{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from __future__ import absolute_import, division, print_function\n", "from collections import defaultdict\n", "from ipywidgets import interact\n", "import matplotlib.pyplot as plt\n", "from chempy import Reaction, Substance, ReactionSystem\n", "from chempy.kinetics.ode import get_odesys\n", "from chempy.kinetics.analysis import plot_reaction_contributions\n", "from chempy.printing.tables import UnimolecularTable, BimolecularTable\n", "from chempy.util.graph import rsys2graph\n", "import sympy\n", "sympy.init_printing()\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "A, B, C, D = map(Substance, 'ABCD')\n", "One = sympy.S.One\n", "reactions = r0, r1, r2 = [\n", " Reaction({'A'}, {'B'}, 4*One/100, name='R1: A cons.'),\n", " Reaction({'B', 'C'}, {'A', 'C'}, 10**(4*One), name='R2: A reform.'),\n", " Reaction({'B': 2}, {'B', 'C'}, 3*10**(7*One), name='R3: C form.')\n", "]\n", "rsys = ReactionSystem(reactions, (A, B, C))\n", "rsys" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "uni, not_uni = UnimolecularTable.from_ReactionSystem(rsys)\n", "bi, not_bi = BimolecularTable.from_ReactionSystem(rsys)\n", "assert not (not_uni & not_bi), \"There are only uni- & bi-molecular reactions in this set\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "uni" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "bi" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "rsys2graph(rsys, 'robertson.png', save='.')\n", "from IPython.display import Image; Image('robertson.png')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "odesys, extra = get_odesys(rsys, include_params=True)\n", "odesys.exprs" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "odesys.get_jac()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "c0 = defaultdict(float, {'A': 1})\n", "result = odesys.integrate(1e10, c0, integrator='cvode', nsteps=2000)\n", "{k: v for k, v in result.info.items() if not k.startswith('internal')}" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "extra['rate_exprs_cb'](result.xout, result.yout)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "result.plot(xscale='log', yscale='log')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fig, axes = plt.subplots(2, 2, figsize=(14, 6))\n", "plot_reaction_contributions(result, rsys, extra['rate_exprs_cb'], 'AB', axes=axes[0, :])\n", "plot_reaction_contributions(result, rsys, extra['rate_exprs_cb'], 'AB', axes=axes[1, :],\n", " relative=True, yscale='linear')\n", "plt.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We could also have parsed the reactions from a string:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "str_massaction = \"\"\"\n", "A -> B; 'k1'\n", "B + C -> A + C; 'k2'\n", "2 B -> B + C; 'k3'\n", "\"\"\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "rsys3 = ReactionSystem.from_string(str_massaction, substance_factory=lambda formula: Substance(formula))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "rsys3.substance_names()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "odesys3, extra3 = get_odesys(rsys3, include_params=False, lower_bounds=[0, 0, 0])\n", "extra3['param_keys'], extra3['unique']" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "odesys3.exprs, odesys3.params, odesys3.names, odesys3.param_names" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def integrate_and_plot(A0=1.0, B0=0.0, C0=0.0, lg_k1=-2, lg_k2=4, lg_k3=7, lg_tend=9):\n", " plt.figure(figsize=(14, 4))\n", " tout, yout, info = odesys3.integrate(\n", " 10**lg_tend, {'A': A0, 'B': B0, 'C': C0},\n", " {'k1': 10**lg_k1, 'k2': 10**lg_k2, 'k3': 10**lg_k3},\n", " integrator='cvode', nsteps=3000)\n", " plt.subplot(1, 2, 1)\n", " odesys3.plot_result(xscale='log', yscale='log')\n", " plt.legend(loc='best')\n", " plt.subplot(1, 2, 2)\n", " plt.plot(tout[tout<.05], yout[tout<.05, odesys3.names.index('B')])\n", " _ = plt.legend('best')\n", "interact(integrate_and_plot) #, **kw)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# We could also have used SymPy to construct symbolic rates:\n", "import sympy\n", "rsys_sym = ReactionSystem.from_string(\"\"\"\n", "A -> B; sp.Symbol('k1')\n", "B + C -> A + C; sp.Symbol('k2')\n", "2 B -> B + C; sp.Symbol('k3')\n", "\"\"\", rxn_parse_kwargs=dict(globals_={'sp': sympy}), substance_factory=lambda formula: Substance(formula))\n", "odesys_sym, _ = get_odesys(rsys_sym, params=True)\n", "for attr in 'exprs params names param_names'.split():\n", " print(getattr(odesys_sym, attr))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For larger systems it is easy to loose track of what substances are actually playing a part, here the html tables can help (note the yellow background color):" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "rsys.substances['D'] = D\n", "uni, not_uni = UnimolecularTable.from_ReactionSystem(rsys)\n", "uni" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "bi, not_bi = BimolecularTable.from_ReactionSystem(rsys)\n", "bi" ] }, { "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": "3.6.1" }, "widgets": { "state": { "6296ca2fc06a4f0c9d9390c4dd51a493": { "views": [ { "cell_index": 16 } ] } }, "version": "1.2.0" } }, "nbformat": 4, "nbformat_minor": 1 }