def test_mass(self):
traj = self.traj
# cpptraj output
cm = '''
reference avg.pdb
rms R0 reference @CA,C,N,O savematrices mass
'''
state = pt.load_batch(traj, cm)
state.run()
saved_mat = state.data[-1].values
# pytraj output
avg = pt.mean_structure(traj)
mat = pt.calc_rotation_matrix(traj,
ref=avg,
mask='@CA,C,N,O',
mass=True)
assert mat.shape == (traj.n_frames, 3, 3), 'mat shape'
aa_eq(mat.flatten(), saved_mat.flatten())
# with rmsd
avg = pt.mean_structure(traj)
mat2, rmsd_ = pt.calc_rotation_matrix(traj,
ref=avg,
mask='@CA,C,N,O',
mass=True,
with_rmsd=True)
aa_eq(mat2.flatten(), saved_mat.flatten())
assert pt.tools.rmsd(rmsd_, state.data['R0']) < 1E-3
def test_nomass(self):
traj = self.traj
# cpptraj output
cm = '''
reference avg.pdb
rms R0 reference @CA,C,N,O savematrices
'''
state = pt.load_batch(traj, cm)
state.run()
saved_mat = state.data[-1].values
# pytraj output
avg = pt.mean_structure(traj)
mat = pt.calc_rotation_matrix(traj, ref=avg, mask='@CA,C,N,O')
assert mat.shape == (traj.n_frames, 3, 3), 'mat shape'
aa_eq(mat.flatten(), saved_mat.flatten())
# not specify reference
cm = '''
rms R0 @CA,C,N,O savematrices
'''
state = pt.load_batch(traj, cm)
state.run()
saved_mat = state.data[-1].values
mat = pt.calc_rotation_matrix(traj, mask='@CA,C,N,O')
aa_eq(mat.flatten(), saved_mat.flatten())
def test_DatasetMatrix3x3(self):
# test _append_from_array
mat0 = pt.calc_rotation_matrix(self.traj, ref=0)
shape2d = (mat0.shape[0], mat0.shape[1] * mat0.shape[2])
dmat3x3 = c_datasets.DatasetMatrix3x3()
dmat3x3._append_from_array(mat0.reshape(shape2d))
aa_eq(mat0, dmat3x3.values)
def test_DatasetMatrix3x3(self):
# test _append_from_array
mat0 = pt.calc_rotation_matrix(self.traj, ref=0)
shape2d = (mat0.shape[0], mat0.shape[1] * mat0.shape[2])
dmat3x3 = c_datasets.DatasetMatrix3x3()
dmat3x3._append_from_array(mat0.reshape(shape2d))
aa_eq(mat0, dmat3x3.values)
def test_add_new_for_CpptrajDatasetList(self):
# TODO:
dslist = CpptrajDatasetList()
# integer
dslist.add_new(dtype='integer', name='my_int')
dslist[-1].data = [2, 3]
aa_eq(dslist[-1].values, [2, 3])
# double
dslist.add_new(dtype='double', name='my_double')
dslist[-1].data = [2, 3]
aa_eq(dslist[-1].values, [2, 3])
# float
dslist.add_new(dtype='float', name='my_float')
dslist[-1].data = [2, 3]
aa_eq(dslist[-1].values, [2, 3])
# string
dslist.add_new(dtype='string', name='my_string')
dslist[-1].data = ['H', 'T']
assert dslist[-1].values.tolist() == ['H', 'T'], 'string must be equal'
# reference
dslist.add_new(dtype='reference', name='my_reference')
dslist[-1].data = self.traj[-2]
aa_eq(dslist[-1].xyz, self.traj[-2].xyz)
# matrix3x3
dslist.add_new(dtype='matrix3x3', name='my_mat3x3')
mat = pt.calc_rotation_matrix(self.traj, ref=0, mask='@CA')
# there is no assignment. Need to update by another method
dslist[-1]._append_from_array(mat)
aa_eq(dslist[-1].values, mat)
# TRAJ
dslist.add_new(dtype='traj', name='my_traj')
dslist[-1].top = self.traj.top
dslist[-1]._load(self.traj.filename)
traj_new = dslist[-1]
# FIXME: segmentation fault
# CRD
dslist.add_new(dtype='coords', name='my_crd')
dslist[-1].top = self.traj.top
dslist[-1].load(self.traj.filename)
traj_new = dslist[-1]
aa_eq(traj_new.xyz, self.traj.xyz)
aa_eq(pt.rmsd(traj_new), pt.rmsd(self.traj))
# vector
dslist.add_new(dtype='vector', name='my_vec')
vecs = pt.vector.vector_mask(self.traj, ':3 :2')
dslist[-1].data = vecs
aa_eq(dslist[-1].values, vecs)
# grid
dslist.add_new(dtype='grid', name='my_grid')
arr = np.random.rand(8, 9, 3).astype('f4')
dslist[-1].data = arr
aa_eq(dslist[-1].values, arr)
# mesh
dslist.add_new(dtype='xymesh', name='my_mesh')
arr = np.random.rand(8, 2).astype('f8')
# there is not easy method to update, use _append_from_array
dslist[-1]._append_from_array(arr)
aa_eq(dslist[-1].values, arr)
# modes
mat = pt.matrix.covar(self.traj, '@CA')
modes = pt.matrix.diagonalize(mat, n_vecs=mat.shape[0], dtype='dataset')[0]
modes2 = modes.__class__()
# dummy test to set name and scalar_type
# (prepare for pca)
modes2.name = 'test_mode'
modes2.scalar_type = 'covar'
modes2._set_modes(False, mat.shape[0], modes.eigenvectors.shape[0],
modes.eigenvalues, modes.eigenvectors.flatten())
aa_eq(modes.eigenvalues, modes2.eigenvalues)
aa_eq(modes.eigenvectors, modes2.eigenvectors)
def test_add_new_for_CpptrajDatasetList(self):
# TODO:
dslist = CpptrajDatasetList()
# integer
dslist.add_new(dtype='integer', name='my_int')
dslist[-1].data = [2, 3]
aa_eq(dslist[-1].values, [2, 3])
# double
dslist.add_new(dtype='double', name='my_double')
dslist[-1].data = [2, 3]
aa_eq(dslist[-1].values, [2, 3])
# float
dslist.add_new(dtype='float', name='my_float')
dslist[-1].data = [2, 3]
aa_eq(dslist[-1].values, [2, 3])
# string
dslist.add_new(dtype='string', name='my_string')
dslist[-1].data = ['H', 'T']
assert dslist[-1].values.tolist() == ['H', 'T'], 'string must be equal'
# reference
dslist.add_new(dtype='reference', name='my_reference')
dslist[-1].data = self.traj[-2]
aa_eq(dslist[-1].xyz, self.traj[-2].xyz)
# matrix3x3
dslist.add_new(dtype='matrix3x3', name='my_mat3x3')
mat = pt.calc_rotation_matrix(self.traj, ref=0, mask='@CA')
# there is no assignment. Need to update by another method
dslist[-1]._append_from_array(mat)
aa_eq(dslist[-1].values, mat)
# TRAJ
dslist.add_new(dtype='traj', name='my_traj')
dslist[-1].top = self.traj.top
dslist[-1]._load(self.traj.filename)
traj_new = dslist[-1]
# FIXME: segmentation fault
# CRD
dslist.add_new(dtype='coords', name='my_crd')
dslist[-1].top = self.traj.top
dslist[-1].load(self.traj.filename)
traj_new = dslist[-1]
aa_eq(traj_new.xyz, self.traj.xyz)
aa_eq(pt.rmsd(traj_new), pt.rmsd(self.traj))
# vector
dslist.add_new(dtype='vector', name='my_vec')
vecs = pt.vector.vector_mask(self.traj, ':3 :2')
dslist[-1].data = vecs
aa_eq(dslist[-1].values, vecs)
# grid
dslist.add_new(dtype='grid', name='my_grid')
arr = np.random.rand(8, 9, 3).astype('f4')
dslist[-1].data = arr
aa_eq(dslist[-1].values, arr)
# mesh
dslist.add_new(dtype='xymesh', name='my_mesh')
arr = np.random.rand(8, 2).astype('f8')
# there is not easy method to update, use _append_from_array
dslist[-1]._append_from_array(arr)
aa_eq(dslist[-1].values, arr)
# modes
mat = pt.matrix.covar(self.traj, '@CA')
modes = pt.matrix.diagonalize(mat,
n_vecs=mat.shape[0],
dtype='dataset')[0]
modes2 = modes.__class__()
# dummy test to set name and scalar_type
# (prepare for pca)
modes2.name = 'test_mode'
modes2.scalar_type = 'covar'
modes2._set_modes(False, mat.shape[0], modes.eigenvectors.shape[0],
modes.eigenvalues, modes.eigenvectors.flatten())
aa_eq(modes.eigenvalues, modes2.eigenvalues)
aa_eq(modes.eigenvectors, modes2.eigenvectors)