multipanel

name	multipanel
description	Assemble multiple plots into ONE publication-ready multi-panel journal figure (e.g. Figure 1 with panels A, B, C). Use whenever the user asks to combine, compose, or lay out several plots as a single composite figure — newly plotted from data or from already-rendered panels the user supplies (PNG/PDF). Ask the user to pick one of two approaches: (1) redraw every panel into one unified figure using independent, tightly packed `subfigures` (each sized to its own labels, so axes need NOT align), consistent style, correctly placed panel letters, and per-panel legends/colorbars; (2) composite already-rendered PNG/PDF panels onto a mosaic canvas and add panel letters (image compositing, not plotting). Both export vector PDF + high-DPI PNG. For a SINGLE plot from a data table, use the sibling `omics-plotting` skill instead.
license	Proprietary (HITS Inc.)

multipanel

Overview

A multi-panel figure is one figure, built one of two ways depending on what you have:

• Option 1 — redraw every panel (you have the data or plotting code): draw each data panel with a python script into its own subfigure so it packs to its own labels — no empty bands, and axes need NOT align across the grid. Follow the discipline below so legends stay inside their panels, panel letters sit at each panel's own top-left, and text never overlaps.
• Option 2 — composite finished images (you only have rendered PNG/PDF panels): paste them onto a plt.subplot_mosaic canvas — fine here, since images carry no tick labels to misalign — add panel letters, and export.

A mix is allowed: if one or two panels are image-only (no data/code), imshow them onto their own subfigure axes and redraw the rest into the same figure. Both modes export a vector PDF and a high-DPI PNG.

Always export the individual panels AND the composite. Every run outputs both: one standalone figure per panel (figure1A.png, figure1B.png, …) and the combined figure (combined_figure1.pdf + .png) — not just the composite. Because a matplotlib subfigure cannot be saved on its own, factor every data panel's plotting body into a draw_<letter>(ax) function (option 1); the same function then draws onto the composite's subfigure axis AND onto a fresh standalone figure, so the panels stay identical across both outputs with no duplicated drawing code. See "Exporting individual panels" below.

This skill covers composition. For how to draw each individual plot type (volcano, GSEA bar, heatmap, box/violin, PCA, Kaplan–Meier, …), use the sibling omics-plotting skill — copy each recipe's body onto a subfigure's axis rather than calling it as a standalone figure. Everything you need here (shared style, composite recipe, panel-label helper) is in this document.

When to use

• The user asks for a multi-panel / composite / journal figure (panels A, B, C…) combining two or more plots into one page of image.
• The user hands you or points out already-rendered panels (PNG/PDF) and wants them combined into one figure (image assembly — see "Assembling user-provided panels").
• You are assembling a figure for a report, a paper submission, or a presentation and want all panels to read as one consistent system.

Do NOT use for

• A single plot from a data table — use the sibling omics-plotting skill.
• Interactive dashboards or web charts (this is static matplotlib output).
• 3D molecular structure rendering (that is the structure viewer, not a plot).

Key Concepts

Redraw vs composite — two composition modes

There are two fundamentally different ways to build a composite, and the user chooses. Redraw (option 1) rebuilds every panel from data or code in one script, giving uniform style, fonts, colors, and panel letters — best when you hold the underlying data/DataFrame or the plotting code. Composite (option 2) pastes already-rendered PNG/PDF panels onto a canvas and only adds panel letters — image assembly, not plotting — best when you have only the finished images. A mix is allowed: image-only panels are imshow-pasted while data panels are redrawn, all into one figure.

Independent subfigures vs shared mosaic

The central layout decision. Giving each panel its own subfigure lets it run its own constrained_layout and pack tightly to its OWN labels — panels sit flush with no empty bands, and axes deliberately do NOT align across the grid. A single shared subplot_mosaic gridspec instead equalizes every column's margin to its widest y-label, leaving wide empty bands beside short-label panels. Independent subfigures are the default here because composites usually mix heterogeneous plot types; a shared mosaic is correct only when panels genuinely share a scale and are meant to be read against each other.

Panel letters in the subfigure frame

Panel letters (bold A, B, C…) must sit at each panel's OWN outer top-left, left of that panel's y-axis labels — never merged into the title and never snapped to a shared column x-position. Placing each letter at (0, 1) in its subfigure's coordinate frame (transform=sf.transSubfigure) guarantees it hugs its panel regardless of neighbors' label widths.

Decision Framework

Start from what you have, then how panels relate:

What sources do you have?
├─ Data / code for every panel .................. Option 1: redraw all
├─ Only finished PNG/PDF images ................. Option 2: composite images
└─ Mix (some data, some image-only) ............. Option 1 + imshow the image-only panels
        │
        ▼
How do the panels relate?
├─ Heterogeneous plot types (default) ........... Independent subfigures (tight pack, axes need NOT align)
└─ Same scale, read against each other .......... Shared subplot_mosaic (aligned axes)
        │
        ▼
Layout: sketch the grid [[...]], nest subfigures for spanning panels, fill every cell

Situation	Approach	Layout primitive	Panel letters
Have data/code for all panels	Redraw (option 1)	fig.subfigures(...) per panel	subfigure frame (0,1)
Only rendered images	Composite (option 2)	plt.subplot_mosaic + imshow	mosaic axes top-left
Some data, some image-only	Redraw + paste	subfigures + imshow leaf	subfigure frame (0,1)
Panels share a common scale	Shared mosaic	subplot_mosaic aligned	axes top-left
Spanning panel (e.g. bottom row)	Nested subfigures	top[0].subfigures(1, 2)	leaf subfigure frame

Workflow

1. Ask which approach first — ask the user, then wait. Both approaches below are usually viable and the choice is the user's, so before drawing or writing any script, ask the user to choose between these two concrete options:

   • Option 1 — Redraw every panel into one unified figure (from data/code): consistent style, fonts, colors, and panel letters across all panels. Best when you have the underlying data (CSV/TSV/DataFrame) or the plotting code.
   • Option 2 — Composite already-rendered images: paste the finished PNG/PDF panels onto a canvas and add panel letters — image assembly, not plotting. Best when you only have the finished images (no data/code) or the user wants to keep the originals as-is.

   Skip the question only when one option is impossible (e.g. only images and no data/code → option 2 is forced; or a data table with no rendered images → option 1) and say why. If a mix (some panels have data, one or two are images-only), tell the user that the image-only panels will be pasted regardless (discipline in the intro).

2. Decide the layout (the grid [[...]] sketch is just to plan the tiling; you build it with nested subfigures, not subplot_mosaic — see discipline #1). Fill every cell. — e.g. two on top, one spanning the bottom → [["A", "B"], ["C", "C"]] → top = fig.subfigures(2, 1); tc = top[0].subfigures(1, 2) (A,B in tc; C in top[1]). — e.g. three on top, two on the bottom → [["A", "B", "C"], ["D", "E", "E"]].

• e.g. one big panel on the left, two stacked on the right → [["A", "B"], ["A", "C"]] → lr = fig.subfigures(1, 2); A = lr[0]; rr = lr[1].subfigures(2, 1).

3. Gather each panel's source — a workspace-relative CSV/TSV (or DataFrame) for data panels, or a user-supplied PNG/PDF for image panels.
4. Write one python script: paste the style block, factor each data panel's plotting body into a draw_<letter>(ax) function (so it can render onto both a subfigure axis and a standalone figure), build the subfigures (nest for spanning panels), call each draw_<letter> onto its axis (data) or imshow the image, collect the subfigures into a panels dict, and add panel letters with the helper. Then always save both outputs to workspace-relative paths under plots/:
   • the composite as plots/combined_figure1.pdf + plots/combined_figure1.png, and
   • each individual panel as plots/figure1A.png, plots/figure1B.png, … (plus matching .pdf) by rendering every draw_<letter> onto a fresh standalone figure. See "Exporting individual panels" for the exact loop.
5. Report the saved paths back to the user — the combined figure and every individual panel file.

Shared style — paste at the top of the script

import matplotlib.pyplot as plt

# Publication style (colorblind-friendly, editable vector text, no top/right spines)
PUB_STYLE = {
    "figure.dpi": 110, "savefig.dpi": 300, "savefig.bbox": "tight",
    "font.family": "sans-serif",
    "font.sans-serif": ["Arial", "Liberation Sans", "Nimbus Sans", "Helvetica", "DejaVu Sans"],
    "font.size": 11, "axes.titlesize": 13, "axes.titleweight": "bold",
    "figure.titlesize": 13, "figure.titleweight": "bold",
    "axes.labelsize": 12, "axes.linewidth": 1.0,
    "axes.spines.top": False, "axes.spines.right": False,
    "xtick.labelsize": 10, "ytick.labelsize": 10,
    "xtick.direction": "out", "ytick.direction": "out",
    "legend.frameon": False, "legend.fontsize": 9,
    "svg.fonttype": "none", "pdf.fonttype": 42, : ,
}
plt.rcParams.update(PUB_STYLE)


UP, DOWN, NS = , ,    
PALETTE = [, , , , ,
           , , , , ]  
DIVERGING_CMAP =     
SEQUENTIAL_CMAP =   

For a dense composite, lower the font: plt.rcParams.update({"font.size": 7, "axes.titlesize": 8, "axes.labelsize": 7, "legend.fontsize": 6}).

Multi-panel discipline

This is what keeps a composite clean — every rule prevents a specific failure.

1. One figure, independent subfigures, constrained layout. Give each panel its own subfigure so it packs to its OWN labels: fig = plt.figure(layout="constrained", figsize=(width_mm/25.4, height_mm/25.4)), then sfs = fig.subfigures(nrows, ncols, width_ratios=..., height_ratios=...) and ax = sfs[r, c].subplots() per panel. Each subfigure runs its own constrained_layout, so a panel with long y-tick labels no longer shoves its column-neighbors' plots sideways — axes deliberately do NOT align across the grid; panels sit flush with no empty bands (a single shared subplot_mosaic gridspec, by contrast, equalizes each column's margin to its widest y-label and leaves a wide gap beside the short-label panels). Reserve a hair of margin so panel letters never clip: fig.get_layout_engine().set(rect=(0.012, 0, 0.988, 0.985)). Never add tight_layout() or manual subplots_adjust. Size in mm (single column = 88 mm, double = 180 mm).
   • Spanning panels: nest subfigures — e.g. two panels on top, one spanning the bottom → top = fig.subfigures(2, 1); tc = top[0].subfigures(1, 2) (A, B in tc[0], tc[1]; C in top[1]). One .subplots() per leaf subfigure.
   • Match each panel to its plot's shape via the subfigures' width_ratios/ height_ratios (pin with ax.set_box_aspect(...) if it still deforms): scatter panels (volcano/PCA) near-square; for bar / box / histogram, protect the value axis in both orientations — horizontal (barh, horizontal box) kept wide, vertical (bar, box, hist) kept tall. Never let a neighbor squeeze that axis flat.
   • When panels genuinely share a scale (same y-range, meant to be read against each other), a shared subplot_mosaic with aligned axes is the right choice instead — but this skill usually combines heterogeneous plot types, so independent subfigures are the default.
2. Fill every cell. No empty grid slots. If a panel would be blank, span a neighbor across it: [["A", "B"], ["C", "C"]].
3. Legends & colorbars belong to their own panel — a legend in that panel's free corner (ax.legend(loc="lower right", frameon=False)) or a colorbar on that one axis (fig.colorbar(im, ax=ax, fraction=0.025, pad=0.02)). Never float a figure-level legend in empty space or stack two in a margin; for a dot plot, keep only the colorbar and drop the size legend (count range → panel title).

Panel-label helper

Place each letter at the top-left corner of its own subfigure. Because every panel lives in its own tightly-packed subfigure, that corner is always left of the panel's y-labels and hugs the panel — so letters never float over an empty band (the shared-column failure) and never overlap a wide y-label, no matter how the panels' label widths differ:

def add_panel_labels(panels, size=11):
    """Bold letter at each panel's OWN outer top-left, in its subfigure frame.

    panels : dict {letter: subfigure} — the subfigure that holds each panel's axes,
    collected as you build them (for a spanning panel, its leaf subfigure). Placing
    the letter at (0, 1) in the subfigure's coordinates puts it at that cell's top-left
    corner: always LEFT of the panel's y-labels and hugging the panel, with no
    dependence on any neighbor's label width. Reserve a hair of figure margin first
    (`fig.get_layout_engine().set(rect=(0.012, 0, 0.988, 0.985))`, discipline #1) so the
    letters of edge panels are not clipped at the canvas edge.
    """
    for letter, sf in panels.items():
        sf.text(0.0, 1.0, letter, transform=sf.transSubfigure,
                fontsize=size, fontweight="bold", va="top", ha="left")

Usage: collect the subfigures as you create them, e.g. panels = {"A": sfs[0, 0], "B": sfs[0, 1], "C": top[1]}, then call add_panel_labels(panels).

Exporting individual panels

Every run produces both the individual panels (figure1A.png, figure1B.png, …) and the composite (combined_figure1.pdf + .png) — this is the default output, not an extra. A matplotlib subfigure cannot be saved on its own, so put each panel's plotting body in a draw_<letter>(ax) function and call it twice: once onto the composite's subfigure axis, and once onto a fresh standalone figure. One source of truth per panel — the panels stay identical across both outputs.

import os

os.makedirs("plots", exist_ok=True)

# 1) Factor each DATA panel's body into a function of a single Axes.
#    (Copy the omics-plotting recipe body here, drawing onto `ax` instead of a new figure.)
def draw_A(ax):
    ax.scatter(df["log2FC"], -np.log10(df["padj"]), s=8, c=NS)  # volcano, etc.
    ax.set_xlabel("log2 fold change"); ax.set_ylabel("-log10 FDR")

def draw_B(ax):
    ...   # PCA / box / heatmap body onto ax

def draw_C(ax):
    ...

DATA_PANELS = {"A": draw_A, "B": draw_B, "C": draw_C}
# Per-panel standalone figure size (mm) — match each plot's shape (discipline #1).
PANEL_SIZE_MM = {"A": (88, 75), "B": (88, 75), "C": (180, 70)}
# Image-only panels stay separate: keep the PNG/PDF the user supplied as their
# standalone file, and only imshow them onto the composite axis (see intro).

# 2) Composite — draw each function onto its subfigure axis, add letters, save.
panels = {"A": sfs[0, 0], "B": sfs[0, 1], "C": top[1]}
for letter, sf  panels.items():
    DATA_PANELS[letter](sf.subplots())
add_panel_labels(panels)
fig.savefig()
fig.savefig(, dpi=)


 letter, draw  DATA_PANELS.items():
    w_mm, h_mm = PANEL_SIZE_MM[letter]
    fp = plt.figure(layout=, figsize=(w_mm / , h_mm / ))
    draw(fp.subplots())
    fp.savefig()
    fp.savefig(, dpi=)
    plt.close(fp)

Output files (Figure 1 with panels A, B, C): plots/combined_figure1.pdf, plots/combined_figure1.png, plots/figure1A.{pdf,png}, plots/figure1B.{pdf,png}, plots/figure1C.{pdf,png}.

Notes:

• No panel letter on standalones — the A/B/C label belongs to the composite frame only; a lone figure1A.png needs no letter baked in.
• Size each standalone to its plot's shape (discipline #1) via PANEL_SIZE_MM: scatter/PCA near-square, barh/horizontal-box wide, vertical bar/box/hist tall — don't reuse one size for all.
• Legends/colorbars still belong to their own axis (discipline #3) — since the body lives in draw_<letter>, attach them inside that function so they appear in both the composite and the standalone.
• Image-only panels are already standalone files (the user's PNG/PDF); don't re-export them — just reference the originals.

Best Practices

• One figure, one style. Never stitch separate PNGs or call standalone plot functions for a composite; copy their bodies onto each subfigure's axis.
• Workspace-relative paths only. Save under plots/ (create it if needed); never absolute paths like /tmp or /home/....
• Only plot data that exists. Never invent columns, groups, or values.
• Label every axis, keep every legend inside its panel, fill every cell.
• Always export both a vector .pdf and a .png (dpi≥300) under plots/.

Common Pitfalls

• Building the whole figure as one shared subplot_mosaic gridspec. It equalizes each column's margin to its widest y-label, so a long-label panel shoves neighbors sideways, leaves empty bands, and strands letters snapped to the shared column edge. How to avoid: give each panel its own subfigure so it packs to its own labels (discipline #1); reserve a shared mosaic only for panels that genuinely share a scale.
• Panel letters merged into titles, snapped to a shared column-x, or clipped at the edge. They then sit right of the y-labels, float far from their plot, or vanish off-canvas. How to avoid: place each letter at (0, 1) in its own subfigure frame (transform=sf.transSubfigure) with the add_panel_labels helper, and reserve a hair of margin (rect=(0.012, 0, 0.988, 0.985), discipline #1) so edge letters stay in-canvas.
• Floating or bulky legends and colorbars. Per-plot figure-level legends collide in the margins, or a colorbar eats half the panel. How to avoid: attach each legend/colorbar to its own panel's axis, drop a composite dot plot's size legend, and thin a wide colorbar (aspect=40, shrink=0.6, small fraction) (discipline #3).
• Value axis flattened — scatter dots merge or bars/boxes squash. A neighbor steals the space the plot's value direction needs. How to avoid: widen or heighten that cell via width_ratios / height_ratios (or pin with ax.set_box_aspect) instead of shrinking the plot; only then bump marker size.
• Cramming long category names onto a narrow x-axis. Pathway/gene-set names collide into unreadable text. How to avoid: put long names on the horizontal y-axis, strip DB prefixes (HALLMARK_, GO_) and abbreviate, or rotate 90° and wrap to ≤26 chars with enough panel width.
• Fixing cramped panels with manual spacing. Adding tight_layout() or subplots_adjust fights constrained_layout and makes it worse. How to avoid: instead increase figsize (in mm), adjust the ratios, or lower the font, and let constrained layout re-space.
• Inconsistent or unreadable text. Over-labeled points overlap, heatmap cell numbers are too dense, and font sizes drift between panels. cap point labels to ≤5 (repel with , else skip); annotate heatmap cells only at ~4–5 pt with no decimals or drop them for the colorbar; keep the same font sizes across all panels, including any the user supplies (discipline #5).

Further Reading

• Matplotlib subfigures / Figure.subfigures — https://matplotlib.org/stable/gallery/subplots_axes_and_figures/subfigures.html
• Matplotlib constrained layout guide — https://matplotlib.org/stable/users/explain/axes/constrained_layout_guide.html
• Matplotlib subplot_mosaic tutorial — https://matplotlib.org/stable/users/explain/axes/mosaic.html
• adjustText (label de-overlap) — https://github.com/Phlya/adjustText