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_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-3)
print np.sum(diff)
assert np.sum(diff) < 300
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 simulate(self, n_repeats, compare='rmsd', plot_rings=False):
"""
Simualtes one molecule on both a perfect "ring" detector, and also the
real `detector` object that the class owns. Then apply whatever "issues"
we're going to look at, interpolate to polar, and compare the results.
Repeat `n_repeats` times.
Parameters
----------
n_repeats : int
How many times to repeat the simulation
compare : function
A function that takes two one-d arrays of variable length, and
returns a float that indicates how well the two simulations match.
Can also be a str to note some common functions: {'rmsd','corr_rmsd'}
"""
xyzlist = self.traj.xyz[0,:,:] * 10.0 # convert nm -> ang. / first snapshot
atomic_numbers = np.array([ a.element.atomic_number for a in self.traj.topology.atoms() ])
comparison = np.zeros(n_repeats)
for i in range(n_repeats):
print "Simulation %d/%d" % (i+1, n_repeats)
# 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(self.num_molecules, 3)
# --- first, scatter onto a perfect ring
q_grid = xray._q_grid_as_xyz(self.q_values, self.num_phi, self.detector.k)
ring_i = _cpuscatter.simulate(self.num_molecules, q_grid, xyzlist,
atomic_numbers, rfloats=rfloats)
perf = xray.Rings(self.q_values, ring_i[None,None,:], self.detector.k)
# --- next, to the full detector
q_grid2 = self.detector.reciprocal
real_i = _cpuscatter.simulate(self.num_molecules, q_grid2, xyzlist,
atomic_numbers, rfloats=rfloats)
# apply "issues"
# for issue in self.issues:
# real_i = self.__dict__['_' + issue](real_i)
# interpolate
ss = xray.Shotset(real_i, self.detector)
real = ss.to_rings(self.q_values, self.num_phi)
# --- plot if requested (mostly for debugging)
if plot_rings:
plt.figure()
plt.plot(perf.polar_intensities[0,0,:])
plt.plot(real.polar_intensities[0,0,:])
plt.legend(['perfect', 'interpolated from detector'])
plt.xlabel(r'$\phi$')
plt.ylabel('Intensity')
plt.show()
# --- compare them on some grounds
if compare == 'rmsd':
comparison[i] = self._rmsd(perf.polar_intensities[0,0,:],
real.polar_intensities[0,0,:])
elif compare == 'corr_rmsd':
c1 = perf.correlate_intra(self.q_values[0], self.q_values[0], mean_only=True)
c2 = real.correlate_intra(self.q_values[0], self.q_values[0], mean_only=True)
comparison[i] = self._rmsd(c1, c2)
else:
try:
comparison[i] = compare(perf.polar_intensities[0,0,:],
real.polar_intensities[0,0,:])
except:
raise Exception('bad compare function')
return comparison
from odin import xray
from odin import _cpuscatter
from mdtraj import trajectory
import matplotlib.pyplot as plt
q_values = [1.4]
traj = trajectory.load('3LYZ.pdb')
xyzlist = traj.xyz[0]
atomic_numbers = np.array([ a.element.atomic_number for a in traj.topology.atoms() ])
b = xray.Beam(None, energy=10.) # same as was used to make reference.dtc
pol_q_grid = xray._q_grid_as_xyz(q_values, 360, b.k)
dtc = xray.load('reference.dtc')
dtc_q_grid = dtc.reciprocal
num_molecules = 1
rfloats = np.random.rand(num_molecules, 3)
pol_i = _cpuscatter.simulate(num_molecules, pol_q_grid, xyzlist,
atomic_numbers, rfloats=rfloats)
dtc_i = _cpuscatter.simulate(num_molecules, dtc_q_grid, xyzlist,
atomic_numbers, rfloats=rfloats)
ss = xray.Shotset(dtc_i, dtc)
r = ss.to_rings(q_values, num_phi=360)
