Refact: flux have 3 components source, tranformation, destination

Makefile

@@ -1,10 +1,49 @@
+DATA_BASE=./datas
+
+PDF_BASE=$(DATA_BASE)/pdfs
+PDF_YEARS=$(wildcard $(PDF_BASE)/*)
+
+RAW_BASE=$(DATA_BASE)/raw
+RAW_CRG=$(RAW_BASE)/CRG
+RAW_CRG_YEARS=$(subst $(PDF_BASE), $(RAW_CRG), $(PDF_YEARS))
+
+
+$(RAW_CRG)/%/: $(wildcard $(PDF_BASE)/%/*)
+	echo $(wildcard $(PDF_BASE)/$*/*)
+	@echo ----
+	ls $(PDF_BASE)/$*/
+	@echo ----
+	echo $*
+	@echo ----
+	echo $^
+	@echo ----
+	echo $?
+
+#./datas/raw/CRG/%: 
+#pdf-oralia extract all --src $$year --dest $$(subst $$PDF_BASE, $$RAW_CRG, $$year)
+# $(RAW_CRG_YEARS): $(PDF_PATHS)
+# 	for year in $(PDF_PATHS); do \
+# 		echo $$year; \
+# 		echo $$(subst $$PDF_BASE, $$RAW_CRG, $$year); \
+# 		echo "----"; \
+# 	done;
+
+extract_pdfs:
+	for year in 2021 2022 2023 2024; do \
+		mkdir -p $(RAW_CRG)/$$year/extracted;\
+		pdf-oralia extract all --src $(PDF_BASE)/$$year/ --dest $(RAW_CRG)/$$year/extracted; \
+		pdf-oralia join --src $(RAW_CRG)/$$year/extracted/ --dest $(RAW_CRG)/$$year/; \
+	done
+
 clean_raw:
 	rm -rf ./PLESNA Compta SYSTEM/raw/**/*.csv
 
 clean_built:
-	rm -rf ./PLESNA Compta SYSTEM/staging/**/*.csv
-	rm -rf ./PLESNA Compta SYSTEM/gold/**/*.csv
-	rm -rf ./PLESNA Compta SYSTEM/datamart/**/*.csv
+	rm -rf $(DATA_BASE)/staging/**/*.csv
+	rm -rf $(DATA_BASE)/gold/**/*.csv
+	rm -rf $(DATA_BASE)/datamart/**/*.csv
 
 clean_all: clean_built clean_raw
 
+import_nextcloud:
+	rsync -a ~/Nextcloud/PLESNA\ Compta\ SYSTEM/Histoire/ ./datas/Histoire

requirements.txt

@@ -0,0 +1,4 @@
+jupyter==1.0.0 
+pandas==1.5.0
+pdf-oralia==0.3.11
+pydantic==2.6.1

scripts/flux.py

@@ -0,0 +1,146 @@
+import logging
+from abc import abstractmethod
+from collections.abc import Callable
+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
+
+    @abstractmethod
+    def get_df(self) -> pd.DataFrame:
+        raise NotImplementedError
+
+
+class ExcelSource(Source):
+    sheet_name: str
+
+    def get_df(self, base_path: Path) -> pd.DataFrame:
+        filepath = base_path / self.filename
+        logger.debug(f"Get content of {filepath}")
+        return pd.read_excel(filepath, sheet_name=self.sheet_name)
+
+
+class CSVSource(Source):
+    options: dict = {}
+
+    def get_df(self, base_path: Path) -> pd.DataFrame:
+        filepath = base_path / self.filename
+        logger.debug(f"Get content of {filepath}")
+        return pd.read_csv(filepath, **self.options)
+
+
+class Transformation(BaseModel):
+    function: Callable
+    extra_kwrds: dict = {}
+
+
+class Destination(BaseModel):
+    name: str
+
+    def write(
+        self, df: pd.DataFrame, dest_path: Path, writing_func: Callable
+    ) -> list[Path]:
+        dest_basename = dest_path / self.name
+        return [writing_func(df, dest_basename)]
+
+
+class SplitDestination(Destination):
+    split_column: str
+
+    def write(
+        self, df: pd.DataFrame, dest_path: Path, writing_func: Callable
+    ) -> list[Path]:
+        wrote_files = []
+
+        for col_value in df[self.split_column].unique():
+            filtered_df = df[df[self.split_column] == col_value]
+
+            dest_basename = dest_path / f"{self.name}-{col_value}"
+            dest = writing_func(filtered_df, dest_basename)
+            wrote_files.append(dest)
+
+        return wrote_files
+
+
+class Flux(BaseModel):
+    sources: list[Source]
+    transformation: Transformation
+    destination: Destination
+
+
+def to_csv(df, dest_basename: Path) -> Path:
+    dest = dest_basename.parent / (dest_basename.stem + ".csv")
+    if dest.exists():
+        df.to_csv(dest, mode="a", header=False, index=False)
+    else:
+        df.to_csv(dest, index=False)
+    return dest
+
+
+def write_split_by(
+    df: pd.DataFrame, column: str, dest_path: Path, name: str, writing_func
+) -> list[Path]:
+    wrote_files = []
+
+    for col_value in df[column].unique():
+        filtered_df = df[df[column] == col_value]
+
+        dest_basename = dest_path / f"{name}-{col_value}"
+        dest = writing_func(filtered_df, dest_basename)
+        wrote_files.append(dest)
+
+    return wrote_files
+
+
+def extract_sources(sources: list[Source], base_path: Path = Path()):
+    for src in sources:
+        if "*" in src.filename:
+            expanded_src = [
+                src.model_copy(update={"filename": str(p.relative_to(base_path))})
+                for p in base_path.glob(src.filename)
+            ]
+            yield from extract_sources(expanded_src, base_path)
+        else:
+            filepath = base_path / src.filename
+            assert filepath.exists
+            yield src.filename, src.get_df(base_path)
+
+
+def split_duplicates(
+    df, origin: str, duplicated: dict[str, pd.DataFrame]
+) -> [pd.DataFrame, dict[str, pd.DataFrame]]:
+    duplicates = df.duplicated()
+    no_duplicates = df[~duplicates]
+    duplicated[origin] = df[duplicates]
+    return no_duplicates, duplicated
+
+
+def consume_fluxes(
+    fluxes: dict[str, Flux],
+    origin_path: Path,
+    dest_path: Path,
+    writing_func=to_csv,
+):
+    duplicated = {}
+    wrote_files = []
+
+    for name, flux in fluxes.items():
+        print(name)
+        logger.info(f"Processing flux {name}")
+        src_df = []
+        for filename, df in extract_sources(flux.sources, origin_path):
+            df, duplicated = split_duplicates(df, str(filename), duplicated)
+            src_df.append(df)
+
+        df = flux.transformation.function(src_df, **flux.transformation.extra_kwrds)
+
+        files = flux.destination.write(df, dest_path, writing_func)
+        wrote_files += files
+    return wrote_files

scripts/gold_mart.py

@@ -0,0 +1,75 @@
+import logging
+from collections.abc import Callable
+from pathlib import Path
+
+import pandas as pd
+
+from scripts.flux import (
+    CSVSource,
+    Destination,
+    Flux,
+    SplitDestination,
+    Transformation,
+    consume_fluxes,
+)
+
+logger = logging.getLogger(__name__)
+logger.setLevel(logging.DEBUG)
+
+
+def build_lots(dfs: list[pd.DataFrame]) -> pd.DataFrame:
+    df = dfs[0]
+    df = df.assign(
+        Impact=df["Crédit"] - df["Débit"],
+    )
+    return df
+
+
+def build_pnl(dfs: list[pd.DataFrame], year: int) -> pd.DataFrame:
+    df = pd.concat(dfs)
+    df = df[df["Année"] == year]
+    pt = df.groupby(["Catégorie", "Mois"]).agg("sum").unstack().fillna(0)
+    pt.columns = [c[1] for c in pt.columns]
+    pt.reset_index(["Catégorie"])
+    return pt
+
+
+def build_pnl_flux(year: int) -> Flux:
+    return Flux(
+        sources=[
+            CSVSource(filename=f"CRG/crg-{year}.csv"),
+            CSVSource(filename=f"banque/banque-{year}.csv"),
+        ],
+        transformation=build_pnl,
+        extra_kwrds={"year": year},
+        destination=Destination(name=f"pnl/{year}"),
+    )
+
+
+if __name__ == "__main__":
+    data_path = Path("datas/")
+    assert data_path.exists()
+
+    gold_path = data_path / "gold"
+    assert gold_path.exists()
+    gold_crg_path = gold_path / "CRG"
+    assert gold_crg_path.exists()
+
+    mart_path = data_path / "datamart"
+    assert mart_path.exists()
+
+    lot_fluxes = {
+        "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={**lot_fluxes, **pnl_fluxes}, origin_path=gold_path, dest_path=mart_path
+    )
+    print(files)

scripts/history_stagging.py

@@ -0,0 +1,186 @@
+import logging
+from pathlib import Path
+
+import pandas as pd
+
+from scripts.flux import consume_fluxes
+
+from .flux import Destination, ExcelSource, Flux, SplitDestination, Transformation
+
+logger = logging.getLogger(__name__)
+logger.setLevel(logging.DEBUG)
+
+
+def extract_cat(cat: pd.DataFrame):
+    cat_drop = list(cat[cat["Nouvelles"] == "NE PAS IMPORTER"]["Anciennes"])
+    cat_trans = cat[cat["Nouvelles"] != "NE PAS IMPORTER"]
+
+    trans = {}
+    for _, (old, new) in cat_trans.iterrows():
+        trans[old] = new
+
+    return trans, cat_drop
+
+
+def trans_2017_2021(
+    dfs: list[pd.DataFrame], stagging_columns: list[str], **kwrds
+) -> pd.DataFrame:
+    df, cat = dfs
+    cat_trans, cat_drop = extract_cat(cat)
+
+    df = df[~df["Categorie"].isin(cat_drop)]
+
+    df = df.assign(
+        Immeuble=df["immeuble"],
+        Porte=df["porte"],
+        Débit=df["Débit"].fillna(0),
+        Crédit=df["Crédit"].fillna(0),
+        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),
+        Fournisseur="",
+    )
+
+    return df[stagging_columns]
+
+
+def trans_2022_charge(
+    dfs: list[pd.DataFrame], stagging_columns: list[str], **kwrds
+) -> pd.DataFrame:
+    df = dfs[0]
+    df = df.assign(
+        Immeuble=df["immeuble"],
+        Porte=df["lot"],
+        Débit=df["Débits"].fillna(0),
+        Crédit=df["Crédits"].fillna(0),
+        Lot=df["immeuble"].astype(str)[0] + df["lot"].astype("str").str.zfill(2),
+        Année=df["annee"],
+        Mois=df["mois"],
+        Catégorie=df["Catégorie Charge"],
+        # Catégorie=df["Catégorie Charge"].replace(trans),
+        Fournisseur="",
+        Régie="Oralia - Gelas",
+        Libellé="",
+    )
+    return df[stagging_columns]
+
+
+def trans_2022_loc(
+    dfs: list[pd.DataFrame], stagging_columns: list[str], **kwrds
+) -> pd.DataFrame:
+    df = dfs[0]
+    df = df.assign(
+        Immeuble=df["immeuble"],
+        Porte=df["lot"],
+        Débit=0,
+        Crédit=df["Réglés"].fillna(0),
+        Lot=df["immeuble"].astype(str)[0] + df["lot"].astype("str").str.zfill(2),
+        Année=df["annee"],
+        Mois=df["mois"],
+        Catégorie="Loyer Charge",
+        Fournisseur="",
+        Régie="Oralia - Gelas",
+        Libellé="",
+    )
+    return df[stagging_columns]
+
+
+def trans_2023(
+    dfs: list[pd.DataFrame], year: str, stagging_columns: list[str], **kwrds
+) -> pd.DataFrame:
+    df = dfs[0]
+    df = df.assign(
+        Débit=df["Débit"].fillna(0),
+        Crédit=df["Crédit"].fillna(0),
+        Lot=df["Immeuble"].astype(str) + df["Porte"].astype("str").str.zfill(2),
+        Année=year,
+    )
+    return df[stagging_columns]
+
+
+STAGGING_COLUMNS = [
+    "Régie",
+    "Immeuble",
+    "Porte",
+    "Lot",
+    "Année",
+    "Mois",
+    "Catégorie",
+    "Fournisseur",
+    "Libellé",
+    "Débit",
+    "Crédit",
+]
+
+FLUXES = {
+    "2017 2021 - charge et locataire.xlsx": Flux(
+        sources=[
+            ExcelSource(
+                filename="2017 2021 - charge et locataire.xlsx", sheet_name="DB CRG"
+            ),
+            ExcelSource(
+                filename="2017 2021 - charge et locataire.xlsx",
+                sheet_name="Catégories",
+            ),
+        ],
+        transformation=Transformation(
+            function=trans_2017_2021,
+            extra_kwrds={"stagging_columns": STAGGING_COLUMNS},
+        ),
+        destination=SplitDestination(name="crg", split_column="Année"),
+    ),
+    "2022 - charge.xlsx": Flux(
+        sources=[
+            ExcelSource(filename="2022 - charge.xlsx", sheet_name="Sheet1"),
+        ],
+        transformation=Transformation(
+            function=trans_2022_charge,
+            extra_kwrds={"stagging_columns": STAGGING_COLUMNS},
+        ),
+        destination=SplitDestination(name="crg", split_column="Année"),
+    ),
+    "2022 - locataire.xlsx": Flux(
+        sources=[
+            ExcelSource(filename="2022 - locataire.xlsx", sheet_name="Sheet1"),
+        ],
+        transformation=Transformation(
+            function=trans_2022_loc,
+            extra_kwrds={"stagging_columns": STAGGING_COLUMNS},
+        ),
+        destination=SplitDestination(name="crg", split_column="Année"),
+    ),
+    "2023 - charge et locataire.xlsx": Flux(
+        sources=[
+            ExcelSource(
+                filename="2023 - charge et locataire.xlsx",
+                sheet_name="DB CRG 2023 ...",
+            ),
+        ],
+        transformation=Transformation(
+            function=trans_2023,
+            extra_kwrds={"year": 2023, "stagging_columns": STAGGING_COLUMNS},
+        ),
+        destination=SplitDestination(name="crg", split_column="Année"),
+    ),
+}
+
+if __name__ == "__main__":
+    data_path = Path("datas/")
+    assert data_path.exists()
+    history_path = data_path / "Histoire"
+    assert history_path.exists()
+    history_crg_path = history_path / "CRG"
+    assert history_crg_path.exists()
+
+    staging_path = data_path / "staging"
+    assert staging_path.exists()
+    staging_crg_path = staging_path / "CRG"
+    assert staging_crg_path.exists()
+
+    crg_files = consume_fluxes(
+        fluxes=FLUXES,
+        origin_path=history_crg_path,
+        dest_path=staging_crg_path,
+    )
+    print(crg_files)

scripts/intersept_not_valid.py

@@ -4,7 +4,7 @@ import pandas as pd
 from pydantic import BaseModel, ValidationError
 
 
-class Interseptor:
+class ValidationInterseptor:
     def __init__(self, model: BaseModel):
         self.model = model
         self.not_valid_rows = []
@@ -18,8 +18,10 @@ class Interseptor:
                 try:
                     self.model(**r)
                 except ValidationError:
-                    r["InterseptorOrigin"] = func.__name__
-                    r["InterseptorIndex"] = i
+                    r["ValidationInterseptorFunc"] = func.__name__
+                    r["ValidationInterseptorArgs"] = args
+                    r["ValidationInterseptorKwrds"] = kwrds
+                    r["ValidationInterseptorIndex"] = i
                     self.not_valid_rows.append(r)
                 else:
                     valid_rows.append(r)

scripts/stagging_gold.py

@@ -0,0 +1,70 @@
+import logging
+from collections.abc import Callable
+from pathlib import Path
+
+import pandas as pd
+
+from scripts.flux import CSVSource, Destination, Flux, Transformation, consume_fluxes
+
+logger = logging.getLogger(__name__)
+logger.setLevel(logging.DEBUG)
+
+
+def feature_crg(dfs: list[pd.DataFrame]) -> pd.DataFrame:
+    df = dfs[0]
+    df = df.assign(
+        Impact=df["Crédit"] - df["Débit"],
+    )
+    return df
+
+
+GOLD_COLUMNS = [
+    "Régie",
+    "Immeuble",
+    "Porte",
+    "Lot",
+    "Année",
+    "Mois",
+    "Catégorie",
+    "Fournisseur",
+    "Libellé",
+    "Débit",
+    "Crédit",
+    "Impact",
+]
+
+
+def build_crg_fluxes(
+    crg_path: Path, pattern: str, transformation: Callable, csv_options: dict = {}
+) -> dict[str, Flux]:
+    fluxes = {}
+    for file in crg_path.glob(pattern):
+        fluxes[f"CRG - {file.name}"] = Flux(
+            sources=[CSVSource(filename=file.name, options=csv_options)],
+            transformation=Transformation(function=transformation),
+            destination=Destination(name=file.name),
+        )
+    return fluxes
+
+
+if __name__ == "__main__":
+    data_path = Path("datas/")
+    assert data_path.exists()
+
+    staging_path = data_path / "staging"
+    assert staging_path.exists()
+    staging_crg_path = staging_path / "CRG"
+    assert staging_crg_path.exists()
+
+    gold_path = data_path / "gold"
+    assert gold_path.exists()
+    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, origin_path=staging_crg_path, dest_path=gold_crg_path
+    )
+    print(crg_files)

tests/test_intersept_not_valid.py

@@ -4,7 +4,7 @@ import pandas as pd
 import pytest
 from pydantic import BaseModel
 
-from scripts.intersept_not_valid import Interseptor
+from scripts.intersept_not_valid import ValidationInterseptor
 
 
 class FakeModel(BaseModel):
@@ -13,7 +13,7 @@ class FakeModel(BaseModel):
 
 
 def test_init_composed():
-    interceptor = Interseptor(FakeModel)
+    interceptor = ValidationInterseptor(FakeModel)
 
     def df_generator(nrows=3):
         records = [{"name": "plop", "age": random.randint(1, 50)} for _ in range(nrows)]
@@ -27,7 +27,7 @@ def test_init_composed():
 
 
 def test_init_decorator():
-    interceptor = Interseptor(FakeModel)
+    interceptor = ValidationInterseptor(FakeModel)
 
     @interceptor
     def df_generator(nrows=3):
@@ -40,7 +40,7 @@ def test_init_decorator():
 
 
 def test_intersept_not_valid():
-    interceptor = Interseptor(FakeModel)
+    interceptor = ValidationInterseptor(FakeModel)
 
     @interceptor
     def df_generator():
@@ -57,7 +57,9 @@ def test_intersept_not_valid():
         {
             "name": "hop",
             "age": "ui",
-            "InterseptorOrigin": "df_generator",
-            "InterseptorIndex": 1,
+            "ValidationInterseptorFunc": "df_generator",
+            "ValidationInterseptorArgs": (),
+            "ValidationInterseptorKwrds": {},
+            "ValidationInterseptorIndex": 1,
         }
     ]