{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"http://landlab.github.io\"><img style=\"float: left\" src=\"../../landlab_header.png\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For more Landlab tutorials, click here: https://landlab.readthedocs.io/en/latest/user_guide/tutorials.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Application of the flow__distance utility on a Sicilian basin**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook illustrates how to run the flow**__**distance utility on a digital elevation model (DEM) that represents a real basin in Sicily. First, a watershed will be extracted from the input DEM by using the watershed utility. Then, the distances from each node to the watershed's outlet will be obtained with the flow**__**distance utility. Flow is routed using the D8 algorithm."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, import what we'll need:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from landlab.io import read_esri_ascii\n",
    "from landlab.components import FlowAccumulator\n",
    "from landlab.plot import imshow_grid\n",
    "from matplotlib.pyplot import figure\n",
    "%matplotlib inline\n",
    "from landlab.utils import watershed\n",
    "import numpy as np\n",
    "from landlab.utils.flow__distance import calculate_flow__distance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import a square DEM that includes the watershed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "(mg, z) = read_esri_ascii('nocella_resampled.txt',\n",
    "                          name='topographic__elevation')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the FlowAccumulator and the DepressionFinderAndRouter components to find depressions, to route the flow across them and to calculate flow direction and drainage area:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fr = FlowAccumulator(mg,\n",
    "                     flow_director='D8',\n",
    "                     depression_finder='DepressionFinderAndRouter')\n",
    "fr.run_one_step()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set the id of the outlet. The value indicated here is the node id of the entire watershed's outlet:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "outlet_id = 15324"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the watershed utility and show the watershed mask:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ws_mask = watershed.get_watershed_mask(mg, outlet_id)\n",
    "figure()\n",
    "imshow_grid(mg, ws_mask, allow_colorbar=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the flow**__**distance utility:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "flow__distance = calculate_flow__distance(mg, add_to_grid=True, clobber=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mask the flow**__**distance to the watershed mask. This operation has to be done because the flow**__**distance utility is applied to the entire grid that contains other streams not connected with our stream network and, for this reason, not belonging to our watershed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "flow_distance = np.zeros(mg.number_of_nodes)\n",
    "flow_distance[ws_mask] = flow__distance[ws_mask] - flow__distance[outlet_id]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add the flow**__**distance field to the grid and show the spatial distribution of the distances from each node to the watershed's outlet:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mg.add_field('flow_distance', flow_distance, at='node', clobber=True)\n",
    "figure()\n",
    "imshow_grid(mg,\n",
    "            mg.at_node['flow_distance'],\n",
    "            colorbar_label='flow distance (m)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}