latools/helpers/plot.py
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
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