def _get_suboptions(opt, rest):
if opt.harpy:
harpy_opt, rest = _harpy_entrypoint(rest)
if opt.optics:
rest = add_to_arguments(rest,
entry_params=optics_params(),
files=harpy_opt.files,
outputdir=harpy_opt.outputdir)
harpy_opt.outputdir = join(harpy_opt.outputdir, 'lin_files')
if harpy_opt.model is not None:
rest = add_to_arguments(
rest,
entry_params={"model_dir": {
"flags": "--model_dir"
}},
model_dir=dirname(abspath(harpy_opt.model)))
else:
harpy_opt = None
if opt.optics:
optics_opt, rest = _optics_entrypoint(rest)
accel_opt = manager.get_parsed_opt(rest)
optics_opt.accelerator = manager.get_accelerator(rest)
if not optics_opt.accelerator.excitation and optics_opt.compensation != "none":
raise AttributeError(
"Compensation requested and no driven model was provided.")
else:
optics_opt = None
accel_opt = None
return harpy_opt, optics_opt, accel_opt
def global_correction_entrypoint(opt: DotDict, accel_opt) -> None:
"""Do the global correction. Iteratively."""
LOG.info("Starting Iterative Global Correction.")
opt = _check_opt_add_dicts(opt)
opt = _add_hardcoded_paths(opt)
opt.output_dir.mkdir(parents=True, exist_ok=True)
accel_inst = manager.get_accelerator(accel_opt)
handler.correct(accel_inst, opt)
def _create_input(motion):
dpps = [0, 0, 0, -4e-4, -4e-4, 4e-4, 4e-4, 5e-5, -3e-5, -2e-5]
print(f"\nInput creation: {dpps}")
opt_dict = dict(accel="lhc", year="2018", ats=True, beam=1, files=[""],
model_dir=join(dirname(__file__), "..", "inputs", "models", "25cm_beam1"),
outputdir=BASE_PATH)
optics_opt, rest = _optics_entrypoint(opt_dict)
optics_opt.accelerator = manager.get_accelerator(rest)
lins = optics_measurement_test_files(opt_dict["model_dir"], dpps, motion)
return lins, optics_opt
def test_varmap_creation(model_inj_beams):
""" Tests the creation of varmap files and compares them with earlier created ones.
Hint: the `model_inj_beam` fixture is defined in `conftest.py`."""
accel_inst = get_accelerator(**model_inj_beams)
varmap_old = read_varmap(accel_inst.model_dir / "varmap_MQSl.h5")
varmap_new = evaluate_for_variables(accel_inst, ['MQSl'])
for order in varmap_new.keys():
for circuit in varmap_new[order].keys():
assert all(varmap_new[order][circuit] == varmap_old[order][circuit]
), f"Varmap values not identical in {order}/{circuit}."
def test_lhc_creation_nominal_driven_check_output(model_25cm_beam1):
accel = get_accelerator(**model_25cm_beam1)
LhcModelCreator.check_run_output(accel)
for dat_file in (TWISS_AC_DAT, TWISS_DAT, TWISS_ELEMENTS_DAT,
TWISS_ADT_DAT):
file_path: Path = accel.model_dir / dat_file
file_path_moved: Path = file_path.with_suffix(f"{file_path.suffix}0")
if dat_file == TWISS_ADT_DAT:
accel.excitation = AccExcitationMode.ADT # Test ACD before
else:
shutil.move(file_path, file_path_moved)
# Run test
with pytest.raises(FileNotFoundError) as creation_error:
LhcModelCreator.check_run_output(accel)
assert str(dat_file) in str(creation_error.value)
if file_path_moved.exists():
shutil.move(file_path_moved, file_path)
def check_accel_from_dir_vs_options(model_dir, accel_options, accel_from_opt,
required_keys):
# creation via model_from_dir tests that all files are in place:
accel_from_dir = get_accelerator(
accel=accel_options["accel"],
model_dir=model_dir,
**{k: accel_options[k]
for k in required_keys},
)
_check_arrays(accel_from_opt.nat_tunes,
accel_from_dir.nat_tunes,
eps=1e-4,
tunes=True)
_check_arrays(accel_from_opt.drv_tunes,
accel_from_dir.drv_tunes,
eps=1e-4,
tunes=True)
_check_arrays(accel_from_opt.modifiers, accel_from_dir.modifiers)
assert accel_from_opt.excitation == accel_from_dir.excitation
assert accel_from_opt.model_dir == accel_from_dir.model_dir
def create_response_entrypoint(opt: DotDict,
other_opt) -> Dict[str, pd.DataFrame]:
"""Entry point for creating pandas-based response matrices.
The response matrices can be either created by response_madx or TwissResponse.
"""
LOG.info("Creating response.")
accel_inst = manager.get_accelerator(other_opt)
if opt.creator == "madx":
fullresponse = response_madx.create_fullresponse(
accel_inst, opt.variable_categories, delta_k=opt.delta_k)
elif opt.creator == "twiss":
fullresponse = response_twiss.create_response(accel_inst,
opt.variable_categories,
opt.optics_params)
if opt.outfile_path is not None:
write_fullresponse(opt.outfile_path, fullresponse)
return fullresponse
def create_instance_and_model(opt, accel_opt):
if sys.flags.debug:
numeric_level = getattr(logging, "DEBUG", None)
ch = logging.StreamHandler(sys.stdout)
formatter = logging.Formatter(
' %(asctime)s %(levelname)s | %(name)s : %(message)s')
ch.setFormatter(formatter)
logging.getLogger().addHandler(ch)
logging.getLogger().setLevel(numeric_level)
else:
numeric_level = getattr(logging, "WARNING", None)
logging.basicConfig(level=numeric_level) # warning level to stderr
create_dirs(opt.outputdir)
accel_inst = manager.get_accelerator(accel_opt)
LOGGER.info(
f"Accelerator Instance {accel_inst.NAME}, model type {opt.type}")
accel_inst.verify_object()
creator = CREATORS[accel_inst.NAME][opt.type]
creator.prepare_run(accel_inst, opt.outputdir)
madx_script = creator.get_madx_script(accel_inst, opt.outputdir)
run_string(madx_script, output_file=opt.writeto, log_file=opt.logfile)
def input_data(request, tmp_path_factory):
"""Creates the input lin data and optics_options."""
data = {}
beam = request.param
for motion in ("free", "driven"):
np.random.seed(12345678)
output_path = tmp_path_factory.mktemp(f"input_{motion}_b{beam}")
opt_dict = dict(accel="lhc",
year="2018",
ats=True,
beam=beam,
files=[""],
model_dir=INPUTS / "models" / f"25cm_beam{beam}",
outputdir=output_path)
optics_opt, rest = _optics_entrypoint(opt_dict)
optics_opt.accelerator = manager.get_accelerator(rest)
lins = optics_measurement_test_files(
opt_dict["model_dir"],
DPPS,
motion,
beam_direction=(1 if beam == 1 else -1))
data[motion] = {'lins': lins, 'optics_opt': optics_opt}
return data
def create_instance_and_model(opt, accel_opt) -> Accelerator:
"""
Manager Keyword Args:
*--Required--*
- **accel**:
Choose the accelerator to use.Can be the class already.
choices: ``['lhc', 'ps', 'esrf', 'psbooster', 'skekb', 'JPARC', 'petra', 'iota']``
Creator Keyword Args:
*--Required--*
- **outputdir** *(str)*:
Output path for model, twiss files will be writen here.
*--Optional--*
- **logfile** *(str)*:
Path to the file where to write the MAD-X script output.If not
provided it will be written to sys.stdout.
- **type**:
Type of model to create.
choices: ``('nominal', 'best_knowledge', 'coupling_correction')``
Accelerator Keyword Args:
lhc: :mod:`omc3.model.accelerators.lhc`
ps: :mod:`omc3.model.accelerators.ps`
esrf: :mod:`omc3.model.accelerators.esrf`
psbooster: :mod:`omc3.model.accelerators.psbooster`
skekb: :mod:`omc3.model.accelerators.skekb`
iota: :mod:`omc3.model.accelerators.iota`
petra: :mod:`omc3.model.accelerators.petra` (not implemented)
JPARC: Not implemented
"""
# Prepare paths
create_dirs(opt.outputdir)
accel_inst = manager.get_accelerator(accel_opt)
LOG.info(f"Accelerator Instance {accel_inst.NAME}, model type {opt.type}")
creator = CREATORS[accel_inst.NAME][opt.type]
# Prepare model-dir output directory
accel_inst.model_dir = opt.outputdir
creator.prepare_run(accel_inst)
# get madx-script with relative output-paths
# as `cwd` changes run to correct directory.
# The resulting model-dir is then more self-contained. (jdilly)
accel_inst.model_dir = Path()
madx_script = creator.get_madx_script(accel_inst)
# Run madx to create model
run_string(madx_script,
output_file=opt.outputdir / JOB_MODEL_MADX,
log_file=opt.logfile,
cwd=opt.outputdir)
# Check output and return accelerator instance
accel_inst.model_dir = opt.outputdir
creator.check_run_output(accel_inst)
return accel_inst
def create_instance_and_model(opt, accel_opt):
"""
Manager Keyword Args:
*--Required--*
- **accel**:
Choose the accelerator to use.Can be the class already.
choices: ``['lhc', 'ps', 'esrf', 'psbooster', 'skekb', 'JPARC', 'petra', 'iota']``
Creator Keyword Args:
*--Required--*
- **outputdir** *(str)*:
Output path for model, twiss files will be writen here.
*--Optional--*
- **logfile** *(str)*:
Path to the file where to write the MAD-X script output.If not
provided it will be written to sys.stdout.
- **type**:
Type of model to create, either nominal or best_knowledge
choices: ``('nominal', 'best_knowledge', 'coupling_correction')``
- **writeto** *(str)*:
Path to the file where to write the resulting MAD-X script.
Accelerator Keyword Args:
lhc: :mod:`omc3.model.accelerators.lhc`
ps: :mod:`omc3.model.accelerators.ps`
esrf: :mod:`omc3.model.accelerators.esrf`
psbooster: :mod:`omc3.model.accelerators.psbooster`
skekb: :mod:`omc3.model.accelerators.skekb`
iota: :mod:`omc3.model.accelerators.iota`
petra: :mod:`omc3.model.accelerators.petra` (not implemented)
JPARC: Not implemented
"""
if sys.flags.debug:
numeric_level = getattr(logging, "DEBUG", None)
ch = logging.StreamHandler(sys.stdout)
formatter = logging.Formatter(' %(asctime)s %(levelname)s | %(name)s : %(message)s')
ch.setFormatter(formatter)
logging.getLogger().addHandler(ch)
logging.getLogger().setLevel(numeric_level)
else:
numeric_level = getattr(logging, "WARNING", None)
logging.basicConfig(level=numeric_level) # warning level to stderr
create_dirs(opt.outputdir)
accel_inst = manager.get_accelerator(accel_opt)
LOGGER.info(f"Accelerator Instance {accel_inst.NAME}, model type {opt.type}")
accel_inst.verify_object()
creator = CREATORS[accel_inst.NAME][opt.type]
creator.prepare_run(accel_inst, opt.outputdir)
madx_script = creator.get_madx_script(accel_inst, opt.outputdir)
run_string(madx_script, output_file=opt.writeto, log_file=opt.logfile)
