generated from dopt-python/py311
456 lines
16 KiB
Python
456 lines
16 KiB
Python
import dataclasses as dc
|
|
import datetime
|
|
import enum
|
|
import json
|
|
from typing import Any, Final
|
|
|
|
import polars as pl
|
|
import sqlalchemy as sql
|
|
|
|
from wattanalyse import db
|
|
|
|
# 1. cleanup obtained new data
|
|
# ~~2. load data from internal database~~
|
|
# ~~3. integrate with with new data (whole snapshot)~~
|
|
# 2. process on order level
|
|
# 3. save results to internal database
|
|
# 4. post-process results
|
|
# 5. write to external database
|
|
|
|
|
|
@dc.dataclass(slots=True, eq=False)
|
|
class PreProcessResult:
|
|
data: pl.LazyFrame
|
|
filtered: pl.DataFrame
|
|
|
|
|
|
class QualityPsm(enum.StrEnum):
|
|
FEHLEND = enum.auto()
|
|
UNPLAUSIBEL = enum.auto()
|
|
PLAUSIBEL = enum.auto()
|
|
|
|
|
|
PSM_SCORES: dict[QualityPsm, int] = {
|
|
QualityPsm.FEHLEND: 1,
|
|
QualityPsm.UNPLAUSIBEL: 0,
|
|
QualityPsm.PLAUSIBEL: 2,
|
|
}
|
|
|
|
RENAMING_SCHEME: dict[str, str] = {
|
|
"PA Pos": "PA_Pos",
|
|
"PSM gemeldet am": "Meldezeitpunkt_Historie",
|
|
"Import Ist": "Import-Ist_Historie",
|
|
"1.bestät. Import Konfektionär": "Bestaetigter-Import_Historie",
|
|
"Zuschnitt am": "Prod-Start_Historie",
|
|
"Teile in Zuschnitt": "Prod-EP10_Historie",
|
|
"Teile im Nähband": "Prod-EP20_Historie",
|
|
"Fertigware aus Nähband": "Prod-EP30_Historie",
|
|
"Teile kontrolliert": "Prod-EP40_Historie",
|
|
"Teile verpackt in Karton": "Prod-EP50_Historie",
|
|
}
|
|
|
|
PRIM_KEYS: Final[list[str]] = ["PA", "PA_Pos"]
|
|
|
|
LOWER_BOUND_DATE_DEVIATION: Final[int] = 0
|
|
UPPER_BOUND_DATE_DEVIATION: Final[int] = 0
|
|
NUMBER_YEARS_UPPER_BOUND_DATES: Final[int] = 4
|
|
|
|
|
|
# // (1) preprocess
|
|
def preprocess_psm(
|
|
data: pl.LazyFrame,
|
|
) -> PreProcessResult:
|
|
data = data.rename(RENAMING_SCHEME)
|
|
REGEX_PATTERN = r"^[\s\-#+/$]+$"
|
|
data = data.with_columns(
|
|
pl.when(pl.col(pl.String).str.contains(REGEX_PATTERN))
|
|
.then(None)
|
|
.otherwise(pl.col(pl.String))
|
|
.name.keep()
|
|
)
|
|
data = data.with_columns(pl.col("Konfektionär").str.strip_chars(" \n\t"))
|
|
filtered_data = pl.LazyFrame(schema=data.collect_schema())
|
|
|
|
# drop duplicates
|
|
# use null count as information measure, least amount of nulls should be contained
|
|
data = data.with_columns(pl.sum_horizontal(pl.all().is_null()).alias("null_count"))
|
|
data = data.sort(PRIM_KEYS + ["Meldezeitpunkt_Historie", "null_count"], descending=False)
|
|
filtered_data = pl.concat(
|
|
[
|
|
filtered_data,
|
|
data.filter(
|
|
~pl.struct(PRIM_KEYS + ["Meldezeitpunkt_Historie"]).is_first_distinct()
|
|
).drop("null_count"),
|
|
]
|
|
)
|
|
data = data.filter(pl.struct(PRIM_KEYS + ["Meldezeitpunkt_Historie"]).is_first_distinct())
|
|
data = data.drop("null_count")
|
|
|
|
# any NULL values in critical columns
|
|
NOT_NULL_COLS = ("PA", "PA_Pos", "Meldezeitpunkt_Historie")
|
|
conds = [pl.col(col).is_null() for col in NOT_NULL_COLS]
|
|
filtered_data = pl.concat([filtered_data, data.filter(pl.any_horizontal(*conds))])
|
|
data = data.filter(~pl.any_horizontal(*conds))
|
|
|
|
# implausible dates
|
|
# dates not allowed to be in the future
|
|
current_datetime = datetime.datetime.now()
|
|
current_date = current_datetime.date()
|
|
NOT_IN_FUTURE_COLS_DATETIME = ("Meldezeitpunkt_Historie",)
|
|
NOT_IN_FUTURE_COLS_DATE = ("Wareneingang am", "Prod-Start_Historie")
|
|
conds = [
|
|
(pl.col(col) > current_datetime).fill_null(False)
|
|
for col in NOT_IN_FUTURE_COLS_DATETIME
|
|
]
|
|
conds.extend(
|
|
[(pl.col(col) > current_date).fill_null(False) for col in NOT_IN_FUTURE_COLS_DATE]
|
|
)
|
|
filtered_data = pl.concat([filtered_data, data.filter(pl.any_horizontal(*conds))])
|
|
data = data.filter(~pl.any_horizontal(*conds))
|
|
|
|
# too much in the future or the past
|
|
# dates
|
|
future_limit = current_date + datetime.timedelta(
|
|
days=(365 * NUMBER_YEARS_UPPER_BOUND_DATES)
|
|
)
|
|
past_limit = datetime.date(1990, 1, 1)
|
|
cond = (pl.col(pl.Date) > future_limit).fill_null(False) | (
|
|
pl.col(pl.Date) < past_limit
|
|
).fill_null(False)
|
|
filtered_data = pl.concat([filtered_data, data.filter(pl.any_horizontal(cond))])
|
|
data = data.filter(~pl.any_horizontal(cond))
|
|
# datetimes
|
|
future_limit = current_datetime + datetime.timedelta(
|
|
days=(365 * NUMBER_YEARS_UPPER_BOUND_DATES)
|
|
)
|
|
past_limit = datetime.datetime(1990, 1, 1)
|
|
cond = (pl.col(pl.Datetime) > future_limit).fill_null(False) | (
|
|
pl.col(pl.Datetime) < past_limit
|
|
).fill_null(False)
|
|
filtered_data = pl.concat([filtered_data, data.filter(pl.any_horizontal(cond))])
|
|
data = data.filter(~pl.any_horizontal(cond))
|
|
|
|
return PreProcessResult(data=data, filtered=filtered_data.collect())
|
|
|
|
|
|
# // (2) process on order level
|
|
def process_order_level(
|
|
data: pl.LazyFrame,
|
|
) -> pl.LazyFrame:
|
|
# ** renaming
|
|
# data = data.rename(RENAMING_SCHEME) # TODO delete, done in pre-processing
|
|
data = data.sort(PRIM_KEYS + ["Meldezeitpunkt_Historie"], descending=False)
|
|
|
|
# ** plausibility check of order quantities
|
|
PLAUSI_FEATURES: list[str] = [
|
|
"Prod-EP10_Historie",
|
|
"Prod-EP20_Historie",
|
|
"Prod-EP30_Historie",
|
|
"Prod-EP40_Historie",
|
|
"Prod-EP50_Historie",
|
|
]
|
|
data = data.with_columns(
|
|
pl.all_horizontal(
|
|
pl.col(PLAUSI_FEATURES).is_null() | (pl.col(PLAUSI_FEATURES) == 0)
|
|
).alias("is_empty")
|
|
)
|
|
conditions = [
|
|
pl.col(PLAUSI_FEATURES[i]) >= pl.col(PLAUSI_FEATURES[i + 1])
|
|
for i in range(len(PLAUSI_FEATURES) - 1)
|
|
]
|
|
data = data.with_columns(
|
|
pl.when(pl.all_horizontal(conditions) | pl.col("is_empty"))
|
|
.then(pl.lit(True))
|
|
.otherwise(pl.lit(False))
|
|
.alias("Prod-Qty_is_valid")
|
|
).with_columns(
|
|
pl.when(pl.col("is_empty"))
|
|
.then(pl.lit(PSM_SCORES[QualityPsm.FEHLEND]))
|
|
.when(pl.col("Prod-Qty_is_valid"))
|
|
.then(pl.lit(PSM_SCORES[QualityPsm.PLAUSIBEL]))
|
|
.otherwise(pl.lit(PSM_SCORES[QualityPsm.UNPLAUSIBEL]))
|
|
.alias("Prod-Qualitaet_Historie")
|
|
)
|
|
# aggregate hint for "Prod-Qualitaet_Durchschnitt": use "drop_nulls" "last"
|
|
# aggregate "Prod-Qualitaet_Historie" and use "mean"
|
|
# need additional "alias" on "Prod-Qualitaet_Historie"
|
|
|
|
# ** planned or target delivery date
|
|
current_date = datetime.datetime.now().date()
|
|
print(f"{current_date=}")
|
|
data = data.with_columns(
|
|
pl.coalesce(["Bestaetigter-Import_Historie", "Import-Ist_Historie"]).alias(
|
|
"Liefertermin_Soll"
|
|
)
|
|
)
|
|
# aggregate hint for "Liefertermin_Soll": use "drop_nulls" "first"
|
|
# first filled field for "Liefertermin Soll" is the relevant target date
|
|
# should be first confirmed date, but if this field is not filled we use the first
|
|
# filled import by the supplier
|
|
|
|
# ** actual delivery date
|
|
# logic of Wattana: set date is before current date --> becomes actual value
|
|
data = data.with_columns(
|
|
pl.when(pl.col("Import-Ist_Historie") < current_date)
|
|
.then(pl.col("Import-Ist_Historie"))
|
|
.otherwise(None)
|
|
.alias("Liefertermin_Ist")
|
|
)
|
|
# aggregate hint for "Liefertermin_Ist": use "drop_nulls" "last"
|
|
# keep last because that is the latest value set by the supplier
|
|
# if all values are NULL then NULL is returned (no actual date available)
|
|
|
|
# ** duration since last report in days
|
|
data = data.sort(PRIM_KEYS + ["Meldezeitpunkt_Historie"], descending=False).with_columns(
|
|
(
|
|
pl.col("Meldezeitpunkt_Historie")
|
|
- pl.col("Meldezeitpunkt_Historie").shift(1).over(PRIM_KEYS)
|
|
)
|
|
.dt.total_days()
|
|
.alias("Tage_zu_letzter_PSM_Historie")
|
|
)
|
|
# aggregate hint for "Tage_zu_letzter_PSM_Durchschnitt"
|
|
# aggregate "Tage_zu_letzter_PSM_Historie" and use "mean" (NULL is ignored automatically)
|
|
# need additional "alias" on "Tage_zu_letzter_PSM_Historie"
|
|
|
|
data = data.sort(PRIM_KEYS + ["Meldezeitpunkt_Historie"], descending=False).with_columns(
|
|
# Prüfen: Ist das aktuelle Datum ungleich dem vorherigen Datum derselben Position?
|
|
(
|
|
pl.col("Import-Ist_Historie")
|
|
!= pl.col("Import-Ist_Historie").shift(1).over(PRIM_KEYS)
|
|
)
|
|
.fill_null(False) # Der allererste Eintrag hat keinen Vorgänger -> Ist keine Änderung
|
|
.alias("Import-Ist_geaendert")
|
|
)
|
|
# aggregate hint for "Import-Ist_geaendert"
|
|
# aggregate "Import-Ist_geaendert" and use "last"
|
|
|
|
# aggregate hint for "Import-Ist_letzter_Wert"
|
|
# aggregate "Import-Ist_Historie" and use "drop_nulls" "last"
|
|
# need additional "alias" on "Import-Ist_Historie"
|
|
|
|
# aggregate hint for "Import-Ist_Anzahl_Aenderungen"
|
|
# aggregate "Import-Ist_geaendert" and use "sum"
|
|
# need additional "alias" on "Import-Ist_geaendert"
|
|
|
|
# aggregate hint for "Prod-Start"
|
|
# aggregate "Prod-Start_Historie" and use "drop_nulls" "first"
|
|
# first entry should be treated as the truth value, changing later does not make sense
|
|
# need additional "alias" on "Prod-Start_Historie"
|
|
|
|
# whole aggregates see DB schema
|
|
data = (
|
|
data.sort(PRIM_KEYS + ["Meldezeitpunkt_Historie"], descending=False)
|
|
.group_by(PRIM_KEYS + ["Konfektionär"])
|
|
.agg(
|
|
pl.col("Meldezeitpunkt_Historie"),
|
|
pl.col("Liefertermin_Soll").drop_nulls().first(),
|
|
pl.col("Bestaetigter-Import_Historie"),
|
|
pl.col("Liefertermin_Ist").drop_nulls().last(),
|
|
pl.col("Import-Ist_Historie"),
|
|
pl.col("Import-Ist_Historie")
|
|
.drop_nulls()
|
|
.last()
|
|
.alias("Import-Ist_letzter_Wert"),
|
|
pl.col("Import-Ist_geaendert").last(),
|
|
pl.col("Import-Ist_geaendert").sum().alias("Import-Ist_Anzahl_Aenderungen"),
|
|
pl.col("Tage_zu_letzter_PSM_Historie"),
|
|
pl.col("Tage_zu_letzter_PSM_Historie")
|
|
.mean()
|
|
.alias("Tage_zu_letzter_PSM_Durchschnitt"),
|
|
pl.col("Prod-EP10_Historie"),
|
|
pl.col("Prod-EP20_Historie"),
|
|
pl.col("Prod-EP30_Historie"),
|
|
pl.col("Prod-EP40_Historie"),
|
|
pl.col("Prod-EP50_Historie"),
|
|
pl.col("Prod-Qualitaet_Historie"),
|
|
pl.col("Prod-Qualitaet_Historie").mean().alias("Prod-Qualitaet_Durchschnitt"),
|
|
pl.col("Prod-Start_Historie"),
|
|
pl.col("Prod-Start_Historie").drop_nulls().first().alias("Prod-Start"),
|
|
)
|
|
)
|
|
# ** order specific aggregates
|
|
data = (
|
|
data.with_columns(
|
|
(pl.col("Liefertermin_Ist") - pl.col("Liefertermin_Soll"))
|
|
.dt.total_days()
|
|
.alias("Terminabweichung_Anzahl_Tage")
|
|
)
|
|
.with_columns(
|
|
(pl.col("Terminabweichung_Anzahl_Tage") < LOWER_BOUND_DATE_DEVIATION).alias(
|
|
"Terminunterschreitung"
|
|
),
|
|
(pl.col("Terminabweichung_Anzahl_Tage") > UPPER_BOUND_DATE_DEVIATION).alias(
|
|
"Terminüberschreitung"
|
|
),
|
|
(pl.col("Liefertermin_Ist") - pl.col("Prod-Start"))
|
|
.dt.total_days()
|
|
.alias("Durchlaufzeit_Anzahl_Tage"),
|
|
)
|
|
.with_columns(
|
|
pl.when(pl.col("Durchlaufzeit_Anzahl_Tage") < 0)
|
|
.then(None)
|
|
.otherwise(pl.col("Durchlaufzeit_Anzahl_Tage"))
|
|
.alias("Durchlaufzeit_Anzahl_Tage")
|
|
)
|
|
)
|
|
|
|
# data = (
|
|
# data.with_columns(
|
|
# pl.when(
|
|
# (pl.col("Liefertermin_Ist").is_not_null())
|
|
# & (pl.col("Liefertermin_Soll").is_not_null())
|
|
# )
|
|
# .then((pl.col("Liefertermin_Ist") - pl.col("Liefertermin_Soll")).dt.total_days())
|
|
# .otherwise(None)
|
|
# .alias("Terminabweichung_Anzahl_Tage")
|
|
# )
|
|
# .with_columns(
|
|
# pl.when(pl.col("Terminabweichung_Anzahl_Tage") < LOWER_BOUND_DATE_DEVIATION)
|
|
# .then(pl.lit(True))
|
|
# .otherwise(pl.lit(False))
|
|
# .alias("Terminunterschreitung"),
|
|
# pl.when(pl.col("Terminabweichung_Anzahl_Tage") > UPPER_BOUND_DATE_DEVIATION)
|
|
# .then(pl.lit(True))
|
|
# .otherwise(pl.lit(False))
|
|
# .alias("Terminüberschreitung"),
|
|
# pl.when(
|
|
# (pl.col("Liefertermin_Ist").is_not_null())
|
|
# & (pl.col("Prod-Start").is_not_null())
|
|
# )
|
|
# .then((pl.col("Liefertermin_Ist") - pl.col("Prod-Start")).dt.total_days())
|
|
# .otherwise(None)
|
|
# .alias("Durchlaufzeit_Anzahl_Tage"),
|
|
# )
|
|
# .with_columns(
|
|
# pl.when(
|
|
# (pl.col("Durchlaufzeit_Anzahl_Tage").is_not_null())
|
|
# & (pl.col("Durchlaufzeit_Anzahl_Tage") < 0)
|
|
# )
|
|
# .then(None)
|
|
# .otherwise(pl.col("Durchlaufzeit_Anzahl_Tage"))
|
|
# .alias("Durchlaufzeit_Anzahl_Tage")
|
|
# )
|
|
# )
|
|
|
|
return data
|
|
|
|
|
|
# // (3) dump order level to internal database
|
|
def _json_default(
|
|
value: Any,
|
|
) -> str:
|
|
if isinstance(value, (datetime.date, datetime.datetime)):
|
|
return value.isoformat()
|
|
raise TypeError
|
|
|
|
|
|
def _parse_to_json(
|
|
x: pl.Series | None,
|
|
) -> str | None:
|
|
if x is None:
|
|
return None
|
|
|
|
return json.dumps(x.to_list(), default=_json_default)
|
|
|
|
|
|
def dump_order_level_to_internal_database_staging(
|
|
data: pl.LazyFrame,
|
|
) -> None:
|
|
|
|
staging_data = data.with_columns(
|
|
pl.col(pl.List)
|
|
.map_elements(
|
|
_parse_to_json,
|
|
return_dtype=pl.String,
|
|
)
|
|
.name.keep()
|
|
)
|
|
staging_data = staging_data.collect()
|
|
rows_inserted = staging_data.write_database(
|
|
"Produktionsauftrag-Einzelsicht_Staging",
|
|
connection=db.DB_URI,
|
|
engine="adbc",
|
|
if_table_exists="replace",
|
|
)
|
|
if rows_inserted != staging_data.height:
|
|
raise RuntimeError("Number of inserted rows and length of staging data do not match.")
|
|
|
|
all_columns = staging_data.columns
|
|
update_columns = [col for col in all_columns if col not in PRIM_KEYS]
|
|
|
|
sql_column_list_str = ", ".join([f'"{c}"' for c in all_columns])
|
|
sql_pk_list_str = ", ".join([f'"{c}"' for c in PRIM_KEYS])
|
|
sql_update_rules_str = ", ".join([f'"{c}" = EXCLUDED."{c}"' for c in update_columns])
|
|
|
|
upsert_sql = f"""
|
|
INSERT INTO "Produktionsauftrag-Einzelsicht" ({sql_column_list_str})
|
|
SELECT {sql_column_list_str} FROM "Produktionsauftrag-Einzelsicht_Staging" WHERE 1=1
|
|
ON CONFLICT({sql_pk_list_str}) DO UPDATE SET
|
|
{sql_update_rules_str};
|
|
"""
|
|
|
|
with db.ENGINE_INTERNAL.begin() as conn:
|
|
conn.execute(sql.text(upsert_sql))
|
|
conn.execute(
|
|
sql.text('DROP TABLE IF EXISTS "Produktionsauftrag-Einzelsicht_Staging";')
|
|
)
|
|
|
|
|
|
def dump_order_level_to_internal_database_wipe(
|
|
data: pl.LazyFrame,
|
|
) -> None:
|
|
|
|
staging_data = data.with_columns(
|
|
pl.col(pl.List)
|
|
.map_elements(
|
|
_parse_to_json,
|
|
return_dtype=pl.String,
|
|
)
|
|
.name.keep()
|
|
)
|
|
# empty table
|
|
with db.ENGINE_INTERNAL.begin() as conn:
|
|
conn.execute(sql.text('DELETE FROM "Produktionsauftrag-Einzelsicht";'))
|
|
|
|
staging_data = staging_data.collect()
|
|
rows_inserted = staging_data.write_database(
|
|
"Produktionsauftrag-Einzelsicht",
|
|
connection=db.DB_URI,
|
|
engine="adbc",
|
|
if_table_exists="append",
|
|
)
|
|
if rows_inserted != staging_data.height:
|
|
raise RuntimeError("Number of inserted rows and length of staging data do not match.")
|
|
|
|
|
|
# ** load order level data from internal database
|
|
def load_order_level_from_internal_database() -> pl.DataFrame:
|
|
data = pl.read_database_uri(
|
|
'SELECT * FROM "Produktionsauftrag-Einzelsicht"',
|
|
uri=db.DB_URI,
|
|
engine="adbc",
|
|
schema_overrides=db.intern_prod_order_t_schema,
|
|
)
|
|
|
|
list_cols_to_type: dict[str, type[pl.DataType]] = {
|
|
"Meldezeitpunkt_Historie": pl.Datetime,
|
|
"Bestaetigter-Import_Historie": pl.Date,
|
|
"Import-Ist_Historie": pl.Date,
|
|
"Tage_zu_letzter_PSM_Historie": pl.Int64,
|
|
"Prod-EP10_Historie": pl.UInt64,
|
|
"Prod-EP20_Historie": pl.UInt64,
|
|
"Prod-EP30_Historie": pl.UInt64,
|
|
"Prod-EP40_Historie": pl.UInt64,
|
|
"Prod-EP50_Historie": pl.UInt64,
|
|
"Prod-Qualitaet_Historie": pl.Int32,
|
|
"Prod-Start_Historie": pl.Date,
|
|
}
|
|
|
|
list_col_parse_conds = {
|
|
col: pl.col(col).str.json_decode(pl.List(list_type))
|
|
for col, list_type in list_cols_to_type.items()
|
|
}
|
|
|
|
return data.with_columns(**list_col_parse_conds)
|