Python API

Cube I/O

Cube generation

xcube.core.new.new_cube(title='Test Cube', width=360, height=180, x_name='lon', y_name='lat', x_dtype='float64', y_dtype=None, x_units='degrees_east', y_units='degrees_north', x_res=1.0, y_res=None, x_start=-180.0, y_start=-90.0, inverse_y=False, time_name='time', time_dtype='datetime64[s]', time_units='seconds since 1970-01-01T00:00:00', time_calendar='proleptic_gregorian', time_periods=5, time_freq='D', time_start='2010-01-01T00:00:00', drop_bounds=False, variables=None)

Create a new empty cube. Useful for creating cubes templates with predefined coordinate variables and metadata. The function is also heavily used by xcube’s unit tests.

The values of the variables dictionary can be either constants, array-like objects, or functions that compute their return value from passed coordinate indexes. The expected signature is::

def my_func(time: int, y: int, x: int) -> Union[bool, int, float]
Parameters

  • title – A title. Defaults to ‘Test Cube’.

  • width – Horizontal number of grid cells. Defaults to 360.

  • height – Vertical number of grid cells. Defaults to 180.

  • x_name – Name of the x coordinate variable. Defaults to ‘lon’.

  • y_name – Name of the y coordinate variable. Defaults to ‘lat’.

  • x_dtype – Data type of x coordinates. Defaults to ‘float64’.

  • y_dtype – Data type of y coordinates. Defaults to ‘float64’.

  • x_units – Units of the x coordinates. Defaults to ‘degrees_east’.

  • y_units – Units of the y coordinates. Defaults to ‘degrees_north’.

  • x_start – Minimum x value. Defaults to -180.

  • y_start – Minimum y value. Defaults to -90.

  • x_res – Spatial resolution in x-direction. Defaults to 1.0.

  • y_res – Spatial resolution in y-direction. Defaults to 1.0.

  • inverse_y – Whether to create an inverse y axis. Defaults to False.

  • time_name – Name of the time coordinate variable. Defaults to ‘time’.

  • time_periods – Number of time steps. Defaults to 5.

  • time_freq – Duration of each time step. Defaults to `1D’.

  • time_start – First time value. Defaults to ‘2010-01-01T00:00:00’.

  • time_dtype – Numpy data type for time coordinates. Defaults to ‘datetime64[s]’.

  • time_units – Units for time coordinates. Defaults to ‘seconds since 1970-01-01T00:00:00’.

  • time_calendar – Calender for time coordinates. Defaults to ‘proleptic_gregorian’.

  • drop_bounds – If True, coordinate bounds variables are not created. Defaults to False.

  • variables – Dictionary of data variables to be added. None by default.

Returns

A cube instance

Cube computation

Cube data extraction

Cube manipulation

xcube.core.unchunk.unchunk_dataset(dataset_path: str, var_names: Sequence[str] = None, coords_only: bool = False)

Unchunk dataset variables in-place.

Parameters

  • dataset_path – Path to ZARR dataset directory.

  • var_names – Optional list of variable names.

  • coords_only – Un-chunk coordinate variables only.

xcube.core.optimize.optimize_dataset(input_path: str, output_path: str = None, in_place: bool = False, unchunk_coords: bool = False, exception_type: Type[Exception] = <class 'ValueError'>)

Optimize a dataset for faster access.

Reduces the number of metadata and coordinate data files in xcube dataset given by given by dataset_path. Consolidated cubes open much faster from remote locations, e.g. in object storage, because obviously much less HTTP requests are required to fetch initial cube meta information. That is, it merges all metadata files into a single top-level JSON file “.zmetadata”. If unchunk_coords is set, it also removes any chunking of coordinate variables so they comprise a single binary data file instead of one file per data chunk. The primary usage of this function is to optimize data cubes for cloud object storage. The function currently works only for data cubes using ZARR format.

Parameters

  • input_path – Path to input dataset with ZARR format.

  • output_path – Path to output dataset with ZARR format. May contain “{input}” template string, which is replaced by the input path’s file name without file name extentsion.

  • in_place – Whether to modify the dataset in place. If False, a copy is made and output_path must be given.

  • unchunk_coords – Whether to also consolidate coordinate chunk files.

  • exception_type – Type of exception to be used on value errors.

Cube subsetting

xcube.core.select.select_vars(dataset: xarray.Dataset, var_names: Collection[str] = None) → xarray.Dataset

Select data variable from given dataset and create new dataset.

Parameters

  • dataset – The dataset from which to select variables.

  • var_names – The names of data variables to select.

Returns

A new dataset. It is empty, if var_names is empty. It is dataset, if var_names is None.

Cube masking

class xcube.core.maskset.MaskSet(flag_var: xarray.DataArray)

A set of mask variables derived from a variable flag_var with CF attributes “flag_masks” and “flag_meanings”.

Each mask is represented by an xarray.DataArray and has the name of the flag, is of type numpy.unit8, and has the dimensions of the given flag_var.

Parameters

flag_var – an xarray.DataArray that defines flag values. The CF attributes “flag_masks” and “flag_meanings” are expected to exists and be valid.

classmethod get_mask_sets(dataset: xarray.Dataset) → Dict[str, xcube.core.maskset.MaskSet]

For each “flag” variable in given dataset, turn it into a MaskSet, store it in a dictionary.

Parameters

dataset – The dataset

Returns

A mapping of flag names to MaskSet. Will be empty if there are no flag variables in dataset.

Rasterisation of Features

Cube metadata

Cube verification

Multi-resolution pyramids

Utilities

class xcube.core.store.CubeStore(dims: Sequence[str], shape: Sequence[int], chunks: Sequence[int], attrs: Dict[str, Any] = None, get_chunk: Callable[[CubeStore, str, Tuple[int, ...]], bytes] = None, trace_store_calls: bool = False)

A Zarr Store that generates data cubes by allowing data variables to fetch or compute their chunks by a user-defined function get_chunk. Implements the standard Python MutableMapping interface.

This is how the get_chunk function is called::

data = get_chunk(cube_store, var_name, chunk_indexes)

where cube_store is this store, var_name is the name of the variable for which data is fetched, and chunk_indexes is a tuple of zero-based, integer chunk indexes. The result must be a Python bytes object.

Parameters

  • dims – Dimension names of all data variables, e.g. (‘time’, ‘lat’, ‘lon’).

  • shape – Shape of all data variables according to dims, e.g. (512, 720, 1480).

  • chunks – Chunk sizes of all data variables according to dims, e.g. (128, 180, 180).

  • attrs – Global dataset attributes.

  • get_chunk – Default chunk fetching/computing function.

  • trace_store_calls – Whether to print calls into the MutableMapping interface.

keys() → a set-like object providing a view on D's keys
class xcube.core.schema.CubeSchema(shape: Sequence[int], coords: Mapping[str, numpy.array], x_name: str = 'lon', y_name: str = 'lat', time_name: str = 'time', dims: Sequence[str] = None, chunks: Sequence[int] = None)

A schema that can be used to create new xcube datasets. The given shape, dims, and chunks, coords apply to all data variables.

Parameters

  • shape – A tuple of dimension sizes.

  • coords – A dictionary of coordinate variables. Must have values for all dims.

  • dims – A sequence of dimension names. Defaults to ('time', 'lat', 'lon').

  • chunks – A tuple of chunk sizes in each dimension.

property ndim

Number of dimensions.

property dims

Tuple of dimension names.

property x_name

Name of the spatial x coordinate variable.

property y_name

Name of the spatial y coordinate variable.

property time_name

Name of the time coordinate variable.

property x_var

Spatial x coordinate variable.

property y_var

Spatial y coordinate variable.

property time_var

Time coordinate variable.

property x_dim

Name of the spatial x dimension.

property y_dim

Name of the spatial y dimension.

property time_dim

Name of the time dimension.

property shape

Tuple of dimension sizes.

property chunks

Tuple of dimension chunk sizes.

property coords

Dictionary of coordinate variables.

classmethod new(cube: xarray.Dataset) → xcube.core.schema.CubeSchema

Create a cube schema from given cube.

Plugin Development

class xcube.util.extension.ExtensionRegistry

A registry of extensions. Typically used by plugins to register extensions.

has_extension(point: str, name: str) → bool

Test if an extension with given point and name is registered.

Parameters

  • point – extension point identifier

  • name – extension name

Returns

True, if extension exists

get_extension(point: str, name: str) → Optional[xcube.util.extension.Extension]

Get registered extension for given point and name.

Parameters

  • point – extension point identifier

  • name – extension name

Returns

the extension or None, if no such exists

get_component(point: str, name: str) → Any

Get extension component for given point and name. Raises a ValueError if no such extension exists.

Parameters

  • point – extension point identifier

  • name – extension name

Returns

extension component

find_extensions(point: str, predicate: Callable[[Extension], bool] = None) → List[xcube.util.extension.Extension]

Find extensions for point and optional filter function predicate.

The filter function is called with an extension and should return a truth value to indicate a match or mismatch.

Parameters

  • point – extension point identifier

  • predicate – optional filter function

Returns

list of matching extensions

find_components(point: str, predicate: Callable[[Extension], bool] = None) → List[Any]

Find extension components for point and optional filter function predicate.

The filter function is called with an extension and should return a truth value to indicate a match or mismatch.

Parameters

  • point – extension point identifier

  • predicate – optional filter function

Returns

list of matching extension components

add_extension(point: str, name: str, component: Any = None, loader: Callable[[Extension], Any] = None, **metadata) → xcube.util.extension.Extension

Register an extension component or an extension component loader for the given extension point, name, and additional metadata.

Either component or loader must be specified, but not both.

A given loader must be a callable with one positional argument extension of type Extension and is expected to return the actual extension component, which may be of any type. The loader will only be called once and only when the actual extension component is requested for the first time. Consider using the function import_component() to create a loader that lazily imports a component from a module and optionally executes it.

Parameters

  • point – extension point identifier

  • name – extension name

  • component – extension component

  • loader – extension component loader function

  • metadata – extension metadata

Returns

a registered extension

remove_extension(point: str, name: str)

Remove registered extension name from given point.

Parameters

  • point – extension point identifier

  • name – extension name

class xcube.util.extension.Extension(point: str, name: str, component: Any = None, loader: Callable[[Extension], Any] = None, **metadata)

An extension that provides a component of any type.

Extensions are registered in a ExtensionRegistry.

Extension objects are not meant to be instantiated directly. Instead, ExtensionRegistry.add_extension() is used to register extensions.

Parameters

  • point – extension point identifier

  • name – extension name

  • component – extension component

  • loader – extension component loader function

  • metadata – extension metadata

property is_lazy

Whether this is a lazy extension that uses a loader.

property component

Extension component.

property point

Extension point identifier.

property name

Extension name.

property metadata

Extension metadata.

xcube.util.extension.import_component(spec: str, transform: Callable[[Any, Extension], Any] = None, call: bool = False, call_args: Sequence[Any] = None, call_kwargs: Mapping[str, Any] = None) → Callable[[xcube.util.extension.Extension], Any]

Return a component loader that imports a module or module component from spec. To import a module, spec should be the fully qualified module name. To import a component, spec must also append the component name to the fully qualified module name separated by a color (“:”) character.

An optional transform callable my be used to transform the imported component. If given, a new component is computed:

component = transform(component, extension)

If the call flag is set, the component is expected to be a callable which will be called using the given call_args and call_kwargs to produce a new component:

component = component(*call_kwargs, **call_kwargs)

Finally, the component is returned.

Parameters

  • spec – String of the form “module_path” or “module_path:component_name”

  • transform – callable that takes two positional arguments, the imported component and the extension of type Extension

  • call – Whether to finally call the component with given call_args and call_kwargs

  • call_args – arguments passed to a callable component if call flag is set

  • call_kwargs – keyword arguments passed to callable component if call flag is set

Returns

a component loader

xcube.constants.EXTENSION_POINT_INPUT_PROCESSORS = 'xcube.core.gen.iproc'

The extension point identifier for input processor extensions

xcube.constants.EXTENSION_POINT_DATASET_IOS = 'xcube.core.dsio'

The extension point identifier for dataset I/O extensions

xcube.constants.EXTENSION_POINT_CLI_COMMANDS = 'xcube.cli'

The extension point identifier for CLI command extensions

xcube.util.plugin.get_extension_registry()

Get populated extension registry.

xcube.util.plugin.get_plugins() → Dict[str, Dict]

Get mapping of “xcube_plugins” entry point names to JSON-serializable plugin meta-information.