def pick_stable_features(R, shape, nboot=500, connectivity=None):
# generate the boots
boots = [
np.random.random_integers(0,
len(R) - 1, len(R)) for i in xrange(nboot)
]
# run tfce on each subj and cond
if True:
Rt = np.zeros([R.shape[0], R.shape[1]] + list(shape))
for i in range(Rt.shape[0]):
for j in range(Rt.shape[1]):
Rt[i, j] = cluster.tfce(np.arctanh(R[i, j]).reshape(*shape),
dt=.05,
tail=1,
connectivity=connectivity,
E=1.0,
H=2.0)
Rt[i, j] += cluster.tfce(np.arctanh(R[i, j]).reshape(*shape),
dt=.05,
tail=-1,
connectivity=connectivity,
E=1.0,
H=2.0)
else:
# convert to Z
Rt = np.arctanh(R)
# calc bootstrap ratio
Rtb = np.array([Rt[boots[b]].mean(0) for b in range(len(boots))])
Rtbr = Rt.mean(0) / Rtb.std(0)
return Rtbr
def R_to_tfce(R, connectivity=None, shape=None, dt=.01, E=2 / 3., H=2.0):
"""Apply TFCE to the R values."""
# allocate for tfce
Rt = np.zeros_like(R)
# Z = np.arctanh(R)
Z = R
# loop
for i in range(Rt.shape[0]):
for j in range(Rt.shape[1]):
# apply tfce in pos and neg direction
Zin = Z[i, j]
if connectivity is None:
# reshape back
Zin = Zin.reshape(*shape)
Rt[i, j] += cluster.tfce(Zin,
dt=dt,
tail=1,
connectivity=connectivity,
E=E,
H=H).flatten()
Rt[i, j] += cluster.tfce(Zin,
dt=dt,
tail=-1,
connectivity=connectivity,
E=E,
H=H).flatten()
return Rt
def R_to_tfce(R, connectivity=None, shape=None,
dt=.01, E=2/3., H=2.0):
"""Apply TFCE to the R values."""
# allocate for tfce
Rt = np.zeros_like(R)
Z = np.arctanh(R)
# loop
for i in range(Rt.shape[0]):
for j in range(Rt.shape[1]):
# apply tfce in pos and neg direction
Rt[i,j] += cluster.tfce(Z[i,j].reshape(*shape),
dt=dt,tail=1,connectivity=connectivity,
E=E,H=H).flatten()
Rt[i,j] += cluster.tfce(Z[i,j].reshape(*shape),
dt=dt,tail=-1,connectivity=connectivity,
E=E,H=H).flatten()
return Rt
def R_to_tfce(R, connectivity=None, shape=None,
dt=.01, E=2/3., H=2.0):
"""Apply TFCE to the R values."""
# allocate for tfce
#Rt = np.zeros([R.shape[0],R.shape[1]]+list(shape))
#Rt = np.zeros_like(R)
#Rt = R.copy()
Rt = np.arctanh(R)
# loop
for i in range(Rt.shape[0]):
for j in range(Rt.shape[1]):
Rt[i,j] = cluster.tfce(Rt[i,j].reshape(*shape),
dt=dt,tail=1,connectivity=connectivity,
E=E,H=H).flatten()
Rt[i,j] += cluster.tfce(Rt[i,j].reshape(*shape),
dt=dt,tail=-1,connectivity=connectivity,
E=E,H=H).flatten()
return Rt
def R_to_tfce(R, connectivity=None, shape=None, dt=.01, E=2 / 3., H=2.0):
"""Apply TFCE to the R values."""
# allocate for tfce
#Rt = np.zeros([R.shape[0],R.shape[1]]+list(shape))
#Rt = np.zeros_like(R)
#Rt = R.copy()
Rt = np.arctanh(R)
# loop
for i in range(Rt.shape[0]):
for j in range(Rt.shape[1]):
Rt[i, j] = cluster.tfce(Rt[i, j].reshape(*shape),
dt=dt,
tail=1,
connectivity=connectivity,
E=E,
H=H).flatten()
Rt[i, j] += cluster.tfce(Rt[i, j].reshape(*shape),
dt=dt,
tail=-1,
connectivity=connectivity,
E=E,
H=H).flatten()
return Rt
def pick_stable_features(R, shape, nboot=500, connectivity=None):
# generate the boots
boots = [np.random.random_integers(0,len(R)-1,len(R))
for i in xrange(nboot)]
# run tfce on each subj and cond
if True:
Rt = np.zeros([R.shape[0],R.shape[1]]+list(shape))
for i in range(Rt.shape[0]):
for j in range(Rt.shape[1]):
Rt[i,j] = cluster.tfce(np.arctanh(R[i,j]).reshape(*shape),
dt=.05,tail=1,connectivity=connectivity,
E=1.0,H=2.0)
Rt[i,j] += cluster.tfce(np.arctanh(R[i,j]).reshape(*shape),
dt=.05,tail=-1,connectivity=connectivity,
E=1.0,H=2.0)
else:
# convert to Z
Rt = np.arctanh(R)
# calc bootstrap ratio
Rtb = np.array([Rt[boots[b]].mean(0) for b in range(len(boots))])
Rtbr = Rt.mean(0)/Rtb.std(0)
return Rtbr
