labforge 0.3.0

Wrap plain Python functions into a small scientific Flet desktop app: theory, simulation, visualization, analysis.

labforge

By Daniel La Rocco

Turn plain Python scripts into small scientific desktop apps.

labforge wraps a simulation you already have — a function that produces data, a function that plots it, a function that summarizes it — into a polished four-section app following the theory → simulation → visualization → analysis workflow. Pure Python, rendered with Flet; no HTML, no JavaScript, no callbacks to wire up.

You provide the science and labforge supplies the app shell, the parameter controls generated from your function signatures, the parameter-scan engine, LaTeX rendering for your theory notes, and eight instrument-panel themes, four dark and four light.

Quick start

import matplotlib.pyplot as plt
import numpy as np

import labforge
from labforge import Lab, Param, ScanResult

lab = Lab("gausslab")
lab.set_theory("theory.md")   # markdown file or string; $$...$$ becomes LaTeX


def sample(mu=0.0, sigma=1.0, n=2000, seed=42):
    """Draw n Gaussian variates; reproducible for a given seed."""
    return np.random.default_rng(seed).normal(mu, sigma, n)


lab.add_worker(sample, {
    "mu": Param(default=0.0, bounds=(-5, 5), scan=True, help="Mean of the distribution"),
    "sigma": Param(default=1.0, bounds=(0.1, 4), scan=True),
    "n": Param(kind="int", default=2000, bounds=(10, 100_000)),
    "seed": "int",
})


def histogram(data, bins=40):
    fig, ax = plt.subplots(figsize=(7, 3.4))
    if isinstance(data, ScanResult):   # a parameter scan: one histogram per grid point
        for params, draws in data:
            ax.hist(draws, bins=bins, density=True, alpha=0.5,
                    label=", ".join(f"{k} = {params[k]:g}" for k in data.keys))
        ax.legend()
    else:
        ax.hist(data, bins=bins, density=True, color=labforge.palette().data)
    labforge.style(fig, ax)            # optional house treatment
    return fig, ax


lab.add_viz(histogram, "Histogram", "Density histogram of the draw.",
            {"bins": Param(kind="int", default=40, bounds=(5, 200))})


def moments(data):
    if isinstance(data, ScanResult):   # one table row per grid point
        return [{**params, "mean": float(np.mean(d)), "std": float(np.std(d))}
                for params, d in data]
    return {"mean": float(np.mean(data)), "std": float(np.std(data, ddof=1))}


lab.add_analysis(moments, "Moments", "Sample moments of the draw.")

lab.open()

That is the whole app. lab.open() opens a native window with four pages — Theory, Simulation, Visualization, Analysis — a slider for every bounded parameter, a Run button, and tabs for each registered visualization and analysis. Content keeps a book-like reading measure, navigation and result updates cross-fade rather than cut, and every page is explorable before the first Run.

An extended version of this example — same lab plus a fitted-density overlay and a Q-Q plot tab — lives at examples/demo_lab.py:

python examples/demo_lab.py                 # native window
python examples/demo_lab.py --browser 8550  # serve at http://localhost:8550
python examples/demo_lab.py --scroll        # one continuous scrolling page
python examples/demo_lab.py --theme lavender

Concepts

Worker. One function produces the data. Each keyword argument gets a UI control from its Param spec — or from the signature default alone, if you spec nothing:

spec	control
Param(default=1.0, bounds=(0, 5))	slider with live readout
Param(kind="int", default=100, bounds=(10, 1000))	integer slider
Param(default=1.0) / "scalar" / "int"	validated text field
Param(..., scan=True) / "scalar or array" / "int or array"	scannable (see below)
"N-tuple" (e.g. "2-tuple")	one field per element
Param(kind="choice", options=["a", "b"])	dropdown

Every Param also takes label= to rename its control and help= for a tooltip on the control's label. Pressing Enter in any text field runs the section it belongs to, and an entry that fails to parse flags the field while the last good value stays live.

Validation happens at registration: unknown spec keys, defaults outside bounds, or a scan spec on a non-worker function raise ValueError when the app is assembled, never mid-use.

Parameter scans. A kwarg declared scan=True gets a scan toggle (bounded) or a comma-separated field (unbounded). Enter 0, 1, 2 and labforge calls the worker once per point of the cartesian grid across all scanned parameters — the worker itself always receives scalars. A long scan reports its progress in the status line as the grid fills in. Downstream functions then receive a ScanResult: a list of (params, result) records with keys, values() and axis(name) helpers, distinguished with isinstance(data, ScanResult).

Visualizations. Functions viz(data, **kwargs) returning a matplotlib figure (bare or (fig, ax)). Each gets a tab with its own controls and a Render button. Figures are yours — labforge only serializes them; the house style (labforge.style(fig, ax)) is strictly opt-in.

Analyses. Functions analysis(data, **kwargs) — the return shape picks the rendering:

return	rendered as
dict	two-column quantity/value table
list of dicts, or a DataFrame	full table
str	markdown
Figure or (fig, ax)	image
anything else	its repr

Multiple workers. Call add_worker more than once for a lab with several workers. Each worker keeps its own workspace — its controls, its last result and its per-tab settings — and the add_viz / add_analysis calls after each add_worker attach that worker's own tabs, so switching workers and back restores exactly what was there. Several workers need a way to choose among them (checked at build): a selects_worker model selector, or worker_view="tabs" to lay the workers out as Simulation-page tabs.

The clean way is a Theory-page selector with selects_worker=True: its options name the workers, so choosing one both swaps the theory and makes that worker active. There is no top-bar dropdown; the Simulation page shows the active worker as a single tab, matching the tabbed Visualization and Analysis pages. The demo (examples/demo_lab.py) uses this to switch between a normal and a gamma model:

lab.set_theory_selector(
    "model",
    Param(kind="choice", options=["Normal", "Gamma"]),
    theory_for,                 # theory_for(selection) -> markdown
    label="Distribution",
    selects_worker=True,
)
lab.add_worker(sample, {...}, name="Normal")   # its own viz/analysis tabs
lab.add_worker(sample_gamma, {...}, name="Gamma")

Theory. A markdown file or string. Displayed $$...$$ equations render as crisp images — through your system LaTeX toolchain when one is installed (with the Euler math font), falling back to matplotlib's mathtext otherwise.

Layouts, views and themes

lab.open() takes four independent knobs:

• view="app" (native window, default) or "browser" — serve the same app and open it in your web browser; with a fixed port the URL is stable across relaunches, and every browser tab gets its own isolated session.
• layout="pages" (rail navigation, default) or "scroll" — the whole lab as one continuous scrolling page.
• theme — one of eight palettes, four dark and four light. A theme sets the chrome, the plot palette and the equation ink together, so figures never drift from the app around them. Colour your own figures with labforge.palette().data, .model, .highlight; list the options with labforge.themes():

name	mode	look
paper	light	warm paper surfaces, graphite ink, vermilion accent (default)
mint	light	light mint greens on white
glacier	light	pale glacier blues, deep slate accent
lavender	light	soft lavender neutrals, deep violet accent
retro_green	dark	matrix mood, calmer green, legible olive card
instrument	dark	near-black teal-tinted surfaces, one electric accent
neon_violet	dark	violet chrome, hot-pink data
neon_gold	dark	the violet palette with pale gold as the accent

The Theory page under each theme — the equations are real LaTeX, rendered in the theme's ink:

paper	mint
glacier	lavender
retro_green	instrument
neon_violet	neon_gold

Install

Requires Python ≥ 3.10.

pip install labforge

Or from a clone, if you want to work on the code directly:

git clone https://github.com/laroccod/labforge.git
cd labforge
pip install -e .

Dependencies: flet (pinned), numpy, matplotlib. A system LaTeX toolchain (latex + dvipng) is optional and only affects equation typography.

Development

pip install -e ".[dev]"
pytest                                        # unit + offline page-tree smoke tests
black --check --line-length 100 src tests examples

The test suite builds every page and layout headlessly — no window needed — and asserts that equations and figures actually rendered, so most mistakes are caught without launching the app.

License

MIT