caf_svd_time.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
'''
Script to perform the svd time calibration with the CoG6, CoG3 and ELS3 algorithms
'''
 
import sys
import datetime
import random
 
from ROOT.Belle2 import SVDCoGTimeCalibrationAlgorithm
from ROOT.Belle2 import SVD3SampleCoGTimeCalibrationAlgorithm
from ROOT.Belle2 import SVD3SampleELSTimeCalibrationAlgorithm
from ROOT.Belle2 import SVDTimeValidationAlgorithm
 
import basf2 as b2
 
import rawdata as raw
from softwaretrigger.constants import HLT_INPUT_OBJECTS
from tracking import add_tracking_reconstruction
 
from caf.framework import Calibration
from caf import strategies
from caf.utils import IoV
from prompt import CalibrationSettings, input_data_filters
from prompt.utils import filter_by_max_events_per_run
 
b2.set_log_level(b2.LogLevel.INFO)
 
random.seed(42)
 
now = datetime.datetime.now()
 
settings = CalibrationSettings(name="caf_svd_time",
                               expert_username="gdujany",
                               description=__doc__,
                               input_data_formats=["raw"],
                               input_data_names=["hadron_calib"],
                               input_data_filters={"hadron_calib": [input_data_filters["Data Tag"]["hadron_calib"],
                                                                    input_data_filters["Beam Energy"]["4S"],
                                                                    input_data_filters["Beam Energy"]["Continuum"],
                                                                    input_data_filters["Run Type"]["physics"],
                                                                    input_data_filters["Magnet"]["On"]]},
                               depends_on=[],
                               expert_config={
                                   "max_events_per_run": 10000,
                                   "isMC": False,
                               })
 
 
 
 
def remove_module(path, name):
 
    new_path = b2.create_path()
    for m in path.modules():
        if name != m.name():
            new_path.add_module(m)
    return new_path
 
 
 
NEW_RECO_DIGITS_NAME = "SVDRecoDigitsFromTracks"
NEW_SHAPER_DIGITS_NAME = "SVDShaperDigitsFromTracks"
 
 
def create_collector(name="SVDTimeCalibrationCollector",
                     clusters="SVDClustersFromTracks",
                     event_info="SVDEventInfo",
                     event_t0="EventT0",
                     granularity="run"):
    """
    Simply creates a SVDTimeCalibrationCollector module with some options.
 
    Returns:
        pybasf2.Module
    """
 
    collector = b2.register_module("SVDTimeCalibrationCollector")
    collector.set_name(name)
    collector.param("SVDClustersFromTracksName", clusters)
    collector.param("SVDEventInfoName", event_info)
    collector.param("EventT0Name", event_t0)
    collector.param("granularity", granularity)
 
    return collector
 
 
def create_validation_collector(name="SVDTimeValidationCollector",
                                clusters="SVDClusters",
                                clusters_onTracks="SVDClustersOnTracks",
                                event_info="SVDEventInfo",
                                event_t0="EventT0",
                                granularity="run"):
    """
    Simply creates a SVDTimeCalibrationCollector module with some options.
 
    Returns:
        pybasf2.Module
    """
 
    collector = b2.register_module("SVDTimeValidationCollector")
    collector.set_name(name)
    collector.param("SVDClustersName", clusters)
    collector.param("SVDClustersOnTracksName", clusters_onTracks)
    collector.param("SVDEventInfoName", event_info)
    collector.param("EventT0Name", event_t0)
    collector.param("granularity", granularity)
 
    return collector
 
 
def create_algorithm(unique_id, prefix="", min_entries=10000):
    """
    Simply creates a SVDCoGTimeCalibrationAlgorithm class with some options.
 
    Returns:
        ROOT.Belle2.SVDCoGTimeCalibrationAlgorithm
    """
    if "CoG6" in prefix:
        algorithm = SVDCoGTimeCalibrationAlgorithm(unique_id)
    if "CoG3" in prefix:
        algorithm = SVD3SampleCoGTimeCalibrationAlgorithm(unique_id)
    if "ELS3" in prefix:
        algorithm = SVD3SampleELSTimeCalibrationAlgorithm(unique_id)
    if prefix:
        algorithm.setPrefix(prefix)
    algorithm.setMinEntries(min_entries)
 
    return algorithm
 
 
def create_validation_algorithm(prefix="", min_entries=10000):
    """
    Simply creates a SVDCoGTimeValidationAlgorithm class with some options.
 
    Returns:
        ROOT.Belle2.SVDCoGTimeValidationAlgorithm
    """
    algorithm = SVDTimeValidationAlgorithm()
    if prefix:
        algorithm.setPrefix(prefix)
    algorithm.setMinEntries(min_entries)
 
    return algorithm
 
 
def create_svd_clusterizer(name="ClusterReconstruction",
                           clusters="SVDClustersFromTracks",
                           reco_digits=None,
                           shaper_digits=None,
                           time_algorithm=0,
                           get_3sample_raw_time=False):
    """
    Simply creates a SVDSimpleClusterizer module with some options.
 
    Returns:
        pybasf2.Module
    """
 
    cluster = b2.register_module("SVDSimpleClusterizer")
    cluster.set_name(name)
    cluster.param("Clusters", clusters)
    if reco_digits is not None:
        cluster.param("RecoDigits", reco_digits)
    if shaper_digits is not None:
        cluster.param("ShaperDigits", shaper_digits)
    cluster.param("timeAlgorithm", time_algorithm)
    if get_3sample_raw_time:
        cluster.param("Calibrate3SampleWithEventT0", False)
    return cluster
 
 
def create_pre_collector_path(clusterizers, isMC=False, is_validation=False):
    """
    Create a basf2 path that runs a common reconstruction path and also runs several SVDSimpleClusterizer
    modules with different configurations. This way they re-use the same reconstructed objects.
 
    Parameters:
        clusterizers (list[pybasf2.Module]): All the differently configured SVDSimpleClusterizer modules.
        They should output to different datastore objects.
 
    returns:
        pybasf2.Path
    """
    # Set-up re-processing path
    path = b2.create_path()
 
    # Read from file only what is needed
    path.add_module("RootInput", branchNames=HLT_INPUT_OBJECTS)
 
    # unpack raw data to do the tracking
    if not isMC:
        raw.add_unpackers(path, components=['PXD', 'SVD', 'CDC'])
    else:
        path.add_module("Gearbox")
        path.add_module("Geometry")
 
    # proceed only if we acquired 6-sample strips
    skim6SampleEvents = b2.register_module("SVD6SampleEventSkim")
    path.add_module(skim6SampleEvents)
    emptypath = b2.create_path()
    skim6SampleEvents.if_false(emptypath)
 
    # run tracking reconstruction
    add_tracking_reconstruction(path)
    path = remove_module(path, "V0Finder")
    if not is_validation:
        b2.set_module_parameters(path, 'SVDCoGTimeEstimator', CalibrationWithEventT0=False)
 
    # repeat svd reconstruction using only SVDShaperDigitsFromTracks
    path.add_module("SVDShaperDigitsFromTracks")
 
    cog = b2.register_module("SVDCoGTimeEstimator")
    cog.set_name("CoGReconstruction")
    path.add_module(cog)
    b2.set_module_parameters(path, 'CoGReconstruction',
                             ShaperDigits=NEW_SHAPER_DIGITS_NAME,
                             RecoDigits=NEW_RECO_DIGITS_NAME,
                             CalibrationWithEventT0=is_validation)
 
    for cluster in clusterizers:
        path.add_module(cluster)
 
    path = remove_module(path, "SVDMissingAPVsClusterCreator")
 
    return path
 
 
def get_calibrations(input_data, **kwargs):
 
    file_to_iov_physics = input_data["hadron_calib"]
    expert_config = kwargs.get("expert_config")
    max_events_per_run = expert_config["max_events_per_run"]  # Maximum number of events selected per each run
    isMC = expert_config["isMC"]
 
    reduced_file_to_iov_physics = filter_by_max_events_per_run(file_to_iov_physics,
                                                               max_events_per_run, random_select=True)
    good_input_files = list(reduced_file_to_iov_physics.keys())
 
    b2.B2INFO(f"Total number of files used as input = {len(good_input_files)}")
 
    exps = [i.exp_low for i in reduced_file_to_iov_physics.values()]
    runs = sorted([i.run_low for i in reduced_file_to_iov_physics.values()])
 
    firstRun = runs[0]
    lastRun = runs[-1]
    expNum = exps[0]
 
    if not len(good_input_files):
        print("No good input files found! Check that the input files have entries != 0!")
        sys.exit(1)
 
    cog6_suffix = "_CoG6"
    cog3_suffix = "_CoG3"
    els3_suffix = "_ELS3"
 
    calType = "Prompt"
    if isMC:
        calType = "MC"
    unique_id_cog6 = f"SVDCoGTimeCalibrations_{calType}_{now.isoformat()}_INFO:_3rdOrderPol_TBindep_" \
                     f"Exp{expNum}_runsFrom{firstRun}to{lastRun}"
    print(f"\nUniqueID_CoG6:\n{unique_id_cog6}")
 
    unique_id_cog3 = f"SVD3SampleCoGTimeCalibrations_{calType}_{now.isoformat()}_INFO:_3rdOrderPol_TBindep_" \
                     f"Exp{expNum}_runsFrom{firstRun}to{lastRun}"
    print(f"\nUniqueID_CoG3:\n{unique_id_cog3}")
 
    unique_id_els3 = f"SVD3SampleELSTimeCalibrations_{calType}_{now.isoformat()}_INFO:_TBindep_" \
                     f"Exp{expNum}_runsFrom{firstRun}to{lastRun}"
    print(f"\nUniqueID_ELS3:\n{unique_id_els3}")
 
    requested_iov = kwargs.get("requested_iov", None)
    output_iov = IoV(requested_iov.exp_low, requested_iov.run_low, -1, -1)
 
    
 
    cog6 = create_svd_clusterizer(
        name=f"ClusterReconstruction{cog6_suffix}",
        clusters=f"SVDClustersFromTracks{cog6_suffix}",
        reco_digits=NEW_RECO_DIGITS_NAME,
        shaper_digits=NEW_SHAPER_DIGITS_NAME,
        time_algorithm=0,
        get_3sample_raw_time=True)
 
    cog3 = create_svd_clusterizer(
        name=f"ClusterReconstruction{cog3_suffix}",
        clusters=f"SVDClustersFromTracks{cog3_suffix}",
        reco_digits=NEW_RECO_DIGITS_NAME,
        shaper_digits=NEW_SHAPER_DIGITS_NAME,
        time_algorithm=1,
        get_3sample_raw_time=True)
 
    els3 = create_svd_clusterizer(
        name=f"ClusterReconstruction{els3_suffix}",
        clusters=f"SVDClustersFromTracks{els3_suffix}",
        reco_digits=NEW_RECO_DIGITS_NAME,
        shaper_digits=NEW_SHAPER_DIGITS_NAME,
        time_algorithm=2,
        get_3sample_raw_time=True)
 
    
    eventInfo = "SVDEventInfo"
    if isMC:
        eventInfo = "SVDEventInfoSim"
 
    coll_cog6 = create_collector(
        name=f"SVDTimeCalibrationCollector{cog6_suffix}",
        clusters=f"SVDClustersFromTracks{cog6_suffix}",
        event_info=eventInfo,
        event_t0="EventT0")
 
    algo_cog6 = create_algorithm(
        unique_id_cog6,
        prefix=coll_cog6.name(),
        min_entries=10000)
 
    coll_cog3 = create_collector(
        name=f"SVDTimeCalibrationCollector{cog3_suffix}",
        clusters=f"SVDClustersFromTracks{cog3_suffix}",
        event_info=eventInfo,
        event_t0="EventT0")
 
    algo_cog3 = create_algorithm(
        unique_id_cog3,
        prefix=coll_cog3.name(),
        min_entries=10000)
 
    coll_els3 = create_collector(
        name=f"SVDTimeCalibrationCollector{els3_suffix}",
        clusters=f"SVDClustersFromTracks{els3_suffix}",
        event_info=eventInfo,
        event_t0="EventT0")
 
    algo_els3 = create_algorithm(
        unique_id_els3,
        prefix=coll_els3.name(),
        min_entries=10000)
 
    
 
    pre_collector_path = create_pre_collector_path(
        clusterizers=[cog6, cog3, els3],
        isMC=isMC)
    pre_collector_path.add_module(coll_cog6)
    pre_collector_path.add_module(coll_cog3)
    # We leave the coll_els3 to be the one "managed" by the CAF
 
    # calibration setup
    calibration = Calibration("SVDTime",
                              collector=coll_els3,   # The other collectors are in the pre_collector_path itself
                              algorithms=[algo_cog3, algo_cog6, algo_els3],
                              input_files=good_input_files,
                              pre_collector_path=pre_collector_path)
 
    calibration.strategies = strategies.SequentialBoundaries
 
    for algorithm in calibration.algorithms:
        algorithm.params = {"iov_coverage": output_iov}
 
    
 
    val_cog6 = create_svd_clusterizer(
        name=f"ClusterReconstruction{cog6_suffix}",
        clusters=f"SVDClusters{cog6_suffix}",
        time_algorithm=0)
 
    val_cog6_onTracks = create_svd_clusterizer(
        name=f"ClusterReconstruction{cog6_suffix}_onTracks",
        clusters=f"SVDClusters{cog6_suffix}_onTracks",
        reco_digits=NEW_RECO_DIGITS_NAME,
        shaper_digits=NEW_SHAPER_DIGITS_NAME,
        time_algorithm=0)
 
    val_cog3 = create_svd_clusterizer(
        name=f"ClusterReconstruction{cog3_suffix}",
        clusters=f"SVDClusters{cog3_suffix}",
        time_algorithm=1)
 
    val_cog3_onTracks = create_svd_clusterizer(
        name=f"ClusterReconstruction{cog3_suffix}_onTracks",
        clusters=f"SVDClusters{cog3_suffix}_onTracks",
        reco_digits=NEW_RECO_DIGITS_NAME,
        shaper_digits=NEW_SHAPER_DIGITS_NAME,
        time_algorithm=1)
 
    val_els3 = create_svd_clusterizer(
        name=f"ClusterReconstruction{els3_suffix}",
        clusters=f"SVDClusters{els3_suffix}",
        time_algorithm=2)
 
    val_els3_onTracks = create_svd_clusterizer(
        name=f"ClusterReconstruction{els3_suffix}_onTracks",
        clusters=f"SVDClusters{els3_suffix}_onTracks",
        reco_digits=NEW_RECO_DIGITS_NAME,
        shaper_digits=NEW_SHAPER_DIGITS_NAME,
        time_algorithm=2)
 
    val_coll_cog6 = create_validation_collector(
        name=f"SVDTimeValidationCollector{cog6_suffix}",
        clusters=f"SVDClusters{cog6_suffix}",
        clusters_onTracks=f"SVDClusters{cog6_suffix}_onTracks",
        event_info=eventInfo,
        event_t0="EventT0")
 
    val_algo_cog6 = create_validation_algorithm(
        prefix=val_coll_cog6.name(),
        min_entries=10000)
 
    val_coll_cog3 = create_validation_collector(
        name=f"SVDTimeValidationCollector{cog3_suffix}",
        clusters=f"SVDClusters{cog3_suffix}",
        clusters_onTracks=f"SVDClusters{cog3_suffix}_onTracks",
        event_info=eventInfo,
        event_t0="EventT0")
 
    val_algo_cog3 = create_validation_algorithm(
        prefix=val_coll_cog3.name(),
        min_entries=10000)
 
    val_coll_els3 = create_validation_collector(
        name=f"SVDTimeValidationCollector{els3_suffix}",
        clusters=f"SVDClusters{els3_suffix}",
        clusters_onTracks=f"SVDClusters{els3_suffix}_onTracks",
        event_info=eventInfo,
        event_t0="EventT0")
 
    val_algo_els3 = create_validation_algorithm(
        prefix=val_coll_els3.name(),
        min_entries=10000)
 
    val_pre_collector_path = create_pre_collector_path(
        clusterizers=[val_cog6, val_cog6_onTracks,
                      val_cog3, val_cog3_onTracks,
                      val_els3, val_els3_onTracks],
        isMC=isMC, is_validation=True)
    val_pre_collector_path.add_module(val_coll_cog6)
    val_pre_collector_path.add_module(val_coll_cog3)
 
    val_calibration = Calibration("SVDTimeValidation",
                                  collector=val_coll_els3,
                                  algorithms=[val_algo_cog3, val_algo_cog6,
                                              val_algo_els3],
                                  input_files=good_input_files,
                                  pre_collector_path=val_pre_collector_path)
 
    val_calibration.strategies = strategies.SequentialRunByRun
 
    for algorithm in val_calibration.algorithms:
        algorithm.params = {"iov_coverage": output_iov}
 
    val_calibration.depends_on(calibration)
 
    return [calibration, val_calibration]