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 "metadata": {
  "name": ""
 },
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 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import scipy.sparse as sp\n",
      "import scipy.sparse.linalg as alg\n",
      "import math\n",
      "from time import time\n",
      "\n",
      "import pylab as pl\n",
      "\n",
      "%pylab inline --no-import-all"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# x - bezwymiarowa siatka polozen\n",
      "\n",
      "# Stale 'rho' zadeklerowane dla ponizszych typow potencjalow to bezwymiarowe zmienne\n",
      "# z bezwymiarowego rownania Schrodingera.\n",
      "\n",
      "def rho_Morse(m, V_c, C, h):\n",
      "    # 1/C - stala o wymiarze dlugosci\n",
      "    # m - masa czastki\n",
      "    # V_c - charakterystyczna energia\n",
      "    # h - stala Diraca\n",
      "    return 2.0 * m * V_c / (C * C * h * h)\n",
      "def rho_Morse2():\n",
      "    return 25.0\n",
      "def Morse(x):\n",
      "    # potencjal Morse'a: v(x) = V_c * ( exp(-2*c*x) - 2*exp(-c*x) )   \n",
      "    # bezwymiarowy potencjal Morse'a: V(x) = exp(-2*x) - 2*exp(-x)   \n",
      "    #return np.exp(-2.0 * x) - 2.0 * np.exp(-x)\n",
      "    return (1.0 - np.exp(-x)) ** 2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# rozmiar macierzy pieciodiagonalnej:\n",
      "N = 1500\n",
      "    \n",
      "# konce bezwymiarowego przedzialu obustronnie domknietego [x_min,x_max]:\n",
      "x_min = -3.0\n",
      "x_max = 9.0\n",
      "\n",
      "# ilosc wartosci wlasnych i wektorow wlasnych, ktore chcemy wyznaczyc:\n",
      "N_eigvals = 3\n",
      "\n",
      "# wybor potencjalu:\n",
      "potential = Morse\n",
      "# opcjonalne argumenty wybranego potencjalu:\n",
      "args_potential = ()\n",
      "\n",
      "# wybor parametru rho (odpowiedni dla danego potencjalu):\n",
      "#rho = rho_Konwent\n",
      "rho = rho_Morse2\n",
      "# opcjonalne argumety wejsciowe funkcji zwracajacej wartosc bezwymiarowej zmiennej rho\n",
      "args_rho = ()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def EigenvalEigenvecProblem5(N, x_min, x_max, N_eigvals, potential, *args_potential):\n",
      "    \n",
      "    \"\"\"\n",
      "    funkcja 'EigenvalEigenvecProblem5' rozwiazuje algebraiczne zagadnienie wlasne \n",
      "    wykorzystujac pieciopunktowa metode centralnych roznic skonczonych do przyblizenia \n",
      "    drugiej pochodnej.\n",
      "    \"\"\"\n",
      "    \n",
      "    # dyskretna siatka polozen, gdzie N - ilosc punktow siatki:\n",
      "    x = np.linspace(x_min, x_max, N)\n",
      "    \n",
      "    # krok dyskretnej siatki:\n",
      "    dx = (x_max - x_min) / (N - 1)\n",
      "    \n",
      "    # elementy na glownej diagonali macierzy pentadiagonalnej:\n",
      "    diagonal = 30.0 * np.ones(N) + 12.0 * dx * dx * rho(*args_rho) * potential(x, *args_potential)\n",
      "    # elementy na pierwszych diagonalach macierzy pentadiagonalnej:\n",
      "    diag_edge1 = -16.0 * np.ones(N - 1)\n",
      "    # elementy na drugich diagonalach macierzy pentadiagonalnej:\n",
      "    diag_edge2 = 1.0 * np.ones(N - 2)\n",
      "    # tablica niezerowych element\u00f3w macierzy pentadiagonalnej:\n",
      "    pentadiagonal = np.array([diag_edge2, diag_edge1, diagonal, diag_edge1, diag_edge2])\n",
      "    \n",
      "    # numery diagonali, gdzie 0 oznacza diagonale glowna, \n",
      "    # -1 -pierwsza diagonale dolna, 1 -pierwsza diagonale gorna,\n",
      "    # -2 -druga diagonale dolna, 2 - druga diagonale gorna:\n",
      "    offsets = [-2, -1, 0, 1, 2]\n",
      "    # macierz trojdiagonalna (hamiltonian) zapisana w postaci macierzy rzadkiej:\n",
      "    hamilton = sp.diags(pentadiagonal, offsets)\n",
      "        \n",
      "    start = time()\n",
      "    \n",
      "    # wyznaczenie N_eigvals wartosci wlasnych i odpowiadajacych im wektorow wlasnych\n",
      "    eigvals, eigvecs = alg.eigsh(hamilton, k = N_eigvals, which = \"SM\", tol = 1.0E-4)\n",
      "    # argument \"SM\" oznacza, \u017ce wyznaczone zostanie N_eigvals najmniejszych wartosci\n",
      "    # wlasnych i odpowiadajacych im wektorow wlasnych, a argument \"tol\" oznacza wzgledna \n",
      "    # dokladnosc, z jaka wyznaczone zostana wartosci wlasne\n",
      "    \n",
      "    # !UWAGA! Argument 'eigvecs' stanowi macierz o 'N' wierszach i 'k' kolumnach,\n",
      "    # wiec i-ty wektor wlasny ma 'i' kolumn o dlugosci 'N'.\n",
      "    \n",
      "    #print \"'alg.eigsh(...)' time: %.2fs\" % (time() - start)\n",
      "    \n",
      "    eigvals = eigvals / (12.0 * dx * dx * rho(*args_rho))\n",
      "    \n",
      "    return eigvals, eigvecs"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# DYSKUSJA BLEDOW\n",
      "\n",
      "eigvals_a, eigvecs_a = EigenvalEigenvecProblem5(N, x_min, x_max, N_eigvals, potential, *args_potential)\n",
      "\n",
      "print eigvals_a\n",
      "\n",
      "N_max = 500\n",
      "N_list = range(100, N_max + 100, 100)\n",
      "measures = 5\n",
      "\n",
      "# blad wzgledny:\n",
      "time_list = []\n",
      "digits_list = []\n",
      "\n",
      "for N in N_list:\n",
      "    eigvals_num, eigvecs_num = EigenvalEigenvecProblem5(N, x_min, x_max, N_eigvals, potential, *args_potential)\n",
      "    \n",
      "    time_start = time()\n",
      "    for m in xrange(measures):\n",
      "        EigenvalEigenvecProblem5(N, x_min, x_max, N_eigvals, potential, *args_potential)\n",
      "    exec_time = (time() - time_start) / measures\n",
      "    \n",
      "    time_list.append( exec_time )\n",
      "    digits = [-math.log10( abs( (e_num - e_a) / e_a ) )\n",
      "                  for e_num, e_a in zip(eigvals_num, eigvals_a) ]\n",
      "    digits_list.append(digits)\n",
      "    \n",
      "for idx in xrange(N_eigvals):\n",
      "    \n",
      "    digit_data = [digit[idx] for digit in digits_list]\n",
      "    pl.plot(N_list, digit_data, \".-\", label = \"$\\widetilde{E}_{%d}$\" % idx)\n",
      "    \n",
      "pl.xlabel(\"$N_{}^2$\", {\"fontsize\": \"xx-large\"})\n",
      "pl.ylabel(\"$l_n$\", {\"fontsize\": \"xx-large\", \"rotation\": \"horizontal\"})\n",
      "pl.xlim(None,N_max)\n",
      "pl.show()\n",
      "\n",
      "for idx in xrange(N_eigvals):\n",
      "    \n",
      "    digit_data = [digit[idx] for digit in digits_list]\n",
      "    pl.plot(time_list, digit_data, \".-\")\n",
      "    \n",
      "pl.xlabel(\"$t$\", {\"fontsize\": \"xx-large\"})\n",
      "pl.ylabel(\"$l_n$\", {\"fontsize\": \"xx-large\", \"rotation\": \"horizontal\"})\n",
      "#pl.xlim(None,1000)\n",
      "pl.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[ 0.19  0.51  0.75]\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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RJpfIBi1AlwZdcHzycdjUssm7cXS0XCLr4yOD+OsvoFIBhx0RUb60nvQ+evQo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\nuXMHVlZWWe2srKyyPmgM5bfffoO9vT0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       "text": [
        "<matplotlib.figure.Figure at 0xac31f8c>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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BkQXu6oZBkZGRmDhxIgAgMDAQOTk5yMjIkKs0MkJCAjBgADBnTvVBAQCJmYmI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\ngxgWRERkEMOCiIgM+n+HaxnqqMYQXAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0xac7660c>"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# WYKRES ZALEZNOSCI ENERGII DLA KOLEJNYCH POZIOMOW ENERGETYCZNYCH:\n",
      "\n",
      "N_eigvals = 5\n",
      "\n",
      "eigvals, eigvecs = EigenvalEigenvecProblem5(N, x_min, x_max, N_eigvals, potential, *args_potential)\n",
      "\n",
      "fig, ax1 = pl.subplots(figsize = (8, 5))\n",
      "ax1.set_yticklabels([])\n",
      "ax1.set_ylabel(\"$\\psi_n$\", {\"fontsize\": \"xx-large\", \"rotation\": \"horizontal\"})\n",
      "\n",
      "ax2 = ax1.twinx()\n",
      "\n",
      "num_x = len(eigvecs)\n",
      "dx = (x_max - x_min) / (num_x - 1) \n",
      "x_list = [x_min + i * dx for i in xrange(num_x)]\n",
      "h_list = [potential(x_i) for x_i in x_list]\n",
      "ax2.plot(x_list, h_list, color = \"black\")\n",
      "\n",
      "#ax2.plot((eigvals), \"o-\")\n",
      "for i in xrange(N_eigvals):\n",
      "    ax2.plot([x_min, x_max], [eigvals[i],eigvals[i]], color = \"orange\")\n",
      "    ax2.plot(x_list, eigvecs[:,i] + eigvals[i], color = \"green\")\n",
      "#plt.savefig(\"num_har_E.png\")\n",
      "\n",
      "ax2.set_xticks([x_min, x_max])\n",
      "ax2.set_xticklabels([\"$x_{min}$\", \"$x_{max}$\"])\n",
      "ax2.set_xlim(x_min, x_max)\n",
      "ax2.set_yticks(eigvals)\n",
      "ax2.set_yticklabels([\"$n = %d$\" % i for i in range(0,len(eigvals))])\n",
      "ax2.set_ylim(None, eigvals[-1] + 0.2)\n",
      "\n",
      "pl.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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WH8a1mGv43fLjYfJDwpPDSc9Jp4pFFZytnKliUQVHc0ccTB1wMHtxKcu5HQSh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\nOqlwBBUJrXAAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0xc1cfcac>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = np.linspace(x_min, x_max, N)\n",
      "eigvals, eigvecs = EigenvalEigenvecProblem5(N, x_min, x_max, N_eigvals, potential, *args_potential)\n",
      "\n",
      "# WYKRES FUNKCJI FALOWEJ:\n",
      "\n",
      "fig, ax1 = pl.subplots()\n",
      "ax2 = ax1.twinx()\n",
      "\n",
      "#ax1.set_figure(figsize = (10, 6))\n",
      "\n",
      "\n",
      "for idx in range(N_eigvals)[0:4]:\n",
      "    \n",
      "    psi = eigvecs[:,idx]\n",
      "    ax2.plot(x, psi + eigvals[idx], label = \"$\\psi_{%d}(x)$\" % idx)\n",
      "    \n",
      "#pl.legend(bbox_to_anchor=(1.16, 0.7))\n",
      "ax1.set_ylabel(\"$\\psi_n(x)$\", {\"fontsize\": \"xx-large\", \"rotation\": \"horizontal\"})\n",
      "ax1.set_yticks([])\n",
      "ax2.set_xlabel(\"$x$\", {\"fontsize\": \"xx-large\"})\n",
      "ax2.set_xlim(x_min, x_max)\n",
      "#pl.ylim(-shift, shift * N_eigvals)\n",
      "ax2.set_xticklabels([x_min, x_max], [\"$x_{min}$\", \"$x_{max}$\"], size = 15)\n",
      "\n",
      "ax2.set_yticks(eigvals)\n",
      "ax2.set_yticklabels([\"$n = %d$\" % i for i in range(0,len(eigvals))])\n",
      "\n",
      "#plt.savefig(\"num_harr_psi3.png\")\n",
      "pl.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0xcdd396c>"
       ]
      }
     ],
     "prompt_number": 92
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = np.linspace(x_min, x_max, N)\n",
      "eigvals, eigvecs = EigenvalEigenvecProblem5(N, x_min, x_max, N_eigvals, potential, *args_potential)\n",
      "\n",
      "\n",
      "# WYKRES FUNKCJI FALOWEJ:\n",
      "\n",
      "pl.figure(figsize = (10, 6))\n",
      "\n",
      "shift = 0.3\n",
      "\n",
      "for idx in range(N_eigvals)[0:3]:\n",
      "    \n",
      "    psi = eigvecs[:,idx]\n",
      "    pl.plot(x, psi + idx * shift, label = \"$\\psi_{%d}(x)$\" % idx)\n",
      "    \n",
      "pl.legend(bbox_to_anchor=(1.16, 0.7))\n",
      "pl.xlabel(\"$x$\", {\"fontsize\": \"xx-large\"})\n",
      "pl.ylabel(\"$\\psi_n(x)$\", {\"fontsize\": \"xx-large\", \"rotation\": \"horizontal\"})\n",
      "pl.xlim(x_min, x_max)\n",
      "#pl.ylim(-shift, shift * N_eigvals)\n",
      "pl.xticks([x_min, 0.0, x_max], [\"$x_{min}$\", \"$0.0$\", \"$x_{max}$\"], size = 15)\n",
      "pl.yticks([])\n",
      "#plt.savefig(\"num_harr_psi3.png\")\n",
      "pl.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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jdzUAchi5xXUIwgC8z4EDptXdnj3mZEhP98orprvA2LF2VwLABcgtrkMQBuCd+vY13UFG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\nAwBQSUIYAIBKEsIAAFTSb14Romzj+akWAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0xcd808ec>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pl.figure(figsize = (10, 6))\n",
      "x = np.linspace(-3.0, 9.0, 300)\n",
      "pl.ylim(0.0,2.0)\n",
      "#pl.xlim(0.0, 200.0)\n",
      "pl.plot(x, (1.0 - np.exp(-x)) ** 2)\n",
      "#plt.plot(np.exp(-2.0 * x) - 2.0 * np.exp(-x))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "[<matplotlib.lines.Line2D at 0xbcd582c>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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a3ntPGjlSKiiwdnIHAPgLj56Bb8XGSv37S5s3m67E6k7dfz9hCgBwCoEKnnHV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\nTUSgAgAAaCICFQAAQBMRqAAAAJro/wERsQL8ycUTjwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0xbcd506c>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# WYKRES GESTOSCI PRAWDOPODOBIENSTWA:\n",
      "\n",
      "pl.figure(figsize = (10, 6))\n",
      "\n",
      "#shift2 = shift**2\n",
      "shift2 = 0.012\n",
      "\n",
      "for idx in range(N_eigvals)[0:4]:\n",
      "    \n",
      "    psi = eigvecs[:,idx]\n",
      "    pl.plot(x, psi**2 + idx * shift2, label = \"$P_{%d}(x)$\" % idx)\n",
      "    \n",
      "pl.legend(bbox_to_anchor=(1.16, 0.7))\n",
      "pl.xlabel(\"$x$\", {\"fontsize\": \"xx-large\"})\n",
      "pl.ylabel(\"$P_i(x)$\", {\"fontsize\": \"xx-large\", \"rotation\": \"horizontal\"})\n",
      "#pl.xlim(x_min, x_max)\n",
      "#pl.ylim(-shift2, shift2 * N_eigvals)\n",
      "pl.xticks([x_min, 0.0, x_max], [\"$x_{min}$\", \"$0.0$\", \"$x_{max}$\"], size = 15)\n",
      "pl.yticks([])\n",
      "#pl.savefig(\"num_harr_P3.png\")\n",
      "pl.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "ValueError",
       "evalue": "x and y must have same first dimension",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
        "\u001b[1;32m<ipython-input-11-73ec2a18dac3>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m     \u001b[0mpsi\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0meigvecs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0midx\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 11\u001b[1;33m     \u001b[0mpl\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpsi\u001b[0m\u001b[1;33m**\u001b[0m\u001b[1;36m2\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0midx\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mshift2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"$P_{%d}(x)$\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0midx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     12\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     13\u001b[0m \u001b[0mpl\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlegend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbbox_to_anchor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1.16\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0.7\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/usr/lib/pymodules/python2.7/matplotlib/pyplot.pyc\u001b[0m in \u001b[0;36mplot\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m   2465\u001b[0m         \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhold\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhold\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2466\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2467\u001b[1;33m         \u001b[0mret\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2468\u001b[0m         \u001b[0mdraw_if_interactive\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2469\u001b[0m     \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/usr/lib/pymodules/python2.7/matplotlib/axes.pyc\u001b[0m in \u001b[0;36mplot\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   3891\u001b[0m         \u001b[0mlines\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3892\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3893\u001b[1;33m         \u001b[1;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_lines\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3894\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_line\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mline\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3895\u001b[0m             \u001b[0mlines\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mline\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/usr/lib/pymodules/python2.7/matplotlib/axes.pyc\u001b[0m in \u001b[0;36m_grab_next_args\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    320\u001b[0m                 \u001b[1;32mreturn\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    321\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mremaining\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m<=\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 322\u001b[1;33m                 \u001b[1;32mfor\u001b[0m \u001b[0mseg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_plot_args\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mremaining\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    323\u001b[0m                     \u001b[1;32myield\u001b[0m \u001b[0mseg\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    324\u001b[0m                 \u001b[1;32mreturn\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/usr/lib/pymodules/python2.7/matplotlib/axes.pyc\u001b[0m in \u001b[0;36m_plot_args\u001b[1;34m(self, tup, kwargs)\u001b[0m\n\u001b[0;32m    298\u001b[0m             \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfloat\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    299\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 300\u001b[1;33m         \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_xy_from_xy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    301\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    302\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcommand\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'plot'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/usr/lib/pymodules/python2.7/matplotlib/axes.pyc\u001b[0m in \u001b[0;36m_xy_from_xy\u001b[1;34m(self, x, y)\u001b[0m\n\u001b[0;32m    238\u001b[0m         \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0matleast_1d\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    239\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 240\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"x and y must have same first dimension\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    241\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m2\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    242\u001b[0m             \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"x and y can be no greater than 2-D\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;31mValueError\u001b[0m: x and y must have same first dimension"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0xbcd5c2c>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "g = lambda x: x**2\n",
      "x = 1\n",
      "result = %timeit -o g(x)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "aaa = 1\n",
      "b = print aaa"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%timeit EigenvalEigenvecProblem5"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%timeit EigenvalEigenvecProblem5(N, x_min, x_max, N_eigvals, potential, *args_potential)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print eigvals\n",
      "print [\"n = %d\" % i for i in range(0,eigvals)]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}