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
@@ -23,7 +23,7 @@ LOGGER = logging.getLogger(__name__)
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def setup_basic_logging(level: str = "INFO") -> None:
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"""
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Configuración básica de logging, por si se necesita desde scripts.
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Basic logging configuration, if needed from scripts.
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"""
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logging.basicConfig(
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level=getattr(logging, level.upper(), logging.INFO),
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@@ -33,10 +33,10 @@ def setup_basic_logging(level: str = "INFO") -> None:
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def _import_class(path: str) -> type:
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"""
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Import dinámico de una clase a partir de un string tipo:
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Dynamic import of a class from a string like:
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"beyond_metrics.dimensions.VolumetriaMetrics"
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"""
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LOGGER.debug("Importando clase %s", path)
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LOGGER.debug("Importing class %s", path)
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module_name, class_name = path.rsplit(".", 1)
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module = import_module(module_name)
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cls = getattr(module, class_name)
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@@ -45,7 +45,7 @@ def _import_class(path: str) -> type:
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def _serialize_for_json(obj: Any) -> Any:
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"""
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Convierte objetos típicos de numpy/pandas en tipos JSON-friendly.
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Converts typical numpy/pandas objects to JSON-friendly types.
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"""
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if obj is None or isinstance(obj, (str, int, float, bool)):
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return obj
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@@ -73,12 +73,12 @@ PostRunCallback = Callable[[Dict[str, Any], str, ResultsSink], None]
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@dataclass
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class BeyondMetricsPipeline:
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"""
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Pipeline principal de BeyondMetrics.
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Main BeyondMetrics pipeline.
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- Lee un CSV desde un DataSource (local, S3, Google Drive, etc.).
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- Ejecuta dimensiones configuradas en un dict de configuración.
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- Serializa resultados numéricos/tabulares a JSON.
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- Guarda las imágenes de los métodos que comienzan por 'plot_'.
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- Reads a CSV from a DataSource (local, S3, Google Drive, etc.).
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- Executes dimensions configured in a config dict.
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- Serializes numeric/tabular results to JSON.
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- Saves images from methods starting with 'plot_'.
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"""
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datasource: DataSource
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@@ -95,39 +95,39 @@ class BeyondMetricsPipeline:
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write_results_json: bool = True,
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) -> Dict[str, Any]:
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LOGGER.info("Inicio de ejecución de BeyondMetricsPipeline")
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LOGGER.info("Leyendo CSV de entrada: %s", input_path)
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LOGGER.info("Starting BeyondMetricsPipeline execution")
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LOGGER.info("Reading input CSV: %s", input_path)
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# 1) Leer datos
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# 1) Read data
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df = self.datasource.read_csv(input_path)
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LOGGER.info("CSV leído con %d filas y %d columnas", df.shape[0], df.shape[1])
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LOGGER.info("CSV read with %d rows and %d columns", df.shape[0], df.shape[1])
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# 2) Determinar carpeta/base de salida para esta ejecución
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# 2) Determine output folder/base for this execution
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run_base = run_dir.rstrip("/")
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LOGGER.info("Ruta base de esta ejecución: %s", run_base)
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LOGGER.info("Base path for this execution: %s", run_base)
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# 3) Ejecutar dimensiones
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# 3) Execute dimensions
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dimensions_cfg = self.dimensions_config
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if not isinstance(dimensions_cfg, dict):
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raise ValueError("El bloque 'dimensions' debe ser un dict.")
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raise ValueError("The 'dimensions' block must be a dict.")
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all_results: Dict[str, Any] = {}
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for dim_name, dim_cfg in dimensions_cfg.items():
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if not isinstance(dim_cfg, dict):
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raise ValueError(f"Config inválida para dimensión '{dim_name}' (debe ser dict).")
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raise ValueError(f"Invalid config for dimension '{dim_name}' (must be dict).")
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if not dim_cfg.get("enabled", True):
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LOGGER.info("Dimensión '%s' desactivada; se omite.", dim_name)
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LOGGER.info("Dimension '%s' disabled; skipping.", dim_name)
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continue
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class_path = dim_cfg.get("class")
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if not class_path:
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raise ValueError(f"Falta 'class' en la dimensión '{dim_name}'.")
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raise ValueError(f"Missing 'class' in dimension '{dim_name}'.")
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metrics: List[str] = dim_cfg.get("metrics", [])
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if not metrics:
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LOGGER.info("Dimensión '%s' sin métricas configuradas; se omite.", dim_name)
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LOGGER.info("Dimension '%s' has no configured metrics; skipping.", dim_name)
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continue
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cls = _import_class(class_path)
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@@ -136,35 +136,35 @@ class BeyondMetricsPipeline:
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if self.dimension_params is not None:
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extra_kwargs = self.dimension_params.get(dim_name, {}) or {}
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# Las dimensiones reciben df en el constructor
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# Dimensions receive df in the constructor
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instance = cls(df, **extra_kwargs)
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dim_results: Dict[str, Any] = {}
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for metric_name in metrics:
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LOGGER.info(" - Ejecutando métrica '%s.%s'", dim_name, metric_name)
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LOGGER.info(" - Executing metric '%s.%s'", dim_name, metric_name)
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result = self._execute_metric(instance, metric_name, run_base, dim_name)
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dim_results[metric_name] = result
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all_results[dim_name] = dim_results
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# 4) Guardar JSON de resultados (opcional)
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# 4) Save results JSON (optional)
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if write_results_json:
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results_json_path = f"{run_base}/results.json"
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LOGGER.info("Guardando resultados en JSON: %s", results_json_path)
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LOGGER.info("Saving results to JSON: %s", results_json_path)
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self.sink.write_json(results_json_path, all_results)
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# 5) Ejecutar callbacks post-run (scorers, agentes, etc.)
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# 5) Execute post-run callbacks (scorers, agents, etc.)
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if self.post_run:
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LOGGER.info("Ejecutando %d callbacks post-run...", len(self.post_run))
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LOGGER.info("Executing %d post-run callbacks...", len(self.post_run))
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for cb in self.post_run:
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try:
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LOGGER.info("Ejecutando post-run callback: %s", cb)
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LOGGER.info("Executing post-run callback: %s", cb)
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cb(all_results, run_base, self.sink)
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except Exception:
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LOGGER.exception("Error ejecutando post-run callback %s", cb)
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LOGGER.exception("Error executing post-run callback %s", cb)
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LOGGER.info("Ejecución completada correctamente.")
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LOGGER.info("Execution completed successfully.")
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return all_results
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@@ -176,42 +176,42 @@ class BeyondMetricsPipeline:
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dim_name: str,
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) -> Any:
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"""
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Ejecuta una métrica:
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Executes a metric:
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- Si empieza por 'plot_' -> se asume que devuelve Axes:
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- se guarda la figura como PNG
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- se devuelve {"type": "image", "path": "..."}
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- Si no, se serializa el valor a JSON.
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- If it starts with 'plot_' -> assumed to return Axes:
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- the figure is saved as PNG
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- returns {"type": "image", "path": "..."}
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- Otherwise, the value is serialized to JSON.
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Además, para métricas categóricas (por skill/canal) de la dimensión
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'volumetry', devolvemos explícitamente etiquetas y valores para que
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el frontend pueda saber a qué pertenece cada número.
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Additionally, for categorical metrics (by skill/channel) from the
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'volumetry' dimension, we explicitly return labels and values so
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the frontend can know what each number belongs to.
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"""
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method = getattr(instance, metric_name, None)
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if method is None or not callable(method):
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raise ValueError(
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f"La métrica '{metric_name}' no existe en {type(instance).__name__}"
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f"Metric '{metric_name}' does not exist in {type(instance).__name__}"
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)
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# Caso plots
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# Plot case
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if metric_name.startswith("plot_"):
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ax = method()
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if not isinstance(ax, Axes):
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raise TypeError(
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f"La métrica '{metric_name}' de '{type(instance).__name__}' "
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f"debería devolver un matplotlib.axes.Axes"
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f"Metric '{metric_name}' of '{type(instance).__name__}' "
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f"should return a matplotlib.axes.Axes"
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)
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fig = ax.get_figure()
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if fig is None:
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raise RuntimeError(
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"Axes.get_figure() devolvió None, lo cual no debería pasar."
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"Axes.get_figure() returned None, which should not happen."
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)
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fig = cast(Figure, fig)
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filename = f"{dim_name}_{metric_name}.png"
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img_path = f"{run_base}/{filename}"
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LOGGER.debug("Guardando figura en %s", img_path)
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LOGGER.debug("Saving figure to %s", img_path)
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self.sink.write_figure(img_path, fig)
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plt.close(fig)
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@@ -220,12 +220,12 @@ class BeyondMetricsPipeline:
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"path": img_path,
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}
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# Caso numérico/tabular
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# Numeric/tabular case
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value = method()
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# Caso especial: series categóricas de volumetría (por skill / canal)
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# Devolvemos {"labels": [...], "values": [...]} para mantener la
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# información de etiquetas en el JSON.
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# Special case: categorical series from volumetry (by skill / channel)
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# Return {"labels": [...], "values": [...]} to maintain
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# label information in the JSON.
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if (
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dim_name == "volumetry"
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and isinstance(value, pd.Series)
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@@ -238,7 +238,7 @@ class BeyondMetricsPipeline:
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}
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):
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labels = [str(idx) for idx in value.index.tolist()]
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# Aseguramos que todos los valores sean numéricos JSON-friendly
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# Ensure all values are JSON-friendly numeric
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values = [float(v) for v in value.astype(float).tolist()]
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return {
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"labels": labels,
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@@ -251,7 +251,7 @@ class BeyondMetricsPipeline:
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def load_dimensions_config(path: str) -> Dict[str, Any]:
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"""
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Carga un JSON de configuración que contiene solo el bloque 'dimensions'.
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Loads a JSON configuration file containing only the 'dimensions' block.
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"""
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import json
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from pathlib import Path
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@@ -261,7 +261,7 @@ def load_dimensions_config(path: str) -> Dict[str, Any]:
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dimensions = cfg.get("dimensions")
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if dimensions is None:
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raise ValueError("El fichero de configuración debe contener un bloque 'dimensions'.")
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raise ValueError("The configuration file must contain a 'dimensions' block.")
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return dimensions
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@@ -274,12 +274,12 @@ def build_pipeline(
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post_run: Optional[List[PostRunCallback]] = None,
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) -> BeyondMetricsPipeline:
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"""
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Crea un BeyondMetricsPipeline a partir de:
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- ruta al JSON con dimensiones/métricas
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- un DataSource ya construido (local/S3/Drive)
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- un ResultsSink ya construido (local/S3/Drive)
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- una lista opcional de callbacks post_run que se ejecutan al final
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(útil para scorers, agentes de IA, etc.)
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Creates a BeyondMetricsPipeline from:
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- path to JSON with dimensions/metrics
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- an already constructed DataSource (local/S3/Drive)
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- an already constructed ResultsSink (local/S3/Drive)
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- an optional list of post_run callbacks that execute at the end
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(useful for scorers, AI agents, etc.)
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"""
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dims_cfg = load_dimensions_config(dimensions_config_path)
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return BeyondMetricsPipeline(
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