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keewiskeewis
authored
vélin (#4872)
1 parent a5f53e2 commit c83dfd1

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‎.pre-commit-config.yaml

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@@ -24,6 +24,11 @@ repos:
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rev: 3.8.4
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hooks:
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- id: flake8
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# - repo: https://github.com/Carreau/velin
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# rev: 0.0.8
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# hooks:
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# - id: velin
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# args: ["--write", "--compact"]
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- repo: https://github.com/pre-commit/mirrors-mypy
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rev: v0.790 # Must match ci/requirements/*.yml
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hooks:

‎xarray/backends/locks.py

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@@ -67,7 +67,7 @@ def _get_scheduler(get=None, collection=None) -> Optional[str]:
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None is returned if no dask scheduler is active.
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See also
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See Also
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--------
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dask.base.get_scheduler
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"""

‎xarray/backends/lru_cache.py

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@@ -34,7 +34,7 @@ def __init__(self, maxsize: int, on_evict: Callable[[K, V], Any] = None):
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----------
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maxsize : int
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Integer maximum number of items to hold in the cache.
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on_evict: callable, optional
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on_evict : callable, optional
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Function to call like ``on_evict(key, value)`` when items are
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evicted.
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"""

‎xarray/backends/rasterio_.py

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@@ -52,9 +52,9 @@ def shape(self):
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def _get_indexer(self, key):
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"""Get indexer for rasterio array.
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Parameter
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---------
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key: tuple of int
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Parameters
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----------
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key : tuple of int
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Returns
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-------
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squeeze_axis: axes to be squeezed
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np_ind: indexer for loaded numpy array
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See also
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See Also
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--------
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indexing.decompose_indexer
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"""
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nx, ny = da.sizes['x'], da.sizes['y']
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x, y = np.meshgrid(np.arange(nx)+0.5, np.arange(ny)+0.5) * transform
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Parameters
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----------
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filename : str, rasterio.DatasetReader, or rasterio.WarpedVRT

‎xarray/backends/zarr.py

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@@ -462,7 +462,7 @@ def set_variables(self, variables, check_encoding_set, writer, unlimited_dims=No
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check_encoding_set : list-like
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List of variables that should be checked for invalid encoding
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values
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writer :
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writer
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unlimited_dims : list-like
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List of dimension names that should be treated as unlimited
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dimensions.
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based on the variable's zarr chunks. If `chunks=None`, zarr array
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data will lazily convert to numpy arrays upon access. This accepts
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all the chunk specifications as Dask does.
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overwrite_encoded_chunks: bool, optional
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overwrite_encoded_chunks : bool, optional
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Whether to drop the zarr chunks encoded for each variable when a
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dataset is loaded with specified chunk sizes (default: False)
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decode_cf : bool, optional
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{'days', 'hours', 'minutes', 'seconds', 'milliseconds', 'microseconds'}
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into timedelta objects. If False, leave them encoded as numbers.
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If None (default), assume the same value of decode_time.
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use_cftime: bool, optional
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use_cftime : bool, optional
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Only relevant if encoded dates come from a standard calendar
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(e.g. "gregorian", "proleptic_gregorian", "standard", or not
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specified). If None (default), attempt to decode times to

‎xarray/coding/cftime_offsets.py

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@@ -796,7 +796,7 @@ def cftime_range(
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periods : int, optional
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Number of periods to generate.
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freq : str or None, default: "D"
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Frequency strings can have multiples, e.g. "5H".
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Frequency strings can have multiples, e.g. "5H".
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normalize : bool, default: False
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Normalize start/end dates to midnight before generating date range.
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name : str, default: None
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Notes
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-----
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This function is an analog of ``pandas.date_range`` for use in generating
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sequences of ``cftime.datetime`` objects. It supports most of the
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features of ``pandas.date_range`` (e.g. specifying how the index is
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| Q(S)-DEC | Quarter frequency, anchored at the end (or beginning) of December |
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+----------+--------------------------------------------------------------------+
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Finally, the following calendar aliases are supported.
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+--------------------------------+---------------------------------------+
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Examples
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--------
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This function returns a ``CFTimeIndex``, populated with ``cftime.datetime``
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objects associated with the specified calendar type, e.g.
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‎xarray/coding/cftimeindex.py

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@@ -516,7 +516,7 @@ def shift(self, n, freq):
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-------
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CFTimeIndex
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See Also
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--------
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pandas.DatetimeIndex.shift
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‎xarray/coding/frequencies.py

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@@ -62,8 +62,8 @@ def infer_freq(index):
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Parameters
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----------
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index : CFTimeIndex, DataArray, DatetimeIndex, TimedeltaIndex, Series
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If not passed a CFTimeIndex, this simply calls `pandas.infer_freq`.
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If passed a Series or a DataArray will use the values of the series (NOT THE INDEX).
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If not passed a CFTimeIndex, this simply calls `pandas.infer_freq`.
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If passed a Series or a DataArray will use the values of the series (NOT THE INDEX).
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Returns
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-------

‎xarray/coding/times.py

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@@ -191,7 +191,7 @@ def decode_cf_datetime(num_dates, units, calendar=None, use_cftime=None):
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Note that time unit in `units` must not be smaller than microseconds and
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not larger than days.
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See Also
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--------
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cftime.num2date
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"""
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Unlike `date2num`, this function can handle datetime64 arrays.
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--------
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cftime.date2num
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"""

‎xarray/conventions.py

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@@ -624,7 +624,7 @@ def cf_decoder(
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concat_characters : bool
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Should character arrays be concatenated to strings, for
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example: ["h", "e", "l", "l", "o"] -> "hello"
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mask_and_scale: bool
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mask_and_scale : bool
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Lazily scale (using scale_factor and add_offset) and mask
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(using _FillValue).
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decode_times : bool
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decoded_attributes : dict
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A dictionary mapping from attribute name to values.
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--------
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decode_cf_variable
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"""
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This includes masking, scaling, character array handling,
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and CF-time encoding.
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A dictionary mapping from attribute name to value
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--------
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decode_cf_variable, encode_cf_variable
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"""

‎xarray/core/accessor_dt.py

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@@ -322,8 +322,8 @@ def strftime(self, date_format):
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def isocalendar(self):
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"""Dataset containing ISO year, week number, and weekday.
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Note
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----
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Notes
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-----
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The iso year and weekday differ from the nominal year and weekday.
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"""
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‎xarray/core/alignment.py

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@@ -135,7 +135,6 @@ def align(
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Examples
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--------
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>>> import xarray as xr
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>>> x = xr.DataArray(
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... [[25, 35], [10, 24]],
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the input. In either case, new xarray objects are always returned.
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fill_value : scalar, optional
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Value to use for newly missing values
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sparse: bool, optional
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sparse : bool, optional
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Use an sparse-array
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Returns
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Examples
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Broadcast two data arrays against one another to fill out their dimensions:
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>>> a = xr.DataArray([1, 2, 3], dims="x")

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