back again with future stack true

setup.py

@@ -16,6 +16,7 @@
     long_description_content_type="text/markdown",
     url="https://github.com/FZJ-IEK3-VSA/tsam",
     include_package_data=True,
+    python_requires='>=3.9',
     packages=setuptools.find_packages(),
     install_requires=required_packages,
     setup_requires=["setuptools-git"],

test/test_cluster_order.py

@@ -103,21 +103,21 @@ def test_cluster_order():
         orig_raw_predefClusterOrder[typPeriods_predefClusterOrder.columns]
         .unstack()
         .loc[sortedDaysOrig1, :]
-        .stack()
+        .stack(future_stack=True,)
     )
-    test1 = typPeriods_predefClusterOrder.unstack().loc[sortedDaysTest1, :].stack()
+    test1 = typPeriods_predefClusterOrder.unstack().loc[sortedDaysTest1, :].stack(future_stack=True,)
     orig2 = (
         orig_raw_predefClusterOrderAndClusterCenters[
             typPeriods_predefClusterOrderAndClusterCenters.columns
         ]
         .unstack()
         .loc[sortedDaysOrig2, :]
-        .stack()
+        .stack(future_stack=True,)
     )
     test2 = (
         typPeriods_predefClusterOrderAndClusterCenters.unstack()
         .loc[sortedDaysTest2, :]
-        .stack()
+        .stack(future_stack=True,)
     )
 
     np.testing.assert_array_almost_equal(

test/test_hierarchical.py

@@ -46,8 +46,8 @@ def test_hierarchical():
     sortedDaysTest = typPeriods.groupby(level=0).sum().sort_values("GHI").index
 
     # rearange their order
-    orig = orig_raw[typPeriods.columns].unstack().loc[sortedDaysOrig, :].stack()
-    test = typPeriods.unstack().loc[sortedDaysTest, :].stack()
+    orig = orig_raw[typPeriods.columns].unstack().loc[sortedDaysOrig, :].stack(future_stack=True,)
+    test = typPeriods.unstack().loc[sortedDaysTest, :].stack(future_stack=True,)
 
     np.testing.assert_array_almost_equal(orig.values, test.values, decimal=4)
 

test/test_k_medoids.py

@@ -43,8 +43,8 @@ def test_k_medoids():
     sortedDaysTest = typPeriods.groupby(level=0).sum().sort_values("GHI").index
 
     # rearange their order
-    orig = orig_raw[typPeriods.columns].unstack().loc[sortedDaysOrig, :].stack()
-    test = typPeriods.unstack().loc[sortedDaysTest, :].stack()
+    orig = orig_raw[typPeriods.columns].unstack().loc[sortedDaysOrig, :].stack(future_stack=True,)
+    test = typPeriods.unstack().loc[sortedDaysTest, :].stack(future_stack=True,)
 
     np.testing.assert_array_almost_equal(orig.values, test.values, decimal=4)
 

test/test_segmentation.py

@@ -46,8 +46,8 @@ def test_segmentation():
     sortedDaysTest = typPeriods.groupby(level=0).sum().sort_values("GHI").index
 
     # rearange their order
-    orig = orig_raw[typPeriods.columns].unstack().loc[sortedDaysOrig, :].stack()
-    test = typPeriods.unstack().loc[sortedDaysTest, :].stack()
+    orig = orig_raw[typPeriods.columns].unstack().loc[sortedDaysOrig, :].stack(future_stack=True,)
+    test = typPeriods.unstack().loc[sortedDaysTest, :].stack(future_stack=True,)
 
     np.testing.assert_array_almost_equal(orig.values, test.values, decimal=4)
 

tsam/__init__.py

@@ -1,11 +0,0 @@
-import sys
-
-if not sys.warnoptions:
-    import warnings
-
-    warnings.filterwarnings(
-        action="ignore",
-        category=FutureWarning,
-        append=True,
-        message=r".*The previous implementation of stack is deprecated and will be removed in a future version of pandas.*",
-    )

tsam/timeseriesaggregation.py

@@ -969,7 +969,7 @@ def createTypicalPeriods(self):
         # check for additional cluster parameters
         if self.evalSumPeriods:
             evaluationValues = (
-                self.normalizedPeriodlyProfiles.stack(level=0)
+                self.normalizedPeriodlyProfiles.stack(future_stack=True,level=0)
                 .sum(axis=1)
                 .unstack(level=1)
             )
@@ -1239,7 +1239,7 @@ def predictOriginalData(self):
             columns=self.normalizedPeriodlyProfiles.columns,
             index=self.normalizedPeriodlyProfiles.index,
         )
-        clustered_data_df = clustered_data_df.stack(level="TimeStep")
+        clustered_data_df = clustered_data_df.stack(future_stack=True,level="TimeStep")
 
         # back in form
         self.normalizedPredictedData = pd.DataFrame(

tsam/utils/durationRepresentation.py

@@ -57,7 +57,7 @@ def durationRepresentation(
                 # get all the values of a certain attribute and cluster
                 candidateValues = candidates.loc[indice[0], a]
                 # sort all values
-                sortedAttr = candidateValues.stack().sort_values()
+                sortedAttr = candidateValues.stack(future_stack=True,).sort_values()
                 # reindex and arrange such that every sorted segment gets represented by its mean
                 sortedAttr.index = pd.MultiIndex.from_tuples(clean_index)
                 representationValues = sortedAttr.unstack(level=0).mean(axis=1)
@@ -97,8 +97,8 @@ def durationRepresentation(
             # concat centroid values and cluster weights for all clusters
             meansAndWeights = pd.concat(
                 [
-                    pd.DataFrame(np.array(meanVals)).stack(),
-                    pd.DataFrame(np.array(clusterLengths)).stack(),
+                    pd.DataFrame(np.array(meanVals)).stack(future_stack=True,),
+                    pd.DataFrame(np.array(clusterLengths)).stack(future_stack=True,),
                 ],
                 axis=1,
             )
@@ -107,7 +107,7 @@ def durationRepresentation(
             # save order of the sorted centroid values across all clusters
             order = meansAndWeightsSorted.index
             # sort all values of the original time series
-            sortedAttr = candidates.loc[:, a].stack().sort_values().values
+            sortedAttr = candidates.loc[:, a].stack(future_stack=True,).sort_values().values
             # take mean of sections of the original duration curve according to the cluster and its weight the
             # respective section is assigned to
             representationValues = []