diff --git a/.github/workflows/python-package-pip.yml b/.github/workflows/python-package-pip.yml index 6611adb..e164819 100644 --- a/.github/workflows/python-package-pip.yml +++ b/.github/workflows/python-package-pip.yml @@ -29,7 +29,7 @@ jobs: - name: Install package with pip run: | - python -m pip install . "zarr${{ matrix.zarr-version }}" + python -m pip install ".[latlon-domain-crop]" "zarr${{ matrix.zarr-version }}" python -m pip install pytest - name: Run tests (non-distributed) diff --git a/CHANGELOG.md b/CHANGELOG.md index 105c9d4..19765aa 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -5,6 +5,14 @@ All notable changes to this project will be documented in this file. The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/), and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html). +## [Unreleased](https://github.com/mllam/mllam-data-prep) + +[All changes](https://github.com/mllam/mllam-data-prep/compare/HEAD...v0.6.1) + +### Added + +- add support for cropping a dataset using the convex hull of the lat/lon coordinates of another dataset (can be used for creating boundary data in Limited Area Modelling setups) [\#45](https://github.com/mllam/mllam-data-prep/pull/45), @leifdenby + ## [v0.6.1](https://github.com/mllam/mllam-data-prep/release/tag/v0.6.1) [All changes](https://github.com/mllam/mllam-data-prep/compare/v0.6.1...v0.6.0) diff --git a/README.md b/README.md index 7350562..9203101 100644 --- a/README.md +++ b/README.md @@ -285,6 +285,27 @@ The `output` section defines three things: 3. `chunking`: the chunk sizes to use when writing the training dataset to zarr. This is optional, but can be used to optimise the performance of the zarr dataset. By default the chunk sizes are set to the size of the dimension, but this can be overridden by setting the chunk size in the configuration file. A common choice is to set the dimension along which you are batching to align with the of each training item (e.g. if you are training a model with time-step roll-out of 10 timesteps, you might choose a chunksize of 10 along the time dimension). 4. Splitting and calculation of statistics of the output variables, using the `splitting` section. The `output.splitting.splits` attribute defines the individual splits to create (for example `train`, `val` and `test`) and `output.splitting.dim` defines the dimension to split along. The `compute_statistics` can be optionally set for a given split to calculate the statistical properties requested (for example `mean`, `std`) any method available on `xarray.Dataset.{op}` can be used. In addition methods prefixed by `diff_` (so the operational would be listed as `diff_{op}`) to compute a statistic based on difference of consecutive time-steps, e.g. `diff_mean` to compute the `mean` of the difference between consecutive timesteps (these are used for normalisating increments). The `dims` attribute defines the dimensions to calculate the statistics over (for example `grid_index` and `time`). +In addition the `output` section can also contain a configuration for cropping the output dataset using the convex hull of coordinates from a different dataset. This is used for example when creating training datasets from limited area modelling (LAM) setups, where a separate dataset is used for the boundary data. The example above doesn't include this section, but [see below](#cropping-the-output-dataset-using-convex-hull-of-another-dataset) for an example of how to use this feature. + +#### Cropping the output dataset using convex hull of another dataset + +When creating training datasets for limited area models (LAMs) it is often useful to crop the training dataset to the convex hull of the coordinates of another dataset (for example a dataset containing boundary data). This can be done by adding a `domain_cropping` section to the `output` section of the configuration file. A full example where ERA5 is cropped is given in [example.danra.yaml](example.danra.yaml). The relevant section is reproduced here for completeness: + +```yaml +output: + ... + domain_cropping: + margin_width_degrees: 10 + interior_dataset_config_path: example.danra.yaml +``` + +The `domain_cropping` section has two required attributes, and one optional attribute: +- `margin_width_degrees`: the width (in degrees) of the margin to add around the convex hull of the coordinates of the interior dataset. This allows you to control how much extra area to include around the convex hull of the interior dataset (e.g. how thick to make the boundary region for LAM boundary forcing datasets). +- `interior_dataset_config_path`: the path to the configuration file of the interior dataset. This is used to load the interior dataset and extract the coordinates to calculate the convex hull from. +- `include_interior_points`: optional, defaults to `false`. If set to `true` the points inside the convex hull of the interior dataset will also be included in the cropped output dataset. If set to `false` only the margin region around the convex hull will be included. This can be useful if you not only want to create a boundary forcing dataset from a global simulation for LAM, but also want to include the interior points of the global simulation that overlap with the LAM domain. + +Details on how the convex hull cropping is actually done can be found in the jupyter notebook in [docs/domain-cropping.ipynb](docs/domain-cropping.ipynb). + ### The `inputs` section ```yaml diff --git a/docs/domain-cropping.ipynb b/docs/domain-cropping.ipynb new file mode 100644 index 0000000..d8ead71 --- /dev/null +++ b/docs/domain-cropping.ipynb @@ -0,0 +1,263 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Cropping a domain using the lat/lon convex hull of another domain\n", + "\n", + "This notebook demonstrates how convex hull based cropping is implemented in `mllam-data-prep`. To actually use this feature see [the details in the README](../README.md#cropping-a-domain-using-the-latlon-convex-hull-of-another-domain).\n", + "\n", + "This method crops a domain using the lat/lon convex hull of another domain. This is useful when a) you have two datasets on two different overlapping domains where you only want to keep the overlapping part, or b) in the case where you want to run limited-area simulations and need to create a dataset that provides the boundary conditions for the limited-area domain." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import tests.data as testdata\n", + "import tempfile\n", + "\n", + "import xarray as xr\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import cartopy.crs as ccrs\n", + "\n", + "import mllam_data_prep as mdp\n", + "import tests.data as testdata\n", + "from mllam_data_prep.ops import cropping " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will start by creating the some synthetic data on two different domains where one sits within the other, mimicking the scenario where you have a high-resolution dataset within a coarser dataset." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tmpdir = tempfile.TemporaryDirectory()\n", + "l = 500 * 1.0e3 # length and width of domain in meters\n", + "N = 50 # number of grid points in each direction\n", + "config_lam = testdata.create_input_datasets_and_config(\n", + " identifier=\"lam\",\n", + " data_categories=[\"state\"],\n", + " tmpdir=tmpdir,\n", + " xlim=[-l/2.0, l/2.0],\n", + " ylim=[-l/2.0, l/2.0],\n", + " nx=N, ny=N,\n", + " add_latlon=True\n", + "\n", + ")\n", + "# make the global domain twice as large as the LAM domain so that the lam\n", + "# domain is contained within the global domain, but half the number of grid\n", + "# points in each direction\n", + "config_global = testdata.create_input_datasets_and_config(\n", + " identifier=\"global\",\n", + " data_categories=[\"state\"],\n", + " tmpdir=tmpdir,\n", + " xlim=[-l, l],\n", + " ylim=[-l, l],\n", + " nx=N//2, ny=N//2,\n", + " add_latlon=True\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ds_lam = mdp.create_dataset(config=config_lam)\n", + "ds_global = mdp.create_dataset(config=config_global)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ds_lam" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ds_global" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's first make a mask that indicates which of the coarser domain grid-points are within the convex hull of the high-resolution grid points coordinates" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "da_interior_mask = cropping.create_convex_hull_mask(ds=ds_global, ds_reference=ds_lam)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "da_interior_mask = da_interior_mask.where(da_interior_mask, drop=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ds_reference = ds_lam\n", + "ds = ds_global\n", + "\n", + "fig, ax = plt.subplots(subplot_kw=dict(projection=ccrs.PlateCarree()))\n", + "xr.plot.scatter(ax=ax, ds=ds, x=\"lon\", y=\"lat\", label=\"ds\", transform=ccrs.PlateCarree())\n", + "xr.plot.scatter(ax=ax, ds=ds_reference, x=\"lon\", y=\"lat\", label=\"ds_ref\", transform=ccrs.PlateCarree())\n", + "xr.plot.scatter(ax=ax, c=da_interior_mask, ds=da_interior_mask.to_dataset(name=\"mask\"), x=\"lon\", y=\"lat\", marker=\"x\", label=\"ds (interior)\")\n", + "ax.coastlines()\n", + "ax.gridlines(draw_labels=True)\n", + "ax.legend()\n", + "plt.show()\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next we will for the points from the larger domain and exterior to this convex hull (i.e. points excluded by the mask created above) compute the distance to the nearest point of the inner domain." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "da_dist = cropping.distance_to_convex_hull_boundary(ds=ds_global, ds_reference=ds_lam)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(subplot_kw=dict(projection=ccrs.PlateCarree()), figsize=(10, 4))\n", + "xr.plot.scatter(ax=ax, ds=ds_reference, x=\"lon\", y=\"lat\", label=\"ds_ref\", marker=\".\", transform=ccrs.PlateCarree())\n", + "xr.plot.scatter(ax=ax, hue=\"dist\", ds=da_dist.to_dataset(name=\"dist\"), x=\"lon\", y=\"lat\", marker=\"x\", label=\"ds (distance)\", add_colorbar=True)\n", + "ax.coastlines()\n", + "ax.gridlines(draw_labels=[\"top\", \"left\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And finally we will use this distance to create a plot that includes a margin around the convex hull of the inner domain." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "max_dist = 1.5 # in degrees\n", + "da_global_margin_crop = cropping.crop_with_convex_hull(\n", + " ds=ds_global, ds_reference=ds_lam, margin_thickness=max_dist\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(subplot_kw=dict(projection=ccrs.PlateCarree()), figsize=(10, 4))\n", + "xr.plot.scatter(ax=ax, ds=ds_reference, x=\"lon\", y=\"lat\", label=\"ds_ref\", marker=\".\", transform=ccrs.PlateCarree())\n", + "xr.plot.scatter(ax=ax, ds=da_global_margin_crop, x=\"lon\", y=\"lat\", marker=\"x\", label=f\"ds (margin crop, {max_dist}deg)\", transform=ccrs.PlateCarree())\n", + "ax.coastlines()\n", + "ax.legend()\n", + "ax.gridlines(draw_labels=[\"top\", \"left\"])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "max_dist = 1.5 # in degrees\n", + "ds_global_margin_crop_without_interior = cropping.crop_with_convex_hull(\n", + " ds=ds_global, ds_reference=ds_lam, margin_thickness=max_dist, include_interior_points=False\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(subplot_kw=dict(projection=ccrs.PlateCarree()), figsize=(10, 4))\n", + "xr.plot.scatter(ax=ax, ds=ds_reference, x=\"lon\", y=\"lat\", label=\"ds_ref\", marker=\".\", transform=ccrs.PlateCarree())\n", + "xr.plot.scatter(ax=ax, ds=ds_global_margin_crop_without_interior, x=\"lon\", y=\"lat\", marker=\"x\", label=f\"ds (margin crop, {max_dist}deg)\", transform=ccrs.PlateCarree())\n", + "ax.coastlines()\n", + "ax.legend()\n", + "ax.gridlines(draw_labels=[\"top\", \"left\"])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "mllam-data-prep", + "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": "3.10.16" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/example.era5_cropped.yaml b/example.era5_cropped.yaml new file mode 100644 index 0000000..dcd92be --- /dev/null +++ b/example.era5_cropped.yaml @@ -0,0 +1,91 @@ +schema_version: v0.5.0 +dataset_version: v1.0.0 + +output: + variables: + static: [grid_index, static_feature] + forcing: [time, grid_index, forcing_feature] + coord_ranges: + time: + start: 1990-09-03T00:00 + end: 1990-09-09T00:00 + step: PT6H + chunking: + time: 1 + splitting: + dim: time + splits: + train: + start: 1990-09-03T00:00 + end: 1990-09-06T00:00 + compute_statistics: + ops: [mean, std, diff_mean, diff_std] + dims: [grid_index, time] + val: + start: 1990-09-06T00:00 + end: 1990-09-07T00:00 + test: + start: 1990-09-07T00:00 + end: 1990-09-09T00:00 + domain_cropping: + margin_width_degrees: 10 + interior_dataset_config_path: example.danra.yaml + +inputs: + era_height_levels: + path: 'simplecache::gs://weatherbench2/datasets/era5/1959-2023_01_10-6h-64x32_equiangular_conservative.zarr' + dims: [time, longitude, latitude, level] + variables: + u_component_of_wind: + level: + values: [1000,] + units: hPa + dim_mapping: + time: + method: rename + dim: time + forcing_feature: + method: stack_variables_by_var_name + dims: [level] + name_format: "{var_name}{level}hPa" + grid_index: + method: stack + dims: [longitude, latitude] + target_output_variable: forcing + + era5_surface: + path: 'simplecache::gs://weatherbench2/datasets/era5/1959-2023_01_10-6h-64x32_equiangular_conservative.zarr' + dims: [time, longitude, latitude, level] + variables: + - mean_sea_level_pressure + dim_mapping: + time: + method: rename + dim: time + forcing_feature: + method: stack_variables_by_var_name + name_format: "{var_name}" + grid_index: + method: stack + dims: [longitude, latitude] + target_output_variable: forcing + + era5_static: + path: 'simplecache::gs://weatherbench2/datasets/era5/1959-2023_01_10-6h-64x32_equiangular_conservative.zarr' + dims: [time, longitude, latitude, level] + variables: + - land_sea_mask + dim_mapping: + static_feature: + method: stack_variables_by_var_name + name_format: "{var_name}" + grid_index: + method: stack + dims: [longitude, latitude] + target_output_variable: static + +extra: + projection: + class_name: PlateCarree + kwargs: + central_longitude: 0.0 diff --git a/mllam_data_prep/config.py b/mllam_data_prep/config.py index ebdba90..b7a0ab4 100644 --- a/mllam_data_prep/config.py +++ b/mllam_data_prep/config.py @@ -297,6 +297,38 @@ class Splitting: splits: Dict[str, Split] +@dataclass +class ConvexHullCropping: + """ + Define the method applied for cropping the spatial domain before writing + the transformed output dataset. This is typically used when you want to + create a dataset to provide data in a boundary around a limited-area + domain. + + The cropping is done by creating a convex hull around the spatial + coordinates of an *interior* dataset (this will typically be the + "limited-area" domain when doing Limited Area Modelling) and then including + all points that are within a margin of the convex hull boundary. In addition + to including the points inside the defined margin around the convex hull, + you can also include the points inside the convex hull of the interior + dataset by setting the `include_interior` attribute to `True`. + + Attributes + ---------- + margin_width_degrees: float + The width (in degrees) of the margin applied to the convex hull + boundary of the interior dataset used to define the cropping domain. + interior_dataset_config_path: str + The path to the configuration file for the dataset defining the interior domain + include_interior_points: bool + Whether to include the points inside the convex hull of the interior dataset + """ + + margin_width_degrees: float + interior_dataset_config_path: str + include_interior_points: bool = False + + @dataclass class Output: """ @@ -334,12 +366,19 @@ class Output: splitting: Splitting Defines the splits of the dataset (e.g. train, test, validation), the dimension to split the dataset along, and optionally the statistics to compute for each split. + + domain_cropping: ConvexHullCropping + Defines the method applied for cropping the spatial domain before writing + the transformed output dataset. This is typically used when you want to + create a dataset to provide data in a boundary around a limited-area + domain. """ variables: Dict[str, List[str]] coord_ranges: Dict[str, Range] = field(default_factory=dict) chunking: Dict[str, int] = field(default_factory=dict) splitting: Optional[Splitting] = None + domain_cropping: Optional[ConvexHullCropping] = None @dataclass diff --git a/mllam_data_prep/create_dataset.py b/mllam_data_prep/create_dataset.py index 3daf321..5f12612 100644 --- a/mllam_data_prep/create_dataset.py +++ b/mllam_data_prep/create_dataset.py @@ -21,6 +21,7 @@ find_config_differences, ) from .ops.chunking import chunk_dataset +from .ops.cropping import crop_with_convex_hull from .ops.derive_variable import derive_variable from .ops.loading import load_input_dataset from .ops.mapping import map_dims_and_variables @@ -141,6 +142,14 @@ def create_dataset(config: Config): "update the schema version used in your config to v0.5.0." ) + # parse the interior domain config already here if domain cropping is + # enabled, so that we can alert the user quickly if the config is invalid + ds_interior_domain = None + if config.output.domain_cropping is not None: + config_interior_domain = Config.from_yaml_file( + file=config.output.domain_cropping.interior_dataset_config_path + ) + output_config = config.output output_coord_ranges = output_config.coord_ranges chunking_config = config.output.chunking @@ -288,6 +297,30 @@ def create_dataset(config: Config): ) ds["splits"] = da_splits + # ensure any dimensions for which coordinate values aren't yet set that + # these are given integer values. This will for example apply when stacking + # (x, y)-coordinates to a grid-index coordinate. These need unique values + # for later reference. + for d in ds.dims: + if d not in ds.coords: + ds[d] = np.arange(ds[d].size) + + if config.output.domain_cropping is not None: + domain_cropping = config.output.domain_cropping + ds_interior_domain = create_dataset(config=config_interior_domain) + logger.info( + f"Cropping dataset using convex hull " + f"({'including' if domain_cropping.include_interior_points else 'excluding'} interior points " + f"and including margin of {domain_cropping.margin_width_degrees} degrees) " + f"of {config.output.domain_cropping.interior_dataset_config_path} dataset " + ) + ds = crop_with_convex_hull( + ds=ds, + ds_reference=ds_interior_domain, + margin_thickness=domain_cropping.margin_width_degrees, + include_interior_points=domain_cropping.include_interior_points, + ) + ds.attrs = {} ds.attrs["schema_version"] = config.schema_version ds.attrs["dataset_version"] = config.dataset_version diff --git a/mllam_data_prep/ops/cropping.py b/mllam_data_prep/ops/cropping.py new file mode 100644 index 0000000..213215d --- /dev/null +++ b/mllam_data_prep/ops/cropping.py @@ -0,0 +1,370 @@ +from typing import Tuple, Union + +import numpy as np +import spherical_geometry as sg +import xarray as xr +from spherical_geometry.polygon import SphericalPolygon + + +def _get_latlon_coords(da: xr.DataArray) -> tuple: + """ + Get the latlon coordinates of a DataArray. + + Parameters + ---------- + da : xarray.DataArray + The data. + + Returns + ------- + tuple (xarray.DataArray, xarray.DataArray) + The latitude and longitude coordinates. + """ + if "latitude" in da.coords and "longitude" in da.coords: + return (da.longitude, da.latitude) + elif "lat" in da.coords and "lon" in da.coords: + return (da.lon, da.lat) + else: + raise Exception("Could not find lat/lon coordinates in DataArray.") + + +def create_convex_hull_mask(ds: xr.Dataset, ds_reference: xr.Dataset) -> xr.DataArray: + """ + Create a grid-point mask for lat/lon coordinates in `da` indicating which + points are interior to the convex hull of the lat/lon coordinates of + `da_ref`. + + Parameters + ---------- + ds : xarray.Dataset + The dataset for which to create the mask. + ds_reference : xarray.Dataset + The reference dataset from which to create the convex hull of the coordinates. + + Returns + ------- + xarray.DataArray + A boolean mask indicating which points in `ds_reference` are interior + to the convex hull. + xarray.Dataset + A dataset containing lat lon coordinates for points in `ds` making up + the convex hull. + """ + da_lon, da_lat = _get_latlon_coords(ds) + da_lon_ref, da_lat_ref = _get_latlon_coords(ds_reference) + + assert da_lat.dims == da_lon.dims + assert da_lat_ref.dims == da_lon_ref.dims + + # latlon to (x, y, z) on unit sphere + da_ref_xyz = _latlon_to_unit_sphere_xyz(da_lat=da_lat_ref, da_lon=da_lon_ref) + + chull_lam = SphericalPolygon.convex_hull(da_ref_xyz.values) + + # call .load() to avoid using dask arrays in the following apply_ufunc + da_interior_mask = xr.apply_ufunc( + chull_lam.contains_lonlat, da_lon.load(), da_lat.load(), vectorize=True + ).astype(bool) + da_interior_mask.attrs[ + "long_name" + ] = "contained in convex hull of source dataset (da_ref)" + + # Get points at edge of convex hull + chull_lam_lon, chull_lam_lat = list(chull_lam.to_lonlat())[0] + chull_lat_lons = xr.Dataset( + coords={ + "lon": (["grid_index_ref"], chull_lam_lon), + "lat": (["grid_index_ref"], chull_lam_lat), + } + ) + + return da_interior_mask, chull_lat_lons + + +def _latlon_to_unit_sphere_xyz( + da_lat: xr.DataArray, da_lon: xr.DataArray +) -> xr.DataArray: + """ + Convert lat/lon coordinates to (x, y, z) on the unit sphere. + + Parameters + ---------- + da_lat : xarray.DataArray + Latitude coordinates. + da_lon : xarray.DataArray + Longitude coordinates. + + Returns + ------- + xr.DataArray + The (x, y, z) coordinates on the unit sphere as an xarray.DataArray + with dimensions (grid_index, component). + """ + pts_xyz = np.array(sg.vector.lonlat_to_vector(da_lon, da_lat)).T + da_xyz = xr.DataArray( + pts_xyz, coords=da_lat.coords, dims=list(da_lat.dims) + ["xyz"] + ) + return da_xyz + + +def shortest_distance_to_arc( + point_cartesian: np.ndarray, + arc_start_cartesian: np.ndarray, + arc_end_cartesian: np.ndarray, +) -> np.ndarray: + """ + Compute shortest haversine distance from a set of points to an arc on the + surface of the sphere. All points are assumed to be on the surface of a + sphere of the same radius (e.g. the unit sphere) given in Cartesian (x, y, + z) coordinates. + + Parameters + ---------- + point_cartesian : np.ndarray, shape (num_points, 3) + Points to measure distance from + arc_start_cartesian : np.ndarray, shape (3,) + Start point of arc + arc_end_cartesian : np.ndarray, shape (3,) + End point of arc + + Returns + ------- + np.ndarray, shape (num_points,) + The distances in radians + """ + # Calculate normal vector to the plane of the great circle + normal_vector = np.cross(arc_start_cartesian, arc_end_cartesian) + normal_vector = normal_vector / np.linalg.norm(normal_vector) # Normalize + + # Project point onto the plane + point_projection = ( + point_cartesian + - np.dot(point_cartesian, normal_vector)[:, np.newaxis] * normal_vector + ) + + # Normalize to get the projected point on the sphere's surface + projected_point = ( + point_projection / np.linalg.norm(point_projection, axis=1)[:, np.newaxis] + ) + + # Calculate the angle between the original point and the projected point + angle_point_to_projection = np.arccos( + np.clip(np.sum(point_cartesian * projected_point, axis=1), -1, 1) + ) + + # Check if the projected point is between the start and end points of the arc + is_between_arc = ( + np.dot(np.cross(arc_start_cartesian, projected_point), normal_vector) >= 0 + ) & (np.dot(np.cross(projected_point, arc_end_cartesian), normal_vector) >= 0) + + # Calculate distances from the point to the start and end points of the arc + distance_to_start = np.arccos( + np.clip(np.dot(point_cartesian, arc_start_cartesian), -1, 1) + ) + distance_to_end = np.arccos( + np.clip(np.dot(point_cartesian, arc_end_cartesian), -1, 1) + ) + + # Choose the appropriate distance + distances = np.where( + is_between_arc, + angle_point_to_projection, + np.minimum(distance_to_start, distance_to_end), + ) + + # Distance returned in radians + return distances + + +def distance_to_convex_hull_boundary( + ds: xr.Dataset, + ds_reference: xr.Dataset, + grid_index_dim: str = "grid_index", + include_convex_hull_mask: bool = False, +) -> Union[xr.DataArray, Tuple[xr.DataArray, xr.DataArray]]: + """ + For all points in `ds` that are external to the convex hull of the points in + `ds_reference`, calculate the minimum distance to the convex hull boundary. + + The method goes through the following steps: + 1. Create a mask for the points in `ds` from the convex hull of the points + in `ds_reference`. + 2. Find the points in `ds` that are external to the convex hull. + 3. For each point in `ds` external to the convex hull, calculate the + minimum distance to the convex hull boundary. The distance is calculated + as the shortest distance to any of the arcs making up the convex hull + boundary. + + + Parameters + ---------- + ds : xarray.Dataset + The dataset for which to calculate the distance. + ds_reference : xarray.Dataset + The reference dataset from which to calculate the convex hull boundary. + grid_index_dim : str, optional + The name of the grid index dimension in `ds` and `ds_reference`. + include_convex_hull_mask : bool, optional + Whether to include the convex hull mask in the output. + + Returns + ------- + da_mindist_to_ref : xarray.DataArray + The minimum distance to the convex hull boundary. + da_ch_mask : xarray.DataArray + The convex hull mask (only if `include_convex_hull_mask` is True). + + """ + # rename the grid index dimension in ds_reference to avoid conflicts (since + # the grid index dimension otherwise has the same name in both datasets, + # and later we will want to find the minimum distance for each point in ds + # to all points in ds_reference) + ds_reference_separate_gridindex = ds_reference.rename( + {grid_index_dim: "grid_index_ref"} + ) + + # create a mask from the convex hull of ds_reference for the grid points in ds + da_ch_mask, ds_chull_lat_lons = create_convex_hull_mask( + ds=ds, ds_reference=ds_reference_separate_gridindex + ) + + # only consider points that are external to the convex hull + ds_exterior = ds.where(~da_ch_mask, drop=True) + ds_exterior_lon, ds_exterior_lat = _get_latlon_coords(ds_exterior) + + da_xyz = _latlon_to_unit_sphere_xyz(ds_exterior_lon, ds_exterior_lat) + + da_xyz_chull = _latlon_to_unit_sphere_xyz(*_get_latlon_coords(ds_chull_lat_lons)) + + # Collect arcs making up chull + chull_arcs = list(zip(da_xyz_chull[:-1], da_xyz_chull[1:])) + [ + (da_xyz_chull[-1], da_xyz_chull[0]) + ] # Add arc from last to first point + + # Calculate minimum distance to each arc and take the minimum + # distance over all arcs + mindist_to_ref = np.stack( + [ + shortest_distance_to_arc(da_xyz, arc_start, arc_end) + for arc_start, arc_end in chull_arcs + ], + axis=0, + ).min(axis=0) + + da_mindist_to_ref = xr.DataArray( + mindist_to_ref, coords=ds_exterior_lat.coords, dims=ds_exterior_lat.dims + ) + da_mindist_to_ref.attrs[ + "long_name" + ] = "minimum distance to convex hull boundary of reference dataset" + da_mindist_to_ref.attrs["units"] = "radians" + + if include_convex_hull_mask: + return da_mindist_to_ref, da_ch_mask + + return da_mindist_to_ref + + +def _mask_with_common_dim(da_mask, ds): + """ + Apply mask to all variables in `ds` that share dimension(s) with `da_mask`. + + Parameters + ---------- + da_mask : xarray.DataArray + The mask. + ds : xarray.Dataset + The dataset to mask. + + Returns + ------- + xarray.Dataset + The masked dataset including the variables that don't share + dimension(s) with the mask (these are simply copied over). + """ + mask_dims = da_mask.dims + vars_with_dims = [ + v for v in ds.data_vars if all(d in ds[v].dims for d in mask_dims) + ] + vars_without_dims = [v for v in ds.data_vars if v not in vars_with_dims] + + ds_masked = ds.drop_vars(vars_without_dims).where(da_mask, drop=True) + ds_masked = xr.merge([ds_masked, ds[vars_without_dims]]) + return ds_masked + + +def crop_with_convex_hull( + ds: xr.Dataset, + ds_reference: xr.Dataset, + grid_index_dim: str = "grid_index", + margin_thickness: float = 2.0, + include_interior_points: bool = True, + return_mask=False, +) -> xr.Dataset: + """ + Crop grid points (with coordinates given in lat/lon) in `ds` that are + within a certain distance (within the margin of a given width) of the + convex hull boundary of the points in `ds_reference`. The margin is + measured in degrees. + + ┌──────────────────────────────────────┐ + │ Margin │ + │ ┌────── Convex hull ───────┐ │ + │ │ │ │ + │ │ included if │ │ + │ │ include_interior == True │ │ + │ │ │ │ + │<--->│ │ │ + │ : └──────────────────────────┘ │ + │ :... margin width │ + └──────────────────────────────────────┘ + + Parameters + ---------- + ds : xarray.Dataset + The dataset to crop. + ds_reference : xarray.Dataset + The reference dataset from which to calculate the convex hull boundary. + grid_index_dim : str, optional + The name of the grid index dimension in `ds` and `ds_reference`. + margin_thickness : float, optional + The thickness of the margin to apply to the convex hull boundary in + degrees. Points within this margin will be included in the output. + """ + if margin_thickness == 0.0: + if not include_interior_points: + raise Exception( + "With no margin and exclude_interior=False, all points would be excluded." + ) + da_mask = create_convex_hull_mask(ds=ds, ds_reference=ds_reference) + else: + da_min_dist_to_ref, da_ch_mask = distance_to_convex_hull_boundary( + ds, + ds_reference, + grid_index_dim=grid_index_dim, + include_convex_hull_mask=True, + ) + + max_dist_radians = margin_thickness * np.pi / 180.0 + da_boundary_mask = da_min_dist_to_ref < max_dist_radians + + if not include_interior_points: + da_mask = da_boundary_mask + else: + da_interior_points = da_ch_mask.where(da_ch_mask, drop=True) + da_mask = xr.concat( + [da_interior_points, da_boundary_mask], dim="grid_index" + ) + + # it is unclear if there is a bug in xr.Dataset.where(), but its default + # behaviour seems to be broadcast (i.e. add) the dimensions of the mask to + # any data variables that don't have those dimensions already. 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nt_analysis, nt_forecast, nx, ny, var_names=DEFAULT_FORECAST_VARS + nt_analysis, + nt_forecast, + nx, + ny, + var_names=DEFAULT_FORECAST_VARS, + xlim=DEFAULT_XLIM, + ylim=DEFAULT_YLIM, ): """ Create a fake forecast dataset with `nt_analysis` analysis times, `nt_forecast` @@ -44,8 +61,8 @@ def create_surface_forecast_dataset( T_START, periods=nt_forecast, freq=DT_FORECAST ).tz_localize(None) - x = np.arange(nx) - y = np.arange(ny) + x = np.linspace(*xlim, nx) + y = np.linspace(*ylim, ny) dataarrays = {} for var_name in var_names: @@ -66,7 +83,12 @@ def create_surface_forecast_dataset( def create_surface_analysis_dataset( - nt_analysis, nx, ny, var_names=DEFAULT_SURFACE_ANALYSIS_VARS + nt_analysis, + nx, + ny, + var_names=DEFAULT_SURFACE_ANALYSIS_VARS, + xlim=DEFAULT_XLIM, + ylim=DEFAULT_YLIM, ): """ Create a fake analysis dataset with `nt_analysis` analysis times, `nx` grid points @@ -76,8 +98,8 @@ def create_surface_analysis_dataset( T_START, periods=nt_analysis, freq=DT_ANALYSIS ).tz_localize(None) - x = np.arange(nx) - y = np.arange(ny) + x = np.linspace(*xlim, nx) + y = np.linspace(*ylim, ny) dataarrays = {} for var_name in var_names: @@ -103,6 +125,9 @@ def create_analysis_dataset_on_levels( nz, level_dim="altitude", var_names=DEFAULT_ATMOSPHERIC_ANALYSIS_VARS, + xlim=DEFAULT_XLIM, + ylim=DEFAULT_YLIM, + zlim=DEFAULT_ZLIM, ): """ Create a fake analysis dataset with `nt_analysis` analysis times, `nx` grid points in x-direction, @@ -120,14 +145,25 @@ def create_analysis_dataset_on_levels( Number of levels level_dim : str, optional Name of the level dimension, by default "altitude" + xlim : tuple, optional + Tuple of the form (xmin, xmax) defining the x-limits, by default DEFAULT_XLIM + ylim : tuple, optional + Tuple of the form (ymin, ymax) defining the y-limits, by default DEFAULT_YLIM + zlim : tuple, optional + Tuple of the form (zmin, zmax) defining the z-limits, by default DEFAULT_ZLIM + + Returns + ------- + xarray.Dataset + The created dataset """ ts_analysis = pd.date_range( T_START, periods=nt_analysis, freq=DT_ANALYSIS ).tz_localize(None) - x = np.arange(nx) - y = np.arange(ny) - z = np.arange(nz) + x = np.linspace(*xlim, nx) + y = np.linspace(*ylim, ny) + z = np.linspace(*zlim, nz) dataarrays = {} for var_name in var_names: @@ -155,6 +191,9 @@ def create_forecast_dataset_on_levels( nz, level_dim="altitude", var_names=DEFAULT_FORECAST_VARS, + xlim=DEFAULT_XLIM, + ylim=DEFAULT_YLIM, + zlim=DEFAULT_ZLIM, ): """ Create a fake forecast dataset with `nt_analysis` analysis times, `nt_forecast` @@ -175,6 +214,17 @@ def create_forecast_dataset_on_levels( Number of levels level_dim : str, optional Name of the level dimension, by default "altitude" + xlim : tuple, optional + Tuple of the form (xmin, xmax) defining the x-limits, by default DEFAULT_XLIM + ylim : tuple, optional + Tuple of the form (ymin, ymax) defining the y-limits, by default DEFAULT_YLIM + zlim : tuple, optional + Tuple of the form (zmin, zmax) defining the z-limits, by default DEFAULT_ZLIM + + Returns + ------- + xarray.Dataset + The created dataset """ ts_analysis = pd.date_range( @@ -184,9 +234,9 @@ def create_forecast_dataset_on_levels( T_START, periods=nt_forecast, freq=DT_FORECAST ).tz_localize(None) - x = np.arange(nx) - y = np.arange(ny) - z = np.arange(nz) + x = np.linspace(*xlim, nx) + y = np.linspace(*ylim, ny) + z = np.linspace(*zlim, nz) dataarrays = {} for var_name in var_names: @@ -207,12 +257,32 @@ def create_forecast_dataset_on_levels( return ds -def create_static_dataset(nx, ny, var_names=DEFAULT_STATIC_VARS): +def create_static_dataset( + nx, ny, var_names=DEFAULT_STATIC_VARS, xlim=DEFAULT_XLIM, ylim=DEFAULT_YLIM +): """ Create a fake static dataset with `nx` grid points in x-direction and `ny` grid points in y-direction. + + Parameters + ---------- + nx : int + Number of grid points in x-direction + ny : int + Number of grid points in y-direction + var_names : list, optional + List of variable names to create, by default DEFAULT_STATIC_VARS + xlim : tuple, optional + Tuple of the form (xmin, xmax) defining the x-limits, by default DEFAULT_XLIM + ylim : tuple, optional + Tuple of the form (ymin, ymax) defining the y-limits, by default DEFAULT_YLIM + + Returns + ------- + xarray.Dataset + The created dataset """ - x = np.arange(nx) - y = np.arange(ny) + x = np.linspace(*xlim, nx) + y = np.linspace(*ylim, ny) dataarrays = {} for var_name in var_names: @@ -230,7 +300,53 @@ def create_static_dataset(nx, ny, var_names=DEFAULT_STATIC_VARS): return ds -def create_data_collection(data_kinds, fp_root): +def _add_latlon(ds: xr.Dataset) -> xr.Dataset: + """ + Add latitude and longitude coordinates (named `lat` and `lon`) to the + dataset using a local equal area projection centered on Denmark with the + coordinates `x` and `y` being the projected coordinates in meters. + + Parameters + ---------- + ds : xarray.Dataset + The input dataset with `x` and `y` coordinates, modified in-place + + Returns + ------- + xarray.Dataset + The dataset with added `lat` and `lon` coordinates + """ + if pyproj is None: + raise ImportError("pyproj is required for this function") + + da_x = ds.coords["x"] + da_y = ds.coords["y"] + xs, ys = np.meshgrid(da_x, da_y, indexing="ij") + proj = pyproj.Proj(proj="laea", lon_0=12.25, lat_0=55.65) + lon, lat = proj(xs, ys, inverse=True) + dims = da_x.dims + da_y.dims + coords = {d: ds.coords[d] for d in dims} + da_lat = xr.DataArray(lat, coords=coords, dims=dims) + da_lon = xr.DataArray(lon, coords=coords, dims=dims) + + ds.coords["lon"] = da_lon + ds.coords["lat"] = da_lat + + return ds + + +def create_data_collection( + data_kinds, + fp_root, + xlim=DEFAULT_XLIM, + ylim=DEFAULT_YLIM, + nx=NX, + ny=NY, + nz=NZ, + nt_analysis=NT_ANALYSIS, + nt_forecast=NT_FORECAST, + add_latlon=False, +): """ Create a fake data collection with the given `data_kinds` and save it to `fp_root`, with each dataset having the `data_kind` name with a unique suffix and saved in `.zarr` format. @@ -242,6 +358,25 @@ def create_data_collection(data_kinds, fp_root): List of data kinds to create, e.g. ["surface_forecast", "static"] fp_root : str Root directory to save the data collection + xlim : tuple, optional + Tuple of the form (xmin, xmax) defining the x-limits, by default DEFAULT_XLIM + ylim : tuple, optional + Tuple of the form (ymin, ymax) defining the y-limits, by default DEFAULT_YLIM + nx : int, optional + Number of grid points in x-direction, by default NX + ny : int, optional + Number of grid points in y-direction, by default NY + nz : int, optional + Number of levels, by default NZ + nt_analysis : int, optional + Number of analysis times, by default NT_ANALYSIS + nt_forecast : int, optional + Number of forecast times, by default NT_FORECAST + add_latlon : bool, optional + Whether to add latitude and longitude coordinates to the datasets. In + this case, the x- and y-coordinates will be interpreted as local equal + area projection coordinates (in meters) centered on Copenhagen, by + default False Returns ------- @@ -259,23 +394,159 @@ def create_data_collection(data_kinds, fp_root): for data_kind in data_kinds: if data_kind == "surface_forecast": - ds = create_surface_forecast_dataset(NT_ANALYSIS, NT_FORECAST, NX, NY) + ds = create_surface_forecast_dataset( + nt_analysis=nt_analysis, + nt_forecast=nt_forecast, + nx=nx, + ny=ny, + xlim=xlim, + ylim=ylim, + ) elif data_kind == "surface_analysis": - ds = create_surface_analysis_dataset(NT_ANALYSIS, NX, NY) + ds = create_surface_analysis_dataset( + nt_analysis=nt_analysis, nx=nx, ny=ny, xlim=xlim, ylim=ylim + ) elif data_kind == "analysis_on_levels": - ds = create_analysis_dataset_on_levels(NT_ANALYSIS, NX, NY, NZ) + ds = create_analysis_dataset_on_levels( + nt_analysis=nt_analysis, nx=nx, ny=ny, nz=nz, xlim=xlim, ylim=ylim + ) elif data_kind == "forecast_on_levels": - ds = create_forecast_dataset_on_levels(NT_ANALYSIS, NT_FORECAST, NX, NY, NZ) + ds = create_forecast_dataset_on_levels( + nt_analysis=nt_analysis, + nt_forecast=nt_forecast, + nx=nx, + ny=ny, + nz=nz, + xlim=xlim, + ylim=ylim, + ) elif data_kind == "static": - ds = create_static_dataset(NX, NY) + ds = create_static_dataset(nx=nx, ny=ny, xlim=xlim, ylim=ylim) else: raise ValueError(f"Unknown data kind: {data_kind}") identifier = str(uuid.uuid4()) dataset_name = f"{data_kind}_{identifier}" + if add_latlon: + ds = _add_latlon(ds) + fp = f"{fp_root}/{dataset_name}.zarr" ds.to_zarr(fp, mode="w") datasets[data_kind] = fp return datasets + + +def create_input_datasets_and_config( + identifier: str, + tmpdir: Path, + data_categories: List[str], + xlim: List[float] = DEFAULT_XLIM, + ylim: List[float] = DEFAULT_YLIM, + nx: int = NX, + ny: int = NY, + add_latlon: bool = False, +): + """ + Create a config and input datasets with test data for it with a given set + of data categories. + + Parameters + ---------- + identifier : str + Named identifier for the data collection + tmpdir : Path + Temporary directory to save the data collection + data_catagories : List[str] + List of categories of data to create, from state/forcing/static. + xlim : List[float], optional + List of the form [xmin, xmax] defining the x-limits, by default DEFAULT_XLIM + ylim : List[float], optional + List of the form [ymin, ymax] defining the y-limits, by default DEFAULT_YLIM + nx : int, optional + Number of grid points in x-direction, by default NX + ny : int, optional + Number of grid points in y-direction, by default NY + add_latlon : bool, optional + Whether to add latitude and longitude coordinates to the datasets. In + this case, the x- and y-coordinates will be interpreted as local equal + area projection coordinates (in meters) centered on Copenhagen, by + default False + + Returns + ------- + mdp.Config + The created config + """ + + output_variables = {} + inputs = {} + + for data_category in data_categories: + input_dims = [] + output_dims = [] + if data_category in ["state", "forcing"]: + data_kinds = ["surface_analysis"] + variable_names = DEFAULT_SURFACE_ANALYSIS_VARS + input_dims.append("analysis_time") + output_dims.append("time") + elif data_category == "static": + data_kinds = ["static"] + variable_names = DEFAULT_STATIC_VARS + else: + raise NotImplementedError(f"Unknown data category: {data_category}") + input_dims.extend(["x", "y"]) + output_dims += ["grid_index", f"{data_category}_feature"] + + datasets = create_data_collection( + data_kinds=data_kinds, + fp_root=Path(tmpdir.name) / identifier, + xlim=xlim, + ylim=ylim, + nx=nx, + ny=ny, + add_latlon=add_latlon, + ) + + assert len(datasets) == 1 + dataset_kind, dataset_path = datasets.popitem() + + output_variables[data_category] = output_dims + dim_mapping = {} + + for d in output_dims: + if d == "time": + dim_mapping[d] = mdp.config.DimMapping( + method="rename", + dim="analysis_time", + ) + elif d == "grid_index": + dim_mapping[d] = mdp.config.DimMapping( + method="stack", + dims=["x", "y"], + ) + else: + dim_mapping[d] = mdp.config.DimMapping( + method="stack_variables_by_var_name", + name_format="{var_name}", + ) + + inputs[f"{identifier}_{dataset_kind}"] = mdp.config.InputDataset( + path=dataset_path, + dims=input_dims, + variables=variable_names, + dim_mapping=dim_mapping, + target_output_variable=data_category, + ) + + config = mdp.Config( + schema_version=SCHEMA_VERSION, + dataset_version="v0.1.0", + output=mdp.config.Output( + variables=output_variables, + ), + inputs=inputs, + ) + + return config diff --git a/tests/test_convex_hull_cropping.py b/tests/test_convex_hull_cropping.py new file mode 100644 index 0000000..d9d3042 --- /dev/null +++ b/tests/test_convex_hull_cropping.py @@ -0,0 +1,210 @@ +import copy +import tempfile +from pathlib import Path + +import numpy as np +import pytest + +import mllam_data_prep as mdp +import mllam_data_prep.config as mdp_config +import tests.data as testdata +from mllam_data_prep.ops import cropping + + +def test_create_convex_hull_mask(): + tmpdir = tempfile.TemporaryDirectory() + domain_size = 500 * 1.0e3 # length and width of domain in meters + N = 200 + config_lam = testdata.create_input_datasets_and_config( + identifier="lam", + data_categories=["state"], + tmpdir=tmpdir, + xlim=[-domain_size / 2.0, domain_size / 2.0], + ylim=[-domain_size / 2.0, domain_size / 2.0], + nx=N, + ny=N, + add_latlon=True, + ) + # make the global domain twice as large as the LAM domain so that the lam + # domain is contained within the global domain + config_global = testdata.create_input_datasets_and_config( + identifier="global", + data_categories=["state"], + tmpdir=tmpdir, + xlim=[-domain_size, domain_size], + ylim=[-domain_size, domain_size], + add_latlon=True, + nx=N // 2, + ny=N // 2, + ) + + ds_lam = mdp.create_dataset(config=config_lam) + ds_global = mdp.create_dataset(config=config_global) + + da_ch_mask, ds_ch_latlons = cropping.create_convex_hull_mask( + ds=ds_global, ds_reference=ds_lam + ) + + # just check that some of the points make up the convex hull for now + # (this doesn't check that the convex hull is correct of course...) + assert 0 < ds_ch_latlons.grid_index_ref.size < ds_lam.grid_index.size + + # Given that the outer domain is 4x larger than the inner domain and the + # inner domain sits completely within the outer domain, then approximately + # 1/4 of the outer domain points should be within the convex hull of the + # inner domain, and 3/4 outside. + n_inside = da_ch_mask.where(da_ch_mask).count().values + n_outside = da_ch_mask.where(~da_ch_mask).count().values + np.testing.assert_almost_equal(n_inside / n_outside, 1.0 / 3.0, decimal=2) + + da_dist = cropping.distance_to_convex_hull_boundary( + ds=ds_global, ds_reference=ds_lam + ) + da_dist_unstacked = da_dist.set_index(grid_index=["x", "y"]).unstack("grid_index") + + # check that the distance decreases towards the middle of the domain in + # both x and y directions + da_dist_x = da_dist_unstacked.sel(y=0, method="nearest") + da_dist_change = da_dist_x.diff("x").dropna("x") + da_dist_x = da_dist_x.sel(x=da_dist_change.x) + np.testing.assert_array_equal(np.sign(da_dist_change), np.sign(da_dist_x.x)) + + da_dist_y = da_dist_unstacked.sel(x=0, method="nearest") + da_dist_change = da_dist_y.diff("y").dropna("y") + da_dist_y = da_dist_y.sel(y=da_dist_change.y) + np.testing.assert_array_equal(np.sign(da_dist_change), np.sign(da_dist_y.y)) + + da_convex_hull_margin_crop = cropping.crop_with_convex_hull( + ds=ds_global, + ds_reference=ds_lam, + margin_thickness=2.0, + include_interior_points=False, + ) + + # check that there are fewer points in this margin region + n_points_margin_region = da_convex_hull_margin_crop.count() + assert n_points_margin_region < n_outside + + +@pytest.mark.parametrize("include_interior_points", [True, False]) +def test_create_cropped_dataset(include_interior_points): + + tmpdir = tempfile.TemporaryDirectory() + d_len = 500 * 1.0e3 # length and width of domain in meters + N = 50 # number of grid points in each direction + config_lam = testdata.create_input_datasets_and_config( + identifier="lam", + data_categories=["state"], + tmpdir=tmpdir, + xlim=[-d_len / 2.0, d_len / 2.0], + ylim=[-d_len / 2.0, d_len / 2.0], + nx=N, + ny=N, + add_latlon=True, + ) + # make the global domain twice as large as the LAM domain so that the lam + # domain is contained within the global domain, but half the number of grid + # points in each direction + config_global = testdata.create_input_datasets_and_config( + identifier="global", + data_categories=["state"], + tmpdir=tmpdir, + xlim=[-d_len, d_len], + ylim=[-d_len, d_len], + nx=N // 2, + ny=N // 2, + add_latlon=True, + ) + + fp_lam_config = str(Path(tmpdir.name) / "lam_config.yml") + config_lam.to_yaml_file(fp_lam_config) + + config_global.output.domain_cropping = mdp.config.ConvexHullCropping( + margin_width_degrees=2.0, + include_interior_points=include_interior_points, + interior_dataset_config_path=fp_lam_config, + ) + mdp.create_dataset(config=config_global) + + +def test_crop_era5_with_generated_lam_domain(): + """ + Test cropping an ERA5 dataset with a generated LAM domain to ensure that + coordinates of ERA5 domain have been carried over into resulting dataset, + i.e. the same variables should be present whether the domain is cropped or + not. + """ + + era5_config = mdp_config.Config( + schema_version="v0.5.0", + dataset_version="v1.0.0", + output=mdp_config.Output( + variables=dict( + state=["time", "grid_index", "state_feature"], + ), + coord_ranges=dict( + time=mdp_config.Range( + start="1990-09-03T00:00", end="1990-09-09T00:00", step="PT6H" + ) + ), + chunking=dict(time=1), + ), + inputs={ + "era_height_levels": mdp_config.InputDataset( + path="simplecache::gs://weatherbench2/datasets/era5/1959-2023_01_10-6h-64x32_equiangular_conservative.zarr", + dims=["time", "longitude", "latitude", "level"], + variables={ + "u_component_of_wind": dict( + level=mdp_config.ValueSelection(values=[1000], units="hPa") + ) + }, + dim_mapping={ + "time": mdp_config.DimMapping(method="rename", dim="time"), + "state_feature": mdp_config.DimMapping( + method="stack_variables_by_var_name", + dims=["level"], + name_format="{var_name}{level}hPa", + ), + "grid_index": mdp_config.DimMapping( + method="stack", dims=["longitude", "latitude"] + ), + }, + target_output_variable="state", + ) + }, + ) + + # create uncropped dataset + ds_uncropped = mdp.create_dataset(config=era5_config) + + tmpdir = tempfile.TemporaryDirectory() + + lam_config = testdata.create_input_datasets_and_config( + identifier="danra_lam", + data_categories=["state"], + tmpdir=tmpdir, + xlim=[-2.0, 2.0], + ylim=[-2.0, 2.0], + nx=50, + ny=50, + add_latlon=True, + ) + # save the LAM config to a temporary file + lam_config_path = Path(tmpdir.name) / "danra_lam_config.yaml" + lam_config.to_yaml_file(lam_config_path) + + # create cropped dataset + era5_config_cropped = copy.deepcopy(era5_config) + era5_config_cropped.output.domain_cropping = mdp_config.ConvexHullCropping( + margin_width_degrees=10, + interior_dataset_config_path=lam_config_path.as_posix(), + ) + + ds_cropped = mdp.create_dataset(config=era5_config_cropped) + + # check that the cropped dataset has the same variables and coordinates as + # the uncropped one, allowing for fewer grid points + for var in ds_uncropped.data_vars: + assert var in ds_cropped.data_vars + for coord in ds_uncropped[var].coords: + assert coord in ds_cropped[var].coords diff --git a/tests/test_from_config.py b/tests/test_from_config.py index 1a89361..5f564e3 100644 --- a/tests/test_from_config.py +++ b/tests/test_from_config.py @@ -10,6 +10,22 @@ import tests.data as testdata +def _find_example_config_files(): + fps = Path(__file__).parent.parent.glob("example.*.yaml") + examples = {fp.name.split(".")[1]: fp for fp in fps} + return examples + + +EXAMPLE_YAML_FILES = _find_example_config_files() + + +@pytest.mark.parametrize("example_name", EXAMPLE_YAML_FILES.keys()) +def test_yaml_example(example_name): + fp_config = EXAMPLE_YAML_FILES[example_name] + with tempfile.TemporaryDirectory(suffix=".zarr") as tmpdir: + mdp.create_dataset_zarr(fp_config=fp_config, fp_zarr=tmpdir) + + def test_gen_data(): tmpdir = tempfile.TemporaryDirectory() testdata.create_data_collection(