change: set to list for new pandas version

src/agora/io/signal.py

@@ -178,8 +178,8 @@ class Signal(BridgeH5):
             else:
                 return merged
         if len(picks):
-            picked_indices = set(picks).intersection(
-                [tuple(x) for x in merged.index]
+            picked_indices = list(
+                set(picks).intersection([tuple(x) for x in merged.index])
             )
             return merged.loc[picked_indices]
         else:

src/agora/io/writer.py

@@ -595,7 +595,7 @@ class Writer(BridgeH5):
             new_tps = set(df.columns)
             if path + "/timepoint" in f:
                 new_tps = new_tps.difference(f[path + "/timepoint"][()])
-            df = df[new_tps]
+            df = df[list(new_tps)]
 
             if (
                 not hasattr(self, "id_cache")

src/postprocessor/core/reshapers/buddings.py

@@ -57,7 +57,7 @@ class buddings(LineageProcess):
                 traps_mothers[(trap, mother)].append(daughter)
         # a new dataframe with dimensions (n_mother_cells * n_tps)
         mothers = signal.loc[
-            set(signal.index).intersection(traps_mothers.keys())
+            list(set(signal.index).intersection(traps_mothers.keys()))
         ]
         buddings = pd.DataFrame(
             np.zeros(mothers.shape).astype(bool),
@@ -76,6 +76,6 @@ class buddings(LineageProcess):
                 fvi.index.intersection(trap_daughter_ids)
             ].values
             # ignore zeros - buds in first image are not budding events
-            daughters_idx = set(times_of_bud_appearance).difference({0})
+            daughters_idx = list(set(times_of_bud_appearance).difference({0}))
             buddings.loc[trap_mother_id, daughters_idx] = True
         return buddings

src/postprocessor/core/reshapers/tracks.py

@@ -62,7 +62,9 @@ def get_merges(tracks, smooth=False, tol=0.2, window=5, degree=3) -> dict:
     # remove traps with no contiguous tracks
     contigs = contigs.loc[contigs.apply(len) > 0]
     # flatten to (trap, cell_id) pairs
-    flat = set([k for v in contigs.values for i in v for j in i for k in j])
+    flat = list(
+        set([k for v in contigs.values for i in v for j in i for k in j])
+    )
     # make a data frame of contiguous tracks with the tracks as arrays
     if smooth:
         smoothed_tracks = clean.loc[flat].apply(