def test_vs_reference(self):
qs = np.arange(2, 3.52, 0.02)
silver = structure.load_coor(ref_file('SilverSphere.coor'))
cl = scatter.sph_hrm_coefficients(silver, q_magnitudes=qs,
num_coefficients=2)[1,:,:]
ref = np.loadtxt(ref_file('ag_kam.dat')) # computed in matlab
assert_allclose(cl, ref)
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_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, 10, 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_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, 10, 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_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=q_values)
ip = np.sum(pi[0, :, :], axis=1)
assert ip[1] > ip[0]
assert ip[1] > ip[2]
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=q_values)
ip = np.sum(pi[0,:,:], axis=1)
assert ip[1] > ip[0]
assert ip[1] > ip[2]
def test():
d = xray.Detector.load('ds1_jun2011.dtc')
traj = structure.load_coor('gold_2nm.coor')
tst = LclsTester(d, traj)
print tst.simulate(1, plot_rings=True)
return
def test_load_coor():
s = structure.load_coor( ref_file('goldBenchMark.coor') )
s.save('s.pdb')
t = trajectory.load('s.pdb')
assert_array_almost_equal(s.xyz, t.xyz, decimal=3)
for a in t.topology.atoms():
assert a.element.symbol == 'Au'
if os.path.exists('s.pdb'):
os.remove('s.pdb')
def test_load_coor():
s = structure.load_coor( ref_file('goldBenchMark.coor') )
s.save('s.pdb')
t = trajectory.load('s.pdb')
assert_array_almost_equal(s.xyz, t.xyz, decimal=3)
for a in t.topology.atoms():
assert a.element.symbol == 'Au'
if os.path.exists('s.pdb'):
os.remove('s.pdb')
def test_multiply_conformations():
traj = structure.load_coor(ref_file('goldBenchMark.coor'))
n_samples = 150
otraj = structure.multiply_conformations(traj, n_samples, 0.1)
# iterate over x,y,z and check if any of the bins are more than 3 STD from the mean
for i in [0,1,2]:
h = np.histogram(otraj.xyz[:,0,i])[0]
cutoff = h.std() * 3.0 # chosen arbitrarily
deviations = np.abs(h - h.mean())
print deviations / h.std()
if np.any( deviations > cutoff ):
raise RuntimeError('Highly unlikely centers of mass are randomly '
'distributed in space. Test is stochastic, though, so'
' try running again to make sure you didn\'t hit a '
'statistical anomaly')
def test_multiply_conformations():
traj = structure.load_coor(ref_file('goldBenchMark.coor'))
n_samples = 150
otraj = structure.multiply_conformations(traj, n_samples, 0.1)
# iterate over x,y,z and check if any of the bins are more than 3 STD from the mean
for i in [0,1,2]:
h = np.histogram(otraj.xyz[:,0,i])[0]
cutoff = h.std() * 3.0 # chosen arbitrarily
deviations = np.abs(h - h.mean())
print deviations / h.std()
if np.any( deviations > cutoff ):
raise RuntimeError('Highly unlikely centers of mass are randomly '
'distributed in space. Test is stochastic, though, so'
' try running again to make sure you didn\'t hit a '
'statistical anomaly')
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_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_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_to_rings(self):
t = structure.load_coor(ref_file('gold1k.coor'))
shot = xray.Shotset.simulate(t, self.d, 1, 1)
shot_ip = shot.intensity_profile(0.1)
q_values = shot_ip[:,0]
rings = shot.to_rings(q_values)
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_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_to_rings(self):
t = structure.load_coor(ref_file('gold1k.coor'))
shot = xray.Shotset.simulate(t, self.d, 1, 1)
shot_ip = shot.intensity_profile(0.1)
q_values = shot_ip[:, 0]
rings = shot.to_rings(q_values)
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_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)
import numpy as np
import matplotlib.pyplot as plt
from odin import structure, scatter
qs = np.arange(2, 3.52, 0.02)
silver = structure.load_coor('reference/SilverSphere.coor')
cl = scatter.sph_hrm_coefficients(silver, q_magnitudes=qs,
num_coefficients=2)[1,:,:]
plt.imshow(cl.T)
plt.show()
from matplotlib.pylab import *
from odin import structure, xray
gold = structure.load_coor('reference/gold1k.coor')
r = xray.Rings.simulate(gold, 10, [2.67], 360, 100)
figure()
c = r.correlate_intra(2.67, 2.67, 4, mean_only = True )
# plot(c, lw=2)
cl = r._convert_to_kam(2.67, 2.67, c)
plot(cl[:,0], cl[:,1], lw=2)
xlabel(r'$\cos \psi$ / rad')
ylabel('correlation')
show()
in the simulated pattern!
The output is a rings file, containing the simulated stuff.
-- TJL May 14 2013
"""
import numpy as np
from odin import xray
from odin import structure
from odin.math2 import ER_rotation_matrix
# --------------------------------------------------------- #
# Manually set parameters -- change these #
traj = structure.load_coor('gold1k.coor') # or whatever
shp = (201,201) # choose number of pixels
distance = 20.0 # detector distance, pixel units
energy = 10.0 # keV
detector_tilt = 1.0 # in degrees
num_molecules = 10
num_shots = 1
q_values = [2.66, 3.07] # inv. Ang.
num_phi = 3600
output_fn = 'tilted.ring'
