490 lines
24 KiB
Python
490 lines
24 KiB
Python
import logging
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import os
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import subprocess
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import sys
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from datetime import datetime, timezone
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from typing import List
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import h5py
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import numpy as np
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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 .instrument import Detector
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from .options import ExperimentConfig, ReaderConfig
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try:
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from . import nb_helpers
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except Exception:
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nb_helpers = None
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class AmorData:
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"""read meta-data and event streams from .hdf file(s), apply filters and conversions"""
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chopperDetectorDistance: float
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chopperDistance: float
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chopperPhase: float
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chopperSpeed: float
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div: float
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data_file_numbers: List[int]
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delta_z: np.ndarray
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detZ_e: np.ndarray
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lamda_e: np.ndarray
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wallTime_e: np.ndarray
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kad: float
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kap: float
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lambdaMax: float
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lambda_e: np.ndarray
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#monitor: float
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mu: float
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nu: float
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tau: float
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tofCut: float
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start_date: str
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monitorType: str
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seriesStartTime = None
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#-------------------------------------------------------------------------------------------------
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def __init__(self, header: Header, reader_config: ReaderConfig, config: ExperimentConfig,
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short_notation:str, norm=False):
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#self.startTime = reader_config.startTime
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self.header = header
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self.config = config
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self.reader_config = reader_config
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self.expand_file_list(short_notation)
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self.read_data(norm=norm)
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#-------------------------------------------------------------------------------------------------
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def read_data(self, norm=False):
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self.file_list = []
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for number in self.data_file_numbers:
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self.file_list.append(self.path_generator(number))
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## read specific meta data and measurement from first file
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if norm:
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self.readHeaderInfo = False
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else:
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self.readHeaderInfo = True
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_detZ_e = []
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_lamda_e = []
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_wallTime_e = []
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#_monitor = 0
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_monitorPerPulse = []
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_pulseTimeS = []
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for file in self.file_list:
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self.read_individual_data(file, norm)
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_detZ_e = np.append(_detZ_e, self.detZ_e)
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_lamda_e = np.append(_lamda_e, self.lamda_e)
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_wallTime_e = np.append(_wallTime_e, self.wallTime_e)
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_monitorPerPulse = np.append(_monitorPerPulse, self.monitorPerPulse)
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_pulseTimeS = np.append(_pulseTimeS, self.pulseTimeS)
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#_monitor += self.monitor
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self.detZ_e = _detZ_e
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self.lamda_e = _lamda_e
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self.wallTime_e = _wallTime_e
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#self.monitor = _monitor
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self.monitorPerPulse = _monitorPerPulse
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self.pulseTimeS = _pulseTimeS
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print(f' number of pulses: {np.shape(self.pulseTimeS)[0]}')
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#-------------------------------------------------------------------------------------------------
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#def path_generator(self, number):
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# fileName = f'amor{self.reader_config.year}n{number:06d}.hdf'
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# if os.path.exists(os.path.join(self.reader_config.dataPath,fileName)):
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# path = self.reader_config.dataPath
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# elif os.path.exists(fileName):
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# path = '.'
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# elif os.path.exists(os.path.join('.','raw', fileName)):
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# path = os.path.join('.','raw')
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# elif os.path.exists(os.path.join('..','raw', fileName)):
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# path = os.path.join('..','raw')
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# elif os.path.exists(f'/afs/psi.ch/project/sinqdata/{self.reader_config.year}/amor/{int(number/1000)}/{fileName}'):
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# path = f'/afs/psi.ch/project/sinqdata/{self.reader_config.year}/amor/{int(number/1000)}'
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# else:
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# sys.exit(f'# ERROR: the file {fileName} is nowhere to be found!')
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# return os.path.join(path, fileName)
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#-------------------------------------------------------------------------------------------------
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def path_generator(self, number):
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fileName = f'amor{self.reader_config.year}n{number:06d}.hdf'
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path = ''
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for rawd in self.reader_config.rawPath:
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if os.path.exists(os.path.join(rawd,fileName)):
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path = rawd
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break
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if not path:
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if os.path.exists(f'/afs/psi.ch/project/sinqdata/{self.reader_config.year}/amor/{int(number/1000)}/{fileName}'):
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path = f'/afs/psi.ch/project/sinqdata/{self.reader_config.year}/amor/{int(number/1000)}'
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else:
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sys.exit(f'# ERROR: the file {fileName} can not be found in {self.reader_config.rawPath}')
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return os.path.join(path, fileName)
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#-------------------------------------------------------------------------------------------------
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def expand_file_list(self, short_notation):
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"""Evaluate string entry for file number lists"""
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#log().debug('Executing get_flist')
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file_list=[]
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for i in short_notation.split(','):
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if '-' in i:
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if ':' in i:
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step = i.split(':', 1)[1]
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file_list += range(int(i.split('-', 1)[0]), int((i.rsplit('-', 1)[1]).split(':', 1)[0])+1, int(step))
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else:
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step = 1
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file_list += range(int(i.split('-', 1)[0]), int(i.split('-', 1)[1])+1, int(step))
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else:
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file_list += [int(i)]
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self.data_file_numbers=sorted(file_list)
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#-------------------------------------------------------------------------------------------------
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def resolve_pixels(self):
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"""determine spatial coordinats and angles from pixel number"""
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nPixel = Detector.nWires * Detector.nStripes * Detector.nBlades
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pixelID = np.arange(nPixel)
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(bladeNr, bPixel) = np.divmod(pixelID, Detector.nWires * Detector.nStripes)
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(bZi, detYi) = np.divmod(bPixel, Detector.nStripes) # z index on blade, y index on detector
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detZi = bladeNr * Detector.nWires + bZi # z index on detector
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detX = bZi * Detector.dX # x position in detector
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# detZ = Detector.zero - bladeNr * Detector.bladeZ - bZi * Detector.dZ # z position on detector
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bladeAngle = np.rad2deg( 2. * np.arcsin(0.5*Detector.bladeZ / Detector.distance) )
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delta = (Detector.nBlades/2. - bladeNr) * bladeAngle \
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- np.rad2deg( np.arctan(bZi*Detector.dZ / ( Detector.distance + bZi * Detector.dX) ) )
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self.delta_z = delta[detYi==1]
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return np.vstack((detYi.T, detZi.T, detX.T, delta.T)).T
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#-------------------------------------------------------------------------------------------------
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def read_individual_data(self, fileName, norm=False):
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self.hdf = h5py.File(fileName, 'r', swmr=True)
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if self.readHeaderInfo:
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self.read_header_info()
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logging.warning(f' from file: {fileName}')
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self.read_individual_header()
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# add header content
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if self.readHeaderInfo:
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self.readHeaderInfo = False
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self.header.measurement_instrument_settings = fileio.InstrumentSettings(
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incident_angle = fileio.ValueRange(round(self.mu+self.kap+self.kad-0.5*self.div, 3),
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round(self.mu+self.kap+self.kad+0.5*self.div, 3),
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'deg'),
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wavelength = fileio.ValueRange(const.lamdaCut, self.config.lambdaRange[1], 'angstrom'),
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polarization = fileio.Polarization.unpolarized,
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)
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self.header.measurement_instrument_settings.mu = fileio.Value(round(self.mu, 3), 'deg', comment='sample angle to horizon')
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self.header.measurement_instrument_settings.nu = fileio.Value(round(self.nu, 3), 'deg', comment='detector angle to horizon')
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self.header.measurement_instrument_settings.div = fileio.Value(round(self.div, 3), 'deg', comment='incoming beam divergence')
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self.header.measurement_instrument_settings.kap = fileio.Value(round(self.kap, 3), 'deg', comment='incoming beam inclination')
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if abs(self.kad)>0.02:
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self.header.measurement_instrument_settings.kad = fileio.Value(round(self.kad, 3), 'deg', comment='incoming beam angular offset')
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if norm:
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self.header.measurement_additional_files.append(fileio.File(file=fileName.split('/')[-1], timestamp=self.fileDate))
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else:
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self.header.measurement_data_files.append(fileio.File(file=fileName.split('/')[-1], timestamp=self.fileDate))
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logging.info(f' mu = {self.mu:6.3f}, nu = {self.nu:6.3f}, kap = {self.kap:6.3f}, kad = {self.kad:6.3f}')
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self.read_event_stream()
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totalNumber = np.shape(self.tof_e)[0]
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# check for empty event stream
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if totalNumber == 0:
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logging.error('empty event stream: can not determine end time')
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sys.exit()
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self.sort_pulses()
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self.associate_pulse_with_monitor()
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self.extract_walltime(norm)
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# following lines: debugging output to trace the time-offset of proton current and neutron pulses
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if self.config.monitorType == 'p':
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cpp, t_bins = np.histogram(self.wallTime_e, self.pulseTimeS)
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np.savetxt('tme.hst', np.vstack((self.pulseTimeS[:-1], cpp, self.monitorPerPulse[:-1])).T)
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#self.average_events_per_pulse() # for debugging only. VERY time consuming!!!
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self.monitor_threshold()
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self.filter_strange_times()
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self.merge_frames()
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self.filter_project_x()
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self.correct_for_chopper_opening()
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self.calculate_derived_properties()
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self.filter_qz_range(norm)
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logging.info(f' number of events: total = {totalNumber:7d}, filtered = {np.shape(self.lamda_e)[0]:7d}')
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def sort_pulses(self):
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chopperPeriod = np.int64(2*self.tau*1e9)
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pulseTime = np.sort(self.dataPacketTime_p)
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pulseTime = pulseTime[np.abs(pulseTime[:]-np.roll(pulseTime, 1)[:])>5]
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pulseTime -= np.int64(self.seriesStartTime)
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self.stopTime = pulseTime[-1]
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pulseTime = pulseTime[pulseTime>=0]
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# fill in missing pulse times
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# TODO: check for real end time
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self.pulseTimeS = np.array([], dtype=np.int64)
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try:
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# further files
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# TODO: use the first pulse of the respective measurement
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#nextPulseTime = startTime % np.int64(self.tau*2e9)
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#nextPulseTime = self.pulseTimeS[-1] + chopperPeriod
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nextPulseTime = pulseTime[0]
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except AttributeError:
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# first file
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nextPulseTime = pulseTime[0] % np.int64(self.tau*2e9)
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print(f' start time: {nextPulseTime/1e9}')
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for tt in pulseTime:
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while tt - nextPulseTime > self.tau*1e9:
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self.pulseTimeS = np.append(self.pulseTimeS, nextPulseTime)
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nextPulseTime += chopperPeriod
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self.pulseTimeS = np.append(self.pulseTimeS, tt)
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nextPulseTime = self.pulseTimeS[-1] + chopperPeriod
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print(f' number of pulses: {np.shape(self.pulseTimeS)[0]}')
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def get_current_per_pulse(self, pulseTimeS, currentTimeS, currents):
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# add currents for early pulses and current time value after last pulse (j+1)
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currentTimeS = np.hstack([[0], currentTimeS, [pulseTimeS[-1]+1]])
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currents = np.hstack([[0], currents])
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pulseCurrentS = np.zeros(pulseTimeS.shape[0], dtype=float)
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j = 0
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for i, ti in enumerate(pulseTimeS):
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if ti >= currentTimeS[j+1]:
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j += 1
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pulseCurrentS[i] = currents[j]
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#print(f' {i} {pulseTimeS[i]} {pulseCurrentS[i]}')
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return pulseCurrentS
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def associate_pulse_with_monitor(self):
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if self.config.monitorType == 'p': # protonCharge
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self.currentTime -= np.int64(self.seriesStartTime)
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self.currentTime -= np.int64(16e9) # time offset of proton current signal
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self.monitorPerPulse = self.get_current_per_pulse(self.pulseTimeS, self.currentTime, self.current) * 2*self.tau * 1e-3
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# filter low-current pulses
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self.monitorPerPulse = np.where(self.monitorPerPulse > 2*self.tau * self.config.lowCurrentThreshold * 1e-3, self.monitorPerPulse, 0)
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elif self.config.monitorType == 't': # countingTime
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self.monitorPerPulse = np.ones(np.shape(self.pulseTimeS)[0])*self.tau
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else:
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self.monitorPerPulse = 1./np.shape(pulseTimeS)[1]
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def extract_walltime(self, norm):
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if nb_helpers:
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self.wallTime_e = nb_helpers.extract_walltime(self.tof_e, self.dataPacket_p, self.dataPacketTime_p)
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else:
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self.wallTime_e = np.empty(np.shape(self.tof_e)[0], dtype=np.int64)
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for i in range(len(self.dataPacket_p)-1):
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self.wallTime_e[self.dataPacket_p[i]:self.dataPacket_p[i+1]] = self.dataPacketTime_p[i]
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self.wallTime_e[self.dataPacket_p[-1]:] = self.dataPacketTime_p[-1]
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self.wallTime_e -= np.int64(self.seriesStartTime)
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logging.debug(f' wall time from {self.wallTime_e[0]/1e9:6.1f} s to {self.wallTime_e[-1]/1e9:6.1f} s')
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def average_events_per_pulse(self):
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if self.config.monitorType == 'p':
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for i, time in enumerate(self.pulseTimeS):
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events = np.shape(self.wallTime_e[self.wallTime_e == time])[0]
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logging.info(f'pulse: {i:6.0f}, events: {events:6.0f}, monitor: {self.monitorPerPulse[i]:6.2f}')
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def monitor_threshold(self):
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if self.config.monitorType == 'p': # fix to check for file compatibility
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goodTimeS = self.pulseTimeS[self.monitorPerPulse!=0]
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filter_e = np.where(np.isin(self.wallTime_e, goodTimeS), True, False)
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self.tof_e = self.tof_e[filter_e]
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self.pixelID_e = self.pixelID_e[filter_e]
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self.wallTime_e = self.wallTime_e[filter_e]
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logging.info(f' rejected {np.shape(self.monitorPerPulse)[0]-np.shape(goodTimeS)[0]} out of {np.shape(self.monitorPerPulse)[0]} pulses')
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logging.info(f' with {np.shape(filter_e)[0]-np.shape(self.tof_e)[0]} events due to low beam current')
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logging.info(f' average counts per pulse = {np.shape(self.tof_e)[0] / np.shape(goodTimeS[goodTimeS!=0])[0]:7.1f}')
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def filter_qz_range(self, norm):
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if self.config.qzRange[1]<0.3 and not norm:
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self.mask_e = np.logical_and(self.mask_e,
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(self.config.qzRange[0]<=self.qz_e) & (self.qz_e<=self.config.qzRange[1]))
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self.detZ_e = self.detZ_e[self.mask_e]
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self.lamda_e = self.lamda_e[self.mask_e]
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self.wallTime_e = self.wallTime_e[self.mask_e]
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def calculate_derived_properties(self):
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self.lamdaMax = const.lamdaCut+1.e13*self.tau*const.hdm/(self.chopperDetectorDistance+124.)
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if nb_helpers:
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self.lamda_e, self.qz_e, self.mask_e = nb_helpers.calculate_derived_properties_focussing(
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self.tof_e, self.detXdist_e, self.delta_e, self.mask_e,
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self.config.lambdaRange[0], self.config.lambdaRange[1], self.nu, self.mu,
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self.chopperDetectorDistance, const.hdm
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)
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return
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# lambda
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self.lamda_e = (1.e13*const.hdm)*self.tof_e/(self.chopperDetectorDistance+self.detXdist_e)
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self.mask_e = np.logical_and(self.mask_e, (self.config.lambdaRange[0]<=self.lamda_e) & (
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self.lamda_e<=self.config.lambdaRange[1]))
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# alpha_f
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# q_z
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if self.config.incidentAngle == 'alphaF':
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alphaF_e = self.nu - self.mu + self.delta_e
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self.qz_e = 4*np.pi*(np.sin(np.deg2rad(alphaF_e))/self.lamda_e)
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# qx_e = 0.
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self.header.measurement_scheme = 'angle- and energy-dispersive'
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elif self.config.incidentAngle == 'nu':
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alphaF_e = (self.nu + self.delta_e + self.kap + self.kad) / 2.
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self.qz_e = 4*np.pi*(np.sin(np.deg2rad(alphaF_e))/self.lamda_e)
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# qx_e = 0.
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self.header.measurement_scheme = 'energy-dispersive'
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else:
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alphaF_e = self.nu - self.mu + self.delta_e
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alphaI = self.kap + self.kad + self.mu
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self.qz_e = 2*np.pi * ((np.sin(np.deg2rad(alphaF_e)) + np.sin(np.deg2rad(alphaI)))/self.lamda_e)
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self.qx_e = 2*np.pi * ((np.cos(np.deg2rad(alphaF_e)) - np.cos(np.deg2rad(alphaI)))/self.lamda_e)
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self.header.measurement_scheme = 'energy-dispersive'
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def correct_for_chopper_opening(self):
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# correct tof for beam size effect at chopper: t_cor = (delta / 180 deg) * tau
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if self.config.incidentAngle == 'alphaF':
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self.tof_e -= ( self.delta_e / 180. ) * self.tau
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else:
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# TODO: check sign of correction
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self.tof_e -= ( self.kad / 180. ) * self.tau
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def filter_project_x(self):
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pixelLookUp = self.resolve_pixels()
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if nb_helpers:
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(self.detZ_e, self.detXdist_e, self.delta_e, self.mask_e) = nb_helpers.filter_project_x(
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pixelLookUp, self.pixelID_e.astype(np.int64), self.config.yRange[0], self.config.yRange[1]
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)
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else:
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# resolve pixel ID into y and z indicees, x position and angle
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(detY_e, self.detZ_e, self.detXdist_e, self.delta_e) = pixelLookUp[np.int_(self.pixelID_e)-1, :].T
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# define mask and filter y range
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self.mask_e = (self.config.yRange[0]<=detY_e) & (detY_e<=self.config.yRange[1])
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def merge_frames(self):
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total_offset = self.tofCut+self.tau*self.config.chopperPhaseOffset/180.
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if nb_helpers:
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self.tof_e = nb_helpers.merge_frames(self.tof_e, self.tofCut, self.tau, total_offset)
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else:
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self.tof_e = np.remainder(self.tof_e-(self.tofCut-self.tau), self.tau)+total_offset # tof shifted to 1 frame
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def filter_strange_times(self):
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# 'strange' tof times are those with t > 2 tau (originating from the efu)
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filter_e = (self.tof_e<=2*self.tau)
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self.tof_e = self.tof_e[filter_e]
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self.pixelID_e = self.pixelID_e[filter_e]
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self.wallTime_e = self.wallTime_e[filter_e]
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if np.shape(filter_e)[0]-np.shape(self.tof_e)[0]>0.5:
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logging.warning(f'# strange times: {np.shape(filter_e)[0]-np.shape(self.tof_e)[0]}')
|
|
|
|
def read_event_stream(self):
|
|
self.tof_e = np.array(self.hdf['/entry1/Amor/detector/data/event_time_offset'][:])/1.e9
|
|
self.pixelID_e = np.array(self.hdf['/entry1/Amor/detector/data/event_id'][:], dtype=np.int64)
|
|
self.dataPacket_p = np.array(self.hdf['/entry1/Amor/detector/data/event_index'][:], dtype=np.uint64)
|
|
self.dataPacketTime_p = np.array(self.hdf['/entry1/Amor/detector/data/event_time_zero'][:], dtype=np.int64)
|
|
if self.config.monitorType in ['auto', 'p']:
|
|
try:
|
|
self.currentTime = np.array(self.hdf['entry1/Amor/detector/proton_current/time'][:], dtype=np.int64)
|
|
self.current = np.array(self.hdf['entry1/Amor/detector/proton_current/value'][:,0], dtype=float)
|
|
if len(self.current)>4:
|
|
self.config.monitorType = 'p'
|
|
else:
|
|
self.config.monitorType = 't'
|
|
except(KeyError, IndexError):
|
|
self.config.monitorType = 't'
|
|
else:
|
|
self.config.monitorType = 't'
|
|
#TODO: protonMonitor
|
|
|
|
def read_individual_header(self):
|
|
self.chopperDistance = float(np.take(self.hdf['entry1/Amor/chopper/pair_separation'], 0))
|
|
self.detectorDistance = float(np.take(self.hdf['entry1/Amor/detector/transformation/distance'], 0))
|
|
self.chopperDetectorDistance = self.detectorDistance-float(np.take(self.hdf['entry1/Amor/chopper/distance'], 0))
|
|
self.tofCut = const.lamdaCut*self.chopperDetectorDistance/const.hdm*1.e-13
|
|
|
|
try:
|
|
self.mu = float(np.take(self.hdf['/entry1/Amor/master_parameters/mu/value'], 0))
|
|
self.nu = float(np.take(self.hdf['/entry1/Amor/master_parameters/nu/value'], 0))
|
|
self.kap = float(np.take(self.hdf['/entry1/Amor/master_parameters/kap/value'], 0))
|
|
self.kad = float(np.take(self.hdf['/entry1/Amor/master_parameters/kad/value'], 0))
|
|
self.div = float(np.take(self.hdf['/entry1/Amor/master_parameters/div/value'], 0))
|
|
self.chopperSpeed = float(np.take(self.hdf['/entry1/Amor/chopper/rotation_speed/value'], 0))
|
|
self.chopperPhase = float(np.take(self.hdf['/entry1/Amor/chopper/phase/value'], 0))
|
|
except(KeyError, IndexError):
|
|
logging.warning(" using parameters from nicos cache")
|
|
year_date = str(self.start_date).replace('-', '/', 1)
|
|
#cachePath = '/home/amor/nicosdata/amor/cache/'
|
|
#cachePath = '/home/nicos/amorcache/'
|
|
cachePath = '/home/amor/cache/'
|
|
value = str(subprocess.getoutput(f'/usr/bin/grep "value" {cachePath}nicos-mu/{year_date}')).split('\t')[-1]
|
|
self.mu = float(value)
|
|
value = str(subprocess.getoutput(f'/usr/bin/grep "value" {cachePath}nicos-nu/{year_date}')).split('\t')[-1]
|
|
self.nu = float(value)
|
|
value = str(subprocess.getoutput(f'/usr/bin/grep "value" {cachePath}nicos-kap/{year_date}')).split('\t')[-1]
|
|
self.kap = float(value)
|
|
value = str(subprocess.getoutput(f'/usr/bin/grep "value" {cachePath}nicos-kad/{year_date}')).split('\t')[-1]
|
|
self.kad = float(value)
|
|
value = str(subprocess.getoutput(f'/usr/bin/grep "value" {cachePath}nicos-div/{year_date}')).split('\t')[-1]
|
|
self.div = float(value)
|
|
value = str(subprocess.getoutput(f'/usr/bin/grep "value" {cachePath}nicos-ch1_speed/{year_date}')).split('\t')[-1]
|
|
self.chopperSpeed = float(value)
|
|
self.chopperPhase = self.config.chopperPhase
|
|
self.tau = 30. / self.chopperSpeed
|
|
|
|
if self.config.muOffset:
|
|
self.mu += self.config.muOffset
|
|
if self.config.mu:
|
|
self.mu = self.config.mu
|
|
if self.config.nu:
|
|
self.nu = self.config.nu
|
|
|
|
# extract start time as unix time, adding UTC offset of 1h to time string
|
|
self.fileDate = datetime.fromisoformat( self.hdf['/entry1/start_time'][0].decode('utf-8')+"+01:00" )
|
|
self.startTime = np.int64( (self.fileDate.timestamp() ) * 1e9 )
|
|
if self.seriesStartTime is None:
|
|
self.seriesStartTime = self.startTime
|
|
|
|
def read_header_info(self):
|
|
# read general information and first data set
|
|
logging.info(f' meta data from: {self.file_list[0]}')
|
|
self.hdf = h5py.File(self.file_list[0], 'r', swmr=True)
|
|
title = self.hdf['entry1/title'][0].decode('utf-8')
|
|
proposal_id = self.hdf['entry1/proposal_id'][0].decode('utf-8')
|
|
user_name = self.hdf['entry1/user/name'][0].decode('utf-8')
|
|
user_affiliation = 'unknown'
|
|
user_email = self.hdf['entry1/user/email'][0].decode('utf-8')
|
|
user_orcid = None
|
|
sampleName = self.hdf['entry1/sample/name'][0].decode('utf-8')
|
|
model = self.hdf['entry1/sample/model'][0].decode('utf-8')
|
|
instrumentName = 'Amor'
|
|
source = self.hdf['entry1/Amor/source/name'][0].decode('utf-8')
|
|
sourceProbe = 'neutron'
|
|
start_time = self.hdf['entry1/start_time'][0].decode('utf-8')
|
|
self.start_date = start_time.split(' ')[0]
|
|
if self.config.sampleModel:
|
|
model = self.config.sampleModel
|
|
# assembling orso header information
|
|
self.header.owner = fileio.Person(
|
|
name=user_name,
|
|
affiliation=user_affiliation,
|
|
contact=user_email,
|
|
)
|
|
if user_orcid:
|
|
self.header.owner.orcid = user_orcid
|
|
self.header.experiment = fileio.Experiment(
|
|
title=title,
|
|
instrument=instrumentName,
|
|
start_date=self.start_date,
|
|
probe=sourceProbe,
|
|
facility=source,
|
|
proposalID=proposal_id
|
|
)
|
|
self.header.sample = fileio.Sample(
|
|
name=sampleName,
|
|
model=SampleModel(stack=model),
|
|
sample_parameters=None,
|
|
)
|
|
self.header.measurement_scheme = 'angle- and energy-dispersive'
|
|
|