Start refactoring reduction into multiple methods
This commit is contained in:
+9
-1
@@ -13,7 +13,6 @@ from . import __version__, const
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from .instrument import Detector
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from .options import ExperimentConfig, ReaderConfig
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class Header:
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"""orso compatible output file header content"""
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@@ -58,6 +57,15 @@ class Header:
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]
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return cols
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def orso_header(self, columns=None, extra_columns=[]):
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"""
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Generate ORSO header from a copy of this class' data.
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"""
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ds = fileio.DataSource.from_dict(self.data_source().to_dict())
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red = fileio.Reduction.from_dict(self.reduction.to_dict())
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if columns is None:
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columns = self.columns()
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return fileio.Orso(ds, red, columns+extra_columns)
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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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+192
-188
@@ -24,197 +24,202 @@ class AmorReduction:
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if not os.path.exists(f'{self.reader_config.dataPath}'):
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logging.debug(f'Creating destination path {self.reader_config.dataPath}')
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os.system(f'mkdir {self.reader_config.dataPath}')
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fromHDF = AmorData(self.startTime, header=self.header, reader_config=self.reader_config, config=self.experiment_config)
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# load or create normalisation matrix
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if self.reduction_config.normalisationFileIdentifier:
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normalise = True
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norm_lz, normAngle, normFileList = self.normalisation_map(self.reduction_config.normalisationFileIdentifier[0])
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self.header.reduction.corrections.append('normalisation with \'additional files\'')
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self.create_normalisation_map(self.reduction_config.normalisationFileIdentifier[0])
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else:
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normalise = False
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norm_lz = self.grid.lz()
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normAngle = 1.
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self.norm_lz = self.grid.lz()
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self.normAngle = 1.
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logging.warning('normalisation matrix: none requested')
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# load R(q_z) curve to be subtracted:
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if self.reduction_config.subtract:
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sq_q, sR_q, sdR_q, sFileName = self.loadRqz(self.reduction_config.subtract)
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subtract = True
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logging.warning(f'loaded background file: {sFileName}')
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self.header.reduction.corrections.append(f'background from \'{sFileName}\' subtracted')
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self.sq_q, self.sR_q, self.sdR_q, self.sFileName = self.loadRqz(self.reduction_config.subtract)
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logging.warning(f'loaded background file: {self.sFileName}')
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self.header.reduction.corrections.append(f'background from \'{self.sFileName}\' subtracted')
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self.subtract = True
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else:
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subtract = False
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self.subtract = False
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# load measurement data and do the reduction
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datasetsRqz = []
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datasetsRlt = []
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self.datasetsRqz = []
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self.datasetsRlt = []
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self.file_reader = AmorData(self.startTime, header=self.header, reader_config=self.reader_config, config=self.experiment_config)
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for i, short_notation in enumerate(self.reduction_config.fileIdentifier):
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logging.warning('reading input:')
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self.header.measurement_data_files = []
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fromHDF.read_data(short_notation)
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if self.reduction_config.timeSlize:
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wallTime_e = fromHDF.wallTime_e
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columns = self.header.columns()+[fileio.Column('time', 's', 'time relative to start of measurement series')]
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headerRqz = fileio.Orso(self.header.data_source(), self.header.reduction, columns)
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interval = self.reduction_config.timeSlize[0]
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try:
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start = self.reduction_config.timeSlize[1]
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except:
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start = 0
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try:
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stop = self.reduction_config.timeSlize[2]
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except:
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stop = wallTime_e[-1]
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# make overwriting log lines possible by removing newline at the end
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logging.StreamHandler.terminator = "\r"
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for i, time in enumerate(np.arange(start, stop, interval)):
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logging.warning(f' time slize {i:4d}')
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filter_e = np.where((time<wallTime_e) & (wallTime_e<time+interval), True, False)
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lamda_e = fromHDF.lamda_e[filter_e]
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detZ_e = fromHDF.detZ_e[filter_e]
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qz_lz, ref_lz, err_lz, res_lz, lamda_lz, theta_lz, int_lz, mask_lz = self.project_on_lz(
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fromHDF, norm_lz, normAngle, lamda_e, detZ_e)
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q_q, R_q, dR_q, dq_q = self.project_on_qz(qz_lz, ref_lz, err_lz, res_lz, norm_lz, mask_lz)
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filter_q = np.where((self.experiment_config.qzRange[0]<q_q) & (q_q<self.experiment_config.qzRange[1]),
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True, False)
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q_q = q_q[filter_q]
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R_q = R_q[filter_q]
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dR_q = dR_q[filter_q]
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dq_q = dq_q[filter_q]
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if self.reduction_config.autoscale:
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R_q, dR_q = self.autoscale(q_q, R_q, dR_q)
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if subtract:
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if len(q_q)==len(sq_q):
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R_q -= sR_q
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dR_q = np.sqrt(dR_q**2+sdR_q**2)
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else:
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subtract = False
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logging.warning(
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f'background file {sFileName} not compatible with q_z scale ({len(sq_q)} vs. {len(q_q)})')
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tme_q = np.ones(np.shape(q_q))*time
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data = np.array([q_q, R_q, dR_q, dq_q, tme_q]).T
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headerRqz.data_set = f'{i}: time = {time:8.1f} s to {time+interval:8.1f} s'
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orso_data = fileio.OrsoDataset(headerRqz, data)
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# make a copy of the header for the next iteration
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headerRqz = fileio.Orso.from_dict(headerRqz.to_dict())
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datasetsRqz.append(orso_data)
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# reset normal logging behavior
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logging.StreamHandler.terminator = "\n"
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logging.warning(f' time slize {i:4d}, done')
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else:
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lamda_e = fromHDF.lamda_e
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detZ_e = fromHDF.detZ_e
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qz_lz, ref_lz, err_lz, res_lz, lamda_lz, theta_lz, int_lz, mask_lz = self.project_on_lz(
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fromHDF, norm_lz, normAngle, lamda_e, detZ_e)
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try:
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ref_lz *= self.reduction_config.scale[i]
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err_lz *= self.reduction_config.scale[i]
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except IndexError:
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ref_lz *= self.reduction_config.scale[-1]
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err_lz *= self.reduction_config.scale[-1]
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if 'Rqz.ort' in self.output_config.outputFormats:
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headerRqz = fileio.Orso(self.header.data_source(), self.header.reduction, self.header.columns())
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headerRqz.data_set = f'Nr {i} : mu = {fromHDF.mu:6.3f} deg'
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# projection on q-grid
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q_q, R_q, dR_q, dq_q = self.project_on_qz(qz_lz, ref_lz, err_lz, res_lz, norm_lz, mask_lz, self.grid)
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filter_q = np.where((self.experiment_config.qzRange[0]<q_q) & (q_q<self.experiment_config.qzRange[1]), True, False)
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q_q = q_q[filter_q]
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R_q = R_q[filter_q]
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dR_q = dR_q[filter_q]
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dq_q = dq_q[filter_q]
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if self.reduction_config.autoscale:
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if i==0:
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R_q, dR_q = self.autoscale(q_q, R_q, dR_q)
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else:
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pRq_z = datasetsRqz[i-1].data[:, 1]
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pdRq_z = datasetsRqz[i-1].data[:, 2]
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R_q, dR_q = self.autoscale(q_q, R_q, dR_q, pRq_z, pdRq_z)
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if subtract:
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if len(q_q)==len(sq_q):
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R_q -= sR_q
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dR_q = np.sqrt(dR_q**2+sdR_q**2)
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else:
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logging.warning(
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f'backgroung file {sFileName} not compatible with q_z scale ({len(sq_q)} vs. {len(q_q)})')
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data = np.array([q_q, R_q, dR_q, dq_q]).T
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orso_data = fileio.OrsoDataset(headerRqz, data)
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headerRqz = fileio.Orso(**headerRqz.to_dict())
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datasetsRqz.append(orso_data)
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if 'Rlt.ort' in self.output_config.outputFormats:
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columns = [
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fileio.Column('Qz', '1/angstrom', 'normal momentum transfer'),
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fileio.Column('R', '', 'specular reflectivity'),
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fileio.ErrorColumn(error_of='R', error_type='uncertainty', value_is='sigma'),
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fileio.ErrorColumn(error_of='Qz', error_type='resolution', value_is='sigma'),
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fileio.Column('lambda', 'angstrom', 'wavelength'),
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fileio.Column('alpha_f', 'deg', 'final angle'),
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fileio.Column('l', '', 'index of lambda-bin'),
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fileio.Column('t', '', 'index of theta bin'),
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fileio.Column('intensity', '', 'filtered neutron events per pixel'),
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fileio.Column('norm', '', 'normalisation matrix'),
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fileio.Column('mask', '', 'pixels used for calculating R(q_z)'),
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]
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# data_source = fromHDF.data_source
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headerRlt = fileio.Orso(self.header.data_source, self.header.reduction, columns)
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ts, zs = ref_lz.shape
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lindex_lz = np.tile(np.arange(1, ts+1), (zs, 1)).T
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tindex_lz = np.tile(np.arange(1, zs+1), (ts, 1))
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j = 0
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for item in zip(
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qz_lz.T,
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ref_lz.T,
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err_lz.T,
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res_lz.T,
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lamda_lz.T,
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theta_lz.T,
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lindex_lz.T,
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tindex_lz.T,
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int_lz.T,
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norm_lz.T,
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np.where(mask_lz, 1, 0).T
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):
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data = np.array(list(item)).T
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headerRlt = fileio.Orso(**headerRlt.to_dict())
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headerRlt.data_set = f'dataset_{i}_{j+1} : alpha_f = {theta_lz[0, j]:6.3f} deg'
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orso_data = fileio.OrsoDataset(headerRlt, data)
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datasetsRlt.append(orso_data)
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j += 1
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self.read_single_file(i, short_notation)
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# output
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logging.warning('output:')
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if 'Rqz.ort' in self.output_config.outputFormats:
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logging.warning(f' {self.reader_config.dataPath}/{self.output_config.outputName}.Rqz.ort')
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theSecondLine = f' {self.header.experiment.title} | {self.header.experiment.start_date} | sample {self.header.sample.name} | R(q_z)'
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fileio.save_orso(datasetsRqz, f'{self.reader_config.dataPath}/{self.output_config.outputName}.Rqz.ort', data_separator='\n',
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comment=theSecondLine)
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self.save_Rqz()
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if 'Rlt.ort' in self.output_config.outputFormats:
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logging.warning(f' {self.reader_config.dataPath}/{self.output_config.outputName}.Rlt.ort')
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theSecondLine = f' {self.header.experiment.title} | {self.header.experiment.start_date} | sample {self.header.sample.name} | R(lambda, theta)'
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fileio.save_orso(datasetsRlt, f'{self.reader_config.dataPath}/{self.output_config.outputName}.Rlt.ort', data_separator='\n',
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comment=theSecondLine)
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self.save_Rtl()
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def read_single_file(self, i, short_notation):
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logging.warning('reading input:')
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self.header.measurement_data_files = []
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self.file_reader.read_data(short_notation)
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if self.reduction_config.timeSlize:
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self.read_timeslices(i)
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else:
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self.read_unsliced(i)
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def read_unsliced(self, i):
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lamda_e = self.file_reader.lamda_e
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detZ_e = self.file_reader.detZ_e
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qz_lz, ref_lz, err_lz, res_lz, lamda_lz, theta_lz, int_lz, self.mask_lz = self.project_on_lz(
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self.file_reader, self.norm_lz, self.normAngle, lamda_e, detZ_e)
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try:
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ref_lz *= self.reduction_config.scale[i]
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err_lz *= self.reduction_config.scale[i]
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except IndexError:
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ref_lz *= self.reduction_config.scale[-1]
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err_lz *= self.reduction_config.scale[-1]
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if 'Rqz.ort' in self.output_config.outputFormats:
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headerRqz = self.header.orso_header()
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headerRqz.data_set = f'Nr {i} : mu = {self.file_reader.mu:6.3f} deg'
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# projection on q-grid
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q_q, R_q, dR_q, dq_q = self.project_on_qz(qz_lz, ref_lz, err_lz, res_lz, self.norm_lz, self.mask_lz)
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filter_q = np.where((self.experiment_config.qzRange[0]<q_q) & (q_q<self.experiment_config.qzRange[1]),
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True, False)
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q_q = q_q[filter_q]
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R_q = R_q[filter_q]
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dR_q = dR_q[filter_q]
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dq_q = dq_q[filter_q]
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if self.reduction_config.autoscale:
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if i==0:
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R_q, dR_q = self.autoscale(q_q, R_q, dR_q)
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else:
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pRq_z = self.datasetsRqz[i-1].data[:, 1]
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pdRq_z = self.datasetsRqz[i-1].data[:, 2]
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R_q, dR_q = self.autoscale(q_q, R_q, dR_q, pRq_z, pdRq_z)
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if self.subtract:
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if len(q_q)==len(self.sq_q):
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R_q -= self.sR_q
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dR_q = np.sqrt(dR_q**2+self.sdR_q**2)
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else:
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logging.warning(
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f'backgroung file {self.sFileName} not compatible with q_z scale ({len(self.sq_q)} vs. {len(q_q)})')
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data = np.array([q_q, R_q, dR_q, dq_q]).T
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orso_data = fileio.OrsoDataset(headerRqz, data)
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self.datasetsRqz.append(orso_data)
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if 'Rlt.ort' in self.output_config.outputFormats:
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columns = [
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fileio.Column('Qz', '1/angstrom', 'normal momentum transfer'),
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fileio.Column('R', '', 'specular reflectivity'),
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fileio.ErrorColumn(error_of='R', error_type='uncertainty', value_is='sigma'),
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fileio.ErrorColumn(error_of='Qz', error_type='resolution', value_is='sigma'),
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fileio.Column('lambda', 'angstrom', 'wavelength'),
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fileio.Column('alpha_f', 'deg', 'final angle'),
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fileio.Column('l', '', 'index of lambda-bin'),
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fileio.Column('t', '', 'index of theta bin'),
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fileio.Column('intensity', '', 'filtered neutron events per pixel'),
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fileio.Column('norm', '', 'normalisation matrix'),
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fileio.Column('mask', '', 'pixels used for calculating R(q_z)'),
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]
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# data_source = file_reader.data_source
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ts, zs = ref_lz.shape
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lindex_lz = np.tile(np.arange(1, ts+1), (zs, 1)).T
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tindex_lz = np.tile(np.arange(1, zs+1), (ts, 1))
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j = 0
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for item in zip(
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qz_lz.T,
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ref_lz.T,
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err_lz.T,
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res_lz.T,
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lamda_lz.T,
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theta_lz.T,
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lindex_lz.T,
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tindex_lz.T,
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int_lz.T,
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self.norm_lz.T,
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np.where(self.mask_lz, 1, 0).T
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):
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data = np.array(list(item)).T
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headerRlt = self.header.orso_header(columns=columns)
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headerRlt.data_set = f'dataset_{i}_{j+1} : alpha_f = {theta_lz[0, j]:6.3f} deg'
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orso_data = fileio.OrsoDataset(headerRlt, data)
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self.datasetsRlt.append(orso_data)
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j += 1
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def read_timeslices(self, i):
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wallTime_e = self.file_reader.wallTime_e
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interval = self.reduction_config.timeSlize[0]
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try:
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start = self.reduction_config.timeSlize[1]
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except:
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start = 0
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try:
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stop = self.reduction_config.timeSlize[2]
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except:
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stop = wallTime_e[-1]
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# make overwriting log lines possible by removing newline at the end
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logging.StreamHandler.terminator = "\r"
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for ti, time in enumerate(np.arange(start, stop, interval)):
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logging.warning(f' time slize {ti:4d}')
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filter_e = np.where((time<wallTime_e) & (wallTime_e<time+interval), True, False)
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lamda_e = self.file_reader.lamda_e[filter_e]
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detZ_e = self.file_reader.detZ_e[filter_e]
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qz_lz, ref_lz, err_lz, res_lz, lamda_lz, theta_lz, int_lz, mask_lz = self.project_on_lz(
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self.file_reader, self.norm_lz, self.normAngle, lamda_e, detZ_e)
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q_q, R_q, dR_q, dq_q = self.project_on_qz(qz_lz, ref_lz, err_lz, res_lz, self.norm_lz, mask_lz)
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filter_q = np.where((self.experiment_config.qzRange[0]<q_q) & (q_q<self.experiment_config.qzRange[1]),
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True, False)
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q_q = q_q[filter_q]
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R_q = R_q[filter_q]
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dR_q = dR_q[filter_q]
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dq_q = dq_q[filter_q]
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if self.reduction_config.autoscale:
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R_q, dR_q = self.autoscale(q_q, R_q, dR_q)
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if self.subtract:
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if len(q_q)==len(self.sq_q):
|
||||
R_q -= self.sR_q
|
||||
dR_q = np.sqrt(dR_q**2+self.sdR_q**2)
|
||||
else:
|
||||
self.subtract = False
|
||||
logging.warning(
|
||||
f'background file {self.sFileName} not compatible with q_z scale ({len(self.sq_q)} vs. {len(q_q)})')
|
||||
|
||||
tme_q = np.ones(np.shape(q_q))*time
|
||||
data = np.array([q_q, R_q, dR_q, dq_q, tme_q]).T
|
||||
headerRqz = self.header.orso_header(
|
||||
extra_columns=[fileio.Column('time', 's', 'time relative to start of measurement series')])
|
||||
headerRqz.data_set = f'{i}_{ti}: time = {time:8.1f} s to {time+interval:8.1f} s'
|
||||
orso_data = fileio.OrsoDataset(headerRqz, data)
|
||||
self.datasetsRqz.append(orso_data)
|
||||
# reset normal logging behavior
|
||||
logging.StreamHandler.terminator = "\n"
|
||||
logging.warning(f' time slize {ti:4d}, done')
|
||||
|
||||
def save_Rqz(self):
|
||||
logging.warning(f' {self.reader_config.dataPath}/{self.output_config.outputName}.Rqz.ort')
|
||||
theSecondLine = f' {self.header.experiment.title} | {self.header.experiment.start_date} | sample {self.header.sample.name} | R(q_z)'
|
||||
fileio.save_orso(self.datasetsRqz, f'{self.reader_config.dataPath}/{self.output_config.outputName}.Rqz.ort',
|
||||
data_separator='\n',
|
||||
comment=theSecondLine)
|
||||
|
||||
def save_Rtl(self):
|
||||
logging.warning(f' {self.reader_config.dataPath}/{self.output_config.outputName}.Rlt.ort')
|
||||
theSecondLine = f' {self.header.experiment.title} | {self.header.experiment.start_date} | sample {self.header.sample.name} | R(lambda, theta)'
|
||||
fileio.save_orso(self.datasetsRlt, f'{self.reader_config.dataPath}/{self.output_config.outputName}.Rlt.ort',
|
||||
data_separator='\n',
|
||||
comment=theSecondLine)
|
||||
|
||||
def autoscale(self, q_q, R_q, dR_q, pR_q=[], pdR_q=[]):
|
||||
autoscale = self.reduction_config.autoscale
|
||||
@@ -282,7 +287,7 @@ class AmorReduction:
|
||||
|
||||
return q_q, Sq_q, dS_q, fileName
|
||||
|
||||
def normalisation_map(self, short_notation):
|
||||
def create_normalisation_map(self, short_notation):
|
||||
dataPath = self.reader_config.dataPath
|
||||
fromHDF = AmorData(self.startTime, header=self.header, reader_config=self.reader_config, config=self.experiment_config)
|
||||
normalisation_list = expand_file_list(short_notation)
|
||||
@@ -291,47 +296,46 @@ class AmorReduction:
|
||||
name = f'{name}_{normalisation_list[i]}'
|
||||
if os.path.exists(f'{dataPath}/{name}.norm'):
|
||||
logging.info(f'# normalisation matrix: found and using {dataPath}/{name}.norm')
|
||||
norm_lz = np.loadtxt(f'{dataPath}/{name}.norm')
|
||||
self.norm_lz = np.loadtxt(f'{dataPath}/{name}.norm')
|
||||
fh = open(f'{dataPath}/{name}.norm', 'r')
|
||||
fh.readline()
|
||||
normFileList = fh.readline().split('[')[1].split(']')[0].replace('\'', '').split(', ')
|
||||
normAngle = float(fh.readline().split('= ')[1])
|
||||
self.normFileList = fh.readline().split('[')[1].split(']')[0].replace('\'', '').split(', ')
|
||||
self.normAngle = float(fh.readline().split('= ')[1])
|
||||
fh.close()
|
||||
for i, entry in enumerate(normFileList):
|
||||
normFileList[i] = entry.split('/')[-1]
|
||||
self.header.measurement_additional_files = normFileList
|
||||
for i, entry in enumerate(self.normFileList):
|
||||
self.normFileList[i] = entry.split('/')[-1]
|
||||
self.header.measurement_additional_files = self.normFileList
|
||||
else:
|
||||
logging.info(f'# normalisation matrix: using the files {normalisation_list}')
|
||||
fromHDF.read_data(short_notation, norm=True)
|
||||
normAngle = fromHDF.nu - fromHDF.mu
|
||||
self.normAngle = fromHDF.nu - fromHDF.mu
|
||||
lamda_e = fromHDF.lamda_e
|
||||
detZ_e = fromHDF.detZ_e
|
||||
norm_lz, bins_l, bins_z = np.histogram2d(lamda_e, detZ_e, bins = (self.grid.lamda(), self.grid.z()))
|
||||
norm_lz = np.where(norm_lz>0, norm_lz, np.nan)
|
||||
self.norm_lz, bins_l, bins_z = np.histogram2d(lamda_e, detZ_e, bins = (self.grid.lamda(), self.grid.z()))
|
||||
self.norm_lz = np.where(self.norm_lz>0, self.norm_lz, np.nan)
|
||||
# correct for the SM reflectivity
|
||||
lamda_l = self.grid.lamda()
|
||||
theta_z = normAngle + fromHDF.delta_z
|
||||
theta_z = self.normAngle + fromHDF.delta_z
|
||||
lamda_lz = (self.grid.lz().T*lamda_l[:-1]).T
|
||||
theta_lz = self.grid.lz()*theta_z
|
||||
qz_lz = 4.0*np.pi * np.sin(np.deg2rad(theta_lz)) / lamda_lz
|
||||
Rsm_lz = np.ones(np.shape(qz_lz))
|
||||
Rsm_lz = np.where(qz_lz>0.0217, 1-(qz_lz-0.0217)*(0.0625/0.0217), Rsm_lz)
|
||||
Rsm_lz = np.where(qz_lz>0.0217*5, np.nan, Rsm_lz)
|
||||
norm_lz = norm_lz / Rsm_lz
|
||||
self.norm_lz = self.norm_lz / Rsm_lz
|
||||
if len(lamda_e) > 1e6:
|
||||
head = ('normalisation matrix based on the measurements\n'
|
||||
f'{fromHDF.file_list}\n'
|
||||
f'nu - mu = {normAngle}\n'
|
||||
f'shape= {np.shape(norm_lz)} (lambda, z)\n'
|
||||
f'nu - mu = {self.normAngle}\n'
|
||||
f'shape= {np.shape(self.norm_lz)} (lambda, z)\n'
|
||||
f'measured at mu = {fromHDF.mu:6.3f} deg\n'
|
||||
f'N(l_lambda, z) = theta(z) / sum_i=-1..1 I(l_lambda+i, z)')
|
||||
head = head.replace('../', '')
|
||||
head = head.replace('./', '')
|
||||
head = head.replace('raw/', '')
|
||||
np.savetxt(f'{dataPath}/{name}.norm', norm_lz, header = head)
|
||||
normFileList = fromHDF.file_list
|
||||
return norm_lz, normAngle, normFileList
|
||||
|
||||
np.savetxt(f'{dataPath}/{name}.norm', self.norm_lz, header = head)
|
||||
self.normFileList = fromHDF.file_list
|
||||
self.header.reduction.corrections.append('normalisation with \'additional files\'')
|
||||
|
||||
def project_on_lz(self, fromHDF, norm_lz, normAngle, lamda_e, detZ_e):
|
||||
# projection on lambda-z-grid
|
||||
|
||||
Reference in New Issue
Block a user