def test_accuracy(self):
confs = align_array(np.vstack(map(np.array, (struct_1, struct_2))),
'axis')
conf_obj = Conformations(confs, 'axis', 7)
rmsds = conf_obj.rmsds_to_reference(conf_obj, 0)
self.assertAlmostEqual(rmsds[0], 0)
self.assertAlmostEqual(rmsds[1], 0.719106, places=6)
def test_zeros(self):
return
confs = align_array(np.zeros((2, 3, 8)), 'axis')
conf_obj = Conformations(confs, 'axis', 8)
rmsds = conf_obj.rmsds_to_reference(conf_obj, 1)
self.assertEqual(rmsds[0], 0)
self.assertEqual(rmsds[1], 0)
def test_data_type(self):
f32_structure = align_array(np.array(struct_1), 'axis')
f64_structure = np.empty(f32_structure.shape, dtype=np.float64)
f64_structure[:, :, :] = f32_structure
self.assertRaises(IRMSD.FloatPrecisionError,
Conformations, f64_structure, 'axis', 7)
def test_simple_structure(self):
return
confs = align_array(np.zeros((2, 3, 8)), 'axis')
confs[1,:,0] += 1
conf_obj = Conformations(confs, 'axis', 2)
rmsds = conf_obj.rmsds_to_reference(conf_obj, 1)
self.assertAlmostEqual(rmsds[0], sqrt(3.0/4))
self.assertEqual(rmsds[1], 0)
conformation = data_frame.reshape((data_frame.shape[0] / n, n, dim + 1))
del data_frame
#shuffle conformation
conformation = np.take(conformation,
np.random.permutation(conformation.shape[0]),
axis=0,
out=conformation)
#keep only m sample
conformation = conformation[:m]
#keep only k sample in order to keep k distance
conformation_part = conformation[:k]
#create conformation object
confs = align_array(conformation[:, :, 1:], 'atom')
conf_obj = Conformations(confs, 'atom', 10)
confs_part = align_array(conformation_part[:, :, 1:], 'atom')
conf_obj_part = Conformations(confs, 'atom', 10)
del conformation_part
del confs
del confs_part
fout = open(folder + "output_1", 'w')
for i in range(m):
# we compute the rmsd between conformation i and all other confomations
rmsds = conf_obj_part.rmsds_to_reference(conf_obj, i)
line = str(conformation[i, 0, 0]) + "," # we add key for each line
#rmsds_sorted=sorted(rmsds)
for atom in range(n):
for coord in range(dim):
conformation[m*n*coord+m*atom+li]=float(key_value1[atom+1][3+coord*7:9+coord*7])
#data.append(float(key_value1[atom+1][3+coord*7:9+coord*7]))
li+=1
# we convert data into numpy array
conformation=np.array(conformation)
#we reshape the one dimensional array into 100000 x 3 x 10
conformation=conformation.reshape((len(conformation)/3/n,3,n))
# we create conformation array
confs = align_array(conformation, 'axis')
# we create conformation object
conf_obj = Conformations(confs, 'axis', 10)
#for each conformation
for i in range(conformation.shape[0]): #data_frame.shape[0]/n
rmsd_dic={}
# we compute the rmsd between conformation i and all other confomations
rmsds = conf_obj.rmsds_to_reference(conf_obj,i)
# we sort the conformation
rmsds_sorted=sorted(rmsds)
#we keep only the k nearest conformation
for l in range(k):
rmsd_dic[l]=rmsds_sorted[l]
