def test_svd_argfail_basis_set(self):
"""The proper failure of the align_tensor.svd() user function for the basis_set argument."""
# Add an alignment tensor.
align_tensor.init(align_id='a', params=(0.0, 0.0, 0.0, 0.0, 0.0))
# Loop over the data types.
for data in DATA_TYPES:
# Catch the int and bin arguments, and skip them.
if data[0] == 'int' or data[0] == 'bin':
continue
# The argument test.
self.assertRaises(RelaxIntError, self.align_tensor_fns.svd, basis_set=data[1])
def test_svd_argfail_basis_tensors(self):
"""The tensors arg unit test of the align_tensor.svd() user function."""
# Add an alignment tensor.
align_tensor.init(align_id='a', params=(0.0, 0.0, 0.0, 0.0, 0.0))
# Loop over the data types.
for data in DATA_TYPES:
# Catch the None and str list arguments, and skip them.
if data[0] == 'None' or data[0] == 'str list':
continue
# The argument test.
self.assertRaises(RelaxNoneListStrError, self.align_tensor_fns.svd, tensors=data[1])
def test_matrix_angles_argfail_basis_set(self):
"""The proper failure of the align_tensor.matrix_angles() user function for the basis_set argument."""
# Add an alignment tensor.
align_tensor.init(align_id='a', params=(0.0, 0.0, 0.0, 0.0, 0.0))
# Loop over the data types.
for data in DATA_TYPES:
# Catch the str argument, and skip it.
if data[0] == 'str':
continue
# The argument test.
self.assertRaises(RelaxStrError, self.align_tensor_fns.matrix_angles, basis_set=data[1])
def test_svd_argfail_basis_tensors(self):
"""The tensors arg unit test of the align_tensor.svd() user function."""
# Add an alignment tensor.
align_tensor.init(align_id='a', params=(0.0, 0.0, 0.0, 0.0, 0.0))
# Loop over the data types.
for data in DATA_TYPES:
# Catch the None and str list arguments, and skip them.
if data[0] == 'None' or data[0] == 'str list':
continue
# The argument test.
self.assertRaises(RelaxNoneListStrError, self.align_tensor_fns.svd, tensors=data[1])
def test_svd_argfail_basis_set(self):
"""The proper failure of the align_tensor.svd() user function for the basis_set argument."""
# Add an alignment tensor.
align_tensor.init(align_id='a', params=(0.0, 0.0, 0.0, 0.0, 0.0))
# Loop over the data types.
for data in DATA_TYPES:
# Catch the str argument, and skip it.
if data[0] == 'str':
continue
# The argument test.
self.assertRaises(RelaxStrError, self.align_tensor_fns.svd, basis_set=data[1])
def update_model():
"""Update the model parameters as necessary."""
# Initialise the list of model parameters.
if not hasattr(cdp, 'params'):
cdp.params = []
# Determine the number of states (loaded as structural models), if not already set.
if not hasattr(cdp, 'N'):
# Set the number.
if hasattr(cdp, 'structure'):
cdp.N = cdp.structure.num_models()
# Otherwise return as the rest cannot be updated without N.
else:
return
# Set up the parameter arrays.
if not cdp.params:
# Add the probability or population weight parameters.
if cdp.model in ['2-domain', 'population']:
for i in range(cdp.N - 1):
cdp.params.append('p' + repr(i))
# Add the Euler angle parameters.
if cdp.model == '2-domain':
for i in range(cdp.N):
cdp.params.append('alpha' + repr(i))
cdp.params.append('beta' + repr(i))
cdp.params.append('gamma' + repr(i))
# Initialise the probability and Euler angle arrays.
if cdp.model in ['2-domain', 'population']:
if not hasattr(cdp, 'probs'):
cdp.probs = [None] * cdp.N
if cdp.model == '2-domain':
if not hasattr(cdp, 'alpha'):
cdp.alpha = [None] * cdp.N
if not hasattr(cdp, 'beta'):
cdp.beta = [None] * cdp.N
if not hasattr(cdp, 'gamma'):
cdp.gamma = [None] * cdp.N
# Determine the data type.
data_types = base_data_types()
# Set up tensors for each alignment.
if hasattr(cdp, 'align_ids'):
for id in cdp.align_ids:
# No tensors initialised.
if not hasattr(cdp, 'align_tensors'):
align_tensor.init(tensor=id, align_id=id)
# Find if the tensor corresponding to the id exists.
exists = False
for tensor in cdp.align_tensors:
if id == tensor.align_id:
exists = True
# Initialise the tensor.
if not exists:
align_tensor.init(tensor=id, align_id=id)
def update_model():
"""Update the model parameters as necessary."""
# Initialise the list of model parameters.
if not hasattr(cdp, 'params'):
cdp.params = []
# Determine the number of states (loaded as structural models), if not already set.
if not hasattr(cdp, 'N'):
# Set the number.
if hasattr(cdp, 'structure'):
cdp.N = cdp.structure.num_models()
# Otherwise return as the rest cannot be updated without N.
else:
return
# Set up the parameter arrays.
if not cdp.params:
# Add the probability or population weight parameters.
if cdp.model in ['2-domain', 'population']:
for i in range(cdp.N-1):
cdp.params.append('p' + repr(i))
# Add the Euler angle parameters.
if cdp.model == '2-domain':
for i in range(cdp.N):
cdp.params.append('alpha' + repr(i))
cdp.params.append('beta' + repr(i))
cdp.params.append('gamma' + repr(i))
# Initialise the probability and Euler angle arrays.
if cdp.model in ['2-domain', 'population']:
if not hasattr(cdp, 'probs'):
cdp.probs = [None] * cdp.N
if cdp.model == '2-domain':
if not hasattr(cdp, 'alpha'):
cdp.alpha = [None] * cdp.N
if not hasattr(cdp, 'beta'):
cdp.beta = [None] * cdp.N
if not hasattr(cdp, 'gamma'):
cdp.gamma = [None] * cdp.N
# Determine the data type.
data_types = base_data_types()
# Set up tensors for each alignment.
if hasattr(cdp, 'align_ids'):
for id in cdp.align_ids:
# No tensors initialised.
if not hasattr(cdp, 'align_tensors'):
align_tensor.init(tensor=id, align_id=id, params=[0.0, 0.0, 0.0, 0.0, 0.0])
# Find if the tensor corresponding to the id exists.
exists = False
for tensor in cdp.align_tensors:
if id == tensor.align_id:
exists = True
# Initialise the tensor.
if not exists:
align_tensor.init(tensor=id, align_id=id, params=[0.0, 0.0, 0.0, 0.0, 0.0])
