latools/latools.py
def apply_classifier(self, name, samples=None, subset=None):
        """
        Apply a clustering classifier based on all samples, or a subset.

        Parameters
        ----------
        name : str
            The name of the classifier to apply.
        subset : str
            The subset of samples to apply the classifier to.
        Returns
        -------
        name : str
        """
        if samples is not None:
            subset = self.make_subset(samples)

        samples = self._get_samples(subset)

        c = self.classifiers[name]
        labs = c.classifier.ulabels_

        with self.pbar.set(total=len(samples), desc='Applying ' + name + ' classifier') as prog:
            for s in samples:
                d = self.data[s]
                try:
                    f = c.predict(d.focus)
                except ValueError:
                    # in case there's no data
                    f = np.array([-2] * len(d.Time))
                for l in labs:
                    ind = f == l
                    d.filt.add(name=name + '_{:.0f}'.format(l),
                            filt=ind,
                            info=name + ' ' + c.method + ' classifier',
                            params=(c.analytes, c.method))
                prog.update()

        return name
def apply_classifier(self, name, samples=None, subset=None):
        """
        Apply a clustering classifier based on all samples, or a subset.

        Parameters
        ----------
        name : str
            The name of the classifier to apply.
        subset : str
            The subset of samples to apply the classifier to.
        Returns
        -------
        name : str
        """
        if samples is not None:
            subset = self.make_subset(samples)

        samples = self._get_samples(subset)

        c = self.classifiers[name]
        labs = c.classifier.ulabels_

        with self.pbar.set(total=len(samples), desc='Applying ' + name + ' classifier') as prog:
            for s in samples:
                d = self.data[s]
                try:
                    f = c.predict(d.focus)
                except ValueError:
                    # in case there's no data
                    f = np.array([-2] * len(d.Time))
                for l in labs:
                    ind = f == l
                    d.filt.add(name=name + '_{:.0f}'.format(l),
                            filt=ind,
                            info=name + ' ' + c.method + ' classifier',
                            params=(c.analytes, c.method))
                prog.update()

        return name
Apply a clustering classifier based on all samples, or a subset.

        Parameters
        ----------
        name : str
            The name of the classifier to apply.
        subset : str
            The subset of samples to apply the classifier to.
        Returns
        -------
        name : str