KLMStripEfficiency Class Reference

Inheritance diagram for KLMStripEfficiency:

Public Member Functions
	__init__ (self, algorithm)
	run (self, iov, iteration, queue)
	execute_over_run_list (self, run_list, iteration, forced_calibration, calibration_stage, output_file)
	process_experiment (self, experiment, experiment_runs, iteration, lowest_exprun, highest_exprun)

Public Attributes
	machine = AlgorithmMachine(self.algorithm)
	:py:class:caf.state_machines.AlgorithmMachine used to help set up and execute CalibrationAlgorithm.
bool	first_execution = True
	Flag for the first execution of this AlgorithmStrategy.
	queue = queue
	The multiprocessing queue used to pass back results one at a time.

Static Public Attributes
dict	usable_params = {'iov_coverage': IoV}
	The parameters of Algorithm object which this Strategy would use.
list	ignored_runs [int]
	COMPLETED = AlgorithmStrategy.COMPLETED
	FAILED = AlgorithmStrategy.FAILED

Detailed Description

Custom strategy for executing the KLM strip efficiency. Requires complex
run merging rules.

This uses a `caf.state_machines.AlgorithmMachine` to actually execute
the various steps rather than operating on a CalibrationAlgorithm
C++ class directly.

Definition at line 26 of file klm_strip_efficiency.py.

Constructor & Destructor Documentation

__init__()

__init__	(		self,
			algorithm )

Definition at line 47 of file klm_strip_efficiency.py.

    def __init__(self, algorithm):
        """
        """
        super().__init__(algorithm)
        
        self.machine = AlgorithmMachine(self.algorithm)
        
        self.first_execution = True
 

Member Function Documentation

execute_over_run_list()

execute_over_run_list	(		self,
			run_list,
			iteration,
			forced_calibration,
			calibration_stage,
			output_file )

Execute over run list.

Definition at line 132 of file klm_strip_efficiency.py.

                              calibration_stage, output_file):
        """
        Execute over run list.
        """
        if not self.first_execution:
            self.machine.setup_algorithm()
        else:
            self.first_execution = False
        self.machine.algorithm.algorithm.setForcedCalibration(
            forced_calibration)
        self.machine.algorithm.algorithm.setCalibrationStage(calibration_stage)
        if (output_file is not None):
            self.machine.algorithm.algorithm.setOutputFileName(output_file)
        self.machine.execute_runs(runs=run_list, iteration=iteration,
                                  apply_iov=None)
        if (self.machine.result.result == AlgResult.ok.value) or \
           (self.machine.result.result == AlgResult.iterate.value):
            self.machine.complete()
        else:
            self.machine.fail()
 

process_experiment()

process_experiment	(		self,
			experiment,
			experiment_runs,
			iteration,
			lowest_exprun,
			highest_exprun )

Process runs from experiment.

Definition at line 154 of file klm_strip_efficiency.py.

                           lowest_exprun, highest_exprun):
        """
        Process runs from experiment.
        """
        # Run lists. They have the following format: run number,
        # calibration result code, ExpRun, algorithm results,
        # merge information, payload.
        run_data = []
 
        # Initial run.
        for exp_run in experiment_runs:
            self.execute_over_run_list(
                [exp_run], iteration, False,
                KLMStripEfficiencyAlgorithm.c_MeasurablePlaneCheck, None)
            result = self.machine.result.result
            algorithm_results = KLMStripEfficiencyAlgorithm.Results(
                self.machine.algorithm.algorithm.getResults())
            # If number of hits is 0, then KLM is excluded. Such runs
            # can be ignored safely.
            if (algorithm_results.getExtHits() > 0):
                run_data.append([exp_run.run, result, [exp_run],
                                 algorithm_results, '', None])
            result_str = calibration_result_string(result)
            basf2.B2INFO(f'Run {int(exp_run.run)}: {result_str}.')
 
        # Sort by run number.
        run_data.sort(key=lambda x: x[0])
 
        # Create list of runs that do not have enough data.
        run_ranges = []
        i = 0
        while (i < len(run_data)):
            if (run_data[i][1] == 2):
                j = i
                while (run_data[j][1] == 2):
                    j += 1
                    if (j >= len(run_data)):
                        break
                run_ranges.append([i, j])
                i = j
            else:
                i += 1
 
        # Determine whether the runs with insufficient data can be merged to
        # the next or previous normal run.
        def can_merge(run_data, run_not_enough_data, run_normal):
            return run_data[run_not_enough_data][3].newExtHitsPlanes(
                run_data[run_normal][3].getExtHitsPlane()) == 0
 
        for run_range in run_ranges:
            next_run = run_range[1]
            # To mark as 'none' at the end if there are no normal runs.
            j = run_range[0]
            i = next_run - 1
            if (next_run < len(run_data)):
                while (i >= run_range[0]):
                    if (can_merge(run_data, i, next_run)):
                        basf2.B2INFO(f'Run {int(run_data[i][0])} (not enough data) can be merged into the next normal run ' +
                                     f'{int(run_data[next_run][0])}.')
                        run_data[i][4] = 'next'
                    else:
                        basf2.B2INFO(f'Run {int(run_data[i][0])} (not enough data) cannot be merged into the next normal run ' +
                                     f'{int(run_data[next_run][0])}, will try the previous one.')
                        break
                    i -= 1
                if (i < run_range[0]):
                    continue
            previous_run = run_range[0] - 1
            if (previous_run >= 0):
                while (j <= i):
                    if (can_merge(run_data, j, previous_run)):
                        basf2.B2INFO(f'Run {int(run_data[j][0])} (not enough data) can be merged into the previous normal run ' +
                                     f'{int(run_data[previous_run][0])}.')
                        run_data[j][4] = 'previous'
                    else:
                        basf2.B2INFO(f'Run {int(run_data[j][0])} (not enough data) cannot be merged into the previous normal ' +
                                     f'run {int(run_data[previous_run][0])}.')
                        break
                    j += 1
                if (j > i):
                    continue
            basf2.B2INFO('A range of runs with not enough data is found that cannot be merged into neither previous nor ' +
                         f'next normal run: from {int(run_data[j][0])} to {int(run_data[i][0])}.')
            while (j <= i):
                run_data[j][4] = 'none'
                j += 1
 
        # Merge runs that do not have enough data. If both this and next
        # run do not have enough data, then merge the collected data.
        i = 0
        j = 0
        while (i < len(run_data) - 1):
            while ((run_data[i][1] == 2) and (run_data[i + 1][1] == 2)):
                if (run_data[i][4] != run_data[i + 1][4]):
                    break
                basf2.B2INFO(f'Merging run {int(run_data[i + 1][0])} (not enough data) into run {int(run_data[i][0])} ' +
                             '(not enough data).')
                run_data[i][2].extend(run_data[i + 1][2])
                del run_data[i + 1]
                self.execute_over_run_list(
                    run_data[i][2], iteration, False,
                    KLMStripEfficiencyAlgorithm.c_MeasurablePlaneCheck, None)
                run_data[i][1] = self.machine.result.result
                run_data[i][3] = KLMStripEfficiencyAlgorithm.Results(
                    self.machine.algorithm.algorithm.getResults())
                result_str = calibration_result_string(run_data[i][1])
                basf2.B2INFO(f'Run {int(run_data[i][0])}: {result_str}.')
                if (i >= len(run_data) - 1):
                    break
            i += 1
 
        # Merge runs that do not have enough data into normal runs.
        def merge_runs(run_data, run_not_enough_data, run_normal, forced):
            basf2.B2INFO(f'Merging run {int(run_data[run_not_enough_data][0])} (not enough data) into run ' +
                         f'{int(run_data[run_normal][0])} (normal).')
            run_data[run_normal][2].extend(run_data[run_not_enough_data][2])
            self.execute_over_run_list(
                run_data[run_normal][2], iteration, forced,
                KLMStripEfficiencyAlgorithm.c_MeasurablePlaneCheck, None)
            run_data[run_normal][1] = self.machine.result.result
            run_data[run_normal][3] = KLMStripEfficiencyAlgorithm.Results(
                self.machine.algorithm.algorithm.getResults())
            result_str = calibration_result_string(run_data[run_normal][1])
            basf2.B2INFO(f'Run {int(run_data[run_normal][0])}: {result_str}.')
            if (run_data[run_normal][1] != 0):
                basf2.B2FATAL(f'Merging run {int(run_data[run_not_enough_data][0])} into ' +
                              f'run {int(run_data[run_normal][0])} failed.')
            del run_data[run_not_enough_data]
 
        i = 0
        while (i < len(run_data)):
            if (run_data[i][1] == 2):
                if (run_data[i][4] == 'next'):
                    merge_runs(run_data, i, i + 1, False)
                elif (run_data[i][4] == 'previous'):
                    merge_runs(run_data, i, i - 1, False)
                else:
                    i += 1
            else:
                i += 1
        i = 0
        while (i < len(run_data)):
            if (run_data[i][1] == 2 and run_data[i][4] == 'none'):
                new_planes_previous = -1
                new_planes_next = -1
                if (i < len(run_data) - 1):
                    new_planes_next = run_data[i][3].newExtHitsPlanes(
                        run_data[i + 1][3].getExtHitsPlane())
                    basf2.B2INFO(f'There are {int(new_planes_next)} new active modules in run {int(run_data[i][0])} ' +
                                 f'relatively to run {int(run_data[i + 1][0])}.')
                if (i > 0):
                    new_planes_previous = run_data[i][3].newExtHitsPlanes(
                        run_data[i - 1][3].getExtHitsPlane())
                    basf2.B2INFO(f'There are {int(new_planes_previous)} new active modules in run {int(run_data[i][0])} ' +
                                 f'relatively to run {int(run_data[i - 1][0])}.')
                run_for_merging = -1
                # If a forced merge of the normal run with another run from
                # a different range of runs with not enough data has already
                # been performed, then the list of active modules may change
                # and there would be 0 new modules. Consequently, the number
                # of modules is checked to be greater or equal than 0. However,
                # there is no guarantee that the same added module would be
                # calibrated normally. Thus, a forced merge is performed anyway.
                if (new_planes_previous >= 0 and new_planes_next < 0):
                    run_for_merging = i - 1
                elif (new_planes_previous < 0 and new_planes_next >= 0):
                    run_for_merging = i + 1
                elif (new_planes_previous >= 0 and new_planes_next >= 0):
                    if (new_planes_previous < new_planes_next):
                        run_for_merging = i - 1
                    else:
                        run_for_merging = i + 1
                else:
                    basf2.B2INFO(f'Cannot determine run for merging for run {int(run_data[i][0])}, performing its' +
                                 ' forced calibration.')
                    self.execute_over_run_list(
                        run_data[i][2], iteration, True,
                        KLMStripEfficiencyAlgorithm.c_MeasurablePlaneCheck,
                        None)
                    run_data[i][1] = self.machine.result.result
                    run_data[i][3] = KLMStripEfficiencyAlgorithm.Results(
                        self.machine.algorithm.algorithm.getResults())
                    result_str = calibration_result_string(run_data[i][1])
                    basf2.B2INFO(f'Run {int(run_data[i][0])}: {result_str}.')
                    if (run_data[i][1] != 0):
                        basf2.B2FATAL(f'Forced calibration of run {int(run_data[i][0])} failed.')
                if (run_for_merging >= 0):
                    merge_runs(run_data, i, run_for_merging, True)
            else:
                i += 1
 
        # Stage 2: determination of maximal run ranges.
        # The set of calibrated planes should be the same for all small
        # run ranges within the large run range.
        run_ranges.clear()
        i = 0
        while (i < len(run_data)):
            j = i + 1
            while (j < len(run_data)):
                planes_differ = False
                if (run_data[j][3].newMeasuredPlanes(
                        run_data[i][3].getEfficiency()) != 0):
                    planes_differ = True
                if (run_data[i][3].newMeasuredPlanes(
                        run_data[j][3].getEfficiency()) != 0):
                    planes_differ = True
                if (planes_differ):
                    basf2.B2INFO(f'Run {int(run_data[j][0])}: the set of planes is different from run {int(run_data[i][0])}.')
                    break
                else:
                    basf2.B2INFO(f'Run {int(run_data[j][0])}: the set of planes is the same as for run {int(run_data[i][0])}.')
                    j = j + 1
            run_ranges.append([i, j])
            i = j
 
        # Stage 3: final calibration.
 
        # Output directory.
        if (not os.path.isdir('efficiency')):
            os.mkdir('efficiency')
 
        # Merge runs.
        def merge_runs_2(run_data, run_1, run_2, forced):
            basf2.B2INFO(f'Merging run {int(run_data[run_2][0])} into run {int(run_data[run_1][0])}.')
            run_data[run_1][2].extend(run_data[run_2][2])
            output_file = f'efficiency/efficiency_{int(run_data[run_1][2][0].exp)}_{int(run_data[run_1][2][0].run)}.root'
            self.execute_over_run_list(
                run_data[run_1][2], iteration, forced,
                KLMStripEfficiencyAlgorithm.c_EfficiencyMeasurement,
                output_file)
            run_data[run_1][1] = self.machine.result.result
            run_data[run_1][3] = KLMStripEfficiencyAlgorithm.Results(
                self.machine.algorithm.algorithm.getResults())
            run_data[run_1][5] = \
                self.machine.algorithm.algorithm.getPayloadValues()
            result_str = calibration_result_string(run_data[run_1][1])
            basf2.B2INFO(f'Run {int(run_data[run_1][0])}: {result_str}; requested precision {0.02:f}, achieved precision ' +
                         f'{run_data[run_1][3].getAchievedPrecision():f}.')
 
        for run_range in run_ranges:
            i = run_range[0]
            while (i < run_range[1]):
                output_file = f'efficiency/efficiency_{int(run_data[i][2][0].exp)}_{int(run_data[i][2][0].run)}.root'
                # Force calibration if there are no more runs in the range.
                if (i == run_range[1] - 1):
                    forced_calibration = True
                else:
                    forced_calibration = False
                self.execute_over_run_list(
                    run_data[i][2], iteration, forced_calibration,
                    KLMStripEfficiencyAlgorithm.c_EfficiencyMeasurement,
                    output_file)
                run_data[i][1] = self.machine.result.result
                run_data[i][3] = KLMStripEfficiencyAlgorithm.Results(
                    self.machine.algorithm.algorithm.getResults())
                run_data[i][5] = \
                    self.machine.algorithm.algorithm.getPayloadValues()
                result_str = calibration_result_string(run_data[i][1])
                basf2.B2INFO(f'Run {int(run_data[i][0])}: {result_str}; requested precision {0.02:f}, achieved precision ' +
                             f'{run_data[i][3].getAchievedPrecision():f}.')
                if (run_data[i][1] == 2):
                    j = i + 1
                    while (j < run_range[1]):
                        # Force calibration if there are no more runs
                        # in the range.
                        if (j == run_range[1] - 1):
                            forced_calibration = True
                        else:
                            forced_calibration = False
                        merge_runs_2(run_data, i, j, forced_calibration)
                        run_data[j][1] = -1
                        j = j + 1
                        if (run_data[i][1] == 0):
                            break
                    i = j
                else:
                    i = i + 1
 
        i = 0
        while (i < len(run_data)):
            if (run_data[i][1] == -1):
                del run_data[i]
            else:
                i = i + 1
 
        # Stage 4: write the results to the database.
        def commit_payload(run_data, run):
            basf2.B2INFO(f'Writing run {int(run_data[run][0])}.')
            self.machine.algorithm.algorithm.commit(run_data[run][5])
 
        for i in range(0, len(run_data)):
            # Get first run again due to possible mergings.
            run_data[i][2].sort(key=lambda x: x.run)
            first_run = run_data[i][2][0].run
            # Set IOV for the current run.
            # The last run will be overwritten when writing the result.
            run_data[i][5].front().iov = \
                Belle2.IntervalOfValidity(experiment, first_run, experiment, -1)
            # Write the previous run.
            if (i > 0):
                iov = run_data[previous_run][5].front().iov
                if (previous_run == 0):
                    run_data[previous_run][5].front().iov = \
                        Belle2.IntervalOfValidity(
                            lowest_exprun.exp, lowest_exprun.run,
                            experiment, first_run - 1)
                else:
                    run_data[previous_run][5].front().iov = \
                        Belle2.IntervalOfValidity(experiment, iov.getRunLow(),
                                                  experiment, first_run - 1)
                commit_payload(run_data, previous_run)
                previous_run = i
            if (i == 0):
                previous_run = 0
            # Write the current run if it is the last run.
            if (i == len(run_data) - 1):
                iov = run_data[i][5].front().iov
                run_data[i][5].front().iov = \
                    Belle2.IntervalOfValidity(
                        experiment, iov.getRunLow(),
                        highest_exprun.exp, highest_exprun.run)
                commit_payload(run_data, i)

run()

run	(		self,
			iov,
			iteration,
			queue )

Runs the algorithm machine over the collected data and
fills the results.

Definition at line 58 of file klm_strip_efficiency.py.

    def run(self, iov, iteration, queue):
        """
        Runs the algorithm machine over the collected data and
        fills the results.
        """
        if not self.is_valid():
            raise StrategyError('The strategy KLMStripEfficiency was not '
                                'set up correctly.')
        
        self.queue = queue
 
        basf2.B2INFO(f'Setting up {self.__class__.__name__} strategy '
                     f'for {self.algorithm.name}')
        # Add all the necessary parameters for a strategy to run.
        machine_params = {}
        machine_params['database_chain'] = self.database_chain
        machine_params['dependent_databases'] = self.dependent_databases
        machine_params['output_dir'] = self.output_dir
        machine_params['output_database_dir'] = self.output_database_dir
        machine_params['input_files'] = self.input_files
        machine_params['ignored_runs'] = self.ignored_runs
        self.machine.setup_from_dict(machine_params)
        # Start moving through machine states.
        basf2.B2INFO(f'Starting AlgorithmMachine of {self.algorithm.name}')
        self.algorithm.algorithm.setCalibrationStage(
            KLMStripEfficiencyAlgorithm.c_EfficiencyMeasurement)
        # This sets up the logging and database chain and assigns all
        # input files from collector jobs.
        self.machine.setup_algorithm(iteration=iteration)
        # After this point, the logging is in the stdout of the algorithm.
        basf2.B2INFO(f'Beginning execution of {self.algorithm.name} using '
                     f'strategy {self.__class__.__name__}')
 
        # Select of runs for calibration.
        runs = self.algorithm.algorithm.getRunListFromAllData()
        all_runs_collected = set(runs_from_vector(runs))
        # Select runs overlapping with the calibration IOV if it is specified.
        if iov:
            runs_to_execute = runs_overlapping_iov(iov, all_runs_collected)
        else:
            runs_to_execute = all_runs_collected
        # Remove the ignored runs.
        if self.ignored_runs:
            basf2.B2INFO(f'Removing the ignored_runs from the runs '
                         f'to execute for {self.algorithm.name}')
            runs_to_execute.difference_update(set(self.ignored_runs))
 
        # Creation of sorted run list split by experiment.
        runs_to_execute = sorted(runs_to_execute)
        runs_to_execute = split_runs_by_exp(runs_to_execute)
 
        # Get IOV coverage,
        iov_coverage = None
        if 'iov_coverage' in self.algorithm.params:
            iov_coverage = self.algorithm.params['iov_coverage']
 
        # Iterate over experiment run lists.
        number_of_experiments = len(runs_to_execute)
        for i_exp, run_list in enumerate(runs_to_execute, start=1):
            lowest_exprun = get_lowest_exprun(number_of_experiments, i_exp,
                                              run_list, iov_coverage)
            highest_exprun = get_highest_exprun(number_of_experiments, i_exp,
                                                run_list, iov_coverage)
            self.process_experiment(run_list[0].exp, run_list, iteration,
                                    lowest_exprun, highest_exprun)
 
        # Send final state and result to CAF.
        self.send_result(self.machine.result)
        if (self.machine.result.result == AlgResult.ok.value) or \
           (self.machine.result.result == AlgResult.iterate.value):
            self.send_final_state(self.COMPLETED)
        else:
            self.send_final_state(self.FAILED)
 

Member Data Documentation

COMPLETED

COMPLETED = AlgorithmStrategy.COMPLETED

static

Definition at line 43 of file klm_strip_efficiency.py.

FAILED

FAILED = AlgorithmStrategy.FAILED

static

Definition at line 45 of file klm_strip_efficiency.py.

first_execution

bool first_execution = True

Flag for the first execution of this AlgorithmStrategy.

Definition at line 56 of file klm_strip_efficiency.py.

ignored_runs

ignored_runs [int]

static

Definition at line 41 of file klm_strip_efficiency.py.

machine

machine = AlgorithmMachine(self.algorithm)

:py:class:caf.state_machines.AlgorithmMachine used to help set up and execute CalibrationAlgorithm.

It gets set up properly in :py:func:run.

Definition at line 54 of file klm_strip_efficiency.py.

queue

queue = queue

The multiprocessing queue used to pass back results one at a time.

Definition at line 67 of file klm_strip_efficiency.py.

usable_params

dict usable_params = {'iov_coverage': IoV}

static

The parameters of Algorithm object which this Strategy would use.

Just here for documentation reasons.

Definition at line 38 of file klm_strip_efficiency.py.

---

The documentation for this class was generated from the following file:

• klm/calibration/scripts/klm_strip_efficiency.py