diff --git a/Functions/functions.py b/Functions/functions.py
index 16cfb8a41c69465ec132f0bff9dcf4bc8bb32197..b8b938951b2576ddb4834d184e7532d738abdda1 100644
--- a/Functions/functions.py
+++ b/Functions/functions.py
@@ -304,8 +304,8 @@ def grafica_data_ybestfit(path, gal, df_best_parmeters, ss="ra"):
 
     ax2 = plt.subplot(grilla[2:,:])
     # -- graficar residuo
-    ax2.plot(gal["r(kpc)"],(ynfw(gal["r(kpc)"]*1000) - gal["v(km/s)"]) / np.sqrt(gal["err_v(km/s)"]) ,'--r',color="darkorange")
-    ax2.plot(gal["r(kpc)"],(yiso(gal["r(kpc)"]*1000) - gal["v(km/s)"]) / np.sqrt(gal["err_v(km/s)"]) ,'--r',color="blue")
+    ax2.plot(gal["r(kpc)"],(ynfw(gal["r(kpc)"]*1000) - gal["v(km/s)"]) / (np.sqrt(gal["err_v(km/s)"])**2) ,'--r',color="darkorange")
+    ax2.plot(gal["r(kpc)"],(yiso(gal["r(kpc)"]*1000) - gal["v(km/s)"]) / (np.sqrt(gal["err_v(km/s)"])**2) ,'--r',color="blue")
 
         
     # -- poner nombre a ejes y título a la gráfica
diff --git a/notebook/confidence_interval_ellipse1-short - 1.ipynb b/notebook/confidence_interval_ellipse1-short - 1.ipynb
index 4d176c8bbdebc983a4c7a178a80a9d98999980bf..37c6fc0d178115ba7d817ec689a85d5e0bcbcdd7 100644
--- a/notebook/confidence_interval_ellipse1-short - 1.ipynb	
+++ b/notebook/confidence_interval_ellipse1-short - 1.ipynb	
@@ -278,6 +278,118 @@
     "ch2_inter_ISO_ = chi2_intervals(best_parmeters, df1, chis_ISO_ra, i = 1, ss = \"ra\")"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m01 = np.exp(-np.array(list(zip(*ch2_inter_NFW_[1]))[1])/2).sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m12 = np.exp(-np.array(list(zip(*ch2_inter_NFW_[2]))[1])/2).sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m23 = np.exp(-np.array(list(zip(*ch2_inter_NFW_[3]))[1])/2).sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m33 = np.exp(-np.array(list(zip(*ch2_inter_NFW_[4]))[1])/2).sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "total = m01+m12+m23+m33"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.09570840914791515"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m01 / total"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.16337829265551743"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m12 / total"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6.147853929639663e-97"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.exp(-chi2_NFW_bestfit/2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": 10,
@@ -382,16 +494,6 @@
     "plot_ellipse_confidence_interval(path,para_range_ISO, best_parmeters, ss=\"ra\", i=1 )"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# -- gráfica, en functions.py, toca descomentar la línea que guarda la gráfica\n",
-    "#grafica_data_ybestfit(path, df1, best_parmeters, ss=\"ra\", mtype = \"new\", new_para_min_ch2_b = new_para_min_ch2_b)"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 17,
@@ -407,10 +509,10 @@
     "# ,len(df1) # saca la longitud de los datos\n",
     "# ,best_parmeters[\"M200_NFW(Msun)\"][0] # aca toca cambiarle el 0 (ra), por 1(r) o 2(a)\n",
     "# ,best_parmeters[\"c_NFW\"][0] # aca toca cambiarle el 0 (ra), por 1(r) o 2(a)\n",
-    "# ,chi2_NFW_bestfit # chi2 para NFW con los parámetros que da leastsq\n",
+    "# ,chi2_NFW_bestfit_red # chi2 para NFW con los parámetros que da leastsq\n",
     "# ,abs(best_parmeters[\"rho0_ISO(Msun/pc3)\"][0]) # aaca toca cambiarle el 0 (ra), por 1(r) o 2(a)\n",
     "# ,abs(best_parmeters[\"Rc_ISO(pc)\"][0]) # aca toca cambiarle el 0 (ra), por 1(r) o 2(a)\n",
-    "# ,chi2_ISO_bestfit)) # chi2 para ISO con los parámetros que da leastsq\n",
+    "# ,chi2_ISO_bestfit_red)) # chi2 para ISO con los parámetros que da leastsq\n",
     "# file1.close()\n"
    ]
   },
diff --git a/notebook/confidence_interval_ellipse1.ipynb b/notebook/confidence_interval_ellipse1.ipynb
index e91c1627cd2615a9f0fa57555010e868ff79c6b1..46864ab6fded0d3355940256ff789c8886429323 100644
--- a/notebook/confidence_interval_ellipse1.ipynb
+++ b/notebook/confidence_interval_ellipse1.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -12,7 +12,8 @@
     "\n",
     "from imports import *\n",
     "from constants import *\n",
-    "from functions import *"
+    "from functions import *\n",
+    "import math"
    ]
   },
   {
@@ -49,7 +50,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -58,7 +59,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -70,7 +71,7 @@
        "<Quantity 3.e+10 solMass>"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -81,7 +82,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -123,16 +124,209 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 24,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>r(kpc)</th>\n",
+       "      <th>err_r(kpc)</th>\n",
+       "      <th>r(arcsec)</th>\n",
+       "      <th>err_r(arcsec)</th>\n",
+       "      <th>v(km/s)</th>\n",
+       "      <th>err_v(km/s)</th>\n",
+       "      <th>Numberofbins</th>\n",
+       "      <th>Side</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>1.2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>17</td>\n",
+       "      <td>1</td>\n",
+       "      <td>a</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>1.2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>20</td>\n",
+       "      <td>17</td>\n",
+       "      <td>1</td>\n",
+       "      <td>r</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.06</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>17</td>\n",
+       "      <td>2</td>\n",
+       "      <td>r</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.08</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-27</td>\n",
+       "      <td>17</td>\n",
+       "      <td>1</td>\n",
+       "      <td>a</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.09</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>18</td>\n",
+       "      <td>17</td>\n",
+       "      <td>1</td>\n",
+       "      <td>r</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99</th>\n",
+       "      <td>2.92</td>\n",
+       "      <td>0.04</td>\n",
+       "      <td>114.3</td>\n",
+       "      <td>1.6</td>\n",
+       "      <td>116</td>\n",
+       "      <td>2</td>\n",
+       "      <td>24</td>\n",
+       "      <td>r</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>100</th>\n",
+       "      <td>3.05</td>\n",
+       "      <td>0.04</td>\n",
+       "      <td>119.4</td>\n",
+       "      <td>1.6</td>\n",
+       "      <td>117</td>\n",
+       "      <td>2</td>\n",
+       "      <td>24</td>\n",
+       "      <td>r</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>101</th>\n",
+       "      <td>3.27</td>\n",
+       "      <td>0.08</td>\n",
+       "      <td>128.0</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>113</td>\n",
+       "      <td>1</td>\n",
+       "      <td>24</td>\n",
+       "      <td>r</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>102</th>\n",
+       "      <td>3.57</td>\n",
+       "      <td>0.10</td>\n",
+       "      <td>139.7</td>\n",
+       "      <td>3.9</td>\n",
+       "      <td>117</td>\n",
+       "      <td>2</td>\n",
+       "      <td>24</td>\n",
+       "      <td>r</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>103</th>\n",
+       "      <td>3.85</td>\n",
+       "      <td>0.09</td>\n",
+       "      <td>150.7</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>113</td>\n",
+       "      <td>3</td>\n",
+       "      <td>13</td>\n",
+       "      <td>r</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>103 rows × 8 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     r(kpc)  err_r(kpc)  r(arcsec)  err_r(arcsec)  v(km/s)  err_v(km/s)  \\\n",
+       "0      0.03        0.00        1.2            0.0        5           17   \n",
+       "1      0.03        0.00        1.2            0.0       20           17   \n",
+       "2      0.06        0.00        2.3            0.0        5           17   \n",
+       "3      0.08        0.00        3.1            0.0      -27           17   \n",
+       "4      0.09        0.00        3.5            0.0       18           17   \n",
+       "..      ...         ...        ...            ...      ...          ...   \n",
+       "99     2.92        0.04      114.3            1.6      116            2   \n",
+       "100    3.05        0.04      119.4            1.6      117            2   \n",
+       "101    3.27        0.08      128.0            3.1      113            1   \n",
+       "102    3.57        0.10      139.7            3.9      117            2   \n",
+       "103    3.85        0.09      150.7            3.5      113            3   \n",
+       "\n",
+       "     Numberofbins Side  \n",
+       "0               1    a  \n",
+       "1               1    r  \n",
+       "2               2    r  \n",
+       "3               1    a  \n",
+       "4               1    r  \n",
+       "..            ...  ...  \n",
+       "99             24    r  \n",
+       "100            24    r  \n",
+       "101            24    r  \n",
+       "102            24    r  \n",
+       "103            13    r  \n",
+       "\n",
+       "[103 rows x 8 columns]"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "#df1"
+    "df1"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -181,40 +375,40 @@
        "      <th>0</th>\n",
        "      <td>rcugc11012</td>\n",
        "      <td>ra</td>\n",
-       "      <td>2.593063e+11</td>\n",
-       "      <td>6.733116e+10</td>\n",
-       "      <td>26.697951</td>\n",
-       "      <td>2.326108</td>\n",
-       "      <td>1.115894</td>\n",
-       "      <td>0.062761</td>\n",
-       "      <td>575.867694</td>\n",
-       "      <td>24.665902</td>\n",
+       "      <td>2.114442e+11</td>\n",
+       "      <td>4.573093e+10</td>\n",
+       "      <td>29.255863</td>\n",
+       "      <td>2.202504</td>\n",
+       "      <td>1.194693</td>\n",
+       "      <td>0.061866</td>\n",
+       "      <td>552.632360</td>\n",
+       "      <td>21.654125</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>rcugc11012</td>\n",
        "      <td>r</td>\n",
-       "      <td>2.022418e+11</td>\n",
-       "      <td>5.126962e+10</td>\n",
-       "      <td>28.991113</td>\n",
-       "      <td>2.633132</td>\n",
-       "      <td>1.175025</td>\n",
-       "      <td>0.078288</td>\n",
-       "      <td>550.177818</td>\n",
-       "      <td>27.099124</td>\n",
+       "      <td>1.842470e+11</td>\n",
+       "      <td>4.454677e+10</td>\n",
+       "      <td>30.514076</td>\n",
+       "      <td>2.667114</td>\n",
+       "      <td>1.208183</td>\n",
+       "      <td>0.079800</td>\n",
+       "      <td>-544.153626</td>\n",
+       "      <td>26.661269</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>rcugc11012</td>\n",
        "      <td>a</td>\n",
-       "      <td>7.406665e+15</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>1.580118</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.914885</td>\n",
-       "      <td>0.087632</td>\n",
-       "      <td>-703.128652</td>\n",
-       "      <td>58.884282</td>\n",
+       "      <td>5.058350e+12</td>\n",
+       "      <td>1.189198e+13</td>\n",
+       "      <td>12.567347</td>\n",
+       "      <td>7.271629</td>\n",
+       "      <td>1.073731</td>\n",
+       "      <td>0.078976</td>\n",
+       "      <td>-622.489979</td>\n",
+       "      <td>37.382289</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -222,17 +416,17 @@
       ],
       "text/plain": [
        "           ID Side  M200_NFW(Msun)  err_M200_NFW(Msun)      c_NFW  err_c_NFW  \\\n",
-       "0  rcugc11012   ra    2.593063e+11        6.733116e+10  26.697951   2.326108   \n",
-       "1  rcugc11012    r    2.022418e+11        5.126962e+10  28.991113   2.633132   \n",
-       "2  rcugc11012    a    7.406665e+15        0.000000e+00   1.580118   0.000000   \n",
+       "0  rcugc11012   ra    2.114442e+11        4.573093e+10  29.255863   2.202504   \n",
+       "1  rcugc11012    r    1.842470e+11        4.454677e+10  30.514076   2.667114   \n",
+       "2  rcugc11012    a    5.058350e+12        1.189198e+13  12.567347   7.271629   \n",
        "\n",
        "   rho0_ISO(Msun/pc3)  err_rho0_ISO(Msun/pc3)  Rc_ISO(pc)  err_Rc_ISO(pc)  \n",
-       "0            1.115894                0.062761  575.867694       24.665902  \n",
-       "1            1.175025                0.078288  550.177818       27.099124  \n",
-       "2            0.914885                0.087632 -703.128652       58.884282  "
+       "0            1.194693                0.061866  552.632360       21.654125  \n",
+       "1            1.208183                0.079800 -544.153626       26.661269  \n",
+       "2            1.073731                0.078976 -622.489979       37.382289  "
       ]
      },
-     "execution_count": 6,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -244,7 +438,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(50000000000.0, 684246962388.9861)"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "order_c = math.floor(math.log10(dff[\"M200_NFW(Msun)\"][1]))\n",
+    "aa = np.linspace(((dff[\"M200_NFW(Msun)\"][1]/(10**order_c)) - ((dff[\"M200_NFW(Msun)\"][1]/(10**order_c))-0.5))*(10**order_c),\n",
+    "                              ((dff[\"M200_NFW(Msun)\"][1]/(10**order_c)) + 5)*(10**order_c),\n",
+    "                              300)\n",
+    "min(aa), max(aa)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -253,7 +471,7 @@
        "3.85"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -264,12 +482,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 792x504 with 1 Axes>"
       ]