How to use the rampy.gaussian function in rampy
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charlesll / rampy / rampy / baseline.py View on Github 
print('ERROR: Install gcvspline to use this mode (needs a working FORTRAN compiler).')
# optional parameters
splinesmooth = kwargs.get('s',2.0)
# Spline baseline with mode 1 of gcvspl.f, see gcvspline documentation
c, wk, ier = gcvspline(yafit[:,0],yafit[:,1],np.sqrt(np.abs(yafit[:,1])),splinesmooth,splmode = 1) # gcvspl with mode 1 and smooth factor
baseline_fitted = splderivative(x,yafit[:,0],c)
elif method == 'gaussian':
### Baseline is of the type y = a*exp(-log(2)*((x-b)/c)**2)
# optional parameters
p0_gauss = kwargs.get('p0_gaussian',[1.,1.,1.])
## fit of the baseline
coeffs, pcov = curve_fit(rampy.gaussian,yafit[:,0],yafit[:,1],p0 = p0_gauss)
baseline_fitted = rampy.gaussian(x,coeffs[0],coeffs[1],coeffs[2])
elif method == 'exp':
### Baseline is of the type y = a*exp(b*(x-xo))
# optional parameters
p0_exp = kwargs.get('p0_exp',[1.,1.,1.])
## fit of the baseline
coeffs, pcov = curve_fit(rampy.funexp,yafit[:,0],yafit[:,1],p0 = p0_exp)
baseline_fitted = rampy.funexp(x,coeffs[0],coeffs[1],coeffs[2])
elif method == 'log':
### Baseline is of the type y = a*exp(b*(x-xo))
# optional parameters
p0_log = kwargs.get('p0_log',[1.,1.,1.,1.])# optional parameters
splinesmooth = kwargs.get('s',2.0)
# Spline baseline with mode 1 of gcvspl.f, see gcvspline documentation
c, wk, ier = gcvspline(yafit[:,0],yafit[:,1],np.sqrt(np.abs(yafit[:,1])),splinesmooth,splmode = 1) # gcvspl with mode 1 and smooth factor
baseline_fitted = splderivative(x,yafit[:,0],c)
elif method == 'gaussian':
### Baseline is of the type y = a*exp(-log(2)*((x-b)/c)**2)
# optional parameters
p0_gauss = kwargs.get('p0_gaussian',[1.,1.,1.])
## fit of the baseline
coeffs, pcov = curve_fit(rampy.gaussian,yafit[:,0],yafit[:,1],p0 = p0_gauss)
baseline_fitted = rampy.gaussian(x,coeffs[0],coeffs[1],coeffs[2])
elif method == 'exp':
### Baseline is of the type y = a*exp(b*(x-xo))
# optional parameters
p0_exp = kwargs.get('p0_exp',[1.,1.,1.])
## fit of the baseline
coeffs, pcov = curve_fit(rampy.funexp,yafit[:,0],yafit[:,1],p0 = p0_exp)
baseline_fitted = rampy.funexp(x,coeffs[0],coeffs[1],coeffs[2])
elif method == 'log':
### Baseline is of the type y = a*exp(b*(x-xo))
# optional parameters
p0_log = kwargs.get('p0_log',[1.,1.,1.,1.])
## fit of the baseline
coeffs, pcov = curve_fit(rampy.funlog,yafit[:,0],yafit[:,1],p0 = p0_log)rampy
A Python module containing functions to treat spectroscopic (XANES, Raman, IR...) data
