Feat: add excel export for mart

scripts/__main__.py

@@ -79,7 +79,11 @@ def feature():
 def datamart():
     from .gold_mart import FLUXES_LOT
 
-    consume_fluxes(fluxes=FLUXES_LOT, origin_path=GOLD_PATH, dest_path=MART_PATH)
+    consume_fluxes(
+        fluxes=FLUXES_LOT,
+        origin_path=GOLD_PATH,
+        dest_path=MART_PATH,
+    )
 
 
 if __name__ == "__main__":

scripts/flux.py

@@ -4,7 +4,7 @@ from collections.abc import Callable
 from pathlib import Path
 
 import pandas as pd
-from pydantic import BaseModel
+from pydantic import BaseModel, Field
 
 
 class Source(BaseModel):
@@ -38,21 +38,51 @@ class Transformation(BaseModel):
     extra_kwrds: dict = {}
 
 
+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 to_excel(df, dest_basename: Path) -> Path:
+    dest = dest_basename.parent / (dest_basename.stem + ".xlsx")
+    if dest.exists():
+        raise ValueError(f"The destination exits {dest}")
+    else:
+        df.to_excel(dest)
+    return dest
+
+
 class Destination(BaseModel):
     name: str
+    writer: Callable = Field(to_csv)
+
+    def _write(
+        self,
+        df: pd.DataFrame,
+        dest_basename: Path,
+        writing_func: Callable | None = None,
+    ) -> Path:
+        if writing_func is None:
+            writing_func = self.writer
+
+        return writing_func(df, dest_basename)
 
     def write(
-        self, df: pd.DataFrame, dest_path: Path, writing_func: Callable
+        self, df: pd.DataFrame, dest_path: Path, writing_func: Callable | None = None
     ) -> list[Path]:
         dest_basename = dest_path / self.name
-        return [writing_func(df, dest_basename)]
+        return [self._write(df, dest_basename, writing_func)]
 
 
 class SplitDestination(Destination):
     split_column: str
 
     def write(
-        self, df: pd.DataFrame, dest_path: Path, writing_func: Callable
+        self, df: pd.DataFrame, dest_path: Path, writing_func: Callable | None = None
     ) -> list[Path]:
         wrote_files = []
 
@@ -60,7 +90,7 @@ class SplitDestination(Destination):
             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)
+            dest = self._write(filtered_df, dest_basename, writing_func)
             wrote_files.append(dest)
 
         return wrote_files
@@ -72,15 +102,6 @@ class Flux(BaseModel):
     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]:
@@ -119,26 +140,38 @@ def split_duplicates(
     return no_duplicates, duplicated
 
 
+def consume_flux(
+    name: str,
+    flux: Flux,
+    origin_path: Path,
+    dest_path: Path,
+    duplicated={},
+):
+    logging.info(f"Consume {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)
+
+    logging.info(f"{files} written")
+    return files
+
+
 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():
-        logging.info(f"Consume {name}")
+        files = consume_flux(name, flux, origin_path, dest_path, duplicated)
-        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

@@ -11,6 +11,7 @@ from scripts.flux import (
     SplitDestination,
     Transformation,
     consume_fluxes,
+    to_excel,
 )
 
 logger = logging.getLogger(__name__)
@@ -26,7 +27,9 @@ FLUXES_LOT = {
     "Lots": Flux(
         sources=[CSVSource(filename="CRG/crg-*.csv")],
         transformation=Transformation(function=build_lots),
-        destination=SplitDestination(name="Lot/lot", split_column="Lot"),
+        destination=SplitDestination(
+            name="Lot/lot", split_column="Lot", writer=to_excel
+        ),
     ),
 }
 
@@ -75,6 +78,8 @@ if __name__ == "__main__":
     pnl_fluxes = {}
 
     files = consume_fluxes(
-        fluxes=pnl_fluxes, origin_path=gold_path, dest_path=mart_path
+        fluxes=pnl_fluxes,
+        origin_path=gold_path,
+        dest_path=mart_path,
     )
     print(files)