selimb/ipython_notebook

## ipython_notebook
{
 "metadata": {
  "name": "",
  "signature": "sha256:c34996a1ca2e1b0e7373db4ee21a08e4854fd46d7e9e02beb3210cc176ea8573"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import matplotlib.pyplot as plt\n",
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from math import sqrt\n",
      "SPINE_COLOR = 'gray'\n",
      "import matplotlib"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def latexify(fig_width=None, fig_height=None, columns=1):\n",
      "    \"\"\"Set up matplotlib's RC params for LaTeX plotting.\n",
      "    Call this before plotting a figure.\n",
      "\n",
      "    Parameters\n",
      "    ----------\n",
      "    fig_width : float, optional, inches\n",
      "    fig_height : float,  optional, inches\n",
      "    columns : {1, 2}\n",
      "    \"\"\"\n",
      "\n",
      "    # code adapted from http://www.scipy.org/Cookbook/Matplotlib/LaTeX_Examples\n",
      "\n",
      "    # Width and max height in inches for IEEE journals taken from\n",
      "    # computer.org/cms/Computer.org/Journal%20templates/transactions_art_guide.pdf\n",
      "\n",
      "    assert(columns in [1,2])\n",
      "\n",
      "    if fig_width is None:\n",
      "        fig_width = 3.39 if columns==1 else 6.9 # width in inches\n",
      "\n",
      "    if fig_height is None:\n",
      "        golden_mean = (sqrt(5)-1.0)/2.0    # Aesthetic ratio\n",
      "        fig_height = fig_width*golden_mean # height in inches\n",
      "\n",
      "    MAX_HEIGHT_INCHES = 8.0\n",
      "    if fig_height > MAX_HEIGHT_INCHES:\n",
      "        print(\"WARNING: fig_height too large:\" + fig_height + \n",
      "              \"so will reduce to\" + MAX_HEIGHT_INCHES + \"inches.\")\n",
      "        fig_height = MAX_HEIGHT_INCHES\n",
      "\n",
      "    params = {'backend': 'ps',\n",
      "              'text.latex.preamble': [r'\\usepackage{gensymb}'],\n",
      "              'axes.labelsize': 8, # fontsize for x and y labels (was 10)\n",
      "              'axes.titlesize': 8,\n",
      "              'font.size': 8, # was 10\n",
      "              'legend.fontsize': 8, # was 10\n",
      "              'xtick.labelsize': 8,\n",
      "              'ytick.labelsize': 8,\n",
      "              'text.usetex': True,\n",
      "              'figure.figsize': [fig_width,fig_height],\n",
      "              'font.family': 'serif'\n",
      "    }\n",
      "\n",
      "    matplotlib.rcParams.update(params)\n",
      "\n",
      "\n",
      "def format_axes(ax):\n",
      "\n",
      "    for spine in ['top', 'right']:\n",
      "        ax.spines[spine].set_visible(False)\n",
      "\n",
      "    for spine in ['left', 'bottom']:\n",
      "        ax.spines[spine].set_color(SPINE_COLOR)\n",
      "        ax.spines[spine].set_linewidth(0.5)\n",
      "\n",
      "    ax.xaxis.set_ticks_position('bottom')\n",
      "    ax.yaxis.set_ticks_position('left')\n",
      "\n",
      "    for axis in [ax.xaxis, ax.yaxis]:\n",
      "        axis.set_tick_params(direction='out', color=SPINE_COLOR)\n",
      "\n",
      "    return ax"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "latexify()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "C1_exp = np.array([120.635, 38.1294964, 80.51948052])\n",
      "C2_exp = np.array([97.6, 55.66801619, 62.8])\n",
      "C3_exp = np.array([82.56578947, 42.07207207, 99.6497373])\n",
      "L1_exp = np.array([38.05970149, 58.11965812])\n",
      "L2_exp = np.array([22.58883249, 25.78125])\n",
      "L3_exp = np.array([36.56207367, 36.19047619])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "C3_th = np.array([23, 13, 22])\n",
      "C2_th = np.array([21, 14, 21])\n",
      "C1_th = np.array([20, 11, 20])\n",
      "L3_th = np.array([21, 20])\n",
      "L2_th = np.array([16, 12])\n",
      "L1_th = np.array([15, 13])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure( figsize=(12, 8 ) )\n",
      "ax = fig.add_axes( [.055, .055, .94, .935] )\n",
      "\n",
      "width = 0.15\n",
      "N = 3*3 + 2*3\n",
      "sep = width*2\n",
      "\n",
      "C_exp = np.concatenate((C1_exp, C2_exp, C3_exp))\n",
      "C_th = np.concatenate((C1_th, C2_th, C3_th))\n",
      "C_exp_coords = np.arange(6,N) + width*0.9\n",
      "C_th_coords = C_exp_coords + width*1.3\n",
      "# C_exp_coords = np.arange(9) + width*0.9\n",
      "# C_th_coords = C_exp_coords + width*1.3\n",
      "\n",
      "L_exp = np.concatenate((L1_exp, L2_exp, L2_exp))\n",
      "L_th = np.concatenate((L1_th, L2_th, L3_th))\n",
      "L_exp_coords = np.arange(6) + width*0.9\n",
      "L_th_coords = L_exp_coords + width*1.3\n",
      "# L_exp_coords = np.arange(9,N) + width*0.9\n",
      "# L_th_coords = L_exp_coords + width*1.3\n",
      "\n",
      "C_exp_bars = ax.bar(C_exp_coords, C_exp, width=width, color='c')\n",
      "C_th_bars = ax.bar(C_th_coords, C_th, width=width, color='darkorange')\n",
      "L_exp_bars = ax.bar(L_exp_coords, L_exp, width=width, color='c')\n",
      "L_th_bars = ax.bar(L_th_coords, L_th, width=width, color='darkorange')\n",
      "\n",
      "xticks = np.arange(N)+sep\n",
      "xticks_minor = np.array([0.0, 1.5, 3.5, 5.5, 8.5, 11.5, 14.5])\n",
      "xticks_minor[1:] = xticks_minor[1:] + sep\n",
      "xlbls=  ['Left', 'Right']*3 + ['Left', 'Web', 'Right']*3\n",
      "\n",
      "ax.set_xticks(xticks)\n",
      "ax.set_xticks(xticks_minor, minor=True)\n",
      "ax.set_xticklabels(xlbls, fontsize='large')\n",
      "ax.tick_params(axis='x', which='both', length=0, bottom='off', top='off')\n",
      "ax.tick_params(axis='x', which='minor', direction='out', length=30, bottom='on')\n",
      "for t in ax.get_xticklabels():\n",
      "    t.set_y(0)\n",
      "\n",
      "# annotations = ['C1', 'C2', 'C3', 'L1', 'L2', 'L3']\n",
      "# x_coords = np.array([1, 4, 7, 9.5, 11.5, 13.5])+sep\n",
      "annotations = ['L1', 'L2', 'L3', 'C1', 'C2', 'C3', ]\n",
      "x_coords = np.array([0.5, 2.5, 4.5, 7, 10, 13])+sep\n",
      "for annotation, xcoord in zip(annotations, x_coords):\n",
      "    ax.annotate(annotation, (0,0), (xcoord, -7), horizontalalignment='center', \n",
      "                fontsize='x-large', fontweight='bold')\n",
      "\n",
      "ax.set_xlim(0,xticks_minor[-1])\n",
      "ax.set_ylabel(r'Corner Thickness Deviation [\\%]', fontsize='x-large')\n",
      "ax.tick_params(axis='y', labelsize='large')\n",
      "ax.legend((C_exp_bars[0], C_th_bars[0]),\n",
      "          ('Experimental', 'Theoretical'),\n",
      "          loc='best', frameon=False, numpoints=2, scatterpoints=2, fontsize='x-large')\n",
      "\n",
      "parting_x = 5.5+sep\n",
      "y_min, y_max = ax.get_ylim()\n",
      "parting_vline = ax.plot((parting_x, parting_x), (y_max, y_min), 'k-')\n",
      "\n",
      "ax.text(parting_x*1.02, y_max*0.95, 'convex\\ntooling', fontsize='x-large')\n",
      "ax.text(parting_x*0.98, y_max*0.95, 'concave\\ntooling', fontsize='x-large', ha='right', multialignment='right')\n",
      "\n",
      "format_axes(ax)\n",
      "fig.savefig('pretty_fucking_plot.pdf')\n",
      "\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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br6rH6QWy3ye53lpbHGtlAAAAE2LkURlba8vpDZmfJKmqL1pr346lKgAAgAky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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x75fa518>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!cp pretty_fucking_plot.pdf tex/figs/"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 32
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, ax = plt.subplots(figsize=(10,6))\n",
      "N = 6\n",
      "width = 0.35\n",
      "L = np.arange(3)\n",
      "C = np.arange(3, N)\n",
      "L_angles = (-0.229, -0.145, -0.502)\n",
      "C_angles_left = (1.209,0.861,0.843)\n",
      "C_angles_right = (1.149, 0.873, 0.517)\n",
      "L_bars = ax.bar(L+width/2, L_angles, width, color='darkorange')\n",
      "C_bars_left = ax.bar(C, C_angles_left, width, color='c')\n",
      "C_bars_right = ax.bar(C+width*1.08, C_angles_right, width, color='seagreen')\n",
      "\n",
      "# Theoretical values\n",
      "theor_value = -6.79*10**-1\n",
      "parting_x = 3-width/2\n",
      "convex_range = np.arange(parting_x+0.05,6+width*2)\n",
      "theor_convex = ax.plot(convex_range,\n",
      "                         np.ones_like(convex_range)*-theor_value, 'k--')\n",
      "concave_range = np.array([0,parting_x])\n",
      "theor_concave = ax.plot(concave_range, \n",
      "                        np.ones_like(concave_range)*-theor_value, 'k--')\n",
      "zero_line = ax.plot((0, N), (0,0), 'k')\n",
      "\n",
      "y_max = 1.5\n",
      "y_min = -1.0\n",
      "parting_vline = ax.plot((parting_x, parting_x), (y_max, y_min), 'k-')\n",
      "\n",
      "ax.text(parting_x*1.02, y_min*0.85, 'convex\\ntooling', fontsize='x-large')\n",
      "ax.text(parting_x*0.98, y_min*0.85, 'concave\\ntooling', fontsize='x-large', ha='right', multialignment='right')\n",
      "\n",
      "ax.set_ylabel(r'Spring In ($^\\circ$)', fontsize='x-large')\n",
      "ax.set_xticks(np.arange(N)+width)\n",
      "ax.set_xticklabels(('L1', 'L2', 'L3',\n",
      "                    'C1', 'C2', 'C3'))\n",
      "ax.legend((C_bars_left[0], C_bars_right[0], theor_convex[0]),\n",
      "          ('Left', 'Right', r'$\\Delta \\theta_{CTE}$'), \n",
      "          loc='best', frameon=False, numpoints=2, scatterpoints=2, fontsize='x-large')\n",
      "ax.set_ylim(y_min, y_max)\n",
      "# ax.legend((theor_positive[0],), (r'$\\Delta \\theta_{CTE}$',),\n",
      "#           loc='best', frameon=False, fontsize='x-large')\n",
      "\n",
      "format_axes(ax)\n",
      "fig.savefig('spring.pdf')\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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smfaYpLSkoqSiux9HVCIAAEDXRNZT5u4VVYPX3XOr0pJeuvuupIWuFwYAABCB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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7eda080>"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!cp spring.pdf tex/figs/"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%reset"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "name": "stdout",
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Once deleted, variables cannot be recovered. Proceed (y/[n])? y\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}