def test_fixes(self): flow = Flow(workdir=test_dir, manager=TaskManager.from_file( os.path.join(test_dir, "taskmanager.yml"))) inp = {} flow.register_task(input=inp) flow.allocate()
def add_pseudo(self, pseudo): """Add a pseudo to the Dojo.""" pseudo = Pseudo.as_pseudo(pseudo) dojo_report = pseudo.dojo_report # Construct the flow flow_workdir = os.path.join(self.workdir, pseudo.basename) flow = Flow(workdir=flow_workdir, manager=self.manager) # Construct the flow according to the info found in the dojo report. if not pseudo.has_hints: # We need the hints in order to run the other tests factory = PPConvergenceFactory() ecut_work = factory.work_for_pseudo(pseudo, ecut_slice=slice(4, None, 1), nlaunch=4) flow.register_work(ecut_work) else: # Hints are available --> construct a flow for the different trials. dojo_trial = "deltafactor" if dojo_trial in self.trials: # Do we have this element in the deltafactor database? #if not df_database().has_symbol(pseudo.symbol): # logger.warning("Cannot find %s in deltafactor database." % pseudo.symbol) delta_factory = DeltaFactory() kppa = 6750 # 6750 is the value used in the deltafactor code. kppa = 1 for accuracy in self.accuracies: if dojo_report.has_trial(dojo_trial, accuracy): continue ecut, pawecutdg = self._ecut_pawecutdg(pseudo, accuracy) work = delta_factory.work_for_pseudo(pseudo, accuracy=accuracy, kppa=kppa, ecut=ecut, pawecutdg=pawecutdg) logger.info("Adding work for %s with accuracy %s" % (dojo_trial, accuracy)) work.set_dojo_accuracy(accuracy) flow.register_work(work) # Test if GBRV tests are wanted. gbrv_structs = [s.split("_")[1] for s in self.trials if s.startswith("gbrv_")] if gbrv_structs: gbrv_factory = GbrvFactory() for struct_type in gbrv_structs: dojo_trial = "gbrv_" + struct_type for accuracy in self.accuracies: if dojo_report.has_trial(dojo_trial, accuracy): continue ecut, pawecutdg = self._ecut_pawecutdg(pseudo, accuracy) work = gbrv_factory.relax_and_eos_work(pseudo, struct_type, ecut=ecut, pawecutdg=pawecutdg) logger.info("Adding work for %s with accuracy %s" % (dojo_trial, accuracy)) work.set_dojo_accuracy(accuracy) flow.register_work(work) flow.allocate() self.pseudos.append(pseudo) self.flows.append(flow)
def create(self): """ create single abinit G0W0 flow """ manager = 'slurm' if 'ceci' in self.spec['mode'] else 'shell' # an AbiStructure object has an overwritten version of get_sorted_structure that sorts according to Z # this could also be pulled into the constructor of Abistructure #abi_structure = self.structure.get_sorted_structure() from abipy import abilab item = copy.copy(self.structure.item) self.structure.__class__ = abilab.Structure self.structure = self.structure.get_sorted_structure_z() self.structure.item = item abi_structure = self.structure manager = TaskManager.from_user_config() # Initialize the flow. flow = Flow(self.work_dir, manager, pickle_protocol=0) # flow = Flow(self.work_dir, manager) # kpoint grid defined over density 40 > ~ 3 3 3 if self.spec['converge'] and not self.all_converged: # (2x2x2) gamma centered mesh for the convergence test on nbands and ecuteps # if kp_in is present in the specs a kp_in X kp_in x kp_in mesh is used for the convergence studie if 'kp_in' in self.spec.keys(): if self.spec['kp_in'] > 9: print('WARNING:\nkp_in should be < 10 to generate an n x n x n mesh\nfor larger values a grid with ' 'density kp_in will be generated') scf_kppa = self.spec['kp_in'] else: scf_kppa = 2 else: # use the specified density for the final calculation with the converged nbands and ecuteps of other # stand alone calculations scf_kppa = self.spec['kp_grid_dens'] gamma = True # 'standard' parameters for stand alone calculation nb = self.get_bands(self.structure) nscf_nband = [10 * nb] nksmall = None ecuteps = [8] ecutsigx = 44 extra_abivars = dict( paral_kgb=1, inclvkb=2, ecut=44, pawecutdg=88, gwmem='10', getden=-1, istwfk="*1", timopt=-1, nbdbuf=8 ) # read user defined extra abivars from file 'extra_abivars' should be dictionary extra_abivars.update(read_extra_abivars()) #self.bands_fac = 0.5 if 'gwcomp' in extra_abivars.keys() else 1 #self.convs['nscf_nbands']['test_range'] = tuple([self.bands_fac*x for x in self.convs['nscf_nbands']['test_range']]) response_models = ['godby'] if 'ppmodel' in extra_abivars.keys(): response_models = [extra_abivars.pop('ppmodel')] if self.option is not None: for k in self.option.keys(): if k in ['ecuteps', 'nscf_nbands']: pass else: extra_abivars.update({k: self.option[k]}) if k == 'ecut': extra_abivars.update({'pawecutdg': self.option[k]*2}) try: grid = read_grid_from_file(s_name(self.structure)+".full_res")['grid'] all_done = read_grid_from_file(s_name(self.structure)+".full_res")['all_done'] workdir = os.path.join(s_name(self.structure), 'w'+str(grid)) except (IOError, OSError): grid = 0 all_done = False workdir = None if not all_done: if (self.spec['test'] or self.spec['converge']) and not self.all_converged: if self.spec['test']: print('| setting test calculation') tests = SingleAbinitGWWork(self.structure, self.spec).tests response_models = [] else: if grid == 0: print('| setting convergence calculations for grid 0') #tests = SingleAbinitGWWorkFlow(self.structure, self.spec).convs tests = self.convs else: print('| extending grid') #tests = expand(SingleAbinitGWWorkFlow(self.structure, self.spec).convs, grid) tests = expand(self.convs, grid) ecuteps = [] nscf_nband = [] for test in tests: if tests[test]['level'] == 'scf': if self.option is None: extra_abivars.update({test + '_s': tests[test]['test_range']}) elif test in self.option: extra_abivars.update({test: self.option[test]}) else: extra_abivars.update({test + '_s': tests[test]['test_range']}) else: for value in tests[test]['test_range']: if test == 'nscf_nbands': nscf_nband.append(value * self.get_bands(self.structure)) #scr_nband takes nscf_nbands if not specified #sigma_nband takes scr_nbands if not specified if test == 'ecuteps': ecuteps.append(value) if test == 'response_model': response_models.append(value) elif self.all_converged: print('| setting up for testing the converged values at the high kp grid ') # add a bandstructure and dos calculation nksmall = 30 # in this case a convergence study has already been performed. # The resulting parameters are passed as option ecuteps = [self.option['ecuteps'], self.option['ecuteps'] + self.convs['ecuteps']['test_range'][1] - self.convs['ecuteps']['test_range'][0]] nscf_nband = [self.option['nscf_nbands'], self.option['nscf_nbands'] + self.convs['nscf_nbands'][ 'test_range'][1] - self.convs['nscf_nbands']['test_range'][0]] # for option in self.option: # if option not in ['ecuteps', 'nscf_nband']: # extra_abivars.update({option + '_s': self.option[option]}) else: print('| all is done for this material') return logger.info('ecuteps : ', ecuteps) logger.info('extra : ', extra_abivars) logger.info('nscf_nb : ', nscf_nband) work = g0w0_extended(abi_structure, self.pseudo_table, scf_kppa, nscf_nband, ecuteps, ecutsigx, accuracy="normal", spin_mode="unpolarized", smearing=None, response_models=response_models, charge=0.0, sigma_nband=None, scr_nband=None, gamma=gamma, nksmall=nksmall, **extra_abivars) flow.register_work(work, workdir=workdir) return flow.allocate()
from pymatgen.io.abinitio.strategies import RelaxStrategy from pymatgen.io.abinitio.abiobjects import KSampling, RelaxationMethod, AbiStructure from pymatgen.core import Structure from myscripts.pseudos import all_pseudos scratchdir = '/p/lscratchd/damewood' basename = '2014_Trilayer/abinit_6z' workdir = os.path.join(scratchdir,basename) logging.basicConfig() structure = Structure.from_file('trilayer_6z.json') manager = TaskManager.from_user_config() ksampling = KSampling(mode='monkhorst',kpts=((6,6,2),), kpt_shifts=((0.5,0.5,0.5),(0.5,0.0,0.0),(0.0,0.5,0.0),(0.0,0.0,0.5))) relax_ion = RelaxationMethod(ionmov = 2, optcell = 0) relax_ioncell = RelaxationMethod(ionmov = 2, optcell = 1) pseudos = all_pseudos() flow = Flow(manager = manager, workdir = os.path.join(workdir, 'trilayer_6z')) spins = numpy.zeros([len(structure),3]) spins[:4,2] = 3. ion_input = RelaxStrategy(structure, pseudos, ksampling, relax_ion, accuracy="high", smearing = "fermi_dirac:0.025 eV", ecut = 40., pawecutdg = 80., chkprim = 0, tolmxf = 5.e-6, spinat = spins, restartxf = -2, nband = 60, nstep = 100) ioncell_input = ion_input.copy() ioncell_input.relax_algo = relax_ioncell work = RelaxWork(ion_input, ioncell_input, manager = manager) flow.register_work(work, workdir = 'relax') flow = flow.allocate()
def test_fixes(self): flow = Flow(workdir=test_dir, manager=TaskManager.from_file(os.path.join(test_dir, "taskmanager.yml"))) inp = {} flow.register_task(input=inp) flow.allocate()
def create(self): """ create single abinit G0W0 flow """ manager = 'slurm' if 'ceci' in self.spec['mode'] else 'shell' # an AbiStructure object has an overwritten version of get_sorted_structure that sorts according to Z # this could also be pulled into the constructor of Abistructure #abi_structure = self.structure.get_sorted_structure() from abipy import abilab item = copy.copy(self.structure.item) self.structure.__class__ = abilab.Structure self.structure = self.structure.get_sorted_structure_z() self.structure.item = item abi_structure = self.structure manager = TaskManager.from_user_config() # Initialize the flow. flow = Flow(self.work_dir, manager, pickle_protocol=0) # flow = Flow(self.work_dir, manager) # kpoint grid defined over density 40 > ~ 3 3 3 if self.spec['converge'] and not self.all_converged: # (2x2x2) gamma centered mesh for the convergence test on nbands and ecuteps # if kp_in is present in the specs a kp_in X kp_in x kp_in mesh is used for the convergence studie if 'kp_in' in self.spec.keys(): if self.spec['kp_in'] > 9: print( 'WARNING:\nkp_in should be < 10 to generate an n x n x n mesh\nfor larger values a grid with ' 'density kp_in will be generated') scf_kppa = self.spec['kp_in'] else: scf_kppa = 2 else: # use the specified density for the final calculation with the converged nbands and ecuteps of other # stand alone calculations scf_kppa = self.spec['kp_grid_dens'] gamma = True # 'standard' parameters for stand alone calculation scf_nband = self.get_bands(self.structure) nscf_nband = [10 * scf_nband] nksmall = None ecuteps = [8] ecutsigx = 44 extra_abivars = dict(paral_kgb=1, inclvkb=2, ecut=44, pawecutdg=88, gwmem='10', getden=-1, istwfk="*1", timopt=-1, nbdbuf=8, prtsuscep=0) # read user defined extra abivars from file 'extra_abivars' should be dictionary extra_abivars.update(read_extra_abivars()) #self.bands_fac = 0.5 if 'gwcomp' in extra_abivars.keys() else 1 #self.convs['nscf_nbands']['test_range'] = tuple([self.bands_fac*x for x in self.convs['nscf_nbands']['test_range']]) response_models = ['godby'] if 'ppmodel' in extra_abivars.keys(): response_models = [extra_abivars.pop('ppmodel')] if self.option is not None: for k in self.option.keys(): if k in ['ecuteps', 'nscf_nbands']: pass else: extra_abivars.update({k: self.option[k]}) if k == 'ecut': extra_abivars.update({'pawecutdg': self.option[k] * 2}) try: grid = read_grid_from_file(s_name(self.structure) + ".full_res")['grid'] all_done = read_grid_from_file( s_name(self.structure) + ".full_res")['all_done'] workdir = os.path.join(s_name(self.structure), 'w' + str(grid)) except (IOError, OSError): grid = 0 all_done = False workdir = None if not all_done: if (self.spec['test'] or self.spec['converge']) and not self.all_converged: if self.spec['test']: print('| setting test calculation') tests = SingleAbinitGWWork(self.structure, self.spec).tests response_models = [] else: if grid == 0: print('| setting convergence calculations for grid 0') #tests = SingleAbinitGWWorkFlow(self.structure, self.spec).convs tests = self.convs else: print('| extending grid') #tests = expand(SingleAbinitGWWorkFlow(self.structure, self.spec).convs, grid) tests = expand(self.convs, grid) ecuteps = [] nscf_nband = [] for test in tests: if tests[test]['level'] == 'scf': if self.option is None: extra_abivars.update( {test + '_s': tests[test]['test_range']}) elif test in self.option: extra_abivars.update({test: self.option[test]}) else: extra_abivars.update( {test + '_s': tests[test]['test_range']}) else: for value in tests[test]['test_range']: if test == 'nscf_nbands': nscf_nband.append( value * self.get_bands(self.structure)) #scr_nband takes nscf_nbands if not specified #sigma_nband takes scr_nbands if not specified if test == 'ecuteps': ecuteps.append(value) if test == 'response_model': response_models.append(value) elif self.all_converged: print( '| setting up for testing the converged values at the high kp grid ' ) # add a bandstructure and dos calculation if os.path.isfile('bands'): nksmall = -30 #negative value > only bandstructure else: nksmall = 30 # in this case a convergence study has already been performed. # The resulting parameters are passed as option ecuteps = [ self.option['ecuteps'], self.option['ecuteps'] + self.convs['ecuteps']['test_range'][1] - self.convs['ecuteps']['test_range'][0] ] nscf_nband = [ self.option['nscf_nbands'], self.option['nscf_nbands'] + self.convs['nscf_nbands']['test_range'][1] - self.convs['nscf_nbands']['test_range'][0] ] # for option in self.option: # if option not in ['ecuteps', 'nscf_nband']: # extra_abivars.update({option + '_s': self.option[option]}) else: print('| all is done for this material') return logger.info('ecuteps : ', ecuteps) logger.info('extra : ', extra_abivars) logger.info('nscf_nb : ', nscf_nband) work = g0w0_extended_work(abi_structure, self.pseudo_table, scf_kppa, nscf_nband, ecuteps, ecutsigx, scf_nband, accuracy="normal", spin_mode="unpolarized", smearing=None, response_models=response_models, charge=0.0, sigma_nband=None, scr_nband=None, gamma=gamma, nksmall=nksmall, **extra_abivars) flow.register_work(work, workdir=workdir) return flow.allocate()