Untitled.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from pylab import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "xv = [1,2,3,4]; yv = [5,1,4,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10b3c8090>]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ycrvdvLF5JXCRE7ZqNaZ225X2yK3SbluKaExKa7fdHtGulm23LUU0NiW1207a2tUy7bZn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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109cfc910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(xv,yv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10bd12110>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW0AAAEACAYAAAB4ayemAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSY0YbUlqxGhLUiNGW5IaMdqS1Mj/A7VWeTlBRWBHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109cfca90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(xv,yv,'ro')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[None, None, None]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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StyStmjsWG1+aleqLvGlw0i5Bqt1uvflytKR180QzGFL3mQXAPsBNkjbJHJKNkZoAD+Pa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\nki0PGQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109cfc9d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "myplot = plot(xv,yv,'k--')\n",
    "setp(myplot, linewidth = 3.0, marker = '+', markersize = 30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.5, 4.5, -0.5, 5.5]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x109cfcb50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "axis ([0.5,4.5,-0.5,5.5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[None]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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wAbi6qrYnuaRfvw34XeC5wJVJAPZV1elD1iVJWtqgl6seLl6uKkkrN6uXq0qS1hiDQZLU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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10bf68f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ti = title('my own title')\n",
    "xl = xlabel('time'); yl = ylabel ('values')\n",
    "setp(xl, fontweight='bold')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10bf688d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "savefig('foo.ps', dpi=600, format='ps', orientation='landscape')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10bf431d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "savefig('foo.ps', dpi=600, format='pdf', orientation='landscape')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "myfig = gcf()\n",
    "close(myfig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "close('all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10c17e590>]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xK6MrZPtPpSZd0b0LGIuI3wLWAteWHE/fJM0jW0zg7PwMd68hbc9rdfy67F+tj19EPBcR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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10bf43bd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig2 = figure()\n",
    "subplot(2,1,1)\n",
    "plot(xv, yv, 'b-')\n",
    "\n",
    "subplot(2,1,2)\n",
    "plot(yv, xv, 'ro')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "close(fig2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10bf5fc90>]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2qvqnrNwyvLXjeubG4JbUp4D3D66ivOSQkcd+1zdpjc/c73pjVfViVb2Olf8T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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c040dd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig2= figure(figsize=(10,10))\n",
    "subplot(2,1,1)\n",
    "plot(xv, yv, 'b-')\n",
    "subplot(2,1,2)\n",
    "plot(yv, xv, 'ro')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-10. ,  -9.5,  -9. ,  -8.5,  -8. ,  -7.5,  -7. ,  -6.5,  -6. ,\n",
       "        -5.5,  -5. ,  -4.5,  -4. ,  -3.5,  -3. ,  -2.5,  -2. ,  -1.5,\n",
       "        -1. ,  -0.5,   0. ,   0.5,   1. ,   1.5,   2. ,   2.5,   3. ,\n",
       "         3.5,   4. ,   4.5,   5. ,   5.5,   6. ,   6.5,   7. ,   7.5,\n",
       "         8. ,   8.5,   9. ,   9.5,  10. ])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xv = np.arange(-10,10.5,0.5); xv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x10c7917d0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NCeNVzSz/3bIJTwzlSEsDV0GcPY6lL8pw3AfsY0ZDbRLkUvUvYNtkvCWPPDGE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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c19ea10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(xv, 2*xv**3-5*xv**2+7*xv)\n",
    "plot(xv, 2000*cos(xv), 'r--')\n",
    "text(-9.5,-2800,'curve A')\n",
    "text(3,1500,'curve B')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10c7de8d0>]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9Kkk+nkbiecAwsBuwLcWCctcCi4A7gN9RLE1xP/BA+efycl37iOgxSfLRlMSzgVcBLwU2\nBv4e2AB4DrBe+ec6FLtTPQI8XP453uth4KHy1c77hyg2LF9O8cvksS5XOWKgJMlHxySeAazV8Fp7\nnHOrXs8a9Vp7nPerjtcBnl2+HqP4H8Ty8vXAqD/vB/44xusemxXd+xuI6F1J8tE3yumez6L4H8Sq\npD/6/fOA5wMvaHjNAv5CsUHNneXrdw3v78j/EmIQJcnHwCv/p/Fcig1VNipfGze8n00x1nBzw+sm\nYKnd1t4IET0nST4CkHgm8CKKsYdVr82AzSnWaLqm4bUos46iHyTJR7Qg8ULglRQ7l20FbE0x+Pxr\n4LLydbnNn2sLMmIcSfIRkyDxHOC1wA7A64FtgFuAC4DzgEvL7RgjapUkH1GBsrtnG+CfKJ4zeAXw\nS+AnwI9s/lBjeDGNdT3JSxoGjqLYGepE20eOUWYI+BawBnCv7aGqA42YShLPBXYB9gB2B66jWAF0\noc1ddcYW00tXk7ykGRSzE3am2NT716y+x+t6FH2au9peKmmW7XurDjSiLhJrUfwMvBN4K8U+xicD\nP7Z5pM7YYvB1e6nh7YAltm+3vQJYAOzZUGZf4EzbSwHGSvAR/czmEZtzbN5PMVXzZOAAYKnEMRJb\n1BpgRBOtkvxsiodNVllanhttU+C5ki6SdJWk91YZYEQvKRP+AptdgFdTLPV8vsTPJHYr5/RH9IxW\n/yDbGZVdg2JtlN2BXYHPSdq008Aiep3N7TZzKebnfw/4KnCDxAfLgdyI2rXayPsuiicKV9mIojU/\n2p0Ug60PAw9L+iXFnORbGm8mae6owxHbIxMNOKLXlFMtT5U4DXgTMAf4PxL/DpyWh65iIsqJLEOV\n3a/FwOtMioHXnYC7gStZfeD1ZcAxFK34NYErgHfbvrHhXhl4jWlD4vXAlygeujocWGDzeL1RRT/q\n6sCr7ZXAwcD5wI3AD20vknSQpIPKMospHh65jiLBn9CY4COmG5tLKVr1H6H4GbqqTPwRUyoPQ0V0\nWbn65t7A14ERYE7m2ke7uj2FMiI6ZGOb+cDLKJZFvk5ijsQaNYcW00Ba8hFTTOIlwLEUa+fvb3NN\nzSFFD0tLPqLP2CyhWB/naOBnEl+SWLPmsGJAJclH1KDswvkOxXTjLYFfSby03qhiECXJR9TI5vcU\nS4WcBFwm8f5yoDaiEumTj+gR5Ro484HrgQ/bLK85pOgB6ZOPGBA21wPbAvcD/52Fz6IKSfIRPcTm\nYZt/BeYCv5B4Z80hRZ9Ld01Ej5J4FbAQ+AHwuSyLMD1l+7+IASbxfOA/gIeBfdJPP/2kTz5igNnc\nQ7Hv7G3ApdLTVoWNaClJPqLH2aygWOTsOxTz6beqN6LoJ0nyEX2gfHjqG8C/AT+XGK47pugPSfIR\nfcTmdOBtwHck9q87nuh9rXaGiogeY3OZxI4Ue8uuZ/PNumOK3pUkH9GHbG6SeCNF182zgbl2W3sy\nxzTTsrtG0rCkxZJukTSnSbltJa2U9I5qQ4yIsdj8DngDsAdwlJTu11hd038UkmZQ7N86DLwc2EfS\n5uOUO5JiG8DMhY+YIjZ/pNhm8NXASVL+dx5P1+o3/3bAEtu3214BLKBYMa/RIcAZwD0VxxcRLdg8\nQDGXfgPgh1mbPkZrleRnA3eOOl5annuSpNkUif+48lT6BSOmmM2DFN02An4ksXbNIUWPaPVfu3YS\n9lHAp21bkmjSXSNp7qjDEdsjbdw/Itpg86jEXsCpwDkSe5TJP/qIpCFgqLL7NVu7RtL2wFzbw+Xx\nYcATto8cVeZWnkrss4CHgA/ZPqvhXlm7JmIKSMwATgBeCuxu8+eaQ4oOdHWBMkkzgZuAnYC7gSuB\nfWwvGqf8KcDZthdWHWhEtK+caXMMsA0wbHN/zSHFJHV1gTLbKynWzDgfuBH4oe1Fkg6SdNBkv2lE\ndJfNE8BHgcso1qV/fs0hRU2y1HDEACv3i/0i8HZg53JP2egjnebOzKmNGGDlU7CflXgEuFhiJ/tp\nM+ZiwCXJR0wDNl+SeJinEv1tdccUUyNJPmKasPnGqBb9zjY31x1TdF+SfMQ0YnNs2aK/SGIXmxvr\njim6K0k+YpqxOVniUeBCid1srqk7puieJPmIacjm+2XXzfkSb7W5su6YojuS5COmKZszJR6jWALh\nHTaX1h1TVC/rT0dMYzZnA+8BFkr8Y93xRPWS5COmOZufA+8CFkjsVnc8Ua0k+YjA5mKKJcO/K/G2\nuuOJ6iTJRwQANpcDuwHHSXyk7niiGlm7JiKeRuLFwDkUCxN+0ubxmkOa1rq6CmVETD82vwVeB2wB\n/FhinZpDig4kyUfEasr154eBu4BfS7yi5pBikpLkI2JMNitsPgwcAYxI7FN3TDFx6ZOPiJYktgLO\nAC6g6KfP3rFTZEr65CUNS1os6RZJc8a4/h5J10q6TtJlkracbEAR0XvK9W22AdYGrpXYoeaQok0t\nW/KSZlDs87ozZf8cDfu8SnotcKPt5ZKGKTb/3r7hPmnJRwyAch79ccD3gLlp1XfXVLTktwOW2L7d\n9gpgAcVDE0+yfbnt5eXhFcCGkw0oInqbzY+BLYENgEUS7y63GYwe1E6Snw1P2y5saXluPAcA53YS\nVET0Npt7bN4D7At8mmJgdtuaw4oxtLMKZdsjs5LeBOwPY/fXSZo76nDE9ki7946I3mNzqcSrKRp3\nZ0rcABxuc0XNofUtSUPAUGX3a6NPfnuKPvbh8vgw4AnbRzaU2xJYCAzbXjLGfdInHzHAJNYEPgAc\nBiwBjgHOtllZa2B9rtPc2U6Sn0kx8LoTcDdwJasPvG4M/ALYz/Z/dSPQiOgPEs8E3gn8K7AJcAJw\nms2tdcbVr7qe5MtvshtwFDADOMn2VyUdBGB7nqQTgbcDvyu/ZIXt7aoMNCL6j8QrgQMpljK+FZgP\nLLSfNs4XTUxJkq9CknzE9CWxBkVvwN7AWygmcJwD/AS4MougjS9JPiL6isQMYHuKZP8W4IXAzylW\nvTzf5g81htdzkuQjoq9JbAzsWr52Au4AzqNI+pfZPFZjeLVLko+IgSExE3gNRcIfBjYDLqZI+OeV\nyyBPK0nyETGwJGZRLKkyTJH4H6RM+MBFNn+tMbwpkSQfEdNCuXTCljzVtbMdxVpa5wBn2E/O7hso\nSfIRMS1J/C3wJuBtFOtpLQFOp0j4d9QZW5WS5CNi2iunaL6JYj7+24DbKBL+gn6fk58kHxExSpnw\nh4C9gHcA1wCnAWfa/KXG0CYlST4iYhwSa1HMxX8vsCPFw1enARf0y5o6SfIREW2QeD7wboqEvzHF\nEgunlrte9awk+YiICZLYjCLZ7wcsB04FfmDz+1oDG0OSfETEJEk8A3gD8D6K/vv/okj4/2nzUJ2x\nrZIkHxFRAYlnUUzFfB/F2joLKRL+JTZP1BdXknxERKUk/o5ia8N/AdalGKw9zebmqY8lST4iomvK\nNfHfR5H0l1G08H8E3GC3vz3q5L9/Z7mz5UbekoYlLZZ0i6Q545Q5urx+raStJxtMRESvsbnW5pPA\nhsAhwHOAs4GbJb4msUO5sFpPaprkJc2g2KdxGHg5sI+kzRvK7A68xPamFDvAHNelWHtaufnuwBrk\n+g1y3SD1q4rN4zaX2HwCeBHFw1aPUOTIeyXOkviYxCvKdXZ6QquW/HbAEtu3214BLKAYmBhtD+C7\nALavANaTtH7lkfa+oboD6LKhugPooqG6A+iyoboD6LKhqf6GNra52ubzNlsDmwLfB/4XRSv/TxI/\nk/iyxJ4SG9WV+Fsl+dnwtHUflpbnWpXZsPPQVtfub+xm5ca61niu2fF476vQzv0mWrexztdRv259\ndmOdH6T6TfTf66DVb7y61vGz16yczT2gZTYH2vwDsDlwNBw/G/gwxWqZf5F+crPEfIm5EgdIc+ZI\nbCWxvrT2P040nna0SvLtDio0/obq1mDEUAXlxrrWeK7Z8Xjvq9DO/ZqVGe9a4/lmx+O971S792pW\nbrxrjeebHY/3vlPt3qtZubGutXNuqI33nWr3Xs3KjXWt1bnG60PjnO9Uu/drVu7JazbLbM6Bg263\n2c3mhcBsmDNCsbTCDOD18NcPUfSE3ACHXiDxF4m74FOnS1wh8fPJVGa0prNrJG0PzLU9XB4fBjxh\n+8hRZf4fMGJ7QXm8GNjR9rKGe03NNJ6IiAHTyeyaViPCVwGbStoEuJti3Yd9GsqcBRwMLCh/KTzQ\nmOA7DTIiIianaZK3vVLSwRTbbc0ATrK9SNJB5fV5ts+VtLukJRRbc32g61FHRERbpuxhqIiImHot\nH4aKiIj+lSQfETHAak3ykl4m6ThJ/yHpgDpj6QZJe0o6XtICSbvUHU/VJL1I0omSTq87lipJ+htJ\n3y0/u33rjqdqg/q5rTLIP3eTyZk90Scv6RnAAtt71R1LN0haD/i/tj9YdyzdIOl02++qO46qSHov\ncJ/tn0haYHvvumPqhkH73BoN8s/dRHJmJS15SSdLWibp+obz7Sxu9laKhwMWVBFLN3RSv9JnKda3\n6EkV1K/nTbCOo5/ifnxKA52kQf8MJ1m/nv65W2WidZtwznS5CEMnL4qdVbYGrh91bgawBNgEWINi\nx/TNKbbc+hawQcM9/rOKWLrxmmz9KJ4EPhLYqe46dPPzA06vuw4V13E/4M1lmfl1x151/frpc5vk\n59cXP3edfHZlmbZyZiXLY9q+pHxgarQnFzcDkLQA2NP2ERQL8CNpR4ott9YCLqoilm7ooH4fA3YC\n1pX0EtvzpizoCeigfs8FvgJsJWmORz0J3WsmUkfgaOAYSW+meNiv502kfpKW0Sef2yoT/Px2pg9+\n7laZ4Gf3AiaYM7u5BvJYC5e9ZnQB2xcDF3cxhm5qp35HUySMftRO/e6jWHypX41ZR9sPAfvXE1Kl\nxqtfv39uq4xXv0OAb9cTUmXGq9uEc2Y3Z9fUP6LbXalf/xv0OqZ+/auyunUzyd8FbDTqeCOK30aD\nIvXrf4Nex9Svf1VWt24m+ScXN5P0TIrFzfqif7NNqV//G/Q6pn79q7q6VTQ6PJ9ilcpHKfqRPlCe\n3w24iWKU+LC6R7FTv+lZv+lQx9Svf+vX7br1xMNQERHRHVm7JiJigCXJR0QMsCT5iIgBliQfETHA\nkuQjIgZYknxExABLko+IGGBJ8hERAyxJPiJigP1/B3UUoEwGYdgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c5ec850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "close('all'); xv_lin=np.arange(-3,3.01,0.02)\n",
    "xv = 10**xv_lin\n",
    "semilogx(xv, exp(-xv/0.01)+0.5*exp(-xv/10)+0.2*exp(-xv/200))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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n9SjWqptadgA9lnJ+KecGzq8QETwVwXUR/BPwcmBv4HGyGvmQxEUSh0m8TqIy\nXYt2Z/LbAksjYllEPAnMI1sjYqj3AOcARMQvgImSNiw80uqbWHYAPZZyfinnBs6vcBFEBDfnq2Vu\nDWwGnAf8A9lZ/h8kfiLxZYk9JDYpq/C3K/KT4e/WfViR72s3ZuPuQ1tTp3+WtRrX7LXGfa22h3te\nhE7eb6S5NdtfRn69OnbN9qeU30j/e00tv+FyLeNnr9W4CB4ErYzgoAheAWwBnAKnTwY+CdwI/Fn6\n8Z0ScyWOlThQmjVLYqrEhtI6bx9pPJ1oV+Q7vajQ+BuqVxcjphcwrtlrjftabQ/3fEqLz+xU4+eO\ndMxwrzXub7U93PMpLT63E42fOZpxw73WuL/VdrPnU1p8ZqcaP3M045q91sm+6W2eT2nxmZ1qFsdI\nxzV7rd2+xtenN9k/pcVndqpZHCMd98xrEayM4BKYuSyCXSN4KTAZZi0gu6/tOOAf4S+fIOuE3ApH\nXiXxZ4n74IgLJH4hceVokhmq5ewaSdsBx0bEQL59NPB0RJw4ZMx/AAsiYl6+fTswLSJWNrzX2Ezj\nMTNLTDeza9pdEb4J2EzSFOB+snUf9m0YcxFwCDAv/6XwcGOB7zZIMzMbnZZFPiJWSzqE7P6K44Az\nI2KJpJn567Mj4lJJu0laCjwKfLTnUZuZWUfG7MtQZmY29vp6TQYzM2vNRd7MLGGlFnlJr5F0mqT/\nknRgmbH0gqQ9JJ0uaZ6kXcqOp2iSXi7pO5IuKDuWIkl6vqRz8mO3X9nxFC3V4zYo5Z+70dTMSvTk\nJT0HmBcRe5cdSy9Imgj8a0R8vOxYekHSBRGxV9lxFEXSh4E/RsSPJc2LiH3KjqkXUjtujVL+uRtJ\nzSzkTF7SWZJWSlrcsL+Txc12J/tywLwiYumFbvLLfYFsfYtKKiC/yhthjkO/xf3UmAY6Sqkfw1Hm\nV+mfu0EjzW3ENTPyRRi6eQA7AFsDi4fsGwcsJfs22gRgIdlXfT8MnAxs1PAePyoill48Rpsf2TeB\nTwR2KjuHXh4/4IKycyg4x/2Bd+Vj5pYde9H59dNxG+Xx64ufu26OXT6mo5pZyPKYEXFd/oWpoZ5Z\n3AxA0jxgj4j4GtkC/EiaBrwPeC5wTRGx9EIX+R0G7ASsJ+lVETF7zIIegS7y2wD4CjBV0qwY8k3o\nqhlJjsApwKmS3kX2Zb/KG0l+klbSJ8dt0AiP3870wc/doBEeu5cwwprZyzWQmy1c9uahAyLiWuDa\nHsbQS53n8T+7AAAA4klEQVTkdwpZwehHneT3R7LFl/pV0xwj4q/Ax8oJqVDD5dfvx23QcPkdCnyr\nnJAKM1xuI66ZvZxdU/4V3d5yfv0v9RydX/8qLLdeFvn7gE2GbG9C9tsoFc6v/6Weo/PrX4Xl1ssi\n/8ziZpLWIlvcrC/6mx1yfv0v9RydX/8qLreCrg7PJVul8gmyPtJH8/27AneQXSU+uuyr2M6vnvnV\nIUfn17/59Tq3SnwZyszMesNr15iZJcxF3swsYS7yZmYJc5E3M0uYi7yZWcJc5M3MEuYib2aWMBd5\nM7OEucibmSXsfwH8WIHneRl2kQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10bd12cd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "semilogx(xv, exp(-xv/0.01)+0.5*exp(-xv/10)+0.2*exp(-xv/200))\n",
    "grid(color='k')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10cd75150>]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10983de90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "semilogy(xv, exp(-xv/0.01)+0.5*exp(-xv/10)+0.2*exp(-xv/200))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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GgEQobQBIhNIGgEQobQBIhNIGgEQobQBIhNIGgER+BpynNoB/0MEZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10cf69a50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "close('all')\n",
    "xv = [0.5,1.5,2.5,3.5]; yv=[2,5,1,6]\n",
    "mybar = bar(xv, yv, width=1, yerr=0.5, color='y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW0AAAEACAYAAAB4ayemAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngEQobQBIhNIGgET+HwmH8q4HyeqpAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c165290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xv = [0.5,1.5,2.5,3.5]; yv=[2,5,1,6]\n",
    "mybar = bar(range(4), yv, width=1, yerr=0.5, color='g')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[None, None, None, None, None, None, None, None]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWYAAAEACAYAAACAi9xRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c7b3350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mybar = bar(xv, yv, width=1, yerr=0.5)\n",
    "xticks(range(1,5), ['A', 'B', 'C', 'D'])\n",
    "setp(mybar, color='r', edgecolor='k')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "close('all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x10d156c90>,\n",
       "  <matplotlib.patches.Wedge at 0x10d1dab90>,\n",
       "  <matplotlib.patches.Wedge at 0x10d1a3e50>,\n",
       "  <matplotlib.patches.Wedge at 0x10d0dbbd0>],\n",
       " [<matplotlib.text.Text at 0x10d221650>,\n",
       "  <matplotlib.text.Text at 0x10d1a3fd0>,\n",
       "  <matplotlib.text.Text at 0x10d0dbc90>,\n",
       "  <matplotlib.text.Text at 0x10d0e5950>])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DNarKvyfZ75yyfVQVIn23wYML4OQ14LH7rzBVNQGFQPPPsU5BUYkXAz4gxidEyeMVCrmu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\n/4qiyb0sY3RSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c165410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "close('all')\n",
    "figure(figsize=(5,5))\n",
    "handles = pie([1,2,3,4], explode=[0.2,0,0,0], shadow=True, labels = ['A', 'B', 'C', 'D'])\n",
    "handles\n",
    "# handles[0] [2] --> 3. Text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[None]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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wlQQES7XHNeN8A2Te+ySbztjN/H0Ct3f1PRyK+Tsl3GhqzccdPZ7IuD7AeOBo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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d22b050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure(figsize=(5,5))\n",
    "handles = pie([1,2,3,4], explode=[0.2,0,0,0], shadow=True, labels = ['A', 'B', 'C', 'D'])\n",
    "setp(handles[0] [0], color='y')\n",
    "setp(handles[1] [0], text='lila Stueck')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from mpl_toolkits.mplot3d import Axes3D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using matplotlib backend: MacOSX\n"
     ]
    }
   ],
   "source": [
    "%matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "close('all')\n",
    "fig=figure(); ax=Axes3D(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "from pylab import*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pylab import*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<mpl_toolkits.mplot3d.art3d.Path3DCollection at 0x10c8e0f90>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "close('all')\n",
    "fig=figure(); ax=Axes3D(fig)\n",
    "import random as rn\n",
    "xv=[]; yv=[]; zv=[]\n",
    "\n",
    "for c in range(100):\n",
    "    xv.append(rn.random()); yv.append(rn.random()); zv.append(rn.random())\n",
    "    \n",
    "ax.scatter(xv,yv,zv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "close('all')\n",
    "fig=figure(); ax=Axes3D(fig)\n",
    "xv=linspace(-10,10,100); yv=linspace(-10,10,100)\n",
    "\n",
    "cx,cy= meshgrid(xv, yv)\n",
    "cz= 0.5*cx+exp(-cy**2)\n",
    "tilt= ax.plot_surface(cx, cy, cz, linewidth=0, cmap=cm.jet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}