{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example: Working with models in Python"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The main feature of HydroMT is to facilitate the process of building and analyzing spatial geoscientific models with a focus on water system models. It does so by automating the workflow to go from raw data to a complete model instance which is ready to run and to analyse model results once the simulation has finished. \n",
    "\n",
    "This notebook will explore how to work with HydroMT models in Python."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import hydromt and setup logging\n",
    "import hydromt\n",
    "from hydromt.log import setuplog\n",
    "\n",
    "# other imports\n",
    "import matplotlib.pyplot as plt\n",
    "import geopandas as gpd\n",
    "\n",
    "logger = setuplog(\"working with models\", log_level=10)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Available models in HydroMT"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To know which models are available within your active environment, you can use global `MODELS` variable in hydromt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# generic model classes\n",
    "print(f\"Generic model classes: {hydromt.MODELS.generic}\")\n",
    "# model classes from external plugin\n",
    "print(f\"Model classes from plugins: {hydromt.MODELS.plugins}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here you may only see the generic models ``grid_model``, ``vector_model`` and ``network_model``. There is one more generic model within HydroMT ``mesh_model`` which is only available if the additional python mesh dependency *xugrid* is available in the activated environment."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model components"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "HydroMT defines any model through the model-agnostic Model API based on several *general* model components and *computational unit* components, see [Model API](https://deltares.github.io/hydromt/latest/user_guide/model_overview.html#model-api). Below is a scheme representing the Model API and general model classes available in HydroMT (without any plugin):"
   ]
  },
  {
   "attachments": {
    "hydromt-models.jpg": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![hydromt-models.jpg](attachment:hydromt-models.jpg)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's discover how models are constructed within HydroMT and take the example of ``grid_model``. We will first instantiate a ``GridModel`` object called `mod`. \n",
    "The `api` property helps us discover the available components and their type. You can do the same with any other HydroMT Model class or plugin (give it a try!)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mod = hydromt.GridModel()\n",
    "mod.api"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here you see all the general components from the ``Model`` class like config, geoms, forcing etc. as well as the ``GridModel`` specific computational unit `grid`. \n",
    "You can see that most components are dictionaries of either xarray DataArray or Datasets or of geopandas GeoDataFrame. \n",
    "For now we are starting from an empty model so all these components will be empty but here is how you can access them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(type(mod.grid))\n",
    "mod.grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mod.geoms"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model setup_* methods"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To fill in our model components with data, HydroMT uses **setup_ methods**. These methods go from reading input data using the DataAdapter, transforming the data using workflows (e.g. reprojection, deriving model parameters, etc...) and adding the new model data to the right model component. An overview of available setup methods can be found in the API reference for the [GridModel](https://deltares.github.io/hydromt/latest/api.html#gridmodel), [VectorModel](https://deltares.github.io/hydromt/latest/api.html#vectormodel), and [MeshModel](https://deltares.github.io/hydromt/latest/api.html#meshmodel)\n",
    "\n",
    "Note that these methods for the generic model classes are still quite limited. To get an idea of potential setup_ methods, checkout the [model plugins](https://deltares.github.io/hydromt/latest/plugins.html)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Let's have a look at some examples of the *setup_* functions to start populating our model like ``setup_grid``. This method parses the [HydroMT region option](https://deltares.github.io/hydromt/latest/user_guide/model_region.html) to define the geographic region of interest and grid of the GridModel to build and once done adds **region** into the `geoms` component and **grid** mask into the `grid` component. You can check the required arguments in the [docs](https://deltares.github.io/hydromt/latest/_generated/hydromt.GridModel.setup_grid.html)."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now setup a region for our model using for example a subbasin for any point in the Piave basin. We first initialize a `GridModel` instance in writing mode at a model root folder. Data is sourced from the *artifact_data* catalog."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "root = \"tmp_grid_model_py\"\n",
    "mod = hydromt.GridModel(\n",
    "    root=root,\n",
    "    mode=\"w\",\n",
    "    data_libs=[\"artifact_data=v0.0.8\", \"data/vito_reclass.yml\"],\n",
    "    logger=logger,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "xy = [12.2051, 45.8331]\n",
    "region = {\"subbasin\": xy, \"uparea\": 50}\n",
    "mod.setup_grid(\n",
    "    region=region,\n",
    "    res=1000,\n",
    "    crs=\"utm\",\n",
    "    hydrography_fn=\"merit_hydro\",\n",
    "    basin_index_fn=\"merit_hydro_index\",\n",
    ")\n",
    "print(mod.geoms)\n",
    "print(mod.grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot\n",
    "fig = plt.figure(figsize=(5, 6))\n",
    "ax = plt.subplot()\n",
    "mod.region.boundary.plot(ax=ax)\n",
    "# grid mask\n",
    "mod.grid[\"mask\"].plot(ax=ax)\n",
    "# grid vector cells using hydromt.raster.vector_grid method\n",
    "mod.grid[\"mask\"].raster.vector_grid().boundary.plot(ax=ax, color=\"black\", linewidth=0.1)\n",
    "# the outlet point we used to derive region\n",
    "gdf_xy = gpd.GeoDataFrame(geometry=gpd.points_from_xy(x=[xy[0]], y=[xy[1]]), crs=4326)\n",
    "gdf_xy.to_crs(mod.crs).plot(ax=ax, markersize=40, c=\"red\", zorder=2)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly, we can also populate the **config** component using the ``setup_config`` method. For HydroMT **config** represents the configuration of the model kernel, e.g. the file that would fix your model kernel run settings or list of outputs etc. For most models, this is usually a text file (for example .yaml, .ini, .toml, .inp formats) that can be ordered in sections. Within HydroMT we then use the dictionary object to represent each header/setting/value.\n",
    "\n",
    "Let's populate our config with some simple settings:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "config = {\n",
    "    \"header\": {\"setting\": \"value\"},\n",
    "    \"timers\": {\"start\": \"2010-02-05\", \"end\": \"2010-02-15\"},\n",
    "}\n",
    "\n",
    "mod.setup_config(**config)\n",
    "mod.config"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can setup maps data using the ``setup_maps_from_raster`` and ``setup_maps_from_raster_reclass`` methods. Both methods add data to the **maps** component based on input raster data (RasterDataset type), but the second method additionally reclassifies the input data based on a reclassification table. The **maps** component gathers any raster input data without any requirements for a specific grid (CRS and resolution). It can contain, for example, direct model input data for models like Delft3D-FM that will interpolate input data on the fly to the model mesh, or auxiliary data that are not used by the model kernel but can be used by HydroMT to build the model (e.g. a gridded DEM), etc.\n",
    "\n",
    "For models that require all their input data to be resampled to the exact computation grid (all raster at the same resolution and projection), then the input data would go into the **grid** component. The corresponding ``setup_grid_from_raster`` and ``setup_grid_from_raster_reclass`` functions for the **grid** components are also available.\n",
    "\n",
    "But back to our example, let's add both a DEM map from the data source *merit_hydro_1k* and a manning roughness map based on reclassified landuse data from the *vito* dataset to our model grid object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mod.setup_grid_from_rasterdataset(\n",
    "    raster_fn=\"merit_hydro_1k\",\n",
    "    variables=\"elevtn\",\n",
    "    fill_method=None,\n",
    "    reproject_method=\"bilinear\",\n",
    "    rename={\"elevtn\": \"DEM\"},\n",
    ")\n",
    "mod.setup_grid_from_raster_reclass(\n",
    "    raster_fn=\"vito\",\n",
    "    fill_method=\"nearest\",\n",
    "    reclass_table_fn=\"vito_reclass\",  # Note: from local data catalog\n",
    "    reclass_variables=[\"manning\"],\n",
    "    reproject_method=\"average\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# check which maps are read\n",
    "print(f\"model grid: {list(mod.grid.data_vars)}\")\n",
    "\n",
    "mod.grid[\"manning\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot\n",
    "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(10, 4))\n",
    "mod.grid[\"DEM\"].raster.mask_nodata().plot(ax=axes[0], cmap=\"terrain\")\n",
    "mod.region.boundary.plot(ax=axes[0], color=\"k\")\n",
    "gdf_xy = gpd.GeoDataFrame(geometry=gpd.points_from_xy(x=[xy[0]], y=[xy[1]]), crs=4326).to_crs(mod.crs)\n",
    "gdf_xy.plot(ax=axes[0], markersize=40, c=\"red\", zorder=2)\n",
    "axes[0].set_title(\"Elevation\")\n",
    "\n",
    "mod.grid[\"manning\"].plot(ax=axes[1], cmap=\"viridis\")\n",
    "mod.region.boundary.plot(ax=axes[1], color=\"k\")\n",
    "gdf_xy.plot(ax=axes[1], markersize=40, c=\"red\", zorder=2)\n",
    "axes[1].set_title(\"Manning roughness\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model read & write methods"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once our model is filled up with data, we can then write it down using either the general ``write`` method or component specific *write_* methods. Similarly, our model can be read back with the general ``read`` method or component specific ones."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now write our model into a model root folder. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mod.write(components=[\"grid\", \"geoms\", \"config\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print MODEL_ROOT folder\n",
    "import os\n",
    "\n",
    "\n",
    "def print_dir(root):\n",
    "    for path, _, files in os.walk(root):\n",
    "        print(path)\n",
    "        for name in files:\n",
    "            if name.endswith(\".xml\"):\n",
    "                continue\n",
    "            print(f\" - {name}\")\n",
    "\n",
    "\n",
    "print_dir(root)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And now let's read it back in a new `GridModel` instance:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mod2 = hydromt.GridModel(root=root, mode=\"r\", logger=logger)\n",
    "mod2.read(components=[\"config\", \"geoms\", \"grid\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# check which grid are read\n",
    "print(f\"model grid: {list(mod2.grid.data_vars)}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Building / updating a model with python"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the same functionalities, it is also possible to build or update a model within python instead of using the command line, using the ``build`` and ``update`` methods. Let's see how we could rebuild our previous `GridModel` with the ``build`` method.\n",
    "\n",
    "First let's start with writing a HydroMT build configuration (ini-file) with the GridModel (setup) methods we want to use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from hydromt.config import configread\n",
    "from pprint import pprint\n",
    "\n",
    "# Read the build configuration\n",
    "config = configread(\"grid_model_build.yaml\")\n",
    "pprint(\n",
    "    config,\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And now let's build our model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# First we instantiate GridModel with the output folder and use the write mode (build from scratch)\n",
    "root3 = \"tmp_grid_model_py1\"\n",
    "mod3 = hydromt.GridModel(\n",
    "    root=root3,\n",
    "    mode=\"w\",\n",
    "    data_libs=[\"artifact_data=v0.0.8\", \"data/vito_reclass.yml\"],\n",
    "    logger=logger,\n",
    ")\n",
    "\n",
    "# Now let's build it with the config file\n",
    "mod3.build(region=region, opt=config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print_dir(root3)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And check that the results are similar to our one-by-one setup earlier:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mod3.config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot\n",
    "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(10, 4))\n",
    "mod3.grid[\"elevtn\"].raster.mask_nodata().plot(ax=axes[0], cmap=\"terrain\")\n",
    "mod3.region.boundary.plot(ax=axes[0], color=\"k\")\n",
    "axes[0].set_title(\"Elevation\")\n",
    "\n",
    "mod3.grid[\"manning\"].plot(ax=axes[1], cmap=\"viridis\")\n",
    "mod3.region.boundary.plot(ax=axes[1], color=\"k\")\n",
    "axes[1].set_title(\"Manning roughness\")"
   ]
  }
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