diff --git a/.ipynb_checkpoints/Ejercicios_clase_5-checkpoint.ipynb b/.ipynb_checkpoints/entrega-checkpoint.ipynb
similarity index 97%
rename from .ipynb_checkpoints/Ejercicios_clase_5-checkpoint.ipynb
rename to .ipynb_checkpoints/entrega-checkpoint.ipynb
index bae957d0c6b14ff0485b2f2fab035504d97ba138..ddea917ad65f6a21554d31a50993b4cb2d9ba64a 100644
--- a/.ipynb_checkpoints/Ejercicios_clase_5-checkpoint.ipynb
+++ b/.ipynb_checkpoints/entrega-checkpoint.ipynb
@@ -1011,7 +1011,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 103,
+   "execution_count": 145,
    "metadata": {
     "id": "-wDngj8nv0t7"
    },
@@ -1072,7 +1072,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 104,
+   "execution_count": 146,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -1114,7 +1114,17 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>90</th>\n",
+       "      <th>132</th>\n",
+       "      <td>22738.039821</td>\n",
+       "      <td>-239.832795</td>\n",
+       "      <td>3.010661</td>\n",
+       "      <td>75.973813</td>\n",
+       "      <td>2.731824</td>\n",
+       "      <td>178.538460</td>\n",
+       "      <td>6.419787</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>133</th>\n",
        "      <td>14409.841451</td>\n",
        "      <td>-222.262629</td>\n",
        "      <td>2.343065</td>\n",
@@ -1124,7 +1134,17 @@
        "      <td>5.848958</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>516</th>\n",
+       "      <th>578</th>\n",
+       "      <td>31219.909353</td>\n",
+       "      <td>105.300441</td>\n",
+       "      <td>2.585564</td>\n",
+       "      <td>31.073563</td>\n",
+       "      <td>4.308750</td>\n",
+       "      <td>73.022872</td>\n",
+       "      <td>10.125563</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>687</th>\n",
        "      <td>267271.868327</td>\n",
        "      <td>115.166949</td>\n",
        "      <td>2.769692</td>\n",
@@ -1134,34 +1154,14 @@
        "      <td>5.758053</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>477</th>\n",
-       "      <td>107493.971955</td>\n",
-       "      <td>96.620752</td>\n",
-       "      <td>3.101467</td>\n",
-       "      <td>26.096038</td>\n",
-       "      <td>3.044724</td>\n",
-       "      <td>61.325689</td>\n",
-       "      <td>7.155100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>288</th>\n",
-       "      <td>169.559297</td>\n",
-       "      <td>-16.954423</td>\n",
-       "      <td>2.975433</td>\n",
-       "      <td>25.863356</td>\n",
-       "      <td>3.902506</td>\n",
-       "      <td>60.778886</td>\n",
-       "      <td>9.170890</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>441</th>\n",
-       "      <td>21979.573625</td>\n",
-       "      <td>66.797420</td>\n",
-       "      <td>2.603415</td>\n",
-       "      <td>20.616066</td>\n",
-       "      <td>2.744205</td>\n",
-       "      <td>48.447755</td>\n",
-       "      <td>6.448882</td>\n",
+       "      <th>600</th>\n",
+       "      <td>325077.772735</td>\n",
+       "      <td>112.668325</td>\n",
+       "      <td>120.781525</td>\n",
+       "      <td>28.345852</td>\n",
+       "      <td>191.375356</td>\n",
+       "      <td>66.612752</td>\n",
+       "      <td>449.732087</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -1174,7 +1174,7 @@
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>21</th>\n",
+       "      <th>27</th>\n",
        "      <td>202.186462</td>\n",
        "      <td>2.289328</td>\n",
        "      <td>2.644426</td>\n",
@@ -1184,7 +1184,7 @@
        "      <td>4.316290</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>29</th>\n",
+       "      <th>37</th>\n",
        "      <td>210.588381</td>\n",
        "      <td>1.978485</td>\n",
        "      <td>3.167382</td>\n",
@@ -1194,7 +1194,7 @@
        "      <td>4.304869</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>30</th>\n",
+       "      <th>38</th>\n",
        "      <td>217.389734</td>\n",
        "      <td>1.980785</td>\n",
        "      <td>2.331454</td>\n",
@@ -1204,7 +1204,7 @@
        "      <td>4.014588</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>26</th>\n",
+       "      <th>33</th>\n",
        "      <td>216.603135</td>\n",
        "      <td>2.821754</td>\n",
        "      <td>3.173432</td>\n",
@@ -1214,7 +1214,7 @@
        "      <td>4.278035</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>27</th>\n",
+       "      <th>34</th>\n",
        "      <td>222.917653</td>\n",
        "      <td>2.823663</td>\n",
        "      <td>2.336885</td>\n",
@@ -1225,40 +1225,40 @@
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>524 rows × 7 columns</p>\n",
+       "<p>695 rows × 7 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "            height      mean_x    mean_y      std_x     std_y      FWHM_x  \\\n",
-       "90    14409.841451 -222.262629  2.343065  74.058552  2.488918  174.037597   \n",
-       "516  267271.868327  115.166949  2.769692  29.110955  2.450235   68.410745   \n",
-       "477  107493.971955   96.620752  3.101467  26.096038  3.044724   61.325689   \n",
-       "288     169.559297  -16.954423  2.975433  25.863356  3.902506   60.778886   \n",
-       "441   21979.573625   66.797420  2.603415  20.616066  2.744205   48.447755   \n",
-       "..             ...         ...       ...        ...       ...         ...   \n",
-       "21      202.186462    2.289328  2.644426   2.005263  1.836719    4.712369   \n",
-       "29      210.588381    1.978485  3.167382   1.943865  1.831859    4.568083   \n",
-       "30      217.389734    1.980785  2.331454   1.931921  1.708335    4.540015   \n",
-       "26      216.603135    2.821754  3.173432   1.804289  1.820440    4.240079   \n",
-       "27      222.917653    2.823663  2.336885   1.803631  1.702241    4.238533   \n",
+       "            height      mean_x      mean_y      std_x       std_y      FWHM_x  \\\n",
+       "132   22738.039821 -239.832795    3.010661  75.973813    2.731824  178.538460   \n",
+       "133   14409.841451 -222.262629    2.343065  74.058552    2.488918  174.037597   \n",
+       "578   31219.909353  105.300441    2.585564  31.073563    4.308750   73.022872   \n",
+       "687  267271.868327  115.166949    2.769692  29.110955    2.450235   68.410745   \n",
+       "600  325077.772735  112.668325  120.781525  28.345852  191.375356   66.612752   \n",
+       "..             ...         ...         ...        ...         ...         ...   \n",
+       "27      202.186462    2.289328    2.644426   2.005263    1.836719    4.712369   \n",
+       "37      210.588381    1.978485    3.167382   1.943865    1.831859    4.568083   \n",
+       "38      217.389734    1.980785    2.331454   1.931921    1.708335    4.540015   \n",
+       "33      216.603135    2.821754    3.173432   1.804289    1.820440    4.240079   \n",
+       "34      222.917653    2.823663    2.336885   1.803631    1.702241    4.238533   \n",
        "\n",
-       "       FWHM_y  \n",
-       "90   5.848958  \n",
-       "516  5.758053  \n",
-       "477  7.155100  \n",
-       "288  9.170890  \n",
-       "441  6.448882  \n",
-       "..        ...  \n",
-       "21   4.316290  \n",
-       "29   4.304869  \n",
-       "30   4.014588  \n",
-       "26   4.278035  \n",
-       "27   4.000267  \n",
+       "         FWHM_y  \n",
+       "132    6.419787  \n",
+       "133    5.848958  \n",
+       "578   10.125563  \n",
+       "687    5.758053  \n",
+       "600  449.732087  \n",
+       "..          ...  \n",
+       "27     4.316290  \n",
+       "37     4.304869  \n",
+       "38     4.014588  \n",
+       "33     4.278035  \n",
+       "34     4.000267  \n",
        "\n",
-       "[524 rows x 7 columns]"
+       "[695 rows x 7 columns]"
       ]
      },
-     "execution_count": 104,
+     "execution_count": 146,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1276,7 +1276,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 105,
+   "execution_count": 147,
    "metadata": {},
    "outputs": [
     {
@@ -1311,7 +1311,7 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>27</th>\n",
+       "      <th>34</th>\n",
        "      <td>222.917653</td>\n",
        "      <td>2.823663</td>\n",
        "      <td>2.336885</td>\n",
@@ -1321,7 +1321,7 @@
        "      <td>4.000267</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>26</th>\n",
+       "      <th>33</th>\n",
        "      <td>216.603135</td>\n",
        "      <td>2.821754</td>\n",
        "      <td>3.173432</td>\n",
@@ -1331,7 +1331,7 @@
        "      <td>4.278035</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>30</th>\n",
+       "      <th>38</th>\n",
        "      <td>217.389734</td>\n",
        "      <td>1.980785</td>\n",
        "      <td>2.331454</td>\n",
@@ -1341,7 +1341,7 @@
        "      <td>4.014588</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>29</th>\n",
+       "      <th>37</th>\n",
        "      <td>210.588381</td>\n",
        "      <td>1.978485</td>\n",
        "      <td>3.167382</td>\n",
@@ -1351,7 +1351,7 @@
        "      <td>4.304869</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>21</th>\n",
+       "      <th>27</th>\n",
        "      <td>202.186462</td>\n",
        "      <td>2.289328</td>\n",
        "      <td>2.644426</td>\n",
@@ -1371,47 +1371,47 @@
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>309</th>\n",
-       "      <td>151.959876</td>\n",
-       "      <td>6.048875</td>\n",
-       "      <td>3.048254</td>\n",
-       "      <td>6.335821</td>\n",
-       "      <td>3.106507</td>\n",
-       "      <td>14.889180</td>\n",
-       "      <td>7.300292</td>\n",
+       "      <th>80</th>\n",
+       "      <td>126.345557</td>\n",
+       "      <td>7.503358</td>\n",
+       "      <td>2.717318</td>\n",
+       "      <td>8.605995</td>\n",
+       "      <td>3.441961</td>\n",
+       "      <td>20.224088</td>\n",
+       "      <td>8.088608</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>351</th>\n",
-       "      <td>195.582213</td>\n",
-       "      <td>7.582757</td>\n",
-       "      <td>2.703236</td>\n",
-       "      <td>6.548991</td>\n",
-       "      <td>3.505496</td>\n",
-       "      <td>15.390128</td>\n",
-       "      <td>8.237915</td>\n",
+       "      <th>98</th>\n",
+       "      <td>191.987149</td>\n",
+       "      <td>10.993017</td>\n",
+       "      <td>2.720394</td>\n",
+       "      <td>9.802730</td>\n",
+       "      <td>3.154057</td>\n",
+       "      <td>23.036415</td>\n",
+       "      <td>7.412033</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>425</th>\n",
-       "      <td>251.594399</td>\n",
-       "      <td>7.994551</td>\n",
-       "      <td>1.593031</td>\n",
-       "      <td>6.646625</td>\n",
-       "      <td>3.216397</td>\n",
-       "      <td>15.619568</td>\n",
-       "      <td>7.558533</td>\n",
+       "      <th>261</th>\n",
+       "      <td>220.500255</td>\n",
+       "      <td>12.860922</td>\n",
+       "      <td>2.878011</td>\n",
+       "      <td>9.887140</td>\n",
+       "      <td>2.760608</td>\n",
+       "      <td>23.234779</td>\n",
+       "      <td>6.487429</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>47</th>\n",
-       "      <td>115.774855</td>\n",
-       "      <td>4.630637</td>\n",
-       "      <td>2.210311</td>\n",
-       "      <td>7.270685</td>\n",
-       "      <td>5.813427</td>\n",
-       "      <td>17.086110</td>\n",
-       "      <td>13.661553</td>\n",
+       "      <th>218</th>\n",
+       "      <td>229.036678</td>\n",
+       "      <td>21.346080</td>\n",
+       "      <td>2.787659</td>\n",
+       "      <td>17.666989</td>\n",
+       "      <td>2.664696</td>\n",
+       "      <td>41.517424</td>\n",
+       "      <td>6.262035</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>288</th>\n",
+       "      <th>372</th>\n",
        "      <td>169.559297</td>\n",
        "      <td>-16.954423</td>\n",
        "      <td>2.975433</td>\n",
@@ -1422,40 +1422,27 @@
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>462 rows × 7 columns</p>\n",
+       "<p>585 rows × 7 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "         height     mean_x    mean_y      std_x     std_y     FWHM_x  \\\n",
-       "27   222.917653   2.823663  2.336885   1.803631  1.702241   4.238533   \n",
-       "26   216.603135   2.821754  3.173432   1.804289  1.820440   4.240079   \n",
-       "30   217.389734   1.980785  2.331454   1.931921  1.708335   4.540015   \n",
-       "29   210.588381   1.978485  3.167382   1.943865  1.831859   4.568083   \n",
-       "21   202.186462   2.289328  2.644426   2.005263  1.836719   4.712369   \n",
-       "..          ...        ...       ...        ...       ...        ...   \n",
-       "309  151.959876   6.048875  3.048254   6.335821  3.106507  14.889180   \n",
-       "351  195.582213   7.582757  2.703236   6.548991  3.505496  15.390128   \n",
-       "425  251.594399   7.994551  1.593031   6.646625  3.216397  15.619568   \n",
-       "47   115.774855   4.630637  2.210311   7.270685  5.813427  17.086110   \n",
-       "288  169.559297 -16.954423  2.975433  25.863356  3.902506  60.778886   \n",
-       "\n",
-       "        FWHM_y  \n",
-       "27    4.000267  \n",
-       "26    4.278035  \n",
-       "30    4.014588  \n",
-       "29    4.304869  \n",
-       "21    4.316290  \n",
-       "..         ...  \n",
-       "309   7.300292  \n",
-       "351   8.237915  \n",
-       "425   7.558533  \n",
-       "47   13.661553  \n",
-       "288   9.170890  \n",
+       "         height     mean_x    mean_y      std_x     std_y     FWHM_x    FWHM_y\n",
+       "34   222.917653   2.823663  2.336885   1.803631  1.702241   4.238533  4.000267\n",
+       "33   216.603135   2.821754  3.173432   1.804289  1.820440   4.240079  4.278035\n",
+       "38   217.389734   1.980785  2.331454   1.931921  1.708335   4.540015  4.014588\n",
+       "37   210.588381   1.978485  3.167382   1.943865  1.831859   4.568083  4.304869\n",
+       "27   202.186462   2.289328  2.644426   2.005263  1.836719   4.712369  4.316290\n",
+       "..          ...        ...       ...        ...       ...        ...       ...\n",
+       "80   126.345557   7.503358  2.717318   8.605995  3.441961  20.224088  8.088608\n",
+       "98   191.987149  10.993017  2.720394   9.802730  3.154057  23.036415  7.412033\n",
+       "261  220.500255  12.860922  2.878011   9.887140  2.760608  23.234779  6.487429\n",
+       "218  229.036678  21.346080  2.787659  17.666989  2.664696  41.517424  6.262035\n",
+       "372  169.559297 -16.954423  2.975433  25.863356  3.902506  60.778886  9.170890\n",
        "\n",
-       "[462 rows x 7 columns]"
+       "[585 rows x 7 columns]"
       ]
      },
-     "execution_count": 105,
+     "execution_count": 147,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1467,7 +1454,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 106,
+   "execution_count": 148,
    "metadata": {},
    "outputs": [
     {
@@ -1482,7 +1469,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 1080x360 with 2 Axes>"
       ]
@@ -1521,7 +1508,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 107,
+   "execution_count": 149,
    "metadata": {},
    "outputs": [
     {
@@ -1545,7 +1532,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 108,
+   "execution_count": 150,
    "metadata": {},
    "outputs": [
     {
@@ -1571,7 +1558,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 109,
+   "execution_count": 151,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -1585,7 +1572,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'MEAN_FWHM_x': 7.940323515966517, 'MEAN_FWHM_y': 7.337040419590766}\n"
+      "{'MEAN_FWHM_x': 8.379209150177722, 'MEAN_FWHM_y': 7.576010416841125}\n"
      ]
     }
    ],
@@ -1602,14 +1589,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 110,
+   "execution_count": 152,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'MEDIAN_FWHM_x': 7.396254460793973, 'MEDIAN_FWHM_y': 6.900833212383647}\n"
+      "{'MEDIAN_FWHM_x': 7.670810006638417, 'MEDIAN_FWHM_y': 6.99528444605464}\n"
      ]
     }
    ],
@@ -1628,7 +1615,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 111,
+   "execution_count": 153,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -1642,7 +1629,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'INCERTIDUMBRE_FWHM_x': 0.15200550254891215, 'INCERTIDUMBRE_FWHM_y': 0.12480846830393083}\n"
+      "{'INCERTIDUMBRE_FWHM_x': 0.15034065812442832, 'INCERTIDUMBRE_FWHM_y': 0.12213295193042271}\n"
      ]
     }
    ],
@@ -1659,7 +1646,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 112,
+   "execution_count": 163,
    "metadata": {},
    "outputs": [
     {
@@ -1694,54 +1681,54 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>222.917653</td>\n",
-       "      <td>2.823663</td>\n",
-       "      <td>2.336885</td>\n",
-       "      <td>1.803631</td>\n",
-       "      <td>1.702241</td>\n",
-       "      <td>4.238533</td>\n",
-       "      <td>4.000267</td>\n",
+       "      <th>33</th>\n",
+       "      <td>215.742228</td>\n",
+       "      <td>2.844406</td>\n",
+       "      <td>3.220690</td>\n",
+       "      <td>1.602668</td>\n",
+       "      <td>1.513167</td>\n",
+       "      <td>3.766270</td>\n",
+       "      <td>3.555943</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>216.603135</td>\n",
-       "      <td>2.821754</td>\n",
-       "      <td>3.173432</td>\n",
-       "      <td>1.804289</td>\n",
-       "      <td>1.820440</td>\n",
-       "      <td>4.240079</td>\n",
-       "      <td>4.278035</td>\n",
+       "      <th>34</th>\n",
+       "      <td>216.970464</td>\n",
+       "      <td>2.845412</td>\n",
+       "      <td>2.329821</td>\n",
+       "      <td>1.614164</td>\n",
+       "      <td>1.493453</td>\n",
+       "      <td>3.793286</td>\n",
+       "      <td>3.509615</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>30</th>\n",
-       "      <td>217.389734</td>\n",
-       "      <td>1.980785</td>\n",
-       "      <td>2.331454</td>\n",
-       "      <td>1.931921</td>\n",
-       "      <td>1.708335</td>\n",
-       "      <td>4.540015</td>\n",
-       "      <td>4.014588</td>\n",
+       "      <th>37</th>\n",
+       "      <td>209.845863</td>\n",
+       "      <td>1.956937</td>\n",
+       "      <td>3.215590</td>\n",
+       "      <td>1.708841</td>\n",
+       "      <td>1.524611</td>\n",
+       "      <td>4.015777</td>\n",
+       "      <td>3.582837</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>210.588381</td>\n",
-       "      <td>1.978485</td>\n",
-       "      <td>3.167382</td>\n",
-       "      <td>1.943865</td>\n",
-       "      <td>1.831859</td>\n",
-       "      <td>4.568083</td>\n",
-       "      <td>4.304869</td>\n",
+       "      <th>335</th>\n",
+       "      <td>174.734848</td>\n",
+       "      <td>2.770882</td>\n",
+       "      <td>2.760025</td>\n",
+       "      <td>1.710865</td>\n",
+       "      <td>1.624143</td>\n",
+       "      <td>4.020532</td>\n",
+       "      <td>3.816736</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>202.186462</td>\n",
-       "      <td>2.289328</td>\n",
-       "      <td>2.644426</td>\n",
-       "      <td>2.005263</td>\n",
-       "      <td>1.836719</td>\n",
-       "      <td>4.712369</td>\n",
-       "      <td>4.316290</td>\n",
+       "      <th>38</th>\n",
+       "      <td>211.627049</td>\n",
+       "      <td>1.961099</td>\n",
+       "      <td>2.326209</td>\n",
+       "      <td>1.715538</td>\n",
+       "      <td>1.499931</td>\n",
+       "      <td>4.031515</td>\n",
+       "      <td>3.524837</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -1754,90 +1741,91 @@
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>400</th>\n",
-       "      <td>148.694932</td>\n",
-       "      <td>3.251515</td>\n",
-       "      <td>3.323809</td>\n",
-       "      <td>3.311014</td>\n",
-       "      <td>3.089162</td>\n",
-       "      <td>7.780883</td>\n",
-       "      <td>7.259531</td>\n",
+       "      <th>543</th>\n",
+       "      <td>131.093045</td>\n",
+       "      <td>3.193123</td>\n",
+       "      <td>2.503308</td>\n",
+       "      <td>4.758416</td>\n",
+       "      <td>2.706784</td>\n",
+       "      <td>11.182277</td>\n",
+       "      <td>6.360943</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>32</th>\n",
-       "      <td>120.009106</td>\n",
-       "      <td>2.582832</td>\n",
-       "      <td>2.669800</td>\n",
-       "      <td>3.323717</td>\n",
-       "      <td>2.781075</td>\n",
-       "      <td>7.810735</td>\n",
-       "      <td>6.535527</td>\n",
+       "      <th>23</th>\n",
+       "      <td>224.503205</td>\n",
+       "      <td>7.472670</td>\n",
+       "      <td>2.648100</td>\n",
+       "      <td>4.762666</td>\n",
+       "      <td>1.649086</td>\n",
+       "      <td>11.192264</td>\n",
+       "      <td>3.875353</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>150</th>\n",
-       "      <td>140.401733</td>\n",
-       "      <td>2.814937</td>\n",
-       "      <td>2.461857</td>\n",
-       "      <td>3.330074</td>\n",
-       "      <td>3.020962</td>\n",
-       "      <td>7.825674</td>\n",
-       "      <td>7.099261</td>\n",
+       "      <th>397</th>\n",
+       "      <td>117.626414</td>\n",
+       "      <td>4.050571</td>\n",
+       "      <td>2.602593</td>\n",
+       "      <td>4.788907</td>\n",
+       "      <td>2.907534</td>\n",
+       "      <td>11.253932</td>\n",
+       "      <td>6.832705</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>281</th>\n",
-       "      <td>192.304218</td>\n",
-       "      <td>3.922074</td>\n",
-       "      <td>2.380541</td>\n",
-       "      <td>3.330268</td>\n",
-       "      <td>2.254765</td>\n",
-       "      <td>7.826129</td>\n",
-       "      <td>5.298697</td>\n",
+       "      <th>331</th>\n",
+       "      <td>120.648167</td>\n",
+       "      <td>3.944006</td>\n",
+       "      <td>2.661164</td>\n",
+       "      <td>4.870656</td>\n",
+       "      <td>2.857321</td>\n",
+       "      <td>11.446041</td>\n",
+       "      <td>6.714704</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>228</th>\n",
-       "      <td>123.573138</td>\n",
-       "      <td>2.781581</td>\n",
-       "      <td>2.546152</td>\n",
-       "      <td>3.334843</td>\n",
-       "      <td>2.962802</td>\n",
-       "      <td>7.836880</td>\n",
-       "      <td>6.962584</td>\n",
+       "      <th>622</th>\n",
+       "      <td>240.208357</td>\n",
+       "      <td>6.285389</td>\n",
+       "      <td>1.606621</td>\n",
+       "      <td>4.905659</td>\n",
+       "      <td>2.470669</td>\n",
+       "      <td>11.528298</td>\n",
+       "      <td>5.806073</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>206 rows × 7 columns</p>\n",
+       "<p>529 rows × 7 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "         height    mean_x    mean_y     std_x     std_y    FWHM_x    FWHM_y\n",
-       "27   222.917653  2.823663  2.336885  1.803631  1.702241  4.238533  4.000267\n",
-       "26   216.603135  2.821754  3.173432  1.804289  1.820440  4.240079  4.278035\n",
-       "30   217.389734  1.980785  2.331454  1.931921  1.708335  4.540015  4.014588\n",
-       "29   210.588381  1.978485  3.167382  1.943865  1.831859  4.568083  4.304869\n",
-       "21   202.186462  2.289328  2.644426  2.005263  1.836719  4.712369  4.316290\n",
-       "..          ...       ...       ...       ...       ...       ...       ...\n",
-       "400  148.694932  3.251515  3.323809  3.311014  3.089162  7.780883  7.259531\n",
-       "32   120.009106  2.582832  2.669800  3.323717  2.781075  7.810735  6.535527\n",
-       "150  140.401733  2.814937  2.461857  3.330074  3.020962  7.825674  7.099261\n",
-       "281  192.304218  3.922074  2.380541  3.330268  2.254765  7.826129  5.298697\n",
-       "228  123.573138  2.781581  2.546152  3.334843  2.962802  7.836880  6.962584\n",
+       "         height    mean_x    mean_y     std_x     std_y     FWHM_x    FWHM_y\n",
+       "33   215.742228  2.844406  3.220690  1.602668  1.513167   3.766270  3.555943\n",
+       "34   216.970464  2.845412  2.329821  1.614164  1.493453   3.793286  3.509615\n",
+       "37   209.845863  1.956937  3.215590  1.708841  1.524611   4.015777  3.582837\n",
+       "335  174.734848  2.770882  2.760025  1.710865  1.624143   4.020532  3.816736\n",
+       "38   211.627049  1.961099  2.326209  1.715538  1.499931   4.031515  3.524837\n",
+       "..          ...       ...       ...       ...       ...        ...       ...\n",
+       "543  131.093045  3.193123  2.503308  4.758416  2.706784  11.182277  6.360943\n",
+       "23   224.503205  7.472670  2.648100  4.762666  1.649086  11.192264  3.875353\n",
+       "397  117.626414  4.050571  2.602593  4.788907  2.907534  11.253932  6.832705\n",
+       "331  120.648167  3.944006  2.661164  4.870656  2.857321  11.446041  6.714704\n",
+       "622  240.208357  6.285389  1.606621  4.905659  2.470669  11.528298  5.806073\n",
        "\n",
-       "[206 rows x 7 columns]"
+       "[529 rows x 7 columns]"
       ]
      },
-     "execution_count": 112,
+     "execution_count": 163,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "parameters_df=parameters_df[(parameters_df[\"FWHM_x\"]<np.median(parameters_df[\"FWHM_x\"])+3*np.std(parameters_df[\"FWHM_x\"])/math.sqrt(len(parameters_df[\"FWHM_x\"]))) & (parameters_df[\"FWHM_y\"]<np.median(parameters_df[\"FWHM_y\"])+3*np.std(parameters_df[\"FWHM_y\"])/math.sqrt(len(parameters_df[\"FWHM_y\"])))]\n",
+    "#elimina valores extremos del dataset\n",
+    "parameters_df=parameters_df[(parameters_df[\"FWHM_x\"]<np.mean(parameters_df[\"FWHM_x\"])+2.5*np.std(parameters_df[\"FWHM_x\"])) & (parameters_df[\"FWHM_y\"]<np.mean(parameters_df[\"FWHM_y\"])+2.5*np.std(parameters_df[\"FWHM_y\"]))]\n",
     "parameters_df.sort_values('FWHM_x',ascending=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 113,
+   "execution_count": 164,
    "metadata": {},
    "outputs": [
     {
@@ -1852,7 +1840,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1080x360 with 2 Axes>"
       ]
@@ -1883,14 +1871,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 114,
+   "execution_count": 165,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'MEDIAN_FWHM_x': 6.361155779828058, 'MEDIAN_FWHM_y': 5.865753036777117}\n"
+      "{'MEDIAN_FWHM_x': 6.61783565447222, 'MEDIAN_FWHM_y': 6.0911794566979856}\n"
      ]
     }
    ],
@@ -1900,14 +1888,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 115,
+   "execution_count": 166,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'INCERTIDUMBRE_FWHM_x': 0.05897470200906251, 'INCERTIDUMBRE_FWHM_y': 0.0583083389179374}\n"
+      "{'INCERTIDUMBRE_FWHM_x': 0.07111893293495501, 'INCERTIDUMBRE_FWHM_y': 0.059722316688425}\n"
      ]
     }
    ],
@@ -1936,7 +1924,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 116,
+   "execution_count": 167,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -1971,7 +1959,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 117,
+   "execution_count": 168,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -2031,7 +2019,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 118,
+   "execution_count": 169,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -2054,14 +2042,14 @@
      "output_type": "stream",
      "text": [
       "\u001b[91mFWHM FOR CHANNEL RED\u001b[0m\n",
-      "{'MEAN_FWHM_x': 6.85040177317909, 'MEAN_FWHM_y': 6.286265858550066}\n",
-      "{'INCERTIDUMBRE_FWHM_x': 0.07397839012804115, 'INCERTIDUMBRE_FWHM_y': 0.06519770408323534}\n",
+      "{'MEDIAN_FWHM_x': 7.9183883547135165, 'MEDIAN_FWHM_y': 7.35922763918757}\n",
+      "{'INCERTIDUMBRE_FWHM_x': 0.1159148719780544, 'INCERTIDUMBRE_FWHM_y': 0.08836013787487608}\n",
       "\u001b[91mFWHM FOR CHANNEL GREEN\u001b[0m\n",
-      "{'MEAN_FWHM_x': 6.245164160492783, 'MEAN_FWHM_y': 5.852337602343364}\n",
-      "{'INCERTIDUMBRE_FWHM_x': 0.05644305840123783, 'INCERTIDUMBRE_FWHM_y': 0.05873235620560445}\n",
+      "{'MEDIAN_FWHM_x': 7.2816934101996456, 'MEDIAN_FWHM_y': 6.823136964119058}\n",
+      "{'INCERTIDUMBRE_FWHM_x': 0.09670234009426094, 'INCERTIDUMBRE_FWHM_y': 0.07626498914594818}\n",
       "\u001b[91mFWHM FOR CHANNEL BLUE\u001b[0m\n",
-      "{'MEAN_FWHM_x': 5.656095446703643, 'MEAN_FWHM_y': 5.248410485649535}\n",
-      "{'INCERTIDUMBRE_FWHM_x': 0.0511572917957581, 'INCERTIDUMBRE_FWHM_y': 0.04996212650827945}\n"
+      "{'MEDIAN_FWHM_x': 6.707228331976005, 'MEDIAN_FWHM_y': 6.143125344552874}\n",
+      "{'INCERTIDUMBRE_FWHM_x': 0.0772819315486433, 'INCERTIDUMBRE_FWHM_y': 0.06542214124518661}\n"
      ]
     }
    ],
@@ -2085,11 +2073,11 @@
     "    # calcula parametros\n",
     "    parameters_df = star_parameters(temp[:,:,i],stars)\n",
     "    parameters_df=parameters_df[(parameters_df[\"height\"]<255)]\n",
-    "    parameters_df=parameters_df[(parameters_df[\"FWHM_x\"]<np.median(parameters_df[\"FWHM_x\"])+3*np.std(parameters_df[\"FWHM_x\"])/math.sqrt(len(parameters_df[\"FWHM_x\"]))) & (parameters_df[\"FWHM_y\"]<np.median(parameters_df[\"FWHM_y\"])+3*np.std(parameters_df[\"FWHM_y\"])/math.sqrt(len(parameters_df[\"FWHM_y\"])))]\n",
+    "    parameters_df=parameters_df[(parameters_df[\"FWHM_x\"]<np.mean(parameters_df[\"FWHM_x\"])+2.5*np.std(parameters_df[\"FWHM_x\"])) & (parameters_df[\"FWHM_y\"]<np.median(parameters_df[\"FWHM_y\"])+2.5*np.std(parameters_df[\"FWHM_y\"]))]\n",
     "    \n",
     "    #muestra resultados\n",
     "    print(bcolors.RED +\"FWHM FOR CHANNEL \"+color[i] + bcolors.ENDC)\n",
-    "    print({\"MEAN_FWHM_x\":np.mean(parameters_df[\"FWHM_x\"]),\"MEAN_FWHM_y\":np.mean(parameters_df[\"FWHM_y\"])})\n",
+    "    print({\"MEDIAN_FWHM_x\":np.median(parameters_df[\"FWHM_x\"]),\"MEDIAN_FWHM_y\":np.median(parameters_df[\"FWHM_y\"])})\n",
     "    print({\"INCERTIDUMBRE_FWHM_x\":np.std(parameters_df[\"FWHM_x\"])/math.sqrt(len(parameters_df[\"FWHM_x\"])),\"INCERTIDUMBRE_FWHM_y\":np.std(parameters_df[\"FWHM_y\"])/math.sqrt(len(parameters_df[\"FWHM_y\"]))})\n"
    ]
   },
@@ -2097,7 +2085,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Encontramos que el mejor canal es el azul con $$FWHM \\approx 6 $$"
+    "Encontramos que el mejor canal es el azul con $$FWHM \\approx 7 $$"
    ]
   },
   {
diff --git a/entrega.html b/entrega.html
index 3c195439f5b461601cc7e15e5829650ee80d5df7..b1aa17760addaa096b964eb3f14fe5ac03cf8c69 100644
--- a/entrega.html
+++ b/entrega.html
@@ -2,7 +2,7 @@
 <html>
 <head><meta charset="utf-8" />
 
-<title>Ejercicios_clase_5</title>
+<title>entrega</title>
 
 <script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script>
 <script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/2.0.3/jquery.min.js"></script>
@@ -14150,7 +14150,7 @@ $$estrella=mean_{pixels}+3*std_{pixels}$$
 </div>
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+<div class="prompt input_prompt">In&nbsp;[145]:</div>
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 <div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">star_parameters</span><span class="p">(</span><span class="n">image</span><span class="p">,</span><span class="n">stars</span><span class="p">):</span>
@@ -14221,7 +14221,7 @@ $$estrella=mean_{pixels}+3*std_{pixels}$$
 </div>
 <div class="cell border-box-sizing code_cell rendered">
 <div class="input">
-<div class="prompt input_prompt">In&nbsp;[104]:</div>
+<div class="prompt input_prompt">In&nbsp;[146]:</div>
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 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">parameters_df</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="s1">&#39;FWHM_x&#39;</span><span class="p">,</span><span class="n">ascending</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
@@ -14237,7 +14237,7 @@ $$estrella=mean_{pixels}+3*std_{pixels}$$
 
 <div class="output_area">
 
-    <div class="prompt output_prompt">Out[104]:</div>
+    <div class="prompt output_prompt">Out[146]:</div>
 
 
 
@@ -14271,7 +14271,17 @@ $$estrella=mean_{pixels}+3*std_{pixels}$$
   </thead>
   <tbody>
     <tr>
-      <th>90</th>
+      <th>132</th>
+      <td>22738.039821</td>
+      <td>-239.832795</td>
+      <td>3.010661</td>
+      <td>75.973813</td>
+      <td>2.731824</td>
+      <td>178.538460</td>
+      <td>6.419787</td>
+    </tr>
+    <tr>
+      <th>133</th>
       <td>14409.841451</td>
       <td>-222.262629</td>
       <td>2.343065</td>
@@ -14281,7 +14291,17 @@ $$estrella=mean_{pixels}+3*std_{pixels}$$
       <td>5.848958</td>
     </tr>
     <tr>
-      <th>516</th>
+      <th>578</th>
+      <td>31219.909353</td>
+      <td>105.300441</td>
+      <td>2.585564</td>
+      <td>31.073563</td>
+      <td>4.308750</td>
+      <td>73.022872</td>
+      <td>10.125563</td>
+    </tr>
+    <tr>
+      <th>687</th>
       <td>267271.868327</td>
       <td>115.166949</td>
       <td>2.769692</td>
@@ -14291,34 +14311,14 @@ $$estrella=mean_{pixels}+3*std_{pixels}$$
       <td>5.758053</td>
     </tr>
     <tr>
-      <th>477</th>
-      <td>107493.971955</td>
-      <td>96.620752</td>
-      <td>3.101467</td>
-      <td>26.096038</td>
-      <td>3.044724</td>
-      <td>61.325689</td>
-      <td>7.155100</td>
-    </tr>
-    <tr>
-      <th>288</th>
-      <td>169.559297</td>
-      <td>-16.954423</td>
-      <td>2.975433</td>
-      <td>25.863356</td>
-      <td>3.902506</td>
-      <td>60.778886</td>
-      <td>9.170890</td>
-    </tr>
-    <tr>
-      <th>441</th>
-      <td>21979.573625</td>
-      <td>66.797420</td>
-      <td>2.603415</td>
-      <td>20.616066</td>
-      <td>2.744205</td>
-      <td>48.447755</td>
-      <td>6.448882</td>
+      <th>600</th>
+      <td>325077.772735</td>
+      <td>112.668325</td>
+      <td>120.781525</td>
+      <td>28.345852</td>
+      <td>191.375356</td>
+      <td>66.612752</td>
+      <td>449.732087</td>
     </tr>
     <tr>
       <th>...</th>
@@ -14331,7 +14331,7 @@ $$estrella=mean_{pixels}+3*std_{pixels}$$
       <td>...</td>
     </tr>
     <tr>
-      <th>21</th>
+      <th>27</th>
       <td>202.186462</td>
       <td>2.289328</td>
       <td>2.644426</td>
@@ -14341,7 +14341,7 @@ $$estrella=mean_{pixels}+3*std_{pixels}$$
       <td>4.316290</td>
     </tr>
     <tr>
-      <th>29</th>
+      <th>37</th>
       <td>210.588381</td>
       <td>1.978485</td>
       <td>3.167382</td>
@@ -14351,7 +14351,7 @@ $$estrella=mean_{pixels}+3*std_{pixels}$$
       <td>4.304869</td>
     </tr>
     <tr>
-      <th>30</th>
+      <th>38</th>
       <td>217.389734</td>
       <td>1.980785</td>
       <td>2.331454</td>
@@ -14361,7 +14361,7 @@ $$estrella=mean_{pixels}+3*std_{pixels}$$
       <td>4.014588</td>
     </tr>
     <tr>
-      <th>26</th>
+      <th>33</th>
       <td>216.603135</td>
       <td>2.821754</td>
       <td>3.173432</td>
@@ -14371,7 +14371,7 @@ $$estrella=mean_{pixels}+3*std_{pixels}$$
       <td>4.278035</td>
     </tr>
     <tr>
-      <th>27</th>
+      <th>34</th>
       <td>222.917653</td>
       <td>2.823663</td>
       <td>2.336885</td>
@@ -14382,7 +14382,7 @@ $$estrella=mean_{pixels}+3*std_{pixels}$$
     </tr>
   </tbody>
 </table>
-<p>524 rows × 7 columns</p>
+<p>695 rows × 7 columns</p>
 </div>
 </div>
 
@@ -14401,7 +14401,7 @@ $$estrella=mean_{pixels}+3*std_{pixels}$$
 </div>
 <div class="cell border-box-sizing code_cell rendered">
 <div class="input">
-<div class="prompt input_prompt">In&nbsp;[105]:</div>
+<div class="prompt input_prompt">In&nbsp;[147]:</div>
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     <div class="input_area">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">parameters_df</span><span class="o">=</span><span class="n">parameters_df</span><span class="p">[(</span><span class="n">parameters_df</span><span class="p">[</span><span class="s2">&quot;height&quot;</span><span class="p">]</span><span class="o">&lt;</span><span class="mi">255</span><span class="p">)]</span>
@@ -14418,7 +14418,7 @@ $$estrella=mean_{pixels}+3*std_{pixels}$$
 
 <div class="output_area">
 
-    <div class="prompt output_prompt">Out[105]:</div>
+    <div class="prompt output_prompt">Out[147]:</div>
 
 
 
@@ -14452,7 +14452,7 @@ $$estrella=mean_{pixels}+3*std_{pixels}$$
   </thead>
   <tbody>
     <tr>
-      <th>27</th>
+      <th>34</th>
       <td>222.917653</td>
       <td>2.823663</td>
       <td>2.336885</td>
@@ -14462,7 +14462,7 @@ $$estrella=mean_{pixels}+3*std_{pixels}$$
       <td>4.000267</td>
     </tr>
     <tr>
-      <th>26</th>
+      <th>33</th>
       <td>216.603135</td>
       <td>2.821754</td>
       <td>3.173432</td>
@@ -14472,7 +14472,7 @@ $$estrella=mean_{pixels}+3*std_{pixels}$$
       <td>4.278035</td>
     </tr>
     <tr>
-      <th>30</th>
+      <th>38</th>
       <td>217.389734</td>
       <td>1.980785</td>
       <td>2.331454</td>
@@ -14482,7 +14482,7 @@ $$estrella=mean_{pixels}+3*std_{pixels}$$
       <td>4.014588</td>
     </tr>
     <tr>
-      <th>29</th>
+      <th>37</th>
       <td>210.588381</td>
       <td>1.978485</td>
       <td>3.167382</td>
@@ -14492,7 +14492,7 @@ $$estrella=mean_{pixels}+3*std_{pixels}$$
       <td>4.304869</td>
     </tr>
     <tr>
-      <th>21</th>
+      <th>27</th>
       <td>202.186462</td>
       <td>2.289328</td>
       <td>2.644426</td>
@@ -14512,47 +14512,47 @@ $$estrella=mean_{pixels}+3*std_{pixels}$$
       <td>...</td>
     </tr>
     <tr>
-      <th>309</th>
-      <td>151.959876</td>
-      <td>6.048875</td>
-      <td>3.048254</td>
-      <td>6.335821</td>
-      <td>3.106507</td>
-      <td>14.889180</td>
-      <td>7.300292</td>
+      <th>80</th>
+      <td>126.345557</td>
+      <td>7.503358</td>
+      <td>2.717318</td>
+      <td>8.605995</td>
+      <td>3.441961</td>
+      <td>20.224088</td>
+      <td>8.088608</td>
     </tr>
     <tr>
-      <th>351</th>
-      <td>195.582213</td>
-      <td>7.582757</td>
-      <td>2.703236</td>
-      <td>6.548991</td>
-      <td>3.505496</td>
-      <td>15.390128</td>
-      <td>8.237915</td>
+      <th>98</th>
+      <td>191.987149</td>
+      <td>10.993017</td>
+      <td>2.720394</td>
+      <td>9.802730</td>
+      <td>3.154057</td>
+      <td>23.036415</td>
+      <td>7.412033</td>
     </tr>
     <tr>
-      <th>425</th>
-      <td>251.594399</td>
-      <td>7.994551</td>
-      <td>1.593031</td>
-      <td>6.646625</td>
-      <td>3.216397</td>
-      <td>15.619568</td>
-      <td>7.558533</td>
+      <th>261</th>
+      <td>220.500255</td>
+      <td>12.860922</td>
+      <td>2.878011</td>
+      <td>9.887140</td>
+      <td>2.760608</td>
+      <td>23.234779</td>
+      <td>6.487429</td>
     </tr>
     <tr>
-      <th>47</th>
-      <td>115.774855</td>
-      <td>4.630637</td>
-      <td>2.210311</td>
-      <td>7.270685</td>
-      <td>5.813427</td>
-      <td>17.086110</td>
-      <td>13.661553</td>
+      <th>218</th>
+      <td>229.036678</td>
+      <td>21.346080</td>
+      <td>2.787659</td>
+      <td>17.666989</td>
+      <td>2.664696</td>
+      <td>41.517424</td>
+      <td>6.262035</td>
     </tr>
     <tr>
-      <th>288</th>
+      <th>372</th>
       <td>169.559297</td>
       <td>-16.954423</td>
       <td>2.975433</td>
@@ -14563,7 +14563,7 @@ $$estrella=mean_{pixels}+3*std_{pixels}$$
     </tr>
   </tbody>
 </table>
-<p>462 rows × 7 columns</p>
+<p>585 rows × 7 columns</p>
 </div>
 </div>
 
@@ -14575,7 +14575,7 @@ $$estrella=mean_{pixels}+3*std_{pixels}$$
 </div>
 <div class="cell border-box-sizing code_cell rendered">
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+<div class="prompt input_prompt">In&nbsp;[148]:</div>
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 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="p">(</span><span class="n">ax1</span><span class="p">,</span><span class="n">ax2</span><span class="p">)</span> <span class="o">=</span> <span class="n">pyplot</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span><span class="mi">5</span><span class="p">))</span>
@@ -14626,7 +14626,7 @@ C:\Users\ECF0124A\Anaconda3\lib\site-packages\seaborn\distributions.py:2557: Fut
 
 
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
 "
 >
 </div>
@@ -14646,7 +14646,7 @@ C:\Users\ECF0124A\Anaconda3\lib\site-packages\seaborn\distributions.py:2557: Fut
 </div>
 <div class="cell border-box-sizing code_cell rendered">
 <div class="input">
-<div class="prompt input_prompt">In&nbsp;[107]:</div>
+<div class="prompt input_prompt">In&nbsp;[149]:</div>
 <div class="inner_cell">
     <div class="input_area">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">({</span><span class="s2">&quot;MIN_FWHM_x&quot;</span><span class="p">:</span><span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">parameters_df</span><span class="p">[</span><span class="s2">&quot;FWHM_x&quot;</span><span class="p">]),</span><span class="s2">&quot;MIN_FWHM_y&quot;</span><span class="p">:</span><span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">parameters_df</span><span class="p">[</span><span class="s2">&quot;FWHM_y&quot;</span><span class="p">]),</span><span class="s2">&quot;MAX_FWHM_x&quot;</span><span class="p">:</span><span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">parameters_df</span><span class="p">[</span><span class="s2">&quot;FWHM_x&quot;</span><span class="p">]),</span><span class="s2">&quot;MAX_FWHM_y&quot;</span><span class="p">:</span><span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">parameters_df</span><span class="p">[</span><span class="s2">&quot;FWHM_y&quot;</span><span class="p">])})</span>
@@ -14684,7 +14684,7 @@ C:\Users\ECF0124A\Anaconda3\lib\site-packages\seaborn\distributions.py:2557: Fut
 </div>
 <div class="cell border-box-sizing code_cell rendered">
 <div class="input">
-<div class="prompt input_prompt">In&nbsp;[108]:</div>
+<div class="prompt input_prompt">In&nbsp;[150]:</div>
 <div class="inner_cell">
     <div class="input_area">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">({</span><span class="s2">&quot;MODE_FWHM_x&quot;</span><span class="p">:</span><span class="n">st</span><span class="o">.</span><span class="n">mode</span><span class="p">(</span><span class="n">parameters_df</span><span class="p">[</span><span class="s2">&quot;FWHM_x&quot;</span><span class="p">]),</span><span class="s2">&quot;MODE_FWHM_y&quot;</span><span class="p">:</span><span class="n">st</span><span class="o">.</span><span class="n">mode</span><span class="p">(</span><span class="n">parameters_df</span><span class="p">[</span><span class="s2">&quot;FWHM_y&quot;</span><span class="p">])})</span>
@@ -14722,7 +14722,7 @@ C:\Users\ECF0124A\Anaconda3\lib\site-packages\seaborn\distributions.py:2557: Fut
 </div>
 <div class="cell border-box-sizing code_cell rendered">
 <div class="input">
-<div class="prompt input_prompt">In&nbsp;[109]:</div>
+<div class="prompt input_prompt">In&nbsp;[151]:</div>
 <div class="inner_cell">
     <div class="input_area">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">({</span><span class="s2">&quot;MEAN_FWHM_x&quot;</span><span class="p">:</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">parameters_df</span><span class="p">[</span><span class="s2">&quot;FWHM_x&quot;</span><span class="p">]),</span><span class="s2">&quot;MEAN_FWHM_y&quot;</span><span class="p">:</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">parameters_df</span><span class="p">[</span><span class="s2">&quot;FWHM_y&quot;</span><span class="p">])})</span>
@@ -14742,7 +14742,7 @@ C:\Users\ECF0124A\Anaconda3\lib\site-packages\seaborn\distributions.py:2557: Fut
 
 
 <div class="output_subarea output_stream output_stdout output_text">
-<pre>{&#39;MEAN_FWHM_x&#39;: 7.940323515966517, &#39;MEAN_FWHM_y&#39;: 7.337040419590766}
+<pre>{&#39;MEAN_FWHM_x&#39;: 8.379209150177722, &#39;MEAN_FWHM_y&#39;: 7.576010416841125}
 </pre>
 </div>
 </div>
@@ -14760,7 +14760,7 @@ C:\Users\ECF0124A\Anaconda3\lib\site-packages\seaborn\distributions.py:2557: Fut
 </div>
 <div class="cell border-box-sizing code_cell rendered">
 <div class="input">
-<div class="prompt input_prompt">In&nbsp;[110]:</div>
+<div class="prompt input_prompt">In&nbsp;[152]:</div>
 <div class="inner_cell">
     <div class="input_area">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">({</span><span class="s2">&quot;MEDIAN_FWHM_x&quot;</span><span class="p">:</span><span class="n">np</span><span class="o">.</span><span class="n">median</span><span class="p">(</span><span class="n">parameters_df</span><span class="p">[</span><span class="s2">&quot;FWHM_x&quot;</span><span class="p">]),</span><span class="s2">&quot;MEDIAN_FWHM_y&quot;</span><span class="p">:</span><span class="n">np</span><span class="o">.</span><span class="n">median</span><span class="p">(</span><span class="n">parameters_df</span><span class="p">[</span><span class="s2">&quot;FWHM_y&quot;</span><span class="p">])})</span>
@@ -14780,7 +14780,7 @@ C:\Users\ECF0124A\Anaconda3\lib\site-packages\seaborn\distributions.py:2557: Fut
 
 
 <div class="output_subarea output_stream output_stdout output_text">
-<pre>{&#39;MEDIAN_FWHM_x&#39;: 7.396254460793973, &#39;MEDIAN_FWHM_y&#39;: 6.900833212383647}
+<pre>{&#39;MEDIAN_FWHM_x&#39;: 7.670810006638417, &#39;MEDIAN_FWHM_y&#39;: 6.99528444605464}
 </pre>
 </div>
 </div>
@@ -14798,7 +14798,7 @@ C:\Users\ECF0124A\Anaconda3\lib\site-packages\seaborn\distributions.py:2557: Fut
 </div>
 <div class="cell border-box-sizing code_cell rendered">
 <div class="input">
-<div class="prompt input_prompt">In&nbsp;[111]:</div>
+<div class="prompt input_prompt">In&nbsp;[153]:</div>
 <div class="inner_cell">
     <div class="input_area">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">({</span><span class="s2">&quot;INCERTIDUMBRE_FWHM_x&quot;</span><span class="p">:</span><span class="n">np</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">parameters_df</span><span class="p">[</span><span class="s2">&quot;FWHM_x&quot;</span><span class="p">])</span><span class="o">/</span><span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">parameters_df</span><span class="p">[</span><span class="s2">&quot;FWHM_x&quot;</span><span class="p">])),</span><span class="s2">&quot;INCERTIDUMBRE_FWHM_y&quot;</span><span class="p">:</span><span class="n">np</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">parameters_df</span><span class="p">[</span><span class="s2">&quot;FWHM_y&quot;</span><span class="p">])</span><span class="o">/</span><span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">parameters_df</span><span class="p">[</span><span class="s2">&quot;FWHM_y&quot;</span><span class="p">]))})</span>
@@ -14818,7 +14818,7 @@ C:\Users\ECF0124A\Anaconda3\lib\site-packages\seaborn\distributions.py:2557: Fut
 
 
 <div class="output_subarea output_stream output_stdout output_text">
-<pre>{&#39;INCERTIDUMBRE_FWHM_x&#39;: 0.15200550254891215, &#39;INCERTIDUMBRE_FWHM_y&#39;: 0.12480846830393083}
+<pre>{&#39;INCERTIDUMBRE_FWHM_x&#39;: 0.15034065812442832, &#39;INCERTIDUMBRE_FWHM_y&#39;: 0.12213295193042271}
 </pre>
 </div>
 </div>
@@ -14837,10 +14837,11 @@ C:\Users\ECF0124A\Anaconda3\lib\site-packages\seaborn\distributions.py:2557: Fut
 </div>
 <div class="cell border-box-sizing code_cell rendered">
 <div class="input">
-<div class="prompt input_prompt">In&nbsp;[112]:</div>
+<div class="prompt input_prompt">In&nbsp;[163]:</div>
 <div class="inner_cell">
     <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">parameters_df</span><span class="o">=</span><span class="n">parameters_df</span><span class="p">[(</span><span class="n">parameters_df</span><span class="p">[</span><span class="s2">&quot;FWHM_x&quot;</span><span class="p">]</span><span class="o">&lt;</span><span class="n">np</span><span class="o">.</span><span class="n">median</span><span class="p">(</span><span class="n">parameters_df</span><span class="p">[</span><span class="s2">&quot;FWHM_x&quot;</span><span class="p">])</span><span class="o">+</span><span class="mi">3</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">parameters_df</span><span class="p">[</span><span class="s2">&quot;FWHM_x&quot;</span><span class="p">])</span><span class="o">/</span><span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">parameters_df</span><span class="p">[</span><span class="s2">&quot;FWHM_x&quot;</span><span class="p">])))</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">parameters_df</span><span class="p">[</span><span class="s2">&quot;FWHM_y&quot;</span><span class="p">]</span><span class="o">&lt;</span><span class="n">np</span><span class="o">.</span><span class="n">median</span><span class="p">(</span><span class="n">parameters_df</span><span class="p">[</span><span class="s2">&quot;FWHM_y&quot;</span><span class="p">])</span><span class="o">+</span><span class="mi">3</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">parameters_df</span><span class="p">[</span><span class="s2">&quot;FWHM_y&quot;</span><span class="p">])</span><span class="o">/</span><span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">parameters_df</span><span class="p">[</span><span class="s2">&quot;FWHM_y&quot;</span><span class="p">])))]</span>
+<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#elimina valores extremos del dataset</span>
+<span class="n">parameters_df</span><span class="o">=</span><span class="n">parameters_df</span><span class="p">[(</span><span class="n">parameters_df</span><span class="p">[</span><span class="s2">&quot;FWHM_x&quot;</span><span class="p">]</span><span class="o">&lt;</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">parameters_df</span><span class="p">[</span><span class="s2">&quot;FWHM_x&quot;</span><span class="p">])</span><span class="o">+</span><span class="mf">2.5</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">parameters_df</span><span class="p">[</span><span class="s2">&quot;FWHM_x&quot;</span><span class="p">]))</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">parameters_df</span><span class="p">[</span><span class="s2">&quot;FWHM_y&quot;</span><span class="p">]</span><span class="o">&lt;</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">parameters_df</span><span class="p">[</span><span class="s2">&quot;FWHM_y&quot;</span><span class="p">])</span><span class="o">+</span><span class="mf">2.5</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">parameters_df</span><span class="p">[</span><span class="s2">&quot;FWHM_y&quot;</span><span class="p">]))]</span>
 <span class="n">parameters_df</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="s1">&#39;FWHM_x&#39;</span><span class="p">,</span><span class="n">ascending</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 </pre></div>
 
@@ -14854,7 +14855,7 @@ C:\Users\ECF0124A\Anaconda3\lib\site-packages\seaborn\distributions.py:2557: Fut
 
 <div class="output_area">
 
-    <div class="prompt output_prompt">Out[112]:</div>
+    <div class="prompt output_prompt">Out[163]:</div>
 
 
 
@@ -14888,54 +14889,54 @@ C:\Users\ECF0124A\Anaconda3\lib\site-packages\seaborn\distributions.py:2557: Fut
   </thead>
   <tbody>
     <tr>
-      <th>27</th>
-      <td>222.917653</td>
-      <td>2.823663</td>
-      <td>2.336885</td>
-      <td>1.803631</td>
-      <td>1.702241</td>
-      <td>4.238533</td>
-      <td>4.000267</td>
+      <th>33</th>
+      <td>215.742228</td>
+      <td>2.844406</td>
+      <td>3.220690</td>
+      <td>1.602668</td>
+      <td>1.513167</td>
+      <td>3.766270</td>
+      <td>3.555943</td>
     </tr>
     <tr>
-      <th>26</th>
-      <td>216.603135</td>
-      <td>2.821754</td>
-      <td>3.173432</td>
-      <td>1.804289</td>
-      <td>1.820440</td>
-      <td>4.240079</td>
-      <td>4.278035</td>
+      <th>34</th>
+      <td>216.970464</td>
+      <td>2.845412</td>
+      <td>2.329821</td>
+      <td>1.614164</td>
+      <td>1.493453</td>
+      <td>3.793286</td>
+      <td>3.509615</td>
     </tr>
     <tr>
-      <th>30</th>
-      <td>217.389734</td>
-      <td>1.980785</td>
-      <td>2.331454</td>
-      <td>1.931921</td>
-      <td>1.708335</td>
-      <td>4.540015</td>
-      <td>4.014588</td>
+      <th>37</th>
+      <td>209.845863</td>
+      <td>1.956937</td>
+      <td>3.215590</td>
+      <td>1.708841</td>
+      <td>1.524611</td>
+      <td>4.015777</td>
+      <td>3.582837</td>
     </tr>
     <tr>
-      <th>29</th>
-      <td>210.588381</td>
-      <td>1.978485</td>
-      <td>3.167382</td>
-      <td>1.943865</td>
-      <td>1.831859</td>
-      <td>4.568083</td>
-      <td>4.304869</td>
+      <th>335</th>
+      <td>174.734848</td>
+      <td>2.770882</td>
+      <td>2.760025</td>
+      <td>1.710865</td>
+      <td>1.624143</td>
+      <td>4.020532</td>
+      <td>3.816736</td>
     </tr>
     <tr>
-      <th>21</th>
-      <td>202.186462</td>
-      <td>2.289328</td>
-      <td>2.644426</td>
-      <td>2.005263</td>
-      <td>1.836719</td>
-      <td>4.712369</td>
-      <td>4.316290</td>
+      <th>38</th>
+      <td>211.627049</td>
+      <td>1.961099</td>
+      <td>2.326209</td>
+      <td>1.715538</td>
+      <td>1.499931</td>
+      <td>4.031515</td>
+      <td>3.524837</td>
     </tr>
     <tr>
       <th>...</th>
@@ -14948,58 +14949,58 @@ C:\Users\ECF0124A\Anaconda3\lib\site-packages\seaborn\distributions.py:2557: Fut
       <td>...</td>
     </tr>
     <tr>
-      <th>400</th>
-      <td>148.694932</td>
-      <td>3.251515</td>
-      <td>3.323809</td>
-      <td>3.311014</td>
-      <td>3.089162</td>
-      <td>7.780883</td>
-      <td>7.259531</td>
+      <th>543</th>
+      <td>131.093045</td>
+      <td>3.193123</td>
+      <td>2.503308</td>
+      <td>4.758416</td>
+      <td>2.706784</td>
+      <td>11.182277</td>
+      <td>6.360943</td>
     </tr>
     <tr>
-      <th>32</th>
-      <td>120.009106</td>
-      <td>2.582832</td>
-      <td>2.669800</td>
-      <td>3.323717</td>
-      <td>2.781075</td>
-      <td>7.810735</td>
-      <td>6.535527</td>
+      <th>23</th>
+      <td>224.503205</td>
+      <td>7.472670</td>
+      <td>2.648100</td>
+      <td>4.762666</td>
+      <td>1.649086</td>
+      <td>11.192264</td>
+      <td>3.875353</td>
     </tr>
     <tr>
-      <th>150</th>
-      <td>140.401733</td>
-      <td>2.814937</td>
-      <td>2.461857</td>
-      <td>3.330074</td>
-      <td>3.020962</td>
-      <td>7.825674</td>
-      <td>7.099261</td>
+      <th>397</th>
+      <td>117.626414</td>
+      <td>4.050571</td>
+      <td>2.602593</td>
+      <td>4.788907</td>
+      <td>2.907534</td>
+      <td>11.253932</td>
+      <td>6.832705</td>
     </tr>
     <tr>
-      <th>281</th>
-      <td>192.304218</td>
-      <td>3.922074</td>
-      <td>2.380541</td>
-      <td>3.330268</td>
-      <td>2.254765</td>
-      <td>7.826129</td>
-      <td>5.298697</td>
+      <th>331</th>
+      <td>120.648167</td>
+      <td>3.944006</td>
+      <td>2.661164</td>
+      <td>4.870656</td>
+      <td>2.857321</td>
+      <td>11.446041</td>
+      <td>6.714704</td>
     </tr>
     <tr>
-      <th>228</th>
-      <td>123.573138</td>
-      <td>2.781581</td>
-      <td>2.546152</td>
-      <td>3.334843</td>
-      <td>2.962802</td>
-      <td>7.836880</td>
-      <td>6.962584</td>
+      <th>622</th>
+      <td>240.208357</td>
+      <td>6.285389</td>
+      <td>1.606621</td>
+      <td>4.905659</td>
+      <td>2.470669</td>
+      <td>11.528298</td>
+      <td>5.806073</td>
     </tr>
   </tbody>
 </table>
-<p>206 rows × 7 columns</p>
+<p>529 rows × 7 columns</p>
 </div>
 </div>
 
@@ -15011,7 +15012,7 @@ C:\Users\ECF0124A\Anaconda3\lib\site-packages\seaborn\distributions.py:2557: Fut
 </div>
 <div class="cell border-box-sizing code_cell rendered">
 <div class="input">
-<div class="prompt input_prompt">In&nbsp;[113]:</div>
+<div class="prompt input_prompt">In&nbsp;[164]:</div>
 <div class="inner_cell">
     <div class="input_area">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="p">(</span><span class="n">ax1</span><span class="p">,</span><span class="n">ax2</span><span class="p">)</span> <span class="o">=</span> <span class="n">pyplot</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span><span class="mi">5</span><span class="p">))</span>
@@ -15061,7 +15062,7 @@ C:\Users\ECF0124A\Anaconda3\lib\site-packages\seaborn\distributions.py:2557: Fut
 
 
 <div class="output_png output_subarea ">
-<img 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pKTjvHzn/+c1atXn7DsoosuYsOGDWzYsIE777yTiooKNmzYQFpaGuvWrTu+3dy5c9m2bdvxz/0FeqAbbriBRx55hFdeeYXvf//7Q/6sAB/72Md49NFH+d73vnfCMUVk6jvbehzrtXiy1MfTdZSuW7eO1tZWQqEQX/va17j33nv53e9+B0QaYFu3buWNN94gMzOT0tLSE47x6quv8uabb5KYmMiaNWt45zvfSXZ29rBZbr75Zu666y7e/e5309nZSTgcPiHXwI7S3bt3c9VVVx2vj9u2beO1114jPj6ehQsX8jd/8zdD1sebb76ZhIQEAP785z8P2VFaUVHByy+/TFpa2pAdpf2OHj3KP/zDPxz/vGDBAp544onjHaW33HILv//970/K8P73v5+vfvWrfOMb3yAYDPLjH//4lLV0pCbyiuBaYL9z7qBzrht4BDjhb55zrnnAxyTARd9fDzzinOtyzh0C9kePJyIyLg4ePIjf7yc3N/eE5XfddRc//OEP6ejoYN26dezevXvI/ZOSkoY99uCRvcyMQCBw/JkLYMymSIiPjz/+3u/309vbO+J9+29/2b59O0uXLuWCCy7g5ZdfHtVtL/1uvPFG/vmf/5krr7wSn2/40tPe3k55eTkAra2tozqHiIh4p7+j9Dvf+Q6NjY1DXtmCkXWUJiQkHO8oHc5QHaWJiYknbPPiiy9yyy23AMN3lIZCoeMdpUMZeGtofyfuwI7SCy+8kAsvvPD456E6Svtft99++0nHH9hRevHFFw+ZITk5mcsuu4zf/e537N69m56eHpYtWzbsdzNSEzmhfBEw8J6kcuD8wRuZ2WeAO4E44LIB+w7sRiqPLhORaWasrtydjZqaGm6//XbuuOOOkxptBw4cYNmyZSxbtoxNmzaxe/duSkpKTnjY+3Sefvpp6uvrSUhI4PHHH+fBBx8kLy+P6upq6urqSE5O5ne/+x1XX301KSkpFBcX8/jjj/Oud72Lrq4u+vpOvBni4osv5uc//zmXXXYZe/fupaysjIULF7J169az+h4uuugi7r33XubMmYPf7yczM5PGxkZ27NjBD37wg1Eda+bMmXz1q1/liiuuOOV2/c9xzJw5k49//OPHe5FFRGLdZKiPcGJH6a5du44vv+uuu3jnO9/Jk08+ybp163jqqaeG3H86dpSWlJTwzW9+k9TUVD7ykY+MKseNN97Ieeedx6233nrKjtKPfexj/L//9/9YtGjRqM8xnEk3WIxz7n7n3FzgC8A/jWZfM/uEmW02s801NTXjE1BEpqWOjo7jo6JdccUVXHXVVXzxi188abv77ruPpUuXsnz5coLBINdccw3Lly/H7/ezYsUKvv3tb5/2XGvXruU973kPy5cv5z3veQ+rV68mGAxy9913s3btWq688koWLVp0fPuf/exnfOc732H58uVcdNFFHD169ITjffrTnyYcDrNs2TJuvPFGHnrooRMK3JlatmwZtbW1XHDBBScsS0tLO+WtOsP55Cc/ydy5c4dd//zzz7Np06bjjcG4uDh+/OMfn1F2EREZeyPpKP3CF77AmjVr2L17NykpKWfUUdrR0cHjjz/OunXrTugo7erqOt5BOLCjFKCrq4v29vYTjtffUQqc0FF6ti666CJ+97vfkZmZeUJH6csvvzzqO2b6O0o//elPn3K7888/nyNHjvDwww/zgQ984GziHzeRVwQrgIE33hZHlw3nEeA/R7Ovc+4B4AGA1atXu8HrRUSGM/gq20CXXnopl156KQD//u//PuQ2zzzzzAmfb7vttuPvZ82axZtvvnl8+cB1A332s58dchju+fPnn3R84PgxQ6HQkA2mweca7urac889N+Ryv99Pc3PzCcseeuihEz4P/Nn6DXxGcPD2/QY/FwLw1re+lY0b/3Lzx29+85sh9xURkYnT31Ha09NDIBDgQx/6EHfeeedJ29133308++yz+Hw+lixZwjXXXIPP5zveUXrbbbeRkZFxynP1d5SWl5dzyy23HH92vb+jtKio6KSO0k9+8pPcfffdBINBHn300ROuqn3605/mU5/6FMuWLSMQCIx5R+kHP/jBE5a1traecUfpSLz//e9n27Ztp/0eR8qcm5j2kpkFgL3A5UQacZuADzrndgzYZr5zbl/0/V8BX3TOrTazJcDDRJ4LLAT+DMw/1WAxq1evdqcbiU9igwaLmfx27drFOeec43UMmSKG+vtiZlucc6uH2UUGUY0UL2iwmNFTfZSBrr32Wj73uc9x+eWXD7l+tPVxwm4Ndc71AncATwG7gF8553aY2T3REUIB7jCzHWa2jchzgrdG990B/ArYCfwB+IxGDBURERERkemusbGRBQsWkJCQMGwj8ExM5K2hOOeeBJ4ctOzuAe//9hT7fhX46vilExERERERmVzS09OPj3Y6libdYDEiIiIiIiIyvtQQFBERERERiTFqCIqITCM//OEPqa+v9zqGiIiITHJqCIqIEJkqoX8ewRUrVvDNb37z+AS2mzdvHnJah36lpaU8/PDDw66vrKzkve99LxCZTuGOO+4467wDj9nv61//OgkJCWRmZp718Ufjtttu47HHHpvQc4qIiIyGOkpPNqGDxYiIjMRYTPkx0EiGHE9ISGDbtm0AVFdX88EPfpDm5ma+/OUvs3r16uNzGQ2lvyE4cD6hfr29vRQWFo55Q2moY37hC1846+P29fXh9/vP+jgiIjI9+P1+li1bdnwewQ9/+MN87nOfw+fzsXnzZn7605/yne98Z8h9S0tL2bBhw5D1ESKdmp/97Gd57LHHeOihh9i8eTPf/e53zyrvwGP2+/rXv05xcbEnHaXXXnvtSR23k4UagiIig+Tm5vLAAw+wZs0avvSlL/H8889z77338rvf/Y7nn3+ev/3byADHZsYLL7zAXXfdxa5du1i5ciW33norGRkZ/OY3v6G1tZW+vj5+8pOfcO211x6feP3IkSNceumlVFRUcMstt/DFL36R0tLSE7a59957aW1t5Utf+hL79+/n9ttvp6amBr/fz6OPPorf7z++fWdnJ5/61KfYvHkzgUCAb33rW7ztbW/joYce4oknnqC9vZ0DBw7w7ne/m3/913896eedNWsWN954I08//TSf//znyczM5Itf/CJdXV3MnTuXH//4xyQnJ3PPPffwP//zP3R0dHDRRRfx/e9/HzObuP8wIiIxTh2lp6eO0pHTraEiIkOYM2cOfX19VFdXn7D83nvv5f7772fbtm2sX7+ehIQEvva1r3HxxRezbds2Pve5zwGwdetWHnvsMZ5//vmTjv3qq6/y61//mjfeeINHH32U003sffPNN/OZz3yG119/nQ0bNlBQUHDC+vvvvx8zY/v27fziF7/g1ltvpbOzE4Bt27bxy1/+ku3bt/PLX/6SI0eODHmOrKwstm7dyhVXXMFXvvIV/vSnP7F161ZWr17Nt771LQDuuOMONm3axJtvvklHRwe/+93vRvZlymmZ2dVmtsfM9pvZXUOsv93MtpvZNjN70cwWR5fPMrOO6PJtZva9iU8vIrGiv6P0u9/9Ls45nnvuOa699loAnn/+eVauXMnKlStZtWoVLS0t3HXXXaxfv56VK1fy7W9/m4ceeojrrruOyy67jMsvv5zS0lKWLl16/Pj9HaXz58/ny1/+MsBJ29x777186UtfAmD//v1cccUVrFixgnPPPZcDBw6csH1nZycf+chHWLZsGatWreLZZ58FIo9p3HDDDVx99dXMnz+fz3/+80P+vLNmzeILX/gC5557Lo8++ih//OMfufDCCzn33HN53/veR2trKwD33HMPa9asYenSpXziE5/AOTe2X/w4UUNQRGQU1q1bx5133sl3vvMdGhsbCQSGvrHiyiuvHPYWlCuvvJKsrCwSEhK44YYbePHFF4c9X0tLCxUVFbz73e8GIBQKkZiYeMI2L774IrfccgsAixYtYubMmcfnG7r88stJS0sjFAqxePFiDh8+POR5brzxRgA2btzIzp07WbduHStXruQnP/nJ8X2effZZzj//fJYtW8YzzzzDjh07hs0tI2dmfuB+4BpgMfCB/obeAA8755Y551YC/wp8a8C6A865ldHX7RMSWkRiljpKp09HqRqCIiJDOHjwIH6/n9zc3BOW33XXXfzwhz+ko6ODdevWsXv37iH3T0pKGvbYg2+nNDMCgcDxwWmA44XqbMXHxx9/7/f76e3tHXK7/rzOOa688kq2bdvGtm3b2LlzJz/60Y/o7Ozk05/+NI899hjbt2/n4x//+JhlFNYC+51zB51z3cAjwPUDN3DONQ/4mARMje5mEYkZ6iideh2lagiKiAxSU1PD7bffzh133HFSo+3AgQMsW7aML3zhC6xZs4bdu3eTkpJCS0vLiI//9NNPU19fT0dHB48//jjr1q0jLy+P6upq6urq6OrqOt6bmJKSQnFxMY8//jgAXV1dtLe3n3C8iy++mJ///OcA7N27l7KyMhYuXHhGP/sFF1zASy+9xP79+wFoa2tj7969xxt92dnZtLa2apTQsVUEDOyKLo8uO4GZfcbMDhC5IjhwGNvZZvaamT1vZhePb1QRiXXqKJ0+HaVqCIqIAB0dHcenj7jiiiu46qqr+OIXv3jSdvfddx9Lly5l+fLlBINBrrnmGpYvX47f72fFihV8+9vfPu251q5dy3ve8x6WL1/Oe97zHlavXk0wGOTuu+9m7dq1XHnllSxatOj49j/72c/4zne+w/Lly7nooos4evToCcf79Kc/TTgcZtmyZdx444089NBDJxS40cjJyeGhhx7iAx/4AMuXL+fCCy9k9+7dpKen8/GPf5ylS5fy9re/nTVr1pzR8eXMOefud87NBb4A/FN0cRUwwzm3CrgTeNjMUofa38w+YWabzWxzTU3NxIQWkWlFHaXTq6NUo4aKyKQzklHMxlpfX9+w6y699FIuvfRSAP793/99yG2eeeaZEz7fdtttx9/PmjXr+Gigt9122wnrBvrsZz875HyF8+fPP+n4wPFjhkIhfvzjH5+0fvC5hntmobS09ITPl112GZs2bTppu6985St85StfOWn5Qw89NORxZcQqgJIBn4ujy4bzCPCfAM65LqAr+n5L9IrhAuCkB2uccw8ADwCsXr1at5aKyIj0d5T2Tx/xoQ99iDvvvPOk7e677z6effZZfD4fS5Ys4ZprrsHn8x3vKL3tttvIyMg45bn6O0rLy8u55ZZbjo9I2t9RWlRUdFJH6Sc/+UnuvvtugsEgjz76KD7fX65zffrTn+ZTn/oUy5YtIxAIjFlHaVdXFxCpiwsWLDjeUZqfnz+lOkptqoxqM1qrV692p3vAVGLDWAy17EXDJJbs2rWLc845x+sYMkUM9ffFzLY454Yfw3wSM7MAsBe4nEgDcBPwQefcjgHbzHfO7Yu+/yvgi8651WaWA9Q75/rMbA6wHljmnDvlrMmqkeKFs63HsViLVR9lNEZbH3VFUERExEPOuV4zuwN4CvADDzrndpjZPcBm59wTwB1mdgXQAzQAt0Z3vwS4x8x6gDBw++kagSIiIqCGoIiIiOecc08CTw5adveA9387zH6/Bn49vulERGQ60mAxIjIpTNfb1GVs6e+JiIjI2FBDUEQ8FwqFqKur0z/y5ZScc9TV1REKhbyOIiIyYVQbZSTO5O+Jbg0VEc8VFxdTXl6OhrSX0wmFQhQXF3sdQ0RkQvR3lGZlZZ00XYNIvzPtKFVDUEQ8FwwGmT17ttcxREREJhV1lMpInUlHqRqCIiIiIiKTkDpKZTzpGUEREREREZEYo4agiIiIiIhIjFFDUEREREREJMaoISgiIiIiIhJj1BAUERERERGJMWoIioiIiIiIxBhNHyEyAjXlG8/6GDnFF4xBEhEREW+MRS0UkclDDUGRQZxzdLXX0dl6lJ6uZnyBOOJC6SSlzcTnD3odT0RERETkrKkhKDJAb3cb9Ue30dl6FDCC8SmEOxtoazxMY/VO0nOXkJwxBzPzOqqIiIiIyBlTQ1AkqqujnprDL+JcmPS8ZSSnzz5+BbCrvZ6mmh00HN1Gd2cjmQWrMNMjtiIiIiIyNakhKEKkoVddth6fP468mRcTjEs+YX18YiY5M95CU81Ommt341yYrMLVujIoIiIiIlOSGoIS8/p6Oqg5sgG/P57cWZcQCCYOuZ2ZkZ67BDMfTTU7CQQTSc9dMsFpRURERETOnu5tk5jmXJja8ldw4V6ySy4athE4UGr2IpLSZ9Fcu5uOlqMTkFJEREREZGypISgxrbl2D10ddWQWnktcKHVE+5gZmfkrCcanUl+1hb6+7nFOKSIiIiIytia0IWhmV5vZHjPbb2Z3DbH+TjPbaWZvmNmfzWzmgHV9ZrYt+npiInPL9NTT1UJT7W4SU4tJSpsxqn3N5yercDV9vV00HH19nBKKiIiIiIyPCWsImpkfuB+4BlgMfMDMFg/a7DVgtXNuOfAY8K8D1nU451ZGX9dNSGiZtpxz1B99DTM/GXnLz+gYcQkZpGYvpL2pjK722jFOKCIiMj309XXT3dFId2czzjmv44hI1EQOFrMW2O+cOwhgZo8A1wM7+zdwzj07YPuNwC0TmE9iSEdrFV1tNWTkr8QfTDjj46RmL6St8TANR18nb/ZlGkVURESESIdrR2sVLXX7TugsNV+ApLQS0rLPOav6KyJnbyJvDS0Cjgz4XB5dNpyPAr8f8DlkZpvNbKOZvWuoHczsE9FtNtfU1Jx1YJmenAvTVP0mgbhkkjNmn9WxfL4A6blL6e5spK2pbIwSioiITF3hvh7qKjZRe+RlenvaSctZTHbxBWQWriYxtYjWhlIq9z+luinisUk5fYSZ3QKsBt46YPFM51yFmc0BnjGz7c65AwP3c849ADwAsHr1at17IENqayyjp6uF7OILxmRS+MS0Elrq99FUs5OktBJNNC8iIjGrr7eL6sMv0NPVTFrOElKzF5xQF5PTZ5KavYj6yi3UVWyit7udtJxFHiYWiV0T+S/WCqBkwOfi6LITmNkVwP8FrnPOdfUvd85VRP88CDwHrBrPsDI9ORemqXYXcaEMElIKx+SYZkZazmL6etppazw8JscUERGZasJ9PVSXvUhvdys5M95CWs6iITtHg3HJ5M54C4lpJTTV7KClbr8HaUVkIhuCm4D5ZjbbzOKAm4ATRv80s1XA94k0AqsHLM8ws/jo+2xgHQOeLRQZqbamMvp62knLOWdMn+cLJecTF8qgqXY3zoXH7LgiIiJTgXOO2vKN9HQ2kV18IQnJeafcPjL69hoSUgpoOPY67S2VE5RURPpNWEPQOdcL3AE8BewCfuWc22Fm95hZ/yig3wCSgUcHTRNxDrDZzF4HngW+5pxTQ1BGxTlHc+0egvFphJLzx/TYkauC50SuCuqZBxE5AyOYYul2M9serY8vDhx528z+T3S/PWb29olNLgLNtbvpbKsms2AVCSkjq7FmRlbRWuJCGdRVbKa3p32cU4rIQBP6jKBz7kngyUHL7h7w/oph9tsALBvfdDLddbRU0tvdSlbR2nEZ3TOUnE8wPo2Wun0kpc3UCKIiMmIDpli6kshgapvM7IlBnZ4PO+e+F93+OuBbwNXRBuFNwBKgEPiTmS1wzvVN6A8hMauzrYammp0kps0gKX3WqPb1+QJkF6+l6uCfqavYTO7Mi1U/RSaIRrWQmNFct49AMInE1OJxOb6ZkZo1n56uZjpbj43LOURk2jo+xZJzrhvon2LpOOdc84CPSUD/oGjXA48457qcc4eA/dHjiYy7cLiP+qqtBIJJZBasOqNGXCAumYy85XS119DacOD0O4jImFBDUGJCV0cD3R11JGfOHdeexsS0EvyBBJrr9o7bOURkWhrRFEtm9hkzOwD8K/DZUe6rKZZkzDXX7qK3u5WMglX4fGd+o1lS+ixCSbk0Vu+gr7dzDBOKyHDUEJSY0FK/H/MFSB7lLSujZeYjJXMuXe01dHc2jeu5RCT2OOfud87NBb4A/NMo933AObfaObc6JydnfAJKTOnpaqa5di9JaTNOOzjM6ZgZGfkrceE+GqvfHKOEInIqagjKtNfX20l70xGS0mfi8wfH/XxJ6bPAfLQ2HBz3c4nItDGiKZYGeAR41xnuKzImGo+9ifn8pOeNzTAOwfgUUrPm09Z4mK6O+jE5pogMTw1BmfZaG0oBR0rG3Ak5nz8QT1JqCW1NZYT7eibknCIy5Y1kiqX5Az6+E9gXff8EcJOZxZvZbGA+8OoEZJYY1tlWTUdrFWnZi/AHQmN23NTsRfj8cTRV7xizY4rI0NQQlGnNuTCtjYeIT8whGJ8yYedNzpyDC/dqKgkRGZERTrF0h5ntMLNtwJ3ArdF9dwC/IjK/7h+Az2jEUBlPzjkajm3HH0wkJXPemB7b5w+Smr2IzrZqOtuqT7+DiJyxCZ0+QmSitdTvp6+nnfTcJRN63rhQBnGhdFobDpKcMUdDYYvIaY1giqW/PcW+XwW+On7pRP6io6WSns5GsgpXYz7/mB8/JWMOLXX7aKzeQd6sHNVQkXGiK4IyrdWWv4rPH0diykkD6I0rMyM5Yw49Xc10tddN6LlFRETGi3OOppqdBOKSSUybMS7nMJ+ftJxFdHfU06WrgiLjRg1BmbZ6ultprH6TpLQZ49JjeTqJaSWYL6g5kUREZNpoby6np6uZtJzF43qlLiltJv5AiKbaPeN2DpFYp4agTFv1lVtwri8yiqcHfL4AyekzaW+u0JxIIiIy5TnnaK7dQzA+hcTU4nE9l/n8pGTNp6u9RnfWiIwTNQRlWnLOUVvxKklpM4gLpXmWIzljDuCiI5eKiIhMXZ2tx+jpaiIla+GEPLeXnDEHnz+OZl0VFBkXagjKtNTWWEpnWzXZxed7miMYn0J8Yg6tjYdwLuxpFhERkbPRXLcXfyCBpLSS0288Bny+AMkZc+horaKnu3VCzikSS9QQlGmptuIVfP54MvJWeh2F5IzZ9PW001K/3+soIiIiZ6StqYyu9hpSsuZhNnH/fIzcWWO01ut5e5GxpoagTDt9vV00HH2DzPwV+ANxXschMaUQnz+O2vJXvI4iIiJyRo4eeg7zBUlOnz2h5w0EE0hMLaa1sZS+3q4JPbfIdKeGoEw7jTU7CId7yCw4z+soQOSB96S0GTRW76CnS7e2iIjI1NLZVkNj9ZukZM7B5w9O+PlTMufiwr3UVW6e8HOLTGdqCMq0U1+5lbhQOskZs7yOclxyxmyc61MRExGRKefY4ecjo3hmzvPk/PGJWcSFMqgue0nP24uMITUEZVrp6WqhuW4vmQXnTugzDKcTjE8lOX02tRWv4JzzOo6IiMiI9HS1Ule5hazC1fgDIc9ypGTNo6u9hua6fZ5lEJluJs+/lEXGQP3RbYAjs+Bcr6OcJLv4fLraa2ltOOh1FBERkRGprdiIC/eSN/NiT3MkphYTiEuhuuxFT3OITCdqCMq0Ul+1lYSUIhKS87yOcpKMvOX4AwkaNEZERKYEF+6j5sjLpGYtIJSU62kWMx85JRfSXLubzrYaT7OITBdqCMq00dlWTXtzOVkFq7yOMiSfP0hm4bk0HHuD3u42r+OIiIicUkP1m/R0NZMzY53XUQDIKT4fzEdthTpURcaCGoIybdRXvQYYGfkrvY4yrJyi8yODxlRt9TqKiIjIKdWUvURcQiZp2Yu8jgJEnrdPz1lMXcVmwuFer+OITHlqCMq04JyjrmorKVnziAuleR1nWAkpBSSlzaC2fKMGjRERkUmrvbmC1sZD5JZcNKkGX8suPp/enjaaqnd4HUVkyps8/2eLnIW2psN0d9STNQkHiRksu/h8OtuqaWss9TqKiIjIkKqPvITPFySraK3XUU6QmrWAuFAGNXreXuSsqSEo00J95VbMFyQ9d6nXUU4rI28lPn+8ipiIiExKvd1t1Fe9RmbheQSCCV7HOYGZj+yiNbTU76OrvdbrOCJTmhqCMuWFw73UH3ud9Nwlns5xNFL+QByZBatoOPY6vT3tXscRERE5QW3Fq7hwL7klk2OQmMEiVymN2opNXkcRmdLUEJQpr7l2D3097VPittB+OcUX4MK90QFuREREJofIlBEbSMmYS0JKvtdxhhQXSiMt55xog7XP6zgiU5YagjLl1VdtJRBMIjVrgddRRiwxtYjE1GJqy1/RoDEiIjJpNNbspLuzcdJMGTGc7OLz6e1upbFmp9dRRKYsNQRlSuvr6aCxZicZBSsxn9/rOKOSXXw+Ha1VtDcf8TqKiIgIEJ0yIpROes5ir6OcUlrWQoLxadTqeXuRM6aGoExpDdXbceHeKXVbaL/M/JX4/HEaNEZERCaFjpajtDQcIKfkoknfuWo+P1lFq2mu20t3Z6PXcUSmJDUEZUqrr9xKfGI2iaklXkcZNX8gRGb+ShqObqOvt9PrOCIiEuOqj7yE+QJkT7IpI4aTXbgGcNRVbvY6isiUpIagTFndnY20NBwks+BczMzrOGcku/h8wn3dGjRGREQ81dvTTn3lFjILVhGIS/I6zojEJ2aRkjGX2opNOBf2Oo7IlKOGoExZkcaTI6tglddRzlhiagkJKQXUVrzqdRQREYlhdRWbCId7Ju2UEcPJKl5Ld0c9LfUHvY4iMuWoIShTVn3VVpLSZhKfmO11lDNmZmQXnU97czltTWVexxERj5jZ1Wa2x8z2m9ldQ6y/08x2mtkbZvZnM5s5YF2fmW2Lvp6Y2OQyHTgXpvrIBpLTZ5OYWuR1nFHJyF2GPxCiTh2qIqMWGOmGZrYMeC9QCAx8gtg55z46wmNcDfxbdP8fOue+Nmj9ncDHgF6gBvhr59zh6LpbgX+KbvoV59xPRppdpp/2lko6Wo9SsujdXkc5a1mF51G5/w8cO/wCc5bf4nUcETkDZ1MjzcwP3A9cCZQDm8zsCefcwHHxXwNWO+fazexTwL8CN0bXdTjnVo7NTyKxqKl2N90d9RTNf4fXUUbN5w+SWbCK2opNlPS0Ewgmeh1JZMoYUUMw2oD77RDbG+CA0zYEz6bQmVkm8EVgdfR8W6L7Nowkv0w/9VVbwXxk5q/wOspZ8wdCZBdfwLHS5+maX098QqbXkURkFMagRq4F9jvnDkaP9whwPXC8Pjrnnh2w/UZAvUYyZqrLXiQYn0ZG7lKvo5yRrKK11Bx5mfqqbeTOuMjrOCJTxkhvDf1HIAi0EilsPUA30AYcHuExjhc651w30F/ojnPOPeuca49+3AgUR9+/HXjaOVcfbfw9DVw9wvPKNONcmPqqbaRlL5oyD7SfTu6MdWBG9eH1XkcRkdE72xpZBAycULQ8umw4HwV+P+BzyMw2m9lGM3vXKHKL0NlWTUvdPnJKLpz0U0YMJzGliISUAt0eKjJKI20IrgBagP5nErYCi4gUuk+N8BhnU+hGu69MYy31B+npaiJzCs4dOJy4UDqZ+SuprXiV3p720+8gIpPJWNTIETGzW4jcHfONAYtnOudWAx8E7jOzucPs+4log3FzTU3NWMaSKay67CXM/GQXne91lDMWed5+Le0tFbQ3V3gdR2TKGGlDMATsc841AmEgPvrsXgVw71iHGqbQjWQ/FbkYUF+1BZ8/nvScxV5HGVN5s95KuK+bWk0wLzLVnG2NrAAGToZaHF12AjO7Avi/wHXOua7+5c65iuifB4HngCGHUnbOPeCcW+2cW52TkzOCWDLd9fV2Ule5hYz8lQTjk72Oc1YiU0n5qa3c5HUUkSljpA3BRiA1+r4OWGpmXwAWAnNGeIyzKXQj2ldFbvoL93XTcGw7GfnL8fmDXscZU4kphaRkzqe67EXC4V6v44jIyDVydjVyEzDfzGabWRxwE3DC6J9mtgr4PpHaWD1geYaZxUffZwPrGPBsocip1FVsJtzXFXk8YYoLBBNJz1tKfeVWwn09XscRmRJG2hDcC8wws1TgZSLPQvw/Ig/Gbx/hMc640AFPAVdFC14GcFV0mcSYxuodhPu6yCo4z+so4yJv1lvp6WrWBPMiU8tZ1UjnXC9wB5G6tgv4lXNuh5ndY2bXRTf7BpAMPDpomohzgM1m9jrwLPC1QYOwiQwpMmXESySlzSQpreT0O0wB2UVr6evtoLF6h9dRRKaEkU4f8RVgKZAO/H/AEmAukWf17hjJAZxzvWbWX+j8wIP9hQ7Y7Jx7ghMLHUCZc+4651y9mf0LkcYkwD3OufoRZpdppK5yC3GhdJIzZnsdZVykZi0gITmfo4eeJavwPMw01afIFDAWNfJJ4MlBy+4e8P6KYfbbACw7k9AS25pr99LVXkvhsqu8jjJmUjLnERfKoLbiVTILVnodR2TSG1FD0Dn3FCdegZtvZpmjbYydaaGLrnsQeHA055Pppaermea6veTPvmzaNpDMjII5V3LwjZ9RX/UaWYXT88qnyHQyVjVSZCJVH3mJQFwK6XnTpx/BzEdW0WqqDvyJrg5NxyRyOsP+a9rMZphZ3oD3J7yA5AHvRcZd5HZJR1bh9BktdCjpeUtJSC6g6uCfcOE+r+OIyBBUI2Uq62yrobl2NzklF+DzjfTmsKkhq3ANEHn+UURO7VT/95cSedZhXfS9G2Y7d5rjiIyJuqotJKaWEErK9TrKuDLzUTjvKg5s+wl1VVvJLlrjdSQROVkpqpEyRdUc2YCZn5ziC7yOMubiEzJIyZpHXeUmCuZeMW3vIBIZC6f7v8MGvR/uJTKu2lsq6WipiplbJdNylpCUNoPK/X8g3NftdRwRGZpqpEw5fb2d1FZsIiNvOcH41NPvMAVlF62lu7ORlrp9XkcRmdRO1Uv5EaBmwHsRz9RXbgXzkZm/0usoE8LMKF5wLXs2/QfHDr9AwZxhH58VEW+oRsqUVFe5JTplxFu8jjJu0nOX4g8mUlu5idTshV7HEZm0hm0IOud+MtR7kYlUU74R5xy1Fa+QkJxHQ/VIZyuZ+pIzZpOeuzQ6guga4kJpXkcSkSjVSJmKqo+8TNXBPxMXyqC9tZL21kqvI40Lny9AZsEqao9spLe7jUBckteRRCalEd04bWYfN7MHzWzJgGVLoss+Pn7xRKCzrZq+3k6S0mZ6HWXCFS14J86FKd/7P15HEZFhqEbKVNHZVk1vdwspmXO9jjLusovW4lyf5uUVOYWRPkH7/wHXE5nott8u4DrgH8Y6lMhAbU2HMV+QhOR8r6NMuFBiNvmz30bD0ddprtvrdRwRGZpqpEwJLfX78fnjSUwt9jrKuEtMKSQxtZjaildxbrixnERi20hHMisB9jrnwv0LnHNhM6sAFoxLMhEg3NdDR3MlSekzMJ/f6zhnpaZ84xntFwgmEYhL4tAbD7P04n/EH4gb42QicpZUI2XS62yrobP1KKnZi6ZsPR1tHY1PzKbh6DYq9/+BuISMaTlKqsjZGOkVwU5gXv+cSQDR9/Oi60TGRXtLJc71xeRtof3M5yez4Dx6e9qo2Pe/XscRkZOpRsqkV132IpgvJm4L7ZeUVoKZj9bGUq+jiExKI20IvgqEgA1m9s9m9s/AS9Flr45XOJG2xsMEgknEJWR6HcVToaQcUjLnUXNkg24RFZl8VCNlUuvtaaeuYhNJaSX4AyGv40wYnz+OhNQi2pqOEA73eR1HZNIZaUPwG9E/ZwFfir7mEJko9+tjHUoEoKu9jq72GpLSZ2GmqbjScpcSSsrl0PZH6Olq8TqOiPyFaqRMajVHNhIO95CSOd/rKBMuOX0WLtxDR0uF11FEJp0RNQSdc38CbgJK+csEuYeAm5xzz4xbOolptZWbAEhKn+FxksnB5/MzZ/kt9PV2cGj7wwx4HElEPKQaKZNZONxLzZGXSMmaH5PTEMUn5uAPJtLaUOp1FJFJZ6RXBHHOPeqcmwvkArnOubnOucfGL5rEMufC1FVsJpScRyCY6HWcSSMhpYAZ57yblvr9lO/V84Iik4VqpExWDUe30dPVTN7MS7yO4gkzIzl9Fl3tNXS113odR2RSGXFDEMDM4oEEINHMZvS/xieaxLLmur30dDWRnD7L6yiTTlbhGnJmrKP68AtUl73kdRwRiVKNlMnGOcexwy8QSsojNWuh13E8k5QeGXCutmKzx0lEJpeRTii/wMzWA+1Ebn05NOB1cNzSScyqrdhEIJhEQkqh11EmHTOjZOF1pOUs5sju39JYvcPrSCIxTTVSJquW+gN0tFSRN/OSmH7WPhBMJJScR13lZj1WITLASK8I/gBYx1+efRj8Ehkzvd1tNFXvILPwXMxGddE6Zpj5mL3sZhJTizj4xs9pazridSSRWKYaKZPSscPPE4hLJrNglddRPJecPoueriaaazXytki/kU4ofx4QBv4N2An0jlsiiXl1VVtxro/swrW0NpV6HWfS8gfimLfqr9n9yr+zf+uPWLDmUyQk551+RxEZa6qRMul0tB6juXY3BXOvwucPeh3HcwkphQSCSdRWvEpaziKv44hMCiNtCJYDfc65vx/PMCLOOWorXiUxtYSElHw1BE8jGJ/C/PM+zp5N/8nezd9n4ZpPEUrK8TqWSKxRjZRJp7psPeYLkFNyoddRJgUzH5mF51JTtoGe7laCccleRxLx3Ejvu/snYK6ZvWM8w4i0Nx2hs/Uo2UVrvI4yZYSScliw+hPgwuzd8gBdHfVeRxKJNaqRMqn0dLdSV7mFrILz1OAZILtwLc71UV+11esoIpPCSK8IfoPIcw7/Y2ZNQOOAdS46ZLbIWasp34jPH6/nGYZRU75x2HXZJRdwrPQFdm38N/JmvXXYaTdyii8Yr3gisUo1UiaV6rIXceE+cmde7HWUSSUhJZ/E1BJqy18ld8bFMT2AjgiM/IrgTCBIpNClA7MGvUTOWm9PO/VHt5FZsAp/IOR1nCknLpRO7sy3EO7rpvrwevp6O72OJBIrVCNl0ujr7aSmbAPpuUv03PgQsovX0tl2jHYNsiYy4iuCPwXceAYRqa/cigv36IrVWYhPyCR3xjqqD79I9eH15M68BH8g3utYItOdaqRMGrXlr9DX20H+rLd5HWVSysxfSfnuJ6ip2EhSuqb5lNg2ooagc+62cc4hMc45R035yySlzSAxtcjrOFNafGI2OTMuoqbsJarL1pM38xJ8/jivY4lMW6qRMlmEw70cO/wCKRlz1cgZhj8QIrPgXOqqtlA8/50E4pK8jiTimRFP0mZmfjP7gJn9m5n9o5nNMLNLzCxzPANKbGhtOERnWzXZuho4JkJJuWSXXEhPVwvVh18k3NfjdSSRaU01UiaD+sot9HQ1kz/nMq+jTGq5M9bhwr3UVrzqdRQRT42oIWhmacBG4L+AO4BrgcXAs8Bnxy2dxIya8pfxBxLIzF/hdZRpIyE5n+zi8+nubKSm7CXCYU1tJjIexqJGmtnVZrbHzPab2V1DrL/TzHaa2Rtm9mczmzlg3a1mti/6unVMfiiZcpwLc7T0ORJTikjJnO91nEktIaWA5Iw51Bx5GefCXscR8cxIrwh+jb9MmNs/xNLTQAdw9TjkkhjS09VK47HtZBWep1sYx1hiSiFZRWvp6qijrvxVnNNjTCLj4KxqpJn5gfuBa4g0ID9gZosHbfYasNo5txx4DPjX6L6ZwBeB84G1wBfNLONsfyCZehqPvUlXey35s9+m0TBHIHfGOro7G2iq2el1FBHPjLQheD3QChwfAts51weUAXPGIZfEkLrKTTjXp9tCx0lSWjEZ+SvoaK1SwRMZH2dbI9cC+51zB51z3cAj0WMe55x71jnXHv24ESiOvn878LRzrt4510CkAaoO2hjjnONo6bPEJ2aTnrfM6zhTQnrOEoKhdKrLXvI6iohnRtoQzAIOOOfKBi0PA6ljG0liiXNhao68THLGHA1zPY6SM+aSlD6L5trd1B/d5nUckenmbGtkETBwLPvy6LLhfBT4/RnuK9NQS/0+2pvLyZt1KWYjHv4hppnPT07xhbTU76ej9ZjXcUQ8MdLfFhXAIjM7p3+Bma0FziFSdETOSFPNTro7G8idsc7rKNOamZFZsIr4hCxK3/wV7c3631ZkDE1YjTSzW4DVRCaxH+2+nzCzzWa2uaamZixjiceOHnqWYHwqWYXneR1lSskpPh/zBajRVUGJUSOdR/Bx4O+AbUTmSjoX2BBd999jHUpiR3XZSwRD6aTnLPE6yrRn5iO75AKqy15i/7afsPjCzxEIJnodS2Q6eJyzq5EVQMmAz8XRZScwsyuA/wu81TnXNWDfSwft+9xQJ3HOPQA8ALB69Wo9MDwF1ZRvPGlZV0c9LfX7Sc9dRl3lZg9STV2BuCQy81dSV7WFwvlXqyZKzBnpFcG7iTyoHiTyIHxcdN/XgXvGJ5pMdx2tR2mp309uyYWYz+91nJjgD4SYu+LD9Ha1cHjHoxo8RmRsnG2N3ATMN7PZZhYH3AQ8MXADM1sFfB+4zjlXPWDVU8BVZpYRHSTmqugyiRHNNbvx+YIkZ8z2OsqUlDvzYsJ93dQcednrKCITbqQTyrea2QXAB4g81A6RwvWL6IPtIqNWXfYS5guQXXS+11FiSlJaCYXzr6Zi7/9SW76RnJILvY4kMqWdbY10zvWa2R1EGnB+4EHn3A4zuwfY7Jx7gsitoMnAo9ERIcucc9c55+rN7F+i5wO4xzlXP6Y/oExa3R0NdLRWkZazGJ8/6HWcKSkxpZDUrIVUl71I3sxL9D1KTBnpraE453qAn0ZfImelt6ed+sotZBasIhCX5HWcmFJTvhGfP55QUi5Hdv+W3p72Uf83yNEIryInONsa6Zx7Enhy0LK7B7y/4hT7Pgg8eCbnlamtqWYX5guSkjnP6yhTWv7st7F38/eoq9yszlGJKSNqCJrZqQqMc859dIzySIyoq9hEONxDbokGifFCZPCYc6k68DT1Va+RM2Od5p0SOUOqkeKF7o5GXQ0cI8kZc0hMLeFY6fNkF5+vkVclZoz0iuBtRB6AH8yiy0dU5MzsauDfiNz68kPn3NcGrb8EuA9YDtzknHtswLo+YHv0Y5lz7roRZpdJxrkw1Uc2kJw+m8RUjXLulUBcEul5S2k4+jrtTWUkpc/0OpLIVHUbY1AjRUajqbb/auDc028sp2Rm5M++lIOv/4yGY9vJzF/hdSSRCTHShmAZJxa5NCCdyBxJg+dNGpKZ+YH7gSuJDKe9ycyecM4NnOG6jEhB/YchDtHhnFs5wrwyiTVW76C7o57i+e/0OkrMS86YS1vTERqOvUEoOQ9/IOR1JJGp6KxrpMhodHc20tFSSVrOOfj8cV7HmRbSc5cSn5jNsUPPkpG3XHfJSEwY0bVv59ws59zsAa9M4DKgHfjiCM+1FtjvnDsYfXj+EeD6Qecpdc69QaR4yjR17PALxIUySM/VlBFeMzOyCs8jHO6l4ejrXscRmZLGqEaKjJieDRx7Zj7yZl1Ke0sFLfX7vI4jMiHO+CZo59xzwGbgH0e4SxFwZMDn8uiykQpFJ8LdaGbvGsV+Mom0NZbR1lhK7syLNWXEJBGMTyUtexHtzeV0tB71Oo7ItHAGNVJkRLo7m+hoqSQlc56uBo6xrMLzCManUnXgT5peSWLCSAeL+fCgRX5gLrAO6BnrUMOY6ZyrMLM5wDNmtt05d2BQzk8AnwCYMWPGBMWS0Th2+Hn8gRDZRWu8jiIDpGYtoK3xMA1H3yA0N1cPyouMwiSpkRIjIlcDA6Rm6WrgWPP5AuTPvpwju/+b5rq9pGUv9DqSyLga6TOCDzH8g/AjnYGzAigZ8Lk4umxEnHMV0T8PmtlzwCrgwKBtHgAeAFi9erW6ciaZrvZ6Go5tJ2/WW/Us2iRjPj/p+cupPfIyrQ0HdbuRyOg8xNnXSJHTilwNrCA1e5GuBo6T7OK1HCt9jsr9fyA1a4GeFZRpbTTd/jbE62XgYyPcfxMw38xmm1kccBPwxIhObJZhZvHR99lEell3nnovmWyqy9aDGbkzNGXEZJSQXEB8Ug5N1Tvp6zvtHNgicqKzrZEip9VUsxPzBUjJmu91lGnL5wtQMPdK2pvLaax+0+s4IuNqpA3B2YNes4BE59w659z+kRzAOdcL3AE8BewCfuWc22Fm95jZdQBmtsbMyoH3Ad83sx3R3c8BNpvZ68CzwNcGjTYqk1xvTwe1FZvIzF9JXCjd6zgyBDMjI28F4XAPTTX630tkFM66RoqcTmvjYTpaKknNWoBfVwPHVVbBucQn5lC5/ymc0/iFMn2N6NZQ59zhsTiZc+5J4MlBy+4e8H4TkVtGB++3AVg2FhnEG7XlGwn3dZE38xKvo8gpxIXSSM6YQ2v9QVIy5hCMT/U6ksikN1Y1UmQ4zjkq9v4vPn+8rgZOAPP5KZx3FYfe+DkNR18ns2CV15FExsWIrgia2ffM7KCZrRywbIWZHTCz/xi3dDIthMO9VJe9RErmPE0gPwWk5SzGfAEajm33OorIlKAaKeOtuXY3rY2HIvMG+kY6vIOcjYy85SSkFFB54I+Ew71exxEZFyP9bfJXQJ9zblv/Aufc62YWAK4DPj0O2WQaqCnfSFtjGT1dTaTnLqGmfKPXkeQ0/IF40rIX0lj9Jp3ttYQSs72OJDLZqUbKuHEuTPm+J4lPzCY5Y7bXcWKGmY+iee9g/2s/orrsRfJnXep1JJExN9JnBLOBliGWtwI5YxdHphvnHM31ewnEpRBKzvc6joxQcuZc/IEQTcfe1FxKIqenGinjpr5yK52tRymad7Wm9plgaTmLSMs+h6oDf6Knq9nrOCJjbqS/UeqABWZ2fLhHM7sIWBhdJzKkrvYaejqbSM2aryGYpxCfL0Bq9iK6Ouro1CTzIqejGinjItzXQ8WBp0hMLSE9b7nXcWJS8aLrcOFeKvY9efqNRaaYkTYEnyEyQe4zZvZnM/szkdE7DfjzeIWTqa+5bh8+fzxJaTO8jiKjlJwxm0AwicbqHboqKHJqqpEyLmqObKCns5HiBe9QZ6pHQonZ5M26hLrKLbQ2lnodR2RMjbQh+EWgCQgCl0ZfQaAxuk7kJB2tx+hsPUpK5hzM5/c6joySmY+03MX0dDXR3lzudRyRyUw1UsZcb08HVQf/TGrWQlIy53kdJ6blz76cYHwqR3b/VtNJyLQyooagc+4AsBp4iMgcgLuAHwNrnXMHxy2dTGnVh9dj5iM5Y67XUeQMJaaWEIxPpalmh4qfyDBUI2U8HCt9lr7eTooWvMPrKDHPH4ineMG1tDeXU1v+itdxRMbMiMcgjha6vx7HLDLJnM0In329ndRWbiI5bSb+QPwYppKJZGak5S6h9sjLtDWWkpwxx+tIIpOSaqSMpe7OJo4dXk9mwSoSUwq9jiNARv5KastfoXzv/5KWcw5xoXSvI4mctREPP2Vms8zs+2b2upk9amYXmNndZrZkPAPK1NTacBBcmJQs3c4y1SUkFxCXkElTzS7C4T6v44hMSqqRMpaqDvwRnKNw3tu9jiJRZsbMJe8DF+bwjsf07LxMCyOdUP4cYCvwMWAZUAR0A18Cbh+vcDI1uXAfLfUHCSXnE4xP9TqOnCUzIz13KX29nbTWH/A6jsikoxopY6mj9Ri1FZvIKbmI+IRMr+PIAPGJWRQteCfNdXuoq9zsdRyRszbSK4JfA9KBnf0LnHNbgQYiD8WLHNfWdIRwXxepmfO9jiJjJJSUQygpj+a6PYT7eryOIzLZqEbKmKnc/3t8/jgK5lzudRQZQk7JhSRnzKF8zxN0dzZ5HUfkrIz0GcG3AkeB84DOAcuPALPGOJNMYc45Wur3EYxPIz5J8yhPJ+m5Szh66Bma6/aRN/Nir+OITCaqkTImWhtLaazeQeG8qwnEJXkdRwbpHzshJWs+bY2H2bf1h+SUXDTiqT1yii8Yz3giozbSK4JxQL1zrnvQ8nQiQ2SLANDZVk1PVzMpmkB+2olLyCAhpYiW+n30dLd6HUdkMlGNlLPmnKNi75ME4lLInaHOtsksGJdMeu5SOluPRsZEEJmiRtoQ3AecY2Yfi34Omdn/B8wA9oxLMpmSWur24QuESEot9jqKjIP03MW4cC9HDz3rdRSRyUQ1Us5aU80uWhsPUTj3SvyBOK/jyGkkZ84llJxPw7E3dIuoTFkjvTX0AeDfge8DDlgRfTngR+MTTaaa7s5mOtuOkZazWBPIT1PB+FSS0mdSc2QDeTMv1vDZIhGqkTIiw03L5Jzj6MGnCcQl43BnNX2TDG8sv1czI6vwPKoO/pna8lfIn3MZPt+IZ2UTmRRGOqH8/cD90Y8WfQF8L7pOhJb6fdEJ5DXX3HSWln0OOEfVgT95HUVkUlCNlLPV1nSYnq4W0nOXYDbimb3EY/5AiOzC1fR2t9B49A2v44iM2oh/2zjn/gaYA7w/+prrnPvMeAWTqaWvt5O2pjKS0jWB/HQXiEsip+RCais30dlW43UckUnhbGukmV1tZnvMbL+Z3TXE+kvMbKuZ9ZrZewet6zOzbdHXE2f7s8jECof7aKreSVwo8hy2TC2h5DxSsxbQ2niI9uZyr+OIjMppr2GbWRA4BtQBC5xzh8c9lUw5xyeQz9QE8rEgf/Zl1Ja/QuWBPzJn+c1exxHxzFjUSDPzE7mieCVQDmwysyecczsHbFYG3Ab8wxCH6HDOrRzteWVyaK3fT19vB1lFazTI2hSVlruEzvZa6iq3EAylE4xL9jqSyIic9oqgc66HyHDYnc45N/6RZKrRBPKxJxifQu7Mi2k4uo32lkqv44h4Zoxq5Fpgv3PuYHTk0UeA6wedp9Q59wYQPqvAMqmE+7ppqt1DKDmPkKZcmrLMfGQXrcUwastfwYX7vI4kMiIjvTX0PmChmV05jllkimpr1gTysShv1qX4AwlU7vuD11FEvHYfZ1cji4jMOdivPLpspEJmttnMNprZu84wg3igqXYPLtxDeu5Sr6PIWQrEJZFZtJqezkYajul5QZkaRjq80TVEeiH/YGZ7iNwG09/z6Zxzl49HOJn8nHO01h8gGJ+iCeRjTCCYQP7sS6nY93ta6g+QkjnX60giXvG6Rs50zlWY2RzgGTPb7pw7MHgjM/sE8AmAGTNmjHMkOZ3ennZa6veTmDZDIzBPE4kphaRkzaelbh/xidkkpZV4HUnklEZ6RfCtRCbMNWBR9PNbgUujL4lR3R0NdHc2kpwxV882xKDcGRcTDKVTvud/cE53rEnMOtsaWQEM/BdjcXTZiDjnKqJ/HgSeA1YNs90DzrnVzrnVOTnquPNaU80uANJzFnucRMZSeu5S4hKyqK/aSk9Xi9dxRE5p2CuCZrYcaIv2Kj4/cZFkKmlpOID5AiSlqXc5Fvn8QYrmX0Pp9l9QX7mVrKLVXkcSmRBjXCM3AfPNbDaRBuBNwAdHmCMDaHfOdZlZNrAO+NezzCPjrLuzmbbGUlIy5xGIS/I6jowhMx/ZxWs5Gp1fMG/22/BpbmWZpE51a+g24GUiReWtwEbn3EUTEUqmhr7eTtqby0lOn43PH/Q6jngkM38l1YdfpGL/70nPW44/EOd1JJGJsI0xqpHOuV4zuwN4CvADDzrndpjZPcBm59wTZrYG+G8gA/grM/uyc24JcA7wfTMLE7nL52uDRhuVSaip+k3MFyA1e5HXUWQcBIKJZBWuoebISzQefZ3MwnO9jiQypFM1BB2QY2YjfY5QYkxrY2l0yghNIB/LzHyULPwr9mz6D44dfo7CuVd5HUlkIoxpjXTOPQk8OWjZ3QPebyJyy+jg/TYAy8Yig0yMzvZaOlqrSMtZonl3p7GElHxSsxbSXLeH+KRs3Tklk9KpnhGsAuYCXUQK3vnRSWsHv3onJKlMKs6Faa0/SCgpV1NGCMkZs8nIW86xQ8/R3dnkdRyRiaAaKaPmnKPx2Jv4AyFSsjTv7nSXlruY+IQs6iu30tPV7HUckZOcqiH4X0QefO8fAcRO8ZIY09FSRV9vB8kZuhooEUXz34FzYSr2/q/XUUQmgmqkjFpHaxXdHXWk5pyDz6cbrqY7Mx9ZxedjPj+15a8Q7uv2OpLICYb9LeScu8vMXgaWAF8hMq/RjyYqmExurQ0H8QcSSEgp8DqKTBLxiVnkz34bVQf/RFbRalKzFngdSWTcqEbKaDkXpql6B4G4ZJLTZ3kdRyZIIJhAVtEaaspeomz348xa8n6vI4kcd8ruKOfcb4HfmtlVwJvOuS9PTCyZzHq6W+lsqyYtZzFmI52BRGJB/uzLqD+6jbJd/83iC+/UIEIyralGymg0HH2dnq5msorWqnbGmITkfFKzF1FXsYmUjDlkFWqEbZkcRvSbyDl3qXPujvEOI1NDW0MpAEnq0ZRBfP4gM855N13ttRw99IzXcUQmhGqknI4L91G5/ymC8Wkkpp405o/EgLScc0jOmEPZzt/Q0XrM6zgiwMgnlBcBooPENB0mIbmAQDDB6zgyCaVmLSCzYBVHDz2rYiciAtRWbqKro4703CWY6bHRWGTmY87ym/EF4jn4+s8I9/V4HUlEDUEZnY6WKsK9nSRlzPI6ikxixQuuw+ePo2zXb3Au7HUcERHPhPt6qDrwJ5LSZhJKzvc6jngoGJ/KrKU30dl2jIp9T55+B5FxpoagjEpr46HIIDEqZnIKwfhkihdcS2vDQarLXvI6joiIZ2qObKCnq4mi+dfoaqCQlr2QnBnrqC57kea6vV7HkRinhqCMWG9PO52tx0hKn6kH3eW0sorWkJazmIp9T9LRUuV1HBGRCdfX20nVoWdIyZpPSuZcr+PIJFE8/52EknIpffOX9Pa0ex1HYtiE/mvezK42sz1mtt/M7hpi/SVmttXMes3svYPW3Wpm+6KvWycutfRrjQ4So2GvZSTMjJmL34c/EOLQ9of1PISIxJxjh9fT19NO0bxrvI4ik4jPH2T2sg/S291G2c5f45zzOpLEqAlrCJqZH7gfuAZYDHzAzBYP2qwMuA14eNC+mcAXgfOBtcAXzSxjvDPLXzjnaGssJZSURyAuyes4MkUE45OZteT9dLQepWL/H7yOIyIyYXq72zhW+jzpuUtJSivxOo5MMompRRTOezsNx96gvmqr13EkRk3kFcG1wH7n3EHnXDfwCHD9wA2cc6XOuTeAwaNLvB142jlX75xrAJ4Grp6I0BLR2XqMvt4OkjNmex1Fppi0nHPIKbmQ6sMv6HkIEYkZR0ufJ9zXTeG8t3sdRSapvFlvJTl9NmW7/puujnqv40gMmsiGYBFwZMDn8uiyMdvXzD5hZpvNbHNNTc0ZB5WTtTUdxuePIyGlwOsoMgUVL7iWUFIeB9/4uYqdiEx7vd1t1Bx5iYz8FRpcTYZl5mPWspvAjNLtj2iUbZlw02rED+fcA8651c651Tk5OV7HmTbCfd20t1SSmFaiQWLkjPj8ccxdeSu4MAe2PUS4r9vrSCIi4+bY4RcI9/VQMOcKr6PIJBefkMmMRe+itfEQ1YfXex1HYkxgAs9VAQy8Sb44umyk+146aN/nxiSVnFZbczm4MMlpM72OIlNYKCmH2ctuZv9rD1K641GSM+ac9VDqOcUXjFE6EZGx0dvdRnXZS2TkLychOc/rODIFZBacS8OxN6jY/wfScs4hlJTrdSSJERN5eWcTMN/MZptZHHAT8MQI930KuMrMMqKDxFwVXSYToK3xMMH4VIKhdK+jyBSXlrOIwnlX03B0Gy16XlBEpqFjh9cT7uvS1UAZscgo2+/B5wtS+uavdIuoTJgJawg653qBO4g04HYBv3LO7TCze8zsOgAzW2Nm5cD7gO+b2Y7ovvXAvxBpTG4C7okuk3HW09VCd0d9dO5ATYQrZy9/9ttIz1tOY/WbtDeP9KYAEZHJr7enneqyF0nPW65nA2VUgvGplJzzLtqaDusWUZkwE3lrKM65J4EnBy27e8D7TURu+xxq3weBB8c1oJykrfEwYCSlzfA6ikwTZsbspTeyo7mc2opXyfVfTCgp2+tYIiJnrTp6NbBQVwPlDGTmr6LhqG4RlYmjkT9kWM452prKCCXn4Q+EvI4j04jPH0dOyUUEgonUHNlAd2eT15FERM5Kb09H5Gpg7lKNsC1nJHKL6A34/HGUvvlL3SIq404NQRlWZ1t1ZO7AdA0SI2PPH4gnd8Zb8Pn81JS9RG9Pu9eRRETOWHXZevp6OymYe6XXUWQKC8anMmPRu2hrKuNY6Qtex5FpbkJvDZWppa3pMD5fkIRk9WzK+AjEJZEz4y0cK32O6sPryZv1Vl19FpEpp6+ng+rDL5Keu4TElEKv48gkVVO+cUTbOedISCmkYv/vCYe7CcanAhopW8aergjKkPp6u+horiQxrRjz+b2OI9NYXCiNnBnrIv+QKntRcwyKyJRTfeQl+no7KJijq4Fy9syMzIJV+HwB6iq34JzzOpJMU2oIypAaq9/EuT4SNUiMTIBQYjbZJRfQ09lMddkGwuFeryOJiIxIX283xw6vJy37HBJTi7yOI9OEPxAiI38l3R31mm5Jxo1uDZUh1Ve9hj+YSHxCltdRZBIa6e0to5GQnE928Vpqy1+h9sjL5JRcpKvRIjKpDPW7r7luH3097YSS88bld6PErsTUYtqby2ms2UlCSsFZ//3SraUymK4Iykl6ulporttLUlqJ5g6UCZWYWkxm4Xl0tlVTW/GqRkwTkUnNuTAtdfuIT8wmPlEdpzK2TrhFtGKzaqKMOTUE5SQNR18HnOYOFE8kp88iPW85HS2V1Fdu1bMREhPM7Goz22Nm+83sriHWX2JmW82s18zeO2jdrWa2L/q6deJSS1vTEfp6O0jNWuh1FJmmjt8i2tlAc90+r+PINKOGoJykrmorCSmFx0epEploqVnzSctZTFvTYRqOva7GoExrZuYH7geuARYDHzCzxYM2KwNuAx4etG8m8EXgfGAt8EUzyxjvzBIZ2bG5dg/B+DRCyXlex5FpLDG1mISUIppqdmreXRlTagjKCTrbamhvPkJWwbleR5EYl5q9iJTM+bTWH6CpZqfXcUTG01pgv3PuoHOuG3gEuH7gBs65UufcG8Dge8PeDjztnKt3zjUATwNXT0ToWNfRUklvdwup2Qv1GIWMq7/cIhqkrlK3iMrYUUNQTlBf9RpgZOSv9DqKxDgzIz1vGUnps2iu3U1zrUZNk2mrCDgy4HN5dNl47ytnqP9qYCCYpJFCZUL4A/FkFqyip7OR5trdXseRaUINQTnOOUd91VZSMucSF0rzOo5ItBf0XBJTi2ms3k5rw0GvI4lMWWb2CTPbbGaba2pqvI4zpXW119Ld2UBK1gLM9E8pmRiJqUUkppXQVLOb7o4Gr+PINKDfXnJce9MRujrqyNRtoTKJmBlZRWsIJedRX/UabU1HTr+TyNRSAZQM+FwcXTam+zrnHnDOrXbOrc7JyTmjoBLRXLsHnz+e5PSZXkeRGJOZvxJ/ID5yi2i4z+s4MsWpISjH1R3divkCZOQu9TqKyAnMfGQXX0B8YjZ1FZvoaKnyOpLIWNoEzDez2WYWB9wEPDHCfZ8CrjKzjOggMVdFl8k46e5ooLPtGKlZ8zXXqUw4nz+OzIJz6elqpqlml9dxZIpTQ1AAcOE+Gqq2kZazGH8wwes4Iifx+QLklFxEXCiNmvKNdLbp1jaZHpxzvcAdRBpwu4BfOed2mNk9ZnYdgJmtMbNy4H3A981sR3TfeuBfiDQmNwH3RJfJOGmu24v5AiRnzPE6isSohJSCyPPzdXvoaq/zOo5MYQGvA8jk0Fy3j96eNrIKVnkdRWRYPn+QnBlv4Vjp89QeeZmc4gsIJekWN5n6nHNPAk8OWnb3gPebiNz2OdS+DwIPjmtAAaCnq4X25nJSsxbi8we9jiMxLCNvOZ1t1dRVbiZ/zuX4fPonvYyerggKAPVVW/EHEkjNXuR1FJFT8gfiyZ1xEZix/7Uf09vT4XUkEYkRzXV7wXykZM3zOorEOJ8/SFbBefR2t9JUvcPrODJFqSEo9PV20Vj9Jhn5y9WjJFNCIC6Z7OIL6e6o59Ab/6UH5kVk3HV3NtHWVEZy+iz8gZDXcUQIJeeSnDGHlvr9elxCzogagkJj9ZuEwz0aLVSmlFBSNjMW30Bz3V6O7P0fr+OIyDRXXbYeXJjUrPleRxE5Lj1vGYFgEnWVmwmHe72OI1OMGoJCfdVrxIUySE6f5XUUkVHJLlpL7sxLqCl7idqKTV7HEZFpqrennZojG0lMLSEQl+x1HJHjfL4AmUWr6etpp/HYG17HkSlGDcEY19PVQnPdXjILVmlSXJmSiue/g5TMeZTt+g3tzSOdek1EZORqjrxMuK+L1OyFXkcROUkoMZuUzPm0Nhyio/WY13FkCtG//GNc/dFtgNNtoTJlmc/P7GU3EwgmceD1n9Lb0+51JBGZRsJ93VQfXk9q9iLiQmlexxEZUnruEoLxKdRXbqavt8vrODJFqCEY4+qrXiMhpYiE5Dyvo4icsWB8MnNW3EJPZxOl2x/BubDXkURkmqit2ERvTxv5s9/mdRSRYZnPT1bR+fT1dVNfuQXnnNeRZApQQzCGdbZV0958RHMHyrSQnD6L4oV/RVPtLo4eetbrOCIyDbhwH8dKnycpfSbJ6bO9jiNySnGhNNJzl9LRWkVrwyGv48gUoIZgDKuveg0wMvJXeh1FZEzklFxERv5KKg/8kdbGUq/jiMgUV390G92dDeTPvgwz8zqOyGmlZM4jlJRH47E36Olq9jqOTHJqCMYo5xx1VVtJyZynZx5k2jAzZp5zA3GhdA698XM9LygiZ8y5MEdLnyOUnE9a9iKv44iMiJmRVbQa8wWoLX9V8+zKKakhGKPamg7T3VFPlgaJkWnGH0xgzvKb6e5q5vCOR/WchIickaaa3XS2HiV/1qUaVVumFH8gRFbhefR0NdFYvcPrODKJBbwOIN6or9qK+YKk5y31OorIGasp3zjsuvScJTRWb+fQ9l+Qkjln2O1yii8Yj2giMgmd6nfGQM45jpU+hz+YSF9f14j3E5ksElIKSM6YQ0v9PkLJeRoUUIakLq4Y5MJ91B99nfTcxfgDIa/jiIyLlKz5hJLyaDj2Ot2dTV7HEZEppKu9lu6OelKzFuhqoExZ6XnLCcanUlexid6eDq/jyCSk324xqKluD3097Zo7UKa1/uckfP44astfIRzu9TqSiEwRzbW78fnjSUqf5XUUkTPm8/nJLj4fF+6lrkLPC8rJ1BCMQfVVW/EHE0nLWuh1FJFx5Q+EyC5cTW93Cw1HX/c6johMAd0dDXS2VZOSNR+fz+91HJGzEoxPJbPwXLraa6nY/wev48gko4ZgjOnr7aSxegeZ+SswFTiJAaHkPFKzFtLWWEpb0xGv44jIJNdUuwfzBUjJGP7ZYpGpJCltBskZszlW+pwGj5ETqCEYYxqOvo4L95JZcJ7XUUQmTFruYuISMqmv2kpvd6vXcURkkurpaqGjpYKUjLn4/EGv44iMmYy8FSSmFlP65i/pbKvxOo5MEhPaEDSzq81sj5ntN7O7hlgfb2a/jK5/xcxmRZfPMrMOM9sWfX1vInNPJ7UVmwgl5ZKUNsPrKCITxsxHdtFawCLzKrmw15FEZBJqrtuLmY+UrHleRxEZU+bzM2f5h8CMA9seok+DxwgT2BA0Mz9wP3ANsBj4gJktHrTZR4EG59w84NvA1wesO+CcWxl93T4hoaeZjtZjtDUdJqtoDWbmdRyRCRWISyKr8Dy6OxtoOPaG13FEZJLp7WmnrfEwSemzNKK2TEvxiZnMXfFhOttrOfjGz9UpKhN6RXAtsN85d9A51w08Alw/aJvrgZ9E3z8GXG5qsYyZusrNYD6ydFuoxKjE1CJSMufRWn+A9uYKr+OIyCTSUrcPgNSsBR4nERk/KZlzmXHOu2mu20PF3v/1Oo54bCIbgkXAwJEayqPLhtzGOdcLNAFZ0XWzzew1M3vezC4e77DTjQv3UVe5hbTsRQTjU7yOI+KZ9LxlxIUyqKvcoucFRQSAvt4uWhsOkZRWQiAuyes4IuMqp/gCckrWcezwC9SWv+p1HPHQVBkspgqY4ZxbBdwJPGxmqYM3MrNPmNlmM9tcU6MHYQdqqttDb3cL2UVrvI4i4ikzH9nF5wNofkERAaClfh/O9ZGarWmVJDaULPwrUrMWcHjXr2msftPrOOKRiWwIVgAlAz4XR5cNuY2ZBYA0oM451+WcqwNwzm0BDgAn3bvhnHvAObfaObc6JydnHH6EqauuYhOBuGTSss/xOoqI5/7yvGAj5Xt/53UcEfFQX28XLfUHSEwtIhh/Uh+zyLRkPj9zVnyYpNRiDr7xc1rqD3gdSTwQmMBzbQLmm9lsIg2+m4APDtrmCeBW4GXgvcAzzjlnZjlAvXOuz8zmAPOBgxMXfeqpKd94/H3/3IEpmfOordzkYSqRyaP/ecGaspdISZ9DRv5yryOJiAda6vfjwr2kqqNUYow/EM+8VX/Nnk3/yf7XfszCNbeTmFrsdSyZQBN2RTD6zN8dwFPALuBXzrkdZnaPmV0X3exHQJaZ7SdyC2j/FBOXAG+Y2TYig8jc7pyrn6jsU11bUxngSEqf5XUUkUklPW8ZSWkzKN3xK9pbKr2OIyITrK+vm5b6/SSkFBEXSvM6jsiEC8QlMf+8jxMIJrJvyw9VC2PMhD4j6Jx70jm3wDk31zn31eiyu51zT0Tfdzrn3uecm+ecW+ucOxhd/mvn3JLo1BHnOuf+ZyJzT2XOOVobDhGXkElcSLe8iAxk5mPOig/jD8Rz4LUf09OlwWNEYklL3T5cuJe0HF0NlNgVF0pj/nkfx/xB9m76T1obDnkdSSbIRN4aKh7obKumt7uVrEINEiMylLhQGnNX3saeTf/Jgdd/woLVn8Tnm/hfjQNv5z5TOcUXjEES8YKZXQ38G+AHfuic+9qg9fHAT4HzgDrgRudcqZnNInKXzZ7ophs11+7IhI9fDSzU1UCJeaGkHBat+TR7tzzAvi0/YO7KWzV4UgyYKqOGyhlqrT+Azx9PYurgmTpEpF9SWgmzl95IW2MpZTsfwznndSSJIWbmB+4HrgEWAx8ws8WDNvso0OCcmwd8G/j6gHUHonfMrFQjcOSa6/braqDIAHEJGSxc82nik7LZ/9qPqa96zetIMs7UEJzGervb6GitIjljFubzex1HZFLLyF9BwdyrqKvcQtWBP3odR2LLWmC/c+6gc64beAS4ftA21wM/ib5/DLjczGwCM04rkZFC90WvBqZ7HUdk0gjGp7Bg9adISpvBoe0Pc2T3E7hwn9exZJyoITiNtTZEBlZNzpjjcRKRqaFgzhVkFa2l6uCfOHZ4vddxJHYUAUcGfC6PLhtym+jga01AVnTdbDN7zcyeN7OLhzuJ5tr9i+a6PdGrgUu8jiIy6QSCCSxY/UlyZ7yF6rL17N38fXq6mr2OJeNADcFpyoX7aG0sJSGlkEAw0es4IlOCmTFz8XtIz11G+Z4nqC57yetIIqdTBcxwzq0iMtr2w2Y25Mhgmms3orennZb6AySlzdQgaiLDMJ+fkkXXM3vZB2lvLmfny9+m/ug2PToxzaghOE21NZcT7usmJXOu11FEphQzH7OXf5C0nCUc2f04x0qf9zqSTH8VQMmAz8XRZUNuY2YBIA2oc851OefqAJxzW4ADwIJxTzyFNdXsAuf0bKDICGQWrGLRBZ8lLpTOoTd+zv7XHqSrQzO4TRdqCE5TrfUHCMSlEJ8Yu72+ImfK5wswd8WHyMhbTvne30WekXBhr2PJ9LUJmG9ms80sDrgJeGLQNk8At0bfvxd4xjnnzCwnOtgMZjYHmA8cnKDcU05PVwttjYdJzpxDIC7J6zgiU0JCcj6L1t5B8cLraG04yM6X7qVy/x/p7enwOpqcJU0fMQ21NpbS3dlARv5KNJaAyJkxn5/Zy28muCeV6rL1dLbXMHvpTfrHo4w551yvmd0BPEVk+ogHnXM7zOweYHN0rt0fAT8zs/1APZHGIsAlwD1m1gOEgdudc+quH0ZTzQ7MfKRlL/I6isiUYj4/eTMvJiN3KUf2/A9VB5/mWNl68ma8hdyZF4/oMaSznSZJUySNPTUEp6Gjh57F548jKX2m11FEpjQzHyWLrieUlMuR3b9l18b7mLnk/aRmzfc6mkwzzrkngScHLbt7wPtO4H1D7Pdr4NfjHnAaaGs6QntzBanZi/AHQl7HEZmS4hIymLvyw7S3VFJ14Ono4GovkJG/guyi80lKm6GLEFOIGoLTTEfrMZpqdpKafY4nk2KLTEc5JReSmFrMoe0Ps2/LA2QVrqFw3ts1CbXIFOGc48ju3+Lzx5OapUcoRc5WYkohc1feSntLJdVlL9FwdBt1FZtISM4ns+Bc0nOXEkrS40mTnVoK08yx0ucxX1CDxIiMsaS0EhZfeGfkdpjSF6g/+ho5xReQM2MdocRsr+OJyCnUV22lrekwmQXn4fMHvY4jMiUNd2tnUloJCcn5tDeX09pYSsW+J6nY9yTB+FQSUopITC0kGJ+mK4WTkBqC00hXez11VVvIKbkQfyDe6zgi047PH6Ro/jvILrqAygN/pPrIBqrLXiQpfSbpOUtIyZhLQmqhrsaLTCJ9vV1U7HuSxNRiPTIhMk58/iDJGbNJzphNb087HS2VtDdX0ly7i+baXfiDiSSmFJKQUkh8YrYahZOE/rUyjRw99GfMfOTPehtNtbu8jiMybcUnZjJ72U0UL3gHtRWbaDi2nYp9/Y93GcH4VOISMogPZeAPJuIPhPAH4qN/DnoFEwnGay4zkfFy9NAz9HQ1M2fFh+hoPep1HJFpLxBMJCVzHimZ8+jr7aSjpYr2lkpaGg7SUr8fnz+OhJRCElIKCCXl4fP5vY4cs9QQnCa62uuprdxMTsmFem5JZIIE41MpmHM5BXMup7uzibbGUjpaj9Ld2UBXRwOtTYfp6+mgr7cTGH4SXp8/nkBcMvGJmYSS8ggl5WCm2X1EzlZXey3HSp8ns+BcktNnqSEoMsH8gdDxK4Xhvh46247R3lxJe3MFbY2lmPlJSCkgKW0moeRc1b4JpobgNFF18OnjVwNFZOLFhdKIy19BBitOWuecI9zXTV9vJ329nYR7u+jr7aCvt4ue7hY622poqt1FS/0BWur2Yb4gSWnFJKfPJi4hw4OfRmTqiwwQ8wTm81M0/x1exxGJeT5/kMTUYhJTi3EuTGdbDR0tFbQ3V9DeXI7PH09S2gySM2YTjE/xOm5MUENwGmhvqaSucgt5My/R1UCRScjMoreGxgND/z9aU55PONwX7S2toK2xjNaGQ8Qn5ZCWvZhQkgakERmNhqPbaKrdRdGCd6o2ikwyZj4SkvNISM4jI38lHS1VtDWV0VK/n5b6fYSS8kjJnEsoOV/PE44jNQSngYq9T+IPhMifc5nXUUTkLPh8fhJTCklMKSScv5LWhkM01++j+vDzJKaWkJ63jEAwweuYIpNeT1cLZbsfJyltBnkzL/E6jsikcLYTuo8XMx+JqUUkphbR19NBa2MprQ0HqTmygUBcMqnZi0hKK/E65rSkhuAU11y7h+a6PRQvuJZAMNHrOCIyRnz+IKnZC0jOnENz7V6a6/bQ0VJJas45pGbN13MUIqdQtvtxwr1dzFzyfv2/IjKF+IMJpOWcQ2r2QtqbK2iu3UN95WaaanZiGFlFazQy9xjSNzmFhcO9lO1+nPjEbHJmrPM6joiMA58vQHruYpLTZ9Jw7A2aqt+ko7mCrOK1BOOSvY4nMuk0HH2DxmNvUDjvGhKS87yOIyJnwMxHUloJianFdLYepal2N2W7fkPVwT+RP+ttZJdcoAbhGNA3OIUdK32ervZa5p37Uf3PIHKGJuutMoMF4pLIKbmQ9uZy6iq3cvTgn8nIX0FS2kw9PyES1dPdStmu35CYWkz+rLd6HUdEzpKZRaaZSM4nlJhF1cE/c2TPbzlWtp6ieVeTkb9CV/3PgloPU1Rney1VB/9Meu5S0rIXeR1HRCZIYmoxcQmZ1FVsor5yC52tx8gsOBefP+h1NBFPORem9M1f0tfXxawl78c0N5nItGFmpGYtICVzPi11eynf9ySHtj/M0dLnKJ7/DlKzF3odcUpSQ3AK6i92Pp+fkkXXex1HRCZYIJhI7sxLaK7dQ1PNDro7m8gpucDrWCKeOnb4BZprdzPjnHeTkFLgdRwRGQdmRmr2Qs7Jmk/90W1U7vsD+7b+kJTM+RQteAdJqcVeR5xS1BCcgqoPr6etsZRZS28kLpTudRwR8YCZkZaziPjETGrLX+XowWcIJeaQWbDK62giE66l/gAV+35Pet5ysosv9DqOiIyDoR7lyJv1VloaDtJcu5vdG/+NxNRi0nOXEBjiGfqcYnWYDqabaqeYtqYjVOz7PWk5i8ksOM/rOCLisVBSLvlzLiculM6h7Q9TtutxwuFer2OJTJiujnoOvP5TQonZzFryPj0zKxJDzOcnNWs+hfOuJjV7ER0tVVTu/yMNR1+nr7fL63iTnq4ITiG9Pe0cfP1nBONTIs8/qNiJCBAIJpA76xK62uuoPvwC7c1HmLPiQ7pjQKa93p4O9r/2Y3Bh5q68DX8g5HUkEfGAzx8kPXcJyRlzaKrZSUv9flobD5OWvZDkzHn49MzwkHRFcIpw4T4OvfFzerqambP8QwTikryOJCKTiJmPkoV/xZzlH6Kj9Ri7Xr6P5rq9XscSGTfhvh4ObHuIrrYa5qz4MKGkHK8jiYjHAsEEsgrPI3/OFcQnZtFY/SZV+5+irfEwzoW9jjfpqCE4BTjnKNv13zTX7WXG4htISp/hdSQRmaQy8pdzzgV/SyA+hX1bfkjVgadV/GTaCYd7Ofj6z2htOMispTeRmjXf60giMonEhdLInbGO3JkX4w/EU1e5mV0b/00dpIOoITjJOeeo3P8HaiteIX/2ZWQXrfU6kohMcqGkHBat/RsyC1ZReeCP7H/tx/R2t3kdS2RMhPt6OPj6z2iq3cWMc24gs2Cl15FEZJIKJeWSN/sysorW0tfTwb4tP2Dflh/Q3lLpdbRJQQ3BSSzSCHyKo4eeIbvofArnvd3rSCIyRfgDccxaehMzzrmBlrp97Np4H811+7yOJXJWenva2bf1hzTV7GTGOe8mp0QjhIrIqZkZSWklLHnL5ylecC1tTUfY9fJ9HHz9Z3S0VHkdz1MaLGaScuE+ynb9N7UVr5BVtJYZi2/ATO12ERk5MyOn5EISU4s5tP0X7NvyANnF51M8/534gwlexxMZlc62ag5s+wld7XXMXvZBTZUiIqPi8wXIm/VWsorWcKz0earLXqLh2Buk5y6lYM4VJKYWeR1xwqkhOAn1dLVwaPvDtNTvJ3/25RTOe7tGCBWRM5aUVsLiCz9H5YE/cqz0eZpqdlOy6HrSc5fqd4tMCQ1HX6d0x6P4fAHmn/cxUjLneR1JRKaoQDCRovnXkDfrrVQfXk912Ys0Vr9JWs5i8mddSlL6rJipjWoITjJNNbso3fEofb0dzFzyfrKL1ngdSUSmAZ8/SPGCd5KRt5zDO37Fwdd/SlL6TIrnv5PkjNlexxMZUm93G0d2/5b6o6+RlDZD06KIyJgJBBMpnPd2cmdeQk3Zixw7vJ49NTtJTC0md8ZbyMhfgc83vZtK0/unm0K6Ouqp2Pu/NBx7g1BSHgvO+zgJKQVexxKRaSYprYRzLvg76iq3UHngKfZs+g/SchaTN/OtJGfMjpleUJncwuFeao68TNWBp+nr66Jw3tvJn/U2THOBicgYCwQTKJh7Jbkz30p91Waqy16i9M1HKN/zP2QWnktW4WoSUwq9jjku1BD0WGdbDdVl66ktfxXMKJx7FXmz3zbteyBExDvm85NdvJbMgpUcO7w+ervoThJSCsgteQsZ+SvxB+K8jikxqK+ng7rKLRwtfY6eriZSsxZQvPA6EpLzvI4mItOcPxBHTslFZBdfQHPdPmrLX6GmbAPVh9eTkFJIRt4K0nOXEErKnTadphPa2jCzq4F/A/zAD51zXxu0Ph74KXAeUAfc6Jwrja77P8BHgT7gs865pyYw+pjq7emgqWYn9VVbaa7bh5mPrKLVFMy5Qre8iMiE8fnjKJhzOXkzL6au6jVqyl7k8M5HKdv936RlLyQ9dxlpOecQCCZ6HTUmxGqNDPd101S7h8bqN2msfpNwXzfJ6bOZtfT9pGTOnzb/4BKRqcHMR1r2QtKyF9Lb3Ub90W2Ru2j2/57K/b8nPjGbtOxFJGfMISVjDoG4JK8jn7EJawiamR+4H7gSKAc2mdkTzrmdAzb7KNDgnJtnZjcBXwduNLPFwE3AEqAQ+JOZLXDO9U1U/jPlnKOnq4m2piORV2MprU2HwYWJC2VQMOcKckouIBif6nVUEYlRPn8cOcXnk120ltbGQzQce4PGY2/SWL0DMBKS80hKn0Vy2kwSUgoIJeXg8+uK4ViKpRrZ29NOe3N5tC6W0Vy3DxfuwR9IICN/BTnFF5KUVuJ1TBERAnFJkYnpZ6yju7OJppqdNFbvoKZ8I9VlLwIQSsojMbWYxJQCElIKSEjOJxCXMiU6sSbyiuBaYL9z7iCAmT0CXA8MLHLXA1+Kvn8M+K5FvsXrgUecc13AITPbHz3ey+Md2rkwLtyHC/cSdn3R93246PtwuIe+3g56e9rp62mnt6eD3p42ujsa6Oqop6ujnnBvJ5Gf2U9CSiH5sy4lLeccktJmaEoIEZk0zIyUaA9nycLraG8up6l2N22Nh6k/uo3a8o3Htw2G0gklZBGMTyUYn0IwPhV/MAG/P4Q/EI8vEP3TF8TMh/n8mPnBfJj5MZ8vsly/A/tNqRoZqY29hPt6CId7CYd7cP3v+3oI93XR29NOb08bvd1tdHc20NUeqYm93S3HjxOfmE120VrSc5eSkjFbzwCKyKQVF0ojp+RCckouJBzupb3pCC0NB2ltLKWlfh/1VVuOb2u+APEJmcSFMgjGJxMIJhGISyIQTCYQl4jPH4/PF8DnD2K+ID5/EJ8viM8XiPwePF4fbVwblBPZECwCjgz4XA6cP9w2zrleM2sCsqLLNw7ad1wn+3AuzNan7wLcqPf1+eOIC6UTl5BJcvpsQkk5JKWVkJBSqGf/RGRKMPORlDaDpLQZQOR3YmdbNZ2tx+hsr6GzrYaujjpamw7T09WMC/ee6ZmYtewmsgrOHbvwU9OUqpHHSp+jYt/vR7ax+YiLTyM+IZO0nEWEEnMiveepxQQ0n6WITEE+X4DkjNknjLrd091KR0slnW21dHfU09VRR3dnI51tx+jtbiMc7hn1eQJxyay49ItjGf3E44/bkT1gZp8APhH92Gpme4bYLBuonbhUk5K+gwh9D/oO+ul78PQ7+MZYHGTmWBxkOlONHBf6vkZP39no6PsavWn2nX3pbA8wbH2cyIZgBTDwpv/i6LKhtik3swCQRuSB+JHsi3PuAeCBU4Uws83OudWjTj+N6DuI0Peg76Cfvgd9B5OAauQUpO9r9PSdjY6+r9HTdzZyE/lwxiZgvpnNNrM4Ig+2PzFomyeAW6Pv3ws845xz0eU3mVm8mc0G5gOvTlBuERGR8aYaKSIiE2rCrghGn2e4A3iKyNDYDzrndpjZPcBm59wTwI+An0UfdK8nUgiJbvcrIg/N9wKfmayjoYmIiIyWaqSIiEw0i3Qmxg4z+0T09piYpe8gQt+DvoN++h70HUiE/h6Mjr6v0dN3Njr6vkZP39nIxVxDUEREREREJNZpAicREREREZEYExMNQTMrMbNnzWynme0ws7/1OpOXzMxvZq+Z2e+8zuIFM0s3s8fMbLeZ7TKzC73O5AUz+1z0/4c3zewXZhbyOtNEMLMHzazazN4csCzTzJ42s33RPzO8zDjehvkOvhH9f+INM/tvM0v3MKJMINXIMxPrtXS0VHtHL1br9Giopp+dmGgIEnl4/u+dc4uBC4DPmNlijzN56W+BXV6H8NC/AX9wzi0CVhCD34WZFQGfBVY755YSGZziJm9TTZiHgKsHLbsL+LNzbj7w5+jn6ewhTv4OngaWOueWA3uB/zPRocQzqpFnJtZr6WjFfO0djRiv06PxEKrpZywmGoLOuSrn3Nbo+xYiv3yKvE3lDTMrBt4J/NDrLF4wszTgEiKj7+Gc63bONXoayjsBICE6H1kiUOlxngnhnHuByIiLA10P/CT6/ifAuyYy00Qb6jtwzv3ROdcb/biRyFx0EgNUI0cv1mvpaKn2nrGYrNOjoZp+dmKiITiQmc0CVgGveBzFK/cBnwfCHufwymygBvhx9JaeH5pZktehJppzrgK4FygDqoAm59wfvU3lqTznXFX0/VEgz8swk8BfA7/3OoRMPNXIEbuP2K6lo6XaO0qq02dFNX2EYqohaGbJwK+Bv3PONXudZ6KZ2bVAtXNui9dZPBQAzgX+0zm3CmgjBm8ZiN4vfz2R4lwIJJnZLd6mmhyiE3TH7HDKZvZ/idwq+HOvs8jEivUaOVKqpWdEtXeUVKfHRqzX9NOJmYagmQWJFLifO+d+43Uej6wDrjOzUuAR4DIz+y9vI024cqDcOdff2/0YkeIUa64ADjnnapxzPcBvgIs8zuSlY2ZWABD9s9rjPJ4ws9uAa4GbneYWiimqkaOiWjp6qr2jpzp95lTTRygmGoJmZkTuS9/lnPuW13m84pz7P865YufcLCIPHD/jnIup3iXn3FHgiJktjC66HNjpYSSvlAEXmFli9P+Py4ntB/efAG6Nvr8V+K2HWTxhZlcTudXtOudcu9d5ZOKoRo6OaunoqfaeEdXpMxfzNX2kYqIhSKT37kNEeu22RV/v8DqUeOZvgJ+b2RvASuD/eRtn4kV7ZR8DtgLbifwueMDTUBPEzH4BvAwsNLNyM/so8DXgSjPbR6QX9mteZhxvw3wH3wVSgKejvyO/52lImUiqkTIRYr72jkYs1+nRUE0/O6a7f0RERERERGJLrFwRFBERERERkSg1BEVERERERGKMGoIiIiIiIiIxRg1BERERERGRGKOGoIiIiIiISIxRQ1BERERERCTGqCEoMsHM7Dkzc8O8PmpmTdH33x6wz5IB2zwwYPnMAcs/Muj4pYPOe+mAbW+LLps16Pz/OGifmwetv3T8vhkREYllqo8iE0sNQRHvdAOvDHodBTZE118yYNvh3r91wPsXxiDTp8wsMODzZ8fgmCIiIqOh+igyAQKn30RExkmVc+6CwQvNbDlwNbDCzFKdc83AxdHV1cBCM8t1zlXzl6JX6Zw7cJZ5eoBi4AbgV2Z2PrA2ujx4lscWEREZKdVHkQmgK4Iik09/z6UfWBd9fzHQBPxowGf4S6FbPwbnfSz6598M+vOxIbYVERGZaKqPImNIDUER78y0k5+BSAc2AR3RbS4xszlEeiJfAp4bsDwfmB/9PNRtLyccH3j2NHk2AJuBt5jZNcD7gCrg0TP8+URERM6E6qPIBNCtoSLe6QZeG7Ss1znXbWavAJcSecZhd3TdC0SKUS+RHs8NA/Ybqsdz8PFTgXNOk+k7wE+BR4A44PtEbn0RERGZKKqPIhNADUER7wz5DETUC0QK3WqgtH+Zc67VzF4DzgOujS6vB9483fGjI5qdrtfzl8A3gDwihfJ7wJrT7CMiIjKWVB9FJoBuDRWZnPpvZQkSuQWlnchtKf3rfMBN0c8vOufcWJzUOddf3AB+5Zw7NhbHFRERGSOqjyJjRA1BkcnpZf5yy0kA2Oic6//8woDlMDYPwg/0VSAH+PgYH1dERORsqT6KjBE1BEUmIedcO7BlwKKBD7uvB8LDrBuLc/c452qdc51jeVwREZGzpfooMnZsjK6Yi4iIiIiIyBShK4IiIiIiIiIxRg1BERERERGRGKOGoIiIiIiISIxRQ1BERERERCTGqCEoIiIiIiISY9QQFBERERERiTFqCIqIiIiIiMQYNQRFRERERERijBqCIiIiIiIiMeb/D+9n1NxOrzgqAAAAAElFTkSuQmCC
 "
 >
 </div>
@@ -15074,7 +15075,7 @@ C:\Users\ECF0124A\Anaconda3\lib\site-packages\seaborn\distributions.py:2557: Fut
 </div>
 <div class="cell border-box-sizing code_cell rendered">
 <div class="input">
-<div class="prompt input_prompt">In&nbsp;[114]:</div>
+<div class="prompt input_prompt">In&nbsp;[165]:</div>
 <div class="inner_cell">
     <div class="input_area">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">({</span><span class="s2">&quot;MEDIAN_FWHM_x&quot;</span><span class="p">:</span><span class="n">np</span><span class="o">.</span><span class="n">median</span><span class="p">(</span><span class="n">parameters_df</span><span class="p">[</span><span class="s2">&quot;FWHM_x&quot;</span><span class="p">]),</span><span class="s2">&quot;MEDIAN_FWHM_y&quot;</span><span class="p">:</span><span class="n">np</span><span class="o">.</span><span class="n">median</span><span class="p">(</span><span class="n">parameters_df</span><span class="p">[</span><span class="s2">&quot;FWHM_y&quot;</span><span class="p">])})</span>
@@ -15094,7 +15095,7 @@ C:\Users\ECF0124A\Anaconda3\lib\site-packages\seaborn\distributions.py:2557: Fut
 
 
 <div class="output_subarea output_stream output_stdout output_text">
-<pre>{&#39;MEDIAN_FWHM_x&#39;: 6.361155779828058, &#39;MEDIAN_FWHM_y&#39;: 5.865753036777117}
+<pre>{&#39;MEDIAN_FWHM_x&#39;: 6.61783565447222, &#39;MEDIAN_FWHM_y&#39;: 6.0911794566979856}
 </pre>
 </div>
 </div>
@@ -15105,7 +15106,7 @@ C:\Users\ECF0124A\Anaconda3\lib\site-packages\seaborn\distributions.py:2557: Fut
 </div>
 <div class="cell border-box-sizing code_cell rendered">
 <div class="input">
-<div class="prompt input_prompt">In&nbsp;[115]:</div>
+<div class="prompt input_prompt">In&nbsp;[166]:</div>
 <div class="inner_cell">
     <div class="input_area">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">({</span><span class="s2">&quot;INCERTIDUMBRE_FWHM_x&quot;</span><span class="p">:</span><span class="n">np</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">parameters_df</span><span class="p">[</span><span class="s2">&quot;FWHM_x&quot;</span><span class="p">])</span><span class="o">/</span><span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">parameters_df</span><span class="p">[</span><span class="s2">&quot;FWHM_x&quot;</span><span class="p">])),</span><span class="s2">&quot;INCERTIDUMBRE_FWHM_y&quot;</span><span class="p">:</span><span class="n">np</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">parameters_df</span><span class="p">[</span><span class="s2">&quot;FWHM_y&quot;</span><span class="p">])</span><span class="o">/</span><span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">parameters_df</span><span class="p">[</span><span class="s2">&quot;FWHM_y&quot;</span><span class="p">]))})</span>
@@ -15125,7 +15126,7 @@ C:\Users\ECF0124A\Anaconda3\lib\site-packages\seaborn\distributions.py:2557: Fut
 
 
 <div class="output_subarea output_stream output_stdout output_text">
-<pre>{&#39;INCERTIDUMBRE_FWHM_x&#39;: 0.05897470200906251, &#39;INCERTIDUMBRE_FWHM_y&#39;: 0.0583083389179374}
+<pre>{&#39;INCERTIDUMBRE_FWHM_x&#39;: 0.07111893293495501, &#39;INCERTIDUMBRE_FWHM_y&#39;: 0.059722316688425}
 </pre>
 </div>
 </div>
@@ -15153,7 +15154,7 @@ $$FWHM\approx 6 pixeles$$</p>
 </div>
 <div class="cell border-box-sizing code_cell rendered">
 <div class="input">
-<div class="prompt input_prompt">In&nbsp;[116]:</div>
+<div class="prompt input_prompt">In&nbsp;[167]:</div>
 <div class="inner_cell">
     <div class="input_area">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">figure</span><span class="p">,</span> <span class="n">plots</span> <span class="o">=</span> <span class="n">pyplot</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">ncols</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">nrows</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span><span class="mi">10</span><span class="p">))</span>
@@ -15194,7 +15195,7 @@ $$FWHM\approx 6 pixeles$$</p>
 </div>
 <div class="cell border-box-sizing code_cell rendered">
 <div class="input">
-<div class="prompt input_prompt">In&nbsp;[117]:</div>
+<div class="prompt input_prompt">In&nbsp;[168]:</div>
 <div class="inner_cell">
     <div class="input_area">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">color</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;b&#39;</span><span class="p">,</span><span class="s1">&#39;r&#39;</span><span class="p">,</span><span class="s1">&#39;g&#39;</span><span class="p">]</span>
@@ -15266,7 +15267,7 @@ $$FWHM\approx 6 pixeles$$</p>
 </div>
 <div class="cell border-box-sizing code_cell rendered">
 <div class="input">
-<div class="prompt input_prompt">In&nbsp;[118]:</div>
+<div class="prompt input_prompt">In&nbsp;[169]:</div>
 <div class="inner_cell">
     <div class="input_area">
 <div class=" highlight hl-ipython3"><pre><span></span><span class="n">color</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;RED&#39;</span><span class="p">,</span><span class="s1">&#39;GREEN&#39;</span><span class="p">,</span><span class="s1">&#39;BLUE&#39;</span><span class="p">]</span>
@@ -15288,11 +15289,11 @@ $$FWHM\approx 6 pixeles$$</p>
     <span class="c1"># calcula parametros</span>
     <span class="n">parameters_df</span> <span class="o">=</span> <span class="n">star_parameters</span><span class="p">(</span><span class="n">temp</span><span class="p">[:,:,</span><span class="n">i</span><span class="p">],</span><span class="n">stars</span><span class="p">)</span>
     <span class="n">parameters_df</span><span class="o">=</span><span class="n">parameters_df</span><span class="p">[(</span><span class="n">parameters_df</span><span class="p">[</span><span class="s2">&quot;height&quot;</span><span class="p">]</span><span class="o">&lt;</span><span class="mi">255</span><span class="p">)]</span>
-    <span class="n">parameters_df</span><span class="o">=</span><span class="n">parameters_df</span><span class="p">[(</span><span class="n">parameters_df</span><span class="p">[</span><span class="s2">&quot;FWHM_x&quot;</span><span class="p">]</span><span class="o">&lt;</span><span class="n">np</span><span class="o">.</span><span class="n">median</span><span class="p">(</span><span class="n">parameters_df</span><span class="p">[</span><span class="s2">&quot;FWHM_x&quot;</span><span class="p">])</span><span class="o">+</span><span class="mi">3</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">parameters_df</span><span class="p">[</span><span class="s2">&quot;FWHM_x&quot;</span><span class="p">])</span><span class="o">/</span><span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">parameters_df</span><span class="p">[</span><span class="s2">&quot;FWHM_x&quot;</span><span class="p">])))</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">parameters_df</span><span class="p">[</span><span class="s2">&quot;FWHM_y&quot;</span><span class="p">]</span><span class="o">&lt;</span><span class="n">np</span><span class="o">.</span><span class="n">median</span><span class="p">(</span><span class="n">parameters_df</span><span class="p">[</span><span class="s2">&quot;FWHM_y&quot;</span><span class="p">])</span><span class="o">+</span><span class="mi">3</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">parameters_df</span><span class="p">[</span><span class="s2">&quot;FWHM_y&quot;</span><span class="p">])</span><span class="o">/</span><span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">parameters_df</span><span class="p">[</span><span class="s2">&quot;FWHM_y&quot;</span><span class="p">])))]</span>
+    <span class="n">parameters_df</span><span class="o">=</span><span class="n">parameters_df</span><span class="p">[(</span><span class="n">parameters_df</span><span class="p">[</span><span class="s2">&quot;FWHM_x&quot;</span><span class="p">]</span><span class="o">&lt;</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">parameters_df</span><span class="p">[</span><span class="s2">&quot;FWHM_x&quot;</span><span class="p">])</span><span class="o">+</span><span class="mf">2.5</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">parameters_df</span><span class="p">[</span><span class="s2">&quot;FWHM_x&quot;</span><span class="p">]))</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">parameters_df</span><span class="p">[</span><span class="s2">&quot;FWHM_y&quot;</span><span class="p">]</span><span class="o">&lt;</span><span class="n">np</span><span class="o">.</span><span class="n">median</span><span class="p">(</span><span class="n">parameters_df</span><span class="p">[</span><span class="s2">&quot;FWHM_y&quot;</span><span class="p">])</span><span class="o">+</span><span class="mf">2.5</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">parameters_df</span><span class="p">[</span><span class="s2">&quot;FWHM_y&quot;</span><span class="p">]))]</span>
     
     <span class="c1">#muestra resultados</span>
     <span class="nb">print</span><span class="p">(</span><span class="n">bcolors</span><span class="o">.</span><span class="n">RED</span> <span class="o">+</span><span class="s2">&quot;FWHM FOR CHANNEL &quot;</span><span class="o">+</span><span class="n">color</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">+</span> <span class="n">bcolors</span><span class="o">.</span><span class="n">ENDC</span><span class="p">)</span>
-    <span class="nb">print</span><span class="p">({</span><span class="s2">&quot;MEAN_FWHM_x&quot;</span><span class="p">:</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">parameters_df</span><span class="p">[</span><span class="s2">&quot;FWHM_x&quot;</span><span class="p">]),</span><span class="s2">&quot;MEAN_FWHM_y&quot;</span><span class="p">:</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">parameters_df</span><span class="p">[</span><span class="s2">&quot;FWHM_y&quot;</span><span class="p">])})</span>
+    <span class="nb">print</span><span class="p">({</span><span class="s2">&quot;MEDIAN_FWHM_x&quot;</span><span class="p">:</span><span class="n">np</span><span class="o">.</span><span class="n">median</span><span class="p">(</span><span class="n">parameters_df</span><span class="p">[</span><span class="s2">&quot;FWHM_x&quot;</span><span class="p">]),</span><span class="s2">&quot;MEDIAN_FWHM_y&quot;</span><span class="p">:</span><span class="n">np</span><span class="o">.</span><span class="n">median</span><span class="p">(</span><span class="n">parameters_df</span><span class="p">[</span><span class="s2">&quot;FWHM_y&quot;</span><span class="p">])})</span>
     <span class="nb">print</span><span class="p">({</span><span class="s2">&quot;INCERTIDUMBRE_FWHM_x&quot;</span><span class="p">:</span><span class="n">np</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">parameters_df</span><span class="p">[</span><span class="s2">&quot;FWHM_x&quot;</span><span class="p">])</span><span class="o">/</span><span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">parameters_df</span><span class="p">[</span><span class="s2">&quot;FWHM_x&quot;</span><span class="p">])),</span><span class="s2">&quot;INCERTIDUMBRE_FWHM_y&quot;</span><span class="p">:</span><span class="n">np</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">parameters_df</span><span class="p">[</span><span class="s2">&quot;FWHM_y&quot;</span><span class="p">])</span><span class="o">/</span><span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">parameters_df</span><span class="p">[</span><span class="s2">&quot;FWHM_y&quot;</span><span class="p">]))})</span>
 </pre></div>
 
@@ -15323,14 +15324,14 @@ $$FWHM\approx 6 pixeles$$</p>
 
 <div class="output_subarea output_stream output_stdout output_text">
 <pre><span class="ansi-red-intense-fg">FWHM FOR CHANNEL RED</span>
-{&#39;MEAN_FWHM_x&#39;: 6.85040177317909, &#39;MEAN_FWHM_y&#39;: 6.286265858550066}
-{&#39;INCERTIDUMBRE_FWHM_x&#39;: 0.07397839012804115, &#39;INCERTIDUMBRE_FWHM_y&#39;: 0.06519770408323534}
+{&#39;MEDIAN_FWHM_x&#39;: 7.9183883547135165, &#39;MEDIAN_FWHM_y&#39;: 7.35922763918757}
+{&#39;INCERTIDUMBRE_FWHM_x&#39;: 0.1159148719780544, &#39;INCERTIDUMBRE_FWHM_y&#39;: 0.08836013787487608}
 <span class="ansi-red-intense-fg">FWHM FOR CHANNEL GREEN</span>
-{&#39;MEAN_FWHM_x&#39;: 6.245164160492783, &#39;MEAN_FWHM_y&#39;: 5.852337602343364}
-{&#39;INCERTIDUMBRE_FWHM_x&#39;: 0.05644305840123783, &#39;INCERTIDUMBRE_FWHM_y&#39;: 0.05873235620560445}
+{&#39;MEDIAN_FWHM_x&#39;: 7.2816934101996456, &#39;MEDIAN_FWHM_y&#39;: 6.823136964119058}
+{&#39;INCERTIDUMBRE_FWHM_x&#39;: 0.09670234009426094, &#39;INCERTIDUMBRE_FWHM_y&#39;: 0.07626498914594818}
 <span class="ansi-red-intense-fg">FWHM FOR CHANNEL BLUE</span>
-{&#39;MEAN_FWHM_x&#39;: 5.656095446703643, &#39;MEAN_FWHM_y&#39;: 5.248410485649535}
-{&#39;INCERTIDUMBRE_FWHM_x&#39;: 0.0511572917957581, &#39;INCERTIDUMBRE_FWHM_y&#39;: 0.04996212650827945}
+{&#39;MEDIAN_FWHM_x&#39;: 6.707228331976005, &#39;MEDIAN_FWHM_y&#39;: 6.143125344552874}
+{&#39;INCERTIDUMBRE_FWHM_x&#39;: 0.0772819315486433, &#39;INCERTIDUMBRE_FWHM_y&#39;: 0.06542214124518661}
 </pre>
 </div>
 </div>
@@ -15342,7 +15343,7 @@ $$FWHM\approx 6 pixeles$$</p>
 <div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
 </div><div class="inner_cell">
 <div class="text_cell_render border-box-sizing rendered_html">
-<p>Encontramos que el mejor canal es el azul con $$FWHM \approx 6 $$</p>
+<p>Encontramos que el mejor canal es el azul con $$FWHM \approx 7 $$</p>
 
 </div>
 </div>
diff --git a/entrega.ipynb b/entrega.ipynb
index bae957d0c6b14ff0485b2f2fab035504d97ba138..ddea917ad65f6a21554d31a50993b4cb2d9ba64a 100644
--- a/entrega.ipynb
+++ b/entrega.ipynb
@@ -1011,7 +1011,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 103,
+   "execution_count": 145,
    "metadata": {
     "id": "-wDngj8nv0t7"
    },
@@ -1072,7 +1072,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 104,
+   "execution_count": 146,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -1114,7 +1114,17 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>90</th>\n",
+       "      <th>132</th>\n",
+       "      <td>22738.039821</td>\n",
+       "      <td>-239.832795</td>\n",
+       "      <td>3.010661</td>\n",
+       "      <td>75.973813</td>\n",
+       "      <td>2.731824</td>\n",
+       "      <td>178.538460</td>\n",
+       "      <td>6.419787</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>133</th>\n",
        "      <td>14409.841451</td>\n",
        "      <td>-222.262629</td>\n",
        "      <td>2.343065</td>\n",
@@ -1124,7 +1134,17 @@
        "      <td>5.848958</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>516</th>\n",
+       "      <th>578</th>\n",
+       "      <td>31219.909353</td>\n",
+       "      <td>105.300441</td>\n",
+       "      <td>2.585564</td>\n",
+       "      <td>31.073563</td>\n",
+       "      <td>4.308750</td>\n",
+       "      <td>73.022872</td>\n",
+       "      <td>10.125563</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>687</th>\n",
        "      <td>267271.868327</td>\n",
        "      <td>115.166949</td>\n",
        "      <td>2.769692</td>\n",
@@ -1134,34 +1154,14 @@
        "      <td>5.758053</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>477</th>\n",
-       "      <td>107493.971955</td>\n",
-       "      <td>96.620752</td>\n",
-       "      <td>3.101467</td>\n",
-       "      <td>26.096038</td>\n",
-       "      <td>3.044724</td>\n",
-       "      <td>61.325689</td>\n",
-       "      <td>7.155100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>288</th>\n",
-       "      <td>169.559297</td>\n",
-       "      <td>-16.954423</td>\n",
-       "      <td>2.975433</td>\n",
-       "      <td>25.863356</td>\n",
-       "      <td>3.902506</td>\n",
-       "      <td>60.778886</td>\n",
-       "      <td>9.170890</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>441</th>\n",
-       "      <td>21979.573625</td>\n",
-       "      <td>66.797420</td>\n",
-       "      <td>2.603415</td>\n",
-       "      <td>20.616066</td>\n",
-       "      <td>2.744205</td>\n",
-       "      <td>48.447755</td>\n",
-       "      <td>6.448882</td>\n",
+       "      <th>600</th>\n",
+       "      <td>325077.772735</td>\n",
+       "      <td>112.668325</td>\n",
+       "      <td>120.781525</td>\n",
+       "      <td>28.345852</td>\n",
+       "      <td>191.375356</td>\n",
+       "      <td>66.612752</td>\n",
+       "      <td>449.732087</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -1174,7 +1174,7 @@
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>21</th>\n",
+       "      <th>27</th>\n",
        "      <td>202.186462</td>\n",
        "      <td>2.289328</td>\n",
        "      <td>2.644426</td>\n",
@@ -1184,7 +1184,7 @@
        "      <td>4.316290</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>29</th>\n",
+       "      <th>37</th>\n",
        "      <td>210.588381</td>\n",
        "      <td>1.978485</td>\n",
        "      <td>3.167382</td>\n",
@@ -1194,7 +1194,7 @@
        "      <td>4.304869</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>30</th>\n",
+       "      <th>38</th>\n",
        "      <td>217.389734</td>\n",
        "      <td>1.980785</td>\n",
        "      <td>2.331454</td>\n",
@@ -1204,7 +1204,7 @@
        "      <td>4.014588</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>26</th>\n",
+       "      <th>33</th>\n",
        "      <td>216.603135</td>\n",
        "      <td>2.821754</td>\n",
        "      <td>3.173432</td>\n",
@@ -1214,7 +1214,7 @@
        "      <td>4.278035</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>27</th>\n",
+       "      <th>34</th>\n",
        "      <td>222.917653</td>\n",
        "      <td>2.823663</td>\n",
        "      <td>2.336885</td>\n",
@@ -1225,40 +1225,40 @@
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>524 rows × 7 columns</p>\n",
+       "<p>695 rows × 7 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "            height      mean_x    mean_y      std_x     std_y      FWHM_x  \\\n",
-       "90    14409.841451 -222.262629  2.343065  74.058552  2.488918  174.037597   \n",
-       "516  267271.868327  115.166949  2.769692  29.110955  2.450235   68.410745   \n",
-       "477  107493.971955   96.620752  3.101467  26.096038  3.044724   61.325689   \n",
-       "288     169.559297  -16.954423  2.975433  25.863356  3.902506   60.778886   \n",
-       "441   21979.573625   66.797420  2.603415  20.616066  2.744205   48.447755   \n",
-       "..             ...         ...       ...        ...       ...         ...   \n",
-       "21      202.186462    2.289328  2.644426   2.005263  1.836719    4.712369   \n",
-       "29      210.588381    1.978485  3.167382   1.943865  1.831859    4.568083   \n",
-       "30      217.389734    1.980785  2.331454   1.931921  1.708335    4.540015   \n",
-       "26      216.603135    2.821754  3.173432   1.804289  1.820440    4.240079   \n",
-       "27      222.917653    2.823663  2.336885   1.803631  1.702241    4.238533   \n",
+       "            height      mean_x      mean_y      std_x       std_y      FWHM_x  \\\n",
+       "132   22738.039821 -239.832795    3.010661  75.973813    2.731824  178.538460   \n",
+       "133   14409.841451 -222.262629    2.343065  74.058552    2.488918  174.037597   \n",
+       "578   31219.909353  105.300441    2.585564  31.073563    4.308750   73.022872   \n",
+       "687  267271.868327  115.166949    2.769692  29.110955    2.450235   68.410745   \n",
+       "600  325077.772735  112.668325  120.781525  28.345852  191.375356   66.612752   \n",
+       "..             ...         ...         ...        ...         ...         ...   \n",
+       "27      202.186462    2.289328    2.644426   2.005263    1.836719    4.712369   \n",
+       "37      210.588381    1.978485    3.167382   1.943865    1.831859    4.568083   \n",
+       "38      217.389734    1.980785    2.331454   1.931921    1.708335    4.540015   \n",
+       "33      216.603135    2.821754    3.173432   1.804289    1.820440    4.240079   \n",
+       "34      222.917653    2.823663    2.336885   1.803631    1.702241    4.238533   \n",
        "\n",
-       "       FWHM_y  \n",
-       "90   5.848958  \n",
-       "516  5.758053  \n",
-       "477  7.155100  \n",
-       "288  9.170890  \n",
-       "441  6.448882  \n",
-       "..        ...  \n",
-       "21   4.316290  \n",
-       "29   4.304869  \n",
-       "30   4.014588  \n",
-       "26   4.278035  \n",
-       "27   4.000267  \n",
+       "         FWHM_y  \n",
+       "132    6.419787  \n",
+       "133    5.848958  \n",
+       "578   10.125563  \n",
+       "687    5.758053  \n",
+       "600  449.732087  \n",
+       "..          ...  \n",
+       "27     4.316290  \n",
+       "37     4.304869  \n",
+       "38     4.014588  \n",
+       "33     4.278035  \n",
+       "34     4.000267  \n",
        "\n",
-       "[524 rows x 7 columns]"
+       "[695 rows x 7 columns]"
       ]
      },
-     "execution_count": 104,
+     "execution_count": 146,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1276,7 +1276,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 105,
+   "execution_count": 147,
    "metadata": {},
    "outputs": [
     {
@@ -1311,7 +1311,7 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>27</th>\n",
+       "      <th>34</th>\n",
        "      <td>222.917653</td>\n",
        "      <td>2.823663</td>\n",
        "      <td>2.336885</td>\n",
@@ -1321,7 +1321,7 @@
        "      <td>4.000267</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>26</th>\n",
+       "      <th>33</th>\n",
        "      <td>216.603135</td>\n",
        "      <td>2.821754</td>\n",
        "      <td>3.173432</td>\n",
@@ -1331,7 +1331,7 @@
        "      <td>4.278035</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>30</th>\n",
+       "      <th>38</th>\n",
        "      <td>217.389734</td>\n",
        "      <td>1.980785</td>\n",
        "      <td>2.331454</td>\n",
@@ -1341,7 +1341,7 @@
        "      <td>4.014588</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>29</th>\n",
+       "      <th>37</th>\n",
        "      <td>210.588381</td>\n",
        "      <td>1.978485</td>\n",
        "      <td>3.167382</td>\n",
@@ -1351,7 +1351,7 @@
        "      <td>4.304869</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>21</th>\n",
+       "      <th>27</th>\n",
        "      <td>202.186462</td>\n",
        "      <td>2.289328</td>\n",
        "      <td>2.644426</td>\n",
@@ -1371,47 +1371,47 @@
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>309</th>\n",
-       "      <td>151.959876</td>\n",
-       "      <td>6.048875</td>\n",
-       "      <td>3.048254</td>\n",
-       "      <td>6.335821</td>\n",
-       "      <td>3.106507</td>\n",
-       "      <td>14.889180</td>\n",
-       "      <td>7.300292</td>\n",
+       "      <th>80</th>\n",
+       "      <td>126.345557</td>\n",
+       "      <td>7.503358</td>\n",
+       "      <td>2.717318</td>\n",
+       "      <td>8.605995</td>\n",
+       "      <td>3.441961</td>\n",
+       "      <td>20.224088</td>\n",
+       "      <td>8.088608</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>351</th>\n",
-       "      <td>195.582213</td>\n",
-       "      <td>7.582757</td>\n",
-       "      <td>2.703236</td>\n",
-       "      <td>6.548991</td>\n",
-       "      <td>3.505496</td>\n",
-       "      <td>15.390128</td>\n",
-       "      <td>8.237915</td>\n",
+       "      <th>98</th>\n",
+       "      <td>191.987149</td>\n",
+       "      <td>10.993017</td>\n",
+       "      <td>2.720394</td>\n",
+       "      <td>9.802730</td>\n",
+       "      <td>3.154057</td>\n",
+       "      <td>23.036415</td>\n",
+       "      <td>7.412033</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>425</th>\n",
-       "      <td>251.594399</td>\n",
-       "      <td>7.994551</td>\n",
-       "      <td>1.593031</td>\n",
-       "      <td>6.646625</td>\n",
-       "      <td>3.216397</td>\n",
-       "      <td>15.619568</td>\n",
-       "      <td>7.558533</td>\n",
+       "      <th>261</th>\n",
+       "      <td>220.500255</td>\n",
+       "      <td>12.860922</td>\n",
+       "      <td>2.878011</td>\n",
+       "      <td>9.887140</td>\n",
+       "      <td>2.760608</td>\n",
+       "      <td>23.234779</td>\n",
+       "      <td>6.487429</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>47</th>\n",
-       "      <td>115.774855</td>\n",
-       "      <td>4.630637</td>\n",
-       "      <td>2.210311</td>\n",
-       "      <td>7.270685</td>\n",
-       "      <td>5.813427</td>\n",
-       "      <td>17.086110</td>\n",
-       "      <td>13.661553</td>\n",
+       "      <th>218</th>\n",
+       "      <td>229.036678</td>\n",
+       "      <td>21.346080</td>\n",
+       "      <td>2.787659</td>\n",
+       "      <td>17.666989</td>\n",
+       "      <td>2.664696</td>\n",
+       "      <td>41.517424</td>\n",
+       "      <td>6.262035</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>288</th>\n",
+       "      <th>372</th>\n",
        "      <td>169.559297</td>\n",
        "      <td>-16.954423</td>\n",
        "      <td>2.975433</td>\n",
@@ -1422,40 +1422,27 @@
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>462 rows × 7 columns</p>\n",
+       "<p>585 rows × 7 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "         height     mean_x    mean_y      std_x     std_y     FWHM_x  \\\n",
-       "27   222.917653   2.823663  2.336885   1.803631  1.702241   4.238533   \n",
-       "26   216.603135   2.821754  3.173432   1.804289  1.820440   4.240079   \n",
-       "30   217.389734   1.980785  2.331454   1.931921  1.708335   4.540015   \n",
-       "29   210.588381   1.978485  3.167382   1.943865  1.831859   4.568083   \n",
-       "21   202.186462   2.289328  2.644426   2.005263  1.836719   4.712369   \n",
-       "..          ...        ...       ...        ...       ...        ...   \n",
-       "309  151.959876   6.048875  3.048254   6.335821  3.106507  14.889180   \n",
-       "351  195.582213   7.582757  2.703236   6.548991  3.505496  15.390128   \n",
-       "425  251.594399   7.994551  1.593031   6.646625  3.216397  15.619568   \n",
-       "47   115.774855   4.630637  2.210311   7.270685  5.813427  17.086110   \n",
-       "288  169.559297 -16.954423  2.975433  25.863356  3.902506  60.778886   \n",
-       "\n",
-       "        FWHM_y  \n",
-       "27    4.000267  \n",
-       "26    4.278035  \n",
-       "30    4.014588  \n",
-       "29    4.304869  \n",
-       "21    4.316290  \n",
-       "..         ...  \n",
-       "309   7.300292  \n",
-       "351   8.237915  \n",
-       "425   7.558533  \n",
-       "47   13.661553  \n",
-       "288   9.170890  \n",
+       "         height     mean_x    mean_y      std_x     std_y     FWHM_x    FWHM_y\n",
+       "34   222.917653   2.823663  2.336885   1.803631  1.702241   4.238533  4.000267\n",
+       "33   216.603135   2.821754  3.173432   1.804289  1.820440   4.240079  4.278035\n",
+       "38   217.389734   1.980785  2.331454   1.931921  1.708335   4.540015  4.014588\n",
+       "37   210.588381   1.978485  3.167382   1.943865  1.831859   4.568083  4.304869\n",
+       "27   202.186462   2.289328  2.644426   2.005263  1.836719   4.712369  4.316290\n",
+       "..          ...        ...       ...        ...       ...        ...       ...\n",
+       "80   126.345557   7.503358  2.717318   8.605995  3.441961  20.224088  8.088608\n",
+       "98   191.987149  10.993017  2.720394   9.802730  3.154057  23.036415  7.412033\n",
+       "261  220.500255  12.860922  2.878011   9.887140  2.760608  23.234779  6.487429\n",
+       "218  229.036678  21.346080  2.787659  17.666989  2.664696  41.517424  6.262035\n",
+       "372  169.559297 -16.954423  2.975433  25.863356  3.902506  60.778886  9.170890\n",
        "\n",
-       "[462 rows x 7 columns]"
+       "[585 rows x 7 columns]"
       ]
      },
-     "execution_count": 105,
+     "execution_count": 147,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1467,7 +1454,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 106,
+   "execution_count": 148,
    "metadata": {},
    "outputs": [
     {
@@ -1482,7 +1469,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 1080x360 with 2 Axes>"
       ]
@@ -1521,7 +1508,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 107,
+   "execution_count": 149,
    "metadata": {},
    "outputs": [
     {
@@ -1545,7 +1532,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 108,
+   "execution_count": 150,
    "metadata": {},
    "outputs": [
     {
@@ -1571,7 +1558,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 109,
+   "execution_count": 151,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -1585,7 +1572,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'MEAN_FWHM_x': 7.940323515966517, 'MEAN_FWHM_y': 7.337040419590766}\n"
+      "{'MEAN_FWHM_x': 8.379209150177722, 'MEAN_FWHM_y': 7.576010416841125}\n"
      ]
     }
    ],
@@ -1602,14 +1589,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 110,
+   "execution_count": 152,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'MEDIAN_FWHM_x': 7.396254460793973, 'MEDIAN_FWHM_y': 6.900833212383647}\n"
+      "{'MEDIAN_FWHM_x': 7.670810006638417, 'MEDIAN_FWHM_y': 6.99528444605464}\n"
      ]
     }
    ],
@@ -1628,7 +1615,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 111,
+   "execution_count": 153,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -1642,7 +1629,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'INCERTIDUMBRE_FWHM_x': 0.15200550254891215, 'INCERTIDUMBRE_FWHM_y': 0.12480846830393083}\n"
+      "{'INCERTIDUMBRE_FWHM_x': 0.15034065812442832, 'INCERTIDUMBRE_FWHM_y': 0.12213295193042271}\n"
      ]
     }
    ],
@@ -1659,7 +1646,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 112,
+   "execution_count": 163,
    "metadata": {},
    "outputs": [
     {
@@ -1694,54 +1681,54 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>222.917653</td>\n",
-       "      <td>2.823663</td>\n",
-       "      <td>2.336885</td>\n",
-       "      <td>1.803631</td>\n",
-       "      <td>1.702241</td>\n",
-       "      <td>4.238533</td>\n",
-       "      <td>4.000267</td>\n",
+       "      <th>33</th>\n",
+       "      <td>215.742228</td>\n",
+       "      <td>2.844406</td>\n",
+       "      <td>3.220690</td>\n",
+       "      <td>1.602668</td>\n",
+       "      <td>1.513167</td>\n",
+       "      <td>3.766270</td>\n",
+       "      <td>3.555943</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>216.603135</td>\n",
-       "      <td>2.821754</td>\n",
-       "      <td>3.173432</td>\n",
-       "      <td>1.804289</td>\n",
-       "      <td>1.820440</td>\n",
-       "      <td>4.240079</td>\n",
-       "      <td>4.278035</td>\n",
+       "      <th>34</th>\n",
+       "      <td>216.970464</td>\n",
+       "      <td>2.845412</td>\n",
+       "      <td>2.329821</td>\n",
+       "      <td>1.614164</td>\n",
+       "      <td>1.493453</td>\n",
+       "      <td>3.793286</td>\n",
+       "      <td>3.509615</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>30</th>\n",
-       "      <td>217.389734</td>\n",
-       "      <td>1.980785</td>\n",
-       "      <td>2.331454</td>\n",
-       "      <td>1.931921</td>\n",
-       "      <td>1.708335</td>\n",
-       "      <td>4.540015</td>\n",
-       "      <td>4.014588</td>\n",
+       "      <th>37</th>\n",
+       "      <td>209.845863</td>\n",
+       "      <td>1.956937</td>\n",
+       "      <td>3.215590</td>\n",
+       "      <td>1.708841</td>\n",
+       "      <td>1.524611</td>\n",
+       "      <td>4.015777</td>\n",
+       "      <td>3.582837</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>210.588381</td>\n",
-       "      <td>1.978485</td>\n",
-       "      <td>3.167382</td>\n",
-       "      <td>1.943865</td>\n",
-       "      <td>1.831859</td>\n",
-       "      <td>4.568083</td>\n",
-       "      <td>4.304869</td>\n",
+       "      <th>335</th>\n",
+       "      <td>174.734848</td>\n",
+       "      <td>2.770882</td>\n",
+       "      <td>2.760025</td>\n",
+       "      <td>1.710865</td>\n",
+       "      <td>1.624143</td>\n",
+       "      <td>4.020532</td>\n",
+       "      <td>3.816736</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>202.186462</td>\n",
-       "      <td>2.289328</td>\n",
-       "      <td>2.644426</td>\n",
-       "      <td>2.005263</td>\n",
-       "      <td>1.836719</td>\n",
-       "      <td>4.712369</td>\n",
-       "      <td>4.316290</td>\n",
+       "      <th>38</th>\n",
+       "      <td>211.627049</td>\n",
+       "      <td>1.961099</td>\n",
+       "      <td>2.326209</td>\n",
+       "      <td>1.715538</td>\n",
+       "      <td>1.499931</td>\n",
+       "      <td>4.031515</td>\n",
+       "      <td>3.524837</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -1754,90 +1741,91 @@
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>400</th>\n",
-       "      <td>148.694932</td>\n",
-       "      <td>3.251515</td>\n",
-       "      <td>3.323809</td>\n",
-       "      <td>3.311014</td>\n",
-       "      <td>3.089162</td>\n",
-       "      <td>7.780883</td>\n",
-       "      <td>7.259531</td>\n",
+       "      <th>543</th>\n",
+       "      <td>131.093045</td>\n",
+       "      <td>3.193123</td>\n",
+       "      <td>2.503308</td>\n",
+       "      <td>4.758416</td>\n",
+       "      <td>2.706784</td>\n",
+       "      <td>11.182277</td>\n",
+       "      <td>6.360943</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>32</th>\n",
-       "      <td>120.009106</td>\n",
-       "      <td>2.582832</td>\n",
-       "      <td>2.669800</td>\n",
-       "      <td>3.323717</td>\n",
-       "      <td>2.781075</td>\n",
-       "      <td>7.810735</td>\n",
-       "      <td>6.535527</td>\n",
+       "      <th>23</th>\n",
+       "      <td>224.503205</td>\n",
+       "      <td>7.472670</td>\n",
+       "      <td>2.648100</td>\n",
+       "      <td>4.762666</td>\n",
+       "      <td>1.649086</td>\n",
+       "      <td>11.192264</td>\n",
+       "      <td>3.875353</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>150</th>\n",
-       "      <td>140.401733</td>\n",
-       "      <td>2.814937</td>\n",
-       "      <td>2.461857</td>\n",
-       "      <td>3.330074</td>\n",
-       "      <td>3.020962</td>\n",
-       "      <td>7.825674</td>\n",
-       "      <td>7.099261</td>\n",
+       "      <th>397</th>\n",
+       "      <td>117.626414</td>\n",
+       "      <td>4.050571</td>\n",
+       "      <td>2.602593</td>\n",
+       "      <td>4.788907</td>\n",
+       "      <td>2.907534</td>\n",
+       "      <td>11.253932</td>\n",
+       "      <td>6.832705</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>281</th>\n",
-       "      <td>192.304218</td>\n",
-       "      <td>3.922074</td>\n",
-       "      <td>2.380541</td>\n",
-       "      <td>3.330268</td>\n",
-       "      <td>2.254765</td>\n",
-       "      <td>7.826129</td>\n",
-       "      <td>5.298697</td>\n",
+       "      <th>331</th>\n",
+       "      <td>120.648167</td>\n",
+       "      <td>3.944006</td>\n",
+       "      <td>2.661164</td>\n",
+       "      <td>4.870656</td>\n",
+       "      <td>2.857321</td>\n",
+       "      <td>11.446041</td>\n",
+       "      <td>6.714704</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>228</th>\n",
-       "      <td>123.573138</td>\n",
-       "      <td>2.781581</td>\n",
-       "      <td>2.546152</td>\n",
-       "      <td>3.334843</td>\n",
-       "      <td>2.962802</td>\n",
-       "      <td>7.836880</td>\n",
-       "      <td>6.962584</td>\n",
+       "      <th>622</th>\n",
+       "      <td>240.208357</td>\n",
+       "      <td>6.285389</td>\n",
+       "      <td>1.606621</td>\n",
+       "      <td>4.905659</td>\n",
+       "      <td>2.470669</td>\n",
+       "      <td>11.528298</td>\n",
+       "      <td>5.806073</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>206 rows × 7 columns</p>\n",
+       "<p>529 rows × 7 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "         height    mean_x    mean_y     std_x     std_y    FWHM_x    FWHM_y\n",
-       "27   222.917653  2.823663  2.336885  1.803631  1.702241  4.238533  4.000267\n",
-       "26   216.603135  2.821754  3.173432  1.804289  1.820440  4.240079  4.278035\n",
-       "30   217.389734  1.980785  2.331454  1.931921  1.708335  4.540015  4.014588\n",
-       "29   210.588381  1.978485  3.167382  1.943865  1.831859  4.568083  4.304869\n",
-       "21   202.186462  2.289328  2.644426  2.005263  1.836719  4.712369  4.316290\n",
-       "..          ...       ...       ...       ...       ...       ...       ...\n",
-       "400  148.694932  3.251515  3.323809  3.311014  3.089162  7.780883  7.259531\n",
-       "32   120.009106  2.582832  2.669800  3.323717  2.781075  7.810735  6.535527\n",
-       "150  140.401733  2.814937  2.461857  3.330074  3.020962  7.825674  7.099261\n",
-       "281  192.304218  3.922074  2.380541  3.330268  2.254765  7.826129  5.298697\n",
-       "228  123.573138  2.781581  2.546152  3.334843  2.962802  7.836880  6.962584\n",
+       "         height    mean_x    mean_y     std_x     std_y     FWHM_x    FWHM_y\n",
+       "33   215.742228  2.844406  3.220690  1.602668  1.513167   3.766270  3.555943\n",
+       "34   216.970464  2.845412  2.329821  1.614164  1.493453   3.793286  3.509615\n",
+       "37   209.845863  1.956937  3.215590  1.708841  1.524611   4.015777  3.582837\n",
+       "335  174.734848  2.770882  2.760025  1.710865  1.624143   4.020532  3.816736\n",
+       "38   211.627049  1.961099  2.326209  1.715538  1.499931   4.031515  3.524837\n",
+       "..          ...       ...       ...       ...       ...        ...       ...\n",
+       "543  131.093045  3.193123  2.503308  4.758416  2.706784  11.182277  6.360943\n",
+       "23   224.503205  7.472670  2.648100  4.762666  1.649086  11.192264  3.875353\n",
+       "397  117.626414  4.050571  2.602593  4.788907  2.907534  11.253932  6.832705\n",
+       "331  120.648167  3.944006  2.661164  4.870656  2.857321  11.446041  6.714704\n",
+       "622  240.208357  6.285389  1.606621  4.905659  2.470669  11.528298  5.806073\n",
        "\n",
-       "[206 rows x 7 columns]"
+       "[529 rows x 7 columns]"
       ]
      },
-     "execution_count": 112,
+     "execution_count": 163,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "parameters_df=parameters_df[(parameters_df[\"FWHM_x\"]<np.median(parameters_df[\"FWHM_x\"])+3*np.std(parameters_df[\"FWHM_x\"])/math.sqrt(len(parameters_df[\"FWHM_x\"]))) & (parameters_df[\"FWHM_y\"]<np.median(parameters_df[\"FWHM_y\"])+3*np.std(parameters_df[\"FWHM_y\"])/math.sqrt(len(parameters_df[\"FWHM_y\"])))]\n",
+    "#elimina valores extremos del dataset\n",
+    "parameters_df=parameters_df[(parameters_df[\"FWHM_x\"]<np.mean(parameters_df[\"FWHM_x\"])+2.5*np.std(parameters_df[\"FWHM_x\"])) & (parameters_df[\"FWHM_y\"]<np.mean(parameters_df[\"FWHM_y\"])+2.5*np.std(parameters_df[\"FWHM_y\"]))]\n",
     "parameters_df.sort_values('FWHM_x',ascending=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 113,
+   "execution_count": 164,
    "metadata": {},
    "outputs": [
     {
@@ -1852,7 +1840,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1080x360 with 2 Axes>"
       ]
@@ -1883,14 +1871,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 114,
+   "execution_count": 165,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'MEDIAN_FWHM_x': 6.361155779828058, 'MEDIAN_FWHM_y': 5.865753036777117}\n"
+      "{'MEDIAN_FWHM_x': 6.61783565447222, 'MEDIAN_FWHM_y': 6.0911794566979856}\n"
      ]
     }
    ],
@@ -1900,14 +1888,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 115,
+   "execution_count": 166,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'INCERTIDUMBRE_FWHM_x': 0.05897470200906251, 'INCERTIDUMBRE_FWHM_y': 0.0583083389179374}\n"
+      "{'INCERTIDUMBRE_FWHM_x': 0.07111893293495501, 'INCERTIDUMBRE_FWHM_y': 0.059722316688425}\n"
      ]
     }
    ],
@@ -1936,7 +1924,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 116,
+   "execution_count": 167,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -1971,7 +1959,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 117,
+   "execution_count": 168,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -2031,7 +2019,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 118,
+   "execution_count": 169,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -2054,14 +2042,14 @@
      "output_type": "stream",
      "text": [
       "\u001b[91mFWHM FOR CHANNEL RED\u001b[0m\n",
-      "{'MEAN_FWHM_x': 6.85040177317909, 'MEAN_FWHM_y': 6.286265858550066}\n",
-      "{'INCERTIDUMBRE_FWHM_x': 0.07397839012804115, 'INCERTIDUMBRE_FWHM_y': 0.06519770408323534}\n",
+      "{'MEDIAN_FWHM_x': 7.9183883547135165, 'MEDIAN_FWHM_y': 7.35922763918757}\n",
+      "{'INCERTIDUMBRE_FWHM_x': 0.1159148719780544, 'INCERTIDUMBRE_FWHM_y': 0.08836013787487608}\n",
       "\u001b[91mFWHM FOR CHANNEL GREEN\u001b[0m\n",
-      "{'MEAN_FWHM_x': 6.245164160492783, 'MEAN_FWHM_y': 5.852337602343364}\n",
-      "{'INCERTIDUMBRE_FWHM_x': 0.05644305840123783, 'INCERTIDUMBRE_FWHM_y': 0.05873235620560445}\n",
+      "{'MEDIAN_FWHM_x': 7.2816934101996456, 'MEDIAN_FWHM_y': 6.823136964119058}\n",
+      "{'INCERTIDUMBRE_FWHM_x': 0.09670234009426094, 'INCERTIDUMBRE_FWHM_y': 0.07626498914594818}\n",
       "\u001b[91mFWHM FOR CHANNEL BLUE\u001b[0m\n",
-      "{'MEAN_FWHM_x': 5.656095446703643, 'MEAN_FWHM_y': 5.248410485649535}\n",
-      "{'INCERTIDUMBRE_FWHM_x': 0.0511572917957581, 'INCERTIDUMBRE_FWHM_y': 0.04996212650827945}\n"
+      "{'MEDIAN_FWHM_x': 6.707228331976005, 'MEDIAN_FWHM_y': 6.143125344552874}\n",
+      "{'INCERTIDUMBRE_FWHM_x': 0.0772819315486433, 'INCERTIDUMBRE_FWHM_y': 0.06542214124518661}\n"
      ]
     }
    ],
@@ -2085,11 +2073,11 @@
     "    # calcula parametros\n",
     "    parameters_df = star_parameters(temp[:,:,i],stars)\n",
     "    parameters_df=parameters_df[(parameters_df[\"height\"]<255)]\n",
-    "    parameters_df=parameters_df[(parameters_df[\"FWHM_x\"]<np.median(parameters_df[\"FWHM_x\"])+3*np.std(parameters_df[\"FWHM_x\"])/math.sqrt(len(parameters_df[\"FWHM_x\"]))) & (parameters_df[\"FWHM_y\"]<np.median(parameters_df[\"FWHM_y\"])+3*np.std(parameters_df[\"FWHM_y\"])/math.sqrt(len(parameters_df[\"FWHM_y\"])))]\n",
+    "    parameters_df=parameters_df[(parameters_df[\"FWHM_x\"]<np.mean(parameters_df[\"FWHM_x\"])+2.5*np.std(parameters_df[\"FWHM_x\"])) & (parameters_df[\"FWHM_y\"]<np.median(parameters_df[\"FWHM_y\"])+2.5*np.std(parameters_df[\"FWHM_y\"]))]\n",
     "    \n",
     "    #muestra resultados\n",
     "    print(bcolors.RED +\"FWHM FOR CHANNEL \"+color[i] + bcolors.ENDC)\n",
-    "    print({\"MEAN_FWHM_x\":np.mean(parameters_df[\"FWHM_x\"]),\"MEAN_FWHM_y\":np.mean(parameters_df[\"FWHM_y\"])})\n",
+    "    print({\"MEDIAN_FWHM_x\":np.median(parameters_df[\"FWHM_x\"]),\"MEDIAN_FWHM_y\":np.median(parameters_df[\"FWHM_y\"])})\n",
     "    print({\"INCERTIDUMBRE_FWHM_x\":np.std(parameters_df[\"FWHM_x\"])/math.sqrt(len(parameters_df[\"FWHM_x\"])),\"INCERTIDUMBRE_FWHM_y\":np.std(parameters_df[\"FWHM_y\"])/math.sqrt(len(parameters_df[\"FWHM_y\"]))})\n"
    ]
   },
@@ -2097,7 +2085,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Encontramos que el mejor canal es el azul con $$FWHM \\approx 6 $$"
+    "Encontramos que el mejor canal es el azul con $$FWHM \\approx 7 $$"
    ]
   },
   {
diff --git a/entrega/Ejercicios_clase_5.md b/entrega/entrega.md
similarity index 85%
rename from entrega/Ejercicios_clase_5.md
rename to entrega/entrega.md
index dced20f56726bc3f978d2f75592ea6649b5abc31..adfde9faeb34822bdfdcbc8745c041629b182f93 100644
--- a/entrega/Ejercicios_clase_5.md
+++ b/entrega/entrega.md
@@ -627,7 +627,17 @@ parameters_df.sort_values('FWHM_x',ascending=False)
   </thead>
   <tbody>
     <tr>
-      <th>90</th>
+      <th>132</th>
+      <td>22738.039821</td>
+      <td>-239.832795</td>
+      <td>3.010661</td>
+      <td>75.973813</td>
+      <td>2.731824</td>
+      <td>178.538460</td>
+      <td>6.419787</td>
+    </tr>
+    <tr>
+      <th>133</th>
       <td>14409.841451</td>
       <td>-222.262629</td>
       <td>2.343065</td>
@@ -637,7 +647,17 @@ parameters_df.sort_values('FWHM_x',ascending=False)
       <td>5.848958</td>
     </tr>
     <tr>
-      <th>516</th>
+      <th>578</th>
+      <td>31219.909353</td>
+      <td>105.300441</td>
+      <td>2.585564</td>
+      <td>31.073563</td>
+      <td>4.308750</td>
+      <td>73.022872</td>
+      <td>10.125563</td>
+    </tr>
+    <tr>
+      <th>687</th>
       <td>267271.868327</td>
       <td>115.166949</td>
       <td>2.769692</td>
@@ -647,34 +667,14 @@ parameters_df.sort_values('FWHM_x',ascending=False)
       <td>5.758053</td>
     </tr>
     <tr>
-      <th>477</th>
-      <td>107493.971955</td>
-      <td>96.620752</td>
-      <td>3.101467</td>
-      <td>26.096038</td>
-      <td>3.044724</td>
-      <td>61.325689</td>
-      <td>7.155100</td>
-    </tr>
-    <tr>
-      <th>288</th>
-      <td>169.559297</td>
-      <td>-16.954423</td>
-      <td>2.975433</td>
-      <td>25.863356</td>
-      <td>3.902506</td>
-      <td>60.778886</td>
-      <td>9.170890</td>
-    </tr>
-    <tr>
-      <th>441</th>
-      <td>21979.573625</td>
-      <td>66.797420</td>
-      <td>2.603415</td>
-      <td>20.616066</td>
-      <td>2.744205</td>
-      <td>48.447755</td>
-      <td>6.448882</td>
+      <th>600</th>
+      <td>325077.772735</td>
+      <td>112.668325</td>
+      <td>120.781525</td>
+      <td>28.345852</td>
+      <td>191.375356</td>
+      <td>66.612752</td>
+      <td>449.732087</td>
     </tr>
     <tr>
       <th>...</th>
@@ -687,7 +687,7 @@ parameters_df.sort_values('FWHM_x',ascending=False)
       <td>...</td>
     </tr>
     <tr>
-      <th>21</th>
+      <th>27</th>
       <td>202.186462</td>
       <td>2.289328</td>
       <td>2.644426</td>
@@ -697,7 +697,7 @@ parameters_df.sort_values('FWHM_x',ascending=False)
       <td>4.316290</td>
     </tr>
     <tr>
-      <th>29</th>
+      <th>37</th>
       <td>210.588381</td>
       <td>1.978485</td>
       <td>3.167382</td>
@@ -707,7 +707,7 @@ parameters_df.sort_values('FWHM_x',ascending=False)
       <td>4.304869</td>
     </tr>
     <tr>
-      <th>30</th>
+      <th>38</th>
       <td>217.389734</td>
       <td>1.980785</td>
       <td>2.331454</td>
@@ -717,7 +717,7 @@ parameters_df.sort_values('FWHM_x',ascending=False)
       <td>4.014588</td>
     </tr>
     <tr>
-      <th>26</th>
+      <th>33</th>
       <td>216.603135</td>
       <td>2.821754</td>
       <td>3.173432</td>
@@ -727,7 +727,7 @@ parameters_df.sort_values('FWHM_x',ascending=False)
       <td>4.278035</td>
     </tr>
     <tr>
-      <th>27</th>
+      <th>34</th>
       <td>222.917653</td>
       <td>2.823663</td>
       <td>2.336885</td>
@@ -738,7 +738,7 @@ parameters_df.sort_values('FWHM_x',ascending=False)
     </tr>
   </tbody>
 </table>
-<p>524 rows × 7 columns</p>
+<p>695 rows × 7 columns</p>
 </div>
 
 
@@ -783,7 +783,7 @@ parameters_df.sort_values('FWHM_x',ascending=True)
   </thead>
   <tbody>
     <tr>
-      <th>27</th>
+      <th>34</th>
       <td>222.917653</td>
       <td>2.823663</td>
       <td>2.336885</td>
@@ -793,7 +793,7 @@ parameters_df.sort_values('FWHM_x',ascending=True)
       <td>4.000267</td>
     </tr>
     <tr>
-      <th>26</th>
+      <th>33</th>
       <td>216.603135</td>
       <td>2.821754</td>
       <td>3.173432</td>
@@ -803,7 +803,7 @@ parameters_df.sort_values('FWHM_x',ascending=True)
       <td>4.278035</td>
     </tr>
     <tr>
-      <th>30</th>
+      <th>38</th>
       <td>217.389734</td>
       <td>1.980785</td>
       <td>2.331454</td>
@@ -813,7 +813,7 @@ parameters_df.sort_values('FWHM_x',ascending=True)
       <td>4.014588</td>
     </tr>
     <tr>
-      <th>29</th>
+      <th>37</th>
       <td>210.588381</td>
       <td>1.978485</td>
       <td>3.167382</td>
@@ -823,7 +823,7 @@ parameters_df.sort_values('FWHM_x',ascending=True)
       <td>4.304869</td>
     </tr>
     <tr>
-      <th>21</th>
+      <th>27</th>
       <td>202.186462</td>
       <td>2.289328</td>
       <td>2.644426</td>
@@ -843,47 +843,47 @@ parameters_df.sort_values('FWHM_x',ascending=True)
       <td>...</td>
     </tr>
     <tr>
-      <th>309</th>
-      <td>151.959876</td>
-      <td>6.048875</td>
-      <td>3.048254</td>
-      <td>6.335821</td>
-      <td>3.106507</td>
-      <td>14.889180</td>
-      <td>7.300292</td>
+      <th>80</th>
+      <td>126.345557</td>
+      <td>7.503358</td>
+      <td>2.717318</td>
+      <td>8.605995</td>
+      <td>3.441961</td>
+      <td>20.224088</td>
+      <td>8.088608</td>
     </tr>
     <tr>
-      <th>351</th>
-      <td>195.582213</td>
-      <td>7.582757</td>
-      <td>2.703236</td>
-      <td>6.548991</td>
-      <td>3.505496</td>
-      <td>15.390128</td>
-      <td>8.237915</td>
+      <th>98</th>
+      <td>191.987149</td>
+      <td>10.993017</td>
+      <td>2.720394</td>
+      <td>9.802730</td>
+      <td>3.154057</td>
+      <td>23.036415</td>
+      <td>7.412033</td>
     </tr>
     <tr>
-      <th>425</th>
-      <td>251.594399</td>
-      <td>7.994551</td>
-      <td>1.593031</td>
-      <td>6.646625</td>
-      <td>3.216397</td>
-      <td>15.619568</td>
-      <td>7.558533</td>
+      <th>261</th>
+      <td>220.500255</td>
+      <td>12.860922</td>
+      <td>2.878011</td>
+      <td>9.887140</td>
+      <td>2.760608</td>
+      <td>23.234779</td>
+      <td>6.487429</td>
     </tr>
     <tr>
-      <th>47</th>
-      <td>115.774855</td>
-      <td>4.630637</td>
-      <td>2.210311</td>
-      <td>7.270685</td>
-      <td>5.813427</td>
-      <td>17.086110</td>
-      <td>13.661553</td>
+      <th>218</th>
+      <td>229.036678</td>
+      <td>21.346080</td>
+      <td>2.787659</td>
+      <td>17.666989</td>
+      <td>2.664696</td>
+      <td>41.517424</td>
+      <td>6.262035</td>
     </tr>
     <tr>
-      <th>288</th>
+      <th>372</th>
       <td>169.559297</td>
       <td>-16.954423</td>
       <td>2.975433</td>
@@ -894,7 +894,7 @@ parameters_df.sort_values('FWHM_x',ascending=True)
     </tr>
   </tbody>
 </table>
-<p>462 rows × 7 columns</p>
+<p>585 rows × 7 columns</p>
 </div>
 
 
@@ -956,7 +956,7 @@ print({"MODE_FWHM_x":st.mode(parameters_df["FWHM_x"]),"MODE_FWHM_y":st.mode(para
 print({"MEAN_FWHM_x":np.mean(parameters_df["FWHM_x"]),"MEAN_FWHM_y":np.mean(parameters_df["FWHM_y"])})
 ```
 
-    {'MEAN_FWHM_x': 7.940323515966517, 'MEAN_FWHM_y': 7.337040419590766}
+    {'MEAN_FWHM_x': 8.379209150177722, 'MEAN_FWHM_y': 7.576010416841125}
     
 
 #### Mediana de FHWM
@@ -966,7 +966,7 @@ print({"MEAN_FWHM_x":np.mean(parameters_df["FWHM_x"]),"MEAN_FWHM_y":np.mean(para
 print({"MEDIAN_FWHM_x":np.median(parameters_df["FWHM_x"]),"MEDIAN_FWHM_y":np.median(parameters_df["FWHM_y"])})
 ```
 
-    {'MEDIAN_FWHM_x': 7.396254460793973, 'MEDIAN_FWHM_y': 6.900833212383647}
+    {'MEDIAN_FWHM_x': 7.670810006638417, 'MEDIAN_FWHM_y': 6.99528444605464}
     
 
 #### Incertidumbre
@@ -976,14 +976,15 @@ print({"MEDIAN_FWHM_x":np.median(parameters_df["FWHM_x"]),"MEDIAN_FWHM_y":np.med
 print({"INCERTIDUMBRE_FWHM_x":np.std(parameters_df["FWHM_x"])/math.sqrt(len(parameters_df["FWHM_x"])),"INCERTIDUMBRE_FWHM_y":np.std(parameters_df["FWHM_y"])/math.sqrt(len(parameters_df["FWHM_y"]))})
 ```
 
-    {'INCERTIDUMBRE_FWHM_x': 0.15200550254891215, 'INCERTIDUMBRE_FWHM_y': 0.12480846830393083}
+    {'INCERTIDUMBRE_FWHM_x': 0.15034065812442832, 'INCERTIDUMBRE_FWHM_y': 0.12213295193042271}
     
 
 Vemos que hay una gran diferencia entre la media y la mediana de la distribución, esto se debe a que nuestros resultados para el FWHM tiene unos valores extremos inesperados y muy altos. Por ejemplo, uno de los FWHM es 60, esto significaría que no podemos distinguir dos estrellas en una imagen de 60x60, lo cual sabemos que es falso. Una manera de evitar este problema es tomar la mediana como referente en lugar de la media, ya que esta ordena los valores y toma el valor que está en la posición central, ignorando los valores extremos. 
 
 
 ```
-parameters_df=parameters_df[(parameters_df["FWHM_x"]<np.median(parameters_df["FWHM_x"])+3*np.std(parameters_df["FWHM_x"])/math.sqrt(len(parameters_df["FWHM_x"]))) & (parameters_df["FWHM_y"]<np.median(parameters_df["FWHM_y"])+3*np.std(parameters_df["FWHM_y"])/math.sqrt(len(parameters_df["FWHM_y"])))]
+#elimina valores extremos del dataset
+parameters_df=parameters_df[(parameters_df["FWHM_x"]<np.mean(parameters_df["FWHM_x"])+2.5*np.std(parameters_df["FWHM_x"])) & (parameters_df["FWHM_y"]<np.mean(parameters_df["FWHM_y"])+2.5*np.std(parameters_df["FWHM_y"]))]
 parameters_df.sort_values('FWHM_x',ascending=True)
 ```
 
@@ -1019,54 +1020,54 @@ parameters_df.sort_values('FWHM_x',ascending=True)
   </thead>
   <tbody>
     <tr>
-      <th>27</th>
-      <td>222.917653</td>
-      <td>2.823663</td>
-      <td>2.336885</td>
-      <td>1.803631</td>
-      <td>1.702241</td>
-      <td>4.238533</td>
-      <td>4.000267</td>
+      <th>33</th>
+      <td>215.742228</td>
+      <td>2.844406</td>
+      <td>3.220690</td>
+      <td>1.602668</td>
+      <td>1.513167</td>
+      <td>3.766270</td>
+      <td>3.555943</td>
     </tr>
     <tr>
-      <th>26</th>
-      <td>216.603135</td>
-      <td>2.821754</td>
-      <td>3.173432</td>
-      <td>1.804289</td>
-      <td>1.820440</td>
-      <td>4.240079</td>
-      <td>4.278035</td>
+      <th>34</th>
+      <td>216.970464</td>
+      <td>2.845412</td>
+      <td>2.329821</td>
+      <td>1.614164</td>
+      <td>1.493453</td>
+      <td>3.793286</td>
+      <td>3.509615</td>
     </tr>
     <tr>
-      <th>30</th>
-      <td>217.389734</td>
-      <td>1.980785</td>
-      <td>2.331454</td>
-      <td>1.931921</td>
-      <td>1.708335</td>
-      <td>4.540015</td>
-      <td>4.014588</td>
+      <th>37</th>
+      <td>209.845863</td>
+      <td>1.956937</td>
+      <td>3.215590</td>
+      <td>1.708841</td>
+      <td>1.524611</td>
+      <td>4.015777</td>
+      <td>3.582837</td>
     </tr>
     <tr>
-      <th>29</th>
-      <td>210.588381</td>
-      <td>1.978485</td>
-      <td>3.167382</td>
-      <td>1.943865</td>
-      <td>1.831859</td>
-      <td>4.568083</td>
-      <td>4.304869</td>
+      <th>335</th>
+      <td>174.734848</td>
+      <td>2.770882</td>
+      <td>2.760025</td>
+      <td>1.710865</td>
+      <td>1.624143</td>
+      <td>4.020532</td>
+      <td>3.816736</td>
     </tr>
     <tr>
-      <th>21</th>
-      <td>202.186462</td>
-      <td>2.289328</td>
-      <td>2.644426</td>
-      <td>2.005263</td>
-      <td>1.836719</td>
-      <td>4.712369</td>
-      <td>4.316290</td>
+      <th>38</th>
+      <td>211.627049</td>
+      <td>1.961099</td>
+      <td>2.326209</td>
+      <td>1.715538</td>
+      <td>1.499931</td>
+      <td>4.031515</td>
+      <td>3.524837</td>
     </tr>
     <tr>
       <th>...</th>
@@ -1079,58 +1080,58 @@ parameters_df.sort_values('FWHM_x',ascending=True)
       <td>...</td>
     </tr>
     <tr>
-      <th>400</th>
-      <td>148.694932</td>
-      <td>3.251515</td>
-      <td>3.323809</td>
-      <td>3.311014</td>
-      <td>3.089162</td>
-      <td>7.780883</td>
-      <td>7.259531</td>
+      <th>543</th>
+      <td>131.093045</td>
+      <td>3.193123</td>
+      <td>2.503308</td>
+      <td>4.758416</td>
+      <td>2.706784</td>
+      <td>11.182277</td>
+      <td>6.360943</td>
     </tr>
     <tr>
-      <th>32</th>
-      <td>120.009106</td>
-      <td>2.582832</td>
-      <td>2.669800</td>
-      <td>3.323717</td>
-      <td>2.781075</td>
-      <td>7.810735</td>
-      <td>6.535527</td>
+      <th>23</th>
+      <td>224.503205</td>
+      <td>7.472670</td>
+      <td>2.648100</td>
+      <td>4.762666</td>
+      <td>1.649086</td>
+      <td>11.192264</td>
+      <td>3.875353</td>
     </tr>
     <tr>
-      <th>150</th>
-      <td>140.401733</td>
-      <td>2.814937</td>
-      <td>2.461857</td>
-      <td>3.330074</td>
-      <td>3.020962</td>
-      <td>7.825674</td>
-      <td>7.099261</td>
+      <th>397</th>
+      <td>117.626414</td>
+      <td>4.050571</td>
+      <td>2.602593</td>
+      <td>4.788907</td>
+      <td>2.907534</td>
+      <td>11.253932</td>
+      <td>6.832705</td>
     </tr>
     <tr>
-      <th>281</th>
-      <td>192.304218</td>
-      <td>3.922074</td>
-      <td>2.380541</td>
-      <td>3.330268</td>
-      <td>2.254765</td>
-      <td>7.826129</td>
-      <td>5.298697</td>
+      <th>331</th>
+      <td>120.648167</td>
+      <td>3.944006</td>
+      <td>2.661164</td>
+      <td>4.870656</td>
+      <td>2.857321</td>
+      <td>11.446041</td>
+      <td>6.714704</td>
     </tr>
     <tr>
-      <th>228</th>
-      <td>123.573138</td>
-      <td>2.781581</td>
-      <td>2.546152</td>
-      <td>3.334843</td>
-      <td>2.962802</td>
-      <td>7.836880</td>
-      <td>6.962584</td>
+      <th>622</th>
+      <td>240.208357</td>
+      <td>6.285389</td>
+      <td>1.606621</td>
+      <td>4.905659</td>
+      <td>2.470669</td>
+      <td>11.528298</td>
+      <td>5.806073</td>
     </tr>
   </tbody>
 </table>
-<p>206 rows × 7 columns</p>
+<p>529 rows × 7 columns</p>
 </div>
 
 
@@ -1169,7 +1170,7 @@ pyplot.show()
 print({"MEDIAN_FWHM_x":np.median(parameters_df["FWHM_x"]),"MEDIAN_FWHM_y":np.median(parameters_df["FWHM_y"])})
 ```
 
-    {'MEDIAN_FWHM_x': 6.361155779828058, 'MEDIAN_FWHM_y': 5.865753036777117}
+    {'MEDIAN_FWHM_x': 6.61783565447222, 'MEDIAN_FWHM_y': 6.0911794566979856}
     
 
 
@@ -1177,7 +1178,7 @@ print({"MEDIAN_FWHM_x":np.median(parameters_df["FWHM_x"]),"MEDIAN_FWHM_y":np.med
 print({"INCERTIDUMBRE_FWHM_x":np.std(parameters_df["FWHM_x"])/math.sqrt(len(parameters_df["FWHM_x"])),"INCERTIDUMBRE_FWHM_y":np.std(parameters_df["FWHM_y"])/math.sqrt(len(parameters_df["FWHM_y"]))})
 ```
 
-    {'INCERTIDUMBRE_FWHM_x': 0.05897470200906251, 'INCERTIDUMBRE_FWHM_y': 0.0583083389179374}
+    {'INCERTIDUMBRE_FWHM_x': 0.07111893293495501, 'INCERTIDUMBRE_FWHM_y': 0.059722316688425}
     
 
 Tomaremos el promedi de los resultados en MEAN_FWHM y diremos que:
@@ -1247,11 +1248,11 @@ for i in range(3):
     # calcula parametros
     parameters_df = star_parameters(temp[:,:,i],stars)
     parameters_df=parameters_df[(parameters_df["height"]<255)]
-    parameters_df=parameters_df[(parameters_df["FWHM_x"]<np.median(parameters_df["FWHM_x"])+3*np.std(parameters_df["FWHM_x"])/math.sqrt(len(parameters_df["FWHM_x"]))) & (parameters_df["FWHM_y"]<np.median(parameters_df["FWHM_y"])+3*np.std(parameters_df["FWHM_y"])/math.sqrt(len(parameters_df["FWHM_y"])))]
+    parameters_df=parameters_df[(parameters_df["FWHM_x"]<np.mean(parameters_df["FWHM_x"])+2.5*np.std(parameters_df["FWHM_x"])) & (parameters_df["FWHM_y"]<np.median(parameters_df["FWHM_y"])+2.5*np.std(parameters_df["FWHM_y"]))]
     
     #muestra resultados
     print(bcolors.RED +"FWHM FOR CHANNEL "+color[i] + bcolors.ENDC)
-    print({"MEAN_FWHM_x":np.mean(parameters_df["FWHM_x"]),"MEAN_FWHM_y":np.mean(parameters_df["FWHM_y"])})
+    print({"MEDIAN_FWHM_x":np.median(parameters_df["FWHM_x"]),"MEDIAN_FWHM_y":np.median(parameters_df["FWHM_y"])})
     print({"INCERTIDUMBRE_FWHM_x":np.std(parameters_df["FWHM_x"])/math.sqrt(len(parameters_df["FWHM_x"])),"INCERTIDUMBRE_FWHM_y":np.std(parameters_df["FWHM_y"])/math.sqrt(len(parameters_df["FWHM_y"]))})
 
 ```
@@ -1261,17 +1262,17 @@ for i in range(3):
     
 
     FWHM FOR CHANNEL RED
-    {'MEAN_FWHM_x': 6.85040177317909, 'MEAN_FWHM_y': 6.286265858550066}
-    {'INCERTIDUMBRE_FWHM_x': 0.07397839012804115, 'INCERTIDUMBRE_FWHM_y': 0.06519770408323534}
+    {'MEDIAN_FWHM_x': 7.9183883547135165, 'MEDIAN_FWHM_y': 7.35922763918757}
+    {'INCERTIDUMBRE_FWHM_x': 0.1159148719780544, 'INCERTIDUMBRE_FWHM_y': 0.08836013787487608}
     FWHM FOR CHANNEL GREEN
-    {'MEAN_FWHM_x': 6.245164160492783, 'MEAN_FWHM_y': 5.852337602343364}
-    {'INCERTIDUMBRE_FWHM_x': 0.05644305840123783, 'INCERTIDUMBRE_FWHM_y': 0.05873235620560445}
+    {'MEDIAN_FWHM_x': 7.2816934101996456, 'MEDIAN_FWHM_y': 6.823136964119058}
+    {'INCERTIDUMBRE_FWHM_x': 0.09670234009426094, 'INCERTIDUMBRE_FWHM_y': 0.07626498914594818}
     FWHM FOR CHANNEL BLUE
-    {'MEAN_FWHM_x': 5.656095446703643, 'MEAN_FWHM_y': 5.248410485649535}
-    {'INCERTIDUMBRE_FWHM_x': 0.0511572917957581, 'INCERTIDUMBRE_FWHM_y': 0.04996212650827945}
+    {'MEDIAN_FWHM_x': 6.707228331976005, 'MEDIAN_FWHM_y': 6.143125344552874}
+    {'INCERTIDUMBRE_FWHM_x': 0.0772819315486433, 'INCERTIDUMBRE_FWHM_y': 0.06542214124518661}
     
 
-Encontramos que el mejor canal es el azul con $$FWHM \approx 6 $$
+Encontramos que el mejor canal es el azul con $$FWHM \approx 7 $$
 
 ## Resultados
 
diff --git a/entrega/output_49_1.png b/entrega/output_49_1.png
index e76a62efeaf66e38c14be3b977fe0b3d28d7df95..6a129b844a136f32fcac99e310fac45a983e520d 100644
Binary files a/entrega/output_49_1.png and b/entrega/output_49_1.png differ
diff --git a/entrega/output_62_1.png b/entrega/output_62_1.png
index 9dc53f19b8e8adb9afd1eb6218289e834fe8586a..112aed9a4925db90072ec5956fb44d3540b26eda 100644
Binary files a/entrega/output_62_1.png and b/entrega/output_62_1.png differ