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Index

• gmxplot
  • 📖 Overview
  • 🚀 Features
  • 📦 Installation
  • ⚡ Quick Start
  • 🧠 Concepts: family vs kind
  • 🛠️ Commands
    • detect
    • plot
    • compare / multi-compare
    • replicas
    • stats
    • dashboard
    • batch
  • 🎨 Plot Kinds
  • 🕐 Time Axis & Windowing
  • 🌊 Smoothing
  • 📊 Statistics
  • 🧬 Replica Aggregation
  • 🧩 Dashboard Manifest
  • 🎛️ Shared Style Options
  • 🔁 Reproduce the Examples
  • 🏗️ Architecture
  • 📄 License

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📖 Overview

gmxplot reads GROMACS XVG files, classifies each one by what the data is (family) and how it should be rendered and analyzed (kind), then produces publication-ready matplotlib figures and rigorous statistics. It normalizes physical time axes, applies configurable windows and smoothing, aggregates independent replicas into a mean±band figure, and assembles heterogeneous XVG files into a single dashboard from a JSON manifest.

The classification is what makes the tool trustworthy: a hydrogen-bond distance histogram is drawn as a step plot with no rolling average, an eigenvalue spectrum is a marker plot (optionally log-scaled), and cluster sizes are bars — while a time series (RMSD, Rg, energy, …) gets a rolling average and drift statistics. Nothing is smoothed by accident.

🚀 Features

• Two-level classification — family (energy, rmsd, hbond_dist, …) is decoupled from kind (timeseries, histogram, spectrum, bar, profile, pca, scatter, xy). Rolling averages and drift statistics apply only to timeseries.
• Physical time axes — Time (ps|ns|us|fs) is detected and normalized to ns; --start/--end/--t-eq and --roll-window are expressed in physical units.
• Configurable windows — statistics are computed on a [start, end] window; --plot-full shows the full trajectory with the out-of-window region shaded.
• Rigorous statistics — naive and block-averaged SEM, half-window drift test, count-weighted histogram statistics, and correct PCA (both PCs analyzed).
• Replica aggregation — faint per-replica lines + mean with an SD/SEM band, with union/first/intersection alignment and long/summary CSV export.
• JSON dashboard — assemble any mix of single-file and replica panels into one figure with shared time axes, t_eq lines, and panel labels.
• GROMACS escape codes — xmgrace \S, \s, \f{} converted to mathtext.
• Multiple output formats — PNG (default), PDF, SVG, EPS, JPG, TIFF.

---

📦 Installation

pip install gmxplot

or

git clone https://github.com/MaybeBio/gmxplot.git
cd gmxplot
pip install -e .

Requirements: Python ≥ 3.10, numpy, pandas, matplotlib, seaborn, typer, rich.

---

⚡ Quick Start

# Inspect how a file is classified
gmxplot detect rmsd.xvg

# Plot with automatic detection, 1 ns rolling window
gmxplot plot rmsd.xvg -o rmsd.png --roll-window 1.0

# Statistics on the equilibrated segment, 10 ps blocks
gmxplot stats rmsd.xvg --start 100 --end 200 --block-time 0.01

# Compare two runs
gmxplot compare rep1/rmsd.xvg rep2/rmsd.xvg -l1 WT -l2 Mutant

# Aggregate three replicas into a mean±SD band
gmxplot replicas rep1/rmsd.xvg rep2/rmsd.xvg rep3/rmsd.xvg \
  --labels r1,r2,r3 --band sd --summary-csv rmsd_summary.csv

# Render a dashboard from a manifest
gmxplot dashboard manifest.json -o overview.pdf --stats-csv panel_stats.csv

---

🧠 Concepts: family vs kind

Two orthogonal ideas replace the old single plot_type:

• family — the semantic content of the file: what the numbers mean.
• kind — the rendering / statistics semantics: how it should be drawn and what statistics are valid.

family	meaning
energy	energies, temperature, pressure, density (multi-column)
rmsd / rmsf	deviation / per-residue fluctuation
gyration	radius of gyration (Rg and components)
sasa	solvent-accessible surface (time series or per-residue)
pca	principal-component projection
hbond_num / hbond_dist	hydrogen-bond count / distance distribution
mindist / contacts	minimum distance / contact count
eigenvalue	covariance eigenvalue spectrum
cluster_size	cluster-size distribution
dssp	secondary-structure content
generic / unknown	anything else

kind	meaning	smoothing	drift
timeseries	ordered time series	yes	yes
profile	per-residue / per-atom profile	no	no
histogram	bin coordinate + counts	no	no (weighted x-stats)
spectrum	eigenvalue / index vs intensity	no	no
bar	discrete categories / cluster sizes	no	no
pca	PC1–PC2 scatter / hexbin	no	no
scatter	unordered x–y scatter	no	no
xy	ordered non-time line	no	no

Smoothing is only offered to timeseries; requesting it on any other kind is silently ignored (those kinds never smooth).

Overrides and precedence: --kind > --type-implied kind > automatic detection. --type sets family, --kind sets kind. Both appear in gmxplot detect.

---

🛠️ Commands

command	purpose
detect	print family / kind / columns / units without plotting
plot	render one file with automatic detection
compare	overlay two files
multi-compare	overlay 3+ files
replicas	aggregate 2+ replicas into mean ± band
stats	write a statistics CSV
dashboard	render a multi-panel JSON manifest
batch	plot every XVG in a directory

🔍 detect FILE

Print the resolved family, kind, columns, time unit, and primary y columns.

gmxplot detect rmsd.xvg
  File: rmsd.xvg
  Family: rmsd
  Kind:   timeseries
  X column: Time   X is time: True   Time unit: ns
  Y columns: RMSD

📈 plot FILE

Render a single XVG file.

Key options

• -o/--output — output path (PNG, PDF, SVG, EPS, JPG, TIFF).
• --kind, --type — override rendering kind / data family.
• --x-col, --y-col (repeatable), --all-cols — column selection.
• --start, --end, --t-eq, --plot-full — statistics window + shading.
• --time-unit {auto,ps,ns,us,fs}, --no-time-normalize.
• --roll-samples N / --roll-avg N / --roll-window T / --roll-fraction F / --no-roll / --no-raw.
• --mean, --std — horizontal mean / ±std reference lines.
• --csv PATH — write the resolved windowed data (x + y columns).
• plus the shared style options.

Example — RMSD with a 5 ns rolling window, an equilibration marker at 50 ns, full trajectory shown with the pre-equilibration region shaded:

gmxplot plot examples/data/rmsd_r1.xvg \
  --roll-window 5 --t-eq 50 --plot-full --mean \
  -o figs/plot_timeseries.png

🆚 compare FILE1 FILE2 / multi-compare FILES...

Overlay two (compare) or three-plus (multi-compare) files. Accepts the same dataset options as plot; --label1/--label2 (compare) or --labels (multi-compare) set trace labels. Colors cycle the default tab10 palette, or a custom one via --palette.

gmxplot compare examples/data/rmsd_r1.xvg examples/data/rmsd_r2.xvg \
  -l1 "Run 1" -l2 "Run 2" --roll-window 5 -o figs/compare.png

🧬 replicas FILES...

Aggregate 2+ replicas into faint per-replica lines + a mean with an SD/SEM band.

Key options

• --labels — comma-separated or repeated labels.
• --align {union,first,intersection} — x-axis alignment (default union).
• --band {sd,sem,none} — band type (default sd).
• --min-replicas N — suppress the mean where fewer than N replicas overlap (default 2).
• --csv — long-format tidy CSV (x, series, replica, value).
• --summary-csv — per-replica window statistics + cross-replica mean±SD.

gmxplot replicas examples/data/rmsd_r1.xvg \
  examples/data/rmsd_r2.xvg examples/data/rmsd_r3.xvg \
  --labels r1,r2,r3 --band sd --roll-window 5 -o figs/replicas.png

📊 stats FILES...

Write a statistics CSV (also printed as a Rich table).

Key options

• --start/--end, --time-unit — the analysis window.
• --block-size N or --block-time T — block-averaging size.
• --flat-threshold F — drift flag threshold (default 2.0).
• --ignore-series REGEX (repeatable) — skip matching series (e.g. RgX|RgY|RgZ).
• -o/--out — output CSV path.

gmxplot stats examples/data/rmsd_r1.xvg --start 50 --block-time 5

See Statistics for the exact columns and definitions.

🧩 dashboard MANIFEST

Render a multi-panel figure from a JSON manifest. See the full manifest schema.

gmxplot dashboard examples/dashboard.json -o figs/dashboard.png

Key options: -o, --dpi, --figsize, --cols, --sharex/--no-sharex, --strict, --stats-csv, --latex/--no-latex.

📁 batch INPUT_DIR

Plot every XVG in a directory using the full plot pipeline.

Key options: -o/--output-dir, --recursive, --pattern (default *.xvg), --skip-errors, --roll-avg, --dpi, --ext, --stats/--no-stats, --stats-file, --mean, --std.

With --recursive, the relative directory structure under INPUT_DIR is preserved in the output directory (so sub/a.xvg → <out>/sub/a_plot.png), avoiding stem collisions across subdirectories.

gmxplot batch examples/data -o figs/batch --recursive --no-stats

---

🎨 Plot Kinds

Each kind gets its own primitive. Sample figures below were rendered from the synthetic data in examples/data/.

timeseries — ordered time series; raw (faint) + rolling average, mean/±std lines, t_eq marker, window shading.

profile — per-residue / per-atom line (RMSF, per-residue SASA). No smoothing.

gmxplot plot examples/data/rmsf.xvg -o figs/plot_profile.png

histogram — bin coordinate + counts as a step plot. No rolling; weighted x-statistics only.

gmxplot plot examples/data/hbond_dist.xvg -o figs/plot_histogram.png

spectrum — eigenvalue / index vs intensity. No smoothing; --ylog is handy.

gmxplot plot examples/data/eigenvalue.xvg --ylog -o figs/plot_spectrum.png

bar — discrete categories / cluster sizes.

gmxplot plot examples/data/cluster_size.xvg -o figs/plot_bar.png

pca — PC1–PC2 projection as a hexbin density (or --kind scatter).

gmxplot plot examples/data/proj2d.xvg -o figs/plot_pca.png

---

🕐 Time Axis & Windowing

• The x axis is detected as time when its label contains Time (...) or the family is a time-series family. --time-unit auto converts to ns via ps→1e-3, ns→1, us→1e3, fs→1e-6; --no-time-normalize keeps the original unit.
• --start/--end trim both the plot and the statistics window.
• --plot-full keeps the full trajectory on the plot and shades the region outside the window; statistics still use [start, end].
• --t-eq draws a vertical line; without an explicit --start, the statistics window starts at t_eq (plot full, analyze [t_eq, end]).

---

🌊 Smoothing

Only timeseries is smoothed. Window resolution order:

--no-roll > --roll-samples > --roll-window > --roll-fraction > auto.

The default is 1% of the x-span; --roll-window is converted to samples via the median Δx. Samples are clamped to [3, min(100, n//2)].

---

📊 Statistics

For a timeseries (NaN dropped pairwise):

• n, mean, sd (ddof=1), sem = sd/√n
• block_size (default round(n**(1/3)), override with --block-size/--block-time), n_blocks = floor(n/block_size), sem_block = SD(block means)/√n_blocks
• sem_used = max(sem, sem_block)
• mean_first_half / mean_second_half split at the window midpoint
• drift = mean_second − mean_first
• drift_over_sem = |drift| / √(sem1_used² + sem2_used²) (plus a naive ratio)
• flag = flat if drift_over_sem ≤ --flat-threshold (default 2), else DRIFT
• min/q10/q50/q90/max

profile/spectrum/bar/xy/scatter output descriptive statistics only. histogram adds count-weighted x statistics: x_weighted_mean, x_weighted_sd, x_weighted_median, x_mode, n_eff = (Σw)²/Σw², sem_weighted. pca analyzes every PC column.

--ignore-series REGEX skips matching series (e.g. RgX|RgY|RgZ).

---

🧬 Replica Aggregation

• Each replica is time-normalized and windowed independently.
• --align union (default) uses the union of x points; first uses the first file's grid; intersection uses shared points. Values are np.interp-ed within each replica's support (NaN outside, no extrapolation).
• Smoothing (if any) is applied per replica before aggregation; the summary CSV always uses the raw windowed data.
• --band sd (default) fills mean±SD, sem fills mean±SEM, none draws only the mean. --min-replicas (default 2) suppresses the mean where too few replicas overlap.

---

🧩 Dashboard Manifest

{
  "title": "Equilibration overview",
  "time_unit": "auto",
  "start": 0, "end": 200, "t_eq": 100,
  "cols": 2, "figsize": [14, 10], "sharex": true,
  "panels": [
    { "file": "rep1/rmsd.xvg", "title": "Backbone RMSD",
      "kind": "timeseries", "y_col": "RMSD", "ylim": [0, 0.6] },
    { "files": ["rep1/hbnum.xvg", "rep2/hbnum.xvg", "rep3/hbnum.xvg"],
      "labels": ["r1", "r2", "r3"], "title": "Interface H-bonds", "band": "sd" }
  ]
}

• Global keys (title, time_unit, start, end, t_eq, cols, figsize, sharex) are inherited by every panel and overridable per-panel.
• A panel has either file (single) or files + labels (replica).
• Panel keys: kind, type, x_col, y_col/y_cols, all_cols, title, xlabel, ylabel, ylog, ylim, xlim, hline, vline, start, end, t_eq, roll_window, roll_samples, legend, band, align, min_replicas.
• Multi-column panels are expanded into one sub-plot per y column so each axis carries a single self-consistent unit. t_eq lines and panel letters (A, B, C…) are drawn automatically; sharex applies only when every panel is a time axis.
• Unknown keys warn by default and error under --strict.

A complete, runnable example lives in examples/dashboard.json.

---

🎛️ Shared Style Options

Every plotting command shares one style parameter set:

option	default	description
--figsize "W H"	10 6	figure size in inches
--title	(file title)	plot title
--xlabel / --ylabel	(axis label)	override axis labels
--xlim "MIN MAX" / --ylim "MIN MAX"	auto	axis limits
--xlog/--no-xlog	off	log x axis
--ylog/--no-ylog	off	log y axis
--grid/--no-grid	on	grid lines
--legend/--no-legend	on	show legend
--legend-loc	best	legend location
--cmap	viridis	colormap for PCA hexbins
--palette	tab10	multi-series line colors (see below)
--line-width	1.5	smoothed / main line width
--raw-alpha	0.5	raw line opacity
--hline (repeatable)	—	horizontal reference line
--vline (repeatable)	—	vertical reference line
--dpi	300	output resolution
--latex/--no-latex	off	LaTeX text rendering

--palette sets the multi-series line colors (used by compare, multi-compare, and multi-column xy). It accepts:

• a comma-separated color list, cycled as needed — --palette '#1f77b4,#d62728,green';
• a matplotlib colormap name — --palette tab10;
• a seaborn palette name — --palette colorblind.

---

🔁 Reproduce the Examples

All figures in this README are generated from synthetic data shipped in the repo:

python examples/generate_data.py     # (re)write examples/data/*.xvg
bash   examples/make_figures.sh      # render figs/*.png  (requires gmxplot)

The synthetic files carry genuine GROMACS-style headers, so they exercise the full auto-detection path — including the kinds (histogram, spectrum, bar, pca) that must not be smoothed.

---

🏗️ Architecture

gmxplot/
├── cli.py          # Typer CLI: 8 commands, lazy matplotlib backend
├── parser.py       # XVG parsing, family/kind detection, column naming, time unit
├── dataset.py      # Dataset/DatasetOptions: columns, time, window, rolling
├── plotting.py     # per-kind primitives + StyleOptions + palette
├── comparison.py   # multi-dataset overlay
├── replicas.py     # alignment + mean±SD band + CSV export
├── dashboard.py    # JSON manifest → multi-panel figure
├── stats.py        # SEM/block-SEM/drift/histogram-weighting + CSV
├── escape_codes.py # xmgrace → mathtext
└── config.py       # defaults (colors, rolling, block, thresholds)

Data flow: parser.read_xvg → dataset.prepare_dataset (resolve family/kind, normalize time, apply window, resolve rolling) → plotting.plot_dataset / comparison.compare_datasets / replicas.replica_plot / dashboard.render_dashboard → stats.compute_dataset_stats.

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📄 License

GPL-3.0 — see LICENSE.