applied suggestions by @riley-brady

.github/workflows/cicd.yml

@@ -11,6 +11,8 @@ on:
   push:
     branches:
       - "**"
+  schedule:
+    - cron: "20 4 * * 0"
 
 concurrency:
   group: CICD-${{ github.ref }}
@@ -37,7 +39,7 @@ jobs:
       fail-fast: false
       matrix:
         os: [ubuntu-latest, macos-latest, windows-latest]
-        python-version: ["3.11"]
+        python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"]
     with:
       os: ${{ matrix.os }}
       python-version: ${{ matrix.python-version }}
@@ -59,7 +61,7 @@ jobs:
     strategy:
       matrix:
         os: [ubuntu-latest, windows-latest]
-        python-version: ["3.11"]
+        python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"]
     with:
       os: ${{ matrix.os }}
       python-version: ${{ matrix.python-version }}

.github/workflows/release.yml

@@ -29,7 +29,7 @@ jobs:
       fail-fast: false
       matrix:
         os: [macos-latest, ubuntu-latest, windows-latest]
-        python-version: ["3.11"]
+        python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"]
     with:
       os: ${{ matrix.os }}
       python-version: ${{ matrix.python-version }}
@@ -51,7 +51,7 @@ jobs:
     strategy:
       matrix:
         os: [ubuntu-latest, windows-latest]
-        python-version: ["3.11"]
+        python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"]
     with:
       os: ${{ matrix.os }}
       python-version: ${{ matrix.python-version }}

cmethods/__init__.py

@@ -5,7 +5,8 @@
 #
 
 r"""
-    Module to apply different bias correction techniques to time-series climate data
+    Module to apply different bias correction techniques to time-series
+    climate data.
 
     T = Temperatures ($T$)
     X = Some climate variable ($X$)
@@ -23,7 +24,7 @@
 
 from __future__ import annotations
 
-from typing import Any, Callable, Optional
+from typing import Any, Callable, List, Optional, Tuple, Union
 
 import numpy as np
 import xarray as xr
@@ -92,7 +93,7 @@ class CMethods:
       due to complex atmospheric and meteorological processes.
     """
 
-    MAX_SCALING_FACTOR: int | float = 10
+    MAX_SCALING_FACTOR: Union[int, float] = 10
 
     # ? -----========= L I N E A R - S C A L I N G =========------
     def linear_scaling(
@@ -442,7 +443,7 @@ def adjust(
         self: CMethods, method: str, obs: XRData, simh: XRData, simp: XRData, **kwargs
     ) -> XRData:
         """
-        Function to apply a bias correction technique on 1-and 3-dimensional
+        Function to apply a bias correction technique on 1-and multidimensional
         data sets. For more information please refer to the method specific
         requirements and execution examples.
 
@@ -472,6 +473,10 @@ def adjust(
             )
 
         # No grouped correction | distribution-based technique
+        # NOTE: This is disabled since using groups like "time.month" will lead
+        #       to unrealistic monthly transitions. If such behavior is wanted,
+        #       mock this function or apply ``CMethods.__apply_ufunc` directly
+        #       on your data sets.
         if kwargs.get("group", None) is None:
             return self.__apply_ufunc(method, obs, simh, simp, **kwargs).to_dataset()
 
@@ -484,9 +489,9 @@ def adjust(
         group: str = kwargs["group"]
         del kwargs["group"]
 
-        obs_g: list[tuple[int, XRData]] = list(obs.groupby(group))
-        simh_g: list[tuple[int, XRData]] = list(simh.groupby(group))
-        simp_g: list[tuple[int, XRData]] = list(simp.groupby(group))
+        obs_g: List[Tuple[int, XRData]] = list(obs.groupby(group))
+        simh_g: List[Tuple[int, XRData]] = list(simh.groupby(group))
+        simp_g: List[Tuple[int, XRData]] = list(simp.groupby(group))
 
         result: Optional[XRData] = None
         for index in range(len(list(obs_g))):
@@ -505,28 +510,6 @@ def adjust(
 
         return result
 
-    def __get_function(self: CMethods, method: str) -> Callable:
-        """
-        Returns the bias correction function corresponding to the ``method`` name.
-
-        :param method: The method name to get the function for
-        :type method: str
-        :raises UnknownMethodError: If the function is not implemented
-        :return: The function of the corresponding method
-        :rtype: function
-        """
-        if method == "linear_scaling":
-            return self.linear_scaling
-        if method == "variance_scaling":
-            return self.variance_scaling
-        if method == "delta_method":
-            return self.delta_method
-        if method == "quantile_mapping":
-            return self.quantile_mapping
-        if method == "quantile_delta_mapping":
-            return self.quantile_delta_mapping
-        raise UnknownMethodError(method, METHODS)
-
     def __apply_ufunc(
         self: CMethods,
         method: str,
@@ -536,9 +519,11 @@ def __apply_ufunc(
         **kwargs: dict,
     ) -> XRData:
         check_xr_types(obs=obs, simh=simh, simp=simp)
+        if method not in METHODS:
+            raise UnknownMethodError(method, METHODS)
 
         result: XRData = xr.apply_ufunc(
-            self.__get_function(method),
+            getattr(self, method),
             obs,
             simh,
             # Need to spoof a fake time axis since 'time' coord on full dataset is different
@@ -562,7 +547,7 @@ def __apply_ufunc(
         # order where time is commonly first.
         return result.transpose(*obs.dims)
 
-    def get_available_methods(self: CMethods) -> list[str]:
+    def get_available_methods(self: CMethods) -> List[str]:
         """
         Function to return the available adjustment methods of the CMethods class.
 

cmethods/static.py

@@ -6,16 +6,17 @@
 
 """Module providing static information for the python-cmethods package"""
 
+from typing import List
 
-SCALING_METHODS: list[str] = ["linear_scaling", "variance_scaling", "delta_method"]
+SCALING_METHODS: List[str] = ["linear_scaling", "variance_scaling", "delta_method"]
 DISTRIBUTION_METHODS: list[str] = [
     "quantile_mapping",
     "detrended_quantile_mapping",
     "quantile_delta_mapping",
 ]
 
-CUSTOM_METHODS: list[str] = SCALING_METHODS + DISTRIBUTION_METHODS
-METHODS: list[str] = CUSTOM_METHODS
+CUSTOM_METHODS: List[str] = SCALING_METHODS + DISTRIBUTION_METHODS
+METHODS: List[str] = CUSTOM_METHODS
 
-ADDITIVE: list[str] = ["+", "add"]
-MULTIPLICATIVE: list[str] = ["*", "mult"]
+ADDITIVE: List[str] = ["+", "add"]
+MULTIPLICATIVE: List[str] = ["*", "mult"]

cmethods/utils.py

@@ -9,7 +9,7 @@
 from __future__ import annotations
 
 import warnings
-from typing import Optional
+from typing import Optional, Union
 
 import numpy as np
 
@@ -96,8 +96,8 @@ def nan_or_equal(value1: float, value2: float) -> bool:
 
 
 def ensure_devidable(
-    numerator: float | np.ndarray,
-    denominator: float | np.ndarray,
+    numerator: Union[float, np.ndarray],
+    denominator: Union[float, np.ndarray],
     max_scaling_factor: float,
 ) -> np.ndarray:
     """
@@ -128,7 +128,10 @@ def ensure_devidable(
     return result
 
 
-def get_pdf(x: list | np.ndarray, xbins: list | np.ndarray) -> np.ndarray:
+def get_pdf(
+    x: Union[list, np.ndarray],
+    xbins: Union[list, np.ndarray],
+) -> np.ndarray:
     r"""
     Compuites and returns the the probability density function :math:`P(x)`
     of ``x`` based on ``xbins``.
@@ -155,7 +158,10 @@ def get_pdf(x: list | np.ndarray, xbins: list | np.ndarray) -> np.ndarray:
     return pdf
 
 
-def get_cdf(x: list | np.ndarray, xbins: list | np.ndarray) -> np.ndarray:
+def get_cdf(
+    x: Union[list, np.ndarray],
+    xbins: Union[list, np.ndarray],
+) -> np.ndarray:
     r"""
     Computes and returns returns the cumulative distribution function :math:`F(x)`
     of ``x`` based on ``xbins``.
@@ -184,9 +190,9 @@ def get_cdf(x: list | np.ndarray, xbins: list | np.ndarray) -> np.ndarray:
 
 
 def get_inverse_of_cdf(
-    base_cdf: list | np.ndarray,
-    insert_cdf: list | np.ndarray,
-    xbins: list | np.ndarray,
+    base_cdf: Union[list, np.ndarray],
+    insert_cdf: Union[list, np.ndarray],
+    xbins: Union[list, np.ndarray],
 ) -> np.ndarray:
     r"""
     Returns the inverse cumulative distribution function as:
@@ -206,7 +212,8 @@ def get_inverse_of_cdf(
 
 
 def get_adjusted_scaling_factor(
-    factor: int | float, max_scaling_factor: int | float
+    factor: Union[int, float],
+    max_scaling_factor: Union[int, float],
 ) -> float:
     r"""
     Returns:

pyproject.toml

@@ -11,7 +11,7 @@ authors = [
 description = "Collection of bias correction procedures for single and multidimensional climate data"
 readme = "README.md"
 license = { file = "LICENSE" }
-requires-python = ">=3.11"
+requires-python = ">=3.8"
 dependencies = ["xarray>=2022.11.0", "netCDF4>=1.6.1", "numpy"]
 keywords = [
   "climate-science",
@@ -30,7 +30,11 @@ keywords = [
 classifiers = [
   "License :: OSI Approved :: GNU General Public License v3 (GPLv3)",
   "Programming Language :: Python",
+  "Programming Language :: Python :: 3.8",
+  "Programming Language :: Python :: 3.9",
+  "Programming Language :: Python :: 3.10",
   "Programming Language :: Python :: 3.11",
+  "Programming Language :: Python :: 3.12",
   "Natural Language :: English",
   "Operating System :: MacOS",
   "Operating System :: Unix",

tests/helper.py

@@ -8,6 +8,8 @@
 
 from __future__ import annotations
 
+from typing import List
+
 import numpy as np
 import xarray as xr
 from sklearn.metrics import mean_squared_error
@@ -45,9 +47,7 @@ def is_3d_rmse_better(result, obsp, simp) -> bool:
     return (rmse_values_new_ds < rmse_values_old_ds).all()
 
 
-def get_datasets(
-    kind: str,
-) -> tuple[xr.Dataset, xr.Dataset, xr.Dataset, xr.Dataset]:
+def get_datasets(kind: str) -> tuple[xr.Dataset, xr.Dataset, xr.Dataset, xr.Dataset]:
     historical_time = xr.cftime_range(
         "1971-01-01", "2000-12-31", freq="D", calendar="noleap"
     )
@@ -56,7 +56,7 @@ def get_datasets(
     )
     latitudes = np.arange(23, 27, 1)
 
-    def get_hist_temp_for_lat(lat: int) -> list[float]:
+    def get_hist_temp_for_lat(lat: int) -> List[float]:
         """Returns a fake interval time series by latitude value"""
         return 273.15 - (
             lat * np.cos(2 * np.pi * historical_time.dayofyear / 365)
@@ -65,7 +65,7 @@ def get_hist_temp_for_lat(lat: int) -> list[float]:
             + 0.1 * (historical_time - historical_time[0]).days / 365
         )
 
-    def get_fake_hist_precipitation_data() -> list[float]:
+    def get_fake_hist_precipitation_data() -> List[float]:
         """Returns ratio based fake time series"""
         pr = (
             np.cos(2 * np.pi * historical_time.dayofyear / 365)

tests/test_misc.py

@@ -107,8 +107,3 @@ def test_adjust_failing_no_group_for_distribution(cm: CMethods, datasets: dict)
             n_quantiles=100,
             group="time.month",
         )
-
-
-def test_get_function_unknown_method(cm: CMethods) -> None:
-    with pytest.raises(UnknownMethodError):
-        cm._CMethods__get_function("not-existing")