comparison consecutive_peaks.py @ 0:7f827a8e4ec5 draft

planemo upload for repository https://github.com/bardin-lab/damid_galaxy_tools commit c753dd4f3e1863aae7ba45dcc7efdf6937b03542-dirty

0:7f827a8e4ec5

import click
import numpy as np
import pandas as pd

SHIFTED_PADJ_COLUMN = 'shifted'
CONSECUTIVE_MAX = 'consecutive_max'
PEAKS_PER_GROUP = 'peaks_per_group'


@click.command()
@click.argument('input_file', type=click.Path(exists=True))
@click.argument('output_file', type=click.Path())
@click.argument('padj_column', default=8)
@click.argument('groupby_column', default=9)
@click.argument('add_number_of_peaks', default=True)
def determine_consecutive_peaks(input_file, output_file, padj_column, groupby_column, add_number_of_peaks):
    """Finds the two lowest consecutives peaks for a group and reports"""
    df = pd.read_csv(input_file, sep='\t', header=None)
    grouped = df.groupby(groupby_column, sort=False)
    if add_number_of_peaks:
        df[PEAKS_PER_GROUP] = grouped[groupby_column].transform(np.size)
    df[SHIFTED_PADJ_COLUMN] = grouped[8].shift()
    df[CONSECUTIVE_MAX] = df[[padj_column, SHIFTED_PADJ_COLUMN]].max(axis=1)
    grouped = df.groupby(groupby_column, sort=False)
    idx = grouped[CONSECUTIVE_MAX].transform(min)  # index of groupwise consecutive minimum
    new_df = df[df[CONSECUTIVE_MAX] == idx]
    new_df.sort_values(by=CONSECUTIVE_MAX)
    new_df[padj_column].replace(new_df[CONSECUTIVE_MAX])
    new_df = new_df.drop(labels=[CONSECUTIVE_MAX, SHIFTED_PADJ_COLUMN], axis=1)
    new_df.to_csv(output_file, sep='\t', header=None, na_rep="NaN")


if __name__ == '__main__':
    determine_consecutive_peaks()