test-conda/HessRogers.ipynb
2020-04-29 23:46:10 +02:00

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106 KiB
Plaintext

{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.gridspec as gridspec\n",
"import pandas as pd\n",
"from scipy.integrate import odeint,quad\n",
"from scipy.stats import kde,beta\n",
"import seaborn as sns\n",
"%matplotlib inline\n",
"from importlib import reload\n",
"pi=np.pi\n",
"\n",
"#mardi 31 mars 2020\n",
"#essayons tout d'abord d'écrire des fonctions qui calculent le rayon spectral\n",
"#et l'abcisse de convergence d'une matrice\n",
"\n",
"from numpy import linalg as LA\n",
"from scipy.linalg import expm\n",
"from scipy.optimize import brentq\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import ucovid\n",
"sns.set()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"#reload(ucovid)\n",
"#ucovid.hesrog(beta=1,b=1,mu=1,r=1,eps=0.2)\n",
"\n",
" \n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"#reload(ucovid)\n",
"#ucovid.lamvsp(beta=1.5,b=1,mu=0.9,r=1,epsmax=0.5)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#reload(ucovid)\n",
"#ucovid.phesrog(beta=1,b=1,mu=2,r=0.5,voir=True,eps=0.1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Pour le modèle de Hessterbeek et Rogers\n",
"\\begin{equation}\n",
" \\frac{dx}{dt} = A(t) x(t)\\qquad \n",
" A(t)=\\begin{pmatrix}-r & b/s(t)\\\\\n",
"\\beta s(t) & -\\mu\\end{pmatrix}\n",
"\\end{equation}\n",
"avec $s(t) = e^{\\epsilon \\sin(2\\pi t)}$. $\\lambda_d(E)$ est le rayon spectral de ma matrice de monodromie $E=\\phi(1)$, et $P$ leur quantité qui vérifient bien \n",
"\\begin{equation}\n",
" R_0 > 1 \\iff \\lambda_d(E) >1 \\iff P >1\\,.\n",
"\\end{equation}\n",
"On voit que contrairement à leur affirmation/prédiction les indicateurs augmentent quand $\\epsilon$ augmente. En outre on voit que l'augmentation est en $\\epsilon^2$ pour $\\epsilon$ petit."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8b8c9a4c7f4644b9a7b7812d1c805fd2",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"interactive(children=(FloatSlider(value=1.0, continuous_update=False, description='beta', max=2.0), FloatSlide…"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<function ucovid.lamvsp>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from ipywidgets import interact, interactive, fixed, interact_manual\n",
"import ipywidgets as widgets\n",
"interact(ucovid.lamvsp,\n",
" beta=widgets.FloatSlider(min=0.0, max=2.0, step=0.1, value=1.0, continuous_update=False),\n",
" b=widgets.FloatSlider(min=0.0, max=2.0, step=0.1, value=1.0, continuous_update=False),\n",
" mu=widgets.FloatSlider(min=0.0, max=2.0, step=0.1, value=1.0, continuous_update=False),\n",
"r=widgets.FloatSlider(min=0.0, max=2.0, step=0.1, value=1.0, continuous_update=False),\n",
" epsmax=widgets.FloatSlider(min=0.0, max=0.5, step=0.01, value=0.2, continuous_update=False)\n",
" )\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1.5131802505966265"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#ucovid.hesrog(beta=2.0,b=1.0,mu=1,r=1,eps=0.0)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1.5131802505966268"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#ucovid.hesrog(beta=1,b=2.0,mu=1,r=1,eps=0.0)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#reload(ucovid)\n",
"#ucovid.blamvsp(vlam=True,epsmax=0.01)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1.0302204550794536"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#reload(ucovid)\n",
"#ucovid.bhesrog(eps=0.5,voir=True)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"I7/I2,I1,exp(eps*sin) 0.9999850643923525 -0.007604639952131169 1.0000002497502654\n"
]
},
{
"data": {
"text/plain": [
"(1.1001486039235775, 1.199982077270823, 1.2006095672731398, 1.1999817775900015)"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#reload(ucovid)\n",
"#ucovid.baphesrog(beta=1.2,eps=0.001,nbpts=1000,voir=True)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"epsilon= 0.01 moyennes xz, yz 1.0000199005907962 0.9999950248756219\n",
"epsilon= 0.001 moyennes xz, yz 0.9999997512437345 0.9999995024875622\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x7f82c20edc10>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def testinfluencevecteurpropre():\n",
" nbpts=200\n",
" T=1\n",
" eps=0.01\n",
" tt=np.linspace(0,T,nbpts+1)\n",
" stt=np.sin(2*pi*tt/T)\n",
" x=np.exp(eps*stt)\n",
" y=1+eps*stt\n",
" z=1-0.001*stt\n",
" print(\"epsilon=\",eps,\"moyennes xz, yz\",(x*z).mean(),(y*z).mean()) #xz > 1 > yz\n",
" eps=0.001\n",
" x=np.exp(eps*stt)\n",
" y=1+eps*stt\n",
" z=1-0.001*stt\n",
" print(\"epsilon=\",eps,\"moyennes xz, yz\",(x*z).mean(),(y*z).mean()) #1> xz et 1 > yz\n",
" plt.plot(tt,x,label=r\"$e^{\\epsilon*sin}$\")\n",
" plt.plot(tt,y,label=r\"1+$\\epsilon*sin$\")\n",
" plt.plot(tt,x*z)\n",
" plt.legend()\n",
"#testinfluencevecteurpropre():"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Mercredi 29 avril 2020. \n",
"\n",
"Essai de la dynamique proposée par Sylvain. On remplace $A(t)$ par\n",
"$$ B(t) =\\begin{pmatrix}-r & b(1 + \\delta \\sin(2\\pi t)\\\\\n",
"\\beta(1-\\delta \\sin(2\\pi t)) & -\\mu\\end{pmatrix}$$\n",
"\n",
"avec comme paramètres typiques : $\\beta=b=1.5$, $r=2$, $\\mu=1$.\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<module 'ucovid' from '/home/philippe/pca/EPIDEMIE/ucovid.py'>"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"reload(ucovid)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#ucovid.lamvsylvain()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "241735df33b94828a9a97eeacb46bcaf",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"interactive(children=(FloatSlider(value=1.5, continuous_update=False, description='beta', max=2.0), FloatSlide…"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<function ucovid.lamvsylvain>"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"interact(ucovid.lamvsylvain,\n",
" beta=widgets.FloatSlider(min=0.0, max=2.0, step=0.1, value=1.5, continuous_update=False),\n",
" b=widgets.FloatSlider(min=0.0, max=2.0, step=0.1, value=1.5, continuous_update=False),\n",
" mu=widgets.FloatSlider(min=0.0, max=2.0, step=0.1, value=1.0, continuous_update=False),\n",
"r=widgets.FloatSlider(min=0.0, max=2.0, step=0.1, value=2.0, continuous_update=False),\n",
" deltamax=widgets.FloatSlider(min=0.0, max=0.5, step=0.01, value=0.2, continuous_update=False)\n",
" )\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"On voit que la l'indicateur $\\lambda_d(E)$ décroît avec $\\delta$ ( et en $\\delta^2$ au voisinage de $0$)"
]
},
{
"cell_type": "code",
"execution_count": null,
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