def read_image_to_register(registername, is_imzml, to_flip, moving):
mz = None
if is_imzml:
imzml = imzmlio.open_imzml(registername)
current_s = imzmlio.get_full_spectra(imzml)
max_x = max(imzml.coordinates, key=lambda item: item[0])[0]
max_y = max(imzml.coordinates, key=lambda item: item[1])[1]
max_z = max(imzml.coordinates, key=lambda item: item[2])[2]
array = imzmlio.get_images_from_spectra(current_s,
(max_x, max_y, max_z)).T
register = sitk.GetImageFromArray(array)
mz, _ = imzml.getspectrum(0)
else:
register = sitk.ReadImage(registername, sitk.sitkFloat32)
if to_flip:
dim = register.GetDimension()
flip = (True, False) + ((False, ) if dim > 2 else ())
register = sitk.Flip(register, flip)
dim = register.GetDimension()
register.SetSpacing([1 for i in range(dim)])
identity = np.identity(dim).tolist()
flat_list = [item for sublist in identity for item in sublist]
direction = tuple(flat_list)
register.SetDirection(flat_list)
return register, mz
def read_imzml(inputname):
imzml = imzmlio.open_imzml(inputname)
spectra = imzmlio.get_full_spectra(imzml)
max_x = max(imzml.coordinates, key=lambda item:item[0])[0]
max_y = max(imzml.coordinates, key=lambda item:item[1])[1]
max_z = max(imzml.coordinates, key=lambda item:item[2])[2]
image = imzmlio.get_images_from_spectra(spectra, (max_x, max_y, max_z))
mzs, intensities = imzml.getspectrum(0)
return spectra, image
parser.add_argument("-i", "--input", help="Input 3D imzml")
parser.add_argument("-o", "--output", help="Output directory for 2D imzml")
args = parser.parse_args()
inputname = args.input
outname = args.output
imzml = imzmlio.open_imzml(inputname)
mz, I = imzml.getspectrum(0)
spectra = imzmlio.get_full_spectra(imzml)
max_x = max(imzml.coordinates, key=lambda item: item[0])[0]
max_y = max(imzml.coordinates, key=lambda item: item[1])[1]
max_z = max(imzml.coordinates, key=lambda item: item[2])[2]
image = imzmlio.get_images_from_spectra(spectra, (max_x, max_y, max_z))
coordinates = imzml.coordinates
print("Parsing all spectra")
intensities, new_coords = imzmlio.get_spectra_from_images(image)
previous_index = 0
array_coords = np.array(new_coords)
array_intensities = np.array(intensities)
split_intensities, split_coords = [], []
print("Splitting")
for i in range(max_z):
indices = np.where(array_coords.T[2] == i + 1)
current_intensities = list(map(list, array_intensities[indices]))
current_coordinates = array_coords[indices]
plt.axis("off")
plt.savefig(current_name,
transparent=True,
bbox_inches='tight',
pad_inches=0,
dpi=150)
plt.close()
## Open imzML
## This is a MS image where peaks are selected
imzml = io.open_imzml("data/wheat/msi.imzML")
spectra = io.get_full_spectra(imzml)
max_x = max(imzml.coordinates, key=lambda item: item[0])[0]
max_y = max(imzml.coordinates, key=lambda item: item[1])[1]
msi_images = io.get_images_from_spectra(spectra, shape=(max_x, max_y))
## Open MRI image
mri_itk_image = sitk.ReadImage("data/wheat/mri.png")
mri_image = sitk.GetArrayFromImage(mri_itk_image)
## Curvature values obtained by the VCM estimator
## of Lachaud et al. available in DGtal:
## More at https://dgtal.org/doc/stable/moduleVCM.html
## here, parameters r=4, R=5 for VCM computation
## The actual script used is available at https://github.com/fgrelard/MyDGtalContrib/blob/master/curvature.sh
## Requires DGtal library compilation
curvature_mri = np.loadtxt("data/wheat/mri_curvature.txt")
curvature_msi_coherence = np.loadtxt("data/wheat/msi_coherence_curvature.txt")
curvature_msi_chaos = np.loadtxt("data/wheat/msi_chaos_curvature.txt")
points_mri = np.loadtxt("data/wheat/mri_curvature_points.sdp",
