def calc_pk_integrated_intensities(p, x, pktype, num_pks):
"""
Calculate the area under the curve (integrated intensities) for fit peaks.
Parameters
----------
p : array_like
(m x u + v) peak parameters for u peaks, m is the number of
parameters per peak:
"gaussian" and "lorentzian" = 3;
"pvoigt" = 4;
"split_pvoigt" = 5.
v is the number of parameters for chosen bgtype
x : array_like
(n, ) ndarray of abcissa coordinates.
pktype : str
type of analytic function that will be used to fit the data; current
options are "gaussian", "gaussian_rot" (gaussian with arbitrary axes)
and "split_pvoigt_rot" (split psuedo voigt with arbitrary axes).
num_pks : int
the number of peaks in the specified interval.
Returns
-------
ints : array_like
(m, ) array of integrated intensity values.
"""
ints = np.zeros(num_pks)
if pktype == 'gaussian' or pktype == 'lorentzian':
p_fit = np.reshape(p[:3*num_pks], [num_pks, 3])
elif pktype == 'pvoigt':
p_fit = np.reshape(p[:4*num_pks], [num_pks, 4])
elif pktype == 'split_pvoigt':
p_fit = np.reshape(p[:6*num_pks], [num_pks, 6])
for ii in np.arange(num_pks):
if pktype == 'gaussian':
ints[ii] = integrate.simps(
pkfuncs._gaussian1d_no_bg(p_fit[ii], x),
x
)
elif pktype == 'lorentzian':
ints[ii] = integrate.simps(
pkfuncs._lorentzian1d_no_bg(p_fit[ii], x),
x
)
elif pktype == 'pvoigt':
ints[ii] = integrate.simps(
pkfuncs._pvoigt1d_no_bg(p_fit[ii], x),
x
)
elif pktype == 'split_pvoigt':
ints[ii] = integrate.simps(
pkfuncs._split_pvoigt1d_no_bg(p_fit[ii], x),
x
)
return ints
def fit_pk_obj_1d_mpeak(p, x, f0, pktype, num_pks):
f = np.zeros(len(x))
p = np.reshape(p, [num_pks, p.shape[0]/num_pks])
for ii in np.arange(num_pks):
if pktype == 'gaussian':
f += pkfuncs._gaussian1d_no_bg(p[ii], x)
elif pktype == 'lorentzian':
f += pkfuncs._lorentzian1d_no_bg(p[ii], x)
elif pktype == 'pvoigt':
f += pkfuncs._pvoigt1d_no_bg(p[ii], x)
elif pktype == 'split_pvoigt':
f += pkfuncs._split_pvoigt1d_no_bg(p[ii], x)
return f - f0
def fit_pk_obj_1d_mpeak(p,x,f0,pktype,num_pks):
f=np.zeros(len(x))
p=np.reshape(p,[num_pks,p.shape[0]/num_pks])
for ii in np.arange(num_pks):
if pktype == 'gaussian':
f=f+pkfuncs._gaussian1d_no_bg(p[ii],x)
elif pktype == 'lorentzian':
f=f+pkfuncs._lorentzian1d_no_bg(p[ii],x)
elif pktype == 'pvoigt':
f=f+pkfuncs._pvoigt1d_no_bg(p[ii],x)
elif pktype == 'split_pvoigt':
f=f+pkfuncs._split_pvoigt1d_no_bg(p[ii],x)
resd = f-f0
return resd
def calc_pk_integrated_intensities(p, x, pktype, num_pks):
"""
Calculates the area under the curve (integrated intensities) for fit peaks
Required Arguments:
p -- (m x u + v) peak parameters for number of peaks, m is the number of
parameters per peak ("gaussian" and "lorentzian" - 3, "pvoigt" - 4, "split_pvoigt"
- 5), v is the number of parameters for chosen bgtype
x -- (n) ndarray of coordinate positions
f -- (n) ndarray of intensity measurements at coordinate positions x
pktype -- string, type of analytic function that will be used to fit the data,
current options are "gaussian","lorentzian","pvoigt" (psuedo voigt), and
"split_pvoigt" (split psuedo voigt)
num_pks -- integer 'u' indicating the number of pks, must match length of p
Outputs:
ints -- (m) integrated intensities for m fit peaks
"""
ints = np.zeros(num_pks)
if pktype == 'gaussian' or pktype == 'lorentzian':
p_fit = np.reshape(p[:3 * num_pks], [num_pks, 3])
elif pktype == 'pvoigt':
p_fit = np.reshape(p[:4 * num_pks], [num_pks, 4])
elif pktype == 'split_pvoigt':
p_fit = np.reshape(p[:6 * num_pks], [num_pks, 6])
for ii in np.arange(num_pks):
if pktype == 'gaussian':
ints[ii] = integrate.simps(pkfuncs._gaussian1d_no_bg(p_fit[ii], x),
x)
elif pktype == 'lorentzian':
ints[ii] = integrate.simps(
pkfuncs._lorentzian1d_no_bg(p_fit[ii], x), x)
elif pktype == 'pvoigt':
ints[ii] = integrate.simps(pkfuncs._pvoigt1d_no_bg(p_fit[ii], x),
x)
elif pktype == 'split_pvoigt':
ints[ii] = integrate.simps(
pkfuncs._split_pvoigt1d_no_bg(p_fit[ii], x), x)
return ints
def calc_pk_integrated_intensities(p,x,pktype,num_pks):
"""
Calculates the area under the curve (integrated intensities) for fit peaks
Required Arguments:
p -- (m x u + v) peak parameters for number of peaks, m is the number of
parameters per peak ("gaussian" and "lorentzian" - 3, "pvoigt" - 4, "split_pvoigt"
- 5), v is the number of parameters for chosen bgtype
x -- (n) ndarray of coordinate positions
f -- (n) ndarray of intensity measurements at coordinate positions x
pktype -- string, type of analytic function that will be used to fit the data,
current options are "gaussian","lorentzian","pvoigt" (psuedo voigt), and
"split_pvoigt" (split psuedo voigt)
num_pks -- integer 'u' indicating the number of pks, must match length of p
Outputs:
ints -- (m) integrated intensities for m fit peaks
"""
ints=np.zeros(num_pks)
if pktype == 'gaussian' or pktype == 'lorentzian':
p_fit=np.reshape(p[:3*num_pks],[num_pks,3])
elif pktype == 'pvoigt':
p_fit=np.reshape(p[:4*num_pks],[num_pks,4])
elif pktype == 'split_pvoigt':
p_fit=np.reshape(p[:6*num_pks],[num_pks,6])
for ii in np.arange(num_pks):
if pktype == 'gaussian':
ints[ii]=integrate.simps(pkfuncs._gaussian1d_no_bg(p_fit[ii],x),x)
elif pktype == 'lorentzian':
ints[ii]=integrate.simps(pkfuncs._lorentzian1d_no_bg(p_fit[ii],x),x)
elif pktype == 'pvoigt':
ints[ii]=integrate.simps(pkfuncs._pvoigt1d_no_bg(p_fit[ii],x),x)
elif pktype == 'split_pvoigt':
ints[ii]=integrate.simps(pkfuncs._split_pvoigt1d_no_bg(p_fit[ii],x),x)
return ints