Merge branch 'master' into faster_first_last_run

Ouranosinc · Nov 30, 2023 · e305e34 · e305e34
2 parents 6cde4a8 + a7165ab
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diff --git a/CHANGES.rst b/CHANGES.rst
@@ -8,25 +8,26 @@ Contributors to this version: Juliette Lavoie (:user:`juliettelavoie`), Pascal B
 
 Announcements
 ^^^^^^^^^^^^^
-* To circumvent issues stemming from changes to the frequency code convention in `pandas` v2.2, we have pinned `xarray` (<2023.11.0) and `pandas` (< 2.2) for this release. This change will be reverted in `xclim` v0.48.0 to support the newer versions (`xarray>= 2023.11.0` and `pandas>= 2.2`).
+* To circumvent issues stemming from changes to the frequency code convention in `pandas` v2.2, we have pinned `xarray` (<2023.11.0) and `pandas` (< 2.2) for this release. This change will be reverted in `xclim` v0.48.0 to support the newer versions (`xarray >= 2023.11.0` and `pandas >= 2.2`).
 * `xclim` v0.47.0 will be the last release supporting Python3.8.
 
 New features and enhancements
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-* New functions ``xclim.ensembles.robustness_fractions`` and ``xclim.ensembles.robustness_categories``. The former will replace ``xclim.ensembles.change_significance`` which is now deprecated and will be removed in xclim 0.49 (:pull:`1514`).
-* Add indicator ID to searched terms in the indicator search documentation page (:issue:`1525`, :pull:`1528`).
+* New functions ``xclim.ensembles.robustness_fractions`` and ``xclim.ensembles.robustness_categories``. The former will replace ``xclim.ensembles.change_significance`` which is now deprecated and will be removed in xclim 0.49. (:pull:`1514`).
+* Add indicator ID to searched terms in the indicator search documentation page. (:issue:`1525`, :pull:`1528`).
 
 Bug fixes
 ^^^^^^^^^
-* Fixed a bug with ``n_escore=-1`` in ``xclim.sdba.adjustment.NpdfTransform`` (:issue:`1515`, :pull:`1515`).
-* In the documentaion, fix the tooltips in the indicator search results (:issue:`1524`, :pull:`1527`).
-* If chunked inputs are passed to indicators ``mean_radiant_temperature`` and ``potential_evapotranspiration``, subcalculations of the solar angle will also use the same chunks, instead of a single one of the same size as the data (:issue:`1536`, :pull:`1542`).
+* Fixed a bug with ``n_escore=-1`` in ``xclim.sdba.adjustment.NpdfTransform``. (:issue:`1515`, :pull:`1515`).
+* In the documentation, fix the tooltips in the indicator search results. (:issue:`1524`, :pull:`1527`).
+* If chunked inputs are passed to indicators ``mean_radiant_temperature`` and ``potential_evapotranspiration``, sub-calculations of the solar angle will also use the same chunks, instead of a single one of the same size as the data. (:issue:`1536`, :pull:`1542`).
 
 Internal changes
 ^^^^^^^^^^^^^^^^
 * Pinned `cf-xarray` below v0.8.5 in Python3.8 installation to further extend legacy support. (:pull:`1519`).
 * `pip check` in conda builds in GitHub workflows have been temporarily set to always pass. (:pull:`1531`).
 * Configure RtD search rankings to emphasize notebooks and indicators over indices and raw source code. (:pull:`1526`).
+* Addressed around 100 very basic `mypy` typing errors and call signature errors. (:pull:`1532`).
 * Use the intermediate step ``_cumsum_reset_on_zero`` instead of ``rle`` which is sufficient in ``_boundary_run``. (:issue:`1405`, :pull:`1530`).
 
 v0.46.0 (2023-10-24)

diff --git a/pyproject.toml b/pyproject.toml
@@ -183,13 +183,16 @@ module = [
   "clisops.core.subset.*",
   "dask.*",
   "lmoments3.*",
+  "matplotlib.*",
   "numba.*",
   "numpy.*",
   "pandas.*",
   "pint.*",
+  "SBCK.*",
   "scipy.*",
   "sklearn.cluster.*",
-  "xarray.*"
+  "xarray.*",
+  "yaml.*"
 ]
 ignore_missing_imports = true
 

diff --git a/setup.cfg b/setup.cfg
@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 0.46.8-beta
+current_version = 0.46.9-beta
 commit = True
 tag = False
 parse = (?P<major>\d+)\.(?P<minor>\d+).(?P<patch>\d+)(\-(?P<release>[a-z]+))?

diff --git a/xclim/__init__.py b/xclim/__init__.py
@@ -15,7 +15,7 @@
 
 __author__ = """Travis Logan"""
 __email__ = "logan.travis@ouranos.ca"
-__version__ = "0.46.8-beta"
+__version__ = "0.46.9-beta"
 
 
 _module_data = _files("xclim.data")

diff --git a/xclim/analog.py b/xclim/analog.py
@@ -5,7 +5,7 @@
 # Code adapted from flyingpigeon.dissimilarity, Nov 2020.
 from __future__ import annotations
 
-from typing import Sequence
+from typing import Any, Sequence
 
 import numpy as np
 import pandas as pd
@@ -16,7 +16,7 @@
 from scipy import spatial
 from scipy.spatial import cKDTree as KDTree
 
-metrics = {}
+metrics: dict[str, Any] = {}
 
 
 def spatial_analogs(
@@ -61,7 +61,7 @@ def spatial_analogs(
         raise RuntimeError(f"Spatial analogue method ({method}) requires scipy>=1.6.0.")
 
     # Create the target DataArray:
-    target = target.to_array("_indices", "target")
+    target_array = target.to_array("_indices", "target")
 
     # Create the target DataArray
     # drop any (sub-)index along "dist_dim" that could conflict with target, and rename it.
@@ -86,13 +86,13 @@ def spatial_analogs(
 
     if candidates.chunks is not None:
         candidates = candidates.chunk({"_indices": -1})
-    if target.chunks is not None:
-        target = target.chunk({"_indices": -1})
+    if target_array.chunks is not None:
+        target_array = target_array.chunk({"_indices": -1})
 
     # Compute dissimilarity
     diss = xr.apply_ufunc(
         metric_func,
-        target,
+        target_array,
         candidates,
         input_core_dims=[(dist_dim, "_indices"), ("_dist_dim", "_indices")],
         output_core_dims=[()],
@@ -104,7 +104,7 @@ def spatial_analogs(
     diss.name = "dissimilarity"
     diss.attrs.update(
         long_name=f"Dissimilarity between target and candidates, using metric {method}.",
-        indices=",".join(target._indices.values),  # noqa
+        indices=",".join(target_array._indices.values),  # noqa
         metric=method,
     )
 

diff --git a/xclim/core/calendar.py b/xclim/core/calendar.py
@@ -1135,14 +1135,14 @@ def climatological_mean_doy(
     Parameters
     ----------
     arr : xarray.DataArray
-      Input array.
+        Input array.
     window : int
-      Window size in days.
+        Window size in days.
 
     Returns
     -------
     xarray.DataArray, xarray.DataArray
-      Mean and standard deviation.
+        Mean and standard deviation.
     """
     rr = arr.rolling(min_periods=1, center=True, time=window).construct("window")
 
@@ -1163,11 +1163,11 @@ def within_bnds_doy(
     Parameters
     ----------
     arr : xarray.DataArray
-      Input array.
+        Input array.
     low : xarray.DataArray
-      Low bound with dayofyear coordinate.
+        Low bound with dayofyear coordinate.
     high : xarray.DataArray
-      High bound with dayofyear coordinate.
+        High bound with dayofyear coordinate.
 
     Returns
     -------
@@ -1185,20 +1185,20 @@ def _doy_days_since_doys(
 
     Parameters
     ----------
-    base: xr.DataArray
-      1D time coordinate.
-    start: DayOfYearStr, optional
-      A date to compute the offset relative to. If note given, start_doy is the same as base_doy.
+    base : xr.DataArray
+        1D time coordinate.
+    start : DayOfYearStr, optional
+        A date to compute the offset relative to. If note given, start_doy is the same as base_doy.
 
     Returns
     -------
     base_doy : xr.DataArray
-      Day of year for each element in base.
+        Day of year for each element in base.
     start_doy : xr.DataArray
-      Day of year of the "start" date.
-      The year used is the one the start date would take as a doy for the corresponding base element.
+        Day of year of the "start" date.
+        The year used is the one the start date would take as a doy for the corresponding base element.
     doy_max : xr.DataArray
-      Number of days (maximum doy) for the year of each value in base.
+        Number of days (maximum doy) for the year of each value in base.
     """
     calendar = get_calendar(base)
 
@@ -1234,24 +1234,23 @@ def doy_to_days_since(
 
     Parameters
     ----------
-    da: xr.DataArray
-      Array of "day-of-year", usually int dtype, must have a `time` dimension.
-      Sampling frequency should be finer or similar to yearly and coarser then daily.
-    start: date of year str, optional
-      A date in "MM-DD" format, the base day of the new array.
-      If None (default), the `time` axis is used.
-      Passing `start` only makes sense if `da` has a yearly sampling frequency.
-    calendar: str, optional
-      The calendar to use when computing the new interval.
-      If None (default), the calendar attribute of the data or of its `time` axis is used.
-      All time coordinates of `da` must exist in this calendar.
-      No check is done to ensure doy values exist in this calendar.
+    da : xr.DataArray
+        Array of "day-of-year", usually int dtype, must have a `time` dimension.
+        Sampling frequency should be finer or similar to yearly and coarser than daily.
+    start : date of year str, optional
+        A date in "MM-DD" format, the base day of the new array. If None (default), the `time` axis is used.
+        Passing `start` only makes sense if `da` has a yearly sampling frequency.
+    calendar : str, optional
+        The calendar to use when computing the new interval.
+        If None (default), the calendar attribute of the data or of its `time` axis is used.
+        All time coordinates of `da` must exist in this calendar.
+        No check is done to ensure doy values exist in this calendar.
 
     Returns
     -------
     xr.DataArray
-      Same shape as `da`, int dtype, day-of-year data translated to a number of days since a given date.
-      If start is not None, there might be negative values.
+        Same shape as `da`, int dtype, day-of-year data translated to a number of days since a given date.
+        If start is not None, there might be negative values.
 
     Notes
     -----
@@ -1306,14 +1305,14 @@ def days_since_to_doy(
 
     Parameters
     ----------
-    da: xr.DataArray
-      The result of :py:func:`doy_to_days_since`.
-    start: DateOfYearStr, optional
-      `da` is considered as days since that start date (in the year of the time index).
-      If None (default), it is read from the attributes.
-    calendar: str, optional
-      Calendar the "days since" were computed in.
-      If None (default), it is read from the attributes.
+    da : xr.DataArray
+        The result of :py:func:`doy_to_days_since`.
+    start : DateOfYearStr, optional
+        `da` is considered as days since that start date (in the year of the time index).
+        If None (default), it is read from the attributes.
+    calendar : str, optional
+        Calendar the "days since" were computed in.
+        If None (default), it is read from the attributes.
 
     Returns
     -------
@@ -1364,23 +1363,23 @@ def date_range_like(source: xr.DataArray, calendar: str) -> xr.DataArray:
     Parameters
     ----------
     source : xr.DataArray
-      1D datetime coordinate DataArray
+        1D datetime coordinate DataArray
     calendar : str
-      New calendar name.
+        New calendar name.
 
     Raises
     ------
     ValueError
-      If the source's frequency was not found.
+        If the source's frequency was not found.
 
     Returns
     -------
     xr.DataArray
-      1D datetime coordinate with the same start, end and frequency as the source, but in the new calendar.
-        The start date is assumed to exist in the target calendar.
-        If the end date doesn't exist, the code tries 1 and 2 calendar days before.
-        Exception when the source is in 360_day and the end of the range is the 30th of a 31-days month,
-        then the 31st is appended to the range.
+        1D datetime coordinate with the same start, end and frequency as the source, but in the new calendar.
+            The start date is assumed to exist in the target calendar.
+            If the end date doesn't exist, the code tries 1 and 2 calendar days before.
+            Exception when the source is in 360_day and the end of the range is the 30th of a 31-days month,
+            then the 31st is appended to the range.
     """
     freq = xr.infer_freq(source)
     if freq is None:
@@ -1428,20 +1427,20 @@ def _convert_datetime(
 
     Parameters
     ----------
-    datetime: Union[datetime.datetime, cftime.datetime]
-      A datetime object to convert.
-    new_doy:  Optional[Union[float, int]]
-      Allows for redefining the day of year (thus ignoring month and day information from the source datetime).
-      -1 is understood as a nan.
-    calendar: str
-      The target calendar
+    datetime: datetime.datetime or cftime.datetime
+        A datetime object to convert.
+    new_doy : float or int, optional
+        Allows for redefining the day of year (thus ignoring month and day information from the source datetime).
+        -1 is understood as a nan.
+    calendar : str
+        The target calendar
 
     Returns
     -------
     Union[cftime.datetime, datetime.datetime, np.nan]
-      A datetime object of the target calendar with the same year, month, day and time
-      as the source (month and day according to `new_doy` if given).
-      If the month and day doesn't exist in the target calendar, returns np.nan. (Ex. 02-29 in "noleap")
+        A datetime object of the target calendar with the same year, month, day and time as the source
+        (month and day according to `new_doy` if given).
+        If the month and day doesn't exist in the target calendar, returns np.nan. (Ex. 02-29 in "noleap")
     """
     if new_doy in [np.nan, -1]:
         return np.nan
@@ -1470,10 +1469,10 @@ def _convert_datetime(
 def select_time(
     da: xr.DataArray | xr.Dataset,
     drop: bool = False,
-    season: str | Sequence[str] = None,
-    month: int | Sequence[int] = None,
-    doy_bounds: tuple[int, int] = None,
-    date_bounds: tuple[str, str] = None,
+    season: str | Sequence[str] | None = None,
+    month: int | Sequence[int] | None = None,
+    doy_bounds: tuple[int, int] | None = None,
+    date_bounds: tuple[str, str] | None = None,
     include_bounds: bool | tuple[bool, bool] = True,
 ) -> xr.DataArray | xr.Dataset:
     """Select entries according to a time period.
@@ -1486,25 +1485,22 @@ def select_time(
     Parameters
     ----------
     da : xr.DataArray or xr.Dataset
-      Input data.
-    drop: boolean
-      Whether to drop elements outside the period of interest or
-      to simply mask them (default).
-    season: string or sequence of strings
-      One or more of 'DJF', 'MAM', 'JJA' and 'SON'.
-    month: integer or sequence of integers
-      Sequence of month numbers (January = 1 ... December = 12)
-    doy_bounds: 2-tuple of integers
-      The bounds as (start, end) of the period of interest expressed in day-of-year,
-      integers going from 1 (January 1st) to 365 or 366 (December 31st). If calendar
-      awareness is needed, consider using ``date_bounds`` instead.
-    date_bounds: 2-tuple of strings
-      The bounds as (start, end) of the period of interest expressed as dates in the
-      month-day (%m-%d) format.
-    include_bounds: bool or 2-tuple of booleans
-      Whether the bounds of `doy_bounds` or `date_bounds` should be inclusive or not.
-      Either one value for both or a tuple.
-      Default is True, meaning bounds are inclusive.
+        Input data.
+    drop : bool
+        Whether to drop elements outside the period of interest or to simply mask them (default).
+    season : string or sequence of strings, optional
+        One or more of 'DJF', 'MAM', 'JJA' and 'SON'.
+    month : integer or sequence of integers, optional
+        Sequence of month numbers (January = 1 ... December = 12)
+    doy_bounds : 2-tuple of integers, optional
+        The bounds as (start, end) of the period of interest expressed in day-of-year, integers going from
+        1 (January 1st) to 365 or 366 (December 31st).
+        If calendar awareness is needed, consider using ``date_bounds`` instead.
+    date_bounds : 2-tuple of strings, optional
+        The bounds as (start, end) of the period of interest expressed as dates in the month-day (%m-%d) format.
+    include_bounds : bool or 2-tuple of booleans
+        Whether the bounds of `doy_bounds` or `date_bounds` should be inclusive or not.
+        Either one value for both or a tuple. Default is True, meaning bounds are inclusive.
 
     Returns
     -------

diff --git a/xclim/core/cfchecks.py b/xclim/core/cfchecks.py
@@ -31,7 +31,7 @@ def check_valid(var, key: str, expected: str | Sequence[str]):
         )
 
 
-def cfcheck_from_name(varname, vardata, attrs: list[str] = None):
+def cfcheck_from_name(varname, vardata, attrs: list[str] | None = None):
     """Perform cfchecks on a DataArray using specifications from xclim's default variables."""
     if attrs is None:
         attrs = ["cell_methods", "standard_name"]

diff --git a/xclim/core/formatting.py b/xclim/core/formatting.py
@@ -556,14 +556,14 @@ def unprefix_attrs(source: dict, keys: Sequence, prefix: str):
 }
 
 
-def _gen_parameters_section(parameters: dict, allowed_periods: list[str] = None):
+def _gen_parameters_section(parameters: dict, allowed_periods: list[str] | None = None):
     """Generate the "parameters" section of the indicator docstring.
 
     Parameters
     ----------
     parameters : dict
         Parameters dictionary (`Ind.parameters`).
-    allowed_periods : List[str], optional
+    allowed_periods : list of str, optional
         Restrict parameters to specific periods. Default: None.
     """
     section = "Parameters\n----------\n"