def test_fromfiles(self):
ss = xray.Shotset.fromfiles( [ref_file('test_cbf.cbf'),
ref_file('test_cbf2.cbf')] )
assert ss.num_shots == 2
n = 0
for i in ss.intensities:
n += 1
assert ss.num_shots == n
assert np.sum(ss.mask) > 1000
def setup(self):
self.nq = 2 # number of detector vectors to do
xyzQ = np.loadtxt(ref_file('512_atom_benchmark.xyz'))
self.xyzlist = xyzQ[:,:3] * 10.0 # nm -> ang.
self.atomic_numbers = xyzQ[:,3].flatten()
self.q_grid = np.loadtxt(ref_file('512_q.xyz'))[:self.nq]
self.rfloats = np.loadtxt(ref_file('512_x_3_random_floats.txt'))
self.num_molecules = self.rfloats.shape[0]
def setup(self):
self.nq = 5 # number of detector vectors to do
self.nr = 10 # number of atoms to use
xyzZ = np.loadtxt(ref_file('512_atom_benchmark.xyz'))
self.xyzlist = xyzZ[:self.nr,:3] * 10.0 # nm -> ang.
self.atomic_numbers = xyzZ[:self.nr,3].flatten()
self.q_grid = np.loadtxt(ref_file('512_q.xyz'))[:self.nq]
self.num_molecules = 512
self.ref_A = ref_simulate_shot(self.xyzlist, self.atomic_numbers,
self.num_molecules, self.q_grid)
def test_intensity_profile(self):
q_values = [2.4, 2.67, 3.0] # should be a peak at |q|=2.67
t = structure.load_coor(ref_file('gold1k.coor'))
rings = xray.Rings.simulate(t, 20, q_values, self.num_phi, 1) # 3 molec, 1 shots
ip = rings.intensity_profile()
assert ip[1,1] > ip[0,1]
assert ip[1,1] > ip[2,1]
def test_simulate_density(self):
# generate a rings object both from an atomic and density model and
# ensure the correlations match
num_shots = 100
num_phi = 1024
nq = 100 # number of q vectors
q_values = [1.0, 2.0]
# atomic model
traj = mdtraj.load(ref_file('pentagon.pdb'))
r1 = xray.Rings.simulate(traj, 1, q_values, num_phi, num_shots)
# density model
grid_dimensions = [151,] * 3
grid_spacing = 1.0 # Angstroms
grid = structure.atomic_to_density(traj, grid_dimensions,
grid_spacing)
r2 = xray.Rings.simulate_density(grid, grid_spacing, num_shots,
q_values, num_phi)
# compute correlations & ensure match
c1 = r1.correlate_intra(1.0, 1.0)
c2 = r2.correlate_intra(1.0, 1.0)
R = np.corrcoef(c1, c2)[0,1]
assert R > 0.95
c1 = r1.correlate_intra(2.0, 2.0)
c2 = r2.correlate_intra(2.0, 2.0)
R = np.corrcoef(c1, c2)[0,1]
assert R > 0.95
def test_i_profile(self):
# doubles as a test for intensity_maxima()
t = structure.load_coor(ref_file('gold1k.coor'))
s = xray.Shotset.simulate(t, self.d, 5, 1)
p = s.intensity_profile()
m = s.intensity_maxima()
assert np.any(np.abs(p[m,0] - 2.67) < 1e-1) # |q| = 2.67 is in {maxima}
def test_from_atomic(self):
"""
simulate both from a grid and from an atomic model and ensure match
"""
num_molecules = 1
nq = 100 # number of q vectors
q_grid = np.zeros((nq, 3))
q_grid[:,1] = np.linspace(.01, 2.0, nq)
traj = mdtraj.load(ref_file('pentagon.pdb'))
atomic_numbers = np.array([ a.element.atomic_number for a in traj.topology.atoms ])
cp, at = get_cromermann_parameters(atomic_numbers)
rxyz = traj.xyz[0] * 10.0
ref_A = ref_simulate_shot(rxyz, atomic_numbers, num_molecules, q_grid,
dont_rotate=True)
grid_dimensions = [125,] * 3
grid_spacing = 0.1 # Angstroms
grid = structure.atomic_to_density(traj, grid_dimensions,
grid_spacing)
A = scatter.simulate_density(grid, grid_spacing,
num_molecules, q_grid,
dont_rotate=True)
tst = np.abs(A)
ref = np.abs(ref_A)
R = np.corrcoef(tst, ref)[0,1]
assert R > 0.95, 'atomic and grid models significantly different'
def setup(self):
self.q_values = np.array([1.0, 2.0])
self.num_phi = 360
self.traj = Trajectory.load(ref_file('ala2.pdb'))
self.num_shots = 2
# generate the tables file on disk, then re-open it
intensities = np.abs( np.random.randn(self.num_shots, len(self.q_values),
self.num_phi) / 100.0 + \
np.cos( np.linspace(0.0, 4.0*np.pi, self.num_phi) ) )
if os.path.exists('tmp_tables.h5'):
os.remove('tmp_tables.h5')
hdf = tables.File('tmp_tables.h5', 'w')
a = tables.Atom.from_dtype(np.dtype(np.float64))
node = hdf.create_earray(where='/', name='data',
shape=(0, len(self.q_values), self.num_phi),
atom=a, filters=io.COMPRESSION)
node.append(intensities)
hdf.close()
self.tables_file = tables.File('tmp_tables.h5', 'r+')
pi = self.tables_file.root.data
pm = np.random.binomial(1, 0.9, size=(len(self.q_values), self.num_phi))
k = 1.0
self.rings = xray.Rings(self.q_values, pi, k, pm)
return
def setup(self):
self.q_values = np.array([1.0, 2.0])
self.num_phi = 360
self.traj = Trajectory.load(ref_file('ala2.pdb'))
self.num_shots = 4
self.rings = xray.Rings.simulate(self.traj, 1, self.q_values,
self.num_phi, self.num_shots) # 1 molec
def test_interpolate_to_polar(self):
# doubles as a test for _implicit_interpolation
q_values = np.array([2.0, 2.67, 3.7]) # should be a peak at |q|=2.67
t = structure.load_coor(ref_file('gold1k.coor'))
s = xray.Shotset.simulate(t, self.d, 3, 1)
pi, pm = s.interpolate_to_polar(q_values, self.num_phi)
ip = np.sum(pi[0,:,:], axis=1)
assert ip[1] > ip[0]
assert ip[1] > ip[2]
def setup(self):
self.q_values = np.array([1.0, 2.0])
self.num_phi = 360
self.l = 50.0
self.d = xray.Detector.generic(spacing=0.4, l=self.l)
self.num_shots = 2
self.i = np.abs( np.random.randn(self.num_shots, self.d.num_pixels) )
self.t = Trajectory.load(ref_file('ala2.pdb'))
self.shot = xray.Shotset(self.i, self.d)
def test_kabsch():
fn = ref_file('gold1k.coor')
obj = structure.load_coor(fn).xyz[0,:,:]
rot_obj = structure.rand_rotate_molecule2(obj)
U = math2.kabsch(rot_obj, obj)
obj2 = np.dot(rot_obj, U)
assert_almost_equal(obj, obj2)
def setup(self):
self.nq = 100 # number of detector vectors to do
self.q_grid = np.loadtxt(ref_file('512_q.xyz'))[:self.nq]
self.traj = mdtraj.load(ref_file('ala2.pdb'))
atomic_numbers = np.array([ a.element.atomic_number for a in self.traj.topology.atoms ])
cp, at = get_cromermann_parameters(atomic_numbers)
self.num_molecules = 32
rxyz = self.traj.xyz[0] * 10.0
self.ref_A = ref_simulate_shot(rxyz,
atomic_numbers,
self.num_molecules,
self.q_grid)
self.cpp_A = _cppscatter.cpp_scatter(self.num_molecules,
rxyz,
self.q_grid,
at, cp,
random_state=np.random.RandomState(RANDOM_SEED))
def test_multi_panel_interp(self):
# regression test ensuring detectors w/multiple basisgrid panels
# are handled correctly
t = structure.load_coor(ref_file('gold1k.coor'))
q_values = np.array([2.66])
multi_d = xray.Detector.load(ref_file('lcls_test.dtc'))
num_phi = 1080
num_molecules = 1
xyzlist = t.xyz[0,:,:] * 10.0 # convert nm -> ang. / first snapshot
atomic_numbers = np.array([ a.element.atomic_number for a in t.topology.atoms ])
# generate a set of random numbers that we can use to make sure the
# two simulations have the same molecular orientation (and therefore)
# output
rfloats = np.random.rand(num_molecules, 3)
# --- first, scatter onto a perfect ring
q_grid = xray._q_grid_as_xyz(q_values, num_phi, multi_d.k)
ring_i = _cpuscatter.simulate(num_molecules, q_grid, xyzlist,
atomic_numbers, rfloats=rfloats)
perf = xray.Rings(q_values, ring_i[None,None,:], multi_d.k)
# --- next, to the full detector
q_grid2 = multi_d.reciprocal
real_i = _cpuscatter.simulate(num_molecules, q_grid2, xyzlist,
atomic_numbers, rfloats=rfloats)
# interpolate
ss = xray.Shotset(real_i, multi_d)
real = ss.to_rings(q_values, num_phi)
# count the number of points that differ significantly between the two
diff = ( np.abs((perf.polar_intensities[0,0,:] - real.polar_intensities[0,0,:]) \
/ (real.polar_intensities[0,0,:] + 1e-300) ) > 1e-3)
print np.sum(diff)
assert np.sum(diff) < 300
def test_sph_harm():
# -----------------------
traj = Trajectory.load(ref_file('pentagon.pdb'))
q_magnitudes = [1.6]
num_coefficients = 44
num_molecules = 1
num_shots = 20000
num_phi = 2048
# -----------------------
q = q_magnitudes[0]
# compute the Kam-theoretic values of the Legendre coefficients C_ell, which
# we will call "coeffsh"
coeffsh_even = scatter.sph_harm_coefficients(traj, q_magnitudes,
num_coefficients=num_coefficients/2)
coeffsh_even = np.nan_to_num(coeffsh_even)
coeffsh_even /= coeffsh_even[1]
coeffsh = np.zeros(num_coefficients)
coeffsh[0::2] = coeffsh_even.flatten()
# next, preform a simulation of the scattering and empirically compute the
# correlation function
rings = xray.Rings.simulate(traj, num_molecules, q_magnitudes,
num_phi, num_shots)
c = rings.correlate_intra(q, q, mean_only=True)
# it seems best to compare the solutions in the expanded basis
c_sh = np.polynomial.legendre.legval(rings.cospsi(q, q), coeffsh.flatten())
c = c - c.mean()
c_sh = c_sh - c_sh.mean()
# plt.figure()
# plt.plot(c_sh / c_sh[0])
# plt.plot(c / c[0])
# plt.show()
# if these are more than 10% different, fail the test
error = (np.sum(np.abs( (c_sh / c_sh[0]) - (c / c[0]) )) / float(num_phi))
assert error < 0.1, 'simulation and analytical computation >10%% different (%f %%)' % error
return
def test_py_cpu_smoke(self):
traj = Trajectory.load(ref_file('ala2.pdb'))
num_molecules = 1
detector = xray.Detector.generic()
detector.beam.photons_scattered_per_shot = 1e3
I = scatter.simulate_shot(traj, num_molecules, detector,
finite_photon=True)
# simple statistical sanity check
assert np.abs(I.sum() - detector.beam.photons_scattered_per_shot) < \
np.sqrt(detector.beam.photons_scattered_per_shot)*6.0
def test_python_call(self):
"""
Test the GPU scattering simulation interface (scatter.simulate)
"""
if not GPU: raise SkipTest
print "testing python wrapper fxn..."
traj = Trajectory.load(ref_file('ala2.pdb'))
num_molecules = 512
detector = xray.Detector.generic()
py_I = scatter.simulate_shot(traj, num_molecules, detector)
assert not np.all( py_I == 0.0 )
assert not np.isnan(np.sum( py_I ))
def test_to_rings_on_disk(self):
# this test uses the Rings `rings_filename` flag
t = structure.load_coor(ref_file('gold1k.coor'))
shot = xray.Shotset.simulate(t, self.d, 1, 1)
q_values = [1.0, 2.0]
rings_ref = shot.to_rings(q_values)
if os.path.exists('tmp.ring'): os.remove('tmp.ring')
shot.to_rings(q_values, rings_filename='tmp.ring')
rings = xray.Rings.load('tmp.ring')
assert_array_almost_equal(rings_ref.polar_intensities,
rings.polar_intensities)
if os.path.exists('tmp.ring'): os.remove('tmp.ring')
def setup(self):
self.q_values = np.array([1.0, 2.0])
self.num_phi = 360
self.l = 50.0
self.d = xray.Detector.generic(spacing=0.4, l=self.l)
self.t = Trajectory.load(ref_file('ala2.pdb'))
self.num_shots = 2
intensities = np.abs(np.random.randn(self.num_shots, self.d.num_pixels))
io.saveh('tmp_tables.h5', data=intensities)
self.tables_file = tables.File('tmp_tables.h5')
self.i = self.tables_file.root.data
self.shot = xray.Shotset(self.i, self.d)
return
def test_iprofile_consistency(self):
t = structure.load_coor(ref_file('gold1k.coor'))
d = xray.Detector.generic()
s = xray.Shotset.simulate(t, d, 5, 1)
q_values = np.arange(1.0, 4.0, 0.02)
num_phi = 360
# compute from polar interp
pi, pm = s._implicit_interpolation(q_values, num_phi)
pi = pi.reshape(len(q_values), num_phi)
ip1 = np.zeros((len(q_values), 2))
ip1[:,0] = q_values
ip1[:,1] = pi.sum(1)
# compute from detector
ip2 = s.intensity_profile(0.02)
# compute from rings
r = xray.Rings.simulate(t, 10, q_values, 360, 1)
ip3 = r.intensity_profile()
# make sure maxima are all similar
ind1 = utils.maxima( math2.smooth(ip1[:,1], beta=15.0, window_size=21) )
ind2 = utils.maxima( math2.smooth(ip2[:,1], beta=15.0, window_size=21) )
ind3 = utils.maxima( math2.smooth(ip3[:,1], beta=15.0, window_size=21) )
m1 = ip1[ind1,0]
m2 = ip2[ind2,0]
m3 = ip3[ind3,0]
# discard the tails of the sim -- they have weak/noisy peaks
# there should be strong peaks at |q| ~ 2.66, 3.06
m1 = m1[(m1 > 2.0) * (m1 < 3.2)]
m2 = m2[(m2 > 2.0) * (m2 < 3.2)]
m3 = m3[(m3 > 2.0) * (m3 < 3.2)]
# I'll let them be two q-brackets off
assert_allclose(m1, m2, atol=0.045)
assert_allclose(m1, m3, atol=0.045)
assert_allclose(m2, m3, atol=0.045)
def test_no_hydrogens():
traj = Trajectory.load(ref_file('ala2.pdb'))
num_molecules = 1
detector = xray.Detector.generic()
detector.beam.photons_scattered_per_shot = 1e3
I_noH = scatter.simulate_shot(traj, num_molecules, detector,
ignore_hydrogens=True,
dont_rotate=True)
I_wH = scatter.simulate_shot(traj, num_molecules, detector,
ignore_hydrogens=False,
dont_rotate=True)
assert not np.all(I_noH == I_wH)
# compute the differece -- we're not setting random numbers here so just
# looking at radially averaged stuff...
diff = np.sum(np.abs(I_noH - I_wH) / I_wH) / float(len(I_wH))
print diff
assert diff < 1.0, 'ignoring hydrogens makes too big of a difference...'
def test_to_rings(self):
t = structure.load_coor(ref_file('gold1k.coor'))
shot = xray.Shotset.simulate(t, self.d, 1, 2)
shot_ip = shot.intensity_profile(0.1)
q_values = shot_ip[:,0]
rings = shot.to_rings(q_values)
assert rings.num_shots == shot.num_shots
rings_ip = rings.intensity_profile()
# normalize to the 6th entry, and discard values before that
# which are usually just large + uninformative
rings_ip[:,1] /= rings_ip[5,1]
shot_ip[:,1] /= shot_ip[5,1]
# for some reason assert_allclose not working, but this is
x = np.sum( np.abs(rings_ip[5:,1] - shot_ip[5:,1]) )
x /= float(len(rings_ip[5:,1]))
print x
assert x < 0.2 # intensity mismatch
assert_allclose(rings_ip[:,0], shot_ip[:,0], err_msg='test impl error')
def test_rotated_beam(self):
# shift a detector up (in x) a bit and test to make sure there's no diff
t = structure.load_coor(ref_file('gold1k.coor'))
s = xray.Shotset.simulate(t, self.d, 5, 1)
sh = 50.0 # the shift mag
xyz = self.d.xyz.copy()
shift = np.zeros_like(xyz)
shift[:,0] += sh
beam_vector = np.array([ sh/self.l, 0.0, 1.0 ])
# note that the detector du is further from the interaction site
du = xray.Detector(xyz + shift, self.d.k, beam_vector=beam_vector)
su = xray.Shotset.simulate(t, du, 5, 1)
p1 = s.intensity_profile(q_spacing=0.05)
p2 = su.intensity_profile(q_spacing=0.05)
p1 /= p1.max()
p2 /= p2.max()
p1 = p2[:10,:]
p2 = p2[:p1.shape[0],:]
assert_allclose(p1, p2, rtol=0.1)
def test_multimodel(self):
""" regression test """
t = mdtraj.load(ref_file('3LYZ_x2.pdb'))
A = scatter.simulate_atomic(t, 1, self.q_grid)
