example_simple.py

# -*- coding: utf-8 -*-
 
 
 
"""A simple example calibration that takes one input data list from raw data and performs
a single calibration."""
 
from prompt import CalibrationSettings, INPUT_DATA_FILTERS
 
 
 
 
settings = CalibrationSettings(name="Example Simple",
                               expert_username="ddossett",
                               description=__doc__,
                               input_data_formats=["raw"],
                               input_data_names=["physics"],
                               input_data_filters={"physics": [f"NOT {INPUT_DATA_FILTERS['Magnet']['On']}",
                                                               INPUT_DATA_FILTERS["Data Tag"]["hadron_calib"],
                                                               INPUT_DATA_FILTERS["Data Quality Tag"]["Good"],
                                                               INPUT_DATA_FILTERS["Beam Energy"]["4S"],
                                                               INPUT_DATA_FILTERS["Run Type"]["physics"]]},
                               depends_on=[],
                               expert_config={})
 
 
 
 
 
 
def get_calibrations(input_data, **kwargs):
    """
    Parameters:
      input_data (dict): Should contain every name from the 'input_data_names' variable as a key.
        Each value is a dictionary with {"/path/to/file_e1_r5.root": IoV(1,5,1,5), ...}. Useful for
        assigning to calibration.files_to_iov
 
      **kwargs: Configuration options to be sent in. Since this may change we use kwargs as a way to help prevent
        backwards compatibility problems. But you could use the correct arguments in b2caf-prompt-run for this
        release explicitly if you want to.
 
        Currently only kwargs["requested_iov"] and kwargs["expert_config"] are used.
 
        "requested_iov" is the IoV range of the bucket and your payloads should correspond to this range.
        However your highest payload IoV should be open ended e.g. IoV(3,4,-1,-1)
 
        "expert_config" is the input configuration. It takes default values from your `CalibrationSettings` but these are
        overwritten by values from the 'expert_config' key in your input `caf_config.json` file when running ``b2caf-prompt-run``.
 
    Returns:
      list(caf.framework.Calibration): All of the calibration objects we want to assign to the CAF process
    """
    import basf2
    # Set up config options
 
    # In this script we want to use one sources of input data.
    # Get the input files  from the input_data variable
    file_to_iov_physics = input_data["physics"]
 
    # We might have requested an enormous amount of data across a run range.
    # There's a LOT more files than runs!
    # Lets set some limits because this calibration doesn't need that much to run.
    max_files_per_run = 2
 
    # If you are using Raw data there's a chance that input files could have zero events.
    # This causes a B2FATAL in basf2 RootInput so the collector job will fail.
    # Currently we don't have a good way of filtering this on the automated side, so we can check here.
    min_events_per_file = 1
 
    # We filter out any more than 2 files per run. The input data files are sorted alphabetically by b2caf-prompt-run
    # already. This procedure respects that ordering
    from prompt.utils import filter_by_max_files_per_run
 
    reduced_file_to_iov_physics = filter_by_max_files_per_run(file_to_iov_physics, max_files_per_run, min_events_per_file)
    input_files_physics = list(reduced_file_to_iov_physics.keys())
    basf2.B2INFO(f"Total number of files actually used as input = {len(input_files_physics)}")
 
    # Get the overall IoV we our process should cover. Includes the end values that we may want to ignore since our output
    # IoV should be open ended. We could also use this as part of the input data selection in some way.
    requested_iov = kwargs.get("requested_iov", None)
 
    # Get the expert configurations if you have something you might configure from them. It should always be available
    # expert_config = kwargs.get("expert_config")
 
    from caf.utils import IoV
    # The actual value our output IoV payload should have. Notice that we've set it open ended.
    output_iov = IoV(requested_iov.exp_low, requested_iov.run_low, -1, -1)
 
    
 
    from ROOT.Belle2 import TestCalibrationAlgorithm
 
    alg_test = TestCalibrationAlgorithm()
 
    
 
    from caf.framework import Calibration
 
    cal_test = Calibration("TestCalibration_physics",
                           collector="CaTest",
                           algorithms=[alg_test],
                           input_files=input_files_physics
                           )
    # Do this for the default AlgorithmStrategy to force the output payload IoV
    # It may be different if you are using another strategy like SequentialRunByRun
    for algorithm in cal_test.algorithms:
        algorithm.params = {"apply_iov": output_iov}
 
    # Most other options like database chain and backend args will be overwritten by b2caf-prompt-run.
    # So we don't bother setting them.
 
    # You must return all calibrations you want to run in the prompt process, even if it's only one
    return [cal_test]