The climpact stand alone package

climapct is both a freva plugin and a stand alone library that can be used to process climate data in order to select regions defined by coordinates stored in geo reference data, like shape or geojson files.

Installation

The best method to install the library is cloning the repository and creating a conda environment:

git clone https://gitlab.dkrz.de/ch1187/plugins4freva/climpact.git
cd climpact
conda env create -f conda-env.yml -n climpact

These commands would create a fresh conda environment named climpact after activating the environment

conda activate climpact

You can make use of the stand alone library.

The RunDirectory class is a class for reading data.

It offers easy control over selecting complex regions defined in geo-reference datasets like shape or geojson files. See also the Api Reference section for more details.

Parameters

files: list[str]: List of files that should be opened by the reader
variables: list[str]: list variables that should be considered
shape_file: str, default: None: Path to the shape file that is used to define the mask region. If None given (default) no masking will be applied.
region: str, default: “”: Select the name of a sub region within the shape file. If None given (default) the whole geometry defined in the shape file is taken.
mask_method: str, default: centres: String representing the method how the masked region (if given) should be applied. The string can either be centres or corners. Where centres selects only those grid-boxes that have the grid-box centres within the coordinates of the mask region. corners will select grid-boxes that have at least one of the four corners of the grid-boxes within the coordinates of the mask region. This means that corners, compared to centres will slightly increase the selected area.
mask: tuple[str, int], default: None: If additionally a land or sea region should be mask this variable can be used to set the path to a land-sea mask file (first entry in the tuple) and the type of the mask (second entry - 0: land, 1: sea).
abort_on_error: bool, default: False: Exit if something goes wrong while loading the dataset
reindex: bool, default: True: Apply nearest neighbor re-indexing to a larger grid in order to achieve a more precise region selection.
kwargs:: Additional information about the run

Examples

First we read a dataset without applying a mask at all

from climpact import RunDirectory
rd = RundDirectory(["~/orog.nc"], ["orog"])
print(rd.variables)
['orog']
print(type(rd.dataset))
<class 'xarray.core.dataset.Dataset'>
print(rd.dataset["orog"])
<xarray.DataArray 'orog' (rlat: 412, rlon: 424)>
dask.array<mul, shape=(412, 424), dtype=float64, chunksize=(412, 424), chunktype=numpy.ndarray>
Coordinates:
    lon      (rlat, rlon) float64 dask.array<chunksize=(412, 424), meta=np.ndarray>
    lat      (rlat, rlon) float64 dask.array<chunksize=(412, 424), meta=np.ndarray>
  * rlon     (rlon) float64 -28.38 -28.26 -28.16 -28.05 ... 17.93 18.05 18.16
  * rlat     (rlat) float64 -23.38 -23.26 -23.16 -23.05 ... 21.61 21.73 21.83
    Y        (rlat, rlon) float64 21.99 22.03 22.07 22.11 ... 66.81 66.75 66.69
    X        (rlat, rlon) float64 -10.06 -9.964 -9.864 ... 64.55 64.76 64.96
Attributes:
    standard_name:  surface_altitude
    long_name:      Surface Altitude
    units:          m
    grid_mapping:   rotated_pole

Now let’s apply a mask defined in a shape file

from climpact import RunDirectory
rd = RundDirectory(["~/orog.nc"], ["orog"], shape_file="Germany.shp")
print(rd.dataset["orog"])
<xarray.DataArray 'orog' (rlat: 365, rlon: 275)>
dask.array<mul, shape=(365, 275), dtype=float64, chunksize=(365, 275), chunktype=numpy.ndarray>
Coordinates:
  * rlon     (rlon) float64 -7.695 -7.673 -7.652 -7.63 ... -1.798 -1.777 -1.755
  * rlat     (rlat) float64 -3.245 -3.223 -3.201 -3.18 ... 4.631 4.653 4.675
    lon      (rlat, rlon) float64 dask.array<chunksize=(365, 275), meta=np.ndarray>
    lat      (rlat, rlon) float64 dask.array<chunksize=(365, 275), meta=np.ndarray>
    Y        (rlat, rlon) float64 46.92 46.92 46.92 46.93 ... 55.39 55.39 55.39
    X        (rlat, rlon) float64 6.713 6.745 6.776 6.807 ... 14.84 14.88 14.92
Attributes:
    standard_name:  surface_altitude
    long_name:      Surface Altitude
    units:          m
    grid_mapping:   rotated_pole

Finally we can only select a sub region in the shape file by giving the key or index to the sub region.

from climpact import RunDirectory
rd = RundDirectory(["~/orog.nc"],
                   ["orog"],
                   shape_file="Germany.shp",
                   region="Schaumburg",
                   )
print(rd.dataset["orog"])
<xarray.DataArray 'orog' (rlat: 25, rlon: 25)>
dask.array<mul, shape=(25, 25), dtype=float64, chunksize=(25, 25), chunktype=numpy.ndarray>
Coordinates:
  * rlon     (rlon) float64 -5.605 -5.587 -5.568 -5.55 ... -5.202 -5.183 -5.165
  * rlat     (rlat) float64 1.595 1.613 1.632 1.65 ... 1.98 1.998 2.017 2.035
    lon      (rlat, rlon) float64 dask.array<chunksize=(25, 25), meta=np.ndarray>
    lat      (rlat, rlon) float64 dask.array<chunksize=(25, 25), meta=np.ndarray>
    Y        (rlat, rlon) float64 52.0 52.0 52.0 52.01 ... 52.48 52.49 52.49
    X        (rlat, rlon) float64 8.876 8.905 8.935 8.964 ... 9.444 9.474 9.504
Attributes:
    standard_name:  surface_altitude
    long_name:      Surface Altitude
    units:          m
    grid_mapping:   rotated_pole