def _store_float_array(array_data, subject_name, operation_id):
"""Create Float Array and DB persist it"""
datatype_inst = MappedArray(user_tag_1=subject_name)
datatype_inst.set_operation_id(operation_id)
datatype_inst.array_data = array_data
datatype_inst.type = "MappedArray"
datatype_inst.module = "tvb.datatypes.arrays"
datatype_inst.subject = subject_name
datatype_inst.state = "RAW"
dao.store_entity(datatype_inst)
def _store_float_array(array_data, subject_name, operation_id):
"""Create Float Array and DB persist it"""
datatype_inst = MappedArray(user_tag_1=subject_name)
datatype_inst.set_operation_id(operation_id)
datatype_inst.array_data = array_data
datatype_inst.type = "MappedArray"
datatype_inst.module = "tvb.datatypes.arrays"
datatype_inst.subject = subject_name
datatype_inst.state = "RAW"
dao.store_entity(datatype_inst)
def launch(self, input_data=None):
"""
Saves in the db the following array.
"""
self.launch_param = input_data
array_inst = MappedArray()
array_inst.storage_path = self.storage_path
array_inst.array_data = numpy.array(range(1, 46)).reshape((5, 3, 3))
array_inst.type = "MappedArray"
array_inst.module = "tvb.datatypes.arrays"
array_inst.subject = "John Doe"
array_inst.state = "RAW"
return array_inst
def launch(self, input_data=None):
"""
Saves in the db the following array.
"""
self.launch_param = input_data
array_inst = MappedArray()
array_inst.storage_path = self.storage_path
array_inst.array_data = numpy.array(range(1, 46)).reshape((5, 3, 3))
array_inst.type = "MappedArray"
array_inst.module = "tvb.datatypes.arrays"
array_inst.subject = "John Doe"
array_inst.state = "RAW"
return array_inst
def test_read_write_arrays(self):
"""
Test the filter function when retrieving dataTypes with a filter
after a column from a class specific table (e.g. DATA_arraywrapper).
"""
test_array = numpy.array(range(16))
shapes = [test_array.shape, (2, 8), (2, 2, 4), (2, 2, 2, 2)]
storage_path = self.flow_service.file_helper.get_project_folder(
self.operation.project, str(self.operation.id))
for i in range(4):
datatype_inst = MappedArray(title="dim_" + str(i + 1),
d_type="MappedArray",
storage_path=storage_path,
module="tvb.datatypes.arrays",
subject="John Doe",
state="RAW",
operation_id=self.operation.id)
datatype_inst.array_data = test_array.reshape(shapes[i])
result = dao.store_entity(datatype_inst)
result.array_data = None
inserted_data = self.flow_service.get_available_datatypes(
self.test_project.id, "tvb.datatypes.arrays.MappedArray")[0]
self.assertEqual(len(inserted_data), 4,
"Found " + str(len(inserted_data)))
for i in range(4):
## inserted_data will be retrieved in the opposite order than the insert order
actual_datatype = dao.get_generic_entity(MappedArray,
inserted_data[3 - i][2],
'gid')[0]
self.assertEqual(actual_datatype.length_1d, shapes[i][0])
if i > 0:
self.assertEqual(actual_datatype.length_2d, shapes[i][1])
expected_arr = test_array.reshape(shapes[i])
self.assertTrue(
numpy.equal(actual_datatype.array_data, expected_arr).all(),
str(i + 1) + "D Data not read correctly")
actual_datatype.array_data = None
### Check that meta-data are also written for Array attributes.
metadata = actual_datatype.get_metadata('array_data')
self.assertTrue(actual_datatype.METADATA_ARRAY_MAX in metadata)
self.assertEqual(metadata[actual_datatype.METADATA_ARRAY_MAX], 15)
self.assertTrue(actual_datatype.METADATA_ARRAY_MIN in metadata)
self.assertEqual(metadata[actual_datatype.METADATA_ARRAY_MIN], 0)
self.assertTrue(actual_datatype.METADATA_ARRAY_MEAN in metadata)
self.assertEqual(metadata[actual_datatype.METADATA_ARRAY_MEAN],
7.5)
def test_read_write_arrays(self):
"""
Test the filter function when retrieving dataTypes with a filter
after a column from a class specific table (e.g. DATA_arraywrapper).
"""
test_array = numpy.array(range(16))
shapes = [test_array.shape, (2, 8), (2, 2, 4), (2, 2, 2, 2)]
storage_path = self.flow_service.file_helper.get_project_folder(self.operation.project, str(self.operation.id))
for i in range(4):
datatype_inst = MappedArray(title="dim_" + str(i + 1), d_type="MappedArray",
storage_path=storage_path, module="tvb.datatypes.arrays",
subject="John Doe", state="RAW", operation_id=self.operation.id)
datatype_inst.array_data = test_array.reshape(shapes[i])
result = dao.store_entity(datatype_inst)
result.array_data = None
inserted_data = self.flow_service.get_available_datatypes(self.test_project.id,
"tvb.datatypes.arrays.MappedArray")[0]
self.assertEqual(len(inserted_data), 4, "Found " + str(len(inserted_data)))
for i in range(4):
## inserted_data will be retrieved in the opposite order than the insert order
actual_datatype = dao.get_generic_entity(MappedArray, inserted_data[3 - i][2], 'gid')[0]
self.assertEqual(actual_datatype.length_1d, shapes[i][0])
if i > 0:
self.assertEqual(actual_datatype.length_2d, shapes[i][1])
expected_arr = test_array.reshape(shapes[i])
self.assertTrue(numpy.equal(actual_datatype.array_data, expected_arr).all(),
str(i + 1) + "D Data not read correctly")
actual_datatype.array_data = None
### Check that meta-data are also written for Array attributes.
metadata = actual_datatype.get_metadata('array_data')
self.assertTrue(actual_datatype.METADATA_ARRAY_MAX in metadata)
self.assertEqual(metadata[actual_datatype.METADATA_ARRAY_MAX], 15)
self.assertTrue(actual_datatype.METADATA_ARRAY_MIN in metadata)
self.assertEqual(metadata[actual_datatype.METADATA_ARRAY_MIN], 0)
self.assertTrue(actual_datatype.METADATA_ARRAY_MEAN in metadata)
self.assertEqual(metadata[actual_datatype.METADATA_ARRAY_MEAN], 7.5)
