blob: 1b779a615ec41a042a0a8e93fd631321dd9f9f43 (
plain)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
|
import datetime
import logging
import re
from sheets_client import SheetsRow
INTERVAL_ABBREVS = {
"pv": datetime.timedelta(days=1),
"vk": datetime.timedelta(days=7),
"kk": datetime.timedelta(days=30),
"v": datetime.timedelta(days=365),
}
def _calculate_interval(interval_str: str):
match = re.search(r"(\d+)(\w+)", interval_str)
number = int(match[1])
abbrev = match[2]
try:
interval = INTERVAL_ABBREVS[abbrev]
except KeyError:
raise Exception(f'"{interval_str}" is an invalid interval string!')
total_interval = datetime.timedelta(seconds=interval.total_seconds() * number)
return total_interval
def _timestamp_to_date(timestamp: str):
return datetime.datetime.strptime(timestamp, "%Y-%m-%d")
def _date_to_timestamp(date):
return datetime.datetime.strftime(date, "%Y-%m-%d")
def _process_row(row: SheetsRow):
interval = _calculate_interval(row.interval)
last_done_date = _timestamp_to_date(row.last_done)
next_date = last_done_date + interval
next_date_str = _date_to_timestamp(next_date)
return (next_date, row.name, row.interval)
def _is_this_week(processed_row):
next_date = processed_row[0]
now = datetime.datetime.now()
return (next_date - now) < datetime.timedelta(weeks=1)
def filter_only_this_weeks_rows(rows: list[SheetsRow]):
processed_rows = map(_process_row, rows)
return filter(_is_this_week, processed_rows)
|
