latools/latools.py
def optimisation_plots(self, overlay_alpha=0.5, samples=None, subset=None, **kwargs):
        """
        Plot the result of signal_optimise.

        `signal_optimiser` must be run first, and the output
        stored in the `opt` attribute of the latools.D object.

        Parameters
        ----------
        d : latools.D object
            A latools data object.
        overlay_alpha : float
            The opacity of the threshold overlays. Between 0 and 1.
        **kwargs
            Passed to `tplot`
        """
        if samples is not None:
            subset = self.make_subset(samples)
        samples = self._get_samples(subset)

        outdir=self.report_dir + '/optimisation_plots/'
        if not os.path.isdir(outdir):
            os.mkdir(outdir)
        
        with self.pbar.set(total=len(samples), desc='Drawing Plots') as prog:
            for s in samples:
                figs = self.data[s].optimisation_plot(overlay_alpha, **kwargs)
                
                n = 1
                for f, _ in figs:
                    if f is not None:
                        f.savefig(outdir + '/' + s + '_optim_{:.0f}.pdf'.format(n))
                        plt.close(f)
                    n += 1
                prog.update()
        return
def optimisation_plots(self, overlay_alpha=0.5, samples=None, subset=None, **kwargs):
        """
        Plot the result of signal_optimise.

        `signal_optimiser` must be run first, and the output
        stored in the `opt` attribute of the latools.D object.

        Parameters
        ----------
        d : latools.D object
            A latools data object.
        overlay_alpha : float
            The opacity of the threshold overlays. Between 0 and 1.
        **kwargs
            Passed to `tplot`
        """
        if samples is not None:
            subset = self.make_subset(samples)
        samples = self._get_samples(subset)

        outdir=self.report_dir + '/optimisation_plots/'
        if not os.path.isdir(outdir):
            os.mkdir(outdir)
        
        with self.pbar.set(total=len(samples), desc='Drawing Plots') as prog:
            for s in samples:
                figs = self.data[s].optimisation_plot(overlay_alpha, **kwargs)
                
                n = 1
                for f, _ in figs:
                    if f is not None:
                        f.savefig(outdir + '/' + s + '_optim_{:.0f}.pdf'.format(n))
                        plt.close(f)
                    n += 1
                prog.update()
        return
def optimisation_plots ( self , overlay_alpha = 0.5 , samples = None , subset = None , * * kwargs ) : if samples is not None : subset = self . make_subset ( samples ) samples = self . _get_samples ( subset ) outdir = self . report_dir + '/optimisation_plots/' if not os . path . isdir ( outdir ) : os . mkdir ( outdir ) with self . pbar . set ( total = len ( samples ) , desc = 'Drawing Plots' ) as prog : for s in samples : figs = self . data [ s ] . optimisation_plot ( overlay_alpha , * * kwargs ) n = 1 for f , _ in figs : if f is not None : f . savefig ( outdir + '/' + s + '_optim_{:.0f}.pdf' . format ( n ) ) plt . close ( f ) n += 1 prog . update ( ) return
Plot the result of signal_optimise.

        `signal_optimiser` must be run first, and the output
        stored in the `opt` attribute of the latools.D object.

        Parameters
        ----------
        d : latools.D object
            A latools data object.
        overlay_alpha : float
            The opacity of the threshold overlays. Between 0 and 1.
        **kwargs
            Passed to `tplot`
Plot the result of signal_optimise .