INSTRUCTION stringlengths 1 46.3k | RESPONSE stringlengths 75 80.2k |
|---|---|
Iterate over each element in this group | def _iter_grouped(self):
"""Iterate over each element in this group"""
for indices in self._group_indices:
yield self._obj.isel(**{self._group_dim: indices}) |
Our index contained empty groups (e.g., from a resampling). If we
reduced on that dimension, we want to restore the full index. | 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
self._group.name in combined.dims):
ind... |
This gets called if we are applying on an array with a
multidimensional group. | 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 ... |
Return the first element of each group along the group dimension | 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) |
Return the last element of each group along the group dimension | 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) |
Fast version of `_iter_grouped` that yields Variables without
metadata | def _iter_grouped_shortcut(self):
"""Fast version of `_iter_grouped` that yields Variables without
metadata
"""
var = self._obj.variable
for indices in self._group_indices:
yield var[{self._group_dim: indices}] |
Apply a function over each array in the group and concatenate them
together into a new array.
`func` is called like `func(ar, *args, **kwargs)` for each array `ar`
in this group.
Apply uses heuristics (like `pandas.GroupBy.apply`) to figure out how
to stack together the array. ... | def apply(self, func, shortcut=False, args=(), **kwargs):
"""Apply a function over each array in the group and concatenate them
together into a new array.
`func` is called like `func(ar, *args, **kwargs)` for each array `ar`
in this group.
Apply uses heuristics (like `pandas.Gr... |
Recombine the applied objects like the original. | def _combine(self, applied, shortcut=False):
"""Recombine the applied objects like the original."""
applied_example, applied = peek_at(applied)
coord, dim, positions = self._infer_concat_args(applied_example)
if shortcut:
combined = self._concat_shortcut(applied, dim, positio... |
Apply a function over each Dataset in the group and concatenate them
together into a new Dataset.
`func` is called like `func(ds, *args, **kwargs)` for each dataset `ds`
in this group.
Apply uses heuristics (like `pandas.GroupBy.apply`) to figure out how
to stack together the d... | def apply(self, func, args=(), **kwargs):
"""Apply a function over each Dataset in the group and concatenate them
together into a new Dataset.
`func` is called like `func(ds, *args, **kwargs)` for each dataset `ds`
in this group.
Apply uses heuristics (like `pandas.GroupBy.appl... |
Reduce the items in this group by applying `func` along some
dimension(s).
Parameters
----------
func : function
Function which can be called in the form
`func(x, axis=axis, **kwargs)` to return the result of collapsing
an np.ndarray over an integer v... | def reduce(self, func, dim=None, axis=None,
keep_attrs=None, shortcut=True, **kwargs):
"""Reduce the items in this group by applying `func` along some
dimension(s).
Parameters
----------
func : function
Function which can be called in the form
... |
Recombine the applied objects like the original. | def _combine(self, applied):
"""Recombine the applied objects like the original."""
applied_example, applied = peek_at(applied)
coord, dim, positions = self._infer_concat_args(applied_example)
combined = concat(applied, dim)
combined = _maybe_reorder(combined, dim, positions)
... |
Reduce the items in this group by applying `func` along some
dimension(s).
Parameters
----------
func : function
Function which can be called in the form
`func(x, axis=axis, **kwargs)` to return the result of collapsing
an np.ndarray over an integer v... | def reduce(self, func, dim=None, keep_attrs=None, **kwargs):
"""Reduce the items in this group by applying `func` along some
dimension(s).
Parameters
----------
func : function
Function which can be called in the form
`func(x, axis=axis, **kwargs)` to ret... |
Return attrs that are not in ignored_attrs | def _filter_attrs(attrs, ignored_attrs):
""" Return attrs that are not in ignored_attrs
"""
return dict((k, v) for k, v in attrs.items() if k not in ignored_attrs) |
Convert a cdms2 variable into an DataArray | def from_cdms2(variable):
"""Convert a cdms2 variable into an DataArray
"""
values = np.asarray(variable)
name = variable.id
dims = variable.getAxisIds()
coords = {}
for axis in variable.getAxisList():
coords[axis.id] = DataArray(
np.asarray(axis), dims=[axis.id],
... |
Convert a DataArray into a cdms2 variable | def to_cdms2(dataarray, copy=True):
"""Convert a DataArray into a cdms2 variable
"""
# we don't want cdms2 to be a hard dependency
import cdms2
def set_cdms2_attrs(var, attrs):
for k, v in attrs.items():
setattr(var, k, v)
# 1D axes
axes = []
for dim in dataarray.di... |
Return attrs with keys in keys list | def _pick_attrs(attrs, keys):
""" Return attrs with keys in keys list
"""
return dict((k, v) for k, v in attrs.items() if k in keys) |
Converts the xarray attrs into args that can be passed into Iris | 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... |
Convert a DataArray into a Iris Cube | 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])
... |
Return a dictionary of attrs when given a Iris object | 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... |
Converts a Iris cell methods into a string | 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)
... |
Convert a Iris cube into an DataArray | 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... |
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. | 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 ... |
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
operations, which makes it much faster than cftime.num2date. In such a
case, the returned array will be of type np.datetime64.
Not... | 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
operations, which makes it much faster than cftime.num2dat... |
Given an array of numeric timedeltas in netCDF format, convert it into a
numpy timedelta64[ns] array. | 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... |
Given an array of datetimes, infer the CF 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 |
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',
'hours', 'minutes' or 'seconds' (the first one that can evenly divide all
unique time deltas in `dates`) | 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',
'hours', 'minutes' or 'seconds' (the first one that can evenly divide all
unique time deltas in `dates`)
"""
dates = n... |
Converts a cftime.datetime object to a string with the format:
YYYY-MM-DD HH:MM:SS.UUUUUU | def format_cftime_datetime(date):
"""Converts a cftime.datetime object to a string with the format:
YYYY-MM-DD HH:MM:SS.UUUUUU
"""
return '{:04d}-{:02d}-{:02d} {:02d}:{:02d}:{:02d}.{:06d}'.format(
date.year, date.month, date.day, date.hour, date.minute, date.second,
date.microsecond) |
Given an array of timedeltas, returns a CF compatible time-unit from
{'days', 'hours', 'minutes' 'seconds'} (the first one that can evenly
divide all unique time deltas in `deltas`) | def infer_timedelta_units(deltas):
"""Given an array of timedeltas, returns a CF compatible time-unit from
{'days', 'hours', 'minutes' 'seconds'} (the first one that can evenly
divide all unique time deltas in `deltas`)
"""
deltas = pd.to_timedelta(np.asarray(deltas).ravel(), box=False)
unique_t... |
Given an array of cftime.datetime objects, return an array of
numpy.datetime64 objects of the same size | 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]')
for i, t in np.ndenumerate(times):
try:
# Use pandas.Timestamp in place... |
Fallback method for encoding dates using cftime.
This method is more flexible than xarray's parsing using datetime64[ns]
arrays but also slower because it loops over each element. | def _encode_datetime_with_cftime(dates, units, calendar):
"""Fallback method for encoding dates using cftime.
This method is more flexible than xarray's parsing using datetime64[ns]
arrays but also slower because it loops over each element.
"""
cftime = _import_cftime()
if np.issubdtype(dates.... |
Given an array of datetime objects, returns the tuple `(num, units,
calendar)` suitable for a CF compliant time variable.
Unlike `date2num`, this function can handle datetime64 arrays.
See also
--------
cftime.date2num | def encode_cf_datetime(dates, units=None, calendar=None):
"""Given an array of datetime objects, returns the tuple `(num, units,
calendar)` suitable for a CF compliant time variable.
Unlike `date2num`, this function can handle datetime64 arrays.
See also
--------
cftime.date2num
"""
da... |
DataArray.name and Dataset keys must be a string or None | def _validate_dataset_names(dataset):
"""DataArray.name and Dataset keys must be a string or None"""
def check_name(name):
if isinstance(name, str):
if not name:
raise ValueError('Invalid name for DataArray or Dataset key: '
'string must be le... |
`attrs` must have a string key and a value which is either: a number,
a string, an ndarray or a list/tuple of numbers/strings. | def _validate_attrs(dataset):
"""`attrs` must have a string key and a value which is either: a number,
a string, an ndarray or a list/tuple of numbers/strings.
"""
def check_attr(name, value):
if isinstance(name, str):
if not name:
raise ValueError('Invalid name for a... |
Load and decode a dataset from a file or file-like object.
Parameters
----------
filename_or_obj : str, Path, file or xarray.backends.*DataStore
Strings and Path objects are interpreted as a path to a netCDF file
or an OpenDAP URL and opened with python-netCDF4, unless the filename
... | def open_dataset(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_kwargs=None,... |
Open an DataArray from a netCDF file containing a single data variable.
This is designed to read netCDF files with only one data variable. If
multiple variables are present then a ValueError is raised.
Parameters
----------
filename_or_obj : str, Path, file or xarray.backends.*DataStore
St... | 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... |
Open multiple files as a single dataset.
Requires dask to be installed. See documentation for details on dask [1].
Attributes from the first dataset file are used for the combined dataset.
Parameters
----------
paths : str or sequence
Either a string glob in the form "path/to/my/files/*.nc... | 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):
"""Open multiple files as a single dataset.
... |
This function creates an appropriate datastore for writing a dataset to
disk as a netCDF file
See `Dataset.to_netcdf` for full API docs.
The ``multifile`` argument is only for the private use of save_mfdataset. | def to_netcdf(dataset, path_or_file=None, mode='w', format=None, group=None,
engine=None, encoding=None, unlimited_dims=None, compute=True,
multifile=False):
"""This function creates an appropriate datastore for writing a dataset to
disk as a netCDF file
See `Dataset.to_netcdf` ... |
Store dataset contents to a backends.*DataStore object. | def dump_to_store(dataset, store, writer=None, encoder=None,
encoding=None, unlimited_dims=None):
"""Store dataset contents to a backends.*DataStore object."""
if writer is None:
writer = ArrayWriter()
if encoding is None:
encoding = {}
variables, attrs = conventions.... |
Write multiple datasets to disk as netCDF files simultaneously.
This function is intended for use with datasets consisting of dask.array
objects, in which case it can write the multiple datasets to disk
simultaneously using a shared thread pool.
When not using dask, it is no different than calling ``t... | def save_mfdataset(datasets, paths, mode='w', format=None, groups=None,
engine=None, compute=True):
"""Write multiple datasets to disk as netCDF files simultaneously.
This function is intended for use with datasets consisting of dask.array
objects, in which case it can write the multiple... |
This function creates an appropriate datastore for writing a dataset to
a zarr ztore
See `Dataset.to_zarr` for full API docs. | def to_zarr(dataset, store=None, mode='w-', synchronizer=None, group=None,
encoding=None, compute=True, consolidated=False):
"""This function creates an appropriate datastore for writing a dataset to
a zarr ztore
See `Dataset.to_zarr` for full API docs.
"""
if isinstance(store, Path):
... |
create a new MultiIndex from the current that removing
unused levels, meaning that they are not expressed in the labels
The resulting MultiIndex will have the same outward
appearance, meaning the same .values and ordering. It will also
be .equals() to the original.
.. versionadded:: 0.20.0
Retur... | def remove_unused_levels(self):
"""
create a new MultiIndex from the current that removing
unused levels, meaning that they are not expressed in the labels
The resulting MultiIndex will have the same outward
appearance, meaning the same .values and ordering. It will also
be .equals() to the orig... |
Robustly index an array, using retry logic with exponential backoff if any
of the errors ``catch`` are raised. The initial_delay is measured in ms.
With the default settings, the maximum delay will be in the range of 32-64
seconds. | def robust_getitem(array, key, catch=Exception, max_retries=6,
initial_delay=500):
"""
Robustly index an array, using retry logic with exponential backoff if any
of the errors ``catch`` are raised. The initial_delay is measured in ms.
With the default settings, the maximum delay will... |
This loads the variables and attributes simultaneously.
A centralized loading function makes it easier to create
data stores that do automatic encoding/decoding.
For example::
class SuffixAppendingDataStore(AbstractDataStore):
def load(self):
va... | def load(self):
"""
This loads the variables and attributes simultaneously.
A centralized loading function makes it easier to create
data stores that do automatic encoding/decoding.
For example::
class SuffixAppendingDataStore(AbstractDataStore):
de... |
Encode the variables and attributes in this store
Parameters
----------
variables : dict-like
Dictionary of key/value (variable name / xr.Variable) pairs
attributes : dict-like
Dictionary of key/value (attribute name / attribute) pairs
Returns
--... | def encode(self, variables, attributes):
"""
Encode the variables and attributes in this store
Parameters
----------
variables : dict-like
Dictionary of key/value (variable name / xr.Variable) pairs
attributes : dict-like
Dictionary of key/value (... |
Top level method for putting data on this store, this method:
- encodes variables/attributes
- sets dimensions
- sets variables
Parameters
----------
variables : dict-like
Dictionary of key/value (variable name / xr.Variable) pairs
attributes : ... | 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:
- encodes variables/attributes
- sets dimensions
- sets variables
Parameters
... |
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 | 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
"""
for k, v ... |
This provides a centralized method to set the variables on the data
store.
Parameters
----------
variables : dict-like
Dictionary of key/value (variable name / xr.Variable) pairs
check_encoding_set : list-like
List of variables that should be checked for ... | def set_variables(self, variables, check_encoding_set, writer,
unlimited_dims=None):
"""
This provides a centralized method to set the variables on the data
store.
Parameters
----------
variables : dict-like
Dictionary of key/value (vari... |
This provides a centralized method to set the dimensions on the data
store.
Parameters
----------
variables : dict-like
Dictionary of key/value (variable name / xr.Variable) pairs
unlimited_dims : list-like
List of dimension names that should be treated a... | def set_dimensions(self, variables, unlimited_dims=None):
"""
This provides a centralized method to set the dimensions on the data
store.
Parameters
----------
variables : dict-like
Dictionary of key/value (variable name / xr.Variable) pairs
unlimited... |
Compute season (DJF, MAM, JJA, SON) from month ordinal | 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] |
Coerce an array of datetime-like values to a CFTimeIndex
and access requested datetime component | def _access_through_cftimeindex(values, name):
"""Coerce an array of datetime-like values to a CFTimeIndex
and access requested datetime component
"""
from ..coding.cftimeindex import CFTimeIndex
values_as_cftimeindex = CFTimeIndex(values.ravel())
if name == 'season':
months = values_as_... |
Coerce an array of datetime-like values to a pandas Series and
access requested datetime component | 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
field_values = _season_from_... |
Indirectly access pandas' libts.get_date_field 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
Name of datetime field to access
dtype : dtype-li... | def _get_date_field(values, name, dtype):
"""Indirectly access pandas' libts.get_date_field 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
Name... |
Coerce an array of datetime-like values to a pandas Series and
apply requested rounding | def _round_series(values, name, freq):
"""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(... |
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)
Name of rounding function
freq ... | 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... |
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 passed objects along each
dimension
- 'outer': us... | 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... |
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
Value to use for locations in this object where ``cond`` is... | 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
Value... |
Load a dataset from the online repository (requires internet).
If a local copy is found then always use that to avoid network traffic.
Parameters
----------
name : str
Name of the netcdf file containing the dataset
ie. 'air_temperature'
cache_dir : string, optional
The dire... | 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).
If a local copy is found then always use that to avoid network t... |
`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 | 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(
"load_dataset` will be remov... |
Make a key for caching files in the LRU cache. | def _make_key(self):
"""Make a key for caching files in the LRU cache."""
value = (self._opener,
self._args,
'a' if self._mode == 'w' else self._mode,
tuple(sorted(self._kwargs.items())))
return _HashedSequence(value) |
Acquiring a file object from the manager.
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
safely acquire a file in multiple threads at the same time, as long as
the underlying... | def acquire(self, needs_lock=True):
"""Acquiring a file object from the manager.
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
safely acquire a file in multiple threads at t... |
Explicitly close any associated file object (if necessary). | 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
... |
Shrink the cache if necessary, evicting the oldest items. | def _enforce_size_limit(self, capacity):
"""Shrink the cache if necessary, evicting the oldest items."""
while len(self._cache) > capacity:
key, value = self._cache.popitem(last=False)
if self._on_evict is not None:
self._on_evict(key, value) |
Resize the cache, evicting the oldest items if necessary. | def maxsize(self, size):
"""Resize the cache, evicting the oldest items if necessary."""
if size < 0:
raise ValueError('maxsize must be non-negative')
with self._lock:
self._enforce_size_limit(size)
self._maxsize = size |
Returns system information as a dict | def get_sys_info():
"Returns system information as a dict"
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... |
Given a key for indexing an ndarray, return an equivalent key which is a
tuple with length equal to the number of dimensions.
The expansion is done by replacing all `Ellipsis` items with the right
number of full slices and then padding the key with full slices so that it
reaches the appropriate dimensi... | def expanded_indexer(key, ndim):
"""Given a key for indexing an ndarray, return an equivalent key which is a
tuple with length equal to the number of dimensions.
The expansion is done by replacing all `Ellipsis` items with the right
number of full slices and then padding the key with full slices so tha... |
Convert values into a numpy array of at most 1-dimension, while preserving
tuples.
Adapted from pandas.core.common._asarray_tuplesafe | def _asarray_tuplesafe(values):
"""
Convert values into a numpy array of at most 1-dimension, while preserving
tuples.
Adapted from pandas.core.common._asarray_tuplesafe
"""
if isinstance(values, tuple):
result = utils.to_0d_object_array(values)
else:
result = np.asarray(val... |
Call pd.Index.get_indexer(labels). | 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 ... |
Given a pandas.Index and labels (e.g., from __getitem__) for one
dimension, return an indexer suitable for indexing an ndarray along that
dimension. If `index` is a pandas.MultiIndex and depending on `label`,
return a new pandas.Index or pandas.MultiIndex (otherwise return None). | def convert_label_indexer(index, label, index_name='', method=None,
tolerance=None):
"""Given a pandas.Index and labels (e.g., from __getitem__) for one
dimension, return an indexer suitable for indexing an ndarray along that
dimension. If `index` is a pandas.MultiIndex and dependi... |
Given a xarray data object and label based indexers, return a mapping
of label indexers with only dimension names as keys.
It groups multiple level indexers given on a multi-index dimension
into a single, dictionary indexer for that dimension (Raise a ValueError
if it is not possible). | def get_dim_indexers(data_obj, indexers):
"""Given a xarray data object and label based indexers, return a mapping
of label indexers with only dimension names as keys.
It groups multiple level indexers given on a multi-index dimension
into a single, dictionary indexer for that dimension (Raise a ValueE... |
Given an xarray data object and label based indexers, return a mapping
of equivalent location based indexers. Also return a mapping of updated
pandas index objects (in case of multi-index level drop). | def remap_label_indexers(data_obj, indexers, method=None, tolerance=None):
"""Given an xarray data object and label based indexers, return a mapping
of equivalent location based indexers. Also return a mapping of updated
pandas index objects (in case of multi-index level drop).
"""
if method is not ... |
Given a slice and the size of the dimension to which it will be applied,
index it with another slice to return a new slice equivalent to applying
the slices sequentially | def slice_slice(old_slice, applied_slice, size):
"""Given a slice and the size of the dimension to which it will be applied,
index it with another slice to return a new slice equivalent to applying
the slices sequentially
"""
step = (old_slice.step or 1) * (applied_slice.step or 1)
# For now, u... |
This function always returns a ExplicitlyIndexed subclass,
so that the vectorized indexing is always possible with the returned
object. | def as_indexable(array):
"""
This function always returns a ExplicitlyIndexed subclass,
so that the vectorized indexing is always possible with the returned
object.
"""
if isinstance(array, ExplicitlyIndexed):
return array
if isinstance(array, np.ndarray):
return NumpyIndexin... |
Convert an OuterIndexer into an vectorized indexer.
Parameters
----------
key : Outer/Basic Indexer
An indexer to convert.
shape : tuple
Shape of the array subject to the indexing.
Returns
-------
VectorizedIndexer
Tuple suitable for use to index a NumPy array with ... | def _outer_to_vectorized_indexer(key, shape):
"""Convert an OuterIndexer into an vectorized indexer.
Parameters
----------
key : Outer/Basic Indexer
An indexer to convert.
shape : tuple
Shape of the array subject to the indexing.
Returns
-------
VectorizedIndexer
... |
Convert an OuterIndexer into an indexer for NumPy.
Parameters
----------
key : Basic/OuterIndexer
An indexer to convert.
shape : tuple
Shape of the array subject to the indexing.
Returns
-------
tuple
Tuple suitable for use to index a NumPy array. | def _outer_to_numpy_indexer(key, shape):
"""Convert an OuterIndexer into an indexer for NumPy.
Parameters
----------
key : Basic/OuterIndexer
An indexer to convert.
shape : tuple
Shape of the array subject to the indexing.
Returns
-------
tuple
Tuple suitable fo... |
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:
The second indexer for indexing original[old_key] | def _combine_indexers(old_key, shape, new_key):
""" 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:
The second indexer for ind... |
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
----------
key : ExplicitIndexer
Explicit indexing object.
shape : Tuple[int, ...]
Shape of the indexed arra... | 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
----------
key : ExplicitIndex... |
convert a slice to successive two slices. The first slice always has
a positive step. | def _decompose_slice(key, size):
""" 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
return key, slice(None)
else:
... |
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.
Parameters
----------
indexer: VectorizedIndexer
indexing_support: one of IndexerSupport entries
Ret... | 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.
Parameters
----------
indexe... |
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
----------
indexer: VectorizedIndexer
indexing_support: One of the entries of IndexingSupport
... | 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
----------
indexer: Vec... |
Return an identical vindex but slices are replaced by arrays | def _arrayize_vectorized_indexer(indexer, shape):
""" Return an identical vindex but slices are replaced by arrays """
slices = [v for v in indexer.tuple if isinstance(v, slice)]
if len(slices) == 0:
return indexer
arrays = [v for v in indexer.tuple if isinstance(v, np.ndarray)]
n_dim = arr... |
Create a dask array using the chunks hint for dimensions of size > 1. | 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):
... |
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 the array being indexed.
chunks_hint : tuple, optional
... | 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... |
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 replaced by an
adjacent non-masked eleme... | 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... |
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
Input indexer.
Returns
-------... | 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
... |
import seaborn and handle deprecation of apionly module | 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
... |
Build a discrete colormap and normalization of the data. | def _build_discrete_cmap(cmap, levels, extend, filled):
"""
Build a discrete colormap and normalization of the data.
"""
import matplotlib as mpl
if not filled:
# non-filled contour plots
extend = 'max'
if extend == 'both':
ext_n = 2
elif extend in ['min', 'max']:
... |
Use some heuristics to set good defaults for colorbar and range.
Parameters
==========
plot_data: Numpy array
Doesn't handle xarray objects
Returns
=======
cmap_params : dict
Use depends on the type of the plotting function | def _determine_cmap_params(plot_data, vmin=None, vmax=None, cmap=None,
center=None, robust=False, extend=None,
levels=None, filled=True, norm=None):
"""
Use some heuristics to set good defaults for colorbar and range.
Parameters
==========
plot_... |
Determine x and y labels for showing RGB images.
Attempts to infer which dimension is RGB/RGBA by size and order of dims. | def _infer_xy_labels_3d(darray, x, y, rgb):
"""
Determine x and y labels for showing RGB images.
Attempts to infer which dimension is RGB/RGBA by size and order of dims.
"""
assert rgb is None or rgb != x
assert rgb is None or rgb != y
# Start by detecting and reporting invalid combination... |
Determine x and y labels. For use in _plot2d
darray must be a 2 dimensional data array, or 3d for imshow only. | def _infer_xy_labels(darray, x, y, imshow=False, rgb=None):
"""
Determine x and y labels. For use in _plot2d
darray must be a 2 dimensional data array, or 3d for imshow only.
"""
assert x is None or x != y
if imshow and darray.ndim == 3:
return _infer_xy_labels_3d(darray, x, y, rgb)
... |
Makes informative labels if variable metadata (attrs) follows
CF conventions. | def label_from_attrs(da, extra=''):
''' Makes informative labels if variable metadata (attrs) follows
CF conventions. '''
if da.attrs.get('long_name'):
name = da.attrs['long_name']
elif da.attrs.get('standard_name'):
name = da.attrs['standard_name']
elif da.name is not None:
... |
Helper function to deal with a xarray consisting of pd.Intervals. Each
interval is replaced with both boundaries. I.e. the length of xarray
doubles. yarray is modified so it matches the new shape of xarray. | def _interval_to_double_bound_points(xarray, yarray):
"""
Helper function to deal with a xarray consisting of pd.Intervals. Each
interval is replaced with both boundaries. I.e. the length of xarray
doubles. yarray is modified so it matches the new shape of xarray.
"""
xarray1 = np.array([x.left... |
Helper function which returns an array
with the Intervals' boundaries. | def _interval_to_bound_points(array):
"""
Helper function which returns an array
with the Intervals' boundaries.
"""
array_boundaries = np.array([x.left for x in array])
array_boundaries = np.concatenate(
(array_boundaries, np.array([array[-1].right])))
return array_boundaries |
Helper function to replace the values of a coordinate array containing
pd.Interval with their mid-points or - for pcolormesh - boundaries which
increases length by 1. | def _resolve_intervals_2dplot(val, func_name):
"""
Helper function to replace the values of a coordinate array containing
pd.Interval with their mid-points or - for pcolormesh - boundaries which
increases length by 1.
"""
label_extra = ''
if _valid_other_type(val, [pd.Interval]):
if ... |
Do all elements of x have a type from types? | def _valid_other_type(x, types):
"""
Do all elements of x have a type from types?
"""
return all(any(isinstance(el, t) for t in types) for el in np.ravel(x)) |
Is any dtype from numpy_types superior to the dtype of x? | def _valid_numpy_subdtype(x, numpy_types):
"""
Is any dtype from numpy_types superior to the dtype of x?
"""
# If any of the types given in numpy_types is understood as numpy.generic,
# all possible x will be considered valid. This is probably unwanted.
for t in numpy_types:
assert not ... |
Raise exception if there is anything in args that can't be plotted on an
axis by matplotlib. | def _ensure_plottable(*args):
"""
Raise exception if there is anything in args that can't be plotted on an
axis by matplotlib.
"""
numpy_types = [np.floating, np.integer, np.timedelta64, np.datetime64]
other_types = [datetime]
try:
import cftime
cftime_datetime = [cftime.date... |
Update axes with provided parameters | def _update_axes(ax, xincrease, yincrease,
xscale=None, yscale=None,
xticks=None, yticks=None,
xlim=None, ylim=None):
"""
Update axes with provided parameters
"""
if xincrease is None:
pass
elif xincrease and ax.xaxis_inverted():
ax.... |
>>> _is_monotonic(np.array([0, 1, 2]))
True
>>> _is_monotonic(np.array([2, 1, 0]))
True
>>> _is_monotonic(np.array([0, 2, 1]))
False | def _is_monotonic(coord, axis=0):
"""
>>> _is_monotonic(np.array([0, 1, 2]))
True
>>> _is_monotonic(np.array([2, 1, 0]))
True
>>> _is_monotonic(np.array([0, 2, 1]))
False
"""
if coord.shape[axis] < 3:
return True
else:
n = coord.shape[axis]
delta_pos = (co... |
>>> _infer_interval_breaks(np.arange(5))
array([-0.5, 0.5, 1.5, 2.5, 3.5, 4.5])
>>> _infer_interval_breaks([[0, 1], [3, 4]], axis=1)
array([[-0.5, 0.5, 1.5],
[ 2.5, 3.5, 4.5]]) | def _infer_interval_breaks(coord, axis=0, check_monotonic=False):
"""
>>> _infer_interval_breaks(np.arange(5))
array([-0.5, 0.5, 1.5, 2.5, 3.5, 4.5])
>>> _infer_interval_breaks([[0, 1], [3, 4]], axis=1)
array([[-0.5, 0.5, 1.5],
[ 2.5, 3.5, 4.5]])
"""
coord = np.asarray(co... |
Parameters
==========
func : plotting function
kwargs : dict,
Dictionary with arguments that need to be parsed
data : ndarray,
Data values
Returns
=======
cmap_params
cbar_kwargs | def _process_cmap_cbar_kwargs(func, kwargs, data):
"""
Parameters
==========
func : plotting function
kwargs : dict,
Dictionary with arguments that need to be parsed
data : ndarray,
Data values
Returns
=======
cmap_params
cbar_kwargs
"""
cmap = kwargs.p... |
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