def setup_analysis(self, path, roi_list, directory, conditions, subjects):
self.directory = directory
self.conditions = conditions
self.subjects = subjects
conn = ConnectivityLoader(path, self.subjects, self.directory, roi_list)
conn.get_results(self.conditions)
self.ds = conn.get_dataset()
return self
def get_analysis_mask(path, subjects, directory, roi_list):
######## Get matrix infos ###############
conn_test = ConnectivityLoader(path, subjects, directory, roi_list)
# Get nan mask to correctly fill matrix
nan_mask = conn_test.get_results(['Samatha', 'Vipassana'])
# Transform matrix into float of ones
mask_ = np.float_(~np.bool_(nan_mask))
# Get the upper part of the matrix
mask_ = np.triu(mask_, k=1)
return mask_
def get_analysis_mask(path, subjects, directory, roi_list):
######## Get matrix infos ###############
conn_test = ConnectivityLoader(path,
subjects,
directory,
roi_list)
# Get nan mask to correctly fill matrix
nan_mask = conn_test.get_results(['Samatha', 'Vipassana'])
# Transform matrix into float of ones
mask_ = np.float_(~np.bool_(nan_mask))
# Get the upper part of the matrix
mask_ = np.triu(mask_, k=1)
return mask_
def write_correlation_matrices(directory, condition):
subjects = np.loadtxt(
'/media/robbis/DATA/fmri/monks/attributes_struct.txt', dtype=np.str)
roi_list = np.loadtxt(
'/media/robbis/DATA/fmri/templates_fcmri/findlab_rois.txt',
delimiter=',',
dtype=np.str)
path = '/media/robbis/DATA/fmri/monks/0_results/'
conn = ConnectivityLoader(path, subjects, directory, roi_list)
nan_mask = conn.get_results(['Samatha', 'Vipassana'])
#nan_mask = conn.get_results(['Rest'])
ds = conn.get_dataset()
mask_ = np.float_(~np.bool_(nan_mask))
mask_ = np.triu(mask_, k=1)
mask_indices = np.nonzero(mask_)
ds_ = ds[np.logical_and(ds.targets == condition, ds.sa.groups == 'E')]
array_ = ds_.samples.mean(0)
mask_[mask_indices] = array_
matrix = np.nan_to_num(copy_matrix(mask_, diagonal_filler=0))
names_lr, colors_lr, index_, coords, networks = get_atlas_info('findlab')
plot_connectomics(
matrix,
20 + 8 * np.abs(matrix.sum(axis=1))**2,
save_path=os.path.join(path, directory),
prename=condition + '_correlation',
save=True,
colormap='bwr',
vmin=np.abs(matrix).max() * -1,
vmax=np.abs(matrix).max(),
node_names=names_lr,
node_colors=colors_lr,
node_coords=coords,
node_order=index_,
networks=networks,
threshold=0.5,
title=condition + ' Correlation',
zscore=False,
)
w_aggregate = aggregate_networks(matrix, roi_list.T[-2])
_, idx = np.unique(networks, return_index=True)
plot_connectomics(w_aggregate,
5 * np.abs(w_aggregate.sum(axis=1))**2,
save_path=os.path.join(path, directory),
prename=condition + '_aggregate_correlation',
save=True,
colormap='bwr',
vmin=-1 * w_aggregate.max(),
vmax=w_aggregate.max(),
node_names=np.unique(networks),
node_colors=colors_lr[idx],
node_coords=coords[idx],
node_order=np.arange(0, len(idx)),
networks=np.unique(networks),
threshold=4,
zscore=False)
def write_correlation_matrices(directory, condition):
subjects = np.loadtxt('/media/robbis/DATA/fmri/monks/attributes_struct.txt',
dtype=np.str)
roi_list = np.loadtxt('/media/robbis/DATA/fmri/templates_fcmri/findlab_rois.txt',
delimiter=',',
dtype=np.str)
path = '/media/robbis/DATA/fmri/monks/0_results/'
conn = ConnectivityLoader(path, subjects, directory, roi_list)
nan_mask = conn.get_results(['Samatha', 'Vipassana'])
#nan_mask = conn.get_results(['Rest'])
ds = conn.get_dataset()
mask_ = np.float_(~np.bool_(nan_mask))
mask_ = np.triu(mask_, k=1)
mask_indices = np.nonzero(mask_)
ds_ = ds[np.logical_and(ds.targets == condition, ds.sa.groups == 'E')]
array_ = ds_.samples.mean(0)
mask_[mask_indices] = array_
matrix = np.nan_to_num(copy_matrix(mask_, diagonal_filler=0))
names_lr, colors_lr, index_, coords, networks = get_atlas_info('findlab')
plot_connectomics(matrix,
20+8*np.abs(matrix.sum(axis=1))**2,
save_path=os.path.join(path, directory),
prename=condition+'_correlation',
save=True,
colormap='bwr',
vmin=np.abs(matrix).max()*-1,
vmax=np.abs(matrix).max(),
node_names=names_lr,
node_colors=colors_lr,
node_coords=coords,
node_order=index_,
networks=networks,
threshold=0.5,
title=condition+' Correlation',
zscore=False,
)
w_aggregate = aggregate_networks(matrix, roi_list.T[-2])
_, idx = np.unique(networks, return_index=True)
plot_connectomics(w_aggregate,
5*np.abs(w_aggregate.sum(axis=1))**2,
save_path=os.path.join(path, directory),
prename=condition+'_aggregate_correlation',
save=True,
colormap='bwr',
vmin=-1*w_aggregate.max(),
vmax=w_aggregate.max(),
node_names=np.unique(networks),
node_colors=colors_lr[idx],
node_coords=coords[idx],
node_order=np.arange(0, len(idx)),
networks=np.unique(networks),
threshold=4,
zscore=False
)
# for g in np.unique(fdata['groups']):
for g in ["Samatha", "Vipassana"]:
print "---------------------------"
print r + " ----- " + g
err = cv(ds[ds.sa.meditation == g])
print cv.ca.stats
##########################################
file_ = open(os.path.join("/media/robbis/DATA/fmri/monks/", "0_results", "results_decoding_new.txt"), "w")
line_ = ""
results_dir = ["20140513_163451_connectivity_fmri"]
# results_dir = ['20151030_141350_connectivity_filtered_first_no_gsr_findlab_fmri']
for r in results_dir:
print "··········· " + r + " ·············"
conn = ConnectivityLoader(path, subjects, r, roi_list)
nan_mask = conn.get_results(["Samatha", "Vipassana"])
# nan_mask = conn.get_results(['Rest'])
ds = conn.get_dataset()
"""
fx = mean_group_sample(['subjects', 'meditation'])
ds = ds.get_mapped(fx)
"""
clf = LinearCSVMC(C=1)
# clf = RbfCSVMC()
ds.targets = ds.sa.groups
# ds.samples = decomposition.KernelPCA(kernel="poly", n_components=30).fit_transform(ds.samples)
fsel = SensitivityBasedFeatureSelection(OneWayAnova(), FractionTailSelector(0.01, mode="select", tail="upper"))
delimiter=',',
dtype=np.str)
style_ = 'Samatha'
cv_repetitions = 250
cv_fraction = 0.5
num_exp_subjects = subjects[subjects.T[1] == group_].shape[0]
cv = ShuffleSplit(num_exp_subjects,
n_iter=cv_repetitions,
test_size=cv_fraction)
algorithm = SVR(kernel='linear', C=1)
# Load data
conn = ConnectivityLoader(path, subjects, r, roi_list)
conn.get_results(conditions)
ds = conn.get_dataset()
ds = ds[np.logical_and(ds.sa.meditation == style_, ds.sa.groups == group_)]
# Select data
X = ds.samples
y = np.float_(ds.sa.expertise) * 0.01
# preprocess
X_ = zscore(X, axis=1) # Sample-wise
y_ = zscore(y)
c = Correlation(X_)
corr = c.transform(X_, y_)[0]
delimiter=',',
dtype=np.str)
style_ = 'Samatha'
cv_repetitions = 250
cv_fraction = 0.5
num_exp_subjects = subjects[subjects.T[1] == group_].shape[0]
cv = ShuffleSplit(num_exp_subjects, n_iter=cv_repetitions, test_size=cv_fraction)
algorithm = SVR(kernel='linear', C=1)
# Load data
conn = ConnectivityLoader(path, subjects, r, roi_list)
conn.get_results(conditions)
ds = conn.get_dataset()
ds = ds[np.logical_and(ds.sa.meditation == style_, ds.sa.groups == group_)]
# Select data
X = ds.samples
y = np.float_(ds.sa.expertise)*0.01
# preprocess
X_ = zscore(X, axis=1) # Sample-wise
y_ = zscore(y)
c = Correlation(X_)
corr = c.run(X_, y_)[0]
def analyze_results(directory,
conditions,
n_permutations=1000.):
"""Write the results of the regression analysis
Parameters
----------
directory : string or list of strings
Path or list of paths where put results.
condition : string or list of strings
Conditions to be analyzed.
Returns
-------
fig : instance of matplotlib.pyplot.Figure
The figure handle.
"""
res_path = '/media/robbis/DATA/fmri/monks/0_results/'
subjects = np.loadtxt('/media/robbis/DATA/fmri/monks/attributes_struct.txt',
dtype=np.str)
path = '/media/robbis/DATA/fmri/monks/'
roi_list = []
roi_list = np.loadtxt('/media/robbis/DATA/fmri/templates_fcmri/findlab_rois.txt',
delimiter=',',
dtype=np.str)
if isinstance(directory, str):
directory = [directory]
if isinstance(conditions, str):
conditions = [conditions]
for dir_ in directory:
for cond_ in conditions:
fname_ = os.path.join(res_path, dir_, cond_+'_values_1000_50.npz')
results_ = np.load(fname_)
values_ = results_['arr_0'].tolist()
errors_ = values_['error'] #values_['errors_']
sets_ = values_['features'] #values_['sets_']
weights_ = values_['weights'] #values_['weights_']
samples_ = values_['subjects'] #values_['samples_']
fname_ = os.path.join(res_path, dir_, cond_+'_permutation_1000_50.npz')
results_ = np.load(fname_)
values_p = results_['arr_0'].tolist()
errors_p = values_p['error'] #values_p['errors_p']
sets_p = values_p['features'] #values_p['sets_p']
weights_p = values_p['weights'] #values_p['weights_p']
samples_p = values_p['subjects'] #values_p['samples_p']
errors_p = np.nanmean(errors_p, axis=1)
print('-----------'+dir_+'-------------')
print(cond_)
print ('MSE = '+str(errors_[:,0].mean())+' -- p '+ \
str(np.count_nonzero(errors_p[:,0] < errors_[:,0].mean())/n_permutations))
print('COR = '+str(np.nanmean(errors_[:,1]))+' -- p '+ \
str(np.count_nonzero(errors_p[:,1] > np.nanmean(errors_[:,1]))/n_permutations))
directory_ = dir_
learner_ = "SVR_C_1"
prename = "%s_%s" %(cond_, learner_)
######## Get matrix infos ###############
conn_test = ConnectivityLoader(res_path,
subjects,
directory_,
roi_list)
# Get nan mask to correctly fill matrix
nan_mask = conn_test.get_results(['Samatha', 'Vipassana'])
# Transform matrix into float of ones
mask_ = np.float_(~np.bool_(nan_mask))
# Get the upper part of the matrix
mask_ = np.triu(mask_, k=1)
mask_indices = np.nonzero(mask_)
n_bins = np.count_nonzero(mask_)
###### Plot of distributions of errors and permutations #########
#errors_p = np.nanmean(errors_p, axis=1)
fig_ = pl.figure()
bpp = pl.boxplot(errors_p, showfliers=False, showmeans=True, patch_artist=True)
bpv = pl.boxplot(errors_, showfliers=False, showmeans=True, patch_artist=True)
fname = "%s_perm_1000_boxplot.png" %(prename)
for box_, boxp_ in zip(bpv['boxes'], bpp['boxes']):
box_.set_facecolor('lightgreen')
boxp_.set_facecolor('lightslategrey')
pl.xticks(np.array([1,2]), ['MSE', 'COR'])
pl.savefig(os.path.join(res_path, directory_, fname))
pl.close()
n_permutations = np.float(errors_p[:,0].shape[0])
##### Plot of connection distributions ########
pl.figure()
h_values_p, _ = np.histogram(sets_p.flatten(), bins=np.arange(0, n_bins+1))
#pl.plot(zscore(h_values_p))
pl.hist(zscore(h_values_p), bins=25)
fname = "%s_features_set_dist.png" %(prename)
pl.savefig(os.path.join(res_path, directory_, fname))
pl.figure()
h_values_, _ = np.histogram(sets_.flatten(), bins=np.arange(0, n_bins+1))
pl.plot(zscore(h_values_))
fname = "%s_features_set_cross_validation.png" %(prename)
pl.savefig(os.path.join(res_path, directory_, fname))
pl.close('all')
######## Plot connectivity stuff ###########
weights_ = weights_.squeeze()
filling_vector = np.zeros(np.count_nonzero(mask_))
counting_vector = np.zeros(np.count_nonzero(mask_))
for s, w in zip(sets_, weights_):
filling_vector[s] += zscore(w)
counting_vector[s] += 1
# Calculate the average weights and then zscore
avg_weigths = np.nan_to_num(filling_vector/counting_vector)
mask_[mask_indices] = avg_weigths
matrix_ = np.nan_to_num(copy_matrix(mask_, diagonal_filler=0))
names_lr, colors_lr, index_, coords, _ = get_atlas_info(dir_)
'''
matrix_[matrix_ == 0] = np.nan
matrix_[np.abs(matrix_) < 1] = np.nan
'''
size_w = np.zeros_like(matrix_)
size_w[mask_indices] = np.abs(avg_weigths)
size_w = np.nan_to_num(copy_matrix(size_w, diagonal_filler=0))
size_w = np.sum(size_w, axis=0)
f, _ = plot_connectivity_circle_edited(matrix_[index_][:,index_],
names_lr[index_],
node_colors=colors_lr[index_],
node_size=2*size_w[index_]**2,
con_thresh = 1.4,
title=cond_,
node_angles=circular_layout(names_lr,
list(names_lr),
),
fontsize_title=19,
fontsize_names=13,
fontsize_colorbar=13,
colorbar_size=0.3,
colormap='bwr',
#colormap=cm_,
vmin=-3.,
vmax=3.,
fig=pl.figure(figsize=(16,16))
)
fname = "%s_features_weight.png" %(prename)
f.savefig(os.path.join(res_path, directory_, fname),
facecolor='black',
dpi=150)
for d_ in ['x', 'y', 'z']:
fname = "%s_connectome_feature_weight_%s.png" %(prename, d_)
fname = os.path.join(res_path, directory_, fname)
plot_connectome(matrix_,
coords,
colors_lr,
2*size_w**2,
1.4,
fname,
#cmap=pl.cm.bwr,
title=None,
display_=d_,
#max_=3.,
#min_=3.
)
fname = "%s_connections_list_feature_weights.txt" %(prename)
fname = os.path.join(res_path, directory_, fname)
#print_connections(matrix_, names_lr, fname)
#########
mask_ = np.float_(~np.bool_(nan_mask))
mask_ = np.triu(mask_, k=1)
mask_indices = np.nonzero(mask_)
mask_[mask_indices] = h_values_
matrix_ = np.nan_to_num(copy_matrix(mask_, diagonal_filler=0))
size_ = np.zeros_like(matrix_)
size_[mask_indices] = counting_vector
size_ = np.nan_to_num(copy_matrix(size_, diagonal_filler=0))
size_ = np.sum(size_, axis=0)
f, _ = plot_connectivity_circle_edited(matrix_[index_][:,index_],
names_lr[index_],
node_colors=colors_lr[index_],
node_size=size_[index_]*5,
con_thresh = 15.,
title=cond_,
node_angles=circular_layout(names_lr,
list(names_lr),
),
fontsize_title=19,
fontsize_names=13,
fontsize_colorbar=13,
colorbar_size=0.3,
#colormap='bwr',
#colormap='terrain',
#vmin=40,
fig=pl.figure(figsize=(16,16))
)
fname = "%s_features_choices.png" %(prename)
f.savefig(os.path.join(res_path, directory_, fname),
facecolor='black',
dpi=150)
for d_ in ['x', 'y', 'z']:
fname = "%s_connectome_feature_choices_%s.png" %(prename, d_)
fname = os.path.join(res_path, directory_, fname)
plot_connectome(matrix_,
coords,
colors_lr,
4.*size_,
15.,
fname,
title=None,
max_=50.,
min_=0.,
display_=d_
)
fname = "%s_connections_list_feature_choices.txt" %(prename)
fname = os.path.join(res_path, directory_, fname)
#print_connections(matrix_, names_lr,fname)
pl.close('all')
