getting_started/synth_data.py [71:128]:
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        return self.remaining_time <= 0

    def get(self):
        return self.offset

class Item:

    def __init__( self, dimension ):
        
        #print( dimension )
        
        self.dimension = dimension
        
        self.daily_pattern = DailyPattern()
        self.random_factor = RandomFactor()
        self.anomaly = None
    
    def get( self, t ):
    
        if random.random() < anomaly_possibility:
            self.anomaly = Anomaly()
        
        value = self.daily_pattern.get(t)
        
        value += self.random_factor.get()

        is_anomaly = bool(self.anomaly)
        if self.anomaly:
            value += self.anomaly.get()
            if self.anomaly.proceed_time():
                self.anomaly = None
        
        metric_values = []
        for i, metric in enumerate(metrics):
            value = introduce_metric_from_upstream[i](value)
            metric_values.append(value)
        
        return metric_values, is_anomaly


def synthesize():

    # create item list
    item_list = []
    for dimension_values in itertools.product( *dimensions.values() ):
        item = Item( dict( zip( dimensions.keys(), dimension_values ) ) )
        item_list.append(item)
    
    # itereate and prepare data    
    dimension_values_list = []
    for i in range( len(dimensions) ):
        dimension_values_list.append([])

    timestamp_list = []

    metric_values_list = []
    for i, metric in enumerate(metrics):
        metric_values_list.append([])
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next_steps/kinesis_stream_connector/l4m_detector/src/synth_live_data_csv.py [78:129]:
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        return self.remaining_time <= 0

    def get(self):
        return self.offset

class Item:
    def __init__( self, dimension ):
        self.dimension = dimension
        
        self.daily_pattern = DailyPattern()
        self.random_factor = RandomFactor()
        self.anomaly = None
    
    def get( self, t ):    
        if random.random() < anomaly_possibility:
            self.anomaly = Anomaly()
        
        value = self.daily_pattern.get(t)
        
        value += self.random_factor.get()

        is_anomaly = bool(self.anomaly)
        if self.anomaly:
            value += self.anomaly.get()
            if self.anomaly.proceed_time():
                self.anomaly = None
        
        metric_values = []
        for i, metric in enumerate(metrics):
            value = introduce_metric_from_upstream[i](value)
            metric_values.append(value)
        
        return metric_values, is_anomaly


def synthesize():
    # create item list - for the given set of dimensions and values
    item_list = []
    for dimension_values in itertools.product( *dimensions.values() ):
        item = Item( dict( zip( dimensions.keys(), dimension_values ) ) )
        item_list.append(item)
    
    # itereate and prepare data    
    dimension_values_list = []
    for i in range( len(dimensions) ):
        dimension_values_list.append([])

    timestamp_list = []

    metric_values_list = []
    for i, metric in enumerate(metrics):
        metric_values_list.append([])
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