docs(signal): act on peter comments

src/agora/io/signal.py

@@ -42,21 +42,15 @@ class Signal(BridgeH5):
             "Cy5",
             "pHluorin405",
         )
-        # Alan: why  "equivalences"? this variable is unused.
-        equivalences = {
-            "m5m": ("extraction/GFP/max/max5px", "extraction/GFP/max/median")
-        }
 
     def __getitem__(self, dsets: t.Union[str, t.Collection]):
         """Get and potentially pre-process data from h5 file and return as a dataframe."""
-        if isinstance(dsets, str):
-            # no pre-processing
+        if isinstance(dsets, str):  # no pre-processing
             df = self.get_raw(dsets)
             return self.add_name(df, dsets)
-        elif isinstance(dsets, list):
-            # pre-processing
+        elif isinstance(dsets, list):  # pre-processing
             is_bgd = [dset.endswith("imBackground") for dset in dsets]
-            # Alan: what does this error message mean?
+            # Check we are not comaring tile-indexed and cell-indexed data
             assert sum(is_bgd) == 0 or sum(is_bgd) == len(
                 dsets
             ), "Tile data and cell data can't be mixed"
@@ -72,14 +66,6 @@ class Signal(BridgeH5):
         df.name = name
         return df
 
-    # def cols_in_mins_old(self, df: pd.DataFrame):
-    #     """Convert numerical columns in a dataframe to minutes."""
-    #     try:
-    #         df.columns = (df.columns * self.tinterval // 60).astype(int)
-    #     except Exception as e:
-    #         print(f"Warning:Signal: Unable to convert columns to minutes: {e}")
-    #     return df
-
     def cols_in_mins(self, df: pd.DataFrame):
         # Convert numerical columns in a dataframe to minutes
         try:
@@ -292,10 +278,8 @@ class Signal(BridgeH5):
                     if in_minutes:
                         df = self.cols_in_mins(df)
             elif isinstance(dataset, list):
-                # Alan: no mother_labels in this case?
                 return [self.get_raw(dset) for dset in dataset]
-            if lineage:
-                # assumes that df is sorted
+            if lineage:  # assume that df is sorted
                 mother_label = np.zeros(len(df), dtype=int)
                 lineage = self.lineage()
                 a, b = validate_association(
@@ -307,7 +291,7 @@ class Signal(BridgeH5):
                 df = add_index_levels(df, {"mother_label": mother_label})
             return df
         except Exception as e:
-            self._log(f"Could not fetch dataset {dataset}", "error")
+            self._log(f"Could not fetch dataset {dataset}: {e}", "error")
             raise e
 
     def get_merges(self):

src/postprocessor/chainer.py

@@ -22,9 +22,9 @@ class Chainer(Signal):
     them when called.
     """
 
-    # these no longer seem to be used
-    #process_types = ("multisignal", "processes", "reshapers")
-    #common_chains = {}
+    equivalences = {
+        "m5m": ("extraction/GFP/max/max5px", "extraction/GFP/max/median")
+    }
 
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
@@ -83,7 +83,7 @@ class Chainer(Signal):
         **kwargs,
     ):
         """Load data from an h5 file."""
-        1/0
+        1 / 0
         if dataset in self.common_chains:
             # get dataset for composite chains
             data = self.common_chains[dataset](**kwargs)
@@ -96,7 +96,7 @@ class Chainer(Signal):
             # keep data only from early time points
             data = self.get_retained(data, retain)
             # data = data.loc[data.notna().sum(axis=1) > data.shape[1] * retain]
-        if (stages and "stage" not in data.columns.names):
+        if stages and "stage" not in data.columns.names:
             # return stages as additional column level
             stages_index = [
                 x