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ANTsX/ANTsPy
ants/contrib/sampling/transforms.py
LocallyBlurIntensity.transform
def transform(self, X, y=None): """ Locally blur an image by applying a gradient anisotropic diffusion filter. Arguments --------- X : ANTsImage image to transform y : ANTsImage (optional) another image to transform. Example ------- >>> import ants >>> blur = ants.contrib.LocallyBlurIntensity(1,5) >>> img2d = ants.image_read(ants.get_data('r16')) >>> img2d_b = blur.transform(img2d) >>> ants.plot(img2d) >>> ants.plot(img2d_b) >>> img3d = ants.image_read(ants.get_data('mni')) >>> img3d_b = blur.transform(img3d) >>> ants.plot(img3d) >>> ants.plot(img3d_b) """ #if X.pixeltype != 'float': # raise ValueError('image.pixeltype must be float ... use TypeCast transform or clone to float') insuffix = X._libsuffix cast_fn = utils.get_lib_fn('locallyBlurAntsImage%s' % (insuffix)) casted_ptr = cast_fn(X.pointer, self.iters, self.conductance) return iio.ANTsImage(pixeltype=X.pixeltype, dimension=X.dimension, components=X.components, pointer=casted_ptr)
python
def transform(self, X, y=None): #if X.pixeltype != 'float': # raise ValueError('image.pixeltype must be float ... use TypeCast transform or clone to float') insuffix = X._libsuffix cast_fn = utils.get_lib_fn('locallyBlurAntsImage%s' % (insuffix)) casted_ptr = cast_fn(X.pointer, self.iters, self.conductance) return iio.ANTsImage(pixeltype=X.pixeltype, dimension=X.dimension, components=X.components, pointer=casted_ptr)
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Locally blur an image by applying a gradient anisotropic diffusion filter. Arguments --------- X : ANTsImage image to transform y : ANTsImage (optional) another image to transform. Example ------- >>> import ants >>> blur = ants.contrib.LocallyBlurIntensity(1,5) >>> img2d = ants.image_read(ants.get_data('r16')) >>> img2d_b = blur.transform(img2d) >>> ants.plot(img2d) >>> ants.plot(img2d_b) >>> img3d = ants.image_read(ants.get_data('mni')) >>> img3d_b = blur.transform(img3d) >>> ants.plot(img3d) >>> ants.plot(img3d_b)
[ "Locally", "blur", "an", "image", "by", "applying", "a", "gradient", "anisotropic", "diffusion", "filter", "." ]
638020af2cdfc5ff4bdb9809ffe67aa505727a3b
https://github.com/ANTsX/ANTsPy/blob/638020af2cdfc5ff4bdb9809ffe67aa505727a3b/ants/contrib/sampling/transforms.py#L242-L273
251,001
ANTsX/ANTsPy
ants/utils/get_ants_data.py
get_data
def get_data(name=None): """ Get ANTsPy test data filename ANTsR function: `getANTsRData` Arguments --------- name : string name of test image tag to retrieve Options: - 'r16' - 'r27' - 'r64' - 'r85' - 'ch2' - 'mni' - 'surf' Returns ------- string filepath of test image Example ------- >>> import ants >>> mnipath = ants.get_ants_data('mni') """ if name is None: files = [] for fname in os.listdir(data_path): if (fname.endswith('.nii.gz')) or (fname.endswith('.jpg') or (fname.endswith('.csv'))): fname = os.path.join(data_path, fname) files.append(fname) return files else: datapath = None for fname in os.listdir(data_path): if (name == fname.split('.')[0]) or ((name+'slice') == fname.split('.')[0]): datapath = os.path.join(data_path, fname) if datapath is None: raise ValueError('File doesnt exist. Options: ' , os.listdir(data_path)) return datapath
python
def get_data(name=None): if name is None: files = [] for fname in os.listdir(data_path): if (fname.endswith('.nii.gz')) or (fname.endswith('.jpg') or (fname.endswith('.csv'))): fname = os.path.join(data_path, fname) files.append(fname) return files else: datapath = None for fname in os.listdir(data_path): if (name == fname.split('.')[0]) or ((name+'slice') == fname.split('.')[0]): datapath = os.path.join(data_path, fname) if datapath is None: raise ValueError('File doesnt exist. Options: ' , os.listdir(data_path)) return datapath
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Get ANTsPy test data filename ANTsR function: `getANTsRData` Arguments --------- name : string name of test image tag to retrieve Options: - 'r16' - 'r27' - 'r64' - 'r85' - 'ch2' - 'mni' - 'surf' Returns ------- string filepath of test image Example ------- >>> import ants >>> mnipath = ants.get_ants_data('mni')
[ "Get", "ANTsPy", "test", "data", "filename" ]
638020af2cdfc5ff4bdb9809ffe67aa505727a3b
https://github.com/ANTsX/ANTsPy/blob/638020af2cdfc5ff4bdb9809ffe67aa505727a3b/ants/utils/get_ants_data.py#L11-L54
251,002
ANTsX/ANTsPy
ants/utils/invariant_image_similarity.py
convolve_image
def convolve_image(image, kernel_image, crop=True): """ Convolve one image with another ANTsR function: `convolveImage` Arguments --------- image : ANTsImage image to convolve kernel_image : ANTsImage image acting as kernel crop : boolean whether to automatically crop kernel_image Returns ------- ANTsImage Example ------- >>> import ants >>> fi = ants.image_read(ants.get_ants_data('r16')) >>> convimg = ants.make_image( (3,3), (1,0,1,0,-4,0,1,0,1) ) >>> convout = ants.convolve_image( fi, convimg ) >>> convimg2 = ants.make_image( (3,3), (0,1,0,1,0,-1,0,-1,0) ) >>> convout2 = ants.convolve_image( fi, convimg2 ) """ if not isinstance(image, iio.ANTsImage): raise ValueError('image must be ANTsImage type') if not isinstance(kernel_image, iio.ANTsImage): raise ValueError('kernel must be ANTsImage type') orig_ptype = image.pixeltype if image.pixeltype != 'float': image = image.clone('float') if kernel_image.pixeltype != 'float': kernel_image = kernel_image.clone('float') if crop: kernel_image_mask = utils.get_mask(kernel_image) kernel_image = utils.crop_image(kernel_image, kernel_image_mask) kernel_image_mask = utils.crop_image(kernel_image_mask, kernel_image_mask) kernel_image[kernel_image_mask==0] = kernel_image[kernel_image_mask==1].mean() libfn = utils.get_lib_fn('convolveImageF%i' % image.dimension) conv_itk_image = libfn(image.pointer, kernel_image.pointer) conv_ants_image = iio.ANTsImage(pixeltype=image.pixeltype, dimension=image.dimension, components=image.components, pointer=conv_itk_image) if orig_ptype != 'float': conv_ants_image = conv_ants_image.clone(orig_ptype) return conv_ants_image
python
def convolve_image(image, kernel_image, crop=True): if not isinstance(image, iio.ANTsImage): raise ValueError('image must be ANTsImage type') if not isinstance(kernel_image, iio.ANTsImage): raise ValueError('kernel must be ANTsImage type') orig_ptype = image.pixeltype if image.pixeltype != 'float': image = image.clone('float') if kernel_image.pixeltype != 'float': kernel_image = kernel_image.clone('float') if crop: kernel_image_mask = utils.get_mask(kernel_image) kernel_image = utils.crop_image(kernel_image, kernel_image_mask) kernel_image_mask = utils.crop_image(kernel_image_mask, kernel_image_mask) kernel_image[kernel_image_mask==0] = kernel_image[kernel_image_mask==1].mean() libfn = utils.get_lib_fn('convolveImageF%i' % image.dimension) conv_itk_image = libfn(image.pointer, kernel_image.pointer) conv_ants_image = iio.ANTsImage(pixeltype=image.pixeltype, dimension=image.dimension, components=image.components, pointer=conv_itk_image) if orig_ptype != 'float': conv_ants_image = conv_ants_image.clone(orig_ptype) return conv_ants_image
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Convolve one image with another ANTsR function: `convolveImage` Arguments --------- image : ANTsImage image to convolve kernel_image : ANTsImage image acting as kernel crop : boolean whether to automatically crop kernel_image Returns ------- ANTsImage Example ------- >>> import ants >>> fi = ants.image_read(ants.get_ants_data('r16')) >>> convimg = ants.make_image( (3,3), (1,0,1,0,-4,0,1,0,1) ) >>> convout = ants.convolve_image( fi, convimg ) >>> convimg2 = ants.make_image( (3,3), (0,1,0,1,0,-1,0,-1,0) ) >>> convout2 = ants.convolve_image( fi, convimg2 )
[ "Convolve", "one", "image", "with", "another" ]
638020af2cdfc5ff4bdb9809ffe67aa505727a3b
https://github.com/ANTsX/ANTsPy/blob/638020af2cdfc5ff4bdb9809ffe67aa505727a3b/ants/utils/invariant_image_similarity.py#L200-L255
251,003
ANTsX/ANTsPy
ants/utils/ndimage_to_list.py
ndimage_to_list
def ndimage_to_list(image): """ Split a n dimensional ANTsImage into a list of n-1 dimensional ANTsImages Arguments --------- image : ANTsImage n-dimensional image to split Returns ------- list of ANTsImage types Example ------- >>> import ants >>> image = ants.image_read(ants.get_ants_data('r16')) >>> image2 = ants.image_read(ants.get_ants_data('r16')) >>> imageTar = ants.make_image( ( *image2.shape, 2 ) ) >>> image3 = ants.list_to_ndimage( imageTar, [image,image2]) >>> image3.dimension == 3 >>> images_unmerged = ants.ndimage_to_list( image3 ) >>> len(images_unmerged) == 2 >>> images_unmerged[0].dimension == 2 """ inpixeltype = image.pixeltype dimension = image.dimension components = 1 imageShape = image.shape nSections = imageShape[ dimension - 1 ] subdimension = dimension - 1 suborigin = iio.get_origin( image )[0:subdimension] subspacing = iio.get_spacing( image )[0:subdimension] subdirection = np.eye( subdimension ) for i in range( subdimension ): subdirection[i,:] = iio.get_direction( image )[i,0:subdimension] subdim = image.shape[ 0:subdimension ] imagelist = [] for i in range( nSections ): img = utils.slice_image( image, axis = subdimension, idx = i ) iio.set_spacing( img, subspacing ) iio.set_origin( img, suborigin ) iio.set_direction( img, subdirection ) imagelist.append( img ) return imagelist
python
def ndimage_to_list(image): inpixeltype = image.pixeltype dimension = image.dimension components = 1 imageShape = image.shape nSections = imageShape[ dimension - 1 ] subdimension = dimension - 1 suborigin = iio.get_origin( image )[0:subdimension] subspacing = iio.get_spacing( image )[0:subdimension] subdirection = np.eye( subdimension ) for i in range( subdimension ): subdirection[i,:] = iio.get_direction( image )[i,0:subdimension] subdim = image.shape[ 0:subdimension ] imagelist = [] for i in range( nSections ): img = utils.slice_image( image, axis = subdimension, idx = i ) iio.set_spacing( img, subspacing ) iio.set_origin( img, suborigin ) iio.set_direction( img, subdirection ) imagelist.append( img ) return imagelist
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Split a n dimensional ANTsImage into a list of n-1 dimensional ANTsImages Arguments --------- image : ANTsImage n-dimensional image to split Returns ------- list of ANTsImage types Example ------- >>> import ants >>> image = ants.image_read(ants.get_ants_data('r16')) >>> image2 = ants.image_read(ants.get_ants_data('r16')) >>> imageTar = ants.make_image( ( *image2.shape, 2 ) ) >>> image3 = ants.list_to_ndimage( imageTar, [image,image2]) >>> image3.dimension == 3 >>> images_unmerged = ants.ndimage_to_list( image3 ) >>> len(images_unmerged) == 2 >>> images_unmerged[0].dimension == 2
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638020af2cdfc5ff4bdb9809ffe67aa505727a3b
https://github.com/ANTsX/ANTsPy/blob/638020af2cdfc5ff4bdb9809ffe67aa505727a3b/ants/utils/ndimage_to_list.py#L67-L113
251,004
ANTsX/ANTsPy
ants/utils/process_args.py
_int_antsProcessArguments
def _int_antsProcessArguments(args): """ Needs to be better validated. """ p_args = [] if isinstance(args, dict): for argname, argval in args.items(): if '-MULTINAME-' in argname: # have this little hack because python doesnt support # multiple dict entries w/ the same key like R lists argname = argname[:argname.find('-MULTINAME-')] if argval is not None: if len(argname) > 1: p_args.append('--%s' % argname) else: p_args.append('-%s' % argname) if isinstance(argval, iio.ANTsImage): p_args.append(_ptrstr(argval.pointer)) elif isinstance(argval, list): for av in argval: if isinstance(av, iio.ANTsImage): av = _ptrstr(av.pointer) p_args.append(av) else: p_args.append(str(argval)) elif isinstance(args, list): for arg in args: if isinstance(arg, iio.ANTsImage): pointer_string = _ptrstr(arg.pointer) p_arg = pointer_string elif arg is None: pass else: p_arg = str(arg) p_args.append(p_arg) return p_args
python
def _int_antsProcessArguments(args): p_args = [] if isinstance(args, dict): for argname, argval in args.items(): if '-MULTINAME-' in argname: # have this little hack because python doesnt support # multiple dict entries w/ the same key like R lists argname = argname[:argname.find('-MULTINAME-')] if argval is not None: if len(argname) > 1: p_args.append('--%s' % argname) else: p_args.append('-%s' % argname) if isinstance(argval, iio.ANTsImage): p_args.append(_ptrstr(argval.pointer)) elif isinstance(argval, list): for av in argval: if isinstance(av, iio.ANTsImage): av = _ptrstr(av.pointer) p_args.append(av) else: p_args.append(str(argval)) elif isinstance(args, list): for arg in args: if isinstance(arg, iio.ANTsImage): pointer_string = _ptrstr(arg.pointer) p_arg = pointer_string elif arg is None: pass else: p_arg = str(arg) p_args.append(p_arg) return p_args
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638020af2cdfc5ff4bdb9809ffe67aa505727a3b
https://github.com/ANTsX/ANTsPy/blob/638020af2cdfc5ff4bdb9809ffe67aa505727a3b/ants/utils/process_args.py#L34-L71
251,005
ANTsX/ANTsPy
ants/learn/decomposition.py
initialize_eigenanatomy
def initialize_eigenanatomy(initmat, mask=None, initlabels=None, nreps=1, smoothing=0): """ InitializeEigenanatomy is a helper function to initialize sparseDecom and sparseDecom2. Can be used to estimate sparseness parameters per eigenvector. The user then only chooses nvecs and optional regularization parameters. Arguments --------- initmat : np.ndarray or ANTsImage input matrix where rows provide initial vector values. alternatively, this can be an antsImage which contains labeled regions. mask : ANTsImage mask if available initlabels : list/tuple of integers which labels in initmat to use as initial components nreps : integer nrepetitions to use smoothing : float if using an initial label image, optionally smooth each roi Returns ------- dict w/ the following key/value pairs: `initlist` : list of ANTsImage types initialization list(s) for sparseDecom(2) `mask` : ANTsImage mask(s) for sparseDecom(2) `enames` : list of strings string names of components for sparseDecom(2) Example ------- >>> import ants >>> import numpy as np >>> mat = np.random.randn(4,100).astype('float32') >>> init = ants.initialize_eigenanatomy(mat) """ if isinstance(initmat, iio.ANTsImage): # create initmat from each of the unique labels if mask is not None: selectvec = mask > 0 else: selectvec = initmat > 0 initmatvec = initmat[selectvec] if initlabels is None: ulabs = np.sort(np.unique(initmatvec)) ulabs = ulabs[ulabs > 0] else: ulabs = initlabels nvox = len(initmatvec) temp = np.zeros((len(ulabs), nvox)) for x in range(len(ulabs)): timg = utils.threshold_image(initmat, ulabs[x]-1e-4, ulabs[x]+1e-4) if smoothing > 0: timg = utils.smooth_image(timg, smoothing) temp[x,:] = timg[selectvec] initmat = temp nclasses = initmat.shape[0] classlabels = ['init%i'%i for i in range(nclasses)] initlist = [] if mask is None: maskmat = np.zeros(initmat.shape) maskmat[0,:] = 1 mask = core.from_numpy(maskmat.astype('float32')) eanatnames = ['A'] * (nclasses*nreps) ct = 0 for i in range(nclasses): vecimg = mask.clone('float') initf = initmat[i,:] vecimg[mask==1] = initf for nr in range(nreps): initlist.append(vecimg) eanatnames[ct+nr-1] = str(classlabels[i]) ct = ct + 1 return {'initlist': initlist, 'mask': mask, 'enames': eanatnames}
python
def initialize_eigenanatomy(initmat, mask=None, initlabels=None, nreps=1, smoothing=0): if isinstance(initmat, iio.ANTsImage): # create initmat from each of the unique labels if mask is not None: selectvec = mask > 0 else: selectvec = initmat > 0 initmatvec = initmat[selectvec] if initlabels is None: ulabs = np.sort(np.unique(initmatvec)) ulabs = ulabs[ulabs > 0] else: ulabs = initlabels nvox = len(initmatvec) temp = np.zeros((len(ulabs), nvox)) for x in range(len(ulabs)): timg = utils.threshold_image(initmat, ulabs[x]-1e-4, ulabs[x]+1e-4) if smoothing > 0: timg = utils.smooth_image(timg, smoothing) temp[x,:] = timg[selectvec] initmat = temp nclasses = initmat.shape[0] classlabels = ['init%i'%i for i in range(nclasses)] initlist = [] if mask is None: maskmat = np.zeros(initmat.shape) maskmat[0,:] = 1 mask = core.from_numpy(maskmat.astype('float32')) eanatnames = ['A'] * (nclasses*nreps) ct = 0 for i in range(nclasses): vecimg = mask.clone('float') initf = initmat[i,:] vecimg[mask==1] = initf for nr in range(nreps): initlist.append(vecimg) eanatnames[ct+nr-1] = str(classlabels[i]) ct = ct + 1 return {'initlist': initlist, 'mask': mask, 'enames': eanatnames}
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InitializeEigenanatomy is a helper function to initialize sparseDecom and sparseDecom2. Can be used to estimate sparseness parameters per eigenvector. The user then only chooses nvecs and optional regularization parameters. Arguments --------- initmat : np.ndarray or ANTsImage input matrix where rows provide initial vector values. alternatively, this can be an antsImage which contains labeled regions. mask : ANTsImage mask if available initlabels : list/tuple of integers which labels in initmat to use as initial components nreps : integer nrepetitions to use smoothing : float if using an initial label image, optionally smooth each roi Returns ------- dict w/ the following key/value pairs: `initlist` : list of ANTsImage types initialization list(s) for sparseDecom(2) `mask` : ANTsImage mask(s) for sparseDecom(2) `enames` : list of strings string names of components for sparseDecom(2) Example ------- >>> import ants >>> import numpy as np >>> mat = np.random.randn(4,100).astype('float32') >>> init = ants.initialize_eigenanatomy(mat)
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638020af2cdfc5ff4bdb9809ffe67aa505727a3b
https://github.com/ANTsX/ANTsPy/blob/638020af2cdfc5ff4bdb9809ffe67aa505727a3b/ants/learn/decomposition.py#L251-L338
251,006
ANTsX/ANTsPy
ants/learn/decomposition.py
eig_seg
def eig_seg(mask, img_list, apply_segmentation_to_images=False, cthresh=0, smooth=1): """ Segment a mask into regions based on the max value in an image list. At a given voxel the segmentation label will contain the index to the image that has the largest value. If the 3rd image has the greatest value, the segmentation label will be 3 at that voxel. Arguments --------- mask : ANTsImage D-dimensional mask > 0 defining segmentation region. img_list : collection of ANTsImage or np.ndarray images to use apply_segmentation_to_images : boolean determines if original image list is modified by the segmentation. cthresh : integer throw away isolated clusters smaller than this value smooth : float smooth the input data first by this value Returns ------- ANTsImage Example ------- >>> import ants >>> mylist = [ants.image_read(ants.get_ants_data('r16')), ants.image_read(ants.get_ants_data('r27')), ants.image_read(ants.get_ants_data('r85'))] >>> myseg = ants.eig_seg(ants.get_mask(mylist[0]), mylist) """ maskvox = mask > 0 maskseg = mask.clone() maskseg[maskvox] = 0 if isinstance(img_list, np.ndarray): mydata = img_list elif isinstance(img_list, (tuple, list)): mydata = core.image_list_to_matrix(img_list, mask) if (smooth > 0): for i in range(mydata.shape[0]): temp_img = core.make_image(mask, mydata[i,:], pixeltype='float') temp_img = utils.smooth_image(temp_img, smooth, sigma_in_physical_coordinates=True) mydata[i,:] = temp_img[mask >= 0.5] segids = np.argmax(np.abs(mydata), axis=0)+1 segmax = np.max(np.abs(mydata), axis=0) maskseg[maskvox] = (segids * (segmax > 1e-09)) if cthresh > 0: for kk in range(int(maskseg.max())): timg = utils.threshold_image(maskseg, kk, kk) timg = utils.label_clusters(timg, cthresh) timg = utils.threshold_image(timg, 1, 1e15) * float(kk) maskseg[maskseg == kk] = timg[maskseg == kk] if (apply_segmentation_to_images) and (not isinstance(img_list, np.ndarray)): for i in range(len(img_list)): img = img_list[i] img[maskseg != float(i)] = 0 img_list[i] = img return maskseg
python
def eig_seg(mask, img_list, apply_segmentation_to_images=False, cthresh=0, smooth=1): maskvox = mask > 0 maskseg = mask.clone() maskseg[maskvox] = 0 if isinstance(img_list, np.ndarray): mydata = img_list elif isinstance(img_list, (tuple, list)): mydata = core.image_list_to_matrix(img_list, mask) if (smooth > 0): for i in range(mydata.shape[0]): temp_img = core.make_image(mask, mydata[i,:], pixeltype='float') temp_img = utils.smooth_image(temp_img, smooth, sigma_in_physical_coordinates=True) mydata[i,:] = temp_img[mask >= 0.5] segids = np.argmax(np.abs(mydata), axis=0)+1 segmax = np.max(np.abs(mydata), axis=0) maskseg[maskvox] = (segids * (segmax > 1e-09)) if cthresh > 0: for kk in range(int(maskseg.max())): timg = utils.threshold_image(maskseg, kk, kk) timg = utils.label_clusters(timg, cthresh) timg = utils.threshold_image(timg, 1, 1e15) * float(kk) maskseg[maskseg == kk] = timg[maskseg == kk] if (apply_segmentation_to_images) and (not isinstance(img_list, np.ndarray)): for i in range(len(img_list)): img = img_list[i] img[maskseg != float(i)] = 0 img_list[i] = img return maskseg
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Segment a mask into regions based on the max value in an image list. At a given voxel the segmentation label will contain the index to the image that has the largest value. If the 3rd image has the greatest value, the segmentation label will be 3 at that voxel. Arguments --------- mask : ANTsImage D-dimensional mask > 0 defining segmentation region. img_list : collection of ANTsImage or np.ndarray images to use apply_segmentation_to_images : boolean determines if original image list is modified by the segmentation. cthresh : integer throw away isolated clusters smaller than this value smooth : float smooth the input data first by this value Returns ------- ANTsImage Example ------- >>> import ants >>> mylist = [ants.image_read(ants.get_ants_data('r16')), ants.image_read(ants.get_ants_data('r27')), ants.image_read(ants.get_ants_data('r85'))] >>> myseg = ants.eig_seg(ants.get_mask(mylist[0]), mylist)
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638020af2cdfc5ff4bdb9809ffe67aa505727a3b
https://github.com/ANTsX/ANTsPy/blob/638020af2cdfc5ff4bdb9809ffe67aa505727a3b/ants/learn/decomposition.py#L342-L409
251,007
ANTsX/ANTsPy
ants/utils/label_stats.py
label_stats
def label_stats(image, label_image): """ Get label statistics from image ANTsR function: `labelStats` Arguments --------- image : ANTsImage Image from which statistics will be calculated label_image : ANTsImage Label image Returns ------- ndarray ? Example ------- >>> import ants >>> image = ants.image_read( ants.get_ants_data('r16') , 2 ) >>> image = ants.resample_image( image, (64,64), 1, 0 ) >>> mask = ants.get_mask(image) >>> segs1 = ants.kmeans_segmentation( image, 3 ) >>> stats = ants.label_stats(image, segs1['segmentation']) """ image_float = image.clone('float') label_image_int = label_image.clone('unsigned int') libfn = utils.get_lib_fn('labelStats%iD' % image.dimension) df = libfn(image_float.pointer, label_image_int.pointer) #df = df[order(df$LabelValue), ] return pd.DataFrame(df)
python
def label_stats(image, label_image): image_float = image.clone('float') label_image_int = label_image.clone('unsigned int') libfn = utils.get_lib_fn('labelStats%iD' % image.dimension) df = libfn(image_float.pointer, label_image_int.pointer) #df = df[order(df$LabelValue), ] return pd.DataFrame(df)
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Get label statistics from image ANTsR function: `labelStats` Arguments --------- image : ANTsImage Image from which statistics will be calculated label_image : ANTsImage Label image Returns ------- ndarray ? Example ------- >>> import ants >>> image = ants.image_read( ants.get_ants_data('r16') , 2 ) >>> image = ants.resample_image( image, (64,64), 1, 0 ) >>> mask = ants.get_mask(image) >>> segs1 = ants.kmeans_segmentation( image, 3 ) >>> stats = ants.label_stats(image, segs1['segmentation'])
[ "Get", "label", "statistics", "from", "image" ]
638020af2cdfc5ff4bdb9809ffe67aa505727a3b
https://github.com/ANTsX/ANTsPy/blob/638020af2cdfc5ff4bdb9809ffe67aa505727a3b/ants/utils/label_stats.py#L8-L41
251,008
ANTsX/ANTsPy
ants/core/ants_image.py
ANTsImage.spacing
def spacing(self): """ Get image spacing Returns ------- tuple """ libfn = utils.get_lib_fn('getSpacing%s'%self._libsuffix) return libfn(self.pointer)
python
def spacing(self): libfn = utils.get_lib_fn('getSpacing%s'%self._libsuffix) return libfn(self.pointer)
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Get image spacing Returns ------- tuple
[ "Get", "image", "spacing" ]
638020af2cdfc5ff4bdb9809ffe67aa505727a3b
https://github.com/ANTsX/ANTsPy/blob/638020af2cdfc5ff4bdb9809ffe67aa505727a3b/ants/core/ants_image.py#L95-L104
251,009
ANTsX/ANTsPy
ants/core/ants_image.py
ANTsImage.set_spacing
def set_spacing(self, new_spacing): """ Set image spacing Arguments --------- new_spacing : tuple or list updated spacing for the image. should have one value for each dimension Returns ------- None """ if not isinstance(new_spacing, (tuple, list)): raise ValueError('arg must be tuple or list') if len(new_spacing) != self.dimension: raise ValueError('must give a spacing value for each dimension (%i)' % self.dimension) libfn = utils.get_lib_fn('setSpacing%s'%self._libsuffix) libfn(self.pointer, new_spacing)
python
def set_spacing(self, new_spacing): if not isinstance(new_spacing, (tuple, list)): raise ValueError('arg must be tuple or list') if len(new_spacing) != self.dimension: raise ValueError('must give a spacing value for each dimension (%i)' % self.dimension) libfn = utils.get_lib_fn('setSpacing%s'%self._libsuffix) libfn(self.pointer, new_spacing)
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Set image spacing Arguments --------- new_spacing : tuple or list updated spacing for the image. should have one value for each dimension Returns ------- None
[ "Set", "image", "spacing" ]
638020af2cdfc5ff4bdb9809ffe67aa505727a3b
https://github.com/ANTsX/ANTsPy/blob/638020af2cdfc5ff4bdb9809ffe67aa505727a3b/ants/core/ants_image.py#L106-L126
251,010
ANTsX/ANTsPy
ants/core/ants_image.py
ANTsImage.origin
def origin(self): """ Get image origin Returns ------- tuple """ libfn = utils.get_lib_fn('getOrigin%s'%self._libsuffix) return libfn(self.pointer)
python
def origin(self): libfn = utils.get_lib_fn('getOrigin%s'%self._libsuffix) return libfn(self.pointer)
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Get image origin Returns ------- tuple
[ "Get", "image", "origin" ]
638020af2cdfc5ff4bdb9809ffe67aa505727a3b
https://github.com/ANTsX/ANTsPy/blob/638020af2cdfc5ff4bdb9809ffe67aa505727a3b/ants/core/ants_image.py#L129-L138
251,011
ANTsX/ANTsPy
ants/core/ants_image.py
ANTsImage.set_origin
def set_origin(self, new_origin): """ Set image origin Arguments --------- new_origin : tuple or list updated origin for the image. should have one value for each dimension Returns ------- None """ if not isinstance(new_origin, (tuple, list)): raise ValueError('arg must be tuple or list') if len(new_origin) != self.dimension: raise ValueError('must give a origin value for each dimension (%i)' % self.dimension) libfn = utils.get_lib_fn('setOrigin%s'%self._libsuffix) libfn(self.pointer, new_origin)
python
def set_origin(self, new_origin): if not isinstance(new_origin, (tuple, list)): raise ValueError('arg must be tuple or list') if len(new_origin) != self.dimension: raise ValueError('must give a origin value for each dimension (%i)' % self.dimension) libfn = utils.get_lib_fn('setOrigin%s'%self._libsuffix) libfn(self.pointer, new_origin)
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Set image origin Arguments --------- new_origin : tuple or list updated origin for the image. should have one value for each dimension Returns ------- None
[ "Set", "image", "origin" ]
638020af2cdfc5ff4bdb9809ffe67aa505727a3b
https://github.com/ANTsX/ANTsPy/blob/638020af2cdfc5ff4bdb9809ffe67aa505727a3b/ants/core/ants_image.py#L140-L160
251,012
ANTsX/ANTsPy
ants/core/ants_image.py
ANTsImage.direction
def direction(self): """ Get image direction Returns ------- tuple """ libfn = utils.get_lib_fn('getDirection%s'%self._libsuffix) return libfn(self.pointer)
python
def direction(self): libfn = utils.get_lib_fn('getDirection%s'%self._libsuffix) return libfn(self.pointer)
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Get image direction Returns ------- tuple
[ "Get", "image", "direction" ]
638020af2cdfc5ff4bdb9809ffe67aa505727a3b
https://github.com/ANTsX/ANTsPy/blob/638020af2cdfc5ff4bdb9809ffe67aa505727a3b/ants/core/ants_image.py#L163-L172
251,013
ANTsX/ANTsPy
ants/core/ants_image.py
ANTsImage.set_direction
def set_direction(self, new_direction): """ Set image direction Arguments --------- new_direction : numpy.ndarray or tuple or list updated direction for the image. should have one value for each dimension Returns ------- None """ if isinstance(new_direction, (tuple,list)): new_direction = np.asarray(new_direction) if not isinstance(new_direction, np.ndarray): raise ValueError('arg must be np.ndarray or tuple or list') if len(new_direction) != self.dimension: raise ValueError('must give a origin value for each dimension (%i)' % self.dimension) libfn = utils.get_lib_fn('setDirection%s'%self._libsuffix) libfn(self.pointer, new_direction)
python
def set_direction(self, new_direction): if isinstance(new_direction, (tuple,list)): new_direction = np.asarray(new_direction) if not isinstance(new_direction, np.ndarray): raise ValueError('arg must be np.ndarray or tuple or list') if len(new_direction) != self.dimension: raise ValueError('must give a origin value for each dimension (%i)' % self.dimension) libfn = utils.get_lib_fn('setDirection%s'%self._libsuffix) libfn(self.pointer, new_direction)
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Set image direction Arguments --------- new_direction : numpy.ndarray or tuple or list updated direction for the image. should have one value for each dimension Returns ------- None
[ "Set", "image", "direction" ]
638020af2cdfc5ff4bdb9809ffe67aa505727a3b
https://github.com/ANTsX/ANTsPy/blob/638020af2cdfc5ff4bdb9809ffe67aa505727a3b/ants/core/ants_image.py#L174-L197
251,014
ANTsX/ANTsPy
ants/core/ants_image.py
ANTsImage.astype
def astype(self, dtype): """ Cast & clone an ANTsImage to a given numpy datatype. Map: uint8 : unsigned char uint32 : unsigned int float32 : float float64 : double """ if dtype not in _supported_dtypes: raise ValueError('Datatype %s not supported. Supported types are %s' % (dtype, _supported_dtypes)) pixeltype = _npy_to_itk_map[dtype] return self.clone(pixeltype)
python
def astype(self, dtype): if dtype not in _supported_dtypes: raise ValueError('Datatype %s not supported. Supported types are %s' % (dtype, _supported_dtypes)) pixeltype = _npy_to_itk_map[dtype] return self.clone(pixeltype)
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Cast & clone an ANTsImage to a given numpy datatype. Map: uint8 : unsigned char uint32 : unsigned int float32 : float float64 : double
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638020af2cdfc5ff4bdb9809ffe67aa505727a3b
https://github.com/ANTsX/ANTsPy/blob/638020af2cdfc5ff4bdb9809ffe67aa505727a3b/ants/core/ants_image.py#L302-L316
251,015
ANTsX/ANTsPy
ants/core/ants_image.py
ANTsImage.new_image_like
def new_image_like(self, data): """ Create a new ANTsImage with the same header information, but with a new image array. Arguments --------- data : ndarray or py::capsule New array or pointer for the image. It must have the same shape as the current image data. Returns ------- ANTsImage """ if not isinstance(data, np.ndarray): raise ValueError('data must be a numpy array') if not self.has_components: if data.shape != self.shape: raise ValueError('given array shape (%s) and image array shape (%s) do not match' % (data.shape, self.shape)) else: if (data.shape[-1] != self.components) or (data.shape[:-1] != self.shape): raise ValueError('given array shape (%s) and image array shape (%s) do not match' % (data.shape[1:], self.shape)) return iio2.from_numpy(data, origin=self.origin, spacing=self.spacing, direction=self.direction, has_components=self.has_components)
python
def new_image_like(self, data): if not isinstance(data, np.ndarray): raise ValueError('data must be a numpy array') if not self.has_components: if data.shape != self.shape: raise ValueError('given array shape (%s) and image array shape (%s) do not match' % (data.shape, self.shape)) else: if (data.shape[-1] != self.components) or (data.shape[:-1] != self.shape): raise ValueError('given array shape (%s) and image array shape (%s) do not match' % (data.shape[1:], self.shape)) return iio2.from_numpy(data, origin=self.origin, spacing=self.spacing, direction=self.direction, has_components=self.has_components)
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Create a new ANTsImage with the same header information, but with a new image array. Arguments --------- data : ndarray or py::capsule New array or pointer for the image. It must have the same shape as the current image data. Returns ------- ANTsImage
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638020af2cdfc5ff4bdb9809ffe67aa505727a3b
https://github.com/ANTsX/ANTsPy/blob/638020af2cdfc5ff4bdb9809ffe67aa505727a3b/ants/core/ants_image.py#L318-L345
251,016
ANTsX/ANTsPy
ants/core/ants_image.py
ANTsImage.to_file
def to_file(self, filename): """ Write the ANTsImage to file Args ---- filename : string filepath to which the image will be written """ filename = os.path.expanduser(filename) libfn = utils.get_lib_fn('toFile%s'%self._libsuffix) libfn(self.pointer, filename)
python
def to_file(self, filename): filename = os.path.expanduser(filename) libfn = utils.get_lib_fn('toFile%s'%self._libsuffix) libfn(self.pointer, filename)
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Write the ANTsImage to file Args ---- filename : string filepath to which the image will be written
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638020af2cdfc5ff4bdb9809ffe67aa505727a3b
https://github.com/ANTsX/ANTsPy/blob/638020af2cdfc5ff4bdb9809ffe67aa505727a3b/ants/core/ants_image.py#L347-L358
251,017
ANTsX/ANTsPy
ants/core/ants_image.py
ANTsImage.apply
def apply(self, fn): """ Apply an arbitrary function to ANTsImage. Args ---- fn : python function or lambda function to apply to ENTIRE image at once Returns ------- ANTsImage image with function applied to it """ this_array = self.numpy() new_array = fn(this_array) return self.new_image_like(new_array)
python
def apply(self, fn): this_array = self.numpy() new_array = fn(this_array) return self.new_image_like(new_array)
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Apply an arbitrary function to ANTsImage. Args ---- fn : python function or lambda function to apply to ENTIRE image at once Returns ------- ANTsImage image with function applied to it
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638020af2cdfc5ff4bdb9809ffe67aa505727a3b
https://github.com/ANTsX/ANTsPy/blob/638020af2cdfc5ff4bdb9809ffe67aa505727a3b/ants/core/ants_image.py#L361-L377
251,018
ANTsX/ANTsPy
ants/core/ants_image.py
ANTsImage.sum
def sum(self, axis=None, keepdims=False): """ Return sum along specified axis """ return self.numpy().sum(axis=axis, keepdims=keepdims)
python
def sum(self, axis=None, keepdims=False): return self.numpy().sum(axis=axis, keepdims=keepdims)
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Return sum along specified axis
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638020af2cdfc5ff4bdb9809ffe67aa505727a3b
https://github.com/ANTsX/ANTsPy/blob/638020af2cdfc5ff4bdb9809ffe67aa505727a3b/ants/core/ants_image.py#L395-L397
251,019
ANTsX/ANTsPy
ants/core/ants_image.py
ANTsImage.range
def range(self, axis=None): """ Return range tuple along specified axis """ return (self.min(axis=axis), self.max(axis=axis))
python
def range(self, axis=None): return (self.min(axis=axis), self.max(axis=axis))
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Return range tuple along specified axis
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638020af2cdfc5ff4bdb9809ffe67aa505727a3b
https://github.com/ANTsX/ANTsPy/blob/638020af2cdfc5ff4bdb9809ffe67aa505727a3b/ants/core/ants_image.py#L404-L406
251,020
ANTsX/ANTsPy
ants/core/ants_image.py
ANTsImage.argrange
def argrange(self, axis=None): """ Return argrange along specified axis """ amin = self.argmin(axis=axis) amax = self.argmax(axis=axis) if axis is None: return (amin, amax) else: return np.stack([amin, amax]).T
python
def argrange(self, axis=None): amin = self.argmin(axis=axis) amax = self.argmax(axis=axis) if axis is None: return (amin, amax) else: return np.stack([amin, amax]).T
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Return argrange along specified axis
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638020af2cdfc5ff4bdb9809ffe67aa505727a3b
https://github.com/ANTsX/ANTsPy/blob/638020af2cdfc5ff4bdb9809ffe67aa505727a3b/ants/core/ants_image.py#L413-L420
251,021
ANTsX/ANTsPy
ants/core/ants_image.py
ANTsImage.unique
def unique(self, sort=False): """ Return unique set of values in image """ unique_vals = np.unique(self.numpy()) if sort: unique_vals = np.sort(unique_vals) return unique_vals
python
def unique(self, sort=False): unique_vals = np.unique(self.numpy()) if sort: unique_vals = np.sort(unique_vals) return unique_vals
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Return unique set of values in image
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638020af2cdfc5ff4bdb9809ffe67aa505727a3b
https://github.com/ANTsX/ANTsPy/blob/638020af2cdfc5ff4bdb9809ffe67aa505727a3b/ants/core/ants_image.py#L427-L432
251,022
ANTsX/ANTsPy
ants/core/ants_image.py
LabelImage.uniquekeys
def uniquekeys(self, metakey=None): """ Get keys for a given metakey """ if metakey is None: return self._uniquekeys else: if metakey not in self.metakeys(): raise ValueError('metakey %s does not exist' % metakey) return self._uniquekeys[metakey]
python
def uniquekeys(self, metakey=None): if metakey is None: return self._uniquekeys else: if metakey not in self.metakeys(): raise ValueError('metakey %s does not exist' % metakey) return self._uniquekeys[metakey]
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Get keys for a given metakey
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638020af2cdfc5ff4bdb9809ffe67aa505727a3b
https://github.com/ANTsX/ANTsPy/blob/638020af2cdfc5ff4bdb9809ffe67aa505727a3b/ants/core/ants_image.py#L731-L740
251,023
ANTsX/ANTsPy
ants/utils/label_clusters.py
label_clusters
def label_clusters(image, min_cluster_size=50, min_thresh=1e-6, max_thresh=1, fully_connected=False): """ This will give a unique ID to each connected component 1 through N of size > min_cluster_size ANTsR function: `labelClusters` Arguments --------- image : ANTsImage input image e.g. a statistical map min_cluster_size : integer throw away clusters smaller than this value min_thresh : scalar threshold to a statistical map max_thresh : scalar threshold to a statistical map fully_connected : boolean boolean sets neighborhood connectivity pattern Returns ------- ANTsImage Example ------- >>> import ants >>> image = ants.image_read( ants.get_ants_data('r16') ) >>> timageFully = ants.label_clusters( image, 10, 128, 150, True ) >>> timageFace = ants.label_clusters( image, 10, 128, 150, False ) """ dim = image.dimension clust = threshold_image(image, min_thresh, max_thresh) temp = int(fully_connected) args = [dim, clust, clust, min_cluster_size, temp] processed_args = _int_antsProcessArguments(args) libfn = utils.get_lib_fn('LabelClustersUniquely') libfn(processed_args) return clust
python
def label_clusters(image, min_cluster_size=50, min_thresh=1e-6, max_thresh=1, fully_connected=False): dim = image.dimension clust = threshold_image(image, min_thresh, max_thresh) temp = int(fully_connected) args = [dim, clust, clust, min_cluster_size, temp] processed_args = _int_antsProcessArguments(args) libfn = utils.get_lib_fn('LabelClustersUniquely') libfn(processed_args) return clust
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This will give a unique ID to each connected component 1 through N of size > min_cluster_size ANTsR function: `labelClusters` Arguments --------- image : ANTsImage input image e.g. a statistical map min_cluster_size : integer throw away clusters smaller than this value min_thresh : scalar threshold to a statistical map max_thresh : scalar threshold to a statistical map fully_connected : boolean boolean sets neighborhood connectivity pattern Returns ------- ANTsImage Example ------- >>> import ants >>> image = ants.image_read( ants.get_ants_data('r16') ) >>> timageFully = ants.label_clusters( image, 10, 128, 150, True ) >>> timageFace = ants.label_clusters( image, 10, 128, 150, False )
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638020af2cdfc5ff4bdb9809ffe67aa505727a3b
https://github.com/ANTsX/ANTsPy/blob/638020af2cdfc5ff4bdb9809ffe67aa505727a3b/ants/utils/label_clusters.py#L11-L53
251,024
ANTsX/ANTsPy
ants/registration/make_points_image.py
make_points_image
def make_points_image(pts, mask, radius=5): """ Create label image from physical space points Creates spherical points in the coordinate space of the target image based on the n-dimensional matrix of points that the user supplies. The image defines the dimensionality of the data so if the input image is 3D then the input points should be 2D or 3D. ANTsR function: `makePointsImage` Arguments --------- pts : numpy.ndarray input powers points mask : ANTsImage mask defining target space radius : integer radius for the points Returns ------- ANTsImage Example ------- >>> import ants >>> import pandas as pd >>> mni = ants.image_read(ants.get_data('mni')).get_mask() >>> powers_pts = pd.read_csv(ants.get_data('powers_mni_itk')) >>> powers_labels = ants.make_points_image(powers_pts.iloc[:,:3].values, mni, radius=3) """ powers_lblimg = mask * 0 npts = len(pts) dim = mask.dimension if pts.shape[1] != dim: raise ValueError('points dimensionality should match that of images') for r in range(npts): pt = pts[r,:] idx = tio.transform_physical_point_to_index(mask, pt.tolist() ).astype(int) in_image = (np.prod(idx <= mask.shape)==1) and (len(np.where(idx<0)[0])==0) if ( in_image == True ): if (dim == 3): powers_lblimg[idx[0],idx[1],idx[2]] = r + 1 elif (dim == 2): powers_lblimg[idx[0],idx[1]] = r + 1 return utils.morphology( powers_lblimg, 'dilate', radius, 'grayscale' )
python
def make_points_image(pts, mask, radius=5): powers_lblimg = mask * 0 npts = len(pts) dim = mask.dimension if pts.shape[1] != dim: raise ValueError('points dimensionality should match that of images') for r in range(npts): pt = pts[r,:] idx = tio.transform_physical_point_to_index(mask, pt.tolist() ).astype(int) in_image = (np.prod(idx <= mask.shape)==1) and (len(np.where(idx<0)[0])==0) if ( in_image == True ): if (dim == 3): powers_lblimg[idx[0],idx[1],idx[2]] = r + 1 elif (dim == 2): powers_lblimg[idx[0],idx[1]] = r + 1 return utils.morphology( powers_lblimg, 'dilate', radius, 'grayscale' )
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Create label image from physical space points Creates spherical points in the coordinate space of the target image based on the n-dimensional matrix of points that the user supplies. The image defines the dimensionality of the data so if the input image is 3D then the input points should be 2D or 3D. ANTsR function: `makePointsImage` Arguments --------- pts : numpy.ndarray input powers points mask : ANTsImage mask defining target space radius : integer radius for the points Returns ------- ANTsImage Example ------- >>> import ants >>> import pandas as pd >>> mni = ants.image_read(ants.get_data('mni')).get_mask() >>> powers_pts = pd.read_csv(ants.get_data('powers_mni_itk')) >>> powers_labels = ants.make_points_image(powers_pts.iloc[:,:3].values, mni, radius=3)
[ "Create", "label", "image", "from", "physical", "space", "points" ]
638020af2cdfc5ff4bdb9809ffe67aa505727a3b
https://github.com/ANTsX/ANTsPy/blob/638020af2cdfc5ff4bdb9809ffe67aa505727a3b/ants/registration/make_points_image.py#L11-L60
251,025
ANTsX/ANTsPy
ants/utils/weingarten_image_curvature.py
weingarten_image_curvature
def weingarten_image_curvature(image, sigma=1.0, opt='mean'): """ Uses the weingarten map to estimate image mean or gaussian curvature ANTsR function: `weingartenImageCurvature` Arguments --------- image : ANTsImage image from which curvature is calculated sigma : scalar smoothing parameter opt : string mean by default, otherwise `gaussian` or `characterize` Returns ------- ANTsImage Example ------- >>> import ants >>> image = ants.image_read(ants.get_ants_data('mni')).resample_image((3,3,3)) >>> imagecurv = ants.weingarten_image_curvature(image) """ if image.dimension not in {2,3}: raise ValueError('image must be 2D or 3D') if image.dimension == 2: d = image.shape temp = np.zeros(list(d)+[10]) for k in range(1,7): voxvals = image[:d[0],:d[1]] temp[:d[0],:d[1],k] = voxvals temp = core.from_numpy(temp) myspc = image.spacing myspc = list(myspc) + [min(myspc)] temp.set_spacing(myspc) temp = temp.clone('float') else: temp = image.clone('float') optnum = 0 if opt == 'gaussian': optnum = 6 if opt == 'characterize': optnum = 5 libfn = utils.get_lib_fn('weingartenImageCurvature') mykout = libfn(temp.pointer, sigma, optnum) mykout = iio.ANTsImage(pixeltype=image.pixeltype, dimension=3, components=image.components, pointer=mykout) if image.dimension == 3: return mykout elif image.dimension == 2: subarr = core.from_numpy(mykout.numpy()[:,:,4]) return core.copy_image_info(image, subarr)
python
def weingarten_image_curvature(image, sigma=1.0, opt='mean'): if image.dimension not in {2,3}: raise ValueError('image must be 2D or 3D') if image.dimension == 2: d = image.shape temp = np.zeros(list(d)+[10]) for k in range(1,7): voxvals = image[:d[0],:d[1]] temp[:d[0],:d[1],k] = voxvals temp = core.from_numpy(temp) myspc = image.spacing myspc = list(myspc) + [min(myspc)] temp.set_spacing(myspc) temp = temp.clone('float') else: temp = image.clone('float') optnum = 0 if opt == 'gaussian': optnum = 6 if opt == 'characterize': optnum = 5 libfn = utils.get_lib_fn('weingartenImageCurvature') mykout = libfn(temp.pointer, sigma, optnum) mykout = iio.ANTsImage(pixeltype=image.pixeltype, dimension=3, components=image.components, pointer=mykout) if image.dimension == 3: return mykout elif image.dimension == 2: subarr = core.from_numpy(mykout.numpy()[:,:,4]) return core.copy_image_info(image, subarr)
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Uses the weingarten map to estimate image mean or gaussian curvature ANTsR function: `weingartenImageCurvature` Arguments --------- image : ANTsImage image from which curvature is calculated sigma : scalar smoothing parameter opt : string mean by default, otherwise `gaussian` or `characterize` Returns ------- ANTsImage Example ------- >>> import ants >>> image = ants.image_read(ants.get_ants_data('mni')).resample_image((3,3,3)) >>> imagecurv = ants.weingarten_image_curvature(image)
[ "Uses", "the", "weingarten", "map", "to", "estimate", "image", "mean", "or", "gaussian", "curvature" ]
638020af2cdfc5ff4bdb9809ffe67aa505727a3b
https://github.com/ANTsX/ANTsPy/blob/638020af2cdfc5ff4bdb9809ffe67aa505727a3b/ants/utils/weingarten_image_curvature.py#L11-L69
251,026
ANTsX/ANTsPy
ants/core/ants_image_io.py
from_numpy
def from_numpy(data, origin=None, spacing=None, direction=None, has_components=False, is_rgb=False): """ Create an ANTsImage object from a numpy array ANTsR function: `as.antsImage` Arguments --------- data : ndarray image data array origin : tuple/list image origin spacing : tuple/list image spacing direction : list/ndarray image direction has_components : boolean whether the image has components Returns ------- ANTsImage image with given data and any given information """ data = data.astype('float32') if data.dtype.name == 'float64' else data img = _from_numpy(data.T.copy(), origin, spacing, direction, has_components, is_rgb) return img
python
def from_numpy(data, origin=None, spacing=None, direction=None, has_components=False, is_rgb=False): data = data.astype('float32') if data.dtype.name == 'float64' else data img = _from_numpy(data.T.copy(), origin, spacing, direction, has_components, is_rgb) return img
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Create an ANTsImage object from a numpy array ANTsR function: `as.antsImage` Arguments --------- data : ndarray image data array origin : tuple/list image origin spacing : tuple/list image spacing direction : list/ndarray image direction has_components : boolean whether the image has components Returns ------- ANTsImage image with given data and any given information
[ "Create", "an", "ANTsImage", "object", "from", "a", "numpy", "array" ]
638020af2cdfc5ff4bdb9809ffe67aa505727a3b
https://github.com/ANTsX/ANTsPy/blob/638020af2cdfc5ff4bdb9809ffe67aa505727a3b/ants/core/ants_image_io.py#L76-L106
251,027
ANTsX/ANTsPy
ants/core/ants_image_io.py
_from_numpy
def _from_numpy(data, origin=None, spacing=None, direction=None, has_components=False, is_rgb=False): """ Internal function for creating an ANTsImage """ if is_rgb: has_components = True ndim = data.ndim if has_components: ndim -= 1 dtype = data.dtype.name ptype = _npy_to_itk_map[dtype] data = np.array(data) if origin is None: origin = tuple([0.]*ndim) if spacing is None: spacing = tuple([1.]*ndim) if direction is None: direction = np.eye(ndim) libfn = utils.get_lib_fn('fromNumpy%s%i' % (_ntype_type_map[dtype], ndim)) if not has_components: itk_image = libfn(data, data.shape[::-1]) ants_image = iio.ANTsImage(pixeltype=ptype, dimension=ndim, components=1, pointer=itk_image) ants_image.set_origin(origin) ants_image.set_spacing(spacing) ants_image.set_direction(direction) ants_image._ndarr = data else: arrays = [data[i,...].copy() for i in range(data.shape[0])] data_shape = arrays[0].shape ants_images = [] for i in range(len(arrays)): tmp_ptr = libfn(arrays[i], data_shape[::-1]) tmp_img = iio.ANTsImage(pixeltype=ptype, dimension=ndim, components=1, pointer=tmp_ptr) tmp_img.set_origin(origin) tmp_img.set_spacing(spacing) tmp_img.set_direction(direction) tmp_img._ndarr = arrays[i] ants_images.append(tmp_img) ants_image = utils.merge_channels(ants_images) if is_rgb: ants_image = ants_image.vector_to_rgb() return ants_image
python
def _from_numpy(data, origin=None, spacing=None, direction=None, has_components=False, is_rgb=False): if is_rgb: has_components = True ndim = data.ndim if has_components: ndim -= 1 dtype = data.dtype.name ptype = _npy_to_itk_map[dtype] data = np.array(data) if origin is None: origin = tuple([0.]*ndim) if spacing is None: spacing = tuple([1.]*ndim) if direction is None: direction = np.eye(ndim) libfn = utils.get_lib_fn('fromNumpy%s%i' % (_ntype_type_map[dtype], ndim)) if not has_components: itk_image = libfn(data, data.shape[::-1]) ants_image = iio.ANTsImage(pixeltype=ptype, dimension=ndim, components=1, pointer=itk_image) ants_image.set_origin(origin) ants_image.set_spacing(spacing) ants_image.set_direction(direction) ants_image._ndarr = data else: arrays = [data[i,...].copy() for i in range(data.shape[0])] data_shape = arrays[0].shape ants_images = [] for i in range(len(arrays)): tmp_ptr = libfn(arrays[i], data_shape[::-1]) tmp_img = iio.ANTsImage(pixeltype=ptype, dimension=ndim, components=1, pointer=tmp_ptr) tmp_img.set_origin(origin) tmp_img.set_spacing(spacing) tmp_img.set_direction(direction) tmp_img._ndarr = arrays[i] ants_images.append(tmp_img) ants_image = utils.merge_channels(ants_images) if is_rgb: ants_image = ants_image.vector_to_rgb() return ants_image
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Internal function for creating an ANTsImage
[ "Internal", "function", "for", "creating", "an", "ANTsImage" ]
638020af2cdfc5ff4bdb9809ffe67aa505727a3b
https://github.com/ANTsX/ANTsPy/blob/638020af2cdfc5ff4bdb9809ffe67aa505727a3b/ants/core/ants_image_io.py#L109-L152
251,028
ANTsX/ANTsPy
ants/core/ants_image_io.py
make_image
def make_image(imagesize, voxval=0, spacing=None, origin=None, direction=None, has_components=False, pixeltype='float'): """ Make an image with given size and voxel value or given a mask and vector ANTsR function: `makeImage` Arguments --------- shape : tuple/ANTsImage input image size or mask voxval : scalar input image value or vector, size of mask spacing : tuple/list image spatial resolution origin : tuple/list image spatial origin direction : list/ndarray direction matrix to convert from index to physical space components : boolean whether there are components per pixel or not pixeltype : float data type of image values Returns ------- ANTsImage """ if isinstance(imagesize, iio.ANTsImage): img = imagesize.clone() sel = imagesize > 0 if voxval.ndim > 1: voxval = voxval.flatten() if (len(voxval) == int((sel>0).sum())) or (len(voxval) == 0): img[sel] = voxval else: raise ValueError('Num given voxels %i not same as num positive values %i in `imagesize`' % (len(voxval), int((sel>0).sum()))) return img else: if isinstance(voxval, (tuple,list,np.ndarray)): array = np.asarray(voxval).astype('float32').reshape(imagesize) else: array = np.full(imagesize,voxval,dtype='float32') image = from_numpy(array, origin=origin, spacing=spacing, direction=direction, has_components=has_components) return image.clone(pixeltype)
python
def make_image(imagesize, voxval=0, spacing=None, origin=None, direction=None, has_components=False, pixeltype='float'): if isinstance(imagesize, iio.ANTsImage): img = imagesize.clone() sel = imagesize > 0 if voxval.ndim > 1: voxval = voxval.flatten() if (len(voxval) == int((sel>0).sum())) or (len(voxval) == 0): img[sel] = voxval else: raise ValueError('Num given voxels %i not same as num positive values %i in `imagesize`' % (len(voxval), int((sel>0).sum()))) return img else: if isinstance(voxval, (tuple,list,np.ndarray)): array = np.asarray(voxval).astype('float32').reshape(imagesize) else: array = np.full(imagesize,voxval,dtype='float32') image = from_numpy(array, origin=origin, spacing=spacing, direction=direction, has_components=has_components) return image.clone(pixeltype)
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Make an image with given size and voxel value or given a mask and vector ANTsR function: `makeImage` Arguments --------- shape : tuple/ANTsImage input image size or mask voxval : scalar input image value or vector, size of mask spacing : tuple/list image spatial resolution origin : tuple/list image spatial origin direction : list/ndarray direction matrix to convert from index to physical space components : boolean whether there are components per pixel or not pixeltype : float data type of image values Returns ------- ANTsImage
[ "Make", "an", "image", "with", "given", "size", "and", "voxel", "value", "or", "given", "a", "mask", "and", "vector" ]
638020af2cdfc5ff4bdb9809ffe67aa505727a3b
https://github.com/ANTsX/ANTsPy/blob/638020af2cdfc5ff4bdb9809ffe67aa505727a3b/ants/core/ants_image_io.py#L155-L205
251,029
ANTsX/ANTsPy
ants/core/ants_image_io.py
matrix_to_images
def matrix_to_images(data_matrix, mask): """ Unmasks rows of a matrix and writes as images ANTsR function: `matrixToImages` Arguments --------- data_matrix : numpy.ndarray each row corresponds to an image array should have number of columns equal to non-zero voxels in the mask mask : ANTsImage image containing a binary mask. Rows of the matrix are unmasked and written as images. The mask defines the output image space Returns ------- list of ANTsImage types """ if data_matrix.ndim > 2: data_matrix = data_matrix.reshape(data_matrix.shape[0], -1) numimages = len(data_matrix) numVoxelsInMatrix = data_matrix.shape[1] numVoxelsInMask = (mask >= 0.5).sum() if numVoxelsInMask != numVoxelsInMatrix: raise ValueError('Num masked voxels %i must match data matrix %i' % (numVoxelsInMask, numVoxelsInMatrix)) imagelist = [] for i in range(numimages): img = mask.clone() img[mask >= 0.5] = data_matrix[i,:] imagelist.append(img) return imagelist
python
def matrix_to_images(data_matrix, mask): if data_matrix.ndim > 2: data_matrix = data_matrix.reshape(data_matrix.shape[0], -1) numimages = len(data_matrix) numVoxelsInMatrix = data_matrix.shape[1] numVoxelsInMask = (mask >= 0.5).sum() if numVoxelsInMask != numVoxelsInMatrix: raise ValueError('Num masked voxels %i must match data matrix %i' % (numVoxelsInMask, numVoxelsInMatrix)) imagelist = [] for i in range(numimages): img = mask.clone() img[mask >= 0.5] = data_matrix[i,:] imagelist.append(img) return imagelist
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Unmasks rows of a matrix and writes as images ANTsR function: `matrixToImages` Arguments --------- data_matrix : numpy.ndarray each row corresponds to an image array should have number of columns equal to non-zero voxels in the mask mask : ANTsImage image containing a binary mask. Rows of the matrix are unmasked and written as images. The mask defines the output image space Returns ------- list of ANTsImage types
[ "Unmasks", "rows", "of", "a", "matrix", "and", "writes", "as", "images" ]
638020af2cdfc5ff4bdb9809ffe67aa505727a3b
https://github.com/ANTsX/ANTsPy/blob/638020af2cdfc5ff4bdb9809ffe67aa505727a3b/ants/core/ants_image_io.py#L208-L243
251,030
ANTsX/ANTsPy
ants/core/ants_image_io.py
images_to_matrix
def images_to_matrix(image_list, mask=None, sigma=None, epsilon=0.5 ): """ Read images into rows of a matrix, given a mask - much faster for large datasets as it is based on C++ implementations. ANTsR function: `imagesToMatrix` Arguments --------- image_list : list of ANTsImage types images to convert to ndarray mask : ANTsImage (optional) image containing binary mask. voxels in the mask are placed in the matrix sigma : scaler (optional) smoothing factor epsilon : scalar threshold for mask Returns ------- ndarray array with a row for each image shape = (N_IMAGES, N_VOXELS) Example ------- >>> import ants >>> img = ants.image_read(ants.get_ants_data('r16')) >>> img2 = ants.image_read(ants.get_ants_data('r16')) >>> img3 = ants.image_read(ants.get_ants_data('r16')) >>> mat = ants.image_list_to_matrix([img,img2,img3]) """ def listfunc(x): if np.sum(np.array(x.shape) - np.array(mask.shape)) != 0: x = reg.resample_image_to_target(x, mask, 2) return x[mask] if mask is None: mask = utils.get_mask(image_list[0]) num_images = len(image_list) mask_arr = mask.numpy() >= epsilon num_voxels = np.sum(mask_arr) data_matrix = np.empty((num_images, num_voxels)) do_smooth = sigma is not None for i,img in enumerate(image_list): if do_smooth: data_matrix[i, :] = listfunc(utils.smooth_image(img, sigma, sigma_in_physical_coordinates=True)) else: data_matrix[i,:] = listfunc(img) return data_matrix
python
def images_to_matrix(image_list, mask=None, sigma=None, epsilon=0.5 ): def listfunc(x): if np.sum(np.array(x.shape) - np.array(mask.shape)) != 0: x = reg.resample_image_to_target(x, mask, 2) return x[mask] if mask is None: mask = utils.get_mask(image_list[0]) num_images = len(image_list) mask_arr = mask.numpy() >= epsilon num_voxels = np.sum(mask_arr) data_matrix = np.empty((num_images, num_voxels)) do_smooth = sigma is not None for i,img in enumerate(image_list): if do_smooth: data_matrix[i, :] = listfunc(utils.smooth_image(img, sigma, sigma_in_physical_coordinates=True)) else: data_matrix[i,:] = listfunc(img) return data_matrix
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Read images into rows of a matrix, given a mask - much faster for large datasets as it is based on C++ implementations. ANTsR function: `imagesToMatrix` Arguments --------- image_list : list of ANTsImage types images to convert to ndarray mask : ANTsImage (optional) image containing binary mask. voxels in the mask are placed in the matrix sigma : scaler (optional) smoothing factor epsilon : scalar threshold for mask Returns ------- ndarray array with a row for each image shape = (N_IMAGES, N_VOXELS) Example ------- >>> import ants >>> img = ants.image_read(ants.get_ants_data('r16')) >>> img2 = ants.image_read(ants.get_ants_data('r16')) >>> img3 = ants.image_read(ants.get_ants_data('r16')) >>> mat = ants.image_list_to_matrix([img,img2,img3])
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638020af2cdfc5ff4bdb9809ffe67aa505727a3b
https://github.com/ANTsX/ANTsPy/blob/638020af2cdfc5ff4bdb9809ffe67aa505727a3b/ants/core/ants_image_io.py#L247-L301
251,031
ANTsX/ANTsPy
ants/core/ants_image_io.py
timeseries_to_matrix
def timeseries_to_matrix( image, mask=None ): """ Convert a timeseries image into a matrix. ANTsR function: `timeseries2matrix` Arguments --------- image : image whose slices we convert to a matrix. E.g. a 3D image of size x by y by z will convert to a z by x*y sized matrix mask : ANTsImage (optional) image containing binary mask. voxels in the mask are placed in the matrix Returns ------- ndarray array with a row for each image shape = (N_IMAGES, N_VOXELS) Example ------- >>> import ants >>> img = ants.make_image( (10,10,10,5 ) ) >>> mat = ants.timeseries_to_matrix( img ) """ temp = utils.ndimage_to_list( image ) if mask is None: mask = temp[0]*0 + 1 return image_list_to_matrix( temp, mask )
python
def timeseries_to_matrix( image, mask=None ): temp = utils.ndimage_to_list( image ) if mask is None: mask = temp[0]*0 + 1 return image_list_to_matrix( temp, mask )
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Convert a timeseries image into a matrix. ANTsR function: `timeseries2matrix` Arguments --------- image : image whose slices we convert to a matrix. E.g. a 3D image of size x by y by z will convert to a z by x*y sized matrix mask : ANTsImage (optional) image containing binary mask. voxels in the mask are placed in the matrix Returns ------- ndarray array with a row for each image shape = (N_IMAGES, N_VOXELS) Example ------- >>> import ants >>> img = ants.make_image( (10,10,10,5 ) ) >>> mat = ants.timeseries_to_matrix( img )
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638020af2cdfc5ff4bdb9809ffe67aa505727a3b
https://github.com/ANTsX/ANTsPy/blob/638020af2cdfc5ff4bdb9809ffe67aa505727a3b/ants/core/ants_image_io.py#L307-L336
251,032
ANTsX/ANTsPy
ants/core/ants_image_io.py
matrix_to_timeseries
def matrix_to_timeseries( image, matrix, mask=None ): """ converts a matrix to a ND image. ANTsR function: `matrix2timeseries` Arguments --------- image: reference ND image matrix: matrix to convert to image mask: mask image defining voxels of interest Returns ------- ANTsImage Example ------- >>> import ants >>> img = ants.make_image( (10,10,10,5 ) ) >>> mask = ants.ndimage_to_list( img )[0] * 0 >>> mask[ 4:8, 4:8, 4:8 ] = 1 >>> mat = ants.timeseries_to_matrix( img, mask = mask ) >>> img2 = ants.matrix_to_timeseries( img, mat, mask) """ if mask is None: mask = temp[0]*0 + 1 temp = matrix_to_images( matrix, mask ) newImage = utils.list_to_ndimage( image, temp) iio.copy_image_info( image, newImage) return(newImage)
python
def matrix_to_timeseries( image, matrix, mask=None ): if mask is None: mask = temp[0]*0 + 1 temp = matrix_to_images( matrix, mask ) newImage = utils.list_to_ndimage( image, temp) iio.copy_image_info( image, newImage) return(newImage)
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converts a matrix to a ND image. ANTsR function: `matrix2timeseries` Arguments --------- image: reference ND image matrix: matrix to convert to image mask: mask image defining voxels of interest Returns ------- ANTsImage Example ------- >>> import ants >>> img = ants.make_image( (10,10,10,5 ) ) >>> mask = ants.ndimage_to_list( img )[0] * 0 >>> mask[ 4:8, 4:8, 4:8 ] = 1 >>> mat = ants.timeseries_to_matrix( img, mask = mask ) >>> img2 = ants.matrix_to_timeseries( img, mat, mask)
[ "converts", "a", "matrix", "to", "a", "ND", "image", "." ]
638020af2cdfc5ff4bdb9809ffe67aa505727a3b
https://github.com/ANTsX/ANTsPy/blob/638020af2cdfc5ff4bdb9809ffe67aa505727a3b/ants/core/ants_image_io.py#L339-L374
251,033
ANTsX/ANTsPy
ants/core/ants_image_io.py
image_header_info
def image_header_info(filename): """ Read file info from image header ANTsR function: `antsImageHeaderInfo` Arguments --------- filename : string name of image file from which info will be read Returns ------- dict """ if not os.path.exists(filename): raise Exception('filename does not exist') libfn = utils.get_lib_fn('antsImageHeaderInfo') retval = libfn(filename) retval['dimensions'] = tuple(retval['dimensions']) retval['origin'] = tuple([round(o,4) for o in retval['origin']]) retval['spacing'] = tuple([round(s,4) for s in retval['spacing']]) retval['direction'] = np.round(retval['direction'],4) return retval
python
def image_header_info(filename): if not os.path.exists(filename): raise Exception('filename does not exist') libfn = utils.get_lib_fn('antsImageHeaderInfo') retval = libfn(filename) retval['dimensions'] = tuple(retval['dimensions']) retval['origin'] = tuple([round(o,4) for o in retval['origin']]) retval['spacing'] = tuple([round(s,4) for s in retval['spacing']]) retval['direction'] = np.round(retval['direction'],4) return retval
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Read file info from image header ANTsR function: `antsImageHeaderInfo` Arguments --------- filename : string name of image file from which info will be read Returns ------- dict
[ "Read", "file", "info", "from", "image", "header" ]
638020af2cdfc5ff4bdb9809ffe67aa505727a3b
https://github.com/ANTsX/ANTsPy/blob/638020af2cdfc5ff4bdb9809ffe67aa505727a3b/ants/core/ants_image_io.py#L377-L401
251,034
ANTsX/ANTsPy
ants/core/ants_image_io.py
image_read
def image_read(filename, dimension=None, pixeltype='float', reorient=False): """ Read an ANTsImage from file ANTsR function: `antsImageRead` Arguments --------- filename : string Name of the file to read the image from. dimension : int Number of dimensions of the image read. This need not be the same as the dimensions of the image in the file. Allowed values: 2, 3, 4. If not provided, the dimension is obtained from the image file pixeltype : string C++ datatype to be used to represent the pixels read. This datatype need not be the same as the datatype used in the file. Options: unsigned char, unsigned int, float, double reorient : boolean | string if True, the image will be reoriented to RPI if it is 3D if False, nothing will happen if string, this should be the 3-letter orientation to which the input image will reoriented if 3D. if the image is 2D, this argument is ignored Returns ------- ANTsImage """ if filename.endswith('.npy'): filename = os.path.expanduser(filename) img_array = np.load(filename) if os.path.exists(filename.replace('.npy', '.json')): with open(filename.replace('.npy', '.json')) as json_data: img_header = json.load(json_data) ants_image = from_numpy(img_array, origin=img_header.get('origin', None), spacing=img_header.get('spacing', None), direction=np.asarray(img_header.get('direction',None)), has_components=img_header.get('components',1)>1) else: img_header = {} ants_image = from_numpy(img_array) else: filename = os.path.expanduser(filename) if not os.path.exists(filename): raise ValueError('File %s does not exist!' % filename) hinfo = image_header_info(filename) ptype = hinfo['pixeltype'] pclass = hinfo['pixelclass'] ndim = hinfo['nDimensions'] ncomp = hinfo['nComponents'] is_rgb = True if pclass == 'rgb' else False if dimension is not None: ndim = dimension # error handling on pixelclass if pclass not in _supported_pclasses: raise ValueError('Pixel class %s not supported!' % pclass) # error handling on pixeltype if ptype in _unsupported_ptypes: ptype = _unsupported_ptype_map.get(ptype, 'unsupported') if ptype == 'unsupported': raise ValueError('Pixeltype %s not supported' % ptype) # error handling on dimension if (ndim < 2) or (ndim > 4): raise ValueError('Found %i-dimensional image - not supported!' % ndim) libfn = utils.get_lib_fn(_image_read_dict[pclass][ptype][ndim]) itk_pointer = libfn(filename) ants_image = iio.ANTsImage(pixeltype=ptype, dimension=ndim, components=ncomp, pointer=itk_pointer, is_rgb=is_rgb) if pixeltype is not None: ants_image = ants_image.clone(pixeltype) if (reorient != False) and (ants_image.dimension == 3): if reorient == True: ants_image = ants_image.reorient_image2('RPI') elif isinstance(reorient, str): ants_image = ants_image.reorient_image2(reorient) return ants_image
python
def image_read(filename, dimension=None, pixeltype='float', reorient=False): if filename.endswith('.npy'): filename = os.path.expanduser(filename) img_array = np.load(filename) if os.path.exists(filename.replace('.npy', '.json')): with open(filename.replace('.npy', '.json')) as json_data: img_header = json.load(json_data) ants_image = from_numpy(img_array, origin=img_header.get('origin', None), spacing=img_header.get('spacing', None), direction=np.asarray(img_header.get('direction',None)), has_components=img_header.get('components',1)>1) else: img_header = {} ants_image = from_numpy(img_array) else: filename = os.path.expanduser(filename) if not os.path.exists(filename): raise ValueError('File %s does not exist!' % filename) hinfo = image_header_info(filename) ptype = hinfo['pixeltype'] pclass = hinfo['pixelclass'] ndim = hinfo['nDimensions'] ncomp = hinfo['nComponents'] is_rgb = True if pclass == 'rgb' else False if dimension is not None: ndim = dimension # error handling on pixelclass if pclass not in _supported_pclasses: raise ValueError('Pixel class %s not supported!' % pclass) # error handling on pixeltype if ptype in _unsupported_ptypes: ptype = _unsupported_ptype_map.get(ptype, 'unsupported') if ptype == 'unsupported': raise ValueError('Pixeltype %s not supported' % ptype) # error handling on dimension if (ndim < 2) or (ndim > 4): raise ValueError('Found %i-dimensional image - not supported!' % ndim) libfn = utils.get_lib_fn(_image_read_dict[pclass][ptype][ndim]) itk_pointer = libfn(filename) ants_image = iio.ANTsImage(pixeltype=ptype, dimension=ndim, components=ncomp, pointer=itk_pointer, is_rgb=is_rgb) if pixeltype is not None: ants_image = ants_image.clone(pixeltype) if (reorient != False) and (ants_image.dimension == 3): if reorient == True: ants_image = ants_image.reorient_image2('RPI') elif isinstance(reorient, str): ants_image = ants_image.reorient_image2(reorient) return ants_image
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Read an ANTsImage from file ANTsR function: `antsImageRead` Arguments --------- filename : string Name of the file to read the image from. dimension : int Number of dimensions of the image read. This need not be the same as the dimensions of the image in the file. Allowed values: 2, 3, 4. If not provided, the dimension is obtained from the image file pixeltype : string C++ datatype to be used to represent the pixels read. This datatype need not be the same as the datatype used in the file. Options: unsigned char, unsigned int, float, double reorient : boolean | string if True, the image will be reoriented to RPI if it is 3D if False, nothing will happen if string, this should be the 3-letter orientation to which the input image will reoriented if 3D. if the image is 2D, this argument is ignored Returns ------- ANTsImage
[ "Read", "an", "ANTsImage", "from", "file" ]
638020af2cdfc5ff4bdb9809ffe67aa505727a3b
https://github.com/ANTsX/ANTsPy/blob/638020af2cdfc5ff4bdb9809ffe67aa505727a3b/ants/core/ants_image_io.py#L425-L515
251,035
ANTsX/ANTsPy
ants/core/ants_image_io.py
dicom_read
def dicom_read(directory, pixeltype='float'): """ Read a set of dicom files in a directory into a single ANTsImage. The origin of the resulting 3D image will be the origin of the first dicom image read. Arguments --------- directory : string folder in which all the dicom images exist Returns ------- ANTsImage Example ------- >>> import ants >>> img = ants.dicom_read('~/desktop/dicom-subject/') """ slices = [] imgidx = 0 for imgpath in os.listdir(directory): if imgpath.endswith('.dcm'): if imgidx == 0: tmp = image_read(os.path.join(directory,imgpath), dimension=3, pixeltype=pixeltype) origin = tmp.origin spacing = tmp.spacing direction = tmp.direction tmp = tmp.numpy()[:,:,0] else: tmp = image_read(os.path.join(directory,imgpath), dimension=2, pixeltype=pixeltype).numpy() slices.append(tmp) imgidx += 1 slices = np.stack(slices, axis=-1) return from_numpy(slices, origin=origin, spacing=spacing, direction=direction)
python
def dicom_read(directory, pixeltype='float'): slices = [] imgidx = 0 for imgpath in os.listdir(directory): if imgpath.endswith('.dcm'): if imgidx == 0: tmp = image_read(os.path.join(directory,imgpath), dimension=3, pixeltype=pixeltype) origin = tmp.origin spacing = tmp.spacing direction = tmp.direction tmp = tmp.numpy()[:,:,0] else: tmp = image_read(os.path.join(directory,imgpath), dimension=2, pixeltype=pixeltype).numpy() slices.append(tmp) imgidx += 1 slices = np.stack(slices, axis=-1) return from_numpy(slices, origin=origin, spacing=spacing, direction=direction)
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Read a set of dicom files in a directory into a single ANTsImage. The origin of the resulting 3D image will be the origin of the first dicom image read. Arguments --------- directory : string folder in which all the dicom images exist Returns ------- ANTsImage Example ------- >>> import ants >>> img = ants.dicom_read('~/desktop/dicom-subject/')
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638020af2cdfc5ff4bdb9809ffe67aa505727a3b
https://github.com/ANTsX/ANTsPy/blob/638020af2cdfc5ff4bdb9809ffe67aa505727a3b/ants/core/ants_image_io.py#L518-L555
251,036
ANTsX/ANTsPy
ants/core/ants_image_io.py
image_write
def image_write(image, filename, ri=False): """ Write an ANTsImage to file ANTsR function: `antsImageWrite` Arguments --------- image : ANTsImage image to save to file filename : string name of file to which image will be saved ri : boolean if True, return image. This allows for using this function in a pipeline: >>> img2 = img.smooth_image(2.).image_write(file1, ri=True).threshold_image(0,20).image_write(file2, ri=True) if False, do not return image """ if filename.endswith('.npy'): img_array = image.numpy() img_header = {'origin': image.origin,'spacing': image.spacing, 'direction': image.direction.tolist(), 'components': image.components} np.save(filename, img_array) with open(filename.replace('.npy','.json'), 'w') as outfile: json.dump(img_header, outfile) else: image.to_file(filename) if ri: return image
python
def image_write(image, filename, ri=False): if filename.endswith('.npy'): img_array = image.numpy() img_header = {'origin': image.origin,'spacing': image.spacing, 'direction': image.direction.tolist(), 'components': image.components} np.save(filename, img_array) with open(filename.replace('.npy','.json'), 'w') as outfile: json.dump(img_header, outfile) else: image.to_file(filename) if ri: return image
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Write an ANTsImage to file ANTsR function: `antsImageWrite` Arguments --------- image : ANTsImage image to save to file filename : string name of file to which image will be saved ri : boolean if True, return image. This allows for using this function in a pipeline: >>> img2 = img.smooth_image(2.).image_write(file1, ri=True).threshold_image(0,20).image_write(file2, ri=True) if False, do not return image
[ "Write", "an", "ANTsImage", "to", "file" ]
638020af2cdfc5ff4bdb9809ffe67aa505727a3b
https://github.com/ANTsX/ANTsPy/blob/638020af2cdfc5ff4bdb9809ffe67aa505727a3b/ants/core/ants_image_io.py#L558-L589
251,037
ANTsX/ANTsPy
ants/segmentation/otsu.py
otsu_segmentation
def otsu_segmentation(image, k, mask=None): """ Otsu image segmentation This is a very fast segmentation algorithm good for quick explortation, but does not return probability maps. ANTsR function: `thresholdImage(image, 'Otsu', k)` Arguments --------- image : ANTsImage input image k : integer integer number of classes. Note that a background class will be added to this, so the resulting segmentation will have k+1 unique values. mask : ANTsImage segment inside this mask Returns ------- ANTsImage Example ------- >>> import ants >>> mni = ants.image_read(ants.get_data('mni')) >>> seg = mni.otsu_segmentation(k=3) #0=bg,1=csf,2=gm,3=wm """ if mask is not None: image = image.mask_image(mask) seg = image.threshold_image('Otsu', k) return seg
python
def otsu_segmentation(image, k, mask=None): if mask is not None: image = image.mask_image(mask) seg = image.threshold_image('Otsu', k) return seg
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Otsu image segmentation This is a very fast segmentation algorithm good for quick explortation, but does not return probability maps. ANTsR function: `thresholdImage(image, 'Otsu', k)` Arguments --------- image : ANTsImage input image k : integer integer number of classes. Note that a background class will be added to this, so the resulting segmentation will have k+1 unique values. mask : ANTsImage segment inside this mask Returns ------- ANTsImage Example ------- >>> import ants >>> mni = ants.image_read(ants.get_data('mni')) >>> seg = mni.otsu_segmentation(k=3) #0=bg,1=csf,2=gm,3=wm
[ "Otsu", "image", "segmentation" ]
638020af2cdfc5ff4bdb9809ffe67aa505727a3b
https://github.com/ANTsX/ANTsPy/blob/638020af2cdfc5ff4bdb9809ffe67aa505727a3b/ants/segmentation/otsu.py#L7-L43
251,038
ANTsX/ANTsPy
ants/utils/crop_image.py
crop_image
def crop_image(image, label_image=None, label=1): """ Use a label image to crop a smaller ANTsImage from within a larger ANTsImage ANTsR function: `cropImage` Arguments --------- image : ANTsImage image to crop label_image : ANTsImage image with label values. If not supplied, estimated from data. label : integer the label value to use Returns ------- ANTsImage Example ------- >>> import ants >>> fi = ants.image_read( ants.get_ants_data('r16') ) >>> cropped = ants.crop_image(fi) >>> cropped = ants.crop_image(fi, fi, 100 ) """ inpixeltype = image.pixeltype ndim = image.dimension if image.pixeltype != 'float': image = image.clone('float') if label_image is None: label_image = get_mask(image) if label_image.pixeltype != 'float': label_image = label_image.clone('float') libfn = utils.get_lib_fn('cropImageF%i' % ndim) itkimage = libfn(image.pointer, label_image.pointer, label, 0, [], []) return iio.ANTsImage(pixeltype='float', dimension=ndim, components=image.components, pointer=itkimage).clone(inpixeltype)
python
def crop_image(image, label_image=None, label=1): inpixeltype = image.pixeltype ndim = image.dimension if image.pixeltype != 'float': image = image.clone('float') if label_image is None: label_image = get_mask(image) if label_image.pixeltype != 'float': label_image = label_image.clone('float') libfn = utils.get_lib_fn('cropImageF%i' % ndim) itkimage = libfn(image.pointer, label_image.pointer, label, 0, [], []) return iio.ANTsImage(pixeltype='float', dimension=ndim, components=image.components, pointer=itkimage).clone(inpixeltype)
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Use a label image to crop a smaller ANTsImage from within a larger ANTsImage ANTsR function: `cropImage` Arguments --------- image : ANTsImage image to crop label_image : ANTsImage image with label values. If not supplied, estimated from data. label : integer the label value to use Returns ------- ANTsImage Example ------- >>> import ants >>> fi = ants.image_read( ants.get_ants_data('r16') ) >>> cropped = ants.crop_image(fi) >>> cropped = ants.crop_image(fi, fi, 100 )
[ "Use", "a", "label", "image", "to", "crop", "a", "smaller", "ANTsImage", "from", "within", "a", "larger", "ANTsImage" ]
638020af2cdfc5ff4bdb9809ffe67aa505727a3b
https://github.com/ANTsX/ANTsPy/blob/638020af2cdfc5ff4bdb9809ffe67aa505727a3b/ants/utils/crop_image.py#L14-L56
251,039
ANTsX/ANTsPy
ants/utils/crop_image.py
decrop_image
def decrop_image(cropped_image, full_image): """ The inverse function for `ants.crop_image` ANTsR function: `decropImage` Arguments --------- cropped_image : ANTsImage cropped image full_image : ANTsImage image in which the cropped image will be put back Returns ------- ANTsImage Example ------- >>> import ants >>> fi = ants.image_read(ants.get_ants_data('r16')) >>> mask = ants.get_mask(fi) >>> cropped = ants.crop_image(fi, mask, 1) >>> cropped = ants.smooth_image(cropped, 1) >>> decropped = ants.decrop_image(cropped, fi) """ inpixeltype = 'float' if cropped_image.pixeltype != 'float': inpixeltype= cropped_image.pixeltype cropped_image = cropped_image.clone('float') if full_image.pixeltype != 'float': full_image = full_image.clone('float') libfn = utils.get_lib_fn('cropImageF%i' % cropped_image.dimension) itkimage = libfn(cropped_image.pointer, full_image.pointer, 1, 1, [], []) ants_image = iio.ANTsImage(pixeltype='float', dimension=cropped_image.dimension, components=cropped_image.components, pointer=itkimage) if inpixeltype != 'float': ants_image = ants_image.clone(inpixeltype) return ants_image
python
def decrop_image(cropped_image, full_image): inpixeltype = 'float' if cropped_image.pixeltype != 'float': inpixeltype= cropped_image.pixeltype cropped_image = cropped_image.clone('float') if full_image.pixeltype != 'float': full_image = full_image.clone('float') libfn = utils.get_lib_fn('cropImageF%i' % cropped_image.dimension) itkimage = libfn(cropped_image.pointer, full_image.pointer, 1, 1, [], []) ants_image = iio.ANTsImage(pixeltype='float', dimension=cropped_image.dimension, components=cropped_image.components, pointer=itkimage) if inpixeltype != 'float': ants_image = ants_image.clone(inpixeltype) return ants_image
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The inverse function for `ants.crop_image` ANTsR function: `decropImage` Arguments --------- cropped_image : ANTsImage cropped image full_image : ANTsImage image in which the cropped image will be put back Returns ------- ANTsImage Example ------- >>> import ants >>> fi = ants.image_read(ants.get_ants_data('r16')) >>> mask = ants.get_mask(fi) >>> cropped = ants.crop_image(fi, mask, 1) >>> cropped = ants.smooth_image(cropped, 1) >>> decropped = ants.decrop_image(cropped, fi)
[ "The", "inverse", "function", "for", "ants", ".", "crop_image" ]
638020af2cdfc5ff4bdb9809ffe67aa505727a3b
https://github.com/ANTsX/ANTsPy/blob/638020af2cdfc5ff4bdb9809ffe67aa505727a3b/ants/utils/crop_image.py#L108-L149
251,040
ANTsX/ANTsPy
ants/segmentation/kmeans.py
kmeans_segmentation
def kmeans_segmentation(image, k, kmask=None, mrf=0.1): """ K-means image segmentation that is a wrapper around `ants.atropos` ANTsR function: `kmeansSegmentation` Arguments --------- image : ANTsImage input image k : integer integer number of classes kmask : ANTsImage (optional) segment inside this mask mrf : scalar smoothness, higher is smoother Returns ------- ANTsImage Example ------- >>> import ants >>> fi = ants.image_read(ants.get_ants_data('r16'), 'float') >>> fi = ants.n3_bias_field_correction(fi, 2) >>> seg = ants.kmeans_segmentation(fi, 3) """ dim = image.dimension kmimage = utils.iMath(image, 'Normalize') if kmask is None: kmask = utils.get_mask(kmimage, 0.01, 1, cleanup=2) kmask = utils.iMath(kmask, 'FillHoles').threshold_image(1,2) nhood = 'x'.join(['1']*dim) mrf = '[%s,%s]' % (str(mrf), nhood) kmimage = atropos(a = kmimage, m = mrf, c = '[5,0]', i = 'kmeans[%s]'%(str(k)), x = kmask) kmimage['segmentation'] = kmimage['segmentation'].clone(image.pixeltype) return kmimage
python
def kmeans_segmentation(image, k, kmask=None, mrf=0.1): dim = image.dimension kmimage = utils.iMath(image, 'Normalize') if kmask is None: kmask = utils.get_mask(kmimage, 0.01, 1, cleanup=2) kmask = utils.iMath(kmask, 'FillHoles').threshold_image(1,2) nhood = 'x'.join(['1']*dim) mrf = '[%s,%s]' % (str(mrf), nhood) kmimage = atropos(a = kmimage, m = mrf, c = '[5,0]', i = 'kmeans[%s]'%(str(k)), x = kmask) kmimage['segmentation'] = kmimage['segmentation'].clone(image.pixeltype) return kmimage
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K-means image segmentation that is a wrapper around `ants.atropos` ANTsR function: `kmeansSegmentation` Arguments --------- image : ANTsImage input image k : integer integer number of classes kmask : ANTsImage (optional) segment inside this mask mrf : scalar smoothness, higher is smoother Returns ------- ANTsImage Example ------- >>> import ants >>> fi = ants.image_read(ants.get_ants_data('r16'), 'float') >>> fi = ants.n3_bias_field_correction(fi, 2) >>> seg = ants.kmeans_segmentation(fi, 3)
[ "K", "-", "means", "image", "segmentation", "that", "is", "a", "wrapper", "around", "ants", ".", "atropos" ]
638020af2cdfc5ff4bdb9809ffe67aa505727a3b
https://github.com/ANTsX/ANTsPy/blob/638020af2cdfc5ff4bdb9809ffe67aa505727a3b/ants/segmentation/kmeans.py#L9-L49
251,041
ANTsX/ANTsPy
ants/registration/reorient_image.py
reorient_image2
def reorient_image2(image, orientation='RAS'): """ Reorient an image. Example ------- >>> import ants >>> mni = ants.image_read(ants.get_data('mni')) >>> mni2 = mni.reorient_image2() """ if image.dimension != 3: raise ValueError('image must have 3 dimensions') inpixeltype = image.pixeltype ndim = image.dimension if image.pixeltype != 'float': image = image.clone('float') libfn = utils.get_lib_fn('reorientImage2') itkimage = libfn(image.pointer, orientation) new_img = iio.ANTsImage(pixeltype='float', dimension=ndim, components=image.components, pointer=itkimage)#.clone(inpixeltype) if inpixeltype != 'float': new_img = new_img.clone(inpixeltype) return new_img
python
def reorient_image2(image, orientation='RAS'): if image.dimension != 3: raise ValueError('image must have 3 dimensions') inpixeltype = image.pixeltype ndim = image.dimension if image.pixeltype != 'float': image = image.clone('float') libfn = utils.get_lib_fn('reorientImage2') itkimage = libfn(image.pointer, orientation) new_img = iio.ANTsImage(pixeltype='float', dimension=ndim, components=image.components, pointer=itkimage)#.clone(inpixeltype) if inpixeltype != 'float': new_img = new_img.clone(inpixeltype) return new_img
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Reorient an image. Example ------- >>> import ants >>> mni = ants.image_read(ants.get_data('mni')) >>> mni2 = mni.reorient_image2()
[ "Reorient", "an", "image", "." ]
638020af2cdfc5ff4bdb9809ffe67aa505727a3b
https://github.com/ANTsX/ANTsPy/blob/638020af2cdfc5ff4bdb9809ffe67aa505727a3b/ants/registration/reorient_image.py#L56-L82
251,042
ANTsX/ANTsPy
ants/registration/reorient_image.py
reorient_image
def reorient_image(image, axis1, axis2=None, doreflection=False, doscale=0, txfn=None): """ Align image along a specified axis ANTsR function: `reorientImage` Arguments --------- image : ANTsImage image to reorient axis1 : list/tuple of integers vector of size dim, might need to play w/axis sign axis2 : list/tuple of integers vector of size dim for 3D doreflection : boolean whether to reflect doscale : scalar value 1 allows automated estimate of scaling txfn : string file name for transformation Returns ------- ANTsImage Example ------- >>> import ants >>> image = ants.image_read(ants.get_ants_data('r16')) >>> ants.reorient_image(image, (1,0)) """ inpixeltype = image.pixeltype if image.pixeltype != 'float': image = image.clone('float') axis_was_none = False if axis2 is None: axis_was_none = True axis2 = [0]*image.dimension axis1 = np.array(axis1) axis2 = np.array(axis2) axis1 = axis1 / np.sqrt(np.sum(axis1*axis1)) * (-1) axis1 = axis1.astype('int') if not axis_was_none: axis2 = axis2 / np.sqrt(np.sum(axis2*axis2)) * (-1) axis2 = axis2.astype('int') else: axis2 = np.array([0]*image.dimension).astype('int') if txfn is None: txfn = mktemp(suffix='.mat') if isinstance(doreflection, tuple): doreflection = list(doreflection) if not isinstance(doreflection, list): doreflection = [doreflection] if isinstance(doscale, tuple): doscale = list(doscale) if not isinstance(doscale, list): doscale = [doscale] if len(doreflection) == 1: doreflection = [doreflection[0]]*image.dimension if len(doscale) == 1: doscale = [doscale[0]]*image.dimension libfn = utils.get_lib_fn('reorientImage%s' % image._libsuffix) libfn(image.pointer, txfn, axis1.tolist(), axis2.tolist(), doreflection, doscale) image2 = apply_transforms(image, image, transformlist=[txfn]) if image.pixeltype != inpixeltype: image2 = image2.clone(inpixeltype) return {'reoimage':image2, 'txfn':txfn}
python
def reorient_image(image, axis1, axis2=None, doreflection=False, doscale=0, txfn=None): inpixeltype = image.pixeltype if image.pixeltype != 'float': image = image.clone('float') axis_was_none = False if axis2 is None: axis_was_none = True axis2 = [0]*image.dimension axis1 = np.array(axis1) axis2 = np.array(axis2) axis1 = axis1 / np.sqrt(np.sum(axis1*axis1)) * (-1) axis1 = axis1.astype('int') if not axis_was_none: axis2 = axis2 / np.sqrt(np.sum(axis2*axis2)) * (-1) axis2 = axis2.astype('int') else: axis2 = np.array([0]*image.dimension).astype('int') if txfn is None: txfn = mktemp(suffix='.mat') if isinstance(doreflection, tuple): doreflection = list(doreflection) if not isinstance(doreflection, list): doreflection = [doreflection] if isinstance(doscale, tuple): doscale = list(doscale) if not isinstance(doscale, list): doscale = [doscale] if len(doreflection) == 1: doreflection = [doreflection[0]]*image.dimension if len(doscale) == 1: doscale = [doscale[0]]*image.dimension libfn = utils.get_lib_fn('reorientImage%s' % image._libsuffix) libfn(image.pointer, txfn, axis1.tolist(), axis2.tolist(), doreflection, doscale) image2 = apply_transforms(image, image, transformlist=[txfn]) if image.pixeltype != inpixeltype: image2 = image2.clone(inpixeltype) return {'reoimage':image2, 'txfn':txfn}
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Align image along a specified axis ANTsR function: `reorientImage` Arguments --------- image : ANTsImage image to reorient axis1 : list/tuple of integers vector of size dim, might need to play w/axis sign axis2 : list/tuple of integers vector of size dim for 3D doreflection : boolean whether to reflect doscale : scalar value 1 allows automated estimate of scaling txfn : string file name for transformation Returns ------- ANTsImage Example ------- >>> import ants >>> image = ants.image_read(ants.get_ants_data('r16')) >>> ants.reorient_image(image, (1,0))
[ "Align", "image", "along", "a", "specified", "axis" ]
638020af2cdfc5ff4bdb9809ffe67aa505727a3b
https://github.com/ANTsX/ANTsPy/blob/638020af2cdfc5ff4bdb9809ffe67aa505727a3b/ants/registration/reorient_image.py#L85-L168
251,043
ANTsX/ANTsPy
ants/registration/reorient_image.py
get_center_of_mass
def get_center_of_mass(image): """ Compute an image center of mass in physical space which is defined as the mean of the intensity weighted voxel coordinate system. ANTsR function: `getCenterOfMass` Arguments --------- image : ANTsImage image from which center of mass will be computed Returns ------- scalar Example ------- >>> fi = ants.image_read( ants.get_ants_data("r16")) >>> com1 = ants.get_center_of_mass( fi ) >>> fi = ants.image_read( ants.get_ants_data("r64")) >>> com2 = ants.get_center_of_mass( fi ) """ if image.pixeltype != 'float': image = image.clone('float') libfn = utils.get_lib_fn('centerOfMass%s' % image._libsuffix) com = libfn(image.pointer) return tuple(com)
python
def get_center_of_mass(image): if image.pixeltype != 'float': image = image.clone('float') libfn = utils.get_lib_fn('centerOfMass%s' % image._libsuffix) com = libfn(image.pointer) return tuple(com)
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Compute an image center of mass in physical space which is defined as the mean of the intensity weighted voxel coordinate system. ANTsR function: `getCenterOfMass` Arguments --------- image : ANTsImage image from which center of mass will be computed Returns ------- scalar Example ------- >>> fi = ants.image_read( ants.get_ants_data("r16")) >>> com1 = ants.get_center_of_mass( fi ) >>> fi = ants.image_read( ants.get_ants_data("r64")) >>> com2 = ants.get_center_of_mass( fi )
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638020af2cdfc5ff4bdb9809ffe67aa505727a3b
https://github.com/ANTsX/ANTsPy/blob/638020af2cdfc5ff4bdb9809ffe67aa505727a3b/ants/registration/reorient_image.py#L171-L200
251,044
ANTsX/ANTsPy
ants/utils/convert_nibabel.py
to_nibabel
def to_nibabel(image): """ Convert an ANTsImage to a Nibabel image """ if image.dimension != 3: raise ValueError('Only 3D images currently supported') import nibabel as nib array_data = image.numpy() affine = np.hstack([image.direction*np.diag(image.spacing),np.array(image.origin).reshape(3,1)]) affine = np.vstack([affine, np.array([0,0,0,1.])]) affine[:2,:] *= -1 new_img = nib.Nifti1Image(array_data, affine) return new_img
python
def to_nibabel(image): if image.dimension != 3: raise ValueError('Only 3D images currently supported') import nibabel as nib array_data = image.numpy() affine = np.hstack([image.direction*np.diag(image.spacing),np.array(image.origin).reshape(3,1)]) affine = np.vstack([affine, np.array([0,0,0,1.])]) affine[:2,:] *= -1 new_img = nib.Nifti1Image(array_data, affine) return new_img
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Convert an ANTsImage to a Nibabel image
[ "Convert", "an", "ANTsImage", "to", "a", "Nibabel", "image" ]
638020af2cdfc5ff4bdb9809ffe67aa505727a3b
https://github.com/ANTsX/ANTsPy/blob/638020af2cdfc5ff4bdb9809ffe67aa505727a3b/ants/utils/convert_nibabel.py#L10-L23
251,045
ANTsX/ANTsPy
ants/utils/convert_nibabel.py
from_nibabel
def from_nibabel(nib_image): """ Convert a nibabel image to an ANTsImage """ tmpfile = mktemp(suffix='.nii.gz') nib_image.to_filename(tmpfile) new_img = iio2.image_read(tmpfile) os.remove(tmpfile) return new_img
python
def from_nibabel(nib_image): tmpfile = mktemp(suffix='.nii.gz') nib_image.to_filename(tmpfile) new_img = iio2.image_read(tmpfile) os.remove(tmpfile) return new_img
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Convert a nibabel image to an ANTsImage
[ "Convert", "a", "nibabel", "image", "to", "an", "ANTsImage" ]
638020af2cdfc5ff4bdb9809ffe67aa505727a3b
https://github.com/ANTsX/ANTsPy/blob/638020af2cdfc5ff4bdb9809ffe67aa505727a3b/ants/utils/convert_nibabel.py#L26-L34
251,046
ANTsX/ANTsPy
ants/contrib/sampling/affine3d.py
RandomShear3D.transform
def transform(self, X=None, y=None): """ Transform an image using an Affine transform with shear parameters randomly generated from the user-specified range. Return the transform if X=None. Arguments --------- X : ANTsImage Image to transform y : ANTsImage (optional) Another image to transform Returns ------- ANTsImage if y is None, else a tuple of ANTsImage types Examples -------- >>> import ants >>> img = ants.image_read(ants.get_data('ch2')) >>> tx = ants.contrib.RandomShear3D(shear_range=(-10,10)) >>> img2 = tx.transform(img) """ # random draw in shear range shear_x = random.gauss(self.shear_range[0], self.shear_range[1]) shear_y = random.gauss(self.shear_range[0], self.shear_range[1]) shear_z = random.gauss(self.shear_range[0], self.shear_range[1]) self.params = (shear_x, shear_y, shear_z) tx = Shear3D((shear_x, shear_y, shear_z), reference=self.reference, lazy=self.lazy) return tx.transform(X,y)
python
def transform(self, X=None, y=None): # random draw in shear range shear_x = random.gauss(self.shear_range[0], self.shear_range[1]) shear_y = random.gauss(self.shear_range[0], self.shear_range[1]) shear_z = random.gauss(self.shear_range[0], self.shear_range[1]) self.params = (shear_x, shear_y, shear_z) tx = Shear3D((shear_x, shear_y, shear_z), reference=self.reference, lazy=self.lazy) return tx.transform(X,y)
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Transform an image using an Affine transform with shear parameters randomly generated from the user-specified range. Return the transform if X=None. Arguments --------- X : ANTsImage Image to transform y : ANTsImage (optional) Another image to transform Returns ------- ANTsImage if y is None, else a tuple of ANTsImage types Examples -------- >>> import ants >>> img = ants.image_read(ants.get_data('ch2')) >>> tx = ants.contrib.RandomShear3D(shear_range=(-10,10)) >>> img2 = tx.transform(img)
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638020af2cdfc5ff4bdb9809ffe67aa505727a3b
https://github.com/ANTsX/ANTsPy/blob/638020af2cdfc5ff4bdb9809ffe67aa505727a3b/ants/contrib/sampling/affine3d.py#L304-L339
251,047
ANTsX/ANTsPy
ants/contrib/sampling/affine3d.py
RandomZoom3D.transform
def transform(self, X=None, y=None): """ Transform an image using an Affine transform with zoom parameters randomly generated from the user-specified range. Return the transform if X=None. Arguments --------- X : ANTsImage Image to transform y : ANTsImage (optional) Another image to transform Returns ------- ANTsImage if y is None, else a tuple of ANTsImage types Examples -------- >>> import ants >>> img = ants.image_read(ants.get_data('ch2')) >>> tx = ants.contrib.RandomZoom3D(zoom_range=(0.8,0.9)) >>> img2 = tx.transform(img) """ # random draw in zoom range zoom_x = np.exp( random.gauss( np.log( self.zoom_range[0] ), np.log( self.zoom_range[1] ) ) ) zoom_y = np.exp( random.gauss( np.log( self.zoom_range[0] ), np.log( self.zoom_range[1] ) ) ) zoom_z = np.exp( random.gauss( np.log( self.zoom_range[0] ), np.log( self.zoom_range[1] ) ) ) self.params = (zoom_x, zoom_y, zoom_z) tx = Zoom3D((zoom_x,zoom_y,zoom_z), reference=self.reference, lazy=self.lazy) return tx.transform(X,y)
python
def transform(self, X=None, y=None): # random draw in zoom range zoom_x = np.exp( random.gauss( np.log( self.zoom_range[0] ), np.log( self.zoom_range[1] ) ) ) zoom_y = np.exp( random.gauss( np.log( self.zoom_range[0] ), np.log( self.zoom_range[1] ) ) ) zoom_z = np.exp( random.gauss( np.log( self.zoom_range[0] ), np.log( self.zoom_range[1] ) ) ) self.params = (zoom_x, zoom_y, zoom_z) tx = Zoom3D((zoom_x,zoom_y,zoom_z), reference=self.reference, lazy=self.lazy) return tx.transform(X,y)
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Transform an image using an Affine transform with zoom parameters randomly generated from the user-specified range. Return the transform if X=None. Arguments --------- X : ANTsImage Image to transform y : ANTsImage (optional) Another image to transform Returns ------- ANTsImage if y is None, else a tuple of ANTsImage types Examples -------- >>> import ants >>> img = ants.image_read(ants.get_data('ch2')) >>> tx = ants.contrib.RandomZoom3D(zoom_range=(0.8,0.9)) >>> img2 = tx.transform(img)
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638020af2cdfc5ff4bdb9809ffe67aa505727a3b
https://github.com/ANTsX/ANTsPy/blob/638020af2cdfc5ff4bdb9809ffe67aa505727a3b/ants/contrib/sampling/affine3d.py#L629-L670
251,048
ANTsX/ANTsPy
ants/contrib/sampling/affine2d.py
Translate2D.transform
def transform(self, X=None, y=None): """ Transform an image using an Affine transform with the given translation parameters. Return the transform if X=None. Arguments --------- X : ANTsImage Image to transform y : ANTsImage (optional) Another image to transform Returns ------- ANTsImage if y is None, else a tuple of ANTsImage types Examples -------- >>> import ants >>> img = ants.image_read(ants.get_data('r16')) >>> tx = ants.contrib.Translate2D(translation=(10,0)) >>> img2_x = tx.transform(img) >>> tx = ants.contrib.Translate2D(translation=(-10,0)) # other direction >>> img2_x = tx.transform(img) >>> tx = ants.contrib.Translate2D(translation=(0,10)) >>> img2_z = tx.transform(img) >>> tx = ants.contrib.Translate2D(translation=(10,10)) >>> img2 = tx.transform(img) """ # convert to radians and unpack translation_x, translation_y = self.translation translation_matrix = np.array([[1, 0, translation_x], [0, 1, translation_y]]) self.tx.set_parameters(translation_matrix) if self.lazy or X is None: return self.tx else: return self.tx.apply_to_image(X, reference=self.reference)
python
def transform(self, X=None, y=None): # convert to radians and unpack translation_x, translation_y = self.translation translation_matrix = np.array([[1, 0, translation_x], [0, 1, translation_y]]) self.tx.set_parameters(translation_matrix) if self.lazy or X is None: return self.tx else: return self.tx.apply_to_image(X, reference=self.reference)
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Transform an image using an Affine transform with the given translation parameters. Return the transform if X=None. Arguments --------- X : ANTsImage Image to transform y : ANTsImage (optional) Another image to transform Returns ------- ANTsImage if y is None, else a tuple of ANTsImage types Examples -------- >>> import ants >>> img = ants.image_read(ants.get_data('r16')) >>> tx = ants.contrib.Translate2D(translation=(10,0)) >>> img2_x = tx.transform(img) >>> tx = ants.contrib.Translate2D(translation=(-10,0)) # other direction >>> img2_x = tx.transform(img) >>> tx = ants.contrib.Translate2D(translation=(0,10)) >>> img2_z = tx.transform(img) >>> tx = ants.contrib.Translate2D(translation=(10,10)) >>> img2 = tx.transform(img)
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638020af2cdfc5ff4bdb9809ffe67aa505727a3b
https://github.com/ANTsX/ANTsPy/blob/638020af2cdfc5ff4bdb9809ffe67aa505727a3b/ants/contrib/sampling/affine2d.py#L62-L101
251,049
ANTsX/ANTsPy
ants/contrib/sampling/affine2d.py
RandomTranslate2D.transform
def transform(self, X=None, y=None): """ Transform an image using an Affine transform with translation parameters randomly generated from the user-specified range. Return the transform if X=None. Arguments --------- X : ANTsImage Image to transform y : ANTsImage (optional) Another image to transform Returns ------- ANTsImage if y is None, else a tuple of ANTsImage types Examples -------- >>> import ants >>> img = ants.image_read(ants.get_data('r16')) >>> tx = ants.contrib.RandomShear2D(translation_range=(-10,10)) >>> img2 = tx.transform(img) """ # random draw in translation range translation_x = random.gauss(self.translation_range[0], self.translation_range[1]) translation_y = random.gauss(self.translation_range[0], self.translation_range[1]) self.params = (translation_x, translation_y) tx = Translate2D((translation_x, translation_y), reference=self.reference, lazy=self.lazy) return tx.transform(X,y)
python
def transform(self, X=None, y=None): # random draw in translation range translation_x = random.gauss(self.translation_range[0], self.translation_range[1]) translation_y = random.gauss(self.translation_range[0], self.translation_range[1]) self.params = (translation_x, translation_y) tx = Translate2D((translation_x, translation_y), reference=self.reference, lazy=self.lazy) return tx.transform(X,y)
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Transform an image using an Affine transform with translation parameters randomly generated from the user-specified range. Return the transform if X=None. Arguments --------- X : ANTsImage Image to transform y : ANTsImage (optional) Another image to transform Returns ------- ANTsImage if y is None, else a tuple of ANTsImage types Examples -------- >>> import ants >>> img = ants.image_read(ants.get_data('r16')) >>> tx = ants.contrib.RandomShear2D(translation_range=(-10,10)) >>> img2 = tx.transform(img)
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638020af2cdfc5ff4bdb9809ffe67aa505727a3b
https://github.com/ANTsX/ANTsPy/blob/638020af2cdfc5ff4bdb9809ffe67aa505727a3b/ants/contrib/sampling/affine2d.py#L141-L175
251,050
ANTsX/ANTsPy
ants/contrib/sampling/affine2d.py
Shear2D.transform
def transform(self, X=None, y=None): """ Transform an image using an Affine transform with the given shear parameters. Return the transform if X=None. Arguments --------- X : ANTsImage Image to transform y : ANTsImage (optional) Another image to transform Returns ------- ANTsImage if y is None, else a tuple of ANTsImage types Examples -------- >>> import ants >>> img = ants.image_read(ants.get_data('r16')) >>> tx = ants.contrib.Shear2D(shear=(10,0,0)) >>> img2_x = tx.transform(img)# x axis stays same >>> tx = ants.contrib.Shear2D(shear=(-10,0,0)) # other direction >>> img2_x = tx.transform(img)# x axis stays same >>> tx = ants.contrib.Shear2D(shear=(0,10,0)) >>> img2_y = tx.transform(img) # y axis stays same >>> tx = ants.contrib.Shear2D(shear=(0,0,10)) >>> img2_z = tx.transform(img) # z axis stays same >>> tx = ants.contrib.Shear2D(shear=(10,10,10)) >>> img2 = tx.transform(img) """ # convert to radians and unpack shear = [math.pi / 180 * s for s in self.shear] shear_x, shear_y = shear shear_matrix = np.array([[1, shear_x, 0], [shear_y, 1, 0]]) self.tx.set_parameters(shear_matrix) if self.lazy or X is None: return self.tx else: return self.tx.apply_to_image(X, reference=self.reference)
python
def transform(self, X=None, y=None): # convert to radians and unpack shear = [math.pi / 180 * s for s in self.shear] shear_x, shear_y = shear shear_matrix = np.array([[1, shear_x, 0], [shear_y, 1, 0]]) self.tx.set_parameters(shear_matrix) if self.lazy or X is None: return self.tx else: return self.tx.apply_to_image(X, reference=self.reference)
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Transform an image using an Affine transform with the given shear parameters. Return the transform if X=None. Arguments --------- X : ANTsImage Image to transform y : ANTsImage (optional) Another image to transform Returns ------- ANTsImage if y is None, else a tuple of ANTsImage types Examples -------- >>> import ants >>> img = ants.image_read(ants.get_data('r16')) >>> tx = ants.contrib.Shear2D(shear=(10,0,0)) >>> img2_x = tx.transform(img)# x axis stays same >>> tx = ants.contrib.Shear2D(shear=(-10,0,0)) # other direction >>> img2_x = tx.transform(img)# x axis stays same >>> tx = ants.contrib.Shear2D(shear=(0,10,0)) >>> img2_y = tx.transform(img) # y axis stays same >>> tx = ants.contrib.Shear2D(shear=(0,0,10)) >>> img2_z = tx.transform(img) # z axis stays same >>> tx = ants.contrib.Shear2D(shear=(10,10,10)) >>> img2 = tx.transform(img)
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638020af2cdfc5ff4bdb9809ffe67aa505727a3b
https://github.com/ANTsX/ANTsPy/blob/638020af2cdfc5ff4bdb9809ffe67aa505727a3b/ants/contrib/sampling/affine2d.py#L217-L259
251,051
ANTsX/ANTsPy
ants/contrib/sampling/affine2d.py
Zoom2D.transform
def transform(self, X=None, y=None): """ Transform an image using an Affine transform with the given zoom parameters. Return the transform if X=None. Arguments --------- X : ANTsImage Image to transform y : ANTsImage (optional) Another image to transform Returns ------- ANTsImage if y is None, else a tuple of ANTsImage types Examples -------- >>> import ants >>> img = ants.image_read(ants.get_data('r16')) >>> tx = ants.contrib.Zoom2D(zoom=(0.8,0.8,0.8)) >>> img2 = tx.transform(img) """ # unpack zoom range zoom_x, zoom_y= self.zoom self.params = (zoom_x, zoom_y) zoom_matrix = np.array([[zoom_x, 0, 0], [0, zoom_y, 0]]) self.tx.set_parameters(zoom_matrix) if self.lazy or X is None: return self.tx else: return self.tx.apply_to_image(X, reference=self.reference)
python
def transform(self, X=None, y=None): # unpack zoom range zoom_x, zoom_y= self.zoom self.params = (zoom_x, zoom_y) zoom_matrix = np.array([[zoom_x, 0, 0], [0, zoom_y, 0]]) self.tx.set_parameters(zoom_matrix) if self.lazy or X is None: return self.tx else: return self.tx.apply_to_image(X, reference=self.reference)
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Transform an image using an Affine transform with the given zoom parameters. Return the transform if X=None. Arguments --------- X : ANTsImage Image to transform y : ANTsImage (optional) Another image to transform Returns ------- ANTsImage if y is None, else a tuple of ANTsImage types Examples -------- >>> import ants >>> img = ants.image_read(ants.get_data('r16')) >>> tx = ants.contrib.Zoom2D(zoom=(0.8,0.8,0.8)) >>> img2 = tx.transform(img)
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638020af2cdfc5ff4bdb9809ffe67aa505727a3b
https://github.com/ANTsX/ANTsPy/blob/638020af2cdfc5ff4bdb9809ffe67aa505727a3b/ants/contrib/sampling/affine2d.py#L526-L560
251,052
ANTsX/ANTsPy
ants/segmentation/kelly_kapowski.py
kelly_kapowski
def kelly_kapowski(s, g, w, its=50, r=0.025, m=1.5, **kwargs): """ Compute cortical thickness using the DiReCT algorithm. Diffeomorphic registration-based cortical thickness based on probabilistic segmentation of an image. This is an optimization algorithm. Arguments --------- s : ANTsimage segmentation image g : ANTsImage gray matter probability image w : ANTsImage white matter probability image its : integer convergence params - controls iterations r : scalar gradient descent update parameter m : scalar gradient field smoothing parameter kwargs : keyword arguments anything else, see KellyKapowski help in ANTs Returns ------- ANTsImage Example ------- >>> import ants >>> img = ants.image_read( ants.get_ants_data('r16') ,2) >>> img = ants.resample_image(img, (64,64),1,0) >>> mask = ants.get_mask( img ) >>> segs = ants.kmeans_segmentation( img, k=3, kmask = mask) >>> thick = ants.kelly_kapowski(s=segs['segmentation'], g=segs['probabilityimages'][1], w=segs['probabilityimages'][2], its=45, r=0.5, m=1) """ if isinstance(s, iio.ANTsImage): s = s.clone('unsigned int') d = s.dimension outimg = g.clone() kellargs = {'d': d, 's': s, 'g': g, 'w': w, 'c': its, 'r': r, 'm': m, 'o': outimg} for k, v in kwargs.items(): kellargs[k] = v processed_kellargs = utils._int_antsProcessArguments(kellargs) libfn = utils.get_lib_fn('KellyKapowski') libfn(processed_kellargs) return outimg
python
def kelly_kapowski(s, g, w, its=50, r=0.025, m=1.5, **kwargs): if isinstance(s, iio.ANTsImage): s = s.clone('unsigned int') d = s.dimension outimg = g.clone() kellargs = {'d': d, 's': s, 'g': g, 'w': w, 'c': its, 'r': r, 'm': m, 'o': outimg} for k, v in kwargs.items(): kellargs[k] = v processed_kellargs = utils._int_antsProcessArguments(kellargs) libfn = utils.get_lib_fn('KellyKapowski') libfn(processed_kellargs) return outimg
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Compute cortical thickness using the DiReCT algorithm. Diffeomorphic registration-based cortical thickness based on probabilistic segmentation of an image. This is an optimization algorithm. Arguments --------- s : ANTsimage segmentation image g : ANTsImage gray matter probability image w : ANTsImage white matter probability image its : integer convergence params - controls iterations r : scalar gradient descent update parameter m : scalar gradient field smoothing parameter kwargs : keyword arguments anything else, see KellyKapowski help in ANTs Returns ------- ANTsImage Example ------- >>> import ants >>> img = ants.image_read( ants.get_ants_data('r16') ,2) >>> img = ants.resample_image(img, (64,64),1,0) >>> mask = ants.get_mask( img ) >>> segs = ants.kmeans_segmentation( img, k=3, kmask = mask) >>> thick = ants.kelly_kapowski(s=segs['segmentation'], g=segs['probabilityimages'][1], w=segs['probabilityimages'][2], its=45, r=0.5, m=1)
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638020af2cdfc5ff4bdb9809ffe67aa505727a3b
https://github.com/ANTsX/ANTsPy/blob/638020af2cdfc5ff4bdb9809ffe67aa505727a3b/ants/segmentation/kelly_kapowski.py#L11-L77
251,053
ANTsX/ANTsPy
ants/core/ants_transform_io.py
new_ants_transform
def new_ants_transform(precision='float', dimension=3, transform_type='AffineTransform', parameters=None): """ Create a new ANTsTransform ANTsR function: None Example ------- >>> import ants >>> tx = ants.new_ants_transform() """ libfn = utils.get_lib_fn('newAntsTransform%s%i' % (utils.short_ptype(precision), dimension)) itk_tx = libfn(precision, dimension, transform_type) ants_tx = tio.ANTsTransform(precision=precision, dimension=dimension, transform_type=transform_type, pointer=itk_tx) if parameters is not None: ants_tx.set_parameters(parameters) return ants_tx
python
def new_ants_transform(precision='float', dimension=3, transform_type='AffineTransform', parameters=None): libfn = utils.get_lib_fn('newAntsTransform%s%i' % (utils.short_ptype(precision), dimension)) itk_tx = libfn(precision, dimension, transform_type) ants_tx = tio.ANTsTransform(precision=precision, dimension=dimension, transform_type=transform_type, pointer=itk_tx) if parameters is not None: ants_tx.set_parameters(parameters) return ants_tx
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Create a new ANTsTransform ANTsR function: None Example ------- >>> import ants >>> tx = ants.new_ants_transform()
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638020af2cdfc5ff4bdb9809ffe67aa505727a3b
https://github.com/ANTsX/ANTsPy/blob/638020af2cdfc5ff4bdb9809ffe67aa505727a3b/ants/core/ants_transform_io.py#L16-L35
251,054
ANTsX/ANTsPy
ants/core/ants_transform_io.py
create_ants_transform
def create_ants_transform(transform_type='AffineTransform', precision='float', dimension=3, matrix=None, offset=None, center=None, translation=None, parameters=None, fixed_parameters=None, displacement_field=None, supported_types=False): """ Create and initialize an ANTsTransform ANTsR function: `createAntsrTransform` Arguments --------- transform_type : string type of transform(s) precision : string numerical precision dimension : integer spatial dimension of transform matrix : ndarray matrix for linear transforms offset : tuple/list offset for linear transforms center : tuple/list center for linear transforms translation : tuple/list translation for linear transforms parameters : ndarray/list array of parameters fixed_parameters : ndarray/list array of fixed parameters displacement_field : ANTsImage multichannel ANTsImage for non-linear transform supported_types : boolean flag that returns array of possible transforms types Returns ------- ANTsTransform or list of ANTsTransform types Example ------- >>> import ants >>> translation = (3,4,5) >>> tx = ants.create_ants_transform( type='Euler3DTransform', translation=translation ) """ def _check_arg(arg, dim=1): if arg is None: if dim == 1: return [] elif dim == 2: return [[]] elif isinstance(arg, np.ndarray): return arg.tolist() elif isinstance(arg, (tuple, list)): return list(arg) else: raise ValueError('Incompatible input argument') matrix = _check_arg(matrix, dim=2) offset = _check_arg(offset) center = _check_arg(center) translation = _check_arg(translation) parameters = _check_arg(parameters) fixed_parameters = _check_arg(fixed_parameters) matrix_offset_types = {'AffineTransform', 'CenteredAffineTransform', 'Euler2DTransform', 'Euler3DTransform', 'Rigid3DTransform', 'Rigid2DTransform', 'QuaternionRigidTransform', 'Similarity2DTransform', 'CenteredSimilarity2DTransform', 'Similarity3DTransform', 'CenteredRigid2DTransform', 'CenteredEuler3DTransform'} #user_matrix_types = {'Affine','CenteredAffine', # 'Euler', 'CenteredEuler', # 'Rigid', 'CenteredRigid', 'QuaternionRigid', # 'Similarity', 'CenteredSimilarity'} if supported_types: return set(list(matrix_offset_types) + ['DisplacementFieldTransform']) # Check for valid dimension if (dimension < 2) or (dimension > 4): raise ValueError('Unsupported dimension: %i' % dimension) # Check for valid precision precision_types = ('float', 'double') if precision not in precision_types: raise ValueError('Unsupported Precision %s' % str(precision)) # Check for supported transform type if (transform_type not in matrix_offset_types) and (transform_type != 'DisplacementFieldTransform'): raise ValueError('Unsupported type %s' % str(transform_type)) # Check parameters with type if (transform_type=='Euler3DTransform'): dimension = 3 elif (transform_type=='Euler2DTransform'): dimension = 2 elif (transform_type=='Rigid3DTransform'): dimension = 3 elif (transform_type=='QuaternionRigidTransform'): dimension = 3 elif (transform_type=='Rigid2DTransform'): dimension = 2 elif (transform_type=='CenteredRigid2DTransform'): dimension = 2 elif (transform_type=='CenteredEuler3DTransform'): dimension = 3 elif (transform_type=='Similarity3DTransform'): dimension = 3 elif (transform_type=='Similarity2DTransform'): dimension = 2 elif (transform_type=='CenteredSimilarity2DTransform'): dimension = 2 # If displacement field if displacement_field is not None: raise ValueError('Displacement field transform not currently supported') # itk_tx = transform_from_displacement_field(displacement_field) # return tio.ants_transform(itk_tx) # Transforms that derive from itk::MatrixOffsetTransformBase libfn = utils.get_lib_fn('matrixOffset%s%i' % (utils.short_ptype(precision), dimension)) itk_tx = libfn(transform_type, precision, dimension, matrix, offset, center, translation, parameters, fixed_parameters) return tio.ANTsTransform(precision=precision, dimension=dimension, transform_type=transform_type, pointer=itk_tx)
python
def create_ants_transform(transform_type='AffineTransform', precision='float', dimension=3, matrix=None, offset=None, center=None, translation=None, parameters=None, fixed_parameters=None, displacement_field=None, supported_types=False): def _check_arg(arg, dim=1): if arg is None: if dim == 1: return [] elif dim == 2: return [[]] elif isinstance(arg, np.ndarray): return arg.tolist() elif isinstance(arg, (tuple, list)): return list(arg) else: raise ValueError('Incompatible input argument') matrix = _check_arg(matrix, dim=2) offset = _check_arg(offset) center = _check_arg(center) translation = _check_arg(translation) parameters = _check_arg(parameters) fixed_parameters = _check_arg(fixed_parameters) matrix_offset_types = {'AffineTransform', 'CenteredAffineTransform', 'Euler2DTransform', 'Euler3DTransform', 'Rigid3DTransform', 'Rigid2DTransform', 'QuaternionRigidTransform', 'Similarity2DTransform', 'CenteredSimilarity2DTransform', 'Similarity3DTransform', 'CenteredRigid2DTransform', 'CenteredEuler3DTransform'} #user_matrix_types = {'Affine','CenteredAffine', # 'Euler', 'CenteredEuler', # 'Rigid', 'CenteredRigid', 'QuaternionRigid', # 'Similarity', 'CenteredSimilarity'} if supported_types: return set(list(matrix_offset_types) + ['DisplacementFieldTransform']) # Check for valid dimension if (dimension < 2) or (dimension > 4): raise ValueError('Unsupported dimension: %i' % dimension) # Check for valid precision precision_types = ('float', 'double') if precision not in precision_types: raise ValueError('Unsupported Precision %s' % str(precision)) # Check for supported transform type if (transform_type not in matrix_offset_types) and (transform_type != 'DisplacementFieldTransform'): raise ValueError('Unsupported type %s' % str(transform_type)) # Check parameters with type if (transform_type=='Euler3DTransform'): dimension = 3 elif (transform_type=='Euler2DTransform'): dimension = 2 elif (transform_type=='Rigid3DTransform'): dimension = 3 elif (transform_type=='QuaternionRigidTransform'): dimension = 3 elif (transform_type=='Rigid2DTransform'): dimension = 2 elif (transform_type=='CenteredRigid2DTransform'): dimension = 2 elif (transform_type=='CenteredEuler3DTransform'): dimension = 3 elif (transform_type=='Similarity3DTransform'): dimension = 3 elif (transform_type=='Similarity2DTransform'): dimension = 2 elif (transform_type=='CenteredSimilarity2DTransform'): dimension = 2 # If displacement field if displacement_field is not None: raise ValueError('Displacement field transform not currently supported') # itk_tx = transform_from_displacement_field(displacement_field) # return tio.ants_transform(itk_tx) # Transforms that derive from itk::MatrixOffsetTransformBase libfn = utils.get_lib_fn('matrixOffset%s%i' % (utils.short_ptype(precision), dimension)) itk_tx = libfn(transform_type, precision, dimension, matrix, offset, center, translation, parameters, fixed_parameters) return tio.ANTsTransform(precision=precision, dimension=dimension, transform_type=transform_type, pointer=itk_tx)
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Create and initialize an ANTsTransform ANTsR function: `createAntsrTransform` Arguments --------- transform_type : string type of transform(s) precision : string numerical precision dimension : integer spatial dimension of transform matrix : ndarray matrix for linear transforms offset : tuple/list offset for linear transforms center : tuple/list center for linear transforms translation : tuple/list translation for linear transforms parameters : ndarray/list array of parameters fixed_parameters : ndarray/list array of fixed parameters displacement_field : ANTsImage multichannel ANTsImage for non-linear transform supported_types : boolean flag that returns array of possible transforms types Returns ------- ANTsTransform or list of ANTsTransform types Example ------- >>> import ants >>> translation = (3,4,5) >>> tx = ants.create_ants_transform( type='Euler3DTransform', translation=translation )
[ "Create", "and", "initialize", "an", "ANTsTransform" ]
638020af2cdfc5ff4bdb9809ffe67aa505727a3b
https://github.com/ANTsX/ANTsPy/blob/638020af2cdfc5ff4bdb9809ffe67aa505727a3b/ants/core/ants_transform_io.py#L38-L187
251,055
ANTsX/ANTsPy
ants/core/ants_transform_io.py
read_transform
def read_transform(filename, dimension=2, precision='float'): """ Read a transform from file ANTsR function: `readAntsrTransform` Arguments --------- filename : string filename of transform dimension : integer spatial dimension of transform precision : string numerical precision of transform Returns ------- ANTsTransform Example ------- >>> import ants >>> tx = ants.new_ants_transform(dimension=2) >>> tx.set_parameters((0.9,0,0,1.1,10,11)) >>> ants.write_transform(tx, '~/desktop/tx.mat') >>> tx2 = ants.read_transform('~/desktop/tx.mat') """ filename = os.path.expanduser(filename) if not os.path.exists(filename): raise ValueError('filename does not exist!') libfn1 = utils.get_lib_fn('getTransformDimensionFromFile') dimension = libfn1(filename) libfn2 = utils.get_lib_fn('getTransformNameFromFile') transform_type = libfn2(filename) libfn3 = utils.get_lib_fn('readTransform%s%i' % (utils.short_ptype(precision), dimension)) itk_tx = libfn3(filename, dimension, precision) return tio.ANTsTransform(precision=precision, dimension=dimension, transform_type=transform_type, pointer=itk_tx)
python
def read_transform(filename, dimension=2, precision='float'): filename = os.path.expanduser(filename) if not os.path.exists(filename): raise ValueError('filename does not exist!') libfn1 = utils.get_lib_fn('getTransformDimensionFromFile') dimension = libfn1(filename) libfn2 = utils.get_lib_fn('getTransformNameFromFile') transform_type = libfn2(filename) libfn3 = utils.get_lib_fn('readTransform%s%i' % (utils.short_ptype(precision), dimension)) itk_tx = libfn3(filename, dimension, precision) return tio.ANTsTransform(precision=precision, dimension=dimension, transform_type=transform_type, pointer=itk_tx)
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Read a transform from file ANTsR function: `readAntsrTransform` Arguments --------- filename : string filename of transform dimension : integer spatial dimension of transform precision : string numerical precision of transform Returns ------- ANTsTransform Example ------- >>> import ants >>> tx = ants.new_ants_transform(dimension=2) >>> tx.set_parameters((0.9,0,0,1.1,10,11)) >>> ants.write_transform(tx, '~/desktop/tx.mat') >>> tx2 = ants.read_transform('~/desktop/tx.mat')
[ "Read", "a", "transform", "from", "file" ]
638020af2cdfc5ff4bdb9809ffe67aa505727a3b
https://github.com/ANTsX/ANTsPy/blob/638020af2cdfc5ff4bdb9809ffe67aa505727a3b/ants/core/ants_transform_io.py#L223-L266
251,056
ANTsX/ANTsPy
ants/core/ants_transform_io.py
write_transform
def write_transform(transform, filename): """ Write ANTsTransform to file ANTsR function: `writeAntsrTransform` Arguments --------- transform : ANTsTransform transform to save filename : string filename of transform (file extension is ".mat" for affine transforms) Returns ------- N/A Example ------- >>> import ants >>> tx = ants.new_ants_transform(dimension=2) >>> tx.set_parameters((0.9,0,0,1.1,10,11)) >>> ants.write_transform(tx, '~/desktop/tx.mat') >>> tx2 = ants.read_transform('~/desktop/tx.mat') """ filename = os.path.expanduser(filename) libfn = utils.get_lib_fn('writeTransform%s' % (transform._libsuffix)) libfn(transform.pointer, filename)
python
def write_transform(transform, filename): filename = os.path.expanduser(filename) libfn = utils.get_lib_fn('writeTransform%s' % (transform._libsuffix)) libfn(transform.pointer, filename)
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Write ANTsTransform to file ANTsR function: `writeAntsrTransform` Arguments --------- transform : ANTsTransform transform to save filename : string filename of transform (file extension is ".mat" for affine transforms) Returns ------- N/A Example ------- >>> import ants >>> tx = ants.new_ants_transform(dimension=2) >>> tx.set_parameters((0.9,0,0,1.1,10,11)) >>> ants.write_transform(tx, '~/desktop/tx.mat') >>> tx2 = ants.read_transform('~/desktop/tx.mat')
[ "Write", "ANTsTransform", "to", "file" ]
638020af2cdfc5ff4bdb9809ffe67aa505727a3b
https://github.com/ANTsX/ANTsPy/blob/638020af2cdfc5ff4bdb9809ffe67aa505727a3b/ants/core/ants_transform_io.py#L269-L297
251,057
ANTsX/ANTsPy
ants/registration/reflect_image.py
reflect_image
def reflect_image(image, axis=None, tx=None, metric='mattes'): """ Reflect an image along an axis ANTsR function: `reflectImage` Arguments --------- image : ANTsImage image to reflect axis : integer (optional) which dimension to reflect across, numbered from 0 to imageDimension-1 tx : string (optional) transformation type to estimate after reflection metric : string similarity metric for image registration. see antsRegistration. Returns ------- ANTsImage Example ------- >>> import ants >>> fi = ants.image_read( ants.get_ants_data('r16'), 'float' ) >>> axis = 2 >>> asym = ants.reflect_image(fi, axis, 'Affine')['warpedmovout'] >>> asym = asym - fi """ if axis is None: axis = image.dimension - 1 if (axis > image.dimension) or (axis < 0): axis = image.dimension - 1 rflct = mktemp(suffix='.mat') libfn = utils.get_lib_fn('reflectionMatrix%s'%image._libsuffix) libfn(image.pointer, axis, rflct) if tx is not None: rfi = registration(image, image, type_of_transform=tx, syn_metric=metric, outprefix=mktemp(), initial_transform=rflct) return rfi else: return apply_transforms(image, image, rflct)
python
def reflect_image(image, axis=None, tx=None, metric='mattes'): if axis is None: axis = image.dimension - 1 if (axis > image.dimension) or (axis < 0): axis = image.dimension - 1 rflct = mktemp(suffix='.mat') libfn = utils.get_lib_fn('reflectionMatrix%s'%image._libsuffix) libfn(image.pointer, axis, rflct) if tx is not None: rfi = registration(image, image, type_of_transform=tx, syn_metric=metric, outprefix=mktemp(), initial_transform=rflct) return rfi else: return apply_transforms(image, image, rflct)
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Reflect an image along an axis ANTsR function: `reflectImage` Arguments --------- image : ANTsImage image to reflect axis : integer (optional) which dimension to reflect across, numbered from 0 to imageDimension-1 tx : string (optional) transformation type to estimate after reflection metric : string similarity metric for image registration. see antsRegistration. Returns ------- ANTsImage Example ------- >>> import ants >>> fi = ants.image_read( ants.get_ants_data('r16'), 'float' ) >>> axis = 2 >>> asym = ants.reflect_image(fi, axis, 'Affine')['warpedmovout'] >>> asym = asym - fi
[ "Reflect", "an", "image", "along", "an", "axis" ]
638020af2cdfc5ff4bdb9809ffe67aa505727a3b
https://github.com/ANTsX/ANTsPy/blob/638020af2cdfc5ff4bdb9809ffe67aa505727a3b/ants/registration/reflect_image.py#L12-L61
251,058
ANTsX/ANTsPy
ants/contrib/bids/cohort.py
BIDSCohort.create_sampler
def create_sampler(self, inputs, targets, input_reader=None, target_reader=None, input_transform=None, target_transform=None, co_transform=None, input_return_processor=None, target_return_processor=None, co_return_processor=None): """ Create a BIDSSampler that can be used to generate infinite augmented samples """ pass
python
def create_sampler(self, inputs, targets, input_reader=None, target_reader=None, input_transform=None, target_transform=None, co_transform=None, input_return_processor=None, target_return_processor=None, co_return_processor=None): pass
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Create a BIDSSampler that can be used to generate infinite augmented samples
[ "Create", "a", "BIDSSampler", "that", "can", "be", "used", "to", "generate", "infinite", "augmented", "samples" ]
638020af2cdfc5ff4bdb9809ffe67aa505727a3b
https://github.com/ANTsX/ANTsPy/blob/638020af2cdfc5ff4bdb9809ffe67aa505727a3b/ants/contrib/bids/cohort.py#L88-L94
251,059
ANTsX/ANTsPy
ants/utils/slice_image.py
slice_image
def slice_image(image, axis=None, idx=None): """ Slice an image. Example ------- >>> import ants >>> mni = ants.image_read(ants.get_data('mni')) >>> mni2 = ants.slice_image(mni, axis=1, idx=100) """ if image.dimension < 3: raise ValueError('image must have at least 3 dimensions') inpixeltype = image.pixeltype ndim = image.dimension if image.pixeltype != 'float': image = image.clone('float') libfn = utils.get_lib_fn('sliceImageF%i' % ndim) itkimage = libfn(image.pointer, axis, idx) return iio.ANTsImage(pixeltype='float', dimension=ndim-1, components=image.components, pointer=itkimage).clone(inpixeltype)
python
def slice_image(image, axis=None, idx=None): if image.dimension < 3: raise ValueError('image must have at least 3 dimensions') inpixeltype = image.pixeltype ndim = image.dimension if image.pixeltype != 'float': image = image.clone('float') libfn = utils.get_lib_fn('sliceImageF%i' % ndim) itkimage = libfn(image.pointer, axis, idx) return iio.ANTsImage(pixeltype='float', dimension=ndim-1, components=image.components, pointer=itkimage).clone(inpixeltype)
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Slice an image. Example ------- >>> import ants >>> mni = ants.image_read(ants.get_data('mni')) >>> mni2 = ants.slice_image(mni, axis=1, idx=100)
[ "Slice", "an", "image", "." ]
638020af2cdfc5ff4bdb9809ffe67aa505727a3b
https://github.com/ANTsX/ANTsPy/blob/638020af2cdfc5ff4bdb9809ffe67aa505727a3b/ants/utils/slice_image.py#L10-L32
251,060
ANTsX/ANTsPy
ants/utils/pad_image.py
pad_image
def pad_image(image, shape=None, pad_width=None, value=0.0, return_padvals=False): """ Pad an image to have the given shape or to be isotropic. Arguments --------- image : ANTsImage image to pad shape : tuple - if shape is given, the image will be padded in each dimension until it has this shape - if shape is not given, the image will be padded along each dimension to match the largest existing dimension so that it has isotropic dimension pad_width : list of pad_value : scalar value with which image will be padded Example ------- >>> import ants >>> img = ants.image_read(ants.get_data('r16')) >>> img2 = ants.pad_image(img, shape=(300,300)) >>> mni = ants.image_read(ants.get_data('mni')) >>> mni2 = ants.pad_image(mni) >>> mni3 = ants.pad_image(mni, pad_width=[(0,4),(0,4),(0,4)]) >>> mni4 = ants.pad_image(mni, pad_width=(4,4,4)) """ inpixeltype = image.pixeltype ndim = image.dimension if image.pixeltype != 'float': image = image.clone('float') if pad_width is None: if shape is None: shape = [max(image.shape)] * image.dimension lower_pad_vals = [math.floor(max(ns-os,0)/2) for os,ns in zip(image.shape, shape)] upper_pad_vals = [math.ceil(max(ns-os,0)/2) for os,ns in zip(image.shape, shape)] else: if shape is not None: raise ValueError('Cannot give both `shape` and `pad_width`. Pick one!') if len(pad_width) != image.dimension: raise ValueError('Must give pad width for each image dimension') lower_pad_vals = [] upper_pad_vals = [] for p in pad_width: if isinstance(p, (list, tuple)): lower_pad_vals.append(p[0]) upper_pad_vals.append(p[1]) else: lower_pad_vals.append(math.floor(p/2)) upper_pad_vals.append(math.ceil(p/2)) libfn = utils.get_lib_fn('padImageF%i' % ndim) itkimage = libfn(image.pointer, lower_pad_vals, upper_pad_vals, value) new_image = iio.ANTsImage(pixeltype='float', dimension=ndim, components=image.components, pointer=itkimage).clone(inpixeltype) if return_padvals: return new_image, lower_pad_vals, upper_pad_vals else: return new_image
python
def pad_image(image, shape=None, pad_width=None, value=0.0, return_padvals=False): inpixeltype = image.pixeltype ndim = image.dimension if image.pixeltype != 'float': image = image.clone('float') if pad_width is None: if shape is None: shape = [max(image.shape)] * image.dimension lower_pad_vals = [math.floor(max(ns-os,0)/2) for os,ns in zip(image.shape, shape)] upper_pad_vals = [math.ceil(max(ns-os,0)/2) for os,ns in zip(image.shape, shape)] else: if shape is not None: raise ValueError('Cannot give both `shape` and `pad_width`. Pick one!') if len(pad_width) != image.dimension: raise ValueError('Must give pad width for each image dimension') lower_pad_vals = [] upper_pad_vals = [] for p in pad_width: if isinstance(p, (list, tuple)): lower_pad_vals.append(p[0]) upper_pad_vals.append(p[1]) else: lower_pad_vals.append(math.floor(p/2)) upper_pad_vals.append(math.ceil(p/2)) libfn = utils.get_lib_fn('padImageF%i' % ndim) itkimage = libfn(image.pointer, lower_pad_vals, upper_pad_vals, value) new_image = iio.ANTsImage(pixeltype='float', dimension=ndim, components=image.components, pointer=itkimage).clone(inpixeltype) if return_padvals: return new_image, lower_pad_vals, upper_pad_vals else: return new_image
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Pad an image to have the given shape or to be isotropic. Arguments --------- image : ANTsImage image to pad shape : tuple - if shape is given, the image will be padded in each dimension until it has this shape - if shape is not given, the image will be padded along each dimension to match the largest existing dimension so that it has isotropic dimension pad_width : list of pad_value : scalar value with which image will be padded Example ------- >>> import ants >>> img = ants.image_read(ants.get_data('r16')) >>> img2 = ants.pad_image(img, shape=(300,300)) >>> mni = ants.image_read(ants.get_data('mni')) >>> mni2 = ants.pad_image(mni) >>> mni3 = ants.pad_image(mni, pad_width=[(0,4),(0,4),(0,4)]) >>> mni4 = ants.pad_image(mni, pad_width=(4,4,4))
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638020af2cdfc5ff4bdb9809ffe67aa505727a3b
https://github.com/ANTsX/ANTsPy/blob/638020af2cdfc5ff4bdb9809ffe67aa505727a3b/ants/utils/pad_image.py#L10-L75
251,061
ANTsX/ANTsPy
ants/core/ants_metric.py
ANTsImageToImageMetric.set_fixed_image
def set_fixed_image(self, image): """ Set Fixed ANTsImage for metric """ if not isinstance(image, iio.ANTsImage): raise ValueError('image must be ANTsImage type') if image.dimension != self.dimension: raise ValueError('image dim (%i) does not match metric dim (%i)' % (image.dimension, self.dimension)) self._metric.setFixedImage(image.pointer, False) self.fixed_image = image
python
def set_fixed_image(self, image): if not isinstance(image, iio.ANTsImage): raise ValueError('image must be ANTsImage type') if image.dimension != self.dimension: raise ValueError('image dim (%i) does not match metric dim (%i)' % (image.dimension, self.dimension)) self._metric.setFixedImage(image.pointer, False) self.fixed_image = image
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Set Fixed ANTsImage for metric
[ "Set", "Fixed", "ANTsImage", "for", "metric" ]
638020af2cdfc5ff4bdb9809ffe67aa505727a3b
https://github.com/ANTsX/ANTsPy/blob/638020af2cdfc5ff4bdb9809ffe67aa505727a3b/ants/core/ants_metric.py#L48-L59
251,062
ANTsX/ANTsPy
ants/core/ants_metric.py
ANTsImageToImageMetric.set_moving_image
def set_moving_image(self, image): """ Set Moving ANTsImage for metric """ if not isinstance(image, iio.ANTsImage): raise ValueError('image must be ANTsImage type') if image.dimension != self.dimension: raise ValueError('image dim (%i) does not match metric dim (%i)' % (image.dimension, self.dimension)) self._metric.setMovingImage(image.pointer, False) self.moving_image = image
python
def set_moving_image(self, image): if not isinstance(image, iio.ANTsImage): raise ValueError('image must be ANTsImage type') if image.dimension != self.dimension: raise ValueError('image dim (%i) does not match metric dim (%i)' % (image.dimension, self.dimension)) self._metric.setMovingImage(image.pointer, False) self.moving_image = image
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Set Moving ANTsImage for metric
[ "Set", "Moving", "ANTsImage", "for", "metric" ]
638020af2cdfc5ff4bdb9809ffe67aa505727a3b
https://github.com/ANTsX/ANTsPy/blob/638020af2cdfc5ff4bdb9809ffe67aa505727a3b/ants/core/ants_metric.py#L74-L85
251,063
ANTsX/ANTsPy
ants/utils/image_to_cluster_images.py
image_to_cluster_images
def image_to_cluster_images(image, min_cluster_size=50, min_thresh=1e-06, max_thresh=1): """ Converts an image to several independent images. Produces a unique image for each connected component 1 through N of size > min_cluster_size ANTsR function: `image2ClusterImages` Arguments --------- image : ANTsImage input image min_cluster_size : integer throw away clusters smaller than this value min_thresh : scalar threshold to a statistical map max_thresh : scalar threshold to a statistical map Returns ------- list of ANTsImage types Example ------- >>> import ants >>> image = ants.image_read(ants.get_ants_data('r16')) >>> image = ants.threshold_image(image, 1, 1e15) >>> image_cluster_list = ants.image_to_cluster_images(image) """ if not isinstance(image, iio.ANTsImage): raise ValueError('image must be ANTsImage type') clust = label_clusters(image, min_cluster_size, min_thresh, max_thresh) labs = np.unique(clust[clust > 0]) clustlist = [] for i in range(len(labs)): labimage = image.clone() labimage[clust != labs[i]] = 0 clustlist.append(labimage) return clustlist
python
def image_to_cluster_images(image, min_cluster_size=50, min_thresh=1e-06, max_thresh=1): if not isinstance(image, iio.ANTsImage): raise ValueError('image must be ANTsImage type') clust = label_clusters(image, min_cluster_size, min_thresh, max_thresh) labs = np.unique(clust[clust > 0]) clustlist = [] for i in range(len(labs)): labimage = image.clone() labimage[clust != labs[i]] = 0 clustlist.append(labimage) return clustlist
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Converts an image to several independent images. Produces a unique image for each connected component 1 through N of size > min_cluster_size ANTsR function: `image2ClusterImages` Arguments --------- image : ANTsImage input image min_cluster_size : integer throw away clusters smaller than this value min_thresh : scalar threshold to a statistical map max_thresh : scalar threshold to a statistical map Returns ------- list of ANTsImage types Example ------- >>> import ants >>> image = ants.image_read(ants.get_ants_data('r16')) >>> image = ants.threshold_image(image, 1, 1e15) >>> image_cluster_list = ants.image_to_cluster_images(image)
[ "Converts", "an", "image", "to", "several", "independent", "images", "." ]
638020af2cdfc5ff4bdb9809ffe67aa505727a3b
https://github.com/ANTsX/ANTsPy/blob/638020af2cdfc5ff4bdb9809ffe67aa505727a3b/ants/utils/image_to_cluster_images.py#L9-L51
251,064
ANTsX/ANTsPy
ants/utils/threshold_image.py
threshold_image
def threshold_image(image, low_thresh=None, high_thresh=None, inval=1, outval=0, binary=True): """ Converts a scalar image into a binary image by thresholding operations ANTsR function: `thresholdImage` Arguments --------- image : ANTsImage Input image to operate on low_thresh : scalar (optional) Lower edge of threshold window hight_thresh : scalar (optional) Higher edge of threshold window inval : scalar Output value for image voxels in between lothresh and hithresh outval : scalar Output value for image voxels lower than lothresh or higher than hithresh binary : boolean if true, returns binary thresholded image if false, return binary thresholded image multiplied by original image Returns ------- ANTsImage Example ------- >>> import ants >>> image = ants.image_read( ants.get_ants_data('r16') ) >>> timage = ants.threshold_image(image, 0.5, 1e15) """ if high_thresh is None: high_thresh = image.max() + 0.01 if low_thresh is None: low_thresh = image.min() - 0.01 dim = image.dimension outimage = image.clone() args = [dim, image, outimage, low_thresh, high_thresh, inval, outval] processed_args = _int_antsProcessArguments(args) libfn = utils.get_lib_fn('ThresholdImage') libfn(processed_args) if binary: return outimage else: return outimage*image
python
def threshold_image(image, low_thresh=None, high_thresh=None, inval=1, outval=0, binary=True): if high_thresh is None: high_thresh = image.max() + 0.01 if low_thresh is None: low_thresh = image.min() - 0.01 dim = image.dimension outimage = image.clone() args = [dim, image, outimage, low_thresh, high_thresh, inval, outval] processed_args = _int_antsProcessArguments(args) libfn = utils.get_lib_fn('ThresholdImage') libfn(processed_args) if binary: return outimage else: return outimage*image
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Converts a scalar image into a binary image by thresholding operations ANTsR function: `thresholdImage` Arguments --------- image : ANTsImage Input image to operate on low_thresh : scalar (optional) Lower edge of threshold window hight_thresh : scalar (optional) Higher edge of threshold window inval : scalar Output value for image voxels in between lothresh and hithresh outval : scalar Output value for image voxels lower than lothresh or higher than hithresh binary : boolean if true, returns binary thresholded image if false, return binary thresholded image multiplied by original image Returns ------- ANTsImage Example ------- >>> import ants >>> image = ants.image_read( ants.get_ants_data('r16') ) >>> timage = ants.threshold_image(image, 0.5, 1e15)
[ "Converts", "a", "scalar", "image", "into", "a", "binary", "image", "by", "thresholding", "operations" ]
638020af2cdfc5ff4bdb9809ffe67aa505727a3b
https://github.com/ANTsX/ANTsPy/blob/638020af2cdfc5ff4bdb9809ffe67aa505727a3b/ants/utils/threshold_image.py#L10-L60
251,065
ANTsX/ANTsPy
ants/registration/symmetrize_image.py
symmetrize_image
def symmetrize_image(image): """ Use registration and reflection to make an image symmetric ANTsR function: N/A Arguments --------- image : ANTsImage image to make symmetric Returns ------- ANTsImage Example ------- >>> import ants >>> image = ants.image_read( ants.get_ants_data('r16') , 'float') >>> simage = ants.symimage(image) """ imager = reflect_image(image, axis=0) imageavg = imager * 0.5 + image for i in range(5): w1 = registration(imageavg, image, type_of_transform='SyN') w2 = registration(imageavg, imager, type_of_transform='SyN') xavg = w1['warpedmovout']*0.5 + w2['warpedmovout']*0.5 nada1 = apply_transforms(image, image, w1['fwdtransforms'], compose=w1['fwdtransforms'][0]) nada2 = apply_transforms(image, image, w2['fwdtransforms'], compose=w2['fwdtransforms'][0]) wavg = (iio.image_read(nada1) + iio.image_read(nada2)) * (-0.5) wavgfn = mktemp(suffix='.nii.gz') iio.image_write(wavg, wavgfn) xavg = apply_transforms(image, imageavg, wavgfn) return xavg
python
def symmetrize_image(image): imager = reflect_image(image, axis=0) imageavg = imager * 0.5 + image for i in range(5): w1 = registration(imageavg, image, type_of_transform='SyN') w2 = registration(imageavg, imager, type_of_transform='SyN') xavg = w1['warpedmovout']*0.5 + w2['warpedmovout']*0.5 nada1 = apply_transforms(image, image, w1['fwdtransforms'], compose=w1['fwdtransforms'][0]) nada2 = apply_transforms(image, image, w2['fwdtransforms'], compose=w2['fwdtransforms'][0]) wavg = (iio.image_read(nada1) + iio.image_read(nada2)) * (-0.5) wavgfn = mktemp(suffix='.nii.gz') iio.image_write(wavg, wavgfn) xavg = apply_transforms(image, imageavg, wavgfn) return xavg
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Use registration and reflection to make an image symmetric ANTsR function: N/A Arguments --------- image : ANTsImage image to make symmetric Returns ------- ANTsImage Example ------- >>> import ants >>> image = ants.image_read( ants.get_ants_data('r16') , 'float') >>> simage = ants.symimage(image)
[ "Use", "registration", "and", "reflection", "to", "make", "an", "image", "symmetric" ]
638020af2cdfc5ff4bdb9809ffe67aa505727a3b
https://github.com/ANTsX/ANTsPy/blob/638020af2cdfc5ff4bdb9809ffe67aa505727a3b/ants/registration/symmetrize_image.py#L13-L49
251,066
freakboy3742/pyxero
xero/basemanager.py
BaseManager._get_attachments
def _get_attachments(self, id): """Retrieve a list of attachments associated with this Xero object.""" uri = '/'.join([self.base_url, self.name, id, 'Attachments']) + '/' return uri, {}, 'get', None, None, False
python
def _get_attachments(self, id): uri = '/'.join([self.base_url, self.name, id, 'Attachments']) + '/' return uri, {}, 'get', None, None, False
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Retrieve a list of attachments associated with this Xero object.
[ "Retrieve", "a", "list", "of", "attachments", "associated", "with", "this", "Xero", "object", "." ]
5566f17fa06ed1f2fb9426c112951a72276b0f9a
https://github.com/freakboy3742/pyxero/blob/5566f17fa06ed1f2fb9426c112951a72276b0f9a/xero/basemanager.py#L240-L243
251,067
freakboy3742/pyxero
xero/basemanager.py
BaseManager._put_attachment_data
def _put_attachment_data(self, id, filename, data, content_type, include_online=False): """Upload an attachment to the Xero object.""" uri = '/'.join([self.base_url, self.name, id, 'Attachments', filename]) params = {'IncludeOnline': 'true'} if include_online else {} headers = {'Content-Type': content_type, 'Content-Length': str(len(data))} return uri, params, 'put', data, headers, False
python
def _put_attachment_data(self, id, filename, data, content_type, include_online=False): uri = '/'.join([self.base_url, self.name, id, 'Attachments', filename]) params = {'IncludeOnline': 'true'} if include_online else {} headers = {'Content-Type': content_type, 'Content-Length': str(len(data))} return uri, params, 'put', data, headers, False
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Upload an attachment to the Xero object.
[ "Upload", "an", "attachment", "to", "the", "Xero", "object", "." ]
5566f17fa06ed1f2fb9426c112951a72276b0f9a
https://github.com/freakboy3742/pyxero/blob/5566f17fa06ed1f2fb9426c112951a72276b0f9a/xero/basemanager.py#L280-L285
251,068
freakboy3742/pyxero
examples/partner_oauth_flow/runserver.py
PartnerCredentialsHandler.page_response
def page_response(self, title='', body=''): """ Helper to render an html page with dynamic content """ f = StringIO() f.write('<!DOCTYPE html>\n') f.write('<html>\n') f.write('<head><title>{}</title><head>\n'.format(title)) f.write('<body>\n<h2>{}</h2>\n'.format(title)) f.write('<div class="content">{}</div>\n'.format(body)) f.write('</body>\n</html>\n') length = f.tell() f.seek(0) self.send_response(200) encoding = sys.getfilesystemencoding() self.send_header("Content-type", "text/html; charset=%s" % encoding) self.send_header("Content-Length", str(length)) self.end_headers() self.copyfile(f, self.wfile) f.close()
python
def page_response(self, title='', body=''): f = StringIO() f.write('<!DOCTYPE html>\n') f.write('<html>\n') f.write('<head><title>{}</title><head>\n'.format(title)) f.write('<body>\n<h2>{}</h2>\n'.format(title)) f.write('<div class="content">{}</div>\n'.format(body)) f.write('</body>\n</html>\n') length = f.tell() f.seek(0) self.send_response(200) encoding = sys.getfilesystemencoding() self.send_header("Content-type", "text/html; charset=%s" % encoding) self.send_header("Content-Length", str(length)) self.end_headers() self.copyfile(f, self.wfile) f.close()
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Helper to render an html page with dynamic content
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5566f17fa06ed1f2fb9426c112951a72276b0f9a
https://github.com/freakboy3742/pyxero/blob/5566f17fa06ed1f2fb9426c112951a72276b0f9a/examples/partner_oauth_flow/runserver.py#L22-L41
251,069
freakboy3742/pyxero
examples/partner_oauth_flow/runserver.py
PartnerCredentialsHandler.redirect_response
def redirect_response(self, url, permanent=False): """ Generate redirect response """ if permanent: self.send_response(301) else: self.send_response(302) self.send_header("Location", url) self.end_headers()
python
def redirect_response(self, url, permanent=False): if permanent: self.send_response(301) else: self.send_response(302) self.send_header("Location", url) self.end_headers()
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Generate redirect response
[ "Generate", "redirect", "response" ]
5566f17fa06ed1f2fb9426c112951a72276b0f9a
https://github.com/freakboy3742/pyxero/blob/5566f17fa06ed1f2fb9426c112951a72276b0f9a/examples/partner_oauth_flow/runserver.py#L43-L52
251,070
freakboy3742/pyxero
xero/auth.py
PublicCredentials._init_credentials
def _init_credentials(self, oauth_token, oauth_token_secret): "Depending on the state passed in, get self._oauth up and running" if oauth_token and oauth_token_secret: if self.verified: # If provided, this is a fully verified set of # credentials. Store the oauth_token and secret # and initialize OAuth around those self._init_oauth(oauth_token, oauth_token_secret) else: # If provided, we are reconstructing an initalized # (but non-verified) set of public credentials. self.oauth_token = oauth_token self.oauth_token_secret = oauth_token_secret else: # This is a brand new set of credentials - we need to generate # an oauth token so it's available for the url property. oauth = OAuth1( self.consumer_key, client_secret=self.consumer_secret, callback_uri=self.callback_uri, rsa_key=self.rsa_key, signature_method=self._signature_method ) url = self.base_url + REQUEST_TOKEN_URL headers = {'User-Agent': self.user_agent} response = requests.post(url=url, headers=headers, auth=oauth) self._process_oauth_response(response)
python
def _init_credentials(self, oauth_token, oauth_token_secret): "Depending on the state passed in, get self._oauth up and running" if oauth_token and oauth_token_secret: if self.verified: # If provided, this is a fully verified set of # credentials. Store the oauth_token and secret # and initialize OAuth around those self._init_oauth(oauth_token, oauth_token_secret) else: # If provided, we are reconstructing an initalized # (but non-verified) set of public credentials. self.oauth_token = oauth_token self.oauth_token_secret = oauth_token_secret else: # This is a brand new set of credentials - we need to generate # an oauth token so it's available for the url property. oauth = OAuth1( self.consumer_key, client_secret=self.consumer_secret, callback_uri=self.callback_uri, rsa_key=self.rsa_key, signature_method=self._signature_method ) url = self.base_url + REQUEST_TOKEN_URL headers = {'User-Agent': self.user_agent} response = requests.post(url=url, headers=headers, auth=oauth) self._process_oauth_response(response)
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Depending on the state passed in, get self._oauth up and running
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5566f17fa06ed1f2fb9426c112951a72276b0f9a
https://github.com/freakboy3742/pyxero/blob/5566f17fa06ed1f2fb9426c112951a72276b0f9a/xero/auth.py#L134-L163
251,071
freakboy3742/pyxero
xero/auth.py
PublicCredentials._init_oauth
def _init_oauth(self, oauth_token, oauth_token_secret): "Store and initialize a verified set of OAuth credentials" self.oauth_token = oauth_token self.oauth_token_secret = oauth_token_secret self._oauth = OAuth1( self.consumer_key, client_secret=self.consumer_secret, resource_owner_key=self.oauth_token, resource_owner_secret=self.oauth_token_secret, rsa_key=self.rsa_key, signature_method=self._signature_method )
python
def _init_oauth(self, oauth_token, oauth_token_secret): "Store and initialize a verified set of OAuth credentials" self.oauth_token = oauth_token self.oauth_token_secret = oauth_token_secret self._oauth = OAuth1( self.consumer_key, client_secret=self.consumer_secret, resource_owner_key=self.oauth_token, resource_owner_secret=self.oauth_token_secret, rsa_key=self.rsa_key, signature_method=self._signature_method )
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Store and initialize a verified set of OAuth credentials
[ "Store", "and", "initialize", "a", "verified", "set", "of", "OAuth", "credentials" ]
5566f17fa06ed1f2fb9426c112951a72276b0f9a
https://github.com/freakboy3742/pyxero/blob/5566f17fa06ed1f2fb9426c112951a72276b0f9a/xero/auth.py#L165-L177
251,072
freakboy3742/pyxero
xero/auth.py
PublicCredentials._process_oauth_response
def _process_oauth_response(self, response): "Extracts the fields from an oauth response" if response.status_code == 200: credentials = parse_qs(response.text) # Initialize the oauth credentials self._init_oauth( credentials.get('oauth_token')[0], credentials.get('oauth_token_secret')[0] ) # If tokens are refreshable, we'll get a session handle self.oauth_session_handle = credentials.get( 'oauth_session_handle', [None])[0] # Calculate token/auth expiry oauth_expires_in = credentials.get( 'oauth_expires_in', [OAUTH_EXPIRY_SECONDS])[0] oauth_authorisation_expires_in = credentials.get( 'oauth_authorization_expires_in', [OAUTH_EXPIRY_SECONDS])[0] self.oauth_expires_at = datetime.datetime.now() + \ datetime.timedelta(seconds=int( oauth_expires_in)) self.oauth_authorization_expires_at = \ datetime.datetime.now() + \ datetime.timedelta(seconds=int( oauth_authorisation_expires_in)) else: self._handle_error_response(response)
python
def _process_oauth_response(self, response): "Extracts the fields from an oauth response" if response.status_code == 200: credentials = parse_qs(response.text) # Initialize the oauth credentials self._init_oauth( credentials.get('oauth_token')[0], credentials.get('oauth_token_secret')[0] ) # If tokens are refreshable, we'll get a session handle self.oauth_session_handle = credentials.get( 'oauth_session_handle', [None])[0] # Calculate token/auth expiry oauth_expires_in = credentials.get( 'oauth_expires_in', [OAUTH_EXPIRY_SECONDS])[0] oauth_authorisation_expires_in = credentials.get( 'oauth_authorization_expires_in', [OAUTH_EXPIRY_SECONDS])[0] self.oauth_expires_at = datetime.datetime.now() + \ datetime.timedelta(seconds=int( oauth_expires_in)) self.oauth_authorization_expires_at = \ datetime.datetime.now() + \ datetime.timedelta(seconds=int( oauth_authorisation_expires_in)) else: self._handle_error_response(response)
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Extracts the fields from an oauth response
[ "Extracts", "the", "fields", "from", "an", "oauth", "response" ]
5566f17fa06ed1f2fb9426c112951a72276b0f9a
https://github.com/freakboy3742/pyxero/blob/5566f17fa06ed1f2fb9426c112951a72276b0f9a/xero/auth.py#L179-L210
251,073
freakboy3742/pyxero
xero/auth.py
PublicCredentials.state
def state(self): """Obtain the useful state of this credentials object so that we can reconstruct it independently. """ return dict( (attr, getattr(self, attr)) for attr in ( 'consumer_key', 'consumer_secret', 'callback_uri', 'verified', 'oauth_token', 'oauth_token_secret', 'oauth_session_handle', 'oauth_expires_at', 'oauth_authorization_expires_at', 'scope' ) if getattr(self, attr) is not None )
python
def state(self): return dict( (attr, getattr(self, attr)) for attr in ( 'consumer_key', 'consumer_secret', 'callback_uri', 'verified', 'oauth_token', 'oauth_token_secret', 'oauth_session_handle', 'oauth_expires_at', 'oauth_authorization_expires_at', 'scope' ) if getattr(self, attr) is not None )
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Obtain the useful state of this credentials object so that we can reconstruct it independently.
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5566f17fa06ed1f2fb9426c112951a72276b0f9a
https://github.com/freakboy3742/pyxero/blob/5566f17fa06ed1f2fb9426c112951a72276b0f9a/xero/auth.py#L245-L258
251,074
freakboy3742/pyxero
xero/auth.py
PublicCredentials.verify
def verify(self, verifier): "Verify an OAuth token" # Construct the credentials for the verification request oauth = OAuth1( self.consumer_key, client_secret=self.consumer_secret, resource_owner_key=self.oauth_token, resource_owner_secret=self.oauth_token_secret, verifier=verifier, rsa_key=self.rsa_key, signature_method=self._signature_method ) # Make the verification request, gettiung back an access token url = self.base_url + ACCESS_TOKEN_URL headers = {'User-Agent': self.user_agent} response = requests.post(url=url, headers=headers, auth=oauth) self._process_oauth_response(response) self.verified = True
python
def verify(self, verifier): "Verify an OAuth token" # Construct the credentials for the verification request oauth = OAuth1( self.consumer_key, client_secret=self.consumer_secret, resource_owner_key=self.oauth_token, resource_owner_secret=self.oauth_token_secret, verifier=verifier, rsa_key=self.rsa_key, signature_method=self._signature_method ) # Make the verification request, gettiung back an access token url = self.base_url + ACCESS_TOKEN_URL headers = {'User-Agent': self.user_agent} response = requests.post(url=url, headers=headers, auth=oauth) self._process_oauth_response(response) self.verified = True
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Verify an OAuth token
[ "Verify", "an", "OAuth", "token" ]
5566f17fa06ed1f2fb9426c112951a72276b0f9a
https://github.com/freakboy3742/pyxero/blob/5566f17fa06ed1f2fb9426c112951a72276b0f9a/xero/auth.py#L260-L279
251,075
freakboy3742/pyxero
xero/auth.py
PublicCredentials.url
def url(self): "Returns the URL that can be visited to obtain a verifier code" # The authorize url is always api.xero.com query_string = {'oauth_token': self.oauth_token} if self.scope: query_string['scope'] = self.scope url = XERO_BASE_URL + AUTHORIZE_URL + '?' + \ urlencode(query_string) return url
python
def url(self): "Returns the URL that can be visited to obtain a verifier code" # The authorize url is always api.xero.com query_string = {'oauth_token': self.oauth_token} if self.scope: query_string['scope'] = self.scope url = XERO_BASE_URL + AUTHORIZE_URL + '?' + \ urlencode(query_string) return url
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Returns the URL that can be visited to obtain a verifier code
[ "Returns", "the", "URL", "that", "can", "be", "visited", "to", "obtain", "a", "verifier", "code" ]
5566f17fa06ed1f2fb9426c112951a72276b0f9a
https://github.com/freakboy3742/pyxero/blob/5566f17fa06ed1f2fb9426c112951a72276b0f9a/xero/auth.py#L282-L292
251,076
freakboy3742/pyxero
xero/auth.py
PartnerCredentials.refresh
def refresh(self): "Refresh an expired token" # Construct the credentials for the verification request oauth = OAuth1( self.consumer_key, client_secret=self.consumer_secret, resource_owner_key=self.oauth_token, resource_owner_secret=self.oauth_token_secret, rsa_key=self.rsa_key, signature_method=self._signature_method ) # Make the verification request, getting back an access token headers = {'User-Agent': self.user_agent} params = {'oauth_session_handle': self.oauth_session_handle} response = requests.post(url=self.base_url + ACCESS_TOKEN_URL, params=params, headers=headers, auth=oauth) self._process_oauth_response(response)
python
def refresh(self): "Refresh an expired token" # Construct the credentials for the verification request oauth = OAuth1( self.consumer_key, client_secret=self.consumer_secret, resource_owner_key=self.oauth_token, resource_owner_secret=self.oauth_token_secret, rsa_key=self.rsa_key, signature_method=self._signature_method ) # Make the verification request, getting back an access token headers = {'User-Agent': self.user_agent} params = {'oauth_session_handle': self.oauth_session_handle} response = requests.post(url=self.base_url + ACCESS_TOKEN_URL, params=params, headers=headers, auth=oauth) self._process_oauth_response(response)
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Refresh an expired token
[ "Refresh", "an", "expired", "token" ]
5566f17fa06ed1f2fb9426c112951a72276b0f9a
https://github.com/freakboy3742/pyxero/blob/5566f17fa06ed1f2fb9426c112951a72276b0f9a/xero/auth.py#L375-L393
251,077
freakboy3742/pyxero
xero/filesmanager.py
FilesManager._get_files
def _get_files(self, folderId): """Retrieve the list of files contained in a folder""" uri = '/'.join([self.base_url, self.name, folderId, 'Files']) return uri, {}, 'get', None, None, False, None
python
def _get_files(self, folderId): uri = '/'.join([self.base_url, self.name, folderId, 'Files']) return uri, {}, 'get', None, None, False, None
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Retrieve the list of files contained in a folder
[ "Retrieve", "the", "list", "of", "files", "contained", "in", "a", "folder" ]
5566f17fa06ed1f2fb9426c112951a72276b0f9a
https://github.com/freakboy3742/pyxero/blob/5566f17fa06ed1f2fb9426c112951a72276b0f9a/xero/filesmanager.py#L118-L121
251,078
dmulcahey/zha-device-handlers
zhaquirks/hivehome/__init__.py
MotionCluster.handle_cluster_request
def handle_cluster_request(self, tsn, command_id, args): """Handle the cluster command.""" if command_id == 0: if self._timer_handle: self._timer_handle.cancel() loop = asyncio.get_event_loop() self._timer_handle = loop.call_later(30, self._turn_off)
python
def handle_cluster_request(self, tsn, command_id, args): if command_id == 0: if self._timer_handle: self._timer_handle.cancel() loop = asyncio.get_event_loop() self._timer_handle = loop.call_later(30, self._turn_off)
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Handle the cluster command.
[ "Handle", "the", "cluster", "command", "." ]
bab2a53724c6fb5caee2e796dd46ebcb45400f93
https://github.com/dmulcahey/zha-device-handlers/blob/bab2a53724c6fb5caee2e796dd46ebcb45400f93/zhaquirks/hivehome/__init__.py#L21-L27
251,079
dmulcahey/zha-device-handlers
zhaquirks/xiaomi/__init__.py
BasicCluster._parse_attributes
def _parse_attributes(self, value): """Parse non standard atrributes.""" from zigpy.zcl import foundation as f attributes = {} attribute_names = { 1: BATTERY_VOLTAGE_MV, 3: TEMPERATURE, 4: XIAOMI_ATTR_4, 5: XIAOMI_ATTR_5, 6: XIAOMI_ATTR_6, 10: PATH } result = {} while value: skey = int(value[0]) svalue, value = f.TypeValue.deserialize(value[1:]) result[skey] = svalue.value for item, value in result.items(): key = attribute_names[item] \ if item in attribute_names else "0xff01-" + str(item) attributes[key] = value if BATTERY_VOLTAGE_MV in attributes: attributes[BATTERY_LEVEL] = int( self._calculate_remaining_battery_percentage( attributes[BATTERY_VOLTAGE_MV] ) ) return attributes
python
def _parse_attributes(self, value): from zigpy.zcl import foundation as f attributes = {} attribute_names = { 1: BATTERY_VOLTAGE_MV, 3: TEMPERATURE, 4: XIAOMI_ATTR_4, 5: XIAOMI_ATTR_5, 6: XIAOMI_ATTR_6, 10: PATH } result = {} while value: skey = int(value[0]) svalue, value = f.TypeValue.deserialize(value[1:]) result[skey] = svalue.value for item, value in result.items(): key = attribute_names[item] \ if item in attribute_names else "0xff01-" + str(item) attributes[key] = value if BATTERY_VOLTAGE_MV in attributes: attributes[BATTERY_LEVEL] = int( self._calculate_remaining_battery_percentage( attributes[BATTERY_VOLTAGE_MV] ) ) return attributes
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Parse non standard atrributes.
[ "Parse", "non", "standard", "atrributes", "." ]
bab2a53724c6fb5caee2e796dd46ebcb45400f93
https://github.com/dmulcahey/zha-device-handlers/blob/bab2a53724c6fb5caee2e796dd46ebcb45400f93/zhaquirks/xiaomi/__init__.py#L64-L91
251,080
dmulcahey/zha-device-handlers
zhaquirks/xiaomi/__init__.py
BasicCluster._calculate_remaining_battery_percentage
def _calculate_remaining_battery_percentage(self, voltage): """Calculate percentage.""" min_voltage = 2500 max_voltage = 3000 percent = (voltage - min_voltage) / (max_voltage - min_voltage) * 200 return min(200, percent)
python
def _calculate_remaining_battery_percentage(self, voltage): min_voltage = 2500 max_voltage = 3000 percent = (voltage - min_voltage) / (max_voltage - min_voltage) * 200 return min(200, percent)
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Calculate percentage.
[ "Calculate", "percentage", "." ]
bab2a53724c6fb5caee2e796dd46ebcb45400f93
https://github.com/dmulcahey/zha-device-handlers/blob/bab2a53724c6fb5caee2e796dd46ebcb45400f93/zhaquirks/xiaomi/__init__.py#L93-L98
251,081
dmulcahey/zha-device-handlers
zhaquirks/xiaomi/__init__.py
PowerConfigurationCluster.battery_reported
def battery_reported(self, voltage, rawVoltage): """Battery reported.""" self._update_attribute(BATTERY_PERCENTAGE_REMAINING, voltage) self._update_attribute(self.BATTERY_VOLTAGE_ATTR, int(rawVoltage / 100))
python
def battery_reported(self, voltage, rawVoltage): self._update_attribute(BATTERY_PERCENTAGE_REMAINING, voltage) self._update_attribute(self.BATTERY_VOLTAGE_ATTR, int(rawVoltage / 100))
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Battery reported.
[ "Battery", "reported", "." ]
bab2a53724c6fb5caee2e796dd46ebcb45400f93
https://github.com/dmulcahey/zha-device-handlers/blob/bab2a53724c6fb5caee2e796dd46ebcb45400f93/zhaquirks/xiaomi/__init__.py#L122-L126
251,082
dmulcahey/zha-device-handlers
zhaquirks/smartthings/tag_v4.py
FastPollingPowerConfigurationCluster.configure_reporting
async def configure_reporting(self, attribute, min_interval, max_interval, reportable_change): """Configure reporting.""" result = await super().configure_reporting( PowerConfigurationCluster.BATTERY_VOLTAGE_ATTR, self.FREQUENCY, self.FREQUENCY, self.MINIMUM_CHANGE ) return result
python
async def configure_reporting(self, attribute, min_interval, max_interval, reportable_change): result = await super().configure_reporting( PowerConfigurationCluster.BATTERY_VOLTAGE_ATTR, self.FREQUENCY, self.FREQUENCY, self.MINIMUM_CHANGE ) return result
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Configure reporting.
[ "Configure", "reporting", "." ]
bab2a53724c6fb5caee2e796dd46ebcb45400f93
https://github.com/dmulcahey/zha-device-handlers/blob/bab2a53724c6fb5caee2e796dd46ebcb45400f93/zhaquirks/smartthings/tag_v4.py#L23-L32
251,083
F5Networks/f5-common-python
f5/bigip/tm/sys/folder.py
Folder.update
def update(self, **kwargs): '''Update the object, removing device group if inherited If inheritedDevicegroup is the string "true" we need to remove deviceGroup from the args before we update or we get the following error: The floating traffic-group: /Common/traffic-group-1 can only be set on /testfolder if its device-group is inherited from the root folder ''' inherit_device_group = self.__dict__.get('inheritedDevicegroup', False) if inherit_device_group == 'true': self.__dict__.pop('deviceGroup') return self._update(**kwargs)
python
def update(self, **kwargs): '''Update the object, removing device group if inherited If inheritedDevicegroup is the string "true" we need to remove deviceGroup from the args before we update or we get the following error: The floating traffic-group: /Common/traffic-group-1 can only be set on /testfolder if its device-group is inherited from the root folder ''' inherit_device_group = self.__dict__.get('inheritedDevicegroup', False) if inherit_device_group == 'true': self.__dict__.pop('deviceGroup') return self._update(**kwargs)
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Update the object, removing device group if inherited If inheritedDevicegroup is the string "true" we need to remove deviceGroup from the args before we update or we get the following error: The floating traffic-group: /Common/traffic-group-1 can only be set on /testfolder if its device-group is inherited from the root folder
[ "Update", "the", "object", "removing", "device", "group", "if", "inherited" ]
7e67d5acd757a60e3d5f8c88c534bd72208f5494
https://github.com/F5Networks/f5-common-python/blob/7e67d5acd757a60e3d5f8c88c534bd72208f5494/f5/bigip/tm/sys/folder.py#L90-L103
251,084
F5Networks/f5-common-python
f5/bigip/tm/cm/device_group.py
Device_Group.sync_to
def sync_to(self): """Wrapper method that synchronizes configuration to DG. Executes the containing object's cm :meth:`~f5.bigip.cm.Cm.exec_cmd` method to sync the configuration TO the device-group. :note:: Both sync_to, and sync_from methods are convenience methods which usually are not what this SDK offers. It is best to execute config-sync with the use of exec_cmd() method on the cm endpoint. """ device_group_collection = self._meta_data['container'] cm = device_group_collection._meta_data['container'] sync_cmd = 'config-sync to-group %s' % self.name cm.exec_cmd('run', utilCmdArgs=sync_cmd)
python
def sync_to(self): device_group_collection = self._meta_data['container'] cm = device_group_collection._meta_data['container'] sync_cmd = 'config-sync to-group %s' % self.name cm.exec_cmd('run', utilCmdArgs=sync_cmd)
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Wrapper method that synchronizes configuration to DG. Executes the containing object's cm :meth:`~f5.bigip.cm.Cm.exec_cmd` method to sync the configuration TO the device-group. :note:: Both sync_to, and sync_from methods are convenience methods which usually are not what this SDK offers. It is best to execute config-sync with the use of exec_cmd() method on the cm endpoint.
[ "Wrapper", "method", "that", "synchronizes", "configuration", "to", "DG", "." ]
7e67d5acd757a60e3d5f8c88c534bd72208f5494
https://github.com/F5Networks/f5-common-python/blob/7e67d5acd757a60e3d5f8c88c534bd72208f5494/f5/bigip/tm/cm/device_group.py#L62-L77
251,085
F5Networks/f5-common-python
f5/bigip/tm/sys/application.py
Service._create
def _create(self, **kwargs): '''Create service on device and create accompanying Python object. :params kwargs: keyword arguments passed in from create call :raises: HTTPError :returns: Python Service object ''' try: return super(Service, self)._create(**kwargs) except HTTPError as ex: if "The configuration was updated successfully but could not be " \ "retrieved" not in ex.response.text: raise # BIG-IP® will create in Common partition if none is given. # In order to create the uri properly in this class's load, # drop in Common as the partition in kwargs. if 'partition' not in kwargs: kwargs['partition'] = 'Common' # Pop all but the necessary load kwargs from the kwargs given to # create. Otherwise, load may fail. kwargs_copy = kwargs.copy() for key in kwargs_copy: if key not in self._meta_data['required_load_parameters']: kwargs.pop(key) # If response was created successfully, do a local_update. # If not, call to overridden _load method via load return self.load(**kwargs)
python
def _create(self, **kwargs): '''Create service on device and create accompanying Python object. :params kwargs: keyword arguments passed in from create call :raises: HTTPError :returns: Python Service object ''' try: return super(Service, self)._create(**kwargs) except HTTPError as ex: if "The configuration was updated successfully but could not be " \ "retrieved" not in ex.response.text: raise # BIG-IP® will create in Common partition if none is given. # In order to create the uri properly in this class's load, # drop in Common as the partition in kwargs. if 'partition' not in kwargs: kwargs['partition'] = 'Common' # Pop all but the necessary load kwargs from the kwargs given to # create. Otherwise, load may fail. kwargs_copy = kwargs.copy() for key in kwargs_copy: if key not in self._meta_data['required_load_parameters']: kwargs.pop(key) # If response was created successfully, do a local_update. # If not, call to overridden _load method via load return self.load(**kwargs)
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Create service on device and create accompanying Python object. :params kwargs: keyword arguments passed in from create call :raises: HTTPError :returns: Python Service object
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7e67d5acd757a60e3d5f8c88c534bd72208f5494
https://github.com/F5Networks/f5-common-python/blob/7e67d5acd757a60e3d5f8c88c534bd72208f5494/f5/bigip/tm/sys/application.py#L105-L133
251,086
F5Networks/f5-common-python
f5/bigip/tm/sys/application.py
Service._build_service_uri
def _build_service_uri(self, base_uri, partition, name): '''Build the proper uri for a service resource. This follows the scheme: <base_uri>/~<partition>~<<name>.app>~<name> :param base_uri: str -- base uri for container :param partition: str -- partition for this service :param name: str -- name of the service :returns: str -- uri to access this service ''' name = name.replace('/', '~') return '%s~%s~%s.app~%s' % (base_uri, partition, name, name)
python
def _build_service_uri(self, base_uri, partition, name): '''Build the proper uri for a service resource. This follows the scheme: <base_uri>/~<partition>~<<name>.app>~<name> :param base_uri: str -- base uri for container :param partition: str -- partition for this service :param name: str -- name of the service :returns: str -- uri to access this service ''' name = name.replace('/', '~') return '%s~%s~%s.app~%s' % (base_uri, partition, name, name)
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Build the proper uri for a service resource. This follows the scheme: <base_uri>/~<partition>~<<name>.app>~<name> :param base_uri: str -- base uri for container :param partition: str -- partition for this service :param name: str -- name of the service :returns: str -- uri to access this service
[ "Build", "the", "proper", "uri", "for", "a", "service", "resource", "." ]
7e67d5acd757a60e3d5f8c88c534bd72208f5494
https://github.com/F5Networks/f5-common-python/blob/7e67d5acd757a60e3d5f8c88c534bd72208f5494/f5/bigip/tm/sys/application.py#L168-L180
251,087
F5Networks/f5-common-python
f5/bigiq/cm/device/licensing/pool/utility.py
Members.delete
def delete(self, **kwargs): """Deletes a member from a license pool You need to be careful with this method. When you use it, and it succeeds on the remote BIG-IP, the configuration of the BIG-IP will be reloaded. During this process, you will not be able to access the REST interface. This method overrides the Resource class's method because it requires that extra json kwargs be supplied. This is not a behavior that is part of the normal Resource class's delete method. :param kwargs: :return: """ if 'id' not in kwargs: # BIG-IQ requires that you provide the ID of the members to revoke # a license from. This ID is already part of the deletion URL though. # Therefore, if you do not provide it, we enumerate it for you. delete_uri = self._meta_data['uri'] if delete_uri.endswith('/'): delete_uri = delete_uri[0:-1] kwargs['id'] = os.path.basename(delete_uri) uid = uuid.UUID(kwargs['id'], version=4) if uid.hex != kwargs['id'].replace('-', ''): raise F5SDKError( "The specified ID is invalid" ) requests_params = self._handle_requests_params(kwargs) kwargs = self._check_for_python_keywords(kwargs) kwargs = self._prepare_request_json(kwargs) delete_uri = self._meta_data['uri'] session = self._meta_data['bigip']._meta_data['icr_session'] # Check the generation for match before delete force = self._check_force_arg(kwargs.pop('force', True)) if not force: self._check_generation() response = session.delete(delete_uri, json=kwargs, **requests_params) if response.status_code == 200: self.__dict__ = {'deleted': True} # This sleep is necessary to prevent BIG-IQ from being able to remove # a license. It happens in certain cases that assignments can be revoked # (and license deletion started) too quickly. Therefore, we must introduce # an artificial delay here to prevent revoking from returning before # BIG-IQ would be ready to remove the license. time.sleep(1)
python
def delete(self, **kwargs): if 'id' not in kwargs: # BIG-IQ requires that you provide the ID of the members to revoke # a license from. This ID is already part of the deletion URL though. # Therefore, if you do not provide it, we enumerate it for you. delete_uri = self._meta_data['uri'] if delete_uri.endswith('/'): delete_uri = delete_uri[0:-1] kwargs['id'] = os.path.basename(delete_uri) uid = uuid.UUID(kwargs['id'], version=4) if uid.hex != kwargs['id'].replace('-', ''): raise F5SDKError( "The specified ID is invalid" ) requests_params = self._handle_requests_params(kwargs) kwargs = self._check_for_python_keywords(kwargs) kwargs = self._prepare_request_json(kwargs) delete_uri = self._meta_data['uri'] session = self._meta_data['bigip']._meta_data['icr_session'] # Check the generation for match before delete force = self._check_force_arg(kwargs.pop('force', True)) if not force: self._check_generation() response = session.delete(delete_uri, json=kwargs, **requests_params) if response.status_code == 200: self.__dict__ = {'deleted': True} # This sleep is necessary to prevent BIG-IQ from being able to remove # a license. It happens in certain cases that assignments can be revoked # (and license deletion started) too quickly. Therefore, we must introduce # an artificial delay here to prevent revoking from returning before # BIG-IQ would be ready to remove the license. time.sleep(1)
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Deletes a member from a license pool You need to be careful with this method. When you use it, and it succeeds on the remote BIG-IP, the configuration of the BIG-IP will be reloaded. During this process, you will not be able to access the REST interface. This method overrides the Resource class's method because it requires that extra json kwargs be supplied. This is not a behavior that is part of the normal Resource class's delete method. :param kwargs: :return:
[ "Deletes", "a", "member", "from", "a", "license", "pool" ]
7e67d5acd757a60e3d5f8c88c534bd72208f5494
https://github.com/F5Networks/f5-common-python/blob/7e67d5acd757a60e3d5f8c88c534bd72208f5494/f5/bigiq/cm/device/licensing/pool/utility.py#L137-L187
251,088
F5Networks/f5-common-python
f5/multi_device/trust_domain.py
TrustDomain._set_attributes
def _set_attributes(self, **kwargs): '''Set attributes for instance in one place :param kwargs: dict -- dictionary of keyword arguments ''' self.devices = kwargs['devices'][:] self.partition = kwargs['partition'] self.device_group_name = 'device_trust_group' self.device_group_type = 'sync-only'
python
def _set_attributes(self, **kwargs): '''Set attributes for instance in one place :param kwargs: dict -- dictionary of keyword arguments ''' self.devices = kwargs['devices'][:] self.partition = kwargs['partition'] self.device_group_name = 'device_trust_group' self.device_group_type = 'sync-only'
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Set attributes for instance in one place :param kwargs: dict -- dictionary of keyword arguments
[ "Set", "attributes", "for", "instance", "in", "one", "place" ]
7e67d5acd757a60e3d5f8c88c534bd72208f5494
https://github.com/F5Networks/f5-common-python/blob/7e67d5acd757a60e3d5f8c88c534bd72208f5494/f5/multi_device/trust_domain.py#L76-L85
251,089
F5Networks/f5-common-python
f5/multi_device/trust_domain.py
TrustDomain.validate
def validate(self): '''Validate that devices are each trusted by one another :param kwargs: dict -- keyword args for devices and partition :raises: DeviceNotTrusted ''' self._populate_domain() missing = [] for domain_device in self.domain: for truster, trustees in iteritems(self.domain): if domain_device not in trustees: missing.append((domain_device, truster, trustees)) if missing: msg = '' for item in missing: msg += '\n%r is not trusted by %r, which trusts: %r' % \ (item[0], item[1], item[2]) raise DeviceNotTrusted(msg) self.device_group = DeviceGroup( devices=self.devices, device_group_name=self.device_group_name, device_group_type=self.device_group_type, device_group_partition=self.partition )
python
def validate(self): '''Validate that devices are each trusted by one another :param kwargs: dict -- keyword args for devices and partition :raises: DeviceNotTrusted ''' self._populate_domain() missing = [] for domain_device in self.domain: for truster, trustees in iteritems(self.domain): if domain_device not in trustees: missing.append((domain_device, truster, trustees)) if missing: msg = '' for item in missing: msg += '\n%r is not trusted by %r, which trusts: %r' % \ (item[0], item[1], item[2]) raise DeviceNotTrusted(msg) self.device_group = DeviceGroup( devices=self.devices, device_group_name=self.device_group_name, device_group_type=self.device_group_type, device_group_partition=self.partition )
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Validate that devices are each trusted by one another :param kwargs: dict -- keyword args for devices and partition :raises: DeviceNotTrusted
[ "Validate", "that", "devices", "are", "each", "trusted", "by", "one", "another" ]
7e67d5acd757a60e3d5f8c88c534bd72208f5494
https://github.com/F5Networks/f5-common-python/blob/7e67d5acd757a60e3d5f8c88c534bd72208f5494/f5/multi_device/trust_domain.py#L87-L112
251,090
F5Networks/f5-common-python
f5/multi_device/trust_domain.py
TrustDomain._populate_domain
def _populate_domain(self): '''Populate TrustDomain's domain attribute. This entails an inspection of each device's certificate-authority devices in its trust domain and recording them. After which, we get a dictionary of who trusts who in the domain. ''' self.domain = {} for device in self.devices: device_name = get_device_info(device).name ca_devices = \ device.tm.cm.trust_domains.trust_domain.load( name='Root' ).caDevices self.domain[device_name] = [ d.replace('/%s/' % self.partition, '') for d in ca_devices ]
python
def _populate_domain(self): '''Populate TrustDomain's domain attribute. This entails an inspection of each device's certificate-authority devices in its trust domain and recording them. After which, we get a dictionary of who trusts who in the domain. ''' self.domain = {} for device in self.devices: device_name = get_device_info(device).name ca_devices = \ device.tm.cm.trust_domains.trust_domain.load( name='Root' ).caDevices self.domain[device_name] = [ d.replace('/%s/' % self.partition, '') for d in ca_devices ]
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Populate TrustDomain's domain attribute. This entails an inspection of each device's certificate-authority devices in its trust domain and recording them. After which, we get a dictionary of who trusts who in the domain.
[ "Populate", "TrustDomain", "s", "domain", "attribute", "." ]
7e67d5acd757a60e3d5f8c88c534bd72208f5494
https://github.com/F5Networks/f5-common-python/blob/7e67d5acd757a60e3d5f8c88c534bd72208f5494/f5/multi_device/trust_domain.py#L114-L131
251,091
F5Networks/f5-common-python
f5/multi_device/trust_domain.py
TrustDomain.create
def create(self, **kwargs): '''Add trusted peers to the root bigip device. When adding a trusted device to a device, the trust is reflexive. That is, the truster trusts the trustee and the trustee trusts the truster. So we only need to add the trusted devices to one device. :param kwargs: dict -- devices and partition ''' self._set_attributes(**kwargs) for device in self.devices[1:]: self._add_trustee(device) pollster(self.validate)()
python
def create(self, **kwargs): '''Add trusted peers to the root bigip device. When adding a trusted device to a device, the trust is reflexive. That is, the truster trusts the trustee and the trustee trusts the truster. So we only need to add the trusted devices to one device. :param kwargs: dict -- devices and partition ''' self._set_attributes(**kwargs) for device in self.devices[1:]: self._add_trustee(device) pollster(self.validate)()
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Add trusted peers to the root bigip device. When adding a trusted device to a device, the trust is reflexive. That is, the truster trusts the trustee and the trustee trusts the truster. So we only need to add the trusted devices to one device. :param kwargs: dict -- devices and partition
[ "Add", "trusted", "peers", "to", "the", "root", "bigip", "device", "." ]
7e67d5acd757a60e3d5f8c88c534bd72208f5494
https://github.com/F5Networks/f5-common-python/blob/7e67d5acd757a60e3d5f8c88c534bd72208f5494/f5/multi_device/trust_domain.py#L133-L146
251,092
F5Networks/f5-common-python
f5/multi_device/trust_domain.py
TrustDomain.teardown
def teardown(self): '''Teardown trust domain by removing trusted devices.''' for device in self.devices: self._remove_trustee(device) self._populate_domain() self.domain = {}
python
def teardown(self): '''Teardown trust domain by removing trusted devices.''' for device in self.devices: self._remove_trustee(device) self._populate_domain() self.domain = {}
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Teardown trust domain by removing trusted devices.
[ "Teardown", "trust", "domain", "by", "removing", "trusted", "devices", "." ]
7e67d5acd757a60e3d5f8c88c534bd72208f5494
https://github.com/F5Networks/f5-common-python/blob/7e67d5acd757a60e3d5f8c88c534bd72208f5494/f5/multi_device/trust_domain.py#L148-L154
251,093
F5Networks/f5-common-python
f5/multi_device/trust_domain.py
TrustDomain._add_trustee
def _add_trustee(self, device): '''Add a single trusted device to the trust domain. :param device: ManagementRoot object -- device to add to trust domain ''' device_name = get_device_info(device).name if device_name in self.domain: msg = 'Device: %r is already in this trust domain.' % device_name raise DeviceAlreadyInTrustDomain(msg) self._modify_trust(self.devices[0], self._get_add_trustee_cmd, device)
python
def _add_trustee(self, device): '''Add a single trusted device to the trust domain. :param device: ManagementRoot object -- device to add to trust domain ''' device_name = get_device_info(device).name if device_name in self.domain: msg = 'Device: %r is already in this trust domain.' % device_name raise DeviceAlreadyInTrustDomain(msg) self._modify_trust(self.devices[0], self._get_add_trustee_cmd, device)
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Add a single trusted device to the trust domain. :param device: ManagementRoot object -- device to add to trust domain
[ "Add", "a", "single", "trusted", "device", "to", "the", "trust", "domain", "." ]
7e67d5acd757a60e3d5f8c88c534bd72208f5494
https://github.com/F5Networks/f5-common-python/blob/7e67d5acd757a60e3d5f8c88c534bd72208f5494/f5/multi_device/trust_domain.py#L156-L166
251,094
F5Networks/f5-common-python
f5/multi_device/trust_domain.py
TrustDomain._remove_trustee
def _remove_trustee(self, device): '''Remove a trustee from the trust domain. :param device: MangementRoot object -- device to remove ''' trustee_name = get_device_info(device).name name_object_map = get_device_names_to_objects(self.devices) delete_func = self._get_delete_trustee_cmd for truster in self.domain: if trustee_name in self.domain[truster] and \ truster != trustee_name: truster_obj = name_object_map[truster] self._modify_trust(truster_obj, delete_func, trustee_name) self._populate_domain() for trustee in self.domain[trustee_name]: if trustee_name != trustee: self._modify_trust(device, delete_func, trustee) self.devices.remove(name_object_map[trustee_name])
python
def _remove_trustee(self, device): '''Remove a trustee from the trust domain. :param device: MangementRoot object -- device to remove ''' trustee_name = get_device_info(device).name name_object_map = get_device_names_to_objects(self.devices) delete_func = self._get_delete_trustee_cmd for truster in self.domain: if trustee_name in self.domain[truster] and \ truster != trustee_name: truster_obj = name_object_map[truster] self._modify_trust(truster_obj, delete_func, trustee_name) self._populate_domain() for trustee in self.domain[trustee_name]: if trustee_name != trustee: self._modify_trust(device, delete_func, trustee) self.devices.remove(name_object_map[trustee_name])
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Remove a trustee from the trust domain. :param device: MangementRoot object -- device to remove
[ "Remove", "a", "trustee", "from", "the", "trust", "domain", "." ]
7e67d5acd757a60e3d5f8c88c534bd72208f5494
https://github.com/F5Networks/f5-common-python/blob/7e67d5acd757a60e3d5f8c88c534bd72208f5494/f5/multi_device/trust_domain.py#L168-L189
251,095
F5Networks/f5-common-python
f5/multi_device/trust_domain.py
TrustDomain._modify_trust
def _modify_trust(self, truster, mod_peer_func, trustee): '''Modify a trusted peer device by deploying an iapp. :param truster: ManagementRoot object -- device on which to perform commands :param mod_peer_func: function -- function to call to modify peer :param trustee: ManagementRoot object or str -- device to modify ''' iapp_name = 'trusted_device' mod_peer_cmd = mod_peer_func(trustee) iapp_actions = self.iapp_actions.copy() iapp_actions['definition']['implementation'] = mod_peer_cmd self._deploy_iapp(iapp_name, iapp_actions, truster) self._delete_iapp(iapp_name, truster)
python
def _modify_trust(self, truster, mod_peer_func, trustee): '''Modify a trusted peer device by deploying an iapp. :param truster: ManagementRoot object -- device on which to perform commands :param mod_peer_func: function -- function to call to modify peer :param trustee: ManagementRoot object or str -- device to modify ''' iapp_name = 'trusted_device' mod_peer_cmd = mod_peer_func(trustee) iapp_actions = self.iapp_actions.copy() iapp_actions['definition']['implementation'] = mod_peer_cmd self._deploy_iapp(iapp_name, iapp_actions, truster) self._delete_iapp(iapp_name, truster)
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Modify a trusted peer device by deploying an iapp. :param truster: ManagementRoot object -- device on which to perform commands :param mod_peer_func: function -- function to call to modify peer :param trustee: ManagementRoot object or str -- device to modify
[ "Modify", "a", "trusted", "peer", "device", "by", "deploying", "an", "iapp", "." ]
7e67d5acd757a60e3d5f8c88c534bd72208f5494
https://github.com/F5Networks/f5-common-python/blob/7e67d5acd757a60e3d5f8c88c534bd72208f5494/f5/multi_device/trust_domain.py#L191-L206
251,096
F5Networks/f5-common-python
f5/multi_device/trust_domain.py
TrustDomain._delete_iapp
def _delete_iapp(self, iapp_name, deploying_device): '''Delete an iapp service and template on the root device. :param iapp_name: str -- name of iapp :param deploying_device: ManagementRoot object -- device where the iapp will be deleted ''' iapp = deploying_device.tm.sys.application iapp_serv = iapp.services.service.load( name=iapp_name, partition=self.partition ) iapp_serv.delete() iapp_tmpl = iapp.templates.template.load( name=iapp_name, partition=self.partition ) iapp_tmpl.delete()
python
def _delete_iapp(self, iapp_name, deploying_device): '''Delete an iapp service and template on the root device. :param iapp_name: str -- name of iapp :param deploying_device: ManagementRoot object -- device where the iapp will be deleted ''' iapp = deploying_device.tm.sys.application iapp_serv = iapp.services.service.load( name=iapp_name, partition=self.partition ) iapp_serv.delete() iapp_tmpl = iapp.templates.template.load( name=iapp_name, partition=self.partition ) iapp_tmpl.delete()
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Delete an iapp service and template on the root device. :param iapp_name: str -- name of iapp :param deploying_device: ManagementRoot object -- device where the iapp will be deleted
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7e67d5acd757a60e3d5f8c88c534bd72208f5494
https://github.com/F5Networks/f5-common-python/blob/7e67d5acd757a60e3d5f8c88c534bd72208f5494/f5/multi_device/trust_domain.py#L208-L224
251,097
F5Networks/f5-common-python
f5/multi_device/trust_domain.py
TrustDomain._deploy_iapp
def _deploy_iapp(self, iapp_name, actions, deploying_device): '''Deploy iapp to add trusted device :param iapp_name: str -- name of iapp :param actions: dict -- actions definition of iapp sections :param deploying_device: ManagementRoot object -- device where the iapp will be created ''' tmpl = deploying_device.tm.sys.application.templates.template serv = deploying_device.tm.sys.application.services.service tmpl.create(name=iapp_name, partition=self.partition, actions=actions) pollster(deploying_device.tm.sys.application.templates.template.load)( name=iapp_name, partition=self.partition ) serv.create( name=iapp_name, partition=self.partition, template='/%s/%s' % (self.partition, iapp_name) )
python
def _deploy_iapp(self, iapp_name, actions, deploying_device): '''Deploy iapp to add trusted device :param iapp_name: str -- name of iapp :param actions: dict -- actions definition of iapp sections :param deploying_device: ManagementRoot object -- device where the iapp will be created ''' tmpl = deploying_device.tm.sys.application.templates.template serv = deploying_device.tm.sys.application.services.service tmpl.create(name=iapp_name, partition=self.partition, actions=actions) pollster(deploying_device.tm.sys.application.templates.template.load)( name=iapp_name, partition=self.partition ) serv.create( name=iapp_name, partition=self.partition, template='/%s/%s' % (self.partition, iapp_name) )
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Deploy iapp to add trusted device :param iapp_name: str -- name of iapp :param actions: dict -- actions definition of iapp sections :param deploying_device: ManagementRoot object -- device where the iapp will be created
[ "Deploy", "iapp", "to", "add", "trusted", "device" ]
7e67d5acd757a60e3d5f8c88c534bd72208f5494
https://github.com/F5Networks/f5-common-python/blob/7e67d5acd757a60e3d5f8c88c534bd72208f5494/f5/multi_device/trust_domain.py#L226-L245
251,098
F5Networks/f5-common-python
f5/multi_device/trust_domain.py
TrustDomain._get_add_trustee_cmd
def _get_add_trustee_cmd(self, trustee): '''Get tmsh command to add a trusted device. :param trustee: ManagementRoot object -- device to add as trusted :returns: str -- tmsh command to add trustee ''' trustee_info = pollster(get_device_info)(trustee) username = trustee._meta_data['username'] password = trustee._meta_data['password'] return 'tmsh::modify cm trust-domain Root ca-devices add ' \ '\\{ %s \\} name %s username %s password %s' % \ (trustee_info.managementIp, trustee_info.name, username, password)
python
def _get_add_trustee_cmd(self, trustee): '''Get tmsh command to add a trusted device. :param trustee: ManagementRoot object -- device to add as trusted :returns: str -- tmsh command to add trustee ''' trustee_info = pollster(get_device_info)(trustee) username = trustee._meta_data['username'] password = trustee._meta_data['password'] return 'tmsh::modify cm trust-domain Root ca-devices add ' \ '\\{ %s \\} name %s username %s password %s' % \ (trustee_info.managementIp, trustee_info.name, username, password)
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Get tmsh command to add a trusted device. :param trustee: ManagementRoot object -- device to add as trusted :returns: str -- tmsh command to add trustee
[ "Get", "tmsh", "command", "to", "add", "a", "trusted", "device", "." ]
7e67d5acd757a60e3d5f8c88c534bd72208f5494
https://github.com/F5Networks/f5-common-python/blob/7e67d5acd757a60e3d5f8c88c534bd72208f5494/f5/multi_device/trust_domain.py#L247-L259
251,099
F5Networks/f5-common-python
f5/bigip/tm/vcmp/virtual_disk.py
Virtual_Disk.load
def load(self, **kwargs): """Loads a given resource Loads a given resource provided a 'name' and an optional 'slot' parameter. The 'slot' parameter is not a required load parameter because it is provided as an optional way of constructing the correct 'name' of the vCMP resource. :param kwargs: :return: """ kwargs['transform_name'] = True kwargs = self._mutate_name(kwargs) return self._load(**kwargs)
python
def load(self, **kwargs): kwargs['transform_name'] = True kwargs = self._mutate_name(kwargs) return self._load(**kwargs)
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Loads a given resource Loads a given resource provided a 'name' and an optional 'slot' parameter. The 'slot' parameter is not a required load parameter because it is provided as an optional way of constructing the correct 'name' of the vCMP resource. :param kwargs: :return:
[ "Loads", "a", "given", "resource" ]
7e67d5acd757a60e3d5f8c88c534bd72208f5494
https://github.com/F5Networks/f5-common-python/blob/7e67d5acd757a60e3d5f8c88c534bd72208f5494/f5/bigip/tm/vcmp/virtual_disk.py#L53-L66