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_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_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 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 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 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_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_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)
#!/usr/bin/env python
import numpy as np
from thor import structure
from mayavi import mlab
# grid parameters
size = 30.0 # angstroms
resolution = 0.25 # angstroms
# compute the number of grid pts necessary
grid_length = int(size / resolution)
gd = (grid_length,)*3
print 'grid dimensions:', gd
t = structure.load_coor('fcc_sphere_2nm.coor')
m = structure.atomic_to_density(t, gd, resolution)
np.save('fcc_sphere_2nm_map.npy', m)
s = mlab.contour3d( m, contours=5, line_width=1.0,
transparent=True )
mlab.show()
