egutierrez 9cdde4a341 feat(eda): núcleo AutomaticEDA — documento por capítulos + renderers PDF/PPTX anti-corte ...

Introduce la capa intermedia entre el contenido de un EDA y su formato de
salida. Un documento es una lista de capítulos versionados; cada capítulo es
un conjunto ordenado de bloques (heading, markdown, kv_table, data_table,
figure, image, caption, note) independientes del formato.

Núcleo (paquete de soporte python/functions/datascience/automatic_eda/):
- model.py: dataclasses de bloques + Chapter, normalizadores defensivos
  (aceptan dataclass o dict, nunca lanzan), ENGINE_VERSION y el manifiesto
  por capítulo (automatic_eda_manifest.json).
- text_layout.py: medición/wrapping por rejilla de caracteres compartida.
- chapters_registry.py: CHAPTER_ORDER pre-declarado + build_document con
  auto-discovery de capítulos por convención (permite añadir capítulos en
  paralelo sin editar el registro).
- render_pdf_impl.py: paginador A5 retrato móvil que MIDE cada bloque y nunca
  corta: texto a líneas completas, tablas largas partidas por filas repitiendo
  cabecera, figuras/imágenes escaladas para caber enteras. Pie versionado por
  capítulo.
- render_pptx_impl.py: mismo principio sobre slides 16:9 (continúa en slide
  "(cont.)"; tablas repiten cabecera; figuras exportadas a PNG escaladas).
- chapters/portada.py y chapters/overview.py: capítulos de referencia. Portada
  con nombre, rótulo Automatic-EDA, fuente, almacenamiento (inferido de
  source), fecha europea, filas×cols, descripción, granularidad y calidad con
  criterios. Overview con df.head (placeholder honesto si falta head_rows),
  diccionario de columnas (tipo/nulos/ejemplos) y describe numérico.

Funciones públicas del registry (grupo eda, dict-no-throw):
- render_automatic_eda_pdf / render_automatic_eda_pptx: aceptan capítulos o un
  TableProfile (construyen los capítulos con build_document) y escriben el
  manifiesto. Aditivas — no reemplazan render_eda_pdf.

Tests self-contained (sin DuckDB) para ambos renderers: golden (portada +
overview), partición de tablas largas repitiendo cabecera, no-corte de celdas
y markdown largos, profile None/{} válido de 1 página/slide, y error path en
directorio no escribible. 23 tests verdes (incluye los previos de
render_eda_pdf, intactos).

Dependencia nueva python-pptx>=1.0.2 declarada en python/pyproject.toml.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

2026-06-30 14:30:31 +02:00

..

automatic_eda

feat(eda): núcleo AutomaticEDA — documento por capítulos + renderers PDF/PPTX anti-corte

2026-06-30 14:30:31 +02:00

tests

fix(eda): hallazgos de comportamiento del benchmark (H2,H3,H6,H7,H8,H10,H11)

2026-06-29 06:37:47 +02:00

__init__.py

feat(eda): núcleo AutomaticEDA — documento por capítulos + renderers PDF/PPTX anti-corte

2026-06-30 14:30:31 +02:00

acf_pacf_test.py

fix(eda): bugs de bajo riesgo del benchmark (H1,H5,H12,H13,H14) + tests faltantes

2026-06-29 03:51:11 +02:00

acf_pacf.md

feat(eda): series temporales + rigor anti-data-mining + PDF movil + /eda + benchmark issues

2026-06-29 03:34:01 +02:00

acf_pacf.py

feat(eda): series temporales + rigor anti-data-mining + PDF movil + /eda + benchmark issues

2026-06-29 03:34:01 +02:00

adf_kpss_stationarity_test.py

fix(eda): bugs de bajo riesgo del benchmark (H1,H5,H12,H13,H14) + tests faltantes

2026-06-29 03:51:11 +02:00

adf_kpss_stationarity.md

feat(eda): series temporales + rigor anti-data-mining + PDF movil + /eda + benchmark issues

2026-06-29 03:34:01 +02:00

adf_kpss_stationarity.py

feat(eda): series temporales + rigor anti-data-mining + PDF movil + /eda + benchmark issues

2026-06-29 03:34:01 +02:00

aggregate_by_group_test.py

feat: funciones Python datascience, finance, cybersecurity y pipelines

2026-04-05 17:11:32 +02:00

aggregate_by_group.md

chore: auto-commit (286 archivos)

2026-05-16 16:33:22 +02:00

aggregate_by_group.py

feat: funciones Python datascience, finance, cybersecurity y pipelines

2026-04-05 17:11:32 +02:00

align_relations_to_entities.md

chore: auto-commit (286 archivos)

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align_relations_to_entities.py

feat: extraccion masiva footprint_aurgi (41 funcs + 4 types + stack Docker geo)

2026-05-04 23:35:22 +02:00

alpha_shape_concave_hull.md

chore: auto-commit (799 archivos)

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alpha_shape_concave_hull.py

feat: extraccion masiva footprint_aurgi (41 funcs + 4 types + stack Docker geo)

2026-05-04 23:35:22 +02:00

association_matrix_test.py

fix(eda): hallazgos de comportamiento del benchmark (H2,H3,H6,H7,H8,H10,H11)

2026-06-29 06:37:47 +02:00

association_matrix.md

feat(eda): series temporales + rigor anti-data-mining + PDF movil + /eda + benchmark issues

2026-06-29 03:34:01 +02:00

association_matrix.py

fix(eda): hallazgos de comportamiento del benchmark (H2,H3,H6,H7,H8,H10,H11)

2026-06-29 06:37:47 +02:00

autocorrelation.md

chore: auto-commit (799 archivos)

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best_central_tendency.md

chore: auto-commit (799 archivos)

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best_central_tendency.py

feat: extraccion masiva footprint_aurgi (41 funcs + 4 types + stack Docker geo)

2026-05-04 23:35:22 +02:00

build_eda_notebook_test.py

feat(browser): auto-commit con 178 cambios

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build_eda_notebook.md

feat(browser): auto-commit con 178 cambios

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build_eda_notebook.py

feat(browser): auto-commit con 178 cambios

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build_entity_schema_prompt_test.py

feat: funciones Python datascience, finance, cybersecurity y pipelines

2026-04-05 17:11:32 +02:00

build_entity_schema_prompt.md

docs: params/output semántico en 506 funciones para composabilidad

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build_entity_schema_prompt.py

feat: funciones Python datascience, finance, cybersecurity y pipelines

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build_join_graph_test.py

feat(browser): auto-commit con 178 cambios

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build_join_graph.md

feat(browser): auto-commit con 178 cambios

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build_join_graph.py

feat(browser): auto-commit con 178 cambios

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build_relation_schema_prompt_test.py

feat: funciones Python datascience, finance, cybersecurity y pipelines

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build_relation_schema_prompt.md

docs: params/output semántico en 506 funciones para composabilidad

2026-04-05 18:45:16 +02:00

build_relation_schema_prompt.py

feat: funciones Python datascience, finance, cybersecurity y pipelines

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cdp_scrape_aliexpress_trending.py

feat(shell): auto-commit con 31 cambios

2026-06-14 23:55:16 +02:00

clip.md

chore: auto-commit (286 archivos)

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column_quality_score_test.py

feat(browser): auto-commit con 178 cambios

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column_quality_score.md

feat(browser): auto-commit con 178 cambios

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column_quality_score.py

feat(browser): auto-commit con 178 cambios

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correlation_matrix_duckdb_test.py

feat(browser): auto-commit con 178 cambios

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correlation_matrix_duckdb.md

feat(browser): auto-commit con 178 cambios

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correlation_matrix_duckdb.py

feat(browser): auto-commit con 178 cambios

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correlation_ratio_test.py

feat(browser): auto-commit con 178 cambios

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correlation_ratio.md

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correlation_ratio.py

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cramers_v_test.py

feat(browser): auto-commit con 178 cambios

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cramers_v.md

feat(browser): auto-commit con 178 cambios

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cramers_v.py

feat(browser): auto-commit con 178 cambios

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datascience.py

feat: funciones Python datascience, finance, cybersecurity y pipelines

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decode_qr_image.py

feat(datascience): auto-commit con 5 cambios

2026-06-28 18:16:24 +02:00

deduplicate_entities_test.py

feat: funciones Python datascience, finance, cybersecurity y pipelines

2026-04-05 17:11:32 +02:00

deduplicate_entities.md

chore: auto-commit (286 archivos)

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deduplicate_entities.py

feat: funciones Python datascience, finance, cybersecurity y pipelines

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deduplicate_relations_test.py

feat: funciones Python datascience, finance, cybersecurity y pipelines

2026-04-05 17:11:32 +02:00

deduplicate_relations.md

chore: auto-commit (286 archivos)

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deduplicate_relations.py

feat: funciones Python datascience, finance, cybersecurity y pipelines

2026-04-05 17:11:32 +02:00

depth_to_relief_glb.md

feat(datascience): promover remove_background al registry + mask en depth_to_relief_glb (grupo img-to-3d)

2026-06-21 21:43:08 +02:00

depth_to_relief_glb.py

feat(datascience): promover remove_background al registry + mask en depth_to_relief_glb (grupo img-to-3d)

2026-06-21 21:43:08 +02:00

describe_numeric_test.py

feat(browser): auto-commit con 178 cambios

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describe_numeric.md

feat(browser): auto-commit con 178 cambios

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describe_numeric.py

feat(browser): auto-commit con 178 cambios

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detect_distribution_type.md

fix(eda): hallazgos de comportamiento del benchmark (H2,H3,H6,H7,H8,H10,H11)

2026-06-29 06:37:47 +02:00

detect_distribution_type.py

fix(eda): hallazgos de comportamiento del benchmark (H2,H3,H6,H7,H8,H10,H11)

2026-06-29 06:37:47 +02:00

detect_drift_test.py

feat: funciones Python datascience, finance, cybersecurity y pipelines

2026-04-05 17:11:32 +02:00

detect_drift.md

chore: auto-commit (286 archivos)

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detect_drift.py

feat: funciones Python datascience, finance, cybersecurity y pipelines

2026-04-05 17:11:32 +02:00

detect_outliers.md

chore: auto-commit (286 archivos)

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diff_entities_test.py

feat: funciones Python datascience, finance, cybersecurity y pipelines

2026-04-05 17:11:32 +02:00

diff_entities.md

chore: auto-commit (286 archivos)

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diff_entities.py

feat: funciones Python datascience, finance, cybersecurity y pipelines

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diff_relations_test.py

feat: funciones Python datascience, finance, cybersecurity y pipelines

2026-04-05 17:11:32 +02:00

diff_relations.md

chore: auto-commit (286 archivos)

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diff_relations.py

feat: funciones Python datascience, finance, cybersecurity y pipelines

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eda_llm_insights_test.py

feat(infra): auto-commit con 56 cambios

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eda_llm_insights.md

feat(browser): auto-commit con 178 cambios

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eda_llm_insights.py

feat(infra): auto-commit con 56 cambios

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estimate_hawkes.md

chore: auto-commit (799 archivos)

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estimate_image_depth.md

feat(infra): auto-commit con 56 cambios

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estimate_image_depth.py

feat(infra): auto-commit con 56 cambios

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estimate_pareto_alpha.md

chore: auto-commit (799 archivos)

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exploratory_caveats_test.py

fix(eda): bugs de bajo riesgo del benchmark (H1,H5,H12,H13,H14) + tests faltantes

2026-06-29 03:51:11 +02:00

exploratory_caveats.md

feat(eda): series temporales + rigor anti-data-mining + PDF movil + /eda + benchmark issues

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exploratory_caveats.py

feat(eda): series temporales + rigor anti-data-mining + PDF movil + /eda + benchmark issues

2026-06-29 03:34:01 +02:00

extract_entities_gliner.md

feat(datascience): GLiNER entity extractor (zero-shot NER) drop-in con LLM

2026-04-30 16:33:38 +02:00

extract_entities_gliner.py

feat(datascience): GLiNER entity extractor (zero-shot NER) drop-in con LLM

2026-04-30 16:33:38 +02:00

extract_entities_llm_test.py

feat: funciones Python datascience, finance, cybersecurity y pipelines

2026-04-05 17:11:32 +02:00

extract_entities_llm.md

docs: params/output semántico en 506 funciones para composabilidad

2026-04-05 18:45:16 +02:00

extract_entities_llm.py

feat: funciones Python datascience, finance, cybersecurity y pipelines

2026-04-05 17:11:32 +02:00

extract_graph_gliner2.md

feat: extraccion masiva footprint_aurgi (41 funcs + 4 types + stack Docker geo)

2026-05-04 23:35:22 +02:00

extract_graph_gliner2.py

feat: extraccion masiva footprint_aurgi (41 funcs + 4 types + stack Docker geo)

2026-05-04 23:35:22 +02:00

extract_relations_glirel.md

feat(datascience): GLiREL relation extractor (zero-shot triplets) drop-in con LLM

2026-04-30 16:41:09 +02:00

extract_relations_glirel.py

feat(datascience): GLiREL relation extractor (zero-shot triplets) drop-in con LLM

2026-04-30 16:41:09 +02:00

extract_relations_llm_test.py

feat: funciones Python datascience, finance, cybersecurity y pipelines

2026-04-05 17:11:32 +02:00

extract_relations_llm.md

docs: params/output semántico en 506 funciones para composabilidad

2026-04-05 18:45:16 +02:00

extract_relations_llm.py

feat: funciones Python datascience, finance, cybersecurity y pipelines

2026-04-05 17:11:32 +02:00

extract_relations_mrebel.md

chore: auto-commit (799 archivos)

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extract_relations_mrebel.py

feat: extraccion masiva footprint_aurgi (41 funcs + 4 types + stack Docker geo)

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extract_triples_spacy_es.md

chore: auto-commit (799 archivos)

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extract_triples_spacy_es.py

feat: extraccion masiva footprint_aurgi (41 funcs + 4 types + stack Docker geo)

2026-05-04 23:35:22 +02:00

fdr_correction_test.py

fix(eda): bugs de bajo riesgo del benchmark (H1,H5,H12,H13,H14) + tests faltantes

2026-06-29 03:51:11 +02:00

fdr_correction.md

feat(eda): series temporales + rigor anti-data-mining + PDF movil + /eda + benchmark issues

2026-06-29 03:34:01 +02:00

fdr_correction.py

feat(eda): series temporales + rigor anti-data-mining + PDF movil + /eda + benchmark issues

2026-06-29 03:34:01 +02:00

fetch_hackernews_search_test.py

feat(browser): auto-commit con 178 cambios

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fetch_hackernews_search.md

feat(browser): auto-commit con 178 cambios

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fetch_hackernews_search.py

feat(browser): auto-commit con 178 cambios

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fetch_reddit_search_test.py

feat(browser): auto-commit con 178 cambios

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fetch_reddit_search.md

feat(browser): auto-commit con 178 cambios

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fetch_reddit_search.py

feat(browser): auto-commit con 178 cambios

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fuzzy_merge_adaptive.md

chore: auto-commit (799 archivos)

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fuzzy_merge_adaptive.py

feat: extraccion masiva footprint_aurgi (41 funcs + 4 types + stack Docker geo)

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geometric_mean.md

feat: extraccion masiva footprint_aurgi (41 funcs + 4 types + stack Docker geo)

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geometric_mean.py

feat: extraccion masiva footprint_aurgi (41 funcs + 4 types + stack Docker geo)

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gliner2_load_model.md

feat: extraccion masiva footprint_aurgi (41 funcs + 4 types + stack Docker geo)

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gliner2_load_model.py

feat: extraccion masiva footprint_aurgi (41 funcs + 4 types + stack Docker geo)

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gliner_load_model.md

feat(datascience): GLiNER entity extractor (zero-shot NER) drop-in con LLM

2026-04-30 16:33:38 +02:00

gliner_load_model.py

feat(datascience): GLiNER entity extractor (zero-shot NER) drop-in con LLM

2026-04-30 16:33:38 +02:00

glirel_load_model.md

feat(datascience): GLiREL relation extractor (zero-shot triplets) drop-in con LLM

2026-04-30 16:41:09 +02:00

glirel_load_model.py

fix(datascience): glirel_load_model compatible con huggingface_hub 1.x

2026-05-04 14:43:35 +02:00

histogram.md

chore: auto-commit (286 archivos)

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hotness_score_test.py

feat: funciones Python datascience, finance, cybersecurity y pipelines

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hotness_score.md

chore: auto-commit (799 archivos)

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hotness_score.py

feat: funciones Python datascience, finance, cybersecurity y pipelines

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impute.md

chore: auto-commit (286 archivos)

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infer_fk_containment_duckdb_test.py

fix(eda): hallazgos de comportamiento del benchmark (H2,H3,H6,H7,H8,H10,H11)

2026-06-29 06:37:47 +02:00

infer_fk_containment_duckdb.md

fix(eda): hallazgos de comportamiento del benchmark (H2,H3,H6,H7,H8,H10,H11)

2026-06-29 06:37:47 +02:00

infer_fk_containment_duckdb.py

fix(eda): hallazgos de comportamiento del benchmark (H2,H3,H6,H7,H8,H10,H11)

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infer_semantic_type_test.py

feat(browser): auto-commit con 178 cambios

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infer_semantic_type.md

feat(browser): auto-commit con 178 cambios

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infer_semantic_type.py

feat(browser): auto-commit con 178 cambios

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isolation_forest_outliers_test.py

feat(browser): auto-commit con 178 cambios

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isolation_forest_outliers.md

feat(browser): auto-commit con 178 cambios

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isolation_forest_outliers.py

feat(browser): auto-commit con 178 cambios

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kde_density_levels.md

chore: auto-commit (799 archivos)

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kde_density_levels.py

feat: extraccion masiva footprint_aurgi (41 funcs + 4 types + stack Docker geo)

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kmeans_segments_test.py

feat(browser): auto-commit con 178 cambios

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kmeans_segments.md

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kmeans_segments.py

feat(browser): auto-commit con 178 cambios

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linspace.md

chore: auto-commit (799 archivos)

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marianmt_es_en_load_model.md

feat: extraccion masiva footprint_aurgi (41 funcs + 4 types + stack Docker geo)

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marianmt_es_en_load_model.py

feat: extraccion masiva footprint_aurgi (41 funcs + 4 types + stack Docker geo)

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melt_test.py

feat: funciones Python datascience, finance, cybersecurity y pipelines

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melt.md

chore: auto-commit (799 archivos)

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melt.py

feat: funciones Python datascience, finance, cybersecurity y pipelines

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merge_graphs_test.py

feat: funciones Python datascience, finance, cybersecurity y pipelines

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merge_graphs.md

chore: auto-commit (799 archivos)

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merge_graphs.py

feat: funciones Python datascience, finance, cybersecurity y pipelines

2026-04-05 17:11:32 +02:00

min_max_scale.md

chore: auto-commit (799 archivos)

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mrebel_base_load_model.md

chore: auto-commit (799 archivos)

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mrebel_base_load_model.py

feat: extraccion masiva footprint_aurgi (41 funcs + 4 types + stack Docker geo)

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mrebel_load_model.md

feat: extraccion masiva footprint_aurgi (41 funcs + 4 types + stack Docker geo)

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mrebel_load_model.py

feat: extraccion masiva footprint_aurgi (41 funcs + 4 types + stack Docker geo)

2026-05-04 23:35:22 +02:00

mutual_info_columns_test.py

feat(browser): auto-commit con 178 cambios

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mutual_info_columns.md

feat(browser): auto-commit con 178 cambios

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mutual_info_columns.py

feat(browser): auto-commit con 178 cambios

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normality_tests_test.py

feat(browser): auto-commit con 178 cambios

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normality_tests.md

feat(browser): auto-commit con 178 cambios

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normality_tests.py

feat(browser): auto-commit con 178 cambios

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ops_to_rdf_triples.md

chore: auto-commit (799 archivos)

2026-05-14 00:28:20 +02:00

ops_to_rdf_triples.py

feat: funciones datascience — ops_to_rdf_triples, ops_to_sigma_json, render_sigma_html

2026-04-02 22:03:51 +02:00

ops_to_sigma_json.md

docs: params/output semántico en 506 funciones para composabilidad

2026-04-05 18:45:16 +02:00

ops_to_sigma_json.py

feat: funciones datascience — ops_to_rdf_triples, ops_to_sigma_json, render_sigma_html

2026-04-02 22:03:51 +02:00

parse_amazon_ranking_html_test.py

feat(browser): auto-commit con 178 cambios

2026-06-20 18:22:23 +02:00

parse_amazon_ranking_html.md

feat(browser): auto-commit con 178 cambios

2026-06-20 18:22:23 +02:00

parse_amazon_ranking_html.py

feat(browser): auto-commit con 178 cambios

2026-06-20 18:22:23 +02:00

parse_rebel_output.md

feat: extraccion masiva footprint_aurgi (41 funcs + 4 types + stack Docker geo)

2026-05-04 23:35:22 +02:00

parse_rebel_output.py

feat: extraccion masiva footprint_aurgi (41 funcs + 4 types + stack Docker geo)

2026-05-04 23:35:22 +02:00

pca_explained_test.py

feat(browser): auto-commit con 178 cambios

2026-06-20 18:22:23 +02:00

pca_explained.md

feat(browser): auto-commit con 178 cambios

2026-06-20 18:22:23 +02:00

pca_explained.py

feat(browser): auto-commit con 178 cambios

2026-06-20 18:22:23 +02:00

pearson.md

chore: auto-commit (799 archivos)

2026-05-14 00:28:20 +02:00

pivot_test.py

feat: funciones Python datascience, finance, cybersecurity y pipelines

2026-04-05 17:11:32 +02:00

pivot.md

chore: auto-commit (799 archivos)

2026-05-14 00:28:20 +02:00

pivot.py

feat: funciones Python datascience, finance, cybersecurity y pipelines

2026-04-05 17:11:32 +02:00

plot_heatmap_log.md

chore: auto-commit (799 archivos)

2026-05-14 00:28:20 +02:00

plot_heatmap_log.py

feat: extraccion masiva footprint_aurgi (41 funcs + 4 types + stack Docker geo)

2026-05-04 23:35:22 +02:00

plot_kde_2d.md

chore: auto-commit (799 archivos)

2026-05-14 00:28:20 +02:00

plot_kde_2d.py

feat: extraccion masiva footprint_aurgi (41 funcs + 4 types + stack Docker geo)

2026-05-04 23:35:22 +02:00

pull_gsc_search_analytics_test.py

feat(browser): auto-commit con 178 cambios

2026-06-20 18:22:23 +02:00

pull_gsc_search_analytics.md

feat(browser): auto-commit con 178 cambios

2026-06-20 18:22:23 +02:00

pull_gsc_search_analytics.py

feat(browser): auto-commit con 178 cambios

2026-06-20 18:22:23 +02:00

query_osint_db_test.py

feat(infra): auto-commit con 56 cambios

2026-06-21 14:22:55 +02:00

query_osint_db.md

feat(infra): auto-commit con 56 cambios

2026-06-21 14:22:55 +02:00

query_osint_db.py

feat(infra): auto-commit con 56 cambios

2026-06-21 14:22:55 +02:00

rebel_load_model.md

chore: auto-commit (799 archivos)

2026-05-14 00:28:20 +02:00

rebel_load_model.py

feat: extraccion masiva footprint_aurgi (41 funcs + 4 types + stack Docker geo)

2026-05-04 23:35:22 +02:00

remove_background.md

feat(datascience): promover remove_background al registry + mask en depth_to_relief_glb (grupo img-to-3d)

2026-06-21 21:43:08 +02:00

remove_background.py

feat(datascience): promover remove_background al registry + mask en depth_to_relief_glb (grupo img-to-3d)

2026-06-21 21:43:08 +02:00

remove_words_from_column.md

chore: auto-commit (799 archivos)

2026-05-14 00:28:20 +02:00

remove_words_from_column.py

feat: extraccion masiva footprint_aurgi (41 funcs + 4 types + stack Docker geo)

2026-05-04 23:35:22 +02:00

render_automatic_eda_pdf_test.py

feat(eda): núcleo AutomaticEDA — documento por capítulos + renderers PDF/PPTX anti-corte

2026-06-30 14:30:31 +02:00

render_automatic_eda_pdf.md

feat(eda): núcleo AutomaticEDA — documento por capítulos + renderers PDF/PPTX anti-corte

2026-06-30 14:30:31 +02:00

render_automatic_eda_pdf.py

feat(eda): núcleo AutomaticEDA — documento por capítulos + renderers PDF/PPTX anti-corte

2026-06-30 14:30:31 +02:00

render_automatic_eda_pptx_test.py

feat(eda): núcleo AutomaticEDA — documento por capítulos + renderers PDF/PPTX anti-corte

2026-06-30 14:30:31 +02:00

render_automatic_eda_pptx.md

feat(eda): núcleo AutomaticEDA — documento por capítulos + renderers PDF/PPTX anti-corte

2026-06-30 14:30:31 +02:00

render_automatic_eda_pptx.py

feat(eda): núcleo AutomaticEDA — documento por capítulos + renderers PDF/PPTX anti-corte

2026-06-30 14:30:31 +02:00

render_eda_markdown_test.py

feat(eda): render de models en markdown + PDF DB-level para profile_database (H4,H9)

2026-06-29 04:05:38 +02:00

render_eda_markdown.md

feat(browser): auto-commit con 178 cambios

2026-06-20 18:22:23 +02:00

render_eda_markdown.py

feat(eda): render de models en markdown + PDF DB-level para profile_database (H4,H9)

2026-06-29 04:05:38 +02:00

render_eda_pdf_test.py

feat(eda): render de models en markdown + PDF DB-level para profile_database (H4,H9)

2026-06-29 04:05:38 +02:00

render_eda_pdf.md

feat(eda): series temporales + rigor anti-data-mining + PDF movil + /eda + benchmark issues

2026-06-29 03:34:01 +02:00

render_eda_pdf.py

feat(eda): render de models en markdown + PDF DB-level para profile_database (H4,H9)

2026-06-29 04:05:38 +02:00

render_sigma_html.md

chore: auto-commit (799 archivos)

2026-05-14 00:28:20 +02:00

render_sigma_html.py

feat: funciones datascience — ops_to_rdf_triples, ops_to_sigma_json, render_sigma_html

2026-04-02 22:03:51 +02:00

rolling_window.md

chore: auto-commit (799 archivos)

2026-05-14 00:28:20 +02:00

run_eda_models_test.py

feat(browser): auto-commit con 178 cambios

2026-06-20 18:22:23 +02:00

run_eda_models.md

feat(browser): auto-commit con 178 cambios

2026-06-20 18:22:23 +02:00

run_eda_models.py

feat(infra): auto-commit con 56 cambios

2026-06-21 14:22:55 +02:00

score_demand_signal_test.py

feat(browser): auto-commit con 178 cambios

2026-06-20 18:22:23 +02:00

score_demand_signal.md

feat(browser): auto-commit con 178 cambios

2026-06-20 18:22:23 +02:00

score_demand_signal.py

feat(browser): auto-commit con 178 cambios

2026-06-20 18:22:23 +02:00

scrape_aliexpress_trending.md

feat(shell): auto-commit con 31 cambios

2026-06-14 23:55:16 +02:00

scrape_aliexpress_trending.py

feat(shell): auto-commit con 31 cambios

2026-06-14 23:55:16 +02:00

scrape_amazon_bestsellers.md

feat(browser): auto-commit con 178 cambios

2026-06-20 18:22:23 +02:00

scrape_amazon_bestsellers.py

feat(browser): auto-commit con 178 cambios

2026-06-20 18:22:23 +02:00

scrape_competitor_prices.md

feat(shell): auto-commit con 31 cambios

2026-06-14 23:55:16 +02:00

scrape_competitor_prices.py

feat(shell): auto-commit con 31 cambios

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scrape_google_trends.md

feat(shell): auto-commit con 31 cambios

2026-06-14 23:55:16 +02:00

scrape_google_trends.py

feat(shell): auto-commit con 31 cambios

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scrape_tiktok_creative.md

feat(shell): auto-commit con 31 cambios

2026-06-14 23:55:16 +02:00

scrape_tiktok_creative.py

feat(shell): auto-commit con 31 cambios

2026-06-14 23:55:16 +02:00

spacy_es_load_model.md

feat: extraccion masiva footprint_aurgi (41 funcs + 4 types + stack Docker geo)

2026-05-04 23:35:22 +02:00

spacy_es_load_model.py

feat: extraccion masiva footprint_aurgi (41 funcs + 4 types + stack Docker geo)

2026-05-04 23:35:22 +02:00

spearman_corr_test.py

feat(browser): auto-commit con 178 cambios

2026-06-20 18:22:23 +02:00

spearman_corr.md

feat(browser): auto-commit con 178 cambios

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spearman_corr.py

feat(browser): auto-commit con 178 cambios

2026-06-20 18:22:23 +02:00

standardize.md

chore: auto-commit (799 archivos)

2026-05-14 00:28:20 +02:00

stl_decompose_test.py

fix(eda): hallazgos de comportamiento del benchmark (H2,H3,H6,H7,H8,H10,H11)

2026-06-29 06:37:47 +02:00

stl_decompose.md

feat(eda): series temporales + rigor anti-data-mining + PDF movil + /eda + benchmark issues

2026-06-29 03:34:01 +02:00

stl_decompose.py

fix(eda): hallazgos de comportamiento del benchmark (H2,H3,H6,H7,H8,H10,H11)

2026-06-29 06:37:47 +02:00

suggest_reexpression_test.py

fix(eda): bugs de bajo riesgo del benchmark (H1,H5,H12,H13,H14) + tests faltantes

2026-06-29 03:51:11 +02:00

suggest_reexpression.md

feat(eda): series temporales + rigor anti-data-mining + PDF movil + /eda + benchmark issues

2026-06-29 03:34:01 +02:00

suggest_reexpression.py

feat(eda): series temporales + rigor anti-data-mining + PDF movil + /eda + benchmark issues

2026-06-29 03:34:01 +02:00

summarize_categorical_test.py

feat(browser): auto-commit con 178 cambios

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summarize_categorical.md

feat(browser): auto-commit con 178 cambios

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summarize_categorical.py

feat(browser): auto-commit con 178 cambios

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summarize_table_duckdb_test.py

feat(browser): auto-commit con 178 cambios

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summarize_table_duckdb.md

feat(browser): auto-commit con 178 cambios

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summarize_table_duckdb.py

feat(browser): auto-commit con 178 cambios

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summarize_table_pg_test.py

feat(infra): auto-commit con 56 cambios

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summarize_table_pg.md

feat(infra): auto-commit con 56 cambios

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summarize_table_pg.py

feat(infra): auto-commit con 56 cambios

2026-06-21 14:22:55 +02:00

summary_stats.md

feat: extraccion masiva footprint_aurgi (41 funcs + 4 types + stack Docker geo)

2026-05-04 23:35:22 +02:00

summary_stats.py

feat: extraccion masiva footprint_aurgi (41 funcs + 4 types + stack Docker geo)

2026-05-04 23:35:22 +02:00

theils_u_test.py

feat(browser): auto-commit con 178 cambios

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theils_u.md

feat(browser): auto-commit con 178 cambios

2026-06-20 18:22:23 +02:00

theils_u.py

feat(browser): auto-commit con 178 cambios

2026-06-20 18:22:23 +02:00

to_returns_test.py

fix(eda): bugs de bajo riesgo del benchmark (H1,H5,H12,H13,H14) + tests faltantes

2026-06-29 03:51:11 +02:00

to_returns.md

feat(eda): series temporales + rigor anti-data-mining + PDF movil + /eda + benchmark issues

2026-06-29 03:34:01 +02:00

to_returns.py

feat(eda): series temporales + rigor anti-data-mining + PDF movil + /eda + benchmark issues

2026-06-29 03:34:01 +02:00

translate_es_to_en.md

chore: auto-commit (799 archivos)

2026-05-14 00:28:20 +02:00

translate_es_to_en.py

feat: extraccion masiva footprint_aurgi (41 funcs + 4 types + stack Docker geo)

2026-05-04 23:35:22 +02:00

trend_slope_test.py

feat(browser): auto-commit con 178 cambios

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trend_slope.md

feat(browser): auto-commit con 178 cambios

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trend_slope.py

feat(browser): auto-commit con 178 cambios

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trimmed_mean.md

feat: extraccion masiva footprint_aurgi (41 funcs + 4 types + stack Docker geo)

2026-05-04 23:35:22 +02:00

trimmed_mean.py

feat: extraccion masiva footprint_aurgi (41 funcs + 4 types + stack Docker geo)

2026-05-04 23:35:22 +02:00

vault_csv_profile.md

chore: auto-commit (95 archivos)

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vault_csv_profile.py

chore: auto-commit (95 archivos)

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vault_pdf_extract.md

chore: auto-commit (95 archivos)

2026-05-13 00:50:34 +02:00

vault_pdf_extract.py

chore: auto-commit (95 archivos)

2026-05-13 00:50:34 +02:00

words_to_dataset.md

chore: auto-commit (799 archivos)

2026-05-14 00:28:20 +02:00

words_to_dataset.py

feat: extraccion masiva footprint_aurgi (41 funcs + 4 types + stack Docker geo)

2026-05-04 23:35:22 +02:00