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Exporting Results ​

Jupiter provides multiple export formats for integrating simulation outputs, charts, and model definitions into downstream tools, papers, and executive reports.

Data Export Formats ​

1. CSV Spreadsheets ​

Ideal for data processing with Python (Pandas/Matplotlib), R, or spreadsheet software:

  • Stationary Single Run: Generates a .csv with one row per metric containing estimated means, margins of error, and confidence bounds.
  • Transient Time Series: Generates a .csv with one row per simulation time instant and one column per metric.
  • Scenario Studies: The chart CSV button exports the series for the selected metric. The global export button on the Scenarios tab downloads a ZIP package containing individual CSV files for every metric.

2. Vector PDF Figures ​

The PDF button on charts generates vector graphics suitable for academic publishing:

  • Crisp vector curves, clean typography, and exact color palettes.
  • Incorporates custom axis and legend titles configured in the chart settings (see Comparing Scenarios).
  • Ready for direct inclusion in LaTeX documents.

3. Execution Summary Text Report ​

Generates a plaintext summary detailing estimated metrics, net statistics (counts of places, transitions, and arcs), and execution parameters.

Model and Diagram Export ​

From the File menu in the editor:

  • Export (Local) [.json]: Exports the structured model definition file. Use this for Git version control, team sharing, or backups.
  • Export PNG / Export PDF: Produces high-resolution images of the Petri net diagram for architecture diagrams and methodology sections.

Pre-Export Checklist ​

  1. Verify Global Time Unit: Values exported to CSV reflect the model's global time unit (ms, s, min, h, d). Ensure that headers in your external papers reflect this unit.
  2. Confirm Statistical Convergence: Make sure confidence interval margins meet acceptable precision thresholds before freezing data.
  3. Save Run in History: While CSV files store numbers, saving the run in Jupiter's History preserves the complete snapshot of the model, parameter settings, and random seeds, enabling future reproducibility.

Next steps ​

Inspect an end-to-end real-world case study in 6 · Example.