JorgeVB/code_unlearning_dataset · Datasets at Hugging Face

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Return a new DataArray whose dataset is given by selecting index labels along the specified dimension(s). .. warning:: Do not try to assign values when using any of the indexing methods ``isel`` or ``sel``:: da = xr.DataArray([0, 1, 2, 3], dims=['x']) # DO ...
Return a new DataArray whose dataset is given by pointwise integer indexing along the specified dimension(s). See Also -------- Dataset.isel_points def isel_points(self, dim='points', **indexers): """Return a new DataArray whose dataset is given by pointwise integer ind...
Return a new DataArray whose dataset is given by pointwise selection of index labels along the specified dimension(s). See Also -------- Dataset.sel_points def sel_points(self, dim='points', method=None, tolerance=None, **indexers): """Return a new DataArray ...
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 ...
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 broadc...
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 c...
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 ...
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 ----...
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, sequence of str,...
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 ...
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 mu...
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, optiona...
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, ...) ...
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 Mult...
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 retu...
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 ``di...
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', 'a...
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'``). ...
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 ...
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...
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 : ...
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. ...
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. def to_dataframe(self, name=None): ...
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`). def to_series(self): """Convert this array into a pandas.Series. The Series is indexed by the Cartesian product of index coord...
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 ...
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....
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. Parameters ...
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'}, ...
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. def from_series(c...
Helper function for equals and identical 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 ...
Like equals, but also checks the array name and attributes, and attributes on all coordinates. See Also -------- DataArray.broadcast_equals DataArray.equal def identical(self, other): """Like equals, but also checks the array name and attributes, and attributes ...
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 ti...
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 ...
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 : Mappin...
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...
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 Al...
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 ...
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 ...
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 `bottl...
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 comp...
integrate the array with the trapezoidal rule. .. note:: This feature is limited to simple cartesian geometry, i.e. coord must be one dimensional. Parameters ---------- dim: str, or a sequence of str Coordinate(s) used for the integration. da...
Convert this rolling object to xr.DataArray, where the window dimension is stacked as a new dimension Parameters ---------- window_dim: str New name of the window dimension. stride: integer, optional Size of stride for the rolling window. fill_val...
Reduce the items in this group by applying `func` along some dimension(s). Parameters ---------- func : function Function which can be called in the form `func(x, **kwargs)` to return the result of collapsing an np.ndarray over an the rolling dimensio...
Number of non-nan entries in each rolling window. def _counts(self): """ Number of non-nan entries in each rolling window. """ rolling_dim = utils.get_temp_dimname(self.obj.dims, '_rolling_dim') # We use False as the fill_value instead of np.nan, since boolean # array is faster to be r...
Methods to return a wrapped function for any function `func` for numpy methods. def _reduce_method(cls, func): """ Methods to return a wrapped function for any function `func` for numpy methods. """ def wrapped_func(self, **kwargs): return self.reduce(func, ...
Methods to return a wrapped function for any function `func` for bottoleneck method, except for `median`. def _bottleneck_reduce(cls, func): """ Methods to return a wrapped function for any function `func` for bottoleneck method, except for `median`. """ def wrapped_fun...
Reduce the items in this group by applying `func` along some dimension(s). Parameters ---------- func : function Function which can be called in the form `func(x, **kwargs)` to return the result of collapsing an np.ndarray over an the rolling dimensio...
Return a wrapped function for injecting numpy and bottoleneck methods. see ops.inject_datasetrolling_methods def _reduce_method(cls, func): """ Return a wrapped function for injecting numpy and bottoleneck methods. see ops.inject_datasetrolling_methods """ def wrapped_f...
Convert this rolling object to xr.Dataset, where the window dimension is stacked as a new dimension Parameters ---------- window_dim: str New name of the window dimension. stride: integer, optional size of stride for the rolling window. fill_value...
Return a wrapped function for injecting numpy methods. see ops.inject_coarsen_methods def _reduce_method(cls, func): """ Return a wrapped function for injecting numpy methods. see ops.inject_coarsen_methods """ def wrapped_func(self, **kwargs): from .dataarra...
Return a wrapped function for injecting numpy methods. see ops.inject_coarsen_methods def _reduce_method(cls, func): """ Return a wrapped function for injecting numpy methods. see ops.inject_coarsen_methods """ def wrapped_func(self, **kwargs): from .dataset ...
Ensure that a variable with vlen bytes is converted to fixed width. def ensure_fixed_length_bytes(var): """Ensure that a variable with vlen bytes is converted to fixed width.""" dims, data, attrs, encoding = unpack_for_encoding(var) if check_vlen_dtype(data.dtype) == bytes: # TODO: figure out how t...
Convert numpy/dask arrays from fixed width bytes to characters. def bytes_to_char(arr): """Convert numpy/dask arrays from fixed width bytes to characters.""" if arr.dtype.kind != 'S': raise ValueError('argument must have a fixed-width bytes dtype') if isinstance(arr, dask_array_type): impo...
Like netCDF4.stringtochar, but faster and more flexible. def _numpy_bytes_to_char(arr): """Like netCDF4.stringtochar, but faster and more flexible. """ # ensure the array is contiguous arr = np.array(arr, copy=False, order='C', dtype=np.string_) return arr.reshape(arr.shape + (1,)).view('S1')
Convert numpy/dask arrays from characters to fixed width bytes. def char_to_bytes(arr): """Convert numpy/dask arrays from characters to fixed width bytes.""" if arr.dtype != 'S1': raise ValueError("argument must have dtype='S1'") if not arr.ndim: # no dimension to concatenate along ...
Like netCDF4.chartostring, but faster and more flexible. def _numpy_char_to_bytes(arr): """Like netCDF4.chartostring, but faster and more flexible. """ # based on: http://stackoverflow.com/a/10984878/809705 arr = np.array(arr, copy=False, order='C') dtype = 'S' + str(arr.shape[-1]) return arr.v...
Given an array, safely cast it to a pandas.Index. If it is already a pandas.Index, return it unchanged. Unlike pandas.Index, if the array has dtype=object or dtype=timedelta64, this function will not attempt to do automatic type conversion but will always return an index with dtype=object. def safe_c...
Creating a MultiIndex from a product without refactorizing levels. Keeping levels the same gives back the original labels when we unstack. Parameters ---------- levels : sequence of pd.Index Values for each MultiIndex level. names : optional sequence of objects Names for each level...
Wrap a transformed array with __array_wrap__ is it can be done safely. This lets us treat arbitrary functions that take and return ndarray objects like ufuncs, as long as they return an array with the same shape. def maybe_wrap_array(original, new_array): """Wrap a transformed array with __array_wrap__ is...
Compare two objects for equivalence (identity or equality), using array_equiv if either object is an ndarray def equivalent(first: T, second: T) -> bool: """Compare two objects for equivalence (identity or equality), using array_equiv if either object is an ndarray """ # TODO: refactor to avoid cir...
Returns the first value from iterable, as well as a new iterator with the same content as the original iterable def peek_at(iterable: Iterable[T]) -> Tuple[T, Iterator[T]]: """Returns the first value from iterable, as well as a new iterator with the same content as the original iterable """ gen = i...
Check the safety of updating one dictionary with another. Raises ValueError if dictionaries have non-compatible values for any key, where compatibility is determined by identity (they are the same item) or the `compat` function. Parameters ---------- first_dict, second_dict : dict-like ...
Remove incompatible items from the first dictionary in-place. Items are retained if their keys are found in both dictionaries and the values are compatible. Parameters ---------- first_dict, second_dict : dict-like Mappings to merge. compat : function, optional Binary operator ...
Whether to treat a value as a scalar. Any non-iterable, string, or 0-D array def is_scalar(value: Any) -> bool: """Whether to treat a value as a scalar. Any non-iterable, string, or 0-D array """ return ( getattr(value, 'ndim', None) == 0 or isinstance(value, (str, bytes)) or not ...
Given a value, wrap it in a 0-D numpy.ndarray with dtype=object. def to_0d_object_array(value: Any) -> np.ndarray: """Given a value, wrap it in a 0-D numpy.ndarray with dtype=object. """ result = np.empty((), dtype=object) result[()] = value return result
Given a value, wrap it in a 0-D numpy.ndarray. def to_0d_array(value: Any) -> np.ndarray: """Given a value, wrap it in a 0-D numpy.ndarray. """ if np.isscalar(value) or (isinstance(value, np.ndarray) and value.ndim == 0): return np.array(value) else: return...
Test equivalence of two dict-like objects. If any of the values are numpy arrays, compare them correctly. Parameters ---------- first, second : dict-like Dictionaries to compare for equality compat : function, optional Binary operator to determine if two values are compatible. By de...
Return the intersection of two dictionaries as a new OrderedDict. Items are retained if their keys are found in both dictionaries and the values are compatible. Parameters ---------- first_dict, second_dict : dict-like Mappings to merge. compat : function, optional Binary opera...
Return True if values of an array are uniformly spaced and sorted. >>> is_uniform_spaced(range(5)) True >>> is_uniform_spaced([-4, 0, 100]) False kwargs are additional arguments to ``np.isclose`` def is_uniform_spaced(arr, **kwargs) -> bool: """Return True if values of an array are uniformly ...
Convert attribute values from numpy objects to native Python objects, for use in to_dict def decode_numpy_dict_values(attrs: Mapping[K, V]) -> Dict[K, V]: """Convert attribute values from numpy objects to native Python objects, for use in to_dict """ attrs = dict(attrs) for k, v in attrs.items(...
Convert val out of numpy time, for use in to_dict. Needed because of numpy bug GH#7619 def ensure_us_time_resolution(val): """Convert val out of numpy time, for use in to_dict. Needed because of numpy bug GH#7619""" if np.issubdtype(val.dtype, np.datetime64): val = val.astype('datetime64[us]') ...
Get an new dimension name based on new_dim, that is not used in dims. If the same name exists, we add an underscore(s) in the head. Example1: dims: ['a', 'b', 'c'] new_dim: ['_rolling'] -> ['_rolling'] Example2: dims: ['a', 'b', 'c', '_rolling'] new_dim: ['_rolling']...
Given an object array with no missing values, infer its dtype from its first element def _infer_dtype(array, name=None): """Given an object array with no missing values, infer its dtype from its first element """ if array.dtype.kind != 'O': raise TypeError('infer_type must be called on a dt...
Create a copy of an array with the given dtype. We use this instead of np.array() to ensure that custom object dtypes end up on the resulting array. def _copy_with_dtype(data, dtype): """Create a copy of an array with the given dtype. We use this instead of np.array() to ensure that custom object dty...
Converts an Variable into an Variable which follows some of the CF conventions: - Nans are masked using _FillValue (or the deprecated missing_value) - Rescaling via: scale_factor and add_offset - datetimes are converted to the CF 'units since time' format - dtype encodings are enfor...
Decodes a variable which may hold CF encoded information. This includes variables that have been masked and scaled, which hold CF style time variables (this is almost always the case if the dataset has been serialized) and which have strings encoded as character arrays. Parameters ---------- ...
Adds time attributes to time bounds variables. Variables handling time bounds ("Cell boundaries" in the CF conventions) do not necessarily carry the necessary attributes to be decoded. This copies the attributes from the time variable to the associated boundaries. See Also: http://cfconventio...
Decode several CF encoded variables. See: decode_cf_variable def decode_cf_variables(variables, attributes, concat_characters=True, mask_and_scale=True, decode_times=True, decode_coords=True, drop_variables=None, use_cftime=None): """ ...
Decode the given Dataset or Datastore according to CF conventions into a new Dataset. Parameters ---------- obj : Dataset or DataStore Object to decode. concat_characters : bool, optional Should character arrays be concatenated to strings, for example: ['h', 'e', 'l', 'l', '...
Decode a set of CF encoded variables and attributes. See Also, decode_cf_variable Parameters ---------- variables : dict A dictionary mapping from variable name to xarray.Variable attributes : dict A dictionary mapping from attribute name to value concat_characters : bool ...
Encode coordinates on the given dataset object into variable specific and global attributes. When possible, this is done according to CF conventions. Parameters ---------- dataset : Dataset Object to encode. Returns ------- variables : dict attrs : dict def encode_dataset...
A function which takes a dicts of variables and attributes and encodes them to conform to CF conventions as much as possible. This includes masking, scaling, character array handling, and CF-time encoding. Decode a set of CF encoded variables and attributes. See Also, decode_cf_variable Para...
Coerce an array to a data type that can be stored in a netCDF-3 file This function performs the following dtype conversions: int64 -> int32 bool -> int8 Data is checked for equality, or equivalence (non-NaN values) with `np.allclose` with the default keyword arguments. def coerce_nc3_dtyp...
Test whether an object can be validly converted to a netCDF-3 dimension, variable or attribute name Earlier versions of the netCDF C-library reference implementation enforced a more restricted set of characters in creating new names, but permitted reading names containing arbitrary bytes. This spec...
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...
Convert arrays of datetime.datetime and datetime.timedelta objects into datetime64 and timedelta64, according to the pandas convention. def _possibly_convert_objects(values): """Convert arrays of datetime.datetime and datetime.timedelta objects into datetime64 and timedelta64, according to the pandas conve...
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 Ind...
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-dime...
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 the size of the broadcast dimension. def _broadcast_compat_variables(*variables): """Create broadcast compatible variables, with the same...
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 ...
Concatenate variables along a new or existing dimension. Parameters ---------- variables : iterable of Array 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 ...
Check for uniqueness of MultiIndex level names in all given variables. Not public API. Used for checking consistency of DataArray and Dataset objects. def assert_unique_multiindex_level_names(variables): """Check for uniqueness of MultiIndex level names in all given variables. Not public API....
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. ...
Return this variable as a base xarray.Variable def to_base_variable(self): """Return this variable as a base xarray.Variable""" return Variable(self.dims, self._data, self._attrs, encoding=self._encoding, fastpath=True)
Return this variable as an xarray.IndexVariable def to_index_variable(self): """Return this variable as an xarray.IndexVariable""" return IndexVariable(self.dims, self._data, self._attrs, encoding=self._encoding, fastpath=True)
Dictionary representation of variable. def to_dict(self, data=True): """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() ...
Prepare an indexing key for an indexing operation. Parameters ----------- key: int, slice, array, dict or tuple of integer, slices and arrays Any valid input for indexing. Returns ------- dims: tuple Dimension of the resultant variable. i...
Make sanity checks def _validate_indexers(self, key): """ Make sanity checks """ for dim, k in zip(self.dims, key): if isinstance(k, BASIC_INDEXING_TYPES): pass else: if not isinstance(k, Variable): k = np.asarray(k) ...
Equivalent numpy's nonzero but returns a tuple of Varibles. def _nonzero(self): """ Equivalent numpy's nonzero but returns a tuple of Varibles. """ # TODO we should replace dask's native nonzero # after https://github.com/dask/dask/issues/1076 is implemented. nonzeros = np.nonzero(self....
Used by IndexVariable to return IndexVariable objects when possible. def _finalize_indexing_result(self, dims, data): """Used by IndexVariable to return IndexVariable objects when possible. """ return type(self)(dims, data, self._attrs, self._encoding, fastpath=True)

Return a new DataArray whose dataset is given by selecting index labels along the specified dimension(s). .. warning:: Do not try to assign values when using any of the indexing methods ``isel`` or ``sel``:: da = xr.DataArray([0, 1, 2, 3], dims=['x']) # DO ...

Return a new DataArray whose dataset is given by pointwise integer indexing along the specified dimension(s). See Also -------- Dataset.isel_points def isel_points(self, dim='points', **indexers): """Return a new DataArray whose dataset is given by pointwise integer ind...

Return a new DataArray whose dataset is given by pointwise selection of index labels along the specified dimension(s). See Also -------- Dataset.sel_points def sel_points(self, dim='points', method=None, tolerance=None, **indexers): """Return a new DataArray ...

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 ...

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 broadc...

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 c...

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 ...

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 ----...

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, sequence of str,...

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 ...

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 mu...

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, optiona...

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, ...) ...

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 Mult...

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 retu...

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 ``di...

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', 'a...

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'``). ...

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 ...

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...

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 : ...

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. ...

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. def to_dataframe(self, name=None): ...

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`). def to_series(self): """Convert this array into a pandas.Series. The Series is indexed by the Cartesian product of index coord...

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 ...

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....

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. Parameters ...

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'}, ...

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. def from_series(c...

Helper function for equals and identical 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 ...

Like equals, but also checks the array name and attributes, and attributes on all coordinates. See Also -------- DataArray.broadcast_equals DataArray.equal def identical(self, other): """Like equals, but also checks the array name and attributes, and attributes ...

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 ti...

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 ...

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 : Mappin...

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...

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 Al...

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 ...

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 ...

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 `bottl...

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 comp...

integrate the array with the trapezoidal rule. .. note:: This feature is limited to simple cartesian geometry, i.e. coord must be one dimensional. Parameters ---------- dim: str, or a sequence of str Coordinate(s) used for the integration. da...

Convert this rolling object to xr.DataArray, where the window dimension is stacked as a new dimension Parameters ---------- window_dim: str New name of the window dimension. stride: integer, optional Size of stride for the rolling window. fill_val...

Reduce the items in this group by applying `func` along some dimension(s). Parameters ---------- func : function Function which can be called in the form `func(x, **kwargs)` to return the result of collapsing an np.ndarray over an the rolling dimensio...

Number of non-nan entries in each rolling window. def _counts(self): """ Number of non-nan entries in each rolling window. """ rolling_dim = utils.get_temp_dimname(self.obj.dims, '_rolling_dim') # We use False as the fill_value instead of np.nan, since boolean # array is faster to be r...

Methods to return a wrapped function for any function `func` for numpy methods. def _reduce_method(cls, func): """ Methods to return a wrapped function for any function `func` for numpy methods. """ def wrapped_func(self, **kwargs): return self.reduce(func, ...

Methods to return a wrapped function for any function `func` for bottoleneck method, except for `median`. def _bottleneck_reduce(cls, func): """ Methods to return a wrapped function for any function `func` for bottoleneck method, except for `median`. """ def wrapped_fun...

Reduce the items in this group by applying `func` along some dimension(s). Parameters ---------- func : function Function which can be called in the form `func(x, **kwargs)` to return the result of collapsing an np.ndarray over an the rolling dimensio...

Return a wrapped function for injecting numpy and bottoleneck methods. see ops.inject_datasetrolling_methods def _reduce_method(cls, func): """ Return a wrapped function for injecting numpy and bottoleneck methods. see ops.inject_datasetrolling_methods """ def wrapped_f...

Convert this rolling object to xr.Dataset, where the window dimension is stacked as a new dimension Parameters ---------- window_dim: str New name of the window dimension. stride: integer, optional size of stride for the rolling window. fill_value...

Return a wrapped function for injecting numpy methods. see ops.inject_coarsen_methods def _reduce_method(cls, func): """ Return a wrapped function for injecting numpy methods. see ops.inject_coarsen_methods """ def wrapped_func(self, **kwargs): from .dataarra...

Return a wrapped function for injecting numpy methods. see ops.inject_coarsen_methods def _reduce_method(cls, func): """ Return a wrapped function for injecting numpy methods. see ops.inject_coarsen_methods """ def wrapped_func(self, **kwargs): from .dataset ...

Ensure that a variable with vlen bytes is converted to fixed width. def ensure_fixed_length_bytes(var): """Ensure that a variable with vlen bytes is converted to fixed width.""" dims, data, attrs, encoding = unpack_for_encoding(var) if check_vlen_dtype(data.dtype) == bytes: # TODO: figure out how t...

Convert numpy/dask arrays from fixed width bytes to characters. def bytes_to_char(arr): """Convert numpy/dask arrays from fixed width bytes to characters.""" if arr.dtype.kind != 'S': raise ValueError('argument must have a fixed-width bytes dtype') if isinstance(arr, dask_array_type): impo...

Like netCDF4.stringtochar, but faster and more flexible. def _numpy_bytes_to_char(arr): """Like netCDF4.stringtochar, but faster and more flexible. """ # ensure the array is contiguous arr = np.array(arr, copy=False, order='C', dtype=np.string_) return arr.reshape(arr.shape + (1,)).view('S1')

Convert numpy/dask arrays from characters to fixed width bytes. def char_to_bytes(arr): """Convert numpy/dask arrays from characters to fixed width bytes.""" if arr.dtype != 'S1': raise ValueError("argument must have dtype='S1'") if not arr.ndim: # no dimension to concatenate along ...

Like netCDF4.chartostring, but faster and more flexible. def _numpy_char_to_bytes(arr): """Like netCDF4.chartostring, but faster and more flexible. """ # based on: http://stackoverflow.com/a/10984878/809705 arr = np.array(arr, copy=False, order='C') dtype = 'S' + str(arr.shape[-1]) return arr.v...

Given an array, safely cast it to a pandas.Index. If it is already a pandas.Index, return it unchanged. Unlike pandas.Index, if the array has dtype=object or dtype=timedelta64, this function will not attempt to do automatic type conversion but will always return an index with dtype=object. def safe_c...

Creating a MultiIndex from a product without refactorizing levels. Keeping levels the same gives back the original labels when we unstack. Parameters ---------- levels : sequence of pd.Index Values for each MultiIndex level. names : optional sequence of objects Names for each level...

Wrap a transformed array with __array_wrap__ is it can be done safely. This lets us treat arbitrary functions that take and return ndarray objects like ufuncs, as long as they return an array with the same shape. def maybe_wrap_array(original, new_array): """Wrap a transformed array with __array_wrap__ is...

Compare two objects for equivalence (identity or equality), using array_equiv if either object is an ndarray def equivalent(first: T, second: T) -> bool: """Compare two objects for equivalence (identity or equality), using array_equiv if either object is an ndarray """ # TODO: refactor to avoid cir...

Returns the first value from iterable, as well as a new iterator with the same content as the original iterable def peek_at(iterable: Iterable[T]) -> Tuple[T, Iterator[T]]: """Returns the first value from iterable, as well as a new iterator with the same content as the original iterable """ gen = i...

Check the safety of updating one dictionary with another. Raises ValueError if dictionaries have non-compatible values for any key, where compatibility is determined by identity (they are the same item) or the `compat` function. Parameters ---------- first_dict, second_dict : dict-like ...

Remove incompatible items from the first dictionary in-place. Items are retained if their keys are found in both dictionaries and the values are compatible. Parameters ---------- first_dict, second_dict : dict-like Mappings to merge. compat : function, optional Binary operator ...

Whether to treat a value as a scalar. Any non-iterable, string, or 0-D array def is_scalar(value: Any) -> bool: """Whether to treat a value as a scalar. Any non-iterable, string, or 0-D array """ return ( getattr(value, 'ndim', None) == 0 or isinstance(value, (str, bytes)) or not ...

Given a value, wrap it in a 0-D numpy.ndarray with dtype=object. def to_0d_object_array(value: Any) -> np.ndarray: """Given a value, wrap it in a 0-D numpy.ndarray with dtype=object. """ result = np.empty((), dtype=object) result[()] = value return result

Given a value, wrap it in a 0-D numpy.ndarray. def to_0d_array(value: Any) -> np.ndarray: """Given a value, wrap it in a 0-D numpy.ndarray. """ if np.isscalar(value) or (isinstance(value, np.ndarray) and value.ndim == 0): return np.array(value) else: return...

Test equivalence of two dict-like objects. If any of the values are numpy arrays, compare them correctly. Parameters ---------- first, second : dict-like Dictionaries to compare for equality compat : function, optional Binary operator to determine if two values are compatible. By de...

Return the intersection of two dictionaries as a new OrderedDict. Items are retained if their keys are found in both dictionaries and the values are compatible. Parameters ---------- first_dict, second_dict : dict-like Mappings to merge. compat : function, optional Binary opera...

Return True if values of an array are uniformly spaced and sorted. >>> is_uniform_spaced(range(5)) True >>> is_uniform_spaced([-4, 0, 100]) False kwargs are additional arguments to ``np.isclose`` def is_uniform_spaced(arr, **kwargs) -> bool: """Return True if values of an array are uniformly ...

Convert attribute values from numpy objects to native Python objects, for use in to_dict def decode_numpy_dict_values(attrs: Mapping[K, V]) -> Dict[K, V]: """Convert attribute values from numpy objects to native Python objects, for use in to_dict """ attrs = dict(attrs) for k, v in attrs.items(...

Convert val out of numpy time, for use in to_dict. Needed because of numpy bug GH#7619 def ensure_us_time_resolution(val): """Convert val out of numpy time, for use in to_dict. Needed because of numpy bug GH#7619""" if np.issubdtype(val.dtype, np.datetime64): val = val.astype('datetime64[us]') ...

Get an new dimension name based on new_dim, that is not used in dims. If the same name exists, we add an underscore(s) in the head. Example1: dims: ['a', 'b', 'c'] new_dim: ['_rolling'] -> ['_rolling'] Example2: dims: ['a', 'b', 'c', '_rolling'] new_dim: ['_rolling']...

Given an object array with no missing values, infer its dtype from its first element def _infer_dtype(array, name=None): """Given an object array with no missing values, infer its dtype from its first element """ if array.dtype.kind != 'O': raise TypeError('infer_type must be called on a dt...

Create a copy of an array with the given dtype. We use this instead of np.array() to ensure that custom object dtypes end up on the resulting array. def _copy_with_dtype(data, dtype): """Create a copy of an array with the given dtype. We use this instead of np.array() to ensure that custom object dty...

Converts an Variable into an Variable which follows some of the CF conventions: - Nans are masked using _FillValue (or the deprecated missing_value) - Rescaling via: scale_factor and add_offset - datetimes are converted to the CF 'units since time' format - dtype encodings are enfor...

Decodes a variable which may hold CF encoded information. This includes variables that have been masked and scaled, which hold CF style time variables (this is almost always the case if the dataset has been serialized) and which have strings encoded as character arrays. Parameters ---------- ...

Adds time attributes to time bounds variables. Variables handling time bounds ("Cell boundaries" in the CF conventions) do not necessarily carry the necessary attributes to be decoded. This copies the attributes from the time variable to the associated boundaries. See Also: http://cfconventio...

Decode several CF encoded variables. See: decode_cf_variable def decode_cf_variables(variables, attributes, concat_characters=True, mask_and_scale=True, decode_times=True, decode_coords=True, drop_variables=None, use_cftime=None): """ ...

Decode the given Dataset or Datastore according to CF conventions into a new Dataset. Parameters ---------- obj : Dataset or DataStore Object to decode. concat_characters : bool, optional Should character arrays be concatenated to strings, for example: ['h', 'e', 'l', 'l', '...

Decode a set of CF encoded variables and attributes. See Also, decode_cf_variable Parameters ---------- variables : dict A dictionary mapping from variable name to xarray.Variable attributes : dict A dictionary mapping from attribute name to value concat_characters : bool ...

Encode coordinates on the given dataset object into variable specific and global attributes. When possible, this is done according to CF conventions. Parameters ---------- dataset : Dataset Object to encode. Returns ------- variables : dict attrs : dict def encode_dataset...

A function which takes a dicts of variables and attributes and encodes them to conform to CF conventions as much as possible. This includes masking, scaling, character array handling, and CF-time encoding. Decode a set of CF encoded variables and attributes. See Also, decode_cf_variable Para...

Coerce an array to a data type that can be stored in a netCDF-3 file This function performs the following dtype conversions: int64 -> int32 bool -> int8 Data is checked for equality, or equivalence (non-NaN values) with `np.allclose` with the default keyword arguments. def coerce_nc3_dtyp...

Test whether an object can be validly converted to a netCDF-3 dimension, variable or attribute name Earlier versions of the netCDF C-library reference implementation enforced a more restricted set of characters in creating new names, but permitted reading names containing arbitrary bytes. This spec...

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...

Convert arrays of datetime.datetime and datetime.timedelta objects into datetime64 and timedelta64, according to the pandas convention. def _possibly_convert_objects(values): """Convert arrays of datetime.datetime and datetime.timedelta objects into datetime64 and timedelta64, according to the pandas conve...

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 Ind...

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-dime...

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 the size of the broadcast dimension. def _broadcast_compat_variables(*variables): """Create broadcast compatible variables, with the same...

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 ...

Concatenate variables along a new or existing dimension. Parameters ---------- variables : iterable of Array 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 ...

Check for uniqueness of MultiIndex level names in all given variables. Not public API. Used for checking consistency of DataArray and Dataset objects. def assert_unique_multiindex_level_names(variables): """Check for uniqueness of MultiIndex level names in all given variables. Not public API....

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. ...

Return this variable as a base xarray.Variable def to_base_variable(self): """Return this variable as a base xarray.Variable""" return Variable(self.dims, self._data, self._attrs, encoding=self._encoding, fastpath=True)

Return this variable as an xarray.IndexVariable def to_index_variable(self): """Return this variable as an xarray.IndexVariable""" return IndexVariable(self.dims, self._data, self._attrs, encoding=self._encoding, fastpath=True)

Dictionary representation of variable. def to_dict(self, data=True): """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() ...

Prepare an indexing key for an indexing operation. Parameters ----------- key: int, slice, array, dict or tuple of integer, slices and arrays Any valid input for indexing. Returns ------- dims: tuple Dimension of the resultant variable. i...

Make sanity checks def _validate_indexers(self, key): """ Make sanity checks """ for dim, k in zip(self.dims, key): if isinstance(k, BASIC_INDEXING_TYPES): pass else: if not isinstance(k, Variable): k = np.asarray(k) ...

Equivalent numpy's nonzero but returns a tuple of Varibles. def _nonzero(self): """ Equivalent numpy's nonzero but returns a tuple of Varibles. """ # TODO we should replace dask's native nonzero # after https://github.com/dask/dask/issues/1076 is implemented. nonzeros = np.nonzero(self....

Used by IndexVariable to return IndexVariable objects when possible. def _finalize_indexing_result(self, dims, data): """Used by IndexVariable to return IndexVariable objects when possible. """ return type(self)(dims, data, self._attrs, self._encoding, fastpath=True)