def _do_two_gaussian_fit(freqs, signal, bounds=None):
"""
Helper function for the two gaussian fit
"""
initial = _two_func_initializer(freqs, signal)
# Edit out the ones we want in the order we want them:
initial = (initial[0], initial[1],
initial[6], initial[7],
initial[2], initial[3],
initial[10], initial[11])
# We want to preferntially weight the error on estimating the height of the
# individual peaks, so we formulate an error-weighting function based on
# these peaks, which is simply a two-gaussian bumpety-bump:
w = (ut.gaussian(freqs, initial[0], 0.075, 1, 0, 0) +
ut.gaussian(freqs, initial[1], 0.075, 1, 0, 0))
# Further, we want to also optimize on the individual gaussians error, to
# restrict the fit space a bit more. For this purpose, we will pass a list
# of gaussians with indices into the parameter list, so that we can do
# that (see mopt.err_func for the mechanics).
func_list = [[ut.gaussian, [0,2,4,6,7],
ut.gaussian(freqs, initial[0], 0.075, 1, 0, 0)],
[ut.gaussian, [1,3,5,6,7],
ut.gaussian(freqs, initial[1], 0.075, 1, 0, 0)]]
params, _ = lsq.leastsqbound(mopt.err_func, initial,
args=(freqs, np.real(signal),
ut.two_gaussian, w, func_list),
bounds=bounds)
return params
def _do_two_gaussian_fit(freqs, signal, bounds=None):
"""
Helper function for the two gaussian fit
"""
initial = _two_func_initializer(freqs, signal)
# Edit out the ones we want in the order we want them:
initial = (initial[0], initial[1], initial[6], initial[7], initial[2],
initial[3], initial[10], initial[11])
# We want to preferntially weight the error on estimating the height of the
# individual peaks, so we formulate an error-weighting function based on
# these peaks, which is simply a two-gaussian bumpety-bump:
w = (ut.gaussian(freqs, initial[0], 0.075, 1, 0, 0) +
ut.gaussian(freqs, initial[1], 0.075, 1, 0, 0))
# Further, we want to also optimize on the individual gaussians error, to
# restrict the fit space a bit more. For this purpose, we will pass a list
# of gaussians with indices into the parameter list, so that we can do
# that (see mopt.err_func for the mechanics).
func_list = [[
ut.gaussian, [0, 2, 4, 6, 7],
ut.gaussian(freqs, initial[0], 0.075, 1, 0, 0)
],
[
ut.gaussian, [1, 3, 5, 6, 7],
ut.gaussian(freqs, initial[1], 0.075, 1, 0, 0)
]]
params, _ = lsq.leastsqbound(mopt.err_func,
initial,
args=(freqs, np.real(signal), ut.two_gaussian,
w, func_list),
bounds=bounds)
return params
def fit_glx2(self,
reject_outliers=3.0,
fit_lb=3.6,
fit_ub=3.9,
phase_correct=True,
scalefit=False):
"""
Parameters
----------
reject_outliers : float or bool
If set to a float, this is the z score threshold for rejection (on
any of the parameters). If set to False, no outlier rejection
fit_lb, fit_ub : float
What part of the spectrum (in ppm) contains the creatine peak.
Default (3.5, 4.2)
scalefit : boolean
If this is set to true, attempt is made to tighten the fit to the
peak with a second round of fitting where the fitted curve
is fit with a scale factor. (default false)
"""
if not hasattr(self, 'creatine_params'):
self.fit_creatine()
fit_spectra = self.diff_spectra
# We fit a two-gaussian function to this entire chunk of the spectrum,
# to catch both glx peaks
model, signal, params = ana.fit_two_gaussian(fit_spectra,
self.f_ppm,
lb=fit_lb,
ub=fit_ub)
# Use an array of ones to index everything but the outliers and nans:
ii = np.ones(signal.shape[0], dtype=bool)
# Reject outliers:
if reject_outliers:
model, signal, params, ii = self._outlier_rejection(
params, model, signal, ii)
# We'll keep around a private attribute to tell us which transients
# were good:
self._glx2_transients = np.where(ii)
# Now we separate params of the two glx peaks from each other
# (remember that they both share offset and drift!):
self.glxp1_params = params[:, (0, 2, 4, 6, 7)]
self.glxp2_params = params[:, (1, 3, 5, 6, 7)]
self.glx2_idx = ut.make_idx(self.f_ppm, fit_lb, fit_ub)
# We'll need to generate the model predictions from these parameters,
# because what we're holding in 'model' is for both together:
self.glxp1_model = np.zeros(
(self.glxp1_params.shape[0],
np.abs(self.glx2_idx.stop - self.glx2_idx.start)))
self.glxp2_model = np.zeros(
(self.glxp2_params.shape[0],
np.abs(self.glx2_idx.stop - self.glx2_idx.start)))
for idx in range(self.glxp2_params.shape[0]):
self.glxp2_model[idx] = ut.gaussian(self.f_ppm[self.glx2_idx],
*self.glxp2_params[idx])
self.glxp1_model[idx] = ut.gaussian(self.f_ppm[self.glx2_idx],
*self.glxp1_params[idx])
if scalefit:
combinedmodel = self.glxp2_model + self.glxp1_model
scalefac, scalemodel = ana._do_scale_fit(self.f_ppm[self.glx2_idx],
signal, combinedmodel)
# Reject outliers:
scalemodel, signal, params, ii = self._rm_outlier_by_amp(
params, scalemodel, signal, ii)
self.glx2_model = scalemodel
else:
self.glx2_model = self.glxp1_model + self.glxp2_model
self.glx2_signal = signal
self.glx2_auc = (
self._calc_auc(ut.gaussian, self.glxp2_params, self.glx2_idx) +
self._calc_auc(ut.gaussian, self.glxp1_params, self.glx2_idx))
def fit_glx2(self, reject_outliers=3.0, fit_lb=3.6, fit_ub=3.9,
phase_correct=True, scalefit=False):
"""
Parameters
----------
reject_outliers : float or bool
If set to a float, this is the z score threshold for rejection (on
any of the parameters). If set to False, no outlier rejection
fit_lb, fit_ub : float
What part of the spectrum (in ppm) contains the creatine peak.
Default (3.5, 4.2)
scalefit : boolean
If this is set to true, attempt is made to tighten the fit to the
peak with a second round of fitting where the fitted curve
is fit with a scale factor. (default false)
"""
if not hasattr(self, 'creatine_params'):
self.fit_creatine()
fit_spectra = self.diff_spectra
# We fit a two-gaussian function to this entire chunk of the spectrum,
# to catch both glx peaks
model, signal, params = ana.fit_two_gaussian(fit_spectra,
self.f_ppm,
lb=fit_lb,
ub=fit_ub)
# Use an array of ones to index everything but the outliers and nans:
ii = np.ones(signal.shape[0], dtype=bool)
# Reject outliers:
if reject_outliers:
model, signal, params, ii = self._outlier_rejection(params,
model,
signal,
ii)
# We'll keep around a private attribute to tell us which transients
# were good:
self._glx2_transients = np.where(ii)
# Now we separate params of the two glx peaks from each other
# (remember that they both share offset and drift!):
self.glxp1_params = params[:, (0, 2, 4, 6, 7)]
self.glxp2_params = params[:, (1, 3, 5, 6, 7)]
self.glx2_idx = ut.make_idx(self.f_ppm, fit_lb, fit_ub)
# We'll need to generate the model predictions from these parameters,
# because what we're holding in 'model' is for both together:
self.glxp1_model = np.zeros((self.glxp1_params.shape[0],
np.abs(self.glx2_idx.stop-self.glx2_idx.start)))
self.glxp2_model = np.zeros((self.glxp2_params.shape[0],
np.abs(self.glx2_idx.stop-self.glx2_idx.start)))
for idx in range(self.glxp2_params.shape[0]):
self.glxp2_model[idx] = ut.gaussian(self.f_ppm[self.glx2_idx],
*self.glxp2_params[idx])
self.glxp1_model[idx] = ut.gaussian(self.f_ppm[self.glx2_idx],
*self.glxp1_params[idx])
if scalefit:
combinedmodel = self.glxp2_model + self.glxp1_model
scalefac, scalemodel = ana._do_scale_fit(
self.f_ppm[self.glx2_idx], signal,combinedmodel)
# Reject outliers:
scalemodel, signal, params, ii = self._rm_outlier_by_amp(params,
scalemodel,
signal,
ii)
self.glx2_model = scalemodel
else:
self.glx2_model = self.glxp1_model + self.glxp2_model
self.glx2_signal = signal
self.glx2_auc = (
self._calc_auc(ut.gaussian, self.glxp2_params, self.glx2_idx) +
self._calc_auc(ut.gaussian, self.glxp1_params, self.glx2_idx))
