df["oversample"] = params.simulator.oversample
if params.opt_det is not None:
df["opt_det"] = params.opt_det
df["stage1_refls"] = stg1_refls
df["stage1_output_img"] = stg1_img_path
df.to_pickle(pandas_path)
return df
if __name__ == '__main__':
from dials.util import show_mail_on_error
with show_mail_on_error():
script = Script()
RUN = script.run
lp = None
if LineProfiler is not None and script.params.profile:
lp = LineProfiler()
lp.add_function(hopper_utils.model)
lp.add_function(hopper_utils.target_func)
RUN = lp(script.run)
elif script.params.profile:
print("Install line_profiler in order to use logging: libtbx.python -m pip install line_profiler")
RUN()
if lp is not None:
stats = lp.get_stats()
hopper_utils.print_profile(stats, ["model", "target_func"])
lp.add_function(stage_two_refiner.StageTwoRefiner._mpi_aggregation)
lp.add_function(stage_two_refiner.StageTwoRefiner._setup)
lp.add_function(hopper_utils.DataModeler.GatherFromExperiment)
RUN = lp(script.run)
if script.params.outdir is None:
od = script.params.refiner.io.output_dir
script.params.outdir = od if od is not None else '.'
if script.params.logging.logname is None:
script.params.logging.logname = "main_stage2.log"
if script.params.profile_name is None:
script.params.profile_name = "prof_stage2.log"
if script.params.logging.disable:
logging.disable(
level=logging.CRITICAL) # disables CRITICAL and below
else:
mpi_logger.setup_logging_from_params(script.params)
RUN()
if lp is not None:
stats = lp.get_stats()
from simtbx.diffBragg import hopper_utils
hopper_utils.print_profile(stats, [
"launch_refiner", "_compute_functional_and_gradients",
"_run_diffBragg_current", "_update_Fcell", "_scale_pixel_data",
"_Fcell_derivatives", "_mpi_aggregation",
"GatherFromExperiment", "_setup"
])