def similarity(self, image, method='correlation'):
""" Calculate similarity of Brain_Data() instance with single Brain_Data or Nibabel image
Args:
image: Brain_Data or Nibabel instance of weight map
Returns:
pexp: Outputs a vector of pattern expression values
"""
if not isinstance(image, Brain_Data):
if isinstance(image, nib.Nifti1Image):
image = Brain_Data(image)
else:
raise ValueError(
"Image is not a Brain_Data or nibabel instance")
dim = image.shape()
# Check to make sure masks are the same for each dataset and if not create a union mask
# This might be handy code for a new Brain_Data method
if np.sum(self.nifti_masker.mask_img.get_data() == 1) != np.sum(
image.nifti_masker.mask_img.get_data() == 1):
new_mask = intersect_masks(
[self.nifti_masker.mask_img, image.nifti_masker.mask_img],
threshold=1,
connected=False)
new_nifti_masker = NiftiMasker(mask_img=new_mask)
data2 = new_nifti_masker.fit_transform(self.to_nifti())
image2 = new_nifti_masker.fit_transform(image.to_nifti())
else:
data2 = self.data
image2 = image.data
# Calculate pattern expression
if method is 'dot_product':
if len(image2.shape) > 1:
if image2.shape[0] > 1:
pexp = []
for i in range(image2.shape[0]):
pexp.append(np.dot(data2, image2[i, :]))
pexp = np.array(pexp)
else:
pexp = np.dot(data2, image2)
else:
pexp = np.dot(data2, image2)
elif method is 'correlation':
if len(image2.shape) > 1:
if image2.shape[0] > 1:
pexp = []
for i in range(image2.shape[0]):
pexp.append(pearson(image2[i, :], data2))
pexp = np.array(pexp)
else:
pexp = pearson(image2, data2)
else:
pexp = pearson(image2, data2)
return pexp
def similarity(self, image, method='correlation'):
""" Calculate similarity of Brain_Data() instance with single Brain_Data or Nibabel image
Args:
self: Brain_Data instance of data to be applied
image: Brain_Data or Nibabel instance of weight map
Returns:
pexp: Outputs a vector of pattern expression values
"""
if not isinstance(image, Brain_Data):
if isinstance(image, nib.Nifti1Image):
image = Brain_Data(image)
else:
raise ValueError("Image is not a Brain_Data or nibabel instance")
dim = image.shape()
# Check to make sure masks are the same for each dataset and if not create a union mask
# This might be handy code for a new Brain_Data method
if np.sum(self.nifti_masker.mask_img.get_data()==1)!=np.sum(image.nifti_masker.mask_img.get_data()==1):
new_mask = intersect_masks([self.nifti_masker.mask_img, image.nifti_masker.mask_img], threshold=1, connected=False)
new_nifti_masker = NiftiMasker(mask_img=new_mask)
data2 = new_nifti_masker.fit_transform(self.to_nifti())
image2 = new_nifti_masker.fit_transform(image.to_nifti())
else:
data2 = self.data
image2 = image.data
# Calculate pattern expression
if method is 'dot_product':
if len(image2.shape) > 1:
if image2.shape[0]>1:
pexp = []
for i in range(image2.shape[0]):
pexp.append(np.dot(data2, image2[i,:]))
pexp = np.array(pexp)
else:
pexp = np.dot(data2, image2)
else:
pexp = np.dot(data2, image2)
elif method is 'correlation':
if len(image2.shape) > 1:
if image2.shape[0]>1:
pexp = []
for i in range(image2.shape[0]):
pexp.append(pearson(image2[i,:], data2))
pexp = np.array(pexp)
else:
pexp = pearson(image2, data2)
else:
pexp = pearson(image2, data2)
return pexp
def apply_mask(data=None,
weight_map=None,
mask=None,
method='dot_product',
save_output=False,
output_dir='.'):
""" Apply Nifti weight map to Nifti Images.
Args:
data: nibabel instance of data to be applied
weight_map: nibabel instance of weight map
mask: binary nibabel mask
method: type of pattern expression (e.g,. 'dot_product','correlation')
save_output: Boolean indicating whether or not to save output to csv file.
output_dir: Directory to use for writing all outputs
**kwargs: Additional parameters to pass
Returns:
pexp: Outputs a vector of pattern expression values
"""
if mask is not None:
if type(mask) is not nib.nifti1.Nifti1Image:
raise ValueError("Mask is not a nibabel instance")
else:
mask = nib.load(
os.path.join(resource_dir, 'MNI152_T1_2mm_brain_mask_dil.nii.gz'))
if type(data) is not nib.nifti1.Nifti1Image:
raise ValueError("Data is not a nibabel instance")
nifti_masker = NiftiMasker(mask_img=mask)
data_masked = nifti_masker.fit_transform(data)
if type(weight_map) is not nib.nifti1.Nifti1Image:
raise ValueError("Weight_map is not a nibabel instance")
weight_map_masked = nifti_masker.fit_transform(weight_map)
# Calculate pattern expression
if method is 'dot_product':
pexp = np.dot(data_masked, np.transpose(weight_map_masked)).squeeze()
elif method is 'correlation':
pexp = pearson(data_masked, weight_map_masked)
if save_output:
np.savetxt(os.path.join(output_dir,
"Pattern_Expression_" + method + ".csv"),
pexp,
delimiter=",")
return pexp
def apply_mask(data=None, weight_map=None, mask=None, method='dot_product', save_output=False, output_dir='.'):
""" Apply Nifti weight map to Nifti Images.
Args:
data: nibabel instance of data to be applied
weight_map: nibabel instance of weight map
mask: binary nibabel mask
method: type of pattern expression (e.g,. 'dot_product','correlation')
save_output: Boolean indicating whether or not to save output to csv file.
output_dir: Directory to use for writing all outputs
**kwargs: Additional parameters to pass
Returns:
pexp: Outputs a vector of pattern expression values
"""
if mask is not None:
if type(mask) is not nib.nifti1.Nifti1Image:
raise ValueError("Mask is not a nibabel instance")
else:
mask = nib.load(os.path.join(resource_dir,'MNI152_T1_2mm_brain_mask_dil.nii.gz'))
if type(data) is not nib.nifti1.Nifti1Image:
raise ValueError("Data is not a nibabel instance")
nifti_masker = NiftiMasker(mask_img=mask)
data_masked = nifti_masker.fit_transform(data)
if type(weight_map) is not nib.nifti1.Nifti1Image:
raise ValueError("Weight_map is not a nibabel instance")
weight_map_masked = nifti_masker.fit_transform(weight_map)
# Calculate pattern expression
if method is 'dot_product':
pexp = np.dot(data_masked,np.transpose(weight_map_masked)).squeeze()
elif method is 'correlation':
pexp = pearson(data_masked,weight_map_masked)
if save_output:
np.savetxt(os.path.join(output_dir,"Pattern_Expression_" + method + ".csv"), pexp, delimiter=",")
return pexp
def apply_mask(data=None,
weight_map=None,
mask=None,
method='dot_product',
save_output=False,
output_dir='.'):
""" Apply Nifti weight map to Nifti Images.
Args:
data: nibabel instance of data to be applied
weight_map: nibabel instance of weight map
mask: binary nibabel mask
method: type of pattern expression (e.g,. 'dot_product','correlation')
save_output: Boolean indicating whether or not to save output to csv file.
output_dir: Directory to use for writing all outputs
**kwargs: Additional parameters to pass
Returns:
pexp: Outputs a vector of pattern expression values
"""
if mask is not None:
if type(mask) is not nib.nifti1.Nifti1Image:
raise ValueError("Mask is not a nibabel instance")
else:
mask = nib.load(
os.path.join(get_resource_path(),
'MNI152_T1_2mm_brain_mask.nii.gz'))
if type(data) is not nib.nifti1.Nifti1Image:
if type(data) is str:
if os.path.isfile(data):
data = nib.load(data)
elif type(data) is list:
data = nib.funcs.concat_images(data)
else:
raise ValueError(
"Data is not a nibabel instance, list of files, or a valid file name."
)
nifti_masker = NiftiMasker(mask_img=mask)
data_masked = nifti_masker.fit_transform(data)
if len(data_masked.shape) > 2:
data_masked = data_masked.squeeze()
if type(weight_map) is not nib.nifti1.Nifti1Image:
if type(weight_map) is str:
if os.path.isfile(weight_map):
data = nib.load(weight_map)
elif type(weight_map) is list:
weight_map = nib.funcs.concat_images(weight_map)
else:
raise ValueError(
"Weight_map is not a nibabel instance, list of files, or a valid file name."
)
weight_map_masked = nifti_masker.fit_transform(weight_map)
if len(weight_map_masked.shape) > 2:
weight_map_masked = weight_map_masked.squeeze()
# Calculate pattern expression
pexp = pd.DataFrame()
for w in range(0, weight_map_masked.shape[0]):
if method == 'dot_product':
pexp = pexp.append(pd.Series(
np.dot(data_masked, np.transpose(weight_map_masked[w, :]))),
ignore_index=True)
elif method == 'correlation':
pexp = pexp.append(pd.Series(
pearson(data_masked, weight_map_masked[w, :])),
ignore_index=True)
pexp = pexp.T
if save_output:
pexp.to_csv(
os.path.join(output_dir, "Pattern_Expression_" + method + ".csv"))
# np.savetxt(os.path.join(output_dir,"Pattern_Expression_" + method + ".csv"), pexp, delimiter=",")
return pexp
def apply_mask(data=None, weight_map=None, mask=None, method='dot_product', save_output=False, output_dir='.'):
""" Apply Nifti weight map to Nifti Images.
Args:
data: nibabel instance of data to be applied
weight_map: nibabel instance of weight map
mask: binary nibabel mask
method: type of pattern expression (e.g,. 'dot_product','correlation')
save_output: Boolean indicating whether or not to save output to csv file.
output_dir: Directory to use for writing all outputs
**kwargs: Additional parameters to pass
Returns:
pexp: Outputs a vector of pattern expression values
"""
if mask is not None:
if type(mask) is not nib.nifti1.Nifti1Image:
raise ValueError("Mask is not a nibabel instance")
else:
mask = nib.load(os.path.join(get_resource_path(),'MNI152_T1_2mm_brain_mask.nii.gz'))
if type(data) is not nib.nifti1.Nifti1Image:
if type(data) is str:
if os.path.isfile(data):
data = nib.load(data)
elif type(data) is list:
data = nib.funcs.concat_images(data)
else:
raise ValueError("Data is not a nibabel instance, list of files, or a valid file name.")
nifti_masker = NiftiMasker(mask_img=mask)
data_masked = nifti_masker.fit_transform(data)
if len(data_masked.shape) > 2:
data_masked = data_masked.squeeze()
if type(weight_map) is not nib.nifti1.Nifti1Image:
if type(weight_map) is str:
if os.path.isfile(weight_map):
data = nib.load(weight_map)
elif type(weight_map) is list:
weight_map = nib.funcs.concat_images(weight_map)
else:
raise ValueError("Weight_map is not a nibabel instance, list of files, or a valid file name.")
weight_map_masked = nifti_masker.fit_transform(weight_map)
if len(weight_map_masked.shape) > 2:
weight_map_masked = weight_map_masked.squeeze()
# Calculate pattern expression
pexp = pd.DataFrame()
for w in range(0, weight_map_masked.shape[0]):
if method == 'dot_product':
pexp = pexp.append(pd.Series(np.dot(data_masked,np.transpose(weight_map_masked[w,:]))), ignore_index=True)
elif method == 'correlation':
pexp = pexp.append(pd.Series(pearson(data_masked,weight_map_masked[w,:])), ignore_index=True)
pexp = pexp.T
if save_output:
pexp.to_csv(os.path.join(output_dir,"Pattern_Expression_" + method + ".csv"))
# np.savetxt(os.path.join(output_dir,"Pattern_Expression_" + method + ".csv"), pexp, delimiter=",")
return pexp
