PyMieSimX 0.1.1

Source-model note

The Gaussian source exposed by PyMieSimX is not a generalized Lorenz–Mie theory (GLMT) implementation. It is a convenience object for defining a Gaussian illumination through its numerical aperture and optical power in watts. The Gaussian and GaussianSet source options should therefore be interpreted as a practical source parameterization, not as a separate GLMT solver.

Installation

Install the GUI and its PyMieSim dependency with:

pip install PyMieSimX

Launch the dashboard with:

pymiesimx

Use pymiesimx --help to see the available host, port, browser, and debug options.

Development

Install an editable checkout with:

pip install -e .

The GUI source lives in PyMieSimX/gui. Scientific calculations are provided by the installed PyMieSim package rather than duplicated here.

Testing and automation

Run the GUI test suite with:

pip install -e ".[testing]"
python -m pytest

GitHub Actions includes quality checks, GUI tests, PyPI publishing, Conda recipe publishing, and coverage deployment. The Conda recipe is in meta.yaml and the container entry point is defined in Dockerfile.

Python API

The computational API can be used without constructing the Dash application:

from PyMieSimX import run_experiment

result = run_experiment(
    source_type="GaussianSet",
    source_values={
        "wavelength": "650",
        "polarization": "0",
        "optical_power": "1e-3",
        "numerical_aperture": "0.2",
    },
    scatterer_type="SphereSet",
    scatterer_values={"diameter": "500", "material": "1.4", "medium": "1.0"},
    detector_type="None",
    detector_values={},
    measure="Qsca",
)

PyMieSimX.create_dash_app and PyMieSimX.OpticalSetupGUI remain available for applications that need the graphical interface.

Background calculations and limits

Parameter sweeps are submitted to a bounded background worker and reported to the dashboard through a polling status indicator. Results are guarded by both an in-memory dataframe limit and a serialized Dash-payload limit. Large-result warnings are logged before execution; oversized results are rejected with an actionable message asking you to reduce the sweep.

Run the command-line launcher with --debug to enable detailed logs. Log messages use the format timestamp | level | logger | message and include experiment job identifiers, input configuration, queue transitions, dataframe memory usage, and serialized result sizes.

Server-side usage metrics

The dashboard can share a PostgreSQL metrics database with RosettaX. Configure the Render service with:

PYMIESIMX_USAGE_METRICS_BACKEND=postgres
DATABASE_URL=<the shared PostgreSQL URL>

PyMieSimX writes namespaced counters to the shared metrics_counters table: pymiesimx_home_page_visit_count, pymiesimx_experiment_run_count, and pymiesimx_single_run_count. If PostgreSQL is unavailable, local development falls back to a JSON file under the platform’s application-data directory.