Add base functions to compute_day_segments.py

pull/103/head
JulioV 2020-07-23 13:53:28 -04:00
parent b59f1715fc
commit c2d011fb6a
2 changed files with 36 additions and 12 deletions

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@ -1,4 +1,4 @@
label,start,end label,start,end
daily,00:00, 23:59 daily,00:00, 23:59
morning,06:00, 11:59 morning,06:00, 11:59
eveningblue3,18:00, 21:59 eveningblue,18:00, 21:59

1 label start end
2 daily 00:00 23:59
3 morning 06:00 11:59
4 eveningblue3 eveningblue 18:00 21:59

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@ -1,19 +1,43 @@
import pandas as pd import pandas as pd
def is_valid_frequency_segments(day_segments):
return False
def parse_day_segments(day_segments): def is_valid_interval_segments(day_segments):
# Add code to parse frequencies, intervals, and events return True
# Expected formats:
# Frequency: label, length columns (e.g. my_prefix, 5) length has to be in minutes (int) def is_valid_event_segments(day_segments):
# Interval: label, start, end columns (e.g. daily, 00:00, 23:59) start and end should be valid hours in 24 hour format return False
# Event: label, timestamp, length, shift (e.g., survey1, 1532313215463, 60, -30), timestamp is a UNIX timestamp in ms, length is in minutes (int), shift is in minutes (+/-int) and is add/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 def parse_frequency_segments(day_segments):
return day_segments
def parse_interval_segments(day_segments):
day_segments["local_date"] = 1 day_segments["local_date"] = 1
day_segments = day_segments.rename(columns={"start": "start_time", "end":"end_time"}) day_segments = day_segments.rename(columns={"start": "start_time", "end":"end_time"})
return day_segments return day_segments
##########################
day_segments = pd.read_csv(snakemake.input[0]) def parse_event_segments(day_segments):
day_segments = parse_day_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) day_segments.to_csv(snakemake.output["segments_file"], index=False)