Call calculation functions and write stats file.

    This function takes a list of DataFrames,
    and will create a column for each in the tab separated output.

def write_stats(datadfs, outputfile, names=[]):
    """Call calculation functions and write stats file.

    This function takes a list of DataFrames,
    and will create a column for each in the tab separated output.
    """
    if outputfile == 'stdout':
        output = sys.stdout
    else:
        output = open(outputfile, 'wt')

    stats = [Stats(df) for df in datadfs]
    features = {
        "Number of reads": "number_of_reads",
        "Total bases": "number_of_bases",
        "Total bases aligned": "number_of_bases_aligned",
        "Median read length": "median_read_length",
        "Mean read length": "mean_read_length",
        "Read length N50": "n50",
        "Average percent identity": "average_identity",
        "Median percent identity": "median_identity",
        "Active channels": "active_channels",
        "Mean read quality": "mean_qual",
        "Median read quality": "median_qual",
    }
    max_len = max([len(k) for k in features.keys()])
    try:
        max_num = max(max([len(str(s.number_of_bases)) for s in stats]),
                      max([len(str(n)) for n in names])) + 6
    except ValueError:
        max_num = max([len(str(s.number_of_bases)) for s in stats]) + 6
    output.write("{:<{}}{}\n".format('General summary:', max_len,
                                     " ".join(['{:>{}}'.format(n, max_num) for n in names])))
    for f in sorted(features.keys()):
        try:
            output.write("{f:{pad}}{v}\n".format(
                f=f + ':',
                pad=max_len,
                v=feature_list(stats, features[f], padding=max_num)))
        except KeyError:
            pass
    if all(["quals" in df for df in datadfs]):
        long_features = {
            "Top 5 longest reads and their mean basecall quality score":
            ["top5_lengths", range(1, 6)],
            "Top 5 highest mean basecall quality scores and their read lengths":
            ["top5_quals", range(1, 6)],
            "Number, percentage and megabases of reads above quality cutoffs":
            ["reads_above_qual", [">Q" + str(q) for q in stats[0].qualgroups]],
        }
        for lf in sorted(long_features.keys()):
            output.write(lf + "\n")
            for i in range(5):
                output.write("{}:\t{}\n".format(
                    long_features[lf][1][i], feature_list(stats, long_features[lf][0], index=i)))