예제 #1
0
def test_phase_correct_zero():
    """
    Test that phase correction works 
    """
    # Make some complex numbers, with the same phase (pi):
    arr = np.random.rand(10,10,10,1024) * np.exp(1j * np.pi)
    corr_arr = ut.phase_correct_zero(arr, -np.pi)
    npt.assert_array_almost_equal(np.angle(corr_arr), 0)
예제 #2
0
파일: mrs-gui.py 프로젝트: arokem/MRS-old
    def on_show(self):
        self.axes.clear()
        self.axes.set_xlabel('ppm')
        self.axes.grid(True)
        has_series = False
        self.data.get_spectra(self.line_spin.value(), self.cutoff_spin.value())

        for row in range(self.series_list_model.rowCount()):
            model_index = self.series_list_model.index(row, 0)
            checked = self.series_list_model.data(
                model_index, QtCore.Qt.CheckStateRole) == QtCore.Qt.Checked
            name = str(self.series_list_model.data(model_index))

            if checked:
                has_series = True
                phase_zero = self.phase_spin.value()
                series = self.data.get_series_data(name)
                series = ut.phase_correct_zero(series, phase_zero)
                self.axes.plot(self.data.f_ppm, series, label=name)

        if has_series and self.legend_cb.isChecked():
            self.axes.legend()
        self.canvas.draw()
예제 #3
0
    def fit_gaba(self,
                 reject_outliers=3.0,
                 fit_lb=2.8,
                 fit_ub=3.4,
                 phase_correct=True,
                 fit_func=None):
        """
        Fit either a single Gaussian, or a two-Gaussian to the GABA 3 PPM
        peak.

        Parameters
        ----------
        reject_outliers : float
            Z-score criterion for rejection of outliers, based on their model
            parameter

        fit_lb, fit_ub : float
            Frequency bounds (in ppm) for the region of the spectrum to be
            fit.

        phase_correct : bool
            Where to perform zero-order phase correction based on the fit of
            the creatine peaks in the sum spectra

        fit_func : None or callable (default None).
            If this is set to `False`, an automatic selection will take place,
            choosing between a two-Gaussian and a single Gaussian, based on a
            split-half cross-validation procedure. Otherwise, the requested
            callable function will be fit. Needs to conform to the conventions
            of `fit_gaussian`/`fit_two_gaussian` and
            `ut.gaussian`/`ut.two_gaussian`.

        """
        # We need to fit the creatine, so that we know which transients to
        # exclude in fitting this peak:
        if not hasattr(self, 'creatine_params'):
            self.fit_creatine()

        fit_spectra = np.ones(self.diff_spectra.shape) * np.nan
        # Silence warnings:
        with warnings.catch_warnings():
            warnings.simplefilter("ignore")
            fit_spectra =\
                self.diff_spectra[self._cr_transients].copy()

        if phase_correct:
            for ii, this_spec in enumerate(fit_spectra):
                # Silence warnings:
                with warnings.catch_warnings():
                    warnings.simplefilter("ignore")
                    fit_spectra[ii] = ut.phase_correct_zero(
                        this_spec,
                        self.creatine_params[self._cr_transients][ii, 3])

        if fit_func is None:
            # Cross-validate!
            fitter, self.gaba_func, self.gaba_model_err, self.gaba_signal_err=\
                self._xval_choose_funcs(fit_spectra,
                                        reject_outliers,
                                        fit_lb, fit_ub)
        # Otherwise, you had better supply a couple of callables that can be
        # used to fit these spectra!
        else:
            fitter = fit_func[0]
            self.gaba_func = fit_func[1]
            self.gaba_model_err, self.gaba_signal_err = \
                self._xval_model_error(fit_spectra, reject_outliers,
                                       fit_lb, fit_ub, fitter, self.gaba_func)
        # Either way, we end up fitting to everything in the end:
        choose_transients, model, signal, params, this_idx = self._fit_helper(
            fit_spectra, reject_outliers, fit_lb, fit_ub, fitter)

        self._gaba_transients = choose_transients
        self.gaba_model = model
        self.gaba_signal = signal
        self.gaba_params = params
        self.gaba_idx = this_idx
        mean_params = stats.nanmean(params, 0)

        self.gaba_auc = self._calc_auc(self.gaba_func, params, self.gaba_idx)
예제 #4
0
파일: api.py 프로젝트: amadeuskanaan/MRS
    def fit_gaba(self, reject_outliers=3.0, fit_lb=2.8, fit_ub=3.4,
                 phase_correct=True, fit_func=None):
        """
        Fit either a single Gaussian, or a two-Gaussian to the GABA 3 PPM
        peak.

        Parameters
        ----------
        reject_outliers : float
            Z-score criterion for rejection of outliers, based on their model
            parameter

        fit_lb, fit_ub : float
            Frequency bounds (in ppm) for the region of the spectrum to be
            fit.

        phase_correct : bool
            Where to perform zero-order phase correction based on the fit of
            the creatine peaks in the sum spectra

        fit_func : None or callable (default None).
            If this is set to `False`, an automatic selection will take place,
            choosing between a two-Gaussian and a single Gaussian, based on a
            split-half cross-validation procedure. Otherwise, the requested
            callable function will be fit. Needs to conform to the conventions
            of `fit_gaussian`/`fit_two_gaussian` and
            `ut.gaussian`/`ut.two_gaussian`.

        """
        # We need to fit the creatine, so that we know which transients to
        # exclude in fitting this peak:
        if not hasattr(self, 'creatine_params'):
            self.fit_creatine()

        fit_spectra = np.ones(self.diff_spectra.shape) * np.nan
        # Silence warnings:
        with warnings.catch_warnings():
            warnings.simplefilter("ignore")
            fit_spectra =\
                self.diff_spectra[self._cr_transients].copy()

        if phase_correct:
            for ii, this_spec in enumerate(fit_spectra):
                # Silence warnings:
                with warnings.catch_warnings():
                    warnings.simplefilter("ignore")
                    fit_spectra[ii] = ut.phase_correct_zero(this_spec,
                            self.creatine_params[self._cr_transients][ii, 3])

        if fit_func is None:
            # Cross-validate!
            fitter, self.gaba_func, self.gaba_model_err, self.gaba_signal_err=\
                self._xval_choose_funcs(fit_spectra,
                                        reject_outliers,
                                        fit_lb, fit_ub)
        # Otherwise, you had better supply a couple of callables that can be
        # used to fit these spectra!
        else:
            fitter = fit_func[0]
            self.gaba_func = fit_func[1]
            self.gaba_model_err, self.gaba_signal_err = \
                self._xval_model_error(fit_spectra, reject_outliers,
                                       fit_lb, fit_ub, fitter, self.gaba_func)
        # Either way, we end up fitting to everything in the end: 
        choose_transients, model, signal, params, this_idx = self._fit_helper(
                                         fit_spectra, reject_outliers,
                                         fit_lb, fit_ub, fitter)

        self._gaba_transients = choose_transients
        self.gaba_model = model
        self.gaba_signal = signal
        self.gaba_params = params
        self.gaba_idx = this_idx
        mean_params = stats.nanmean(params, 0)

        self.gaba_auc =  self._calc_auc(self.gaba_func, params, self.gaba_idx)