def test_data(tmpdir): sim = Simulator() r = 10 sigma = 1 y = [0, 1] n_reps = 3 output_dir = str(tmpdir) sim.create_data(y, sigma, reps=n_reps, output_dir=output_dir) shape_3d = (91, 109, 91) shape_2d = (6, 238955) y=pd.read_csv(os.path.join(str(tmpdir.join('y.csv'))),header=None,index_col=None).T flist = glob.glob(str(tmpdir.join('centered*.nii.gz'))) dat = Brain_Data(data=flist,Y=y) # Test shape assert dat.shape() == shape_2d # Test Mean assert dat.mean().shape()[0] == shape_2d[1] # Test Std assert dat.std().shape()[0] == shape_2d[1] # Test to_nifti d = dat.to_nifti assert d().shape[0:3] == shape_3d # # Test T-test out = dat.ttest() assert out['t'].shape()[0]==shape_2d[1] # Test Regress dat.X = pd.DataFrame({'Intercept':np.ones(len(dat.Y)),'X1':np.array(dat.Y).flatten()},index=None) out = dat.regress() assert out['beta'].shape() == (2,shape_2d[1]) # Test indexing assert out['t'][1].shape()[0] == shape_2d[1] # Test threshold i=1 tt = threshold(out['t'][i], out['p'][i], threshold_dict={'fdr':.05}) assert tt.shape()[0] == shape_2d[1]
def test_data(tmpdir): sim = Simulator() r = 10 sigma = 1 y = [0, 1] n_reps = 3 output_dir = str(tmpdir) sim.create_data(y, sigma, reps=n_reps, output_dir=output_dir) shape_3d = (91, 109, 91) shape_2d = (6, 238955) y = pd.read_csv(os.path.join(str(tmpdir.join('y.csv'))), header=None, index_col=None).T flist = glob.glob(str(tmpdir.join('centered*.nii.gz'))) # Test load list dat = Brain_Data(data=flist, Y=y) # Test load file assert Brain_Data(flist[0]) # Test to_nifti d = dat.to_nifti() assert d.shape[0:3] == shape_3d # Test load nibabel assert Brain_Data(d) # Test shape assert dat.shape() == shape_2d # Test Mean assert dat.mean().shape()[0] == shape_2d[1] # Test Std assert dat.std().shape()[0] == shape_2d[1] # Test add new = dat + dat assert new.shape() == shape_2d # Test subtract new = dat - dat assert new.shape() == shape_2d # Test multiply new = dat * dat assert new.shape() == shape_2d # Test Iterator x = [x for x in dat] assert len(x) == len(dat) assert len(x[0].data.shape) == 1 # # Test T-test out = dat.ttest() assert out['t'].shape()[0] == shape_2d[1] # # # Test T-test - permutation method # out = dat.ttest(threshold_dict={'permutation':'tfce','n_permutations':50,'n_jobs':1}) # assert out['t'].shape()[0]==shape_2d[1] # Test Regress dat.X = pd.DataFrame( { 'Intercept': np.ones(len(dat.Y)), 'X1': np.array(dat.Y).flatten() }, index=None) out = dat.regress() assert out['beta'].shape() == (2, shape_2d[1]) # Test indexing assert out['t'][1].shape()[0] == shape_2d[1] # Test threshold i = 1 tt = threshold(out['t'][i], out['p'][i], .05) assert isinstance(tt, Brain_Data) # Test write dat.write(os.path.join(str(tmpdir.join('test_write.nii')))) assert Brain_Data(os.path.join(str(tmpdir.join('test_write.nii')))) # Test append assert dat.append(dat).shape()[0] == shape_2d[0] * 2 # Test distance distance = dat.distance(method='euclidean') assert distance.shape == (shape_2d[0], shape_2d[0]) # Test predict stats = dat.predict(algorithm='svm', cv_dict={ 'type': 'kfolds', 'n_folds': 2, 'n': len(dat.Y) }, plot=False, **{'kernel': "linear"}) # Support Vector Regression, with 5 fold cross-validation with Platt Scaling # This will output probabilities of each class stats = dat.predict(algorithm='svm', cv_dict=None, plot=False, **{ 'kernel': 'linear', 'probability': True }) assert isinstance(stats['weight_map'], Brain_Data) # Logistic classificiation, with 5 fold stratified cross-validation. stats = dat.predict(algorithm='logistic', cv_dict={ 'type': 'kfolds', 'n_folds': 5, 'n': len(dat.Y) }, plot=False) assert isinstance(stats['weight_map'], Brain_Data) # Ridge classificiation, with 5 fold between-subject cross-validation, where data for each subject is held out together. stats = dat.predict(algorithm='ridgeClassifier', cv_dict=None, plot=False) assert isinstance(stats['weight_map'], Brain_Data) # Test Similarity r = dat.similarity(stats['weight_map']) assert len(r) == shape_2d[0] r2 = dat.similarity(stats['weight_map'].to_nifti()) assert len(r2) == shape_2d[0] # Test apply_mask - might move part of this to test mask suite s1 = create_sphere([41, 64, 55], radius=10) assert isinstance(s1, nb.Nifti1Image) s2 = Brain_Data(s1) masked_dat = dat.apply_mask(s1) assert masked_dat.shape()[1] == np.sum(s2.data != 0) # Test extract_roi mask = create_sphere([41, 64, 55], radius=10) assert len(dat.extract_roi(mask)) == shape_2d[0] # Test r_to_z z = dat.r_to_z() assert z.shape() == dat.shape() # Test copy d_copy = dat.copy() assert d_copy.shape() == dat.shape() # Test detrend detrend = dat.detrend() assert detrend.shape() == dat.shape()
def test_brain_data(tmpdir): # Add 3mm to list to test that resolution as well for resolution in ['2mm']: MNI_Template["resolution"] = resolution sim = Simulator() r = 10 sigma = 1 y = [0, 1] n_reps = 3 output_dir = str(tmpdir) dat = sim.create_data(y, sigma, reps=n_reps, output_dir=output_dir) if MNI_Template["resolution"] == '2mm': shape_3d = (91, 109, 91) shape_2d = (6, 238955) elif MNI_Template["resolution"] == '3mm': shape_3d = (60, 72, 60) shape_2d = (6, 71020) y = pd.read_csv(os.path.join(str(tmpdir.join('y.csv'))),header=None, index_col=None) holdout = pd.read_csv(os.path.join(str(tmpdir.join('rep_id.csv'))),header=None,index_col=None) # Test load list of 4D images file_list = [str(tmpdir.join('data.nii.gz')), str(tmpdir.join('data.nii.gz'))] dat = Brain_Data(file_list) dat = Brain_Data([nb.load(x) for x in file_list]) # Test load list dat = Brain_Data(data=str(tmpdir.join('data.nii.gz')), Y=y) # Test concatenate out = Brain_Data([x for x in dat]) assert isinstance(out, Brain_Data) assert len(out)==len(dat) # Test to_nifti d = dat.to_nifti() assert d.shape[0:3] == shape_3d # Test load nibabel assert Brain_Data(d) # Test shape assert dat.shape() == shape_2d # Test Mean assert dat.mean().shape()[0] == shape_2d[1] # Test Std assert dat.std().shape()[0] == shape_2d[1] # Test add new = dat + dat assert new.shape() == shape_2d # Test subtract new = dat - dat assert new.shape() == shape_2d # Test multiply new = dat * dat assert new.shape() == shape_2d # Test Indexing index = [0, 3, 1] assert len(dat[index]) == len(index) index = range(4) assert len(dat[index]) == len(index) index = dat.Y == 1 assert len(dat[index.values.flatten()]) == index.values.sum() assert len(dat[index]) == index.values.sum() assert len(dat[:3]) == 3 # Test Iterator x = [x for x in dat] assert len(x) == len(dat) assert len(x[0].data.shape) == 1 # # Test T-test out = dat.ttest() assert out['t'].shape()[0] == shape_2d[1] # # # Test T-test - permutation method # out = dat.ttest(threshold_dict={'permutation':'tfce','n_permutations':50,'n_jobs':1}) # assert out['t'].shape()[0]==shape_2d[1] # Test Regress dat.X = pd.DataFrame({'Intercept':np.ones(len(dat.Y)), 'X1':np.array(dat.Y).flatten()}, index=None) # Standard OLS out = dat.regress() assert type(out['beta'].data) == np.ndarray assert type(out['t'].data) == np.ndarray assert type(out['p'].data) == np.ndarray assert type(out['residual'].data) == np.ndarray assert type(out['df'].data) == np.ndarray assert out['beta'].shape() == (2, shape_2d[1]) assert out['t'][1].shape()[0] == shape_2d[1] # Robust OLS out = dat.regress(mode='robust') assert type(out['beta'].data) == np.ndarray assert type(out['t'].data) == np.ndarray assert type(out['p'].data) == np.ndarray assert type(out['residual'].data) == np.ndarray assert type(out['df'].data) == np.ndarray assert out['beta'].shape() == (2, shape_2d[1]) assert out['t'][1].shape()[0] == shape_2d[1] # Test threshold i=1 tt = threshold(out['t'][i], out['p'][i], .05) assert isinstance(tt, Brain_Data) # Test write dat.write(os.path.join(str(tmpdir.join('test_write.nii')))) assert Brain_Data(os.path.join(str(tmpdir.join('test_write.nii')))) # Test append assert dat.append(dat).shape()[0] == shape_2d[0]*2 # Test distance distance = dat.distance(method='euclidean') assert isinstance(distance, Adjacency) assert distance.square_shape()[0] == shape_2d[0] # Test predict stats = dat.predict(algorithm='svm', cv_dict={'type': 'kfolds', 'n_folds': 2}, plot=False, **{'kernel':"linear"}) # Support Vector Regression, with 5 fold cross-validation with Platt Scaling # This will output probabilities of each class stats = dat.predict(algorithm='svm', cv_dict=None, plot=False, **{'kernel':'linear', 'probability':True}) assert isinstance(stats['weight_map'], Brain_Data) # Logistic classificiation, with 2 fold cross-validation. stats = dat.predict(algorithm='logistic', cv_dict={'type': 'kfolds', 'n_folds': 2}, plot=False) assert isinstance(stats['weight_map'], Brain_Data) # Ridge classificiation, stats = dat.predict(algorithm='ridgeClassifier', cv_dict=None, plot=False) assert isinstance(stats['weight_map'], Brain_Data) # Ridge stats = dat.predict(algorithm='ridge', cv_dict={'type': 'kfolds', 'n_folds': 2, 'subject_id':holdout}, plot=False, **{'alpha':.1}) # Lasso stats = dat.predict(algorithm='lasso', cv_dict={'type': 'kfolds', 'n_folds': 2, 'stratified':dat.Y}, plot=False, **{'alpha':.1}) # PCR stats = dat.predict(algorithm='pcr', cv_dict=None, plot=False) # Test Similarity r = dat.similarity(stats['weight_map']) assert len(r) == shape_2d[0] r2 = dat.similarity(stats['weight_map'].to_nifti()) assert len(r2) == shape_2d[0] r = dat.similarity(stats['weight_map'], method='dot_product') assert len(r) == shape_2d[0] r = dat.similarity(stats['weight_map'], method='cosine') assert len(r) == shape_2d[0] r = dat.similarity(dat, method='correlation') assert r.shape == (dat.shape()[0],dat.shape()[0]) r = dat.similarity(dat, method='dot_product') assert r.shape == (dat.shape()[0],dat.shape()[0]) r = dat.similarity(dat, method='cosine') assert r.shape == (dat.shape()[0],dat.shape()[0]) # Test apply_mask - might move part of this to test mask suite s1 = create_sphere([12, 10, -8], radius=10) assert isinstance(s1, nb.Nifti1Image) masked_dat = dat.apply_mask(s1) assert masked_dat.shape()[1] == np.sum(s1.get_data() != 0) # Test extract_roi mask = create_sphere([12, 10, -8], radius=10) assert len(dat.extract_roi(mask)) == shape_2d[0] # Test r_to_z z = dat.r_to_z() assert z.shape() == dat.shape() # Test copy d_copy = dat.copy() assert d_copy.shape() == dat.shape() # Test detrend detrend = dat.detrend() assert detrend.shape() == dat.shape() # Test standardize s = dat.standardize() assert s.shape() == dat.shape() assert np.isclose(np.sum(s.mean().data), 0, atol=.1) s = dat.standardize(method='zscore') assert s.shape() == dat.shape() assert np.isclose(np.sum(s.mean().data), 0, atol=.1) # Test Sum s = dat.sum() assert s.shape() == dat[1].shape() # Test Groupby s1 = create_sphere([12, 10, -8], radius=10) s2 = create_sphere([22, -2, -22], radius=10) mask = Brain_Data([s1, s2]) d = dat.groupby(mask) assert isinstance(d, Groupby) # Test Aggregate mn = dat.aggregate(mask, 'mean') assert isinstance(mn, Brain_Data) assert len(mn.shape()) == 1 # Test Threshold s1 = create_sphere([12, 10, -8], radius=10) s2 = create_sphere([22, -2, -22], radius=10) mask = Brain_Data(s1)*5 mask = mask + Brain_Data(s2) m1 = mask.threshold(upper=.5) m2 = mask.threshold(upper=3) m3 = mask.threshold(upper='98%') m4 = Brain_Data(s1)*5 + Brain_Data(s2)*-.5 m4 = mask.threshold(upper=.5,lower=-.3) assert np.sum(m1.data > 0) > np.sum(m2.data > 0) assert np.sum(m1.data > 0) == np.sum(m3.data > 0) assert np.sum(m4.data[(m4.data > -.3) & (m4.data <.5)]) == 0 assert np.sum(m4.data[(m4.data < -.3) | (m4.data >.5)]) > 0 # Test Regions r = mask.regions(min_region_size=10) m1 = Brain_Data(s1) m2 = r.threshold(1, binarize=True) # assert len(r)==2 assert len(np.unique(r.to_nifti().get_data())) == 2 diff = m2-m1 assert np.sum(diff.data) == 0 # Test Bootstrap masked = dat.apply_mask(create_sphere(radius=10, coordinates=[0, 0, 0])) n_samples = 3 b = masked.bootstrap('mean', n_samples=n_samples) assert isinstance(b['Z'], Brain_Data) b = masked.bootstrap('std', n_samples=n_samples) assert isinstance(b['Z'], Brain_Data) b = masked.bootstrap('predict', n_samples=n_samples, plot=False) assert isinstance(b['Z'], Brain_Data) b = masked.bootstrap('predict', n_samples=n_samples, plot=False, cv_dict={'type':'kfolds','n_folds':3}) assert isinstance(b['Z'], Brain_Data) b = masked.bootstrap('predict', n_samples=n_samples, save_weights=True, plot=False) assert len(b['samples'])==n_samples # Test decompose n_components = 3 stats = dat.decompose(algorithm='pca', axis='voxels', n_components=n_components) assert n_components == len(stats['components']) assert stats['weights'].shape == (len(dat), n_components) stats = dat.decompose(algorithm='ica', axis='voxels', n_components=n_components) assert n_components == len(stats['components']) assert stats['weights'].shape == (len(dat), n_components) dat.data = dat.data + 2 dat.data[dat.data<0] = 0 stats = dat.decompose(algorithm='nnmf', axis='voxels', n_components=n_components) assert n_components == len(stats['components']) assert stats['weights'].shape == (len(dat), n_components) stats = dat.decompose(algorithm='fa', axis='voxels', n_components=n_components) assert n_components == len(stats['components']) assert stats['weights'].shape == (len(dat), n_components) stats = dat.decompose(algorithm='pca', axis='images', n_components=n_components) assert n_components == len(stats['components']) assert stats['weights'].shape == (len(dat), n_components) stats = dat.decompose(algorithm='ica', axis='images', n_components=n_components) assert n_components == len(stats['components']) assert stats['weights'].shape == (len(dat), n_components) dat.data = dat.data + 2 dat.data[dat.data<0] = 0 stats = dat.decompose(algorithm='nnmf', axis='images', n_components=n_components) assert n_components == len(stats['components']) assert stats['weights'].shape == (len(dat), n_components) stats = dat.decompose(algorithm='fa', axis='images', n_components=n_components) assert n_components == len(stats['components']) assert stats['weights'].shape == (len(dat), n_components) # Test Hyperalignment Method sim = Simulator() y = [0, 1] n_reps = 10 s1 = create_sphere([0, 0, 0], radius=3) d1 = sim.create_data(y, 1, reps=n_reps, output_dir=None).apply_mask(s1) d2 = sim.create_data(y, 2, reps=n_reps, output_dir=None).apply_mask(s1) d3 = sim.create_data(y, 3, reps=n_reps, output_dir=None).apply_mask(s1) # Test procrustes using align data = [d1, d2, d3] out = align(data, method='procrustes') assert len(data) == len(out['transformed']) assert len(data) == len(out['transformation_matrix']) assert data[0].shape() == out['common_model'].shape() transformed = np.dot(d1.data, out['transformation_matrix'][0]) centered = d1.data - np.mean(d1.data, 0) transformed = (np.dot(centered/np.linalg.norm(centered), out['transformation_matrix'][0])*out['scale'][0]) np.testing.assert_almost_equal(0, np.sum(out['transformed'][0].data - transformed), decimal=5) # Test deterministic brain_data bout = d1.align(out['common_model'], method='deterministic_srm') assert d1.shape() == bout['transformed'].shape() assert d1.shape() == bout['common_model'].shape() assert d1.shape()[1] == bout['transformation_matrix'].shape[0] btransformed = np.dot(d1.data, bout['transformation_matrix']) np.testing.assert_almost_equal(0, np.sum(bout['transformed'].data - btransformed)) # Test deterministic brain_data bout = d1.align(out['common_model'], method='probabilistic_srm') assert d1.shape() == bout['transformed'].shape() assert d1.shape() == bout['common_model'].shape() assert d1.shape()[1] == bout['transformation_matrix'].shape[0] btransformed = np.dot(d1.data, bout['transformation_matrix']) np.testing.assert_almost_equal(0, np.sum(bout['transformed'].data-btransformed)) # Test procrustes brain_data bout = d1.align(out['common_model'], method='procrustes') assert d1.shape() == bout['transformed'].shape() assert d1.shape() == bout['common_model'].shape() assert d1.shape()[1] == bout['transformation_matrix'].shape[0] centered = d1.data - np.mean(d1.data, 0) btransformed = (np.dot(centered/np.linalg.norm(centered), bout['transformation_matrix'])*bout['scale']) np.testing.assert_almost_equal(0, np.sum(bout['transformed'].data-btransformed), decimal=5) np.testing.assert_almost_equal(0, np.sum(out['transformed'][0].data - bout['transformed'].data)) # Test hyperalignment on Brain_Data over time (axis=1) sim = Simulator() y = [0, 1] n_reps = 10 s1 = create_sphere([0, 0, 0], radius=5) d1 = sim.create_data(y, 1, reps=n_reps, output_dir=None).apply_mask(s1) d2 = sim.create_data(y, 2, reps=n_reps, output_dir=None).apply_mask(s1) d3 = sim.create_data(y, 3, reps=n_reps, output_dir=None).apply_mask(s1) data = [d1, d2, d3] out = align(data, method='procrustes', axis=1) assert len(data) == len(out['transformed']) assert len(data) == len(out['transformation_matrix']) assert data[0].shape() == out['common_model'].shape() centered = data[0].data.T-np.mean(data[0].data.T, 0) transformed = (np.dot(centered/np.linalg.norm(centered), out['transformation_matrix'][0])*out['scale'][0]) np.testing.assert_almost_equal(0,np.sum(out['transformed'][0].data-transformed.T), decimal=5) bout = d1.align(out['common_model'], method='deterministic_srm', axis=1) assert d1.shape() == bout['transformed'].shape() assert d1.shape() == bout['common_model'].shape() assert d1.shape()[0] == bout['transformation_matrix'].shape[0] btransformed = np.dot(d1.data.T, bout['transformation_matrix']) np.testing.assert_almost_equal(0, np.sum(bout['transformed'].data-btransformed.T)) bout = d1.align(out['common_model'], method='probabilistic_srm', axis=1) assert d1.shape() == bout['transformed'].shape() assert d1.shape() == bout['common_model'].shape() assert d1.shape()[0] == bout['transformation_matrix'].shape[0] btransformed = np.dot(d1.data.T, bout['transformation_matrix']) np.testing.assert_almost_equal(0, np.sum(bout['transformed'].data-btransformed.T)) bout = d1.align(out['common_model'], method='procrustes', axis=1) assert d1.shape() == bout['transformed'].shape() assert d1.shape() == bout['common_model'].shape() assert d1.shape()[0] == bout['transformation_matrix'].shape[0] centered = d1.data.T-np.mean(d1.data.T, 0) btransformed = (np.dot(centered/np.linalg.norm(centered), bout['transformation_matrix'])*bout['scale']) np.testing.assert_almost_equal(0, np.sum(bout['transformed'].data-btransformed.T), decimal=5) np.testing.assert_almost_equal(0, np.sum(out['transformed'][0].data-bout['transformed'].data))
import glob con1_name = 'horizontal_checkerboard' con1_file_list = glob.glob( os.path.join(data_dir, 'derivatives', 'fmriprep', '*', 'func', f'sub*_{con1_name}*nii.gz')) con1_file_list.sort() con1_dat = Brain_Data(con1_file_list) # Now that we have the data loaded, we can run quick operations such as, what is the mean activation in each voxel across participants? Or, what is the standard deviation of the voxel activity across participants? # # Notice how we can chain different commands like `.mean()` and `.plot()`. This makes it easy to quickly manipulate the data similar to how we use tools like pandas. # In[89]: con1_dat.mean().plot() # We can use the `ttest()` method to run a quick t-test across each voxel in the brain. # In[90]: con1_stats = con1_dat.ttest() print(con1_stats.keys()) # This return a dictionary of a map of the t-values and a separate one containing the p-value for each voxel. # # For now, let's look at the results of the t-ttest and threshold them to something like t>4. # In[91]:
print(data[[1, 6, 2]].shape()) print(data[0:10].shape()) index = np.zeros(len(data), dtype=bool) index[[1, 5, 9, 16, 20, 22]] = True print(data[index].shape()) # ### Simple Arithmetic Operations # Calculate the mean for every voxel over images # In[52]: data.mean() # Calculate the standard deviation for every voxel over images # In[53]: data.std() # Methods can be chained. Here we get the shape of the mean. # In[54]: print(data.mean().shape()) # Brain_Data instances can be added and subtracted
def test_brain_data(tmpdir): sim = Simulator() r = 10 sigma = 1 y = [0, 1] n_reps = 3 output_dir = str(tmpdir) sim.create_data(y, sigma, reps=n_reps, output_dir=output_dir) shape_3d = (91, 109, 91) shape_2d = (6, 238955) y=pd.read_csv(os.path.join(str(tmpdir.join('y.csv'))), header=None,index_col=None).T holdout=pd.read_csv(os.path.join(str(tmpdir.join('rep_id.csv'))),header=None,index_col=None).T flist = glob.glob(str(tmpdir.join('centered*.nii.gz'))) # Test load list dat = Brain_Data(data=flist,Y=y) # Test load file assert Brain_Data(flist[0]) # Test to_nifti d = dat.to_nifti() assert d.shape[0:3] == shape_3d # Test load nibabel assert Brain_Data(d) # Test shape assert dat.shape() == shape_2d # Test Mean assert dat.mean().shape()[0] == shape_2d[1] # Test Std assert dat.std().shape()[0] == shape_2d[1] # Test add new = dat + dat assert new.shape() == shape_2d # Test subtract new = dat - dat assert new.shape() == shape_2d # Test multiply new = dat * dat assert new.shape() == shape_2d # Test Iterator x = [x for x in dat] assert len(x) == len(dat) assert len(x[0].data.shape) == 1 # # Test T-test out = dat.ttest() assert out['t'].shape()[0] == shape_2d[1] # # # Test T-test - permutation method # out = dat.ttest(threshold_dict={'permutation':'tfce','n_permutations':50,'n_jobs':1}) # assert out['t'].shape()[0]==shape_2d[1] # Test Regress dat.X = pd.DataFrame({'Intercept':np.ones(len(dat.Y)), 'X1':np.array(dat.Y).flatten()},index=None) out = dat.regress() assert out['beta'].shape() == (2,shape_2d[1]) # Test indexing assert out['t'][1].shape()[0] == shape_2d[1] # Test threshold i=1 tt = threshold(out['t'][i], out['p'][i], .05) assert isinstance(tt,Brain_Data) # Test write dat.write(os.path.join(str(tmpdir.join('test_write.nii')))) assert Brain_Data(os.path.join(str(tmpdir.join('test_write.nii')))) # Test append assert dat.append(dat).shape()[0]==shape_2d[0]*2 # Test distance distance = dat.distance(method='euclidean') assert isinstance(distance,Adjacency) assert distance.square_shape()[0]==shape_2d[0] # Test predict stats = dat.predict(algorithm='svm', cv_dict={'type': 'kfolds','n_folds': 2}, plot=False,**{'kernel':"linear"}) # Support Vector Regression, with 5 fold cross-validation with Platt Scaling # This will output probabilities of each class stats = dat.predict(algorithm='svm', cv_dict=None, plot=False,**{'kernel':'linear', 'probability':True}) assert isinstance(stats['weight_map'],Brain_Data) # Logistic classificiation, with 2 fold cross-validation. stats = dat.predict(algorithm='logistic', cv_dict={'type': 'kfolds', 'n_folds': 2}, plot=False) assert isinstance(stats['weight_map'],Brain_Data) # Ridge classificiation, stats = dat.predict(algorithm='ridgeClassifier', cv_dict=None,plot=False) assert isinstance(stats['weight_map'],Brain_Data) # Ridge stats = dat.predict(algorithm='ridge', cv_dict={'type': 'kfolds', 'n_folds': 2,'subject_id':holdout}, plot=False,**{'alpha':.1}) # Lasso stats = dat.predict(algorithm='lasso', cv_dict={'type': 'kfolds', 'n_folds': 2,'stratified':dat.Y}, plot=False,**{'alpha':.1}) # PCR stats = dat.predict(algorithm='pcr', cv_dict=None, plot=False) # Test Similarity r = dat.similarity(stats['weight_map']) assert len(r) == shape_2d[0] r2 = dat.similarity(stats['weight_map'].to_nifti()) assert len(r2) == shape_2d[0] # Test apply_mask - might move part of this to test mask suite s1 = create_sphere([12, 10, -8], radius=10) assert isinstance(s1, nb.Nifti1Image) s2 = Brain_Data(s1) masked_dat = dat.apply_mask(s1) assert masked_dat.shape()[1] == np.sum(s2.data != 0) # Test extract_roi mask = create_sphere([12, 10, -8], radius=10) assert len(dat.extract_roi(mask)) == shape_2d[0] # Test r_to_z z = dat.r_to_z() assert z.shape() == dat.shape() # Test copy d_copy = dat.copy() assert d_copy.shape() == dat.shape() # Test detrend detrend = dat.detrend() assert detrend.shape() == dat.shape() # Test standardize s = dat.standardize() assert s.shape() == dat.shape() assert np.isclose(np.sum(s.mean().data), 0, atol=.1) s = dat.standardize(method='zscore') assert s.shape() == dat.shape() assert np.isclose(np.sum(s.mean().data), 0, atol=.1) # Test Sum s = dat.sum() assert s.shape() == dat[1].shape() # Test Groupby s1 = create_sphere([12, 10, -8], radius=10) s2 = create_sphere([22, -2, -22], radius=10) mask = Brain_Data([s1, s2]) d = dat.groupby(mask) assert isinstance(d, Groupby) # Test Aggregate mn = dat.aggregate(mask, 'mean') assert isinstance(mn, Brain_Data) assert len(mn.shape()) == 1 # Test Threshold s1 = create_sphere([12, 10, -8], radius=10) s2 = create_sphere([22, -2, -22], radius=10) mask = Brain_Data(s1)*5 mask = mask + Brain_Data(s2) m1 = mask.threshold(thresh=.5) m2 = mask.threshold(thresh=3) m3 = mask.threshold(thresh='98%') assert np.sum(m1.data > 0) > np.sum(m2.data > 0) assert np.sum(m1.data > 0) == np.sum(m3.data > 0) # Test Regions r = mask.regions(min_region_size=10) m1 = Brain_Data(s1) m2 = r.threshold(1, binarize=True) # assert len(r)==2 assert len(np.unique(r.to_nifti().get_data())) == 2 # JC edit: I think this is what you were trying to do diff = m2-m1 assert np.sum(diff.data) == 0
# # Here we will convolve the image with a 3-D gaussian kernel, with a 6mm full width half maximum (FWHM) using the `.smooth()` method. # In[50]: fwhm=6 smoothed = data.smooth(fwhm=fwhm) # Let's take a look and see how this changes the image. # In[40]: data.mean().plot() # In[41]: smoothed.mean().plot() # ## Estimate GLM for all voxels # Now we are ready to estimate the regression model for all voxels. # # We will assign the design_matrix object to the `.X` attribute of our `Brain_Data` instance. # # Then we simply need to run the `.regress()` method.
For our first group analysis, let's try to examine which regions of the brain are consistently activated across participants. We will just load the first regressor in the design matrix - *horizontal_checkerboard*. We will use the `glob` function to search for all files that contain the name *horizontal_checkerboard* in each subject's folder. We will then sort the list and load all of the files using the `Brain_Data` class. This will take a little bit to load all of the data into ram. import glob con1_name = 'horizontal_checkerboard' con1_file_list = glob.glob(os.path.join(data_dir, 'derivatives','fmriprep','*', 'func', f'sub*_{con1_name}*nii.gz')) con1_file_list.sort() con1_dat = Brain_Data(con1_file_list) Now that we have the data loaded, we can run quick operations such as, what is the mean activation in each voxel across participants? Or, what is the standard deviation of the voxel activity across participants? Notice how we can chain different commands like `.mean()` and `.plot()`. This makes it easy to quickly manipulate the data similar to how we use tools like pandas. con1_dat.mean().plot() We can use the `ttest()` method to run a quick t-test across each voxel in the brain. con1_stats = con1_dat.ttest() print(con1_stats.keys()) This return a dictionary of a map of the t-values and a separate one containing the p-value for each voxel. For now, let's look at the results of the t-ttest and threshold them to something like t>4. con1_stats['t'].iplot() As you can see we see very clear activation in various parts of visual cortex, which we expected from the visual stimulation.
import pandas as pd #could do it here or could grab a set of raw (per-study) images from neurosynth #and repeatedly pdata = fetch_pain() pdata = Brain_Data(pdata) print(len(pdata)) #data_nifti=pdata.to_nifti() #some interesting things about how to plot brains with this package from nilearn.plotting import plot_glass_brain plot_glass_brain(pdata.mean().to_nifti()) pdata.mean().plot() from nltools.plotting import plotBrain plotBrain(pdata.X) data_subjs=pdata.X["SubjectID"].unique() len(data_subjs) train_subjs=pandas.DataFrame(data_subjs).sample(frac=0.9,random_state=2057) test_subjs= [s for s in data_subjs if s not in train_subjs[0]] pdata.Y=pdata.X["PainLevel"] data_train = pdata[train_subjs[0]]