diff --git a/intervene_test/Enhancer_predications_updated.ipynb b/intervene_test/Enhancer_predications_updated.ipynb
index 31eb030b5210b12844c030b46b22604062f21400..fdf7479ebf0b15dd490ae076a7478efc5cdd4424 100644
--- a/intervene_test/Enhancer_predications_updated.ipynb
+++ b/intervene_test/Enhancer_predications_updated.ipynb
@@ -2,40 +2,16 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "([<matplotlib.axis.YTick at 0x10f6ed710>,\n",
-       "  <matplotlib.axis.YTick at 0x10fdad550>,\n",
-       "  <matplotlib.axis.YTick at 0x10fd9d8d0>,\n",
-       "  <matplotlib.axis.YTick at 0x10fee4350>,\n",
-       "  <matplotlib.axis.YTick at 0x10fee48d0>,\n",
-       "  <matplotlib.axis.YTick at 0x10fee4e50>,\n",
-       "  <matplotlib.axis.YTick at 0x10fee4ad0>,\n",
-       "  <matplotlib.axis.YTick at 0x10feee210>,\n",
-       "  <matplotlib.axis.YTick at 0x10feee790>,\n",
-       "  <matplotlib.axis.YTick at 0x10feeed10>,\n",
-       "  <matplotlib.axis.YTick at 0x10fef72d0>],\n",
-       " <a list of 11 Text yticklabel objects>)"
+       "<Figure size 720x360 with 0 Axes>"
       ]
      },
-     "execution_count": 9,
      "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 720x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
      "output_type": "display_data"
     }
    ],
@@ -93,7 +69,7 @@
     "p3 = plt.bar(ind, meth_3, width,\n",
     "              bottom=[i+j for i,j in zip(meth_1, meth_2)],color='#D52114')\n",
     "p4 = plt.bar(ind, meth_4, width,\n",
-    "              bottom=[i+j+l for i,j,l in zip(meth_1, meth_2, meth_3)],color='orange')\n",
+    "              bottom=[i+j+l for i,j,l in zip(meth_1, meth_2, meth_3)],color='#007517')\n",
     "\n",
     "plt.ylabel('% total enhancers')\n",
     "plt.title('Stage')\n",
@@ -102,45 +78,9 @@
     "\n",
     "\n",
     "\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 432x288 with 0 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "N = 4\n",
-    "ind = np.arange(N)    # the x locations for the groups\n",
-    "width = 0.5       # the width of the bars: can also be len(x) sequence\n",
-    "\n",
-    "p1 = plt.bar(ind, meth_1, width, color='#6DA2DB')\n",
-    "p2 = plt.bar(ind, meth_2, width,\n",
-    "             bottom=meth_1,color='#D2D5D4')\n",
-    "p3 = plt.bar(ind, meth_3, width,\n",
-    "              bottom=[i+j for i,j in zip(meth_1, meth_2)],color='#D52114')\n",
-    "p4 = plt.bar(ind, meth_3, width,\n",
-    "              bottom=[i+j for i,j in zip(meth_1, meth_2)],color='#D52114')\n",
-    "\n",
-    "plt.ylabel('% total enhancers')\n",
-    "plt.title('Stage')\n",
-    "plt.xticks(ind, ('H3K4me1', 'H3K27ac', 'H3K4me1+H3K27ac' 'GR0-seq'))\n",
-    "plt.yticks(np.arange(0, 110, 10))\n",
-    "\n",
     "plt.savefig('Enhancer_percentages.png')\n",
-    "plt.clf()\n"
+    "plt.clf()\n",
+    "\n"
    ]
   },
   {