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pydata/xarray | xarray/core/dataset.py | Dataset.unstack | def unstack(self, dim=None):
"""
Unstack existing dimensions corresponding to MultiIndexes into
multiple new dimensions.
New dimensions will be added at the end.
Parameters
----------
dim : str or sequence of str, optional
Dimension(s) over which to ... | python | def unstack(self, dim=None):
"""
Unstack existing dimensions corresponding to MultiIndexes into
multiple new dimensions.
New dimensions will be added at the end.
Parameters
----------
dim : str or sequence of str, optional
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pydata/xarray | xarray/core/dataset.py | Dataset.update | def update(self, other, inplace=None):
"""Update this dataset's variables with those from another dataset.
Parameters
----------
other : Dataset or castable to Dataset
Dataset or variables with which to update this dataset.
inplace : bool, optional
If Tru... | python | def update(self, other, inplace=None):
"""Update this dataset's variables with those from another dataset.
Parameters
----------
other : Dataset or castable to Dataset
Dataset or variables with which to update this dataset.
inplace : bool, optional
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pydata/xarray | xarray/core/dataset.py | Dataset.merge | def merge(self, other, inplace=None, overwrite_vars=frozenset(),
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"""Merge the arrays of two datasets into a single dataset.
This method generally not allow for overriding data, with the exception
of attributes, which are ignored on the second ... | python | def merge(self, other, inplace=None, overwrite_vars=frozenset(),
compat='no_conflicts', join='outer'):
"""Merge the arrays of two datasets into a single dataset.
This method generally not allow for overriding data, with the exception
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pydata/xarray | xarray/core/dataset.py | Dataset.drop | def drop(self, labels, dim=None):
"""Drop variables or index labels from this dataset.
Parameters
----------
labels : scalar or list of scalars
Name(s) of variables or index labels to drop.
dim : None or str, optional
Dimension along which to drop index l... | python | def drop(self, labels, dim=None):
"""Drop variables or index labels from this dataset.
Parameters
----------
labels : scalar or list of scalars
Name(s) of variables or index labels to drop.
dim : None or str, optional
Dimension along which to drop index l... | [
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pydata/xarray | xarray/core/dataset.py | Dataset.drop_dims | def drop_dims(self, drop_dims):
"""Drop dimensions and associated variables from this dataset.
Parameters
----------
drop_dims : str or list
Dimension or dimensions to drop.
Returns
-------
obj : Dataset
The dataset without the given dime... | python | def drop_dims(self, drop_dims):
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Parameters
----------
drop_dims : str or list
Dimension or dimensions to drop.
Returns
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obj : Dataset
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pydata/xarray | xarray/core/dataset.py | Dataset.transpose | def transpose(self, *dims):
"""Return a new Dataset object with all array dimensions transposed.
Although the order of dimensions on each array will change, the dataset
dimensions themselves will remain in fixed (sorted) order.
Parameters
----------
*dims : str, optiona... | python | def transpose(self, *dims):
"""Return a new Dataset object with all array dimensions transposed.
Although the order of dimensions on each array will change, the dataset
dimensions themselves will remain in fixed (sorted) order.
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pydata/xarray | xarray/core/dataset.py | Dataset.dropna | def dropna(self, dim, how='any', thresh=None, subset=None):
"""Returns a new dataset with dropped labels for missing values along
the provided dimension.
Parameters
----------
dim : str
Dimension along which to drop missing values. Dropping along
multiple... | python | def dropna(self, dim, how='any', thresh=None, subset=None):
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Parameters
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dim : str
Dimension along which to drop missing values. Dropping along
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pydata/xarray | xarray/core/dataset.py | Dataset.fillna | def fillna(self, value):
"""Fill missing values in this object.
This operation follows the normal broadcasting and alignment rules that
xarray uses for binary arithmetic, except the result is aligned to this
object (``join='left'``) instead of aligned to the intersection of
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"""Fill missing values in this object.
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pydata/xarray | xarray/core/dataset.py | Dataset.interpolate_na | def interpolate_na(self, dim=None, method='linear', limit=None,
use_coordinate=True,
**kwargs):
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Parameters
----------
dim : str
Specifies the dimension along which to interpol... | python | def interpolate_na(self, dim=None, method='linear', limit=None,
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pydata/xarray | xarray/core/dataset.py | Dataset.ffill | def ffill(self, dim, limit=None):
'''Fill NaN values by propogating values forward
*Requires bottleneck.*
Parameters
----------
dim : str
Specifies the dimension along which to propagate values when
filling.
limit : int, default None
... | python | def ffill(self, dim, limit=None):
'''Fill NaN values by propogating values forward
*Requires bottleneck.*
Parameters
----------
dim : str
Specifies the dimension along which to propagate values when
filling.
limit : int, default None
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pydata/xarray | xarray/core/dataset.py | Dataset.bfill | def bfill(self, dim, limit=None):
'''Fill NaN values by propogating values backward
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dim : str
Specifies the dimension along which to propagate values when
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dim : str
Specifies the dimension along which to propagate values when
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limit : int, default None
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pydata/xarray | xarray/core/dataset.py | Dataset.combine_first | def combine_first(self, other):
"""Combine two Datasets, default to data_vars of self.
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"""Combine two Datasets, default to data_vars of self.
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filled with np.nan.
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"""Assign new data variables to a Dataset, returning a new object
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variables : mapping, value pairs
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Pa... | python | def to_array(self, dim='variable', name=None):
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pydata/xarray | xarray/core/dataset.py | Dataset.to_dask_dataframe | def to_dask_dataframe(self, dim_order=None, set_index=False):
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----------
dim_order : list, opti... | python | def to_dask_dataframe(self, dim_order=None, set_index=False):
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d = {'coords': {'t': {'... | python | def from_dict(cls, d):
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pydata/xarray | xarray/core/dataset.py | Dataset.diff | def diff(self, dim, n=1, label='upper'):
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----------
dim : str, optional
Dimension over which to calculate the finite difference.
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pydata/xarray | xarray/core/dataset.py | Dataset.shift | def shift(self, shifts=None, fill_value=dtypes.NA, **shifts_kwargs):
"""Shift this dataset by an offset along one or more dimensions.
Only data variables are moved; coordinates stay in place. This is
consistent with the behavior of ``shift`` in pandas.
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... | python | def shift(self, shifts=None, fill_value=dtypes.NA, **shifts_kwargs):
"""Shift this dataset by an offset along one or more dimensions.
Only data variables are moved; coordinates stay in place. This is
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pydata/xarray | xarray/core/dataset.py | Dataset.roll | def roll(self, shifts=None, roll_coords=None, **shifts_kwargs):
"""Roll this dataset by an offset along one or more dimensions.
Unlike shift, roll may rotate all variables, including coordinates
if specified. The direction of rotation is consistent with
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P... | python | def roll(self, shifts=None, roll_coords=None, **shifts_kwargs):
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Unlike shift, roll may rotate all variables, including coordinates
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pydata/xarray | xarray/core/dataset.py | Dataset.sortby | def sortby(self, variables, ascending=True):
"""
Sort object by labels or values (along an axis).
Sorts the dataset, either along specified dimensions,
or according to values of 1-D dataarrays that share dimension
with calling object.
If the input variables are dataarra... | python | def sortby(self, variables, ascending=True):
"""
Sort object by labels or values (along an axis).
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pydata/xarray | xarray/core/dataset.py | Dataset.quantile | def quantile(self, q, dim=None, interpolation='linear',
numeric_only=False, keep_attrs=None):
"""Compute the qth quantile of the data along the specified dimension.
Returns the qth quantiles(s) of the array elements for each variable
in the Dataset.
Parameters
... | python | def quantile(self, q, dim=None, interpolation='linear',
numeric_only=False, keep_attrs=None):
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Returns the qth quantiles(s) of the array elements for each variable
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pydata/xarray | xarray/core/dataset.py | Dataset.rank | def rank(self, dim, pct=False, keep_attrs=None):
"""Ranks the data.
Equal values are assigned a rank that is the average of the ranks that
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pydata/xarray | xarray/core/dataset.py | Dataset.differentiate | def differentiate(self, coord, edge_order=1, datetime_unit=None):
""" Differentiate with the second order accurate central
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.. note::
This feature is limited to simple cartesian geometry, i.e. coord
must be one dimensional.
Parameters
-------... | python | def differentiate(self, coord, edge_order=1, datetime_unit=None):
""" Differentiate with the second order accurate central
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.. note::
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pydata/xarray | xarray/core/dataset.py | Dataset.integrate | def integrate(self, coord, datetime_unit=None):
""" integrate the array with the trapezoidal rule.
.. note::
This feature is limited to simple cartesian geometry, i.e. coord
must be one dimensional.
Parameters
----------
dim: str, or a sequence of str
... | python | def integrate(self, coord, datetime_unit=None):
""" integrate the array with the trapezoidal rule.
.. note::
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----------
dim: str, or a sequence of str
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pydata/xarray | xarray/coding/variables.py | lazy_elemwise_func | def lazy_elemwise_func(array, func, dtype):
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Parameters
----------
array : any valid value of Variable._data
func : callable
Function to apply to indexed slices of an array. For use with dask,
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... | python | def lazy_elemwise_func(array, func, dtype):
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array : any valid value of Variable._data
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pydata/xarray | xarray/coding/variables.py | pop_to | def pop_to(source, dest, key, name=None):
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value = source.pop(key, None)
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pydata/xarray | xarray/coding/variables.py | _apply_mask | def _apply_mask(
data: np.ndarray,
encoded_fill_values: list,
decoded_fill_value: Any,
dtype: Any,
) -> np.ndarray:
"""Mask all matching values in a NumPy arrays."""
data = np.asarray(data, dtype=dtype)
condition = False
for fv in encoded_fill_values:
condition |= data == fv
... | python | def _apply_mask(
data: np.ndarray,
encoded_fill_values: list,
decoded_fill_value: Any,
dtype: Any,
) -> np.ndarray:
"""Mask all matching values in a NumPy arrays."""
data = np.asarray(data, dtype=dtype)
condition = False
for fv in encoded_fill_values:
condition |= data == fv
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pydata/xarray | xarray/coding/variables.py | _choose_float_dtype | def _choose_float_dtype(dtype, has_offset):
"""Return a float dtype that can losslessly represent `dtype` values."""
# Keep float32 as-is. Upcast half-precision to single-precision,
# because float16 is "intended for storage but not computation"
if dtype.itemsize <= 4 and np.issubdtype(dtype, np.floati... | python | def _choose_float_dtype(dtype, has_offset):
"""Return a float dtype that can losslessly represent `dtype` values."""
# Keep float32 as-is. Upcast half-precision to single-precision,
# because float16 is "intended for storage but not computation"
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pydata/xarray | xarray/core/missing.py | _apply_over_vars_with_dim | def _apply_over_vars_with_dim(func, self, dim=None, **kwargs):
'''wrapper for datasets'''
ds = type(self)(coords=self.coords, attrs=self.attrs)
for name, var in self.data_vars.items():
if dim in var.dims:
ds[name] = func(var, dim=dim, **kwargs)
else:
ds[name] = var
... | python | def _apply_over_vars_with_dim(func, self, dim=None, **kwargs):
'''wrapper for datasets'''
ds = type(self)(coords=self.coords, attrs=self.attrs)
for name, var in self.data_vars.items():
if dim in var.dims:
ds[name] = func(var, dim=dim, **kwargs)
else:
ds[name] = var
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pydata/xarray | xarray/core/missing.py | get_clean_interp_index | def get_clean_interp_index(arr, dim, use_coordinate=True, **kwargs):
'''get index to use for x values in interpolation.
If use_coordinate is True, the coordinate that shares the name of the
dimension along which interpolation is being performed will be used as the
x values.
If use_coordinate is Fa... | python | def get_clean_interp_index(arr, dim, use_coordinate=True, **kwargs):
'''get index to use for x values in interpolation.
If use_coordinate is True, the coordinate that shares the name of the
dimension along which interpolation is being performed will be used as the
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pydata/xarray | xarray/core/missing.py | interp_na | def interp_na(self, dim=None, use_coordinate=True, method='linear', limit=None,
**kwargs):
'''Interpolate values according to different methods.'''
if dim is None:
raise NotImplementedError('dim is a required argument')
if limit is not None:
valids = _get_valid_fill_mask(self... | python | def interp_na(self, dim=None, use_coordinate=True, method='linear', limit=None,
**kwargs):
'''Interpolate values according to different methods.'''
if dim is None:
raise NotImplementedError('dim is a required argument')
if limit is not None:
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pydata/xarray | xarray/core/missing.py | func_interpolate_na | def func_interpolate_na(interpolator, x, y, **kwargs):
'''helper function to apply interpolation along 1 dimension'''
# it would be nice if this wasn't necessary, works around:
# "ValueError: assignment destination is read-only" in assignment below
out = y.copy()
nans = pd.isnull(y)
nonans = ~n... | python | def func_interpolate_na(interpolator, x, y, **kwargs):
'''helper function to apply interpolation along 1 dimension'''
# it would be nice if this wasn't necessary, works around:
# "ValueError: assignment destination is read-only" in assignment below
out = y.copy()
nans = pd.isnull(y)
nonans = ~n... | [
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pydata/xarray | xarray/core/missing.py | _bfill | def _bfill(arr, n=None, axis=-1):
'''inverse of ffill'''
import bottleneck as bn
arr = np.flip(arr, axis=axis)
# fill
arr = bn.push(arr, axis=axis, n=n)
# reverse back to original
return np.flip(arr, axis=axis) | python | def _bfill(arr, n=None, axis=-1):
'''inverse of ffill'''
import bottleneck as bn
arr = np.flip(arr, axis=axis)
# fill
arr = bn.push(arr, axis=axis, n=n)
# reverse back to original
return np.flip(arr, axis=axis) | [
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pydata/xarray | xarray/core/missing.py | ffill | def ffill(arr, dim=None, limit=None):
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# work around for bottleneck 178
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'''forward fill missing values'''
import bottleneck as bn
axis = arr.get_axis_num(dim)
# work around for bottleneck 178
_limit = limit if limit is not None else arr.shape[axis]
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pydata/xarray | xarray/core/missing.py | bfill | def bfill(arr, dim=None, limit=None):
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# work around for bottleneck 178
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'''backfill missing values'''
axis = arr.get_axis_num(dim)
# work around for bottleneck 178
_limit = limit if limit is not None else arr.shape[axis]
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pydata/xarray | xarray/core/missing.py | _get_interpolator | def _get_interpolator(method, vectorizeable_only=False, **kwargs):
'''helper function to select the appropriate interpolator class
returns interpolator class and keyword arguments for the class
'''
interp1d_methods = ['linear', 'nearest', 'zero', 'slinear', 'quadratic',
'cubic',... | python | def _get_interpolator(method, vectorizeable_only=False, **kwargs):
'''helper function to select the appropriate interpolator class
returns interpolator class and keyword arguments for the class
'''
interp1d_methods = ['linear', 'nearest', 'zero', 'slinear', 'quadratic',
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pydata/xarray | xarray/core/missing.py | _get_interpolator_nd | def _get_interpolator_nd(method, **kwargs):
'''helper function to select the appropriate interpolator class
returns interpolator class and keyword arguments for the class
'''
valid_methods = ['linear', 'nearest']
try:
from scipy import interpolate
except ImportError:
raise Impo... | python | def _get_interpolator_nd(method, **kwargs):
'''helper function to select the appropriate interpolator class
returns interpolator class and keyword arguments for the class
'''
valid_methods = ['linear', 'nearest']
try:
from scipy import interpolate
except ImportError:
raise Impo... | [
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pydata/xarray | xarray/core/missing.py | _get_valid_fill_mask | def _get_valid_fill_mask(arr, dim, limit):
'''helper function to determine values that can be filled when limit is not
None'''
kw = {dim: limit + 1}
# we explicitly use construct method to avoid copy.
new_dim = utils.get_temp_dimname(arr.dims, '_window')
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'''helper function to determine values that can be filled when limit is not
None'''
kw = {dim: limit + 1}
# we explicitly use construct method to avoid copy.
new_dim = utils.get_temp_dimname(arr.dims, '_window')
return (arr.isnull().rolling(min_periods=... | [
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pydata/xarray | xarray/core/missing.py | _localize | def _localize(var, indexes_coords):
""" Speed up for linear and nearest neighbor method.
Only consider a subspace that is needed for the interpolation
"""
indexes = {}
for dim, [x, new_x] in indexes_coords.items():
index = x.to_index()
imin = index.get_loc(np.min(new_x.values), metho... | python | def _localize(var, indexes_coords):
""" Speed up for linear and nearest neighbor method.
Only consider a subspace that is needed for the interpolation
"""
indexes = {}
for dim, [x, new_x] in indexes_coords.items():
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pydata/xarray | xarray/core/missing.py | _floatize_x | def _floatize_x(x, new_x):
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This is particulary useful for datetime dtype.
x, new_x: tuple of np.ndarray
"""
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new_x = list(new_x)
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# Scipy casts coordinates to np.float6... | python | def _floatize_x(x, new_x):
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This is particulary useful for datetime dtype.
x, new_x: tuple of np.ndarray
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""" Make an interpolation of Variable
Parameters
----------
var: Variable
index_coords:
Mapping from dimension name to a pair of original and new coordinates.
Original coordinates should be sorted in strictly ascending order.
... | python | def interp(var, indexes_coords, method, **kwargs):
""" Make an interpolation of Variable
Parameters
----------
var: Variable
index_coords:
Mapping from dimension name to a pair of original and new coordinates.
Original coordinates should be sorted in strictly ascending order.
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pydata/xarray | xarray/core/missing.py | interp_func | def interp_func(var, x, new_x, method, kwargs):
"""
multi-dimensional interpolation for array-like. Interpolated axes should be
located in the last position.
Parameters
----------
var: np.ndarray or dask.array.Array
Array to be interpolated. The final dimension is interpolated.
x: a... | python | def interp_func(var, x, new_x, method, kwargs):
"""
multi-dimensional interpolation for array-like. Interpolated axes should be
located in the last position.
Parameters
----------
var: np.ndarray or dask.array.Array
Array to be interpolated. The final dimension is interpolated.
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pydata/xarray | xarray/plot/facetgrid.py | _nicetitle | def _nicetitle(coord, value, maxchar, template):
"""
Put coord, value in template and truncate at maxchar
"""
prettyvalue = format_item(value, quote_strings=False)
title = template.format(coord=coord, value=prettyvalue)
if len(title) > maxchar:
title = title[:(maxchar - 3)] + '...'
... | python | def _nicetitle(coord, value, maxchar, template):
"""
Put coord, value in template and truncate at maxchar
"""
prettyvalue = format_item(value, quote_strings=False)
title = template.format(coord=coord, value=prettyvalue)
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pydata/xarray | xarray/plot/facetgrid.py | _easy_facetgrid | def _easy_facetgrid(data, plotfunc, kind, x=None, y=None, row=None,
col=None, col_wrap=None, sharex=True, sharey=True,
aspect=None, size=None, subplot_kws=None, **kwargs):
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Convenience method to call xarray.plot.FacetGrid from 2d plotting methods
kwargs are the ar... | python | def _easy_facetgrid(data, plotfunc, kind, x=None, y=None, row=None,
col=None, col_wrap=None, sharex=True, sharey=True,
aspect=None, size=None, subplot_kws=None, **kwargs):
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pydata/xarray | xarray/plot/facetgrid.py | FacetGrid.map_dataarray | def map_dataarray(self, func, x, y, **kwargs):
"""
Apply a plotting function to a 2d facet's subset of the data.
This is more convenient and less general than ``FacetGrid.map``
Parameters
----------
func : callable
A plotting function with the same signature... | python | def map_dataarray(self, func, x, y, **kwargs):
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pydata/xarray | xarray/plot/facetgrid.py | FacetGrid._finalize_grid | def _finalize_grid(self, *axlabels):
"""Finalize the annotations and layout."""
if not self._finalized:
self.set_axis_labels(*axlabels)
self.set_titles()
self.fig.tight_layout()
for ax, namedict in zip(self.axes.flat, self.name_dicts.flat):
... | python | def _finalize_grid(self, *axlabels):
"""Finalize the annotations and layout."""
if not self._finalized:
self.set_axis_labels(*axlabels)
self.set_titles()
self.fig.tight_layout()
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pydata/xarray | xarray/plot/facetgrid.py | FacetGrid.add_colorbar | def add_colorbar(self, **kwargs):
"""Draw a colorbar
"""
kwargs = kwargs.copy()
if self._cmap_extend is not None:
kwargs.setdefault('extend', self._cmap_extend)
if 'label' not in kwargs:
kwargs.setdefault('label', label_from_attrs(self.data))
self.... | python | def add_colorbar(self, **kwargs):
"""Draw a colorbar
"""
kwargs = kwargs.copy()
if self._cmap_extend is not None:
kwargs.setdefault('extend', self._cmap_extend)
if 'label' not in kwargs:
kwargs.setdefault('label', label_from_attrs(self.data))
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pydata/xarray | xarray/plot/facetgrid.py | FacetGrid.set_axis_labels | def set_axis_labels(self, x_var=None, y_var=None):
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if x_var is not None:
if x_var in self.data.coords:
self._x_var = x_var
self.set_xlabels(label_from_attrs(self.data[x_var]))
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"""Set axis labels on the left column and bottom row of the grid."""
if x_var is not None:
if x_var in self.data.coords:
self._x_var = x_var
self.set_xlabels(label_from_attrs(self.data[x_var]))
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pydata/xarray | xarray/plot/facetgrid.py | FacetGrid.set_xlabels | def set_xlabels(self, label=None, **kwargs):
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label = label_from_attrs(self.data[self._x_var])
for ax in self._bottom_axes:
ax.set_xlabel(label, **kwargs)
return self | python | def set_xlabels(self, label=None, **kwargs):
"""Label the x axis on the bottom row of the grid."""
if label is None:
label = label_from_attrs(self.data[self._x_var])
for ax in self._bottom_axes:
ax.set_xlabel(label, **kwargs)
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pydata/xarray | xarray/plot/facetgrid.py | FacetGrid.set_ylabels | def set_ylabels(self, label=None, **kwargs):
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label = label_from_attrs(self.data[self._y_var])
for ax in self._left_axes:
ax.set_ylabel(label, **kwargs)
return self | python | def set_ylabels(self, label=None, **kwargs):
"""Label the y axis on the left column of the grid."""
if label is None:
label = label_from_attrs(self.data[self._y_var])
for ax in self._left_axes:
ax.set_ylabel(label, **kwargs)
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pydata/xarray | xarray/plot/facetgrid.py | FacetGrid.set_titles | def set_titles(self, template="{coord} = {value}", maxchar=30,
**kwargs):
"""
Draw titles either above each facet or on the grid margins.
Parameters
----------
template : string
Template for plot titles containing {coord} and {value}
maxcha... | python | def set_titles(self, template="{coord} = {value}", maxchar=30,
**kwargs):
"""
Draw titles either above each facet or on the grid margins.
Parameters
----------
template : string
Template for plot titles containing {coord} and {value}
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pydata/xarray | xarray/plot/facetgrid.py | FacetGrid.set_ticks | def set_ticks(self, max_xticks=_NTICKS, max_yticks=_NTICKS,
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"""
Set and control tick behavior
Parameters
----------
max_xticks, max_yticks : int, optional
Maximum number of labeled ticks to plot on x, y axes
fontsize : s... | python | def set_ticks(self, max_xticks=_NTICKS, max_yticks=_NTICKS,
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"""
Set and control tick behavior
Parameters
----------
max_xticks, max_yticks : int, optional
Maximum number of labeled ticks to plot on x, y axes
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pydata/xarray | xarray/plot/facetgrid.py | FacetGrid.map | def map(self, func, *args, **kwargs):
"""
Apply a plotting function to each facet's subset of the data.
Parameters
----------
func : callable
A plotting function that takes data and keyword arguments. It
must plot to the currently active matplotlib Axes a... | python | def map(self, func, *args, **kwargs):
"""
Apply a plotting function to each facet's subset of the data.
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----------
func : callable
A plotting function that takes data and keyword arguments. It
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pydata/xarray | xarray/core/resample_cftime.py | _get_time_bins | def _get_time_bins(index, freq, closed, label, base):
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Parameters
----------
index : CFTimeIndex
Index object to be resampled (e.g., CFTimeIndex named 'time').
freq : xarray.coding.cftime_offsets.BaseCFTimeOffset
... | python | def _get_time_bins(index, freq, closed, label, base):
"""Obtain the bins and their respective labels for resampling operations.
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----------
index : CFTimeIndex
Index object to be resampled (e.g., CFTimeIndex named 'time').
freq : xarray.coding.cftime_offsets.BaseCFTimeOffset
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pydata/xarray | xarray/core/resample_cftime.py | _adjust_bin_edges | def _adjust_bin_edges(datetime_bins, offset, closed, index, labels):
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daily frequencies greater than one day, month end, and year end
frequencies.
Consider the following example. Let's say you want to downsample the
time series with ... | python | def _adjust_bin_edges(datetime_bins, offset, closed, index, labels):
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pydata/xarray | xarray/core/resample_cftime.py | _get_range_edges | def _get_range_edges(first, last, offset, closed='left', base=0):
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CFTimeIndex range.
Parameters
----------
first : cftime.datetime
Uncorrected starting datetime object for resampled CFTimeIndex range.
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first : cftime.datetime
Uncorrected starting datetime object for resampled CFTimeIndex range.
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pydata/xarray | xarray/core/resample_cftime.py | _adjust_dates_anchored | def _adjust_dates_anchored(first, last, offset, closed='right', base=0):
""" First and last offsets should be calculated from the start day to fix
an error cause by resampling across multiple days when a one day period is
not a multiple of the frequency.
See https://github.com/pandas-dev/pandas/issues/8... | python | def _adjust_dates_anchored(first, last, offset, closed='right', base=0):
""" First and last offsets should be calculated from the start day to fix
an error cause by resampling across multiple days when a one day period is
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pydata/xarray | xarray/core/resample_cftime.py | exact_cftime_datetime_difference | def exact_cftime_datetime_difference(a, b):
"""Exact computation of b - a
Assumes:
a = a_0 + a_m
b = b_0 + b_m
Here a_0, and b_0 represent the input dates rounded
down to the nearest second, and a_m, and b_m represent
the remaining microseconds associated with date a and
date ... | python | def exact_cftime_datetime_difference(a, b):
"""Exact computation of b - a
Assumes:
a = a_0 + a_m
b = b_0 + b_m
Here a_0, and b_0 represent the input dates rounded
down to the nearest second, and a_m, and b_m represent
the remaining microseconds associated with date a and
date ... | [
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pydata/xarray | xarray/core/resample_cftime.py | CFTimeGrouper.first_items | def first_items(self, index):
"""Meant to reproduce the results of the following
grouper = pandas.Grouper(...)
first_items = pd.Series(np.arange(len(index)),
index).groupby(grouper).first()
with index being a CFTimeIndex instead of a DatetimeIndex.
... | python | def first_items(self, index):
"""Meant to reproduce the results of the following
grouper = pandas.Grouper(...)
first_items = pd.Series(np.arange(len(index)),
index).groupby(grouper).first()
with index being a CFTimeIndex instead of a DatetimeIndex.
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pydata/xarray | xarray/core/alignment.py | align | def align(*objects, **kwargs):
"""align(*objects, join='inner', copy=True, indexes=None,
exclude=frozenset())
Given any number of Dataset and/or DataArray objects, returns new
objects with aligned indexes and dimension sizes.
Array from the aligned objects are suitable as input to mathema... | python | def align(*objects, **kwargs):
"""align(*objects, join='inner', copy=True, indexes=None,
exclude=frozenset())
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pydata/xarray | xarray/core/alignment.py | deep_align | def deep_align(objects, join='inner', copy=True, indexes=None,
exclude=frozenset(), raise_on_invalid=True):
"""Align objects for merging, recursing into dictionary values.
This function is not public API.
"""
from .dataarray import DataArray
from .dataset import Dataset
if index... | python | def deep_align(objects, join='inner', copy=True, indexes=None,
exclude=frozenset(), raise_on_invalid=True):
"""Align objects for merging, recursing into dictionary values.
This function is not public API.
"""
from .dataarray import DataArray
from .dataset import Dataset
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pydata/xarray | xarray/core/alignment.py | reindex_like_indexers | def reindex_like_indexers(target, other):
"""Extract indexers to align target with other.
Not public API.
Parameters
----------
target : Dataset or DataArray
Object to be aligned.
other : Dataset or DataArray
Object to be aligned with.
Returns
-------
Dict[Any, pan... | python | def reindex_like_indexers(target, other):
"""Extract indexers to align target with other.
Not public API.
Parameters
----------
target : Dataset or DataArray
Object to be aligned.
other : Dataset or DataArray
Object to be aligned with.
Returns
-------
Dict[Any, pan... | [
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pydata/xarray | xarray/core/alignment.py | reindex_variables | def reindex_variables(
variables: Mapping[Any, Variable],
sizes: Mapping[Any, int],
indexes: Mapping[Any, pd.Index],
indexers: Mapping,
method: Optional[str] = None,
tolerance: Any = None,
copy: bool = True,
) -> 'Tuple[OrderedDict[Any, Variable], OrderedDict[Any, pd.Index]]':
"""Conform... | python | def reindex_variables(
variables: Mapping[Any, Variable],
sizes: Mapping[Any, int],
indexes: Mapping[Any, pd.Index],
indexers: Mapping,
method: Optional[str] = None,
tolerance: Any = None,
copy: bool = True,
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pydata/xarray | xarray/core/alignment.py | broadcast | def broadcast(*args, **kwargs):
"""Explicitly broadcast any number of DataArray or Dataset objects against
one another.
xarray objects automatically broadcast against each other in arithmetic
operations, so this function should not be necessary for normal use.
If no change is needed, the input dat... | python | def broadcast(*args, **kwargs):
"""Explicitly broadcast any number of DataArray or Dataset objects against
one another.
xarray objects automatically broadcast against each other in arithmetic
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pydata/xarray | xarray/core/coordinates.py | assert_coordinate_consistent | def assert_coordinate_consistent(obj, coords):
""" Maeke sure the dimension coordinate of obj is
consistent with coords.
obj: DataArray or Dataset
coords: Dict-like of variables
"""
for k in obj.dims:
# make sure there are no conflict in dimension coordinates
if k in coords and ... | python | def assert_coordinate_consistent(obj, coords):
""" Maeke sure the dimension coordinate of obj is
consistent with coords.
obj: DataArray or Dataset
coords: Dict-like of variables
"""
for k in obj.dims:
# make sure there are no conflict in dimension coordinates
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pydata/xarray | xarray/core/coordinates.py | remap_label_indexers | def remap_label_indexers(obj, indexers=None, method=None, tolerance=None,
**indexers_kwargs):
"""
Remap **indexers from obj.coords.
If indexer is an instance of DataArray and it has coordinate, then this
coordinate will be attached to pos_indexers.
Returns
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pydata/xarray | xarray/core/coordinates.py | AbstractCoordinates._merge_inplace | def _merge_inplace(self, other):
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priority_vars = OrderedDict(
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"""For use with in-place binary arithmetic."""
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yield
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pydata/xarray | xarray/core/dask_array_ops.py | dask_rolling_wrapper | def dask_rolling_wrapper(moving_func, a, window, min_count=None, axis=-1):
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dtype, fill_value = dtypes.maybe_promote(a.dtype)
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axis = a.ndim + axis
depth = {d: 0 for d... | python | def dask_rolling_wrapper(moving_func, a, window, min_count=None, axis=-1):
'''wrapper to apply bottleneck moving window funcs on dask arrays'''
dtype, fill_value = dtypes.maybe_promote(a.dtype)
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pydata/xarray | xarray/core/dask_array_ops.py | rolling_window | def rolling_window(a, axis, window, center, fill_value):
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pydata/xarray | xarray/backends/zarr.py | open_zarr | def open_zarr(store, group=None, synchronizer=None, chunks='auto',
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concat_characters=True, decode_coords=True,
drop_variables=None, consolidated=False,
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decode_cf=True, mask_and_scale=True, decode_times=True,
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drop_variables=None, consolidated=False,
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pydata/xarray | xarray/core/merge.py | broadcast_dimension_size | def broadcast_dimension_size(
variables: List[Variable],
) -> 'OrderedDict[Any, int]':
"""Extract dimension sizes from a dictionary of variables.
Raises ValueError if any dimensions have different sizes.
"""
dims = OrderedDict() # type: OrderedDict[Any, int]
for var in variables:
for d... | python | def broadcast_dimension_size(
variables: List[Variable],
) -> 'OrderedDict[Any, int]':
"""Extract dimension sizes from a dictionary of variables.
Raises ValueError if any dimensions have different sizes.
"""
dims = OrderedDict() # type: OrderedDict[Any, int]
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pydata/xarray | xarray/core/merge.py | unique_variable | def unique_variable(name, variables, compat='broadcast_equals'):
# type: (Any, List[Variable], str) -> Variable
"""Return the unique variable from a list of variables or raise MergeError.
Parameters
----------
name : hashable
Name for this variable.
variables : list of xarray.Variable
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# type: (Any, List[Variable], str) -> Variable
"""Return the unique variable from a list of variables or raise MergeError.
Parameters
----------
name : hashable
Name for this variable.
variables : list of xarray.Variable
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pydata/xarray | xarray/core/merge.py | merge_variables | def merge_variables(
list_of_variables_dicts, # type: List[Mapping[Any, Variable]]
priority_vars=None, # type: Optional[Mapping[Any, Variable]]
compat='minimal', # type: str
):
# type: (...) -> OrderedDict[Any, Variable]
"""Merge dicts of variables, while resolving conflic... | python | def merge_variables(
list_of_variables_dicts, # type: List[Mapping[Any, Variable]]
priority_vars=None, # type: Optional[Mapping[Any, Variable]]
compat='minimal', # type: str
):
# type: (...) -> OrderedDict[Any, Variable]
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pydata/xarray | xarray/core/merge.py | expand_variable_dicts | def expand_variable_dicts(
list_of_variable_dicts: 'List[Union[Dataset, OrderedDict]]',
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"""Given a list of dicts with xarray object values, expand the values.
Parameters
----------
list_of_variable_dicts : list of dict or Dataset objects
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list_of_variable_dicts: 'List[Union[Dataset, OrderedDict]]',
) -> 'List[Mapping[Any, Variable]]':
"""Given a list of dicts with xarray object values, expand the values.
Parameters
----------
list_of_variable_dicts : list of dict or Dataset objects
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pydata/xarray | xarray/core/merge.py | determine_coords | def determine_coords(list_of_variable_dicts):
# type: (List[Dict]) -> Tuple[Set, Set]
"""Given a list of dicts with xarray object values, identify coordinates.
Parameters
----------
list_of_variable_dicts : list of dict or Dataset objects
Of the same form as the arguments to expand_variable... | python | def determine_coords(list_of_variable_dicts):
# type: (List[Dict]) -> Tuple[Set, Set]
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pydata/xarray | xarray/core/merge.py | coerce_pandas_values | def coerce_pandas_values(objects):
"""Convert pandas values found in a list of labeled objects.
Parameters
----------
objects : list of Dataset or mappings
The mappings may contain any sort of objects coercible to
xarray.Variables as keys, including pandas objects.
Returns
----... | python | def coerce_pandas_values(objects):
"""Convert pandas values found in a list of labeled objects.
Parameters
----------
objects : list of Dataset or mappings
The mappings may contain any sort of objects coercible to
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pydata/xarray | xarray/core/merge.py | merge_coords_for_inplace_math | def merge_coords_for_inplace_math(objs, priority_vars=None):
"""Merge coordinate variables without worrying about alignment.
This function is used for merging variables in coordinates.py.
"""
expanded = expand_variable_dicts(objs)
variables = merge_variables(expanded, priority_vars)
assert_uniq... | python | def merge_coords_for_inplace_math(objs, priority_vars=None):
"""Merge coordinate variables without worrying about alignment.
This function is used for merging variables in coordinates.py.
"""
expanded = expand_variable_dicts(objs)
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pydata/xarray | xarray/core/merge.py | _get_priority_vars | def _get_priority_vars(objects, priority_arg, compat='equals'):
"""Extract the priority variable from a list of mappings.
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pydata/xarray | xarray/core/merge.py | expand_and_merge_variables | def expand_and_merge_variables(objs, priority_arg=None):
"""Merge coordinate variables without worrying about alignment.
This function is used for merging variables in computation.py.
"""
expanded = expand_variable_dicts(objs)
priority_vars = _get_priority_vars(objs, priority_arg)
variables = m... | python | def expand_and_merge_variables(objs, priority_arg=None):
"""Merge coordinate variables without worrying about alignment.
This function is used for merging variables in computation.py.
"""
expanded = expand_variable_dicts(objs)
priority_vars = _get_priority_vars(objs, priority_arg)
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pydata/xarray | xarray/core/merge.py | merge_coords | def merge_coords(objs, compat='minimal', join='outer', priority_arg=None,
indexes=None):
"""Merge coordinate variables.
See merge_core below for argument descriptions. This works similarly to
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... | python | def merge_coords(objs, compat='minimal', join='outer', priority_arg=None,
indexes=None):
"""Merge coordinate variables.
See merge_core below for argument descriptions. This works similarly to
merge_core, except everything we don't worry about whether variables are
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pydata/xarray | xarray/core/merge.py | merge_data_and_coords | def merge_data_and_coords(data, coords, compat='broadcast_equals',
join='outer'):
"""Used in Dataset.__init__."""
objs = [data, coords]
explicit_coords = coords.keys()
indexes = dict(extract_indexes(coords))
return merge_core(objs, compat, join, explicit_coords=explicit_coo... | python | def merge_data_and_coords(data, coords, compat='broadcast_equals',
join='outer'):
"""Used in Dataset.__init__."""
objs = [data, coords]
explicit_coords = coords.keys()
indexes = dict(extract_indexes(coords))
return merge_core(objs, compat, join, explicit_coords=explicit_coo... | [
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pydata/xarray | xarray/core/merge.py | extract_indexes | def extract_indexes(coords):
"""Yields the name & index of valid indexes from a mapping of coords"""
for name, variable in coords.items():
variable = as_variable(variable, name=name)
if variable.dims == (name,):
yield name, variable.to_index() | python | def extract_indexes(coords):
"""Yields the name & index of valid indexes from a mapping of coords"""
for name, variable in coords.items():
variable = as_variable(variable, name=name)
if variable.dims == (name,):
yield name, variable.to_index() | [
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pydata/xarray | xarray/core/merge.py | assert_valid_explicit_coords | def assert_valid_explicit_coords(variables, dims, explicit_coords):
"""Validate explicit coordinate names/dims.
Raise a MergeError if an explicit coord shares a name with a dimension
but is comprised of arbitrary dimensions.
"""
for coord_name in explicit_coords:
if coord_name in dims and v... | python | def assert_valid_explicit_coords(variables, dims, explicit_coords):
"""Validate explicit coordinate names/dims.
Raise a MergeError if an explicit coord shares a name with a dimension
but is comprised of arbitrary dimensions.
"""
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pydata/xarray | xarray/core/merge.py | merge_core | def merge_core(objs,
compat='broadcast_equals',
join='outer',
priority_arg=None,
explicit_coords=None,
indexes=None):
"""Core logic for merging labeled objects.
This is not public API.
Parameters
----------
objs : list of m... | python | def merge_core(objs,
compat='broadcast_equals',
join='outer',
priority_arg=None,
explicit_coords=None,
indexes=None):
"""Core logic for merging labeled objects.
This is not public API.
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----------
objs : list of m... | [
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pydata/xarray | xarray/core/merge.py | merge | def merge(objects, compat='no_conflicts', join='outer'):
"""Merge any number of xarray objects into a single Dataset as variables.
Parameters
----------
objects : Iterable[Union[xarray.Dataset, xarray.DataArray, dict]]
Merge together all variables from these objects. If any of them are
... | python | def merge(objects, compat='no_conflicts', join='outer'):
"""Merge any number of xarray objects into a single Dataset as variables.
Parameters
----------
objects : Iterable[Union[xarray.Dataset, xarray.DataArray, dict]]
Merge together all variables from these objects. If any of them are
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pydata/xarray | xarray/core/merge.py | dataset_merge_method | def dataset_merge_method(dataset, other, overwrite_vars, compat, join):
"""Guts of the Dataset.merge method."""
# we are locked into supporting overwrite_vars for the Dataset.merge
# method due for backwards compatibility
# TODO: consider deprecating it?
if isinstance(overwrite_vars, str):
... | python | def dataset_merge_method(dataset, other, overwrite_vars, compat, join):
"""Guts of the Dataset.merge method."""
# we are locked into supporting overwrite_vars for the Dataset.merge
# method due for backwards compatibility
# TODO: consider deprecating it?
if isinstance(overwrite_vars, str):
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pydata/xarray | xarray/core/merge.py | dataset_update_method | def dataset_update_method(dataset, other):
"""Guts of the Dataset.update method.
This drops a duplicated coordinates from `other` if `other` is not an
`xarray.Dataset`, e.g., if it's a dict with DataArray values (GH2068,
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"""
from .dataset import Dataset
from .dataarray import DataA... | python | def dataset_update_method(dataset, other):
"""Guts of the Dataset.update method.
This drops a duplicated coordinates from `other` if `other` is not an
`xarray.Dataset`, e.g., if it's a dict with DataArray values (GH2068,
GH2180).
"""
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pydata/xarray | xarray/core/nanops.py | _replace_nan | def _replace_nan(a, val):
"""
replace nan in a by val, and returns the replaced array and the nan
position
"""
mask = isnull(a)
return where_method(val, mask, a), mask | python | def _replace_nan(a, val):
"""
replace nan in a by val, and returns the replaced array and the nan
position
"""
mask = isnull(a)
return where_method(val, mask, a), mask | [
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pydata/xarray | xarray/core/nanops.py | _maybe_null_out | def _maybe_null_out(result, axis, mask, min_count=1):
"""
xarray version of pandas.core.nanops._maybe_null_out
"""
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"""
xarray version of pandas.core.nanops._maybe_null_out
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pydata/xarray | xarray/core/nanops.py | _nan_argminmax_object | def _nan_argminmax_object(func, fill_value, value, axis=None, **kwargs):
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"""
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type
"""
valid_count = count(value, axis=axis)
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type. | 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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