Adjust exponent text after setting scientific limits on matplotlib axis

AdjustExponent_QUESTION.py

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1st offset printout: Text(0,0.5,u'')\n",
      "2nd offset printout: Text(0,291.333,u'1e\\u22125')\n",
      "3rd offset printout: Text(0,291.333,u'$\\\\mathregular{10^{-5}}$')\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAagAAAEaCAYAAABEsMO+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9cbb128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pylab as plt\n",
    "import numpy as np\n",
    "\n",
    "# Create a figure and axis\n",
    "fig, ax = plt.subplots()\n",
    " \n",
    "# Plot 100 random points \n",
    "# the y values of which are very small\n",
    "ax.scatter(np.random.rand(100), np.random.rand(100)/100000.0)\n",
    " \n",
    "# Set the y limits appropriately\n",
    "ax.set_ylim(0, 1/100000.0)\n",
    " \n",
    "# Change the y ticklabel format to scientific format\n",
    "ax.ticklabel_format(axis='y', style='sci', scilimits=(-2, 2))\n",
    " \n",
    "# Get the offset value\n",
    "offset = ax.yaxis.get_offset_text()\n",
    " \n",
    "# Print it out\n",
    "print '1st offset printout: {}'.format(offset)\n",
    " \n",
    "# Run plt.tight_layout()\n",
    "plt.tight_layout()\n",
    " \n",
    "# Print out offset again - you can see the value now!\n",
    "print '2nd offset printout: {}'.format(offset)\n",
    " \n",
    "# Change it to latex format\n",
    "offset.set_text(r'$\\mathregular{10^{-5}}$')\n",
    " \n",
    "# Print it out\n",
    "print '3rd offset printout: {}'.format(offset)\n",
    " \n",
    "# Add some text to the middle of the figure just to \n",
    "# check that it isn't the latex format that's the problem\n",
    "ax.text(0.5, 0.5/100000.0, r'$\\mathregular{10^{-2}}$')\n",
    " \n",
    "# And show the figure\n",
    "plt.show()"
   ]
  },
  {
   "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": "2.7.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}