Digitize Plots

Digitize plots

Goal: turn a plotted curve in an image into numbers (a CSV of x,y), from the command line. This is the scriptable counterpart to WebPlotDigitizer (automeris.io), which is GUI-only.

The engine is the plotdigitizer CLI, but always drive it through the bundled scripts/digitize.py wrapper rather than calling plotdigitizer directly. The raw tool has two silent footguns that produce inf/nan or wrong-by-a- factor output, and the wrapper hides both (see "Why the wrapper" below). The wrapper also bundles the surrounding workflow — PDF rendering, color isolation, and a verification overlay — so the whole job is two or three commands.

Setup

pip install plotdigitizer numpy Pillow   # plotdigitizer pulls in opencv
# PDF rendering uses PyMuPDF if installed, else the `pdftoppm` binary (poppler)

Reference the script as ${CLAUDE_SKILL_DIR}/scripts/digitize.py.

Workflow

1. Get a clean PNG of the figure

If the figure is in a PDF, render the page and crop to just the plot (a tight crop makes calibration and tracing far more reliable):

python ${CLAUDE_SKILL_DIR}/scripts/digitize.py render paper.pdf --page 3 --dpi 150 -o page.png
# then crop to the figure with PIL, or read the page image and crop the axes box

2. Find the calibration: data value ↔ pixel for two points per axis

You need two reference points on each axis, each as DATA:PIXEL — the data value and where it sits in the image. x pixels are columns; y pixels are top-origin rows (what an image viewer or PIL reports — the wrapper flips them to the convention plotdigitizer wants).

Two ways to get the pixel positions, both fine:

• Read them by eye. Look at the figure (Read the PNG) and read off, e.g., "the Kℓ=0.0 tick is at about column 420, Kℓ=3.5 at 784; E=0.00 at row 362, E=0.14 at row 106." For most figures this is accurate enough.
• Assist with inspect. It prints candidate axis-spine pixel positions and writes zoomed margin strips so ticks are easy to read:

  python ${CLAUDE_SKILL_DIR}/scripts/digitize.py inspect fig.png
  

Pick tick marks that are far apart (e.g. first and last labeled tick) — a long baseline minimizes calibration error.

3. Isolate ONE series if the plot is a multi-color scatter

plotdigitizer traces a single curve. If the figure overlays several series, reduce it to one with --color, which keeps only pixels near a target color and writes a black-on-white image internally. Give the color as R,G,B, #hex, or @col,row to sample the marker color straight from the image:

--color '@470,150'      # sample whatever marker is at column 470, row 150
--color '200,40,40'     # or name the RGB directly (here: red)  --tol 80

A single clean curve needs no --color.

4. Extract, then VERIFY with the overlay

python ${CLAUDE_SKILL_DIR}/scripts/digitize.py extract fig.png \
  --xref 0:420 3.5:784 \
  --yref 0:362 0.14:106 \
  --color '200,40,40' --tol 80 \
  -o data.csv --overlay overlay.png

Then read overlay.png (it marks each extracted point on the original figure) and confirm the crosshairs sit on the data. This catches calibration mistakes immediately — do not report numbers without looking at the overlay.

Why the wrapper (do not call plotdigitizer directly)

1. It int-truncates every calibration coordinate. plotdigitizer's geometry.Point does int(x), so a data value of 0.14 becomes 0 and 3.5 becomes 3. A fractional axis silently collapses (scale → 0, output all inf) or rescales by the wrong factor. The wrapper multiplies each axis by a power of ten so calibration values are integers, then divides the result back down.
2. Its calibration pixel-y is bottom-origin. It internally computes image_height - y, so passing the natural top-origin row breaks the y-scale. The wrapper accepts top-origin rows and flips them for you.

Both bugs are silent — the tool emits a CSV either way — which is exactly why the overlay verification in step 4 is mandatory.

When NOT to use this — read the figure directly instead

For a quick one-off, or when you need the lower envelope across many overlaid series (which a single-curve tracer cannot follow), it is faster and often just as good to read the image yourself and report a handful of anchor points. In practice the two approaches agree to within a percent or two of the axis range. Reach for plotdigitizer when you need many dense points, a reproducible script, or batch extraction over many similar figures.

For interactive, messy, or genuinely multi-dataset figures where you want to click points, WebPlotDigitizer (automeris.io) in a browser remains the better tool — it is just not CLI-driven.