notebooks/roughness_map.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Input Layer - Manning's Roughness"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Goal\n",
    "\n",
    "Create a `.dfsu` roughness map as a MIKE21 HD model input layer corresponding to elements from an unstructured `.mesh`, taking roughness values from a GIS polygon vector `.shp` file that outlines regions of roughness values.\n",
    "\n",
    "**This can be extened to any sort of data from any provied polygon ie. eddy viscosity. The final exported `.dfsu` final can have the appropriate units set by using the [Unit](https://dhitools.readthedocs.io/en/latest/api.html#module-dhitools.units) functions when calling `lyr_to_dfsu()`.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dhitools import mesh"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "## tl;dr\n",
    "\n",
    "Minimum steps to create roughness `.dfsu` from input `.mesh` and roughness `.shp`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: CRS does not match!\n"
     ]
    }
   ],
   "source": [
    "mesh_f = 'data/example_mesh_no_elevation.mesh' # Mesh files\n",
    "roughness_shp = 'data/shp/roughness.shp' # Roughness map\n",
    "dfsu_out = 'data/roughness.dfsu' # Output dfsu\n",
    "\n",
    "field_attribute = 'Manning_n'\n",
    "\n",
    "m = mesh.Mesh(mesh_f) # Load mesh\n",
    "m.lyr_from_shape('roughness', roughness_shp, field_attribute) # Get roughness values for mesh elements from roughness map\n",
    "m.lyr_to_dfsu('roughness', dfsu_out) # Write dfsu"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Outline\n",
    "\n",
    "- Load `.mesh` and roughness map `.shp` file\n",
    "- Inspect these files\n",
    "- Assign roughness values from `.shp` file to mesh elements\n",
    "- Write a new `.dfsu` file\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import geopandas as gpd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cm as cm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "mesh_f = 'data/example_mesh_no_elevation.mesh' # Mesh files\n",
    "roughness_shp = 'data/shp/roughness.shp' # Roughness map"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the mesh and roughness map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "m = mesh.Mesh(mesh_f)\n",
    "roughness_map = gpd.read_file(roughness_shp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mesh summary:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input mesh file: data/example_mesh_no_elevation.mesh\n",
      "Num. Elmts = 800\n",
      "Num. Nodes = 512\n",
      "Mean elevation = 0.0\n",
      "Projection = \n",
      " PROJCS[\"GDA_1994_MGA_Zone_56\",GEOGCS[\"GCS_GDA_1994\",DATUM[\"D_GDA_1994\",SPHEROID[\"GRS_1980\",6378137,298.257222101]],PRIMEM[\"Greenwich\",0],UNIT[\"Degree\",0.017453292519943295]],PROJECTION[\"Transverse_Mercator\"],PARAMETER[\"False_Easting\",500000],PARAMETER[\"False_Northing\",10000000],PARAMETER[\"Central_Meridian\",153],PARAMETER[\"Scale_Factor\",0.9996],PARAMETER[\"Latitude_Of_Origin\",0],UNIT[\"Meter\",1]]\n"
     ]
    }
   ],
   "source": [
    "m.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `Geopandas` library is used to load the roughness file, which allows for `pandas` style dataframe functionality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(304, 14)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roughness_map.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>OBJECTID</th>\n",
       "      <th>QLUMP_CODE</th>\n",
       "      <th>PRIMARY</th>\n",
       "      <th>SECONDARY</th>\n",
       "      <th>TERTIARY</th>\n",
       "      <th>Manning_n</th>\n",
       "      <th>inv_mann_n</th>\n",
       "      <th>Class_Name</th>\n",
       "      <th>Id</th>\n",
       "      <th>ROAD</th>\n",
       "      <th>Shape_Leng</th>\n",
       "      <th>Shape_Le_1</th>\n",
       "      <th>Shape_Area</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.025</td>\n",
       "      <td>40.0</td>\n",
       "      <td>Coastal Water</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>893089.340340</td>\n",
       "      <td>2.948710e+09</td>\n",
       "      <td>POLYGON ((538366.4013483515 7232355.935799949,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.025</td>\n",
       "      <td>40.0</td>\n",
       "      <td>Coastal Water</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1507.683439</td>\n",
       "      <td>3.696424e+04</td>\n",
       "      <td>POLYGON ((447845.9320874052 7249123.273189012,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.025</td>\n",
       "      <td>40.0</td>\n",
       "      <td>Coastal Water</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32577.239684</td>\n",
       "      <td>4.024320e+06</td>\n",
       "      <td>POLYGON ((448050.3153323128 7241192.393661046,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.025</td>\n",
       "      <td>40.0</td>\n",
       "      <td>Coastal Water</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17094.300845</td>\n",
       "      <td>3.056761e+06</td>\n",
       "      <td>POLYGON ((523660.2675399155 7238256.11602405, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.025</td>\n",
       "      <td>40.0</td>\n",
       "      <td>Coastal Water</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9822.985870</td>\n",
       "      <td>6.001529e+05</td>\n",
       "      <td>POLYGON ((448569.4988675959 7238605.131837157,...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   OBJECTID  QLUMP_CODE PRIMARY SECONDARY TERTIARY  Manning_n  inv_mann_n  \\\n",
       "0         0           0    None      None     None      0.025        40.0   \n",
       "1         0           0    None      None     None      0.025        40.0   \n",
       "2         0           0    None      None     None      0.025        40.0   \n",
       "3         0           0    None      None     None      0.025        40.0   \n",
       "4         0           0    None      None     None      0.025        40.0   \n",
       "\n",
       "      Class_Name  Id  ROAD  Shape_Leng     Shape_Le_1    Shape_Area  \\\n",
       "0  Coastal Water   0   0.0         0.0  893089.340340  2.948710e+09   \n",
       "1  Coastal Water   0   0.0         0.0    1507.683439  3.696424e+04   \n",
       "2  Coastal Water   0   0.0         0.0   32577.239684  4.024320e+06   \n",
       "3  Coastal Water   0   0.0         0.0   17094.300845  3.056761e+06   \n",
       "4  Coastal Water   0   0.0         0.0    9822.985870  6.001529e+05   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((538366.4013483515 7232355.935799949,...  \n",
       "1  POLYGON ((447845.9320874052 7249123.273189012,...  \n",
       "2  POLYGON ((448050.3153323128 7241192.393661046,...  \n",
       "3  POLYGON ((523660.2675399155 7238256.11602405, ...  \n",
       "4  POLYGON ((448569.4988675959 7238605.131837157,...  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roughness_map.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These are all of the attributes within the roughness map with each row of the dataframe corresponding to a polygon. We're interested in obtaining the `Manning_n` value for each mesh element\n",
    "\n",
    "*For demo purposes, the roughness map covers a subset of the mesh to keep file size reasonable.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(18,10))\n",
    "roughness_map.plot(column = 'Class_Name', ax=ax, legend=True, categorical=True)#, cmap=cm.cool)\n",
    "shp_x = ax.get_xlim()\n",
    "shp_y = ax.get_ylim()\n",
    "ax.scatter(m.elements[:,0], m.elements[:,1], color='k', s=40, marker='x')\n",
    "ax.set_ylim(shp_y)\n",
    "ax.set_xlim(shp_x)\n",
    "ax.set_aspect('equal')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The figure shows the different polygon classes and the crosses show the centre of mesh elements. A spatial join will be performed on the mesh element points and the Manning's n value for whichever polygon an element is within."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Roughness Values for Mesh Elements"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the `dhitools` `mesh` object we can call the method `lyr_from_shape` which accepts a layer name to save to, path to the `.shp` file we're interested in (ie. the roughness map) and the field in the shape files attribute table to extract values from after the spatial join."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "lyr_name = 'roughness' # This is just a dictionary key to access the extracted results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Roughness `.shp` file is as before"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'data/shp/roughness.shp'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roughness_shp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Field attribute is the 'Manning_n', as seen in the `roughness_map` GeoDataFrame above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "field_attribute = 'Manning_n'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Call the `lyr_from_shape` function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: CRS does not match!\n"
     ]
    }
   ],
   "source": [
    "m.lyr_from_shape(lyr_name, roughness_shp, field_attribute)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This saves the values in the mesh `lyrs` attribute in a dictionary, accessed by the `lyr_name` (the dictionary key) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
       "         nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
       "         nan,   nan,   nan,   nan, 0.025,   nan,   nan, 0.025, 0.025,\n",
       "         nan, 0.025,   nan,   nan, 0.025,   nan,   nan,   nan,   nan,\n",
       "         nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
       "         nan, 0.025,   nan,   nan, 0.025,   nan,   nan,   nan,   nan,\n",
       "         nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
       "         nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
       "         nan,   nan,   nan,   nan,   nan,   nan,   nan, 0.025,   nan,\n",
       "         nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
       "         nan,   nan,   nan,   nan,   nan, 0.025,   nan,   nan,   nan,\n",
       "         nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,   nan,\n",
       "         nan,   nan, 0.07 , 0.07 ,   nan,   nan,   nan,   nan,   nan,\n",
       "         nan,   nan,   nan,   nan, 0.025,   nan,   nan,   nan,   nan,\n",
       "         nan,   nan,   nan,   nan,   nan,   nan,   nan, 0.025,   nan,\n",
       "         nan,   nan,   nan, 0.025,   nan,   nan,   nan,   nan,   nan,\n",
       "         nan, 0.025,   nan, 0.025,   nan,   nan])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.lyrs['roughness'][:150]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A lot of these values are `nan` because the roughness map only covers a subset of the mesh."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(m.lyrs['roughness'][~np.isnan(m.lyrs['roughness'])], bins=10)\n",
    "ax = plt.gca()\n",
    "ax.set_xlabel('Manning (n)')\n",
    "ax.set_ylabel('Count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Output .dfsu"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Call `lyr_to_dfsu` to write the roughness values for each mesh element to a `.dfsu`.\n",
    "\n",
    "Accepts input, `lyr_name` to get the values, and a path to the output `.dfsu`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfsu_out = 'data/roughness.dfsu'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "m.lyr_to_dfsu(lyr_name, dfsu_out)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "dhitools_py2",
   "language": "python",
   "name": "dhitools_py2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}