75 lines
2.9 KiB
Python
75 lines
2.9 KiB
Python
"""
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Defines how to normalize a focusing reflectometry dataset by a reference measurement.
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"""
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import logging
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import os
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import numpy as np
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from typing import List
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from .event_data_types import EventDatasetProtocol
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from .header import Header
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from .options import NormalisationMethod
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from .instrument import Grid
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class Normalisation:
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normFileList = List[str]
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normAngle: float
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normMonitor: float
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norm_lz: np.ndarray
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def __init__(self, reference:EventDatasetProtocol, normalisationMethod: NormalisationMethod, grid: Grid):
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self.normAngle = reference.geometry.nu-reference.geometry.mu
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lamda_e = reference.data.events.lamda
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detZ_e = reference.data.events.detZ
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self.normMonitor = np.sum(reference.data.pulses.monitor)
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norm_lz, _, _ = np.histogram2d(lamda_e, detZ_e, bins=(grid.lamda(), grid.z()))
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norm_lz = np.where(norm_lz>2, norm_lz, np.nan)
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if normalisationMethod==NormalisationMethod.direct_beam:
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self.norm_lz = np.flip(norm_lz, 1)
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else:
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# correct for reference sm reflectivity
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lamda_l = grid.lamda()
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theta_z = self.normAngle+reference.geometry.delta_z
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lamda_lz = (grid.lz().T*lamda_l[:-1]).T
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theta_lz = grid.lz()*theta_z
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qz_lz = 4.0*np.pi*np.sin(np.deg2rad(theta_lz))/lamda_lz
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# TODO: introduce variable for `m` and propably for the slope
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Rsm_lz = np.ones(np.shape(qz_lz))
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Rsm_lz = np.where(qz_lz>0.0217, 1-(qz_lz-0.0217)*(0.0625/0.0217), Rsm_lz)
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Rsm_lz = np.where(qz_lz>0.0217*5, np.nan, Rsm_lz)
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self.norm_lz = norm_lz/Rsm_lz
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self.normFileList = [os.path.basename(entry) for entry in reference.file_list]
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@classmethod
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def from_file(cls, filename) -> 'Normalisation':
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logging.warning(f'normalisation matrix: found and using {filename}')
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self = super().__new__(cls)
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with open(filename, 'rb') as fh:
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self.normFileList = np.load(fh, allow_pickle=True)
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self.normAngle = np.load(fh, allow_pickle=True)
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self.norm_lz = np.load(fh, allow_pickle=True)
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self.normMonitor = np.load(fh, allow_pickle=True)
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return self
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@classmethod
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def unity(cls, grid:Grid) -> 'Normalisation':
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logging.warning(f'normalisation is unity')
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self = super().__new__(cls)
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self.norm_lz = grid.lz()
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self.normFileList = []
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self.normAngle = 1.
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self.normMonitor = 1.
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return self
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def safe(self, filename):
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with open(filename, 'wb') as fh:
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np.save(fh, np.array(self.normFileList), allow_pickle=False)
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np.save(fh, np.array(self.normAngle), allow_pickle=False)
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np.save(fh, self.norm_lz, allow_pickle=False)
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np.save(fh, self.normMonitor, allow_pickle=False)
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def update_header(self, header:Header):
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header.measurement_additional_files = self.normFileList
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