from __future__ import annotations
import warnings
from collections.abc import Hashable, Iterable, Iterator, Mapping
from contextlib import suppress
from html import escape
from textwrap import dedent
from typing import TYPE_CHECKING, Any, Callable, TypeVar, Union, overload
import numpy as np
import pandas as pd
from xarray.core import dtypes, duck_array_ops, formatting, formatting_html, ops
from xarray.core.indexing import BasicIndexer, ExplicitlyIndexed
from xarray.core.options import OPTIONS, _get_keep_attrs
from xarray.core.utils import (
Frozen,
either_dict_or_kwargs,
is_scalar,
)
from xarray.namedarray.core import _raise_if_any_duplicate_dimensions
from xarray.namedarray.parallelcompat import get_chunked_array_type, guess_chunkmanager
from xarray.namedarray.pycompat import is_chunked_array
try:
import cftime
except ImportError:
cftime = None
# Used as a sentinel value to indicate a all dimensions
ALL_DIMS = ...
if TYPE_CHECKING:
import datetime
from numpy.typing import DTypeLike
from xarray.core.dataarray import DataArray
from xarray.core.dataset import Dataset
from xarray.core.indexes import Index
from xarray.core.resample import Resample
from xarray.core.rolling_exp import RollingExp
from xarray.core.types import (
DatetimeLike,
DTypeLikeSave,
ScalarOrArray,
Self,
SideOptions,
T_Chunks,
T_DataWithCoords,
T_Variable,
)
from xarray.core.variable import Variable
DTypeMaybeMapping = Union[DTypeLikeSave, Mapping[Any, DTypeLikeSave]]
T_Resample = TypeVar("T_Resample", bound="Resample")
C = TypeVar("C")
T = TypeVar("T")
class ImplementsArrayReduce:
__slots__ = ()
@classmethod
def _reduce_method(cls, func: Callable, include_skipna: bool, numeric_only: bool):
if include_skipna:
def wrapped_func(self, dim=None, axis=None, skipna=None, **kwargs):
return self.reduce(
func=func, dim=dim, axis=axis, skipna=skipna, **kwargs
)
else:
def wrapped_func(self, dim=None, axis=None, **kwargs): # type: ignore[misc]
return self.reduce(func=func, dim=dim, axis=axis, **kwargs)
return wrapped_func
_reduce_extra_args_docstring = dedent(
"""\
dim : str or sequence of str, optional
Dimension(s) over which to apply `{name}`.
axis : int or sequence of int, optional
Axis(es) over which to apply `{name}`. Only one of the 'dim'
and 'axis' arguments can be supplied. If neither are supplied, then
`{name}` is calculated over axes."""
)
_cum_extra_args_docstring = dedent(
"""\
dim : str or sequence of str, optional
Dimension over which to apply `{name}`.
axis : int or sequence of int, optional
Axis over which to apply `{name}`. Only one of the 'dim'
and 'axis' arguments can be supplied."""
)
class ImplementsDatasetReduce:
__slots__ = ()
@classmethod
def _reduce_method(cls, func: Callable, include_skipna: bool, numeric_only: bool):
if include_skipna:
def wrapped_func(self, dim=None, skipna=None, **kwargs):
return self.reduce(
func=func,
dim=dim,
skipna=skipna,
numeric_only=numeric_only,
**kwargs,
)
else:
def wrapped_func(self, dim=None, **kwargs): # type: ignore[misc]
return self.reduce(
func=func, dim=dim, numeric_only=numeric_only, **kwargs
)
return wrapped_func
_reduce_extra_args_docstring = dedent(
"""
dim : str or sequence of str, optional
Dimension(s) over which to apply `{name}`. By default `{name}` is
applied over all dimensions.
"""
).strip()
_cum_extra_args_docstring = dedent(
"""
dim : str or sequence of str, optional
Dimension over which to apply `{name}`.
axis : int or sequence of int, optional
Axis over which to apply `{name}`. Only one of the 'dim'
and 'axis' arguments can be supplied.
"""
).strip()
class AbstractArray:
"""Shared base class for DataArray and Variable."""
__slots__ = ()
def __bool__(self: Any) -> bool:
return bool(self.values)
def __float__(self: Any) -> float:
return float(self.values)
def __int__(self: Any) -> int:
return int(self.values)
def __complex__(self: Any) -> complex:
return complex(self.values)
def __array__(self: Any, dtype: DTypeLike | None = None) -> np.ndarray:
return np.asarray(self.values, dtype=dtype)
def __repr__(self) -> str:
return formatting.array_repr(self)
def _repr_html_(self):
if OPTIONS["display_style"] == "text":
return f"<pre>{escape(repr(self))}</pre>"
return formatting_html.array_repr(self)
def __format__(self: Any, format_spec: str = "") -> str:
if format_spec != "":
if self.shape == ():
# Scalar values might be ok use format_spec with instead of repr:
return self.data.__format__(format_spec)
else:
# TODO: If it's an array the formatting.array_repr(self) should
# take format_spec as an input. If we'd only use self.data we
# lose all the information about coords for example which is
# important information:
raise NotImplementedError(
"Using format_spec is only supported"
f" when shape is (). Got shape = {self.shape}."
)
else:
return self.__repr__()
def _iter(self: Any) -> Iterator[Any]:
for n in range(len(self)):
yield self[n]
def __iter__(self: Any) -> Iterator[Any]:
if self.ndim == 0:
raise TypeError("iteration over a 0-d array")
return self._iter()
@overload
def get_axis_num(self, dim: Iterable[Hashable]) -> tuple[int, ...]: ...
@overload
def get_axis_num(self, dim: Hashable) -> int: ...
def get_axis_num(self, dim: Hashable | Iterable[Hashable]) -> int | tuple[int, ...]:
"""Return axis number(s) corresponding to dimension(s) in this array.
Parameters
----------
dim : str or iterable of str
Dimension name(s) for which to lookup axes.
Returns
-------
int or tuple of int
Axis number or numbers corresponding to the given dimensions.
"""
if not isinstance(dim, str) and isinstance(dim, Iterable):
return tuple(self._get_axis_num(d) for d in dim)
else:
return self._get_axis_num(dim)
def _get_axis_num(self: Any, dim: Hashable) -> int:
_raise_if_any_duplicate_dimensions(self.dims)
try:
return self.dims.index(dim)
except ValueError:
raise ValueError(f"{dim!r} not found in array dimensions {self.dims!r}")
@property
def sizes(self: Any) -> Mapping[Hashable, int]:
"""Ordered mapping from dimension names to lengths.
Immutable.
See Also
--------
Dataset.sizes
"""
return Frozen(dict(zip(self.dims, self.shape)))
class AttrAccessMixin:
"""Mixin class that allows getting keys with attribute access"""
__slots__ = ()
def __init_subclass__(cls, **kwargs):
"""Verify that all subclasses explicitly define ``__slots__``. If they don't,
raise error in the core xarray module and a FutureWarning in third-party
extensions.
"""
if not hasattr(object.__new__(cls), "__dict__"):
pass
elif cls.__module__.startswith("xarray."):
raise AttributeError(f"{cls.__name__} must explicitly define __slots__")
else:
cls.__setattr__ = cls._setattr_dict
warnings.warn(
f"xarray subclass {cls.__name__} should explicitly define __slots__",
FutureWarning,
stacklevel=2,
)
super().__init_subclass__(**kwargs)
@property
def _attr_sources(self) -> Iterable[Mapping[Hashable, Any]]:
"""Places to look-up items for attribute-style access"""
yield from ()
@property
def _item_sources(self) -> Iterable[Mapping[Hashable, Any]]:
"""Places to look-up items for key-autocompletion"""
yield from ()
def __getattr__(self, name: str) -> Any:
if name not in {"__dict__", "__setstate__"}:
# this avoids an infinite loop when pickle looks for the
# __setstate__ attribute before the xarray object is initialized
for source in self._attr_sources:
with suppress(KeyError):
return source[name]
raise AttributeError(
f"{type(self).__name__!r} object has no attribute {name!r}"
)
# This complicated two-method design boosts overall performance of simple operations
# - particularly DataArray methods that perform a _to_temp_dataset() round-trip - by
# a whopping 8% compared to a single method that checks hasattr(self, "__dict__") at
# runtime before every single assignment. All of this is just temporary until the
# FutureWarning can be changed into a hard crash.
def _setattr_dict(self, name: str, value: Any) -> None:
"""Deprecated third party subclass (see ``__init_subclass__`` above)"""
object.__setattr__(self, name, value)
if name in self.__dict__:
# Custom, non-slotted attr, or improperly assigned variable?
warnings.warn(
f"Setting attribute {name!r} on a {type(self).__name__!r} object. Explicitly define __slots__ "
"to suppress this warning for legitimate custom attributes and "
"raise an error when attempting variables assignments.",
FutureWarning,
stacklevel=2,
)
def __setattr__(self, name: str, value: Any) -> None:
"""Objects with ``__slots__`` raise AttributeError if you try setting an
undeclared attribute. This is desirable, but the error message could use some
improvement.
"""
try:
object.__setattr__(self, name, value)
except AttributeError as e:
# Don't accidentally shadow custom AttributeErrors, e.g.
# DataArray.dims.setter
if str(e) != f"{type(self).__name__!r} object has no attribute {name!r}":
raise
raise AttributeError(
f"cannot set attribute {name!r} on a {type(self).__name__!r} object. Use __setitem__ style"
"assignment (e.g., `ds['name'] = ...`) instead of assigning variables."
) from e
def __dir__(self) -> list[str]:
"""Provide method name lookup and completion. Only provide 'public'
methods.
"""
extra_attrs = {
item
for source in self._attr_sources
for item in source
if isinstance(item, str)
}
return sorted(set(dir(type(self))) | extra_attrs)
def _ipython_key_completions_(self) -> list[str]:
"""Provide method for the key-autocompletions in IPython.
See http://ipython.readthedocs.io/en/stable/config/integrating.html#tab-completion
For the details.
"""
items = {
item
for source in self._item_sources
for item in source
if isinstance(item, str)
}
return list(items)
def get_squeeze_dims(
xarray_obj,
dim: Hashable | Iterable[Hashable] | None = None,
axis: int | Iterable[int] | None = None,
) -> list[Hashable]:
"""Get a list of dimensions to squeeze out."""
if dim is not None and axis is not None:
raise ValueError("cannot use both parameters `axis` and `dim`")
if dim is None and axis is None:
return [d for d, s in xarray_obj.sizes.items() if s == 1]
if isinstance(dim, Iterable) and not isinstance(dim, str):
dim = list(dim)
elif dim is not None:
dim = [dim]
else:
assert axis is not None
if isinstance(axis, int):
axis = [axis]
axis = list(axis)
if any(not isinstance(a, int) for a in axis):
raise TypeError("parameter `axis` must be int or iterable of int.")
alldims = list(xarray_obj.sizes.keys())
dim = [alldims[a] for a in axis]
if any(xarray_obj.sizes[k] > 1 for k in dim):
raise ValueError(
"cannot select a dimension to squeeze out "
"which has length greater than one"
)
return dim
class DataWithCoords(AttrAccessMixin):
"""Shared base class for Dataset and DataArray."""
_close: Callable[[], None] | None
_indexes: dict[Hashable, Index]
__slots__ = ("_close",)
def squeeze(
self,
dim: Hashable | Iterable[Hashable] | None = None,
drop: bool = False,
axis: int | Iterable[int] | None = None,
) -> Self:
"""Return a new object with squeezed data.
Parameters
----------
dim : None or Hashable or iterable of Hashable, optional
Selects a subset of the length one dimensions. If a dimension is
selected with length greater than one, an error is raised. If
None, all length one dimensions are squeezed.
drop : bool, default: False
If ``drop=True``, drop squeezed coordinates instead of making them
scalar.
axis : None or int or iterable of int, optional
Like dim, but positional.
Returns
-------
squeezed : same type as caller
This object, but with with all or a subset of the dimensions of
length 1 removed.
See Also
--------
numpy.squeeze
"""
dims = get_squeeze_dims(self, dim, axis)
return self.isel(drop=drop, **{d: 0 for d in dims})
def clip(
self,
min: ScalarOrArray | None = None,
max: ScalarOrArray | None = None,
*,
keep_attrs: bool | None = None,
) -> Self:
"""
Return an array whose values are limited to ``[min, max]``.
At least one of max or min must be given.
Parameters
----------
min : None or Hashable, optional
Minimum value. If None, no lower clipping is performed.
max : None or Hashable, optional
Maximum value. If None, no upper clipping is performed.
keep_attrs : bool or None, optional
If True, the attributes (`attrs`) will be copied from
the original object to the new one. If False, the new
object will be returned without attributes.
Returns
-------
clipped : same type as caller
This object, but with with values < min are replaced with min,
and those > max with max.
See Also
--------
numpy.clip : equivalent function
"""
from xarray.core.computation import apply_ufunc
if keep_attrs is None:
# When this was a unary func, the default was True, so retaining the
# default.
keep_attrs = _get_keep_attrs(default=True)
return apply_ufunc(
np.clip, self, min, max, keep_attrs=keep_attrs, dask="allowed"
)
def get_index(self, key: Hashable) -> pd.Index:
"""Get an index for a dimension, with fall-back to a default RangeIndex"""
if key not in self.dims:
raise KeyError(key)
try:
return self._indexes[key].to_pandas_index()
except KeyError:
return pd.Index(range(self.sizes[key]), name=key)
def _calc_assign_results(
self: C, kwargs: Mapping[Any, T | Callable[[C], T]]
) -> dict[Hashable, T]:
return {k: v(self) if callable(v) else v for k, v in kwargs.items()}
def assign_coords(
self,
coords: Mapping | None = None,
**coords_kwargs: Any,
) -> Self:
"""Assign new coordinates to this object.
Returns a new object with all the original data in addition to the new
coordinates.
Parameters
----------
coords : mapping of dim to coord, optional
A mapping whose keys are the names of the coordinates and values are the
coordinates to assign. The mapping will generally be a dict or
:class:`Coordinates`.
* If a value is a standard data value — for example, a ``DataArray``,
scalar, or array — the data is simply assigned as a coordinate.
* If a value is callable, it is called with this object as the only
parameter, and the return value is used as new coordinate variables.
* A coordinate can also be defined and attached to an existing dimension
using a tuple with the first element the dimension name and the second
element the values for this new coordinate.
**coords_kwargs : optional
The keyword arguments form of ``coords``.
One of ``coords`` or ``coords_kwargs`` must be provided.
Returns
-------
assigned : same type as caller
A new object with the new coordinates in addition to the existing
data.
Examples
--------
Convert `DataArray` longitude coordinates from 0-359 to -180-179:
>>> da = xr.DataArray(
... np.random.rand(4),
... coords=[np.array([358, 359, 0, 1])],
... dims="lon",
... )
>>> da
<xarray.DataArray (lon: 4)> Size: 32B
array([0.5488135 , 0.71518937, 0.60276338, 0.54488318])
Coordinates:
* lon (lon) int64 32B 358 359 0 1
>>> da.assign_coords(lon=(((da.lon + 180) % 360) - 180))
<xarray.DataArray (lon: 4)> Size: 32B
array([0.5488135 , 0.71518937, 0.60276338, 0.54488318])
Coordinates:
* lon (lon) int64 32B -2 -1 0 1
The function also accepts dictionary arguments:
>>> da.assign_coords({"lon": (((da.lon + 180) % 360) - 180)})
<xarray.DataArray (lon: 4)> Size: 32B
array([0.5488135 , 0.71518937, 0.60276338, 0.54488318])
Coordinates:
* lon (lon) int64 32B -2 -1 0 1
New coordinate can also be attached to an existing dimension:
>>> lon_2 = np.array([300, 289, 0, 1])
>>> da.assign_coords(lon_2=("lon", lon_2))
<xarray.DataArray (lon: 4)> Size: 32B
array([0.5488135 , 0.71518937, 0.60276338, 0.54488318])
Coordinates:
* lon (lon) int64 32B 358 359 0 1
lon_2 (lon) int64 32B 300 289 0 1
Note that the same result can also be obtained with a dict e.g.
>>> _ = da.assign_coords({"lon_2": ("lon", lon_2)})
Note the same method applies to `Dataset` objects.
Convert `Dataset` longitude coordinates from 0-359 to -180-179:
>>> temperature = np.linspace(20, 32, num=16).reshape(2, 2, 4)
>>> precipitation = 2 * np.identity(4).reshape(2, 2, 4)
>>> ds = xr.Dataset(
... data_vars=dict(
... temperature=(["x", "y", "time"], temperature),
... precipitation=(["x", "y", "time"], precipitation),
... ),
... coords=dict(
... lon=(["x", "y"], [[260.17, 260.68], [260.21, 260.77]]),
... lat=(["x", "y"], [[42.25, 42.21], [42.63, 42.59]]),
... time=pd.date_range("2014-09-06", periods=4),
... reference_time=pd.Timestamp("2014-09-05"),
... ),
... attrs=dict(description="Weather-related data"),
... )
>>> ds
<xarray.Dataset> Size: 360B
Dimensions: (x: 2, y: 2, time: 4)
Coordinates:
lon (x, y) float64 32B 260.2 260.7 260.2 260.8
lat (x, y) float64 32B 42.25 42.21 42.63 42.59
* time (time) datetime64[ns] 32B 2014-09-06 ... 2014-09-09
reference_time datetime64[ns] 8B 2014-09-05
Dimensions without coordinates: x, y
Data variables:
temperature (x, y, time) float64 128B 20.0 20.8 21.6 ... 30.4 31.2 32.0
precipitation (x, y, time) float64 128B 2.0 0.0 0.0 0.0 ... 0.0 0.0 2.0
Attributes:
description: Weather-related data
>>> ds.assign_coords(lon=(((ds.lon + 180) % 360) - 180))
<xarray.Dataset> Size: 360B
Dimensions: (x: 2, y: 2, time: 4)
Coordinates:
lon (x, y) float64 32B -99.83 -99.32 -99.79 -99.23
lat (x, y) float64 32B 42.25 42.21 42.63 42.59
* time (time) datetime64[ns] 32B 2014-09-06 ... 2014-09-09
reference_time datetime64[ns] 8B 2014-09-05
Dimensions without coordinates: x, y
Data variables:
temperature (x, y, time) float64 128B 20.0 20.8 21.6 ... 30.4 31.2 32.0
precipitation (x, y, time) float64 128B 2.0 0.0 0.0 0.0 ... 0.0 0.0 2.0
Attributes:
description: Weather-related data
See Also
--------
Dataset.assign
Dataset.swap_dims
Dataset.set_coords
"""
from xarray.core.coordinates import Coordinates
coords_combined = either_dict_or_kwargs(coords, coords_kwargs, "assign_coords")
data = self.copy(deep=False)
results: Coordinates | dict[Hashable, Any]
if isinstance(coords, Coordinates):
results = coords
else:
results = self._calc_assign_results(coords_combined)
data.coords.update(results)
return data
def assign_attrs(self, *args: Any, **kwargs: Any) -> Self:
"""Assign new attrs to this object.
Returns a new object equivalent to ``self.attrs.update(*args, **kwargs)``.
Parameters
----------
*args
positional arguments passed into ``attrs.update``.
**kwargs
keyword arguments passed into ``attrs.update``.
Examples
--------
>>> dataset = xr.Dataset({"temperature": [25, 30, 27]})
>>> dataset
<xarray.Dataset> Size: 24B
Dimensions: (temperature: 3)
Coordinates:
* temperature (temperature) int64 24B 25 30 27
Data variables:
*empty*
>>> new_dataset = dataset.assign_attrs(
... units="Celsius", description="Temperature data"
... )
>>> new_dataset
<xarray.Dataset> Size: 24B
Dimensions: (temperature: 3)
Coordinates:
* temperature (temperature) int64 24B 25 30 27
Data variables:
*empty*
Attributes:
units: Celsius
description: Temperature data
# Attributes of the new dataset
>>> new_dataset.attrs
{'units': 'Celsius', 'description': 'Temperature data'}
Returns
-------
assigned : same type as caller
A new object with the new attrs in addition to the existing data.
See Also
--------
Dataset.assign
"""
out = self.copy(deep=False)
out.attrs.update(*args, **kwargs)
return out
def pipe(
self,
func: Callable[..., T] | tuple[Callable[..., T], str],
*args: Any,
**kwargs: Any,
) -> T:
"""
Apply ``func(self, *args, **kwargs)``
This method replicates the pandas method of the same name.
Parameters
----------
func : callable
function to apply to this xarray object (Dataset/DataArray).
``args``, and ``kwargs`` are passed into ``func``.
Alternatively a ``(callable, data_keyword)`` tuple where
``data_keyword`` is a string indicating the keyword of
``callable`` that expects the xarray object.
*args
positional arguments passed into ``func``.
**kwargs
a dictionary of keyword arguments passed into ``func``.
Returns
-------
object : Any
the return type of ``func``.
Notes
-----
Use ``.pipe`` when chaining together functions that expect
xarray or pandas objects, e.g., instead of writing
.. code:: python
f(g(h(ds), arg1=a), arg2=b, arg3=c)
You can write
.. code:: python
(ds.pipe(h).pipe(g, arg1=a).pipe(f, arg2=b, arg3=c))
If you have a function that takes the data as (say) the second
argument, pass a tuple indicating which keyword expects the
data. For example, suppose ``f`` takes its data as ``arg2``:
.. code:: python
(ds.pipe(h).pipe(g, arg1=a).pipe((f, "arg2"), arg1=a, arg3=c))
Examples
--------
>>> x = xr.Dataset(
... {
... "temperature_c": (
... ("lat", "lon"),
... 20 * np.random.rand(4).reshape(2, 2),
... ),
... "precipitation": (("lat", "lon"), np.random.rand(4).reshape(2, 2)),
... },
... coords={"lat": [10, 20], "lon": [150, 160]},
... )
>>> x
<xarray.Dataset> Size: 96B
Dimensions: (lat: 2, lon: 2)
Coordinates:
* lat (lat) int64 16B 10 20
* lon (lon) int64 16B 150 160
Data variables:
temperature_c (lat, lon) float64 32B 10.98 14.3 12.06 10.9
precipitation (lat, lon) float64 32B 0.4237 0.6459 0.4376 0.8918
>>> def adder(data, arg):
... return data + arg
...
>>> def div(data, arg):
... return data / arg
...
>>> def sub_mult(data, sub_arg, mult_arg):
... return (data * mult_arg) - sub_arg
...
>>> x.pipe(adder, 2)
<xarray.Dataset> Size: 96B
Dimensions: (lat: 2, lon: 2)
Coordinates:
* lat (lat) int64 16B 10 20
* lon (lon) int64 16B 150 160
Data variables:
temperature_c (lat, lon) float64 32B 12.98 16.3 14.06 12.9
precipitation (lat, lon) float64 32B 2.424 2.646 2.438 2.892
>>> x.pipe(adder, arg=2)
<xarray.Dataset> Size: 96B
Dimensions: (lat: 2, lon: 2)
Coordinates:
* lat (lat) int64 16B 10 20
* lon (lon) int64 16B 150 160
Data variables:
temperature_c (lat, lon) float64 32B 12.98 16.3 14.06 12.9
precipitation (lat, lon) float64 32B 2.424 2.646 2.438 2.892
>>> (
... x.pipe(adder, arg=2)
... .pipe(div, arg=2)
... .pipe(sub_mult, sub_arg=2, mult_arg=2)
... )
<xarray.Dataset> Size: 96B
Dimensions: (lat: 2, lon: 2)
Coordinates:
* lat (lat) int64 16B 10 20
* lon (lon) int64 16B 150 160
Data variables:
temperature_c (lat, lon) float64 32B 10.98 14.3 12.06 10.9
precipitation (lat, lon) float64 32B 0.4237 0.6459 0.4376 0.8918
See Also
--------
pandas.DataFrame.pipe
"""
if isinstance(func, tuple):
func, target = func
if target in kwargs:
raise ValueError(
f"{target} is both the pipe target and a keyword argument"
)
kwargs[target] = self
return func(*args, **kwargs)
else:
return func(self, *args, **kwargs)
def rolling_exp(
self: T_DataWithCoords,
window: Mapping[Any, int] | None = None,
window_type: str = "span",
**window_kwargs,
) -> RollingExp[T_DataWithCoords]:
"""
Exponentially-weighted moving window.
Similar to EWM in pandas
Requires the optional Numbagg dependency.
Parameters
----------
window : mapping of hashable to int, optional
A mapping from the name of the dimension to create the rolling
exponential window along (e.g. `time`) to the size of the moving window.
window_type : {"span", "com", "halflife", "alpha"}, default: "span"
The format of the previously supplied window. Each is a simple
numerical transformation of the others. Described in detail:
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.ewm.html
**window_kwargs : optional
The keyword arguments form of ``window``.
One of window or window_kwargs must be provided.
See Also
--------
core.rolling_exp.RollingExp
"""
from xarray.core import rolling_exp
if "keep_attrs" in window_kwargs:
warnings.warn(
"Passing ``keep_attrs`` to ``rolling_exp`` has no effect. Pass"
" ``keep_attrs`` directly to the applied function, e.g."
" ``rolling_exp(...).mean(keep_attrs=False)``."
)
window = either_dict_or_kwargs(window, window_kwargs, "rolling_exp")
return rolling_exp.RollingExp(self, window, window_type)
def _resample(
self,
resample_cls: type[T_Resample],
indexer: Mapping[Any, str] | None,
skipna: bool | None,
closed: SideOptions | None,
label: SideOptions | None,
base: int | None,
offset: pd.Timedelta | datetime.timedelta | str | None,
origin: str | DatetimeLike,
loffset: datetime.timedelta | str | None,
restore_coord_dims: bool | None,
**indexer_kwargs: str,
) -> T_Resample:
"""Returns a Resample object for performing resampling operations.
Handles both downsampling and upsampling. The resampled
dimension must be a datetime-like coordinate. If any intervals
contain no values from the original object, they will be given
the value ``NaN``.
Parameters
----------
indexer : {dim: freq}, optional
Mapping from the dimension name to resample frequency [1]_. The
dimension must be datetime-like.
skipna : bool, optional
Whether to skip missing values when aggregating in downsampling.
closed : {"left", "right"}, optional
Side of each interval to treat as closed.
label : {"left", "right"}, optional
Side of each interval to use for labeling.
base : int, optional
For frequencies that evenly subdivide 1 day, the "origin" of the
aggregated intervals. For example, for "24H" frequency, base could
range from 0 through 23.
.. deprecated:: 2023.03.0
Following pandas, the ``base`` parameter is deprecated in favor
of the ``origin`` and ``offset`` parameters, and will be removed
in a future version of xarray.
origin : {'epoch', 'start', 'start_day', 'end', 'end_day'}, pd.Timestamp, datetime.datetime, np.datetime64, or cftime.datetime, default 'start_day'
The datetime on which to adjust the grouping. The timezone of origin
must match the timezone of the index.
If a datetime is not used, these values are also supported:
- 'epoch': `origin` is 1970-01-01
- 'start': `origin` is the first value of the timeseries
- 'start_day': `origin` is the first day at midnight of the timeseries
- 'end': `origin` is the last value of the timeseries
- 'end_day': `origin` is the ceiling midnight of the last day
offset : pd.Timedelta, datetime.timedelta, or str, default is None
An offset timedelta added to the origin.
loffset : timedelta or str, optional
Offset used to adjust the resampled time labels. Some pandas date
offset strings are supported.
.. deprecated:: 2023.03.0
Following pandas, the ``loffset`` parameter is deprecated in favor
of using time offset arithmetic, and will be removed in a future
version of xarray.
restore_coord_dims : bool, optional
If True, also restore the dimension order of multi-dimensional
coordinates.
**indexer_kwargs : {dim: freq}
The keyword arguments form of ``indexer``.
One of indexer or indexer_kwargs must be provided.
Returns
-------
resampled : same type as caller
This object resampled.
Examples
--------
Downsample monthly time-series data to seasonal data:
>>> da = xr.DataArray(
... np.linspace(0, 11, num=12),
... coords=[
... pd.date_range(
... "1999-12-15",
... periods=12,
... freq=pd.DateOffset(months=1),
... )
... ],
... dims="time",
... )
>>> da
<xarray.DataArray (time: 12)> Size: 96B
array([ 0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11.])
Coordinates:
* time (time) datetime64[ns] 96B 1999-12-15 2000-01-15 ... 2000-11-15
>>> da.resample(time="QS-DEC").mean()
<xarray.DataArray (time: 4)> Size: 32B
array([ 1., 4., 7., 10.])
Coordinates:
* time (time) datetime64[ns] 32B 1999-12-01 2000-03-01 ... 2000-09-01
Upsample monthly time-series data to daily data:
>>> da.resample(time="1D").interpolate("linear") # +doctest: ELLIPSIS
<xarray.DataArray (time: 337)> Size: 3kB
array([ 0. , 0.03225806, 0.06451613, 0.09677419, 0.12903226,
0.16129032, 0.19354839, 0.22580645, 0.25806452, 0.29032258,
0.32258065, 0.35483871, 0.38709677, 0.41935484, 0.4516129 ,
0.48387097, 0.51612903, 0.5483871 , 0.58064516, 0.61290323,
0.64516129, 0.67741935, 0.70967742, 0.74193548, 0.77419355,
0.80645161, 0.83870968, 0.87096774, 0.90322581, 0.93548387,
0.96774194, 1. , 1.03225806, 1.06451613, 1.09677419,
1.12903226, 1.16129032, 1.19354839, 1.22580645, 1.25806452,
1.29032258, 1.32258065, 1.35483871, 1.38709677, 1.41935484,
1.4516129 , 1.48387097, 1.51612903, 1.5483871 , 1.58064516,
1.61290323, 1.64516129, 1.67741935, 1.70967742, 1.74193548,
1.77419355, 1.80645161, 1.83870968, 1.87096774, 1.90322581,
1.93548387, 1.96774194, 2. , 2.03448276, 2.06896552,
2.10344828, 2.13793103, 2.17241379, 2.20689655, 2.24137931,
2.27586207, 2.31034483, 2.34482759, 2.37931034, 2.4137931 ,
2.44827586, 2.48275862, 2.51724138, 2.55172414, 2.5862069 ,
2.62068966, 2.65517241, 2.68965517, 2.72413793, 2.75862069,
2.79310345, 2.82758621, 2.86206897, 2.89655172, 2.93103448,
2.96551724, 3. , 3.03225806, 3.06451613, 3.09677419,
3.12903226, 3.16129032, 3.19354839, 3.22580645, 3.25806452,
...
7.87096774, 7.90322581, 7.93548387, 7.96774194, 8. ,
8.03225806, 8.06451613, 8.09677419, 8.12903226, 8.16129032,
8.19354839, 8.22580645, 8.25806452, 8.29032258, 8.32258065,
8.35483871, 8.38709677, 8.41935484, 8.4516129 , 8.48387097,
8.51612903, 8.5483871 , 8.58064516, 8.61290323, 8.64516129,
8.67741935, 8.70967742, 8.74193548, 8.77419355, 8.80645161,
8.83870968, 8.87096774, 8.90322581, 8.93548387, 8.96774194,
9. , 9.03333333, 9.06666667, 9.1 , 9.13333333,
9.16666667, 9.2 , 9.23333333, 9.26666667, 9.3 ,
9.33333333, 9.36666667, 9.4 , 9.43333333, 9.46666667,
9.5 , 9.53333333, 9.56666667, 9.6 , 9.63333333,
9.66666667, 9.7 , 9.73333333, 9.76666667, 9.8 ,
9.83333333, 9.86666667, 9.9 , 9.93333333, 9.96666667,
10. , 10.03225806, 10.06451613, 10.09677419, 10.12903226,
10.16129032, 10.19354839, 10.22580645, 10.25806452, 10.29032258,
10.32258065, 10.35483871, 10.38709677, 10.41935484, 10.4516129 ,
10.48387097, 10.51612903, 10.5483871 , 10.58064516, 10.61290323,
10.64516129, 10.67741935, 10.70967742, 10.74193548, 10.77419355,
10.80645161, 10.83870968, 10.87096774, 10.90322581, 10.93548387,
10.96774194, 11. ])
Coordinates:
* time (time) datetime64[ns] 3kB 1999-12-15 1999-12-16 ... 2000-11-15
Limit scope of upsampling method
>>> da.resample(time="1D").nearest(tolerance="1D")
<xarray.DataArray (time: 337)> Size: 3kB
array([ 0., 0., nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, 1., 1., 1., nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, 2., 2., 2., nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 3.,
3., 3., nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, 4., 4., 4., nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, 5., 5., 5., nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
6., 6., 6., nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, 7., 7., 7., nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, 8., 8., 8., nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, 9., 9., 9., nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, 10., 10., 10., nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 11., 11.])
Coordinates:
* time (time) datetime64[ns] 3kB 1999-12-15 1999-12-16 ... 2000-11-15
See Also
--------
pandas.Series.resample
pandas.DataFrame.resample
References
----------
.. [1] https://pandas.pydata.org/docs/user_guide/timeseries.html#dateoffset-objects
"""
# TODO support non-string indexer after removing the old API.
from xarray.core.dataarray import DataArray
from xarray.core.groupby import ResolvedGrouper, TimeResampler
from xarray.core.resample import RESAMPLE_DIM
# note: the second argument (now 'skipna') use to be 'dim'
if (
(skipna is not None and not isinstance(skipna, bool))
or ("how" in indexer_kwargs and "how" not in self.dims)
or ("dim" in indexer_kwargs and "dim" not in self.dims)
):
raise TypeError(
"resample() no longer supports the `how` or "
"`dim` arguments. Instead call methods on resample "
"objects, e.g., data.resample(time='1D').mean()"
)
indexer = either_dict_or_kwargs(indexer, indexer_kwargs, "resample")
if len(indexer) != 1:
raise ValueError("Resampling only supported along single dimensions.")
dim, freq = next(iter(indexer.items()))
dim_name: Hashable = dim
dim_coord = self[dim]
group = DataArray(
dim_coord,
coords=dim_coord.coords,
dims=dim_coord.dims,
name=RESAMPLE_DIM,
)
grouper = TimeResampler(
freq=freq,
closed=closed,
label=label,
origin=origin,
offset=offset,
loffset=loffset,
base=base,
)
rgrouper = ResolvedGrouper(grouper, group, self)
return resample_cls(
self,
(rgrouper,),
dim=dim_name,
resample_dim=RESAMPLE_DIM,
restore_coord_dims=restore_coord_dims,
)
def where(self, cond: Any, other: Any = dtypes.NA, drop: bool = False) -> Self:
"""Filter elements from this object according to a condition.
Returns elements from 'DataArray', where 'cond' is True,
otherwise fill in 'other'.
This operation follows the normal broadcasting and alignment rules that
xarray uses for binary arithmetic.
Parameters
----------
cond : DataArray, Dataset, or callable
Locations at which to preserve this object's values. dtype must be `bool`.
If a callable, the callable is passed this object, and the result is used as
the value for cond.
other : scalar, DataArray, Dataset, or callable, optional
Value to use for locations in this object where ``cond`` is False.
By default, these locations are filled with NA. If a callable, it must
expect this object as its only parameter.
drop : bool, default: False
If True, coordinate labels that only correspond to False values of
the condition are dropped from the result.
Returns
-------
DataArray or Dataset
Same xarray type as caller, with dtype float64.
Examples
--------
>>> a = xr.DataArray(np.arange(25).reshape(5, 5), dims=("x", "y"))
>>> a
<xarray.DataArray (x: 5, y: 5)> Size: 200B
array([[ 0, 1, 2, 3, 4],
[ 5, 6, 7, 8, 9],
[10, 11, 12, 13, 14],
[15, 16, 17, 18, 19],
[20, 21, 22, 23, 24]])
Dimensions without coordinates: x, y
>>> a.where(a.x + a.y < 4)
<xarray.DataArray (x: 5, y: 5)> Size: 200B
array([[ 0., 1., 2., 3., nan],
[ 5., 6., 7., nan, nan],
[10., 11., nan, nan, nan],
[15., nan, nan, nan, nan],
[nan, nan, nan, nan, nan]])
Dimensions without coordinates: x, y
>>> a.where(a.x + a.y < 5, -1)
<xarray.DataArray (x: 5, y: 5)> Size: 200B
array([[ 0, 1, 2, 3, 4],
[ 5, 6, 7, 8, -1],
[10, 11, 12, -1, -1],
[15, 16, -1, -1, -1],
[20, -1, -1, -1, -1]])
Dimensions without coordinates: x, y
>>> a.where(a.x + a.y < 4, drop=True)
<xarray.DataArray (x: 4, y: 4)> Size: 128B
array([[ 0., 1., 2., 3.],
[ 5., 6., 7., nan],
[10., 11., nan, nan],
[15., nan, nan, nan]])
Dimensions without coordinates: x, y
>>> a.where(lambda x: x.x + x.y < 4, lambda x: -x)
<xarray.DataArray (x: 5, y: 5)> Size: 200B
array([[ 0, 1, 2, 3, -4],
[ 5, 6, 7, -8, -9],
[ 10, 11, -12, -13, -14],
[ 15, -16, -17, -18, -19],
[-20, -21, -22, -23, -24]])
Dimensions without coordinates: x, y
>>> a.where(a.x + a.y < 4, drop=True)
<xarray.DataArray (x: 4, y: 4)> Size: 128B
array([[ 0., 1., 2., 3.],
[ 5., 6., 7., nan],
[10., 11., nan, nan],
[15., nan, nan, nan]])
Dimensions without coordinates: x, y
See Also
--------
numpy.where : corresponding numpy function
where : equivalent function
"""
from xarray.core.alignment import align
from xarray.core.dataarray import DataArray
from xarray.core.dataset import Dataset
if callable(cond):
cond = cond(self)
if callable(other):
other = other(self)
if drop:
if not isinstance(cond, (Dataset, DataArray)):
raise TypeError(
f"cond argument is {cond!r} but must be a {Dataset!r} or {DataArray!r} (or a callable than returns one)."
)
self, cond = align(self, cond)
def _dataarray_indexer(dim: Hashable) -> DataArray:
return cond.any(dim=(d for d in cond.dims if d != dim))
def _dataset_indexer(dim: Hashable) -> DataArray:
cond_wdim = cond.drop_vars(
var for var in cond if dim not in cond[var].dims
)
keepany = cond_wdim.any(dim=(d for d in cond.dims if d != dim))
return keepany.to_dataarray().any("variable")
_get_indexer = (
_dataarray_indexer if isinstance(cond, DataArray) else _dataset_indexer
)
indexers = {}
for dim in cond.sizes.keys():
indexers[dim] = _get_indexer(dim)
self = self.isel(**indexers)
cond = cond.isel(**indexers)
return ops.where_method(self, cond, other)
def set_close(self, close: Callable[[], None] | None) -> None:
"""Register the function that releases any resources linked to this object.
This method controls how xarray cleans up resources associated
with this object when the ``.close()`` method is called. It is mostly
intended for backend developers and it is rarely needed by regular
end-users.
Parameters
----------
close : callable
The function that when called like ``close()`` releases
any resources linked to this object.
"""
self._close = close
def close(self) -> None:
"""Release any resources linked to this object."""
if self._close is not None:
self._close()
self._close = None
def isnull(self, keep_attrs: bool | None = None) -> Self:
"""Test each value in the array for whether it is a missing value.
Parameters
----------
keep_attrs : bool or None, optional
If True, the attributes (`attrs`) will be copied from
the original object to the new one. If False, the new
object will be returned without attributes.
Returns
-------
isnull : DataArray or Dataset
Same type and shape as object, but the dtype of the data is bool.
See Also
--------
pandas.isnull
Examples
--------
>>> array = xr.DataArray([1, np.nan, 3], dims="x")
>>> array
<xarray.DataArray (x: 3)> Size: 24B
array([ 1., nan, 3.])
Dimensions without coordinates: x
>>> array.isnull()
<xarray.DataArray (x: 3)> Size: 3B
array([False, True, False])
Dimensions without coordinates: x
"""
from xarray.core.computation import apply_ufunc
if keep_attrs is None:
keep_attrs = _get_keep_attrs(default=False)
return apply_ufunc(
duck_array_ops.isnull,
self,
dask="allowed",
keep_attrs=keep_attrs,
)
def notnull(self, keep_attrs: bool | None = None) -> Self:
"""Test each value in the array for whether it is not a missing value.
Parameters
----------
keep_attrs : bool or None, optional
If True, the attributes (`attrs`) will be copied from
the original object to the new one. If False, the new
object will be returned without attributes.
Returns
-------
notnull : DataArray or Dataset
Same type and shape as object, but the dtype of the data is bool.
See Also
--------
pandas.notnull
Examples
--------
>>> array = xr.DataArray([1, np.nan, 3], dims="x")
>>> array
<xarray.DataArray (x: 3)> Size: 24B
array([ 1., nan, 3.])
Dimensions without coordinates: x
>>> array.notnull()
<xarray.DataArray (x: 3)> Size: 3B
array([ True, False, True])
Dimensions without coordinates: x
"""
from xarray.core.computation import apply_ufunc
if keep_attrs is None:
keep_attrs = _get_keep_attrs(default=False)
return apply_ufunc(
duck_array_ops.notnull,
self,
dask="allowed",
keep_attrs=keep_attrs,
)
def isin(self, test_elements: Any) -> Self:
"""Tests each value in the array for whether it is in test elements.
Parameters
----------
test_elements : array_like
The values against which to test each value of `element`.
This argument is flattened if an array or array_like.
See numpy notes for behavior with non-array-like parameters.
Returns
-------
isin : DataArray or Dataset
Has the same type and shape as this object, but with a bool dtype.
Examples
--------
>>> array = xr.DataArray([1, 2, 3], dims="x")
>>> array.isin([1, 3])
<xarray.DataArray (x: 3)> Size: 3B
array([ True, False, True])
Dimensions without coordinates: x
See Also
--------
numpy.isin
"""
from xarray.core.computation import apply_ufunc
from xarray.core.dataarray import DataArray
from xarray.core.dataset import Dataset
from xarray.core.variable import Variable
if isinstance(test_elements, Dataset):
raise TypeError(
f"isin() argument must be convertible to an array: {test_elements}"
)
elif isinstance(test_elements, (Variable, DataArray)):
# need to explicitly pull out data to support dask arrays as the
# second argument
test_elements = test_elements.data
return apply_ufunc(
duck_array_ops.isin,
self,
kwargs=dict(test_elements=test_elements),
dask="allowed",
)
def astype(
self,
dtype,
*,
order=None,
casting=None,
subok=None,
copy=None,
keep_attrs=True,
) -> Self:
"""
Copy of the xarray object, with data cast to a specified type.
Leaves coordinate dtype unchanged.
Parameters
----------
dtype : str or dtype
Typecode or data-type to which the array is cast.
order : {'C', 'F', 'A', 'K'}, optional
Controls the memory layout order of the result. ‘C’ means C order,
‘F’ means Fortran order, ‘A’ means ‘F’ order if all the arrays are
Fortran contiguous, ‘C’ order otherwise, and ‘K’ means as close to
the order the array elements appear in memory as possible.
casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional
Controls what kind of data casting may occur.
* 'no' means the data types should not be cast at all.
* 'equiv' means only byte-order changes are allowed.
* 'safe' means only casts which can preserve values are allowed.
* 'same_kind' means only safe casts or casts within a kind,
like float64 to float32, are allowed.
* 'unsafe' means any data conversions may be done.
subok : bool, optional
If True, then sub-classes will be passed-through, otherwise the
returned array will be forced to be a base-class array.
copy : bool, optional
By default, astype always returns a newly allocated array. If this
is set to False and the `dtype` requirement is satisfied, the input
array is returned instead of a copy.
keep_attrs : bool, optional
By default, astype keeps attributes. Set to False to remove
attributes in the returned object.
Returns
-------
out : same as object
New object with data cast to the specified type.
Notes
-----
The ``order``, ``casting``, ``subok`` and ``copy`` arguments are only passed
through to the ``astype`` method of the underlying array when a value
different than ``None`` is supplied.
Make sure to only supply these arguments if the underlying array class
supports them.
See Also
--------
numpy.ndarray.astype
dask.array.Array.astype
sparse.COO.astype
"""
from xarray.core.computation import apply_ufunc
kwargs = dict(order=order, casting=casting, subok=subok, copy=copy)
kwargs = {k: v for k, v in kwargs.items() if v is not None}
return apply_ufunc(
duck_array_ops.astype,
self,
dtype,
kwargs=kwargs,
keep_attrs=keep_attrs,
dask="allowed",
)
def __enter__(self) -> Self:
return self
def __exit__(self, exc_type, exc_value, traceback) -> None:
self.close()
def __getitem__(self, value):
# implementations of this class should implement this method
raise NotImplementedError()
@overload
def full_like(
other: DataArray,
fill_value: Any,
dtype: DTypeLikeSave | None = None,
*,
chunks: T_Chunks = None,
chunked_array_type: str | None = None,
from_array_kwargs: dict[str, Any] | None = None,
) -> DataArray: ...
@overload
def full_like(
other: Dataset,
fill_value: Any,
dtype: DTypeMaybeMapping | None = None,
*,
chunks: T_Chunks = None,
chunked_array_type: str | None = None,
from_array_kwargs: dict[str, Any] | None = None,
) -> Dataset: ...
@overload
def full_like(
other: Variable,
fill_value: Any,
dtype: DTypeLikeSave | None = None,
*,
chunks: T_Chunks = None,
chunked_array_type: str | None = None,
from_array_kwargs: dict[str, Any] | None = None,
) -> Variable: ...
@overload
def full_like(
other: Dataset | DataArray,
fill_value: Any,
dtype: DTypeMaybeMapping | None = None,
*,
chunks: T_Chunks = {},
chunked_array_type: str | None = None,
from_array_kwargs: dict[str, Any] | None = None,
) -> Dataset | DataArray: ...
@overload
def full_like(
other: Dataset | DataArray | Variable,
fill_value: Any,
dtype: DTypeMaybeMapping | None = None,
*,
chunks: T_Chunks = None,
chunked_array_type: str | None = None,
from_array_kwargs: dict[str, Any] | None = None,
) -> Dataset | DataArray | Variable: ...
[docs]
def full_like(
other: Dataset | DataArray | Variable,
fill_value: Any,
dtype: DTypeMaybeMapping | None = None,
*,
chunks: T_Chunks = None,
chunked_array_type: str | None = None,
from_array_kwargs: dict[str, Any] | None = None,
) -> Dataset | DataArray | Variable:
"""
Return a new object with the same shape and type as a given object.
Returned object will be chunked if if the given object is chunked, or if chunks or chunked_array_type are specified.
Parameters
----------
other : DataArray, Dataset or Variable
The reference object in input
fill_value : scalar or dict-like
Value to fill the new object with before returning it. If
other is a Dataset, may also be a dict-like mapping data
variables to fill values.
dtype : dtype or dict-like of dtype, optional
dtype of the new array. If a dict-like, maps dtypes to
variables. If omitted, it defaults to other.dtype.
chunks : int, "auto", tuple of int or mapping of Hashable to int, optional
Chunk sizes along each dimension, e.g., ``5``, ``"auto"``, ``(5, 5)`` or
``{"x": 5, "y": 5}``.
chunked_array_type: str, optional
Which chunked array type to coerce the underlying data array to.
Defaults to 'dask' if installed, else whatever is registered via the `ChunkManagerEnetryPoint` system.
Experimental API that should not be relied upon.
from_array_kwargs: dict, optional
Additional keyword arguments passed on to the `ChunkManagerEntrypoint.from_array` method used to create
chunked arrays, via whichever chunk manager is specified through the `chunked_array_type` kwarg.
For example, with dask as the default chunked array type, this method would pass additional kwargs
to :py:func:`dask.array.from_array`. Experimental API that should not be relied upon.
Returns
-------
out : same as object
New object with the same shape and type as other, with the data
filled with fill_value. Coords will be copied from other.
If other is based on dask, the new one will be as well, and will be
split in the same chunks.
Examples
--------
>>> x = xr.DataArray(
... np.arange(6).reshape(2, 3),
... dims=["lat", "lon"],
... coords={"lat": [1, 2], "lon": [0, 1, 2]},
... )
>>> x
<xarray.DataArray (lat: 2, lon: 3)> Size: 48B
array([[0, 1, 2],
[3, 4, 5]])
Coordinates:
* lat (lat) int64 16B 1 2
* lon (lon) int64 24B 0 1 2
>>> xr.full_like(x, 1)
<xarray.DataArray (lat: 2, lon: 3)> Size: 48B
array([[1, 1, 1],
[1, 1, 1]])
Coordinates:
* lat (lat) int64 16B 1 2
* lon (lon) int64 24B 0 1 2
>>> xr.full_like(x, 0.5)
<xarray.DataArray (lat: 2, lon: 3)> Size: 48B
array([[0, 0, 0],
[0, 0, 0]])
Coordinates:
* lat (lat) int64 16B 1 2
* lon (lon) int64 24B 0 1 2
>>> xr.full_like(x, 0.5, dtype=np.double)
<xarray.DataArray (lat: 2, lon: 3)> Size: 48B
array([[0.5, 0.5, 0.5],
[0.5, 0.5, 0.5]])
Coordinates:
* lat (lat) int64 16B 1 2
* lon (lon) int64 24B 0 1 2
>>> xr.full_like(x, np.nan, dtype=np.double)
<xarray.DataArray (lat: 2, lon: 3)> Size: 48B
array([[nan, nan, nan],
[nan, nan, nan]])
Coordinates:
* lat (lat) int64 16B 1 2
* lon (lon) int64 24B 0 1 2
>>> ds = xr.Dataset(
... {"a": ("x", [3, 5, 2]), "b": ("x", [9, 1, 0])}, coords={"x": [2, 4, 6]}
... )
>>> ds
<xarray.Dataset> Size: 72B
Dimensions: (x: 3)
Coordinates:
* x (x) int64 24B 2 4 6
Data variables:
a (x) int64 24B 3 5 2
b (x) int64 24B 9 1 0
>>> xr.full_like(ds, fill_value={"a": 1, "b": 2})
<xarray.Dataset> Size: 72B
Dimensions: (x: 3)
Coordinates:
* x (x) int64 24B 2 4 6
Data variables:
a (x) int64 24B 1 1 1
b (x) int64 24B 2 2 2
>>> xr.full_like(ds, fill_value={"a": 1, "b": 2}, dtype={"a": bool, "b": float})
<xarray.Dataset> Size: 51B
Dimensions: (x: 3)
Coordinates:
* x (x) int64 24B 2 4 6
Data variables:
a (x) bool 3B True True True
b (x) float64 24B 2.0 2.0 2.0
See Also
--------
zeros_like
ones_like
"""
from xarray.core.dataarray import DataArray
from xarray.core.dataset import Dataset
from xarray.core.variable import Variable
if not is_scalar(fill_value) and not (
isinstance(other, Dataset) and isinstance(fill_value, dict)
):
raise ValueError(
f"fill_value must be scalar or, for datasets, a dict-like. Received {fill_value} instead."
)
if isinstance(other, Dataset):
if not isinstance(fill_value, dict):
fill_value = {k: fill_value for k in other.data_vars.keys()}
dtype_: Mapping[Any, DTypeLikeSave]
if not isinstance(dtype, Mapping):
dtype_ = {k: dtype for k in other.data_vars.keys()}
else:
dtype_ = dtype
data_vars = {
k: _full_like_variable(
v.variable,
fill_value.get(k, dtypes.NA),
dtype_.get(k, None),
chunks,
chunked_array_type,
from_array_kwargs,
)
for k, v in other.data_vars.items()
}
return Dataset(data_vars, coords=other.coords, attrs=other.attrs)
elif isinstance(other, DataArray):
if isinstance(dtype, Mapping):
raise ValueError("'dtype' cannot be dict-like when passing a DataArray")
return DataArray(
_full_like_variable(
other.variable,
fill_value,
dtype,
chunks,
chunked_array_type,
from_array_kwargs,
),
dims=other.dims,
coords=other.coords,
attrs=other.attrs,
name=other.name,
)
elif isinstance(other, Variable):
if isinstance(dtype, Mapping):
raise ValueError("'dtype' cannot be dict-like when passing a Variable")
return _full_like_variable(
other, fill_value, dtype, chunks, chunked_array_type, from_array_kwargs
)
else:
raise TypeError("Expected DataArray, Dataset, or Variable")
def _full_like_variable(
other: Variable,
fill_value: Any,
dtype: DTypeLike | None = None,
chunks: T_Chunks = None,
chunked_array_type: str | None = None,
from_array_kwargs: dict[str, Any] | None = None,
) -> Variable:
"""Inner function of full_like, where other must be a variable"""
from xarray.core.variable import Variable
if fill_value is dtypes.NA:
fill_value = dtypes.get_fill_value(dtype if dtype is not None else other.dtype)
if (
is_chunked_array(other.data)
or chunked_array_type is not None
or chunks is not None
):
if chunked_array_type is None:
chunkmanager = get_chunked_array_type(other.data)
else:
chunkmanager = guess_chunkmanager(chunked_array_type)
if dtype is None:
dtype = other.dtype
if from_array_kwargs is None:
from_array_kwargs = {}
data = chunkmanager.array_api.full(
other.shape,
fill_value,
dtype=dtype,
chunks=chunks if chunks else other.data.chunks,
**from_array_kwargs,
)
else:
data = np.full_like(other.data, fill_value, dtype=dtype)
return Variable(dims=other.dims, data=data, attrs=other.attrs)
@overload
def zeros_like(
other: DataArray,
dtype: DTypeLikeSave | None = None,
*,
chunks: T_Chunks = None,
chunked_array_type: str | None = None,
from_array_kwargs: dict[str, Any] | None = None,
) -> DataArray: ...
@overload
def zeros_like(
other: Dataset,
dtype: DTypeMaybeMapping | None = None,
*,
chunks: T_Chunks = None,
chunked_array_type: str | None = None,
from_array_kwargs: dict[str, Any] | None = None,
) -> Dataset: ...
@overload
def zeros_like(
other: Variable,
dtype: DTypeLikeSave | None = None,
*,
chunks: T_Chunks = None,
chunked_array_type: str | None = None,
from_array_kwargs: dict[str, Any] | None = None,
) -> Variable: ...
@overload
def zeros_like(
other: Dataset | DataArray,
dtype: DTypeMaybeMapping | None = None,
*,
chunks: T_Chunks = None,
chunked_array_type: str | None = None,
from_array_kwargs: dict[str, Any] | None = None,
) -> Dataset | DataArray: ...
@overload
def zeros_like(
other: Dataset | DataArray | Variable,
dtype: DTypeMaybeMapping | None = None,
*,
chunks: T_Chunks = None,
chunked_array_type: str | None = None,
from_array_kwargs: dict[str, Any] | None = None,
) -> Dataset | DataArray | Variable: ...
[docs]
def zeros_like(
other: Dataset | DataArray | Variable,
dtype: DTypeMaybeMapping | None = None,
*,
chunks: T_Chunks = None,
chunked_array_type: str | None = None,
from_array_kwargs: dict[str, Any] | None = None,
) -> Dataset | DataArray | Variable:
"""Return a new object of zeros with the same shape and
type as a given dataarray or dataset.
Parameters
----------
other : DataArray, Dataset or Variable
The reference object. The output will have the same dimensions and coordinates as this object.
dtype : dtype, optional
dtype of the new array. If omitted, it defaults to other.dtype.
chunks : int, "auto", tuple of int or mapping of Hashable to int, optional
Chunk sizes along each dimension, e.g., ``5``, ``"auto"``, ``(5, 5)`` or
``{"x": 5, "y": 5}``.
chunked_array_type: str, optional
Which chunked array type to coerce the underlying data array to.
Defaults to 'dask' if installed, else whatever is registered via the `ChunkManagerEnetryPoint` system.
Experimental API that should not be relied upon.
from_array_kwargs: dict, optional
Additional keyword arguments passed on to the `ChunkManagerEntrypoint.from_array` method used to create
chunked arrays, via whichever chunk manager is specified through the `chunked_array_type` kwarg.
For example, with dask as the default chunked array type, this method would pass additional kwargs
to :py:func:`dask.array.from_array`. Experimental API that should not be relied upon.
Returns
-------
out : DataArray, Dataset or Variable
New object of zeros with the same shape and type as other.
Examples
--------
>>> x = xr.DataArray(
... np.arange(6).reshape(2, 3),
... dims=["lat", "lon"],
... coords={"lat": [1, 2], "lon": [0, 1, 2]},
... )
>>> x
<xarray.DataArray (lat: 2, lon: 3)> Size: 48B
array([[0, 1, 2],
[3, 4, 5]])
Coordinates:
* lat (lat) int64 16B 1 2
* lon (lon) int64 24B 0 1 2
>>> xr.zeros_like(x)
<xarray.DataArray (lat: 2, lon: 3)> Size: 48B
array([[0, 0, 0],
[0, 0, 0]])
Coordinates:
* lat (lat) int64 16B 1 2
* lon (lon) int64 24B 0 1 2
>>> xr.zeros_like(x, dtype=float)
<xarray.DataArray (lat: 2, lon: 3)> Size: 48B
array([[0., 0., 0.],
[0., 0., 0.]])
Coordinates:
* lat (lat) int64 16B 1 2
* lon (lon) int64 24B 0 1 2
See Also
--------
ones_like
full_like
"""
return full_like(
other,
0,
dtype,
chunks=chunks,
chunked_array_type=chunked_array_type,
from_array_kwargs=from_array_kwargs,
)
@overload
def ones_like(
other: DataArray,
dtype: DTypeLikeSave | None = None,
*,
chunks: T_Chunks = None,
chunked_array_type: str | None = None,
from_array_kwargs: dict[str, Any] | None = None,
) -> DataArray: ...
@overload
def ones_like(
other: Dataset,
dtype: DTypeMaybeMapping | None = None,
*,
chunks: T_Chunks = None,
chunked_array_type: str | None = None,
from_array_kwargs: dict[str, Any] | None = None,
) -> Dataset: ...
@overload
def ones_like(
other: Variable,
dtype: DTypeLikeSave | None = None,
*,
chunks: T_Chunks = None,
chunked_array_type: str | None = None,
from_array_kwargs: dict[str, Any] | None = None,
) -> Variable: ...
@overload
def ones_like(
other: Dataset | DataArray,
dtype: DTypeMaybeMapping | None = None,
*,
chunks: T_Chunks = None,
chunked_array_type: str | None = None,
from_array_kwargs: dict[str, Any] | None = None,
) -> Dataset | DataArray: ...
@overload
def ones_like(
other: Dataset | DataArray | Variable,
dtype: DTypeMaybeMapping | None = None,
*,
chunks: T_Chunks = None,
chunked_array_type: str | None = None,
from_array_kwargs: dict[str, Any] | None = None,
) -> Dataset | DataArray | Variable: ...
[docs]
def ones_like(
other: Dataset | DataArray | Variable,
dtype: DTypeMaybeMapping | None = None,
*,
chunks: T_Chunks = None,
chunked_array_type: str | None = None,
from_array_kwargs: dict[str, Any] | None = None,
) -> Dataset | DataArray | Variable:
"""Return a new object of ones with the same shape and
type as a given dataarray or dataset.
Parameters
----------
other : DataArray, Dataset, or Variable
The reference object. The output will have the same dimensions and coordinates as this object.
dtype : dtype, optional
dtype of the new array. If omitted, it defaults to other.dtype.
chunks : int, "auto", tuple of int or mapping of Hashable to int, optional
Chunk sizes along each dimension, e.g., ``5``, ``"auto"``, ``(5, 5)`` or
``{"x": 5, "y": 5}``.
chunked_array_type: str, optional
Which chunked array type to coerce the underlying data array to.
Defaults to 'dask' if installed, else whatever is registered via the `ChunkManagerEnetryPoint` system.
Experimental API that should not be relied upon.
from_array_kwargs: dict, optional
Additional keyword arguments passed on to the `ChunkManagerEntrypoint.from_array` method used to create
chunked arrays, via whichever chunk manager is specified through the `chunked_array_type` kwarg.
For example, with dask as the default chunked array type, this method would pass additional kwargs
to :py:func:`dask.array.from_array`. Experimental API that should not be relied upon.
Returns
-------
out : same as object
New object of ones with the same shape and type as other.
Examples
--------
>>> x = xr.DataArray(
... np.arange(6).reshape(2, 3),
... dims=["lat", "lon"],
... coords={"lat": [1, 2], "lon": [0, 1, 2]},
... )
>>> x
<xarray.DataArray (lat: 2, lon: 3)> Size: 48B
array([[0, 1, 2],
[3, 4, 5]])
Coordinates:
* lat (lat) int64 16B 1 2
* lon (lon) int64 24B 0 1 2
>>> xr.ones_like(x)
<xarray.DataArray (lat: 2, lon: 3)> Size: 48B
array([[1, 1, 1],
[1, 1, 1]])
Coordinates:
* lat (lat) int64 16B 1 2
* lon (lon) int64 24B 0 1 2
See Also
--------
zeros_like
full_like
"""
return full_like(
other,
1,
dtype,
chunks=chunks,
chunked_array_type=chunked_array_type,
from_array_kwargs=from_array_kwargs,
)
def get_chunksizes(
variables: Iterable[Variable],
) -> Mapping[Any, tuple[int, ...]]:
chunks: dict[Any, tuple[int, ...]] = {}
for v in variables:
if hasattr(v._data, "chunks"):
for dim, c in v.chunksizes.items():
if dim in chunks and c != chunks[dim]:
raise ValueError(
f"Object has inconsistent chunks along dimension {dim}. "
"This can be fixed by calling unify_chunks()."
)
chunks[dim] = c
return Frozen(chunks)
def is_np_datetime_like(dtype: DTypeLike) -> bool:
"""Check if a dtype is a subclass of the numpy datetime types"""
return np.issubdtype(dtype, np.datetime64) or np.issubdtype(dtype, np.timedelta64)
def is_np_timedelta_like(dtype: DTypeLike) -> bool:
"""Check whether dtype is of the timedelta64 dtype."""
return np.issubdtype(dtype, np.timedelta64)
def _contains_cftime_datetimes(array: Any) -> bool:
"""Check if a array inside a Variable contains cftime.datetime objects"""
if cftime is None:
return False
if array.dtype == np.dtype("O") and array.size > 0:
first_idx = (0,) * array.ndim
if isinstance(array, ExplicitlyIndexed):
first_idx = BasicIndexer(first_idx)
sample = array[first_idx]
return isinstance(np.asarray(sample).item(), cftime.datetime)
return False
def contains_cftime_datetimes(var: T_Variable) -> bool:
"""Check if an xarray.Variable contains cftime.datetime objects"""
return _contains_cftime_datetimes(var._data)
def _contains_datetime_like_objects(var: T_Variable) -> bool:
"""Check if a variable contains datetime like objects (either
np.datetime64, np.timedelta64, or cftime.datetime)
"""
return is_np_datetime_like(var.dtype) or contains_cftime_datetimes(var)
