Diffraction

The polartools.diffraction module fits Bragg peaks and helps with reciprocal- space data. Peak shapes are powered by lmfit (Gaussian, Lorentzian, Pseudo-Voigt). The module also includes 2D plotting helpers for mesh scans and a databroker-aware dbplot shortcut.

Fitting a single Bragg peak

from polartools.diffraction import fit_peak, Model
from polartools.load_data import load_table

df = load_table(scan_id=42, source="myfile.dat", folder="/data/2024-1")

result = fit_peak(
    xdata=df["th"],
    ydata=df["apd"] / df["I0"],  # normalize by monitor
    model=Model.PseudoVoigt,
)

print(result.fit_report())
result.plot()

Model is an enum: Model.Gaussian, Model.Lorentzian, Model.PseudoVoigt. The result is an lmfit.model.ModelResult.

Loading scan metadata

from polartools.diffraction import load_info, get_type

info = load_info(
    scan_id=42,
    info=["sample", "T", "B"],
    source="myfile.dat",
    folder="/data/2024-1",
)
scan_type = get_type(scan_id=42, source="myfile.dat", folder="/data/2024-1")

Fitting a series of scans

fit_series fits the same peak shape across a range of scans (e.g. a temperature or field series) and returns a DataFrame of fit parameters:

from polartools.diffraction import fit_series

# scan_series: [start1, end1, step1, start2, end2, step2, ...]
fits = fit_series(
    scan_series=[100, 119, 1],
    source="myfile.dat",
    folder="/data/2024-1",
    x="th",
    y="apd",
    monitor="I0",
    model=Model.PseudoVoigt,
)

# fits is a DataFrame indexed by scan_id with columns:
#   center, sigma, amplitude, height, fwhm, ...
fits.plot(y="center", marker="o")

load_series returns the raw concatenated data without fitting if you want to plot or process the series differently.

Mesh scans (2D maps)

For mesh scans (typically motor1 × motor2 with intensity at each grid point):

from polartools.diffraction import load_mesh, plot_2d

mesh = load_mesh(
    scan_id=200,
    source="myfile.dat",
    folder="/data/2024-1",
    x="h",
    y="k",
    intensity="apd",
    monitor="I0",
)

plot_2d(mesh)  # heatmap with axis labels

For paired ±-field mesh scans:

from polartools.diffraction import load_dichromesh
mesh_plus, mesh_minus, dichro = load_dichromesh(scan_plus=200, scan_minus=201, ...)

Quick plotting

load_axes infers sensible defaults for the x/y/monitor axes from the scan metadata; plot_data and plot_fit give one-line plots. dbplot is the databroker shortcut:

from polartools.diffraction import plot_data, plot_fit, dbplot

plot_data(scan_id=42, source="myfile.dat", folder="/data/2024-1")
plot_fit(
    scan_id=42,
    source="myfile.dat",
    folder="/data/2024-1",
    model=Model.PseudoVoigt,
)

# straight from a databroker catalog
dbplot(db, scan_id=42)

Notes

• Peak fitting expects a single peak per scan. For multi-peak fits, build the composite model directly with lmfit.

• monitor should be a column name; the monitor is divided into the intensity column before fitting/plotting.

• fit_series accepts the SPEC-style [start, end, step] triplets so you can stitch together discontiguous scan ranges.