(self, generated_metrics, *args, **kwargs)
:param generated_metrics: dict of generated timeseries to use.
            Metrics not found here will be read from regular data.
            Should be in this format:
            {
                metric_type: {
                    metric_key: [(timestamp, value), ...],
                    ...
                },
                ...
            }
:param generated_metrics: dict of generated timeseries to use.
            Metrics not found here will be read from regular data.
            Should be in this format:
            {
                metric_type: {
                    metric_key: [(timestamp, value), ...],
                    ...
                },
                ...
            }
: param generated_metrics : dict of generated timeseries to use . Metrics not found here will be read from regular data . Should be in this format : { metric_type : { metric_key : [ ( timestamp value ) ... ] ... } ... }
def __init__(self, generated_metrics, *args, **kwargs):
        """
        :param generated_metrics: dict of generated timeseries to use.
            Metrics not found here will be read from regular data.
            Should be in this format:
            {
                metric_type: {
                    metric_key: [(timestamp, value), ...],
                    ...
                },
                ...
            }
        """
        super(ClustermanMetricsSimulationClient, self).__init__(*args, **kwargs)
        _validate_metrics_object(generated_metrics)
        self.generated_metrics = generated_metrics
def __init__ ( self , generated_metrics , * args , * * kwargs ) : super ( ClustermanMetricsSimulationClient , self ) . __init__ ( * args , * * kwargs ) _validate_metrics_object ( generated_metrics ) self . generated_metrics = generated_metrics