from io import StringIO

import polars as pl

import data
from data import etl_cache

d = data.load()

def property_capacities(id: int):

	file = f"etl_property_capacities_{id}.obj"
	obj = etl_cache.openObj(file)
	if obj:
		return obj

	extractions = d.extractions_for(id).pl()
	df_dates = pl.DataFrame()

	for row in extractions.rows(named=True):
		df_calendar = pl.read_json(StringIO(row['calendar']))
		#df_calendar.insert_column(0, pl.Series("created_at", [row['created_at']]))
		df_dates = pl.concat([df_calendar, df_dates], how="diagonal")
	
	# order = sorted(df_dates.columns)
	# df_dates = df_dates.select(order)
	sum_hor = df_dates.sum_horizontal()
	#print(sum_hor)
	# Get the available dates per extraction
	count_days = []
	for dates in df_dates.rows():
		# Remove all None values
		liste = [x for x in dates if x is not None]
		count_days.append(len(liste))

	counts = pl.DataFrame({"count_days" : count_days, "sum" : sum_hor})
	result = {"capacities": [], "dates": extractions['created_at'].cast(pl.Date).to_list() }
	
	for row in counts.rows(named=True):
		max_capacity = row['count_days'] * 2
		max_capacity_perc = 100 / max_capacity
		result['capacities'].append(round(max_capacity_perc * row['sum'], 2))
	result['capacities'].reverse()

	etl_cache.saveObj(file, result)
	return result