def generate_split_load_metrics()

in docker/services/mocked_data_service.py [0:0]

• 60 lines of code
• 1 McCabe index (conditional complexity)

    def generate_split_load_metrics(self, config, metric_file_path):
        period_days = config.get(TAG_PERIOD_DAYS)
        length = POINTS_IN_DAY * period_days

        instance_id_series, instance_type_series, timestamp_series, \
        shape_series, shape_size_koef_series = self.generate_common_columns(
            metric_file_path=metric_file_path, length=length
        )

        deviation = config.get(TAG_STD)
        cpu_avg = config.get(TAG_CPU)
        memory_avg = config.get(TAG_MEMORY)
        avg_iops_avg = config.get(TAG_AVG_DISK_IOPS)
        max_iops_avg = config.get(TAG_MAX_DISK_IOPS)
        net_output_avg = config.get(TAG_NET_OUTPUT_LOAD)
        probability = config.get(TAG_PROBABILITY)

        cpu_load_series = generate_split_series(
            distribution='normal',
            avg_loads=cpu_avg,
            probabilities=probability,
            scale=deviation,
            size=length
        )
        memory_load_series = generate_split_series(
            distribution='normal',
            avg_loads=memory_avg,
            probabilities=probability,
            scale=deviation,
            size=length
        )
        net_output_load_series = generate_constant_metric_series(
            distribution='normal',
            loc=net_output_avg,
            scale=deviation,
            size=length
        )
        avg_iops_series = generate_constant_metric_series(
            distribution='normal',
            loc=avg_iops_avg,
            scale=deviation,
            size=length
        )
        max_iops_series = generate_constant_metric_series(
            distribution='normal',
            loc=max_iops_avg,
            scale=deviation,
            size=length
        )

        df_data = {
            'instance_id': instance_id_series,
            'instance_type': instance_type_series,
            'shape': shape_series,
            'shape_size_koef': shape_size_koef_series,
            'timestamp': timestamp_series,
            'cpu_load': cpu_load_series,
            'memory_load': memory_load_series,
            'net_output_load': net_output_load_series,
            'avg_disk_iops': avg_iops_series,
            'max_disk_iops': max_iops_series,
        }
        df = pd.DataFrame(df_data)
        df.to_csv(metric_file_path, sep=',', index=False)