diff --git a/Entrega.ipynb b/Entrega.ipynb
index 3a61447c43b6b3e9489b2fcd489ad06e4b6f7245..3df5bf4dabae59e0a152090507120d8751f040b1 100644
--- a/Entrega.ipynb
+++ b/Entrega.ipynb
@@ -66,7 +66,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from Clase_estrella import *"
+    "from Clase_estrella import *\n",
+    "from Clase_estrella_mod import *"
    ]
   },
   {
@@ -113,7 +114,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7fc7c5f59898>"
+       "<matplotlib.image.AxesImage at 0x7f219d239a20>"
       ]
      },
      "execution_count": 4,
@@ -312,7 +313,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7fc7c5e86d30>"
+       "<matplotlib.image.AxesImage at 0x7f219cf6d470>"
       ]
      },
      "execution_count": 8,
@@ -936,7 +937,7 @@
     "    for i in range(0,len(clase_estrella_todas)):\n",
     "        uno, dos = clase_estrella_todas[i].ajusteGauss(p1)\n",
     "\n",
-    "        parametros_todas.append(dos)\n",
+    "        parametros_todas.append(dos[2])\n",
     "\n",
     "\n",
     "    print('En total se estan analizando '+str(len(clase_estrella_todas))+' estrellas')\n",
@@ -1000,15 +1001,15 @@
      "text": [
       "En total se estan analizando 33 estrellas\n",
       "  \n",
-      "Mediana:  1.4018568345763582\n",
-      "Media:  -60.02889934017261\n",
-      "Desviacion estandar 1:  257.23862885662066\n",
-      "Desviacion estandar 2:  256.4579331901813\n"
+      "Mediana:  3.6431319397092845\n",
+      "Media:  10.582431466176326\n",
+      "Desviacion estandar 1:  35.16548117352378\n",
+      "Desviacion estandar 2:  34.62857206923758\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 648x288 with 1 Axes>"
       ]
@@ -1057,15 +1058,15 @@
      "text": [
       "En total se estan analizando 45 estrellas\n",
       "  \n",
-      "Mediana:  1.46501995989867\n",
-      "Media:  -24.20043926725717\n",
-      "Desviacion estandar 1:  159.60019232223172\n",
-      "Desviacion estandar 2:  159.24513027547198\n"
+      "Mediana:  2.8120597260178433\n",
+      "Media:  7.613224897789264\n",
+      "Desviacion estandar 1:  20.98882031359488\n",
+      "Desviacion estandar 2:  20.754300994080676\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 648x288 with 1 Axes>"
       ]
@@ -1098,15 +1099,15 @@
      "text": [
       "En total se estan analizando 32 estrellas\n",
       "  \n",
-      "Mediana:  1.4056342445779744\n",
-      "Media:  -124.0466362617896\n",
-      "Desviacion estandar 1:  654.9121084711437\n",
-      "Desviacion estandar 2:  652.8623002867687\n"
+      "Mediana:  6.049476951128224\n",
+      "Media:  11.492052569549994\n",
+      "Desviacion estandar 1:  38.2468281989113\n",
+      "Desviacion estandar 2:  37.64447829927557\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 648x288 with 1 Axes>"
       ]
@@ -1139,15 +1140,15 @@
      "text": [
       "En total se estan analizando 45 estrellas\n",
       "  \n",
-      "Mediana:  1.3063649806304618\n",
-      "Media:  -4233247.385112735\n",
-      "Desviacion estandar 1:  45270472.137159556\n",
-      "Desviacion estandar 2:  45169759.059932515\n"
+      "Mediana:  6.294989035387612\n",
+      "Media:  23.880080331840503\n",
+      "Desviacion estandar 1:  46.0442986200702\n",
+      "Desviacion estandar 2:  45.52982103540607\n"
      ]
     },
     {
      "data": {
-      "image/png": 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5yTmT5382yVFJdi+3T59FAgCHmap6f5JfzNKZiO8meUOSjyd5e5KTkxyZ5APd/UdVdUaSdybZnKULPl/T3Vcu+xoCAwAYzVskAMBwAgMAGE5gAADDCQwAYDiBAQAMJzAAgOEEBgAw3P8Dp59v2sRLpm4AAAAASUVORK5CYII=\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 648x288 with 1 Axes>"
       ]
@@ -1169,15 +1170,145 @@
     "Se puede observar que el azul es el que tiene más dispersión de datos, seguido del verde y luego el rojo. Sin embargo, la dispersión presentada por la imagen completa, no es tan grande como se ve con el azul, ni tan pequeña como se ve con el rojo."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Considerando la incertidumbre**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Se hace el análisis considerando la incertidumbre. Se modifica la función error, cada uno de los errores se pesa con $1/\\sqrt(intensidad)$. Por efectos de practicidad se creó una nueva clase con esta modificación y se modifica la siguiente función."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
+   "source": [
+    "def analisis2(imagen_grisss):\n",
+    "\n",
+    "    #Haciendo el análisis para toda la imagen\n",
+    "\n",
+    "    estrellas_para_estadistica = estrellas_recortadas(imagen_grisss, 250)\n",
+    "\n",
+    "    #Conversion a la clase estrella\n",
+    "\n",
+    "    clase_estrella_todas=[]\n",
+    "\n",
+    "\n",
+    "    for item in estrellas_para_estadistica:\n",
+    "        clase_estrella_todas.append(Estrella_a_mod(item))\n",
+    "\n",
+    "    #Recoleccion de parámetros\n",
+    "\n",
+    "    p1=np.array([1,0,1,5,5])   #Para recordar: p = [a, b, c, x0, y0]\n",
+    "\n",
+    "    parametros_todas=[]\n",
+    "\n",
+    "    for i in range(0,len(clase_estrella_todas)):\n",
+    "        uno, dos = clase_estrella_todas[i].ajusteGauss_mod(p1)\n",
+    "\n",
+    "        parametros_todas.append(dos)\n",
+    "\n",
+    "\n",
+    "    print('En total se estan analizando '+str(len(clase_estrella_todas))+' estrellas')\n",
+    "\n",
+    "    #Para encontrar la mediana, media, moda y desviacion estandar del ajuste hecho se hace lo siguiente:\n",
+    "\n",
+    "    evaluar=np.array(parametros_todas)\n",
+    "\n",
+    "\n",
+    "    FWHM=evaluar*2*math.sqrt(2*math.log(2))\n",
+    "\n",
+    "    zz=FWHM\n",
+    "\n",
+    "    #Media\n",
+    "    media=np.mean(zz)\n",
+    "\n",
+    "    #Mediana\n",
+    "    mediana=np.median(zz)\n",
+    "\n",
+    "    #Moda\n",
+    "    #moda=stat.mode(np.round(zz,3).reshape(-1))\n",
+    "    \n",
+    "    #moda=max(set(np.round(zz,2)), key=list(np.round(zz,2)).count)\n",
+    "\n",
+    "    #Desviacion estandar con scipy\n",
+    "    desviacion=stat.stdev(zz.reshape(-1))\n",
+    "\n",
+    "    #Desviacion estandar con numpy\n",
+    "    desviacion1=zz.std()\n",
+    "\n",
+    "    print('  ')\n",
+    "    print('Mediana: ', mediana)\n",
+    "    print('Media: ', media)\n",
+    "    #print('Moda: ', moda)\n",
+    "    print('Desviacion estandar 1: ', desviacion)\n",
+    "    print('Desviacion estandar 2: ', desviacion1)\n",
+    "\n",
+    "    #Histograma\n",
+    "\n",
+    "    plt.figure(figsize=(9,4))\n",
+    "    plt.hist(zz, bins=20, histtype='bar', alpha=0.7, edgecolor = 'black', linewidth=0.2)\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#FALTA CORREGIR ESTA FUNCIÓN\n",
+    "\n",
+    "#analisis2(imagen_grisss)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[NbConvertApp] Converting notebook Entrega.ipynb to markdown\n",
+      "[NbConvertApp] Support files will be in Entrega_files/\n",
+      "[NbConvertApp] Making directory Entrega_files\n",
+      "[NbConvertApp] Making directory Entrega_files\n",
+      "[NbConvertApp] Making directory Entrega_files\n",
+      "[NbConvertApp] Making directory Entrega_files\n",
+      "[NbConvertApp] Making directory Entrega_files\n",
+      "[NbConvertApp] Making directory Entrega_files\n",
+      "[NbConvertApp] Making directory Entrega_files\n",
+      "[NbConvertApp] Making directory Entrega_files\n",
+      "[NbConvertApp] Making directory Entrega_files\n",
+      "[NbConvertApp] Making directory Entrega_files\n",
+      "[NbConvertApp] Making directory Entrega_files\n",
+      "[NbConvertApp] Making directory Entrega_files\n",
+      "[NbConvertApp] Making directory Entrega_files\n",
+      "[NbConvertApp] Making directory Entrega_files\n",
+      "[NbConvertApp] Writing 19233 bytes to Entrega.md\n"
+     ]
+    }
+   ],
    "source": [
     "#Para guardar el notebook a .md\n",
     "! jupyter nbconvert --to markdown Entrega.ipynb"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/Entrega.md b/Entrega.md
index 9fef3624d1863c4870d5c06bddc3ec7a77e9feb1..16ee48cba81364915c9980fa168ad41d45ed35d0 100644
--- a/Entrega.md
+++ b/Entrega.md
@@ -27,6 +27,7 @@ _Por facilidad, se creó una clase para el posterior análisis de todas las estr
 
 ```python
 from Clase_estrella import *
+from Clase_estrella_mod import *
 ```
 
 ### Paso 1 - Leer la imagen almacenada 
@@ -57,7 +58,7 @@ plt.imshow(imagen)
 
 
 
-    <matplotlib.image.AxesImage at 0x7fc7c5f59898>
+    <matplotlib.image.AxesImage at 0x7f31bea10a20>
 
 
 
@@ -187,7 +188,7 @@ plt.imshow(imagen_grisss, cmap='gray')
 
 
 
-    <matplotlib.image.AxesImage at 0x7fc7c5e86d30>
+    <matplotlib.image.AxesImage at 0x7f31be7445c0>
 
 
 
@@ -747,8 +748,113 @@ analisis(B)
 
 Se puede observar que el azul es el que tiene más dispersión de datos, seguido del verde y luego el rojo. Sin embargo, la dispersión presentada por la imagen completa, no es tan grande como se ve con el azul, ni tan pequeña como se ve con el rojo.
 
+**Considerando la incertidumbre**
+
+Se hace el análisis considerando la incertidumbre. Se modifica la función error, cada uno de los errores se pesa con $1/\sqrt(intensidad)$. Por efectos de practicidad se creó una nueva clase con esta modificación y se modifica la siguiente función.
+
+
+```python
+def analisis2(imagen_grisss):
+
+    #Haciendo el análisis para toda la imagen
+
+    estrellas_para_estadistica = estrellas_recortadas(imagen_grisss, 250)
+
+    #Conversion a la clase estrella
+
+    clase_estrella_todas=[]
+
+
+    for item in estrellas_para_estadistica:
+        clase_estrella_todas.append(Estrella_a_mod(item))
+
+    #Recoleccion de parámetros
+
+    p1=np.array([1,0,1,5,5])   #Para recordar: p = [a, b, c, x0, y0]
+
+    parametros_todas=[]
+
+    for i in range(0,len(clase_estrella_todas)):
+        uno, dos = clase_estrella_todas[i].ajusteGauss_mod(p1)
+
+        parametros_todas.append(dos)
+
+
+    print('En total se estan analizando '+str(len(clase_estrella_todas))+' estrellas')
+
+    #Para encontrar la mediana, media, moda y desviacion estandar del ajuste hecho se hace lo siguiente:
+
+    evaluar=np.array(parametros_todas)
+
+
+    FWHM=evaluar*2*math.sqrt(2*math.log(2))
+
+    zz=FWHM
+
+    #Media
+    media=np.mean(zz)
+
+    #Mediana
+    mediana=np.median(zz)
+
+    #Moda
+    #moda=stat.mode(np.round(zz,3).reshape(-1))
+    
+    #moda=max(set(np.round(zz,2)), key=list(np.round(zz,2)).count)
+
+    #Desviacion estandar con scipy
+    desviacion=stat.stdev(zz.reshape(-1))
+
+    #Desviacion estandar con numpy
+    desviacion1=zz.std()
+
+    print('  ')
+    print('Mediana: ', mediana)
+    print('Media: ', media)
+    #print('Moda: ', moda)
+    print('Desviacion estandar 1: ', desviacion)
+    print('Desviacion estandar 2: ', desviacion1)
+
+    #Histograma
+
+    plt.figure(figsize=(9,4))
+    plt.hist(zz, bins=20, histtype='bar', alpha=0.7, edgecolor = 'black', linewidth=0.2)
+    plt.show()
+```
+
+
+```python
+#FALTA CORREGIR ESTA FUNCIÓN
+
+#analisis2(imagen_grisss)
+```
+
 
 ```python
 #Para guardar el notebook a .md
 ! jupyter nbconvert --to markdown Entrega.ipynb
+```
+
+    [NbConvertApp] Converting notebook Entrega.ipynb to markdown
+    [NbConvertApp] Support files will be in Entrega_files/
+    [NbConvertApp] Making directory Entrega_files
+    [NbConvertApp] Making directory Entrega_files
+    [NbConvertApp] Making directory Entrega_files
+    [NbConvertApp] Making directory Entrega_files
+    [NbConvertApp] Making directory Entrega_files
+    [NbConvertApp] Making directory Entrega_files
+    [NbConvertApp] Making directory Entrega_files
+    [NbConvertApp] Making directory Entrega_files
+    [NbConvertApp] Making directory Entrega_files
+    [NbConvertApp] Making directory Entrega_files
+    [NbConvertApp] Making directory Entrega_files
+    [NbConvertApp] Making directory Entrega_files
+    [NbConvertApp] Making directory Entrega_files
+    [NbConvertApp] Making directory Entrega_files
+    [NbConvertApp] Writing 19233 bytes to Entrega.md
+
+
+
+```python
+
 ```