from __future__ import division
from rstbx.diffraction.fastbragg import detector,standard_camera,crystal_structure
from rstbx.simulation.constants import electron_radius
from rstbx.simulation import xfel1
from rstbx.simulation.sim_pdf import PDF
from rstbx.simulation.sim_utils import generate_random_rotation
# Instantiation of the Detector, array size & pixel edge in meters
Det = detector(slowpixels=1516,fastpixels=1516,pixel_size=0.00022)
Det.show()
Cam = standard_camera(detector = Det,
mean_xtd_distance_m = 0.150,
mean_xray_wavelength_m = 1.24E-10)
Cam.show()
Pdb = crystal_structure(pdb_code='2alp',standard_camera=Cam)
Pdb.show()
square_crystal_edge = 5.E-6 # in meters
square_focus_edge = 5.E-6 # in meters
vol_crystal = square_crystal_edge**3 # volume of the crystal in meters**3
I_beam = 2E12 #number of photons per LCLS pulse
I_beam_flux = I_beam/(square_focus_edge**2)
darwin_numerator = I_beam_flux * (electron_radius**2) * vol_crystal
class Params:pass
Sim = Params()
Sim.mosaicity = 0.10 #degrees
Sim.bandpass = 0.002
Sim.tracing_impacts = 1000
def tst_all():
F = foo()
assert F == (1, 2, 3, 4)
size = 1516
D = detector(slowpixels=1516, fastpixels=1516, pixel_size=0.00011)
D.set_region_of_interest(0, int(0.6 * size), int(0.4 * size),
int(0.6 * size))
D.set_oversampling(1)
C = camera()
C.distance = 0.18166
C.Ybeam = 0.08338
C.Zbeam = 0.08338
C.lambda0 = 6.2E-10
C.dispersion = 0.002
C.dispsteps = 4
C.hdivrange = 0
C.vdivrange = 0
C.hdivstep = 1
C.vdivstep = 1
C.source_distance = 10.
C.fluence = 1.E24
Amat = sqr((127.6895065259495, 0.6512077339887, -0.4403031342553,
-1.1449112128916, 225.3922539826207, 1.8393136632579,
1.0680694468752, -2.4923062985132, 306.0953037195841))
PSII = amplitudes_from_pdb(8., "fft", True)
from cctbx import crystal_orientation
X = crystal()
X.orientation = crystal_orientation.crystal_orientation(
Amat, crystal_orientation.basis_type.direct)
X.miller = PSII.indices()
X.amplitudes = PSII.data()
X.Na = 6
X.Nb = 6
X.Nc = 6
SIM = fast_bragg_simulation()
SIM.set_detector(D)
SIM.set_camera(C)
SIM.set_crystal(X)
SIM.sweep_over_detector()
data = D.raw
scale_factor = 55000. / flex.max(data)
#print "scale_factor",scale_factor
fileout = "intimage_001.img"
SIM.to_smv_format(fileout=fileout,
intfile_scale=scale_factor,
saturation=40000)
import os
assert os.path.isfile(fileout)
os.remove(fileout)
#simulation is complete, now we'll autoindex the image fragment and verify
# that the indexed cell is similar to the input cell.
if (not libtbx.env.has_module("annlib")):
print "Skipping some tests: annlib not available."
return
# 1. Analysis of the image to identify the Bragg peak centers.
# This step uses an inefficient algorithm and implementation and
# is most time consuming; but the code is only for testing, not production
from rstbx.diffraction.fastbragg.tst_utils_clustering import specific_libann_cluster
M = specific_libann_cluster(scale_factor * data,
intensity_cutoff=25,
distance_cutoff=17)
# M is a dictionary of peak intensities indexed by pixel coordinates
# 2. Now autoindex the pattern
from rstbx.diffraction.fastbragg.tst_utils_clustering import index_wrapper
SIM.C = C
SIM.D = D
ai, ref_uc = index_wrapper(M.keys(), SIM, PSII)
tst_uc = ai.getOrientation().unit_cell()
#print ref_uc # (127.692, 225.403, 306.106, 90, 90, 90)
#print tst_uc # (106.432, 223.983, 303.102, 90.3185, 91.5998, 90.5231)
# 3. Final assertion. In the given orientation,
# the unit cell A vector is into the beam and is not well sampled,
# so tolerances have to be fairly relaxed, 5%.
# Labelit does better with the target_cell restraint, but this improved
# algorithm is not used here for the test
assert ref_uc.is_similar_to(tst_uc,
relative_length_tolerance=0.20,
absolute_angle_tolerance=2.0)
from __future__ import absolute_import, division, print_function
from rstbx.diffraction.fastbragg import detector, standard_camera, crystal_structure
from rstbx.simulation.constants import electron_radius
from rstbx.simulation import xfel1
from rstbx.simulation.sim_pdf import PDF
from rstbx.simulation.sim_utils import generate_random_rotation
# Instantiation of the Detector, array size & pixel edge in meters
Det = detector(slowpixels=1516, fastpixels=1516, pixel_size=0.00022)
Det.show()
Cam = standard_camera(detector=Det,
mean_xtd_distance_m=0.150,
mean_xray_wavelength_m=1.24E-10)
Cam.show()
Pdb = crystal_structure(pdb_code='2alp', standard_camera=Cam)
Pdb.show()
square_crystal_edge = 5.E-6 # in meters
square_focus_edge = 5.E-6 # in meters
vol_crystal = square_crystal_edge**3 # volume of the crystal in meters**3
I_beam = 2E12 #number of photons per LCLS pulse
I_beam_flux = I_beam / (square_focus_edge**2)
darwin_numerator = I_beam_flux * (electron_radius**2) * vol_crystal
class Params:
pass
def tst_all():
F = foo()
assert F == (1,2,3,4)
size=1516
D = detector(slowpixels=1516,fastpixels=1516,pixel_size=0.00011)
D.set_region_of_interest(0,int(0.6*size),int(0.4*size),int(0.6*size))
D.set_oversampling(1)
C = camera()
C.distance = 0.18166
C.Ybeam = 0.08338
C.Zbeam = 0.08338
C.lambda0 = 6.2E-10
C.dispersion = 0.002
C.dispsteps = 4
C.hdivrange = 0
C.vdivrange = 0
C.hdivstep = 1
C.vdivstep = 1
C.source_distance = 10.
C.fluence = 1.E24
Amat = sqr((127.6895065259495, 0.6512077339887,-0.4403031342553,
-1.1449112128916,225.3922539826207,1.8393136632579,
1.0680694468752,-2.4923062985132,306.0953037195841))
PSII = amplitudes_from_pdb(8.,"fft",True)
from cctbx import crystal_orientation
X = crystal()
X.orientation = crystal_orientation.crystal_orientation(
Amat,crystal_orientation.basis_type.direct)
X.miller = PSII.indices()
X.amplitudes = PSII.data()
X.Na = 6; X.Nb = 6; X.Nc = 6
SIM = fast_bragg_simulation()
SIM.set_detector(D)
SIM.set_camera(C)
SIM.set_crystal(X)
SIM.sweep_over_detector()
data = D.raw
scale_factor = 55000./flex.max(data)
#print "scale_factor",scale_factor
fileout="intimage_001.img"
SIM.to_smv_format(fileout=fileout,
intfile_scale = scale_factor, saturation=40000)
import os
assert os.path.isfile(fileout)
os.remove(fileout)
#simulation is complete, now we'll autoindex the image fragment and verify
# that the indexed cell is similar to the input cell.
if (not libtbx.env.has_module("annlib")):
print "Skipping some tests: annlib not available."
return
# 1. Analysis of the image to identify the Bragg peak centers.
# This step uses an inefficient algorithm and implementation and
# is most time consuming; but the code is only for testing, not production
from rstbx.diffraction.fastbragg.tst_utils_clustering import specific_libann_cluster
M=specific_libann_cluster(scale_factor*data,intensity_cutoff = 25,distance_cutoff=17)
# M is a dictionary of peak intensities indexed by pixel coordinates
# 2. Now autoindex the pattern
from rstbx.diffraction.fastbragg.tst_utils_clustering import index_wrapper
SIM.C = C
SIM.D = D
ai,ref_uc = index_wrapper(M.keys(), SIM, PSII)
tst_uc = ai.getOrientation().unit_cell()
#print ref_uc # (127.692, 225.403, 306.106, 90, 90, 90)
#print tst_uc # (106.432, 223.983, 303.102, 90.3185, 91.5998, 90.5231)
# 3. Final assertion. In the given orientation,
# the unit cell A vector is into the beam and is not well sampled,
# so tolerances have to be fairly relaxed, 5%.
# Labelit does better with the target_cell restraint, but this improved
# algorithm is not used here for the test
assert ref_uc.is_similar_to(tst_uc, relative_length_tolerance=0.20,
absolute_angle_tolerance= 2.0)
