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pydata/xarray | xarray/core/nanops.py | _nan_minmax_object | def _nan_minmax_object(func, fill_value, value, axis=None, **kwargs):
""" In house nanmin and nanmax for object array """
valid_count = count(value, axis=axis)
filled_value = fillna(value, fill_value)
data = getattr(np, func)(filled_value, axis=axis, **kwargs)
if not hasattr(data, 'dtype'): # scala... | python | def _nan_minmax_object(func, fill_value, value, axis=None, **kwargs):
""" In house nanmin and nanmax for object array """
valid_count = count(value, axis=axis)
filled_value = fillna(value, fill_value)
data = getattr(np, func)(filled_value, axis=axis, **kwargs)
if not hasattr(data, 'dtype'): # scala... | [
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pydata/xarray | xarray/core/nanops.py | _nanmean_ddof_object | def _nanmean_ddof_object(ddof, value, axis=None, **kwargs):
""" In house nanmean. ddof argument will be used in _nanvar method """
from .duck_array_ops import (count, fillna, _dask_or_eager_func,
where_method)
valid_count = count(value, axis=axis)
value = fillna(value, ... | python | def _nanmean_ddof_object(ddof, value, axis=None, **kwargs):
""" In house nanmean. ddof argument will be used in _nanvar method """
from .duck_array_ops import (count, fillna, _dask_or_eager_func,
where_method)
valid_count = count(value, axis=axis)
value = fillna(value, ... | [
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pydata/xarray | xarray/backends/netCDF4_.py | _disable_auto_decode_variable | def _disable_auto_decode_variable(var):
"""Disable automatic decoding on a netCDF4.Variable.
We handle these types of decoding ourselves.
"""
var.set_auto_maskandscale(False)
# only added in netCDF4-python v1.2.8
with suppress(AttributeError):
var.set_auto_chartostring(False) | python | def _disable_auto_decode_variable(var):
"""Disable automatic decoding on a netCDF4.Variable.
We handle these types of decoding ourselves.
"""
var.set_auto_maskandscale(False)
# only added in netCDF4-python v1.2.8
with suppress(AttributeError):
var.set_auto_chartostring(False) | [
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pydata/xarray | xarray/core/dtypes.py | maybe_promote | def maybe_promote(dtype):
"""Simpler equivalent of pandas.core.common._maybe_promote
Parameters
----------
dtype : np.dtype
Returns
-------
dtype : Promoted dtype that can hold missing values.
fill_value : Valid missing value for the promoted dtype.
"""
# N.B. these casting rul... | python | def maybe_promote(dtype):
"""Simpler equivalent of pandas.core.common._maybe_promote
Parameters
----------
dtype : np.dtype
Returns
-------
dtype : Promoted dtype that can hold missing values.
fill_value : Valid missing value for the promoted dtype.
"""
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pydata/xarray | xarray/core/dtypes.py | get_pos_infinity | def get_pos_infinity(dtype):
"""Return an appropriate positive infinity for this dtype.
Parameters
----------
dtype : np.dtype
Returns
-------
fill_value : positive infinity value corresponding to this dtype.
"""
if issubclass(dtype.type, (np.floating, np.integer)):
return ... | python | def get_pos_infinity(dtype):
"""Return an appropriate positive infinity for this dtype.
Parameters
----------
dtype : np.dtype
Returns
-------
fill_value : positive infinity value corresponding to this dtype.
"""
if issubclass(dtype.type, (np.floating, np.integer)):
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pydata/xarray | xarray/core/dtypes.py | get_neg_infinity | def get_neg_infinity(dtype):
"""Return an appropriate positive infinity for this dtype.
Parameters
----------
dtype : np.dtype
Returns
-------
fill_value : positive infinity value corresponding to this dtype.
"""
if issubclass(dtype.type, (np.floating, np.integer)):
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"""Return an appropriate positive infinity for this dtype.
Parameters
----------
dtype : np.dtype
Returns
-------
fill_value : positive infinity value corresponding to this dtype.
"""
if issubclass(dtype.type, (np.floating, np.integer)):
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pydata/xarray | xarray/core/dtypes.py | is_datetime_like | def is_datetime_like(dtype):
"""Check if a dtype is a subclass of the numpy datetime types
"""
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np.issubdtype(dtype, np.timedelta64)) | python | def is_datetime_like(dtype):
"""Check if a dtype is a subclass of the numpy datetime types
"""
return (np.issubdtype(dtype, np.datetime64) or
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pydata/xarray | xarray/core/dtypes.py | result_type | def result_type(*arrays_and_dtypes):
"""Like np.result_type, but with type promotion rules matching pandas.
Examples of changed behavior:
number + string -> object (not string)
bytes + unicode -> object (not unicode)
Parameters
----------
*arrays_and_dtypes : list of arrays and dtypes
... | python | def result_type(*arrays_and_dtypes):
"""Like np.result_type, but with type promotion rules matching pandas.
Examples of changed behavior:
number + string -> object (not string)
bytes + unicode -> object (not unicode)
Parameters
----------
*arrays_and_dtypes : list of arrays and dtypes
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pydata/xarray | xarray/backends/rasterio_.py | _parse_envi | def _parse_envi(meta):
"""Parse ENVI metadata into Python data structures.
See the link for information on the ENVI header file format:
http://www.harrisgeospatial.com/docs/enviheaderfiles.html
Parameters
----------
meta : dict
Dictionary of keys and str values to parse, as returned by... | python | def _parse_envi(meta):
"""Parse ENVI metadata into Python data structures.
See the link for information on the ENVI header file format:
http://www.harrisgeospatial.com/docs/enviheaderfiles.html
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----------
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pydata/xarray | xarray/backends/rasterio_.py | open_rasterio | def open_rasterio(filename, parse_coordinates=None, chunks=None, cache=None,
lock=None):
"""Open a file with rasterio (experimental).
This should work with any file that rasterio can open (most often:
geoTIFF). The x and y coordinates are generated automatically from the
file's geoinf... | python | def open_rasterio(filename, parse_coordinates=None, chunks=None, cache=None,
lock=None):
"""Open a file with rasterio (experimental).
This should work with any file that rasterio can open (most often:
geoTIFF). The x and y coordinates are generated automatically from the
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pydata/xarray | xarray/backends/rasterio_.py | RasterioArrayWrapper._get_indexer | def _get_indexer(self, key):
""" Get indexer for rasterio array.
Parameter
---------
key: tuple of int
Returns
-------
band_key: an indexer for the 1st dimension
window: two tuples. Each consists of (start, stop).
squeeze_axis: axes to be squeeze... | python | def _get_indexer(self, key):
""" Get indexer for rasterio array.
Parameter
---------
key: tuple of int
Returns
-------
band_key: an indexer for the 1st dimension
window: two tuples. Each consists of (start, stop).
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pydata/xarray | xarray/core/groupby.py | unique_value_groups | def unique_value_groups(ar, sort=True):
"""Group an array by its unique values.
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ar : array-like
Input array. This will be flattened if it is not already 1-D.
sort : boolean, optional
Whether or not to sort unique values.
Returns
-------
values : np... | python | def unique_value_groups(ar, sort=True):
"""Group an array by its unique values.
Parameters
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ar : array-like
Input array. This will be flattened if it is not already 1-D.
sort : boolean, optional
Whether or not to sort unique values.
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pydata/xarray | xarray/core/groupby.py | _consolidate_slices | def _consolidate_slices(slices):
"""Consolidate adjacent slices in a list of slices.
"""
result = []
last_slice = slice(None)
for slice_ in slices:
if not isinstance(slice_, slice):
raise ValueError('list element is not a slice: %r' % slice_)
if (result and last_slice.sto... | python | def _consolidate_slices(slices):
"""Consolidate adjacent slices in a list of slices.
"""
result = []
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pydata/xarray | xarray/core/groupby.py | _inverse_permutation_indices | def _inverse_permutation_indices(positions):
"""Like inverse_permutation, but also handles slices.
Parameters
----------
positions : list of np.ndarray or slice objects.
If slice objects, all are assumed to be slices.
Returns
-------
np.ndarray of indices or None, if no permutation... | python | def _inverse_permutation_indices(positions):
"""Like inverse_permutation, but also handles slices.
Parameters
----------
positions : list of np.ndarray or slice objects.
If slice objects, all are assumed to be slices.
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pydata/xarray | xarray/core/groupby.py | _apply_loffset | def _apply_loffset(grouper, result):
"""
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if loffset is set, offset the result index
This is NOT an idempotent routine, it will be applied
exactly once to the result.
Parameters
----------
result : Series or DataFrame
the result of resample
"""
need... | python | def _apply_loffset(grouper, result):
"""
(copied from pandas)
if loffset is set, offset the result index
This is NOT an idempotent routine, it will be applied
exactly once to the result.
Parameters
----------
result : Series or DataFrame
the result of resample
"""
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pydata/xarray | xarray/core/groupby.py | GroupBy._iter_grouped | def _iter_grouped(self):
"""Iterate over each element in this group"""
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yield self._obj.isel(**{self._group_dim: indices}) | python | def _iter_grouped(self):
"""Iterate over each element in this group"""
for indices in self._group_indices:
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pydata/xarray | xarray/core/groupby.py | GroupBy._maybe_restore_empty_groups | def _maybe_restore_empty_groups(self, combined):
"""Our index contained empty groups (e.g., from a resampling). If we
reduced on that dimension, we want to restore the full index.
"""
if (self._full_index is not None and
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ind... | python | def _maybe_restore_empty_groups(self, combined):
"""Our index contained empty groups (e.g., from a resampling). If we
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"""
if (self._full_index is not None and
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pydata/xarray | xarray/core/groupby.py | GroupBy._maybe_unstack | def _maybe_unstack(self, obj):
"""This gets called if we are applying on an array with a
multidimensional group."""
if self._stacked_dim is not None and self._stacked_dim in obj.dims:
obj = obj.unstack(self._stacked_dim)
for dim in self._inserted_dims:
if ... | python | def _maybe_unstack(self, obj):
"""This gets called if we are applying on an array with a
multidimensional group."""
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obj = obj.unstack(self._stacked_dim)
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pydata/xarray | xarray/core/groupby.py | GroupBy.first | def first(self, skipna=None, keep_attrs=None):
"""Return the first element of each group along the group dimension
"""
return self._first_or_last(duck_array_ops.first, skipna, keep_attrs) | python | def first(self, skipna=None, keep_attrs=None):
"""Return the first element of each group along the group dimension
"""
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pydata/xarray | xarray/core/groupby.py | GroupBy.last | def last(self, skipna=None, keep_attrs=None):
"""Return the last element of each group along the group dimension
"""
return self._first_or_last(duck_array_ops.last, skipna, keep_attrs) | python | def last(self, skipna=None, keep_attrs=None):
"""Return the last element of each group along the group dimension
"""
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pydata/xarray | xarray/core/groupby.py | DataArrayGroupBy.apply | def apply(self, func, shortcut=False, args=(), **kwargs):
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pydata/xarray | xarray/core/groupby.py | DataArrayGroupBy._combine | def _combine(self, applied, shortcut=False):
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pydata/xarray | xarray/convert.py | from_cdms2 | def from_cdms2(variable):
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"""Convert a cdms2 variable into an DataArray
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name = variable.id
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pydata/xarray | xarray/convert.py | to_cdms2 | def to_cdms2(dataarray, copy=True):
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def set_cdms2_attrs(var, attrs):
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# 1D axes
axes = []
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pydata/xarray | xarray/convert.py | _pick_attrs | def _pick_attrs(attrs, keys):
""" Return attrs with keys in keys list
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pydata/xarray | xarray/convert.py | _get_iris_args | def _get_iris_args(attrs):
""" Converts the xarray attrs into args that can be passed into Iris
"""
# iris.unit is deprecated in Iris v1.9
import cf_units
args = {'attributes': _filter_attrs(attrs, iris_forbidden_keys)}
args.update(_pick_attrs(attrs, ('standard_name', 'long_name',)))
unit_ar... | python | def _get_iris_args(attrs):
""" Converts the xarray attrs into args that can be passed into Iris
"""
# iris.unit is deprecated in Iris v1.9
import cf_units
args = {'attributes': _filter_attrs(attrs, iris_forbidden_keys)}
args.update(_pick_attrs(attrs, ('standard_name', 'long_name',)))
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pydata/xarray | xarray/convert.py | to_iris | def to_iris(dataarray):
""" Convert a DataArray into a Iris Cube
"""
# Iris not a hard dependency
import iris
from iris.fileformats.netcdf import parse_cell_methods
dim_coords = []
aux_coords = []
for coord_name in dataarray.coords:
coord = encode(dataarray.coords[coord_name])
... | python | def to_iris(dataarray):
""" Convert a DataArray into a Iris Cube
"""
# Iris not a hard dependency
import iris
from iris.fileformats.netcdf import parse_cell_methods
dim_coords = []
aux_coords = []
for coord_name in dataarray.coords:
coord = encode(dataarray.coords[coord_name])
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pydata/xarray | xarray/convert.py | _iris_obj_to_attrs | def _iris_obj_to_attrs(obj):
""" Return a dictionary of attrs when given a Iris object
"""
attrs = {'standard_name': obj.standard_name,
'long_name': obj.long_name}
if obj.units.calendar:
attrs['calendar'] = obj.units.calendar
if obj.units.origin != '1' and not obj.units.is_unkno... | python | def _iris_obj_to_attrs(obj):
""" Return a dictionary of attrs when given a Iris object
"""
attrs = {'standard_name': obj.standard_name,
'long_name': obj.long_name}
if obj.units.calendar:
attrs['calendar'] = obj.units.calendar
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pydata/xarray | xarray/convert.py | _iris_cell_methods_to_str | def _iris_cell_methods_to_str(cell_methods_obj):
""" Converts a Iris cell methods into a string
"""
cell_methods = []
for cell_method in cell_methods_obj:
names = ''.join(['{}: '.format(n) for n in cell_method.coord_names])
intervals = ' '.join(['interval: {}'.format(interval)
... | python | def _iris_cell_methods_to_str(cell_methods_obj):
""" Converts a Iris cell methods into a string
"""
cell_methods = []
for cell_method in cell_methods_obj:
names = ''.join(['{}: '.format(n) for n in cell_method.coord_names])
intervals = ' '.join(['interval: {}'.format(interval)
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pydata/xarray | xarray/convert.py | _name | def _name(iris_obj, default='unknown'):
""" Mimicks `iris_obj.name()` but with different name resolution order.
Similar to iris_obj.name() method, but using iris_obj.var_name first to
enable roundtripping.
"""
return (iris_obj.var_name or iris_obj.standard_name or
iris_obj.long_name or ... | python | def _name(iris_obj, default='unknown'):
""" Mimicks `iris_obj.name()` but with different name resolution order.
Similar to iris_obj.name() method, but using iris_obj.var_name first to
enable roundtripping.
"""
return (iris_obj.var_name or iris_obj.standard_name or
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pydata/xarray | xarray/convert.py | from_iris | def from_iris(cube):
""" Convert a Iris cube into an DataArray
"""
import iris.exceptions
from xarray.core.pycompat import dask_array_type
name = _name(cube)
if name == 'unknown':
name = None
dims = []
for i in range(cube.ndim):
try:
dim_coord = cube.coord(di... | python | def from_iris(cube):
""" Convert a Iris cube into an DataArray
"""
import iris.exceptions
from xarray.core.pycompat import dask_array_type
name = _name(cube)
if name == 'unknown':
name = None
dims = []
for i in range(cube.ndim):
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dim_coord = cube.coord(di... | [
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pydata/xarray | xarray/coding/times.py | decode_cf_datetime | def decode_cf_datetime(num_dates, units, calendar=None, use_cftime=None):
"""Given an array of numeric dates in netCDF format, convert it into a
numpy array of date time objects.
For standard (Gregorian) calendars, this function uses vectorized
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"""Given an array of numeric dates in netCDF format, convert it into a
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pydata/xarray | xarray/coding/times.py | decode_cf_timedelta | def decode_cf_timedelta(num_timedeltas, units):
"""Given an array of numeric timedeltas in netCDF format, convert it into a
numpy timedelta64[ns] array.
"""
num_timedeltas = np.asarray(num_timedeltas)
units = _netcdf_to_numpy_timeunit(units)
shape = num_timedeltas.shape
num_timedeltas = num... | python | def decode_cf_timedelta(num_timedeltas, units):
"""Given an array of numeric timedeltas in netCDF format, convert it into a
numpy timedelta64[ns] array.
"""
num_timedeltas = np.asarray(num_timedeltas)
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pydata/xarray | xarray/coding/times.py | infer_calendar_name | def infer_calendar_name(dates):
"""Given an array of datetimes, infer the CF calendar name"""
if np.asarray(dates).dtype == 'datetime64[ns]':
return 'proleptic_gregorian'
else:
return np.asarray(dates).ravel()[0].calendar | python | def infer_calendar_name(dates):
"""Given an array of datetimes, infer the CF calendar name"""
if np.asarray(dates).dtype == 'datetime64[ns]':
return 'proleptic_gregorian'
else:
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pydata/xarray | xarray/coding/times.py | infer_datetime_units | def infer_datetime_units(dates):
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'hours', 'minutes' or 'seconds' (the first one that can evenly divide all
unique time deltas in `dates`)
"""
dates = n... | python | def infer_datetime_units(dates):
"""Given an array of datetimes, returns a CF compatible time-unit string of
the form "{time_unit} since {date[0]}", where `time_unit` is 'days',
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"""
return '{:04d}-{:02d}-{:02d} {:02d}:{:02d}:{:02d}.{:06d}'.format(
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divide all unique time deltas in `deltas`)
"""
deltas = pd.to_timedelta(np.asarray(deltas).ravel(), box=False)
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"""Given an array of timedeltas, returns a CF compatible time-unit from
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times = np.asarray(times)
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# Use pandas.Timestamp in place... | python | def cftime_to_nptime(times):
"""Given an array of cftime.datetime objects, return an array of
numpy.datetime64 objects of the same size"""
times = np.asarray(times)
new = np.empty(times.shape, dtype='M8[ns]')
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pydata/xarray | xarray/coding/times.py | _encode_datetime_with_cftime | def _encode_datetime_with_cftime(dates, units, calendar):
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"""
cftime = _import_cftime()
if np.issubdtype(dates.... | python | def _encode_datetime_with_cftime(dates, units, calendar):
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pydata/xarray | xarray/coding/times.py | encode_cf_datetime | def encode_cf_datetime(dates, units=None, calendar=None):
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Unlike `date2num`, this function can handle datetime64 arrays.
See also
--------
cftime.date2num
"""
da... | python | def encode_cf_datetime(dates, units=None, calendar=None):
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Unlike `date2num`, this function can handle datetime64 arrays.
See also
--------
cftime.date2num
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pydata/xarray | xarray/backends/api.py | _validate_dataset_names | def _validate_dataset_names(dataset):
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pydata/xarray | xarray/backends/api.py | open_dataset | def open_dataset(filename_or_obj, group=None, decode_cf=True,
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backend_kwargs=None,... | python | def open_dataset(filename_or_obj, group=None, decode_cf=True,
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pydata/xarray | xarray/backends/api.py | open_dataarray | def open_dataarray(filename_or_obj, group=None, decode_cf=True,
mask_and_scale=None, decode_times=True, autoclose=None,
concat_characters=True, decode_coords=True, engine=None,
chunks=None, lock=None, cache=None, drop_variables=None,
backend_kw... | python | def open_dataarray(filename_or_obj, group=None, decode_cf=True,
mask_and_scale=None, decode_times=True, autoclose=None,
concat_characters=True, decode_coords=True, engine=None,
chunks=None, lock=None, cache=None, drop_variables=None,
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pydata/xarray | xarray/backends/api.py | open_mfdataset | def open_mfdataset(paths, chunks=None, concat_dim=_CONCAT_DIM_DEFAULT,
compat='no_conflicts', preprocess=None, engine=None,
lock=None, data_vars='all', coords='different',
autoclose=None, parallel=False, **kwargs):
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... | python | def open_mfdataset(paths, chunks=None, concat_dim=_CONCAT_DIM_DEFAULT,
compat='no_conflicts', preprocess=None, engine=None,
lock=None, data_vars='all', coords='different',
autoclose=None, parallel=False, **kwargs):
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pydata/xarray | xarray/backends/api.py | to_netcdf | def to_netcdf(dataset, path_or_file=None, mode='w', format=None, group=None,
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multifile=False):
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writer = ArrayWriter()
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pydata/xarray | xarray/backends/api.py | save_mfdataset | def save_mfdataset(datasets, paths, mode='w', format=None, groups=None,
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pydata/xarray | xarray/backends/api.py | to_zarr | def to_zarr(dataset, store=None, mode='w-', synchronizer=None, group=None,
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a zarr ztore
See `Dataset.to_zarr` for full API docs.
"""
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pydata/xarray | xarray/core/pdcompat.py | remove_unused_levels | def remove_unused_levels(self):
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pydata/xarray | xarray/backends/common.py | AbstractDataStore.load | def load(self):
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class SuffixAppendingDataStore(AbstractDataStore):
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Encode the variables and attributes in this store
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variables : dict-like
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Encode the variables and attributes in this store
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variables : dict-like
Dictionary of key/value (variable name / xr.Variable) pairs
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pydata/xarray | xarray/backends/common.py | AbstractWritableDataStore.store | def store(self, variables, attributes, check_encoding_set=frozenset(),
writer=None, unlimited_dims=None):
"""
Top level method for putting data on this store, this method:
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- sets dimensions
- sets variables
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... | python | def store(self, variables, attributes, check_encoding_set=frozenset(),
writer=None, unlimited_dims=None):
"""
Top level method for putting data on this store, this method:
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- sets dimensions
- sets variables
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"""
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Parameters
----------
attributes : dict-like
Dictionary of key/value (attribute name / attribute) pairs
"""
for k, v ... | python | def set_attributes(self, attributes):
"""
This provides a centralized method to set the dataset attributes on the
data store.
Parameters
----------
attributes : dict-like
Dictionary of key/value (attribute name / attribute) pairs
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pydata/xarray | xarray/backends/common.py | AbstractWritableDataStore.set_variables | def set_variables(self, variables, check_encoding_set, writer,
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"""
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store.
Parameters
----------
variables : dict-like
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"""
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store.
Parameters
----------
variables : dict-like
Dictionary of key/value (variable name / xr.Variable) pairs
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variables : dict-like
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pydata/xarray | xarray/core/accessors.py | _season_from_months | def _season_from_months(months):
"""Compute season (DJF, MAM, JJA, SON) from month ordinal
"""
# TODO: Move "season" accessor upstream into pandas
seasons = np.array(['DJF', 'MAM', 'JJA', 'SON'])
months = np.asarray(months)
return seasons[(months // 3) % 4] | python | def _season_from_months(months):
"""Compute season (DJF, MAM, JJA, SON) from month ordinal
"""
# TODO: Move "season" accessor upstream into pandas
seasons = np.array(['DJF', 'MAM', 'JJA', 'SON'])
months = np.asarray(months)
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pydata/xarray | xarray/core/accessors.py | _access_through_cftimeindex | def _access_through_cftimeindex(values, name):
"""Coerce an array of datetime-like values to a CFTimeIndex
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"""
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values_as_cftimeindex = CFTimeIndex(values.ravel())
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"""Coerce an array of datetime-like values to a CFTimeIndex
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"""
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values_as_cftimeindex = CFTimeIndex(values.ravel())
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pydata/xarray | xarray/core/accessors.py | _access_through_series | def _access_through_series(values, name):
"""Coerce an array of datetime-like values to a pandas Series and
access requested datetime component
"""
values_as_series = pd.Series(values.ravel())
if name == "season":
months = values_as_series.dt.month.values
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"""Coerce an array of datetime-like values to a pandas Series and
access requested datetime component
"""
values_as_series = pd.Series(values.ravel())
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pydata/xarray | xarray/core/accessors.py | _get_date_field | def _get_date_field(values, name, dtype):
"""Indirectly access pandas' libts.get_date_field by wrapping data
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Parameters
----------
values : np.ndarray or dask.array-like
Array-like container of datetime-like values
name : str
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"""Indirectly access pandas' libts.get_date_field by wrapping data
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"""Coerce an array of datetime-like values to a pandas Series and
apply requested rounding
"""
values_as_series = pd.Series(values.ravel())
method = getattr(values_as_series.dt, name)
field_values = method(freq=freq).values
return field_values.reshape(... | python | def _round_series(values, name, freq):
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pydata/xarray | xarray/core/accessors.py | _round_field | def _round_field(values, name, freq):
"""Indirectly access pandas rounding functions by wrapping data
as a Series and calling through `.dt` attribute.
Parameters
----------
values : np.ndarray or dask.array-like
Array-like container of datetime-like values
name : str (ceil, floor, round... | python | def _round_field(values, name, freq):
"""Indirectly access pandas rounding functions by wrapping data
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values : np.ndarray or dask.array-like
Array-like container of datetime-like values
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pydata/xarray | xarray/core/ops.py | fillna | def fillna(data, other, join="left", dataset_join="left"):
"""Fill missing values in this object with data from the other object.
Follows normal broadcasting and alignment rules.
Parameters
----------
join : {'outer', 'inner', 'left', 'right'}, optional
Method for joining the indexes of the... | python | def fillna(data, other, join="left", dataset_join="left"):
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pydata/xarray | xarray/core/ops.py | where_method | def where_method(self, cond, other=dtypes.NA):
"""Return elements from `self` or `other` depending on `cond`.
Parameters
----------
cond : DataArray or Dataset with boolean dtype
Locations at which to preserve this objects values.
other : scalar, DataArray or Dataset, optional
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pydata/xarray | xarray/tutorial.py | open_dataset | def open_dataset(name, cache=True, cache_dir=_default_cache_dir,
github_url='https://github.com/pydata/xarray-data',
branch='master', **kws):
"""
Load a dataset from the online repository (requires internet).
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github_url='https://github.com/pydata/xarray-data',
branch='master', **kws):
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pydata/xarray | xarray/tutorial.py | load_dataset | def load_dataset(*args, **kwargs):
"""
`load_dataset` will be removed a future version of xarray. The current
behavior of this function can be achived by using
`tutorial.open_dataset(...).load()`.
See Also
--------
open_dataset
"""
warnings.warn(
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"""
`load_dataset` will be removed a future version of xarray. The current
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`tutorial.open_dataset(...).load()`.
See Also
--------
open_dataset
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pydata/xarray | xarray/backends/file_manager.py | CachingFileManager._make_key | def _make_key(self):
"""Make a key for caching files in the LRU cache."""
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tuple(sorted(self._kwargs.items())))
return _HashedSequence(value) | python | def _make_key(self):
"""Make a key for caching files in the LRU cache."""
value = (self._opener,
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pydata/xarray | xarray/backends/file_manager.py | CachingFileManager.acquire | def acquire(self, needs_lock=True):
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A new file is only opened if it has expired from the
least-recently-used cache.
This method uses a lock, which ensures that it is thread-safe. You can
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pydata/xarray | xarray/backends/file_manager.py | CachingFileManager.close | def close(self, needs_lock=True):
"""Explicitly close any associated file object (if necessary)."""
# TODO: remove needs_lock if/when we have a reentrant lock in
# dask.distributed: https://github.com/dask/dask/issues/3832
with self._optional_lock(needs_lock):
default = None
... | python | def close(self, needs_lock=True):
"""Explicitly close any associated file object (if necessary)."""
# TODO: remove needs_lock if/when we have a reentrant lock in
# dask.distributed: https://github.com/dask/dask/issues/3832
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pydata/xarray | xarray/backends/lru_cache.py | LRUCache._enforce_size_limit | def _enforce_size_limit(self, capacity):
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self._on_evict(key, value) | python | def _enforce_size_limit(self, capacity):
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pydata/xarray | xarray/backends/lru_cache.py | LRUCache.maxsize | def maxsize(self, size):
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"""Resize the cache, evicting the oldest items if necessary."""
if size < 0:
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pydata/xarray | xarray/util/print_versions.py | get_sys_info | def get_sys_info():
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blob = []
# get full commit hash
commit = None
if os.path.isdir(".git") and os.path.isdir("xarray"):
try:
pipe = subprocess.Popen('git log --format="%H" -n 1'.split(" "),
stdout=subpr... | python | def get_sys_info():
"Returns system information as a dict"
blob = []
# get full commit hash
commit = None
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"""Given a key for indexing an ndarray, return an equivalent key which is a
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pydata/xarray | xarray/core/indexing.py | _asarray_tuplesafe | def _asarray_tuplesafe(values):
"""
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tuples.
Adapted from pandas.core.common._asarray_tuplesafe
"""
if isinstance(values, tuple):
result = utils.to_0d_object_array(values)
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result = np.asarray(val... | python | def _asarray_tuplesafe(values):
"""
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Adapted from pandas.core.common._asarray_tuplesafe
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pydata/xarray | xarray/core/indexing.py | get_indexer_nd | def get_indexer_nd(index, labels, method=None, tolerance=None):
""" Call pd.Index.get_indexer(labels). """
kwargs = _index_method_kwargs(method, tolerance)
flat_labels = np.ravel(labels)
flat_indexer = index.get_indexer(flat_labels, **kwargs)
indexer = flat_indexer.reshape(labels.shape)
return ... | python | def get_indexer_nd(index, labels, method=None, tolerance=None):
""" Call pd.Index.get_indexer(labels). """
kwargs = _index_method_kwargs(method, tolerance)
flat_labels = np.ravel(labels)
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pydata/xarray | xarray/core/indexing.py | convert_label_indexer | def convert_label_indexer(index, label, index_name='', method=None,
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pydata/xarray | xarray/core/indexing.py | remap_label_indexers | def remap_label_indexers(data_obj, indexers, method=None, tolerance=None):
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"""
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pydata/xarray | xarray/core/indexing.py | slice_slice | def slice_slice(old_slice, applied_slice, size):
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the slices sequentially
"""
step = (old_slice.step or 1) * (applied_slice.step or 1)
# For now, u... | python | def slice_slice(old_slice, applied_slice, size):
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pydata/xarray | xarray/core/indexing.py | as_indexable | def as_indexable(array):
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"""
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pydata/xarray | xarray/core/indexing.py | _outer_to_vectorized_indexer | def _outer_to_vectorized_indexer(key, shape):
"""Convert an OuterIndexer into an vectorized indexer.
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An indexer to convert.
shape : tuple
Shape of the array subject to the indexing.
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"""Convert an OuterIndexer into an vectorized indexer.
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----------
key : Outer/Basic Indexer
An indexer to convert.
shape : tuple
Shape of the array subject to the indexing.
Returns
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----------
key : Basic/OuterIndexer
An indexer to convert.
shape : tuple
Shape of the array subject to the indexing.
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-------
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""" Combine two indexers.
Parameters
----------
old_key: ExplicitIndexer
The first indexer for the original array
shape: tuple of ints
Shape of the original array to be indexed by old_key
new_key:
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""" Combine two indexers.
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----------
old_key: ExplicitIndexer
The first indexer for the original array
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Shape of the original array to be indexed by old_key
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pydata/xarray | xarray/core/indexing.py | explicit_indexing_adapter | def explicit_indexing_adapter(
key, shape, indexing_support, raw_indexing_method):
"""Support explicit indexing by delegating to a raw indexing method.
Outer and/or vectorized indexers are supported by indexing a second time
with a NumPy array.
Parameters
----------
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key, shape, indexing_support, raw_indexing_method):
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pydata/xarray | xarray/core/indexing.py | _decompose_slice | def _decompose_slice(key, size):
""" convert a slice to successive two slices. The first slice always has
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"""
start, stop, step = key.indices(size)
if step > 0:
# If key already has a positive step, use it as is in the backend
return key, slice(None)
else:
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""" convert a slice to successive two slices. The first slice always has
a positive step.
"""
start, stop, step = key.indices(size)
if step > 0:
# If key already has a positive step, use it as is in the backend
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pydata/xarray | xarray/core/indexing.py | _decompose_vectorized_indexer | def _decompose_vectorized_indexer(indexer, shape, indexing_support):
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Decompose vectorized indexer to the successive two indexers, where the
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is used to index loaded on-memory np.ndarray.
Parameters
----------
indexe... | python | def _decompose_vectorized_indexer(indexer, shape, indexing_support):
"""
Decompose vectorized indexer to the successive two indexers, where the
first indexer will be used to index backend arrays, while the second one
is used to index loaded on-memory np.ndarray.
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pydata/xarray | xarray/core/indexing.py | _decompose_outer_indexer | def _decompose_outer_indexer(indexer, shape, indexing_support):
"""
Decompose outer indexer to the successive two indexers, where the
first indexer will be used to index backend arrays, while the second one
is used to index the loaded on-memory np.ndarray.
Parameters
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indexer: Vec... | python | def _decompose_outer_indexer(indexer, shape, indexing_support):
"""
Decompose outer indexer to the successive two indexers, where the
first indexer will be used to index backend arrays, while the second one
is used to index the loaded on-memory np.ndarray.
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""" Return an identical vindex but slices are replaced by arrays """
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if len(slices) == 0:
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""" Return an identical vindex but slices are replaced by arrays """
slices = [v for v in indexer.tuple if isinstance(v, slice)]
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pydata/xarray | xarray/core/indexing.py | _dask_array_with_chunks_hint | def _dask_array_with_chunks_hint(array, chunks):
"""Create a dask array using the chunks hint for dimensions of size > 1."""
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if len(chunks) < array.ndim:
raise ValueError('not enough chunks in hint')
new_chunks = []
for chunk, size in zip(chunks, array.shape):
... | python | def _dask_array_with_chunks_hint(array, chunks):
"""Create a dask array using the chunks hint for dimensions of size > 1."""
import dask.array as da
if len(chunks) < array.ndim:
raise ValueError('not enough chunks in hint')
new_chunks = []
for chunk, size in zip(chunks, array.shape):
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pydata/xarray | xarray/core/indexing.py | create_mask | def create_mask(indexer, shape, chunks_hint=None):
"""Create a mask for indexing with a fill-value.
Parameters
----------
indexer : ExplicitIndexer
Indexer with -1 in integer or ndarray value to indicate locations in
the result that should be masked.
shape : tuple
Shape of t... | python | def create_mask(indexer, shape, chunks_hint=None):
"""Create a mask for indexing with a fill-value.
Parameters
----------
indexer : ExplicitIndexer
Indexer with -1 in integer or ndarray value to indicate locations in
the result that should be masked.
shape : tuple
Shape of t... | [
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pydata/xarray | xarray/core/indexing.py | _posify_mask_subindexer | def _posify_mask_subindexer(index):
"""Convert masked indices in a flat array to the nearest unmasked index.
Parameters
----------
index : np.ndarray
One dimensional ndarray with dtype=int.
Returns
-------
np.ndarray
One dimensional ndarray with all values equal to -1 repla... | python | def _posify_mask_subindexer(index):
"""Convert masked indices in a flat array to the nearest unmasked index.
Parameters
----------
index : np.ndarray
One dimensional ndarray with dtype=int.
Returns
-------
np.ndarray
One dimensional ndarray with all values equal to -1 repla... | [
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pydata/xarray | xarray/core/indexing.py | posify_mask_indexer | def posify_mask_indexer(indexer):
"""Convert masked values (-1) in an indexer to nearest unmasked values.
This routine is useful for dask, where it can be much faster to index
adjacent points than arbitrary points from the end of an array.
Parameters
----------
indexer : ExplicitIndexer
... | python | def posify_mask_indexer(indexer):
"""Convert masked values (-1) in an indexer to nearest unmasked values.
This routine is useful for dask, where it can be much faster to index
adjacent points than arbitrary points from the end of an array.
Parameters
----------
indexer : ExplicitIndexer
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Parameters
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pydata/xarray | xarray/plot/utils.py | import_seaborn | def import_seaborn():
'''import seaborn and handle deprecation of apionly module'''
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
try:
import seaborn.apionly as sns
if (w and issubclass(w[-1].category, UserWarning) and
... | python | def import_seaborn():
'''import seaborn and handle deprecation of apionly module'''
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
try:
import seaborn.apionly as sns
if (w and issubclass(w[-1].category, UserWarning) and
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