export_format

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Axis export_format on sub-layer L8_A_export_format (layer l8).

Sub-layer

L8_A_export_format

Axis metadata

  • Default: 'json_csv'

  • Sweepable: False

  • Status: operational

Operational status summary

  • Operational: 9 option(s)

  • Future: 0 option(s)

Options

all – operational

Emit every supported export format together.

Comprehensive option emitting JSON + CSV + Parquet + LaTeX + Markdown + HTML for every applicable artifact. Largest disk footprint but covers every downstream consumer in one run.

When to use

Comprehensive reproducibility / sharing – single run that covers every audience.

References

  • macroforecast design Part 3, L8: ‘reproducibility = manifest + provenance + bit-exact replicate.’

Related options: json, csv, parquet, json_csv, json_parquet

Last reviewed 2026-05-05 by macroforecast author.

csv – operational

CSV tables for tabular artifacts (forecasts, metrics, importance).

Standard comma-separated values, UTF-8 encoded. The lowest-common-denominator format for spreadsheet / R workflows. Loses dtype information (everything becomes string on round-trip); for analytics workloads prefer parquet.

When to use

Spreadsheet / R workflows; collaborators who avoid JSON.

References

  • macroforecast design Part 3, L8: ‘reproducibility = manifest + provenance + bit-exact replicate.’

Related options: json, parquet, json_csv, json_parquet, latex_tables

Last reviewed 2026-05-05 by macroforecast author.

html_report – operational

Self-contained HTML report with embedded plots and tables.

Renders a single .html file combining tables (via pandas’ to_html) and base64-embedded matplotlib figures. Opens in any browser without a server; ideal for stakeholder-shareable reports without LaTeX tooling.

When to use

Stakeholder-shareable reports without LaTeX tooling.

References

  • macroforecast design Part 3, L8: ‘reproducibility = manifest + provenance + bit-exact replicate.’

Related options: json, csv, parquet, json_csv, json_parquet

Last reviewed 2026-05-05 by macroforecast author.

json – operational

JSON dump of every artifact (default).

Default round-trip-safe format; native Python / JS / R support; preserves nested structure (dicts of dicts of DataFrames). All numeric values rendered as floats with full precision; date-like values rendered as ISO 8601 strings.

When to use

Default; round-trips cleanly into Python / JS / R.

References

  • macroforecast design Part 3, L8: ‘reproducibility = manifest + provenance + bit-exact replicate.’

Related options: csv, parquet, json_csv, json_parquet, latex_tables

Last reviewed 2026-05-05 by macroforecast author.

json_csv – operational

Both JSON and CSV for every applicable artifact.

Convenience option emitting both formats. Used when downstream consumers vary – Python users want JSON round-trip, R / Excel users want CSV. Doubles the artifact-directory size.

When to use

When downstream consumers vary across both Python and Excel / R.

References

  • macroforecast design Part 3, L8: ‘reproducibility = manifest + provenance + bit-exact replicate.’

Related options: json, csv, parquet, json_parquet, latex_tables

Last reviewed 2026-05-05 by macroforecast author.

json_parquet – operational

Both JSON and Parquet for every applicable artifact.

Hybrid option for runs that combine reproducibility (JSON for the manifest / small artifacts) with analytics (Parquet for large forecast tables). Recommended for production sweeps.

When to use

Hybrid analytics + reproducibility setups.

References

Related options: json, csv, parquet, json_csv, latex_tables

Last reviewed 2026-05-05 by macroforecast author.

latex_tables – operational

LaTeX tabular snippets ready to \input into a paper.

Emits one .tex file per tabular artifact (forecasts, metrics, ranking). Booktabs-friendly column alignment and column-name escaping; uses pandas’ to_latex backend.

When to use

Paper-draft pipelines. Selecting latex_tables on l8.export_format activates this branch of the layer’s runtime.

References

Related options: json, csv, parquet, json_csv, json_parquet

Last reviewed 2026-05-05 by macroforecast author.

markdown_report – operational

Single Markdown report bundling tables and figure references.

Renders a self-contained .md document with pipe-aligned tables and embedded image references. Intended as the human-readable summary for stakeholder reports and GitHub / wiki documentation.

When to use

Lightweight Markdown / GitHub-rendered reports.

References

  • macroforecast design Part 3, L8: ‘reproducibility = manifest + provenance + bit-exact replicate.’

Related options: json, csv, parquet, json_csv, json_parquet

Last reviewed 2026-05-05 by macroforecast author.

parquet – operational

Apache Parquet (pyarrow); columnar binary tabular format.

Columnar binary format with full dtype preservation, automatic dictionary encoding for low-cardinality columns, and per-column compression. 5-10× smaller than CSV for typical macro panels; an order of magnitude faster to read for column-subset queries. Requires pyarrow (already a transitive dependency).

When to use

Large-scale analytics; preserving dtypes; cross-language workflows (Spark, DuckDB, R arrow).

References

Related options: json, csv, json_csv, json_parquet, latex_tables

Last reviewed 2026-05-05 by macroforecast author.