def launch(self,
data_file,
dataset_name,
structure_path='',
transpose=False,
slice=None,
sampling_rate=1000,
start_time=0,
tstype=None,
tstype_parameters=None):
try:
data = read_nested_mat_file(data_file, dataset_name,
structure_path)
if transpose:
data = data.T
if slice:
data = data[parse_slice(slice)]
ts = self.ts_builder[tstype](self, data, **tstype_parameters)
ts.start_time = start_time
ts.sample_period = 1.0 / sampling_rate
ts.sample_period_unit = 's'
ts.write_time_slice(numpy.r_[:data.shape[0]] * ts.sample_period)
# we expect empirical data shape to be time, channel.
# But tvb expects time, state, channel, mode. Introduce those dimensions
ts.write_data_slice(data[:, numpy.newaxis, :, numpy.newaxis])
ts.close_file()
return ts
except ParseException as ex:
self.log.exception(ex)
raise LaunchException(ex)
def get_mapped_array_volume_view(self,
mapped_array,
x_plane,
y_plane,
z_plane,
mapped_array_slice=None,
**kwargs):
x_plane, y_plane, z_plane = preprocess_space_parameters(
x_plane, y_plane, z_plane, self.length_1d, self.length_2d,
self.length_3d)
slice_x, slice_y, slice_z = self.get_volume_slice(
x_plane, y_plane, z_plane)
if mapped_array_slice:
matrix_slice = parse_slice(mapped_array_slice)
measure = mapped_array.get_data('array_data', matrix_slice)
else:
measure = mapped_array.get_data('array_data')
if measure.shape != (self.connectivity.number_of_regions, ):
raise ValueError('cannot project measure on the space')
result_x = measure[slice_x]
result_y = measure[slice_y]
result_z = measure[slice_z]
# Voxels outside the brain are -1. The indexing above is incorrect for those voxels as it
# associates the values of the last region measure[-1] to them.
# Here we replace those values with an out of scale value.
result_x[slice_x == -1] = measure.min() - 1
result_y[slice_y == -1] = measure.min() - 1
result_z[slice_z == -1] = measure.min() - 1
return [[result_x.tolist()], [result_y.tolist()], [result_z.tolist()]]
def compute_2d_view(matrix, slice_s):
"""
Create a 2d view of the matrix using the suggested slice
If the given slice is invalid or fails to produce a 2d array the default is used
which selects the first 2 dimensions.
If the matrix is complex the real part is shown
:param slice_s: a string representation of a slice
:return: (a 2d array, the slice used to make it, is_default_returned)
"""
default = (slice(None), slice(None)) + tuple(
0 for _ in range(matrix.ndim - 2)) # [:,:,0,0,0,0 etc]
try:
if slice_s is not None:
matrix_slice = parse_slice(slice_s)
else:
matrix_slice = slice(None)
m = matrix[matrix_slice]
if m.ndim > 2: # the slice did not produce a 2d array, treat as error
raise ValueError(str(matrix.shape))
except (
IndexError, ValueError
): # if the slice could not be parsed or it failed to produce a 2d array
matrix_slice = default
slice_used = slice_str(matrix_slice)
return matrix[matrix_slice].astype(
float), slice_used, matrix_slice == default
def compute_2d_view(matrix, slice_s):
"""
Create a 2d view of the matrix using the suggested slice
If the given slice is invalid or fails to produce a 2d array the default is used
which selects the first 2 dimensions.
If the matrix is complex the real part is shown
:param slice_s: a string representation of a slice
:return: (a 2d array, the slice used to make it, is_default_returned)
"""
default = (slice(None), slice(None)) + tuple(0 for _ in range(matrix.ndim - 2)) # [:,:,0,0,0,0 etc]
try:
if slice_s is not None:
matrix_slice = parse_slice(slice_s)
else:
matrix_slice = slice(None)
m = matrix[matrix_slice]
if m.ndim > 2: # the slice did not produce a 2d array, treat as error
raise ValueError(str(matrix.shape))
except (IndexError, ValueError): # if the slice could not be parsed or it failed to produce a 2d array
matrix_slice = default
slice_used = slice_str(matrix_slice)
return matrix[matrix_slice].astype(float), slice_used, matrix_slice == default
def launch(self, data_file, dataset_name, structure_path='',
transpose=False, slice=None, sampling_rate=1000,
start_time=0, tstype=None, tstype_parameters=None):
try:
data = read_nested_mat_file(data_file, dataset_name, structure_path)
if transpose:
data = data.T
if slice:
data = data[parse_slice(slice)]
ts = self.ts_builder[tstype](self, data, **tstype_parameters)
ts.start_time = start_time
ts.sample_period = 1.0 / sampling_rate
ts.sample_period_unit = 's'
ts.write_time_slice(numpy.r_[:data.shape[0]] * ts.sample_period)
# we expect empirical data shape to be time, channel.
# But tvb expects time, state, channel, mode. Introduce those dimensions
ts.write_data_slice(data[:, numpy.newaxis, :, numpy.newaxis])
ts.close_file()
return ts
except ParseException as ex:
self.log.exception(ex)
raise LaunchException(ex)
def test_parse_slice(self):
assert parse_slice("::1, :") == (slice(None, None,
1), slice(None, None, None))
assert parse_slice("2") == 2
assert parse_slice("[2]") == 2
assert parse_slice("[]") == slice(None)
try:
parse_slice(":::::")
except ValueError:
pass
