def compute_2d_view(self, dtm_index, slice_s):
# type: (DataTypeMatrix, str) -> (numpy.array, str, bool)
"""
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 dtm_index: main input. It can have more then 2D
: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(dtm_index.ndim - 2)) # [:,:,0,0,0,0 etc]
slice_used = default
try:
if slice_s is not None and slice_s != "":
slice_used = parse_slice(slice_s)
except ValueError: # if the slice could not be parsed
self.log.warning("failed to parse the slice")
try:
with h5.h5_file_for_index(dtm_index) as h5_file:
result_2d = h5_file.array_data[slice_used]
result_2d = result_2d.astype(float)
if result_2d.ndim > 2: # the slice did not produce a 2d array, treat as error
raise ValueError(str(dtm_index.shape))
except (ValueError, IndexError,
TypeError): # if the slice failed to produce a 2d array
self.log.warning("failed to produce a 2d array")
return self.compute_2d_view(dtm_index, "")
return result_2d, slice_str(slice_used), slice_used == 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 matrix: main input. It can have more then 2D
: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_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, ts_idx, ts_h5 = self.ts_builder[self.tstype](self, data.shape,
tstype_parameters)
ts.start_time = start_time
ts.sample_period_unit = 's'
ts_h5.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_h5.write_data_slice(data[:, numpy.newaxis, :, numpy.newaxis])
data_shape = ts_h5.read_data_shape()
ts_h5.nr_dimensions.store(len(data_shape))
ts_h5.gid.store(uuid.UUID(ts_idx.gid))
ts_h5.sample_period.store(ts.sample_period)
ts_h5.sample_period_unit.store(ts.sample_period_unit)
ts_h5.sample_rate.store(ts.sample_rate)
ts_h5.start_time.store(ts.start_time)
ts_h5.labels_ordering.store(ts.labels_ordering)
ts_h5.labels_dimensions.store(ts.labels_dimensions)
ts_h5.title.store(ts.title)
ts_h5.close()
ts_idx.title = ts.title
ts_idx.time_series_type = type(ts).__name__
ts_idx.sample_period_unit = ts.sample_period_unit
ts_idx.sample_period = ts.sample_period
ts_idx.sample_rate = ts.sample_rate
ts_idx.labels_dimensions = json.dumps(ts.labels_dimensions)
ts_idx.labels_ordering = json.dumps(ts.labels_ordering)
ts_idx.data_ndim = len(data_shape)
ts_idx.data_length_1d, ts_idx.data_length_2d, ts_idx.data_length_3d, ts_idx.data_length_4d = prepare_array_shape_meta(
data_shape)
return ts_idx
except ParseException as ex:
self.log.exception(ex)
raise LaunchException(ex)
def launch(self, view_model):
# type: (RegionMatTimeSeriesImporterModel) -> [TimeSeriesRegionIndex, TimeSeriesEEGIndex]
try:
data = read_nested_mat_file(view_model.data_file,
view_model.dataset_name,
view_model.structure_path)
if view_model.transpose:
data = data.T
if view_model.slice:
data = data[parse_slice(view_model.slice)]
datatype_index = self.load_entity_by_gid(view_model.datatype)
ts, ts_idx, ts_h5 = self.ts_builder[self.tstype](self, data.shape,
datatype_index)
ts.start_time = view_model.start_time
ts.sample_period_unit = 's'
ts_h5.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_h5.write_data_slice(data[:, numpy.newaxis, :, numpy.newaxis])
data_shape = ts_h5.read_data_shape()
ts_h5.nr_dimensions.store(len(data_shape))
ts_h5.gid.store(uuid.UUID(ts_idx.gid))
ts_h5.sample_period.store(ts.sample_period)
ts_h5.sample_period_unit.store(ts.sample_period_unit)
ts_h5.sample_rate.store(ts.sample_rate)
ts_h5.start_time.store(ts.start_time)
ts_h5.labels_ordering.store(ts.labels_ordering)
ts_h5.labels_dimensions.store(ts.labels_dimensions)
ts_h5.title.store(ts.title)
ts_h5.close()
ts_idx.title = ts.title
ts_idx.time_series_type = type(ts).__name__
ts_idx.sample_period_unit = ts.sample_period_unit
ts_idx.sample_period = ts.sample_period
ts_idx.sample_rate = ts.sample_rate
ts_idx.labels_dimensions = json.dumps(ts.labels_dimensions)
ts_idx.labels_ordering = json.dumps(ts.labels_ordering)
ts_idx.data_ndim = len(data_shape)
ts_idx.data_length_1d, ts_idx.data_length_2d, ts_idx.data_length_3d, ts_idx.data_length_4d = prepare_array_shape_meta(
data_shape)
return ts_idx
except ParseException as ex:
self.log.exception(ex)
raise LaunchException(ex)
