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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 get_sec_overview(self):
 """ Generate the "overview" section of the report. """


        logger.debug("Calculating Overview metrics.")

        data_path = os.path.join(self.data_dir, "overview")
        if not os.path.exists(data_path):
            os.makedirs(data_path)

        overview_config = {
            "activity_metrics": [],
            "author_metrics": [],
            "bmi_metrics": [],
            "time_to_close_metrics": [],
            "projects_metrics": []
        }

        for ds in self.data_sources:
            metric_file = self.ds2class[ds]
            metric_index = self.get_metric_index(ds)
            overview = metric_file.overview(metric_index, self.start_date, self.end_date)
            for section in overview_config:
                overview_config[section] += overview[section]

        overview_config['activity_file_csv'] = "data_source_evolution.csv"
        overview_config['efficiency_file_csv'] = "efficiency.csv"

        # ACTIVITY METRICS
        metrics = overview_config['activity_metrics']
        file_name = overview_config['activity_file_csv']
        file_name = os.path.join(data_path, file_name)

        csv = "metricsnames, netvalues, relativevalues, datasource\n"

        for metric in metrics:
            (last, percentage) = get_trend(metric.timeseries())
            csv += "{}, {}, {}, {}\n".format(metric.name, last,
                                             percentage, metric.DS_NAME)
        csv = csv.replace("_", "\_")
        create_csv(file_name, csv)

        # AUTHOR METRICS
        """
        Git Authors:
        -----------
        Description: average number of developers per month by quarters
        (so we have the average number of developers per month during
        those three months). If the approach is to work at the level of month,
        then just the number of developers per month.
        """

        author = overview_config['author_metrics']
        if author:
            authors_by_period = author[0]
            title_label = file_label = authors_by_period.name + ' per ' + self.interval
            file_path = os.path.join(data_path, file_label)
            csv_data = authors_by_period.timeseries(dataframe=True)
            # generate the CSV and the image file displaying the data
            self.create_csv_fig_from_df([csv_data], file_path, [authors_by_period.name],
                                        fig_type="bar", title=title_label, xlabel="time_period",
                                        ylabel=authors_by_period.id)
        # BMI METRICS
        bmi = []
        bmi_metrics = overview_config['bmi_metrics']
        csv = ""
        for metric in bmi_metrics:
            bmi.append(metric.aggregations())
            csv += metric.id + ", "

        # Time to close METRICS
        ttc = []
        ttc_metrics = overview_config['time_to_close_metrics']
        for metric in ttc_metrics:
            ttc.append(metric.aggregations())
            csv += metric.id + ", "

        # generate efficiency file
        csv = csv[:-2] + "\n"
        csv = csv.replace("_", "")
        bmi.extend(ttc)
        for val in bmi:
            csv += "%s, " % str_val(val)
        if csv[-2:] == ", ":
            csv = csv[:-2]

        file_name = os.path.join(data_path, 'efficiency.csv')
        create_csv(file_name, csv)
        logger.debug("Overview metrics generation complete!")