docs/examples/06_mesh2d_refinement_gridded_samples_gebco.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mesh2d refinement based on Gebco gridded samples with variable depth precision\n",
    "\n",
    "This is a brief introduction to the process of mesh refinement using gridded samples from Gebco global dataset. \n",
    "\n",
    "It is also an example of \n",
    "1. Generating a mesh of defined extension\n",
    "2. Saving the generated mesh to UGrid file\n",
    "3. Reading bathymetric data form a large NetDFC file\n",
    "4. Performing mesh refinement based on uniformly spaced gridded bathymetric data (fast), non-uniformly spaced bathymetric data (slow). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import necessary libraries, close all figures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2839ee5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import meshkernel\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.close(\"all\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b32722f0",
   "metadata": {},
   "source": [
    "# 1. Generating a mesh of defined extension"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "04a588ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# general settings\n",
    "lon_min, lon_max = -1, -0.2\n",
    "lat_min, lat_max = 49.1, 49.6\n",
    "lon_res, lat_res = 0.1, 0.1\n",
    "figsize = (10, 4)\n",
    "crs = \"EPSG:4326\"\n",
    "\n",
    "\"\"\"\n",
    "Make a regular (potentially rotated) rectilinear grid. First generate a curvilinear grid than convert the curvilinear grid into unstructured grid. The steps are the following:\n",
    "- curvilinear_compute_uniform_on_extension, see the following notebook: https://github.com/Deltares/MeshKernelPy/blob/AddCurvilinearGridSupport/docs/examples/04_curvilineargrid_basics.ipynb\n",
    "- curvilinear_convert_to_mesh2d: https://github.com/Deltares/MeshKernelPy/blob/118cb4953c4e95d5b18ed283bb37f391134b2bb2/meshkernel/meshkernel.py#L1399 \n",
    "\"\"\"\n",
    "\n",
    "# Create an instance of MakeGridParameters and set the values\n",
    "make_grid_parameters = meshkernel.MakeGridParameters()\n",
    "make_grid_parameters.origin_x = lon_min\n",
    "make_grid_parameters.origin_y = lat_min\n",
    "make_grid_parameters.upper_right_x = lon_max\n",
    "make_grid_parameters.upper_right_y = lat_max\n",
    "make_grid_parameters.block_size_x = lon_res\n",
    "make_grid_parameters.block_size_y = lat_res\n",
    "\n",
    "\n",
    "mk2 = meshkernel.MeshKernel(projection=meshkernel.ProjectionType.SPHERICAL)\n",
    "mk2.curvilinear_compute_rectangular_grid_on_extension(make_grid_parameters)\n",
    "mk2.curvilinear_convert_to_mesh2d()  # convert to ugrid/mesh2d\n",
    "\n",
    "mesh2d = mk2.mesh2d_get()\n",
    "fig, ax = plt.subplots()\n",
    "mesh2d.plot_edges(ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44b5a9a3",
   "metadata": {},
   "source": [
    "# 2. Saving the generated mesh to UGrid file"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ecb3baf",
   "metadata": {},
   "source": [
    "To save the generated mesh to a UGrid file, you can use the following code snippet. Please note that in order to execute the code below, the UGrid Python package needs to be installed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8f7e2102",
   "metadata": {},
   "outputs": [],
   "source": [
    "ugrid_installed = False\n",
    "if ugrid_installed:\n",
    "    from ugrid import UGrid, UGridMesh2D\n",
    "\n",
    "    mesh2d_ugrid = UGrid.from_meshkernel_mesh2d_to_ugrid_mesh2d(\n",
    "        mesh2d=mesh2d, name=\"mesh2d\", is_spherical=True\n",
    "    )\n",
    "\n",
    "    attribute_dict = {\n",
    "        \"name\": \"Unknown projected\",\n",
    "        \"epsg\": np.array([4326], dtype=int),\n",
    "        \"grid_mapping_name\": \"Unknown projected\",\n",
    "        \"longitude_of_prime_meridian\": np.array([0.0], dtype=float),\n",
    "        \"semi_major_axis\": np.array([6378137.0], dtype=float),\n",
    "        \"semi_minor_axis\": np.array([6356752.314245], dtype=float),\n",
    "        \"inverse_flattening\": np.array([6356752.314245], dtype=float),\n",
    "        \"EPSG_code\": \"EPSG:4326\",\n",
    "        \"value\": \"value is equal to EPSG code\",\n",
    "    }\n",
    "\n",
    "    with UGrid(\"./gebco_mesh2d_net.nc\", \"w+\") as ug:\n",
    "        # 1. Define a new mesh2d\n",
    "        topology_id = ug.mesh2d_define(mesh2d_ugrid)\n",
    "        # 3. Put a new mesh2d\n",
    "        ug.mesh2d_put(topology_id, mesh2d_ugrid)\n",
    "        # 3. Add crs to file\n",
    "        ug.variable_int_with_attributes_define(\"wgs84\", attribute_dict)\n",
    "        # 4. Add conventions (global attributes)\n",
    "        conventions = {\n",
    "            \"institution\": \"Deltares\",\n",
    "            \"references\": \"Unknown\",\n",
    "            \"source\": \"Unknown Unknown. Model: Unknown\",\n",
    "            \"history\": \"Created on 2017-11-27T18:05:09+0100, Unknown\",\n",
    "            \"Conventions\": \"CF-1.6 UGRID-1.0/Deltares-0.8\",\n",
    "        }\n",
    "        ug.attribute_global_define(conventions)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24ae6fe1",
   "metadata": {},
   "source": [
    "# 3. Reading bathymetric data form a large NetDFC file"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99c45809",
   "metadata": {},
   "source": [
    "Bathymetric data can be read from a large dataset using the xarray package, which allows you to load only a selected portion of the data. When the longitude and latitude arrays are assumed not uniformly spaced, bilinear interpolation is slower"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f25b1525",
   "metadata": {},
   "outputs": [],
   "source": [
    "# select and plot bathy\n",
    "uniform_grid_spacing = False\n",
    "if not uniform_grid_spacing:\n",
    "    import xarray as xr\n",
    "\n",
    "    file_nc_bathy = r\"p:\\metocean-data\\open\\GEBCO\\2021\\GEBCO_2021.nc\"\n",
    "    data_bathy = xr.open_dataset(file_nc_bathy)\n",
    "    data_bathy_sel = data_bathy.sel(\n",
    "        lon=slice(lon_min - 1 / 4, lon_max + 1 / 4),\n",
    "        lat=slice(lat_min - 1 / 4, lat_max + 1 / 4),\n",
    "    )\n",
    "\n",
    "    lon_np = data_bathy_sel.lon.to_numpy().flatten().astype(\"float\")\n",
    "    lat_np = data_bathy_sel.lat.to_numpy().flatten().astype(\"float\")\n",
    "    values_np = data_bathy_sel.elevation.to_numpy().flatten().astype(\"float32\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6063f5a9",
   "metadata": {},
   "source": [
    "Otherwise, read from ascii, assuming uniform spacing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "19cf9896",
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_asc_file(file_path, dtype=np.float32):\n",
    "    \"\"\"Reads asc file and returns headers and data as numpy array\n",
    "    Args:\n",
    "            file_path (str): The file path\n",
    "    Returns:\n",
    "            header: The ascii header\n",
    "            data: The ascii data as a numpy array of doubles\n",
    "    \"\"\"\n",
    "\n",
    "    header = {}\n",
    "    data = []\n",
    "\n",
    "    with open(file_path, \"r\") as file:\n",
    "        # Read header information\n",
    "        for _ in range(6):\n",
    "            line = file.readline().strip().split()\n",
    "            header[line[0]] = float(line[1])\n",
    "\n",
    "        # Read data values\n",
    "        for line in file:\n",
    "            data_row = [float(value) for value in line.strip().split()]\n",
    "            data.insert(0, data_row)  # Insert row at the beginning\n",
    "\n",
    "        # Flatten the data\n",
    "        data = np.array(data).flatten().astype(dtype)\n",
    "\n",
    "    return header, data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "977b9d4d",
   "metadata": {},
   "source": [
    "# 4. Performing mesh refinement based on gridded data, depths as np.float32 or np.int16"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12487948",
   "metadata": {},
   "source": [
    "Read the depths in file as np.float32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "360b6ef8",
   "metadata": {},
   "outputs": [],
   "source": [
    "header, values_np = read_asc_file(\"./data_examples/gebco.asc\", dtype=np.float32)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59f52827",
   "metadata": {},
   "source": [
    "Read the depths in file as np.int16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "56bd777a",
   "metadata": {},
   "outputs": [],
   "source": [
    "header, values_np = read_asc_file(\"./data_examples/gebco.asc\", dtype=np.int16)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d089097",
   "metadata": {},
   "source": [
    "Grid properties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a2d80b66",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_x = int(header[\"ncols\"])\n",
    "num_y = int(header[\"nrows\"])\n",
    "x_origin = header[\"xllcenter\"]\n",
    "y_origin = header[\"yllcenter\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "856a428a",
   "metadata": {},
   "source": [
    "Gridded samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "258e8353",
   "metadata": {},
   "outputs": [],
   "source": [
    "gridded_samples = meshkernel.GriddedSamples(\n",
    "    num_x=num_x,\n",
    "    num_y=num_y,\n",
    "    x_origin=x_origin,\n",
    "    y_origin=y_origin,\n",
    "    cell_size=0.0041666666666,\n",
    "    values=values_np,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34d296cc",
   "metadata": {},
   "source": [
    "Another option is to generate gridded samples from an array of x and y coordinates, not uniformly spaced"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "de067008",
   "metadata": {},
   "outputs": [],
   "source": [
    "if not uniform_grid_spacing:\n",
    "    gridded_samples = meshkernel.GriddedSamples(\n",
    "        x_coordinates=lon_np,\n",
    "        y_coordinates=lat_np,\n",
    "        num_x=len(lon_np),\n",
    "        num_y=len(lat_np),\n",
    "        values=values_np,\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c662e624",
   "metadata": {},
   "source": [
    "Define the mesh refinement parameters "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b8c8825f",
   "metadata": {},
   "outputs": [],
   "source": [
    "mesh_refinement_parameters = meshkernel.MeshRefinementParameters(\n",
    "    refine_intersected=False,\n",
    "    use_mass_center_when_refining=False,\n",
    "    min_edge_size=500,\n",
    "    refinement_type=meshkernel.RefinementType.WAVE_COURANT,\n",
    "    connect_hanging_nodes=True,\n",
    "    account_for_samples_outside_face=False,\n",
    "    max_refinement_iterations=3,\n",
    "    smoothing_iterations=5,\n",
    "    max_courant_time=120.0,\n",
    "    directional_refinement=0,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b71101a",
   "metadata": {},
   "source": [
    "Perform the refinement (Bilinear interpolation will be used)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7ba9e478",
   "metadata": {},
   "outputs": [],
   "source": [
    "mk2.mesh2d_refine_based_on_gridded_samples(\n",
    "    gridded_samples=gridded_samples,\n",
    "    mesh_refinement_params=mesh_refinement_parameters,\n",
    "    use_nodal_refinement=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d798907",
   "metadata": {},
   "source": [
    "Plot the refined mesh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "63921e46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# zoomed in plot to focus on patchy coastlines\n",
    "fig1 = plt.figure(figsize=(16, 12))\n",
    "ax1 = fig1.add_subplot(111)\n",
    "\n",
    "mesh2d_grid2 = mk2.mesh2d_get()\n",
    "mesh2d_grid2.plot_edges(ax1, linewidth=1)"
   ]
  }
 ],
 "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": "3.8.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}