def fit_pk_obj_1d(p, x, f0, pktype):
if pktype == 'gaussian':
f = pkfuncs.gaussian1d(p, x)
elif pktype == 'lorentzian':
f = pkfuncs.lorentzian1d(p, x)
elif pktype == 'pvoigt':
f = pkfuncs.pvoigt1d(p, x)
elif pktype == 'split_pvoigt':
f = pkfuncs.split_pvoigt1d(p, x)
elif pktype == 'tanh_stepdown':
f = pkfuncs.tanh_stepdown_nobg(p, x)
resd = f - f0
return resd
def fit_pk_obj_1d(p,x,f0,pktype):
if pktype == 'gaussian':
f=pkfuncs.gaussian1d(p,x)
elif pktype == 'lorentzian':
f=pkfuncs.lorentzian1d(p,x)
elif pktype == 'pvoigt':
f=pkfuncs.pvoigt1d(p,x)
elif pktype == 'split_pvoigt':
f=pkfuncs.split_pvoigt1d(p,x)
elif pktype == 'tanh_stepdown':
f=pkfuncs.tanh_stepdown_nobg(p,x)
resd = f-f0
return resd
def fit_pk_obj_1d_bnded(p, x, f0, pktype, weight, lb, ub):
if pktype == 'gaussian':
f = pkfuncs.gaussian1d(p, x)
elif pktype == 'lorentzian':
f = pkfuncs.lorentzian1d(p, x)
elif pktype == 'pvoigt':
f = pkfuncs.pvoigt1d(p, x)
elif pktype == 'split_pvoigt':
f = pkfuncs.split_pvoigt1d(p, x)
num_data = len(f)
num_parm = len(p)
resd = np.zeros(num_data + num_parm)
#tub bnds implementation
resd[:num_data] = f - f0
for ii in range(num_parm):
if lb[ii] is not None:
resd[num_data + ii] = weight * np.max(
[-(p[ii] - lb[ii]), 0., (p[ii] - ub[ii])])
return resd
def fit_pk_obj_1d_bnded(p,x,f0,pktype,weight,lb,ub):
if pktype == 'gaussian':
f=pkfuncs.gaussian1d(p,x)
elif pktype == 'lorentzian':
f=pkfuncs.lorentzian1d(p,x)
elif pktype == 'pvoigt':
f=pkfuncs.pvoigt1d(p,x)
elif pktype == 'split_pvoigt':
f=pkfuncs.split_pvoigt1d(p,x)
num_data=len(f)
num_parm=len(p)
resd=np.zeros(num_data+num_parm)
#tub bnds implementation
resd[:num_data] = f-f0
for ii in range(num_parm):
if lb[ii] is not None:
resd[num_data+ii]=weight*np.max([-(p[ii]-lb[ii]),0.,(p[ii]-ub[ii])])
return resd