Step 4 — Aggregate Error Stackup

The per-layer error CSVs produced by Step 2 accumulate in the state directory over the course of a print job. Step 4 aggregates them into a single interactive visualization that confirms the proportional controller's performance: error should diminish as layers progress.

What it produces

3D Pancake Stackup

An interactive Plotly scene showing every layer's robot path and error surface at their physical Z heights — a literal "pancake stack" of the build. Each layer has two traces:

Trace	Visual	Data
Robot path	Cool→warm gradient by layer progression	X, Y, Z from CSV
Error surface	Red-Yellow-Green colorscale by error magnitude	X, Y, Z + err_mm

Layers are toggle-able via the legend. Hover reveals coordinates and error values.

Error Trend Chart

A 2D subplot below the 3D scene charting per-layer error statistics against layer number:

Series	Metric
RMS error	`sqrt(mean(err_mm²))`
Mean	err
Max	err

A downward slope confirms the controller is converging.

Summary CSV

stackup_summary.csv — one row per layer with columns: layer, nominal_z, n_samples, mean_abs_err, rms_err, max_abs_err, std_err

Command

# from ros2_ws/lib/control/aggregate_error_stackup/
uv run python aggregate_error_stackup.py <state_dir> [options]

Working directory: ros2_ws/lib/control/aggregate_error_stackup/

Arguments

Flag	Default	Purpose
Positional 1	—	State directory containing `L*.csv` files
`-o`, `--output`	`<state_dir>/stackup.html`	Output HTML path
`--summary`	`<state_dir>/stackup_summary.csv`	Summary CSV path
`--title`	auto from dir name	Custom plot title
`--layers`	all discovered	Comma-separated layer subset
`--no-trend`	false	Omit the 2D trend subplot

Inputs

All L{N}.csv files in the state directory, each with schema:

X,Y,Z,err_mm
12.2021,47.3637,43.2479,4.0644
10.7972,47.6810,43.2649,4.0644

These are produced by Step 2 (map_surface_err_to_xyz_pos.py) and accumulate naturally as the print loop runs.

When to run

During a print job — run between layer batches to check that error is trending downward. If not, consider adjusting k_p.
After a completed job — produce a final report of controller performance across all layers.
Comparing configurations — run on state directories from different print jobs to compare k_p values or other parameter changes.

Remote viewing

The output HTML is self-contained (Plotly JS is inlined). Serve it over the network from the robot host:

cd /path/to/STATE/ERR/job_dir/
python3 -m http.server 8080 --bind 0.0.0.0
# browse to http://<robot-host>:8080/stackup.html

Relationship to Steps 1–3

Steps 1–3 form the real-time closed loop. Step 4 is a post-hoc analysis tool that reads the same artifacts the loop produces. It does not modify any state and can be run repeatedly as new layers become available.

Steps 1–3 (per layer, real-time)     Step 4 (aggregate, any time)
       │                                       │
       ▼                                       ▼
 state_dir/L{N}.csv  ──────────────→  stackup.html
                                      stackup_summary.csv

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