diff --git a/ejercicio1-clase05.ipynb b/ejercicio1-clase05.ipynb
index 7694067ebd61dcc21bc2f711db57ab6994f0134e..75673dea7c822cb32a4976bc6ed9f1382d99e1df 100644
--- a/ejercicio1-clase05.ipynb
+++ b/ejercicio1-clase05.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 206,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -12,16 +12,17 @@
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 207,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "a=np.zeros([3,3])\n",
     "estrella=plt.imread('data/zapatocaImage.jpeg')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 208,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -30,16 +31,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 209,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f274ec1fd68>"
+       "<matplotlib.image.AxesImage at 0x7f274cd21c18>"
       ]
      },
-     "execution_count": 80,
+     "execution_count": 209,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -62,16 +63,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = a.astype(np.int32)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 210,
    "metadata": {},
    "outputs": [
     {
@@ -80,7 +72,7 @@
        "(789, 1184, 3)"
       ]
      },
-     "execution_count": 55,
+     "execution_count": 210,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -91,16 +83,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 142,
+   "execution_count": 211,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f274da29978>"
+       "<matplotlib.image.AxesImage at 0x7f274cc914e0>"
       ]
      },
-     "execution_count": 142,
+     "execution_count": 211,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -124,16 +116,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 141,
+   "execution_count": 212,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f274da45390>"
+       "<matplotlib.image.AxesImage at 0x7f274cbf3a20>"
       ]
      },
-     "execution_count": 141,
+     "execution_count": 212,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -157,16 +149,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 140,
+   "execution_count": 213,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f274dad4da0>"
+       "<matplotlib.image.AxesImage at 0x7f274cbe40b8>"
       ]
      },
-     "execution_count": 140,
+     "execution_count": 213,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -190,7 +182,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 195,
+   "execution_count": 214,
    "metadata": {},
    "outputs": [
     {
@@ -199,7 +191,7 @@
        "61.46883456650567"
       ]
      },
-     "execution_count": 195,
+     "execution_count": 214,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -211,16 +203,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 197,
+   "execution_count": 215,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f274cdd2cc0>"
+       "<matplotlib.image.AxesImage at 0x7f274cb4b588>"
       ]
      },
-     "execution_count": 197,
+     "execution_count": 215,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -240,117 +232,6 @@
    "source": [
     "plt.imshow(gray1,cmap='gray')"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 181,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "255.0"
-      ]
-     },
-     "execution_count": 181,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "gray1.max()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 186,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f274d4788d0>"
-      ]
-     },
-     "execution_count": 186,
-     "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": [
-    "d=np.array([[0,10],[256,0],[300,300],[500,500]])\n",
-    "plt.imshow(d,cmap='gray')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 187,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f274d25ef98>"
-      ]
-     },
-     "execution_count": 187,
-     "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": [
-    "plt.imshow(d/3,cmap='gray')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 189,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "AttributeError",
-     "evalue": "'numpy.ndarray' object has no attribute 'type'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-189-43b00e1cf40a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mR\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mG\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mB\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\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;31mAttributeError\u001b[0m: 'numpy.ndarray' object has no attribute 'type'"
-     ]
-    }
-   ],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {