Interactive plots using Plotly.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import plotly\n",
    "plotly.session.sign_in('aswinr','sxbl9y5px2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plotly.tools.set_credentials_file(username='aswinr',api_key='sxbl9y5px2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import plotly.plotly as ply\n",
    "from plotly.graph_objs import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "trace0 = Scatter(x=[1,2,3,4],y=[10,15,14,12])\n",
    "trace1 = Scatter(x=[1,2,3,4],y=[16,15,4,9])\n",
    "data = Data([trace0,trace1])\n",
    "unique_url = ply.plot(data, filename = 'basic-line')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'https://plot.ly/~aswinr/4'"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique_url"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~aswinr/4.embed\" height=\"525\" width=\"100%\"></iframe>"
      ],
      "text/plain": [
       "<plotly.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ply.iplot(data,filename='basic-line')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import plotly.plotly as ply"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.1613895   0.97463698  0.4124306   0.37158103  0.93815165  0.17029207\n",
      "   0.89418211  0.76825208  0.38209573  0.8590017 ]\n",
      " [ 0.15755888  0.96113387  0.81232861  0.76230854  0.06549112  0.79156768\n",
      "   0.61939443  0.67539755  0.07940353  0.59798269]\n",
      " [ 0.47241475  0.96942618  0.07014375  0.18537726  0.17865129  0.67867289\n",
      "   0.64375211  0.18262788  0.20774076  0.71779128]\n",
      " [ 0.37127263  0.80377592  0.21288741  0.01433436  0.18217687  0.12681522\n",
      "   0.2614761   0.67702073  0.89849091  0.72265941]\n",
      " [ 0.21057012  0.9363168   0.56513995  0.91890095  0.66285346  0.75929819\n",
      "   0.32333355  0.77683583  0.20349724  0.65387629]]\n",
      "[ 0.1613895   0.97463698  0.4124306   0.37158103  0.93815165  0.17029207\n",
      "  0.89418211  0.76825208  0.38209573  0.8590017 ]\n",
      "[ 0.15755888  0.96113387  0.81232861  0.76230854  0.06549112  0.79156768\n",
      "  0.61939443  0.67539755  0.07940353  0.59798269]\n",
      "[ 0.47241475  0.96942618  0.07014375  0.18537726  0.17865129  0.67867289\n",
      "  0.64375211  0.18262788  0.20774076  0.71779128]\n",
      "[ 0.37127263  0.80377592  0.21288741  0.01433436  0.18217687  0.12681522\n",
      "  0.2614761   0.67702073  0.89849091  0.72265941]\n",
      "[ 0.21057012  0.9363168   0.56513995  0.91890095  0.66285346  0.75929819\n",
      "  0.32333355  0.77683583  0.20349724  0.65387629]\n"
     ]
    }
   ],
   "source": [
    "arr = np.random.rand(5,10)\n",
    "print(arr)\n",
    "x,y,z,s,ew = arr # Splitting the array into vectors\n",
    "print(x)\n",
    "print(y)\n",
    "print(z)\n",
    "print(s)\n",
    "print(ew)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[ 0.27637566  0.13920589  0.14877975  0.42072478]\n",
      "  [ 0.62284734  0.82258401  0.33666568  0.56583183]\n",
      "  [ 0.94277598  0.96113354  0.11816266  0.52942324]\n",
      "  [ 0.91672706  0.46988645  0.6201772   0.47742835]\n",
      "  [ 0.74274957  0.63828066  0.83337444  0.24133222]\n",
      "  [ 0.37954008  0.92672476  0.97443701  0.10365988]\n",
      "  [ 0.18650967  0.25832942  0.82079131  0.82171577]\n",
      "  [ 0.06522562  0.85408983  0.92610992  0.86124553]\n",
      "  [ 0.08509751  0.25542741  0.50756225  0.52787451]\n",
      "  [ 0.74524743  0.05399797  0.58798267  0.21481183]]\n",
      "\n",
      " [[ 0.38799559  0.25460151  0.59095405  0.29364364]\n",
      "  [ 0.08151028  0.99413604  0.6754089   0.81337344]\n",
      "  [ 0.75899161  0.11369832  0.94245182  0.4020336 ]\n",
      "  [ 0.58195573  0.62377303  0.11768991  0.5054034 ]\n",
      "  [ 0.37063942  0.45536109  0.77539551  0.17758226]\n",
      "  [ 0.01567334  0.98411305  0.08372579  0.19372837]\n",
      "  [ 0.96534612  0.96380669  0.23340525  0.52283354]\n",
      "  [ 0.89876134  0.59548531  0.08938935  0.20325691]\n",
      "  [ 0.80228039  0.03941594  0.71671739  0.79732129]\n",
      "  [ 0.54718171  0.24307435  0.95955259  0.02845179]]]\n",
      "[[ 0.27637566  0.13920589  0.14877975  0.42072478]\n",
      " [ 0.62284734  0.82258401  0.33666568  0.56583183]\n",
      " [ 0.94277598  0.96113354  0.11816266  0.52942324]\n",
      " [ 0.91672706  0.46988645  0.6201772   0.47742835]\n",
      " [ 0.74274957  0.63828066  0.83337444  0.24133222]\n",
      " [ 0.37954008  0.92672476  0.97443701  0.10365988]\n",
      " [ 0.18650967  0.25832942  0.82079131  0.82171577]\n",
      " [ 0.06522562  0.85408983  0.92610992  0.86124553]\n",
      " [ 0.08509751  0.25542741  0.50756225  0.52787451]\n",
      " [ 0.74524743  0.05399797  0.58798267  0.21481183]]\n",
      "[[ 0.38799559  0.25460151  0.59095405  0.29364364]\n",
      " [ 0.08151028  0.99413604  0.6754089   0.81337344]\n",
      " [ 0.75899161  0.11369832  0.94245182  0.4020336 ]\n",
      " [ 0.58195573  0.62377303  0.11768991  0.5054034 ]\n",
      " [ 0.37063942  0.45536109  0.77539551  0.17758226]\n",
      " [ 0.01567334  0.98411305  0.08372579  0.19372837]\n",
      " [ 0.96534612  0.96380669  0.23340525  0.52283354]\n",
      " [ 0.89876134  0.59548531  0.08938935  0.20325691]\n",
      " [ 0.80228039  0.03941594  0.71671739  0.79732129]\n",
      " [ 0.54718171  0.24307435  0.95955259  0.02845179]]\n"
     ]
    }
   ],
   "source": [
    "threed_arr = np.random.rand(2,10,4)\n",
    "print(threed_arr)\n",
    "c,ec = threed_arr\n",
    "print(c)\n",
    "print(ec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "area_scale, width_scale = 500,5\n",
    "\n",
    "fig,ax=plt.subplots()\n",
    "sc = ax.scatter(x,y,c=c,s=np.square(s)*area_scale,edgecolor=ec,\n",
    "               linewidth=ew*width_scale)\n",
    "ax.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~aswinr/11.embed\" height=\"525\" width=\"100%\"></iframe>"
      ],
      "text/plain": [
       "<plotly.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ply.iplot_mpl(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.049162</td>\n",
       "      <td> 0.149485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.532408</td>\n",
       "      <td> 0.834197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td> 0.687212</td>\n",
       "      <td> 0.620891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td> 0.042034</td>\n",
       "      <td> 0.034430</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          a         b\n",
       "0 -0.049162  0.149485\n",
       "1 -0.532408  0.834197\n",
       "2  0.687212  0.620891\n",
       "3  0.042034  0.034430"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.DataFrame({\"a\":np.random.randn(4),\"b\":np.random.randn(4)})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f147080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.plot(df)\n",
    "plt.grid()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "min() arg is an empty sequence",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-66-445854fc0d39>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mmpl_fig_obj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mply\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot_mpl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmpl_fig_obj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/Users/aswinrajkumar/anaconda/lib/python3.4/site-packages/plotly/plotly/plotly.py\u001b[0m in \u001b[0;36mplot_mpl\u001b[0;34m(fig, resize, strip_style, update, **plot_options)\u001b[0m\n\u001b[1;32m    258\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    259\u001b[0m     \"\"\"\n\u001b[0;32m--> 260\u001b[0;31m     \u001b[0mfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtools\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmpl_to_plotly\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mresize\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstrip_style\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstrip_style\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    261\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mupdate\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    262\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/aswinrajkumar/anaconda/lib/python3.4/site-packages/plotly/tools.py\u001b[0m in \u001b[0;36mmpl_to_plotly\u001b[0;34m(fig, resize, strip_style, verbose)\u001b[0m\n\u001b[1;32m    376\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0m_matplotlylib_imported\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    377\u001b[0m         \u001b[0mrenderer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmatplotlylib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPlotlyRenderer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 378\u001b[0;31m         \u001b[0mmatplotlylib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mExporter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    379\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mresize\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    380\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/aswinrajkumar/anaconda/lib/python3.4/site-packages/plotly/matplotlylib/mplexporter/exporter.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fig)\u001b[0m\n\u001b[1;32m     47\u001b[0m             \u001b[0;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     48\u001b[0m             \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 49\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcrawl_fig\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     50\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     51\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mstaticmethod\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/aswinrajkumar/anaconda/lib/python3.4/site-packages/plotly/matplotlylib/mplexporter/exporter.py\u001b[0m in \u001b[0;36mcrawl_fig\u001b[0;34m(self, fig)\u001b[0m\n\u001b[1;32m    112\u001b[0m         \u001b[0;34m\"\"\"Crawl the figure and process all axes\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    113\u001b[0m         with self.renderer.draw_figure(fig=fig,\n\u001b[0;32m--> 114\u001b[0;31m                                        props=utils.get_figure_properties(fig)):\n\u001b[0m\u001b[1;32m    115\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0max\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    116\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcrawl_ax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/aswinrajkumar/anaconda/lib/python3.4/contextlib.py\u001b[0m in \u001b[0;36m__enter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     57\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__enter__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     58\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 59\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgen\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     60\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mStopIteration\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     61\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"generator didn't yield\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/aswinrajkumar/anaconda/lib/python3.4/site-packages/plotly/matplotlylib/mplexporter/renderers/base.py\u001b[0m in \u001b[0;36mdraw_figure\u001b[0;34m(self, fig, props)\u001b[0m\n\u001b[1;32m     41\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_current_fig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     42\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fig_props\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprops\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 43\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprops\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mprops\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     44\u001b[0m         \u001b[0;32myield\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     45\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/aswinrajkumar/anaconda/lib/python3.4/site-packages/plotly/matplotlylib/renderer.py\u001b[0m in \u001b[0;36mopen_figure\u001b[0;34m(self, fig, props)\u001b[0m\n\u001b[1;32m     83\u001b[0m             \u001b[0mautosize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     84\u001b[0m             hovermode='closest')\n\u001b[0;32m---> 85\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmpl_x_bounds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmpl_y_bounds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmpltools\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_axes_bounds\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     86\u001b[0m         margin = go.Margin(\n\u001b[1;32m     87\u001b[0m             \u001b[0ml\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmpl_x_bounds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplotly_fig\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'layout'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'width'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/aswinrajkumar/anaconda/lib/python3.4/site-packages/plotly/matplotlylib/mpltools.py\u001b[0m in \u001b[0;36mget_axes_bounds\u001b[0;34m(fig)\u001b[0m\n\u001b[1;32m    204\u001b[0m         \u001b[0my_min\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbounds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    205\u001b[0m         \u001b[0my_max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbounds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mbounds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 206\u001b[0;31m     \u001b[0mx_min\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_min\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_min\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_min\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_max\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_max\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    207\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mx_min\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_max\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0my_min\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_max\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    208\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: min() arg is an empty sequence"
     ]
    }
   ],
   "source": [
    "mpl_fig_obj = plt.figure()\n",
    "ply.plot_mpl(mpl_fig_obj)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11176bd30>]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ffcf908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "arr = np.array([1,2.5,3,2.2,5])\n",
    "plt.plot(arr)\n",
    "#plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/aswinrajkumar/anaconda/lib/python3.4/site-packages/plotly/matplotlylib/renderer.py:296: UserWarning:\n",
      "\n",
      "found box chart data with length <= 1, assuming data redundancy, not plotting.\n",
      "\n"
     ]
    },
    {
     "ename": "KeyError",
     "evalue": "'data'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-81-665ca64f1472>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmpl_fig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_subplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m111\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mply\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miplot_mpl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmpl_fig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilename\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'test'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/Users/aswinrajkumar/anaconda/lib/python3.4/site-packages/plotly/plotly/plotly.py\u001b[0m in \u001b[0;36miplot_mpl\u001b[0;34m(fig, resize, strip_style, update, **plot_options)\u001b[0m\n\u001b[1;32m    234\u001b[0m             \u001b[0;34m\"object. Run 'help(plotly.graph_objs.Figure)' for more info.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    235\u001b[0m         )\n\u001b[0;32m--> 236\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0miplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mplot_options\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    237\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    238\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/aswinrajkumar/anaconda/lib/python3.4/site-packages/plotly/plotly/plotly.py\u001b[0m in \u001b[0;36miplot\u001b[0;34m(figure_or_data, **plot_options)\u001b[0m\n\u001b[1;32m    137\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m'auto_open'\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mplot_options\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    138\u001b[0m         \u001b[0mplot_options\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'auto_open'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 139\u001b[0;31m     \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigure_or_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mplot_options\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    140\u001b[0m     \u001b[0murlsplit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    141\u001b[0m     \u001b[0musername\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mplot_id\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0murlsplit\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murlsplit\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m  \u001b[0;31m# TODO: HACKY!\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/aswinrajkumar/anaconda/lib/python3.4/site-packages/plotly/plotly/plotly.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(figure_or_data, validate, **plot_options)\u001b[0m\n\u001b[1;32m    166\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    167\u001b[0m     \"\"\"\n\u001b[0;32m--> 168\u001b[0;31m     \u001b[0mfigure\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtools\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreturn_figure_from_figure_or_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigure_or_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    169\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    170\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mentry\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mfigure\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'data'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/aswinrajkumar/anaconda/lib/python3.4/site-packages/plotly/tools.py\u001b[0m in \u001b[0;36mreturn_figure_from_figure_or_data\u001b[0;34m(figure_or_data, validate_figure)\u001b[0m\n\u001b[1;32m   1262\u001b[0m                                          \u001b[0;34m\"seeing this error:\\n\\n{0}\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1263\u001b[0m                                          \"\".format(err))\n\u001b[0;32m-> 1264\u001b[0;31m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mfigure\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'data'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1265\u001b[0m             raise exceptions.PlotlyEmptyDataError(\n\u001b[1;32m   1266\u001b[0m                 \u001b[0;34m\"Empty data list found. Make sure that you populated the \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'data'"
     ]
    }
   ],
   "source": [
    "mpl_fig = plt.figure()\n",
    "ax = mpl_fig.add_subplot(111)\n",
    "ax.bar(10,100)\n",
    "ply.iplot_mpl(mpl_fig, filename='test')"
   ]
  }
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