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 imzml_to_npy(inputname):
p = io.open_imzml(inputname)
size = len(p.coordinates)
spectra = io.get_spectra_intensities(p)
np.save("data/intensities_250DJ.npy", spectra)
spectra = io.get_spectra_mzs(p)
np.save("data/mzs_250DJ.npy", spectra)
def test_normalize(self):
self.imzml = io.open_imzml(
"/mnt/d/MALDI/imzML/MSI_20190419_01/00/peaksel_small.imzML")
image = io.to_image_array(self.imzml)
norm = io.normalize(image)
print(image)
print(norm.dtype)
print(norm.shape)
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
import argparse
import random
import esmraldi.imzmlio as imzmlio
import numpy as np
import os
parser = argparse.ArgumentParser()
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)
import esmraldi.imzmlio as io
import esmraldi.spectraprocessing as sp
import numpy as np
from scipy.stats import pearsonr
parser = argparse.ArgumentParser()
parser.add_argument("-i", "--input", help="Input MALDI imzML")
parser.add_argument("-p",
"--prominence",
help="Prominence factor for peak selection")
args = parser.parse_args()
inputname = args.input
prominence = float(args.prominence)
p = io.open_imzml(inputname)
spectra = io.get_spectra(p)
mz, I = spectra[0]
min_diff = mz[1] - mz[0]
wlen = max(10, int(50.0 / min_diff))
indices_mzs = sp.spectra_peak_indices_adaptative(spectra,
factor=prominence,
wlen=wlen)
width_peak_indices = 10
tic = []
medians = []
sic = []
sic_comp = []
for i in range(image.shape[-1]):
n, ext = os.path.splitext(name)
current_name = n + "_" + str(i + 1) + ext
plt.imshow(image[..., i], cmap="gray")
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
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")
parser = argparse.ArgumentParser()
parser.add_argument("-i", "--input", help="Input MALDI imzML")
parser.add_argument("-o", "--output", help="Output nii file")
args = parser.parse_args()
inputname = args.input
outname = args.output
root, ext = os.path.splitext(outname)
image = imzmlio.open_imzml(inputname)
spectra = imzmlio.get_full_spectra(image)
max_x = max(image.coordinates, key=lambda item: item[0])[0]
max_y = max(image.coordinates, key=lambda item: item[1])[1]
max_z = max(image.coordinates, key=lambda item: item[2])[2]
img_array = imzmlio.get_images_from_spectra(spectra, (max_x, max_y, max_z))
mzs, y = image.getspectrum(0)
imzmlio.to_nifti(img_array, outname)
imzmlio.to_csv(mzs, root + ".csv")
#seg.max_variance_sort(image)
import esmraldi.imzmlio as io
import SimpleITK as sitk
import argparse
import numpy as np
parser = argparse.ArgumentParser()
parser.add_argument("-i", "--input", help="Input spectrum")
parser.add_argument("-o", "--output", help="Output spectrum")
parser.add_argument("-f", "--factor", help="Scale factor")
args = parser.parse_args()
input_name = args.input
outname = args.output
factor = float(args.factor)
imzml = io.open_imzml(input_name)
original_spectra = io.get_spectra(imzml)
mzs = original_spectra[:, 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))
itk_image = sitk.GetImageFromArray(image)
size = image.shape[:2]
new_size = [d*factor for d in size]
new_size = new_size[::-1]
resized_itk = utils.resize(itk_image, new_size)
def test_to_image_array(self):
self.imzml = io.open_imzml(
"/mnt/d/MALDI/imzML/MSI_20190419_01/00/peaksel_small.imzML")
image2 = io.to_image_array(self.imzml)
print(image2.shape)
def test_get_spectra_from_images(self):
self.imzml = io.open_imzml(
"/mnt/d/MALDI/imzML/MSI_20190419_01/00/peaksel.imzML")
coords = self.imzml.coordinates
i, newcoords = io.get_spectra_from_images(self.data)
self.assertEqual(coords, newcoords)
