def reduce_image(image):
mzs = []
intensities = []
for i, (x, y, z) in enumerate(image.coordinates):
mz, ints = image.getspectrum(i)
mz = mz[:1]
ints = ints[:1]
mzs.append(mz)
intensities.append(ints)
imzmlio.write_imzml(
mzs, intensities, image.coordinates,
"/mnt/d/MALDI/imzML/MSI_20190419_01/00/peaksel_small.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]
current_coordinates[..., 2] = 1
current_coordinates = list(map(tuple, current_coordinates))
mzs = np.tile(mz, (len(indices[0]), 1)).tolist()
outroot, outext = os.path.splitext(outname)
current_name = outroot + "_" + str(i) + outext
print(current_name)
imzmlio.write_imzml(mzs, current_intensities, current_coordinates,
current_name)
sitk.sitkNearestNeighbor, 0)
outRegister_array[..., i] = sitk.GetArrayFromImage(register_image_3D).T
fig, ax = plt.subplots(1, 2)
before = sitk.GetArrayFromImage(image).T
index = np.unravel_index(np.argmax(np.mean(before, axis=-1), axis=None),
before.shape)
print(index)
after = outRegister_array
print(outRegister_array.shape)
if len(shape) == 3:
before = before[:, :, index[-1]]
after = after[:, :, index[-1]]
elif len(shape) == 4:
before = before[:, :, index[-2], index[-1]]
after = after[:, :, index[-2], index[-1]]
# ax[0].imshow(before.T)
# ax[1].imshow(after.T)
# plt.show()
if is_imzml:
mz, y = imzml.getspectrum(0)
intensities, coordinates = imzmlio.get_spectra_from_images(
outRegister_array)
mzs = [mz] * len(coordinates)
imzmlio.write_imzml(mzs, intensities, coordinates, outputname)
else:
outRegister = sitk.Cast(outRegister, sitk.sitkFloat32)
sitk.WriteImage(outRegister, outputname)
outname = args.output
factor = int(args.factor)
noise_level = float(args.level)
nb_peaks = int(args.nbpeaks)
step_mz = float(args.step)
imzml = io.open_imzml(input_name)
spectra = io.get_spectra(imzml)
peaks = sp.spectra_peak_mzs_adaptative_noiselevel(spectra, factor=factor, noise_level=noise_level, wlen=100)
realigned_spectra = sp.realign_mzs(spectra, peaks, reference="median", nb_occurrence=nb_peaks, step=step_mz)
print(realigned_spectra.shape)
av = sp.spectra_mean(realigned_spectra)
mzs = realigned_spectra[0, 0, :]
plt.plot(mzs, av)
plt.show()
mzs = []
intensities = []
for spectrum in realigned_spectra:
x, y = spectrum
mzs.append(x)
intensities.append(y)
io.write_imzml(mzs, intensities, imzml.coordinates, outname)
