latools/D_obj.py
def autorange(self, analyte='total_counts', gwin=5, swin=3, win=30,
                  on_mult=[1., 1.], off_mult=[1., 1.5],
                  ploterrs=True, transform='log', **kwargs):
        """
        Automatically separates signal and background data regions.

        Automatically detect signal and background regions in the laser
        data, based on the behaviour of a single analyte. The analyte used
        should be abundant and homogenous in the sample.

        **Step 1: Thresholding.**
        The background signal is determined using a gaussian kernel density
        estimator (kde) of all the data. Under normal circumstances, this
        kde should find two distinct data distributions, corresponding to
        'signal' and 'background'. The minima between these two distributions
        is taken as a rough threshold to identify signal and background
        regions. Any point where the trace crosses this thrshold is identified
        as a 'transition'.

        **Step 2: Transition Removal.**
        The width of the transition regions between signal and background are
        then determined, and the transitions are excluded from analysis. The
        width of the transitions is determined by fitting a gaussian to the
        smoothed first derivative of the analyte trace, and determining its
        width at a point where the gaussian intensity is at at `conf` time the
        gaussian maximum. These gaussians are fit to subsets of the data
        centered around the transitions regions determined in Step 1, +/- `win`
        data points. The peak is further isolated by finding the minima and
        maxima of a second derivative within this window, and the gaussian is
        fit to the isolated peak.

        Parameters
        ----------
        analyte : str
            The analyte that autorange should consider. For best results,
            choose an analyte that is present homogeneously in high
            concentrations.
        gwin : int
            The smoothing window used for calculating the first derivative.
            Must be odd.
        win : int
            Determines the width (c +/- win) of the transition data subsets.
        on_mult and off_mult : tuple, len=2
            Factors to control the width of the excluded transition regions.
            A region n times the full - width - half - maximum of the transition
            gradient will be removed either side of the transition center.
            `on_mult` and `off_mult` refer to the laser - on and laser - off
            transitions, respectively. See manual for full explanation.
            Defaults to (1.5, 1) and (1, 1.5).


        Returns
        -------
        Outputs added as instance attributes. Returns None.
        bkg, sig, trn : iterable, bool
            Boolean arrays identifying background, signal and transision
            regions
        bkgrng, sigrng and trnrng : iterable
            (min, max) pairs identifying the boundaries of contiguous
            True regions in the boolean arrays.
        """
        if analyte is None:
            # sig = self.focus[self.internal_standard]
            sig = self.data['total_counts']
        elif analyte == 'total_counts':
            sig = self.data['total_counts']
        elif analyte in self.analytes:
            sig = self.focus[analyte]
        else:
            raise ValueError('Invalid analyte.')

        (self.bkg, self.sig,
         self.trn, failed) = proc.autorange(self.Time, sig, gwin=gwin, swin=swin, win=win,
                                            on_mult=on_mult, off_mult=off_mult,
                                            transform=transform)

        self.mkrngs()

        errs_to_plot = False
        if len(failed) > 0:
            errs_to_plot = True
            plotlines = []
            for f in failed:
                if f != self.Time[-1]:
                    plotlines.append(f)
            # warnings.warn(("\n\nSample {:s}: ".format(self.sample) +
            #                "Transition identification at " +
            #                "{:.1f} failed.".format(f) +
            #                "\n  **This is not necessarily a problem**"
            #                "\nBut please check the data plots and make sure " +
            #                "everything is OK.\n"))

        if ploterrs and errs_to_plot and len(plotlines) > 0:
            f, ax = self.tplot(ranges=True)
            for pl in plotlines:
                ax.axvline(pl, c='r', alpha=0.6, lw=3, ls='dashed')
            return f, plotlines
        else:
            return
def autorange(self, analyte='total_counts', gwin=5, swin=3, win=30,
                  on_mult=[1., 1.], off_mult=[1., 1.5],
                  ploterrs=True, transform='log', **kwargs):
        """
        Automatically separates signal and background data regions.

        Automatically detect signal and background regions in the laser
        data, based on the behaviour of a single analyte. The analyte used
        should be abundant and homogenous in the sample.

        **Step 1: Thresholding.**
        The background signal is determined using a gaussian kernel density
        estimator (kde) of all the data. Under normal circumstances, this
        kde should find two distinct data distributions, corresponding to
        'signal' and 'background'. The minima between these two distributions
        is taken as a rough threshold to identify signal and background
        regions. Any point where the trace crosses this thrshold is identified
        as a 'transition'.

        **Step 2: Transition Removal.**
        The width of the transition regions between signal and background are
        then determined, and the transitions are excluded from analysis. The
        width of the transitions is determined by fitting a gaussian to the
        smoothed first derivative of the analyte trace, and determining its
        width at a point where the gaussian intensity is at at `conf` time the
        gaussian maximum. These gaussians are fit to subsets of the data
        centered around the transitions regions determined in Step 1, +/- `win`
        data points. The peak is further isolated by finding the minima and
        maxima of a second derivative within this window, and the gaussian is
        fit to the isolated peak.

        Parameters
        ----------
        analyte : str
            The analyte that autorange should consider. For best results,
            choose an analyte that is present homogeneously in high
            concentrations.
        gwin : int
            The smoothing window used for calculating the first derivative.
            Must be odd.
        win : int
            Determines the width (c +/- win) of the transition data subsets.
        on_mult and off_mult : tuple, len=2
            Factors to control the width of the excluded transition regions.
            A region n times the full - width - half - maximum of the transition
            gradient will be removed either side of the transition center.
            `on_mult` and `off_mult` refer to the laser - on and laser - off
            transitions, respectively. See manual for full explanation.
            Defaults to (1.5, 1) and (1, 1.5).


        Returns
        -------
        Outputs added as instance attributes. Returns None.
        bkg, sig, trn : iterable, bool
            Boolean arrays identifying background, signal and transision
            regions
        bkgrng, sigrng and trnrng : iterable
            (min, max) pairs identifying the boundaries of contiguous
            True regions in the boolean arrays.
        """
        if analyte is None:
            # sig = self.focus[self.internal_standard]
            sig = self.data['total_counts']
        elif analyte == 'total_counts':
            sig = self.data['total_counts']
        elif analyte in self.analytes:
            sig = self.focus[analyte]
        else:
            raise ValueError('Invalid analyte.')

        (self.bkg, self.sig,
         self.trn, failed) = proc.autorange(self.Time, sig, gwin=gwin, swin=swin, win=win,
                                            on_mult=on_mult, off_mult=off_mult,
                                            transform=transform)

        self.mkrngs()

        errs_to_plot = False
        if len(failed) > 0:
            errs_to_plot = True
            plotlines = []
            for f in failed:
                if f != self.Time[-1]:
                    plotlines.append(f)
            # warnings.warn(("\n\nSample {:s}: ".format(self.sample) +
            #                "Transition identification at " +
            #                "{:.1f} failed.".format(f) +
            #                "\n  **This is not necessarily a problem**"
            #                "\nBut please check the data plots and make sure " +
            #                "everything is OK.\n"))

        if ploterrs and errs_to_plot and len(plotlines) > 0:
            f, ax = self.tplot(ranges=True)
            for pl in plotlines:
                ax.axvline(pl, c='r', alpha=0.6, lw=3, ls='dashed')
            return f, plotlines
        else:
            return
def autorange ( self , analyte = 'total_counts' , gwin = 5 , swin = 3 , win = 30 , on_mult = [ 1. , 1. ] , off_mult = [ 1. , 1.5 ] , ploterrs = True , transform = 'log' , * * kwargs ) : if analyte is None : # sig = self.focus[self.internal_standard] sig = self . data [ 'total_counts' ] elif analyte == 'total_counts' : sig = self . data [ 'total_counts' ] elif analyte in self . analytes : sig = self . focus [ analyte ] else : raise ValueError ( 'Invalid analyte.' ) ( self . bkg , self . sig , self . trn , failed ) = proc . autorange ( self . Time , sig , gwin = gwin , swin = swin , win = win , on_mult = on_mult , off_mult = off_mult , transform = transform ) self . mkrngs ( ) errs_to_plot = False if len ( failed ) > 0 : errs_to_plot = True plotlines = [ ] for f in failed : if f != self . Time [ - 1 ] : plotlines . append ( f ) # warnings.warn(("\n\nSample {:s}: ".format(self.sample) + #                "Transition identification at " + #                "{:.1f} failed.".format(f) + #                "\n  **This is not necessarily a problem**" #                "\nBut please check the data plots and make sure " + #                "everything is OK.\n")) if ploterrs and errs_to_plot and len ( plotlines ) > 0 : f , ax = self . tplot ( ranges = True ) for pl in plotlines : ax . axvline ( pl , c = 'r' , alpha = 0.6 , lw = 3 , ls = 'dashed' ) return f , plotlines else : return
Automatically separates signal and background data regions.

        Automatically detect signal and background regions in the laser
        data, based on the behaviour of a single analyte. The analyte used
        should be abundant and homogenous in the sample.

        **Step 1: Thresholding.**
        The background signal is determined using a gaussian kernel density
        estimator (kde) of all the data. Under normal circumstances, this
        kde should find two distinct data distributions, corresponding to
        'signal' and 'background'. The minima between these two distributions
        is taken as a rough threshold to identify signal and background
        regions. Any point where the trace crosses this thrshold is identified
        as a 'transition'.

        **Step 2: Transition Removal.**
        The width of the transition regions between signal and background are
        then determined, and the transitions are excluded from analysis. The
        width of the transitions is determined by fitting a gaussian to the
        smoothed first derivative of the analyte trace, and determining its
        width at a point where the gaussian intensity is at at `conf` time the
        gaussian maximum. These gaussians are fit to subsets of the data
        centered around the transitions regions determined in Step 1, +/- `win`
        data points. The peak is further isolated by finding the minima and
        maxima of a second derivative within this window, and the gaussian is
        fit to the isolated peak.

        Parameters
        ----------
        analyte : str
            The analyte that autorange should consider. For best results,
            choose an analyte that is present homogeneously in high
            concentrations.
        gwin : int
            The smoothing window used for calculating the first derivative.
            Must be odd.
        win : int
            Determines the width (c +/- win) of the transition data subsets.
        on_mult and off_mult : tuple, len=2
            Factors to control the width of the excluded transition regions.
            A region n times the full - width - half - maximum of the transition
            gradient will be removed either side of the transition center.
            `on_mult` and `off_mult` refer to the laser - on and laser - off
            transitions, respectively. See manual for full explanation.
            Defaults to (1.5, 1) and (1, 1.5).


        Returns
        -------
        Outputs added as instance attributes. Returns None.
        bkg, sig, trn : iterable, bool
            Boolean arrays identifying background, signal and transision
            regions
        bkgrng, sigrng and trnrng : iterable
            (min, max) pairs identifying the boundaries of contiguous
            True regions in the boolean arrays.
Automatically separates signal and background data regions .