Advanced Data Visualization Techniques Using gnuplot

Automating Plot Generation with gnuplot and ScriptsAutomating plot generation saves time, ensures reproducibility, and scales visualization workflows. gnuplot — a compact, scriptable plotting utility available on Linux, macOS, and Windows — is ideal for batch plotting and integration with other tools. This article walks through principles, practical examples, integrations, and best practices for automating plot creation using gnuplot and scripts.

---

Why automate plotting?

Automated plotting is valuable when you need to:

• Reproduce figures consistently across runs and datasets.
• Generate hundreds or thousands of plots without manual intervention.
• Integrate plotting into data-processing pipelines (ETL, nightly jobs, CI).
• Produce publication-quality figures with consistent style and metadata.

gnuplot is well-suited because it:

• Accepts commands from files or standard input.
• Supports many output formats (PNG, SVG, PDF, EPS, LaTeX-compatible).
• Can be called from shell scripts, Python, R, Makefiles, and CI systems.
• Has powerful data transformation and formatting options built in.

---

Basic gnuplot script structure

A gnuplot script is a plain text file containing gnuplot commands. Typical structure:

• Set terminal/output options (format, size, font).
• Set labels, ranges, style settings, and key/legend.
• Load or plot data using plot/splot commands.
• Optionally, save or reset settings.

Example (save as plot.gnuplot):

set terminal pngcairo size 800,600 enhanced font 'DejaVu Sans,12' set output 'output.png' set title 'Sample Data' set xlabel 'X' set ylabel 'Y' set grid plot 'data.csv' using 1:2 with lines title 'Series A' 

Run from shell:

gnuplot plot.gnuplot 

---

Embedding variables and templates

For automation you often need to inject variables (filenames, ranges, titles). Methods:

• Use gnuplot’s string handling and system() if needed.
• Generate the gnuplot script dynamically from a template using a scripting language (bash, Python, Perl).
• Use command-line -e to pass simple variable assignments.

Example (bash template generation):

#!/bin/bash DATAFILE="$1" OUTFILE="${2:-output.png}" TITLE="${3:-My Plot}" cat > tmp.gnuplot <<EOF set terminal pngcairo size 1000,600 enhanced font 'DejaVu Sans,12' set output '${OUTFILE}' set title '${TITLE}' set xlabel 'X' set ylabel 'Y' plot '${DATAFILE}' using 1:2 with lines lw 2 title 'Series' EOF gnuplot tmp.gnuplot rm tmp.gnuplot 

Example (passing vars with -e):

gnuplot -e "datafile='data.csv'; outfile='out.png'; title='Auto Plot'" - <<'GNUPLOT' set terminal pngcairo size 800,600 set output outfile set title title plot datafile using 1:2 with lines GNUPLOT 

---

Generating multiple plots in batches

Common tasks:

• Plot multiple columns from one file into separate images.
• Plot many files with consistent styles.
• Produce time series snapshots (one plot per timestamp).

Bash loop example (plot each column separately):

#!/bin/bash DATA=data.csv N_COLS=4 for col in $(seq 2 $N_COLS); do   outfile="col${col}.png"   gnuplot -e "datafile='${DATA}'; col=${col}; outfile='${outfile}'" - <<'GP' set terminal pngcairo size 800,600 set output outfile set title sprintf('Column %d', col) plot datafile using 1:col with lines title columnheader(col) GP done 

If you prefer a single gnuplot script that loops internally, use gnuplot’s iteration:

DATA='data.csv' do for [col=2:4] {   set output sprintf('col%d.png', col)   plot DATA using 1:col with lines title columnheader(col) } 

---

Producing multi-panel figures and layouts

gnuplot supports multiplot mode for arranging several subplots into a single output. Example 2×2 layout:

set terminal pngcairo size 1200,900 set output 'multipanel.png' set multiplot layout 2,2 title 'Multi-panel Example' do for [col=2:5] {   set title sprintf('Column %d', col)   plot 'data.csv' using 1:col with lines } unset multiplot 

For publication-quality composite figures, combine gnuplot’s EPS/PDF output with LaTeX or use SVG outputs and an external vector editor.

---

Styling and consistent themes

Create a style header script to reuse in all plots:

# style.gnuplot set terminal pngcairo enhanced font 'DejaVu Sans,11' set grid lt 1 lc rgb '#dddddd' set key right top set style line 1 lw 2 lc rgb '#1f77b4' set style line 2 lw 2 lc rgb '#ff7f0e' 

Include via:

load 'style.gnuplot' 

Use set format to control axis tick formatting and set xtics/ytics for custom ticks.

---

Data preprocessing and piping

For complex transformations, preprocess data in a script (awk, Python, R) and pipe to gnuplot. Example: compute moving average with awk then plot:

awk 'NR>1{sum+=$2; buf[NR%5]=$2; if(NR>5) sum-=buf[(NR+5)%5]; if(NR>=5) print $1, sum/5}' data.csv |  gnuplot -p -e "plot '-' using 1:2 with lines title '5-pt MA';" 

Alternatively, call gnuplot from Python using subprocess and pass data via stdin.

---

Integrating with other languages and tools

• Python: Use subprocess to call gnuplot or write gnuplot files. For tighter integration, use matplotlib for advanced styling but gnuplot is lighter for quick numeric-driven plots.
• R: System calls or write gnuplot scripts from R. R’s ggplot2 may be preferable for layered grammar, but gnuplot excels in portability and scriptability.
• Makefile/CI: Include a rule that creates plots as build artifacts; run on changes to data or scripts.

Makefile example:

plots: plot1.png plot2.png plot1.png: data.csv plot1.gnuplot 	gnuplot plot1.gnuplot plot2.png: data.csv plot2.gnuplot 	gnuplot plot2.gnuplot 

---

Export formats and LaTeX integration

Common terminals:

• pngcairo — high-quality PNG with anti-aliasing.
• svg — scalable vector, good for web.
• pdfcairo / eps — publication vector formats.
• epslatex / cairolatex — split text for LaTeX rendering (useful for font consistency in papers).

Example using epslatex:

set terminal epslatex color size 4,3 standalone set output 'figure.tex' plot 'data.csv' using 1:2 with lines 

Then compile with LaTeX to get a vector PDF.

---

Error handling and logging

• Check gnuplot exit codes in scripts.
• Redirect stderr/stdout to log files for debugging.
• Use small test datasets when developing scripts.

Example:

gnuplot script.gnuplot 2> gnuplot.log || { echo "gnuplot failed — check gnuplot.log"; exit 1; } 

---

Best practices

• Keep style settings in reusable files and load them.
• Use clear variable names when templating scripts.
• Prefer vector formats for publication and PNG/SVG for web.
• Pin fonts and sizes for consistent cross-platform appearance.
• Version-control your gnuplot scripts and generated plots’ source templates.
• Add metadata (titles, captions) programmatically if generating many images.

---

Example: full automation pipeline

1. Preprocess raw CSVs with Python (clean, aggregate).
2. For each processed CSV, generate a gnuplot script from a Jinja2 template.
3. Run gnuplot in parallel (GNU parallel or xargs -P) to render images.
4. Combine or post-process (ImageMagick for thumbnails, pdftk for PDF merging).
5. Upload artifacts to web or archive.

Snippet (Python subprocess example):

import subprocess, pathlib for csv in pathlib.Path('processed').glob('*.csv'):     out = csv.with_suffix('.png').name     script = f""" set terminal pngcairo size 1200,800 set output '{out}' set title '{csv.stem}' plot '{csv}' using 1:2 with lines lw 2 """     subprocess.run(['gnuplot'], input=script.encode()) 

---

Troubleshooting common issues

• Fonts look different: specify font family and size explicitly and ensure the font is installed.
• Data parsing errors: ensure delimiters and headers are handled; use using and firstrow/skip options.
• Slow rendering: reduce image size, simplify styles, or switch terminals.
• Legends/clipping: adjust margins with set lmargin/rmargin/tmargin/bmargin.

---

Automating plots with gnuplot and scripts provides a lightweight, reproducible, and flexible approach to visualization. With templating, batching, and integration into pipelines, you can reliably produce consistent figures for monitoring, reporting, and publication.