Fit Functions

py4syn.utils.fit.fitGauss(xarray, yarray)

This function mix a Linear Model with a Gaussian Model (LMFit).

See also: Lmfit Documentation

Parameters:

xarray : array

X data

yarray : array

Y data

Returns:

peak value: `float`

peak position: `float`

min value: `float`

min position: `float`

fwhm: `float`

fwhm positon: `float`

center of mass: `float`

fit_Y: `array`

fit_result: `ModelFit`

Examples

>>> import pylab as pl
>>> data = 'testdata.txt'
>>> X = pl.loadtxt(data);
>>> x = X[:,0];
>>> y = X[:,7];
>>>
>>> pkv, pkp, minv, minp, fwhm, fwhmp, com = fitGauss(x, y)
>>> print("Peak ", pkv, " at ", pkp)
>>> print("Min ", minv, " at ", minp)
>>> print("Fwhm ", fwhm, " at ", fwhmp)
>>> print("COM = ", com)
>>>

py4syn.utils.fit.tvDenoising1D(data, lamb)

This function implements a 1-D Total Variation denoising according to Condat, L. (2013) “A direct algorithm for 1-D total variation denoising.”

See also: Condat, L. (2013). A direct algorithm for 1-D total variation denoising. IEEE Signal Processing Letters, 20(11), 1054–1057. doi:10.1109/LSP.2013.2278339

Parameters:

data : array

Data to be fit

lamb : float

Note

lamb must be nonnegative. lamb = 0 will result in output = data.

Returns:

fitData: `array`

Examples

>>> import pylab as pl
>>> data = 'testdata.txt'
>>> X = pl.loadtxt(data);
>>> x = X[:,0];
>>> data = X[:,7];
>>>
>>> denoised = tvDenoising1D(data, lamb=200)
>>>
>>> pl.plot(x, data, 'b')
>>> pl.hold(True)
>>> pl.plot(x, denoised, 'r--')
>>> pl.show()