raise RuntimeError("This script needs the matplotlib library") import matplotlib as mpl from nipy.labs.spatial_models.hroi import HROI_from_watershed from nipy.labs.spatial_models.discrete_domain import grid_domain_from_shape import nipy.labs.utils.simul_multisubject_fmri_dataset as simul ############################################################################### # data simulation shape = (60, 60) pos = np.array([[12, 14], [20, 20], [30, 20]]) ampli = np.array([3, 4, 4]) x = simul.surrogate_2d_dataset(n_subj=1, shape=shape, pos=pos, ampli=ampli, width=10.0).squeeze() th = 2.36 # compute the field structure and perform the watershed domain = grid_domain_from_shape(shape) nroi = HROI_from_watershed(domain, np.ravel(x), threshold=th) label = nroi.label #compute the region-based signal average bfm = np.array([np.mean(x.ravel()[label == k]) for k in range(label.max() + 1)]) bmap = np.zeros(x.size) if label.max() > - 1: bmap[label > - 1] = bfm[label[label > - 1]] label = np.reshape(label, shape)
return AF, BF ############################################################################### # Main script ############################################################################### # generate the data n_subj = 10 shape = (60, 60) pos = np.array([[12, 14], [20, 20], [30, 20]]) ampli = np.array([5, 7, 6]) sjitter = 1.0 betas = simul.surrogate_2d_dataset(n_subj=n_subj, shape=shape, pos=pos, ampli=ampli, width=5.0) # set various parameters theta = float(st.t.isf(0.01, 100)) dmax = 5. / 1.5 ths = n_subj / 4 thq = 0.9 verbose = 1 smin = 5 method = 'simple'#'loo'#'quick'# # run the algo AF, BF = make_bsa_2d(betas, theta, dmax, ths, thq, smin, method, verbose=verbose) #mp.show()
plt.colorbar(shrink=.8) plt.savefig('bsa_results.png') ############################################################################### # Main script ############################################################################### # generate the data n_subjects = 10 shape = (60, 60) pos = np.array([[12, 14], [20, 20], [30, 20]]) ampli = np.array([5, 7, 6]) sjitter = 1.0 stats = simul.surrogate_2d_dataset(n_subj=n_subjects, shape=shape, pos=pos, ampli=ampli, width=5.0, seed=1) # set various parameters threshold = float(st.t.isf(0.01, 100)) sigma = 4. / 1.5 prevalence_threshold = n_subjects * .25 prevalence_pval = 0.9 smin = 5 algorithm = 'co-occurrence' # 'density' domain = grid_domain_from_shape(shape) # get the functional information stats_ = np.array([np.ravel(stats[k]) for k in range(n_subjects)]).T # run the algo
plt.colorbar(shrink=.8) ############################################################################### # Main script ############################################################################### # generate the data n_subjects = 10 shape = (60, 60) pos = np.array([[12, 14], [20, 20], [30, 20]]) ampli = np.array([5, 7, 6]) sjitter = 1.0 stats = simul.surrogate_2d_dataset(n_subj=n_subjects, shape=shape, pos=pos, ampli=ampli, width=5.0) # set various parameters threshold = float(st.t.isf(0.01, 100)) sigma = 4. / 1.5 prevalence_threshold = n_subjects * .25 prevalence_pval = 0.9 smin = 5 algorithm = 'co-occurrence' # 'density' domain = grid_domain_from_shape(shape) # get the functional information stats_ = np.array([np.ravel(stats[k]) for k in range(n_subjects)]).T # run the algo
import nipy.labs.utils.simul_multisubject_fmri_dataset as simul import nipy.labs.spatial_models.hroi as hroi from nipy.labs.spatial_models.discrete_domain import domain_from_array # --------------------------------------------------------- # simulate an activation image # --------------------------------------------------------- dimx = 60 dimy = 60 pos = np.array([[12, 14], [20, 20], [30, 20]]) ampli = np.array([3, 4, 4]) nbvox = dimx * dimy dataset = simul.surrogate_2d_dataset(nbsubj=1, dimx=dimx, dimy=dimy, pos=pos, ampli=ampli, width=10.0).squeeze() values = dataset.ravel() #------------------------------------------------------- # Computations #------------------------------------------------------- # create a domain descriptor associated with this domain = domain_from_array(dataset ** 2 > 0) nroi = hroi.HROI_as_discrete_domain_blobs(domain, dataset.ravel(), threshold=2.0, smin=3) label = np.reshape(nroi.label, ((dimx, dimy))) # create an average activaion image
try: import matplotlib.pyplot as plt except ImportError: raise RuntimeError("This script needs the matplotlib library") import nipy.labs.spatial_models.hroi as hroi import nipy.labs.utils.simul_multisubject_fmri_dataset as simul from nipy.labs.spatial_models.discrete_domain import domain_from_binary_array ############################################################################## # simulate the data shape = (60, 60) pos = np.array([[12, 14], [20, 20], [30, 20]]) ampli = np.array([3, 4, 4]) dataset = simul.surrogate_2d_dataset(n_subj=1, shape=shape, pos=pos, ampli=ampli, width=10.0).squeeze() # create a domain descriptor associated with this domain = domain_from_binary_array(dataset ** 2 > 0) nroi = hroi.HROI_as_discrete_domain_blobs(domain, dataset.ravel(), threshold=2., smin=5) n1 = nroi.copy() nroi.reduce_to_leaves() td = n1.make_forest().depth_from_leaves() root = np.argmax(td) lv = n1.make_forest().get_descendants(root) u = nroi.make_graph().cc()
import nipy.labs.utils.simul_multisubject_fmri_dataset as simul from nipy.labs.utils.reproducibility_measures import \ voxel_reproducibility, cluster_reproducibility, map_reproducibility,\ peak_reproducibility from nipy.labs.spatial_models.discrete_domain import grid_domain_from_array ############################################################################### # Generate the data nsubj = 105 dimx = 60 dimy = 60 pos = np.array([[12, 14], [20, 20], [30, 20]]) ampli = np.array([2.5, 3.5, 3]) dataset = simul.surrogate_2d_dataset(nbsubj=nsubj, dimx=dimx, dimy=dimy, pos=pos, ampli=ampli, width=5.0) betas = np.reshape(dataset, (nsubj, dimx, dimy)) # set the variance at 1 everywhere func = np.reshape(betas, (nsubj, dimx * dimy)).T var = np.ones((dimx * dimy, nsubj)) domain = grid_domain_from_array(np.ones((dimx, dimy, 1))) ############################################################################### # Run reproducibility analysis ngroups = 10 thresholds = np.arange(.5, 6., .5) sigma = 2.0 csize = 10 niter = 10
# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: import numpy as np import pylab as pl from nipy.labs.utils.simul_multisubject_fmri_dataset import \ surrogate_2d_dataset pos = np.array([[10, 10], [14, 20], [23, 18]]) ampli = np.array([4, 5, 2]) # First generate some noiseless data noiseless_data = surrogate_2d_dataset(n_subj=1, noise_level=0, spatial_jitter=0, signal_jitter=0, pos=pos, ampli=ampli) pl.figure(figsize=(10, 3)) pl.subplot(1, 4, 1) pl.imshow(noiseless_data[0]) pl.title('Noise-less data') # Second, generate some group data, with default noise parameters group_data = surrogate_2d_dataset(n_subj=3, pos=pos, ampli=ampli) pl.subplot(1, 4, 2) pl.imshow(group_data[0]) pl.title('Subject 1') pl.subplot(1, 4, 3)
# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: import numpy as np import pylab as pl from nipy.labs.utils.simul_multisubject_fmri_dataset import \ surrogate_2d_dataset pos = np.array([[10, 10], [14, 20], [23, 18]]) ampli = np.array([4, 5, 2]) # First generate some noiseless data noiseless_data = surrogate_2d_dataset(n_subj=1, noise_level=0, spatial_jitter=0, signal_jitter=0, pos=pos, ampli=ampli) pl.figure(figsize=(10, 3)) pl.subplot(1, 4, 1) pl.imshow(noiseless_data[0]) pl.title('Noise-less data') # Second, generate some group data, with default noise parameters group_data = surrogate_2d_dataset(n_subj=3, pos=pos, ampli=ampli) pl.subplot(1, 4, 2) pl.imshow(group_data[0]) pl.title('Subject 1') pl.subplot(1, 4, 3) pl.title('Subject 2') pl.imshow(group_data[1])