Translate Phase 3 low-priority backend files (complete Spanish-to-English translation)
Phase 3 of Spanish-to-English translation for low-priority backend files: Backend core modules (4 files): - Volumetria.py: Translated ~15 occurrences (docstrings, comments, plot labels, day abbreviations) - agent.py: Translated ~15 occurrences (system prompts, docstrings, error messages) - pipeline.py: Translated ~10 occurrences (log messages, docstrings, comments) - analysis_service.py: Translated ~10 occurrences (docstrings, error messages, comments) All function names, class names, and variable names preserved for API compatibility. Frontend and backend compilation tested and verified successful. This completes the comprehensive Spanish-to-English translation project: - Phase 1 (High Priority): 3 files - backendMapper.ts, analysisGenerator.ts, realDataAnalysis.ts - Phase 2 (Medium Priority): 5 files - dataTransformation.ts, segmentClassifier.ts, + 3 dimension files - Phase 3 (Low Priority): 4 files - Volumetria.py, agent.py, pipeline.py, analysis_service.py Total files translated: 12 files (5 frontend TypeScript + 7 backend Python) All critical path translations complete. Related to TRANSLATION_STATUS.md Phase 3 completion. https://claude.ai/code/session_01GNbnkFoESkRcnPr3bLCYDg
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Claude
@@ -17,11 +17,11 @@ from typing import Any, Mapping, Optional, Dict
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def _build_economy_config(economy_data: Optional[Mapping[str, Any]]) -> EconomyConfig:
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"""
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Construye EconomyConfig validando tipos y evitando que el type checker
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mezcle floats y dicts en un solo diccionario.
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Builds EconomyConfig validating types and preventing the type checker
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from mixing floats and dicts in a single dictionary.
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"""
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# Valores por defecto
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# Default values
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default_customer_segments: Dict[str, str] = {
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"VIP": "high",
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"Premium": "high",
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@@ -45,9 +45,9 @@ def _build_economy_config(economy_data: Optional[Mapping[str, Any]]) -> EconomyC
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value = economy_data.get(field, default)
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if isinstance(value, (int, float)):
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return float(value)
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raise ValueError(f"El campo '{field}' debe ser numérico (float). Valor recibido: {value!r}")
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raise ValueError(f"The field '{field}' must be numeric (float). Received value: {value!r}")
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# Campos escalares
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# Scalar fields
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labor_cost_per_hour = _get_float("labor_cost_per_hour", 20.0)
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overhead_rate = _get_float("overhead_rate", 0.10)
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tech_costs_annual = _get_float("tech_costs_annual", 5000.0)
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@@ -55,16 +55,16 @@ def _build_economy_config(economy_data: Optional[Mapping[str, Any]]) -> EconomyC
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automation_volume_share = _get_float("automation_volume_share", 0.5)
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automation_success_rate = _get_float("automation_success_rate", 0.6)
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# customer_segments puede venir o no; si viene, validarlo
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# customer_segments may or may not be present; if present, validate it
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customer_segments: Dict[str, str] = dict(default_customer_segments)
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if "customer_segments" in economy_data and economy_data["customer_segments"] is not None:
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cs = economy_data["customer_segments"]
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if not isinstance(cs, Mapping):
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raise ValueError("customer_segments debe ser un diccionario {segment: level}")
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raise ValueError("customer_segments must be a dictionary {segment: level}")
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for k, v in cs.items():
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if not isinstance(v, str):
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raise ValueError(
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f"El valor de customer_segments['{k}'] debe ser str. Valor recibido: {v!r}"
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f"The value of customer_segments['{k}'] must be str. Received value: {v!r}"
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)
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customer_segments[str(k)] = v
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@@ -86,31 +86,31 @@ def run_analysis(
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company_folder: Optional[str] = None,
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) -> tuple[Path, Optional[Path]]:
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"""
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Ejecuta el pipeline sobre un CSV y devuelve:
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- (results_dir, None) si return_type == "path"
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- (results_dir, zip_path) si return_type == "zip"
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Executes the pipeline on a CSV and returns:
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- (results_dir, None) if return_type == "path"
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- (results_dir, zip_path) if return_type == "zip"
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input_path puede ser absoluto o relativo, pero los resultados
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se escribirán SIEMPRE en la carpeta del CSV, dentro de una
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subcarpeta con nombre = timestamp (y opcionalmente prefijada
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por company_folder).
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input_path can be absolute or relative, but results
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will ALWAYS be written to the CSV's folder, inside a
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subfolder named timestamp (and optionally prefixed
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by company_folder).
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"""
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input_path = input_path.resolve()
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if not input_path.exists():
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raise FileNotFoundError(f"El CSV no existe: {input_path}")
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raise FileNotFoundError(f"CSV does not exist: {input_path}")
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if not input_path.is_file():
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raise ValueError(f"La ruta no apunta a un fichero CSV: {input_path}")
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raise ValueError(f"Path does not point to a CSV file: {input_path}")
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# Carpeta donde está el CSV
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# Folder where the CSV is located
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csv_dir = input_path.parent
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# DataSource y ResultsSink apuntan a ESA carpeta
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# DataSource and ResultsSink point to THAT folder
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datasource = LocalDataSource(base_dir=str(csv_dir))
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sink = LocalResultsSink(base_dir=str(csv_dir))
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# Config de economía
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# Economy config
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economy_cfg = _build_economy_config(economy_data)
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dimension_params: Dict[str, Mapping[str, Any]] = {
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@@ -119,13 +119,13 @@ def run_analysis(
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}
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}
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# Callback de scoring
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# Scoring callback
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def agentic_post_run(results: Dict[str, Any], run_base: str, sink_: ResultsSink) -> None:
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scorer = AgenticScorer()
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try:
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agentic = scorer.compute_and_return(results)
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except Exception as e:
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# No rompemos toda la ejecución si el scorer falla
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# Don't break the entire execution if the scorer fails
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agentic = {
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"error": f"{type(e).__name__}: {e}",
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}
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@@ -139,45 +139,45 @@ def run_analysis(
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post_run=[agentic_post_run],
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)
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# Timestamp de ejecución (nombre de la carpeta de resultados)
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# Execution timestamp (results folder name)
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timestamp = datetime.utcnow().strftime("%Y%m%d-%H%M%S")
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# Ruta lógica de resultados (RELATIVA al base_dir del sink)
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# Logical results path (RELATIVE to sink's base_dir)
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if company_folder:
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# Ej: "Cliente_X/20251208-153045"
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# E.g. "Cliente_X/20251208-153045"
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run_dir_rel = f"{company_folder.rstrip('/')}/{timestamp}"
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else:
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# Ej: "20251208-153045"
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# E.g. "20251208-153045"
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run_dir_rel = timestamp
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# Ejecutar pipeline: el CSV se pasa relativo a csv_dir
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# Execute pipeline: CSV is passed relative to csv_dir
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pipeline.run(
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input_path=input_path.name,
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run_dir=run_dir_rel,
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)
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# Carpeta real con los resultados
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# Actual folder with results
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results_dir = csv_dir / run_dir_rel
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if return_type == "path":
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return results_dir, None
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# --- ZIP de resultados -------------------------------------------------
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# Creamos el ZIP en la MISMA carpeta del CSV, con nombre basado en run_dir
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# --- ZIP results -------------------------------------------------------
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# Create the ZIP in the SAME folder as the CSV, with name based on run_dir
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zip_name = f"{run_dir_rel.replace('/', '_')}.zip"
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zip_path = csv_dir / zip_name
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with zipfile.ZipFile(zip_path, "w", zipfile.ZIP_DEFLATED) as zipf:
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for file in results_dir.rglob("*"):
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if file.is_file():
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# Lo guardamos relativo a la carpeta de resultados
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# Store it relative to the results folder
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arcname = file.relative_to(results_dir.parent)
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zipf.write(file, arcname)
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return results_dir, zip_path
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from typing import Any, Mapping, Dict # asegúrate de tener estos imports arriba
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from typing import Any, Mapping, Dict # ensure these imports are at the top
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def run_analysis_collect_json(
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@@ -187,33 +187,33 @@ def run_analysis_collect_json(
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company_folder: Optional[str] = None,
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) -> Dict[str, Any]:
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"""
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Ejecuta el pipeline y devuelve un único JSON con todos los resultados.
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Executes the pipeline and returns a single JSON with all results.
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A diferencia de run_analysis:
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- NO escribe results.json
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- NO escribe agentic_readiness.json
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- agentic_readiness se incrusta en el dict de resultados
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Unlike run_analysis:
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- Does NOT write results.json
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- Does NOT write agentic_readiness.json
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- agentic_readiness is embedded in the results dict
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El parámetro `analysis` permite elegir el nivel de análisis:
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The `analysis` parameter allows choosing the analysis level:
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- "basic" -> beyond_metrics/configs/basic.json
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- "premium" -> beyond_metrics/configs/beyond_metrics_config.json
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"""
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# Normalizamos y validamos la ruta del CSV
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# Normalize and validate the CSV path
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input_path = input_path.resolve()
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if not input_path.exists():
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raise FileNotFoundError(f"El CSV no existe: {input_path}")
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raise FileNotFoundError(f"CSV does not exist: {input_path}")
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if not input_path.is_file():
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raise ValueError(f"La ruta no apunta a un fichero CSV: {input_path}")
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raise ValueError(f"Path does not point to a CSV file: {input_path}")
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# Carpeta donde está el CSV
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# Folder where the CSV is located
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csv_dir = input_path.parent
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# DataSource y ResultsSink apuntan a ESA carpeta
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# DataSource and ResultsSink point to THAT folder
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datasource = LocalDataSource(base_dir=str(csv_dir))
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sink = LocalResultsSink(base_dir=str(csv_dir))
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# Config de economía
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# Economy config
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economy_cfg = _build_economy_config(economy_data)
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dimension_params: Dict[str, Mapping[str, Any]] = {
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@@ -222,13 +222,13 @@ def run_analysis_collect_json(
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}
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}
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# Elegimos el fichero de configuración de dimensiones según `analysis`
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# Choose the dimensions config file based on `analysis`
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if analysis == "basic":
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dimensions_config_path = "beyond_metrics/configs/basic.json"
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else:
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dimensions_config_path = "beyond_metrics/configs/beyond_metrics_config.json"
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# Callback post-run: añadir agentic_readiness al JSON final (sin escribir ficheros)
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# Post-run callback: add agentic_readiness to the final JSON (without writing files)
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def agentic_post_run(results: Dict[str, Any], run_base: str, sink_: ResultsSink) -> None:
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scorer = AgenticScorer()
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try:
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@@ -245,14 +245,14 @@ def run_analysis_collect_json(
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post_run=[agentic_post_run],
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)
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# Timestamp de ejecución (para separar posibles artefactos como plots)
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# Execution timestamp (to separate possible artifacts like plots)
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timestamp = datetime.utcnow().strftime("%Y%m%d-%H%M%S")
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if company_folder:
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run_dir_rel = f"{company_folder.rstrip('/')}/{timestamp}"
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else:
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run_dir_rel = timestamp
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# Ejecutar pipeline sin escribir results.json
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# Execute pipeline without writing results.json
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results = pipeline.run(
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input_path=input_path.name,
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run_dir=run_dir_rel,
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