def test_plot_leafs(self, small_tree):
tree = small_tree['tree']
ss_props = list()
for i in range(tree.nleafs):
seq = ''.join(random.sample(1000*SSP.dssp_codes, 42))
ss_props.append(SSP().from_dssp_sequence(seq))
ps.propagator_size_weighted_sum(ss_props, tree)
tree.root['ss'].plot('leafs')
def test_instance_decorated_as_node_property(self):
ss = 'GTEL'
v = np.random.rand(len(ss), SSP.n_codes)
v /= np.sum(v, axis=1)[:, np.newaxis] # normalize rows
profile_prop = SSP(name='foo', aa=ss, profile=v, errors=0.1*v)
assert profile_prop.name == 'foo'
assert np.array_equal(profile_prop.x, ss)
assert np.array_equal(profile_prop.y, v)
assert np.array_equal(profile_prop.e, 0.1*v)
def test_propagator_size_weighted_sum(self, small_tree):
r"""Create random secondary sequences by shufling all codes and
assign to the leafs of the tree. Then, propagate the profiles up
the tree hiearchy. Finally, compare the profile of the root with
expected profile.
"""
tree = small_tree['tree']
ss_props = list()
for i in range(tree.nleafs):
seq = ''.join(random.sample(SSP.dssp_codes, SSP.n_codes))
ss_props.append(SSP().from_dssp_sequence(seq))
ps.propagator_size_weighted_sum(ss_props, tree)
# Manually calculate the average profile for the last residue
y = np.asarray([ss_props[i].y for i in range(tree.nleafs)])
average_profile = np.mean(y, axis=0)
np.testing.assert_array_almost_equal(average_profile,
tree.root['ss'].y, decimal=12)
def test_plot_node(self):
profile = np.random.rand(42, SSP.n_codes) # not normalized
profile /= np.sum(profile, axis=1)[:, np.newaxis] # normalized
prop = SSP(profile=profile)
prop.plot('node')
def test_disparity(self):
p = np.random.rand(42, SSP.n_codes) # not normalized
o = np.zeros((42, SSP.n_codes))
pr = SSP(profile=p)
assert pr.disparity(SSP(profile=-p)) == 4 * \
pr.disparity(SSP(profile=o))
def test_collapse(self):
profile = np.random.rand(42, SSP.n_codes) # not normalized
prop = SSP(profile=profile)
c = prop.collapsed
assert c[0] == np.argmax(profile[0])
def test_fractions(self):
profile = np.random.rand(42, SSP.n_codes) # not normalized
prop = SSP(profile=profile)
f = prop.fractions
assert f['H'] == np.sum(profile, axis=0)[0] / 42
def test_from_dssp_pdb(self, ss_benchmark):
name = ss_benchmark['pdb_file']
ss_prop = SSP().from_dssp_pdb(name)
np.testing.assert_array_equal(ss_prop.y[-1], SSP.code2profile(' '))
def test_from_dssp_sequence(self):
seq = ''.join(random.sample(SSP.dssp_codes, SSP.n_codes))
ss_prop = SSP().from_dssp_sequence(seq)
np.testing.assert_array_equal(ss_prop.y[-1], SSP.code2profile(seq[-1]))
def test_default_name(self):
ss_prop = SSP()
assert ss_prop.name == 'ss'
