import datetime import functools import warnings from numbers import Number from typing import ( TYPE_CHECKING, Any, Callable, Dict, Hashable, Iterable, List, Mapping, Optional, Sequence, Tuple, TypeVar, Union, cast, ) import numpy as np import pandas as pd from ..plot.plot import _PlotMethods from . import ( computation, dtypes, groupby, indexing, ops, pdcompat, resample, rolling, utils, weighted, ) from .accessor_dt import CombinedDatetimelikeAccessor from .accessor_str import StringAccessor from .alignment import ( _broadcast_helper, _get_broadcast_dims_map_common_coords, align, reindex_like_indexers, ) from .common import AbstractArray, DataWithCoords from .coordinates import ( DataArrayCoordinates, LevelCoordinatesSource, assert_coordinate_consistent, remap_label_indexers, ) from .dataset import Dataset, split_indexes from .formatting import format_item from .indexes import Indexes, default_indexes, propagate_indexes from .indexing import is_fancy_indexer from .merge import PANDAS_TYPES, MergeError, _extract_indexes_from_coords from .options import OPTIONS, _get_keep_attrs from .utils import Default, ReprObject, _check_inplace, _default, either_dict_or_kwargs from .variable import ( IndexVariable, Variable, as_compatible_data, as_variable, assert_unique_multiindex_level_names, ) if TYPE_CHECKING: T_DSorDA = TypeVar("T_DSorDA", "DataArray", Dataset) try: from dask.delayed import Delayed except ImportError: Delayed = None try: from cdms2 import Variable as cdms2_Variable except ImportError: cdms2_Variable = None try: from iris.cube import Cube as iris_Cube except ImportError: iris_Cube = None def _infer_coords_and_dims( shape, coords, dims ) -> "Tuple[Dict[Any, Variable], Tuple[Hashable, ...]]": """All the logic for creating a new DataArray""" if ( coords is not None and not utils.is_dict_like(coords) and len(coords) != len(shape) ): raise ValueError( "coords is not dict-like, but it has %s items, " "which does not match the %s dimensions of the " "data" % (len(coords), len(shape)) ) if isinstance(dims, str): dims = (dims,) if dims is None: dims = ["dim_%s" % n for n in range(len(shape))] if coords is not None and len(coords) == len(shape): # try to infer dimensions from coords if utils.is_dict_like(coords): # deprecated in GH993, removed in GH1539 raise ValueError( "inferring DataArray dimensions from " "dictionary like ``coords`` is no longer " "supported. Use an explicit list of " "``dims`` instead." ) for n, (dim, coord) in enumerate(zip(dims, coords)): coord = as_variable(coord, name=dims[n]).to_index_variable() dims[n] = coord.name dims = tuple(dims) elif len(dims) != len(shape): raise ValueError( "different number of dimensions on data " "and dims: %s vs %s" % (len(shape), len(dims)) ) else: for d in dims: if not isinstance(d, str): raise TypeError("dimension %s is not a string" % d) new_coords: Dict[Any, Variable] = {} if utils.is_dict_like(coords): for k, v in coords.items(): new_coords[k] = as_variable(v, name=k) elif coords is not None: for dim, coord in zip(dims, coords): var = as_variable(coord, name=dim) var.dims = (dim,) new_coords[dim] = var.to_index_variable() sizes = dict(zip(dims, shape)) for k, v in new_coords.items(): if any(d not in dims for d in v.dims): raise ValueError( "coordinate %s has dimensions %s, but these " "are not a subset of the DataArray " "dimensions %s" % (k, v.dims, dims) ) for d, s in zip(v.dims, v.shape): if s != sizes[d]: raise ValueError( "conflicting sizes for dimension %r: " "length %s on the data but length %s on " "coordinate %r" % (d, sizes[d], s, k) ) if k in sizes and v.shape != (sizes[k],): raise ValueError( "coordinate %r is a DataArray dimension, but " "it has shape %r rather than expected shape %r " "matching the dimension size" % (k, v.shape, (sizes[k],)) ) assert_unique_multiindex_level_names(new_coords) return new_coords, dims def _check_data_shape(data, coords, dims): if data is dtypes.NA: data = np.nan if coords is not None and utils.is_scalar(data, include_0d=False): if utils.is_dict_like(coords): if dims is None: return data else: data_shape = tuple( as_variable(coords[k], k).size if k in coords.keys() else 1 for k in dims ) else: data_shape = tuple(as_variable(coord, "foo").size for coord in coords) data = np.full(data_shape, data) return data class _LocIndexer: __slots__ = ("data_array",) def __init__(self, data_array: "DataArray"): self.data_array = data_array def __getitem__(self, key) -> "DataArray": if not utils.is_dict_like(key): # expand the indexer so we can handle Ellipsis labels = indexing.expanded_indexer(key, self.data_array.ndim) key = dict(zip(self.data_array.dims, labels)) return self.data_array.sel(**key) def __setitem__(self, key, value) -> None: if not utils.is_dict_like(key): # expand the indexer so we can handle Ellipsis labels = indexing.expanded_indexer(key, self.data_array.ndim) key = dict(zip(self.data_array.dims, labels)) pos_indexers, _ = remap_label_indexers(self.data_array, key) self.data_array[pos_indexers] = value # Used as the key corresponding to a DataArray's variable when converting # arbitrary DataArray objects to datasets _THIS_ARRAY = ReprObject("") class DataArray(AbstractArray, DataWithCoords): """N-dimensional array with labeled coordinates and dimensions. DataArray provides a wrapper around numpy ndarrays that uses labeled dimensions and coordinates to support metadata aware operations. The API is similar to that for the pandas Series or DataFrame, but DataArray objects can have any number of dimensions, and their contents have fixed data types. Additional features over raw numpy arrays: - Apply operations over dimensions by name: ``x.sum('time')``. - Select or assign values by integer location (like numpy): ``x[:10]`` or by label (like pandas): ``x.loc['2014-01-01']`` or ``x.sel(time='2014-01-01')``. - Mathematical operations (e.g., ``x - y``) vectorize across multiple dimensions (known in numpy as "broadcasting") based on dimension names, regardless of their original order. - Keep track of arbitrary metadata in the form of a Python dictionary: ``x.attrs`` - Convert to a pandas Series: ``x.to_series()``. Getting items from or doing mathematical operations with a DataArray always returns another DataArray. Parameters ---------- data : array_like Values for this array. Must be an ``numpy.ndarray``, ndarray like, or castable to an ``ndarray``. If a self-described xarray or pandas object, attempts are made to use this array's metadata to fill in other unspecified arguments. A view of the array's data is used instead of a copy if possible. coords : sequence or dict of array_like, optional Coordinates (tick labels) to use for indexing along each dimension. The following notations are accepted: - mapping {dimension name: array-like} - sequence of tuples that are valid arguments for ``xarray.Variable()`` - (dims, data) - (dims, data, attrs) - (dims, data, attrs, encoding) Additionally, it is possible to define a coord whose name does not match the dimension name, or a coord based on multiple dimensions, with one of the following notations: - mapping {coord name: DataArray} - mapping {coord name: Variable} - mapping {coord name: (dimension name, array-like)} - mapping {coord name: (tuple of dimension names, array-like)} dims : hashable or sequence of hashable, optional Name(s) of the data dimension(s). Must be either a hashable (only for 1D data) or a sequence of hashables with length equal to the number of dimensions. If this argument is omitted, dimension names default to ``['dim_0', ... 'dim_n']``. name : str or None, optional Name of this array. attrs : dict_like or None, optional Attributes to assign to the new instance. By default, an empty attribute dictionary is initialized. Examples -------- Create data: >>> np.random.seed(0) >>> temperature = 15 + 8 * np.random.randn(2, 2, 3) >>> precipitation = 10 * np.random.rand(2, 2, 3) >>> lon = [[-99.83, -99.32], [-99.79, -99.23]] >>> lat = [[42.25, 42.21], [42.63, 42.59]] >>> time = pd.date_range("2014-09-06", periods=3) >>> reference_time = pd.Timestamp("2014-09-05") Initialize a dataarray with multiple dimensions: >>> da = xr.DataArray( ... data=temperature, ... dims=["x", "y", "time"], ... coords=dict( ... lon=(["x", "y"], lon), ... lat=(["x", "y"], lat), ... time=time, ... reference_time=reference_time, ... ), ... attrs=dict( ... description="Ambient temperature.", ... units="degC", ... ), ... ) >>> da array([[[29.11241877, 18.20125767, 22.82990387], [32.92714559, 29.94046392, 7.18177696]], [[22.60070734, 13.78914233, 14.17424919], [18.28478802, 16.15234857, 26.63418806]]]) Coordinates: lon (x, y) float64 -99.83 -99.32 -99.79 -99.23 lat (x, y) float64 42.25 42.21 42.63 42.59 * time (time) datetime64[ns] 2014-09-06 2014-09-07 2014-09-08 reference_time datetime64[ns] 2014-09-05 Dimensions without coordinates: x, y Attributes: description: Ambient temperature. units: degC Find out where the coldest temperature was: >>> da.isel(da.argmin(...)) array(7.18177696) Coordinates: lon float64 -99.32 lat float64 42.21 time datetime64[ns] 2014-09-08 reference_time datetime64[ns] 2014-09-05 Attributes: description: Ambient temperature. units: degC """ _cache: Dict[str, Any] _coords: Dict[Any, Variable] _indexes: Optional[Dict[Hashable, pd.Index]] _name: Optional[Hashable] _variable: Variable __slots__ = ( "_cache", "_coords", "_file_obj", "_indexes", "_name", "_variable", "__weakref__", ) _groupby_cls = groupby.DataArrayGroupBy _rolling_cls = rolling.DataArrayRolling _coarsen_cls = rolling.DataArrayCoarsen _resample_cls = resample.DataArrayResample _weighted_cls = weighted.DataArrayWeighted dt = utils.UncachedAccessor(CombinedDatetimelikeAccessor) def __init__( self, data: Any = dtypes.NA, coords: Union[Sequence[Tuple], Mapping[Hashable, Any], None] = None, dims: Union[Hashable, Sequence[Hashable], None] = None, name: Hashable = None, attrs: Mapping = None, # internal parameters indexes: Dict[Hashable, pd.Index] = None, fastpath: bool = False, ): if fastpath: variable = data assert dims is None assert attrs is None else: # try to fill in arguments from data if they weren't supplied if coords is None: if isinstance(data, DataArray): coords = data.coords elif isinstance(data, pd.Series): coords = [data.index] elif isinstance(data, pd.DataFrame): coords = [data.index, data.columns] elif isinstance(data, (pd.Index, IndexVariable)): coords = [data] elif isinstance(data, pdcompat.Panel): coords = [data.items, data.major_axis, data.minor_axis] if dims is None: dims = getattr(data, "dims", getattr(coords, "dims", None)) if name is None: name = getattr(data, "name", None) if attrs is None and not isinstance(data, PANDAS_TYPES): attrs = getattr(data, "attrs", None) data = _check_data_shape(data, coords, dims) data = as_compatible_data(data) coords, dims = _infer_coords_and_dims(data.shape, coords, dims) variable = Variable(dims, data, attrs, fastpath=True) indexes = dict( _extract_indexes_from_coords(coords) ) # needed for to_dataset # These fully describe a DataArray self._variable = variable assert isinstance(coords, dict) self._coords = coords self._name = name # TODO(shoyer): document this argument, once it becomes part of the # public interface. self._indexes = indexes self._file_obj = None def _replace( self, variable: Variable = None, coords=None, name: Union[Hashable, None, Default] = _default, indexes=None, ) -> "DataArray": if variable is None: variable = self.variable if coords is None: coords = self._coords if name is _default: name = self.name return type(self)(variable, coords, name=name, fastpath=True, indexes=indexes) def _replace_maybe_drop_dims( self, variable: Variable, name: Union[Hashable, None, Default] = _default ) -> "DataArray": if variable.dims == self.dims and variable.shape == self.shape: coords = self._coords.copy() indexes = self._indexes elif variable.dims == self.dims: # Shape has changed (e.g. from reduce(..., keepdims=True) new_sizes = dict(zip(self.dims, variable.shape)) coords = { k: v for k, v in self._coords.items() if v.shape == tuple(new_sizes[d] for d in v.dims) } changed_dims = [ k for k in variable.dims if variable.sizes[k] != self.sizes[k] ] indexes = propagate_indexes(self._indexes, exclude=changed_dims) else: allowed_dims = set(variable.dims) coords = { k: v for k, v in self._coords.items() if set(v.dims) <= allowed_dims } indexes = propagate_indexes( self._indexes, exclude=(set(self.dims) - allowed_dims) ) return self._replace(variable, coords, name, indexes=indexes) def _overwrite_indexes(self, indexes: Mapping[Hashable, Any]) -> "DataArray": if not len(indexes): return self coords = self._coords.copy() for name, idx in indexes.items(): coords[name] = IndexVariable(name, idx) obj = self._replace(coords=coords) # switch from dimension to level names, if necessary dim_names: Dict[Any, str] = {} for dim, idx in indexes.items(): if not isinstance(idx, pd.MultiIndex) and idx.name != dim: dim_names[dim] = idx.name if dim_names: obj = obj.rename(dim_names) return obj def _to_temp_dataset(self) -> Dataset: return self._to_dataset_whole(name=_THIS_ARRAY, shallow_copy=False) def _from_temp_dataset( self, dataset: Dataset, name: Union[Hashable, None, Default] = _default ) -> "DataArray": variable = dataset._variables.pop(_THIS_ARRAY) coords = dataset._variables indexes = dataset._indexes return self._replace(variable, coords, name, indexes=indexes) def _to_dataset_split(self, dim: Hashable) -> Dataset: """ splits dataarray along dimension 'dim' """ def subset(dim, label): array = self.loc[{dim: label}] array.attrs = {} return as_variable(array) variables = {label: subset(dim, label) for label in self.get_index(dim)} variables.update({k: v for k, v in self._coords.items() if k != dim}) indexes = propagate_indexes(self._indexes, exclude=dim) coord_names = set(self._coords) - {dim} dataset = Dataset._construct_direct( variables, coord_names, indexes=indexes, attrs=self.attrs ) return dataset def _to_dataset_whole( self, name: Hashable = None, shallow_copy: bool = True ) -> Dataset: if name is None: name = self.name if name is None: raise ValueError( "unable to convert unnamed DataArray to a " "Dataset without providing an explicit name" ) if name in self.coords: raise ValueError( "cannot create a Dataset from a DataArray with " "the same name as one of its coordinates" ) # use private APIs for speed: this is called by _to_temp_dataset(), # which is used in the guts of a lot of operations (e.g., reindex) variables = self._coords.copy() variables[name] = self.variable if shallow_copy: for k in variables: variables[k] = variables[k].copy(deep=False) indexes = self._indexes coord_names = set(self._coords) dataset = Dataset._construct_direct(variables, coord_names, indexes=indexes) return dataset def to_dataset( self, dim: Hashable = None, *, name: Hashable = None, promote_attrs: bool = False, ) -> Dataset: """Convert a DataArray to a Dataset. Parameters ---------- dim : hashable, optional Name of the dimension on this array along which to split this array into separate variables. If not provided, this array is converted into a Dataset of one variable. name : hashable, optional Name to substitute for this array's name. Only valid if ``dim`` is not provided. promote_attrs : bool, default: False Set to True to shallow copy attrs of DataArray to returned Dataset. Returns ------- dataset : Dataset """ if dim is not None and dim not in self.dims: raise TypeError( f"{dim} is not a dim. If supplying a ``name``, pass as a kwarg." ) if dim is not None: if name is not None: raise TypeError("cannot supply both dim and name arguments") result = self._to_dataset_split(dim) else: result = self._to_dataset_whole(name) if promote_attrs: result.attrs = dict(self.attrs) return result @property def name(self) -> Optional[Hashable]: """The name of this array.""" return self._name @name.setter def name(self, value: Optional[Hashable]) -> None: self._name = value @property def variable(self) -> Variable: """Low level interface to the Variable object for this DataArray.""" return self._variable @property def dtype(self) -> np.dtype: return self.variable.dtype @property def shape(self) -> Tuple[int, ...]: return self.variable.shape @property def size(self) -> int: return self.variable.size @property def nbytes(self) -> int: return self.variable.nbytes @property def ndim(self) -> int: return self.variable.ndim def __len__(self) -> int: return len(self.variable) @property def data(self) -> Any: """The array's data as a dask or numpy array""" return self.variable.data @data.setter def data(self, value: Any) -> None: self.variable.data = value @property def values(self) -> np.ndarray: """The array's data as a numpy.ndarray""" return self.variable.values @values.setter def values(self, value: Any) -> None: self.variable.values = value @property def _in_memory(self) -> bool: return self.variable._in_memory def to_index(self) -> pd.Index: """Convert this variable to a pandas.Index. Only possible for 1D arrays. """ return self.variable.to_index() @property def dims(self) -> Tuple[Hashable, ...]: """Tuple of dimension names associated with this array. Note that the type of this property is inconsistent with `Dataset.dims`. See `Dataset.sizes` and `DataArray.sizes` for consistently named properties. """ return self.variable.dims @dims.setter def dims(self, value): raise AttributeError( "you cannot assign dims on a DataArray. Use " ".rename() or .swap_dims() instead." ) def _item_key_to_dict(self, key: Any) -> Mapping[Hashable, Any]: if utils.is_dict_like(key): return key else: key = indexing.expanded_indexer(key, self.ndim) return dict(zip(self.dims, key)) @property def _level_coords(self) -> Dict[Hashable, Hashable]: """Return a mapping of all MultiIndex levels and their corresponding coordinate name. """ level_coords: Dict[Hashable, Hashable] = {} for cname, var in self._coords.items(): if var.ndim == 1 and isinstance(var, IndexVariable): level_names = var.level_names if level_names is not None: (dim,) = var.dims level_coords.update({lname: dim for lname in level_names}) return level_coords def _getitem_coord(self, key): from .dataset import _get_virtual_variable try: var = self._coords[key] except KeyError: dim_sizes = dict(zip(self.dims, self.shape)) _, key, var = _get_virtual_variable( self._coords, key, self._level_coords, dim_sizes ) return self._replace_maybe_drop_dims(var, name=key) def __getitem__(self, key: Any) -> "DataArray": if isinstance(key, str): return self._getitem_coord(key) else: # xarray-style array indexing return self.isel(indexers=self._item_key_to_dict(key)) def __setitem__(self, key: Any, value: Any) -> None: if isinstance(key, str): self.coords[key] = value else: # Coordinates in key, value and self[key] should be consistent. # TODO Coordinate consistency in key is checked here, but it # causes unnecessary indexing. It should be optimized. obj = self[key] if isinstance(value, DataArray): assert_coordinate_consistent(value, obj.coords.variables) # DataArray key -> Variable key key = { k: v.variable if isinstance(v, DataArray) else v for k, v in self._item_key_to_dict(key).items() } self.variable[key] = value def __delitem__(self, key: Any) -> None: del self.coords[key] @property def _attr_sources(self) -> List[Mapping[Hashable, Any]]: """List of places to look-up items for attribute-style access""" return self._item_sources + [self.attrs] @property def _item_sources(self) -> List[Mapping[Hashable, Any]]: """List of places to look-up items for key-completion""" return [ self.coords, {d: self.coords[d] for d in self.dims}, LevelCoordinatesSource(self), ] def __contains__(self, key: Any) -> bool: return key in self.data @property def loc(self) -> _LocIndexer: """Attribute for location based indexing like pandas.""" return _LocIndexer(self) @property def attrs(self) -> Dict[Hashable, Any]: """Dictionary storing arbitrary metadata with this array.""" return self.variable.attrs @attrs.setter def attrs(self, value: Mapping[Hashable, Any]) -> None: # Disable type checking to work around mypy bug - see mypy#4167 self.variable.attrs = value # type: ignore @property def encoding(self) -> Dict[Hashable, Any]: """Dictionary of format-specific settings for how this array should be serialized.""" return self.variable.encoding @encoding.setter def encoding(self, value: Mapping[Hashable, Any]) -> None: self.variable.encoding = value @property def indexes(self) -> Indexes: """Mapping of pandas.Index objects used for label based indexing""" if self._indexes is None: self._indexes = default_indexes(self._coords, self.dims) return Indexes(self._indexes) @property def coords(self) -> DataArrayCoordinates: """Dictionary-like container of coordinate arrays.""" return DataArrayCoordinates(self) def reset_coords( self, names: Union[Iterable[Hashable], Hashable, None] = None, drop: bool = False, inplace: bool = None, ) -> Union[None, "DataArray", Dataset]: """Given names of coordinates, reset them to become variables. Parameters ---------- names : hashable or iterable of hashable, optional Name(s) of non-index coordinates in this dataset to reset into variables. By default, all non-index coordinates are reset. drop : bool, optional If True, remove coordinates instead of converting them into variables. Returns ------- Dataset, or DataArray if ``drop == True`` """ _check_inplace(inplace) if names is None: names = set(self.coords) - set(self.dims) dataset = self.coords.to_dataset().reset_coords(names, drop) if drop: return self._replace(coords=dataset._variables) else: if self.name is None: raise ValueError( "cannot reset_coords with drop=False on an unnamed DataArrray" ) dataset[self.name] = self.variable return dataset def __dask_tokenize__(self): from dask.base import normalize_token return normalize_token((type(self), self._variable, self._coords, self._name)) def __dask_graph__(self): return self._to_temp_dataset().__dask_graph__() def __dask_keys__(self): return self._to_temp_dataset().__dask_keys__() def __dask_layers__(self): return self._to_temp_dataset().__dask_layers__() @property def __dask_optimize__(self): return self._to_temp_dataset().__dask_optimize__ @property def __dask_scheduler__(self): return self._to_temp_dataset().__dask_scheduler__ def __dask_postcompute__(self): func, args = self._to_temp_dataset().__dask_postcompute__() return self._dask_finalize, (func, args, self.name) def __dask_postpersist__(self): func, args = self._to_temp_dataset().__dask_postpersist__() return self._dask_finalize, (func, args, self.name) @staticmethod def _dask_finalize(results, func, args, name): ds = func(results, *args) variable = ds._variables.pop(_THIS_ARRAY) coords = ds._variables return DataArray(variable, coords, name=name, fastpath=True) def load(self, **kwargs) -> "DataArray": """Manually trigger loading of this array's data from disk or a remote source into memory and return this array. Normally, it should not be necessary to call this method in user code, because all xarray functions should either work on deferred data or load data automatically. However, this method can be necessary when working with many file objects on disk. Parameters ---------- **kwargs : dict Additional keyword arguments passed on to ``dask.array.compute``. See Also -------- dask.array.compute """ ds = self._to_temp_dataset().load(**kwargs) new = self._from_temp_dataset(ds) self._variable = new._variable self._coords = new._coords return self def compute(self, **kwargs) -> "DataArray": """Manually trigger loading of this array's data from disk or a remote source into memory and return a new array. The original is left unaltered. Normally, it should not be necessary to call this method in user code, because all xarray functions should either work on deferred data or load data automatically. However, this method can be necessary when working with many file objects on disk. Parameters ---------- **kwargs : dict Additional keyword arguments passed on to ``dask.array.compute``. See Also -------- dask.array.compute """ new = self.copy(deep=False) return new.load(**kwargs) def persist(self, **kwargs) -> "DataArray": """Trigger computation in constituent dask arrays This keeps them as dask arrays but encourages them to keep data in memory. This is particularly useful when on a distributed machine. When on a single machine consider using ``.compute()`` instead. Parameters ---------- **kwargs : dict Additional keyword arguments passed on to ``dask.persist``. See Also -------- dask.persist """ ds = self._to_temp_dataset().persist(**kwargs) return self._from_temp_dataset(ds) def copy(self, deep: bool = True, data: Any = None) -> "DataArray": """Returns a copy of this array. If `deep=True`, a deep copy is made of the data array. Otherwise, a shallow copy is made, and the returned data array's values are a new view of this data array's values. Use `data` to create a new object with the same structure as original but entirely new data. Parameters ---------- deep : bool, optional Whether the data array and its coordinates are loaded into memory and copied onto the new object. Default is True. data : array_like, optional Data to use in the new object. Must have same shape as original. When `data` is used, `deep` is ignored for all data variables, and only used for coords. Returns ------- object : DataArray New object with dimensions, attributes, coordinates, name, encoding, and optionally data copied from original. Examples -------- Shallow versus deep copy >>> array = xr.DataArray([1, 2, 3], dims="x", coords={"x": ["a", "b", "c"]}) >>> array.copy() array([1, 2, 3]) Coordinates: * x (x) >> array_0 = array.copy(deep=False) >>> array_0[0] = 7 >>> array_0 array([7, 2, 3]) Coordinates: * x (x) >> array array([7, 2, 3]) Coordinates: * x (x) >> array.copy(data=[0.1, 0.2, 0.3]) array([0.1, 0.2, 0.3]) Coordinates: * x (x) >> array array([7, 2, 3]) Coordinates: * x (x) "DataArray": return self.copy(deep=False) def __deepcopy__(self, memo=None) -> "DataArray": # memo does nothing but is required for compatibility with # copy.deepcopy return self.copy(deep=True) # mutable objects should not be hashable # https://github.com/python/mypy/issues/4266 __hash__ = None # type: ignore @property def chunks(self) -> Optional[Tuple[Tuple[int, ...], ...]]: """Block dimensions for this array's data or None if it's not a dask array. """ return self.variable.chunks def chunk( self, chunks: Union[ None, Number, Tuple[Number, ...], Tuple[Tuple[Number, ...], ...], Mapping[Hashable, Union[None, Number, Tuple[Number, ...]]], ] = None, name_prefix: str = "xarray-", token: str = None, lock: bool = False, ) -> "DataArray": """Coerce this array's data into a dask arrays with the given chunks. If this variable is a non-dask array, it will be converted to dask array. If it's a dask array, it will be rechunked to the given chunk sizes. If neither chunks is not provided for one or more dimensions, chunk sizes along that dimension will not be updated; non-dask arrays will be converted into dask arrays with a single block. Parameters ---------- chunks : int, tuple of int or mapping of hashable to int, optional Chunk sizes along each dimension, e.g., ``5``, ``(5, 5)`` or ``{'x': 5, 'y': 5}``. name_prefix : str, optional Prefix for the name of the new dask array. token : str, optional Token uniquely identifying this array. lock : optional Passed on to :py:func:`dask.array.from_array`, if the array is not already as dask array. Returns ------- chunked : xarray.DataArray """ if isinstance(chunks, (tuple, list)): chunks = dict(zip(self.dims, chunks)) ds = self._to_temp_dataset().chunk( chunks, name_prefix=name_prefix, token=token, lock=lock ) return self._from_temp_dataset(ds) def isel( self, indexers: Mapping[Hashable, Any] = None, drop: bool = False, missing_dims: str = "raise", **indexers_kwargs: Any, ) -> "DataArray": """Return a new DataArray whose data is given by integer indexing along the specified dimension(s). Parameters ---------- indexers : dict, optional A dict with keys matching dimensions and values given by integers, slice objects or arrays. indexer can be a integer, slice, array-like or DataArray. If DataArrays are passed as indexers, xarray-style indexing will be carried out. See :ref:`indexing` for the details. One of indexers or indexers_kwargs must be provided. drop : bool, optional If ``drop=True``, drop coordinates variables indexed by integers instead of making them scalar. missing_dims : {"raise", "warn", "ignore"}, default: "raise" What to do if dimensions that should be selected from are not present in the DataArray: - "raise": raise an exception - "warning": raise a warning, and ignore the missing dimensions - "ignore": ignore the missing dimensions **indexers_kwargs : {dim: indexer, ...}, optional The keyword arguments form of ``indexers``. See Also -------- Dataset.isel DataArray.sel """ indexers = either_dict_or_kwargs(indexers, indexers_kwargs, "isel") if any(is_fancy_indexer(idx) for idx in indexers.values()): ds = self._to_temp_dataset()._isel_fancy( indexers, drop=drop, missing_dims=missing_dims ) return self._from_temp_dataset(ds) # Much faster algorithm for when all indexers are ints, slices, one-dimensional # lists, or zero or one-dimensional np.ndarray's variable = self._variable.isel(indexers, missing_dims=missing_dims) coords = {} for coord_name, coord_value in self._coords.items(): coord_indexers = { k: v for k, v in indexers.items() if k in coord_value.dims } if coord_indexers: coord_value = coord_value.isel(coord_indexers) if drop and coord_value.ndim == 0: continue coords[coord_name] = coord_value return self._replace(variable=variable, coords=coords) def sel( self, indexers: Mapping[Hashable, Any] = None, method: str = None, tolerance=None, drop: bool = False, **indexers_kwargs: Any, ) -> "DataArray": """Return a new DataArray whose data is given by selecting index labels along the specified dimension(s). In contrast to `DataArray.isel`, indexers for this method should use labels instead of integers. Under the hood, this method is powered by using pandas's powerful Index objects. This makes label based indexing essentially just as fast as using integer indexing. It also means this method uses pandas's (well documented) logic for indexing. This means you can use string shortcuts for datetime indexes (e.g., '2000-01' to select all values in January 2000). It also means that slices are treated as inclusive of both the start and stop values, unlike normal Python indexing. .. warning:: Do not try to assign values when using any of the indexing methods ``isel`` or ``sel``:: da = xr.DataArray([0, 1, 2, 3], dims=['x']) # DO NOT do this da.isel(x=[0, 1, 2])[1] = -1 Assigning values with the chained indexing using ``.sel`` or ``.isel`` fails silently. Parameters ---------- indexers : dict, optional A dict with keys matching dimensions and values given by scalars, slices or arrays of tick labels. For dimensions with multi-index, the indexer may also be a dict-like object with keys matching index level names. If DataArrays are passed as indexers, xarray-style indexing will be carried out. See :ref:`indexing` for the details. One of indexers or indexers_kwargs must be provided. method : {None, "nearest", "pad", "ffill", "backfill", "bfill"}, optional Method to use for inexact matches: * None (default): only exact matches * pad / ffill: propagate last valid index value forward * backfill / bfill: propagate next valid index value backward * nearest: use nearest valid index value tolerance : optional Maximum distance between original and new labels for inexact matches. The values of the index at the matching locations must satisfy the equation ``abs(index[indexer] - target) <= tolerance``. drop : bool, optional If ``drop=True``, drop coordinates variables in `indexers` instead of making them scalar. **indexers_kwargs : {dim: indexer, ...}, optional The keyword arguments form of ``indexers``. One of indexers or indexers_kwargs must be provided. Returns ------- obj : DataArray A new DataArray with the same contents as this DataArray, except the data and each dimension is indexed by the appropriate indexers. If indexer DataArrays have coordinates that do not conflict with this object, then these coordinates will be attached. In general, each array's data will be a view of the array's data in this DataArray, unless vectorized indexing was triggered by using an array indexer, in which case the data will be a copy. See Also -------- Dataset.sel DataArray.isel """ ds = self._to_temp_dataset().sel( indexers=indexers, drop=drop, method=method, tolerance=tolerance, **indexers_kwargs, ) return self._from_temp_dataset(ds) def head( self, indexers: Union[Mapping[Hashable, int], int] = None, **indexers_kwargs: Any, ) -> "DataArray": """Return a new DataArray whose data is given by the the first `n` values along the specified dimension(s). Default `n` = 5 See Also -------- Dataset.head DataArray.tail DataArray.thin """ ds = self._to_temp_dataset().head(indexers, **indexers_kwargs) return self._from_temp_dataset(ds) def tail( self, indexers: Union[Mapping[Hashable, int], int] = None, **indexers_kwargs: Any, ) -> "DataArray": """Return a new DataArray whose data is given by the the last `n` values along the specified dimension(s). Default `n` = 5 See Also -------- Dataset.tail DataArray.head DataArray.thin """ ds = self._to_temp_dataset().tail(indexers, **indexers_kwargs) return self._from_temp_dataset(ds) def thin( self, indexers: Union[Mapping[Hashable, int], int] = None, **indexers_kwargs: Any, ) -> "DataArray": """Return a new DataArray whose data is given by each `n` value along the specified dimension(s). See Also -------- Dataset.thin DataArray.head DataArray.tail """ ds = self._to_temp_dataset().thin(indexers, **indexers_kwargs) return self._from_temp_dataset(ds) def broadcast_like( self, other: Union["DataArray", Dataset], exclude: Iterable[Hashable] = None ) -> "DataArray": """Broadcast this DataArray against another Dataset or DataArray. This is equivalent to xr.broadcast(other, self)[1] xarray objects are broadcast against each other in arithmetic operations, so this method is not be necessary for most uses. If no change is needed, the input data is returned to the output without being copied. If new coords are added by the broadcast, their values are NaN filled. Parameters ---------- other : Dataset or DataArray Object against which to broadcast this array. exclude : iterable of hashable, optional Dimensions that must not be broadcasted Returns ------- new_da : DataArray The caller broadcasted against ``other``. Examples -------- >>> arr1 = xr.DataArray( ... np.random.randn(2, 3), ... dims=("x", "y"), ... coords={"x": ["a", "b"], "y": ["a", "b", "c"]}, ... ) >>> arr2 = xr.DataArray( ... np.random.randn(3, 2), ... dims=("x", "y"), ... coords={"x": ["a", "b", "c"], "y": ["a", "b"]}, ... ) >>> arr1 array([[ 1.76405235, 0.40015721, 0.97873798], [ 2.2408932 , 1.86755799, -0.97727788]]) Coordinates: * x (x) >> arr2 array([[ 0.95008842, -0.15135721], [-0.10321885, 0.4105985 ], [ 0.14404357, 1.45427351]]) Coordinates: * x (x) >> arr1.broadcast_like(arr2) array([[ 1.76405235, 0.40015721, 0.97873798], [ 2.2408932 , 1.86755799, -0.97727788], [ nan, nan, nan]]) Coordinates: * x (x) object 'a' 'b' 'c' * y (y) object 'a' 'b' 'c' """ if exclude is None: exclude = set() else: exclude = set(exclude) args = align(other, self, join="outer", copy=False, exclude=exclude) dims_map, common_coords = _get_broadcast_dims_map_common_coords(args, exclude) return _broadcast_helper(args[1], exclude, dims_map, common_coords) def reindex_like( self, other: Union["DataArray", Dataset], method: str = None, tolerance=None, copy: bool = True, fill_value=dtypes.NA, ) -> "DataArray": """Conform this object onto the indexes of another object, filling in missing values with ``fill_value``. The default fill value is NaN. Parameters ---------- other : Dataset or DataArray Object with an 'indexes' attribute giving a mapping from dimension names to pandas.Index objects, which provides coordinates upon which to index the variables in this dataset. The indexes on this other object need not be the same as the indexes on this dataset. Any mis-matched index values will be filled in with NaN, and any mis-matched dimension names will simply be ignored. method : {None, "nearest", "pad", "ffill", "backfill", "bfill"}, optional Method to use for filling index values from other not found on this data array: * None (default): don't fill gaps * pad / ffill: propagate last valid index value forward * backfill / bfill: propagate next valid index value backward * nearest: use nearest valid index value tolerance : optional Maximum distance between original and new labels for inexact matches. The values of the index at the matching locations must satisfy the equation ``abs(index[indexer] - target) <= tolerance``. copy : bool, optional If ``copy=True``, data in the return value is always copied. If ``copy=False`` and reindexing is unnecessary, or can be performed with only slice operations, then the output may share memory with the input. In either case, a new xarray object is always returned. fill_value : scalar or dict-like, optional Value to use for newly missing values. If a dict-like, maps variable names (including coordinates) to fill values. Use this data array's name to refer to the data array's values. Returns ------- reindexed : DataArray Another dataset array, with this array's data but coordinates from the other object. See Also -------- DataArray.reindex align """ indexers = reindex_like_indexers(self, other) return self.reindex( indexers=indexers, method=method, tolerance=tolerance, copy=copy, fill_value=fill_value, ) def reindex( self, indexers: Mapping[Hashable, Any] = None, method: str = None, tolerance=None, copy: bool = True, fill_value=dtypes.NA, **indexers_kwargs: Any, ) -> "DataArray": """Conform this object onto the indexes of another object, filling in missing values with ``fill_value``. The default fill value is NaN. Parameters ---------- indexers : dict, optional Dictionary with keys given by dimension names and values given by arrays of coordinates tick labels. Any mis-matched coordinate values will be filled in with NaN, and any mis-matched dimension names will simply be ignored. One of indexers or indexers_kwargs must be provided. copy : bool, optional If ``copy=True``, data in the return value is always copied. If ``copy=False`` and reindexing is unnecessary, or can be performed with only slice operations, then the output may share memory with the input. In either case, a new xarray object is always returned. method : {None, 'nearest', 'pad'/'ffill', 'backfill'/'bfill'}, optional Method to use for filling index values in ``indexers`` not found on this data array: * None (default): don't fill gaps * pad / ffill: propagate last valid index value forward * backfill / bfill: propagate next valid index value backward * nearest: use nearest valid index value tolerance : optional Maximum distance between original and new labels for inexact matches. The values of the index at the matching locations must satisfy the equation ``abs(index[indexer] - target) <= tolerance``. fill_value : scalar or dict-like, optional Value to use for newly missing values. If a dict-like, maps variable names (including coordinates) to fill values. Use this data array's name to refer to the data array's values. **indexers_kwargs : {dim: indexer, ...}, optional The keyword arguments form of ``indexers``. One of indexers or indexers_kwargs must be provided. Returns ------- reindexed : DataArray Another dataset array, with this array's data but replaced coordinates. See Also -------- DataArray.reindex_like align """ indexers = either_dict_or_kwargs(indexers, indexers_kwargs, "reindex") if isinstance(fill_value, dict): fill_value = fill_value.copy() sentinel = object() value = fill_value.pop(self.name, sentinel) if value is not sentinel: fill_value[_THIS_ARRAY] = value ds = self._to_temp_dataset().reindex( indexers=indexers, method=method, tolerance=tolerance, copy=copy, fill_value=fill_value, ) return self._from_temp_dataset(ds) def interp( self, coords: Mapping[Hashable, Any] = None, method: str = "linear", assume_sorted: bool = False, kwargs: Mapping[str, Any] = None, **coords_kwargs: Any, ) -> "DataArray": """Multidimensional interpolation of variables. Parameters ---------- coords : dict, optional Mapping from dimension names to the new coordinates. New coordinate can be an scalar, array-like or DataArray. If DataArrays are passed as new coordinates, their dimensions are used for the broadcasting. Missing values are skipped. method : str, default: "linear" The method used to interpolate. Choose from - {"linear", "nearest"} for multidimensional array, - {"linear", "nearest", "zero", "slinear", "quadratic", "cubic"} for 1-dimensional array. assume_sorted : bool, optional If False, values of x can be in any order and they are sorted first. If True, x has to be an array of monotonically increasing values. kwargs : dict Additional keyword arguments passed to scipy's interpolator. Valid options and their behavior depend on if 1-dimensional or multi-dimensional interpolation is used. **coords_kwargs : {dim: coordinate, ...}, optional The keyword arguments form of ``coords``. One of coords or coords_kwargs must be provided. Returns ------- interpolated : DataArray New dataarray on the new coordinates. Notes ----- scipy is required. See Also -------- scipy.interpolate.interp1d scipy.interpolate.interpn Examples -------- >>> da = xr.DataArray( ... data=[[1, 4, 2, 9], [2, 7, 6, np.nan], [6, np.nan, 5, 8]], ... dims=("x", "y"), ... coords={"x": [0, 1, 2], "y": [10, 12, 14, 16]}, ... ) >>> da array([[ 1., 4., 2., 9.], [ 2., 7., 6., nan], [ 6., nan, 5., 8.]]) Coordinates: * x (x) int64 0 1 2 * y (y) int64 10 12 14 16 1D linear interpolation (the default): >>> da.interp(x=[0, 0.75, 1.25, 1.75]) array([[1. , 4. , 2. , nan], [1.75, 6.25, 5. , nan], [3. , nan, 5.75, nan], [5. , nan, 5.25, nan]]) Coordinates: * y (y) int64 10 12 14 16 * x (x) float64 0.0 0.75 1.25 1.75 1D nearest interpolation: >>> da.interp(x=[0, 0.75, 1.25, 1.75], method="nearest") array([[ 1., 4., 2., 9.], [ 2., 7., 6., nan], [ 2., 7., 6., nan], [ 6., nan, 5., 8.]]) Coordinates: * y (y) int64 10 12 14 16 * x (x) float64 0.0 0.75 1.25 1.75 1D linear extrapolation: >>> da.interp( ... x=[1, 1.5, 2.5, 3.5], ... method="linear", ... kwargs={"fill_value": "extrapolate"}, ... ) array([[ 2. , 7. , 6. , nan], [ 4. , nan, 5.5, nan], [ 8. , nan, 4.5, nan], [12. , nan, 3.5, nan]]) Coordinates: * y (y) int64 10 12 14 16 * x (x) float64 1.0 1.5 2.5 3.5 2D linear interpolation: >>> da.interp(x=[0, 0.75, 1.25, 1.75], y=[11, 13, 15], method="linear") array([[2.5 , 3. , nan], [4. , 5.625, nan], [ nan, nan, nan], [ nan, nan, nan]]) Coordinates: * x (x) float64 0.0 0.75 1.25 1.75 * y (y) int64 11 13 15 """ if self.dtype.kind not in "uifc": raise TypeError( "interp only works for a numeric type array. " "Given {}.".format(self.dtype) ) ds = self._to_temp_dataset().interp( coords, method=method, kwargs=kwargs, assume_sorted=assume_sorted, **coords_kwargs, ) return self._from_temp_dataset(ds) def interp_like( self, other: Union["DataArray", Dataset], method: str = "linear", assume_sorted: bool = False, kwargs: Mapping[str, Any] = None, ) -> "DataArray": """Interpolate this object onto the coordinates of another object, filling out of range values with NaN. Parameters ---------- other : Dataset or DataArray Object with an 'indexes' attribute giving a mapping from dimension names to an 1d array-like, which provides coordinates upon which to index the variables in this dataset. Missing values are skipped. method : str, default: "linear" The method used to interpolate. Choose from - {"linear", "nearest"} for multidimensional array, - {"linear", "nearest", "zero", "slinear", "quadratic", "cubic"} for 1-dimensional array. assume_sorted : bool, optional If False, values of coordinates that are interpolated over can be in any order and they are sorted first. If True, interpolated coordinates are assumed to be an array of monotonically increasing values. kwargs : dict, optional Additional keyword passed to scipy's interpolator. Returns ------- interpolated : DataArray Another dataarray by interpolating this dataarray's data along the coordinates of the other object. Notes ----- scipy is required. If the dataarray has object-type coordinates, reindex is used for these coordinates instead of the interpolation. See Also -------- DataArray.interp DataArray.reindex_like """ if self.dtype.kind not in "uifc": raise TypeError( "interp only works for a numeric type array. " "Given {}.".format(self.dtype) ) ds = self._to_temp_dataset().interp_like( other, method=method, kwargs=kwargs, assume_sorted=assume_sorted ) return self._from_temp_dataset(ds) def rename( self, new_name_or_name_dict: Union[Hashable, Mapping[Hashable, Hashable]] = None, **names: Hashable, ) -> "DataArray": """Returns a new DataArray with renamed coordinates or a new name. Parameters ---------- new_name_or_name_dict : str or dict-like, optional If the argument is dict-like, it used as a mapping from old names to new names for coordinates. Otherwise, use the argument as the new name for this array. **names : hashable, optional The keyword arguments form of a mapping from old names to new names for coordinates. One of new_name_or_name_dict or names must be provided. Returns ------- renamed : DataArray Renamed array or array with renamed coordinates. See Also -------- Dataset.rename DataArray.swap_dims """ if names or utils.is_dict_like(new_name_or_name_dict): new_name_or_name_dict = cast( Mapping[Hashable, Hashable], new_name_or_name_dict ) name_dict = either_dict_or_kwargs(new_name_or_name_dict, names, "rename") dataset = self._to_temp_dataset().rename(name_dict) return self._from_temp_dataset(dataset) else: new_name_or_name_dict = cast(Hashable, new_name_or_name_dict) return self._replace(name=new_name_or_name_dict) def swap_dims(self, dims_dict: Mapping[Hashable, Hashable]) -> "DataArray": """Returns a new DataArray with swapped dimensions. Parameters ---------- dims_dict : dict-like Dictionary whose keys are current dimension names and whose values are new names. Returns ------- swapped : DataArray DataArray with swapped dimensions. Examples -------- >>> arr = xr.DataArray( ... data=[0, 1], ... dims="x", ... coords={"x": ["a", "b"], "y": ("x", [0, 1])}, ... ) >>> arr array([0, 1]) Coordinates: * x (x) >> arr.swap_dims({"x": "y"}) array([0, 1]) Coordinates: x (y) >> arr.swap_dims({"x": "z"}) array([0, 1]) Coordinates: x (z) "DataArray": """Return a new object with an additional axis (or axes) inserted at the corresponding position in the array shape. The new object is a view into the underlying array, not a copy. If dim is already a scalar coordinate, it will be promoted to a 1D coordinate consisting of a single value. Parameters ---------- dim : hashable, sequence of hashable, dict, or None, optional Dimensions to include on the new variable. If provided as str or sequence of str, then dimensions are inserted with length 1. If provided as a dict, then the keys are the new dimensions and the values are either integers (giving the length of the new dimensions) or sequence/ndarray (giving the coordinates of the new dimensions). axis : int, list of int or tuple of int, or None, default: None Axis position(s) where new axis is to be inserted (position(s) on the result array). If a list (or tuple) of integers is passed, multiple axes are inserted. In this case, dim arguments should be same length list. If axis=None is passed, all the axes will be inserted to the start of the result array. **dim_kwargs : int or sequence or ndarray The keywords are arbitrary dimensions being inserted and the values are either the lengths of the new dims (if int is given), or their coordinates. Note, this is an alternative to passing a dict to the dim kwarg and will only be used if dim is None. Returns ------- expanded : same type as caller This object, but with an additional dimension(s). """ if isinstance(dim, int): raise TypeError("dim should be hashable or sequence/mapping of hashables") elif isinstance(dim, Sequence) and not isinstance(dim, str): if len(dim) != len(set(dim)): raise ValueError("dims should not contain duplicate values.") dim = dict.fromkeys(dim, 1) elif dim is not None and not isinstance(dim, Mapping): dim = {cast(Hashable, dim): 1} dim = either_dict_or_kwargs(dim, dim_kwargs, "expand_dims") ds = self._to_temp_dataset().expand_dims(dim, axis) return self._from_temp_dataset(ds) def set_index( self, indexes: Mapping[Hashable, Union[Hashable, Sequence[Hashable]]] = None, append: bool = False, inplace: bool = None, **indexes_kwargs: Union[Hashable, Sequence[Hashable]], ) -> Optional["DataArray"]: """Set DataArray (multi-)indexes using one or more existing coordinates. Parameters ---------- indexes : {dim: index, ...} Mapping from names matching dimensions and values given by (lists of) the names of existing coordinates or variables to set as new (multi-)index. append : bool, optional If True, append the supplied index(es) to the existing index(es). Otherwise replace the existing index(es) (default). **indexes_kwargs : optional The keyword arguments form of ``indexes``. One of indexes or indexes_kwargs must be provided. Returns ------- obj : DataArray Another DataArray, with this data but replaced coordinates. Examples -------- >>> arr = xr.DataArray( ... data=np.ones((2, 3)), ... dims=["x", "y"], ... coords={"x": range(2), "y": range(3), "a": ("x", [3, 4])}, ... ) >>> arr array([[1., 1., 1.], [1., 1., 1.]]) Coordinates: * x (x) int64 0 1 * y (y) int64 0 1 2 a (x) int64 3 4 >>> arr.set_index(x="a") array([[1., 1., 1.], [1., 1., 1.]]) Coordinates: * x (x) int64 3 4 * y (y) int64 0 1 2 See Also -------- DataArray.reset_index """ ds = self._to_temp_dataset().set_index( indexes, append=append, inplace=inplace, **indexes_kwargs ) return self._from_temp_dataset(ds) def reset_index( self, dims_or_levels: Union[Hashable, Sequence[Hashable]], drop: bool = False, inplace: bool = None, ) -> Optional["DataArray"]: """Reset the specified index(es) or multi-index level(s). Parameters ---------- dims_or_levels : hashable or sequence of hashable Name(s) of the dimension(s) and/or multi-index level(s) that will be reset. drop : bool, optional If True, remove the specified indexes and/or multi-index levels instead of extracting them as new coordinates (default: False). Returns ------- obj : DataArray Another dataarray, with this dataarray's data but replaced coordinates. See Also -------- DataArray.set_index """ _check_inplace(inplace) coords, _ = split_indexes( dims_or_levels, self._coords, set(), self._level_coords, drop=drop ) return self._replace(coords=coords) def reorder_levels( self, dim_order: Mapping[Hashable, Sequence[int]] = None, inplace: bool = None, **dim_order_kwargs: Sequence[int], ) -> "DataArray": """Rearrange index levels using input order. Parameters ---------- dim_order : optional Mapping from names matching dimensions and values given by lists representing new level orders. Every given dimension must have a multi-index. **dim_order_kwargs : optional The keyword arguments form of ``dim_order``. One of dim_order or dim_order_kwargs must be provided. Returns ------- obj : DataArray Another dataarray, with this dataarray's data but replaced coordinates. """ _check_inplace(inplace) dim_order = either_dict_or_kwargs(dim_order, dim_order_kwargs, "reorder_levels") replace_coords = {} for dim, order in dim_order.items(): coord = self._coords[dim] index = coord.to_index() if not isinstance(index, pd.MultiIndex): raise ValueError("coordinate %r has no MultiIndex" % dim) replace_coords[dim] = IndexVariable(coord.dims, index.reorder_levels(order)) coords = self._coords.copy() coords.update(replace_coords) return self._replace(coords=coords) def stack( self, dimensions: Mapping[Hashable, Sequence[Hashable]] = None, **dimensions_kwargs: Sequence[Hashable], ) -> "DataArray": """ Stack any number of existing dimensions into a single new dimension. New dimensions will be added at the end, and the corresponding coordinate variables will be combined into a MultiIndex. Parameters ---------- dimensions : mapping of hashable to sequence of hashable Mapping of the form `new_name=(dim1, dim2, ...)`. Names of new dimensions, and the existing dimensions that they replace. An ellipsis (`...`) will be replaced by all unlisted dimensions. Passing a list containing an ellipsis (`stacked_dim=[...]`) will stack over all dimensions. **dimensions_kwargs The keyword arguments form of ``dimensions``. One of dimensions or dimensions_kwargs must be provided. Returns ------- stacked : DataArray DataArray with stacked data. Examples -------- >>> arr = xr.DataArray( ... np.arange(6).reshape(2, 3), ... coords=[("x", ["a", "b"]), ("y", [0, 1, 2])], ... ) >>> arr array([[0, 1, 2], [3, 4, 5]]) Coordinates: * x (x) >> stacked = arr.stack(z=("x", "y")) >>> stacked.indexes["z"] MultiIndex([('a', 0), ('a', 1), ('a', 2), ('b', 0), ('b', 1), ('b', 2)], names=['x', 'y']) See Also -------- DataArray.unstack """ ds = self._to_temp_dataset().stack(dimensions, **dimensions_kwargs) return self._from_temp_dataset(ds) def unstack( self, dim: Union[Hashable, Sequence[Hashable], None] = None, fill_value: Any = dtypes.NA, sparse: bool = False, ) -> "DataArray": """ Unstack existing dimensions corresponding to MultiIndexes into multiple new dimensions. New dimensions will be added at the end. Parameters ---------- dim : hashable or sequence of hashable, optional Dimension(s) over which to unstack. By default unstacks all MultiIndexes. fill_value : scalar or dict-like, default: nan value to be filled. If a dict-like, maps variable names to fill values. Use the data array's name to refer to its name. If not provided or if the dict-like does not contain all variables, the dtype's NA value will be used. sparse : bool, default: False use sparse-array if True Returns ------- unstacked : DataArray Array with unstacked data. Examples -------- >>> arr = xr.DataArray( ... np.arange(6).reshape(2, 3), ... coords=[("x", ["a", "b"]), ("y", [0, 1, 2])], ... ) >>> arr array([[0, 1, 2], [3, 4, 5]]) Coordinates: * x (x) >> stacked = arr.stack(z=("x", "y")) >>> stacked.indexes["z"] MultiIndex([('a', 0), ('a', 1), ('a', 2), ('b', 0), ('b', 1), ('b', 2)], names=['x', 'y']) >>> roundtripped = stacked.unstack() >>> arr.identical(roundtripped) True See Also -------- DataArray.stack """ ds = self._to_temp_dataset().unstack(dim, fill_value, sparse) return self._from_temp_dataset(ds) def to_unstacked_dataset(self, dim, level=0): """Unstack DataArray expanding to Dataset along a given level of a stacked coordinate. This is the inverse operation of Dataset.to_stacked_array. Parameters ---------- dim : str Name of existing dimension to unstack level : int or str The MultiIndex level to expand to a dataset along. Can either be the integer index of the level or its name. label : int, default: 0 Label of the level to expand dataset along. Overrides the label argument if given. Returns ------- unstacked: Dataset Examples -------- >>> import xarray as xr >>> arr = xr.DataArray( ... np.arange(6).reshape(2, 3), ... coords=[("x", ["a", "b"]), ("y", [0, 1, 2])], ... ) >>> data = xr.Dataset({"a": arr, "b": arr.isel(y=0)}) >>> data Dimensions: (x: 2, y: 3) Coordinates: * x (x) >> stacked = data.to_stacked_array("z", ["x"]) >>> stacked.indexes["z"] MultiIndex([('a', 0.0), ('a', 1.0), ('a', 2.0), ('b', nan)], names=['variable', 'y']) >>> roundtripped = stacked.to_unstacked_dataset(dim="z") >>> data.identical(roundtripped) True See Also -------- Dataset.to_stacked_array """ idx = self.indexes[dim] if not isinstance(idx, pd.MultiIndex): raise ValueError(f"'{dim}' is not a stacked coordinate") level_number = idx._get_level_number(level) variables = idx.levels[level_number] variable_dim = idx.names[level_number] # pull variables out of datarray data_dict = {} for k in variables: data_dict[k] = self.sel({variable_dim: k}, drop=True).squeeze(drop=True) # unstacked dataset return Dataset(data_dict) def transpose(self, *dims: Hashable, transpose_coords: bool = True) -> "DataArray": """Return a new DataArray object with transposed dimensions. Parameters ---------- *dims : hashable, optional By default, reverse the dimensions. Otherwise, reorder the dimensions to this order. transpose_coords : bool, default: True If True, also transpose the coordinates of this DataArray. Returns ------- transposed : DataArray The returned DataArray's array is transposed. Notes ----- This operation returns a view of this array's data. It is lazy for dask-backed DataArrays but not for numpy-backed DataArrays -- the data will be fully loaded. See Also -------- numpy.transpose Dataset.transpose """ if dims: dims = tuple(utils.infix_dims(dims, self.dims)) variable = self.variable.transpose(*dims) if transpose_coords: coords: Dict[Hashable, Variable] = {} for name, coord in self.coords.items(): coord_dims = tuple(dim for dim in dims if dim in coord.dims) coords[name] = coord.variable.transpose(*coord_dims) return self._replace(variable, coords) else: return self._replace(variable) @property def T(self) -> "DataArray": return self.transpose() def drop_vars( self, names: Union[Hashable, Iterable[Hashable]], *, errors: str = "raise" ) -> "DataArray": """Returns an array with dropped variables. Parameters ---------- names : hashable or iterable of hashable Name(s) of variables to drop. errors: {"raise", "ignore"}, optional If 'raise' (default), raises a ValueError error if any of the variable passed are not in the dataset. If 'ignore', any given names that are in the DataArray are dropped and no error is raised. Returns ------- dropped : Dataset New Dataset copied from `self` with variables removed. """ ds = self._to_temp_dataset().drop_vars(names, errors=errors) return self._from_temp_dataset(ds) def drop( self, labels: Mapping = None, dim: Hashable = None, *, errors: str = "raise", **labels_kwargs, ) -> "DataArray": """Backward compatible method based on `drop_vars` and `drop_sel` Using either `drop_vars` or `drop_sel` is encouraged See Also -------- DataArray.drop_vars DataArray.drop_sel """ ds = self._to_temp_dataset().drop(labels, dim, errors=errors) return self._from_temp_dataset(ds) def drop_sel( self, labels: Mapping[Hashable, Any] = None, *, errors: str = "raise", **labels_kwargs, ) -> "DataArray": """Drop index labels from this DataArray. Parameters ---------- labels : mapping of hashable to Any Index labels to drop errors : {"raise", "ignore"}, optional If 'raise' (default), raises a ValueError error if any of the index labels passed are not in the dataset. If 'ignore', any given labels that are in the dataset are dropped and no error is raised. **labels_kwargs : {dim: label, ...}, optional The keyword arguments form of ``dim`` and ``labels`` Returns ------- dropped : DataArray """ if labels_kwargs or isinstance(labels, dict): labels = either_dict_or_kwargs(labels, labels_kwargs, "drop") ds = self._to_temp_dataset().drop_sel(labels, errors=errors) return self._from_temp_dataset(ds) def dropna( self, dim: Hashable, how: str = "any", thresh: int = None ) -> "DataArray": """Returns a new array with dropped labels for missing values along the provided dimension. Parameters ---------- dim : hashable Dimension along which to drop missing values. Dropping along multiple dimensions simultaneously is not yet supported. how : {"any", "all"}, optional * any : if any NA values are present, drop that label * all : if all values are NA, drop that label thresh : int, default: None If supplied, require this many non-NA values. Returns ------- DataArray """ ds = self._to_temp_dataset().dropna(dim, how=how, thresh=thresh) return self._from_temp_dataset(ds) def fillna(self, value: Any) -> "DataArray": """Fill missing values in this object. This operation follows the normal broadcasting and alignment rules that xarray uses for binary arithmetic, except the result is aligned to this object (``join='left'``) instead of aligned to the intersection of index coordinates (``join='inner'``). Parameters ---------- value : scalar, ndarray or DataArray Used to fill all matching missing values in this array. If the argument is a DataArray, it is first aligned with (reindexed to) this array. Returns ------- DataArray """ if utils.is_dict_like(value): raise TypeError( "cannot provide fill value as a dictionary with " "fillna on a DataArray" ) out = ops.fillna(self, value) return out def interpolate_na( self, dim: Hashable = None, method: str = "linear", limit: int = None, use_coordinate: Union[bool, str] = True, max_gap: Union[ int, float, str, pd.Timedelta, np.timedelta64, datetime.timedelta ] = None, keep_attrs: bool = None, **kwargs: Any, ) -> "DataArray": """Fill in NaNs by interpolating according to different methods. Parameters ---------- dim : str Specifies the dimension along which to interpolate. method : str, optional String indicating which method to use for interpolation: - 'linear': linear interpolation (Default). Additional keyword arguments are passed to :py:func:`numpy.interp` - 'nearest', 'zero', 'slinear', 'quadratic', 'cubic', 'polynomial': are passed to :py:func:`scipy.interpolate.interp1d`. If ``method='polynomial'``, the ``order`` keyword argument must also be provided. - 'barycentric', 'krog', 'pchip', 'spline', 'akima': use their respective :py:class:`scipy.interpolate` classes. use_coordinate : bool or str, default: True Specifies which index to use as the x values in the interpolation formulated as `y = f(x)`. If False, values are treated as if eqaully-spaced along ``dim``. If True, the IndexVariable `dim` is used. If ``use_coordinate`` is a string, it specifies the name of a coordinate variariable to use as the index. limit : int, default: None Maximum number of consecutive NaNs to fill. Must be greater than 0 or None for no limit. This filling is done regardless of the size of the gap in the data. To only interpolate over gaps less than a given length, see ``max_gap``. max_gap: int, float, str, pandas.Timedelta, numpy.timedelta64, datetime.timedelta, default: None Maximum size of gap, a continuous sequence of NaNs, that will be filled. Use None for no limit. When interpolating along a datetime64 dimension and ``use_coordinate=True``, ``max_gap`` can be one of the following: - a string that is valid input for pandas.to_timedelta - a :py:class:`numpy.timedelta64` object - a :py:class:`pandas.Timedelta` object - a :py:class:`datetime.timedelta` object Otherwise, ``max_gap`` must be an int or a float. Use of ``max_gap`` with unlabeled dimensions has not been implemented yet. Gap length is defined as the difference between coordinate values at the first data point after a gap and the last value before a gap. For gaps at the beginning (end), gap length is defined as the difference between coordinate values at the first (last) valid data point and the first (last) NaN. For example, consider:: array([nan, nan, nan, 1., nan, nan, 4., nan, nan]) Coordinates: * x (x) int64 0 1 2 3 4 5 6 7 8 The gap lengths are 3-0 = 3; 6-3 = 3; and 8-6 = 2 respectively keep_attrs : bool, default: True If True, the dataarray's attributes (`attrs`) will be copied from the original object to the new one. If False, the new object will be returned without attributes. kwargs : dict, optional parameters passed verbatim to the underlying interpolation function Returns ------- interpolated: DataArray Filled in DataArray. See also -------- numpy.interp scipy.interpolate Examples -------- >>> da = xr.DataArray( ... [np.nan, 2, 3, np.nan, 0], dims="x", coords={"x": [0, 1, 2, 3, 4]} ... ) >>> da array([nan, 2., 3., nan, 0.]) Coordinates: * x (x) int64 0 1 2 3 4 >>> da.interpolate_na(dim="x", method="linear") array([nan, 2. , 3. , 1.5, 0. ]) Coordinates: * x (x) int64 0 1 2 3 4 >>> da.interpolate_na(dim="x", method="linear", fill_value="extrapolate") array([1. , 2. , 3. , 1.5, 0. ]) Coordinates: * x (x) int64 0 1 2 3 4 """ from .missing import interp_na return interp_na( self, dim=dim, method=method, limit=limit, use_coordinate=use_coordinate, max_gap=max_gap, keep_attrs=keep_attrs, **kwargs, ) def ffill(self, dim: Hashable, limit: int = None) -> "DataArray": """Fill NaN values by propogating values forward *Requires bottleneck.* Parameters ---------- dim : hashable Specifies the dimension along which to propagate values when filling. limit : int, default: None The maximum number of consecutive NaN values to forward fill. In other words, if there is a gap with more than this number of consecutive NaNs, it will only be partially filled. Must be greater than 0 or None for no limit. Returns ------- DataArray """ from .missing import ffill return ffill(self, dim, limit=limit) def bfill(self, dim: Hashable, limit: int = None) -> "DataArray": """Fill NaN values by propogating values backward *Requires bottleneck.* Parameters ---------- dim : str Specifies the dimension along which to propagate values when filling. limit : int, default: None The maximum number of consecutive NaN values to backward fill. In other words, if there is a gap with more than this number of consecutive NaNs, it will only be partially filled. Must be greater than 0 or None for no limit. Returns ------- DataArray """ from .missing import bfill return bfill(self, dim, limit=limit) def combine_first(self, other: "DataArray") -> "DataArray": """Combine two DataArray objects, with union of coordinates. This operation follows the normal broadcasting and alignment rules of ``join='outer'``. Default to non-null values of array calling the method. Use np.nan to fill in vacant cells after alignment. Parameters ---------- other : DataArray Used to fill all matching missing values in this array. Returns ------- DataArray """ return ops.fillna(self, other, join="outer") def reduce( self, func: Callable[..., Any], dim: Union[None, Hashable, Sequence[Hashable]] = None, axis: Union[None, int, Sequence[int]] = None, keep_attrs: bool = None, keepdims: bool = False, **kwargs: Any, ) -> "DataArray": """Reduce this array by applying `func` along some dimension(s). Parameters ---------- func : callable Function which can be called in the form `f(x, axis=axis, **kwargs)` to return the result of reducing an np.ndarray over an integer valued axis. dim : hashable or sequence of hashable, optional Dimension(s) over which to apply `func`. axis : int or sequence of int, optional Axis(es) over which to repeatedly apply `func`. Only one of the 'dim' and 'axis' arguments can be supplied. If neither are supplied, then the reduction is calculated over the flattened array (by calling `f(x)` without an axis argument). keep_attrs : bool, optional If True, the variable's attributes (`attrs`) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes. keepdims : bool, default: False If True, the dimensions which are reduced are left in the result as dimensions of size one. Coordinates that use these dimensions are removed. **kwargs : dict Additional keyword arguments passed on to `func`. Returns ------- reduced : DataArray DataArray with this object's array replaced with an array with summarized data and the indicated dimension(s) removed. """ var = self.variable.reduce(func, dim, axis, keep_attrs, keepdims, **kwargs) return self._replace_maybe_drop_dims(var) def to_pandas(self) -> Union["DataArray", pd.Series, pd.DataFrame]: """Convert this array into a pandas object with the same shape. The type of the returned object depends on the number of DataArray dimensions: * 0D -> `xarray.DataArray` * 1D -> `pandas.Series` * 2D -> `pandas.DataFrame` Only works for arrays with 2 or fewer dimensions. The DataArray constructor performs the inverse transformation. """ # TODO: consolidate the info about pandas constructors and the # attributes that correspond to their indexes into a separate module? constructors = {0: lambda x: x, 1: pd.Series, 2: pd.DataFrame} try: constructor = constructors[self.ndim] except KeyError: raise ValueError( "cannot convert arrays with %s dimensions into " "pandas objects" % self.ndim ) indexes = [self.get_index(dim) for dim in self.dims] return constructor(self.values, *indexes) def to_dataframe( self, name: Hashable = None, dim_order: List[Hashable] = None ) -> pd.DataFrame: """Convert this array and its coordinates into a tidy pandas.DataFrame. The DataFrame is indexed by the Cartesian product of index coordinates (in the form of a :py:class:`pandas.MultiIndex`). Other coordinates are included as columns in the DataFrame. Parameters ---------- name Name to give to this array (required if unnamed). dim_order Hierarchical dimension order for the resulting dataframe. Array content is transposed to this order and then written out as flat vectors in contiguous order, so the last dimension in this list will be contiguous in the resulting DataFrame. This has a major influence on which operations are efficient on the resulting dataframe. If provided, must include all dimensions of this DataArray. By default, dimensions are sorted according to the DataArray dimensions order. Returns ------- result DataArray as a pandas DataFrame. """ if name is None: name = self.name if name is None: raise ValueError( "cannot convert an unnamed DataArray to a " "DataFrame: use the ``name`` parameter" ) if self.ndim == 0: raise ValueError("cannot convert a scalar to a DataFrame") # By using a unique name, we can convert a DataArray into a DataFrame # even if it shares a name with one of its coordinates. # I would normally use unique_name = object() but that results in a # dataframe with columns in the wrong order, for reasons I have not # been able to debug (possibly a pandas bug?). unique_name = "__unique_name_identifier_z98xfz98xugfg73ho__" ds = self._to_dataset_whole(name=unique_name) if dim_order is None: ordered_dims = dict(zip(self.dims, self.shape)) else: ordered_dims = ds._normalize_dim_order(dim_order=dim_order) df = ds._to_dataframe(ordered_dims) df.columns = [name if c == unique_name else c for c in df.columns] return df def to_series(self) -> pd.Series: """Convert this array into a pandas.Series. The Series is indexed by the Cartesian product of index coordinates (in the form of a :py:class:`pandas.MultiIndex`). """ index = self.coords.to_index() return pd.Series(self.values.reshape(-1), index=index, name=self.name) def to_masked_array(self, copy: bool = True) -> np.ma.MaskedArray: """Convert this array into a numpy.ma.MaskedArray Parameters ---------- copy : bool, default: True If True make a copy of the array in the result. If False, a MaskedArray view of DataArray.values is returned. Returns ------- result : MaskedArray Masked where invalid values (nan or inf) occur. """ values = self.values # only compute lazy arrays once isnull = pd.isnull(values) return np.ma.MaskedArray(data=values, mask=isnull, copy=copy) def to_netcdf(self, *args, **kwargs) -> Union[bytes, "Delayed", None]: """Write DataArray contents to a netCDF file. All parameters are passed directly to :py:meth:`xarray.Dataset.to_netcdf`. Notes ----- Only xarray.Dataset objects can be written to netCDF files, so the xarray.DataArray is converted to a xarray.Dataset object containing a single variable. If the DataArray has no name, or if the name is the same as a coordinate name, then it is given the name ``"__xarray_dataarray_variable__"``. See Also -------- Dataset.to_netcdf """ from ..backends.api import DATAARRAY_NAME, DATAARRAY_VARIABLE if self.name is None: # If no name is set then use a generic xarray name dataset = self.to_dataset(name=DATAARRAY_VARIABLE) elif self.name in self.coords or self.name in self.dims: # The name is the same as one of the coords names, which netCDF # doesn't support, so rename it but keep track of the old name dataset = self.to_dataset(name=DATAARRAY_VARIABLE) dataset.attrs[DATAARRAY_NAME] = self.name else: # No problems with the name - so we're fine! dataset = self.to_dataset() return dataset.to_netcdf(*args, **kwargs) def to_dict(self, data: bool = True) -> dict: """ Convert this xarray.DataArray into a dictionary following xarray naming conventions. Converts all variables and attributes to native Python objects. Useful for converting to json. To avoid datetime incompatibility use decode_times=False kwarg in xarrray.open_dataset. Parameters ---------- data : bool, optional Whether to include the actual data in the dictionary. When set to False, returns just the schema. See also -------- DataArray.from_dict """ d = self.variable.to_dict(data=data) d.update({"coords": {}, "name": self.name}) for k in self.coords: d["coords"][k] = self.coords[k].variable.to_dict(data=data) return d @classmethod def from_dict(cls, d: dict) -> "DataArray": """ Convert a dictionary into an xarray.DataArray Input dict can take several forms: .. code:: python d = {"dims": ("t"), "data": x} d = { "coords": {"t": {"dims": "t", "data": t, "attrs": {"units": "s"}}}, "attrs": {"title": "air temperature"}, "dims": "t", "data": x, "name": "a", } where "t" is the name of the dimesion, "a" is the name of the array, and x and t are lists, numpy.arrays, or pandas objects. Parameters ---------- d : dict Mapping with a minimum structure of {"dims": [...], "data": [...]} Returns ------- obj : xarray.DataArray See also -------- DataArray.to_dict Dataset.from_dict """ coords = None if "coords" in d: try: coords = { k: (v["dims"], v["data"], v.get("attrs")) for k, v in d["coords"].items() } except KeyError as e: raise ValueError( "cannot convert dict when coords are missing the key " "'{dims_data}'".format(dims_data=str(e.args[0])) ) try: data = d["data"] except KeyError: raise ValueError("cannot convert dict without the key 'data''") else: obj = cls(data, coords, d.get("dims"), d.get("name"), d.get("attrs")) return obj @classmethod def from_series(cls, series: pd.Series, sparse: bool = False) -> "DataArray": """Convert a pandas.Series into an xarray.DataArray. If the series's index is a MultiIndex, it will be expanded into a tensor product of one-dimensional coordinates (filling in missing values with NaN). Thus this operation should be the inverse of the `to_series` method. If sparse=True, creates a sparse array instead of a dense NumPy array. Requires the pydata/sparse package. See also -------- xarray.Dataset.from_dataframe """ temp_name = "__temporary_name" df = pd.DataFrame({temp_name: series}) ds = Dataset.from_dataframe(df, sparse=sparse) result = cast(DataArray, ds[temp_name]) result.name = series.name return result def to_cdms2(self) -> "cdms2_Variable": """Convert this array into a cdms2.Variable""" from ..convert import to_cdms2 return to_cdms2(self) @classmethod def from_cdms2(cls, variable: "cdms2_Variable") -> "DataArray": """Convert a cdms2.Variable into an xarray.DataArray""" from ..convert import from_cdms2 return from_cdms2(variable) def to_iris(self) -> "iris_Cube": """Convert this array into a iris.cube.Cube""" from ..convert import to_iris return to_iris(self) @classmethod def from_iris(cls, cube: "iris_Cube") -> "DataArray": """Convert a iris.cube.Cube into an xarray.DataArray""" from ..convert import from_iris return from_iris(cube) def _all_compat(self, other: "DataArray", compat_str: str) -> bool: """Helper function for equals, broadcast_equals, and identical""" def compat(x, y): return getattr(x.variable, compat_str)(y.variable) return utils.dict_equiv(self.coords, other.coords, compat=compat) and compat( self, other ) def broadcast_equals(self, other: "DataArray") -> bool: """Two DataArrays are broadcast equal if they are equal after broadcasting them against each other such that they have the same dimensions. See Also -------- DataArray.equals DataArray.identical """ try: return self._all_compat(other, "broadcast_equals") except (TypeError, AttributeError): return False def equals(self, other: "DataArray") -> bool: """True if two DataArrays have the same dimensions, coordinates and values; otherwise False. DataArrays can still be equal (like pandas objects) if they have NaN values in the same locations. This method is necessary because `v1 == v2` for ``DataArray`` does element-wise comparisons (like numpy.ndarrays). See Also -------- DataArray.broadcast_equals DataArray.identical """ try: return self._all_compat(other, "equals") except (TypeError, AttributeError): return False def identical(self, other: "DataArray") -> bool: """Like equals, but also checks the array name and attributes, and attributes on all coordinates. See Also -------- DataArray.broadcast_equals DataArray.equals """ try: return self.name == other.name and self._all_compat(other, "identical") except (TypeError, AttributeError): return False def _result_name(self, other: Any = None) -> Optional[Hashable]: # use the same naming heuristics as pandas: # https://github.com/ContinuumIO/blaze/issues/458#issuecomment-51936356 other_name = getattr(other, "name", _default) if other_name is _default or other_name == self.name: return self.name else: return None def __array_wrap__(self, obj, context=None) -> "DataArray": new_var = self.variable.__array_wrap__(obj, context) return self._replace(new_var) def __matmul__(self, obj): return self.dot(obj) def __rmatmul__(self, other): # currently somewhat duplicative, as only other DataArrays are # compatible with matmul return computation.dot(other, self) @staticmethod def _unary_op(f: Callable[..., Any]) -> Callable[..., "DataArray"]: @functools.wraps(f) def func(self, *args, **kwargs): keep_attrs = kwargs.pop("keep_attrs", None) if keep_attrs is None: keep_attrs = _get_keep_attrs(default=True) with warnings.catch_warnings(): warnings.filterwarnings("ignore", r"All-NaN (slice|axis) encountered") warnings.filterwarnings( "ignore", r"Mean of empty slice", category=RuntimeWarning ) with np.errstate(all="ignore"): da = self.__array_wrap__(f(self.variable.data, *args, **kwargs)) if keep_attrs: da.attrs = self.attrs return da return func @staticmethod def _binary_op( f: Callable[..., Any], reflexive: bool = False, join: str = None, # see xarray.align **ignored_kwargs, ) -> Callable[..., "DataArray"]: @functools.wraps(f) def func(self, other): if isinstance(other, (Dataset, groupby.GroupBy)): return NotImplemented if isinstance(other, DataArray): align_type = OPTIONS["arithmetic_join"] if join is None else join self, other = align(self, other, join=align_type, copy=False) other_variable = getattr(other, "variable", other) other_coords = getattr(other, "coords", None) variable = ( f(self.variable, other_variable) if not reflexive else f(other_variable, self.variable) ) coords, indexes = self.coords._merge_raw(other_coords) name = self._result_name(other) return self._replace(variable, coords, name, indexes=indexes) return func @staticmethod def _inplace_binary_op(f: Callable) -> Callable[..., "DataArray"]: @functools.wraps(f) def func(self, other): if isinstance(other, groupby.GroupBy): raise TypeError( "in-place operations between a DataArray and " "a grouped object are not permitted" ) # n.b. we can't align other to self (with other.reindex_like(self)) # because `other` may be converted into floats, which would cause # in-place arithmetic to fail unpredictably. Instead, we simply # don't support automatic alignment with in-place arithmetic. other_coords = getattr(other, "coords", None) other_variable = getattr(other, "variable", other) try: with self.coords._merge_inplace(other_coords): f(self.variable, other_variable) except MergeError as exc: raise MergeError( "Automatic alignment is not supported for in-place operations.\n" "Consider aligning the indices manually or using a not-in-place operation.\n" "See https://github.com/pydata/xarray/issues/3910 for more explanations." ) from exc return self return func def _copy_attrs_from(self, other: Union["DataArray", Dataset, Variable]) -> None: self.attrs = other.attrs plot = utils.UncachedAccessor(_PlotMethods) def _title_for_slice(self, truncate: int = 50) -> str: """ If the dataarray has 1 dimensional coordinates or comes from a slice we can show that info in the title Parameters ---------- truncate : int, default: 50 maximum number of characters for title Returns ------- title : string Can be used for plot titles """ one_dims = [] for dim, coord in self.coords.items(): if coord.size == 1: one_dims.append( "{dim} = {v}".format(dim=dim, v=format_item(coord.values)) ) title = ", ".join(one_dims) if len(title) > truncate: title = title[: (truncate - 3)] + "..." return title def diff(self, dim: Hashable, n: int = 1, label: Hashable = "upper") -> "DataArray": """Calculate the n-th order discrete difference along given axis. Parameters ---------- dim : hashable Dimension over which to calculate the finite difference. n : int, optional The number of times values are differenced. label : hashable, optional The new coordinate in dimension ``dim`` will have the values of either the minuend's or subtrahend's coordinate for values 'upper' and 'lower', respectively. Other values are not supported. Returns ------- difference : same type as caller The n-th order finite difference of this object. .. note:: `n` matches numpy's behavior and is different from pandas' first argument named `periods`. Examples -------- >>> arr = xr.DataArray([5, 5, 6, 6], [[1, 2, 3, 4]], ["x"]) >>> arr.diff("x") array([0, 1, 0]) Coordinates: * x (x) int64 2 3 4 >>> arr.diff("x", 2) array([ 1, -1]) Coordinates: * x (x) int64 3 4 See Also -------- DataArray.differentiate """ ds = self._to_temp_dataset().diff(n=n, dim=dim, label=label) return self._from_temp_dataset(ds) def shift( self, shifts: Mapping[Hashable, int] = None, fill_value: Any = dtypes.NA, **shifts_kwargs: int, ) -> "DataArray": """Shift this array by an offset along one or more dimensions. Only the data is moved; coordinates stay in place. Values shifted from beyond array bounds are replaced by NaN. This is consistent with the behavior of ``shift`` in pandas. Parameters ---------- shifts : mapping of hashable to int, optional Integer offset to shift along each of the given dimensions. Positive offsets shift to the right; negative offsets shift to the left. fill_value: scalar, optional Value to use for newly missing values **shifts_kwargs The keyword arguments form of ``shifts``. One of shifts or shifts_kwargs must be provided. Returns ------- shifted : DataArray DataArray with the same coordinates and attributes but shifted data. See also -------- roll Examples -------- >>> arr = xr.DataArray([5, 6, 7], dims="x") >>> arr.shift(x=1) array([nan, 5., 6.]) Dimensions without coordinates: x """ variable = self.variable.shift( shifts=shifts, fill_value=fill_value, **shifts_kwargs ) return self._replace(variable=variable) def roll( self, shifts: Mapping[Hashable, int] = None, roll_coords: bool = None, **shifts_kwargs: int, ) -> "DataArray": """Roll this array by an offset along one or more dimensions. Unlike shift, roll may rotate all variables, including coordinates if specified. The direction of rotation is consistent with :py:func:`numpy.roll`. Parameters ---------- shifts : mapping of hashable to int, optional Integer offset to rotate each of the given dimensions. Positive offsets roll to the right; negative offsets roll to the left. roll_coords : bool Indicates whether to roll the coordinates by the offset The current default of roll_coords (None, equivalent to True) is deprecated and will change to False in a future version. Explicitly pass roll_coords to silence the warning. **shifts_kwargs The keyword arguments form of ``shifts``. One of shifts or shifts_kwargs must be provided. Returns ------- rolled : DataArray DataArray with the same attributes but rolled data and coordinates. See also -------- shift Examples -------- >>> arr = xr.DataArray([5, 6, 7], dims="x") >>> arr.roll(x=1) array([7, 5, 6]) Dimensions without coordinates: x """ ds = self._to_temp_dataset().roll( shifts=shifts, roll_coords=roll_coords, **shifts_kwargs ) return self._from_temp_dataset(ds) @property def real(self) -> "DataArray": return self._replace(self.variable.real) @property def imag(self) -> "DataArray": return self._replace(self.variable.imag) def dot( self, other: "DataArray", dims: Union[Hashable, Sequence[Hashable], None] = None ) -> "DataArray": """Perform dot product of two DataArrays along their shared dims. Equivalent to taking taking tensordot over all shared dims. Parameters ---------- other : DataArray The other array with which the dot product is performed. dims : ..., hashable or sequence of hashable, optional Which dimensions to sum over. Ellipsis (`...`) sums over all dimensions. If not specified, then all the common dimensions are summed over. Returns ------- result : DataArray Array resulting from the dot product over all shared dimensions. See also -------- dot numpy.tensordot Examples -------- >>> da_vals = np.arange(6 * 5 * 4).reshape((6, 5, 4)) >>> da = xr.DataArray(da_vals, dims=["x", "y", "z"]) >>> dm_vals = np.arange(4) >>> dm = xr.DataArray(dm_vals, dims=["z"]) >>> dm.dims ('z',) >>> da.dims ('x', 'y', 'z') >>> dot_result = da.dot(dm) >>> dot_result.dims ('x', 'y') """ if isinstance(other, Dataset): raise NotImplementedError( "dot products are not yet supported with Dataset objects." ) if not isinstance(other, DataArray): raise TypeError("dot only operates on DataArrays.") return computation.dot(self, other, dims=dims) def sortby( self, variables: Union[Hashable, "DataArray", Sequence[Union[Hashable, "DataArray"]]], ascending: bool = True, ) -> "DataArray": """Sort object by labels or values (along an axis). Sorts the dataarray, either along specified dimensions, or according to values of 1-D dataarrays that share dimension with calling object. If the input variables are dataarrays, then the dataarrays are aligned (via left-join) to the calling object prior to sorting by cell values. NaNs are sorted to the end, following Numpy convention. If multiple sorts along the same dimension is given, numpy's lexsort is performed along that dimension: https://docs.scipy.org/doc/numpy/reference/generated/numpy.lexsort.html and the FIRST key in the sequence is used as the primary sort key, followed by the 2nd key, etc. Parameters ---------- variables : hashable, DataArray, or sequence of hashable or DataArray 1D DataArray objects or name(s) of 1D variable(s) in coords whose values are used to sort this array. ascending : bool, optional Whether to sort by ascending or descending order. Returns ------- sorted : DataArray A new dataarray where all the specified dims are sorted by dim labels. Examples -------- >>> da = xr.DataArray( ... np.random.rand(5), ... coords=[pd.date_range("1/1/2000", periods=5)], ... dims="time", ... ) >>> da array([0.5488135 , 0.71518937, 0.60276338, 0.54488318, 0.4236548 ]) Coordinates: * time (time) datetime64[ns] 2000-01-01 2000-01-02 ... 2000-01-05 >>> da.sortby(da) array([0.4236548 , 0.54488318, 0.5488135 , 0.60276338, 0.71518937]) Coordinates: * time (time) datetime64[ns] 2000-01-05 2000-01-04 ... 2000-01-02 """ ds = self._to_temp_dataset().sortby(variables, ascending=ascending) return self._from_temp_dataset(ds) def quantile( self, q: Any, dim: Union[Hashable, Sequence[Hashable], None] = None, interpolation: str = "linear", keep_attrs: bool = None, skipna: bool = True, ) -> "DataArray": """Compute the qth quantile of the data along the specified dimension. Returns the qth quantiles(s) of the array elements. Parameters ---------- q : float or array-like of float Quantile to compute, which must be between 0 and 1 inclusive. dim : hashable or sequence of hashable, optional Dimension(s) over which to apply quantile. interpolation : {"linear", "lower", "higher", "midpoint", "nearest"}, default: "linear" This optional parameter specifies the interpolation method to use when the desired quantile lies between two data points ``i < j``: - linear: ``i + (j - i) * fraction``, where ``fraction`` is the fractional part of the index surrounded by ``i`` and ``j``. - lower: ``i``. - higher: ``j``. - nearest: ``i`` or ``j``, whichever is nearest. - midpoint: ``(i + j) / 2``. keep_attrs : bool, optional If True, the dataset's attributes (`attrs`) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes. skipna : bool, optional Whether to skip missing values when aggregating. Returns ------- quantiles : DataArray If `q` is a single quantile, then the result is a scalar. If multiple percentiles are given, first axis of the result corresponds to the quantile and a quantile dimension is added to the return array. The other dimensions are the dimensions that remain after the reduction of the array. See Also -------- numpy.nanquantile, numpy.quantile, pandas.Series.quantile, Dataset.quantile Examples -------- >>> da = xr.DataArray( ... data=[[0.7, 4.2, 9.4, 1.5], [6.5, 7.3, 2.6, 1.9]], ... coords={"x": [7, 9], "y": [1, 1.5, 2, 2.5]}, ... dims=("x", "y"), ... ) >>> da.quantile(0) # or da.quantile(0, dim=...) array(0.7) Coordinates: quantile float64 0.0 >>> da.quantile(0, dim="x") array([0.7, 4.2, 2.6, 1.5]) Coordinates: * y (y) float64 1.0 1.5 2.0 2.5 quantile float64 0.0 >>> da.quantile([0, 0.5, 1]) array([0.7, 3.4, 9.4]) Coordinates: * quantile (quantile) float64 0.0 0.5 1.0 >>> da.quantile([0, 0.5, 1], dim="x") array([[0.7 , 4.2 , 2.6 , 1.5 ], [3.6 , 5.75, 6. , 1.7 ], [6.5 , 7.3 , 9.4 , 1.9 ]]) Coordinates: * y (y) float64 1.0 1.5 2.0 2.5 * quantile (quantile) float64 0.0 0.5 1.0 """ ds = self._to_temp_dataset().quantile( q, dim=dim, keep_attrs=keep_attrs, interpolation=interpolation, skipna=skipna, ) return self._from_temp_dataset(ds) def rank( self, dim: Hashable, pct: bool = False, keep_attrs: bool = None ) -> "DataArray": """Ranks the data. Equal values are assigned a rank that is the average of the ranks that would have been otherwise assigned to all of the values within that set. Ranks begin at 1, not 0. If pct, computes percentage ranks. NaNs in the input array are returned as NaNs. The `bottleneck` library is required. Parameters ---------- dim : hashable Dimension over which to compute rank. pct : bool, optional If True, compute percentage ranks, otherwise compute integer ranks. keep_attrs : bool, optional If True, the dataset's attributes (`attrs`) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes. Returns ------- ranked : DataArray DataArray with the same coordinates and dtype 'float64'. Examples -------- >>> arr = xr.DataArray([5, 6, 7], dims="x") >>> arr.rank("x") array([1., 2., 3.]) Dimensions without coordinates: x """ ds = self._to_temp_dataset().rank(dim, pct=pct, keep_attrs=keep_attrs) return self._from_temp_dataset(ds) def differentiate( self, coord: Hashable, edge_order: int = 1, datetime_unit: str = None ) -> "DataArray": """ Differentiate the array with the second order accurate central differences. .. note:: This feature is limited to simple cartesian geometry, i.e. coord must be one dimensional. Parameters ---------- coord : hashable The coordinate to be used to compute the gradient. edge_order : {1, 2}, default: 1 N-th order accurate differences at the boundaries. datetime_unit : {"Y", "M", "W", "D", "h", "m", "s", "ms", \ "us", "ns", "ps", "fs", "as"} or None, optional Unit to compute gradient. Only valid for datetime coordinate. Returns ------- differentiated: DataArray See also -------- numpy.gradient: corresponding numpy function Examples -------- >>> da = xr.DataArray( ... np.arange(12).reshape(4, 3), ... dims=["x", "y"], ... coords={"x": [0, 0.1, 1.1, 1.2]}, ... ) >>> da array([[ 0, 1, 2], [ 3, 4, 5], [ 6, 7, 8], [ 9, 10, 11]]) Coordinates: * x (x) float64 0.0 0.1 1.1 1.2 Dimensions without coordinates: y >>> >>> da.differentiate("x") array([[30. , 30. , 30. ], [27.54545455, 27.54545455, 27.54545455], [27.54545455, 27.54545455, 27.54545455], [30. , 30. , 30. ]]) Coordinates: * x (x) float64 0.0 0.1 1.1 1.2 Dimensions without coordinates: y """ ds = self._to_temp_dataset().differentiate(coord, edge_order, datetime_unit) return self._from_temp_dataset(ds) def integrate( self, dim: Union[Hashable, Sequence[Hashable]], datetime_unit: str = None ) -> "DataArray": """ integrate the array with the trapezoidal rule. .. note:: This feature is limited to simple cartesian geometry, i.e. dim must be one dimensional. Parameters ---------- dim : hashable, or sequence of hashable Coordinate(s) used for the integration. datetime_unit : {"Y", "M", "W", "D", "h", "m", "s", "ms", "us", "ns", \ "ps", "fs", "as"}, optional Can be used to specify the unit if datetime coordinate is used. Returns ------- integrated: DataArray See also -------- numpy.trapz: corresponding numpy function Examples -------- >>> da = xr.DataArray( ... np.arange(12).reshape(4, 3), ... dims=["x", "y"], ... coords={"x": [0, 0.1, 1.1, 1.2]}, ... ) >>> da array([[ 0, 1, 2], [ 3, 4, 5], [ 6, 7, 8], [ 9, 10, 11]]) Coordinates: * x (x) float64 0.0 0.1 1.1 1.2 Dimensions without coordinates: y >>> >>> da.integrate("x") array([5.4, 6.6, 7.8]) Dimensions without coordinates: y """ ds = self._to_temp_dataset().integrate(dim, datetime_unit) return self._from_temp_dataset(ds) def unify_chunks(self) -> "DataArray": """Unify chunk size along all chunked dimensions of this DataArray. Returns ------- DataArray with consistent chunk sizes for all dask-array variables See Also -------- dask.array.core.unify_chunks """ ds = self._to_temp_dataset().unify_chunks() return self._from_temp_dataset(ds) def map_blocks( self, func: "Callable[..., T_DSorDA]", args: Sequence[Any] = (), kwargs: Mapping[str, Any] = None, template: Union["DataArray", "Dataset"] = None, ) -> "T_DSorDA": """ Apply a function to each block of this DataArray. .. warning:: This method is experimental and its signature may change. Parameters ---------- func : callable User-provided function that accepts a DataArray as its first parameter. The function will receive a subset or 'block' of this DataArray (see below), corresponding to one chunk along each chunked dimension. ``func`` will be executed as ``func(subset_dataarray, *subset_args, **kwargs)``. This function must return either a single DataArray or a single Dataset. This function cannot add a new chunked dimension. args : sequence Passed to func after unpacking and subsetting any xarray objects by blocks. xarray objects in args must be aligned with this object, otherwise an error is raised. kwargs : mapping Passed verbatim to func after unpacking. xarray objects, if any, will not be subset to blocks. Passing dask collections in kwargs is not allowed. template : DataArray or Dataset, optional xarray object representing the final result after compute is called. If not provided, the function will be first run on mocked-up data, that looks like this object but has sizes 0, to determine properties of the returned object such as dtype, variable names, attributes, new dimensions and new indexes (if any). ``template`` must be provided if the function changes the size of existing dimensions. When provided, ``attrs`` on variables in `template` are copied over to the result. Any ``attrs`` set by ``func`` will be ignored. Returns ------- A single DataArray or Dataset with dask backend, reassembled from the outputs of the function. Notes ----- This function is designed for when ``func`` needs to manipulate a whole xarray object subset to each block. In the more common case where ``func`` can work on numpy arrays, it is recommended to use ``apply_ufunc``. If none of the variables in this object is backed by dask arrays, calling this function is equivalent to calling ``func(obj, *args, **kwargs)``. See Also -------- dask.array.map_blocks, xarray.apply_ufunc, xarray.Dataset.map_blocks, xarray.DataArray.map_blocks Examples -------- Calculate an anomaly from climatology using ``.groupby()``. Using ``xr.map_blocks()`` allows for parallel operations with knowledge of ``xarray``, its indices, and its methods like ``.groupby()``. >>> def calculate_anomaly(da, groupby_type="time.month"): ... gb = da.groupby(groupby_type) ... clim = gb.mean(dim="time") ... return gb - clim ... >>> time = xr.cftime_range("1990-01", "1992-01", freq="M") >>> month = xr.DataArray(time.month, coords={"time": time}, dims=["time"]) >>> np.random.seed(123) >>> array = xr.DataArray( ... np.random.rand(len(time)), ... dims=["time"], ... coords={"time": time, "month": month}, ... ).chunk() >>> array.map_blocks(calculate_anomaly, template=array).compute() array([ 0.12894847, 0.11323072, -0.0855964 , -0.09334032, 0.26848862, 0.12382735, 0.22460641, 0.07650108, -0.07673453, -0.22865714, -0.19063865, 0.0590131 , -0.12894847, -0.11323072, 0.0855964 , 0.09334032, -0.26848862, -0.12382735, -0.22460641, -0.07650108, 0.07673453, 0.22865714, 0.19063865, -0.0590131 ]) Coordinates: * time (time) object 1990-01-31 00:00:00 ... 1991-12-31 00:00:00 month (time) int64 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 Note that one must explicitly use ``args=[]`` and ``kwargs={}`` to pass arguments to the function being applied in ``xr.map_blocks()``: >>> array.map_blocks( ... calculate_anomaly, kwargs={"groupby_type": "time.year"}, template=array ... ) # doctest: +ELLIPSIS dask.array Coordinates: * time (time) object 1990-01-31 00:00:00 ... 1991-12-31 00:00:00 month (time) int64 dask.array """ from .parallel import map_blocks return map_blocks(func, self, args, kwargs, template) def polyfit( self, dim: Hashable, deg: int, skipna: bool = None, rcond: float = None, w: Union[Hashable, Any] = None, full: bool = False, cov: bool = False, ): """ Least squares polynomial fit. This replicates the behaviour of `numpy.polyfit` but differs by skipping invalid values when `skipna = True`. Parameters ---------- dim : hashable Coordinate along which to fit the polynomials. deg : int Degree of the fitting polynomial. skipna : bool, optional If True, removes all invalid values before fitting each 1D slices of the array. Default is True if data is stored in a dask.array or if there is any invalid values, False otherwise. rcond : float, optional Relative condition number to the fit. w : hashable or array-like, optional Weights to apply to the y-coordinate of the sample points. Can be an array-like object or the name of a coordinate in the dataset. full : bool, optional Whether to return the residuals, matrix rank and singular values in addition to the coefficients. cov : bool or str, optional Whether to return to the covariance matrix in addition to the coefficients. The matrix is not scaled if `cov='unscaled'`. Returns ------- polyfit_results : Dataset A single dataset which contains: polyfit_coefficients The coefficients of the best fit. polyfit_residuals The residuals of the least-square computation (only included if `full=True`). When the matrix rank is deficient, np.nan is returned. [dim]_matrix_rank The effective rank of the scaled Vandermonde coefficient matrix (only included if `full=True`) [dim]_singular_value The singular values of the scaled Vandermonde coefficient matrix (only included if `full=True`) polyfit_covariance The covariance matrix of the polynomial coefficient estimates (only included if `full=False` and `cov=True`) See also -------- numpy.polyfit """ return self._to_temp_dataset().polyfit( dim, deg, skipna=skipna, rcond=rcond, w=w, full=full, cov=cov ) def pad( self, pad_width: Mapping[Hashable, Union[int, Tuple[int, int]]] = None, mode: str = "constant", stat_length: Union[ int, Tuple[int, int], Mapping[Hashable, Tuple[int, int]] ] = None, constant_values: Union[ int, Tuple[int, int], Mapping[Hashable, Tuple[int, int]] ] = None, end_values: Union[ int, Tuple[int, int], Mapping[Hashable, Tuple[int, int]] ] = None, reflect_type: str = None, **pad_width_kwargs: Any, ) -> "DataArray": """Pad this array along one or more dimensions. .. warning:: This function is experimental and its behaviour is likely to change especially regarding padding of dimension coordinates (or IndexVariables). When using one of the modes ("edge", "reflect", "symmetric", "wrap"), coordinates will be padded with the same mode, otherwise coordinates are padded using the "constant" mode with fill_value dtypes.NA. Parameters ---------- pad_width : mapping of hashable to tuple of int Mapping with the form of {dim: (pad_before, pad_after)} describing the number of values padded along each dimension. {dim: pad} is a shortcut for pad_before = pad_after = pad mode : str, default: "constant" One of the following string values (taken from numpy docs) 'constant' (default) Pads with a constant value. 'edge' Pads with the edge values of array. 'linear_ramp' Pads with the linear ramp between end_value and the array edge value. 'maximum' Pads with the maximum value of all or part of the vector along each axis. 'mean' Pads with the mean value of all or part of the vector along each axis. 'median' Pads with the median value of all or part of the vector along each axis. 'minimum' Pads with the minimum value of all or part of the vector along each axis. 'reflect' Pads with the reflection of the vector mirrored on the first and last values of the vector along each axis. 'symmetric' Pads with the reflection of the vector mirrored along the edge of the array. 'wrap' Pads with the wrap of the vector along the axis. The first values are used to pad the end and the end values are used to pad the beginning. stat_length : int, tuple or mapping of hashable to tuple, default: None Used in 'maximum', 'mean', 'median', and 'minimum'. Number of values at edge of each axis used to calculate the statistic value. {dim_1: (before_1, after_1), ... dim_N: (before_N, after_N)} unique statistic lengths along each dimension. ((before, after),) yields same before and after statistic lengths for each dimension. (stat_length,) or int is a shortcut for before = after = statistic length for all axes. Default is ``None``, to use the entire axis. constant_values : scalar, tuple or mapping of hashable to tuple, default: 0 Used in 'constant'. The values to set the padded values for each axis. ``{dim_1: (before_1, after_1), ... dim_N: (before_N, after_N)}`` unique pad constants along each dimension. ``((before, after),)`` yields same before and after constants for each dimension. ``(constant,)`` or ``constant`` is a shortcut for ``before = after = constant`` for all dimensions. Default is 0. end_values : scalar, tuple or mapping of hashable to tuple, default: 0 Used in 'linear_ramp'. The values used for the ending value of the linear_ramp and that will form the edge of the padded array. ``{dim_1: (before_1, after_1), ... dim_N: (before_N, after_N)}`` unique end values along each dimension. ``((before, after),)`` yields same before and after end values for each axis. ``(constant,)`` or ``constant`` is a shortcut for ``before = after = constant`` for all axes. Default is 0. reflect_type : {"even", "odd"}, optional Used in "reflect", and "symmetric". The "even" style is the default with an unaltered reflection around the edge value. For the "odd" style, the extended part of the array is created by subtracting the reflected values from two times the edge value. **pad_width_kwargs The keyword arguments form of ``pad_width``. One of ``pad_width`` or ``pad_width_kwargs`` must be provided. Returns ------- padded : DataArray DataArray with the padded coordinates and data. See also -------- DataArray.shift, DataArray.roll, DataArray.bfill, DataArray.ffill, numpy.pad, dask.array.pad Notes ----- By default when ``mode="constant"`` and ``constant_values=None``, integer types will be promoted to ``float`` and padded with ``np.nan``. To avoid type promotion specify ``constant_values=np.nan`` Examples -------- >>> arr = xr.DataArray([5, 6, 7], coords=[("x", [0, 1, 2])]) >>> arr.pad(x=(1, 2), constant_values=0) array([0, 5, 6, 7, 0, 0]) Coordinates: * x (x) float64 nan 0.0 1.0 2.0 nan nan >>> da = xr.DataArray( ... [[0, 1, 2, 3], [10, 11, 12, 13]], ... dims=["x", "y"], ... coords={"x": [0, 1], "y": [10, 20, 30, 40], "z": ("x", [100, 200])}, ... ) >>> da.pad(x=1) array([[nan, nan, nan, nan], [ 0., 1., 2., 3.], [10., 11., 12., 13.], [nan, nan, nan, nan]]) Coordinates: * x (x) float64 nan 0.0 1.0 nan * y (y) int64 10 20 30 40 z (x) float64 nan 100.0 200.0 nan >>> da.pad(x=1, constant_values=np.nan) array([[-9223372036854775808, -9223372036854775808, -9223372036854775808, -9223372036854775808], [ 0, 1, 2, 3], [ 10, 11, 12, 13], [-9223372036854775808, -9223372036854775808, -9223372036854775808, -9223372036854775808]]) Coordinates: * x (x) float64 nan 0.0 1.0 nan * y (y) int64 10 20 30 40 z (x) float64 nan 100.0 200.0 nan """ ds = self._to_temp_dataset().pad( pad_width=pad_width, mode=mode, stat_length=stat_length, constant_values=constant_values, end_values=end_values, reflect_type=reflect_type, **pad_width_kwargs, ) return self._from_temp_dataset(ds) def idxmin( self, dim: Hashable = None, skipna: bool = None, fill_value: Any = dtypes.NA, keep_attrs: bool = None, ) -> "DataArray": """Return the coordinate label of the minimum value along a dimension. Returns a new `DataArray` named after the dimension with the values of the coordinate labels along that dimension corresponding to minimum values along that dimension. In comparison to :py:meth:`~DataArray.argmin`, this returns the coordinate label while :py:meth:`~DataArray.argmin` returns the index. Parameters ---------- dim : str, optional Dimension over which to apply `idxmin`. This is optional for 1D arrays, but required for arrays with 2 or more dimensions. skipna : bool or None, default: None If True, skip missing values (as marked by NaN). By default, only skips missing values for ``float``, ``complex``, and ``object`` dtypes; other dtypes either do not have a sentinel missing value (``int``) or ``skipna=True`` has not been implemented (``datetime64`` or ``timedelta64``). fill_value : Any, default: NaN Value to be filled in case all of the values along a dimension are null. By default this is NaN. The fill value and result are automatically converted to a compatible dtype if possible. Ignored if ``skipna`` is False. keep_attrs : bool, default: False If True, the attributes (``attrs``) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes. Returns ------- reduced : DataArray New `DataArray` object with `idxmin` applied to its data and the indicated dimension removed. See also -------- Dataset.idxmin, DataArray.idxmax, DataArray.min, DataArray.argmin Examples -------- >>> array = xr.DataArray( ... [0, 2, 1, 0, -2], dims="x", coords={"x": ["a", "b", "c", "d", "e"]} ... ) >>> array.min() array(-2) >>> array.argmin() array(4) >>> array.idxmin() array('e', dtype='>> array = xr.DataArray( ... [ ... [2.0, 1.0, 2.0, 0.0, -2.0], ... [-4.0, np.NaN, 2.0, np.NaN, -2.0], ... [np.NaN, np.NaN, 1.0, np.NaN, np.NaN], ... ], ... dims=["y", "x"], ... coords={"y": [-1, 0, 1], "x": np.arange(5.0) ** 2}, ... ) >>> array.min(dim="x") array([-2., -4., 1.]) Coordinates: * y (y) int64 -1 0 1 >>> array.argmin(dim="x") array([4, 0, 2]) Coordinates: * y (y) int64 -1 0 1 >>> array.idxmin(dim="x") array([16., 0., 4.]) Coordinates: * y (y) int64 -1 0 1 """ return computation._calc_idxminmax( array=self, func=lambda x, *args, **kwargs: x.argmin(*args, **kwargs), dim=dim, skipna=skipna, fill_value=fill_value, keep_attrs=keep_attrs, ) def idxmax( self, dim: Hashable = None, skipna: bool = None, fill_value: Any = dtypes.NA, keep_attrs: bool = None, ) -> "DataArray": """Return the coordinate label of the maximum value along a dimension. Returns a new `DataArray` named after the dimension with the values of the coordinate labels along that dimension corresponding to maximum values along that dimension. In comparison to :py:meth:`~DataArray.argmax`, this returns the coordinate label while :py:meth:`~DataArray.argmax` returns the index. Parameters ---------- dim : hashable, optional Dimension over which to apply `idxmax`. This is optional for 1D arrays, but required for arrays with 2 or more dimensions. skipna : bool or None, default: None If True, skip missing values (as marked by NaN). By default, only skips missing values for ``float``, ``complex``, and ``object`` dtypes; other dtypes either do not have a sentinel missing value (``int``) or ``skipna=True`` has not been implemented (``datetime64`` or ``timedelta64``). fill_value : Any, default: NaN Value to be filled in case all of the values along a dimension are null. By default this is NaN. The fill value and result are automatically converted to a compatible dtype if possible. Ignored if ``skipna`` is False. keep_attrs : bool, default: False If True, the attributes (``attrs``) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes. Returns ------- reduced : DataArray New `DataArray` object with `idxmax` applied to its data and the indicated dimension removed. See also -------- Dataset.idxmax, DataArray.idxmin, DataArray.max, DataArray.argmax Examples -------- >>> array = xr.DataArray( ... [0, 2, 1, 0, -2], dims="x", coords={"x": ["a", "b", "c", "d", "e"]} ... ) >>> array.max() array(2) >>> array.argmax() array(1) >>> array.idxmax() array('b', dtype='>> array = xr.DataArray( ... [ ... [2.0, 1.0, 2.0, 0.0, -2.0], ... [-4.0, np.NaN, 2.0, np.NaN, -2.0], ... [np.NaN, np.NaN, 1.0, np.NaN, np.NaN], ... ], ... dims=["y", "x"], ... coords={"y": [-1, 0, 1], "x": np.arange(5.0) ** 2}, ... ) >>> array.max(dim="x") array([2., 2., 1.]) Coordinates: * y (y) int64 -1 0 1 >>> array.argmax(dim="x") array([0, 2, 2]) Coordinates: * y (y) int64 -1 0 1 >>> array.idxmax(dim="x") array([0., 4., 4.]) Coordinates: * y (y) int64 -1 0 1 """ return computation._calc_idxminmax( array=self, func=lambda x, *args, **kwargs: x.argmax(*args, **kwargs), dim=dim, skipna=skipna, fill_value=fill_value, keep_attrs=keep_attrs, ) def argmin( self, dim: Union[Hashable, Sequence[Hashable]] = None, axis: int = None, keep_attrs: bool = None, skipna: bool = None, ) -> Union["DataArray", Dict[Hashable, "DataArray"]]: """Index or indices of the minimum of the DataArray over one or more dimensions. If a sequence is passed to 'dim', then result returned as dict of DataArrays, which can be passed directly to isel(). If a single str is passed to 'dim' then returns a DataArray with dtype int. If there are multiple minima, the indices of the first one found will be returned. Parameters ---------- dim : hashable, sequence of hashable or ..., optional The dimensions over which to find the minimum. By default, finds minimum over all dimensions - for now returning an int for backward compatibility, but this is deprecated, in future will return a dict with indices for all dimensions; to return a dict with all dimensions now, pass '...'. axis : int, optional Axis over which to apply `argmin`. Only one of the 'dim' and 'axis' arguments can be supplied. keep_attrs : bool, optional If True, the attributes (`attrs`) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes. skipna : bool, optional If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64). Returns ------- result : DataArray or dict of DataArray See also -------- Variable.argmin, DataArray.idxmin Examples -------- >>> array = xr.DataArray([0, 2, -1, 3], dims="x") >>> array.min() array(-1) >>> array.argmin() array(2) >>> array.argmin(...) {'x': array(2)} >>> array.isel(array.argmin(...)) array(-1) >>> array = xr.DataArray( ... [[[3, 2, 1], [3, 1, 2], [2, 1, 3]], [[1, 3, 2], [2, -5, 1], [2, 3, 1]]], ... dims=("x", "y", "z"), ... ) >>> array.min(dim="x") array([[ 1, 2, 1], [ 2, -5, 1], [ 2, 1, 1]]) Dimensions without coordinates: y, z >>> array.argmin(dim="x") array([[1, 0, 0], [1, 1, 1], [0, 0, 1]]) Dimensions without coordinates: y, z >>> array.argmin(dim=["x"]) {'x': array([[1, 0, 0], [1, 1, 1], [0, 0, 1]]) Dimensions without coordinates: y, z} >>> array.min(dim=("x", "z")) array([ 1, -5, 1]) Dimensions without coordinates: y >>> array.argmin(dim=["x", "z"]) {'x': array([0, 1, 0]) Dimensions without coordinates: y, 'z': array([2, 1, 1]) Dimensions without coordinates: y} >>> array.isel(array.argmin(dim=["x", "z"])) array([ 1, -5, 1]) Dimensions without coordinates: y """ result = self.variable.argmin(dim, axis, keep_attrs, skipna) if isinstance(result, dict): return {k: self._replace_maybe_drop_dims(v) for k, v in result.items()} else: return self._replace_maybe_drop_dims(result) def argmax( self, dim: Union[Hashable, Sequence[Hashable]] = None, axis: int = None, keep_attrs: bool = None, skipna: bool = None, ) -> Union["DataArray", Dict[Hashable, "DataArray"]]: """Index or indices of the maximum of the DataArray over one or more dimensions. If a sequence is passed to 'dim', then result returned as dict of DataArrays, which can be passed directly to isel(). If a single str is passed to 'dim' then returns a DataArray with dtype int. If there are multiple maxima, the indices of the first one found will be returned. Parameters ---------- dim : hashable, sequence of hashable or ..., optional The dimensions over which to find the maximum. By default, finds maximum over all dimensions - for now returning an int for backward compatibility, but this is deprecated, in future will return a dict with indices for all dimensions; to return a dict with all dimensions now, pass '...'. axis : int, optional Axis over which to apply `argmax`. Only one of the 'dim' and 'axis' arguments can be supplied. keep_attrs : bool, optional If True, the attributes (`attrs`) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes. skipna : bool, optional If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64). Returns ------- result : DataArray or dict of DataArray See also -------- Variable.argmax, DataArray.idxmax Examples -------- >>> array = xr.DataArray([0, 2, -1, 3], dims="x") >>> array.max() array(3) >>> array.argmax() array(3) >>> array.argmax(...) {'x': array(3)} >>> array.isel(array.argmax(...)) array(3) >>> array = xr.DataArray( ... [[[3, 2, 1], [3, 1, 2], [2, 1, 3]], [[1, 3, 2], [2, 5, 1], [2, 3, 1]]], ... dims=("x", "y", "z"), ... ) >>> array.max(dim="x") array([[3, 3, 2], [3, 5, 2], [2, 3, 3]]) Dimensions without coordinates: y, z >>> array.argmax(dim="x") array([[0, 1, 1], [0, 1, 0], [0, 1, 0]]) Dimensions without coordinates: y, z >>> array.argmax(dim=["x"]) {'x': array([[0, 1, 1], [0, 1, 0], [0, 1, 0]]) Dimensions without coordinates: y, z} >>> array.max(dim=("x", "z")) array([3, 5, 3]) Dimensions without coordinates: y >>> array.argmax(dim=["x", "z"]) {'x': array([0, 1, 0]) Dimensions without coordinates: y, 'z': array([0, 1, 2]) Dimensions without coordinates: y} >>> array.isel(array.argmax(dim=["x", "z"])) array([3, 5, 3]) Dimensions without coordinates: y """ result = self.variable.argmax(dim, axis, keep_attrs, skipna) if isinstance(result, dict): return {k: self._replace_maybe_drop_dims(v) for k, v in result.items()} else: return self._replace_maybe_drop_dims(result) # this needs to be at the end, or mypy will confuse with `str` # https://mypy.readthedocs.io/en/latest/common_issues.html#dealing-with-conflicting-names str = utils.UncachedAccessor(StringAccessor) # priority most be higher than Variable to properly work with binary ufuncs ops.inject_all_ops_and_reduce_methods(DataArray, priority=60)