def fit_water(self,
line_broadening=5,
zerofill=100,
filt_method=None,
min_ppm=-5.0,
max_ppm=5.0):
"""
"""
# Get the water spectrum as well:
f_hz, w_spectra = ana.get_spectra(self.water_fid,
line_broadening=line_broadening,
zerofill=zerofill,
filt_method=filt_method)
f_ppm = ut.freq_to_ppm(f_hz)
# Averaging across echos:
self.water_spectra = np.mean(w_spectra, 1)
model, signal, params = ana.fit_lorentzian(self.water_spectra,
self.f_ppm,
lb=min_ppm,
ub=max_ppm)
# Store the params:
self.water_model = model
self.water_signal = signal
self.water_params = params
self.water_idx = ut.make_idx(self.f_ppm, min_ppm, max_ppm)
mean_params = stats.nanmean(params, 0)
self.water_auc = self._calc_auc(ut.lorentzian, params, self.water_idx)
def fit_water(self, line_broadening=5, zerofill=100,
filt_method=None, min_ppm=-5.0, max_ppm=5.0):
"""
"""
# Get the water spectrum as well:
f_hz, w_spectra = ana.get_spectra(self.water_fid,
line_broadening=line_broadening,
zerofill=zerofill,
filt_method=filt_method)
f_ppm = ut.freq_to_ppm(f_hz)
# Averaging across echos:
self.water_spectra = np.mean(w_spectra, 1)
model, signal, params = ana.fit_lorentzian(self.water_spectra,
self.f_ppm,
lb=min_ppm,
ub=max_ppm)
# Store the params:
self.water_model = model
self.water_signal = signal
self.water_params = params
self.water_idx = ut.make_idx(self.f_ppm, min_ppm, max_ppm)
mean_params = stats.nanmean(params, 0)
self.water_auc = self._calc_auc(ut.lorentzian, params, self.water_idx)
def fit_naa(self, reject_outliers=3.0, fit_lb=1.8, fit_ub=2.4,
phase_correct=True):
"""
Fit a Lorentzian function to the NAA peak at ~ 2 ppm. Example of
fitting inverted peak: Foerster et al. 2013, An imbalance between
excitatory and inhibitory neurotransmitters in amyothrophic lateral
sclerosis revealed by use of 3T proton MRS
"""
model, signal, params = ana.fit_lorentzian(self.diff_spectra,
self.f_ppm,
lb=fit_lb,
ub=fit_ub)
# Store the params:
self.naa_model = model
self.naa_signal = signal
self.naa_params = params
self.naa_idx = ut.make_idx(self.f_ppm, fit_lb, fit_ub)
mean_params = stats.nanmean(params, 0)
self.naa_auc = self._calc_auc(ut.lorentzian, params, self.naa_idx)
def fit_naa(self, reject_outliers=3.0, fit_lb=1.8, fit_ub=2.4,
phase_correct=True):
"""
Fit a Lorentzian function to the NAA peak at ~ 2 ppm. Example of
fitting inverted peak: Foerster et al. 2013, An imbalance between
excitatory and inhibitory neurotransmitters in amyothrophic lateral
sclerosis revealed by use of 3T proton MRS
"""
model, signal, params = ana.fit_lorentzian(self.diff_spectra,
self.f_ppm,
lb=fit_lb,
ub=fit_ub)
# Store the params:
self.naa_model = model
self.naa_signal = signal
self.naa_params = params
self.naa_idx = ut.make_idx(self.f_ppm, fit_lb, fit_ub)
mean_params = stats.nanmean(params, 0)
self.naa_auc = self._calc_auc(ut.lorentzian, params, self.naa_idx)
