from nomadcore.simple_parser import SimpleMatcher as SM
from nomadcore.baseclasses import MainHierarchicalParser
from commonmatcher import CommonMatcher
import cp2kparser.generic.configurationreading
import cp2kparser.generic.csvparsing
from nomadcore.caching_backend import CachingLevel
import logging
logger = logging.getLogger("nomad")
#===============================================================================
class CP2KGeoOptParser(MainHierarchicalParser):
"""Used to parse the CP2K calculation with run types:
-GEO_OPT/GEOMETRY_OPTIMIZATION
"""
def __init__(self, file_path, parser_context):
"""
"""
super(CP2KGeoOptParser, self).__init__(file_path, parser_context)
self.setup_common_matcher(CommonMatcher(parser_context))
self.traj_iterator = None
self.energy_reeval_quickstep = None
#=======================================================================
# Globally cached values
self.cache_service.add("number_of_frames_in_sequence", 0)
self.cache_service.add("frame_sequence_potential_energy", [])
self.cache_service.add("frame_sequence_local_frames_ref", [])
self.cache_service.add("geometry_optimization_method")
#=======================================================================
# Cache levels
self.caching_level_for_metaname.update({
'x_cp2k_section_geometry_optimization_step': CachingLevel.ForwardAndCache,
'x_cp2k_section_quickstep_calculation': CachingLevel.ForwardAndCache,
'x_cp2k_section_geometry_optimization': CachingLevel.ForwardAndCache,
# 'x_cp2k_section_geometry_optimization_energy_reevaluation': CachingLevel.ForwardAndCache,
})
#=======================================================================
# SimpleMatchers
self.geo_opt = SM(
" *** STARTING GEOMETRY OPTIMIZATION ***".replace("*", "\*"),
sections=["section_frame_sequence", "x_cp2k_section_geometry_optimization"],
subMatchers=[
SM( " *** CONJUGATE GRADIENTS ***".replace("*", "\*"),
adHoc=self.adHoc_conjugate_gradient(),
otherMetaInfo=["geometry_optimization_method"],
),
SM( " *** BFGS ***".replace("*", "\*"),
adHoc=self.adHoc_bfgs(),
otherMetaInfo=["geometry_optimization_method"],
),
SM( " *** L-BFGS ***".replace("*", "\*"),
adHoc=self.adHoc_bfgs(),
otherMetaInfo=["geometry_optimization_method"],
),
# SM( "",
# forwardMatch=True,
# sections=["section_single_configuration_calculation", "section_system", "x_cp2k_section_geometry_optimization_step"],
# subMatchers=[
# self.cm.quickstep_calculation(),
# SM( " -------- Informations at step"),
# SM( " Optimization Method =\s+(?P<x_cp2k_optimization_method>{})".format(self.cm.regex_word)),
# SM( " Total Energy =\s+(?P<x_cp2k_optimization_energy__hartree>{})".format(self.cm.regex_f),
# otherMetaInfo=["frame_sequence_potential_energy"]
# ),
# ],
# otherMetaInfo=["atom_positions"],
# adHoc=self.adHoc_step(),
# ),
SM( " OPTIMIZATION STEP:",
endReStr=" Conv. in RMS gradients =",
name="geooptstep",
repeats=True,
subMatchers=[
SM( "",
forwardMatch=True,
sections=["x_cp2k_section_geometry_optimization_step"],
otherMetaInfo=[
"atom_positions",
],
subMatchers=[
# SM( "",
# forwardMatch=True,
# endReStr=" *** MNBRACK - NUMBER OF ENERGY EVALUATIONS :\s+{}\s+***".replace("*", "\*").format(self.cm.regex_i),
# subMatchers=[
# SM(" SCF WAVEFUNCTION OPTIMIZATION",
# forwardMatch=True,
# repeats=True,
# subMatchers=[
# self.cm.quickstep_calculation(),
# ]
# )
# ]
# ),
# SM( "",
# forwardMatch=True,
# endReStr=" *** BRENT - NUMBER OF ENERGY EVALUATIONS :\s+{}\s+***".replace("*", "\*").format(self.cm.regex_i),
# subMatchers=[
# SM(" SCF WAVEFUNCTION OPTIMIZATION",
# forwardMatch=True,
# repeats=True,
# subMatchers=[
# self.cm.quickstep_calculation(),
# ]
# )
# ]
# ),
SM( " -------- Informations at step"),
SM( " Optimization Method =\s+(?P<x_cp2k_optimization_method>{})".format(self.cm.regex_word)),
SM( " Total Energy =\s+(?P<x_cp2k_optimization_energy__hartree>{})".format(self.cm.regex_f),
otherMetaInfo=["frame_sequence_potential_energy"]
),
SM( " Real energy change =\s+(?P<x_cp2k_optimization_energy_change__hartree>{})".format(self.cm.regex_f)),
SM( " Decrease in energy =\s+(?P<x_cp2k_optimization_energy_decrease>{})".format(self.cm.regex_word)),
SM( " Used time =\s+(?P<x_cp2k_optimization_used_time>{})".format(self.cm.regex_f)),
SM( " Max. step size =\s+(?P<x_cp2k_optimization_max_step_size__bohr>{})".format(self.cm.regex_f)),
SM( " Conv. limit for step size =\s+(?P<x_cp2k_optimization_step_size_convergence_limit__bohr>{})".format(self.cm.regex_f),
otherMetaInfo=["geometry_optimization_geometry_change"]
),
SM( " Convergence in step size =\s+(?P<x_cp2k_optimization_step_size_convergence>{})".format(self.cm.regex_word)),
SM( " RMS step size =\s+(?P<x_cp2k_optimization_rms_step_size__bohr>{})".format(self.cm.regex_f)),
SM( " Convergence in RMS step =\s+(?P<x_cp2k_optimization_rms_step_size_convergence>{})".format(self.cm.regex_word)),
SM( " Max. gradient =\s+(?P<x_cp2k_optimization_max_gradient__bohr_1hartree>{})".format(self.cm.regex_f)),
SM( " Conv. limit for gradients =\s+(?P<x_cp2k_optimization_gradient_convergence_limit__bohr_1hartree>{})".format(self.cm.regex_f),
otherMetaInfo=["geometry_optimization_threshold_force"]
),
SM( " Conv. for gradients =\s+(?P<x_cp2k_optimization_max_gradient_convergence>{})".format(self.cm.regex_word)),
SM( " RMS gradient =\s+(?P<x_cp2k_optimization_rms_gradient__bohr_1hartree>{})".format(self.cm.regex_f)),
SM( " Conv. in RMS gradients =\s+(?P<x_cp2k_optimization_rms_gradient_convergence>{})".format(self.cm.regex_word)),
],
# adHoc=self.adHoc_step()
),
]
),
SM( " *** GEOMETRY OPTIMIZATION COMPLETED ***".replace("*", "\*"),
adHoc=self.adHoc_geo_opt_converged(),
otherMetaInfo=["geometry_optimization_converged"]
),
SM( " Reevaluating energy at the minimum",
# sections=["x_cp2k_section_geometry_optimization_energy_reevaluation"],
subMatchers=[
self.cm.quickstep_calculation(),
# SM("",
# adHoc=self.adHoc_save_energy_reeval_quickstep()
# )
],
# adHoc=self.adHoc_save_energy_reeval_quickstep()
),
# SM( "",
# forwardMatch=True,
# adHoc=self.adHoc_save_energy_reeval_quickstep()
# )
],
)
# Compose root matcher according to the run type. This way the
# unnecessary regex parsers will not be compiled and searched. Saves
# computational time.
self.root_matcher = SM("",
forwardMatch=True,
sections=["section_run", "section_sampling_method"],
subMatchers=[
SM( "",
forwardMatch=True,
sections=["section_method"],
subMatchers=[
self.cm.header(),
self.cm.quickstep_header(),
],
),
self.geo_opt
]
)
#===========================================================================
# onClose triggers
def onClose_x_cp2k_section_geometry_optimization(self, backend, gIndex, section):
# Get the re-evaluated energy and add it to frame_sequence_potential_energy
reeval_quickstep = self.energy_reeval_quickstep
if reeval_quickstep is not None:
energy = reeval_quickstep.get_latest_value("x_cp2k_energy_total")
if energy is not None:
self.cache_service["frame_sequence_potential_energy"].append(energy)
# Push values from cache
self.cache_service.push_array_values("frame_sequence_potential_energy")
self.cache_service.push_value("geometry_optimization_method")
self.backend.addValue("frame_sequence_to_sampling_ref", 0)
# Get the optimization convergence criteria from the last optimization
# step
section.add_latest_value([
"x_cp2k_section_geometry_optimization_step",
"x_cp2k_optimization_step_size_convergence_limit"],
"geometry_optimization_geometry_change",
)
section.add_latest_value([
"x_cp2k_section_geometry_optimization_step",
"x_cp2k_optimization_gradient_convergence_limit"],
"geometry_optimization_threshold_force",
)
# Push the information into single configuration and system
steps = section["x_cp2k_section_geometry_optimization_step"]
each = self.cache_service["each_geo_opt"]
add_last = False
add_last_setting = self.cache_service["traj_add_last"]
if add_last_setting == "NUMERIC" or add_last_setting == "SYMBOLIC":
add_last = True
# Push the trajectory
n_steps = len(steps) + 1
last_step = n_steps - 1
for i_step in range(n_steps):
singleId = backend.openSection("section_single_configuration_calculation")
systemId = backend.openSection("section_system")
if self.traj_iterator is not None:
if (i_step + 1) % each == 0 or (i_step == last_step and add_last):
try:
pos = next(self.traj_iterator)
except StopIteration:
logger.error("Could not get the next geometries from an external file. It seems that the number of optimization steps in the CP2K outpufile doesn't match the number of steps found in the external trajectory file.")
else:
backend.addArrayValues("atom_positions", pos, unit="angstrom")
backend.closeSection("section_system", systemId)
backend.closeSection("section_single_configuration_calculation", singleId)
self.cache_service.push_array_values("frame_sequence_local_frames_ref")
backend.addValue("number_of_frames_in_sequence", n_steps)
def onClose_section_sampling_method(self, backend, gIndex, section):
self.backend.addValue("sampling_method", "geometry_optimization")
def onClose_x_cp2k_section_quickstep_calculation(self, backend, gIndex, section):
self.energy_reeval_quickstep = section
def onClose_x_cp2k_section_geometry_optimization_step(self, backend, gIndex, section):
energy = section["x_cp2k_optimization_energy"]
if energy is not None:
self.cache_service["frame_sequence_potential_energy"].append(energy[0])
def onClose_section_method(self, backend, gIndex, section):
traj_file = self.file_service.get_file_by_id("trajectory")
traj_format = self.cache_service["trajectory_format"]
if traj_format is not None and traj_file is not None:
# Use special parsing for CP2K pdb files because they don't follow the proper syntax
if traj_format == "PDB":
self.traj_iterator = cp2kparser.generic.csvparsing.iread(traj_file, columns=[3, 4, 5], start="CRYST", end="END")
else:
try:
self.traj_iterator = cp2kparser.generic.configurationreading.iread(traj_file)
except ValueError:
pass
def onClose_section_single_configuration_calculation(self, backend, gIndex, section):
self.cache_service["frame_sequence_local_frames_ref"].append(gIndex)
#===========================================================================
# adHoc functions
def adHoc_geo_opt_converged(self):
"""Called when the geometry optimization converged.
"""
def wrapper(parser):
parser.backend.addValue("geometry_optimization_converged", True)
return wrapper
def adHoc_geo_opt_not_converged(self):
"""Called when the geometry optimization did not converge.
"""
def wrapper(parser):
parser.backend.addValue("geometry_optimization_converged", False)
return wrapper
def adHoc_conjugate_gradient(self):
"""Called when conjugate gradient method is used.
"""
def wrapper(parser):
self.cache_service["geometry_optimization_method"] = "conjugate_gradient"
return wrapper
def adHoc_bfgs(self):
"""Called when conjugate gradient method is used.
"""
def wrapper(parser):
self.cache_service["geometry_optimization_method"] = "bfgs"
return wrapper
# def adHoc_save_energy_reeval_quickstep(self):
# def wrapper(parser):
# section_managers = parser.backend.sectionManagers
# section_run_manager = section_managers["section_run"]
# section_run = section_run_manager.openSections[0]
# print section_run.subSectionValues
# # quickstep = section_run.get_latest_value("x_cp2k_section_quickstep_calculation")
# # print quickstep
# # self.energy_reeval_quickstep = quickstep
# return wrapper
def debug(self):
def wrapper(parser):
print "DEBUG"
return wrapper