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-3)
print np.sum(diff)
assert np.sum(diff) < 300
def test_gpu(self):
# just makes sure that CPU and GPU match
if not GPU: raise SkipTest
gpu_I = gpuscatter.simulate(self.num_molecules, self.q_grid, self.xyzlist,
self.atomic_numbers, rfloats=self.rfloats,
poisson_parameter=1.0)
cpu_I = cpuscatter.simulate(self.num_molecules, self.q_grid, self.xyzlist,
self.atomic_numbers, rfloats=self.rfloats,
poisson_parameter=1.0)
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-3)
print np.sum(diff)
assert np.sum(diff) < 300
def test_cpu_scatter(self):
print "testing c cpu code..."
cpu_I = cpuscatter.simulate(self.num_molecules, self.q_grid, self.xyzlist,
self.atomic_numbers, rfloats=self.rfloats)
print "CPU", cpu_I
print "REF", self.ref_I
assert_allclose(cpu_I, self.ref_I, rtol=1e-03,
err_msg='scatter: c-cpu/cpu reference mismatch')
assert not np.all( cpu_I == 0.0 )
assert not np.sum( cpu_I == np.nan )
def test_cpu(self):
cpu_I = cpuscatter.simulate(self.num_molecules, self.q_grid, self.xyzlist,
self.atomic_numbers, rfloats=self.rfloats,
poisson_parameter=1.0)