fitting

Module for data fitting routines

Functions

scilightcon.fitting.detect_peaks(x, y, method, n_max, options={})

Peak detection function using different algorithms.

Examples:

>>> from scilightcon.datasets import load_csv_data
>>> fname = 'data_test_detect_peaks.csv'
>>> data, header = load_csv_data(fname)
>>> x = data[:,0]
>>> y = data[:,1]
>>> method = "above_average"
>>> clusters = detect_peaks(x, y, method = method, n_max=2)
>>> print('clusters', clusters)
clusters [[22, 25], [12, 15]]

Parameters:

Name	Type	Description	Default
x	float	x axis data	required
y	float	y axis data	required
method	str	[above_average, z_score, dispersion]	required
n_max	int	Maximum number of peaks	required
options	dict	Additional options to pass to the chosen algorithm	{}

Returns:

Type	Description
List[Tuple[int, int]]	A list of tuples indicating the begining and the end of peaks

scilightcon.fitting.fit_beam_profile_2d(matrix, method, options={'decimation': 1})

Fitting of two-dimensional beam profiles using the Gaussian or ISO method.

Examples:

>>> import scilightcon
>>> from scilightcon.utils._fixes import _open_binary
>>> from scilightcon.fitting import fit_beam_profile_2d
>>> import pickle
>>> filepath = _open_binary(scilightcon.datasets.DATA_MODULE, 'test_beam_profile.pkl')
>>> matrix = pickle.load(filepath)['matrix']
>>> method = 'gauss'

Parameters:

Name	Type	Description	Default
matrix	array	matrix	required
method	str	iso for ISO, gauss for Gaussian	required
options	dict	Additional options (decimation)	{'decimation': 1}

Returns:

Name	Type	Description
mean_x	float	position of the beam center in x axis
mean_y	float	position of the beam center in y axis
sigma_x	float	variance of beam along x axis
sigma_y	float	variance of beam along y axis
sigma_xy	float	standard deviation of x-y beam product
sigma_p	float	variance of beam along its primary axis
sigma_s	float	variance of beam along its secondary axis
phi	float	beam angle (principal axis)