Some fixes to protocol

src/aiida_quantumespresso_hp/workflows/protocols/hubbard.yaml

@@ -5,9 +5,13 @@ default_inputs:
  tolerance_onsite: 0.1
  tolerance_intersite: 0.01
  radial_analysis:
- radius_max: 10.0 # in Angstrom
- thr: 0.1
  nn_finder: 'crystal'
+ nn_inputs:
+ distance_cutoffs: null # in Angstrom
+ x_diff_weight: 0
+ porous_adjustment: False
+ radius_max: 10.0 # in Angstrom
+ thr: 0.01 # in Angstrom
  scf:
  kpoints_distance: 0.4
 

tests/conftest.py

@@ -494,6 +494,8 @@ def generate_inputs_hubbard(generate_inputs_pw, generate_inputs_hp, generate_hub
 
  def _generate_inputs_hubbard(hubbard_structure=None):
  """Generate default inputs for a `SelfConsistentHubbardWorkChain."""
+ from aiida.orm import Bool
+
  hubbard_structure = hubbard_structure or generate_hubbard_structure()
  inputs_pw = generate_inputs_pw(structure=hubbard_structure)
  inputs_relax = generate_inputs_pw(structure=hubbard_structure)
@@ -508,6 +510,7 @@ def _generate_inputs_hubbard(hubbard_structure=None):
  inputs_hp.pop('parent_scf')
 
  inputs = {
+ 'meta_convergence': Bool(True),
  'hubbard_structure': hubbard_structure,
  'relax': {
  'base': {

tests/workflows/protocols/test_hubbard/test_default.yml

@@ -24,6 +24,10 @@ max_iterations: 10
 meta_convergence: true
 radial_analysis:
  nn_finder: crystal
+ nn_inputs:
+ distance_cutoffs: null
+ porous_adjustment: false
+ x_diff_weight: 0
  radius_max: 10.0
  thr: 0.01
 relax:

tests/workflows/test_hubbard.py

@@ -2,7 +2,7 @@
 # pylint: disable=no-member,redefined-outer-name
 """Tests for the `SelfConsistentHubbardWorkChain` class."""
 from aiida.common import AttributeDict
-from aiida.orm import Dict
+from aiida.orm import Bool, Dict, Int, load_node
 from plumpy import ProcessState
 import pytest
 
@@ -119,8 +119,6 @@ def test_validate_inputs_invalid_inputs(generate_workchain_hubbard, generate_inp
 @pytest.mark.usefixtures('aiida_profile')
 def test_validate_invalid_positve_input(generate_workchain_hubbard, generate_inputs_hubbard, parameters):
  """Test `SelfConsistentHubbardWorkChain` for invalid positive inputs."""
- from aiida.orm import Int
-
  inputs = AttributeDict(generate_inputs_hubbard())
  inputs.update({parameters: Int(-1)})
 
@@ -178,8 +176,6 @@ def test_magnetic_setup(generate_workchain_hubbard, generate_inputs_hubbard):
 @pytest.mark.usefixtures('aiida_profile')
 def test_skip_relax_iterations(generate_workchain_hubbard, generate_inputs_hubbard, generate_hp_workchain_node):
  """Test `SelfConsistentHubbardWorkChain` when skipping the first relax iterations."""
- from aiida.orm import Bool, Int
-
  inputs = generate_inputs_hubbard()
  inputs['skip_relax_iterations'] = Int(1)
  inputs['meta_convergence'] = Bool(True)
@@ -230,8 +226,6 @@ def test_skip_relax_iterations(generate_workchain_hubbard, generate_inputs_hubba
 @pytest.mark.usefixtures('aiida_profile')
 def test_relax_frequency(generate_workchain_hubbard, generate_inputs_hubbard):
  """Test `SelfConsistentHubbardWorkChain` when `relax_frequency` is different from 1."""
- from aiida.orm import Int
-
  inputs = generate_inputs_hubbard()
  inputs['relax_frequency'] = Int(3)
  process = generate_workchain_hubbard(inputs=inputs)
@@ -257,8 +251,6 @@ def test_radial_analysis(
 
  We want to make sure `rmax` is in `hp.parameters`.
  """
- from aiida.orm import load_node
-
  inputs = generate_inputs_hubbard()
  inputs['radial_analysis'] = Dict({}) # no need to specify inputs, it will use the defaults
  process = generate_workchain_hubbard(inputs=inputs)
@@ -275,7 +267,6 @@ def test_radial_analysis(
 @pytest.mark.usefixtures('aiida_profile')
 def test_should_check_convergence(generate_workchain_hubbard, generate_inputs_hubbard):
  """Test `SelfConsistentHubbardWorkChain.should_check_convergence`."""
- from aiida.orm import Bool
  inputs = generate_inputs_hubbard()
  inputs['meta_convergence'] = Bool(True)
  process = generate_workchain_hubbard(inputs=inputs)
@@ -292,12 +283,12 @@ def test_outline_without_metaconvergence(
 
  We want to make sure the `outputs.hubbard_structure` is the last computed.
  """
- from aiida.orm import Bool
  inputs = generate_inputs_hubbard()
  inputs['meta_convergence'] = Bool(False)
  process = generate_workchain_hubbard(inputs=inputs)
 
  process.setup()
+ process.update_iteration()
 
  process.ctx.workchains_hp = [generate_hp_workchain_node()]
  assert process.inspect_hp() is None
@@ -313,12 +304,12 @@ def test_outline(
  generate_workchain_hubbard, generate_inputs_hubbard, generate_scf_workchain_node, generate_hp_workchain_node
 ):
  """Test `SelfConsistentHubbardWorkChain` outline."""
- from aiida.orm import Bool
  inputs = generate_inputs_hubbard()
  inputs['meta_convergence'] = Bool(True)
  process = generate_workchain_hubbard(inputs=inputs)
 
  process.setup()
+ process.update_iteration()
 
  process.run_relax()
  # assert 'workchains_relax' in process.ctx
@@ -347,6 +338,7 @@ def test_outline(
 
  process.ctx.workchains_hp = [generate_hp_workchain_node()]
  assert process.inspect_hp() is None
+ assert process.should_check_convergence()
  process.check_convergence()
  assert process.ctx.is_converged
 
@@ -358,9 +350,7 @@ def test_outline(
 @pytest.mark.usefixtures('aiida_profile')
 def test_should_run_relax(generate_workchain_hubbard, generate_inputs_hubbard):
  """Test `SelfConsistentHubbardWorkChain.should_run_relax` method."""
- from aiida.orm import Bool
  inputs = generate_inputs_hubbard()
- inputs['meta_convergence'] = Bool(True)
  inputs.pop('relax')
  process = generate_workchain_hubbard(inputs=inputs)
 
@@ -378,6 +368,7 @@ def test_converged_check_convergence(
  process = generate_workchain_hubbard(inputs=inputs)
 
  process.setup()
+ process.update_iteration()
 
  # Mocking current (i.e. "old") and "new" HubbardStructureData,
  # containing different Hubbard parameters
@@ -433,25 +424,29 @@ def test_relabel_check_convergence(
  process = generate_workchain_hubbard(inputs=inputs)
 
  process.setup()
+ process.update_iteration()
 
  current_hubbard_structure = generate_hubbard_structure(u_value=1, only_u=True)
  process.ctx.current_hubbard_structure = current_hubbard_structure
  process.ctx.workchains_hp = [generate_hp_workchain_node(relabel=True, u_value=100, only_u=True)]
  process.check_convergence()
+ assert process.should_check_convergence()
  assert not process.ctx.is_converged
  assert process.ctx.current_hubbard_structure.get_kind_names() != current_hubbard_structure.get_kind_names()
 
  current_hubbard_structure = generate_hubbard_structure(u_value=99.99, only_u=True)
  process.ctx.current_hubbard_structure = current_hubbard_structure
  process.ctx.workchains_hp = [generate_hp_workchain_node(relabel=True, u_value=100, only_u=True)]
  process.check_convergence()
+ assert process.should_check_convergence()
  assert process.ctx.is_converged
  assert process.ctx.current_hubbard_structure.get_kind_names() != current_hubbard_structure.get_kind_names()
 
  current_hubbard_structure = generate_hubbard_structure(u_value=99.99)
  process.ctx.current_hubbard_structure = current_hubbard_structure
  process.ctx.workchains_hp = [generate_hp_workchain_node(relabel=True, u_value=100)]
  process.check_convergence()
+ assert process.should_check_convergence()
  assert process.ctx.is_converged
  assert process.ctx.current_hubbard_structure.get_kind_names() == current_hubbard_structure.get_kind_names()