314 lines
16 KiB
Python
314 lines
16 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
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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 . 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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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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ctime: 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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monitor1: float
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monitor2: 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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#-------------------------------------------------------------------------------------------------
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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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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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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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#-------------------------------------------------------------------------------------------------
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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(f'{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 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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#return matr
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#-------------------------------------------------------------------------------------------------
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def read_individual_data(self, fileName, norm=False):
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pixelLookUp = self.resolve_pixels()
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self.hdf = h5py.File(fileName, 'r', swmr=True)
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if self.readHeaderInfo:
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# read general information and first data set
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logging.info(f'# meta data from: {self.file_list[0]}')
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self.hdf = h5py.File(self.file_list[0], 'r', swmr=True)
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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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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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self.start_date = start_time.split(' ')[0]
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if self.config.sampleModel:
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model = self.config.sampleModel
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else:
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model = None
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# assembling orso header information
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self.header.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.header.owner.orcid = user_orcid
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self.header.experiment = fileio.Experiment(
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title = title,
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instrument = instrumentName,
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start_date = self.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.header.sample = fileio.Sample(
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name = sampleName,
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model = model,
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sample_parameters = None,
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)
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self.header.measurement_scheme = 'angle- and energy-dispersive'
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self.chopperDistance = float(np.take(self.hdf['entry1/Amor/chopper/pair_separation'], 0))
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self.detectorDistance = float(np.take(self.hdf['entry1/Amor/detector/transformation/distance'], 0))
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self.chopperDetectorDistance = self.detectorDistance - float(np.take(self.hdf['entry1/Amor/chopper/distance'], 0))
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self.tofCut = const.lamdaCut * self.chopperDetectorDistance / const.hdm * 1.e-13
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logging.info(f'# data from file: {fileName}')
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try:
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self.mu = float(np.take(self.hdf['/entry1/Amor/master_parameters/mu/value'], 0))
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self.nu = float(np.take(self.hdf['/entry1/Amor/master_parameters/nu/value'], 0))
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self.kap = float(np.take(self.hdf['/entry1/Amor/master_parameters/kap/value'], 0))
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self.kad = float(np.take(self.hdf['/entry1/Amor/master_parameters/kad/value'], 0))
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self.div = float(np.take(self.hdf['/entry1/Amor/master_parameters/div/value'], 0))
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self.chopperSpeed = float(np.take(self.hdf['/entry1/Amor/chopper/rotation_speed/value'], 0))
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self.chopperPhase = float(np.take(self.hdf['/entry1/Amor/chopper/phase/value'], 0))
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except(KeyError, IndexError):
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logging.warning(" using parameters from nicos cache")
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year_date = str(self.start_date).replace('-', '/', 1)
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cachePath = '/home/amor/nicosdata/amor/cache/'
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value = str(subprocess.getoutput(f'/usr/bin/grep "value" {cachePath}nicos-mu/{year_date}')).split('\t')[-1]
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self.mu = float(value)
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value = str(subprocess.getoutput(f'/usr/bin/grep "value" {cachePath}nicos-nu/{year_date}')).split('\t')[-1]
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self.nu = float(value)
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value = str(subprocess.getoutput(f'/usr/bin/grep "value" {cachePath}nicos-kap/{year_date}')).split('\t')[-1]
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self.kap = float(value)
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value = str(subprocess.getoutput(f'/usr/bin/grep "value" {cachePath}nicos-kad/{year_date}')).split('\t')[-1]
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self.kad = float(value)
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value = str(subprocess.getoutput(f'/usr/bin/grep "value" {cachePath}nicos-div/{year_date}')).split('\t')[-1]
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self.div = float(value)
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value = str(subprocess.getoutput(f'/usr/bin/grep "value" {cachePath}nicos-ch1_speed/{year_date}')).split('\t')[-1]
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self.chopperSpeed = float(value)
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self.chopperPhase = self.config.chopperPhase
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self.tau = 30. / self.chopperSpeed
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if self.config.muOffset:
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self.mu += self.config.muOffset
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if self.config.mu:
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self.mu = self.config.mu
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if self.config.nu:
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self.nu = self.config.nu
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# TODO: figure out real stop time....
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self.ctime=(self.hdf['/entry1/Amor/detector/data/event_time_zero'][-1]
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- self.hdf['/entry1/Amor/detector/data/event_time_zero'][0]) / 1.e9
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fileDate = datetime.fromisoformat( self.hdf['/entry1/start_time'][0].decode('utf-8') )
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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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if norm:
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self.header.measurement_additional_files.append(fileio.File(file=fileName.split('/')[-1], timestamp=fileDate))
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else:
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self.header.measurement_data_files.append(fileio.File(file=fileName.split('/')[-1], timestamp=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.kap:6.3f}')
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# TODO: should extract monitor from counts or beam current times time
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self.monitor1 = self.ctime
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self.monitor2 = self.monitor1
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# read data event streams
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tof_e = np.array(self.hdf['/entry1/Amor/detector/data/event_time_offset'][:])/1.e9
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pixelID_e = np.array(self.hdf['/entry1/Amor/detector/data/event_id'][:], dtype=int)
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totalNumber = np.shape(tof_e)[0]
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wallTime_e = np.empty(totalNumber)
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dataPacket_p = np.array(self.hdf['/entry1/Amor/detector/data/event_index'][:], dtype=np.uint64)
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dataPacketTime_p = np.array(self.hdf['/entry1/Amor/detector/data/event_time_zero'][:], dtype=np.uint64)/1e9
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#for i, index in enumerate(dataPacket_p):
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# wallTime_e[index:] = dataPacketTime_p[i]
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for i in range(len(dataPacket_p)-1):
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wallTime_e[dataPacket_p[i]:dataPacket_p[i+1]] = dataPacketTime_p[i]
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wallTime_e[dataPacket_p[-1]:] = dataPacketTime_p[-1]
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if not self.startTime and not norm:
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self.startTime = wallTime_e[0]
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wallTime_e -= self.startTime
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logging.debug(f'wall time from {wallTime_e[0]} to {wallTime_e[-1]}')
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# filter 'strange' tof times > 2 tau
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if True:
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filter_e = (tof_e <= 2*self.tau)
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tof_e = tof_e[filter_e]
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pixelID_e = pixelID_e[filter_e]
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wallTime_e = wallTime_e[filter_e]
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if np.shape(filter_e)[0]-np.shape(tof_e)[0] > 0.5 :
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logging.warning(f'# strange times: {np.shape(filter_e)[0]-np.shape(tof_e)[0]}')
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tof_e = np.remainder( tof_e - self.tofCut + self.tau, self.tau) + self.tofCut # tof shifted to 1 frame
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tof_e = tof_e + self.tau * self.config.chopperPhaseOffset / 180. # correction for time offset between chopper pulse and tof zero
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# resolve pixel ID into y and z indicees, x position and angle
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(detY_e, detZ_e, detXdist_e, delta_e) = pixelLookUp[np.int_(pixelID_e)-1,:].T
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# define mask and filter y range
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mask_e = (self.config.yRange[0] <= detY_e) & (detY_e <= self.config.yRange[1])
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# correct tof for beam size effect at chopper: t_cor = (delta / 180 deg) * tau
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# TODO: check for correctness
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if not self.config.offSpecular:
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tof_e -= ( delta_e / 180. ) * self.tau
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# lambda
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lamda_e = 1.e13 * tof_e * const.hdm / (self.chopperDetectorDistance + detXdist_e)
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self.lamdaMax = const.lamdaCut + 1.e13 * self.tau * const.hdm / (self.chopperDetectorDistance + 124.)
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mask_e = np.logical_and(mask_e, (self.config.lambdaRange[0] <= lamda_e) & (lamda_e <= self.config.lambdaRange[1]))
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# alpha_f
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alphaF_e = self.nu - self.mu + delta_e
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# q_z
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if self.config.offSpecular:
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alphaI = self.kap + self.kad + self.mu
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qz_e = 2 * np.pi * ( np.sin( np.deg2rad(alphaF_e) ) + np.sin( np.deg2rad( alphaI ) ) ) / lamda_e
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qx_e = 2 * np.pi * ( np.cos( np.deg2rad(alphaF_e) ) - np.cos( np.deg2rad( alphaI ) ) ) / lamda_e
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self.header.measurement_scheme = 'energy-dispersive',
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else:
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qz_e = 4 * np.pi * np.sin( np.deg2rad(alphaF_e) ) / 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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# filter q_z range
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if self.config.qzRange[1] < 0.3 and not norm:
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mask_e = np.logical_and(mask_e, (self.config.qzRange[0] <= qz_e) & (qz_e <= self.config.qzRange[1]))
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self.detZ_e = detZ_e[mask_e]
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self.lamda_e = lamda_e[mask_e]
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self.wallTime_e = wallTime_e[mask_e]
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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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