{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Covariant Lyapunov Vectors"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"ndarray-odeint has calculator of Covariant Lyapunov Vector (CLV).\n",
"The algorithm of CLV has introduced in [Ginelli et al. PRL(2007)] to analyze collective motions.\n",
"\n",
"[Ginelli et al. PRL(2007)]:http://link.aps.org/doi/10.1103/PhysRevLett.99.130601"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A sample script for calculate CLV of Lorenz 63 model is placed at `src/bin/clv.rs`"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[m\u001b[32m\u001b[1m Finished\u001b[m release [optimized] target(s) in 0.0 secs\n",
"\u001b[m\u001b[32m\u001b[1m Running\u001b[m `target/release/clv`\n",
"exponents = [0.9055708184196481, -0.0007741520360882003, -14.572521136716762] shape=[3], strides=[1]\n"
]
}
],
"source": [
"! cargo run --release --bin clv > clv.csv"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Tangency of CLVs"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df = np.arccos(pd.read_csv(\"clv.csv\"))"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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KqdFZaakb6Iag9hfa+S+z9nhP/WDEI6eR9OtUxUWLFvX08YuCnHzIyYecfIqSU+sBhOka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\nAABANAQAAEA0BAAAQDQEAABA0v8HRxQVcE8vUDMAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fbd26940400>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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3VxWNVy2+3uK8q2YdWazfH5/61Kc67ndLpdLp342rVq2iUChQLEZO9wwhdPxB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\niIgIKghEREQEFQQiIiIC/H/hGGCxwPFjSgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fbd269605f8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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YO3e257jL5/PK5XIaGRmJ2j+pToVjY2NauzbtGag67wqx0EqMvZKOeBeRAHKK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\nSUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fbd2616c908>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"for col in df.columns:\n",
" plt.figure()\n",
" plt.title(col)\n",
" df[col].hist(bins=100)\n",
" plt.xlim(0, np.pi)\n",
" plt.yscale(\"log\")"
]
}
],
"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": "3.5.2"
},
"toc": {
"colors": {
"hover_highlight": "#DAA520",
"running_highlight": "#FF0000",
"selected_highlight": "#FFD700"
},
"moveMenuLeft": true,
"nav_menu": {
"height": "48px",
"width": "252px"
},
"navigate_menu": true,
"number_sections": true,
"sideBar": true,
"threshold": 4,
"toc_cell": false,
"toc_section_display": "block",
"toc_window_display": false
}
},
"nbformat": 4,
"nbformat_minor": 2
}
