Merge branch 'deprecateFlagMVScores' into 'develop'

Deprecate flag mv scores

See merge request !845

CHANGELOG.md

@@ -32,6 +32,7 @@ SPDX-License-Identifier: GPL-3.0-or-later
 - `flagManual` in favor of `setFlags`
 - `flagRaise` with delegation to better replacements `flagZScore`, `flagUniLOF`, `flagJumps` or `flagOffset`
 - `flagByGrubbs` with delegation to better replacements `flagZScore`, `flagUniLOF`s
+- `flagMVScore` with delegation to manual application of the steps
 ## [2.5.0](https://git.ufz.de/rdm-software/saqc/-/tags/v2.4.1) - 2023-06-22
 [List of commits](https://git.ufz.de/rdm-software/saqc/-/compare/v2.4.1...v2.5.0)
 ### Added

docs/funcs/multivariateAnalysis.rst

@@ -12,7 +12,7 @@ Multivariate outlier detection.
 .. autosummary::
    :nosignatures:
 
-   ~SaQC.flagMVScores
    ~SaQC.flagCrossStatistics
    ~SaQC.flagLOF
+   ~SaQC.flagZScore
 

saqc/funcs/outliers.py

@@ -723,6 +723,17 @@ class OutliersMixin:
                 DeprecationWarning,
             )
 
+        warnings.warn(
+            """
+                FlagMVScores is deprecated and will be removed with Version 2.8.
+                To replicate the function, transform the different fields involved 
+                via explicit applications of some transformations, than calculate the 
+                kNN scores via `saqc.SaQC.assignkNScores` and finally assign the STRAY
+                algorithm via `saqc.SaQC.flagByStray`.
+                """,
+            DeprecationWarning,
+        )
+
         # Hint: checking is delegated to the called functions
 
         fields = toSequence(field)

tests/funcs/test_outlier_detection.py

@@ -93,13 +93,17 @@ def test_flagMVScores(course_3):
     s2 = pd.Series(data=s2.values, index=s1.index)
     data = DictOfSeries(field1=s1, field2=s2)
     flags = initFlagsLike(data)
-    qc = SaQC(data, flags).flagMVScores(
-        field=fields,
-        trafo=np.log,
-        iter_start=0.95,
-        n=10,
-        flag=BAD,
+    qc = SaQC(data, flags)
+    qc = qc.processGeneric("field1", func=lambda x: np.log(x))
+    qc = qc.processGeneric("field2", func=lambda x: np.log(x))
+    qc = qc.assignKNNScore(
+        ["field1", "field2"],
+        target="kNNScores",
     )
+    qc = qc.flagByStray("kNNScores", iter_start=0.95)
+    qc = qc.transferFlags("kNNScores", target="field1")
+    qc = qc.transferFlags("kNNScores", target="field2")
+
     _check(fields, qc.flags, characteristics)