Apply ``feature`` to all recordings in ``raw_datasets``. Store the results
    in two files. One file stores the raw result, the other one groups the
    results by symbols and stores the mean, standard deviation and the name of
    the symbol as a csv file.

    Parameters
    ----------
    raw_datasets : List of dictionaries
        Each dictionary is a raw_dataset.
    feature : An instance of the feature class type
        The `feature` which gets analyzed on `raw_datasets`.
    basename : string
        Name for the file in which the data gets written.

def analyze_feature(raw_datasets, feature, basename="aspect_ratios"):
    """
    Apply ``feature`` to all recordings in ``raw_datasets``. Store the results
    in two files. One file stores the raw result, the other one groups the
    results by symbols and stores the mean, standard deviation and the name of
    the symbol as a csv file.

    Parameters
    ----------
    raw_datasets : List of dictionaries
        Each dictionary is a raw_dataset.
    feature : An instance of the feature class type
        The `feature` which gets analyzed on `raw_datasets`.
    basename : string
        Name for the file in which the data gets written.
    """

    # Prepare files
    csv_file = dam.prepare_file(basename + '.csv')
    raw_file = dam.prepare_file(basename + '.raw')

    csv_file = open(csv_file, 'a')
    raw_file = open(raw_file, 'a')

    csv_file.write("label,mean,std\n")  # Write header
    raw_file.write("latex,raw_data_id,value\n")  # Write header

    print_data = []
    for _, datasets in dam.sort_by_formula_id(raw_datasets).items():
        values = []
        for data in datasets:
            value = feature(data)[0]
            values.append(value)
            raw_file.write("%s,%i,%0.2f\n" % (datasets[0].formula_in_latex,
                                              data.raw_data_id,
                                              value))
        label = filter_label(datasets[0].formula_in_latex)
        print_data.append((label, numpy.mean(values), numpy.std(values)))

    # Sort the data by highest mean, descending
    print_data = sorted(print_data, key=lambda n: n[1], reverse=True)

    # Write data to file
    for label, mean, std in print_data:
        csv_file.write("%s,%0.2f,%0.2f\n" % (label, mean, std))
    csv_file.close()