FlyFigures.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>chrom</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>name</th>\n",
       "      <th>enrichmentVal</th>\n",
       "      <th>conservation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>chr2L</td>\n",
       "      <td>46918</td>\n",
       "      <td>52708</td>\n",
       "      <td>MACS_peak_2</td>\n",
       "      <td>381.76</td>\n",
       "      <td>0.091850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>chr2L</td>\n",
       "      <td>108918</td>\n",
       "      <td>110485</td>\n",
       "      <td>MACS_peak_3</td>\n",
       "      <td>151.61</td>\n",
       "      <td>0.399072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>chr2L</td>\n",
       "      <td>119520</td>\n",
       "      <td>122136</td>\n",
       "      <td>MACS_peak_4</td>\n",
       "      <td>1606.61</td>\n",
       "      <td>0.346234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>chr2L</td>\n",
       "      <td>160262</td>\n",
       "      <td>162966</td>\n",
       "      <td>MACS_peak_5</td>\n",
       "      <td>235.12</td>\n",
       "      <td>0.567218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>chr2L</td>\n",
       "      <td>164982</td>\n",
       "      <td>167431</td>\n",
       "      <td>MACS_peak_6</td>\n",
       "      <td>506.72</td>\n",
       "      <td>0.476483</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   chrom   start     end         name  enrichmentVal  conservation\n",
       "0  chr2L   46918   52708  MACS_peak_2         381.76      0.091850\n",
       "1  chr2L  108918  110485  MACS_peak_3         151.61      0.399072\n",
       "2  chr2L  119520  122136  MACS_peak_4        1606.61      0.346234\n",
       "3  chr2L  160262  162966  MACS_peak_5         235.12      0.567218\n",
       "4  chr2L  164982  167431  MACS_peak_6         506.72      0.476483"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('/Users/ethanbaker/Documents/GersteinLab/STARRseqAnalysis/featureConservation.td',sep='\\t',index_col=0)\n",
    "chroms = df.chrom.unique()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 6000.0)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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VTlJ1SU5ObrwTaYbk/G5vLfn8NCUZnPvHK2jLcgCwefhjiP8thbYYOHLEzdE1TEseu3MX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\nzv6RyCiwkWPNZ1/5cTBeT3ht1B9uec8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ybNZ1GFZjRJ3ZtqnlWijlqkaslDgreOfhXTt0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\nSklJSUlZEFIBSklJSUlZEP5/dio49PX4TvAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x107532710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"whitegrid\", color_codes=True)\n",
    "sns.lmplot('conservation','enrichmentVal', data=df, hue='chrom',fit_reg=True,ci=None)\n",
    "plt.xlim(xmin=0)\n",
    "plt.ylim(ymin=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x107f67050>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HO3/+vNm2SBzVk7EnkZdAjRgxwmybHEMNu1a6MtUneZbZVFbnzp3NtkVy6NAh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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1088a85d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.clf()\n",
    "sns.set(style=\"whitegrid\")\n",
    "sns.boxplot(x='chrom',y='enrichmentVal',data=df, hue='chrom')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x10804ac90>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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jpZdesl5v/kdmuQxBqq60tDTyrsDZ2KsnqORlQFq+bj0rIpVLkWX+6KOPAvDU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\nELaIdJoAAAABSURBVClqiuWEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1044efd10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "top10 = np.round(len(df.index)/10,decimals=0)\n",
    "#print n\n",
    "top10enrichment = df.sort_values('enrichmentVal',ascending=False).head(n=top10)\n",
    "top10enrichment.sort_values('chrom',ascending=True,inplace=True)\n",
    "plt.clf()\n",
    "sns.set(style=\"whitegrid\")\n",
    "sns.boxplot(x='chrom',y='enrichmentVal',data=top10enrichment, hue='chrom')\n",
    "ax=plt.subplot(111)\n",
    "box=ax.get_position()\n",
    "ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])\n",
    "for tick in ax.get_xticklabels():\n",
    "    tick.set_rotation(-30)\n",
    "plt.legend(bbox_to_anchor=(1,0.5),loc='center left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>chrom</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>chr2L</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chr3L</th>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chr3R</th>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chrX</th>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chr2RHet</th>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chr3LHet</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chr3RHet</th>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chr2LHet</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chrYHet</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          chrom\n",
       "chr2L        49\n",
       "chr3L        43\n",
       "chr3R        39\n",
       "chrX         33\n",
       "chr2RHet     13\n",
       "chr3LHet     12\n",
       "chr3RHet     11\n",
       "chr2LHet      3\n",
       "chrYHet       2"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = top10enrichment.chrom.value_counts().to_frame()\n",
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1080b1b10>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2hg4dqpycHOfzuuPdxcXFOnr0qOLj42UYhmpqanTy5Emvze1aBDcAwBSVF877dF2edAfb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\nCG4AACyE4AYAwEIIbgAALITgBgDAQv4PjnbyHSShkHMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1089a12d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.clf()\n",
    "sns.barplot(x=t.index,y='chrom',data=t,hue=t.index)\n",
    "plt.ylabel(\"Count\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1089a15d0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EAVtiHyZwIiIX5uPjU23bkeS4Z9syKK2m5w4dOgQA4v8BoFWrVtWOPN+yZYs0QdqJp9CJ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\nAiciIlIgJnAiIiIFYgInIiJSoP8PFEqyM8fNbkQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108a84150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.clf()\n",
    "sns.set(style=\"whitegrid\")\n",
    "sns.boxplot(x='chrom',y='conservation',data=df, hue='chrom')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1082c6250>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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077331qpNZZxO53nLajtjWlFRUaX7QdXK+5h+8x3fSzQ1dZldku+l/5ka4H37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\ncAAALIgABwDAgghwAAAs6P8D2m2LvUrOVUcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109011610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.clf()\n",
    "top10 = np.round(len(df.index)/10,decimals=0)\n",
    "#print n\n",
    "top10conservation = df.sort_values('conservation',ascending=False).head(n=top10)\n",
    "top10conservation.sort_values('chrom',ascending=True,inplace=True)\n",
    "plt.clf()\n",
    "sns.set(style=\"whitegrid\")\n",
    "sns.boxplot(x='chrom',y='conservation',data=top10conservation, hue='chrom')\n",
    "ax=plt.subplot(111)\n",
    "box=ax.get_position()\n",
    "ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])\n",
    "for tick in ax.get_xticklabels():\n",
    "    tick.set_rotation(-30)\n",
    "plt.legend(bbox_to_anchor=(1,0.5),loc='center left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>chrom</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>chr3R</th>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chr3L</th>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chr2L</th>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chrX</th>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chr3RHet</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          chrom\n",
       "chr3R        85\n",
       "chr3L        55\n",
       "chr2L        35\n",
       "chrX         29\n",
       "chr3RHet      1"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = top10conservation.chrom.value_counts().to_frame()\n",
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x10dbe94d0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5nU79PcC2HB97OZ1Orf2/3QG15diYr60T1pAGd0ZGhubOnSufzye3262kpKRQ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\nAIP8PxQJxgWozlWMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10dab1950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.clf()\n",
    "sns.barplot(x=t.index,y='chrom',data=t,hue=t.index)\n",
    "plt.ylabel(\"Count\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "num must be 1 <= num <= 0, not 1",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-19-26daf3c224fb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclf\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[0mgrid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mFacetGrid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhue\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"chrom\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcol_wrap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mgrid\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjointplot\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"conservation\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"enrichmentVal\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmarker\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"o\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mms\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/seaborn/axisgrid.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, row, col, hue, col_wrap, sharex, sharey, size, aspect, palette, row_order, col_order, hue_order, hue_kws, dropna, legend_out, despine, margin_titles, xlim, ylim, subplot_kws, gridspec_kws)\u001b[0m\n\u001b[1;32m    316\u001b[0m             \u001b[0mfig\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[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    317\u001b[0m             \u001b[0maxes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mempty\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_axes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 318\u001b[0;31m             \u001b[0maxes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_subplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnrow\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mncol\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0msubplot_kws\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    319\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0msharex\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    320\u001b[0m                 \u001b[0msubplot_kws\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"sharex\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/matplotlib/figure.pyc\u001b[0m in \u001b[0;36madd_subplot\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1003\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_axstack\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremove\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[1;32m   1004\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1005\u001b[0;31m             \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msubplot_class_factory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprojection_class\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1006\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1007\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_axstack\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/matplotlib/axes/_subplots.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, fig, *args, **kwargs)\u001b[0m\n\u001b[1;32m     62\u001b[0m                     raise ValueError(\n\u001b[1;32m     63\u001b[0m                         \"num must be 1 <= num <= {maxn}, not {num}\".format(\n\u001b[0;32m---> 64\u001b[0;31m                             maxn=rows*cols, num=num))\n\u001b[0m\u001b[1;32m     65\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_subplotspec\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mGridSpec\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrows\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcols\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum\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[0m\n\u001b[1;32m     66\u001b[0m                 \u001b[0;31m# num - 1 for converting from MATLAB to python indexing\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: num must be 1 <= num <= 0, not 1"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1093e5c90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1094055d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.clf()\n",
    "grid = sns.FacetGrid(df, hue=\"chrom\", col_wrap=6)\n",
    "grid.map(sns.jointplot, x=\"conservation\", y=\"enrichmentVal\", data=df, marker=\"o\", ms=4)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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