Merge and formatting

doped/interface/fermi_solver.py

@@ -803,15 +803,15 @@ def pseudo_equilibrium_solve(
 
  Returns:
  pd.DataFrame: DataFrame containing defect and carrier concentrations
- and the self consistent Fermi energy
+ and the self-consistent Fermi energy
  """
  (
  fermi_level,
  electrons,
  holes,
  concentrations,
  ) = self.defect_thermodynamics.get_quenched_fermi_level_and_concentrations(
- bulk_dos_vr=self.bulk_dos,
+ bulk_dos=self.bulk_dos,
  chempots=chempots,
  limit=None,
  annealing_temperature=annealing_temperature,

doped/thermodynamics.py

@@ -1179,13 +1179,13 @@ def get_equilibrium_concentrations(
  dilute limit approximation.
 
  Note that these are the `equilibrium` defect concentrations!
- DefectThermodynamics.get_quenched_fermi_level_and_concentrations() can
- instead be used to calculate the Fermi level and defect concentrations
- for a material grown/annealed at higher temperatures and then cooled
- (quenched) to room/operating temperature (where defect concentrations
- are assumed to remain fixed) - this is known as the frozen defect
- approach and is typically the most valid approximation (see its
- docstring for more information).
+ ``DefectThermodynamics.get_quenched_fermi_level_and_concentrations()``
+ can instead be used to calculate the Fermi level and defect
+ concentrations for a material grown/annealed at higher temperatures
+ and then cooled (quenched) to room/operating temperature (where defect
+ concentrations are assumed to remain fixed) - this is known as the
+ frozen defect approach and is typically the most valid approximation
+ (see its docstring for more information).
 
  The degeneracy/multiplicity factor "g" is an important parameter in the defect
  concentration equation (see discussion in https://doi.org/10.1039/D2FD00043A
@@ -1375,39 +1375,6 @@ def _parse_fermi_dos(
  )
  return fdos
 
- def _add_effective_dopant_concentration(
- self, conc_df: pd.DataFrame, effective_dopant_concentration: Optional[float] = None
- ):
- """
- Add the effective dopant concentration to the concentration
- ``DataFrame``.
- """
- if effective_dopant_concentration is not None:
- eff_dopant_df = pd.DataFrame(
- {
- "Defect": "Effective Dopant",
- "Charge": np.sign(effective_dopant_concentration),
- "Formation Energy (eV)": "N/A",
- "Concentration (cm^-3)": np.abs(effective_dopant_concentration),
- "Charge State Population": "100.0%",
- },
- index=[0],
- )
- for col in conc_df.columns:
- if col not in eff_dopant_df.columns:
- eff_dopant_df[col] = "N/A" # e.g. concentration per site, if per_site=True
-
- if "Charge" not in conc_df.columns:
- eff_dopant_df = eff_dopant_df.drop(
- columns=["Charge", "Formation Energy (eV)", "Charge State Population"]
- )
- eff_dopant_df = eff_dopant_df.set_index("Defect") # Defect is index with summed conc df
- return pd.concat([conc_df, eff_dopant_df])
-
- return pd.concat([conc_df, eff_dopant_df], ignore_index=True)
-
- return conc_df
-
  def get_equilibrium_fermi_level(
  self,
  bulk_dos: Optional[Union[FermiDos, Vasprun, PathLike]] = None,
@@ -1431,13 +1398,13 @@ def get_equilibrium_fermi_level(
  default, unless ``bulk_band_gap_vr`` is set during defect parsing.
 
  Note that this assumes `equilibrium` defect concentrations!
- DefectThermodynamics.get_quenched_fermi_level_and_concentrations() can
- instead be used to calculate the Fermi level and defect concentrations
- for a material grown/annealed at higher temperatures and then cooled
- (quenched) to room/operating temperature (where defect concentrations
- are assumed to remain fixed) - this is known as the frozen defect
- approach and is typically the most valid approximation (see its
- docstring for more information).
+ ``DefectThermodynamics.get_quenched_fermi_level_and_concentrations()``
+ can instead be used to calculate the Fermi level and defect
+ concentrations for a material grown/annealed at higher temperatures and
+ then cooled (quenched) to room/operating temperature (where defect
+ concentrations are assumed to remain fixed) - this is known as the
+ frozen defect approach and is typically the most valid approximation
+ (see its docstring for more information).
 
  Note that the bulk DOS calculation should be well-converged with respect to
  k-points for accurate Fermi level predictions!
@@ -3141,6 +3108,49 @@ def __repr__(self):
  )
 
 
+def _add_effective_dopant_concentration(
+ conc_df: pd.DataFrame, effective_dopant_concentration: Optional[float] = None
+):
+ """
+ Add the effective dopant concentration to the concentration ``DataFrame``.
+
+ Args:
+ conc_df (pd.DataFrame):
+ DataFrame of defect concentrations.
+ effective_dopant_concentration (float):
+ The effective dopant concentration to add to the DataFrame.
+
+ Returns:
+ pd.DataFrame: DataFrame of defect concentrations with the effective
+ dopant concentration
+ """
+ if effective_dopant_concentration is None:
+ return conc_df
+
+ eff_dopant_df = pd.DataFrame(
+ {
+ "Defect": "Effective Dopant",
+ "Charge": np.sign(effective_dopant_concentration),
+ "Formation Energy (eV)": "N/A",
+ "Concentration (cm^-3)": np.abs(effective_dopant_concentration),
+ "Charge State Population": "100.0%",
+ },
+ index=[0],
+ )
+ for col in conc_df.columns:
+ if col not in eff_dopant_df.columns:
+ eff_dopant_df[col] = "N/A" # e.g. concentration per site, if per_site=True
+
+ if "Charge" not in conc_df.columns:
+ eff_dopant_df = eff_dopant_df.drop(
+ columns=["Charge", "Formation Energy (eV)", "Charge State Population"]
+ )
+ eff_dopant_df = eff_dopant_df.set_index("Defect") # Defect is index with summed conc df
+ return pd.concat([conc_df, eff_dopant_df])
+
+ return pd.concat([conc_df, eff_dopant_df], ignore_index=True)
+
+
 def _group_defect_charge_state_concentrations(
  conc_df: pd.DataFrame, per_site: bool = False, skip_formatting: bool = False
 ):

tests/test_interface_fermi_solver.py

@@ -55,7 +55,7 @@ def test_assert_scan_temperature_raises(self):
  annealing_temperature_range=[1, 2, 3],
  quenching_temperature_range=None,
  )
-  print(exc.value) # for debugging
+ print(exc.value) # for debugging
 
  with pytest.raises(ValueError) as exc:
  self.fs.scan_temperature(
@@ -64,7 +64,7 @@ def test_assert_scan_temperature_raises(self):
  annealing_temperature_range=None,
  quenching_temperature_range=[1, 2, 3],
  )
-  print(exc.value) # for debugging
+ print(exc.value) # for debugging
 
  with pytest.raises(ValueError) as exc:
  self.fs.scan_temperature(
@@ -73,7 +73,7 @@ def test_assert_scan_temperature_raises(self):
  annealing_temperature_range=None,
  quenching_temperature_range=None,
  )
-  print(exc.value) # for debugging
+ print(exc.value) # for debugging
 
  def test_get_interpolated_chempots(self):