20191206_netcdf_DataDriven_spatial_Depth_Synthetic_LandlabLandslide.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Landlab Landslide Probability Model with Saturation \n",
    "\n",
    "<img src=\"https://www.washington.edu/brand/files/2014/09/W-Logo_Purple_Hex.png\" style=\"float:right;width:200px;padding:20px\">   \n",
    "\n",
    "\n",
    "<br />\n",
    "This Jupyter Notebook runs the Landlab LandslideProbability component on a synthetic \n",
    "Landlab grid using four depth to water table options to replace recharge options described in the paper: <br />\n",
    "#### Strauch et al. 2018. A hydro-climatological approach to predicting regional landslide probability using Landlab. Earth Surface Dynamics, 6, 1-26. <br /> \n",
    "This notebook performs the following functions:<br >\n",
    "* Import libraries and set HydroShare variables<br />\n",
    "* Create a grid and data fields used to calculate landslide probability<br />\n",
    "* Specify Depth to Water Table Distributions to compare four options<br /> \n",
    "* Run LandslideProbability function from Landlab landslide component<br /> \n",
    "* Compare the sensitivity based on four Depth to Water Table options<br /> \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.  HydroShare Setup and Preparation\n",
    "\n",
    "To run this notebook, we must import several libraries.\n",
    "The hs_utils library provides functions for interacting with HydroShare, including resource querying, dowloading, and creation.  Additional libraries support the functions of Landlab. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ignore deprecation warnings\n",
      "Successfully established a connection with HydroShare\n",
      "Successfully established a connection with HydroShare\n",
      "Data will be loaded from and saved to:/home/jovyan/work/notebooks/data/4cac25933f6448409cab97b293129b4f/4cac25933f6448409cab97b293129b4f/data/contents\n",
      "/home/jovyan/work/notebooks/data/4cac25933f6448409cab97b293129b4f/4cac25933f6448409cab97b293129b4f/data/contents\n",
      "You are not going crazy...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/python2/lib/python2.7/site-packages/landlab/__init__.py:27: UserWarning: \n",
      "This call to matplotlib.use() has no effect because the backend has already\n",
      "been chosen; matplotlib.use() must be called *before* pylab, matplotlib.pyplot,\n",
      "or matplotlib.backends is imported for the first time.\n",
      "\n",
      "The backend was *originally* set to 'module://ipykernel.pylab.backend_inline' by the following code:\n",
      "  File \"/opt/conda/envs/python2/lib/python2.7/runpy.py\", line 174, in _run_module_as_main\n",
      "    \"__main__\", fname, loader, pkg_name)\n",
      "  File \"/opt/conda/envs/python2/lib/python2.7/runpy.py\", line 72, in _run_code\n",
      "    exec code in run_globals\n",
      "  File \"/opt/conda/envs/python2/lib/python2.7/site-packages/ipykernel_launcher.py\", line 16, in <module>\n",
      "    app.launch_new_instance()\n",
      "  File \"/opt/conda/envs/python2/lib/python2.7/site-packages/traitlets/config/application.py\", line 658, in launch_instance\n",
      "    app.start()\n",
      "  File \"/opt/conda/envs/python2/lib/python2.7/site-packages/ipykernel/kernelapp.py\", line 486, in start\n",
      "    self.io_loop.start()\n",
      "  File \"/opt/conda/envs/python2/lib/python2.7/site-packages/tornado/ioloop.py\", line 1064, in start\n",
      "    handler_func(fd_obj, events)\n",
      "  File \"/opt/conda/envs/python2/lib/python2.7/site-packages/tornado/stack_context.py\", line 276, in null_wrapper\n",
      "    return fn(*args, **kwargs)\n",
      "  File \"/opt/conda/envs/python2/lib/python2.7/site-packages/zmq/eventloop/zmqstream.py\", line 450, in _handle_events\n",
      "    self._handle_recv()\n",
      "  File \"/opt/conda/envs/python2/lib/python2.7/site-packages/zmq/eventloop/zmqstream.py\", line 480, in _handle_recv\n",
      "    self._run_callback(callback, msg)\n",
      "  File \"/opt/conda/envs/python2/lib/python2.7/site-packages/zmq/eventloop/zmqstream.py\", line 432, in _run_callback\n",
      "    callback(*args, **kwargs)\n",
      "  File \"/opt/conda/envs/python2/lib/python2.7/site-packages/tornado/stack_context.py\", line 276, in null_wrapper\n",
      "    return fn(*args, **kwargs)\n",
      "  File \"/opt/conda/envs/python2/lib/python2.7/site-packages/ipykernel/kernelbase.py\", line 283, in dispatcher\n",
      "    return self.dispatch_shell(stream, msg)\n",
      "  File \"/opt/conda/envs/python2/lib/python2.7/site-packages/ipykernel/kernelbase.py\", line 233, in dispatch_shell\n",
      "    handler(stream, idents, msg)\n",
      "  File \"/opt/conda/envs/python2/lib/python2.7/site-packages/ipykernel/kernelbase.py\", line 399, in execute_request\n",
      "    user_expressions, allow_stdin)\n",
      "  File \"/opt/conda/envs/python2/lib/python2.7/site-packages/ipykernel/ipkernel.py\", line 208, in do_execute\n",
      "    res = shell.run_cell(code, store_history=store_history, silent=silent)\n",
      "  File \"/opt/conda/envs/python2/lib/python2.7/site-packages/ipykernel/zmqshell.py\", line 537, in run_cell\n",
      "    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n",
      "  File \"/opt/conda/envs/python2/lib/python2.7/site-packages/IPython/core/interactiveshell.py\", line 2714, in run_cell\n",
      "    interactivity=interactivity, compiler=compiler, result=result)\n",
      "  File \"/opt/conda/envs/python2/lib/python2.7/site-packages/IPython/core/interactiveshell.py\", line 2818, in run_ast_nodes\n",
      "    if self.run_code(code, result):\n",
      "  File \"/opt/conda/envs/python2/lib/python2.7/site-packages/IPython/core/interactiveshell.py\", line 2878, in run_code\n",
      "    exec(code_obj, self.user_global_ns, self.user_ns)\n",
      "  File \"<ipython-input-1-e6a39570703b>\", line 7, in <module>\n",
      "    get_ipython().magic(u'matplotlib inline')\n",
      "  File \"/opt/conda/envs/python2/lib/python2.7/site-packages/IPython/core/interactiveshell.py\", line 2160, in magic\n",
      "    return self.run_line_magic(magic_name, magic_arg_s)\n",
      "  File \"/opt/conda/envs/python2/lib/python2.7/site-packages/IPython/core/interactiveshell.py\", line 2081, in run_line_magic\n",
      "    result = fn(*args,**kwargs)\n",
      "  File \"<decorator-gen-105>\", line 2, in matplotlib\n",
      "  File \"/opt/conda/envs/python2/lib/python2.7/site-packages/IPython/core/magic.py\", line 188, in <lambda>\n",
      "    call = lambda f, *a, **k: f(*a, **k)\n",
      "  File \"/opt/conda/envs/python2/lib/python2.7/site-packages/IPython/core/magics/pylab.py\", line 100, in matplotlib\n",
      "    gui, backend = self.shell.enable_matplotlib(args.gui)\n",
      "  File \"/opt/conda/envs/python2/lib/python2.7/site-packages/IPython/core/interactiveshell.py\", line 2966, in enable_matplotlib\n",
      "    pt.activate_matplotlib(backend)\n",
      "  File \"/opt/conda/envs/python2/lib/python2.7/site-packages/IPython/core/pylabtools.py\", line 315, in activate_matplotlib\n",
      "    matplotlib.pyplot.switch_backend(backend)\n",
      "  File \"/opt/conda/envs/python2/lib/python2.7/site-packages/matplotlib/pyplot.py\", line 233, in switch_backend\n",
      "    _backend_mod, new_figure_manager, draw_if_interactive, _show = pylab_setup()\n",
      "  File \"/opt/conda/envs/python2/lib/python2.7/site-packages/matplotlib/backends/__init__.py\", line 62, in pylab_setup\n",
      "    [backend_name], 0)\n",
      "  File \"/opt/conda/envs/python2/lib/python2.7/site-packages/ipykernel/pylab/backend_inline.py\", line 165, in <module>\n",
      "    _enable_matplotlib_integration()\n",
      "  File \"/opt/conda/envs/python2/lib/python2.7/site-packages/ipykernel/pylab/backend_inline.py\", line 155, in _enable_matplotlib_integration\n",
      "    activate_matplotlib(backend)\n",
      "  File \"/opt/conda/envs/python2/lib/python2.7/site-packages/IPython/core/pylabtools.py\", line 315, in activate_matplotlib\n",
      "    matplotlib.pyplot.switch_backend(backend)\n",
      "  File \"/opt/conda/envs/python2/lib/python2.7/site-packages/matplotlib/pyplot.py\", line 231, in switch_backend\n",
      "    matplotlib.use(newbackend, warn=False, force=True)\n",
      "  File \"/opt/conda/envs/python2/lib/python2.7/site-packages/matplotlib/__init__.py\", line 1410, in use\n",
      "    reload(sys.modules['matplotlib.backends'])\n",
      "  File \"/opt/conda/envs/python2/lib/python2.7/site-packages/matplotlib/backends/__init__.py\", line 16, in <module>\n",
      "    line for line in traceback.format_stack()\n",
      "\n",
      "\n",
      "  matplotlib.use('Agg')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "You are not going crazy...\n"
     ]
    }
   ],
   "source": [
    "#import Python utilities for calculating and plotting\n",
    "import six\n",
    "import os\n",
    "import matplotlib as mpl\n",
    "mpl.use('agg')\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "\n",
    "import warnings\n",
    "print('Ignore deprecation warnings')\n",
    "warnings.filterwarnings(action='ignore', category=DeprecationWarning)\n",
    "\n",
    "#import utilities for importing and exporting to HydroShare\n",
    "from utilities import hydroshare\n",
    "# set variables for interacting with HydroShare from this notebook\n",
    "hs=hydroshare.hydroshare()\n",
    "# Create object to map the home directory\n",
    "hs=hydroshare.hydroshare()\n",
    "homedir = os.getcwd()\n",
    "print('Data will be loaded from and saved to:'+homedir)\n",
    "os.chdir('/home/jovyan/work/notebooks/data/4cac25933f6448409cab97b293129b4f/4cac25933f6448409cab97b293129b4f/data/contents')\n",
    "print(homedir)\n",
    "\n",
    "# Import Landlab libraries\n",
    "import landslide_probability\n",
    "from landslide_probability_20191202 import LandslideProbability\n",
    "from landlab import RasterModelGrid\n",
    "from landlab import imshow_grid_at_node\n",
    "\n",
    "from landlab.io.netcdf import read_netcdf\n",
    "from landlab.io.netcdf import write_netcdf\n",
    "\n",
    "import numpy as np\n",
    "import xarray as xr\n",
    "import pandas as pd\n",
    "\n",
    "from landlab.io import read_esri_ascii\n",
    "from landlab.io import write_esri_ascii"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you are curious about where the data is being downloaded, click on the Jupyter Notebook dashboard icon to see a File System view.  The homedir directory location printed above is where you can find the data and contents you will download to a HydroShare JupyterHub server.  At the end of this work session, you can migrate this data to the HydroShare iRods server as a Generic Resource. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This example uses a synthetic model domain. As a first example, make a synthetic grid with 5 rows and 4 columns space 0.2 units apart.   <br /> \n",
    "##### Synthetic Grid Restart opportunity: After completing this entire notebook, go to the Dashboard, Kernal, and select Restart and Clear Output.  Execute the code from the beginning, but change the cell below to the rows, columns, and spacing of your choice.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid = read_netcdf('synthetic_data_driven_spatial_depth.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['YR1', 'YR2', 'YR3']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.at_node.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Confirm the size of the grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.number_of_nodes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To see what the grid id of our nodes are, execute the command below.  However, note that this command gives you the rows of the grid, but these are technically up-side-down from how the RasterModelGrid nodes are numbered (e.g,. node id 0 is at the bottom left corner of the grid). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3],\n",
       "       [ 4,  5,  6,  7],\n",
       "       [ 8,  9, 10, 11],\n",
       "       [12, 13, 14, 15],\n",
       "       [16, 17, 18, 19]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.nodes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also see how many core nodes we have (i.e., the nodes that calculations are performed on) in the center of the grid...not the edges, which are 'closed nodes'. Compare this array to the full grid above.  Asign these to the variable 'core_nodes' so we can use these later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 5,  6,  9, 10, 13, 14])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "core_nodes = grid.core_nodes\n",
    "core_nodes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will create the required fields for the landslide component and add data as fields to the grid.  Thus, the data arrays will need to be as the length of the number of nodes.<br /> \n",
    "##### Data Fields Restart opportunity: After completing this entire notebook, go to the Dashboard, Kernal, and select Restart and Clear Output.  Execute the code from the beginning, but change the cell below to the data inputs of your choice.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "gridnodes = grid.number_of_nodes\n",
    "grid_size = grid.number_of_nodes\n",
    "\n",
    "grid['node']['soil__density']= \\\n",
    "         2000. * np.ones(gridnodes)\n",
    "\n",
    "grid['node']['soil__internal_friction_angle']= \\\n",
    "         np.sort(np.random.randint(26, 37, gridnodes))\n",
    "\n",
    "grid['node']['soil__mode_total_cohesion']= \\\n",
    "         np.sort(np.random.randint(30, 900, gridnodes))\n",
    "    \n",
    "scatter_dat = np.random.randint(1, 10, gridnodes)    \n",
    "grid['node']['soil__maximum_total_cohesion']= \\\n",
    "         grid.at_node['soil__mode_total_cohesion'] + scatter_dat\n",
    "\n",
    "grid['node']['soil__minimum_total_cohesion']= \\\n",
    "         grid.at_node['soil__mode_total_cohesion'] - scatter_dat\n",
    "\n",
    "grid['node']['soil__thickness']= \\\n",
    "         np.sort(np.random.randint(1, 10, gridnodes))\n",
    "\n",
    "grid['node']['soil__transmissivity']= \\\n",
    "         np.sort(np.random.randint(5, 20, gridnodes),-1)\n",
    "        \n",
    "grid['node']['topographic__slope'] = np.random.rand(gridnodes)\n",
    "\n",
    "grid['node']['topographic__specific_contributing_area']= \\\n",
    "         np.sort(np.random.randint(30, 900, gridnodes))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see what one of these fields (slope) looks like on the grid."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure('Slope')\n",
    "imshow_grid_at_node(grid,'topographic__slope', cmap='copper_r',\n",
    "                 grid_units=('coordinates', 'coordinates'), shrink=0.75,\n",
    "                 var_name='Slope', var_units='m/m')\n",
    "plt.savefig('Slope.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.0999999999999996"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(grid['node']['soil__thickness'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{0: (3.1348855160345486, 2.1339028896492409, 4.2298493546765314)}, {1: (4.3641055473531214, 3.3839620238107222, 4.4822002297488588)}, {2: (4.9997363861010431, 4.4987411920372287, 3.8892047488581669)}, {3: (3.9480286943080589, 2.007680398674899, 4.3143715531551994)}, {4: (3.8487040702022735, 2.48373836203718, 2.3989945374607911)}, {5: (4.176460110679832, 2.3597464046292518, 3.86995565015072)}, {6: (3.9938494706376044, 4.9504547555157892, 3.1362133315826775)}, {7: (4.2026147447657127, 4.8803142543445528, 2.9848784481643795)}, {8: (4.1163454826398613, 3.746951123011975, 2.1198065039185501)}, {9: (4.6215269665299221, 2.7099028438244708, 4.829102153571891)}, {10: (3.7740425056733122, 2.0782994936051171, 3.8324204869673371)}, {11: (2.7359464420021391, 2.8233773035209206, 4.8240207409403384)}, {12: (4.9040529801858987, 4.7971356536503436, 2.6893029622097657)}, {13: (2.0149565917571559, 3.5470104394242314, 2.7042667872425739)}, {14: (3.4423020872620453, 3.7744471204150347, 2.0271274892017592)}, {15: (4.4711137780420813, 4.1925682324790561, 2.1644931070588127)}, {16: (4.2663012232093855, 3.9762677759984459, 4.3539971907449413)}, {17: (4.3407503897934561, 4.9119388775808348, 2.5880043715323349)}, {18: (4.2689057625043292, 4.8282254881463036, 4.0076949260173054)}, {19: (2.6426233884811143, 2.8315266942441326, 2.5452952714296782)}]\n",
      "[ 2.64262339  2.83152669  2.54529527]\n"
     ]
    }
   ],
   "source": [
    "key=np.arange(grid.number_of_nodes)\n",
    "HSD_dict_annualmaxDWT=zip(grid['node']['YR1'],grid['node']['YR2'],grid['node']['YR3'])\n",
    "#i=1\n",
    "for i in range(grid.number_of_nodes):\n",
    "    HSD_dict_annualmaxDWT[i] = {key[i]:HSD_dict_annualmaxDWT[i]} \n",
    "\n",
    "print(HSD_dict_annualmaxDWT)\n",
    "\n",
    "hwdist=pd.DataFrame.from_dict(HSD_dict_annualmaxDWT[i], orient='index')\n",
    "print(hwdist.values[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 2, 2, 2, 2, 3, 4, 5, 5, 6, 6, 7, 8, 8, 8, 9])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid['node']['soil__thickness']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input read for groundwater__recharge_distribution\n",
      "None\n",
      "Input read for  groundwater__depth_distribution\n",
      "data_driven_spatial\n"
     ]
    }
   ],
   "source": [
    "\n",
    "n=50\n",
    "\n",
    "distribution1 = 'data_driven_spatial'\n",
    "\n",
    "\n",
    "LS_prob1=LandslideProbability(grid, number_of_iterations=n,\n",
    "                     groundwater__depth_distribution='data_driven_spatial',\n",
    "                     groundwater__depth_HSD_inputs=[HSD_dict_annualmaxDWT])\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.66666667  1.          0.33333333]\n",
      "Input Depth Annual Year Max Data = 3 years\n",
      "[ 2.64262339  2.83152669  2.54529527]\n",
      "Input Depth Annual Year Max Interpolated Data = n years\n",
      "[ 2.54529527  2.54529527  2.54529527  2.54529527  2.54529527  2.54529527\n",
      "  2.54529527  2.54529527  2.54529527  2.54529527  2.54529527  2.55293784\n",
      "  2.55416501  2.55992608  2.56904195  2.57166773  2.57557365  2.57573577\n",
      "  2.58115591  2.58271191  2.6003387   2.60239619  2.60706452  2.60821211\n",
      "  2.61382728  2.61394097  2.62396463  2.62668953  2.62810223  2.62830705\n",
      "  2.63481408  2.63655813  2.64268009  2.64487391  2.6512111   2.66017129\n",
      "  2.67012168  2.70358269  2.70580592  2.7071504   2.71349521  2.71770786\n",
      "  2.7366707   2.75786129  2.77019338  2.78936157  2.79964817  2.80017002\n",
      "  2.81093402  2.8194095 ]\n"
     ]
    }
   ],
   "source": [
    "from statsmodels.distributions.empirical_distribution import ECDF\n",
    "import scipy.constants\n",
    "from scipy import interpolate\n",
    "\n",
    "Yrand = np.sort(np.random.rand(n))\n",
    "Fx = ECDF(hwdist.values[0])\n",
    "Fx_ = Fx(hwdist.values[0])\n",
    "print(Fx_)\n",
    "f = interpolate.interp1d(Fx_, hwdist.values[0], bounds_error=False,\n",
    "                                     fill_value=min(hwdist.values[0]))\n",
    "\n",
    "De_interpolated = f(Yrand)\n",
    "print('Input Depth Annual Year Max Data = 3 years')\n",
    "print(hwdist.values[0])\n",
    "print('Input Depth Annual Year Max Interpolated Data = n years')\n",
    "print(De_interpolated)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Number of iterations to run Monte Carlo simulation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The landslide component employes the infinite slope model to calculate factor-of-safety values using a Monte Carlo simulation approach, which randomly selects input values from parameter distributions. You can pick the number of iterations to run Monte Carlo simulations, but the default is 250. The higher the number of iteration, the longer the program runs, but the more precise the probability of failure result becomes.<br /> \n",
    "##### Random iterations Restart opportunity: After completing this entire notebook, go to the Dashboard, Kernal, and select Restart and Clear Output.  Execute the code from the beginning, but change the cell below to the number of iterations of your choice.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Specify Lognormal Spatial Depth to Water Table Recharge Distribution\n",
    "\n",
    "There are _four_ different options for providing recharge for the Monte Carlo simulation.  We'll provide each of these recharge options below by specifying a recharge distribution and the necessary parameters. <br /> \n",
    "##### Restart opportunity: After completing this entire notebook, go to the Dashboard, Kernal, and select Restart and Clear Output.  Execute the code from the beginning, but change the cells below to adjust the recharge distributions.\n",
    "\n",
    " ### 1. Saturation Option - 'lognormal_spatial' distribution \n",
    " \n",
    "In this option, we'll apply a uniform distribution of recharge based on minimum and maximum recharge values.  We will then instantiate the LandslideProbability component with these parameters, as well as the grid and number of iterations we specified above.  Instantiate creates an instance of a class.  (For example, the _iPhone_ is a class and _each phone_ is an instance.)\n",
    "\n",
    "No outputs are generated by these commands as they are setting up the recharge and instantiating the component."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run the Landslide Component "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In calculate HSD loop - _hwdist.values[0]\n",
      "[ 4.17646011  2.3597464   3.86995565]\n",
      "[ 1.          0.33333333  0.66666667]\n",
      "Input Depth Annual Year Max Data = 3 years\n",
      "[ 4.17646011  2.3597464   3.86995565]\n",
      "Input Depth Annual Year Max Interpolated Data = n years\n",
      "[ 2.3597464   2.3597464   2.3597464   2.3597464   2.3597464   2.3597464\n",
      "  2.3597464   2.3597464   2.3597464   2.3597464   2.3597464   2.3597464\n",
      "  2.3597464   2.3597464   2.3597464   2.3597464   2.3597464   2.3597464\n",
      "  2.3597464   2.3597464   2.3597464   2.3597464   2.3597464   2.3597464\n",
      "  2.3597464   2.3597464   2.52009364  2.72640857  2.83667734  2.96221768\n",
      "  2.97282129  3.2201793   3.41659742  3.43288646  3.45893633  3.50680893\n",
      "  3.58986705  3.80852785  3.8231222   3.8706395   3.89368411  3.91561918\n",
      "  3.93670948  4.01186099  4.01916818  4.02744828  4.11144702  4.15481427\n",
      "  4.15509162  4.1657697 ]\n",
      "In calculate HSD loop -interpolate _hwdist.values[0]\n",
      "[ 2.3597464   2.3597464   2.3597464   2.3597464   2.3597464   2.3597464\n",
      "  2.3597464   2.3597464   2.3597464   2.3597464   2.3597464   2.3597464\n",
      "  2.3597464   2.3597464   2.3597464   2.3597464   2.3597464   2.3597464\n",
      "  2.3597464   2.3597464   2.3597464   2.3597464   2.3597464   2.3597464\n",
      "  2.3597464   2.3597464   2.52009364  2.72640857  2.83667734  2.96221768\n",
      "  2.97282129  3.2201793   3.41659742  3.43288646  3.45893633  3.50680893\n",
      "  3.58986705  3.80852785  3.8231222   3.8706395   3.89368411  3.91561918\n",
      "  3.93670948  4.01186099  4.01916818  4.02744828  4.11144702  4.15481427\n",
      "  4.15509162  4.1657697 ]\n",
      "interpolated Depth dist FOS input\n",
      "[ 2.3597464   2.3597464   2.3597464   2.3597464   2.3597464   2.3597464\n",
      "  2.3597464   2.3597464   2.3597464   2.3597464   2.3597464   2.3597464\n",
      "  2.3597464   2.3597464   2.3597464   2.3597464   2.3597464   2.3597464\n",
      "  2.3597464   2.3597464   2.3597464   2.3597464   2.3597464   2.3597464\n",
      "  2.3597464   2.3597464   2.52009364  2.72640857  2.83667734  2.96221768\n",
      "  2.97282129  3.2201793   3.41659742  3.43288646  3.45893633  3.50680893\n",
      "  3.58986705  3.80852785  3.8231222   3.8706395   3.89368411  3.91561918\n",
      "  3.93670948  4.01186099  4.01916818  4.02744828  4.11144702  4.15481427\n",
      "  4.15509162  4.1657697 ]\n",
      "i\n",
      "5\n",
      "self._HSD_dict\n",
      "[{0: (3.1348855160345486, 2.1339028896492409, 4.2298493546765314)}, {1: (4.3641055473531214, 3.3839620238107222, 4.4822002297488588)}, {2: (4.9997363861010431, 4.4987411920372287, 3.8892047488581669)}, {3: (3.9480286943080589, 2.007680398674899, 4.3143715531551994)}, {4: (3.8487040702022735, 2.48373836203718, 2.3989945374607911)}, {5: (4.176460110679832, 2.3597464046292518, 3.86995565015072)}, {6: (3.9938494706376044, 4.9504547555157892, 3.1362133315826775)}, {7: (4.2026147447657127, 4.8803142543445528, 2.9848784481643795)}, {8: (4.1163454826398613, 3.746951123011975, 2.1198065039185501)}, {9: (4.6215269665299221, 2.7099028438244708, 4.829102153571891)}, {10: (3.7740425056733122, 2.0782994936051171, 3.8324204869673371)}, {11: (2.7359464420021391, 2.8233773035209206, 4.8240207409403384)}, {12: (4.9040529801858987, 4.7971356536503436, 2.6893029622097657)}, {13: (2.0149565917571559, 3.5470104394242314, 2.7042667872425739)}, {14: (3.4423020872620453, 3.7744471204150347, 2.0271274892017592)}, {15: (4.4711137780420813, 4.1925682324790561, 2.1644931070588127)}, {16: (4.2663012232093855, 3.9762677759984459, 4.3539971907449413)}, {17: (4.3407503897934561, 4.9119388775808348, 2.5880043715323349)}, {18: (4.2689057625043292, 4.8282254881463036, 4.0076949260173054)}, {19: (2.6426233884811143, 2.8315266942441326, 2.5452952714296782)}]\n",
      "self._De\n",
      "[ 2.3597464   2.3597464   2.3597464   2.3597464   2.3597464   2.3597464\n",
      "  2.3597464   2.3597464   2.3597464   2.3597464   2.3597464   2.3597464\n",
      "  2.3597464   2.3597464   2.3597464   2.3597464   2.3597464   2.3597464\n",
      "  2.3597464   2.3597464   2.3597464   2.3597464   2.3597464   2.3597464\n",
      "  2.3597464   2.3597464   2.52009364  2.72640857  2.83667734  2.96221768\n",
      "  2.97282129  3.2201793   3.41659742  3.43288646  3.45893633  3.50680893\n",
      "  3.58986705  3.80852785  3.8231222   3.8706395   3.89368411  3.91561918\n",
      "  3.93670948  4.01186099  4.01916818  4.02744828  4.11144702  4.15481427\n",
      "  4.15509162  4.1657697 ]\n",
      "self._HSD_dict\n",
      "[{0: (3.1348855160345486, 2.1339028896492409, 4.2298493546765314)}, {1: (4.3641055473531214, 3.3839620238107222, 4.4822002297488588)}, {2: (4.9997363861010431, 4.4987411920372287, 3.8892047488581669)}, {3: (3.9480286943080589, 2.007680398674899, 4.3143715531551994)}, {4: (3.8487040702022735, 2.48373836203718, 2.3989945374607911)}, {5: (4.176460110679832, 2.3597464046292518, 3.86995565015072)}, {6: (3.9938494706376044, 4.9504547555157892, 3.1362133315826775)}, {7: (4.2026147447657127, 4.8803142543445528, 2.9848784481643795)}, {8: (4.1163454826398613, 3.746951123011975, 2.1198065039185501)}, {9: (4.6215269665299221, 2.7099028438244708, 4.829102153571891)}, {10: (3.7740425056733122, 2.0782994936051171, 3.8324204869673371)}, {11: (2.7359464420021391, 2.8233773035209206, 4.8240207409403384)}, {12: (4.9040529801858987, 4.7971356536503436, 2.6893029622097657)}, {13: (2.0149565917571559, 3.5470104394242314, 2.7042667872425739)}, {14: (3.4423020872620453, 3.7744471204150347, 2.0271274892017592)}, {15: (4.4711137780420813, 4.1925682324790561, 2.1644931070588127)}, {16: (4.2663012232093855, 3.9762677759984459, 4.3539971907449413)}, {17: (4.3407503897934561, 4.9119388775808348, 2.5880043715323349)}, {18: (4.2689057625043292, 4.8282254881463036, 4.0076949260173054)}, {19: (2.6426233884811143, 2.8315266942441326, 2.5452952714296782)}]\n",
      "Depth of Water Distribution (SoilDepth-DTWdist)\n",
      "[-0.3597464  -0.3597464  -0.3597464  -0.3597464  -0.3597464  -0.3597464\n",
      " -0.3597464  -0.3597464  -0.3597464  -0.3597464  -0.3597464  -0.3597464\n",
      " -0.3597464  -0.3597464  -0.3597464  -0.3597464  -0.3597464  -0.3597464\n",
      " -0.3597464  -0.3597464  -0.3597464  -0.3597464  -0.3597464  -0.3597464\n",
      " -0.3597464  -0.3597464  -0.52009364 -0.72640857 -0.83667734 -0.96221768\n",
      " -0.97282129 -1.2201793  -1.41659742 -1.43288646 -1.45893633 -1.50680893\n",
      " -1.58986705 -1.80852785 -1.8231222  -1.8706395  -1.89368411 -1.91561918\n",
      " -1.93670948 -2.01186099 -2.01916818 -2.02744828 -2.11144702 -2.15481427\n",
      " -2.15509162 -2.1657697 ]\n",
      "Depth distribution is not none - count saturated cells\n",
      "Soil Thickness This Node\n",
      "2\n",
      "Depth of Water Distribution (SoilDepth-DTWdist) hw>hs correction\n",
      "[ 2.  2.  2.  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.  2.  2.  2.\n",
      "  2.  2.  2.  2.  2.  2.  2.  2.  2.  2.  2.  2.  2.  2.]\n",
      "Soil thickness All\n",
      "[1 1 1 1 1 2 2 2 2 3 4 5 5 6 6 7 8 8 8 9]\n",
      "Mean water table depth\n",
      "2.95781605876\n",
      "In calculate HSD loop - _hwdist.values[0]\n",
      "[ 4.17646011  2.3597464   3.86995565]\n",
      "[ 1.          0.33333333  0.66666667]\n",
      "Input Depth Annual Year Max Data = 3 years\n",
      "[ 4.17646011  2.3597464   3.86995565]\n",
      "Input Depth Annual Year Max Interpolated Data = n years\n",
      "[ 2.3597464   2.3597464   2.3597464   2.3597464   2.3597464   2.3597464\n",
      "  2.3597464   2.3597464   2.3597464   2.3597464   2.51482316  2.64595707\n",
      "  2.7698666   2.79212853  2.80413349  2.82427888  2.8752958   3.12085131\n",
      "  3.43183599  3.44219752  3.45159106  3.45989156  3.48306816  3.52656168\n",
      "  3.53185875  3.59687907  3.649535    3.76721702  3.80397432  3.80894691\n",
      "  3.8405377   3.8802081   3.89373879  3.90012728  3.9041817   3.91370116\n",
      "  3.92382774  3.93296747  3.99825289  4.00524471  4.03523083  4.06713291\n",
      "  4.06771419  4.07708219  4.08133332  4.1024233   4.13301219  4.13607843\n",
      "  4.14169197  4.17539992]\n",
      "In calculate HSD loop -interpolate _hwdist.values[0]\n",
      "[ 2.3597464   2.3597464   2.3597464   2.3597464   2.3597464   2.3597464\n",
      "  2.3597464   2.3597464   2.3597464   2.3597464   2.51482316  2.64595707\n",
      "  2.7698666   2.79212853  2.80413349  2.82427888  2.8752958   3.12085131\n",
      "  3.43183599  3.44219752  3.45159106  3.45989156  3.48306816  3.52656168\n",
      "  3.53185875  3.59687907  3.649535    3.76721702  3.80397432  3.80894691\n",
      "  3.8405377   3.8802081   3.89373879  3.90012728  3.9041817   3.91370116\n",
      "  3.92382774  3.93296747  3.99825289  4.00524471  4.03523083  4.06713291\n",
      "  4.06771419  4.07708219  4.08133332  4.1024233   4.13301219  4.13607843\n",
      "  4.14169197  4.17539992]\n",
      "interpolated Depth dist FOS input\n",
      "[ 2.3597464   2.3597464   2.3597464   2.3597464   2.3597464   2.3597464\n",
      "  2.3597464   2.3597464   2.3597464   2.3597464   2.51482316  2.64595707\n",
      "  2.7698666   2.79212853  2.80413349  2.82427888  2.8752958   3.12085131\n",
      "  3.43183599  3.44219752  3.45159106  3.45989156  3.48306816  3.52656168\n",
      "  3.53185875  3.59687907  3.649535    3.76721702  3.80397432  3.80894691\n",
      "  3.8405377   3.8802081   3.89373879  3.90012728  3.9041817   3.91370116\n",
      "  3.92382774  3.93296747  3.99825289  4.00524471  4.03523083  4.06713291\n",
      "  4.06771419  4.07708219  4.08133332  4.1024233   4.13301219  4.13607843\n",
      "  4.14169197  4.17539992]\n",
      "self._rel_wetness\n",
      "[ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
      "self._soil__mean_relative_wetness\n",
      "1.0\n",
      "self._rel_wetness\n",
      "[ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
      "self._phi\n",
      "[ 26.66293354  29.688656    27.12066755  25.5727536   23.41246665\n",
      "  24.24053525  29.52480361  27.65746907  28.2878982   32.16836344\n",
      "  34.57846094  25.91234823  29.37114377  26.29651069  23.30400691\n",
      "  30.33126062  26.72217473  27.15986018  28.71044793  31.2007987\n",
      "  29.06158395  31.78590769  26.37640094  30.78656644  25.62992468\n",
      "  33.29343205  29.60250321  25.90024716  33.16236114  30.03705625\n",
      "  26.22180244  25.88104248  28.14352958  23.43758658  25.82945953\n",
      "  27.44825495  27.09618273  27.72997076  26.40792917  28.69755995\n",
      "  31.65504302  24.91691647  28.13714048  25.08363525  29.89321944\n",
      "  27.24693165  28.52036109  25.60770674  25.22276848  27.91257382]\n",
      "In calculate HSD loop - _hwdist.values[0]\n",
      "[ 3.99384947  4.95045476  3.13621333]\n",
      "[ 0.66666667  1.          0.33333333]\n",
      "Input Depth Annual Year Max Data = 3 years\n",
      "[ 3.99384947  4.95045476  3.13621333]\n",
      "Input Depth Annual Year Max Interpolated Data = n years\n",
      "[ 3.13621333  3.13621333  3.13621333  3.13621333  3.13621333  3.13621333\n",
      "  3.13621333  3.13621333  3.13621333  3.13621333  3.13621333  3.13621333\n",
      "  3.13621333  3.19290869  3.19635135  3.28889575  3.30371205  3.3052328\n",
      "  3.36957334  3.38596325  3.45416844  3.45497126  3.45732644  3.45852319\n",
      "  3.59080384  3.65811278  3.6777401   3.7599322   3.79912533  3.89667528\n",
      "  3.91510404  3.93957031  4.03610993  4.06088364  4.08842536  4.15886667\n",
      "  4.25245425  4.36613578  4.42104209  4.4360894   4.5172225   4.54214494\n",
      "  4.61193532  4.67490639  4.67507983  4.68885301  4.81234337  4.82534721\n",
      "  4.83274272  4.83327043]\n",
      "In calculate HSD loop -interpolate _hwdist.values[0]\n",
      "[ 3.13621333  3.13621333  3.13621333  3.13621333  3.13621333  3.13621333\n",
      "  3.13621333  3.13621333  3.13621333  3.13621333  3.13621333  3.13621333\n",
      "  3.13621333  3.19290869  3.19635135  3.28889575  3.30371205  3.3052328\n",
      "  3.36957334  3.38596325  3.45416844  3.45497126  3.45732644  3.45852319\n",
      "  3.59080384  3.65811278  3.6777401   3.7599322   3.79912533  3.89667528\n",
      "  3.91510404  3.93957031  4.03610993  4.06088364  4.08842536  4.15886667\n",
      "  4.25245425  4.36613578  4.42104209  4.4360894   4.5172225   4.54214494\n",
      "  4.61193532  4.67490639  4.67507983  4.68885301  4.81234337  4.82534721\n",
      "  4.83274272  4.83327043]\n",
      "interpolated Depth dist FOS input\n",
      "[ 3.13621333  3.13621333  3.13621333  3.13621333  3.13621333  3.13621333\n",
      "  3.13621333  3.13621333  3.13621333  3.13621333  3.13621333  3.13621333\n",
      "  3.13621333  3.19290869  3.19635135  3.28889575  3.30371205  3.3052328\n",
      "  3.36957334  3.38596325  3.45416844  3.45497126  3.45732644  3.45852319\n",
      "  3.59080384  3.65811278  3.6777401   3.7599322   3.79912533  3.89667528\n",
      "  3.91510404  3.93957031  4.03610993  4.06088364  4.08842536  4.15886667\n",
      "  4.25245425  4.36613578  4.42104209  4.4360894   4.5172225   4.54214494\n",
      "  4.61193532  4.67490639  4.67507983  4.68885301  4.81234337  4.82534721\n",
      "  4.83274272  4.83327043]\n",
      "i\n",
      "6\n",
      "self._HSD_dict\n",
      "[{0: (3.1348855160345486, 2.1339028896492409, 4.2298493546765314)}, {1: (4.3641055473531214, 3.3839620238107222, 4.4822002297488588)}, {2: (4.9997363861010431, 4.4987411920372287, 3.8892047488581669)}, {3: (3.9480286943080589, 2.007680398674899, 4.3143715531551994)}, {4: (3.8487040702022735, 2.48373836203718, 2.3989945374607911)}, {5: (4.176460110679832, 2.3597464046292518, 3.86995565015072)}, {6: (3.9938494706376044, 4.9504547555157892, 3.1362133315826775)}, {7: (4.2026147447657127, 4.8803142543445528, 2.9848784481643795)}, {8: (4.1163454826398613, 3.746951123011975, 2.1198065039185501)}, {9: (4.6215269665299221, 2.7099028438244708, 4.829102153571891)}, {10: (3.7740425056733122, 2.0782994936051171, 3.8324204869673371)}, {11: (2.7359464420021391, 2.8233773035209206, 4.8240207409403384)}, {12: (4.9040529801858987, 4.7971356536503436, 2.6893029622097657)}, {13: (2.0149565917571559, 3.5470104394242314, 2.7042667872425739)}, {14: (3.4423020872620453, 3.7744471204150347, 2.0271274892017592)}, {15: (4.4711137780420813, 4.1925682324790561, 2.1644931070588127)}, {16: (4.2663012232093855, 3.9762677759984459, 4.3539971907449413)}, {17: (4.3407503897934561, 4.9119388775808348, 2.5880043715323349)}, {18: (4.2689057625043292, 4.8282254881463036, 4.0076949260173054)}, {19: (2.6426233884811143, 2.8315266942441326, 2.5452952714296782)}]\n",
      "self._De\n",
      "[ 3.13621333  3.13621333  3.13621333  3.13621333  3.13621333  3.13621333\n",
      "  3.13621333  3.13621333  3.13621333  3.13621333  3.13621333  3.13621333\n",
      "  3.13621333  3.19290869  3.19635135  3.28889575  3.30371205  3.3052328\n",
      "  3.36957334  3.38596325  3.45416844  3.45497126  3.45732644  3.45852319\n",
      "  3.59080384  3.65811278  3.6777401   3.7599322   3.79912533  3.89667528\n",
      "  3.91510404  3.93957031  4.03610993  4.06088364  4.08842536  4.15886667\n",
      "  4.25245425  4.36613578  4.42104209  4.4360894   4.5172225   4.54214494\n",
      "  4.61193532  4.67490639  4.67507983  4.68885301  4.81234337  4.82534721\n",
      "  4.83274272  4.83327043]\n",
      "self._HSD_dict\n",
      "[{0: (3.1348855160345486, 2.1339028896492409, 4.2298493546765314)}, {1: (4.3641055473531214, 3.3839620238107222, 4.4822002297488588)}, {2: (4.9997363861010431, 4.4987411920372287, 3.8892047488581669)}, {3: (3.9480286943080589, 2.007680398674899, 4.3143715531551994)}, {4: (3.8487040702022735, 2.48373836203718, 2.3989945374607911)}, {5: (4.176460110679832, 2.3597464046292518, 3.86995565015072)}, {6: (3.9938494706376044, 4.9504547555157892, 3.1362133315826775)}, {7: (4.2026147447657127, 4.8803142543445528, 2.9848784481643795)}, {8: (4.1163454826398613, 3.746951123011975, 2.1198065039185501)}, {9: (4.6215269665299221, 2.7099028438244708, 4.829102153571891)}, {10: (3.7740425056733122, 2.0782994936051171, 3.8324204869673371)}, {11: (2.7359464420021391, 2.8233773035209206, 4.8240207409403384)}, {12: (4.9040529801858987, 4.7971356536503436, 2.6893029622097657)}, {13: (2.0149565917571559, 3.5470104394242314, 2.7042667872425739)}, {14: (3.4423020872620453, 3.7744471204150347, 2.0271274892017592)}, {15: (4.4711137780420813, 4.1925682324790561, 2.1644931070588127)}, {16: (4.2663012232093855, 3.9762677759984459, 4.3539971907449413)}, {17: (4.3407503897934561, 4.9119388775808348, 2.5880043715323349)}, {18: (4.2689057625043292, 4.8282254881463036, 4.0076949260173054)}, {19: (2.6426233884811143, 2.8315266942441326, 2.5452952714296782)}]\n",
      "Depth of Water Distribution (SoilDepth-DTWdist)\n",
      "[-1.13621333 -1.13621333 -1.13621333 -1.13621333 -1.13621333 -1.13621333\n",
      " -1.13621333 -1.13621333 -1.13621333 -1.13621333 -1.13621333 -1.13621333\n",
      " -1.13621333 -1.19290869 -1.19635135 -1.28889575 -1.30371205 -1.3052328\n",
      " -1.36957334 -1.38596325 -1.45416844 -1.45497126 -1.45732644 -1.45852319\n",
      " -1.59080384 -1.65811278 -1.6777401  -1.7599322  -1.79912533 -1.89667528\n",
      " -1.91510404 -1.93957031 -2.03610993 -2.06088364 -2.08842536 -2.15886667\n",
      " -2.25245425 -2.36613578 -2.42104209 -2.4360894  -2.5172225  -2.54214494\n",
      " -2.61193532 -2.67490639 -2.67507983 -2.68885301 -2.81234337 -2.82534721\n",
      " -2.83274272 -2.83327043]\n",
      "Depth distribution is not none - count saturated cells\n",
      "Soil Thickness This Node\n",
      "2\n",
      "Depth of Water Distribution (SoilDepth-DTWdist) hw>hs correction\n",
      "[ 2.  2.  2.  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.  2.  2.  2.\n",
      "  2.  2.  2.  2.  2.  2.  2.  2.  2.  2.  2.  2.  2.  2.]\n",
      "Soil thickness All\n",
      "[1 1 1 1 1 2 2 2 2 3 4 5 5 6 6 7 8 8 8 9]\n",
      "Mean water table depth\n",
      "3.77418633179\n",
      "In calculate HSD loop - _hwdist.values[0]\n",
      "[ 3.99384947  4.95045476  3.13621333]\n",
      "[ 0.66666667  1.          0.33333333]\n",
      "Input Depth Annual Year Max Data = 3 years\n",
      "[ 3.99384947  4.95045476  3.13621333]\n",
      "Input Depth Annual Year Max Interpolated Data = n years\n",
      "[ 3.13621333  3.13621333  3.13621333  3.13621333  3.13621333  3.13621333\n",
      "  3.13621333  3.13621333  3.13621333  3.13621333  3.13621333  3.13621333\n",
      "  3.13621333  3.13621333  3.13621333  3.13621333  3.13621333  3.13621333\n",
      "  3.13621333  3.13621333  3.17528561  3.2062279   3.31098114  3.33103592\n",
      "  3.34596592  3.36971932  3.38182932  3.44709545  3.54008795  3.60956463\n",
      "  3.70573812  3.85953861  3.86987026  3.92205269  3.98314078  3.99177172\n",
      "  4.1083782   4.32837751  4.41944262  4.45873165  4.57591087  4.67767401\n",
      "  4.73512361  4.74753414  4.79348023  4.86140926  4.87657483  4.90881782\n",
      "  4.92006277  4.92274669]\n",
      "In calculate HSD loop -interpolate _hwdist.values[0]\n",
      "[ 3.13621333  3.13621333  3.13621333  3.13621333  3.13621333  3.13621333\n",
      "  3.13621333  3.13621333  3.13621333  3.13621333  3.13621333  3.13621333\n",
      "  3.13621333  3.13621333  3.13621333  3.13621333  3.13621333  3.13621333\n",
      "  3.13621333  3.13621333  3.17528561  3.2062279   3.31098114  3.33103592\n",
      "  3.34596592  3.36971932  3.38182932  3.44709545  3.54008795  3.60956463\n",
      "  3.70573812  3.85953861  3.86987026  3.92205269  3.98314078  3.99177172\n",
      "  4.1083782   4.32837751  4.41944262  4.45873165  4.57591087  4.67767401\n",
      "  4.73512361  4.74753414  4.79348023  4.86140926  4.87657483  4.90881782\n",
      "  4.92006277  4.92274669]\n",
      "interpolated Depth dist FOS input\n",
      "[ 3.13621333  3.13621333  3.13621333  3.13621333  3.13621333  3.13621333\n",
      "  3.13621333  3.13621333  3.13621333  3.13621333  3.13621333  3.13621333\n",
      "  3.13621333  3.13621333  3.13621333  3.13621333  3.13621333  3.13621333\n",
      "  3.13621333  3.13621333  3.17528561  3.2062279   3.31098114  3.33103592\n",
      "  3.34596592  3.36971932  3.38182932  3.44709545  3.54008795  3.60956463\n",
      "  3.70573812  3.85953861  3.86987026  3.92205269  3.98314078  3.99177172\n",
      "  4.1083782   4.32837751  4.41944262  4.45873165  4.57591087  4.67767401\n",
      "  4.73512361  4.74753414  4.79348023  4.86140926  4.87657483  4.90881782\n",
      "  4.92006277  4.92274669]\n",
      "self._rel_wetness\n",
      "[ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
      "self._soil__mean_relative_wetness\n",
      "1.0\n",
      "self._rel_wetness\n",
      "[ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
      "self._phi\n",
      "[ 27.63979391  28.09171343  29.24069254  31.36666823  27.81100884\n",
      "  33.87559985  32.80418943  24.81353373  27.19048656  28.62472788\n",
      "  25.6770837   27.91867581  31.25016932  30.62941839  30.07755151\n",
      "  30.93411275  26.76255379  27.87021578  30.58723427  33.06785518\n",
      "  29.39422014  27.42623862  24.42061624  30.9321191   23.70585678\n",
      "  28.08919747  29.28605631  33.83424597  25.47291367  28.32758463\n",
      "  24.269786    27.87677039  30.07615398  27.3974581   26.80707115\n",
      "  25.81727495  29.67690122  30.7773363   30.5451971   34.42723993\n",
      "  23.3967365   30.30750786  29.87997191  29.77748628  34.79896065\n",
      "  24.05108445  30.78964799  32.12552483  29.11845772  27.81433427]\n",
      "In calculate HSD loop - _hwdist.values[0]\n",
      "[ 4.62152697  2.70990284  4.82910215]\n",
      "[ 0.66666667  0.33333333  1.        ]\n",
      "Input Depth Annual Year Max Data = 3 years\n",
      "[ 4.62152697  2.70990284  4.82910215]\n",
      "Input Depth Annual Year Max Interpolated Data = n years\n",
      "[ 2.70990284  2.70990284  2.70990284  2.70990284  2.70990284  2.70990284\n",
      "  2.70990284  2.70990284  2.70990284  2.70990284  2.70990284  2.70990284\n",
      "  2.70990284  2.70990284  2.70990284  2.77519318  2.89207992  3.08741694\n",
      "  3.09115032  3.11397855  3.11656428  3.12110627  3.12304448  3.22818058\n",
      "  3.27436795  3.41945238  3.49722954  3.65296576  3.76980177  3.80440626\n",
      "  3.86595861  3.8774636   4.13070942  4.27537848  4.32251665  4.43869726\n",
      "  4.46393401  4.54556568  4.62446425  4.6442881   4.65085747  4.65868861\n",
      "  4.66548365  4.71851029  4.73119529  4.74013019  4.74165955  4.74638375\n",
      "  4.80365899  4.81662418]\n",
      "In calculate HSD loop -interpolate _hwdist.values[0]\n",
      "[ 2.70990284  2.70990284  2.70990284  2.70990284  2.70990284  2.70990284\n",
      "  2.70990284  2.70990284  2.70990284  2.70990284  2.70990284  2.70990284\n",
      "  2.70990284  2.70990284  2.70990284  2.77519318  2.89207992  3.08741694\n",
      "  3.09115032  3.11397855  3.11656428  3.12110627  3.12304448  3.22818058\n",
      "  3.27436795  3.41945238  3.49722954  3.65296576  3.76980177  3.80440626\n",
      "  3.86595861  3.8774636   4.13070942  4.27537848  4.32251665  4.43869726\n",
      "  4.46393401  4.54556568  4.62446425  4.6442881   4.65085747  4.65868861\n",
      "  4.66548365  4.71851029  4.73119529  4.74013019  4.74165955  4.74638375\n",
      "  4.80365899  4.81662418]\n",
      "interpolated Depth dist FOS input\n",
      "[ 2.70990284  2.70990284  2.70990284  2.70990284  2.70990284  2.70990284\n",
      "  2.70990284  2.70990284  2.70990284  2.70990284  2.70990284  2.70990284\n",
      "  2.70990284  2.70990284  2.70990284  2.77519318  2.89207992  3.08741694\n",
      "  3.09115032  3.11397855  3.11656428  3.12110627  3.12304448  3.22818058\n",
      "  3.27436795  3.41945238  3.49722954  3.65296576  3.76980177  3.80440626\n",
      "  3.86595861  3.8774636   4.13070942  4.27537848  4.32251665  4.43869726\n",
      "  4.46393401  4.54556568  4.62446425  4.6442881   4.65085747  4.65868861\n",
      "  4.66548365  4.71851029  4.73119529  4.74013019  4.74165955  4.74638375\n",
      "  4.80365899  4.81662418]\n",
      "i\n",
      "9\n",
      "self._HSD_dict\n",
      "[{0: (3.1348855160345486, 2.1339028896492409, 4.2298493546765314)}, {1: (4.3641055473531214, 3.3839620238107222, 4.4822002297488588)}, {2: (4.9997363861010431, 4.4987411920372287, 3.8892047488581669)}, {3: (3.9480286943080589, 2.007680398674899, 4.3143715531551994)}, {4: (3.8487040702022735, 2.48373836203718, 2.3989945374607911)}, {5: (4.176460110679832, 2.3597464046292518, 3.86995565015072)}, {6: (3.9938494706376044, 4.9504547555157892, 3.1362133315826775)}, {7: (4.2026147447657127, 4.8803142543445528, 2.9848784481643795)}, {8: (4.1163454826398613, 3.746951123011975, 2.1198065039185501)}, {9: (4.6215269665299221, 2.7099028438244708, 4.829102153571891)}, {10: (3.7740425056733122, 2.0782994936051171, 3.8324204869673371)}, {11: (2.7359464420021391, 2.8233773035209206, 4.8240207409403384)}, {12: (4.9040529801858987, 4.7971356536503436, 2.6893029622097657)}, {13: (2.0149565917571559, 3.5470104394242314, 2.7042667872425739)}, {14: (3.4423020872620453, 3.7744471204150347, 2.0271274892017592)}, {15: (4.4711137780420813, 4.1925682324790561, 2.1644931070588127)}, {16: (4.2663012232093855, 3.9762677759984459, 4.3539971907449413)}, {17: (4.3407503897934561, 4.9119388775808348, 2.5880043715323349)}, {18: (4.2689057625043292, 4.8282254881463036, 4.0076949260173054)}, {19: (2.6426233884811143, 2.8315266942441326, 2.5452952714296782)}]\n",
      "self._De\n",
      "[ 2.70990284  2.70990284  2.70990284  2.70990284  2.70990284  2.70990284\n",
      "  2.70990284  2.70990284  2.70990284  2.70990284  2.70990284  2.70990284\n",
      "  2.70990284  2.70990284  2.70990284  2.77519318  2.89207992  3.08741694\n",
      "  3.09115032  3.11397855  3.11656428  3.12110627  3.12304448  3.22818058\n",
      "  3.27436795  3.41945238  3.49722954  3.65296576  3.76980177  3.80440626\n",
      "  3.86595861  3.8774636   4.13070942  4.27537848  4.32251665  4.43869726\n",
      "  4.46393401  4.54556568  4.62446425  4.6442881   4.65085747  4.65868861\n",
      "  4.66548365  4.71851029  4.73119529  4.74013019  4.74165955  4.74638375\n",
      "  4.80365899  4.81662418]\n",
      "self._HSD_dict\n",
      "[{0: (3.1348855160345486, 2.1339028896492409, 4.2298493546765314)}, {1: (4.3641055473531214, 3.3839620238107222, 4.4822002297488588)}, {2: (4.9997363861010431, 4.4987411920372287, 3.8892047488581669)}, {3: (3.9480286943080589, 2.007680398674899, 4.3143715531551994)}, {4: (3.8487040702022735, 2.48373836203718, 2.3989945374607911)}, {5: (4.176460110679832, 2.3597464046292518, 3.86995565015072)}, {6: (3.9938494706376044, 4.9504547555157892, 3.1362133315826775)}, {7: (4.2026147447657127, 4.8803142543445528, 2.9848784481643795)}, {8: (4.1163454826398613, 3.746951123011975, 2.1198065039185501)}, {9: (4.6215269665299221, 2.7099028438244708, 4.829102153571891)}, {10: (3.7740425056733122, 2.0782994936051171, 3.8324204869673371)}, {11: (2.7359464420021391, 2.8233773035209206, 4.8240207409403384)}, {12: (4.9040529801858987, 4.7971356536503436, 2.6893029622097657)}, {13: (2.0149565917571559, 3.5470104394242314, 2.7042667872425739)}, {14: (3.4423020872620453, 3.7744471204150347, 2.0271274892017592)}, {15: (4.4711137780420813, 4.1925682324790561, 2.1644931070588127)}, {16: (4.2663012232093855, 3.9762677759984459, 4.3539971907449413)}, {17: (4.3407503897934561, 4.9119388775808348, 2.5880043715323349)}, {18: (4.2689057625043292, 4.8282254881463036, 4.0076949260173054)}, {19: (2.6426233884811143, 2.8315266942441326, 2.5452952714296782)}]\n",
      "Depth of Water Distribution (SoilDepth-DTWdist)\n",
      "[ 0.29009716  0.29009716  0.29009716  0.29009716  0.29009716  0.29009716\n",
      "  0.29009716  0.29009716  0.29009716  0.29009716  0.29009716  0.29009716\n",
      "  0.29009716  0.29009716  0.29009716  0.22480682  0.10792008 -0.08741694\n",
      " -0.09115032 -0.11397855 -0.11656428 -0.12110627 -0.12304448 -0.22818058\n",
      " -0.27436795 -0.41945238 -0.49722954 -0.65296576 -0.76980177 -0.80440626\n",
      " -0.86595861 -0.8774636  -1.13070942 -1.27537848 -1.32251665 -1.43869726\n",
      " -1.46393401 -1.54556568 -1.62446425 -1.6442881  -1.65085747 -1.65868861\n",
      " -1.66548365 -1.71851029 -1.73119529 -1.74013019 -1.74165955 -1.74638375\n",
      " -1.80365899 -1.81662418]\n",
      "Depth distribution is not none - count saturated cells\n",
      "Soil Thickness This Node\n",
      "3\n",
      "Depth of Water Distribution (SoilDepth-DTWdist) hw>hs correction\n",
      "[ 0.29009716  0.29009716  0.29009716  0.29009716  0.29009716  0.29009716\n",
      "  0.29009716  0.29009716  0.29009716  0.29009716  0.29009716  0.29009716\n",
      "  0.29009716  0.29009716  0.29009716  0.22480682  0.10792008  3.          3.\n",
      "  3.          3.          3.          3.          3.          3.          3.\n",
      "  3.          3.          3.          3.          3.          3.          3.\n",
      "  3.          3.          3.          3.          3.          3.          3.\n",
      "  3.          3.          3.          3.          3.          3.          3.\n",
      "  3.          3.          3.        ]\n",
      "Soil thickness All\n",
      "[1 1 1 1 1 2 2 2 2 3 4 5 5 6 6 7 8 8 8 9]\n",
      "Mean water table depth\n",
      "3.60155297721\n",
      "In calculate HSD loop - _hwdist.values[0]\n",
      "[ 4.62152697  2.70990284  4.82910215]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.66666667  0.33333333  1.        ]\n",
      "Input Depth Annual Year Max Data = 3 years\n",
      "[ 4.62152697  2.70990284  4.82910215]\n",
      "Input Depth Annual Year Max Interpolated Data = n years\n",
      "[ 2.70990284  2.70990284  2.70990284  2.70990284  2.70990284  2.70990284\n",
      "  2.70990284  2.70990284  2.70990284  2.70990284  2.70990284  2.70990284\n",
      "  2.70990284  2.70990284  2.70990284  2.70990284  2.70990284  2.70990284\n",
      "  2.70990284  2.70990284  2.8022742   2.88889562  2.89100153  2.93862459\n",
      "  2.95682249  3.04961062  3.28138788  3.41572933  3.64260506  3.67181207\n",
      "  3.70950379  3.80553001  3.8276338   4.00798589  4.31299133  4.63091348\n",
      "  4.63654593  4.65450246  4.67405697  4.67920118  4.68802713  4.69302392\n",
      "  4.73960166  4.7533616   4.75555609  4.76414645  4.78377509  4.80817233\n",
      "  4.81087587  4.8212239 ]\n",
      "In calculate HSD loop -interpolate _hwdist.values[0]\n",
      "[ 2.70990284  2.70990284  2.70990284  2.70990284  2.70990284  2.70990284\n",
      "  2.70990284  2.70990284  2.70990284  2.70990284  2.70990284  2.70990284\n",
      "  2.70990284  2.70990284  2.70990284  2.70990284  2.70990284  2.70990284\n",
      "  2.70990284  2.70990284  2.8022742   2.88889562  2.89100153  2.93862459\n",
      "  2.95682249  3.04961062  3.28138788  3.41572933  3.64260506  3.67181207\n",
      "  3.70950379  3.80553001  3.8276338   4.00798589  4.31299133  4.63091348\n",
      "  4.63654593  4.65450246  4.67405697  4.67920118  4.68802713  4.69302392\n",
      "  4.73960166  4.7533616   4.75555609  4.76414645  4.78377509  4.80817233\n",
      "  4.81087587  4.8212239 ]\n",
      "interpolated Depth dist FOS input\n",
      "[ 2.70990284  2.70990284  2.70990284  2.70990284  2.70990284  2.70990284\n",
      "  2.70990284  2.70990284  2.70990284  2.70990284  2.70990284  2.70990284\n",
      "  2.70990284  2.70990284  2.70990284  2.70990284  2.70990284  2.70990284\n",
      "  2.70990284  2.70990284  2.8022742   2.88889562  2.89100153  2.93862459\n",
      "  2.95682249  3.04961062  3.28138788  3.41572933  3.64260506  3.67181207\n",
      "  3.70950379  3.80553001  3.8276338   4.00798589  4.31299133  4.63091348\n",
      "  4.63654593  4.65450246  4.67405697  4.67920118  4.68802713  4.69302392\n",
      "  4.73960166  4.7533616   4.75555609  4.76414645  4.78377509  4.80817233\n",
      "  4.81087587  4.8212239 ]\n",
      "self._rel_wetness\n",
      "[ 0.09669905  0.09669905  0.09669905  0.09669905  0.09669905  0.09669905\n",
      "  0.09669905  0.09669905  0.09669905  0.09669905  0.09669905  0.09669905\n",
      "  0.09669905  0.09669905  0.09669905  0.07493561  0.03597336  1.          1.\n",
      "  1.          1.          1.          1.          1.          1.          1.\n",
      "  1.          1.          1.          1.          1.          1.          1.\n",
      "  1.          1.          1.          1.          1.          1.          1.\n",
      "  1.          1.          1.          1.          1.          1.          1.\n",
      "  1.          1.          1.        ]\n",
      "self._soil__mean_relative_wetness\n",
      "0.691227894936\n",
      "self._rel_wetness\n",
      "[ 0.09669905  0.09669905  0.09669905  0.09669905  0.09669905  0.09669905\n",
      "  0.09669905  0.09669905  0.09669905  0.09669905  0.09669905  0.09669905\n",
      "  0.09669905  0.09669905  0.09669905  0.07493561  0.03597336  1.          1.\n",
      "  1.          1.          1.          1.          1.          1.          1.\n",
      "  1.          1.          1.          1.          1.          1.          1.\n",
      "  1.          1.          1.          1.          1.          1.          1.\n",
      "  1.          1.          1.          1.          1.          1.          1.\n",
      "  1.          1.          1.        ]\n",
      "self._phi\n",
      "[ 25.51984343  31.31714994  29.35706547  29.1270676   33.21523451\n",
      "  32.02282904  35.80112086  28.94120665  34.56696275  32.7164386\n",
      "  28.98129521  31.14496679  28.45352759  34.16894817  26.69712592\n",
      "  27.78988239  27.50206796  29.31736528  32.42496346  30.30132006\n",
      "  29.97709244  23.98328326  29.48693774  25.97325904  27.75022526\n",
      "  35.2638648   27.81775805  33.91398496  33.1299837   27.25070291\n",
      "  30.99608628  26.73879149  32.22893651  31.29551176  33.24953808\n",
      "  29.91017234  34.89532529  25.71267219  28.48836867  32.08786608\n",
      "  29.4313151   27.39810558  26.62864552  33.32229396  29.8601496\n",
      "  34.30006167  32.28928935  29.36780376  29.09847736  30.2068529 ]\n",
      "In calculate HSD loop - _hwdist.values[0]\n",
      "[ 3.77404251  2.07829949  3.83242049]\n",
      "[ 0.66666667  0.33333333  1.        ]\n",
      "Input Depth Annual Year Max Data = 3 years\n",
      "[ 3.77404251  2.07829949  3.83242049]\n",
      "Input Depth Annual Year Max Interpolated Data = n years\n",
      "[ 2.07829949  2.07829949  2.07829949  2.07829949  2.07829949  2.07829949\n",
      "  2.07829949  2.07829949  2.07829949  2.07829949  2.07829949  2.07829949\n",
      "  2.07829949  2.07829949  2.07829949  2.07829949  2.07829949  2.07829949\n",
      "  2.07829949  2.14022289  2.71744128  2.82473617  2.83757305  2.88731173\n",
      "  2.95828273  2.96108444  3.00208366  3.03898472  3.103757    3.22720158\n",
      "  3.38944145  3.55136109  3.77587694  3.78407326  3.78510414  3.78597738\n",
      "  3.78633454  3.7923543   3.79831558  3.80334504  3.80678194  3.81032422\n",
      "  3.81125964  3.81305906  3.81871084  3.81881232  3.82163434  3.8217655\n",
      "  3.82783525  3.83238696]\n",
      "In calculate HSD loop -interpolate _hwdist.values[0]\n",
      "[ 2.07829949  2.07829949  2.07829949  2.07829949  2.07829949  2.07829949\n",
      "  2.07829949  2.07829949  2.07829949  2.07829949  2.07829949  2.07829949\n",
      "  2.07829949  2.07829949  2.07829949  2.07829949  2.07829949  2.07829949\n",
      "  2.07829949  2.14022289  2.71744128  2.82473617  2.83757305  2.88731173\n",
      "  2.95828273  2.96108444  3.00208366  3.03898472  3.103757    3.22720158\n",
      "  3.38944145  3.55136109  3.77587694  3.78407326  3.78510414  3.78597738\n",
      "  3.78633454  3.7923543   3.79831558  3.80334504  3.80678194  3.81032422\n",
      "  3.81125964  3.81305906  3.81871084  3.81881232  3.82163434  3.8217655\n",
      "  3.82783525  3.83238696]\n",
      "interpolated Depth dist FOS input\n",
      "[ 2.07829949  2.07829949  2.07829949  2.07829949  2.07829949  2.07829949\n",
      "  2.07829949  2.07829949  2.07829949  2.07829949  2.07829949  2.07829949\n",
      "  2.07829949  2.07829949  2.07829949  2.07829949  2.07829949  2.07829949\n",
      "  2.07829949  2.14022289  2.71744128  2.82473617  2.83757305  2.88731173\n",
      "  2.95828273  2.96108444  3.00208366  3.03898472  3.103757    3.22720158\n",
      "  3.38944145  3.55136109  3.77587694  3.78407326  3.78510414  3.78597738\n",
      "  3.78633454  3.7923543   3.79831558  3.80334504  3.80678194  3.81032422\n",
      "  3.81125964  3.81305906  3.81871084  3.81881232  3.82163434  3.8217655\n",
      "  3.82783525  3.83238696]\n",
      "i\n",
      "10\n",
      "self._HSD_dict\n",
      "[{0: (3.1348855160345486, 2.1339028896492409, 4.2298493546765314)}, {1: (4.3641055473531214, 3.3839620238107222, 4.4822002297488588)}, {2: (4.9997363861010431, 4.4987411920372287, 3.8892047488581669)}, {3: (3.9480286943080589, 2.007680398674899, 4.3143715531551994)}, {4: (3.8487040702022735, 2.48373836203718, 2.3989945374607911)}, {5: (4.176460110679832, 2.3597464046292518, 3.86995565015072)}, {6: (3.9938494706376044, 4.9504547555157892, 3.1362133315826775)}, {7: (4.2026147447657127, 4.8803142543445528, 2.9848784481643795)}, {8: (4.1163454826398613, 3.746951123011975, 2.1198065039185501)}, {9: (4.6215269665299221, 2.7099028438244708, 4.829102153571891)}, {10: (3.7740425056733122, 2.0782994936051171, 3.8324204869673371)}, {11: (2.7359464420021391, 2.8233773035209206, 4.8240207409403384)}, {12: (4.9040529801858987, 4.7971356536503436, 2.6893029622097657)}, {13: (2.0149565917571559, 3.5470104394242314, 2.7042667872425739)}, {14: (3.4423020872620453, 3.7744471204150347, 2.0271274892017592)}, {15: (4.4711137780420813, 4.1925682324790561, 2.1644931070588127)}, {16: (4.2663012232093855, 3.9762677759984459, 4.3539971907449413)}, {17: (4.3407503897934561, 4.9119388775808348, 2.5880043715323349)}, {18: (4.2689057625043292, 4.8282254881463036, 4.0076949260173054)}, {19: (2.6426233884811143, 2.8315266942441326, 2.5452952714296782)}]\n",
      "self._De\n",
      "[ 2.07829949  2.07829949  2.07829949  2.07829949  2.07829949  2.07829949\n",
      "  2.07829949  2.07829949  2.07829949  2.07829949  2.07829949  2.07829949\n",
      "  2.07829949  2.07829949  2.07829949  2.07829949  2.07829949  2.07829949\n",
      "  2.07829949  2.14022289  2.71744128  2.82473617  2.83757305  2.88731173\n",
      "  2.95828273  2.96108444  3.00208366  3.03898472  3.103757    3.22720158\n",
      "  3.38944145  3.55136109  3.77587694  3.78407326  3.78510414  3.78597738\n",
      "  3.78633454  3.7923543   3.79831558  3.80334504  3.80678194  3.81032422\n",
      "  3.81125964  3.81305906  3.81871084  3.81881232  3.82163434  3.8217655\n",
      "  3.82783525  3.83238696]\n",
      "self._HSD_dict\n",
      "[{0: (3.1348855160345486, 2.1339028896492409, 4.2298493546765314)}, {1: (4.3641055473531214, 3.3839620238107222, 4.4822002297488588)}, {2: (4.9997363861010431, 4.4987411920372287, 3.8892047488581669)}, {3: (3.9480286943080589, 2.007680398674899, 4.3143715531551994)}, {4: (3.8487040702022735, 2.48373836203718, 2.3989945374607911)}, {5: (4.176460110679832, 2.3597464046292518, 3.86995565015072)}, {6: (3.9938494706376044, 4.9504547555157892, 3.1362133315826775)}, {7: (4.2026147447657127, 4.8803142543445528, 2.9848784481643795)}, {8: (4.1163454826398613, 3.746951123011975, 2.1198065039185501)}, {9: (4.6215269665299221, 2.7099028438244708, 4.829102153571891)}, {10: (3.7740425056733122, 2.0782994936051171, 3.8324204869673371)}, {11: (2.7359464420021391, 2.8233773035209206, 4.8240207409403384)}, {12: (4.9040529801858987, 4.7971356536503436, 2.6893029622097657)}, {13: (2.0149565917571559, 3.5470104394242314, 2.7042667872425739)}, {14: (3.4423020872620453, 3.7744471204150347, 2.0271274892017592)}, {15: (4.4711137780420813, 4.1925682324790561, 2.1644931070588127)}, {16: (4.2663012232093855, 3.9762677759984459, 4.3539971907449413)}, {17: (4.3407503897934561, 4.9119388775808348, 2.5880043715323349)}, {18: (4.2689057625043292, 4.8282254881463036, 4.0076949260173054)}, {19: (2.6426233884811143, 2.8315266942441326, 2.5452952714296782)}]\n",
      "Depth of Water Distribution (SoilDepth-DTWdist)\n",
      "[ 1.92170051  1.92170051  1.92170051  1.92170051  1.92170051  1.92170051\n",
      "  1.92170051  1.92170051  1.92170051  1.92170051  1.92170051  1.92170051\n",
      "  1.92170051  1.92170051  1.92170051  1.92170051  1.92170051  1.92170051\n",
      "  1.92170051  1.85977711  1.28255872  1.17526383  1.16242695  1.11268827\n",
      "  1.04171727  1.03891556  0.99791634  0.96101528  0.896243    0.77279842\n",
      "  0.61055855  0.44863891  0.22412306  0.21592674  0.21489586  0.21402262\n",
      "  0.21366546  0.2076457   0.20168442  0.19665496  0.19321806  0.18967578\n",
      "  0.18874036  0.18694094  0.18128916  0.18118768  0.17836566  0.1782345\n",
      "  0.17216475  0.16761304]\n",
      "Depth distribution is not none - count saturated cells\n",
      "Soil Thickness This Node\n",
      "4\n",
      "Depth of Water Distribution (SoilDepth-DTWdist) hw>hs correction\n",
      "[ 1.92170051  1.92170051  1.92170051  1.92170051  1.92170051  1.92170051\n",
      "  1.92170051  1.92170051  1.92170051  1.92170051  1.92170051  1.92170051\n",
      "  1.92170051  1.92170051  1.92170051  1.92170051  1.92170051  1.92170051\n",
      "  1.92170051  1.85977711  1.28255872  1.17526383  1.16242695  1.11268827\n",
      "  1.04171727  1.03891556  0.99791634  0.96101528  0.896243    0.77279842\n",
      "  0.61055855  0.44863891  0.22412306  0.21592674  0.21489586  0.21402262\n",
      "  0.21366546  0.2076457   0.20168442  0.19665496  0.19321806  0.18967578\n",
      "  0.18874036  0.18694094  0.18128916  0.18118768  0.17836566  0.1782345\n",
      "  0.17216475  0.16761304]\n",
      "Soil thickness All\n",
      "[1 1 1 1 1 2 2 2 2 3 4 5 5 6 6 7 8 8 8 9]\n",
      "Mean water table depth\n",
      "2.93242246796\n",
      "In calculate HSD loop - _hwdist.values[0]\n",
      "[ 3.77404251  2.07829949  3.83242049]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.66666667  0.33333333  1.        ]\n",
      "Input Depth Annual Year Max Data = 3 years\n",
      "[ 3.77404251  2.07829949  3.83242049]\n",
      "Input Depth Annual Year Max Interpolated Data = n years\n",
      "[ 2.07829949  2.07829949  2.07829949  2.07829949  2.07829949  2.07829949\n",
      "  2.07829949  2.07829949  2.07829949  2.07829949  2.07829949  2.07829949\n",
      "  2.07829949  2.07829949  2.07829949  2.07829949  2.07829949  2.07829949\n",
      "  2.16305024  2.47653887  2.57065501  2.70591243  2.78096785  2.80064252\n",
      "  2.80319332  2.87254466  2.87743488  3.02832977  3.05746706  3.34495034\n",
      "  3.3770979   3.39867421  3.53918159  3.57075586  3.63646133  3.70006366\n",
      "  3.7814163   3.78327599  3.78470412  3.78920446  3.79358912  3.8058606\n",
      "  3.80671888  3.81132061  3.82258687  3.82340815  3.82430753  3.82627765\n",
      "  3.82702739  3.8316876 ]\n",
      "In calculate HSD loop -interpolate _hwdist.values[0]\n",
      "[ 2.07829949  2.07829949  2.07829949  2.07829949  2.07829949  2.07829949\n",
      "  2.07829949  2.07829949  2.07829949  2.07829949  2.07829949  2.07829949\n",
      "  2.07829949  2.07829949  2.07829949  2.07829949  2.07829949  2.07829949\n",
      "  2.16305024  2.47653887  2.57065501  2.70591243  2.78096785  2.80064252\n",
      "  2.80319332  2.87254466  2.87743488  3.02832977  3.05746706  3.34495034\n",
      "  3.3770979   3.39867421  3.53918159  3.57075586  3.63646133  3.70006366\n",
      "  3.7814163   3.78327599  3.78470412  3.78920446  3.79358912  3.8058606\n",
      "  3.80671888  3.81132061  3.82258687  3.82340815  3.82430753  3.82627765\n",
      "  3.82702739  3.8316876 ]\n",
      "interpolated Depth dist FOS input\n",
      "[ 2.07829949  2.07829949  2.07829949  2.07829949  2.07829949  2.07829949\n",
      "  2.07829949  2.07829949  2.07829949  2.07829949  2.07829949  2.07829949\n",
      "  2.07829949  2.07829949  2.07829949  2.07829949  2.07829949  2.07829949\n",
      "  2.16305024  2.47653887  2.57065501  2.70591243  2.78096785  2.80064252\n",
      "  2.80319332  2.87254466  2.87743488  3.02832977  3.05746706  3.34495034\n",
      "  3.3770979   3.39867421  3.53918159  3.57075586  3.63646133  3.70006366\n",
      "  3.7814163   3.78327599  3.78470412  3.78920446  3.79358912  3.8058606\n",
      "  3.80671888  3.81132061  3.82258687  3.82340815  3.82430753  3.82627765\n",
      "  3.82702739  3.8316876 ]\n",
      "self._rel_wetness\n",
      "[ 0.48042513  0.48042513  0.48042513  0.48042513  0.48042513  0.48042513\n",
      "  0.48042513  0.48042513  0.48042513  0.48042513  0.48042513  0.48042513\n",
      "  0.48042513  0.48042513  0.48042513  0.48042513  0.48042513  0.48042513\n",
      "  0.48042513  0.46494428  0.32063968  0.29381596  0.29060674  0.27817207\n",
      "  0.26042932  0.25972889  0.24947909  0.24025382  0.22406075  0.1931996\n",
      "  0.15263964  0.11215973  0.05603076  0.05398169  0.05372397  0.05350566\n",
      "  0.05341636  0.05191142  0.05042111  0.04916374  0.04830452  0.04741895\n",
      "  0.04718509  0.04673523  0.04532229  0.04529692  0.04459141  0.04455863\n",
      "  0.04304119  0.04190326]\n",
      "self._soil__mean_relative_wetness\n",
      "0.26689438301\n",
      "self._rel_wetness\n",
      "[ 0.48042513  0.48042513  0.48042513  0.48042513  0.48042513  0.48042513\n",
      "  0.48042513  0.48042513  0.48042513  0.48042513  0.48042513  0.48042513\n",
      "  0.48042513  0.48042513  0.48042513  0.48042513  0.48042513  0.48042513\n",
      "  0.48042513  0.46494428  0.32063968  0.29381596  0.29060674  0.27817207\n",
      "  0.26042932  0.25972889  0.24947909  0.24025382  0.22406075  0.1931996\n",
      "  0.15263964  0.11215973  0.05603076  0.05398169  0.05372397  0.05350566\n",
      "  0.05341636  0.05191142  0.05042111  0.04916374  0.04830452  0.04741895\n",
      "  0.04718509  0.04673523  0.04532229  0.04529692  0.04459141  0.04455863\n",
      "  0.04304119  0.04190326]\n",
      "self._phi\n",
      "[ 28.51291062  35.19543963  28.2662453   33.47119644  36.86445201\n",
      "  32.8812828   30.23483274  26.01632758  29.85432492  29.58584796\n",
      "  27.69786069  29.47306821  28.98371927  27.29983831  29.61001181\n",
      "  28.23996958  30.26056856  25.41133838  34.72788793  33.30908268\n",
      "  30.52083407  33.81450595  36.0305292   26.67230573  31.23193134\n",
      "  37.76045478  37.00342906  30.97405323  26.89286037  30.79096985\n",
      "  24.17531287  27.6601545   35.85017127  28.78110457  31.30445505\n",
      "  28.39066754  35.63026664  32.23639699  28.77512724  32.98655281\n",
      "  26.6413019   29.23734754  27.87194331  31.7563684   26.56462907\n",
      "  29.27243629  28.35598274  30.1297548   28.92623702  31.99466845]\n",
      "In calculate HSD loop - _hwdist.values[0]\n",
      "[ 2.01495659  3.54701044  2.70426679]\n",
      "[ 0.33333333  1.          0.66666667]\n",
      "Input Depth Annual Year Max Data = 3 years\n",
      "[ 2.01495659  3.54701044  2.70426679]\n",
      "Input Depth Annual Year Max Interpolated Data = n years\n",
      "[ 2.01495659  2.01495659  2.01495659  2.01495659  2.01495659  2.01495659\n",
      "  2.01495659  2.01495659  2.01495659  2.01495659  2.01495659  2.01495659\n",
      "  2.01495659  2.01495659  2.01495659  2.06823425  2.11717623  2.14378936\n",
      "  2.18908848  2.21484989  2.2766364   2.30445212  2.36396588  2.46062251\n",
      "  2.47395607  2.51183154  2.56292229  2.64624585  2.69158936  2.72565372\n",
      "  2.77273218  2.90995388  2.93566831  2.96584125  2.9947986   3.00644558\n",
      "  3.01081248  3.02510107  3.04338329  3.11981807  3.15552421  3.19708043\n",
      "  3.26309222  3.33549947  3.41252668  3.43335807  3.43441803  3.49464147\n",
      "  3.50400551  3.53707771]\n",
      "In calculate HSD loop -interpolate _hwdist.values[0]\n",
      "[ 2.01495659  2.01495659  2.01495659  2.01495659  2.01495659  2.01495659\n",
      "  2.01495659  2.01495659  2.01495659  2.01495659  2.01495659  2.01495659\n",
      "  2.01495659  2.01495659  2.01495659  2.06823425  2.11717623  2.14378936\n",
      "  2.18908848  2.21484989  2.2766364   2.30445212  2.36396588  2.46062251\n",
      "  2.47395607  2.51183154  2.56292229  2.64624585  2.69158936  2.72565372\n",
      "  2.77273218  2.90995388  2.93566831  2.96584125  2.9947986   3.00644558\n",
      "  3.01081248  3.02510107  3.04338329  3.11981807  3.15552421  3.19708043\n",
      "  3.26309222  3.33549947  3.41252668  3.43335807  3.43441803  3.49464147\n",
      "  3.50400551  3.53707771]\n",
      "interpolated Depth dist FOS input\n",
      "[ 2.01495659  2.01495659  2.01495659  2.01495659  2.01495659  2.01495659\n",
      "  2.01495659  2.01495659  2.01495659  2.01495659  2.01495659  2.01495659\n",
      "  2.01495659  2.01495659  2.01495659  2.06823425  2.11717623  2.14378936\n",
      "  2.18908848  2.21484989  2.2766364   2.30445212  2.36396588  2.46062251\n",
      "  2.47395607  2.51183154  2.56292229  2.64624585  2.69158936  2.72565372\n",
      "  2.77273218  2.90995388  2.93566831  2.96584125  2.9947986   3.00644558\n",
      "  3.01081248  3.02510107  3.04338329  3.11981807  3.15552421  3.19708043\n",
      "  3.26309222  3.33549947  3.41252668  3.43335807  3.43441803  3.49464147\n",
      "  3.50400551  3.53707771]\n",
      "i\n",
      "13\n",
      "self._HSD_dict\n",
      "[{0: (3.1348855160345486, 2.1339028896492409, 4.2298493546765314)}, {1: (4.3641055473531214, 3.3839620238107222, 4.4822002297488588)}, {2: (4.9997363861010431, 4.4987411920372287, 3.8892047488581669)}, {3: (3.9480286943080589, 2.007680398674899, 4.3143715531551994)}, {4: (3.8487040702022735, 2.48373836203718, 2.3989945374607911)}, {5: (4.176460110679832, 2.3597464046292518, 3.86995565015072)}, {6: (3.9938494706376044, 4.9504547555157892, 3.1362133315826775)}, {7: (4.2026147447657127, 4.8803142543445528, 2.9848784481643795)}, {8: (4.1163454826398613, 3.746951123011975, 2.1198065039185501)}, {9: (4.6215269665299221, 2.7099028438244708, 4.829102153571891)}, {10: (3.7740425056733122, 2.0782994936051171, 3.8324204869673371)}, {11: (2.7359464420021391, 2.8233773035209206, 4.8240207409403384)}, {12: (4.9040529801858987, 4.7971356536503436, 2.6893029622097657)}, {13: (2.0149565917571559, 3.5470104394242314, 2.7042667872425739)}, {14: (3.4423020872620453, 3.7744471204150347, 2.0271274892017592)}, {15: (4.4711137780420813, 4.1925682324790561, 2.1644931070588127)}, {16: (4.2663012232093855, 3.9762677759984459, 4.3539971907449413)}, {17: (4.3407503897934561, 4.9119388775808348, 2.5880043715323349)}, {18: (4.2689057625043292, 4.8282254881463036, 4.0076949260173054)}, {19: (2.6426233884811143, 2.8315266942441326, 2.5452952714296782)}]\n",
      "self._De\n",
      "[ 2.01495659  2.01495659  2.01495659  2.01495659  2.01495659  2.01495659\n",
      "  2.01495659  2.01495659  2.01495659  2.01495659  2.01495659  2.01495659\n",
      "  2.01495659  2.01495659  2.01495659  2.06823425  2.11717623  2.14378936\n",
      "  2.18908848  2.21484989  2.2766364   2.30445212  2.36396588  2.46062251\n",
      "  2.47395607  2.51183154  2.56292229  2.64624585  2.69158936  2.72565372\n",
      "  2.77273218  2.90995388  2.93566831  2.96584125  2.9947986   3.00644558\n",
      "  3.01081248  3.02510107  3.04338329  3.11981807  3.15552421  3.19708043\n",
      "  3.26309222  3.33549947  3.41252668  3.43335807  3.43441803  3.49464147\n",
      "  3.50400551  3.53707771]\n",
      "self._HSD_dict\n",
      "[{0: (3.1348855160345486, 2.1339028896492409, 4.2298493546765314)}, {1: (4.3641055473531214, 3.3839620238107222, 4.4822002297488588)}, {2: (4.9997363861010431, 4.4987411920372287, 3.8892047488581669)}, {3: (3.9480286943080589, 2.007680398674899, 4.3143715531551994)}, {4: (3.8487040702022735, 2.48373836203718, 2.3989945374607911)}, {5: (4.176460110679832, 2.3597464046292518, 3.86995565015072)}, {6: (3.9938494706376044, 4.9504547555157892, 3.1362133315826775)}, {7: (4.2026147447657127, 4.8803142543445528, 2.9848784481643795)}, {8: (4.1163454826398613, 3.746951123011975, 2.1198065039185501)}, {9: (4.6215269665299221, 2.7099028438244708, 4.829102153571891)}, {10: (3.7740425056733122, 2.0782994936051171, 3.8324204869673371)}, {11: (2.7359464420021391, 2.8233773035209206, 4.8240207409403384)}, {12: (4.9040529801858987, 4.7971356536503436, 2.6893029622097657)}, {13: (2.0149565917571559, 3.5470104394242314, 2.7042667872425739)}, {14: (3.4423020872620453, 3.7744471204150347, 2.0271274892017592)}, {15: (4.4711137780420813, 4.1925682324790561, 2.1644931070588127)}, {16: (4.2663012232093855, 3.9762677759984459, 4.3539971907449413)}, {17: (4.3407503897934561, 4.9119388775808348, 2.5880043715323349)}, {18: (4.2689057625043292, 4.8282254881463036, 4.0076949260173054)}, {19: (2.6426233884811143, 2.8315266942441326, 2.5452952714296782)}]\n",
      "Depth of Water Distribution (SoilDepth-DTWdist)\n",
      "[ 3.98504341  3.98504341  3.98504341  3.98504341  3.98504341  3.98504341\n",
      "  3.98504341  3.98504341  3.98504341  3.98504341  3.98504341  3.98504341\n",
      "  3.98504341  3.98504341  3.98504341  3.93176575  3.88282377  3.85621064\n",
      "  3.81091152  3.78515011  3.7233636   3.69554788  3.63603412  3.53937749\n",
      "  3.52604393  3.48816846  3.43707771  3.35375415  3.30841064  3.27434628\n",
      "  3.22726782  3.09004612  3.06433169  3.03415875  3.0052014   2.99355442\n",
      "  2.98918752  2.97489893  2.95661671  2.88018193  2.84447579  2.80291957\n",
      "  2.73690778  2.66450053  2.58747332  2.56664193  2.56558197  2.50535853\n",
      "  2.49599449  2.46292229]\n",
      "Depth distribution is not none - count saturated cells\n",
      "Soil Thickness This Node\n",
      "6\n",
      "Depth of Water Distribution (SoilDepth-DTWdist) hw>hs correction\n",
      "[ 3.98504341  3.98504341  3.98504341  3.98504341  3.98504341  3.98504341\n",
      "  3.98504341  3.98504341  3.98504341  3.98504341  3.98504341  3.98504341\n",
      "  3.98504341  3.98504341  3.98504341  3.93176575  3.88282377  3.85621064\n",
      "  3.81091152  3.78515011  3.7233636   3.69554788  3.63603412  3.53937749\n",
      "  3.52604393  3.48816846  3.43707771  3.35375415  3.30841064  3.27434628\n",
      "  3.22726782  3.09004612  3.06433169  3.03415875  3.0052014   2.99355442\n",
      "  2.98918752  2.97489893  2.95661671  2.88018193  2.84447579  2.80291957\n",
      "  2.73690778  2.66450053  2.58747332  2.56664193  2.56558197  2.50535853\n",
      "  2.49599449  2.46292229]\n",
      "Soil thickness All\n",
      "[1 1 1 1 1 2 2 2 2 3 4 5 5 6 6 7 8 8 8 9]\n",
      "Mean water table depth\n",
      "2.59054282657\n",
      "In calculate HSD loop - _hwdist.values[0]\n",
      "[ 2.01495659  3.54701044  2.70426679]\n",
      "[ 0.33333333  1.          0.66666667]\n",
      "Input Depth Annual Year Max Data = 3 years\n",
      "[ 2.01495659  3.54701044  2.70426679]\n",
      "Input Depth Annual Year Max Interpolated Data = n years\n",
      "[ 2.01495659  2.01495659  2.01495659  2.01495659  2.01495659  2.01495659\n",
      "  2.01495659  2.01495659  2.01495659  2.01495659  2.01495659  2.01495659\n",
      "  2.01495659  2.03309346  2.09918194  2.14500753  2.15374617  2.15463751\n",
      "  2.18131517  2.22592715  2.41692382  2.42644788  2.47952561  2.59694785\n",
      "  2.61012147  2.70262223  2.73167708  2.75410723  2.77359267  2.78987763\n",
      "  2.79660372  2.81855239  2.96095301  2.96338229  2.97752038  2.99381541\n",
      "  3.0298773   3.09674239  3.10070617  3.20856806  3.21104531  3.2514036\n",
      "  3.25464673  3.32777134  3.38991723  3.41877831  3.44793546  3.46250459\n",
      "  3.50262705  3.53243034]\n",
      "In calculate HSD loop -interpolate _hwdist.values[0]\n",
      "[ 2.01495659  2.01495659  2.01495659  2.01495659  2.01495659  2.01495659\n",
      "  2.01495659  2.01495659  2.01495659  2.01495659  2.01495659  2.01495659\n",
      "  2.01495659  2.03309346  2.09918194  2.14500753  2.15374617  2.15463751\n",
      "  2.18131517  2.22592715  2.41692382  2.42644788  2.47952561  2.59694785\n",
      "  2.61012147  2.70262223  2.73167708  2.75410723  2.77359267  2.78987763\n",
      "  2.79660372  2.81855239  2.96095301  2.96338229  2.97752038  2.99381541\n",
      "  3.0298773   3.09674239  3.10070617  3.20856806  3.21104531  3.2514036\n",
      "  3.25464673  3.32777134  3.38991723  3.41877831  3.44793546  3.46250459\n",
      "  3.50262705  3.53243034]\n",
      "interpolated Depth dist FOS input\n",
      "[ 2.01495659  2.01495659  2.01495659  2.01495659  2.01495659  2.01495659\n",
      "  2.01495659  2.01495659  2.01495659  2.01495659  2.01495659  2.01495659\n",
      "  2.01495659  2.03309346  2.09918194  2.14500753  2.15374617  2.15463751\n",
      "  2.18131517  2.22592715  2.41692382  2.42644788  2.47952561  2.59694785\n",
      "  2.61012147  2.70262223  2.73167708  2.75410723  2.77359267  2.78987763\n",
      "  2.79660372  2.81855239  2.96095301  2.96338229  2.97752038  2.99381541\n",
      "  3.0298773   3.09674239  3.10070617  3.20856806  3.21104531  3.2514036\n",
      "  3.25464673  3.32777134  3.38991723  3.41877831  3.44793546  3.46250459\n",
      "  3.50262705  3.53243034]\n",
      "self._rel_wetness\n",
      "[ 0.6641739   0.6641739   0.6641739   0.6641739   0.6641739   0.6641739\n",
      "  0.6641739   0.6641739   0.6641739   0.6641739   0.6641739   0.6641739\n",
      "  0.6641739   0.6641739   0.6641739   0.65529429  0.64713729  0.64270177\n",
      "  0.63515192  0.63085835  0.6205606   0.61592465  0.60600569  0.58989625\n",
      "  0.58767399  0.58136141  0.57284629  0.55895903  0.55140177  0.54572438\n",
      "  0.53787797  0.51500769  0.51072195  0.50569313  0.5008669   0.49892574\n",
      "  0.49819792  0.49581649  0.49276945  0.48003032  0.4740793   0.46715326\n",
      "  0.4561513   0.44408342  0.43124555  0.42777366  0.42759699  0.41755975\n",
      "  0.41599908  0.41048705]\n",
      "self._soil__mean_relative_wetness\n",
      "0.568242862239\n",
      "self._rel_wetness\n",
      "[ 0.6641739   0.6641739   0.6641739   0.6641739   0.6641739   0.6641739\n",
      "  0.6641739   0.6641739   0.6641739   0.6641739   0.6641739   0.6641739\n",
      "  0.6641739   0.6641739   0.6641739   0.65529429  0.64713729  0.64270177\n",
      "  0.63515192  0.63085835  0.6205606   0.61592465  0.60600569  0.58989625\n",
      "  0.58767399  0.58136141  0.57284629  0.55895903  0.55140177  0.54572438\n",
      "  0.53787797  0.51500769  0.51072195  0.50569313  0.5008669   0.49892574\n",
      "  0.49819792  0.49581649  0.49276945  0.48003032  0.4740793   0.46715326\n",
      "  0.4561513   0.44408342  0.43124555  0.42777366  0.42759699  0.41755975\n",
      "  0.41599908  0.41048705]\n",
      "self._phi\n",
      "[ 31.61591953  32.04440852  30.54682541  33.07344935  40.98380681\n",
      "  38.44616449  29.54258745  27.7636591   38.16480621  33.53195509\n",
      "  30.53528813  34.92777405  34.60518012  29.59431208  31.13129405\n",
      "  28.59303744  30.63781096  29.73188932  30.46003042  35.06134673\n",
      "  28.37585965  29.30380831  29.75645311  31.64115078  31.4871734\n",
      "  31.08836864  35.84687285  41.67083706  33.50197637  39.21645461\n",
      "  32.29719359  29.37557878  32.39792218  31.46409184  33.12955526\n",
      "  34.74400347  32.84311051  38.98065931  37.89487267  35.25229717\n",
      "  33.0961483   29.72371926  32.30051909  35.28077218  31.36396815\n",
      "  29.33572951  38.3554258   35.33571761  34.30066059  36.40930713]\n",
      "In calculate HSD loop - _hwdist.values[0]\n",
      "[ 3.44230209  3.77444712  2.02712749]\n",
      "[ 0.66666667  1.          0.33333333]\n",
      "Input Depth Annual Year Max Data = 3 years\n",
      "[ 3.44230209  3.77444712  2.02712749]\n",
      "Input Depth Annual Year Max Interpolated Data = n years\n",
      "[ 2.02712749  2.02712749  2.02712749  2.02712749  2.02712749  2.02712749\n",
      "  2.02712749  2.02712749  2.02712749  2.02712749  2.02712749  2.02712749\n",
      "  2.02712749  2.02712749  2.02712749  2.0558587   2.09903764  2.13178221\n",
      "  2.19576477  2.37153797  2.45935855  2.53370422  2.79619142  2.93369476\n",
      "  3.10287458  3.10899028  3.12074637  3.27947421  3.33148511  3.46420538\n",
      "  3.48918545  3.49576342  3.49782829  3.51561812  3.51571995  3.52394932\n",
      "  3.54419034  3.54505682  3.58002116  3.59610038  3.61565613  3.62229914\n",
      "  3.63152591  3.67363302  3.69015633  3.69447123  3.73535346  3.75353286\n",
      "  3.77280786  3.77297895]\n",
      "In calculate HSD loop -interpolate _hwdist.values[0]\n",
      "[ 2.02712749  2.02712749  2.02712749  2.02712749  2.02712749  2.02712749\n",
      "  2.02712749  2.02712749  2.02712749  2.02712749  2.02712749  2.02712749\n",
      "  2.02712749  2.02712749  2.02712749  2.0558587   2.09903764  2.13178221\n",
      "  2.19576477  2.37153797  2.45935855  2.53370422  2.79619142  2.93369476\n",
      "  3.10287458  3.10899028  3.12074637  3.27947421  3.33148511  3.46420538\n",
      "  3.48918545  3.49576342  3.49782829  3.51561812  3.51571995  3.52394932\n",
      "  3.54419034  3.54505682  3.58002116  3.59610038  3.61565613  3.62229914\n",
      "  3.63152591  3.67363302  3.69015633  3.69447123  3.73535346  3.75353286\n",
      "  3.77280786  3.77297895]\n",
      "interpolated Depth dist FOS input\n",
      "[ 2.02712749  2.02712749  2.02712749  2.02712749  2.02712749  2.02712749\n",
      "  2.02712749  2.02712749  2.02712749  2.02712749  2.02712749  2.02712749\n",
      "  2.02712749  2.02712749  2.02712749  2.0558587   2.09903764  2.13178221\n",
      "  2.19576477  2.37153797  2.45935855  2.53370422  2.79619142  2.93369476\n",
      "  3.10287458  3.10899028  3.12074637  3.27947421  3.33148511  3.46420538\n",
      "  3.48918545  3.49576342  3.49782829  3.51561812  3.51571995  3.52394932\n",
      "  3.54419034  3.54505682  3.58002116  3.59610038  3.61565613  3.62229914\n",
      "  3.63152591  3.67363302  3.69015633  3.69447123  3.73535346  3.75353286\n",
      "  3.77280786  3.77297895]\n",
      "i\n",
      "14\n",
      "self._HSD_dict\n",
      "[{0: (3.1348855160345486, 2.1339028896492409, 4.2298493546765314)}, {1: (4.3641055473531214, 3.3839620238107222, 4.4822002297488588)}, {2: (4.9997363861010431, 4.4987411920372287, 3.8892047488581669)}, {3: (3.9480286943080589, 2.007680398674899, 4.3143715531551994)}, {4: (3.8487040702022735, 2.48373836203718, 2.3989945374607911)}, {5: (4.176460110679832, 2.3597464046292518, 3.86995565015072)}, {6: (3.9938494706376044, 4.9504547555157892, 3.1362133315826775)}, {7: (4.2026147447657127, 4.8803142543445528, 2.9848784481643795)}, {8: (4.1163454826398613, 3.746951123011975, 2.1198065039185501)}, {9: (4.6215269665299221, 2.7099028438244708, 4.829102153571891)}, {10: (3.7740425056733122, 2.0782994936051171, 3.8324204869673371)}, {11: (2.7359464420021391, 2.8233773035209206, 4.8240207409403384)}, {12: (4.9040529801858987, 4.7971356536503436, 2.6893029622097657)}, {13: (2.0149565917571559, 3.5470104394242314, 2.7042667872425739)}, {14: (3.4423020872620453, 3.7744471204150347, 2.0271274892017592)}, {15: (4.4711137780420813, 4.1925682324790561, 2.1644931070588127)}, {16: (4.2663012232093855, 3.9762677759984459, 4.3539971907449413)}, {17: (4.3407503897934561, 4.9119388775808348, 2.5880043715323349)}, {18: (4.2689057625043292, 4.8282254881463036, 4.0076949260173054)}, {19: (2.6426233884811143, 2.8315266942441326, 2.5452952714296782)}]\n",
      "self._De\n",
      "[ 2.02712749  2.02712749  2.02712749  2.02712749  2.02712749  2.02712749\n",
      "  2.02712749  2.02712749  2.02712749  2.02712749  2.02712749  2.02712749\n",
      "  2.02712749  2.02712749  2.02712749  2.0558587   2.09903764  2.13178221\n",
      "  2.19576477  2.37153797  2.45935855  2.53370422  2.79619142  2.93369476\n",
      "  3.10287458  3.10899028  3.12074637  3.27947421  3.33148511  3.46420538\n",
      "  3.48918545  3.49576342  3.49782829  3.51561812  3.51571995  3.52394932\n",
      "  3.54419034  3.54505682  3.58002116  3.59610038  3.61565613  3.62229914\n",
      "  3.63152591  3.67363302  3.69015633  3.69447123  3.73535346  3.75353286\n",
      "  3.77280786  3.77297895]\n",
      "self._HSD_dict\n",
      "[{0: (3.1348855160345486, 2.1339028896492409, 4.2298493546765314)}, {1: (4.3641055473531214, 3.3839620238107222, 4.4822002297488588)}, {2: (4.9997363861010431, 4.4987411920372287, 3.8892047488581669)}, {3: (3.9480286943080589, 2.007680398674899, 4.3143715531551994)}, {4: (3.8487040702022735, 2.48373836203718, 2.3989945374607911)}, {5: (4.176460110679832, 2.3597464046292518, 3.86995565015072)}, {6: (3.9938494706376044, 4.9504547555157892, 3.1362133315826775)}, {7: (4.2026147447657127, 4.8803142543445528, 2.9848784481643795)}, {8: (4.1163454826398613, 3.746951123011975, 2.1198065039185501)}, {9: (4.6215269665299221, 2.7099028438244708, 4.829102153571891)}, {10: (3.7740425056733122, 2.0782994936051171, 3.8324204869673371)}, {11: (2.7359464420021391, 2.8233773035209206, 4.8240207409403384)}, {12: (4.9040529801858987, 4.7971356536503436, 2.6893029622097657)}, {13: (2.0149565917571559, 3.5470104394242314, 2.7042667872425739)}, {14: (3.4423020872620453, 3.7744471204150347, 2.0271274892017592)}, {15: (4.4711137780420813, 4.1925682324790561, 2.1644931070588127)}, {16: (4.2663012232093855, 3.9762677759984459, 4.3539971907449413)}, {17: (4.3407503897934561, 4.9119388775808348, 2.5880043715323349)}, {18: (4.2689057625043292, 4.8282254881463036, 4.0076949260173054)}, {19: (2.6426233884811143, 2.8315266942441326, 2.5452952714296782)}]\n",
      "Depth of Water Distribution (SoilDepth-DTWdist)\n",
      "[ 3.97287251  3.97287251  3.97287251  3.97287251  3.97287251  3.97287251\n",
      "  3.97287251  3.97287251  3.97287251  3.97287251  3.97287251  3.97287251\n",
      "  3.97287251  3.97287251  3.97287251  3.9441413   3.90096236  3.86821779\n",
      "  3.80423523  3.62846203  3.54064145  3.46629578  3.20380858  3.06630524\n",
      "  2.89712542  2.89100972  2.87925363  2.72052579  2.66851489  2.53579462\n",
      "  2.51081455  2.50423658  2.50217171  2.48438188  2.48428005  2.47605068\n",
      "  2.45580966  2.45494318  2.41997884  2.40389962  2.38434387  2.37770086\n",
      "  2.36847409  2.32636698  2.30984367  2.30552877  2.26464654  2.24646714\n",
      "  2.22719214  2.22702105]\n",
      "Depth distribution is not none - count saturated cells\n",
      "Soil Thickness This Node\n",
      "6\n",
      "Depth of Water Distribution (SoilDepth-DTWdist) hw>hs correction\n",
      "[ 3.97287251  3.97287251  3.97287251  3.97287251  3.97287251  3.97287251\n",
      "  3.97287251  3.97287251  3.97287251  3.97287251  3.97287251  3.97287251\n",
      "  3.97287251  3.97287251  3.97287251  3.9441413   3.90096236  3.86821779\n",
      "  3.80423523  3.62846203  3.54064145  3.46629578  3.20380858  3.06630524\n",
      "  2.89712542  2.89100972  2.87925363  2.72052579  2.66851489  2.53579462\n",
      "  2.51081455  2.50423658  2.50217171  2.48438188  2.48428005  2.47605068\n",
      "  2.45580966  2.45494318  2.41997884  2.40389962  2.38434387  2.37770086\n",
      "  2.36847409  2.32636698  2.30984367  2.30552877  2.26464654  2.24646714\n",
      "  2.22719214  2.22702105]\n",
      "Soil thickness All\n",
      "[1 1 1 1 1 2 2 2 2 3 4 5 5 6 6 7 8 8 8 9]\n",
      "Mean water table depth\n",
      "2.87314933257\n",
      "In calculate HSD loop - _hwdist.values[0]\n",
      "[ 3.44230209  3.77444712  2.02712749]\n",
      "[ 0.66666667  1.          0.33333333]\n",
      "Input Depth Annual Year Max Data = 3 years\n",
      "[ 3.44230209  3.77444712  2.02712749]\n",
      "Input Depth Annual Year Max Interpolated Data = n years\n",
      "[ 2.02712749  2.02712749  2.02712749  2.02712749  2.02712749  2.02712749\n",
      "  2.02712749  2.02712749  2.02712749  2.02712749  2.02712749  2.02712749\n",
      "  2.02712749  2.02712749  2.02712749  2.0834846   2.23827024  2.28096346\n",
      "  2.35952282  2.37437704  2.38375068  2.53011034  2.57379895  2.7624452\n",
      "  2.81956154  3.11558845  3.28434551  3.32213775  3.37542378  3.40057572\n",
      "  3.42612584  3.44862294  3.4600704   3.50754423  3.55327724  3.574117\n",
      "  3.57499064  3.57696949  3.58007808  3.60237596  3.6203629   3.65058254\n",
      "  3.65643659  3.6649504   3.69390627  3.70376615  3.71771236  3.73511025\n",
      "  3.76357446  3.76513214]\n",
      "In calculate HSD loop -interpolate _hwdist.values[0]\n",
      "[ 2.02712749  2.02712749  2.02712749  2.02712749  2.02712749  2.02712749\n",
      "  2.02712749  2.02712749  2.02712749  2.02712749  2.02712749  2.02712749\n",
      "  2.02712749  2.02712749  2.02712749  2.0834846   2.23827024  2.28096346\n",
      "  2.35952282  2.37437704  2.38375068  2.53011034  2.57379895  2.7624452\n",
      "  2.81956154  3.11558845  3.28434551  3.32213775  3.37542378  3.40057572\n",
      "  3.42612584  3.44862294  3.4600704   3.50754423  3.55327724  3.574117\n",
      "  3.57499064  3.57696949  3.58007808  3.60237596  3.6203629   3.65058254\n",
      "  3.65643659  3.6649504   3.69390627  3.70376615  3.71771236  3.73511025\n",
      "  3.76357446  3.76513214]\n",
      "interpolated Depth dist FOS input\n",
      "[ 2.02712749  2.02712749  2.02712749  2.02712749  2.02712749  2.02712749\n",
      "  2.02712749  2.02712749  2.02712749  2.02712749  2.02712749  2.02712749\n",
      "  2.02712749  2.02712749  2.02712749  2.0834846   2.23827024  2.28096346\n",
      "  2.35952282  2.37437704  2.38375068  2.53011034  2.57379895  2.7624452\n",
      "  2.81956154  3.11558845  3.28434551  3.32213775  3.37542378  3.40057572\n",
      "  3.42612584  3.44862294  3.4600704   3.50754423  3.55327724  3.574117\n",
      "  3.57499064  3.57696949  3.58007808  3.60237596  3.6203629   3.65058254\n",
      "  3.65643659  3.6649504   3.69390627  3.70376615  3.71771236  3.73511025\n",
      "  3.76357446  3.76513214]\n",
      "self._rel_wetness\n",
      "[ 0.66214542  0.66214542  0.66214542  0.66214542  0.66214542  0.66214542\n",
      "  0.66214542  0.66214542  0.66214542  0.66214542  0.66214542  0.66214542\n",
      "  0.66214542  0.66214542  0.66214542  0.65735688  0.65016039  0.64470296\n",
      "  0.6340392   0.60474367  0.59010691  0.57771596  0.5339681   0.51105087\n",
      "  0.48285424  0.48183495  0.47987561  0.45342097  0.44475248  0.42263244\n",
      "  0.41846909  0.41737276  0.41702862  0.41406365  0.41404667  0.41267511\n",
      "  0.40930161  0.4091572   0.40332981  0.40064994  0.39739065  0.39628348\n",
      "  0.39474568  0.38772783  0.38497394  0.3842548   0.37744109  0.37441119\n",
      "  0.37119869  0.37117017]\n",
      "self._soil__mean_relative_wetness\n",
      "0.521141777905\n",
      "self._rel_wetness\n",
      "[ 0.66214542  0.66214542  0.66214542  0.66214542  0.66214542  0.66214542\n",
      "  0.66214542  0.66214542  0.66214542  0.66214542  0.66214542  0.66214542\n",
      "  0.66214542  0.66214542  0.66214542  0.65735688  0.65016039  0.64470296\n",
      "  0.6340392   0.60474367  0.59010691  0.57771596  0.5339681   0.51105087\n",
      "  0.48285424  0.48183495  0.47987561  0.45342097  0.44475248  0.42263244\n",
      "  0.41846909  0.41737276  0.41702862  0.41406365  0.41404667  0.41267511\n",
      "  0.40930161  0.4091572   0.40332981  0.40064994  0.39739065  0.39628348\n",
      "  0.39474568  0.38772783  0.38497394  0.3842548   0.37744109  0.37441119\n",
      "  0.37119869  0.37117017]\n",
      "self._phi\n",
      "[ 38.20597362  36.09962126  37.27745925  27.98248459  31.58673892\n",
      "  33.3298124   32.45786276  30.85654161  31.56669474  39.24991809\n",
      "  31.44689062  31.95102467  26.82471596  37.20189703  30.01758113\n",
      "  30.57750348  31.09027606  37.8921819   30.60194476  36.48037492\n",
      "  36.63204172  38.7821177   29.50345379  30.71486649  35.74973469\n",
      "  30.49010935  31.37693791  30.16006429  30.22945192  33.02358538\n",
      "  29.41936959  31.6849658   32.54738148  27.7441125   32.2292311\n",
      "  29.18522049  33.13109812  36.89668126  36.78567026  28.96289096\n",
      "  34.15700277  37.03410265  30.92311323  35.99452481  38.00949536\n",
      "  36.60398909  29.38465627  27.15376669  30.44348593  31.23271387]\n",
      "Landslide probability successfully calculated\n"
     ]
    }
   ],
   "source": [
    "LS_prob1.calculate_landslide_probability()\n",
    "print('Landslide probability successfully calculated')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To generate the outputs from running the component, we call the 'calculate_landslide_probability' method inside the component using the class instance (e.g., LS_prob1) for each recharge option specified above. The following cell runs the model; in the following section we will assessing the results."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using recharge option 1 as an example, run the first recharge option simulation of probability of failure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50,)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LS_prob1._De.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LS_prob1.grid.number_of_nodes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The outputs of model simulations are:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['landslide__probability_of_failure',\n",
       " 'soil__mean_relative_wetness',\n",
       " 'soil__mean_watertable_depth',\n",
       " 'soil__probability_of_saturation']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(LS_prob1.output_var_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#grid['node']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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PMdDPLZJ2ypHnCcAJwLsJEwx3Jcwyb5muqAEAU22fJelgANuLJb1QtFCJ/FSoAUyVlJ3YdZztauP89wDO6IhgxZL0uteRYDhfOTnWZH8MbE+Y8XudpNm2b88k+zJwlu2fxhrh7wgl+1psZnujhmXPQbcYgCclrUL0iSFpM+DRYkVK5EWq2AdQ0xfQkmM1AXg7cHAnZCuYpNf9QP4moDcAd9n+F0Cc7fsOIGsADEyO/1cknxuIqyRtVGZI2kK3GID9CVOk15X0Z2AasHuxIiXy0qQvoBI7AjfYbt/Qk+4h6XWvMyA8YTBv6kqePjctSzMLuFjSZ4DlCD6x6nEywQg8ADxLeOVs+5V5BatGtxiA24CtgA0IF3cn3dM/kaiH1NRM4Mie9GfzDyS97g9G1wBqNW3m8fS5J3CS7SMkvRE4VdImtmuFKzyBMIHwr9SIftgM3WIArrL9WsILA4CkG4BCw6Ul8iFgsMFhoADREdz2wMfbLVOXkPS61xlYqg+gVtNmHk+fHyFGDLN9VfQMO5XavqHusT27IblzUrQ76FUJ1aZJkl7DEgs6mRAhKtEDiKZ9AT0FrNJ2gQom6XX/YMg1+idyHbC+pJcRhjXvAbyvLM09hFFvJ0nakDCzd0GdfP8W/Ur9ltAEFGRrwzDQomsAbwM+RLCUR2a2Pw58qQiBEk2gxr2B9jlJr/sFCYbz9QHEUV6fJgz9HQROsH2bpMOAObEUfwDwM0mfI9iXD0UHcbWYRPjwvzV7OqC3DYDtk4GTJb3b9rlFypJoHiEGmu8D6DuSXvcRIvcwUADbvyMM7cxu+0rm/+2EmM+N5Nkxl9FF1wAAsH2upJ2BjQlVotL2w4qTKpGXKsNAxz1Jr/uAxkYBtRVJXyY4fKsYZF7SNsCyts9v9hxdYQAkHUNoG30LcDywG3BtoUIlGqKZYaCSphCe9yaEKu3etq9qs2iFkfS6D2iwBtBm/gr8VtIzhMBJJW+g6xPcqPyB4Gqkabql2PYm2x8AFto+lOAvZa06xyS6hJI30OySk6OB39t+BfAq4I6OCVkMSa97HYGHB0aWscT2ebY3JwQVuo3Qr/AYIW72G2x/rhRTu1m6ogZAiAkL8JSk1YGHgZcVKE+iAYQYajAIjKTJwJsJnaXYfg54ru3CFUvS615HxTUBlbD9D+Afnci7WwzA+bE54LuEqo4JVeZEj1ChE7ieL6B1CFXaEyW9iuDadl/bT3ZW0jEl6XWvU2wTUMfpCgNg+2vx77mSzgcm2k4+U3oEVR4GWs8X0BBhQtRnYoD4o4GDgP/tkJhjTtLrPkDCE7riM9kRuubKJL2JjL9rSdg+pVChErlopgmIMGvyPtvXxPVzCAagr0h63eM04A00JNcOhL6tQeB424dXSPMegk8gAzfbLp8sVp5+ou1nGhE7L11hACSdCqwL3ASU3OWaEA6tbUxdcVn2blNIyIfbHHKx3SEm2x0S8uZ7H66+UzDQ4Cgg2w9IulfSBjE4zLaM9prY84yVXrebx29tXyXlqH3b3XwytiEhLXL3AeRxBy1pfYLn281tL5SU54JulfQg8CfgCuDP7apJdoUBAGYCG+WYEZfoQgQMNecN9DPAL6JL6H8BHZvwUhBJr3scCQaGc+t2HnfQHwN+bHshgO1aPoCIadaTNB3YkhAI5ieSFtluuTTbLQbgVmBVoHrk8UT30kQNAMD2TYSPZL+S9LrHkcTQ0CjdrjW4IY876JfHfP9MaCaaZfv3dWRYkzB7eEvCcOnbgCsbvJSKdIsBmArcLulaRjs7entxIiXy0mQfwHgg6XWvIxgc/ZWsNbghjzvoIcJErq0JvqL+FN1B12qzvYfgaO6btj+RR+y8dIsBmFW0AInmaaEJqN+ZVbQAidaQYHgot27ncQd9H3C17eeBf0u6k2AQrquR72uALYD3xTjD/wAut/3zvIJVoysMgO3Li5Yh0QJNNgH1O0mvex8tXQOoRR530L8hBoWRNJXQJPSvWpnavlnSP4F/EpqB9iJMouxtAyDpSttbSHqc0VWlUsizyVUOTXQRoQaQmoBKJL3uHwQM5ZwInNMd9EXAWyXdThgZ9nnbNYbYQexzWAb4C6Ht/822727ykkZRtDvoLeLvCo0eK2ktwnC6VQlh0o6zfXR7JUzkQmqqBiBpLsFH/gvA4jxB5HuBVvQakm53FYKh/KOA8riDNiFW9P65Ti8NAEfbPjW3EA1QdA1g5Vr7q7lBjSwGDrB9g6QVgOslXZIdc5sYG1rsA3iL7YfaKE7htKjXkHS7awiz3Is7v+0XJX0M6D8DQPD/YsI3ZDqwMP6fQuj5ruo4y/Y84vA6249LuoMwDCu9JGOMSH0AZTSt15B0u5tQgzWADnGJpAOBM4ERX1k5ChJ1KboJ6GUw4jd9dqw+IWlHYLu8+UiaQegpv6bCvn2AfQCmT2uqRp6ohyr2AdRzBgfhI3mxJAPHVtjfk7RLr+MxM6ig26P0evrqLcucqEwjfQAdZO/4+6nMNhMcKrZE0TWAEq/Pjm+1faGkr9U6oISk5YFzgf1sP1a+P35UjgOYud5L04zMDhDmATTsDA7CdPj/xOnwl0j6m+0rOiNlITSt11Bbt0fp9cxNkl53CAkmDhZ7e0sFimpI2t72Jc3k3S1DNx6S9GVJMyStLekQgu/0mkgaJrwgv7DdcoDkRHMoDgPNLnmw/Z/4Ox/4NWEqfT/RlF5D0u1uYQCYMOCRpUv5drMHdosB2BOYRvgI/Dr+37PWAZJEGAd7h+0jOy5hoiqlYaDZpe4x0nKxgxNJywFvJbhO6Cca1mtIut1NSLDMoEeWLqXpTorCm4CiB72Dbe/b4KGbA+8H/irpprjtS6X21sTY0kQn8EuBX4dvHUPA6fV8ovQSLeg1JN3uGkRjJf887qBjut2AswnNhHMqpWmApi1T4QbA9guSXtfEcVfSguVLtA+pYh9ATaLHxFd1RqLiaVav47FJt7uEgQb6APK4g47pVgA+S4VBK2NN4QYgcqOk2QSLmB3mlNo+e4A0E7gqSa97HAkm5G/6yeMOGuBrwHeAA9sk5txmD+wWA7AyoXNsm8w2A+lF6QWSL6BqJL3ucQTlTUAtuYOW9BpgLdvnx7H9+eQoiywHjESWs/2uvPmU0xUGwHa/BQIZVyRvoJVJet37DMRO4AxNu4OObh2+D3yoERk6GVmuKwxADHjwQ0LnlwkOj/a1fV+hgiVGmLrisjX2isEmDUBsN50D3G97l6Yy6VKSXvc+DXYC13MHvQKwCXBZHPywKjBb0tvrdAR3LLJcVxgA4ETgdGD3uL5X3LZ9W88yPAirTWlLVqu0JZclbN4muUbya2tu8JPL7qi6T4LB5vsA9gXuAPrRQ+bY6HWbeeCMX7Qtr53PaFtWADx9z6HtzbAOA4JJ+fsAarqDjnF8p5bWJV0GHJhjFFDHIst1S8/dNNsn2l4cl5MIY6YTPUCYLq9RS67jQgl5Z+D4TspXIEmvexwBywwuWWphezFQcgd9B3BWyR20pFaiwJUiy10kaXZpaSG/EbqlBvCQpL2AUnlhT3LOmEx0AYIBLfXRz+ML6CjgC4SqcT+S9LrHabAGUNcddNn2rXNmOyu3AA3SLQZgb+BHhA4SgD+zxAFSossRYmhwqcpkTV9AknYB5tu+XtLWnZSvQJJe9zihBlC4L6CORZbrCgNg+x4gBcruUQQMLl0DqMfmwNsl7QRMBCZLOs32Xu2WryiSXvc+jdYAOoGkzQiDCTYEJhBmGT/ZjshyXdEHIGlNSb+WNF/Sg5LOje3DiV5AMDigUUs9bB9se03bMwidZZf208cfkl73AwNyN/gC+hGh+fAfwCTgo3Fby3SFASCMjJgNrE6YTPHbuC3RA5SagLJLAkh63fMMAJOGlixFYfsuYND2C7ZPBLZuR77d8qam0RK9TOwEzi6NYPuyfpsDEEl63eNIXeEO+ilJE4CbJH1H0ueA5dqRcbcYgIck7SVpMC57kUZL9AzNDgMdByS97nFKncAFNwG9n/Ct/jTBp9RawLvbkXG3GIC9gfcADxAmO+xGGi3RM5Q6gbNLAkh63fOEGsCSpX567SDpTkl3STqowv79Jd0u6RZJ/ydp7Xp52r6b8JqtZvtQ2/vHJqGWSaOAEq0jMZja/Zci6XXvE/xctdUd9I3ATNtPSfokwSvoe+vkuyvwPcIIoJdJejVwmO2Wdasr3lpJJ0uakllfSdIJRcqUyE8zNQBJEyVdK+lmSbdJGts5/mNA0uveR4LhgSVLHUbcQdt+Dii5gx7B9h9tPxVXryb4C6rHrJj3opjHTQTPoC3TFTUA4JW2F5VWbC+MblMTPYBEM+3+zwLb2H4ixr+9UtKFtq9uv4SFkfS6xwmFm1GbWnIHXcZHgAtziLHY9qPqQNNqtxiAAUkr2V4IIGlluke2RF3UsDO46Nnwibg6HJeuDbraJEmvexwBwwPtcQc9KmEYEDAT2CqHGLdKeh8wKGl9QjSxv+Q4ri7dooxHAH+RdA7hhr0H+EaxIiXyUmUmcF1fQLHN9HpgPeDHtgsPkddmkl73OBIM5S9413MHHfPUdsAhwFa2n82R72di+mcJfqUuIkQVa5muMAC2T4kfi20I35N3lcfRTHQvEgwu3QRU0xcQhLi5wKtjO/mvJW1i+9ZOyTnWJL3uD4byV25ruoOGkYhgxwI72J6fJ9PYZ3BIXNpKoQZA0vK2nwCIL8ZSL0c2TaJ7aWXop+1F0Tf6DgTf5z1N0uv+QTj3KCDbiyWV3EEPAieU3EEDc2zPBr4LLA+cHdv076k3mkfSTOBLLB0S8pWNX9Foiq4BnCfpJuA84HrbTwJIWgd4C6HK/DPgnOJETNRDqugNtN4x04Dn48d/ErAd8O1OyFcASa/7BAmGGyjb1HMHbXu7JsT4BfB54K/Ai00cX5VCDYDtbaM3yI8Dm8dOsueBO4ELgA/afqBIGRP1ETQTEnI14OTYDzBACJ5xfrtlK4Kk1/2DEAMqfLT8glh7aDtF1wAqWsxEFzJcIxySGjcAtm8B+nZIZNLrfkEMqPDP5FclHQ/8H6EjGADbv2o148KvrGdpcwzfXqbJGkCiSxnruLvdTKgBDBctxoeBVxCGSpeagAwkA5DoDjoxSSWR6Aa6oAbwKtv/rxMZF9q4Jel3kmYUKUOiDUgwODB6Gcckve4nxADDI0tBXC1po05kXPSbehJwsaRDojuARK8yoNHL+OYkkl73BVLoAygtBbEFIRbAndGL6F8l3dKOjIseBXSWpAuArwBzJJ1KZpiT7SMLEy6RH9HwR1/SWsApwKqEZ36c7aPbL9zYk/S6n2isD0DSDsDRhHkAx9s+vGz/MgS9fx0hNsR7bc+tk+0OjUjcCIU3bhGGxz0JLAOsQJvHuSbGAkGDvoCAxcABtm+QtAJwvaRL+mimbNLrPiB0AtcYAZdNm88d9EeAhbbXk7QHYe5LTXfQMR5ARyh6JvAOwJGEuKmvzbhJbeT4qtY2MUY0UQOwPY8QJAXbj0u6g+BNsecNQKt6nckj6XbhNFQDGHEHDSCp5A46q9PvILh3hjAR8EeSFJ0jjjlF1wAOAXa3fVujB+a0tomxYmkDUNcZXInYYfoaoF+cwTWt15B0u5u4/vrbLhrQRlMzmya26A56JE10HfEosArwUHslz0fRfQBbtnB4HmubGCuWbgKq6wwOgk8c4FxgP9uPdUK0saZFvYak212D7Uba3/O4g87tMnosKHoUUCtUsrZrlCeStI+kOZLmLFjUcE08kQepqVFAcYTMucAv2jGrsY+oq9uj9HrBwjEVLlGVPO6gR9JIGgJWBB4ZE+kq0MsGIJcltX2c7Zm2Z06bsuwYiDVOadAAKMwc+zlwRxoVsxR1dXuUXk9baYzEStRhxB20pAkEd9DlPnxmAx+M/3cDLi2q/R962wDkCr6QGANKncCN1QA2B94PbCPpprjs1FE5e4ek2z2I7cVAyR30HQQHh7dJOkxSyeXzz4FVJN0F7A8cVIy0gaI7gVuhbvCFxBjSeEjIK6lc0k0k3e5ZcriDfgbYfazlqkbPGoBqwRcKFmuckmb/tpOk24mxomcNACSXu11DE/MAErVJup0YC3raACS6iMZnAicSiYJJb22idZrrBEZ5S45LAAAgAElEQVTSCZLmS+r5OMCJRC+SDECiDTQ3D4DgNbNjjq4SiURtkgFItIcmDIDtKyhwEkwiMd5RgXMQxhxJC4COedbrc9a2Pa3SDkm/B6aWbZ4IPJNZr+gLKPoBOt/2Jm2Sc9yRQ6+n0pqvmaKP7yRV9Xo8MK46gcfzg+4kDfpLSbSZenotaU4ev0zdenyic6QmoEQikRinJAOQSCQS45RkABKFIekM4CpgA0n3SfpI0TL1KRXjMPTQ8YkO0REDIGlLSXdm1udK2q4T52oUSSdJ+nqTx86SdFqN/bdJ2ro8raTpkp6IgT7GBEmTJP1W0qOSzh6r8zaC7T1tr2Z72Paatn9e75jsPW6ETuigpBmSHN361kr3IUlXZtafkLROnrTtoFognlaPl3SZpI82c3y99zB7j7Jpy78tY4Gkl0q6QtLjko6okU6STpS0UNK1OfK9UNIH4/+2P/c8dKQT2PafgA06kXc3Y3vjKtvvAZYvrUu6DDjN9vEdFGc34KXAKtFLYVuRNAtYz/Ze7c47c46TgPtsf7m0rdo97iVsL18/1fim2j0q/7ZImgt81PYfOijOPoRRTJPruG7eghDFbU3bT9bL1PaObZKvafqqCaheSWycsTbw9058/NtBelbdTXo+o1gbuD2H3/61gbl5Pv7tpJVnlcsASPqipPtjFehOSdtKWkbSUZL+E5ejJC0T028t6b5mhSo79yxJ50g6M57/BkmvyuyfG+W7BXhS0pCkDWP1dFFsMnh7WbZTJV0S87tc0tqZ/I6WdK+kxyRdL6k8vN/EOrIs1cyQbSqQ9A1gS0Iw6Cck/UjSj8urlrH5Zr8696bidUo6FPgK8N54jqpt65LWi/fgUUkPSTqz3r1QCFj+pUz+N1e6fo1uBivdg49Iuge4NG4/W9ID8fxXSNo4bt8H+G/gC/Ecvy0/Rx4dlHSApPkEn/pvk/QGSVfFezYv3v8JGZkt6ROS/hGr8j+WpLhvUNL34n36F7Bz2b38kKR/Rd34t6T/rnLPLWm9+H8VSbPjPb4WWLcs7Suirj6i8O69p9qzzByzcjzmH/F3pbj9JEnHZHT/kXjfz8/I9aCk54D5cdubJF0X0z0g6e8KsRv+TijxrivpWknPSXpW0h0ZObLP9uZ4zpsk3QS8itrv4cg9Kru2kW+LpFOB6cBvo458QdIFkj5Tdswtkt5Z555lr/M6SW8q3TNCAJeSHlZsRlR4x44H3hjTHSppJUnnS1oQdel8SWtmjqnYhKYKTYvZtFHP/izp+5IeIQaZl7S3pDviuS7K3s+q2K65EKpb9wKrx/UZBCU9DLgaeAkwDfgL8LWYZmtC1b2Ux1xgu3rnqnL+WcDzhCaNYeBA4N/AcCbvmwgBNCbFNHcRPlATgG2Ax4ENYvqT4vqbgWWAo4ErM+fbixCkeQg4AHgAmNiALNtl0p6WuWcGhuL6ZYRqa+mcbyAE/BiI61OBp4CX1rgv9a5z5Px17u8ZhCDmA4TJW1s0cC9OK8tr1HOucg9OAZYDJsXtewMrxGdxFHBT5viTgK9XOwf1dXBxTDMMPEiYmLY1sFm8phmEwB37ZfI3cD4whfBxWQDsEPd9AvgbQddWBv5Yeq7xmh7L3P/VgI3j/w8xWsdMaD4D+CVwVjx+E4L//yvjvuUI796H4zleS2iK2LjOM/0OcFD8fxDw7Sq6/ytCnOHzCW6nDVxJaDq8BXgjsJAQuGcI2DOurwJ8BpgX5d0EeCvwf8CijBzZZ3sO8GjZs631Hmbv0YgeUOfbArwHuCaz/irgYWBCjfu1crXrrKaHVfIpf86rAO8Glo334WzgN5n9lxG/A9ljKfteVEm7OD6DIcJ3752E78GGcduXgb/UkzlPDeCF+IA2kjRse67tfxJKZ4fZnm97AXBovIGd4Hrb59h+HjiS8KHaLLP/B7bvtf103L48cLjt52xfSlDwPTPpL7B9he1nCR+/N0paC8D2abYftr3Y9hHx2rP9GfVkaRjb1wKPAtvGTXsAl9l+sMZhea4zD88Tqq6r237GIVBLSa5696IZZtl+Mj4rbJ9g+/H4LGYBr5K0Ys686ung83H/88DTcXnG9tXxmuYCxwJbleV7uO1FDn03fwReHbe/Bzgq6tojwLfKjnsR2ETSJNvzXMeHv8KggHcDX4n35Fbg5EySXQhNCidGeW8gxFDerc59eUcmn5MJH4cSWd3/AOF5TiQUQoiyPEgoGHwO+IftU+P5zyAYwF0JejYfONX2rbYvjvdjcryu8md7UtyXfbZV38MWOI8QTGf9uP5+4Ezbz9U4Zuca19k08d051/ZTth8HvsHSutYs/7H9wyjv08DHgW/ZvsOh2febwKvr1QLqGgDbdwH7EV7O+ZJ+KWl1YHVGTz+/O27rBCMBsm2/SAiZt3ql/XH7vTFdVrY1KqW3/QTBH83qAApNBnfEquAiQtDmqVWOrSRLs5xMKHETf0+tkz7PdebhCwR/ntcqNCPtXdqR4140w8j9i00qh0v6p6THCKU5GjhHPR182KP7QJ4lDDk9PzZNPEZ4UcrP90Dm/1Ms6cBfndG6NnJuh3bf9xJqCfNiU8Qr6sg/jVBaq5gnwTBvqtBctSg+g/8GVq2T70ttz4tyzSPUkEqU6/7jBAOwRtn++wjNZuUuJu4GNgZeRiglZ2W/n6BLUys821/GNFdLupBQw6r6HjZLNCZnAXtJGiAYqjzvUqXrbPRdGoWkZSUdK+nueA+uAKaoPaMB7y1bXxs4OqMnjxCeRc1ryNUHYPt021vEkxj4NqHJImtdptO5uKUjpYL4UMtjpGY7Z/4DrBXTZWW7v0p+yxOqgP9RaOP+IqGkt5LtKYSSuaocW0mWPFTqTDoNeIdCn8KGwG/q5JHnOusLYj9g+2O2VyeUIn6i0C9Q715UuoYnCdXdEpU+VNnj3kcorW5HMC4z4vZa58jSjA7uSyjdrW97MqEJLa/70nmMjtU7PbvT9kW2tyc0//wN+Fmd/BYQqvLV8rwXuNz2lMyyvO1PSvqDpFsrLO+oc85y3V+B0DRW6Z4/wej7W5JvbUKTzqj8CB9SE5qpyp/thjHNrsAPCU2WFd/DOvKXU0lHTiYYym2Bp2xfVSePcj2CJt6lChxAqGFtGnXtzXF7PX0rdSLXepfKr/te4ONlujLJ9l9qnaiuAZC0gaRtFDrXniFUo18gVBG/LGmapKmETseqY+Rb5HWS3hU7RfYjlOSurpL2GsIN/IKkYYUx47uypAQCsJOkLRQ6/75GaDO8l/AyLCa8mEOSvgJMbkGWajwIjBoHbvs+QizYU4FzS00kNchznXWRtHumY2ohQbFeoP69eBCYUWaAbgL2iPLMpH5TxQqE+/cwQdm/WbZ/qftURjM6uCyhrf6JWEL/ZJ30Wc4CPitpTYWO1ZGA3gpjxd8uabl4TU8Q7mNVbL9AaIefFUuLGxE6HEucD7xc0vvjPR2W9HpJG9rezvYmFZbzgAclrRblWo3YoRsp1/07CO91+aCNNQk69nJJ71MYwPBeYCPgFYR7D6GkvZGkZQlBzh+L11X+bA/OXPfvCN+enau8h41Q6V26itAcdwT1S/8QIq9Vus7zG5SlnBUI38tFklYGvprnoNiceT/h3g7GWvm6dQ47BjhYSwZRrCipbuzhPDWAZYDDCVb9AUJ18kvA14E5hM6ivwI3xG2d4DxC9brUUfOu2K67FLGt7+3AjlHmnwAfsP23TLLTCQ/jEeB1hNIChBisFwJ/J1QBn2HpqlZuWWpwNLBb7K3/QWb7ycD/I4fS5rzOPLweuEbSE8BsYF/b/6b+vShNLntY0g3x//8SFHUhoT3+9DrnPiXmfT+hM7LckP6c0Pe0SFKlGlEzOngsoXT6OKGEfmbt5KP4GeG+3BzP9avMvgFCie8/BL3aCvifHHl+mtDE9AChnfzE0o7YbvxWQp/Qf2KabxPeyVrMZokh+SBBZ0uU637pfl0Xf9eMH+Q9CIWJXeJ1PUxoLvwkoSBQKlWfGuV+IMo1L24vf7bZ0UGl/oZfUPk9bIRvEQoBiyQdmNl+CuFdqlsotf0wS1/nLrZb9WB6FKGD9iGCbv++gWM/Bnw+yrMxYYBDVWz/mqAbv4zNTbcSvg016Xp30BqDCUfdgqQ3ExR2RlnbfiKRG0mrEGor04F7gN1tP6IwlHZN26+J6f5EKM0vT/jQHEPoGC4Fov+GpMOAObZnx2NmEUaCHVR2zjMII3SmEkrlXyWMvsL2MQpB7j9JqFU+Dexfr3mixXvwAWCf2HSdqEIyAF2CpGFCietm24cVLU+i/1CFmdX9SGyOuhT4ie1TipanmxnTmcAKvi+eqLC4yvYvjaV8RSFpQ2ARofPwqMz26VXuyxOSplfNcOn8j6mSxzEduJzEGKAwYqvSM/13le3NNK/0HJLeRui3epBME6SCD6GK71KD+Vf7hvXkt6rrawCJRCKR6Ax95QsokUgkEvkZVw6fpk5dyTNmtDS3Y9wyd+79PPTQwrzj5RNjyNQpy3rGalOKFqMnmTtvEQ8temrc6vW4MgAzZqzBnDm/qp8wsRQzZ76raBESVZix2hTmnLhP0WL0JDM/PL5j1aQmoEQikRinJAOQSCQS45RkABKJRGKckgxAIpFIjFOSAUgkEolxSs8bgOgt70bFsHaJRL+QdDvRaXreABD8u99RN1Ui0Xsk3U50lJ42ANGP/c6EYMyJHkHSCZLmS7q1yn5J+oGkuxQCer92rGUsmqTbvUmv6XZPGwCC47QvEII/VETSPpLmSJqzYMHCsZMsUYuTgB1q7N8RWD8u+wA/HQOZuo2auj1Krxc9NbaSJWpxEj2k2z1rACTtAsy3fX2tdLaPsz3T9sxp01YaI+kStbB9BSEISDXeAZziwNWEOKqrjY10xZNHt0fp9ZRlqyVLjDG9ptu97Apic+DtknYiBLWeLOm0fo8b0I0MTlrNfuHZkXU/v/A2QgSxEsfZbmTO/RqMjj52X9w2r3LyviPpdpcgqdxdcl/pds8aANsHE+OMKsTDPTC9IAXx4nNMWm3nkdWn7jntGdszW8ixknOuceO3POl2dzEwsEQdX3zRfaXbhRsASRMJ8Ti3BFYnhIu7FbjA9m1FypbIixgcHG5nhvcBa2XW1yTExO0Zkl73BwKGMgbguRdb/lY3rNuSXkKoFWb1aE47wsYWagBiuMddgcuAa4D5hCrvy4HD40t0gO1bauVj+7KYR6IApAEGBya2M8vZwKcl/RLYFHjUds80/7RLryHpdtFIMDy4pKv0ucUtf3Nz67aktwAHASsDN7JEj94JrCvpHOAI2481K0zRNYDrbM+qsu/IaPlyhz5MFIUYHJyQP3UmgLik+ygLIA78DtgJuAt4CvhwmwXuNEmv+wQhhgbyj5Vps27vBHzM9j0VzjNEqGFuD5ybW8AyCjUAti8o3yZpAFje9mO25xOsXqKLkcTQ4DK509ves85+A59qVa6iSHrdP5TXAOrRTt22/fka+xYDv8ktWBW6YhiopNMlTZa0HHA7cKekqhef6C7EAAMDE0aWRCDpdR8gGBzUyFI07XYL0hUGANgotmO9k1BFmg68v1iRErmJNYDSkhgh6XWPI8Tw4MDI0gW0NaZt0X0AJYYlDRNelB/Zfr7C+NtElyLEYCr5V2Lc67XeeGhb8/NVX21rfvWQaKgPYAy4sZ2ZdcuVHQvMBZYDrpC0NtB0z3ZijJFSE1Blkl73OAKGBjWyFI3tvduZX1cYANs/sL2G7Z3iFOm7gbcULVciH2KAwcEJI0sikPS695GKawKS9FtJu8ZaZPm+dSQdJqklg9AVTUCSXgp8E1jd9o6SNgLeCPy8WMkSuQizZYo5dRdPuEp63R8MFtcE9DFgf+AoSY8ACwjzAGYA/yQ0K57Xygm6ogZA8KB3EeEFBvg7sF9h0iQaQ+AJAyNLrkOkHSTdGd3iHlRh/3RJf4wBUW6JfnHK08wC/kz4qF5DaHI5C1hMmHB1iaRXtnBlrXISSa97mkZrAO3Q6xK2H7D9BdvrArsDXyMYhE1sb9/qxx+6pAYATLV9lqSDIYxxlfRC0UIlchJ6yhpIrkHgx4RJLPcB10mabfv2TLIvA2fZ/mksOf+OUPLJ0u0TrpJe9zilPoBcadun10they6hP6mtdEsN4ElJqxCdIknaDHi0WJESuREwPLBkqc8bgLts/8v2c8AvCW5ysxiYHP+vSAV/KVUmXK2a2T/f9px8F9ERkl73OKWJYDlrAG3R67GkW2oA+xN8ZKwr6c/ANEKVJ9ELSHh0DWCqpOyHt9xlbiWXuJuW5ToLuFjSZwijaLbLKc3vgG6JIJb0usep4Aqilm53Uq87QrcYgNuArYANCOXJO+me2kmiHgKGB7NbHqrjMjePS9w9gZNsHyHpjcCpkjbJ4QGx+LF6S0h63eNUcAVRS7c7pteSJgHTbd+ZV/Y8dIsyXmV7se3bbN9q+3ngqqKFSuSk1AdQWuqTxyXuRwgduti+ijD6YWqOvH+WR4AxIul1j6PGXEF0RK8l7QrcBPw+rr9a0uzcF1GDot1Br0qoNk2S9BqWWNDJQIpz1yNY4Hxt/yWuA9aX9DLgfmAP4H1lae4BtgVOkrQh4UVZkE0gaXnbT4ySxf5JvTSdJul1/1ByBZGTtuh1BWYR+hcuA7B9k6QZeYWqRdFNQG8DPkSwlEdmtj8OfKkIgRJN0OAooDga5tOEIZKDwAm2b5N0GCHQxWzgAOBnkj5HqEZ/KHpSzHKepJuA84DrbT8ZxNE6hAlX7yHUCM5p7QIbJul1v9CAK4g26nU5i20/KrW/dbNod9AnAydLerftpn1aJwqm8RoAtn9H6LDNbvtK5v/thChItfLYNo6j/jiwuaSVCHMA7gQuAD5o+4GGBGsDSa/7h/KIYPVoh15X4FZJ7wMGJa0PfBb4S4N5VKToGgAAts+VtDOwMaFKVNp+WHFSJXIjwTKD9dN1gEovXLeQ9Lr3KU0EK5jPAIcAzwKnE2oYX29Hxl1hACQdQ2gbfQtwPLAbcG2hQiXyI/BQMQagm0l63fsIGCzQAMTJZYfG4DCHtDv/wk1b5E22PwAstH0oYWr/WnWOSXQLpRpAaUmUSHrd4zQ4Eazt2H4BeF2n8u+KGgDBgRfAU5JWBx4GXlagPIlGaKIPYJyQ9LrHCRPBCp9acmMc9nk28GRpo+1ftZpxtxiA8yVNAb4L3EDoHT++WJESuZFgQnElf0mn2n5/vW0FkPS6x2k0JnCHWJlQeNgms81AfxgA21+Lf8+NMS8n2k4+U3qF4msAG2dXYrtpx6rNeUl63fsEZ3DFGgDbH+5U3l1hAAAkvYngFW8ormP7lEKFSuRjoPEagKQdgKMJ46WPt314hTTvIUyCMXCz7feV7T+YMK5+kqRSpC0BzwFZ30OFkfS6t2l0FFA79LpC+hNZ2qVEW6KDdYUBkHQqsC5hunPJXa6B9KL0AGEmcH4DkMdtbhzvfDCwue2F0bXz6PPa3wK+Jelbtg9u9TraTa/qdbvj+PY6gzn7ANql1xU4P/N/IvBftMmLaFcYAGAmsFGOGXGJLkSCoaWC1tVkxG1uOF4lt7lZv+kfA35seyEE187VMrN9cJwEtj6jx9tf0ZBU7SfpdY/TYB9AW/W6RPlkQklnAH/IK1QtCu/diNwKrFo3VaIrESp3mDVV0pzMsk/ZIZXc5q5RlublwMsl/VnS1bFqXfn80keBKwgTZA6Nv7Nau6q2kPS6x5HE0ODAyEJt3W6rXtdgfdoU6KhbagBTgdslXUuY7QaA7bcXJ1IiLxIMjdakdriDHiIo+tYEnzp/im5zF1U4dl/g9cDVtt8i6RUEQ1A0Sa97nAquIFp1B92IXodMpcdjPoq/DwBfzCN/PbrFAMwqWoBECwiGhhsaK53Hbe59hA/688C/Jd1JeHGuq5DfM7afkYSkZWz/TdIGjQjUIWYVLUCiNRpsAmq3XgNge4X8EjdGVxgA25cXLUOieYLP9IYOyeM29zfE4BmSphKqzv+qkt99cbz9b4BLJC2k4FB7kPS6PxCDOb2B0ma9llQzsp3tG/IKVo2i4wFcaXuLTBVnZBdg25OrHIqktQijKVYFXiSEZju6owInKiLBcAM1gJxucy8C3irpdsIIms/bfrhKfv8V/86S9EdCrNXfN39FrdGKXsfjk253CRIM53R13m69Bo6odTpGTwxriqLdQW8Rf5up4iwGDrB9g6QVgOslXZIdcpUYG4LDrMaOyeE214SYuvtXPa+0LPB8rE4Tm31eC9ztEJS7EFrUa0i63TV0wB10Xb3OpH1L7hM3SdE1gJVr7bf9SI1984B58f/jku4g9Linl2SMCTWAQk79e0KIvX9IWo8QbvEXwC6S3mD7oCKEakWv4/6k211Cke6gJW1j+1JJ76q0vx98AV3Pkt7t6cDC+H8KIXRaLsdZMTzaa4BrKuzbB9gHYPr01dsgcmIpYtzUAljJ9j/i/w8CZ9j+jKQJBN0qxADQJr2G6ro9Sq9XXbENIicqEWoAhY2W3wq4FNi1wr7e9wVk+2Uw4jd9dqw+IWlHYLs8eUhaHjgX2M/2Y+X7bR9HdAswc+YmaUJOBxgQLDNUyK3NnnQbgtM1bD8n6cUiBIrnb1mvY/qquj1KrzdcPel1h5CUuw+g3dj+avzte19Ar7f9idKK7Qslfa3WAQCShgkvyC/aUR1KNIeACQOFfINukfQ9woiL9YCLAeKIoG6gKb2GpNvdgpTfFURn5ehMZLlumQn8kKQvS5ohaW1JhxDcn1ZFkoCfA3fYPrJW2kRnCfFgPLKMIR8DHiI4W3ur7afi9o2A742lIFVoWK8h6XY3IYoNCAMjNcn3EkJDCtgdWLsdeXeLAdgTmAb8Oi7T4rZabA68H9hG0k1x2amzYiYqMYCZMLBkGStsP237cNv72r45s/0vtk8dM0Gq04xeQ9LtrkESQwMDI0tBdCyyXOFNQNGD3sG2923kONtXUnnqdWKMkWBCg+9GHre5Md1uhEhIr7c9p0VRx4xm9RqSbncbQw0McOiQXj8Tf9seWa5wA2D7BUmFB+9INI+AiYP5+1zzuM2N6VYAPkuF0V3dTtLr/qARVxAd1OvfaunIcj/LeWxNCjcAkY7FvEx0noHGawB53OYCfA34DnBgnkwlTSbMtXm8IWk6R9LrHkeNuYJoq15L2t322cBp0Vlc2yPLdUsfQDbm5a5x2aVQiRK5CTUAjyy0wR20pNcAa9nOBsOofH5ppqS/ArcAt0q6uUtK30mve5xSDSDTCdySO+hG9JoQOAbCaDAAbD/bro8/dEkNoJPjXBOdR4Lh0Z2/LbmDljQAfB/4UE4RTgD+x/af4vFbACcCr8x5fEdIet37hJjA7XEH3YRePxx9W70s1iRHZ9wGt+JdYQAkrQn8kDD6wcCVwL627ytUsEQuBjCTGhv+Wc9t7grAJsBlYUQkqwKzJb29SofZ46WPP4RO1OiIrVCSXvc+DbqCaLde70zwbXUqtR3DNU1XGABCae10wvhWgL3itu0Lk6gOk6Z/tWgRavL0PWMXD0WCCY0ZgJpuc2MVd+qS/HUZcGD5S5Jxl3utpGOBMwgf2vcClzV8Ie2n5/S63fiq7n5P6tGgK4i26HUm/XPA1ZLeZHtBVRmlH9r+TF4hs3SLAZhm+8TM+kmS9itMmkRDDAgmNeAKIqfb3DyUl4qyX5tucI+Q9LrHaaQG0Ea9Ls+36sc/snkz+UL3GICHJO1FKMFBmCxTd8ZkojsQsEyDE8Dquc0t2751le0dd5fbIkmve50GHR22Q6/Hkm4ZBbQ38B5CrMsHgN3itkQPEGoAS5axRtKKko7MjMw4QlI3uMhMet3jdIMriE7SFTUA2/cAKVB2jzJA4zWANnMCcCvhYwvBjcKJQEU/6mNF0uvep+QKostpetZ4V1yZpDUl/VrSfEkPSjo3jqBI9ACSi3IGV2Jd21+1/a+4HAqsU4QgWZJe9z6liGClpVBZpMlxFnE5TYcL7QoDQCitzQZWJ0yc+G3clugBSp3ApaUAno5j/wGQtDnwdBGClJH0uscpxQQuLcXIUHuio+2Tms27K5qASKMleprQCVyoCJ8ATsm0+y8kRAgrmqTXPU9DriA6RccmOhZ+ZZGHJO0laTAue5FGS/QMA1rKFURdJO0g6U5Jd0laKnSjpP0l3S7pFkn/J6mi//M4u3ID268ivBCvtP0a27e0ck1tIul1j1PBFUSd9O3R6zKWmugItGWiY7cYgOxoiXmk0RI9hYDBgSVL3fRLvCbuSAjesqekjcqS3QjMtP1K4ByC86ylsP0i8On4/7FKYUELJOl1j9NIH0A79Trm99o42fFaScdK2lrSVpJ+QpsmOnZFE1AaLdHbiKV8AdWjrtdE23/MpL+aMIu2GpdIOhA4k9FeNx9pRKh2k/S692nQFUS79brjEx27wgBIOpngI2VRXF8JOMJ2Ki31AoKhxgZIVPKauGmN9B8BLqyxv6Qnn8psMwWPBEp63ftUcAZXi7bq9VhMdOwKA0Bot11UWrG9MLpNTfQAoQYwatNUSVn/JsfZPq7skHIqlmhiu/lMYKtq57fdluhIHSDpdY8jwdDoGkAt3W6rXmfSrkgo/b85brocOKwdbqG7xQAMSFrJ9kIASSvTPbIl6iBgUA25g67nNTHkK20HHAJsZfvZqueXJgL/A2xBeOH+BBxj+5lqx4wRSa97HQmNHgVUS7fbqtcZOjbRsVuU8QjgL5LOIbzA7wG+UaxIibyEeAANHVLTa2LIU68BjgV2sD2/Tn6nEEZF/DCu70lwobt71SPGhqTX/UD+PoB263WJdW2/O7N+qKSb8gpVi64wALZPidWqbQgFyneVx9FMdC9hpET+9Dm9Jn4XWB44O/pOv6dGAIzSMNASf5R0c+NX0l6SXvcBoXqbK2kH9LrE05K2iMM/2zrRsVADIGl5208AxBdjqZcjmybRrTHrV+QAAAm2SURBVLi8Caj+EXW8JtreroHsbpS0me2rASRtCvy5IYHaSNLrfkLQwESwNut1iY5NdCy6BnBerMqcB1xv+0kASesAbyFUmX9GGC+b6FKaaAJqN5sCH5B0T1yfDtwRp887jrkeS5Je9wulSS5FnT4z0VHSZAjzXdqVf6EGwPa2knYCPg5sHjvJngfuBC4APmj7gSJlTNRHiAEVqko7FHnycpJe9xkNxANoN7ZfjM1KZ3VikmPRNYCKVaZEryEGNFzY2W3fXdjJq5D0uk+QCq0BRDo20bFwA9CrjGXM3W5HiAEGixYj0SZ6PY5v2yneGVzHJjomA5BoA8XWABKJjlFwHwB0dqJjoVcm6XeSZhQpQ6J1pNAHUFrGO0mv+wkFd7elpQgJpInRi+ivYlCh/eLkx5Ypum5zEnCxpEOkVITsXUINoLTkOqK+29xlJJ0Z91/TYx/Uk0h63R+UJrmUlnrJO6PXpwAbEyY6/ojgafTURi6jGkWPAjpL0gXAV4A5kk4FXszsP7Iw4RINoQZUKeM2d3vC9PnrJM0umyT1EWCh7fUk7QF8G3hvG0XuGEmv+wiRuw+gg3rdsYmORdcAIAyPexJYBlihbEn0AEIManhkycGI21zbzwElt7lZ3gGcHP+fA2yrOHWyR0h63ReokWAXndLrGyVtNiJRGyc6Fj0TeAfgSELc1NfafqqJ448mTLs+3vbh7ZcyUY/rr7/9osGBTaZmNk2s4w00j9vckTRxiv2jwCrAQ+2TvDO0qteZPJJuF8z1d867SFt8La9ud0qvOzbRsegeu0OA3W3f1uiBOatbiTHAdqMTsfK4zc3tWrcLaVqvIel2N9GgbndKrzs20bHoPoAtWzi8bvSdRNeSx21uKc19koaAFYFCI3zlpUW9hqTbvUpH9LqTEx27oQ+gWSpVt9YoTyRpH0lzJM1ZsGDhmAmXqMmI21xJEwhuc2eXpZnNEodXuwGX2u6VGkCr1NXtUXq9qOEWpkRn6Dm97mUDkKsqZfs42zNtz5w2baUxECtRD9uLCYHcLwLuIPg5uU3SYZJKrnF/Dqwi6S5gf2CpIXV9TF3dHqXXU5YdI7EStehFvS66D6AVckXfSXQnOdzmPkPxAV2KIul2j9Jret3LNYA81a1EohdJup0YE3q2BlAt+k7BYiUSLZN0OzFW9KwBgORyN9G/JN1OjAW93ASUSCQSiRZIBiCRSCTGKckAJBKJxDglGYBEIpEYp2j8TK4ESQuAWtOqp9Kas7FWjm/13J1mbdvTihYisTQ59DpRnXGt1+PKANRD0hzbM4s4vtVzJxKJRKOkJqBEIpEYpyQDkEgkEuOUZABGc1z9JB07vtVzJxKJREOkPoBEIpEYp6QaQCKRSIxTkgFIJBKJccq4NgCSVpZ0iaR/xN+KEWMk/V7SIknnS9pB0p2S7pK0VDAHSd+XdFNc/i5pUWbfCZLmS7q1ynm2lvRo5vivVEqXSCQS7WBc9wFI+g7wiO3D48d8JdtfrJBuW2BZ4OPAhmSCdQN7VgvWLekzwGts7x3X3ww8AZxie5MK6bcGDrS9SzuuL5FIJGoxrmsAhEDbJ8f/JwPvrJTI9v8BjwNTiMG6bT8HlIJ1V2NP4IxMPlfQI4HNE4lE/zPeDcBLbc8DiL8vqZN+IjkC0QNIWht4GXBpgzK9UdLNki6UtHGDxyYSiURuejogTB4k/QFYtcKuQ5rJrsK2am1oewDn2H6hgfxvIPgmeULSTsBvgPUblDGRSCRy0fcGwPZ21fZJelDSarbnSVoNmF8nu6fJH6x7D+BTDcr6WOb/7yT9RNJU293sJC6RSPQo470JaDbwwfj/g8B5ddI/So5g3ZI2AFYCrmpEGEmrSlL8/wbC83m4kTwSiUQiL31fA6jD4cBZkj4C3APsDiBpJvAJ2x+N638CXgEsDzwJ/Al4lhisW9JhwBzbJWOwJ/BLlw2xknQGsDUwVdJ9wFeBYQDbxwC7AZ+UtJhQ29ijPI9EIpFoF+N6GGgikUiMZ8Z7E1AikUiMW5IBSCQSiXFKMgCJRCIxTkkGIJFIJMYpyQAkEonEOGXcGQBJa0n6t6SV4/pKcX3tCmknSbpc0uAYyvdE/F1d0jkt5LOfpGWbPPbTkj7c7LkTiURvMC6HgUr6ArCe7X0kHQvMtf2tCuk+BQzZPrpDcgzZXly27f+3d36hXVZhHP98MUT8Nxk/CIJogYxQkAkJg2VCpeGtF4HESgSvFLdK7CISnBDCFnU1ISIVL1IEhRCLWcyJ08HGVqsNIoi5m6SgtVo6L+Lp4jxve5377Y//tvY+Hzj8znnec877/H4/OM/7nPOe54yZ2cqH0PcQ8Pz97CJ2w9FpZhsfVI8gCBYuhfMAnI+AWkmNwAvAh2XqvU5ud7Ckg5K+92BtR11WI6lLUr+k89mZAtPIL0v6QFIH0OC7iq9L6pZ0JHevquzcAEm7JJ3zcwl+8jDWWb1jknokDUg67LL9wFNAu6R2l23z+/RKOitppcuPShp0PVsAzOwWMOS7kYMgWKyYWSET8CopkNvWMteXAjdz5e3ANWC5lyv9sx/Y4vkm4OMZ5JeB1ly/XwBveH4vMOb5KuAHz+8CfgYqSBFJbwBPT9Jjife9wctDQMnzJeAKsMLL7wKHgErgRyY8wTU5vd4D3pnv/ylSpEiPLhXVA4A0oP8C3HMwi1MC/siVXwGOW3o6xsx+l1RBGjQ7vM5J4MVy8lxfZ3L5OibODDg1jb7fmNmomY0Dg0C2ZvGapF6gD1gPrJuiba3LOyV9S4p79AzwJzAOfCppB3Ar1+ZXkhcRBMEipZCxgCTVkE71qgWuSjptfi5Ajtukp+3/mlE+9PNc+XtSeTb93snl/wGekPQscADYZGYjkk5wt84ZAi6Z2c57LqRpnpdJge32AS/5pWWk3yAIgkVK4TwAj7Z5DGg0s2GgGWiZXM/MRoAlkrIBtQ3Ynb1ZI6nSzEaBEUmbvU490FFOXkalTtLgC2nNYS6sJhmTUUlPkryajL+AVZ7vAuokrXXdl0uq9nWACjO7CDQCNbn21cCUZxcHQbA4KJwBAPYAw2Z2ycutwHOStkxRt420SIyZfUWar+/xaZQDXudNoFlSP2kAbZpBPpkGYK+kbtIc/6wxs+9IUz8DwGckY5LxCfClpHYz+420jvC569NFim66Crjgsg7grVz7OuDruegTBMH/i0K+BjpbJG0E3jaz+vnW5XFS1O8dBEWjiB7ArDGzPtKrlI9tI9gCoQS8P99KBEHwaAkPIAiCoKCEBxAEQVBQwgAEQRAUlDAAQRAEBSUMQBAEQUEJAxAEQVBQ/gVsosAMATG6gAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure('Water Table Depth Calculations')\n",
    "xticks = np.arange(-0.1, 1.6, 0.8)\n",
    "\n",
    "\n",
    "ax1 = fig.add_subplot(221)\n",
    "ax1.xaxis.set_visible(False)\n",
    "imshow_grid_at_node(grid, 'soil__thickness', plot_name='soil (m)',\n",
    "                    allow_colorbar=True, cmap='copper_r',\n",
    "                    grid_units=('coordinates', 'coordinates'))\n",
    "\n",
    "ax2 = fig.add_subplot(222)\n",
    "ax2.set_xticks(xticks)\n",
    "imshow_grid_at_node(grid, 'soil__mean_relative_wetness', cmap='YlGnBu',\n",
    "                 grid_units=('coordinates', 'coordinates'), shrink=0.75,\n",
    "                 var_name='mean_rw', var_units='m')\n",
    "\n",
    "ax3 = fig.add_subplot(223)\n",
    "ax3.set_xticks(xticks)\n",
    "imshow_grid_at_node(grid, 'soil__probability_of_saturation', cmap='YlGnBu',\n",
    "                 grid_units=('coordinates', 'coordinates'), shrink=0.75,\n",
    "                 var_name='prob Sat', var_units='-')\n",
    "ax4 = fig.add_subplot(224)\n",
    "ax4.xaxis.set_visible(False)\n",
    "imshow_grid_at_node(grid,'landslide__probability_of_failure', cmap='copper_r',\n",
    "                 grid_units=('coordinates', 'coordinates'), shrink=0.75,\n",
    "                 var_name='prob_failure', var_units='-')\n",
    "\n",
    "plt.figure('Slope')\n",
    "imshow_grid_at_node(grid,'topographic__slope', cmap='copper_r',\n",
    "                 grid_units=('coordinates', 'coordinates'), shrink=0.75,\n",
    "                 var_name='Slope', var_units='m/m')\n",
    "plt.savefig('Slope.png')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save the arrays as variables by 'attaching the fields to the grid' and view the outputs. \n",
    "\n",
    "This simulation generates a probability value for each core node. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
       "        1.        ,  1.        ,  0.        ,  0.        ,  0.69122789,\n",
       "        0.26689438,  0.        ,  0.        ,  0.56824286,  0.52114178,\n",
       "        0.        ,  0.        ,  0.        ,  0.        ,  0.        ])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LS_prob1_soil__probability_of_saturation = grid.at_node['soil__mean_relative_wetness']\n",
    "grid.at_node['soil__mean_relative_wetness']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LS_prob1_soil__probability_of_saturation = grid.at_node['soil__probability_of_saturation']\n",
    "grid.at_node['soil__probability_of_saturation']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
       "        2.95781606,  3.77418633,  0.        ,  0.        ,  3.60155298,\n",
       "        2.93242247,  0.        ,  0.        ,  2.59054283,  2.87314933,\n",
       "        0.        ,  0.        ,  0.        ,  0.        ,  0.        ])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LS_prob1_soil__mean_watertable_depth = grid.at_node['soil__mean_watertable_depth']\n",
    "grid.at_node['soil__mean_watertable_depth']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LS_prob1_probability_of_failure = grid.at_node['landslide__probability_of_failure']\n",
    "grid.at_node['landslide__probability_of_failure']"
   ]
  }
 ],
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