def add_phase_shift(self, pattern, displ):
"""
Add phase shift corresponding to displ to complex pattern.
:param pattern: complex field pattern.
:param displ: displ(acement) (position) of the particle (m).
:return: modified complex field pattern.
"""
self.ensure_beam()
pattern = xp.asarray(pattern)
displ = xp.asarray(displ)
return pattern * xp.exp(
1j * xp.dot(2 * np.pi * self.pixel_position_reciprocal, displ))
def solid_angle(pixel_center, pixel_area, orientation):
"""
Calculate the solid angle for each pixel.
:param pixel_center: The position of each pixel in real space. In pixel stack format.
:param orientation: The orientation of the detector.
:param pixel_area: The pixel area for each pixel. In pixel stack format.
:return: Solid angle of each pixel.
"""
orientation = xp.asarray(orientation)
# Use 1D format
pixel_center_1d = reshape_pixels_position_arrays_to_1d(pixel_center)
pixel_center_norm_1d = xp.sqrt(xp.sum(xp.square(pixel_center_1d), axis=-1))
pixel_area_1d = xp.reshape(pixel_area, np.prod(pixel_area.shape))
# Calculate the direction of each pixel.
pixel_center_direction_1d = pixel_center_1d / pixel_center_norm_1d[:, xp.newaxis]
# Normalize the orientation vector
orientation_norm = xp.sqrt(xp.sum(xp.square(orientation)))
orientation_normalized = orientation / orientation_norm
# The correction induced by projection which is a factor of cosine.
cosine_1d = xp.abs(xp.dot(pixel_center_direction_1d, orientation_normalized))
# Calculate the solid angle ignoring the projection
solid_angle_1d = xp.divide(pixel_area_1d, xp.square(pixel_center_norm_1d))
solid_angle_1d = xp.multiply(cosine_1d, solid_angle_1d)
# Restore the pixel stack format
solid_angle_stack = xp.reshape(solid_angle_1d, pixel_area.shape)
return solid_angle_stack
def calculate_atomic_factor(particle, q_space, pixel_num):
"""
Calculate the atomic form factor for each atom at each momentum
:param particle: The particle object
:param q_space: The reciprocal to calculate
:param pixel_num: The number of pixels.
:return:
"""
q_space = asnumpy(q_space) # CubicSpline is not compatible with Cupy
f_hkl = np.zeros((particle.num_atom_types, pixel_num))
q_space_1d = np.reshape(q_space, [
pixel_num,
])
if particle.num_atom_types == 1:
cs = CubicSpline(particle.q_sample,
particle.ff_table[:]) # Use cubic spline
f_hkl[0, :] = cs(q_space_1d) # interpolate
else:
for atm in range(particle.num_atom_types):
cs = CubicSpline(particle.q_sample,
particle.ff_table[atm, :]) # Use cubic spline
f_hkl[atm, :] = cs(q_space_1d) # interpolate
f_hkl = np.reshape(f_hkl, (particle.num_atom_types, ) + q_space.shape)
return xp.asarray(f_hkl)
def get_reciprocal_space_pixel_position(pixel_center, wave_vector):
"""
Obtain the coordinate of each pixel in the reciprocal space.
:param pixel_center: The coordinate of the pixel in real space.
:param wave_vector: The wavevector.
:return: The array containing the pixel coordinates.
"""
wave_vector = xp.asarray(wave_vector)
# reshape the array into a 1d position array
pixel_center_1d = reshape_pixels_position_arrays_to_1d(pixel_center)
# Calculate the reciprocal position of each pixel
wave_vector_norm = xp.sqrt(xp.sum(xp.square(wave_vector)))
wave_vector_direction = wave_vector / wave_vector_norm
pixel_center_norm = xp.sqrt(xp.sum(xp.square(pixel_center_1d), axis=1))
pixel_center_direction = pixel_center_1d / pixel_center_norm[:, np.newaxis]
pixel_position_reciprocal_1d = wave_vector_norm * (
pixel_center_direction - wave_vector_direction)
# restore the pixels shape
pixel_position_reciprocal = xp.reshape(pixel_position_reciprocal_1d, pixel_center.shape)
return pixel_position_reciprocal
def extract_slice(local_index, local_weight, volume):
"""
Take one slice from the volume given the index and weight map.
:param local_index: The index containing values to take.
:param local_weight: The weight for each index.
:param volume: The volume to slice from.
:return: The slice or an array of zeros is if dimensions are incompatible.
"""
local_index = xp.asarray(local_index)
local_weight = xp.asarray(local_weight)
volume = xp.asarray(volume)
# Convert the index of the 3D diffraction volume to 1D
pattern_shape = local_index.shape[:3]
pixel_num = int(np.prod(pattern_shape))
volume_num_1d = volume.shape[0]
convertion_factor = xp.array(
[volume_num_1d * volume_num_1d, volume_num_1d, 1], dtype=np.int64)
index_2d = xp.reshape(local_index, [pixel_num, 8, 3])
index_2d = xp.matmul(index_2d, convertion_factor)
volume_1d = xp.reshape(volume, volume_num_1d ** 3)
weight_2d = xp.reshape(local_weight, [pixel_num, 8])
# Expand the data to merge
try:
data_to_merge = volume_1d[index_2d]
except IndexError:
print("Requested slice and diffraction volume have incompatible dimensions")
return np.zeros(pattern_shape)
# Merge the data
data_merged = xp.sum(xp.multiply(weight_2d, data_to_merge), axis=-1)
data = xp.reshape(data_merged, pattern_shape)
return data
def rotate_pixels_in_reciprocal_space(rot_mat, pixels_position):
"""
Rotate the pixel positions according to the rotation matrix
Note that for np.dot(a,b)
If a is an N-D array and b is an M-D array (where M>=2),
it is a sum product over the last axis of a and the second-to-last axis of b.
:param rot_mat: The rotation matrix for M v
:param pixels_position: [the other dimensions, 3 for x,y,z]
:return: np.dot(pixels_position, rot_mat.T)
"""
rot_mat = xp.asarray(rot_mat)
return xp.dot(pixels_position, rot_mat.T)
def get_reciprocal_mesh(voxel_number_1d, max_value):
"""
Get a centered, symetric mesh of given dimensions.
:param voxel_number_1d: Number of voxel per axis.
:param max_value: Max (absolute) value on each axis.
:return: The mesh grid, the voxel lenght.
"""
linspace = xp.linspace(-max_value, max_value, voxel_number_1d)
reciprocal_mesh_stack = xp.asarray(
xp.meshgrid(linspace, linspace, linspace, indexing='ij'))
reciprocal_mesh = xp.moveaxis(reciprocal_mesh_stack, 0, -1)
voxel_length = 2 * max_value / (voxel_number_1d - 1)
return reciprocal_mesh, voxel_length
def calculate_diffraction_pattern_gpu(reciprocal_space, particle, return_type='intensity'):
"""
Calculate the diffraction field of the specified reciprocal space.
:param reciprocal_space: The reciprocal space over which to calculate the diffraction field as s-vectors, where q=2*pi*s
:param particle: The particle object to calculate the diffraction field.
:param return_type: 'intensity' to return the intensity field. 'complex_field' to return the full diffraction field.
:return: The diffraction field.
"""
"""This function can be used to calculate the diffraction field for
arbitrary reciprocal space """
# convert the reciprocal space into a 1d series.
shape = reciprocal_space.shape
pixel_number = int(np.prod(shape[:-1]))
reciprocal_space_1d = xp.reshape(reciprocal_space, [pixel_number, 3])
reciprocal_norm_1d = xp.sqrt(xp.sum(xp.square(reciprocal_space_1d), axis=-1))
qvectors_1d = 2*np.pi*reciprocal_space_1d
# Calculate atom form factor for the reciprocal space, passing in sin(theta)/lambda in per Angstrom
form_factor = pd.calculate_atomic_factor(particle=particle,
q_space=reciprocal_norm_1d * (1e-10 / 2.), # For unit compatibility
pixel_num=pixel_number)
# Get atom position
atom_position = np.ascontiguousarray(particle.atom_pos[:])
atom_type_num = len(particle.split_idx) - 1
# create
pattern_cos = xp.zeros(pixel_number, dtype=xp.float64)
pattern_sin = xp.zeros(pixel_number, dtype=xp.float64)
# atom_number = atom_position.shape[0]
split_index = xp.array(particle.split_idx)
cuda_split_index = cuda.to_device(split_index)
cuda_atom_position = cuda.to_device(atom_position)
cuda_reciprocal_position = cuda.to_device(qvectors_1d)
cuda_form_factor = cuda.to_device(form_factor)
# Calculate the pattern
calculate_pattern_gpu_back_engine[(pixel_number + 511) // 512, 512](
cuda_form_factor, cuda_reciprocal_position, cuda_atom_position,
pattern_cos, pattern_sin, atom_type_num, cuda_split_index,
pixel_number)
# Add the hydration layer
if particle.mesh is not None:
water_position = np.ascontiguousarray(particle.mesh[particle.solvent_mask,:])
water_num = np.sum(particle.solvent_mask)
water_prefactor = particle.solvent_mean_electron_density * particle.mesh_voxel_size**3
cuda_water_position = cuda.to_device(water_position)
calculate_solvent_pattern_gpu_back_engine[(pixel_number + 511) // 512, 512](
cuda_reciprocal_position, cuda_water_position,
pattern_cos, pattern_sin, water_prefactor, water_num,
pixel_number)
# Add another contribution if defined, e.g. virus void...
if particle.other_mask is not None:
other_position = np.ascontiguousarray(particle.mesh[particle.other_mask,:])
other_num = np.sum(particle.other_mask)
other_prefactor = particle.other_mean_electron_density * particle.mesh_voxel_size**3
cuda_other_position = cuda.to_device(other_position)
calculate_solvent_pattern_gpu_back_engine[(pixel_number + 511) // 512, 512](
cuda_reciprocal_position, cuda_other_position,
pattern_cos, pattern_sin, other_prefactor, other_num,
pixel_number)
if return_type == "intensity":
pattern = np.reshape(np.square(np.abs(pattern_cos + 1j * pattern_sin)), shape[:-1])
return xp.asarray(pattern)
elif return_type == "complex_field":
pattern = np.reshape(pattern_cos + 1j * pattern_sin, shape[:-1])
return xp.asarray(pattern)
else:
print("Please set the parameter return_type = 'intensity' or 'complex_field'")
print("This time, this program return the complex field.")
pattern = np.reshape(pattern_cos + 1j * pattern_sin, shape[:-1])
return xp.asarray(pattern)
def get_weight_and_index(pixel_position, voxel_length, voxel_num_1d):
"""
Obtain the weight of the pixel for adjacent voxels.
In this function, pixel position is first cast to the shape [pixel number,3].
:param pixel_position: The position of each pixel in the space.
:param voxel_length:
:param voxel_num_1d:
:return:
"""
# Extract the detector shape
detector_shape = pixel_position.shape[:-1]
pixel_num = np.prod(detector_shape)
# Cast the position infor to the shape [pixel number, 3]
pixel_position = xp.asarray(pixel_position)
pixel_position_1d = xp.reshape(pixel_position, (pixel_num, 3))
# convert_to_voxel_unit
pixel_position_1d_voxel_unit = pixel_position_1d / voxel_length
# shift the center position
shift = (voxel_num_1d - 1) / 2.
pixel_position_1d_voxel_unit += shift
# Get one nearest neighbor
tmp_index = xp.floor(pixel_position_1d_voxel_unit).astype(np.int64)
# Generate the holders
indexes = xp.zeros((pixel_num, 8, 3), dtype=np.int64)
weight = xp.ones((pixel_num, 8), dtype=np.float64)
# Calculate the floors and the ceilings
dfloor = pixel_position_1d_voxel_unit - tmp_index
dceiling = 1 - dfloor
# Assign the correct values to the indexes
indexes[:, 0, :] = tmp_index
indexes[:, 1, 0] = tmp_index[:, 0]
indexes[:, 1, 1] = tmp_index[:, 1]
indexes[:, 1, 2] = tmp_index[:, 2] + 1
indexes[:, 2, 0] = tmp_index[:, 0]
indexes[:, 2, 1] = tmp_index[:, 1] + 1
indexes[:, 2, 2] = tmp_index[:, 2]
indexes[:, 3, 0] = tmp_index[:, 0]
indexes[:, 3, 1] = tmp_index[:, 1] + 1
indexes[:, 3, 2] = tmp_index[:, 2] + 1
indexes[:, 4, 0] = tmp_index[:, 0] + 1
indexes[:, 4, 1] = tmp_index[:, 1]
indexes[:, 4, 2] = tmp_index[:, 2]
indexes[:, 5, 0] = tmp_index[:, 0] + 1
indexes[:, 5, 1] = tmp_index[:, 1]
indexes[:, 5, 2] = tmp_index[:, 2] + 1
indexes[:, 6, 0] = tmp_index[:, 0] + 1
indexes[:, 6, 1] = tmp_index[:, 1] + 1
indexes[:, 6, 2] = tmp_index[:, 2]
indexes[:, 7, :] = tmp_index + 1
# Assign the correct values to the weight
weight[:, 0] = xp.prod(dceiling, axis=-1)
weight[:, 1] = dceiling[:, 0] * dceiling[:, 1] * dfloor[:, 2]
weight[:, 2] = dceiling[:, 0] * dfloor[:, 1] * dceiling[:, 2]
weight[:, 3] = dceiling[:, 0] * dfloor[:, 1] * dfloor[:, 2]
weight[:, 4] = dfloor[:, 0] * dceiling[:, 1] * dceiling[:, 2]
weight[:, 5] = dfloor[:, 0] * dceiling[:, 1] * dfloor[:, 2]
weight[:, 6] = dfloor[:, 0] * dfloor[:, 1] * dceiling[:, 2]
weight[:, 7] = xp.prod(dfloor, axis=-1)
# Change the shape of the index and weight variable
indexes = xp.reshape(indexes, detector_shape + (8, 3))
weight = xp.reshape(weight, detector_shape + (8, ))
return indexes, weight
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 = sk.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 = sk.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 = sk.get_uniform_quat(dataset_size, True)
sys.stdout.flush()
# Load PDB
print("(GPU 0) Reading PDB file: {}".format(pdb_file))
particle = sk.Particle()
particle.read_pdb(pdb_file, ff='WK')
# Calculate diffraction volume
print("(GPU 0) Calculating diffraction volume")
experiment = sk.SPIExperiment(det, beam, particle)
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] = asnumpy(np_diffraction_pattern)
# Save diffraction pattern as PNG file
data_index = batch_start + i
save_diffraction_pattern_as_image(
data_index, img_dir, asnumpy(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] = asnumpy(np_photons)
# Add the batch of diffraction patterns to the HDF5 file
h5_diffraction_patterns[batch_start:batch_end] = asnumpy(
np_diffraction_patterns)
if with_intensities:
h5_intensities[batch_start:batch_end] = asnumpy(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']
numParticles = int(params['numParticles'])
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 = sk.Beam(beam)
# load and initialize the detector
det = sk.PnccdDetector(geom=geom, beam=beam)
# create particle object(s)
particle = sk.Particle()
particle.read_pdb(pdb, ff='WK')
experiment = sk.SPIExperiment(det, beam, particle)
f = h5.File(os.path.join(outDir, "sim_data.h5"), "w")
f.attrs['numParticles'] = numParticles
experiment.volumes[0] = xp.asarray(experiment.volumes[0])
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_positions = f.create_dataset("positions",
shape=(numPatterns, ) +
(numParticles, 3),
dtype=np.float32,
chunks=(1, ) + (numParticles, 3),
compression="gzip",
compression_opts=4)
dset_orientations = f.create_dataset("orientations",
shape=(numPatterns, ) +
(numParticles, 4),
chunks=(1, ) + (numParticles, 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_intensities, img_slice_positions,
img_slice_orientations) = 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_intensities)
dset_photons[ind, :, :, :] = det.add_quantization(
img_slice_intensities)
dset_positions[ind, :, :] = np.asarray(img_slice_positions)
dset_orientations[ind, :, :] = np.asarray(img_slice_orientations)
n += 1
else:
det = dct['detector']
beam = dct['beam']
particle = dct['particle']
experiment = sk.SPIExperiment(det, beam, particle)
for i in range((rank - 1), numPatterns, size - 1):
img_slice = experiment.generate_image_stack(
return_intensities=True,
return_positions=True,
return_orientations=True,
always_tuple=True)
img_slice_intensities = img_slice[0]
img_slice_positions = img_slice[1]
img_slice_orientations = img_slice[2]
comm.ssend((i, img_slice_intensities, img_slice_positions,
img_slice_orientations),
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 initialize(self, geom, beam):
"""
Initialize the detector with user defined parameters
:param geom: The dictionary containing all the necessary information to initialized the
detector.
:param beam: The beam object
:return: None
"""
"""
Doc:
To use this class, the user has to provide the necessary information to initialize
the detector.
All the necessary entries are listed in the example notebook.
"""
# 'detector distance': detector distance in (m)
##########################################################################################
# Extract necessary information
##########################################################################################
# Define the hierarchy system. For simplicity, we only use two-layer structure.
for key in {'panel number', 'panel pixel num x', 'panel pixel num y'}:
if key not in geom:
raise KeyError("Missing required '{}' key.".format(key))
self.panel_num = int(geom['panel number'])
self.panel_pixel_num_x = int(geom['panel pixel num x'])
self.panel_pixel_num_y = int(geom['panel pixel num y'])
self._shape = (self.panel_num, self.panel_pixel_num_x,
self.panel_pixel_num_y)
# Define all properties the detector should have
self._distance = None
if 'pixel center z' in geom:
if 'detector distance' in geom:
raise ValueError("Please provide one of "
"'pixel center z' or 'detector distance'.")
self.center_z = xp.asarray(geom['pixel center z'],
dtype=xp.float64)
self._distance = float(self.center_z.mean())
else:
if 'detector distance' not in geom:
KeyError("Missing required 'detector distance' key.")
self._distance = float(geom['detector distance'])
self.center_z = self._distance * xp.ones(self._shape,
dtype=xp.float64)
# Below: [panel number, pixel num x, pixel num y] in (m)
# Change dtype and make numpy/cupy array
self.pixel_width = xp.asarray(geom['pixel width'], dtype=xp.float64)
self.pixel_height = xp.asarray(geom['pixel height'], dtype=xp.float64)
self.center_x = xp.asarray(geom['pixel center x'], dtype=xp.float64)
self.center_y = xp.asarray(geom['pixel center y'], dtype=xp.float64)
self.orientation = np.array([0, 0, 1])
# construct the the pixel position array
self.pixel_position = xp.zeros(self._shape + (3, ))
self.pixel_position[..., 0] = self.center_x
self.pixel_position[..., 1] = self.center_y
self.pixel_position[..., 2] = self.center_z
# Pixel map
if 'pixel map' in geom:
# [panel number, pixel num x, pixel num y]
self.pixel_index_map = xp.asarray(geom['pixel map'],
dtype=xp.int64)
# Detector pixel number info
self.detector_pixel_num_x = asnumpy(
xp.max(self.pixel_index_map[..., 0]) + 1)
self.detector_pixel_num_y = asnumpy(
xp.max(self.pixel_index_map[..., 1]) + 1)
# Panel pixel number info
# number of pixels in each panel in x/y direction
self.panel_pixel_num_x = self.pixel_index_map.shape[1]
self.panel_pixel_num_y = self.pixel_index_map.shape[2]
# total number of pixels (px*py)
self.pixel_num_total = np.prod(self._shape)
###########################################################################################
# Do necessary calculation to finishes the initialization
###########################################################################################
# self.geometry currently only work for the pre-defined detectors
self.geometry = geom
# Calculate the pixel area
self.pixel_area = xp.multiply(self.pixel_height, self.pixel_width)
# Get reciprocal space configurations and corrections.
self.initialize_pixels_with_beam(beam=beam)
##########################################################################################
# Do necessary calculation to finishes the initialization
##########################################################################################
# Detector effects
if 'pedestal' in geom:
self._pedestal = xp.asarray(geom['pedestal'], dtype=xp.float64)
else:
self._pedestal = xp.zeros((self.panel_num, self.panel_pixel_num_x,
self.panel_pixel_num_y))
if 'pixel rms' in geom:
self._pixel_rms = xp.asarray(geom['pixel rms'], dtype=xp.float64)
else:
self._pixel_rms = xp.zeros((self.panel_num, self.panel_pixel_num_x,
self.panel_pixel_num_y))
if 'pixel bkgd' in geom:
self._pixel_bkgd = xp.asarray(geom['pixel bkgd'], dtype=xp.float64)
else:
self._pixel_bkgd = xp.zeros(
(self.panel_num, self.panel_pixel_num_x,
self.panel_pixel_num_y))
if 'pixel status' in geom:
self._pixel_status = xp.asarray(geom['pixel status'],
dtype=xp.float64)
else:
self._pixel_status = xp.zeros(
(self.panel_num, self.panel_pixel_num_x,
self.panel_pixel_num_y))
if 'pixel mask' in geom:
self._pixel_mask = xp.asarray(geom['pixel mask'], dtype=xp.float64)
else:
self._pixel_mask = xp.zeros(
(self.panel_num, self.panel_pixel_num_x,
self.panel_pixel_num_y))
if 'pixel gain' in geom:
self._pixel_gain = xp.asarray(geom['pixel gain'], dtype=xp.float64)
else:
self._pixel_gain = xp.ones((self.panel_num, self.panel_pixel_num_x,
self.panel_pixel_num_y))
def main():
# parse user input
params = parse_input_arguments(sys.argv)
# Particle parameters
pdb = params['pdb']
numParticles = int(params['numParticles'])
mesh_voxel_size = params['meshVoxelSize'] / 10**10
hydration_layer_thickness = params['hydrationLayerThickness'] / 10**10
solvent_mean_electron_density = params[
'solventMeanElectronDensity'] * 10**30
other_mean_electron_density = params['otherMeanElectronDensity'] * 10**30
other_mask_probe_scale = params['otherMaskProbeScale']
other_mask_name = params['otherMaskName']
# Geometry parameters
geom = params['geom']
# Beam parameters
beam = params['beam']
wavelength = params['wavelength']
if wavelength is not None:
wavelength /= 10**10
# Dataset informations
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 = sk.Beam(beam)
if wavelength is not None:
print('New wavelength: {} m'.format(wavelength))
beam.set_wavelength(wavelength)
# load and initialize the detector
det = sk.PnccdDetector(geom=geom, beam=beam)
# create particle object(s)
particle = sk.Particle()
particle.read_pdb(pdb, ff='WK')
print(
'Hydration layer: [ {} m (thickness) ] [ {} m (mesh voxel size) ] [ {} (density e/m**3) ]'
.format(hydration_layer_thickness, mesh_voxel_size,
solvent_mean_electron_density))
particle.set_hydration_layer_thickness(hydration_layer_thickness)
particle.set_mesh_voxel_size(mesh_voxel_size)
particle.set_solvent_mean_electron_density(
solvent_mean_electron_density)
if other_mask_name is not None:
print(
'Additional mask: [ {} ] [ {} (probe scale) ] [ {} (density e/m**3) ]'
.format(other_mask_name, other_mask_probe_scale,
other_mean_electron_density))
particle.set_other_mean_electron_density(
other_mean_electron_density)
particle.set_other_mask_probe_scale(other_mask_probe_scale)
particle.set_other_mask_name(other_mask_name)
particle.create_masks()
experiment = sk.SPIExperiment(det, beam, particle)
f = h5.File(os.path.join(outDir, saveName), "w")
f.attrs['numParticles'] = numParticles
if xp is np:
experiment.volumes[0] = xp.asarray(experiment.volumes[0])
else:
experiment.volumes[0] = xp.asarray(experiment.volumes[0]).get()
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_positions = f.create_dataset("positions",
shape=(numPatterns, ) +
(numParticles, 3),
dtype=np.float32,
chunks=(1, ) + (numParticles, 3),
compression="gzip",
compression_opts=4)
dset_orientations = f.create_dataset("orientations",
shape=(numPatterns, ) +
(numParticles, 4),
chunks=(1, ) + (numParticles, 4),
compression="gzip",
compression_opts=4)
if xp is np:
pixel_index_map = det.pixel_index_map
pixel_position_reciprocal = det.pixel_position_reciprocal
else:
pixel_index_map = det.pixel_index_map.get()
pixel_position_reciprocal = det.pixel_position_reciprocal.get()
dset_pixel_index_map = f.create_dataset("pixel_index_map",
data=pixel_index_map,
compression="gzip",
compression_opts=4)
dset_pixel_position_reciprocal = f.create_dataset(
"pixel_position_reciprocal",
data=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_intensities, img_slice_positions,
img_slice_orientations) = comm.recv(source=MPI.ANY_SOURCE,
status=status1)
i = status1.Get_source()
print("Rank 0: Received image %d from rank %d" % (ind, i))
if xp is np:
dset_intensities[ind, :, :, :] = xp.asarray(
img_slice_intensities)
dset_photons[ind, :, :, :] = det.add_quantization(
img_slice_intensities)
dset_positions[ind, :, :] = xp.asarray(img_slice_positions)
dset_orientations[ind, :, :] = xp.asarray(
img_slice_orientations)
else:
dset_intensities[ind, :, :, :] = xp.asarray(
img_slice_intensities).get()
dset_photons[ind, :, :, :] = det.add_quantization(
img_slice_intensities).get()
dset_positions[ind, :, :] = xp.asarray(
img_slice_positions).get()
dset_orientations[ind, :, :] = xp.asarray(
img_slice_orientations).get()
n += 1
else:
det = dct['detector']
beam = dct['beam']
particle = dct['particle']
experiment = sk.SPIExperiment(det, beam, particle)
for i in range((rank - 1), numPatterns, size - 1):
img_slice = experiment.generate_image_stack(
return_intensities=True,
return_positions=True,
return_orientations=True,
always_tuple=True)
img_slice_intensities = img_slice[0]
img_slice_positions = img_slice[1]
img_slice_orientations = img_slice[2]
comm.ssend((i, img_slice_intensities, img_slice_positions,
img_slice_orientations),
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 initialize(self, geom, run_num=0, cframe=0):
"""
Initialize the detector
:param geom: The *-end.data file which characterizes the geometry profile.
:param run_num: The run_num containing the background, rms and gain and the other
pixel pixel properties.
:param cframe: The desired coordinate frame, 0 for psana and 1 for lab conventions.
:return: None
"""
# Redirect the output stream
old_stdout = sys.stdout
f = six.StringIO()
# f = open('Detector_initialization.log', 'w')
sys.stdout = f
###########################################################################################
# Initialize the geometry configuration
############################################################################################
self.geometry = GeometryAccess(geom, cframe=cframe)
self.run_num = run_num
# Set coordinate in real space (convert to m)
temp = [xp.asarray(t) * 1e-6 for t in self.geometry.get_pixel_coords(cframe=cframe)]
temp_index = [xp.asarray(t)
for t in self.geometry.get_pixel_coord_indexes(cframe=cframe)]
self.panel_num = np.prod(temp[0].shape[:-2])
self._distance = float(temp[2].mean())
self._shape = (self.panel_num, temp[0].shape[-2], temp[0].shape[-1])
self.pixel_position = xp.zeros(self._shape + (3,))
self.pixel_index_map = xp.zeros(self._shape + (2,))
for n in range(3):
self.pixel_position[..., n] = temp[n].reshape(self._shape)
for n in range(2):
self.pixel_index_map[..., n] = temp_index[n].reshape(self._shape)
self.pixel_index_map = self.pixel_index_map.astype(xp.int64)
# Get the range of the pixel index
self.detector_pixel_num_x = asnumpy(
xp.max(self.pixel_index_map[..., 0]) + 1)
self.detector_pixel_num_y = asnumpy(
xp.max(self.pixel_index_map[..., 1]) + 1)
self.panel_pixel_num_x = np.array([self.pixel_index_map.shape[1], ] * self.panel_num)
self.panel_pixel_num_y = np.array([self.pixel_index_map.shape[2], ] * self.panel_num)
self.pixel_num_total = np.sum(np.multiply(self.panel_pixel_num_x, self.panel_pixel_num_y))
tmp = float(self.geometry.get_pixel_scale_size() * 1e-6) # Convert to m
self.pixel_width = xp.ones(
(self.panel_num, self.panel_pixel_num_x[0], self.panel_pixel_num_y[0])) * tmp
self.pixel_height = xp.ones(
(self.panel_num, self.panel_pixel_num_x[0], self.panel_pixel_num_y[0])) * tmp
# Calculate the pixel area
self.pixel_area = xp.multiply(self.pixel_height, self.pixel_width)
###########################################################################################
# Initialize the pixel effects
###########################################################################################
# first we should parse the path
parsed_path = geom.split('/')
self.exp = parsed_path[-5]
if self.exp == 'calib':
self.exp = parsed_path[-6]
self.group = parsed_path[-4]
self.source = parsed_path[-3]
self._pedestals = None
self._pixel_rms = None
self._pixel_mask = None
self._pixel_bkgd = None
self._pixel_status = None
self._pixel_gain = None
if psana_version==1:
try:
cbase = self._get_cbase()
self.calibdir = '/'.join(parsed_path[:-4])
pbits = 255
gcp = GenericCalibPars(cbase, self.calibdir, self.group, self.source, run_num, pbits)
self._pedestals = gcp.pedestals()
self._pixel_rms = gcp.pixel_rms()
self._pixel_mask = gcp.pixel_mask()
self._pixel_bkgd = gcp.pixel_bkgd()
self._pixel_status = gcp.pixel_status()
self._pixel_gain = gcp.pixel_gain()
except NotImplementedError:
# No GenericCalibPars information.
pass
else:
try:
self.det = self._get_det_id(self.group)
except NotImplementedError:
# No GenericCalibPars information.
self.det = None
# Redirect the output stream
sys.stdout = old_stdout