def fishplot(img, overl = '', orient = 'axial', sliceno = 20, al = .5, col = 'magma'):
#===============================================================================
# N.B This breaks frequently - I have no idea what is wrong with the implementation
import ants
import numpy as np
r = img.shape
if orient == 'coronal': axs = 0; ri = 0
elif orient == 'sagittal': axs = 1; ri = 1
elif orient == 'axial': axs = 2; ri = 2
# ALERT ALERT I HAVE NO IDEA WHAT THE SLICE INDEXING WANTS FROM ME
# Does it live in the physical space?
# Does it live in the voxel space?
# I DON'T KNOW - so I'm fudging it
sliceno = min([sliceno,r[ri]])
if orient == 'axial': mx_dim = r[ri] - 1
else: mx_dim = r[ri] * img.spacing[ri]-1
sli = list(map(int, np.ndarray.tolist(np.linspace(0, mx_dim, sliceno))))
if not overl: ants.plot(img, axis = axs, slices = sli, figsize = 6)
else: ants.plot(img, overl, overlay_cmap = col, overlay_alpha= al, axis = axs, slices = sli, figsize = 6)
def load_ants(args):
import ants
images = load_patients(args)
for i in images:
img = ants.image_read(i)
# Create save dir in parent folder
save_dir = output_dir(args, i)
ants.plot(img, filename=f'{save_dir}.png', cmap='plasma')
def cde_mot_meancalc(imgs,
Fimg,
noimages=100,
delfirst=True,
crop=False,
plot='do'):
import numpy as np
import ants
import os
print('I found ' + str(len(imgs)) + ' images')
# Load subsection of tifs
#---------------------------------------------------------------------------
maxno = np.min([len(imgs), noimages])
loadi = np.linspace(0, len(imgs) - 1, maxno)
loadi = loadi.astype(int)
print('Of these I' 'm loading ' + str(maxno))
if delfirst:
loadi = np.delete(loadi, 0)
print('I' 'm ignoring the first volume')
# Load initial image for dimensions
#---------------------------------------------------------------------------
if type(imgs[0]) == str:
templ = ants.image_read(Fimg + os.sep + imgs[0])
elif type(imgs[0]) == ants.core.ants_image.ANTsImage:
templ = imgs[0]
if crop:
templ = ants.crop_indices(templ, [0, 0, 1], templ.shape)
mean_arr = np.multiply(templ.numpy(), 0)
imglist = []
for i in loadi:
if type(imgs[0]) == str:
img = ants.image_read(Fimg + os.sep + imgs[i])
elif type(imgs[0]) == ants.core.ants_image.ANTsImage:
img = imgs[i]
if crop: img = ants.crop_indices(img, [0, 0, 1], img.shape)
mean_arr = mean_arr + img.numpy() / maxno
imglist.append(img)
mimg = ants.from_numpy(mean_arr)
if plot == 'do':
ants.plot(mimg, axis=2, slices=range(mimg.shape[2]), figsize=3)
return mimg, imglist
def fishplot(img, overl='', orient='axial', sliceno=20, al=.5, col='magma'):
#===============================================================================
"""
This function plots output of registration.
Inputs:
im (ants image): Atlas to plot
overl (ants image): warped image to overlay onto atlas
orient (str): plotting orientation
sliceno (int): number of slices to visualise
al (int): alpha
col (str): color map
"""
import ants
import numpy as np
r = img.shape
if orient == 'coronal':
axs = 0
ri = 0
elif orient == 'sagittal':
axs = 1
ri = 1
elif orient == 'axial':
axs = 2
ri = 2
sliceno = min([sliceno, r[ri]])
if orient == 'axial': mx_dim = r[ri] - 1
else: mx_dim = r[ri] * img.spacing[ri] - 1
sli = list(map(int, np.ndarray.tolist(np.linspace(0, mx_dim, sliceno))))
if not overl: ants.plot(img, axis=axs, slices=sli, figsize=6)
else:
ants.plot(img,
overl,
overlay_cmap=col,
overlay_alpha=al,
axis=axs,
slices=sli,
figsize=6)
nuisance = np.c_[ nuisance, motCorr['FD'] ]
nuisance = np.c_[ nuisance, mycompcor['basis'] ]
gmmat = regress_components( gmmat[goodtimes[0],:], nuisance[goodtimes[0],:] )
dfnmat = ants.timeseries_to_matrix( simg, ants.threshold_image( dfnImg, 1, dfnImg.max() ) )
dfnmat = regress_components( dfnmat[goodtimes[0],:], nuisance[goodtimes[0],:] )
# dfnmatf = frequencyFilterfMRI( dfnmat, tr = tr, freqLo = 0.01, freqHi = 0.1, opt = "trig" )
dfnsignal = dfnmat.mean( axis = 1 )
gmmatDFNCorr = np.zeros( gmmat.shape[1] )
for k in range( gmmat.shape[1] ):
gmmatDFNCorr[ k ] = pearsonr( dfnsignal, gmmat[:,k] )[0]
corrImg = ants.make_image( gmseg, gmmatDFNCorr )
corrImgPos = corrImg * ants.threshold_image( corrImg, 0.25, 1 )
ants.plot( avgBold, corrImgPos, axis=2, overlay_alpha = 0.6, cbar=False, nslices = 24, ncol=8, cbar_length=0.3, cbar_vertical=True )
"""
needs to be translated to python still
TODO:
ilr - image based linear regression
frequencyFilterfMRI - scipy probably butter filtfilt
getNetworkBeta <-function( motcorrIn, networkName = 'Default Mode' ) {
*implement* noiseu = compcor( ccmat, ncompcor = 10 )
smth = c( 1.0, 1.0, 1.0, 2.0 ) # this is for sigmaInPhysicalCoordinates = F
def show(self, **kwargs):
ants.plot(self.obj)
import ants
import os
import glob
dataDirectory = './'
populationFiles = glob.glob(dataDirectory + "OASIS*Slice121.nii.gz")
population = list()
for i in range(len(populationFiles)):
population.append(ants.image_read(populationFiles[i], dimension=2))
btp = ants.build_template(initialTemplate=None,
image_list=population,
iterations=4,
gradient_step=0.2,
verbose=True,
syn_metric='CC',
reg_iterations=(100, 70, 50, 0))
ants.plot(btp)
fixedImage = ants.image_read(
'../mmwhs/CT_test/205_5/ct-image_crop_man_reg_resize/ct_train_1013_image.nii.gz'
)
fixedMask = ants.image_read(
'../mmwhs/CT_test/205_5/ct-label_crop_man_reg_resize/ct_train_1013_label.nii.gz'
)
movingImage = ants.image_read(
'../mmwhs/CT_test/205_5/ct-image_crop_man_reg_resize/ct_train_1014_image.nii.gz'
)
movingMask = ants.image_read(
'../mmwhs/CT_test/205_5/ct-label_crop_man_reg_resize/ct_train_1014_label.nii.gz'
)
# Plot the fixed image/mask
ants.plot(fixedImage, overlay=fixedMask, overlay_cmap="viridis", alpha=0.9)
# Plot the moving image/mask
ants.plot(movingImage, overlay=movingMask, overlay_cmap="viridis", alpha=0.9)
#######
#
# Perform registration with no mask
#
outputDirectory = './OutputNoMaskANTsPy/'
if not os.path.isdir(outputDirectory):
os.mkdir(outputDirectory)
outputPrefix = outputDirectory + 'antsr'
import ants import os import glob dataDirectory = './' populationFiles = glob.glob( dataDirectory + "OASIS*Slice121.nii.gz" ) population = list() for i in range( len( populationFiles ) ): population.append( ants.image_read( populationFiles[i], dimension = 2 ) ) btp = ants.build_template( initialTemplate = None, image_list = population, iterations = 4, gradient_step = 0.2, verbose = True, syn_metric = 'CC', reg_iterations = ( 100, 70, 50, 0 ) ) ants.plot( btp )
def load_prereg(experiment, file, modality, condition, prefix, disk, Fdata):
#===============================================================================
"""
This function loads prereg data in ants and also loads trace and coord data, and plots output.
Inputs:
experiment (str): experiment subfolder name
file (str): fish name
modality (str): imaging modality
condition (str): imaging condition
prefix (str): genotype, if None no genotype, if 'Yes' genotype is added
disk (str): path to mean images
Fdata (str): path to trace and coords data
Returns:
f (ants image): image stack to register
Fish (dict): dictionary containing cell and trace data
meanimg_fold (list): list containing the folder with the image tif
"""
import numpy as np
import os
#Find fish image
#--------------------------------------------------------
img_type = adfn.return_files(disk + 'Project/',
file + os.sep + modality + os.sep, '**')[0]
meanimg_fold = adfn.return_files(
disk + 'Project/', file + os.sep + modality + os.sep + img_type,
'*' + condition + '*meanimgstack*')
num = meanimg_fold[0][meanimg_fold[0].find(modality) -
3:meanimg_fold[0].find(modality) - 1]
dpf = meanimg_fold[0][meanimg_fold[0].find('dpf') -
1:meanimg_fold[0].find('dpf') + 3]
if prefix == 'Yes':
pref = file[file.find(modality) - 4:file[0].find(modality) - 2]
else:
pref = ''
#Find coords and trace files
#--------------------------------------------------------
name_list = [
'*_realcoord.npy', '*_realtrace.npy', '*_binarised.npy',
'*_deltaff.npy'
]
dict_list = ['coord', 'trace', 'bind', 'dff']
Fish = {}
def grab(Fdata, experiment, condition, pref, number, age, name):
#out = adfn.return_files(Fdata, experiment, '*' + experiment + pref + '-' + number + '*' + age + '*' + condition + name
out = adfn.return_files(
Fdata, 'PTZ-WILDTYPE-CCM/comb_2/', '*' + 'PTZ-WILDTYPE' + pref +
'-' + number + '*' + age + '*' + condition + name)
if len(out) > 1:
print('More than one file found - check list length')
return ()
if len(out) < 1:
print('No files found found - check list length')
return ()
return (out)
for i, nom in enumerate(name_list):
Fish.update({
dict_list[i]:
np.load(
grab(Fdata, experiment, condition, pref, num, dpf, nom)[0])
})
# Visualise fish
#---------------------------------------------------------------------------
f = ants.image_read(disk + 'Project/' + file + os.sep + modality + os.sep +
img_type + os.sep + meanimg_fold[0])
f.set_spacing([.3, .3, 15.])
f.set_direction(np.array([[-1., 0., 0.], [0., 1., 0.], [0., 0., 1.]]))
ants.plot(f, axis=2, nslices=5, slices=np.arange(0, 5, 1), figsize=10)
savename = adfn.save_name(
grab(Fdata, experiment, condition, pref, num, dpf, nom)[0])
return (f, Fish, meanimg_fold, savename)
import ants import os # Read in the images dataDirectory = './data/' fixedImageFile = dataDirectory + 'chalf.nii.gz' fixedImage = ants.image_read( fixedImageFile, dimension = 2 ) movingImageFile = dataDirectory + 'c.nii.gz' movingImage = ants.image_read( movingImageFile, dimension = 2 ) # Plot the fixed and moving images ants.plot( movingImage, overlay = fixedImage, overlay_cmap = "viridis", alpha = 0.9 ) ####### # # Perform registration # outputDirectory = './OutputANTsPy/' if not os.path.isdir( outputDirectory ): os.mkdir( outputDirectory ) outputPrefix = outputDirectory + 'antsr' registration = ants.registration( fixed = fixedImage, moving = movingImage, type_of_transform = 'TVMSQC', grad_step = 2.0, verbose = True, outprefix = outputPrefix )
def test_plot_example(self):
for img in self.imgs:
ants.plot(img)
import ants
import os
# Read in the images
dataDirectory = './data/'
fixedImageFile = dataDirectory + 'chalf.nii.gz'
fixedImage = ants.image_read(fixedImageFile, dimension=2)
movingImageFile = dataDirectory + 'c.nii.gz'
movingImage = ants.image_read(movingImageFile, dimension=2)
# Plot the fixed and moving images
ants.plot(movingImage, overlay=fixedImage, overlay_cmap="viridis", alpha=0.9)
#######
#
# Perform registration
#
outputDirectory = './OutputANTsPy/'
if not os.path.isdir(outputDirectory):
os.mkdir(outputDirectory)
outputPrefix = outputDirectory + 'antsr'
registration = ants.registration(fixed=fixedImage,
moving=movingImage,
type_of_transform='TVMSQC',
grad_step=2.0,
verbose=True,