コード例 #1
0
ファイル: pdb.py プロジェクト: arokem/pdb
def fg_from_trk(trk_file, affine=None):
    """
    Read data from a trackvis .trk file and create a FiberGroup object
    according to the information in it.
    """

    # Generate right away, since we're going to do it anyway:
    read_trk = tv.read(trk_file, as_generator=False)
    fibers_trk = read_trk[0]

    # Per default read from the affine from the file header:
    if affine is not None:
        aff = affine
    else:
        hdr = read_trk[1]
        aff = tv.aff_from_hdr(hdr)
        # If the header contains a bogus affine, we revert to np.eye(4), so we
        # don't get into trouble later:
        try:
            np.matrix(aff).getI()
        except np.linalg.LinAlgError:
            e_s = "trk file contains bogus header, reverting to np.eye(4)"
            warnings.warn(e_s)
            aff = np.eye(4)

    fibers = []
    for f in fibers_trk:
        fibers.append(ozf.Fiber(np.array(f[0]).T, affine=aff))

    return ozf.FiberGroup(fibers, affine=aff)
コード例 #2
0
ファイル: io.py プロジェクト: qytian/osmosis
def fg_from_trk(trk_file, affine=None):
    """
    Read data from a trackvis .trk file and create a FiberGroup object
    according to the information in it.
    """

    # Generate right away, since we're going to do it anyway:
    read_trk = tv.read(trk_file, as_generator=False)
    fibers_trk = read_trk[0]

    # Per default read from the affine from the file header:
    if affine is not None:
        aff = affine
    else: 
        hdr = read_trk[1]
        aff= tv.aff_from_hdr(hdr)
        # If the header contains a bogus affine, we revert to np.eye(4), so we
        # don't get into trouble later:
        try:
            np.matrix(aff).getI()
        except np.linalg.LinAlgError:
            e_s = "trk file contains bogus header, reverting to np.eye(4)" 
            warnings.warn(e_s)
            aff = np.eye(4)

    fibers = []
    for f in fibers_trk:
        fibers.append(ozf.Fiber(np.array(f[0]).T,affine=aff))

    return ozf.FiberGroup(fibers, affine=aff)
コード例 #3
0
	def _run_interface(self, runtime):
		# Loading the ROI file
	    import nibabel as nib
	    import numpy as np
	    from dipy.tracking import utils

	    img = nib.load(self.inputs.ROI_file)
	    data = img.get_data()
	    affine = img.get_affine()

	    # Getting the FA file
	    img = nib.load(self.inputs.FA_file)
	    FA_data = img.get_data()
	    FA_affine = img.get_affine()

	    # Loading the streamlines
	    from nibabel import trackvis
	    streams, hdr = trackvis.read(self.inputs.trackfile,points_space='rasmm')
	    streamlines = [s[0] for s in streams]
	    streamlines_affine = trackvis.aff_from_hdr(hdr,atleast_v2=True)

	    # Checking for negative values
	    from dipy.tracking._utils import _mapping_to_voxel, _to_voxel_coordinates
	    endpoints = [sl[0::len(sl)-1] for sl in streamlines]
	    lin_T, offset = _mapping_to_voxel(affine, (1.,1.,1.))
	    inds = np.dot(endpoints, lin_T)
	    inds += offset
	    negative_values = np.where(inds <0)[0]
	    for negative_value in sorted(negative_values, reverse=True):
			del streamlines[negative_value]

	    # Constructing the streamlines matrix
	    matrix,mapping = utils.connectivity_matrix(streamlines=streamlines,label_volume=data,affine=streamlines_affine,symmetric=True,return_mapping=True,mapping_as_streamlines=True)
	    matrix[matrix < 10] = 0

	    # Constructing the FA matrix
	    dimensions = matrix.shape
	    FA_matrix = np.empty(shape=dimensions)

	    for i in range(0,dimensions[0]):
	        for j in range(0,dimensions[1]):
	            if matrix[i,j]:
	                dm = utils.density_map(mapping[i,j], FA_data.shape, affine=streamlines_affine)
            		FA_matrix[i,j] = np.mean(FA_data[dm>0])
	            else:
	                FA_matrix[i,j] = 0

	    FA_matrix[np.tril_indices(n=len(FA_matrix))] = 0
	    FA_matrix = FA_matrix.T + FA_matrix - np.diagonal(FA_matrix)

	    from nipype.utils.filemanip import split_filename
	    _, base, _ = split_filename(self.inputs.trackfile)
	    np.savetxt(base + '_FA_matrix.txt',FA_matrix,delimiter='\t')
	    return runtime
コード例 #4
0
	def _run_interface(self, runtime):
		# Loading the ROI file
	    from dipy.tracking import utils 
	    import nibabel as nib
	    import numpy as np
	    import os 
	    
	    img = nib.load(self.inputs.ROI_file)
	    data = img.get_data()
	    affine = img.get_affine()

	    # Getting ROI volumes if they haven't been generated
		if not os.path.isfile('/imaging/jb07/CALM/DWI/FA_connectome/Atlas_volumes.csv'):
			import nibabel as nib
			import numpy as np
			import os 
			import pandas as pd
			import subprocess

			atlas_file = ROI_file
			img = nib.load(atlas_file)
			data = img.get_data()
			affine = img.get_affine()
			volumes = pd.DataFrame()

			atlas_labels = np.unique(data)

			for atlas_label in atlas_labels:
				data = nib.load(atlas_file).get_data()
				data[data != atlas_label] = 0 
				data[data == atlas_label] = 1
				nib.save(nib.Nifti1Image(data, affine), 'temp.nii.gz')
				volumes.set_value(atlas_label, 'volume', subprocess.check_output(os.environ['FSLDIR'] + '/bin/fslstats temp.nii.gz -V', shell=True).split(' ')[0])

			os.remove('temp.nii.gz')
			volumes.to_csv('/imaging/jb07/CALM/DWI/FA_connectome/Atlas_volumes.csv')

		ROI_volumes = pd.read_csv('/home/jb07/CALM/DWI/FA_connectome/Atlas_volumes.csv')

		# Getting the FA file
		img = nib.load(self.inputs.FA_file)
		FA_data = img.get_data()
		FA_affine = img.get_affine()

		# Loading the streamlines
		from nibabel import trackvis
		streams, hdr = trackvis.read(self.inputs.trackfile,points_space='rasmm')
		streamlines = [s[0] for s in streams]
		streamlines_affine = trackvis.aff_from_hdr(hdr,atleast_v2=True)

		# Checking for negative values
		from dipy.tracking._utils import _mapping_to_voxel, _to_voxel_coordinates
		endpoints = [sl[0::len(sl)-1] for sl in streamlines]
		lin_T, offset = _mapping_to_voxel(affine, (1.,1.,1.))
		inds = np.dot(endpoints, lin_T)
		inds += offset
		negative_values = np.where(inds <0)[0]
		for negative_value in sorted(negative_values, reverse=True):
			del streamlines[negative_value]

		# Constructing the streamlines matrix
		matrix,mapping = utils.connectivity_matrix(streamlines=streamlines,label_volume=data,affine=streamlines_affine,symmetric=True,return_mapping=True,mapping_as_streamlines=True)
		matrix[matrix < 10] = 0

		# Constructing the FA matrix
		dimensions = matrix.shape
		FA_matrix = np.empty(shape=dimensions)
		density_matrix = np.empty(shape=dimensions)
		density_corrected_matrix = np.empty(shape=dimensions)

		for i in range(0,dimensions[0]):
		    for j in range(0,dimensions[1]):
		        if matrix[i,j]:
		            dm = utils.density_map(mapping[i,j], FA_data.shape, affine=streamlines_affine)
		            FA_matrix[i,j] = np.mean(FA_data[dm>0])
		            if np.sum(dm > 0) > 0:
		            	density_matrix[i,j] = np.sum(dm[dm > 0])
		            	density_corrected_matrix[i,j] = np.sum(dm[dm > 0])/np.sum([ROI_volumes.iloc[i].values.astype('int'), ROI_volumes.iloc[j].values.astype('int')])
		            else: 
		            	density_matrix[i,j] = 0
		            	density_corrected_matrix[i,j] = 0
		        else:
		            FA_matrix[i,j] = 0
		            density_matrix[i,j] = 0
		            density_corrected_matrix[i,j] = 0

		FA_matrix[np.tril_indices(n=len(FA_matrix))] = 0
		FA_matrix = FA_matrix.T + FA_matrix - np.diagonal(FA_matrix)

		density_matrix[np.tril_indices(n=len(density_matrix))] = 0
		density_matrix = density_matrix.T + density_matrix - np.diagonal(density_matrix)

		density_corrected_matrix[np.tril_indices(n=len(density_corrected_matrix))] = 0
		density_corrected_matrix = density_corrected_matrix.T + density_corrected_matrix - np.diagonal(density_corrected_matrix)

		from nipype.utils.filemanip import split_filename
		_, base, _ = split_filename(self.inputs.trackfile)
		np.savetxt(base + '_FA_matrix.txt',FA_matrix,delimiter='\t')
		np.savetxt(base + '_density_matrix.txt',density_matrix,delimiter='\t')
		np.savetxt(base + '_volume_corrected_density_matrix.txt',density_corrected_matrix,delimiter='\t')