fixed typos and assertion types in tests

Author: btschwertfeger

README.md

@@ -1,4 +1,4 @@
-# python-cmethods: A collection of bias correction techniques wirtten in Python
+# python-cmethods
 
 <div align="center">
 
@@ -18,7 +18,7 @@
 
 </div>
 
-This Python module contains a collection of different scale- and distribution-based bias adjustment techniques for climatic research (see `/examples/examples.ipynb` for help).
+This Python module serves as a collection of different scale- and distribution-based bias correction techniques for climatic research
 
 The documentation is available at: [https://python-kraken-sdk.readthedocs.io/en/stable/](https://python-kraken-sdk.readthedocs.io/en/stable/)
 
@@ -67,7 +67,7 @@ In this way, for example, modeled data, which on average represent values that a
 
 ## 2. Available methods
 
-All methods except the `adjust_3d` function requires the application on one time series.
+All methods except the `adjust_3d` function requires that the input data sets only contain one dimension.
 
 | Function name            | Description                                                                                                                                                  |
 | ------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------ |
@@ -149,7 +149,7 @@ Help:
     --scen input_data/scenario.nc     \
     --kind "+"                        \
     --variable "tas"                  \
-    -method quantile_mapping
+    --method quantile_mapping
 ```
 
 (2.) Example - Linear Scaling bias correction on the provided example data:
@@ -162,7 +162,7 @@ Help:
     --kind "+"                        \
     --variable "tas"                  \
     --group "time.month"              \
-    -method linear_scaling
+    --method linear_scaling
 ```
 
 Notes:

cmethods/__init__.py

@@ -53,7 +53,7 @@ class CMethods:
     The following bias correction techniques are available:
         Scaling-based techniques:
             * Linear Scaling :func:`cmethods.CMethods.linear_scaling`
-            * Vairance Scaling :func:`cmethods.CMethods.variance_scaling`
+            * Variance Scaling :func:`cmethods.CMethods.variance_scaling`
             * Delta (change) Method :func:`cmethods.CMethods.delta_method`
 
         Distribution-based techniques:
@@ -137,7 +137,7 @@ def adjust_3d(
         group: Union[str, None] = None,
         n_jobs: int = 1,
         **kwargs,
-    ) -> xr.core.dataarray.Dataset:
+    ) -> xr.core.dataarray.DataArray:
         """
         Function to apply a bias correction method on 3-dimensional climate data.
 
@@ -166,7 +166,7 @@ def adjust_3d(
         :type n_jobs: int, optional
         :raises UnknownMethodError: If the correction method is not implemented
         :return: The bias-corrected time series
-        :rtype: xr.core.dataarray.Dataset
+        :rtype: xr.core.dataarray.DataArray
 
         .. code-block:: python
             :linenos:
@@ -348,7 +348,7 @@ def grouped_correction(
             for i, index in enumerate(groups[month]):
                 result[index] = computed_result[i]
 
-        return result
+        return np.array(result)
 
     # ? -----========= L I N E A R - S C A L I N G =========------
     @classmethod
@@ -711,7 +711,7 @@ def quantile_mapping(
         kind: str = "+",
         detrended: bool = False,
         **kwargs,
-    ) -> xr.core.dataarray.DataArray:
+    ) -> np.array:
         r"""
         The Quantile Mapping bias correction technique can be used to minimize distributional
         biases between modeled and observed time-series climate data. Its interval-independant
@@ -767,7 +767,7 @@ def quantile_mapping(
         :type detrended: bool, optional
         :raises NotImplementedError: If the kind is not in (``+``, ``*``, ``add``, ``mult``)
         :return: The bias-corrected time series
-        :rtype: xr.core.dataarray.DataArray
+        :rtype: np.array
 
         .. code-block:: python
             :linenos:
@@ -903,7 +903,7 @@ def quantile_delta_mapping(
         n_quantiles: int,
         kind: str = "+",
         **kwargs,
-    ) -> xr.core.dataarray.DataArray:
+    ) -> np.array:
         r"""
         The Quantile Delta Mapping bias correction technique can be used to minimize distributional
         biases between modeled and observed time-series climate data. Its interval-independant
@@ -1019,7 +1019,6 @@ def quantile_delta_mapping(
             ... )
         """
         if kind in cls.ADDITIVE:
-            # res = simp.copy(deep=True)
             obs, simh, simp = (
                 np.array(obs),
                 np.array(simh),

docs/src/introduction.rst

@@ -45,7 +45,7 @@ Available Methods
 The following bias correction techniques are available:
     Scaling-based techniques:
         * Linear Scaling :func:`cmethods.CMethods.linear_scaling`
-        * Vairance Scaling :func:`cmethods.CMethods.variance_scaling`
+        * Variance Scaling :func:`cmethods.CMethods.variance_scaling`
         * Delta (change) Method :func:`cmethods.CMethods.delta_method`
 
     Distribution-based techniques:

tests/test_methods.py

@@ -122,7 +122,7 @@ def test_linear_scaling(self) -> None:
                 simp=self.data[kind]["simp"][:, 0, 0],
                 kind=kind,
             )
-            assert isinstance(ls_result, xr.core.dataarray.DataArray)
+            assert isinstance(ls_result, (np.ndarray, np.generic))
             assert mean_squared_error(
                 ls_result, self.data[kind]["obsp"][:, 0, 0], squared=False
             ) < mean_squared_error(
@@ -140,7 +140,7 @@ def test_variance_scaling(self) -> None:
             simp=self.data[kind]["simp"][:, 0, 0],
             kind="+",
         )
-        assert isinstance(vs_result, xr.core.dataarray.DataArray)
+        assert isinstance(vs_result, (np.ndarray, np.generic))
         assert mean_squared_error(
             vs_result, self.data[kind]["obsp"][:, 0, 0], squared=False
         ) < mean_squared_error(
@@ -159,7 +159,7 @@ def test_delta_method(self) -> None:
                 simp=self.data[kind]["simp"][:, 0, 0],
                 kind=kind,
             )
-            assert isinstance(dm_result, xr.core.dataarray.DataArray)
+            assert isinstance(dm_result, (np.ndarray, np.generic))
             assert mean_squared_error(
                 dm_result, self.data[kind]["obsp"][:, 0, 0], squared=False
             ) < mean_squared_error(
@@ -179,7 +179,7 @@ def test_quantile_mapping(self) -> None:
                 n_quantiles=100,
                 kind=kind,
             )
-            assert isinstance(qm_result, xr.core.dataarray.DataArray)
+            assert isinstance(qm_result, (np.ndarray, np.generic))
             assert mean_squared_error(
                 qm_result, self.data[kind]["obsp"][:, 0, 0], squared=False
             ) < mean_squared_error(
@@ -200,7 +200,7 @@ def test_detrended_quantile_mapping(self) -> None:
                 kind=kind,
                 detrended=True,
             )
-            assert isinstance(dqm_result, xr.core.dataarray.DataArray)
+            assert isinstance(dqm_result, (np.ndarray, np.generic))
             assert mean_squared_error(
                 dqm_result, self.data[kind]["obsp"][:, 0, 0], squared=False
             ) < mean_squared_error(
@@ -221,7 +221,7 @@ def test_quantile_delta_mapping(self) -> None:
                 kind=kind,
             )
 
-            assert isinstance(qdm_result, xr.core.dataarray.DataArray)
+            assert isinstance(qdm_result, (np.ndarray, np.generic))
             assert mean_squared_error(
                 qdm_result, self.data[kind]["obsp"][:, 0, 0], squared=False
             ) < mean_squared_error(