Augmenting Standard Analysis with Historical Caching 3.0

Historical Cache Functionality

This article describes how to use the historical cache functionality built into Sepasoft analysis to speed up part of gathering historical data. Note that this only works with sequential historical data, such as a list of downtime events, or a list of runs, or analysis comparing consecutive shifts. If the analysis is grouped by some sequential data point (Operation UUID, State Event Sequence, Line State Event Begin, etc), this may help. It does respect existing setting values, if any are used.

Python Function

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Python
def run_historical_analysis(lookback_hours, cache_key, analysis_name=None, analysis_setting=None):
    # first we check if the user has passed either an analysis setting object or an analysis setting name
    if not analysis_setting:
        analysis_setting = system.mes.analysis.getMESAnalysisSettings(analysis_name)
        
    # end date is always now
    current_end_date = system.date.now()
    # getting top of current hour
    current_start_date = system.date.setTime(current_end_date, system.date.getHour24(current_end_date), 0, 0)
    current_analysis_dataset = system.mes.analysis.executeAnalysis(current_start_date, current_end_date, analysis_setting).getDataset()
    
    # historical start date is top of current hour minus lookback_hours
    historical_start_date = system.date.addHours(current_start_date, -lookback_hours)
    # checking for existing custom setting values on the analysis setting
    existing_setting_values = analysis_setting.getSettingValues()
    # appending the cache settings to the existing setting values
    analysis_setting.setSettingValues("%s,Cache Expiration=43200,Historical Cache Key=%s" % (existing_setting_values, cache_key))
    historical_analysis_dataset = system.mes.analysis.executeAnalysis(historical_start_date, current_start_date, analysis_setting).getDataset()
    
    # looping over the current_analysis_dataset and appending it to the historical_analysis_dataset
    data_to_add = []
    for row_num in range(current_analysis_dataset.getRowCount()):
        row_data = []
        for col_num in range(current_analysis_dataset.getColumnCount()):
            row_data.append(current_analysis_dataset.getValueAt(row_num, col_num))
        data_to_add.append(row_data)
        
    # returning the combined dataset
    return system.dataset.addRows(historical_analysis_dataset, data_to_add)

Usage:

Do the following to execute this function with an existing analysis setting object or one that you have created from scratch:

Usage with a pre-loaded or existing analysis setting

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Python
analysis = system.mes.analysis.getMESAnalysisSettings("Downtime Events")
results = run_historical_analysis(400, "blah", analysis_setting=analysis)

Otherwise, you can simply name the analysis you wish to use, like so:

Usage with just the name of an analysis setting

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Python
results = run_historical_analysis(400, "blah", analysis_name="Downtime Events")