def test_square_y_clash():
with pytest.raises(TypeError):
beam = ps.Beam(photon_energy=PHOTON_ENERGY,
focus_x=DIM,
focus_y=2 * DIM,
focus_shape="square",
fluence=FLUENCE)
def test_circle_double_diameter_clash():
with pytest.raises(TypeError):
beam = ps.Beam(photon_energy=PHOTON_ENERGY,
focus_x=DIM,
focus_y=1.1 * DIM,
focus_shape="circle",
fluence=FLUENCE)
def test_take_n_slice():
ex_dir_ = os.path.dirname(__file__) + '/../../../examples'
# Load beam
beam = ps.Beam(ex_dir_ + '/input/exp_chuck.beam')
# Load and initialize the detector
det = ps.PnccdDetector(geom=ex_dir_ + '/lcls/amo86615/'
'PNCCD::CalibV1/Camp.0:pnCCD.1/geometry/0-end.data',
beam=beam)
mesh_length = 128
mesh, voxel_length = det.get_reciprocal_mesh(voxel_number_1d=mesh_length)
with h5.File('imStack-test.hdf5', 'r') as f:
volume_in = f['volume'][:]
slices_in = f['imUniform'][:]
orientations_in = f['imOrientations'][:]
slices_rec = slice_.take_n_slices(
volume=volume_in,
voxel_length=voxel_length,
pixel_momentum=det.pixel_position_reciprocal,
orientations=orientations_in)
# Note: This does not work if orientations is stored as float32
assert np.allclose(slices_in, slices_rec)
def test_photon_energy():
beam = ps.Beam(photon_energy=PHOTON_ENERGY,
focus_radius=DIM,
fluence=FLUENCE)
assert np.isclose(beam.wavelength, WAVELENGTH, atol=1e-14)
assert np.isclose(beam.wavenumber, WAVENUMBER)
assert np.isclose(beam.photon_energy, PHOTON_ENERGY)
def test_circle_diameter():
beam = ps.Beam(photon_energy=PHOTON_ENERGY, focus_x=DIM,
focus_shape="circle", fluence=FLUENCE)
focus_x, focus_y, focus_shape = beam.get_focus()
assert np.isclose(focus_x, DIM, atol=1e-14)
assert np.isclose(focus_y, DIM, atol=1e-14)
assert focus_shape == "circle"
assert np.isclose(beam.get_focus_area(), np.pi/4 * DIM**2, atol=1e-21)
def test_fluence():
beam = ps.Beam(photon_energy=PHOTON_ENERGY,
focus_radius=DIM,
focus_shape="circle",
fluence=FLUENCE)
assert np.isclose(beam.get_photons_per_pulse(), FLUENCE)
assert np.isclose(beam.get_photons_per_pulse_per_area(),
FLUENCE / (np.pi * DIM**2))
def test_square():
beam = ps.Beam(photon_energy=PHOTON_ENERGY, focus_x=DIM,
focus_shape="square", fluence=FLUENCE)
focus_x, focus_y, focus_shape = beam.get_focus()
assert np.isclose(focus_x, DIM, atol=1e-14)
assert np.isclose(focus_y, DIM, atol=1e-14)
assert focus_shape == "square"
assert np.isclose(beam.get_focus_area(), DIM**2, atol=1e-21)
def test_file():
ex_dir_ = os.path.dirname(__file__) + '/../../../examples'
beam = ps.Beam(ex_dir_+'/input/beam/amo86615.beam')
focus_x, focus_y, focus_shape = beam.get_focus()
assert np.isclose(beam.photon_energy, 4600)
assert np.isclose(beam.get_photons_per_pulse(), 1e12)
assert np.isclose(focus_x, 2e-7)
assert np.isclose(focus_y, 2e-7)
assert focus_shape == "circle"
def load_pixels(pixels):
beam = ps.Beam('data/exp_chuck.beam')
det = ps.PnccdDetector(
geom='data/lcls/amo86615/PNCCD::CalibV1/Camp.0:pnCCD.1/'
'geometry/0-end.data',
beam=beam)
pixel_momentum = det.pixel_position_reciprocal
numpy.copyto(pixels.momentum, det.pixel_position_reciprocal, casting='no')
max_pixel_dist = numpy.max(det.pixel_distance_reciprocal)
return max_pixel_dist
def setup_class(cls):
ex_dir_ = os.path.dirname(__file__) + '/../../../examples'
# Load beam
beam = ps.Beam(ex_dir_ + '/input/beam/amo86615.beam')
# Load and initialize the detector
np.random.seed(0)
det = ps.Epix10kDetector(
geom=ex_dir_ + '/input/lcls/xcsx35617/'
'Epix10ka2M::CalibV1/XcsEndstation.0:Epix10ka2M.0/geometry/0-end.data',
run_num=0,
beam=beam,
cameraConfig='fixedMedium')
cls.det = det
cls.pos_recip = det.pixel_position_reciprocal
# Ref Particle
cls.particle_0 = ps.Particle()
cls.particle_0.create_from_atoms([ # Angstrom
("O", cst.vecx),
("O", 2 * cst.vecy),
("O", 3 * cst.vecz),
])
cls.pattern_0 = pg.calculate_diffraction_pattern_gpu(
cls.pos_recip, cls.particle_0, return_type="complex_field")
# Second Particle
cls.part_coord_1 = np.array((0.5, 0.2, 0.1)) # Angstrom
cls.particle_1 = ps.Particle()
cls.particle_1.create_from_atoms([ # Angstrom
("O", cst.vecx + cls.part_coord_1),
("O", 2 * cst.vecy + cls.part_coord_1),
("O", 3 * cst.vecz + cls.part_coord_1),
])
cls.part_coord_1 *= 1e-10 # Angstrom -> meter
cls.pattern_1 = pg.calculate_diffraction_pattern_gpu(
cls.pos_recip, cls.particle_1, return_type="complex_field")
# Flat Field
cls.flatField = np.ones(
(cls.det.panel_num, cls.det.panel_pixel_num_x[0],
cls.det.panel_pixel_num_y[0])) * 1.0
cls.I0width = 0.03
cls.I0min = 0
cls.I0max = 150000
cls.bauf = BuildAutoRangeFrames(cls.det, cls.I0width, cls.I0min,
cls.I0max, cls.flatField)
cls.bauf.makeFrame()
def setup_class(cls):
ex_dir_ = os.path.dirname(__file__) + '/../../examples'
# Load beam
beam = ps.Beam(ex_dir_+'/input/beam/amo86615.beam')
# Load and initialize the detector
det = ps.PnccdDetector(
geom=ex_dir_+'/input/lcls/amo86615/'
'PNCCD::CalibV1/Camp.0:pnCCD.1/geometry/0-end.data',
beam=beam)
cls.mesh_length = 15
cls.mesh, voxel_length = det.get_reciprocal_mesh(
voxel_number_1d=cls.mesh_length)
# 1 Atom
cls.particle_1 = ps.Particle()
cls.particle_1.create_from_atoms([
("O", np.array([0., 0., 0.]))
])
cls.volume_1 = pg.calculate_diffraction_pattern_gpu(
cls.mesh, cls.particle_1)
# 2 Atoms x
cls.particle_2x = ps.Particle()
cls.particle_2x.create_from_atoms([
("O", cst.vecx),
("O", -cst.vecx)
])
cls.volume_2x = pg.calculate_diffraction_pattern_gpu(
cls.mesh, cls.particle_2x)
# 2 Atoms y
cls.particle_2y = ps.Particle()
cls.particle_2y.create_from_atoms([
("O", cst.vecy),
("O", -cst.vecy)
])
cls.volume_2y = pg.calculate_diffraction_pattern_gpu(
cls.mesh, cls.particle_2y)
# 2 Atoms z
cls.particle_2z = ps.Particle()
cls.particle_2z.create_from_atoms([
("O", cst.vecz),
("O", -cst.vecz)
])
cls.volume_2z = pg.calculate_diffraction_pattern_gpu(
cls.mesh, cls.particle_2z)
def setup_class(cls):
ex_dir_ = os.path.dirname(__file__) + '/../../../examples'
# Load beam
beam = ps.Beam(ex_dir_+'/input/beam/amo86615.beam')
# Load and initialize the detector
det = ps.PnccdDetector(
geom=ex_dir_+'/input/lcls/amo86615/'
'PNCCD::CalibV1/Camp.0:pnCCD.1/geometry/0-end.data',
beam=beam)
cls.det = det
cls.pos_recip = det.pixel_position_reciprocal
# Ref Particle
cls.particle_0 = ps.Particle()
cls.particle_0.create_from_atoms([ # Angstrom
("O", cst.vecx),
("O", 2*cst.vecy),
("O", 3*cst.vecz),
])
cls.pattern_0 = pg.calculate_diffraction_pattern_gpu(
cls.pos_recip, cls.particle_0, return_type="complex_field")
# Second Particle
cls.part_coord_1 = np.array((0.5, 0.2, 0.1)) # Angstrom
cls.particle_1 = ps.Particle()
cls.particle_1.create_from_atoms([ # Angstrom
("O", cst.vecx + cls.part_coord_1),
("O", 2*cst.vecy + cls.part_coord_1),
("O", 3*cst.vecz + cls.part_coord_1),
])
cls.part_coord_1 *= 1e-10 # Angstrom -> meter
cls.pattern_1 = pg.calculate_diffraction_pattern_gpu(
cls.pos_recip, cls.particle_1, return_type="complex_field")
def test_ellipse_lack_y():
with pytest.raises(TypeError):
beam = ps.Beam(photon_energy=PHOTON_ENERGY, focus_x=DIM,
focus_shape="ellipse", fluence=FLUENCE)
import sys
sys.path.append("/reg/neh/home/yoon82/Software/pysingfel/")
import numpy as np
import matplotlib.pyplot as plt
import h5py as h5
import pysingfel as ps
import time
# Create a particle object
particleOp = ps.Particle()
particleOp.read_pdb('../input/pyrene.pdb', ff='WK')
# Load beam
beam = ps.Beam('../input/exp_chuck.beam')
# Load and initialize the detector
det = ps.PnccdDetector(
geom=
'../../lcls_detectors/amo86615/PNCCD::CalibV1/Camp.0:pnCCD.1/geometry/hydrocarbon.data',
beam=beam)
tic = time.time()
patternOp = det.get_photons(device='gpu', particle=particleOp)
toc = time.time()
print("It takes {:.2f} seconds to finish the calculation.".format(toc - tic))
fig = plt.figure(figsize=(10, 8))
plt.imshow(det.assemble_image_stack(patternOp), vmin=0, vmax=5)
plt.title('Open state')
plt.show()
def test_wavenumber_photon_energy_clash():
with pytest.raises(TypeError):
beam = ps.Beam(wavenumber=WAVENUMBER, photon_energy=PHOTON_ENERGY,
focus_radius=DIM, fluence=FLUENCE)
def main():
# Parse user input for config file and dataset name
user_input = parse_input_arguments(sys.argv)
config_file = user_input['config']
dataset_name = user_input['dataset']
# Get the Config file parameters
with open(config_file) as config_file:
config_params = json.load(config_file)
# Check if dataset in Config file
if dataset_name not in config_params:
raise Exception("Dataset {} not in Config file.".format(dataset_name))
# Get the dataset parameters from Config file parameters
dataset_params = config_params[dataset_name]
# Get the input and output dataset parameters
pdb_file = dataset_params['pdb']
beam_file = dataset_params['beam']
beam_fluence_increase_factor = dataset_params['beamFluenceIncreaseFactor']
geom_file = dataset_params['geom']
dataset_size = dataset_params['numPatterns']
img_dir = dataset_params['imgDir']
output_dir = dataset_params['outDir']
# PDB
print("Load PDB: {}".format(pdb_file))
particle = ps.Particle()
particle.read_pdb(pdb_file, ff='WK')
atomic_coordinates = particle.atom_pos
# Beam parameters
print("Load beam parameters: {}".format(pdb_file))
beam = ps.Beam(beam_file)
# Increase the beam fluence
if not np.isclose(beam_fluence_increase_factor, 1.0):
print('BEFORE: # of photons per pulse {}'.format(
beam.get_photons_per_pulse()))
print('>>> Increasing the number of photons per pulse by a factor {}'.
format(beam_fluence_increase_factor))
beam.set_photons_per_pulse(beam_fluence_increase_factor *
beam.get_photons_per_pulse())
print('AFTER : # of photons per pulse {}'.format(
beam.get_photons_per_pulse()))
# Geometry of detector
print("Load detector geometry: {}".format(geom_file))
det = ps.PnccdDetector(geom=geom_file, beam=beam)
# Simulate the SPI Experiment
print("Calculating diffraction volume")
tic = time.time()
experiment = ps.SPIExperiment(det, beam, particle)
toc = time.time()
print("It takes {:.2f} seconds to finish the calculation.".format(toc -
tic))
# Generate random orientations
print("Generating random orientations as uniform quaternions")
orientations = ps.get_uniform_quat(dataset_size, True)
# Get diffraction pattern shape
diffraction_pattern_height = det.detector_pixel_num_x.item()
diffraction_pattern_width = det.detector_pixel_num_y.item()
# Use orientations to generate diffraction patterns
print("Using orientations to generate diffraction patterns")
diffraction_patterns = np.zeros(
(dataset_size, diffraction_pattern_height, diffraction_pattern_width))
experiment.set_orientations(orientations)
tic = time.time()
for data_index in tqdm.tqdm(range(dataset_size)):
diffraction_pattern = experiment.generate_image()
diffraction_patterns[data_index] = diffraction_pattern
save_diffraction_pattern_as_image(data_index, img_dir,
diffraction_pattern)
toc = time.time()
print(
"It takes {:.2f} seconds to generate the diffraction patterns.".format(
toc - tic))
# Create output directory if it does not exist
if not os.path.exists(output_dir):
print("Creating output directory: {}".format(output_dir))
os.makedirs(output_dir)
# Define path to output HDF5 file
output_file = get_output_file_name(dataset_name, dataset_size,
diffraction_pattern_height,
diffraction_pattern_width)
cspi_synthetic_dataset_file = os.path.join(output_dir, output_file)
print("Saving dataset to: {}".format(cspi_synthetic_dataset_file))
# Define dataset names for HDF5 file
diffraction_patterns_dataset_name = "diffraction_patterns"
orientations_dataset_name = "orientations"
atomic_coordinates_dataset_name = "atomic_coordinates"
# Create and write datasets to HDF5 file
with h5.File(cspi_synthetic_dataset_file,
"w") as cspi_synthetic_dataset_file_handle:
dset_diffraction_patterns = cspi_synthetic_dataset_file_handle.create_dataset(
diffraction_patterns_dataset_name,
diffraction_patterns.shape,
dtype='f')
dset_diffraction_patterns[...] = diffraction_patterns
dset_orientations = cspi_synthetic_dataset_file_handle.create_dataset(
orientations_dataset_name, orientations.shape, dtype='f')
dset_orientations[...] = orientations
dset_atomic_coordinates = cspi_synthetic_dataset_file_handle.create_dataset(
atomic_coordinates_dataset_name,
atomic_coordinates.shape,
dtype='f')
dset_atomic_coordinates[...] = atomic_coordinates
# Load datasets from HDF5 file to verify write
with h5.File(cspi_synthetic_dataset_file,
"r") as cspi_synthetic_dataset_file_handle:
print("cspi_synthetic_dataset_file keys:",
list(cspi_synthetic_dataset_file_handle.keys()))
print(cspi_synthetic_dataset_file_handle[
diffraction_patterns_dataset_name])
print(cspi_synthetic_dataset_file_handle[orientations_dataset_name])
print(
cspi_synthetic_dataset_file_handle[atomic_coordinates_dataset_name]
)
diffraction_patterns = cspi_synthetic_dataset_file_handle[
diffraction_patterns_dataset_name][:]
# compute statistics
print("Diffraction pattern statistics:")
diffraction_pattern_statistics = {
'min': diffraction_patterns.min(),
'max': diffraction_patterns.max(),
'mean': diffraction_patterns.mean()
}
pp = pprint.PrettyPrinter(indent=4)
pp.pprint(diffraction_pattern_statistics)
def test_wavelength_wavenumber_clash():
with pytest.raises(TypeError):
beam = ps.Beam(wavelength=WAVELENGTH, wavenumber=WAVENUMBER,
focus_radius=DIM, fluence=FLUENCE)
def test_wavelength_photon_energy_clash():
with pytest.raises(TypeError):
beam = ps.Beam(wavelength=WAVELENGTH, photon_energy=PHOTON_ENERGY,
focus_radius=DIM, fluence=FLUENCE)
def main():
random.seed(10000)
# Parse user input
params = parse_input_arguments(sys.argv)
pdb = params['pdb']
geom = params['geom']
beam = params['beam']
orient = int(params['UniformOrientation'])
number = int(params['numSlices'])
outDir = params['outDir']
saveName = params['saveNameHDF5']
savePhotons = params['savePhotons']
# Initialize MPI
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
sz = comm.size
det = None
data = None
if rank==0:
print("====================================================================")
print("Running %d parallel MPI processes" % (comm.size))
t_start = MPI.Wtime()
orientations = np.zeros((2*number,4))
particle = ps.Particle()
if rank==0:
if orient== 1:
orientations = ps.geometry.get_uniform_quat(num_pts=number).astype(np.float64)
elif orient== 0:
orientations = ps.geometry.get_random_quat(num_pts=number).astype(np.float64)
print "O=",orientations.shape
print "ODtype=", orientations.dtype
#sys.exit(0)
print("Reading PDB file...")
particle.read_pdb(pdb, ff='WK')
# reading beam and detector files
beam= ps.Beam(beam)
#beam.set_wavelength(1.0e-10)
print beam.get_wavelength()
det = ps.PnccdDetector(geom=geom, beam=beam)
print("Broadcasting input to processes...")
data = {'particle': particle, 'orientations': orientations, 'detector': det}
dct = comm.bcast(data,root=0)
if rank==0:
pattern_shape = det.pedestals.shape
fin = h5.File(os.path.join(outDir,'test_saveHDF5_parallel_intens_combined.h5'),'w')
if savePhotons == 1:
fph = h5.File(os.path.join(outDir,'test_saveHDF5_parallel_photons_combined.h5'),'w')
if savePhotons == 1:
dset_photons = fph.create_dataset('imgPhot', shape=(number,)+pattern_shape,dtype=np.int32, chunks=(1,)+pattern_shape, compression="gzip", compression_opts=4)
dset_intens = fin.create_dataset('imgIntens', shape=(number,)+pattern_shape,dtype=np.float32, chunks=(1,)+pattern_shape, compression="gzip", compression_opts=4)
if savePhotons == 1:
fph.create_dataset('orientation', data=orientations, compression="gzip", compression_opts=4)
fin.create_dataset('orientation', data=orientations, compression="gzip", compression_opts=4)
print("Done creating HDF5 file and datasets...")
c = 0
while c < number:
status1 = MPI.Status()
result = comm.recv(source=MPI.ANY_SOURCE,status=status1) # (index,photImg)
i = status1.Get_source()
dd = det.add_correction(result[1])
print("Rank 0: Received image %d from rank %d" % (result[0],i))
dset_intens[result[0],:,:,:] = dd #result[1]
#photoImg = det.add_correction_and_quantization(pattern=result[1])
if savePhotons == 1:
photoImg = det.add_quantization(pattern=dd)
dset_photons[result[0],:,:,:] = photoImg
c += 1
else: # slave
# initialize intensity volume
ori = dct['orientations']
det = dct['detector']
particle = dct['particle']
slices_num = ori.shape[0]
pattern_shape = det.pedestals.shape
pixel_momentum = det.pixel_position_reciprocal
sliceOne = np.zeros((pattern_shape)) #left out dtype=np.float32
mesh_length = 128
mesh,voxel_length = det.get_reciprocal_mesh(voxel_number_1d=mesh_length)
print "MeshDtype=",mesh.dtype
intensVol = pg.diffraction.calculate_diffraction_pattern_gpu(mesh, particle, return_type='intensity')
# lft out mesh.astype(np.float32)
for i in range((rank-1),number,sz-1):
# transform quaternion (set of orientations) into 3D rotation
rotmat = ps.geometry.quaternion2rot3d(ori[i,:])
intensSlice = slave_calc_intensity(rot3d = rotmat,
pixel_momentum = pixel_momentum,
pattern_shape = pattern_shape,
volume = intensVol,
voxel_length = voxel_length)
# intensVol.astype(np.float32)
# Convert the one image to photons
#photImg = det.add_correction_and_quantization(pattern=intensSlice)
# astype(np.int32)
print("Sending slice %d from rank %d" % (i,rank))
comm.ssend((i,intensSlice),dest=0)
if rank==0:
t_end = MPI.Wtime()
print("Finishing constructing %d patterns in %f seconds" % (number,t_end-t_start))
import matplotlib.pyplot as plt
fin.flush()
if savePhotons == 1:
fph.flush()
# Display first diffraction image
photImgAssem = det.assemble_image_stack(image_stack=fph['imgPhot'][0,:,:,:])
intensImgAssemb = det.assemble_image_stack(image_stack=fin['imgIntens'][0,:,:,:])
#diff = photoImg2 - photoImg
#print np.nonzero(diff)
#print np.max
#diffImgAssemb = det.assemble_image_stack(image_stack=diff)
#fig = plt.figure()
#ax1 = fig.add_subplot(2,1,1)
#plt.imshow(diffImgAssemb)
#plt.colorbar()
#ax1.colorbar()
#ax2 = fig.add_subplot(2,1,2)
#ax2.imshow(np.log(photImgAssem+1), interpolation='none')
#ax2.colorbar()
plt.show()
fin.close()
if savePhotons == 1:
fph.close()
sys.exit()
import matplotlib.pyplot as plt
from matplotlib.colors import LogNorm
import os
from pysingfel import *
import pysingfel as ps
from pysingfel.util import asnumpy, xp
from pysingfel.build_autoranging_frames import BuildAutoRangeFrames
pwd = os.path.dirname(__file__)
# create particle object(s)
particle = ps.Particle()
particle.read_pdb(os.path.join(pwd, '../input/pdb/3iyf.pdb'), ff='WK')
# load beam
beam = ps.Beam(os.path.join(pwd, '../input/beam/amo86615.beam'))
#beam._n_phot = 1e14 # detector normal
beam._n_phot = 1e17 # detector saturates
#beam._n_phot = 1e20 # detector gets fried
geom = os.path.join(
pwd,
'../input/lcls/amo86615/PNCCD::CalibV1/Camp.0:pnCCD.1/geometry/0-end.data')
# load and initialize the detector
det = ps.Epix10kDetector(geom=geom,
run_num=0,
beam=beam,
cameraConfig='fixedMedium')
# reset detector distance for desired resolution
det.distance = 0.25
def __init__(self, pdb_file, colors=False, debug=False):
super(ApplicationWindow, self).__init__()
self.debug = debug
# Create a particle object
self.particle = ps.Particle()
self.particle.read_pdb(pdb_file, ff='WK')
# Load beam
beam = ps.Beam('../input/beam/amo86615.beam')
# Load and initialize the detector
self.det = ps.PnccdDetector(
geom='../input/lcls/amo86615/PNCCD::CalibV1/'
'Camp.0:pnCCD.1/geometry/0-end.data',
beam=beam)
mesh_length = 151 if not debug else 31
mesh, self.voxel_length = self.det.get_reciprocal_mesh(
voxel_number_1d=mesh_length)
self.volume = pg.calculate_diffraction_pattern_gpu(
mesh, self.particle, return_type='intensity')
self.pixel_momentum = self.det.pixel_position_reciprocal
if colors:
color_map = collections.defaultdict(lambda: "#000000", {
"C": "#ff0000",
"N": "#00ff00",
"O": "#0000ff",
})
colors = [color_map[s] for s in self.particle.atomic_symbol]
else:
colors = None
self._azim = None
self._elev = None
self._time = 0.
self._uptodate = False
self._main = QtWidgets.QWidget()
self.setCentralWidget(self._main)
layout = QtWidgets.QHBoxLayout(self._main)
real3d_canvas = FigureCanvas(Figure(figsize=(4, 4)))
layout.addWidget(real3d_canvas)
self.addToolBar(NavigationToolbar(real3d_canvas, self))
self._real3d_ax = real3d_canvas.figure.subplots(
subplot_kw={"projection": '3d'})
self._real3d_ax.scatter(
-self.particle.atom_pos[:, 2],
self.particle.atom_pos[:, 1],
self.particle.atom_pos[:, 0],
s=1,
c=colors,
)
self._real3d_ax.set_title("3D Protein")
self._real3d_ax.set_xlabel('-Z')
self._real3d_ax.set_ylabel('Y')
self._real3d_ax.set_zlabel('X')
if self.debug:
real2d_canvas = FigureCanvas(Figure(figsize=(4, 4)))
layout.addWidget(real2d_canvas)
self.addToolBar(NavigationToolbar(real2d_canvas, self))
self._real2d_ax = real2d_canvas.figure.subplots()
recip_canvas = FigureCanvas(Figure(figsize=(4, 4)))
layout.addWidget(recip_canvas)
self.addToolBar(NavigationToolbar(recip_canvas, self))
self._recip_ax = recip_canvas.figure.subplots()
self._timer = recip_canvas.new_timer(100,
[(self._update_canvas, (), {})])
self._timer.start()
numDinitro = 500
numPyrene = 1000
pwd = os.path.dirname(__file__)
# Create a particle object
particleC = ps.Particle()
particleC.read_pdb(os.path.join(pwd, '../input/pdb/cyclohexane.pdb'), ff='WK')
particleD = ps.Particle()
particleD.read_pdb(os.path.join(pwd, '../input/pdb/dinitro.pdb'), ff='WK')
particleP = ps.Particle()
particleP.read_pdb(os.path.join(pwd, '../input/pdb/pyrene.pdb'), ff='WK')
# Load beam
beam = ps.Beam(os.path.join(pwd, '../input/beam/temp.beam'))
geom = os.path.join(
pwd,
'../input/lcls/amo86615/PNCCD::CalibV1/Camp.0:pnCCD.1/geometry/0-end.data')
# Load and initialize the detector
det = ps.PnccdDetector(geom=geom, beam=beam)
tic = time.time()
patternC = det.get_photons(device='gpu', particle=particleC)
toc = time.time()
print("It took {:.2f} seconds to finish SPI calculation.".format(toc - tic))
patternD = det.get_photons(device='gpu', particle=particleD)
patternP = det.get_photons(device='gpu', particle=particleP)
def main():
# Parse user input for config file and dataset name
user_input = parse_input_arguments(sys.argv)
config_file = user_input['config']
dataset_name = user_input['dataset']
# Get the Config file parameters
with open(config_file) as config_file:
config_params = json.load(config_file)
# Check if dataset in Config file
if dataset_name not in config_params:
raise Exception("Dataset {} not in Config file.".format(dataset_name))
# Get the dataset parameters from Config file parameters
dataset_params = config_params[dataset_name]
# Get the input dataset parameters
pdb_file = dataset_params["pdb"]
beam_file = dataset_params["beam"]
beam_fluence_increase_factor = dataset_params["beamFluenceIncreaseFactor"]
geom_file = dataset_params["geom"]
dataset_size = dataset_params["numPatterns"]
# Divide up the task of creating the dataset to be executed simultaneously by multiple ranks
batch_size = dataset_params["batchSize"]
# Get the output dataset parameters
img_dir = dataset_params["imgDir"]
output_dir = dataset_params["outDir"]
# raise exception if batch_size does not divide into dataset_size
if dataset_size % batch_size != 0:
if RANK == MASTER_RANK:
raise ValueError(
"(Master) batch_size {} should divide dataset_size {}.".format(
batch_size, dataset_size))
else:
sys.exit(1)
# Compute number of batches to process
n_batches = dataset_size // batch_size
# Flags
save_volume = False
with_intensities = False
given_orientations = True
# Constants
photons_dtype = np.uint8
photons_max = np.iinfo(photons_dtype).max
# Load beam parameters
beam = ps.Beam(beam_file)
# Increase the beam fluence
if not np.isclose(beam_fluence_increase_factor, 1.0):
beam.set_photons_per_pulse(beam_fluence_increase_factor *
beam.get_photons_per_pulse())
# Load geometry of detector
det = ps.PnccdDetector(geom=geom_file, beam=beam)
# Get the shape of the diffraction pattern
diffraction_pattern_height = det.detector_pixel_num_x.item()
diffraction_pattern_width = det.detector_pixel_num_y.item()
# Define path to output HDF5 file
output_file = get_output_file_name(dataset_name, dataset_size,
diffraction_pattern_height,
diffraction_pattern_width)
cspi_synthetic_dataset_file = os.path.join(output_dir, output_file)
# Generate uniform orientations
if given_orientations and RANK == MASTER_RANK:
print("(Master) Generate {} uniform orientations".format(dataset_size))
orientations = ps.get_uniform_quat(dataset_size, True)
sys.stdout.flush()
# Create a particle object
if RANK == GPU_RANKS[0]:
# Load PDB
print("(GPU 0) Reading PDB file: {}".format(pdb_file))
particle = ps.Particle()
particle.read_pdb(pdb_file, ff='WK')
# Calculate diffraction volume
print("(GPU 0) Calculating diffraction volume")
experiment = ps.SPIExperiment(det, beam, particle)
else:
experiment = ps.SPIExperiment(det, beam, None)
sys.stdout.flush()
# Transfer diffraction volume to CPU memory
buffer = asnumpy(experiment.volumes[0])
# GPU rank broadcasts diffraction volume to other ranks
COMM.Bcast(buffer, root=1)
# This condition is necessary if the script is run on more than one machine (each machine having 1 GPU and 9 CPU)
if RANK in GPU_RANKS[1:]:
experiment.volumes[0] = xp.asarray(experiment.volumes[0])
if RANK == MASTER_RANK:
# Create output directory if it does not exist
if not os.path.exists(output_dir):
print("(Master) Creating output directory: {}".format(output_dir))
os.makedirs(output_dir)
# Create image directory if it does not exist
if not os.path.exists(img_dir):
print(
"(Master) Creating image output directory: {}".format(img_dir))
os.makedirs(img_dir)
print("(Master) Creating HDF5 file to store the datasets: {}".format(
cspi_synthetic_dataset_file))
f = h5.File(cspi_synthetic_dataset_file, "w")
f.create_dataset("pixel_position_reciprocal",
data=det.pixel_position_reciprocal)
f.create_dataset("pixel_index_map", data=det.pixel_index_map)
if given_orientations:
f.create_dataset("orientations", data=orientations)
f.create_dataset("photons", (dataset_size, 4, 512, 512), photons_dtype)
# Create a dataset to store the diffraction patterns
f.create_dataset("diffraction_patterns",
(dataset_size, diffraction_pattern_height,
diffraction_pattern_width),
dtype='f')
if save_volume:
f.create_dataset("volume", data=experiment.volumes[0])
if with_intensities:
f.create_dataset("intensities", (dataset_size, 4, 512, 512),
np.float32)
f.close()
sys.stdout.flush()
# Make sure file is created before others open it
COMM.barrier()
# Add the atomic coordinates of the particle to the HDF5 file
if RANK == GPU_RANKS[0]:
atomic_coordinates = particle.atom_pos
f = h5.File(cspi_synthetic_dataset_file, "a")
dset_atomic_coordinates = f.create_dataset("atomic_coordinates",
atomic_coordinates.shape,
dtype='f')
dset_atomic_coordinates[...] = atomic_coordinates
f.close()
# Make sure file is closed before others open it
COMM.barrier()
# Keep track of the number of images processed
n_images_processed = 0
if RANK == MASTER_RANK:
# Send batch numbers to non-Master ranks
for batch_n in tqdm(range(n_batches)):
# Receive query for batch number from a rank
i_rank = COMM.recv(source=MPI.ANY_SOURCE)
# Send batch number to that rank
COMM.send(batch_n, dest=i_rank)
# Send orientations as well
if given_orientations:
batch_start = batch_n * batch_size
batch_end = (batch_n + 1) * batch_size
COMM.send(orientations[batch_start:batch_end], dest=i_rank)
# Tell non-Master ranks to stop asking for more data since there are no more batches to process
for _ in range(N_RANKS - 1):
# Send one "None" to each rank as final flag
i_rank = COMM.recv(source=MPI.ANY_SOURCE)
COMM.send(None, dest=i_rank)
else:
# Get the HDF5 file
f = h5.File(cspi_synthetic_dataset_file, "r+")
# Get the dataset used to store the photons
h5_photons = f["photons"]
# Get the dataset used to store the diffraction patterns
h5_diffraction_patterns = f["diffraction_patterns"]
# Get the dataset used to store intensities
if with_intensities:
h5_intensities = f["intensities"]
while True:
# Ask for batch number from Master rank
COMM.send(RANK, dest=MASTER_RANK)
# Receive batch number from Master rank
batch_n = COMM.recv(source=MASTER_RANK)
# If batch number is final flag, stop
if batch_n is None:
break
# Receive orientations as well from Master rank
if given_orientations:
orientations = COMM.recv(source=MASTER_RANK)
experiment.set_orientations(orientations)
# Define a Numpy array to hold a batch of photons
np_photons = np.zeros((batch_size, 4, 512, 512), photons_dtype)
# Define a Numpy array to hold a batch of diffraction patterns
np_diffraction_patterns = np.zeros(
(batch_size, diffraction_pattern_height,
diffraction_pattern_width))
# Define a Numpy array to hold a batch of intensities
if with_intensities:
np_intensities = np.zeros((batch_size, 4, 512, 512),
np.float32)
# Define the batch start and end offsets
batch_start = batch_n * batch_size
batch_end = (batch_n + 1) * batch_size
# Generate batch of snapshots
for i in range(batch_size):
# Generate image stack
image_stack_tuple = experiment.generate_image_stack(
return_photons=True,
return_intensities=with_intensities,
always_tuple=True)
# Photons
photons = image_stack_tuple[0]
# # Raise exception if photon max exceeds max of uint8
# if photons.max() > photons_max:
# raise RuntimeError("Value of photons too large for type {}.".format(photons_dtype))
np_photons[i] = asnumpy(photons.astype(photons_dtype))
# Assemble the image stack into a 2D diffraction pattern
np_diffraction_pattern = experiment.det.assemble_image_stack(
image_stack_tuple)
# Add the assembled diffraction pattern to the batch
np_diffraction_patterns[i] = np_diffraction_pattern
# Save diffraction pattern as PNG file
data_index = batch_start + i
save_diffraction_pattern_as_image(data_index, img_dir,
np_diffraction_pattern)
# Intensities
if with_intensities:
np_intensities[i] = asnumpy(image_stack_tuple[1].astype(
np.float32))
# Update the number of images processed
n_images_processed += 1
# Add the batch of photons to the HDF5 file
h5_photons[batch_start:batch_end] = np_photons
# Add the batch of diffraction patterns to the HDF5 file
h5_diffraction_patterns[
batch_start:batch_end] = np_diffraction_patterns
if with_intensities:
h5_intensities[batch_start:batch_end] = np_intensities
# Close the HDF5 file
f.close()
sys.stdout.flush()
# Wait for ranks to finish
COMM.barrier()
def main():
# parse user input
params = parse_input_arguments(sys.argv)
pdb = params['pdb']
geom = params['geom']
beam = params['beam']
numPatterns = int(params['numPatterns'])
outDir = params['outDir']
saveName = params['saveNameHDF5']
data = None
if rank == 0:
print(
"===================================================================="
)
print("Running %d parallel MPI processes" % size)
t_start = MPI.Wtime()
# load beam
beam = ps.Beam(beam)
# load and initialize the detector
det = ps.PnccdDetector(geom=geom, beam=beam)
# create particle object(s)
particle = ps.Particle()
particle.read_pdb(pdb, ff='WK')
data = {"detector": det, "beam": beam, "particle": particle}
print("Broadcasting input to processes...")
dct = comm.bcast(data, root=0)
if rank == 0:
pattern_shape = det.pedestals.shape # (4, 512, 512)
f = h5.File(os.path.join(outDir, "SPI_MPI.h5"), "w")
dset = f.create_dataset(
"intensity",
shape=(numPatterns, ) + pattern_shape,
dtype=np.float32,
chunks=(1, ) + pattern_shape,
compression="gzip",
compression_opts=4) # (numPatterns, 4, 512, 512)
print("Done creating HDF5 file and dataset...")
n = 0
while n < numPatterns:
status1 = MPI.Status()
(ind, img) = comm.recv(source=MPI.ANY_SOURCE, status=status1)
i = status1.Get_source()
print("Rank 0: Received image %d from rank %d" % (ind, i))
dset[ind, :, :, :] = img
n += 1
else:
det = dct['detector']
beam = dct['beam']
particle = dct['particle']
experiment = ps.SPIExperiment(det, beam, particle)
for i in range((rank - 1), numPatterns, size - 1):
img_intensity = experiment.generate_image_stack()
print("Sending slice %d from rank %d" % (i, rank))
comm.ssend((i, img_intensity), dest=0)
if rank == 0:
t_end = MPI.Wtime()
print("Finishing constructing %d patterns in %f seconds" %
(numPatterns, t_end - t_start))
f.close()
sys.exit()
def main():
# Parse user input
params = parse_input_arguments(sys.argv)
pdb = params['pdb']
geom = params['geom']
beam = params['beam']
orient = int(params['UniformOrientation'])
number = int(params['numSlices'])
outDir = params['outDir']
# Initialize MPI
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
sz = comm.size
det = None
data = None
if rank==0:
print("====================================================================")
print("Running %d parallel MPI processes" % (comm.size))
t_start = MPI.Wtime()
orientations = np.zeros((number,4))
particle = ps.Particle()
if rank==0:
if orient== 1:
orientations = ps.geometry.get_uniform_quat(num_pts=number).astype(np.float32)
elif orient== 0:
orientations = ps.geometry.get_random_quat(num_pts=number).astype(np.float32)
print("Reading PDB file...")
particle.read_pdb(pdb, ff='WK')
# reading beam and detector files
beam= ps.Beam(beam)
det = ps.PnccdDetector(geom=geom, beam=beam)
print("Broadcasting input to processes...")
data = {'particle': particle, 'orientations': orientations, 'detector': det}
dct = comm.bcast(data,root=0)
if rank==0:
pattern_shape = det.pedestal.shape
f = h5.File(os.path.join(outDir,'saveHDF5_parallel.h5'),'w')
dset = f.create_dataset('img', shape=(number,)+pattern_shape,dtype=np.int32, chunks=(1,)+pattern_shape, compression="gzip", compression_opts=4)
f.create_dataset('orientation', data=orientations, compression="gzip", compression_opts=4)
print("Done creating HDF5 file and datasets...")
c = 0
while c < number:
status1 = MPI.Status()
result = comm.recv(source=MPI.ANY_SOURCE,status=status1) # (index,photImg)
i = status1.Get_source()
print("Rank 0: Received image %d from rank %d" % (result[0],i))
dset[result[0],:,:,:] = result[1]
c += 1
else: # slave
# initialize intensity volume
ori = dct['orientations']
det = dct['detector']
particle = dct['particle']
slices_num = ori.shape[0]
pattern_shape = det.pedestal.shape
pixel_momentum = det.pixel_position_reciprocal
sliceOne = np.zeros((pattern_shape))
mesh_length = 128
mesh,voxel_length = det.get_reciprocal_mesh(voxel_number_1d=mesh_length)
intensVol = pg.diffraction.calculate_diffraction_pattern_gpu(mesh, particle, return_type='intensity')
for i in range((rank-1),number,sz-1):
# transform quaternion (set of orientations) into 3D rotation
rotmat = ps.geometry.quaternion2rot3d(ori[i,:])
intensSlice = slave_calc_intensity(rot3d = rotmat,
pixel_momentum = pixel_momentum,
pattern_shape = pattern_shape,
volume = intensVol,
voxel_length = voxel_length)
# Convert the one image to photons
photImg = det.add_correction_and_quantization(pattern=intensSlice).astype(np.int32)
print("Sending slice %d from rank %d" % (i,rank))
comm.send((i,photImg),dest=0)
if rank==0:
t_end = MPI.Wtime()
print("Finishing constructing %d patterns in %f seconds" % (number,t_end-t_start))
import matplotlib.pyplot as plt
# Display first diffraction image
photImgAssem = det.assemble_image_stack(image_stack=f['img'][0,:,:,:])
plt.imshow(photImgAssem, interpolation='none', vmin=0,vmax=4)
plt.colorbar()
plt.show()
f.close()
x = np.cos(u) * np.sin(v)
y = np.sin(u) * np.sin(v)
z = np.cos(v)
x = r * x + xCenter
y = r * y + yCenter
z = r * z + zCenter
return (x, y, z)
num = 4
input_dir = '../input'
beamfile = input_dir + '/beam/amo86615.beam'
pdbfile = input_dir + '/pdb/3iyf.pdb'
beam = ps.Beam(beamfile)
particle = ps.Particle()
particle.read_pdb(pdbfile, ff='WK')
particles = {particle: num}
part_states, part_positions = distribute_particles(particles,
beam.get_focus()[0] / 2,
jet_radius=1e-4,
gamma=1.)
radius = max_radius(particles)
x = []
y = []
z = []
for i in range(num):
x.append(part_positions[i, 0])
def main():
# parse user input
params = parse_input_arguments(sys.argv)
pdb = params['pdb']
geom = params['geom']
beam = params['beam']
numPatterns = int(params['numPatterns'])
outDir = params['outDir']
saveName = params['saveNameHDF5']
data = None
if rank == 0:
print(
"===================================================================="
)
print("Running %d parallel MPI processes" % size)
t_start = MPI.Wtime()
# load beam
beam = ps.Beam(beam)
# load and initialize the detector
det = ps.PnccdDetector(geom=geom, beam=beam)
highest_k_beam = beam.get_highest_wavenumber_beam()
recidet = ReciprocalDetector(det, highest_k_beam)
# create particle object(s)
particle = ps.Particle()
particle.read_pdb(pdb, ff='WK')
experiment = ps.SPIExperiment(det, beam, particle)
f = h5.File(os.path.join(outDir, "SPI_MPI.h5"), "w")
f.attrs['numParticles'] = 1
experiment.volumes[0] = xp.asarray(experiment.volumes[0])
dset_volume = f.create_dataset("volume",
data=experiment.volumes[0],
compression="gzip",
compression_opts=4)
data = {"detector": det, "beam": beam, "particle": particle}
print("Broadcasting input to processes...")
dct = comm.bcast(data, root=0)
if rank == 0:
pattern_shape = det.pedestals.shape # (4, 512, 512)
dset_intensities = f.create_dataset(
"intensities",
shape=(numPatterns, ) + pattern_shape,
dtype=np.float32,
chunks=(1, ) + pattern_shape,
compression="gzip",
compression_opts=4) # (numPatterns, 4, 512, 512)
dset_photons = f.create_dataset("photons",
shape=(numPatterns, ) + pattern_shape,
dtype=np.float32,
chunks=(1, ) + pattern_shape,
compression="gzip",
compression_opts=4)
dset_orientations = f.create_dataset("orientations",
shape=(numPatterns, ) + (1, 4),
dtype=np.float32,
chunks=(1, ) + (1, 4),
compression="gzip",
compression_opts=4)
dset_pixel_index_map = f.create_dataset("pixel_index_map",
data=det.pixel_index_map,
compression="gzip",
compression_opts=4)
dset_pixel_position_reciprocal = f.create_dataset(
"pixel_position_reciprocal",
data=det.pixel_position_reciprocal,
compression="gzip",
compression_opts=4)
print("Done creating HDF5 file and dataset...")
n = 0
while n < numPatterns:
status1 = MPI.Status()
(ind, img_slice_intensity,
img_slice_orientation) = comm.recv(source=MPI.ANY_SOURCE,
status=status1)
i = status1.Get_source()
print("Rank 0: Received image %d from rank %d" % (ind, i))
dset_intensities[ind, :, :, :] = np.asarray(img_slice_intensity)
dset_photons[ind, :, :, :] = recidet.add_quantization(
img_slice_intensity)
dset_orientations[ind, :, :] = np.asarray(img_slice_orientation)
n += 1
else:
det = dct['detector']
beam = dct['beam']
particle = dct['particle']
experiment = ps.SPIExperiment(det, beam, particle)
for i in range((rank - 1), numPatterns, size - 1):
img_slice = experiment.generate_image_stack(
return_intensities=True,
return_orientation=True,
always_tuple=True)
img_slice_intensity = img_slice[0]
img_slice_orientation = img_slice[1]
comm.ssend((i, img_slice_intensity, img_slice_orientation), dest=0)
if rank == 0:
t_end = MPI.Wtime()
print("Finishing constructing %d patterns in %f seconds" %
(numPatterns, t_end - t_start))
f.close()
sys.exit()
