diff --git a/SCoPe_classification_demo.ipynb b/SCoPe_classification_demo.ipynb
index 0b4bc63..9cd87f8 100644
--- a/SCoPe_classification_demo.ipynb
+++ b/SCoPe_classification_demo.ipynb
@@ -78,16 +78,16 @@
        "      <th>ccd</th>\n",
        "      <th>quad</th>\n",
        "      <th>...</th>\n",
-       "      <th>eb_xgb</th>\n",
        "      <th>hp_xgb</th>\n",
+       "      <th>i_xgb</th>\n",
        "      <th>blyr_xgb</th>\n",
-       "      <th>rscvn_xgb</th>\n",
-       "      <th>rrd_xgb</th>\n",
-       "      <th>mir_xgb</th>\n",
-       "      <th>rrc_xgb</th>\n",
-       "      <th>bogus_xgb</th>\n",
+       "      <th>yso_xgb</th>\n",
+       "      <th>saw_xgb</th>\n",
+       "      <th>ceph2_xgb</th>\n",
        "      <th>rrab_xgb</th>\n",
-       "      <th>agn_xgb</th>\n",
+       "      <th>sin_xgb</th>\n",
+       "      <th>osarg_xgb</th>\n",
+       "      <th>rrblz_xgb</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -109,11 +109,11 @@
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.00</td>\n",
+       "      <td>0.23</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.00</td>\n",
-       "      <td>0.01</td>\n",
-       "      <td>0.19</td>\n",
-       "      <td>0.09</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.33</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
@@ -132,12 +132,12 @@
        "      <td>0.00</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.77</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.34</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0.19</td>\n",
        "      <td>0.00</td>\n",
+       "      <td>0.23</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.33</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
@@ -157,11 +157,11 @@
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.00</td>\n",
+       "      <td>0.23</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.00</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0.19</td>\n",
-       "      <td>0.00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.33</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
@@ -176,16 +176,16 @@
        "      <td>9</td>\n",
        "      <td>4</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.00</td>\n",
+       "      <td>0.01</td>\n",
        "      <td>0.01</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.00</td>\n",
+       "      <td>0.23</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.00</td>\n",
-       "      <td>0.01</td>\n",
-       "      <td>0.20</td>\n",
-       "      <td>0.00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.33</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
@@ -205,11 +205,11 @@
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.00</td>\n",
+       "      <td>0.23</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.00</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0.19</td>\n",
-       "      <td>0.00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.33</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -248,16 +248,16 @@
        "      <td>8</td>\n",
        "      <td>4</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.00</td>\n",
+       "      <td>0.01</td>\n",
        "      <td>0.01</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.00</td>\n",
+       "      <td>0.23</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.00</td>\n",
-       "      <td>0.01</td>\n",
-       "      <td>0.19</td>\n",
-       "      <td>0.00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.33</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>96</th>\n",
@@ -273,15 +273,15 @@
        "      <td>1</td>\n",
        "      <td>...</td>\n",
        "      <td>0.01</td>\n",
-       "      <td>0.01</td>\n",
+       "      <td>0.03</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.00</td>\n",
+       "      <td>0.23</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0.03</td>\n",
-       "      <td>0.20</td>\n",
-       "      <td>0.02</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.33</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>97</th>\n",
@@ -296,16 +296,16 @@
        "      <td>9</td>\n",
        "      <td>2</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.05</td>\n",
        "      <td>0.00</td>\n",
+       "      <td>0.13</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.00</td>\n",
+       "      <td>0.02</td>\n",
+       "      <td>0.23</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.00</td>\n",
-       "      <td>0.03</td>\n",
-       "      <td>0.32</td>\n",
-       "      <td>0.00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.33</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>98</th>\n",
@@ -321,15 +321,15 @@
        "      <td>1</td>\n",
        "      <td>...</td>\n",
        "      <td>0.00</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>0.01</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.00</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.00</td>\n",
        "      <td>0.02</td>\n",
-       "      <td>0.24</td>\n",
+       "      <td>0.23</td>\n",
+       "      <td>0.0</td>\n",
        "      <td>0.00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.33</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>99</th>\n",
@@ -349,11 +349,11 @@
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.00</td>\n",
+       "      <td>0.23</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.00</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0.20</td>\n",
-       "      <td>0.00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.33</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -387,31 +387,31 @@
        "98  92000149780744640  14.978093 -13.329795    0.034946    296   15     1   \n",
        "99  88190168016806816  16.801518 -16.503011    0.366488    296   10     4   \n",
        "\n",
-       "    ...  eb_xgb  hp_xgb  blyr_xgb  rscvn_xgb  rrd_xgb  mir_xgb  rrc_xgb  \\\n",
-       "0   ...    0.00    0.00       0.0        0.0     0.00      0.0     0.00   \n",
-       "1   ...    0.00    0.00       0.0        0.0     0.77      0.0     0.34   \n",
-       "2   ...    0.00    0.00       0.0        0.0     0.00      0.0     0.00   \n",
-       "3   ...    0.00    0.01       0.0        0.0     0.00      0.0     0.00   \n",
-       "4   ...    0.00    0.00       0.0        0.0     0.00      0.0     0.00   \n",
-       "..  ...     ...     ...       ...        ...      ...      ...      ...   \n",
-       "95  ...    0.00    0.01       0.0        0.0     0.00      0.0     0.00   \n",
-       "96  ...    0.01    0.01       0.0        0.0     0.00      0.0     0.00   \n",
-       "97  ...    0.05    0.00       0.0        0.0     0.00      0.0     0.00   \n",
-       "98  ...    0.00    0.00       0.0        0.0     0.00      0.0     0.00   \n",
-       "99  ...    0.00    0.00       0.0        0.0     0.00      0.0     0.00   \n",
+       "    ...  hp_xgb  i_xgb  blyr_xgb  yso_xgb  saw_xgb  ceph2_xgb  rrab_xgb  \\\n",
+       "0   ...    0.00   0.00       0.0      0.0     0.00       0.23       0.0   \n",
+       "1   ...    0.00   0.00       0.0      0.0     0.00       0.23       0.0   \n",
+       "2   ...    0.00   0.00       0.0      0.0     0.00       0.23       0.0   \n",
+       "3   ...    0.01   0.01       0.0      0.0     0.00       0.23       0.0   \n",
+       "4   ...    0.00   0.00       0.0      0.0     0.00       0.23       0.0   \n",
+       "..  ...     ...    ...       ...      ...      ...        ...       ...   \n",
+       "95  ...    0.01   0.01       0.0      0.0     0.00       0.23       0.0   \n",
+       "96  ...    0.01   0.03       0.0      0.0     0.00       0.23       0.0   \n",
+       "97  ...    0.00   0.13       0.0      0.0     0.02       0.23       0.0   \n",
+       "98  ...    0.00   0.01       0.0      0.0     0.02       0.23       0.0   \n",
+       "99  ...    0.00   0.00       0.0      0.0     0.00       0.23       0.0   \n",
        "\n",
-       "    bogus_xgb  rrab_xgb  agn_xgb  \n",
-       "0        0.01      0.19     0.09  \n",
-       "1        0.00      0.19     0.00  \n",
-       "2        0.00      0.19     0.00  \n",
-       "3        0.01      0.20     0.00  \n",
-       "4        0.00      0.19     0.00  \n",
-       "..        ...       ...      ...  \n",
-       "95       0.01      0.19     0.00  \n",
-       "96       0.03      0.20     0.02  \n",
-       "97       0.03      0.32     0.00  \n",
-       "98       0.02      0.24     0.00  \n",
-       "99       0.00      0.20     0.00  \n",
+       "    sin_xgb  osarg_xgb  rrblz_xgb  \n",
+       "0      0.00        0.0       0.33  \n",
+       "1      0.01        0.0       0.33  \n",
+       "2      0.00        0.0       0.33  \n",
+       "3      0.00        0.0       0.33  \n",
+       "4      0.00        0.0       0.33  \n",
+       "..      ...        ...        ...  \n",
+       "95     0.00        0.0       0.33  \n",
+       "96     0.01        0.0       0.33  \n",
+       "97     0.00        0.0       0.33  \n",
+       "98     0.00        0.0       0.33  \n",
+       "99     0.00        0.0       0.33  \n",
        "\n",
        "[100 rows x 99 columns]"
       ]
@@ -471,16 +471,16 @@
        "      <th>ccd</th>\n",
        "      <th>quad</th>\n",
        "      <th>...</th>\n",
-       "      <th>eb_xgb</th>\n",
        "      <th>hp_xgb</th>\n",
+       "      <th>i_xgb</th>\n",
        "      <th>blyr_xgb</th>\n",
-       "      <th>rscvn_xgb</th>\n",
-       "      <th>rrd_xgb</th>\n",
-       "      <th>mir_xgb</th>\n",
-       "      <th>rrc_xgb</th>\n",
-       "      <th>bogus_xgb</th>\n",
+       "      <th>yso_xgb</th>\n",
+       "      <th>saw_xgb</th>\n",
+       "      <th>ceph2_xgb</th>\n",
        "      <th>rrab_xgb</th>\n",
-       "      <th>agn_xgb</th>\n",
+       "      <th>sin_xgb</th>\n",
+       "      <th>osarg_xgb</th>\n",
+       "      <th>rrblz_xgb</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -501,12 +501,12 @@
        "      <td>100.000000</td>\n",
        "      <td>100.0</td>\n",
        "      <td>100.000000</td>\n",
-       "      <td>100.0000</td>\n",
-       "      <td>100.0</td>\n",
-       "      <td>100.000000</td>\n",
+       "      <td>100.00000</td>\n",
        "      <td>100.000000</td>\n",
        "      <td>100.000000</td>\n",
        "      <td>100.000000</td>\n",
+       "      <td>100.0000</td>\n",
+       "      <td>100.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
@@ -521,16 +521,16 @@
        "      <td>9.160000</td>\n",
        "      <td>2.550000</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.001000</td>\n",
        "      <td>0.001100</td>\n",
+       "      <td>0.009100</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.000800</td>\n",
-       "      <td>0.0077</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.004100</td>\n",
-       "      <td>0.010700</td>\n",
-       "      <td>0.197500</td>\n",
-       "      <td>0.017300</td>\n",
+       "      <td>0.000400</td>\n",
+       "      <td>0.00060</td>\n",
+       "      <td>0.230300</td>\n",
+       "      <td>0.001000</td>\n",
+       "      <td>0.001700</td>\n",
+       "      <td>0.0002</td>\n",
+       "      <td>0.33</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
@@ -545,16 +545,16 @@
        "      <td>4.416955</td>\n",
        "      <td>1.085953</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.005412</td>\n",
        "      <td>0.003145</td>\n",
+       "      <td>0.036628</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.004422</td>\n",
-       "      <td>0.0770</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.034468</td>\n",
-       "      <td>0.014788</td>\n",
-       "      <td>0.021945</td>\n",
-       "      <td>0.081239</td>\n",
+       "      <td>0.003153</td>\n",
+       "      <td>0.00312</td>\n",
+       "      <td>0.001714</td>\n",
+       "      <td>0.007177</td>\n",
+       "      <td>0.004507</td>\n",
+       "      <td>0.0020</td>\n",
+       "      <td>0.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
@@ -573,12 +573,12 @@
        "      <td>0.000000</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.230000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>0.190000</td>\n",
-       "      <td>0.000000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.33</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
@@ -597,12 +597,12 @@
        "      <td>0.000000</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.000000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.230000</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>0.190000</td>\n",
        "      <td>0.000000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.33</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
@@ -621,12 +621,12 @@
        "      <td>0.000000</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.230000</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>0.010000</td>\n",
-       "      <td>0.190000</td>\n",
        "      <td>0.000000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.33</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
@@ -642,15 +642,15 @@
        "      <td>4.000000</td>\n",
        "      <td>...</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
+       "      <td>0.010000</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.230000</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>0.010000</td>\n",
-       "      <td>0.200000</td>\n",
        "      <td>0.000000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.33</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
@@ -665,16 +665,16 @@
        "      <td>16.000000</td>\n",
        "      <td>4.000000</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.050000</td>\n",
        "      <td>0.010000</td>\n",
+       "      <td>0.330000</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.040000</td>\n",
-       "      <td>0.7700</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.340000</td>\n",
-       "      <td>0.100000</td>\n",
-       "      <td>0.320000</td>\n",
-       "      <td>0.580000</td>\n",
+       "      <td>0.030000</td>\n",
+       "      <td>0.02000</td>\n",
+       "      <td>0.240000</td>\n",
+       "      <td>0.070000</td>\n",
+       "      <td>0.020000</td>\n",
+       "      <td>0.0200</td>\n",
+       "      <td>0.33</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -692,35 +692,35 @@
        "75%    1.029647e+13    2.371505e+18  1.720156e+17  8.981017e+16   17.482961   \n",
        "max    1.029663e+13    2.468292e+18  1.930213e+17  9.200015e+16   19.534744   \n",
        "\n",
-       "              dec      period  field         ccd        quad  ...      eb_xgb  \\\n",
+       "              dec      period  field         ccd        quad  ...      hp_xgb  \\\n",
        "count  100.000000  100.000000  100.0  100.000000  100.000000  ...  100.000000   \n",
-       "mean   -16.696696   20.546433  296.0    9.160000    2.550000  ...    0.001000   \n",
-       "std      2.128283  104.364753    0.0    4.416955    1.085953  ...    0.005412   \n",
+       "mean   -16.696696   20.546433  296.0    9.160000    2.550000  ...    0.001100   \n",
+       "std      2.128283  104.364753    0.0    4.416955    1.085953  ...    0.003145   \n",
        "min    -20.651207    0.021168  296.0    1.000000    1.000000  ...    0.000000   \n",
        "25%    -18.323814    0.026841  296.0    5.750000    2.000000  ...    0.000000   \n",
        "50%    -16.293143    0.039651  296.0    9.500000    2.000000  ...    0.000000   \n",
        "75%    -15.151479    0.096073  296.0   12.250000    4.000000  ...    0.000000   \n",
-       "max    -13.329795  637.168335  296.0   16.000000    4.000000  ...    0.050000   \n",
+       "max    -13.329795  637.168335  296.0   16.000000    4.000000  ...    0.010000   \n",
        "\n",
-       "           hp_xgb  blyr_xgb   rscvn_xgb   rrd_xgb  mir_xgb     rrc_xgb  \\\n",
-       "count  100.000000     100.0  100.000000  100.0000    100.0  100.000000   \n",
-       "mean     0.001100       0.0    0.000800    0.0077      0.0    0.004100   \n",
-       "std      0.003145       0.0    0.004422    0.0770      0.0    0.034468   \n",
-       "min      0.000000       0.0    0.000000    0.0000      0.0    0.000000   \n",
-       "25%      0.000000       0.0    0.000000    0.0000      0.0    0.000000   \n",
-       "50%      0.000000       0.0    0.000000    0.0000      0.0    0.000000   \n",
-       "75%      0.000000       0.0    0.000000    0.0000      0.0    0.000000   \n",
-       "max      0.010000       0.0    0.040000    0.7700      0.0    0.340000   \n",
+       "            i_xgb  blyr_xgb     yso_xgb    saw_xgb   ceph2_xgb    rrab_xgb  \\\n",
+       "count  100.000000     100.0  100.000000  100.00000  100.000000  100.000000   \n",
+       "mean     0.009100       0.0    0.000400    0.00060    0.230300    0.001000   \n",
+       "std      0.036628       0.0    0.003153    0.00312    0.001714    0.007177   \n",
+       "min      0.000000       0.0    0.000000    0.00000    0.230000    0.000000   \n",
+       "25%      0.000000       0.0    0.000000    0.00000    0.230000    0.000000   \n",
+       "50%      0.000000       0.0    0.000000    0.00000    0.230000    0.000000   \n",
+       "75%      0.010000       0.0    0.000000    0.00000    0.230000    0.000000   \n",
+       "max      0.330000       0.0    0.030000    0.02000    0.240000    0.070000   \n",
        "\n",
-       "        bogus_xgb    rrab_xgb     agn_xgb  \n",
-       "count  100.000000  100.000000  100.000000  \n",
-       "mean     0.010700    0.197500    0.017300  \n",
-       "std      0.014788    0.021945    0.081239  \n",
-       "min      0.000000    0.190000    0.000000  \n",
-       "25%      0.000000    0.190000    0.000000  \n",
-       "50%      0.010000    0.190000    0.000000  \n",
-       "75%      0.010000    0.200000    0.000000  \n",
-       "max      0.100000    0.320000    0.580000  \n",
+       "          sin_xgb  osarg_xgb  rrblz_xgb  \n",
+       "count  100.000000   100.0000     100.00  \n",
+       "mean     0.001700     0.0002       0.33  \n",
+       "std      0.004507     0.0020       0.00  \n",
+       "min      0.000000     0.0000       0.33  \n",
+       "25%      0.000000     0.0000       0.33  \n",
+       "50%      0.000000     0.0000       0.33  \n",
+       "75%      0.000000     0.0000       0.33  \n",
+       "max      0.020000     0.0200       0.33  \n",
        "\n",
        "[8 rows x 99 columns]"
       ]
@@ -762,94 +762,94 @@
        " 'ccd',\n",
        " 'quad',\n",
        " 'filter',\n",
-       " 'e_dnn',\n",
-       " 'cv_dnn',\n",
-       " 'el_dnn',\n",
-       " 'osarg_dnn',\n",
-       " 'ea_dnn',\n",
-       " 'rrblz_dnn',\n",
-       " 'ceph2_dnn',\n",
-       " 'bis_dnn',\n",
-       " 'puls_dnn',\n",
-       " 'agn_dnn',\n",
-       " 'longt_dnn',\n",
-       " 'mir_dnn',\n",
-       " 'dscu_dnn',\n",
-       " 'rrc_dnn',\n",
-       " 'eb_dnn',\n",
-       " 'wp_dnn',\n",
-       " 'rrd_dnn',\n",
-       " 'hp_dnn',\n",
-       " 'blend_dnn',\n",
-       " 'wvir_dnn',\n",
-       " 'lpv_dnn',\n",
-       " 'emsms_dnn',\n",
-       " 'i_dnn',\n",
-       " 'rrlyr_dnn',\n",
-       " 'rrab_dnn',\n",
        " 'sin_dnn',\n",
        " 'rscvn_dnn',\n",
-       " 'bright_dnn',\n",
+       " 'emsms_dnn',\n",
+       " 'ea_dnn',\n",
+       " 'rrlyr_dnn',\n",
+       " 'el_dnn',\n",
+       " 'saw_dnn',\n",
        " 'bogus_dnn',\n",
+       " 'wuma_dnn',\n",
+       " 'e_dnn',\n",
        " 'yso_dnn',\n",
-       " 'blyr_dnn',\n",
-       " 'dp_dnn',\n",
-       " 'saw_dnn',\n",
        " 'ceph_dnn',\n",
-       " 'blher_dnn',\n",
-       " 'ext_dnn',\n",
-       " 'wuma_dnn',\n",
-       " 'ew_dnn',\n",
+       " 'cv_dnn',\n",
        " 'srv_dnn',\n",
        " 'fla_dnn',\n",
+       " 'blyr_dnn',\n",
+       " 'wp_dnn',\n",
+       " 'eb_dnn',\n",
+       " 'blher_dnn',\n",
+       " 'hp_dnn',\n",
+       " 'ext_dnn',\n",
+       " 'dip_dnn',\n",
+       " 'rrab_dnn',\n",
        " 'vnv_dnn',\n",
        " 'pnp_dnn',\n",
-       " 'dip_dnn',\n",
+       " 'wvir_dnn',\n",
+       " 'dp_dnn',\n",
+       " 'bright_dnn',\n",
+       " 'ceph2_dnn',\n",
+       " 'dscu_dnn',\n",
+       " 'bis_dnn',\n",
+       " 'osarg_dnn',\n",
+       " 'rrblz_dnn',\n",
+       " 'i_dnn',\n",
+       " 'puls_dnn',\n",
        " 'mp_dnn',\n",
-       " 'saw_xgb',\n",
-       " 'dp_xgb',\n",
-       " 'i_xgb',\n",
-       " 'longt_xgb',\n",
-       " 'yso_xgb',\n",
+       " 'rrc_dnn',\n",
+       " 'mir_dnn',\n",
+       " 'agn_dnn',\n",
+       " 'longt_dnn',\n",
+       " 'blend_dnn',\n",
+       " 'ew_dnn',\n",
+       " 'lpv_dnn',\n",
+       " 'rrd_dnn',\n",
+       " 'rrd_xgb',\n",
        " 'wvir_xgb',\n",
-       " 'puls_xgb',\n",
-       " 'sin_xgb',\n",
+       " 'dp_xgb',\n",
+       " 'blher_xgb',\n",
+       " 'lpv_xgb',\n",
+       " 'agn_xgb',\n",
        " 'dscu_xgb',\n",
-       " 'blend_xgb',\n",
-       " 'dip_xgb',\n",
+       " 'puls_xgb',\n",
+       " 'rrc_xgb',\n",
+       " 'mir_xgb',\n",
+       " 'e_xgb',\n",
+       " 'rscvn_xgb',\n",
+       " 'rrlyr_xgb',\n",
+       " 'bis_xgb',\n",
+       " 'emsms_xgb',\n",
        " 'mp_xgb',\n",
-       " 'pnp_xgb',\n",
-       " 'vnv_xgb',\n",
-       " 'rrblz_xgb',\n",
        " 'ew_xgb',\n",
-       " 'osarg_xgb',\n",
-       " 'fla_xgb',\n",
-       " 'srv_xgb',\n",
-       " 'ext_xgb',\n",
-       " 'ceph2_xgb',\n",
-       " 'bis_xgb',\n",
-       " 'blher_xgb',\n",
-       " 'e_xgb',\n",
+       " 'bogus_xgb',\n",
        " 'ea_xgb',\n",
+       " 'vnv_xgb',\n",
+       " 'pnp_xgb',\n",
+       " 'longt_xgb',\n",
+       " 'dip_xgb',\n",
+       " 'cv_xgb',\n",
+       " 'blend_xgb',\n",
+       " 'ext_xgb',\n",
        " 'wuma_xgb',\n",
-       " 'ceph_xgb',\n",
-       " 'el_xgb',\n",
+       " 'srv_xgb',\n",
+       " 'fla_xgb',\n",
        " 'bright_xgb',\n",
-       " 'cv_xgb',\n",
-       " 'lpv_xgb',\n",
-       " 'emsms_xgb',\n",
-       " 'rrlyr_xgb',\n",
-       " 'wp_xgb',\n",
+       " 'el_xgb',\n",
+       " 'ceph_xgb',\n",
        " 'eb_xgb',\n",
+       " 'wp_xgb',\n",
        " 'hp_xgb',\n",
+       " 'i_xgb',\n",
        " 'blyr_xgb',\n",
-       " 'rscvn_xgb',\n",
-       " 'rrd_xgb',\n",
-       " 'mir_xgb',\n",
-       " 'rrc_xgb',\n",
-       " 'bogus_xgb',\n",
+       " 'yso_xgb',\n",
+       " 'saw_xgb',\n",
+       " 'ceph2_xgb',\n",
        " 'rrab_xgb',\n",
-       " 'agn_xgb']"
+       " 'sin_xgb',\n",
+       " 'osarg_xgb',\n",
+       " 'rrblz_xgb']"
       ]
      },
      "execution_count": 5,
@@ -966,50 +966,50 @@
     {
      "data": {
       "text/plain": [
-       "['e_dnn',\n",
-       " 'cv_dnn',\n",
-       " 'el_dnn',\n",
-       " 'osarg_dnn',\n",
-       " 'ea_dnn',\n",
-       " 'rrblz_dnn',\n",
-       " 'ceph2_dnn',\n",
-       " 'bis_dnn',\n",
-       " 'puls_dnn',\n",
-       " 'agn_dnn',\n",
-       " 'longt_dnn',\n",
-       " 'mir_dnn',\n",
-       " 'dscu_dnn',\n",
-       " 'rrc_dnn',\n",
-       " 'eb_dnn',\n",
-       " 'wp_dnn',\n",
-       " 'rrd_dnn',\n",
-       " 'hp_dnn',\n",
-       " 'blend_dnn',\n",
-       " 'wvir_dnn',\n",
-       " 'lpv_dnn',\n",
+       "['sin_dnn',\n",
+       " 'rscvn_dnn',\n",
        " 'emsms_dnn',\n",
-       " 'i_dnn',\n",
+       " 'ea_dnn',\n",
        " 'rrlyr_dnn',\n",
-       " 'rrab_dnn',\n",
-       " 'sin_dnn',\n",
-       " 'rscvn_dnn',\n",
-       " 'bright_dnn',\n",
+       " 'el_dnn',\n",
+       " 'saw_dnn',\n",
        " 'bogus_dnn',\n",
+       " 'wuma_dnn',\n",
+       " 'e_dnn',\n",
        " 'yso_dnn',\n",
-       " 'blyr_dnn',\n",
-       " 'dp_dnn',\n",
-       " 'saw_dnn',\n",
        " 'ceph_dnn',\n",
-       " 'blher_dnn',\n",
-       " 'ext_dnn',\n",
-       " 'wuma_dnn',\n",
-       " 'ew_dnn',\n",
+       " 'cv_dnn',\n",
        " 'srv_dnn',\n",
        " 'fla_dnn',\n",
+       " 'blyr_dnn',\n",
+       " 'wp_dnn',\n",
+       " 'eb_dnn',\n",
+       " 'blher_dnn',\n",
+       " 'hp_dnn',\n",
+       " 'ext_dnn',\n",
+       " 'dip_dnn',\n",
+       " 'rrab_dnn',\n",
        " 'vnv_dnn',\n",
        " 'pnp_dnn',\n",
-       " 'dip_dnn',\n",
-       " 'mp_dnn']"
+       " 'wvir_dnn',\n",
+       " 'dp_dnn',\n",
+       " 'bright_dnn',\n",
+       " 'ceph2_dnn',\n",
+       " 'dscu_dnn',\n",
+       " 'bis_dnn',\n",
+       " 'osarg_dnn',\n",
+       " 'rrblz_dnn',\n",
+       " 'i_dnn',\n",
+       " 'puls_dnn',\n",
+       " 'mp_dnn',\n",
+       " 'rrc_dnn',\n",
+       " 'mir_dnn',\n",
+       " 'agn_dnn',\n",
+       " 'longt_dnn',\n",
+       " 'blend_dnn',\n",
+       " 'ew_dnn',\n",
+       " 'lpv_dnn',\n",
+       " 'rrd_dnn']"
       ]
      },
      "execution_count": 9,
@@ -1038,50 +1038,50 @@
     {
      "data": {
       "text/plain": [
-       "['saw_xgb',\n",
-       " 'dp_xgb',\n",
-       " 'i_xgb',\n",
-       " 'longt_xgb',\n",
-       " 'yso_xgb',\n",
+       "['rrd_xgb',\n",
        " 'wvir_xgb',\n",
-       " 'puls_xgb',\n",
-       " 'sin_xgb',\n",
+       " 'dp_xgb',\n",
+       " 'blher_xgb',\n",
+       " 'lpv_xgb',\n",
+       " 'agn_xgb',\n",
        " 'dscu_xgb',\n",
-       " 'blend_xgb',\n",
-       " 'dip_xgb',\n",
+       " 'puls_xgb',\n",
+       " 'rrc_xgb',\n",
+       " 'mir_xgb',\n",
+       " 'e_xgb',\n",
+       " 'rscvn_xgb',\n",
+       " 'rrlyr_xgb',\n",
+       " 'bis_xgb',\n",
+       " 'emsms_xgb',\n",
        " 'mp_xgb',\n",
-       " 'pnp_xgb',\n",
-       " 'vnv_xgb',\n",
-       " 'rrblz_xgb',\n",
        " 'ew_xgb',\n",
-       " 'osarg_xgb',\n",
-       " 'fla_xgb',\n",
-       " 'srv_xgb',\n",
-       " 'ext_xgb',\n",
-       " 'ceph2_xgb',\n",
-       " 'bis_xgb',\n",
-       " 'blher_xgb',\n",
-       " 'e_xgb',\n",
+       " 'bogus_xgb',\n",
        " 'ea_xgb',\n",
+       " 'vnv_xgb',\n",
+       " 'pnp_xgb',\n",
+       " 'longt_xgb',\n",
+       " 'dip_xgb',\n",
+       " 'cv_xgb',\n",
+       " 'blend_xgb',\n",
+       " 'ext_xgb',\n",
        " 'wuma_xgb',\n",
-       " 'ceph_xgb',\n",
-       " 'el_xgb',\n",
+       " 'srv_xgb',\n",
+       " 'fla_xgb',\n",
        " 'bright_xgb',\n",
-       " 'cv_xgb',\n",
-       " 'lpv_xgb',\n",
-       " 'emsms_xgb',\n",
-       " 'rrlyr_xgb',\n",
-       " 'wp_xgb',\n",
+       " 'el_xgb',\n",
+       " 'ceph_xgb',\n",
        " 'eb_xgb',\n",
+       " 'wp_xgb',\n",
        " 'hp_xgb',\n",
+       " 'i_xgb',\n",
        " 'blyr_xgb',\n",
-       " 'rscvn_xgb',\n",
-       " 'rrd_xgb',\n",
-       " 'mir_xgb',\n",
-       " 'rrc_xgb',\n",
-       " 'bogus_xgb',\n",
+       " 'yso_xgb',\n",
+       " 'saw_xgb',\n",
+       " 'ceph2_xgb',\n",
        " 'rrab_xgb',\n",
-       " 'agn_xgb']"
+       " 'sin_xgb',\n",
+       " 'osarg_xgb',\n",
+       " 'rrblz_xgb']"
       ]
      },
      "execution_count": 10,
@@ -1110,7 +1110,7 @@
     {
      "data": {
       "text/plain": [
-       "'\\nimport random\\nfield_296 = pd.read_csv(\"field_296.csv\")\\nrandom = field_296.sample(n=100, frac=None, replace=False, weights=None, random_state=1, axis=None, ignore_index=False)\\nrandom.to_csv(\\'field_296_100rows.csv\\', index=False)\\n'"
+       "'\\nfield_296 = pd.read_csv(\"preds_dnn_xgb/field_296.csv\")\\nrandom = field_296.sample(n=100, frac=None, replace=False, weights=None, random_state=1, axis=None, ignore_index=False)\\nrandom.to_csv(\\'field_296_100rows.csv\\', index=False)\\n'"
       ]
      },
      "execution_count": 11,
@@ -1120,8 +1120,7 @@
    ],
    "source": [
     "\"\"\"\n",
-    "import random\n",
-    "field_296 = pd.read_csv(\"field_296.csv\")\n",
+    "field_296 = pd.read_csv(\"preds_dnn_xgb/field_296.csv\")\n",
     "random = field_296.sample(n=100, frac=None, replace=False, weights=None, random_state=1, axis=None, ignore_index=False)\n",
     "random.to_csv('field_296_100rows.csv', index=False)\n",
     "\"\"\""
@@ -1143,7 +1142,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1189,7 +1188,7 @@
     },
     {
      "data": {
-      "image/png": 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F5hEFWVlZGjlypNq0aaPGjRtbp/ft21fVq1dXYGCg9u/fr3HjxikpKUkrVqzIcTszZszQtGnTCis2AABwIYeLzMmTJ2WxWFS1alVJ0s6dO7V48WI1bNhQzz33XL6DREZG6scff9QPP/xgM/2P22zSpIkCAgLUsWNHHTt2TLVr1862nQkTJmj06NHW92lpaQoKCsp3LgAAUHQ5fGqpb9++Wr9+vSQpJSVFjz76qHbu3KmJEyfq1VdfzVeI4cOHa82aNVq/fr21IOWmVatWkqSjR4/mON/Ly0tly5a1eQEAgOLJ4SLz448/qmXLlpKkZcuWqXHjxtq6datiY2MVExPj0LYMw9Dw4cO1cuVKff/996pZs+Yd10lISJAkBQQEOBodAAAUMw6fWsrIyJCXl5ck6bvvvlO3bt0kSfXr19eZM2cc2lZkZKQWL16sf//73/Lx8VFKSookydfXVyVLltSxY8e0ePFidenSRRUrVtT+/fs1atQoPfzww2ratKmj0QEAQDHj8IhMo0aN9NFHH2nz5s369ttv1blzZ0nS6dOnVbFiRYe2NXfuXF26dEnt2rVTQECA9bV06VJJkqenp7777js99thjql+/vsaMGaPevXvrq6++cjQ2AAAohhwekZk5c6Z69uypt956SwMHDtS9994rSVq9erX1lJO9DMPIc35QUJA2btzoaEQAAPAX4XCRadeunX777TelpaWpfPny1unPPfecSpUq5dRwAAAAecnXDfEMw9CePXv08ccf6/Lly5JunQaiyAAAgMLk8IjML7/8os6dO+vEiRNKT0/Xo48+Kh8fH82cOVPp6en66KOPCiInAABANg6PyIwYMUItWrRQamqqSpYsaZ3es2dPxcfHOzUcAABAXhwekdm8ebO2bt0qT09Pm+k1atTQ//73P6cFAwAAuBOHR2SysrKUmZmZbfqpU6fk4+PjlFAAAAD2cLjIPPbYY5o9e7b1vcVi0ZUrVzRlyhR16dLFmdkAAADy5PCppbfffluhoaFq2LChrl+/rr59++rIkSOqVKmSPv/884LICAAAkCOHi0zVqlW1b98+LVmyRPv379eVK1cUERGhfv362Vz8CwAAUNAcLjLXr1+Xt7e3nn766YLIAwAAYDeHr5GpUqWKBg4cqG+//VZZWVkFkQkAAMAuDheZhQsX6tq1a+revbvuuecejRw5Urt37y6IbAAAAHlyuMj07NlTy5cv19mzZ/XGG2/o4MGDevDBB1WvXj29+uqrBZERAAAgR/l61pIk+fj4aPDgwfrmm2+0f/9+lS5dWtOmTXNmNgAAgDzlu8hcv35dy5YtU48ePXT//ffrwoULevnll52ZDQAAIE8Of2tp3bp1Wrx4sVatWiV3d3f97W9/0zfffKOHH364IPIBAADkyuEi07NnT3Xt2lWLFi1Sly5d5OHhURC5AAAA7sihInPz5k3NnDlTTz75pPz9/QsqEwAAgF0cukbG3d1d48aNU3p6ekHlAQAAsJvDF/u2bNlSe/fuLYgsAAAADnH4Gplhw4ZpzJgxOnXqlJo3b67SpUvbzG/atKnTwgEAAOTF4SLTp08fSdJLL71knWaxWGQYhiwWizIzM52XDgAAIA8OF5nk5OSCyAEAAOAwh4tM9erVCyIHAACAwxwuMosWLcpz/oABA/IdBgAAwBEOF5kRI0bYvM/IyNC1a9fk6empUqVKUWQAAEChcfjr16mpqTavK1euKCkpSQ899JA+//zzgsgIAACQo3w/NPKP6tatq6ioqGyjNQAAAAXJKUVGunXX39OnTztrcwAAAHfk8DUyq1evtnlvGIbOnDmjDz74QG3atHFaMAAAgDtxuMj06NHD5r3FYlHlypXVoUMHvf32287KBQAAcEcOF5msrKyCyAEAAOAwh66RSUtLy7HIZGVlKS0tzWmhAAAA7GF3kVm5cqVatGih69evZ5v3+++/64EHHtBXX33l1HAAAAB5sbvIzJ07V2PHjlWpUqWyzStdurTGjRunDz74wKnhAAAA8mJ3kfnxxx/Vrl27XOc//PDDOnDggDMyAQAA2MXui31TU1N18+bNXOdnZGQoNTXVKaFQwBaHuzqB4/oudXUCAEARZPeITI0aNbR79+5c5+/evZsnYwMAgEJld5Hp1auXJk6cqLNnz2abl5KSoldeeUW9e/d2ajgAAIC82H1qafz48fr3v/+tunXr6umnn1ZwcLAk6dChQ4qNjVVQUJDGjx9fYEEBAAD+zO4i4+Pjoy1btmjChAlaunSp9XqYcuXK6emnn9b06dPl4+NTYEEBAAD+zKE7+/r6+urDDz9UdHS0fvvtNxmGocqVK8tisRRUPgAAgFw5/IgC6f+erwQAAOBKDj2iwNlmzJihBx54QD4+PqpSpYp69OihpKQkm2WuX7+uyMhIVaxYUWXKlFHv3r1zvOAYAAD89bi0yGzcuFGRkZHavn27vv32W2VkZOixxx7T1atXrcuMGjVKX331lZYvX66NGzfq9OnT6tWrlwtTAwCAoiJfp5acJS4uzuZ9TEyMqlSpoj179ujhhx/WpUuXNH/+fC1evFgdOnSQJC1YsEANGjTQ9u3b9eCDD2bbZnp6utLT063veZglAADFl8MjMosWLbIpCrfduHFDixYtuqswly5dkiRVqFBBkrRnzx5lZGSoU6dO1mXq16+vatWqadu2bTluY8aMGfL19bW+goKC7ioTAAAouhwuMoMHD7YWjj+6fPmyBg8enO8gWVlZGjlypNq0aaPGjRtLunWjPU9PT5UrV85mWT8/P6WkpOS4nQkTJujSpUvW18mTJ/OdCQAAFG0On1oyDCPHr1ufOnVKvr6++Q4SGRmpH3/8UT/88EO+tyFJXl5e8vLyuqttAAAAc7C7yNx3332yWCyyWCzq2LGj3N3/b9XMzEwlJyerc+fO+QoxfPhwrVmzRps2bVLVqlWt0/39/XXjxg1dvHjRZlTm7Nmz8vf3z9e+AABA8WF3kenRo4ckKSEhQaGhoSpTpox1nqenp2rUqOHws5YMw9CLL76olStXasOGDapZs6bN/ObNm8vDw0Px8fHWbSclJenEiRMKCQlxaF8AAKD4sbvITJkyRdKtp2CHh4fL29v7rnceGRmpxYsX69///rd8fHys1734+vqqZMmS8vX1VUREhEaPHq0KFSqobNmyevHFFxUSEpLjN5YAAMBfi8PXyAwcOFDSrW8pnTt3TllZWTbzq1WrZve25s6dK0lq166dzfQFCxZo0KBBkqR3331Xbm5u6t27t9LT0xUaGqoPP/zQ0dgAAKAYcrjIHDlyRM8884y2bt1qM/32RcCZmZl2b8swjDsu4+3trejoaEVHRzsaFQAAFHMOF5lBgwbJ3d1da9asUUBAAA+MBAAALuNwkUlISNCePXtUv379gsgDAABgN4dviNewYUP99ttvBZEFAADAIXYVmbS0NOtr5syZGjt2rDZs2KDz58/bzOO5RgAAoDDZdWqpXLlyNtfCGIahjh072iyTn4t9AQAA7oZdRWb9+vUFnQMAAMBhdhWZRx55pKBzAAAAOMzhby3t378/x+kWi0Xe3t6qVq0aD20EAACFwuEi06xZszzvHePh4aHw8HB9/PHHTnmMAQAAQG4c/vr1ypUrVbduXc2bN08JCQlKSEjQvHnzFBwcrMWLF2v+/Pn6/vvv9corrxREXgAAACuHR2SmT5+u9957T6GhodZpTZo0UdWqVTVp0iTt3LlTpUuX1pgxYzRr1iynhgUAAPgjh0dkDhw4oOrVq2ebXr16dR04cEDSrdNPZ86cuft0AAAAeXC4yNSvX19RUVG6ceOGdVpGRoaioqKsjy343//+Jz8/P+elBAAAyIHDp5aio6PVrVs3Va1aVU2bNpV0a5QmMzNTa9askSQdP35cw4YNc25SAACAP3G4yLRu3VrJycmKjY3V4cOHJUlPPvmk+vbtKx8fH0lS//79nZsSAAAgBw4XGUny8fHR0KFDnZ0FAADAIXYVmdWrVyssLEweHh5avXp1nst269bNKcEAAADuxK4i06NHD6WkpKhKlSrq0aNHrsvx0EgAAFCY7CoyWVlZOf4MAADgSg5//To3p06d0nPPPeeszQEAANyR04rM+fPnNX/+fGdtDgAA4I6cVmQAAAAKG0UGAACYFkUGAACYlt03xOvVq1ee8y9evHi3WQAAABxid5Hx9fW94/wBAwbcdSAAAAB72V1kFixYUJA5AAAAHMY1MgAAwLQoMgAAwLQoMgAAwLQoMgAAwLTsKjL333+/UlNTJUmvvvqqrl27VqChAAAA7GFXkUlMTNTVq1clSdOmTdOVK1cKNBQAAIA97Pr6dbNmzTR48GA99NBDMgxDs2bNUpkyZXJcdvLkyU4NCAAAkBu7ikxMTIymTJmiNWvWyGKx6D//+Y/c3bOvarFYKDIAAKDQ2FVkgoODtWTJEkmSm5ub4uPjVaVKlQINBgAAcCd239n3tqysrILIAQAA4DCHi4wkHTt2TLNnz1ZiYqIkqWHDhhoxYoRq167t1HAAAAB5cfg+MuvWrVPDhg21c+dONW3aVE2bNtWOHTvUqFEjffvttwWREQAAIEcOj8iMHz9eo0aNUlRUVLbp48aN06OPPuq0cAAAAHlxeEQmMTFRERER2aY/88wzOnjwoFNCAQAA2MPhIlO5cmUlJCRkm56QkMA3mQAAQKFyuMgMGTJEzz33nGbOnKnNmzdr8+bNioqK0vPPP68hQ4Y4tK1Nmzapa9euCgwMlMVi0apVq2zmDxo0SBaLxebVuXNnRyMDAIBiyuFrZCZNmiQfHx+9/fbbmjBhgiQpMDBQU6dO1UsvveTQtq5evap7771XzzzzjHr16pXjMp07d9aCBQus7728vByNDAAAiimHi4zFYtGoUaM0atQoXb58WZLk4+OTr52HhYUpLCwsz2W8vLzk7++fr+0DAIDizeFTS3/k4+OT7xJjrw0bNqhKlSoKDg7WCy+8oPPnz+e5fHp6utLS0mxeAACgeLqrIlPQOnfurEWLFik+Pl4zZ87Uxo0bFRYWpszMzFzXmTFjhnx9fa2voKCgQkwMAAAKU77u7FtY+vTpY/25SZMmatq0qWrXrq0NGzaoY8eOOa4zYcIEjR492vo+LS2NMgMAQDFVpEdk/qxWrVqqVKmSjh49musyXl5eKlu2rM0LAAAUTw4VmYyMDHXs2FFHjhwpqDx5OnXqlM6fP6+AgACX7B8AABQtDp1a8vDw0P79+5228ytXrtiMriQnJyshIUEVKlRQhQoVNG3aNPXu3Vv+/v46duyYxo4dqzp16ig0NNRpGQAAgHk5fGrp6aef1vz5852y8927d+u+++7TfffdJ0kaPXq07rvvPk2ePFklSpTQ/v371a1bN9WrV08RERFq3ry5Nm/ezL1kAACApHxc7Hvz5k19+umn+u6779S8eXOVLl3aZv4777xj97batWsnwzBynb9u3TpH4wEAgL8Qh4vMjz/+qPvvv1+SdPjwYZt5FovFOakAAADs4HCRWb9+fUHkAAAAcFi+v3599OhRrVu3Tr///rsk5XmKCAAAoCA4PCJz/vx5PfXUU1q/fr0sFouOHDmiWrVqKSIiQuXLl9fbb79dEDkBACj2/uXxlqsj5MPjLt27wyMyo0aNkoeHh06cOKFSpUpZp4eHhysuLs6p4QAAAPLi8IjMN998o3Xr1qlq1ao20+vWratffvnFacEAAADuxOERmatXr9qMxNx24cIF7u8CAAAKlcNFpm3btlq0aJH1vcViUVZWlt588021b9/eqeEAAADy4vCppTfffFMdO3bU7t27dePGDY0dO1Y//fSTLly4oC1bthRERgAAgBw5PCLTuHFjHT58WA899JC6d++uq1evqlevXtq7d69q165dEBkBAABy5PCIjCT5+vpq4sSJzs4CAADgkHwVmdTUVM2fP1+JiYmSpIYNG2rw4MGqUKGCU8MBAADkxeFTS5s2bVKNGjU0Z84cpaamKjU1VXPmzFHNmjW1adOmgsgIAACQI4dHZCIjIxUeHq65c+eqRIkSkqTMzEwNGzZMkZGROnDggNNDAgAA5MThEZmjR49qzJgx1hIjSSVKlNDo0aN19OhRp4YDAADIi8NF5v7777deG/NHiYmJuvfee50SCgAAwB52nVrav3+/9eeXXnpJI0aM0NGjR/Xggw9KkrZv367o6GhFRUUVTEoAAIAc2FVkmjVrJovFIsMwrNPGjh2bbbm+ffsqPDzceekAAADyYFeRSU5OLugcAAAADrOryFSvXr2gcwAAADgsXzfEO336tH744QedO3dOWVlZNvNeeuklpwQDAAC4E4eLTExMjJ5//nl5enqqYsWKslgs1nkWi4UiAwAACo3DRWbSpEmaPHmyJkyYIDc3h7+9DQAA4DQON5Fr166pT58+lBgAAOByDreRiIgILV++vCCyAAAAOMThU0szZszQE088obi4ODVp0kQeHh4289955x2nhQMAAMhLvorMunXrFBwcLEnZLvYFAAAoLA4XmbfffluffvqpBg0aVABxAAAA7OfwNTJeXl5q06ZNQWQBAABwiMNFZsSIEXr//fcLIgsAAIBDHD61tHPnTn3//fdas2aNGjVqlO1i3xUrVjgtHAAAQF4cLjLlypVTr169CiILAACAQxwuMgsWLCiIHAAAAA7j9rwAAMC0HB6RqVmzZp73izl+/PhdBQIAALCXw0Vm5MiRNu8zMjK0d+9excXF6eWXX3ZWLhSg7xLPujqCwzq5OgAAoEhyuMiMGDEix+nR0dHavXv3XQcCAACwl9OukQkLC9OXX37prM0BAADckdOKzBdffKEKFSo4a3MAAAB35PCppfvuu8/mYl/DMJSSkqJff/1VH374oVPDAQAA5MXhItOjRw+b925ubqpcubLatWun+vXrOysXAADAHTlcZKZMmVIQOQAAABzm0hvibdq0SV27dlVgYKAsFotWrVplM98wDE2ePFkBAQEqWbKkOnXqpCNHjrgmLAAAKHLsLjJubm4qUaJEni93d8cGeK5evap7771X0dHROc5/8803NWfOHH300UfasWOHSpcurdDQUF2/ft2h/QAAgOLJ7uaxcuXKXOdt27ZNc+bMUVZWlkM7DwsLU1hYWI7zDMPQ7Nmz9corr6h79+6SpEWLFsnPz0+rVq1Snz59HNoXAAAofuwuMrfLxB8lJSVp/Pjx+uqrr9SvXz+9+uqrTguWnJyslJQUder0f/d09fX1VatWrbRt27Zci0x6errS09Ot79PS0pyWCQAAFC35ukbm9OnTGjJkiJo0aaKbN28qISFBCxcuVPXq1Z0WLCUlRZLk5+dnM93Pz886LyczZsyQr6+v9RUUFOS0TAAAoGhxqMhcunRJ48aNU506dfTTTz8pPj5eX331lRo3blxQ+Rw2YcIEXbp0yfo6efKkqyMBAIACYveppTfffFMzZ86Uv7+/Pv/88xxPNTmTv7+/JOns2bMKCAiwTj979qyaNWuW63peXl7y8vIq0GwAAKBosLvIjB8/XiVLllSdOnW0cOFCLVy4MMflVqxY4ZRgNWvWlL+/v+Lj463FJS0tTTt27NALL7zglH0AAABzs7vIDBgwwObRBM5w5coVHT161Po+OTlZCQkJqlChgqpVq6aRI0fq9ddfV926dVWzZk1NmjRJgYGB2e4uDAAA/prsLjIxMTFO3/nu3bvVvn176/vRo0dLkgYOHKiYmBiNHTtWV69e1XPPPaeLFy/qoYceUlxcnLy9vZ2eBQAAmI/Djyhwpnbt2skwjFznWywWvfrqq079WjcAACg+XPqIAgAAgLtBkQEAAKZFkQEAAKZFkQEAAKZFkQEAAKZFkQEAAKZFkQEAAKZFkQEAAKZFkQEAAKZFkQEAAKZFkQEAAKZFkQEAAKZFkQEAAKZFkQEAAKbl7uoAgF0Wh7s6geP6LnV1AgAo9hiRAQAApkWRAQAApkWRAQAApkWRAQAApkWRAQAApkWRAQAApkWRAQAApkWRAQAApkWRAQAApkWRAQAApsUjCoCCwmMVCkWN8V+7OoLDfo563NURgGKDERkAAGBaFBkAAGBaFBkAAGBaFBkAAGBaFBkAAGBaFBkAAGBaFBkAAGBaFBkAAGBaFBkAAGBaFBkAAGBaPKIAgKn9y+MtV0fIBx5RUBjM+PiKf3m4OoH5MCIDAABMiyIDAABMiyIDAABMiyIDAABMiyIDAABMiyIDAABMq0gXmalTp8pisdi86tev7+pYAACgiCjy95Fp1KiRvvvuO+t7d/ciHxkAABSSIt8K3N3d5e/vb/fy6enpSk9Pt75PS0sriFgAAKAIKPJF5siRIwoMDJS3t7dCQkI0Y8YMVatWLdflZ8yYoWnTphViQqD44E6oyNXicFcncNi/PM66OgIKQZG+RqZVq1aKiYlRXFyc5s6dq+TkZLVt21aXL1/OdZ0JEybo0qVL1tfJkycLMTEAAChMRXpEJiwszPpz06ZN1apVK1WvXl3Lli1TREREjut4eXnJy8ursCICAAAXKtIjMn9Wrlw51atXT0ePHnV1FAAAUASYqshcuXJFx44dU0BAgKujAACAIqBIF5l//OMf2rhxo37++Wdt3bpVPXv2VIkSJfT3v//d1dEAAEARUKSvkTl16pT+/ve/6/z586pcubIeeughbd++XZUrV3Z1NAAAUAQU6SKzZMkSV0cAAABFWJE+tQQAAJAXigwAADAtigwAADCtIn2NDHDbd4ncahwAkB0jMgAAwLQoMgAAwLQoMgAAwLQoMgAAwLQoMgAAwLQoMgAAwLQoMgAAwLQoMgAAwLQoMgAAwLQoMgAAwLR4RAEAq395vOXqCH8J301q5+oIDuvUwM/VEYAcMSIDAABMiyIDAABMiyIDAABMiyIDAABMiyIDAABMiyIDAABMiyIDAABMiyIDAABMiyIDAABMiyIDAABMiyIDAABMiyIDAABMiyIDAABMiyIDAABMiyIDAABMiyIDAABMiyIDAABMiyIDAABMiyIDAABMiyIDAABMy93VAQAARd93iWddHQHIESMyAADAtCgyAADAtCgyAADAtCgyAADAtCgyAADAtCgyAADAtExRZKKjo1WjRg15e3urVatW2rlzp6sjAQCAIqDIF5mlS5dq9OjRmjJliv773//q3nvvVWhoqM6dO+fqaAAAwMWKfJF55513NGTIEA0ePFgNGzbURx99pFKlSunTTz91dTQAAOBiRfrOvjdu3NCePXs0YcIE6zQ3Nzd16tRJ27Zty3Gd9PR0paenW99funRJkpSWlub0fFfTbzp9mwAAmElB/H3943YNw8hzuSJdZH777TdlZmbKz8/PZrqfn58OHTqU4zozZszQtGnTsk0PCgoqkIwAAPylveVboJu/fPmyfH1z30eRLjL5MWHCBI0ePdr6PisrSxcuXFDFihVlsVictp+0tDQFBQXp5MmTKlu2rNO2i+w41oWD41w4OM6Fg+NcOAryOBuGocuXLyswMDDP5Yp0kalUqZJKlCihs2dtH1Z29uxZ+fv757iOl5eXvLy8bKaVK1euoCKqbNmy/I+kkHCsCwfHuXBwnAsHx7lwFNRxzmsk5rYifbGvp6enmjdvrvj4eOu0rKwsxcfHKyQkxIXJAABAUVCkR2QkafTo0Ro4cKBatGihli1bavbs2bp69aoGDx7s6mgAAMDFinyRCQ8P16+//qrJkycrJSVFzZo1U1xcXLYLgAubl5eXpkyZku00FpyPY104OM6Fg+NcODjOhaMoHGeLcafvNQEAABRRRfoaGQAAgLxQZAAAgGlRZAAAgGlRZAAAgGlRZPIQHR2tGjVqyNvbW61atdLOnTvzXH758uWqX7++vL291aRJE61du7aQkpqfI8f6k08+Udu2bVW+fHmVL19enTp1uuPvBrc4+m/6tiVLlshisahHjx4FG7CYcPQ4X7x4UZGRkQoICJCXl5fq1avHfz/s4Ohxnj17toKDg1WyZEkFBQVp1KhRun79eiGlNadNmzapa9euCgwMlMVi0apVq+64zoYNG3T//ffLy8tLderUUUxMTMGGNJCjJUuWGJ6ensann35q/PTTT8aQIUOMcuXKGWfPns1x+S1bthglSpQw3nzzTePgwYPGK6+8Ynh4eBgHDhwo5OTm4+ix7tu3rxEdHW3s3bvXSExMNAYNGmT4+voap06dKuTk5uLocb4tOTnZuOeee4y2bdsa3bt3L5ywJubocU5PTzdatGhhdOnSxfjhhx+M5ORkY8OGDUZCQkIhJzcXR49zbGys4eXlZcTGxhrJycnGunXrjICAAGPUqFGFnNxc1q5da0ycONFYsWKFIclYuXJlnssfP37cKFWqlDF69Gjj4MGDxvvvv2+UKFHCiIuLK7CMFJlctGzZ0oiMjLS+z8zMNAIDA40ZM2bkuPxTTz1lPP744zbTWrVqZTz//PMFmrM4cPRY/9nNmzcNHx8fY+HChQUVsVjIz3G+efOm0bp1a+Nf//qXMXDgQIqMHRw9znPnzjVq1apl3Lhxo7AiFguOHufIyEijQ4cONtNGjx5ttGnTpkBzFif2FJmxY8cajRo1spkWHh5uhIaGFlguTi3l4MaNG9qzZ486depknebm5qZOnTpp27ZtOa6zbds2m+UlKTQ0NNflcUt+jvWfXbt2TRkZGapQoUJBxTS9/B7nV199VVWqVFFERERhxDS9/Bzn1atXKyQkRJGRkfLz81Pjxo31xhtvKDMzs7Bim05+jnPr1q21Z88e6+mn48ePa+3aterSpUuhZP6rcMXfwiJ/Z19X+O2335SZmZnt7sF+fn46dOhQjuukpKTkuHxKSkqB5SwO8nOs/2zcuHEKDAzM9j8e/J/8HOcffvhB8+fPV0JCQiEkLB7yc5yPHz+u77//Xv369dPatWt19OhRDRs2TBkZGZoyZUphxDad/Bznvn376rffftNDDz0kwzB08+ZNDR06VP/85z8LI/JfRm5/C9PS0vT777+rZMmSTt8nIzIwtaioKC1ZskQrV66Ut7e3q+MUG5cvX1b//v31ySefqFKlSq6OU6xlZWWpSpUqmjdvnpo3b67w8HBNnDhRH330kaujFSsbNmzQG2+8oQ8//FD//e9/tWLFCn399dd67bXXXB0Nd4kRmRxUqlRJJUqU0NmzZ22mnz17Vv7+/jmu4+/v79DyuCU/x/q2WbNmKSoqSt99952aNm1akDFNz9HjfOzYMf3888/q2rWrdVpWVpYkyd3dXUlJSapdu3bBhjah/Px7DggIkIeHh0qUKGGd1qBBA6WkpOjGjRvy9PQs0MxmlJ/jPGnSJPXv31/PPvusJKlJkya6evWqnnvuOU2cOFFubvz/emfI7W9h2bJlC2Q0RmJEJkeenp5q3ry54uPjrdOysrIUHx+vkJCQHNcJCQmxWV6Svv3221yXxy35OdaS9Oabb+q1115TXFycWrRoURhRTc3R41y/fn0dOHBACQkJ1le3bt3Uvn17JSQkKCgoqDDjm0Z+/j23adNGR48etRZFSTp8+LACAgIoMbnIz3G+du1atrJyuzwaPHLQaVzyt7DALiM2uSVLlhheXl5GTEyMcfDgQeO5554zypUrZ6SkpBiGYRj9+/c3xo8fb11+y5Ythru7uzFr1iwjMTHRmDJlCl+/tpOjxzoqKsrw9PQ0vvjiC+PMmTPW1+XLl131EUzB0eP8Z3xryT6OHucTJ04YPj4+xvDhw42kpCRjzZo1RpUqVYzXX3/dVR/BFBw9zlOmTDF8fHyMzz//3Dh+/LjxzTffGLVr1zaeeuopV30EU7h8+bKxd+9eY+/evYYk45133jH27t1r/PLLL4ZhGMb48eON/v37W5e//fXrl19+2UhMTDSio6P5+rUrvf/++0a1atUMT09Po2XLlsb27dut8x555BFj4MCBNssvW7bMqFevnuHp6Wk0atTI+Prrrws5sXk5cqyrV69uSMr2mjJlSuEHNxlH/03/EUXGfo4e561btxqtWrUyvLy8jFq1ahnTp083bt68WcipzceR45yRkWFMnTrVqF27tuHt7W0EBQUZw4YNM1JTUws/uImsX78+x//e3j62AwcONB555JFs6zRr1szw9PQ0atWqZSxYsKBAM1oMgzE1AABgTlwjAwAATIsiAwAATIsiAwAATIsiAwAATIsiAwAATIsiAwAATIsiAwAATIsiAwAATIsiAxRRFotFq1atKvD9bNiwQRaLRRcvXrROW7VqlerUqaMSJUpo5MiRiomJUbly5Qo8S7t27TRy5MgC309B+Pnnn2WxWJSQkHBX2xk0aJB69OiR5zJ/Pk41atTQ7Nmzre8L698OUBRQZAAXSElJ0YsvvqhatWrJy8tLQUFB6tq1a7aHrRWG1q1b68yZM/L19bVOe/755/W3v/1NJ0+e1Guvvabw8HAdPnzYafvMqTxJ0ooVK/Taa685bT85uV04br8qVqyoxx57THv37i3Q/TrTnY7TmTNnFBYWJsl5BQsoqtxdHQD4q/n555/Vpk0blStXTm+99ZaaNGmijIwMrVu3TpGRkTp06FCh5vH09JS/v7/1/ZUrV3Tu3DmFhoYqMDDQOr1kyZIFnqVChQoFvo/bvvvuOzVq1EinTp3SSy+9pLCwMB06dCjHkaeMjAx5eHgUWrY7udNx+uPvEyjuGJEBCtmwYcNksVi0c+dO9e7dW/Xq1VOjRo00evRobd++Pdf1xo0bp3r16qlUqVKqVauWJk2apIyMDOv8ffv2qX379vLx8VHZsmXVvHlz7d69W5L0yy+/qGvXripfvrxKly6tRo0aae3atZJsR0c2bNggHx8fSVKHDh1ksVi0YcOGHE8tffXVV3rggQfk7e2tSpUqqWfPntZ5n332mVq0aCEfHx/5+/urb9++OnfunKRbRa59+/aSpPLly8tisWjQoEGSsp8ySU1N1YABA1S+fHmVKlVKYWFhOnLkiHX+7Vzr1q1TgwYNVKZMGXXu3Flnzpy54++hYsWK8vf3V4sWLTRr1iydPXtWO3bssI5gLF26VI888oi8vb0VGxurrKwsvfrqq6pataq8vLzUrFkzxcXFZdvuoUOH1Lp1a3l7e6tx48bauHGjdV5mZqYiIiJUs2ZNlSxZUsHBwXrvvfdyzDdt2jRVrlxZZcuW1dChQ3Xjxg3rvDudgvvjqaWaNWtKku677z5ZLBa1a9dOmzZtkoeHh1JSUmzWGzlypNq2bXvHYwcUJRQZoBBduHBBcXFxioyMVOnSpbPNz+s6FB8fH8XExOjgwYN677339Mknn+jdd9+1zu/Xr5+qVq2qXbt2ac+ePRo/frx1FCEyMlLp6enatGmTDhw4oJkzZ6pMmTLZ9tG6dWslJSVJkr788kudOXNGrVu3zrbc119/rZ49e6pLly7au3ev4uPj1bJlS+v8jIwMvfbaa9q3b59WrVqln3/+2VpWgoKC9OWXX0qSkpKSdObMmVz/mA8aNEi7d+/W6tWrtW3bNhmGoS5dutgUuGvXrmnWrFn67LPPtGnTJp04cUL/+Mc/cj2OObk92vTHsjB+/HiNGDFCiYmJCg0N1Xvvvae3335bs2bN0v79+xUaGqpu3brZFCtJevnllzVmzBjt3btXISEh6tq1q86fPy9JysrKUtWqVbV8+XIdPHhQkydP1j//+U8tW7bMZhvx8fFKTEzUhg0b9Pnnn2vFihWaNm2aQ5/ptp07d0q6NQJ15swZrVixQg8//LBq1aqlzz77zLpcRkaGYmNj9cwzz+RrP4DLFOiztQHY2LFjhyHJWLFixR2XlWSsXLky1/lvvfWW0bx5c+t7Hx8fIyYmJsdlmzRpYkydOjXHeevXrzckGampqYZhGEZqaqohyVi/fr11mQULFhi+vr7W9yEhIUa/fv3u+Blu27VrlyHJuHz5co77vO2RRx4xRowYYRiGYRw+fNiQZGzZssU6/7fffjNKlixpLFu2zJpLknH06FHrMtHR0Yafn1+uWZKTkw1Jxt69e62ft2fPnkaZMmWMlJQU6/zZs2fbrBcYGGhMnz7dZtoDDzxgDBs2zGa7UVFR1vkZGRlG1apVjZkzZ+aaJzIy0ujdu7f1/cCBA40KFSoYV69etU6bO3euUaZMGSMzMzPbcTIMw6hevbrx7rvvWt//8d/Onz/vbTNnzjQaNGhgff/ll18aZcqUMa5cuZJrVqAoYkQGKESGYeR73aVLl6pNmzby9/dXmTJl9Morr+jEiRPW+aNHj9azzz6rTp06KSoqSseOHbPOe+mll/T666+rTZs2mjJlivbv339XnyMhIUEdO3bMdf6ePXvUtWtXVatWTT4+PnrkkUckySbvnSQmJsrd3V2tWrWyTqtYsaKCg4OVmJhonVaqVCnVrl3b+j4gIMB6GisvrVu3VpkyZVS+fHnt27dPS5culZ+fn3V+ixYtrD+npaXp9OnTatOmjc022rRpY5NFkkJCQqw/u7u7q0WLFjbLREdHq3nz5qpcubLKlCmjefPmZTsu9957r0qVKmWzzStXrujkyZN3/Fz2GjRokI4ePWo9nRkTE6Onnnoqx5FCoCijyACFqG7durJYLA5f0Ltt2zb169dPXbp00Zo1a7R3715NnDjR5lTI1KlT9dNPP+nxxx/X999/r4YNG2rlypWSpGeffVbHjx9X//79deDAAbVo0ULvv/9+vj9HXhf+Xr16VaGhoSpbtqxiY2O1a9cua44/5nWWP1+Ea7FY7CqMS5cu1b59+5Samqpjx46pS5cuNvML4g/6kiVL9I9//EMRERH65ptvlJCQoMGDBxfIcbmTKlWqqGvXrlqwYIHOnj2r//znP5xWgilRZIBCVKFCBYWGhio6OlpXr17NNv/PX0e+bevWrapevbomTpyoFi1aqG7duvrll1+yLVevXj2NGjVK33zzjXr16qUFCxZY5wUFBWno0KFasWKFxowZo08++STfn6Np06a5flX80KFDOn/+vKKiotS2bVvVr18/2wiJp6enpFsXv+amQYMGunnzpnbs2GGddv78eSUlJalhw4b5zn5bUFCQateubdf9ccqWLavAwEBt2bLFZvqWLVuyZfnjBds3b97Unj171KBBA+vyrVu31rBhw3TfffepTp06NiNnt+3bt0+///67zTbLlCmjoKAgRz6ipLyP9bPPPqulS5dq3rx5ql27drYRJ8AMKDJAIYuOjlZmZqZatmypL7/8UkeOHFFiYqLmzJljc1rij+rWrasTJ05oyZIlOnbsmObMmWMd5ZCk33//XcOHD9eGDRv0yy+/aMuWLdq1a5f1D+jIkSO1bt06JScn67///a/Wr19vnZcfU6ZM0eeff64pU6YoMTHRegGxJFWrVk2enp56//33dfz4ca1evTrbPU+qV68ui8WiNWvW6Ndff9WVK1dy/Mzdu3fXkCFD9MMPP2jfvn16+umndc8996h79+75zp5fL7/8smbOnKmlS5cqKSlJ48ePV0JCgkaMGGGzXHR0tFauXKlDhw4pMjJSqamp1pGOunXravfu3Vq3bp0OHz6sSZMmadeuXdn2dePGDUVEROjgwYNau3atpkyZouHDh8vNzfH/ZFepUkUlS5ZUXFyczp49q0uXLlnn3R45e/311zV48GCHtw0UBRQZoJDVqlVL//3vf9W+fXuNGTNGjRs31qOPPqr4+HjNnTs3x3W6deumUaNGafjw4WrWrJm2bt2qSZMmWeeXKFFC58+f14ABA1SvXj099dRTCgsLs37TJTMzU5GRkWrQoIE6d+6sevXq6cMPP8z3Z2jXrp2WL1+u1atXq1mzZurQoYP12zGVK1dWTEyMli9froYNGyoqKkqzZs2yWf+ee+7RtGnTNH78ePn5+Wn48OE57mfBggVq3ry5nnjiCYWEhMgwDK1du9Yl93R56aWXNHr0aI0ZM0ZNmjRRXFycVq9erbp169osFxUVpaioKN1777364YcftHr1alWqVEnSrRsN9urVS+Hh4WrVqpXOnz+vYcOGZdtXx44dVbduXT388MMKDw9Xt27dNHXq1Hzldnd315w5c/Txxx8rMDDQpgS6ublp0KBByszM1IABA/K1fcDVLMbdXH0IADC1iIgI/frrr1q9erWrowD5wp19AeAv6NKlSzpw4IAWL15MiYGpUWQA4C+oe/fu2rlzp4YOHapHH33U1XGAfOPUEgAAMC0u9gUAAKZFkQEAAKZFkQEAAKZFkQEAAKZFkQEAAKZFkQEAAKZFkQEAAKZFkQEAAKb1/wH7kfODIlQ+xgAAAABJRU5ErkJggg==",
+      "image/png": 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AYFoUGQAAYFoUGQAAYFoUGQAAYFoUGQAAYFoUGQAAYFoUGQAAYFoUGQAAYFoUGQAAYFoUGQAAYFoUGQAAYFoUGQAAYFoUGQAAYFoUGQAAYFoUGQAAYFoUGQAAYFoUGQAAYFoUGQAAYFoUGQAAYFoUGQAAYFoUGQAAYFoUGQAAYFoUGQAAYFoUGQAAYFoUGQAAYFoUGQAAYFoUGQAAYFoUGQAAYFoUGQAAYFoUGQAAYFoUGQAAYFoUGQAAYFoUGQAAYFpuLTLz5s1TTEyMgoODFRwcrNjYWH366afW+ZcvX1Z8fLyqVq2qwMBA9e3bV1lZWW5MDAAAPIlbi0zNmjWVmJio1NRU7d69W507d1bPnj31/fffS5LGjh2rTz75REuXLtXmzZt18uRJ9enTx52RAQCAB7EYhmG4O8QfhYaG6uWXX9YDDzyg6tWra9GiRXrggQckSQcPHlSTJk20bds23X777XZtLycnRyEhIcrOzlZwcLBTs9aZuNqp2ysvPybe6+4IAACUyN7f3x5zjUx+fr4WL16sixcvKjY2VqmpqcrLy1PXrl2tyzRu3Fi1atXStm3bit1Obm6ucnJybD4AAODG5PYis2/fPgUGBsrPz08jRozQ8uXL1bRpU2VmZsrX11eVK1e2WT4sLEyZmZnFbm/mzJkKCQmxfqKiolz8EwAAAHdxe5GJjo5WWlqaduzYoSeeeEJDhgzR/v37y7y9hIQEZWdnWz8nTpxwYloAAOBJvN0dwNfXVw0aNJAktWrVSrt27dJrr72mfv366cqVKzp37pzNqExWVpbCw8OL3Z6fn5/8/PxcHRsAAHgAt4/I/FlBQYFyc3PVqlUr+fj4aP369dZ56enpOn78uGJjY92YEAAAeAq3jsgkJCSoW7duqlWrls6fP69FixZp06ZNWrdunUJCQjR8+HCNGzdOoaGhCg4O1pNPPqnY2Fi771gCAAA3NoeLzIkTJ2SxWFSzZk1J0s6dO7Vo0SI1bdpUjz32mEPbOnXqlAYPHqyMjAyFhIQoJiZG69at01133SVJevXVV+Xl5aW+ffsqNzdXcXFxeuONNxyNDAAAblAOP0emffv2euyxxzRo0CBlZmYqOjpazZo106FDh/Tkk0/q+eefd1XWMuE5MoXxHBkAgKdz2XNkvvvuO912222SpA8//FDNmzfX119/rZSUFCUnJ5c5MAAAgKMcLjJ5eXnWu4K++OIL3X///ZJ+f1hdRkaGc9MBAACUwOEi06xZM7355pv68ssv9fnnn+uee+6RJJ08eVJVq1Z1ekAAAIDiOFxkZs2apfnz56tjx47661//qptvvlmStHLlSuspJwAAgPLg8F1LHTt21K+//qqcnBxVqVLFOv2xxx5TQECAU8MBAACUpEwPxDMMQ6mpqZo/f77Onz8v6fcn9FJkAABAeXJ4ROann37SPffco+PHjys3N1d33XWXgoKCNGvWLOXm5urNN990RU4AAIBCHB6RGT16tFq3bq2zZ8+qYsWK1um9e/e2eZ0AAACAqzk8IvPll1/q66+/lq+vr830OnXq6L///a/TggEAAJTG4RGZgoIC5efnF5r+888/KygoyCmhAAAA7OFwkbn77rs1Z84c63eLxaILFy5oypQp6t69uzOzAQAAlMjhU0uvvPKK4uLi1LRpU12+fFkDBgzQoUOHVK1aNX3wwQeuyOix/u3zsrsjlBHvWgIA3BgcLjI1a9bUt99+q8WLF2vv3r26cOGChg8froEDB9pc/AsAAOBqDheZy5cvy9/fXw8//LAr8gAAANjN4WtkatSooSFDhujzzz9XQUGBKzIBAADYxeEis3DhQl26dEk9e/bUTTfdpDFjxmj37t2uyAYAAFAih4tM7969tXTpUmVlZekf//iH9u/fr9tvv12NGjXS9OnTXZERAACgSGV615IkBQUFadiwYfrss8+0d+9eVapUSdOmTXNmNgAAgBKVuchcvnxZH374oXr16qVbbrlFZ86c0dNPP+3MbAAAACVy+K6ldevWadGiRVqxYoW8vb31wAMP6LPPPlOHDh1ckQ8AAKBYDheZ3r17q0ePHnrvvffUvXt3+fj4uCIXAABAqRwqMlevXtWsWbP04IMPKjw83FWZAAAA7OLQNTLe3t6aMGGCcnNzXZUHAADAbg5f7Hvbbbdpz549rsgCAADgEIevkRk5cqTGjx+vn3/+Wa1atVKlSpVs5sfExDgtHAAAQEkcLjL9+/eXJD311FPWaRaLRYZhyGKxKD8/33npAAAASuBwkTl27JgrcgAAADjM4SJTu3ZtV+QAAABwmMNF5r333itx/uDBg8scBgAAwBEOF5nRo0fbfM/Ly9OlS5fk6+urgIAAigwAACg3Dt9+ffbsWZvPhQsXlJ6erjvuuEMffPCBKzICAAAUqcwvjfyjhg0bKjExsdBoDQAAgCs5pchIvz/19+TJk87aHAAAQKkcvkZm5cqVNt8Nw1BGRob+9a9/qV27dk4LBgAAUBqHi0yvXr1svlssFlWvXl2dO3fWK6+84tC2Zs6cqWXLlungwYOqWLGi2rZtq1mzZik6Otq6TMeOHbV582ab9R5//HG9+eabjkYHAAA3GIeLTEFBgdN2vnnzZsXHx+vWW2/V1atX9eyzz+ruu+/W/v37bV598Oijj2r69OnW7wEBAU7LAAAAzMuhIpOTk6PAwEB5edleWlNQUKALFy4oODjYoZ2vXbvW5ntycrJq1Kih1NRUdejQwTo9ICBA4eHhDm0bAADc+Oy+2Hf58uVq3bq1Ll++XGjeb7/9pltvvVWffPLJdYXJzs6WJIWGhtpMT0lJUbVq1dS8eXMlJCTo0qVLxW4jNzdXOTk5Nh8AAHBjsrvIzJs3T88880yRp3UqVaqkCRMm6F//+leZgxQUFGjMmDFq166dmjdvbp0+YMAA/ec//9HGjRuVkJCg999/Xw8//HCx25k5c6ZCQkKsn6ioqDJnAgAAns1iGIZhz4KRkZHasmWLGjRoUOT8w4cPq0OHDmW+BfuJJ57Qp59+qq+++ko1a9YsdrkNGzaoS5cuOnz4sOrXr19ofm5urnJzc63fc3JyFBUVpezsbIdPfZXmi8kdnbq98tJ1xiZ3RwAAoEQ5OTkKCQkp9fe33dfInD17VlevXi12fl5ens6ePetYyv/fqFGjtGrVKm3ZsqXEEiNJbdq0kaRii4yfn5/8/PzKlAMAAJiL3aeW6tSpo927dxc7f/fu3Q6/GdswDI0aNUrLly/Xhg0bVLdu3VLXSUtLkyRFREQ4tC8AAHDjsbvI9OnTR5MmTVJWVlaheZmZmXruuefUt29fh3YeHx+v//znP1q0aJGCgoKUmZmpzMxM/fbbb5KkI0eOaMaMGUpNTdWPP/6olStXavDgwerQoYNiYmIc2hcAALjx2H2NzPnz5xUbG6vjx4/r4Ycftj607uDBg0pJSVFUVJS2b9+uoKAg+3dusRQ5fcGCBRo6dKhOnDihhx9+WN99950uXryoqKgo9e7dW88995zd17vYe46tLLhGBgAA13D6NTJBQUHaunWrEhIStGTJEuv1MJUrV9bDDz+sF1980aESI/1+aqkkUVFRhZ7qCwAAcI1DD8QLCQnRG2+8oaSkJP36668yDEPVq1cvdmQFAADAlRx+RYH0f+9XAgAAcCe7L/YFAADwNBQZAABgWhQZAABgWg4Xmffee8/mFQDXXLlyRe+9955TQgEAANjD4SIzbNgw61uq/+j8+fMaNmyYU0IBAADYw+EiYxhGkbdb//zzzwoJCXFKKAAAAHvYffv1X/7yF1ksFlksFnXp0kXe3v+3an5+vo4dO6Z77rnHJSEBAACKYneR6dWrl6TfX9oYFxenwMBA6zxfX1/VqVPH4XctAQAAXA+7i8yUKVMk/f4W7H79+snf399loQAAAOzh8JN9hwwZIun3u5ROnTqlgoICm/m1atVyTjIAAIBSOFxkDh06pEceeURff/21zfRrFwHn5+c7LRwAAEBJHC4yQ4cOlbe3t1atWqWIiAheGAkAANzG4SKTlpam1NRUNW7c2BV5AAAA7Obwc2SaNm2qX3/91RVZAAAAHGJXkcnJybF+Zs2apWeeeUabNm3S6dOnbebl5OS4Oi8AAICVXaeWKleubHMtjGEY6tKli80yXOwLAADKm11FZuPGja7OAQAA4DC7isydd97p6hwAAAAOc/iupb179xY53WKxyN/fX7Vq1ZKfn991BwMAACiNw0WmZcuWJT47xsfHR/369dP8+fN5jQEAAHAph2+/Xr58uRo2bKi33npLaWlpSktL01tvvaXo6GgtWrRI77zzjjZs2KDnnnvOFXkBAACsHB6RefHFF/Xaa68pLi7OOq1FixaqWbOmJk+erJ07d6pSpUoaP368Zs+e7dSwAAAAf+TwiMy+fftUu3btQtNr166tffv2Sfr99FNGRsb1pwMAACiBw0WmcePGSkxM1JUrV6zT8vLylJiYaH1twX//+1+FhYU5LyUAAEARHD61lJSUpPvvv181a9ZUTEyMpN9HafLz87Vq1SpJ0tGjRzVy5EjnJgUAAPgTh4tM27ZtdezYMaWkpOiHH36QJD344IMaMGCAgoKCJEmDBg1ybkoAAIAiOFxkJCkoKEgjRoxwdhYAAACH2FVkVq5cqW7dusnHx0crV64scdn777/fKcHgQov6uTuB4wYscXcCAIAHsqvI9OrVS5mZmapRo4Z69epV7HK8NBIAAJQnu4pMQUFBkX8GAABwJ4dvvy7Ozz//rMcee8xZmwMAACiV04rM6dOn9c477zhrcwAAAKVyWpEBAAAob24tMjNnztStt96qoKAg64XE6enpNstcvnxZ8fHxqlq1qgIDA9W3b19lZWW5KTEAAPAkbi0ymzdvVnx8vLZv367PP/9ceXl5uvvuu3Xx4kXrMmPHjtUnn3yipUuXavPmzTp58qT69OnjxtQAAMBT2P1AvNLKw7lz5xze+dq1a22+Jycnq0aNGkpNTVWHDh2UnZ2td955R4sWLVLnzp0lSQsWLFCTJk20fft23X777YW2mZubq9zcXOv3nJwch3MBAABzsLvIhISElDp/8ODB1xUmOztbkhQaGipJSk1NVV5enrp27WpdpnHjxqpVq5a2bdtWZJGZOXOmpk2bdl05AACAOdhdZBYsWODKHCooKNCYMWPUrl07NW/eXJKUmZkpX19fVa5c2WbZsLAwZWZmFrmdhIQEjRs3zvo9JydHUVFRLssNAADcp0zvWnKF+Ph4fffdd/rqq6+uazt+fn7y8/NzUioAAODJPOL261GjRmnVqlXauHGjatasaZ0eHh6uK1euFLr+JisrS+Hh4eWcEgAAeBq3FhnDMDRq1CgtX75cGzZsUN26dW3mt2rVSj4+Plq/fr11Wnp6uo4fP67Y2NjyjgsAADyMW08txcfHa9GiRfp//+//KSgoyHrdS0hIiCpWrKiQkBANHz5c48aNU2hoqIKDg/Xkk08qNja2yAt9AQDA/xa7RmRuueUWnT17VpI0ffp0Xbp0ySk7nzdvnrKzs9WxY0dFRERYP0uWLLEu8+qrr+q+++5T37591aFDB4WHh2vZsmVO2T8AADA3i2EYRmkLVaxYUYcOHVLNmjVVoUIFZWRkqEaNGuWR77rl5OQoJCRE2dnZCg4Oduq2v5jc0anbKy9dm4S5O4LjBiwpfRkAwA3D3t/fdp1aatmypYYNG6Y77rhDhmFo9uzZCgwMLHLZ559/vmyJAQAAHGRXkUlOTtaUKVO0atUqWSwWffrpp/L2LryqxWKhyAAAUEZ1Jq52dwSH/Zh4r1v3b1eRiY6O1uLFiyVJXl5eWr9+vWlOLQEAgBuXw3ctFRQUuCIHAACAw8p0+/WRI0c0Z84cHThwQJLUtGlTjR49WvXr13dqOAAAgJI4/EC8devWqWnTptq5c6diYmIUExOjHTt2qFmzZvr8889dkREAAKBIDo/ITJw4UWPHjlViYmKh6RMmTNBdd93ltHAAAAAlcXhE5sCBAxo+fHih6Y888oj279/vlFAAAAD2cLjIVK9eXWlpaYWmp6WlcScTAAAoVw6fWnr00Uf12GOP6ejRo2rbtq0kaevWrZo1a5bGjRvn9IAAAADFcbjITJ48WUFBQXrllVeUkJAgSYqMjNTUqVP11FNPOT0gAABAcRwuMhaLRWPHjtXYsWN1/vx5SVJQUJDTgwEAAJSmTM+RuYYCAwAA3Mnhi30BAAA8BUUGAACYFkUGAACYlkNFJi8vT126dNGhQ4dclQcAAMBuDhUZHx8f7d2711VZAAAAHOLwqaWHH35Y77zzjiuyAAAAOMTh26+vXr2qd999V1988YVatWqlSpUq2cz/5z//6bRwAAAAJXG4yHz33Xe65ZZbJEk//PCDzTyLxeKcVAAAAHZwuMhs3LjRFTkAAAAcVubbrw8fPqx169bpt99+kyQZhuG0UAAAAPZwuMicPn1aXbp0UaNGjdS9e3dlZGRIkoYPH67x48c7PSAAAEBxHC4yY8eOlY+Pj44fP66AgADr9H79+mnt2rVODQcAAFASh6+R+eyzz7Ru3TrVrFnTZnrDhg31008/OS0YAABAaRwekbl48aLNSMw1Z86ckZ+fn1NCAQAA2MPhItO+fXu999571u8Wi0UFBQV66aWX1KlTJ6eGAwAAKInDp5ZeeukldenSRbt379aVK1f0zDPP6Pvvv9eZM2e0detWV2QEAAAoksMjMs2bN9cPP/ygO+64Qz179tTFixfVp08f7dmzR/Xr13dFRgAAgCI5PCIjSSEhIZo0aZKzswAAADikTEXm7Nmzeuedd3TgwAFJUtOmTTVs2DCFhoY6NRwAAEBJHD61tGXLFtWpU0dz587V2bNndfbsWc2dO1d169bVli1bXJERAACgSA6PyMTHx6tfv36aN2+eKlSoIEnKz8/XyJEjFR8fr3379jk9JAAAQFEcHpE5fPiwxo8fby0xklShQgWNGzdOhw8fdmhbW7ZsUY8ePRQZGSmLxaIVK1bYzB86dKgsFovN55577nE0MgAAuEE5XGRuueUW67Uxf3TgwAHdfPPNDm3r4sWLuvnmm5WUlFTsMvfcc48yMjKsnw8++MDRyAAA4AZl16mlvXv3Wv/81FNPafTo0Tp8+LBuv/12SdL27duVlJSkxMREh3berVs3devWrcRl/Pz8FB4ebvc2c3NzlZuba/2ek5PjUCYAAGAedhWZli1bymKxyDAM67Rnnnmm0HIDBgxQv379nJdO0qZNm1SjRg1VqVJFnTt31gsvvKCqVasWu/zMmTM1bdo0p2YAAACeya4ic+zYMVfnKNI999yjPn36qG7dujpy5IieffZZdevWTdu2bbO5RuePEhISNG7cOOv3nJwcRUVFlVdkAABQjuwqMrVr13Z1jiL179/f+ucWLVooJiZG9evX16ZNm9SlS5ci1/Hz8+PllQAA/I8o0wPxTp48qa+++kqnTp1SQUGBzbynnnrKKcGKUq9ePVWrVk2HDx8utsgAAID/HQ4XmeTkZD3++OPy9fVV1apVZbFYrPMsFotLi8zPP/+s06dPKyIiwmX7AAAA5uFwkZk8ebKef/55JSQkyMvL4bu3bVy4cMHm2TPHjh1TWlqaQkNDFRoaqmnTpqlv374KDw/XkSNH9Mwzz6hBgwaKi4u7rv0CAIAbg8NF5tKlS+rfv/91lxhJ2r17tzp16mT9fu0i3SFDhmjevHnau3evFi5cqHPnzikyMlJ33323ZsyYwTUwAABAUhmKzPDhw7V06VJNnDjxunfesWNHm1u6/2zdunXXvQ8AAHDjcrjIzJw5U/fdd5/Wrl2rFi1ayMfHx2b+P//5T6eFAwAAKEmZisy6desUHR0tSYUu9oXn++JAlrsjOKyruwMAQDn4t8/L7o5QBve6de8OF5lXXnlF7777roYOHeqCOAAAAPZz+IpdPz8/tWvXzhVZAAAAHOJwkRk9erRef/11V2QBAABwiMOnlnbu3KkNGzZo1apVatasWaGLfZctW+a0cAAAACVxuMhUrlxZffr0cUUWAAAAhzhcZBYsWOCKHAAAAA67/sfzAgAAuInDIzJ169Yt8XkxR48eva5AAAAA9nK4yIwZM8bme15envbs2aO1a9fq6aefdlYuAACAUjlcZEaPHl3k9KSkJO3evfu6AwEAANjLadfIdOvWTR9//LGzNgcAAFAqpxWZjz76SKGhoc7aHAAAQKkcPrX0l7/8xeZiX8MwlJmZqV9++UVvvPGGU8MBAACUxOEi06tXL5vvXl5eql69ujp27KjGjRs7KxcAAECpHC4yU6ZMcUUOAAAAh/FAPAAAYFp2j8h4eXmV+CA8SbJYLLp69ep1hwIAALCH3UVm+fLlxc7btm2b5s6dq4KCAqeEAgAAsIfdRaZnz56FpqWnp2vixIn65JNPNHDgQE2fPt2p4QAAAEpSpmtkTp48qUcffVQtWrTQ1atXlZaWpoULF6p27drOzgcAAFAsh4pMdna2JkyYoAYNGuj777/X+vXr9cknn6h58+auygcAAFAsu08tvfTSS5o1a5bCw8P1wQcfFHmqCQAAoDzZXWQmTpyoihUrqkGDBlq4cKEWLlxY5HLLli1zWjgAAICS2F1kBg8eXOrt1wAAAOXJ7iKTnJzswhgAAACO48m+AADAtCgyAADAtCgyAADAtCgyAADAtCgyAADAtCgyAADAtCgyAADAtNxaZLZs2aIePXooMjJSFotFK1assJlvGIaef/55RUREqGLFiuratasOHTrknrAAAMDjuLXIXLx4UTfffLOSkpKKnP/SSy9p7ty5evPNN7Vjxw5VqlRJcXFxunz5cjknBQAAnsjuJ/u6Qrdu3dStW7ci5xmGoTlz5ui5556zvqDyvffeU1hYmFasWKH+/fsXuV5ubq5yc3Ot33NycpwfHAAAeASPvUbm2LFjyszMVNeuXa3TQkJC1KZNG23btq3Y9WbOnKmQkBDrJyoqqjziAgAAN/DYIpOZmSlJCgsLs5keFhZmnVeUhIQEZWdnWz8nTpxwaU4AAOA+bj215Ap+fn7y8/NzdwwAAFAOPHZEJjw8XJKUlZVlMz0rK8s6DwAA/G/z2CJTt25dhYeHa/369dZpOTk52rFjh2JjY92YDAAAeAq3nlq6cOGCDh8+bP1+7NgxpaWlKTQ0VLVq1dKYMWP0wgsvqGHDhqpbt64mT56syMhI9erVy32hAQCAx3Brkdm9e7c6depk/T5u3DhJ0pAhQ5ScnKxnnnlGFy9e1GOPPaZz587pjjvu0Nq1a+Xv7++uyAAAwIO4tch07NhRhmEUO99isWj69OmaPn16OaYCAABm4bHXyAAAAJSGIgMAAEyLIgMAAEyLIgMAAEyLIgMAAEyLIgMAAEyLIgMAAEyLIgMAAEyLIgMAAEyLIgMAAEyLIgMAAEzLre9aAuz1xeSO7o7gsK4zNrk7AgDc8BiRAQAApkWRAQAApkWRAQAApkWRAQAApkWRAQAApkWRAQAApkWRAQAApkWRAQAApsUD8QBXWdTP3QkcVmfvYHdHcNiPife6OwIAN2JEBgAAmBZFBgAAmBZFBgAAmBZFBgAAmBZFBgAAmBZFBgAAmBZFBgAAmBZFBgAAmBYPxANgbiZ88KAGLHF3AuCGwYgMAAAwLYoMAAAwLYoMAAAwLYoMAAAwLY8uMlOnTpXFYrH5NG7c2N2xAACAh/D4u5aaNWumL774wvrd29vjIwMAgHLi8a3A29tb4eHh7o4BAAA8kEefWpKkQ4cOKTIyUvXq1dPAgQN1/PjxEpfPzc1VTk6OzQcAANyYPHpEpk2bNkpOTlZ0dLQyMjI0bdo0tW/fXt99952CgoKKXGfmzJmaNm1aOScFgBucGR88iP8JHj0i061bNz344IOKiYlRXFyc1qxZo3PnzunDDz8sdp2EhARlZ2dbPydOnCjHxAAAoDx59IjMn1WuXFmNGjXS4cOHi13Gz89Pfn5+5ZgKAAC4i0ePyPzZhQsXdOTIEUVERLg7CgAA8AAeXWT+/ve/a/Pmzfrxxx/19ddfq3fv3qpQoYL++te/ujsaAADwAB59aunnn3/WX//6V50+fVrVq1fXHXfcoe3bt6t69erujgYAADyARxeZxYsXuzsCAADwYB59agkAAKAkFBkAAGBaHn1qCQBuRHUmrnZ3BIf9GOPuBEDRGJEBAACmRZEBAACmRZEBAACmRZEBAACmRZEBAACmRZEBAACmRZEBAACmRZEBAACmxQPxABf54kCWuyM47N8+L7s7gsO+OODuBI4z43GWwtwdACgSIzIAAMC0KDIAAMC0KDIAAMC0KDIAAMC0KDIAAMC0KDIAAMC0KDIAAMC0KDIAAMC0eCAeAOCGZMaHUsJxjMgAAADTosgAAADTosgAAADTosgAAADTosgAAADTosgAAADTosgAAADTosgAAADT4oF4AIBS8XA5eCpGZAAAgGlRZAAAgGlRZAAAgGlRZAAAgGmZosgkJSWpTp068vf3V5s2bbRz5053RwIAAB7A44vMkiVLNG7cOE2ZMkXffPONbr75ZsXFxenUqVPujgYAANzM44vMP//5Tz366KMaNmyYmjZtqjfffFMBAQF699133R0NAAC4mUc/R+bKlStKTU1VQkKCdZqXl5e6du2qbdu2FblObm6ucnNzrd+zs7MlSTk5OU7PdzH3qtO3CQCAmbji9+sft2sYRonLeXSR+fXXX5Wfn6+wsDCb6WFhYTp48GCR68ycOVPTpk0rND0qKsolGQEA+J/2cohLN3/+/HmFhBS/D48uMmWRkJCgcePGWb8XFBTozJkzqlq1qiwWi9P2k5OTo6ioKJ04cULBwcFO2y4K41iXD45z+eA4lw+Oc/lw5XE2DEPnz59XZGRkict5dJGpVq2aKlSooKws20djZ2VlKTw8vMh1/Pz85OfnZzOtcuXKroqo4OBg/kdSTjjW5YPjXD44zuWD41w+XHWcSxqJucajL/b19fVVq1attH79euu0goICrV+/XrGxsW5MBgAAPIFHj8hI0rhx4zRkyBC1bt1at912m+bMmaOLFy9q2LBh7o4GAADczOOLTL9+/fTLL7/o+eefV2Zmplq2bKm1a9cWugC4vPn5+WnKlCmFTmPB+TjW5YPjXD44zuWD41w+POE4W4zS7msCAADwUB59jQwAAEBJKDIAAMC0KDIAAMC0KDIAAMC0KDIlSEpKUp06deTv7682bdpo586dJS6/dOlSNW7cWP7+/mrRooXWrFlTTknNz5Fj/fbbb6t9+/aqUqWKqlSpoq5du5b6d4PfOfpv+prFixfLYrGoV69erg14g3D0OJ87d07x8fGKiIiQn5+fGjVqxH8/7ODocZ4zZ46io6NVsWJFRUVFaezYsbp8+XI5pTWnLVu2qEePHoqMjJTFYtGKFStKXWfTpk265ZZb5OfnpwYNGig5Odm1IQ0UafHixYavr6/x7rvvGt9//73x6KOPGpUrVzaysrKKXH7r1q1GhQoVjJdeesnYv3+/8dxzzxk+Pj7Gvn37yjm5+Th6rAcMGGAkJSUZe/bsMQ4cOGAMHTrUCAkJMX7++edyTm4ujh7na44dO2bcdNNNRvv27Y2ePXuWT1gTc/Q45+bmGq1btza6d+9ufPXVV8axY8eMTZs2GWlpaeWc3FwcPc4pKSmGn5+fkZKSYhw7dsxYt26dERERYYwdO7ack5vLmjVrjEmTJhnLli0zJBnLly8vcfmjR48aAQEBxrhx44z9+/cbr7/+ulGhQgVj7dq1LstIkSnGbbfdZsTHx1u/5+fnG5GRkcbMmTOLXP6hhx4y7r33Xptpbdq0MR5//HGX5rwROHqs/+zq1atGUFCQsXDhQldFvCGU5ThfvXrVaNu2rfHvf//bGDJkCEXGDo4e53nz5hn16tUzrly5Ul4RbwiOHuf4+Hijc+fONtPGjRtntGvXzqU5byT2FJlnnnnGaNasmc20fv36GXFxcS7LxamlIly5ckWpqanq2rWrdZqXl5e6du2qbdu2FbnOtm3bbJaXpLi4uGKXx+/Kcqz/7NKlS8rLy1NoaKirYppeWY/z9OnTVaNGDQ0fPrw8YppeWY7zypUrFRsbq/j4eIWFhal58+b6xz/+ofz8/PKKbTplOc5t27ZVamqq9fTT0aNHtWbNGnXv3r1cMv+vcMfvQo9/sq87/Prrr8rPzy/09OCwsDAdPHiwyHUyMzOLXD4zM9NlOW8EZTnWfzZhwgRFRkYW+h8P/k9ZjvNXX32ld955R2lpaeWQ8MZQluN89OhRbdiwQQMHDtSaNWt0+PBhjRw5Unl5eZoyZUp5xDadshznAQMG6Ndff9Udd9whwzB09epVjRgxQs8++2x5RP6fUdzvwpycHP3222+qWLGi0/fJiAxMLTExUYsXL9by5cvl7+/v7jg3jPPnz2vQoEF6++23Va1aNXfHuaEVFBSoRo0aeuutt9SqVSv169dPkyZN0ptvvunuaDeUTZs26R//+IfeeOMNffPNN1q2bJlWr16tGTNmuDsarhMjMkWoVq2aKlSooKysLJvpWVlZCg8PL3Kd8PBwh5bH78pyrK+ZPXu2EhMT9cUXXygmJsaVMU3P0eN85MgR/fjjj+rRo4d1WkFBgSTJ29tb6enpql+/vmtDm1BZ/j1HRETIx8dHFSpUsE5r0qSJMjMzdeXKFfn6+ro0sxmV5ThPnjxZgwYN0t/+9jdJUosWLXTx4kU99thjmjRpkry8+P/1zlDc78Lg4GCXjMZIjMgUydfXV61atdL69eut0woKCrR+/XrFxsYWuU5sbKzN8pL0+eefF7s8fleWYy1JL730kmbMmKG1a9eqdevW5RHV1Bw9zo0bN9a+ffuUlpZm/dx///3q1KmT0tLSFBUVVZ7xTaMs/57btWunw4cPW4uiJP3www+KiIigxBSjLMf50qVLhcrKtfJo8MpBp3HL70KXXUZscosXLzb8/PyM5ORkY//+/cZjjz1mVK5c2cjMzDQMwzAGDRpkTJw40br81q1bDW9vb2P27NnGgQMHjClTpnD7tZ0cPdaJiYmGr6+v8dFHHxkZGRnWz/nz5931I5iCo8f5z7hryT6OHufjx48bQUFBxqhRo4z09HRj1apVRo0aNYwXXnjBXT+CKTh6nKdMmWIEBQUZH3zwgXH06FHjs88+M+rXr2889NBD7voRTOH8+fPGnj17jD179hiSjH/+85/Gnj17jJ9++skwDMOYOHGiMWjQIOvy126/fvrpp40DBw4YSUlJ3H7tTq+//rpRq1Ytw9fX17jtttuM7du3W+fdeeedxpAhQ2yW//DDD41GjRoZvr6+RrNmzYzVq1eXc2LzcuRY165d25BU6DNlypTyD24yjv6b/iOKjP0cPc5ff/210aZNG8PPz8+oV6+e8eKLLxpXr14t59Tm48hxzsvLM6ZOnWrUr1/f8Pf3N6KiooyRI0caZ8+eLf/gJrJx48Yi/3t77dgOGTLEuPPOOwut07JlS8PX19eoV6+esWDBApdmtBgGY2oAAMCcuEYGAACYFkUGAACYFkUGAACYFkUGAACYFkUGAACYFkUGAACYFkUGAACYFkUGAACYFkUG8FAWi0UrVqxw+X42bdoki8Wic+fOWaetWLFCDRo0UIUKFTRmzBglJyercuXKLs/SsWNHjRkzxuX7cYUff/xRFotFaWlp17WdoUOHqlevXiUu8+fjVKdOHc2ZM8f6vbz+7QCegCIDuEFmZqaefPJJ1atXT35+foqKilKPHj0KvWytPLRt21YZGRkKCQmxTnv88cf1wAMP6MSJE5oxY4b69eunH374wWn7LKo8SdKyZcs0Y8YMp+2nKNcKx7VP1apVdffdd2vPnj0u3a8zlXacMjIy1K1bN0nOK1iAp/J2dwDgf82PP/6odu3aqXLlynr55ZfVokUL5eXlad26dYqPj9fBgwfLNY+vr6/Cw8Ot3y9cuKBTp04pLi5OkZGR1ukVK1Z0eZbQ0FCX7+OaL774Qs2aNdPPP/+sp556St26ddPBgweLHHnKy8uTj49PuWUrTWnH6Y9/n8CNjhEZoJyNHDlSFotFO3fuVN++fdWoUSM1a9ZM48aN0/bt24tdb8KECWrUqJECAgJUr149TZ48WXl5edb53377rTp16qSgoCAFBwerVatW2r17tyTpp59+Uo8ePVSlShVVqlRJzZo105o1ayTZjo5s2rRJQUFBkqTOnTvLYrFo06ZNRZ5a+uSTT3TrrbfK399f1apVU+/eva3z3n//fbVu3VpBQUEKDw/XgAEDdOrUKUm/F7lOnTpJkqpUqSKLxaKhQ4dKKnzK5OzZsxo8eLCqVKmigIAAdevWTYcOHbLOv5Zr3bp1atKkiQIDA3XPPfcoIyOj1L+HqlWrKjw8XK1bt9bs2bOVlZWlHTt2WEcwlixZojvvvFP+/v5KSUlRQUGBpk+frpo1a8rPz08tW7bU2rVrC2334MGDatu2rfz9/dW8eXNt3rzZOi8/P1/Dhw9X3bp1VbFiRUVHR+u1114rMt+0adNUvXp1BQcHa8SIEbpy5Yp1Xmmn4P54aqlu3bqSpL/85S+yWCzq2LGjtmzZIh8fH2VmZtqsN2bMGLVv377UYwd4EooMUI7OnDmjtWvXKj4+XpUqVSo0v6TrUIKCgpScnKz9+/frtdde09tvv61XX33VOn/gwIGqWbOmdu3apdTUVE2cONE6ihAfH6/c3Fxt2bJF+/bt06xZsxQYGFhoH23btlV6erok6eOPP1ZGRobatm1baLnVq1erd+/e6t69u/bs2aP169frtttus87Py8vTjBkz9O2332rFihX68ccfrWUlKipKH3/8sSQpPT1dGRkZxf4yHzp0qHbv3q2VK1dq27ZtMgxD3bt3tylwly5d0uzZs/X+++9ry5YtOn78uP7+978XexyLcm206Y9lYeLEiRo9erQOHDiguLg4vfbaa3rllVc0e/Zs7d27V3Fxcbr//vttipUkPf300xo/frz27Nmj2NhY9ejRQ6dPn5YkFRQUqGbNmlq6dKn279+v559/Xs8++6w+/PBDm22sX79eBw4c0KZNm/TBBx9o2bJlmjZtmkM/0zU7d+6U9PsIVEZGhpYtW6YOHTqoXr16ev/9963L5eXlKSUlRY888kiZ9gO4jUvfrQ3Axo4dOwxJxrJly0pdVpKxfPnyYue//PLLRqtWrazfg4KCjOTk5CKXbdGihTF16tQi523cuNGQZJw9e9YwDMM4e/asIcnYuHGjdZkFCxYYISEh1u+xsbHGwIEDS/0Zrtm1a5chyTh//nyR+7zmzjvvNEaPHm0YhmH88MMPhiRj69at1vm//vqrUbFiRePDDz+05pJkHD582LpMUlKSERYWVmyWY8eOGZKMPXv2WH/e3r17G4GBgUZmZqZ1/pw5c2zWi4yMNF588UWbabfeeqsxcuRIm+0mJiZa5+fl5Rk1a9Y0Zs2aVWye+Ph4o2/fvtbvQ4YMMUJDQ42LFy9ap82bN88IDAw08vPzCx0nwzCM2rVrG6+++qr1+x//7fz5571m1qxZRpMmTazfP/74YyMwMNC4cOFCsVkBT8SIDFCODMMo87pLlixRu3btFB4ersDAQD333HM6fvy4df64ceP0t7/9TV27dlViYqKOHDlinffUU0/phRdeULt27TRlyhTt3bv3un6OtLQ0denSpdj5qamp6tGjh2rVqqWgoCDdeeedkmSTtzQHDhyQt7e32rRpY51WtWpVRUdH68CBA9ZpAQEBql+/vvV7RESE9TRWSdq2bavAwEBVqVJF3377rZYsWaKwsDDr/NatW1v/nJOTo5MnT6pdu3Y222jXrp1NFkmKjY21/tnb21utW7e2WSYpKUmtWrVS9erVFRgYqLfeeqvQcbn55psVEBBgs80LFy7oxIkTpf5c9ho6dKgOHz5sPZ2ZnJyshx56qMiRQsCTUWSActSwYUNZLBaHL+jdtm2bBg4cqO7du2vVqlXas2ePJk2aZHMqZOrUqfr+++917733asOGDWratKmWL18uSfrb3/6mo0ePatCgQdq3b59at26t119/vcw/R0kX/l68eFFxcXEKDg5WSkqKdu3aZc3xx7zO8ueLcC0Wi12FccmSJfr222919uxZHTlyRN27d7eZ74pf6IsXL9bf//53DR8+XJ999pnS0tI0bNgwlxyX0tSoUUM9evTQggULlJWVpU8//ZTTSjAligxQjkJDQxUXF6ekpCRdvHix0Pw/3458zddff63atWtr0qRJat26tRo2bKiffvqp0HKNGjXS2LFj9dlnn6lPnz5asGCBdV5UVJRGjBihZcuWafz48Xr77bfL/HPExMQUe6v4wYMHdfr0aSUmJqp9+/Zq3LhxoRESX19fSb9f/FqcJk2a6OrVq9qxY4d12unTp5Wenq6mTZuWOfs1UVFRql+/vl3PxwkODlZkZKS2bt1qM33r1q2Fsvzxgu2rV68qNTVVTZo0sS7ftm1bjRw5Un/5y1/UoEEDm5Gza7799lv99ttvNtsMDAxUVFSUIz+ipJKP9d/+9jctWbJEb731lurXr19oxAkwA4oMUM6SkpKUn5+v2267TR9//LEOHTqkAwcOaO7cuTanJf6oYcOGOn78uBYvXqwjR45o7ty51lEOSfrtt980atQobdq0ST/99JO2bt2qXbt2WX+BjhkzRuvWrdOxY8f0zTffaOPGjdZ5ZTFlyhR98MEHmjJlig4cOGC9gFiSatWqJV9fX73++us6evSoVq5cWeiZJ7Vr15bFYtGqVav0yy+/6MKFC0X+zD179tSjjz6qr776St9++60efvhh3XTTTerZs2eZs5fV008/rVmzZmnJkiVKT0/XxIkTlZaWptGjR9ssl5SUpOXLl+vgwYOKj4/X2bNnrSMdDRs21O7du7Vu3Tr98MMPmjx5snbt2lVoX1euXNHw4cO1f/9+rVmzRlOmTNGoUaPk5eX4f7Jr1KihihUrau3atcrKylJ2drZ13rWRsxdeeEHDhg1zeNuAJ6DIAOWsXr16+uabb9SpUyeNHz9ezZs311133aX169dr3rx5Ra5z//33a+zYsRo1apRatmypr7/+WpMnT7bOr1Chgk6fPq3BgwerUaNGeuihh9StWzfrnS75+fmKj49XkyZNdM8996hRo0Z64403yvwzdOzYUUuXLtXKlSvVsmVLde7c2Xp3TPXq1ZWcnKylS5eqadOmSkxM1OzZs23Wv+mmmzRt2jRNnDhRYWFhGjVqVJH7WbBggVq1aqX77rtPsbGxMgxDa9asccszXZ566imNGzdO48ePV4sWLbR27VqtXLlSDRs2tFkuMTFRiYmJuvnmm/XVV19p5cqVqlatmqTfHzTYp08f9evXT23atNHp06c1cuTIQvvq0qWLGjZsqA4dOqhfv366//77NXXq1DLl9vb21ty5czV//nxFRkbalEAvLy8NHTpU+fn5Gjx4cJm2D7ibxbieqw8BAKY2fPhw/fLLL1q5cqW7owBlwpN9AeB/UHZ2tvbt26dFixZRYmBqFBkA+B/Us2dP7dy5UyNGjNBdd93l7jhAmXFqCQAAmBYX+wIAANOiyAAAANOiyAAAANOiyAAAANOiyAAAANOiyAAAANOiyAAAANOiyAAAANP6/wCm5I6WPB6oiwAAAABJRU5ErkJggg==",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1232,7 +1231,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.6"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,
diff --git a/field_296_100rows.csv b/field_296_100rows.csv
index 7421a92..d60b1ae 100644
--- a/field_296_100rows.csv
+++ b/field_296_100rows.csv
@@ -1,101 +1,101 @@
-_id,Gaia_EDR3___id,AllWISE___id,PS1_DR1___id,ra,dec,period,field,ccd,quad,filter,e_dnn,cv_dnn,el_dnn,osarg_dnn,ea_dnn,rrblz_dnn,ceph2_dnn,bis_dnn,puls_dnn,agn_dnn,longt_dnn,mir_dnn,dscu_dnn,rrc_dnn,eb_dnn,wp_dnn,rrd_dnn,hp_dnn,blend_dnn,wvir_dnn,lpv_dnn,emsms_dnn,i_dnn,rrlyr_dnn,rrab_dnn,sin_dnn,rscvn_dnn,bright_dnn,bogus_dnn,yso_dnn,blyr_dnn,dp_dnn,saw_dnn,ceph_dnn,blher_dnn,ext_dnn,wuma_dnn,ew_dnn,srv_dnn,fla_dnn,vnv_dnn,pnp_dnn,dip_dnn,mp_dnn,saw_xgb,dp_xgb,i_xgb,longt_xgb,yso_xgb,wvir_xgb,puls_xgb,sin_xgb,dscu_xgb,blend_xgb,dip_xgb,mp_xgb,pnp_xgb,vnv_xgb,rrblz_xgb,ew_xgb,osarg_xgb,fla_xgb,srv_xgb,ext_xgb,ceph2_xgb,bis_xgb,blher_xgb,e_xgb,ea_xgb,wuma_xgb,ceph_xgb,el_xgb,bright_xgb,cv_xgb,lpv_xgb,emsms_xgb,rrlyr_xgb,wp_xgb,eb_xgb,hp_xgb,blyr_xgb,rscvn_xgb,rrd_xgb,mir_xgb,rrc_xgb,bogus_xgb,rrab_xgb,agn_xgb
-10296582009946,0,140015201351056624,90610143756592496,14.375673299999988,-14.4896686,5.751998771499175,296,15,3,2,0.01,0.0,0.0,0.0,0.07,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.03,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.05,0.01,0.0,0.0,0.0,0.03,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.29,0.0,0.0,0.03,0.1,0.07,0.0,0.0,0.07,0.0,0.0,0.0,0.0,0.0,0.01,0.02,0.0,0.01,0.0,0.0,0.0,0.07,0.0,0.0,0.23,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01,0.19,0.09
-10296531000371,2468292420539961088,170013701351041248,91920166559039984,16.6555788,-13.3924335,0.2536717465673753,296,14,2,1,0.44,0.0,0.0,0.0,0.01,0.0,0.0,0.61,0.41,0.0,0.0,0.0,0.01,0.05,0.0,0.02,0.23,0.0,0.0,0.0,0.0,0.0,0.0,0.37,0.01,0.02,0.02,0.0,0.01,0.0,0.0,0.0,0.02,0.01,0.0,0.0,0.59,0.5,0.0,0.0,0.98,0.88,0.0,0.0,0.0,0.03,0.0,0.0,0.0,0.0,0.9,0.01,0.0,0.29,0.0,0.01,0.94,0.99,0.07,0.84,0.0,0.07,0.0,0.0,0.0,0.09,0.0,0.83,0.02,0.01,0.0,0.0,0.0,0.0,0.07,0.0,0.88,0.22,0.0,0.0,0.0,0.0,0.77,0.0,0.34,0.0,0.19,0.0
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