notebooks/learning/gwf_bcf2ss.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Contents\n",
    "\n",
    "- 1. Load example\n",
    "\t- 1(a). Load standard formulation simulation\n",
    "\t- 1(b). Load Newton-Raphson formulation simulation\n",
    "- 2. Explore example\n",
    "- 3. Check and run simulations\n",
    "\t- 3(a). Standard formulation simulation\n",
    "\t- 3(b). Newton-Raphson formulation simulation\n",
    "- 4. Plot results\n",
    "\t- 4(a). Standard formulation simulation\n",
    "\t- 4(b). Newton-Raphson formulation simulation\n",
    "- 5. Breakdown the standard formulation simulation\n",
    "\t- 5(a). Simulation level packages\n",
    "\t\t- 5(a)(i). Temporal Discretization (TDIS) Package\n",
    "\t\t- 5(a)(ii). Iterative Model Solution (IMS)\n",
    "\t- 5(b). Model level packages\n",
    "\t\t- 5(b)(i). Structured Discretization (DIS)\n",
    "\t\t- 5(b)(ii). Node Property Flow (NPF) Package\n",
    "\t\t- 5(b)(iii). Initial Conditions (IC) Package\n",
    "\t\t- 5(b)(iv). River (RIV) Package\n",
    "\t\t- 5(b)(v). Well (WEL) Package\n",
    "\t\t- 5(b)(vi). Recharge (RCH) Package\n",
    "\t\t- 5(b)(vii). Output Control (OC)\n",
    "- 6. Breakdown the Newton-Raphson formulation simulation\n",
    "\t- 6(a). Iterative Model Solution (IMS)\n",
    "\t- 6(b). Node Property Flow (NPF) Package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "import flopy as fp\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Load example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ex-gwf-bcf2ss'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim_name = \"ex-gwf-bcf2ss\"\n",
    "sim_name"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1(a). Load standard formulation simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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 riv...\n",
      "    loading package wel...\n",
      "    loading package rch...\n",
      "    loading package oc...\n",
      "  loading solution package ex-gwf-bcf2ss...\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "sim_name = ex-gwf-bcf2ss\n",
       "sim_path = C:\\Users\\buckl\\Documents\\Software\\mf6.4.1\\examples\\ex-gwf-bcf2ss-p01a\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-bcf2ss\n",
       "\n",
       "\n",
       "###################\n",
       "Package ex-gwf-bcf2ss.tdis\n",
       "###################\n",
       "\n",
       "package_name = ex-gwf-bcf2ss.tdis\n",
       "filename = ex-gwf-bcf2ss.tdis\n",
       "package_type = tdis\n",
       "model_or_simulation_package = simulation\n",
       "simulation_name = ex-gwf-bcf2ss\n",
       "\n",
       "\n",
       "###################\n",
       "Package ex-gwf-bcf2ss\n",
       "###################\n",
       "\n",
       "package_name = ex-gwf-bcf2ss\n",
       "filename = ex-gwf-bcf2ss.ims\n",
       "package_type = ims\n",
       "model_or_simulation_package = simulation\n",
       "simulation_name = ex-gwf-bcf2ss\n",
       "\n",
       "\n",
       "@@@@@@@@@@@@@@@@@@@@\n",
       "Model ex-gwf-bcf2ss\n",
       "@@@@@@@@@@@@@@@@@@@@\n",
       "\n",
       "name = ex-gwf-bcf2ss\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-bcf2ss.dis\n",
       "package_type = dis\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-bcf2ss\n",
       "\n",
       "\n",
       "###################\n",
       "Package npf\n",
       "###################\n",
       "\n",
       "package_name = npf\n",
       "filename = ex-gwf-bcf2ss.npf\n",
       "package_type = npf\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-bcf2ss\n",
       "\n",
       "\n",
       "###################\n",
       "Package ic\n",
       "###################\n",
       "\n",
       "package_name = ic\n",
       "filename = ex-gwf-bcf2ss.ic\n",
       "package_type = ic\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-bcf2ss\n",
       "\n",
       "\n",
       "###################\n",
       "Package riv_0\n",
       "###################\n",
       "\n",
       "package_name = riv_0\n",
       "filename = ex-gwf-bcf2ss.riv\n",
       "package_type = riv\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-bcf2ss\n",
       "\n",
       "\n",
       "###################\n",
       "Package wel_0\n",
       "###################\n",
       "\n",
       "package_name = wel_0\n",
       "filename = ex-gwf-bcf2ss.wel\n",
       "package_type = wel\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-bcf2ss\n",
       "\n",
       "\n",
       "###################\n",
       "Package rcha_0\n",
       "###################\n",
       "\n",
       "package_name = rcha_0\n",
       "filename = ex-gwf-bcf2ss.rcha\n",
       "package_type = rcha\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-bcf2ss\n",
       "\n",
       "\n",
       "###################\n",
       "Package oc\n",
       "###################\n",
       "\n",
       "package_name = oc\n",
       "filename = ex-gwf-bcf2ss.oc\n",
       "package_type = oc\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-bcf2ss\n",
       "\n",
       "\n"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# simulation using standard formulation\n",
    "wksp_p1a = Path(\"C:\\\\Users\\\\buckl\\\\Documents\\\\Software\\\\mf6.4.1\\\\examples\\\\ex-gwf-bcf2ss-p01a\")\n",
    "sim_p1a = 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=wksp_p1a\n",
    ")\n",
    "sim_p1a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ex-gwf-bcf2ss']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim_p1a.model_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name = ex-gwf-bcf2ss\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-bcf2ss.dis\n",
       "package_type = dis\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-bcf2ss\n",
       "\n",
       "\n",
       "###################\n",
       "Package npf\n",
       "###################\n",
       "\n",
       "package_name = npf\n",
       "filename = ex-gwf-bcf2ss.npf\n",
       "package_type = npf\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-bcf2ss\n",
       "\n",
       "\n",
       "###################\n",
       "Package ic\n",
       "###################\n",
       "\n",
       "package_name = ic\n",
       "filename = ex-gwf-bcf2ss.ic\n",
       "package_type = ic\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-bcf2ss\n",
       "\n",
       "\n",
       "###################\n",
       "Package riv_0\n",
       "###################\n",
       "\n",
       "package_name = riv_0\n",
       "filename = ex-gwf-bcf2ss.riv\n",
       "package_type = riv\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-bcf2ss\n",
       "\n",
       "\n",
       "###################\n",
       "Package wel_0\n",
       "###################\n",
       "\n",
       "package_name = wel_0\n",
       "filename = ex-gwf-bcf2ss.wel\n",
       "package_type = wel\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-bcf2ss\n",
       "\n",
       "\n",
       "###################\n",
       "Package rcha_0\n",
       "###################\n",
       "\n",
       "package_name = rcha_0\n",
       "filename = ex-gwf-bcf2ss.rcha\n",
       "package_type = rcha\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-bcf2ss\n",
       "\n",
       "\n",
       "###################\n",
       "Package oc\n",
       "###################\n",
       "\n",
       "package_name = oc\n",
       "filename = ex-gwf-bcf2ss.oc\n",
       "package_type = oc\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-bcf2ss\n",
       "\n"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_p1a = sim_p1a.model_dict['ex-gwf-bcf2ss']\n",
    "model_p1a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1(b). Load Newton-Raphson formulation simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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 riv...\n",
      "    loading package wel...\n",
      "    loading package rch...\n",
      "    loading package oc...\n",
      "  loading solution package ex-gwf-bcf2ss...\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "sim_name = ex-gwf-bcf2ss\n",
       "sim_path = C:\\Users\\buckl\\Documents\\Software\\mf6.4.1\\examples\\ex-gwf-bcf2ss-p02a\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-bcf2ss\n",
       "\n",
       "\n",
       "###################\n",
       "Package ex-gwf-bcf2ss.tdis\n",
       "###################\n",
       "\n",
       "package_name = ex-gwf-bcf2ss.tdis\n",
       "filename = ex-gwf-bcf2ss.tdis\n",
       "package_type = tdis\n",
       "model_or_simulation_package = simulation\n",
       "simulation_name = ex-gwf-bcf2ss\n",
       "\n",
       "\n",
       "###################\n",
       "Package ex-gwf-bcf2ss\n",
       "###################\n",
       "\n",
       "package_name = ex-gwf-bcf2ss\n",
       "filename = ex-gwf-bcf2ss.ims\n",
       "package_type = ims\n",
       "model_or_simulation_package = simulation\n",
       "simulation_name = ex-gwf-bcf2ss\n",
       "\n",
       "\n",
       "@@@@@@@@@@@@@@@@@@@@\n",
       "Model ex-gwf-bcf2ss\n",
       "@@@@@@@@@@@@@@@@@@@@\n",
       "\n",
       "name = ex-gwf-bcf2ss\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-bcf2ss.dis\n",
       "package_type = dis\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-bcf2ss\n",
       "\n",
       "\n",
       "###################\n",
       "Package npf\n",
       "###################\n",
       "\n",
       "package_name = npf\n",
       "filename = ex-gwf-bcf2ss.npf\n",
       "package_type = npf\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-bcf2ss\n",
       "\n",
       "\n",
       "###################\n",
       "Package ic\n",
       "###################\n",
       "\n",
       "package_name = ic\n",
       "filename = ex-gwf-bcf2ss.ic\n",
       "package_type = ic\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-bcf2ss\n",
       "\n",
       "\n",
       "###################\n",
       "Package riv_0\n",
       "###################\n",
       "\n",
       "package_name = riv_0\n",
       "filename = ex-gwf-bcf2ss.riv\n",
       "package_type = riv\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-bcf2ss\n",
       "\n",
       "\n",
       "###################\n",
       "Package wel_0\n",
       "###################\n",
       "\n",
       "package_name = wel_0\n",
       "filename = ex-gwf-bcf2ss.wel\n",
       "package_type = wel\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-bcf2ss\n",
       "\n",
       "\n",
       "###################\n",
       "Package rcha_0\n",
       "###################\n",
       "\n",
       "package_name = rcha_0\n",
       "filename = ex-gwf-bcf2ss.rcha\n",
       "package_type = rcha\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-bcf2ss\n",
       "\n",
       "\n",
       "###################\n",
       "Package oc\n",
       "###################\n",
       "\n",
       "package_name = oc\n",
       "filename = ex-gwf-bcf2ss.oc\n",
       "package_type = oc\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-bcf2ss\n",
       "\n",
       "\n"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# simulation using Newton-Raphson formulation\n",
    "wksp_p2a = Path(\"C:\\\\Users\\\\buckl\\\\Documents\\\\Software\\\\mf6.4.1\\\\examples\\\\ex-gwf-bcf2ss-p02a\")\n",
    "sim_p2a = 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=wksp_p2a\n",
    ")\n",
    "sim_p2a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ex-gwf-bcf2ss']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim_p2a.model_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name = ex-gwf-bcf2ss\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-bcf2ss.dis\n",
       "package_type = dis\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-bcf2ss\n",
       "\n",
       "\n",
       "###################\n",
       "Package npf\n",
       "###################\n",
       "\n",
       "package_name = npf\n",
       "filename = ex-gwf-bcf2ss.npf\n",
       "package_type = npf\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-bcf2ss\n",
       "\n",
       "\n",
       "###################\n",
       "Package ic\n",
       "###################\n",
       "\n",
       "package_name = ic\n",
       "filename = ex-gwf-bcf2ss.ic\n",
       "package_type = ic\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-bcf2ss\n",
       "\n",
       "\n",
       "###################\n",
       "Package riv_0\n",
       "###################\n",
       "\n",
       "package_name = riv_0\n",
       "filename = ex-gwf-bcf2ss.riv\n",
       "package_type = riv\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-bcf2ss\n",
       "\n",
       "\n",
       "###################\n",
       "Package wel_0\n",
       "###################\n",
       "\n",
       "package_name = wel_0\n",
       "filename = ex-gwf-bcf2ss.wel\n",
       "package_type = wel\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-bcf2ss\n",
       "\n",
       "\n",
       "###################\n",
       "Package rcha_0\n",
       "###################\n",
       "\n",
       "package_name = rcha_0\n",
       "filename = ex-gwf-bcf2ss.rcha\n",
       "package_type = rcha\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-bcf2ss\n",
       "\n",
       "\n",
       "###################\n",
       "Package oc\n",
       "###################\n",
       "\n",
       "package_name = oc\n",
       "filename = ex-gwf-bcf2ss.oc\n",
       "package_type = oc\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-bcf2ss\n",
       "\n"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_p2a = sim_p2a.model_dict['ex-gwf-bcf2ss']\n",
    "model_p2a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Explore example\n",
    "\n",
    "Both simulations should have the same boundary conditions, so only the first simulation is looked at here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.set_title(\"Map View of Lower Aquifer (1st stress period)\", fontweight=\"bold\")\n",
    "mapview = fp.plot.PlotMapView(model=model_p1a, ax=ax, layer=1)\n",
    "mapview.plot_ibound()\n",
    "mapview.plot_grid()\n",
    "\n",
    "# plot wells\n",
    "# wel_bc = mapview.plot_bc(\"WEL\")\n",
    "\n",
    "# plot stream\n",
    "riv_bc = mapview.plot_bc(\"RIV\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1: rec.array([((1, 2, 3), -35000.), ((1, 7, 3), -35000.)],\n",
       "           dtype=[('cellid', 'O'), ('q', '<f8')])}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_p1a.package_dict['wel_0'].stress_period_data.get_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wells aren't active until second stress period..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.set_title(\"Map View of Lower Aquifer (2nd stress period)\", fontweight=\"bold\")\n",
    "mapview = fp.plot.PlotMapView(model=model_p1a, ax=ax, layer=1)\n",
    "mapview.plot_ibound()\n",
    "mapview.plot_grid()\n",
    "\n",
    "# plot wells\n",
    "wel_bc = mapview.plot_bc(\"WEL\", kper=1)\n",
    "\n",
    "# plot stream\n",
    "riv_bc = mapview.plot_bc(\"RIV\", kper=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Check and run simulations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3(a). Standard formulation simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checking model \"ex-gwf-bcf2ss\"...\n",
      "\n",
      "ex-gwf-bcf2ss 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",
      "    riv_0 package: BC indices valid\n",
      "    riv_0 package: not a number (Nan) entries\n",
      "    riv_0 package: BC in inactive cells\n",
      "    wel_0 package: BC indices valid\n",
      "    wel_0 package: not a number (Nan) entries\n",
      "    wel_0 package: BC in inactive cells\n",
      "\n",
      "Checking for missing simulation packages...\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<flopy.utils.check.mf6check at 0x1ff923bb510>]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim_p1a.check()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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): 2024/01/13 11:48:51\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",
      " \n",
      " Run end date and time (yyyy/mm/dd hh:mm:ss): 2024/01/13 11:48:52\n",
      " Elapsed run time:  0.041 Seconds\n",
      " \n",
      " Normal termination of simulation.\n"
     ]
    }
   ],
   "source": [
    "success, buff = sim_p1a.run_simulation()\n",
    "assert success, \"MODFLOW did not terminate normally!\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3(b). Newton-Raphson formulation simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checking model \"ex-gwf-bcf2ss\"...\n",
      "\n",
      "ex-gwf-bcf2ss 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",
      "    riv_0 package: BC indices valid\n",
      "    riv_0 package: not a number (Nan) entries\n",
      "    riv_0 package: BC in inactive cells\n",
      "    wel_0 package: BC indices valid\n",
      "    wel_0 package: not a number (Nan) entries\n",
      "    wel_0 package: BC in inactive cells\n",
      "\n",
      "Checking for missing simulation packages...\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<flopy.utils.check.mf6check at 0x1ff9379ed10>]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim_p2a.check()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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): 2024/01/13 11:49:41\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",
      " \n",
      " Run end date and time (yyyy/mm/dd hh:mm:ss): 2024/01/13 11:49:41\n",
      " Elapsed run time:  0.043 Seconds\n",
      " \n",
      " Normal termination of simulation.\n"
     ]
    }
   ],
   "source": [
    "success, buff = sim_p2a.run_simulation()\n",
    "assert success, \"MODFLOW did not terminate normally!\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Plot results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4(a). Standard formulation simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "hds_p1a = fp.utils.binaryfile.HeadFile(Path(wksp_p1a, f\"{sim_name}.hds\"))\n",
    "times_p1a = hds_p1a.get_times()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x900 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(\n",
    "    nrows=len(times_p1a),\n",
    "    ncols=2,\n",
    "    sharex=True,\n",
    "    sharey=True,\n",
    "    figsize=(12, 9)\n",
    ")\n",
    "\n",
    "fig.suptitle(\"Results of Standard Formulation\", fontsize=20, fontweight=\"bold\")\n",
    "aquifer_nm = [\"Upper\", \"Lower\"]\n",
    "for i, t in enumerate(times_p1a):\n",
    "    for layer in [0, 1]:        \n",
    "        mapview = fp.plot.PlotMapView(model=model_p1a, ax=ax[i, layer], layer=layer)\n",
    "        ax[i, layer].set_title(f\"{aquifer_nm[layer]} aquifer, t={t:.1f} d\", fontweight=\"bold\")\n",
    "        mapview.plot_ibound()\n",
    "        mapview.plot_grid()\n",
    "\n",
    "        cb = mapview.plot_array(hds_p1a.get_data(totim=t), cmap=\"winter\", alpha=0.6, vmin=0, vmax=140)\n",
    "        plt.colorbar(cb)\n",
    "        hdcn = mapview.contour_array(hds_p1a.get_data(totim=t), colors=\"black\" if i == 0 else \"white\")\n",
    "        plt.clabel(hdcn, fmt=\"%1.1f\")\n",
    "\n",
    "        if layer == 1:\n",
    "            mapview.plot_bc(\"WEL\", kper=i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(\n",
    "    nrows=len(times_p1a),\n",
    "    ncols=1,\n",
    "    squeeze=True,\n",
    "    figsize=(6, 10)\n",
    ")\n",
    "for i, t in enumerate(times_p1a):\n",
    "    xsect = fp.plot.PlotCrossSection(model=model_p1a, ax=ax[i], line={\"row\": 7})\n",
    "    ax[i].set_title(f\"Row 8 Cross-Section w/ Heads, t={t:.1f} d\", fontweight=\"bold\")\n",
    "    xsect.plot_ibound()\n",
    "    xsect.plot_grid()\n",
    "    hds_t = hds_p1a.get_data(totim=t)    \n",
    "    cb = xsect.plot_array(hds_t, head=hds_t, cmap=\"winter\", alpha=0.8, vmin=0, vmax=130)\n",
    "    plt.colorbar(cb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4(b). Newton-Raphson formulation simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "hds_p2a = fp.utils.binaryfile.HeadFile(Path(wksp_p2a, f\"{sim_name}.hds\"))\n",
    "times_p2a = hds_p2a.get_times()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x900 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(\n",
    "    nrows=len(times_p2a),\n",
    "    ncols=2,\n",
    "    sharex=True,\n",
    "    sharey=True,\n",
    "    figsize=(12, 9)\n",
    ")\n",
    "\n",
    "fig.suptitle(\"Results of Newton-Raphson Formulation\", fontsize=20, fontweight=\"bold\")\n",
    "aquifer_nm = [\"Upper\", \"Lower\"]\n",
    "for i, t in enumerate(times_p2a):\n",
    "    for layer in [0, 1]:        \n",
    "        mapview = fp.plot.PlotMapView(model=model_p2a, ax=ax[i, layer], layer=layer)\n",
    "        ax[i, layer].set_title(f\"{aquifer_nm[layer]} aquifer, t={t:.1f} d\", fontweight=\"bold\")\n",
    "        mapview.plot_ibound()\n",
    "        mapview.plot_grid()\n",
    "\n",
    "        cb = mapview.plot_array(hds_p2a.get_data(totim=t), cmap=\"winter\", alpha=0.6, vmin=0, vmax=140)\n",
    "        plt.colorbar(cb)\n",
    "        hdcn = mapview.contour_array(hds_p2a.get_data(totim=t), colors=\"black\" if i == 0 else \"white\")\n",
    "        plt.clabel(hdcn, fmt=\"%1.1f\")\n",
    "\n",
    "        if layer == 1:\n",
    "            mapview.plot_bc(\"WEL\", kper=i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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L+BTvJyEE2rVrh/feew9HjhxBVlYWtm7dKrW7+EfN/v37Ie4cbpKW4l79qVOnSvXI7OzsEBkZKT0vLCyUtqnke3fhwoVS5QohkJCQUCrvN998U+a+KlbyR1xVgkt196mpWFtb48EHHwQAHD16FIcPH4ZGo0FAQID0mV2yZImUv+Rn9ubNmzh48CAefPBB2WiCqVXnPbp69aqU99VXX8WwYcPQp08fZGdnlyq35GmCycnJ0uPCwkLZ82JV/RzXRHU/L+bUp08f6XHJH9zFPwzvzlOW8vZ1dnY2UlNTTdFMqgHVBPtiEydOhL29PQDgxIkT+O677wDcORe9+EssOTkZkZGR+PbbbzFx4kTpA9ukSRM8/PDDUlklvwxPnTqFxo0bo3379tIXZ8kP9t29pLK0a9dOerx8+XKsW7cOX331FV555RUpveQQblmsra2xcOFC6fnmzZsRGhqKLVu2YN++fVi3bh0effRRPPPMM5W2B7jzxTRy5EjpeXh4OLZv3441a9Zg9uzZUvrTTz8NAPj888/Rr18/LFq0CNu3b8fevXtloxHFIxcLFixAaGgo3n//fezcuRMJCQnYvXt3qXwVOXLkCO6//34MHz4cq1atwp49e/D1119j1KhRUp7u3bvL2l7s0Ucfxfr165GQkIDPPvsMr7/+Ojp06IAtW7YAAJ544glotVoAwPfff4+wsDDs2LED27dvx0svvYQNGzZIZZXs5Xz00Uc4dOhQmQGkWHX3qSkVf2Zv3bqFCxcuwN/fH87OzpV+Zr/77jvk5+eb/JDG3arzHvn6+kp5ly5diu+++w5vvPEGVq9eXarchx9+GLa2tgDu/HifPHkyvv32Wzz//POlfjACVf8cA9W/BGzJz8tnn32GAwcO4PDhw1LaunXryvyRc/dy96V9k5OTsXXrVmzdulXWvm+//RZbt26VXTV0//79UpuLfzwDwL///W9p9Gf16tVYvHgxNm/ejP/85z9SngkTJlS4fSUPBX755Zd444038M033+Dpp5++56/dYdHMMTGgLpR36p0Q8tNCgoODpfTY2Ngqn3onxJ1T5UrmGTBggLTOz89PStdqtSIvL6/SNuv1enH//feXO5HJwcFBnDp1qkrbX50r6FV27utff/1V6WlixeeFlzz7oKyl+AyIkjO+716srKzE4cOHK93Gktc1KGuxtraWncFgMBgqPK0LuHNKXcnyyzs9sGS+kpPqihdfX18hRNkT9Kq7T4UQpcqt6nt3t71798rqef7556V9U/Lsg+bNm8teN2bMGAFAnDhxotI67t7usr5myjv1rjrv0aVLl2QTvoqX3r17S49nz54tlV3yugwlP2slJ6UVT5ir6ue4Ju/BV199VWaZtVXZLP6Sn52Sp43e/f1oiivolTzbpHixs7MT9913X7X2FZmOKoP92bNnZV/iJWfJJycni8cff1y4ubkJa2tr0bRpUzFixAhx9OjRMutp1qyZVE7Jc52feeYZKb1v375Vbveff/4ppk6dKl1oxdraWjRr1kz8+9//FqdPn67WPjh9+rR48cUXRevWrYW9vb1o3Lix8Pf3F+PHjxdJSUlSvqp8WWVlZYno6GjRunVrodVqRaNGjUSPHj3EihUrZEHp3Llz4uWXXxZdu3YVTZo0EQ0aNBCOjo6ib9++YvPmzVK+lJQU8cILL4j27dsLZ2dn0aBBA+Hi4iIGDRok4uPjq7yvPv74Y/HEE0+INm3aCCcnJ2FtbS08PDzEiBEjZKdoFcvPzxdLly4VPXv2FA4ODsLW1lb4+fmJRx55RKxevbrUjPbvv/9ejBgxQri7uwtra2vh5uYmXVSnmNFoFNOnTxc+Pj7S56qyYF+dfSqE6YL9rVu3hI2NjfSajz76SFrXq1cvKf3f//63lF5UVCTc3d2rfCrn3dtdnWAvRPXeo8OHD4uePXsKOzs70bJlS/HBBx/Iyi4Z7IUQ4r333hPNmzcXWq1WdO7cWXz11Vdlzo6v6udYCCH69u0rvf7atWtV2j8LFy4ULVu2lP0gry1TBXshhPjuu+/EwIEDhYODg7CzsxNdunQRK1asEIWFhVVqS35+vpg5c6bw9PQUtra2onfv3uKHH37gRXXqkEaIasxeIiLVOXr0KHr27IkJEyZgxYoVdd2ce0pRURFcXV2RlZWFZ555RnZ4h+heospT74io6oQQmD17Nv71r3/VdVPuOSdOnEBWVhYcHBxkc2WI7jUM9kRUoZ49e8rO/6d/FF8DYtasWfD09Kzj1hCVj8P4REREFq7Gp94dPHgQQ4cOhZeXFzQaTamLvYwePVo6taN4GTx4sCxPZmYmwsPDodPp4OTkhLFjx1Z6HjkRERFVT42DfW5uLjp16oTly5eXm2fw4MG4fv26tHz++eey9eHh4fjll18QHx+PXbt24eDBgxg/fnxNm0RERERlqPEx+9DQ0Eovm6nVauHh4VHmutOnTyMuLg5Hjx6VLnyybNkyDBkyBAsXLoSXl1dNm0ZEREQlKDpBb//+/XBzc4OzszMGDBiAN998U7pKXWJiIpycnGRXOAsODoaVlRWSkpLKnflrMBhkV4cqKipCZmYmXF1ded1lIqJ6TAiBmzdvwsvLq9J7idREXl5eqZv61ISNjY10Rcb6QrFgP3jwYIwYMQJ+fn44f/48Xn31VYSGhiIxMRENGjRAeno63Nzc5I2xtoaLi4vspgt3i4mJwdy5c5VqNhER1bErV66gWbNmJi0zLy8PDn7uKEgv/y6JVeXh4YG0tLR6FfAVC/Ylr/3doUMHdOzYES1btsT+/fsxcODAGpcbHR2NqKgo6Xl2djZ8fHzw3Xffya6VbUrnz5/HwYMH0a9fP7Rs2bLelc867q06LGEbWMe9U74l1XHp0iUMGjQIDg4OJi/baDSiIF2PDlfmo4Gu5kG6UJ+Hn71nwGg0MtiXpUWLFmjSpAnOnTuHgQMHwsPDAzdu3JDlKSgoQGZmZrnH+YE78wCKb1BSkq+vr8nvElbMYDDA1tYWzZo1U6QOpctnHfdWHZawDazj3infkuoopuQh2QYOtmjgYFfzAurpyepmu+vd77//jr/++ku68ERgYCCysrKQkpIi5dm7dy+KiooQEBBgrmYREZGaCE3tl3qoxj37nJwcnDt3TnqelpaG48ePw8XFBS4uLpg7dy7CwsLg4eGB8+fPY9q0abj//vsREhICAGjTpg0GDx6McePGYeXKlcjPz0dkZCRGjhzJmfhERKSM4lvx1Ob19VCNe/bJycno0qULunTpAgCIiopCly5dMGvWLDRo0AAnT57EY489hgceeABjx45Ft27dcOjQIdkQ/IYNG+Dv74+BAwdiyJAh6NOnDz788MPabxURERFJatyzDwoKQkVX2t29e3elZbi4uGDjxo01bQIREVH11HYovp4O45vtmD0RERHVDd71joiI1IM9eyIiIrJE7NkTEZF6qHQ2PoM9ERGph0qDPYfxiYiILBx79kREpB7s2RMREZElYs+eiIjUg6feERERkSViz56IiNRDpT17BnsiIlIPlU7QY7AnIiL1UGnPnsfsiYiILByDPRERkYXjMD4REakHh/GJiIjIlA4ePIihQ4fCy8sLGo0GsbGx0rr8/HxMnz4dHTp0QKNGjeDl5YXnnnsO165dk5WRmZmJ8PBw6HQ6ODk5YezYscjJyalWOxjsiYhIPYQJlmrIzc1Fp06dsHz58lLrbt26hWPHjmHmzJk4duwYtm3bhtTUVDz22GOyfOHh4fjll18QHx+PXbt24eDBgxg/fny12sFhfCIiUg8zn3oXGhqK0NDQMtc5OjoiPj5elvb++++jZ8+euHz5Mnx8fHD69GnExcXh6NGj6N69OwBg2bJlGDJkCBYuXAgvL68qtYM9eyIiUg8T9ez1er1sMRgMJmlednY2NBoNnJycAACJiYlwcnKSAj0ABAcHw8rKCklJSVUul8GeiIiomry9veHo6CgtMTExtS4zLy8P06dPx9NPPw2dTgcASE9Ph5ubmyyftbU1XFxckJ6eXuWyOYxPREQqovnfUpvXA1euXJECMgBotdpatSo/Px9PPvkkhBBYsWJFrcoqi8UE+xEuO2ELXeUZa+B2y9t4UpGSiYjIrEx0zF6n08mCfW0UB/pLly5h7969snI9PDxw48YNWf6CggJkZmbCw8OjynVYTLBX2qpxwBanvbBDosnL5o8JIiIzucfOsy8O9GfPnsW+ffvg6uoqWx8YGIisrCykpKSgW7duAIC9e/eiqKgIAQEBVa6HwZ6IiEghOTk5OHfunPQ8LS0Nx48fh4uLCzw9PfH444/j2LFj2LVrFwoLC6Xj8C4uLrCxsUGbNm0wePBgjBs3DitXrkR+fj4iIyMxcuTIKs/EBxjsiYhIbcx457rk5GT0799feh4VFQUAGDVqFObMmYOdO3cCADp37ix73b59+xAUFAQA2LBhAyIjIzFw4EBYWVkhLCwMS5curVY7GOzvEUoeJgB4qICICIDZh/GDgoIgRPm/LipaV8zFxQUbN26sVr1346l3REREFo7BnoiIyMJxGJ+IiNRDoJbD+CZriVkx2KvIqv8DtjjvhZ3gvAAiUikzXxv/XsFhfCIiIgvHnj0REakHe/bVc/DgQQwdOhReXl7QaDSIjY2V1uXn52P69Ono0KEDGjVqBC8vLzz33HO4du2arIzmzZtDo9HIlvnz59d4Y4iIiCpk5vvZ3ytqHOxzc3PRqVMnLF++vNS6W7du4dixY5g5cyaOHTuGbdu2ITU1FY899lipvPPmzcP169elZeLEiTVtEhERUSU0JljqnxoP44eGhiI0NLTMdY6OjoiPj5elvf/+++jZsycuX74MHx8fKd3BwaFaF/One9uqUVbY4rwfdsYfFSn/ts8tTgIkIqoms03Qy87OhkajgZOTkyx9/vz5cHV1RZcuXfDuu++ioKCgwnIMBgP0er1sISIiqhKVDuObZYJeXl4epk+fjqefflp2675Jkyaha9eucHFxwQ8//IDo6Ghcv34dixYtKresmJgYzJ071xzNJiIiS3OP3fXOXBQP9sW37xNCYMWKFbJ1xTcEAICOHTvCxsYGL7zwAmJiYqDVasssLzo6WvY6vV4Pb29vZRpPRESWRaWz8RUN9sWB/tKlS9i7d6+sV1+WgIAAFBQU4OLFi2jdunWZebRabbk/BIiIiKg0xYJ9caA/e/Ys9u3bB1dX10pfc/z4cVhZWcHNzU2pZhERkZpxGL96cnJycO7cOel5Wloajh8/DhcXF3h6euLxxx/HsWPHsGvXLhQWFiI9PR3AnVv12djYIDExEUlJSejfvz8cHByQmJiIKVOm4Nlnn4Wzs3Ptt4yIiIgA1CLYJycno3///tLz4uPoo0aNwpw5c7Bz504AQOfOnWWv27dvH4KCgqDVarFp0ybMmTMHBoMBfn5+mDJliux4PFFZVj3dEFucDsMuJ0WR8m+78xr/RBaLPfvqCQoKghDlz1SoaB0AdO3aFUeOHKlp9URERFRFvDY+ERGpB2fjExERWTiVBnve4paIiMjCsWdPRETqwZ49ERERWSL27ImISD146h0RFVs1wg5bnI7A7s8TJi/7thPP4yci82KwJyIi9VDpMXsGeyIiUg8O4xMREVk4lQZ7zsYnIiKycAz2REREFo7D+EREpCK1HMYHh/GJiIjoHsSePRERqQdPvSMiIrJwDPZEZC6rBuuwRZsMuyu/KlL+be0tXqWPqCwqDfY8Zk9ERGTh2LMnIiIV0aB2M+rr52x8BnsiIlIPlQ7jM9gTEZF68HK5REREZInYsyciIvXgMD4REZGF4zA+ERERWSIGeyIiIgvHYE9EROpRPIxfm6UaDh48iKFDh8LLywsajQaxsbHy5giBWbNmwdPTE3Z2dggODsbZs2dleTIzMxEeHg6dTgcnJyeMHTsWOTk51WoHgz0REamHMMFSDbm5uejUqROWL19e5voFCxZg6dKlWLlyJZKSktCoUSOEhIQgLy9PyhMeHo5ffvkF8fHx2LVrFw4ePIjx48dXqx2coEdERKSQ0NBQhIaGlrlOCIElS5bg9ddfx7BhwwAAn3zyCdzd3REbG4uRI0fi9OnTiIuLw9GjR9G9e3cAwLJlyzBkyBAsXLgQXl5eVWoHgz2RhVrVzxVbCk7A7sxvipR/u+A2b7ZD9Y+JTr3T6/WyZK1WC61WW62i0tLSkJ6ejuDgYCnN0dERAQEBSExMxMiRI5GYmAgnJycp0ANAcHAwrKyskJSUhH/9619VqovD+EREpB4mGsb39vaGo6OjtMTExFS7Kenp6QAAd3d3Wbq7u7u0Lj09HW5ubrL11tbWcHFxkfJUBXv2RESkIqa5Ec6VK1eg0+mk1Or26s2txj37e2WGIRERkbnpdDrZUpNg7+HhAQDIyMiQpWdkZEjrPDw8cOPGDdn6goICZGZmSnmqosbB/l6ZYUhERFRlZp6NXxE/Pz94eHggISFBStPr9UhKSkJgYCAAIDAwEFlZWUhJSZHy7N27F0VFRQgICKhyXTUexr9XZhgSERFVmZkvl5uTk4Nz585Jz9PS0nD8+HG4uLjAx8cHkydPxptvvolWrVrBz88PM2fOhJeXF4YPHw4AaNOmDQYPHoxx48Zh5cqVyM/PR2RkJEaOHFmtOKnIBL3KZhgCqHSGIRERkcmZuWefnJyMLl26oEuXLgCAqKgodOnSBbNmzQIATJs2DRMnTsT48ePRo0cP5OTkIC4uDra2tlIZGzZsgL+/PwYOHIghQ4agT58++PDDD6vVDkUm6Ck5w9BgMMBgMEjP7z79gYiI6F4RFBQEIcr/haDRaDBv3jzMmzev3DwuLi7YuHFjrdpR7069i4mJkZ3u4O3tXddNIiKi+sLMl8u9VygS7JWcYRgdHY3s7GxpuXLliolbT0REZFkUCfZKzjDUarWlTnkgIiKi8tX4mP29MsOQiIioysw8G/9eUeNgn5ycjP79+0vPo6KiAACjRo3CunXrMG3aNOTm5mL8+PHIyspCnz59ypxhGBkZiYEDB8LKygphYWFYunRpLTaHiIioAia6Nn59U+Ngf6/MMCQiIqqWehqwa4PXxieiGlvV2Qtb/j4Du6OXFSn/9u1bvLMekQkw2BMRkXpwGJ+IiMjSmeaud/UNgz0REamHSnv29e4KekRERFQ97NkTEZF6qLRnz2BPRETqwYvqEBERWTiV9ux5zJ6IiMjCsWdPREQqUz+H4muDwZ6IiNRDpcfsOYxPRERk4dizJyIi9VDpBD0GeyIiUg8GeyIiIgun0mDPY/ZEREQWjj17IiJSEd71jojonrPKzw9bLl6EXUaGIuXfvn0bTypSMt2TVDqMz2BPRETqwfPsiYiIyBKxZ09EROrBYXwiIiJLxwl6RERElk2lPXsesyciIrJw7NkTEZF6qHQ2PoM9ERGpB4fxiYiIyBKxZ09EROrBnj0RERFZIvbsiYhIPVQ6QY89eyIiIgvHnj0REalLPT3uXhsM9kREpB4cxje95s2bQ6PRlFoiIiIAAEFBQaXWTZgwQckmERGRmgkTLPWQoj37o0ePorCwUHp+6tQpPPzww3jiiSektHHjxmHevHnSc3t7eyWbREREpDqKBvumTZvKns+fPx8tW7bEQw89JKXZ29vDw8NDyWYQERH9jzrveme22fhGoxGfffYZnn/+eWg0/+ysDRs2oEmTJmjfvj2io6Nx69YtczWJiIjUxozD+IWFhZg5cyb8/PxgZ2eHli1b4o033oAQ/xQihMCsWbPg6ekJOzs7BAcH4+zZsybYUDmzTdCLjY1FVlYWRo8eLaU988wz8PX1hZeXF06ePInp06cjNTUV27ZtK7ccg8EAg8EgPdfr9Uo2m4hUYJVjO2xJyYDdr6b/Prl9+zaeNHmpVB+88847WLFiBdavX4927dohOTkZY8aMgaOjIyZNmgQAWLBgAZYuXYr169fDz88PM2fOREhICH799VfY2tqarC1mC/arV69GaGgovLy8pLTx48dLjzt06ABPT08MHDgQ58+fR8uWLcssJyYmBnPnzlW8vUREZIHMeLncH374AcOGDcMjjzwC4M6k9c8//xw//vjjnaKEwJIlS/D6669j2LBhAIBPPvkE7u7uiI2NxciRI2vRUDmzDONfunQJe/bswf/93/9VmC8gIAAAcO7cuXLzREdHIzs7W1quXLli0rYSEZEFM+Mwfq9evZCQkIDffvsNAHDixAkcPnwYoaGhAIC0tDSkp6cjODhYeo2joyMCAgKQmJhYq828m1l69mvXroWbm5v066Y8x48fBwB4enqWm0er1UKr1ZqyeUREpBYmOs/+7kPIZcWmGTNmQK/Xw9/fHw0aNEBhYSHeeusthIeHAwDS09MBAO7u7rLXubu7S+tMRfGefVFREdauXYtRo0bB2vqf3xbnz5/HG2+8gZSUFFy8eBE7d+7Ec889h379+qFjx45KN4uIiKjGvL294ejoKC0xMTGl8mzZsgUbNmzAxo0bcezYMaxfvx4LFy7E+vXrzd5exXv2e/bsweXLl/H888/L0m1sbLBnzx4sWbIEubm58Pb2RlhYGF5//XWlm0RERFQrV65cgU6nk56XNeL8yiuvYMaMGdKx9w4dOuDSpUuIiYnBqFGjpNPOMzIyZCPaGRkZ6Ny5s0nbq3iwHzRokOw0g2Le3t44cOCA0tUTERH9w0TD+DqdThbsy3Lr1i1YWckH0Bs0aICioiIAgJ+fHzw8PJCQkCAFd71ej6SkJLz44os1b2MZeG18IiJSDzPOxh86dCjeeust+Pj4oF27dvjpp5+waNEiaaRbo9Fg8uTJePPNN9GqVSvp1DsvLy8MHz68Fo0sjcGeiIhIAcuWLcPMmTPx0ksv4caNG/Dy8sILL7yAWbNmSXmmTZuG3NxcjB8/HllZWejTpw/i4uJMeo49wGBPRERqYsa73jk4OGDJkiVYsmRJuXk0Gg3mzZsnu0eMEhjsiYhIPcw4jH8vMdu18YmIiKhuMNgTERFZOA7jExGRepjxmP29hMGeiIjUg8fsiYiIyBKxZ09EROqh0p49gz0REamHSo/ZcxifiIjIwjHYExERWTgO4xMRkXqodBifwZ6IiNSDE/SIiIgsnEp79jxmT0REZOHYsyciIvXgMD4RESlhlaEHtsTlwO7AcUXKv337Np5UpGRLpPnfUpvX1z8cxiciIrJw7NkTEZG61NOh+NpgsCciIvXgMXsiIiILp9Jgz2P2REREFo49eyIiUg9eVIeIiIgsEXv2RESkHgK17NmbrCVmxZ49ERGRhWPPnoiI1EOls/EZ7ImISD1UOkGPwZ6IiNRDpT17HrMnIiKycOzZExGRivCudyY3Z84caDQa2eLv7y+tz8vLQ0REBFxdXdG4cWOEhYUhIyNDySYREZGaCRMs9ZDiw/jt2rXD9evXpeXw4cPSuilTpuCrr77CF198gQMHDuDatWsYMWKE0k0iIiJSFcWH8a2treHh4VEqPTs7G6tXr8bGjRsxYMAAAMDatWvRpk0bHDlyBA8++KDSTSMiIrVR6QQ9xYP92bNn4eXlBVtbWwQGBiImJgY+Pj5ISUlBfn4+goODpbz+/v7w8fFBYmJiucHeYDDAYDBIz/V6/f/SjRC3biuyDUajUfa3vpXPOu6tOixhG1hHTco3VJKzNnXcKfvy5cuK1VFctpJ1/P7774qVLWGwN72AgACsW7cOrVu3xvXr1zF37lz07dsXp06dQnp6OmxsbODk5CR7jbu7O9LT08stMyYmBnPnzi2VfjPnJqw0+abeBJncW7eQe+tWvS0fRRrk3ryNXCjzowgAUGiFXL3SdVgrvx0F1spuR4E1crMV3oZ8G+Rm5yEXeQrWoUVulsJ15NkhN8+AXCgXLJFrj9xcI3Kh0I+KLB1ys4qUfb/dgeTkZCQnJytXB5StIy9Pwc9RMZ5nb3qhoaHS444dOyIgIAC+vr7YsmUL7OzsalRmdHQ0oqKipOd6vR7e3t5waOwAG6dGtW5zWYxGI3Jv3UIje3vY2NjUu/KlOm7eRiP7RsrWYbyNRo2UrsOg/L7KMyr8fhuV30+5+Xfeb61CdRiMyNUXKP+ZyixSfl/9IRSrw2g0IhdAo0b2sLHRmrz8O3Xc+SHUvXt3+Pj4KFLH5cuXkZycrGgdv//+O+bPn69I2Wpn1lPvnJyc8MADD+DcuXN4+OGHYTQakZWVJevdZ2RklHmMv5hWq4VWW/ofRqu1ga19zX5AVEXurVuwsbGBnUJ1KF0+AOTi9p06avhD696pw2CGOoyK1qF0+XfqyIeNVuk6CsywHXn1/jOVi0LY2GgV3QYA8PHxQYcOHRQrPzk5WdE6yvpuJ9Mw60V1cnJycP78eXh6eqJbt25o2LAhEhISpPWpqam4fPkyAgMDzdksIiJSi+Jh/Nos9ZCiPfv//Oc/GDp0KHx9fXHt2jXMnj0bDRo0wNNPPw1HR0eMHTsWUVFRcHFxgU6nw8SJExEYGMiZ+EREpAxO0DO933//HU8//TT++usvNG3aFH369MGRI0fQtGlTAMDixYthZWWFsLAwGAwGhISE4IMPPlCySURERKqjaLDftGlThettbW2xfPlyLF++XMlmEBER3cHZ+ERERBaOw/hERESWjjfCISIiIhO6evUqnn32Wbi6usLOzg4dOnSQXZRICIFZs2bB09MTdnZ2CA4OxtmzZ03eDgZ7IiJSDzPe9e7vv/9G79690bBhQ3z77bf49ddf8d///hfOzs5SngULFmDp0qVYuXIlkpKS0KhRI4SEhJj8aoIcxiciIvUw4zH7d955B97e3li7dq2U5ufn909RQmDJkiV4/fXXMWzYMADAJ598And3d8TGxmLkyJG1aKgce/ZEREQK2LlzJ7p3744nnngCbm5u6NKlCz766CNpfVpaGtLT02U3hHN0dERAQAASExNN2hYGeyIiUhcTDOHr9XrZUvJurMUuXLiAFStWoFWrVti9ezdefPFFTJo0CevXrwcA6aZv7u7ustdVdkO4mmCwJyIi9TDR5XK9vb3h6OgoLTExMaWqKioqQteuXfH222+jS5cuGD9+PMaNG4eVK1eae6t5zJ6IiKi6rly5Ap1OJz0v6yY+np6eaNu2rSytTZs2+PLLLwFAuulbRkYGPD09pTwZGRno3LmzSdvLnj0REVE16XQ62VJWsO/duzdSU1Nlab/99ht8fX0B3Jms5+HhIbshnF6vR1JSkslvCMeePRERqYcZL5c7ZcoU9OrVC2+//TaefPJJ/Pjjj/jwww/x4YcfAgA0Gg0mT56MN998E61atYKfnx9mzpwJLy8vDB8+vOZtLAODPRERqYcZT73r0aMHtm/fjujoaMybNw9+fn5YsmQJwsPDpTzTpk1Dbm4uxo8fj6ysLPTp0wdxcXGwtbWtRSNLY7AnIiL1MPONcB599FE8+uij5a7XaDSYN28e5s2bV/M2VQGP2RMREVk49uyJiEg9eNc7IiIiS8e73hEREZEFYs+eiIjUg8P4REREFo7BnoiIyMKpNNjzmD0REZGFY8+eiIjUw8wX1blXsGdPRERk4dizJyIi9WDPnoiIiCwRe/ZERKQeKp2Nz2BPRETqodJhfAZ7IiJSD5X27HnMnoiIyMKxZ09EZAFWHXkMW840hp1duiLl377dGE/6K1K0manzrncM9kREpB4cxiciIiJLxJ49ERGpB3v2phcTE4MePXrAwcEBbm5uGD58OFJTU2V5goKCoNFoZMuECROUbBYREamVMMFSDyka7A8cOICIiAgcOXIE8fHxyM/Px6BBg5CbmyvLN27cOFy/fl1aFixYoGSziIhIrYrPs6/NUg8pOowfFxcne75u3Tq4ubkhJSUF/fr1k9Lt7e3h4eGhZFOIiIhUy6wT9LKzswEALi4usvQNGzagSZMmaN++PaKjo3Hr1q1yyzAYDNDr9bKFiIiIyme2CXpFRUWYPHkyevfujfbt20vpzzzzDHx9feHl5YWTJ09i+vTpSE1NxbZt28osJyYmBnPnzjVXs4mIyKLUdiiew/gVioiIwKlTp3D48GFZ+vjx46XHHTp0gKenJwYOHIjz58+jZcuWpcqJjo5GVFSU9Fyv18Pb21u5hhMRkeVQ6Wx8swT7yMhI7Nq1CwcPHkSzZs0qzBsQEAAAOHfuXJnBXqvVQqvVKtJOIiIiS6RosBdCYOLEidi+fTv2798PPz+/Sl9z/PhxAICnp6eSTSMiIjXiXe9MLyIiAhs3bsSOHTvg4OCA9PQ712x2dHSEnZ0dzp8/j40bN2LIkCFwdXXFyZMnMWXKFPTr1w8dO3ZUsmlERESqoWiwX7FiBYA7F84pae3atRg9ejRsbGywZ88eLFmyBLm5ufD29kZYWBhef/11JZtFRERqJVDLnr3JWmJWig/jV8Tb2xsHDhxQsglERESqx2vjExGRenA2PhERkYVTabDnLW6JiIgsHHv2RESkHuzZExERkSViz56IiNRDpRfVYc+eiIjIwrFnT0RE6qHSnj2DPRERqYdKJ+gx2BMRkXqotGfPY/ZEREQWjsGeiIjIwjHYExGRehQP49dmqaH58+dDo9Fg8uTJUlpeXh4iIiLg6uqKxo0bIywsDBkZGSbYUDkGeyIiIoUdPXoUq1atQseOHWXpU6ZMwVdffYUvvvgCBw4cwLVr1zBixAiT189gT0RE6iFMsFRTTk4OwsPD8dFHH8HZ2VlKz87OxurVq7Fo0SIMGDAA3bp1w9q1a/HDDz/gyJEjtdjI0hjsiYhIPeog2EdEROCRRx5BcHCwLD0lJQX5+fmydH9/f/j4+CAxMbH6FVWAp94REZF6mOg8e71eL0vWarXQarWlsm/atAnHjh3D0aNHS61LT0+HjY0NnJycZOnu7u5IT0+vRSNLY8+eiIiomry9veHo6CgtMTExpfJcuXIFL7/8MjZs2ABbW9s6aOU/2LMnIiIV0fxvqc3r7wRynU4npZbVq09JScGNGzfQtWtXKa2wsBAHDx7E+++/j927d8NoNCIrK0vWu8/IyICHh0ct2lgagz0REamHiYbxdTqdLNiXZeDAgfj5559laWPGjIG/vz+mT58Ob29vNGzYEAkJCQgLCwMApKam4vLlywgMDKxFI0tjsCciIvUw4+VyHRwc0L59e1lao0aN4OrqKqWPHTsWUVFRcHFxgU6nw8SJExEYGIgHH3yw5m0sA4M9ERFRHVm8eDGsrKwQFhYGg8GAkJAQfPDBByavh8GeiIjUo47verd//37Zc1tbWyxfvhzLly+vXcGVYLAnIiIVqeUwfq0m99UdnnpHRERk4RjsiYiILByH8YmISD3MOBv/XsJgT0RE6lHHE/TqCofxiYiILBx79kREpB4q7dkz2BMRkXow2BMREVk609wIp765J47ZL1++HM2bN4etrS0CAgLw448/1nWTiIiILEadB/vNmzcjKioKs2fPxrFjx9CpUyeEhITgxo0bdd00IiKyNMIESz1U58P4ixYtwrhx4zBmzBgAwMqVK/H1119jzZo1mDFjRh23joiIiq366P+wZasz7OyUiXh5eS6KlCuj0vPs67RnbzQakZKSguDgYCnNysoKwcHBSExMrMOWERGRRWLP3vz+/PNPFBYWwt3dXZbu7u6OM2fOlPkag8EAg8EgPdfr9Yq2kYiIqL6r82P21RUTEwNHR0dp8fb2rusmERFRfVE8jF+bpR6q02DfpEkTNGjQABkZGbL0jIwMeHh4lPma6OhoZGdnS8uVK1fM0VQiIqJ6q06DvY2NDbp164aEhAQpraioCAkJCQgMDCzzNVqtFjqdTrYQERFR+ep8Nn5UVBRGjRqF7t27o2fPnliyZAlyc3Ol2flEREQmo9LZ+HUe7J966in88ccfmDVrFtLT09G5c2fExcWVmrRHRERUa7xcbt2JjIxEZGRkXTeDiIgsnUqDfb2bjU9ERETVc0/07ImIiMxCpT17BnsiIlIRdd71jsGeiIjUQ6U9ex6zJyIisnDs2RMRkXqotGfPYE9EROrBi+oQERFZOJUGex6zJyIisnAM9kRERBaOw/hERKQeHMYnIiIiS8SePRERqUs9PX2uNhjsiYhIPXiePRERkYVTabDnMXsiIiILx549ERGpCO96R0REZNlUOozPYE9EROohUMvz7E3WErPiMXsiIiILx549ERGpB4fxiYiILBwvl0tERESWiMGeiIhIATExMejRowccHBzg5uaG4cOHIzU1VZYnLy8PERERcHV1RePGjREWFoaMjAyTt4XBnoiI1KN4GL82SxUdOHAAEREROHLkCOLj45Gfn49BgwYhNzdXyjNlyhR89dVX+OKLL3DgwAFcu3YNI0aMMPlm85g9ERGphxkn6MXFxcmer1u3Dm5ubkhJSUG/fv2QnZ2N1atXY+PGjRgwYAAAYO3atWjTpg2OHDmCBx98sBYNlWPPnoiIqJr0er1sMRgMlb4mOzsbAODi4gIASElJQX5+PoKDg6U8/v7+8PHxQWJioknby2BPRETqIUywAPD29oajo6O0xMTEVFhtUVERJk+ejN69e6N9+/YAgPT0dNjY2MDJyUmW193dHenp6abYWgmH8YmISD1MNIx/5coV6HQ6KVmr1Vb4soiICJw6dQqHDx+uReU1x2BPREQqYpob4eh0Olmwr0hkZCR27dqFgwcPolmzZlK6h4cHjEYjsrKyZL37jIwMeHh41KKNpXEYn4iISAFCCERGRmL79u3Yu3cv/Pz8ZOu7deuGhg0bIiEhQUpLTU3F5cuXERgYaNK2sGdPRETqYcbZ+BEREdi4cSN27NgBBwcH6Ti8o6Mj7Ozs4OjoiLFjxyIqKgouLi7Q6XSYOHEiAgMDTToTH2CwJyIiNTHj5XJXrFgBAAgKCpKlr127FqNHjwYALF68GFZWVggLC4PBYEBISAg++OCDmrevHIoM41+8eBFjx46Fn58f7Ozs0LJlS8yePRtGo1GWR6PRlFqOHDmiRJOIiIhMNhu/SlUJUeZSHOgBwNbWFsuXL0dmZiZyc3Oxbds2kx+vBxTq2Z85cwZFRUVYtWoV7r//fpw6dQrjxo1Dbm4uFi5cKMu7Z88etGvXTnru6uqqRJOIiIhUS5FgP3jwYAwePFh63qJFC6SmpmLFihWlgr2rq6siv2KIiIhK4V3vlJWdnS1dNaikxx57DG5ubujTpw927txpruYQERGphlkm6J07dw7Lli2T9eobN26M//73v+jduzesrKzw5ZdfYvjw4YiNjcVjjz1WblkGg0F2WUK9Xq9o24mIiOq7avXsZ8yYUeakupLLmTNnZK+5evUqBg8ejCeeeALjxo2T0ps0aYKoqCgEBASgR48emD9/Pp599lm8++67FbYhJiZGdolCb2/v6mwCERGpmRnvencvqVbPfurUqbJZhGVp0aKF9PjatWvo378/evXqhQ8//LDS8gMCAhAfH19hnujoaERFRUnP9Xo9Az4REVWNGc+zv5dUK9g3bdoUTZs2rVLeq1evon///ujWrRvWrl0LK6vKBxGOHz8OT0/PCvNotdpKr0FMRERUJgZ707l69SqCgoLg6+uLhQsX4o8//pDWFc+8X79+PWxsbNClSxcAwLZt27BmzRp8/PHHSjSJiIhItRQJ9vHx8Th37hzOnTsnu+g/cOciA8XeeOMNXLp0CdbW1vD398fmzZvx+OOPK9EkIiIi9uxNafTo0ZUe2x81ahRGjRqlRPVERETlMM1d7+obXhufiIjUpZ72zmuDt7glIiKycOzZExGReqj0crkM9kREpB6coEdERGThVNqz5zF7IiIiC8dgT0REZOE4jE9EROrBYXwiIiKyROzZExGRenA2PhERkYVjsCciIrJwKg32PGZPRERk4dizJyIiFeFd74iIiCybSofxGeyJiEg9eJ49ERERWSL27ImISD04jE9ERGTp1DlBj8P4REREFo49eyIiUg8O4xMREVk4lc7GZ7AnIiL1UGnPnsfsiYiILBx79kREpB7s2RMREZElYs+eiIjUQ6UT9NizJyIisnDs2RMRkXqo9Jg9gz0REamHSofxGeyJiEhd6mnvvDZ4zJ6IiMjCsWdPREQqwrvemVTz5s2h0Whky/z582V5Tp48ib59+8LW1hbe3t5YsGCBUs0hIiL6Z4JebZZqWr58OZo3bw5bW1sEBATgxx9/rP12VJOiw/jz5s3D9evXpWXixInSOr1ej0GDBsHX1xcpKSl49913MWfOHHz44YdKNomIiMhsNm/ejKioKMyePRvHjh1Dp06dEBISghs3bpi1HYoGewcHB3h4eEhLo0aNpHUbNmyA0WjEmjVr0K5dO4wcORKTJk3CokWLlGwSERGpWfFs/Nos1bBo0SKMGzcOY8aMQdu2bbFy5UrY29tjzZo1Cm1g2RQ9Zj9//ny88cYb8PHxwTPPPIMpU6bA2vpOlYmJiejXrx9sbGyk/CEhIXjnnXfw999/w9nZucwyDQYDDAaD9Dw7O/tO+t+5im2H0WBEUU4ejCIXmlv59a58qY6beTCKHAXrMNypAwpvR04ejMiB5raC26FgHUaDAUW5eTBqcqDJM5q8fAAw5hnv1GFlrVwdBgOKbhlhtGoAjUGpOvJQdLsAxgYNoDEaKn9BTerIM6AorxDGnAbQ5Ju+DqPBgCIjYMzVQFOYZ/LyAcCYl4eiAmsY8wBN0W1l6jAaUFRkB6NBQKOxVaQOQ14mAEAI5abLFxboazUbv7BQD+DO6HRJWq0WWq1WlmY0GpGSkoLo6GgpzcrKCsHBwUhMTKx5I2pAsWA/adIkdO3aFS4uLvjhhx8QHR2N69evSz339PR0+Pn5yV7j7u4urSsv2MfExGDu3Lml0s89MMe0G1CGjHpePuu4t+qwhG1gHfdO+ZZUx82bN+Ho6GjSMm1sbODh4YGff/audVmNGzeGt7e8nNmzZ2POnDmytD///BOFhYVSbCvm7u6OM2fO1Lod1VGtYD9jxgy88847FeY5ffo0/P39ERUVJaV17NgRNjY2eOGFFxATE1Pq1091REdHy8rOysqCr68vLl++bPIPh7no9Xp4e3vjypUr0Ol0dd2cGuN23DssYRsAy9gOS9gGwDzbIYTAzZs34eXlZfKybW1tkZaWBqOx9qNQQghoNPLh/NrENXOoVrCfOnUqRo8eXWGeFi1alJkeEBCAgoICXLx4Ea1bt4aHhwcyMuS/EYufe3h4lFt+WUMlAODo6Fiv/5EAQKfT1fttALgd9xJL2AbAMrbDErYBUH47lOy02drawtZWmUMQZWnSpAkaNGhQZqyrKM4poVrBvmnTpmjatGmNKjp+/DisrKzg5uYGAAgMDMRrr72G/Px8NGzYEAAQHx+P1q1blzuET0REVF/Y2NigW7duSEhIwPDhwwEARUVFSEhIQGRkpFnboshs/MTERCxZsgQnTpzAhQsXsGHDBkyZMgXPPvusFMifeeYZ2NjYYOzYsfjll1+wefNmvPfee7IheiIiovosKioKH330EdavX4/Tp0/jxRdfRG5uLsaMGWPWdigyQU+r1WLTpk2YM2cODAYD/Pz8MGXKFFkgd3R0xHfffYeIiAh069YNTZo0waxZszB+/Phq1zV79ux7/nhJRSxhGwBux73EErYBsIztsIRtACxnO8ztqaeewh9//IFZs2YhPT0dnTt3RlxcXKlJe0rTCCXPcSAiIqI6xxvhEBERWTgGeyIiIgvHYE9ERGThGOyJiIgsXL0O9vfCbQOLHTx4EEOHDoWXlxc0Gg1iY2Nl64UQmDVrFjw9PWFnZ4fg4GCcPXtWliczMxPh4eHQ6XRwcnLC2LFjkZOTI8uj9G2BY2Ji0KNHDzg4OMDNzQ3Dhw9HamqqLE9eXh4iIiLg6uqKxo0bIywsrNRFIy5fvoxHHnkE9vb2cHNzwyuvvIKCggJZnv3796Nr167QarW4//77sW7dOpNsw4oVK9CxY0fp4h+BgYH49ttv6037yzJ//nxoNBpMnjy5Xm3HnDlzSt3q2t/fv15tQ7GrV6/i2WefhaurK+zs7NChQwckJydL6+vD/3hZtx7XaDSIiIgAUL/eD6omUU9t2rRJ2NjYiDVr1ohffvlFjBs3Tjg5OYmMjIw6ac8333wjXnvtNbFt2zYBQGzfvl22fv78+cLR0VHExsaKEydOiMcee0z4+fmJ27dvS3kGDx4sOnXqJI4cOSIOHTok7r//fvH0009L67Ozs4W7u7sIDw8Xp06dEp9//rmws7MTq1atMtl2hISEiLVr14pTp06J48ePiyFDhggfHx+Rk5Mj5ZkwYYLw9vYWCQkJIjk5WTz44IOiV69e0vqCggLRvn17ERwcLH766SfxzTffiCZNmojo6Ggpz4ULF4S9vb2IiooSv/76q1i2bJlo0KCBiIuLq/U27Ny5U3z99dfit99+E6mpqeLVV18VDRs2FKdOnaoX7b/bjz/+KJo3by46duwoXn75ZSm9PmzH7NmzRbt27cT169el5Y8//qhX2yCEEJmZmcLX11eMHj1aJCUliQsXLojdu3eLc+fOSXnqw//4jRs3ZO9FfHy8ACD27dsnhKg/7wdVX70N9j179hQRERHS88LCQuHl5SViYmLqsFV33B3si4qKhIeHh3j33XeltKysLKHVasXnn38uhBDi119/FQDE0aNHpTzffvut0Gg04urVq0IIIT744APh7OwsDAaDlGf69OmidevWim3LjRs3BABx4MABqd0NGzYUX3zxhZTn9OnTAoBITEwUQtz54WNlZSXS09OlPCtWrBA6nU5q+7Rp00S7du1kdT311FMiJCREke1wdnYWH3/8cb1r/82bN0WrVq1EfHy8eOihh6RgX1+2Y/bs2aJTp05lrqsv2yDEnf+zPn36lLu+vv6Pv/zyy6Jly5aiqKioXr0fVH31chi/+LaBwcHBUlpd3TawKtLS0pCeni5rr6OjIwICAqT2JiYmwsnJCd27d5fyBAcHw8rKCklJSVKesm4LnJqair///luRthffQtjFxQUAkJKSgvz8fNm2+Pv7w8fHR7YtHTp0kF00IiQkBHq9Hr/88ouUp2QZxXlM/f4VFhZi06ZNyM3NRWBgYL1rf0REBB555JFSddWn7Th79iy8vLzQokULhIeH4/Lly/VuG3bu3Inu3bvjiSeegJubG7p06YKPPvpIWl8f/8eNRiM+++wzPP/889BoNPXq/aDqq5fBvqLbBqanp9dRq8pX3KaK2pueni7dN6CYtbU1XFxcZHnKKqNkHaZUVFSEyZMno3fv3mjfvr1Uj42NDZycnCrclsraWV4evV6P27drfz/un3/+GY0bN4ZWq8WECROwfft2tG3btt60HwA2bdqEY8eOISYmptS6+rIdAQEBWLduHeLi4rBixQqkpaWhb9++uHnzZr3ZBgC4cOECVqxYgVatWmH37t148cUXMWnSJKxfv17Wlvr0Px4bG4usrCzp5mb16f2g6lPsfvZU/0VERODUqVM4fPhwXTel2lq3bo3jx48jOzsbW7duxahRo3DgwIG6blaVXblyBS+//DLi4+PNepcuUwsNDZUed+zYEQEBAfD19cWWLVtgZ2dXhy2rnqKiInTv3h1vv/02AKBLly44deoUVq5ciVGjRtVx62pm9erVCA0NVeR2snTvqZc9+3vptoFVUdymitrr4eGBGzduyNYXFBQgMzNTlqcmtwWuicjISOzatQv79u1Ds2bNZNtiNBqRlZVV4bZU1s7y8uh0OpMEARsbG9x///3o1q0bYmJi0KlTJ7z33nv1pv0pKSm4ceMGunbtCmtra1hbW+PAgQNYunQprK2t4e7uXi+2425OTk544IEHcO7cuXrzXgCAp6cn2rZtK0tr06aNdEiivv2PX7p0CXv27MH//d//SWn16f2g6quXwb7kbQOLFd82MDAwsA5bVjY/Pz94eHjI2qvX65GUlCS1NzAwEFlZWUhJSZHy7N27F0VFRQgICJDyHDx4EPn5+VIeU98WWAiByMhIbN++HXv37oWfn59sfbdu3dCwYUPZtqSmpuLy5cuybfn5559lX2zx8fHQ6XTSF2ZgYKCsjOI8Sr1/RUVFMBgM9ab9AwcOxM8//4zjx49LS/fu3REeHi49rg/bcbecnBycP38enp6e9ea9AIDevXuXOgX1t99+g6+vL4D69T8OAGvXroWbmxseeeQRKa0+vR9UA3U9Q7CmNm3aJLRarVi3bp349ddfxfjx44WTk5Nslqg53bx5U/z000/ip59+EgDEokWLxE8//SQuXbokhLhzWo6Tk5PYsWOHOHnypBg2bFiZp+V06dJFJCUlicOHD4tWrVrJTsvJysoS7u7u4t///rc4deqU2LRpk7C3tzfpqXcvvviicHR0FPv375edonPr1i0pz4QJE4SPj4/Yu3evSE5OFoGBgSIwMFBaX3x6zqBBg8Tx48dFXFycaNq0aZmn57zyyivi9OnTYvny5SY7PWfGjBniwIEDIi0tTZw8eVLMmDFDaDQa8d1339WL9pen5Gz8+rIdU6dOFfv37xdpaWni+++/F8HBwaJJkybixo0b9WYbhLhz+qO1tbV46623xNmzZ8WGDRuEvb29+Oyzz6Q89eV/vLCwUPj4+Ijp06eXWldf3g+qvnob7IUQYtmyZcLHx0fY2NiInj17iiNHjtRZW/bt2ycAlFpGjRolhLhzas7MmTOFu7u70Gq1YuDAgSI1NVVWxl9//SWefvpp0bhxY6HT6cSYMWPEzZs3ZXlOnDgh+vTpI7RarbjvvvvE/PnzTbodZW0DALF27Vopz+3bt8VLL70knJ2dhb29vfjXv/4lrl+/Livn4sWLIjQ0VNjZ2YkmTZqIqVOnivz8fFmeffv2ic6dOwsbGxvRokULWR218fzzzwtfX19hY2MjmjZtKgYOHCgF+vrQ/vLcHezrw3Y89dRTwtPTU9jY2Ij77rtPPPXUU7Jz0+vDNhT76quvRPv27YVWqxX+/v7iww8/lK2vL//ju3fvFgBKtU2I+vV+UPXwFrdEREQWrl4esyciIqKqY7AnIiKycAz2REREFo7BnoiIyMIx2BMREVk4BnsiIiILx2BPRERk4RjsiYiILByDPRERkYVjsCciIrJwDPZEREQWjsGeiIjIwjHYExERWTgGeyIiIgvHYE9ERGThGOyJiIgsHIM9ERGRhWOwJyIisnAM9kRERBaOwZ6IiMjCMdgTERFZOAZ7IiIiC8dgT0REZOEY7ImIiCwcgz0REZGFY7AnIiKycAz2RAq6ePEiNBoNNBoNgoKC6ro5VIbRo0dL79H+/fvrujkWjfu67lhssJ8zZ470oSperK2t4ebmhsGDB+Pbb7+t6yaWKTMzE9OmTUObNm1gZ2eHhg0bolmzZnjqqafw008/Vaus1NRUREREwN/fH40bN4ZOp0OHDh3w0ksv4ejRowptgXndvn0b8+bNQ7t27WBnZwd7e3v4+PggKCgIU6dOxfXr1xVvw7p16zBnzhzMmTMHWVlZitdX19asWQONRoOlS5eWm6fkjxyNRlNq/bp161T7Iyg2Nlb6vFy8eNEkZRqNRqxatQpPPvkk/P39odPpYGdnh7Zt22LWrFnIzc2tVnkpKSkYNmwYXF1dYWtri7Zt2yImJgZGo9Ek7aU6ICzU7NmzBYByF41GI2JjY+u6mTJ5eXmiXbt25bbZ1tZWHDt2rEplLVu2TFhbW5dbVqdOnZTdGDMoKioSAwYMqPB9PnTokOLteOihh6T60tLSZOvy8vLEoUOHxKFDh8TJkycVb4s5jBgxQgAQ58+fLzdPWlqa7H2429q1a6V1Dz30kIKtrdyoUaOktuzbt69e1nf9+vUK/w969OghDAZDlcravXu3sLGxKbOcQYMGiYKCghq309z7mv5hsT37kkJDQ3Ho0CFs374dnTp1AgAIIbBs2bI6bpnc7t278csvvwAAnJyc8Omnn2LXrl1Sm/Py8rBmzZpKy9m6dSsmTpyIgoICAMCgQYOwadMmJCQkYM2aNRgyZEiZva27Vbc3YG579uzB3r17AQAtWrTAunXrkJCQgE8++QTTpk1DixYt6riFgFarRZ8+fdCnTx906NChrptTa/n5+YiPj0ebNm3uif1L/9BoNAgNDcW6deuwe/duTJ06VVp39OhRbNiwodIybt++jTFjxkg9+Ndffx1ffvkl2rdvDwD47rvvsHLlSmU2gJRV1782lFKyZz9q1Cgp/csvv5TSH3jggVKvS0lJEY8//rhwd3cXDRs2FO7u7iIsLEwkJydLebKysoSVlZUAIHr37i2lf/TRR2X+anV3dxcAhLu7e4Vt/uSTT6TXP/7441L6+++/L6VPmDChwjLy8/OFj4+PrJyioqJS+X799VfpccmeaUpKihgzZoxwdXWV9ciys7PFq6++Kvz9/YWtra1o3Lix6Nmzp1i5cmWp8vft2ycGDhwonJ2dhbW1tWjSpIno0aOHmDRpksjKypLybd26VfTu3VvodDppX/fu3VtMmzatzDbfbf78+VK7ly5dWmp9YWGhuH37tizNaDSK//73v6Jr167C3t5e2Nvbi549e4pPP/20zDoSExPF448/Ljw9PaU2hoaGip9++kns27evwt5UWlqarId7dw+2Ovu0uAxfX1/x22+/iaFDh4pGjRoJZ2dn8cILL5TazrudPHlSKiM8PFxKf+2110qNSty8eVM0aNBAABA9e/aUlRMfHy8AiP/85z8V1lebnn113qOYmBjx0EMPifvuu0/Y2toKOzs70aZNG/Haa6+J3NzcUvmXLVsmWrRoIWxtbUWPHj1EQkJCub3Nqn6Oq+rufXL3Ulz3b7/9Jo0GVbSUHCnS6/Xihx9+KFXn8OHDpfJffPHFStu4efNmKX9ISIiUnpiYKKW3b9++SttbnX1NylNdsN+6dauUHhQUJHvNjh07RMOGDcv8R2zYsKHYsWOHlLdTp07S0LrRaBRCCPH8889L+d9++20hhBDnz58vM4CX5cKFC9LQu5OTk/j000/Frl27pLqsrKzEgQMHKizj4MGDUn1WVlbiwoULle6rksG+RYsWpb6kMzMzhb+/f7lfUiNHjpTKOnPmjLCzsys379mzZ4UQQuzfv1/6wVTWkp+fX2m7ly9fLuVv27atiI2NrfBL2Gg0ioEDB5Zb57Rp02T516xZIwW9u5e1a9fWKthXZ58K8U+w1+l00g+xkstrr71W4b4qKioSzs7OAoBo2bKllF7yMMjGjRuFEEIkJCRIaXcH9cmTJwsAYv/+/RXWV9NgX933qHXr1uXm7d+/vyzvu+++W+b/dZs2bUoFoKp+jqujqsG+ZECsaKnK4Y9XXnml3PeyLBMnTpTyz507V0rPz8+XfTdmZmZWWE519jWZhyqCfWhoqDh06JDYvn27FDgBiM8//1zKn5OTI5o0aSKte/HFF8U333wjXnrpJSmtSZMmIicnRwgh/6f48ccfhRBC9kF+9NFHhRBCfPrpp1Lae++9V2m7N2/eLNzc3Er9o7Rv317s2bOn0tcvW7ZMeo23t3eV9lXJYG9tbS1mz54tdu/eLRYvXiyEEGLChAnS+g4dOoht27aJjz/+WAoeAMSmTZtK1f/yyy+LhIQEsXXrVvHmm2+K7t27i3PnzgkhhJg6daqU7+233xYJCQli06ZN4vXXXxdt27at0nHBX375pVQw1mg0ol27duKVV14RFy9elOUv+QX04IMPiu3bt4utW7fKAsaRI0eEEEL8/vvvQqvVSunDhw+X8o8bN05s2LBBZGVliUOHDonOnTtL+b744gup55WXl1dusK/OPhVCyLbR399ffPnll+KNN96QfTYrM3ToUCn/jRs3REFBgWjcuLGUFhkZKYQQsnJL/sAVQohWrVoJJyenSn+MVRbYygta1XmPhBBi8eLF4tNPPxXffPON2L9/v9i5c6cYMmSIlPf7778XQtz5cVUyeE+cOFF8/fXX4tlnny0z4Fb1c1wdxfM3QkNDpbKXLl0qfV6Kf6iaKtgbjUbZD8qEhIRK21jyM7Jy5UrZuuIRSgAVzh2q7r4m81BFsL97cXNzE+vXr5fl37Ztm7S+W7dusnXdunWT1m3fvl0IIcSWLVtkQfzvv/8WGo1G+Pn5CTs7O+nLt+SPhapMrvv+++9FQEBAqTZbW1uLl156qdIv2TfffFN6TUBAQJX2Vclg/+qrr8rWFRYWygLQzz//LK0r+YU4bNgwIYQQK1eulNKWLFkirl+/XmadM2bMkAXIP//8s8x8J0+eLDV8mZGRIa1funRpuaMxjRo1kg1tlvyht2XLFqm8efPmlQp4ixcvltJ69epV5f139wS9soJ9dfepEPJg/9NPP0npJb/MKxtaXrBggSyIHz9+XACQJoUWf+6Lg6VGoxF//fWX9PrU1FQBQDz11FMV1nP3dlcnaFXnPRJCiFOnTomRI0eKZs2alfk5KP6BXXJ4ukePHtLrCwoKZIe9igNQVT/HNWGOoezCwkLx3HPPSfWEhYVV6XUlR3rWrFkjW+ft7S2tq2jia3X3NZmHNVTojz/+kCbCFfvtt9+kxwEBAbJ1PXv2REpKiixfv379pPWJiYl44IEHIIRA3759kZaWhkOHDuG3337DDz/8AADQ6XTSRLvynDlzBgMGDIDBYIC/vz927NiBpk2bYsKECdiyZQs++OAD3HfffXj11VfLLcPR0VF6fO3atQrrK8vQoUNlz//44w/8/fffAAB7e3tpog5wZ78UK94vw4YNw2uvvYa//voLkydPxuTJk+Hs7IyAgAA8//zzeOKJJwAA4eHhWLx4MQwGg5Tm5uaG3r1746WXXkJwcDAAYOLEiThw4ICsTWvXrsXo0aOl9Y8++ig2b96MuLg4JCUlIS8vD8CdCYZTp06V3oOS7/GTTz5Z5vafPn26VN5HHnmk0v1WHdXdpyXpdDp07txZeu7q6io9zsrKkr3/d7v7M1v8+QgLC4Ner8eJEyeQm5uLI0eOAADatm0LFxcX6TVff/01AODRRx+t0naWdOjQIdnzb7/9Fm+//XapfNV5jy5duoRevXpBr9eXW2/xqZAXLlyQ0nr06CE9btCgAbp164bLly/LXlfVz7ESzp49i4yMjErzOTo6ljnpMz8/H//+97+xefNmAEDfvn3xySefVKnuRo0aSY8NBoNsXcnT7krmu1t19zWZhypm448aNQr5+fmIi4uDvb09hBBYsGABvvrqqyq9vqyZ6+7u7njggQcA3PniTExMBAAEBgYiMDAQABAfH4+ff/4ZANCrVy9YWVW8uz/99FPpH2zMmDF44IEH4OzsLJtVGxsbW2EZJX9QXL16tdrn8bq7u5e77u79UNZ+8fDwQEpKCqZPn44+ffrA1dUVf//9N+Li4vDkk09i06ZNAID27dsjJSUFkyZNQkBAABwdHXHjxg1s374dISEhUoCuCj8/P8yYMQP79+9HZmYm3nrrLWndTz/9BCFElcsy9xkIVdmnJTk7O8ueW1v/83u9su3s1q2b9CVd1me2oKAAn376KTIzMwHcCRIlff3117CyskJoaGiF9ZSl+IyE4qVVq1bVLqNY8Xu0fv16KdAHBgYiNjYWhw4dwrRp06S8RUVFlZZXm8+xEt566y307du30mXixImlXpuXl4cRI0ZIgX7gwIH49ttvYW9vX6W6mzdvLj0u+YOjoKAAf/31V5n5qqMqZwGRMlQR7IE7X4ohISGyL4KZM2dKj4sDNwD8+OOPsteWfF4yX/GX4aVLl7Bt2zYA8mC/bNkyFBYWApD3qsrz559/So9zcnKkxzdv3iwzvSyBgYHw8fEBcOeLbsaMGWXmK+4d3e3uf8amTZvCyckJwJ0v2ZIjIklJSdLj4v0ihICvry/mz5+PQ4cO4c8//5RdwKd4Pwkh0K5dO7z33ns4cuQIsrKysHXrVqndxT9q9u/fD3HncJO0FPfqT506VaqXYGdnh8jISOl5YWGhtE0l37sLFy6UKlcIgYSEhFJ5v/nmmzL3VbGSP+KqElyqu09NxdraGg8++CCAO6diHT58GBqNBgEBAdJndsmSJVL+kp/Zmzdv4uDBg3jwwQdlowmmVp336OrVq1LeV199FcOGDUOfPn2QnZ1dqtySpwkmJydLjwsLC2XPi1X1c1wT1f28VFVOTg4eeeQR7Nq1C8Cd0Ymvv/66wl743fr06SM9LvmD++jRo9KpvO3bty/1o7Ok6u5rMg/VDeNPnDgRCxYswK1bt3DixAl89913GDRoEAYNGgRXV1f89ddfSE5ORmRkJB555BF888030ge0SZMmePjhh6Wy+vXrh9WrVwO4E3gaN26M9u3bw8PDA8CdK9gVu7uXVJZ27dpJj5cvX44WLVrA1dUVs2fPltJLDuGWxdraGgsXLpSGQDdv3ozs7GyMGTMGTZs2xaVLl7B161ZcvXq1Slfks7KywsiRI6Vza8PDwzF79mz8/fffsnY9/fTTAIDPP/8cK1euxPDhw+Hn5wdHR0fpXHjgn6HBBQsWYP/+/XjkkUfg4+ODRo0aYffu3aXyVeTIkSN46aWXMGTIEISGhqJly5YwGAz4+OOPpTzdu3eXHoeHh+PEiRMA7gxFT5s2Dc2aNcP169dx5swZ7NixA1OnTsXo0aPxxBNPYMaMGTAYDPj+++8RFhaG5557DkVFRYiPj0fv3r0RHh4OQN7b/uijjzBkyBDY2dnJ6q7NPjWlfv36ISEhAbdu3cKFCxfQpk0bODs7S8G+vM/sd999h/z8fJMf0rhbdd4jX19f6XVLly6FjY0NkpKSpP/Jkh5++GHY2toiLy8PP/74IyZPnoyQkBBs2rSpzGHlqn6OASAoKEg61JSWllZpr7fk5+Wzzz5DgwYN0KBBAynQrlu3DuvWrat8Z5Vw+/ZtPPzww9IhmI4dO2Ly5MmyHyju7u7SiMrFixfh5+cHAHjooYekS9cOHToUXl5euHbtGr777ju89tpr6NatG2bNmiWVM2HChArbUt19TWZi1hkCZlTeqXdCCBERESGtCw4OltJjY2OrfOqdEHdOlSuZZ8CAAdI6Pz8/KV2r1Yq8vLxK26zX68X9999f7kQmBwcHcerUqSptf3WuoFfRBDMhhPjrr78qPU2s+LzwkmcflLUUnwFRcsb33YuVlZU4fPhwpdtY8roGZS3W1tayMxgMBkOFp3UBd06pK1l+eacHlsxXclJd8eLr6yuEKHuCXnX3qRCiVLlVfe/utnfvXlk9zz//vLRvSp590Lx5c9nrxowZIwCIEydOVFrH3dtd1tdMeafeVec9unTpkrC3ty+1vnfv3tLj2bNnS2WXvC5Dyc9aydNNiyeNVfVzXJP34KuvviqzzNqoyoTIkt+DFV3/wRRX0KvOvibzUGWwP3v2rOxLvOQs+eTkZPH4448LNzc3YW1tLZo2bSpGjBghjh49WmY9zZo1k8opea7zM888I6X37du3yu3+888/xdSpU6ULrVhbW4tmzZqJf//73+L06dPV2genT58WL774omjdurWwt7cXjRs3Fv7+/mL8+PEiKSlJyleVL6usrCwRHR0tWrduLbRarWjUqJHo0aOHWLFihSwonTt3Trz88suia9euokmTJqJBgwbC0dFR9O3bV2zevFnKl5KSIl544QXRvn174ezsLBo0aCBcXFzEoEGDRHx8fJX31ccffyyeeOIJ0aZNG+Hk5CSsra2Fh4eHGDFihOwUrWL5+fli6dKlomfPnsLBwUHY2toKPz8/8cgjj4jVq1eXmtH+/fffixEjRgh3d3dhbW0t3NzcpIvqFDMajWL69OnCx8dH+lxVFuyrs0+FMF2wv3XrluyL/KOPPpLW9erVS0r/97//LaUXFRUJd3f3Kp/Kefd2VyfYC1G99+jw4cOiZ8+ews7OTrRs2VJ88MEHsrJLBnshhHjvvfdE8+bNhVarFZ07dxZfffVVmbPjq/o5FkKIvn37Sq+/du1alfbPwoULRcuWLWU/yGvDlMFeiDvfg0OHDhXOzs5Cq9WKNm3aiLfffrvKl9wVour7msxDI0Q1Zi8RkeocPXoUPXv2xIQJE7BixYq6bs49paioCK6ursjKysIzzzxTpUvSEtUF1R2zJ6LqEUJg9uzZ+Ne//lXXTbnnnDhxAllZWXBwcMDChQvrujlE5WKwJ6IK9ezZU3b+P/2jeGLerFmz4OnpWcetISofh/GJiIgsXI3Psz948KB0moZGoyl1sZfRo0dDo9HIlsGDB8vyZGZmIjw8HDqdDk5OThg7dmyl55ETERFR9dQ42Ofm5qJTp05Yvnx5uXkGDx6M69evS8vnn38uWx8eHo5ffvkF8fHx2LVrFw4ePIjx48fXtElERERUhhofsw8NDa30splarVa6wMzdTp8+jbi4OBw9elS6+MiyZcswZMgQLFy4EF5eXjVtGhEREZWg6AS9/fv3w83NDc7OzhgwYADefPNN6VKbiYmJcHJykl1lLDg4GFZWVkhKSip35q/BYJBdvaqoqAiZmZlwdXXldZeJiOoxIQRu3rwJLy+vSu8lUhN5eXmyG/rUlI2NDWxtbU3QIvNRLNgPHjwYI0aMgJ+fH86fP49XX30VoaGhSExMRIMGDZCeng43Nzd5Y6yt4eLigvT09HLLjYmJwdy5c5VqNhER1bErV66gWbNmJi0zLy8PDn7uKEgv/y6JVeXh4YG0tLR6FfAVC/YjR46UHnfo0AEdO3ZEy5YtsX//fgwcOLDG5UZHRyMqKkp6np2dDR8fH3z33Xeya2Wb0vnz53Hw4EH069cPLVu2rHfls457qw5L2AbWce+Ub0l1XLp0CYMGDYKDg4PJyzYajShI16PDlflooKt5kC7U5+Fn7xkwGo0M9mVp0aIFmjRpgnPnzmHgwIHw8PDAjRs3ZHkKCgqQmZlZ7nF+4M48AK1WWyrd19fX5HcJK2YwGGBra4tmzZopUofS5bOOe6sOS9gG1nHvlG9JdRRT8pBsAwdbNHCwq3kB9fRkdbPd4vb333/HX3/9JV14IjAwEFlZWUhJSZHy7N27F0VFRQgICDBXs4iISE2EpvZLPVTjnn1OTg7OnTsnPU9LS8Px48fh4uICFxcXzJ07F2FhYfDw8MD58+cxbdo03H///QgJCQEAtGnTBoMHD8a4ceOwcuVK5OfnIzIyEiNHjuRMfCIiUkbxrXhq8/p6qMY9++TkZHTp0gVdunQBAERFRaFLly6YNWsWGjRogJMnT+Kxxx7DAw88gLFjx6Jbt244dOiQbAh+w4YN8Pf3x8CBAzFkyBD06dMHH374Ye23ioiIiCQ17tkHBQWhoivt7t69u9IyXFxcsHHjxpo2gYiIqHpqOxRfT4fxzXbMnoiIiOoG73pHRETqwZ49ERERWSL27ImISD1UOhufwZ6IiNRDpcGew/hEREQWjj17IiJSD/bsiYiIyBKxZ09EROrBU++IiIjIErFnT0RE6qHSnj2DPRERqYdKJ+gx2BMRkXqotGfPY/ZEREQWjsGeiIjIwnEYn4iI1IPD+ERERGRKBw8exNChQ+Hl5QWNRoPY2FhpXX5+PqZPn44OHTqgUaNG8PLywnPPPYdr167JysjMzER4eDh0Oh2cnJwwduxY5OTkVKsdDPZERKQewgRLNeTm5qJTp05Yvnx5qXW3bt3CsWPHMHPmTBw7dgzbtm1DamoqHnvsMVm+8PBw/PLLL4iPj8euXbtw8OBBjB8/vlrt4DA+ERGph5lPvQsNDUVoaGiZ6xwdHREfHy9Le//999GzZ09cvnwZPj4+OH36NOLi4nD06FF0794dALBs2TIMGTIECxcuhJeXV5XawZ49ERGph4l69nq9XrYYDAaTNC87OxsajQZOTk4AgMTERDg5OUmBHgCCg4NhZWWFpKSkKpfLYE9ERFRN3t7ecHR0lJaYmJhal5mXl4fp06fj6aefhk6nAwCkp6fDzc1Nls/a2houLi5IT0+vctkcxiciIhXR/G+pzeuBK1euSAEZALRaba1alZ+fjyeffBJCCKxYsaJWZZWFwZ6IiNTDRMfsdTqdLNjXRnGgv3TpEvbu3Ssr18PDAzdu3JDlLygoQGZmJjw8PKpcB4fxiYhIPYrPs6/NYkLFgf7s2bPYs2cPXF1dZesDAwORlZWFlJQUKW3v3r0oKipCQEBAlethz56IiEghOTk5OHfunPQ8LS0Nx48fh4uLCzw9PfH444/j2LFj2LVrFwoLC6Xj8C4uLrCxsUGbNm0wePBgjBs3DitXrkR+fj4iIyMxcuTIKs/EBxjsiYhIbcx457rk5GT0799feh4VFQUAGDVqFObMmYOdO3cCADp37ix73b59+xAUFAQA2LBhAyIjIzFw4EBYWVkhLCwMS5curVY7GOyJiEg9zHy53KCgIAhR/q+LitYVc3FxwcaNG6tV7914zJ6IiMjCMdgTERFZOA7jExGRegjUchjfZC0xKwZ7IiJSDzNfG/9ewWF8IiIiC8eePRERqQd79tVz8OBBDB06FF5eXtBoNIiNjZXW5efnY/r06ejQoQMaNWoELy8vPPfcc7h27ZqsjObNm0Oj0ciW+fPn13hjiIiIKmTm+9nfK2oc7HNzc9GpUycsX7681Lpbt27h2LFjmDlzJo4dO4Zt27YhNTUVjz32WKm88+bNw/Xr16Vl4sSJNW0SERFRJTQmWOqfGg/jh4aGIjQ0tMx1jo6OiI+Pl6W9//776NmzJy5fvgwfHx8p3cHBoVoX8yciIqLqMdsEvezsbGg0Gjg5OcnS58+fD1dXV3Tp0gXvvvsuCgoKKizHYDBAr9fLFiIioipR6TC+WSbo5eXlYfr06Xj66adlt+6bNGkSunbtChcXF/zwww+Ijo7G9evXsWjRonLLiomJwdy5c83RbCIisjRmvlzuvULxYF98+z4hBFasWCFbV3xDAADo2LEjbGxs8MILLyAmJgZarbbM8qKjo2Wv0+v18Pb2VqbxRERkWVQ6G1/RYF8c6C9duoS9e/fKevVlCQgIQEFBAS5evIjWrVuXmUer1Zb7Q4CIiIhKUyzYFwf6s2fPYt++fXB1da30NcePH4eVlRXc3NyUahYREakZh/GrJycnB+fOnZOep6Wl4fjx43BxcYGnpycef/xxHDt2DLt27UJhYSHS09MB3LlVn42NDRITE5GUlIT+/fvDwcEBiYmJmDJlCp599lk4OzvXfsuIiIgIQC2CfXJyMvr37y89Lz6OPmrUKMyZMwc7d+4EAHTu3Fn2un379iEoKAharRabNm3CnDlzYDAY4OfnhylTpsiOxxMREZkUe/bVExQUBCHKn6lQ0ToA6Nq1K44cOVLT6omIiKiKeG18IiJSD87GJyIisnAqDfa8xS0REZGFY8+eiIjUgz17IiIiskTs2RMRkXqo9NQ79uyJiIgsHHv2RESkHio9Zs9gT0RE6qHSYXwGeyIiUg+VBnsesyciIrJwDPZEREQWjsP4RESkIrUcxgeH8YmIiOgexJ49ERGpB0+9IyIisnAM9kRERBZOpcGex+yJiIgsHHv2RESkIhrUbkZ9/ZyNz2BPRETqodJhfAZ7IiJSD14ul4iIiCwRe/ZERKQeHMYnIiKycBzGJyIiIkvEYE9ERGThGOyJiEg9iofxa7NUw8GDBzF06FB4eXlBo9EgNjZW3hwhMGvWLHh6esLOzg7BwcE4e/asLE9mZibCw8Oh0+ng5OSEsWPHIicnp1rtYLAnIiL1ECZYqiE3NxedOnXC8uXLy1y/YMECLF26FCtXrkRSUhIaNWqEkJAQ5OXlSXnCw8Pxyy+/ID4+Hrt27cLBgwcxfvz4arWDE/SIiIgUEhoaitDQ0DLXCSGwZMkSvP766xg2bBgA4JNPPoG7uztiY2MxcuRInD59GnFxcTh69Ci6d+8OAFi2bBmGDBmChQsXwsvLq0rtYM+eiIjUw0Q9e71eL1sMBkO1m5KWlob09HQEBwdLaY6OjggICEBiYiIAIDExEU5OTlKgB4Dg4GBYWVkhKSmpynUx2BMRkXqYKNh7e3vD0dFRWmJiYqrdlPT0dACAu7u7LN3d3V1al56eDjc3N9l6a2truLi4SHmqgsP4RESkIqa5Ec6VK1eg0+mkVK1WW7tmKazGPft7ZYYhERGRuel0OtlSk2Dv4eEBAMjIyJClZ2RkSOs8PDxw48YN2fqCggJkZmZKeaqixsH+XplhSEREVGVmno1fET8/P3h4eCAhIUFK0+v1SEpKQmBgIAAgMDAQWVlZSElJkfLs3bsXRUVFCAgIqHJdNR7Gv1dmGBIREVWZmS+Xm5OTg3PnzknP09LScPz4cbi4uMDHxweTJ0/Gm2++iVatWsHPzw8zZ86El5cXhg8fDgBo06YNBg8ejHHjxmHlypXIz89HZGQkRo4cWa04qcgEPXPOMCQiIqoyM/fsk5OT0aVLF3Tp0gUAEBUVhS5dumDWrFkAgGnTpmHixIkYP348evTogZycHMTFxcHW1lYqY8OGDfD398fAgQMxZMgQ9OnTBx9++GG12qHIBD0lZxgaDAbZKQ56vd5UzSYiIjKpoKAgCFH+LwSNRoN58+Zh3rx55eZxcXHBxo0ba9WOenfqXUxMjOx0B29v77puEhER1RdmvlzuvUKRYK/kDMPo6GhkZ2dLy5UrV0zceiIiIsuiSLBXcoahVqstdcoDERERla/Gx+zvlRmGREREVWbm2fj3ihoH++TkZPTv3196HhUVBQAYNWoU1q1bh2nTpiE3Nxfjx49HVlYW+vTpU+YMw8jISAwcOBBWVlYICwvD0qVLa7E5REREFajtufImPM/enGoc7O+VGYZERETVUk8Ddm3Uu9n4REREVD28EQ4REakHh/GJiIgsnWnuelffMNgTEZF6qLRnz2P2REREFo49eyIiUg+V9uwZ7ImISD14UR0iIiILp9KePY/ZExERWTj27ImISGXq51B8bTDYExGReqj0mD2H8YmIiCwce/ZERKQeKp2gx2BPRETqwWBPRERk4VQa7HnMnoiIyMKxZ09ERCrCu94RERFZNpUO4zPYExGRevA8eyIiIrJE7NkTEZF6cBifiIjI0nGCHhERkWVTac+ex+yJiIgsHHv2RESkHiqdjc9gT0RE6sFhfCIiIrJE7NkTEZF6sGdPRERElog9eyIiUg+VTtBjz56IiMjCsWdPRETqUk+Pu9cGgz0REakHh/FNr3nz5tBoNKWWiIgIAEBQUFCpdRMmTFCySUREpGbCBEs9pGjP/ujRoygsLJSenzp1Cg8//DCeeOIJKW3cuHGYN2+e9Nze3l7JJhEREamOosG+adOmsufz589Hy5Yt8dBDD0lp9vb28PDwULIZRERE/6POu96ZbTa+0WjEZ599hueffx4azT87a8OGDWjSpAnat2+P6Oho3Lp1y1xNIiIitTHjMH5hYSFmzpwJPz8/2NnZoWXLlnjjjTcgxD+FCCEwa9YseHp6ws7ODsHBwTh79qwJNlTObBP0YmNjkZWVhdGjR0tpzzzzDHx9feHl5YWTJ09i+vTpSE1NxbZt28otx2AwwGAwSM/1er2SzSYiIqqRd955BytWrMD69evRrl07JCcnY8yYMXB0dMSkSZMAAAsWLMDSpUuxfv16+Pn5YebMmQgJCcGvv/4KW1tbk7XFbMF+9erVCA0NhZeXl5Q2fvx46XGHDh3g6emJgQMH4vz582jZsmWZ5cTExGDu3LmKt5eIiCyQGS+X+8MPP2DYsGF45JFHANyZtP7555/jxx9/vFOUEFiyZAlef/11DBs2DADwySefwN3dHbGxsRg5cmQtGipnlmH8S5cuYc+ePfi///u/CvMFBAQAAM6dO1dunujoaGRnZ0vLlStXTNpWIiKyYGYcxu/VqxcSEhLw22+/AQBOnDiBw4cPIzQ0FACQlpaG9PR0BAcHS69xdHREQEAAEhMTa7WZdzNLz37t2rVwc3OTft2U5/jx4wAAT0/PcvNotVpotVpTNo+IiNTCROfZ330IuazYNGPGDOj1evj7+6NBgwYoLCzEW2+9hfDwcABAeno6AMDd3V32Ond3d2mdqSjesy8qKsLatWsxatQoWFv/89vi/PnzeOONN5CSkoKLFy9i586deO6559CvXz907NhR6WYRERHVmLe3NxwdHaUlJiamVJ4tW7Zgw4YN2LhxI44dO4b169dj4cKFWL9+vdnbq3jPfs+ePbh8+TKef/55WbqNjQ327NmDJUuWIDc3F97e3ggLC8Prr7+udJOIiIhq5cqVK9DpdNLzskacX3nlFcyYMUM69t6hQwdcunQJMTExGDVqlHTaeUZGhmxEOyMjA507dzZpexUP9oMGDZKdZlDM29sbBw4cULp6IiKif5hoGF+n08mCfVlu3boFKyv5AHqDBg1QVFQEAPDz84OHhwcSEhKk4K7X65GUlIQXX3yx5m0sA6+NT0RE6mHG2fhDhw7FW2+9BR8fH7Rr1w4//fQTFi1aJI10azQaTJ48GW+++SZatWolnXrn5eWF4cOH16KRpTHYExERKWDZsmWYOXMmXnrpJdy4cQNeXl544YUXMGvWLCnPtGnTkJubi/HjxyMrKwt9+vRBXFycSc+xBxjsiYhITcx41zsHBwcsWbIES5YsKTePRqPBvHnzZPeIUQKDPRERqYcZh/HvJWa7Nj4RERHVDQZ7IiIiC8dhfCIiUg8zHrO/lzDYExGRevCYPREREVki9uyJiEg9VNqzZ7AnIiL1UOkxew7jExERWTgGeyIiIgvHYXwiIlIPlQ7jM9gTEZF6cIIeERGRhVNpz57H7ImIiCwce/ZERKQeHMYnIiKydJr/LbV5ff3DYXwiIiILx549ERGpSz0diq8NBnsiIlIPHrOv30Zsvg5bhzxFyr59+zaeVKRkuletyg3All23YJdw0uRl3759G3/fCsCWr3Nht/eEycsvroOfWaIyqDTY85g9ERGRhbOYnj0Rya3KeRBbdt6GXfzPipTP0QOql3hRHSIiIrJE7NlX0Sp9ILbsyIPdd6dMXvbt23nsIVWDku8FcOf9ICILJVDLnr3JWmJW7NkTERFZOPbsiYhIPVQ6G5/BnoiI1EOlE/QY7Imoxlb93RtbvjTA7ptfFCn/9m0Dnqyf3610r1Jpz57H7ImIiCwce/ZERKQivOudyc2ZMwcajUa2+Pv7S+vz8vIQEREBV1dXNG7cGGFhYcjIyFCySUREpGbCBEs9pHjPvl27dtizZ88/FVr/U+WUKVPw9ddf44svvoCjoyMiIyMxYsQIfP/990o3656z6kZfbNlcALudZxQp//btAjzZUJGiZcyxHUREVD2KB3tra2t4eHiUSs/Ozsbq1auxceNGDBgwAACwdu1atGnTBkeOHMGDDz6odNOIiEhtVDpBT/Fgf/bsWXh5ecHW1haBgYGIiYmBj48PUlJSkJ+fj+DgYCmvv78/fHx8kJiYWG6wNxgMMBgM0nO9Xn8nPc8AYX1bkW0wGo13/pao16Tl/69cpcqXym4IXL58WbE67pRtp/x2wAz7Cv+87yYvX/o8KVN+ybKV2oaSZSteh9Ycn1vl6lC6fEuq4/fff1esbIlKg71GCKFY07/99lvk5OSgdevWuH79OubOnYurV6/i1KlT+OqrrzBmzBhZ4AaAnj17on///njnnXfKLHPOnDmYO3duqXT3F7fDSttIke0AABQoe+LCC00OK1q+JVn1Zx/lK7EuUr4OpRWZYSKRsYHiVbzgcVDxOujekJeXh/nz5yM7Oxs6nc6kZev1ejg6OqLz4v1oYNe4xuUU3s7B8SlBirRRSYr27ENDQ6XHHTt2REBAAHx9fbFlyxbY2dnVqMzo6GhERUVJz/V6Pby9veHg4AAbe2V2vNFoRK4+D43sG8HGxkaR8gGge/fu8PHxMXn5wJ1f48nJyRZRx6o4KPZeAP97v4030cjeXrH3O/fWLTRqpEz5Uh03b9/ZBq1CdRiMyDUaFN8OoH7/b1jS/57Sdfz++++YP3++ImWrnVlPvXNycsIDDzyAc+fO4eGHH4bRaERWVhacnJykPBkZGWUe4y+m1Wqh1WpLp9vYwLaGPyCqIhd5sLGxqfGPlKrw8fFBhw4dFCs/OTnZIuoAbij+XuQaoWgdubduKb8NuA0brdJ1GBTfDqD+/29Yyv+e0nWU9d1OpmHWi+rk5OTg/Pnz8PT0RLdu3dCwYUMkJCRI61NTU3H58mUEBgaas1lERKQWxZfLrc1SDynas//Pf/6DoUOHwtfXF9euXcPs2bPRoEEDPP3003B0dMTYsWMRFRUFFxcX6HQ6TJw4EYGBgZyJT0REylDpBD1Fg/3vv/+Op59+Gn/99ReaNm2KPn364MiRI2jatCkAYPHixbCyskJYWBgMBgNCQkLwwQcfKNkkIiIi1VE02G/atKnC9ba2tli+fDmWL1+uZDOIiIju4F3vqC6tutwfW9YDdnbnFCn/9m3gSQdFipYxx3YQEdUYh/GJiIgsnTpvhMNgT0T3tFUXB2DLGoVHvZwUKZoIV69exfTp0/Htt9/i1q1buP/++7F27Vp0794dACCEwOzZs/HRRx8hKysLvXv3xooVK9CqVSuTtoP3syciIvUw413v/v77b/Tu3RsNGzbEt99+i19//RX//e9/4ezsLOVZsGABli5dipUrVyIpKQmNGjVCSEgI8vLyTLCx/2DPnoiI1MOMx+zfeecdeHt7Y+3atVKan5/fP0UJgSVLluD111/HsGHDAACffPIJ3N3dERsbi5EjR9aioXLs2RMRESlg586d6N69O5544gm4ubmhS5cu+Oijj6T1aWlpSE9Pl90QztHREQEBAUhMTDRpWxjsiYhIXUwwhK/X62XL3Td1A4ALFy5Ix993796NF198EZMmTcL69esBAOnp6QAAd3d32evc3d2ldabCYE9EROphosvlent7w9HRUVpiYmJKVVVUVISuXbvi7bffRpcuXTB+/HiMGzcOK1euNPdW85g9ERFRdV25ckV2i9uybuLj6emJtm3bytLatGmDL7/8EgCkm75lZGTA09NTypORkYHOnTubtL3s2RMREVWTTqeTLWUF+969eyM1NVWW9ttvv8HX1xfAncl6Hh4eshvC6fV6JCUlmfyGcOzZExGRepjxcrlTpkxBr1698Pbbb+PJJ5/Ejz/+iA8//BAffvghAECj0WDy5Ml488030apVK/j5+WHmzJnw8vLC8OHDa97GMjDYExGRepjx1LsePXpg+/btiI6Oxrx58+Dn54clS5YgPDxcyjNt2jTk5uZi/PjxyMrKQp8+fRAXFwdbW9taNLI0BnsiIlIPM98I59FHH8Wjjz5a7nqNRoN58+Zh3rx5NW9TFfCYPRERkYVjz56IiNSDd70jIiKydLzrHRGRKq36eTC2XNHCzv6Kycu+fUuLJ71NXixRtTDYExGRenAYn4iIyMIx2BMREVk4lQZ7nnpHRERk4dizJyIi9TDzRXXuFezZExERWTj27ImISD3YsyciIiJLxJ49ERGph0pn4zPYExGReqh0GJ/BnoiI1EOlPXsesyciIrJw7NmryKojj2HLmcaws0tXpPzbtxsD9n8rUjYRkWnwrndERESWjcP4REREZInYsyciIvVgz970YmJi0KNHDzg4OMDNzQ3Dhw9HamqqLE9QUBA0Go1smTBhgpLNIiIitRImWOohRXv2Bw4cQEREBHr06IGCggK8+uqrGDRoEH799Vc0atRIyjdu3DjMmzdPem5vb69ks4iIzGrVoeHY8rMj7Oz/UKT827cc8WQHRYq2PDzP3vTi4uJkz9etWwc3NzekpKSgX79+Urq9vT08PDyUbAoREZFqmXWCXnZ2NgDAxcVFlr5hwwY0adIE7du3R3R0NG7dulVuGQaDAXq9XrYQERFR+cw2Qa+oqAiTJ09G79690b59eyn9mWeega+vL7y8vHDy5ElMnz4dqamp2LZtW5nlxMTEYO7cueZqNhERWZRaDuPzPPuKRURE4NSpUzh8+LAsffz48dLjDh06wNPTEwMHDsT58+fRsmXLUuVER0cjKipKeq7X6+Ht7a1cw4mIyHKodDa+WYJ9ZGQkdu3ahYMHD6JZs2YV5g0ICAAAnDt3rsxgr9VqodVqFWknERGRJVI02AshMHHiRGzfvh379++Hn59fpa85fvw4AMDT01PJphERkRpxNr7pRUREYOPGjdixYwccHByQnn7nmuyOjo6ws7PD+fPnsXHjRgwZMgSurq44efIkpkyZgn79+qFjx45KNo2IiEg1FA32K1asAHDnwjklrV27FqNHj4aNjQ327NmDJUuWIDc3F97e3ggLC8Prr7+uZLOIiEitBGrZszdZS8xK8WH8inh7e+PAgQNKNoGIiEj1eG18IiJSD87GJyIisnAqDfa8xS0REZGFY8+eiIjUgz17IiIiskTs2RMRkXqo9KI67NkTERFZOPbsiYhIPVTas2ewJyIi9VDpBD0GeyIiUg+V9ux5zJ6IiMjCsWdPRGQBVu14BlsOOMHOLkeR8m/f9sCTDylSNJkBe/ZERKQexcP4tVlqaP78+dBoNJg8ebKUlpeXh4iICLi6uqJx48YICwtDRkaGCTZUjsGeiIhIYUePHsWqVavQsWNHWfqUKVPw1Vdf4YsvvsCBAwdw7do1jBgxwuT1M9gTEZF6CBMs1ZSTk4Pw8HB89NFHcHZ2ltKzs7OxevVqLFq0CAMGDEC3bt2wdu1a/PDDDzhy5EgtNrI0BnsiIlKPOgj2EREReOSRRxAcHCxLT0lJQX5+vizd398fPj4+SExMrH5FFeAEPSIiUg8TnWev1+tlyVqtFlqttlT2TZs24dixYzh69Gipdenp6bCxsYGTk5Ms3d3dHenp6bVoZGns2RMREVWTt7c3HB0dpSUmJqZUnitXruDll1/Ghg0bYGtrWwet/Ad79kREpCKa/y21ef2dQK7T6aTUsnr1KSkpuHHjBrp27SqlFRYW4uDBg3j//fexe/duGI1GZGVlyXr3GRkZ8PDwqEUbS2OwJyIi9TDRML5Op5MF+7IMHDgQP//8syxtzJgx8Pf3x/Tp0+Ht7Y2GDRsiISEBYWFhAIDU1FRcvnwZgYGBtWhkaQz2RESkHma8XK6DgwPat28vS2vUqBFcXV2l9LFjxyIqKgouLi7Q6XSYOHEiAgMD8eCDD9a8jWVgsCciIqojixcvhpWVFcLCwmAwGBASEoIPPvjA5PUw2BMRkXrU8V3v9u/fL3tua2uL5cuXY/ny5bUruBIM9kREpCK1HMav1eS+usNT74iIiCwcgz0REZGF4zA+ERGphxln499LGOyJiEg96niCXl3hMD4REZGFY8+eiIjUQ6U9ewZ7IiJSDwZ7IiIiS2eaG+HUN/fEMfvly5ejefPmsLW1RUBAAH788ce6bhIREZHFqPNgv3nzZkRFRWH27Nk4duwYOnXqhJCQENy4caOum0ZERJZGmGCph+o82C9atAjjxo3DmDFj0LZtW6xcuRL29vZYs2ZNXTeNiIgsTfF59rVZ6qE6PWZvNBqRkpKC6OhoKc3KygrBwcFITEysw5YREdHdVn04Dlu+cIKdnTLl5+W5KFNwSZygZ35//vknCgsL4e7uLkt3d3fHmTNnynyNwWCAwWCQnuv1ekXbSEREVN/V+TB+dcXExMDR0VFavL2967pJRERUX6h0GL9Og32TJk3QoEEDZGRkyNIzMjLg4eFR5muio6ORnZ0tLVeuXDFHU4mIiOqtOg32NjY26NatGxISEqS0oqIiJCQkIDAwsMzXaLVa6HQ62UJERETlq/OL6kRFRWHUqFHo3r07evbsiSVLliA3Nxdjxoyp66YREZGl4V3v6sZTTz2FP/74A7NmzUJ6ejo6d+6MuLi4UpP2iIiIao2z8etOZGQkIiMj67oZRERk6VQa7OvdbHwiIiKqnnuiZ09ERGQWKu3ZM9gTEZGKqPOudwz2RESkHirt2fOYPRERkYVjz56IiNRDpT17BnsiIlIPXlSHiIjIwqk02POYPRERkYVjsCciIrJwHMYnIiL14DA+ERERWSL27ImISF3q6elztcFgT0RE6sHz7ImIiCycSoM9j9kTERFZOPbsiYhIRXjXOyIiIsum0mF8BnsiIlIPgVqeZ2+ylpgVj9kTERFZOPbsiYhIPTiMT0REZOF4uVwiIiKyRAz2RERECoiJiUGPHj3g4OAANzc3DB8+HKmpqbI8eXl5iIiIgKurKxo3boywsDBkZGSYvC0M9kREpB7Fw/i1WarowIEDiIiIwJEjRxAfH4/8/HwMGjQIubm5Up4pU6bgq6++whdffIEDBw7g2rVrGDFihMk3m8fsiYhIPcw4QS8uLk72fN26dXBzc0NKSgr69euH7OxsrF69Ghs3bsSAAQMAAGvXrkWbNm1w5MgRPPjgg7VoqBx79kRERNWk1+tli8FgqPQ12dnZAAAXFxcAQEpKCvLz8xEcHCzl8ff3h4+PDxITE03aXgZ7IiJSD2GCBYC3tzccHR2lJSYmpsJqi4qKMHnyZPTu3Rvt27cHAKSnp8PGxgZOTk6yvO7u7khPTzfF1ko4jE9EROphomH8K1euQKfTSclarbbCl0VERODUqVM4fPhwLSqvOQZ7IiJSEdPcCEen08mCfUUiIyOxa9cuHDx4EM2aNZPSPTw8YDQakZWVJevdZ2RkwMPDoxZtLI3D+ERERAoQQiAyMhLbt2/H3r174efnJ1vfrVs3NGzYEAkJCVJaamoqLl++jMDAQJO2hT17IiJSDzPOxo+IiMDGjRuxY8cOODg4SMfhHR0dYWdnB0dHR4wdOxZRUVFwcXGBTqfDxIkTERgYaNKZ+ACDPRERqYkZL5e7YsUKAEBQUJAsfe3atRg9ejQAYPHixbCyskJYWBgMBgNCQkLwwQcf1Lx95VBkGP/ixYsYO3Ys/Pz8YGdnh5YtW2L27NkwGo2yPBqNptRy5MgRJZpERERkstn4VapKiDKX4kAPALa2tli+fDkyMzORm5uLbdu2mfx4PaBQz/7MmTMoKirCqlWrcP/99+PUqVMYN24ccnNzsXDhQlnePXv2oF27dtJzV1dXJZpERESkWooE+8GDB2Pw4MHS8xYtWiA1NRUrVqwoFexdXV0V+RVDRERUCu96p6zs7GzpqkElPfbYY3Bzc0OfPn2wc+dOczWHiIhINcwyQe/cuXNYtmyZrFffuHFj/Pe//0Xv3r1hZWWFL7/8EsOHD0dsbCwee+yxcssyGAyyyxLq9XpF205ERFTfVatnP2PGjDIn1ZVczpw5I3vN1atXMXjwYDzxxBMYN26clN6kSRNERUUhICAAPXr0wPz58/Hss8/i3XffrbANMTExsksUent7V2cTiIhIzcx417t7SbV69lOnTpXNIixLixYtpMfXrl1D//790atXL3z44YeVlh8QEID4+PgK80RHRyMqKkp6rtfrGfCJiKhqzHie/b2kWsG+adOmaNq0aZXyXr16Ff3790e3bt2wdu1aWFlVPohw/PhxeHp6VphHq9VWeg1iIiKiMjHYm87Vq1cRFBQEX19fLFy4EH/88Ye0rnjm/fr162FjY4MuXboAALZt24Y1a9bg448/VqJJREREqqVIsI+Pj8e5c+dw7tw52UX/gTsXGSj2xhtv4NKlS7C2toa/vz82b96Mxx9/XIkmERERsWdvSqNHj6702P6oUaMwatQoJaonIiIqh2nuelff8Nr4RESkLvW0d14bvMUtERGRhWPPnoiI1EOll8tlsCciIvXgBD0iIiILp9KePY/ZExERWTgGeyIiIgvHYXwiIlIPDuMTERGRJWLPnoiI1IOz8YmIiCwcgz0REZGFU2mw5zF7IiIiC8eePRERqQjvekdERGTZVDqMz2BPRETqwfPsiYiIyBKxZ09EROrBYXwiIiJLp84JehzGJyIisnDs2RMRkXpwGJ+IiMjCqXQ2PoM9ERGph0p79jxmT0REZOHYsyciIvVgz56IiIgsEXv2RESkHiqdoMeePRERkYVjz56IiNRDpcfsGeyJiEg9VDqMz2BPRETqUk9757XBY/ZEREQWjj17IiJSEd71zqSaN28OjUYjW+bPny/Lc/LkSfTt2xe2trbw9vbGggULlGoOERHRPxP0arNU0/Lly9G8eXPY2toiICAAP/74Y+23o5oUHcafN28erl+/Li0TJ06U1un1egwaNAi+vr5ISUnBu+++izlz5uDDDz9UsklERERms3nzZkRFRWH27Nk4duwYOnXqhJCQENy4ccOs7VA02Ds4OMDDw0NaGjVqJK3bsGEDjEYj1qxZg3bt2mHkyJGYNGkSFi1apGSTiIhIzYpn49dmqYZFixZh3LhxGDNmDNq2bYuVK1fC3t4ea9asUWgDy6boMfv58+fjjTfegI+PD5555hlMmTIF1tZ3qkxMTES/fv1gY2Mj5Q8JCcE777yDv//+G87OzmWWaTAYYDAYpOfZ2dl30nOzFdsOo9GIImMujLc00BTkKVB+HoqMBhhzNdAUmr58ADDm5aGoADDmAZqi28rUYTSgyDpX+e0oUu69AP73fhhyYbxlpdD7bVS0fFkduQrWYTCiyHjrznuh1PttMKDImAfjbQXrMBpQlG+8U4cC/xtGgwFFBfl3/veg0P9eXh6KCotgNAAa3FKmDoMRRUVFMBoENBpl6jAY/gYACKHcdPnCAn2tZuMXFuoB3BmdLkmr1UKr1crSjEYjUlJSEB0dLaVZWVkhODgYiYmJNW9EDSgW7CdNmoSuXbvCxcUFP/zwA6Kjo3H9+nWp556eng4/Pz/Za9zd3aV15QX7mJgYzJ07t1T6ucVDTbwFpWXU8/JZx71VhyVsA+u4d8q3pDpu3rwJR0dHk5ZpY2MDDw8P/Pyzd63Laty4Mby95eXMnj0bc+bMkaX9+eefKCwslGJbMXd3d5w5c6bW7aiOagX7GTNm4J133qkwz+nTp+Hv74+oqCgprWPHjrCxscELL7yAmJiYUr9+qiM6OlpWdlZWFnx9fXH58mWTfzjMRa/Xw9vbG1euXIFOp6vr5tQYt+PeYQnbAFjGdljCNgDm2Q4hBG7evAkvLy+Tl21ra4u0tDQYjcZalyWEgEYjH86vTVwzh2oF+6lTp2L06NEV5mnRokWZ6QEBASgoKMDFixfRunVreHh4ICND/hux+LmHh0e55Zc1VAIAjo6O9fofCQB0Ol293waA23EvsYRtACxjOyxhG/6/vfuPqap+4wD+vgj3CiO46OWnBWEQpAgiJN209QcsIlbZWmOGjdRy0GXhj5mWC/ynYGtrmSsqK3DTefuxICsFiV9mQwoC5AJDDJLmJFyEgBEC9/39w3HmEfQbBuK5e17b3fB8np3zPPvs8Mi953M/wOzXMZt/tM2fPx/z58+ftfNfy2QyYd68eVP2uhv1udkwrWbv7e0Nb2/vm7pQY2MjnJyc4OPjAwAwm83YtWsXRkdH4eLiAgAoKytDWFjYdd/CF0IIIbRCr9cjJiYG5eXlWLNmDQDAbrejvLwcmZmZtzSXWXkav6amBu+88w6amprQ2dmJgwcPYsuWLVi3bp3SyJ999lno9Xps3LgRLS0t+Oyzz7Bnzx7VW/RCCCGElm3duhX79u3D/v370dbWhoyMDFy6dAnr16+/pXnMygN6BoMBVqsVu3fvxsjICIKDg7FlyxZVI/f09MSxY8dgsVgQExMDk8mE7OxsbNq0adrXysnJue0/L7kRR6gBkDpuJ45QA+AYdThCDYDj1HGrpaSk4MKFC8jOzkZPTw+WL1+OkpKSSQ/tzTYdZ3ONgxBCCCHmnGyEI4QQQjg4afZCCCGEg5NmL4QQQjg4afZCCCGEg9N0s78dtg2ccPz4cTz++OMICAiATqdDcXGxapwksrOz4e/vD1dXVyQkJKCjo0MV09fXh9TUVHh4eMBoNGLjxo0YGhpSxcz2tsC5ubm4//77cccdd8DHxwdr1qxBe3u7Kuaff/6BxWLBwoUL4e7ujqeffnrSl0Z0d3cjOTkZbm5u8PHxwfbt2zE2NqaKqaqqwooVK2AwGBASEoLCwsIZqSE/Px+RkZHKl3+YzWYcPXpUM/lPJS8vDzqdDps3b9ZUHbt375601XV4eLimaphw7tw5rFu3DgsXLoSrqyuWLVuGuro6ZVwL9/hUW4/rdDpYLBYA2poPMU3UKKvVSr1ez08//ZQtLS188cUXaTQa+ccff8xJPkeOHOGuXbv41VdfEQCLiopU43l5efT09GRxcTGbmpr4xBNPMDg4mMPDw0rMo48+yqioKJ48eZI//PADQ0JCuHbtWmX84sWL9PX1ZWpqKm02Gw8dOkRXV1d++OGHM1ZHYmIiCwoKaLPZ2NjYyMcee4yBgYEcGhpSYtLT03nXXXexvLycdXV1fOCBB/jggw8q42NjY4yIiGBCQgIbGhp45MgRmkwmvvrqq0pMZ2cn3dzcuHXrVra2tnLv3r2cN28eS0pK/nMNhw8f5nfffcfTp0+zvb2dr732Gl1cXGiz2TSR/7V++ukn3n333YyMjGRWVpZyXAt15OTkcOnSpTx//rzyunDhgqZqIMm+vj4GBQXx+eefZ21tLTs7O1laWsozZ84oMVq4x3t7e1VzUVZWRgCsrKwkqZ35ENOn2Wa/cuVKWiwW5d/j4+MMCAhgbm7uHGZ1xbXN3m6308/Pj2+99ZZyrL+/nwaDgYcOHSJJtra2EgB//vlnJebo0aPU6XQ8d+4cSfL999+nl5cXR0ZGlJgdO3YwLCxs1mrp7e0lAFZXVyt5u7i48IsvvlBi2traCIA1NTUkr/zHx8nJiT09PUpMfn4+PTw8lNxfeeUVLl26VHWtlJQUJiYmzkodXl5e/PjjjzWX/+DgIENDQ1lWVsaHH35YafZaqSMnJ4dRUVFTjmmlBvLKfbZ69errjmv1Hs/KyuI999xDu92uqfkQ06fJt/Entg1MSEhQjs3VtoH/RldXF3p6elT5enp6Ii4uTsm3pqYGRqMRsbGxSkxCQgKcnJxQW1urxEy1LXB7ezv++uuvWcl9YgvhBQsWAADq6+sxOjqqqiU8PByBgYGqWpYtW6b60ojExEQMDAygpaVFibn6HBMxMz1/4+PjsFqtuHTpEsxms+byt1gsSE5OnnQtLdXR0dGBgIAALF68GKmpqeju7tZcDYcPH0ZsbCyeeeYZ+Pj4IDo6Gvv27VPGtXiPX758GQcOHMCGDRug0+k0NR9i+jTZ7G+0bWBPT88cZXV9EzndKN+enh5l34AJzs7OWLBggSpmqnNcfY2ZZLfbsXnzZqxatQoRERHKdfR6PYxG4w1r+X95Xi9mYGAAw8P/fc/v5uZmuLu7w2AwID09HUVFRViyZIlm8gcAq9WKX375Bbm5uZPGtFJHXFwcCgsLUVJSgvz8fHR1deGhhx7C4OCgZmoAgM7OTuTn5yM0NBSlpaXIyMjAyy+/jP3796ty0dI9XlxcjP7+fmVzMy3Nh5i+WdvPXmifxWKBzWbDiRMn5jqVaQsLC0NjYyMuXryIL7/8Emlpaaiurp7rtP6133//HVlZWSgrK7ulu3TNtKSkJOXnyMhIxMXFISgoCJ9//jlcXV3nMLPpsdvtiI2NxZtvvgkAiI6Ohs1mwwcffIC0tLQ5zu7mfPLJJ0hKSpqV7WTF7UeTf9nfTtsG/hsTOd0oXz8/P/T29qrGx8bG0NfXp4q5mW2Bb0ZmZia+/fZbVFZW4s4771TVcvnyZfT399+wlv+X5/ViPDw8ZqQJ6PV6hISEICYmBrm5uYiKisKePXs0k399fT16e3uxYsUKODs7w9nZGdXV1Xj33Xfh7OwMX19fTdRxLaPRiHvvvRdnzpzRzFwAgL+/P5YsWaI6dt999ykfSWjtHj979iy+//57vPDCC8oxLc2HmD5NNvurtw2cMLFtoNlsnsPMphYcHAw/Pz9VvgMDA6itrVXyNZvN6O/vR319vRJTUVEBu92OuLg4Jeb48eMYHR1VYmZ6W2CSyMzMRFFRESoqKhAcHKwaj4mJgYuLi6qW9vZ2dHd3q2ppbm5W/WIrKyuDh4eH8gvTbDarzjERM1vzZ7fbMTIyopn84+Pj0dzcjMbGRuUVGxuL1NRU5Wct1HGtoaEh/Prrr/D399fMXADAqlWrJi1BPX36NIKCggBo6x4HgIKCAvj4+CA5OVk5pqX5EDdhrp8QvFlWq5UGg4GFhYVsbW3lpk2baDQaVU+J3kqDg4NsaGhgQ0MDAfDtt99mQ0MDz549S/LKshyj0civv/6ap06d4pNPPjnlspzo6GjW1tbyxIkTDA0NVS3L6e/vp6+vL5977jnabDZarVa6ubnN6NK7jIwMenp6sqqqSrVE5++//1Zi0tPTGRgYyIqKCtbV1dFsNtNsNivjE8tzHnnkETY2NrKkpITe3t5TLs/Zvn0729ra+N57783Y8pydO3eyurqaXV1dPHXqFHfu3EmdTsdjx45pIv/rufppfK3UsW3bNlZVVbGrq4s//vgjExISaDKZ2Nvbq5kayCvLH52dnfnGG2+wo6ODBw8epJubGw8cOKDEaOUeHx8fZ2BgIHfs2DFpTCvzIaZPs82eJPfu3cvAwEDq9XquXLmSJ0+enLNcKisrCWDSKy0tjeSVpTmvv/46fX19aTAYGB8fz/b2dtU5/vzzT65du5bu7u708PDg+vXrOTg4qIppamri6tWraTAYuGjRIubl5c1oHVPVAIAFBQVKzPDwMF966SV6eXnRzc2NTz31FM+fP686z2+//cakpCS6urrSZDJx27ZtHB0dVcVUVlZy+fLl1Ov1XLx4seoa/8WGDRsYFBREvV5Pb29vxsfHK41eC/lfz7XNXgt1pKSk0N/fn3q9nosWLWJKSopqbboWapjwzTffMCIiggaDgeHh4fzoo49U41q5x0tLSwlgUm6ktuZDTI9scSuEEEI4OE1+Zi+EEEKIf0+avRBCCOHgpNkLIYQQDk6avRBCCOHgpNkLIYQQDk6avRBCCOHgpNkLIYQQDk6avRBCCOHgpNkLIYQQDk6avRBCCOHgpNkLIYQQDk6avRBCCOHg/gcKyKNf2Nb/MwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 600x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(\n",
    "    nrows=len(times_p2a),\n",
    "    ncols=1,\n",
    "    squeeze=True,\n",
    "    figsize=(6, 10)\n",
    ")\n",
    "for i, t in enumerate(times_p2a):\n",
    "    xsect = fp.plot.PlotCrossSection(model=model_p2a, ax=ax[i], line={\"row\": 7})\n",
    "    ax[i].set_title(f\"Row 8 Cross-Section w/ Heads, t={t:.1f} d\", fontweight=\"bold\")\n",
    "    xsect.plot_ibound()\n",
    "    xsect.plot_grid()\n",
    "    hds_t = hds_p2a.get_data(totim=t)    \n",
    "    cb = xsect.plot_array(hds_t, head=hds_t, cmap=\"winter\", alpha=0.8, vmin=0, vmax=130)\n",
    "    plt.colorbar(cb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Breakdown the standard formulation simulation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5(a). Simulation level packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['nam', 'tdis', 'ims'])"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim_p1a.package_type_dict.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5(a)(i). [Temporal Discretization (TDIS) Package](https://flopy.readthedocs.io/en/latest/source/flopy.mf6.modflow.mftdis.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "package_name = ex-gwf-bcf2ss.tdis\n",
       "filename = ex-gwf-bcf2ss.tdis\n",
       "package_type = tdis\n",
       "model_or_simulation_package = simulation\n",
       "simulation_name = ex-gwf-bcf2ss\n",
       "\n",
       "Block options\n",
       "--------------------\n",
       "time_units\n",
       "{internal}\n",
       "('days')\n",
       "\n",
       "\n",
       "Block dimensions\n",
       "--------------------\n",
       "nper\n",
       "{internal}\n",
       "(2)\n",
       "\n",
       "\n",
       "Block perioddata\n",
       "--------------------\n",
       "perioddata\n",
       "{internal}\n",
       "(rec.array([(1., 1, 1.), (1., 1, 1.)],\n",
       "          dtype=[('perlen', '<f8'), ('nstp', '<i4'), ('tsmult', '<f8')]))\n",
       "\n"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim_p1a.package_type_dict['tdis'][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Reverse-engineer class summary!\n",
    "\n",
    "This simulation has two stress periods, both with the same setup.\n",
    "\n",
    "```python\n",
    "time_units = \"days\"\n",
    "nper = 2\n",
    "# list of (perlen, nstp, tsmult)\n",
    "# perlen (double): length of a stress period\n",
    "# nstp (int): number of time steps in a stress period\n",
    "# tsmult (double): multiplier for the length of successive time steps\n",
    "perioddata = [\n",
    "    # first stress period\n",
    "    (1.0, 1, 1.0),\n",
    "    # second stress period\n",
    "    (1.0, 1, 1.0)\n",
    "]\n",
    "filename = \"ex-gwf-bcf2ss.tdis\"\n",
    "pname = \"ex-gwf-bcf2ss.tdis\"\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5(a)(ii). [Iterative Model Solution (IMS)](https://flopy.readthedocs.io/en/latest/source/flopy.mf6.modflow.mfims.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "package_name = ex-gwf-bcf2ss\n",
       "filename = ex-gwf-bcf2ss.ims\n",
       "package_type = ims\n",
       "model_or_simulation_package = simulation\n",
       "simulation_name = ex-gwf-bcf2ss\n",
       "\n",
       "Block nonlinear\n",
       "--------------------\n",
       "outer_dvclose\n",
       "{internal}\n",
       "(1e-06)\n",
       "\n",
       "outer_maximum\n",
       "{internal}\n",
       "(500)\n",
       "\n",
       "\n",
       "Block linear\n",
       "--------------------\n",
       "inner_maximum\n",
       "{internal}\n",
       "(100)\n",
       "\n",
       "inner_dvclose\n",
       "{internal}\n",
       "(1e-06)\n",
       "\n",
       "rcloserecord\n",
       "{internal}\n",
       "(rec.array([('inner_rclose', 0.001, 'strict')],\n",
       "          dtype=[('inner_rclose_label', 'O'), ('inner_rclose', '<f8'), ('rclose_option', 'O')]))\n",
       "\n",
       "linear_acceleration\n",
       "{internal}\n",
       "('cg')\n",
       "\n",
       "relaxation_factor\n",
       "{internal}\n",
       "(0.97)\n",
       "\n"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim_p1a.package_type_dict['ims'][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Reverse-engineer class summary!\n",
    "\n",
    "```python\n",
    "# dependent-variable change criterion for convergance of the outer (nonlinear) iterations\n",
    "outer_dvclose = 1e-6\n",
    "# maximum number of outer (nonlinear) iterations\n",
    "outer_maximum = 500\n",
    "# maximum number of inner (linear) iterations\n",
    "inner_maximum = 100\n",
    "# dependent-variable change criterion for convergance of the inner (linear) iterations\n",
    "inner_dvclose = 1e-6\n",
    "# rcloserecord = [inner_rclose, rclose_option]\n",
    "# inner_rclose: flow residual tolerance for convergence of the IMS linear solver\n",
    "# rclose_option: specific flow residual criterion used\n",
    "rcloserecord = [0.001, \"strict\"]\n",
    "# preconditioned conjugate gradient method\n",
    "linear_acceleration = \"cg\"\n",
    "# relaxation factor used by the incomplete LU factorization preconditioners\n",
    "relaxation_factor = 0.97\n",
    "filename = \"ex-gwf-bcf2ss.ims\n",
    "pname = \"ex-gwf-bcf2ss\"\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5(b). Model level packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['dis', 'npf', 'ic', 'riv_0', 'wel_0', 'rcha_0', 'oc']"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_p1a.package_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5(b)(i). [Structured Discretization (DIS)](https://flopy.readthedocs.io/en/latest/source/flopy.mf6.modflow.mfgwfdis.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "package_name = dis\n",
       "filename = ex-gwf-bcf2ss.dis\n",
       "package_type = dis\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-bcf2ss\n",
       "\n",
       "Block options\n",
       "--------------------\n",
       "length_units\n",
       "{internal}\n",
       "('feet')\n",
       "\n",
       "\n",
       "Block dimensions\n",
       "--------------------\n",
       "nlay\n",
       "{internal}\n",
       "(2)\n",
       "\n",
       "nrow\n",
       "{internal}\n",
       "(10)\n",
       "\n",
       "ncol\n",
       "{internal}\n",
       "(15)\n",
       "\n",
       "\n",
       "Block griddata\n",
       "--------------------\n",
       "delr\n",
       "{constant 500.0}\n",
       "\n",
       "delc\n",
       "{constant 500.0}\n",
       "\n",
       "top\n",
       "{constant 150.0}\n",
       "\n",
       "botm\n",
       "Layer_1{constant 50.0}\n",
       "Layer_2{constant -50.0}\n",
       "\n"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_p1a.package_dict['dis']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Reverse-engineer class summary!\n",
    "\n",
    "This is a two-layer model with 10 rows and 15 columns. Cells are square 500.0 ft by 500.0 ft.\n",
    "\n",
    "```python\n",
    "length_units = \"FEET\"\n",
    "nlay = 2\n",
    "nrow = 10\n",
    "ncol = 15\n",
    "# column spacing in the row direction\n",
    "delr = 500.0\n",
    "# row spacing in the column direction\n",
    "delc = 500.0\n",
    "# top elevation for each cell in the top model layer\n",
    "top = 150.0\n",
    "botm = [\n",
    "    # bottom of upper aquifer\n",
    "    50.0,\n",
    "    # bottom of lower aquifer\n",
    "    -50.0\n",
    "]\n",
    "filename = \"ex-gwf-bcf2ss.dis\"\n",
    "pname = \"dis\"\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5(b)(ii). [Node Property Flow (NPF) Package](https://flopy.readthedocs.io/en/latest/source/flopy.mf6.modflow.mfgwfnpf.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "package_name = npf\n",
       "filename = ex-gwf-bcf2ss.npf\n",
       "package_type = npf\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-bcf2ss\n",
       "\n",
       "Block options\n",
       "--------------------\n",
       "rewet_record\n",
       "{internal}\n",
       "(rec.array([('wetfct', 1., 'iwetit', 1, 'ihdwet', 0)],\n",
       "          dtype=[('wetfct_label', 'O'), ('wetfct', '<f8'), ('iwetit_label', 'O'), ('iwetit', '<i4'), ('ihdwet_label', 'O'), ('ihdwet', '<i4')]))\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",
       "\n",
       "k\n",
       "Layer_1{constant 10.0}\n",
       "Layer_2{constant 5.0}\n",
       "\n",
       "k33\n",
       "{constant 0.1}\n",
       "\n",
       "wetdry\n",
       "Layer_1{internal}\n",
       "(array([[ 2.,  2.,  2.,  2.,  2.,  2.,  2.,  2., -2., -2., -2., -2., -2.,\n",
       "        -2., -2.],\n",
       "       [ 2.,  2.,  2.,  2.,  2.,  2.,  2.,  2., -2., -2., -2., -2., -2.,\n",
       "        -2., -2.],\n",
       "       [ 2.,  2.,  2., -2.,  2.,  2.,  2.,  2., -2., -2., -2., -2., -2.,\n",
       "        -2., -2.],\n",
       "       [ 2.,  2.,  2.,  2.,  2.,  2.,  2.,  2., -2., -2., -2., -2., -2.,\n",
       "        -2., -2.],\n",
       "       [ 2.,  2.,  2.,  2.,  2.,  2.,  2.,  2., -2., -2., -2., -2., -2.,\n",
       "        -2., -2.],\n",
       "       [ 2.,  2.,  2.,  2.,  2.,  2.,  2.,  2., -2., -2., -2., -2., -2.,\n",
       "        -2., -2.],\n",
       "       [ 2.,  2.,  2.,  2.,  2.,  2.,  2.,  2., -2., -2., -2., -2., -2.,\n",
       "        -2., -2.],\n",
       "       [ 2.,  2.,  2., -2.,  2.,  2.,  2.,  2., -2., -2., -2., -2., -2.,\n",
       "        -2., -2.],\n",
       "       [ 2.,  2.,  2.,  2.,  2.,  2.,  2.,  2., -2., -2., -2., -2., -2.,\n",
       "        -2., -2.],\n",
       "       [ 2.,  2.,  2.,  2.,  2.,  2.,  2.,  2., -2., -2., -2., -2., -2.,\n",
       "        -2., -2.]]))\n",
       "Layer_2{constant 0.0}\n",
       "\n"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_p1a.package_dict['npf']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Reverse-engineer class summary!\n",
    "\n",
    "The standard formulation simulation makes use of the WETDRY feature in the NPF package. The same vertical hydraulic conductivity is used for both layers.\n",
    "\n",
    "```python\n",
    "rewet_record = [\n",
    "    # factor included in the calculation of head that is initially established at a cell when that cell is converted from dry to wet\n",
    "    'wetfct', 1.0,\n",
    "    # iteration interval for attempting to wet cells\n",
    "    'iwetit', 1,\n",
    "    # flag that determines which equation is used to define the initial head at cells that become wet\n",
    "    'ihdwet', 0\n",
    "]\n",
    "save_specific_discharge = True\n",
    "icelltype = [\n",
    "    # upper aquifer is unconfined/convertible\n",
    "    1,\n",
    "    # lower aquifer is confined\n",
    "    0\n",
    "]\n",
    "# (horizontal) hydraulic conductivity\n",
    "k = [\n",
    "    # upper aquifer\n",
    "    10.0,\n",
    "    # lower aquifer\n",
    "    5.0\n",
    "]\n",
    "# (vertical) hydraulic conductivity\n",
    "k33 = 0.1\n",
    "# a combination of the wetting threshold and a flag to indicate which neighboring cells can cause a cell to become wet\n",
    "# wetdry < 0: only a cell below a dry cell can cause the cell to become wet\n",
    "# wetdry > 0: the cell below a dry cell and horizontally adjacent cells can cause a cell to become wet\n",
    "wetdry = [\n",
    "    # upper aquifer\n",
    "    [\n",
    "        [ 2.,  2.,  2.,  2.,  2.,  2.,  2.,  2., -2., -2., -2., -2., -2., -2., -2.],\n",
    "        [ 2.,  2.,  2.,  2.,  2.,  2.,  2.,  2., -2., -2., -2., -2., -2., -2., -2.],\n",
    "        [ 2.,  2.,  2., -2.,  2.,  2.,  2.,  2., -2., -2., -2., -2., -2., -2., -2.],\n",
    "        [ 2.,  2.,  2.,  2.,  2.,  2.,  2.,  2., -2., -2., -2., -2., -2., -2., -2.],\n",
    "        [ 2.,  2.,  2.,  2.,  2.,  2.,  2.,  2., -2., -2., -2., -2., -2., -2., -2.],\n",
    "        [ 2.,  2.,  2.,  2.,  2.,  2.,  2.,  2., -2., -2., -2., -2., -2., -2., -2.],\n",
    "        [ 2.,  2.,  2.,  2.,  2.,  2.,  2.,  2., -2., -2., -2., -2., -2., -2., -2.],\n",
    "        [ 2.,  2.,  2., -2.,  2.,  2.,  2.,  2., -2., -2., -2., -2., -2., -2., -2.],\n",
    "        [ 2.,  2.,  2.,  2.,  2.,  2.,  2.,  2., -2., -2., -2., -2., -2., -2., -2.],\n",
    "        [ 2.,  2.,  2.,  2.,  2.,  2.,  2.,  2., -2., -2., -2., -2., -2., -2., -2.]\n",
    "    ],\n",
    "    # lower aquifer\n",
    "    0.0\n",
    "]\n",
    "filename = \"ex-gwf-bcf2ss.npf\"\n",
    "pname = \"npf\"\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5(b)(iii). [Initial Conditions (IC) Package](https://flopy.readthedocs.io/en/latest/source/flopy.mf6.modflow.mfgwfic.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "package_name = ic\n",
       "filename = ex-gwf-bcf2ss.ic\n",
       "package_type = ic\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-bcf2ss\n",
       "\n",
       "Block griddata\n",
       "--------------------\n",
       "strt\n",
       "{constant 0.0}\n",
       "\n"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_p1a.package_dict['ic']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Reverse-engineer class summary!\n",
    "\n",
    "```python\n",
    "# initial (starting) head\n",
    "strt = 0.0\n",
    "filename = \"ex-gwf-bcf2ss.ic\"\n",
    "pname = \"ic\"\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5(b)(iv). [River (RIV) Package](https://flopy.readthedocs.io/en/latest/source/flopy.mf6.modflow.mfgwfriv.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "flopy.mf6.modflow.mfgwfriv.ModflowGwfriv"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_p1a.package_dict['riv_0'].__class__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "package_name = riv_0\n",
       "filename = ex-gwf-bcf2ss.riv\n",
       "package_type = riv\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-bcf2ss\n",
       "\n",
       "Block dimensions\n",
       "--------------------\n",
       "maxbound\n",
       "{internal}\n",
       "(10)\n",
       "\n",
       "\n",
       "Block period\n",
       "--------------------\n",
       "stress_period_data\n",
       "{0: <flopy.mf6.data.mfdataplist.PandasListStorage object at 0x000001FF92177090>}\n"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_p1a.package_dict['riv_0']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rec.array([((1, 0, 14), 0., 10000., -5.), ((1, 1, 14), 0., 10000., -5.),\n",
       "           ((1, 2, 14), 0., 10000., -5.), ((1, 3, 14), 0., 10000., -5.),\n",
       "           ((1, 4, 14), 0., 10000., -5.), ((1, 5, 14), 0., 10000., -5.),\n",
       "           ((1, 6, 14), 0., 10000., -5.), ((1, 7, 14), 0., 10000., -5.),\n",
       "           ((1, 8, 14), 0., 10000., -5.), ((1, 9, 14), 0., 10000., -5.)],\n",
       "          dtype=[('cellid', 'O'), ('stage', '<f8'), ('cond', '<f8'), ('rbot', '<f8')])"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_p1a.package_dict['riv_0'].stress_period_data.get_data(key=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Reverse-engineer class summary!\n",
    "\n",
    "```python\n",
    "# maximum number of river cells that will be specified for use during any stress period\n",
    "maxbound = 10\n",
    "# list of (cellid, stage, cond, rbot)\n",
    "# cellid: (layer, row, column)\n",
    "# stage: head in the river\n",
    "# cond: riverbed hydraulic conductance\n",
    "# rbot: elevation of the bottom of the riverbed\n",
    "stress_period_data = [\n",
    "    # last column of lower aquifer\n",
    "    ((1, 0, 14), 0.0, 10000.0, -5.0), ((1, 1, 14), 0.0, 10000.0, -5.0),\n",
    "    ((1, 2, 14), 0.0, 10000.0, -5.0), ((1, 3, 14), 0.0, 10000.0, -5.0),\n",
    "    ((1, 4, 14), 0.0, 10000.0, -5.0), ((1, 5, 14), 0.0, 10000.0, -5.0),\n",
    "    ((1, 6, 14), 0.0, 10000.0, -5.0), ((1, 7, 14), 0.0, 10000.0, -5.0),\n",
    "    ((1, 8, 14), 0.0, 10000.0, -5.0), ((1, 9, 14), 0.0, 10000.0, -5.0)\n",
    "]\n",
    "filename = \"ex-gwf-bcf2ss.riv\"\n",
    "pname = \"riv_0\"\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5(b)(v). [Well (WEL) Package](https://flopy.readthedocs.io/en/latest/source/flopy.mf6.modflow.mfgwfwel.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "package_name = wel_0\n",
       "filename = ex-gwf-bcf2ss.wel\n",
       "package_type = wel\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-bcf2ss\n",
       "\n",
       "Block dimensions\n",
       "--------------------\n",
       "maxbound\n",
       "{internal}\n",
       "(2)\n",
       "\n",
       "\n",
       "Block period\n",
       "--------------------\n",
       "stress_period_data\n",
       "{1: <flopy.mf6.data.mfdataplist.PandasListStorage object at 0x000001FF926A7410>}\n"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_p1a.package_dict['wel_0']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rec.array([((1, 2, 3), -35000.), ((1, 7, 3), -35000.)],\n",
       "          dtype=[('cellid', 'O'), ('q', '<f8')])"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_p1a.package_dict['wel_0'].stress_period_data.get_data(key=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Reverse-engineer class summary!\n",
    "\n",
    "Well data isn't set until the second stress period.\n",
    "\n",
    "```python\n",
    "maxbound = 2\n",
    "stress_period_data = {\n",
    "    1: [\n",
    "        # both wells in lower aquifer\n",
    "        ((1, 2, 3), -35000.0),\n",
    "        ((1, 7, 3), -35000.0)\n",
    "    ]\n",
    "}\n",
    "filename = \"ex-gwf-bcf2ss.wel\"\n",
    "pname = \"wel_0\"\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5(b)(vi). [Recharge (RCH) Package](https://flopy.readthedocs.io/en/latest/source/flopy.mf6.modflow.mfgwfrcha.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "package_name = rcha_0\n",
       "filename = ex-gwf-bcf2ss.rcha\n",
       "package_type = rcha\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-bcf2ss\n",
       "\n",
       "Block options\n",
       "--------------------\n",
       "readasarrays\n",
       "{internal}\n",
       "(True)\n",
       "\n",
       "\n",
       "Block period\n",
       "--------------------\n",
       "irch\n",
       "None\n",
       "recharge\n",
       "{constant 0.004}\n",
       "\n",
       "aux\n",
       "None\n"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_p1a.package_dict['rcha_0']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Reverse-engineer class summary!\n",
    "\n",
    "```python\n",
    "readasarrays = True # default value\n",
    "irch = None # default value\n",
    "# recharge flux rate (L / T)\n",
    "recharge = 0.004\n",
    "aux = None # default value\n",
    "filename = \"ex-gwf-bcf2ss.rcha\"\n",
    "pname = \"rcha_0\"\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5(b)(vii). [Output Control (OC)](https://flopy.readthedocs.io/en/latest/source/flopy.mf6.modflow.mfgwfoc.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "package_name = oc\n",
       "filename = ex-gwf-bcf2ss.oc\n",
       "package_type = oc\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-bcf2ss\n",
       "\n",
       "Block options\n",
       "--------------------\n",
       "budget_filerecord\n",
       "{internal}\n",
       "(rec.array([('ex-gwf-bcf2ss.cbc',)],\n",
       "          dtype=[('budgetfile', 'O')]))\n",
       "\n",
       "head_filerecord\n",
       "{internal}\n",
       "(rec.array([('ex-gwf-bcf2ss.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": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_p1a.package_dict['oc']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Reverse-engineer class summary!\n",
    "\n",
    "```python\n",
    "budget_filerecord = [\"ex-gwf-bcf2ss.cbc\"]\n",
    "head_filerecord = [\"ex-gwf-bcf2ss.hds\"]\n",
    "saverecord = [\n",
    "    (\"head\", \"all\"),\n",
    "    (\"budget\", \"all\")\n",
    "]\n",
    "printrecord = None # default value\n",
    "filename = \"ex-gwf-bcf2ss.oc\"\n",
    "pname = \"oc\"\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. Breakdown the Newton-Raphson formulation simulation\n",
    "\n",
    "There are two packages that differ in the Newton-Raphson version: IMS and NPF. The \"NEWTON\" flag is also specified in the model `.nam` file."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6(a). [Iterative Model Solution (IMS)](https://flopy.readthedocs.io/en/latest/source/flopy.mf6.modflow.mfims.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "package_name = ex-gwf-bcf2ss\n",
       "filename = ex-gwf-bcf2ss.ims\n",
       "package_type = ims\n",
       "model_or_simulation_package = simulation\n",
       "simulation_name = ex-gwf-bcf2ss\n",
       "\n",
       "Block nonlinear\n",
       "--------------------\n",
       "outer_dvclose\n",
       "{internal}\n",
       "(1e-06)\n",
       "\n",
       "outer_maximum\n",
       "{internal}\n",
       "(500)\n",
       "\n",
       "\n",
       "Block linear\n",
       "--------------------\n",
       "inner_maximum\n",
       "{internal}\n",
       "(100)\n",
       "\n",
       "inner_dvclose\n",
       "{internal}\n",
       "(1e-06)\n",
       "\n",
       "rcloserecord\n",
       "{internal}\n",
       "(rec.array([('inner_rclose', 0.001, 'strict')],\n",
       "          dtype=[('inner_rclose_label', 'O'), ('inner_rclose', '<f8'), ('rclose_option', 'O')]))\n",
       "\n",
       "linear_acceleration\n",
       "{internal}\n",
       "('bicgstab')\n",
       "\n",
       "relaxation_factor\n",
       "{internal}\n",
       "(0.97)\n",
       "\n"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim_p2a.package_type_dict['ims'][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Reverse-engineer class summary!\n",
    "\n",
    "```python\n",
    "outer_dvclose = 1e-6\n",
    "outer_maximum = 500\n",
    "inner_maximum = 100\n",
    "inner_dvclose = 1e-6\n",
    "rcloserecord = [0.001, \"strict\"]\n",
    "# preconditioned bi-conjugate gradient stabilized method\n",
    "linear_acceleration = \"bicgstab\"\n",
    "relaxation_factor = 0.97\n",
    "filename = \"ex-gwf-bcf2ss.ims\"\n",
    "pname = \"ex-gwf-bcf2ss\"\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6(b). [Node Property Flow (NPF) Package](https://flopy.readthedocs.io/en/latest/source/flopy.mf6.modflow.mfgwfnpf.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "package_name = npf\n",
       "filename = ex-gwf-bcf2ss.npf\n",
       "package_type = npf\n",
       "model_or_simulation_package = model\n",
       "model_name = ex-gwf-bcf2ss\n",
       "\n",
       "Block options\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",
       "\n",
       "k\n",
       "Layer_1{constant 10.0}\n",
       "Layer_2{constant 5.0}\n",
       "\n",
       "k33\n",
       "{constant 0.1}\n",
       "\n"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_p2a.package_dict['npf']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Reverse-engineer class summary!\n",
    "\n",
    "```python\n",
    "save_specific_discharge = True\n",
    "icelltype = [1, 0]\n",
    "k = [10.0, 5.0]\n",
    "k33 = 0.1\n",
    "filename = \"ex-gwf-bcf2ss.npf\"\n",
    "pname = \"npf\"\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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