_period_data.get_data(key=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "package_name = rch_2_ts\n",
       "filename = ex-gwf-advtidal.rch3.ts\n",
       "package_type = ts\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-advtidal\n",
       "parent_file = rch-zone_3\n",
       "\n",
       "Block attributes\n",
       "--------------------\n",
       "time_series_namerecord\n",
       "{internal}\n",
       "(rec.array([('rch_3',)],\n",
       "          dtype=[('time_series_names', 'O')]))\n",
       "\n",
       "interpolation_methodrecord\n",
       "{internal}\n",
       "(rec.array([('stepwise',)],\n",
       "          dtype=[('interpolation_method', 'O')]))\n",
       "\n",
       "\n",
       "Block timeseries\n",
       "--------------------\n",
       "timeseries\n",
       "{internal}\n",
       "(rec.array([( 0., 0.0017), ( 1., 0.002 ), (11., 0.0017), (21., 0.0018),\n",
       "           (31., 0.002 )],\n",
       "          dtype=[('ts_time', '<f8'), ('ts_array', '<f8')]))\n",
       "\n"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.package_dict['rch_2_ts']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5(i). [Evapotranspiration (EVT) Package](https://flopy.readthedocs.io/en/latest/source/flopy.mf6.modflow.mfgwfevt.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "flopy.mf6.modflow.mfgwfevt.ModflowGwfevt"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.package_dict['evt'].__class__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "package_name = evt\n",
       "filename = ex-gwf-advtidal.evt\n",
       "package_type = evt\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-advtidal\n",
       "\n",
       "Block dimensions\n",
       "--------------------\n",
       "maxbound\n",
       "{internal}\n",
       "(150)\n",
       "\n",
       "nseg\n",
       "{internal}\n",
       "(3)\n",
       "\n",
       "\n",
       "Block period\n",
       "--------------------\n",
       "stress_period_data\n",
       "{internal}\n",
       "(rec.array([((0, 0, 0), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 0, 1), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 0, 2), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 0, 3), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 0, 4), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 0, 5), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 0, 6), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 0, 7), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 0, 8), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 0, 9), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 1, 0), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 1, 1), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 1, 2), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 1, 3), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 1, 4), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 1, 5), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 1, 6), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 1, 7), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 1, 8), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 1, 9), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 2, 0), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 2, 1), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 2, 2), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 2, 3), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 2, 4), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 2, 5), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 2, 6), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 2, 7), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 2, 8), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 2, 9), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 3, 0), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 3, 1), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 3, 2), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 3, 3), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 3, 4), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 3, 5), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 3, 6), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 3, 7), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 3, 8), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 3, 9), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 4, 0), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 4, 1), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 4, 2), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 4, 3), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 4, 4), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 4, 5), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 4, 6), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 4, 7), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 4, 8), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 4, 9), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 5, 0), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 5, 1), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 5, 2), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 5, 3), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 5, 4), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 5, 5), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 5, 6), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 5, 7), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 5, 8), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 5, 9), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 6, 0), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 6, 1), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 6, 2), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 6, 3), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 6, 4), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 6, 5), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 6, 6), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 6, 7), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 6, 8), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 6, 9), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 7, 0), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 7, 1), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 7, 2), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 7, 3), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 7, 4), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 7, 5), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 7, 6), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 7, 7), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 7, 8), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 7, 9), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 8, 0), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 8, 1), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 8, 2), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 8, 3), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 8, 4), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 8, 5), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 8, 6), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 8, 7), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 8, 8), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 8, 9), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 9, 0), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 9, 1), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 9, 2), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 9, 3), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 9, 4), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 9, 5), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 9, 6), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 9, 7), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 9, 8), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 9, 9), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 10, 0), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 10, 1), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 10, 2), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 10, 3), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 10, 4), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 10, 5), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 10, 6), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 10, 7), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 10, 8), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 10, 9), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 11, 0), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 11, 1), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 11, 2), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 11, 3), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 11, 4), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 11, 5), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 11, 6), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 11, 7), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 11, 8), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 11, 9), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 12, 0), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 12, 1), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 12, 2), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 12, 3), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 12, 4), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 12, 5), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 12, 6), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 12, 7), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 12, 8), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 12, 9), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 13, 0), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 13, 1), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 13, 2), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 13, 3), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 13, 4), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 13, 5), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 13, 6), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 13, 7), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 13, 8), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 13, 9), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 14, 0), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 14, 1), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 14, 2), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 14, 3), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 14, 4), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 14, 5), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 14, 6), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 14, 7), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 14, 8), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
       "           ((0, 14, 9), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan)],\n",
       "          dtype=[('cellid', 'O'), ('surface', '<f8'), ('rate', '<f8'), ('depth', '<f8'), ('pxdp_0', '<f8'), ('pxdp_1', '<f8'), ('petm_0', '<f8'), ('petm_1', '<f8'), ('petm0', '<f8')]))\n",
       "\n"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.package_dict['evt']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Reverse-engineer class summary!\n",
    "\n",
    "```python\n",
    "maxbound = 150\n",
    "# number of ET segments\n",
    "# NSEG > 1: the PXDP and PETM arrays must be of size NSEG - 1 and be listed in order from the uppermost segment down\n",
    "nseg = 3\n",
    "# list of (cellid, surface, rate, depth, pxdp_0, pxdp_1, petm_0, petm_1, petm0)\n",
    "# surface: elevation of the ET surface (L)\n",
    "# rate: the maximum ET flux rate (L / T)\n",
    "# depth: ET extinction depth (L)\n",
    "# pxdp: the proportion of the ET extinction depth at the bottom of a segment\n",
    "# petm: the proportion of the maximum ET flux rate at the bottom of a segment\n",
    "# petm0: the proportion of the maximum ET flux rate that will apply when head is at or above the ET surface\n",
    "stress_period_data = [\n",
    "    ((0, 0, 0), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 0, 1), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 0, 2), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 0, 3), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 0, 4), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 0, 5), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 0, 6), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 0, 7), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 0, 8), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 0, 9), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 1, 0), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 1, 1), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 1, 2), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 1, 3), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 1, 4), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 1, 5), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 1, 6), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 1, 7), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 1, 8), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 1, 9), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 2, 0), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 2, 1), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 2, 2), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 2, 3), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 2, 4), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 2, 5), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 2, 6), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 2, 7), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 2, 8), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 2, 9), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 3, 0), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 3, 1), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 3, 2), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 3, 3), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 3, 4), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 3, 5), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 3, 6), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 3, 7), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 3, 8), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 3, 9), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 4, 0), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 4, 1), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 4, 2), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 4, 3), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 4, 4), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 4, 5), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 4, 6), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 4, 7), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 4, 8), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 4, 9), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 5, 0), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 5, 1), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 5, 2), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 5, 3), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 5, 4), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 5, 5), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 5, 6), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 5, 7), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 5, 8), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 5, 9), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 6, 0), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 6, 1), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 6, 2), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 6, 3), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 6, 4), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 6, 5), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 6, 6), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 6, 7), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 6, 8), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 6, 9), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 7, 0), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 7, 1), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 7, 2), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 7, 3), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 7, 4), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 7, 5), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 7, 6), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 7, 7), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 7, 8), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 7, 9), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 8, 0), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 8, 1), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 8, 2), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 8, 3), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 8, 4), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 8, 5), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 8, 6), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 8, 7), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 8, 8), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 8, 9), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 9, 0), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 9, 1), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 9, 2), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 9, 3), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 9, 4), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 9, 5), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 9, 6), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 9, 7), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 9, 8), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 9, 9), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 10, 0), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 10, 1), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 10, 2), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 10, 3), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 10, 4), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 10, 5), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 10, 6), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 10, 7), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 10, 8), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 10, 9), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 11, 0), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 11, 1), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 11, 2), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 11, 3), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 11, 4), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 11, 5), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 11, 6), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 11, 7), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 11, 8), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 11, 9), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 12, 0), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 12, 1), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 12, 2), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 12, 3), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 12, 4), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 12, 5), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 12, 6), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 12, 7), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 12, 8), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 12, 9), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 13, 0), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 13, 1), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 13, 2), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 13, 3), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 13, 4), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 13, 5), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 13, 6), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 13, 7), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 13, 8), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 13, 9), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 14, 0), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 14, 1), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 14, 2), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 14, 3), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 14, 4), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 14, 5), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 14, 6), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 14, 7), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 14, 8), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan),\n",
    "    ((0, 14, 9), 50., 0.0004, 10., 0.2, 0.5, 0.3, 0.1, nan)\n",
    "]\n",
    "filename = \"ex-gwf-advtidal.evt\"\n",
    "pname = \"evt\"\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5(j). [Output Control (OC)](https://flopy.readthedocs.io/en/latest/source/flopy.mf6.modflow.mfgwfoc.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "package_name = oc\n",
       "filename = ex-gwf-advtidal.oc\n",
       "package_type = oc\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-advtidal\n",
       "\n",
       "Block options\n",
       "--------------------\n",
       "budget_filerecord\n",
       "{internal}\n",
       "(rec.array([('ex-gwf-advtidal.cbc',)],\n",
       "          dtype=[('budgetfile', 'O')]))\n",
       "\n",
       "head_filerecord\n",
       "{internal}\n",
       "(rec.array([('ex-gwf-advtidal.hds',)],\n",
       "          dtype=[('headfile', 'O')]))\n",
       "\n",
       "\n",
       "Block period\n",
       "--------------------\n",
       "saverecord\n",
       "{internal}\n",
       "(rec.array([('head', 'all', None), ('budget', 'all', None)],\n",
       "          dtype=[('rtype', 'O'), ('ocsetting', 'O'), ('ocsetting_data', 'O')]))\n",
       "\n",
       "printrecord\n",
       "None\n"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.package_dict['oc']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Reverse-engineer class summary!\n",
    "\n",
    "```python\n",
    "budget_filerecord = [\"ex-gwf-advtidal.cbc\"],\n",
    "head_filerecord = [\"ex-gwf-advtidal.hds\"],\n",
    "# list of (rtype, ocsetting)\n",
    "saverecord = [\n",
    "    (\"head\", \"all\"),\n",
    "    (\"budget\", \"all\")\n",
    "]\n",
    "printrecord = None # default value\n",
    "filename = \"ex-gwf-advtidal.oc\"\n",
    "pname = \"oc\"\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5(k). [Observation (OBS)](https://flopy.readthedocs.io/en/latest/source/flopy.mf6.modflow.mfutlobs.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "flopy.mf6.modflow.mfutlobs.ModflowUtlobs"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.package_dict['obs_1'].__class__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "package_name = obs_1\n",
       "filename = ex-gwf-advtidal.obs\n",
       "package_type = obs\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-advtidal\n",
       "\n",
       "Block continuous\n",
       "--------------------\n",
       "continuous\n",
       "{internal}\n",
       "(rec.array([('h1_13_8', 'head', (2, 12, 7), None)],\n",
       "          dtype=[('obsname', 'O'), ('obstype', 'O'), ('id', 'O'), ('id2', 'O')]))\n",
       "\n"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.package_dict['obs_1']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Reverse-engineer class summary!\n",
    "\n",
    "```python\n",
    "# list of (obsname, obstype, id, id2)\n",
    "continuous = [('h1_13_8', 'head', (2, 12, 7), None)]\n",
    "filename = \"ex-gwf-advtidal.obs\"\n",
    "pname = \"obs_1\"\n",
    "```\n",
    "\n",
    "This simulation has a flow observation that doesn't show up in the summary. See `ex-gwf-advtidal.obs`\n",
    "\n",
    "```python\n",
    "# list of (obsname, obstype, id, id2)\n",
    "continuous = [(\"icf1\", \"flow-ja-face\", (0, 4, 5), (0, 5, 5))]\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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