'Cannot resolve dtype error' preventing writing nwb to file

[mudd54]

Hello,

I'm trying to convert csv files into nwbs. The csv files become TimeInterval objects. I'm planning to link the nwb's I make back to the data they are derived from (hence all the fields being labelled 'refer to original file').

However, everytime I try to write the nwbs to file I get this error:

The nwbfile variable seems to have the data I would like:

I'm assuming there is an issue with how I'm writing the csv files to time intervals? Any help would be appreciated. Reduced version of the code I'm using is below:

def create_nwb_file(session_description, experimenter, lab, institution, experiment_description, session_id):
    return NWBFile(
        session_description=session_description,
        identifier=session_id,  # Use session ID as the identifier
        session_start_time=datetime.now(tzlocal()), # TODO: replace with actual session start time 
        experimenter=experimenter,
        lab=lab,
        institution=institution,
        experiment_description=experiment_description,
        session_id=session_id,
    )

def add_intervals_from_csv(nwbfile, df, name, description, probe_id, channel_id, additional_columns=None):
    # Create a TimeIntervals object
    time_intervals = TimeIntervals(
        name=name,
        description=description,
    )

    # Add columns for probe and channel IDs
    time_intervals.add_column(name="probe_id", description="ID of the probe")
    time_intervals.add_column(name="channel_id", description="ID of the channel")

    # Add additional columns if provided
    if additional_columns:
        for col_name, col_desc in additional_columns.items():
            time_intervals.add_column(name=col_name, description=col_desc)

    # Add rows to the TimeIntervals object
    for _, row in df.iterrows():
        if additional_columns is not None:
            kwargs = {col: row[col] for col in df.columns if col in additional_columns}
        else:
            kwargs = {}

        time_intervals.add_row(
            start_time=row['start_time'],
            stop_time=row['end_time'],
            probe_id=probe_id,
            channel_id=channel_id,
            **kwargs,
        )

    return time_intervals

session_id = '1234'
probe_id = '5678'
channel_id = '91011'
nwbfile = create_nwb_file(session_description, experimenter, lab, institution, experiment_description, session_id)

# Generate a random number of rows between 200 and 800
num_rows = np.random.randint(200, 801)

# Generate data for the dataframe
toy_data = {
    'event_number': np.arange(1, num_rows + 1),
    'start_time': np.random.uniform(0, 4000, num_rows),
    'end_time': np.random.uniform(0, 4000, num_rows),
    'duration': np.random.uniform(0, 1, num_rows),
    'mean_zscore': np.random.uniform(0, 10, num_rows),
    'median_zscore': np.random.uniform(0, 2, num_rows),
    'max_zscore': np.random.uniform(0, 10, num_rows),
    'min_zscore': np.random.uniform(0, 1, num_rows)
}

# Create the dataframe
toy_df = pd.DataFrame(toy_data)

probe_module = nwbfile.create_processing_module(name="probe_" + probe_id, description='Probe events')

csv_module = add_intervals_from_csv(nwbfile, toy_df, 
                        name = 'movement_artifacts_channel_' + channel_id,
                        description="Motion artifact events",
                        probe_id=probe_id,
                        channel_id=channel_id,
                        additional_columns={
                            'mean_zscore': "Mean z-score of the event",
                            'median_zscore': "Median z-score of the event",
                            'max_zscore': "Max z-score of the event",
                            'min_zscore': "Min z-score of the event",
                        })

probe_module.add_data_interface(csv_module)

try:
    # Save the NWB file
    with NWBHDF5IO('test_nwb.nwb', 'w') as io:
        io.write(nwbfile)
    print("Saved NWB file for " + str(session_id))

except Exception as e:
    print(e)