from io import StringIO

import polars as pl

import data
from data import etl_cache

d = data.load()

def property_capacities_weekdays(id: int, scrapeDate: str):
	
	file = f"etl_property_capacities_weekdays_{id}.obj"
	obj = etl_cache.openObj(file)
	if obj:
		return obj

	extractions = d.extractions_propId_scrapeDate(id, scrapeDate).pl()
	weekdays = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']
	df_calendar = pl.DataFrame()
	numWeeks = 0

	for row in extractions.rows(named=True):
		scrapeDate = row['created_at']
		df_calendar = pl.read_json(StringIO(row['calendar']))
		columnTitles = df_calendar.columns
		df_calendar = df_calendar.transpose()
		df_calendar = df_calendar.with_columns(pl.Series(name="dates", values=columnTitles))
		df_calendar = df_calendar.with_columns((pl.col("dates").str.to_date()))
		numWeeks = round((df_calendar.get_column("dates").max() - df_calendar.get_column("dates").min()).days / 7, 0)
		df_calendar = df_calendar.with_columns(pl.col("dates").dt.weekday().alias("weekday_num"))
		df_calendar = df_calendar.with_columns(pl.col("dates").dt.strftime("%A").alias("weekday"))
		df_calendar = df_calendar.drop("dates")

	df_calendar = df_calendar.group_by(["weekday", "weekday_num"]).agg(pl.col("column_0").sum())
	df_calendar = df_calendar.with_columns((pl.col("column_0") / numWeeks / 2 * 100).alias("column_0"))
	df_calendar = df_calendar.sort('weekday_num')
	df_calendar = df_calendar.drop('weekday_num')

	result = {"scraping-date": scrapeDate, "weekdays": df_calendar['weekday'].to_list(), 'capacities': df_calendar['column_0'].to_list()}
	etl_cache.saveObj(file, result)
	return result