data.utils.xarray.engine

Classes

EarthkitBackendEntrypoint

BackendEntrypoint is a class container and it is the main interface

Module Contents

class data.utils.xarray.engine.EarthkitBackendEntrypoint

Bases: xarray.backends.BackendEntrypoint

BackendEntrypoint is a class container and it is the main interface for the backend plugins, see BackendEntrypoint subclassing. It shall implement:

open_dataset method: it shall implement reading from file, variables decoding and it returns an instance of Dataset. It shall take in input at least filename_or_obj argument and drop_variables keyword argument. For more details see open_dataset.
guess_can_open method: it shall return True if the backend is able to open filename_or_obj, False otherwise. The implementation of this method is not mandatory.
open_datatree method: it shall implement reading from file, variables decoding and it returns an instance of DataTree. It shall take in input at least filename_or_obj argument. The implementation of this method is not mandatory. For more details see <reference to open_datatree documentation>.

open_dataset_parameters

A list of open_dataset method parameters. The setting of this attribute is not mandatory.

Type:: tuple, default: None

description

A short string describing the engine. The setting of this attribute is not mandatory.

Type:: str, default: ""

url

A string with the URL to the backend’s documentation. The setting of this attribute is not mandatory.

Type:: str, default: ""

classmethod guess_can_open(filename_or_obj): Backend open_dataset method used by Xarray in open_dataset().

open_dataset(filename_or_obj, source_type='file', profile='mars', variable_key=None, drop_variables=None, rename_variables=None, mono_variable=None, extra_dims=None, drop_dims=None, ensure_dims=None, fixed_dims=None, dim_roles=None, dim_name_from_role_name=None, rename_dims=None, dims_as_attrs=None, time_dim_mode=None, level_dim_mode=None, squeeze=None, add_valid_time_coord=None, decode_times=None, decode_timedelta=None, add_geo_coords=None, attrs_mode=None, attrs=None, variable_attrs=None, global_attrs=None, coord_attrs=None, add_earthkit_attrs=None, rename_attrs=None, fill_metadata=None, remapping=None, flatten_values=None, lazy_load=None, release_source=None, allow_holes=None, strict=None, dtype=None, array_module=None, array_backend=None, array_namespace=None, errors=None)

filename_or_obj, str, Path or earthkit object

Input GRIB file or object to be converted to an xarray dataset.

profile: str, dict or None

Provide custom default values for most of the kwargs. Currently, the “mars” and “grid” built-in profiles are available, otherwise an explicit dict can be used. None is equivalent to an empty dict. When a kwarg is specified it will update a default value if it is a dict otherwise it will overwrite it. See: Xarray engine: profiles for more information.

variable_key: str, None

Metadata key to specify the dataset variables. It cannot be defined as a dimension. Default is “param” (in earthkit-data this is the same as “shortName”). Only enabled when mono_variable is False or None.

drop_variables: str, or iterable of str, None

A variable or list of variables to drop from the dataset. Default is None. Only used when variable_key is enabled.

rename_variables: dict, None

Mapping to rename variables. Default is None. Only used when variable_key is enabled.

mono_variable: bool, str, None

If True or str, the dataset will contain a single variable called “data” (or the value of the mono_variable kwarg when it is a str). If False, the dataset will contain one variable for each distinct value of variable_key metadata key. The default value (None) expands to False unless the profile overwrites it.

extra_dims: str, or iterable of str, None

Define additional dimensions on top of the predefined dimensions. Only enabled when no fixed_dims is specified. Default is None. It can be a single item or a list. Each item is either a metadata key, or a dict/tuple defining mapping between the dimension name and the metadata key. The whole option can be a dict. E.g.

# use key "expver" as a dimension
extra_dims = "expver"
# use keys "expver" and "steam" as a dimension
extra_dims = ["expver", "stream"]
# define dimensions "expver", mars_stream" and "mars_type" from
# metadata keys "expver", "stream" and "type"
extra_dims = [
    "expver",
    {"mars_stream": "stream"},
    ("mars_type", "type"),
]
extra_dims = [
    {
        "expver": "expver",
        "mars_stream": "stream",
        "mars_type": "type",
    }
]

drop_dims: str, or iterable of str, None

Single or multiple dimensions to be ignored. Default is None. Default is None.

ensure_dims: str, or iterable of str, None

Dimension or dimensions that should be kept even when squeeze=True and their size is only 1. Default is None.

fixed_dims: str, or iterable of str, None

Define all the dimensions to be generated. When used no other dimensions will be created. Might be incompatible with other settings. Default is None. It can be a single item or a list. Each item is either a metadata key, or a dict/tuple defining mapping between the dimension name and the metadata key. The whole option can be a dict. E.g.

# use key "step" as a dimension
fixed_dims = "step"
# use keys "step" and "levelist" as a dimension
extra_dims = ["step", "levelist"]
# define dimensions "step", level" and "level_type" from
# metadata keys "step", "levelist" and "levtype"
extra_dims = [
    "step",
    {"level": "levelist"},
    ("level_type", "levtype"),
]
extra_dims = [
    {"step": "step", "level": "levelist", "level_type": "levtype"}
]

dim_roles: dict, None

Specify the “roles” used to form the predefined dimensions. The predefined dimensions are automatically generated when no fixed_dims specified and comprise the following (in a fixed order):

ensemble forecast member dimension
temporal dimensions (controlled by time_dim_mode)
vertical dimensions (controlled by level_dim_mode)

dim_roles is a mapping between the “roles” and the metadata keys representing the roles. The possible roles are as follows:

“number”: metadata key interpreted as ensemble forecast members
“date”: metadata key interpreted as date part of the “forecast_reference_time”
“time”: metadata key interpreted as time part of the “forecast_reference_time”
“step”: metadata key interpreted as forecast step
“forecast_reference_time”: if not specified or None or empty the forecast reference time is built using the “date” and “time” roles
“valid_time”: if not specified or None or empty the valid time is built using the “validityDate” and “validityTime” metadata keys
“level”: metadata key interpreted as level
“level_type”: metadata key interpreted as level type

The default values are as follows:

{
    "number": "number",
    "date": "dataDate",
    "time": "dataTime",
    "step": "step",
    "forecast_reference_time": None,
    "valid_date": None,
    "level": "level",
    "level_type": "typeOfLevel",
}

dims_roles behaves differently to the other kwargs in the sense that it does not override but update the default values. So e.g. to change only “number” in the defaults it is enough to specify: “dim_roles={“number”: “perturbationNumber”}.

dim_name_from_role_name: bool, None

If True, the dimension names are formed from the role names. Otherwise the dimension names are formed from the metadata keys specified in dim_roles. Its default value (None) expands to True unless the profile overwrites it. Only used when no fixed_dims` are specified. New in version 0.15.0.

rename_dims: dict, None

Mapping to rename dimensions. Default is None.

dims_as_attrs: str, or iterable of str, None

Dimension or list of dimensions which should be turned to variable attributes if they have only one value for the given variable. Default is None.

time_dim_mode: str, None

Define how predefined temporal dimensions are formed. The default is “forecast”. The possible values are as follows:

“forecast”: adds two dimensions:
- “forecast_reference_time”: built from the “date” and “time” roles (see dim_roles) as np.datetime64 values
- “step”: built from the “step” role. When decode_times=True the values are np.timedelta64
“valid_time”: adds a dimension called “valid_time” as described by the “valid_time” role (see dim_roles). Will contain np.datetime64 values.
“raw”: the “date”, “time” and “step” roles are turned into 3 separate dimensions

level_dim_mode: str, None

Define how predefined vertical dimensions are formed. The default is “level”. The possible values are:

“level”: adds a single dimension according to the “level” role (see dim_roles)
“level_per_type”: adds a separate dimensions for each level type based on the “level” and “level_type” roles.
“level_and_type”: Use a single dimension for combined level and type of level.

squeeze: bool, None

Remove dimensions which have only one valid value. Not applies to dimensions in ensure_dims. Its default value (None) expands to True unless the profile overwrites it.

add_valid_time_coord: bool, None

Add the valid_time coordinate containing np.datetime64 values to the dataset. Only makes effect when time_dim_mode is not “valid_time”. Its default value (None) expands to False unless the profile overwrites it.

decode_times: bool, None

If True, decode date and datetime coordinates into datetime64 values. If False, leave coordinates representing date-like GRIB keys (e.g. “date”, “validityDate”) encoded as native int values. The default value (None) expands to True unless the profile overwrites it.

decode_timedelta: bool, None

If True, decode coordinates representing time-like or duration-like GRIB keys (e.g. “time”, “validityTime”, “step”) into timedelta64 values. If False, leave time-like coordinates encoded as native int values, while duration-like coordinates will be encoded as int with the units attached to the coordinate as the “units” attribute. If None (default), assume the same value of decode_times unless the profile overwrites it.

add_geo_coords: bool, None

If True, add geographic coordinates to the dataset when field values are represented by a single “values” dimension. Its default value (None) expands to True unless the profile overwrites it.

flatten_values: bool, None

if True, flatten the values per field resulting in a single dimension called “values” representing a field. Otherwise the field shape is used to form the field dimensions. When the fields are defined on an unstructured grid (e.g. reduced Gaussian) or are spectral (e.g. spherical harmonics) this option is ignored and the field values are always represented by a single “values” dimension. Its default value (None) expands to False unless the profile overwrites it.

attrs_mode: str, None

Define how attributes are generated. Default is “fixed”. The possible values are:

“fixed”: Use the attributes defined in variable_attrs as variables attributes and global_attrs as global attributes.
“unique”: Use all the attributes defined in attrs, variable_attrs and global_attrs. When an attribute has unique value for a dataset it will be a global attribute, otherwise it will be a variable attribute. However, this logic is only applied if a unique variable attribute can be a global attribute according to the CF conventions Appendix A. (e.g. “units” cannot be a global attribute). Additionally, keys in variable_attrs are always used as variable attributes, while keys in global_attrs are always used as global attributes.

attrs: str, number, callable, dict or list of these, None

Attribute or list of attributes. Only used when attrs_mode is unique. Its default value (None) expands to [] unless the profile overwrites it. The following attributes are supported:

str: Name of the attribute used as a metadata key to generate the value of the attribute. Can also be specified by prefixing with “key=” (e.g. “key=level”). When prefixed with “namespace=” it specifies a metadata namespace (e.g. “namespace=parameter”), which will be added as a dict to the attribute.
callable: A callable that takes a Metadata object and returns a dict of attributes
dict: A dictionary of attributes with the keys as the attribute names. If the value is a callable it takes the attribute name and a Metadata object and returns the value of the attribute. A str value prefixed with “key=” or “namespace=” is interpreted as explained above. Any other values are used as the pre-defined value for the attribute.

variable_attrs: str, number, callable, dict or list of these, None

Variable attribute or attributes. For the allowed values see attrs. Its default value (None) expands to [] unless the profile overwrites it.

global_attrs: str, number, dict or list of these, None

Global attribute or attributes. For the allowed values see attrs. Its default value (None) expands to [] unless the profile overwrites it.

coord_attrs: dict, None

To be documented. Default is None.

add_earthkit_attrs: bool, None

If True, add earthkit specific attributes to the dataset. Its default value (None) expands to True unless the profile overwrites it.

rename_attrs: dict, None

A dictionary of attribute to rename. Default is None.

fill_metadata: dict, None

Define fill values to metadata keys. Default is None.

remapping: dict, None

Define new metadata keys for indexing. Default is None.

lazy_load: bool, None

If True, the resulting Dataset will load data lazily from the underlying data source. If False, a DataSet holding all the data in memory and decoupled from the backend source will be created. Using lazy_load=False with release_source=True can provide optimised memory usage in certain cases. The default value of lazy_load (None) expands to True unless the profile overwrites it.

release_source: bool, None

Only used when lazy_load=False. If True, memory held in the input fields are released as soon as their values are copied into the resulting Dataset. This is done per field to avoid memory spikes. The release operation is currently only supported for GRIB fields stored entirely in memory, e.g. when read from a stream. When a field does not support the release operation, this option is ignored. Having run to_xarray the input data becomes unusable, so use this option carefully. The default value of release_source (None) expands to False unless the profile overwrites it.

allow_holes: bool, None

If False, GRIB fields must form a full hypercube (without holes). If True, a dataset will be created from any GRIB fields and its coordinates will be a union of coordinates of the fields (outer join). Values corresponding to missing GRIB fields will be filled with NaN. The default value of allow_holes (None) expands to False unless the profile overwrites it.

strict: bool, None

If True, perform stricter checks on hypercube consistency. Its default value (None) expands to False unless the profile overwrites it.

dtype: str, numpy.dtype or None

Typecode or data-type of the array data.

array_namespace: str, array namespace, None

The array namespace to use for array operations. The default value (None) is expanded to “numpy”.

abstract open_datatree(filename_or_obj, *, drop_variables=None): Backend open_datatree method used by Xarray in open_datatree().

abstract open_groups_as_dict(filename_or_obj, *, drop_variables=None)

Opens a dictionary mapping from group names to Datasets.

Called by open_groups(). This function exists to provide a universal way to open all groups in a file, before applying any additional consistency checks or requirements necessary to create a DataTree object (typically done using from_dict()).