rapids/src/data/compute_day_segments.py

import pandas as pd

def is_valid_frequency_segments(day_segments):
    return False

def is_valid_interval_segments(day_segments):
    return True

def is_valid_event_segments(day_segments):
    return False

def parse_frequency_segments(day_segments):
    return day_segments

def parse_interval_segments(day_segments):
    day_segments["local_date"] = 1
    day_segments = day_segments.rename(columns={"start": "start_time", "end":"end_time"})
    return day_segments

def parse_event_segments(day_segments):
    return day_segments

def parse_day_segments(day_segments_file):
    # Add code to validate and parse frequencies, intervals, and events
    # Expected formats:
    # Frequency: label, length columns (e.g. my_prefix, 5) length has to be in minutes (int)
    # Interval: label, start, end columns (e.g. daily, 00:00, 23:59) start and end should be valid hours in 24 hour format
    # Event: label, timestamp, length, shift (e.g., survey1, 1532313215463, 60, -30), timestamp is a UNIX timestamp in ms (we could take a date time string instead), length is in minutes (int), shift is in minutes (+/-int) and is added/substracted from timestamp
    # Our output should have local_date, start_time, end_time, label. In the readable_datetime script, If local_date has the same value for all rows, every segment will be applied for all days, otherwise each segment will be applied only to its local_date
    day_segments = pd.read_csv(day_segments_file)

    if(is_valid_frequency_segments(day_segments)):
        day_segments = parse_frequency_segments(day_segments)
    elif(is_valid_interval_segments(day_segments)):
        day_segments = parse_interval_segments(day_segments)
    elif(is_valid_event_segments(day_segments)):
        day_segments = parse_event_segments(day_segments)
    else:
        raise ValueError("{} does not have a format compatible with frequency, interval or event day segments. Please refer to [LINK]".format(day_segments_file))
    return day_segments

day_segments = parse_day_segments(snakemake.input[0])
day_segments.to_csv(snakemake.output["segments_file"], index=False)