Peak Fitting

lmfit is used to perform fits, however specific wrappers are provided for peak fitting.

multipeakfit

A wrapper is provided for multipeak fitting, providing a simple and powerful tool for most uses.

import numpy as np
import matplotlib.pyplot as plt
from mmg_toolbox.fitting import multipeakfit, gauss

# Create an interesting spectrum
x = np.arange(200, 300, 0.1)
background = 6.0
peaks = [
    dict(height=60, cen=250, fwhm=23),
    dict(height=12, cen=230, fwhm=5),
    dict(height=30, cen=280, fwhm=30),
]
y = np.ones_like(x) * background
for peak in peaks:
    y += gauss(x, **peak)

# Fit peak
result = multipeakfit(x, y, min_peak_power=0, peak_distance_idx=3)

# View Results
print(result)
result.plot()

# Evaluate results
print(f"\n\nNumber of peaks fit: {len(result)}")
print(f"Total amplitude: {result.amplitude: .2f} +/- {result.stderr_amplitude: .2f}")
for peak in result:
    print(f"Peak {peak.model_name} has amplitude: {peak.amplitude:.1f}, and width: {peak.fwhm:.3}")

plt.show()

The result object contains all the fitted data and information about the fit.

Scan wrappers

A specific wrapper is provided within the scan object for fitting data directly from a file. Fit results are stored within the scan object namespace.

import matplotlib.pyplot as plt
from mmg_toolbox import Experiment

data_dir = '/experiment/data/dir'
scan_numbers = [12345, 12346, 12348]

exp = Experiment(data_dir, instrument='i16')
exp.plot.set_plot_defaults()

scans = exp.scans(*scan_numbers)
# Fitting
amplitude = []
amplitude_err = []
metadata = []
for scan in scans:
    result = scan.fit.multi_peak_fit(
        xaxis='axes',  # default scan axes
        yaxis='signal',  # default scan values
        npeaks=1,
        min_peak_power=None,
        peak_distance_idx=6,
        model='Gaussian',
        background='Slope'
    )
    print(result)
    amp, err = scan.fit.fit_parameter('amplitude')
    amplitude.append(amp)
    amplitude_err.append(err)
    value, = scan.get_data('Ta', default=0)
    metadata.append(value)

fig, ax = plt.subplots()
ax.errorbar(metadata, amplitude, amplitude_err, fmt='.-', label='Ta')
ax.set_xlabel('Ta')
ax.set_ylabel('amplitude')
ax.set_title(exp.generate_scans_title(*scan_numbers))

plt.show()