diff --git a/ENTREGA.ipynb b/ENTREGA.ipynb
index 524869ca1aa41d57d1989cf72f3e4224844fd0e8..3e739c18882098de541f1b864b44d100f0a9f05a 100644
--- a/ENTREGA.ipynb
+++ b/ENTREGA.ipynb
@@ -972,7 +972,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": 96,
    "metadata": {},
    "outputs": [
     {
@@ -982,27 +982,22 @@
       "[ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16]\n",
       "(17,)\n",
       "(17,)\n",
-      "[0.97452698 8.0371231  1.54176471]\n"
+      "[2.55545105 8.01323096 0.10128328]\n"
      ]
     },
     {
-     "ename": "ValueError",
-     "evalue": "x and y must have same first dimension, but have shapes (17,) and (200,)",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-92-bf73db0682fb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlum_model\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlumx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'o'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"darkorange\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlum_model_prueba\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'--r'\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     23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m \u001b[0;31m#plt.plot(x,ymodel - y_ruido,'--r')\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.local/lib/python3.7/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2840\u001b[0m     return gca().plot(\n\u001b[1;32m   2841\u001b[0m         \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscalex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mscalex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscaley\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mscaley\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2842\u001b[0;31m         **({\"data\": data} if data is not None else {}), **kwargs)\n\u001b[0m\u001b[1;32m   2843\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2844\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.local/lib/python3.7/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(self, scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1741\u001b[0m         \"\"\"\n\u001b[1;32m   1742\u001b[0m         \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcbook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnormalize_kwargs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmlines\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLine2D\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1743\u001b[0;31m         \u001b[0mlines\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m*\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[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\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[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1744\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[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1745\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[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.local/lib/python3.7/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m    271\u001b[0m                 \u001b[0mthis\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0margs\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[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    272\u001b[0m                 \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0margs\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[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 273\u001b[0;31m             \u001b[0;32myield\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_plot_args\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mthis\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    274\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    275\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_next_color\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[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.local/lib/python3.7/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m_plot_args\u001b[0;34m(self, tup, kwargs)\u001b[0m\n\u001b[1;32m    397\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    398\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[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 399\u001b[0;31m             raise ValueError(f\"x and y must have same first dimension, but \"\n\u001b[0m\u001b[1;32m    400\u001b[0m                              f\"have shapes {x.shape} and {y.shape}\")\n\u001b[1;32m    401\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[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mValueError\u001b[0m: x and y must have same first dimension, but have shapes (17,) and (200,)"
-     ]
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f5acb9b65c0>]"
+      ]
+     },
+     "execution_count": 96,
+     "metadata": {},
+     "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1020,9 +1015,9 @@
     "y = np.arange(0, estrella2.shape[1], 1)\n",
     "print(x)\n",
     "#print(y)\n",
-    "#x_prueba = np.arange(0,20,0.1)\n",
+    "x_prueba = np.arange(0,16,0.1)\n",
     "\n",
-    "lumx = estrella2[:,17]/100\n",
+    "lumx = estrella2[:,17]/1000\n",
     "print(lumx.shape)\n",
     "print(x.shape)\n",
     "\n",
@@ -1035,7 +1030,7 @@
     "lum_model = func_gauss_C(best,x)\n",
     "plt.plot(x,lum_model)\n",
     "plt.plot(x,lumx,'o',color=\"darkorange\")\n",
-    "plt.plot(x,lum_model_prueba,'--r')\n",
+    "plt.plot(x_prueba,lum_model_prueba,'--r')\n",
     "\n",
     "#plt.plot(x,ymodel - y_ruido,'--r')\n",
     "#plt.axhline(y=0,color=\"gray\")\n",