diff --git a/Figures For Paper.ipynb b/Figures For Paper.ipynb
index 962f9cc..93b76d5 100644
--- a/Figures For Paper.ipynb	
+++ b/Figures For Paper.ipynb	
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 291,
    "metadata": {
     "collapsed": false
    },
@@ -34,6 +34,7 @@
     "from Bio import SeqIO\n",
     "from string import ascii_lowercase, ascii_uppercase\n",
     "from matplotlib.colors import LogNorm, NoNorm\n",
+    "from mpl_toolkits.basemap import Basemap\n",
     "\n",
     "mpl.rcParams['pdf.fonttype']=42\n",
     "%matplotlib inline\n",
@@ -52,7 +53,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {
     "collapsed": false
    },
@@ -60,10 +61,10 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x10c4daad0>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x10a35ead0>"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -820,7 +821,7 @@
        "C4p2QkcDD10AAAAASUVORK5CYII=\n"
       ],
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10c4da190>"
+       "<matplotlib.figure.Figure at 0x10a35e1d0>"
       ]
      },
      "metadata": {},
@@ -848,7 +849,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {
     "collapsed": false
    },
@@ -866,7 +867,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {
     "collapsed": false
    },
@@ -874,10 +875,10 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x10cf1b950>"
+       "<matplotlib.collections.PathCollection at 0x10a95d0d0>"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -984,7 +985,7 @@
        "fxqaqyzsyLMLAAAAAElFTkSuQmCC\n"
       ],
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10cb451d0>"
+       "<matplotlib.figure.Figure at 0x105b03910>"
       ]
      },
      "metadata": {},
@@ -1002,7 +1003,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {
     "collapsed": false
    },
@@ -1121,7 +1122,7 @@
        "Q1gmAAAAAElFTkSuQmCC\n"
       ],
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10cf921d0>"
+       "<matplotlib.figure.Figure at 0x10a9e8290>"
       ]
      },
      "metadata": {},
@@ -1174,299 +1175,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 8,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [
-    {
-     "ename": "ValueError",
-     "evalue": "x and y must have same first dimension",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-22-192293ce9ff3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m    175\u001b[0m                      horizontalalignment='center', verticalalignment='center')\n\u001b[1;32m    176\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 177\u001b[0;31m             \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtheta\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    178\u001b[0m             \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfill\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtheta\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfacecolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.25\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    179\u001b[0m         \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_varlabels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mspoke_labels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m<ipython-input-22-192293ce9ff3>\u001b[0m in \u001b[0;36mplot\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     58\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mplot\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[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m             \u001b[0;34m\"\"\"Override plot so that line is closed by default\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 60\u001b[0;31m             \u001b[0mlines\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mRadarAxes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\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     61\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mlines\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_close_line\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/Users/harrietalexander/anaconda/lib/python2.7/site-packages/matplotlib/axes/_axes.pyc\u001b[0m in \u001b[0;36mplot\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1371\u001b[0m         \u001b[0mlines\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   1372\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1373\u001b[0;31m         \u001b[0;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_lines\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[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1374\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_line\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1375\u001b[0m             \u001b[0mlines\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/Users/harrietalexander/anaconda/lib/python2.7/site-packages/matplotlib/axes/_base.pyc\u001b[0m in \u001b[0;36m_grab_next_args\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    302\u001b[0m                 \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    303\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mremaining\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 304\u001b[0;31m                 \u001b[0;32mfor\u001b[0m \u001b[0mseg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_plot_args\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mremaining\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\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    305\u001b[0m                     \u001b[0;32myield\u001b[0m \u001b[0mseg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    306\u001b[0m                 \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/Users/harrietalexander/anaconda/lib/python2.7/site-packages/matplotlib/axes/_base.pyc\u001b[0m in \u001b[0;36m_plot_args\u001b[0;34m(self, tup, kwargs)\u001b[0m\n\u001b[1;32m    280\u001b[0m             \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    281\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 282\u001b[0;31m         \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_xy_from_xy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    283\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    284\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcommand\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'plot'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/Users/harrietalexander/anaconda/lib/python2.7/site-packages/matplotlib/axes/_base.pyc\u001b[0m in \u001b[0;36m_xy_from_xy\u001b[0;34m(self, x, y)\u001b[0m\n\u001b[1;32m    221\u001b[0m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0matleast_1d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    222\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 223\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"x and y must have same first dimension\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    224\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    225\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"x and y can be no greater than 2-D\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mValueError\u001b[0m: x and y must have same first dimension"
-     ]
-    },
-    {
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-      ],
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x10fe0a810>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "\"\"\"\n",
     "Functions to create a radar chart\n",
@@ -1626,37 +1339,37 @@
     "    return data\n",
     "\n",
     "\n",
-    "if __name__ == '__main__':\n",
-    "    N = 6\n",
-    "    theta = radar_factory(N, frame='polygon')\n",
-    "\n",
-    "    data = example_data()\n",
-    "    spoke_labels = data.pop('column names')\n",
-    "\n",
-    "    fig = plt.figure(figsize=(9, 9))\n",
-    "    fig.subplots_adjust(wspace=0.25, hspace=0.20, top=0.85, bottom=0.05)\n",
-    "\n",
-    "    colors = ['b', 'r', 'g', 'm', 'y']\n",
-    "    # Plot the four cases from the example data on separate axes\n",
-    "    for n, title in enumerate(data.keys()):\n",
-    "        ax = fig.add_subplot(2, 2, n+1, projection='radar')\n",
-    "        plt.rgrids([0.2, 0.4, 0.6, 0.8])\n",
-    "        ax.set_title(title, weight='bold', size='medium', position=(0.5, 1.1),\n",
-    "                     horizontalalignment='center', verticalalignment='center')\n",
-    "        for d, color in zip(data[title], colors):\n",
-    "            ax.plot(theta, d, color=color)\n",
-    "            ax.fill(theta, d, facecolor=color, alpha=0.25)\n",
-    "        ax.set_varlabels(spoke_labels)\n",
-    "\n",
-    "    # add legend relative to top-left plot\n",
-    "    plt.subplot(2, 2, 1)\n",
-    "    labels = ('Factor 1', 'Factor 2', 'Factor 3', 'Factor 4', 'Factor 5')\n",
-    "    legend = plt.legend(labels, loc=(0.9, .95), labelspacing=0.1)\n",
-    "    plt.setp(legend.get_texts(), fontsize='small')\n",
-    "\n",
-    "    plt.figtext(0.5, 0.965, '5-Factor Solution Profiles Across Four Scenarios',\n",
-    "                ha='center', color='black', weight='bold', size='large')\n",
-    "    plt.show()"
+    "# if __name__ == '__main__':\n",
+    "#     N = 6\n",
+    "#     theta = radar_factory(N, frame='polygon')\n",
+    "\n",
+    "#     data = example_data()\n",
+    "#     spoke_labels = data.pop('column names')\n",
+    "\n",
+    "#     fig = plt.figure(figsize=(9, 9))\n",
+    "#     fig.subplots_adjust(wspace=0.25, hspace=0.20, top=0.85, bottom=0.05)\n",
+    "\n",
+    "#     colors = ['b', 'r', 'g', 'm', 'y']\n",
+    "#     # Plot the four cases from the example data on separate axes\n",
+    "#     for n, title in enumerate(data.keys()):\n",
+    "#         ax = fig.add_subplot(2, 2, n+1, projection='radar')\n",
+    "#         plt.rgrids([0.2, 0.4, 0.6, 0.8])\n",
+    "#         ax.set_title(title, weight='bold', size='medium', position=(0.5, 1.1),\n",
+    "#                      horizontalalignment='center', verticalalignment='center')\n",
+    "#         for d, color in zip(data[title], colors):\n",
+    "#             ax.plot(theta, d, color=color)\n",
+    "#             ax.fill(theta, d, facecolor=color, alpha=0.25)\n",
+    "#         ax.set_varlabels(spoke_labels)\n",
+    "\n",
+    "#     # add legend relative to top-left plot\n",
+    "#     plt.subplot(2, 2, 1)\n",
+    "#     labels = ('Factor 1', 'Factor 2', 'Factor 3', 'Factor 4', 'Factor 5')\n",
+    "#     legend = plt.legend(labels, loc=(0.9, .95), labelspacing=0.1)\n",
+    "#     plt.setp(legend.get_texts(), fontsize='small')\n",
+    "\n",
+    "#     plt.figtext(0.5, 0.965, '5-Factor Solution Profiles Across Four Scenarios',\n",
+    "#                 ha='center', color='black', weight='bold', size='large')\n",
+    "#     plt.show()"
    ]
   },
   {
@@ -3576,6 +3289,1958 @@
     "fig.savefig('GroupProfile.pdf')"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 253,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
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+       "yfs5ITYvUKGE9atPEtawQL8neIwrByv3BAJdx8xOzz0EDpa0Lc5QEHCkmX0K3CSpBnhC0iLcQ+lP\n",
+       "/A0F4AFgZzNb5I/vAS4HDi7F+wgEekhYv3opYQ0LBFojs7CeBQKBQCAQCAQCIZQiEAgEAoFAIBAg\n",
+       "GMaBQCAQCAQCgQAQDONAIBAIBAKBQAAIhnEgEAgEAoFAIAAEwzgQCAQCgUAgEACCYRwIBAKBQCAQ\n",
+       "CADBMA4EAoFAIBAIBIBgGAcCgUAgEAgEAgD8P3gyCtuCkXX1AAAAAElFTkSuQmCC\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11c464e10>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "import matplotlib.ticker as mtick\n",
+    "DiaColor=pal.colorbrewer.sequential.Oranges_9_r.mpl_colormap(np.linspace(0,.7,10))\n",
+    "HapColor=pal.colorbrewer.sequential.Blues_9_r.mpl_colormap(np.linspace(0,.7,9))\n",
+    "EmiColor=pal.colorbrewer.sequential.Greens_9_r.mpl_colormap(0.1)\n",
+    "#Only plot the taxa that are >5 in any of the treatments\n",
+    "Dia_10=Dia_rank[Dia_rank[Dia_rank<=5].any(axis=1)].sort('E1-X').index\n",
+    "Hap_10=Hap_rank[Hap_rank[Hap_rank<=5].any(axis=1)].sort('E1-X').index\n",
+    "Hap_10=Hap_10[1:]\n",
+    "E1=[u'E1-Z1',u'E1-Q', u'E1-R', u'E1-V', u'E1-X', u'E1-T', u'E1-S']\n",
+    "E2=[u'E2-Z5',u'E2-Q', u'E2-R', u'E2-V', u'E2-X', u'E2-T', u'E2-S']\n",
+    "Dia_pd.sum()\n",
+    "\n",
+    "l=1\n",
+    "el=3\n",
+    "\n",
+    "fig = plt.figure(figsize=(10, 10))\n",
+    "\n",
+    "#Plot insitu data\n",
+    "\n",
+    "ax3 = plt.subplot2grid((2,2), (0,0), colspan=2)\n",
+    "\n",
+    "\n",
+    "Dia_pd.loc[Dia_10,insitu].T.plot(ax=ax3, colors=DiaColor, lw=l)\n",
+    "Hap_pd.loc[Hap_10,insitu].T.plot(ax=ax3, colors=HapColor, lw=l)\n",
+    "Hap_pd.loc['Emihux',insitu].T.plot(ax=ax3, colors=EmiColor, lw=el)\n",
+    "\n",
+    "ax3.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
+    "#create radar figure for E1\n",
+    "ax = fig.add_subplot(2,2, 3, projection='radar')\n",
+    "N = 7\n",
+    "theta = radar_factory(N, frame='polygon')\n",
+    "data=Dia_pd.loc[Dia_10,E1]\n",
+    "spoke_labels = data.columns\n",
+    "for d,c in zip(data.index, DiaColor):\n",
+    "    ax.plot(theta, data.loc[d], color=c,lw=l)\n",
+    "\n",
+    "data=Hap_pd.loc[Hap_10,E1]\n",
+    "for d,c in zip(data.index,HapColor):\n",
+    "    ax.plot(theta, data.loc[d], color=c,lw=l)\n",
+    "ax.plot(theta, Hap_pd.loc['Emihux',E1], color=EmiColor, lw=el)\n",
+    "ax.set_varlabels(spoke_labels)\n",
+    "\n",
+    "\n",
+    "#create radar figure for E2\n",
+    "\n",
+    "ax2 = fig.add_subplot(2,2, 4, projection='radar')\n",
+    "\n",
+    "N = 7\n",
+    "theta = radar_factory(N, frame='polygon')\n",
+    "data=Dia_pd.loc[Dia_10,E2]\n",
+    "spoke_labels = data.columns\n",
+    "for d,c in zip(data.index,DiaColor):\n",
+    "    ax2.plot(theta, data.loc[d], color=c, lw=l)\n",
+    "data=Hap_pd.loc[Hap_10,E2]\n",
+    "spoke_labels = data.columns\n",
+    "for d,c in zip(data.index,HapColor):\n",
+    "    ax2.plot(theta, data.loc[d], color=c, lw=l)\n",
+    "ax2.plot(theta, Hap_pd.loc['Emihux',E2], color=EmiColor, lw=3)\n",
+    "# ax2.fill(theta, Hap_pd.loc['Emihux',E2], color=EmiColor, alpha=.2)\n",
+    "    \n",
+    "    \n",
+    "spoke_labels=['T=0',u'CONTROL', u'+N', u'-P', u'+DSW', u'-N', u'+P']\n",
+    "ax.set_varlabels(spoke_labels)\n",
+    "ax2.set_varlabels(spoke_labels)\n",
+    "\n",
+    "\n",
+    "ax.set_ylim([0,.04])      \n",
+    "ax2.set_ylim([0,.04])      \n",
+    "\n",
+    "ax.set_yticks(np.arange(0,.045,.01))\n",
+    "ax2.set_yticks(np.arange(0,.045,.01))\n",
+    "ax.set_yticks(np.arange(0,.045,.01))\n",
+    "ax2.set_yticks(np.arange(0,.045,.01))\n",
+    "\n",
+    "\n",
+    "# Create the formatter using the function to_percent. This multiplies all the\n",
+    "# default labels by 100, making them all percentages\n",
+    "formatter = mtick.FuncFormatter(to_percent)\n",
+    "\n",
+    "# Set the formatter\n",
+    "ax.yaxis.set_major_formatter(formatter)\n",
+    "ax2.yaxis.set_major_formatter(formatter)\n",
+    "ax3.yaxis.set_major_formatter(formatter)\n",
+    "\n",
+    "fig.savefig('GroupProfile.pdf')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "\n"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -3585,7 +5250,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 9,
    "metadata": {
     "collapsed": false
    },
@@ -3597,7 +5262,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 10,
    "metadata": {
     "collapsed": false
    },
@@ -3620,7 +5285,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 11,
    "metadata": {
     "collapsed": false
    },
@@ -3685,434 +5350,63 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 969,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
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-       "WDdvRUjtqLVehIce6qB1DHrIId9+wAAA2ZYX5DHNkYOHQT3gkUgkEolEMjgI9mvpEolEIpFIJN85\n",
-       "5IBHIpFIJBLJgEcOeCQSiUQikQx45IBHIpFIJBLJgEcOeCQSiUQikQx45IBHIpFIJBLJgEcOeCQS\n",
-       "iUQikQx45IBHIpFIJBLJgOf/AZgoojihf+FzAAAAAElFTkSuQmCC\n"
-      ],
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10ea86190>"
+       "[('Emi219', 'Emi379', 'Emihu1'),\n",
+       " ('Emi219',),\n",
+       " ('Emi219', 'Emi379'),\n",
+       " ('Emi219', 'Emihu1'),\n",
+       " ('Emi219', 'Emi370', 'Emi374'),\n",
+       " ('Emi219', 'Emi370', 'Emi374', 'Emi379', 'Emihu1'),\n",
+       " ('Emi370',),\n",
+       " ('Emi219', 'Emi374', 'Emi379'),\n",
+       " ('Emi219', 'Emi370', 'Emihu1'),\n",
+       " ('Emihu1',),\n",
+       " ('Emi374',),\n",
+       " ('Emi370', 'Emi379'),\n",
+       " ('Emi219', 'Emi374', 'Emihu1'),\n",
+       " ('Emi370', 'Emi374'),\n",
+       " ('Emi219', 'Emi370', 'Emi379', 'Emihu1'),\n",
+       " ('Emi379',),\n",
+       " ('Emi370', 'Emi374', 'Emi379', 'Emihu1'),\n",
+       " ('Emi370', 'Emi379', 'Emihu1'),\n",
+       " ('Emi374', 'Emihu1'),\n",
+       " ('Emi219', 'Emi374', 'Emi379', 'Emihu1'),\n",
+       " ('Emi219', 'Emi374'),\n",
+       " ('Emi379', 'Emihu1'),\n",
+       " ('Emi374', 'Emi379', 'Emihu1'),\n",
+       " ('Emi370', 'Emi374', 'Emi379'),\n",
+       " ('Emi219', 'Emi370', 'Emi379'),\n",
+       " ('Emi370', 'Emi374', 'Emihu1'),\n",
+       " ('Emi219', 'Emi370', 'Emi374', 'Emi379'),\n",
+       " ('Emi219', 'Emi370'),\n",
+       " ('Emi370', 'Emihu1'),\n",
+       " ('Emi219', 'Emi370', 'Emi374', 'Emihu1'),\n",
+       " ('Emi374', 'Emi379')]"
       ]
      },
+     "execution_count": 969,
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "execute_result"
     }
    ],
+   "source": [
+    "Genes_Uniq_Venn.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 369,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
    "source": [
     "\n",
     "def plot_Scatter_histogram(Hash_in,s=200):\n",
@@ -4182,12 +5476,12 @@
     "            ax1.plot()\n",
     "    return fig, xs\n",
     "    \n",
-    "fig1,EHX_SortedList=plot_Scatter_histogram(Genes_Uniq_Venn,s=200)\n"
+    "# fig1,EHX_SortedList=plot_Scatter_histogram(Genes_Uniq_Venn,s=200)\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 13,
    "metadata": {
     "collapsed": true
    },
@@ -4205,7 +5499,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 14,
    "metadata": {
     "collapsed": true
    },
@@ -4223,24 +5517,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 15,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "E1-Z1\n",
-      "E1-Z4\n",
-      "E2-Z5\n",
-      "E2-Z9\n",
-      "E3-Z10\n",
-      "E3-Z11\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "def plot_Scatter_histogram_fieldTPM(Hash_in,s=200, title=None, filetype='.pdf', plotType=None):\n",
     "    #takes a hash of the form (set, numbers)\n",
@@ -4350,7 +5631,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 16,
    "metadata": {
     "collapsed": false
    },
@@ -4359,190 +5640,46 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[('Emi219', 'Emi370', 'Emi374', 'Emi379', 'Emihu1'), ('Emi219', 'Emi370', 'Emi374', 'Emi379'), ('Emi219', 'Emi370', 'Emi374', 'Emihu1'), ('Emi219', 'Emi370', 'Emi379', 'Emihu1'), ('Emi219', 'Emi374', 'Emi379', 'Emihu1'), ('Emi370', 'Emi374', 'Emi379', 'Emihu1'), ('Emi219', 'Emi370', 'Emi374'), ('Emi219', 'Emi370', 'Emi379'), ('Emi219', 'Emi370', 'Emihu1'), ('Emi219', 'Emi374', 'Emi379'), ('Emi219', 'Emi374', 'Emihu1'), ('Emi219', 'Emi379', 'Emihu1'), ('Emi370', 'Emi374', 'Emi379'), ('Emi370', 'Emi374', 'Emihu1'), ('Emi370', 'Emi379', 'Emihu1'), ('Emi374', 'Emi379', 'Emihu1'), ('Emi219', 'Emi370'), ('Emi219', 'Emi374'), ('Emi219', 'Emi379'), ('Emi219', 'Emihu1'), ('Emi370', 'Emi374'), ('Emi370', 'Emi379'), ('Emi370', 'Emihu1'), ('Emi374', 'Emi379'), ('Emi374', 'Emihu1'), ('Emi379', 'Emihu1'), ('Emi219',), ('Emi370',), ('Emi374',), ('Emi379',), ('Emihu1',)]\n"
+      "E1-Z1\n",
+      "E1-Z4\n",
+      "E2-Z5\n",
+      "E2-Z9\n",
+      "E3-Z10\n",
+      "E3-Z11\n"
      ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x1135cfb90>"
-      ]
-     },
-     "execution_count": 39,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
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-      ],
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x1135cf690>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     }
    ],
+   "source": [
+    "InsituHash=ParseField_RSEM_Files('output_RSEM_Bowtie2/*Z*genes.results')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 284,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
    "source": [
     "def plot_Scatter_histogram_fieldTPM_grid(Hash_in,s=200, title=None, filetype='.pdf', plotType=None, ax1=None):\n",
     "    #takes a hash of the form (set, numbers)\n",
     "    #get the number of sets and the number of groups: get the lengths there in\n",
-    "    num_sets=len(Hash_in)\n",
-    "    grpNames=np.sort(list(set(np.hstack(Hash_in.keys()))))\n",
+    "    num_sets=len(Hash_in) #take in the number of samples\n",
+    "    grpNames=np.sort(list(set(np.hstack(Hash_in.keys())))) #sort the names by group length\n",
     "    num_groups=len(grpNames)\n",
     "    y=np.sort(num_sets*range(1,num_groups+1))\n",
     "    x=num_groups*range(1,num_sets+1)\n",
-    "\n",
+    "    \n",
+    "    #Define the color schemes\n",
+    "    specColorKey={'Emi219':0, 'Emi370':1, 'Emi374':2, 'Emi379':3, 'Emihu1':4}#key to convert name to list\n",
     "    color=b2m.sequential.Greys_5.hex_colors\n",
-    "    color_bar=b2m.sequential.Greens_9.hex_colors\n",
-    "#   fig=plt.figure(figsize=(num_sets/3, num_groups))\n",
-    "    if ax1==None:\n",
-    "        \n",
+    "    color_bar=b2m.sequential.Greys_7.hex_colors\n",
+    "    color_ind=b2m.qualitative.Set2_6.hex_colors\n",
+    "    del color_ind[4]    \n",
+    "    if ax1==None: \n",
     "        fig = plt.figure(figsize=(8, 6)) \n",
-    "#     fig.suptitle(title)\n",
     "        ax1=fig.add_subplot(111)\n",
-    "#     gs = gridspec.GridSpec(2, 1, height_ratios=[3,1]) \n",
-    "#     ax1 = plt.subplot(gs[0])\n",
-    "#     ax2 = plt.subplot(gs[1])\n",
-    "    #create grid of dots\n",
-    "#     ax2.scatter(x,y, c=color[1], linewidth=0, s=s)\n",
-    "    #alter the range etc. of pltos\n",
     "    ax1.set_xlim([0, np.max(x)+.7])\n",
-    "#     ax2.set_xlim([0, np.max(x)+.7])\n",
-    "#     ax2.set_ylim([.5, np.max(y)+.5])\n",
-    "#     ax2.set_yticks(range(1,num_groups+1))\n",
-    "#     ax2.set_yticklabels(grpNames)\n",
-    "#     ax2.spines['left'].set_color('none')\n",
-    "#     ax2.spines['right'].set_color('none')\n",
-    "#     ax2.spines['top'].set_color('none')\n",
-    "#     ax2.spines['bottom'].set_color('none')\n",
-    "#     ax2.tick_params(axis='both',         \n",
-    "#         which='both',      \n",
-    "#         bottom='off',      \n",
-    "#         top='off',  \n",
-    "#         right='off',\n",
-    "#         left='off',\n",
-    "#         labelbottom='off') \n",
     "    ax1.set_title(title)\n",
     "    ax1.tick_params(axis='both',         \n",
     "        which='both',      \n",
@@ -4557,7 +5694,6 @@
     "    grpHash={}\n",
     "    for i,x in enumerate(grpNames):\n",
     "        grpHash[x]=i+1\n",
-    "    #sort he list of keys (sets) by the length of the set\n",
     "    xs=Hash_in.keys()\n",
     "    xs.sort()\n",
     "    xs.sort(key = lambda s: len(s), reverse=True)\n",
@@ -4568,39 +5704,4235 @@
     "        i+=1#number along the x axis\n",
     "        for j in key:\n",
     "            n=grpHash[j]#number along the y axis\n",
-    "#             ax2.scatter(i,n, s=s, c=color_bar[len(key)+3], lw=0)\n",
+    "\n",
+    "            #Define the color scheme\n",
+    "            if len(key)>1:\n",
+    "                fc=color_bar[len(key)+1]\n",
+    "            else:\n",
+    "                fc=color_ind[specColorKey[key[0]]]\n",
+    "                \n",
+    "            #Plot different type of plots\n",
     "            if plotType=='tpm':\n",
-    "                rect=plt.Rectangle((i-.5,0), 1, Hash_in[key]['TPM'], fc=color_bar[len(key)+3],lw=0)\n",
+    "                rect=plt.Rectangle((i-.5,0), 1, Hash_in[key]['TPM'], fc=fc,lw=0)\n",
     "                ax1.set_ylabel('Total TPM', fontsize='large', weight='bold')\n",
     "\n",
     "            elif plotType=='% of genes':\n",
-    "                rect=plt.Rectangle((i-.5,0), 1, float(Hash_in[key]['Greater0'])/Hash_in[key]['Len_total'], fc=color_bar[len(key)+3],lw=0)\n",
+    "                rect=plt.Rectangle((i-.5,0), 1, float(Hash_in[key]['Greater0'])/Hash_in[key]['Len_total'], fc=fc,lw=0)\n",
     "                ax1.set_ylabel('% of genes expressed', fontsize='large', weight='bold')\n",
     "            elif plotType=='TPM norm to % of genes in field':\n",
-    "                rect=plt.Rectangle((i-.5,0), 1, float(Hash_in[key]['TPM'])/Hash_in[key]['Greater0'], fc=color_bar[len(key)+3],lw=0)\n",
+    "                rect=plt.Rectangle((i-.5,0), 1, float(Hash_in[key]['TPM'])/Hash_in[key]['Greater0'], fc=fc,lw=0)\n",
     "                ax1.set_ylabel('TPM normalized to expressed genes #', fontsize='large', weight='bold')\n",
-    "             \n",
+    "            elif plotType=='Total':\n",
+    "                rect=plt.Rectangle((i-.5,0), 1, float(Hash_in[key]['Len_total']), fc=fc,lw=0)\n",
+    "                ax1.set_ylabel('Number of Orthologus Genes', fontsize='large', weight='bold')\n",
     "            else:    \n",
-    "                rect=plt.Rectangle((i-.5,0), 1, Hash_in[key]['TPM']/Hash_in[key]['Len_total'], fc=color_bar[len(key)+3],lw=0)\n",
+    "                rect=plt.Rectangle((i-.5,0), 1, Hash_in[key]['TPM']/Hash_in[key]['Len_total'], fc=fc,lw=0)\n",
     "                ax1.set_ylabel('Average TPM within Group', fontsize='large', weight='bold')\n",
     "\n",
     "            ax1.add_patch(rect)\n",
     "            ax1.plot()\n",
     "    return ax1\n",
-    "\n",
-    "plot_Scatter_histogram_fieldTPM_grid(InsituHash['E1-Z1'],s=200, title=None, filetype='.pdf', plotType=None, ax1=None)\n"
+    "\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 285,
    "metadata": {
-    "collapsed": true
+    "collapsed": false
    },
    "outputs": [],
    "source": [
-    "InsituHash=ParseField_RSEM_Files('output_RSEM_Bowtie2/*Z*genes.results')\n"
-   ]
+    "def plot_Scatter_grid(Hash_in,s=200, title=None, ax2=None):\n",
+    "    #given an axis plot the scatter of species \n",
+    "    #takes a hash of the form (set, numbers)\n",
+    "    #get the number of sets and the number of groups: get the lengths there in\n",
+    "    num_sets=len(Hash_in)\n",
+    "    grpNames=np.sort(list(set(np.hstack(Hash_in.keys()))))\n",
+    "    num_groups=len(grpNames)\n",
+    "    y=np.sort(num_sets*range(1,num_groups+1))\n",
+    "    x=num_groups*range(1,num_sets+1)\n",
+    "    \n",
+    "    #Change the color scheme to be grey --> colors\n",
+    "    specColorKey={'Emi219':0, 'Emi370':1, 'Emi374':2, 'Emi379':3, 'Emihu1':4}#key to convert name to list\n",
+    "    color=b2m.sequential.Greys_6.hex_colors\n",
+    "    color_bar=b2m.sequential.Greys_7.hex_colors\n",
+    "    color_ind=b2m.qualitative.Set2_6.hex_colors\n",
+    "    del color_ind[4]\n",
+    "    if ax2==None:\n",
+    "        fig = plt.figure(figsize=(8, 6)) \n",
+    "        ax2=fig.add_subplot(111)\n",
+    "    ax2.scatter(x,y, c=color[1], linewidth=0, s=s)\n",
+    "\n",
+    "    ax2.set_xlim([0, np.max(x)+.7])\n",
+    "    ax2.set_ylim([.5, np.max(y)+.5])\n",
+    "    ax2.set_yticks(range(1,num_groups+1))\n",
+    "    ax2.set_yticklabels(grpNames)\n",
+    "    ax2.spines['left'].set_color('none')\n",
+    "    ax2.spines['right'].set_color('none')\n",
+    "    ax2.spines['top'].set_color('none')\n",
+    "    ax2.spines['bottom'].set_color('none')\n",
+    "    ax2.tick_params(axis='both',         \n",
+    "        which='both',      \n",
+    "        bottom='off',      \n",
+    "        top='off',  \n",
+    "        right='off',\n",
+    "        left='off',\n",
+    "        labelbottom='off') \n",
+    "    grpHash={}\n",
+    "    for i,x in enumerate(grpNames):\n",
+    "        grpHash[x]=i+1\n",
+    "    #sort he list of keys (sets) by the length of the set\n",
+    "    xs=Hash_in.keys()\n",
+    "    xs.sort()\n",
+    "    xs.sort(key = lambda s: len(s), reverse=True)\n",
+    "    plt.tight_layout()\n",
+    "    for i,key in enumerate(xs):\n",
+    "        nGenes=len(Hash_in[key])\n",
+    "        i+=1#number along the x axis\n",
+    "        c=color_bar[len(key)+1]\n",
+    "        if len(key)==1:\n",
+    "            c=color_ind[specColorKey[key[0]]]\n",
+    "        for j in key:\n",
+    "            n=grpHash[j]#number along the y axis\n",
+    "            ax2.scatter(i,n, s=s, c=c, lw=0)\n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 282,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "E1-Q\n",
+      "E1-R\n",
+      "E1-S\n",
+      "E1-T\n",
+      "E1-V\n",
+      "E1-X\n",
+      "E1-Z1\n",
+      "E1-Z4\n",
+      "E2-Q\n",
+      "E2-R\n",
+      "E2-S\n",
+      "E2-T\n",
+      "E2-V\n",
+      "E2-X\n",
+      "E2-Z5\n",
+      "E2-Z9\n",
+      "E3-Q\n",
+      "E3-R\n",
+      "E3-S\n",
+      "E3-T\n",
+      "E3-V\n",
+      "E3-X\n",
+      "E3-Z10\n",
+      "E3-Z11\n"
+     ]
+    }
+   ],
+   "source": [
+    "AllHash=ParseField_RSEM_Files('output_RSEM_Bowtie2/*genes.results')\n",
+    "Ehx_TPM_df=pd.DataFrame(columns=AllHash.keys(), index=AllHash['E1-Q'].keys())\n",
+    "for k in AllHash.keys():\n",
+    "    for x in AllHash[k].keys():\n",
+    "        val=AllHash[k][x]['TPM']\n",
+    "        Ehx_TPM_df.loc[x,k]=val\n",
+    "Ehx_TPMPercent=Ehx_TPM_df/Ehx_TPM_df.sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 368,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[('Emi219', 'Emi370', 'Emi374', 'Emi379', 'Emihu1'), ('Emi219', 'Emi370', 'Emi374', 'Emi379'), ('Emi219', 'Emi370', 'Emi374', 'Emihu1'), ('Emi219', 'Emi370', 'Emi379', 'Emihu1'), ('Emi219', 'Emi374', 'Emi379', 'Emihu1'), ('Emi370', 'Emi374', 'Emi379', 'Emihu1'), ('Emi219', 'Emi370', 'Emi374'), ('Emi219', 'Emi370', 'Emi379'), ('Emi219', 'Emi370', 'Emihu1'), ('Emi219', 'Emi374', 'Emi379'), ('Emi219', 'Emi374', 'Emihu1'), ('Emi219', 'Emi379', 'Emihu1'), ('Emi370', 'Emi374', 'Emi379'), ('Emi370', 'Emi374', 'Emihu1'), ('Emi370', 'Emi379', 'Emihu1'), ('Emi374', 'Emi379', 'Emihu1'), ('Emi219', 'Emi370'), ('Emi219', 'Emi374'), ('Emi219', 'Emi379'), ('Emi219', 'Emihu1'), ('Emi370', 'Emi374'), ('Emi370', 'Emi379'), ('Emi370', 'Emihu1'), ('Emi374', 'Emi379'), ('Emi374', 'Emihu1'), ('Emi379', 'Emihu1'), ('Emi219',), ('Emi370',), ('Emi374',), ('Emi379',), ('Emihu1',)]\n"
+     ]
+    },
+    {
+     "data": {
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+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11db16490>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#define order and colors\n",
+    "E1=[u'E1-Z1',u'E1-R', u'E1-V', u'E1-X', u'E1-T', u'E1-S',u'E1-Q']\n",
+    "E2=[u'E2-Z5', u'E2-R', u'E2-V', u'E2-X', u'E2-T', u'E2-S',u'E2-Q']\n",
+    "\n",
+    "Order=[('Emi219', 'Emi370', 'Emi374', 'Emi379', 'Emihu1'), \n",
+    "                          ('Emi219', 'Emi370', 'Emi374', 'Emi379'), ('Emi219', 'Emi370', 'Emi374', 'Emihu1'), \n",
+    "                          ('Emi219', 'Emi370', 'Emi379', 'Emihu1'), ('Emi219', 'Emi374', 'Emi379', 'Emihu1'), \n",
+    "                          ('Emi370', 'Emi374', 'Emi379', 'Emihu1'), ('Emi219', 'Emi370', 'Emi374'), \n",
+    "                          ('Emi219', 'Emi370', 'Emi379'), ('Emi219', 'Emi370', 'Emihu1'), \n",
+    "                          ('Emi219', 'Emi374', 'Emi379'), ('Emi219', 'Emi374', 'Emihu1'), \n",
+    "                          ('Emi219', 'Emi379', 'Emihu1'), ('Emi370', 'Emi374', 'Emi379'), \n",
+    "                          ('Emi370', 'Emi374', 'Emihu1'), ('Emi370', 'Emi379', 'Emihu1'), \n",
+    "                          ('Emi374', 'Emi379', 'Emihu1'), ('Emi219', 'Emi370'), \n",
+    "                          ('Emi219', 'Emi374'), ('Emi219', 'Emi379'), ('Emi219', 'Emihu1'), \n",
+    "                          ('Emi370', 'Emi374'), ('Emi370', 'Emi379'), ('Emi370', 'Emihu1'), \n",
+    "                          ('Emi374', 'Emi379'), ('Emi374', 'Emihu1'), ('Emi379', 'Emihu1'), \n",
+    "                          ('Emi219',), ('Emi370',), ('Emi374',), ('Emi379',), ('Emihu1',)]\n",
+    "\n",
+    "specColorKey={'Emi219':0, 'Emi370':1, 'Emi374':2, 'Emi379':3, 'Emihu1':4}#key to convert name to list\n",
+    "color=b2m.sequential.Greys_6.hex_colors\n",
+    "color_bar=b2m.sequential.Greys_7.hex_colors\n",
+    "color_ind=b2m.qualitative.Set2_6.hex_colors\n",
+    "del color_ind[4]\n",
+    "colors=[]\n",
+    "for key in Order:\n",
+    "        if len(key)>1:\n",
+    "            colors.append(color_bar[len(key)+1])\n",
+    "        else:\n",
+    "            colors.append(color_ind[specColorKey[key[0]]])\n",
+    "\n",
+    "l=2\n",
+    "            \n",
+    "#create the grid spec space\n",
+    "fig=plt.figure(figsize=(11,9))\n",
+    "gs = gridspec.GridSpec(7, 7)\n",
+    "ax1 = fig.add_subplot(gs[0:3, 0:3])\n",
+    "ax2 = fig.add_subplot(gs[0:3,3:])\n",
+    "ax3 = fig.add_subplot(gs[3:6, 0:3])\n",
+    "ax3b = fig.add_subplot(gs[6:, 0:3])\n",
+    "ax4 = fig.add_subplot(gs[3:, 3:5],projection='radar')\n",
+    "ax5 = fig.add_subplot(gs[3:, 5:],projection='radar')\n",
+    "\n",
+    "\"\"\"\n",
+    "Plot the map\n",
+    "\"\"\"\n",
+    "ax1 = fig.add_subplot(gs[0:3, 0:3])\n",
+    "lons=Emi_location['Longitude']\n",
+    "lats=Emi_location['Latitude']\n",
+    "m = Basemap(projection='mill',llcrnrlat=-60,urcrnrlat=70,\\\n",
+    "            llcrnrlon=-150,urcrnrlon=180,resolution='c')\n",
+    "# m.drawcoastlines()\n",
+    "# m.fillcontinents(color='coral',lake_color='aqua')\n",
+    "m.bluemarble()\n",
+    "for Ehx in Emi_location.index:\n",
+    "    lon=lons.loc[Ehx]\n",
+    "    lat=lats.loc[Ehx]\n",
+    "    x, y = m(lon,lat)\n",
+    "    m.scatter(x,y,100,marker='o',color=color_ind[specColorKey[Ehx]])\n",
+    "\"\"\"\n",
+    "Plot the time series\n",
+    "\"\"\"\n",
+    "Ehx_TPMPercent=Ehx_TPMPercent.loc[Order]\n",
+    "Ehx_TPMPercent[insitu].T.plot(ax=ax2, color=colors, lw=2, legend=False)\n",
+    "ax2.set_xticklabels(insitu)\n",
+    "ax2.yaxis.set_major_formatter(formatter)\n",
+    "\n",
+    "\"\"\" \n",
+    "Plot the break down of the ortho mcl clusters\n",
+    "\"\"\"\n",
+    "\n",
+    "plot_Scatter_grid(InsituHash['E1-Z1'], s=50, ax2=ax3b)\n",
+    "plot_Scatter_histogram_fieldTPM_grid(InsituHash['E1-Z1'],s=200, title=None, filetype='.pdf', plotType='Total', ax1=ax3)\n",
+    "\n",
+    "\n",
+    "\"\"\"\n",
+    "Plot the radar plots for the experiments\n",
+    "\"\"\"\n",
+    "\n",
+    "N = 7\n",
+    "theta = radar_factory(N, frame='polygon')\n",
+    "data=Ehx_TPMPercent[E1]\n",
+    "spoke_labels = data.columns\n",
+    "for d,c in zip(range(len(data.index)),colors):\n",
+    "    ax4.plot(theta, data.loc[[Order[d]]].T, color=c, lw=l)\n",
+    "spoke_labels=['T=0', u'+N', u'-P', u'+DSW', u'-N', u'+P',u'CONTROL',]\n",
+    "ax4.set_varlabels(spoke_labels)\n",
+    "ax4.yaxis.set_major_formatter(formatter)\n",
+    "\n",
+    "\n",
+    "N = 7\n",
+    "theta = radar_factory(N, frame='polygon')\n",
+    "data=Ehx_TPMPercent[E2]\n",
+    "spoke_labels = data.columns\n",
+    "for d,c in zip(range(len(data.index)),colors):\n",
+    "    ax5.plot(theta, data.loc[[Order[d]]].T, color=c, lw=l)\n",
+    "spoke_labels=['T=0', u'+N', u'-P', u'+DSW', u'-N', u'+P',u'CONTROL',]\n",
+    "ax5.set_varlabels(spoke_labels)\n",
+    "\n",
+    "ax4.set_ylim([0,.65])\n",
+    "ax5.set_ylim([0,.65])\n",
+    "ax4.set_yticks(np.arange(0,.65,.2))\n",
+    "ax5.set_yticks(np.arange(0,.65,.2))\n",
+    "ax4.yaxis.set_major_formatter(formatter)\n",
+    "ax5.yaxis.set_major_formatter(formatter)\n",
+    "\n",
+    "fig.savefig('EmilianaDynamics.pdf')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 340,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "Emi_location=pd.DataFrame(columns=['Latitude', 'Longitude'], index=('Emi219', 'Emi370', 'Emi374', 'Emi379', 'Emihu1'))\n",
+    "Emi_location.loc['Emi374']=[42.5, -69]\n",
+    "Emi_location.loc['Emi370']=[59.5, 10.6]\n",
+    "Emi_location.loc['Emi379']=[50.1669, -4.2504]\n",
+    "Emi_location.loc['Emihu1']=[-2.6667, -82.7167]\n",
+    "Emi_location.loc['Emi219']=[-36.26, 174.8]\n",
+    "\n",
+    "\n",
+    "lons=Emi_location['Longitude']\n",
+    "lats=Emi_location['Latitude']\n",
+    "m = Basemap(projection='mill',llcrnrlat=-60,urcrnrlat=70,\\\n",
+    "            llcrnrlon=-150,urcrnrlon=180,resolution='c')\n",
+    "# m.drawcoastlines()\n",
+    "# m.fillcontinents(color='coral',lake_color='aqua')\n",
+    "m.bluemarble()\n",
+    "for Ehx in Emi_location.index:\n",
+    "    lon=lons.loc[Ehx]\n",
+    "    lat=lats.loc[Ehx]\n",
+    "    x, y = m(lon,lat)\n",
+    "    m.scatter(x,y,100,marker='o',color=color_ind[specColorKey[Ehx]])\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##Section 2: Functional shifts between taxonomic groups\n",
+    "###KOG Orthology analaysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 964,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "#Read in KOG table and create a dictionary of dictionaries to describe all the data. \n",
+    "KOG_MasterDict={}\n",
+    "file=open('TranscriptomeAnnotation/KOG/output/output.2', 'r')\n",
+    "header=next(file)\n",
+    "header= header.split('\\t')\n",
+    "for l in file:\n",
+    "    line=l.split('\\t')\n",
+    "    PID=line[0]\n",
+    "    a={}\n",
+    "    for i,e in enumerate(line):\n",
+    "        a[header[i].strip()]=e.strip()\n",
+    "    KOG_MasterDict[PID]=a\n",
+    "KOG_MasterDict\n",
+    "\n",
+    "##Create a pandas DF from the master dict and adjust it\n",
+    "\n",
+    "KOG_MasterPD=pd.DataFrame.from_dict(KOG_MasterDict, orient='columns').T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 965,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "#Create a new Hash that goes from orhtologus group to KOG description\n",
+    "newOG_KOGHash={}\n",
+    "for gene in gFHash:\n",
+    "    newOG_KOGHash[gene]=[]\n",
+    "    for transcript in gFHash[gene]:\n",
+    "        if transcript in KOG_MasterDict:\n",
+    "            KOG=KOG_MasterDict[transcript]['class description']\n",
+    "            if KOG in newOG_KOGHash[gene]:\n",
+    "                pass\n",
+    "            else: \n",
+    "                newOG_KOGHash[gene].append(KOG)\n",
+    "KOG_cts={}                \n",
+    "def invert(d):\n",
+    "    return dict( (v,k) for k in d for v in d[k] )\n",
+    "    \n",
+    "gFHash_inverted=invert(gFHash)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 967,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "#add some useful columns to the dictionary: species column/ Orthologus group assignemnt\n",
+    "\n",
+    "KOG_MasterPD=KOG_MasterPD.loc[gFHash_inverted.keys()]\n",
+    "KOG_MasterPD['Organism']=[item.split('|')[0] for item in KOG_MasterPD.index]\n",
+    "KOG_MasterPD['OrthoMCLGroups']=KOG_MasterPD['#Query'].map(gFHash_inverted)\n",
+    "KOG_MasterPD=KOG_MasterPD.dropna()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "###Look at the distribution of KOG classes by strain"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 878,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 915,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# \"\"\"\n",
+    "# I would like to add the multiple class catagories into the regular set of A-Z... but I can't seem to get it to work.\n",
+    "# Below is my attempt. \n",
+    "# \"\"\"\n",
+    "# Emi_spec=['Emi379', 'Emihu1', 'Emi219', 'Emi374', 'Emi370']\n",
+    "# Grouping_ClassOrg_SingleOnly=Grouping_ClassOrg.copy()\n",
+    "# for val, df in Grouping_ClassOrg.groupby(level=0):\n",
+    "#     l=len(val)\n",
+    "#     if l>1:\n",
+    "#         for letter in val:\n",
+    "#             a=Grouping_ClassOrg.loc[(letter, Emi_spec), 'Size'].groupby(level=1).sum()\n",
+    "#             b=(Grouping_ClassOrg.loc[(val, Emi_spec), 'Size'].groupby(level=1).sum())/l\n",
+    "#             test=b.add(a, fill_value=0)\n",
+    "#             test=pd.DataFrame(test)\n",
+    "#             for ind in test.index:\n",
+    "#                 print ind\n",
+    "#                 Grouping_ClassOrg_SingleOnly.loc[(letter,ind[0]), 'Size']=test.loc[ind]\n",
+    "#         Grouping_ClassOrg_SingleOnly=Grouping_ClassOrg_SingleOnly.drop(val)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First, look at the functional distribution by species across KOGS: it looks quite similar"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 945,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.text.Text at 0x19c4ef5d0>"
+      ]
+     },
+     "execution_count": 945,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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+       "RU5ErkJggg==\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x156ce8050>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "specColorKey={'Emi219':0, 'Emi370':1, 'Emi374':2, 'Emi379':3, 'Emihu1':4}#key to convert name to list\n",
+    "#group the classes to create counts by catagory\n",
+    "Grouping_ClassOrg=pd.DataFrame(KOG_MasterPD.groupby(['class', 'Organism']).size())\n",
+    "Grouping_ClassOrg.columns=['Size']\n",
+    "Grouping_ClassOrg_SingleOnly=Grouping_ClassOrg.copy()\n",
+    "#drop all values that are more than one catagory\n",
+    "for val, df in Grouping_ClassOrg_SingleOnly.groupby(level=0):\n",
+    "    l=len(val)\n",
+    "    if l>1:\n",
+    "        Grouping_ClassOrg_SingleOnly=Grouping_ClassOrg_SingleOnly.drop(val)\n",
+    "\n",
+    "Count=pd.DataFrame.from_dict(gFHash_inverted, orient='index')\n",
+    "Count['Species']=[i.split('|')[0] for i in Count.index]\n",
+    "Species_Transcriptome_Size=Count.groupby('Species').size()\n",
+    "\n",
+    "#Normalize to the total number of transcripts per strain\n",
+    "for s in Grouping_ClassOrg_SingleOnly.index:\n",
+    "    val=Grouping_ClassOrg_SingleOnly.loc[s]\n",
+    "    div=Species_Transcriptome_Size.loc[s[1]]\n",
+    "    Grouping_ClassOrg_SingleOnly.loc[s]=val/div\n",
+    "\n",
+    "fig,ax=plt.subplots(1)\n",
+    "fig.set_figheight(9)\n",
+    "fig.set_figwidth(14)\n",
+    "\n",
+    "Grouping_ClassOrg_SingleOnly.unstack().plot(kind='bar',ax=ax, colors=color_ind, lw=0, grid=False, legend=['Emi219', 'Emi370', 'Emi374', 'Emi379', 'Emihu1'])\n",
+    "ax.yaxis.set_major_formatter(formatter)\n",
+    "ax.set_ylabel('Percent of total peptides', weight='bold', size='x-large')\n",
+    "ax.set_xlabel('KOG Catagory', weight='bold', size='x-large')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##Now, let's compare the content across the different orthologus groups"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 970,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "OrthologusGroup_To_Set=invert(Genes_Uniq_Venn)\n",
+    "KOG_MasterPD['Species_sets']=KOG_MasterPD['OrthoMCLGroups'].map(OrthologusGroup_To_Set)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1032,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "#count the number of A-Z across the different orthologus groupings\n",
+    "SetClass_Count=KOG_MasterPD.groupby(['Species_sets', 'class']).size()\n",
+    "for i in SetClass_Count.index:\n",
+    "    if len(i[1])>1:\n",
+    "        SetClass_Count=SetClass_Count.drop(i)\n",
+    "for s in SetClass_Count.index:\n",
+    "    val=SetClass_Count.loc[s]\n",
+    "    div=float(len(Genes_Uniq_Venn[s[0]]))\n",
+    "    SetClass_Count.loc[s]=val/div"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1033,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
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+       "GPgOcCxZ78hXIuIgsuGc90y0BsaYieEZU40xtSFpc+DjZL0bmwEXAk8GPgp8EbifLKrnVmS9IA/n\n",
+       "eQ8Aj5A1QjYDPkHmJ7IMeEdE3DTRihhjJoIbIcYYY4yZCh6OMcYYY8xUcCPEGGOMMVPBjRBjjDHG\n",
+       "TAU3QowxxhgzFdwIMcYYY8xUcCPEGGOMMVPBjRBjjDHGTAU3QowxxhgzFf4fHaaXmDYDKQwAAAAA\n",
+       "SUVORK5CYII=\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1e750c2d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "GroupsOfInt=[('Emi219', 'Emi370', 'Emi374', 'Emi379', 'Emihu1'), ('Emi219',), ('Emi370',), ('Emi374',), ('Emi379',), ('Emihu1',)]\n",
+    "fig,axs=plt.subplots(6)\n",
+    "fig.set_size_inches(9,12)\n",
+    "for g, ax in zip(GroupsOfInt, axs):\n",
+    "    SetClass_Count.loc[g].plot(kind='bar', ax=ax)\n",
+    "    ax.set_title(g)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1022,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4.0684722487125695"
+      ]
+     },
+     "execution_count": 1022,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g=GroupsOfInt[0]\n",
+    "SetClass_Count=KOG_MasterPD.groupby(['Species_sets', 'class']).size()\n",
+    "SetClass_Count.loc[g].sum()/float(len(Genes_Uniq_Venn[g]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1026,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "139"
+      ]
+     },
+     "execution_count": 1026,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "SetClass_Count.loc[g].size"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1030,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Species_sets\n",
+       "(Emi219,)                                    1415\n",
+       "(Emi219, Emi370)                             1422\n",
+       "(Emi219, Emi370, Emi374)                      502\n",
+       "(Emi219, Emi370, Emi374, Emi379)             2188\n",
+       "(Emi219, Emi370, Emi374, Emi379, Emihu1)    21331\n",
+       "(Emi219, Emi370, Emi374, Emihu1)             2830\n",
+       "(Emi219, Emi370, Emi379)                     1122\n",
+       "(Emi219, Emi370, Emi379, Emihu1)             5459\n",
+       "(Emi219, Emi370, Emihu1)                     4967\n",
+       "(Emi219, Emi374)                              109\n",
+       "(Emi219, Emi374, Emi379)                      137\n",
+       "(Emi219, Emi374, Emi379, Emihu1)              869\n",
+       "(Emi219, Emi374, Emihu1)                      274\n",
+       "(Emi219, Emi379)                              222\n",
+       "(Emi219, Emi379, Emihu1)                      502\n",
+       "(Emi219, Emihu1)                              791\n",
+       "(Emi370,)                                    1759\n",
+       "(Emi370, Emi374)                              160\n",
+       "(Emi370, Emi374, Emi379)                      185\n",
+       "(Emi370, Emi374, Emi379, Emihu1)              562\n",
+       "(Emi370, Emi374, Emihu1)                      296\n",
+       "(Emi370, Emi379)                              223\n",
+       "(Emi370, Emi379, Emihu1)                      483\n",
+       "(Emi370, Emihu1)                              787\n",
+       "(Emi374,)                                     530\n",
+       "(Emi374, Emi379)                              122\n",
+       "(Emi374, Emi379, Emihu1)                      275\n",
+       "(Emi374, Emihu1)                              305\n",
+       "(Emi379,)                                     917\n",
+       "(Emi379, Emihu1)                             1066\n",
+       "(Emihu1,)                                    4075\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 1030,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "KOG_MasterPD.groupby('Species_sets').size()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {