data_set=None,

**kwargs):

if data_set is None:

data_set = "ds107"

df, region_signals = _load_data(root_dir=root_dir,

data_set=data_set,

**kwargs)

data_set="ds107",

cache_dir="/volatile/storage/workspace/parietal_retreat/" +

"covariance_learn/cache/",

n_jobs=1):

from joblib import Memory

mem = Memory(cachedir=cache_dir)

load_data_ = mem.cache(setup_data_paths.run)

df = setup_data_paths.get_all_paths(root_dir=root_dir, data_set=data_set)

# region_signals = joblib.load(os.path.join(root_dir, dump_file))

region_signals = load_data_(root_dir=root_dir, data_set=data_set,

n_jobs=n_jobs,

dump_dir=os.path.join(cache_dir, data_set))

data_set_dir = os.path.join(root_dir,

data_set,

"models/model001/condition_key.txt")

with open(data_set_dir) as f:

conditions = list()

while True:

try:

line = f.readline()

if not line:

raise StopIteration

if len(line.split()) > 3:

conditions.append(" ".join(line.split()[2:]))

else:

conditions.append(line.split()[2])

except StopIteration:

df_ = df.groupby(["condition", "subj_id"])

df_list = list()

for names, group in df_:

data = list()

for ix_ in range(len(group)):

onset_file, TR, region_ix, confd_file =\

[group.iloc[ix_][k]

for k in ["cond_onsets", "TR", "region_ix", "confds"]]

confds = confound.compute_mvt_confounds(confd_file)[0]

signals = _regress(region_signals[region_ix], confds)

data.append(_get_samples(signals, onset_file, TR))

arr = np.vstack(data)

df_list.append({"condition": names[0],

"subj_id": names[1],

"region_signals": arr})

onsets = np.loadtxt(onset_file)

onsets_ = onsets[..., 0]

X1 = np.hstack((onsets_[:-1][np.diff(onsets_) > TR], onsets_[-1]))

X2 = np.hstack((onsets_[0], onsets_[1:][np.diff(onsets_) > TR]))

y1 = np.ones(shape=(X1.shape[0], ))

y2 = np.zeros(shape=(X2.shape[0], ))

X = np.hstack((X1, X2))

y = np.hstack((y1, y2))

y = y[np.argsort(X, kind="mergesort")]

X = np.sort(X, kind="mergesort") + 5.

z = np.arange(0, signals.shape[0] * np.float(TR), np.float(TR))

ix_ = np.array([True

if np.any(np.logical_and(X2 < t, t <= X1)) else False

for t in z])

covs = list()

for ix_ in range(len(df)):

time_series = df.iloc[ix_]["region_signals"]

cov_est = CovEst(assume_centered=True)

covs.append(cov_est.fit(time_series).covariance_)

df["covs"] = covs

"""

Gives the Z-Fisher transformed correlation matrix. Correlations 1 and -1

are transformed to nan.

Parameters

==========

corr: np.array

correlation matrix

Returns

=======

Z: np.array

Z-Fisher transformed correlation matrix

"""

eps = sys.float_info.epsilon # 1/1e9

Z = copy.copy(corr) # to avoid side effects

corr_is_one = 1.0 - abs(corr) < eps

Z[corr_is_one] = np.inf * np.sign(Z[corr_is_one])

#0.5*np.log((1+corr[1.0 - corr >= eps])/(1-corr[1.0 - corr >= eps]))

Z[np.logical_not(corr_is_one)] = \

np.arctanh(corr[np.logical_not(corr_is_one)])

"""Apply to each entry of array the variance stabilizing transform

Parameters

==========

X: array

input data

kind: str

covariance embedding kind

Returns

=======

X: array

transformed data, same shape as Y

"""

if kind in ['correlation', 'partial correlation']:

Y = corr_to_Z(X)

else:

Y = X

'cov_estimator': None},

p_correction="fdr",

n_jobs=1):

grouped = df.groupby(["condition", "subj_id"])

dict_list = list()

entries = ("baseline", "mean signif baseline",

"follow up", "mean signif follow up",

"comparison", "tstat", "pval", "mean signif comparison")

for (ix1_, condition1) in enumerate(conditions):

for (ix2_, condition2) in enumerate(conditions):

if ix1_ <= ix2_:

continue

cond = list()

grouped = df.groupby("subj_id")

for _, group in grouped:

cond.append(group[group["condition"] == condition1]

["region_signals"].iloc[0])

cond.append(group[group["condition"] == condition2]

["region_signals"].iloc[0])

X = CovEmbedding(**estimators).fit_transform(cond)

X = [vec_to_sym(x) for x in X]

X = np.asarray(X)

X = sym_to_vec(X, isometry=False)

Y = var_stabilize(X, estimators['kind'])

t_stat_baseline, p_baseline = scipy.stats.ttest_1samp(Y[::2, ...],

0.0,

axis=0)

q_baseline = pval_correction.correct(p_baseline, correction=p_correction)

q_baseline[np.isnan(q_baseline)] = 0.

baseline_signif = np.tanh(Y[::2, ...]).mean(axis=0) * (q_baseline < 0.05)

t_stat_followup, p_followup = scipy.stats.ttest_1samp(Y[1::2, ...],

0.0,

axis=0)

q_followup = pval_correction.correct(p_followup, correction=p_correction)

q_followup[np.isnan(q_followup)] = 0.

followup_signif = np.tanh(Y[1::2, ...]).mean(axis=0) * (q_followup < 0.05)

t_stat, p = scipy.stats.ttest_rel(Y[::2, ...],

Y[1::2, ...],

axis=0)

q = pval_correction.correct(p, correction=p_correction)

q[np.isnan(q)] = 0.

comp_signif = (np.tanh(Y[1::2, ...])- np.tanh(Y[::2, ...])).mean(axis=0) * \

(q < 0.05) * (np.minimum(q_baseline, q_followup) < 0.05 )

print "{} vs. {}: t_stat = {}, q-val = {}".format(

condition1, condition2, t_stat, q)

dict_list.append(

dict(zip(*[entries,

("{}".format(condition1),

baseline_signif,

"{}".format(condition2),

followup_signif,

"{} vs. {}".format(condition1, condition2),

t_stat, q, comp_signif)])))