def test_q_values(self): q_values = np.array([1.0, 2.0, 3.0]) num_phi = 360 k = 2.0 * np.pi / 1.4 qxyz = xray._q_grid_as_xyz(q_values, num_phi, k) # assert that the above vectors are the correct length assert np.all( np.abs( np.sqrt( np.sum( np.power(qxyz,2), axis=1 ) ) - \ np.repeat(q_values, num_phi)) < 1e-6 )
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