def XXXperform_one_simulation(self, key):
# method uses the old code as used in abc_coverage
#def get_partiality_response(key,one_index,spectra_simulation,ROI,rand_ori,tophat_spectrum=True):
spectra = self.allspectra
from LS49.sim.step5_pad import microcrystal
crystal = microcrystal(
Deff_A=4000, length_um=4.,
beam_diameter_um=1.0) # assume smaller than 10 um crystals
iterator = spectra.generate_recast_renormalized_image(image=key,
energy=7120.,
total_flux=1e12)
file_prefix = "key_slow_nonoise_%06d" % key
Amatrix_rot = self.models4["Amat"]
from LS49.work2_for_aca_lsq.util_partiality import run_sim2smv
pixels = run_sim2smv(self.ROI,
prefix=file_prefix,
crystal=crystal,
spectra=iterator,
rotation=Amatrix_rot,
tophat_spectrum=False,
quick=False,
rank=0)
return pixels["roi_pixels"]
def get_partiality_response(key,
one_index,
spectra_simulation,
ROI,
rand_ori,
tophat_spectrum=True):
spectra = spectra_simulation
crystal = microcrystal(
Deff_A=4000, length_um=4.,
beam_diameter_um=1.0) # assume smaller than 10 um crystals
iterator = spectra.generate_recast_renormalized_image(image=key,
energy=7120.,
total_flux=1e12)
file_prefix = "key_slow_nonoise_%06d" % key
pixels = run_sim2smv(ROI,
prefix=file_prefix,
crystal=crystal,
spectra=iterator,
rotation=rand_ori,
tophat_spectrum=tophat_spectrum,
quick=False,
rank=0)
return pixels
def get_partiality_response(key, one_index, spectra_simulation, ROI):
N_total = 100000 # number of items to simulate
spectra = spectra_simulation
crystal = microcrystal(
Deff_A=4000, length_um=4.,
beam_diameter_um=1.0) # assume smaller than 10 um crystals
mt = flex.mersenne_twister(seed=0)
random_orientations = []
for iteration in range(N_total):
random_orientations.append(mt.random_double_r3_rotation_matrix())
iterator = spectra.generate_recast_renormalized_image(image=key,
energy=7120.,
total_flux=1e12)
file_prefix = "key_slow_nonoise_%06d" % key
rand_ori = sqr(random_orientations[key])
pixels = run_sim2smv(ROI,
prefix=file_prefix,
crystal=crystal,
spectra=iterator,
rotation=rand_ori,
quick=False,
rank=0)
return pixels
def get_partiality_response(key,one_index,spectra_simulation,ROI): N_total = 100000 # number of items to simulate spectra = spectra_simulation crystal = microcrystal(Deff_A = 4000, length_um = 4., beam_diameter_um = 1.0) # assume smaller than 10 um crystals from LS49 import legacy_random_orientations random_orientations = legacy_random_orientations(N_total) iterator = spectra.generate_recast_renormalized_image(image=key,energy=7120.,total_flux=1e12) file_prefix = "key_slow_nonoise_%06d"%key rand_ori = sqr(random_orientations[key]) pixels = run_sim2smv(ROI,prefix = file_prefix,crystal = crystal,spectra=iterator,rotation=rand_ori,quick=False,rank=0) return pixels
workaround_nt = int(os.environ.get("OMP_NUM_THREADS", 1))
omptbx.omp_set_num_threads(workaround_nt)
N_total = 100000 # number of items to simulate
N_stride = size # total number of worker tasks
print("hello from rank %d of %d" % (rank, size), "with omp_threads=",
omp_get_num_procs())
import datetime
start_elapse = time()
if rank == 0:
print("Rank 0 time", datetime.datetime.now())
from LS49.spectra.generate_spectra import spectra_simulation
from LS49.sim.step5_pad import microcrystal
print("hello2 from rank %d of %d" % (rank, size))
SS = spectra_simulation()
C = microcrystal(
Deff_A=4000, length_um=4.,
beam_diameter_um=1.0) # assume smaller than 10 um crystals
mt = flex.mersenne_twister(seed=0)
random_orientations = []
for iteration in range(N_total):
random_orientations.append(mt.random_double_r3_rotation_matrix())
transmitted_info = dict(spectra=SS,
crystal=C,
random_orientations=random_orientations)
else:
transmitted_info = None
transmitted_info = comm.bcast(transmitted_info, root=0)
comm.barrier()
parcels = list(range(rank, N_total, N_stride))
while len(parcels) > 0:
import random
def run(self):
self.parse_input()
N_total = 100000 # self.params.N_total # number of items to simulate, nominally 100000
logical_rank = self.mpi_helper.rank
logical_size = self.mpi_helper.size
if self.mpi_helper.rank == 0 and self.mpi_helper.size == 1: # special case of testing it
try:
logical_rank = self.params.tester.rank
logical_size = self.params.tester.size
except Exception:
pass
N_stride = int(math.ceil(
N_total / logical_size)) # total number of tasks per rank
print("hello from rank %d of %d with stride %d" %
(logical_rank, logical_size, N_stride))
#from scitbx.lbfgs.tst_mpi_split_evaluator import mpi_split_evaluator_run
#from scitbx.lbfgs.tst_mpi_split_evaluator import run_mpi as simple_tester
#simple_tester()
if self.params.starting_model.algorithm == "to_file":
if self.mpi_helper.rank == 0:
HKL_lookup, static_fcalcs = get_static_fcalcs_with_HKL_lookup()
from LS49.work2_for_aca_lsq.remake_range_intensities_with_complex \
import get_intensity_structure
model_intensities = get_intensity_structure(
static_fcalcs,
FE1_model=Fe_oxidized_model,
FE2_model=Fe_reduced_model)
with (open(self.params.starting_model.filename, "wb")) as out:
pickle.dump(HKL_lookup, out, pickle.HIGHEST_PROTOCOL)
pickle.dump(static_fcalcs, out, pickle.HIGHEST_PROTOCOL)
pickle.dump(model_intensities, out,
pickle.HIGHEST_PROTOCOL)
return
else:
if self.mpi_helper.rank == 0:
with (open(self.params.starting_model.filename, "rb")) as inp:
print("the starting model (used for channel weighting) is",
self.params.starting_model.filename)
HKL_lookup = pickle.load(inp)
static_fcalcs = pickle.load(inp)
model_intensities = pickle.load(inp)
from LS49.spectra.generate_spectra import spectra_simulation
from LS49.sim.step5_pad import microcrystal
shuffA = list(range(N_total))
import random
random.shuffle(shuffA)
transmitted_info = dict(
HKL_lookup=HKL_lookup,
static_fcalcs=static_fcalcs,
model_intensities=model_intensities,
spectra_simulation=spectra_simulation(),
crystal=microcrystal(Deff_A=4000,
length_um=4.,
beam_diameter_um=1.0),
shuffA=shuffA)
else:
transmitted_info = None
print("before braodcast with ", self.mpi_helper.rank,
self.mpi_helper.size)
transmitted_info = self.mpi_helper.comm.bcast(transmitted_info, root=0)
self.mpi_helper.comm.barrier()
print("after barrier")
# -----------------------------------------------------------------------
if self.mpi_helper.rank == 0:
print("Finding initial G and abc factors")
per_rank_items = []
per_rank_keys = []
per_rank_G = []
min_spots = 3
N_input = 0
import os, omptbx # cori workaround, which does not get OMP_NUM_THREADS from environment
workaround_nt = int(os.environ.get("OMP_NUM_THREADS", 1))
omptbx.omp_set_num_threads(workaround_nt)
for item, key in get_items(logical_rank, N_total, N_stride,
transmitted_info["shuffA"],
self.params.cohort):
N_input += 1
if len(item) >= min_spots:
try:
FOI = fit_one_image_multispot(
key=key,
list_of_images=item,
HKL_lookup=transmitted_info["HKL_lookup"],
model_intensities=transmitted_info[
"model_intensities"],
spectra=transmitted_info["spectra_simulation"],
crystal=transmitted_info["crystal"])
except RuntimeError as e:
# no recovery from LBFGS error, skip event
continue
metric_P1, metric_C2 = FOI.DRM.get_current_angular_offsets(
FOI.x[-4:-1])
print(
"""LLG Image %06d on %d Bragg spots NLL channels F = %9.1f angular offsets in P1 and C2 (degrees): %8.5f %8.5f"""
%
(key, len(item), FOI.compute_functional_and_gradients()[0],
metric_P1, metric_C2), FOI.x[-4:-1])
# reporting out results to new abc_coverage pickles. Use the old one "item" as a template:
# item := [<LS49.work2_for_aca_lsq.abc_background.fit_roi_multichannel>,... one for each spot]
# each fit_roi has the following attributes and we modify them as follows:
# 'a', the abcG parameters of the original one-spot fit, unused
# 'asu_idx_C2_setting', unmodified
# 'image_no', same as key, unmodified
# 'n', number of parameters for one-spot fit, unused
# 'orig_idx_C2_setting', unmodified
# 'sb_data', original shoebox data [integers as floats], passed along unmodified
# 'simtbx_P1_miller', unmodified
# 'simtbx_intensity_7122', unmodified
# 'x', the abcG parameters of the original one-spot fit, unused
# modify these:
# 'bkgrd_a', the spot abc parameters output here, to be passed on to global data fit
for ispot in range(len(item)):
item[ispot].bkgrd_a = FOI.x[3 * ispot:3 * (ispot + 1)]
# 'channels', need to save the new data that was set during update_roi_model_pixels_with_current_rotation()
# but only for the Amat, not the derivatives.
item[ispot].channels = FOI.new_calc2_dict_last_round[
ispot]["channels"]
# 'roi', get new region of interest summation that was set during update_roi_model_pixels_with_current_rotation()
item[ispot].roi = FOI.roi_model_pixels[ispot]
# put the newly refined background model back into the item
per_rank_keys.append(key)
per_rank_G.append(FOI.a[-1])
print(
"pickling modified abc_coverage file for key %d in rank %d"
% (key, logical_rank), )
with open(abc_glob_pixel_ref % (key), "wb") as F:
pickle.dump(item, F, pickle.HIGHEST_PROTOCOL)
print("rank %d has %d refined images" %
(logical_rank, len(per_rank_keys)))
N_ranks = self.mpi_helper.comm.reduce(1, self.mpi_helper.MPI.SUM, 0)
N_refined_images = self.mpi_helper.comm.reduce(len(per_rank_keys),
self.mpi_helper.MPI.SUM,
0)
N_input_images = self.mpi_helper.comm.reduce(N_input,
self.mpi_helper.MPI.SUM,
0)
self.mpi_helper.comm.barrier()
if self.mpi_helper.rank == 0:
print("final report %d ranks, %d input images, %d refined models" %
(N_ranks, N_input_images, N_refined_images))
print("Finished finding initial G and abc factors")
self.mpi_helper.comm.barrier()