{
"cells": [
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"cell_type": "markdown",
"metadata": {},
"source": [
"ChEn-3170: Computational Methods in Chemical Engineering Fall 2020 UMass Lowell; Prof. V. F. de Almeida **22Oct20**\n",
"\n",
"# 08. Full-Rank Least-Squares Reaction Rates\n",
"$ \n",
" \\newcommand{\\Amtrx}{\\boldsymbol{\\mathsf{A}}}\n",
" \\newcommand{\\Bmtrx}{\\boldsymbol{\\mathsf{B}}}\n",
" \\newcommand{\\Mmtrx}{\\boldsymbol{\\mathsf{M}}}\n",
" \\newcommand{\\Imtrx}{\\boldsymbol{\\mathsf{I}}}\n",
" \\newcommand{\\Pmtrx}{\\boldsymbol{\\mathsf{P}}}\n",
" \\newcommand{\\Lmtrx}{\\boldsymbol{\\mathsf{L}}}\n",
" \\newcommand{\\Umtrx}{\\boldsymbol{\\mathsf{U}}}\n",
" \\newcommand{\\Smtrx}{\\boldsymbol{\\mathsf{S}}}\n",
" \\newcommand{\\xvec}{\\boldsymbol{\\mathsf{x}}}\n",
" \\newcommand{\\avec}{\\boldsymbol{\\mathsf{a}}}\n",
" \\newcommand{\\bvec}{\\boldsymbol{\\mathsf{b}}}\n",
" \\newcommand{\\cvec}{\\boldsymbol{\\mathsf{c}}}\n",
" \\newcommand{\\rvec}{\\boldsymbol{\\mathsf{r}}}\n",
" \\newcommand{\\mvec}{\\boldsymbol{\\mathsf{m}}}\n",
" \\newcommand{\\gvec}{\\boldsymbol{\\mathsf{g}}}\n",
" \\newcommand{\\zerovec}{\\boldsymbol{\\mathsf{0}}}\n",
" \\newcommand{\\norm}[1]{\\bigl\\lVert{#1}\\bigr\\rVert}\n",
" \\newcommand{\\transpose}[1]{{#1}^\\top}\n",
" \\DeclareMathOperator{\\rank}{rank}\n",
"$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"## Table of Contents\n",
"* [Introduction](#intro)\n",
"* [Full-Rank Reaction mechanism](#rxnmech)\n",
"* [Full-Rank Least-Squares Reaction Rates](#lsr)\n",
" - [Random Production Rates](#randomg)\n",
" - [Realistic Production Rates](#realg)\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## [Introduction](#toc)\n",
"\n",
"Recall course notes OneNote [ChEn-3170-stoic](https://studentuml-my.sharepoint.com/:o:/g/personal/valmor_dealmeida_uml_edu/Evkhba9SC3xOmd2q7jj0k-MBEc_Ci1Quzryh3onHBUM1XQ?e=NQ0zT5) on computational stoichiometry including an introduction to the linear, full-rank, least-squares method.\n",
"\n",
"This topic is an application of the least-squares method for calculating *net* reaction rates.\n",
"The relation between the (instantaneous) *net* reaction rates vector, $\\rvec$, and the (instantaneous) species production rates vector, $\\gvec$, is given by $\\transpose{\\Smtrx}\\,\\rvec = \\gvec$. Since this is often a rectangular system, it remains the problem of finding a *solution*. Specifically if $\\Smtrx$ is $m \\times n$ with $m$ reactions and $n$ species, then $\\Smtrx^\\top$ is $n\\times m$, that is,\n",
"\n",
"$\\Smtrx^\\top = \n",
"\\begin{pmatrix}\n",
"S^\\top _{1,1} & S^\\top _{1,2} & \\dots & S^\\top _{1,m} \\\\\n",
"S^\\top _{2,1} & S^\\top _{2,2} & \\dots & S^\\top _{2,m} \\\\\n",
"\\vdots & \\vdots & \\ddots & \\vdots \\\\\n",
"S^\\top _{n,1} & S^\\top _{n,2} & \\dots & S^\\top _{n,m}\n",
"\\end{pmatrix} \n",
"$\n",
"where $S^\\top_{i,j} = S_{j,i}$. \n",
"\n",
"The reaction rates and species production rates are related by the matrix product\n",
"\n",
"\\begin{equation*}\n",
"\\begin{pmatrix}\n",
"S^\\top _{1,1} & S^\\top _{1,2} & \\dots & S^\\top _{1,m} \\\\\n",
"S^\\top _{2,1} & S^\\top _{2,2} & \\dots & S^\\top _{2,m} \\\\\n",
"\\vdots & \\vdots & \\ddots & \\vdots \\\\\n",
"S^\\top _{n,1} & S^\\top _{n,2} & \\dots & S^\\top _{n,m}\n",
"\\end{pmatrix} \n",
"\\,\n",
"\\begin{pmatrix}\n",
"r_1 \\\\ \n",
"r_2 \\\\ \n",
"\\vdots \\\\ \n",
"r_m \n",
"\\end{pmatrix}\n",
"=\n",
"\\begin{pmatrix}\n",
"g_1 \\\\ \n",
"g_2 \\\\ \n",
"\\vdots \\\\ \n",
"g_n \n",
"\\end{pmatrix}\n",
"\\end{equation*}\n",
"\n",
"which shows that each species production rate has a contribution of every reaction:\n",
"\n",
"\\begin{equation*}\n",
"g_j = \\sum\\limits_{i=1}^m S^\\top _{j,i}\\, r_i \\qquad\\ \\forall \\qquad\\ j=1,\\ldots, n.\n",
"\\end{equation*}\n",
"\n",
"Refer to the course notes OneNote [ChEn-3170-stoic](https://studentuml-my.sharepoint.com/:o:/g/personal/valmor_dealmeida_uml_edu/Evihj869hRNHjN6JN4v0xOsBuoAJ3azgu_Q__l76d1W1zw?e=cwPY6I) on computational stoichiometry including an introduction to the linear, full-rank, least-squares method.\n",
"\n",
"To compute the reaction rates vector $\\rvec$ for a given species production vector $\\gvec$ we need to solve:\n",
"\n",
"\\begin{equation*}\n",
"\\Smtrx^\\top\\,\\rvec = \\gvec .\n",
"\\end{equation*}\n",
"\n",
"There exists a **unique** least-squares solution $\\rvec_\\text{LS}$ to this problem if $\\Smtrx$ is full rank, that is,\n",
"\n",
"\\begin{equation*}\n",
" \\min\\limits_\\rvec \\norm{\\gvec - \\Smtrx^\\top\\,\\rvec_\\text{LS}}^2 \\quad\\ \\forall \\quad\\ \\rvec.\n",
"\\end{equation*}\n",
"\n",
"This solution is obtained by solving:\n",
"\n",
"\\begin{equation*}\n",
"\\Smtrx\\,\\Smtrx^\\top\\,\\rvec_\\text{LS} = \\Smtrx\\,\\gvec ,\n",
"\\end{equation*}\n",
"\n",
"where $\\Smtrx\\,\\Smtrx^\\top$ is square, symmetric, and non-singular. The least-squares problem is just $\\Amtrx\\,\\xvec=\\bvec$ with\n",
"$\\Amtrx = \\Smtrx\\,\\Smtrx^\\top$ and $\\bvec = \\Smtrx\\,\\gvec$. The $\\Amtrx$ is called, the normal matrix.\n",
"\n",
"Once the **unique** LS net reaction rates $\\rvec_\\text{LS}$ are computed, they can be used to fit reaction rate constants $k_{\\text{f}i}$ and $k_{\\text{b}i}$ (forward and backward, respectively) to experimental data using the reaction rates expressions\n",
"\n",
"\\begin{equation*}\n",
" r_{\\text{LS}i} = k_{\\text{f}i}\\prod\\limits_{j=1}^n\\,c_j^{\\alpha_{i,j}} - k_{\\text{b}i}\\prod\\limits_{j=1}^n\\,c_j^{\\beta_{i,j}} \n",
" \\quad\\ \\forall \\quad\\ i=1,\\ldots,m ,\n",
"\\end{equation*}\n",
"\n",
"as function of the concentration of the species $c_j(t)$. The exponents $\\alpha_{i,j}$ are values determined by experiments for the dependency of the forward rates of reaction on all species concentrations. Similarly $\\beta_{i,j}$ are the counterparts for the backward rates of reaction. These exponent coefficients are related to the stoichiometric coefficients $S_{i,j}$ so that $\\alpha_{i,j}\\,S_{i,j} \\le 0$ and $\\beta_{i,j}\\,S_{i,j} \\ge 0$. The values of $\\alpha_{i,j}$ and $\\beta_{i,j}$ are not necessarily the same as $S_{i,j}$. These values are determined experimentally and the resulting rate expression is typically called the rate law."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## [Full-Rank Reaction Mechanism](#toc)\n",
"Refer to course Notebook 07."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['H2O(g)', 'C(s)', 'H2(g)', 'O(s)', 'CH2(s)', 'CH4(g)', 'CO(g)', 'CO(s)', 'CH(s)', 'H(s)', 'S', 'CH3(s)']\n",
"# of species = 12\n",
"\n",
"r0 : CO(g) + S <=> CO(s)\n",
"r1 : CO(s) + S <=> C(s) + O(s)\n",
"r2 : O(s) + H2(g) <=> H2O(g) + S\n",
"r3 : H2(g) + 2 S <=> 2 H(s)\n",
"r4 : C(s) + H(s) <=> CH(s) + S\n",
"r5 : CH(s) + H(s) <=> CH2(s) + S\n",
"r6 : CH2(s) + H(s) <=> CH3(s) + S\n",
"r7 : CH3(s) + H(s) <=> CH4(g) + 2 S\n",
"n_reactions = 8\n"
]
}
],
"source": [
"'''Read a reaction mechanism and create data structures'''\n",
"\n",
"# build the stoichiometric matrix\n",
"try: \n",
" from chen_3170.toolkit import reaction_mechanism \n",
"except ModuleNotFoundError:\n",
" assert False, 'You need to provide your own reaction_mechanism function here. Bailing out.'\n",
"\n",
"(species, reactions, stoic_mtrx, _, _) = reaction_mechanism('data/methane-catalyst-rxn.txt')\n",
"\n",
"print(species)\n",
"print('# of species =',len(species))\n",
"print('')\n",
"from chen_3170.help import print_reactions\n",
"\n",
"print_reactions(reactions)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"matrix shape = (8, 12)\n"
]
},
{
"data": {
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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"stoic_mtrx=\n",
" [[ 0. 0. 0. 0. 0. 0. -1. 1. 0. 0. -1. 0.]\n",
" [ 0. 1. 0. 1. 0. 0. 0. -1. 0. 0. -1. 0.]\n",
" [ 1. 0. -1. -1. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
" [ 0. 0. -1. 0. 0. 0. 0. 0. 0. 2. -2. 0.]\n",
" [ 0. -1. 0. 0. 0. 0. 0. 0. 1. -1. 1. 0.]\n",
" [ 0. 0. 0. 0. 1. 0. 0. 0. -1. -1. 1. 0.]\n",
" [ 0. 0. 0. 0. -1. 0. 0. 0. 0. -1. 1. 1.]\n",
" [ 0. 0. 0. 0. 0. 1. 0. 0. 0. -1. 2. -1.]]\n",
"\n",
"mole balance vector =\n",
" [-1. 0. 0. -1. 0. 0. 0. 1.]\n"
]
}
],
"source": [
"'''Check the stoichiometric matrix'''\n",
"\n",
"from chen_3170.help import plot_matrix\n",
"\n",
"plot_matrix(stoic_mtrx, title='Stoichiometric Matrix')\n",
"print('stoic_mtrx=\\n',stoic_mtrx)\n",
"print('')\n",
"print('mole balance vector =\\n', stoic_mtrx.sum(1))"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"S shape = (8, 12)\n",
"Rank of S = 8\n",
"Matrix is full rank.\n"
]
}
],
"source": [
"'''Rank of S'''\n",
"\n",
"try: \n",
" from chen_3170.toolkit import matrix_rank \n",
"except ModuleNotFoundError:\n",
" assert False, 'You need to provide your own matrix_rank function here. Bailing out.'\n",
"\n",
"s_rank = matrix_rank(stoic_mtrx)\n",
"print('S shape = ',stoic_mtrx.shape)\n",
"print('Rank of S = ',s_rank)\n",
"\n",
"if s_rank == min(stoic_mtrx.shape):\n",
" print('Matrix is full rank.')\n",
"else:\n",
" print('Matrix is rank deficient.')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## [Full-Rank Least-Squares Reaction Rates](#toc)\n",
"Refer to course Notebook 07."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### [Random Production Rates](#toc)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"'''Assume a species production rate as random'''\n",
"\n",
"import numpy as np\n",
"a = -1.8\n",
"b = 2.1\n",
"g_vec = (b-a)*np.random.random(len(species)) + a"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here, let's compute $\\rvec_\\text{LS} $ for \n",
"$\n",
"\\Smtrx\\,\\Smtrx^\\top\\,\\rvec_\\text{LS} = \\Smtrx\\,\\gvec .\n",
"$"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"species = ['H2O(g)', 'C(s)', 'H2(g)', 'O(s)', 'CH2(s)', 'CH4(g)', 'CO(g)', 'CO(s)', 'CH(s)', 'H(s)', 'S', 'CH3(s)']\n",
"species production rates g_vec = [ 1.553 1.221 -0.948 1.166 -0.869 0.298 -1.088 -1.702 0.781 -0.864\n",
" 0.474 -1.661]\n",
"reaction rates r_vec = [ 0.706 2.158 1.334 -0.377 0.477 -0.76 -0.342 0.872]\n",
"residual norm ||g - ST r|| = 1.24992e+00\n"
]
}
],
"source": [
"'''Compute the LS reaction rates for random species production rates'''\n",
"\n",
"# make sure the matrix is full rank\n",
"\n",
"try: \n",
" from chen_3170.toolkit import matrix_rank \n",
"except ModuleNotFoundError:\n",
" assert False, 'You need to provide your own matrxi_rank function here. Bailing out.'\n",
" \n",
"assert matrix_rank(stoic_mtrx) == min(stoic_mtrx.shape[0],stoic_mtrx.shape[1])\n",
"assert matrix_rank(stoic_mtrx) < stoic_mtrx.shape[1]\n",
"\n",
"# build A x = b for the LS problem\n",
"a_mtrx = stoic_mtrx @ stoic_mtrx.transpose() # A = S ST, A is the normal matrix\n",
"b_vec = stoic_mtrx @ g_vec # b = S g\n",
"\n",
"try: \n",
" from chen_3170.toolkit import lu_factorization \n",
"except ModuleNotFoundError:\n",
" assert False, 'You need to provide your own lu_factorization function here. Bailing out.'\n",
"\n",
"# matrix LU factorization of A, the normal matrix\n",
"(L, U, P, s_rank) = lu_factorization(a_mtrx, 'partial') # matrix is full rank; partial pivoting works\n",
"assert s_rank == np.linalg.matrix_rank(stoic_mtrx)\n",
"\n",
"from chen_3170.help import forward_solve\n",
"\n",
"try: \n",
" from chen_3170.toolkit import backward_solve \n",
"except ModuleNotFoundError:\n",
" assert False, 'You need to provide your own backward_solve function here. Bailing out.'\n",
"\n",
"# solve the LS problem: A x = b\n",
"y_vec = forward_solve(L, P@b_vec) # L y = P b\n",
"x_vec = backward_solve(U, y_vec) # U x = y\n",
"\n",
"assert np.linalg.norm(x_vec - np.linalg.solve(a_mtrx,b_vec)) < 1e-12\n",
"\n",
"np.set_printoptions(precision=3,threshold=100,edgeitems=3)\n",
"print('species = ',species)\n",
"print('species production rates g_vec =', g_vec)\n",
"\n",
"r_vec = x_vec # r = x\n",
"print('reaction rates r_vec =', r_vec)\n",
"residual_vec = g_vec - stoic_mtrx.transpose() @ r_vec\n",
"print('residual norm ||g - ST r|| = %8.5e'%np.linalg.norm(residual_vec))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"species = ['H2O(g)', 'C(s)', 'H2(g)', 'O(s)', 'CH2(s)', 'CH4(g)', 'CO(g)', 'CO(s)', 'CH(s)', 'H(s)', 'S', 'CH3(s)']\n",
"species production rates g_vec = [ 1.553 1.221 -0.948 1.166 -0.869 0.298 -1.088 -1.702 0.781 -0.864\n",
" 0.474 -1.661]\n"
]
}
],
"source": [
"'''Plot of the input species production rate'''\n",
"\n",
"try: \n",
" from chen_3170.toolkit import plot_rates \n",
"except ModuleNotFoundError:\n",
" assert False, 'You need to provide your plot_rates function here. Bailing out.'\n",
"%matplotlib inline\n",
"plot_rates(species, g_vec, \n",
" title='Input Species Production Rates ('+str(len(species))+')', \n",
" y_label='Species Production Rate Density', \n",
" x_tick_rotation=60, bar_color='lightblue', style='dark')\n",
"\n",
"print('species = ',species)\n",
"print('species production rates g_vec =',g_vec)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"reaction rates r_vec = [ 0.706 2.158 1.334 -0.377 0.477 -0.76 -0.342 0.872]\n"
]
}
],
"source": [
"'''Plot least-squares reaction rates'''\n",
"\n",
"try: \n",
" from chen_3170.toolkit import plot_rates \n",
"except ModuleNotFoundError:\n",
" assert False, 'You need to provide your plot_rates function here. Bailing out.'\n",
"%matplotlib inline \n",
"plot_rates(reactions, r_vec, \n",
" title='Unique LS Reaction Rates (%s)'%str(s_rank), \n",
" y_label='Reaction Rate Density', \n",
" x_tick_rotation=60, bar_color='orange', style='dark')\n",
"\n",
"print('reaction rates r_vec =',r_vec)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### [Realistic Production Rates](#toc)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"'''Assume the following species production rate'''\n",
"\n",
"g_dict = dict()\n",
"\n",
"g_dict['CH4(g)'] = -2.9\n",
"g_dict['O(s)'] = -4.1\n",
"g_dict['S'] = 0.4\n",
"g_dict['CO(s)'] = 0.3\n",
"g_dict['H2O(g)'] = 2.7\n",
"g_dict['C(s)'] = 1.3\n",
"g_dict['CO(g)'] = 1.1\n",
"g_dict['H(s)'] = 1.9\n",
"g_dict['CH3(s)'] = 1.1\n",
"g_dict['CH2(s)'] = -1.3\n",
"g_dict['H2(g)'] = 1.6\n",
"g_dict['CH(s)'] = 0.4\n",
"\n",
"for spc in species:\n",
" g_vec[species.index(spc)] = g_dict[spc]"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"species = ['H2O(g)', 'C(s)', 'H2(g)', 'O(s)', 'CH2(s)', 'CH4(g)', 'CO(g)', 'CO(s)', 'CH(s)', 'H(s)', 'S', 'CH3(s)']\n",
"species production rates g_vec = [ 2.7 1.3 1.6 -4.1 -1.3 -2.9 1.1 0.3 0.4 1.9 0.4 1.1]\n",
"reaction rates r_vec = [-1.1 -1.4 2.7 -4.3 -2.7 -3.1 -1.8 -2.9]\n",
"residual norm ||g - ST r|| = 2.50785e-15\n"
]
}
],
"source": [
"'''Compute the LS reaction rates for random species production rates'''\n",
"\n",
"# build A x = b for the LS problem\n",
"a_mtrx = stoic_mtrx @ stoic_mtrx.transpose() # A = S ST, A is the normal matrix\n",
"b_vec = stoic_mtrx @ g_vec # b = S g\n",
"\n",
"try: \n",
" from chen_3170.toolkit import lu_factorization \n",
"except ModuleNotFoundError:\n",
" assert False, 'You need to provide your own lu_factorization function here. Bailing out.'\n",
"\n",
"# matrix LU factorization of A, the normal matrix\n",
"(L, U, P, s_rank) = lu_factorization( a_mtrx, 'partial' ) # matrix is full rank; partial pivoting works\n",
"assert s_rank == np.linalg.matrix_rank(stoic_mtrx)\n",
"\n",
"from chen_3170.help import forward_solve\n",
"\n",
"try: \n",
" from chen_3170.toolkit import backward_solve \n",
"except ModuleNotFoundError:\n",
" assert False, 'You need to provide your own backward_solve function here. Bailing out.'\n",
"\n",
"# solve the LS problem: A x = b\n",
"y_vec = forward_solve( L, P @ b_vec) # L y = P b\n",
"x_vec = backward_solve( U, y_vec) # U x = y\n",
"assert np.linalg.norm(x_vec - np.linalg.solve(a_mtrx,b_vec)) < 1e-12\n",
"\n",
"np.set_printoptions(precision=3,threshold=100,edgeitems=3)\n",
"print('species = ',species)\n",
"print('species production rates g_vec =',g_vec)\n",
"\n",
"r_vec = x_vec # r = x\n",
"print('reaction rates r_vec =',r_vec)\n",
"residual_vec = g_vec - stoic_mtrx.transpose() @ r_vec\n",
"print('residual norm ||g - ST r|| = %8.5e'%np.linalg.norm(residual_vec))"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"species = ['H2O(g)', 'C(s)', 'H2(g)', 'O(s)', 'CH2(s)', 'CH4(g)', 'CO(g)', 'CO(s)', 'CH(s)', 'H(s)', 'S', 'CH3(s)']\n",
"species production rates g_vec = [ 2.7 1.3 1.6 -4.1 -1.3 -2.9 1.1 0.3 0.4 1.9 0.4 1.1]\n"
]
}
],
"source": [
"'''Plot of the input species production rate'''\n",
"\n",
"try: \n",
" from chen_3170.toolkit import plot_rates \n",
"except ModuleNotFoundError:\n",
" assert False, 'You need to provide your plot_rates function here. Bailing out.'\n",
"%matplotlib inline\n",
"plot_rates(species, g_vec, \n",
" title='Input Species Production Rates ('+str(len(species))+')', \n",
" y_label='Species Production Rate Density', \n",
" x_tick_rotation=60, bar_color='lightblue', style='dark')\n",
"\n",
"print('species = ',species)\n",
"print('species production rates g_vec =',g_vec)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"reaction rates r_vec = [-1.1 -1.4 2.7 -4.3 -2.7 -3.1 -1.8 -2.9]\n"
]
}
],
"source": [
"'''Plot least-squares reaction rates'''\n",
"\n",
"try: \n",
" from chen_3170.toolkit import plot_rates \n",
"except ModuleNotFoundError:\n",
" assert False, 'You need to provide your plot_rates function here. Bailing out.'\n",
"%matplotlib inline \n",
"plot_rates(reactions, r_vec, \n",
" title='Unique LS Reaction Rates (%s)'%str(s_rank), \n",
" y_label='Reaction Rate Density', \n",
" x_tick_rotation=60, bar_color='orange', style='dark')\n",
"\n",
"print('reaction rates r_vec =',r_vec)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.8"
},
"latex_envs": {
"LaTeX_envs_menu_present": true,
"autoclose": false,
"autocomplete": true,
"bibliofile": "biblio.bib",
"cite_by": "apalike",
"current_citInitial": 1,
"eqLabelWithNumbers": true,
"eqNumInitial": 1,
"hotkeys": {
"equation": "Ctrl-E",
"itemize": "Ctrl-I"
},
"labels_anchors": false,
"latex_user_defs": false,
"report_style_numbering": false,
"user_envs_cfg": false
}
},
"nbformat": 4,
"nbformat_minor": 2
}