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[notebooks] add parametric delay example
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{ | ||
"cells": [ | ||
{ | ||
"cell_type": "raw", | ||
"metadata": {}, | ||
"source": [ | ||
"This file is part of the pyMOR project (http://www.pymor.org).\n", | ||
"Copyright 2013-2019 pyMOR developers and contributors. All rights reserved.\n", | ||
"License: BSD 2-Clause License (http://opensource.org/licenses/BSD-2-Clause)" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"import numpy as np\n", | ||
"import scipy.linalg as spla\n", | ||
"import matplotlib as mpl\n", | ||
"import matplotlib.pyplot as plt\n", | ||
"\n", | ||
"from pymor.basic import *" | ||
] | ||
}, | ||
{ | ||
"cell_type": "markdown", | ||
"metadata": {}, | ||
"source": [ | ||
"# Model" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"def H(s, mu):\n", | ||
" tau = mu['tau']\n", | ||
" return np.array([[np.exp(-s) / (tau * s + 1)]])\n", | ||
"\n", | ||
"def dH(s, mu):\n", | ||
" tau = mu['tau']\n", | ||
" return np.array([[-(tau * s + tau + 1) * np.exp(-s) / (tau * s + 1) ** 2]])" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"f = ProjectionParameterFunctional('tau', ())\n", | ||
"parameter_space = CubicParameterSpace(f.parameter_type, 0.01, 1)\n", | ||
"\n", | ||
"fom = TransferFunction(NumpyVectorSpace(1), NumpyVectorSpace(1),\n", | ||
" H, dH,\n", | ||
" parameter_space=parameter_space)" | ||
] | ||
}, | ||
{ | ||
"cell_type": "markdown", | ||
"metadata": {}, | ||
"source": [ | ||
"# Bode plot" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"mu_list_short = [0.01, 0.1, 1]" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"w = np.logspace(-2, 4, 100)\n", | ||
"\n", | ||
"fig, ax = plt.subplots()\n", | ||
"for mu in mu_list_short:\n", | ||
" fom.mag_plot(w, ax=ax, mu=mu, label=fr'$\\tau = {mu}$')\n", | ||
"ax.legend()\n", | ||
"plt.show()" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"w_list = np.logspace(-2, 4, 100)\n", | ||
"mu_list = np.logspace(-2, 0, 50)\n", | ||
"\n", | ||
"fom_w_mu = np.zeros((len(w_list), len(mu_list)))\n", | ||
"for i, mu in enumerate(mu_list):\n", | ||
" fom_w_mu[:, i] = spla.norm(fom.bode(w_list, mu=mu), axis=(1, 2))" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"fig, ax = plt.subplots()\n", | ||
"out = ax.contourf(w_list, mu_list, fom_w_mu.T,\n", | ||
" norm=mpl.colors.LogNorm(),\n", | ||
" levels=np.logspace(np.log10(fom_w_mu.min()), np.log10(fom_w_mu.max()), 100))\n", | ||
"ax.set_xlabel(r'Frequency $\\omega$')\n", | ||
"ax.set_ylabel(r'Parameter $\\mu$')\n", | ||
"ax.set_xscale('log')\n", | ||
"ax.set_yscale('log')\n", | ||
"fig.colorbar(out, ticks=np.logspace(-4, 1, 6))\n", | ||
"plt.show()" | ||
] | ||
}, | ||
{ | ||
"cell_type": "markdown", | ||
"metadata": {}, | ||
"source": [ | ||
"# TF-IRKA" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"r = 10\n", | ||
"roms_tf_irka = []\n", | ||
"for mu in mu_list_short:\n", | ||
" tf_irka = TFIRKAReductor(fom, mu=mu)\n", | ||
" rom = tf_irka.reduce(r, conv_crit='h2', maxit=1000, num_prev=5)\n", | ||
" roms_tf_irka.append(rom)" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"fig, ax = plt.subplots()\n", | ||
"for mu, rom in zip(mu_list_short, roms_tf_irka):\n", | ||
" poles = rom.poles()\n", | ||
" ax.plot(poles.real, poles.imag, '.', label=fr'$\\tau = {mu}$')\n", | ||
"ax.set_title(\"Poles of TF-IRKA's ROMs\")\n", | ||
"ax.legend()\n", | ||
"plt.show()" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"fig, ax = plt.subplots()\n", | ||
"for mu, rom in zip(mu_list_short, roms_tf_irka):\n", | ||
" rom.mag_plot(w, ax=ax, label=fr'$\\tau = {mu}$')\n", | ||
"ax.set_title(\"Bode plot of TF-IRKA's ROMs\")\n", | ||
"ax.legend()\n", | ||
"plt.show()" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"fig, ax = plt.subplots()\n", | ||
"for mu, rom in zip(mu_list_short, roms_tf_irka):\n", | ||
" (fom - rom).mag_plot(w, ax=ax, mu=mu, label=fr'$\\tau = {mu}$')\n", | ||
"ax.set_title(\"Bode plot of error systems\")\n", | ||
"ax.legend()\n", | ||
"plt.show()" | ||
] | ||
} | ||
], | ||
"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": "3.6.8" | ||
} | ||
}, | ||
"nbformat": 4, | ||
"nbformat_minor": 2 | ||
} |