SelfConsistentHubbardWorkChain: Relabeling of HubbardStructure if relaxations are skipped

src/aiida_quantumespresso_hp/workflows/hubbard.py

@@ -421,6 +421,28 @@ def get_pseudos(self) -> dict:
 
  return results
 
+ def relabel_onsite_hubbard(self, workchain):
+ """Relabel the Hubbard structure if new types have been detected."""
+ from aiida_quantumespresso.utils.hubbard import is_intersite_hubbard
+
+ relabeling_message = ''
+
+ if not is_intersite_hubbard(workchain.outputs.hubbard_structure.hubbard):
+ for site in workchain.outputs.hubbard.dict.sites:
+ if not site['type'] == site['new_type']:
+ result = structure_relabel_kinds(
+ self.ctx.current_hubbard_structure, workchain.outputs.hubbard, self.ctx.current_magnetic_moments
+ )
+ self.ctx.current_hubbard_structure = result['hubbard_structure']
+ if self.ctx.current_magnetic_moments is not None:
+ self.ctx.current_magnetic_moments = result['starting_magnetization']
+ relabeling_message = 'new types have been detected: relabeling the structure.'
+ break
+
+ if relabeling_message and self.inputs.meta_convergence:
+ relabeling_message = relabeling_message[:-1] + ' and starting new iteration.'
+ return relabeling_message
+
  def run_relax(self):
  """Run the PwRelaxWorkChain to run a relax PwCalculation."""
  inputs = self.get_inputs(PwRelaxWorkChain, 'relax')
@@ -578,14 +600,17 @@ def inspect_hp(self):
 
  if not self.should_check_convergence():
  self.ctx.current_hubbard_structure = workchain.outputs.hubbard_structure
+ relabeling_message = self.relabel_onsite_hubbard(workchain)
+
  if not self.inputs.meta_convergence:
  self.report('meta convergence is switched off, so not checking convergence of Hubbard parameters.')
  self.ctx.is_converged = True
 
+ if relabeling_message:
+ self.report(relabeling_message)
+
  def check_convergence(self):
  """Check the convergence of the Hubbard parameters."""
- from aiida_quantumespresso.utils.hubbard import is_intersite_hubbard
-
  workchain = self.ctx.workchains_hp[-1]
 
  # We store in memory the parameters before relabelling to make the comparison easier.
@@ -601,18 +626,9 @@ def check_convergence(self):
 
  # We check if new types were created, in which case we relabel the `HubbardStructureData`
  self.ctx.current_hubbard_structure = workchain.outputs.hubbard_structure
-
- if not is_intersite_hubbard(workchain.outputs.hubbard_structure.hubbard):
- for site in workchain.outputs.hubbard.dict.sites:
- if not site['type'] == site['new_type']:
- self.report('new types have been detected: relabeling the structure and starting new iteration.')
- result = structure_relabel_kinds(
- self.ctx.current_hubbard_structure, workchain.outputs.hubbard, self.ctx.current_magnetic_moments
- )
- self.ctx.current_hubbard_structure = result['hubbard_structure']
- if self.ctx.current_magnetic_moments is not None:
- self.ctx.current_magnetic_moments = result['starting_magnetization']
- break
+ relabeling_message = self.relabel_onsite_hubbard(workchain)
+ if relabeling_message:
+ self.report(relabeling_message)
 
  if not len(ref_params) == len(new_params):
  self.report('The new and old Hubbard parameters have different lenghts. Assuming to be at the first cycle.')

tests/workflows/test_hubbard.py

@@ -227,6 +227,41 @@ def test_skip_relax_iterations(generate_workchain_hubbard, generate_inputs_hubba
  assert process.should_check_convergence()
 
 
+@pytest.mark.usefixtures('aiida_profile')
+def test_skip_relax_iterations_relabeling(
+ generate_workchain_hubbard, generate_inputs_hubbard, generate_hp_workchain_node, generate_hubbard_structure
+):
+ """Test `SelfConsistentHubbardWorkChain` when skipping the first relax iterations and relabeling is needed."""
+ from aiida.orm import Bool, Int
+
+ inputs = generate_inputs_hubbard()
+ inputs['skip_relax_iterations'] = Int(1)
+ inputs['meta_convergence'] = Bool(True)
+ process = generate_workchain_hubbard(inputs=inputs)
+ process.setup()
+
+ current_hubbard_structure = generate_hubbard_structure(u_value=1, only_u=True)
+ process.current_hubbard_structure = current_hubbard_structure
+ # 1
+ process.update_iteration()
+ assert process.ctx.skip_relax_iterations == 1
+ assert process.ctx.iteration == 1
+ assert not process.should_run_relax()
+ assert not process.should_check_convergence()
+ process.ctx.workchains_hp = [generate_hp_workchain_node(relabel=True, u_value=1, only_u=True)]
+ process.inspect_hp()
+ assert process.ctx.current_hubbard_structure.get_kind_names(
+ ) != process.ctx.workchains_hp[-1].outputs.hubbard_structure.get_kind_names()
+ # 2
+ process.update_iteration()
+ assert process.should_run_relax()
+ assert process.should_check_convergence()
+ # 3
+ process.update_iteration()
+ assert process.should_run_relax()
+ assert process.should_check_convergence()
+
+
 @pytest.mark.usefixtures('aiida_profile')
 def test_relax_frequency(generate_workchain_hubbard, generate_inputs_hubbard):
  """Test `SelfConsistentHubbardWorkChain` when `relax_frequency` is different from 1."""
@@ -441,3 +476,50 @@ def test_inspect_hp(generate_workchain_hubbard, generate_inputs_hubbard, generat
  process.ctx.workchains_hp = [generate_hp_workchain_node(exit_status=300)]
  result = process.inspect_hp()
  assert result == WorkChain.exit_codes.ERROR_SUB_PROCESS_FAILED_HP.format(iteration=process.ctx.iteration)
+
+
+@pytest.mark.usefixtures('aiida_profile')
+def test_relabel_onsite_hubbard(
+ generate_workchain_hubbard, generate_inputs_hubbard, generate_hp_workchain_node, generate_hubbard_structure
+):
+ """Test `SelfConsistentHubbardWorkChain.relabel_onsite_hubbard`."""
+ from aiida.orm import Bool
+ inputs = generate_inputs_hubbard()
+
+ # Scenario 1: `meta_convergence` is False and intersite parameters are present
+ process = generate_workchain_hubbard(inputs=inputs)
+ process.setup()
+
+ current_hubbard_structure = generate_hubbard_structure(u_value=1, only_u=True)
+ process.ctx.current_hubbard_structure = current_hubbard_structure
+ # Contains intersite parameters --> no relabeling
+ workchain_hp = generate_hp_workchain_node(relabel=True, u_value=100, only_u=False)
+ relabeling_message = process.relabel_onsite_hubbard(workchain_hp)
+ assert relabeling_message == ''
+
+ # Scenario 2: `meta_convergence` is False and only onsite parameters are present
+ process = generate_workchain_hubbard(inputs=inputs)
+ process.setup()
+
+ current_hubbard_structure = generate_hubbard_structure(u_value=1, only_u=True)
+ process.ctx.current_hubbard_structure = current_hubbard_structure
+ # Contains only onsite parameters --> relabeling
+ # `meta_convergence` is False, so relableing message should not contain
+ # new iteration
+ workchain_hp = generate_hp_workchain_node(relabel=True, u_value=100, only_u=True)
+ relabeling_message = process.relabel_onsite_hubbard(workchain_hp)
+ assert relabeling_message == 'new types have been detected: relabeling the structure.'
+
+ # Scenario 3: `meta_convergence` is True and only onsite parameters are present
+ inputs['meta_convergence'] = Bool(True)
+ process = generate_workchain_hubbard(inputs=inputs)
+ process.setup()
+
+ current_hubbard_structure = generate_hubbard_structure(u_value=1, only_u=True)
+ process.ctx.current_hubbard_structure = current_hubbard_structure
+ # Contains only onsite parameters --> relabeling
+ # `meta_convergence` is True, so relableing message should contain
+ # new iteration
+ workchain_hp = generate_hp_workchain_node(relabel=True, u_value=100, only_u=True)
+ relabeling_message = process.relabel_onsite_hubbard(workchain_hp)
+ assert relabeling_message == 'new types have been detected: relabeling the structure and starting new iteration.'