scientific-report-pdf

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Generate a structured scientific PDF report from a JSON description. Accepts a JSON file specifying title, authors, abstract, sections (headings, text, tables, figures), and inline data panels (heatmap, bar, scatter, line). Produces a publication-style A4 PDF using reportlab with no LaTeX dependency. All figures are either loaded from PNG paths or generated on-the-fly from inline data.

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scientific-report-pdf

Generates a structured scientific PDF report from a JSON input file. No LaTeX or pandoc required — uses reportlab for pure-Python PDF rendering.

Usage

python3 skills/scientific-report-pdf/scripts/scientific_report_pdf.py --input-json report.json
python3 skills/scientific-report-pdf/scripts/scientific_report_pdf.py --input-json report.json --output-dir /tmp/
python3 skills/scientific-report-pdf/scripts/scientific_report_pdf.py --describe-schema

Input JSON Structure

{
  "title": "The Sound of Molecules",
  "authors": ["ReportAgent", "MusicAnalyst"],
  "subtitle": "CS1 Investigation | LAMM Research Platform",
  "abstract": "We present ...",
  "sections": [
    {"type": "heading", "level": 1, "text": "1. Introduction"},
    {"type": "text", "text": "Sonification has been applied to ..."},
    {
      "type": "table",
      "label": "Table 1",
      "caption": "RDKit descriptors for 16 compounds.",
      "headers": ["Compound", "MW", "LogP"],
      "rows": [["aspirin", "180.2", "1.19"], ["ibuprofen", "206.3", "3.72"]]
    },
    {
      "type": "figure",
      "label": "Figure 1",
      "caption": "Era-match heatmap.",
      "path": "/path/to/era_match.png"
    },
    {
      "type": "panel",
      "label": "Figure 2",
      "caption": "Mean similarity by drug class.",
      "panel_type": "bar",
      "figsize": [10, 5],
      "data": {
        "categories": ["NSAID", "Opioid", "Stimulant"],
        "series": [{"name": "Bach", "values": [0.4, 0.7, 0.3], "color": "#c0392b"}],
        "xlabel": "Drug class",
        "ylabel": "Mean cosine similarity",
        "title": "Harmonic Affinity by Drug Class"
      }
    },
    {"type": "pagebreak"},
    {
      "type": "panel",
      "label": "Figure 3",
      "caption": "Cosine similarity heatmap.",
      "panel_type": "heatmap",
      "data": {
        "values": [[0.8, 0.3], [0.2, 0.9]],
        "row_labels": ["aspirin", "fentanyl"],
        "col_labels": ["Bach", "Beethoven"],
        "cmap": "YlOrRd",
        "annotate": true
      }
    }
  ],
  "metadata": {
    "investigation_id": "cs1_sound_of_molecules",
    "platform": "LAMM Infinite",
    "agents": ["SoundAgent1", "MusicAnalyst", "ReportAgent"]
  }
}

Section Types

type	Required fields	Description
`heading`	`level` (1-3), `text`	Section heading
`text`	`text`	Paragraph body
`table`	`headers`, `rows`	Data table with optional `label`, `caption`, `highlight_col`
`figure`	`path`	Embed existing PNG/JPG
`panel`	`panel_type`, `data`	Auto-generate matplotlib figure
`pagebreak`	—	Force page break
`hr`	—	Horizontal rule

Panel Types

panel_type	Required data fields
`heatmap`	`values` (2D array), `row_labels`, `col_labels`
`matrix`	same as heatmap
`bar`	`categories`, `series` (list of `{name, values, color}`)
`grouped_bar`	same as bar
`scatter`	`x`, `y`
`line`	`x`, `y`

Output

{
  "pdf_path": "/tmp/The_Sound_of_Molecules_20260403_001234.pdf",
  "n_pages": 8,
  "n_figures": 4,
  "size_kb": 512
}

Dependencies

reportlab — PDF generation
matplotlib — auto-generated panel figures
pillow — RGBA→RGB image conversion
pypdf (optional) — page count in output