More robust handling of events input data

bidscoin/plugins/__init__.py

@@ -164,7 +164,7 @@ def __init__(self, sourcefile: Path, eventsdata: dict, options: dict):
         :param options:     The plugin options
         """
 
-        self.sourcefile = sourcefile
+        self.sourcefile = Path(sourcefile)
         self._data      = eventsdata
         self.options    = options
 
@@ -192,6 +192,8 @@ def eventstable(self) -> pd.DataFrame:
         """Returns the target events.tsv data"""
 
         # Check the parser's data structure
+        if not len(self.logtable):
+            return pd.DataFrame(columns=['onset', 'duration'])
         if not self.isvalid:
             pass
 

bidscoin/plugins/events2bids.py

@@ -179,7 +179,7 @@ def __init__(self, sourcefile: Path, _data: dict, options: dict):
         super().__init__(sourcefile, _data, options)
 
         # Read the log-tables from the Presentation log file
-        self._sourcetable = pd.read_csv(self.sourcefile, sep='\t', skiprows=options.get('skiprows',3), skip_blank_lines=True)
+        self._sourcetable = pd.read_csv(self.sourcefile, sep='\t', skiprows=options.get('skiprows',3), skip_blank_lines=True) if self.sourcefile.is_file() else pd.DataFrame()
         """The Presentation log-tables (https://www.neurobs.com/pres_docs/html/03_presentation/07_data_reporting/01_logfiles/index.html)"""
         self._sourcecols  = self._sourcetable.columns
         """Store the original column names"""
@@ -188,13 +188,16 @@ def __init__(self, sourcefile: Path, _data: dict, options: dict):
     def logtable(self) -> pd.DataFrame:
         """Returns a Presentation log-table"""
 
-        df              = self._sourcetable
-        nrows           = len(df)
+        df = self._sourcetable
+        if not (nrows := len(df)):
+            return df
+
+        # Get the row indices to slice the event, stimulus, video or survey table
         stimulus_header = (df.iloc[:, 0] == 'Event Type').idxmax() or nrows
         video_header    = (df.iloc[:, 0] == 'filename').idxmax() or nrows
         survey_header   = (df.iloc[:, 0] == 'Time').idxmax() or nrows
 
-        # Get the row indices to slice the event, stimulus, video or survey table
+        # Get the first and last row index of the table of interest
         df.columns = self._sourcecols
         if self.options['table'].lower() == 'event':
             begin = 0