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pydata/xarray | xarray/core/utils.py | update_safety_check | def update_safety_check(first_dict: MutableMapping[K, V],
second_dict: Mapping[K, V],
compat: Callable[[V, V], bool] = equivalent) -> None:
"""Check the safety of updating one dictionary with another.
Raises ValueError if dictionaries have non-compatible values f... | python | def update_safety_check(first_dict: MutableMapping[K, V],
second_dict: Mapping[K, V],
compat: Callable[[V, V], bool] = equivalent) -> None:
"""Check the safety of updating one dictionary with another.
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pydata/xarray | xarray/core/utils.py | remove_incompatible_items | def remove_incompatible_items(first_dict: MutableMapping[K, V],
second_dict: Mapping[K, V],
compat: Callable[[V, V], bool] = equivalent
) -> None:
"""Remove incompatible items from the first dictionary in-place.
Items are... | python | def remove_incompatible_items(first_dict: MutableMapping[K, V],
second_dict: Mapping[K, V],
compat: Callable[[V, V], bool] = equivalent
) -> None:
"""Remove incompatible items from the first dictionary in-place.
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pydata/xarray | xarray/core/utils.py | is_scalar | def is_scalar(value: Any) -> bool:
"""Whether to treat a value as a scalar.
Any non-iterable, string, or 0-D array
"""
return (
getattr(value, 'ndim', None) == 0 or
isinstance(value, (str, bytes)) or not
isinstance(value, (Iterable, ) + dask_array_type)) | python | def is_scalar(value: Any) -> bool:
"""Whether to treat a value as a scalar.
Any non-iterable, string, or 0-D array
"""
return (
getattr(value, 'ndim', None) == 0 or
isinstance(value, (str, bytes)) or not
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pydata/xarray | xarray/core/utils.py | to_0d_object_array | def to_0d_object_array(value: Any) -> np.ndarray:
"""Given a value, wrap it in a 0-D numpy.ndarray with dtype=object.
"""
result = np.empty((), dtype=object)
result[()] = value
return result | python | def to_0d_object_array(value: Any) -> np.ndarray:
"""Given a value, wrap it in a 0-D numpy.ndarray with dtype=object.
"""
result = np.empty((), dtype=object)
result[()] = value
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pydata/xarray | xarray/core/utils.py | to_0d_array | def to_0d_array(value: Any) -> np.ndarray:
"""Given a value, wrap it in a 0-D numpy.ndarray.
"""
if np.isscalar(value) or (isinstance(value, np.ndarray) and
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return np.array(value)
else:
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"""Given a value, wrap it in a 0-D numpy.ndarray.
"""
if np.isscalar(value) or (isinstance(value, np.ndarray) and
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return np.array(value)
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pydata/xarray | xarray/core/utils.py | dict_equiv | def dict_equiv(first: Mapping[K, V], second: Mapping[K, V],
compat: Callable[[V, V], bool] = equivalent) -> bool:
"""Test equivalence of two dict-like objects. If any of the values are
numpy arrays, compare them correctly.
Parameters
----------
first, second : dict-like
Dicti... | python | def dict_equiv(first: Mapping[K, V], second: Mapping[K, V],
compat: Callable[[V, V], bool] = equivalent) -> bool:
"""Test equivalence of two dict-like objects. If any of the values are
numpy arrays, compare them correctly.
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----------
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pydata/xarray | xarray/core/utils.py | ordered_dict_intersection | def ordered_dict_intersection(first_dict: Mapping[K, V],
second_dict: Mapping[K, V],
compat: Callable[[V, V], bool] = equivalent
) -> MutableMapping[K, V]:
"""Return the intersection of two dictionaries as a new OrderedDict.
... | python | def ordered_dict_intersection(first_dict: Mapping[K, V],
second_dict: Mapping[K, V],
compat: Callable[[V, V], bool] = equivalent
) -> MutableMapping[K, V]:
"""Return the intersection of two dictionaries as a new OrderedDict.
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pydata/xarray | xarray/core/utils.py | is_uniform_spaced | def is_uniform_spaced(arr, **kwargs) -> bool:
"""Return True if values of an array are uniformly spaced and sorted.
>>> is_uniform_spaced(range(5))
True
>>> is_uniform_spaced([-4, 0, 100])
False
kwargs are additional arguments to ``np.isclose``
"""
arr = np.array(arr, dtype=float)
... | python | def is_uniform_spaced(arr, **kwargs) -> bool:
"""Return True if values of an array are uniformly spaced and sorted.
>>> is_uniform_spaced(range(5))
True
>>> is_uniform_spaced([-4, 0, 100])
False
kwargs are additional arguments to ``np.isclose``
"""
arr = np.array(arr, dtype=float)
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pydata/xarray | xarray/core/utils.py | decode_numpy_dict_values | def decode_numpy_dict_values(attrs: Mapping[K, V]) -> Dict[K, V]:
"""Convert attribute values from numpy objects to native Python objects,
for use in to_dict
"""
attrs = dict(attrs)
for k, v in attrs.items():
if isinstance(v, np.ndarray):
attrs[k] = v.tolist()
elif isinst... | python | def decode_numpy_dict_values(attrs: Mapping[K, V]) -> Dict[K, V]:
"""Convert attribute values from numpy objects to native Python objects,
for use in to_dict
"""
attrs = dict(attrs)
for k, v in attrs.items():
if isinstance(v, np.ndarray):
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pydata/xarray | xarray/core/utils.py | ensure_us_time_resolution | def ensure_us_time_resolution(val):
"""Convert val out of numpy time, for use in to_dict.
Needed because of numpy bug GH#7619"""
if np.issubdtype(val.dtype, np.datetime64):
val = val.astype('datetime64[us]')
elif np.issubdtype(val.dtype, np.timedelta64):
val = val.astype('timedelta64[us]... | python | def ensure_us_time_resolution(val):
"""Convert val out of numpy time, for use in to_dict.
Needed because of numpy bug GH#7619"""
if np.issubdtype(val.dtype, np.datetime64):
val = val.astype('datetime64[us]')
elif np.issubdtype(val.dtype, np.timedelta64):
val = val.astype('timedelta64[us]... | [
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pydata/xarray | xarray/core/utils.py | get_temp_dimname | def get_temp_dimname(dims: Container[Hashable], new_dim: Hashable) -> Hashable:
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new_dim: ['_rolling']
-> ['_rolling... | python | def get_temp_dimname(dims: Container[Hashable], new_dim: Hashable) -> Hashable:
""" Get an new dimension name based on new_dim, that is not used in dims.
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pydata/xarray | xarray/conventions.py | _infer_dtype | def _infer_dtype(array, name=None):
"""Given an object array with no missing values, infer its dtype from its
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pydata/xarray | xarray/conventions.py | _copy_with_dtype | def _copy_with_dtype(data, dtype):
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result = np.empty(data.shape, dtype)
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pydata/xarray | xarray/conventions.py | encode_cf_variable | def encode_cf_variable(var, needs_copy=True, name=None):
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pydata/xarray | xarray/conventions.py | decode_cf_variable | def decode_cf_variable(name, var, concat_characters=True, mask_and_scale=True,
decode_times=True, decode_endianness=True,
stack_char_dim=True, use_cftime=None):
"""
Decodes a variable which may hold CF encoded information.
This includes variables that have been... | python | def decode_cf_variable(name, var, concat_characters=True, mask_and_scale=True,
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decoded. This copies the attributes from the time variable to the
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pydata/xarray | xarray/conventions.py | decode_cf_variables | def decode_cf_variables(variables, attributes, concat_characters=True,
mask_and_scale=True, decode_times=True,
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use_cftime=None):
"""
Decode several CF encoded variables.
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mask_and_scale=True, decode_times=True,
decode_coords=True, drop_variables=None,
use_cftime=None):
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pydata/xarray | xarray/conventions.py | decode_cf | def decode_cf(obj, concat_characters=True, mask_and_scale=True,
decode_times=True, decode_coords=True, drop_variables=None,
use_cftime=None):
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Parameters
----------
obj : Dataset or D... | python | def decode_cf(obj, concat_characters=True, mask_and_scale=True,
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pydata/xarray | xarray/conventions.py | cf_decoder | def cf_decoder(variables, attributes,
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decode_times=True):
"""
Decode a set of CF encoded variables and attributes.
See Also, decode_cf_variable
Parameters
----------
variables : dict
A dictionary mapping from va... | python | def cf_decoder(variables, attributes,
concat_characters=True, mask_and_scale=True,
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Decode a set of CF encoded variables and attributes.
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pydata/xarray | xarray/conventions.py | encode_dataset_coordinates | def encode_dataset_coordinates(dataset):
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Parameters
----------
dataset : Dataset
Object to encode.
Returns
-------
varia... | python | def encode_dataset_coordinates(dataset):
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When possible, this is done according to CF conventions.
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----------
dataset : Dataset
Object to encode.
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A function which takes a dicts of variables and attributes
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"""
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pydata/xarray | xarray/core/variable.py | _broadcast_compat_variables | def _broadcast_compat_variables(*variables):
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dims = tuple(_unified_dims(variables))
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dims = tuple(_unified_dims(variables))
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pydata/xarray | xarray/core/variable.py | concat | def concat(variables, dim='concat_dim', positions=None, shortcut=False):
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Parameters
----------
variables : iterable of Array
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matching dimensions and shape except for a... | python | def concat(variables, dim='concat_dim', positions=None, shortcut=False):
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Parameters
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pydata/xarray | xarray/core/variable.py | assert_unique_multiindex_level_names | def assert_unique_multiindex_level_names(variables):
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Not public API. Used for checking consistency of DataArray and Dataset
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"""
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pydata/xarray | xarray/core/variable.py | Variable.to_dict | def to_dict(self, data=True):
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item['data'] = ensure_us_time_resolution(self.values).tolist()
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pydata/xarray | xarray/core/variable.py | Variable._broadcast_indexes | def _broadcast_indexes(self, key):
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Parameters
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key: int, slice, array, dict or tuple of integer, slices and arrays
Any valid input for indexing.
Returns
-------
dims: tuple
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"""Prepare an indexing key for an indexing operation.
Parameters
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key: int, slice, array, dict or tuple of integer, slices and arrays
Any valid input for indexing.
Returns
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dims: tuple
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pydata/xarray | xarray/core/variable.py | Variable._validate_indexers | def _validate_indexers(self, key):
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pass
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if k.ndim ... | python | def _validate_indexers(self, key):
""" Make sanity checks """
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# TODO we should replace dask's native nonzero
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pydata/xarray | xarray/core/variable.py | Variable._finalize_indexing_result | def _finalize_indexing_result(self, dims, data):
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pydata/xarray | xarray/core/variable.py | Variable._getitem_with_mask | def _getitem_with_mask(self, key, fill_value=dtypes.NA):
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# TODO(shoyer): expose this method in public API somewhere (isel?) and
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# TODO(shoyer): add a sanity check that all other integers are
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pydata/xarray | xarray/core/variable.py | Variable.copy | def copy(self, deep=True, data=None):
"""Returns a copy of this object.
If `deep=True`, the data array is loaded into memory and copied onto
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orig... | python | def copy(self, deep=True, data=None):
"""Returns a copy of this object.
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"""Coerce this array's data into a dask arrays with the given chunks.
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pydata/xarray | xarray/core/variable.py | Variable.isel | def isel(self, indexers=None, drop=False, **indexers_kwargs):
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pydata/xarray | xarray/core/variable.py | Variable.squeeze | def squeeze(self, dim=None):
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dim : None or str or tuple of str, optional
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pydata/xarray | xarray/core/variable.py | Variable.shift | def shift(self, shifts=None, fill_value=dtypes.NA, **shifts_kwargs):
"""
Return a new Variable with shifted data.
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Integer offset to shift along each of the given dimensions.
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"""
Return a new Variable with shifted data.
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shifts : mapping of the form {dim: offset}
Integer offset to shift along each of the given dimensions.
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pydata/xarray | xarray/core/variable.py | Variable.pad_with_fill_value | def pad_with_fill_value(self, pad_widths=None, fill_value=dtypes.NA,
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"""
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pad_width: Mapping of the form {dim: (before, after)}
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Integer offset to roll along each of the given dimensions.
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pydata/xarray | xarray/core/variable.py | Variable.transpose | def transpose(self, *dims) -> 'Variable':
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pydata/xarray | xarray/core/variable.py | Variable.unstack | def unstack(self, dimensions=None, **dimensions_kwargs):
"""
Unstack an existing dimension into multiple new dimensions.
New dimensions will be added at the end, and the order of the data
along each new dimension will be in contiguous (C) order.
Parameters
----------
... | python | def unstack(self, dimensions=None, **dimensions_kwargs):
"""
Unstack an existing dimension into multiple new dimensions.
New dimensions will be added at the end, and the order of the data
along each new dimension will be in contiguous (C) order.
Parameters
----------
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pydata/xarray | xarray/core/variable.py | Variable.reduce | def reduce(self, func, dim=None, axis=None,
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Parameters
----------
func : function
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shortcut=False):
"""Concatenate variables along a new or existing dimension.
Parameters
----------
variables : iterable of Array
Arrays to stack together. Each variable is expected to have
... | python | def concat(cls, variables, dim='concat_dim', positions=None,
shortcut=False):
"""Concatenate variables along a new or existing dimension.
Parameters
----------
variables : iterable of Array
Arrays to stack together. Each variable is expected to have
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pydata/xarray | xarray/core/variable.py | Variable.equals | def equals(self, other, equiv=duck_array_ops.array_equiv):
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Variables can still be equal (like pandas objects) if they have NaN
values in the same locations.
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pydata/xarray | xarray/core/variable.py | Variable.broadcast_equals | def broadcast_equals(self, other, equiv=duck_array_ops.array_equiv):
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each other; otherwise False.
Variables can still be equal (like pandas objects) if they have NaN
values in the same locations.
"""
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try:
return (utils.dict_equiv(self.attrs, other.attrs) and
self.equals(other))
except (TypeError, AttributeError):
return False | python | def identical(self, other):
"""Like equals, but also checks attributes.
"""
try:
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pydata/xarray | xarray/core/variable.py | Variable.quantile | def quantile(self, q, dim=None, interpolation='linear'):
"""Compute the qth quantile of the data along the specified dimension.
Returns the qth quantiles(s) of the array elements.
Parameters
----------
q : float in range of [0,1] (or sequence of floats)
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Returns the qth quantiles(s) of the array elements.
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----------
q : float in range of [0,1] (or sequence of floats)
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pydata/xarray | xarray/core/variable.py | Variable.rank | def rank(self, dim, pct=False):
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set. Ranks begin at 1, not 0. If `pct`, computes percentage ranks.
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pydata/xarray | xarray/core/variable.py | Variable.rolling_window | def rolling_window(self, dim, window, window_dim, center=False,
fill_value=dtypes.NA):
"""
Make a rolling_window along dim and add a new_dim to the last place.
Parameters
----------
dim: str
Dimension over which to compute rolling_window
... | python | def rolling_window(self, dim, window, window_dim, center=False,
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"""
Make a rolling_window along dim and add a new_dim to the last place.
Parameters
----------
dim: str
Dimension over which to compute rolling_window
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pydata/xarray | xarray/core/variable.py | Variable.coarsen | def coarsen(self, windows, func, boundary='exact', side='left'):
"""
Apply
"""
windows = {k: v for k, v in windows.items() if k in self.dims}
if not windows:
return self.copy()
reshaped, axes = self._coarsen_reshape(windows, boundary, side)
if isinsta... | python | def coarsen(self, windows, func, boundary='exact', side='left'):
"""
Apply
"""
windows = {k: v for k, v in windows.items() if k in self.dims}
if not windows:
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pydata/xarray | xarray/core/variable.py | Variable._coarsen_reshape | def _coarsen_reshape(self, windows, boundary, side):
"""
Construct a reshaped-array for corsen
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"""
Construct a reshaped-array for corsen
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pydata/xarray | xarray/core/variable.py | Variable._to_numeric | def _to_numeric(self, offset=None, datetime_unit=None, dtype=float):
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See duck_array_ops.datetime_to_numeric
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""" A (private) method to convert datetime array to numeric dtype
See duck_array_ops.datetime_to_numeric
"""
numeric_array = duck_array_ops.datetime_to_numeric(
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pydata/xarray | xarray/core/variable.py | IndexVariable.concat | def concat(cls, variables, dim='concat_dim', positions=None,
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"""Specialized version of Variable.concat for IndexVariable objects.
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arrays, if possible.
"""
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pydata/xarray | xarray/core/variable.py | IndexVariable.copy | def copy(self, deep=True, data=None):
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pydata/xarray | xarray/core/variable.py | IndexVariable.to_index | def to_index(self):
"""Convert this variable to a pandas.Index"""
# n.b. creating a new pandas.Index from an old pandas.Index is
# basically free as pandas.Index objects are immutable
assert self.ndim == 1
index = self._data.array
if isinstance(index, pd.MultiIndex):
... | python | def to_index(self):
"""Convert this variable to a pandas.Index"""
# n.b. creating a new pandas.Index from an old pandas.Index is
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pydata/xarray | xarray/core/variable.py | IndexVariable.level_names | def level_names(self):
"""Return MultiIndex level names or None if this IndexVariable has no
MultiIndex.
"""
index = self.to_index()
if isinstance(index, pd.MultiIndex):
return index.names
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"""Return MultiIndex level names or None if this IndexVariable has no
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"""
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pydata/xarray | xarray/core/variable.py | IndexVariable.get_level_variable | def get_level_variable(self, level):
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pydata/xarray | xarray/coding/cftimeindex.py | _parsed_string_to_bounds | def _parsed_string_to_bounds(date_type, resolution, parsed):
"""Generalization of
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"""
if resolution == 'year':
return (date_type(parsed.year, 1, 1),
... | python | def _parsed_string_to_bounds(date_type, resolution, parsed):
"""Generalization of
pandas.tseries.index.DatetimeIndex._parsed_string_to_bounds
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objects.
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if resolution == 'year':
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pydata/xarray | xarray/coding/cftimeindex.py | get_date_field | def get_date_field(datetimes, field):
"""Adapted from pandas.tslib.get_date_field"""
return np.array([getattr(date, field) for date in datetimes]) | python | def get_date_field(datetimes, field):
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pydata/xarray | xarray/coding/cftimeindex.py | _field_accessor | def _field_accessor(name, docstring=None, min_cftime_version='0.0'):
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... | python | def _field_accessor(name, docstring=None, min_cftime_version='0.0'):
"""Adapted from pandas.tseries.index._field_accessor"""
def f(self, min_cftime_version=min_cftime_version):
import cftime
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pydata/xarray | xarray/coding/cftimeindex.py | _parse_array_of_cftime_strings | def _parse_array_of_cftime_strings(strings, date_type):
"""Create a numpy array from an array of strings.
For use in generating dates from strings for use with interp. Assumes the
array is either 0-dimensional or 1-dimensional.
Parameters
----------
strings : array of strings
Strings ... | python | def _parse_array_of_cftime_strings(strings, date_type):
"""Create a numpy array from an array of strings.
For use in generating dates from strings for use with interp. Assumes the
array is either 0-dimensional or 1-dimensional.
Parameters
----------
strings : array of strings
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pydata/xarray | xarray/coding/cftimeindex.py | CFTimeIndex._partial_date_slice | def _partial_date_slice(self, resolution, parsed):
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Note that when using a CFTimeIndex, if a partial-date selection
returns a single element, it will never be converted to a scalar
coordinate; this is in sligh... | python | def _partial_date_slice(self, resolution, parsed):
"""Adapted from
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pydata/xarray | xarray/coding/cftimeindex.py | CFTimeIndex._get_string_slice | def _get_string_slice(self, key):
"""Adapted from pandas.tseries.index.DatetimeIndex._get_string_slice"""
parsed, resolution = _parse_iso8601_with_reso(self.date_type, key)
try:
loc = self._partial_date_slice(resolution, parsed)
except KeyError:
raise KeyError(key... | python | def _get_string_slice(self, key):
"""Adapted from pandas.tseries.index.DatetimeIndex._get_string_slice"""
parsed, resolution = _parse_iso8601_with_reso(self.date_type, key)
try:
loc = self._partial_date_slice(resolution, parsed)
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pydata/xarray | xarray/coding/cftimeindex.py | CFTimeIndex.get_loc | def get_loc(self, key, method=None, tolerance=None):
"""Adapted from pandas.tseries.index.DatetimeIndex.get_loc"""
if isinstance(key, str):
return self._get_string_slice(key)
else:
return pd.Index.get_loc(self, key, method=method,
toler... | python | def get_loc(self, key, method=None, tolerance=None):
"""Adapted from pandas.tseries.index.DatetimeIndex.get_loc"""
if isinstance(key, str):
return self._get_string_slice(key)
else:
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pydata/xarray | xarray/coding/cftimeindex.py | CFTimeIndex._maybe_cast_slice_bound | def _maybe_cast_slice_bound(self, label, side, kind):
"""Adapted from
pandas.tseries.index.DatetimeIndex._maybe_cast_slice_bound"""
if isinstance(label, str):
parsed, resolution = _parse_iso8601_with_reso(self.date_type,
label... | python | def _maybe_cast_slice_bound(self, label, side, kind):
"""Adapted from
pandas.tseries.index.DatetimeIndex._maybe_cast_slice_bound"""
if isinstance(label, str):
parsed, resolution = _parse_iso8601_with_reso(self.date_type,
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pydata/xarray | xarray/coding/cftimeindex.py | CFTimeIndex.get_value | def get_value(self, series, key):
"""Adapted from pandas.tseries.index.DatetimeIndex.get_value"""
if np.asarray(key).dtype == np.dtype(bool):
return series.iloc[key]
elif isinstance(key, slice):
return series.iloc[self.slice_indexer(
key.start, key.stop, k... | python | def get_value(self, series, key):
"""Adapted from pandas.tseries.index.DatetimeIndex.get_value"""
if np.asarray(key).dtype == np.dtype(bool):
return series.iloc[key]
elif isinstance(key, slice):
return series.iloc[self.slice_indexer(
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pydata/xarray | xarray/coding/cftimeindex.py | CFTimeIndex.shift | def shift(self, n, freq):
"""Shift the CFTimeIndex a multiple of the given frequency.
See the documentation for :py:func:`~xarray.cftime_range` for a
complete listing of valid frequency strings.
Parameters
----------
n : int
Periods to shift by
freq ... | python | def shift(self, n, freq):
"""Shift the CFTimeIndex a multiple of the given frequency.
See the documentation for :py:func:`~xarray.cftime_range` for a
complete listing of valid frequency strings.
Parameters
----------
n : int
Periods to shift by
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Parameters
----------
unsafe : bool
Flag to turn off warning when converting from a CFTimeIndex with
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"""If possible, convert this index to a pandas.DatetimeIndex.
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----------
unsafe : bool
Flag to turn off warning when converting from a CFTimeIndex with
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pydata/xarray | xarray/core/duck_array_ops.py | _dask_or_eager_func | def _dask_or_eager_func(name, eager_module=np, dask_module=dask_array,
list_of_args=False, array_args=slice(1),
requires_dask=None):
"""Create a function that dispatches to dask for dask array inputs."""
if dask_module is not None:
def f(*args, **kwargs):
... | python | def _dask_or_eager_func(name, eager_module=np, dask_module=dask_array,
list_of_args=False, array_args=slice(1),
requires_dask=None):
"""Create a function that dispatches to dask for dask array inputs."""
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pydata/xarray | xarray/core/duck_array_ops.py | as_shared_dtype | def as_shared_dtype(scalars_or_arrays):
"""Cast a arrays to a shared dtype using xarray's type promotion rules."""
arrays = [asarray(x) for x in scalars_or_arrays]
# Pass arrays directly instead of dtypes to result_type so scalars
# get handled properly.
# Note that result_type() safely gets the dty... | python | def as_shared_dtype(scalars_or_arrays):
"""Cast a arrays to a shared dtype using xarray's type promotion rules."""
arrays = [asarray(x) for x in scalars_or_arrays]
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pydata/xarray | xarray/core/duck_array_ops.py | allclose_or_equiv | def allclose_or_equiv(arr1, arr2, rtol=1e-5, atol=1e-8):
"""Like np.allclose, but also allows values to be NaN in both arrays
"""
arr1, arr2 = as_like_arrays(arr1, arr2)
if arr1.shape != arr2.shape:
return False
return bool(
isclose(arr1, arr2, rtol=rtol, atol=atol, equal_nan=True).a... | python | def allclose_or_equiv(arr1, arr2, rtol=1e-5, atol=1e-8):
"""Like np.allclose, but also allows values to be NaN in both arrays
"""
arr1, arr2 = as_like_arrays(arr1, arr2)
if arr1.shape != arr2.shape:
return False
return bool(
isclose(arr1, arr2, rtol=rtol, atol=atol, equal_nan=True).a... | [
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pydata/xarray | xarray/core/duck_array_ops.py | array_equiv | def array_equiv(arr1, arr2):
"""Like np.array_equal, but also allows values to be NaN in both arrays
"""
arr1, arr2 = as_like_arrays(arr1, arr2)
if arr1.shape != arr2.shape:
return False
with warnings.catch_warnings():
warnings.filterwarnings('ignore', "In the future, 'NAT == x'")
... | python | def array_equiv(arr1, arr2):
"""Like np.array_equal, but also allows values to be NaN in both arrays
"""
arr1, arr2 = as_like_arrays(arr1, arr2)
if arr1.shape != arr2.shape:
return False
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pydata/xarray | xarray/core/duck_array_ops.py | count | def count(data, axis=None):
"""Count the number of non-NA in this array along the given axis or axes
"""
return np.sum(np.logical_not(isnull(data)), axis=axis) | python | def count(data, axis=None):
"""Count the number of non-NA in this array along the given axis or axes
"""
return np.sum(np.logical_not(isnull(data)), axis=axis) | [
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pydata/xarray | xarray/core/duck_array_ops.py | datetime_to_numeric | def datetime_to_numeric(array, offset=None, datetime_unit=None, dtype=float):
"""Convert an array containing datetime-like data to an array of floats.
Parameters
----------
da : np.array
Input data
offset: Scalar with the same type of array or None
If None, subtract minimum values t... | python | def datetime_to_numeric(array, offset=None, datetime_unit=None, dtype=float):
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Parameters
----------
da : np.array
Input data
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pydata/xarray | xarray/core/duck_array_ops.py | mean | def mean(array, axis=None, skipna=None, **kwargs):
"""inhouse mean that can handle np.datetime64 or cftime.datetime
dtypes"""
from .common import _contains_cftime_datetimes
array = asarray(array)
if array.dtype.kind in 'Mm':
offset = min(array)
# xarray always uses np.datetime64[ns]... | python | def mean(array, axis=None, skipna=None, **kwargs):
"""inhouse mean that can handle np.datetime64 or cftime.datetime
dtypes"""
from .common import _contains_cftime_datetimes
array = asarray(array)
if array.dtype.kind in 'Mm':
offset = min(array)
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pydata/xarray | xarray/core/duck_array_ops.py | first | def first(values, axis, skipna=None):
"""Return the first non-NA elements in this array along the given axis
"""
if (skipna or skipna is None) and values.dtype.kind not in 'iSU':
# only bother for dtypes that can hold NaN
_fail_on_dask_array_input_skipna(values)
return nanfirst(value... | python | def first(values, axis, skipna=None):
"""Return the first non-NA elements in this array along the given axis
"""
if (skipna or skipna is None) and values.dtype.kind not in 'iSU':
# only bother for dtypes that can hold NaN
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pydata/xarray | xarray/core/duck_array_ops.py | last | def last(values, axis, skipna=None):
"""Return the last non-NA elements in this array along the given axis
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if (skipna or skipna is None) and values.dtype.kind not in 'iSU':
# only bother for dtypes that can hold NaN
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return nanlast(values, ... | python | def last(values, axis, skipna=None):
"""Return the last non-NA elements in this array along the given axis
"""
if (skipna or skipna is None) and values.dtype.kind not in 'iSU':
# only bother for dtypes that can hold NaN
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pydata/xarray | xarray/core/duck_array_ops.py | rolling_window | def rolling_window(array, axis, window, center, fill_value):
"""
Make an ndarray with a rolling window of axis-th dimension.
The rolling dimension will be placed at the last dimension.
"""
if isinstance(array, dask_array_type):
return dask_array_ops.rolling_window(
array, axis, w... | python | def rolling_window(array, axis, window, center, fill_value):
"""
Make an ndarray with a rolling window of axis-th dimension.
The rolling dimension will be placed at the last dimension.
"""
if isinstance(array, dask_array_type):
return dask_array_ops.rolling_window(
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pydata/xarray | xarray/core/combine.py | concat | def concat(objs, dim=None, data_vars='all', coords='different',
compat='equals', positions=None, indexers=None, mode=None,
concat_over=None):
"""Concatenate xarray objects along a new or existing dimension.
Parameters
----------
objs : sequence of Dataset and DataArray objects
... | python | def concat(objs, dim=None, data_vars='all', coords='different',
compat='equals', positions=None, indexers=None, mode=None,
concat_over=None):
"""Concatenate xarray objects along a new or existing dimension.
Parameters
----------
objs : sequence of Dataset and DataArray objects
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pydata/xarray | xarray/core/combine.py | _calc_concat_dim_coord | def _calc_concat_dim_coord(dim):
"""
Infer the dimension name and 1d coordinate variable (if appropriate)
for concatenating along the new dimension.
"""
from .dataarray import DataArray
if isinstance(dim, str):
coord = None
elif not isinstance(dim, (DataArray, Variable)):
di... | python | def _calc_concat_dim_coord(dim):
"""
Infer the dimension name and 1d coordinate variable (if appropriate)
for concatenating along the new dimension.
"""
from .dataarray import DataArray
if isinstance(dim, str):
coord = None
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pydata/xarray | xarray/core/combine.py | _calc_concat_over | def _calc_concat_over(datasets, dim, data_vars, coords):
"""
Determine which dataset variables need to be concatenated in the result,
and which can simply be taken from the first dataset.
"""
# Return values
concat_over = set()
equals = {}
if dim in datasets[0]:
concat_over.add(... | python | def _calc_concat_over(datasets, dim, data_vars, coords):
"""
Determine which dataset variables need to be concatenated in the result,
and which can simply be taken from the first dataset.
"""
# Return values
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pydata/xarray | xarray/core/combine.py | _dataset_concat | def _dataset_concat(datasets, dim, data_vars, coords, compat, positions):
"""
Concatenate a sequence of datasets along a new or existing dimension
"""
from .dataset import Dataset
if compat not in ['equals', 'identical']:
raise ValueError("compat=%r invalid: must be 'equals' "
... | python | def _dataset_concat(datasets, dim, data_vars, coords, compat, positions):
"""
Concatenate a sequence of datasets along a new or existing dimension
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from .dataset import Dataset
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pydata/xarray | xarray/core/combine.py | _infer_tile_ids_from_nested_list | def _infer_tile_ids_from_nested_list(entry, current_pos):
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"""
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pydata/xarray | xarray/core/combine.py | _combine_nd | def _combine_nd(combined_ids, concat_dims, data_vars='all',
coords='different', compat='no_conflicts'):
"""
Concatenates and merges an N-dimensional structure of datasets.
No checks are performed on the consistency of the datasets, concat_dims or
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coords='different', compat='no_conflicts'):
"""
Concatenates and merges an N-dimensional structure of datasets.
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pydata/xarray | xarray/core/combine.py | _auto_combine | def _auto_combine(datasets, concat_dims, compat, data_vars, coords,
infer_order_from_coords, ids):
"""
Calls logic to decide concatenation order before concatenating.
"""
# Arrange datasets for concatenation
if infer_order_from_coords:
raise NotImplementedError
# T... | python | def _auto_combine(datasets, concat_dims, compat, data_vars, coords,
infer_order_from_coords, ids):
"""
Calls logic to decide concatenation order before concatenating.
"""
# Arrange datasets for concatenation
if infer_order_from_coords:
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pydata/xarray | xarray/core/combine.py | auto_combine | def auto_combine(datasets, concat_dim=_CONCAT_DIM_DEFAULT,
compat='no_conflicts', data_vars='all', coords='different'):
"""Attempt to auto-magically combine the given datasets into one.
This method attempts to combine a list of datasets into a single entity by
inspecting metadata and using ... | python | def auto_combine(datasets, concat_dim=_CONCAT_DIM_DEFAULT,
compat='no_conflicts', data_vars='all', coords='different'):
"""Attempt to auto-magically combine the given datasets into one.
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pydata/xarray | xarray/coding/cftime_offsets.py | get_date_type | def get_date_type(calendar):
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raise ImportError(
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else:
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"""Return the cftime date type for a given calendar name."""
try:
import cftime
except ImportError:
raise ImportError(
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pydata/xarray | xarray/coding/cftime_offsets.py | _get_day_of_month | def _get_day_of_month(other, day_option):
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Parameters
----------
other : cftime.datetime
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'start': returns 1
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"""Find the day in `other`'s month that satisfies a BaseCFTimeOffset's
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----------
other : cftime.datetime
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pydata/xarray | xarray/coding/cftime_offsets.py | _days_in_month | def _days_in_month(date):
"""The number of days in the month of the given date"""
if date.month == 12:
reference = type(date)(date.year + 1, 1, 1)
else:
reference = type(date)(date.year, date.month + 1, 1)
return (reference - timedelta(days=1)).day | python | def _days_in_month(date):
"""The number of days in the month of the given date"""
if date.month == 12:
reference = type(date)(date.year + 1, 1, 1)
else:
reference = type(date)(date.year, date.month + 1, 1)
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pydata/xarray | xarray/coding/cftime_offsets.py | _adjust_n_months | def _adjust_n_months(other_day, n, reference_day):
"""Adjust the number of times a monthly offset is applied based
on the day of a given date, and the reference day provided.
"""
if n > 0 and other_day < reference_day:
n = n - 1
elif n <= 0 and other_day > reference_day:
n = n + 1
... | python | def _adjust_n_months(other_day, n, reference_day):
"""Adjust the number of times a monthly offset is applied based
on the day of a given date, and the reference day provided.
"""
if n > 0 and other_day < reference_day:
n = n - 1
elif n <= 0 and other_day > reference_day:
n = n + 1
... | [
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] | 6d93a95d05bdbfc33fff24064f67d29dd891ab58 | https://github.com/pydata/xarray/blob/6d93a95d05bdbfc33fff24064f67d29dd891ab58/xarray/coding/cftime_offsets.py#L204-L212 | train | Adjust the number of times a monthly offset is applied based
on the day of a given date. | Pu7Z6IJCgH3a,vcEHXBQXuDuh,sHOWSIAKtU58,ZVWAAMjVVHHl,qRin5pdYOdbB,IySsVMyKT3tF,FwEHNICjJCy0,yISIa0MMKKfB,GAtvbI59wr0o,OmNM6rT0Sgul,gu1MSKhYvigU,S2TTo9DhhiSh,aaLV7ZjAfkcR,ker4pIJmdvxf,WaQEaQCVMQ03,xV97BFGi0hY9,YnM1HtHE4j7G,X5FyJb4ToTo6,jLmadlzMdunT,GGFwFLsDF9Fv,prtR0Uw1GMh5,oNamnshN4dFG,QZzQeAYvsoum,VHAt7CcYKC2T,cKsTbNGL... |
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