from __future__ import absolute_import, division, print_function import functools import warnings import numpy as np import pandas as pd from . import ( computation, dtypes, groupby, indexing, ops, resample, rolling, utils) from ..plot.plot import _PlotMethods from .accessors import DatetimeAccessor from .alignment import align, reindex_like_indexers from .common import AbstractArray, DataWithCoords from .coordinates import ( DataArrayCoordinates, Indexes, LevelCoordinatesSource, assert_coordinate_consistent, remap_label_indexers) from .dataset import Dataset, merge_indexes, split_indexes from .formatting import format_item from .options import OPTIONS from .pycompat import OrderedDict, basestring, iteritems, range, zip from .utils import ( _check_inplace, decode_numpy_dict_values, either_dict_or_kwargs, ensure_us_time_resolution) from .variable import ( IndexVariable, Variable, as_compatible_data, as_variable, assert_unique_multiindex_level_names) def _infer_coords_and_dims(shape, coords, dims): """All the logic for creating a new DataArray""" if (coords is not None and not utils.is_dict_like(coords) and len(coords) != len(shape)): raise ValueError('coords is not dict-like, but it has %s items, ' 'which does not match the %s dimensions of the ' 'data' % (len(coords), len(shape))) if isinstance(dims, basestring): dims = (dims,) if dims is None: dims = ['dim_%s' % n for n in range(len(shape))] if coords is not None and len(coords) == len(shape): # try to infer dimensions from coords if utils.is_dict_like(coords): # deprecated in GH993, removed in GH1539 raise ValueError('inferring DataArray dimensions from ' 'dictionary like ``coords`` is no longer ' 'supported. Use an explicit list of ' '``dims`` instead.') for n, (dim, coord) in enumerate(zip(dims, coords)): coord = as_variable(coord, name=dims[n]).to_index_variable() dims[n] = coord.name dims = tuple(dims) else: for d in dims: if not isinstance(d, basestring): raise TypeError('dimension %s is not a string' % d) new_coords = OrderedDict() if utils.is_dict_like(coords): for k, v in coords.items(): new_coords[k] = as_variable(v, name=k) elif coords is not None: for dim, coord in zip(dims, coords): var = as_variable(coord, name=dim) var.dims = (dim,) new_coords[dim] = var sizes = dict(zip(dims, shape)) for k, v in new_coords.items(): if any(d not in dims for d in v.dims): raise ValueError('coordinate %s has dimensions %s, but these ' 'are not a subset of the DataArray ' 'dimensions %s' % (k, v.dims, dims)) for d, s in zip(v.dims, v.shape): if s != sizes[d]: raise ValueError('conflicting sizes for dimension %r: ' 'length %s on the data but length %s on ' 'coordinate %r' % (d, sizes[d], s, k)) if k in sizes and v.shape != (sizes[k],): raise ValueError('coordinate %r is a DataArray dimension, but ' 'it has shape %r rather than expected shape %r ' 'matching the dimension size' % (k, v.shape, (sizes[k],))) assert_unique_multiindex_level_names(new_coords) return new_coords, dims class _LocIndexer(object): def __init__(self, data_array): self.data_array = data_array def __getitem__(self, key): if not utils.is_dict_like(key): # expand the indexer so we can handle Ellipsis labels = indexing.expanded_indexer(key, self.data_array.ndim) key = dict(zip(self.data_array.dims, labels)) return self.data_array.sel(**key) def __setitem__(self, key, value): if not utils.is_dict_like(key): # expand the indexer so we can handle Ellipsis labels = indexing.expanded_indexer(key, self.data_array.ndim) key = dict(zip(self.data_array.dims, labels)) pos_indexers, _ = remap_label_indexers(self.data_array, **key) self.data_array[pos_indexers] = value # Used as the key corresponding to a DataArray's variable when converting # arbitrary DataArray objects to datasets _THIS_ARRAY = utils.ReprObject('') class DataArray(AbstractArray, DataWithCoords): """N-dimensional array with labeled coordinates and dimensions. DataArray provides a wrapper around numpy ndarrays that uses labeled dimensions and coordinates to support metadata aware operations. The API is similar to that for the pandas Series or DataFrame, but DataArray objects can have any number of dimensions, and their contents have fixed data types. Additional features over raw numpy arrays: - Apply operations over dimensions by name: ``x.sum('time')``. - Select or assign values by integer location (like numpy): ``x[:10]`` or by label (like pandas): ``x.loc['2014-01-01']`` or ``x.sel(time='2014-01-01')``. - Mathematical operations (e.g., ``x - y``) vectorize across multiple dimensions (known in numpy as "broadcasting") based on dimension names, regardless of their original order. - Keep track of arbitrary metadata in the form of a Python dictionary: ``x.attrs`` - Convert to a pandas Series: ``x.to_series()``. Getting items from or doing mathematical operations with a DataArray always returns another DataArray. Attributes ---------- dims : tuple Dimension names associated with this array. values : np.ndarray Access or modify DataArray values as a numpy array. coords : dict-like Dictionary of DataArray objects that label values along each dimension. name : str or None Name of this array. attrs : OrderedDict Dictionary for holding arbitrary metadata. """ _groupby_cls = groupby.DataArrayGroupBy _rolling_cls = rolling.DataArrayRolling _resample_cls = resample.DataArrayResample dt = property(DatetimeAccessor) def __init__(self, data, coords=None, dims=None, name=None, attrs=None, encoding=None, fastpath=False): """ Parameters ---------- data : array_like Values for this array. Must be an ``numpy.ndarray``, ndarray like, or castable to an ``ndarray``. If a self-described xarray or pandas object, attempts are made to use this array's metadata to fill in other unspecified arguments. A view of the array's data is used instead of a copy if possible. coords : sequence or dict of array_like objects, optional Coordinates (tick labels) to use for indexing along each dimension. If dict-like, should be a mapping from dimension names to the corresponding coordinates. If sequence-like, should be a sequence of tuples where the first element is the dimension name and the second element is the corresponding coordinate array_like object. dims : str or sequence of str, optional Name(s) of 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. If this argument is omitted, dimension names are taken from ``coords`` (if possible) and otherwise default to ``['dim_0', ... 'dim_n']``. name : str or None, optional Name of this array. attrs : dict_like or None, optional Attributes to assign to the new instance. By default, an empty attribute dictionary is initialized. 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', 'dtype', 'units' and 'calendar' (the later two only for datetime arrays). Unrecognized keys are ignored. """ if fastpath: variable = data assert dims is None assert attrs is None assert encoding is None else: # try to fill in arguments from data if they weren't supplied if coords is None: coords = getattr(data, 'coords', None) if isinstance(data, pd.Series): coords = [data.index] elif isinstance(data, pd.DataFrame): coords = [data.index, data.columns] elif isinstance(data, (pd.Index, IndexVariable)): coords = [data] elif isinstance(data, pd.Panel): coords = [data.items, data.major_axis, data.minor_axis] if dims is None: dims = getattr(data, 'dims', getattr(coords, 'dims', None)) if name is None: name = getattr(data, 'name', None) if attrs is None: attrs = getattr(data, 'attrs', None) if encoding is None: encoding = getattr(data, 'encoding', None) data = as_compatible_data(data) coords, dims = _infer_coords_and_dims(data.shape, coords, dims) variable = Variable(dims, data, attrs, encoding, fastpath=True) # uncomment for a useful consistency check: # assert all(isinstance(v, Variable) for v in coords.values()) # These fully describe a DataArray self._variable = variable self._coords = coords self._name = name self._file_obj = None self._initialized = True __default = object() def _replace(self, variable=None, coords=None, name=__default): if variable is None: variable = self.variable if coords is None: coords = self._coords if name is self.__default: name = self.name return type(self)(variable, coords, name=name, fastpath=True) def _replace_maybe_drop_dims(self, variable, name=__default): if variable.dims == self.dims: coords = self._coords.copy() else: allowed_dims = set(variable.dims) coords = OrderedDict((k, v) for k, v in self._coords.items() if set(v.dims) <= allowed_dims) return self._replace(variable, coords, name) def _replace_indexes(self, indexes): if not len(indexes): return self coords = self._coords.copy() for name, idx in indexes.items(): coords[name] = IndexVariable(name, idx) obj = self._replace(coords=coords) # switch from dimension to level names, if necessary dim_names = {} for dim, idx in indexes.items(): if not isinstance(idx, pd.MultiIndex) and idx.name != dim: dim_names[dim] = idx.name if dim_names: obj = obj.rename(dim_names) return obj def _to_temp_dataset(self): return self._to_dataset_whole(name=_THIS_ARRAY, shallow_copy=False) def _from_temp_dataset(self, dataset, name=__default): variable = dataset._variables.pop(_THIS_ARRAY) coords = dataset._variables return self._replace(variable, coords, name) def _to_dataset_split(self, dim): def subset(dim, label): array = self.loc[{dim: label}] if dim in array.coords: del array.coords[dim] array.attrs = {} return array variables = OrderedDict([(label, subset(dim, label)) for label in self.get_index(dim)]) coords = self.coords.to_dataset() if dim in coords: del coords[dim] return Dataset(variables, coords, self.attrs) def _to_dataset_whole(self, name=None, shallow_copy=True): if name is None: name = self.name if name is None: raise ValueError('unable to convert unnamed DataArray to a ' 'Dataset without providing an explicit name') if name in self.coords: raise ValueError('cannot create a Dataset from a DataArray with ' 'the same name as one of its coordinates') # use private APIs for speed: this is called by _to_temp_dataset(), # which is used in the guts of a lot of operations (e.g., reindex) variables = self._coords.copy() variables[name] = self.variable if shallow_copy: for k in variables: variables[k] = variables[k].copy(deep=False) coord_names = set(self._coords) dataset = Dataset._from_vars_and_coord_names(variables, coord_names) return dataset def to_dataset(self, dim=None, name=None): """Convert a DataArray to a Dataset. Parameters ---------- dim : str, optional Name of the dimension on this array along which to split this array into separate variables. If not provided, this array is converted into a Dataset of one variable. name : str, optional Name to substitute for this array's name. Only valid if ``dim`` is not provided. Returns ------- dataset : Dataset """ if dim is not None and dim not in self.dims: warnings.warn('the order of the arguments on DataArray.to_dataset ' 'has changed; you now need to supply ``name`` as ' 'a keyword argument', FutureWarning, stacklevel=2) name = dim dim = None if dim is not None: if name is not None: raise TypeError('cannot supply both dim and name arguments') return self._to_dataset_split(dim) else: return self._to_dataset_whole(name) @property def name(self): """The name of this array. """ return self._name @name.setter def name(self, value): self._name = value @property def variable(self): """Low level interface to the Variable object for this DataArray.""" return self._variable @property def dtype(self): return self.variable.dtype @property def shape(self): return self.variable.shape @property def size(self): return self.variable.size @property def nbytes(self): return self.variable.nbytes @property def ndim(self): return self.variable.ndim def __len__(self): return len(self.variable) @property def data(self): """The array's data as a dask or numpy array""" return self.variable.data @data.setter def data(self, value): self.variable.data = value @property def values(self): """The array's data as a numpy.ndarray""" return self.variable.values @values.setter def values(self, value): self.variable.values = value @property def _in_memory(self): return self.variable._in_memory def to_index(self): """Convert this variable to a pandas.Index. Only possible for 1D arrays. """ return self.variable.to_index() @property def dims(self): """Tuple of dimension names associated with this array. Note that the type of this property is inconsistent with `Dataset.dims`. See `Dataset.sizes` and `DataArray.sizes` for consistently named properties. """ return self.variable.dims @dims.setter def dims(self, value): raise AttributeError('you cannot assign dims on a DataArray. Use ' '.rename() or .swap_dims() instead.') def _item_key_to_dict(self, key): if utils.is_dict_like(key): return key else: key = indexing.expanded_indexer(key, self.ndim) return dict(zip(self.dims, key)) @property def _level_coords(self): """Return a mapping of all MultiIndex levels and their corresponding coordinate name. """ level_coords = OrderedDict() for cname, var in self._coords.items(): if var.ndim == 1 and isinstance(var, IndexVariable): level_names = var.level_names if level_names is not None: dim, = var.dims level_coords.update({lname: dim for lname in level_names}) return level_coords def _getitem_coord(self, key): from .dataset import _get_virtual_variable try: var = self._coords[key] except KeyError: dim_sizes = dict(zip(self.dims, self.shape)) _, key, var = _get_virtual_variable( self._coords, key, self._level_coords, dim_sizes) return self._replace_maybe_drop_dims(var, name=key) def __getitem__(self, key): if isinstance(key, basestring): return self._getitem_coord(key) else: # xarray-style array indexing return self.isel(indexers=self._item_key_to_dict(key)) def __setitem__(self, key, value): if isinstance(key, basestring): self.coords[key] = value else: # Coordinates in key, value and self[key] should be consistent. # TODO Coordinate consistency in key is checked here, but it # causes unnecessary indexing. It should be optimized. obj = self[key] if isinstance(value, DataArray): assert_coordinate_consistent(value, obj.coords.variables) # DataArray key -> Variable key key = {k: v.variable if isinstance(v, DataArray) else v for k, v in self._item_key_to_dict(key).items()} self.variable[key] = value def __delitem__(self, key): del self.coords[key] @property def _attr_sources(self): """List of places to look-up items for attribute-style access""" return self._item_sources + [self.attrs] @property def _item_sources(self): """List of places to look-up items for key-completion""" return [self.coords, {d: self.coords[d] for d in self.dims}, LevelCoordinatesSource(self)] def __contains__(self, key): return key in self.data @property def loc(self): """Attribute for location based indexing like pandas. """ return _LocIndexer(self) @property def attrs(self): """Dictionary storing arbitrary metadata with this array.""" return self.variable.attrs @attrs.setter def attrs(self, value): self.variable.attrs = value @property def encoding(self): """Dictionary of format-specific settings for how this array should be serialized.""" return self.variable.encoding @encoding.setter def encoding(self, value): self.variable.encoding = value @property def indexes(self): """OrderedDict of pandas.Index objects used for label based indexing """ return Indexes(self._coords, self.sizes) @property def coords(self): """Dictionary-like container of coordinate arrays. """ return DataArrayCoordinates(self) def reset_coords(self, names=None, drop=False, inplace=None): """Given names of coordinates, reset them to become variables. Parameters ---------- names : str or list of str, optional Name(s) of non-index coordinates in this dataset to reset into variables. By default, all non-index coordinates are reset. drop : bool, optional If True, remove coordinates instead of converting them into variables. inplace : bool, optional If True, modify this dataset inplace. Otherwise, create a new object. Returns ------- Dataset, or DataArray if ``drop == True`` """ inplace = _check_inplace(inplace) if inplace and not drop: raise ValueError('cannot reset coordinates in-place on a ' 'DataArray without ``drop == True``') if names is None: names = set(self.coords) - set(self.dims) dataset = self.coords.to_dataset().reset_coords(names, drop) if drop: if inplace: self._coords = dataset._variables else: return self._replace(coords=dataset._variables) else: if self.name is None: raise ValueError('cannot reset_coords with drop=False ' 'on an unnamed DataArrray') dataset[self.name] = self.variable return dataset def __dask_graph__(self): return self._to_temp_dataset().__dask_graph__() def __dask_keys__(self): return self._to_temp_dataset().__dask_keys__() def __dask_layers__(self): return self._to_temp_dataset().__dask_layers__() @property def __dask_optimize__(self): return self._to_temp_dataset().__dask_optimize__ @property def __dask_scheduler__(self): return self._to_temp_dataset().__dask_scheduler__ def __dask_postcompute__(self): func, args = self._to_temp_dataset().__dask_postcompute__() return self._dask_finalize, (func, args, self.name) def __dask_postpersist__(self): func, args = self._to_temp_dataset().__dask_postpersist__() return self._dask_finalize, (func, args, self.name) @staticmethod def _dask_finalize(results, func, args, name): ds = func(results, *args) variable = ds._variables.pop(_THIS_ARRAY) coords = ds._variables return DataArray(variable, coords, name=name, fastpath=True) def load(self, **kwargs): """Manually trigger loading of this array's data from disk or a remote source into memory and return this array. Normally, it should not be necessary to call this method in user code, because all xarray functions should either work on deferred data or load data automatically. However, this method can be necessary when working with many file objects on disk. Parameters ---------- **kwargs : dict Additional keyword arguments passed on to ``dask.array.compute``. See Also -------- dask.array.compute """ ds = self._to_temp_dataset().load(**kwargs) new = self._from_temp_dataset(ds) self._variable = new._variable self._coords = new._coords return self def compute(self, **kwargs): """Manually trigger loading of this array's data from disk or a remote source into memory and return a new array. The original is left unaltered. Normally, it should not be necessary to call this method in user code, because all xarray functions should either work on deferred data or load data automatically. However, this method can be necessary when working with many file objects on disk. Parameters ---------- **kwargs : dict Additional keyword arguments passed on to ``dask.array.compute``. See Also -------- dask.array.compute """ new = self.copy(deep=False) return new.load(**kwargs) def persist(self, **kwargs): """ Trigger computation in constituent dask arrays This keeps them as dask arrays but encourages them to keep data in memory. This is particularly useful when on a distributed machine. When on a single machine consider using ``.compute()`` instead. Parameters ---------- **kwargs : dict Additional keyword arguments passed on to ``dask.persist``. See Also -------- dask.persist """ ds = self._to_temp_dataset().persist(**kwargs) return self._from_temp_dataset(ds) def copy(self, deep=True, data=None): """Returns a copy of this array. If `deep=True`, a deep copy is made of the data array. Otherwise, a shallow copy is made, so each variable in the new array's dataset is also a variable in this array's dataset. Use `data` to create a new object with the same structure as original but entirely new data. Parameters ---------- deep : bool, optional Whether the data array and its coordinates are loaded into memory and copied onto the new object. Default is True. data : array_like, optional Data to use in the new object. Must have same shape as original. When `data` is used, `deep` is ignored for all data variables, and only used for coords. Returns ------- object : DataArray New object with dimensions, attributes, coordinates, name, encoding, and optionally data copied from original. Examples -------- Shallow versus deep copy >>> array = xr.DataArray([1, 2, 3], dims='x', ... coords={'x': ['a', 'b', 'c']}) >>> array.copy() array([1, 2, 3]) Coordinates: * x (x) >> array_0 = array.copy(deep=False) >>> array_0[0] = 7 >>> array_0 array([7, 2, 3]) Coordinates: * x (x) >> array array([7, 2, 3]) Coordinates: * x (x) >> array.copy(data=[0.1, 0.2, 0.3]) array([ 0.1, 0.2, 0.3]) Coordinates: * x (x) >> array array([1, 2, 3]) Coordinates: * x (x) =0.16) tolerance : optional Maximum distance between original and new labels for inexact matches. The values of the index at the matching locations most satisfy the equation ``abs(index[indexer] - target) <= tolerance``. Requires pandas>=0.17. copy : bool, optional If ``copy=True``, data in the return value is always copied. If ``copy=False`` and reindexing is unnecessary, or can be performed with only slice operations, then the output may share memory with the input. In either case, a new xarray object is always returned. Returns ------- reindexed : DataArray Another dataset array, with this array's data but coordinates from the other object. See Also -------- DataArray.reindex align """ indexers = reindex_like_indexers(self, other) return self.reindex(method=method, tolerance=tolerance, copy=copy, **indexers) def reindex(self, indexers=None, method=None, tolerance=None, copy=True, **indexers_kwargs): """Conform this object onto a new set of indexes, filling in missing values with NaN. Parameters ---------- indexers : dict, optional Dictionary with keys given by dimension names and values given by arrays of coordinates tick labels. Any mis-matched coordinate values will be filled in with NaN, and any mis-matched dimension names will simply be ignored. One of indexers or indexers_kwargs must be provided. copy : bool, optional If ``copy=True``, data in the return value is always copied. If ``copy=False`` and reindexing is unnecessary, or can be performed with only slice operations, then the output may share memory with the input. In either case, a new xarray object is always returned. method : {None, 'nearest', 'pad'/'ffill', 'backfill'/'bfill'}, optional Method to use for filling index values in ``indexers`` not found on this data array: * None (default): don't fill gaps * pad / ffill: propagate last valid index value forward * backfill / bfill: propagate next valid index value backward * nearest: use nearest valid index value (requires pandas>=0.16) tolerance : optional Maximum distance between original and new labels for inexact matches. The values of the index at the matching locations most satisfy the equation ``abs(index[indexer] - target) <= tolerance``. **indexers_kwarg : {dim: indexer, ...}, optional The keyword arguments form of ``indexers``. One of indexers or indexers_kwargs must be provided. Returns ------- reindexed : DataArray Another dataset array, with this array's data but replaced coordinates. See Also -------- DataArray.reindex_like align """ indexers = either_dict_or_kwargs( indexers, indexers_kwargs, 'reindex') ds = self._to_temp_dataset().reindex( indexers=indexers, method=method, tolerance=tolerance, copy=copy) return self._from_temp_dataset(ds) def interp(self, coords=None, method='linear', assume_sorted=False, kwargs={}, **coords_kwargs): """ Multidimensional interpolation of variables. coords : dict, optional Mapping from dimension names to the new coordinates. new coordinate can be an scalar, array-like or DataArray. If DataArrays are passed as new coordates, their dimensions are used for the broadcasting. method: {'linear', 'nearest'} for multidimensional array, {'linear', 'nearest', 'zero', 'slinear', 'quadratic', 'cubic'} for 1-dimensional array. assume_sorted: boolean, optional If False, values of x can be in any order and they are sorted first. If True, x has to be an array of monotonically increasing values. kwargs: dictionary Additional keyword passed to scipy's interpolator. **coords_kwarg : {dim: coordinate, ...}, optional The keyword arguments form of ``coords``. One of coords or coords_kwargs must be provided. Returns ------- interpolated: xr.DataArray New dataarray on the new coordinates. Notes ----- scipy is required. See Also -------- scipy.interpolate.interp1d scipy.interpolate.interpn Examples -------- >>> da = xr.DataArray([1, 3], [('x', np.arange(2))]) >>> da.interp(x=0.5) array(2.0) Coordinates: x float64 0.5 """ if self.dtype.kind not in 'uifc': raise TypeError('interp only works for a numeric type array. ' 'Given {}.'.format(self.dtype)) ds = self._to_temp_dataset().interp( coords, method=method, kwargs=kwargs, assume_sorted=assume_sorted, **coords_kwargs) return self._from_temp_dataset(ds) def interp_like(self, other, method='linear', assume_sorted=False, kwargs={}): """Interpolate this object onto the coordinates of another object, filling out of range values with NaN. Parameters ---------- other : Dataset or DataArray Object with an 'indexes' attribute giving a mapping from dimension names to an 1d array-like, which provides coordinates upon which to index the variables in this dataset. method: string, optional. {'linear', 'nearest'} for multidimensional array, {'linear', 'nearest', 'zero', 'slinear', 'quadratic', 'cubic'} for 1-dimensional array. 'linear' is used by default. assume_sorted: boolean, optional If False, values of coordinates that are interpolated over can be in any order and they are sorted first. If True, interpolated coordinates are assumed to be an array of monotonically increasing values. kwargs: dictionary, optional Additional keyword passed to scipy's interpolator. Returns ------- interpolated: xr.DataArray Another dataarray by interpolating this dataarray's data along the coordinates of the other object. Notes ----- scipy is required. If the dataarray has object-type coordinates, reindex is used for these coordinates instead of the interpolation. See Also -------- DataArray.interp DataArray.reindex_like """ if self.dtype.kind not in 'uifc': raise TypeError('interp only works for a numeric type array. ' 'Given {}.'.format(self.dtype)) ds = self._to_temp_dataset().interp_like( other, method=method, kwargs=kwargs, assume_sorted=assume_sorted) return self._from_temp_dataset(ds) def rename(self, new_name_or_name_dict=None, **names): """Returns a new DataArray with renamed coordinates or a new name. Parameters ---------- new_name_or_name_dict : str or dict-like, optional If the argument is dict-like, it it used as a mapping from old names to new names for coordinates. Otherwise, use the argument as the new name for this array. **names, optional The keyword arguments form of a mapping from old names to new names for coordinates. One of new_name_or_name_dict or names must be provided. Returns ------- renamed : DataArray Renamed array or array with renamed coordinates. See Also -------- Dataset.rename DataArray.swap_dims """ if names or utils.is_dict_like(new_name_or_name_dict): name_dict = either_dict_or_kwargs( new_name_or_name_dict, names, 'rename') dataset = self._to_temp_dataset().rename(name_dict) return self._from_temp_dataset(dataset) else: return self._replace(name=new_name_or_name_dict) def swap_dims(self, dims_dict): """Returns a new DataArray with swapped dimensions. Parameters ---------- dims_dict : dict-like Dictionary whose keys are current dimension names and whose values are new names. Each value must already be a coordinate on this array. Returns ------- renamed : Dataset DataArray with swapped dimensions. See Also -------- DataArray.rename Dataset.swap_dims """ ds = self._to_temp_dataset().swap_dims(dims_dict) return self._from_temp_dataset(ds) def expand_dims(self, dim, axis=None): """Return a new object with an additional axis (or axes) inserted at the corresponding position in the array shape. If dim is already a scalar coordinate, it will be promoted to a 1D coordinate consisting of a single value. Parameters ---------- dim : str or sequence of str. Dimensions to include on the new variable. dimensions are inserted with length 1. axis : integer, list (or tuple) of integers, or None Axis position(s) where new axis is to be inserted (position(s) on the result array). If a list (or tuple) of integers is passed, multiple axes are inserted. In this case, dim arguments should be same length list. If axis=None is passed, all the axes will be inserted to the start of the result array. Returns ------- expanded : same type as caller This object, but with an additional dimension(s). """ ds = self._to_temp_dataset().expand_dims(dim, axis) return self._from_temp_dataset(ds) def set_index(self, indexes=None, append=False, inplace=None, **indexes_kwargs): """Set DataArray (multi-)indexes using one or more existing coordinates. Parameters ---------- indexes : {dim: index, ...} Mapping from names matching dimensions and values given by (lists of) the names of existing coordinates or variables to set as new (multi-)index. append : bool, optional If True, append the supplied index(es) to the existing index(es). Otherwise replace the existing index(es) (default). inplace : bool, optional If True, set new index(es) in-place. Otherwise, return a new DataArray object. **indexes_kwargs: optional The keyword arguments form of ``indexes``. One of indexes or indexes_kwargs must be provided. Returns ------- obj : DataArray Another dataarray, with this data but replaced coordinates. See Also -------- DataArray.reset_index """ inplace = _check_inplace(inplace) indexes = either_dict_or_kwargs(indexes, indexes_kwargs, 'set_index') coords, _ = merge_indexes(indexes, self._coords, set(), append=append) if inplace: self._coords = coords else: return self._replace(coords=coords) def reset_index(self, dims_or_levels, drop=False, inplace=None): """Reset the specified index(es) or multi-index level(s). Parameters ---------- dims_or_levels : str or list Name(s) of the dimension(s) and/or multi-index level(s) that will be reset. drop : bool, optional If True, remove the specified indexes and/or multi-index levels instead of extracting them as new coordinates (default: False). inplace : bool, optional If True, modify the dataarray in-place. Otherwise, return a new DataArray object. Returns ------- obj : DataArray Another dataarray, with this dataarray's data but replaced coordinates. See Also -------- DataArray.set_index """ inplace = _check_inplace(inplace) coords, _ = split_indexes(dims_or_levels, self._coords, set(), self._level_coords, drop=drop) if inplace: self._coords = coords else: return self._replace(coords=coords) def reorder_levels(self, dim_order=None, inplace=None, **dim_order_kwargs): """Rearrange index levels using input order. Parameters ---------- dim_order : optional Mapping from names matching dimensions and values given by lists representing new level orders. Every given dimension must have a multi-index. inplace : bool, optional If True, modify the dataarray in-place. Otherwise, return a new DataArray object. **dim_order_kwargs: optional The keyword arguments form of ``dim_order``. One of dim_order or dim_order_kwargs must be provided. Returns ------- obj : DataArray Another dataarray, with this dataarray's data but replaced coordinates. """ inplace = _check_inplace(inplace) dim_order = either_dict_or_kwargs(dim_order, dim_order_kwargs, 'reorder_levels') replace_coords = {} for dim, order in dim_order.items(): coord = self._coords[dim] index = coord.to_index() if not isinstance(index, pd.MultiIndex): raise ValueError("coordinate %r has no MultiIndex" % dim) replace_coords[dim] = IndexVariable(coord.dims, index.reorder_levels(order)) coords = self._coords.copy() coords.update(replace_coords) if inplace: self._coords = coords else: return self._replace(coords=coords) 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 corresponding coordinate variables will be combined into a MultiIndex. Parameters ---------- dimensions : Mapping of the form new_name=(dim1, dim2, ...) 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 : DataArray DataArray with stacked data. Examples -------- >>> arr = DataArray(np.arange(6).reshape(2, 3), ... coords=[('x', ['a', 'b']), ('y', [0, 1, 2])]) >>> arr array([[0, 1, 2], [3, 4, 5]]) Coordinates: * x (x) |S1 'a' 'b' * y (y) int64 0 1 2 >>> stacked = arr.stack(z=('x', 'y')) >>> stacked.indexes['z'] MultiIndex(levels=[[u'a', u'b'], [0, 1, 2]], labels=[[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 1, 2]], names=[u'x', u'y']) See also -------- DataArray.unstack """ ds = self._to_temp_dataset().stack(dimensions, **dimensions_kwargs) return self._from_temp_dataset(ds) def unstack(self, dim=None): """ Unstack existing dimensions corresponding to MultiIndexes into multiple new dimensions. New dimensions will be added at the end. Parameters ---------- dim : str or sequence of str, optional Dimension(s) over which to unstack. By default unstacks all MultiIndexes. Returns ------- unstacked : DataArray Array with unstacked data. Examples -------- >>> arr = DataArray(np.arange(6).reshape(2, 3), ... coords=[('x', ['a', 'b']), ('y', [0, 1, 2])]) >>> arr array([[0, 1, 2], [3, 4, 5]]) Coordinates: * x (x) |S1 'a' 'b' * y (y) int64 0 1 2 >>> stacked = arr.stack(z=('x', 'y')) >>> stacked.indexes['z'] MultiIndex(levels=[[u'a', u'b'], [0, 1, 2]], labels=[[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 1, 2]], names=[u'x', u'y']) >>> roundtripped = stacked.unstack() >>> arr.identical(roundtripped) True See also -------- DataArray.stack """ ds = self._to_temp_dataset().unstack(dim) return self._from_temp_dataset(ds) def transpose(self, *dims): """Return a new DataArray object with transposed dimensions. Parameters ---------- *dims : str, optional By default, reverse the dimensions. Otherwise, reorder the dimensions to this order. Returns ------- transposed : DataArray The returned DataArray's array is transposed. Notes ----- Although this operation returns a view of this array's data, it is not lazy -- the data will be fully loaded. See Also -------- numpy.transpose Dataset.transpose """ variable = self.variable.transpose(*dims) return self._replace(variable) def drop(self, labels, dim=None): """Drop coordinates or index labels from this DataArray. Parameters ---------- labels : scalar or list of scalars Name(s) of coordinate variables or index labels to drop. dim : str, optional Dimension along which to drop index labels. By default (if ``dim is None``), drops coordinates rather than index labels. Returns ------- dropped : DataArray """ if utils.is_scalar(labels): labels = [labels] ds = self._to_temp_dataset().drop(labels, dim) return self._from_temp_dataset(ds) def dropna(self, dim, how='any', thresh=None): """Returns a new array with dropped labels for missing values along the provided dimension. Parameters ---------- dim : str Dimension along which to drop missing values. Dropping along multiple dimensions simultaneously is not yet supported. how : {'any', 'all'}, optional * any : if any NA values are present, drop that label * all : if all values are NA, drop that label thresh : int, default None If supplied, require this many non-NA values. Returns ------- DataArray """ ds = self._to_temp_dataset().dropna(dim, how=how, thresh=thresh) return self._from_temp_dataset(ds) def fillna(self, value): """Fill missing values in this object. This operation follows the normal broadcasting and alignment rules that xarray uses for binary arithmetic, except the result is aligned to this object (``join='left'``) instead of aligned to the intersection of index coordinates (``join='inner'``). Parameters ---------- value : scalar, ndarray or DataArray Used to fill all matching missing values in this array. If the argument is a DataArray, it is first aligned with (reindexed to) this array. Returns ------- DataArray """ if utils.is_dict_like(value): raise TypeError('cannot provide fill value as a dictionary with ' 'fillna on a DataArray') out = ops.fillna(self, value) return out def interpolate_na(self, dim=None, method='linear', limit=None, use_coordinate=True, **kwargs): """Interpolate values according to different methods. Parameters ---------- dim : str Specifies the dimension along which to interpolate. method : {'linear', 'nearest', 'zero', 'slinear', 'quadratic', 'cubic', 'polynomial', 'barycentric', 'krog', 'pchip', 'spline', 'akima'}, optional String indicating which method to use for interpolation: - 'linear': linear interpolation (Default). Additional keyword arguments are passed to ``numpy.interp`` - 'nearest', 'zero', 'slinear', 'quadratic', 'cubic', 'polynomial': are passed to ``scipy.interpolate.interp1d``. If method=='polynomial', the ``order`` keyword argument must also be provided. - 'barycentric', 'krog', 'pchip', 'spline', and `akima`: use their respective``scipy.interpolate`` classes. use_coordinate : boolean or str, default True Specifies which index to use as the x values in the interpolation formulated as `y = f(x)`. If False, values are treated as if eqaully-spaced along `dim`. If True, the IndexVariable `dim` is used. If use_coordinate is a string, it specifies the name of a coordinate variariable to use as the index. limit : int, default None Maximum number of consecutive NaNs to fill. Must be greater than 0 or None for no limit. Returns ------- DataArray See also -------- numpy.interp scipy.interpolate """ from .missing import interp_na return interp_na(self, dim=dim, method=method, limit=limit, use_coordinate=use_coordinate, **kwargs) def ffill(self, dim, limit=None): '''Fill NaN values by propogating values forward *Requires bottleneck.* Parameters ---------- dim : str Specifies the dimension along which to propagate values when filling. limit : int, default None The maximum number of consecutive NaN values to forward fill. In other words, if there is a gap with more than this number of consecutive NaNs, it will only be partially filled. Must be greater than 0 or None for no limit. Returns ------- DataArray ''' from .missing import ffill return ffill(self, dim, limit=limit) def bfill(self, dim, limit=None): '''Fill NaN values by propogating values backward *Requires bottleneck.* Parameters ---------- dim : str Specifies the dimension along which to propagate values when filling. limit : int, default None The maximum number of consecutive NaN values to backward fill. In other words, if there is a gap with more than this number of consecutive NaNs, it will only be partially filled. Must be greater than 0 or None for no limit. Returns ------- DataArray ''' from .missing import bfill return bfill(self, dim, limit=limit) def combine_first(self, other): """Combine two DataArray objects, with union of coordinates. This operation follows the normal broadcasting and alignment rules of ``join='outer'``. Default to non-null values of array calling the method. Use np.nan to fill in vacant cells after alignment. Parameters ---------- other : DataArray Used to fill all matching missing values in this array. Returns ------- DataArray """ return ops.fillna(self, other, join="outer") def reduce(self, func, dim=None, axis=None, keep_attrs=None, **kwargs): """Reduce this array by applying `func` along some dimension(s). Parameters ---------- func : function Function which can be called in the form `f(x, axis=axis, **kwargs)` to return the result of reducing an np.ndarray over an integer valued axis. dim : str or sequence of str, optional Dimension(s) over which to apply `func`. axis : int or sequence of int, optional Axis(es) over which to repeatedly apply `func`. Only one of the 'dim' and 'axis' arguments can be supplied. If neither are supplied, then the reduction is calculated over the flattened array (by calling `f(x)` without an axis argument). keep_attrs : bool, optional If True, the variable's attributes (`attrs`) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes. **kwargs : dict Additional keyword arguments passed on to `func`. Returns ------- reduced : DataArray DataArray with this object's array replaced with an array with summarized data and the indicated dimension(s) removed. """ var = self.variable.reduce(func, dim, axis, keep_attrs, **kwargs) return self._replace_maybe_drop_dims(var) def to_pandas(self): """Convert this array into a pandas object with the same shape. The type of the returned object depends on the number of DataArray dimensions: * 1D -> `pandas.Series` * 2D -> `pandas.DataFrame` * 3D -> `pandas.Panel` Only works for arrays with 3 or fewer dimensions. The DataArray constructor performs the inverse transformation. """ # TODO: consolidate the info about pandas constructors and the # attributes that correspond to their indexes into a separate module? constructors = {0: lambda x: x, 1: pd.Series, 2: pd.DataFrame, 3: pd.Panel} try: constructor = constructors[self.ndim] except KeyError: raise ValueError('cannot convert arrays with %s dimensions into ' 'pandas objects' % self.ndim) indexes = [self.get_index(dim) for dim in self.dims] return constructor(self.values, *indexes) def to_dataframe(self, name=None): """Convert this array and its coordinates into a tidy pandas.DataFrame. The DataFrame is indexed by the Cartesian product of index coordinates (in the form of a :py:class:`pandas.MultiIndex`). Other coordinates are included as columns in the DataFrame. """ if name is None: name = self.name if name is None: raise ValueError('cannot convert an unnamed DataArray to a ' 'DataFrame: use the ``name`` parameter') dims = OrderedDict(zip(self.dims, self.shape)) # By using a unique name, we can convert a DataArray into a DataFrame # even if it shares a name with one of its coordinates. # I would normally use unique_name = object() but that results in a # dataframe with columns in the wrong order, for reasons I have not # been able to debug (possibly a pandas bug?). unique_name = '__unique_name_identifier_z98xfz98xugfg73ho__' ds = self._to_dataset_whole(name=unique_name) df = ds._to_dataframe(dims) df.columns = [name if c == unique_name else c for c in df.columns] return df def to_series(self): """Convert this array into a pandas.Series. The Series is indexed by the Cartesian product of index coordinates (in the form of a :py:class:`pandas.MultiIndex`). """ index = self.coords.to_index() return pd.Series(self.values.reshape(-1), index=index, name=self.name) def to_masked_array(self, copy=True): """Convert this array into a numpy.ma.MaskedArray Parameters ---------- copy : bool If True (default) make a copy of the array in the result. If False, a MaskedArray view of DataArray.values is returned. Returns ------- result : MaskedArray Masked where invalid values (nan or inf) occur. """ isnull = pd.isnull(self.values) return np.ma.MaskedArray(data=self.values, mask=isnull, copy=copy) def to_netcdf(self, *args, **kwargs): """Write DataArray contents to a netCDF file. Parameters ---------- path : str or Path, optional Path to which to save this dataset. If no path is provided, this function returns the resulting netCDF file as a bytes object; in this case, we need to use scipy.io.netcdf, which does not support netCDF version 4 (the default format becomes NETCDF3_64BIT). mode : {'w', 'a'}, optional Write ('w') or append ('a') mode. If mode='w', any existing file at this location will be overwritten. format : {'NETCDF4', 'NETCDF4_CLASSIC', 'NETCDF3_64BIT', 'NETCDF3_CLASSIC'}, optional File format for the resulting netCDF file: * NETCDF4: Data is stored in an HDF5 file, using netCDF4 API features. * NETCDF4_CLASSIC: Data is stored in an HDF5 file, using only netCDF 3 compatible API features. * NETCDF3_64BIT: 64-bit offset version of the netCDF 3 file format, which fully supports 2+ GB files, but is only compatible with clients linked against netCDF version 3.6.0 or later. * NETCDF3_CLASSIC: The classic netCDF 3 file format. It does not handle 2+ GB files very well. All formats are supported by the netCDF4-python library. scipy.io.netcdf only supports the last two formats. The default format is NETCDF4 if you are saving a file to disk and have the netCDF4-python library available. Otherwise, xarray falls back to using scipy to write netCDF files and defaults to the NETCDF3_64BIT format (scipy does not support netCDF4). group : str, optional Path to the netCDF4 group in the given file to open (only works for format='NETCDF4'). The group(s) will be created if necessary. engine : {'netcdf4', 'scipy', 'h5netcdf'}, optional Engine to use when writing netCDF files. If not provided, the default engine is chosen based on available dependencies, with a preference for 'netcdf4' if writing to a file on disk. encoding : dict, optional Nested dictionary with variable names as keys and dictionaries of variable specific encodings as values, e.g., ``{'my_variable': {'dtype': 'int16', 'scale_factor': 0.1, 'zlib': True}, ...}`` Notes ----- Only xarray.Dataset objects can be written to netCDF files, so the xarray.DataArray is converted to a xarray.Dataset object containing a single variable. If the DataArray has no name, or if the name is the same as a co-ordinate name, then it is given the name '__xarray_dataarray_variable__'. All parameters are passed directly to `xarray.Dataset.to_netcdf`. """ from ..backends.api import DATAARRAY_NAME, DATAARRAY_VARIABLE if self.name is None: # If no name is set then use a generic xarray name dataset = self.to_dataset(name=DATAARRAY_VARIABLE) elif self.name in self.coords or self.name in self.dims: # The name is the same as one of the coords names, which netCDF # doesn't support, so rename it but keep track of the old name dataset = self.to_dataset(name=DATAARRAY_VARIABLE) dataset.attrs[DATAARRAY_NAME] = self.name else: # No problems with the name - so we're fine! dataset = self.to_dataset() return dataset.to_netcdf(*args, **kwargs) def to_dict(self): """ Convert this xarray.DataArray into a dictionary following xarray naming conventions. Converts all variables and attributes to native Python objects. Useful for coverting to json. To avoid datetime incompatibility use decode_times=False kwarg in xarrray.open_dataset. See also -------- DataArray.from_dict """ d = {'coords': {}, 'attrs': decode_numpy_dict_values(self.attrs), 'dims': self.dims} for k in self.coords: data = ensure_us_time_resolution(self[k].values).tolist() d['coords'].update({ k: {'data': data, 'dims': self[k].dims, 'attrs': decode_numpy_dict_values(self[k].attrs)}}) d.update({'data': ensure_us_time_resolution(self.values).tolist(), 'name': self.name}) return d @classmethod def from_dict(cls, d): """ Convert a dictionary into an xarray.DataArray Input dict can take several forms:: d = {'dims': ('t'), 'data': x} d = {'coords': {'t': {'dims': 't', 'data': t, 'attrs': {'units':'s'}}}, 'attrs': {'title': 'air temperature'}, 'dims': 't', 'data': x, 'name': 'a'} where 't' is the name of the dimesion, 'a' is the name of the array, and x and t are lists, numpy.arrays, or pandas objects. Parameters ---------- d : dict, with a minimum structure of {'dims': [..], 'data': [..]} Returns ------- obj : xarray.DataArray See also -------- DataArray.to_dict Dataset.from_dict """ coords = None if 'coords' in d: try: coords = OrderedDict([(k, (v['dims'], v['data'], v.get('attrs'))) for k, v in d['coords'].items()]) except KeyError as e: raise ValueError( "cannot convert dict when coords are missing the key " "'{dims_data}'".format(dims_data=str(e.args[0]))) try: data = d['data'] except KeyError: raise ValueError("cannot convert dict without the key 'data''") else: obj = cls(data, coords, d.get('dims'), d.get('name'), d.get('attrs')) return obj @classmethod def from_series(cls, series): """Convert a pandas.Series into an xarray.DataArray. If the series's index is a MultiIndex, it will be expanded into a tensor product of one-dimensional coordinates (filling in missing values with NaN). Thus this operation should be the inverse of the `to_series` method. """ # TODO: add a 'name' parameter name = series.name df = pd.DataFrame({name: series}) ds = Dataset.from_dataframe(df) return ds[name] def to_cdms2(self): """Convert this array into a cdms2.Variable """ from ..convert import to_cdms2 return to_cdms2(self) @classmethod def from_cdms2(cls, variable): """Convert a cdms2.Variable into an xarray.DataArray """ from ..convert import from_cdms2 return from_cdms2(variable) def to_iris(self): """Convert this array into a iris.cube.Cube """ from ..convert import to_iris return to_iris(self) @classmethod def from_iris(cls, cube): """Convert a iris.cube.Cube into an xarray.DataArray """ from ..convert import from_iris return from_iris(cube) def _all_compat(self, other, compat_str): """Helper function for equals and identical""" def compat(x, y): return getattr(x.variable, compat_str)(y.variable) return (utils.dict_equiv(self.coords, other.coords, compat=compat) and compat(self, other)) def broadcast_equals(self, other): """Two DataArrays are broadcast equal if they are equal after broadcasting them against each other such that they have the same dimensions. See Also -------- DataArray.equals DataArray.identical """ try: return self._all_compat(other, 'broadcast_equals') except (TypeError, AttributeError): return False def equals(self, other): """True if two DataArrays have the same dimensions, coordinates and values; otherwise False. DataArrays can still be equal (like pandas objects) if they have NaN values in the same locations. This method is necessary because `v1 == v2` for ``DataArray`` does element-wise comparisons (like numpy.ndarrays). See Also -------- DataArray.broadcast_equals DataArray.identical """ try: return self._all_compat(other, 'equals') except (TypeError, AttributeError): return False def identical(self, other): """Like equals, but also checks the array name and attributes, and attributes on all coordinates. See Also -------- DataArray.broadcast_equals DataArray.equal """ try: return (self.name == other.name and self._all_compat(other, 'identical')) except (TypeError, AttributeError): return False __default_name = object() def _result_name(self, other=None): # use the same naming heuristics as pandas: # https://github.com/ContinuumIO/blaze/issues/458#issuecomment-51936356 other_name = getattr(other, 'name', self.__default_name) if other_name is self.__default_name or other_name == self.name: return self.name else: return None def __array_wrap__(self, obj, context=None): new_var = self.variable.__array_wrap__(obj, context) return self._replace(new_var) @staticmethod def _unary_op(f): @functools.wraps(f) def func(self, *args, **kwargs): with np.errstate(all='ignore'): return self.__array_wrap__(f(self.variable.data, *args, **kwargs)) return func @staticmethod def _binary_op(f, reflexive=False, join=None, **ignored_kwargs): @functools.wraps(f) def func(self, other): if isinstance(other, (Dataset, groupby.GroupBy)): return NotImplemented if isinstance(other, DataArray): align_type = (OPTIONS['arithmetic_join'] if join is None else join) self, other = align(self, other, join=align_type, copy=False) other_variable = getattr(other, 'variable', other) other_coords = getattr(other, 'coords', None) variable = (f(self.variable, other_variable) if not reflexive else f(other_variable, self.variable)) coords = self.coords._merge_raw(other_coords) name = self._result_name(other) return self._replace(variable, coords, name) return func @staticmethod def _inplace_binary_op(f): @functools.wraps(f) def func(self, other): if isinstance(other, groupby.GroupBy): raise TypeError('in-place operations between a DataArray and ' 'a grouped object are not permitted') # n.b. we can't align other to self (with other.reindex_like(self)) # because `other` may be converted into floats, which would cause # in-place arithmetic to fail unpredictably. Instead, we simply # don't support automatic alignment with in-place arithmetic. other_coords = getattr(other, 'coords', None) other_variable = getattr(other, 'variable', other) with self.coords._merge_inplace(other_coords): f(self.variable, other_variable) return self return func def _copy_attrs_from(self, other): self.attrs = other.attrs @property def plot(self): """ Access plotting functions >>> d = DataArray([[1, 2], [3, 4]]) For convenience just call this directly >>> d.plot() Or use it as a namespace to use xarray.plot functions as DataArray methods >>> d.plot.imshow() # equivalent to xarray.plot.imshow(d) """ return _PlotMethods(self) def _title_for_slice(self, truncate=50): """ If the dataarray has 1 dimensional coordinates or comes from a slice we can show that info in the title Parameters ---------- truncate : integer maximum number of characters for title Returns ------- title : string Can be used for plot titles """ one_dims = [] for dim, coord in iteritems(self.coords): if coord.size == 1: one_dims.append('{dim} = {v}'.format( dim=dim, v=format_item(coord.values))) title = ', '.join(one_dims) if len(title) > truncate: title = title[:(truncate - 3)] + '...' return title def diff(self, dim, n=1, label='upper'): """Calculate the n-th order discrete difference along given axis. Parameters ---------- dim : str, optional Dimension over which to calculate the finite difference. n : int, optional The number of times values are differenced. label : str, optional The new coordinate in dimension ``dim`` will have the values of either the minuend's or subtrahend's coordinate for values 'upper' and 'lower', respectively. Other values are not supported. Returns ------- difference : same type as caller The n-th order finite difference of this object. Examples -------- >>> arr = xr.DataArray([5, 5, 6, 6], [[1, 2, 3, 4]], ['x']) >>> arr.diff('x') array([0, 1, 0]) Coordinates: * x (x) int64 2 3 4 >>> arr.diff('x', 2) array([ 1, -1]) Coordinates: * x (x) int64 3 4 See Also -------- DataArray.differentiate """ ds = self._to_temp_dataset().diff(n=n, dim=dim, label=label) return self._from_temp_dataset(ds) def shift(self, shifts=None, fill_value=dtypes.NA, **shifts_kwargs): """Shift this array by an offset along one or more dimensions. Only the data is moved; coordinates stay in place. Values shifted from beyond array bounds are replaced by NaN. This is consistent with the behavior of ``shift`` in pandas. Parameters ---------- shifts : Mapping with the form of {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_kwarg must be provided. Returns ------- shifted : DataArray DataArray with the same coordinates and attributes but shifted data. See also -------- roll Examples -------- >>> arr = xr.DataArray([5, 6, 7], dims='x') >>> arr.shift(x=1) array([ nan, 5., 6.]) Coordinates: * x (x) int64 0 1 2 """ variable = self.variable.shift( shifts=shifts, fill_value=fill_value, **shifts_kwargs) return self._replace(variable=variable) def roll(self, shifts=None, roll_coords=None, **shifts_kwargs): """Roll this array by an offset along one or more dimensions. Unlike shift, roll may rotate all variables, including coordinates if specified. The direction of rotation is consistent with :py:func:`numpy.roll`. Parameters ---------- roll_coords : bool Indicates whether to roll the coordinates by the offset The current default of roll_coords (None, equivalent to True) is deprecated and will change to False in a future version. Explicitly pass roll_coords to silence the warning. **shifts : keyword arguments of the form {dim: offset} Integer offset to rotate each of the given dimensions. Positive offsets roll to the right; negative offsets roll to the left. Returns ------- rolled : DataArray DataArray with the same attributes but rolled data and coordinates. See also -------- shift Examples -------- >>> arr = xr.DataArray([5, 6, 7], dims='x') >>> arr.roll(x=1) array([7, 5, 6]) Coordinates: * x (x) int64 2 0 1 """ ds = self._to_temp_dataset().roll( shifts=shifts, roll_coords=roll_coords, **shifts_kwargs) return self._from_temp_dataset(ds) @property def real(self): return self._replace(self.variable.real) @property def imag(self): return self._replace(self.variable.imag) def dot(self, other, dims=None): """Perform dot product of two DataArrays along their shared dims. Equivalent to taking taking tensordot over all shared dims. Parameters ---------- other : DataArray The other array with which the dot product is performed. dims: list of strings, optional Along which dimensions to be summed over. Default all the common dimensions are summed over. Returns ------- result : DataArray Array resulting from the dot product over all shared dimensions. See also -------- dot numpy.tensordot Examples -------- >>> da_vals = np.arange(6 * 5 * 4).reshape((6, 5, 4)) >>> da = DataArray(da_vals, dims=['x', 'y', 'z']) >>> dm_vals = np.arange(4) >>> dm = DataArray(dm_vals, dims=['z']) >>> dm.dims ('z') >>> da.dims ('x', 'y', 'z') >>> dot_result = da.dot(dm) >>> dot_result.dims ('x', 'y') """ if isinstance(other, Dataset): raise NotImplementedError('dot products are not yet supported ' 'with Dataset objects.') if not isinstance(other, DataArray): raise TypeError('dot only operates on DataArrays.') return computation.dot(self, other, dims=dims) def sortby(self, variables, ascending=True): """ Sort object by labels or values (along an axis). Sorts the dataarray, either along specified dimensions, or according to values of 1-D dataarrays that share dimension with calling object. If the input variables are dataarrays, then the dataarrays are aligned (via left-join) to the calling object prior to sorting by cell values. NaNs are sorted to the end, following Numpy convention. If multiple sorts along the same dimension is given, numpy's lexsort is performed along that dimension: https://docs.scipy.org/doc/numpy/reference/generated/numpy.lexsort.html and the FIRST key in the sequence is used as the primary sort key, followed by the 2nd key, etc. Parameters ---------- variables: str, DataArray, or list of either 1D DataArray objects or name(s) of 1D variable(s) in coords whose values are used to sort this array. ascending: boolean, optional Whether to sort by ascending or descending order. Returns ------- sorted: DataArray A new dataarray where all the specified dims are sorted by dim labels. Examples -------- >>> da = xr.DataArray(np.random.rand(5), ... coords=[pd.date_range('1/1/2000', periods=5)], ... dims='time') >>> da array([ 0.965471, 0.615637, 0.26532 , 0.270962, 0.552878]) Coordinates: * time (time) datetime64[ns] 2000-01-01 2000-01-02 2000-01-03 ... >>> da.sortby(da) array([ 0.26532 , 0.270962, 0.552878, 0.615637, 0.965471]) Coordinates: * time (time) datetime64[ns] 2000-01-03 2000-01-04 2000-01-05 ... """ ds = self._to_temp_dataset().sortby(variables, ascending=ascending) return self._from_temp_dataset(ds) def quantile(self, q, dim=None, interpolation='linear', keep_attrs=None): """Compute the qth quantile of the data along the specified dimension. Returns the qth quantiles(s) of the array elements. Parameters ---------- q : float in range of [0,1] (or sequence of floats) 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. interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'} This optional parameter specifies the interpolation method to use when the desired quantile lies between two data points ``i < j``: - linear: ``i + (j - i) * fraction``, where ``fraction`` is the fractional part of the index surrounded by ``i`` and ``j``. - lower: ``i``. - higher: ``j``. - nearest: ``i`` or ``j``, whichever is nearest. - midpoint: ``(i + j) / 2``. keep_attrs : bool, optional If True, the dataset's attributes (`attrs`) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes. Returns ------- quantiles : DataArray If `q` is a single quantile, then the result is a scalar. If multiple percentiles are given, first axis of the result corresponds to the quantile and a quantile dimension is added to the return array. The other dimensions are the dimensions that remain after the reduction of the array. See Also -------- numpy.nanpercentile, pandas.Series.quantile, Dataset.quantile """ ds = self._to_temp_dataset().quantile( q, dim=dim, keep_attrs=keep_attrs, interpolation=interpolation) return self._from_temp_dataset(ds) def rank(self, dim, pct=False, keep_attrs=None): """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. keep_attrs : bool, optional If True, the dataset's attributes (`attrs`) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes. Returns ------- ranked : DataArray DataArray with the same coordinates and dtype 'float64'. Examples -------- >>> arr = xr.DataArray([5, 6, 7], dims='x') >>> arr.rank('x') array([ 1., 2., 3.]) Dimensions without coordinates: x """ ds = self._to_temp_dataset().rank(dim, pct=pct, keep_attrs=keep_attrs) return self._from_temp_dataset(ds) def differentiate(self, coord, edge_order=1, datetime_unit=None): """ Differentiate the array with the second order accurate central differences. .. note:: This feature is limited to simple cartesian geometry, i.e. coord must be one dimensional. Parameters ---------- coord: str The coordinate to be used to compute the gradient. edge_order: 1 or 2. Default 1 N-th order accurate differences at the boundaries. datetime_unit: None or any of {'Y', 'M', 'W', 'D', 'h', 'm', 's', 'ms', 'us', 'ns', 'ps', 'fs', 'as'} Unit to compute gradient. Only valid for datetime coordinate. Returns ------- differentiated: DataArray See also -------- numpy.gradient: corresponding numpy function Examples -------- >>> da = xr.DataArray(np.arange(12).reshape(4, 3), dims=['x', 'y'], ... coords={'x': [0, 0.1, 1.1, 1.2]}) >>> da array([[ 0, 1, 2], [ 3, 4, 5], [ 6, 7, 8], [ 9, 10, 11]]) Coordinates: * x (x) float64 0.0 0.1 1.1 1.2 Dimensions without coordinates: y >>> >>> da.differentiate('x') array([[30. , 30. , 30. ], [27.545455, 27.545455, 27.545455], [27.545455, 27.545455, 27.545455], [30. , 30. , 30. ]]) Coordinates: * x (x) float64 0.0 0.1 1.1 1.2 Dimensions without coordinates: y """ ds = self._to_temp_dataset().differentiate( coord, edge_order, datetime_unit) return self._from_temp_dataset(ds) # priority most be higher than Variable to properly work with binary ufuncs ops.inject_all_ops_and_reduce_methods(DataArray, priority=60)