optimize the fringes removal function
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6d841ef992
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@ -195,6 +195,10 @@ class FringeRemoval():
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@referenceImages.setter
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def referenceImages(self, value):
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if value is None:
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self._referenceImages = None
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return
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if self.reshape:
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value = self.reshape_data(value)
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elif isinstance(value, type(xr.DataArray())):
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@ -257,6 +261,10 @@ class FringeRemoval():
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raise DataSizeException
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self._mask = value
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self._center = None
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self._span = None
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self.k = np.where(self._mask.flatten() == 1)[0]
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def _auto_mask(self):
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@ -267,7 +275,7 @@ class FringeRemoval():
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y_end = int(self._center[1] + self._span[1] / 2)
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y_start = int(self._center[1] - self._span[1] / 2)
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mask[y_start:y_end, x_start:x_end] = 0
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mask[y_start:y_end, x_start:x_end] = 0
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return mask
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@ -2,6 +2,10 @@ import numpy as np
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import xarray as xr
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import copy
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from DataContainer.ReadData import read_hdf5_file
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from Analyser.FringeRemoval import FringeRemoval
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from ToolFunction.ToolFunction import get_scanAxis
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class ImageAnalyser():
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"""A class for operate with and analyse images
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@ -15,11 +19,15 @@ class ImageAnalyser():
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'background': 'background',
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'dark': 'dark',
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'OD':'OD',
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'optimumBackground':'optimumBackground'
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}
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self._center = None
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self._span = None
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self._fraction = None
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self._fringeRemoval = FringeRemoval()
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self.fringeRemovalReferenceImages = None
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@property
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def image_name(self):
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"""The getter of the names of three standard images for absorption images
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@ -150,7 +158,7 @@ class ImageAnalyser():
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res.attrs = copy.copy(dataArray.attrs)
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return res
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def crop_image(self, dataSet, center=None, span=None):
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def crop_image(self, dataSet, center=None, span=None, fringeRemoval=False):
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"""Crop the image according to the region of interest (ROI).
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:param dataSet: The images
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@ -192,6 +200,13 @@ class ImageAnalyser():
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dataSet[key].attrs['y_center'] = center[1]
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dataSet[key].attrs['x_span'] = span[0]
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dataSet[key].attrs['y_span'] = span[1]
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if fringeRemoval:
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scanAxis = list(get_scanAxis(self.fringeRemovalReferenceImages))
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if not scanAxis[1] is None:
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self._fringeRemoval.referenceImages = self.fringeRemovalReferenceImages.isel(x=slice(x_start, x_end), y=slice(y_start, y_end)).stack(_imgIdx=scanAxis)
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else:
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self._fringeRemoval.referenceImages = self.fringeRemovalReferenceImages.isel(x=slice(x_start, x_end), y=slice(y_start, y_end))
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return dataSet.isel(x=slice(x_start, x_end), y=slice(y_start, y_end))
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@ -220,6 +235,32 @@ class ImageAnalyser():
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return imageOD[0]
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else:
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return imageOD
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def get_OD_no_dark(self, imageAtom, imageBackground):
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"""Calculate the OD image for absorption imaging without dark images.
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:param imageAtom: The image with atoms
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:type imageAtom: numpy array
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:param imageBackground: The image without atoms
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:type imageBackground: numpy array
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:param imageDrak: The image without light
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:type imageDrak: numpy array
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:return: The OD images
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:rtype: numpy array
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"""
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numerator = np.atleast_1d(imageBackground)
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denominator = np.atleast_1d(imageAtom)
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numerator[numerator == 0] = 1
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denominator[denominator == 0] = 1
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imageOD = np.abs(np.divide(denominator, numerator))
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imageOD= -np.log(imageOD)
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if len(imageOD) == 1:
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return imageOD[0]
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else:
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return imageOD
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def get_Ncount(self, dataSet, dim=['x', 'y'], **kwargs):
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"""Sum all the value in the image to give the Ncount.
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@ -233,7 +274,7 @@ class ImageAnalyser():
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"""
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return dataSet.sum(dim=['x', 'y'], **kwargs)
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def get_absorption_images(self, dataSet, dask='allowed', keep_attrs=True, **kwargs):
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def get_absorption_images(self, dataSet, fringeRemoval=False, dask='allowed', keep_attrs=True, **kwargs):
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"""Calculate the OD images for absorption imaging.
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:param dataSet: The data from absorption imaging.
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@ -253,11 +294,31 @@ class ImageAnalyser():
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}
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)
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dataSet = dataSet.assign(
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{
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self._image_name['OD']: xr.apply_ufunc(self.get_OD, dataSet[self._image_name['atoms']], dataSet[self._image_name['background']], dataSet[self._image_name['dark']], **kwargs)
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}
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)
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if fringeRemoval:
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scanAxis = list(get_scanAxis(dataSet))
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if not scanAxis[1] is None:
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OptimumRef = self._fringeRemoval.fringe_removal(dataSet[self._image_name['atoms']].stack(_imgIdx=scanAxis))
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else:
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OptimumRef = self._fringeRemoval.fringe_removal(dataSet[self._image_name['atoms']])
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dataSet = dataSet.assign(
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{
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self._image_name['optimumBackground']: OptimumRef
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}
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)
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dataSet = dataSet.assign(
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{
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self._image_name['OD']: xr.apply_ufunc(self.get_OD_no_dark, dataSet[self._image_name['atoms']], dataSet[self._image_name['optimumBackground']], **kwargs)
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}
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)
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else:
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dataSet = dataSet.assign(
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{
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self._image_name['OD']: xr.apply_ufunc(self.get_OD, dataSet[self._image_name['atoms']], dataSet[self._image_name['background']], dataSet[self._image_name['dark']], **kwargs)
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}
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)
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# dataSet[self._image_name['OD']].attrs.update(dataSet.attrs)
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@ -282,6 +343,68 @@ class ImageAnalyser():
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)
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xr.apply_ufunc(self.get_OD, dataSet[self._image_name['atoms']], dataSet[self._image_name['background']], dataSet[self._image_name['dark']], **kwargs)
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@property
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def fringeRemovalCenter(self):
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"""The getter of the center of region of insterest (ROI)
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:return: The center of region of insterest (ROI)
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:rtype: tuple
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"""
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return self._fringeRemoval.center
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@fringeRemovalCenter.setter
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def fringeRemovalCenter(self, value):
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"""The setter of the center of region of insterest (ROI)
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:param value: The center of region of insterest (ROI)
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:type value: tuple
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"""
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self._fringeRemoval.center = value
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@property
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def fringeRemovalSpan(self):
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"""The getter of the span of region of insterest (ROI)
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:return: The span of region of insterest (ROI)
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:rtype: tuple
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"""
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return self._fringeRemoval.span
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@fringeRemovalSpan.setter
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def fringeRemovalSpan(self, value):
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"""The setter of the span of region of insterest (ROI)
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:param value: The span of region of insterest (ROI)
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:type value: tuple
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"""
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self._fringeRemoval.span = value
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# @property
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# def fringeRemovalReferenceImages(self):
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# return self._fringeRemoval.referenceImages
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# @fringeRemovalReferenceImages.setter
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# def fringeRemovalReferenceImages(self, value):
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# self._fringeRemoval.referenceImages = value
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def load_fringe_removal_background_from_hdf5(self, img_dir, SequenceName, data, shotNum, group, crop=False, load=False, **kwargs):
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folderPath = img_dir + SequenceName + "/" + data
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filePath = folderPath + "/" + shotNum + "/*.h5"
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dataSet = read_hdf5_file(filePath, group, **kwargs)
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scanAxis = dataSet.scanAxis
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dataSet = dataSet[self._image_name['background']]
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dataSet.attrs['scanAxis'] = scanAxis
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if crop:
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dataSet = self.crop_image(dataSet)
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if load:
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self.fringeRemovalReferenceImages = dataSet.load()
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else:
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self.fringeRemovalReferenceImages = dataSet
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def load_fringe_removal_background_from_database():
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pass
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