CLI

The current top-level command is:

dfode-kit --help

Available commands:

• config
• init
• run-case
• sample
• augment
• label
• train
• h52npy

List commands deterministically:

dfode-kit --list-commands

Command overview

config

Store and inspect machine-local runtime configuration such as OpenFOAM, Conda, and DeepFlame activation paths.

Example:

dfode-kit config set openfoam_bashrc /path/to/openfoam/etc/bashrc

init

Initialize canonical cases from explicit presets. The current supported case is oneD-flame, which preserves the current DFODE-kit empirical setup logic while making it previewable, overrideable, and serializable to JSON.

Example:

dfode-kit init oneD-flame \
  --mech /path/to/gri30.yaml \
  --fuel CH4:1 \
  --oxidizer air \
  --phi 1.0 \
  --out /tmp/ch4_phi1_case \
  --preview --json

run-case

Run a case-local runner such as Allrun using the stored runtime configuration from dfode-kit config.

Example:

dfode-kit run-case --case /path/to/case --preview --json

sample

Sample thermochemical states from canonical flame simulation outputs and save them to HDF5.

Example:

dfode-kit sample \
  --mech /path/to/gri30.yaml \
  --case /path/to/case \
  --save /path/to/output.h5 \
  --include_mesh

augment

Apply perturbation-based dataset augmentation to sampled states.

Example:

dfode-kit augment \
  --source /path/to/sample.h5 \
  --mech /path/to/gri30.yaml \
  --save /path/to/augmented.npy \
  --preset random-local-combustion-v1 \
  --target-size 20000 \
  --apply

label

Generate supervised learning targets using Cantera/CVODE time advancement.

Example:

dfode-kit label \
  --mech /path/to/gri30.yaml \
  --time 1e-6 \
  --source /path/to/augmented.npy \
  --save /path/to/labeled.npy

train

Train a neural-network surrogate for chemistry integration.

Example:

dfode-kit train \
  --mech /path/to/gri30.yaml \
  --source_file /path/to/labeled.npy \
  --output_path /path/to/model.pt

h52npy

Convert HDF5 scalar-field datasets into a stacked NumPy array.

Example:

dfode-kit h52npy \
  --source /path/to/sample.h5 \
  --save_to /path/to/sample.npy

Current design notes

Recent CLI refactors improved:

• deterministic command ordering,
• stable top-level command listing,
• lazy command loading for lighter help paths,
• more predictable command dispatch behavior.

The new init command already supports machine-readable JSON output for planning/provenance, and run-case supports JSON output for preview/apply results.

For augmentation specifically, see Data Augmentation. For the end-to-end artifact flow between sample, augment, label, h52npy, and train, see Data Preparation and Training Workflow.

Future work should still add:

• broader machine-readable JSON output across all commands,
• standardized stderr/stdout behavior,
• command-level error normalization.