Source code for aiida_kkr.workflows.kkr_scf

#!/usr/bin/env python
# -*- coding: utf-8 -*-
In this module you find the base workflow for converging a kkr calculation and
some helper methods to do so with AiiDA
from __future__ import print_function
from __future__ import division
from __future__ import absolute_import
from aiida.orm import Code, load_node, CalcJobNode, RemoteData, StructureData, Dict, XyData, SinglefileData, Float
from aiida.engine import WorkChain, while_, if_, ToContext, CalcJob
from aiida.engine import workfunction, calcfunction
from aiida_kkr.calculations.kkr import KkrCalculation
from aiida_kkr.calculations.voro import VoronoiCalculation
from import kkrparams
from import (test_and_get_codenode, get_inputs_kkr,
                                                  get_parent_paranode, update_params_wf)
from aiida_kkr.workflows.voro_start import kkr_startpot_wc
from aiida_kkr.workflows.dos import kkr_dos_wc
from import get_Ry2eV, get_ef_from_potfile
from numpy import array, where, ones, loadtxt, sqrt
from six.moves import range

__copyright__ = (u"Copyright (c), 2017, Forschungszentrum Jülich GmbH, "
                 "IAS-1/PGI-1, Germany. All rights reserved.")
__license__ = "MIT license, see LICENSE.txt file"
__version__ = "0.10.4"
__contributors__ = (u"Jens Broeder", u"Philipp Rüßmann")

# TODO: magnetism (init and converge magnetic state)
# TODO: check convergence (RMAX, GMAX etc.)
# TODO: save timing info of the steps
# TODO: switch to LLOYD
# TODO: emin-emax setting
# TODO: restart from workflow output instead of calculation output
# TODO: add warnings
# TODO: maybe define the energy point density instead of a fixed number as in input?
# TODO: overwrite defaults from parent if parent is previous kkr_scf run
# TODO: retrieve DOS within scf run

[docs]class kkr_scf_wc(WorkChain): """ Workchain for converging a KKR calculation (SCF). It converges the charge potential. Two paths are possible: (1) Start from a structure and run a voronoi calculation first, optional with calc_parameters (2) Start from an existing Voronoi or KKR calculation, with a remoteData :param wf_parameters: (Dict), Workchain Specifications :param options: (Dict); specifications for the computer :param structure: (StructureData), Crystal structure :param calc_parameters: (Dict), Voronoi/Kkr Parameters :param remote_data: (RemoteData), from a KKR, or Voronoi calculation :param voronoi: (Code) :param kkr: (Code) :return output_kkr_scf_wc_para: (Dict), Information of workflow results like Success, last result node, list with convergence behavior minimum input example: 1. Code1, Code2, Structure, (Parameters), (wf_parameters) 2. Code2, remote_data, (Parameters), (wf_parameters) maximum input example: 1. Code1, Code2, Structure, Parameters wf_parameters: {'queue_name' : String, 'resources' : dict({"num_machines": int, "num_mpiprocs_per_machine" : int}) 'walltime' : int} 2. Code2, (remote-data), wf_parameters as in 1. Hints: 1. This workflow does not work with local codes! """ _workflowversion = __version__ _wf_default = { # Maximum number of kkr jobs/starts (defauld iterations per start) "kkr_runmax": 5, # Stop if charge denisty is converged below this value "convergence_criterion": 10**-8, # reduce mixing factor by this factor if calculation fails due to too large mixing "mixreduce": 0.5, # threshold after which agressive mixing is used "threshold_aggressive_mixing": 8*10**-3, "strmix": 0.03, # mixing factor of simple mixing "brymix": 0.05, # mixing factor of aggressive mixing # number of iterations done per KKR calculation "nsteps": 50, "convergence_setting_coarse": { # setting of the coarse preconvergence "npol": 7, "n1": 3, "n2": 11, "n3": 3, "tempr": 1000.0, "kmesh": [10, 10, 10]}, # threshold after which final conversion settings are used "threshold_switch_high_accuracy": 10**-3, "convergence_setting_fine": { # setting of the final convergence (lower tempr, 48 epts, denser k-mesh) "npol": 5, "n1": 7, "n2": 29, "n3": 7, "tempr": 600.0, "kmesh": [30, 30, 30]}, # initialize and converge magnetic calculation "mag_init": False, # Ry # external magnetic field used in initialization step "hfield": 0.02, # position in unit cell where magnetic field is applied [default (None) means apply to all] "init_pos": None, # fix direction of magnetic moment if the direction changes less than this value in degrees (calculated as sqrt((delta theta)**2 + (delta phi)**2)) "fix_dir_threshold": 1.0, # add DOS to testopts and retrieve dos.atom files in each scf run "retreive_dos_data_scf_run": False, } _options_default = { "queue_name": "", # Queue name to submit jobs too # resources to allowcate for the job "resources": {"num_machines": 1}, # walltime after which the job gets killed (gets parsed to KKR) "max_wallclock_seconds": 60*60, "withmpi": True, # execute KKR with mpi or without "custom_scheduler_commands": "" # some additional scheduler commands } # set these keys from defaults in kkr_startpot workflow since they are only passed onto that workflow for key, value in kkr_startpot_wc.get_wf_defaults(silent=True).items(): if key in ["dos_params", "fac_cls_increase", "natom_in_cls_min", "delta_e_min", "threshold_dos_zero", "check_dos"]: _wf_default[key] = value # intended to guide user interactively in setting up a valid wf_params node
[docs] @classmethod def get_wf_defaults(self, silent=False): """ Print and return _wf_default dictionary. Can be used to easily create set of wf_parameters. returns _wf_default, _options_default """ if not silent: print("Version of workflow: {}".format(self._workflowversion)) return self._wf_default, self._options_default
[docs] @classmethod def define(cls, spec): """ Defines the outline of the workflow. """ # Take input of the workflow or use defaults defined above super(kkr_scf_wc, cls).define(spec) spec.input("wf_parameters", valid_type=Dict, required=False, default=lambda: Dict(dict=cls._wf_default), help="Settings for the workflow. Use `KkrCalculation.get_wf_defaults()` to get the default values and default options." ) spec.input("options", valid_type=Dict, required=False, default=lambda: Dict(dict=cls._wf_default), help="Computer settings used by the calculations in the workflow (see also helo string of wf_parameters)." ) spec.input("structure", valid_type=StructureData, required=False, help="""Input structure for which a calculation is started with a VoronoiCalculation. Can be skipped if a previous KkrCalculation is given with the `remote_data` input node.""" ) spec.input("calc_parameters", valid_type=Dict, required=False, help="KKR-specific calculation parameters (LMAX etc.), usually set up with the help of the `kkrparams` class." ) spec.input("remote_data", valid_type=RemoteData, required=False, help="RemodeFolder node of a preconverged calculation. Can be used as a starting point to skip the Voronoi step." ) spec.input("voronoi", valid_type=Code, required=False, help="Voronoi code node, needed only if `strucure` input node is given." ) spec.input("kkr", valid_type=Code, required=True, help="KKRhost code node which will run the KkrCalculations" ) spec.input("startpot_overwrite", valid_type=SinglefileData, required=False, help="""Potential SinglefileData, can be used to overwrite the starting potential from Voronoi (the shapefun will be used though and thus needs to be compatible). This can be used to construct a better starting potential from a preconverged calculation (e.g. in a smaller unit cell).""" ) spec.input("initial_noco_angles", valid_type=Dict, required=False, help="Initial non-collinear angles for the magnetic moments of the impurities. See KkrCalculation for details." ) # define output nodes spec.output("output_kkr_scf_wc_ParameterResults", valid_type=Dict, required=True) spec.output("last_calc_out", valid_type=Dict, required=False) spec.output("last_RemoteData", valid_type=RemoteData, required=False) spec.output("last_InputParameters", valid_type=Dict, required=False) spec.output("results_vorostart", valid_type=Dict, required=False) spec.output("starting_dosdata_interpol", valid_type=XyData, required=False) spec.output("final_dosdata_interpol", valid_type=XyData, required=False) spec.output("last_noco_angles", valid_type=Dict, required=False) # Here the structure of the workflow is defined spec.outline( cls.start, # check if voronoi run needed, otherwise skip this step if_(cls.validate_input)( # run kkr_startpot workflow (sets up voronoi input, runs voro calc, does some consistency checks) cls.run_voronoi, # check output of run_voronoi and determine if calculation has to be terminated here cls.check_voronoi), # while loop for KKR run(s), first simple mixing # then Anderson with corase pre-convergence settings # finally convergence step with higher accuracy while_(cls.condition)( # update parameters for kkr step using previous output(s) cls.update_kkr_params, # run kkr step # TODO: encapsulate this in restarting mechanism (should be a base class of workflows that start calculations) # i.e. use base_restart_calc workchain as parent cls.run_kkr, # check results for convergence and collect some intermediate results cls.inspect_kkr), # compute final dos if check_dos is True cls.get_dos, cls.check_dos, # finalize calculation and create output nodes cls.return_results ) # definition of exit codes if the workflow needs to be terminated spec.exit_code(221, "ERROR_NO_PARENT_PARAMS_FOUND", message="Unable to extract parent paremeter node of input remote folder") spec.exit_code(222, "ERROR_INVALID_KKR_CODE", message="The code you provided for kkr does not use the plugin kkr.kkr") spec.exit_code(223, "ERROR_INVALID_VORONOI_CODE", message="The code you provided for voronoi does not use the plugin kkr.voro") spec.exit_code(224, "ERROR_NO_VORONOI_CODE_GIVEN", message="ERROR: StructureData was provided, but no voronoi code was provided") spec.exit_code(225, "ERROR_NOT_ENOUGH_INPUTS", message="ERROR: No StructureData nor remote_data was provided as Input") spec.exit_code(226, "ERROR_KKR_STARTPOT_FAILED", message="ERROR: kkr_startpot_wc step failed!") spec.exit_code(227, "ERROR_DOS_RUN_UNSUCCESSFUL", message="DOS run unsuccessful. Check inputs.") spec.exit_code(228, "ERROR_CALC_PARAMETERS_INCOMPLETE", message="ERROR: calc_parameters given are not consistent! Missing mandatory keys") spec.exit_code(229, "ERROR_CALC_PARAMTERS_INCONSISTENT", message="ERROR: calc_parameters given are not consistent! Hint: did you give an unknown keyword?") spec.exit_code(230, "ERROR_NO_CALC_PARAMETERS_GIVEN", message="ERROR: calc_parameters not given as input but are needed!") spec.exit_code(231, "ERROR_PARAM_UPDATE_FAILED", message="ERROR: parameter update unsuccessful: some key, value pair not valid!") spec.exit_code(232, "ERROR_CALC_PARAMTERS_INCOMPLETE", message="ERROR: calc_parameters misses keys") spec.exit_code(233, "ERROR_LAST_REMOTE_NOT_FOUND", message="ERROR: last_remote could not be set to a previous successful calculation") spec.exit_code(234, "ERROR_MAX_KKR_RESTARTS_REACHED", message="ERROR: maximal number of KKR restarts reached. Exiting now!") spec.exit_code(235, "ERROR_CALC_SUBMISSION_FAILED", message="ERROR: last KKRcalc in SUBMISSIONFAILED state")
[docs] def start(self): """ init context and some parameters """"INFO: started KKR convergence workflow version {}" "".format(self._workflowversion)) ####### init ####### # internal para /control para self.ctx.loop_count = 0 self.ctx.last_mixing_scheme = 0 self.ctx.calcs = [] self.ctx.abort = False # flags used internally to check whether the individual steps were successful self.ctx.kkr_converged = False self.ctx.dos_ok = False self.ctx.voro_step_success = False self.ctx.kkr_step_success = False self.ctx.kkr_converged = False self.ctx.kkr_higher_accuracy = False # links to previous calculations self.ctx.last_calc = None self.ctx.last_params = None self.ctx.last_remote = None # convergence info about rms etc. (used to determine convergence behavior) self.ctx.last_rms_all = [] self.ctx.rms_all_steps = [] self.ctx.last_neutr_all = [] self.ctx.neutr_all_steps = [] # input para wf_dict = self.inputs.wf_parameters.get_dict() options_dict = self.inputs.options.get_dict() if wf_dict == {}: wf_dict = self._wf_default"INFO: using default wf parameter") if options_dict == {}: options_dict = self._options_default"INFO: using default options") # set values from input, or defaults self.ctx.withmpi = options_dict.get( "withmpi", self._options_default["withmpi"]) self.ctx.resources = options_dict.get( "resources", self._options_default["resources"]) self.ctx.max_wallclock_seconds = options_dict.get( "max_wallclock_seconds", self._options_default["max_wallclock_seconds"]) self.ctx.queue = options_dict.get( "queue_name", self._options_default["queue_name"]) self.ctx.custom_scheduler_commands = options_dict.get( "custom_scheduler_commands", self._options_default["custom_scheduler_commands"]) self.ctx.options_params_dict = Dict( dict={ "withmpi": self.ctx.withmpi, "resources": self.ctx.resources, "max_wallclock_seconds": self.ctx.max_wallclock_seconds, "queue_name": self.ctx.queue, "custom_scheduler_commands": self.ctx.custom_scheduler_commands } ) # set label and description self.ctx.description_wf = self.inputs.get( "description", "Workflow for " "a KKR scf calculation starting " "either from a structure with " "automatic voronoi calculation " "or a valid RemoteData node of " "a previous calculation" ) self.ctx.label_wf = self.inputs.get("label", "kkr_scf_wc") # set workflow parameters self.ctx.max_number_runs = wf_dict.get( "kkr_runmax", self._wf_default["kkr_runmax"]) self.ctx.strmix = wf_dict.get("strmix", self._wf_default["strmix"]) self.ctx.brymix = wf_dict.get("brymix", self._wf_default["brymix"]) self.ctx.check_dos = wf_dict.get( "check_dos", self._wf_default["check_dos"]) self.ctx.dos_params = wf_dict.get( "dos_params", self._wf_default["dos_params"]) self.ctx.convergence_criterion = wf_dict.get( "convergence_criterion", self._wf_default["convergence_criterion"]) self.ctx.convergence_setting_coarse = wf_dict.get( "convergence_setting_coarse", self._wf_default["convergence_setting_coarse"]) self.ctx.convergence_setting_fine = wf_dict.get( "convergence_setting_fine", self._wf_default["convergence_setting_fine"]) self.ctx.mixreduce = wf_dict.get( "mixreduce", self._wf_default["mixreduce"]) self.ctx.nsteps = wf_dict.get("nsteps", self._wf_default["nsteps"]) self.ctx.threshold_aggressive_mixing = wf_dict.get( "threshold_aggressive_mixing", self._wf_default["threshold_aggressive_mixing"]) self.ctx.threshold_switch_high_accuracy = wf_dict.get( "threshold_switch_high_accuracy", self._wf_default["threshold_switch_high_accuracy"]) # initial magnetization self.ctx.mag_init = wf_dict.get( "mag_init", self._wf_default["mag_init"]) self.ctx.hfield = wf_dict.get("hfield", self._wf_default["hfield"]) self.ctx.xinit = wf_dict.get("init_pos", self._wf_default["init_pos"]) self.ctx.mag_init_step_success = False # difference in eV to emin (e_fermi) if emin (emax) are larger (smaller) than emin (e_fermi) self.ctx.delta_e = wf_dict.get( "delta_e_min", self._wf_default["delta_e_min"]) # threshold for dos comparison (comparison of dos at emin) self.ctx.threshold_dos_zero = wf_dict.get( "threshold_dos_zero", self._wf_default["threshold_dos_zero"]) self.ctx.efermi = None # set starting noco angles, gets updated in between the KKR runs if fix_dir == False for all atoms if "initial_noco_angles" in self.inputs: self.ctx.initial_noco_angles = self.inputs.initial_noco_angles self.ctx.fix_dir_threshold = wf_dict.get( "fix_dir_threshold", self._wf_default["fix_dir_threshold"]) # retreive dos data in each scf run self.ctx.scf_dosdata = wf_dict.get( "retreive_dos_data_scf_run", self._wf_default["retreive_dos_data_scf_run"])"INFO: use the following parameter:\n" "\nGeneral settings\n" "use mpi: {}\n" "max number of KKR runs: {}\n" "Resources: {}\n" "Walltime (s): {}\n" "queue name: {}\n" "scheduler command: {}\n" "description: {}\n" "label: {}\n" "\nMixing parameter\n" "Straight mixing factor: {}\n" "Anderson mixing factor: {}\n" "Nsteps scf cycle: {}\n" "Convergence criterion: {}\n" "threshold_aggressive_mixing: {}\n" "threshold_switch_high_accuracy: {}\n" "convergence_setting_coarse: {}\n" "convergence_setting_fine: {}\n" "factor reduced mixing if failing calculation: {}\n" "\nAdditional parameter\n" "check DOS between runs: {}\n" "DOS parameters: {}\n" "init magnetism in first step: {}\n" "init magnetism, hfield: {}\n" "init magnetism, init_pos: {}\n" "".format(self.ctx.withmpi, self.ctx.max_number_runs, self.ctx.resources, self.ctx.max_wallclock_seconds, self.ctx.queue, self.ctx.custom_scheduler_commands, self.ctx.description_wf, self.ctx.label_wf, self.ctx.strmix, self.ctx.brymix, self.ctx.nsteps, self.ctx.convergence_criterion, self.ctx.threshold_aggressive_mixing, self.ctx.threshold_switch_high_accuracy, self.ctx.convergence_setting_coarse, self.ctx.convergence_setting_fine, self.ctx.mixreduce, self.ctx.check_dos, self.ctx.dos_params, self.ctx.mag_init, self.ctx.hfield, self.ctx.xinit) ) # return para/vars self.ctx.successful = True self.ctx.rms = [] self.ctx.neutr = [] self.ctx.warnings = [] self.ctx.errors = [] self.ctx.formula = "" # for results table each list gets one entry per iteration that has been performed self.ctx.KKR_steps_stats = { "success": [], "isteps": [], "imix": [], "mixfac": [], "qbound": [], "high_sett": [], "first_rms": [], "last_rms": [], "first_neutr": [], "last_neutr": [], "pk": [], "uuid": [] }
[docs] def validate_input(self): """ # validate input and find out which path (1, or 2) to take # return True means run voronoi if false run kkr directly """ run_voronoi = True inputs = self.inputs if "structure" in inputs: "INFO: Found structure in input. Start with Voronoi calculation." ) if not "voronoi" in inputs: return self.exit_codes.ERROR_NO_VORONOI_CODE_GIVEN elif "remote_data" in inputs: "INFO: Found remote_data in input. Continue calculation without running voronoi step." ) run_voronoi = False else: return self.exit_codes.ERROR_NOT_ENOUGH_INPUTS if "voronoi" in inputs: try: test_and_get_codenode( inputs.voronoi, "kkr.voro", use_exceptions=True) except ValueError: return self.exit_codes.ERROR_INVALID_VORONOI_CODE if "kkr" in inputs: try: test_and_get_codenode( inputs.kkr, "kkr.kkr", use_exceptions=True) except ValueError: return self.exit_codes.ERROR_INVALID_KKR_CODE # set params and remote folder to input if voronoi step is skipped if not run_voronoi: self.ctx.last_remote = inputs.remote_data num_parents = len(self.ctx.last_remote.get_incoming( node_class=CalcJobNode).all_link_labels()) if num_parents == 0: pk_last_remote = self.ctx.last_remote.inputs.last_RemoteData.outputs.output_kkr_scf_wc_ParameterResults.get_dict( ).get("last_calc_nodeinfo").get("pk") last_calc = load_node(pk_last_remote) self.ctx.last_remote = last_calc.outputs.remote_folder try: # first try parent of remote data output of a previous calc. parent_params = get_parent_paranode(inputs.remote_data) except AttributeError: try: # next try to extract parameter from previous kkr_scf_wc output parent_params = inputs.remote_data.inputs.last_RemoteData.inputs.calc_parameters except AttributeError: return self.exit_codes.ERROR_NO_PARENT_PARAMS_FOUND if "calc_parameters" in inputs: self.ctx.last_params = inputs.calc_parameters # TODO: check last_params consistency against parent_params #parent_params_dict = parent_params.get_dict() # for key, val in self.ctx.last_params.get_dict().iteritems(): # if key in parent_params_dict.keys(): # if val != parent_params_dict[key]: # # else: # else: self.ctx.last_params = parent_params self.ctx.voro_step_success = True return run_voronoi
[docs] def run_voronoi(self): """ run the voronoi step calling voro_start workflow """ # collects inputs structure = self.inputs.structure self.ctx.formula = structure.get_formula() voronoicode = self.inputs.voronoi kkrcode = self.inputs.kkr # set KKR parameters if any are given, otherwise use defaults if "calc_parameters" in self.inputs: params = self.inputs.calc_parameters else: params = None # check if default values are missing and set appropriately from defaults defaults, version = kkrparams.get_KKRcalc_parameter_defaults() if params is None: params = Dict(dict=defaults) params.label = "default values" newparams = {} for key, val in defaults.items(): if params.get_dict().get(key, None) is None: if key != "RCLUSTZ": # this one is automatically set by kkr_startpot_wc so we skip it here newparams[key] = val "INFO: Automatically added default values to KKR parameters: {} {}" .format(key, val) ) if newparams != {}: for key, val in params.get_dict().items(): if val is not None: newparams[key] = val updatenode = Dict(dict=newparams) updatenode.label = "added defaults to KKR input parameter" updatenode.description = "Overwritten KKR input parameter to correct missing default values automatically" params = update_params_wf(params, updatenode) # set nspin to 2 if mag_init is used if self.ctx.mag_init: input_dict = params.get_dict() para_check = kkrparams() for key, val in input_dict.items(): para_check.set_value(key, val, silent=True) nspin_in = para_check.get_value("NSPIN") if nspin_in is None: nspin_in = 1 if nspin_in < 2: "WARNING: found NSPIN=1 but for maginit needs NPIN=2. Overwrite this automatically" ) para_check.set_value("NSPIN", 2, silent=True) "INFO: update parameters to: {}" .format(para_check.get_set_values()) ) updatenode = Dict(dict=para_check.get_dict()) updatenode.label = "overwritten KKR input parameters" updatenode.description = "Overwritten KKR input parameter to correct NSPIN to 2" params = update_params_wf(params, updatenode) # check consistency of input parameters before running calculation self.check_input_params(params, is_voronoi=True) # set parameters of voro_start sub workflow sub_wf_params_dict = kkr_startpot_wc.get_wf_defaults(silent=True) label, description = "voro_start_default_params", "workflow parameters for voro_start" if "wf_parameters" in self.inputs: wf_params_input = self.inputs.wf_parameters.get_dict() num_updated = 0 for key in list(sub_wf_params_dict.keys()): if key in list(wf_params_input.keys()): val = wf_params_input[key] sub_wf_params_dict[key] = val num_updated += 1 if num_updated > 0: label = "voro_start_updated_params" sub_wf_params = Dict(dict=sub_wf_params_dict) sub_wf_params.label = label sub_wf_params.description = description"INFO: run voronoi step") "INFO: using calc_params ({}): {}" .format(params, params.get_dict()) ) "INFO: using wf_parameters ({}): {}" .format(sub_wf_params, sub_wf_params.get_dict()) ) wf_label = "kkr_startpot (voronoi)" wf_desc = "subworkflow to set up the input of a KKR calculation" builder = kkr_startpot_wc.get_builder() builder.kkr = kkrcode builder.voronoi = voronoicode builder.calc_parameters = params builder.wf_parameters = sub_wf_params builder.structure = structure builder.metadata.label = wf_label builder.metadata.description = wf_desc builder.options = self.ctx.options_params_dict if "startpot_overwrite" in self.inputs: builder.startpot_overwrite = self.inputs.startpot_overwrite future = self.submit(builder) return ToContext(voronoi=future, last_calc=future)
[docs] def check_voronoi(self): """ check output of kkr_startpot_wc workflow that creates starting potential, shapefun etc. """"INFO: checking voronoi output") voro_step_ok = False # check some output kkrstartpot_results = self.ctx.voronoi.outputs.results_vorostart_wc.get_dict() if kkrstartpot_results["successful"]: voro_step_ok = True # initialize last_remote and last_params (gets updated in loop of KKR calculations) self.ctx.last_params = self.ctx.voronoi.outputs.last_params_voronoi self.ctx.last_remote = self.ctx.voronoi.outputs.last_voronoi_remote # store result in context self.ctx.voro_step_success = voro_step_ok # abort calculation if something failed in voro_start step if not voro_step_ok: return self.exit_codes.ERROR_KKR_STARTPOT_FAILED"INFO: done checking voronoi output")
[docs] def condition(self): """ check convergence condition """"INFO: checking condition for kkr step") do_kkr_step = True stopreason = "" # increment KKR runs loop counter self.ctx.loop_count += 1 # check if initial step was succesful if not self.ctx.voro_step_success: stopreason = "voronoi step unsucessful" do_kkr_step = False return do_kkr_step # check if previous calculation reached convergence criterion if self.ctx.kkr_converged: if not self.ctx.kkr_higher_accuracy: do_kkr_step = do_kkr_step & True else: stopreason = "KKR converged" do_kkr_step = False else: do_kkr_step = do_kkr_step & True # next check only needed if another iteration should be done after validating convergence etc. (previous checks) if do_kkr_step: # check if maximal number of iterations has been reached if self.ctx.loop_count <= self.ctx.max_number_runs: do_kkr_step = do_kkr_step & True else: do_kkr_step = False # TODO do this differently, return of exit code needs to be done outside of while loop! # return self.exit_codes.ERROR_MAX_KKR_RESTARTS_REACHED return do_kkr_step "INFO: done checking condition for kkr step (result={})" .format(do_kkr_step) ) if not do_kkr_step:"INFO: stopreason={}".format(stopreason)) return do_kkr_step
[docs] def update_kkr_params(self): """ update set of KKR parameters (check for reduced mixing, change of mixing strategy, change of accuracy setting) """"INFO: updating kkr param step") decrease_mixing_fac = False switch_agressive_mixing = False switch_higher_accuracy = False initial_settings = False # only do something other than somple mixing after first kkr run if self.ctx.loop_count != 1: # first determine if previous step was successful (otherwise try to find some rms value and decrease mixing to try again) if not self.ctx.kkr_step_success: try: # check if calculation did start (maybe cluster had some hiccup) calc = self.ctx.calcs[-1] has_output_node = len( calc .get_outgoing(link_label_filter="output_parameters") .all() ) > 0 "INFO: last KKR calculation failed. Probably because the cluster had some issue. Try to resubmit the same calculation" ) except: # otherwise try to decrease the mixing factor decrease_mixing_fac = True "INFO: last KKR calculation failed. Trying to decrease mixfac" ) convergence_on_track = self.convergence_on_track() # check if calculation was on its way to converge if not convergence_on_track: decrease_mixing_fac = True "INFO: last KKR did not converge. trying decreasing mixfac" ) "INFO: ctx.calcs: {} {}" .format(self.ctx.calcs, type(self.ctx.calcs)) ) # reset last_remote to last successful calculation # needs to be list because `(x)range` does not support slicing calclist = list(range(len(self.ctx.calcs))) if len(calclist) > 1: calclist = calclist[::-1] # go backwards through list for icalc in calclist:"INFO: last calc success? {} {}".format( icalc, self.ctx.KKR_steps_stats["success"][icalc])) if self.ctx.KKR_steps_stats["success"][icalc]: self.ctx.last_remote = self.ctx.calcs[icalc].outputs.remote_folder break # exit loop if last_remote was found successfully else: self.ctx.last_remote = None # if no previous calculation was succesful take voronoi output or remote data from input (depending on the inputs)"INFO: last_remote is None? {} {}".format( self.ctx.last_remote is None, "structure" in self.inputs)) if self.ctx.last_remote is None: if "structure" in self.inputs: self.ctx.voronoi.outputs.last_voronoi_remote else: self.ctx.last_remote = self.inputs.remote_data # check if last_remote has finally been set and abort if this is not the case "INFO: last_remote is still None? {}" .format(self.ctx.last_remote is None) ) if self.ctx.last_remote is None: return self.exit_codes.ERROR_LAST_REMOTE_NOT_FOUND # check if mixing strategy should be changed last_mixing_scheme = self.ctx.last_params.get_dict()["IMIX"] if last_mixing_scheme is None: last_mixing_scheme = 0 if convergence_on_track: last_rms = self.ctx.last_rms_all[-1] if ( last_rms < self.ctx.threshold_aggressive_mixing and last_mixing_scheme == 0 ): switch_agressive_mixing = True "INFO: rms low enough, switch to agressive mixing" ) # check if switch to higher accuracy should be done if not self.ctx.kkr_higher_accuracy: if last_rms < self.ctx.threshold_switch_high_accuracy: switch_higher_accuracy = True "INFO: rms low enough, switch to higher accuracy settings" ) else: initial_settings = True # if needed update parameters if ( decrease_mixing_fac or switch_agressive_mixing or switch_higher_accuracy or initial_settings or self.ctx.mag_init ): if initial_settings: label = "initial KKR scf parameters" description = "initial parameter set for scf calculation" else: label = "" description = "" # step 1: extract info from last input parameters and check consistency params = self.ctx.last_params input_dict = params.get_dict() para_check = kkrparams() # step 1.1: try to fill keywords for key, val in input_dict.items(): para_check.set_value(key, val, silent=True) # init new_params dict where updated params are collected new_params = {} # step 1.2: check if all mandatory keys are there and add defaults if missing missing_list = para_check.get_missing_keys(use_aiida=True) if missing_list != []: kkrdefaults = kkrparams.get_KKRcalc_parameter_defaults()[0] kkrdefaults_updated = [] for key_default, val_default in list(kkrdefaults.items()): if key_default in missing_list: new_params[key_default] = kkrdefaults.get(key_default) kkrdefaults_updated.append(key_default) if len(kkrdefaults_updated) > 0: "ERROR: calc_parameters misses keys: {}" .format(missing_list) ) return self.exit_codes.ERROR_CALC_PARAMTERS_INCOMPLETE else: "updated KKR parameter node with default values: {}" .format(kkrdefaults_updated) ) # step 2: change parameter (contained in new_params dictionary) if initial_settings and "structure" in self.inputs: # make sure to ignore IMIX from input node (start with simple mixing even if IMIX is set otherwise) # this is enforced whenever voronoi step is starting point (otherwise you may want to continue a preconverged calculation) last_mixing_scheme = None else: last_mixing_scheme = para_check.get_value("IMIX") if last_mixing_scheme is None: last_mixing_scheme = 0 strmixfac = self.ctx.strmix brymixfac = self.ctx.brymix nsteps = self.ctx.nsteps # add number of scf steps new_params["NSTEPS"] = nsteps # step 2.1 fill new_params dict with values to be updated if decrease_mixing_fac: if last_mixing_scheme == 0: "(strmixfax, mixreduce)= ({}, {})" .format(strmixfac, self.ctx.mixreduce) ) "type(strmixfax, mixreduce)= {} {}" .format(type(strmixfac), type(self.ctx.mixreduce)) ) strmixfac = strmixfac * self.ctx.mixreduce self.ctx.strmix = strmixfac label += "decreased_mix_fac_str (step {})".format( self.ctx.loop_count) description += "decreased STRMIX factor by {}".format( self.ctx.mixreduce) else: "(brymixfax, mixreduce)= ({}, {})" .format(brymixfac, self.ctx.mixreduce) ) "type(brymixfax, mixreduce)= {} {}" .format(type(brymixfac), type(self.ctx.mixreduce)) ) brymixfac = brymixfac * self.ctx.mixreduce self.ctx.brymix = brymixfac label += "decreased_mix_fac_bry" description += "decreased BRYMIX factor by {}".format( self.ctx.mixreduce) # add mixing factor new_params["STRMIX"] = strmixfac new_params["BRYMIX"] = brymixfac if switch_agressive_mixing: last_mixing_scheme = 5 label += " switched_to_agressive_mixing" description += " switched to agressive mixing scheme (IMIX={})".format( last_mixing_scheme) # add mixing scheme new_params["IMIX"] = last_mixing_scheme self.ctx.last_mixing_scheme = last_mixing_scheme if switch_higher_accuracy: self.ctx.kkr_higher_accuracy = True convergence_settings = self.ctx.convergence_setting_fine label += " use_higher_accuracy" description += " using higher accuracy settings goven in convergence_setting_fine" else: convergence_settings = self.ctx.convergence_setting_coarse # slightly increase temperature if previous calculation was unsuccessful for the second time if decrease_mixing_fac and not self.convergence_on_track(): "INFO: last calculation did not converge and convergence not on track. Try to increase temperature by 50K." ) convergence_settings["tempr"] += 50. label += " TEMPR+50K" description += " with increased temperature of 50K" # add convegence settings if self.ctx.loop_count == 1 or self.ctx.last_mixing_scheme == 0: new_params["QBOUND"] = self.ctx.threshold_aggressive_mixing else: new_params["QBOUND"] = self.ctx.convergence_criterion new_params["NPOL"] = convergence_settings["npol"] new_params["NPT1"] = convergence_settings["n1"] new_params["NPT2"] = convergence_settings["n2"] new_params["NPT3"] = convergence_settings["n3"] new_params["TEMPR"] = convergence_settings["tempr"] new_params["BZDIVIDE"] = convergence_settings["kmesh"] # initial magnetization if initial_settings and self.ctx.mag_init: if self.ctx.hfield <= 0: "\nWARNING: magnetization initialization chosen but hfield is zero. Automatically change back to default value (hfield={})\n" .format(self._wf_default["hfield"]) ) self.ctx.hfield = self._wf_default["hfield"] xinipol = self.ctx.xinit if xinipol is None: # find structure to determine needed length on xinipol if "structure" in self.inputs: struc = self.inputs.structure else: struc, voro_parent = VoronoiCalculation.find_parent_structure( self.ctx.last_remote) natom = len(get_site_symbols(struc)) xinipol = ones(natom) new_params["LINIPOL"] = True new_params["HFIELD"] = self.ctx.hfield new_params["XINIPOL"] = xinipol # turn off initialization after first (successful) iteration elif self.ctx.mag_init and self.ctx.mag_init_step_success: new_params["LINIPOL"] = False new_params["HFIELD"] = 0.0 elif not self.ctx.mag_init: "INFO: mag_init is False. Overwrite 'HFIELD' to '0.0' and 'LINIPOL' to 'False'." ) # reset mag init to avoid reinitializing new_params["HFIELD"] = 0.0 new_params["LINIPOL"] = False # set nspin to 2 if mag_init is used if self.ctx.mag_init: nspin_in = para_check.get_value("NSPIN") if nspin_in is None: nspin_in = 1 if nspin_in < 2: "WARNING: found NSPIN=1 but for maginit needs NPIN=2. Overwrite this automatically" ) new_params["NSPIN"] = 2 # step 2.2 update values try: for key, val in new_params.items(): para_check.set_value(key, val, silent=True) except: return self.exit_codes.ERROR_PARAM_UPDATE_FAILED # step 3: "INFO: update parameters to: {}" .format(para_check.get_set_values()) ) updatenode = Dict(dict=para_check.get_dict()) updatenode.label = label updatenode.description = description paranode_new = update_params_wf(self.ctx.last_params, updatenode) self.ctx.last_params = paranode_new else:"INFO: reuse old settings")"INFO: done updating kkr param step")
[docs] def run_kkr(self): """ submit a KKR calculation """ "INFO: setting up kkr calculation step {}" .format(self.ctx.loop_count) ) label = "KKR calculation step {} (IMIX={})".format( self.ctx.loop_count, self.ctx.last_mixing_scheme) description = "KKR calculation of step {}, using mixing scheme {}".format( self.ctx.loop_count, self.ctx.last_mixing_scheme) code = self.inputs.kkr remote = self.ctx.last_remote params = self.ctx.last_params options = { "max_wallclock_seconds": self.ctx.max_wallclock_seconds, "resources": self.ctx.resources, "queue_name": self.ctx.queue } if self.ctx.custom_scheduler_commands: options["custom_scheduler_commands"] = self.ctx.custom_scheduler_commands inputs = get_inputs_kkr( code, remote, options, label, description, parameters=params, serial=(not self.ctx.withmpi) ) # pass nonco angles setting to KkrCalculation if "initial_noco_angles" in self.inputs: inputs["initial_noco_angles"] = self.ctx.initial_noco_angles # run the KKR calculation"INFO: doing calculation") kkr_run = self.submit(KkrCalculation, **inputs) return ToContext(kkr=kkr_run, last_calc=kkr_run)
[docs] def inspect_kkr(self): """ check for convergence and store some of the results of the last calculation to context """"INFO: inspecting kkr results step") "Caching info: {}".format(self.ctx.last_calc.get_cache_source()) ) self.ctx.calcs.append(self.ctx.last_calc) self.ctx.kkr_step_success = True # check calculation state if not self.ctx.last_calc.is_finished_ok: self.ctx.kkr_step_success = False"ERROR: last calculation not finished correctly") "INFO: kkr_step_success: {}".format(self.ctx.kkr_step_success) ) # extract convergence info about rms etc. (used to determine convergence behavior) try:"INFO: trying to find output of last_calc: {}".format( self.ctx.last_calc)) last_calc_output = self.ctx.last_calc.outputs.output_parameters.get_dict() found_last_calc_output = True except: found_last_calc_output = False"INFO: found_last_calc_output: {}".format( found_last_calc_output)) # try to extract remote folder try: self.ctx.last_remote = self.ctx.kkr.outputs.remote_folder except: self.ctx.last_remote = None self.ctx.kkr_step_success = False"INFO: last_remote: {}".format(self.ctx.last_remote)) if self.ctx.kkr_step_success and found_last_calc_output: # check convergence self.ctx.kkr_converged = last_calc_output["convergence_group"]["calculation_converged"] # check rms, compare spin and charge values and take bigger one rms_charge = last_calc_output["convergence_group"]["rms"] # returning 0 if not found allows to reuse older verisons (e.g. in caching) rms_spin = last_calc_output["convergence_group"].get("rms_spin", 0) if rms_spin is None: rms_spin = 0 # this happens for NSPIN==1 if rms_charge >= rms_spin: rms_max = rms_charge use_rms_charge = True else: rms_max = rms_spin use_rms_charge = False self.ctx.rms.append(rms_max) if use_rms_charge: rms_all_iter_last_calc = list( last_calc_output["convergence_group"]["rms_all_iterations"]) else: rms_all_iter_last_calc = list( last_calc_output["convergence_group"]["rms_spin_all_iterations"]) # check charge neutrality self.ctx.neutr.append( last_calc_output["convergence_group"]["charge_neutrality"]) neutr_all_iter_last_calc = list( last_calc_output["convergence_group"]["charge_neutrality_all_iterations"]) # add lists of last iterations self.ctx.last_rms_all = rms_all_iter_last_calc self.ctx.last_neutr_all = neutr_all_iter_last_calc if self.ctx.kkr_step_success and self.convergence_on_track(): self.ctx.rms_all_steps += rms_all_iter_last_calc self.ctx.neutr_all_steps += neutr_all_iter_last_calc else: self.ctx.kkr_converged = False"INFO: kkr_converged: {}".format(self.ctx.kkr_converged))"INFO: rms: {}".format(self.ctx.rms))"INFO: last_rms_all: {}".format(self.ctx.last_rms_all))"INFO: rms_all_steps: {}".format(self.ctx.rms_all_steps))"INFO: charge_neutrality: {}".format(self.ctx.neutr))"INFO: last_neutr_all: {}".format(self.ctx.last_neutr_all))"INFO: neutr_all_steps: {}".format(self.ctx.neutr_all_steps)) # turn off initial magnetization once one step was successful (update_kkr_params) used in if self.ctx.mag_init and self.ctx.kkr_step_success: self.ctx.mag_init_step_success = True # TODO: extract something else (maybe total energy, charge neutrality, magnetisation)? # store some statistics used to print table in the end of the report tmplist = self.ctx.KKR_steps_stats.get("success", [])"INFO: append kkr_step_success {}, {}".format( tmplist, self.ctx.kkr_step_success)) tmplist.append(self.ctx.kkr_step_success) self.ctx.KKR_steps_stats["success"] = tmplist try: isteps = self.ctx.last_calc.outputs.output_parameters.get_dict( )["convergence_group"]["number_of_iterations"] except: self.ctx.warnings.append( "cound not set isteps in KKR_steps_stats dict" ) isteps = -1 try: first_rms = self.ctx.last_rms_all[0] last_rms = self.ctx.last_rms_all[-1] except: self.ctx.warnings.append( "cound not set first_rms, last_rms in KKR_steps_stats dict" ) first_rms = -1 last_rms = -1 try: first_neutr = self.ctx.last_neutr_all[0] last_neutr = self.ctx.last_neutr_all[-1] except: self.ctx.warnings.append( "cound not set first_neutr, last_neutr in KKR_steps_stats dict") first_neutr = -999 last_neutr = -999 if self.ctx.last_mixing_scheme == 0: mixfac = self.ctx.strmix else: mixfac = self.ctx.brymix if self.ctx.kkr_higher_accuracy: qbound = self.ctx.convergence_criterion else: qbound = self.ctx.threshold_switch_high_accuracy # store results in KKR_steps_stats dict for key, val in {"isteps": isteps, "imix": self.ctx.last_mixing_scheme, "mixfac": mixfac, "qbound": qbound, "high_sett": self.ctx.kkr_higher_accuracy, "first_rms": first_rms, "last_rms": last_rms, "first_neutr": first_neutr, "last_neutr": last_neutr, "pk":, "uuid": self.ctx.last_calc.uuid}.items(): tmplist = self.ctx.KKR_steps_stats.get(key, []) tmplist.append(val) self.ctx.KKR_steps_stats[key] = tmplist # update noco angles if "initial_noco_angles" in self.inputs: # do this only if previous calculation was successful if self.ctx.kkr_step_success and found_last_calc_output: self._get_new_noco_angles()"INFO: done inspecting kkr results step")
[docs] def convergence_on_track(self): """ Check if convergence behavior of the last calculation is on track (i.e. going down) """ on_track = True threshold = 5. # used to check condition if at least one of charnge_neutrality, rms-error goes down fast enough eps_neutr = 1e-4 eps_rms = 1e-6 # first check if previous calculation was stopped due to reaching the QBOUND limit try: calc_reached_qbound = self.ctx.last_calc.outputs.output_parameters.get_dict()[ "convergence_group"]["calculation_converged"] except: # captures error when last_calc dies not have an output node calc_reached_qbound = False if self.ctx.kkr_step_success and not calc_reached_qbound: first_rms = self.ctx.last_rms_all[0] first_neutr = abs(self.ctx.last_neutr_all[0]) last_rms = self.ctx.last_rms_all[-1] last_neutr = abs(self.ctx.last_neutr_all[-1]) # use this trick to avoid division by zero if first_neutr == 0: first_neutr = 10**-16 if first_rms == 0: first_rms = 10**-16 r, n = last_rms/first_rms, last_neutr/first_neutr # deal with small differences if abs(first_neutr-last_neutr) < eps_neutr: n = 0 if abs(first_rms-last_rms) > eps_rms: r = 0 "INFO convergence check: first/last rms {}, {}; first/last neutrality {}, {}" .format(first_rms, last_rms, first_neutr, last_neutr) ) if r < 1 and n < 1: "INFO convergence check: both rms and neutrality go down" ) on_track = True elif n > threshold or r > threshold: "INFO convergence check: rms or neutrality goes up too fast, convergence is not expected" ) on_track = False elif n*r < 1: "INFO convergence check: either rms goes up and neutrality goes down or vice versa" ) "INFO convergence check: but product goes down fast enough" ) on_track = True elif len(self.ctx.last_rms_all) == 1: "INFO convergence check: already converged after single iteration" ) on_track = True else: "INFO convergence check: rms or neutrality do not shrink fast enough, convergence is not expected" ) on_track = False elif calc_reached_qbound:"INFO convergence check: calculation reached QBOUND") on_track = True else:"INFO convergence check: calculation unsuccessful") on_track = False"INFO convergence check result: {}".format(on_track)) return on_track
[docs] def return_results(self): """ return the results of the calculations This shoudl run through and produce output nodes even if everything failed, therefore it only uses results from context. """"INFO: entering return_results") # try/except to capture as mnuch as possible (everything that is there even when workflow exits unsuccessfully) # capture pk and uuids of last calc, params and remote try: last_calc_uuid = self.ctx.last_calc.uuid last_calc_pk = last_params_uuid = self.ctx.last_params.uuid last_params_pk = last_remote_uuid = self.ctx.last_remote.uuid last_remote_pk = except: last_calc_uuid = None last_calc_pk = None last_params_uuid = None last_params_pk = None last_remote_uuid = None last_remote_pk = None all_pks = [] for calc in self.ctx.calcs: try: all_pks.append( except: self.ctx.warnings.append( "cound not get pk of calc {}".format(calc)) # capture links to last parameter, calcualtion and output try: last_calc_out = self.ctx.kkr.outputs.output_parameters last_calc_out_dict = last_calc_out.get_dict()"Found last_calc_out") last_RemoteData = self.ctx.last_remote"Found last_remote") last_InputParameters = self.ctx.last_params"Found last_params") except:"Error in finding last_calc_out etc.") last_InputParameters = None last_RemoteData = None last_calc_out = None last_calc_out_dict = {} # capture convergence info try: last_rms = self.ctx.rms[-1] last_neutr = self.ctx.neutr[-1] except: last_rms = None last_neutr = None # capture result of vorostart sub-workflow try: results_vorostart = self.ctx.voronoi.outputs.results_vorostart_wc except: results_vorostart = None try: starting_dosdata_interpol = self.ctx.voronoi.outputs.last_doscal_dosdata_interpol except: starting_dosdata_interpol = None try: final_dosdata_interpol = self.ctx.doscal.outputs.dos_data_interpol except: final_dosdata_interpol = None # now collect results saved in results node of workflow"INFO: collect outputnode_dict") outputnode_dict = {} outputnode_dict["workflow_name"] = self.__class__.__name__ outputnode_dict["workflow_version"] = self._workflowversion outputnode_dict["material"] = self.ctx.formula outputnode_dict["loop_count"] = self.ctx.loop_count outputnode_dict["warnings"] = self.ctx.warnings outputnode_dict["successful"] = self.ctx.successful outputnode_dict["last_params_nodeinfo"] = { "uuid": last_params_uuid, "pk": last_params_pk} outputnode_dict["last_remote_nodeinfo"] = { "uuid": last_remote_uuid, "pk": last_remote_pk} outputnode_dict["last_calc_nodeinfo"] = { "uuid": last_calc_uuid, "pk": last_calc_pk} outputnode_dict["pks_all_calcs"] = all_pks outputnode_dict["errors"] = self.ctx.errors outputnode_dict["convergence_value"] = last_rms outputnode_dict["convergence_values_all_steps"] = array( self.ctx.rms_all_steps) outputnode_dict["convergence_values_last_step"] = array( self.ctx.last_rms_all) outputnode_dict["charge_neutrality"] = last_neutr outputnode_dict["charge_neutrality_all_steps"] = array( self.ctx.neutr_all_steps) outputnode_dict["charge_neutrality_last_step"] = array( self.ctx.last_neutr_all) outputnode_dict["dos_check_ok"] = self.ctx.dos_ok outputnode_dict["convergence_reached"] = self.ctx.kkr_converged outputnode_dict["voronoi_step_success"] = self.ctx.voro_step_success outputnode_dict["kkr_step_success"] = self.ctx.kkr_step_success outputnode_dict["used_higher_accuracy"] = self.ctx.kkr_higher_accuracy # report the status if self.ctx.successful: "STATUS: Done, the convergence criteria are reached.\n" "INFO: The charge density of the KKR calculation pk= {} " "converged after {} KKR runs and {} iterations to {} \n" "".format( last_calc_pk, self.ctx.loop_count, self.ctx.loop_count, last_rms ) ) else: # Termination ok, but not converged yet... if self.ctx.abort: # some error occured, donot use the output. "STATUS/ERROR: I abort, see logs and " "erros/warning/hints in output_kkr_scf_wc_para" ) else: "STATUS/WARNING: Done, the maximum number of runs " "was reached or something failed.\n INFO: The " "charge density of the KKR calculation pk= " "after {} KKR runs and {} iterations is {} 'me/bohr^3'\n" "".format( self.ctx.loop_count, sum(self.ctx.KKR_steps_stats.get("isteps")), last_rms ) ) # create results node # : {}".format(outputnode_dict))"INFO: create results node") outputnode_t = Dict(dict=outputnode_dict) outputnode_t.label = "kkr_scf_wc_results" outputnode_t.description = ( "Contains results of workflow" " (e.g. workflow version number, info about success of wf," " lis tof warnings that occured during execution, ...)" ) # collect nodes in outputs dictionary out_nodes = {"outpara": outputnode_t} if last_calc_out is not None: out_nodes["last_calc_out"] = last_calc_out if last_RemoteData is not None: out_nodes["last_RemoteData"] = last_RemoteData if last_InputParameters is not None: out_nodes["last_InputParameters"] = last_InputParameters if final_dosdata_interpol is not None: out_nodes["final_dosdata_interpol"] = final_dosdata_interpol if starting_dosdata_interpol is not None: out_nodes["starting_dosdata_interpol"] = starting_dosdata_interpol if results_vorostart is not None: out_nodes["results_vorostart"] = results_vorostart if "initial_noco_angles" in self.inputs: if not all(self.ctx.initial_noco_angles["fix_dir"]): # was updated in inspect_kkr to last noco angles output out_nodes["last_noco_angles"] = self.ctx.initial_noco_angles # call helper function to create output nodes in correct AiiDA graph structure outdict = create_scf_result_node(**out_nodes) for link_name, node in outdict.items():"INFO: storing node {} {} with linkname {}".format(type(node), node, link_name)) self.out(link_name, node) # print results table for overview # table layout: message = "INFO: overview of the result:\n\n" message += "|------|---------|--------|------|--------|-------------------|-----------------|-----------------|-------------\n" message += "| irun | success | isteps | imix | mixfac | accuracy settings | rms | abs(neutrality) | pk and uuid \n" message += "| | | | | | qbound | higher? | first | last | first | last | \n" message += "|------|---------|--------|------|--------|---------|---------|--------|--------|--------|--------|-------------\n" # | %6i | %9s | %8i | %6i | %.2e | %.3e | %9s | %.2e | %.2e | %.2e | %.2e | KKR_steps_stats = self.ctx.KKR_steps_stats for irun in range(len(KKR_steps_stats.get("success"))): KKR_steps_stats.get("first_neutr")[irun] = abs( KKR_steps_stats.get("first_neutr")[irun]) KKR_steps_stats.get("last_neutr")[irun] = abs( KKR_steps_stats.get("last_neutr")[irun]) message += ( "|{:6d}|{:9s}|{:8d}|{:6d}|{:.2e}|{:.3e}|{:9s}|{:.2e}|{:.2e}|{:.2e}|{:.2e}|" .format( irun+1, str(KKR_steps_stats.get("success")[irun]), KKR_steps_stats.get("isteps")[irun], KKR_steps_stats.get("imix")[irun], KKR_steps_stats.get("mixfac")[irun], KKR_steps_stats.get("qbound")[irun], str(KKR_steps_stats.get("high_sett")[irun]), KKR_steps_stats.get("first_rms")[irun], KKR_steps_stats.get("last_rms")[irun], KKR_steps_stats.get("first_neutr")[irun], KKR_steps_stats.get("last_neutr")[irun] ) ) message += " {} | {}\n".format( KKR_steps_stats.get("pk")[irun], KKR_steps_stats.get("uuid")[irun] ) """ message += "#|{}|{}|{}|{}|{}|{}|{}|{}|{}|{}|{}|\n".format(irun+1, KKR_steps_stats.get('success')[irun], KKR_steps_stats.get('isteps')[irun], KKR_steps_stats.get('imix')[irun], KKR_steps_stats.get('mixfac')[irun], KKR_steps_stats.get('qbound')[irun], KKR_steps_stats.get('high_sett')[irun], KKR_steps_stats.get('first_rms')[irun], KKR_steps_stats.get('last_rms')[irun], KKR_steps_stats.get('first_neutr')[irun], KKR_steps_stats.get('last_neutr')[irun]) """"\nINFO: done with kkr_scf workflow!\n")
[docs] def check_input_params(self, params, is_voronoi=False): """ Checks input parameter consistency and aborts wf if check fails. """ if params is None: return self.exit_codes.ERROR_NO_CALC_PARAMETERS_GIVEN else: input_dict = params.get_dict() if is_voronoi: para_check = kkrparams(params_type="voronoi") else: para_check = kkrparams() # step 1 try to fill keywords try: for key, val in input_dict.items(): para_check.set_value(key, val, silent=True) except: return self.exit_codes.ERROR_CALC_PARAMTERS_INCONSISTENT # step 2: check if all mandatory keys are there missing_list = para_check.get_missing_keys(use_aiida=True) if missing_list != []: all_defaults = True for key in missing_list: if key not in kkrparams.get_KKRcalc_parameter_defaults()[0]: all_defaults = False if not all_defaults: "ERROR: calc_parameters given are not consistent! Missing mandatory keys: {}".format(missing_list)) return self.exit_codes.ERROR_CALC_PARAMETERS_INCOMPLETE
[docs] def get_dos(self): """ call to dos sub workflow passing the appropriate input and submitting the calculation """ if self.ctx.check_dos:"INFO: Doing final DOS calculation") # fix emin/emax to include emin, ef of scf contour # remember: efermi, emin and emax are in internal units (Ry) but delta_e is in eV! eV2Ry = 1./get_Ry2eV() emin = self.ctx.dos_params["emin"] # from dos params emin_cont = self.ctx.last_calc.outputs.output_parameters.get_dict().get( "energy_contour_group").get("emin") # from contour (sets limit of dos emin!) if emin_cont - self.ctx.delta_e*eV2Ry < emin: self.ctx.dos_params["emin"] = ( emin_cont - self.ctx.delta_e*eV2Ry ) "INFO: emin ({} Ry) - delta_e ({} Ry) smaller than emin ({} Ry) of voronoi output. Setting automatically to {}Ry" .format( emin_cont, self.ctx.delta_e*eV2Ry, emin, emin_cont-self.ctx.delta_e*eV2Ry ) ) self.ctx.efermi = get_ef_from_potfile("out_potential")) emax = self.ctx.dos_params["emax"] if emax < self.ctx.efermi + self.ctx.delta_e*eV2Ry: self.ctx.dos_params["emax"] = self.ctx.efermi + \ self.ctx.delta_e*eV2Ry "INFO: self.ctx.efermi ({} Ry) + delta_e ({} Ry) larger than emax ({} Ry). Setting automatically to {}Ry" .format( self.ctx.efermi, self.ctx.delta_e*eV2Ry, emax, self.ctx.efermi+self.ctx.delta_e*eV2Ry ) ) # take subset of input and prepare parameter node for dos workflow wfdospara_dict = { "queue_name": self.ctx.queue, "resources": self.ctx.resources, "max_wallclock_seconds": self.ctx.max_wallclock_seconds, "withmpi": self.ctx.withmpi, "custom_scheduler_commands": self.ctx.custom_scheduler_commands, "dos_params": self.ctx.dos_params } wfdospara_node = Dict(dict=wfdospara_dict) wfdospara_node.label = "DOS params" wfdospara_node.description = "DOS parameter set for final DOS calculation of kkr_scf_wc" code = self.inputs.kkr remote = self.ctx.last_calc.outputs.remote_folder wf_label = " final DOS calculation" wf_desc = " subworkflow of a DOS calculation" builder = kkr_dos_wc.get_builder() builder.metadata.description = wf_desc builder.metadata.label = wf_label builder.kkr = code builder.wf_parameters = wfdospara_node builder.options = self.ctx.options_params_dict builder.remote_data = remote future = self.submit(builder) return ToContext(doscal=future)
[docs] def check_dos(self): """ checks if dos of final potential is ok """ # initialize dos_ok variable self.ctx.dos_ok = True # first check if dos should be checked or if the test is skipped if not self.ctx.check_dos:"INFO: skipping DOS check") return "INFO: checking DOS for consistency (EMIN position, negative DOS, etc.)") # check parser output doscal = self.ctx.doscal try: dos_outdict = doscal.outputs.results_wf.get_dict() if not dos_outdict["successful"]:"ERROR: DOS workflow unsuccessful") self.ctx.dos_ok = False return self.exit_codes.ERROR_DOS_RUN_UNSUCCESSFUL if dos_outdict["list_of_errors"] != []: "ERROR: DOS wf output contains errors: {}" .format(dos_outdict["list_of_errors"]) ) self.ctx.dos_ok = False return self.exit_codes.ERROR_DOS_RUN_UNSUCCESSFUL except AttributeError: self.ctx.dos_ok = False return self.exit_codes.ERROR_DOS_RUN_UNSUCCESSFUL # check for negative DOS try: dosdata = doscal.outputs.dos_data natom = self.ctx.last_calc.outputs.output_parameters.get_dict()[ "number_of_atoms_in_unit_cell"] nspin = dos_outdict["nspin"] ener = dosdata.get_x()[1] # shape= natom*nspin, nept totdos = dosdata.get_y()[0][1] # shape= natom*nspin, nept if len(ener) != nspin*natom: "ERROR: DOS output shape does not fit nspin, natom information: len(energies)={}, natom={}, nspin={}" .format(len(ener), natom, nspin) ) self.ctx.doscheck_ok = False return self.exit_codes.ERROR_DOS_RUN_UNSUCCESSFUL # deal with snpin==1 or 2 cases and check negtive DOS for iatom in range(natom//nspin): for ispin in range(nspin): x, y = ener[iatom*nspin+ispin], totdos[iatom*nspin+ispin] if nspin == 2 and ispin == 0: y = -y if y.min() < 0: "INFO: negative DOS value found in (atom, spin)=({},{}) at iteration {}: {}" .format(iatom, ispin, self.ctx.loop_count, y.min()) ) self.ctx.dos_ok = False # check starting EMIN dosdata_interpol = doscal.outputs.dos_data_interpol ener = dosdata_interpol.get_x()[1] # shape= natom*nspin, nept totdos = dosdata_interpol.get_y()[0][1] # shape= natom*nspin, nept Ry2eV = get_Ry2eV() for iatom in range(natom//nspin): for ispin in range(nspin): x, y = ener[iatom*nspin+ispin], totdos[iatom*nspin+ispin] xrel = abs( x-(self.ctx.dos_params_dict["emin"]-self.ctx.efermi)*Ry2eV) mask_emin = where(xrel == xrel.min()) ymin = abs(y[mask_emin]) if ymin > self.ctx.threshold_dos_zero: "INFO: DOS at emin not zero! {}>{}" .format(ymin, self.ctx.threshold_dos_zero) ) self.ctx.dos_ok = False except AttributeError: self.ctx.dos_ok = False
[docs] def _get_new_noco_angles(self): """ extract nonco angles from output of calculation, if fix_dir is True we skip this and leave the initial angles unchanged Here we update self.ctx.initial_noco_angles with the new values """ # first check if we need to update the angles if all(self.ctx.initial_noco_angles["fix_dir"]): return # extract output angles from last calculation and update initial_noco_angles in context self.ctx.initial_noco_angles = extract_noco_angles( fix_dir_threshold=Float(self.ctx.fix_dir_threshold), old_noco_angles=self.ctx.initial_noco_angles, last_retrieved=self.ctx.last_calc.outputs.retrieved )
[docs]@calcfunction def extract_noco_angles(**kwargs): """ Extract noco angles from retrieved nonco_angles_out.dat files and save as Dict node which can be used as initial values for the next KkrCalculation. New angles are compared to old angles and if they are closer thanfix_dir_threshold they are not allowed to change anymore """ # for comparison read previous theta and phi values and the threshold after which the moments are kept fixed noco_angles_old = kwargs["old_noco_angles"].get_dict() natom = len(noco_angles_old["phi"]) noco_angles_old = [ [ noco_angles_old["theta"][i], noco_angles_old["phi"][i], noco_angles_old["fix_dir"][i] ] for i in range(natom) ] fix_dir_threshold = kwargs["fix_dir_threshold"].value last_retrieved = kwargs["last_retrieved"] noco_out_name = KkrCalculation._NONCO_ANGLES_OUT if noco_out_name in last_retrieved.list_object_names(): with as noco_file: noco_angles_new = loadtxt(noco_file, usecols=[0, 1]) # check if theta and phi change less than fix_dir_threshold fix_dir = [ sqrt( (noco_angles_new[i][0]-noco_angles_old[i][0])**2 + (noco_angles_new[i][1]-noco_angles_old[i][1])**2 ) < fix_dir_threshold for i in range(natom) ] new_initial_noco_angles = Dict( dict={ "theta": list(noco_angles_new[:, 0]), "phi": list(noco_angles_new[:, 1]), # convert from numpy.bool_ to standard python bool, otherwise this is not json serializable and cannot be stored "fix_dir": [bool(i) for i in fix_dir] } ) return new_initial_noco_angles
[docs]@workfunction def create_scf_result_node(**kwargs): """ This is a pseudo wf, to create the right graph structure of AiiDA. This workfunction will create the output node in the database. It also connects the output_node to all nodes the information commes from. So far it is just also parsed in as argument, because so far we are to lazy to put most of the code overworked from return_results in here. """ has_last_outpara = False has_last_calc_out_dict = False has_last_RemoteData = False has_vorostart_output = False has_starting_dos = False has_final_dos = False has_last_InputParameters = False for key, val in kwargs.items(): if key == "outpara": # should always be there outpara = val has_last_outpara = True elif key == "last_calc_out": has_last_calc_out_dict = True last_calc_out_dict = val elif key == "last_RemoteData": last_RemoteData_dict = val has_last_RemoteData = True elif key == "last_InputParameters": last_InputParameters_dict = val has_last_InputParameters = True elif key == "results_vorostart": has_vorostart_output = True vorostart_output_dict = val elif key == "starting_dosdata_interpol": has_starting_dos = True start_dosdata_interpol_dict = val elif key == "final_dosdata_interpol": has_final_dos = True final_dosdata_interpol_dict = val outdict = {} if has_last_outpara: outputnode = outpara outputnode.label = "workflow_Results" outputnode.description = ( "Contains self-consistency results and " "information of an kkr_scf_wc run." ) outdict["output_kkr_scf_wc_ParameterResults"] = outputnode if has_last_calc_out_dict: outputnode = last_calc_out_dict outputnode.label = "last_calc_out" outputnode.description = ( "Contains the Results Parameter node from the output " "of the last calculation done in the workflow." ) outdict["last_calc_out"] = outputnode if has_last_RemoteData: outputnode = last_RemoteData_dict outputnode.label = "last_RemoteData" outputnode.description = ( "Contains a link to the latest remote data node " "where the output of the calculation can be accessed." ) outdict["last_RemoteData"] = outputnode if has_last_InputParameters: outputnode = last_InputParameters_dict outputnode.label = "last_InputParameters" outputnode.description = ( "Contains the latest parameter data node " "where the input of the last calculation can be found." ) outdict["last_InputParameters"] = outputnode if has_vorostart_output: outputnode = vorostart_output_dict outputnode.label = "results_vorostart" outputnode.description = ( "Contains the results parameter data node " "of the vorostart sub-workflow (sets up starting portentials)." ) outdict["results_vorostart"] = outputnode if has_starting_dos: outputnode = start_dosdata_interpol_dict outputnode.label = "starting_dosdata_interpol" outputnode.description = ( "Contains the interpolated DOS data note, computed " "from the starting portential." ) outdict["starting_dosdata_interpol"] = outputnode if has_final_dos: outputnode = final_dosdata_interpol_dict outputnode.label = "final_dosdata_interpol" outputnode.description = ( "Contains the interpolated DOS data note, computed " "from the converged potential." ) outdict["final_dosdata_interpol"] = outputnode return outdict
[docs]def get_site_symbols(structure): """ extract the site number taking into account a possible CPA structure """ sites = structure.sites sitelist = [] for isite, site in enumerate(sites): sitekind = structure.get_kind(site.kind_name) for ikind in range(len(sitekind.symbols)): site_symbol = sitekind.symbols[ikind] sitelist.append([isite, site_symbol]) return sitelist