EthoClaw ethoclaw-multiparameter-clustermap-generate

Generate journal-style hierarchical clustering clustermaps (heatmap+dendrogram) from a folder of .h5/.hdf5 files (KinematicParameter/ParameterName + ParameterData). Summarize frames per sample (mean/median/max), optionally normalize (column z-score), cluster samples/parameters, and export PNG/PDF + CSV + linkage arrays. Use when user asks for multi-parameter clustering heatmap/clustering tree/full parameter full sample clustering figure generation.

install

source · Clone the upstream repo

git clone https://github.com/penciler-star/EthoClaw

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/penciler-star/EthoClaw "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/ethoclaw-multiparameter-clustermap-generate" ~/.claude/skills/penciler-star-ethoclaw-ethoclaw-multiparameter-clustermap-generate && rm -rf "$T"

manifest: skills/ethoclaw-multiparameter-clustermap-generate/SKILL.md

source content

H5 Multi-parameter Clustermap

Create a Prism-like clustermap (heatmap + dendrogram) for all parameters × all samples from

.h5

files.

Assumptions about H5 structure

Each

.h5

must contain:

```
KinematicParameter/ParameterName
```
(parameter name list)
```
KinematicParameter/ParameterData
```
(2D array: frames/timepoints × parameters)

How it works

For each sample (one
```
.h5
```
): summarize every parameter’s time-series into a single value (
```
mean
```
/
```
median
```
/
```
max
```
).
Stack into a feature matrix: rows = samples, cols = parameters.
Optional normalization: parameter-wise z-score across samples (column z-score).
Hierarchical cluster rows and columns and draw clustermap.

Quick start (no config editing)

Option 1: Run in the project folder (recommended)

cd /d C:\\path\\to\\your_project
python <skill>/scripts/cluster_all_params.py

It will:

auto-read all
```
*.h5
```
under the current folder
write outputs to
```
results/cluster_all_kinematic_params/
```

Option 2: Pass project path explicitly

python scripts/cluster_all_params.py --root "C:\\path\\to\\your_project"

Option 3: Use TOML config (only if you want a saved preset)

python scripts/cluster_all_params_from_config.py --config references/config.example.toml

Outputs

In your configured output folder:

```
clustermap_all_params.png
```
```
clustermap_all_params.pdf
```
```
feature_matrix.csv
```
```
row_linkage.npy
```
,
```
col_linkage.npy
```
```
clustering_meta.json
```

Config knobs you’ll likely change

CLI flags (no file edits):

```
--summary mean|median|max
```
```
--style nature|cell|minimal
```

--cmap RdBu_r|coolwarm|vlag|icefire|...

(used when

style

doesn’t override)

```
--linewidths
```
/
```
--linecolor
```
(grid lines)
```
--metric euclidean|correlation
```

TOML (optional saved preset):

```
features.summary = mean|median|max
```
```
plot.style = nature|cell|minimal
```

plot.cmap = RdBu_r|coolwarm|vlag|icefire|...

```
plot.linewidths
```
/
```
plot.linecolor
```

clustering.metric = euclidean|correlation

Notes

If
```
ParameterName
```
ordering differs between files, the script will stop (to avoid mixing mismatched features).
If you have many parameters, set smaller
```
plot.xtick_fontsize
```
and rotate ticks.