diff --git a/soleprint/station/tools/dataconvert/pyproject.toml b/soleprint/station/tools/dataconvert/pyproject.toml new file mode 100644 index 0000000..08c6c10 --- /dev/null +++ b/soleprint/station/tools/dataconvert/pyproject.toml @@ -0,0 +1,14 @@ +[project] +name = "seed-converter" +version = "0.1.0" +description = "SQL Seed Converter Tool" +readme = "README.md" +requires-python = ">=3.11" +dependencies = [ + "pandas>=2.2.0", + "openpyxl>=3.1.2", + "odfpy>=1.4.1", +] + +[tool.uv] +package = false \ No newline at end of file diff --git a/soleprint/station/tools/dataconvert/seed_converter.py b/soleprint/station/tools/dataconvert/seed_converter.py new file mode 100644 index 0000000..80c05d2 --- /dev/null +++ b/soleprint/station/tools/dataconvert/seed_converter.py @@ -0,0 +1,164 @@ +#!/usr/bin/env python3 +""" +Modular Seed Converter Tool +Converts CSV, Excel (.xlsx, .xls), OpenDocument (.ods), directories, ZIP archives, +or wildcard file patterns into schema-agnostic, individual SQL seed files. + +Usage: + python3 seed_converter.py --input "data/*" "*.xlsx" --out-dir seed/ + python3 seed_converter.py --input path/to/folder/ --out-dir seed/ +""" + +import os +import re +import glob +import argparse +import zipfile +import tempfile +import pandas as pd +from pathlib import Path + + +def sanitize_identifier(identifier: str) -> str: + """Sanitize names for SQL tables, columns, and filenames.""" + clean = re.sub(r"[^\w]", "_", str(identifier).strip().lower()) + clean = re.sub(r"_+", "_", clean) + return clean.strip("_") + + +def dataframe_to_sql(df: pd.DataFrame, table_name: str) -> str: + """Generate SQL INSERT statements for a given DataFrame without schema qualification.""" + if df.empty: + return "" + + table_ref = f'"{table_name}"' + cols = ", ".join([f'"{sanitize_identifier(c)}"' for c in df.columns]) + + lines = [ + f"-- Generated seed data for table: {table_ref}\n", + "BEGIN;\n\n" + ] + + for _, row in df.iterrows(): + vals = [] + for v in row: + if pd.isna(v): + vals.append("NULL") + elif isinstance(v, (bool, int)): + vals.append(str(v)) + elif isinstance(v, float): + vals.append(str(int(v)) if v.is_integer() else str(v)) + else: + escaped = str(v).replace("'", "''") + vals.append(f"'{escaped}'") + + val_str = ", ".join(vals) + lines.append(f"INSERT INTO {table_ref} ({cols}) VALUES ({val_str}) ON CONFLICT DO NOTHING;\n") + + lines.append("\nCOMMIT;\n") + return "".join(lines) + + +def load_dataframes_from_file(file_path: Path) -> dict: + """Load a file (.csv, .xlsx, .ods) into a dictionary of {entity_name: DataFrame}.""" + ext = file_path.suffix.lower() + dfs = {} + + try: + if ext == ".csv": + dfs[file_path.stem] = pd.read_csv(file_path) + elif ext in [".xlsx", ".xls"]: + xls = pd.ExcelFile(file_path) + for sheet in xls.sheet_names: + df_raw = pd.read_excel(xls, sheet_name=sheet, header=None) + if len(df_raw) > 1 and str(df_raw.iloc[1, 0]).strip() in [ + "CONTROLLED_TERMINOLOGY", "DATA_ELEMENT", "DATA_DOMAIN", "DATA_STATE", "DATA_CATEGORY" + ]: + cols = [str(c).strip() for c in df_raw.iloc[1]] + data_df = df_raw.iloc[5:].copy() + data_df.columns = cols + dfs[sheet] = data_df + else: + dfs[sheet] = pd.read_excel(xls, sheet_name=sheet) + elif ext == ".ods": + xls = pd.ExcelFile(file_path, engine="odf") + for sheet in xls.sheet_names: + dfs[sheet] = pd.read_excel(xls, sheet_name=sheet) + except Exception as e: + print(f"[Warning] Could not read '{file_path.name}': {e}") + + return dfs + + +def process_and_write(dfs: dict, out_dir: Path, source_name: str): + """Write individual .sql files per table/sheet into the output directory.""" + for raw_name, df in dfs.items(): + if df.empty: + continue + + clean_table_name = sanitize_identifier(raw_name) + + if len(dfs) > 1 and not clean_table_name.startswith("drm_") and not clean_table_name.startswith("study_"): + filename = f"{sanitize_identifier(source_name)}_{clean_table_name}.sql" + else: + filename = f"{clean_table_name}.sql" + + sql_content = dataframe_to_sql(df, clean_table_name) + out_file = out_dir / filename + + mode = "a" if out_file.exists() else "w" + with open(out_file, mode, encoding="utf-8") as f: + f.write(sql_content) + + print(f"[seed_converter] Generated: {out_file}") + + +def process_path(path: Path, out_dir: Path): + """Process files, directories, or zip archives recursively.""" + supported = {".csv", ".xlsx", ".xls", ".ods"} + + if path.is_file() and path.suffix.lower() == ".zip": + with tempfile.TemporaryDirectory() as tmp_dir: + with zipfile.ZipFile(path, "r") as zip_ref: + zip_ref.extractall(tmp_dir) + process_path(Path(tmp_dir), out_dir) + + elif path.is_dir(): + for root, _, files in os.walk(path): + for f in sorted(files): + f_path = Path(root) / f + if f_path.suffix.lower() in supported: + dfs = load_dataframes_from_file(f_path) + process_and_write(dfs, out_dir, f_path.stem) + + elif path.is_file() and path.suffix.lower() in supported: + dfs = load_dataframes_from_file(path) + process_and_write(dfs, out_dir, path.stem) + + +def main(): + parser = argparse.ArgumentParser(description="Convert data sources into schema-agnostic SQL seed files.") + parser.add_argument("--input", nargs="+", required=True, help="Input file(s), directory, wildcard pattern(s), or ZIP archive(s)") + parser.add_argument("--out-dir", default="seed", help="Directory where individual .sql files will be written") + args = parser.parse_args() + + out_dir = Path(args.out_dir) + out_dir.mkdir(parents=True, exist_ok=True) + + expanded_paths = [] + for in_str in args.input: + if any(c in in_str for c in ["*", "?", "["]): + matched = glob.glob(in_str, recursive=True) + if not matched: + print(f"[Warning] No files matched wildcard pattern: '{in_str}'") + for m in sorted(matched): + expanded_paths.append(Path(m)) + else: + expanded_paths.append(Path(in_str)) + + for in_path in expanded_paths: + process_path(in_path, out_dir) + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git 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