implenment fringe removal class
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Analyser/FringeRemoval.py
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216
Analyser/FringeRemoval.py
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import numpy as np
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from scipy.linalg import lu
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import xarray as xr
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def fringeremoval(absimages, refimages, bgmask=None):
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# Process inputs
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nimgs = absimages.shape[2]
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nimgsR = refimages.shape[2]
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xdim = absimages.shape[1]
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ydim = absimages.shape[0]
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A = (absimages.reshape(xdim * ydim, nimgs).astype(np.float32))
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R = (refimages.reshape(xdim * ydim, nimgsR).astype(np.float32))
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optrefimages = np.zeros_like(absimages, dtype=np.float32)
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if bgmask is None:
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bgmask = np.ones((ydim, xdim), dtype=np.uint8)
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k = np.where(bgmask.flatten() == 1)[0] # Index k specifying the background region
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# Ensure there are no duplicate reference images
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# R = np.unique(R, axis=1) # Comment this line if memory issues arise
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# Decompose B = R * R' using LU decomposition
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P, L, U = lu(R[k, :].T @ R[k, :], permute_l = False, p_indices = True)
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for j in range(nimgs):
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b = R[k, :].T @ A[k, j]
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# Obtain coefficients c which minimize least-square residuals
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c = np.linalg.solve(U, np.linalg.solve(L[P], b))
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# Compute optimized reference image
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optrefimages[:, :, j] = (R @ c).reshape((ydim, xdim))
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return optrefimages
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class InvalidDimException(Exception):
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"Raised when the program can not identify (index of images, x, y) axes."
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def __init__(self, dims):
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if len(dims)>3:
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self.message = 'The input data must have two or three axes: (index of images(alternative), x, y)'
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else:
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self.message = 'Can not identify (index of images(alternative), x, y) from ' + str(dims)
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super().__init__(self.message)
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class DataSizeException(Exception):
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"Raised when the shape of the data is not correct."
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def __init__(self):
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self.message = 'The input data size does not match.'
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super().__init__(self.message)
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class FringeRemoval():
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def __init__(self) -> None:
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self.nimgsR = 0
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self.xdim = 0
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self.ydim = 0
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self._mask = None
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self.reshape=True
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self.P = None
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self.L = None
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self.U = None
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def reshape_data(self, data):
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if data is None:
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return data
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if isinstance(data, type(xr.DataArray())):
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dims = data.dims
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if len(dims)>3:
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raise InvalidDimException(dims)
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xAxis = None
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yAxis = None
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if len(dims) == 2:
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imageAxis = ''
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else:
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imageAxis = None
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for dim in dims:
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if (dim == 'x') or ('_x' in dim):
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xAxis = dim
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elif (dim == 'y') or ('_y' in dim):
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yAxis = dim
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else:
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imageAxis = dim
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if (xAxis is None) or (yAxis is None) or (imageAxis is None):
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raise InvalidDimException(dims)
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if len(dims) == 2:
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data = data.transpose(yAxis, xAxis)
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else:
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data = data.transpose(yAxis, xAxis, imageAxis)
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data = data.to_numpy()
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else:
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data = np.array(data)
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if len(data.shape) == 3:
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data = np.swapaxes(data, 0, 2)
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# data = np.swapaxes(data, 0, 1)
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elif len(data.shape) == 2:
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data = np.swapaxes(data, 0, 1)
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return data
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@property
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def referenceImages(self):
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res = self._referenceImages.reshape(self.ydim, self.xdim, self.nimgsR)
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res = np.swapaxes(res, 0, 2)
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return res
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@referenceImages.setter
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def referenceImages(self, value):
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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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value = value.to_numpy()
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self.nimgsR = value.shape[2]
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self.xdim = value.shape[1]
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self.ydim = value.shape[0]
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self._referenceImages = (value.reshape(self.xdim * self.ydim, self.nimgsR).astype(np.float32))
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def add_reference_images(self, data):
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if self.reshape:
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data = self.reshape_data(data)
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elif isinstance(data, type(xr.DataArray())):
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data = data.to_numpy()
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if not ((data.shape[0]==self.ydim) and (data.shape[1]==self.xdim)):
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raise DataSizeException
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data = data.reshape(self.xdim * self.ydim)
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self._referenceImages = np.append(self._referenceImages, data, axis=1)
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def _remove_first_reference_images(self):
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self._referenceImages = np.delete(self._referenceImages, 0, axis=1)
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def update_reference_images(self, data):
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self._remove_first_reference_images()
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self.add_reference_images(data)
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if self._mask is None:
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self.mask = np.ones((self.ydim, self.xdim), dtype=np.uint8)
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self.decompose_referenceImages()
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@property
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def mask(self):
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return self._mask
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@mask.setter
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def mask(self, value):
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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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value = value.to_numpy()
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if not ((value.shape[0]==self.ydim) and (value.shape[1]==self.xdim)):
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raise DataSizeException
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self._mask = value
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self.k = np.where(self._mask.flatten() == 1)[0]
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def decompose_referenceImages(self):
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self.P, self.L, self.U = lu(self._referenceImages[self.k, :].T @ self._referenceImages[self.k, :], permute_l = False, p_indices = True)
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def _fringe_removal(self, absorptionImages, referenceImages=None, mask=None, reshape=None):
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if not reshape is None:
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self.reshape = reshape
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if not referenceImages is None:
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self.referenceImages = referenceImages
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if not mask is None:
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self.mask = mask
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if self.P is None:
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self.decompose_referenceImages()
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absorptionImages = np.atleast_3d(absorptionImages)
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if self.reshape:
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absorptionImages = self.reshape_data(absorptionImages)
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self.nimgs = absorptionImages.shape[2]
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absorptionImages = (absorptionImages.reshape(self.xdim * self.ydim, self.nimgs).astype(np.float32))
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optrefimages = np.zeros_like(absorptionImages, dtype=np.float32)
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for j in range(self.nimgs):
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b = self._referenceImages[self.k, :].T @ absorptionImages[self.k, j]
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# Obtain coefficients c which minimize least-square residuals
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c = np.linalg.solve(self.U, np.linalg.solve(self.L[self.P], b))
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# Compute optimized reference image
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optrefimages[:, j] = (self._referenceImages @ c)
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return optrefimages
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def fringe_removal(self, absorptionImages, referenceImages=None, mask=None, reshape=None):
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res = self._fringe_removal(absorptionImages, referenceImages, mask, reshape)
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res = res.reshape(self.ydim, self.xdim, self.nimgs)
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return np.swapaxes(res, 0, 2)
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