@_log<EOL><INDENT>def correlation_plots(self, x_analyte, y_analyte, window=<NUM_LIT:15>, filt=True, recalc=False, samples=None, subset=None, outdir=None):<DEDENT>
if outdir is None:<EOL><INDENT>outdir = self.report_dir + '<STR_LIT>'<EOL><DEDENT>if not os.path.isdir(outdir):<EOL><INDENT>os.mkdir(outdir)<EOL><DEDENT>if subset is not None:<EOL><INDENT>samples = self._get_samples(subset)<EOL><DEDENT>elif samples is None:<EOL><INDENT>samples = self.subsets['<STR_LIT>']<EOL><DEDENT>elif isinstance(samples, str):<EOL><INDENT>samples = [samples]<EOL><DEDENT>with self.pbar.set(total=len(samples), desc='<STR_LIT>') as prog:<EOL><INDENT>for s in samples:<EOL><INDENT>f, a = self.data[s].correlation_plot(x_analyte=x_analyte, y_analyte=y_analyte,<EOL>window=window, filt=filt, recalc=recalc)<EOL>f.savefig('<STR_LIT>'.format(outdir, s, x_analyte, y_analyte))<EOL>plt.close(f)<EOL>prog.update()<EOL><DEDENT><DEDENT>return<EOL>
Plot the local correlation between two analytes.

Parameters
----------
x_analyte, y_analyte : str
    The names of the x and y analytes to correlate.
window : int, None
    The rolling window used when calculating the correlation.
filt : bool
    Whether or not to apply existing filters to the data before
    calculating this filter.
recalc : bool
    If True, the correlation is re-calculated, even if it is already present.

Returns
-------
None