chromasurr 0.1.1

Surrogate modeling and Bayesian calibration of chromatographic processes using CADET and Gaussian Processes.

A lightweight Python library that couples the CADET process simulator with modern sensitivity analysis, Gaussian-process surrogates, and diagnostic tools.

✨ Highlights

• Vectorized KPI extractor – Compute retention time, peak width, and number of plates from any simulation in one call (chromasurr.metrics.extract)

• Global Sobol sensitivity – One-liner run_sensitivity_analysis() to rank parameters with Saltelli sampling and automatic metric extraction

• Gaussian-process surrogate manager – The Surrogate class trains, prunes, and re-trains emulators in log-space; includes built-in sensitivity on the GP itself

• Reusable process builder – The BatchElution class provides a ready-to-use CADET process object with configurable parameters like cycle time and feed duration

• End-to-end example – Full demo in examples/chromasurr_demo.py covering sensitivity analysis, calibration, and UQ

🚀 Quick start

Run the demo in ≈ 60 seconds:

# 1. Clone the repository
git clone https://github.com/talasunna/chromasurr.git && cd chromasurr

# 2. Create environment (Python ≥ 3.10)
python3 -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt

# 3. Launch the demo
python examples/chromasurr_demo.py

This runs a full workflow using a preconfigured BatchElution process and guides you through GP surrogate modeling, Sobol sensitivity analysis, and Bayesian calibration.

📦 Installation

pip install git+https://github.com/talasunna/chromasurr.git

Note – CADET-Core must be installed or compiled on your system. See the CADET-Core Installation Guide for details.

CADET-Process is automatically installed via pip when installing chromasurr.

🛠️ Usage at a glance

 from chromasurr.surrogate import Surrogate
 from chromasurr.uq import perform_monte_carlo_uq, latin_hypercube_sampler
 from chromasurr.visualize import sobol_indices, summarize_results, uq_distribution
 from chromasurr.process.batch_elution import BatchElution

 # 1. Instantiate a configurable process object
 proc = BatchElution(cycle_time=600.0, feed_duration=50.0)

 # 2. Set up parameter configuration and choose metric
 param_config = {
      "ax_disp": "flow_sheet.column.axial_dispersion",
      "porosity": "flow_sheet.column.total_porosity",
  }
  bounds = {
      "ax_disp": [1e-6, 1e-2],
      "porosity": [0.35, 0.95],
  }
  metrics = ["peak_width"]

 # 3. Train surrogate
 surr = Surrogate(proc, param_config, bounds, metrics, n_train=128)
 surr.train()

 # 4. Run sensitivity analysis
 surr.analyze_sensitivity(n_samples=1024)
 sobol_results = surr.sensitivity

 # 5. Selection of most important parameters, either based on a threshold or the number of parameters you want, and retrain model based on selection
 surr.select_important_params(threshold=0.05)
 surr.retrain()

 # 6. Set up latin hypercube sampler and run uncertainty quantification
 lhs_sampler = latin_hypercube_sampler(list(surr.bounds.values()))
 uq = perform_monte_carlo_uq(surrogate=surr, sample_input=lhs_sampler, metric=metrics[0], n_samples=1000)

 # 7. Visualize your analyses: sobol indices, uq distribution, and your results
uq_distribution(uq, metric="peak_width")

sobol_indices(sobol_results, metric=metrics[0])

summarize_results(
    surrogate=surr,
    metric=metrics[0],
    uq_result=uq,
)

Sensitivity Analysis Workflow Options

• Option 1: Run Sensitivity Analysis First, Then Train Surrogate

  Use run_sensitivity_analysis on the CADET model to rank parameters, then train a surrogate focusing on the important ones (based on the number of parameters you’d like to retain, or on a threshold you can decide).

• Option 2: Train Surrogate First, Then Run Sensitivity Analysis

  Fit a surrogate with Surrogate, then analyze it with analyze_sensitivity().

Both paths support uncertainty quantification and parameter calibration workflows.

—

All public functions include NumPy-style docstrings and Python 3.10+ type hints for autocompletion and static analysis.

📚 Documentation

Docs are hosted on GitHub Pages: https://talasunna.github.io/chromasurr/

🖇️ Project structure

chromasurr/
│   __init__.py
│   metrics.py              ← KPI extractor
│   sensitivity.py          ← Saltelli driver + helpers
│   surrogate.py            ← Surrogate manager
│   uq.py                   ← Uncertainty quantification tools
│   error_analysis.py       ← Error diagnostics
├── process/
│   └── batch_elution.py    ← Configurable CADET Process class
└── examples/
    └── chromasurr_demo.py  ← End-to-end demo script

docs/
tests/

📜 License

Distributed under the MIT License – see LICENSE for details.

Made with ☕ by Tala Al-Sunna.