requirements.txt

@@ -2,4 +2,3 @@ jupyter==1.0.0
 pandas==1.5.0
 pdf-oralia==0.3.11
 pydantic==2.6.1
-click==8.1.7

scripts/__main__.py

@@ -1,86 +0,0 @@
-import logging
-from logging.config import dictConfig
-from pathlib import Path
-
-import click
-
-from .flux import consume_fluxes
-
-DATA_PATH = Path("datas/")
-assert DATA_PATH.exists()
-HISTORY_PATH = DATA_PATH / "Histoire"
-assert HISTORY_PATH.exists()
-STAGING_PATH = DATA_PATH / "staging"
-assert STAGING_PATH.exists()
-GOLD_PATH = DATA_PATH / "gold"
-assert GOLD_PATH.exists()
-MART_PATH = DATA_PATH / "datamart"
-assert MART_PATH.exists()
-
-
-@click.group()
-@click.option("--debug/--no-debug", default=False)
-def main(debug):
-    if debug:
-        logging_level = logging.DEBUG
-    else:
-        logging_level = logging.INFO
-    logging_config = dict(
-        version=1,
-        formatters={"f": {"format": "%(levelname)-8s %(name)-12s %(message)s"}},
-        handlers={
-            "h": {
-                "class": "logging.StreamHandler",
-                "formatter": "f",
-                "level": logging_level,
-            }
-        },
-        root={
-            "handlers": ["h"],
-            "level": logging_level,
-        },
-    )
-
-    dictConfig(logging_config)
-
-
-@main.command()
-def ingest():
-    from .history_stagging import FLUXES_CRG
-
-    history_crg_path = HISTORY_PATH / "CRG"
-    assert history_crg_path.exists()
-    staging_crg_path = STAGING_PATH / "CRG"
-    assert staging_crg_path.exists()
-    consume_fluxes(
-        fluxes=FLUXES_CRG,
-        origin_path=history_crg_path,
-        dest_path=staging_crg_path,
-    )
-
-
-@main.command()
-def feature():
-    from .stagging_gold import FLUXES_CRG
-
-    staging_crg_path = STAGING_PATH / "CRG"
-    assert staging_crg_path.exists()
-    gold_crg_path = GOLD_PATH / "CRG"
-    assert gold_crg_path.exists()
-
-    consume_fluxes(
-        fluxes=FLUXES_CRG(staging_crg_path),
-        origin_path=staging_crg_path,
-        dest_path=gold_crg_path,
-    )
-
-
-@main.command()
-def datamart():
-    from .gold_mart import FLUXES_LOT
-
-    consume_fluxes(fluxes=FLUXES_LOT, origin_path=GOLD_PATH, dest_path=MART_PATH)
-
-
-if __name__ == "__main__":
-    main()

scripts/flux.py

@@ -6,6 +6,9 @@ from pathlib import Path
 import pandas as pd
 from pydantic import BaseModel
 
+logger = logging.getLogger(__name__)
+logger.setLevel(logging.DEBUG)
+
 
 class Source(BaseModel):
     filename: str
@@ -20,7 +23,7 @@ class ExcelSource(Source):
 
     def get_df(self, base_path: Path) -> pd.DataFrame:
         filepath = base_path / self.filename
-        logging.debug(f"Get content of {filepath}")
+        logger.debug(f"Get content of {filepath}")
         return pd.read_excel(filepath, sheet_name=self.sheet_name)
 
 
@@ -29,7 +32,7 @@ class CSVSource(Source):
 
     def get_df(self, base_path: Path) -> pd.DataFrame:
         filepath = base_path / self.filename
-        logging.debug(f"Get content of {filepath}")
+        logger.debug(f"Get content of {filepath}")
         return pd.read_csv(filepath, **self.options)
 
 
@@ -129,16 +132,15 @@ def consume_fluxes(
     wrote_files = []
 
     for name, flux in fluxes.items():
-        logging.info(f"Consume {name}")
+        print(name)
+        logger.info(f"Processing flux {name}")
         src_df = []
         for filename, df in extract_sources(flux.sources, origin_path):
-            logging.info(f"Extracting {filename}")
             df, duplicated = split_duplicates(df, str(filename), duplicated)
             src_df.append(df)
 
-        logging.info(f"Execute {flux.transformation.function.__name__}")
         df = flux.transformation.function(src_df, **flux.transformation.extra_kwrds)
+
         files = flux.destination.write(df, dest_path, writing_func)
-        logging.info(f"{files} written")
         wrote_files += files
     return wrote_files

scripts/gold_mart.py

@@ -18,22 +18,13 @@ logger.setLevel(logging.DEBUG)
 
 
 def build_lots(dfs: list[pd.DataFrame]) -> pd.DataFrame:
-    df = pd.concat(dfs)
+    df = dfs[0]
     df = df.assign(
         Impact=df["Crédit"] - df["Débit"],
     )
     return df
 
 
-FLUXES_LOT = {
-    "Lots": Flux(
-        sources=[CSVSource(filename="CRG/crg-*.csv")],
-        transformation=Transformation(function=build_lots),
-        destination=SplitDestination(name="Lot/lot", split_column="Lot"),
-    ),
-}
-
-
 def build_pnl(dfs: list[pd.DataFrame], year: int) -> pd.DataFrame:
     df = pd.concat(dfs)
     df = df[df["Année"] == year]
@@ -49,10 +40,8 @@ def build_pnl_flux(year: int) -> Flux:
             CSVSource(filename=f"CRG/crg-{year}.csv"),
             CSVSource(filename=f"banque/banque-{year}.csv"),
         ],
-        transformation=Transformation(
+        transformation=build_pnl,
-            function=build_pnl,
+        extra_kwrds={"year": year},
-            extra_kwrds={"year": year},
-        ),
         destination=Destination(name=f"pnl/{year}"),
     )
 
@@ -69,15 +58,18 @@ if __name__ == "__main__":
     mart_path = data_path / "datamart"
     assert mart_path.exists()
 
-    files = consume_fluxes(
+    lot_fluxes = {
-        fluxes=FLUXES_LOT, origin_path=gold_path, dest_path=mart_path
+        "Lots": Flux(
-    )
+            sources=[CSVSource(filename="CRG/crg-*.csv")],
-
+            transformation=Transformation(function=build_lots),
+            destination=SplitDestination(name="Lot/lot", split_column="Lot"),
+        ),
+    }
     years = list(range(2017, 2024))
     # pnl_fluxes = {f"pnl-{year}": build_pnl_flux(year) for year in years}
     pnl_fluxes = {}
 
     files = consume_fluxes(
-        fluxes=pnl_fluxes, origin_path=gold_path, dest_path=mart_path
+        fluxes={**lot_fluxes, **pnl_fluxes}, origin_path=gold_path, dest_path=mart_path
     )
     print(files)

scripts/history_stagging.py

@@ -22,12 +22,6 @@ def extract_cat(cat: pd.DataFrame):
     return trans, cat_drop
 
 
-def lot_naming(value):
-    if str(value).isnumeric():
-        return str(value).zfill(2)
-    return "PC"
-
-
 def trans_2017_2021(
     dfs: list[pd.DataFrame], stagging_columns: list[str], **kwrds
 ) -> pd.DataFrame:
@@ -41,7 +35,7 @@ def trans_2017_2021(
         Porte=df["porte"],
         Débit=df["Débit"].fillna(0),
         Crédit=df["Crédit"].fillna(0),
-        Lot=df["porte"].apply(lot_naming),
+        Lot=df["immeuble"].astype(str) + df["porte"].astype("str").str.zfill(2),
         Année=df["Date"].astype(str).str.slice(0, 4),
         Mois=df["Date"].astype(str).str.slice(5, 7),
         Catégorie=df["Categorie"].replace(cat_trans),
@@ -60,7 +54,7 @@ def trans_2022_charge(
         Porte=df["lot"],
         Débit=df["Débits"].fillna(0),
         Crédit=df["Crédits"].fillna(0),
-        Lot=df["lot"].apply(lot_naming),
+        Lot=df["immeuble"].astype(str)[0] + df["lot"].astype("str").str.zfill(2),
         Année=df["annee"],
         Mois=df["mois"],
         Catégorie=df["Catégorie Charge"],
@@ -81,7 +75,7 @@ def trans_2022_loc(
         Porte=df["lot"],
         Débit=0,
         Crédit=df["Réglés"].fillna(0),
-        Lot=df["lot"].apply(lot_naming),
+        Lot=df["immeuble"].astype(str)[0] + df["lot"].astype("str").str.zfill(2),
         Année=df["annee"],
         Mois=df["mois"],
         Catégorie="Loyer Charge",
@@ -99,7 +93,7 @@ def trans_2023(
     df = df.assign(
         Débit=df["Débit"].fillna(0),
         Crédit=df["Crédit"].fillna(0),
-        Lot=lot_naming(df["Porte"]),
+        Lot=df["Immeuble"].astype(str) + df["Porte"].astype("str").str.zfill(2),
         Année=year,
     )
     return df[stagging_columns]
@@ -119,7 +113,7 @@ STAGGING_COLUMNS = [
     "Crédit",
 ]
 
-FLUXES_CRG = {
+FLUXES = {
     "2017 2021 - charge et locataire.xlsx": Flux(
         sources=[
             ExcelSource(
@@ -185,7 +179,7 @@ if __name__ == "__main__":
     assert staging_crg_path.exists()
 
     crg_files = consume_fluxes(
-        fluxes=FLUXES_CRG,
+        fluxes=FLUXES,
         origin_path=history_crg_path,
         dest_path=staging_crg_path,
     )

scripts/stagging_gold.py

@@ -14,7 +14,6 @@ def feature_crg(dfs: list[pd.DataFrame]) -> pd.DataFrame:
     df = dfs[0]
     df = df.assign(
         Impact=df["Crédit"] - df["Débit"],
-        Lot=df["Immeuble"].astype(str) + df["Lot"].astype("str"),
     )
     return df
 
@@ -48,12 +47,6 @@ def build_crg_fluxes(
     return fluxes
 
 
-def FLUXES_CRG(staging_crg_path: Path):
-    return build_crg_fluxes(
-        crg_path=staging_crg_path, pattern="*.csv", transformation=feature_crg
-    )
-
-
 if __name__ == "__main__":
     data_path = Path("datas/")
     assert data_path.exists()
@@ -68,9 +61,10 @@ if __name__ == "__main__":
     gold_crg_path = gold_path / "CRG"
     assert gold_crg_path.exists()
 
+    fluxes = build_crg_fluxes(
+        crg_path=staging_crg_path, pattern="*.csv", transformation=feature_crg
+    )
     crg_files = consume_fluxes(
-        fluxes=FLUXES_CRG(staging_crg_path),
+        fluxes=fluxes, origin_path=staging_crg_path, dest_path=gold_crg_path
-        origin_path=staging_crg_path,
-        dest_path=gold_crg_path,
     )
     print(crg_files)