iterative_cbm.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Example of runnning CBM iteratively. CBM is run for several intervals with an opportunity to adjust disturbance events between each interval.  \n",
    "\n",
    "The script creates a seperate CBM-CFS3 project for each simulation interval and injects the previous simulation state into each subsequent interation.\n",
    "\n",
    "Each interval's CBM-CFS3 project has varying disturbance levels, in this case the script sets the harvest C target to be 5% of each previous iteration's end-of-interval total biomass C."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys, os, json, subprocess\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import cbm3_python_helper\n",
    "cbm3_python_helper.load_cbm3_python()\n",
    "\n",
    "import cbm3_python.simulation.projectsimulator\n",
    "from cbm3_python.cbm3data import cbm3_results\n",
    "from cbm3_python.cbm3data.aidb import AIDB\n",
    "from cbm3_python.cbm3data.accessdb import AccessDB\n",
    "from cbm3_python.cbm3data.projectdb import ProjectDB\n",
    "from cbm3_python.simulation.simulator import Simulator\n",
    "from cbm3_python.cbm3data import cbm3_results\n",
    "from cbm3_python.cbm3data import sit_helper\n",
    "standard_import_tool_plugin_path=sit_helper.load_standard_import_tool_plugin()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "local_working_dir = os.path.abspath(\".\")\n",
    "toolbox_path = r'C:\\Program Files (x86)\\Operational-Scale CBM-CFS3'\n",
    "aidb_path=os.path.join(toolbox_path, \"admin\", \"dbs\", \"ArchiveIndex_Beta_Install.mdb\")\n",
    "cbm_exe_path=os.path.join(toolbox_path, \"admin\", \"executables\")\n",
    "cbm_wd = os.path.join(toolbox_path, \"temp\")\n",
    "\n",
    "#the number of steps in each iteration\n",
    "iteration_steps = 10\n",
    "#the number of iterations\n",
    "num_iterations = 5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following methods inject disturbances and transition rules into the iteration import function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_event_factory(events):\n",
    "    def factory(config):\n",
    "        for e in events:\n",
    "            config.add_event(**e)\n",
    "    return factory\n",
    "            \n",
    "def create_transition_rule_factory(transitions):\n",
    "    def factory(config):\n",
    "        for t in transitions:\n",
    "            config.add_transition_rule(**t)\n",
    "    return factory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Updates the number of timesteps to run for the given CBM project (AccessDB instance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def set_project_run_length(proj, n_steps):\n",
    "    proj.ExecuteQuery(\"UPDATE tblRunTableDetails SET tblRunTableDetails.RunLength = ?\", n_steps)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Path helper functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_project_path(iteration_num):\n",
    "    return os.path.join(local_working_dir, \"iterative_cbm\", \"iterative_cbm_{}.mdb\".format(iteration_num))\n",
    "\n",
    "def get_results_db_path(iteration_num):\n",
    "    return os.path.join(local_working_dir, \"iterative_cbm\", \"iterative_cbm_results_{}.mdb\".format(iteration_num))\n",
    "\n",
    "def get_config_save_path(iteration_num):\n",
    "    return os.path.join(local_working_dir, \"iterative_cbm\", \"iterative_cbm_config_{}.json\".format(iteration_num))\n",
    "\n",
    "def get_iteration_tempfiles_dir(iteration_num):\n",
    "    return os.path.join(local_working_dir, \"iterative_cbm\", \"iterative_cbm_tempfiles_{}\".format(iteration_num))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sets up and imports a constant SIT inventory with varying events and transition rules at the specified path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def iteration_import(project_path, config_save_path, events_factory, transition_factory):\n",
    "\n",
    "\n",
    "    c = sit_helper.SITConfig(\n",
    "        imported_project_path=project_path,\n",
    "        initialize_mapping=True\n",
    "    )\n",
    "    c.data_config(\n",
    "        age_class_size=5,\n",
    "        num_age_classes=14,\n",
    "        classifiers=[\"admin\", \"eco\", \"Leading Species\", \"Land Cover\"])\n",
    "\n",
    "    c.set_admin_eco_mapping(\"admin\",\"eco\")\n",
    "    c.set_species_classifier(\"Leading Species\")\n",
    "    c.map_species(\"Nonforest\", \"Not stocked\")\n",
    "\n",
    "    c.add_inventory(classifier_set=[\"British Columbia\", \"Boreal Cordillera\", \"Luvisolic (W. Canada)\", \"Nonforest\"],\n",
    "                    area=100, age=0, unfccc_land_class=1)\n",
    "    c.add_inventory(classifier_set=[\"British Columbia\", \"Boreal Plains\", \"Luvisolic (W. Canada)\", \"Nonforest\"],\n",
    "                    area=100, age=0, unfccc_land_class=1)\n",
    "    c.add_inventory(classifier_set=[\"British Columbia\", \"Taiga Plains\", \"Luvisolic (W. Canada)\", \"Nonforest\"],\n",
    "                    area=100, age=0, unfccc_land_class=1)\n",
    "    c.add_inventory(classifier_set=[\"British Columbia\", \"Pacific Maritime\", \"Luvisolic (W. Canada)\", \"Nonforest\"],\n",
    "                    area=100, age=0, unfccc_land_class=1)\n",
    "    c.add_inventory(classifier_set=[\"British Columbia\", \"Montane Cordillera\", \"Luvisolic (W. Canada)\", \"Nonforest\"],\n",
    "                    area=100, age=0, unfccc_land_class=1)\n",
    "    \n",
    "    c.add_inventory(classifier_set=[\"British Columbia\", \"Boreal Cordillera\", \"Spruce\", \"Forest Only\"],\n",
    "                    area=1000, age=50, unfccc_land_class=0,\n",
    "                    historic_disturbance=\"Wildfire\", last_pass_disturbance=\"Wildfire\")\n",
    "    c.add_inventory(classifier_set=[\"British Columbia\", \"Boreal Plains\", \"Spruce\", \"Forest Only\"],\n",
    "                    area=1000, age=50, unfccc_land_class=0,\n",
    "                    historic_disturbance=\"Wildfire\", last_pass_disturbance=\"Wildfire\")\n",
    "    c.add_inventory(classifier_set=[\"British Columbia\", \"Taiga Plains\", \"Spruce\", \"Forest Only\"],\n",
    "                    area=1000, age=50, unfccc_land_class=0,\n",
    "                    historic_disturbance=\"Wildfire\", last_pass_disturbance=\"Wildfire\")\n",
    "    c.add_inventory(classifier_set=[\"British Columbia\", \"Pacific Maritime\", \"Spruce\", \"Forest Only\"],\n",
    "                    area=1000, age=50, unfccc_land_class=0,\n",
    "                    historic_disturbance=\"Wildfire\", last_pass_disturbance=\"Wildfire\")\n",
    "    c.add_inventory(classifier_set=[\"British Columbia\", \"Montane Cordillera\", \"Spruce\", \"Forest Only\"],\n",
    "                    area=1000, age=50, unfccc_land_class=0,\n",
    "                    historic_disturbance=\"Wildfire\", last_pass_disturbance=\"Wildfire\")\n",
    "\n",
    "    growth_curve_data = [0,0,0,27,84,155,217,271,317,353,382,406,426,426]\n",
    "\n",
    "    not_stocked_growth = [0.0]*len(growth_curve_data)\n",
    "\n",
    "    c.add_yield(classifier_set=[\"?\",\"?\",\"?\",\"Nonforest\"],\n",
    "                leading_species_classifier_value=\"Not stocked\",\n",
    "                values=not_stocked_growth)\n",
    "\n",
    "    c.add_yield(classifier_set=[\"?\",\"?\",\"Red pine\",\"Forest Only\"], \n",
    "                leading_species_classifier_value=\"Red pine\", values=growth_curve_data)\n",
    "    \n",
    "    c.add_yield(classifier_set=[\"?\",\"?\",\"Spruce\",\"Forest Only\"], \n",
    "                leading_species_classifier_value=\"Spruce\", values=growth_curve_data)\n",
    "    \n",
    "    events_factory(c)\n",
    "    transition_factory(c)\n",
    "    \n",
    "    c.import_project(standard_import_tool_plugin_path, config_save_path)\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set up the first iteration.  This is a typical CBM run the since the stand initialization routine (Makelist) is run to estimate initial forest conditions.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "events = [\n",
    "    {\n",
    "    \"classifier_set\": [\"?\",\"?\",\"?\",\"?\"],\n",
    "    \"disturbance_type\": \"Afforestation\", \n",
    "    \"time_step\": 1, \n",
    "    \"target\": 100,\n",
    "    \"target_type\": \"Area\",\n",
    "    \"sort\": \"SORT_BY_SW_AGE\"\n",
    "    }\n",
    "]\n",
    "transitions = [\n",
    "    {\n",
    "    \"classifier_set_source\": [\"?\",\"?\",\"?\",\"?\"],\n",
    "    \"classifier_set_target\": [\"?\",\"?\",\"Red pine\",\"Forest Only\"],\n",
    "    \"disturbance_type\": \"Afforestation\",\n",
    "    \"percent\": 100\n",
    "    }\n",
    "]\n",
    "\n",
    "iteration_num = 1\n",
    "project_path = get_project_path(iteration_num)\n",
    "tempfiles_dir = get_iteration_tempfiles_dir(iteration_num)\n",
    "results_db_path = get_results_db_path(iteration_num)\n",
    "\n",
    "iteration_import(\n",
    "    project_path,\n",
    "    get_config_save_path(iteration_num),\n",
    "    create_event_factory(events),\n",
    "    create_transition_rule_factory(transitions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "with AIDB(aidb_path, False) as aidb, \\\n",
    "     AccessDB(project_path, False) as proj:\n",
    "\n",
    "    set_project_run_length(proj, iteration_steps)\n",
    "    simId = aidb.AddProjectToAIDB(proj)\n",
    "    s = Simulator(cbm_exe_path,\n",
    "                simId,\n",
    "                os.path.dirname(project_path),\n",
    "                cbm_wd,\n",
    "                toolbox_path)\n",
    "    \n",
    "\n",
    "    s.CleanupRunDirectory()\n",
    "    s.CreateMakelistFiles()\n",
    "    s.copyMakelist()\n",
    "    s.runMakelist()\n",
    "    s.loadMakelistSVLS()\n",
    "    s.DumpMakelistSVLs()\n",
    "    s.CreateCBMFiles()\n",
    "    s.CopyCBMExecutable()\n",
    "    s.RunCBM()\n",
    "    s.CopyTempFiles(output_dir=tempfiles_dir)\n",
    "    s.LoadCBMResults(output_path = results_db_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "now run subsequent iterations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "for i in range(2,num_iterations+1):\n",
    "    iteration_num = i\n",
    "    project_path = get_project_path(iteration_num)\n",
    "    tempfiles_dir = get_iteration_tempfiles_dir(iteration_num)\n",
    "    results_db_path = get_results_db_path(iteration_num)\n",
    "\n",
    "    #get the total biomass from a previous iteration\n",
    "    total_biomass = cbm3_results.load_pool_indicators( get_results_db_path(i-1))[\"Total Biomass\"][-1:].values[0]\n",
    "    #set the target to 5% of the total biomass from the previous iteration\n",
    "    targetC = total_biomass * 0.05\n",
    "    \n",
    "    #create a series of events (divided by the number of iterations)\n",
    "    events = [\n",
    "        {\n",
    "        \"classifier_set\": [\"?\",\"?\",\"?\",\"?\"],\n",
    "        \"disturbance_type\": \"Clear-cut with slash-burn\", \n",
    "        \"time_step\": x+1, \n",
    "        \"target\": targetC/iteration_steps,\n",
    "        \"target_type\": \"Merchantable\",\n",
    "        \"sort\": \"MERCHCSORT_TOTAL\"\n",
    "        } for x in range(iteration_steps)\n",
    "    ]\n",
    "    \n",
    "    #import the project with new disturbance events\n",
    "    iteration_import(\n",
    "        project_path,\n",
    "        get_config_save_path(iteration_num),\n",
    "        create_event_factory(events),\n",
    "        create_transition_rule_factory([]))\n",
    "    \n",
    "    with AIDB(aidb_path, False) as aidb, \\\n",
    "         AccessDB(project_path, False) as proj:\n",
    "\n",
    "        proj.ExecuteQuery(\"UPDATE tblRunTableDetails SET tblRunTableDetails.RunLength = ?\", iteration_steps)\n",
    "        simId = aidb.AddProjectToAIDB(proj)\n",
    "        s = Simulator(cbm_exe_path,\n",
    "                    simId,\n",
    "                    os.path.dirname(project_path),\n",
    "                    cbm_wd,\n",
    "                    toolbox_path)\n",
    "\n",
    "        s.CleanupRunDirectory()\n",
    "        s.CopyToWorkingDir(project_path)\n",
    "        s.CreateCBMFiles()\n",
    "        s.CopyCBMExecutable()\n",
    "        s.CopySVLFromPreviousRun(os.path.join(get_iteration_tempfiles_dir(i-1),\"CBMRun\",\"output\"))\n",
    "        s.RunCBM()\n",
    "        s.CopyTempFiles(output_dir=tempfiles_dir)\n",
    "        s.LoadCBMResults(output_path = results_db_path)\n",
    "   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In theory the stock values for the last timestep of a previous iteration should be equal to the stock values for the 0th timestep of a current iteration. The next cell shows a small discrepency between iterations 2 and 3.  Considering that this is a very small relative difference, and noting the fact that CBM3 operates with 32 bit precision (which limits us to 6 to 7 digits) I do not think this indicates a larger issue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>i2</th>\n",
       "      <th>i3</th>\n",
       "      <th>rel_dif</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TimeStep</th>\n",
       "      <td>1.000000e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aboveground Very Fast DOM</th>\n",
       "      <td>4.872035e+04</td>\n",
       "      <td>4.872035e+04</td>\n",
       "      <td>1.002212e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belowground Very Fast DOM</th>\n",
       "      <td>1.040664e+04</td>\n",
       "      <td>1.040664e+04</td>\n",
       "      <td>2.346008e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aboveground Fast DOM</th>\n",
       "      <td>8.772152e+04</td>\n",
       "      <td>8.772152e+04</td>\n",
       "      <td>4.453012e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belowground Fast DOM</th>\n",
       "      <td>1.980821e+04</td>\n",
       "      <td>1.980821e+04</td>\n",
       "      <td>1.232523e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Medium DOM</th>\n",
       "      <td>2.006112e+05</td>\n",
       "      <td>2.006112e+05</td>\n",
       "      <td>1.947174e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aboveground Slow DOM</th>\n",
       "      <td>2.019364e+05</td>\n",
       "      <td>2.019364e+05</td>\n",
       "      <td>9.671981e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belowground Slow DOM</th>\n",
       "      <td>6.523250e+05</td>\n",
       "      <td>6.523250e+05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Softwood Stem Snag</th>\n",
       "      <td>6.008220e+04</td>\n",
       "      <td>6.008221e+04</td>\n",
       "      <td>5.688820e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Softwood Branch Snag</th>\n",
       "      <td>9.901381e+03</td>\n",
       "      <td>9.901382e+03</td>\n",
       "      <td>4.931446e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hardwood Stem Snag</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hardwood Branch Snag</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BlackCarbon</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Peat</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Softwood Merchantable</th>\n",
       "      <td>4.068681e+05</td>\n",
       "      <td>4.068681e+05</td>\n",
       "      <td>5.761081e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Softwood Foliage</th>\n",
       "      <td>4.445775e+04</td>\n",
       "      <td>4.445776e+04</td>\n",
       "      <td>3.295263e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Softwood Other</th>\n",
       "      <td>1.472343e+05</td>\n",
       "      <td>1.472343e+05</td>\n",
       "      <td>3.980051e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Softwood Coarse Roots</th>\n",
       "      <td>1.218671e+05</td>\n",
       "      <td>1.218671e+05</td>\n",
       "      <td>5.612672e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Softwood Fine Roots</th>\n",
       "      <td>1.101326e+04</td>\n",
       "      <td>1.101327e+04</td>\n",
       "      <td>4.440100e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hardwood Merchantable</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hardwood Foliage</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hardwood Other</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hardwood Coarse Roots</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hardwood Find Roots</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total Biomass</th>\n",
       "      <td>7.314405e+05</td>\n",
       "      <td>7.314405e+05</td>\n",
       "      <td>5.208079e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aboveground Biomass</th>\n",
       "      <td>5.985601e+05</td>\n",
       "      <td>5.985601e+05</td>\n",
       "      <td>5.139835e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belowground Biomass</th>\n",
       "      <td>1.328803e+05</td>\n",
       "      <td>1.328804e+05</td>\n",
       "      <td>5.515488e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total Ecosystem</th>\n",
       "      <td>2.022953e+06</td>\n",
       "      <td>2.022953e+06</td>\n",
       "      <td>1.883088e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dead Organic Matter</th>\n",
       "      <td>1.291513e+06</td>\n",
       "      <td>1.291513e+06</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aboveground DOM</th>\n",
       "      <td>6.089731e+05</td>\n",
       "      <td>6.089731e+05</td>\n",
       "      <td>1.282897e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belowground DOM</th>\n",
       "      <td>6.825398e+05</td>\n",
       "      <td>6.825398e+05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Deadwood</th>\n",
       "      <td>2.904030e+05</td>\n",
       "      <td>2.904030e+05</td>\n",
       "      <td>1.345114e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Litter</th>\n",
       "      <td>3.383783e+05</td>\n",
       "      <td>3.383783e+05</td>\n",
       "      <td>2.308807e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Soil C</th>\n",
       "      <td>1.315057e+06</td>\n",
       "      <td>1.315057e+06</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     i2            i3       rel_dif\n",
       "TimeStep                   1.000000e+01  0.000000e+00  2.000000e+00\n",
       "Aboveground Very Fast DOM  4.872035e+04  4.872035e+04  1.002212e-08\n",
       "Belowground Very Fast DOM  1.040664e+04  1.040664e+04  2.346008e-08\n",
       "Aboveground Fast DOM       8.772152e+04  8.772152e+04  4.453012e-08\n",
       "Belowground Fast DOM       1.980821e+04  1.980821e+04  1.232523e-07\n",
       "Medium DOM                 2.006112e+05  2.006112e+05  1.947174e-08\n",
       "Aboveground Slow DOM       2.019364e+05  2.019364e+05  9.671981e-09\n",
       "Belowground Slow DOM       6.523250e+05  6.523250e+05  0.000000e+00\n",
       "Softwood Stem Snag         6.008220e+04  6.008221e+04  5.688820e-08\n",
       "Softwood Branch Snag       9.901381e+03  9.901382e+03  4.931446e-08\n",
       "Hardwood Stem Snag         0.000000e+00  0.000000e+00           NaN\n",
       "Hardwood Branch Snag       0.000000e+00  0.000000e+00           NaN\n",
       "BlackCarbon                0.000000e+00  0.000000e+00           NaN\n",
       "Peat                       0.000000e+00  0.000000e+00           NaN\n",
       "Softwood Merchantable      4.068681e+05  4.068681e+05  5.761081e-08\n",
       "Softwood Foliage           4.445775e+04  4.445776e+04  3.295263e-08\n",
       "Softwood Other             1.472343e+05  1.472343e+05  3.980051e-08\n",
       "Softwood Coarse Roots      1.218671e+05  1.218671e+05  5.612672e-08\n",
       "Softwood Fine Roots        1.101326e+04  1.101327e+04  4.440100e-08\n",
       "Hardwood Merchantable      0.000000e+00  0.000000e+00           NaN\n",
       "Hardwood Foliage           0.000000e+00  0.000000e+00           NaN\n",
       "Hardwood Other             0.000000e+00  0.000000e+00           NaN\n",
       "Hardwood Coarse Roots      0.000000e+00  0.000000e+00           NaN\n",
       "Hardwood Find Roots        0.000000e+00  0.000000e+00           NaN\n",
       "Total Biomass              7.314405e+05  7.314405e+05  5.208079e-08\n",
       "Aboveground Biomass        5.985601e+05  5.985601e+05  5.139835e-08\n",
       "Belowground Biomass        1.328803e+05  1.328804e+05  5.515488e-08\n",
       "Total Ecosystem            2.022953e+06  2.022953e+06  1.883088e-08\n",
       "Dead Organic Matter        1.291513e+06  1.291513e+06  0.000000e+00\n",
       "Aboveground DOM            6.089731e+05  6.089731e+05  1.282897e-08\n",
       "Belowground DOM            6.825398e+05  6.825398e+05  0.000000e+00\n",
       "Deadwood                   2.904030e+05  2.904030e+05  1.345114e-08\n",
       "Litter                     3.383783e+05  3.383783e+05  2.308807e-08\n",
       "Soil C                     1.315057e+06  1.315057e+06  0.000000e+00"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "i2 = cbm3_results.load_pool_indicators( get_results_db_path(2)).iloc[-1] \n",
    "i3 = cbm3_results.load_pool_indicators( get_results_db_path(3)).iloc[0]\n",
    "pd.DataFrame(data={\"i2\": i2, \"i3\": i3, \"rel_dif\": (i2-i3).abs()/((i2+i3)/2)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def merge_iteration_results(results, iteration_steps):\n",
    "    \n",
    "    out_results = []\n",
    "    #correct the timestep\n",
    "    for i, r in enumerate(results):\n",
    "        #drop the 0th timestep from the 2nd iteration onwards (it's the same as the last value from each previous iteration)\n",
    "        out = r.loc[r[\"TimeStep\"]!=0].copy() if i!=0 else r.copy()\n",
    "        out[\"TimeStep\"] = out[\"TimeStep\"] + i*iteration_steps\n",
    "        out_results.append(out)\n",
    "\n",
    "    return pd.concat(out_results, axis=0)\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_pool_indicators = merge_iteration_results([\n",
    "    cbm3_results.load_pool_indicators( get_results_db_path(x))\n",
    "        for x in range(1, num_iterations+1)\n",
    "], iteration_steps)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x69ef7f0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuUAAAHkCAYAAACZjlHGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAIABJREFUeJzs3XmUHVWh7/HfPlOfnrszEUIYJU8g6aRtOpExIDLKIAIakMXwLhcQL1zu9cklKmuJgO+C+nCpV8EAriSKBOU+CJNDLorIlSmTQBhMXggmIZCk5/lM+/1RVefUOX16zFDdyffjqlVVu3btqu40y1/ts88uY60VAAAAgOCEgr4BAAAAYH9HKAcAAAACRigHAAAAAkYoBwAAAAJGKAcAAAACRigHAAAAAjZkKDfGxI0xrxpj/mqMWWeM+ZZbfrgx5hVjzHpjzKPGmJhbXuLub3CPH+Zr62tu+bvGmLN85We7ZRuMMQt95SO+BgAAADDeDKenvE/SadbaOZLqJZ1tjDlO0j2Svm+tnSGpRdI1bv1rJLVYa4+U9H23nowxx0i6VNJMSWdL+okxJmyMCUv6saRzJB0j6TK3rkZ6DQAAAGA8GjKUW0enuxt1FyvpNEmPueVLJF3obn/W3Zd7/NPGGOOWL7PW9llr35O0QdI8d9lgrd1orU1IWibps+45I70GAAAAMO4Ma0y526O9VtJ2SSsk/T9JrdbalFtli6SD3O2DJG2WJPd4m6SJ/vKCcwYqnziKawAAAADjTmQ4lay1aUn1xpgaSY9LOrpYNXddrMfaDlJe7MFgsPqDXSOPMeY6SddJUnl5+bFHHXVUkdMAAACA3WfVqlU7rbWTR3LOsEK5x1rbaox5XtJxkmqMMRG3p3q6pA/calskHSxpizEmIqlaUrOv3OM/p1j5zlFco/B+F0laJEmNjY125cqVI/lxAQAAgBEzxrw/0nOGM/vKZLeHXMaYUkmnS3pb0h8lXeJWu0rScnf7SXdf7vE/WGutW36pO3PK4ZJmSHpV0muSZrgzrcTkfBn0SfeckV4DAAAAGHeG01N+oKQl7iwpIUm/stY+bYx5S9IyY8xdktZIesit/5CknxtjNsjpvb5Ukqy164wxv5L0lqSUpH9yh8XIGHOjpN9JCkv6mbV2ndvWrSO5BgAAADAemf2lg5nhKwAAANgbjDGrrLWNIzlnRGPKAQAA9mXJZFJbtmxRb29v0LeCcSAej2v69OmKRqO73BahHAAAwLVlyxZVVlbqsMMOE69AwWCstWpqatKWLVt0+OGH73J7w5qnHAAAYH/Q29uriRMnEsgxJGOMJk6cuNs+VSGUAwAA+BDIMVy782+FUA4AADBGNDU1qb6+XvX19Zo6daoOOuig7H4ikehXv7m5Wffff/+Q7aZSKdXU1BQtD4fD2WvU19fru9/97m75WUYik8no7rvv3uvXHUuYfQUAAMD19ttv6+iji724fO+7/fbbVVFRoa9+9asD1tmwYYMuueQSrV27dtC2UqmUJk2apNbW1mGV721j5T5Go9jfzGhmX6GnHAAAYBz4zne+o1mzZmnWrFn60Y9+JElauHCh3n33XdXX12vhwoVqb2/XaaedpoaGBs2ePVtPP/30qK/3yiuv6Pjjj9ecOXP0yU9+Ut3d3erp6dFVV12luro6NTQ06IUXXpAkvfHGG5o7d67q6+s1e/Zsbdy4UV/72tf04x//ONverbfeqp/85CfaunWrTjrpJNXX12vWrFn6y1/+ooULF6qjo0P19fW68sorJUlLlizRvHnzVF9fry9/+cvKZDLZHv9bbrlFDQ0NOuuss/TKK6/olFNO0RFHHKFnn312F37DwaKnHAAAwOXv9fzWU+v01gftu7X9Y6ZV6ZvnzxxWXX9P+auvvqprr71WL730ktLptObNm6dHH31UZWVleT3lyWRSvb29qqys1Pbt23XiiSdq/fr1g/aUl5SUqK6uLlt222236bzzztPRRx+t//zP/1RDQ4Pa2tpUUVGh733ve9qwYYMeeOABrVu3Tp/5zGe0fv163XzzzTr11FO1YMEC9fX1yVqrrVu36tJLL9Vrr72mdDqtGTNmaNWqVVq0aJEkJ6Sn02n19PQoHo/n3d+bb76p2267TY899pgikYiuu+46nXrqqfrCF76gaDSq3//+9zrjjDN0/vnnK5VK6amnntJf//pXXX/99drbeW939ZQzJSIAAMAY9+c//1kXX3yxysrKJEkXXnihXnzxRZ155pl59ay1uvXWW/Xiiy8qFApp8+bN2rlzZ9Hx5J7Kysp+w1/WrFmjQw45RA0NDZKk6upqSdKLL76oW265RZI0c+ZMTZs2TRs2bNAJJ5ygu+66S++//74uuugiHXnkkfrYxz6myspKvfHGG3r//fc1b9481dbWau7cubr++uvV29urCy+8UHPmzFEqlcq7/n/913/ptddeU2Ojk2t7enp08MEHS5JKS0t1xhlnSJLq6upUXV2tSCSiuro6bdq0aTS/3jGBUA4AAFDEcHu094bhjmxYunSp2tratHr1akUiEU2fPn1UU/ZZa4vOLDLQfVxxxRU6/vjj9cwzz+iMM87QkiVLNH/+fF1zzTVavHixNm3apOuvv16SdNppp+n555/XM888o8svv1xf+9rXtGDBgn7X+Yd/+AfdeeedeeWpVEqxWCy7HwqFVFJSkt0uDPfjCWPKAQAAxrj58+fr8ccfV09Pjzo7O7V8+XKdfPLJqqysVEdHR7ZeW1ubpkyZokgkohUrVmjr1q2jut7MmTP1/vvva/Xq1ZKk9vZ2pdNpzZ8/Xw8//LAkZ9jGtm3bdOSRR2rjxo068sgjdfPNN+vcc8/V66+/Lkm6+OKL9dRTT2nt2rU6/fTTJUnvv/++pk6dquuuu05XX3211qxZo0jE6Sf2QvXpp5+uX/3qV9q5c6ckZ1aav//976P6WcYLesoBAADGuHnz5umyyy7T3LlzJUk33HBDdhx4Y2Oj6urqdO655+orX/mKzj//fDU2NqqhoUEzZswYsm3vC5aec889V9/+9rf1yCOP6IYbblBvb69KS0v1hz/8QTfddJOuv/561dXVKRqNaunSpYrFYvrlL3+pRx55RNFoVNOmTdNdd90lyXkN/fz58zV16lSFQk5f8HPPPad7771X0WhUFRUV+sUvfiFJuuaaazR79mw1NjZq6dKl+uY3v6nTTz9dmUxG0WhU999/v6ZNm7Zbf69jCV/0BAAAcI2lKRH3BZlMRvX19XriiSd0xBFHBH07ewRTIgIAAGDMeuONN/Sxj31MZ5999j4byHcnhq8AAABgt6urq9N7770X9G2MG/SUAwAAAAEjlAMAAAABI5QDAAAAASOUAwAAAAEjlAMAAIwRTU1Nqq+vV319vaZOnaqDDjoou59IJPrVb25u1v333z9ku6lUSjU1NUXLw+Fw9hrHHnusXn75ZUnS5s2b+71pE3sOs68AAACMERMnTtTatWslSbfffrsqKir01a9+dcD6Xij/0pe+NOprVlZWZq/5zDPP6Bvf+Iaee+45HXzwwXr00UdH3S5Ghp5yAACAceA73/mOZs2apVmzZulHP/qRJGnhwoV69913VV9fr4ULF6q9vV2nnXaaGhoaNHv2bD399NMjukZ7e7tqa2slSRs2bMi+6bOnp0dXXXWV6urq1NDQoBdeeEGS9OCDD+qiiy7Seeedp8MPP1z33Xefvvvd7+oTn/iETjjhBLW2tkqS7r//fs2dO1dz5szR5z//efX09EiSli1bplmzZmnOnDn61Kc+JcmZ33zu3Lmqr6/X7NmztXHjxl3/5Y0D9JQDAAAU85uF0odv7N42p9ZJ59w94tNeffVVPfzww3r11VeVTqc1b948nXLKKbr77ru1YcOGbE93MpnU8uXLVVlZqe3bt+vEE0/UeeedN2jbHR0dqq+vV29vrz788EP98Y9/7Ffnhz/8oWKxmN544w2tW7dOn/nMZ7R+/XpJ0rp167R69Wp1dnZqxowZuvfee7VmzRrddNNN+sUvfqEbb7xRn//857O9+QsXLtTixYt1ww036Fvf+paef/55HXDAAdkA/5Of/ERf/epXtWDBAvX19Wl/efs8PeUAAABj3J///GddfPHFKisrU2VlpS688EK9+OKL/epZa3Xrrbdq9uzZOvPMM7V582bt3Llz0La94SvvvPOOnn76aV155ZX96rz44ou64oorJEkzZ87UtGnTtGHDBknSaaedpvLych1wwAGqqKjQ+eefL8l5edCmTZskSa+//rpOPvlk1dXVadmyZVq3bp0k6cQTT9SVV16pBx98UJlMRpJ0wgkn6K677tJ3vvMdbd68WfF4fHS/tHGGnnIAAIBiRtGjvacMt7d46dKlamtr0+rVqxWJRDR9+nT19vYO+zonnXSSPvjgAzU3Nw/7+iUlJdntUCiU3Q+FQkqlUpKkK6+8Ur/5zW80a9YsPfjgg9kvkz7wwAN65ZVX9PTTT2vOnDl6/fXXdcUVV+j444/XM888ozPOOENLlizR/Pnzh/0zjFf0lAMAAIxx8+fP1+OPP66enh51dnZq+fLlOvnkk1VZWamOjo5svba2Nk2ZMkWRSEQrVqzQ1q1bR3SddevWKRQKZceV+6//8MMPS5Lefvttbdu2TUceeeSw2+3q6tLUqVOVTCb1y1/+Mlu+ceNGHXfccbrzzjtVW1urrVu3auPGjTryyCN1880369xzz9Xrr78+op9hvKKnHAAAYIybN2+eLrvsMs2dO1eSdMMNN6iurk6S1NjYqLq6Op177rn6yle+ovPPP1+NjY1qaGjQjBkzhmzbG1PuWbp0qYwxeXVuuukmXX/99aqrq1M0GtXSpUsVi8WGff933HGH5s2bp0MOOUSzZs3K9t7/67/+q9577z1Za3XmmWdq1qxZuuuuu/TII48oGo1q2rRpuuuuu4Z9nfHM7C+D5xsbG+3KlSuDvg0AADCGvf322zr66KODvg2MI8X+Zowxq6y1jSNph+ErAAAAQMAI5QAAAEDACOUAAABAwAjlAAAAQMAI5QAAAEDACOUAAABAwAjlAAAAY0g4HFZ9fb1mzpypOXPm6N57782+gn5X3X777fre975X9NiiRYt01FFH6aijjtK8efP04osv7pZrDuUf//Ef9dZbbw2r7vPPPy9jjB566KFs2Zo1a2SMGfDn8jzxxBN511m8eLE++OCD0d30HkAoBwAAGENKS0u1du1arVu3TitWrNCzzz6rb33rW3v0mk8//bR++tOf6sUXX9Q777yj+++/X1/84hf14Ycf9qubTqd367UffPBBHXPMMcOuX1dXp0cffTS7v2zZMs2ZM2fI83ZHKE+lUiOqPxKEcgAAgDFqypQpWrRokf7jP/5D1lql02ndcsstmjt3rmbPnq2f/vSnkqTOzk59+tOfVkNDg+rq6rR8+fJsG9/+9rf18Y9/XKeffrrefffdote555579N3vfleTJk2SJDU0NOiqq67Sj3/8Y0nSYYcdpjvuuEMnnXSSfv3rX+u1117T7Nmzdfzxx+uWW27RrFmzJEmbNm3SySefrIaGBjU0NOgvf/mLJKeH+9RTT9Ull1yio446Spdffrm8F1ieeuqp8l7w+Nvf/lYNDQ2aM2eOPv3pTxe910MOOUS9vb366KOPZK3Vb3/7W51zzjnZ4w888IDmzp2rOXPm6OKLL1Z3d7f+8pe/6Mknn9Qtt9yi+vp63XPPPVq5cqUuv/xy1dfXq6enR6tWrdIpp5yiY489VmeddZa2bduWvb+vf/3rOuWUU/SDH/xgdP+QwxDZYy0DAACMY/e8eo/eaX5nt7Z51ISjdOu8W0d0zhFHHKFMJqPt27dr+fLlqq6u1muvvaa+vj6deOKJOvPMM3XwwQfr8ccfV1VVlXbu3KnjjjtOF1xwgVavXq1ly5ZpzZo1SqVSamho0LHHHtvvGuvWretX3tjYqCVLlmT34/F4dkjLrFmztGjRIp1wwglauHBhts6UKVO0YsUKxeNxrV+/Xpdddlk2cK9Zs0br1q3TtGnTdOKJJ+q///u/ddJJJ2XP3bFjh6699lq98MILOvzww9Xc3Dzg7+SSSy7Rr3/9a33iE59QQ0ODSkpKsscuuugiXXvttZKk2267TQ899JBuuukmXXDBBTrvvPN0ySWXSJJ+85vf6Hvf+54aGxuVTCZ10003afny5Zo8ebIeffRRfeMb39DPfvYzSVJra6v+9Kc/De8fbJQI5QAAAGOc16v8+9//Xq+//roee+wxSVJbW5vWr1+v6dOn6+tf/7peeOEFhUIhbd26VR999JH+/Oc/63Of+5zKysokSRdccMGIrmmMye4vWLBAkhNQOzo6dMIJJ0iSvvjFL+rpp5+WJCWTSd14441au3atwuGw/va3v2XPnzdvnqZPny5Jqq+v16ZNm/JC+csvv6z58+fr8MMPlyRNmDBhwHv7whe+oAULFuidd97RZZddlu2Rl6Q333xTt912m1pbW9XZ2amzzjpryJ/13Xff1ZtvvqkzzjhDkjNE58ADD+z3s+9JhHIAAIAiRtqjvads3LhR4XBYU6ZMkbVWP/rRj/oFzcWLF2vHjh1atWqVotGoDjvsMPX29kpSXrAeyDHHHKNVq1bptNNOy5atXr06b6x3eXm5pNwDQjHf//73dcABB+ivf/2rMpmM4vF49pi/NzscDvcbn134EDCYqVOnKhqNasWKFfrBD36QF8qvvvpqPfHEE5ozZ44WL16s559/fsj2rLWaOXOmXnrppaLHvZ99T2JMOQAAwBi1Y8cOfelLX9KNN94oY4zOOuss3XfffUomk5Kkv/3tb+rq6lJbW5umTJmiaDSqP/7xj3r//fclSfPnz9fjjz+unp4edXR06Kmnnip6nX/7t3/TrbfeqqamJknS2rVrtXjxYn35y1/uV7e2tlaVlZV6+eWXJTlftPS0tbXpwAMPVCgU0s9//vMRfSn0+OOP15/+9Ce99957kjTo8BVJuuOOO3TPPfcoHA7nlXd0dOjAAw9UMpnUww8/nC2vrKxUR0dH0f2Pf/zj2rFjRzaUJ5NJrVu3btj3vjvQUw4AADCG9PT0qL6+XslkUpFIRFdccYW+8pWvSHKmD9y0aZMaGhpkrdXkyZP1xBNP6PLLL9f555+vxsZG1dfX66ijjpLkfGFzwYIFqq+v16GHHqqTTz656DUvuOACbd26VSeccIKMMaqsrNQvfvGLvCEcfg899JCuvfZalZeX69RTT1V1dbUk6ctf/rIuvvhi/frXv9anPvWpEfUwT548WYsWLdJFF12kTCaTHZ8+EG/4TKE777xTn/zkJ3XooYeqrq4uG7wvvfRSXXvttfrhD3+oxx57TFdffbW+9KUvqbS0VC+99JIee+wx/fM//7Pa2tqUSqX0L//yL5o5c+aw739XmcE+gtiXNDY2Wu+LBgAAAMW8/fbbOvroo4O+jTGvs7NTFRUVkqS7775b27Zt26Mzk4xlxf5mjDGrrLWNI2mHnnIAAACMyDPPPKN///d/VyqV0qGHHqrFixcHfUvjHqEcAAAAI7JgwYK9MiPJ/oQvegIAAAABI5QDAAD47C/ft8Ou251/K4RyAAAAVzweV1NTE8EcQ7LWqqmpKW8u9l3BmHIAAADX9OnTtWXLFu3YsSPoW8E4EI/Hs28p3VWEcgAAAFc0Gs2+5h3Ymxi+AgAAAASMUA4AAAAEjFAOAAAABIxQDgAAAASMUA4AAAAEjFAOAAAABIxQDgAAAASMUA4AAAAEjFAOAAAABIxQDgAAAASMUA4AAAAEjFAOAAAABIxQDgAAAASMUA4AAAAEbMhQbow52BjzR2PM28aYdcaYm93y240xW40xa93lM75zvmaM2WCMedcYc5av/Gy3bIMxZqGv/HBjzCvGmPXGmEeNMTG3vMTd3+AeP2yoawAAAADjzXB6ylOS/pe19mhJx0n6J2PMMe6x71tr693lWUlyj10qaaaksyX9xBgTNsaEJf1Y0jmSjpF0ma+de9y2ZkhqkXSNW36NpBZr7ZGSvu/WG/Aao/4tAAAAAAEaMpRba7dZa1e72x2S3pZ00CCnfFbSMmttn7X2PUkbJM1zlw3W2o3W2oSkZZI+a4wxkk6T9Jh7/hJJF/raWuJuPybp0279ga4BAAAAjDsjGlPuDh/5hKRX3KIbjTGvG2N+ZoypdcsOkrTZd9oWt2yg8omSWq21qYLyvLbc421u/YHaKrzf64wxK40xK3fs2DGSHxUAAADYa4Ydyo0xFZL+U9K/WGvbJd0n6WOS6iVtk/R/vKpFTrejKB9NW/kF1i6y1jZaaxsnT55c5BQAAAAgeMMK5caYqJxA/rC19v9KkrX2I2tt2lqbkfSAcsNHtkg62Hf6dEkfDFK+U1KNMSZSUJ7Xlnu8WlLzIG0BAAAA485wZl8xkh6S9La19l5f+YG+ap+T9Ka7/aSkS92ZUw6XNEPSq5JekzTDnWklJueLmk9aa62kP0q6xD3/KknLfW1d5W5fIukPbv2BrgEAAACMO5Ghq+hESVdIesMYs9Yt+7qc2VPq5Qwb2STpekmy1q4zxvxK0ltyZm75J2ttWpKMMTdK+p2ksKSfWWvXue3dKmmZMeYuSWvkPATIXf/cGLNBTg/5pUNdAwAAABhvjNPxvO9rbGy0K1euDPo2AAAAsI8zxqyy1jaO5Bze6AkAAAAEjFAOAAAABIxQDgAAAASMUA4AAAAEjFAOAAAABIxQDgAAAASMUA4AAAAEjFAOAAAABIxQDgAAAASMUA4AAAAEjFAOAAAABIxQDgAAAASMUA4AAAAEjFAOAAAABIxQDgAAAASMUA4AAAAEjFAOAAAABIxQDgAAAASMUA4AAAAEjFAOAAAABIxQDgAAAASMUA4AAAAEjFAOAAAABIxQDgAAAASMUA4AAAAEjFAOAAAABIxQDgAAAASMUA4AAAAEjFAOAAAABIxQDgAAAASMUA4AAAAEjFAOAAAABIxQDgAAAASMUA4AAAAEjFAOAAAABIxQDgAAAASMUA4AAAAEjFAOAAAABIxQDgAAAASMUA4AAAAEjFAOAAAABIxQDgAAAASMUA4AAAAEjFAOAAAABIxQDgAAAASMUA4AAAAEjFAOAAAABIxQDgAAAASMUA4AAAAEjFAOAAAABIxQDgAAAAQsEvQNAHtSIpVRe29S7T1Jtfem3HVS7T0ptfcmlc5Y1ZRFVVsWU01pVDVlMdWWO/vxaDjo2wcAAPsJQjnGtEQqo47e4oG6+H5+vZ5ketTXLomEnLDuhfYyN7SXRTWhPKaaspgmuAG+tiym2vKYquIRGWN2428AAADsDwjl2KP6Uum80NzRmyoarDuyYXpkoTocMqqKR1QZj6q6NKqq0oimVFaoKh5VdVlUVfGIqkqjqoo7x5x1bj9kjFq7k2rpTqi1O6nW7oRaupNq7XH2W7qc/baehNZv78weT2fsgPdTW+YP6r4AXxbThPL+S1ksTJAHAGA/RyjHgKy16knmQnVHsV5pX1lHQaDu6E2qL5UZ9BqRkHFDshOeK+MRTa6oyAbsbIguEqir4tHdEminVoc1tTo+ot9LR18qG9hbuhJq7kqopdtZmruccN/cldB7O7u0+u+taulKKDVAkC+JhPoF9dqymCaWO73vE92yiRUlmlgeU3VpVKEQIR4AgH0JoXwfl0pn1NaTdHp/vd7gnvyA3VEYrH2he6AeYU8sEsoLyZXxiA6qLXXKsr3UxXurK+MRlUbHXy+xMcb9+aI6dOLwzvGCfHNnQs3didy6K5EN9c1dTtnfm7vV3JVQR2+qaFteb7wX4CeWl7ih3QvwJZpYEdOkCme7hhAPAMCYRygfJ6y16kqk1dKVcEN2Ihu0W7qc/f7lCbUPEOw85bFwNiBXxaOaXFmiIyaXZwN0pRs+K+MRVXv1SnNlfBlyePxB/jCVD+ucRCqjlu6EmjqdwN7U1eesOxNq6kqo2d1/+8N2NXU6//7FOCHeCekT3aA+sdzbd7adQF+iSZUlKmc4DQAAex2hPAB9qbQ7ftkby+yOX/b1Zrf41z1OeTI9cK91ZUlENeVR1ZQ6X0g8dEKZan1fTKx1xzXXljljr6tLo6ooiSgSZlbMsSoWCemAqrgOqBre0JpkujDEJ9TU2eeG+D7t7HT232hpVVNnQh19xR/YSiIhTarwetu90F6iSd6+F+ArnJ56/oYAANh1hPJdkEpn1N6bGjJYt/Y4vdmtbsDuTgz85cVYJOSEaTdcf2xyhWrLo6oujWW/QOifBaTG3Y8SjPZ70XBIUyrjmlI5vBDfm0z7et5zob2pK6Gdbpj/qL1Xb33QrqauvgEfCmvLonkhflJegM9tT6ooUWmMT1YAACiGUC4pk7Hq6E054dk/9rrbm3mjyOwc3YMPDQkZZQNzTWlUB1bHdfSBVe70erkwXRiyx+MYa4xP8WhY02pKNa2mdMi61lq196ayYb2ps087uxLa2dGXLdvZ2ac3t7YN2gtfHgvnB/VKJ6xPLtifVBFTRQnTSwIA9h/7TSjf3t6nbz21LhusnSEhznZbT1KDfZ+xKh7J65k+bFK5asucWTCKBuzSmCrjEb5ch32GMSY77Oljk4eu35tMOz3uvtC+wxfed3b2aVNTl1a+36KW7oRskf/+vGE0/p72SZXO9uRseC/R5IoSVZUS4AEA49t+E8o/6ujVYyu3qNoXnqfXlrlvcXSDtX/b7eGuLo0yZhYYoXg0rINqSnXQMHrhU+mMmruc0L6z0xfk3VC/o7NPH7T16vWtbWruShSdESgWDmliRcwX1v1B3gnuk91AX10aJcADAMYcY4t1Ue2Djm1stKtWrgz6NgDsgkzGqqU74YT3zj7tcAP8js4+7ezI9cJ7vfPF5oaPhk2/se9ecHeGz8Q0uaIk+9KnMJ94AQBGyBizylrbOJJzhuwpN8YcLGmppKmSMpIWWWt/YIyZIOlRSYdJ2iTpC9baFuN0Qf1A0mckdUu62lq72m3rKkm3uU3fZa1d4pYfK2mxpFJJz0q62VprR3ONAX+OYfwyAIxtoZBxpnGsKNHHVTlo3UzGqq0nmQ3v2Z74gjD/1rb2AQN8yCh/PviKmCYVmQvemyO+Ks5ZhjYwAAAgAElEQVSc8ACA0RnO8JWUpP9lrV1tjKmUtMoYs0LS1ZKes9bebYxZKGmhpFslnSNphrt8UtJ9kj7pBuxvSmqUZN12nrTWtrh1rpP0spxQfrak37htDvsau/rLALDvCIWMat23os44YHgB3ulxd77E2tTpzAO/s9OZE76pM6G3P2jXzs6+Ab/kHXGv6c397s0J700rOaHcC/LOflWcsfAAAMeQodxau03SNne7wxjztqSDJH1W0qlutSWSnpcTmD8raal1xsW8bIypMcYc6NZdYa1tliQ32J9tjHleUpW19iW3fKmkC+WE8hFdw71XABgRf4D/H0MEeCn/xU7eS5288J7bTuiNLa1qGuTtrNGw82InJ6yX+HrlY5rgzgc/sSJXRk88AOy7RvRFT2PMYZI+IekVSQd4Idhau80YM8WtdpCkzb7Ttrhlg5VvKVKuUVyDUA5gjxvpi536Umm1dCXV5Pa4N3tzwXcl1Ox7Q+uWlu5Bp5QMh0wutPsDvDu0ZmJeGWPiAWA8GXYoN8ZUSPpPSf9irW0f5CPXYgfsKMoHvZ3hnGOMuU7OsBgdcsghQzQJAHtGSSSsqdVhTa0eWYjf6Q6h8b+h1dtu7kponTucZqCeeO99CRMKwrq3rvXGyrvDbWrLYopFmG0KAIIwrFBujInKCeQPW2v/r1v8kTdkxB2est0t3yLpYN/p0yV94JafWlD+vFs+vUj90Vwjj7V2kaRFktTY2Lh/TDMDYNwbaYj3D6dxQnt+mG92y//2UYda3LcNDzTxVmVJRBN8Q2Zqy5yhNBN84d6/8JInANg9hjP7ipH0kKS3rbX3+g49KekqSXe76+W+8huNMcvkfPmyzQ3Vv5P0v40xtW69MyV9zVrbbIzpMMYcJ2dYzJWSfjSaa4z8xweA8W+kw2nSGavW7kQ2uPt73/3L1tZevbm1Xc1dCSXSmaJt+cfF+5faslzvu7fv9M5HVRIJ784fHwD2CcPpKT9R0hWS3jDGrHXLvi4nKP/KGHONpL9L+rx77Fk5UxVukDNd4f+UJDd83ynpNbfeHd6XPiXdoNyUiL9xF430GgCAoYV9U0sOh7VWXYm00+Pe7X2hNakWN8xn193OkJqmQWaokaTyWFi15flh3dmOOuVlseywm9py54VvUV7iBmAft9+8PKixsdGu5OVBALBXpNIZtfY4wb3ZDezNXclcoO/2lztLdyI9YHuVJZHsDDm1ZVFNKMtt17rhvsZ9Y7PzJVd65AEEZ4+8PAgAgJGKhEPZN6cOV28yrVZ3zHtLl9Mr39KVUEt3MhvgW7qTaupMaP1HnWrtTqhrkCBfFgurtizX215T5ob4svww74X4CeUxlUbDjJEHEAhCOQBgTIhHR/YFV6kgyHcn1OoG+FY3wLf4gv3m5m61dCfV1pMcsL1YJOQOn4lmh9d4ob4wwNe6vfXlMYI8gF1HKAcAjFujCfKpdEZtPclsz3uLrxe+xTfUprU7obc/bM+G/oFGe8bCoX7B3b9fWx51xsi7+zXlUVUyaw2AAoRyAMB+JRIOjeiLrpKUyVi19ybzxsf7h9m0diWzw22GE+QjIZM/nKbIEJsad4hNTakT6mvKonzhFdiHEcoBABhCyA3RNWWxYZ+Tzli1F+mR9w+3aXHD/aad3Vrd3arW7oSS6YEnYKgsiajaH9rdtRfma8qiqinNldWURlVVGuXNrsA4QCgHAGAPCIdMdsaY4fKmn2zpyoX31h5nKI0X4P1Db4YzTt4YqSruBfaoqt2wXluW264udY+XOdvVpTFVl0Z5wyuwFxHKAQAYI4wxqiiJqKIkooMnDP+8dMaqrSeZDext3Um19njBPqm2bLhPqrUnqfebutTanVR7b3LAITaSM6d8dWn/8F7t9sBXD7DQOw+MHKEcAIBxLhwy2ZcwHa7yYZ+Xzlh19Dph3gvsrd0Jtfv2vWNtPQlt3NmZ7ZlPpIq/5dVTWRIpGtyrSiN5ob4q7m1HstvxKHPMY/9DKAcAYD8V9o2VP3TiyM7tTaazvfNtPUm1dSfz93uSavdt/78dnWp3HwB6k4MH+lgk5AZ2J9h7Yd3br4xH+pVVxXPbJZEQs9tg3CGUAwCAEYtHw4pHwzqgavjTUXr6Umm196SyId0L7+29KbUXhPmO3pRauhP6e3O3c6w3OeiXYSVnmsrKeMRdonnbVb793LbTg19RkqtPsMfeRigHAAB7VUkkrMmVYU2uHP60lB5rrXqTGXX0Jt1Q74T7Di/Q9ybV3pNSh1vmrd/b2eXup9TZlxryOtGwyYZ0Z50L9t5+RTyiypKIymIRlZdEVF4SVrn7nYCyWFgVJU45U1liOAjlAABg3DDGqDQWVmksrCmj6KWXnLH0nX25wN7u9shny/pS6vQFeK/eB6296uzrzO6nMoP32Hti4ZDKS8Iqi0XcoO6E99Kosy6L+daxiMpKnHVpwX6Z+3OXxyKKR+nJ39cQygEAwH4lHDLZL56OlrVWfamMOvtS6upLqasvra6EE+K7+9JOWcI51tmXVrd7LFu3L6UdHX3qTjjHuvrS6kmmh319Y6TSaDgvqJfG3P2oF/Sd7dJYSGWxiOJe/ahzjnd+3NdOWTSieCykWJjQv7cRygEAAEbIGJMdVz9pBG+HHUw6Y9WTTKu7L6UuN6x3J9LZIN+dSDnHE87Sk3Dq9fjq9iTSau7qUY9vvzuZVnqYvfqecMio1P354tFQNsjHo26oL9yPhRSPuPVjYcUjoewxr414QXvePtNnOgjlAAAAY0A4lJunfndLpDLqSaTdUO+E+x4v3LvbXuDvSaTUm8w45cm0er06br3W7oS2Zfcz6nW3Rxr8PdGwUTwSVkleeA+pJOLuu2G/xDsWcbez61D2XO+ckkhYJe6DgX9d4p4TC4cUGmMPA4RyAACAfVwsEnKmmtToh+wMJZl2gnxvMq2+ZG67N287V+aF+b5UJlvWl0yrN+Wc35tyylq7k06bqUy2Tl8qo0R68Kk1hxILh1TiC/B5227Q97ZjkYJj7vFcG/l1RoNQDgAAgF0WDYcUDYdUFd9zwd8vnbFKuIE+G+y9QO8r86+dxamT3U5l3H1fnWRa7T3Ow0Ai7Rx31rkHgsHehjsahHIAAACMO+FQbiaevc1aq2Taqi+VViIb9jPudlpz7hl5m4RyAAAAYASMMYpFjGKjHKpSDLPZAwAAAAEjlAMAAAABI5QDAAAAASOUAwAAAAEjlAMAAAABI5QDAAAAASOUAwAAAAEjlAMAAAABI5QDAAAAASOUAwAAAAEjlAMAAAABI5QDAAAAASOUAwAAAAEjlAMAAAABI5QDAAAAASOUAwAAAAEjlAMAAAABI5QDAAAAASOUAwAAAAEjlAMAAAABI5QDAAAAASOUAwAAAAEjlAMAAAABI5QDAAAAASOUAwAAAAEjlAMAAAABI5QDAAAAASOUAwAAAAEjlAMAAAABI5QDAAAAASOUAwAAAAEjlAMAAAABI5QDAAAAASOUAwAAAAEjlAMAAAABI5QDAAAAASOUAwAAAAEjlAMAAAABI5QDAAAAASOUAwAAAAEjlAMAAAABI5QDAAAAASOUAwAAAAEjlAMAAAABI5QDAAAAARsylBtjfmaM2W6MedNXdrsxZqsxZq27fMZ37GvGmA3GmHeNMWf5ys92yzYYYxb6yg83xrxijFlvjHnUGBNzy0vc/Q3u8cOGugYAAAAwHg2np3yxpLOLlH/fWlvvLs9KkjHmGEmXSprpnvMTY0zYGBOW9GNJ50g6RtJlbl1Jusdta4akFknXuOXXSGqx1h4p6ftuvQGvMbIfGwAAABg7hgzl1toXJDUPs73PSlpmre2z1r4naYOkee6ywVq70VqbkLRM0meNMUbSaZIec89fIulCX1tL3O3HJH3arT/QNQAAAIBxaVfGlN9ojHndHd5S65YdJGmzr84Wt2yg8omSWq21qYLyvLbc421u/YHa6scYc50xZqUxZuWOHTtG91MCAAAAe9hoQ/l9kj4mqV7SNkn/xy03ReraUZSPpq3+hdYustY2WmsbJ0+eXKwKAAAAELhRhXJr7UfW2rS1NiPpAeWGj2yRdLCv6nRJHwxSvlNSjTEmUlCe15Z7vFrOMJqB2gIAAADGpVGFcmPMgb7dz0nyZmZ5UtKl7swph0uaIelVSa9JmuHOtBKT80XNJ621VtIfJV3inn+VpOW+tq5yty+R9Ae3/kDXAAAAAMalyFAVjDGPSDpV0iRjzBZJ35R0qjGmXs6wkU2Srpcka+06Y8yvJL0lKSXpn6y1abedGyX9TlJY0s+stevcS9wqaZkx5i5JayQ95JY/JOnnxpgNcnrILx3qGgAAAMB4ZJzO531fY2OjXblyZdC3AQAAgH2cMWaVtbZxJOfwRk8AAAAgYIRyAAAAIGCEcgAAACBghHIAAAAgYIRyAAAAIGCEcgAAACBghHIAAAAgYIRyAAAAIGCEcgAAACBghHIAAAAgYIRyAAAAIGCEcgAAACBghHIAAAAgYIRyAAAAIGCEcgAAACBghHIAAAAgYIRyAAAAIGCEcgAAACBghHIAAAAgYIRyAAAAIGCEcgAAACBghHIAAAAgYIRyAAAAIGCRoG9gb9nUvkn/+Lt/lDFGIRNy1goV3zYhheTbNiEZ+bbd+gMel8ldx7cdUkgyyruWd16xcwY6FjLOs5S/jWL1/fvedb12JOXade/Lv2+MyV3D156kom0bmX71/Od61ytWN6/cV+Y/r/D6heX++v57LHa+/7iVVTqTVtq6S8F2yqaUzqSVsZnstnfMyipt08pkMsooo4zNOHW9bZuWtW4d65RZa2Vli+77yyXl6shKUrZuv3VBWSHvfK+NYuWFCn9XxcoLf5d5/55O5f7/1r5/u8K/rcK/zbzjRf4bLfxvMrtf5L/NsAnnleUtvnaz9RRSOBRW2IQVMqHcOpTbL1bm3T8AACO134RySUpmkk7QkROG/KHHH5K8416oKna86LavPX873vnAnlQ0GOcO5tUrtu3JC/H+4G6LH/f+xqHsA4AX1sMm3C/ce9uRUCS/nsmv722HTEgRE8nbDoWctiKhSP/zQuFs/cJj2fqFdbx937b/Gv66A7URCUX6HfM/0AEABrffhPLDqg7TknOWBHoPXnAvDO1Fg3xBqC9WL2MzklVeT2te/YLz+7Xj61ktdr73P/91CkOYv7fXOpXyzsuGNat+7XrXLtbTO1Bd7/dY7F7yjvna8cqz7Ra05w8SxUKMPxRlQ1Uo18PqhaWQQtkgkw1nbr3CntpsT7C/h3eAT0+Kfarg9QYXfkIQtIH+/Yr9/RSu/Z8QDPg3W+xBuNh/R2659ymGV+Y9aPuPeW35j3mfiHjnZNeZ/P3sdia/zPs0JVtW0J7XVsqmsucXflqTyCSUTqf7fVKTsZlsB0PepzvuJzteeymbCvJPQZLy/tvJrt3/3vLWvocC/763Xfhw4D+3sL1oKNq/TsF5Xp2BHjoGe/AY6CGHT0oA7Kr9JpSPBd7H48C+qnCoEoLjfxBJ27RSGSewpzKpfsOzvOP+kJ/KpJyQn8kfulU4jCuZSfY/z71W4bGi+wXX8z+U9KR6lLJOe8Xa8K7lnec9lATFe7geKsiP9pOHvP1BHiYGfGgpaMc7PpwHm2go2q+dsdQhAOwLCOUAsA/yOgHC2r86AvyfFHhh3tv2Hjz824UPJYVr78HDe9Ao/ERjoIcc/4OD/8Go38OQzX/g6En35D2EFN5r9ucq2A9CseDvPSxkw7tvf7C6hcf8DwF5yyD1iz1sjLSchw0EiVAOANhnhExIoXBIUUWDvpW9xvtUxP+JQb9PF3wPCHkPLO4nDYUPDN4nEoUPNv4HhX7n+vb9i/+cZCaphE30O+a/nv+Y90C0Nw32UDFUyM9um4ii4Wje+f46hecVrvPOC0cHbKNfmyZXxsPF+EMoBwBgHMv7VGQf/GDE+85HYbgv/BRkoIeCEZUXqzeMst5Ub/YBwjueTCdz2+6xvfmQUexTgKIPEIN8EpFXp8gx/0NIsWNFz/Pdh39IVWGbhZ/E7A8PGYRyAAAwZhljnOCmiErCJUHfzi7zHjL8Dxj+gJ+0yaLleXUyydxxmxqwrbwy70FhgAeUZCapPts34DX9Dxfesjdn3vI/AGQ/xRgk9PvrRE100CFVQ7WZ910LX92871oUPASN6mfczb8zAAAADCD7kDHK4DaWFH5aUeyhwD9EqfAhIp1JZx9Cip1X9MGkyKcb/i9+e+2lM2n1pfrUleka8hz/8SCn+B3/fxEAAADY68IhZ9jUvvAJhsf/JWvvoSBvxqcBvjheGPRP1+kjvjahHAAAAFDuQSMWju31a/OmAwAAACBghHIAAAAgYIRyAAAAIGCEcgAAACBghHIAAAAgYIRyAAAAIGCEcgAAACBghHIAAAAgYIRyAAAAIGCEcgAAACBghHIAAAAgYIRyAAAAIGCEcgAAACBghHIAAAAgYIRyAAAAIGCEcgAAACBghHIAAAAgYIRyAAAAIGCEcgAAACBghHIAAAAgYIRyAAAAIGCEcgAAACBghHIAAAAgYIRyAAAAIGCEcgAAACBghHIAAAAgYIRyAAAAIGCEcgAAACBghHIAAAAgYEOGcmPMz4wx240xb/rKJhhjVhhj1rvrWrfcGGN+aIzZYIx53RjT4DvnKrf+emPMVb7yY40xb7jn/NAYY0Z7DQAAAGA8Gk5P+WJJZxeULZT0nLV2hqTn3H1JOkfSDHe5TtJ9khOwJX1T0iclzZP0TS9ku3Wu85139miuAQAAAIxXQ4Zya+0LkpoLij8raYm7vUTShb7ypdbxsqQaY8yBks6StMJa22ytbZG0QtLZ7rEqa+1L1loraWlBWyO5BgAAADAujXZM+QHW2m2S5K6nuOUHSdrsq7fFLRusfEuR8tFcAwAAABiXdvcXPU2RMjuK8tFco39FY64zxqw0xqzcsWPHEM0CAAAAwRhtKP/IGzLirre75VskHeyrN13SB0OUTy9SPppr9GOtXWStbbTWNk6ePHlEPyAAAACwt4w2lD8pyZtB5SpJy33lV7ozpBwnqc0devI7SWcaY2rdL3ieKel37rEOY8xx7qwrVxa0NZJrAAAAAONSZKgKxphHJJ0qaZIxZoucWVTulvQrY8w1kv4u6fNu9WclfUbSBkndkv6nJFlrm40xd0p6za13h7XW+/LoDXJmeCmV9Bt30UivAQAAAIxXxpn0ZN/X2NhoV65cGfRtAAAAYB9njFllrW0cyTm80RMAAAAIGKEcAAAACBihHAAAAAgYoRwAAAAIGKEcAAAACBihHAAAAAgYoRwAAAAIGKEcAAAACBihHAAAAAgYoRwAAAAIGKEcAAAACBihHAAAAAgYoRwAAAAIGKEcAAAACBihHAAAAAgYoRwAAAAIGKEcAAAACBihHAAAAAgYoRwAAAAIGKEcAAAACBihHAAAAAhYJOgbAPY4a6VEl9TbKvW0+tZtks1I5ZOdpcJdR0uDvmMAALCfIZRjfMhkpL72/GDd21YQsgcpy6SGf61YhS+oT5HKJ7n77nbFlNzxeI0U4gMnAACwawjl2HvSKScg97YW6bUu0oudF6zbJdmB2zZhqbTGCcneuubQ/mXx6vwyGalrp9S1w1225/Y7t0vN70mbX5G6m5xe9UKhiFQ2Kb+nvXDxl0dK9tRvFwAAjGOEcoxMKjFwj/RQvdaJjsHbDpfkB+aKqdKkjw8drOM1UqxcMmZ0P1PtoUPXyaSlnhYnqPuDuxfevf2mDVLnDinVU7ydkupcz/uAId7tkY/XjP5nAgAA4wqhfH+U7B24R3qodbJ78LajZQW91QdL8briQbowZI/lsdyhsBumJ0k6Zuj6fZ1uaN/phPjO7VL3Tiewe2F+5wbp/b9I3c0q+ilAKOobOuMtk3zBfXL+cXrhAQAYtwjl453Xg9vd5ATA7iZ32emEPW+/y7ef7Bq8zVhlfoiecETxMF2s5zoS2zs/91hXUuEsEw4fum46JfU09+91LxxO07R+iF74qv7hfaD90lrGwgMAMIYQyscSa6VEpxuimwoCdlOR8iYnkA801jpWIZVNdJbyydKUo53t0tqCYF2bH7DD/FnsVeGI0/NdMUU6YObQ9RNdRcJ7Qa9888bBx8KbcO7vIi+wDxDmd2V4EAAAGBLpa09KJ/MDdF5PdpEe7O4mKd1XvK1QxA3Yk6SyCU54K5vohCYveGcD+CSpdIIUje/dnxd7R6zc6YEfTi+890lKsfHv2SC/Q9q6ylknOou3EykdJMBPyi8vm8QnJgAAjBChfLisdabky+ut3lkQrAt6tPvaBm4vXu0G7IlS9XTpwDlS+URf8PYCtrsuqaKnEiOXNxb+6KHrJ3t8ob1YT/wOqWOb9OEbznYmWbyd7BdaJ+UH+LKCMu9vnU9nAAD7uf33/wn9vdj+Huzs9s7+w0UGCiDhmBs23ABdc0gubBQL2mUTpHB07/68wHBES50v59YcPHRda50vCnc3FQR3N7x3u+vmjdLmV539YkNpJGdIVV5on5SbarJ8Yv6x0gmEeADAPsdYO8jcz/uQxiMm2JVfbxhmL3aNb1iIO1wkb5hIQeCOVdCLDQwlk8kNpen2h/em/EDvhfmBZqWRcUP8pIFDvL9HnhAPANjLjDGrrLWNIzln//l/qnTCGYtbc6hvLPYEX8B21/wfOLBnhEJur/fE4dXPpHPDwrI98U25QN+909nf8a70/n8PEuLle9CelPtvfbB9ppcEAOxl+0/6nHyUdOXyoO8CwHCFws4Llioma1jj4bNfat2Z3xufHZbm7meH0zRJNl28rVhFbpai0gnOdpm7Lq3NlRWWMywNADBK+08oB7Bvy/tS61FD189knBdiFYb27p1Sd4szd3xPi7N8tC63PVCQl5w5/rNhvnbgxR/k4zXMlAQAIJQD2E+FQu4QtgnSpBnDO8ebhckL6N3N+du9rbn9nhZp+1u57Uxq4HYjpflhPl7j26/JD/ClBe8VCIV3z+8DABAoQjkADJcxThCOV0u1hw3/PO/FYP7A3t3s9sa3+sJ8q7O0bJI+WOOUJ7sHuyEpXpV7EVg2uNf0D/CF65Iq3uoKAGMIoRwA9jRjpJJKZ6k5ZGTnJnsLQrsb6ntb+wf63lapbWvu+GC98/I9YBQG9n5l1W55Te4cXhAFALsVoRwAxrJoXIpOlSqnjuw8a6VEV//Q3tPqzC+fF+rdso5tubJ0Yoj7KvOFdX+4rx54KXF79eNVfCkWAAoQygFgX2SMVFLhLNXTR3autc7bXXu9AO9b/KHef7zzQ2nHO7n9gaan9ETLfYG9qiC4V/m2qwu2q5ztWDnvhwCwTyGUAwDyGSPFypylatrIz89knDH0hYG+r72gzBfqOz6Udv5N6m136g069EaSCecCerxKKvECe6WvzLfO2650An6sgnH1AMYMQjkAYPcKhdze7ipJB4/8/GxPvRfk3TDf1+bb9pd3OPutm3N1+tolmxniQiY/0Bfrke+3XZPfu8+LpgDsJoRyAMDY4u+p14Gja8NaZ+YaL6D3tucHdv/aH/Lbtkjb1w0/2Efi/YfaFB2SU13Qg+/WiVXSWw9AEqEcALAvMsYZdx4r1y4F+0RnQXBvG2JplVrfz42/zySHutH8ITd54b0yf7hN3tCcyvxhOXxxFhj3COUAABTjn8qy+qCRn2+tlOot0ls/RI99xzZnfL1XPmSwl9Nj7wX4gZZYRZE6FU5vfUmFsx8tp+ceCAihHACAPcEYKVrqLJUHjL6dZG9u3Hw2yHv7Hblg39cu9XW6xzqkts257eGGe8kJ7zF35p5YRUGo95XFyt265b79cifkx8qduoR8YNgI5QAAjGXRuLNUTN61dlJ9uZDuXxKdBdudUqLDXbv72YDvlqV6R3D/Zbnwnl2X5fajZUWOudvZY77taJmzEPaxjyGUAwCwP4iUOEv5pF1vK51ywnmiy117YX6I/WR3bpx++7bc8WT3yIK+JEVKnQAfdYO8P7R7+9Ey99MKf1lp/rFYee4TjWiZMxQoWuaM02cufOxFhHIAADAy4YjzBtfSmt3XZjolJbukRLcvzHe5Qb5w3Z2rW1je+aGzTvW65T1Sqmfk92NC+SE9Wup+auEvizsPB5GYFC7JPfhESvL387ZjvjL3vHC0oMwt59OA/QqhHAAABC8ckcLuNJK7WybjBPNkjy/Ud7v7PbmAn+pxxvB7x1Letr+sx3lg6Nrh9vD3OUs64dRPJ3bffYcibkCP5QJ9OJoL8t6xIbejzhLy9t12Q9HcsXDMd72oux3N1cnbjzjrbJnvWCjCw8QoEcoBAMC+LRTyTZG5h2UyTjBP90kpX1BP9fm2e6V00g3zfb7tRG5ddDuZ2/dve98XKHq8z/kUIpPcvQ8MgzEhN5x7gT3i7vsWL8yHwr4w7+1Hhrkfzl8b/37Bedl7CvvqhXznhX31wvnlJuzUzdsP5+oWlofCo/q1EcoBAAB2l1BICrlfzh1rrJUyaSecZ5JucE/mQrwX3LMh3ivz76d85UmnPX/dTNpXJ5Vb0r5jGa+dVP45mbTzgJHpyu3728g7P+0stqDOOEYoBwAA2B8Y4w5d2YfjXyaTH9JtuiDcp31l/lCfdt7gm1cn5bRn0/nn2fTQ5d/68ohvfR/+VwEAAMB+JRSSQjFJsYBvZOShnJH4AAAAQMAI5QAAAEDACOUAAABAwAjlAAAAQMAI5QAAAEDACOUAAABAwAjlAAAAQMAI5QAAAEDACOUAAABAwAjlAAAAQMB2KZQbYzYZY94wxqw1xqx0yyYYY1YYY9a761q33BhjfmiM2WCMed0Y0+Br5yq3/npjzFW+8mPd9je455rBrgEAAACMR7ujp/xT1tp6a22ju79Q0nPW2hmSnnP3JekcSTPc5TpJ90lOwJb0TUmflDRP0jd9Ifs+t6533tlDXAMAAAAYd/bE8JXPSlribi+RdAN5hw4AAAo1SURBVKGvfKl1vCypxhhzoKSzJK2w1jZba1skrZB0tnusylr7krXWSlpa0FaxawAAAADjzq6Gcivp98aYVcaY69yyA6y12yTJXU9xyw+StNl37ha3bLDyLUXKB7tGHmPMdcaYlcaYlTt27BjljwgAAADsWZFdPP9Ea+0HxpgpklYYY94ZpK4pUmZHUT5s1tpFkhZJUmNj44jOBQAAAPaWXeopt9Z+4K63S3pczpjwj9yhJ3LX293qWyQd7Dt9uqQPhiifXqRcg1wDAAAAGHdGHcqNMeXGmEpvW9KZ/7+9uw21rKrjOP79NU5kNuWzmWNoMoZD6kQ6mFqoiIwmKaWkWEpIlqRZGDHVi8oQjCITGihR0ReNJtrkINYoZmkQOuPzU9YoooPiWKYphjL678VZN4/DCb2jM2vOPd8PXM5e+6xz9jrcP3f/7mbtdYD7gOXA1AoqpwDXtO3lwMltFZYDgOfa1JMVwBFJtmk3eB4BrGjPPZ/kgLbqysnrvdeoY0iSJElj561MX9kJWNZWKdwCWFpVv0+yErgyyanAY8Dxrf91wFHAauBF4IsAVfVMkh8CK1u/c6rqmbZ9OnApsCXwu/YDcN7/OYYkSZI0djJY2GTm22+//WrVqlW9hyFJkqQZLsntQ8uFvyl+o6ckSZLUmaFckiRJ6sxQLkmSJHVmKJckSZI6M5RLkiRJnRnKJUmSpM4M5ZIkSVJnhnJJkiSpM0O5JEmS1JmhXJIkSerMUC5JkiR1ZiiXJEmSOjOUS5IkSZ0ZyiVJkqTODOWSJElSZ4ZySZIkqTNDuSRJktSZoVySJEnqzFAuSZIkdWYolyRJkjozlEuSJEmdGcolSZKkzgzlkiRJUmeGckmSJKkzQ7kkSZLUmaFckiRJ6sxQLkmSJHVmKJckSZI6M5RLkiRJnRnKJUmSpM4M5ZIkSVJnhnJJkiSpM0O5JEmS1JmhXJIkSerMUC5JkiR1ZiiXJEmSOjOUS5IkSZ0ZyiVJkqTODOWSJElSZ4ZySZIkqTNDuSRJktSZoVySJEnqzFAuSZIkdWYolyRJkjozlEuSJEmdGcolSZKkzgzlkiRJUmeGckmSJKkzQ7kkSZLUmaFckiRJ6sxQLkmSJHVmKJckSZI6M5RLkiRJnRnKJUmSpM4M5ZIkSVJnhnJJkiSpM0O5JEmS1JmhXJIkSerMUC5JkiR1ZiiXJEmSOjOUS5IkSZ0ZyiVJkqTODOWSJElSZ2MdypMsSvJQktVJFvcejyRJkrQhxjaUJ5kFLAGOBOYDJyaZ33dUkiRJ0vSNbSgHFgKrq+qRqnoZuAI4pvOYJEmSpGkb51C+C/D4UHtN2ydJkiSNlS16D+AtyIh99boOyWnAaa35UpL7NvqoNG62B/7RexDa7FgXGsW60CjWhUb58HRfMM6hfA2w61B7LvDEcIequhC4ECDJqqrab9MNT+PAutAo1oVGsS40inWhUZKsmu5rxnn6ykpgXpLdk7wTOAFY3nlMkiRJ0rSN7ZXyqlqX5AxgBTALuKSq7u88LEmSJGnaxjaUA1TVdcB1b7L7hRtzLBpb1oVGsS40inWhUawLjTLtukhVvXEvSZIkSRvNOM8plyRJkmaEiQjlSRYleSjJ6iSLe49HfSS5JMna4aUxk2yb5IYkf2+P2/QcozatJLsmuSnJg0nuT3JW229dTLAk70pyW5K7W138oO3fPcmtrS5+3RYZ0IRJMivJnUmubW3rYsIleTTJvUnumlp1ZUPOIzM+lCeZBSwBjgTmAycmmd93VOrkUmDRevsWAzdW1TzgxtbW5FgHnF1VewEHAF9tfx+si8n2EnBYVe0LLAAWJTkA+BFwfquLfwGndhyj+jkLeHCobV0I4NCqWjC0POa0zyMzPpQDC4HVVfVIVb0MXAEc03lM6qCqbgaeWW/3McBlbfsy4NhNOih1VVVPVtUdbft5BifaXbAuJloNvNCas9tPAYcBV7X91sUESjIX+BRwUWsH60KjTfs8MgmhfBfg8aH2mrZPAtipqp6EQUADduw8HnWSZDfgo8CtWBcTr01RuAtYC9wAPAw8W1XrWhfPJZPpZ8C3gFdbezusCw3+ab8+ye3t2+RhA84jY70k4puUEftcckbS/yR5D3A18PWq+vfg4pcmWVW9AixIsjWwDNhrVLdNOyr1lORoYG1V3Z7kkKndI7paF5PnoKp6IsmOwA1J/rohbzIJV8rXALsOtecCT3QaizY/TyXZGaA9ru08Hm1iSWYzCOS/qqrftN3WhQCoqmeBPzK452DrJFMXszyXTJ6DgE8neZTBVNjDGFw5ty4mXFU90R7XMvgnfiEbcB6ZhFC+EpjX7o5+J3ACsLzzmLT5WA6c0rZPAa7pOBZtYm0+6MXAg1X106GnrIsJlmSHdoWcJFsChzO43+Am4LjWzbqYMFX17aqaW1W7McgSf6iqk7AuJlqSrZLMmdoGjgDuYwPOIxPx5UFJjmLw3+ws4JKqOrfzkNRBksuBQ4DtgaeA7wG/Ba4EPgg8BhxfVevfDKoZKsnBwC3Avbw2R/Q7DOaVWxcTKsk+DG7MmsXg4tWVVXVOkg8xuEK6LXAn8PmqeqnfSNVLm77yzao62rqYbO33v6w1twCWVtW5SbZjmueRiQjlkiRJ0uZsEqavSJIkSZs1Q7kkSZLUmaFckiRJ6sxQLkmSJHVmKJckSZI6m4Rv9JSkGastu3Vja74feAV4urVfrKoD34Zj7MRgPfddgdnAo1V1VJLdgAOraulbPYYkTTqXRJSkGSLJ94EXquonb/P7/hJ4oKouaO19quqe4bWa387jSdIkcvqKJM1QSV5oj4ck+VOSK5P8Lcl5SU5KcluSe5Ps0frtkOTqJCvbz0HtrXYG1ky9b1Xd0zbPAz6R5K4k30gyK8mP22vvSfLloePfnGRZkgeS/CKJ5x9JGuIfRUmaDPsCZwF7A18A9qyqhcBFwJmtzwXA+VW1P/DZ9hzAEuDiJDcl+W6SD7T9i4FbqmpBVZ0PnAo8116/P/ClJLu3vguBs9vx9wA+sxE/qySNHeeUS9JkWFlVTwIkeRi4vu2/Fzi0bR8OzE8y9Zr3JplTVSvaV0kvAo4E7kzykRHHOALYJ8lxrf0+YB7wMnBbVT3Sjn85cDBw1dv5ASVpnBnKJWkyvDS0/epQ+1VeOxe8A/h4Vf1n/RdX1TPAUmBpkmuBTwL/XK9bgDOrasXrdg7mnq9/A5M3NEnSEKevSJKmXA+cMdVIsqA9Hpbk3W17DoPpJ48BzwNzhl6/Ajg9yezWd88kW7XnFibZvc0l/xzw5439YSRpnHilXJI05WvAkiT3MDg/3Ax8BfgY8PMk6xhczLmoqla28L0uyd3ApQzmpO8G3JHBHJingWPbe/+FwY2he7f3XbapPpQkjQOXRJQkbVQunShJb8zpK5IkSVJnXimXJEmSOvNKuSRJktSZoVySJEnqzFAuSZIkdWYolyRJkjozlEuSJEmdGcolSZKkzv4Lme+3w5qFbp4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "merged_pool_indicators[[\"TimeStep\",\"Total Ecosystem\",\"Total Biomass\",\"Dead Organic Matter\"]] \\\n",
    "    .groupby(\"TimeStep\").sum().plot(ylim=(0,3e6), figsize=(12,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_stock_changes = merge_iteration_results([\n",
    "    cbm3_results.load_stock_changes( get_results_db_path(x))\n",
    "        for x in range(1, num_iterations+1)\n",
    "], iteration_steps)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x53cfc50>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "merged_stock_changes[[\"TimeStep\", \"Total Harvest (Biomass)\"]].groupby(\"TimeStep\").sum().plot(figsize=(12,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xfb22630>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "merged_stock_changes[[\"TimeStep\", 'Net Primary Productivity (NPP)',\n",
    " 'Net Ecosystem Productivity (NEP)',\"Net Biome Productivity (NBP)\"]].groupby(\"TimeStep\").sum().plot(figsize=(12,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}