from __future__ import annotations import copy import itertools import math import numbers import warnings from datetime import timedelta from typing import ( TYPE_CHECKING, Any, Callable, Hashable, Iterable, Literal, Mapping, NoReturn, Sequence, ) import numpy as np import pandas as pd from numpy.typing import ArrayLike from packaging.version import Version import xarray as xr # only for Dataset and DataArray from xarray.core import common, dtypes, duck_array_ops, indexing, nputils, ops, utils from xarray.core.arithmetic import VariableArithmetic from xarray.core.common import AbstractArray from xarray.core.indexing import ( BasicIndexer, OuterIndexer, PandasIndexingAdapter, VectorizedIndexer, as_indexable, ) from xarray.core.options import OPTIONS, _get_keep_attrs from xarray.core.pycompat import array_type, integer_types, is_duck_dask_array from xarray.core.utils import ( Frozen, NdimSizeLenMixin, OrderedSet, _default, decode_numpy_dict_values, drop_dims_from_indexers, either_dict_or_kwargs, ensure_us_time_resolution, infix_dims, is_duck_array, maybe_coerce_to_str, ) NON_NUMPY_SUPPORTED_ARRAY_TYPES = ( indexing.ExplicitlyIndexed, pd.Index, ) # https://github.com/python/mypy/issues/224 BASIC_INDEXING_TYPES = integer_types + (slice,) if TYPE_CHECKING: from xarray.core.types import ( Dims, ErrorOptionsWithWarn, PadModeOptions, PadReflectOptions, QuantileMethods, T_Variable, ) NON_NANOSECOND_WARNING = ( "Converting non-nanosecond precision {case} values to nanosecond precision. " "This behavior can eventually be relaxed in xarray, as it is an artifact from " "pandas which is now beginning to support non-nanosecond precision values. " "This warning is caused by passing non-nanosecond np.datetime64 or " "np.timedelta64 values to the DataArray or Variable constructor; it can be " "silenced by converting the values to nanosecond precision ahead of time." ) class MissingDimensionsError(ValueError): """Error class used when we can't safely guess a dimension name.""" # inherits from ValueError for backward compatibility # TODO: move this to an xarray.exceptions module? def as_variable(obj, name=None) -> Variable | IndexVariable: """Convert an object into a Variable. Parameters ---------- obj : object Object to convert into a Variable. - If the object is already a Variable, return a shallow copy. - Otherwise, if the object has 'dims' and 'data' attributes, convert it into a new Variable. - If all else fails, attempt to convert the object into a Variable by unpacking it into the arguments for creating a new Variable. name : str, optional If provided: - `obj` can be a 1D array, which is assumed to label coordinate values along a dimension of this given name. - Variables with name matching one of their dimensions are converted into `IndexVariable` objects. Returns ------- var : Variable The newly created variable. """ from xarray.core.dataarray import DataArray # TODO: consider extending this method to automatically handle Iris and if isinstance(obj, DataArray): # extract the primary Variable from DataArrays obj = obj.variable if isinstance(obj, Variable): obj = obj.copy(deep=False) elif isinstance(obj, tuple): if isinstance(obj[1], DataArray): raise TypeError( "Using a DataArray object to construct a variable is" " ambiguous, please extract the data using the .data property." ) try: obj = Variable(*obj) except (TypeError, ValueError) as error: # use .format() instead of % because it handles tuples consistently raise error.__class__( "Could not convert tuple of form " "(dims, data[, attrs, encoding]): " "{} to Variable.".format(obj) ) elif utils.is_scalar(obj): obj = Variable([], obj) elif isinstance(obj, (pd.Index, IndexVariable)) and obj.name is not None: obj = Variable(obj.name, obj) elif isinstance(obj, (set, dict)): raise TypeError(f"variable {name!r} has invalid type {type(obj)!r}") elif name is not None: data = as_compatible_data(obj) if data.ndim != 1: raise MissingDimensionsError( f"cannot set variable {name!r} with {data.ndim!r}-dimensional data " "without explicit dimension names. Pass a tuple of " "(dims, data) instead." ) obj = Variable(name, data, fastpath=True) else: raise TypeError( "unable to convert object into a variable without an " f"explicit list of dimensions: {obj!r}" ) if name is not None and name in obj.dims: # convert the Variable into an Index if obj.ndim != 1: raise MissingDimensionsError( f"{name!r} has more than 1-dimension and the same name as one of its " f"dimensions {obj.dims!r}. xarray disallows such variables because they " "conflict with the coordinates used to label dimensions." ) obj = obj.to_index_variable() return obj def _maybe_wrap_data(data): """ Put pandas.Index and numpy.ndarray arguments in adapter objects to ensure they can be indexed properly. NumpyArrayAdapter, PandasIndexingAdapter and LazilyIndexedArray should all pass through unmodified. """ if isinstance(data, pd.Index): return PandasIndexingAdapter(data) return data def _as_nanosecond_precision(data): dtype = data.dtype non_ns_datetime64 = ( dtype.kind == "M" and isinstance(dtype, np.dtype) and dtype != np.dtype("datetime64[ns]") ) non_ns_datetime_tz_dtype = ( isinstance(dtype, pd.DatetimeTZDtype) and dtype.unit != "ns" ) if non_ns_datetime64 or non_ns_datetime_tz_dtype: utils.emit_user_level_warning(NON_NANOSECOND_WARNING.format(case="datetime")) if isinstance(dtype, pd.DatetimeTZDtype): nanosecond_precision_dtype = pd.DatetimeTZDtype("ns", dtype.tz) else: nanosecond_precision_dtype = "datetime64[ns]" return data.astype(nanosecond_precision_dtype) elif dtype.kind == "m" and dtype != np.dtype("timedelta64[ns]"): utils.emit_user_level_warning(NON_NANOSECOND_WARNING.format(case="timedelta")) return data.astype("timedelta64[ns]") else: return data def _possibly_convert_objects(values): """Convert arrays of datetime.datetime and datetime.timedelta objects into datetime64 and timedelta64, according to the pandas convention. For the time being, convert any non-nanosecond precision DatetimeIndex or TimedeltaIndex objects to nanosecond precision. While pandas is relaxing this in version 2.0.0, in xarray we will need to make sure we are ready to handle non-nanosecond precision datetimes or timedeltas in our code before allowing such values to pass through unchanged. Converting to nanosecond precision through pandas.Series objects ensures that datetimes and timedeltas are within the valid date range for ns precision, as pandas will raise an error if they are not. """ as_series = pd.Series(values.ravel()) if as_series.dtype.kind in "mM": as_series = _as_nanosecond_precision(as_series) return np.asarray(as_series).reshape(values.shape) def _possibly_convert_datetime_or_timedelta_index(data): """For the time being, convert any non-nanosecond precision DatetimeIndex or TimedeltaIndex objects to nanosecond precision. While pandas is relaxing this in version 2.0.0, in xarray we will need to make sure we are ready to handle non-nanosecond precision datetimes or timedeltas in our code before allowing such values to pass through unchanged.""" if isinstance(data, (pd.DatetimeIndex, pd.TimedeltaIndex)): return _as_nanosecond_precision(data) else: return data def as_compatible_data(data, fastpath=False): """Prepare and wrap data to put in a Variable. - If data does not have the necessary attributes, convert it to ndarray. - If data has dtype=datetime64, ensure that it has ns precision. If it's a pandas.Timestamp, convert it to datetime64. - If data is already a pandas or xarray object (other than an Index), just use the values. Finally, wrap it up with an adapter if necessary. """ if fastpath and getattr(data, "ndim", 0) > 0: # can't use fastpath (yet) for scalars return _maybe_wrap_data(data) from xarray.core.dataarray import DataArray if isinstance(data, (Variable, DataArray)): return data.data if isinstance(data, NON_NUMPY_SUPPORTED_ARRAY_TYPES): data = _possibly_convert_datetime_or_timedelta_index(data) return _maybe_wrap_data(data) if isinstance(data, tuple): data = utils.to_0d_object_array(data) if isinstance(data, pd.Timestamp): # TODO: convert, handle datetime objects, too data = np.datetime64(data.value, "ns") if isinstance(data, timedelta): data = np.timedelta64(getattr(data, "value", data), "ns") # we don't want nested self-described arrays if isinstance(data, (pd.Series, pd.Index, pd.DataFrame)): data = data.values if isinstance(data, np.ma.MaskedArray): mask = np.ma.getmaskarray(data) if mask.any(): dtype, fill_value = dtypes.maybe_promote(data.dtype) data = np.asarray(data, dtype=dtype) data[mask] = fill_value else: data = np.asarray(data) if not isinstance(data, np.ndarray) and ( hasattr(data, "__array_function__") or hasattr(data, "__array_namespace__") ): return data # validate whether the data is valid data types. data = np.asarray(data) if isinstance(data, np.ndarray) and data.dtype.kind in "OMm": data = _possibly_convert_objects(data) return _maybe_wrap_data(data) def _as_array_or_item(data): """Return the given values as a numpy array, or as an individual item if it's a 0d datetime64 or timedelta64 array. Importantly, this function does not copy data if it is already an ndarray - otherwise, it will not be possible to update Variable values in place. This function mostly exists because 0-dimensional ndarrays with dtype=datetime64 are broken :( https://github.com/numpy/numpy/issues/4337 https://github.com/numpy/numpy/issues/7619 TODO: remove this (replace with np.asarray) once these issues are fixed """ data = np.asarray(data) if data.ndim == 0: if data.dtype.kind == "M": data = np.datetime64(data, "ns") elif data.dtype.kind == "m": data = np.timedelta64(data, "ns") return data class Variable(AbstractArray, NdimSizeLenMixin, VariableArithmetic): """A netcdf-like variable consisting of dimensions, data and attributes which describe a single Array. A single Variable object is not fully described outside the context of its parent Dataset (if you want such a fully described object, use a DataArray instead). The main functional difference between Variables and numpy arrays is that numerical operations on Variables implement array broadcasting by dimension name. For example, adding an Variable with dimensions `('time',)` to another Variable with dimensions `('space',)` results in a new Variable with dimensions `('time', 'space')`. Furthermore, numpy reduce operations like ``mean`` or ``sum`` are overwritten to take a "dimension" argument instead of an "axis". Variables are light-weight objects used as the building block for datasets. They are more primitive objects, so operations with them provide marginally higher performance than using DataArrays. However, manipulating data in the form of a Dataset or DataArray should almost always be preferred, because they can use more complete metadata in context of coordinate labels. """ __slots__ = ("_dims", "_data", "_attrs", "_encoding") def __init__(self, dims, data, attrs=None, encoding=None, fastpath=False): """ Parameters ---------- dims : str or sequence of str Name(s) of the the data dimension(s). Must be either a string (only for 1D data) or a sequence of strings with length equal to the number of dimensions. data : array_like Data array which supports numpy-like data access. attrs : dict_like or None, optional Attributes to assign to the new variable. If None (default), an empty attribute dictionary is initialized. encoding : dict_like or None, optional Dictionary specifying how to encode this array's data into a serialized format like netCDF4. Currently used keys (for netCDF) include '_FillValue', 'scale_factor', 'add_offset' and 'dtype'. Well-behaved code to serialize a Variable should ignore unrecognized encoding items. """ self._data = as_compatible_data(data, fastpath=fastpath) self._dims = self._parse_dimensions(dims) self._attrs = None self._encoding = None if attrs is not None: self.attrs = attrs if encoding is not None: self.encoding = encoding @property def dtype(self): """ Data-type of the array’s elements. See Also -------- ndarray.dtype numpy.dtype """ return self._data.dtype @property def shape(self): """ Tuple of array dimensions. See Also -------- numpy.ndarray.shape """ return self._data.shape @property def nbytes(self) -> int: """ Total bytes consumed by the elements of the data array. If the underlying data array does not include ``nbytes``, estimates the bytes consumed based on the ``size`` and ``dtype``. """ if hasattr(self._data, "nbytes"): return self._data.nbytes else: return self.size * self.dtype.itemsize @property def _in_memory(self): return isinstance( self._data, (np.ndarray, np.number, PandasIndexingAdapter) ) or ( isinstance(self._data, indexing.MemoryCachedArray) and isinstance(self._data.array, indexing.NumpyIndexingAdapter) ) @property def data(self) -> Any: """ The Variable's data as an array. The underlying array type (e.g. dask, sparse, pint) is preserved. See Also -------- Variable.to_numpy Variable.as_numpy Variable.values """ if is_duck_array(self._data): return self._data else: return self.values @data.setter def data(self, data): data = as_compatible_data(data) if data.shape != self.shape: raise ValueError( f"replacement data must match the Variable's shape. " f"replacement data has shape {data.shape}; Variable has shape {self.shape}" ) self._data = data def astype( self: T_Variable, dtype, *, order=None, casting=None, subok=None, copy=None, keep_attrs=True, ) -> T_Variable: """ Copy of the Variable object, with data cast to a specified type. Parameters ---------- dtype : str or dtype Typecode or data-type to which the array is cast. order : {'C', 'F', 'A', 'K'}, optional Controls the memory layout order of the result. ‘C’ means C order, ‘F’ means Fortran order, ‘A’ means ‘F’ order if all the arrays are Fortran contiguous, ‘C’ order otherwise, and ‘K’ means as close to the order the array elements appear in memory as possible. casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional Controls what kind of data casting may occur. * 'no' means the data types should not be cast at all. * 'equiv' means only byte-order changes are allowed. * 'safe' means only casts which can preserve values are allowed. * 'same_kind' means only safe casts or casts within a kind, like float64 to float32, are allowed. * 'unsafe' means any data conversions may be done. subok : bool, optional If True, then sub-classes will be passed-through, otherwise the returned array will be forced to be a base-class array. copy : bool, optional By default, astype always returns a newly allocated array. If this is set to False and the `dtype` requirement is satisfied, the input array is returned instead of a copy. keep_attrs : bool, optional By default, astype keeps attributes. Set to False to remove attributes in the returned object. Returns ------- out : same as object New object with data cast to the specified type. Notes ----- The ``order``, ``casting``, ``subok`` and ``copy`` arguments are only passed through to the ``astype`` method of the underlying array when a value different than ``None`` is supplied. Make sure to only supply these arguments if the underlying array class supports them. See Also -------- numpy.ndarray.astype dask.array.Array.astype sparse.COO.astype """ from xarray.core.computation import apply_ufunc kwargs = dict(order=order, casting=casting, subok=subok, copy=copy) kwargs = {k: v for k, v in kwargs.items() if v is not None} return apply_ufunc( duck_array_ops.astype, self, dtype, kwargs=kwargs, keep_attrs=keep_attrs, dask="allowed", ) def load(self, **kwargs): """Manually trigger loading of this variable's data from disk or a remote source into memory and return this variable. 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. Parameters ---------- **kwargs : dict Additional keyword arguments passed on to ``dask.array.compute``. See Also -------- dask.array.compute """ if is_duck_dask_array(self._data): self._data = as_compatible_data(self._data.compute(**kwargs)) elif not is_duck_array(self._data): self._data = np.asarray(self._data) return self def compute(self, **kwargs): """Manually trigger loading of this variable's data from disk or a remote source into memory and return a new variable. 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. 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 __dask_tokenize__(self): # Use v.data, instead of v._data, in order to cope with the wrappers # around NetCDF and the like from dask.base import normalize_token return normalize_token((type(self), self._dims, self.data, self._attrs)) def __dask_graph__(self): if is_duck_dask_array(self._data): return self._data.__dask_graph__() else: return None def __dask_keys__(self): return self._data.__dask_keys__() def __dask_layers__(self): return self._data.__dask_layers__() @property def __dask_optimize__(self): return self._data.__dask_optimize__ @property def __dask_scheduler__(self): return self._data.__dask_scheduler__ def __dask_postcompute__(self): array_func, array_args = self._data.__dask_postcompute__() return self._dask_finalize, (array_func,) + array_args def __dask_postpersist__(self): array_func, array_args = self._data.__dask_postpersist__() return self._dask_finalize, (array_func,) + array_args def _dask_finalize(self, results, array_func, *args, **kwargs): data = array_func(results, *args, **kwargs) return Variable(self._dims, data, attrs=self._attrs, encoding=self._encoding) @property def values(self): """The variable's data as a numpy.ndarray""" return _as_array_or_item(self._data) @values.setter def values(self, values): self.data = values def to_base_variable(self) -> Variable: """Return this variable as a base xarray.Variable""" return Variable( self._dims, self._data, self._attrs, encoding=self._encoding, fastpath=True ) to_variable = utils.alias(to_base_variable, "to_variable") def to_index_variable(self) -> IndexVariable: """Return this variable as an xarray.IndexVariable""" return IndexVariable( self._dims, self._data, self._attrs, encoding=self._encoding, fastpath=True ) to_coord = utils.alias(to_index_variable, "to_coord") def _to_index(self) -> pd.Index: return self.to_index_variable()._to_index() def to_index(self) -> pd.Index: """Convert this variable to a pandas.Index""" return self.to_index_variable().to_index() def to_dict(self, data: bool = True, encoding: bool = False) -> dict: """Dictionary representation of variable.""" item = {"dims": self.dims, "attrs": decode_numpy_dict_values(self.attrs)} if data: item["data"] = ensure_us_time_resolution(self.values).tolist() else: item.update({"dtype": str(self.dtype), "shape": self.shape}) if encoding: item["encoding"] = dict(self.encoding) return item @property def dims(self) -> tuple[Hashable, ...]: """Tuple of dimension names with which this variable is associated.""" return self._dims @dims.setter def dims(self, value: str | Iterable[Hashable]) -> None: self._dims = self._parse_dimensions(value) def _parse_dimensions(self, dims: str | Iterable[Hashable]) -> tuple[Hashable, ...]: if isinstance(dims, str): dims = (dims,) dims = tuple(dims) if len(dims) != self.ndim: raise ValueError( f"dimensions {dims} must have the same length as the " f"number of data dimensions, ndim={self.ndim}" ) return dims def _item_key_to_tuple(self, key): if utils.is_dict_like(key): return tuple(key.get(dim, slice(None)) for dim in self.dims) else: return key def _broadcast_indexes(self, key): """Prepare an indexing key for an indexing operation. Parameters ---------- key : int, slice, array-like, dict or tuple of integer, slice and array-like Any valid input for indexing. Returns ------- dims : tuple Dimension of the resultant variable. indexers : IndexingTuple subclass Tuple of integer, array-like, or slices to use when indexing self._data. The type of this argument indicates the type of indexing to perform, either basic, outer or vectorized. new_order : Optional[Sequence[int]] Optional reordering to do on the result of indexing. If not None, the first len(new_order) indexing should be moved to these positions. """ key = self._item_key_to_tuple(key) # key is a tuple # key is a tuple of full size key = indexing.expanded_indexer(key, self.ndim) # Convert a scalar Variable to an integer key = tuple( k.data.item() if isinstance(k, Variable) and k.ndim == 0 else k for k in key ) # Convert a 0d-array to an integer key = tuple( k.item() if isinstance(k, np.ndarray) and k.ndim == 0 else k for k in key ) if all(isinstance(k, BASIC_INDEXING_TYPES) for k in key): return self._broadcast_indexes_basic(key) self._validate_indexers(key) # Detect it can be mapped as an outer indexer # If all key is unlabeled, or # key can be mapped as an OuterIndexer. if all(not isinstance(k, Variable) for k in key): return self._broadcast_indexes_outer(key) # If all key is 1-dimensional and there are no duplicate labels, # key can be mapped as an OuterIndexer. dims = [] for k, d in zip(key, self.dims): if isinstance(k, Variable): if len(k.dims) > 1: return self._broadcast_indexes_vectorized(key) dims.append(k.dims[0]) elif not isinstance(k, integer_types): dims.append(d) if len(set(dims)) == len(dims): return self._broadcast_indexes_outer(key) return self._broadcast_indexes_vectorized(key) def _broadcast_indexes_basic(self, key): dims = tuple( dim for k, dim in zip(key, self.dims) if not isinstance(k, integer_types) ) return dims, BasicIndexer(key), None def _validate_indexers(self, key): """Make sanity checks""" for dim, k in zip(self.dims, key): if not isinstance(k, BASIC_INDEXING_TYPES): if not isinstance(k, Variable): k = np.asarray(k) if k.ndim > 1: raise IndexError( "Unlabeled multi-dimensional array cannot be " "used for indexing: {}".format(k) ) if k.dtype.kind == "b": if self.shape[self.get_axis_num(dim)] != len(k): raise IndexError( "Boolean array size {:d} is used to index array " "with shape {:s}.".format(len(k), str(self.shape)) ) if k.ndim > 1: raise IndexError( "{}-dimensional boolean indexing is " "not supported. ".format(k.ndim) ) if getattr(k, "dims", (dim,)) != (dim,): raise IndexError( "Boolean indexer should be unlabeled or on the " "same dimension to the indexed array. Indexer is " "on {:s} but the target dimension is {:s}.".format( str(k.dims), dim ) ) def _broadcast_indexes_outer(self, key): dims = tuple( k.dims[0] if isinstance(k, Variable) else dim for k, dim in zip(key, self.dims) if not isinstance(k, integer_types) ) new_key = [] for k in key: if isinstance(k, Variable): k = k.data if not isinstance(k, BASIC_INDEXING_TYPES): k = np.asarray(k) if k.size == 0: # Slice by empty list; numpy could not infer the dtype k = k.astype(int) elif k.dtype.kind == "b": (k,) = np.nonzero(k) new_key.append(k) return dims, OuterIndexer(tuple(new_key)), None def _nonzero(self): """Equivalent numpy's nonzero but returns a tuple of Variables.""" # TODO we should replace dask's native nonzero # after https://github.com/dask/dask/issues/1076 is implemented. nonzeros = np.nonzero(self.data) return tuple(Variable((dim), nz) for nz, dim in zip(nonzeros, self.dims)) def _broadcast_indexes_vectorized(self, key): variables = [] out_dims_set = OrderedSet() for dim, value in zip(self.dims, key): if isinstance(value, slice): out_dims_set.add(dim) else: variable = ( value if isinstance(value, Variable) else as_variable(value, name=dim) ) if variable.dtype.kind == "b": # boolean indexing case (variable,) = variable._nonzero() variables.append(variable) out_dims_set.update(variable.dims) variable_dims = set() for variable in variables: variable_dims.update(variable.dims) slices = [] for i, (dim, value) in enumerate(zip(self.dims, key)): if isinstance(value, slice): if dim in variable_dims: # We only convert slice objects to variables if they share # a dimension with at least one other variable. Otherwise, # we can equivalently leave them as slices aknd transpose # the result. This is significantly faster/more efficient # for most array backends. values = np.arange(*value.indices(self.sizes[dim])) variables.insert(i - len(slices), Variable((dim,), values)) else: slices.append((i, value)) try: variables = _broadcast_compat_variables(*variables) except ValueError: raise IndexError(f"Dimensions of indexers mismatch: {key}") out_key = [variable.data for variable in variables] out_dims = tuple(out_dims_set) slice_positions = set() for i, value in slices: out_key.insert(i, value) new_position = out_dims.index(self.dims[i]) slice_positions.add(new_position) if slice_positions: new_order = [i for i in range(len(out_dims)) if i not in slice_positions] else: new_order = None return out_dims, VectorizedIndexer(tuple(out_key)), new_order def __getitem__(self: T_Variable, key) -> T_Variable: """Return a new Variable object whose contents are consistent with getting the provided key from the underlying data. NB. __getitem__ and __setitem__ implement xarray-style indexing, where if keys are unlabeled arrays, we index the array orthogonally with them. If keys are labeled array (such as Variables), they are broadcasted with our usual scheme and then the array is indexed with the broadcasted key, like numpy's fancy indexing. If you really want to do indexing like `x[x > 0]`, manipulate the numpy array `x.values` directly. """ dims, indexer, new_order = self._broadcast_indexes(key) data = as_indexable(self._data)[indexer] if new_order: data = np.moveaxis(data, range(len(new_order)), new_order) return self._finalize_indexing_result(dims, data) def _finalize_indexing_result(self: T_Variable, dims, data) -> T_Variable: """Used by IndexVariable to return IndexVariable objects when possible.""" return self._replace(dims=dims, data=data) def _getitem_with_mask(self, key, fill_value=dtypes.NA): """Index this Variable with -1 remapped to fill_value.""" # TODO(shoyer): expose this method in public API somewhere (isel?) and # use it for reindex. # TODO(shoyer): add a sanity check that all other integers are # non-negative # TODO(shoyer): add an optimization, remapping -1 to an adjacent value # that is actually indexed rather than mapping it to the last value # along each axis. if fill_value is dtypes.NA: fill_value = dtypes.get_fill_value(self.dtype) dims, indexer, new_order = self._broadcast_indexes(key) if self.size: if is_duck_dask_array(self._data): # dask's indexing is faster this way; also vindex does not # support negative indices yet: # https://github.com/dask/dask/pull/2967 actual_indexer = indexing.posify_mask_indexer(indexer) else: actual_indexer = indexer data = as_indexable(self._data)[actual_indexer] mask = indexing.create_mask(indexer, self.shape, data) # we need to invert the mask in order to pass data first. This helps # pint to choose the correct unit # TODO: revert after https://github.com/hgrecco/pint/issues/1019 is fixed data = duck_array_ops.where(np.logical_not(mask), data, fill_value) else: # array cannot be indexed along dimensions of size 0, so just # build the mask directly instead. mask = indexing.create_mask(indexer, self.shape) data = np.broadcast_to(fill_value, getattr(mask, "shape", ())) if new_order: data = duck_array_ops.moveaxis(data, range(len(new_order)), new_order) return self._finalize_indexing_result(dims, data) def __setitem__(self, key, value): """__setitem__ is overloaded to access the underlying numpy values with orthogonal indexing. See __getitem__ for more details. """ dims, index_tuple, new_order = self._broadcast_indexes(key) if not isinstance(value, Variable): value = as_compatible_data(value) if value.ndim > len(dims): raise ValueError( f"shape mismatch: value array of shape {value.shape} could not be " f"broadcast to indexing result with {len(dims)} dimensions" ) if value.ndim == 0: value = Variable((), value) else: value = Variable(dims[-value.ndim :], value) # broadcast to become assignable value = value.set_dims(dims).data if new_order: value = duck_array_ops.asarray(value) value = value[(len(dims) - value.ndim) * (np.newaxis,) + (Ellipsis,)] value = np.moveaxis(value, new_order, range(len(new_order))) indexable = as_indexable(self._data) indexable[index_tuple] = value @property def attrs(self) -> dict[Any, Any]: """Dictionary of local attributes on this variable.""" if self._attrs is None: self._attrs = {} return self._attrs @attrs.setter def attrs(self, value: Mapping[Any, Any]) -> None: self._attrs = dict(value) @property def encoding(self) -> dict[Any, Any]: """Dictionary of encodings on this variable.""" if self._encoding is None: self._encoding = {} return self._encoding @encoding.setter def encoding(self, value): try: self._encoding = dict(value) except ValueError: raise ValueError("encoding must be castable to a dictionary") def copy( self: T_Variable, deep: bool = True, data: ArrayLike | None = None ) -> T_Variable: """Returns a copy of this object. If `deep=True`, the data array is loaded into memory and copied onto the new object. Dimensions, attributes and encodings are always copied. Use `data` to create a new object with the same structure as original but entirely new data. Parameters ---------- deep : bool, default: True Whether the data array is 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. Returns ------- object : Variable New object with dimensions, attributes, encodings, and optionally data copied from original. Examples -------- Shallow copy versus deep copy >>> var = xr.Variable(data=[1, 2, 3], dims="x") >>> var.copy() array([1, 2, 3]) >>> var_0 = var.copy(deep=False) >>> var_0[0] = 7 >>> var_0 array([7, 2, 3]) >>> var array([7, 2, 3]) Changing the data using the ``data`` argument maintains the structure of the original object, but with the new data. Original object is unaffected. >>> var.copy(data=[0.1, 0.2, 0.3]) array([0.1, 0.2, 0.3]) >>> var array([7, 2, 3]) See Also -------- pandas.DataFrame.copy """ return self._copy(deep=deep, data=data) def _copy( self: T_Variable, deep: bool = True, data: ArrayLike | None = None, memo: dict[int, Any] | None = None, ) -> T_Variable: if data is None: ndata = self._data if isinstance(ndata, indexing.MemoryCachedArray): # don't share caching between copies ndata = indexing.MemoryCachedArray(ndata.array) if deep: ndata = copy.deepcopy(ndata, memo) else: ndata = as_compatible_data(data) if self.shape != ndata.shape: raise ValueError( "Data shape {} must match shape of object {}".format( ndata.shape, self.shape ) ) attrs = copy.deepcopy(self._attrs, memo) if deep else copy.copy(self._attrs) encoding = ( copy.deepcopy(self._encoding, memo) if deep else copy.copy(self._encoding) ) # note: dims is already an immutable tuple return self._replace(data=ndata, attrs=attrs, encoding=encoding) def _replace( self: T_Variable, dims=_default, data=_default, attrs=_default, encoding=_default, ) -> T_Variable: if dims is _default: dims = copy.copy(self._dims) if data is _default: data = copy.copy(self.data) if attrs is _default: attrs = copy.copy(self._attrs) if encoding is _default: encoding = copy.copy(self._encoding) return type(self)(dims, data, attrs, encoding, fastpath=True) def __copy__(self: T_Variable) -> T_Variable: return self._copy(deep=False) def __deepcopy__( self: T_Variable, memo: dict[int, Any] | None = None ) -> T_Variable: return self._copy(deep=True, memo=memo) # mutable objects should not be hashable # https://github.com/python/mypy/issues/4266 __hash__ = None # type: ignore[assignment] @property def chunks(self) -> tuple[tuple[int, ...], ...] | None: """ Tuple of block lengths for this dataarray's data, in order of dimensions, or None if the underlying data is not a dask array. See Also -------- Variable.chunk Variable.chunksizes xarray.unify_chunks """ return getattr(self._data, "chunks", None) @property def chunksizes(self) -> Mapping[Any, tuple[int, ...]]: """ Mapping from dimension names to block lengths for this variable's data, or None if the underlying data is not a dask array. Cannot be modified directly, but can be modified by calling .chunk(). Differs from variable.chunks because it returns a mapping of dimensions to chunk shapes instead of a tuple of chunk shapes. See Also -------- Variable.chunk Variable.chunks xarray.unify_chunks """ if hasattr(self._data, "chunks"): return Frozen({dim: c for dim, c in zip(self.dims, self.data.chunks)}) else: return {} _array_counter = itertools.count() def chunk( self, chunks: ( int | Literal["auto"] | tuple[int, ...] | tuple[tuple[int, ...], ...] | Mapping[Any, None | int | tuple[int, ...]] ) = {}, name: str | None = None, lock: bool = False, inline_array: bool = False, **chunks_kwargs: Any, ) -> Variable: """Coerce this array's data into a dask array 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 or dict, optional Chunk sizes along each dimension, e.g., ``5``, ``(5, 5)`` or ``{'x': 5, 'y': 5}``. name : str, optional Used to generate the name for this array in the internal dask graph. Does not need not be unique. lock : optional Passed on to :py:func:`dask.array.from_array`, if the array is not already as dask array. inline_array: optional Passed on to :py:func:`dask.array.from_array`, if the array is not already as dask array. **chunks_kwargs : {dim: chunks, ...}, optional The keyword arguments form of ``chunks``. One of chunks or chunks_kwargs must be provided. Returns ------- chunked : xarray.Variable See Also -------- Variable.chunks Variable.chunksizes xarray.unify_chunks dask.array.from_array """ import dask.array as da if chunks is None: warnings.warn( "None value for 'chunks' is deprecated. " "It will raise an error in the future. Use instead '{}'", category=FutureWarning, ) chunks = {} if isinstance(chunks, (float, str, int, tuple, list)): pass # dask.array.from_array can handle these directly else: chunks = either_dict_or_kwargs(chunks, chunks_kwargs, "chunk") if utils.is_dict_like(chunks): chunks = {self.get_axis_num(dim): chunk for dim, chunk in chunks.items()} data = self._data if is_duck_dask_array(data): data = data.rechunk(chunks) else: if isinstance(data, indexing.ExplicitlyIndexed): # Unambiguously handle array storage backends (like NetCDF4 and h5py) # that can't handle general array indexing. For example, in netCDF4 you # can do "outer" indexing along two dimensions independent, which works # differently from how NumPy handles it. # da.from_array works by using lazy indexing with a tuple of slices. # Using OuterIndexer is a pragmatic choice: dask does not yet handle # different indexing types in an explicit way: # https://github.com/dask/dask/issues/2883 data = indexing.ImplicitToExplicitIndexingAdapter( data, indexing.OuterIndexer ) # All of our lazily loaded backend array classes should use NumPy # array operations. kwargs = {"meta": np.ndarray} else: kwargs = {} if utils.is_dict_like(chunks): chunks = tuple(chunks.get(n, s) for n, s in enumerate(self.shape)) data = da.from_array( data, chunks, name=name, lock=lock, inline_array=inline_array, **kwargs ) return self._replace(data=data) def to_numpy(self) -> np.ndarray: """Coerces wrapped data to numpy and returns a numpy.ndarray""" # TODO an entrypoint so array libraries can choose coercion method? data = self.data # TODO first attempt to call .to_numpy() once some libraries implement it if hasattr(data, "chunks"): data = data.compute() if isinstance(data, array_type("cupy")): data = data.get() # pint has to be imported dynamically as pint imports xarray if isinstance(data, array_type("pint")): data = data.magnitude if isinstance(data, array_type("sparse")): data = data.todense() data = np.asarray(data) return data def as_numpy(self: T_Variable) -> T_Variable: """Coerces wrapped data into a numpy array, returning a Variable.""" return self._replace(data=self.to_numpy()) def _as_sparse(self, sparse_format=_default, fill_value=dtypes.NA): """ use sparse-array as backend. """ import sparse # TODO: what to do if dask-backended? if fill_value is dtypes.NA: dtype, fill_value = dtypes.maybe_promote(self.dtype) else: dtype = dtypes.result_type(self.dtype, fill_value) if sparse_format is _default: sparse_format = "coo" try: as_sparse = getattr(sparse, f"as_{sparse_format.lower()}") except AttributeError: raise ValueError(f"{sparse_format} is not a valid sparse format") data = as_sparse(self.data.astype(dtype), fill_value=fill_value) return self._replace(data=data) def _to_dense(self): """ Change backend from sparse to np.array """ if hasattr(self._data, "todense"): return self._replace(data=self._data.todense()) return self.copy(deep=False) def isel( self: T_Variable, indexers: Mapping[Any, Any] | None = None, missing_dims: ErrorOptionsWithWarn = "raise", **indexers_kwargs: Any, ) -> T_Variable: """Return a new array indexed along the specified dimension(s). Parameters ---------- **indexers : {dim: indexer, ...} Keyword arguments with names matching dimensions and values given by integers, slice objects or arrays. 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 - "warn": raise a warning, and ignore the missing dimensions - "ignore": ignore the missing dimensions Returns ------- obj : Array object A new Array with the selected data and dimensions. In general, the new variable's data will be a view of this variable's data, unless numpy fancy indexing was triggered by using an array indexer, in which case the data will be a copy. """ indexers = either_dict_or_kwargs(indexers, indexers_kwargs, "isel") indexers = drop_dims_from_indexers(indexers, self.dims, missing_dims) key = tuple(indexers.get(dim, slice(None)) for dim in self.dims) return self[key] def squeeze(self, dim=None): """Return a new object with squeezed data. Parameters ---------- dim : None or str or tuple of str, optional Selects a subset of the length one dimensions. If a dimension is selected with length greater than one, an error is raised. If None, all length one dimensions are squeezed. Returns ------- squeezed : same type as caller This object, but with with all or a subset of the dimensions of length 1 removed. See Also -------- numpy.squeeze """ dims = common.get_squeeze_dims(self, dim) return self.isel({d: 0 for d in dims}) def _shift_one_dim(self, dim, count, fill_value=dtypes.NA): axis = self.get_axis_num(dim) if count > 0: keep = slice(None, -count) elif count < 0: keep = slice(-count, None) else: keep = slice(None) trimmed_data = self[(slice(None),) * axis + (keep,)].data if fill_value is dtypes.NA: dtype, fill_value = dtypes.maybe_promote(self.dtype) else: dtype = self.dtype width = min(abs(count), self.shape[axis]) dim_pad = (width, 0) if count >= 0 else (0, width) pads = [(0, 0) if d != dim else dim_pad for d in self.dims] data = np.pad( trimmed_data.astype(dtype), pads, mode="constant", constant_values=fill_value, ) if is_duck_dask_array(data): # chunked data should come out with the same chunks; this makes # it feasible to combine shifted and unshifted data # TODO: remove this once dask.array automatically aligns chunks data = data.rechunk(self.data.chunks) return self._replace(data=data) def shift(self, shifts=None, fill_value=dtypes.NA, **shifts_kwargs): """ Return a new Variable with shifted data. Parameters ---------- shifts : mapping of the form {dim: offset} 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 : Variable Variable with the same dimensions and attributes but shifted data. """ shifts = either_dict_or_kwargs(shifts, shifts_kwargs, "shift") result = self for dim, count in shifts.items(): result = result._shift_one_dim(dim, count, fill_value=fill_value) return result def _pad_options_dim_to_index( self, pad_option: Mapping[Any, int | tuple[int, int]], fill_with_shape=False, ): if fill_with_shape: return [ (n, n) if d not in pad_option else pad_option[d] for d, n in zip(self.dims, self.data.shape) ] return [(0, 0) if d not in pad_option else pad_option[d] for d in self.dims] def pad( self, pad_width: Mapping[Any, int | tuple[int, int]] | None = None, mode: PadModeOptions = "constant", stat_length: int | tuple[int, int] | Mapping[Any, tuple[int, int]] | None = None, constant_values: float | tuple[float, float] | Mapping[Any, tuple[float, float]] | None = None, end_values: int | tuple[int, int] | Mapping[Any, tuple[int, int]] | None = None, reflect_type: PadReflectOptions = None, keep_attrs: bool | None = None, **pad_width_kwargs: Any, ): """ Return a new Variable with padded data. 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" See numpy / Dask docs stat_length : int, tuple or mapping of hashable to tuple Used in 'maximum', 'mean', 'median', and 'minimum'. Number of values at edge of each axis used to calculate the statistic value. constant_values : scalar, tuple or mapping of hashable to tuple Used in 'constant'. The values to set the padded values for each axis. end_values : scalar, tuple or mapping of hashable to tuple 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. 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. 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. **pad_width_kwargs One of pad_width or pad_width_kwargs must be provided. Returns ------- padded : Variable Variable with the same dimensions and attributes but padded data. """ pad_width = either_dict_or_kwargs(pad_width, pad_width_kwargs, "pad") # change default behaviour of pad with mode constant if mode == "constant" and ( constant_values is None or constant_values is dtypes.NA ): dtype, constant_values = dtypes.maybe_promote(self.dtype) else: dtype = self.dtype # create pad_options_kwargs, numpy requires only relevant kwargs to be nonempty if isinstance(stat_length, dict): stat_length = self._pad_options_dim_to_index( stat_length, fill_with_shape=True ) if isinstance(constant_values, dict): constant_values = self._pad_options_dim_to_index(constant_values) if isinstance(end_values, dict): end_values = self._pad_options_dim_to_index(end_values) # workaround for bug in Dask's default value of stat_length https://github.com/dask/dask/issues/5303 if stat_length is None and mode in ["maximum", "mean", "median", "minimum"]: stat_length = [(n, n) for n in self.data.shape] # type: ignore[assignment] # change integer values to a tuple of two of those values and change pad_width to index for k, v in pad_width.items(): if isinstance(v, numbers.Number): pad_width[k] = (v, v) pad_width_by_index = self._pad_options_dim_to_index(pad_width) # create pad_options_kwargs, numpy/dask requires only relevant kwargs to be nonempty pad_option_kwargs: dict[str, Any] = {} if stat_length is not None: pad_option_kwargs["stat_length"] = stat_length if constant_values is not None: pad_option_kwargs["constant_values"] = constant_values if end_values is not None: pad_option_kwargs["end_values"] = end_values if reflect_type is not None: pad_option_kwargs["reflect_type"] = reflect_type array = np.pad( self.data.astype(dtype, copy=False), pad_width_by_index, mode=mode, **pad_option_kwargs, ) if keep_attrs is None: keep_attrs = _get_keep_attrs(default=True) attrs = self._attrs if keep_attrs else None return type(self)(self.dims, array, attrs=attrs) def _roll_one_dim(self, dim, count): axis = self.get_axis_num(dim) count %= self.shape[axis] if count != 0: indices = [slice(-count, None), slice(None, -count)] else: indices = [slice(None)] arrays = [self[(slice(None),) * axis + (idx,)].data for idx in indices] data = duck_array_ops.concatenate(arrays, axis) if is_duck_dask_array(data): # chunked data should come out with the same chunks; this makes # it feasible to combine shifted and unshifted data # TODO: remove this once dask.array automatically aligns chunks data = data.rechunk(self.data.chunks) return self._replace(data=data) def roll(self, shifts=None, **shifts_kwargs): """ Return a new Variable with rolld data. Parameters ---------- shifts : mapping of hashable to int Integer offset to roll along each of the given dimensions. Positive offsets roll to the right; negative offsets roll to the left. **shifts_kwargs The keyword arguments form of ``shifts``. One of shifts or shifts_kwargs must be provided. Returns ------- shifted : Variable Variable with the same dimensions and attributes but rolled data. """ shifts = either_dict_or_kwargs(shifts, shifts_kwargs, "roll") result = self for dim, count in shifts.items(): result = result._roll_one_dim(dim, count) return result def transpose( self, *dims: Hashable | ellipsis, missing_dims: ErrorOptionsWithWarn = "raise", ) -> Variable: """Return a new Variable object with transposed dimensions. Parameters ---------- *dims : Hashable, optional By default, reverse the dimensions. Otherwise, reorder the dimensions to this order. missing_dims : {"raise", "warn", "ignore"}, default: "raise" What to do if dimensions that should be selected from are not present in the Variable: - "raise": raise an exception - "warn": raise a warning, and ignore the missing dimensions - "ignore": ignore the missing dimensions Returns ------- transposed : Variable The returned object has transposed data and dimensions with the same attributes as the original. Notes ----- This operation returns a view of this variable's data. It is lazy for dask-backed Variables but not for numpy-backed Variables. See Also -------- numpy.transpose """ if len(dims) == 0: dims = self.dims[::-1] else: dims = tuple(infix_dims(dims, self.dims, missing_dims)) if len(dims) < 2 or dims == self.dims: # no need to transpose if only one dimension # or dims are in same order return self.copy(deep=False) axes = self.get_axis_num(dims) data = as_indexable(self._data).transpose(axes) return self._replace(dims=dims, data=data) @property def T(self) -> Variable: return self.transpose() def set_dims(self, dims, shape=None): """Return a new variable with given set of dimensions. This method might be used to attach new dimension(s) to variable. When possible, this operation does not copy this variable's data. Parameters ---------- dims : str or sequence of str or dict Dimensions to include on the new variable. If a dict, values are used to provide the sizes of new dimensions; otherwise, new dimensions are inserted with length 1. Returns ------- Variable """ if isinstance(dims, str): dims = [dims] if shape is None and utils.is_dict_like(dims): shape = dims.values() missing_dims = set(self.dims) - set(dims) if missing_dims: raise ValueError( f"new dimensions {dims!r} must be a superset of " f"existing dimensions {self.dims!r}" ) self_dims = set(self.dims) expanded_dims = tuple(d for d in dims if d not in self_dims) + self.dims if self.dims == expanded_dims: # don't use broadcast_to unless necessary so the result remains # writeable if possible expanded_data = self.data elif shape is not None: dims_map = dict(zip(dims, shape)) tmp_shape = tuple(dims_map[d] for d in expanded_dims) expanded_data = duck_array_ops.broadcast_to(self.data, tmp_shape) else: expanded_data = self.data[(None,) * (len(expanded_dims) - self.ndim)] expanded_var = Variable( expanded_dims, expanded_data, self._attrs, self._encoding, fastpath=True ) return expanded_var.transpose(*dims) def _stack_once(self, dims: list[Hashable], new_dim: Hashable): if not set(dims) <= set(self.dims): raise ValueError(f"invalid existing dimensions: {dims}") if new_dim in self.dims: raise ValueError( "cannot create a new dimension with the same " "name as an existing dimension" ) if len(dims) == 0: # don't stack return self.copy(deep=False) other_dims = [d for d in self.dims if d not in dims] dim_order = other_dims + list(dims) reordered = self.transpose(*dim_order) new_shape = reordered.shape[: len(other_dims)] + (-1,) new_data = duck_array_ops.reshape(reordered.data, new_shape) new_dims = reordered.dims[: len(other_dims)] + (new_dim,) return Variable(new_dims, new_data, self._attrs, self._encoding, fastpath=True) def stack(self, dimensions=None, **dimensions_kwargs): """ Stack any number of existing dimensions into a single new dimension. New dimensions will be added at the end, and the order of the data along each new dimension will be in contiguous (C) order. Parameters ---------- dimensions : mapping of hashable to tuple of hashable Mapping of form new_name=(dim1, dim2, ...) describing the names of new dimensions, and the existing dimensions that they replace. **dimensions_kwargs The keyword arguments form of ``dimensions``. One of dimensions or dimensions_kwargs must be provided. Returns ------- stacked : Variable Variable with the same attributes but stacked data. See Also -------- Variable.unstack """ dimensions = either_dict_or_kwargs(dimensions, dimensions_kwargs, "stack") result = self for new_dim, dims in dimensions.items(): result = result._stack_once(dims, new_dim) return result def _unstack_once_full( self, dims: Mapping[Any, int], old_dim: Hashable ) -> Variable: """ Unstacks the variable without needing an index. Unlike `_unstack_once`, this function requires the existing dimension to contain the full product of the new dimensions. """ new_dim_names = tuple(dims.keys()) new_dim_sizes = tuple(dims.values()) if old_dim not in self.dims: raise ValueError(f"invalid existing dimension: {old_dim}") if set(new_dim_names).intersection(self.dims): raise ValueError( "cannot create a new dimension with the same " "name as an existing dimension" ) if math.prod(new_dim_sizes) != self.sizes[old_dim]: raise ValueError( "the product of the new dimension sizes must " "equal the size of the old dimension" ) other_dims = [d for d in self.dims if d != old_dim] dim_order = other_dims + [old_dim] reordered = self.transpose(*dim_order) new_shape = reordered.shape[: len(other_dims)] + new_dim_sizes new_data = reordered.data.reshape(new_shape) new_dims = reordered.dims[: len(other_dims)] + new_dim_names return Variable(new_dims, new_data, self._attrs, self._encoding, fastpath=True) def _unstack_once( self, index: pd.MultiIndex, dim: Hashable, fill_value=dtypes.NA, sparse: bool = False, ) -> Variable: """ Unstacks this variable given an index to unstack and the name of the dimension to which the index refers. """ reordered = self.transpose(..., dim) new_dim_sizes = [lev.size for lev in index.levels] new_dim_names = index.names indexer = index.codes # Potentially we could replace `len(other_dims)` with just `-1` other_dims = [d for d in self.dims if d != dim] new_shape = tuple(list(reordered.shape[: len(other_dims)]) + new_dim_sizes) new_dims = reordered.dims[: len(other_dims)] + new_dim_names if fill_value is dtypes.NA: is_missing_values = math.prod(new_shape) > math.prod(self.shape) if is_missing_values: dtype, fill_value = dtypes.maybe_promote(self.dtype) else: dtype = self.dtype fill_value = dtypes.get_fill_value(dtype) else: dtype = self.dtype if sparse: # unstacking a dense multitindexed array to a sparse array from sparse import COO codes = zip(*index.codes) if reordered.ndim == 1: indexes = codes else: sizes = itertools.product(*[range(s) for s in reordered.shape[:-1]]) tuple_indexes = itertools.product(sizes, codes) indexes = map(lambda x: list(itertools.chain(*x)), tuple_indexes) # type: ignore data = COO( coords=np.array(list(indexes)).T, data=self.data.astype(dtype).ravel(), fill_value=fill_value, shape=new_shape, sorted=index.is_monotonic_increasing, ) else: data = np.full_like( self.data, fill_value=fill_value, shape=new_shape, dtype=dtype, ) # Indexer is a list of lists of locations. Each list is the locations # on the new dimension. This is robust to the data being sparse; in that # case the destinations will be NaN / zero. data[(..., *indexer)] = reordered return self._replace(dims=new_dims, data=data) def unstack(self, dimensions=None, **dimensions_kwargs): """ Unstack an existing dimension into multiple new dimensions. New dimensions will be added at the end, and the order of the data along each new dimension will be in contiguous (C) order. Note that unlike ``DataArray.unstack`` and ``Dataset.unstack``, this method requires the existing dimension to contain the full product of the new dimensions. Parameters ---------- dimensions : mapping of hashable to mapping of hashable to int Mapping of the form old_dim={dim1: size1, ...} describing the names of existing dimensions, and the new dimensions and sizes that they map to. **dimensions_kwargs The keyword arguments form of ``dimensions``. One of dimensions or dimensions_kwargs must be provided. Returns ------- unstacked : Variable Variable with the same attributes but unstacked data. See Also -------- Variable.stack DataArray.unstack Dataset.unstack """ dimensions = either_dict_or_kwargs(dimensions, dimensions_kwargs, "unstack") result = self for old_dim, dims in dimensions.items(): result = result._unstack_once_full(dims, old_dim) return result def fillna(self, value): return ops.fillna(self, value) def where(self, cond, other=dtypes.NA): return ops.where_method(self, cond, other) def clip(self, min=None, max=None): """ Return an array whose values are limited to ``[min, max]``. At least one of max or min must be given. Refer to `numpy.clip` for full documentation. See Also -------- numpy.clip : equivalent function """ from xarray.core.computation import apply_ufunc return apply_ufunc(np.clip, self, min, max, dask="allowed") def reduce( self, func: Callable[..., Any], dim: Dims = None, axis: int | Sequence[int] | None = None, keep_attrs: bool | None = None, keepdims: bool = False, **kwargs, ) -> Variable: """Reduce this array by applying `func` along some dimension(s). Parameters ---------- func : callable Function which can be called in the form `func(x, axis=axis, **kwargs)` to return the result of reducing an np.ndarray over an integer valued axis. dim : "...", str, Iterable of Hashable or None, optional Dimension(s) over which to apply `func`. By default `func` is applied over all dimensions. axis : int or Sequence of int, optional Axis(es) over which to 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 `func(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 **kwargs : dict Additional keyword arguments passed on to `func`. Returns ------- reduced : Array Array with summarized data and the indicated dimension(s) removed. """ if dim == ...: dim = None if dim is not None and axis is not None: raise ValueError("cannot supply both 'axis' and 'dim' arguments") if dim is not None: axis = self.get_axis_num(dim) with warnings.catch_warnings(): warnings.filterwarnings( "ignore", r"Mean of empty slice", category=RuntimeWarning ) if axis is not None: if isinstance(axis, tuple) and len(axis) == 1: # unpack axis for the benefit of functions # like np.argmin which can't handle tuple arguments axis = axis[0] data = func(self.data, axis=axis, **kwargs) else: data = func(self.data, **kwargs) if getattr(data, "shape", ()) == self.shape: dims = self.dims else: removed_axes: Iterable[int] if axis is None: removed_axes = range(self.ndim) else: removed_axes = np.atleast_1d(axis) % self.ndim if keepdims: # Insert np.newaxis for removed dims slices = tuple( np.newaxis if i in removed_axes else slice(None, None) for i in range(self.ndim) ) if getattr(data, "shape", None) is None: # Reduce has produced a scalar value, not an array-like data = np.asanyarray(data)[slices] else: data = data[slices] dims = self.dims else: dims = tuple( adim for n, adim in enumerate(self.dims) if n not in removed_axes ) if keep_attrs is None: keep_attrs = _get_keep_attrs(default=False) attrs = self._attrs if keep_attrs else None return Variable(dims, data, attrs=attrs) @classmethod def concat( cls, variables, dim="concat_dim", positions=None, shortcut=False, combine_attrs="override", ): """Concatenate variables along a new or existing dimension. Parameters ---------- variables : iterable of Variable Arrays to stack together. Each variable is expected to have matching dimensions and shape except for along the stacked dimension. dim : str or DataArray, optional Name of the dimension to stack along. This can either be a new dimension name, in which case it is added along axis=0, or an existing dimension name, in which case the location of the dimension is unchanged. Where to insert the new dimension is determined by the first variable. positions : None or list of array-like, optional List of integer arrays which specifies the integer positions to which to assign each dataset along the concatenated dimension. If not supplied, objects are concatenated in the provided order. shortcut : bool, optional This option is used internally to speed-up groupby operations. If `shortcut` is True, some checks of internal consistency between arrays to concatenate are skipped. combine_attrs : {"drop", "identical", "no_conflicts", "drop_conflicts", \ "override"}, default: "override" String indicating how to combine attrs of the objects being merged: - "drop": empty attrs on returned Dataset. - "identical": all attrs must be the same on every object. - "no_conflicts": attrs from all objects are combined, any that have the same name must also have the same value. - "drop_conflicts": attrs from all objects are combined, any that have the same name but different values are dropped. - "override": skip comparing and copy attrs from the first dataset to the result. Returns ------- stacked : Variable Concatenated Variable formed by stacking all the supplied variables along the given dimension. """ from xarray.core.merge import merge_attrs if not isinstance(dim, str): (dim,) = dim.dims # can't do this lazily: we need to loop through variables at least # twice variables = list(variables) first_var = variables[0] arrays = [v.data for v in variables] if dim in first_var.dims: axis = first_var.get_axis_num(dim) dims = first_var.dims data = duck_array_ops.concatenate(arrays, axis=axis) if positions is not None: # TODO: deprecate this option -- we don't need it for groupby # any more. indices = nputils.inverse_permutation(np.concatenate(positions)) data = duck_array_ops.take(data, indices, axis=axis) else: axis = 0 dims = (dim,) + first_var.dims data = duck_array_ops.stack(arrays, axis=axis) attrs = merge_attrs( [var.attrs for var in variables], combine_attrs=combine_attrs ) encoding = dict(first_var.encoding) if not shortcut: for var in variables: if var.dims != first_var.dims: raise ValueError( f"Variable has dimensions {list(var.dims)} but first Variable has dimensions {list(first_var.dims)}" ) return cls(dims, data, attrs, encoding) def equals(self, other, equiv=duck_array_ops.array_equiv): """True if two Variables have the same dimensions and values; otherwise False. Variables can still be equal (like pandas objects) if they have NaN values in the same locations. This method is necessary because `v1 == v2` for Variables does element-wise comparisons (like numpy.ndarrays). """ other = getattr(other, "variable", other) try: return self.dims == other.dims and ( self._data is other._data or equiv(self.data, other.data) ) except (TypeError, AttributeError): return False def broadcast_equals(self, other, equiv=duck_array_ops.array_equiv): """True if two Variables have the values after being broadcast against each other; otherwise False. Variables can still be equal (like pandas objects) if they have NaN values in the same locations. """ try: self, other = broadcast_variables(self, other) except (ValueError, AttributeError): return False return self.equals(other, equiv=equiv) def identical(self, other, equiv=duck_array_ops.array_equiv): """Like equals, but also checks attributes.""" try: return utils.dict_equiv(self.attrs, other.attrs) and self.equals( other, equiv=equiv ) except (TypeError, AttributeError): return False def no_conflicts(self, other, equiv=duck_array_ops.array_notnull_equiv): """True if the intersection of two Variable's non-null data is equal; otherwise false. Variables can thus still be equal if there are locations where either, or both, contain NaN values. """ return self.broadcast_equals(other, equiv=equiv) def quantile( self, q: ArrayLike, dim: str | Sequence[Hashable] | None = None, method: QuantileMethods = "linear", keep_attrs: bool | None = None, skipna: bool | None = None, interpolation: QuantileMethods | None = None, ) -> Variable: """Compute the qth quantile of the data along the specified dimension. Returns the qth quantiles(s) of the array elements. Parameters ---------- q : float or sequence of float Quantile to compute, which must be between 0 and 1 inclusive. dim : str or sequence of str, optional Dimension(s) over which to apply quantile. method : str, default: "linear" This optional parameter specifies the interpolation method to use when the desired quantile lies between two data points. The options sorted by their R type as summarized in the H&F paper [1]_ are: 1. "inverted_cdf" (*) 2. "averaged_inverted_cdf" (*) 3. "closest_observation" (*) 4. "interpolated_inverted_cdf" (*) 5. "hazen" (*) 6. "weibull" (*) 7. "linear" (default) 8. "median_unbiased" (*) 9. "normal_unbiased" (*) The first three methods are discontiuous. The following discontinuous variations of the default "linear" (7.) option are also available: * "lower" * "higher" * "midpoint" * "nearest" See :py:func:`numpy.quantile` or [1]_ for details. The "method" argument was previously called "interpolation", renamed in accordance with numpy version 1.22.0. (*) These methods require numpy version 1.22 or newer. 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. 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 ------- quantiles : Variable 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, pandas.Series.quantile, Dataset.quantile DataArray.quantile References ---------- .. [1] R. J. Hyndman and Y. Fan, "Sample quantiles in statistical packages," The American Statistician, 50(4), pp. 361-365, 1996 """ from xarray.core.computation import apply_ufunc if interpolation is not None: warnings.warn( "The `interpolation` argument to quantile was renamed to `method`.", FutureWarning, ) if method != "linear": raise TypeError("Cannot pass interpolation and method keywords!") method = interpolation if skipna or (skipna is None and self.dtype.kind in "cfO"): _quantile_func = np.nanquantile else: _quantile_func = np.quantile if keep_attrs is None: keep_attrs = _get_keep_attrs(default=False) scalar = utils.is_scalar(q) q = np.atleast_1d(np.asarray(q, dtype=np.float64)) if dim is None: dim = self.dims if utils.is_scalar(dim): dim = [dim] def _wrapper(npa, **kwargs): # move quantile axis to end. required for apply_ufunc return np.moveaxis(_quantile_func(npa, **kwargs), 0, -1) axis = np.arange(-1, -1 * len(dim) - 1, -1) if Version(np.__version__) >= Version("1.22.0"): kwargs = {"q": q, "axis": axis, "method": method} else: if method not in ("linear", "lower", "higher", "midpoint", "nearest"): raise ValueError( f"Interpolation method '{method}' requires numpy >= 1.22 or is not supported." ) kwargs = {"q": q, "axis": axis, "interpolation": method} result = apply_ufunc( _wrapper, self, input_core_dims=[dim], exclude_dims=set(dim), output_core_dims=[["quantile"]], output_dtypes=[np.float64], dask_gufunc_kwargs=dict(output_sizes={"quantile": len(q)}), dask="parallelized", kwargs=kwargs, ) # for backward compatibility result = result.transpose("quantile", ...) if scalar: result = result.squeeze("quantile") if keep_attrs: result.attrs = self._attrs return result def rank(self, dim, pct=False): """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 : str Dimension over which to compute rank. pct : bool, optional If True, compute percentage ranks, otherwise compute integer ranks. Returns ------- ranked : Variable See Also -------- Dataset.rank, DataArray.rank """ if not OPTIONS["use_bottleneck"]: raise RuntimeError( "rank requires bottleneck to be enabled." " Call `xr.set_options(use_bottleneck=True)` to enable it." ) import bottleneck as bn data = self.data if is_duck_dask_array(data): raise TypeError( "rank does not work for arrays stored as dask " "arrays. Load the data via .compute() or .load() " "prior to calling this method." ) elif not isinstance(data, np.ndarray): raise TypeError(f"rank is not implemented for {type(data)} objects.") axis = self.get_axis_num(dim) func = bn.nanrankdata if self.dtype.kind == "f" else bn.rankdata ranked = func(data, axis=axis) if pct: count = np.sum(~np.isnan(data), axis=axis, keepdims=True) ranked /= count return Variable(self.dims, ranked) def rolling_window( self, dim, window, window_dim, center=False, fill_value=dtypes.NA ): """ Make a rolling_window along dim and add a new_dim to the last place. Parameters ---------- dim : str Dimension over which to compute rolling_window. For nd-rolling, should be list of dimensions. window : int Window size of the rolling For nd-rolling, should be list of integers. window_dim : str New name of the window dimension. For nd-rolling, should be list of strings. center : bool, default: False If True, pad fill_value for both ends. Otherwise, pad in the head of the axis. fill_value value to be filled. Returns ------- Variable that is a view of the original array with a added dimension of size w. The return dim: self.dims + (window_dim, ) The return shape: self.shape + (window, ) Examples -------- >>> v = Variable(("a", "b"), np.arange(8).reshape((2, 4))) >>> v.rolling_window("b", 3, "window_dim") array([[[nan, nan, 0.], [nan, 0., 1.], [ 0., 1., 2.], [ 1., 2., 3.]], [[nan, nan, 4.], [nan, 4., 5.], [ 4., 5., 6.], [ 5., 6., 7.]]]) >>> v.rolling_window("b", 3, "window_dim", center=True) array([[[nan, 0., 1.], [ 0., 1., 2.], [ 1., 2., 3.], [ 2., 3., nan]], [[nan, 4., 5.], [ 4., 5., 6.], [ 5., 6., 7.], [ 6., 7., nan]]]) """ if fill_value is dtypes.NA: # np.nan is passed dtype, fill_value = dtypes.maybe_promote(self.dtype) var = self.astype(dtype, copy=False) else: dtype = self.dtype var = self if utils.is_scalar(dim): for name, arg in zip( ["window", "window_dim", "center"], [window, window_dim, center] ): if not utils.is_scalar(arg): raise ValueError( f"Expected {name}={arg!r} to be a scalar like 'dim'." ) dim = [dim] # dim is now a list nroll = len(dim) if utils.is_scalar(window): window = [window] * nroll if utils.is_scalar(window_dim): window_dim = [window_dim] * nroll if utils.is_scalar(center): center = [center] * nroll if ( len(dim) != len(window) or len(dim) != len(window_dim) or len(dim) != len(center) ): raise ValueError( "'dim', 'window', 'window_dim', and 'center' must be the same length. " f"Received dim={dim!r}, window={window!r}, window_dim={window_dim!r}," f" and center={center!r}." ) pads = {} for d, win, cent in zip(dim, window, center): if cent: start = win // 2 # 10 -> 5, 9 -> 4 end = win - 1 - start pads[d] = (start, end) else: pads[d] = (win - 1, 0) padded = var.pad(pads, mode="constant", constant_values=fill_value) axis = [self.get_axis_num(d) for d in dim] new_dims = self.dims + tuple(window_dim) return Variable( new_dims, duck_array_ops.sliding_window_view( padded.data, window_shape=window, axis=axis ), ) def coarsen( self, windows, func, boundary="exact", side="left", keep_attrs=None, **kwargs ): """ Apply reduction function. """ windows = {k: v for k, v in windows.items() if k in self.dims} if keep_attrs is None: keep_attrs = _get_keep_attrs(default=True) if keep_attrs: _attrs = self.attrs else: _attrs = None if not windows: return self._replace(attrs=_attrs) reshaped, axes = self.coarsen_reshape(windows, boundary, side) if isinstance(func, str): name = func func = getattr(duck_array_ops, name, None) if func is None: raise NameError(f"{name} is not a valid method.") return self._replace(data=func(reshaped, axis=axes, **kwargs), attrs=_attrs) def coarsen_reshape(self, windows, boundary, side): """ Construct a reshaped-array for coarsen """ if not utils.is_dict_like(boundary): boundary = {d: boundary for d in windows.keys()} if not utils.is_dict_like(side): side = {d: side for d in windows.keys()} # remove unrelated dimensions boundary = {k: v for k, v in boundary.items() if k in windows} side = {k: v for k, v in side.items() if k in windows} for d, window in windows.items(): if window <= 0: raise ValueError( f"window must be > 0. Given {window} for dimension {d}" ) variable = self for d, window in windows.items(): # trim or pad the object size = variable.shape[self._get_axis_num(d)] n = int(size / window) if boundary[d] == "exact": if n * window != size: raise ValueError( f"Could not coarsen a dimension of size {size} with " f"window {window} and boundary='exact'. Try a different 'boundary' option." ) elif boundary[d] == "trim": if side[d] == "left": variable = variable.isel({d: slice(0, window * n)}) else: excess = size - window * n variable = variable.isel({d: slice(excess, None)}) elif boundary[d] == "pad": # pad pad = window * n - size if pad < 0: pad += window if side[d] == "left": pad_width = {d: (0, pad)} else: pad_width = {d: (pad, 0)} variable = variable.pad(pad_width, mode="constant") else: raise TypeError( "{} is invalid for boundary. Valid option is 'exact', " "'trim' and 'pad'".format(boundary[d]) ) shape = [] axes = [] axis_count = 0 for i, d in enumerate(variable.dims): if d in windows: size = variable.shape[i] shape.append(int(size / windows[d])) shape.append(windows[d]) axis_count += 1 axes.append(i + axis_count) else: shape.append(variable.shape[i]) return variable.data.reshape(shape), tuple(axes) def isnull(self, keep_attrs: bool | None = None): """Test each value in the array for whether it is a missing value. Returns ------- isnull : Variable Same type and shape as object, but the dtype of the data is bool. See Also -------- pandas.isnull Examples -------- >>> var = xr.Variable("x", [1, np.nan, 3]) >>> var array([ 1., nan, 3.]) >>> var.isnull() array([False, True, False]) """ from xarray.core.computation import apply_ufunc if keep_attrs is None: keep_attrs = _get_keep_attrs(default=False) return apply_ufunc( duck_array_ops.isnull, self, dask="allowed", keep_attrs=keep_attrs, ) def notnull(self, keep_attrs: bool | None = None): """Test each value in the array for whether it is not a missing value. Returns ------- notnull : Variable Same type and shape as object, but the dtype of the data is bool. See Also -------- pandas.notnull Examples -------- >>> var = xr.Variable("x", [1, np.nan, 3]) >>> var array([ 1., nan, 3.]) >>> var.notnull() array([ True, False, True]) """ from xarray.core.computation import apply_ufunc if keep_attrs is None: keep_attrs = _get_keep_attrs(default=False) return apply_ufunc( duck_array_ops.notnull, self, dask="allowed", keep_attrs=keep_attrs, ) @property def real(self): """ The real part of the variable. See Also -------- numpy.ndarray.real """ return self._replace(data=self.data.real) @property def imag(self): """ The imaginary part of the variable. See Also -------- numpy.ndarray.imag """ return self._replace(data=self.data.imag) def __array_wrap__(self, obj, context=None): return Variable(self.dims, obj) def _unary_op(self, f, *args, **kwargs): keep_attrs = kwargs.pop("keep_attrs", None) if keep_attrs is None: keep_attrs = _get_keep_attrs(default=True) with np.errstate(all="ignore"): result = self.__array_wrap__(f(self.data, *args, **kwargs)) if keep_attrs: result.attrs = self.attrs return result def _binary_op(self, other, f, reflexive=False): if isinstance(other, (xr.DataArray, xr.Dataset)): return NotImplemented if reflexive and issubclass(type(self), type(other)): other_data, self_data, dims = _broadcast_compat_data(other, self) else: self_data, other_data, dims = _broadcast_compat_data(self, other) keep_attrs = _get_keep_attrs(default=False) attrs = self._attrs if keep_attrs else None with np.errstate(all="ignore"): new_data = ( f(self_data, other_data) if not reflexive else f(other_data, self_data) ) result = Variable(dims, new_data, attrs=attrs) return result def _inplace_binary_op(self, other, f): if isinstance(other, xr.Dataset): raise TypeError("cannot add a Dataset to a Variable in-place") self_data, other_data, dims = _broadcast_compat_data(self, other) if dims != self.dims: raise ValueError("dimensions cannot change for in-place operations") with np.errstate(all="ignore"): self.values = f(self_data, other_data) return self def _to_numeric(self, offset=None, datetime_unit=None, dtype=float): """A (private) method to convert datetime array to numeric dtype See duck_array_ops.datetime_to_numeric """ numeric_array = duck_array_ops.datetime_to_numeric( self.data, offset, datetime_unit, dtype ) return type(self)(self.dims, numeric_array, self._attrs) def _unravel_argminmax( self, argminmax: str, dim: Dims, axis: int | None, keep_attrs: bool | None, skipna: bool | None, ) -> Variable | dict[Hashable, Variable]: """Apply argmin or argmax over one or more dimensions, returning the result as a dict of DataArray that can be passed directly to isel. """ if dim is None and axis is None: warnings.warn( "Behaviour of argmin/argmax with neither dim nor axis argument will " "change to return a dict of indices of each dimension. To get a " "single, flat index, please use np.argmin(da.data) or " "np.argmax(da.data) instead of da.argmin() or da.argmax().", DeprecationWarning, stacklevel=3, ) argminmax_func = getattr(duck_array_ops, argminmax) if dim is ...: # In future, should do this also when (dim is None and axis is None) dim = self.dims if ( dim is None or axis is not None or not isinstance(dim, Sequence) or isinstance(dim, str) ): # Return int index if single dimension is passed, and is not part of a # sequence return self.reduce( argminmax_func, dim=dim, axis=axis, keep_attrs=keep_attrs, skipna=skipna ) # Get a name for the new dimension that does not conflict with any existing # dimension newdimname = "_unravel_argminmax_dim_0" count = 1 while newdimname in self.dims: newdimname = f"_unravel_argminmax_dim_{count}" count += 1 stacked = self.stack({newdimname: dim}) result_dims = stacked.dims[:-1] reduce_shape = tuple(self.sizes[d] for d in dim) result_flat_indices = stacked.reduce(argminmax_func, axis=-1, skipna=skipna) result_unravelled_indices = duck_array_ops.unravel_index( result_flat_indices.data, reduce_shape ) result = { d: Variable(dims=result_dims, data=i) for d, i in zip(dim, result_unravelled_indices) } if keep_attrs is None: keep_attrs = _get_keep_attrs(default=False) if keep_attrs: for v in result.values(): v.attrs = self.attrs return result def argmin( self, dim: Dims = None, axis: int | None = None, keep_attrs: bool | None = None, skipna: bool | None = None, ) -> Variable | dict[Hashable, Variable]: """Index or indices of the minimum of the Variable over one or more dimensions. If a sequence is passed to 'dim', then result returned as dict of Variables, which can be passed directly to isel(). If a single str is passed to 'dim' then returns a Variable with dtype int. If there are multiple minima, the indices of the first one found will be returned. Parameters ---------- dim : "...", str, Iterable of Hashable or None, 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 : Variable or dict of Variable See Also -------- DataArray.argmin, DataArray.idxmin """ return self._unravel_argminmax("argmin", dim, axis, keep_attrs, skipna) def argmax( self, dim: Dims = None, axis: int | None = None, keep_attrs: bool | None = None, skipna: bool | None = None, ) -> Variable | dict[Hashable, Variable]: """Index or indices of the maximum of the Variable over one or more dimensions. If a sequence is passed to 'dim', then result returned as dict of Variables, which can be passed directly to isel(). If a single str is passed to 'dim' then returns a Variable with dtype int. If there are multiple maxima, the indices of the first one found will be returned. Parameters ---------- dim : "...", str, Iterable of Hashable or None, 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 `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 : Variable or dict of Variable See Also -------- DataArray.argmax, DataArray.idxmax """ return self._unravel_argminmax("argmax", dim, axis, keep_attrs, skipna) class IndexVariable(Variable): """Wrapper for accommodating a pandas.Index in an xarray.Variable. IndexVariable preserve loaded values in the form of a pandas.Index instead of a NumPy array. Hence, their values are immutable and must always be one- dimensional. They also have a name property, which is the name of their sole dimension unless another name is given. """ __slots__ = () def __init__(self, dims, data, attrs=None, encoding=None, fastpath=False): super().__init__(dims, data, attrs, encoding, fastpath) if self.ndim != 1: raise ValueError(f"{type(self).__name__} objects must be 1-dimensional") # Unlike in Variable, always eagerly load values into memory if not isinstance(self._data, PandasIndexingAdapter): self._data = PandasIndexingAdapter(self._data) def __dask_tokenize__(self): from dask.base import normalize_token # Don't waste time converting pd.Index to np.ndarray return normalize_token((type(self), self._dims, self._data.array, self._attrs)) def load(self): # data is already loaded into memory for IndexVariable return self # https://github.com/python/mypy/issues/1465 @Variable.data.setter # type: ignore[attr-defined] def data(self, data): raise ValueError( f"Cannot assign to the .data attribute of dimension coordinate a.k.a IndexVariable {self.name!r}. " f"Please use DataArray.assign_coords, Dataset.assign_coords or Dataset.assign as appropriate." ) @Variable.values.setter # type: ignore[attr-defined] def values(self, values): raise ValueError( f"Cannot assign to the .values attribute of dimension coordinate a.k.a IndexVariable {self.name!r}. " f"Please use DataArray.assign_coords, Dataset.assign_coords or Dataset.assign as appropriate." ) def chunk(self, chunks={}, name=None, lock=False, inline_array=False): # Dummy - do not chunk. This method is invoked e.g. by Dataset.chunk() return self.copy(deep=False) def _as_sparse(self, sparse_format=_default, fill_value=_default): # Dummy return self.copy(deep=False) def _to_dense(self): # Dummy return self.copy(deep=False) def _finalize_indexing_result(self, dims, data): if getattr(data, "ndim", 0) != 1: # returns Variable rather than IndexVariable if multi-dimensional return Variable(dims, data, self._attrs, self._encoding) else: return self._replace(dims=dims, data=data) def __setitem__(self, key, value): raise TypeError(f"{type(self).__name__} values cannot be modified") @classmethod def concat( cls, variables, dim="concat_dim", positions=None, shortcut=False, combine_attrs="override", ): """Specialized version of Variable.concat for IndexVariable objects. This exists because we want to avoid converting Index objects to NumPy arrays, if possible. """ from xarray.core.merge import merge_attrs if not isinstance(dim, str): (dim,) = dim.dims variables = list(variables) first_var = variables[0] if any(not isinstance(v, cls) for v in variables): raise TypeError( "IndexVariable.concat requires that all input " "variables be IndexVariable objects" ) indexes = [v._data.array for v in variables] if not indexes: data = [] else: data = indexes[0].append(indexes[1:]) if positions is not None: indices = nputils.inverse_permutation(np.concatenate(positions)) data = data.take(indices) # keep as str if possible as pandas.Index uses object (converts to numpy array) data = maybe_coerce_to_str(data, variables) attrs = merge_attrs( [var.attrs for var in variables], combine_attrs=combine_attrs ) if not shortcut: for var in variables: if var.dims != first_var.dims: raise ValueError("inconsistent dimensions") return cls(first_var.dims, data, attrs) def copy(self, deep: bool = True, data: ArrayLike | None = None): """Returns a copy of this object. `deep` is ignored since data is stored in the form of pandas.Index, which is already immutable. Dimensions, attributes and encodings are always copied. Use `data` to create a new object with the same structure as original but entirely new data. Parameters ---------- deep : bool, default: True Deep is ignored when data is given. Whether the data array is 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. Returns ------- object : Variable New object with dimensions, attributes, encodings, and optionally data copied from original. """ if data is None: ndata = self._data.copy(deep=deep) else: ndata = as_compatible_data(data) if self.shape != ndata.shape: raise ValueError( "Data shape {} must match shape of object {}".format( ndata.shape, self.shape ) ) attrs = copy.deepcopy(self._attrs) if deep else copy.copy(self._attrs) encoding = copy.deepcopy(self._encoding) if deep else copy.copy(self._encoding) return self._replace(data=ndata, attrs=attrs, encoding=encoding) def equals(self, other, equiv=None): # if equiv is specified, super up if equiv is not None: return super().equals(other, equiv) # otherwise use the native index equals, rather than looking at _data other = getattr(other, "variable", other) try: return self.dims == other.dims and self._data_equals(other) except (TypeError, AttributeError): return False def _data_equals(self, other): return self._to_index().equals(other._to_index()) def to_index_variable(self) -> IndexVariable: """Return this variable as an xarray.IndexVariable""" return self.copy(deep=False) to_coord = utils.alias(to_index_variable, "to_coord") def _to_index(self) -> pd.Index: # n.b. creating a new pandas.Index from an old pandas.Index is # basically free as pandas.Index objects are immutable. # n.b.2. this method returns the multi-index instance for # a pandas multi-index level variable. assert self.ndim == 1 index = self._data.array if isinstance(index, pd.MultiIndex): # set default names for multi-index unnamed levels so that # we can safely rename dimension / coordinate later valid_level_names = [ name or f"{self.dims[0]}_level_{i}" for i, name in enumerate(index.names) ] index = index.set_names(valid_level_names) else: index = index.set_names(self.name) return index def to_index(self) -> pd.Index: """Convert this variable to a pandas.Index""" index = self._to_index() level = getattr(self._data, "level", None) if level is not None: # return multi-index level converted to a single index return index.get_level_values(level) else: return index @property def level_names(self) -> list[str] | None: """Return MultiIndex level names or None if this IndexVariable has no MultiIndex. """ index = self.to_index() if isinstance(index, pd.MultiIndex): return index.names else: return None def get_level_variable(self, level): """Return a new IndexVariable from a given MultiIndex level.""" if self.level_names is None: raise ValueError(f"IndexVariable {self.name!r} has no MultiIndex") index = self.to_index() return type(self)(self.dims, index.get_level_values(level)) @property def name(self) -> Hashable: return self.dims[0] @name.setter def name(self, value) -> NoReturn: raise AttributeError("cannot modify name of IndexVariable in-place") def _inplace_binary_op(self, other, f): raise TypeError( "Values of an IndexVariable are immutable and can not be modified inplace" ) # for backwards compatibility Coordinate = utils.alias(IndexVariable, "Coordinate") def _unified_dims(variables): # validate dimensions all_dims = {} for var in variables: var_dims = var.dims if len(set(var_dims)) < len(var_dims): raise ValueError( "broadcasting cannot handle duplicate " f"dimensions: {list(var_dims)!r}" ) for d, s in zip(var_dims, var.shape): if d not in all_dims: all_dims[d] = s elif all_dims[d] != s: raise ValueError( "operands cannot be broadcast together " f"with mismatched lengths for dimension {d!r}: {(all_dims[d], s)}" ) return all_dims def _broadcast_compat_variables(*variables): """Create broadcast compatible variables, with the same dimensions. Unlike the result of broadcast_variables(), some variables may have dimensions of size 1 instead of the size of the broadcast dimension. """ dims = tuple(_unified_dims(variables)) return tuple(var.set_dims(dims) if var.dims != dims else var for var in variables) def broadcast_variables(*variables: Variable) -> tuple[Variable, ...]: """Given any number of variables, return variables with matching dimensions and broadcast data. The data on the returned variables will be a view of the data on the corresponding original arrays, but dimensions will be reordered and inserted so that both broadcast arrays have the same dimensions. The new dimensions are sorted in order of appearance in the first variable's dimensions followed by the second variable's dimensions. """ dims_map = _unified_dims(variables) dims_tuple = tuple(dims_map) return tuple( var.set_dims(dims_map) if var.dims != dims_tuple else var for var in variables ) def _broadcast_compat_data(self, other): if all(hasattr(other, attr) for attr in ["dims", "data", "shape", "encoding"]): # `other` satisfies the necessary Variable API for broadcast_variables new_self, new_other = _broadcast_compat_variables(self, other) self_data = new_self.data other_data = new_other.data dims = new_self.dims else: # rely on numpy broadcasting rules self_data = self.data other_data = other dims = self.dims return self_data, other_data, dims def concat( variables, dim="concat_dim", positions=None, shortcut=False, combine_attrs="override", ): """Concatenate variables along a new or existing dimension. Parameters ---------- variables : iterable of Variable Arrays to stack together. Each variable is expected to have matching dimensions and shape except for along the stacked dimension. dim : str or DataArray, optional Name of the dimension to stack along. This can either be a new dimension name, in which case it is added along axis=0, or an existing dimension name, in which case the location of the dimension is unchanged. Where to insert the new dimension is determined by the first variable. positions : None or list of array-like, optional List of integer arrays which specifies the integer positions to which to assign each dataset along the concatenated dimension. If not supplied, objects are concatenated in the provided order. shortcut : bool, optional This option is used internally to speed-up groupby operations. If `shortcut` is True, some checks of internal consistency between arrays to concatenate are skipped. combine_attrs : {"drop", "identical", "no_conflicts", "drop_conflicts", \ "override"}, default: "override" String indicating how to combine attrs of the objects being merged: - "drop": empty attrs on returned Dataset. - "identical": all attrs must be the same on every object. - "no_conflicts": attrs from all objects are combined, any that have the same name must also have the same value. - "drop_conflicts": attrs from all objects are combined, any that have the same name but different values are dropped. - "override": skip comparing and copy attrs from the first dataset to the result. Returns ------- stacked : Variable Concatenated Variable formed by stacking all the supplied variables along the given dimension. """ variables = list(variables) if all(isinstance(v, IndexVariable) for v in variables): return IndexVariable.concat(variables, dim, positions, shortcut, combine_attrs) else: return Variable.concat(variables, dim, positions, shortcut, combine_attrs) def calculate_dimensions(variables: Mapping[Any, Variable]) -> dict[Hashable, int]: """Calculate the dimensions corresponding to a set of variables. Returns dictionary mapping from dimension names to sizes. Raises ValueError if any of the dimension sizes conflict. """ dims: dict[Hashable, int] = {} last_used = {} scalar_vars = {k for k, v in variables.items() if not v.dims} for k, var in variables.items(): for dim, size in zip(var.dims, var.shape): if dim in scalar_vars: raise ValueError( f"dimension {dim!r} already exists as a scalar variable" ) if dim not in dims: dims[dim] = size last_used[dim] = k elif dims[dim] != size: raise ValueError( f"conflicting sizes for dimension {dim!r}: " f"length {size} on {k!r} and length {dims[dim]} on {last_used!r}" ) return dims