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utils

Climate Data Utilities

Utility functions for working with climate data.

make_raster_template(x_min: int | float, y_min: int | float, stride: int | float, resolution: int | float, crs: str = 'EPSG:4326') -> rt.RasterArray

Create a raster template with the specified dimensions and resolution.

A raster template is a RasterArray with a specified extent, resolution, and CRS. The data values are initialized to zero. This function is useful for creating a template to use when resampling another raster to a common grid.

Parameters

x_min The minimum x-coordinate of the raster. y_min The minimum y-coordinate of the raster. stride The length of one side of the raster in the x and y directions measured in the units of the provided coordinate reference system. resolution The resolution of the raster in the units of the provided coordinate reference system. crs The coordinate reference system of the generated raster.

Returns

rt.RasterArray A raster template with the specified dimensions and resolution.

Source code in src/climate_data/utils.py 
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def make_raster_template(
    x_min: int | float,
    y_min: int | float,
    stride: int | float,
    resolution: int | float,
    crs: str = "EPSG:4326",
) -> rt.RasterArray:
    """Create a raster template with the specified dimensions and resolution.

    A raster template is a RasterArray with a specified extent, resolution, and CRS. The data
    values are initialized to zero. This function is useful for creating a template to use
    when resampling another raster to a common grid.

    Parameters
    ----------
    x_min
        The minimum x-coordinate of the raster.
    y_min
        The minimum y-coordinate of the raster.
    stride
        The length of one side of the raster in the x and y directions measured in the units
        of the provided coordinate reference system.
    resolution
        The resolution of the raster in the units of the provided coordinate reference system.
    crs
        The coordinate reference system of the generated raster.

    Returns
    -------
    rt.RasterArray
        A raster template with the specified dimensions and resolution.
    """
    tolerance = 1e-12
    evenly_divides = (stride % resolution < tolerance) or (
        resolution - stride % resolution < tolerance
    )
    if not evenly_divides:
        msg = "Stride must be a multiple of resolution"
        raise ValueError(msg)

    transform = Affine(
        a=resolution,
        b=0,
        c=x_min,
        d=0,
        e=-resolution,
        f=y_min + stride,
    )

    n_pix = int(stride / resolution)

    data = np.zeros((n_pix, n_pix), dtype=np.int8)
    return rt.RasterArray(
        data,
        transform,
        crs=crs,
        no_data_value=-1,
    )

to_raster(ds: xr.DataArray, no_data_value: float | int, lat_col: str = 'lat', lon_col: str = 'lon', crs: str = 'EPSG:4326') -> rt.RasterArray

Convert an xarray DataArray to a RasterArray.

Parameters

ds The xarray DataArray to convert. no_data_value The value to use for missing data. This should be consistent with the dtype of the data. lat_col The name of the latitude coordinate in the dataset. lon_col The name of the longitude coordinate in the dataset. crs The coordinate reference system of the data.

Returns

rt.RasterArray The RasterArray representation of the input data.

Source code in src/climate_data/utils.py 
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def to_raster(
    ds: xr.DataArray,
    no_data_value: float | int,
    lat_col: str = "lat",
    lon_col: str = "lon",
    crs: str = "EPSG:4326",
) -> rt.RasterArray:
    """Convert an xarray DataArray to a RasterArray.

    Parameters
    ----------
    ds
        The xarray DataArray to convert.
    no_data_value
        The value to use for missing data. This should be consistent with the dtype of the data.
    lat_col
        The name of the latitude coordinate in the dataset.
    lon_col
        The name of the longitude coordinate in the dataset.
    crs
        The coordinate reference system of the data.

    Returns
    -------
    rt.RasterArray
        The RasterArray representation of the input data.
    """
    lat, lon = ds[lat_col].data, ds[lon_col].data

    dlat = (lat[1:] - lat[:-1]).mean()
    dlon = (lon[1:] - lon[:-1]).mean()

    transform = Affine(
        a=dlon,
        b=0.0,
        c=lon[0],
        d=0.0,
        e=-dlat,
        f=lat[-1],
    )
    return rt.RasterArray(
        data=ds.data[::-1],
        transform=transform,
        crs=crs,
        no_data_value=no_data_value,
    )