implenment parallel computing
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@ -32,12 +32,52 @@ class FringeRemoval():
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self.ydim = 0 # The shape of y axis
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self._mask = None # The mask array to choose the region of interest for fringes removal
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self._center = None # Set the mask array by center and span
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self._span = None
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self.reshape=True # If it is necessary to reshape the data from (index of images(alternative), x, y) to (y, x, index of images(alternative))
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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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@property
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def center(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._center
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@center.setter
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def center(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._mask = None
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self._center = value
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@property
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def span(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._span
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@span.setter
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def span(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._mask = None
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self._span = value
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def reshape_data(self, data):
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"""The function is to reshape the data to the correct shape.
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@ -98,6 +138,55 @@ class FringeRemoval():
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return data
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def _reshape_absorption_images(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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self.nimgs = len(data[imageAxis])
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data = data.stack(axis=[yAxis, xAxis])
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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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@ -150,9 +239,6 @@ class FringeRemoval():
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"""
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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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@ -173,46 +259,32 @@ class FringeRemoval():
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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 _auto_mask(self):
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mask = np.ones((self.ydim, self.xdim), dtype=np.uint8)
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if not self._center is None:
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x_start = int(self._center[0] - self._span[0] / 2)
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x_end = int(self._center[0] + self._span[0] / 2)
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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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return mask
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def decompose_referenceImages(self):
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if self._mask is None:
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self.mask = self._auto_mask()
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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 solve_coefficient(self):
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pass
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def _fringe_removal(self, absorptionImages, referenceImages=None, mask=None, reshape=None, dask='forbidden'):
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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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def _fringe_removal(self, absorptionImages):
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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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b = self.temp @ absorptionImages[self.k]
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c = np.linalg.solve(self.U, np.linalg.solve(self.L[self.P], b))
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optrefimages = (self._referenceImages @ c)
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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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if dask=='forbidden':
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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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else:
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pass
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return optrefimages
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def fringe_removal(self, absorptionImages, referenceImages=None, mask=None, reshape=None, dask='forbidden'):
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def fringe_removal(self, absorptionImages, referenceImages=None, mask=None, reshape=None, dask='parallelized'):
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"""
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This function will generate a 'fake' background images, which can help to remove the fringes.
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@ -229,13 +301,22 @@ class FringeRemoval():
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:param dask: Please refer to xarray.apply_ufunc()
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:type dask: {"forbidden", "allowed", "parallelized"}, optional
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:return: The 'fake' background to help removing the fringes
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:rtype: numpy array
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:rtype: xarray array
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"""
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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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res = self._fringe_removal(absorptionImages, referenceImages, mask, reshape, dask)
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res = res.reshape(self.ydim, self.xdim, self.nimgs)
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if self.reshape:
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return np.swapaxes(res, 0, 2)
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else:
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return res
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absorptionImages = self._reshape_absorption_images(absorptionImages)
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self.temp = self._referenceImages[self.k, :].T
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optrefimages = xr.apply_ufunc(self._fringe_removal, absorptionImages, input_core_dims=[['axis']], output_core_dims=[['axis']], dask=dask, vectorize=True, output_dtypes=float)
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return optrefimages.unstack()
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