def get_points_on_spectrum(self):
indices = self.mz_to_indices()
indices_denom = np.empty((0))
if self.current_mz_denom > 0:
indices_denom = self.mz_to_indices(True)
if indices.any():
num = np.mean(image[..., indices.flatten()], axis=-1)
if indices_denom.any():
denom = np.mean(image[..., indices_denom.flatten()], axis=-1)
divided = np.zeros_like(num, dtype=np.float64)
np.divide(num, denom, out=divided, where=denom != 0)
num = divided
vol = vedo.Volume(num)
vol.spacing([1, 1, spacing])
vol.interpolation(1)
self.vp.update(vol)
if not self.is_text_editing:
self.edit_mz.setText(str(round(self.current_mz, 4)))
self.edit_mz_denom.setText(str(round(self.current_mz_denom, 4)))
# self.remove([self.points, self.spline])
# cpt = self.computeWorldPosition(evt.picked2d) # make this 2d-screen point 3d
# self.cpoints.append(cpt)
# self.points = vedo.Points(self.cpoints, r=8).c('black')
# if len(self.cpoints) > 2:
# self.spline = vedo.Line(self.cpoints, closed=True).lw(5).c('red5')
# self.add([self.points, self.spline])
# def onKeyPress(self, evt):
# if evt.keyPressed == 'z' and self.spline: # cut mesh with a ribbon-like surface
# vedo.printc("Cutting the mesh please wait..", invert=True)
# tol = self.mesh.diagonalSize()/2 # size of ribbon
# pts = self.spline.points()
# n = vedo.fitPlane(pts, signed=True).normal # compute normal vector to points
# rib = vedo.Ribbon(pts - tol*n, pts + tol*n, closed=True)
# self.mesh.cutWithMesh(rib)
# self.remove([self.spline, self.points]).render()
# self.cpoints, self.points, self.spline = [], None, None
# def start(self, **kwargs):
# return self.show(self.txt2d, self.mesh, **kwargs)
#
######################################################################################
vedo.settings.useParallelProjection = True # to avoid perspective artifacts
msh = vedo.Volume(vedo.datadir+'embryo.tif').isosurface().color('gold', 0.25) # Mesh
plt = FreeHandCutPlotter(msh).addHoverLegend()
#plt.init(some_list_of_initial_pts) #optional!
plt.start(axes=1, bg2='lightblue').close()
# Sort lists for correct order
f1_list = sorted(f1_files)
f2_list = sorted(f2_files)
# print(f1_list)
# print(f2_list)
index = 3
medium = pv.read(tmp_folder + 'porousMedium.vti')
grains = medium.get_array('tag').reshape([nz, ny, nx])
grains = grains[:, :, n_slices_start:nx-n_slices_end]
grains = grains[:,:,:]
print(grains.shape)
grains = vd.Volume(grains).isosurface(1)
plt = vd.Plotter(axes=9, bg='w', bg2='w', size=(1200,900), offscreen=False) # Create a vedo plotter; axes=9 is just an outline
plt += grains.c("gray").opacity(0.4)
cam = dict(pos=(179, 313, 136),
focalPoint=(45.0, 49.5, 44.0),
viewup=(-0.163, -0.252, 0.954),
distance=309,
clippingRange=(158, 501))
plt.show(camera=cam, interactive=True) # .screenshot(f'animation_and_plotting/gif_images/test{index}.png', scale=4)
plt.close()
# exit()
# for index in range(len(f1_list)):
f1_mesh = pv.read(f1_list[index])
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))
print(image.shape)
if len(image.shape) == 3:
shape_4D = image.shape[:-1] + (1, image.shape[-1])
image = np.reshape(image, shape_4D)
image = np.transpose(image, (2, 1, 0, 3))
if is_memmap:
os.makedirs(memmap_dir, exist_ok=True)
np.save(memmap_image_filename, image)
np.save(memmap_spectra_filename, spectra)
vol = vedo.Volume(image[..., 0])
mean_spectra = sp.spectra_mean(spectra)
else:
vol = vedo.load(inputname) # load Volume
vedo.printHistogram(vol, logscale=True)
vol.spacing([1, 1, spacing])
vol.mode(0).color("jet").jittering(True)
vol.interpolation(1)
app = Qt.QApplication(sys.argv)
window = MainWindow(inputname, vol, spectra[0, 0], mean_spectra)
sys.exit(app.exec_())
import urllib, hdf5storage, scipy.ndimage, vedo # import libs
# download image from the Digital Rocks Portal
drp = 'https://www.digitalrocksportal.org/'
url = drp + 'projects/374/images/309544/download/'
urllib.request.urlretrieve(url, im_name := '374_01_00_256.mat')
# read-in image
bin_im = hdf5.storage.loadmat(im_name)['bin']
# obtain the Euclidean distance of the pore-space
edist = scipy.ndimage.morphology.distance_transform_edt(bin_im == 0)
# remove upper corner
half_size = bin_im.shape[0] // 2
edist[-half_size:, -half_size:, :half_size] = 0
bin_im[-half_size:, -half_size:, :half_size] = -1
# plot using Vedo
plot = vedo.Volume(edist).legosurface(vmin=1, vmax=5, cmap='turbo')
plot += vedo.Volume(bin_im).legosurface(vmin=1,
vmax=2).c('lightgray').opacity(0.05)
vedo.plotter(plot)