def classify_based_on_value(logger_df, field_of_interest, value_of_interest,
    time_step=None, time_symbol='t'):
    """
    Classify the data of a field of interest based on a value of interest.

    Parameters
    ----------
    logger_df: pd.DataFrame
        The logger file in pandas dataframe format.
    field_of_interest: str
        The field of interest that we want to count its different cell types.
    value_of_interest: int
        the value of interest of which the classification will be based on.
    time_step: int (optional)
        The time step between time points. If not specified,
        the time step of the Morpheus file will be used.
    time_symbol: str (default=t)
        The time symbol, as specified in the morpheus model.
    Returns
    -------
    dict_time_series: dict
        A dictionary with cell type as a key, and the occurrences of
        the cell types as a value for different time intervals.
    """
    if not time_step:
        time_steps = logger_df[time_symbol].unique()
        time_step = time_steps[1] - time_steps[0]
    if field_of_interest not in logger_df:
        error_message = 'Column name does not exist!'
        return error_message
    df_size = len(logger_df[time_symbol].unique().tolist())
    dict_time_series = {}
    series_obj = 0
    for j in range(logger_df[time_symbol].unique().tolist()[0], df_size *
        time_step, time_step):
        filtered_data = logger_df[logger_df[time_symbol] == j]
        for i in range(2):
            series_obj = filtered_data.apply(lambda x: True if x[
                field_of_interest] >= value_of_interest else False, axis=1)
            num_of_rows = len(series_obj[series_obj == True].index)
        dict_time_series[j] = [len(series_obj) - num_of_rows, num_of_rows]
    return dict_time_series
