344 lines
16 KiB
Python
344 lines
16 KiB
Python
"""
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Reading of Amor NeXus data files to extract metadata and event stream.
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"""
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from typing import BinaryIO, List, Union
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import h5py
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import numpy as np
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import platform
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import logging
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import subprocess
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from datetime import datetime
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from orsopy import fileio
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from orsopy.fileio.model_language import SampleModel
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from . import const
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from .header import Header
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from .event_data_types import AmorGeometry, AmorTiming, AmorEventStream, PACKET_TYPE, EVENT_TYPE, PULSE_TYPE, PC_TYPE
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try:
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import zoneinfo
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except ImportError:
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# for python versions < 3.9 try to use the backports version
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from backports import zoneinfo
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# Time zone used to interpret time strings
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AMOR_LOCAL_TIMEZONE = zoneinfo.ZoneInfo(key='Europe/Zurich')
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if platform.node().startswith('amor'):
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NICOS_CACHE_DIR = '/home/amor/nicosdata/amor/cache/'
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GREP = '/usr/bin/grep "%s"'
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else:
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NICOS_CACHE_DIR = None
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class AmorEventData:
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"""
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Read one amor NeXus datafile and extract relevant header information.
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Implements EventDatasetProtocol
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"""
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file_list: List[str]
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first_index: int
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last_index: int = -1
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EOF: bool = False
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max_events: int
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owner: fileio.Person
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experiment: fileio.Experiment
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sample: fileio.Sample
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instrument_settings: fileio.InstrumentSettings
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geometry: AmorGeometry
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timing: AmorTiming
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data: AmorEventStream
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eventStartTime: np.int64
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def __init__(self, fileName:Union[str, h5py.File, BinaryIO], first_index:int=0, max_events:int=100_000_000):
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if type(fileName) is str:
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self.file_list = [fileName]
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self.hdf = h5py.File(fileName, 'r', swmr=True)
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elif type(fileName) is h5py.File:
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self.file_list = [fileName.filename]
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self.hdf = fileName
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else:
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self.file_list = [repr(fileName)]
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self.hdf = h5py.File(fileName, 'r')
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self.first_index = first_index
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self.max_events = max_events
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self.read_header_info()
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self.read_instrument_configuration()
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self.read_event_stream()
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# actions applied to any dataset
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self.read_chopper_trigger_stream()
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self.read_proton_current_stream()
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if type(fileName) is str:
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# close the input file to free memory, only if the file was opened in this object
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self.hdf.close()
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del(self.hdf)
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def _replace_if_missing(self, key, nicos_key, dtype=float):
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try:
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return dtype(self.hdf[f'/entry1/Amor/{key}'][0])
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except(KeyError, IndexError):
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if NICOS_CACHE_DIR:
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try:
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logging.warning(f" using parameter {nicos_key} from nicos cache")
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year_date = self.fileDate.strftime('%Y')
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value = str(subprocess.getoutput(f'{GREP} {NICOS_CACHE_DIR}nicos-{nicos_key}/{year_date}')).split('\t')[-1]
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return dtype(value)
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except Exception:
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logging.error("Couldn't get value from nicos cache", exc_info=True)
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return dtype(0)
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else:
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logging.warning(f" parameter {key} not found, relpace by zero")
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return dtype(0)
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def read_header_info(self):
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# read general information and first data set
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title = self.hdf['entry1/title'][0].decode('utf-8')
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proposal_id = self.hdf['entry1/proposal_id'][0].decode('utf-8')
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user_name = self.hdf['entry1/user/name'][0].decode('utf-8')
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user_affiliation = 'unknown'
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user_email = self.hdf['entry1/user/email'][0].decode('utf-8')
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user_orcid = None
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sampleName = self.hdf['entry1/sample/name'][0].decode('utf-8')
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model = self.hdf['entry1/sample/model'][0].decode('utf-8')
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if 'stack:' in model:
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import yaml
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model = yaml.safe_load(model)
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else:
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model = dict(stack=model)
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instrumentName = 'Amor'
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source = self.hdf['entry1/Amor/source/name'][0].decode('utf-8')
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sourceProbe = 'neutron'
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start_time = self.hdf['entry1/start_time'][0].decode('utf-8')
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# extract start time as unix time, adding UTC offset of 1h to time string
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start_date = datetime.fromisoformat(start_time)
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self.fileDate = start_date.replace(tzinfo=AMOR_LOCAL_TIMEZONE)
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self.owner = fileio.Person(
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name=user_name,
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affiliation=user_affiliation,
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contact=user_email,
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)
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if user_orcid:
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self.owner.orcid = user_orcid
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self.experiment = fileio.Experiment(
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title=title,
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instrument=instrumentName,
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start_date=start_date,
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probe=sourceProbe,
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facility=source,
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proposalID=proposal_id
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)
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self.sample = fileio.Sample(
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name=sampleName,
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model=SampleModel.from_dict(model),
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sample_parameters=None,
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)
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def read_instrument_configuration(self):
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chopperSeparation = float(np.take(self.hdf['entry1/Amor/chopper/pair_separation'], 0))
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detectorDistance = float(np.take(self.hdf['entry1/Amor/detector/transformation/distance'], 0))
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chopperDetectorDistance = detectorDistance-float(np.take(self.hdf['entry1/Amor/chopper/distance'], 0))
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polarizationConfigs = ['unpolarized', 'unpolarized', 'po', 'mo', 'op', 'pp', 'mp', 'om', 'pm', 'mm']
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mu = self._replace_if_missing('instrument_control_parameters/mu', 'mu', float)
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nu = self._replace_if_missing('instrument_control_parameters/nu', 'nu', float)
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kap = self._replace_if_missing('instrument_control_parameters/kappa', 'kappa', float)
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kad = self._replace_if_missing('instrument_control_parameters/kappa_offset', 'kad', float)
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div = self._replace_if_missing('instrument_control_parameters/div', 'div', float)
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ch1TriggerPhase = self._replace_if_missing('chopper/ch1_trigger_phase', 'ch1_trigger_phase', float)
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ch2TriggerPhase = self._replace_if_missing('chopper/ch2_trigger_phase', 'ch2_trigger_phase', float)
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try:
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chopperTriggerTime = (float(self.hdf['entry1/Amor/chopper/ch2_trigger/event_time_zero'][7]) \
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-float(self.hdf['entry1/Amor/chopper/ch2_trigger/event_time_zero'][0])) \
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/7
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chopperTriggerTimeDiff = float(self.hdf['entry1/Amor/chopper/ch2_trigger/event_time_offset'][2])
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except (KeyError, IndexError):
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logging.debug(' chopper speed and phase taken from .hdf file')
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chopperSpeed = self._replace_if_missing('chopper/rotation_speed', 'chopper_phase', float)
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chopperPhase = self._replace_if_missing('chopper/phase', 'chopper_phase', float)
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tau = 30/chopperSpeed
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else:
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tau = int(1e-6*chopperTriggerTime/2+0.5)*(1e-3)
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chopperTriggerPhase = 180e-9*chopperTriggerTimeDiff/tau
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chopperSpeed = 30/tau
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chopperPhase = chopperTriggerPhase+ch1TriggerPhase-ch2TriggerPhase
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self.geometry = AmorGeometry(mu, nu, kap, kad, div,
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chopperSeparation, detectorDistance, chopperDetectorDistance)
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self.timing = AmorTiming(ch1TriggerPhase, ch2TriggerPhase, chopperSpeed, chopperPhase, tau)
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polarizationConfigLabel = self._replace_if_missing('polarization/configuration/average_value', 'polarizer_config_label', int)
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polarizationConfig = fileio.Polarization(polarizationConfigs[polarizationConfigLabel])
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logging.debug(f' polarization configuration: {polarizationConfig} (index {polarizationConfigLabel})')
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self.instrument_settings = fileio.InstrumentSettings(
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incident_angle = fileio.ValueRange(round(mu+kap+kad-0.5*div, 3),
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round(mu+kap+kad+0.5*div, 3),
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'deg'),
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wavelength = fileio.ValueRange(const.lamdaCut, const.lamdaMax, 'angstrom'),
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#polarization = fileio.Polarization.unpolarized,
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polarization = fileio.Polarization(polarizationConfig)
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)
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self.instrument_settings.mu = fileio.Value(
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round(mu, 3),
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'deg',
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comment='sample angle to horizon')
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self.instrument_settings.nu = fileio.Value(
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round(nu, 3),
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'deg',
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comment='detector angle to horizon')
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self.instrument_settings.div = fileio.Value(
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round(div, 3),
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'deg',
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comment='incoming beam divergence')
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self.instrument_settings.kap = fileio.Value(
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round(kap, 3),
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'deg',
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comment='incoming beam inclination')
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if abs(kad)>0.02:
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self.instrument_settings.kad = fileio.Value(
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round(kad, 3),
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'deg',
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comment='incoming beam angular offset')
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def update_header(self, header:Header):
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"""
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Add dataset information into an existing header.
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"""
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logging.info(f' meta data from: {self.file_list[0]}')
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header.owner = self.owner
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header.experiment = self.experiment
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header.sample = self.sample
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header.measurement_instrument_settings = self.instrument_settings
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def read_event_stream(self):
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"""
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Read the actual event data from file. If file is too large, find event index from packets
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that allow splitting of file smaller than self.max_events.
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"""
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packets = np.recarray(self.hdf['/entry1/Amor/detector/data/event_index'].shape, dtype=PACKET_TYPE)
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packets.start_index = self.hdf['/entry1/Amor/detector/data/event_index'][:]
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packets.Time = self.hdf['/entry1/Amor/detector/data/event_time_zero'][:]
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try:
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# packet index that matches first event index
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start_packet = int(np.where(packets.start_index==self.first_index)[0][0])
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except IndexError:
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raise IndexError(f'No event packet found starting at event #{self.first_index}')
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packets = packets[start_packet:]
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nevts = self.hdf['/entry1/Amor/detector/data/event_time_offset'].shape[0]
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if (nevts-self.first_index)>self.max_events:
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end_packet = np.where(packets.start_index<=(self.first_index+self.max_events))[0][-1]
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self.last_index = packets.start_index[end_packet]-1
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packets = packets[:end_packet]
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else:
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self.last_index = nevts-1
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self.EOF = True
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nevts = self.last_index+1-self.first_index
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# adapte packet to event index relation
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packets.start_index -= self.first_index
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events = np.recarray(nevts, dtype=EVENT_TYPE)
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events.tof = np.array(self.hdf['/entry1/Amor/detector/data/event_time_offset'][self.first_index:self.last_index+1])/1.e9
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events.pixelID = self.hdf['/entry1/Amor/detector/data/event_id'][self.first_index:self.last_index+1]
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events.mask = 0
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pulses = self.read_chopper_trigger_stream()
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current = self.read_proton_current_stream()
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self.data = AmorEventStream(events, packets, pulses, current)
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if self.first_index>0 and not self.EOF:
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# label the file name if not all events were used
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self.file_list[0] += f'[{self.first_index}:{self.last_index}]'
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def read_chopper_trigger_stream(self):
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chopper1TriggerTime = np.array(self.hdf['entry1/Amor/chopper/ch2_trigger/event_time_zero'][:-2], dtype=np.int64)
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#self.chopper2TriggerTime = self.chopper1TriggerTime + np.array(self.hdf['entry1/Amor/chopper/ch2_trigger/event_time'][:-2], dtype=np.int64)
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# + np.array(self.hdf['entry1/Amor/chopper/ch2_trigger/event_time_offset'][:], dtype=np.int64)
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if np.shape(chopper1TriggerTime)[0] > 2:
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startTime = chopper1TriggerTime[0]
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pulseTimeS = chopper1TriggerTime
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else:
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logging.warn(' no chopper trigger data available, using event steram instead')
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startTime = np.array(self.hdf['/entry1/Amor/detector/data/event_time_zero'][0], dtype=np.int64)
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stopTime = np.array(self.hdf['/entry1/Amor/detector/data/event_time_zero'][-2], dtype=np.int64)
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pulseTimeS = np.arange(startTime, stopTime, self.timing.tau*1e9, dtype=np.int64)
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pulses = np.recarray(pulseTimeS.shape, dtype=PULSE_TYPE)
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pulses.time = pulseTimeS
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pulses.monitor = 1. # default is monitor pulses as it requires no calculation
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# apply filter in case the events were filtered
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if self.first_index>0 or not self.EOF:
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pulses = pulses[(pulses.time>=self.data.packets.Time[0])&(pulses.time<=self.data.packets.Time[-1])]
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self.eventStartTime = startTime
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return pulses
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def read_proton_current_stream(self):
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proton_current = np.recarray(self.hdf['entry1/Amor/detector/proton_current/time'].shape, dtype=PC_TYPE)
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proton_current.time = self.hdf['entry1/Amor/detector/proton_current/time'][:]
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proton_current.current = self.hdf['entry1/Amor/detector/proton_current/value'][:,0]
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if self.first_index>0 or not self.EOF:
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proton_current = proton_current[(proton_current.time>=self.data.packets.Time[0])&
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(proton_current.time<=self.data.packets.Time[-1])]
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return proton_current
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def info(self):
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output = ""
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for key in ['owner', 'experiment', 'sample', 'instrument_settings']:
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value = repr(getattr(self, key)).replace("\n","\n ")
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output += f'\n{key}={value},'
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output += '\n'
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return output
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def append(self, other):
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"""
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Append event streams from another file to this one. Adjusts the event indices in the
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packets to stay valid.
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"""
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new_events = np.concatenate([self.data.events, other.data.events]).view(np.recarray)
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new_pulses = np.concatenate([self.data.pulses, other.data.pulses]).view(np.recarray)
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new_proton_current = np.concatenate([self.data.proton_current, other.data.proton_current]).view(np.recarray)
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new_packets = np.concatenate([self.data.packets, other.data.packets]).view(np.recarray)
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new_packets.start_index[self.data.packets.shape[0]:] += self.data.events.shape[0]
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self.data = AmorEventStream(new_events, new_packets, new_pulses, new_proton_current)
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# Indicate that this is amodified dataset, basically counts number of appends as negative indices
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self.last_index = min(self.last_index-1, -1)
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self.file_list += other.file_list
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def __repr__(self):
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output = (f"AmorEventData({self.file_list!r}) # {self.data.events.shape[0]} events, "
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f"{self.data.pulses.shape[0]} pulses")
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return output
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def get_timeslice(self, start, end)->'AmorEventData':
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# return a new dataset with just events that occured in given time slice
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if not 'wallTime' in self.data.events.dtype.names:
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raise ValueError("This dataset is missing a wallTime that is required for time slicing")
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event_filter = self.data.events.wallTime>=start
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event_filter &= self.data.events.wallTime<end
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pulse_filter = self.data.pulses.time>=start
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pulse_filter &= self.data.pulses.time<end
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output = super().__new__(AmorEventData)
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for key, value in self.__dict__.items():
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if key == 'data':
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continue
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else:
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setattr(output, key, value)
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# TODO: this is not strictly correct, as the packet/event relationship is lost
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output.data = AmorEventStream(self.data.events[event_filter], self.data.packets,
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self.data.pulses[pulse_filter], self.data.proton_current)
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return output
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