From 5dceb75d6256bd4f77f5f441ba0c3859b7923161 Mon Sep 17 00:00:00 2001
From: Jennifer Lorena Ortega Aguilar <ortegaj@jupyterMiLAB>
Date: Wed, 17 Feb 2021 18:25:21 -0500
Subject: [PATCH] avance tarea

---
 ENTREGA.ipynb | 304 +++++++++++++++++++++++++++++++-------------------
 1 file changed, 188 insertions(+), 116 deletions(-)

diff --git a/ENTREGA.ipynb b/ENTREGA.ipynb
index 3e739c1..1fe1c15 100644
--- a/ENTREGA.ipynb
+++ b/ENTREGA.ipynb
@@ -193,26 +193,23 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[24.33333333 24.33333333 24.33333333 ... 43.33333333 55.\n",
-      "  62.        ]\n",
-      " [26.66666667 26.66666667 26.66666667 ... 45.66666667 53.\n",
-      "  57.66666667]\n",
-      " [29.         29.         29.         ... 48.         53.\n",
-      "  55.33333333]\n",
+      "255\n",
+      "[[0.07177974 0.07177974 0.07177974 ... 0.12782694 0.16224189 0.18289086]\n",
+      " [0.07866273 0.07866273 0.07866273 ... 0.13470993 0.15634218 0.17010816]\n",
+      " [0.08554572 0.08554572 0.08554572 ... 0.14159292 0.15634218 0.16322517]\n",
       " ...\n",
-      " [37.         34.66666667 34.66666667 ... 16.33333333 21.\n",
-      "  25.66666667]\n",
-      " [32.33333333 34.66666667 37.         ... 21.         21.\n",
-      "  22.        ]\n",
-      " [27.66666667 32.33333333 39.33333333 ... 26.66666667 24.33333333\n",
-      "  17.33333333]]\n",
+      " [0.10914454 0.10226155 0.10226155 ... 0.04818092 0.0619469  0.07571288]\n",
+      " [0.09537856 0.10226155 0.10914454 ... 0.0619469  0.0619469  0.06489676]\n",
+      " [0.08161259 0.09537856 0.11602753 ... 0.07866273 0.07177974 0.05113078]]\n",
       "(789, 1184)\n"
      ]
     }
    ],
    "source": [
-    "np.max(rgb)\n",
+    "print(np.max(rgb))\n",
     "Grayscale = (r + g + b / 3)\n",
+    "\n",
+    "Grayscale =Grayscale/np.max(Grayscale)\n",
     "print(Grayscale)\n",
     "print(Grayscale.shape)"
    ]
@@ -253,7 +250,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f5ad8353978>"
+       "<matplotlib.image.AxesImage at 0x7f89e25c9a58>"
       ]
      },
      "execution_count": 11,
@@ -281,6 +278,25 @@
     "plt.imshow(Grayscale, cmap=cm.gray, vmax=np.max(Grayscale))\n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.24338912304684565 0.39277252958736175\n"
+     ]
+    }
+   ],
+   "source": [
+    "std = np.std(Grayscale)\n",
+    "mean = np.mean(Grayscale)\n",
+    "print(std,mean)\n"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 12,
@@ -296,7 +312,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f5ad8287dd8>"
+       "<matplotlib.image.AxesImage at 0x7f89e24faef0>"
       ]
      },
      "execution_count": 12,
@@ -332,7 +348,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f5ad81fda90>"
+       "<matplotlib.image.AxesImage at 0x7f89e2474ba8>"
       ]
      },
      "execution_count": 13,
@@ -365,7 +381,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f5ad81ea6a0>"
+       "<matplotlib.image.AxesImage at 0x7f89e245f7b8>"
       ]
      },
      "execution_count": 14,
@@ -374,7 +390,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -398,7 +414,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f5ad6e76128>"
+       "<matplotlib.image.AxesImage at 0x7f89e10ec208>"
       ]
      },
      "execution_count": 15,
@@ -431,7 +447,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f5ad6e5b0f0>"
+       "<matplotlib.image.AxesImage at 0x7f89e10d1198>"
       ]
      },
      "execution_count": 16,
@@ -464,7 +480,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f5ad82c72b0>"
+       "<matplotlib.image.AxesImage at 0x7f89e24d20f0>"
       ]
      },
      "execution_count": 17,
@@ -497,7 +513,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f5ad6d9ad68>"
+       "<matplotlib.image.AxesImage at 0x7f89e100fef0>"
       ]
      },
      "execution_count": 18,
@@ -530,7 +546,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f5ad6d01be0>"
+       "<matplotlib.image.AxesImage at 0x7f89e0f75cc0>"
       ]
      },
      "execution_count": 19,
@@ -563,7 +579,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f5ad6c6ea90>"
+       "<matplotlib.image.AxesImage at 0x7f89e0ee4b00>"
       ]
      },
      "execution_count": 20,
@@ -596,7 +612,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f5ad6c5cbe0>"
+       "<matplotlib.image.AxesImage at 0x7f89e0ed0c88>"
       ]
      },
      "execution_count": 21,
@@ -605,7 +621,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -629,7 +645,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f5ad6bcbe10>"
+       "<matplotlib.image.AxesImage at 0x7f89e0e3ff60>"
       ]
      },
      "execution_count": 22,
@@ -662,7 +678,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f5ad6b3c6d8>"
+       "<matplotlib.image.AxesImage at 0x7f89e0db2748>"
       ]
      },
      "execution_count": 23,
@@ -758,30 +774,44 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 44,
    "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]\n",
-      "[0.52021778 0.1704145 ]\n"
+      "[ 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",
+      "[0.92070473 9.67429279]\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<matplotlib.lines.Line2D at 0x7f5acc90f358>"
+       "<matplotlib.lines.Line2D at 0x7f89d6e64e48>"
       ]
      },
-     "execution_count": 59,
+     "execution_count": 44,
      "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>"
       ]
@@ -793,18 +823,18 @@
     }
    ],
    "source": [
-    "x = np.arange(0,20,1)\n",
+    "x = np.arange(0,20,0.1)\n",
     "print(x)\n",
     "np.random.seed(1)\n",
-    "ruido = np.random.normal(0,0.3,20)\n",
+    "ruido = np.random.normal(0,0.5,200)\n",
     "#print(ruido)\n",
     "\n",
-    "params = [1,1] \n",
+    "params = [1,10] \n",
     "y = func_gauss(params,x)\n",
     "    \n",
     "y_ruido = y + ruido\n",
     "\n",
-    "p1 = [0.5,1]\n",
+    "p1 = [2,5]\n",
     "best,suss = leastsq(Error_min_cuadra, p1, args=(x,y_ruido))\n",
     "print(best)\n",
     "ymodel = func_gauss(best,x)\n",
@@ -816,7 +846,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
@@ -858,7 +888,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
@@ -877,16 +907,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.lines.Line2D at 0x7f5accc0b588>"
+       "<matplotlib.lines.Line2D at 0x7f89d721c588>"
       ]
      },
-     "execution_count": 54,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -913,7 +943,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -930,14 +960,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 109,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "(17, 25)\n"
+      "(16, 25)\n"
      ]
     },
     {
@@ -946,13 +976,13 @@
        "25"
       ]
      },
-     "execution_count": 81,
+     "execution_count": 109,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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ihVkeAPS2CzkSvlnStbNftH25pLdL2t/hNQFAY8wbwhFxj6Tztar/jGaeuMzj7gGgUNE5YdvXSToYEfP+udP2TtsjtkdKHtUDAL0sHcK2l0v6uKQ/vZDxEbErIoYjYnhoaCg7HQD0tJIj4SslbZZ0v+0nJG2QtMf2uk4uDACaIH2VekQ8IGnNix+3gng4Ip7u4LoAoBEu5BK12yR9V9I22wdsv2/hlwUAzTDvkXBE3DDP5zd1bDUA0DBd7R1hW0uXLl3weaamplLj+/ryp8ZL+iCMjo6ma7Zt25aueeaZZ1Ljs70GJGnFihXpmpJ79Evmyd7X39/fn56j5EqfiPzVnNk+IFK+F8SxY+e7AvWllfTbuPLKK9M1Jf0mst+bkq9xSe+UuXDbMgBURAgDQEWEMABURAgDQEWEMABURAgDQEWEMABURAgDQEWEMABURAgDQEWEMABURAgDQEVdbeATETp9+nQ3p7wgJQ1csk2CpLJGQY8//ni65oorrkiNn56eTs9RUlPSvGliYiJdk23gYzs9R0kznpKvWck82YZMDz30UHqOkq9ZSaOckuZC/99wJAwAFRHCAFDRhTxZY7ftMdt7Z73+Qds/sr3P9l8s3BIBoHddyJHwzZKuPfcF22+VdJ2kn4+IqyR9qvNLA4DeN28IR8Q9kmafHf8DSZ+MiBdaY8YWYG0A0PNKzwlvlfTLtu+1/e+2f2GugbZ32h6xPVLySBgA6GWlITwg6SJJ10j6Y0lf8RzXrETErogYjojhoaGhwukAoDeVhvABSbfHjP+WdFbSxZ1bFgA0Q2kIf13SWyXJ9lZJiyU93aE1AUBjzHvHnO3bJL1F0sW2D0j6hKTdkna3LlubknRjlNzaAwANN28IR8QNc3zqPR1eCwA0DnfMAUBF7uZZBNtHJf3kPJ+6WM0+p8z2s/1sf++7IiIumf1iV0N4LrZHImK49jpqYfvZfra/udvP6QgAqIgQBoCKXi4hvKv2Aipj+5uN7W+wl8U5YQBoqpfLkTAANBIhDAAVVQ9h29fafsj2o7Zvqr2ebrP9hO0HbN9ne6T2ehba+Z7UYvsi23fZfqT1dnXNNS6kObb/z2wfbO0D99l+Z801LiTbl9v+tu0HW0/l+VDr9cbsA7NVDWHb/ZI+J+kdkrZLusH29pprquStEfHahlwrebNmPalF0k2S7o6ILZLubn3cq27Wz26/JH2mtQ+8NiK+2eU1ddO0pI9ExHbNtMJ9f+tnvkn7wE+pfST8BkmPRsSPI2JK0pc189gk9Kg5ntRynaRbWu/fIund3VxTN82x/Y0REYciYk/r/ZOSRiWtV4P2gdlqh/B6SU+e8/GB1mtNEpK+Zfv7tnfWXkwlayPiUOv9w5LW1lxMJR+w/cPW6YpG/K+47U2SXifpXjV4H6gdwpDeFBGv18wpmffbfnPtBdXUaonatOsmPy/pSkmvlXRI0l9VXU0X2F4p6WuSPhwRJ879XNP2gdohfFDS5ed8vKH1WmNExMHW2zFJd2jmFE3THLF9qSS13jbqwbERcSQizkTEWUl/px7fB2wv0kwA3xoRt7debuw+UDuEvydpi+3NthdLul7SnZXX1DW2V9gefPF9SW+XtPelq3rSnZJubL1/o6RvVFxL170YPi2/qR7eB1rPovySpNGI+PQ5n2rsPlD9jrnW5TifldQvaXdE/HnVBXWR7Z/TzNGvNNNg/x97ffvPfVKLpCOaeVLL1yV9RdJGzbQ6/e2I6Mk/Xs2x/W/RzKmIkPSEpN8/5/xoT7H9Jkn/IekBzTybUpI+rpnzwo3YB2arHsIA0GS1T0cAQKMRwgBQESEMABURwgBQESEMABURwgBQESEMABX9D6+hlszIsBylAAAAAElFTkSuQmCC\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -964,7 +994,7 @@
     }
    ],
    "source": [
-    "estrella2 = Grayscale[545:562,650:675]\n",
+    "estrella2 = Grayscale[546:562,650:675]\n",
     "plt.imshow(estrella2, cmap=cm.gray, vmax=np.max(estrella2))\n",
     "print(estrella2.shape)\n",
     "estrella2.shape[1]"
@@ -972,7 +1002,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 96,
+   "execution_count": 62,
    "metadata": {},
    "outputs": [
     {
@@ -982,22 +1012,22 @@
       "[ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16]\n",
       "(17,)\n",
       "(17,)\n",
-      "[2.55545105 8.01323096 0.10128328]\n"
+      "[ 4.93765974e+00  8.06395245e+00 -6.62856580e-03]\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f5acb9b65c0>]"
+       "[<matplotlib.lines.Line2D at 0x7f89d6027f60>]"
       ]
      },
-     "execution_count": 96,
+     "execution_count": 62,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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b2MO9k3l2thkWWI5FLRrXqcrndyXw117N+GjVPq5/cwW709IvPtARlSDXr4fISFiypOzPER5HErpwvUuUvD16JoOx7/zO60t3cWOnRnwzsQctIi1ecPlSdu+GnJxyr1JUJcCPyYNb8+64eA6dvsCQ//3KNxsPFD6oHDVrSlS/vlkST26MejVJ6MK1LlHy9rddx7jm9V/ZsP8kLwxvx4sj2lO1igf0DFapAhMnQpcuFXr6lbGRzL+/F60b1GDSnI38a+4mLmTlmp0l1YApT22YevWgRg1J6F5ObooK15oRVWxBLR3ahNcu+4HXluwkOrw6b47pREt3b5U7gX3C1Js/J9MiojpTb+5EixPzLqpZU6HaMN26QWgo/Pij4wMXLlPaTVFpoQvXKnEhif28+uNOru/YkHkTe3heMj9yxPSjV1KAvx8PXR3DB7d15Xh6FkPe+JXPzvVGD5gBoU0BZR4rUugrJkZa6F7OAz7LCq9SQsnbQ7nhPD+8HSM6N3J6IS2nGDbMtH5/+MEhp+vdMpwFk3oxac5G/jl3Eys7tuHJcbuoFlSJX9lrr4WwMDNO3k/act5IXlXhWr2eMt0FBWQQhOr1NCPjG3tmMtfatHztU+wdJKJGMB/f2Y2/92/JNxsPMOR/vxY/CqasRoyAl16SZO7F5JUVrhU7hpwr3+KEf33ytOKEf30Y8DYNEu6wOrKKO3QITp+G2FiHn9rfTzGpfwtm3dmdUxeyGTF9JVsOnK74CTMyTKzCK0lCFy51PiuHO1a3pNPht5nRZSe1Jx0guN0tVodVOYmJ5rGMk4oqIiG6Lp/fnUBwoD83zVjFyuTj5T9JdjbUrAkvvuj4AIVbkIQuXObU+SzGvvM7v+xM4/kb23H3FdGe2cVS1LZt5tEJLfSCosOrM3dCAvVrBjPu/dUs3nq4fCcIDJQiXV5OErpwicOnMxj51kq2HDjDm2M6M7JLY6tDcpzeveGFF8xYbyerXzOEz+5KoHX9Gtz98To+W7u/fCeQkS5eTRK6KJtLzO4szZ5j5xg+fQUHT2Uw8/YuDIxzfuJzqfbt4R//cFmNlNrVqjDrzm70aB7GP+duYsby5LI/OSbGFBDLzXVegMIyZUroSqmBSqntSqldSqmHi9n/gFJqm1Jqk1JqiVKqqeNDFZa5xOzO0mw5cJoR01dwPiuX2X/tzuXRYc6P19V+/hmOHXPpJasFBfDuuC5c264+T89P4tkFSZRpkmCrVpCVBXv3Oj1G4XqXTOhKKX9gKjAIaA2MVkoVvfuzAYjXWrcD5gLPOzpQYaEKFodatfs4o2esIijAn8/vTqBto5pODNIix49D377wwQcuv3SVAD9eu6kjY7s3YfqyZB7+YjM5uZdYNKN3b3j5ZTNmXnidsrTQuwK7tNa7tdZZwBxgWMEDtNY/aa3tv/GrADcuiyfKrQLFoX7YdoS/vLeayJrBzJ3gBut9OosLRriUxt9PMWVYHPdf2YJP1+7n3k/Wk5FdSndKixYwMAK+7lqh7jPh3sqS0BsCBe+8pNq2leQOYEFxO5RS45VSa5VSa9PS0soepbBWOYtDfb52P3d/vI7Y+jX4/K4E6td08aLIruSiES6lUUrxwICW/GdIaxZtPcJt76/hbEYJZQgSZ8GcO2FH+bvPhPtz6E1RpdRYIB54obj9WusZWut4rXV8eHi4Iy8tnKmY2Z0EVDXbi3h7+W4emruJy6Pr8smd3ahdrYqLgrRIYqJZs9MNlne7rUczXh3VgTV7T3Dz279zPD3z4oN+mQwfZcCXBbaVt7a6cFtlSegHgIJjzBrZthWilOoPTAaGaq2LeScJjxU7xhSDKqU4lNaa5xYm8dT8RAa3rc874+IrV3fEU2zbZkaOuMl0+us6NuTtv8Sz8+hZRkxfyYFTFwofcDYFIoCjXLxdeLyy/MatAVoopZphEvlNwM0FD1BKdQTeAgZqrYu+VYQ3iB1TYnW/3DzN5K82M2fNfm7u1oQpw+Lw9/OCCUNl8eKLcOaM1VEU0jcmgo/v6MbtM9dw45sr+OiOrvkLhIQ2gfB9cA7zrxr524XHu2SzQmudA0wEFgGJwGda661Kqf8qpYbaDnsBqA58rpTaqJSa57SIhVvJyM7l3lnrmbNmPxP7Nuep63womQO0bQs9elgdxUXio+rw6V0J5GrNiLdWsiHlpNnR6ymoH2S+tje9Sug+E55HFrgQFZaemcP4D9eyIvk4jw6O5c5el1kdkmulpMDSpTB0KNSpY3U0xUo5fp6x7/7OsfRM3rqlM71ahMPiV+DqB2AE0L+pSeblra0uLCMLXAiHO56eyc1vr+L3PSd4aUR730vmAD/9BLfdBm48YqtJ3arMnZBA07rVuH3mGr7fdAj6T4JPP4VXUmH8XknmXkQSuii3A6cuMOKtlWw/fJa3xnbmxs4+Ou0gMdEUvIqOtjqSUkWEBjNnfHc6NK7FxNnr+Xj1fhg5EhqWNvpYeCJJ6KJcdh09y/BpK0g7k8lHd3Sjf+tIq0OyTmKiWdQiwP1H89QMCeTD27vRr1UEj369hY8/WIS2YHarcC5J6KLM1u07wYjpK8nO1cy5qztdm7lnv7HLbNtm6YSi8gqp4s/0WzpzQ8eGpL4/B3XrrWQdP2l1WMKBJKGLMvl0TQo3zVhFjZBA5t6dQJsGXliXpTwyMmD3bsum/FdUoL8fL45oT6t+XQF44rnPOVbcBCThkSShe7NKlLy1y8nN4/F5W/nXF5vp1qwu39zbg6iwapd+orcLCjJLz02caHUk5ebnp7h+7NUAqG1bGfbGb2w9KMvSeQNJ6N6qEiVv7U6ey2Lc+6uZuWIvt/doxszbulCrqpdP5S8rpSAiAjy1hEVUFISE8Lf6OeRpzfBpK1mw+ZDVUYlKkoTurSpY8tZux5GzDJv6G2v2nOT54e14bEhrAvzl7fKnL7+EZ54Bi+ZxVJqfH8TGEpayi28m9iC2figTZq3nlR92kJfnof8nIQnda1Wg5K3d4q2HuX7qb1zIzmX2+O6MjPei5eIcZc4ceO89l61S5BRz5sCcOUSEBjN7fHeGd27Ea0t2cu8n6zmflVO2czigW084jiR0b1XOkrdgCmz9b8lOxn+0juiI6syb2IPOTWs7KUAPt2WLx90QvUiLFlDbvL5BAf68MLwdjw6OZdHWw9w4bSWpJ8+X/nwHdOsJx5KE7q3KUfIW4HxWDhM/2cBLP+zgug4N+Mzb65hXRmamWZezbVurI6mcQ4fgscf+XKRDKcWdvS7jvVu7kHryPMPe+I01e0+U/PxKdusJx5OE7q3KUPLWLvXkeYZPW8n8LYd4ZFAMr4zqQHCgv+tj9hRJSWaRZU9P6FlZMGUKLF9eaHOfVhF8fW8PaoYEcvPbq5izupzdd1KK1zLuP8VNVFwpJW/tVu85wYSP15GVk8d747rQNybCRcF5sP37oUoVz0/oTZpAjRqwefNFu6LDq/PVPT2YOHs9D3+5maTDZ3l0cGzhG+OhTWzdLUVIKV7LSAvdh33yewo3v72KmiGBfHVvD0nmZXXttXDunFnYwpMpBXFxsGlTsbtrVg3k/Vu7cGfPZsxcsZdb31/DqfNZ+QeUs1tPOJ8kdB+UnZvH/329hX9/tZkezcP46t4eNI/w0kWcnSUgwG1WKaqUdu1MC72E4ZcB/n48em1rnh/ejtV7TnDd1N/YdfSs2VmObj3hGl7wjhTlceJcFre8+zsfrdrHXb3NDbCaIYFWh+VZbrwRPvzQ6igco21byMm5ZAngkfGNmT2+G+mZOVw3dQVLk46YHbFjTAneB/OkFK8bkITuQxIPnWHoG7+yPuUUr4xqzyPXxPrW6kKOcPKkmVR05IjVkTjGnXfC6dNm1usldG5ah28m9qRp3arc8cFapi9LxqoFckTxJKH7iIVbDnHjtBVk5+bx2V0JXN/RR2uYV5b9BqKn3xC1q1KlXF1HDWuF8PndCVwTV59nFyTxwGd/kJGd68QARXnIKBcvl5eneX3pTl79cScdGtdixi2diagRbHVYnsvbEjrAI4+YYmOPP16mw6tWCeCNmzsSszSUl37Ywe5j53h1VAeaSdE2y0kL3Yut23eC69/8jVd/3MmNnRoxZ3x3SeaVtXmzmV3ZoIHVkTjOpk2mG6kclFLcd2ULpo/tTPLRdK56ZRnPLEjkbEa2k4IUZSEtdC904NQFnluQxLw/DhJZI4iXR7bn+o4NUZ5cd8Rd1KgBV13l2TVcimrXDhYvNhONqpSvmubAuHp0alqLFxZu561lu/li3QH+ObAVwzs1wk/uz7icsuqmRnx8vF67dq0l1/ZW57NymP5zMm8t3w3AXb0v464roqkWJH+3RSlmz4abb4aNG6F9+wqf5o/9p3ji262sTzlF24Y1eXxoazo3LceqVomzTNmAsylmclKvp2TUTDGUUuu01vHF7ZPfdC+Ql6f5euMBnluYxJEzmQxp34CHB8XQsJbUYnEorb2rZW7XqZN5XL++Ugm9feNafDHhcr7ZeJBnFiRy47SVDOtg3ouXrAtkL/Rlrw1jL/QFktTLQfrQ3U05y5Gu23eS66et4IHP/iCyRjBfTEjgf6M7SjJ3hk8/hehoSPGyWiUtWphuFwdQSnFdx4YsfbAP9/VrzoIth+n34jL+t2Rn6aNhpNCXQ0gL3Z2Uo5Vy8NQFnrX1k0eEBvHSCNNPLv2WTrR+PaSmQv36VkfiWH5+8McfDj1ltaAAHryqFSPjG/PMgkRe+mEHc9bsZ/LgWAbF1bv4fo4U+nIISejupLRWii2hn8/KYfqy3cxYnozWcF+/5twt/eSusWGDqX0SKDNry6pxnaq8OaYzK5OP88S3W7ln1nq6X1aHx65tQ+sGNfIPlEJfDiFdLu6klFZKXp7mqw2p9HtxGa8v2Un/2EiWPHgFD17VSpK5K2htEnrHjlZH4hyLF0PDhrBrl1NOnxBdl+/u68mT18Wx/fBZrv3fL0z+ajMnztmKfUmhL4eQTOBOSmilZFZtyKhpK9i4/xTtGtXkjZs7Eh9VjtEDovJSU+H4cejQwepInCMsDA4eNH+0mjd3yiUC/P0Y270pQ9o14NUlO/hw5T6+/eMgf+vfklsSRhMIMsqlkqSF7k6KaaVkqWAeOjyKg6cu8OKI9nx9Tw9J5lbIy4Px46FXL6sjcY42bUwFyfXrnX6pmlUD+c+QNiyc1Iv2jWvx3++2MfDV5Szzv0oKfVWSjEN3N4mzyF3+b/zS93MwN5yXLoyjQfc7mNBH+smFk3XsCJGRsHChyy6ptWZJ4lGe/H4be4+f58qYCG7r0Yzul9UpvJiG+JOMQ/cAR89msGjrERZsjub3fdPIzdNc264+Dw+KoVHtqpc+gXCuAwfM6BZvqIFeko4d4fvvXTreXilF/9aR9GoZxszf9vLG0l0sSTpKnWpVuLpNJIPbNqhYcvfRSUqS0C10+HQGC7ccYv6Ww6zZewKt4bKwaky4IprB7eoTW7/GpU8inE9riI+HwYPhnXesjsZ5hgwxpQ2yskyxLhcKCvDnriuiGXd5FD9vT2P+5kN8s/Egs1fv/zO5X9O2PgmX1b10cvfhSUrS5eJiqSfPs3DLYeZvPsT6lFMAtIoMZVDbegyKq0/LyOpSc8XdpKZC48bw+utw331WR+MzMrJz/0zuSxKPcC4rt2zJfUZUCUMgm5q+eQ8nXS4W23f8HAu2HGbB5kP8kXoagDYNavCPq1oyMK6+LP/m7tasMY9dulgbhytkZ8OxY24xeSo40J+BcfUYGFevUHKfZ2u5164ayMC4ehcndx+epCQJ3UmS09JZsPkQ8zcfZtuhMwC0b1SThwfFMCiuHk3rSu1oj7F6tRkB4q1DFgvq29dMnPrpJ6sjKaRocl+2I43vNxWf3HuGNkYVl7x9YJKSJHQH0Vqz/chZFmw+zMIth9l+xCyk27lpbR4dHMvAuHpyc9NTrVljap0E+0At+Y4dYeZMyM0Ff3+roylWcKA/V7epx9Vt8pN7wZb76NDRPBHyGlV0Rv6TfGSSkiT0csrIzmXPsXMkp6WTfPQcu4+lk5yWzu60c5zPykUp6BJVh8eHtGZgXH3q1fSBJODtHnoIMjOtjsI1unaFN96ApCQzNt3NFZ/cGzB5Zx6TgmbSwO8YR3Q4c/3v4cj2djQ/sYfmEaE0j6hOZI0gr7tfJQm9GFprjqVnsTstneQ0W/K2/Us9eQH7fWSlzBqLY2r8yui606mZc5i86o3w7/0MxCZY+58QjnP11VZH4Dpdu5rH1as9IqEXVDi5t2Pl7n/w3eGz7Dqazq6j6SRvOMjZzJw/j68eFEB0eDWiI6rTPKI6zcPNY5M6VT12DLzPjnLJzdOkZ+SQlp55ceI+ms6ZjPwXPjjQj8vCqhMdUd28AcKrEx1enWZh1QhJnlN4iBSYj3dXzfD6IVI+YetWc5OwZ0+37YJwqLw8qFMHRo+GadOsjsahtNaknc00yT3NJPldtscjZ/I/gQX6K6LqVjNJPqL6n7/vYaFVqBEcSNUq/pa27Esb5VKmhK6UGgi8BvgD72itny2yvzfwKtAOuElrPfdS56xsQrcn5DMZ2Zy+kM2ZjGzOXMixPWZzJiPH9lh4+1nb9oJ/qe0iQoPMixeRn7SjI6pTv0ZwyWVpvXyIlM+791748EM4dco3EjrArFmmnku3blZH4jJnM7JJTjuX35q3Nez2nThPbl7hHBngp6gREkjNkEBqBAdQIyTQ/AsOpEZIgG27bb/tmJoFjqkSULnWf6WGLSql/IGpwAAgFVijlJqntd5W4LAU4FbgH5WKtAze/20PLy3eQXoxCbmo0OAA2w/Z/FAb16n65w/dvr121UAuC6/OZeHVqBFcgbKoPjxEyif8+iskJPhOMgcY43ufLEODA+nQuBYdGtcqtD0zJ5d9x8+zO+0cJ89n/dlIPH3BNBTtjcmDpy5w+oJpLGbl5pV6rZBAfx4f2ppRXRw/6qYsfehdgV1a690ASqk5wDDgz4Sutd5r21f6/8QBWkaGMjK+caGk/OdfyeBAk8RDAqkeFIC/KxZ7kDrO3uv0adi8GW680epIXCsz0wxbjI42qxn5sKAAf1pGhtIyMrTMz8nIzr0o8ed/bR6bR5T9fOVRloTeENhf4PtUoEKfxZRS44HxAE2aVCzh9WgeRo/mYRV6rlP0eqr4PnQfGCLl9VauNNP+e/SwOhLXysw0ZQ4eewz+8x+ro/E4wYH+BAf6E1HD9SPcXHorV2s9Q2sdr7WODw8Pd+WlnSd2jLkBGtoUUOZRboh6hxUrTFeLD/UlA6aeS7t28MsvVkciyqksLfQDQOMC3zeybRN2sWMkgXujyZPhhhugug+WZujZE95/35QCkCX3PEZZWuhrgBZKqWZKqSrATcA854YlhBsICvKN6f7F6dULzp1z+OLRwrkumdC11jnARGARkAh8prXeqpT6r1JqKIBSqotSKhUYAbyllNrqzKCFcLqtW80M0QM++mHUft/gt9+sjUOUi89OLBKiVC++mJ/QGzSwOhprbNkCsbG+NWTTA0j5XCHK68cfISbGd5M5QFyc1RGIcvLMggVCOFNWlhnh0b+/1ZFYKyUFJkww3U/CI0hCF6KoVavg/Hm48kqrI7FWYCBMnw7z51sdibUSZ5kSHy/5mcfEWVZHVCLfTege9CIJFzt0CCIioE8fqyOxVv36puLijz9aHYl17OuTnt0H6Pz1Sd00X/hmQvewF0m42KhRcPgw1KpldSTW698fli+HjIxLH+uNfplceBY4mO9/mWxNPJfgmwndw14k4UIFi90Lk9AzMsysWV/kYcX3fDOhe9iLJFzo++9NQart262OxD1ccQU0awYnTlgdiTVKKrLnpsX3fDOhe9iLJFzo229Nd0tUlNWRuIfQUNi9G4YPtzoSa/R6yhTbK8iNi+/5ZkL3sBdJuIjW8N13Zsm5oCCro3EvWpvhnL7G0cX3nDwYwzcnFtlfjF8mm26W0CYmmUuBLd+2fj0cPAhDhlgdiXs5dgzat4d//9us4ORrHFV8zz4Yw37/zj4Yw34NB/DNhA5SIVFc7Ntvzc3Qa66xOhL3EhZmKk7Om+ebCd1RShuM4aBc5JtdLkIUp2dPs6iDt9Tqd6Rhw8wqRmfOWB2J53LBYAzPSugyGUg4U//+8PjjVkfhnoYONbXRFy2yOhLP5YLBGJ6T0GUykHCmVatgxw6ro3BfCQmm6+XLL62OxHO5YDCG5/Shu6D/Sfiw++6DvDxYt87qSNyTv78pKdywodWReC4XDMbwnIQuk4GEs+zcCWvXmoQlSjZunNUReD4nD8bwnC4XmQwknGX2bDO6ZdQoqyNxf4mJ8O67VkchSuA5CV0mAwln0Nok9F69oFEjq6Nxfx99BHfdBUePWh2JKIbnJHRHz9gSAmDvXtizB8bI+6hMxoyB3FzzR1C4HVlTVIhjxyAkBKpVszoSz9C1K6Snm5WMpCqly5W2pqjntNCFcLS8PPMYFibJvDzuvdf0pf/0k9WRiCIkoQvf9corZnx1errVkXiWUaPM/QYpMex2PGfYohCOlJtr1suMjDR1SkTZBQebkrqBgVZHIoqQFrrwTXPnwq5dcP/9VkfimezJfO9eS8MQhUlCF74nLw+efBJiYuDGG62OxnM9/TS0bi1DGN2IJHThe779FrZsgcmTzZR2UTHDh0NmJrzwgtWRCBtJ6ML3XHUVvPMO3HST1ZF4tpYtzbj0qVPhyBGroxFIQhe+Rmsz5vyOOyBAxgRU2v/9nymrO3my1ZEIJKELX3LiBHTsCD/+aHUk3qNFC/j732H+fDh1yupofJ4kdOE7Jk+GzZshIsLqSLzLf/5jJhrVqmV1JD5PErrwDd9/b8adT5oE7dpZHY13qVYNatY0XS/y6cdSktCF9zt0CG691axc//TTVkfjvZ5/Hq6+Gn7+2epIfJYkdOH9Zs+Gc+fMY3Cw1dF4r0mTTJ/6zTdDaqrV0fgkSejC+/397/D77xAba3Uk3q16dfj8c1Mb5+qr4fhxqyPyOZLQhXfKzoZ//csUkFIK2ra1OiLf0LYtzJsHyclw++1WR+NzZCCu8D7p6TByJCxYAOHh0KqV1RH5lj594LPPoEEDqyPxOZLQhXdZs8bMXkxOhhkz4K9/tToi3zR0aP7Xjz4KcXEyM9cFpMtFeI+ffjL1zTMyzPA5SebWy8yEpUth9Gi4/nqpoe5kktCFZ0tOzh8ml5AADzwAf/wBfftaGpawCQqC5cvh2WfNH9k2beCWW2DfPqsj80plSuhKqYFKqe1KqV1KqYeL2R+klPrUtv93pVSUwyMVAiArC1auhGeegW7doHlzuPtuU6MlONiMha5d2+ooRUEBAeYGdXIy3HefubdhHz66YgUsXgxnzlgbo5e45CLRSil/YAcwAEgF1gCjtdbbChxzD9BOa323Uuom4Hqt9ajSziuLRIs/2d+DSplf7IMHIS0t/19KCkyZAn5+phvlnXfM8V26wA03wF/+IjfgPElWFlSpYr4eNAgWLjSvfVSUqeAYH2/q1QNs3Ghe9zp1TFG1kBDzx8DPdzsXSlskuiw3RbsCu7TWu20nmwMMA7YVOGYY8Ljt67nAG0oppS/116KiJkyAZcvM1/ZLNGtmCgQBjB0LRf9YxMWZVWoArrsOtm0rvL97d/jwQ/P1gAEXr8Ry5ZVm6rj92LS0wvuHDjVrVNqvdf58fmxgJls89ZRZXOGyyy7+P40fD//+t0locXEX73/gAfjb38ysx65dL97/2GMm2e3aBVdccfH+F14wMWzcCNdcY7YVjG/aNPNz+fVXU+e6qI8/hv79Tetq3LiL93/zjeny+PxzuOeei/cvXWqGtM2cCf/4h9mWlwcXLpg+7+Rk83N580145JHCzw0MNAsTN2hgPq4PGgQ9e0pNFk9lT+ZgRsOsWmU+dSUmwo4dsHp1/v477oD16ws/v3fv/N//bt3M76qfn/mj4OdnxsC/+67Z3727GQ9v368UDBliPskBdOpk3oMF3XSTqU+Tl1f87+Jtt8FDD8HZs+b6RU2caH4HDh+Gfv3yt3fpAh98UKYfUUWVJaE3BPYX+D4VKPq/+PMYrXWOUuo0UBc4VvAgpdR4YDxAkyZNKhgy0KRJ4R+0UoVbaM2bQ05O4ecUTKKxsVC1auH9LVvmf9227cXJokWL/K87d774I2LB/d27m1aIPbai+/v0uei/RHS0eQwMNImzKHv8wcGmnndRTZuax2rV8hN2QY0bm8dateDaa/O32+Nr2NA8hoWZxF5UZKR5bNCg+IQfFpYfx8iRF++3F26Kjs4f7aBUfqurRg2z7dprzTnCw805w8PNte2lbnv3vvjcwnOFhpoG1IABxe+fOtXMOj15Mv+Pf8Hf9YEDzYpJeXnmn9amxINdp05w+nT+fsj/XQCTRzIzC1/T/rtg319UvXrm0d+/+P323BEYWHh/VFTx/0cHKkuXy3BgoNb6Ttv3twDdtNYTCxyzxXZMqu37ZNsxx4o7J0iXixBCVERpXS5l6Yg6ABT4k0Yj27Zij1FKBQA1AZn3K4QQLlSWhL4GaKGUaqaUqgLcBMwrcsw8wN6xOhxY6rT+cyGEEMW6ZB+6rU98IrAI8Afe01pvVUr9F1irtZ4HvAt8pJTaBZzAJH0hhBAuVKap/1rr+cD8ItseK/B1BjDCsaEJIYQoD98dzCmEEF5GEroQQngJSehCCOElJKELIYSXuOTEIqddWKk0oKIl18IoMgvVTUhc5SNxlZ+7xiZxlU9l4mqqtQ4vbodlCb0ylFJrS5opZSWJq3wkrvJz19gkrvJxVlzS5SKEEF5CEroQQngJT03oM6wOoAQSV/lIXOXnrrFJXOXjlLg8sg9dCCHExTy1hS6EEKIISehCCOElPC6hX2rBaisopRorpX5SSm1TSm1VSk2yOqaClFL+SqkNSqnvrI7FTilVSyk1VymVpJRKVEolWB0TgFLq77bXcItSarZSKtiiON5TSh21LR5j31ZHKfWDUmqn7dHlq2GXENcLttdxk1LqK6VULXeIq8C+B5VSWikV5i5xKaXus/3MtiqlnnfU9TwqodsWrJ4KDAJaA6OVUq2tjQqAHOBBrXVroDtwr5vEZTcJSLQ6iCJeAxZqrWOA9rhBfEqphsD9QLzWOg5TLtqqUtAzgYFFtj0MLNFatwCW2L53tZlcHNcPQJzWuh1mQflHij7JBWZycVwopRoDVwEprg7IZiZF4lJK9cWsw9xea90GeNFRF/OohE6BBau11lmAfcFqS2mtD2mt19u+PotJTg1Lf5ZrKKUaAYOBd6yOxU4pVRPojamjj9Y6S2t9ytKg8gUAIbaVt6oCB60IQmu9HLO2QEHDAPsqwx8A17kyJig+Lq31Yq21fRHfVZhVzSyPy+YV4J+AJaM/SohrAvCs1jrTdsxRR13P0xJ6cQtWu0XitFNKRQEdgd8tDsXuVcwbOs/iOApqBqQB79u6gt5RSlWzOiit9QFMaykFOASc1lovtjaqQiK11odsXx8GIq0MpgS3AwusDgJAKTUMOKC1/sPqWIpoCfRSSv2ulFqmlOriqBN7WkJ3a0qp6sAXwN+01mfcIJ5rgaNa63VWx1JEANAJmKa17gicw5rug0JsfdLDMH9wGgDVlFJjrY2qeLYlHt1qzLFSajKm+3GWG8RSFfg38NiljrVAAFAH0z37EPCZUko54sSeltDLsmC1JZRSgZhkPktr/aXV8dj0AIYqpfZiuqf6KaU+tjYkwHyyStVa2z/FzMUkeKv1B/ZordO01tnAl8DlFsdU0BGlVH0A26PDPqpXllLqVuBaYIybrCccjfnD/Ift/d8IWK+UqmdpVEYq8KU2VmM+PTvkhq2nJfSyLFjtcra/ru8CiVrrl62Ox05r/YjWupHWOgrzs1qqtba8xam1PgzsV0q1sm26EthmYUh2KUB3pVRV22t6JW5ws7aAgouxjwO+sTCWPymlBmK69YZqrc9bHQ+A1nqz1jpCax1le/+nAp1s7z2rfQ30BVBKtQSq4KCKkB6V0G03XuwLVicCn2mtt1obFWBawrdgWsAbbf+usTooN3cfMEsptQnoADxtbThg+8QwF1gPbMb8flgydVwpNRtYCbRSSqUqpe4AngUGKKV2Yj5NPOsmcb0BhAI/2N77090kLsuVENd7wGW2oYxzgHGO+lQjU/+FEMJLeFQLXQghRMkkoQshhJeQhC6EEF5CEroQQngJSehCCOElJKELIYSXkIQuhBBe4v8BfiEiHIrL2GgAAAAASUVORK5CYII=\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1015,9 +1045,8 @@
     "y = np.arange(0, estrella2.shape[1], 1)\n",
     "print(x)\n",
     "#print(y)\n",
-    "x_prueba = np.arange(0,16,0.1)\n",
     "\n",
-    "lumx = estrella2[:,17]/1000\n",
+    "lumx = estrella2[:,17]/10\n",
     "print(lumx.shape)\n",
     "print(x.shape)\n",
     "\n",
@@ -1025,80 +1054,96 @@
     "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_prueba,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",
     "\n"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<img src=\"https://wikimedia.org/api/rest_v1/media/math/render/svg/0c3a59990abb6b35ad9e0e4c3edbe44462fc20c7\">\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 126,
    "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"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "p1 = [1,1,10]\n",
-    "best,suss = leastsq(Error_min_cuadra_C, p1, args=(x,lumx))\n",
-    "print(best)\n",
+    "def func_gauss_2D(params,x,y):\n",
+    "    #params[0]= A\n",
+    "    #params[1] = x0\n",
+    "    #params[2] = SigmaX\n",
+    "    #params[3] = y0\n",
+    "    #params[4] = SigmaY\n",
+    "    #numpy.pi = numero pi\n",
+    "    z = params[0]*(np.exp(-(((x-params[1])**2)/(2*params[2]**2))+(((y-params[3])**2)/(2*params[4]**2))))\n",
+    "    return z\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)"
+    "def Error_gauss_2D(tpl,x,y,z):\n",
+    "    return func_gauss_2D(tpl,x,y)-z"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 121,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = np.arange(0, estrella2.shape[0], 1)\n",
+    "y = np.arange(0, estrella2.shape[1], 1)\n",
+    "\n",
+    "#print(y)\n",
+    "#print(x)\n",
+    "\n",
+    "\n",
+    "X, Y = np.meshgrid(x, y)               \n",
+    "#print(X)\n",
+    "#print(\"-----------------\")\n",
+    "#print(Y)\n",
+    "#print(X.shape,Y.shape)\n",
+    "\n",
+    "#estrella2 = Grayscale[545:562,650:675]\n",
+    "\n",
+    "def lum_2D(estrella,x,y):\n",
+    "    lum_list = []\n",
+    "    x_list = []\n",
+    "    y_list = []\n",
+    "    for i in x:\n",
+    "        for j in y:\n",
+    "            lum = estrella[x[i],y[j]] \n",
+    "            lum_list.append(lum)\n",
+    "            x_list.append(x[i])\n",
+    "            y_list.append(y[j])\n",
+    "    return lum_list,x_list,y_list \n",
+    "\n",
+    "lum_grap, x_grap, y_grap = lum_2D(estrella2,x,y)\n",
+    "lum_array= np.array(lum_grap)\n",
+    "x_array= np.array(x_grap)\n",
+    "y_array= np.array(y_grap)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
    "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",
-      "(25,)\n",
-      "(23,)\n"
-     ]
-    },
     {
      "data": {
+      "image/png": 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\n",
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f5accd14ba8>]"
-      ]
-     },
-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 432x432 with 1 Axes>"
       ]
      },
      "metadata": {
@@ -1108,20 +1153,47 @@
     }
    ],
    "source": [
-    "import matplotlib.cm as cm\n",
-    "\n",
-    "x = np.arange(0, estrella2.shape[0], 1)\n",
-    "y = np.arange(0, estrella2.shape[1], 1)\n",
-    "print(x)\n",
-    "#print(y)\n",
+    "import matplotlib.pyplot as plt\n",
+    "from mpl_toolkits.mplot3d import Axes3D\n",
+    "import numpy as np\n",
     "\n",
-    "lumy = estrella2[6,:]\n",
-    "print(lumy.shape)\n",
-    "print(x.shape)\n",
     "\n",
-    "plt.plot(y,lumy,'o',color=\"darkorange\")"
+    "fig = plt.figure(figsize=(6, 6))\n",
+    "ax = fig.add_subplot(111, projection='3d')\n",
+    "ax.scatter(x_grap, y_grap,lum_grap,\n",
+    "           linewidths=1, alpha=.7,\n",
+    "           edgecolor='k',\n",
+    "           s = 5)\n",
+    "plt.show()\n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 0.60788423  7.07278913  9.82945886 12.49474136 15.61069467]\n"
+     ]
+    }
+   ],
+   "source": [
+    "p1 = [1,2,1,1,20]\n",
+    "best,suss = leastsq(Error_gauss_2D, p1, args=(x_array,y_array,lum_array))\n",
+    "print(best)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": null,
-- 
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