Fix deprecation, future and performance warnings

JuBiotech · Apr 26, 2023 · 357a15c · 357a15c
1 parent 01dff16
commit 357a15c
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Showing 5 changed files with 16 additions and 14 deletions.
diff --git a/bletl/__init__.py b/bletl/__init__.py
@@ -19,4 +19,4 @@
     NoMeasurementData,
 )
 
-__version__ = "1.2.2"
+__version__ = "1.2.3"
diff --git a/bletl/features.py b/bletl/features.py
@@ -429,7 +429,10 @@ def from_bldata(
         data["x"] = narrow[fs]
         _log.info("Applying tsfresh extractor to %s\n", fs)
         df_fs = ts_extractor._extract(data)
-        for i, mname in enumerate(df_fs.columns):
-            df_result[f"{fs}_{mname}"] = df_fs.to_numpy()[:, i]
+        # Rename columns to include the filterset name
+        df_fs.columns = [f"{fs}_{col}" for col in df_fs.columns]
+        # Add columns with new features for this filterset to the result
+        df_fs.index = [i for i, in df_fs.index]
+        df_result = pandas.concat([df_result, df_fs], axis=1)
     _log.info("Extraction finished in: %s minutes", round((time.time() - s_time) / 60, ndigits=1))
     return df_result
diff --git a/bletl/splines.py b/bletl/splines.py
@@ -330,9 +330,9 @@ def get_mue(
     if wells == "all":
         wells = list(bsdata.time.columns)
     if blank == "first":
-        blank_dict = {well: data.iloc[0] for well, data in bsdata.value.iteritems()}
+        blank_dict = {well: data.iloc[0] for well, data in bsdata.value.items()}
     elif isinstance(blank, numbers.Number):
-        blank_dict = {well: blank for well, data in bsdata.value.iteritems()}
+        blank_dict = {well: blank for well, data in bsdata.value.items()}
     elif isinstance(blank, dict):
         if set(blank.keys()) != set(wells):
             raise ValueError("Please provide blanks for every well")

diff --git a/tests/test_core.py b/tests/test_core.py
@@ -268,11 +268,10 @@ def test_get_unified_dataframe(self):
         unified_df = data["BS20"].get_unified_dataframe()
         assert isinstance(unified_df, pandas.DataFrame)
         numpy.testing.assert_approx_equal(unified_df.index[2], 0.35313, significant=4)
-        numpy.testing.assert_approx_equal(
-            unified_df.iloc[unified_df.index.get_loc(5, method="nearest"), unified_df.columns.get_loc("A05")],
-            63.8517,
-            significant=6,
-        )
+        idx = unified_df.index.get_indexer([5], method="nearest")
+        col = unified_df.columns.get_indexer(["A05"])
+        values = unified_df.iloc[idx, col].to_numpy()
+        numpy.testing.assert_approx_equal(values, 63.8517, significant=6)
 
     def test_get_narrow_data(self):
         fp = pathlib.Path(

diff --git a/tests/test_splines.py b/tests/test_splines.py
@@ -66,7 +66,7 @@ def test_get_mue_wells(self):
 
         # dictionary of scalars (first 5 cycles)
         blank_dict = {
-            well: data.iloc[:5].mean() for well, data in bldata["BS3"].value.iteritems() if well in wells
+            well: data.iloc[:5].mean() for well, data in bldata["BS3"].value.items() if well in wells
         }
         mue_blank_dict = bletl.splines.get_mue(bldata["BS3"], wells=wells, blank=blank_dict, method="us")
         mue_median = numpy.median(mue_blank_dict.value.loc[60:75, "B03"])
@@ -98,7 +98,7 @@ def test_get_mue_on_all(self):
         numpy.testing.assert_allclose(mue_median, 0.30, atol=0.01)
 
         # dictionary of scalars (first 5 cycles)
-        blank_dict = {well: data.iloc[:5].mean() for well, data in bldata["BS3"].value.iteritems()}
+        blank_dict = {well: data.iloc[:5].mean() for well, data in bldata["BS3"].value.items()}
         mue_blank_dict = bletl.splines.get_mue(bldata["BS3"], blank=blank_dict, method="us")
         mue_median = numpy.median(mue_blank_dict.value.loc[60:75, "B03"])
         numpy.testing.assert_allclose(mue_median, 0.38, atol=0.02)
@@ -142,7 +142,7 @@ def test_get_mue_wells(self):
 
         # dictionary of scalars (first 5 cycles)
         blank_dict = {
-            well: data.iloc[:5].mean() for well, data in bldata["BS3"].value.iteritems() if well in wells
+            well: data.iloc[:5].mean() for well, data in bldata["BS3"].value.items() if well in wells
         }
         mue_blank_dict = bletl.splines.get_mue(bldata["BS3"], wells=wells, blank=blank_dict, method="ucss")
         mue_median = numpy.median(mue_blank_dict.value.loc[60:75, "B03"])
@@ -174,7 +174,7 @@ def test_get_mue_on_all(self):
         numpy.testing.assert_allclose(mue_median, 0.30, atol=0.02)
 
         # dictionary of scalars (first 5 cycles)
-        blank_dict = {well: data.iloc[:5].mean() for well, data in bldata["BS3"].value.iteritems()}
+        blank_dict = {well: data.iloc[:5].mean() for well, data in bldata["BS3"].value.items()}
         mue_blank_dict = bletl.splines.get_mue(bldata["BS3"], blank=blank_dict, method="ucss")
         mue_median = numpy.median(mue_blank_dict.value.loc[60:75, "B03"])
         numpy.testing.assert_allclose(mue_median, 0.38, atol=0.02)