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data_structure

DistributionParameters

Bases: BaseModel

Source code in src/libeq/data_structure.py 
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class DistributionParameters(BaseModel):
    c0: Np1DArrayFp64 | None = None
    c0_sigma: Np1DArrayFp64 | None = None

    initial_log: float | None = None
    final_log: float | None = None
    log_increments: float | None = None

    independent_component: int | None = None

c0 class-attribute instance-attribute 

c0: Np1DArrayFp64 | None = None

c0_sigma class-attribute instance-attribute 

c0_sigma: Np1DArrayFp64 | None = None

initial_log class-attribute instance-attribute 

initial_log: float | None = None

final_log class-attribute instance-attribute 

final_log: float | None = None

log_increments class-attribute instance-attribute 

log_increments: float | None = None

independent_component class-attribute instance-attribute 

independent_component: int | None = None

TitrationParameters

Bases: BaseModel

Source code in src/libeq/data_structure.py 
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class TitrationParameters(BaseModel):
    c0: Np1DArrayFp64 | None = None
    c0_sigma: Np1DArrayFp64 | None = None
    ct: Np1DArrayFp64 | None = None
    ct_sigma: Np1DArrayFp64 | None = None

c0 class-attribute instance-attribute 

c0: Np1DArrayFp64 | None = None

c0_sigma class-attribute instance-attribute 

c0_sigma: Np1DArrayFp64 | None = None

ct class-attribute instance-attribute 

ct: Np1DArrayFp64 | None = None

ct_sigma class-attribute instance-attribute 

ct_sigma: Np1DArrayFp64 | None = None

SimulationTitrationParameters

Bases: TitrationParameters

Source code in src/libeq/data_structure.py 
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class SimulationTitrationParameters(TitrationParameters):
    v0: float | None = None
    v_increment: float | None = None
    n_add: int | None = None

v0 class-attribute instance-attribute 

v0: float | None = None

v_increment class-attribute instance-attribute 

v_increment: float | None = None

n_add class-attribute instance-attribute 

n_add: int | None = None

PotentiometryTitrationsParameters

Bases: TitrationParameters

Source code in src/libeq/data_structure.py 
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class PotentiometryTitrationsParameters(TitrationParameters):
    electro_active_compoment: int | None = None
    e0: float | None = None
    e0_sigma: float | None = None
    slope: float | None = None
    v0: float | None = None
    v0_sigma: float | None = None
    v_add: Np1DArrayFp64 | None = None
    emf: Np1DArrayFp64 | None = None

electro_active_compoment class-attribute instance-attribute 

electro_active_compoment: int | None = None

e0 class-attribute instance-attribute 

e0: float | None = None

e0_sigma class-attribute instance-attribute 

e0_sigma: float | None = None

slope class-attribute instance-attribute 

slope: float | None = None

v0 class-attribute instance-attribute 

v0: float | None = None

v0_sigma class-attribute instance-attribute 

v0_sigma: float | None = None

v_add class-attribute instance-attribute 

v_add: Np1DArrayFp64 | None = None

emf class-attribute instance-attribute 

emf: Np1DArrayFp64 | None = None

PotentiometryOptions

Bases: BaseModel

Source code in src/libeq/data_structure.py 
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class PotentiometryOptions(BaseModel):
    titrations: List[PotentiometryTitrationsParameters] = []
    px_range: List[float] = [0, 0]
    weights: Literal["constants", "calculated", "given"] = "constants"
    beta_flags: List[int] = []
    conc_flags: List[int] = []
    pot_flags: List[int] = []

titrations class-attribute instance-attribute 

titrations: List[PotentiometryTitrationsParameters] = []

px_range class-attribute instance-attribute 

px_range: List[float] = [0, 0]

weights class-attribute instance-attribute 

weights: Literal["constants", "calculated", "given"] = (
    "constants"
)

beta_flags class-attribute instance-attribute 

beta_flags: List[int] = []

conc_flags class-attribute instance-attribute 

conc_flags: List[int] = []

pot_flags class-attribute instance-attribute 

pot_flags: List[int] = []

SolverData

Bases: BaseModel

Source code in src/libeq/data_structure.py 
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class SolverData(BaseModel):
    model_config = ConfigDict(extra="forbid")

    distribution_opts: DistributionParameters = DistributionParameters()
    titration_opts: SimulationTitrationParameters = SimulationTitrationParameters()
    potentiometry_options: PotentiometryOptions = PotentiometryOptions()

    components: List[str]
    stoichiometry: Np2DArrayInt8
    solid_stoichiometry: Np2DArrayInt8
    log_beta: Np1DArrayFp64
    log_beta_sigma: Np1DArrayFp64 = np.array([])
    log_beta_ref_dbh: Np2DArrayFp64 = np.empty((0, 2))
    log_ks: Np1DArrayFp64 = np.array([])
    log_ks_sigma: Np1DArrayFp64 = np.array([])
    log_ks_ref_dbh: Np2DArrayFp64 = np.empty((0, 2))

    charges: Np1DArrayFp64 = np.array([])

    ionic_strength_dependence: bool = False
    reference_ionic_str_species: Np1DArrayFp64 | float = 0
    reference_ionic_str_solids: Np1DArrayFp64 | float = 0
    dbh_params: Np1DArrayFp64 = np.zeros(8)

    @computed_field
    @cached_property
    def species_charges(self) -> Np1DArrayFp64:
        return (self.stoichiometry * self.charges[:, np.newaxis]).sum(axis=0)

    @computed_field
    @cached_property
    def solid_charges(self) -> Np1DArrayFp64:
        return (self.solid_stoichiometry * self.charges[:, np.newaxis]).sum(axis=0)

    @computed_field(repr=False)
    @cached_property
    def z_star_species(self) -> Np1DArrayFp64:
        return (self.stoichiometry * (self.charges[:, np.newaxis] ** 2)).sum(
            axis=0
        ) - self.species_charges**2

    @computed_field(repr=False)
    @cached_property
    def p_star_species(self) -> Np1DArrayFp64:
        return self.stoichiometry.sum(axis=0) - 1

    @computed_field(repr=False)
    @cached_property
    def z_star_solids(self) -> Np1DArrayFp64:
        return (self.solid_stoichiometry * (self.charges[:, np.newaxis] ** 2)).sum(
            axis=0
        ) - self.solid_charges**2

    @computed_field(repr=False)
    @cached_property
    def p_star_solids(self) -> Np1DArrayFp64:
        return self.solid_stoichiometry.sum(axis=0)

    @computed_field
    @cached_property
    def dbh_values(self) -> Dict[str, Np1DArrayFp64]:
        result = dict()
        for phase, iref, per_species_cde, z, p in zip(
            ("species", "solids"),
            (self.reference_ionic_str_species, self.reference_ionic_str_solids),
            (self.log_beta_ref_dbh, self.log_ks_ref_dbh),
            (self.z_star_species, self.z_star_solids),
            (self.p_star_species, self.p_star_solids),
        ):
            dbh_values = dict()
            dbh_values["azast"] = self.dbh_params[0] * z
            dbh_values["adh"] = self.dbh_params[0]
            dbh_values["bdh"] = self.dbh_params[1]
            dbh_values["cdh"] = self.dbh_params[2] * p + self.dbh_params[3] * z
            dbh_values["ddh"] = self.dbh_params[4] * p + self.dbh_params[5] * z
            dbh_values["edh"] = self.dbh_params[6] * p + self.dbh_params[7] * z
            dbh_values["fib"] = np.sqrt(iref) / (1 + self.dbh_params[1] * np.sqrt(iref))

            not_zero_columns = np.where(np.any(per_species_cde != 0, axis=0))[0]
            for i in not_zero_columns:
                dbh_values["cdh"][i] = per_species_cde[0][i]
                dbh_values["ddh"][i] = per_species_cde[1][i]
                dbh_values["edh"][i] = per_species_cde[2][i]

            result[phase] = dbh_values
        return result

    @computed_field
    @cached_property
    def species_names(self) -> List[str]:
        return self.components + _assemble_species_names(
            self.components, self.stoichiometry
        )

    @computed_field
    @cached_property
    def solids_names(self) -> List[str]:
        return _assemble_species_names(self.components, self.solid_stoichiometry)

    @computed_field
    @cached_property
    def nc(self) -> int:
        return self.stoichiometry.shape[0]

    @computed_field
    @cached_property
    def ns(self) -> int:
        return self.stoichiometry.shape[1]

    @computed_field
    @cached_property
    def nf(self) -> int:
        return self.solid_stoichiometry.shape[1]

    @classmethod
    def load_from_bstac(cls, file_path: str) -> "SolverData":
        data = dict()
        with open(file_path, "r") as file:
            lines = file.readlines()
        parsed_data = parse_BSTAC_file(lines)

        temperature = parsed_data["TEMP"]
        data["stoichiometry"] = np.array(
            [
                [d[key] for key in d if key.startswith("IX")]
                for d in parsed_data["species"]
            ]
        ).T
        data["solid_stoichiometry"] = np.empty(
            (data["stoichiometry"].shape[0], 0), dtype=np.int8
        )
        data["log_beta"] = np.array([d["BLOG"] for d in parsed_data["species"]])

        data["charges"] = np.array(parsed_data.get("charges", []))
        data["components"] = parsed_data["comp_name"]
        data["ionic_strength_dependence"] = parsed_data["ICD"] != 0
        if data["ionic_strength_dependence"]:
            data["reference_ionic_str_species"] = np.array(
                [parsed_data["IREF"] for _ in range(data["stoichiometry"].shape[1])]
            )
            data["reference_ionic_str_solids"] = np.array(
                [
                    parsed_data["IREF"]
                    for _ in range(data["solid_stoichiometry"].shape[1])
                ]
            )
            data["dbh_params"] = [
                parsed_data[i] for i in ["AT", "BT", "c0", "c1", "d0", "d1", "e0", "e1"]
            ]

        titration_options = [
            PotentiometryTitrationsParameters(
                c0=np.array([c["C0"] for c in t["components_concentrations"]]),
                ct=np.array([c["CTT"] for c in t["components_concentrations"]]),
                electro_active_compoment=(
                    t["titration_comp_settings"][1]
                    if t["titration_comp_settings"][1] != 0
                    else len(data["components"]) - 1
                ),
                e0=t["potential_params"][0],
                e0_sigma=t["potential_params"][1],
                slope=(
                    t["potential_params"][4]
                    if t["potential_params"][4] != 0
                    else (temperature + 273.15) / 11.6048 * 2.303
                ),
                v0=t["v_params"][0],
                v0_sigma=t["v_params"][1],
                v_add=np.array(t["volume"]),
                emf=np.array(t["potential"]),
            )
            for t in parsed_data["titrations"]
        ]

        weights = parsed_data.get("MODE", 1)
        match weights:
            case 0:
                weights = "calculated"
            case 1:
                weights = "constants"
            case 2:
                weights = "given"
            case _:
                raise ValueError("Invalid MODE value")

        data["potentiometry_options"] = PotentiometryOptions(
            titrations=titration_options,
            weights=weights,
            px_range=[parsed_data["PHI"], parsed_data["PHF"]],
            beta_flags=[s["KEY"] for s in parsed_data["species"]],
            conc_flags=[],
            pot_flags=[],
        )
        return cls(**data)

    @classmethod
    def load_from_pyes(cls, pyes_data: str | dict) -> "SolverData":
        if isinstance(pyes_data, str):
            with open(pyes_data, "r") as file:
                pyes_data = json.load(file)
        data = dict()
        data["components"] = list(pyes_data["compModel"]["Name"].values())

        data["stoichiometry"] = np.row_stack(
            [
                list(pyes_data["speciesModel"][col].values())
                for col in data["components"]
            ]
        )
        data["log_beta"] = np.array(list(pyes_data["speciesModel"]["LogB"].values()))
        data["log_beta_sigma"] = np.array(
            list(pyes_data["speciesModel"]["Sigma"].values())
        )
        data["log_beta_ref_dbh"] = np.vstack(
            (
                list(pyes_data["speciesModel"]["CGF"].values()),
                list(pyes_data["speciesModel"]["DGF"].values()),
                list(pyes_data["speciesModel"]["EGF"].values()),
            )
        )

        data["solid_stoichiometry"] = np.row_stack(
            [
                list(pyes_data["solidSpeciesModel"][col].values())
                for col in data["components"]
            ]
        )
        data["log_ks"] = np.array(
            list(pyes_data["solidSpeciesModel"]["LogKs"].values())
        )
        data["log_ks_sigma"] = np.array(
            list(pyes_data["solidSpeciesModel"]["Sigma"].values())
        )
        data["log_ks_ref_dbh"] = np.vstack(
            (
                list(pyes_data["solidSpeciesModel"]["CGF"].values()),
                list(pyes_data["solidSpeciesModel"]["DGF"].values()),
                list(pyes_data["solidSpeciesModel"]["EGF"].values()),
            )
        )

        data["charges"] = np.array(list(pyes_data["compModel"]["Charge"].values()))
        data["ionic_strength_dependence"] = pyes_data["imode"] != 0
        data["reference_ionic_str_species"] = np.array(
            list(pyes_data["speciesModel"]["Ref. Ionic Str."].values())
        )
        data["reference_ionic_str_species"] = np.where(
            data["reference_ionic_str_species"] == 0,
            pyes_data["ris"],
            data["reference_ionic_str_species"],
        )

        data["reference_ionic_str_solids"] = np.array(
            list(pyes_data["solidSpeciesModel"]["Ref. Ionic Str."].values())
        )
        data["reference_ionic_str_solids"] = np.where(
            data["reference_ionic_str_solids"] == 0,
            pyes_data["ris"],
            data["reference_ionic_str_solids"],
        )

        data["dbh_params"] = [
            pyes_data[name] for name in ["a", "b", "c0", "c1", "d0", "d1", "e0", "e1"]
        ]

        data["distribution_opts"] = DistributionParameters(
            c0=np.array(list(pyes_data["concModel"]["C0"].values())),
            c0_sigma=np.array(list(pyes_data["concModel"]["Sigma C0"].values())),
            initial_log=pyes_data.get("initialLog"),
            final_log=pyes_data.get("finalLog"),
            log_increments=pyes_data.get("logInc"),
            independent_component=pyes_data.get("ind_comp"),
        )

        data["titration_opts"] = SimulationTitrationParameters(
            c0=np.array(list(pyes_data["concModel"]["C0"].values())),
            c0_sigma=np.array(list(pyes_data["concModel"]["Sigma C0"].values())),
            ct=np.array(list(pyes_data["concModel"]["CT"].values())),
            ct_sigma=np.array(list(pyes_data["concModel"]["Sigma CT"].values())),
            v0=pyes_data.get("v0"),
            v_increment=pyes_data.get("vinc"),
            n_add=pyes_data.get("nop"),
        )
        return cls(**data)

model_config class-attribute instance-attribute 

model_config = ConfigDict(extra='forbid')

distribution_opts class-attribute instance-attribute 

distribution_opts: DistributionParameters = (
    DistributionParameters()
)

titration_opts class-attribute instance-attribute 

titration_opts: SimulationTitrationParameters = (
    SimulationTitrationParameters()
)

potentiometry_options class-attribute instance-attribute 

potentiometry_options: PotentiometryOptions = (
    PotentiometryOptions()
)

components instance-attribute 

components: List[str]

stoichiometry instance-attribute 

stoichiometry: Np2DArrayInt8

solid_stoichiometry instance-attribute 

solid_stoichiometry: Np2DArrayInt8

log_beta instance-attribute 

log_beta: Np1DArrayFp64

log_beta_sigma class-attribute instance-attribute 

log_beta_sigma: Np1DArrayFp64 = array([])

log_beta_ref_dbh class-attribute instance-attribute 

log_beta_ref_dbh: Np2DArrayFp64 = empty((0, 2))

log_ks class-attribute instance-attribute 

log_ks: Np1DArrayFp64 = array([])

log_ks_sigma class-attribute instance-attribute 

log_ks_sigma: Np1DArrayFp64 = array([])

log_ks_ref_dbh class-attribute instance-attribute 

log_ks_ref_dbh: Np2DArrayFp64 = empty((0, 2))

charges class-attribute instance-attribute 

charges: Np1DArrayFp64 = array([])

ionic_strength_dependence class-attribute instance-attribute 

ionic_strength_dependence: bool = False

reference_ionic_str_species class-attribute instance-attribute 

reference_ionic_str_species: Np1DArrayFp64 | float = 0

reference_ionic_str_solids class-attribute instance-attribute 

reference_ionic_str_solids: Np1DArrayFp64 | float = 0

dbh_params class-attribute instance-attribute 

dbh_params: Np1DArrayFp64 = zeros(8)

species_charges cached property 

species_charges: Np1DArrayFp64

solid_charges cached property 

solid_charges: Np1DArrayFp64

z_star_species cached property 

z_star_species: Np1DArrayFp64

p_star_species cached property 

p_star_species: Np1DArrayFp64

z_star_solids cached property 

z_star_solids: Np1DArrayFp64

p_star_solids cached property 

p_star_solids: Np1DArrayFp64

dbh_values cached property 

dbh_values: Dict[str, Np1DArrayFp64]

species_names cached property 

species_names: List[str]

solids_names cached property 

solids_names: List[str]

nc cached property 

nc: int

ns cached property 

ns: int

nf cached property 

nf: int

load_from_bstac classmethod 

load_from_bstac(file_path: str) -> SolverData
Source code in src/libeq/data_structure.py 
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@classmethod
def load_from_bstac(cls, file_path: str) -> "SolverData":
    data = dict()
    with open(file_path, "r") as file:
        lines = file.readlines()
    parsed_data = parse_BSTAC_file(lines)

    temperature = parsed_data["TEMP"]
    data["stoichiometry"] = np.array(
        [
            [d[key] for key in d if key.startswith("IX")]
            for d in parsed_data["species"]
        ]
    ).T
    data["solid_stoichiometry"] = np.empty(
        (data["stoichiometry"].shape[0], 0), dtype=np.int8
    )
    data["log_beta"] = np.array([d["BLOG"] for d in parsed_data["species"]])

    data["charges"] = np.array(parsed_data.get("charges", []))
    data["components"] = parsed_data["comp_name"]
    data["ionic_strength_dependence"] = parsed_data["ICD"] != 0
    if data["ionic_strength_dependence"]:
        data["reference_ionic_str_species"] = np.array(
            [parsed_data["IREF"] for _ in range(data["stoichiometry"].shape[1])]
        )
        data["reference_ionic_str_solids"] = np.array(
            [
                parsed_data["IREF"]
                for _ in range(data["solid_stoichiometry"].shape[1])
            ]
        )
        data["dbh_params"] = [
            parsed_data[i] for i in ["AT", "BT", "c0", "c1", "d0", "d1", "e0", "e1"]
        ]

    titration_options = [
        PotentiometryTitrationsParameters(
            c0=np.array([c["C0"] for c in t["components_concentrations"]]),
            ct=np.array([c["CTT"] for c in t["components_concentrations"]]),
            electro_active_compoment=(
                t["titration_comp_settings"][1]
                if t["titration_comp_settings"][1] != 0
                else len(data["components"]) - 1
            ),
            e0=t["potential_params"][0],
            e0_sigma=t["potential_params"][1],
            slope=(
                t["potential_params"][4]
                if t["potential_params"][4] != 0
                else (temperature + 273.15) / 11.6048 * 2.303
            ),
            v0=t["v_params"][0],
            v0_sigma=t["v_params"][1],
            v_add=np.array(t["volume"]),
            emf=np.array(t["potential"]),
        )
        for t in parsed_data["titrations"]
    ]

    weights = parsed_data.get("MODE", 1)
    match weights:
        case 0:
            weights = "calculated"
        case 1:
            weights = "constants"
        case 2:
            weights = "given"
        case _:
            raise ValueError("Invalid MODE value")

    data["potentiometry_options"] = PotentiometryOptions(
        titrations=titration_options,
        weights=weights,
        px_range=[parsed_data["PHI"], parsed_data["PHF"]],
        beta_flags=[s["KEY"] for s in parsed_data["species"]],
        conc_flags=[],
        pot_flags=[],
    )
    return cls(**data)

load_from_pyes classmethod 

load_from_pyes(pyes_data: str | dict) -> SolverData
Source code in src/libeq/data_structure.py 
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@classmethod
def load_from_pyes(cls, pyes_data: str | dict) -> "SolverData":
    if isinstance(pyes_data, str):
        with open(pyes_data, "r") as file:
            pyes_data = json.load(file)
    data = dict()
    data["components"] = list(pyes_data["compModel"]["Name"].values())

    data["stoichiometry"] = np.row_stack(
        [
            list(pyes_data["speciesModel"][col].values())
            for col in data["components"]
        ]
    )
    data["log_beta"] = np.array(list(pyes_data["speciesModel"]["LogB"].values()))
    data["log_beta_sigma"] = np.array(
        list(pyes_data["speciesModel"]["Sigma"].values())
    )
    data["log_beta_ref_dbh"] = np.vstack(
        (
            list(pyes_data["speciesModel"]["CGF"].values()),
            list(pyes_data["speciesModel"]["DGF"].values()),
            list(pyes_data["speciesModel"]["EGF"].values()),
        )
    )

    data["solid_stoichiometry"] = np.row_stack(
        [
            list(pyes_data["solidSpeciesModel"][col].values())
            for col in data["components"]
        ]
    )
    data["log_ks"] = np.array(
        list(pyes_data["solidSpeciesModel"]["LogKs"].values())
    )
    data["log_ks_sigma"] = np.array(
        list(pyes_data["solidSpeciesModel"]["Sigma"].values())
    )
    data["log_ks_ref_dbh"] = np.vstack(
        (
            list(pyes_data["solidSpeciesModel"]["CGF"].values()),
            list(pyes_data["solidSpeciesModel"]["DGF"].values()),
            list(pyes_data["solidSpeciesModel"]["EGF"].values()),
        )
    )

    data["charges"] = np.array(list(pyes_data["compModel"]["Charge"].values()))
    data["ionic_strength_dependence"] = pyes_data["imode"] != 0
    data["reference_ionic_str_species"] = np.array(
        list(pyes_data["speciesModel"]["Ref. Ionic Str."].values())
    )
    data["reference_ionic_str_species"] = np.where(
        data["reference_ionic_str_species"] == 0,
        pyes_data["ris"],
        data["reference_ionic_str_species"],
    )

    data["reference_ionic_str_solids"] = np.array(
        list(pyes_data["solidSpeciesModel"]["Ref. Ionic Str."].values())
    )
    data["reference_ionic_str_solids"] = np.where(
        data["reference_ionic_str_solids"] == 0,
        pyes_data["ris"],
        data["reference_ionic_str_solids"],
    )

    data["dbh_params"] = [
        pyes_data[name] for name in ["a", "b", "c0", "c1", "d0", "d1", "e0", "e1"]
    ]

    data["distribution_opts"] = DistributionParameters(
        c0=np.array(list(pyes_data["concModel"]["C0"].values())),
        c0_sigma=np.array(list(pyes_data["concModel"]["Sigma C0"].values())),
        initial_log=pyes_data.get("initialLog"),
        final_log=pyes_data.get("finalLog"),
        log_increments=pyes_data.get("logInc"),
        independent_component=pyes_data.get("ind_comp"),
    )

    data["titration_opts"] = SimulationTitrationParameters(
        c0=np.array(list(pyes_data["concModel"]["C0"].values())),
        c0_sigma=np.array(list(pyes_data["concModel"]["Sigma C0"].values())),
        ct=np.array(list(pyes_data["concModel"]["CT"].values())),
        ct_sigma=np.array(list(pyes_data["concModel"]["Sigma CT"].values())),
        v0=pyes_data.get("v0"),
        v_increment=pyes_data.get("vinc"),
        n_add=pyes_data.get("nop"),
    )
    return cls(**data)