def block_test(data, mask, max_blk_edge, rad):
comm = MPI.COMM_WORLD
rank = comm.rank
(dim0, dim1, dim2) = mask.shape
# Initialize dataset with known pattern
for subj in data:
if subj is not None:
for tr in range(subj.shape[3]):
for d1 in range(dim0):
for d2 in range(dim1):
for d3 in range(dim2):
subj[d1, d2, d3, tr] = np.array(
[d1, d2, d3, tr])
sl = Searchlight(sl_rad=rad, max_blk_edge=max_blk_edge)
sl.distribute(data, mask)
sl.broadcast(mask)
global_outputs = sl.run_block_function(block_test_sfn)
if rank == 0:
for d0 in range(rad, global_outputs.shape[0]-rad):
for d1 in range(rad, global_outputs.shape[1]-rad):
for d2 in range(rad, global_outputs.shape[2]-rad):
if mask[d0, d1, d2]:
assert np.array_equal(
np.array(global_outputs[d0, d1, d2]),
np.array([d0, d1, d2, 0]))
def block_test(data, mask, max_blk_edge, rad):
comm = MPI.COMM_WORLD
rank = comm.rank
size = comm.size
nsubj = len(data)
(dim0, dim1, dim2) = mask.shape
# Initialize dataset with known pattern
for subj in data:
if subj is not None:
for tr in range(subj.shape[3]):
for d1 in range(dim0):
for d2 in range(dim1):
for d3 in range(dim2):
subj[d1,d2,d3,tr] = np.array([ d1, d2, d3, tr])
def sfn(l,msk,myrad,bcast_var, extra_params):
outmat = l[0][:,:,:,0]
outmat[~msk] = None
return outmat[rad:-rad,rad:-rad,rad:-rad]
sl = Searchlight(sl_rad=rad, max_blk_edge=max_blk_edge)
sl.distribute(data, mask)
sl.broadcast(mask)
global_outputs = sl.run_block_function(sfn)
if rank == 0:
for d0 in range(rad, global_outputs.shape[0]-rad):
for d1 in range(rad, global_outputs.shape[1]-rad):
for d2 in range(rad, global_outputs.shape[2]-rad):
if mask[d0, d1, d2]:
assert np.array_equal(np.array(global_outputs[d0,d1,d2]), np.array([d0,d1,d2,0]))
def block_test(data, mask, max_blk_edge, rad):
comm = MPI.COMM_WORLD
rank = comm.rank
(dim0, dim1, dim2) = mask.shape
# Initialize dataset with known pattern
for subj in data:
if subj is not None:
for tr in range(subj.shape[3]):
for d1 in range(dim0):
for d2 in range(dim1):
for d3 in range(dim2):
subj[d1, d2, d3, tr] = np.array(
[d1, d2, d3, tr])
sl = Searchlight(sl_rad=rad, max_blk_edge=max_blk_edge)
sl.distribute(data, mask)
sl.broadcast(mask)
global_outputs = sl.run_block_function(block_test_sfn)
if rank == 0:
for d0 in range(rad, global_outputs.shape[0]-rad):
for d1 in range(rad, global_outputs.shape[1]-rad):
for d2 in range(rad, global_outputs.shape[2]-rad):
if mask[d0, d1, d2]:
assert np.array_equal(
np.array(global_outputs[d0, d1, d2]),
np.array([d0, d1, d2, 0]))
def block_test(data, mask, max_blk_edge, rad):
comm = MPI.COMM_WORLD
rank = comm.rank
size = comm.size
nsubj = len(data)
(dim0, dim1, dim2) = mask.shape
# Initialize dataset with known pattern
for subj in data:
if subj is not None:
for tr in range(subj.shape[3]):
for d1 in range(dim0):
for d2 in range(dim1):
for d3 in range(dim2):
subj[d1,d2,d3,tr] = np.array([ d1, d2, d3, tr])
def sfn(l,msk,myrad,bcast_var):
outmat = l[0][:,:,:,0]
outmat[~msk] = None
return outmat[rad:-rad,rad:-rad,rad:-rad]
sl = Searchlight(sl_rad=rad, max_blk_edge=max_blk_edge)
sl.distribute(data, mask)
sl.broadcast(mask)
global_outputs = sl.run_block_function(sfn)
if rank == 0:
for d0 in range(rad, global_outputs.shape[0]-rad):
for d1 in range(rad, global_outputs.shape[1]-rad):
for d2 in range(rad, global_outputs.shape[2]-rad):
if mask[d0, d1, d2]:
assert np.array_equal(np.array(global_outputs[d0,d1,d2]), np.array([d0,d1,d2,0]))
