JorgeVB/code_unlearning_dataset · Datasets at Hugging Face

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Create a dataset as quickly as possible. Beware: the `variables` OrderedDict is modified INPLACE. def _fast_dataset( variables: 'OrderedDict[Any, Variable]', coord_variables: Mapping[Any, Variable], ) -> 'Dataset': """Create a dataset as quickly as possible. Beware: the `variables` OrderedDict is...
Apply a variable level function over Dataset, dict of DataArray, DataArray, Variable and/or ndarray objects. def apply_dataset_vfunc( func, *args, signature, join='inner', dataset_join='exact', fill_value=_NO_FILL_VALUE, exclude_dims=frozenset(), keep_attrs=False ): """Apply a v...
Iterate over selections of an xarray object in the provided order. def _iter_over_selections(obj, dim, values): """Iterate over selections of an xarray object in the provided order.""" from .groupby import _dummy_copy dummy = None for value in values: try: obj_sel = obj.sel(**{dim:...
Apply a dataset or datarray level function over GroupBy, Dataset, DataArray, Variable and/or ndarray objects. def apply_groupby_func(func, *args): """Apply a dataset or datarray level function over GroupBy, Dataset, DataArray, Variable and/or ndarray objects. """ from .groupby import GroupBy, peek_...
Apply a ndarray level function over Variable and/or ndarray objects. def apply_variable_ufunc( func, *args, signature, exclude_dims=frozenset(), dask='forbidden', output_dtypes=None, output_sizes=None, keep_attrs=False ): """Apply a ndarray level function over Variable and/or ndarra...
Apply a ndarray level function over ndarray objects. def apply_array_ufunc(func, *args, dask='forbidden'): """Apply a ndarray level function over ndarray objects.""" if any(isinstance(arg, dask_array_type) for arg in args): if dask == 'forbidden': raise ValueError('apply_ufunc encountered a...
Apply a vectorized function for unlabeled arrays on xarray objects. The function will be mapped over the data variable(s) of the input arguments using xarray's standard rules for labeled computation, including alignment, broadcasting, looping over GroupBy/Dataset variables, and merging of coordinates. ...
Generalized dot product for xarray objects. Like np.einsum, but provides a simpler interface based on array dimensions. Parameters ---------- arrays: DataArray (or Variable) objects Arrays to compute. dims: str or tuple of strings, optional Which dimensions to sum over. If n...
Return elements from `x` or `y` depending on `cond`. Performs xarray-like broadcasting across input arguments. Parameters ---------- cond : scalar, array, Variable, DataArray or Dataset with boolean dtype When True, return values from `x`, otherwise returns values from `y`. x, y : scalar, ...
Create an equivalent signature string for a NumPy gufunc. Unlike __str__, handles dimensions that don't map to Python identifiers. def to_gufunc_string(self): """Create an equivalent signature string for a NumPy gufunc. Unlike __str__, handles dimensions that don't map to Python ...
Return indices for an inverse permutation. Parameters ---------- indices : 1D np.ndarray with dtype=int Integer positions to assign elements to. Returns ------- inverse_permutation : 1D np.ndarray with dtype=int Integer indices to take from the original array to create the ...
Given a non-empty list, does it consist of contiguous integers? def _is_contiguous(positions): """Given a non-empty list, does it consist of contiguous integers?""" previous = positions[0] for current in positions[1:]: if current != previous + 1: return False previous = current ...
Indices of the advanced indexes subspaces for mixed indexing and vindex. def _advanced_indexer_subspaces(key): """Indices of the advanced indexes subspaces for mixed indexing and vindex. """ if not isinstance(key, tuple): key = (key,) advanced_index_positions = [i for i, k in enumerate(key) ...
rolling window with padding. def rolling_window(a, axis, window, center, fill_value): """ rolling window with padding. """ pads = [(0, 0) for s in a.shape] if center: start = int(window / 2) # 10 -> 5, 9 -> 4 end = window - 1 - start pads[axis] = (start, end) else: pad...
Make an ndarray with a rolling window along axis. Parameters ---------- a : array_like Array to add rolling window to axis: int axis position along which rolling window will be applied. window : int Size of rolling window Returns ------- Array that is a view of ...
Get a virtual variable (e.g., 'time.year' or a MultiIndex level) from a dict of xarray.Variable objects (if possible) def _get_virtual_variable(variables, key, level_vars=None, dim_sizes=None): """Get a virtual variable (e.g., 'time.year' or a MultiIndex level) from a dict of xarray.Variable objects (if po...
Calculate the dimensions corresponding to a set of variables. Returns dictionary mapping from dimension names to sizes. Raises ValueError if any of the dimension sizes conflict. def calculate_dimensions(variables): """Calculate the dimensions corresponding to a set of variables. Returns dictionary ma...
Merge variables into multi-indexes. Not public API. Used in Dataset and DataArray set_index methods. def merge_indexes( indexes, # type: Dict[Any, Union[Any, List[Any]]] variables, # type: Dict[Any, Variable] coord_names, # type: Set append=False, # type: bool ): # type...
Extract (multi-)indexes (levels) as variables. Not public API. Used in Dataset and DataArray reset_index methods. def split_indexes( dims_or_levels, # type: Union[Any, List[Any]] variables, # type: OrderedDict[Any, Variable] coord_names, # type: Set level_coords, # type: Dict[Any, Any] ...
Cast the given object to a Dataset. Handles Datasets, DataArrays and dictionaries of variables. A new Dataset object is only created if the provided object is not already one. def as_dataset(obj): """Cast the given object to a Dataset. Handles Datasets, DataArrays and dictionaries of variables. A new...
Provide method for the key-autocompletions in IPython. def _ipython_key_completions_(self): """Provide method for the key-autocompletions in IPython. """ return [key for key in self._dataset._ipython_key_completions_() if key not in self._dataset._coord_names]
Set the initial value of Dataset variables and dimensions def _set_init_vars_and_dims(self, data_vars, coords, compat): """Set the initial value of Dataset variables and dimensions """ both_data_and_coords = [k for k in data_vars if k in coords] if both_data_and_coords: rais...
Create a new dataset from the contents of a backends.DataStore object def load_store(cls, store, decoder=None): """Create a new dataset from the contents of a backends.DataStore object """ variables, attributes = store.load() if decoder: variables, attribut...
Dictionary of global attributes on this dataset def attrs(self) -> Mapping: """Dictionary of global attributes on this dataset """ if self._attrs is None: self._attrs = OrderedDict() return self._attrs
Manually trigger loading of this dataset's data from disk or a remote source into memory and return this dataset. 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, ...
Manually trigger loading of this dataset's data from disk or a remote source into memory and return a new dataset. 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 ...
Persist all Dask arrays in memory def _persist_inplace(self, **kwargs): """ Persist all Dask arrays in memory """ # access .data to coerce everything to numpy or dask arrays lazy_data = {k: v._data for k, v in self.variables.items() if isinstance(v._data, dask_array_type)} ...
Trigger computation, keeping data as dask arrays This operation can be used to trigger computation on underlying dask arrays, similar to ``.compute()``. However this operation keeps the data as dask arrays. This is particularly useful when using the dask.distributed scheduler and you ...
Shortcut around __init__ for internal use when we want to skip costly validation def _construct_direct(cls, variables, coord_names, dims, attrs=None, indexes=None, encoding=None, file_obj=None): """Shortcut around __init__ for internal use when we want to skip costly v...
Fastpath constructor for internal use. Returns an object with optionally with replaced attributes. Explicitly passed arguments are not copied when placed on the new dataset. It is up to the caller to ensure that they have the right type and are not used elsewhere. def _replace( # t...
Replace variables with recalculated dimensions. def _replace_with_new_dims( # type: ignore self: T, variables: 'OrderedDict[Any, Variable]' = None, coord_names: set = None, attrs: 'Optional[OrderedDict]' = __default, indexes: 'Optional[OrderedDict[Any, pd.Index]]' = __default, ...
Returns a copy of this dataset. If `deep=True`, a deep copy is made of each of the component variables. Otherwise, a shallow copy of each of the component variable is made, so that the underlying memory region of the new dataset is the same as in the original dataset. Use `data...
Return a mapping of all MultiIndex levels and their corresponding coordinate name. def _level_coords(self): """Return a mapping of all MultiIndex levels and their corresponding coordinate name. """ level_coords = OrderedDict() for name, index in self.indexes.items(): ...
Create a new Dataset with the listed variables from this dataset and the all relevant coordinates. Skips all validation. def _copy_listed(self: T, names) -> T: """Create a new Dataset with the listed variables from this dataset and the all relevant coordinates. Skips all validation. """...
Construct a DataArray by indexing this dataset def _construct_dataarray(self, name) -> 'DataArray': """Construct a DataArray by indexing this dataset """ from .dataarray import DataArray try: variable = self._variables[name] except KeyError: _, name, var...
List of places to look-up items for key-completion def _item_sources(self): """List of places to look-up items for key-completion""" return [self.data_vars, self.coords, {d: self[d] for d in self.dims}, LevelCoordinatesSource(self)]
Helper function for equals and identical def _all_compat(self, other, compat_str): """Helper function for equals and identical""" # some stores (e.g., scipy) do not seem to preserve order, so don't # require matching order for equality def compat(x, y): return getattr(x, co...
Mapping of pandas.Index objects used for label based indexing def indexes(self) -> 'Mapping[Any, pd.Index]': """Mapping of pandas.Index objects used for label based indexing """ if self._indexes is None: self._indexes = default_indexes(self._variables, self._dims) return Ind...
Given names of one or more variables, set them as coordinates Parameters ---------- names : str or list of str Name(s) of variables in this dataset to convert into coordinates. inplace : bool, optional If True, modify this dataset inplace. Otherwise, create a new...
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 ...
Store dataset contents to a backends.DataStore object. def dump_to_store(self, store, kwargs): """Store dataset contents to a backends.DataStore object.""" from ..backends.api import dump_to_store # TODO: rename and/or cleanup this method to make it more consistent # with to_netcdf(...
Write dataset contents to a netCDF file. Parameters ---------- path : str, Path or file-like object, optional Path to which to save this dataset. File-like objects are only supported by the scipy engine. If no path is provided, this function returns the resul...
Write dataset contents to a zarr group. .. note:: Experimental The Zarr backend is new and experimental. Please report any unexpected behavior via github issues. Parameters ---------- store : MutableMapping or str, optional Store or path ...
Concise summary of a Dataset variables and attributes. Parameters ---------- buf : writable buffer, defaults to sys.stdout See Also -------- pandas.DataFrame.assign netCDF's ncdump def info(self, buf=None): """ Concise summary of a Dataset varia...
Block dimensions for this dataset's data or None if it's not a dask array. def chunks(self): """Block dimensions for this dataset's data or None if it's not a dask array. """ chunks = {} for v in self.variables.values(): if v.chunks is not None: ...
Coerce all arrays in this dataset into dask arrays with the given chunks. Non-dask arrays in this dataset will be converted to dask arrays. Dask arrays will be rechunked to the given chunk sizes. If neither chunks is not provided for one or more dimensions, chunk sizes along th...
Here we make sure + indexer has a valid keys + indexer is in a valid data type + string indexers are cast to the appropriate date type if the associated index is a DatetimeIndex or CFTimeIndex def _validate_indexers( self, indexers: Mapping, ) -> List[Tuple[Any, Union[slic...
Extract coordinates from indexers. Returns an OrderedDict mapping from coordinate name to the coordinate variable. Only coordinate with a name different from any of self.variables will be attached. def _get_indexers_coords_and_indexes(self, indexers): """ Extract coordinates f...
Returns a new dataset with each array indexed along the specified dimension(s). This method selects values from each array using its `__getitem__` method, except this method does not require knowing the order of each array's dimensions. Parameters ---------- ind...
Returns a new dataset with each array indexed by tick labels along the specified dimension(s). In contrast to `Dataset.isel`, indexers for this method should use labels instead of integers. Under the hood, this method is powered by using pandas's powerful Index objects. This ma...
Returns a new dataset with each array indexed pointwise along the specified dimension(s). This method selects pointwise values from each array and is akin to the NumPy indexing behavior of `arr[[0, 1], [0, 1]]`, except this method does not require knowing the order of each array's dimen...
Returns a new dataset with each array indexed pointwise by tick labels along the specified dimension(s). In contrast to `Dataset.isel_points`, indexers for this method should use labels instead of integers. In contrast to `Dataset.sel`, this method selects points along the diag...
Conform this object onto the indexes of another object, filling in missing values with NaN. Parameters ---------- other : Dataset or DataArray Object with an 'indexes' attribute giving a mapping from dimension names to pandas.Index objects, which provides coordin...
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 Dataset. Parameters ---------- coords : dict, optional Mapping from dimension names to the new coordinates. New coordinate can be a scalar, array-like or DataArray. If DataArrays are passed as new coordates, their dimensions ...
Interpolate this object onto the coordinates of another object, filling the 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 provid...
Returns a new object with renamed variables and dimensions. Parameters ---------- name_dict : dict-like, optional Dictionary whose keys are current variable or dimension names and whose values are the desired names. inplace : bool, optional If True, r...
Returns a new object 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 variable in the dataset. inplace : bool, optional ...
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 Dataset (multi-)indexes using one or more existing coordinates or variables. Parameters ---------- indexes : {dim: index, ...} Mapping from names matching dimensions and values given by (lists of) the names of existing coordinates or variables to set ...
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...
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...
Update this dataset's variables with those from another dataset. Parameters ---------- other : Dataset or castable to Dataset Dataset or variables with which to update this dataset. inplace : bool, optional If True, merge the other dataset into this dataset in-pl...
Merge the arrays of two datasets into a single dataset. This method generally not allow for overriding data, with the exception of attributes, which are ignored on the second dataset. Variables with the same name are checked for conflicts via the equals or identical methods. Pa...
Drop variables or index labels from this dataset. Parameters ---------- labels : scalar or list of scalars Name(s) of variables or index labels to drop. dim : None or str, optional Dimension along which to drop index labels. By default (if ``dim is No...
Drop dimensions and associated variables from this dataset. Parameters ---------- drop_dims : str or list Dimension or dimensions to drop. Returns ------- obj : Dataset The dataset without the given dimensions (or any variables contai...
Return a new Dataset object with all array dimensions transposed. Although the order of dimensions on each array will change, the dataset dimensions themselves will remain in fixed (sorted) order. Parameters ---------- *dims : str, optional By default, reverse the d...
Returns a new dataset 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', ...
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'``). ...
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', ...
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...
Combine two Datasets, default to data_vars of self. The new coordinates follow the normal broadcasting and alignment rules of ``join='outer'``. Vacant cells in the expanded coordinates are filled with np.nan. Parameters ---------- other : DataArray Used to ...
Reduce this dataset 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 ...
Apply a function over the data variables in this dataset. Parameters ---------- func : function Function which can be called in the form `func(x, args, *kwargs)` to transform each DataArray `x` in this dataset into another DataArray. keep_attrs : bo...
Assign new data variables to a Dataset, returning a new object with all the original variables in addition to the new ones. Parameters ---------- variables : mapping, value pairs Mapping from variables names to the new values. If the new values are callable, they...
Convert this dataset into an xarray.DataArray The data variables of this dataset will be broadcast against each other and stacked along the first axis of the new array. All coordinates of this dataset will remain coordinates. Parameters ---------- dim : str, optional ...
Convert a pandas.DataFrame into an xarray.Dataset Each column will be converted into an independent variable in the Dataset. If the dataframe's index is a MultiIndex, it will be expanded into a tensor product of one-dimensional indices (filling in missing values with NaN). This method w...
Convert this dataset into a dask.dataframe.DataFrame. The dimensions, coordinates and data variables in this dataset form the columns of the DataFrame. Parameters ---------- dim_order : list, optional Hierarchical dimension order for the resulting dataframe. All ...
Convert this dataset to 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.Dataset. Input dict can take several forms:: d = {'t': {'dims': ('t'), 'data': t}, 'a': {'dims': ('t'), 'data': x}, 'b': {'dims': ('t'), 'data': y}} d = {'coords': {'t': {'dims': 't', 'data': t, ...
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 dataset by an offset along one or more dimensions. Only data variables are moved; coordinates stay in place. This is consistent with the behavior of ``shift`` in pandas. Parameters ---------- shifts : Mapping with the form of {dim: offset} Integer offset ...
Roll this dataset by an offset along one or more dimensions. Unlike shift, roll may rotate all variables, including coordinates if specified. The direction of rotation is consistent with :py:func:`numpy.roll`. Parameters ---------- shifts : dict, optional A...
Sort object by labels or values (along an axis). Sorts the dataset, 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 th...
Compute the qth quantile of the data along the specified dimension. Returns the qth quantiles(s) of the array elements for each variable in the Dataset. Parameters ---------- q : float in range of [0,1] (or sequence of floats) Quantile to compute, which must be betw...
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 is True, computes percentage ranks. NaNs in the input array are returned as NaNs. ...
Differentiate 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 gr...
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...
Returns a ``Dataset`` with variables that match specific conditions. Can pass in ``key=value`` or ``key=callable``. A Dataset is returned containing only the variables for which all the filter tests pass. These tests are either ``key=value`` for which the attribute ``key`` has the exac...
Lazily apply an element-wise function to an array. Parameters ---------- array : any valid value of Variable._data func : callable Function to apply to indexed slices of an array. For use with dask, this should be a pickle-able object. dtype : coercible to np.dtype Dtype for...
A convenience function which pops a key k from source to dest. None values are not passed on. If k already exists in dest an error is raised. def pop_to(source, dest, key, name=None): """ A convenience function which pops a key k from source to dest. None values are not passed on. If k already ex...
Mask all matching values in a NumPy arrays. def _apply_mask( data: np.ndarray, encoded_fill_values: list, decoded_fill_value: Any, dtype: Any, ) -> np.ndarray: """Mask all matching values in a NumPy arrays.""" data = np.asarray(data, dtype=dtype) condition = False for fv in encoded_fill...
Return a float dtype that can losslessly represent `dtype` values. def _choose_float_dtype(dtype, has_offset): """Return a float dtype that can losslessly represent `dtype` values.""" # Keep float32 as-is. Upcast half-precision to single-precision, # because float16 is "intended for storage but not comput...
wrapper for datasets def _apply_over_vars_with_dim(func, self, dim=None, kwargs): '''wrapper for datasets''' ds = type(self)(coords=self.coords, attrs=self.attrs) for name, var in self.data_vars.items(): if dim in var.dims: ds[name] = func(var, dim=dim, kwargs) else: ...
get index to use for x values in interpolation. If use_coordinate is True, the coordinate that shares the name of the dimension along which interpolation is being performed will be used as the x values. If use_coordinate is False, the x values are set as an equally spaced sequence. def get_clean_...
Interpolate values according to different methods. def interp_na(self, dim=None, use_coordinate=True, method='linear', limit=None, **kwargs): '''Interpolate values according to different methods.''' if dim is None: raise NotImplementedError('dim is a required argument') if limit is ...
helper function to apply interpolation along 1 dimension def func_interpolate_na(interpolator, x, y, **kwargs): '''helper function to apply interpolation along 1 dimension''' # it would be nice if this wasn't necessary, works around: # "ValueError: assignment destination is read-only" in assignment below ...
inverse of ffill def _bfill(arr, n=None, axis=-1): '''inverse of ffill''' import bottleneck as bn arr = np.flip(arr, axis=axis) # fill arr = bn.push(arr, axis=axis, n=n) # reverse back to original return np.flip(arr, axis=axis)

Create a dataset as quickly as possible. Beware: the `variables` OrderedDict is modified INPLACE. def _fast_dataset( variables: 'OrderedDict[Any, Variable]', coord_variables: Mapping[Any, Variable], ) -> 'Dataset': """Create a dataset as quickly as possible. Beware: the `variables` OrderedDict is...

Apply a variable level function over Dataset, dict of DataArray, DataArray, Variable and/or ndarray objects. def apply_dataset_vfunc( func, *args, signature, join='inner', dataset_join='exact', fill_value=_NO_FILL_VALUE, exclude_dims=frozenset(), keep_attrs=False ): """Apply a v...

Iterate over selections of an xarray object in the provided order. def _iter_over_selections(obj, dim, values): """Iterate over selections of an xarray object in the provided order.""" from .groupby import _dummy_copy dummy = None for value in values: try: obj_sel = obj.sel(**{dim:...

Apply a dataset or datarray level function over GroupBy, Dataset, DataArray, Variable and/or ndarray objects. def apply_groupby_func(func, *args): """Apply a dataset or datarray level function over GroupBy, Dataset, DataArray, Variable and/or ndarray objects. """ from .groupby import GroupBy, peek_...

Apply a ndarray level function over Variable and/or ndarray objects. def apply_variable_ufunc( func, *args, signature, exclude_dims=frozenset(), dask='forbidden', output_dtypes=None, output_sizes=None, keep_attrs=False ): """Apply a ndarray level function over Variable and/or ndarra...

Apply a ndarray level function over ndarray objects. def apply_array_ufunc(func, *args, dask='forbidden'): """Apply a ndarray level function over ndarray objects.""" if any(isinstance(arg, dask_array_type) for arg in args): if dask == 'forbidden': raise ValueError('apply_ufunc encountered a...

Apply a vectorized function for unlabeled arrays on xarray objects. The function will be mapped over the data variable(s) of the input arguments using xarray's standard rules for labeled computation, including alignment, broadcasting, looping over GroupBy/Dataset variables, and merging of coordinates. ...

Generalized dot product for xarray objects. Like np.einsum, but provides a simpler interface based on array dimensions. Parameters ---------- arrays: DataArray (or Variable) objects Arrays to compute. dims: str or tuple of strings, optional Which dimensions to sum over. If n...

Return elements from `x` or `y` depending on `cond`. Performs xarray-like broadcasting across input arguments. Parameters ---------- cond : scalar, array, Variable, DataArray or Dataset with boolean dtype When True, return values from `x`, otherwise returns values from `y`. x, y : scalar, ...

Create an equivalent signature string for a NumPy gufunc. Unlike __str__, handles dimensions that don't map to Python identifiers. def to_gufunc_string(self): """Create an equivalent signature string for a NumPy gufunc. Unlike __str__, handles dimensions that don't map to Python ...

Return indices for an inverse permutation. Parameters ---------- indices : 1D np.ndarray with dtype=int Integer positions to assign elements to. Returns ------- inverse_permutation : 1D np.ndarray with dtype=int Integer indices to take from the original array to create the ...

Given a non-empty list, does it consist of contiguous integers? def _is_contiguous(positions): """Given a non-empty list, does it consist of contiguous integers?""" previous = positions[0] for current in positions[1:]: if current != previous + 1: return False previous = current ...

Indices of the advanced indexes subspaces for mixed indexing and vindex. def _advanced_indexer_subspaces(key): """Indices of the advanced indexes subspaces for mixed indexing and vindex. """ if not isinstance(key, tuple): key = (key,) advanced_index_positions = [i for i, k in enumerate(key) ...

rolling window with padding. def rolling_window(a, axis, window, center, fill_value): """ rolling window with padding. """ pads = [(0, 0) for s in a.shape] if center: start = int(window / 2) # 10 -> 5, 9 -> 4 end = window - 1 - start pads[axis] = (start, end) else: pad...

Make an ndarray with a rolling window along axis. Parameters ---------- a : array_like Array to add rolling window to axis: int axis position along which rolling window will be applied. window : int Size of rolling window Returns ------- Array that is a view of ...

Get a virtual variable (e.g., 'time.year' or a MultiIndex level) from a dict of xarray.Variable objects (if possible) def _get_virtual_variable(variables, key, level_vars=None, dim_sizes=None): """Get a virtual variable (e.g., 'time.year' or a MultiIndex level) from a dict of xarray.Variable objects (if po...

Calculate the dimensions corresponding to a set of variables. Returns dictionary mapping from dimension names to sizes. Raises ValueError if any of the dimension sizes conflict. def calculate_dimensions(variables): """Calculate the dimensions corresponding to a set of variables. Returns dictionary ma...

Merge variables into multi-indexes. Not public API. Used in Dataset and DataArray set_index methods. def merge_indexes( indexes, # type: Dict[Any, Union[Any, List[Any]]] variables, # type: Dict[Any, Variable] coord_names, # type: Set append=False, # type: bool ): # type...

Extract (multi-)indexes (levels) as variables. Not public API. Used in Dataset and DataArray reset_index methods. def split_indexes( dims_or_levels, # type: Union[Any, List[Any]] variables, # type: OrderedDict[Any, Variable] coord_names, # type: Set level_coords, # type: Dict[Any, Any] ...

Cast the given object to a Dataset. Handles Datasets, DataArrays and dictionaries of variables. A new Dataset object is only created if the provided object is not already one. def as_dataset(obj): """Cast the given object to a Dataset. Handles Datasets, DataArrays and dictionaries of variables. A new...

Provide method for the key-autocompletions in IPython. def _ipython_key_completions_(self): """Provide method for the key-autocompletions in IPython. """ return [key for key in self._dataset._ipython_key_completions_() if key not in self._dataset._coord_names]

Set the initial value of Dataset variables and dimensions def _set_init_vars_and_dims(self, data_vars, coords, compat): """Set the initial value of Dataset variables and dimensions """ both_data_and_coords = [k for k in data_vars if k in coords] if both_data_and_coords: rais...

Create a new dataset from the contents of a backends.*DataStore object def load_store(cls, store, decoder=None): """Create a new dataset from the contents of a backends.*DataStore object """ variables, attributes = store.load() if decoder: variables, attribut...

Dictionary of global attributes on this dataset def attrs(self) -> Mapping: """Dictionary of global attributes on this dataset """ if self._attrs is None: self._attrs = OrderedDict() return self._attrs

Manually trigger loading of this dataset's data from disk or a remote source into memory and return this dataset. 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, ...

Manually trigger loading of this dataset's data from disk or a remote source into memory and return a new dataset. 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 ...

Persist all Dask arrays in memory def _persist_inplace(self, **kwargs): """ Persist all Dask arrays in memory """ # access .data to coerce everything to numpy or dask arrays lazy_data = {k: v._data for k, v in self.variables.items() if isinstance(v._data, dask_array_type)} ...

Trigger computation, keeping data as dask arrays This operation can be used to trigger computation on underlying dask arrays, similar to ``.compute()``. However this operation keeps the data as dask arrays. This is particularly useful when using the dask.distributed scheduler and you ...

Shortcut around __init__ for internal use when we want to skip costly validation def _construct_direct(cls, variables, coord_names, dims, attrs=None, indexes=None, encoding=None, file_obj=None): """Shortcut around __init__ for internal use when we want to skip costly v...

Fastpath constructor for internal use. Returns an object with optionally with replaced attributes. Explicitly passed arguments are *not* copied when placed on the new dataset. It is up to the caller to ensure that they have the right type and are not used elsewhere. def _replace( # t...

Replace variables with recalculated dimensions. def _replace_with_new_dims( # type: ignore self: T, variables: 'OrderedDict[Any, Variable]' = None, coord_names: set = None, attrs: 'Optional[OrderedDict]' = __default, indexes: 'Optional[OrderedDict[Any, pd.Index]]' = __default, ...

Returns a copy of this dataset. If `deep=True`, a deep copy is made of each of the component variables. Otherwise, a shallow copy of each of the component variable is made, so that the underlying memory region of the new dataset is the same as in the original dataset. Use `data...

Return a mapping of all MultiIndex levels and their corresponding coordinate name. def _level_coords(self): """Return a mapping of all MultiIndex levels and their corresponding coordinate name. """ level_coords = OrderedDict() for name, index in self.indexes.items(): ...

Create a new Dataset with the listed variables from this dataset and the all relevant coordinates. Skips all validation. def _copy_listed(self: T, names) -> T: """Create a new Dataset with the listed variables from this dataset and the all relevant coordinates. Skips all validation. """...

Construct a DataArray by indexing this dataset def _construct_dataarray(self, name) -> 'DataArray': """Construct a DataArray by indexing this dataset """ from .dataarray import DataArray try: variable = self._variables[name] except KeyError: _, name, var...

List of places to look-up items for key-completion def _item_sources(self): """List of places to look-up items for key-completion""" return [self.data_vars, self.coords, {d: self[d] for d in self.dims}, LevelCoordinatesSource(self)]

Helper function for equals and identical def _all_compat(self, other, compat_str): """Helper function for equals and identical""" # some stores (e.g., scipy) do not seem to preserve order, so don't # require matching order for equality def compat(x, y): return getattr(x, co...

Mapping of pandas.Index objects used for label based indexing def indexes(self) -> 'Mapping[Any, pd.Index]': """Mapping of pandas.Index objects used for label based indexing """ if self._indexes is None: self._indexes = default_indexes(self._variables, self._dims) return Ind...

Given names of one or more variables, set them as coordinates Parameters ---------- names : str or list of str Name(s) of variables in this dataset to convert into coordinates. inplace : bool, optional If True, modify this dataset inplace. Otherwise, create a new...

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 ...

Store dataset contents to a backends.*DataStore object. def dump_to_store(self, store, **kwargs): """Store dataset contents to a backends.*DataStore object.""" from ..backends.api import dump_to_store # TODO: rename and/or cleanup this method to make it more consistent # with to_netcdf(...

Write dataset contents to a netCDF file. Parameters ---------- path : str, Path or file-like object, optional Path to which to save this dataset. File-like objects are only supported by the scipy engine. If no path is provided, this function returns the resul...

Write dataset contents to a zarr group. .. note:: Experimental The Zarr backend is new and experimental. Please report any unexpected behavior via github issues. Parameters ---------- store : MutableMapping or str, optional Store or path ...

Concise summary of a Dataset variables and attributes. Parameters ---------- buf : writable buffer, defaults to sys.stdout See Also -------- pandas.DataFrame.assign netCDF's ncdump def info(self, buf=None): """ Concise summary of a Dataset varia...

Block dimensions for this dataset's data or None if it's not a dask array. def chunks(self): """Block dimensions for this dataset's data or None if it's not a dask array. """ chunks = {} for v in self.variables.values(): if v.chunks is not None: ...

Coerce all arrays in this dataset into dask arrays with the given chunks. Non-dask arrays in this dataset will be converted to dask arrays. Dask arrays will be rechunked to the given chunk sizes. If neither chunks is not provided for one or more dimensions, chunk sizes along th...

Here we make sure + indexer has a valid keys + indexer is in a valid data type + string indexers are cast to the appropriate date type if the associated index is a DatetimeIndex or CFTimeIndex def _validate_indexers( self, indexers: Mapping, ) -> List[Tuple[Any, Union[slic...

Extract coordinates from indexers. Returns an OrderedDict mapping from coordinate name to the coordinate variable. Only coordinate with a name different from any of self.variables will be attached. def _get_indexers_coords_and_indexes(self, indexers): """ Extract coordinates f...

Returns a new dataset with each array indexed along the specified dimension(s). This method selects values from each array using its `__getitem__` method, except this method does not require knowing the order of each array's dimensions. Parameters ---------- ind...

Returns a new dataset with each array indexed by tick labels along the specified dimension(s). In contrast to `Dataset.isel`, indexers for this method should use labels instead of integers. Under the hood, this method is powered by using pandas's powerful Index objects. This ma...

Returns a new dataset with each array indexed pointwise along the specified dimension(s). This method selects pointwise values from each array and is akin to the NumPy indexing behavior of `arr[[0, 1], [0, 1]]`, except this method does not require knowing the order of each array's dimen...

Returns a new dataset with each array indexed pointwise by tick labels along the specified dimension(s). In contrast to `Dataset.isel_points`, indexers for this method should use labels instead of integers. In contrast to `Dataset.sel`, this method selects points along the diag...

Conform this object onto the indexes of another object, filling in missing values with NaN. Parameters ---------- other : Dataset or DataArray Object with an 'indexes' attribute giving a mapping from dimension names to pandas.Index objects, which provides coordin...

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 Dataset. Parameters ---------- coords : dict, optional Mapping from dimension names to the new coordinates. New coordinate can be a scalar, array-like or DataArray. If DataArrays are passed as new coordates, their dimensions ...

Interpolate this object onto the coordinates of another object, filling the 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 provid...

Returns a new object with renamed variables and dimensions. Parameters ---------- name_dict : dict-like, optional Dictionary whose keys are current variable or dimension names and whose values are the desired names. inplace : bool, optional If True, r...

Returns a new object 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 variable in the dataset. inplace : bool, optional ...

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 Dataset (multi-)indexes using one or more existing coordinates or variables. Parameters ---------- indexes : {dim: index, ...} Mapping from names matching dimensions and values given by (lists of) the names of existing coordinates or variables to set ...

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...

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...

Update this dataset's variables with those from another dataset. Parameters ---------- other : Dataset or castable to Dataset Dataset or variables with which to update this dataset. inplace : bool, optional If True, merge the other dataset into this dataset in-pl...

Merge the arrays of two datasets into a single dataset. This method generally not allow for overriding data, with the exception of attributes, which are ignored on the second dataset. Variables with the same name are checked for conflicts via the equals or identical methods. Pa...

Drop variables or index labels from this dataset. Parameters ---------- labels : scalar or list of scalars Name(s) of variables or index labels to drop. dim : None or str, optional Dimension along which to drop index labels. By default (if ``dim is No...

Drop dimensions and associated variables from this dataset. Parameters ---------- drop_dims : str or list Dimension or dimensions to drop. Returns ------- obj : Dataset The dataset without the given dimensions (or any variables contai...

Return a new Dataset object with all array dimensions transposed. Although the order of dimensions on each array will change, the dataset dimensions themselves will remain in fixed (sorted) order. Parameters ---------- *dims : str, optional By default, reverse the d...

Returns a new dataset 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', ...

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'``). ...

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', ...

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...

Combine two Datasets, default to data_vars of self. The new coordinates follow the normal broadcasting and alignment rules of ``join='outer'``. Vacant cells in the expanded coordinates are filled with np.nan. Parameters ---------- other : DataArray Used to ...

Reduce this dataset 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 ...

Apply a function over the data variables in this dataset. Parameters ---------- func : function Function which can be called in the form `func(x, *args, **kwargs)` to transform each DataArray `x` in this dataset into another DataArray. keep_attrs : bo...

Assign new data variables to a Dataset, returning a new object with all the original variables in addition to the new ones. Parameters ---------- variables : mapping, value pairs Mapping from variables names to the new values. If the new values are callable, they...

Convert this dataset into an xarray.DataArray The data variables of this dataset will be broadcast against each other and stacked along the first axis of the new array. All coordinates of this dataset will remain coordinates. Parameters ---------- dim : str, optional ...

Convert a pandas.DataFrame into an xarray.Dataset Each column will be converted into an independent variable in the Dataset. If the dataframe's index is a MultiIndex, it will be expanded into a tensor product of one-dimensional indices (filling in missing values with NaN). This method w...

Convert this dataset into a dask.dataframe.DataFrame. The dimensions, coordinates and data variables in this dataset form the columns of the DataFrame. Parameters ---------- dim_order : list, optional Hierarchical dimension order for the resulting dataframe. All ...

Convert this dataset to 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.Dataset. Input dict can take several forms:: d = {'t': {'dims': ('t'), 'data': t}, 'a': {'dims': ('t'), 'data': x}, 'b': {'dims': ('t'), 'data': y}} d = {'coords': {'t': {'dims': 't', 'data': t, ...

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 dataset by an offset along one or more dimensions. Only data variables are moved; coordinates stay in place. This is consistent with the behavior of ``shift`` in pandas. Parameters ---------- shifts : Mapping with the form of {dim: offset} Integer offset ...

Roll this dataset by an offset along one or more dimensions. Unlike shift, roll may rotate all variables, including coordinates if specified. The direction of rotation is consistent with :py:func:`numpy.roll`. Parameters ---------- shifts : dict, optional A...

Sort object by labels or values (along an axis). Sorts the dataset, 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 th...

Compute the qth quantile of the data along the specified dimension. Returns the qth quantiles(s) of the array elements for each variable in the Dataset. Parameters ---------- q : float in range of [0,1] (or sequence of floats) Quantile to compute, which must be betw...

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 is True, computes percentage ranks. NaNs in the input array are returned as NaNs. ...

Differentiate 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 gr...

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...

Returns a ``Dataset`` with variables that match specific conditions. Can pass in ``key=value`` or ``key=callable``. A Dataset is returned containing only the variables for which all the filter tests pass. These tests are either ``key=value`` for which the attribute ``key`` has the exac...

Lazily apply an element-wise function to an array. Parameters ---------- array : any valid value of Variable._data func : callable Function to apply to indexed slices of an array. For use with dask, this should be a pickle-able object. dtype : coercible to np.dtype Dtype for...

A convenience function which pops a key k from source to dest. None values are not passed on. If k already exists in dest an error is raised. def pop_to(source, dest, key, name=None): """ A convenience function which pops a key k from source to dest. None values are not passed on. If k already ex...

Mask all matching values in a NumPy arrays. def _apply_mask( data: np.ndarray, encoded_fill_values: list, decoded_fill_value: Any, dtype: Any, ) -> np.ndarray: """Mask all matching values in a NumPy arrays.""" data = np.asarray(data, dtype=dtype) condition = False for fv in encoded_fill...

Return a float dtype that can losslessly represent `dtype` values. def _choose_float_dtype(dtype, has_offset): """Return a float dtype that can losslessly represent `dtype` values.""" # Keep float32 as-is. Upcast half-precision to single-precision, # because float16 is "intended for storage but not comput...

wrapper for datasets def _apply_over_vars_with_dim(func, self, dim=None, **kwargs): '''wrapper for datasets''' ds = type(self)(coords=self.coords, attrs=self.attrs) for name, var in self.data_vars.items(): if dim in var.dims: ds[name] = func(var, dim=dim, **kwargs) else: ...

get index to use for x values in interpolation. If use_coordinate is True, the coordinate that shares the name of the dimension along which interpolation is being performed will be used as the x values. If use_coordinate is False, the x values are set as an equally spaced sequence. def get_clean_...

Interpolate values according to different methods. def interp_na(self, dim=None, use_coordinate=True, method='linear', limit=None, **kwargs): '''Interpolate values according to different methods.''' if dim is None: raise NotImplementedError('dim is a required argument') if limit is ...

helper function to apply interpolation along 1 dimension def func_interpolate_na(interpolator, x, y, **kwargs): '''helper function to apply interpolation along 1 dimension''' # it would be nice if this wasn't necessary, works around: # "ValueError: assignment destination is read-only" in assignment below ...

inverse of ffill def _bfill(arr, n=None, axis=-1): '''inverse of ffill''' import bottleneck as bn arr = np.flip(arr, axis=axis) # fill arr = bn.push(arr, axis=axis, n=n) # reverse back to original return np.flip(arr, axis=axis)