#/*########################################################################## # Copyright (C) 2004-2020 V.A. Sole, European Synchrotron Radiation Facility # # This file is part of the PyMca X-ray Fluorescence Toolkit developed at # the ESRF by the Software group. # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN # THE SOFTWARE. # #############################################################################*/ """ This plugin provides two methods to align stack images, one based on a FFT algorithm and the other one based on the SIFT algorithm (on GPU). The result of the alignment computation may be applied directly to the data, or saved to a file. This plugin also allows to apply the results from a file. """ __author__ = "V.A. Sole - ESRF Data Analysis" __contact__ = "sole@esrf.fr" __license__ = "MIT" __copyright__ = "European Synchrotron Radiation Facility, Grenoble, France" import sys import os import numpy import logging from PyMca5 import StackPluginBase from PyMca5.PyMcaGui import PyMcaQt as qt from PyMca5.PyMcaGui import FFTAlignmentWindow from PyMca5.PyMcaMath import ImageRegistration from PyMca5.PyMcaMath.fitting import SpecfitFuns from PyMca5.PyMcaGui import CalculationThread from PyMca5.PyMcaIO import ArraySave from PyMca5.PyMcaGui.io import PyMcaFileDialogs from PyMca5.PyMcaIO import specfilewrapper from PyMca5.PyMcaGui import HDF5Widget _logger = logging.getLogger(__name__) try: from PyMca5.PyMcaGui.math import SIFTAlignmentWindow sift = SIFTAlignmentWindow.sift ocl = SIFTAlignmentWindow.silx.opencl.ocl SIFT = True except: _logger.info("SIFTAlignmentWindow not successful") SIFT = False try: import h5py HDF5 = True except: HDF5 = False class ImageAlignmentStackPlugin(StackPluginBase.StackPluginBase): def __init__(self, stackWindow, **kw): if _logger.getEffectiveLevel() == logging.DEBUG: StackPluginBase.pluginBaseLogger.setLevel(logging.DEBUG) StackPluginBase.StackPluginBase.__init__(self, stackWindow, **kw) self.methodDict = {'FFT Alignment':[self._fftAlignment, "Align using FFT", None]} self.__methodKeys = ['FFT Alignment'] if SIFT: key = 'SIFT Alignment' self.methodDict[key] = [self._siftAlignment, "Align using SIFT Algorithm", None] self.__methodKeys.append(key) key = 'From File Alignment' self.methodDict[key] = [self._shiftFromFile, "Align using shifts from file", None] self.__methodKeys.append(key) self.widget = None def stackUpdated(self): self.widget = None #Methods implemented by the plugin def getMethods(self): return self.__methodKeys def getMethodToolTip(self, name): return self.methodDict[name][1] def getMethodPixmap(self, name): return self.methodDict[name][2] def applyMethod(self, name): return self.methodDict[name][0]() def _fftAlignment(self): stack = self.getStackDataObject() if stack is None: return mcaIndex = stack.info.get('McaIndex') if not (mcaIndex in [0, -1, 2]): raise IndexError("1D index must be 0, 2, or -1") if self.widget is None: self.widget = FFTAlignmentWindow.FFTAlignmentDialog() self.widget.setStack(stack) ret = self.widget.exec() if ret: ddict = self.widget.getParameters() self.widget.setDummyStack() offsets = [ddict['Dim 0']['offset'], ddict['Dim 1']['offset']] widths = [ddict['Dim 0']['width'], ddict['Dim 1']['width']] if mcaIndex == 0: reference = stack.data[ddict['reference_index']] else: reference = ddict['reference_image'] crop = False if ddict['file_use']: filename = ddict['file_name'] else: filename = None if filename is not None: self.__hdf5 = self.initializeHDF5File(filename) if _logger.getEffectiveLevel() == logging.DEBUG: shifts = self.calculateShiftsFFT(stack, reference, offsets=offsets, widths=widths, crop=crop) result = self.shiftStack(stack, shifts, crop=crop, filename=filename) else: result = self.__calculateShiftsFFT(stack, reference, offsets=offsets, widths=widths, crop=crop) # result[0] contains the string "Exception" in case # of error. However, direct comparison will raise an # error if type(result[0]) == type('Exception'): # exception occurred raise Exception(result[1], result[2], result[3]) else: shifts = result result = self.__shiftStack(stack, shifts, crop=crop, filename=filename) if result is not None: # exception occurred raise Exception(result[1], result[2], result[3]) if filename is not None: hdf = self.__hdf5 alignmentGroup = hdf['/entry_000/Alignment'] outputShifts = self.getHDF5BufferIntoGroup(alignmentGroup, shape=(stack.data.shape[mcaIndex], 2), name="shifts", dtype=numpy.float32) outputShifts[:,:] = shifts attributes={'interpretation':'image'} referenceFrame = self.getHDF5BufferIntoGroup(alignmentGroup, shape=reference.shape, name="reference_frame", dtype=numpy.float32, attributes=attributes) referenceFrame[:,:] = reference[:,:] maskFrame = self.getHDF5BufferIntoGroup(alignmentGroup, shape=reference.shape, name="reference_mask", dtype=numpy.uint8, attributes=attributes) maskData = numpy.zeros(reference.shape, dtype=numpy.uint8) maskData[offsets[0]:offsets[0] + widths[0], offsets[1] : offsets[1] + widths[1]] = 1 maskFrame[:,:] = maskData[:,:] # fill the axes information dataGroup = hdf['/entry_000/Data'] try: activeCurve = self.getActiveCurve() if activeCurve is None: activeCurve = self.getAllCurves()[0] x, y, legend, info = activeCurve dataGroup[info['xlabel']] = numpy.array(x, dtype=numpy.float32) dataGroup[info['xlabel']].attrs['axis'] = numpy.int32(1) axesAttribute = '%s:dim_1:dim_2' % info['xlabel'] except: if _logger.getEffectiveLevel() == logging.DEBUG: raise dataGroup['dim_0'] = numpy.arange(stack.data.shape[mcaIndex]).astype(numpy.float32) dataGroup['dim_0'].attrs['axis'] = numpy.int32(1) axesAttribute = 'dim_0:dim_1:dim_2' dataGroup['dim_1'] = numpy.arange(reference.shape[0]).astype(numpy.float32) dataGroup['dim_1'].attrs['axis'] = numpy.int32(2) dataGroup['dim_2'] = numpy.arange(reference.shape[1]).astype(numpy.float32) dataGroup['dim_2'].attrs['axis'] = numpy.int32(3) dim2 = numpy.arange(reference.shape[1]).astype(numpy.float32) dataGroup['data'].attrs['axes'] = axesAttribute self.finishHDF5File(hdf) else: self.setStack(stack) def __calculateShiftsFFT(self, *var, **kw): self._progress = 0.0 thread = CalculationThread.CalculationThread(\ calculation_method=self.calculateShiftsFFT, calculation_vars=var, calculation_kw=kw) thread.start() CalculationThread.waitingMessageDialog(thread, message="Please wait. Calculation going on.", parent=self.widget, modal=True, update_callback=self._waitingCallback) return thread.result def __shiftStack(self, *var, **kw): self._progress = 0.0 thread = CalculationThread.CalculationThread(\ calculation_method=self.shiftStack, calculation_vars=var, calculation_kw=kw) thread.start() CalculationThread.waitingMessageDialog(thread, message="Please wait. Calculation going on.", parent=self.widget, modal=True, update_callback=self._waitingCallback) return thread.result def __calculateShiftsSIFT(self, *var, **kw): self._progress = 0.0 thread = CalculationThread.CalculationThread(\ calculation_method=self.calculateShiftsSIFT, calculation_vars=var, calculation_kw=kw) thread.start() CalculationThread.waitingMessageDialog(thread, message="Please wait. Calculation going on.", parent=self.widget, modal=True, update_callback=self._waitingCallback) return thread.result def _waitingCallback(self): ddict = {} ddict['message'] = "Calculation Progress = %d %%" % self._progress return ddict def _siftAlignment(self): if not SIFT: try: import pyopencl except: raise ImportError("PyOpenCL does not seem to be installed on your system") if ocl is None: raise ImportError("PyOpenCL does not seem to be installed on your system") stack = self.getStackDataObject() if stack is None: return mcaIndex = stack.info.get('McaIndex') if not (mcaIndex in [0, 2, -1]): raise IndexError("Unsupported 1D index %d" % mcaIndex) widget = SIFTAlignmentWindow.SIFTAlignmentDialog() widget.setStack(stack) mask = self.getStackSelectionMask() widget.setSelectionMask(mask) ret = widget.exec() if ret: ddict = widget.getParameters() widget.setDummyStack() reference = ddict['reference_image'] mask = ddict['mask'] if ddict['file_use']: filename = ddict['file_name'] else: filename = None if filename is not None: self.__hdf5 = self.initializeHDF5File(filename) crop = False device = ddict['opencl_device'] if _logger.getEffectiveLevel() == logging.DEBUG: result = self.calculateShiftsSIFT(stack, reference, mask=mask, device=device, crop=crop, filename=filename) else: result = self.__calculateShiftsSIFT(stack, reference, mask=mask, device=device, crop=crop, filename=filename) if result is not None: if len(result): if result[0] == 'Exception': # exception occurred raise Exception(result[1], result[2], result[3]) if filename is None: self.setStack(stack) def calculateShiftsSIFT(self, stack, reference, mask=None, device=None, crop=None, sigma=None, filename=None): mask = self.getStackSelectionMask() if mask is not None: if mask.sum() == 0: mask = None if device is None: siftInstance = sift.LinearAlign(reference.astype(numpy.float32), devicetype="cpu", init_sigma=sigma) else: siftInstance = sift.LinearAlign(reference.astype(numpy.float32), deviceid=device, init_sigma=sigma) data = stack.data mcaIndex = stack.info['McaIndex'] if not (mcaIndex in [0, 2, -1]): raise IndexError("Unsupported 1D index %d" % mcaIndex) total = float(data.shape[mcaIndex]) if filename is not None: hdf = self.__hdf5 dataGroup = hdf['/entry_000/Data'] attributes = {} attributes['interpretation'] = "image" attributes['signal'] = numpy.int32(1) outputStack = self.getHDF5BufferIntoGroup(dataGroup, shape=(data.shape[mcaIndex], reference.shape[0], reference.shape[1]), name="data", dtype=numpy.float32, attributes=attributes) shifts = numpy.zeros((data.shape[mcaIndex], 2), dtype=numpy.float32) if mcaIndex == 0: for i in range(data.shape[mcaIndex]): _logger.debug("SIFT Shifting image %d", i) result = siftInstance.align(data[i].astype(numpy.float32), shift_only=True, return_all=True) _logger.debug("Index = %d shift = %.4f, %.4f", i, result['offset'][0], result['offset'][1]) if filename is None: stack.data[i] = result['result'] else: outputStack[i] = result['result'] shifts[i, 0] = result['offset'][0] shifts[i, 1] = result['offset'][1] self._progress = (100 * i) / total else: image2 = numpy.zeros(reference.shape, dtype=numpy.float32) for i in range(data.shape[mcaIndex]): _logger.debug("SIFT Shifting image %d", i) image2[:, :] = data[:, :, i] result = siftInstance.align(image2, shift_only=True, return_all=True) _logger.debug("Index = %d shift = %.4f, %.4f", i, result['offset'][0], result['offset'][1]) if filename is None: stack.data[:, :, i] = result['result'] else: outputStack[i] = result['result'] shifts[i, 0] = result['offset'][0] shifts[i, 1] = result['offset'][1] self._progress = (100 * i) / total if filename is not None: hdf = self.__hdf5 alignmentGroup = hdf['/entry_000/Alignment'] outputShifts = self.getHDF5BufferIntoGroup(alignmentGroup, shape=(stack.data.shape[mcaIndex], 2), name="shifts", dtype=numpy.float32) outputShifts[:,:] = shifts attributes={'interpretation':'image'} referenceFrame = self.getHDF5BufferIntoGroup(alignmentGroup, shape=reference.shape, name="reference_frame", dtype=numpy.float32, attributes=attributes) referenceFrame[:,:] = reference[:,:] maskFrame = self.getHDF5BufferIntoGroup(alignmentGroup, shape=reference.shape, name="reference_mask", dtype=numpy.uint8, attributes=attributes) if mask is None: maskData = numpy.ones(reference.shape, dtype=numpy.uint8) else: maskData = mask maskFrame[:,:] = maskData[:,:] # fill the axes information dataGroup = hdf['/entry_000/Data'] try: activeCurve = self.getActiveCurve() if activeCurve is None: activeCurve = self.getAllCurves()[0] x, y, legend, info = activeCurve dataGroup[info['xlabel']] = numpy.array(x, dtype=numpy.float32) dataGroup[info['xlabel']].attrs['axis'] = numpy.int32(1) axesAttribute = '%s:dim_1:dim_2' % info['xlabel'] except: if _logger.getEffectiveLevel() == logging.DEBUG: raise dataGroup['dim_0'] = numpy.arange(stack.data.shape[mcaIndex]).astype(numpy.float32) dataGroup['dim_0'].attrs['axis'] = numpy.int32(1) axesAttribute = 'dim_0:dim_1:dim_2' dataGroup['dim_1'] = numpy.arange(reference.shape[0]).astype(numpy.float32) dataGroup['dim_1'].attrs['axis'] = numpy.int32(2) dataGroup['dim_2'] = numpy.arange(reference.shape[1]).astype(numpy.float32) dataGroup['dim_2'].attrs['axis'] = numpy.int32(3) dim2 = numpy.arange(reference.shape[1]).astype(numpy.float32) dataGroup['data'].attrs['axes'] = axesAttribute self.finishHDF5File(hdf) def calculateShiftsFFT(self, stack, reference, offsets=None, widths=None, crop=False): _logger.debug("Offsets = %s", offsets) _logger.debug("Widths = %s", widths) data = stack.data if offsets is None: offsets = [0.0, 0.0] if widths is None: widths = [reference.shape[0], reference.shape[1]] fft2Function = numpy.fft.fft2 if 1: DTYPE = numpy.float32 else: DTYPE = numpy.float64 image2 = numpy.zeros((widths[0], widths[1]), dtype=DTYPE) shape = image2.shape USE_APODIZATION_WINDOW = False apo = [10, 10] if USE_APODIZATION_WINDOW: # use apodization window window = numpy.outer(SpecfitFuns.slit([0.5, shape[0] * 0.5, shape[0] - 4 * apo[0], apo[0]], numpy.arange(float(shape[0]))), SpecfitFuns.slit([0.5, shape[1] * 0.5, shape[1] - 4 * apo[1], apo[1]], numpy.arange(float(shape[1])))).astype(DTYPE) else: window = numpy.zeros((shape[0], shape[1]), dtype=DTYPE) window[apo[0]:shape[0] - apo[0], apo[1]:shape[1] - apo[1]] = 1 image2[:,:] = window * reference[offsets[0]:offsets[0]+widths[0], offsets[1]:offsets[1]+widths[1]] image2fft2 = fft2Function(image2) mcaIndex = stack.info.get('McaIndex') shifts = numpy.zeros((data.shape[mcaIndex], 2), numpy.float64) image1 = numpy.zeros(image2.shape, dtype=DTYPE) total = float(data.shape[mcaIndex]) if mcaIndex == 0: for i in range(data.shape[mcaIndex]): image1[:,:] = window * data[i][offsets[0]:offsets[0]+widths[0], offsets[1]:offsets[1]+widths[1]] image1fft2 = fft2Function(image1) shifts[i] = ImageRegistration.measure_offset_from_ffts(image2fft2, image1fft2) _logger.debug("Index = %d shift = %.4f, %.4f", i, shifts[i][0], shifts[i][1]) self._progress = (100 * i) / total elif mcaIndex in [2, -1]: for i in range(data.shape[mcaIndex]): image1[:,:] = window * data[:,:,i][offsets[0]:offsets[0]+widths[0], offsets[1]:offsets[1]+widths[1]] image1fft2 = fft2Function(image1) shifts[i] = ImageRegistration.measure_offset_from_ffts(image2fft2, image1fft2) _logger.debug("Index = %d shift = %.4f, %.4f", i, shifts[i][0], shifts[i][1]) self._progress = (100 * i) / total else: raise IndexError("Only stacks of images or spectra supported. 1D index should be 0 or 2") return shifts def _shiftFromFile(self): stack = self.getStackDataObject() if stack is None: return data = stack.data mcaIndex = stack.info.get('McaIndex') if not (mcaIndex in [0, -1, 2]): raise IndexError("1D index must be 0, 2, or -1") filefilter = ['HDF5 Files (*.h5 *.nxs *.hdf *.hdf5)', 'CSV 2-column (*.csv)', 'ASCII 2-column (*)'] filename, ffilter = PyMcaFileDialogs.\ getFileList(parent=None, filetypelist=filefilter, message='Load', mode='OPEN', single=True, getfilter=True, currentfilter=filefilter[0]) if len(filename): _logger.debug("file name = %s file filter = %s", filename, ffilter) else: _logger.debug("nothing selected") return filename = filename[0] if ffilter.startswith('HDF5'): # browse self.__hdf5Dialog = qt.QDialog() self.__hdf5Dialog.setWindowTitle('Select your data set by a double click') self.__hdf5Dialog.mainLayout = qt.QVBoxLayout(self.__hdf5Dialog) self.__hdf5Dialog.mainLayout.setContentsMargins(0, 0, 0, 0) self.__hdf5Dialog.mainLayout.setSpacing(0) fileModel = HDF5Widget.FileModel() fileView = HDF5Widget.HDF5Widget(fileModel) with h5py.File(filename, "r") as hdfFile: fileModel.appendPhynxFile(hdfFile, weakreference=True) self.__shiftsDataset = None fileView.sigHDF5WidgetSignal.connect(self._hdf5WidgetSlot) self.__hdf5Dialog.mainLayout.addWidget(fileView) self.__hdf5Dialog.resize(400, 200) ret = self.__hdf5Dialog.exec() if not ret: return shifts = hdfFile[self.__shitfsDataset].value else: sf = specfilewrapper.Specfile(filename) nScans = len(sf) targetScan = None for scan in sf: if scan.lines() == data.shape[stack.info['McaIndex']]: targetScan = scan break if targetScan is None: scan = None sf = None raise IOError("Number of read lines does not match stack shape") shifts = targetScan.data() targetScan = None scan = None sf = None if shifts.shape[0] == 3 and\ shifts.shape[1] == data.shape[stack.info['McaIndex']]: # one column was added (point number) shifts = shifts[1:].T filename = None if not isinstance(data, numpy.ndarray): filefilter = ['HDF5 Files (*.h5)'] filename = PyMcaFileDialogs.\ getFileList(parent=None, filetypelist=filefilter, message='Select output file', mode='SAVE', single=True, getfilter=False, currentfilter=filefilter[0]) if len(filename): filename = filename[0] _logger.debug("file name = %s", filename) else: raise IOError("No output file selected") if filename is not None: self.__hdf5 = self.initializeHDF5File(filename) crop = False if _logger.getEffectiveLevel() == logging.DEBUG: result = self.shiftStack(stack, shifts, crop=crop, filename=filename) else: result = self.__shiftStack(stack, shifts, crop=crop, filename=filename) if result is not None: # exception occurred raise Exception(result[1], result[2], result[3]) if filename is not None: hdf = self.__hdf5 alignmentGroup = hdf['/entry_000/Alignment'] outputShifts = self.getHDF5BufferIntoGroup(alignmentGroup, shape=(stack.data.shape[mcaIndex], 2), name="shifts", dtype=numpy.float32) outputShifts[:,:] = shifts attributes={'interpretation':'image'} # fill the axes information dataGroup = hdf['/entry_000/Data'] if mcaIndex == 0: reference_shape = data[0].shape else: reference_shape = data.shape[0], data.shape[1] try: activeCurve = self.getActiveCurve() if activeCurve is None: activeCurve = self.getAllCurves()[0] x, y, legend, info = activeCurve dataGroup[info['xlabel']] = numpy.array(x, dtype=numpy.float32) dataGroup[info['xlabel']].attrs['axis'] = numpy.int32(1) axesAttribute = '%s:dim_1:dim_2' % info['xlabel'] except: if _logger.getEffectiveLevel() == logging.DEBUG: raise dataGroup['dim_0'] = numpy.arange(stack.data.shape[mcaIndex]).astype(numpy.float32) dataGroup['dim_0'].attrs['axis'] = numpy.int32(1) axesAttribute = 'dim_0:dim_1:dim_2' dataGroup['dim_1'] = numpy.arange(reference_shape[0]).astype(numpy.float32) dataGroup['dim_1'].attrs['axis'] = numpy.int32(2) dataGroup['dim_2'] = numpy.arange(reference_shape[1]).astype(numpy.float32) dataGroup['dim_2'].attrs['axis'] = numpy.int32(3) dim2 = numpy.arange(reference_shape[1]).astype(numpy.float32) dataGroup['data'].attrs['axes'] = axesAttribute self.finishHDF5File(hdf) else: self.setStack(stack) def _hdf5WidgetSlot(self, ddict): if ddict['event'] == "itemDoubleClicked": if ddict['type'].lower() in ['dataset']: self.__shitfsDataset = ddict['name'] self.__hdf5Dialog.accept() def shiftStack(self, stack, shifts, crop=False, filename=None): """ """ data = stack.data mcaIndex = stack.info['McaIndex'] if mcaIndex not in [0, 2, -1]: raise IndexError("Only stacks of images or spectra supported. 1D index should be 0 or 2") if mcaIndex == 0: shape = data[mcaIndex].shape else: shape = data.shape[0], data.shape[1] d0_start, d0_end, d1_start, d1_end = \ ImageRegistration.get_crop_indices(shape, shifts[:, 0], shifts[:, 1]) window = numpy.zeros(shape, numpy.float32) window[d0_start:d0_end, d1_start:d1_end] = 1.0 self._progress = 0.0 total = float(data.shape[mcaIndex]) if filename is not None: hdf = self.__hdf5 dataGroup = hdf['/entry_000/Data'] attributes = {} attributes['interpretation'] = "image" attributes['signal'] = numpy.int32(1) outputStack = self.getHDF5BufferIntoGroup(dataGroup, shape=(data.shape[mcaIndex], shape[0], shape[1]), name="data", dtype=numpy.float32, attributes=attributes) for i in range(data.shape[mcaIndex]): #tmpImage = ImageRegistration.shiftFFT(data[i], shifts[i]) if mcaIndex == 0: tmpImage = ImageRegistration.shiftBilinear(data[i], shifts[i]) #tmpImage = ImageRegistration.shiftImage(data[i], -shifts[i], method="fft") if filename is None: stack.data[i] = tmpImage * window else: outputStack[i] = tmpImage * window else: tmpImage = ImageRegistration.shiftBilinear(data[:,:,i], shifts[i]) if filename is None: stack.data[:, :, i] = tmpImage * window else: outputStack[i] = tmpImage * window _logger.debug("Index %d bilinear shifted", i) self._progress = (100 * i) / total def initializeHDF5File(self, fname): #for the time being overwriting if os.path.exists(fname): os.remove(fname) hdf = h5py.File(fname, 'w') entryName = "entry_000" nxEntry = hdf.require_group(entryName) if 'NX_class' not in nxEntry.attrs: nxEntry.attrs['NX_class'] = 'NXentry'.encode('utf-8') nxEntry['title'] = numpy.string_("PyMca saved 3D Array".encode('utf-8')) nxEntry['start_time'] = numpy.string_(ArraySave.getDate().encode('utf-8')) alignmentGroup = nxEntry.require_group('Alignment') dataGroup = nxEntry.require_group('Data') dataGroup.attrs['NX_class'] = 'NXdata'.encode('utf-8') return hdf def finishHDF5File(self, hdf): #add final date toplevelEntry = hdf["entry_000"] toplevelEntry['end_time'] = numpy.string_(ArraySave.getDate().encode('utf-8')) hdf.flush() hdf.close() def getHDF5BufferIntoGroup(self, h5Group, shape, name="data", dtype=numpy.float32, attributes=None, compression=None, shuffle=False, chunks=None, chunk_cache=None): dataset = h5Group.require_dataset(name, shape=shape, dtype=dtype, chunks=chunks, shuffle=shuffle, compression=compression) if attributes is None: attributes = {} for attribute in attributes: dataset.attrs[attribute] = attributes[attribute] return dataset MENU_TEXT = "Image Alignment Tool" def getStackPluginInstance(stackWindow, **kw): ob = ImageAlignmentStackPlugin(stackWindow) return ob