assume less about a column named "value"

Author: drammock

mne_bids/read.py

@@ -526,6 +526,26 @@ def _handle_info_reading(sidecar_fname, raw):
     return raw
 
 
+def _str_to_num(sequence):
+    """Convert event values from string to either float or int, if possible.
+
+    Often (but not always!) the `value` column was written by MNE-BIDS and can be
+    assumed to represent integer event IDs (as would be found in MNE-Python events
+    arrays / event_id dicts). But let's be agnostic about that, and meanwhile if the
+    conversion to `float` succeeds but `int` fails we might as well return floats.
+    """
+    try:
+        sequence = np.asarray(sequence).astype(float)
+    except ValueError:
+        pass
+    else:
+        try:
+            sequence = sequence.astype(int)
+        except ValueError:
+            pass
+    return sequence
+
+
 def _handle_events_reading(events_fname, raw):
     """Read associated events.tsv and convert valid events to annotations on Raw."""
     logger.info(f"Reading events from {events_fname}.")
@@ -557,7 +577,10 @@ def _handle_events_reading(events_fname, raw):
         trial_types = events_dict[trial_type_col_name]
         # handle event values (if provided); ensure pairings are 1 value per description
         if "value" in events_dict:
-            values = np.asarray(events_dict["value"], dtype=str)
+            # drop rows where `value` is `n/a` (only when making our `event_id` dict;
+            # `value = n/a` doesn't prevent annotation). Then, try to convert to numeric
+            culled = _drop(events_dict, "n/a", "value")
+            values = _str_to_num(events_dict["value"])
             for trial_type in np.unique(trial_types):
                 idx = np.where(trial_type == np.atleast_1d(trial_types))[0]
                 matching_values = values[idx]
@@ -574,11 +597,8 @@ def _handle_events_reading(events_fname, raw):
                         new_name = f"{trial_type}/{value}"
                         logger.info(f"    Renaming event: {trial_type} -> {new_name}")
                         trial_types[ii] = new_name
-            # drop rows where `value` is `n/a` & convert remaining `value` to int (only
-            # when making our `event_id` dict; `value = n/a` doesn't prevent annotation)
-            culled = _drop(events_dict, "n/a", "value")
             event_id = dict(
-                zip(culled[trial_type_col_name], np.asarray(culled["value"], dtype=int))
+                zip(culled[trial_type_col_name], _str_to_num(culled["value"]))
             )
         else:
             event_id = dict(zip(trial_types, np.arange(len(trial_types))))