Instruction:
Here you have a function in Python 3, explain what it does
def SNM0(T, Tc, Vc, omega, delta_SRK=None):
    r'''Calculates saturated liquid density using the Mchaweh, Moshfeghian
    model [1]_. Designed for simple calculations.

    .. math::
        V_s = V_c/(1+1.169\tau^{1/3}+1.818\tau^{2/3}-2.658\tau+2.161\tau^{4/3}

        \tau = 1-\frac{(T/T_c)}{\alpha_{SRK}}

        \alpha_{SRK} = [1 + m(1-\sqrt{T/T_C}]^2

        m = 0.480+1.574\omega-0.176\omega^2

    If the fit parameter `delta_SRK` is provided, the following is used:

    .. math::
        V_s = V_C/(1+1.169\tau^{1/3}+1.818\tau^{2/3}-2.658\tau+2.161\tau^{4/3})
        /\left[1+\delta_{SRK}(\alpha_{SRK}-1)^{1/3}\right]

    Parameters
    ----------
    T : float
        Temperature of fluid [K]
    Tc : float
        Critical temperature of fluid [K]
    Vc : float
        Critical volume of fluid [m^3/mol]
    omega : float
        Acentric factor for fluid, [-]
    delta_SRK : float, optional
        Fitting parameter [-]

    Returns
    -------
    Vs : float
        Saturation liquid volume, [m^3/mol]

    Notes
    -----
    73 fit parameters have been gathered from the article.

    Examples
    --------
    Argon, without the fit parameter and with it. Tabulated result in Perry's
    is 3.4613e-05. The fit increases the error on this occasion.

    >>> SNM0(121, 150.8, 7.49e-05, -0.004)
    3.4402256402733416e-05
    >>> SNM0(121, 150.8, 7.49e-05, -0.004, -0.03259620)
    3.493288100008123e-05

    References
    ----------
    .. [1] Mchaweh, A., A. Alsaygh, Kh. Nasrifar, and M. Moshfeghian.
       "A Simplified Method for Calculating Saturated Liquid Densities."
       Fluid Phase Equilibria 224, no. 2 (October 1, 2004): 157-67.
       doi:10.1016/j.fluid.2004.06.054
    '''
    Tr = T/Tc
    m = 0.480 + 1.574*omega - 0.176*omega*omega
    alpha_SRK = (1. + m*(1. - Tr**0.5))**2
    tau = 1. - Tr/alpha_SRK

    rho0 = 1. + 1.169*tau**(1/3.) + 1.818*tau**(2/3.) - 2.658*tau + 2.161*tau**(4/3.)
    V0 = 1./rho0

    if not delta_SRK:
        return Vc*V0
    else:
        return Vc*V0/(1. + delta_SRK*(alpha_SRK - 1.)**(1/3.))

Response: r Returns a string that is a simple formula for calculating the saturated liquid density of a given temperature and critical volume.


Instruction:
Can you tell what is the following Python 3 function doing
def COSTALD_compressed(T, P, Psat, Tc, Pc, omega, Vs):
    r'''Calculates compressed-liquid volume, using the COSTALD [1]_ CSP
    method and a chemical's critical properties.

    The molar volume of a liquid is given by:

    .. math::
        V = V_s\left( 1 - C \ln \frac{B + P}{B + P^{sat}}\right)

        \frac{B}{P_c} = -1 + a\tau^{1/3} + b\tau^{2/3} + d\tau + e\tau^{4/3}

        e = \exp(f + g\omega_{SRK} + h \omega_{SRK}^2)

        C = j + k \omega_{SRK}

    Parameters
    ----------
    T : float
        Temperature of fluid [K]
    P : float
        Pressure of fluid [Pa]
    Psat : float
        Saturation pressure of the fluid [Pa]
    Tc : float
        Critical temperature of fluid [K]
    Pc : float
        Critical pressure of fluid [Pa]
    omega : float
        (ideally SRK) Acentric factor for fluid, [-]
        This parameter is alternatively a fit parameter.
    Vs : float
        Saturation liquid volume, [m^3/mol]

    Returns
    -------
    V_dense : float
        High-pressure liquid volume, [m^3/mol]

    Notes
    -----
    Original equation was in terms of density, but it is converted here.

    The example is from DIPPR, and exactly correct.
    This is DIPPR Procedure 4C: Method for Estimating the Density of Pure
    Organic Liquids under Pressure.

    Examples
    --------
    >>> COSTALD_compressed(303., 9.8E7, 85857.9, 466.7, 3640000.0, 0.281, 0.000105047)
    9.287482879788506e-05

    References
    ----------
    .. [1]  Thomson, G. H., K. R. Brobst, and R. W. Hankinson. "An Improved
       Correlation for Densities of Compressed Liquids and Liquid Mixtures."
       AIChE Journal 28, no. 4 (July 1, 1982): 671-76. doi:10.1002/aic.690280420
    '''
    a = -9.070217
    b = 62.45326
    d = -135.1102
    f = 4.79594
    g = 0.250047
    h = 1.14188
    j = 0.0861488
    k = 0.0344483
    tau = 1 - T/Tc
    e = exp(f + g*omega + h*omega**2)
    C = j + k*omega
    B = Pc*(-1 + a*tau**(1/3.) + b*tau**(2/3.) + d*tau + e*tau**(4/3.))
    return Vs*(1 - C*log((B + P)/(B + Psat)))

Response: r Calculates the compressed - liquid volume of a given temperature pressure temperature and critical pressure.


Instruction:
Here you have a function in Python 3, explain what it does
def Amgat(xs, Vms):
    r'''Calculate mixture liquid density using the Amgat mixing rule.
    Highly inacurate, but easy to use. Assumes idea liquids with
    no excess volume. Average molecular weight should be used with it to obtain
    density.

    .. math::
        V_{mix} = \sum_i x_i V_i

    or in terms of density:

    .. math::

        \rho_{mix} = \sum\frac{x_i}{\rho_i}

    Parameters
    ----------
    xs : array
        Mole fractions of each component, []
    Vms : array
        Molar volumes of each fluids at conditions [m^3/mol]

    Returns
    -------
    Vm : float
        Mixture liquid volume [m^3/mol]

    Notes
    -----
    Units are that of the given volumes.
    It has been suggested to use this equation with weight fractions,
    but the results have been less accurate.

    Examples
    --------
    >>> Amgat([0.5, 0.5], [4.057e-05, 5.861e-05])
    4.9590000000000005e-05
    '''
    if not none_and_length_check([xs, Vms]):
        raise Exception('Function inputs are incorrect format')
    return mixing_simple(xs, Vms)

Response: r Calculates the mixture liquid density using the Amgat mixing rule.


Instruction:
Explain what the following Python 3 code does
def Rackett_mixture(T, xs, MWs, Tcs, Pcs, Zrs):
    r'''Calculate mixture liquid density using the Rackett-derived mixing rule
    as shown in [2]_.

    .. math::
        V_m = \sum_i\frac{x_i T_{ci}}{MW_i P_{ci}} Z_{R,m}^{(1 + (1 - T_r)^{2/7})} R \sum_i x_i MW_i

    Parameters
    ----------
    T : float
        Temperature of liquid [K]
    xs: list
        Mole fractions of each component, []
    MWs : list
        Molecular weights of each component [g/mol]
    Tcs : list
        Critical temperatures of each component [K]
    Pcs : list
        Critical pressures of each component [Pa]
    Zrs : list
        Rackett parameters of each component []

    Returns
    -------
    Vm : float
        Mixture liquid volume [m^3/mol]

    Notes
    -----
    Model for pure compounds in [1]_ forms the basis for this model, shown in
    [2]_. Molecular weights are used as weighing by such has been found to
    provide higher accuracy in [2]_. The model can also be used without
    molecular weights, but results are somewhat different.

    As with the Rackett model, critical compressibilities may be used if
    Rackett parameters have not been regressed.

    Critical mixture temperature, and compressibility are all obtained with
    simple mixing rules.

    Examples
    --------
    Calculation in [2]_ for methanol and water mixture. Result matches example.

    >>> Rackett_mixture(T=298., xs=[0.4576, 0.5424], MWs=[32.04, 18.01], Tcs=[512.58, 647.29], Pcs=[8.096E6, 2.209E7], Zrs=[0.2332, 0.2374])
    2.625288603174508e-05

    References
    ----------
    .. [1] Rackett, Harold G. "Equation of State for Saturated Liquids."
       Journal of Chemical & Engineering Data 15, no. 4 (1970): 514-517.
       doi:10.1021/je60047a012
    .. [2] Danner, Ronald P, and Design Institute for Physical Property Data.
       Manual for Predicting Chemical Process Design Data. New York, N.Y, 1982.
    '''
    if not none_and_length_check([xs, MWs, Tcs, Pcs, Zrs]):
        raise Exception('Function inputs are incorrect format')
    Tc = mixing_simple(xs, Tcs)
    Zr = mixing_simple(xs, Zrs)
    MW = mixing_simple(xs, MWs)
    Tr = T/Tc
    bigsum = sum(xs[i]*Tcs[i]/Pcs[i]/MWs[i] for i in range(len(xs)))
    return (R*bigsum*Zr**(1. + (1. - Tr)**(2/7.)))*MW

Response: r Calculates the mixture liquid density using the Rackett - derived mixing rule.


Instruction:
Given the following Python 3 function, write the documentation
def COSTALD_mixture(xs, T, Tcs, Vcs, omegas):
    r'''Calculate mixture liquid density using the COSTALD CSP method.

    A popular and accurate estimation method. If possible, fit parameters are
    used; alternatively critical properties work well.

    The mixing rules giving parameters for the pure component COSTALD
    equation are:

    .. math::
        T_{cm} = \frac{\sum_i\sum_j x_i x_j (V_{ij}T_{cij})}{V_m}

        V_m = 0.25\left[ \sum x_i V_i + 3(\sum x_i V_i^{2/3})(\sum_i x_i V_i^{1/3})\right]

        V_{ij}T_{cij} = (V_iT_{ci}V_{j}T_{cj})^{0.5}

        \omega = \sum_i z_i \omega_i

    Parameters
    ----------
    xs: list
        Mole fractions of each component
    T : float
        Temperature of fluid [K]
    Tcs : list
        Critical temperature of fluids [K]
    Vcs : list
        Critical volumes of fluids [m^3/mol].
        This parameter is alternatively a fit parameter
    omegas : list
        (ideally SRK) Acentric factor of all fluids, [-]
        This parameter is alternatively a fit parameter.

    Returns
    -------
    Vs : float
        Saturation liquid mixture volume

    Notes
    -----
    Range: 0.25 < Tr < 0.95, often said to be to 1.0
    No example has been found.
    Units are that of critical or fit constant volume.

    Examples
    --------
    >>> COSTALD_mixture([0.4576, 0.5424], 298.,  [512.58, 647.29],[0.000117, 5.6e-05], [0.559,0.344] )
    2.706588773271354e-05

    References
    ----------
    .. [1] Hankinson, Risdon W., and George H. Thomson. "A New Correlation for
       Saturated Densities of Liquids and Their Mixtures." AIChE Journal
       25, no. 4 (1979): 653-663. doi:10.1002/aic.690250412
    '''
    cmps = range(len(xs))
    if not none_and_length_check([xs, Tcs, Vcs, omegas]):
        raise Exception('Function inputs are incorrect format')
    sum1 = sum([xi*Vci for xi, Vci in zip(xs, Vcs)])
    sum2 = sum([xi*Vci**(2/3.) for xi, Vci in zip(xs, Vcs)])
    sum3 = sum([xi*Vci**(1/3.) for xi, Vci in zip(xs, Vcs)])
    Vm = 0.25*(sum1 + 3.*sum2*sum3)
    VijTcij = [[(Tcs[i]*Tcs[j]*Vcs[i]*Vcs[j])**0.5 for j in cmps] for i in cmps]
    omega = mixing_simple(xs, omegas)
    Tcm = sum([xs[i]*xs[j]*VijTcij[i][j]/Vm for j in cmps for i in cmps])
    return COSTALD(T, Tcm, Vm, omega)

Response: r Calculates the mixture liquid density using the COSTALD CSP method.


Instruction:
Can you generate a brief explanation for the following Python 3 code
def calculate(self, T, P, zs, ws, method):
        r'''Method to calculate molar volume of a liquid mixture at
        temperature `T`, pressure `P`, mole fractions `zs` and weight fractions
        `ws` with a given method.

        This method has no exception handling; see `mixture_property`
        for that.

        Parameters
        ----------
        T : float
            Temperature at which to calculate the property, [K]
        P : float
            Pressure at which to calculate the property, [Pa]
        zs : list[float]
            Mole fractions of all species in the mixture, [-]
        ws : list[float]
            Weight fractions of all species in the mixture, [-]
        method : str
            Name of the method to use

        Returns
        -------
        Vm : float
            Molar volume of the liquid mixture at the given conditions,
            [m^3/mol]
        '''
        if method == SIMPLE:
            Vms = [i(T, P) for i in self.VolumeLiquids]
            return Amgat(zs, Vms)
        elif method == COSTALD_MIXTURE:
            return COSTALD_mixture(zs, T, self.Tcs, self.Vcs, self.omegas)
        elif method == COSTALD_MIXTURE_FIT:
            return COSTALD_mixture(zs, T, self.Tcs, self.COSTALD_Vchars, self.COSTALD_omegas)
        elif method == RACKETT:
            return Rackett_mixture(T, zs, self.MWs, self.Tcs, self.Pcs, self.Zcs)
        elif method == RACKETT_PARAMETERS:
            return Rackett_mixture(T, zs, self.MWs, self.Tcs, self.Pcs, self.Z_RAs)
        elif method == LALIBERTE:
            ws = list(ws) ; ws.pop(self.index_w)
            rho = Laliberte_density(T, ws, self.wCASs)
            MW = mixing_simple(zs, self.MWs)
            return rho_to_Vm(rho, MW)
        else:
            raise Exception('Method not valid')

Response:
r Method to calculate the molar volume of a liquid mixture at
        temperature pressure P mole fractions zs and weight fractions ws.


Instruction:
Explain what the following Python 3 code does
def calculate_P(self, T, P, method):
        r'''Method to calculate pressure-dependent gas molar volume at
        temperature `T` and pressure `P` with a given method.

        This method has no exception handling; see `TP_dependent_property`
        for that.

        Parameters
        ----------
        T : float
            Temperature at which to calculate molar volume, [K]
        P : float
            Pressure at which to calculate molar volume, [K]
        method : str
            Name of the method to use

        Returns
        -------
        Vm : float
            Molar volume of the gas at T and P, [m^3/mol]
        '''
        if method == EOS:
            self.eos[0] = self.eos[0].to_TP(T=T, P=P)
            Vm = self.eos[0].V_g
        elif method == TSONOPOULOS_EXTENDED:
            B = BVirial_Tsonopoulos_extended(T, self.Tc, self.Pc, self.omega, dipole=self.dipole)
            Vm = ideal_gas(T, P) + B
        elif method == TSONOPOULOS:
            B = BVirial_Tsonopoulos(T, self.Tc, self.Pc, self.omega)
            Vm = ideal_gas(T, P) + B
        elif method == ABBOTT:
            B = BVirial_Abbott(T, self.Tc, self.Pc, self.omega)
            Vm = ideal_gas(T, P) + B
        elif method == PITZER_CURL:
            B = BVirial_Pitzer_Curl(T, self.Tc, self.Pc, self.omega)
            Vm = ideal_gas(T, P) + B
        elif method == CRC_VIRIAL:
            a1, a2, a3, a4, a5 = self.CRC_VIRIAL_coeffs
            t = 298.15/T - 1.
            B = (a1 + a2*t + a3*t**2 + a4*t**3 + a5*t**4)/1E6
            Vm = ideal_gas(T, P) + B
        elif method == IDEAL:
            Vm = ideal_gas(T, P)
        elif method == COOLPROP:
            Vm = 1./PropsSI('DMOLAR', 'T', T, 'P', P, self.CASRN)
        elif method in self.tabular_data:
            Vm = self.interpolate_P(T, P, method)
        return Vm

Response: r Method to calculate the pressure - dependent gas molar volume at a given temperature T and pressure P.


Instruction:
Can you tell what is the following Python 3 function doing
def calculate(self, T, P, zs, ws, method):
        r'''Method to calculate molar volume of a gas mixture at
        temperature `T`, pressure `P`, mole fractions `zs` and weight fractions
        `ws` with a given method.

        This method has no exception handling; see `mixture_property`
        for that.

        Parameters
        ----------
        T : float
            Temperature at which to calculate the property, [K]
        P : float
            Pressure at which to calculate the property, [Pa]
        zs : list[float]
            Mole fractions of all species in the mixture, [-]
        ws : list[float]
            Weight fractions of all species in the mixture, [-]
        method : str
            Name of the method to use

        Returns
        -------
        Vm : float
            Molar volume of the gas mixture at the given conditions, [m^3/mol]
        '''
        if method == SIMPLE:
            Vms = [i(T, P) for i in self.VolumeGases]
            return mixing_simple(zs, Vms)
        elif method == IDEAL:
            return ideal_gas(T, P)
        elif method == EOS:
            self.eos[0] = self.eos[0].to_TP_zs(T=T, P=P, zs=zs)
            return self.eos[0].V_g
        else:
            raise Exception('Method not valid')

Response:
r Method to calculate the molar volume of a gas mixture at
 temperature pressure mole fractions zs and weight fractions of all species in the mixture at a given time.


Instruction:
Here you have a function in Python 3, explain what it does
def enthalpy_Cpg_Hvap(self):
        r'''Method to calculate the enthalpy of an ideal mixture (no pressure
        effects). This routine is based on "route A", where only the gas heat
        capacity and enthalpy of vaporization are used.

        The reference temperature is a property of the class; it defaults to
        298.15 K.

        For a pure gas mixture:

        .. math::
             H = \sum_i z_i \cdot \int_{T_{ref}}^T C_{p}^{ig}(T) dT

        For a pure liquid mixture:

        .. math::
             H = \sum_i z_i \left( \int_{T_{ref}}^T C_{p}^{ig}(T) dT + H_{vap, i}(T) \right)

        For a vapor-liquid mixture:

        .. math::
             H = \sum_i z_i \cdot \int_{T_{ref}}^T C_{p}^{ig}(T) dT
                 + \sum_i x_i\left(1 - \frac{V}{F}\right)H_{vap, i}(T)

        Returns
        -------
        H : float
            Enthalpy of the mixture with respect to the reference temperature,
            [J/mol]

        Notes
        -----
        The object must be flashed before this routine can be used. It
        depends on the properties T, zs, xs, V_over_F, HeatCapacityGases,
        EnthalpyVaporizations, and.
        '''
        H = 0
        T = self.T
        if self.phase == 'g':
            for i in self.cmps:
                H += self.zs[i]*self.HeatCapacityGases[i].T_dependent_property_integral(self.T_REF_IG, T)
        elif self.phase == 'l':
            for i in self.cmps:
                # No further contribution needed
                Hg298_to_T = self.HeatCapacityGases[i].T_dependent_property_integral(self.T_REF_IG, T)
                Hvap = self.EnthalpyVaporizations[i](T) # Do the transition at the temperature of the liquid
                if Hvap is None:
                    Hvap = 0 # Handle the case of a package predicting a transition past the Tc
                H += self.zs[i]*(Hg298_to_T - Hvap)
        elif self.phase == 'l/g':
            for i in self.cmps:
                Hg298_to_T_zi = self.zs[i]*self.HeatCapacityGases[i].T_dependent_property_integral(self.T_REF_IG, T)
                Hvap = self.EnthalpyVaporizations[i](T)
                if Hvap is None:
                    Hvap = 0 # Handle the case of a package predicting a transition past the Tc
                Hvap_contrib = -self.xs[i]*(1-self.V_over_F)*Hvap
                H += (Hg298_to_T_zi + Hvap_contrib)
        return H

Response:
Method to calculate the enthalpy of an ideal mixture. This routine is based on route A where only the gas heat
        capacity and enthalpy of vaporization effects are used.


Instruction:
Explain what the following Python 3 code does
def entropy_Cpg_Hvap(self):
        r'''Method to calculate the entropy of an ideal mixture. This routine
        is based on "route A", where only the gas heat capacity and enthalpy of
        vaporization are used.

        The reference temperature and pressure are properties of the class; it
        defaults to 298.15 K and 101325 Pa.

        There is a contribution due to mixing:

        .. math::
            \Delta S_{mixing} = -R\sum_i z_i \log(z_i)

        The ideal gas pressure contribution is:

        .. math::
            \Delta S_{P} = -R\log\left(\frac{P}{P_{ref}}\right)

        For a liquid mixture or a partially liquid mixture, the entropy
        contribution is not so strong - all such pressure effects find that
        expression capped at the vapor pressure, as shown in [1]_.

        .. math::
            \Delta S_{P} = - \sum_i x_i\left(1 - \frac{V}{F}\right)
            R\log\left(\frac{P_{sat, i}}{P_{ref}}\right) - \sum_i y_i\left(
            \frac{V}{F}\right) R\log\left(\frac{P}{P_{ref}}\right)

        These expressions are combined with the standard heat capacity and
        enthalpy of vaporization expressions to calculate the total entropy:

        For a pure gas mixture:

        .. math::
             S = \Delta S_{mixing} + \Delta S_{P} + \sum_i z_i \cdot
             \int_{T_{ref}}^T \frac{C_{p}^{ig}(T)}{T} dT

        For a pure liquid mixture:

        .. math::
             S = \Delta S_{mixing} + \Delta S_{P} + \sum_i z_i \left(
             \int_{T_{ref}}^T \frac{C_{p}^{ig}(T)}{T} dT + \frac{H_{vap, i}
             (T)}{T} \right)

        For a vapor-liquid mixture:

        .. math::
             S = \Delta S_{mixing} + \Delta S_{P} + \sum_i z_i \cdot
             \int_{T_{ref}}^T \frac{C_{p}^{ig}(T)}{T} dT + \sum_i x_i\left(1
             - \frac{V}{F}\right)\frac{H_{vap, i}(T)}{T}

        Returns
        -------
        S : float
            Entropy of the mixture with respect to the reference temperature,
            [J/mol/K]

        Notes
        -----
        The object must be flashed before this routine can be used. It
        depends on the properties T, P, zs, V_over_F, HeatCapacityGases,
        EnthalpyVaporizations, VaporPressures, and xs.

        References
        ----------
        .. [1] Poling, Bruce E. The Properties of Gases and Liquids. 5th edition.
           New York: McGraw-Hill Professional, 2000.
        '''
        S = 0
        T = self.T
        P = self.P
        S -= R*sum([zi*log(zi) for zi in self.zs if zi > 0]) # ideal composition entropy composition; chemsep checked
        # Both of the mixing and vapor pressure terms have negative signs
        # Equation 6-4.4b in Poling for the vapor pressure component
        # For liquids above their critical temperatures, Psat is equal to the system P (COCO).
        if self.phase == 'g':
            S -= R*log(P/101325.) # Gas-phase ideal pressure contribution (checked repeatedly)
            for i in self.cmps:
                S += self.HeatCapacityGases[i].T_dependent_property_integral_over_T(298.15, T)
        elif self.phase == 'l':
            Psats = self._Psats(T)
            for i in self.cmps:
                Sg298_to_T = self.HeatCapacityGases[i].T_dependent_property_integral_over_T(298.15, T)
                Hvap = self.EnthalpyVaporizations[i](T)
                if Hvap is None:
                    Hvap = 0 # Handle the case of a package predicting a transition past the Tc
                Svap = -Hvap/T # Do the transition at the temperature of the liquid
                S_P = -R*log(Psats[i]/101325.)
                S += self.zs[i]*(Sg298_to_T + Svap + S_P)
        elif self.phase == 'l/g':
            Psats = self._Psats(T)
            S_P_vapor = -R*log(P/101325.) # Gas-phase ideal pressure contribution (checked repeatedly)
            for i in self.cmps:
                Sg298_to_T_zi = self.zs[i]*self.HeatCapacityGases[i].T_dependent_property_integral_over_T(298.15, T)
                Hvap = self.EnthalpyVaporizations[i](T)
                if Hvap is None:
                    Hvap = 0 # Handle the case of a package predicting a transition past the Tc

                Svap_contrib = -self.xs[i]*(1-self.V_over_F)*Hvap/T
                # Pressure contributions from both phases
                S_P_vapor_i = self.V_over_F*self.ys[i]*S_P_vapor
                S_P_liquid_i = -R*log(Psats[i]/101325.)*(1-self.V_over_F)*self.xs[i]
                S += (Sg298_to_T_zi + Svap_contrib + S_P_vapor_i + S_P_liquid_i)
        return S

Response: Method to calculate the entropy of an ideal mixture. This method is based on route A and route B and route A and route B and route A.


Instruction:
Make a summary of the following Python 3 code
def load_economic_data():
    global HPV_data
    if HPV_data is not None:
        return None
    global _EPACDRDict, _ECHATonnageDict

    '''OECD are chemicals produced by and OECD members in > 1000 tonnes/year.'''
    HPV_data = pd.read_csv(os.path.join(folder, 'HPV 2015 March 3.csv'),
                           sep='\t', index_col=0)
    # 13061-29-2 not valid and removed

    _ECHATonnageDict = {}
    with zipfile.ZipFile(os.path.join(folder, 'ECHA Tonnage Bands.csv.zip')) as z:
        with z.open(z.namelist()[0]) as f:
            for line in f.readlines():
                # for some reason, the file must be decoded to UTF8 first
                CAS, band = line.decode("utf-8").strip('\n').split('\t')
                if CAS in _ECHATonnageDict:
                    if band in _ECHATonnageDict[CAS]:
                        pass
                    else:
                        _ECHATonnageDict[CAS].append(band)
                else:
                    _ECHATonnageDict[CAS] = [band]

    _EPACDRDict = {}
    with open(os.path.join(folder, 'EPA 2012 Chemical Data Reporting.csv')) as f:
        '''EPA summed reported chemical usages. In metric tonnes/year after conversion.
        Many producers keep their date confidential.
        This was originally in terms of lb/year, but rounded to the nearest kg.

        '''
        next(f)
        for line in f:
            values = line.rstrip().split('\t')
            CAS, manufactured, imported, exported = to_num(values)
            _EPACDRDict[CAS] = {"Manufactured": manufactured/1000., "Imported": imported/1000.,
                                "Exported": exported/1000.}

Response: Loads the ePA - 2012 ephemeris chemical data from the file HPV_data.


Instruction:
Implement a Python 3 function for
looking up the economic status of a chemical.

Response:
def economic_status(CASRN, Method=None, AvailableMethods=False):  # pragma: no cover
    '''Look up the economic status of a chemical.

    This API is considered experimental, and is expected to be removed in a
    future release in favor of a more complete object-oriented interface.

    >>> pprint(economic_status(CASRN='98-00-0'))
    ["US public: {'Manufactured': 0.0, 'Imported': 10272.711, 'Exported': 184.127}",
     u'10,000 - 100,000 tonnes per annum',
     'OECD HPV Chemicals']

    >>> economic_status(CASRN='13775-50-3')  # SODIUM SESQUISULPHATE
    []
    >>> economic_status(CASRN='98-00-0', Method='OECD high production volume chemicals')
    'OECD HPV Chemicals'
    >>> economic_status(CASRN='98-01-1', Method='European Chemicals Agency Total Tonnage Bands')
    [u'10,000 - 100,000 tonnes per annum']
    '''
    load_economic_data()
    CASi = CAS2int(CASRN)

    def list_methods():
        methods = []
        methods.append('Combined')
        if CASRN in _EPACDRDict:
            methods.append(EPACDR)
        if CASRN in _ECHATonnageDict:
            methods.append(ECHA)
        if CASi in HPV_data.index:
            methods.append(OECD)
        methods.append(NONE)
        return methods
    if AvailableMethods:
        return list_methods()
    if not Method:
        Method = list_methods()[0]
    # This is the calculate, given the method section
    if Method == EPACDR:
        status = 'US public: ' + str(_EPACDRDict[CASRN])
    elif Method == ECHA:
        status = _ECHATonnageDict[CASRN]
    elif Method == OECD:
        status = 'OECD HPV Chemicals'
    elif Method == 'Combined':
        status = []
        if CASRN in _EPACDRDict:
            status += ['US public: ' + str(_EPACDRDict[CASRN])]
        if CASRN in _ECHATonnageDict:
            status += _ECHATonnageDict[CASRN]
        if CASi in HPV_data.index:
            status += ['OECD HPV Chemicals']
    elif Method == NONE:
        status = None
    else:
        raise Exception('Failure in in function')
    return status


Instruction:
Make a summary of the following Python 3 code
def BVirial_Tsonopoulos_extended(T, Tc, Pc, omega, a=0, b=0, species_type='',
                                 dipole=0, order=0):
    r'''Calculates the second virial coefficient using the
    comprehensive model in [1]_. See the notes for the calculation of `a` and
    `b`.

    .. math::
        \frac{BP_c}{RT_c} = B^{(0)} + \omega B^{(1)} + a B^{(2)} + b B^{(3)}

        B^{(0)}=0.1445-0.33/T_r-0.1385/T_r^2-0.0121/T_r^3

        B^{(1)} = 0.0637+0.331/T_r^2-0.423/T_r^3 -0.423/T_r^3 - 0.008/T_r^8

        B^{(2)} = 1/T_r^6

        B^{(3)} = -1/T_r^8

    Parameters
    ----------
    T : float
        Temperature of fluid [K]
    Tc : float
        Critical temperature of fluid [K]
    Pc : float
        Critical pressure of the fluid [Pa]
    omega : float
        Acentric factor for fluid, [-]
    a : float, optional
        Fit parameter, calculated based on species_type if a is not given and
        species_type matches on of the supported chemical classes.
    b : float, optional
        Fit parameter, calculated based on species_type if a is not given and
        species_type matches on of the supported chemical classes.
    species_type : str, optional
        One of .
    dipole : float
        dipole moment, optional, [Debye]
    order : int, optional
        Order of the calculation. 0 for the calculation of B itself; for 1/2/3,
        the first/second/third derivative of B with respect to temperature; and
        for -1/-2, the first/second indefinite integral of B with respect to
        temperature. No other integrals or derivatives are implemented, and an
        exception will be raised if any other order is given.

    Returns
    -------
    B : float
        Second virial coefficient in density form or its integral/derivative if
        specified, [m^3/mol or m^3/mol/K^order]

    Notes
    -----
    Analytical models for derivatives and integrals are available for orders
    -2, -1, 1, 2, and 3, all obtained with SymPy.

    To calculate `a` or `b`, the following rules are used:

    For 'simple' or 'normal' fluids:

    .. math::
        a = 0

        b = 0

    For 'ketone', 'aldehyde', 'alkyl nitrile', 'ether', 'carboxylic acid',
    or 'ester' types of chemicals:

    .. math::
        a = -2.14\times 10^{-4} \mu_r - 4.308 \times 10^{-21} (\mu_r)^8

        b = 0

    For 'alkyl halide', 'mercaptan', 'sulfide', or 'disulfide' types of
    chemicals:

    .. math::
        a = -2.188\times 10^{-4} (\mu_r)^4 - 7.831 \times 10^{-21} (\mu_r)^8

        b = 0

    For 'alkanol' types of chemicals (except methanol):

    .. math::
        a = 0.0878

        b = 0.00908 + 0.0006957 \mu_r

    For methanol:

    .. math::
        a = 0.0878

        b = 0.0525

    For water:

    .. math::
        a = -0.0109

        b = 0

    If required, the form of dipole moment used in the calculation of some
    types of `a` and `b` values is as follows:

    .. math::
        \mu_r = 100000\frac{\mu^2(Pc/101325.0)}{Tc^2}

    For first temperature derivative of B:

    .. math::
        \frac{d B^{(0)}}{dT} = \frac{33 Tc}{100 T^{2}} + \frac{277 Tc^{2}}{1000 T^{3}} + \frac{363 Tc^{3}}{10000 T^{4}} + \frac{607 Tc^{8}}{125000 T^{9}}

        \frac{d B^{(1)}}{dT} = - \frac{331 Tc^{2}}{500 T^{3}} + \frac{1269 Tc^{3}}{1000 T^{4}} + \frac{8 Tc^{8}}{125 T^{9}}

        \frac{d B^{(2)}}{dT} = - \frac{6 Tc^{6}}{T^{7}}

        \frac{d B^{(3)}}{dT} = \frac{8 Tc^{8}}{T^{9}}

    For the second temperature derivative of B:

    .. math::
        \frac{d^2 B^{(0)}}{dT^2} = - \frac{3 Tc}{125000 T^{3}} \left(27500 + \frac{34625 Tc}{T} + \frac{6050 Tc^{2}}{T^{2}} + \frac{1821 Tc^{7}}{T^{7}}\right)

        \frac{d^2 B^{(1)}}{dT^2} = \frac{3 Tc^{2}}{500 T^{4}} \left(331 - \frac{846 Tc}{T} - \frac{96 Tc^{6}}{T^{6}}\right)

        \frac{d^2 B^{(2)}}{dT^2} = \frac{42 Tc^{6}}{T^{8}}

        \frac{d^2 B^{(3)}}{dT^2} = - \frac{72 Tc^{8}}{T^{10}}

    For the third temperature derivative of B:

    .. math::
        \frac{d^3 B^{(0)}}{dT^3} = \frac{3 Tc}{12500 T^{4}} \left(8250 + \frac{13850 Tc}{T} + \frac{3025 Tc^{2}}{T^{2}} + \frac{1821 Tc^{7}}{T^{7}}\right)

        \frac{d^3 B^{(1)}}{dT^3} = \frac{3 Tc^{2}}{250 T^{5}} \left(-662 + \frac{2115 Tc}{T} + \frac{480 Tc^{6}}{T^{6}}\right)

        \frac{d^3 B^{(2)}}{dT^3} = - \frac{336 Tc^{6}}{T^{9}}

        \frac{d^3 B^{(3)}}{dT^3} = \frac{720 Tc^{8}}{T^{11}}

    For the first indefinite integral of B:

    .. math::
        \int{B^{(0)}} dT = \frac{289 T}{2000} - \frac{33 Tc}{100} \log{\left (T \right )} + \frac{1}{7000000 T^{7}} \left(969500 T^{6} Tc^{2} + 42350 T^{5} Tc^{3} + 607 Tc^{8}\right)

        \int{B^{(1)}} dT = \frac{637 T}{10000} - \frac{1}{70000 T^{7}} \left(23170 T^{6} Tc^{2} - 14805 T^{5} Tc^{3} - 80 Tc^{8}\right)

        \int{B^{(2)}} dT = - \frac{Tc^{6}}{5 T^{5}}

        \int{B^{(3)}} dT = \frac{Tc^{8}}{7 T^{7}}

    For the second indefinite integral of B:

    .. math::
        \int\int B^{(0)} dT dT = \frac{289 T^{2}}{4000} - \frac{33 T}{100} Tc \log{\left (T \right )} + \frac{33 T}{100} Tc + \frac{277 Tc^{2}}{2000} \log{\left (T \right )} - \frac{1}{42000000 T^{6}} \left(254100 T^{5} Tc^{3} + 607 Tc^{8}\right)

        \int\int B^{(1)} dT dT = \frac{637 T^{2}}{20000} - \frac{331 Tc^{2}}{1000} \log{\left (T \right )} - \frac{1}{210000 T^{6}} \left(44415 T^{5} Tc^{3} + 40 Tc^{8}\right)

        \int\int B^{(2)} dT dT = \frac{Tc^{6}}{20 T^{4}}

        \int\int B^{(3)} dT dT = - \frac{Tc^{8}}{42 T^{6}}

    Examples
    --------
    Example from Perry's Handbook, 8E, p2-499. Matches to a decimal place.

    >>> BVirial_Tsonopoulos_extended(430., 405.65, 11.28E6, 0.252608, a=0, b=0, species_type='ketone', dipole=1.469)
    -9.679715056695323e-05

    References
    ----------
    .. [1] Tsonopoulos, C., and J. L. Heidman. "From the Virial to the Cubic
       Equation of State." Fluid Phase Equilibria 57, no. 3 (1990): 261-76.
       doi:10.1016/0378-3812(90)85126-U
    .. [2] Tsonopoulos, Constantine, and John H. Dymond. "Second Virial
       Coefficients of Normal Alkanes, Linear 1-Alkanols (and Water), Alkyl
       Ethers, and Their Mixtures." Fluid Phase Equilibria, International
       Workshop on Vapour-Liquid Equilibria and Related Properties in Binary
       and Ternary Mixtures of Ethers, Alkanes and Alkanols, 133, no. 1-2
       (June 1997): 11-34. doi:10.1016/S0378-3812(97)00058-7.
    '''
    Tr = T/Tc
    if order == 0:
        B0 = 0.1445 - 0.33/Tr - 0.1385/Tr**2 - 0.0121/Tr**3 - 0.000607/Tr**8
        B1 = 0.0637 + 0.331/Tr**2 - 0.423/Tr**3 - 0.008/Tr**8
        B2 = 1./Tr**6
        B3 = -1./Tr**8
    elif order == 1:
        B0 = 33*Tc/(100*T**2) + 277*Tc**2/(1000*T**3) + 363*Tc**3/(10000*T**4) + 607*Tc**8/(125000*T**9)
        B1 = -331*Tc**2/(500*T**3) + 1269*Tc**3/(1000*T**4) + 8*Tc**8/(125*T**9)
        B2 = -6.0*Tc**6/T**7
        B3 = 8.0*Tc**8/T**9
    elif order == 2:
        B0 = -3*Tc*(27500 + 34625*Tc/T + 6050*Tc**2/T**2 + 1821*Tc**7/T**7)/(125000*T**3)
        B1 = 3*Tc**2*(331 - 846*Tc/T - 96*Tc**6/T**6)/(500*T**4)
        B2 = 42.0*Tc**6/T**8
        B3 = -72.0*Tc**8/T**10
    elif order == 3:
        B0 = 3*Tc*(8250 + 13850*Tc/T + 3025*Tc**2/T**2 + 1821*Tc**7/T**7)/(12500*T**4)
        B1 = 3*Tc**2*(-662 + 2115*Tc/T + 480*Tc**6/T**6)/(250*T**5)
        B2 = -336.0*Tc**6/T**9
        B3 = 720.0*Tc**8/T**11
    elif order == -1:
        B0 = 289*T/2000. - 33*Tc*log(T)/100. + (969500*T**6*Tc**2 + 42350*T**5*Tc**3 + 607*Tc**8)/(7000000.*T**7)
        B1 = 637*T/10000. - (23170*T**6*Tc**2 - 14805*T**5*Tc**3 - 80*Tc**8)/(70000.*T**7)
        B2 = -Tc**6/(5*T**5)
        B3 = Tc**8/(7*T**7)
    elif order == -2:
        B0 = 289*T**2/4000. - 33*T*Tc*log(T)/100. + 33*T*Tc/100. + 277*Tc**2*log(T)/2000. - (254100*T**5*Tc**3 + 607*Tc**8)/(42000000.*T**6)
        B1 = 637*T**2/20000. - 331*Tc**2*log(T)/1000. - (44415*T**5*Tc**3 + 40*Tc**8)/(210000.*T**6)
        B2 = Tc**6/(20*T**4)
        B3 = -Tc**8/(42*T**6)
    else:
        raise Exception('Only orders -2, -1, 0, 1, 2 and 3 are supported.')
    if a == 0 and b == 0 and species_type != '':
        if species_type == 'simple' or species_type == 'normal':
            a, b = 0, 0
        elif species_type == 'methyl alcohol':
            a, b = 0.0878, 0.0525
        elif species_type == 'water':
            a, b = -0.0109, 0
        elif dipole != 0 and Tc != 0 and Pc != 0:
            dipole_r = 1E5*dipole**2*(Pc/101325.0)/Tc**2

            if (species_type == 'ketone' or species_type == 'aldehyde'
            or species_type == 'alkyl nitrile' or species_type == 'ether'
            or species_type == 'carboxylic acid' or species_type == 'ester'):
                a, b = -2.14E-4*dipole_r-4.308E-21*dipole_r**8, 0
            elif (species_type == 'alkyl halide' or species_type == 'mercaptan'
            or species_type == 'sulfide' or species_type == 'disulfide'):
                a, b = -2.188E-4*dipole_r**4-7.831E-21*dipole_r**8, 0

            elif species_type == 'alkanol':
                a, b = 0.0878, 0.00908+0.0006957*dipole_r
    Br = B0 + omega*B1 + a*B2 + b*B3
    return Br*R*Tc/Pc

Response: r Returns the second virial coefficient using the BVirial_Tsonopoulos_extended method.


Instruction:
Make a summary of the following Python 3 code
def Tb(counts):
        r'''Estimates the normal boiling temperature of an organic compound
        using the Joback method as a function of chemical structure only.

        .. math::
            T_b = 198.2 + \sum_i {T_{b,i}}

        For 438 compounds tested by Joback, the absolute average error was
        12.91 K  and standard deviation was 17.85 K; the average relative error
        was 3.6%.

        Parameters
        ----------
        counts : dict
            Dictionary of Joback groups present (numerically indexed) and their
            counts, [-]

        Returns
        -------
        Tb : float
            Estimated normal boiling temperature, [K]

        Examples
        --------
        >>> Joback.Tb({1: 2, 24: 1})
        322.11
        '''
        tot = 0.0
        for group, count in counts.items():
            tot += joback_groups_id_dict[group].Tb*count
        Tb = 198.2 + tot
        return Tb

Response:
Estimate the normal boiling temperature of an organic compound
        using Joback s method.


Instruction:
Create a Python 3 function for
estimating the melting temperature of an organic compound using Joback method.

Response:
def Tm(counts):
        r'''Estimates the melting temperature of an organic compound using the
        Joback method as a function of chemical structure only.

        .. math::
            T_m = 122.5 + \sum_i {T_{m,i}}

        For 388 compounds tested by Joback, the absolute average error was
        22.6 K  and standard deviation was 24.68 K; the average relative error
        was 11.2%.

        Parameters
        ----------
        counts : dict
            Dictionary of Joback groups present (numerically indexed) and their
            counts, [-]

        Returns
        -------
        Tm : float
            Estimated melting temperature, [K]

        Examples
        --------
        >>> Joback.Tm({1: 2, 24: 1})
        173.5
        '''
        tot = 0.0
        for group, count in counts.items():
            tot += joback_groups_id_dict[group].Tm*count
        Tm = 122.5 + tot
        return Tm


Instruction:
Can you tell what is the following Python 3 function doing
def Tc(counts, Tb=None):
        r'''Estimates the critcal temperature of an organic compound using the
        Joback method as a function of chemical structure only, or optionally
        improved by using an experimental boiling point. If the experimental
        boiling point is not provided it will be estimated with the Joback
        method as well.

        .. math::
            T_c = T_b \left[0.584 + 0.965 \sum_i {T_{c,i}}
            - \left(\sum_i {T_{c,i}}\right)^2 \right]^{-1}

        For 409 compounds tested by Joback, the absolute average error was
        4.76 K  and standard deviation was 6.94 K; the average relative error
        was 0.81%.

        Appendix BI of Joback's work lists 409 estimated critical temperatures.

        Parameters
        ----------
        counts : dict
            Dictionary of Joback groups present (numerically indexed) and their
            counts, [-]
        Tb : float, optional
            Experimental normal boiling temperature, [K]

        Returns
        -------
        Tc : float
            Estimated critical temperature, [K]

        Examples
        --------
        >>> Joback.Tc({1: 2, 24: 1}, Tb=322.11)
        500.5590049525365
        '''
        if Tb is None:
            Tb = Joback.Tb(counts)
        tot = 0.0
        for group, count in counts.items():
            tot += joback_groups_id_dict[group].Tc*count
        Tc = Tb/(0.584 + 0.965*tot - tot*tot)
        return Tc

Response: Return the critcal temperature of an organic compound using Joback s method.


Instruction:
Here you have a function in Python 3, explain what it does
def Vc(counts):
        r'''Estimates the critcal volume of an organic compound using the
        Joback method as a function of chemical structure only.

        .. math::
            V_c = 17.5 + \sum_i {V_{c,i}}

        In the above equation, critical volume is calculated in cm^3/mol; it
        is converted to m^3/mol here.

        310 compounds were used by Joback in this determination, with an
        absolute average error of 7.54 cm^3/mol, standard devaition 13.16
        cm^3/mol, and AARE of 2.27%.

        Parameters
        ----------
        counts : dict
            Dictionary of Joback groups present (numerically indexed) and their
            counts, [-]

        Returns
        -------
        Vc : float
            Estimated critical volume, [m^3/mol]

        Examples
        --------
        >>> Joback.Vc({1: 2, 24: 1})
        0.0002095
        '''
        tot = 0.0
        for group, count in counts.items():
            tot += joback_groups_id_dict[group].Vc*count
        Vc = 17.5 + tot
        return Vc*1E-6

Response: Estimate the critical volume of an organic compound using Joback method.


Instruction:
Write a Python 3 function for
returning an ideal - gas enthalpy of formation at 298. 15 K of an ancestry.

Response:
def Hf(counts):
        r'''Estimates the ideal-gas enthalpy of formation at 298.15 K of an
        organic compound using the Joback method as a function of chemical
        structure only.

        .. math::
            H_{formation} = 68.29 + \sum_i {H_{f,i}}

        In the above equation, enthalpy of formation is calculated in kJ/mol;
        it is converted to J/mol here.

        370 compounds were used by Joback in this determination, with an
        absolute average error of 2.2 kcal/mol, standard devaition 2.0
        kcal/mol, and AARE of 15.2%.

        Parameters
        ----------
        counts : dict
            Dictionary of Joback groups present (numerically indexed) and their
            counts, [-]

        Returns
        -------
        Hf : float
            Estimated ideal-gas enthalpy of formation at 298.15 K, [J/mol]

        Examples
        --------
        >>> Joback.Hf({1: 2, 24: 1})
        -217829.99999999997
        '''
        tot = 0.0
        for group, count in counts.items():
            tot += joback_groups_id_dict[group].Hform*count
        Hf = 68.29 + tot
        return Hf*1000


Instruction:
Can you generate the documentation for the following Python 3 function
def Gf(counts):
        r'''Estimates the ideal-gas Gibbs energy of formation at 298.15 K of an
        organic compound using the Joback method as a function of chemical
        structure only.

        .. math::
            G_{formation} = 53.88 + \sum {G_{f,i}}

        In the above equation, Gibbs energy of formation is calculated in
        kJ/mol; it is converted to J/mol here.

        328 compounds were used by Joback in this determination, with an
        absolute average error of 2.0 kcal/mol, standard devaition 4.37
        kcal/mol, and AARE of 15.7%.

        Parameters
        ----------
        counts : dict
            Dictionary of Joback groups present (numerically indexed) and their
            counts, [-]

        Returns
        -------
        Gf : float
            Estimated ideal-gas Gibbs energy of formation at 298.15 K, [J/mol]

        Examples
        --------
        >>> Joback.Gf({1: 2, 24: 1})
        -154540.00000000003
        '''
        tot = 0.0
        for group, count in counts.items():
            tot += joback_groups_id_dict[group].Gform*count
        Gf = 53.88 + tot
        return Gf*1000

Response: Calculates the ideal - gas Gibbs energy of formation at 298. 15 K of an organic compound.


Instruction:
Write a Python 3 function that can
calculate the enthalpy of fusion of an organic compound at its melting point using Joback s method.

Response:
def Hfus(counts):
        r'''Estimates the enthalpy of fusion of an organic compound at its
        melting point using the Joback method as a function of chemical
        structure only.

        .. math::
            \Delta H_{fus} = -0.88 + \sum_i H_{fus,i}

        In the above equation, enthalpy of fusion is calculated in
        kJ/mol; it is converted to J/mol here.

        For 155 compounds tested by Joback, the absolute average error was
        485.2 cal/mol  and standard deviation was 661.4 cal/mol; the average
        relative error was 38.7%.

        Parameters
        ----------
        counts : dict
            Dictionary of Joback groups present (numerically indexed) and their
            counts, [-]

        Returns
        -------
        Hfus : float
            Estimated enthalpy of fusion of the compound at its melting point,
            [J/mol]

        Examples
        --------
        >>> Joback.Hfus({1: 2, 24: 1})
        5125.0
        '''
        tot = 0.0
        for group, count in counts.items():
            tot += joback_groups_id_dict[group].Hfus*count
        Hfus = -0.88 + tot
        return Hfus*1000


Instruction:
Can you tell what is the following Python 3 function doing
def Hvap(counts):
        r'''Estimates the enthalpy of vaporization of an organic compound at
        its normal boiling point using the Joback method as a function of
        chemical structure only.

        .. math::
            \Delta H_{vap} = 15.30 + \sum_i H_{vap,i}

        In the above equation, enthalpy of fusion is calculated in
        kJ/mol; it is converted to J/mol here.

        For 368 compounds tested by Joback, the absolute average error was
        303.5 cal/mol  and standard deviation was 429 cal/mol; the average
        relative error was 3.88%.

        Parameters
        ----------
        counts : dict
            Dictionary of Joback groups present (numerically indexed) and their
            counts, [-]

        Returns
        -------
        Hvap : float
            Estimated enthalpy of vaporization of the compound at its normal
            boiling point, [J/mol]

        Examples
        --------
        >>> Joback.Hvap({1: 2, 24: 1})
        29018.0
        '''
        tot = 0.0
        for group, count in counts.items():
            tot += joback_groups_id_dict[group].Hvap*count
        Hvap = 15.3 + tot
        return Hvap*1000

Response:
Calculates the enthalpy of vaporization of an organic compound at
        normal boiling point using Joback s method of Joback method.


Instruction:
Explain what the following Python 3 code does
def mul_coeffs(counts):
        r'''Computes the liquid phase viscosity Joback coefficients
        of an organic compound using the Joback method as a function of
        chemical structure only.

        .. math::
            \mu_{liq} = \text{MW} \exp\left( \frac{ \sum_i \mu_a - 597.82}{T}
            + \sum_i \mu_b - 11.202 \right)

        288 compounds were used by Joback in this determination. No overall
        error was reported.

        The liquid viscosity data used was specified to be at "several
        temperatures for each compound" only. A small and unspecified number
        of compounds were used in this estimation.

        Parameters
        ----------
        counts : dict
            Dictionary of Joback groups present (numerically indexed) and their
            counts, [-]

        Returns
        -------
        coefficients : list[float]
            Coefficients which will result in a liquid viscosity in
            in units of Pa*s, [-]

        Examples
        --------
        >>> mu_ab = Joback.mul_coeffs({1: 2, 24: 1})
        >>> mu_ab
        [839.1099999999998, -14.99]
        >>> MW = 58.041864812
        >>> mul = lambda T : MW*exp(mu_ab[0]/T + mu_ab[1])
        >>> mul(300)
        0.0002940378347162687
        '''
        a, b = 0.0, 0.0
        for group, count in counts.items():
            a += joback_groups_id_dict[group].mua*count
            b += joback_groups_id_dict[group].mub*count
        a -= 597.82
        b -= 11.202
        return [a, b]

Response: r Returns the liquid phase viscosity in a given set of temperatures for each Joback group.


Instruction:
Given the following Python 3 function, write the documentation
def Cpig(self, T):
        r'''Computes ideal-gas heat capacity at a specified temperature
        of an organic compound using the Joback method as a function of
        chemical structure only.

        .. math::
            C_p^{ig} = \sum_i a_i - 37.93 + \left[ \sum_i b_i + 0.210 \right] T
            + \left[ \sum_i c_i - 3.91 \cdot 10^{-4} \right] T^2
            + \left[\sum_i d_i + 2.06 \cdot 10^{-7}\right] T^3

        Parameters
        ----------
        T : float
            Temperature, [K]

        Returns
        -------
        Cpig : float
            Ideal-gas heat capacity, [J/mol/K]

        Examples
        --------
        >>> J = Joback('CC(=O)C')
        >>> J.Cpig(300)
        75.32642000000001
        '''
        if self.calculated_Cpig_coeffs is None:
            self.calculated_Cpig_coeffs = Joback.Cpig_coeffs(self.counts)
        return horner(reversed(self.calculated_Cpig_coeffs), T)

Response: r Returns the ideal - gas heat capacity at a specified temperature INCLUSIVE WITHOUT A COVERAGE.


Instruction:
Explain what the following Python 3 code does
def Laliberte_viscosity_i(T, w_w, v1, v2, v3, v4, v5, v6):
    r'''Calculate the viscosity of a solute using the form proposed by [1]_
    Parameters are needed, and a temperature. Units are Kelvin and Pa*s.

    .. math::
        \mu_i = \frac{\exp\left( \frac{v_1(1-w_w)^{v_2}+v_3}{v_4 t +1}\right)}
            {v_5(1-w_w)^{v_6}+1}

    Parameters
    ----------
    T : float
        Temperature of fluid [K]
    w_w : float
        Weight fraction of water in the solution
    v1-v6 : floats
        Function fit parameters

    Returns
    -------
    mu_i : float
        Solute partial viscosity, Pa*s

    Notes
    -----
    Temperature range check is outside of this function.
    Check is performed using NaCl at 5 degC from the first value in [1]_'s spreadsheet.

    Examples
    --------
    >>> d =  _Laliberte_Viscosity_ParametersDict['7647-14-5']
    >>> Laliberte_viscosity_i(273.15+5, 1-0.005810, d["V1"], d["V2"], d["V3"], d["V4"], d["V5"], d["V6"] )
    0.004254025533308794

    References
    ----------
    .. [1] Laliberte, Marc. "A Model for Calculating the Heat Capacity of
       Aqueous Solutions, with Updated Density and Viscosity Data." Journal of
       Chemical & Engineering Data 54, no. 6 (June 11, 2009): 1725-60.
       doi:10.1021/je8008123
    '''
    t = T-273.15
    mu_i = exp((v1*(1-w_w)**v2 + v3)/(v4*t+1))/(v5*(1-w_w)**v6 + 1)
    return mu_i/1000.

Response: A function that calculates the viscosity of a solute in a Laliberte system.


Instruction:
Can you tell what is the following Python 3 function doing
def Laliberte_density_w(T):
    r'''Calculate the density of water using the form proposed by [1]_.
    No parameters are needed, just a temperature. Units are Kelvin and kg/m^3h.

    .. math::
        \rho_w = \frac{\left\{\left([(-2.8054253\times 10^{-10}\cdot t +
        1.0556302\times 10^{-7})t - 4.6170461\times 10^{-5}]t
        -0.0079870401\right)t + 16.945176   \right\}t + 999.83952}
        {1 + 0.01687985\cdot t}

    Parameters
    ----------
    T : float
        Temperature of fluid [K]

    Returns
    -------
    rho_w : float
        Water density, [kg/m^3]

    Notes
    -----
    Original source not cited
    No temperature range is used.

    Examples
    --------
    >>> Laliberte_density_w(298.15)
    997.0448954179155
    >>> Laliberte_density_w(273.15 + 50)
    988.0362916114763

    References
    ----------
    .. [1] Laliberte, Marc. "A Model for Calculating the Heat Capacity of
       Aqueous Solutions, with Updated Density and Viscosity Data." Journal of
       Chemical & Engineering Data 54, no. 6 (June 11, 2009): 1725-60.
       doi:10.1021/je8008123
    '''
    t = T-273.15
    rho_w = (((((-2.8054253E-10*t + 1.0556302E-7)*t - 4.6170461E-5)*t - 0.0079870401)*t + 16.945176)*t + 999.83952) \
        / (1 + 0.01687985*t)
    return rho_w

Response: Calculates the density of water using the form proposed by [ 1 ] Laliberte Marc. A Model for Calculating the Heat Capacity of Aqueous Solutions with Updated Density and Viscosity Data.


Instruction:
Make a summary of the following Python 3 code
def Laliberte_density_i(T, w_w, c0, c1, c2, c3, c4):
    r'''Calculate the density of a solute using the form proposed by Laliberte [1]_.
    Parameters are needed, and a temperature, and water fraction. Units are Kelvin and Pa*s.

    .. math::
        \rho_{app,i} = \frac{(c_0[1-w_w]+c_1)\exp(10^{-6}[t+c_4]^2)}
        {(1-w_w) + c_2 + c_3 t}

    Parameters
    ----------
    T : float
        Temperature of fluid [K]
    w_w : float
        Weight fraction of water in the solution
    c0-c4 : floats
        Function fit parameters

    Returns
    -------
    rho_i : float
        Solute partial density, [kg/m^3]

    Notes
    -----
    Temperature range check is TODO

    Examples
    --------
    >>> d = _Laliberte_Density_ParametersDict['7647-14-5']
    >>> Laliberte_density_i(273.15+0, 1-0.0037838838, d["C0"], d["C1"], d["C2"], d["C3"], d["C4"])
    3761.8917585699983

    References
    ----------
    .. [1] Laliberte, Marc. "A Model for Calculating the Heat Capacity of
       Aqueous Solutions, with Updated Density and Viscosity Data." Journal of
       Chemical & Engineering Data 54, no. 6 (June 11, 2009): 1725-60.
       doi:10.1021/je8008123
    '''
    t = T - 273.15
    return ((c0*(1 - w_w)+c1)*exp(1E-6*(t + c4)**2))/((1 - w_w) + c2 + c3*t)

Response: r Calculates the density of a solute in a Laliberte.


Instruction:
Can you generate a brief explanation for the following Python 3 code
def Laliberte_density(T, ws, CASRNs):
    r'''Calculate the density of an aqueous electrolyte mixture using the form proposed by [1]_.
    Parameters are loaded by the function as needed. Units are Kelvin and Pa*s.

    .. math::
        \rho_m = \left(\frac{w_w}{\rho_w} + \sum_i \frac{w_i}{\rho_{app_i}}\right)^{-1}

    Parameters
    ----------
    T : float
        Temperature of fluid [K]
    ws : array
        Weight fractions of fluid components other than water
    CASRNs : array
        CAS numbers of the fluid components other than water

    Returns
    -------
    rho_i : float
        Solution density, [kg/m^3]

    Notes
    -----
    Temperature range check is not used here.

    Examples
    --------
    >>> Laliberte_density(273.15, [0.0037838838], ['7647-14-5'])
    1002.6250120185854

    References
    ----------
    .. [1] Laliberte, Marc. "A Model for Calculating the Heat Capacity of
       Aqueous Solutions, with Updated Density and Viscosity Data." Journal of
       Chemical & Engineering Data 54, no. 6 (June 11, 2009): 1725-60.
       doi:10.1021/je8008123
    '''
    rho_w = Laliberte_density_w(T)
    w_w = 1 - sum(ws)
    rho = w_w/rho_w
    for i in range(len(CASRNs)):
        d = _Laliberte_Density_ParametersDict[CASRNs[i]]
        rho_i = Laliberte_density_i(T, w_w, d["C0"], d["C1"], d["C2"], d["C3"], d["C4"])
        rho = rho + ws[i]/rho_i
    return 1./rho

Response: r Calculates the density of an aqueous electrolyte mixture using the form proposed by [ 1 ].


Instruction:
Can you generate the documentation for the following Python 3 function
def Laliberte_heat_capacity_i(T, w_w, a1, a2, a3, a4, a5, a6):
    r'''Calculate the heat capacity of a solute using the form proposed by [1]_
    Parameters are needed, and a temperature, and water fraction.

    .. math::
        Cp_i = a_1 e^\alpha + a_5(1-w_w)^{a_6}
        \alpha = a_2 t + a_3 \exp(0.01t) + a_4(1-w_w)

    Parameters
    ----------
    T : float
        Temperature of fluid [K]
    w_w : float
        Weight fraction of water in the solution
    a1-a6 : floats
        Function fit parameters

    Returns
    -------
    Cp_i : float
        Solute partial heat capacity, [J/kg/K]

    Notes
    -----
    Units are Kelvin and J/kg/K.
    Temperature range check is TODO

    Examples
    --------
    >>> d = _Laliberte_Heat_Capacity_ParametersDict['7647-14-5']
    >>> Laliberte_heat_capacity_i(1.5+273.15, 1-0.00398447, d["A1"], d["A2"], d["A3"], d["A4"], d["A5"], d["A6"])
    -2930.7353945880477

    References
    ----------
    .. [1] Laliberte, Marc. "A Model for Calculating the Heat Capacity of
       Aqueous Solutions, with Updated Density and Viscosity Data." Journal of
       Chemical & Engineering Data 54, no. 6 (June 11, 2009): 1725-60.
       doi:10.1021/je8008123
    '''
    t = T - 273.15
    alpha = a2*t + a3*exp(0.01*t) + a4*(1. - w_w)
    Cp_i = a1*exp(alpha) + a5*(1. - w_w)**a6
    return Cp_i*1000.

Response: r Calculates the heat capacity of a solute using the form proposed by [ 1 ] _Laliberte_Heat_Capacity_ParametersDict.


Instruction:
Make a summary of the following Python 3 code
def dilute_ionic_conductivity(ionic_conductivities, zs, rhom):
    r'''This function handles the calculation of the electrical conductivity of
    a dilute electrolytic aqueous solution. Requires the mole fractions of
    each ion, the molar density of the whole mixture, and ionic conductivity
    coefficients for each ion.

    .. math::
        \lambda = \sum_i \lambda_i^\circ z_i \rho_m

    Parameters
    ----------
    ionic_conductivities : list[float]
        Ionic conductivity coefficients of each ion in the mixture [m^2*S/mol]
    zs : list[float]
        Mole fractions of each ion in the mixture, [-]
    rhom : float
        Overall molar density of the solution, [mol/m^3]

    Returns
    -------
    kappa : float
        Electrical conductivity of the fluid, [S/m]

    Notes
    -----
    The ionic conductivity coefficients should not be `equivalent` coefficients;
    for example, 0.0053 m^2*S/mol is the equivalent conductivity coefficient of
    Mg+2, but this method expects twice its value - 0.0106. Both are reported
    commonly in literature.

    Water can be included in this caclulation by specifying a coefficient of
    0. The conductivity of any electrolyte eclipses its own conductivity by
    many orders of magnitude. Any other solvents present will affect the
    conductivity extensively and there are few good methods to predict this
    effect.

    Examples
    --------
    Complex mixture of electrolytes ['Cl-', 'HCO3-', 'SO4-2', 'Na+', 'K+',
    'Ca+2', 'Mg+2']:

    >>> ionic_conductivities = [0.00764, 0.00445, 0.016, 0.00501, 0.00735, 0.0119, 0.01061]
    >>> zs = [0.03104, 0.00039, 0.00022, 0.02413, 0.0009, 0.0024, 0.00103]
    >>> dilute_ionic_conductivity(ionic_conductivities=ionic_conductivities, zs=zs, rhom=53865.9)
    22.05246783663

    References
    ----------
    .. [1] Haynes, W.M., Thomas J. Bruno, and David R. Lide. CRC Handbook of
       Chemistry and Physics, 95E. Boca Raton, FL: CRC press, 2014.
    '''
    return sum([ci*(zi*rhom) for zi, ci in zip(zs, ionic_conductivities)])

Response: r Returns the ionic conductivity of a dilute electrolytic aqueous solution.


Instruction:
Explain what the following Python 3 code does
def conductivity_McCleskey(T, M, lambda_coeffs, A_coeffs, B, multiplier, rho=1000.):
    r'''This function handles the calculation of the electrical conductivity of
    an electrolytic aqueous solution with one electrolyte in solution. It
    handles temperature dependency and concentrated solutions. Requires the
    temperature of the solution; its molality, and four sets of coefficients
    `lambda_coeffs`, `A_coeffs`, `B`, and `multiplier`.

    .. math::
        \Lambda = \frac{\kappa}{C}

        \Lambda = \Lambda^0(t) - A(t) \frac{m^{1/2}}{1+Bm^{1/2}}

        \Lambda^\circ(t) = c_1 t^2 + c_2 t + c_3

        A(t) = d_1 t^2 + d_2 t + d_3

    In the above equations, `t` is temperature in degrees Celcius;
    `m` is molality in mol/kg, and C is the concentration of the elctrolytes
    in mol/m^3, calculated as the product of density and molality.

    Parameters
    ----------
    T : float
        Temperature of the solution, [K]
    M : float
        Molality of the solution with respect to one electrolyte
        (mol solute / kg solvent), [mol/kg]
    lambda_coeffs : list[float]
        List of coefficients for the polynomial used to calculate `lambda`;
        length-3 coefficients provided in [1]_,  [-]
    A_coeffs : list[float]
        List of coefficients for the polynomial used to calculate `A`;
        length-3 coefficients provided in [1]_, [-]
    B : float
        Empirical constant for an electrolyte, [-]
    multiplier : float
        The multiplier to obtain the absolute conductivity from the equivalent
        conductivity; ex 2 for CaCl2, [-]
    rho : float, optional
        The mass density of the aqueous mixture, [kg/m^3]

    Returns
    -------
    kappa : float
        Electrical conductivity of the solution at the specified molality and
        temperature [S/m]

    Notes
    -----
    Coefficients provided in [1]_ result in conductivity being calculated in
    units of mS/cm; they are converted to S/m before returned.

    Examples
    --------
    A 0.5 wt% solution of CaCl2, conductivity calculated in mS/cm

    >>> conductivity_McCleskey(T=293.15, M=0.045053, A_coeffs=[.03918, 3.905,
    ... 137.7], lambda_coeffs=[0.01124, 2.224, 72.36], B=3.8, multiplier=2)
    0.8482584585108555

    References
    ----------
    .. [1] McCleskey, R. Blaine. "Electrical Conductivity of Electrolytes Found
       In Natural Waters from (5 to 90) °C." Journal of Chemical & Engineering
       Data 56, no. 2 (February 10, 2011): 317-27. doi:10.1021/je101012n.
    '''
    t = T - 273.15
    lambda_coeff = horner(lambda_coeffs, t)
    A = horner(A_coeffs, t)
    M_root = M**0.5
    param = lambda_coeff - A*M_root/(1. + B*M_root)
    C = M*rho/1000. # convert to mol/L to get concentration
    return param*C*multiplier*0.1

Response: r Returns the electrical conductivity of a given entry in the order in which the electrolyte is in the same species as the one in the solution.


Instruction:
Given the following Python 3 function, write the documentation
def conductivity(CASRN=None, AvailableMethods=False, Method=None, full_info=True):
    r'''This function handles the retrieval of a chemical's conductivity.
    Lookup is based on CASRNs. Will automatically select a data source to use
    if no Method is provided; returns None if the data is not available.

    Function has data for approximately 100 chemicals.

    Parameters
    ----------
    CASRN : string
        CASRN [-]

    Returns
    -------
    kappa : float
        Electrical conductivity of the fluid, [S/m]
    T : float, only returned if full_info == True
        Temperature at which conductivity measurement was made
    methods : list, only returned if AvailableMethods == True
        List of methods which can be used to obtain RI with the given inputs

    Other Parameters
    ----------------
    Method : string, optional
        A string for the method name to use, as defined by constants in
        conductivity_methods
    AvailableMethods : bool, optional
        If True, function will determine which methods can be used to obtain
        conductivity for the desired chemical, and will return methods instead
        of conductivity
    full_info : bool, optional
        If True, function will return the temperature at which the conductivity
        reading was made

    Notes
    -----
    Only one source is available in this function. It is:

        * 'LANGE_COND' which is from Lange's Handbook, Table 8.34 Electrical
        Conductivity of Various Pure Liquids', a compillation of data in [1]_.

    Examples
    --------
    >>> conductivity('7732-18-5')
    (4e-06, 291.15)

    References
    ----------
    .. [1] Speight, James. Lange's Handbook of Chemistry. 16 edition.
       McGraw-Hill Professional, 2005.
    '''
    def list_methods():
        methods = []
        if CASRN in Lange_cond_pure.index:
            methods.append(LANGE_COND)
        methods.append(NONE)
        return methods
    if AvailableMethods:
        return list_methods()
    if not Method:
        Method = list_methods()[0]
    if Method == LANGE_COND:
        kappa = float(Lange_cond_pure.at[CASRN, 'Conductivity'])
        if full_info:
            T = float(Lange_cond_pure.at[CASRN, 'T'])

    elif Method == NONE:
        kappa, T = None, None
    else:
        raise Exception('Failure in in function')

    if full_info:
        return kappa, T
    else:
        return kappa

Response: r Returns the conductivity of a given chemical.


Instruction:
Can you generate the documentation for the following Python 3 function
def thermal_conductivity_Magomedov(T, P, ws, CASRNs, k_w=None):
    r'''Calculate the thermal conductivity of an aqueous mixture of
    electrolytes using the form proposed by Magomedov [1]_.
    Parameters are loaded by the function as needed. Function will fail if an
    electrolyte is not in the database.

    .. math::
        \lambda = \lambda_w\left[ 1 - \sum_{i=1}^n A_i (w_i + 2\times10^{-4}
        w_i^3)\right] - 2\times10^{-8} PT\sum_{i=1}^n w_i

    Parameters
    ----------
    T : float
        Temperature of liquid [K]
    P : float
        Pressure of the liquid [Pa]
    ws : array
        Weight fractions of liquid components other than water
    CASRNs : array
        CAS numbers of the liquid components other than water
    k_w : float
        Liquid thermal condiuctivity or pure water at T and P, [W/m/K]

    Returns
    -------
    kl : float
        Liquid thermal condiuctivity, [W/m/K]

    Notes
    -----
    Range from 273 K to 473 K, P from 0.1 MPa to 100 MPa. C from 0 to 25 mass%.
    Internal untis are MPa for pressure and weight percent.

    An example is sought for this function. It is not possible to reproduce
    the author's values consistently.

    Examples
    --------
    >>> thermal_conductivity_Magomedov(293., 1E6, [.25], ['7758-94-3'], k_w=0.59827)
    0.548654049375

    References
    ----------
    .. [1] Magomedov, U. B. "The Thermal Conductivity of Binary and
       Multicomponent Aqueous Solutions of Inorganic Substances at High
       Parameters of State." High Temperature 39, no. 2 (March 1, 2001):
       221-26. doi:10.1023/A:1017518731726.
    '''
    P = P/1E6
    ws = [i*100 for i in ws]
    if not k_w:
        raise Exception('k_w correlation must be provided')

    sum1 = 0
    for i, CASRN in enumerate(CASRNs):
        Ai = float(Magomedovk_thermal_cond.at[CASRN, 'Ai'])
        sum1 += Ai*(ws[i] + 2E-4*ws[i]**3)
    return k_w*(1 - sum1) - 2E-8*P*T*sum(ws)

Response: r Calculates the thermal conductivity of an aqueous mixture of an anonymized liquid components other than water at T and P.


Instruction:
Can you tell what is the following Python 3 function doing
def ionic_strength(mis, zis):
    r'''Calculate the ionic strength of a solution in one of two ways,
    depending on the inputs only. For Pitzer and Bromley models,
    `mis` should be molalities of each component. For eNRTL models,
    `mis` should be mole fractions of each electrolyte in the solution.
    This will sum to be much less than 1.

    .. math::
        I = \frac{1}{2} \sum M_i z_i^2

        I = \frac{1}{2} \sum x_i z_i^2

    Parameters
    ----------
    mis : list
        Molalities of each ion, or mole fractions of each ion [mol/kg or -]
    zis : list
        Charges of each ion [-]

    Returns
    -------
    I : float
        ionic strength, [?]

    Examples
    --------
    >>> ionic_strength([0.1393, 0.1393], [1, -1])
    0.1393

    References
    ----------
    .. [1] Chen, Chau-Chyun, H. I. Britt, J. F. Boston, and L. B. Evans. "Local
       Composition Model for Excess Gibbs Energy of Electrolyte Systems.
       Part I: Single Solvent, Single Completely Dissociated Electrolyte
       Systems." AIChE Journal 28, no. 4 (July 1, 1982): 588-96.
       doi:10.1002/aic.690280410
    .. [2] Gmehling, Jurgen. Chemical Thermodynamics: For Process Simulation.
       Weinheim, Germany: Wiley-VCH, 2012.
    '''
    return 0.5*sum([mi*zi*zi for mi, zi in zip(mis, zis)])

Response: r Calculates the ionic strength of a single - system problem in one of two ways.


Instruction:
Can you generate a brief explanation for the following Python 3 code
def Kweq_1981(T, rho_w):
    r'''Calculates equilibrium constant for OH- and H+ in water, according to
    [1]_. Second most recent formulation.

    .. math::
        \log_{10} K_w= A + B/T + C/T^2 + D/T^3 + (E+F/T+G/T^2)\log_{10} \rho_w

    Parameters
    ----------
    T : float
        Temperature of fluid [K]
    rho_w : float
        Density of water, [kg/m^3]

    Returns
    -------
    Kweq : float
        Ionization constant of water, [-]

    Notes
    -----
    Density is internally converted to units of g/cm^3.

    A = -4.098;
    B = -3245.2;
    C = 2.2362E5;
    D = -3.984E7;
    E = 13.957;
    F = -1262.3;
    G = 8.5641E5

    Examples
    --------
    >>> -1*log10(Kweq_1981(600, 700))
    11.274522047458206

    References
    ----------
    .. [1] Marshall, William L., and E. U. Franck. "Ion Product of Water
       Substance, 0-1000  degree C, 1010,000 Bars New International Formulation
       and Its Background." Journal of Physical and Chemical Reference Data 10,
       no. 2 (April 1, 1981): 295-304. doi:10.1063/1.555643.
    '''
    rho_w = rho_w/1000.
    A = -4.098
    B = -3245.2
    C = 2.2362E5
    D = -3.984E7
    E = 13.957
    F = -1262.3
    G = 8.5641E5
    return 10**(A + B/T + C/T**2 + D/T**3 + (E + F/T + G/T**2)*log10(rho_w))

Response: r Returns the Kweq constant for OH - and H + in water.


Instruction:
How would you explain what the following Python 3 function does
def Kweq_IAPWS_gas(T):
    r'''Calculates equilibrium constant for OH- and H+ in water vapor,
    according to [1]_.
    This is the most recent formulation available.

    .. math::
        -log_{10}  K_w^G = \gamma_0 + \gamma_1 T^{-1} + \gamma_2 T^{-2} + \gamma_3 T^{-3}

    Parameters
    ----------
    T : float
        Temperature of H2O [K]

    Returns
    -------
    K_w_G : float

    Notes
    -----
    gamma0 = 6.141500E-1;
    gamma1 = 4.825133E4;
    gamma2 = -6.770793E4;
    gamma3 = 1.010210E7

    Examples
    --------
    >>> Kweq_IAPWS_gas(800)
    1.4379721554798815e-61

    References
    ----------
    .. [1] Bandura, Andrei V., and Serguei N. Lvov. "The Ionization Constant
       of Water over Wide Ranges of Temperature and Density." Journal of Physical
       and Chemical Reference Data 35, no. 1 (March 1, 2006): 15-30.
       doi:10.1063/1.1928231
    '''
    gamma0 = 6.141500E-1
    gamma1 = 4.825133E4
    gamma2 = -6.770793E4
    gamma3 = 1.010210E7
    K_w_G = 10**(-1*(gamma0 + gamma1/T + gamma2/T**2 + gamma3/T**3))
    return K_w_G

Response: rCalculates equilibrium constant for OH - and H + in water vapor.


Instruction:
Can you generate a brief explanation for the following Python 3 code
def calculate(self, T, method):
        r'''Method to calculate permittivity of a liquid at temperature `T`
        with a given method.

        This method has no exception handling; see `T_dependent_property`
        for that.

        Parameters
        ----------
        T : float
            Temperature at which to calculate relative permittivity, [K]
        method : str
            Name of the method to use

        Returns
        -------
        epsilon : float
            Relative permittivity of the liquid at T, [-]
        '''
        if method == CRC:
            A, B, C, D = self.CRC_coeffs
            epsilon = A + B*T + C*T**2 + D*T**3
        elif method == CRC_CONSTANT:
            epsilon = self.CRC_permittivity
        elif method in self.tabular_data:
            epsilon = self.interpolate(T, method)
        return epsilon

Response: r Method to calculate relative permittivity of a liquid at temperature T with a given method.


Instruction:
Implement a Python 3 function for
calculating the heat of combustion in J / mol.

Response:
def Hcombustion(atoms, Hf=None, HfH2O=-285825, HfCO2=-393474,
                HfSO2=-296800, HfBr2=30880, HfI2=62417, HfHCl=-92173,
                HfHF=-272711, HfP4O10=-3009940, HfO2=0, HfN2=0):
    '''Calculates the heat of combustion, in J/mol.
    Value non-hydrocarbons is not correct, but still calculable.

    Parameters
    ----------
    atoms : dict
        Dictionary of atoms and their counts, []
    Hf : float
        Heat of formation of given chemical, [J/mol]
    HfH2O : float, optional
        Heat of formation of water, [J/mol]
    HfCO2 : float, optional
        Heat of formation of carbon dioxide, [J/mol]
    HfSO2 : float, optional
        Heat of formation of sulfur dioxide, [J/mol]
    HfBr2 : float, optional
        Heat of formation of bromine, [J/mol]
    HfI2 : float, optional
        Heat of formation of iodine, [J/mol]
    HfHCl : float, optional
        Heat of formation of chlorine, [J/mol]
    HfHF : float, optional
        Heat of formation of hydrogen fluoride, [J/mol]
    HfP4O10 : float, optional
        Heat of formation of phosphorus pentoxide, [J/mol]
    HfO2 : float, optional
        Heat of formation of oxygen, [J/mol]
    HfN2 : float, optional
        Heat of formation of nitrogen, [J/mol]

    Returns
    -------
    Hc : float
        Heat of combustion of chemical, [J/mol]

    Notes
    -----
    Default heats of formation for chemicals are at 298 K, 1 atm.

    Examples
    --------
    Liquid methanol burning

    >>> Hcombustion({'H': 4, 'C': 1, 'O': 1}, Hf=-239100)
    -726024.0
    '''
    if not Hf or not atoms:
        return None
    nC, nH, nN, nO, nS, nBr, nI, nCl, nF, nP = 0, 0, 0, 0, 0, 0, 0, 0, 0, 0

    if 'C' in atoms and atoms['C'] != 0:
        nC = atoms['C']
    else:
        return None  # C is necessary for this formula
    if 'H' in atoms:
        nH = atoms['H']
    if 'N' in atoms:
        nN = atoms['N']
    if 'O' in atoms:
        nO = atoms['O']
    if 'S' in atoms:
        nS = atoms['S']
    if 'Br' in atoms:
        nBr = atoms['Br']
    if 'I' in atoms:
        nI = atoms['I']
    if 'Cl' in atoms:
        nCl = atoms['Cl']
    if 'F' in atoms:
        nF = atoms['F']
    if 'P' in atoms:
        nP = atoms['P']

    nO2_req = nC + nS + nH/4. + 5*nP/4. - (nCl + nF)/4. - nO/2.
    nCO2 = nC
    nBr2 = nBr/2.
    nI2 = nI/2.

    nHCl = nCl
    nHF = nF

    nSO2 = nS

    nN2 = nN/2.
    nP4O10 = nP/4.
    nH2O = (nH - nCl - nF)/2.

    Hc = (nBr2*HfBr2 + nI2*HfI2) + (nHCl*HfHCl + nHF*HfHF) + nSO2*HfSO2 + \
        nN2*HfN2 + nP4O10*HfP4O10 + nH2O*HfH2O - nO2_req*HfO2 + nCO2*HfCO2 - Hf
    return Hc


Instruction:
Can you tell what is the following Python 3 function doing
def REFPROP(T, Tc, sigma0, n0, sigma1=0, n1=0, sigma2=0, n2=0):
    r'''Calculates air-liquid surface tension  using the REFPROP [1]_
    regression-based method. Relatively recent, and most accurate.

    .. math::
        \sigma(T)=\sigma_0\left(1-\frac{T}{T_c}\right)^{n_0}+
        \sigma_1\left(1-\frac{T}{T_c}\right)^{n_1}+
        \sigma_2\left(1-\frac{T}{T_c}\right)^{n_2}

    Parameters
    ----------
    T : float
        Temperature of fluid [K]
    Tc : float
        Critical temperature of fluid [K]
    sigma0 : float
        First emperical coefficient of a fluid
    n0 : float
        First emperical exponent of a fluid
    sigma1 : float, optional
        Second emperical coefficient of a fluid.
    n1 : float, optional
        Second emperical exponent of a fluid.
    sigma1 : float, optional
        Third emperical coefficient of a fluid.
    n2 : float, optional
        Third emperical exponent of a fluid.

    Returns
    -------
    sigma : float
        Liquid surface tension, N/m

    Notes
    -----
    Function as implemented in [1]_. No example necessary; results match
    literature values perfectly.
    Form of function returns imaginary results when T > Tc; None is returned
    if this is the case.

    Examples
    --------
    Parameters for water at 298.15 K

    >>> REFPROP(298.15, 647.096, -0.1306, 2.471, 0.2151, 1.233)
    0.07205503890847453

    References
    ----------
    .. [1] Diky, Vladimir, Robert D. Chirico, Chris D. Muzny, Andrei F.
       Kazakov, Kenneth Kroenlein, Joseph W. Magee, Ilmutdin Abdulagatov, and
       Michael Frenkel. "ThermoData Engine (TDE): Software Implementation of
       the Dynamic Data Evaluation Concept." Journal of Chemical Information
       and Modeling 53, no. 12 (2013): 3418-30. doi:10.1021/ci4005699.
    '''
    Tr = T/Tc
    sigma = sigma0*(1.-Tr)**n0 + sigma1*(1.-Tr)**n1 + sigma2*(1.-Tr)**n2
    return sigma

Response: r Regression of REFPROP for a given temperature and critical temperature.


Instruction:
Here you have a function in Python 3, explain what it does
def Pitzer(T, Tc, Pc, omega):
    r'''Calculates air-water surface tension using the correlation derived
    by [1]_ from the works of [2]_ and [3]_. Based on critical property CSP
    methods.

    .. math::
        \sigma = P_c^{2/3}T_c^{1/3}\frac{1.86 + 1.18\omega}{19.05}
        \left[ \frac{3.75 + 0.91 \omega}{0.291 - 0.08 \omega}\right]^{2/3} (1-T_r)^{11/9}

    Parameters
    ----------
    T : float
        Temperature of fluid [K]
    Tc : float
        Critical temperature of fluid [K]
    Pc : float
        Critical pressure of fluid [Pa]
    omega : float
        Acentric factor for fluid, [-]

    Returns
    -------
    sigma : float
        Liquid surface tension, N/m

    Notes
    -----
    The source of this equation has not been reviewed.
    Internal units of presure are bar, surface tension of mN/m.

    Examples
    --------
    Chlorobenzene from Poling, as compared with a % error value at 293 K.

    >>> Pitzer(293., 633.0, 4530000.0, 0.249)
    0.03458453513446387

    References
    ----------
    .. [1] Poling, Bruce E. The Properties of Gases and Liquids. 5th edition.
       New York: McGraw-Hill Professional, 2000.
    .. [2] Curl, R. F., and Kenneth Pitzer. "Volumetric and Thermodynamic
       Properties of Fluids-Enthalpy, Free Energy, and Entropy." Industrial &
       Engineering Chemistry 50, no. 2 (February 1, 1958): 265-74.
       doi:10.1021/ie50578a047
    .. [3] Pitzer, K. S.: Thermodynamics, 3d ed., New York, McGraw-Hill,
       1995, p. 521.
    '''
    Tr = T/Tc
    Pc = Pc/1E5  # Convert to bar
    sigma = Pc**(2/3.0)*Tc**(1/3.0)*(1.86+1.18*omega)/19.05 * (
        (3.75+0.91*omega)/(0.291-0.08*omega))**(2/3.0)*(1-Tr)**(11/9.0)
    sigma = sigma/1000  # N/m, please
    return sigma

Response:
r Returns a formula that calculates air - water surface tension using the correlation derived
    by Pitzer.


Instruction:
Can you generate the documentation for the following Python 3 function
def Sastri_Rao(T, Tb, Tc, Pc, chemicaltype=None):
    r'''Calculates air-water surface tension using the correlation derived by
    [1]_ based on critical property CSP methods and chemical classes.

    .. math::
        \sigma = K P_c^xT_b^y T_c^z\left[\frac{1-T_r}{1-T_{br}}\right]^m

    Parameters
    ----------
    T : float
        Temperature of fluid [K]
    Tb : float
        Boiling temperature of the fluid [K]
    Tc : float
        Critical temperature of fluid [K]
    Pc : float
        Critical pressure of fluid [Pa]

    Returns
    -------
    sigma : float
        Liquid surface tension, N/m

    Notes
    -----
    The source of this equation has not been reviewed.
    Internal units of presure are bar, surface tension of mN/m.

    Examples
    --------
    Chlorobenzene from Poling, as compared with a % error value at 293 K.

    >>> Sastri_Rao(293.15, 404.75, 633.0, 4530000.0)
    0.03234567739694441

    References
    ----------
    .. [1] Sastri, S. R. S., and K. K. Rao. "A Simple Method to Predict
       Surface Tension of Organic Liquids." The Chemical Engineering Journal
       and the Biochemical Engineering Journal 59, no. 2 (October 1995): 181-86.
       doi:10.1016/0923-0467(94)02946-6.
    '''
    if chemicaltype == 'alcohol':
        k, x, y, z, m = 2.28, 0.25, 0.175, 0, 0.8
    elif chemicaltype == 'acid':
        k, x, y, z, m = 0.125, 0.50, -1.5, 1.85, 11/9.0
    else:
        k, x, y, z, m = 0.158, 0.50, -1.5, 1.85, 11/9.0
    Tr = T/Tc
    Tbr = Tb/Tc
    Pc = Pc/1E5  # Convert to bar
    sigma = k*Pc**x*Tb**y*Tc**z*((1 - Tr)/(1 - Tbr))**m
    sigma = sigma/1000  # N/m
    return sigma

Response: r A simple method to predict air - water surface tension of the system.


Instruction:
Here you have a function in Python 3, explain what it does
def Hakim_Steinberg_Stiel(T, Tc, Pc, omega, StielPolar=0):
    r'''Calculates air-water surface tension using the reference fluids methods
    of [1]_.

    .. math::
        \sigma = 4.60104\times 10^{-7} P_c^{2/3}T_c^{1/3}Q_p \left(\frac{1-T_r}{0.4}\right)^m

        Q_p = 0.1574+0.359\omega-1.769\chi-13.69\chi^2-0.51\omega^2+1.298\omega\chi

        m = 1.21+0.5385\omega-14.61\chi-32.07\chi^2-1.65\omega^2+22.03\omega\chi

    Parameters
    ----------
    T : float
        Temperature of fluid [K]
    Tc : float
        Critical temperature of fluid [K]
    Pc : float
        Critical pressure of fluid [Pa]
    omega : float
        Acentric factor for fluid, [-]
    StielPolar : float, optional
        Stiel Polar Factor, [-]

    Returns
    -------
    sigma : float
        Liquid surface tension, N/m

    Notes
    -----
    Original equation for m and Q are used. Internal units are atm and mN/m.

    Examples
    --------
    1-butanol, as compared to value in CRC Handbook of 0.02493.

    >>> Hakim_Steinberg_Stiel(298.15, 563.0, 4414000.0, 0.59, StielPolar=-0.07872)
    0.021907902575190447

    References
    ----------
    .. [1] Hakim, D. I., David Steinberg, and L. I. Stiel. "Generalized
       Relationship for the Surface Tension of Polar Fluids." Industrial &
       Engineering Chemistry Fundamentals 10, no. 1 (February 1, 1971): 174-75.
       doi:10.1021/i160037a032.
    '''
    Q = (0.1574 + 0.359*omega - 1.769*StielPolar - 13.69*StielPolar**2
        - 0.510*omega**2 + 1.298*StielPolar*omega)
    m = (1.210 + 0.5385*omega - 14.61*StielPolar - 32.07*StielPolar**2
        - 1.656*omega**2 + 22.03*StielPolar*omega)
    Tr = T/Tc
    Pc = Pc/101325.
    sigma = Pc**(2/3.)*Tc**(1/3.)*Q*((1 - Tr)/0.4)**m
    sigma = sigma/1000.  # convert to N/m
    return sigma

Response: Hakim - Steinberg - Stiel formula for the given temperature and pressure.


Instruction:
Can you tell what is the following Python 3 function doing
def Mersmann_Kind_surface_tension(T, Tm, Tb, Tc, Pc, n_associated=1):
    r'''Estimates the surface tension of organic liquid substances
    according to the method of [1]_.

    .. math::
        \sigma^* = \frac{\sigma n_{ass}^{1/3}} {(kT_c)^{1/3} T_{rm}P_c^{2/3}}

        \sigma^* = \left(\frac{T_b - T_m}{T_m}\right)^{1/3}
        \left[6.25(1-T_r) + 31.3(1-T_r)^{4/3}\right]

    Parameters
    ----------
    T : float
        Temperature of the fluid [K]
    Tm : float
        Melting temperature [K]
    Tb : float
        Boiling temperature of the fluid [K]
    Tc : float
        Critical temperature of the fluid [K]
    Pc : float
        Critical pressure of the fluid [Pa]
    n_associated : float
        Number of associated molecules in a cluster (2 for alcohols, 1
        otherwise), [-]

    Returns
    -------
    sigma : float
        Liquid-vapor surface tension [N/m]

    Notes
    -----
    In the equation, all quantities must be in SI units. `k` is the boltzman
    constant.

    Examples
    --------
    MTBE at STP (the actual value is 0.0181):

    >>> Mersmann_Kind_surface_tension(298.15, 164.15, 328.25, 497.1, 3430000.0)
    0.016744309508833335

    References
    ----------
    .. [1] Mersmann, Alfons, and Matthias Kind. "Prediction of Mechanical and
       Thermal Properties of Pure Liquids, of Critical Data, and of Vapor
       Pressure." Industrial & Engineering Chemistry Research, January 31,
       2017. https://doi.org/10.1021/acs.iecr.6b04323.
    '''
    Tr = T/Tc
    sigma_star = ((Tb - Tm)/Tm)**(1/3.)*(6.25*(1. - Tr) + 31.3*(1. - Tr)**(4/3.))
    sigma = sigma_star*(k*Tc)**(1/3.)*(Tm/Tc)*Pc**(2/3.)*n_associated**(-1/3.)
    return sigma

Response: Mersmann - kind surface tension of organic liquid substances.


Instruction:
Explain what the following Python 3 code does
def calculate(self, T, P, zs, ws, method):
        r'''Method to calculate surface tension of a liquid mixture at
        temperature `T`, pressure `P`, mole fractions `zs` and weight fractions
        `ws` with a given method.

        This method has no exception handling; see `mixture_property`
        for that.

        Parameters
        ----------
        T : float
            Temperature at which to calculate the property, [K]
        P : float
            Pressure at which to calculate the property, [Pa]
        zs : list[float]
            Mole fractions of all species in the mixture, [-]
        ws : list[float]
            Weight fractions of all species in the mixture, [-]
        method : str
            Name of the method to use

        Returns
        -------
        sigma : float
            Surface tension of the liquid at given conditions, [N/m]
        '''
        if method == SIMPLE:
            sigmas = [i(T) for i in self.SurfaceTensions]
            return mixing_simple(zs, sigmas)
        elif method == DIGUILIOTEJA:
            return Diguilio_Teja(T=T, xs=zs, sigmas_Tb=self.sigmas_Tb,
                                 Tbs=self.Tbs, Tcs=self.Tcs)
        elif method == WINTERFELDSCRIVENDAVIS:
            sigmas = [i(T) for i in self.SurfaceTensions]
            rhoms = [1./i(T, P) for i in self.VolumeLiquids]
            return Winterfeld_Scriven_Davis(zs, sigmas, rhoms)
        else:
            raise Exception('Method not valid')

Response:
r Method to calculate the surface tension of a liquid mixture at
 temperature pressure mole fractions and weight fractions of all species in the mixture at
 zs and ws.


Instruction:
How would you code a function in Python 3 to
load group assignments from DDBST file.

Response:
def load_group_assignments_DDBST():
    '''Data is stored in the format
    InChI key\tbool bool bool \tsubgroup count ...\tsubgroup count \tsubgroup count...
    where the bools refer to whether or not the original UNIFAC, modified
    UNIFAC, and PSRK group assignments were completed correctly.
    The subgroups and their count have an indefinite length.
    '''
    # Do not allow running multiple times
    if DDBST_UNIFAC_assignments:
        return None
    with open(os.path.join(folder, 'DDBST UNIFAC assignments.tsv')) as f:
        _group_assignments = [DDBST_UNIFAC_assignments, DDBST_MODIFIED_UNIFAC_assignments, DDBST_PSRK_assignments]
        for line in f.readlines():
            key, valids, original, modified, PSRK = line.split('\t')
            # list of whether or not each method was correctly identified or not
            valids = [True if i == '1' else False for i in valids.split(' ')]
            for groups, storage, valid in zip([original, modified, PSRK], _group_assignments, valids):
                if valid:
                    groups = groups.rstrip().split(' ')
                    d_data = {}
                    for i in range(int(len(groups)/2)):
                        d_data[int(groups[i*2])] = int(groups[i*2+1])
                    storage[key] = d_data


Instruction:
Explain what the following Python 3 code does
def UNIFAC_psi(T, subgroup1, subgroup2, subgroup_data, interaction_data,
               modified=False):
    r'''Calculates the interaction parameter psi(m, n) for two UNIFAC
    subgroups, given the system temperature, the UNIFAC subgroups considered
    for the variant of UNIFAC used, the interaction parameters for the
    variant of UNIFAC used, and whether or not the temperature dependence is
    modified from the original form, as shown below.

    Original temperature dependence:

    .. math::
        \Psi_{mn} = \exp\left(\frac{-a_{mn}}{T}\right)

    Modified temperature dependence:

    .. math::
        \Psi_{mn} = \exp\left(\frac{-a_{mn} - b_{mn}T - c_{mn}T^2}{T}\right)

    Parameters
    ----------
    T : float
        Temperature of the system, [K]
    subgroup1 : int
        First UNIFAC subgroup for identifier, [-]
    subgroup2 : int
        Second UNIFAC subgroup for identifier, [-]
    subgroup_data : dict[UNIFAC_subgroup]
        Normally provided as inputs to `UNIFAC`.
    interaction_data : dict[dict[tuple(a_mn, b_mn, c_mn)]]
        Normally provided as inputs to `UNIFAC`.
    modified : bool
        True if the modified temperature dependence is used by the interaction
        parameters, otherwise False

    Returns
    -------
    psi : float
        UNIFAC interaction parameter term, [-]

    Notes
    -----
    UNIFAC interaction parameters are asymmetric. No warning is raised if an
    interaction parameter is missing.

    Examples
    --------
    >>> from thermo.unifac import UFSG, UFIP, DOUFSG, DOUFIP2006

    >>> UNIFAC_psi(307, 18, 1, UFSG, UFIP)
    0.9165248264184787

    >>> UNIFAC_psi(373.15, 9, 78, DOUFSG, DOUFIP2006, modified=True)
    1.3703140538273264

    References
    ----------
    .. [1] Gmehling, Jurgen. Chemical Thermodynamics: For Process Simulation.
       Weinheim, Germany: Wiley-VCH, 2012.
    .. [2] Fredenslund, Aage, Russell L. Jones, and John M. Prausnitz. "Group
       Contribution Estimation of Activity Coefficients in Nonideal Liquid
       Mixtures." AIChE Journal 21, no. 6 (November 1, 1975): 1086-99.
       doi:10.1002/aic.690210607.
    '''
    main1 = subgroup_data[subgroup1].main_group_id
    main2 = subgroup_data[subgroup2].main_group_id
    if modified:
        try:
            a, b, c = interaction_data[main1][main2]
        except:
            return 1.
        return exp((-a/T -b - c*T))
    else:
        try:
            return exp(-interaction_data[main1][main2]/T)
        except:
            return 1.

Response: r Returns the interaction parameter psi for two subgroups.


Instruction:
Make a summary of the following Python 3 code
def dipole_moment(CASRN, AvailableMethods=False, Method=None):
    r'''This function handles the retrieval of a chemical's dipole moment.
    Lookup is based on CASRNs. Will automatically select a data source to use
    if no Method is provided; returns None if the data is not available.

    Prefered source is 'CCCBDB'. Considerable variation in reported data has
    found.

    Parameters
    ----------
    CASRN : string
        CASRN [-]

    Returns
    -------
    dipole : float
        Dipole moment, [debye]
    methods : list, only returned if AvailableMethods == True
        List of methods which can be used to obtain dipole moment with the
        given inputs

    Other Parameters
    ----------------
    Method : string, optional
        The method name to use. Accepted methods are 'CCCBDB', 'MULLER', or
        'POLING'. All valid values are also held in the list `dipole_methods`.
    AvailableMethods : bool, optional
        If True, function will determine which methods can be used to obtain
        the dipole moment for the desired chemical, and will return methods
        instead of the dipole moment

    Notes
    -----
    A total of three sources are available for this function. They are:

        * 'CCCBDB', a series of critically evaluated data for compounds in
          [1]_, intended for use in predictive modeling.
        * 'MULLER', a collection of data in a
          group-contribution scheme in [2]_.
        * 'POLING', in the appendix in [3].

    This function returns dipole moment in units of Debye. This is actually
    a non-SI unit; to convert to SI, multiply by 3.33564095198e-30 and its
    units will be in ampere*second^2 or equivalently and more commonly given,
    coulomb*second. The constant is the result of 1E-21/c, where c is the
    speed of light.

    Examples
    --------
    >>> dipole_moment(CASRN='64-17-5')
    1.44

    References
    ----------
    .. [1] NIST Computational Chemistry Comparison and Benchmark Database
       NIST Standard Reference Database Number 101 Release 17b, September 2015,
       Editor: Russell D. Johnson III http://cccbdb.nist.gov/
    .. [2] Muller, Karsten, Liudmila Mokrushina, and Wolfgang Arlt. "Second-
       Order Group Contribution Method for the Determination of the Dipole
       Moment." Journal of Chemical & Engineering Data 57, no. 4 (April 12,
       2012): 1231-36. doi:10.1021/je2013395.
    .. [3] Poling, Bruce E. The Properties of Gases and Liquids. 5th edition.
       New York: McGraw-Hill Professional, 2000.
    '''
    def list_methods():
        methods = []
        if CASRN in _dipole_CCDB.index and not np.isnan(_dipole_CCDB.at[CASRN, 'Dipole']):
            methods.append(CCCBDB)
        if CASRN in _dipole_Muller.index and not np.isnan(_dipole_Muller.at[CASRN, 'Dipole']):
            methods.append(MULLER)
        if CASRN in _dipole_Poling.index and not np.isnan(_dipole_Poling.at[CASRN, 'Dipole']):
            methods.append(POLING)
        methods.append(NONE)
        return methods
    if AvailableMethods:
        return list_methods()
    if not Method:
        Method = list_methods()[0]

    if Method == CCCBDB:
        _dipole = float(_dipole_CCDB.at[CASRN, 'Dipole'])
    elif Method == MULLER:
        _dipole = float(_dipole_Muller.at[CASRN, 'Dipole'])
    elif Method == POLING:
        _dipole = float(_dipole_Poling.at[CASRN, 'Dipole'])
    elif Method == NONE:
        _dipole = None
    else:
        raise Exception('Failure in in function')
    return _dipole

Response: r Returns the dipole moment for a given CASRN.