diff --git a/ENTREGA.ipynb b/ENTREGA.ipynb
index e27cb9d076462abd60f0d37bf802b5ce85c223b7..524869ca1aa41d57d1989cf72f3e4224844fd0e8 100644
--- a/ENTREGA.ipynb
+++ b/ENTREGA.ipynb
@@ -70,7 +70,6 @@
     "image = Image.open('data/zapatocaImage.jpeg')\n",
     "# convert image to numpy array\n",
     "data = asarray(image) # abrimos la imagen en forma de array\n",
-    "# summarize shape\n",
     "print(data.shape)\n",
     "print(data)"
    ]
@@ -254,7 +253,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f8415923a90>"
+       "<matplotlib.image.AxesImage at 0x7f5ad8353978>"
       ]
      },
      "execution_count": 11,
@@ -297,7 +296,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f8415858e80>"
+       "<matplotlib.image.AxesImage at 0x7f5ad8287dd8>"
       ]
      },
      "execution_count": 12,
@@ -333,7 +332,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f84157cfb38>"
+       "<matplotlib.image.AxesImage at 0x7f5ad81fda90>"
       ]
      },
      "execution_count": 13,
@@ -366,7 +365,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f84157bc780>"
+       "<matplotlib.image.AxesImage at 0x7f5ad81ea6a0>"
       ]
      },
      "execution_count": 14,
@@ -399,7 +398,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f8415724198>"
+       "<matplotlib.image.AxesImage at 0x7f5ad6e76128>"
       ]
      },
      "execution_count": 15,
@@ -432,7 +431,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f841442e160>"
+       "<matplotlib.image.AxesImage at 0x7f5ad6e5b0f0>"
       ]
      },
      "execution_count": 16,
@@ -465,7 +464,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f8415827a20>"
+       "<matplotlib.image.AxesImage at 0x7f5ad82c72b0>"
       ]
      },
      "execution_count": 17,
@@ -498,7 +497,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f841436ce80>"
+       "<matplotlib.image.AxesImage at 0x7f5ad6d9ad68>"
       ]
      },
      "execution_count": 18,
@@ -531,7 +530,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f84142d3c18>"
+       "<matplotlib.image.AxesImage at 0x7f5ad6d01be0>"
       ]
      },
      "execution_count": 19,
@@ -564,7 +563,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f8414241ac8>"
+       "<matplotlib.image.AxesImage at 0x7f5ad6c6ea90>"
       ]
      },
      "execution_count": 20,
@@ -597,7 +596,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f841422ecc0>"
+       "<matplotlib.image.AxesImage at 0x7f5ad6c5cbe0>"
       ]
      },
      "execution_count": 21,
@@ -630,7 +629,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f841419df28>"
+       "<matplotlib.image.AxesImage at 0x7f5ad6bcbe10>"
       ]
      },
      "execution_count": 22,
@@ -663,7 +662,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f8414112710>"
+       "<matplotlib.image.AxesImage at 0x7f5ad6b3c6d8>"
       ]
      },
      "execution_count": 23,
@@ -735,7 +734,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -749,7 +748,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -759,12 +758,92 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 59,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19]\n",
+      "[0.52021778 0.1704145 ]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.lines.Line2D at 0x7f5acc90f358>"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = np.arange(0,20,1)\n",
+    "print(x)\n",
+    "np.random.seed(1)\n",
+    "ruido = np.random.normal(0,0.3,20)\n",
+    "#print(ruido)\n",
+    "\n",
+    "params = [1,1] \n",
+    "y = func_gauss(params,x)\n",
+    "    \n",
+    "y_ruido = y + ruido\n",
+    "\n",
+    "p1 = [0.5,1]\n",
+    "best,suss = leastsq(Error_min_cuadra, p1, args=(x,y_ruido))\n",
+    "print(best)\n",
+    "ymodel = func_gauss(best,x)\n",
+    "plt.plot(x,ymodel)\n",
+    "plt.plot(x,y,'o',color=\"darkorange\")\n",
+    "#plt.plot(x,ymodel - y_ruido,'--r')\n",
+    "plt.axhline(y=0,color=\"gray\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 0.   0.1  0.2  0.3  0.4  0.5  0.6  0.7  0.8  0.9  1.   1.1  1.2  1.3\n",
+      "  1.4  1.5  1.6  1.7  1.8  1.9  2.   2.1  2.2  2.3  2.4  2.5  2.6  2.7\n",
+      "  2.8  2.9  3.   3.1  3.2  3.3  3.4  3.5  3.6  3.7  3.8  3.9  4.   4.1\n",
+      "  4.2  4.3  4.4  4.5  4.6  4.7  4.8  4.9  5.   5.1  5.2  5.3  5.4  5.5\n",
+      "  5.6  5.7  5.8  5.9  6.   6.1  6.2  6.3  6.4  6.5  6.6  6.7  6.8  6.9\n",
+      "  7.   7.1  7.2  7.3  7.4  7.5  7.6  7.7  7.8  7.9  8.   8.1  8.2  8.3\n",
+      "  8.4  8.5  8.6  8.7  8.8  8.9  9.   9.1  9.2  9.3  9.4  9.5  9.6  9.7\n",
+      "  9.8  9.9 10.  10.1 10.2 10.3 10.4 10.5 10.6 10.7 10.8 10.9 11.  11.1\n",
+      " 11.2 11.3 11.4 11.5 11.6 11.7 11.8 11.9 12.  12.1 12.2 12.3 12.4 12.5\n",
+      " 12.6 12.7 12.8 12.9 13.  13.1 13.2 13.3 13.4 13.5 13.6 13.7 13.8 13.9\n",
+      " 14.  14.1 14.2 14.3 14.4 14.5 14.6 14.7 14.8 14.9 15.  15.1 15.2 15.3\n",
+      " 15.4 15.5 15.6 15.7 15.8 15.9 16.  16.1 16.2 16.3 16.4 16.5 16.6 16.7\n",
+      " 16.8 16.9 17.  17.1 17.2 17.3 17.4 17.5 17.6 17.7 17.8 17.9 18.  18.1\n",
+      " 18.2 18.3 18.4 18.5 18.6 18.7 18.8 18.9 19.  19.1 19.2 19.3 19.4 19.5\n",
+      " 19.6 19.7 19.8 19.9]\n"
+     ]
+    }
+   ],
    "source": [
     "x = np.arange(0,20,0.1)\n",
-    "#print(x)\n",
+    "print(x)\n",
     "np.random.seed(1)\n",
     "ruido = np.random.normal(0,0.3,200)\n",
     "#print(ruido)\n",
@@ -774,46 +853,46 @@
     "    \n",
     "y_ruido = y + ruido\n",
     "\n",
-    "#print(y)"
+    "#print(y)\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[0.96733251 9.79457362]\n"
+      "[0.96732372 9.79457162]\n"
      ]
     }
    ],
    "source": [
-    "p1 = [0.5,4]\n",
+    "p1 = [0.5,1]\n",
     "best,suss = leastsq(Error_min_cuadra, p1, args=(x,y_ruido))\n",
     "print(best)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 54,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.lines.Line2D at 0x7f840a500b00>"
+       "<matplotlib.lines.Line2D at 0x7f5accc0b588>"
       ]
      },
-     "execution_count": 76,
+     "execution_count": 54,
      "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>"
       ]
@@ -834,14 +913,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 123,
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def func_gauss_C(params,x):\n",
+    "    #params[0]= sigma\n",
+    "    #params[1] = x0\n",
+    "    #params[2] = C\n",
+    "    #numpy.pi = numero pi\n",
+    "    y = (1/(params[0]*np.sqrt(2*np.pi)))*(np.exp((-(x-params[1])**2)/(2*params[0]**2)))+params[2]\n",
+    "    return y\n",
+    "def Error_min_cuadra_C(tpl,x,y):\n",
+    "    return func_gauss_C(tpl,x)-y"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "(23, 25)\n"
+      "(17, 25)\n"
      ]
     },
     {
@@ -850,13 +946,13 @@
        "25"
       ]
      },
-     "execution_count": 123,
+     "execution_count": 81,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -868,7 +964,7 @@
     }
    ],
    "source": [
-    "estrella2 = Grayscale[542:565,650:675]\n",
+    "estrella2 = Grayscale[545:562,650:675]\n",
     "plt.imshow(estrella2, cmap=cm.gray, vmax=np.max(estrella2))\n",
     "print(estrella2.shape)\n",
     "estrella2.shape[1]"
@@ -876,31 +972,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 126,
+   "execution_count": 92,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22]\n",
-      "(23,)\n",
-      "(23,)\n"
+      "[ 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"
      ]
     },
     {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f8409d60a20>]"
-      ]
-     },
-     "execution_count": 126,
-     "metadata": {},
-     "output_type": "execute_result"
+     "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": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -918,17 +1020,64 @@
     "y = np.arange(0, estrella2.shape[1], 1)\n",
     "print(x)\n",
     "#print(y)\n",
+    "#x_prueba = np.arange(0,20,0.1)\n",
     "\n",
-    "lumx = estrella2[:,0]\n",
-    "print(lum.shape)\n",
+    "lumx = estrella2[:,17]/100\n",
+    "print(lumx.shape)\n",
     "print(x.shape)\n",
     "\n",
-    "plt.plot(x,lumx,'o',color=\"darkorange\")"
+    "p1 = [1,2,0]\n",
+    "best,suss = leastsq(Error_min_cuadra_C, p1, args=(x,lumx))\n",
+    "print(best)\n",
+    "\n",
+    "best_prueba = [0.96732372,9.79457162]\n",
+    "lum_model_prueba = func_gauss(best_prueba,x_prueba)\n",
+    "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",
+    "\n",
+    "#plt.plot(x,ymodel - y_ruido,'--r')\n",
+    "#plt.axhline(y=0,color=\"gray\")\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[  176.29102851 -1549.37095867   194.23196889]\n",
+      "[-5421.87983623  1516.82161716   194.23200076]\n",
+      "[ 3.57723658e+05 -3.98651738e+04  1.94231883e+02]\n",
+      "[ 2860.79195657 17491.05553753   194.23188406]\n"
+     ]
+    }
+   ],
+   "source": [
+    "p1 = [1,1,10]\n",
+    "best,suss = leastsq(Error_min_cuadra_C, p1, args=(x,lumx))\n",
+    "print(best)\n",
+    "\n",
+    "p1 = [1,10,10]\n",
+    "best,suss = leastsq(Error_min_cuadra_C, p1, args=(x,lumx))\n",
+    "print(best)\n",
+    "p1 = [10,10,10]\n",
+    "best,suss = leastsq(Error_min_cuadra_C, p1, args=(x,lumx))\n",
+    "print(best)\n",
+    "\n",
+    "p1 = [3,5,80]\n",
+    "best,suss = leastsq(Error_min_cuadra_C, p1, args=(x,lumx))\n",
+    "print(best)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 129,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [
     {
@@ -936,23 +1085,23 @@
      "output_type": "stream",
      "text": [
       "[ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22]\n",
-      "(23,)\n",
+      "(25,)\n",
       "(23,)\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f8409825828>]"
+       "[<matplotlib.lines.Line2D at 0x7f5accd14ba8>]"
       ]
      },
-     "execution_count": 129,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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khZ31huPAceDOqtqXZALYm+TRZt2fVdWXT7PPs1W1cbmKlCQtzVl79lV1pKr2NcvHgHlg7aAL68n8LMxMwb3ndB7nZ0ddkSStSH2N2SeZAq4B9jRNn03y/ST3J3lv16aXJfleku8m2bRMtZ5sfhYe2QrHngOq8/jIVgNfkk6j57BPcgGwE9hWVW8AXwXeD2wEjgD3NpseAdZX1TXAvwP+Msl7TvN6W5PMJZk7evRo/5XvvhuOv3ly2/E3O+2j5hmHpBWmp7BPch6doJ+tql0AVfVSVb1dVe8AXweua9rfqqpXm+W9wLPAlae+ZlXNVNV0VU1PTk72X/mx5/trHxbPOCStQL1cjRNgBzBfVfd1tV/ctdnHgKea9skk5zbLlwNXAD9azqIBmFjfX/uwrOQzDkmt1cvVODcAtwJPJtnftN0F3JJkI1DAIeBTzboPAV9M8nPgHeDTVfXaMtbcsWl7p8fcHaxrzu+0j9JKPeOQ1GpnDfuqehzIaVY9fIbtd9IZ8hmsDVs6j7vv7gTpxPpO0J9oH5WJ9c0QzmnaJWlEeunZr1wbtow+3E+1Us84JLWa0yUstw1bYPMMTFwKpPO4eWbl/VGS1Cqru2e/Uq3EMw5JrWbPXpJawLCXpBYw7CWpBQz7s3HqA0ljwA9oF3Ji6oMTl1GemPoA/ABW0qpiz34hTn0gaUwY9gtx6gNJY8KwX8hKnWxNkvpk2C9k0/bOVAfdnPpA0ipk2C/EqQ8kjQmvxjkbpz6QNAbs2UtSCxj2ktQChr0ktYBhL0kt0MsNx9cleSzJwSQHktzRtH8hyYtJ9jc/N3bt8/kkzyT5YZKPDvIAJEln10vP/jhwZ1VdBVwP3J7kqmbdn1XVxubnYYBm3c3A1cDvAf81ybkDqL1/TmomaRAWky1DzqNebjh+BDjSLB9LMg+sXWCXm4BvVdVbwI+TPANcB/yvZah38ZzUTNIgLCZbRpBHfY3ZJ5kCrgH2NE2fTfL9JPcneW/TthZ4oWu3wyz8x2E4nNRM0iAsJltGkEc9h32SC4CdwLaqegP4KvB+YCOdnv+9/fziJFuTzCWZO3r0aD+7Lo6TmkkahMVkywjyqKewT3IenaCfrapdAFX1UlW9XVXvAF+nM1QD8CKwrmv3S5q2k1TVTFVNV9X05OTkUo6hN05qJmkQFpMtI8ijXq7GCbADmK+q+7raL+7a7GPAU83yQ8DNSd6d5DLgCuCJ5St5kZzUTNIgLCZbRpBHvcyNcwNwK/Bkkv1N213ALUk2AgUcAj4FUFUHkjwAHKRzJc/tVfX28pa9CCc+9Nh9d+dUaWJ95z+sH85KWorFZMsI8ihVNbAX79X09HTNzc2NugxJWlWS7K2q6V629Ru0ktQChr0ktYBhL0ktYNhLUgsY9pLUAoa9JJ1qDCdN9B60ktRtTCdNtGcvSd3GdNJEw16Suo3ppImGvSR1G9NJEw17Seo2ppMmGvaS1G3DFtg8AxOXAuk8bp5Z1R/OglfjSNL/b8OWVR/up7JnL0ktYNhLUgsY9pLUAoa9JLWAYS9JLbAibkuY5Cjw3BJe4kLglWUqZ7Xx2Nurzcff5mOHXx7/pVU12csOKyLslyrJXK/3YRw3Hns7jx3affxtPnZY3PE7jCNJLWDYS1ILjEvYz4y6gBHy2Nurzcff5mOHRRz/WIzZS5IWNi49e0nSAlZ12Cf5vSQ/TPJMkv846nqGLcmhJE8m2Z9kbtT1DFKS+5O8nOSprrb3JXk0ydPN43tHWeMgneH4v5Dkxeb935/kxlHWOChJ1iV5LMnBJAeS3NG0j/37v8Cx9/3er9phnCTnAv8H+OfAYeDvgFuq6uBICxuiJIeA6aoa++uNk3wI+HvgG1X1T5q2e4DXqupLzR/791bVfxhlnYNyhuP/AvD3VfXlUdY2aEkuBi6uqn1JJoC9wL8EPsGYv/8LHPvH6fO9X809++uAZ6rqR1X1M+BbwE0jrkkDUlX/E3jtlOabgL9olv+Czj+CsXSG42+FqjpSVfua5WPAPLCWFrz/Cxx731Zz2K8FXuh6fphF/kdYxQp4JMneJFtHXcwIXFRVR5rl/wtcNMpiRuSzSb7fDPOM3TDGqZJMAdcAe2jZ+3/KsUOf7/1qDnvBb1XVPwV+H7i9OdVvpeqMR67OMcnF+yrwfmAjcAS4d6TVDFiSC4CdwLaqeqN73bi//6c59r7f+9Uc9i8C67qeX9K0tUZVvdg8vgz8dzpDW23yUjOmeWJs8+UR1zNUVfVSVb1dVe8AX2eM3/8k59EJu9mq2tU0t+L9P92xL+a9X81h/3fAFUkuS/Iu4GbgoRHXNDRJfqX5wIYkvwJsBp5aeK+x8xBwW7N8G/BXI6xl6E4EXeNjjOn7nyTADmC+qu7rWjX27/+Zjn0x7/2qvRoHoLnc6D8D5wL3V9Xqvv17H5JcTqc3D517Cf/lOB9/km8CH6Yz299LwH8CHgQeANbTmTX141U1lh9inuH4P0znNL6AQ8Cnusawx0aS3wJ2A08C7zTNd9EZux7r93+BY7+FPt/7VR32kqTerOZhHElSjwx7SWoBw16SWsCwl6QWMOwlqQUMe0lqAcNeklrAsJekFvh/iYyPNw/47QQAAAAASUVORK5CYII=\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -971,8 +1120,8 @@
     "print(x)\n",
     "#print(y)\n",
     "\n",
-    "lumy = estrella2[0,:]\n",
-    "print(lum.shape)\n",
+    "lumy = estrella2[6,:]\n",
+    "print(lumy.shape)\n",
     "print(x.shape)\n",
     "\n",
     "plt.plot(y,lumy,'o',color=\"darkorange\")"