workshops/RI2021/ml_ops/lambdas/anomaly-alert-function/anomaly-alert-function.py [42:56]:
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            data2[i]=[]
        #    data2[i]=[]
        for i in metricList:
            data2[i['MetricName']+'AnomalyMetricValue']=[]
            data2[i['MetricName']+'GroupScore']=[]
    
        #Data collection from the event for the CSV
        for i in event['impactedMetric']['relevantTimeSeries']:
            for a in i['dimensions']:
                data2[a['dimensionName']].append(a['dimensionValue'])
            data2[metricName+'AnomalyMetricValue'].append(i['metricValue'])
            data2[metricName+'GroupScore'].append(event['anomalyScore'])
            data2['Timestamp'].append(start_time)
            
        nRow=len(data2['Timestamp'])
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workshops/RI2021/ml_ops/lambdas/anomaly-alert-function/anomaly-alert-function.py [108:120]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
            data2[i]=[]
        for i in metricList:
            data2[i['MetricName']+'AnomalyMetricValue']=[]
            data2[i['MetricName']+'GroupScore']=[]
    
        #Data collection for the CSV
        for i in event['impactedMetric']['relevantTimeSeries']:
            for a in i['dimensions']:
                data2[a['dimensionName']].append(a['dimensionValue'])
            data2[metricName+'AnomalyMetricValue'].append(i['metricValue'])
            data2[metricName+'GroupScore'].append(event['anomalyScore'])
            data2['Timestamp'].append(start_time)
        nRow=len(data2['Timestamp'])
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