imageAs2DArray = np.zeros((height, width), dtype=np.uint16)
starttime = time.clock()
darkImagesDataset = exchangeGrp.create_dataset('data_dark',
(ndrk, height, width),
'uint16') # Create dataset
darkThetaDataset = exchangeGrp.create_dataset('theta_dark', (ndrk, ),
'float') # Create dataset
for darkImageCounter in range(0, ndrk):
print "Dark " + str(darkImageCounter)
darkThetaDataset[darkImageCounter] = rotmin
darkImageName = samplename + str(darkImageCounter +
1).zfill(4) + "." + "tif"
darkImageFullPath = tifdir + "/" + darkImageName
with tifffile(darkImageFullPath) as tif:
imageAs2DArray = tif.asarray() # image is a np.ndarray
darkImagesDataset[
darkImageCounter, :, :] = imageAs2DArray # Fill dataset
flatImagesDataset = exchangeGrp.create_dataset(
'data_white', (2 * nflt * grid_steps, height, width), 'uint16')
flatThetaDataset = exchangeGrp.create_dataset('theta_flat',
(2 * nflt * grid_steps, ),
'float') # Create dataset
for flatImageCounter in range(0, 2 * nflt * grid_steps):
print "Flat " + str(flatImageCounter)
if flatImageCounter < nflt * grid_steps:
offset = ndrk
flatThetaDataset[flatImageCounter] = rotmin
else: