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Hmt Monte Carlo by tomevault-io — Agent Skill

Name: Hmt Monte Carlo
Author: tomevault-io

Hmt Monte Carlo

Run Monte Carlo uncertainty analysis on a hydraulic model by sampling parameters from statistical distributions and computing exceedance probabilities. Use this when the user wants uncertainty analysis, Monte Carlo simulation, or probabilistic results. Use when this capability is needed.

tomevault-io Updated Sep 17, 2026

File contents

Run Monte Carlo uncertainty analysis on a hydraulic model.

Prerequisites

Project in a base_case/ subdirectory
Parameter uncertainty distributions defined by user
Solver paths configured in hmt_config.json

Steps

Check that a project is open. If not, ask the user to run /hmt-open pointing to base_case/<project_file>.
Show available materials.
```
hmt-cli get_materials
```
Ask the user for uncertain parameters. For each:
- Material name or BC ID
- Distribution: "truncated_normal" (default) or "uniform"
- For truncated_normal: mean, std, min, max
- For uniform: min, max

Build validated parameter specifications.

hmt-cli build_param_specs --args '{"specs": [
  {"type": "manning_n", "material_name": "<name>", "distribution": "truncated_normal",
   "mean": <mean>, "std": <std>, "min": <min>, "max": <max>},
  ...
]}'

Confirm the distributions with the user.

Ask for MC settings: number of samples (50–200), random seed (default 42), output directory (default ./mc_runs), parallel processes (default 1).

Generate samples.

hmt-cli generate_mc_samples --args '{"param_specs": <specs>, "n_samples": <N>, "random_seed": <seed>, "output_csv": "mc_samples.csv"}'

Show a preview of the first 5 sample rows.

Run Monte Carlo simulations.

hmt-cli run_monte_carlo --args '{"base_case_dir": "./base_case", "param_specs": <specs>, "n_samples": <N>, "n_processes": <procs>, "random_seed": <seed>, "sample_csv": "mc_samples.csv", "delete_cases": true, "output_dir": "<output_dir>"}'

Periodically show progress while waiting:

tail -20 mc_progress.log

Report: successful/failed runs, results JSON path.

Compute statistics. Ask for observation point coordinates if the user wants point statistics.

hmt-cli get_mc_statistics --args '{"results_json": "<path>", "observation_points": [{"name": "<name>", "x": <x>, "y": <y>}], "exceedance_probabilities": [99, 90, 50, 10, 1]}'

Present exceedance table: Point | P99 | P90 | P50 | P10 | P1

Report the spatial exceedance VTK path for ParaView visualization.

Troubleshooting:

Many failed cases → check pyHMT2D.log
Variable detection fails → pass "variable": "<name>" explicitly

Source: psu-efd/pyHMT2D — distributed by TomeVault.

---name: hmt-monte-carlodescription: Run Monte Carlo uncertainty analysis on a hydraulic model by sampling parameters from statistical distributions and computing exceedance probabilities. Use this when the user wants uncertainty analysis, Monte Carlo simulation, or probabilistic results. Use when this capability is needed.---Run Monte Carlo uncertainty analysis on a hydraulic model.## Prerequisites- Project in a `base_case/` subdirectory- Parameter uncertainty distributions defined by user- Solver paths configured in `hmt_config.json`## Steps1. **Check that a project is open.** If not, ask the user to run `/hmt-open` pointing to `base_case/<project_file>`.2. **Show available materials.** ```bash hmt-cli get_materials ```3. **Ask the user for uncertain parameters.** For each: - Material name or BC ID - Distribution: `"truncated_normal"` (default) or `"uniform"` - For `truncated_normal`: mean, std, min, max - For `uniform`: min, max4. **Build validated parameter specifications.** ```bash hmt-cli build_param_specs --args '{"specs": [ {"type": "manning_n", "material_name": "<name>", "distribution": "truncated_normal", "mean": <mean>, "std": <std>, "min": <min>, "max": <max>}, ... ]}' ``` Confirm the distributions with the user.5. **Ask for MC settings:** number of samples (50–200), random seed (default 42), output directory (default `./mc_runs`), parallel processes (default 1).6. **Generate samples.** ```bash hmt-cli generate_mc_samples --args '{"param_specs": <specs>, "n_samples": <N>, "random_seed": <seed>, "output_csv": "mc_samples.csv"}' ``` Show a preview of the first 5 sample rows.7. **Run Monte Carlo simulations.** ```bash hmt-cli run_monte_carlo --args '{"base_case_dir": "./base_case", "param_specs": <specs>, "n_samples": <N>, "n_processes": <procs>, "random_seed": <seed>, "sample_csv": "mc_samples.csv", "delete_cases": true, "output_dir": "<output_dir>"}' ``` Periodically show progress while waiting: ```bash tail -20 mc_progress.log ```8. **Report:** successful/failed runs, results JSON path.9. **Compute statistics.** Ask for observation point coordinates if the user wants point statistics. ```bash hmt-cli get_mc_statistics --args '{"results_json": "<path>", "observation_points": [{"name": "<name>", "x": <x>, "y": <y>}], "exceedance_probabilities": [99, 90, 50, 10, 1]}' ``` Present exceedance table: Point | P99 | P90 | P50 | P10 | P110. **Report** the spatial exceedance VTK path for ParaView visualization.**Troubleshooting:**- Many failed cases → check `pyHMT2D.log`- Variable detection fails → pass `"variable": "<name>"` explicitly---> Source: [psu-efd/pyHMT2D](https://github.com/psu-efd/pyHMT2D) — distributed by [TomeVault](https://tomevault.io).

Frequently asked questions