diff --git a/Functions/functions.py b/Functions/functions.py
index b8b938951b2576ddb4834d184e7532d738abdda1..73d14988ca9d74b29a6b55a693054f0882a09c97 100644
--- a/Functions/functions.py
+++ b/Functions/functions.py
@@ -445,10 +445,10 @@ def chi2_intervals(df_best_parmeters, gal, chis, i = 0, ss = "ra"):
Retorna 5 listas:
ch2_m: chi2 <= chi2_bestfit
- ch2_p01: chi2_bestfit<ch2<chi2_bestfit+0.1
- ch2_p12: chi2_bestfit+0.1<ch2<=chi2_bestfit+0.3
- ch2_p23: chi2_bestfit+0.3<ch2<=chi2_bestfit+0.9
- ch2_p3p: chi2_bestfit+0.9<ch2
+ ch2_p01: chi2_bestfit<ch2<chi2_bestfit+0.006
+ ch2_p12: chi2_bestfit+0.006<ch2<=chi2_bestfit+0.093
+ ch2_p23: chi2_bestfit+0.093<ch2<=chi2_bestfit+0.765
+ ch2_p3p: chi2_bestfit+0.765<ch2
'''
@@ -482,10 +482,10 @@ def chi2_intervals(df_best_parmeters, gal, chis, i = 0, ss = "ra"):
#chi2_bestfit = chis[i_chi2_bestfit]
ch2_m = [(i,ch2) for i,ch2 in enumerate(chis) if ch2<=chi2_bestfit]
- ch2_p01 = [(i,ch2) for i,ch2 in enumerate(chis) if chi2_bestfit<ch2<=chi2_bestfit+0.1]
- ch2_p12 = [(i,ch2) for i,ch2 in enumerate(chis) if chi2_bestfit+0.1<ch2<=chi2_bestfit+0.3]
- ch2_p23 = [(i,ch2) for i,ch2 in enumerate(chis) if chi2_bestfit+0.3<ch2<=chi2_bestfit+0.9]
- ch2_p3p = [(i,ch2) for i,ch2 in enumerate(chis) if chi2_bestfit+0.9<ch2]
+ ch2_p01 = [(i,ch2) for i,ch2 in enumerate(chis) if chi2_bestfit<ch2<=chi2_bestfit+0.006]
+ ch2_p12 = [(i,ch2) for i,ch2 in enumerate(chis) if chi2_bestfit+0.006<ch2<=chi2_bestfit+0.093]
+ ch2_p23 = [(i,ch2) for i,ch2 in enumerate(chis) if chi2_bestfit+0.093<ch2<=chi2_bestfit+0.765]
+ ch2_p3p = [(i,ch2) for i,ch2 in enumerate(chis) if chi2_bestfit+0.765<ch2]
# if len(ch2_m) == 0:
# ch2_m = (i,chi2_bestfit)
@@ -505,10 +505,10 @@ def parameters_each_interval(combs, ch2_inter, kk = 0):
chi2_intervals(df_best_parmeters, gal, chis, i = 0, ss = "ra")
ch2_m: chi2 < = chi2_bestfit --> kk = 0
- ch2_p01: chi2_bestfit<ch2<chi2_bestfit+0.1 --> kk = 1
- ch2_p12: chi2_bestfit+0.1<ch2<=chi2_bestfit+0.3 --> kk = 2
- ch2_p23: chi2_bestfit+0.3<ch2<=chi2_bestfit+0.9 --> kk = 3
- ch2_p3p: chi2_bestfit+0.9<ch2 --> kk = 4
+ ch2_p01: chi2_bestfit<ch2<chi2_bestfit+0.006 --> kk = 1
+ ch2_p12: chi2_bestfit+0.006<ch2<=chi2_bestfit+0.093 --> kk = 2
+ ch2_p23: chi2_bestfit+0.093<ch2<=chi2_bestfit+0.765 --> kk = 3
+ ch2_p3p: chi2_bestfit+0.765<ch2 --> kk = 4
kk = 0: Que intervalo se quiere de acuerdo a las 5 listas
@@ -538,10 +538,10 @@ def plot_ellipse_confidence_interval(path, para_range, df_best_parmeters, ss="ra
para_range: arreglo de longitud 5, donde se ponen el par de parámetros que cumplen:
chi2 < =chi2_bestfit --> kk = 0
- chi2_bestfit<ch2<chi2_bestfit+0.1 --> kk = 1
- chi2_bestfit+0.1<ch2<=chi2_bestfit+0.3 --> kk = 2
- chi2_bestfit+0.3<ch2<=chi2_bestfit+0.9 --> kk = 3
- chi2_bestfit+0.9<ch2 --> kk = 4
+ chi2_bestfit<ch2<chi2_bestfit+0.006 --> kk = 1
+ chi2_bestfit+0.006<ch2<=chi2_bestfit+0.093 --> kk = 2
+ chi2_bestfit+0.093<ch2<=chi2_bestfit+0.765 --> kk = 3
+ chi2_bestfit+0.765<ch2 --> kk = 4
es el output de la función parameters_each_interval(combs, ch2_inter, kk = 0)
luego de realizar un for para todos los kk (por eso queda de longitud 5)
diff --git a/notebook/Functions.ipynb b/notebook/Functions.ipynb
index acd93f380e3a93e809d74a95e97bcc5b8891cd6f..2f27312fae7ad0b7a3a4d22a72574a9a76d32546 100644
--- a/notebook/Functions.ipynb
+++ b/notebook/Functions.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
@@ -17,7 +17,7 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 21,
"metadata": {},
"outputs": [
{
@@ -38,7 +38,7 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 22,
"metadata": {},
"outputs": [
{
@@ -54,12 +54,7 @@
"../galaxies_data/rcugc5251.txt\n",
"57\n",
"../galaxies_data/rcugc11300.txt\n",
- "58\n",
- "../galaxies_data/rcugc8403.txt\n",
- "122\n",
- "../galaxies_data/rcugc9866.txt\n",
- "35\n",
- "../galaxies_data/rcugc11466.txt\n"
+ "58\n"
]
},
{
@@ -85,12 +80,6 @@
"../Functions/functions.py:52: RuntimeWarning: invalid value encountered in log\n",
" v = (10*G*H0*MM)**(1/3) * ((np.log(1+c*x) - c*x/(1+c*x)) /(x*(np.log(1+c) - c/(1+c))) )**(1/2)\n",
"../Functions/functions.py:67: RuntimeWarning: invalid value encountered in sqrt\n",
- " v = ( 4*np.pi*G*rr*x**(-1) *(x - np.arctan(x) ) )**(1/2)\n",
- "../Functions/functions.py:67: RuntimeWarning: invalid value encountered in sqrt\n",
- " v = ( 4*np.pi*G*rr*x**(-1) *(x - np.arctan(x) ) )**(1/2)\n",
- "../Functions/functions.py:52: RuntimeWarning: invalid value encountered in log\n",
- " v = (10*G*H0*MM)**(1/3) * ((np.log(1+c*x) - c*x/(1+c*x)) /(x*(np.log(1+c) - c/(1+c))) )**(1/2)\n",
- "../Functions/functions.py:67: RuntimeWarning: invalid value encountered in sqrt\n",
" v = ( 4*np.pi*G*rr*x**(-1) *(x - np.arctan(x) ) )**(1/2)\n"
]
},
@@ -98,27 +87,18 @@
"name": "stdout",
"output_type": "stream",
"text": [
+ "../galaxies_data/rcugc8403.txt\n",
+ "122\n",
+ "../galaxies_data/rcugc9866.txt\n",
+ "35\n",
+ "../galaxies_data/rcugc11466.txt\n",
"57\n",
"../galaxies_data/rcugc2800.txt\n",
"12\n",
"../galaxies_data/rcugc9465.txt\n",
"20\n",
"../galaxies_data/rcugc3273.txt\n",
- "19\n",
- "../galaxies_data/rcugc3876.txt\n",
- "33\n",
- "../galaxies_data/rcugc7985.txt\n",
- "97\n",
- "../galaxies_data/rcugc9753.txt\n",
- "59\n",
- "../galaxies_data/rcugc7876.txt\n",
- "33\n",
- "../galaxies_data/rcugc1256.txt\n",
- "127\n",
- "../galaxies_data/rcugc5228.txt\n",
- "53\n",
- "../galaxies_data/rcugc4165.txt\n",
- "128\n"
+ "19\n"
]
},
{
@@ -133,6 +113,12 @@
" v = ( 4*np.pi*G*rr*x**(-1) *(x - np.arctan(x) ) )**(1/2)\n",
"../Functions/functions.py:67: RuntimeWarning: invalid value encountered in sqrt\n",
" v = ( 4*np.pi*G*rr*x**(-1) *(x - np.arctan(x) ) )**(1/2)\n",
+ "../Functions/functions.py:52: RuntimeWarning: invalid value encountered in log\n",
+ " v = (10*G*H0*MM)**(1/3) * ((np.log(1+c*x) - c*x/(1+c*x)) /(x*(np.log(1+c) - c/(1+c))) )**(1/2)\n",
+ "../Functions/functions.py:67: RuntimeWarning: invalid value encountered in sqrt\n",
+ " v = ( 4*np.pi*G*rr*x**(-1) *(x - np.arctan(x) ) )**(1/2)\n",
+ "../Functions/functions.py:67: RuntimeWarning: invalid value encountered in sqrt\n",
+ " v = ( 4*np.pi*G*rr*x**(-1) *(x - np.arctan(x) ) )**(1/2)\n",
"../Functions/functions.py:67: RuntimeWarning: invalid value encountered in sqrt\n",
" v = ( 4*np.pi*G*rr*x**(-1) *(x - np.arctan(x) ) )**(1/2)\n",
"../Functions/functions.py:82: RuntimeWarning: invalid value encountered in double_scalars\n",
@@ -144,7 +130,33 @@
"../Functions/functions.py:52: RuntimeWarning: invalid value encountered in log\n",
" v = (10*G*H0*MM)**(1/3) * ((np.log(1+c*x) - c*x/(1+c*x)) /(x*(np.log(1+c) - c/(1+c))) )**(1/2)\n",
"../Functions/functions.py:67: RuntimeWarning: invalid value encountered in sqrt\n",
- " v = ( 4*np.pi*G*rr*x**(-1) *(x - np.arctan(x) ) )**(1/2)\n",
+ " v = ( 4*np.pi*G*rr*x**(-1) *(x - np.arctan(x) ) )**(1/2)\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "../galaxies_data/rcugc3876.txt\n",
+ "33\n",
+ "../galaxies_data/rcugc7985.txt\n",
+ "97\n",
+ "../galaxies_data/rcugc9753.txt\n",
+ "59\n",
+ "../galaxies_data/rcugc7876.txt\n",
+ "33\n",
+ "../galaxies_data/rcugc1256.txt\n",
+ "127\n",
+ "../galaxies_data/rcugc5228.txt\n",
+ "53\n",
+ "../galaxies_data/rcugc4165.txt\n",
+ "128\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
"../Functions/functions.py:52: RuntimeWarning: invalid value encountered in log\n",
" v = (10*G*H0*MM)**(1/3) * ((np.log(1+c*x) - c*x/(1+c*x)) /(x*(np.log(1+c) - c/(1+c))) )**(1/2)\n",
"../Functions/functions.py:67: RuntimeWarning: invalid value encountered in sqrt\n",
@@ -189,9 +201,17 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 23,
"metadata": {},
"outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "../Functions/functions.py:202: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
+ " A = np.array(AA)\n"
+ ]
+ },
{
"data": {
"text/html": [
@@ -227,6 +247,84 @@
" </thead>\n",
" <tbody>\n",
" <tr>\n",
+ " <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",
+ " </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",
+ " </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",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>rcugc12754</td>\n",
+ " <td>ra</td>\n",
+ " <td>1.199370e+12</td>\n",
+ " <td>6.943164e+11</td>\n",
+ " <td>12.086865</td>\n",
+ " <td>2.165628</td>\n",
+ " <td>0.311694</td>\n",
+ " <td>0.043470</td>\n",
+ " <td>1161.715673</td>\n",
+ " <td>118.998680</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>rcugc12754</td>\n",
+ " <td>r</td>\n",
+ " <td>5.035508e+11</td>\n",
+ " <td>2.355115e+11</td>\n",
+ " <td>16.200361</td>\n",
+ " <td>2.789003</td>\n",
+ " <td>0.485795</td>\n",
+ " <td>0.115488</td>\n",
+ " <td>862.599418</td>\n",
+ " <td>134.056610</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>5</th>\n",
+ " <td>rcugc12754</td>\n",
+ " <td>a</td>\n",
+ " <td>9.880568e+13</td>\n",
+ " <td>1.079205e+15</td>\n",
+ " <td>3.530839</td>\n",
+ " <td>11.233202</td>\n",
+ " <td>0.225455</td>\n",
+ " <td>0.034240</td>\n",
+ " <td>-1501.777726</td>\n",
+ " <td>193.464013</td>\n",
+ " </tr>\n",
+ " <tr>\n",
" <th>6</th>\n",
" <td>rcugc10521</td>\n",
" <td>ra</td>\n",
@@ -266,56 +364,56 @@
" <td>49.658439</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>0</th>\n",
- " <td>rcugc11012</td>\n",
+ " <th>9</th>\n",
+ " <td>rcugc5251</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>8.911271e+11</td>\n",
+ " <td>3.279580e+11</td>\n",
+ " <td>7.847177</td>\n",
+ " <td>1.126197</td>\n",
+ " <td>0.095103</td>\n",
+ " <td>0.009995</td>\n",
+ " <td>1955.859756</td>\n",
+ " <td>141.843616</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>1</th>\n",
- " <td>rcugc11012</td>\n",
+ " <th>10</th>\n",
+ " <td>rcugc5251</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>2.847285e+13</td>\n",
+ " <td>1.418855e+14</td>\n",
+ " <td>2.055816</td>\n",
+ " <td>3.766892</td>\n",
+ " <td>0.055162</td>\n",
+ " <td>0.007339</td>\n",
+ " <td>2803.895034</td>\n",
+ " <td>256.627147</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>2</th>\n",
- " <td>rcugc11012</td>\n",
+ " <th>11</th>\n",
+ " <td>rcugc5251</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>4.044392e+11</td>\n",
+ " <td>1.074778e+11</td>\n",
+ " <td>11.421348</td>\n",
+ " <td>1.301228</td>\n",
+ " <td>0.149151</td>\n",
+ " <td>0.020671</td>\n",
+ " <td>1435.060920</td>\n",
+ " <td>134.239424</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>14</th>\n",
+ " <th>12</th>\n",
" <td>rcugc11300</td>\n",
- " <td>a</td>\n",
- " <td>3.409549e+11</td>\n",
- " <td>2.322841e+11</td>\n",
- " <td>14.467874</td>\n",
- " <td>3.015630</td>\n",
- " <td>0.648142</td>\n",
- " <td>0.119352</td>\n",
- " <td>-530.604094</td>\n",
- " <td>68.924424</td>\n",
+ " <td>ra</td>\n",
+ " <td>6.704159e+11</td>\n",
+ " <td>4.475147e+11</td>\n",
+ " <td>11.867839</td>\n",
+ " <td>2.286667</td>\n",
+ " <td>0.388367</td>\n",
+ " <td>0.053377</td>\n",
+ " <td>-773.361244</td>\n",
+ " <td>81.621591</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
@@ -331,30 +429,95 @@
" <td>153.666617</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>12</th>\n",
+ " <th>14</th>\n",
" <td>rcugc11300</td>\n",
- " <td>ra</td>\n",
- " <td>6.704159e+11</td>\n",
- " <td>4.475147e+11</td>\n",
- " <td>11.867839</td>\n",
- " <td>2.286667</td>\n",
- " <td>0.388367</td>\n",
- " <td>0.053377</td>\n",
- " <td>-773.361244</td>\n",
- " <td>81.621591</td>\n",
+ " <td>a</td>\n",
+ " <td>3.409549e+11</td>\n",
+ " <td>2.322841e+11</td>\n",
+ " <td>14.467874</td>\n",
+ " <td>3.015630</td>\n",
+ " <td>0.648142</td>\n",
+ " <td>0.119352</td>\n",
+ " <td>-530.604094</td>\n",
+ " <td>68.924424</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>23</th>\n",
- " <td>rcugc11466</td>\n",
- " <td>a</td>\n",
- " <td>1.031561e+12</td>\n",
- " <td>1.315092e+12</td>\n",
- " <td>14.433037</td>\n",
- " <td>5.232382</td>\n",
- " <td>0.753391</td>\n",
- " <td>0.241565</td>\n",
- " <td>641.842957</td>\n",
- " <td>145.301383</td>\n",
+ " <th>15</th>\n",
+ " <td>rcugc8403</td>\n",
+ " <td>ra</td>\n",
+ " <td>2.824588e+15</td>\n",
+ " <td>3.174184e+16</td>\n",
+ " <td>0.036598</td>\n",
+ " <td>2.898439</td>\n",
+ " <td>0.024339</td>\n",
+ " <td>0.001006</td>\n",
+ " <td>-5512.731928</td>\n",
+ " <td>260.099331</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>16</th>\n",
+ " <td>rcugc8403</td>\n",
+ " <td>r</td>\n",
+ " <td>2.872633e+15</td>\n",
+ " <td>3.176901e+14</td>\n",
+ " <td>-0.001232</td>\n",
+ " <td>0.001450</td>\n",
+ " <td>0.026152</td>\n",
+ " <td>0.001476</td>\n",
+ " <td>-4959.445607</td>\n",
+ " <td>329.686403</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17</th>\n",
+ " <td>rcugc8403</td>\n",
+ " <td>a</td>\n",
+ " <td>2.973990e+15</td>\n",
+ " <td>6.361679e+16</td>\n",
+ " <td>0.038919</td>\n",
+ " <td>5.529651</td>\n",
+ " <td>0.023915</td>\n",
+ " <td>0.001344</td>\n",
+ " <td>5654.921427</td>\n",
+ " <td>357.334603</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>18</th>\n",
+ " <td>rcugc9866</td>\n",
+ " <td>ra</td>\n",
+ " <td>9.194802e+16</td>\n",
+ " <td>6.484594e+19</td>\n",
+ " <td>0.503489</td>\n",
+ " <td>255.378793</td>\n",
+ " <td>1.128745</td>\n",
+ " <td>0.078333</td>\n",
+ " <td>-596.269259</td>\n",
+ " <td>39.570275</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>19</th>\n",
+ " <td>rcugc9866</td>\n",
+ " <td>r</td>\n",
+ " <td>2.674279e+12</td>\n",
+ " <td>6.544128e+12</td>\n",
+ " <td>14.833173</td>\n",
+ " <td>8.846376</td>\n",
+ " <td>1.563207</td>\n",
+ " <td>0.165678</td>\n",
+ " <td>446.566564</td>\n",
+ " <td>37.755050</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>20</th>\n",
+ " <td>rcugc9866</td>\n",
+ " <td>a</td>\n",
+ " <td>1.028874e+17</td>\n",
+ " <td>6.803796e+19</td>\n",
+ " <td>0.465600</td>\n",
+ " <td>233.903756</td>\n",
+ " <td>0.956111</td>\n",
+ " <td>0.064391</td>\n",
+ " <td>718.974234</td>\n",
+ " <td>54.852587</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
@@ -383,82 +546,30 @@
" <td>45.463928</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>45</th>\n",
- " <td>rcugc1256</td>\n",
- " <td>ra</td>\n",
- " <td>2.746272e+15</td>\n",
- " <td>6.848837e+16</td>\n",
- " <td>0.046261</td>\n",
- " <td>6.478192</td>\n",
- " <td>0.049004</td>\n",
- " <td>0.005195</td>\n",
- " <td>3194.723287</td>\n",
- " <td>436.004347</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>46</th>\n",
- " <td>rcugc1256</td>\n",
- " <td>r</td>\n",
- " <td>3.282509e+15</td>\n",
- " <td>2.965788e+17</td>\n",
- " <td>0.057940</td>\n",
- " <td>23.714046</td>\n",
- " <td>0.058702</td>\n",
- " <td>0.008359</td>\n",
- " <td>2723.998719</td>\n",
- " <td>452.959319</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>47</th>\n",
- " <td>rcugc1256</td>\n",
- " <td>a</td>\n",
- " <td>1.934242e+15</td>\n",
- " <td>0.000000e+00</td>\n",
- " <td>-0.000362</td>\n",
- " <td>0.000000</td>\n",
- " <td>0.031232</td>\n",
- " <td>0.003839</td>\n",
- " <td>5562.440363</td>\n",
- " <td>1424.241175</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>5</th>\n",
- " <td>rcugc12754</td>\n",
+ " <th>23</th>\n",
+ " <td>rcugc11466</td>\n",
" <td>a</td>\n",
- " <td>9.880568e+13</td>\n",
- " <td>1.079205e+15</td>\n",
- " <td>3.530839</td>\n",
- " <td>11.233202</td>\n",
- " <td>0.225455</td>\n",
- " <td>0.034240</td>\n",
- " <td>-1501.777726</td>\n",
- " <td>193.464013</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>rcugc12754</td>\n",
- " <td>r</td>\n",
- " <td>5.035508e+11</td>\n",
- " <td>2.355115e+11</td>\n",
- " <td>16.200361</td>\n",
- " <td>2.789003</td>\n",
- " <td>0.485795</td>\n",
- " <td>0.115488</td>\n",
- " <td>862.599418</td>\n",
- " <td>134.056610</td>\n",
+ " <td>1.031561e+12</td>\n",
+ " <td>1.315092e+12</td>\n",
+ " <td>14.433037</td>\n",
+ " <td>5.232382</td>\n",
+ " <td>0.753391</td>\n",
+ " <td>0.241565</td>\n",
+ " <td>641.842957</td>\n",
+ " <td>145.301383</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>3</th>\n",
- " <td>rcugc12754</td>\n",
+ " <th>24</th>\n",
+ " <td>rcugc2800</td>\n",
" <td>ra</td>\n",
- " <td>1.199370e+12</td>\n",
- " <td>6.943164e+11</td>\n",
- " <td>12.086865</td>\n",
- " <td>2.165628</td>\n",
- " <td>0.311694</td>\n",
- " <td>0.043470</td>\n",
- " <td>1161.715673</td>\n",
- " <td>118.998680</td>\n",
+ " <td>6.004507e+15</td>\n",
+ " <td>9.335974e+17</td>\n",
+ " <td>0.035356</td>\n",
+ " <td>40.008107</td>\n",
+ " <td>0.079353</td>\n",
+ " <td>0.024400</td>\n",
+ " <td>-2139.198772</td>\n",
+ " <td>574.710693</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
@@ -474,19 +585,6 @@
" <td>717.036396</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>24</th>\n",
- " <td>rcugc2800</td>\n",
- " <td>ra</td>\n",
- " <td>6.004507e+15</td>\n",
- " <td>9.335974e+17</td>\n",
- " <td>0.035356</td>\n",
- " <td>40.008107</td>\n",
- " <td>0.079353</td>\n",
- " <td>0.024400</td>\n",
- " <td>-2139.198772</td>\n",
- " <td>574.710693</td>\n",
- " </tr>\n",
- " <tr>\n",
" <th>26</th>\n",
" <td>rcugc2800</td>\n",
" <td>a</td>\n",
@@ -500,6 +598,45 @@
" <td>1238.704841</td>\n",
" </tr>\n",
" <tr>\n",
+ " <th>27</th>\n",
+ " <td>rcugc9465</td>\n",
+ " <td>ra</td>\n",
+ " <td>5.035104e+11</td>\n",
+ " <td>3.372252e+11</td>\n",
+ " <td>7.993004</td>\n",
+ " <td>1.878037</td>\n",
+ " <td>0.101898</td>\n",
+ " <td>0.010593</td>\n",
+ " <td>-1481.011607</td>\n",
+ " <td>111.281476</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>28</th>\n",
+ " <td>rcugc9465</td>\n",
+ " <td>r</td>\n",
+ " <td>7.040458e+12</td>\n",
+ " <td>4.327424e+13</td>\n",
+ " <td>3.650049</td>\n",
+ " <td>6.881350</td>\n",
+ " <td>0.105234</td>\n",
+ " <td>0.016646</td>\n",
+ " <td>-1490.729665</td>\n",
+ " <td>175.334583</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>29</th>\n",
+ " <td>rcugc9465</td>\n",
+ " <td>a</td>\n",
+ " <td>3.039314e+11</td>\n",
+ " <td>1.275605e+11</td>\n",
+ " <td>9.262560</td>\n",
+ " <td>1.542827</td>\n",
+ " <td>0.093547</td>\n",
+ " <td>0.008758</td>\n",
+ " <td>-1523.670478</td>\n",
+ " <td>101.078183</td>\n",
+ " </tr>\n",
+ " <tr>\n",
" <th>30</th>\n",
" <td>rcugc3273</td>\n",
" <td>ra</td>\n",
@@ -539,17 +676,17 @@
" <td>1737.448638</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>35</th>\n",
+ " <th>33</th>\n",
" <td>rcugc3876</td>\n",
- " <td>a</td>\n",
- " <td>4.372452e+12</td>\n",
- " <td>1.065304e+13</td>\n",
- " <td>7.083120</td>\n",
- " <td>4.766984</td>\n",
- " <td>0.236762</td>\n",
- " <td>0.040800</td>\n",
- " <td>-1195.132681</td>\n",
- " <td>158.521584</td>\n",
+ " <td>ra</td>\n",
+ " <td>8.745098e+11</td>\n",
+ " <td>5.538130e+11</td>\n",
+ " <td>11.197992</td>\n",
+ " <td>2.217348</td>\n",
+ " <td>0.264269</td>\n",
+ " <td>0.034809</td>\n",
+ " <td>-1103.088500</td>\n",
+ " <td>105.309866</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
@@ -565,173 +702,30 @@
" <td>140.449918</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>33</th>\n",
+ " <th>35</th>\n",
" <td>rcugc3876</td>\n",
- " <td>ra</td>\n",
- " <td>8.745098e+11</td>\n",
- " <td>5.538130e+11</td>\n",
- " <td>11.197992</td>\n",
- " <td>2.217348</td>\n",
- " <td>0.264269</td>\n",
- " <td>0.034809</td>\n",
- " <td>-1103.088500</td>\n",
- " <td>105.309866</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>53</th>\n",
- " <td>rcugc4165</td>\n",
" <td>a</td>\n",
- " <td>2.407757e+11</td>\n",
- " <td>1.325602e+11</td>\n",
- " <td>11.326505</td>\n",
- " <td>2.052447</td>\n",
- " <td>0.246358</td>\n",
- " <td>0.040932</td>\n",
- " <td>-769.910293</td>\n",
- " <td>88.586216</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>51</th>\n",
- " <td>rcugc4165</td>\n",
- " <td>ra</td>\n",
- " <td>5.346014e+10</td>\n",
- " <td>9.342103e+09</td>\n",
- " <td>20.881720</td>\n",
- " <td>1.868248</td>\n",
- " <td>0.518692</td>\n",
- " <td>0.091846</td>\n",
- " <td>-486.174258</td>\n",
- " <td>51.611642</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>52</th>\n",
- " <td>rcugc4165</td>\n",
- " <td>r</td>\n",
- " <td>3.343517e+10</td>\n",
- " <td>5.687744e+09</td>\n",
- " <td>28.555576</td>\n",
- " <td>3.285971</td>\n",
- " <td>1.060762</td>\n",
- " <td>0.337500</td>\n",
- " <td>-328.093293</td>\n",
- " <td>58.438224</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>50</th>\n",
- " <td>rcugc5228</td>\n",
- " <td>a</td>\n",
- " <td>3.746682e+11</td>\n",
- " <td>1.262943e+11</td>\n",
- " <td>17.299386</td>\n",
- " <td>2.389282</td>\n",
- " <td>0.349202</td>\n",
- " <td>0.036056</td>\n",
- " <td>-1067.857455</td>\n",
- " <td>75.540782</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>49</th>\n",
- " <td>rcugc5228</td>\n",
- " <td>r</td>\n",
- " <td>7.907683e+11</td>\n",
- " <td>4.888149e+11</td>\n",
- " <td>13.703838</td>\n",
- " <td>2.878679</td>\n",
- " <td>0.310904</td>\n",
- " <td>0.032540</td>\n",
- " <td>1192.423188</td>\n",
- " <td>90.474117</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>48</th>\n",
- " <td>rcugc5228</td>\n",
- " <td>ra</td>\n",
- " <td>4.395670e+11</td>\n",
- " <td>1.219716e+11</td>\n",
- " <td>16.457691</td>\n",
- " <td>1.781741</td>\n",
- " <td>0.338096</td>\n",
- " <td>0.025245</td>\n",
- " <td>-1102.220484</td>\n",
- " <td>57.185035</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>11</th>\n",
- " <td>rcugc5251</td>\n",
- " <td>a</td>\n",
- " <td>4.044392e+11</td>\n",
- " <td>1.074778e+11</td>\n",
- " <td>11.421348</td>\n",
- " <td>1.301228</td>\n",
- " <td>0.149151</td>\n",
- " <td>0.020671</td>\n",
- " <td>1435.060920</td>\n",
- " <td>134.239424</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>10</th>\n",
- " <td>rcugc5251</td>\n",
- " <td>r</td>\n",
- " <td>2.847285e+13</td>\n",
- " <td>1.418855e+14</td>\n",
- " <td>2.055816</td>\n",
- " <td>3.766892</td>\n",
- " <td>0.055162</td>\n",
- " <td>0.007339</td>\n",
- " <td>2803.895034</td>\n",
- " <td>256.627147</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>9</th>\n",
- " <td>rcugc5251</td>\n",
- " <td>ra</td>\n",
- " <td>8.911271e+11</td>\n",
- " <td>3.279580e+11</td>\n",
- " <td>7.847177</td>\n",
- " <td>1.126197</td>\n",
- " <td>0.095103</td>\n",
- " <td>0.009995</td>\n",
- " <td>1955.859756</td>\n",
- " <td>141.843616</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>44</th>\n",
- " <td>rcugc7876</td>\n",
- " <td>a</td>\n",
- " <td>1.297424e+12</td>\n",
- " <td>4.821391e+12</td>\n",
- " <td>8.383026</td>\n",
- " <td>8.804274</td>\n",
- " <td>0.247657</td>\n",
- " <td>0.069949</td>\n",
- " <td>-949.003008</td>\n",
- " <td>204.465613</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>43</th>\n",
- " <td>rcugc7876</td>\n",
- " <td>r</td>\n",
- " <td>1.322602e+16</td>\n",
- " <td>9.493859e+17</td>\n",
- " <td>0.094005</td>\n",
- " <td>19.419618</td>\n",
- " <td>0.147842</td>\n",
- " <td>0.011712</td>\n",
- " <td>-1546.791624</td>\n",
- " <td>111.118757</td>\n",
+ " <td>4.372452e+12</td>\n",
+ " <td>1.065304e+13</td>\n",
+ " <td>7.083120</td>\n",
+ " <td>4.766984</td>\n",
+ " <td>0.236762</td>\n",
+ " <td>0.040800</td>\n",
+ " <td>-1195.132681</td>\n",
+ " <td>158.521584</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>42</th>\n",
- " <td>rcugc7876</td>\n",
+ " <th>36</th>\n",
+ " <td>rcugc7985</td>\n",
" <td>ra</td>\n",
- " <td>1.130949e+16</td>\n",
- " <td>8.566252e+17</td>\n",
- " <td>0.126063</td>\n",
- " <td>21.043650</td>\n",
- " <td>0.174573</td>\n",
- " <td>0.019911</td>\n",
- " <td>-1315.279313</td>\n",
- " <td>126.775989</td>\n",
+ " <td>1.498078e+11</td>\n",
+ " <td>1.666427e+10</td>\n",
+ " <td>23.250504</td>\n",
+ " <td>1.116352</td>\n",
+ " <td>0.687378</td>\n",
+ " <td>0.048352</td>\n",
+ " <td>607.842719</td>\n",
+ " <td>27.887561</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
@@ -760,95 +754,17 @@
" <td>31.388210</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>36</th>\n",
- " <td>rcugc7985</td>\n",
- " <td>ra</td>\n",
- " <td>1.498078e+11</td>\n",
- " <td>1.666427e+10</td>\n",
- " <td>23.250504</td>\n",
- " <td>1.116352</td>\n",
- " <td>0.687378</td>\n",
- " <td>0.048352</td>\n",
- " <td>607.842719</td>\n",
- " <td>27.887561</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>15</th>\n",
- " <td>rcugc8403</td>\n",
- " <td>ra</td>\n",
- " <td>2.824588e+15</td>\n",
- " <td>3.174184e+16</td>\n",
- " <td>0.036598</td>\n",
- " <td>2.898439</td>\n",
- " <td>0.024339</td>\n",
- " <td>0.001006</td>\n",
- " <td>-5512.731928</td>\n",
- " <td>260.099331</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>16</th>\n",
- " <td>rcugc8403</td>\n",
- " <td>r</td>\n",
- " <td>2.872633e+15</td>\n",
- " <td>3.176901e+14</td>\n",
- " <td>-0.001232</td>\n",
- " <td>0.001450</td>\n",
- " <td>0.026152</td>\n",
- " <td>0.001476</td>\n",
- " <td>-4959.445607</td>\n",
- " <td>329.686403</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>17</th>\n",
- " <td>rcugc8403</td>\n",
- " <td>a</td>\n",
- " <td>2.973990e+15</td>\n",
- " <td>6.361679e+16</td>\n",
- " <td>0.038919</td>\n",
- " <td>5.529651</td>\n",
- " <td>0.023915</td>\n",
- " <td>0.001344</td>\n",
- " <td>5654.921427</td>\n",
- " <td>357.334603</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>29</th>\n",
- " <td>rcugc9465</td>\n",
- " <td>a</td>\n",
- " <td>3.039314e+11</td>\n",
- " <td>1.275605e+11</td>\n",
- " <td>9.262560</td>\n",
- " <td>1.542827</td>\n",
- " <td>0.093547</td>\n",
- " <td>0.008758</td>\n",
- " <td>-1523.670478</td>\n",
- " <td>101.078183</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>28</th>\n",
- " <td>rcugc9465</td>\n",
- " <td>r</td>\n",
- " <td>7.040458e+12</td>\n",
- " <td>4.327424e+13</td>\n",
- " <td>3.650049</td>\n",
- " <td>6.881350</td>\n",
- " <td>0.105234</td>\n",
- " <td>0.016646</td>\n",
- " <td>-1490.729665</td>\n",
- " <td>175.334583</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>27</th>\n",
- " <td>rcugc9465</td>\n",
+ " <th>39</th>\n",
+ " <td>rcugc9753</td>\n",
" <td>ra</td>\n",
- " <td>5.035104e+11</td>\n",
- " <td>3.372252e+11</td>\n",
- " <td>7.993004</td>\n",
- " <td>1.878037</td>\n",
- " <td>0.101898</td>\n",
- " <td>0.010593</td>\n",
- " <td>-1481.011607</td>\n",
- " <td>111.281476</td>\n",
+ " <td>1.013924e+11</td>\n",
+ " <td>9.292196e+09</td>\n",
+ " <td>62.230141</td>\n",
+ " <td>3.573573</td>\n",
+ " <td>5.578407</td>\n",
+ " <td>0.858502</td>\n",
+ " <td>272.818406</td>\n",
+ " <td>24.360156</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40</th>\n",
@@ -877,56 +793,160 @@
" <td>40.456161</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>39</th>\n",
- " <td>rcugc9753</td>\n",
+ " <th>42</th>\n",
+ " <td>rcugc7876</td>\n",
" <td>ra</td>\n",
- " <td>1.013924e+11</td>\n",
- " <td>9.292196e+09</td>\n",
- " <td>62.230141</td>\n",
- " <td>3.573573</td>\n",
- " <td>5.578407</td>\n",
- " <td>0.858502</td>\n",
- " <td>272.818406</td>\n",
- " <td>24.360156</td>\n",
+ " <td>1.130949e+16</td>\n",
+ " <td>8.566252e+17</td>\n",
+ " <td>0.126063</td>\n",
+ " <td>21.043650</td>\n",
+ " <td>0.174573</td>\n",
+ " <td>0.019911</td>\n",
+ " <td>-1315.279313</td>\n",
+ " <td>126.775989</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>20</th>\n",
- " <td>rcugc9866</td>\n",
+ " <th>43</th>\n",
+ " <td>rcugc7876</td>\n",
+ " <td>r</td>\n",
+ " <td>1.322602e+16</td>\n",
+ " <td>9.493859e+17</td>\n",
+ " <td>0.094005</td>\n",
+ " <td>19.419618</td>\n",
+ " <td>0.147842</td>\n",
+ " <td>0.011712</td>\n",
+ " <td>-1546.791624</td>\n",
+ " <td>111.118757</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>44</th>\n",
+ " <td>rcugc7876</td>\n",
" <td>a</td>\n",
- " <td>1.028874e+17</td>\n",
- " <td>6.803796e+19</td>\n",
- " <td>0.465600</td>\n",
- " <td>233.903756</td>\n",
- " <td>0.956111</td>\n",
- " <td>0.064391</td>\n",
- " <td>718.974234</td>\n",
- " <td>54.852587</td>\n",
+ " <td>1.297424e+12</td>\n",
+ " <td>4.821391e+12</td>\n",
+ " <td>8.383026</td>\n",
+ " <td>8.804274</td>\n",
+ " <td>0.247657</td>\n",
+ " <td>0.069949</td>\n",
+ " <td>-949.003008</td>\n",
+ " <td>204.465613</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>18</th>\n",
- " <td>rcugc9866</td>\n",
+ " <th>45</th>\n",
+ " <td>rcugc1256</td>\n",
" <td>ra</td>\n",
- " <td>9.194802e+16</td>\n",
- " <td>6.484594e+19</td>\n",
- " <td>0.503489</td>\n",
- " <td>255.378793</td>\n",
- " <td>1.128745</td>\n",
- " <td>0.078333</td>\n",
- " <td>-596.269259</td>\n",
- " <td>39.570275</td>\n",
+ " <td>2.746272e+15</td>\n",
+ " <td>6.848837e+16</td>\n",
+ " <td>0.046261</td>\n",
+ " <td>6.478192</td>\n",
+ " <td>0.049004</td>\n",
+ " <td>0.005195</td>\n",
+ " <td>3194.723287</td>\n",
+ " <td>436.004347</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>46</th>\n",
+ " <td>rcugc1256</td>\n",
+ " <td>r</td>\n",
+ " <td>3.282509e+15</td>\n",
+ " <td>2.965788e+17</td>\n",
+ " <td>0.057940</td>\n",
+ " <td>23.714046</td>\n",
+ " <td>0.058702</td>\n",
+ " <td>0.008359</td>\n",
+ " <td>2723.998719</td>\n",
+ " <td>452.959319</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>47</th>\n",
+ " <td>rcugc1256</td>\n",
+ " <td>a</td>\n",
+ " <td>1.934242e+15</td>\n",
+ " <td>0.000000e+00</td>\n",
+ " <td>-0.000362</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.031232</td>\n",
+ " <td>0.003839</td>\n",
+ " <td>5562.440363</td>\n",
+ " <td>1424.241175</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>48</th>\n",
+ " <td>rcugc5228</td>\n",
+ " <td>ra</td>\n",
+ " <td>4.395670e+11</td>\n",
+ " <td>1.219716e+11</td>\n",
+ " <td>16.457691</td>\n",
+ " <td>1.781741</td>\n",
+ " <td>0.338096</td>\n",
+ " <td>0.025245</td>\n",
+ " <td>-1102.220484</td>\n",
+ " <td>57.185035</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>49</th>\n",
+ " <td>rcugc5228</td>\n",
+ " <td>r</td>\n",
+ " <td>7.907683e+11</td>\n",
+ " <td>4.888149e+11</td>\n",
+ " <td>13.703838</td>\n",
+ " <td>2.878679</td>\n",
+ " <td>0.310904</td>\n",
+ " <td>0.032540</td>\n",
+ " <td>1192.423188</td>\n",
+ " <td>90.474117</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50</th>\n",
+ " <td>rcugc5228</td>\n",
+ " <td>a</td>\n",
+ " <td>3.746682e+11</td>\n",
+ " <td>1.262943e+11</td>\n",
+ " <td>17.299386</td>\n",
+ " <td>2.389282</td>\n",
+ " <td>0.349202</td>\n",
+ " <td>0.036056</td>\n",
+ " <td>-1067.857455</td>\n",
+ " <td>75.540782</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>51</th>\n",
+ " <td>rcugc4165</td>\n",
+ " <td>ra</td>\n",
+ " <td>5.346014e+10</td>\n",
+ " <td>9.342103e+09</td>\n",
+ " <td>20.881720</td>\n",
+ " <td>1.868248</td>\n",
+ " <td>0.518692</td>\n",
+ " <td>0.091846</td>\n",
+ " <td>-486.174258</td>\n",
+ " <td>51.611642</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>52</th>\n",
+ " <td>rcugc4165</td>\n",
+ " <td>r</td>\n",
+ " <td>3.343517e+10</td>\n",
+ " <td>5.687744e+09</td>\n",
+ " <td>28.555576</td>\n",
+ " <td>3.285971</td>\n",
+ " <td>1.060762</td>\n",
+ " <td>0.337500</td>\n",
+ " <td>-328.093293</td>\n",
+ " <td>58.438224</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>19</th>\n",
- " <td>rcugc9866</td>\n",
- " <td>r</td>\n",
- " <td>2.674279e+12</td>\n",
- " <td>6.544128e+12</td>\n",
- " <td>14.833173</td>\n",
- " <td>8.846376</td>\n",
- " <td>1.563207</td>\n",
- " <td>0.165678</td>\n",
- " <td>446.566564</td>\n",
- " <td>37.755050</td>\n",
+ " <th>53</th>\n",
+ " <td>rcugc4165</td>\n",
+ " <td>a</td>\n",
+ " <td>2.407757e+11</td>\n",
+ " <td>1.325602e+11</td>\n",
+ " <td>11.326505</td>\n",
+ " <td>2.052447</td>\n",
+ " <td>0.246358</td>\n",
+ " <td>0.040932</td>\n",
+ " <td>-769.910293</td>\n",
+ " <td>88.586216</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
@@ -934,182 +954,182 @@
],
"text/plain": [
" ID Side M200_NFW(Msun) err_M200_NFW(Msun) c_NFW \\\n",
- "6 rcugc10521 ra 3.750712e+11 8.836594e+10 17.823933 \n",
- "7 rcugc10521 r 3.856047e+11 1.138636e+11 17.562688 \n",
- "8 rcugc10521 a 3.528735e+11 1.432616e+11 18.451536 \n",
"0 rcugc11012 ra 2.593063e+11 6.733116e+10 26.697951 \n",
"1 rcugc11012 r 2.022418e+11 5.126962e+10 28.991113 \n",
"2 rcugc11012 a 7.406665e+15 0.000000e+00 1.580118 \n",
- "14 rcugc11300 a 3.409549e+11 2.322841e+11 14.467874 \n",
- "13 rcugc11300 r 1.029698e+12 1.188287e+12 10.508375 \n",
+ "3 rcugc12754 ra 1.199370e+12 6.943164e+11 12.086865 \n",
+ "4 rcugc12754 r 5.035508e+11 2.355115e+11 16.200361 \n",
+ "5 rcugc12754 a 9.880568e+13 1.079205e+15 3.530839 \n",
+ "6 rcugc10521 ra 3.750712e+11 8.836594e+10 17.823933 \n",
+ "7 rcugc10521 r 3.856047e+11 1.138636e+11 17.562688 \n",
+ "8 rcugc10521 a 3.528735e+11 1.432616e+11 18.451536 \n",
+ "9 rcugc5251 ra 8.911271e+11 3.279580e+11 7.847177 \n",
+ "10 rcugc5251 r 2.847285e+13 1.418855e+14 2.055816 \n",
+ "11 rcugc5251 a 4.044392e+11 1.074778e+11 11.421348 \n",
"12 rcugc11300 ra 6.704159e+11 4.475147e+11 11.867839 \n",
- "23 rcugc11466 a 1.031561e+12 1.315092e+12 14.433037 \n",
+ "13 rcugc11300 r 1.029698e+12 1.188287e+12 10.508375 \n",
+ "14 rcugc11300 a 3.409549e+11 2.322841e+11 14.467874 \n",
+ "15 rcugc8403 ra 2.824588e+15 3.174184e+16 0.036598 \n",
+ "16 rcugc8403 r 2.872633e+15 3.176901e+14 -0.001232 \n",
+ "17 rcugc8403 a 2.973990e+15 6.361679e+16 0.038919 \n",
+ "18 rcugc9866 ra 9.194802e+16 6.484594e+19 0.503489 \n",
+ "19 rcugc9866 r 2.674279e+12 6.544128e+12 14.833173 \n",
+ "20 rcugc9866 a 1.028874e+17 6.803796e+19 0.465600 \n",
"21 rcugc11466 ra 1.567651e+11 3.967021e+10 27.311503 \n",
"22 rcugc11466 r 1.211166e+11 3.174498e+10 30.826282 \n",
- "45 rcugc1256 ra 2.746272e+15 6.848837e+16 0.046261 \n",
- "46 rcugc1256 r 3.282509e+15 2.965788e+17 0.057940 \n",
- "47 rcugc1256 a 1.934242e+15 0.000000e+00 -0.000362 \n",
- "5 rcugc12754 a 9.880568e+13 1.079205e+15 3.530839 \n",
- "4 rcugc12754 r 5.035508e+11 2.355115e+11 16.200361 \n",
- "3 rcugc12754 ra 1.199370e+12 6.943164e+11 12.086865 \n",
- "25 rcugc2800 r 4.922941e+15 3.588734e+18 0.066001 \n",
+ "23 rcugc11466 a 1.031561e+12 1.315092e+12 14.433037 \n",
"24 rcugc2800 ra 6.004507e+15 9.335974e+17 0.035356 \n",
+ "25 rcugc2800 r 4.922941e+15 3.588734e+18 0.066001 \n",
"26 rcugc2800 a 7.315716e+15 1.904708e+16 0.007847 \n",
+ "27 rcugc9465 ra 5.035104e+11 3.372252e+11 7.993004 \n",
+ "28 rcugc9465 r 7.040458e+12 4.327424e+13 3.650049 \n",
+ "29 rcugc9465 a 3.039314e+11 1.275605e+11 9.262560 \n",
"30 rcugc3273 ra 1.947548e+15 1.874781e+15 0.006575 \n",
"31 rcugc3273 r 1.307276e+15 1.314852e+17 0.024113 \n",
"32 rcugc3273 a 1.962743e+15 1.048961e+17 0.025251 \n",
- "35 rcugc3876 a 4.372452e+12 1.065304e+13 7.083120 \n",
- "34 rcugc3876 r 4.140602e+11 2.384803e+11 14.372596 \n",
"33 rcugc3876 ra 8.745098e+11 5.538130e+11 11.197992 \n",
- "53 rcugc4165 a 2.407757e+11 1.325602e+11 11.326505 \n",
- "51 rcugc4165 ra 5.346014e+10 9.342103e+09 20.881720 \n",
- "52 rcugc4165 r 3.343517e+10 5.687744e+09 28.555576 \n",
- "50 rcugc5228 a 3.746682e+11 1.262943e+11 17.299386 \n",
- "49 rcugc5228 r 7.907683e+11 4.888149e+11 13.703838 \n",
- "48 rcugc5228 ra 4.395670e+11 1.219716e+11 16.457691 \n",
- "11 rcugc5251 a 4.044392e+11 1.074778e+11 11.421348 \n",
- "10 rcugc5251 r 2.847285e+13 1.418855e+14 2.055816 \n",
- "9 rcugc5251 ra 8.911271e+11 3.279580e+11 7.847177 \n",
- "44 rcugc7876 a 1.297424e+12 4.821391e+12 8.383026 \n",
- "43 rcugc7876 r 1.322602e+16 9.493859e+17 0.094005 \n",
- "42 rcugc7876 ra 1.130949e+16 8.566252e+17 0.126063 \n",
+ "34 rcugc3876 r 4.140602e+11 2.384803e+11 14.372596 \n",
+ "35 rcugc3876 a 4.372452e+12 1.065304e+13 7.083120 \n",
+ "36 rcugc7985 ra 1.498078e+11 1.666427e+10 23.250504 \n",
"37 rcugc7985 r 2.143343e+11 4.480938e+10 20.534732 \n",
"38 rcugc7985 a 1.145157e+11 1.373512e+10 25.628898 \n",
- "36 rcugc7985 ra 1.498078e+11 1.666427e+10 23.250504 \n",
- "15 rcugc8403 ra 2.824588e+15 3.174184e+16 0.036598 \n",
- "16 rcugc8403 r 2.872633e+15 3.176901e+14 -0.001232 \n",
- "17 rcugc8403 a 2.973990e+15 6.361679e+16 0.038919 \n",
- "29 rcugc9465 a 3.039314e+11 1.275605e+11 9.262560 \n",
- "28 rcugc9465 r 7.040458e+12 4.327424e+13 3.650049 \n",
- "27 rcugc9465 ra 5.035104e+11 3.372252e+11 7.993004 \n",
+ "39 rcugc9753 ra 1.013924e+11 9.292196e+09 62.230141 \n",
"40 rcugc9753 r 1.061245e+11 1.034212e+10 60.284106 \n",
"41 rcugc9753 a 9.839894e+10 1.408237e+10 63.662825 \n",
- "39 rcugc9753 ra 1.013924e+11 9.292196e+09 62.230141 \n",
- "20 rcugc9866 a 1.028874e+17 6.803796e+19 0.465600 \n",
- "18 rcugc9866 ra 9.194802e+16 6.484594e+19 0.503489 \n",
- "19 rcugc9866 r 2.674279e+12 6.544128e+12 14.833173 \n",
+ "42 rcugc7876 ra 1.130949e+16 8.566252e+17 0.126063 \n",
+ "43 rcugc7876 r 1.322602e+16 9.493859e+17 0.094005 \n",
+ "44 rcugc7876 a 1.297424e+12 4.821391e+12 8.383026 \n",
+ "45 rcugc1256 ra 2.746272e+15 6.848837e+16 0.046261 \n",
+ "46 rcugc1256 r 3.282509e+15 2.965788e+17 0.057940 \n",
+ "47 rcugc1256 a 1.934242e+15 0.000000e+00 -0.000362 \n",
+ "48 rcugc5228 ra 4.395670e+11 1.219716e+11 16.457691 \n",
+ "49 rcugc5228 r 7.907683e+11 4.888149e+11 13.703838 \n",
+ "50 rcugc5228 a 3.746682e+11 1.262943e+11 17.299386 \n",
+ "51 rcugc4165 ra 5.346014e+10 9.342103e+09 20.881720 \n",
+ "52 rcugc4165 r 3.343517e+10 5.687744e+09 28.555576 \n",
+ "53 rcugc4165 a 2.407757e+11 1.325602e+11 11.326505 \n",
"\n",
" err_c_NFW rho0_ISO(Msun/pc3) err_rho0_ISO(Msun/pc3) Rc_ISO(pc) \\\n",
- "6 1.567258 0.408326 0.029438 -945.391461 \n",
- "7 1.944719 0.378558 0.034688 -995.762972 \n",
- "8 2.742364 0.506888 0.045074 -815.605126 \n",
"0 2.326108 1.115894 0.062761 575.867694 \n",
"1 2.633132 1.175025 0.078288 550.177818 \n",
"2 0.000000 0.914885 0.087632 -703.128652 \n",
- "14 3.015630 0.648142 0.119352 -530.604094 \n",
- "13 3.402518 0.242213 0.035367 -1148.839001 \n",
+ "3 2.165628 0.311694 0.043470 1161.715673 \n",
+ "4 2.789003 0.485795 0.115488 862.599418 \n",
+ "5 11.233202 0.225455 0.034240 -1501.777726 \n",
+ "6 1.567258 0.408326 0.029438 -945.391461 \n",
+ "7 1.944719 0.378558 0.034688 -995.762972 \n",
+ "8 2.742364 0.506888 0.045074 -815.605126 \n",
+ "9 1.126197 0.095103 0.009995 1955.859756 \n",
+ "10 3.766892 0.055162 0.007339 2803.895034 \n",
+ "11 1.301228 0.149151 0.020671 1435.060920 \n",
"12 2.286667 0.388367 0.053377 -773.361244 \n",
- "23 5.232382 0.753391 0.241565 641.842957 \n",
+ "13 3.402518 0.242213 0.035367 -1148.839001 \n",
+ "14 3.015630 0.648142 0.119352 -530.604094 \n",
+ "15 2.898439 0.024339 0.001006 -5512.731928 \n",
+ "16 0.001450 0.026152 0.001476 -4959.445607 \n",
+ "17 5.529651 0.023915 0.001344 5654.921427 \n",
+ "18 255.378793 1.128745 0.078333 -596.269259 \n",
+ "19 8.846376 1.563207 0.165678 446.566564 \n",
+ "20 233.903756 0.956111 0.064391 718.974234 \n",
"21 2.787412 1.485118 0.246255 -416.147974 \n",
"22 3.508471 1.640102 0.293765 395.317109 \n",
- "45 6.478192 0.049004 0.005195 3194.723287 \n",
- "46 23.714046 0.058702 0.008359 2723.998719 \n",
- "47 0.000000 0.031232 0.003839 5562.440363 \n",
- "5 11.233202 0.225455 0.034240 -1501.777726 \n",
- "4 2.789003 0.485795 0.115488 862.599418 \n",
- "3 2.165628 0.311694 0.043470 1161.715673 \n",
- "25 192.740216 0.091274 0.045137 1869.328697 \n",
+ "23 5.232382 0.753391 0.241565 641.842957 \n",
"24 40.008107 0.079353 0.024400 -2139.198772 \n",
+ "25 192.740216 0.091274 0.045137 1869.328697 \n",
"26 0.615002 0.069938 0.032272 -2520.187250 \n",
+ "27 1.878037 0.101898 0.010593 -1481.011607 \n",
+ "28 6.881350 0.105234 0.016646 -1490.729665 \n",
+ "29 1.542827 0.093547 0.008758 -1523.670478 \n",
"30 0.216696 0.029885 0.006360 4026.029331 \n",
"31 25.646476 0.042986 0.012842 -2458.148158 \n",
"32 13.622041 0.027315 0.006775 4754.710382 \n",
- "35 4.766984 0.236762 0.040800 -1195.132681 \n",
- "34 3.050778 0.340805 0.077528 -932.523640 \n",
"33 2.217348 0.264269 0.034809 -1103.088500 \n",
- "53 2.052447 0.246358 0.040932 -769.910293 \n",
- "51 1.868248 0.518692 0.091846 -486.174258 \n",
- "52 3.285971 1.060762 0.337500 -328.093293 \n",
- "50 2.389282 0.349202 0.036056 -1067.857455 \n",
- "49 2.878679 0.310904 0.032540 1192.423188 \n",
- "48 1.781741 0.338096 0.025245 -1102.220484 \n",
- "11 1.301228 0.149151 0.020671 1435.060920 \n",
- "10 3.766892 0.055162 0.007339 2803.895034 \n",
- "9 1.126197 0.095103 0.009995 1955.859756 \n",
- "44 8.804274 0.247657 0.069949 -949.003008 \n",
- "43 19.419618 0.147842 0.011712 -1546.791624 \n",
- "42 21.043650 0.174573 0.019911 -1315.279313 \n",
+ "34 3.050778 0.340805 0.077528 -932.523640 \n",
+ "35 4.766984 0.236762 0.040800 -1195.132681 \n",
+ "36 1.116352 0.687378 0.048352 607.842719 \n",
"37 1.784017 0.580403 0.062182 -698.712375 \n",
"38 1.376470 0.796740 0.071667 -540.927270 \n",
- "36 1.116352 0.687378 0.048352 607.842719 \n",
- "15 2.898439 0.024339 0.001006 -5512.731928 \n",
- "16 0.001450 0.026152 0.001476 -4959.445607 \n",
- "17 5.529651 0.023915 0.001344 5654.921427 \n",
- "29 1.542827 0.093547 0.008758 -1523.670478 \n",
- "28 6.881350 0.105234 0.016646 -1490.729665 \n",
- "27 1.878037 0.101898 0.010593 -1481.011607 \n",
+ "39 3.573573 5.578407 0.858502 272.818406 \n",
"40 3.463964 5.728347 0.920853 -268.177786 \n",
"41 5.954981 5.475560 1.396172 -276.038884 \n",
- "39 3.573573 5.578407 0.858502 272.818406 \n",
- "20 233.903756 0.956111 0.064391 718.974234 \n",
- "18 255.378793 1.128745 0.078333 -596.269259 \n",
- "19 8.846376 1.563207 0.165678 446.566564 \n",
+ "42 21.043650 0.174573 0.019911 -1315.279313 \n",
+ "43 19.419618 0.147842 0.011712 -1546.791624 \n",
+ "44 8.804274 0.247657 0.069949 -949.003008 \n",
+ "45 6.478192 0.049004 0.005195 3194.723287 \n",
+ "46 23.714046 0.058702 0.008359 2723.998719 \n",
+ "47 0.000000 0.031232 0.003839 5562.440363 \n",
+ "48 1.781741 0.338096 0.025245 -1102.220484 \n",
+ "49 2.878679 0.310904 0.032540 1192.423188 \n",
+ "50 2.389282 0.349202 0.036056 -1067.857455 \n",
+ "51 1.868248 0.518692 0.091846 -486.174258 \n",
+ "52 3.285971 1.060762 0.337500 -328.093293 \n",
+ "53 2.052447 0.246358 0.040932 -769.910293 \n",
"\n",
" err_Rc_ISO(pc) \n",
- "6 47.727474 \n",
- "7 64.414017 \n",
- "8 49.658439 \n",
"0 24.665902 \n",
"1 27.099124 \n",
"2 58.884282 \n",
- "14 68.924424 \n",
- "13 153.666617 \n",
+ "3 118.998680 \n",
+ "4 134.056610 \n",
+ "5 193.464013 \n",
+ "6 47.727474 \n",
+ "7 64.414017 \n",
+ "8 49.658439 \n",
+ "9 141.843616 \n",
+ "10 256.627147 \n",
+ "11 134.239424 \n",
"12 81.621591 \n",
- "23 145.301383 \n",
+ "13 153.666617 \n",
+ "14 68.924424 \n",
+ "15 260.099331 \n",
+ "16 329.686403 \n",
+ "17 357.334603 \n",
+ "18 39.570275 \n",
+ "19 37.755050 \n",
+ "20 54.852587 \n",
"21 44.000055 \n",
"22 45.463928 \n",
- "45 436.004347 \n",
- "46 452.959319 \n",
- "47 1424.241175 \n",
- "5 193.464013 \n",
- "4 134.056610 \n",
- "3 118.998680 \n",
- "25 717.036396 \n",
+ "23 145.301383 \n",
"24 574.710693 \n",
+ "25 717.036396 \n",
"26 1238.704841 \n",
+ "27 111.281476 \n",
+ "28 175.334583 \n",
+ "29 101.078183 \n",
"30 1052.107286 \n",
"31 605.531402 \n",
"32 1737.448638 \n",
- "35 158.521584 \n",
- "34 140.449918 \n",
"33 105.309866 \n",
- "53 88.586216 \n",
- "51 51.611642 \n",
- "52 58.438224 \n",
- "50 75.540782 \n",
- "49 90.474117 \n",
- "48 57.185035 \n",
- "11 134.239424 \n",
- "10 256.627147 \n",
- "9 141.843616 \n",
- "44 204.465613 \n",
- "43 111.118757 \n",
- "42 126.775989 \n",
+ "34 140.449918 \n",
+ "35 158.521584 \n",
+ "36 27.887561 \n",
"37 48.421321 \n",
"38 31.388210 \n",
- "36 27.887561 \n",
- "15 260.099331 \n",
- "16 329.686403 \n",
- "17 357.334603 \n",
- "29 101.078183 \n",
- "28 175.334583 \n",
- "27 111.281476 \n",
+ "39 24.360156 \n",
"40 25.200634 \n",
"41 40.456161 \n",
- "39 24.360156 \n",
- "20 54.852587 \n",
- "18 39.570275 \n",
- "19 37.755050 "
+ "42 126.775989 \n",
+ "43 111.118757 \n",
+ "44 204.465613 \n",
+ "45 436.004347 \n",
+ "46 452.959319 \n",
+ "47 1424.241175 \n",
+ "48 57.185035 \n",
+ "49 90.474117 \n",
+ "50 75.540782 \n",
+ "51 51.611642 \n",
+ "52 58.438224 \n",
+ "53 88.586216 "
]
},
- "execution_count": 10,
+ "execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dff = return_values(AA)\n",
- "dff.sort_values(by = [\"ID\"])"
+ "dff"
]
},
{
diff --git a/notebook/confidence_interval_ellipse1-short - 1.ipynb b/notebook/confidence_interval_ellipse1-short - 1.ipynb
index 37c6fc0d178115ba7d817ec689a85d5e0bcbcdd7..231d4aeb8493db13dba91c0e5b1ccc841934fb12 100644
--- a/notebook/confidence_interval_ellipse1-short - 1.ipynb
+++ b/notebook/confidence_interval_ellipse1-short - 1.ipynb
@@ -516,6 +516,1765 @@
"# file1.close()\n"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": 140,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ra = [\"11012&103&2.114e+11&29.26&21.84&1.19&552.63&6.85&\",\n",
+ "\"12754&92&9.574e+11&13.04&5.88&0.33&1114.34&9.29&\",\n",
+ "\"10521&63&3.203e+11&19.06&13.36&0.41&948.78&7.40&\",\n",
+ "\"5251&57&8.431e+11&7.90&19.25&0.10&1903.24&9.76&\",\n",
+ "\"11300&58&4.934e+11&13.06&9.24&0.44&698.36&13.05&\",\n",
+ "\"8403&122&2.848e+15&0.04&29.70&0.02&5487.03&8.27&\",\n",
+ "\"9866&35&9.218e+16&0.50&7.35&1.08&614.23&6.21&\",\n",
+ "\"11466&57&1.063e+11&32.11&14.25&1.81&363.33&15.28&\",\n",
+ "\"2800&12&5.139e+15&0.08&6.84&0.08&2039.29&4.89&\",\n",
+ "\"9465&20&5.92e+11&7.47&9.83&0.10&1464.60&3.33&\", \n",
+ "\"3273&19&1.71e+15&0.03&54.55&0.02&5259.97&14.27&\",\n",
+ "\"3876&33&7.419e+11&11.81&2.70&0.23&1199.68&2.24&\",\n",
+ "\"7985&97&1.456e+11&23.74&6.66&0.71&595.23&6.59&\",\n",
+ "\"9753&59&9.753e+10&64.95&14.92&6.05&263.56&13.94&\",\n",
+ "\"7876&33&1.392e+16&0.09&6.21&0.17&1355.79&3.31&\",\n",
+ "\"1256&127&2.665e+15&0.09&63.15&0.05&3171.60&33.47&\",\n",
+ "\"5228&53&4.594e+11&16.19&51.60&0.34&1098.37&16.07&\",\n",
+ "\"4165&128&6.074e+10&19.14&3.52&0.39&564.17&3.68&\"]\n",
+ "\n",
+ "aa = [\"11012&36&5.058e+12&12.57&8.81&1.07&622.49&3.03&\",\n",
+ "\"12754&49&1.054e+14&3.48&4.63&0.21&1601.60&4.13&\",\n",
+ "\"10521&20&3.028e+11&19.58&5.05&0.51&807.68&1.57&\",\n",
+ "\"5251&30&3.708e+11&11.96&7.25&0.16&1389.47&7.60&\",\n",
+ "\"11300&26&1.860e+11&17.80&7.80&0.78&464.83&8.24&\",\n",
+ "\"8403&80&3.020e+15&0.05&30.17&0.03&5444.29&9.80&\",\n",
+ "\"9866&21&1.360e+17&0.36&9.27&0.96&713.56&5.82&\",\n",
+ "\"11466&22&4.558e+11&18.41&9.31&1.29&441.45&18.66&\",\n",
+ "\"2800&7&6.454e+15&0.07&8.85&0.08&2192.76&5.00&\",\n",
+ "\"9465&9&2.887e+11&9.51&2.56&0.09&1523.35&1.13&\",\n",
+ "\"3273&13&1.873e+15&0.00&83.62&0.02&6151.82&20.23&\",\n",
+ "\"3876&18&1.342e+12&9.85&3.97&0.23&1216.49&3.09&\",\n",
+ "\"7985&57&1.127e+11&26.12&6.39&0.83&530.89&6.54&\",\n",
+ "\"9753&33&9.255e+10&67.98&22.58&6.35&257.76&21.15&\",\n",
+ "\"7876&13&4.047e+11&12.19&3.28&0.27&907.54&2.40&\",\n",
+ "\"1256&49&1.452e+15&0.00&40.57&0.03&6852.55&13.50&\",\n",
+ "\"5228&29&4.20e+11&16.57&73.86&0.34&1094.36&23.38&\",\n",
+ "\"4165&53&3.563e+11&9.81&1.60&0.17&967.56&1.96&\"]\n",
+ "\n",
+ "rr = [\"11012&67&1.842e+11&30.51&26.93&1.21&544.15&8.45&\",\n",
+ "\"12754&43&4.022e+11&17.74&6.53&0.79&634.76&12.87&\",\n",
+ "\"10521&43&3.234e+11&18.97&17.64&0.39&982.07&9.91&\",\n",
+ "\"5251&27&2.119e+15&0.01&16.12&0.05&2780.64&4.79&\",\n",
+ "\"11300&32&1.017e+12&10.56&10.19&0.22&1258.09&8.30&\",\n",
+ "\"8403&42&2.700e+15&0.02&28.09&0.02&5178.19&4.85&\",\n",
+ "\"9866&14&1.970e+12&16.06&5.08&1.48&464.66&3.02&\",\n",
+ "\"11466&35&9.455e+10&34.22&16.10&1.81&367.12&13.77&\",\n",
+ "\"2800&5&5.763e+15&0.02&7.03&0.08&2073.18&7.39&\",\n",
+ "\"9465&11&7.806e+12&3.40&14.67&0.11&1448.91&5.27&\",\n",
+ "\"3273&6&1.441e+15&0.00&1.27&0.04&2612.75&1.41&\",\n",
+ "\"3876&15&4.091e+11&14.54&1.24&0.26&1135.72&1.47&\",\n",
+ "\"7985&40&2.245e+11&20.12&5.64&0.56&712.26&4.45&\",\n",
+ "\"9753&26&1.051e+11&61.02&5.42&5.74&268.99&5.42&\",\n",
+ "\"7876&20&1.408e+16&0.10&7.90&0.14&1595.91&2.07&\",\n",
+ "\"1256&78&3.439e+15&0.10&73.65&0.06&2721.83&40.16&\",\n",
+ "\"5228&24&7.714e+11&13.95&26.53&0.34&1117.07&8.03&\",\n",
+ "\"4165&75&3.300e+10&28.52&3.94&0.98&341.53&3.91&\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 141,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "chinfw_ra = []\n",
+ "chiiso_ra = []\n",
+ "chinfw_rr = []\n",
+ "chiiso_rr = []\n",
+ "chinfw_aa = []\n",
+ "chiiso_aa = []\n",
+ "name = []\n",
+ "for ii in range(len(ra)):\n",
+ " chinfw_ra.append(float(ra[ii].split(\"&\")[4]))\n",
+ " chiiso_ra.append(float(ra[ii].split(\"&\")[-2]))\n",
+ " chinfw_rr.append(float(aa[ii].split(\"&\")[4]))\n",
+ " chiiso_rr.append(float(aa[ii].split(\"&\")[-2]))\n",
+ " chinfw_aa.append(float(rr[ii].split(\"&\")[4]))\n",
+ " chiiso_aa.append(float(rr[ii].split(\"&\")[-2]))\n",
+ " name.append(int(rr[ii].split(\"&\")[0]))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 142,
+ "metadata": {},
+ "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>name</th>\n",
+ " <th>nfwra</th>\n",
+ " <th>isora</th>\n",
+ " <th>nfwrr</th>\n",
+ " <th>isorr</th>\n",
+ " <th>nfwaa</th>\n",
+ " <th>isoaa</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>11012</td>\n",
+ " <td>21.84</td>\n",
+ " <td>6.85</td>\n",
+ " <td>8.81</td>\n",
+ " <td>3.03</td>\n",
+ " <td>26.93</td>\n",
+ " <td>8.45</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>12754</td>\n",
+ " <td>5.88</td>\n",
+ " <td>9.29</td>\n",
+ " <td>4.63</td>\n",
+ " <td>4.13</td>\n",
+ " <td>6.53</td>\n",
+ " <td>12.87</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>10521</td>\n",
+ " <td>13.36</td>\n",
+ " <td>7.40</td>\n",
+ " <td>5.05</td>\n",
+ " <td>1.57</td>\n",
+ " <td>17.64</td>\n",
+ " <td>9.91</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>5251</td>\n",
+ " <td>19.25</td>\n",
+ " <td>9.76</td>\n",
+ " <td>7.25</td>\n",
+ " <td>7.60</td>\n",
+ " <td>16.12</td>\n",
+ " <td>4.79</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>11300</td>\n",
+ " <td>9.24</td>\n",
+ " <td>13.05</td>\n",
+ " <td>7.80</td>\n",
+ " <td>8.24</td>\n",
+ " <td>10.19</td>\n",
+ " <td>8.30</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>5</th>\n",
+ " <td>8403</td>\n",
+ " <td>29.70</td>\n",
+ " <td>8.27</td>\n",
+ " <td>30.17</td>\n",
+ " <td>9.80</td>\n",
+ " <td>28.09</td>\n",
+ " <td>4.85</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>6</th>\n",
+ " <td>9866</td>\n",
+ " <td>7.35</td>\n",
+ " <td>6.21</td>\n",
+ " <td>9.27</td>\n",
+ " <td>5.82</td>\n",
+ " <td>5.08</td>\n",
+ " <td>3.02</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>7</th>\n",
+ " <td>11466</td>\n",
+ " <td>14.25</td>\n",
+ " <td>15.28</td>\n",
+ " <td>9.31</td>\n",
+ " <td>18.66</td>\n",
+ " <td>16.10</td>\n",
+ " <td>13.77</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>8</th>\n",
+ " <td>2800</td>\n",
+ " <td>6.84</td>\n",
+ " <td>4.89</td>\n",
+ " <td>8.85</td>\n",
+ " <td>5.00</td>\n",
+ " <td>7.03</td>\n",
+ " <td>7.39</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>9</th>\n",
+ " <td>9465</td>\n",
+ " <td>9.83</td>\n",
+ " <td>3.33</td>\n",
+ " <td>2.56</td>\n",
+ " <td>1.13</td>\n",
+ " <td>14.67</td>\n",
+ " <td>5.27</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>10</th>\n",
+ " <td>3273</td>\n",
+ " <td>54.55</td>\n",
+ " <td>14.27</td>\n",
+ " <td>83.62</td>\n",
+ " <td>20.23</td>\n",
+ " <td>1.27</td>\n",
+ " <td>1.41</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>11</th>\n",
+ " <td>3876</td>\n",
+ " <td>2.70</td>\n",
+ " <td>2.24</td>\n",
+ " <td>3.97</td>\n",
+ " <td>3.09</td>\n",
+ " <td>1.24</td>\n",
+ " <td>1.47</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>12</th>\n",
+ " <td>7985</td>\n",
+ " <td>6.66</td>\n",
+ " <td>6.59</td>\n",
+ " <td>6.39</td>\n",
+ " <td>6.54</td>\n",
+ " <td>5.64</td>\n",
+ " <td>4.45</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>13</th>\n",
+ " <td>9753</td>\n",
+ " <td>14.92</td>\n",
+ " <td>13.94</td>\n",
+ " <td>22.58</td>\n",
+ " <td>21.15</td>\n",
+ " <td>5.42</td>\n",
+ " <td>5.42</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>14</th>\n",
+ " <td>7876</td>\n",
+ " <td>6.21</td>\n",
+ " <td>3.31</td>\n",
+ " <td>3.28</td>\n",
+ " <td>2.40</td>\n",
+ " <td>7.90</td>\n",
+ " <td>2.07</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>15</th>\n",
+ " <td>1256</td>\n",
+ " <td>63.15</td>\n",
+ " <td>33.47</td>\n",
+ " <td>40.57</td>\n",
+ " <td>13.50</td>\n",
+ " <td>73.65</td>\n",
+ " <td>40.16</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>16</th>\n",
+ " <td>5228</td>\n",
+ " <td>51.60</td>\n",
+ " <td>16.07</td>\n",
+ " <td>73.86</td>\n",
+ " <td>23.38</td>\n",
+ " <td>26.53</td>\n",
+ " <td>8.03</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17</th>\n",
+ " <td>4165</td>\n",
+ " <td>3.52</td>\n",
+ " <td>3.68</td>\n",
+ " <td>1.60</td>\n",
+ " <td>1.96</td>\n",
+ " <td>3.94</td>\n",
+ " <td>3.91</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " name nfwra isora nfwrr isorr nfwaa isoaa\n",
+ "0 11012 21.84 6.85 8.81 3.03 26.93 8.45\n",
+ "1 12754 5.88 9.29 4.63 4.13 6.53 12.87\n",
+ "2 10521 13.36 7.40 5.05 1.57 17.64 9.91\n",
+ "3 5251 19.25 9.76 7.25 7.60 16.12 4.79\n",
+ "4 11300 9.24 13.05 7.80 8.24 10.19 8.30\n",
+ "5 8403 29.70 8.27 30.17 9.80 28.09 4.85\n",
+ "6 9866 7.35 6.21 9.27 5.82 5.08 3.02\n",
+ "7 11466 14.25 15.28 9.31 18.66 16.10 13.77\n",
+ "8 2800 6.84 4.89 8.85 5.00 7.03 7.39\n",
+ "9 9465 9.83 3.33 2.56 1.13 14.67 5.27\n",
+ "10 3273 54.55 14.27 83.62 20.23 1.27 1.41\n",
+ "11 3876 2.70 2.24 3.97 3.09 1.24 1.47\n",
+ "12 7985 6.66 6.59 6.39 6.54 5.64 4.45\n",
+ "13 9753 14.92 13.94 22.58 21.15 5.42 5.42\n",
+ "14 7876 6.21 3.31 3.28 2.40 7.90 2.07\n",
+ "15 1256 63.15 33.47 40.57 13.50 73.65 40.16\n",
+ "16 5228 51.60 16.07 73.86 23.38 26.53 8.03\n",
+ "17 4165 3.52 3.68 1.60 1.96 3.94 3.91"
+ ]
+ },
+ "execution_count": 142,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "dats = pd.DataFrame({\"name\":name, \"nfwra\": chinfw_ra,\"isora\": chiiso_ra,\"nfwrr\": chinfw_rr,\"isorr\": chiiso_rr,\n",
+ " \"nfwaa\": chinfw_aa,\"isoaa\": chiiso_aa })\n",
+ "dats"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 143,
+ "metadata": {},
+ "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>name</th>\n",
+ " <th>meannfw</th>\n",
+ " <th>stdnfw</th>\n",
+ " <th>meaniso</th>\n",
+ " <th>stdiso</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>11012</td>\n",
+ " <td>19.19</td>\n",
+ " <td>9.35</td>\n",
+ " <td>6.11</td>\n",
+ " <td>2.78</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>12754</td>\n",
+ " <td>5.68</td>\n",
+ " <td>0.97</td>\n",
+ " <td>8.76</td>\n",
+ " <td>4.39</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>10521</td>\n",
+ " <td>12.02</td>\n",
+ " <td>6.40</td>\n",
+ " <td>6.29</td>\n",
+ " <td>4.28</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>5251</td>\n",
+ " <td>14.21</td>\n",
+ " <td>6.22</td>\n",
+ " <td>7.38</td>\n",
+ " <td>2.49</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>11300</td>\n",
+ " <td>9.08</td>\n",
+ " <td>1.20</td>\n",
+ " <td>9.86</td>\n",
+ " <td>2.76</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>5</th>\n",
+ " <td>8403</td>\n",
+ " <td>29.32</td>\n",
+ " <td>1.09</td>\n",
+ " <td>7.64</td>\n",
+ " <td>2.53</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>6</th>\n",
+ " <td>9866</td>\n",
+ " <td>7.23</td>\n",
+ " <td>2.10</td>\n",
+ " <td>5.02</td>\n",
+ " <td>1.74</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>7</th>\n",
+ " <td>11466</td>\n",
+ " <td>13.22</td>\n",
+ " <td>3.51</td>\n",
+ " <td>15.90</td>\n",
+ " <td>2.50</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>8</th>\n",
+ " <td>2800</td>\n",
+ " <td>7.57</td>\n",
+ " <td>1.11</td>\n",
+ " <td>5.76</td>\n",
+ " <td>1.41</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>9</th>\n",
+ " <td>9465</td>\n",
+ " <td>9.02</td>\n",
+ " <td>6.10</td>\n",
+ " <td>3.24</td>\n",
+ " <td>2.07</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>10</th>\n",
+ " <td>3273</td>\n",
+ " <td>46.48</td>\n",
+ " <td>41.76</td>\n",
+ " <td>11.97</td>\n",
+ " <td>9.62</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>11</th>\n",
+ " <td>3876</td>\n",
+ " <td>2.64</td>\n",
+ " <td>1.37</td>\n",
+ " <td>2.27</td>\n",
+ " <td>0.81</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>12</th>\n",
+ " <td>7985</td>\n",
+ " <td>6.23</td>\n",
+ " <td>0.53</td>\n",
+ " <td>5.86</td>\n",
+ " <td>1.22</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>13</th>\n",
+ " <td>9753</td>\n",
+ " <td>14.31</td>\n",
+ " <td>8.60</td>\n",
+ " <td>13.50</td>\n",
+ " <td>7.87</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>14</th>\n",
+ " <td>7876</td>\n",
+ " <td>5.80</td>\n",
+ " <td>2.34</td>\n",
+ " <td>2.59</td>\n",
+ " <td>0.64</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>15</th>\n",
+ " <td>1256</td>\n",
+ " <td>59.12</td>\n",
+ " <td>16.90</td>\n",
+ " <td>29.04</td>\n",
+ " <td>13.87</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>16</th>\n",
+ " <td>5228</td>\n",
+ " <td>50.66</td>\n",
+ " <td>23.68</td>\n",
+ " <td>15.83</td>\n",
+ " <td>7.68</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17</th>\n",
+ " <td>4165</td>\n",
+ " <td>3.02</td>\n",
+ " <td>1.25</td>\n",
+ " <td>3.18</td>\n",
+ " <td>1.07</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " name meannfw stdnfw meaniso stdiso\n",
+ "0 11012 19.19 9.35 6.11 2.78\n",
+ "1 12754 5.68 0.97 8.76 4.39\n",
+ "2 10521 12.02 6.40 6.29 4.28\n",
+ "3 5251 14.21 6.22 7.38 2.49\n",
+ "4 11300 9.08 1.20 9.86 2.76\n",
+ "5 8403 29.32 1.09 7.64 2.53\n",
+ "6 9866 7.23 2.10 5.02 1.74\n",
+ "7 11466 13.22 3.51 15.90 2.50\n",
+ "8 2800 7.57 1.11 5.76 1.41\n",
+ "9 9465 9.02 6.10 3.24 2.07\n",
+ "10 3273 46.48 41.76 11.97 9.62\n",
+ "11 3876 2.64 1.37 2.27 0.81\n",
+ "12 7985 6.23 0.53 5.86 1.22\n",
+ "13 9753 14.31 8.60 13.50 7.87\n",
+ "14 7876 5.80 2.34 2.59 0.64\n",
+ "15 1256 59.12 16.90 29.04 13.87\n",
+ "16 5228 50.66 23.68 15.83 7.68\n",
+ "17 4165 3.02 1.25 3.18 1.07"
+ ]
+ },
+ "execution_count": 143,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "dats['meannfw'] = round(dats[[\"nfwra\",\"nfwrr\", \"nfwaa\"]].mean(axis=1),2)\n",
+ "dats['stdnfw'] = round(dats[[\"nfwra\",\"nfwrr\", \"nfwaa\"]].std(axis=1),2)\n",
+ "dats['meaniso'] = round(dats[[\"isora\",\"isorr\", \"isoaa\"]].mean(axis=1),2)\n",
+ "dats['stdiso'] = round(dats[[\"isora\",\"isorr\", \"isoaa\"]].std(axis=1),2)\n",
+ "dats[[\"name\",\"meannfw\", \"stdnfw\", \"meaniso\", \"stdiso\"]]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 144,
+ "metadata": {},
+ "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>name</th>\n",
+ " <th>meannfw</th>\n",
+ " <th>stdnfw</th>\n",
+ " <th>meaniso</th>\n",
+ " <th>stdiso</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>15</th>\n",
+ " <td>1256</td>\n",
+ " <td>59.12</td>\n",
+ " <td>16.90</td>\n",
+ " <td>29.04</td>\n",
+ " <td>13.87</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>8</th>\n",
+ " <td>2800</td>\n",
+ " <td>7.57</td>\n",
+ " <td>1.11</td>\n",
+ " <td>5.76</td>\n",
+ " <td>1.41</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>10</th>\n",
+ " <td>3273</td>\n",
+ " <td>46.48</td>\n",
+ " <td>41.76</td>\n",
+ " <td>11.97</td>\n",
+ " <td>9.62</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>11</th>\n",
+ " <td>3876</td>\n",
+ " <td>2.64</td>\n",
+ " <td>1.37</td>\n",
+ " <td>2.27</td>\n",
+ " <td>0.81</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17</th>\n",
+ " <td>4165</td>\n",
+ " <td>3.02</td>\n",
+ " <td>1.25</td>\n",
+ " <td>3.18</td>\n",
+ " <td>1.07</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>16</th>\n",
+ " <td>5228</td>\n",
+ " <td>50.66</td>\n",
+ " <td>23.68</td>\n",
+ " <td>15.83</td>\n",
+ " <td>7.68</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>5251</td>\n",
+ " <td>14.21</td>\n",
+ " <td>6.22</td>\n",
+ " <td>7.38</td>\n",
+ " <td>2.49</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>14</th>\n",
+ " <td>7876</td>\n",
+ " <td>5.80</td>\n",
+ " <td>2.34</td>\n",
+ " <td>2.59</td>\n",
+ " <td>0.64</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>12</th>\n",
+ " <td>7985</td>\n",
+ " <td>6.23</td>\n",
+ " <td>0.53</td>\n",
+ " <td>5.86</td>\n",
+ " <td>1.22</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>5</th>\n",
+ " <td>8403</td>\n",
+ " <td>29.32</td>\n",
+ " <td>1.09</td>\n",
+ " <td>7.64</td>\n",
+ " <td>2.53</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>9</th>\n",
+ " <td>9465</td>\n",
+ " <td>9.02</td>\n",
+ " <td>6.10</td>\n",
+ " <td>3.24</td>\n",
+ " <td>2.07</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>13</th>\n",
+ " <td>9753</td>\n",
+ " <td>14.31</td>\n",
+ " <td>8.60</td>\n",
+ " <td>13.50</td>\n",
+ " <td>7.87</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>6</th>\n",
+ " <td>9866</td>\n",
+ " <td>7.23</td>\n",
+ " <td>2.10</td>\n",
+ " <td>5.02</td>\n",
+ " <td>1.74</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>10521</td>\n",
+ " <td>12.02</td>\n",
+ " <td>6.40</td>\n",
+ " <td>6.29</td>\n",
+ " <td>4.28</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>11012</td>\n",
+ " <td>19.19</td>\n",
+ " <td>9.35</td>\n",
+ " <td>6.11</td>\n",
+ " <td>2.78</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>11300</td>\n",
+ " <td>9.08</td>\n",
+ " <td>1.20</td>\n",
+ " <td>9.86</td>\n",
+ " <td>2.76</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>7</th>\n",
+ " <td>11466</td>\n",
+ " <td>13.22</td>\n",
+ " <td>3.51</td>\n",
+ " <td>15.90</td>\n",
+ " <td>2.50</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>12754</td>\n",
+ " <td>5.68</td>\n",
+ " <td>0.97</td>\n",
+ " <td>8.76</td>\n",
+ " <td>4.39</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " name meannfw stdnfw meaniso stdiso\n",
+ "15 1256 59.12 16.90 29.04 13.87\n",
+ "8 2800 7.57 1.11 5.76 1.41\n",
+ "10 3273 46.48 41.76 11.97 9.62\n",
+ "11 3876 2.64 1.37 2.27 0.81\n",
+ "17 4165 3.02 1.25 3.18 1.07\n",
+ "16 5228 50.66 23.68 15.83 7.68\n",
+ "3 5251 14.21 6.22 7.38 2.49\n",
+ "14 7876 5.80 2.34 2.59 0.64\n",
+ "12 7985 6.23 0.53 5.86 1.22\n",
+ "5 8403 29.32 1.09 7.64 2.53\n",
+ "9 9465 9.02 6.10 3.24 2.07\n",
+ "13 9753 14.31 8.60 13.50 7.87\n",
+ "6 9866 7.23 2.10 5.02 1.74\n",
+ "2 10521 12.02 6.40 6.29 4.28\n",
+ "0 11012 19.19 9.35 6.11 2.78\n",
+ "4 11300 9.08 1.20 9.86 2.76\n",
+ "7 11466 13.22 3.51 15.90 2.50\n",
+ "1 12754 5.68 0.97 8.76 4.39"
+ ]
+ },
+ "execution_count": 144,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "dats = dats.sort_values(by=[\"name\"])\n",
+ "dats[[\"name\",\"meannfw\", \"stdnfw\", \"meaniso\", \"stdiso\"]]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 145,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "m200_ra = []\n",
+ "c_ra = []\n",
+ "m200_rr = []\n",
+ "c_rr = []\n",
+ "m200_aa = []\n",
+ "c_aa = []\n",
+ "name = []\n",
+ "for ii in range(len(ra)):\n",
+ " m200_ra.append(float(ra[ii].split(\"&\")[2]))\n",
+ " c_ra.append(float(ra[ii].split(\"&\")[3]))\n",
+ " m200_rr.append(float(aa[ii].split(\"&\")[2]))\n",
+ " c_rr.append(float(aa[ii].split(\"&\")[3]))\n",
+ " m200_aa.append(float(rr[ii].split(\"&\")[2]))\n",
+ " c_aa.append(float(rr[ii].split(\"&\")[3]))\n",
+ " name.append(int(rr[ii].split(\"&\")[0]))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 146,
+ "metadata": {},
+ "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>name</th>\n",
+ " <th>m200ra</th>\n",
+ " <th>cra</th>\n",
+ " <th>m200rr</th>\n",
+ " <th>crr</th>\n",
+ " <th>m200a</th>\n",
+ " <th>caa</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>11012</td>\n",
+ " <td>2.114000e+11</td>\n",
+ " <td>29.26</td>\n",
+ " <td>5.058000e+12</td>\n",
+ " <td>12.57</td>\n",
+ " <td>1.842000e+11</td>\n",
+ " <td>30.51</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>12754</td>\n",
+ " <td>9.574000e+11</td>\n",
+ " <td>13.04</td>\n",
+ " <td>1.054000e+14</td>\n",
+ " <td>3.48</td>\n",
+ " <td>4.022000e+11</td>\n",
+ " <td>17.74</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>10521</td>\n",
+ " <td>3.203000e+11</td>\n",
+ " <td>19.06</td>\n",
+ " <td>3.028000e+11</td>\n",
+ " <td>19.58</td>\n",
+ " <td>3.234000e+11</td>\n",
+ " <td>18.97</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>5251</td>\n",
+ " <td>8.431000e+11</td>\n",
+ " <td>7.90</td>\n",
+ " <td>3.708000e+11</td>\n",
+ " <td>11.96</td>\n",
+ " <td>2.119000e+15</td>\n",
+ " <td>0.01</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>11300</td>\n",
+ " <td>4.934000e+11</td>\n",
+ " <td>13.06</td>\n",
+ " <td>1.860000e+11</td>\n",
+ " <td>17.80</td>\n",
+ " <td>1.017000e+12</td>\n",
+ " <td>10.56</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>5</th>\n",
+ " <td>8403</td>\n",
+ " <td>2.848000e+15</td>\n",
+ " <td>0.04</td>\n",
+ " <td>3.020000e+15</td>\n",
+ " <td>0.05</td>\n",
+ " <td>2.700000e+15</td>\n",
+ " <td>0.02</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>6</th>\n",
+ " <td>9866</td>\n",
+ " <td>9.218000e+16</td>\n",
+ " <td>0.50</td>\n",
+ " <td>1.360000e+17</td>\n",
+ " <td>0.36</td>\n",
+ " <td>1.970000e+12</td>\n",
+ " <td>16.06</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>7</th>\n",
+ " <td>11466</td>\n",
+ " <td>1.063000e+11</td>\n",
+ " <td>32.11</td>\n",
+ " <td>4.558000e+11</td>\n",
+ " <td>18.41</td>\n",
+ " <td>9.455000e+10</td>\n",
+ " <td>34.22</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>8</th>\n",
+ " <td>2800</td>\n",
+ " <td>5.139000e+15</td>\n",
+ " <td>0.08</td>\n",
+ " <td>6.454000e+15</td>\n",
+ " <td>0.07</td>\n",
+ " <td>5.763000e+15</td>\n",
+ " <td>0.02</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>9</th>\n",
+ " <td>9465</td>\n",
+ " <td>5.920000e+11</td>\n",
+ " <td>7.47</td>\n",
+ " <td>2.887000e+11</td>\n",
+ " <td>9.51</td>\n",
+ " <td>7.806000e+12</td>\n",
+ " <td>3.40</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>10</th>\n",
+ " <td>3273</td>\n",
+ " <td>1.710000e+15</td>\n",
+ " <td>0.03</td>\n",
+ " <td>1.873000e+15</td>\n",
+ " <td>0.00</td>\n",
+ " <td>1.441000e+15</td>\n",
+ " <td>0.00</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>11</th>\n",
+ " <td>3876</td>\n",
+ " <td>7.419000e+11</td>\n",
+ " <td>11.81</td>\n",
+ " <td>1.342000e+12</td>\n",
+ " <td>9.85</td>\n",
+ " <td>4.091000e+11</td>\n",
+ " <td>14.54</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>12</th>\n",
+ " <td>7985</td>\n",
+ " <td>1.456000e+11</td>\n",
+ " <td>23.74</td>\n",
+ " <td>1.127000e+11</td>\n",
+ " <td>26.12</td>\n",
+ " <td>2.245000e+11</td>\n",
+ " <td>20.12</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>13</th>\n",
+ " <td>9753</td>\n",
+ " <td>9.753000e+10</td>\n",
+ " <td>64.95</td>\n",
+ " <td>9.255000e+10</td>\n",
+ " <td>67.98</td>\n",
+ " <td>1.051000e+11</td>\n",
+ " <td>61.02</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>14</th>\n",
+ " <td>7876</td>\n",
+ " <td>1.392000e+16</td>\n",
+ " <td>0.09</td>\n",
+ " <td>4.047000e+11</td>\n",
+ " <td>12.19</td>\n",
+ " <td>1.408000e+16</td>\n",
+ " <td>0.10</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>15</th>\n",
+ " <td>1256</td>\n",
+ " <td>2.665000e+15</td>\n",
+ " <td>0.09</td>\n",
+ " <td>1.452000e+15</td>\n",
+ " <td>0.00</td>\n",
+ " <td>3.439000e+15</td>\n",
+ " <td>0.10</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>16</th>\n",
+ " <td>5228</td>\n",
+ " <td>4.594000e+11</td>\n",
+ " <td>16.19</td>\n",
+ " <td>4.200000e+11</td>\n",
+ " <td>16.57</td>\n",
+ " <td>7.714000e+11</td>\n",
+ " <td>13.95</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17</th>\n",
+ " <td>4165</td>\n",
+ " <td>6.074000e+10</td>\n",
+ " <td>19.14</td>\n",
+ " <td>3.563000e+11</td>\n",
+ " <td>9.81</td>\n",
+ " <td>3.300000e+10</td>\n",
+ " <td>28.52</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " name m200ra cra m200rr crr m200a caa\n",
+ "0 11012 2.114000e+11 29.26 5.058000e+12 12.57 1.842000e+11 30.51\n",
+ "1 12754 9.574000e+11 13.04 1.054000e+14 3.48 4.022000e+11 17.74\n",
+ "2 10521 3.203000e+11 19.06 3.028000e+11 19.58 3.234000e+11 18.97\n",
+ "3 5251 8.431000e+11 7.90 3.708000e+11 11.96 2.119000e+15 0.01\n",
+ "4 11300 4.934000e+11 13.06 1.860000e+11 17.80 1.017000e+12 10.56\n",
+ "5 8403 2.848000e+15 0.04 3.020000e+15 0.05 2.700000e+15 0.02\n",
+ "6 9866 9.218000e+16 0.50 1.360000e+17 0.36 1.970000e+12 16.06\n",
+ "7 11466 1.063000e+11 32.11 4.558000e+11 18.41 9.455000e+10 34.22\n",
+ "8 2800 5.139000e+15 0.08 6.454000e+15 0.07 5.763000e+15 0.02\n",
+ "9 9465 5.920000e+11 7.47 2.887000e+11 9.51 7.806000e+12 3.40\n",
+ "10 3273 1.710000e+15 0.03 1.873000e+15 0.00 1.441000e+15 0.00\n",
+ "11 3876 7.419000e+11 11.81 1.342000e+12 9.85 4.091000e+11 14.54\n",
+ "12 7985 1.456000e+11 23.74 1.127000e+11 26.12 2.245000e+11 20.12\n",
+ "13 9753 9.753000e+10 64.95 9.255000e+10 67.98 1.051000e+11 61.02\n",
+ "14 7876 1.392000e+16 0.09 4.047000e+11 12.19 1.408000e+16 0.10\n",
+ "15 1256 2.665000e+15 0.09 1.452000e+15 0.00 3.439000e+15 0.10\n",
+ "16 5228 4.594000e+11 16.19 4.200000e+11 16.57 7.714000e+11 13.95\n",
+ "17 4165 6.074000e+10 19.14 3.563000e+11 9.81 3.300000e+10 28.52"
+ ]
+ },
+ "execution_count": 146,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "nfw = pd.DataFrame({\"name\":name, \"m200ra\": m200_ra,\"cra\": c_ra,\"m200rr\": m200_rr,\"crr\": c_rr,\n",
+ " \"m200a\": m200_aa,\"caa\": c_aa })\n",
+ "nfw"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 147,
+ "metadata": {},
+ "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>name</th>\n",
+ " <th>meanm200</th>\n",
+ " <th>stdm200</th>\n",
+ " <th>meanc</th>\n",
+ " <th>stdc</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>15</th>\n",
+ " <td>1256</td>\n",
+ " <td>2.518667e+15</td>\n",
+ " <td>1.001550e+15</td>\n",
+ " <td>0.06</td>\n",
+ " <td>0.06</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>8</th>\n",
+ " <td>2800</td>\n",
+ " <td>5.785333e+15</td>\n",
+ " <td>6.577844e+14</td>\n",
+ " <td>0.06</td>\n",
+ " <td>0.03</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>10</th>\n",
+ " <td>3273</td>\n",
+ " <td>1.674667e+15</td>\n",
+ " <td>2.181567e+14</td>\n",
+ " <td>0.01</td>\n",
+ " <td>0.02</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>11</th>\n",
+ " <td>3876</td>\n",
+ " <td>8.310000e+11</td>\n",
+ " <td>4.727893e+11</td>\n",
+ " <td>12.07</td>\n",
+ " <td>2.36</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17</th>\n",
+ " <td>4165</td>\n",
+ " <td>1.500133e+11</td>\n",
+ " <td>1.791871e+11</td>\n",
+ " <td>19.16</td>\n",
+ " <td>9.36</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>16</th>\n",
+ " <td>5228</td>\n",
+ " <td>5.502667e+11</td>\n",
+ " <td>1.925177e+11</td>\n",
+ " <td>15.57</td>\n",
+ " <td>1.42</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>5251</td>\n",
+ " <td>7.067380e+14</td>\n",
+ " <td>1.223055e+15</td>\n",
+ " <td>6.62</td>\n",
+ " <td>6.08</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>14</th>\n",
+ " <td>7876</td>\n",
+ " <td>9.333468e+15</td>\n",
+ " <td>8.083066e+15</td>\n",
+ " <td>4.13</td>\n",
+ " <td>6.98</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>12</th>\n",
+ " <td>7985</td>\n",
+ " <td>1.609333e+11</td>\n",
+ " <td>5.745558e+10</td>\n",
+ " <td>23.33</td>\n",
+ " <td>3.02</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>5</th>\n",
+ " <td>8403</td>\n",
+ " <td>2.856000e+15</td>\n",
+ " <td>1.601499e+14</td>\n",
+ " <td>0.04</td>\n",
+ " <td>0.02</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>9</th>\n",
+ " <td>9465</td>\n",
+ " <td>2.895567e+12</td>\n",
+ " <td>4.255263e+12</td>\n",
+ " <td>6.79</td>\n",
+ " <td>3.11</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>13</th>\n",
+ " <td>9753</td>\n",
+ " <td>9.839333e+10</td>\n",
+ " <td>6.319386e+09</td>\n",
+ " <td>64.65</td>\n",
+ " <td>3.49</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>6</th>\n",
+ " <td>9866</td>\n",
+ " <td>7.606066e+16</td>\n",
+ " <td>6.941715e+16</td>\n",
+ " <td>5.64</td>\n",
+ " <td>9.02</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>10521</td>\n",
+ " <td>3.155000e+11</td>\n",
+ " <td>1.110720e+10</td>\n",
+ " <td>19.20</td>\n",
+ " <td>0.33</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>11012</td>\n",
+ " <td>1.817867e+12</td>\n",
+ " <td>2.806071e+12</td>\n",
+ " <td>24.11</td>\n",
+ " <td>10.02</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>11300</td>\n",
+ " <td>5.654667e+11</td>\n",
+ " <td>4.201612e+11</td>\n",
+ " <td>13.81</td>\n",
+ " <td>3.68</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>7</th>\n",
+ " <td>11466</td>\n",
+ " <td>2.188833e+11</td>\n",
+ " <td>2.052599e+11</td>\n",
+ " <td>28.25</td>\n",
+ " <td>8.58</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>12754</td>\n",
+ " <td>3.558653e+13</td>\n",
+ " <td>6.046087e+13</td>\n",
+ " <td>11.42</td>\n",
+ " <td>7.27</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " name meanm200 stdm200 meanc stdc\n",
+ "15 1256 2.518667e+15 1.001550e+15 0.06 0.06\n",
+ "8 2800 5.785333e+15 6.577844e+14 0.06 0.03\n",
+ "10 3273 1.674667e+15 2.181567e+14 0.01 0.02\n",
+ "11 3876 8.310000e+11 4.727893e+11 12.07 2.36\n",
+ "17 4165 1.500133e+11 1.791871e+11 19.16 9.36\n",
+ "16 5228 5.502667e+11 1.925177e+11 15.57 1.42\n",
+ "3 5251 7.067380e+14 1.223055e+15 6.62 6.08\n",
+ "14 7876 9.333468e+15 8.083066e+15 4.13 6.98\n",
+ "12 7985 1.609333e+11 5.745558e+10 23.33 3.02\n",
+ "5 8403 2.856000e+15 1.601499e+14 0.04 0.02\n",
+ "9 9465 2.895567e+12 4.255263e+12 6.79 3.11\n",
+ "13 9753 9.839333e+10 6.319386e+09 64.65 3.49\n",
+ "6 9866 7.606066e+16 6.941715e+16 5.64 9.02\n",
+ "2 10521 3.155000e+11 1.110720e+10 19.20 0.33\n",
+ "0 11012 1.817867e+12 2.806071e+12 24.11 10.02\n",
+ "4 11300 5.654667e+11 4.201612e+11 13.81 3.68\n",
+ "7 11466 2.188833e+11 2.052599e+11 28.25 8.58\n",
+ "1 12754 3.558653e+13 6.046087e+13 11.42 7.27"
+ ]
+ },
+ "execution_count": 147,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "nfw['meanm200'] = round(nfw[[\"m200ra\",\"m200rr\", \"m200a\"]].mean(axis=1),2)\n",
+ "nfw['stdm200'] = round(nfw[[\"m200ra\",\"m200rr\", \"m200a\"]].std(axis=1),2)\n",
+ "nfw['meanc'] = round(nfw[[\"cra\",\"crr\", \"caa\"]].mean(axis=1),2)\n",
+ "nfw['stdc'] = round(nfw[[\"cra\",\"crr\", \"caa\"]].std(axis=1),2)\n",
+ "nfw = nfw.sort_values(by=[\"name\"])\n",
+ "nfw[[\"name\",\"meanm200\", \"stdm200\", \"meanc\", \"stdc\"]]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 151,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "rho_ra = []\n",
+ "rc_ra = []\n",
+ "rho_rr = []\n",
+ "rc_rr = []\n",
+ "rho_aa = []\n",
+ "rc_aa = []\n",
+ "name = []\n",
+ "for ii in range(len(ra)):\n",
+ " rho_ra.append(float(ra[ii].split(\"&\")[5]))\n",
+ " rc_ra.append(float(ra[ii].split(\"&\")[6]))\n",
+ " rho_rr.append(float(aa[ii].split(\"&\")[5]))\n",
+ " rc_rr.append(float(aa[ii].split(\"&\")[6]))\n",
+ " rho_aa.append(float(rr[ii].split(\"&\")[5]))\n",
+ " rc_aa.append(float(rr[ii].split(\"&\")[6]))\n",
+ " name.append(int(rr[ii].split(\"&\")[0]))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 152,
+ "metadata": {},
+ "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>name</th>\n",
+ " <th>rhora</th>\n",
+ " <th>rcra</th>\n",
+ " <th>rhorr</th>\n",
+ " <th>rcrr</th>\n",
+ " <th>rhoa</th>\n",
+ " <th>rcaa</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>11012</td>\n",
+ " <td>1.19</td>\n",
+ " <td>552.63</td>\n",
+ " <td>1.07</td>\n",
+ " <td>622.49</td>\n",
+ " <td>1.21</td>\n",
+ " <td>544.15</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>12754</td>\n",
+ " <td>0.33</td>\n",
+ " <td>1114.34</td>\n",
+ " <td>0.21</td>\n",
+ " <td>1601.60</td>\n",
+ " <td>0.79</td>\n",
+ " <td>634.76</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>10521</td>\n",
+ " <td>0.41</td>\n",
+ " <td>948.78</td>\n",
+ " <td>0.51</td>\n",
+ " <td>807.68</td>\n",
+ " <td>0.39</td>\n",
+ " <td>982.07</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>5251</td>\n",
+ " <td>0.10</td>\n",
+ " <td>1903.24</td>\n",
+ " <td>0.16</td>\n",
+ " <td>1389.47</td>\n",
+ " <td>0.05</td>\n",
+ " <td>2780.64</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>11300</td>\n",
+ " <td>0.44</td>\n",
+ " <td>698.36</td>\n",
+ " <td>0.78</td>\n",
+ " <td>464.83</td>\n",
+ " <td>0.22</td>\n",
+ " <td>1258.09</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>5</th>\n",
+ " <td>8403</td>\n",
+ " <td>0.02</td>\n",
+ " <td>5487.03</td>\n",
+ " <td>0.03</td>\n",
+ " <td>5444.29</td>\n",
+ " <td>0.02</td>\n",
+ " <td>5178.19</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>6</th>\n",
+ " <td>9866</td>\n",
+ " <td>1.08</td>\n",
+ " <td>614.23</td>\n",
+ " <td>0.96</td>\n",
+ " <td>713.56</td>\n",
+ " <td>1.48</td>\n",
+ " <td>464.66</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>7</th>\n",
+ " <td>11466</td>\n",
+ " <td>1.81</td>\n",
+ " <td>363.33</td>\n",
+ " <td>1.29</td>\n",
+ " <td>441.45</td>\n",
+ " <td>1.81</td>\n",
+ " <td>367.12</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>8</th>\n",
+ " <td>2800</td>\n",
+ " <td>0.08</td>\n",
+ " <td>2039.29</td>\n",
+ " <td>0.08</td>\n",
+ " <td>2192.76</td>\n",
+ " <td>0.08</td>\n",
+ " <td>2073.18</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>9</th>\n",
+ " <td>9465</td>\n",
+ " <td>0.10</td>\n",
+ " <td>1464.60</td>\n",
+ " <td>0.09</td>\n",
+ " <td>1523.35</td>\n",
+ " <td>0.11</td>\n",
+ " <td>1448.91</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>10</th>\n",
+ " <td>3273</td>\n",
+ " <td>0.02</td>\n",
+ " <td>5259.97</td>\n",
+ " <td>0.02</td>\n",
+ " <td>6151.82</td>\n",
+ " <td>0.04</td>\n",
+ " <td>2612.75</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>11</th>\n",
+ " <td>3876</td>\n",
+ " <td>0.23</td>\n",
+ " <td>1199.68</td>\n",
+ " <td>0.23</td>\n",
+ " <td>1216.49</td>\n",
+ " <td>0.26</td>\n",
+ " <td>1135.72</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>12</th>\n",
+ " <td>7985</td>\n",
+ " <td>0.71</td>\n",
+ " <td>595.23</td>\n",
+ " <td>0.83</td>\n",
+ " <td>530.89</td>\n",
+ " <td>0.56</td>\n",
+ " <td>712.26</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>13</th>\n",
+ " <td>9753</td>\n",
+ " <td>6.05</td>\n",
+ " <td>263.56</td>\n",
+ " <td>6.35</td>\n",
+ " <td>257.76</td>\n",
+ " <td>5.74</td>\n",
+ " <td>268.99</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>14</th>\n",
+ " <td>7876</td>\n",
+ " <td>0.17</td>\n",
+ " <td>1355.79</td>\n",
+ " <td>0.27</td>\n",
+ " <td>907.54</td>\n",
+ " <td>0.14</td>\n",
+ " <td>1595.91</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>15</th>\n",
+ " <td>1256</td>\n",
+ " <td>0.05</td>\n",
+ " <td>3171.60</td>\n",
+ " <td>0.03</td>\n",
+ " <td>6852.55</td>\n",
+ " <td>0.06</td>\n",
+ " <td>2721.83</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>16</th>\n",
+ " <td>5228</td>\n",
+ " <td>0.34</td>\n",
+ " <td>1098.37</td>\n",
+ " <td>0.34</td>\n",
+ " <td>1094.36</td>\n",
+ " <td>0.34</td>\n",
+ " <td>1117.07</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17</th>\n",
+ " <td>4165</td>\n",
+ " <td>0.39</td>\n",
+ " <td>564.17</td>\n",
+ " <td>0.17</td>\n",
+ " <td>967.56</td>\n",
+ " <td>0.98</td>\n",
+ " <td>341.53</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " name rhora rcra rhorr rcrr rhoa rcaa\n",
+ "0 11012 1.19 552.63 1.07 622.49 1.21 544.15\n",
+ "1 12754 0.33 1114.34 0.21 1601.60 0.79 634.76\n",
+ "2 10521 0.41 948.78 0.51 807.68 0.39 982.07\n",
+ "3 5251 0.10 1903.24 0.16 1389.47 0.05 2780.64\n",
+ "4 11300 0.44 698.36 0.78 464.83 0.22 1258.09\n",
+ "5 8403 0.02 5487.03 0.03 5444.29 0.02 5178.19\n",
+ "6 9866 1.08 614.23 0.96 713.56 1.48 464.66\n",
+ "7 11466 1.81 363.33 1.29 441.45 1.81 367.12\n",
+ "8 2800 0.08 2039.29 0.08 2192.76 0.08 2073.18\n",
+ "9 9465 0.10 1464.60 0.09 1523.35 0.11 1448.91\n",
+ "10 3273 0.02 5259.97 0.02 6151.82 0.04 2612.75\n",
+ "11 3876 0.23 1199.68 0.23 1216.49 0.26 1135.72\n",
+ "12 7985 0.71 595.23 0.83 530.89 0.56 712.26\n",
+ "13 9753 6.05 263.56 6.35 257.76 5.74 268.99\n",
+ "14 7876 0.17 1355.79 0.27 907.54 0.14 1595.91\n",
+ "15 1256 0.05 3171.60 0.03 6852.55 0.06 2721.83\n",
+ "16 5228 0.34 1098.37 0.34 1094.36 0.34 1117.07\n",
+ "17 4165 0.39 564.17 0.17 967.56 0.98 341.53"
+ ]
+ },
+ "execution_count": 152,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "iso = pd.DataFrame({\"name\":name, \"rhora\": rho_ra,\"rcra\": rc_ra,\"rhorr\": rho_rr,\"rcrr\": rc_rr,\n",
+ " \"rhoa\": rho_aa,\"rcaa\": rc_aa })\n",
+ "iso"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 154,
+ "metadata": {},
+ "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>name</th>\n",
+ " <th>meanrho</th>\n",
+ " <th>stdrho</th>\n",
+ " <th>meanrc</th>\n",
+ " <th>stdrc</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>15</th>\n",
+ " <td>1256</td>\n",
+ " <td>0.05</td>\n",
+ " <td>0.02</td>\n",
+ " <td>4248.66</td>\n",
+ " <td>2266.22</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>8</th>\n",
+ " <td>2800</td>\n",
+ " <td>0.08</td>\n",
+ " <td>0.00</td>\n",
+ " <td>2101.74</td>\n",
+ " <td>80.62</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>10</th>\n",
+ " <td>3273</td>\n",
+ " <td>0.03</td>\n",
+ " <td>0.01</td>\n",
+ " <td>4674.85</td>\n",
+ " <td>1840.66</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>11</th>\n",
+ " <td>3876</td>\n",
+ " <td>0.24</td>\n",
+ " <td>0.02</td>\n",
+ " <td>1183.96</td>\n",
+ " <td>42.62</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17</th>\n",
+ " <td>4165</td>\n",
+ " <td>0.51</td>\n",
+ " <td>0.42</td>\n",
+ " <td>624.42</td>\n",
+ " <td>317.33</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>16</th>\n",
+ " <td>5228</td>\n",
+ " <td>0.34</td>\n",
+ " <td>0.00</td>\n",
+ " <td>1103.27</td>\n",
+ " <td>12.12</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>5251</td>\n",
+ " <td>0.10</td>\n",
+ " <td>0.06</td>\n",
+ " <td>2024.45</td>\n",
+ " <td>703.46</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>14</th>\n",
+ " <td>7876</td>\n",
+ " <td>0.19</td>\n",
+ " <td>0.07</td>\n",
+ " <td>1286.41</td>\n",
+ " <td>349.39</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>12</th>\n",
+ " <td>7985</td>\n",
+ " <td>0.70</td>\n",
+ " <td>0.14</td>\n",
+ " <td>612.79</td>\n",
+ " <td>91.95</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>5</th>\n",
+ " <td>8403</td>\n",
+ " <td>0.02</td>\n",
+ " <td>0.01</td>\n",
+ " <td>5369.84</td>\n",
+ " <td>167.34</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>9</th>\n",
+ " <td>9465</td>\n",
+ " <td>0.10</td>\n",
+ " <td>0.01</td>\n",
+ " <td>1478.95</td>\n",
+ " <td>39.24</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>13</th>\n",
+ " <td>9753</td>\n",
+ " <td>6.05</td>\n",
+ " <td>0.31</td>\n",
+ " <td>263.44</td>\n",
+ " <td>5.62</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>6</th>\n",
+ " <td>9866</td>\n",
+ " <td>1.17</td>\n",
+ " <td>0.27</td>\n",
+ " <td>597.48</td>\n",
+ " <td>125.29</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>10521</td>\n",
+ " <td>0.44</td>\n",
+ " <td>0.06</td>\n",
+ " <td>912.84</td>\n",
+ " <td>92.58</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>11012</td>\n",
+ " <td>1.16</td>\n",
+ " <td>0.08</td>\n",
+ " <td>573.09</td>\n",
+ " <td>42.99</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>11300</td>\n",
+ " <td>0.48</td>\n",
+ " <td>0.28</td>\n",
+ " <td>807.09</td>\n",
+ " <td>407.65</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>7</th>\n",
+ " <td>11466</td>\n",
+ " <td>1.64</td>\n",
+ " <td>0.30</td>\n",
+ " <td>390.63</td>\n",
+ " <td>44.05</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>12754</td>\n",
+ " <td>0.44</td>\n",
+ " <td>0.31</td>\n",
+ " <td>1116.90</td>\n",
+ " <td>483.43</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " name meanrho stdrho meanrc stdrc\n",
+ "15 1256 0.05 0.02 4248.66 2266.22\n",
+ "8 2800 0.08 0.00 2101.74 80.62\n",
+ "10 3273 0.03 0.01 4674.85 1840.66\n",
+ "11 3876 0.24 0.02 1183.96 42.62\n",
+ "17 4165 0.51 0.42 624.42 317.33\n",
+ "16 5228 0.34 0.00 1103.27 12.12\n",
+ "3 5251 0.10 0.06 2024.45 703.46\n",
+ "14 7876 0.19 0.07 1286.41 349.39\n",
+ "12 7985 0.70 0.14 612.79 91.95\n",
+ "5 8403 0.02 0.01 5369.84 167.34\n",
+ "9 9465 0.10 0.01 1478.95 39.24\n",
+ "13 9753 6.05 0.31 263.44 5.62\n",
+ "6 9866 1.17 0.27 597.48 125.29\n",
+ "2 10521 0.44 0.06 912.84 92.58\n",
+ "0 11012 1.16 0.08 573.09 42.99\n",
+ "4 11300 0.48 0.28 807.09 407.65\n",
+ "7 11466 1.64 0.30 390.63 44.05\n",
+ "1 12754 0.44 0.31 1116.90 483.43"
+ ]
+ },
+ "execution_count": 154,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "iso['meanrho'] = round(iso[[\"rhora\",\"rhorr\", \"rhoa\"]].mean(axis=1),2)\n",
+ "iso['stdrho'] = round(iso[[\"rhora\",\"rhorr\", \"rhoa\"]].std(axis=1),2)\n",
+ "iso['meanrc'] = round(iso[[\"rcra\",\"rcrr\", \"rcaa\"]].mean(axis=1),2)\n",
+ "iso['stdrc'] = round(iso[[\"rcra\",\"rcrr\", \"rcaa\"]].std(axis=1),2)\n",
+ "iso = iso.sort_values(by=[\"name\"])\n",
+ "iso[[\"name\",\"meanrho\", \"stdrho\", \"meanrc\", \"stdrc\"]]"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
diff --git a/notebook/confidence_interval_ellipse1-short.ipynb b/notebook/confidence_interval_ellipse1-short.ipynb
index 7fb61cebee9d375908c33f7ac10b054d6a6d0014..4b586f21a91d5aa672f3723dc1a75c685771ffef 100644
--- a/notebook/confidence_interval_ellipse1-short.ipynb
+++ b/notebook/confidence_interval_ellipse1-short.ipynb
@@ -104,7 +104,7 @@
"metadata": {},
"outputs": [],
"source": [
- "path = [\"../galaxies_data/rcugc4165.txt\"]"
+ "path = [\"../galaxies_data/rcugc3273.txt\"]"
]
},
{
@@ -139,14 +139,16 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "../galaxies_data/rcugc4165.txt\n",
- "128\n"
+ "../galaxies_data/rcugc3273.txt\n",
+ "19\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
+ "../Functions/functions.py:52: RuntimeWarning: invalid value encountered in log\n",
+ " v = (10*G*H0*MM)**(1/3) * ((np.log(1+c*x) - c*x/(1+c*x)) /(x*(np.log(1+c) - c/(1+c))) )**(1/2)\n",
"../Functions/functions.py:67: RuntimeWarning: invalid value encountered in sqrt\n",
" v = ( 4*np.pi*G*rr*x**(-1) *(x - np.arctan(x) ) )**(1/2)\n"
]
@@ -185,7 +187,7 @@
"metadata": {},
"outputs": [],
"source": [
- "#df1"
+ "#df1 = dfa.copy()"
]
},
{
@@ -237,57 +239,57 @@
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
- " <td>rcugc4165</td>\n",
+ " <td>rcugc3273</td>\n",
" <td>ra</td>\n",
- " <td>6.074409e+10</td>\n",
- " <td>1.228760e+10</td>\n",
- " <td>19.137209</td>\n",
- " <td>1.851499</td>\n",
- " <td>0.394941</td>\n",
- " <td>0.073572</td>\n",
- " <td>564.171234</td>\n",
- " <td>65.353228</td>\n",
+ " <td>1.707359e+15</td>\n",
+ " <td>7.661680e+16</td>\n",
+ " <td>0.028256</td>\n",
+ " <td>11.502467</td>\n",
+ " <td>0.024792</td>\n",
+ " <td>0.006482</td>\n",
+ " <td>-5259.971109</td>\n",
+ " <td>2335.110971</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
- " <td>rcugc4165</td>\n",
+ " <td>rcugc3273</td>\n",
" <td>r</td>\n",
- " <td>3.300000e+10</td>\n",
- " <td>6.069498e+09</td>\n",
- " <td>28.515061</td>\n",
- " <td>3.587851</td>\n",
- " <td>0.979249</td>\n",
- " <td>0.371557</td>\n",
- " <td>-341.529478</td>\n",
- " <td>72.642947</td>\n",
+ " <td>1.423936e+15</td>\n",
+ " <td>5.789004e+14</td>\n",
+ " <td>0.004628</td>\n",
+ " <td>0.075467</td>\n",
+ " <td>0.039177</td>\n",
+ " <td>0.013257</td>\n",
+ " <td>-2612.747015</td>\n",
+ " <td>742.684289</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
- " <td>rcugc4165</td>\n",
+ " <td>rcugc3273</td>\n",
" <td>a</td>\n",
- " <td>3.562725e+11</td>\n",
- " <td>2.543976e+11</td>\n",
- " <td>9.808096</td>\n",
- " <td>2.170091</td>\n",
- " <td>0.174522</td>\n",
- " <td>0.027822</td>\n",
- " <td>-967.563968</td>\n",
- " <td>118.061555</td>\n",
+ " <td>1.515592e+15</td>\n",
+ " <td>5.189987e+17</td>\n",
+ " <td>0.063836</td>\n",
+ " <td>90.400464</td>\n",
+ " <td>0.023264</td>\n",
+ " <td>0.007258</td>\n",
+ " <td>6151.817104</td>\n",
+ " <td>4126.193532</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
- " ID Side M200_NFW(Msun) err_M200_NFW(Msun) c_NFW err_c_NFW \\\n",
- "0 rcugc4165 ra 6.074409e+10 1.228760e+10 19.137209 1.851499 \n",
- "1 rcugc4165 r 3.300000e+10 6.069498e+09 28.515061 3.587851 \n",
- "2 rcugc4165 a 3.562725e+11 2.543976e+11 9.808096 2.170091 \n",
+ " ID Side M200_NFW(Msun) err_M200_NFW(Msun) c_NFW err_c_NFW \\\n",
+ "0 rcugc3273 ra 1.707359e+15 7.661680e+16 0.028256 11.502467 \n",
+ "1 rcugc3273 r 1.423936e+15 5.789004e+14 0.004628 0.075467 \n",
+ "2 rcugc3273 a 1.515592e+15 5.189987e+17 0.063836 90.400464 \n",
"\n",
- " rho0_ISO(Msun/pc3) err_rho0_ISO(Msun/pc3) Rc_ISO(pc) err_Rc_ISO(pc) \n",
- "0 0.394941 0.073572 564.171234 65.353228 \n",
- "1 0.979249 0.371557 -341.529478 72.642947 \n",
- "2 0.174522 0.027822 -967.563968 118.061555 "
+ " rho0_ISO(Msun/pc3) err_rho0_ISO(Msun/pc3) Rc_ISO(pc) err_Rc_ISO(pc) \n",
+ "0 0.024792 0.006482 -5259.971109 2335.110971 \n",
+ "1 0.039177 0.013257 -2612.747015 742.684289 \n",
+ "2 0.023264 0.007258 6151.817104 4126.193532 "
]
},
"execution_count": 7,
@@ -308,7 +310,7 @@
{
"data": {
"text/plain": [
- "5.12"
+ "7.29"
]
},
"execution_count": 8,
@@ -329,7 +331,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "10 1 2 -1\n"
+ "15 -2 3 -2\n"
]
}
],
@@ -349,7 +351,7 @@
{
"data": {
"text/plain": [
- "1.9137208755410824"
+ "2.825644140969208"
]
},
"execution_count": 10,
@@ -369,7 +371,7 @@
{
"data": {
"text/plain": [
- "3.449406205382829"
+ "1.9792421281400072"
]
},
"execution_count": 11,
@@ -389,7 +391,7 @@
{
"data": {
"text/plain": [
- "3.449406205382829"
+ "1.9792421281400072"
]
},
"execution_count": 12,
@@ -441,9 +443,17 @@
"execution_count": 15,
"metadata": {},
"outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "../Functions/functions.py:52: RuntimeWarning: invalid value encountered in sqrt\n",
+ " v = (10*G*H0*MM)**(1/3) * ((np.log(1+c*x) - c*x/(1+c*x)) /(x*(np.log(1+c) - c/(1+c))) )**(1/2)\n"
+ ]
+ },
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 792x504 with 1 Axes>"
]
@@ -492,9 +502,17 @@
"execution_count": 17,
"metadata": {},
"outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "../Functions/functions.py:52: RuntimeWarning: invalid value encountered in sqrt\n",
+ " v = (10*G*H0*MM)**(1/3) * ((np.log(1+c*x) - c*x/(1+c*x)) /(x*(np.log(1+c) - c/(1+c))) )**(1/2)\n"
+ ]
+ },
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 792x576 with 2 Axes>"
]
@@ -518,9 +536,9 @@
" color = \"purple\", ecolor=\"black\",capsize=2,elinewidth=0.5)\n",
"\n",
"ax1.plot(x/1000,vnfw(best_parmeters[\"M200_NFW(Msun)\"][0], best_parmeters[\"c_NFW\"][0], x/r200(best_parmeters[\"M200_NFW(Msun)\"][0]), G = G, H0 = H0),\n",
- " color=\"darkorange\", label=\"NFW\\nM200 = {:2e}\\nc = {:2f}\".format(best_parmeters[\"M200_NFW(Msun)\"][0],best_parmeters[\"c_NFW\"][0]))\n",
+ " color=\"darkorange\", label=\"NFW\\nM200 = {:2e}\\nc = {:2f}\".format(best_parmeters[\"M200_NFW(Msun)\"][2],best_parmeters[\"c_NFW\"][0]))\n",
"ax1.plot(x/1000,viso ( (best_parmeters[\"rho0_ISO(Msun/pc3)\"][0]*best_parmeters[\"Rc_ISO(pc)\"][0]**2) , x/abs(best_parmeters[\"Rc_ISO(pc)\"][0]), G = G),\n",
- " color=\"blue\", label=\"ISO\\nrho0 = {:2f}\\nR_c = {:2f}\".format(abs(best_parmeters[\"rho0_ISO(Msun/pc3)\"][0]),abs(best_parmeters[\"Rc_ISO(pc)\"][0])))\n",
+ " color=\"blue\", label=\"ISO\\nrho0 = {:2f}\\nR_c = {:2f}\".format(abs(best_parmeters[\"rho0_ISO(Msun/pc3)\"][2]),abs(best_parmeters[\"Rc_ISO(pc)\"][0])))\n",
"\n",
"ax2 = plt.subplot(grilla[2:,:])\n",
"# -- graficar residuo\n",
@@ -543,7 +561,7 @@
"metadata": {},
"outputs": [],
"source": [
- "preed =vnfw(best_parmeters[\"M200_NFW(Msun)\"][0], best_parmeters[\"c_NFW\"][0], df1[\"r(kpc)\"]*1000/r200(best_parmeters[\"M200_NFW(Msun)\"][0]), G = G, H0 = H0)"
+ "preed =vnfw(best_parmeters[\"M200_NFW(Msun)\"][2], best_parmeters[\"c_NFW\"][0], df1[\"r(kpc)\"]*1000/r200(best_parmeters[\"M200_NFW(Msun)\"][0]), G = G, H0 = H0)"
]
},
{
@@ -604,8 +622,8 @@
"output_type": "stream",
"text": [
"Mejor $\\chi^2$ para \n",
- "NFW: 443.0693019080659 \n",
- "ISO: 464.11643927074226\n"
+ "NFW: 927.3868114498251 \n",
+ "ISO: 242.6627825340533\n"
]
}
],
@@ -628,8 +646,8 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "3.5164230310163958\n",
- "3.68346380373605\n"
+ "54.55216537940148\n",
+ "14.274281325532547\n"
]
}
],
@@ -794,7 +812,7 @@
{
"data": {
"text/plain": [
- "1.9137208755410824"
+ "6.383631446303356"
]
},
"execution_count": 33,
@@ -803,7 +821,7 @@
}
],
"source": [
- "(best_parmeters[\"c_NFW\"][0]/(10**order_c))"
+ "(best_parmeters[\"c_NFW\"][2]/(10**order_c))"
]
},
{
@@ -814,10 +832,10 @@
{
"data": {
"text/latex": [
- "$4.8595269 \\times 10^{11} \\; \\mathrm{M_{\\odot}}$"
+ "$1.2124735 \\times 10^{16} \\; \\mathrm{M_{\\odot}}$"
],
"text/plain": [
- "<Quantity 4.85952692e+11 solMass>"
+ "<Quantity 1.21247353e+16 solMass>"
]
},
"execution_count": 34,
@@ -826,7 +844,7 @@
}
],
"source": [
- "0.8e1*best_parmeters[\"M200_NFW(Msun)\"][0]*units.Msun"
+ "0.8e1*best_parmeters[\"M200_NFW(Msun)\"][2]*units.Msun"
]
},
{
@@ -874,8 +892,8 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "CPU times: user 2 µs, sys: 2 µs, total: 4 µs\n",
- "Wall time: 7.39 µs\n"
+ "CPU times: user 1 µs, sys: 1 µs, total: 2 µs\n",
+ "Wall time: 4.77 µs\n"
]
}
],
@@ -936,10 +954,10 @@
" #chi2_bestfit = chis[i_chi2_bestfit]\n",
" \n",
" ch2_m = [(i,ch2) for i,ch2 in enumerate(chis) if ch2<=chi2_bestfit]\n",
- " ch2_p01 = [(i,ch2) for i,ch2 in enumerate(chis) if chi2_bestfit<ch2<=chi2_bestfit+0.1]\n",
- " ch2_p12 = [(i,ch2) for i,ch2 in enumerate(chis) if chi2_bestfit+0.1<ch2<=chi2_bestfit+0.3]\n",
- " ch2_p23 = [(i,ch2) for i,ch2 in enumerate(chis) if chi2_bestfit+0.3<ch2<=chi2_bestfit+0.9]\n",
- " ch2_p3p = [(i,ch2) for i,ch2 in enumerate(chis) if chi2_bestfit+0.9<ch2]\n",
+ " ch2_p01 = [(i,ch2) for i,ch2 in enumerate(chis) if chi2_bestfit<ch2<=chi2_bestfit+0.006]\n",
+ " ch2_p12 = [(i,ch2) for i,ch2 in enumerate(chis) if chi2_bestfit+0.006<ch2<=chi2_bestfit+0.093]\n",
+ " ch2_p23 = [(i,ch2) for i,ch2 in enumerate(chis) if chi2_bestfit+0.093<ch2<=chi2_bestfit+0.765]\n",
+ " ch2_p3p = [(i,ch2) for i,ch2 in enumerate(chis) if chi2_bestfit+0.765<ch2]\n",
" \n",
"# if len(ch2_m) == 0:\n",
"# ch2_m = (i,chi2_bestfit)\n",
@@ -957,7 +975,7 @@
{
"data": {
"text/plain": [
- "3.68354415285349"
+ "14.27460729650954"
]
},
"execution_count": 38,
@@ -977,7 +995,7 @@
{
"data": {
"text/plain": [
- "685"
+ "645"
]
},
"execution_count": 39,
@@ -998,7 +1016,7 @@
{
"data": {
"text/plain": [
- "0"
+ "151"
]
},
"execution_count": 40,
@@ -1069,32 +1087,19 @@
},
{
"cell_type": "code",
- "execution_count": 43,
+ "execution_count": 59,
"metadata": {},
- "outputs": [
- {
- "ename": "IndexError",
- "evalue": "list index out of range",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)",
- "\u001b[0;32m<ipython-input-43-c81821becdb5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpara_range_NFW_ra_m\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparameters_each_interval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcombs_NFW_ra\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mch2_inter_NFW_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkk\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mpara_range_NFW_ra_p01\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparameters_each_interval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcombs_NFW_ra\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mch2_inter_NFW_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkk\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mpara_range_NFW_ra_p12\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparameters_each_interval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcombs_NFW_ra\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mch2_inter_NFW_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkk\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mpara_range_NFW_ra_p23\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparameters_each_interval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcombs_NFW_ra\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mch2_inter_NFW_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkk\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mpara_range_NFW_ra_p3p\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparameters_each_interval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcombs_NFW_ra\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mch2_inter_NFW_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkk\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m<ipython-input-42-4e57153f7b18>\u001b[0m in \u001b[0;36mparameters_each_interval\u001b[0;34m(combs, ch2_inter, kk)\u001b[0m\n\u001b[1;32m 20\u001b[0m '''\n\u001b[1;32m 21\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m \u001b[0mcombs_interval\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcombs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mch2_inter\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkk\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 23\u001b[0m \u001b[0mpara1_interval\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mcombs_interval\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 24\u001b[0m \u001b[0mpara2_interval\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mcombs_interval\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;31mIndexError\u001b[0m: list index out of range"
- ]
- }
- ],
+ "outputs": [],
"source": [
"\n",
- "para_range_NFW_ra_m = parameters_each_interval(combs_NFW_ra, ch2_inter_NFW_, kk = 0)\n",
+ "#para_range_NFW_ra_m = parameters_each_interval(combs_NFW_ra, ch2_inter_NFW_, kk = 0)\n",
"para_range_NFW_ra_p01 = parameters_each_interval(combs_NFW_ra, ch2_inter_NFW_, kk = 1)\n",
"para_range_NFW_ra_p12 = parameters_each_interval(combs_NFW_ra, ch2_inter_NFW_, kk = 2)\n",
"para_range_NFW_ra_p23 = parameters_each_interval(combs_NFW_ra, ch2_inter_NFW_, kk = 3)\n",
"para_range_NFW_ra_p3p = parameters_each_interval(combs_NFW_ra, ch2_inter_NFW_, kk = 4)\n",
"\n",
"\n",
- "# para_range_ISO_ra_m = parameters_each_interval(combs_ISO_ra, ch2_inter_ISO_, kk = 0)\n",
+ "#para_range_ISO_ra_m = parameters_each_interval(combs_ISO_ra, ch2_inter_ISO_, kk = 0)\n",
"para_range_ISO_ra_p01 = parameters_each_interval(combs_ISO_ra, ch2_inter_ISO_, kk = 1)\n",
"para_range_ISO_ra_p12 = parameters_each_interval(combs_ISO_ra, ch2_inter_ISO_, kk = 2)\n",
"para_range_ISO_ra_p23 = parameters_each_interval(combs_ISO_ra, ch2_inter_ISO_, kk = 3)\n",
@@ -1103,30 +1108,39 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 60,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU times: user 7 µs, sys: 0 ns, total: 7 µs\n",
+ "Wall time: 15.3 µs\n"
+ ]
+ }
+ ],
"source": [
"%time\n",
"# -- Ãndice dentro del arreglo de chi^2s que es el nuevo mÃnimo para NFW e ISO\n",
- "newi_min_ch2_NFW = [i for i in ch2_inter_NFW_[0] if i[1] == min(list(zip(*ch2_inter_NFW_[0]))[1])][0][0]\n",
+ "#newi_min_ch2_NFW = [i for i in ch2_inter_NFW_[0] if i[1] == min(list(zip(*ch2_inter_NFW_[0]))[1])][0][0]\n",
"\n",
"#newi_min_ch2_ISO = [i for i in ch2_inter_ISO_[0] if i[1] == min(list(zip(*ch2_inter_ISO_[0]))[1])][0][0]"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 61,
"metadata": {},
"outputs": [],
"source": [
"# -- valor del nuevo mÃnimo chi^2, se multiplica por (len(df1)-2), para que sea el normal y no el reducido\n",
- "chis_NFW_ra[newi_min_ch2_NFW]"
+ "#chis_NFW_ra[newi_min_ch2_NFW]"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 62,
"metadata": {},
"outputs": [],
"source": [
@@ -1136,40 +1150,55 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 63,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 720x504 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
"source": [
"plt.figure(figsize = (10,7))\n",
- "plt.scatter(para_range_NFW_ra_m[0],para_range_NFW_ra_m[1],color=\"blue\",alpha=0.5,label = r\"$\\chi^2\\leq\\chi^2_{fit}$\")\n",
- "plt.scatter(para_range_NFW_ra_p01[0],para_range_NFW_ra_p01[1],color=\"red\",alpha=0.5,label = r\"$\\chi^2_{fit}<\\chi^2\\leq\\chi^2_{fit}+0.1$\")\n",
- "plt.scatter(para_range_NFW_ra_p12[0],para_range_NFW_ra_p12[1],color=\"green\",alpha=0.5,label = r\"$\\chi^2_{fit}+0.1<\\chi^2\\leq\\chi^2_{fit}+0.3$\")\n",
- "plt.scatter(para_range_NFW_ra_p23[0],para_range_NFW_ra_p23[1],color=\"violet\",alpha=0.5,label = r\"$\\chi^2_{fit}+0.3<\\chi^2\\leq\\chi^2_{fit}+0.9$\")\n",
+ "#plt.scatter(para_range_NFW_ra_m[0],para_range_NFW_ra_m[1],color=\"blue\",alpha=0.5,label = r\"$\\chi^2\\leq\\chi^2_{fit}$\")\n",
+ "plt.scatter(para_range_NFW_ra_p01[0],para_range_NFW_ra_p01[1],color=\"red\",alpha=0.5,label = r\"$\\chi^2_{fit}<\\chi^2\\leq\\chi^2_{fit}+0.006$\")\n",
+ "plt.scatter(para_range_NFW_ra_p12[0],para_range_NFW_ra_p12[1],color=\"green\",alpha=0.5,label = r\"$\\chi^2_{fit}+0.006<\\chi^2\\leq\\chi^2_{fit}+0.093$\")\n",
+ "plt.scatter(para_range_NFW_ra_p23[0],para_range_NFW_ra_p23[1],color=\"violet\",alpha=0.5,label = r\"$\\chi^2_{fit}+0.093<\\chi^2\\leq\\chi^2_{fit}+0.765$\")\n",
"#plt.scatter(para_range_NFW_ra_p3p[0],para_range_NFW_ra_p3p[1],color=\"orange\",alpha=0.5,label = r\"$\\chi^2_{fit}+3<\\chi^2$\")\n",
"\n",
"plt.scatter(best_parmeters[\"M200_NFW(Msun)\"][0],best_parmeters[\"c_NFW\"][0],color=\"black\")\n",
- "plt.scatter(combs_NFW_ra[newi_min_ch2_NFW][0],combs_NFW_ra[newi_min_ch2_NFW][1], color = \"orange\",label=\"New min $\\chi^2$ \" )\n",
+ "#plt.scatter(combs_NFW_ra[newi_min_ch2_NFW][0],combs_NFW_ra[newi_min_ch2_NFW][1], color = \"orange\",label=\"New min $\\chi^2$ \" )\n",
"plt.xlabel(\"M_200\")\n",
"plt.ylabel(\"c\")\n",
- "plt.title(\"NFW\")\n",
+ "plt.title(\"rcugc3273 NFW\")\n",
"# plt.ylim(0,30)\n",
"# plt.xlim(3e10,7e10)\n",
- "plt.legend()\n"
+ "plt.legend()\n",
+ "\n",
+ "plt.savefig(\"rcugc3273RA0.pdf\")"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 64,
"metadata": {},
"outputs": [],
"source": [
"#print(chis_ISO_ra[newi_min_ch2_ISO])\n",
- "print(combs_NFW_ra[newi_min_ch2_NFW])"
+ "#print(combs_NFW_ra[newi_min_ch2_NFW])"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 49,
"metadata": {},
"outputs": [],
"source": [
@@ -1179,15 +1208,28 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 65,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 720x504 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
"source": [
"plt.figure(figsize = (10,7))\n",
- "# plt.scatter(para_range_ISO_ra_m[0],para_range_ISO_ra_m[1],color=\"blue\",alpha=0.5,label = r\"$\\chi^2\\leq\\chi^2_{fit}$\")\n",
+ "#plt.scatter(para_range_ISO_ra_m[0],para_range_ISO_ra_m[1],color=\"blue\",alpha=0.5,label = r\"$\\chi^2\\leq\\chi^2_{fit}$\")\n",
"plt.scatter(para_range_ISO_ra_p01[0],para_range_ISO_ra_p01[1],color=\"red\",alpha=0.5,label = r\"$\\chi^2_{fit}<\\chi^2\\leq\\chi^2_{fit}+0.1$\")\n",
- "plt.scatter(para_range_ISO_ra_p12[0],para_range_ISO_ra_p12[1],color=\"green\",alpha=0.5,label = r\"$\\chi^2_{fit}+0.1<\\chi^2\\leq\\chi^2_{fit}+0.3$\")\n",
- "plt.scatter(para_range_ISO_ra_p23[0],para_range_ISO_ra_p23[1],color=\"violet\",alpha=0.5,label = r\"$\\chi^2_{fit}+0.3<\\chi^2\\leq\\chi^2_{fit}+0.9$\")\n",
+ "plt.scatter(para_range_ISO_ra_p12[0],para_range_ISO_ra_p12[1],color=\"green\",alpha=0.5,label = r\"$\\chi^2_{fit}+0.006<\\chi^2\\leq\\chi^2_{fit}+0.093$\")\n",
+ "plt.scatter(para_range_ISO_ra_p23[0],para_range_ISO_ra_p23[1],color=\"violet\",alpha=0.5,label = r\"$\\chi^2_{fit}+0.093<\\chi^2\\leq\\chi^2_{fit}+0.765$\")\n",
"#plt.scatter(para_range_ISO_ra_p3p[0],para_range_ISO_ra_p3p[1],color=\"orange\",alpha=0.5,label = r\"$\\chi^2_{fit}+3<\\chi^2$\")\n",
"\n",
"\n",
@@ -1195,14 +1237,41 @@
"# plt.scatter(combs_ISO_ra[newi_min_ch2_ISO][0],combs_ISO_ra[newi_min_ch2_ISO][1], color = \"orange\",label=\"New min $\\chi^2$ \" )\n",
"plt.xlabel(\"Rc\")\n",
"plt.ylabel(\"rho0\")\n",
- "plt.title(\"ISO\")\n",
+ "plt.title(\"rcugc3273 ISO\")\n",
"\n",
- "plt.legend()"
+ "plt.legend()\n",
+ "plt.savefig(\"rcugc3273RA1.pdf\")"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 66,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "3273&19&1.71e+15&0.03&54.55&0.02&5259.97&14.27&\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(\"\\n{}&{}&{:.2e}&{:.2f}&{:.2f}&{:.2f}&{:.2f}&{:.2f}&\".format(\n",
+ "path[0].split(\"/\")[-1][:-4][5:] # saca el N de la galaxia\n",
+ ",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 / (len(df1)-2)) # 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 / (len(df1)-2))))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 52,
"metadata": {},
"outputs": [],
"source": [
@@ -1211,7 +1280,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 53,
"metadata": {},
"outputs": [],
"source": [
@@ -1244,7 +1313,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 54,
"metadata": {},
"outputs": [],
"source": [
@@ -1270,6 +1339,181 @@
"Entre mas pequeño el p-value = 1-cdf hay mas razones fuertes para rechazar la hipotesis nula, si pvalue es menor que el nivel de significancia (alpha = 0.05 usualmente ) entonces se rechaza hipotesis nula"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": 55,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Text(0, 0.5, '$\\\\log (\\\\rho_0)$ $(M_{\\\\odot} / pc^3) 10^{-3}$')"
+ ]
+ },
+ "execution_count": 55,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": "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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "rho = np.array([1.19, 0.33\n",
+ ",0.41\n",
+ ",0.10\n",
+ ",0.44\n",
+ ",0.02\n",
+ ",1.08\n",
+ ",1.81\n",
+ ",0.08\n",
+ ",0.10\n",
+ ",0.02\n",
+ ",0.23\n",
+ ",0.71\n",
+ ",6.05\n",
+ ",0.17\n",
+ ",0.05\n",
+ ",0.34\n",
+ ",0.39])\n",
+ "\n",
+ "rc = np.array([552.63\n",
+ ",1114.34\n",
+ ",948.78\n",
+ ",1903.24\n",
+ ",698.36\n",
+ ",5487.03\n",
+ ",614.26\n",
+ ",363.33\n",
+ ",2039.29\n",
+ ",1464.60\n",
+ ",5259.97\n",
+ ",1199.68\n",
+ ",595.23\n",
+ ",263.56\n",
+ ",1355.79\n",
+ ",3171.60\n",
+ ",1098.37\n",
+ ",564.17])\n",
+ "\n",
+ "plt.scatter(np.log10(rc/1000), np.log10(rho*100))\n",
+ "plt.xlabel(r\"log (R$_c$) (kpc)\")\n",
+ "plt.ylabel(r\"$\\log (\\rho_0)$ $(M_{\\odot} / pc^3) 10^{-3}$\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 56,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "M200s = np.array([2.11e+11\n",
+ ",9.57e+11\n",
+ ",3.20e+11\n",
+ ",8.43e+11\n",
+ ",4.93e+11\n",
+ ",3.17e+15\n",
+ ",8.80e+16\n",
+ ",1.06e+11\n",
+ ",5.47e+15\n",
+ ",5.92e+11\n",
+ ",1.71e+15\n",
+ ",7.42e+11\n",
+ ",1.46e+11\n",
+ ",9.75e+10\n",
+ ",1.36e+16\n",
+ ",2.86e+15\n",
+ ",4.59e+11\n",
+ ",6.07e+10])\n",
+ "\n",
+ "V200s = (M200s * 10 * G*H0)**(1/3)\n",
+ "\n",
+ "c = np.array([\n",
+ "29.26\n",
+ ",13.04\n",
+ ",19.06\n",
+ ",7.90\n",
+ ",13.06\n",
+ ",0.01\n",
+ ",0.52\n",
+ ",32.11\n",
+ ",0.07\n",
+ ",7.47\n",
+ ",0.03\n",
+ ",11.8\n",
+ ",23.74\n",
+ ",64.95\n",
+ ",0.10\n",
+ ",0.07\n",
+ ",16.19\n",
+ ",19.14])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 57,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.004300917270036279"
+ ]
+ },
+ "execution_count": 57,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "G"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 58,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Text(0.5, 0, '$\\\\log(V_{200})$ (km/s)')"
+ ]
+ },
+ "execution_count": 58,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.scatter(np.log10(c),np.log10(V200s))\n",
+ "plt.ylabel(r\"log(c)\")\n",
+ "plt.xlabel(r\"$\\log(V_{200})$ (km/s)\")\n",
+ "# plt.xlim(0.5, 2)\n",
+ "# plt.ylim(1, 2.5)"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
diff --git a/notebook/rcugc4165.pdf b/notebook/rcugc4165.pdf
deleted file mode 100644
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