teqp package¶

Submodules¶

teqp.teqp module¶

TEQP: Templated Equation of State Package

class teqp.teqp.AbstractModel¶

Bases: pybind11_object

build_Psi_Hessian_autodiff(self: teqp.teqp.AbstractModel, T: float, rhovec: numpy.ndarray[numpy.float64[m, 1]]) → numpy.ndarray[numpy.float64[m, n]]¶

build_Psir_Hessian_autodiff(self: teqp.teqp.AbstractModel, T: float, rhovec: numpy.ndarray[numpy.float64[m, 1]]) → numpy.ndarray[numpy.float64[m, n]]¶

build_Psir_gradient_autodiff(self: teqp.teqp.AbstractModel, T: float, rhovec: numpy.ndarray[numpy.float64[m, 1]]) → numpy.ndarray[numpy.float64[m, 1]]¶

build_d2PsirdTdrhoi_autodiff(self: teqp.teqp.AbstractModel, T: float, rhovec: numpy.ndarray[numpy.float64[m, 1]]) → numpy.ndarray[numpy.float64[m, 1]]¶

dpsatdT_pure(self: teqp.teqp.AbstractModel, T: float, rhoL: float, rhoV: float) → float¶

eigen_problem(self: teqp.teqp.AbstractModel, T: float, rhovec: numpy.ndarray[numpy.float64[m, 1]], alignment_v0: numpy.ndarray[numpy.float64[m, 1]] | None = None) → teqp::EigenData¶

extrapolate_from_critical(self: teqp.teqp.AbstractModel, Tc: float, rhoc: float, T: float, molefrac: numpy.ndarray[numpy.float64[m, 1]] | None = None) → numpy.ndarray[numpy.float64[2, 1]]¶

find_VLLE_T_binary(self: teqp.teqp.AbstractModel, traces: List[json], options: teqp.teqp.VLLEFinderOptions | None = None) → List[json]¶

find_VLLE_p_binary(self: teqp.teqp.AbstractModel, traces: List[json], options: teqp.teqp.VLLEFinderOptions | None = None) → List[json]¶

get_ATrhoXi(self: teqp.teqp.AbstractModel, T: float, NT: int, rhomolar: float, Nrho: int, molefrac: numpy.ndarray[numpy.float64[m, 1]], i: int, NXi: int) → float¶

get_ATrhoXiXj(self: teqp.teqp.AbstractModel, T: float, NT: int, rhomolar: float, Nrho: int, molefrac: numpy.ndarray[numpy.float64[m, 1]], i: int, NXi: int, j: int, NXj: int) → float¶

get_ATrhoXiXjXk(self: teqp.teqp.AbstractModel, T: float, NT: int, rhomolar: float, Nrho: int, molefrac: numpy.ndarray[numpy.float64[m, 1]], i: int, NXi: int, j: int, NXj: int, k: int, NXk: int) → float¶

get_Ar00(self: teqp.teqp.AbstractModel, T: float, rho: float, molefrac: numpy.ndarray[numpy.float64[m, 1]]) → float¶

get_Ar00n(self: teqp.teqp.AbstractModel, T: float, rho: float, molefrac: numpy.ndarray[numpy.float64[m, 1]]) → numpy.ndarray[numpy.float64[m, 1]]¶

get_Ar01(self: teqp.teqp.AbstractModel, T: float, rho: float, molefrac: numpy.ndarray[numpy.float64[m, 1]]) → float¶

get_Ar01n(self: teqp.teqp.AbstractModel, T: float, rho: float, molefrac: numpy.ndarray[numpy.float64[m, 1]]) → numpy.ndarray[numpy.float64[m, 1]]¶

get_Ar02(self: teqp.teqp.AbstractModel, T: float, rho: float, molefrac: numpy.ndarray[numpy.float64[m, 1]]) → float¶

get_Ar02n(self: teqp.teqp.AbstractModel, T: float, rho: float, molefrac: numpy.ndarray[numpy.float64[m, 1]]) → numpy.ndarray[numpy.float64[m, 1]]¶

get_Ar03(self: teqp.teqp.AbstractModel, T: float, rho: float, molefrac: numpy.ndarray[numpy.float64[m, 1]]) → float¶

get_Ar03n(self: teqp.teqp.AbstractModel, T: float, rho: float, molefrac: numpy.ndarray[numpy.float64[m, 1]]) → numpy.ndarray[numpy.float64[m, 1]]¶

get_Ar04(self: teqp.teqp.AbstractModel, T: float, rho: float, molefrac: numpy.ndarray[numpy.float64[m, 1]]) → float¶

get_Ar04n(self: teqp.teqp.AbstractModel, T: float, rho: float, molefrac: numpy.ndarray[numpy.float64[m, 1]]) → numpy.ndarray[numpy.float64[m, 1]]¶

get_Ar05n(self: teqp.teqp.AbstractModel, T: float, rho: float, molefrac: numpy.ndarray[numpy.float64[m, 1]]) → numpy.ndarray[numpy.float64[m, 1]]¶

get_Ar06n(self: teqp.teqp.AbstractModel, T: float, rho: float, molefrac: numpy.ndarray[numpy.float64[m, 1]]) → numpy.ndarray[numpy.float64[m, 1]]¶

get_Ar10(self: teqp.teqp.AbstractModel, T: float, rho: float, molefrac: numpy.ndarray[numpy.float64[m, 1]]) → float¶

get_Ar11(self: teqp.teqp.AbstractModel, T: float, rho: float, molefrac: numpy.ndarray[numpy.float64[m, 1]]) → float¶

get_Ar12(self: teqp.teqp.AbstractModel, T: float, rho: float, molefrac: numpy.ndarray[numpy.float64[m, 1]]) → float¶

get_Ar13(self: teqp.teqp.AbstractModel, T: float, rho: float, molefrac: numpy.ndarray[numpy.float64[m, 1]]) → float¶

get_Ar14(self: teqp.teqp.AbstractModel, T: float, rho: float, molefrac: numpy.ndarray[numpy.float64[m, 1]]) → float¶

get_Ar20(self: teqp.teqp.AbstractModel, T: float, rho: float, molefrac: numpy.ndarray[numpy.float64[m, 1]]) → float¶

get_Ar21(self: teqp.teqp.AbstractModel, T: float, rho: float, molefrac: numpy.ndarray[numpy.float64[m, 1]]) → float¶

get_Ar22(self: teqp.teqp.AbstractModel, T: float, rho: float, molefrac: numpy.ndarray[numpy.float64[m, 1]]) → float¶

get_Ar23(self: teqp.teqp.AbstractModel, T: float, rho: float, molefrac: numpy.ndarray[numpy.float64[m, 1]]) → float¶

get_Ar24(self: teqp.teqp.AbstractModel, T: float, rho: float, molefrac: numpy.ndarray[numpy.float64[m, 1]]) → float¶

get_Arxy(self: teqp.teqp.AbstractModel, NT: int, ND: int, T: float, rho: float, molefrac: numpy.ndarray[numpy.float64[m, 1]]) → float¶

get_AtaudeltaXi(self: teqp.teqp.AbstractModel, tau: float, Ntau: int, delta: float, Ndelta: int, molefrac: numpy.ndarray[numpy.float64[m, 1]], i: int, NXi: int) → float¶

get_AtaudeltaXiXj(self: teqp.teqp.AbstractModel, tau: float, Ntau: int, delta: float, Ndelta: int, molefrac: numpy.ndarray[numpy.float64[m, 1]], i: int, NXi: int, j: int, NXj: int) → float¶

get_AtaudeltaXiXjXk(self: teqp.teqp.AbstractModel, tau: float, Ntau: int, delta: float, Ndelta: int, molefrac: numpy.ndarray[numpy.float64[m, 1]], i: int, NXi: int, j: int, NXj: int, k: int, NXk: int) → float¶

get_B12vir(self: teqp.teqp.AbstractModel, T: float, molefrac: numpy.ndarray[numpy.float64[m, 1]]) → float¶

get_B2vir(self: teqp.teqp.AbstractModel, T: float, molefrac: numpy.ndarray[numpy.float64[m, 1]]) → float¶

get_Bnvir(self: teqp.teqp.AbstractModel, Nderiv: int, T: float, molefrac: numpy.ndarray[numpy.float64[m, 1]]) → Dict[int, float]¶

get_R(self: teqp.teqp.AbstractModel, molefrac: numpy.ndarray[numpy.float64[m, 1]]) → float¶

get_chempotVLE_autodiff(self: teqp.teqp.AbstractModel, T: float, rhovec: numpy.ndarray[numpy.float64[m, 1]]) → numpy.ndarray[numpy.float64[m, 1]]¶

get_criticality_conditions(self: teqp.teqp.AbstractModel, T: float, rhovec: numpy.ndarray[numpy.float64[m, 1]]) → numpy.ndarray[numpy.float64[2, 1]]¶

get_dchempotdT_autodiff(self: teqp.teqp.AbstractModel, T: float, rhovec: numpy.ndarray[numpy.float64[m, 1]]) → numpy.ndarray[numpy.float64[m, 1]]¶

get_deriv_mat2(self: teqp.teqp.AbstractModel, T: float, rho: float, molefrac: numpy.ndarray[numpy.float64[m, 1]]) → numpy.ndarray[numpy.float64[3, 3]]¶

get_dmBnvirdTm(self: teqp.teqp.AbstractModel, Nderiv: int, NTderiv: int, T: float, molefrac: numpy.ndarray[numpy.float64[m, 1]]) → float¶

get_dp_dT_crit(self: teqp.teqp.AbstractModel, T: float, rhovec: numpy.ndarray[numpy.float64[m, 1]]) → float¶

get_dpsat_dTsat_isopleth(self: teqp.teqp.AbstractModel, T: float, rhovecL: numpy.ndarray[numpy.float64[m, 1]], rhovecV: numpy.ndarray[numpy.float64[m, 1]]) → float¶

get_drhovec_dT_crit(self: teqp.teqp.AbstractModel, T: float, rhovec: numpy.ndarray[numpy.float64[m, 1]]) → numpy.ndarray[numpy.float64[m, 1]]¶

get_drhovecdT_psat(self: teqp.teqp.AbstractModel, T: float, rhovecL: numpy.ndarray[numpy.float64[m, 1]], rhovecV: numpy.ndarray[numpy.float64[m, 1]]) → Tuple[numpy.ndarray[numpy.float64[m, 1]], numpy.ndarray[numpy.float64[m, 1]]]¶

get_drhovecdp_Tsat(self: teqp.teqp.AbstractModel, T: float, rhovecL: numpy.ndarray[numpy.float64[m, 1]], rhovecV: numpy.ndarray[numpy.float64[m, 1]]) → Tuple[numpy.ndarray[numpy.float64[m, 1]], numpy.ndarray[numpy.float64[m, 1]]]¶

get_fugacity_coefficients(self: teqp.teqp.AbstractModel, T: float, rhovec: numpy.ndarray[numpy.float64[m, 1]]) → numpy.ndarray[numpy.float64[m, 1]]¶

get_minimum_eigenvalue_Psi_Hessian(self: teqp.teqp.AbstractModel, T: float, rhovec: numpy.ndarray[numpy.float64[m, 1]]) → float¶

get_neff(self: teqp.teqp.AbstractModel, T: float, rho: float, molefrac: numpy.ndarray[numpy.float64[m, 1]]) → float¶

get_partial_molar_volumes(self: teqp.teqp.AbstractModel, T: float, rhovec: numpy.ndarray[numpy.float64[m, 1]]) → numpy.ndarray[numpy.float64[m, 1]]¶

get_pr(self: teqp.teqp.AbstractModel, T: float, rhovec: numpy.ndarray[numpy.float64[m, 1]]) → float¶

get_pure_critical_conditions_Jacobian(self: teqp.teqp.AbstractModel, T: float, rho: float, alternative_pure_index: int | None = None, alternative_length: int | None = None) → Tuple[numpy.ndarray[numpy.float64[m, 1]], numpy.ndarray[numpy.float64[m, n]]]¶

get_splus(self: teqp.teqp.AbstractModel, T: float, rhovec: numpy.ndarray[numpy.float64[m, 1]]) → float¶

mix_VLE_Tp(self: teqp.teqp.AbstractModel, T: float, p_given: float, rhovecL0: numpy.ndarray[numpy.float64[m, 1]], rhovecV0: numpy.ndarray[numpy.float64[m, 1]], options: teqp.teqp.MixVLETpFlags | None = None) → teqp.teqp.MixVLEReturn¶

mix_VLE_Tx(self: teqp.teqp.AbstractModel, T: float, rhovecL0: numpy.ndarray[numpy.float64[m, 1]], rhovecV0: numpy.ndarray[numpy.float64[m, 1]], xspec: numpy.ndarray[numpy.float64[m, 1]], atol: float, reltol: float, axtol: float, relxtol: float, maxiter: int) → Tuple[teqp.teqp.VLE_return_code, numpy.ndarray[numpy.float64[m, 1]], numpy.ndarray[numpy.float64[m, 1]]]¶

mix_VLLE_T(self: teqp.teqp.AbstractModel, T: float, rhovecVinit: numpy.ndarray[numpy.float64[m, 1]], rhovecL1init: numpy.ndarray[numpy.float64[m, 1]], rhovecL2init: numpy.ndarray[numpy.float64[m, 1]], atol: float, reltol: float, axtol: float, relxtol: float, maxiter: int) → Tuple[teqp::VLLE::VLLE_return_code, numpy.ndarray[numpy.float64[m, 1]], numpy.ndarray[numpy.float64[m, 1]], numpy.ndarray[numpy.float64[m, 1]]]¶

mixture_VLE_px(self: teqp.teqp.AbstractModel, p_spec: float, xmolar_spec: numpy.ndarray[numpy.float64[m, 1]], T0: float, rhovecL0: numpy.ndarray[numpy.float64[m, 1]], rhovecV0: numpy.ndarray[numpy.float64[m, 1]], options: teqp.teqp.MixVLEpxFlags | None = None) → Tuple[teqp.teqp.VLE_return_code, float, numpy.ndarray[numpy.float64[m, 1]], numpy.ndarray[numpy.float64[m, 1]]]¶

pure_VLE_T(self: teqp.teqp.AbstractModel, T: float, rhoL: float, rhoV: float, max_iter: int, molefrac: numpy.ndarray[numpy.float64[m, 1]] | None = None) → numpy.ndarray[numpy.float64[2, 1]]¶

solve_pure_critical(self: teqp.teqp.AbstractModel, T: float, rho: float, flags: json | None = None) → Tuple[float, float]¶

trace_VLE_isobar_binary(self: teqp.teqp.AbstractModel, p: float, T0: float, rhovecL0: numpy.ndarray[numpy.float64[m, 1]], rhovecV0: numpy.ndarray[numpy.float64[m, 1]], options: teqp.teqp.PVLEOptions | None = None) → json¶

trace_VLE_isotherm_binary(self: teqp.teqp.AbstractModel, T: float, rhovecL0: numpy.ndarray[numpy.float64[m, 1]], rhovecV0: numpy.ndarray[numpy.float64[m, 1]], options: teqp.teqp.TVLEOptions | None = None) → json¶

trace_VLLE_binary(self: teqp.teqp.AbstractModel, T: float, rhovecV: numpy.ndarray[numpy.float64[m, 1]], rhovecL1: numpy.ndarray[numpy.float64[m, 1]], rhovecL2: numpy.ndarray[numpy.float64[m, 1]], options: teqp.teqp.VLLETracerOptions | None = None) → json¶

trace_critical_arclength_binary(self: teqp.teqp.AbstractModel, T0: float, rhovec0: numpy.ndarray[numpy.float64[m, 1]], path: str | None = None, options: teqp.teqp.TCABOptions | None = None) → json¶

class teqp.teqp.IterationMatrices¶

Bases: pybind11_object

property J¶

property v¶

property vars¶

class teqp.teqp.MixVLEReturn¶

Bases: pybind11_object

property T¶

property initial_r¶

property message¶

property num_fev¶

property num_iter¶

property r¶

property return_code¶

property rhovecL¶

property rhovecV¶

property success¶

class teqp.teqp.MixVLETpFlags¶

Bases: pybind11_object

property atol¶

property axtol¶

property maxiter¶

property reltol¶

property relxtol¶

class teqp.teqp.MixVLEpxFlags¶

Bases: pybind11_object

property atol¶

property axtol¶

property maxiter¶

property reltol¶

property relxtol¶

class teqp.teqp.MultiFluidVLEAncillaries¶

Bases: pybind11_object

property pL¶

property pV¶

property rhoL¶

property rhoV¶

class teqp.teqp.NRIterator¶

Bases: pybind11_object

get_T(self: teqp.teqp.NRIterator) → float¶

get_molefrac(self: teqp.teqp.NRIterator) → numpy.ndarray[numpy.float64[m, 1]]¶

get_rho(self: teqp.teqp.NRIterator) → float¶

get_vals(self: teqp.teqp.NRIterator) → numpy.ndarray[numpy.float64[m, 1]]¶

get_vars(self: teqp.teqp.NRIterator) → List[str]¶

take_step(self: teqp.teqp.NRIterator) → numpy.ndarray[numpy.float64[m, 1]]¶

take_steps(self: teqp.teqp.NRIterator, arg0: int) → None¶

class teqp.teqp.PVLEOptions¶

Bases: pybind11_object

property abs_err¶

property calc_criticality¶

property crit_termination¶

property init_c¶

property init_dt¶

property integration_order¶

property max_dt¶

property max_steps¶

property polish¶

property polish_exception_on_fail¶

property polish_reltol_rho¶

property rel_err¶

property terminate_unstable¶

property verbosity¶

class teqp.teqp.SAFTCoeffs¶

Bases: pybind11_object

property BibTeXKey¶

property Qstar2¶

property epsilon_over_k¶

property m¶

property mustar2¶

property nQ¶

property name¶

property nmu¶

property sigma_Angstrom¶

class teqp.teqp.TCABOptions¶

Bases: pybind11_object

property T_tol¶

property abs_err¶

property calc_stability¶

property init_c¶

property init_dt¶

property integration_order¶

property max_dt¶

property max_step_count¶

property polish¶

property polish_exception_on_fail¶

property polish_reltol_T¶

property polish_reltol_rho¶

property pure_endpoint_polish¶

property rel_err¶

property skip_dircheck_count¶

property small_T_count¶

property stability_rel_drho¶

property verbosity¶

class teqp.teqp.TVLEOptions¶

Bases: pybind11_object

property abs_err¶

property calc_criticality¶

property crit_termination¶

property init_c¶

property init_dt¶

property integration_order¶

property max_dt¶

property max_steps¶

property p_termination¶

property polish¶

property polish_exception_on_fail¶

property polish_reltol_rho¶

property rel_err¶

property terminate_unstable¶

property verbosity¶

class teqp.teqp.VLEAncillary¶

Bases: pybind11_object

property T_r¶

property Tmax¶

property Tmin¶

class teqp.teqp.VLE_return_code¶

Bases: pybind11_object

Members:

unset

xtol_satisfied

functol_satisfied

maxiter_met

maxfev_met

notfinite_step

functol_satisfied = <VLE_return_code.functol_satisfied: 2>¶

maxfev_met = <VLE_return_code.maxfev_met: 3>¶

maxiter_met = <VLE_return_code.maxiter_met: 4>¶

property name¶

notfinite_step = <VLE_return_code.notfinite_step: 5>¶

unset = <VLE_return_code.unset: 0>¶

property value¶

xtol_satisfied = <VLE_return_code.xtol_satisfied: 1>¶

class teqp.teqp.VLLEFinderOptions¶

Bases: pybind11_object

property max_steps¶

property rho_trivial_threshold¶

class teqp.teqp.VLLETracerOptions¶

Bases: pybind11_object

property T_limit¶

property abs_err¶

property init_dT¶

property max_dT¶

property max_polish_steps¶

property max_step_count¶

property max_step_retries¶

property polish¶

property rel_err¶

property terminate_composition¶

property terminate_composition_tol¶

property verbosity¶

teqp.teqp.attach_model_specific_methods(arg0: object) → None¶

teqp.teqp.build_alias_map(root: str) → Dict[str, str]¶

teqp.teqp.build_ancillaries(model: teqp.teqp.AbstractModel, Tc: float, rhoc: float, Tmin: float, flags: json | None = None) → teqp.teqp.MultiFluidVLEAncillaries¶

teqp.teqp.collect_component_json(identifiers: List[str], root: str) → List[json]¶

teqp.teqp.convert_CoolProp_idealgas(arg0: str, arg1: int) → json¶

teqp.teqp.convert_FLD(component: str, name: str) → json¶

teqp.teqp.convert_HMXBNC(path: str) → Tuple[json, json]¶

teqp.teqp.get_BIPdep(BIPcollection: json, identifiers: List[str], flags: json = None) → Tuple[json, bool]¶

teqp.teqp.get_departure_json(name: str, root: str) → json¶

Module contents¶

teqp.AmmoniaWaterTillnerRoth()¶

teqp.CPAfactory(spec)¶

teqp.IdealHelmholtz(model)¶

teqp.PCSAFTEOS(coeffs, kmat=None)¶

teqp.build_LJ126_TholJPCRD2016()¶

teqp.build_Psi_Hessian_autodiff(model, *args, **kwargs)¶

teqp.build_Psir_Hessian_autodiff(model, *args, **kwargs)¶

teqp.build_Psir_gradient_autodiff(model, *args, **kwargs)¶

teqp.build_d2PsirdTdrhoi_autodiff(model, *args, **kwargs)¶

teqp.build_multifluid_ecs_mutant(*args, **kwargs)¶

teqp.build_multifluid_model(components, coolprop_root, BIPcollectionpath='', flags={}, departurepath='')¶

teqp.build_multifluid_mutant(*args, **kwargs)¶

teqp.canonical_PR(Tc_K, pc_Pa, acentric, kmat=None)¶

teqp.canonical_SRK(Tc_K, pc_Pa, acentric, kmat=None)¶

teqp.deprecated_caller(model, *args, **kwargs)¶

teqp.eigen_problem(model, *args, **kwargs)¶

teqp.extrapolate_from_critical(model, *args, **kwargs)¶

teqp.find_VLLE_T_binary(model, *args, **kwargs)¶

teqp.get_B2virget_B12vir(model, *args, **kwargs)¶

teqp.get_chempotVLE_autodiff(model, *args, **kwargs)¶

teqp.get_criticality_conditions(model, *args, **kwargs)¶

teqp.get_datapath()¶: Get the absolute path to the folder containing the root of multi-fluid data

teqp.get_dchempotdT_autodiff(model, *args, **kwargs)¶

teqp.get_dpsat_dTsat_isopleth(model, *args, **kwargs)¶

teqp.get_drhovec_dT_crit(model, *args, **kwargs)¶

teqp.get_drhovecdT_psat(model, *args, **kwargs)¶

teqp.get_drhovecdp_Tsat(model, *args, **kwargs)¶

teqp.get_fugacity_coefficients(model, *args, **kwargs)¶

teqp.get_minimum_eigenvalue_Psi_Hessian(model, *args, **kwargs)¶

teqp.get_partial_molar_volumes(model, *args, **kwargs)¶

teqp.get_pr(model, *args, **kwargs)¶

teqp.get_pure_critical_conditions_Jacobian(model, *args, **kwargs)¶

teqp.get_splus(model, *args, **kwargs)¶

teqp.make_model(*args, **kwargs)¶: This function is in two parts; first the make_model function (renamed to _make_model in the Python interface) is used to make the model and then the model-specific methods are attached to the instance

teqp.make_vdW1(a, b)¶

teqp.mix_VLE_Tx(model, *args, **kwargs)¶

teqp.mix_VLLE_T(model, *args, **kwargs)¶

teqp.mixture_VLE_px(model, *args, **kwargs)¶

teqp.pure_VLE_T(model, *args, **kwargs)¶

teqp.solve_pure_critical(model, *args, **kwargs)¶

teqp.tolist(a)¶

teqp.trace_VLE_isobar_binary(model, *args, **kwargs)¶

teqp.trace_VLE_isotherm_binary(model, *args, **kwargs)¶

teqp.trace_critical_arclength_binary(model, *args, **kwargs)¶

teqp.vdWEOS(Tc_K, pc_Pa)¶

teqp.vdWEOS1(*args)¶

teqp package¶

Submodules¶

teqp.teqp module¶

Module contents¶

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