diff --git a/examples/arkane/networks/CH2NH2/input.py b/examples/arkane/networks/CH2NH2/input.py new file mode 100644 index 0000000000..32c0805403 --- /dev/null +++ b/examples/arkane/networks/CH2NH2/input.py @@ -0,0 +1,98 @@ +#!/usr/bin/env python3 +# encoding: utf-8 + +title = 'CH2NH2 PES' +description = """ +Calculations done using ARC at the following levels of theory: +opt: wb97xd/6-311++g(d,p) +freq: wb97xd/6-311++g(d,p) +sp: ccsd(t)-f12/cc-pvqz-f12 +1D rotor scans: wb97xd/6-311++g(d,p) +""" + +modelChemistry = "CCSD(T)-F12/cc-pvqz-f12" + +useHinderedRotors = True +useBondCorrections = False + + +species('CH2NH2', 'yaml_files/CH2NH2.yml', + collisionModel = TransportData(sigma=(3.626,'angstrom'), epsilon=(481.8,'J/mol')), + energyTransferModel = SingleExponentialDown(alpha0=(133,'cm^-1'), T0=(300,'K'), n=0.85), # C3H4/N2 +) + +species('CH3NH', 'yaml_files/CH3NH.yml', + collisionModel = TransportData(sigma=(3.626,'angstrom'), epsilon=(481.8,'J/mol')), + energyTransferModel = SingleExponentialDown(alpha0=(133,'cm^-1'), T0=(300,'K'), n=0.85), # C3H4/N2 +) + +species('CH2NH', 'yaml_files/CH2NH.yml', + collisionModel = TransportData(sigma=(3.690,'angstrom'), epsilon=(417.0,'J/mol')), + energyTransferModel = SingleExponentialDown(alpha0=(133,'cm^-1'), T0=(300,'K'), n=0.85), # C3H4/N2 +) + +species('H', 'yaml_files/H.yml', + collisionModel = TransportData(sigma=(2.050,'angstrom'), epsilon=(145.0,'J/mol')), + energyTransferModel = SingleExponentialDown(alpha0=(133,'cm^-1'), T0=(300,'K'), n=0.85), # C3H4/N2 +) + +species( + label = 'Ar', + structure = SMILES('[Ar]'), + E0 = (-6.19426,'kJ/mol'), + spinMultiplicity = 1, + opticalIsomers = 1, + molecularWeight = (39.348,'amu'), + collisionModel = TransportData(shapeIndex=0, epsilon=(1134.93,'J/mol'), sigma=(3.33,'angstroms'), dipoleMoment=(0,'C*m'), polarizability=(0,'angstroms^3'), rotrelaxcollnum=0.0, comment="""GRI-Mech"""), + energyTransferModel = SingleExponentialDown(alpha0=(133,'cm^-1'), T0=(300,'K'), n=0.85), + thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.5,2.64613e-14,-3.72536e-17,1.7192e-20,-2.44483e-24,-745,4.3663], Tmin=(100,'K'), Tmax=(3802.52,'K')), NASAPolynomial(coeffs=[2.5,1.04239e-10,-3.81845e-14,6.18592e-18,-3.73869e-22,-745,4.3663], Tmin=(3802.52,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(-6.19426,'kJ/mol'), Cp0=(20.7862,'J/(mol*K)'), CpInf=(20.7862,'J/(mol*K)'), label="""Ar""", comment="""Thermo library: BurkeH2O2"""), +) + +transitionState('CH2NH_to_CH2NH2','yaml_files/CH2NH_to_CH2NH2.yml') + +transitionState('CH2NH_to_CH3NH','yaml_files/CH2NH_to_CH3NH.yml') + +transitionState('CH3NH_to_CH2NH2','yaml_files/CH3NH_to_CH2NH2.yml') + +reaction( + label = 'CH3NH = CH2NH2', + reactants = ['CH3NH'], + products = ['CH2NH2'], + transitionState = 'CH3NH_to_CH2NH2', + tunneling = 'Eckart', +) + +reaction( + label = 'CH2NH + H = CH2NH2', + reactants = ['CH2NH', 'H'], + products = ['CH2NH2'], + transitionState = 'CH2NH_to_CH2NH2', + tunneling = 'Eckart', +) + +reaction( + label = 'CH2NH + H = CH3NH', + reactants = ['CH2NH', 'H'], + products = ['CH3NH'], + transitionState = 'CH2NH_to_CH3NH', + tunneling = 'Eckart', +) + +network( + label = 'CH2NH2', + isomers = ['CH2NH2', 'CH3NH'], + reactants = [('CH2NH', 'H')], + bathGas = {'Ar': 1} +) + +pressureDependence( + label='CH2NH2', + Tmin=(500.0,'K'), Tmax=(2500.0,'K'), Tcount=25, + Pmin=(0.01,'bar'), Pmax=(100.0,'bar'), Pcount=15, + maximumGrainSize = (0.5,'kcal/mol'), + minimumGrainCount = 250, + method = 'chemically-significant eigenvalues', # use the CSE method which is more expensive, less robust, yet more accurate, see: http://reactionmechanismgenerator.github.io/RMG-Py/theory/pdep/methods.html#the-chemically-signficant-eigenvalues-method + interpolationModel = ('chebyshev', 6, 4), + activeKRotor = True, + activeJRotor = True, +) diff --git a/examples/arkane/networks/CH2NH2/yaml_files/CH2NH.yml b/examples/arkane/networks/CH2NH2/yaml_files/CH2NH.yml new file mode 100644 index 0000000000..d7b6e430c6 --- /dev/null +++ b/examples/arkane/networks/CH2NH2/yaml_files/CH2NH.yml @@ -0,0 +1,245 @@ +RMG_version: 3.0.0 +adjacency_list: | + 1 N u0 p1 c0 {2,D} {5,S} + 2 C u0 p0 c0 {1,D} {3,S} {4,S} + 3 H u0 p0 c0 {2,S} + 4 H u0 p0 c0 {2,S} + 5 H u0 p0 c0 {1,S} +author: Duminda Ranasinghe +chemkin_thermo_string: | + CH2NH_GS C 1H 3N 1 G 10.000 3000.000 929.04 1 + 8.90142813E-01 1.36790591E-02-6.97921272E-06 1.73897882E-09-1.70110463E-13 2 + 1.12137680E+04 1.84259478E+01 4.08222115E+00-4.85090203E-03 3.06667703E-05 3 + -3.08208892E-08 1.00838992E-11 1.08272132E+04 4.37264781E+00 4 +class: ArkaneSpecies +conformer: + E0: + class: ScalarQuantity + units: kJ/mol + value: 89.98686934415784 + class: Conformer + coordinates: + class: ArrayQuantity + units: angstroms + value: + class: np_array + object: + - 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6.0 + - 1.0 + - 1.0 + - 1.0 + - 7.0 + - 1.0 + optical_isomers: 1 + spin_multiplicity: 2 +datetime: 2020-01-15 21:12 +energy_transfer_model: + T0: + class: ScalarQuantity + units: K + value: 300.0 + alpha0: + class: ScalarQuantity + units: kJ/mol + value: 3.5886 + class: SingleExponentialDown + n: 0.85 +frequency_scale_factor: 0.987893763452 +inchi: InChI=1S/CH4N/c1-2/h2H,1H3 +inchi_key: YKPQUSLRUFLVDA-UHFFFAOYSA-N +is_ts: false +label: CH3NH_GS +level_of_theory: ccsd(t)-f12/cc-pvqz-f12 +molecular_weight: + class: ScalarQuantity + units: amu + value: 30.049203150754995 +smiles: C[NH] +thermo: + Cp0: + class: ScalarQuantity + units: J/(mol*K) + value: 33.257888 + CpInf: + class: ScalarQuantity + units: J/(mol*K) + value: 128.874316 + E0: + class: ScalarQuantity + units: kJ/mol + value: 179.01493982401396 + Tmax: + class: ScalarQuantity + units: K + value: 3000.0 + Tmin: + class: ScalarQuantity + units: K + value: 10.0 + class: NASA + polynomials: + polynomial1: + Tmax: + class: ScalarQuantity + units: K + value: 712.8146573649618 + Tmin: + class: ScalarQuantity + units: K + value: 10.0 + class: NASAPolynomial + coeffs: + class: np_array + object: + - 3.996814381017037 + - 3.391033812167941e-05 + - 2.523910246710106e-05 + - -2.893088258244595e-08 + - 1.06075042038419e-11 + - 21530.60274489121 + - 5.880894904305926 + polynomial2: + Tmax: + class: ScalarQuantity + units: K + value: 3000.0 + Tmin: + class: ScalarQuantity + units: K + value: 712.8146573649618 + class: NASAPolynomial + coeffs: + class: np_array + object: + - 1.2167996118646134 + - 0.015634021103968932 + - -7.588527433968901e-06 + - 1.7712722092687994e-09 + - -1.6035710296181866e-13 + - 21926.93234702742 + - 18.351747957449664 +thermo_data: + Cpdata: + class: ArrayQuantity + units: cal/(mol*K) + value: + class: np_array + object: + - 11.095157455549103 + - 12.854464904013188 + - 14.64601765725426 + - 16.352522341773092 + - 19.292929522218 + - 21.607310395224218 + - 25.35651730997513 + - 27.29467100306541 + - 28.207075669944597 + H298: + class: ScalarQuantity + units: kcal/mol + value: 45.494600046199174 + S298: + class: ScalarQuantity + units: cal/(mol*K) + value: 58.71718292451362 + Tdata: + class: ArrayQuantity + units: K + value: + class: np_array + object: + - 300.0 + - 400.0 + - 500.0 + - 600.0 + - 800.0 + - 1000.0 + - 1500.0 + - 2000.0 + - 2400.0 + class: ThermoData +use_bond_corrections: false +use_hindered_rotors: true +xyz: |- + 6 + CH3NH_GS + C 0.05984800 -0.62319600 0.00000000 + H -0.46898100 -1.02444400 0.87886100 + H -0.46898100 -1.02444400 -0.87886100 + H 1.08093800 -1.00826200 0.00000000 + N 0.05980600 0.81236000 0.00000000 + H -0.92102100 1.10943400 0.00000000 diff --git a/examples/arkane/networks/CH2NH2/yaml_files/CH3NH_to_CH2NH2.yml b/examples/arkane/networks/CH2NH2/yaml_files/CH3NH_to_CH2NH2.yml new file mode 100644 index 0000000000..626e095559 --- /dev/null +++ b/examples/arkane/networks/CH2NH2/yaml_files/CH3NH_to_CH2NH2.yml @@ -0,0 +1,141 @@ +RMG_version: 2.4.1 +class: ArkaneSpecies +conformer: + E0: + class: ScalarQuantity + units: kJ/mol + value: 331.76521579681975 + class: Conformer + coordinates: + class: ArrayQuantity + units: angstroms + value: + class: np_array + object: + - - -0.68121 + - -0.032328 + - 0.007869 + - - -1.260575 + - 0.839534 + - -0.273384 + - - -1.149183 + - -1.010521 + - -0.06991 + - - 0.103255 + - 0.273733 + - 0.9644419999999999 + - - 0.747137 + - 0.126948 + - -0.098427 + - - 1.161506 + - -0.7982759999999999 + - 0.019735 + mass: + class: ArrayQuantity + units: amu + value: + class: np_array + object: + - 12.000000000000002 + - 1.00782503224 + - 1.00782503224 + - 1.00782503224 + - 14.00307400443 + - 1.00782503224 + modes: + - class: IdealGasTranslation + mass: + class: ScalarQuantity + units: amu + value: 30.034370000000003 + quantum: false + - class: NonlinearRotor + inertia: + class: ArrayQuantity + units: amu*angstrom^2 + value: + class: np_array + object: + - 3.7652244401803583 + - 18.823711436296357 + - 20.34280723248981 + quantum: false + rotationalConstant: + class: ArrayQuantity + units: cm^-1 + value: + class: np_array + object: + - 4.47719102039532 + - 0.8955528834150734 + - 0.8286776186142547 + symmetry: 1 + - class: HarmonicOscillator + frequencies: + class: ArrayQuantity + units: cm^-1 + value: + class: np_array + object: + - 755.060576651137 + - 888.0597797732784 + - 992.6464530802716 + - 1104.4152206807992 + - 1212.4556161958685 + - 1314.6253705819015 + - 1453.4883485861449 + - 2354.3468126454154 + - 3073.009414296528 + - 3195.050087311136 + - 3458.2058673245165 + quantum: true + number: + class: ArrayQuantity + value: + class: np_array + object: + - 6.0 + - 1.0 + - 1.0 + - 1.0 + - 7.0 + - 1.0 + opticalIsomers: 1 + spinMultiplicity: 2 +datetime: 2019-07-29 22:33 +imaginary_frequency: + class: ScalarQuantity + units: cm^-1 + value: -1969.61447514033 +is_ts: true +label: CH3NH_to_CH2NH2 +products: +- adjacency_list: | + multiplicity 2 + 1 N u0 p1 c0 {2,S} {5,S} {6,S} + 2 C u1 p0 c0 {1,S} {3,S} {4,S} + 3 H u0 p0 c0 {2,S} + 4 H u0 p0 c0 {2,S} + 5 H u0 p0 c0 {1,S} + 6 H u0 p0 c0 {1,S} + label: CH2NH2 +reactants: +- adjacency_list: | + multiplicity 2 + 1 N u1 p1 c0 {2,S} {6,S} + 2 C u0 p0 c0 {1,S} {3,S} {4,S} {5,S} + 3 H u0 p0 c0 {2,S} + 4 H u0 p0 c0 {2,S} + 5 H u0 p0 c0 {2,S} + 6 H u0 p0 c0 {1,S} + label: CH3NH +reaction_label: CH3NH = CH2NH2 +xyz: |- + 6 + CH3NH_to_CH2NH2 + C -6.8121e-11 -3.2328e-12 7.869e-13 + H -1.260575e-10 8.39534e-11 -2.73384e-11 + H -1.149183e-10 -1.010521e-10 -6.991e-12 + H 1.03255e-11 2.73733e-11 9.64442e-11 + N 7.47137e-11 1.26948e-11 -9.8427e-12 + H 1.161506e-10 -7.98276e-11 1.9735e-12 diff --git a/examples/arkane/networks/CH2NH2/yaml_files/H.yml b/examples/arkane/networks/CH2NH2/yaml_files/H.yml new file mode 100644 index 0000000000..f47def3892 --- /dev/null +++ b/examples/arkane/networks/CH2NH2/yaml_files/H.yml @@ -0,0 +1,77 @@ +RMG_version: 2.3.0 +adjacency_list: | + multiplicity 2 + 1 H u1 p0 c0 +author: Duminda Ranasinghe +chemkin_thermo_string: | + H H 1 G 10.000 3000.000 1363.47 1 + 2.50000000E+00-1.01797141E-13 8.37579665E-17-2.93808300E-20 3.72101315E-24 2 + 2.54729349E+04-4.61279342E-01 2.50000000E+00-1.52659729E-15 6.32259992E-18 3 + -7.59355194E-21 2.67358716E-24 2.54729349E+04-4.61279342E-01 4 +class: ArkaneSpecies +conformer: + E0: {class: ScalarQuantity, units: kJ/mol, value: 211.7971222569539} + class: Conformer + coordinates: + class: ArrayQuantity + units: angstroms + value: + - [0.0, 0.0, 0.0] + mass: + class: ArrayQuantity + units: amu + value: [1.00782503224] + modes: + - class: IdealGasTranslation + mass: {class: ScalarQuantity, units: amu, value: 1.00783} + quantum: false + number: + class: ArrayQuantity + value: [1.0] + opticalIsomers: 1 + spinMultiplicity: 2 +datetime: 2019-06-04 18:24 +energy_transfer_model: + T0: {class: ScalarQuantity, units: K, value: 300.0} + alpha0: {class: ScalarQuantity, units: kJ/mol, value: 3.5886} + class: SingleExponentialDown + n: 0.85 +frequency_scale_factor: 0.992 +inchi: InChI=1S/H +inchi_key: YZCKVEUIGOORGS-UHFFFAOYSA-N +is_ts: false +label: H +level_of_theory: ccsd(t)-f12/aug-cc-pvqz +molecular_weight: {class: ScalarQuantity, units: amu, value: 1.0079401224797493} +smiles: '[H]' +thermo: + Cp0: {class: ScalarQuantity, units: J/(mol*K), value: 20.78618} + CpInf: {class: ScalarQuantity, units: J/(mol*K), value: 20.78618} + E0: {class: ScalarQuantity, units: kJ/mol, value: 211.79402511613392} + Tmax: {class: ScalarQuantity, units: K, value: 3000.0} + Tmin: {class: ScalarQuantity, units: K, value: 10.0} + class: NASA + polynomials: + polynomial1: + Tmax: {class: ScalarQuantity, units: K, value: 1363.4652887478705} + Tmin: {class: ScalarQuantity, units: K, value: 10.0} + class: NASAPolynomial + coeffs: [2.5000000000000555, -1.5265972916629936e-15, 6.322599921667203e-18, + -7.59355193728352e-21, 2.6735871640486746e-24, 25472.934941623953, -0.46127934199839227] + polynomial2: + Tmax: {class: ScalarQuantity, units: K, value: 3000.0} + Tmin: {class: ScalarQuantity, units: K, value: 1363.4652887478705} + class: NASAPolynomial + coeffs: [2.5000000000444205, -1.0179714090107131e-13, 8.375796653364967e-17, + -2.938083003866578e-20, 3.721013146781619e-24, 25472.934941609077, -0.4612793422363687] +thermo_data: + Cp (cal/mol*K): {1000 K: '4.97', 1500 K: '4.97', 2000 K: '4.97', 2400 K: '4.97', + 300 K: '4.97', 400 K: '4.97', 500 K: '4.97', 600 K: '4.97', 800 K: '4.97'} + H298: 52.10 kcal/mol + S298: 27.39 cal/mol*K +use_bond_corrections: false +use_hindered_rotors: false +xyz: |- + 1 + H + H 0.0 0.0 0.0 diff --git a/rmgpy/pdep/cse.pyx b/rmgpy/pdep/cse.pyx index 4a3a44900b..c16915d850 100644 --- a/rmgpy/pdep/cse.pyx +++ b/rmgpy/pdep/cse.pyx @@ -47,12 +47,12 @@ from rmgpy.pdep.me import generate_full_me_matrix def apply_chemically_significant_eigenvalues_method(network, list lumping_order=None): """A method for applying the Chemically Significant Eigenvalues approach for solving the master equation.""" - cdef np.ndarray[np.int_t,ndim=1] j_list - cdef np.ndarray[np.int_t,ndim=3] indices - cdef np.ndarray[np.float64_t,ndim=1] e_list, s_mat, s_mat_inv, omega0, omega, eq_ratios - cdef np.ndarray[np.float64_t,ndim=2] me_mat, k, eigen_vectors0, eigen_vectors, z_mat, z_mat_inv, y, x - cdef np.ndarray[np.float64_t,ndim=3] dens_states - cdef np.ndarray[np.float64_t,ndim=4] g_nj, pa + cdef np.ndarray[np.int_t, ndim=1] j_list + cdef np.ndarray[np.int_t, ndim=3] indices + cdef np.ndarray[np.float64_t, ndim=1] e_list, s_mat, s_mat_inv, omega0, omega, eq_ratios + cdef np.ndarray[np.float64_t, ndim=2] me_mat, k, eigen_vectors0, eigen_vectors, z_mat, z_mat_inv, y, x + cdef np.ndarray[np.float64_t, ndim=3] dens_states + cdef np.ndarray[np.float64_t, ndim=4] g_nj, pa cdef list lumping, unlumping cdef double temperature, pressure, ym_b cdef int n_isom, n_reac, n_prod, n_grains, n_j, n_chem, n_cse, n_rows @@ -74,7 +74,7 @@ def apply_chemically_significant_eigenvalues_method(network, list lumping_order= n_grains = len(e_list) n_chem = n_isom + n_reac - ym_b = 1.0e-6 * pressure / constants.R / temperature + ym_b = 1.0e-6 * pressure / (constants.R * temperature) # Generate the full master equation matrix me_mat, indices = generate_full_me_matrix(network, products=False) @@ -89,15 +89,15 @@ def apply_chemically_significant_eigenvalues_method(network, list lumping_order= for s in range(n_j): index = indices[i, r, s] if index > -1: - s_mat[index] = sqrt(dens_states[i, r, s] * (2 * j_list[s] + 1) - * exp(-e_list[r] / constants.R / temperature) * eq_ratios[i]) + s_mat[index] = sqrt(dens_states[i, r, s] * (2 * j_list[s] + 1) \ + * exp(-e_list[r] / (constants.R * temperature)) * eq_ratios[i]) s_mat_inv[index] = 1.0 / s_mat[index] for n in range(n_reac): index = n_rows - n_reac + n s_mat[index] = sqrt(eq_ratios[n + n_isom] / ym_b) s_mat_inv[index] = 1.0 / s_mat[index] - # Symmetrize master equation matrix: me_mat = s_mat * Msymm * s_mat_inv + # Symmetrize master equation matrix: me_mat = s_mat * m_symm * s_mat_inv # Since s_mat and s_mat_inv are diagonal we can do this very efficiently for r in range(n_rows): for s in range(n_rows): @@ -108,9 +108,9 @@ def apply_chemically_significant_eigenvalues_method(network, list lumping_order= for r in range(n_rows): for s in range(r): if me_mat[r, s] != 0: - if abs(me_mat[r, s] - me_mat[s,r]) > 0.01 * me_mat[r, s]: - if me_mat[r, s] > 1e-200 or me_mat[s,r] > 1e-200: - print(r, s, me_mat[r, s], me_mat[s,r]) + if abs(me_mat[r, s] - me_mat[s, r]) > 0.01 * min(me_mat[r, s], me_mat[s, r]) \ + and max(me_mat[r, s], me_mat[s, r]) > 1e-200: + print(r, s, me_mat[r, s], me_mat[s, r]) properly_symmetrized = False if not properly_symmetrized: raise ChemicallySignificantEigenvaluesError('Master equation matrix not properly symmetrized.') @@ -118,7 +118,7 @@ def apply_chemically_significant_eigenvalues_method(network, list lumping_order= # Get eigenvalues and eigenvectors # We only need the slowest n_chem + 1 eigenmodes, so only compute those try: - # omega0, eigen_vectors0 = scipy.linalg.eigh(me_mat, eigvals=(n_rows-n_chem-1,n_rows-1), + # omega0, eigen_vectors0 = scipy.linalg.eigh(me_mat, eigvals=(n_rows - n_chem - 1, n_rows - 1), # overwrite_a=True, overwrite_b=True) omega0, eigen_vectors0 = scipy.linalg.eigh(me_mat, overwrite_a=True, overwrite_b=True) except np.linalg.LinAlgError: @@ -155,7 +155,7 @@ def apply_chemically_significant_eigenvalues_method(network, list lumping_order= # omega0[ind[-1]], omega0[ind[-2]], omega0[ind[-1]] / omega0[ind[-2]])) else: lumping = [] - unlumping = range(n_chem) + unlumping = list(range(n_chem)) # Extract the chemically-significant eigenvalues and eigenvectors omega = omega0.take(ind[-n_cse:]) diff --git a/rmgpy/pdep/me.pyx b/rmgpy/pdep/me.pyx index 9cc40a7301..82ea8d3819 100644 --- a/rmgpy/pdep/me.pyx +++ b/rmgpy/pdep/me.pyx @@ -73,7 +73,7 @@ cpdef generate_full_me_matrix(network, bint products=True): beta = 1. / (constants.R * temperature) # Construct accounting matrix - indices = -np.ones((n_isom,n_grains,n_j), np.int) + indices = -np.ones((n_isom, n_grains, n_j), np.int) n_rows = 0 for r in range(n_grains): for s in range(n_j): @@ -86,7 +86,7 @@ cpdef generate_full_me_matrix(network, bint products=True): n_rows += n_prod # Construct full ME matrix - me_mat = np.zeros([n_rows,n_rows], np.float64) + me_mat = np.zeros([n_rows, n_rows], np.float64) # Collision terms for i in range(n_isom): @@ -95,18 +95,18 @@ cpdef generate_full_me_matrix(network, bint products=True): if indices[i, r, s] > -1: for u in range(r, n_grains): for v in range(s, n_j): - me_mat[indices[i, r, s], indices[i,u,v]] = m_coll[i, r, s, u, v] - me_mat[indices[i, u, v], indices[i,r,s]] = m_coll[i, u, v, r, s] + me_mat[indices[i, r, s], indices[i, u, v]] = m_coll[i, r, s, u, v] + me_mat[indices[i, u, v], indices[i, r, s]] = m_coll[i, u, v, r, s] # Isomerization terms for i in range(n_isom): for j in range(i): - if k_ij[i, j, n_grains - 1,0] > 0 or k_ij[j, i, n_grains - 1,0] > 0: + if k_ij[i, j, n_grains - 1, 0] > 0 or k_ij[j, i, n_grains - 1, 0] > 0: for r in range(n_grains): for s in range(n_j): u, v = indices[i, r, s], indices[j, r, s] if u > -1 and v > -1: - me_mat[u, v] = k_ij[j, i, r, s] + me_mat[v, u] = k_ij[j, i, r, s] me_mat[u, u] -= k_ij[j, i, r, s] me_mat[u, v] = k_ij[i, j, r, s] me_mat[v, v] -= k_ij[i, j, r, s] @@ -114,7 +114,7 @@ cpdef generate_full_me_matrix(network, bint products=True): # Association/dissociation terms for i in range(n_isom): for n in range(n_reac + n_prod): - if g_nj[n, i, n_grains - 1,0] > 0: + if g_nj[n, i, n_grains - 1, 0] > 0: for r in range(n_grains): for s in range(n_j): u = indices[i, r, s] @@ -122,7 +122,7 @@ cpdef generate_full_me_matrix(network, bint products=True): if u > -1: me_mat[u, u] -= g_nj[n, i, r, s] if n < n_reac or products: - me_mat[u, v] = g_nj[n, i, r, s] + me_mat[v, u] = g_nj[n, i, r, s] if n < n_reac: val = f_im[i, n, r, s] * dens_states[n + n_isom, r, s] \ * (2 * j_list[s] + 1) * exp(-e_list[r] * beta)