latools/latools.py
def filter_threshold(self, analyte, threshold,
                         samples=None, subset=None):
        """
        Applies a threshold filter to the data.

        Generates two filters above and below the threshold value for a
        given analyte.

        Parameters
        ----------
        analyte : str
            The analyte that the filter applies to.
        threshold : float
            The threshold value.
        filt : bool
            Whether or not to apply existing filters to the data before
            calculating this filter.
        samples : array_like or None
            Which samples to apply this filter to. If None, applies to all
            samples.
        subset : str or number
            The subset of samples (defined by make_subset) you want to apply
            the filter to.

        Returns
        -------
        None
        """
        if samples is not None:
            subset = self.make_subset(samples)

        samples = self._get_samples(subset)

        self.minimal_analytes.update([analyte])

        with self.pbar.set(total=len(samples), desc='Threshold Filter') as prog:
            for s in samples:
                self.data[s].filter_threshold(analyte, threshold)
                prog.update()
def filter_threshold(self, analyte, threshold,
                         samples=None, subset=None):
        """
        Applies a threshold filter to the data.

        Generates two filters above and below the threshold value for a
        given analyte.

        Parameters
        ----------
        analyte : str
            The analyte that the filter applies to.
        threshold : float
            The threshold value.
        filt : bool
            Whether or not to apply existing filters to the data before
            calculating this filter.
        samples : array_like or None
            Which samples to apply this filter to. If None, applies to all
            samples.
        subset : str or number
            The subset of samples (defined by make_subset) you want to apply
            the filter to.

        Returns
        -------
        None
        """
        if samples is not None:
            subset = self.make_subset(samples)

        samples = self._get_samples(subset)

        self.minimal_analytes.update([analyte])

        with self.pbar.set(total=len(samples), desc='Threshold Filter') as prog:
            for s in samples:
                self.data[s].filter_threshold(analyte, threshold)
                prog.update()
def filter_threshold ( self , analyte , threshold , samples = None , subset = None ) : if samples is not None : subset = self . make_subset ( samples ) samples = self . _get_samples ( subset ) self . minimal_analytes . update ( [ analyte ] ) with self . pbar . set ( total = len ( samples ) , desc = 'Threshold Filter' ) as prog : for s in samples : self . data [ s ] . filter_threshold ( analyte , threshold ) prog . update ( )
Applies a threshold filter to the data.

        Generates two filters above and below the threshold value for a
        given analyte.

        Parameters
        ----------
        analyte : str
            The analyte that the filter applies to.
        threshold : float
            The threshold value.
        filt : bool
            Whether or not to apply existing filters to the data before
            calculating this filter.
        samples : array_like or None
            Which samples to apply this filter to. If None, applies to all
            samples.
        subset : str or number
            The subset of samples (defined by make_subset) you want to apply
            the filter to.

        Returns
        -------
        None
Applies a threshold filter to the data .