idmtools.analysis.tags_analyzer module

class idmtools.analysis.tags_analyzer.TagsAnalyzer(uid=None, working_dir=None, parse=True, output_path='output_tag')

Bases: idmtools.entities.ianalyzer.IAnalyzer

Provides an analyzer for CSV output

Examples

# Example TagsAnalyzer for any experiment
# In this example, we will demonstrate how to use a TagsAnalyzer to put your sim tags in a csv file

# First, import some necessary system and idmtools packages.
from idmtools.analysis.analyze_manager import AnalyzeManager
from idmtools.analysis.tags_analyzer import TagsAnalyzer
from idmtools.core import ItemType
from idmtools.core.platform_factory import Platform

if __name__ == '__main__':

    # Set the platform where you want to run your analysis
    # In this case we are running in COMPS since the Work Item we are analyzing was run on COMPS
    with Platform('COMPS2') as platform:

        # Arg option for analyzer init are uid, working_dir, data in the method map (aka select_simulation_data),
        # and filenames
        # Initialize the analyser class which just requires an experiment id
        analyzers = [TagsAnalyzer(output_path="output_tag")]

        # Set the experiment id you want to analyze
        experiment_id = '36d8bfdc-83f6-e911-a2be-f0921c167861'  # staging exp id JSuresh's Magude exp

        # Specify the id Type, in this case an Experiment on COMPS
        manager = AnalyzeManager(partial_analyze_ok=True,
                                 ids=[(experiment_id, ItemType.EXPERIMENT)],
                                 analyzers=analyzers)
        manager.analyze()

initialize()

Call once after the analyzer has been added to the AnalyzeManager.

Add everything depending on the working directory or unique ID here instead of in __init__.

map(data, simulation)

In parallel for each simulation, consume raw data from filenames and emit selected data.

Parameters

• data – A dictionary associating filename with content for simulation data.

• item – IItem object that the passed data is associated with.

Returns

Selected data for the given item.

reduce(all_data)

Combine the map() data for a set of items into an aggregate result.

Parameters

all_data – A dictionary with entries for the item ID and selected data.