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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def parse_plotCorrelation(self):
 """Find plotCorrelation output"""

        self.deeptools_plotCorrelationData = dict()
        for f in self.find_log_files('deeptools/plotCorrelationData', filehandles=False):
            parsed_data, samples = self.parsePlotCorrelationData(f)
            for k, v in parsed_data.items():
                if k in self.deeptools_plotCorrelationData:
                    log.warning("Replacing duplicate sample {}.".format(k))
                self.deeptools_plotCorrelationData[k] = v
            if len(parsed_data) > 0:
                self.add_data_source(f, section='plotCorrelation')

        if len(self.deeptools_plotCorrelationData) > 0:
            config = {
                'id': 'deeptools_correlation_plot',
                'title': 'deeptools: Correlation Plot',
            }
            data = []
            for s_name in samples:
                try:
                    data.append(self.deeptools_plotCorrelationData[s_name])
                except KeyError:
                    pass
            if len(data) == 0:
                log.debug('No valid data for correlation plot')
                return None

            self.add_section(
                name="Correlation heatmap",
                anchor="deeptools_correlation",
                description="Pairwise correlations of samples based on distribution of sequence reads",
                plot=heatmap.plot(data, samples, samples, config)
            )

        return len(self.deeptools_plotCorrelationData)