65 lines
2.6 KiB
Python
65 lines
2.6 KiB
Python
from datetime import datetime, timedelta
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from io import StringIO
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import polars as pl
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import data
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from data import etl_cache
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d = data.load()
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def region_capacities_monthly(id: int, scrapeDate_start: str):
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file = f"etl_region_capacities_monthly_{id}_{scrapeDate_start}.obj"
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obj = etl_cache.openObj(file)
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if obj:
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return obj
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# Get end date of start search-window
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scrapeDate_end = scrapeDate_start + timedelta(days=1)
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# Get Data
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if id == -1:
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extractions = d.singleScrape_of_global_scrapDate(scrapeDate_start, scrapeDate_end).pl()
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else:
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extractions = d.singleScrape_of_region_scrapDate(id, scrapeDate_start, scrapeDate_end).pl()
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df_calendar = pl.DataFrame()
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numWeeks = 0
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firstExe = True
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counter = 0
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for row in extractions.rows(named=True):
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scrapeDate = row['created_at']
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if row['calendarBody']:
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counter += 1
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df_calendar = pl.read_json(StringIO(row['calendarBody']))
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columnTitles = df_calendar.columns
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df_calendar = df_calendar.transpose()
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df_calendar = df_calendar.with_columns(pl.Series(name="dates", values=columnTitles))
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df_calendar = df_calendar.with_columns((pl.col("dates").str.to_date()))
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df_calendar = df_calendar.with_columns((pl.col("dates").dt.month_end().dt.day().alias('numDays')))
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df_calendar = df_calendar.with_columns((pl.col("dates").dt.strftime("%b") + " " + (pl.col("dates").dt.strftime("%Y"))).alias('date_short'))
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df_calendar = df_calendar.with_columns((pl.col("dates").dt.strftime("%Y") + " " + (pl.col("dates").dt.strftime("%m"))).alias('dates'))
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df_calendar = df_calendar.group_by(['dates', 'date_short','numDays']).agg(pl.col("column_0").sum())
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df_calendar = df_calendar.with_columns((pl.col("column_0") / pl.col("numDays") / 2 * 100).alias("column_0"))
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df_calendar = df_calendar.sort('dates')
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df_calendar = df_calendar.drop('dates')
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df_calendar = df_calendar.drop('numDays')
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df_calendar = df_calendar.rename({'column_0': str(counter)})
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if firstExe:
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outDf = df_calendar
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firstExe = False
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else:
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outDf = outDf.join(df_calendar, on='date_short')
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# Calculate horizontal Mean
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means = outDf.mean_horizontal()
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outDf = outDf.insert_column(1, means)
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outDf = outDf[['date_short', 'mean']]
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result = {"date": scrapeDate, "months": outDf['date_short'].to_list(),'capacities': outDf['mean'].to_list()}
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etl_cache.saveObj(file, result)
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return result |