dantro.groups.labelled module

Implements the LabelledDataGroup, which allows to handle groups and containers that can be associated with further coordinates.

This imitates the xarray selection interface and provides a uniform interface to select data from these groups. Most importantly, it allows to combine all the data of one group, allowing to conveniently work with heterogeneously stored data.

class dantro.groups.labelled.LabelledDataGroup(*args, dims: Tuple[str] = None, mode: str = None, allow_deep_selection: bool = None, **kwargs)[source]

Bases: dantro.groups.ordered.OrderedDataGroup

A group that assumes that the members it contains can be labelled with dimension names and coordinates.

Such a group has the great benefit to provide a selection interface that works fully on the dimension labels and coordinates and can cooperate with the xarray selection interface, i.e. the sel and isel methods.

_NEW_CONTAINER_CLS

alias of dantro.containers.xrdatactr.XrDataContainer

LDG_ALLOW_DEEP_SELECTION = True
LDG_DIMS = ()
LDG_EXTRACT_COORDS_FROM = 'data'
LDG_COORDS_ATTR_PREFIX = 'ext_coords__'
LDG_COORDS_MODE_ATTR_PREFIX = 'ext_coords_mode__'
LDG_COORDS_MODE_DEFAULT = 'scalar'
LDG_STRICT_ATTR_CHECKING = False
LDG_COORDS_SEPARATOR_IN_NAME = ';'
_COLLECTIVE_SELECT_THRESHOLD = 1.8
__init__(*args, dims: Tuple[str] = None, mode: str = None, allow_deep_selection: bool = None, **kwargs)[source]

Initialize a LabelledDataGroup

Parameters
  • *args – Passed on to OrderedDataGroup

  • dims (TDims, optional) – The dimensions associated with this group. If not given, will use those defined in the LDG_DIMS class variable. These can not be changed afterwards!

  • mode (str, optional) – By which coordinate extraction mode to get the coordinates from the group members. Can be attrs, name, data or anything else specified in extract_coords().

  • allow_deep_selection (bool, optional) – Whether to allow deep selection. If not given, will use the LDG_ALLOW_DEEP_SELECTION class variable’s value. Behaviour can be changed via the property of the same name.

  • **kwargs – Passed on to OrderedDataGroup

property dims

The names of the group-level dimensions this group manages.

It _may_ contain dimensions that overlap with dimension names from the members; this is intentional.

property ndim

The rank of the space covered by the group-level dimensions.

property coords

Returns a dict-like container of group-level coordinate values keyed by dimension.

property shape

Return the shape of the space covered by the group-level dimensions.

property allow_deep_selection

Whether deep selection is allowed.

property member_map

Returns an array that represents the space that the members of this group span, where each value (i.e. a specific coordinate combination) is the name of the corresponding member of this group.

Upon first call, this is computed here. If members are added, it is tried to accomodate them in there; if not possible, the cache will be invalidated.

The member map _may_ include empty strings, i.e. coordinate combinations that are not covered by any member. Also, they can contain duplicate names, as one member can cover multiple coordinates.

Note

The member map is invalidated when new members are added that can not be accomodated in it. It will be recalculated when needed.

property member_map_available

Whether the member map is available yet.

isel(indexers: dict = None, *, drop: bool = False, combination_method: str = 'auto', deep: bool = None, **indexers_kwargs) → xarray.core.dataarray.DataArray[source]

Return a new labelled xr.DataArray with an index-selected subset of members of this group.

If deep selection is activated, those indexers that are not available in the group-managed dimensions are looked up in the members of this group.

Note

For data combination (via any combination_method) dimensions that differ in size across group members have to be labelled, such that arrays can be aligned using the respective coordinates. See the xarray documentation for more information about coordinates.

Parameters
  • indexers (dict, optional) – A dict with keys matching dimensions and values given by scalars, slices or arrays of tick indices. As xr.DataArray.isel, uses pandas-like indexing, i.e.: slices do not include the terminal value.

  • drop (bool, optional) – Whether to drop coordinate variables instead of making them scalar.

  • combination_method (str, optional) –

    How to combine group-level data with member-level data. Ignored if data from a single group member is selected, i.e. no data has to be combined. Can be:

    • concat: Concatenate. This can preserve the dtype, but requires that no data is missing.

    • merge: Merge, using xarray.merge. This leads to a type conversion to float64, but allows members being missing or coordinates not fully filling the available space.

    • try_concat: Try concatenation, fall back to merging if that was unsuccessful.

    • auto: Automatically deduce suitably combination method. Use merge if data is non-integer type and try_concat otherwise.

    Note

    Selecting all data (by not passing any indexers) can be significantly faster using the merge combination method than using the concat method.

  • deep (bool, optional) – Whether to allow deep indexing, i.e.: that indexers may contain dimensions that don’t refer to group- level dimensions but to dimensions that are only availble among the member data. If None, will use the value returned by the allow_deep_selection property.

  • **indexers_kwargs – Additional indexers

Returns

The selected data, potentially a combination of data

on group level and member-level data.

Return type

xr.DataArray

sel(indexers: dict = None, *, method: str = None, tolerance: float = None, drop: bool = False, combination_method: str = 'auto', deep: bool = None, **indexers_kwargs) → xarray.core.dataarray.DataArray[source]

Return a new labelled xr.DataArray with a coordinate-selected subset of members of this group.

If deep selection is activated, those indexers that are not available in the group-managed dimensions are looked up in the members of this group.

Note

For data combination (via any combination_method) dimensions that differ in size across group members have to be labelled, such that arrays can be aligned using the respective coordinates. See the xarray documentation for more information about coordinates.

Parameters
  • indexers (dict, optional) – A dict with keys matching dimensions and values given by scalars, slices or arrays of tick labels. As xr.DataArray.sel, uses pandas-like indexing, i.e.: slices include the terminal value.

  • method (str, optional) – Method to use for inexact matches

  • tolerance (float, optional) – Maximum (absolute) distance between original and given label for inexact matches.

  • drop (bool, optional) – Whether to drop coordinate variables instead of making them scalar.

  • combination_method (str, optional) –

    How to combine group-level data with member-level data. Ignored if data from a single group member is selected, i.e. no data has to be combined. Can be:

    • concat: Concatenate. This can preserve the dtype, but requires that no data is missing.

    • merge: Merge, using xarray.merge. This leads to a type conversion to float64, but allows members being missing or coordinates not fully filling the available space.

    • try_concat: Try concatenation, fall back to merging if that was unsuccessful.

    • auto: Automatically deduce suitably combination method. Use merge if data is non-integer type and try_concat otherwise.

    Note

    Selecting all data (by not passing any indexers) can be significantly faster using the merge combination method than using the concat method.

  • deep (bool, optional) – Whether to allow deep indexing, i.e.: that indexers may contain dimensions that don’t refer to group- level dimensions but to dimensions that are only availble among the member data. If None, will use the value returned by the allow_deep_selection property.

  • **indexers_kwargs – Additional indexers

Returns

The selected data, potentially a combination of data

on group level and member-level data.

Return type

xr.DataArray

_get_coords_of(obj: dantro.abc.AbstractDataContainer) → Dict[str, Sequence[TCoord]][source]

Extract the coordinates for the given object using the extract_coords() function.

Parameters

obj (AbstractDataContainer) – The object to get the coordinates of.

Returns

The extracted coordinates

Return type

TCoordsDict

_add_container_callback(cont: dantro.abc.AbstractDataContainer) → None[source]

Called by the base class after adding a container, this method checks whether the member map needs to be invalidated or whether the new container can be accomodated in it.

If it can be accomodated, the member map will be adjusted such that for all coordinates associated with the given cont, the member map points to the newly added container.

Parameters

cont (AbstractDataContainer) – The newly added container

_parse_indexers(indexers: dict, *, allow_deep: bool, **indexers_kwargs) → Tuple[dict, dict][source]

Parses the given indexer arguments and split them into indexers for the selection of group members and deep selection.

Parameters
  • indexers (dict) – The indexers dict, may be empty

  • allow_deep (bool) – Whether to allow deep selection

  • **indexers_kwargs – Additional indexers

Returns

(shallow indexers, deep indexers)

Return type

Tuple[dict, dict]

Raises

ValueError – If deep indexers were given but deep selection was not enabled

_get_cont(name: str, *, combination_method: str) → Optional[dantro.containers.xrdatactr.XrDataContainer][source]

Retrieve the container from the group. If no container could be found, returns None, which denotes that further processing should be skipped.

Parameters
  • name (str) – Name of the container to be extracted

  • combination_method (str) – How the container data will be combined

Returns

The extracted container

Return type

Union[XrDataContainer, None]

Raises

ItemAccessError – If combination_method == "concat", on invalid container name.

_process_cont(cont, *, coords, shallow_indexers: dict, deep_indexers: dict, by_index: bool, drop: bool, **sel_kwargs) → xarray.core.dataarray.DataArray[source]

Process the given container and coordinates into a data array; this applies selection along container dimensions that overlap with the group dimensions as well as deep selection.

Parameters
  • cont – The container to be processed

  • coords – The DataArrayCoordinates of the given container in the preselected member map.

  • shallow_indexers (dict) – Indexers that were used to preselect the member map.

  • deep_indexers (dict) – Indexers to be applied to the container

  • by_index (bool) – Whether to select by index

  • drop (bool) – Whether to drop coordinate variables instead of making them scalar.

  • **sel_kwargs – Passed to .sel.

Returns

The processed container data

Return type

xr.DataArray

Raises

ValueError – In name mode, on conflicting non-dimension container coordinates.

_select(*, combination_method: str, shallow_indexers: dict, deep_indexers: dict, by_index: bool, drop: bool, **sel_kwargs) → xarray.core.dataarray.DataArray[source]

Preselect the member map (if needed) and designate a suitable method for further processing and selection based on the given combination method and indexers.

If possible, take shortcuts when selecting all data or when selecting data from a single group member.

Parameters
  • combination_method (str) – How to combine the member data.

  • shallow_indexers (dict) – Indexers to be applied on the group-level.

  • deep_indexers (dict) – Indexers to be applied on the member-level only.

  • by_index (bool) – Whether to select by index.

  • drop (bool) – Whether to drop coordinate variables instead of making them scalar.

  • **sel_kwargs – Passed to .sel.

Returns

The selected data.

Return type

xr.DataArray

Raises

ValueError – On invalid combination_method.

_select_single(cont_names: xarray.core.dataarray.DataArray, shallow_indexers: dict, deep_indexers: dict, by_index: bool, drop: bool, **sel_kwargs) → xarray.core.dataarray.DataArray[source]

Select data from a single group member. Expects the preselected member map to contain only a single valid container name.

_select_all_merge() → xarray.core.dataarray.DataArray[source]

Select all group data by directly merging all containers. This circumvents building the member map. This might fail, e.g. if there are conflicting or duplicate coordinates.

_ALLOWED_CONT_TYPES = None
_ATTRS_CLS

alias of dantro.base.BaseDataAttrs

_COND_TREE_CONDENSE_THRESH = 10
_COND_TREE_MAX_LEVEL = 10
_DirectInsertionModeMixin__in_direct_insertion_mode = False
_LockDataMixin__locked = False
_MutableMapping__marker = <object object>
_NEW_GROUP_CLS = None
_STORAGE_CLS

alias of collections.OrderedDict

__contains__(cont: Union[str, dantro.abc.AbstractDataContainer]) → bool

Whether the given container is in this group or not.

If this is a data tree object, it will be checked whether this specific instance is part of the group, using is-comparison.

Otherwise, assumes that cont is a valid argument to the __getitem__() method (a key or key sequence) and tries to access the item at that path, returning True if this succeeds and False if not.

Lookup complexity is that of item lookup (scalar) for both name and object lookup.

Parameters

cont (Union[str, AbstractDataContainer]) – The name of the container, a path, or an object to check via identity comparison.

Returns

Whether the given container object is part of this group or

whether the given path is accessible from this group.

Return type

bool

__delitem__(key: str) → None

Deletes an item from the group

__eq__(other) → bool

Evaluates equality by making the following comparisons: identity, strict type equality, and finally: equality of the _data and _attrs attributes, i.e. the private attribute. This ensures that comparison does not trigger any downstream effects like resolution of proxies.

If types do not match exactly, NotImplemented is returned, thus referring the comparison to the other side of the ==.

__format__(spec_str: str) → str

Creates a formatted string from the given specification.

Invokes further methods which are prefixed by _format_.

__getitem__(key: Union[str, List[str]]) → dantro.abc.AbstractDataContainer

Looks up the given key and returns the corresponding item.

This supports recursive relative lookups in two ways:

  • By supplying a path as a string that includes the path separator. For example, foo/bar/spam walks down the tree along the given path segments.

  • By directly supplying a key sequence, i.e. a list or tuple of key strings.

With the last path segment, it is possible to access an element that is no longer part of the data tree; successive lookups thus need to use the interface of the corresponding leaf object of the data tree.

Absolute lookups, i.e. from path /foo/bar, are not possible!

Lookup complexity is that of the underlying data structure: for groups based on dict-like storage containers, lookups happen in constant time.

Note

This method aims to replicate the behavior of POSIX paths.

Thus, it can also be used to access the element itself or the parent element: Use . to refer to this object and .. to access this object’s parent.

Parameters

key (Union[str, List[str]]) – The name of the object to retrieve or a path via which it can be found in the data tree.

Returns

The object at key, which concurs to the

dantro tree interface.

Return type

AbstractDataContainer

Raises

ItemAccessError – If no object could be found at the given key or if an absolute lookup, starting with /, was attempted.

__iter__()

Returns an iterator over the OrderedDict

__len__() → int

The number of members in this group.

__repr__() → str

Same as __str__

__setitem__(key: Union[str, List[str]], val: dantro.base.BaseDataContainer) → None

This method is used to allow access to the content of containers of this group. For adding an element to this group, use the add method!

Parameters
  • key (Union[str, List[str]]) – The key to which to set the value. If this is a path, will recurse down to the lowest level. Note that all intermediate keys need to be present.

  • val (BaseDataContainer) – The value to set

Returns

None

Raises

ValueError – If trying to add an element to this group, which should be done via the add method.

__sizeof__() → int

Returns the size of the data (in bytes) stored in this container’s data and its attributes.

Note that this value is approximate. It is computed by calling the sys.getsizeof function on the data, the attributes, the name and some caching attributes that each dantro data tree class contains. Importantly, this is not a recursive algorithm.

Also, derived classes might implement further attributes that are not taken into account either. To be more precise in a subclass, create a specific __sizeof__ method and invoke this parent method additionally.

For more information, see the documentation of sys.getsizeof:

__str__() → str

An info string, that describes the object. This invokes the formatting helpers to show the log string (type and name) as well as the info string of this object.

_abc_impl = <_abc_data object>
_add_container(cont, *, overwrite: bool)

Private helper method to add a container to this group.

_add_container_to_data(cont: dantro.abc.AbstractDataContainer) → None

Performs the operation of adding the container to the _data. This can be used by subclasses to make more elaborate things while adding data, e.g. specify ordering …

NOTE This method should NEVER be called on its own, but only via the

_add_container method, which takes care of properly linking the container that is to be added.

NOTE After adding, the container need be reachable under its .name!

Parameters

cont – The container to add

_attrs = None
_check_cont(cont) → None

Can be used by a subclass to check a container before adding it to this group. Is called by _add_container before checking whether the object exists or not.

This is not expected to return, but can raise errors, if something did not work out as expected.

Parameters

cont – The container to check

_check_data(data: Any) → None

This method can be used to check the data provided to this container

It is called before the data is stored in the __init__ method and should raise an exception or create a warning if the data is not as desired.

This method can be subclassed to implement more specific behaviour. To propagate the parent classes’ behaviour the subclassed method should always call its parent method using super().

Note

The CheckDataMixin provides a generalised implementation of this method to perform some type checks and react to unexpected types.

Parameters

data (Any) – The data to check

_check_name(new_name: str) → None

Called from name.setter and can be used to check the name that the container is supposed to have. On invalid name, this should raise.

This method can be subclassed to implement more specific behaviour. To propagate the parent classes’ behaviour the subclassed method should always call its parent method using super().

Parameters

new_name (str) – The new name, which is to be checked.

_direct_insertion_mode(*, enabled: bool = True)

A context manager that brings the class this mixin is used in into direct insertion mode. While in that mode, the with_direct_insertion() property will return true.

This context manager additionally invokes two callback functions, which can be specialized to perform certain operations when entering or exiting direct insertion mode: Before entering, _enter_direct_insertion_mode() is called. After exiting, _exit_direct_insertion_mode() is called.

Parameters

enabled (bool, optional) – whether to actually use direct insertion mode. If False, will yield directly without setting the toggle. This is equivalent to a null-context.

_enter_direct_insertion_mode()

Called after entering direct insertion mode; can be overwritten to attach additional behaviour.

_exit_direct_insertion_mode()

Called before exiting direct insertion mode; can be overwritten to attach additional behaviour.

_format_cls_name() → str

A __format__ helper function: returns the class name

_format_info() → str

A __format__ helper function: returns an info string that is used to characterize this object. Does NOT include name and classname!

_format_logstr() → str

A __format__ helper function: returns the log string, a combination of class name and name

_format_name() → str

A __format__ helper function: returns the name

_format_path() → str

A __format__ helper function: returns the path to this container

_format_tree() → str

Returns the default tree representation of this group by invoking the .tree property

_format_tree_condensed() → str

Returns the default tree representation of this group by invoking the .tree property

_ipython_key_completions_() → List[str]

For ipython integration, return a list of available keys

Links the new_child to this class, unlinking the old one.

This method should be called from any method that changes which items are associated with this group.

_lock_hook()

Invoked upon locking.

_select_generic(cont_names: xarray.core.dataarray.DataArray, *, combination_method: str, shallow_indexers: dict, deep_indexers: dict, by_index: bool, drop: bool, **sel_kwargs) → xarray.core.dataarray.DataArray[source]

Select data from group members using the given indexers and combine it via the specified method. If deep indexers are given, apply the deep indexing on each of the members.

This method receives a labelled array of container names, on which the selection already took place. The aim is now to align the objects these names refer to, including their coordinates, and thereby construct an array that contains both the dimensions given by the cont_names array and each members’ data dimensions.

Available combination methods are based either on xarray.merge operations or xarray.concat along each dimension. For both these combination methods, the members of this group need to be prepared such that the operation can be applied, i.e.: they need to already be in an array capable of that operation and they need to directly or indirectly preserve coordinate information.

For that purpose, an object-array is constructed holding the processed member data. As the xarray.Dataset and xarray.DataArray types have issues with handling array-like objects in object arrays, this is done via a numpy.ndarray.

Parameters
  • cont_names (xr.DataArray) – The pre-selected member map object, i.e. a labelled array containing names of the desired members that are to be combined.

  • combination_method (str) – How to combine them: concat, try_concat, or merge. Concatenation will allow preserving the dtype of the underlying data.

  • shallow_indexers (dict) – Indexer arguments that were used for the group member selection.

  • deep_indexers (dict) – Indexer arguments for deep selection to be done before combination.

  • by_index (bool) – Whether the deep indexing should take place by index; if False, will use label-based selection.

  • **sel_kwargs – Passed on to .sel.

Returns

The selected data of the members from cont_names,

combined using the given combination method.

Return type

xr.Dataset

Raises
  • ValueError – On conflicting coordinate information on group-level and member-level.

  • KeyError – In concat mode, upon missing members.

_tree_repr(*, level: int = 0, max_level: int = None, info_fstr='<{:cls_name,info}>', info_ratio: float = 0.6, condense_thresh: Union[int, Callable[[int, int], int]] = None, total_item_count: int = 0) → Union[str, List[str]]

Recursively creates a multi-line string tree representation of this group. This is used by, e.g., the _format_tree method.

Parameters
  • level (int, optional) – The depth within the tree

  • max_level (int, optional) – The maximum depth within the tree; recursion is not continued beyond this level.

  • info_fstr (str, optional) – The format string for the info string

  • info_ratio (float, optional) – The width ratio of the whole line width that the info string takes

  • condense_thresh (Union[int, Callable[[int, int], int]], optional) – If given, this specifies the threshold beyond which the tree view for the current element becomes condensed by hiding the output for some elements. The minimum value for this is 3, indicating that there should be at most 3 lines be generated from this level (excluding the lines coming from recursion), i.e.: two elements and one line for indicating how many values are hidden. If a smaller value is given, this is silently brought up to 3. Half of the elements are taken from the beginning of the item iteration, the other half from the end. If given as integer, that number is used. If a callable is given, the callable will be invoked with the current level, number of elements to be added at this level, and the current total item count along this recursion branch. The callable should then return the number of lines to be shown for the current element.

  • total_item_count (int, optional) – The total number of items already created in this recursive tree representation call. Passed on between recursive calls.

Returns

The (multi-line) tree representation of

this group. If this method was invoked with level == 0, a string will be returned; otherwise, a list of strings will be returned.

Return type

Union[str, List[str]]

Unlink a child from this class.

This method should be called from any method that removes an item from this group, be it through deletion or through

_unlock_hook()

Invoked upon unlocking.

add(*conts, overwrite: bool = False)

Add the given containers to this group.

property attrs

The container attributes.

property classname

Returns the name of this DataContainer-derived class

clear()

Clears all containers from this group.

This is done by unlinking all children and then overwriting _data with an empty _STORAGE_CLS object.

property data

The stored data.

get(key, default=None)

Return the container at key, or default if container with name key is not available.

items()

Returns an iterator over the (name, data container) tuple of this group.

keys()

Returns an iterator over the container names in this group.

lock()

Locks the data of this object

property locked

Whether this object is locked

property logstr

Returns the classname and name of this object

property name

The name of this DataContainer-derived object.

new_container(path: Union[str, List[str]], *, Cls: type = None, **kwargs)

Creates a new container of class Cls and adds it at the given path relative to this group.

If needed, intermediate groups are automatically created.

Parameters
  • path (Union[str, List[str]]) – Where to add the container.

  • Cls (type, optional) – The class of the container to add. If None, the _NEW_CONTAINER_CLS class variable’s value is used.

  • **kwargs – passed on to Cls.__init__

Returns

the created container

Return type

Cls

Raises
  • ValueError – If neither the Cls argument nor the class variable _NEW_CONTAINER_CLS were set or if path was empty.

  • TypeError – When Cls is not compatible to the data tree

new_group(path: Union[str, list], *, Cls: type = None, **kwargs)

Creates a new group at the given path.

Parameters
  • path (Union[str, list]) – The path to create the group at. Note that the whole intermediate path needs to already exist.

  • Cls (type, optional) – If given, use this type to create the group. If not given, uses the class specified in the _NEW_GROUP_CLS class variable or, as last resort, the type of this instance.

  • **kwargs – Passed on to Cls.__init__

Returns

the created group

Return type

Cls

Raises

TypeError – For the given class not being derived from BaseDataGroup

property parent

The associated parent of this container or group

property path

The path to get to this container or group from some root path

pop(k[, d]) → v, remove specified key and return the corresponding value.

If key is not found, d is returned if given, otherwise KeyError is raised.

popitem() → (k, v), remove and return some (key, value) pair

as a 2-tuple; but raise KeyError if D is empty.

raise_if_locked(*, prefix: str = None)

Raises an exception if this object is locked; does nothing otherwise

recursive_update(other, *, overwrite: bool = True)

Recursively updates the contents of this data group with the entries of the given data group

Note

This will create shallow copies of those elements in other that are added to this object.

Parameters
  • other (BaseDataGroup) – The group to update with

  • overwrite (bool, optional) – Whether to overwrite already existing object. If False, a conflict will lead to an error being raised and the update being stopped.

Raises

TypeError – If other was of invalid type

setdefault(key, default=None)

This method is not supported for a data group

property tree

Returns the default (full) tree representation of this group

property tree_condensed

Returns the condensed tree representation of this group. Uses the _COND_TREE_* prefixed class attributes as parameters.

unlock()

Unlocks the data of this object

update([E, ]**F) → None. Update D from mapping/iterable E and F.

If E present and has a .keys() method, does: for k in E: D[k] = E[k] If E present and lacks .keys() method, does: for (k, v) in E: D[k] = v In either case, this is followed by: for k, v in F.items(): D[k] = v

values()

Returns an iterator over the containers in this group.

property with_direct_insertion

Whether the class this mixin is mixed into is currently in direct insertion mode.

classmethod _combine_by_merge(dsets: numpy.ndarray) → xarray.core.dataset.Dataset[source]

Combine the given datasets by merging using xarray.merge.

Parameters

dsets (np.ndarray) – The object-dtype array of xr.Datasets that are to be combined.

Returns

All datasets, aligned and combined via xarray.merge

Return type

xr.Dataset

classmethod _combine_by_concatenation(dsets: numpy.ndarray, *, dims: Tuple[str]) → xarray.core.dataset.Dataset[source]

Combine the given datasets by concatenation using xarray.concat and subsequent application along all dimensions specified in dims.

Parameters
  • dsets (np.ndarray) – The object-dtype array of xr.Dataset objects that are to be combined by concatenation.

  • dims (TDims) – The dimension names corresponding to _all_ the dimensions of the dsets array.

Returns

The dataset resulting from the concatenation

Return type

xr.Dataset