from __future__ import annotations
import warnings
from contextlib import suppress
from html import escape
from textwrap import dedent
from typing import (
TYPE_CHECKING,
Any,
Callable,
Dict,
Hashable,
Iterable,
Iterator,
List,
Mapping,
Optional,
Tuple,
TypeVar,
Union,
overload,
)
import numpy as np
import pandas as pd
from . import dtypes, duck_array_ops, formatting, formatting_html, ops
from .npcompat import DTypeLike
from .options import OPTIONS, _get_keep_attrs
from .pycompat import is_duck_dask_array
from .rolling_exp import RollingExp
from .utils import Frozen, either_dict_or_kwargs, is_scalar
try:
import cftime
except ImportError:
cftime = None
# Used as a sentinel value to indicate a all dimensions
ALL_DIMS = ...
if TYPE_CHECKING:
from .dataarray import DataArray
from .dataset import Dataset
from .types import T_DataWithCoords, T_Xarray
from .variable import Variable
from .weighted import Weighted
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, dim, axis, skipna=skipna, **kwargs)
else:
def wrapped_func(self, dim=None, axis=None, **kwargs): # type: ignore[misc]
return self.reduce(func, dim, 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, dim, skipna=skipna, numeric_only=numeric_only, **kwargs
)
else:
def wrapped_func(self, dim=None, **kwargs): # type: ignore[misc]
return self.reduce(func, 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) -> 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"{escape(repr(self))}"
return formatting_html.array_repr(self)
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()
def get_axis_num(
self, dim: Union[Hashable, Iterable[Hashable]]
) -> Union[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 isinstance(dim, Iterable) and not isinstance(dim, str):
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:
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(
"{!r} object has no attribute {!r}".format(type(self).__name__, name)
)
# 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) != "{!r} object has no attribute {!r}".format(
type(self).__name__, name
):
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 = set(
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 = set(
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: Union[Hashable, Iterable[Hashable], None] = None,
axis: Union[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: Optional[Callable[[], None]]
__slots__ = ("_close",)
def squeeze(
self,
dim: Union[Hashable, Iterable[Hashable], None] = None,
drop: bool = False,
axis: Union[int, Iterable[int], None] = None,
):
"""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, optional
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=None, max=None, *, keep_attrs: bool = None):
"""
Return an array whose values are limited to ``[min, max]``.
At least one of max or min must be given.
Refer to `numpy.clip` for full documentation.
See Also
--------
numpy.clip : equivalent function
"""
from .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.xindexes[key].to_pandas_index()
except KeyError:
return pd.Index(range(self.sizes[key]), name=key)
def _calc_assign_results(
self: C, kwargs: Mapping[Any, Union[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=None, **coords_kwargs):
"""Assign new coordinates to this object.
Returns a new object with all the original data in addition to the new
coordinates.
Parameters
----------
coords : dict, optional
A dict where the keys are the names of the coordinates
with the new values to assign. If the values are callable, they are
computed on this object and assigned to new coordinate variables.
If the values are not callable, (e.g. a ``DataArray``, scalar, or
array), they are simply assigned. A new 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 longitude coordinates from 0-359 to -180-179:
>>> da = xr.DataArray(
... np.random.rand(4),
... coords=[np.array([358, 359, 0, 1])],
... dims="lon",
... )
>>> da
array([0.5488135 , 0.71518937, 0.60276338, 0.54488318])
Coordinates:
* lon (lon) int64 358 359 0 1
>>> da.assign_coords(lon=(((da.lon + 180) % 360) - 180))
array([0.5488135 , 0.71518937, 0.60276338, 0.54488318])
Coordinates:
* lon (lon) int64 -2 -1 0 1
The function also accepts dictionary arguments:
>>> da.assign_coords({"lon": (((da.lon + 180) % 360) - 180)})
array([0.5488135 , 0.71518937, 0.60276338, 0.54488318])
Coordinates:
* lon (lon) int64 -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))
array([0.5488135 , 0.71518937, 0.60276338, 0.54488318])
Coordinates:
* lon (lon) int64 358 359 0 1
lon_2 (lon) int64 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)})
Notes
-----
Since ``coords_kwargs`` is a dictionary, the order of your arguments
may not be preserved, and so the order of the new variables is not well
defined. Assigning multiple variables within the same ``assign_coords``
is possible, but you cannot reference other variables created within
the same ``assign_coords`` call.
See Also
--------
Dataset.assign
Dataset.swap_dims
"""
coords_kwargs = either_dict_or_kwargs(coords, coords_kwargs, "assign_coords")
data = self.copy(deep=False)
results = self._calc_assign_results(coords_kwargs)
data.coords.update(results)
return data
def assign_attrs(self, *args, **kwargs):
"""Assign new attrs to this object.
Returns a new object equivalent to ``self.attrs.update(*args, **kwargs)``.
Parameters
----------
*args
positional arguments passed into ``attrs.update``.
**kwargs
keyword arguments passed into ``attrs.update``.
Returns
-------
assigned : same type as caller
A new object with the new attrs in addition to the existing data.
See Also
--------
Dataset.assign
"""
out = self.copy(deep=False)
out.attrs.update(*args, **kwargs)
return out
def pipe(
self,
func: Union[Callable[..., T], Tuple[Callable[..., T], str]],
*args,
**kwargs,
) -> 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
Dimensions: (lat: 2, lon: 2)
Coordinates:
* lat (lat) int64 10 20
* lon (lon) int64 150 160
Data variables:
temperature_c (lat, lon) float64 10.98 14.3 12.06 10.9
precipitation (lat, lon) float64 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)
Dimensions: (lat: 2, lon: 2)
Coordinates:
* lat (lat) int64 10 20
* lon (lon) int64 150 160
Data variables:
temperature_c (lat, lon) float64 12.98 16.3 14.06 12.9
precipitation (lat, lon) float64 2.424 2.646 2.438 2.892
>>> x.pipe(adder, arg=2)
Dimensions: (lat: 2, lon: 2)
Coordinates:
* lat (lat) int64 10 20
* lon (lon) int64 150 160
Data variables:
temperature_c (lat, lon) float64 12.98 16.3 14.06 12.9
precipitation (lat, lon) float64 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)
... )
Dimensions: (lat: 2, lon: 2)
Coordinates:
* lat (lat) int64 10 20
* lon (lon) int64 150 160
Data variables:
temperature_c (lat, lon) float64 10.98 14.3 12.06 10.9
precipitation (lat, lon) float64 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 groupby(self, group, squeeze: bool = True, restore_coord_dims: bool = None):
"""Returns a GroupBy object for performing grouped operations.
Parameters
----------
group : str, DataArray or IndexVariable
Array whose unique values should be used to group this array. If a
string, must be the name of a variable contained in this dataset.
squeeze : bool, optional
If "group" is a dimension of any arrays in this dataset, `squeeze`
controls whether the subarrays have a dimension of length 1 along
that dimension or if the dimension is squeezed out.
restore_coord_dims : bool, optional
If True, also restore the dimension order of multi-dimensional
coordinates.
Returns
-------
grouped
A `GroupBy` object patterned after `pandas.GroupBy` that can be
iterated over in the form of `(unique_value, grouped_array)` pairs.
Examples
--------
Calculate daily anomalies for daily data:
>>> da = xr.DataArray(
... np.linspace(0, 1826, num=1827),
... coords=[pd.date_range("1/1/2000", "31/12/2004", freq="D")],
... dims="time",
... )
>>> da
array([0.000e+00, 1.000e+00, 2.000e+00, ..., 1.824e+03, 1.825e+03,
1.826e+03])
Coordinates:
* time (time) datetime64[ns] 2000-01-01 2000-01-02 ... 2004-12-31
>>> da.groupby("time.dayofyear") - da.groupby("time.dayofyear").mean("time")
array([-730.8, -730.8, -730.8, ..., 730.2, 730.2, 730.5])
Coordinates:
* time (time) datetime64[ns] 2000-01-01 2000-01-02 ... 2004-12-31
dayofyear (time) int64 1 2 3 4 5 6 7 8 ... 359 360 361 362 363 364 365 366
See Also
--------
core.groupby.DataArrayGroupBy
core.groupby.DatasetGroupBy
"""
# While we don't generally check the type of every arg, passing
# multiple dimensions as multiple arguments is common enough, and the
# consequences hidden enough (strings evaluate as true) to warrant
# checking here.
# A future version could make squeeze kwarg only, but would face
# backward-compat issues.
if not isinstance(squeeze, bool):
raise TypeError(
f"`squeeze` must be True or False, but {squeeze} was supplied"
)
return self._groupby_cls(
self, group, squeeze=squeeze, restore_coord_dims=restore_coord_dims
)
def groupby_bins(
self,
group,
bins,
right: bool = True,
labels=None,
precision: int = 3,
include_lowest: bool = False,
squeeze: bool = True,
restore_coord_dims: bool = None,
):
"""Returns a GroupBy object for performing grouped operations.
Rather than using all unique values of `group`, the values are discretized
first by applying `pandas.cut` [1]_ to `group`.
Parameters
----------
group : str, DataArray or IndexVariable
Array whose binned values should be used to group this array. If a
string, must be the name of a variable contained in this dataset.
bins : int or array-like
If bins is an int, it defines the number of equal-width bins in the
range of x. However, in this case, the range of x is extended by .1%
on each side to include the min or max values of x. If bins is a
sequence it defines the bin edges allowing for non-uniform bin
width. No extension of the range of x is done in this case.
right : bool, default: True
Indicates whether the bins include the rightmost edge or not. If
right == True (the default), then the bins [1,2,3,4] indicate
(1,2], (2,3], (3,4].
labels : array-like or bool, default: None
Used as labels for the resulting bins. Must be of the same length as
the resulting bins. If False, string bin labels are assigned by
`pandas.cut`.
precision : int
The precision at which to store and display the bins labels.
include_lowest : bool
Whether the first interval should be left-inclusive or not.
squeeze : bool, default: True
If "group" is a dimension of any arrays in this dataset, `squeeze`
controls whether the subarrays have a dimension of length 1 along
that dimension or if the dimension is squeezed out.
restore_coord_dims : bool, optional
If True, also restore the dimension order of multi-dimensional
coordinates.
Returns
-------
grouped
A `GroupBy` object patterned after `pandas.GroupBy` that can be
iterated over in the form of `(unique_value, grouped_array)` pairs.
The name of the group has the added suffix `_bins` in order to
distinguish it from the original variable.
References
----------
.. [1] http://pandas.pydata.org/pandas-docs/stable/generated/pandas.cut.html
"""
return self._groupby_cls(
self,
group,
squeeze=squeeze,
bins=bins,
restore_coord_dims=restore_coord_dims,
cut_kwargs={
"right": right,
"labels": labels,
"precision": precision,
"include_lowest": include_lowest,
},
)
def weighted(self: T_DataWithCoords, weights: "DataArray") -> Weighted[T_Xarray]:
"""
Weighted operations.
Parameters
----------
weights : DataArray
An array of weights associated with the values in this Dataset.
Each value in the data contributes to the reduction operation
according to its associated weight.
Notes
-----
``weights`` must be a DataArray and cannot contain missing values.
Missing values can be replaced by ``weights.fillna(0)``.
"""
return self._weighted_cls(self, weights)
def rolling(
self,
dim: Mapping[Any, int] = None,
min_periods: int = None,
center: Union[bool, Mapping[Any, bool]] = False,
**window_kwargs: int,
):
"""
Rolling window object.
Parameters
----------
dim : dict, optional
Mapping from the dimension name to create the rolling iterator
along (e.g. `time`) to its moving window size.
min_periods : int, default: None
Minimum number of observations in window required to have a value
(otherwise result is NA). The default, None, is equivalent to
setting min_periods equal to the size of the window.
center : bool or mapping, default: False
Set the labels at the center of the window.
**window_kwargs : optional
The keyword arguments form of ``dim``.
One of dim or window_kwargs must be provided.
Returns
-------
core.rolling.DataArrayRolling or core.rolling.DatasetRolling
A rolling object (``DataArrayRolling`` for ``DataArray``,
``DatasetRolling`` for ``Dataset``)
Examples
--------
Create rolling seasonal average of monthly data e.g. DJF, JFM, ..., SON:
>>> da = xr.DataArray(
... np.linspace(0, 11, num=12),
... coords=[
... pd.date_range(
... "15/12/1999",
... periods=12,
... freq=pd.DateOffset(months=1),
... )
... ],
... dims="time",
... )
>>> da
array([ 0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11.])
Coordinates:
* time (time) datetime64[ns] 1999-12-15 2000-01-15 ... 2000-11-15
>>> da.rolling(time=3, center=True).mean()
array([nan, 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., nan])
Coordinates:
* time (time) datetime64[ns] 1999-12-15 2000-01-15 ... 2000-11-15
Remove the NaNs using ``dropna()``:
>>> da.rolling(time=3, center=True).mean().dropna("time")
array([ 1., 2., 3., 4., 5., 6., 7., 8., 9., 10.])
Coordinates:
* time (time) datetime64[ns] 2000-01-15 2000-02-15 ... 2000-10-15
See Also
--------
core.rolling.DataArrayRolling
core.rolling.DatasetRolling
"""
dim = either_dict_or_kwargs(dim, window_kwargs, "rolling")
return self._rolling_cls(self, dim, min_periods=min_periods, center=center)
def rolling_exp(
self,
window: Mapping[Any, int] = None,
window_type: str = "span",
**window_kwargs,
):
"""
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
"""
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 RollingExp(self, window, window_type)
def coarsen(
self,
dim: Mapping[Any, int] = None,
boundary: str = "exact",
side: Union[str, Mapping[Any, str]] = "left",
coord_func: str = "mean",
**window_kwargs: int,
):
"""
Coarsen object.
Parameters
----------
dim : mapping of hashable to int, optional
Mapping from the dimension name to the window size.
boundary : {"exact", "trim", "pad"}, default: "exact"
If 'exact', a ValueError will be raised if dimension size is not a
multiple of the window size. If 'trim', the excess entries are
dropped. If 'pad', NA will be padded.
side : {"left", "right"} or mapping of str to {"left", "right"}
coord_func : str or mapping of hashable to str, default: "mean"
function (name) that is applied to the coordinates,
or a mapping from coordinate name to function (name).
Returns
-------
core.rolling.DataArrayCoarsen or core.rolling.DatasetCoarsen
A coarsen object (``DataArrayCoarsen`` for ``DataArray``,
``DatasetCoarsen`` for ``Dataset``)
Examples
--------
Coarsen the long time series by averaging over every four days.
>>> da = xr.DataArray(
... np.linspace(0, 364, num=364),
... dims="time",
... coords={"time": pd.date_range("15/12/1999", periods=364)},
... )
>>> da # +doctest: ELLIPSIS
array([ 0. , 1.00275482, 2.00550964, 3.00826446,
4.01101928, 5.0137741 , 6.01652893, 7.01928375,
8.02203857, 9.02479339, 10.02754821, 11.03030303,
...
356.98071625, 357.98347107, 358.9862259 , 359.98898072,
360.99173554, 361.99449036, 362.99724518, 364. ])
Coordinates:
* time (time) datetime64[ns] 1999-12-15 1999-12-16 ... 2000-12-12
>>> da.coarsen(time=3, boundary="trim").mean() # +doctest: ELLIPSIS
array([ 1.00275482, 4.01101928, 7.01928375, 10.02754821,
13.03581267, 16.04407713, 19.0523416 , 22.06060606,
25.06887052, 28.07713499, 31.08539945, 34.09366391,
...
349.96143251, 352.96969697, 355.97796143, 358.9862259 ,
361.99449036])
Coordinates:
* time (time) datetime64[ns] 1999-12-16 1999-12-19 ... 2000-12-10
>>>
See Also
--------
core.rolling.DataArrayCoarsen
core.rolling.DatasetCoarsen
"""
dim = either_dict_or_kwargs(dim, window_kwargs, "coarsen")
return self._coarsen_cls(
self,
dim,
boundary=boundary,
side=side,
coord_func=coord_func,
)
def resample(
self,
indexer: Mapping[Any, str] = None,
skipna=None,
closed: str = None,
label: str = None,
base: int = 0,
keep_attrs: bool = None,
loffset=None,
restore_coord_dims: bool = None,
**indexer_kwargs: str,
):
"""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.
loffset : timedelta or str, optional
Offset used to adjust the resampled time labels. Some pandas date
offset strings are supported.
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(
... "15/12/1999",
... periods=12,
... freq=pd.DateOffset(months=1),
... )
... ],
... dims="time",
... )
>>> da
array([ 0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11.])
Coordinates:
* time (time) datetime64[ns] 1999-12-15 2000-01-15 ... 2000-11-15
>>> da.resample(time="QS-DEC").mean()
array([ 1., 4., 7., 10.])
Coordinates:
* time (time) datetime64[ns] 1999-12-01 2000-03-01 2000-06-01 2000-09-01
Upsample monthly time-series data to daily data:
>>> da.resample(time="1D").interpolate("linear") # +doctest: ELLIPSIS
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 ,
...
10.80645161, 10.83870968, 10.87096774, 10.90322581, 10.93548387,
10.96774194, 11. ])
Coordinates:
* time (time) datetime64[ns] 1999-12-15 1999-12-16 ... 2000-11-15
Limit scope of upsampling method
>>> da.resample(time="1D").nearest(tolerance="1D")
array([ 0., 0., nan, ..., nan, 11., 11.])
Coordinates:
* time (time) datetime64[ns] 1999-12-15 1999-12-16 ... 2000-11-15
See Also
--------
pandas.Series.resample
pandas.DataFrame.resample
References
----------
.. [1] http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases
"""
# TODO support non-string indexer after removing the old API.
from ..coding.cftimeindex import CFTimeIndex
from .dataarray import DataArray
from .resample import RESAMPLE_DIM
if keep_attrs is not None:
warnings.warn(
"Passing ``keep_attrs`` to ``resample`` has no effect and will raise an"
" error in xarray 0.20. Pass ``keep_attrs`` directly to the applied"
" function, e.g. ``resample(...).mean(keep_attrs=True)``."
)
# 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 = dim
dim_coord = self[dim]
# TODO: remove once pandas=1.1 is the minimum required version
with warnings.catch_warnings():
warnings.filterwarnings(
"ignore",
r"'(base|loffset)' in .resample\(\) and in Grouper\(\) is deprecated.",
category=FutureWarning,
)
# TODO (benbovy - flexible indexes): update when CFTimeIndex is an xarray Index subclass
if isinstance(self.xindexes[dim_name].to_pandas_index(), CFTimeIndex):
from .resample_cftime import CFTimeGrouper
grouper = CFTimeGrouper(freq, closed, label, base, loffset)
else:
grouper = pd.Grouper(
freq=freq, closed=closed, label=label, base=base, loffset=loffset
)
group = DataArray(
dim_coord, coords=dim_coord.coords, dims=dim_coord.dims, name=RESAMPLE_DIM
)
resampler = self._resample_cls(
self,
group=group,
dim=dim_name,
grouper=grouper,
resample_dim=RESAMPLE_DIM,
restore_coord_dims=restore_coord_dims,
)
return resampler
def where(self, cond, other=dtypes.NA, drop: bool = False):
"""Filter elements from this object according to a condition.
This operation follows the normal broadcasting and alignment rules that
xarray uses for binary arithmetic.
Parameters
----------
cond : DataArray, Dataset, or callable
Locations at which to preserve this object's values. dtype must be `bool`.
If a callable, it must expect this object as its only parameter.
other : scalar, DataArray or Dataset, optional
Value to use for locations in this object where ``cond`` is False.
By default, these locations filled with NA.
drop : bool, optional
If True, coordinate labels that only correspond to False values of
the condition are dropped from the result. Mutually exclusive with
``other``.
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
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)
array([[ 0., 1., 2., 3., nan],
[ 5., 6., 7., nan, nan],
[10., 11., nan, nan, nan],
[15., nan, nan, nan, nan],
[nan, nan, nan, nan, nan]])
Dimensions without coordinates: x, y
>>> a.where(a.x + a.y < 5, -1)
array([[ 0, 1, 2, 3, 4],
[ 5, 6, 7, 8, -1],
[10, 11, 12, -1, -1],
[15, 16, -1, -1, -1],
[20, -1, -1, -1, -1]])
Dimensions without coordinates: x, y
>>> a.where(a.x + a.y < 4, drop=True)
array([[ 0., 1., 2., 3.],
[ 5., 6., 7., nan],
[10., 11., nan, nan],
[15., nan, nan, nan]])
Dimensions without coordinates: x, y
>>> a.where(lambda x: x.x + x.y < 4, drop=True)
array([[ 0., 1., 2., 3.],
[ 5., 6., 7., nan],
[10., 11., nan, nan],
[15., nan, nan, nan]])
Dimensions without coordinates: x, y
See Also
--------
numpy.where : corresponding numpy function
where : equivalent function
"""
from .alignment import align
from .dataarray import DataArray
from .dataset import Dataset
if callable(cond):
cond = cond(self)
if drop:
if other is not dtypes.NA:
raise ValueError("cannot set `other` if drop=True")
if not isinstance(cond, (Dataset, DataArray)):
raise TypeError(
f"cond argument is {cond!r} but must be a {Dataset!r} or {DataArray!r}"
)
# align so we can use integer indexing
self, cond = align(self, cond)
# get cond with the minimal size needed for the Dataset
if isinstance(cond, Dataset):
clipcond = cond.to_array().any("variable")
else:
clipcond = cond
# clip the data corresponding to coordinate dims that are not used
nonzeros = zip(clipcond.dims, np.nonzero(clipcond.values))
indexers = {k: np.unique(v) for k, v in nonzeros}
self = self.isel(**indexers)
cond = cond.isel(**indexers)
return ops.where_method(self, cond, other)
def set_close(self, close: Optional[Callable[[], 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: Any) -> 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):
"""Test each value in the array for whether it is a missing value.
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
array([ 1., nan, 3.])
Dimensions without coordinates: x
>>> array.isnull()
array([False, True, False])
Dimensions without coordinates: x
"""
from .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):
"""Test each value in the array for whether it is not a missing value.
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
array([ 1., nan, 3.])
Dimensions without coordinates: x
>>> array.notnull()
array([ True, False, True])
Dimensions without coordinates: x
"""
from .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):
"""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])
array([ True, False, True])
Dimensions without coordinates: x
See Also
--------
numpy.isin
"""
from .computation import apply_ufunc
from .dataarray import DataArray
from .dataset import Dataset
from .variable import Variable
if isinstance(test_elements, Dataset):
raise TypeError(
"isin() argument must be convertible to an array: {}".format(
test_elements
)
)
elif isinstance(test_elements, (Variable, DataArray)):
# need to explicitly pull out data to support dask arrays as the
# second argument
test_elements = test_elements.data
return apply_ufunc(
duck_array_ops.isin,
self,
kwargs=dict(test_elements=test_elements),
dask="allowed",
)
def astype(
self: T,
dtype,
*,
order=None,
casting=None,
subok=None,
copy=None,
keep_attrs=True,
) -> T:
"""
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 .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: T) -> T:
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: "Dataset",
fill_value,
dtype: Union[DTypeLike, Mapping[Any, DTypeLike]] = None,
) -> "Dataset":
...
@overload
def full_like(other: "DataArray", fill_value, dtype: DTypeLike = None) -> "DataArray":
...
@overload
def full_like(other: "Variable", fill_value, dtype: DTypeLike = None) -> "Variable":
...
def full_like(other, fill_value, dtype=None):
"""Return a new object with the same shape and type as a given object.
Parameters
----------
other : DataArray, Dataset or Variable
The reference object in input
fill_value : scalar 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.
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
array([[0, 1, 2],
[3, 4, 5]])
Coordinates:
* lat (lat) int64 1 2
* lon (lon) int64 0 1 2
>>> xr.full_like(x, 1)
array([[1, 1, 1],
[1, 1, 1]])
Coordinates:
* lat (lat) int64 1 2
* lon (lon) int64 0 1 2
>>> xr.full_like(x, 0.5)
array([[0, 0, 0],
[0, 0, 0]])
Coordinates:
* lat (lat) int64 1 2
* lon (lon) int64 0 1 2
>>> xr.full_like(x, 0.5, dtype=np.double)
array([[0.5, 0.5, 0.5],
[0.5, 0.5, 0.5]])
Coordinates:
* lat (lat) int64 1 2
* lon (lon) int64 0 1 2
>>> xr.full_like(x, np.nan, dtype=np.double)
array([[nan, nan, nan],
[nan, nan, nan]])
Coordinates:
* lat (lat) int64 1 2
* lon (lon) int64 0 1 2
>>> ds = xr.Dataset(
... {"a": ("x", [3, 5, 2]), "b": ("x", [9, 1, 0])}, coords={"x": [2, 4, 6]}
... )
>>> ds
Dimensions: (x: 3)
Coordinates:
* x (x) int64 2 4 6
Data variables:
a (x) int64 3 5 2
b (x) int64 9 1 0
>>> xr.full_like(ds, fill_value={"a": 1, "b": 2})
Dimensions: (x: 3)
Coordinates:
* x (x) int64 2 4 6
Data variables:
a (x) int64 1 1 1
b (x) int64 2 2 2
>>> xr.full_like(ds, fill_value={"a": 1, "b": 2}, dtype={"a": bool, "b": float})
Dimensions: (x: 3)
Coordinates:
* x (x) int64 2 4 6
Data variables:
a (x) bool True True True
b (x) float64 2.0 2.0 2.0
See Also
--------
zeros_like
ones_like
"""
from .dataarray import DataArray
from .dataset import Dataset
from .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 not isinstance(other, Dataset) and isinstance(dtype, Mapping):
raise ValueError(
"'dtype' cannot be dict-like when passing a DataArray or Variable"
)
if isinstance(other, Dataset):
if not isinstance(fill_value, dict):
fill_value = {k: fill_value for k in other.data_vars.keys()}
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, fill_value.get(k, dtypes.NA), dtype_.get(k, None))
for k, v in other.data_vars.items()
}
return Dataset(data_vars, coords=other.coords, attrs=other.attrs)
elif isinstance(other, DataArray):
return DataArray(
_full_like_variable(other.variable, fill_value, dtype),
dims=other.dims,
coords=other.coords,
attrs=other.attrs,
name=other.name,
)
elif isinstance(other, Variable):
return _full_like_variable(other, fill_value, dtype)
else:
raise TypeError("Expected DataArray, Dataset, or Variable")
def _full_like_variable(other, fill_value, dtype: DTypeLike = None):
"""Inner function of full_like, where other must be a variable"""
from .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_duck_dask_array(other.data):
import dask.array
if dtype is None:
dtype = other.dtype
data = dask.array.full(
other.shape, fill_value, dtype=dtype, chunks=other.data.chunks
)
else:
data = np.full_like(other.data, fill_value, dtype=dtype)
return Variable(dims=other.dims, data=data, attrs=other.attrs)
def zeros_like(other, dtype: DTypeLike = None):
"""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.
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
array([[0, 1, 2],
[3, 4, 5]])
Coordinates:
* lat (lat) int64 1 2
* lon (lon) int64 0 1 2
>>> xr.zeros_like(x)
array([[0, 0, 0],
[0, 0, 0]])
Coordinates:
* lat (lat) int64 1 2
* lon (lon) int64 0 1 2
>>> xr.zeros_like(x, dtype=float)
array([[0., 0., 0.],
[0., 0., 0.]])
Coordinates:
* lat (lat) int64 1 2
* lon (lon) int64 0 1 2
See Also
--------
ones_like
full_like
"""
return full_like(other, 0, dtype)
def ones_like(other, dtype: DTypeLike = None):
"""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.
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
array([[0, 1, 2],
[3, 4, 5]])
Coordinates:
* lat (lat) int64 1 2
* lon (lon) int64 0 1 2
>>> xr.ones_like(x)
array([[1, 1, 1],
[1, 1, 1]])
Coordinates:
* lat (lat) int64 1 2
* lon (lon) int64 0 1 2
See Also
--------
zeros_like
full_like
"""
return full_like(other, 1, dtype)
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) -> bool:
"""Check if an array contains cftime.datetime objects"""
if cftime is None:
return False
else:
if array.dtype == np.dtype("O") and array.size > 0:
sample = array.ravel()[0]
if is_duck_dask_array(sample):
sample = sample.compute()
if isinstance(sample, np.ndarray):
sample = sample.item()
return isinstance(sample, cftime.datetime)
else:
return False
def contains_cftime_datetimes(var) -> bool:
"""Check if an xarray.Variable contains cftime.datetime objects"""
return _contains_cftime_datetimes(var.data)
def _contains_datetime_like_objects(var) -> 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)