--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/consecutive_peaks.py	Fri Oct 26 11:58:06 2018 -0400
@@ -0,0 +1,34 @@
+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()