diff --git a/ejercicio1-clase05.ipynb b/ejercicio1-clase05.ipynb
index da8f9261709a6396b503da21d2d77517531136b4..0a0663339c78b2a98d5eff431fa3d9dfd2ce8962 100644
--- a/ejercicio1-clase05.ipynb
+++ b/ejercicio1-clase05.ipynb
@@ -2,18 +2,20 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
-    "import matplotlib"
+    "import matplotlib\n",
+    "import scipy\n",
+    "from scipy.optimize import leastsq"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -22,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -31,16 +33,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f60896ce630>"
+       "<matplotlib.image.AxesImage at 0x7f43afdc99e8>"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -63,7 +65,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -72,7 +74,7 @@
        "(789, 1184, 3)"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -83,16 +85,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f605232bba8>"
+       "<matplotlib.image.AxesImage at 0x7f43afd7de80>"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -116,16 +118,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f60522a76a0>"
+       "<matplotlib.image.AxesImage at 0x7f43afcfb908>"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -149,16 +151,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f605220dc88>"
+       "<matplotlib.image.AxesImage at 0x7f43afc5df98>"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -182,7 +184,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -191,7 +193,7 @@
        "61.46883456650567"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -203,16 +205,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f60521fd1d0>"
+       "<matplotlib.image.AxesImage at 0x7f43afbcf518>"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -235,7 +237,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -244,7 +246,7 @@
        "255.0"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -255,16 +257,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f604de11a90>"
+       "<matplotlib.image.AxesImage at 0x7f43afbb0dd8>"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -288,7 +290,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -297,16 +299,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f6052301a90>"
+       "<matplotlib.image.AxesImage at 0x7f43afd87c18>"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -329,22 +331,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f604d9a3b00>"
+       "<matplotlib.image.AxesImage at 0x7f43afadada0>"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 15,
      "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>"
       ]
@@ -356,17 +358,365 @@
     }
    ],
    "source": [
-    "star1=seccion[58:70,95:106]\n",
+    "star1=seccion[58:70,95:107]\n",
     "plt.imshow(star1,cmap='gray')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
+   "source": [
+    "def gauss2D(params,x, y):\n",
+    "    exponente = -((x-params[2])**2 + (y-params[3])**2) / (2*params[4]**2)\n",
+    "    z = (params[0]*np.exp(exponente)) + params[1]\n",
+    "    return z.ravel()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def error(tpl,x,y,z):\n",
+    "    return (gauss2D(tpl,x,y)-z).ravel()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "251.39999999999998"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.min(star1)\n",
+    "np.max(star1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(12, 12)"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.shape(star1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "starshape=np.reshape(star1,144)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "error() missing 1 required positional argument: 'z'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-26-ad918fbb0230>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merror\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mxx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0myy\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[0;31mTypeError\u001b[0m: error() missing 1 required positional argument: 'z'"
+     ]
+    }
+   ],
+   "source": [
+    "plt.plot(error(p0,xx,yy))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x=np.arange(0,12,1)\n",
+    "y=np.arange(0,12,1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "yy, xx = np.meshgrid(x,y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "Argument Z must be 2-dimensional.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-29-9632aa412321>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mfig2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\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      4\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfig2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgca\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprojection\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'3d'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot_surface\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0myy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mzz\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrstride\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcstride\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlinewidth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mantialiased\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"rainbow\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m~/.local/lib/python3.7/site-packages/mpl_toolkits/mplot3d/axes3d.py\u001b[0m in \u001b[0;36mplot_surface\u001b[0;34m(self, X, Y, Z, norm, vmin, vmax, lightsource, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1554\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1555\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mZ\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;32m-> 1556\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Argument Z must be 2-dimensional.\"\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   1557\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misnan\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mZ\u001b[0m\u001b[0;34m)\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   1558\u001b[0m             cbook._warn_external(\n",
+      "\u001b[0;31mValueError\u001b[0m: Argument Z must be 2-dimensional."
+     ]
+    },
+    {
+     "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": [
+    "p0=[180,3,6,6,2]\n",
+    "zz=gauss2D(p0,xx,yy)\n",
+    "fig2 = plt.figure()\n",
+    "ax = fig2.gca(projection='3d')\n",
+    "ax.plot_surface(xx,yy, zz, rstride=3, cstride=3, linewidth=1, antialiased=True,cmap=\"rainbow\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "operands could not be broadcast together with shapes (144,) (12,12) ",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-38-258b1b853cd4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merror\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mxx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0myy\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mstar1\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      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolorbar\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<ipython-input-22-d9489358e90e>\u001b[0m in \u001b[0;36merror\u001b[0;34m(tpl, x, y, z)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0merror\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtpl\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[0mz\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----> 2\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mgauss2D\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtpl\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[0mz\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\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[0;31mValueError\u001b[0m: operands could not be broadcast together with shapes (144,) (12,12) "
+     ]
+    }
+   ],
+   "source": [
+    "plt.imshow(error(p0,xx,yy,star1))\n",
+    "plt.colorbar()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[153.0473449  107.39805619   5.85363172   6.18735579   1.34690923]\n"
+     ]
+    }
+   ],
+   "source": [
+    "best,suss = leastsq(error, p0, args=(xx,yy,starshape))\n",
+    "print(best)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def gauss2D2D(params,x, y):\n",
+    "    exponente = -((x-params[2])**2 + (y-params[3])**2) / (2*params[4]**2)\n",
+    "    z = (params[0]*np.exp(exponente)) + params[1]\n",
+    "    return z"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<mpl_toolkits.mplot3d.art3d.Poly3DCollection at 0x7f43a7773eb8>"
+      ]
+     },
+     "execution_count": 33,
+     "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": [
+    "zz2=gauss2D2D(best,xx,yy)\n",
+    "fig2 = plt.figure()\n",
+    "ax = fig2.gca(projection='3d')\n",
+    "ax.plot_surface(xx,yy, zz2, rstride=3, cstride=3, linewidth=1, antialiased=True,cmap=\"rainbow\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<mpl_toolkits.mplot3d.art3d.Poly3DCollection at 0x7f43a76f9be0>"
+      ]
+     },
+     "execution_count": 34,
+     "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": [
+    "fig3 = plt.figure()\n",
+    "ax = fig3.gca(projection='3d')\n",
+    "ax.plot_surface(xx,yy, star1, rstride=3, cstride=3, linewidth=1, antialiased=True,cmap=\"rainbow\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "operands could not be broadcast together with shapes (144,) (12,12) ",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-37-f6da7a445d93>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merror\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbest\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mxx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0myy\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mstar1\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      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolorbar\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<ipython-input-22-d9489358e90e>\u001b[0m in \u001b[0;36merror\u001b[0;34m(tpl, x, y, z)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0merror\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtpl\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[0mz\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----> 2\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mgauss2D\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtpl\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[0mz\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\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[0;31mValueError\u001b[0m: operands could not be broadcast together with shapes (144,) (12,12) "
+     ]
+    }
+   ],
    "source": []
   },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def error2(tpl,x,y,z):\n",
+    "    return gauss2D2D(tpl,x,y)-z"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.colorbar.Colorbar at 0x7f43a7470208>"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.imshow(error2(best,xx,yy,star1))\n",
+    "plt.colorbar()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.8578851050203109e-09"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "error2(best,xx,yy,star1).mean()"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,