Plot a sinter CSV

Drop the CSV that sinter collect --save_resume_filepath (or sinter.collect called with save_resume_filepath=) wrote onto the plot in the QEC Workbench. Nuclei plots logical error rate against physical error rate for each code distance, estimates the error-suppression factor Λ, and lets you export the figure and the numbers behind it. It works in the desktop app and in the browser build at getnuclei.dev/try.

Which files work

Any CSV in sinter’s stats format. The header must contain these columns (extra columns are ignored):

Column	Meaning
`shots`	Shots sampled for the task.
`errors`	Shots with a logical error.
`discards`	Shots discarded by post-selection (may be 0).
`seconds`	CPU core time sinter spent sampling and decoding the task, summed across workers (not wall-clock). Compare it between runs only on the same hardware and worker count.
`decoder`	Decoder name.
`strong_id`	Hash that identifies the task.
`json_metadata`	JSON object describing the task (`d`, `p`, `r`, basis, noise model, …).
`custom_counts`	Optional extra counts; kept for export, not plotted.

Rows that share a strong_id are summed, exactly as sinter’s own --save_resume_filepath merge does. The loader reports malformed rows by line number and plots the rest; a file with no usable header is rejected and your current data is left alone.

Check the column mapping

Sinter does not define what keys live inside json_metadata, so Nuclei does not assume. After loading, the CSV column mapping strip shows what it will plot:

p: the numeric metadata key for the x-axis (noise strength).
d: the code distance.
rounds: the number of syndrome rounds, used for per-round rates.
decoder: the decoder column, or a metadata key.
Series split by: extra metadata keys (for example basis or noise model) that separate tasks into their own series and their own Λ fits. Only keys with at least two values are offered; keys that are the same in every row are shown as read-only chips.
Replicates: seed-like keys (s, seed, shard, …) that pile several tasks onto one point are summed into one task, as sinter pools replicates. When tasks collide for another reason, the warning offers Merge as replicates or Split by that key.

After a file loads, the mapping collapses to one line (file, counts, what is mapped); Edit mapping opens the controls and the preview.

Conventional key names (p, d, r/rounds, and so on) are pre-selected, but the selection is always shown and editable. If the choice for p is not a conventional name, the strip says it was guessed and asks you to confirm. Nothing is guessed silently: if two tasks land on the same plotted point, or a metadata key splits the tasks into blocks that are not used to separate series, the strip warns you instead of quietly pooling them. A preview table shows the first rows as they will be plotted.

The current mapping is also shown in the sidebar next to the file name and row count.

Per shot or per round

The toggle above the plot switches the y-axis units, and the Decoder Workbench table follows it. Your choice is kept while you move between views. Per round is the default whenever rounds are known; otherwise the plot falls back to per shot.

Per shot is the raw rate: errors / (shots - discards).
Per round converts each point with the equivalent of sinter.shot_error_rate_to_piece_error_rate(rate, pieces=rounds), applied to the best estimate and to both interval ends. It needs known rounds; rows without rounds are hidden in per-round mode rather than mixed in at per-shot units, and the plot says how many were hidden.

Per-round rates are what you want when comparing runs with different numbers of rounds (for example r = d against r = 3d). A per-shot Λ is confounded when rounds change with distance, and the Λ readout says so.

Intervals

Every point’s whiskers are sinter’s likelihood-ratio interval, the one sinter.fit_binomial produces, with max_likelihood_factor = 1000 (about 3.7σ). It is computed on the kept shots, shots - discards. This is a likelihood interval, not a normal approximation, so it stays sensible at zero or few errors. The factor appears in the plot footer and in every export.

Λ and its warnings

Λ is the factor by which the logical error rate drops when the distance goes up by 2. For each series at a fixed p, Nuclei fits an ordinary least-squares line to ln(rate) against distance and reports Λ = exp(-2 · slope). It needs at least three distinct distances with at least one logical error each; points with zero errors have no logarithm and are left out (and listed). The interval shown is a 95% Student-t interval on the slope with n−2 degrees of freedom, which is the same convention the MCP fit tool uses. The fit is unweighted, so it ignores how many errors each point has.

Series and experiments are never pooled: rows with different decoders, metadata or rounds schemes get separate fits.

Λ is shown as a table per decoder: p, Λ, 95% interval and r². A warning triangle on a row lists its caveats on hover. Rows whose interval is not meaningful (saturated or above threshold) fold into one line under the table. The warnings are also written into the SVG metadata, and the SVG <desc> carries one line per Λ; the CSV export does not carry Λ:

Warning	What it means
`saturated`	Some rates are near 0.5, which is pure noise. Λ and its interval carry no information; the interval is not printed.
`Λ ≤ 1`	Logical errors do not fall with distance. You are above threshold at this `p`.
`95% interval includes 1`	Suppression with distance is not established at this confidence.
`r² < 0.5`	`ln(rate)` is poorly described by a line in distance; the Λ number is not a good summary.
tasks collide / rounds vary	Several tasks share one plotted point and were fitted as replicates, or rounds change with distance, which confounds a per-shot Λ.

Nuclei does not estimate or claim a threshold p_th.

Exports

SVG is print-ready: a white page with dark text and the light-theme palette, whatever theme the app is in, and one short caption line (whisker convention and the Λ method). It carries its own data (colors are inlined; fonts are referenced by name, so a machine without Fira Code and Geist Sans falls back to its defaults) and embeds a <metadata> block with the units, rate estimator, interval method and factor, the Λ estimates with their warnings, and the source: file name, task count and a digest of the sorted strong_ids.
CSV (the plot’s export) has one row per task with the rate, interval ends, units, ci_method, max_likelihood_factor, and the original strong_id, json_metadata and custom_counts as they were in your file (not the re-mapped copies). It has no seconds column; opening it again in Nuclei re-imports it with seconds set to 0.
Export stats.csv writes the sinter-format file (shots, errors, discards, seconds, decoder, strong_id, json_metadata, custom_counts). That file can be dropped back onto the plot or fed to sinter plot.
The Decoder Workbench table exports its own CSV with the discard-aware rate, interval and time per shot for each decoder at each (d, p).

Use it from your own agent

To run the same statistics from Claude Code, Cursor or another MCP client, with every number tied to a run ID and manifest, see Use Nuclei from your AI agent (MCP).