fpraktikum/GL/scripts/Untitled.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.hycell": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from SecondaryValue import SecondaryValue\n",
    "from fillplots import plot_regions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.hycell": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "L = 500  # mm\n",
    "R = 1000  # mm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.hycell": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "g = SecondaryValue('1 - L/R', defaults=dict(R=R))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.hycell": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g(L=L)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.hycell": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": [
       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\n"
      ],
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gsh = np.linspace(.01, 10, 1000)\n",
    "fig = plt.figure()\n",
    "ax = plt.subplot(111)\n",
    "\n",
    "ax.fill_between(gsh, 1/gsh, alpha=0.3, label='Stabilitaetsbereich')\n",
    "ax.set_ylim([0,2])\n",
    "ax.set_xlim([-.1,2])\n",
    "ax.set_xlabel('$g_1$')\n",
    "ax.set_ylabel('$g_2$')\n",
    "ls = np.linspace(0, R, 100)\n",
    "ax.plot(np.ones_like(ls), g(L=[ls]), color='orange', linewidth=5, label='g fuer $L\\in (0,1)$m')\n",
    "ax.legend()\n",
    "plt.show()\n",
    "fig.savefig('stabdiag.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.hycell": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.        , 0.98989899, 0.97979798, 0.96969697, 0.95959596,\n       0.94949495, 0.93939394, 0.92929293, 0.91919192, 0.90909091,\n       0.8989899 , 0.88888889, 0.87878788, 0.86868687, 0.85858586,\n       0.84848485, 0.83838384, 0.82828283, 0.81818182, 0.80808081,\n       0.7979798 , 0.78787879, 0.77777778, 0.76767677, 0.75757576,\n       0.74747475, 0.73737374, 0.72727273, 0.71717172, 0.70707071,\n       0.6969697 , 0.68686869, 0.67676768, 0.66666667, 0.65656566,\n       0.64646465, 0.63636364, 0.62626263, 0.61616162, 0.60606061,\n       0.5959596 , 0.58585859, 0.57575758, 0.56565657, 0.55555556,\n       0.54545455, 0.53535354, 0.52525253, 0.51515152, 0.50505051,\n       0.49494949, 0.48484848, 0.47474747, 0.46464646, 0.45454545,\n       0.44444444, 0.43434343, 0.42424242, 0.41414141, 0.4040404 ,\n       0.39393939, 0.38383838, 0.37373737, 0.36363636, 0.35353535,\n       0.34343434, 0.33333333, 0.32323232, 0.31313131, 0.3030303 ,\n       0.29292929, 0.28282828, 0.27272727, 0.26262626, 0.25252525,\n       0.24242424, 0.23232323, 0.22222222, 0.21212121, 0.2020202 ,\n       0.19191919, 0.18181818, 0.17171717, 0.16161616, 0.15151515,\n       0.14141414, 0.13131313, 0.12121212, 0.11111111, 0.1010101 ,\n       0.09090909, 0.08080808, 0.07070707, 0.06060606, 0.05050505,\n       0.04040404, 0.03030303, 0.02020202, 0.01010101, 0.        ])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g(L=[ls])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.hycell": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
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