generated from dopt-python/py311
115 lines
3.1 KiB
Python
115 lines
3.1 KiB
Python
# %%
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import datetime
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import importlib
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from pathlib import Path
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import external_code
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import polars as pl
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import sqlalchemy as sql
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from wattanalyse import db
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importlib.reload(db)
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importlib.reload(external_code)
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# %%
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PROJECT_BASE = Path(__file__).parents[1]
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DATA_PTH = PROJECT_BASE / "data"
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assert DATA_PTH.exists()
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# %%
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# // load data
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target = DATA_PTH / "PSM_20260507.arrow"
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data_raw = pl.scan_ipc(target)
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# %%
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# 0. read data (from customer's database)
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# 1. cleanup obtained new data
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# ~~2. load data from internal database~~
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# ~~3. integrate with with new data (whole snapshot)~~
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# 2. process on order level
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# 3. save results to internal database
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# 4. post-process results
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# 5. write to external database
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# // (1) cleanup obtained new data
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# load data from internal database
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# integrate with with new data (whole snapshot)
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res = external_code.preprocess_psm(data_raw)
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data = res.data
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print(f"Data:\n{data.collect()}\n\n---\n\nFiltered:\n{res.filtered}")
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# %%
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# // (2) processing order level
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df = external_code.process_order_level(data)
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# ?? What is if "Konfektionär" is NULL?
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# If this is NULL, then the aggregates for "Konfektionär" will not work. Instead, they are
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# calculated for all NULL entries which might incorporate different production orders which
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# belong to different "Konfektionär". Thus, these values will be calculated, but should not be
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# considered.
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# %%
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# // (3) save results to internal database
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external_code.dump_order_level_to_internal_database_wipe(df)
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# %%
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# now load data from database
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df = external_code.load_order_level_from_internal_database()
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df
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# %%
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tmp = df.clone()
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# two ways to define the aggregate for date deviations: just use < 0 or use Boolean flag
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# defined by the user-specified boundaries
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USE_BOUNDARIES = False
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filter_date_deviation_early: pl.Expr
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filter_date_deviation_late: pl.Expr
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if USE_BOUNDARIES:
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filter_date_deviation_early = pl.col("Terminunterschreitung")
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filter_date_deviation_late = pl.col("Terminüberschreitung")
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else:
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filter_date_deviation_early = pl.col("Terminabweichung_Anzahl_Tage") < 0
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filter_date_deviation_late = pl.col("Terminabweichung_Anzahl_Tage") > 0
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tmp.select(
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pl.col("Terminabweichung_Anzahl_Tage")
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.filter(filter_date_deviation_early)
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.mean()
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.abs()
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.round(mode="half_away_from_zero")
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.cast(pl.Int64)
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.alias("Mittlere_Tage_Unterschreitung"),
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pl.col("Terminabweichung_Anzahl_Tage")
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.filter(filter_date_deviation_late)
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.mean()
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.abs()
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.round(mode="half_away_from_zero")
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.cast(pl.Int64)
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.alias("Mittlere_Tage_Ueberschreitung"),
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pl.col("Terminabweichung_Anzahl_Tage")
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.std(ddof=1)
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.alias("Standardabweichung_Lieferterminabweichung"),
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pl.col("Import-Ist_Anzahl_Aenderungen")
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.mean()
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.abs()
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.round(mode="half_away_from_zero")
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.cast(pl.Int64)
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.alias("Mittlere_Anzahl_Anpassungen_Liefertermin"),
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pl.col("Tage_zu_letzter_PSM_Historie")
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.list.explode()
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.mean()
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.abs()
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.round(mode="half_away_from_zero")
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.cast(pl.Int64)
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.alias("Mittlere_Abstaende_PSM"),
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pl.col("Durchlaufzeit_Anzahl_Tage")
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.mean()
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.round(mode="half_away_from_zero")
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.cast(pl.Int64)
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.alias("Mittlere_Durchlaufzeit"),
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)
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# %%
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