def gplot(self, analytes=None, win=25, figsize=[10, 4],
              ranges=False, focus_stage=None, ax=None, recalc=True):
        """
        Plot analytes gradients as a function of Time.

        Parameters
        ----------
        analytes : array_like
            list of strings containing names of analytes to plot.
            None = all analytes.
        win : int
            The window over which to calculate the rolling gradient.
        figsize : tuple
            size of final figure.
        ranges : bool
            show signal/background regions.

        Returns
        -------
        figure, axis
        """

        if type(analytes) is str:
            analytes = [analytes]
        if analytes is None:
            analytes = self.analytes

        if focus_stage is None:
            focus_stage = self.focus_stage

        if ax is None:
            fig = plt.figure(figsize=figsize)
            ax = fig.add_axes([.1, .12, .77, .8])
            ret = True
        else:
            fig = ax.figure
            ret = False

        x = self.Time
        if recalc or not self.grads_calced:
            self.grads = calc_grads(x, self.data[focus_stage], analytes, win)
            self.grads_calce = True

        for a in analytes:
            ax.plot(x, self.grads[a], color=self.cmap[a], label=a)

        if ranges:
            for lims in self.bkgrng:
                ax.axvspan(*lims, color='k', alpha=0.1, zorder=-1)
            for lims in self.sigrng:
                ax.axvspan(*lims, color='r', alpha=0.1, zorder=-1)

        ax.text(0.01, 0.99, self.sample + ' : ' + self.focus_stage + ' : gradient',
                transform=ax.transAxes,
                ha='left', va='top')

        ax.set_xlabel('Time (s)')
        ax.set_xlim(np.nanmin(x), np.nanmax(x))

        # y label
        ud = {'rawdata': 'counts/s',
              'despiked': 'counts/s',
              'bkgsub': 'background corrected counts/s',
              'ratios': 'counts/{:s} count/s',
              'calibrated': 'mol/mol {:s}/s'}
        if focus_stage in ['ratios', 'calibrated']:
            ud[focus_stage] = ud[focus_stage].format(self.internal_standard)
        ax.set_ylabel(ud[focus_stage])
        # y tick format

        def yfmt(x, p):
            return '{:.0e}'.format(x)
        ax.yaxis.set_major_formatter(mpl.ticker.FuncFormatter(yfmt))

        ax.legend(bbox_to_anchor=(1.15, 1))

        ax.axhline(0, color='k', lw=1, ls='dashed', alpha=0.5)

        if ret:
            return fig, ax
def gplot(self, analytes=None, win=25, figsize=[10, 4],
              ranges=False, focus_stage=None, ax=None, recalc=True):
        """
        Plot analytes gradients as a function of Time.

        Parameters
        ----------
        analytes : array_like
            list of strings containing names of analytes to plot.
            None = all analytes.
        win : int
            The window over which to calculate the rolling gradient.
        figsize : tuple
            size of final figure.
        ranges : bool
            show signal/background regions.

        Returns
        -------
        figure, axis
        """

        if type(analytes) is str:
            analytes = [analytes]
        if analytes is None:
            analytes = self.analytes

        if focus_stage is None:
            focus_stage = self.focus_stage

        if ax is None:
            fig = plt.figure(figsize=figsize)
            ax = fig.add_axes([.1, .12, .77, .8])
            ret = True
        else:
            fig = ax.figure
            ret = False

        x = self.Time
        if recalc or not self.grads_calced:
            self.grads = calc_grads(x, self.data[focus_stage], analytes, win)
            self.grads_calce = True

        for a in analytes:
            ax.plot(x, self.grads[a], color=self.cmap[a], label=a)

        if ranges:
            for lims in self.bkgrng:
                ax.axvspan(*lims, color='k', alpha=0.1, zorder=-1)
            for lims in self.sigrng:
                ax.axvspan(*lims, color='r', alpha=0.1, zorder=-1)

        ax.text(0.01, 0.99, self.sample + ' : ' + self.focus_stage + ' : gradient',
                transform=ax.transAxes,
                ha='left', va='top')

        ax.set_xlabel('Time (s)')
        ax.set_xlim(np.nanmin(x), np.nanmax(x))

        # y label
        ud = {'rawdata': 'counts/s',
              'despiked': 'counts/s',
              'bkgsub': 'background corrected counts/s',
              'ratios': 'counts/{:s} count/s',
              'calibrated': 'mol/mol {:s}/s'}
        if focus_stage in ['ratios', 'calibrated']:
            ud[focus_stage] = ud[focus_stage].format(self.internal_standard)
        ax.set_ylabel(ud[focus_stage])
        # y tick format

        def yfmt(x, p):
            return '{:.0e}'.format(x)
        ax.yaxis.set_major_formatter(mpl.ticker.FuncFormatter(yfmt))

        ax.legend(bbox_to_anchor=(1.15, 1))

        ax.axhline(0, color='k', lw=1, ls='dashed', alpha=0.5)

        if ret:
            return fig, ax
def gplot ( self , analytes = None , win = 25 , figsize = [ 10 , 4 ] , ranges = False , focus_stage = None , ax = None , recalc = True ) : if type ( analytes ) is str : analytes = [ analytes ] if analytes is None : analytes = self . analytes if focus_stage is None : focus_stage = self . focus_stage if ax is None : fig = plt . figure ( figsize = figsize ) ax = fig . add_axes ( [ .1 , .12 , .77 , .8 ] ) ret = True else : fig = ax . figure ret = False x = self . Time if recalc or not self . grads_calced : self . grads = calc_grads ( x , self . data [ focus_stage ] , analytes , win ) self . grads_calce = True for a in analytes : ax . plot ( x , self . grads [ a ] , color = self . cmap [ a ] , label = a ) if ranges : for lims in self . bkgrng : ax . axvspan ( * lims , color = 'k' , alpha = 0.1 , zorder = - 1 ) for lims in self . sigrng : ax . axvspan ( * lims , color = 'r' , alpha = 0.1 , zorder = - 1 ) ax . text ( 0.01 , 0.99 , self . sample + ' : ' + self . focus_stage + ' : gradient' , transform = ax . transAxes , ha = 'left' , va = 'top' ) ax . set_xlabel ( 'Time (s)' ) ax . set_xlim ( np . nanmin ( x ) , np . nanmax ( x ) ) # y label ud = { 'rawdata' : 'counts/s' , 'despiked' : 'counts/s' , 'bkgsub' : 'background corrected counts/s' , 'ratios' : 'counts/{:s} count/s' , 'calibrated' : 'mol/mol {:s}/s' } if focus_stage in [ 'ratios' , 'calibrated' ] : ud [ focus_stage ] = ud [ focus_stage ] . format ( self . internal_standard ) ax . set_ylabel ( ud [ focus_stage ] ) # y tick format def yfmt ( x , p ) : return '{:.0e}' . format ( x ) ax . yaxis . set_major_formatter ( mpl . ticker . FuncFormatter ( yfmt ) ) ax . legend ( bbox_to_anchor = ( 1.15 , 1 ) ) ax . axhline ( 0 , color = 'k' , lw = 1 , ls = 'dashed' , alpha = 0.5 ) if ret : return fig , ax
Plot analytes gradients as a function of Time.

        Parameters
        ----------
        analytes : array_like
            list of strings containing names of analytes to plot.
            None = all analytes.
        win : int
            The window over which to calculate the rolling gradient.
        figsize : tuple
            size of final figure.
        ranges : bool
            show signal/background regions.

        Returns
        -------
        figure, axis
Plot analytes gradients as a function of Time .