from __future__ import absolute_import, division, print_function from textwrap import dedent import numpy as np import pandas as pd from . import dtypes, duck_array_ops, formatting, ops from .arithmetic import SupportsArithmetic from .options import _get_keep_attrs from .pycompat import OrderedDict, basestring, dask_array_type, suppress from .utils import Frozen, ReprObject, SortedKeysDict, either_dict_or_kwargs # Used as a sentinel value to indicate a all dimensions ALL_DIMS = ReprObject('') class ImplementsArrayReduce(object): @classmethod def _reduce_method(cls, func, include_skipna, numeric_only): if include_skipna: def wrapped_func(self, dim=None, axis=None, skipna=None, **kwargs): return self.reduce(func, dim, axis, skipna=skipna, allow_lazy=True, **kwargs) else: def wrapped_func(self, dim=None, axis=None, **kwargs): return self.reduce(func, dim, axis, allow_lazy=True, **kwargs) return wrapped_func _reduce_extra_args_docstring = dedent("""\ dim : str or sequence of str, optional Dimension(s) over which to apply `{name}`. axis : int or sequence of int, optional Axis(es) over which to apply `{name}`. Only one of the 'dim' and 'axis' arguments can be supplied. If neither are supplied, then `{name}` is calculated over axes.""") _cum_extra_args_docstring = dedent("""\ dim : str or sequence of str, optional Dimension over which to apply `{name}`. axis : int or sequence of int, optional Axis over which to apply `{name}`. Only one of the 'dim' and 'axis' arguments can be supplied.""") class ImplementsDatasetReduce(object): @classmethod def _reduce_method(cls, func, include_skipna, numeric_only): if include_skipna: def wrapped_func(self, dim=None, skipna=None, **kwargs): return self.reduce(func, dim, skipna=skipna, numeric_only=numeric_only, allow_lazy=True, **kwargs) else: def wrapped_func(self, dim=None, **kwargs): return self.reduce(func, dim, numeric_only=numeric_only, allow_lazy=True, **kwargs) return wrapped_func _reduce_extra_args_docstring = \ """dim : str or sequence of str, optional Dimension(s) over which to apply `{name}`. By default `{name}` is applied over all dimensions.""" _cum_extra_args_docstring = \ """dim : str or sequence of str, optional Dimension over which to apply `{name}`. axis : int or sequence of int, optional Axis over which to apply `{name}`. Only one of the 'dim' and 'axis' arguments can be supplied.""" class AbstractArray(ImplementsArrayReduce, formatting.ReprMixin): """Shared base class for DataArray and Variable.""" def __bool__(self): return bool(self.values) # Python 3 uses __bool__, Python 2 uses __nonzero__ __nonzero__ = __bool__ def __float__(self): return float(self.values) def __int__(self): return int(self.values) def __complex__(self): return complex(self.values) def __long__(self): return long(self.values) # noqa def __array__(self, dtype=None): return np.asarray(self.values, dtype=dtype) def __repr__(self): return formatting.array_repr(self) def _iter(self): for n in range(len(self)): yield self[n] def __iter__(self): if self.ndim == 0: raise TypeError('iteration over a 0-d array') return self._iter() @property def T(self): return self.transpose() def get_axis_num(self, dim): """Return axis number(s) corresponding to dimension(s) in this array. Parameters ---------- dim : str or iterable of str Dimension name(s) for which to lookup axes. Returns ------- int or tuple of int Axis number or numbers corresponding to the given dimensions. """ if isinstance(dim, basestring): return self._get_axis_num(dim) else: return tuple(self._get_axis_num(d) for d in dim) def _get_axis_num(self, dim): try: return self.dims.index(dim) except ValueError: raise ValueError("%r not found in array dimensions %r" % (dim, self.dims)) @property def sizes(self): """Ordered mapping from dimension names to lengths. Immutable. See also -------- Dataset.sizes """ return Frozen(OrderedDict(zip(self.dims, self.shape))) class AttrAccessMixin(object): """Mixin class that allows getting keys with attribute access """ _initialized = False @property def _attr_sources(self): """List of places to look-up items for attribute-style access""" return [] @property def _item_sources(self): """List of places to look-up items for key-autocompletion """ return [] def __getattr__(self, name): if name != '__setstate__': # this avoids an infinite loop when pickle looks for the # __setstate__ attribute before the xarray object is initialized for source in self._attr_sources: with suppress(KeyError): return source[name] raise AttributeError("%r object has no attribute %r" % (type(self).__name__, name)) def __setattr__(self, name, value): if self._initialized: try: # Allow setting instance variables if they already exist # (e.g., _attrs). We use __getattribute__ instead of hasattr # to avoid key lookups with attribute-style access. self.__getattribute__(name) except AttributeError: raise AttributeError( "cannot set attribute %r on a %r object. Use __setitem__ " "style assignment (e.g., `ds['name'] = ...`) instead to " "assign variables." % (name, type(self).__name__)) object.__setattr__(self, name, value) def __dir__(self): """Provide method name lookup and completion. Only provide 'public' methods. """ extra_attrs = [item for sublist in self._attr_sources for item in sublist if isinstance(item, basestring)] return sorted(set(dir(type(self)) + extra_attrs)) def _ipython_key_completions_(self): """Provide method for the key-autocompletions in IPython. See http://ipython.readthedocs.io/en/stable/config/integrating.html#tab-completion For the details. """ # noqa item_lists = [item for sublist in self._item_sources for item in sublist if isinstance(item, basestring)] return list(set(item_lists)) def get_squeeze_dims(xarray_obj, dim, axis=None): """Get a list of dimensions to squeeze out. """ if dim is not None and axis is not None: raise ValueError('cannot use both parameters `axis` and `dim`') if dim is None and axis is None: dim = [d for d, s in xarray_obj.sizes.items() if s == 1] else: if isinstance(dim, basestring): dim = [dim] if isinstance(axis, int): axis = (axis, ) if isinstance(axis, tuple): for a in axis: if not isinstance(a, int): raise ValueError( 'parameter `axis` must be int or tuple of int.') alldims = list(xarray_obj.sizes.keys()) dim = [alldims[a] for a in axis] if any(xarray_obj.sizes[k] > 1 for k in dim): raise ValueError('cannot select a dimension to squeeze out ' 'which has length greater than one') return dim class DataWithCoords(SupportsArithmetic, AttrAccessMixin): """Shared base class for Dataset and DataArray.""" def squeeze(self, dim=None, drop=False, axis=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. drop : bool, optional If ``drop=True``, drop squeezed coordinates instead of making them scalar. axis : int, optional Select the dimension to squeeze. Added for compatibility reasons. 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 = get_squeeze_dims(self, dim, axis) return self.isel(drop=drop, **{d: 0 for d in dims}) def get_index(self, key): """Get an index for a dimension, with fall-back to a default RangeIndex """ if key not in self.dims: raise KeyError(key) try: return self.indexes[key] except KeyError: # need to ensure dtype=int64 in case range is empty on Python 2 return pd.Index(range(self.sizes[key]), name=key, dtype=np.int64) def _calc_assign_results(self, kwargs): results = SortedKeysDict() for k, v in kwargs.items(): if callable(v): results[k] = v(self) else: results[k] = v return results def assign_coords(self, **kwargs): """Assign new coordinates to this object. Returns a new object with all the original data in addition to the new coordinates. Parameters ---------- kwargs : keyword, value pairs keywords are the variables names. If the values are callable, they are computed on this object and assigned to new coordinate variables. If the values are not callable, (e.g. a DataArray, scalar, or array), they are simply assigned. Returns ------- assigned : same type as caller A new object with the new coordinates in addition to the existing data. Examples -------- Convert longitude coordinates from 0-359 to -180-179: >>> da = xr.DataArray(np.random.rand(4), ... coords=[np.array([358, 359, 0, 1])], ... dims='lon') >>> da array([0.28298 , 0.667347, 0.657938, 0.177683]) Coordinates: * lon (lon) int64 358 359 0 1 >>> da.assign_coords(lon=(((da.lon + 180) % 360) - 180)) array([0.28298 , 0.667347, 0.657938, 0.177683]) Coordinates: * lon (lon) int64 -2 -1 0 1 Notes ----- Since ``kwargs`` is a dictionary, the order of your arguments may not be preserved, and so the order of the new variables is not well defined. Assigning multiple variables within the same ``assign_coords`` is possible, but you cannot reference other variables created within the same ``assign_coords`` call. See also -------- Dataset.assign Dataset.swap_dims """ data = self.copy(deep=False) results = self._calc_assign_results(kwargs) data.coords.update(results) return data def assign_attrs(self, *args, **kwargs): """Assign new attrs to this object. Returns a new object equivalent to self.attrs.update(*args, **kwargs). Parameters ---------- args : positional arguments passed into ``attrs.update``. kwargs : keyword arguments passed into ``attrs.update``. Returns ------- assigned : same type as caller A new object with the new attrs in addition to the existing data. See also -------- Dataset.assign """ out = self.copy(deep=False) out.attrs.update(*args, **kwargs) return out def pipe(self, func, *args, **kwargs): """ Apply func(self, *args, **kwargs) This method replicates the pandas method of the same name. Parameters ---------- func : function function to apply to this xarray object (Dataset/DataArray). ``args``, and ``kwargs`` are passed into ``func``. Alternatively a ``(callable, data_keyword)`` tuple where ``data_keyword`` is a string indicating the keyword of ``callable`` that expects the xarray object. args : positional arguments passed into ``func``. kwargs : a dictionary of keyword arguments passed into ``func``. Returns ------- object : the return type of ``func``. Notes ----- Use ``.pipe`` when chaining together functions that expect xarray or pandas objects, e.g., instead of writing >>> f(g(h(ds), arg1=a), arg2=b, arg3=c) You can write >>> (ds.pipe(h) ... .pipe(g, arg1=a) ... .pipe(f, arg2=b, arg3=c) ... ) If you have a function that takes the data as (say) the second argument, pass a tuple indicating which keyword expects the data. For example, suppose ``f`` takes its data as ``arg2``: >>> (ds.pipe(h) ... .pipe(g, arg1=a) ... .pipe((f, 'arg2'), arg1=a, arg3=c) ... ) See Also -------- pandas.DataFrame.pipe """ if isinstance(func, tuple): func, target = func if target in kwargs: msg = ('%s is both the pipe target and a keyword argument' % target) raise ValueError(msg) kwargs[target] = self return func(*args, **kwargs) else: return func(self, *args, **kwargs) def groupby(self, group, squeeze=True): """Returns a GroupBy object for performing grouped operations. Parameters ---------- group : str, DataArray or IndexVariable Array whose unique values should be used to group this array. If a string, must be the name of a variable contained in this dataset. squeeze : boolean, optional If "group" is a dimension of any arrays in this dataset, `squeeze` controls whether the subarrays have a dimension of length 1 along that dimension or if the dimension is squeezed out. Returns ------- grouped : GroupBy A `GroupBy` object patterned after `pandas.GroupBy` that can be iterated over in the form of `(unique_value, grouped_array)` pairs. Examples -------- Calculate daily anomalies for daily data: >>> da = xr.DataArray(np.linspace(0, 1826, num=1827), ... coords=[pd.date_range('1/1/2000', '31/12/2004', ... freq='D')], ... dims='time') >>> da array([0.000e+00, 1.000e+00, 2.000e+00, ..., 1.824e+03, 1.825e+03, 1.826e+03]) Coordinates: * time (time) datetime64[ns] 2000-01-01 2000-01-02 2000-01-03 ... >>> da.groupby('time.dayofyear') - da.groupby('time.dayofyear').mean('time') array([-730.8, -730.8, -730.8, ..., 730.2, 730.2, 730.5]) Coordinates: * time (time) datetime64[ns] 2000-01-01 2000-01-02 2000-01-03 ... dayofyear (time) int64 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 ... See Also -------- core.groupby.DataArrayGroupBy core.groupby.DatasetGroupBy """ # noqa return self._groupby_cls(self, group, squeeze=squeeze) def groupby_bins(self, group, bins, right=True, labels=None, precision=3, include_lowest=False, squeeze=True): """Returns a GroupBy object for performing grouped operations. Rather than using all unique values of `group`, the values are discretized first by applying `pandas.cut` [1]_ to `group`. Parameters ---------- group : str, DataArray or IndexVariable Array whose binned values should be used to group this array. If a string, must be the name of a variable contained in this dataset. bins : int or array of scalars If bins is an int, it defines the number of equal-width bins in the range of x. However, in this case, the range of x is extended by .1% on each side to include the min or max values of x. If bins is a sequence it defines the bin edges allowing for non-uniform bin width. No extension of the range of x is done in this case. right : boolean, optional Indicates whether the bins include the rightmost edge or not. If right == True (the default), then the bins [1,2,3,4] indicate (1,2], (2,3], (3,4]. labels : array or boolean, default None Used as labels for the resulting bins. Must be of the same length as the resulting bins. If False, string bin labels are assigned by `pandas.cut`. precision : int The precision at which to store and display the bins labels. include_lowest : bool Whether the first interval should be left-inclusive or not. squeeze : boolean, optional If "group" is a dimension of any arrays in this dataset, `squeeze` controls whether the subarrays have a dimension of length 1 along that dimension or if the dimension is squeezed out. Returns ------- grouped : GroupBy A `GroupBy` object patterned after `pandas.GroupBy` that can be iterated over in the form of `(unique_value, grouped_array)` pairs. The name of the group has the added suffix `_bins` in order to distinguish it from the original variable. References ---------- .. [1] http://pandas.pydata.org/pandas-docs/stable/generated/pandas.cut.html """ # noqa return self._groupby_cls(self, group, squeeze=squeeze, bins=bins, cut_kwargs={'right': right, 'labels': labels, 'precision': precision, 'include_lowest': include_lowest}) def rolling(self, dim=None, min_periods=None, center=False, **dim_kwargs): """ Rolling window object. Parameters ---------- dim: dict, optional Mapping from the dimension name to create the rolling iterator along (e.g. `time`) to its moving window size. min_periods : int, default None Minimum number of observations in window required to have a value (otherwise result is NA). The default, None, is equivalent to setting min_periods equal to the size of the window. center : boolean, default False Set the labels at the center of the window. **dim_kwargs : optional The keyword arguments form of ``dim``. One of dim or dim_kwargs must be provided. Returns ------- Rolling object (core.rolling.DataArrayRolling for DataArray, core.rolling.DatasetRolling for Dataset.) Examples -------- Create rolling seasonal average of monthly data e.g. DJF, JFM, ..., SON: >>> da = xr.DataArray(np.linspace(0, 11, num=12), ... coords=[pd.date_range('15/12/1999', ... periods=12, freq=pd.DateOffset(months=1))], ... dims='time') >>> da array([ 0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11.]) Coordinates: * time (time) datetime64[ns] 1999-12-15 2000-01-15 2000-02-15 ... >>> da.rolling(time=3, center=True).mean() array([nan, 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., nan]) Coordinates: * time (time) datetime64[ns] 1999-12-15 2000-01-15 2000-02-15 ... Remove the NaNs using ``dropna()``: >>> da.rolling(time=3, center=True).mean().dropna('time') array([ 1., 2., 3., 4., 5., 6., 7., 8., 9., 10.]) Coordinates: * time (time) datetime64[ns] 2000-01-15 2000-02-15 2000-03-15 ... See Also -------- core.rolling.DataArrayRolling core.rolling.DatasetRolling """ # noqa dim = either_dict_or_kwargs(dim, dim_kwargs, 'rolling') return self._rolling_cls(self, dim, min_periods=min_periods, center=center) def resample(self, indexer=None, skipna=None, closed=None, label=None, base=0, keep_attrs=None, loffset=None, **indexer_kwargs): """Returns a Resample object for performing resampling operations. Handles both downsampling and upsampling. If any intervals contain no values from the original object, they will be given the value ``NaN``. Parameters ---------- indexer : {dim: freq}, optional Mapping from the dimension name to resample frequency. skipna : bool, optional Whether to skip missing values when aggregating in downsampling. closed : 'left' or 'right', optional Side of each interval to treat as closed. label : 'left or 'right', optional Side of each interval to use for labeling. base : int, optional For frequencies that evenly subdivide 1 day, the "origin" of the aggregated intervals. For example, for '24H' frequency, base could range from 0 through 23. loffset : timedelta or str, optional Offset used to adjust the resampled time labels. Some pandas date offset strings are supported. keep_attrs : bool, optional If True, the object'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. **indexer_kwargs : {dim: freq} The keyword arguments form of ``indexer``. One of indexer or indexer_kwargs must be provided. Returns ------- resampled : same type as caller This object resampled. Examples -------- Downsample monthly time-series data to seasonal data: >>> da = xr.DataArray(np.linspace(0, 11, num=12), ... coords=[pd.date_range('15/12/1999', ... periods=12, freq=pd.DateOffset(months=1))], ... dims='time') >>> da array([ 0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11.]) Coordinates: * time (time) datetime64[ns] 1999-12-15 2000-01-15 2000-02-15 ... >>> da.resample(time="QS-DEC").mean() array([ 1., 4., 7., 10.]) Coordinates: * time (time) datetime64[ns] 1999-12-01 2000-03-01 2000-06-01 2000-09-01 Upsample monthly time-series data to daily data: >>> da.resample(time='1D').interpolate('linear') array([ 0. , 0.032258, 0.064516, ..., 10.935484, 10.967742, 11. ]) Coordinates: * time (time) datetime64[ns] 1999-12-15 1999-12-16 1999-12-17 ... References ---------- .. [1] http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases """ # noqa # TODO support non-string indexer after removing the old API. from .dataarray import DataArray from .resample import RESAMPLE_DIM from ..coding.cftimeindex import CFTimeIndex if keep_attrs is None: keep_attrs = _get_keep_attrs(default=False) # note: the second argument (now 'skipna') use to be 'dim' if ((skipna is not None and not isinstance(skipna, bool)) or ('how' in indexer_kwargs and 'how' not in self.dims) or ('dim' in indexer_kwargs and 'dim' not in self.dims)): raise TypeError( 'resample() no longer supports the `how` or ' '`dim` arguments. Instead call methods on resample ' "objects, e.g., data.resample(time='1D').mean()") indexer = either_dict_or_kwargs(indexer, indexer_kwargs, 'resample') if len(indexer) != 1: raise ValueError( "Resampling only supported along single dimensions." ) dim, freq = indexer.popitem() dim_name = dim dim_coord = self[dim] if isinstance(self.indexes[dim_name], CFTimeIndex): raise NotImplementedError( 'Resample is currently not supported along a dimension ' 'indexed by a CFTimeIndex. For certain kinds of downsampling ' 'it may be possible to work around this by converting your ' 'time index to a DatetimeIndex using ' 'CFTimeIndex.to_datetimeindex. Use caution when doing this ' 'however, because switching to a DatetimeIndex from a ' 'CFTimeIndex with a non-standard calendar entails a change ' 'in the calendar type, which could lead to subtle and silent ' 'errors.' ) group = DataArray(dim_coord, coords=dim_coord.coords, dims=dim_coord.dims, name=RESAMPLE_DIM) # TODO: to_offset() call required for pandas==0.19.2 grouper = pd.Grouper(freq=freq, closed=closed, label=label, base=base, loffset=pd.tseries.frequencies.to_offset(loffset)) resampler = self._resample_cls(self, group=group, dim=dim_name, grouper=grouper, resample_dim=RESAMPLE_DIM) return resampler def where(self, cond, other=dtypes.NA, drop=False): """Filter elements from this object according to a condition. This operation follows the normal broadcasting and alignment rules that xarray uses for binary arithmetic. Parameters ---------- cond : DataArray or Dataset with boolean dtype Locations at which to preserve this object's values. other : scalar, DataArray or Dataset, optional Value to use for locations in this object where ``cond`` is False. By default, these locations filled with NA. drop : boolean, optional If True, coordinate labels that only correspond to False values of the condition are dropped from the result. Mutually exclusive with ``other``. Returns ------- Same type as caller. Examples -------- >>> import numpy as np >>> a = xr.DataArray(np.arange(25).reshape(5, 5), dims=('x', 'y')) >>> a.where(a.x + a.y < 4) array([[ 0., 1., 2., 3., nan], [ 5., 6., 7., nan, nan], [ 10., 11., nan, nan, nan], [ 15., nan, nan, nan, nan], [ nan, nan, nan, nan, nan]]) Dimensions without coordinates: x, y >>> a.where(a.x + a.y < 5, -1) array([[ 0, 1, 2, 3, 4], [ 5, 6, 7, 8, -1], [10, 11, 12, -1, -1], [15, 16, -1, -1, -1], [20, -1, -1, -1, -1]]) Dimensions without coordinates: x, y >>> a.where(a.x + a.y < 4, drop=True) array([[ 0., 1., 2., 3.], [ 5., 6., 7., nan], [ 10., 11., nan, nan], [ 15., nan, nan, nan]]) Dimensions without coordinates: x, y See also -------- numpy.where : corresponding numpy function where : equivalent function """ from .alignment import align from .dataarray import DataArray from .dataset import Dataset if drop: if other is not dtypes.NA: raise ValueError('cannot set `other` if drop=True') if not isinstance(cond, (Dataset, DataArray)): raise TypeError("cond argument is %r but must be a %r or %r" % (cond, Dataset, DataArray)) # align so we can use integer indexing self, cond = align(self, cond) # get cond with the minimal size needed for the Dataset if isinstance(cond, Dataset): clipcond = cond.to_array().any('variable') else: clipcond = cond # clip the data corresponding to coordinate dims that are not used nonzeros = zip(clipcond.dims, np.nonzero(clipcond.values)) indexers = {k: np.unique(v) for k, v in nonzeros} self = self.isel(**indexers) cond = cond.isel(**indexers) return ops.where_method(self, cond, other) def close(self): """Close any files linked to this object """ if self._file_obj is not None: self._file_obj.close() self._file_obj = None def isin(self, test_elements): """Tests each value in the array for whether it is in the supplied list. Parameters ---------- test_elements : array_like The values against which to test each value of `element`. This argument is flattened if an array or array_like. See numpy notes for behavior with non-array-like parameters. Returns ------- isin : same as object, bool Has the same shape as this object. Examples -------- >>> array = xr.DataArray([1, 2, 3], dims='x') >>> array.isin([1, 3]) array([ True, False, True]) Dimensions without coordinates: x See also -------- numpy.isin """ from .computation import apply_ufunc from .dataset import Dataset from .dataarray import DataArray from .variable import Variable if isinstance(test_elements, Dataset): raise TypeError( 'isin() argument must be convertible to an array: {}' .format(test_elements)) elif isinstance(test_elements, (Variable, DataArray)): # need to explicitly pull out data to support dask arrays as the # second argument test_elements = test_elements.data return apply_ufunc( duck_array_ops.isin, self, kwargs=dict(test_elements=test_elements), dask='allowed', ) def __enter__(self): return self def __exit__(self, exc_type, exc_value, traceback): self.close() def full_like(other, fill_value, dtype=None): """Return a new object with the same shape and type as a given object. Parameters ---------- other : DataArray, Dataset, or Variable The reference object in input fill_value : scalar Value to fill the new object with before returning it. dtype : dtype, optional dtype of the new array. If omitted, it defaults to other.dtype. Returns ------- out : same as object New object with the same shape and type as other, with the data filled with fill_value. Coords will be copied from other. If other is based on dask, the new one will be as well, and will be split in the same chunks. """ from .dataarray import DataArray from .dataset import Dataset from .variable import Variable if isinstance(other, Dataset): data_vars = OrderedDict( (k, _full_like_variable(v, fill_value, dtype)) for k, v in other.data_vars.items()) return Dataset(data_vars, coords=other.coords, attrs=other.attrs) elif isinstance(other, DataArray): return DataArray( _full_like_variable(other.variable, fill_value, dtype), dims=other.dims, coords=other.coords, attrs=other.attrs, name=other.name) elif isinstance(other, Variable): return _full_like_variable(other, fill_value, dtype) else: raise TypeError("Expected DataArray, Dataset, or Variable") def _full_like_variable(other, fill_value, dtype=None): """Inner function of full_like, where other must be a variable """ from .variable import Variable if isinstance(other.data, dask_array_type): import dask.array if dtype is None: dtype = other.dtype data = dask.array.full(other.shape, fill_value, dtype=dtype, chunks=other.data.chunks) else: data = np.full_like(other, fill_value, dtype=dtype) return Variable(dims=other.dims, data=data, attrs=other.attrs) def zeros_like(other, dtype=None): """Shorthand for full_like(other, 0, dtype) """ return full_like(other, 0, dtype) def ones_like(other, dtype=None): """Shorthand for full_like(other, 1, dtype) """ return full_like(other, 1, dtype) def is_np_datetime_like(dtype): """Check if a dtype is a subclass of the numpy datetime types """ return (np.issubdtype(dtype, np.datetime64) or np.issubdtype(dtype, np.timedelta64)) def contains_cftime_datetimes(var): """Check if a variable contains cftime datetime objects""" try: from cftime import datetime as cftime_datetime except ImportError: return False else: if var.dtype == np.dtype('O') and var.data.size > 0: sample = var.data.ravel()[0] if isinstance(sample, dask_array_type): sample = sample.compute() if isinstance(sample, np.ndarray): sample = sample.item() return isinstance(sample, cftime_datetime) else: return False def _contains_datetime_like_objects(var): """Check if a variable contains datetime like objects (either np.datetime64, np.timedelta64, or cftime.datetime)""" return is_np_datetime_like(var.dtype) or contains_cftime_datetimes(var)