def analysis(**kwargs):
"""
Method to fast analyze states from matrices.
Parameters
----------
kwargs : dictionary of several parameters
path : string of the data path
filetype : string ('masked', 'original')
if you want to use masked matrix or full rank.
fname : string. pattern of the mat file in input
default ("mat_corr_sub_%s.mat")
conditions : list of string of data conditions.
should cope with file pattern specified in fname.
method : string ('speed', 'variance')
Method used to subsample data.
max_k : integer (default = 15).
The maximum number of cluster to use
state_res_fname : pattern of the output file
(default "clustering_labels_%s_maxk_%s_%s_%s.pyobj")
File used to save labels after clustering.
"""
from mvpa_itab.conn.states.utils import plot_metrics, plot_states_matrices
configuration = {
"path": "/media/robbis/DATA/fmri/movie_viviana/",
"filetype": "masked",
"fname": "mat_corr_sub_%s.mat",
"conditions": ["movie", "scramble", "rest"],
"state_res_fname": "clustering_labels_%s_maxk_%s_%s_%s.pyobj",
"max_k": 15,
"method": "speed",
}
configuration.update(kwargs)
conditions = configuration["conditions"]
max_k = configuration["max_k"]
method = configuration["method"]
filetype = configuration["filetype"]
for cond in conditions:
path = os.path.join(configuration["path"], configuration["filetype"])
data_, n_roi = get_data(os.path.join(path, "mat_corr_sub_%s.mat" % (str.upper(cond))))
X, clustering_ = cluster_state(data_, max_k, method)
metrics_, k_step, metrics_keys = calculate_metrics(X, clustering_)
fig = plot_metrics(metrics_, metrics_keys, k_step)
directory = os.path.join(path, method, cond)
if not os.path.exists(directory):
os.makedirs(directory)
state_res_fname = configuration["state_res_fname"]
pickle.dump(
clustering_, file(os.path.join(directory, state_res_fname % (cond, str(max_k), method, filetype)), "w")
)
fig_fname = os.path.join(directory, "metrics.png")
fig.savefig(fig_fname)
for i, cl in enumerate(clustering_):
if not os.path.exists(os.path.join(directory, str(i + 2))):
os.makedirs(os.path.join(directory, str(i + 2)))
plot_states_matrices(X, cl, os.path.join(directory, str(i + 2)), cond)
max_k = 15
method = 'speed'
X, clustering_ = cluster_state(data_, max_k, method)
metrics_, k_step, metrics_keys = calculate_metrics(X, clustering_)
pickle.dump(clustering_, file(os.path.join(path,
"clustering_labels_%s_maxk_%s_%s_%s.pyobj" %(cond,
str(max_k),
method,
filetype),
),
'w'))
plot_states_matrices(X,
clustering_[3],
path,
cond)
#################
order = {'MOVIE': [0,1,2,3,4],
'REST': [4,3,1,0,2],
'SCRAMBLE' : [1,2,4,0,3]
}
order = {'MOVIE': [0,1,2,3,4],
'REST': [3,1,2,4,0],
'SCRAMBLE' : [0,4,2,1,3]
}
path = '/media/robbis/DATA/fmri/movie_viviana/masked'