From 8e499d9669ffd0e77e3932d3664064d874eb6ba3 Mon Sep 17 00:00:00 2001
From: moonlanderr <sbanik@esri.com>
Date: Mon, 29 Jul 2024 13:22:33 +0530
Subject: [PATCH] predction column name upated

---
 ...y-prediction-using-weather-variables.ipynb | 1081 +++++++++--------
 1 file changed, 548 insertions(+), 533 deletions(-)

diff --git a/samples/04_gis_analysts_data_scientists/solar-energy-prediction-using-weather-variables.ipynb b/samples/04_gis_analysts_data_scientists/solar-energy-prediction-using-weather-variables.ipynb
index a3324896d..06e46edde 100644
--- a/samples/04_gis_analysts_data_scientists/solar-energy-prediction-using-weather-variables.ipynb
+++ b/samples/04_gis_analysts_data_scientists/solar-energy-prediction-using-weather-variables.ipynb
@@ -159,7 +159,7 @@
        "                        </a>\n",
        "                        <br/><img src='https://geosaurus.maps.arcgis.com/home/js/jsapi/esri/css/images/item_type_icons/featureshosted16.png' style=\"vertical-align:middle;\" width=16 height=16>Feature Layer Collection by api_data_owner\n",
        "                        <br/>Last Modified: October 23, 2020\n",
-       "                        <br/>0 comments, 11627 views\n",
+       "                        <br/>0 comments, 11650 views\n",
        "                    </div>\n",
        "                </div>\n",
        "                "
@@ -592,415 +592,415 @@
      "data": {
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+       "#T_8d822_row0_col0, #T_8d822_row1_col1, #T_8d822_row2_col2, #T_8d822_row3_col3, #T_8d822_row4_col4, #T_8d822_row5_col5, #T_8d822_row6_col6, #T_8d822_row7_col7, #T_8d822_row8_col8, #T_8d822_row9_col9 {\n",
        "  background-color: #00441b;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b011b_row0_col1, #T_b011b_row3_col5 {\n",
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        "}\n",
-       "#T_b011b_row2_col7, #T_b011b_row2_col9 {\n",
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        "  background-color: #f4fbf1;\n",
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        "  color: #000000;\n",
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-       "#T_b011b_row4_col2 {\n",
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        "  background-color: #f2faef;\n",
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        "  color: #000000;\n",
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        "  background-color: #cbeac4;\n",
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        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b011b_row7_col0 {\n",
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        "  background-color: #f3faf0;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_b011b_row7_col1 {\n",
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        "  background-color: #c9eac2;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_b011b_row7_col4 {\n",
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        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b011b_row7_col8, #T_b011b_row8_col7 {\n",
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        "  background-color: #006428;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b011b_row7_col9 {\n",
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        "  background-color: #2a924a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b011b_row8_col0 {\n",
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        "  background-color: #f2faf0;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_b011b_row8_col1 {\n",
+       "#T_8d822_row8_col1 {\n",
        "  background-color: #d5efcf;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_b011b_row8_col2 {\n",
+       "#T_8d822_row8_col2 {\n",
        "  background-color: #2b934b;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b011b_row8_col3 {\n",
+       "#T_8d822_row8_col3 {\n",
        "  background-color: #b1e0ab;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_b011b_row8_col5 {\n",
+       "#T_8d822_row8_col5 {\n",
        "  background-color: #f5fbf3;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_b011b_row8_col6 {\n",
+       "#T_8d822_row8_col6 {\n",
        "  background-color: #0d7836;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b011b_row8_col9 {\n",
+       "#T_8d822_row8_col9 {\n",
        "  background-color: #5bb86a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b011b_row9_col1 {\n",
+       "#T_8d822_row9_col1 {\n",
        "  background-color: #e6f5e1;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_b011b_row9_col2 {\n",
+       "#T_8d822_row9_col2 {\n",
        "  background-color: #0a7633;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b011b_row9_col3 {\n",
+       "#T_8d822_row9_col3 {\n",
        "  background-color: #e5f5e1;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_b011b_row9_col5 {\n",
+       "#T_8d822_row9_col5 {\n",
        "  background-color: #d4eece;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_b011b_row9_col7 {\n",
+       "#T_8d822_row9_col7 {\n",
        "  background-color: #1f8742;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b011b_row9_col8 {\n",
+       "#T_8d822_row9_col8 {\n",
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        "</style>\n",
-       "<table id=\"T_b011b\">\n",
+       "<table id=\"T_8d822\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
-       "      <th id=\"T_b011b_level0_col0\" class=\"col_heading level0 col0\" >altitude_m</th>\n",
-       "      <th id=\"T_b011b_level0_col1\" class=\"col_heading level0 col1\" >wind_speed</th>\n",
-       "      <th id=\"T_b011b_level0_col2\" class=\"col_heading level0 col2\" >dayl__s_</th>\n",
-       "      <th id=\"T_b011b_level0_col3\" class=\"col_heading level0 col3\" >prcp__mm_d</th>\n",
-       "      <th id=\"T_b011b_level0_col4\" class=\"col_heading level0 col4\" >srad__W_m_</th>\n",
-       "      <th id=\"T_b011b_level0_col5\" class=\"col_heading level0 col5\" >swe__kg_m_</th>\n",
-       "      <th id=\"T_b011b_level0_col6\" class=\"col_heading level0 col6\" >tmax__deg</th>\n",
-       "      <th id=\"T_b011b_level0_col7\" class=\"col_heading level0 col7\" >tmin__deg</th>\n",
-       "      <th id=\"T_b011b_level0_col8\" class=\"col_heading level0 col8\" >vp__Pa_</th>\n",
-       "      <th id=\"T_b011b_level0_col9\" class=\"col_heading level0 col9\" >capacity_f</th>\n",
+       "      <th id=\"T_8d822_level0_col0\" class=\"col_heading level0 col0\" >altitude_m</th>\n",
+       "      <th id=\"T_8d822_level0_col1\" class=\"col_heading level0 col1\" >wind_speed</th>\n",
+       "      <th id=\"T_8d822_level0_col2\" class=\"col_heading level0 col2\" >dayl__s_</th>\n",
+       "      <th id=\"T_8d822_level0_col3\" class=\"col_heading level0 col3\" >prcp__mm_d</th>\n",
+       "      <th id=\"T_8d822_level0_col4\" class=\"col_heading level0 col4\" >srad__W_m_</th>\n",
+       "      <th id=\"T_8d822_level0_col5\" class=\"col_heading level0 col5\" >swe__kg_m_</th>\n",
+       "      <th id=\"T_8d822_level0_col6\" class=\"col_heading level0 col6\" >tmax__deg</th>\n",
+       "      <th id=\"T_8d822_level0_col7\" class=\"col_heading level0 col7\" >tmin__deg</th>\n",
+       "      <th id=\"T_8d822_level0_col8\" class=\"col_heading level0 col8\" >vp__Pa_</th>\n",
+       "      <th id=\"T_8d822_level0_col9\" class=\"col_heading level0 col9\" >capacity_f</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_b011b_level0_row0\" class=\"row_heading level0 row0\" >altitude_m</th>\n",
-       "      <td id=\"T_b011b_row0_col0\" class=\"data row0 col0\" >1.00</td>\n",
-       "      <td id=\"T_b011b_row0_col1\" class=\"data row0 col1\" >-0.01</td>\n",
-       "      <td id=\"T_b011b_row0_col2\" class=\"data row0 col2\" >0.04</td>\n",
-       "      <td id=\"T_b011b_row0_col3\" class=\"data row0 col3\" >0.01</td>\n",
-       "      <td id=\"T_b011b_row0_col4\" class=\"data row0 col4\" >0.03</td>\n",
-       "      <td id=\"T_b011b_row0_col5\" class=\"data row0 col5\" >0.02</td>\n",
-       "      <td id=\"T_b011b_row0_col6\" class=\"data row0 col6\" >0.02</td>\n",
-       "      <td id=\"T_b011b_row0_col7\" class=\"data row0 col7\" >0.02</td>\n",
-       "      <td id=\"T_b011b_row0_col8\" class=\"data row0 col8\" >0.02</td>\n",
-       "      <td id=\"T_b011b_row0_col9\" class=\"data row0 col9\" >0.03</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_b011b_level0_row1\" class=\"row_heading level0 row1\" >wind_speed</th>\n",
-       "      <td id=\"T_b011b_row1_col0\" class=\"data row1 col0\" >-0.01</td>\n",
-       "      <td id=\"T_b011b_row1_col1\" class=\"data row1 col1\" >1.00</td>\n",
-       "      <td id=\"T_b011b_row1_col2\" class=\"data row1 col2\" >-0.41</td>\n",
-       "      <td id=\"T_b011b_row1_col3\" class=\"data row1 col3\" >-0.17</td>\n",
-       "      <td id=\"T_b011b_row1_col4\" class=\"data row1 col4\" >-0.26</td>\n",
-       "      <td id=\"T_b011b_row1_col5\" class=\"data row1 col5\" >0.02</td>\n",
-       "      <td id=\"T_b011b_row1_col6\" class=\"data row1 col6\" >-0.03</td>\n",
-       "      <td id=\"T_b011b_row1_col7\" class=\"data row1 col7\" >-0.06</td>\n",
-       "      <td id=\"T_b011b_row1_col8\" class=\"data row1 col8\" >-0.13</td>\n",
-       "      <td id=\"T_b011b_row1_col9\" class=\"data row1 col9\" >-0.24</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_b011b_level0_row2\" class=\"row_heading level0 row2\" >dayl__s_</th>\n",
-       "      <td id=\"T_b011b_row2_col0\" class=\"data row2 col0\" >0.04</td>\n",
-       "      <td id=\"T_b011b_row2_col1\" class=\"data row2 col1\" >-0.41</td>\n",
-       "      <td id=\"T_b011b_row2_col2\" class=\"data row2 col2\" >1.00</td>\n",
-       "      <td id=\"T_b011b_row2_col3\" class=\"data row2 col3\" >0.20</td>\n",
-       "      <td id=\"T_b011b_row2_col4\" class=\"data row2 col4\" >0.78</td>\n",
-       "      <td id=\"T_b011b_row2_col5\" class=\"data row2 col5\" >-0.18</td>\n",
-       "      <td id=\"T_b011b_row2_col6\" class=\"data row2 col6\" >0.72</td>\n",
-       "      <td id=\"T_b011b_row2_col7\" class=\"data row2 col7\" >0.73</td>\n",
-       "      <td id=\"T_b011b_row2_col8\" class=\"data row2 col8\" >0.60</td>\n",
-       "      <td id=\"T_b011b_row2_col9\" class=\"data row2 col9\" >0.77</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_b011b_level0_row3\" class=\"row_heading level0 row3\" >prcp__mm_d</th>\n",
-       "      <td id=\"T_b011b_row3_col0\" class=\"data row3 col0\" >0.01</td>\n",
-       "      <td id=\"T_b011b_row3_col1\" class=\"data row3 col1\" >-0.17</td>\n",
-       "      <td id=\"T_b011b_row3_col2\" class=\"data row3 col2\" >0.20</td>\n",
-       "      <td id=\"T_b011b_row3_col3\" class=\"data row3 col3\" >1.00</td>\n",
-       "      <td id=\"T_b011b_row3_col4\" class=\"data row3 col4\" >-0.18</td>\n",
-       "      <td id=\"T_b011b_row3_col5\" class=\"data row3 col5\" >-0.07</td>\n",
-       "      <td id=\"T_b011b_row3_col6\" class=\"data row3 col6\" >-0.03</td>\n",
-       "      <td id=\"T_b011b_row3_col7\" class=\"data row3 col7\" >0.10</td>\n",
-       "      <td id=\"T_b011b_row3_col8\" class=\"data row3 col8\" >0.20</td>\n",
-       "      <td id=\"T_b011b_row3_col9\" class=\"data row3 col9\" >-0.04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_b011b_level0_row4\" class=\"row_heading level0 row4\" >srad__W_m_</th>\n",
-       "      <td id=\"T_b011b_row4_col0\" class=\"data row4 col0\" >0.03</td>\n",
-       "      <td id=\"T_b011b_row4_col1\" class=\"data row4 col1\" >-0.26</td>\n",
-       "      <td id=\"T_b011b_row4_col2\" class=\"data row4 col2\" >0.78</td>\n",
-       "      <td id=\"T_b011b_row4_col3\" class=\"data row4 col3\" >-0.18</td>\n",
-       "      <td id=\"T_b011b_row4_col4\" class=\"data row4 col4\" >1.00</td>\n",
-       "      <td id=\"T_b011b_row4_col5\" class=\"data row4 col5\" >0.04</td>\n",
-       "      <td id=\"T_b011b_row4_col6\" class=\"data row4 col6\" >0.69</td>\n",
-       "      <td id=\"T_b011b_row4_col7\" class=\"data row4 col7\" >0.50</td>\n",
-       "      <td id=\"T_b011b_row4_col8\" class=\"data row4 col8\" >0.28</td>\n",
-       "      <td id=\"T_b011b_row4_col9\" class=\"data row4 col9\" >0.82</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_b011b_level0_row5\" class=\"row_heading level0 row5\" >swe__kg_m_</th>\n",
-       "      <td id=\"T_b011b_row5_col0\" class=\"data row5 col0\" >0.02</td>\n",
-       "      <td id=\"T_b011b_row5_col1\" class=\"data row5 col1\" >0.02</td>\n",
-       "      <td id=\"T_b011b_row5_col2\" class=\"data row5 col2\" >-0.18</td>\n",
-       "      <td id=\"T_b011b_row5_col3\" class=\"data row5 col3\" >-0.07</td>\n",
-       "      <td id=\"T_b011b_row5_col4\" class=\"data row5 col4\" >0.04</td>\n",
-       "      <td id=\"T_b011b_row5_col5\" class=\"data row5 col5\" >1.00</td>\n",
-       "      <td id=\"T_b011b_row5_col6\" class=\"data row5 col6\" >-0.45</td>\n",
-       "      <td id=\"T_b011b_row5_col7\" class=\"data row5 col7\" >-0.48</td>\n",
-       "      <td id=\"T_b011b_row5_col8\" class=\"data row5 col8\" >-0.46</td>\n",
-       "      <td id=\"T_b011b_row5_col9\" class=\"data row5 col9\" >-0.19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_b011b_level0_row6\" class=\"row_heading level0 row6\" >tmax__deg</th>\n",
-       "      <td id=\"T_b011b_row6_col0\" class=\"data row6 col0\" >0.02</td>\n",
-       "      <td id=\"T_b011b_row6_col1\" class=\"data row6 col1\" >-0.03</td>\n",
-       "      <td id=\"T_b011b_row6_col2\" class=\"data row6 col2\" >0.72</td>\n",
-       "      <td id=\"T_b011b_row6_col3\" class=\"data row6 col3\" >-0.03</td>\n",
-       "      <td id=\"T_b011b_row6_col4\" class=\"data row6 col4\" >0.69</td>\n",
-       "      <td id=\"T_b011b_row6_col5\" class=\"data row6 col5\" >-0.45</td>\n",
-       "      <td id=\"T_b011b_row6_col6\" class=\"data row6 col6\" >1.00</td>\n",
-       "      <td id=\"T_b011b_row6_col7\" class=\"data row6 col7\" >0.93</td>\n",
-       "      <td id=\"T_b011b_row6_col8\" class=\"data row6 col8\" >0.75</td>\n",
-       "      <td id=\"T_b011b_row6_col9\" class=\"data row6 col9\" >0.75</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_b011b_level0_row7\" class=\"row_heading level0 row7\" >tmin__deg</th>\n",
-       "      <td id=\"T_b011b_row7_col0\" class=\"data row7 col0\" >0.02</td>\n",
-       "      <td id=\"T_b011b_row7_col1\" class=\"data row7 col1\" >-0.06</td>\n",
-       "      <td id=\"T_b011b_row7_col2\" class=\"data row7 col2\" >0.73</td>\n",
-       "      <td id=\"T_b011b_row7_col3\" class=\"data row7 col3\" >0.10</td>\n",
-       "      <td id=\"T_b011b_row7_col4\" class=\"data row7 col4\" >0.50</td>\n",
-       "      <td id=\"T_b011b_row7_col5\" class=\"data row7 col5\" >-0.48</td>\n",
-       "      <td id=\"T_b011b_row7_col6\" class=\"data row7 col6\" >0.93</td>\n",
-       "      <td id=\"T_b011b_row7_col7\" class=\"data row7 col7\" >1.00</td>\n",
-       "      <td id=\"T_b011b_row7_col8\" class=\"data row7 col8\" >0.85</td>\n",
-       "      <td id=\"T_b011b_row7_col9\" class=\"data row7 col9\" >0.65</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_b011b_level0_row8\" class=\"row_heading level0 row8\" >vp__Pa_</th>\n",
-       "      <td id=\"T_b011b_row8_col0\" class=\"data row8 col0\" >0.02</td>\n",
-       "      <td id=\"T_b011b_row8_col1\" class=\"data row8 col1\" >-0.13</td>\n",
-       "      <td id=\"T_b011b_row8_col2\" class=\"data row8 col2\" >0.60</td>\n",
-       "      <td id=\"T_b011b_row8_col3\" class=\"data row8 col3\" >0.20</td>\n",
-       "      <td id=\"T_b011b_row8_col4\" class=\"data row8 col4\" >0.28</td>\n",
-       "      <td id=\"T_b011b_row8_col5\" class=\"data row8 col5\" >-0.46</td>\n",
-       "      <td id=\"T_b011b_row8_col6\" class=\"data row8 col6\" >0.75</td>\n",
-       "      <td id=\"T_b011b_row8_col7\" class=\"data row8 col7\" >0.85</td>\n",
-       "      <td id=\"T_b011b_row8_col8\" class=\"data row8 col8\" >1.00</td>\n",
-       "      <td id=\"T_b011b_row8_col9\" class=\"data row8 col9\" >0.45</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_b011b_level0_row9\" class=\"row_heading level0 row9\" >capacity_f</th>\n",
-       "      <td id=\"T_b011b_row9_col0\" class=\"data row9 col0\" >0.03</td>\n",
-       "      <td id=\"T_b011b_row9_col1\" class=\"data row9 col1\" >-0.24</td>\n",
-       "      <td id=\"T_b011b_row9_col2\" class=\"data row9 col2\" >0.77</td>\n",
-       "      <td id=\"T_b011b_row9_col3\" class=\"data row9 col3\" >-0.04</td>\n",
-       "      <td id=\"T_b011b_row9_col4\" class=\"data row9 col4\" >0.82</td>\n",
-       "      <td id=\"T_b011b_row9_col5\" class=\"data row9 col5\" >-0.19</td>\n",
-       "      <td id=\"T_b011b_row9_col6\" class=\"data row9 col6\" >0.75</td>\n",
-       "      <td id=\"T_b011b_row9_col7\" class=\"data row9 col7\" >0.65</td>\n",
-       "      <td id=\"T_b011b_row9_col8\" class=\"data row9 col8\" >0.45</td>\n",
-       "      <td id=\"T_b011b_row9_col9\" class=\"data row9 col9\" >1.00</td>\n",
+       "      <th id=\"T_8d822_level0_row0\" class=\"row_heading level0 row0\" >altitude_m</th>\n",
+       "      <td id=\"T_8d822_row0_col0\" class=\"data row0 col0\" >1.00</td>\n",
+       "      <td id=\"T_8d822_row0_col1\" class=\"data row0 col1\" >-0.01</td>\n",
+       "      <td id=\"T_8d822_row0_col2\" class=\"data row0 col2\" >0.04</td>\n",
+       "      <td id=\"T_8d822_row0_col3\" class=\"data row0 col3\" >0.01</td>\n",
+       "      <td id=\"T_8d822_row0_col4\" class=\"data row0 col4\" >0.03</td>\n",
+       "      <td id=\"T_8d822_row0_col5\" class=\"data row0 col5\" >0.02</td>\n",
+       "      <td id=\"T_8d822_row0_col6\" class=\"data row0 col6\" >0.02</td>\n",
+       "      <td id=\"T_8d822_row0_col7\" class=\"data row0 col7\" >0.02</td>\n",
+       "      <td id=\"T_8d822_row0_col8\" class=\"data row0 col8\" >0.02</td>\n",
+       "      <td id=\"T_8d822_row0_col9\" class=\"data row0 col9\" >0.03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_8d822_level0_row1\" class=\"row_heading level0 row1\" >wind_speed</th>\n",
+       "      <td id=\"T_8d822_row1_col0\" class=\"data row1 col0\" >-0.01</td>\n",
+       "      <td id=\"T_8d822_row1_col1\" class=\"data row1 col1\" >1.00</td>\n",
+       "      <td id=\"T_8d822_row1_col2\" class=\"data row1 col2\" >-0.41</td>\n",
+       "      <td id=\"T_8d822_row1_col3\" class=\"data row1 col3\" >-0.17</td>\n",
+       "      <td id=\"T_8d822_row1_col4\" class=\"data row1 col4\" >-0.26</td>\n",
+       "      <td id=\"T_8d822_row1_col5\" class=\"data row1 col5\" >0.02</td>\n",
+       "      <td id=\"T_8d822_row1_col6\" class=\"data row1 col6\" >-0.03</td>\n",
+       "      <td id=\"T_8d822_row1_col7\" class=\"data row1 col7\" >-0.06</td>\n",
+       "      <td id=\"T_8d822_row1_col8\" class=\"data row1 col8\" >-0.13</td>\n",
+       "      <td id=\"T_8d822_row1_col9\" class=\"data row1 col9\" >-0.24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_8d822_level0_row2\" class=\"row_heading level0 row2\" >dayl__s_</th>\n",
+       "      <td id=\"T_8d822_row2_col0\" class=\"data row2 col0\" >0.04</td>\n",
+       "      <td id=\"T_8d822_row2_col1\" class=\"data row2 col1\" >-0.41</td>\n",
+       "      <td id=\"T_8d822_row2_col2\" class=\"data row2 col2\" >1.00</td>\n",
+       "      <td id=\"T_8d822_row2_col3\" class=\"data row2 col3\" >0.20</td>\n",
+       "      <td id=\"T_8d822_row2_col4\" class=\"data row2 col4\" >0.78</td>\n",
+       "      <td id=\"T_8d822_row2_col5\" class=\"data row2 col5\" >-0.18</td>\n",
+       "      <td id=\"T_8d822_row2_col6\" class=\"data row2 col6\" >0.72</td>\n",
+       "      <td id=\"T_8d822_row2_col7\" class=\"data row2 col7\" >0.73</td>\n",
+       "      <td id=\"T_8d822_row2_col8\" class=\"data row2 col8\" >0.60</td>\n",
+       "      <td id=\"T_8d822_row2_col9\" class=\"data row2 col9\" >0.77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_8d822_level0_row3\" class=\"row_heading level0 row3\" >prcp__mm_d</th>\n",
+       "      <td id=\"T_8d822_row3_col0\" class=\"data row3 col0\" >0.01</td>\n",
+       "      <td id=\"T_8d822_row3_col1\" class=\"data row3 col1\" >-0.17</td>\n",
+       "      <td id=\"T_8d822_row3_col2\" class=\"data row3 col2\" >0.20</td>\n",
+       "      <td id=\"T_8d822_row3_col3\" class=\"data row3 col3\" >1.00</td>\n",
+       "      <td id=\"T_8d822_row3_col4\" class=\"data row3 col4\" >-0.18</td>\n",
+       "      <td id=\"T_8d822_row3_col5\" class=\"data row3 col5\" >-0.07</td>\n",
+       "      <td id=\"T_8d822_row3_col6\" class=\"data row3 col6\" >-0.03</td>\n",
+       "      <td id=\"T_8d822_row3_col7\" class=\"data row3 col7\" >0.10</td>\n",
+       "      <td id=\"T_8d822_row3_col8\" class=\"data row3 col8\" >0.20</td>\n",
+       "      <td id=\"T_8d822_row3_col9\" class=\"data row3 col9\" >-0.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_8d822_level0_row4\" class=\"row_heading level0 row4\" >srad__W_m_</th>\n",
+       "      <td id=\"T_8d822_row4_col0\" class=\"data row4 col0\" >0.03</td>\n",
+       "      <td id=\"T_8d822_row4_col1\" class=\"data row4 col1\" >-0.26</td>\n",
+       "      <td id=\"T_8d822_row4_col2\" class=\"data row4 col2\" >0.78</td>\n",
+       "      <td id=\"T_8d822_row4_col3\" class=\"data row4 col3\" >-0.18</td>\n",
+       "      <td id=\"T_8d822_row4_col4\" class=\"data row4 col4\" >1.00</td>\n",
+       "      <td id=\"T_8d822_row4_col5\" class=\"data row4 col5\" >0.04</td>\n",
+       "      <td id=\"T_8d822_row4_col6\" class=\"data row4 col6\" >0.69</td>\n",
+       "      <td id=\"T_8d822_row4_col7\" class=\"data row4 col7\" >0.50</td>\n",
+       "      <td id=\"T_8d822_row4_col8\" class=\"data row4 col8\" >0.28</td>\n",
+       "      <td id=\"T_8d822_row4_col9\" class=\"data row4 col9\" >0.82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_8d822_level0_row5\" class=\"row_heading level0 row5\" >swe__kg_m_</th>\n",
+       "      <td id=\"T_8d822_row5_col0\" class=\"data row5 col0\" >0.02</td>\n",
+       "      <td id=\"T_8d822_row5_col1\" class=\"data row5 col1\" >0.02</td>\n",
+       "      <td id=\"T_8d822_row5_col2\" class=\"data row5 col2\" >-0.18</td>\n",
+       "      <td id=\"T_8d822_row5_col3\" class=\"data row5 col3\" >-0.07</td>\n",
+       "      <td id=\"T_8d822_row5_col4\" class=\"data row5 col4\" >0.04</td>\n",
+       "      <td id=\"T_8d822_row5_col5\" class=\"data row5 col5\" >1.00</td>\n",
+       "      <td id=\"T_8d822_row5_col6\" class=\"data row5 col6\" >-0.45</td>\n",
+       "      <td id=\"T_8d822_row5_col7\" class=\"data row5 col7\" >-0.48</td>\n",
+       "      <td id=\"T_8d822_row5_col8\" class=\"data row5 col8\" >-0.46</td>\n",
+       "      <td id=\"T_8d822_row5_col9\" class=\"data row5 col9\" >-0.19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_8d822_level0_row6\" class=\"row_heading level0 row6\" >tmax__deg</th>\n",
+       "      <td id=\"T_8d822_row6_col0\" class=\"data row6 col0\" >0.02</td>\n",
+       "      <td id=\"T_8d822_row6_col1\" class=\"data row6 col1\" >-0.03</td>\n",
+       "      <td id=\"T_8d822_row6_col2\" class=\"data row6 col2\" >0.72</td>\n",
+       "      <td id=\"T_8d822_row6_col3\" class=\"data row6 col3\" >-0.03</td>\n",
+       "      <td id=\"T_8d822_row6_col4\" class=\"data row6 col4\" >0.69</td>\n",
+       "      <td id=\"T_8d822_row6_col5\" class=\"data row6 col5\" >-0.45</td>\n",
+       "      <td id=\"T_8d822_row6_col6\" class=\"data row6 col6\" >1.00</td>\n",
+       "      <td id=\"T_8d822_row6_col7\" class=\"data row6 col7\" >0.93</td>\n",
+       "      <td id=\"T_8d822_row6_col8\" class=\"data row6 col8\" >0.75</td>\n",
+       "      <td id=\"T_8d822_row6_col9\" class=\"data row6 col9\" >0.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_8d822_level0_row7\" class=\"row_heading level0 row7\" >tmin__deg</th>\n",
+       "      <td id=\"T_8d822_row7_col0\" class=\"data row7 col0\" >0.02</td>\n",
+       "      <td id=\"T_8d822_row7_col1\" class=\"data row7 col1\" >-0.06</td>\n",
+       "      <td id=\"T_8d822_row7_col2\" class=\"data row7 col2\" >0.73</td>\n",
+       "      <td id=\"T_8d822_row7_col3\" class=\"data row7 col3\" >0.10</td>\n",
+       "      <td id=\"T_8d822_row7_col4\" class=\"data row7 col4\" >0.50</td>\n",
+       "      <td id=\"T_8d822_row7_col5\" class=\"data row7 col5\" >-0.48</td>\n",
+       "      <td id=\"T_8d822_row7_col6\" class=\"data row7 col6\" >0.93</td>\n",
+       "      <td id=\"T_8d822_row7_col7\" class=\"data row7 col7\" >1.00</td>\n",
+       "      <td id=\"T_8d822_row7_col8\" class=\"data row7 col8\" >0.85</td>\n",
+       "      <td id=\"T_8d822_row7_col9\" class=\"data row7 col9\" >0.65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_8d822_level0_row8\" class=\"row_heading level0 row8\" >vp__Pa_</th>\n",
+       "      <td id=\"T_8d822_row8_col0\" class=\"data row8 col0\" >0.02</td>\n",
+       "      <td id=\"T_8d822_row8_col1\" class=\"data row8 col1\" >-0.13</td>\n",
+       "      <td id=\"T_8d822_row8_col2\" class=\"data row8 col2\" >0.60</td>\n",
+       "      <td id=\"T_8d822_row8_col3\" class=\"data row8 col3\" >0.20</td>\n",
+       "      <td id=\"T_8d822_row8_col4\" class=\"data row8 col4\" >0.28</td>\n",
+       "      <td id=\"T_8d822_row8_col5\" class=\"data row8 col5\" >-0.46</td>\n",
+       "      <td id=\"T_8d822_row8_col6\" class=\"data row8 col6\" >0.75</td>\n",
+       "      <td id=\"T_8d822_row8_col7\" class=\"data row8 col7\" >0.85</td>\n",
+       "      <td id=\"T_8d822_row8_col8\" class=\"data row8 col8\" >1.00</td>\n",
+       "      <td id=\"T_8d822_row8_col9\" class=\"data row8 col9\" >0.45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_8d822_level0_row9\" class=\"row_heading level0 row9\" >capacity_f</th>\n",
+       "      <td id=\"T_8d822_row9_col0\" class=\"data row9 col0\" >0.03</td>\n",
+       "      <td id=\"T_8d822_row9_col1\" class=\"data row9 col1\" >-0.24</td>\n",
+       "      <td id=\"T_8d822_row9_col2\" class=\"data row9 col2\" >0.77</td>\n",
+       "      <td id=\"T_8d822_row9_col3\" class=\"data row9 col3\" >-0.04</td>\n",
+       "      <td id=\"T_8d822_row9_col4\" class=\"data row9 col4\" >0.82</td>\n",
+       "      <td id=\"T_8d822_row9_col5\" class=\"data row9 col5\" >-0.19</td>\n",
+       "      <td id=\"T_8d822_row9_col6\" class=\"data row9 col6\" >0.75</td>\n",
+       "      <td id=\"T_8d822_row9_col7\" class=\"data row9 col7\" >0.65</td>\n",
+       "      <td id=\"T_8d822_row9_col8\" class=\"data row9 col8\" >0.45</td>\n",
+       "      <td id=\"T_8d822_row9_col9\" class=\"data row9 col9\" >1.00</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x1b34bd58cd0>"
+       "<pandas.io.formats.style.Styler at 0x1fc83f67a90>"
       ]
      },
      "execution_count": 8,
@@ -1051,7 +1051,7 @@
        "                        </a>\n",
        "                        <br/><img src='https://geosaurus.maps.arcgis.com/home/js/jsapi/esri/css/images/item_type_icons/featureshosted16.png' style=\"vertical-align:middle;\" width=16 height=16>Feature Layer Collection by api_data_owner\n",
        "                        <br/>Last Modified: April 27, 2020\n",
-       "                        <br/>0 comments, 415 views\n",
+       "                        <br/>0 comments, 428 views\n",
        "                    </div>\n",
        "                </div>\n",
        "                "
@@ -1530,7 +1530,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1541,7 +1541,7 @@
     {
      "data": {
       "text/plain": [
-       "0.001445439770745928"
+       "0.0005754399373371565"
       ]
      },
      "execution_count": 20,
@@ -1593,603 +1593,603 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <td>0</td>\n",
-       "      <td>0.002915</td>\n",
-       "      <td>0.002427</td>\n",
+       "      <td>0.003993</td>\n",
+       "      <td>0.002826</td>\n",
        "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>1</td>\n",
-       "      <td>0.002403</td>\n",
-       "      <td>0.002272</td>\n",
+       "      <td>0.002635</td>\n",
+       "      <td>0.002453</td>\n",
        "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>2</td>\n",
-       "      <td>0.002272</td>\n",
-       "      <td>0.002147</td>\n",
+       "      <td>0.002427</td>\n",
+       "      <td>0.002265</td>\n",
        "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>3</td>\n",
-       "      <td>0.002125</td>\n",
-       "      <td>0.002053</td>\n",
+       "      <td>0.002233</td>\n",
+       "      <td>0.002168</td>\n",
        "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>4</td>\n",
-       "      <td>0.002148</td>\n",
-       "      <td>0.001997</td>\n",
+       "      <td>0.002187</td>\n",
+       "      <td>0.002147</td>\n",
        "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>5</td>\n",
-       "      <td>0.002077</td>\n",
-       "      <td>0.002052</td>\n",
+       "      <td>0.002235</td>\n",
+       "      <td>0.002035</td>\n",
        "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>6</td>\n",
-       "      <td>0.002157</td>\n",
-       "      <td>0.002114</td>\n",
+       "      <td>0.002045</td>\n",
+       "      <td>0.001987</td>\n",
        "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>7</td>\n",
-       "      <td>0.002050</td>\n",
-       "      <td>0.002056</td>\n",
+       "      <td>0.002112</td>\n",
+       "      <td>0.001990</td>\n",
        "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>8</td>\n",
-       "      <td>0.002078</td>\n",
-       "      <td>0.002049</td>\n",
+       "      <td>0.002057</td>\n",
+       "      <td>0.001926</td>\n",
        "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>9</td>\n",
-       "      <td>0.002174</td>\n",
-       "      <td>0.001880</td>\n",
+       "      <td>0.001978</td>\n",
+       "      <td>0.001881</td>\n",
        "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>10</td>\n",
-       "      <td>0.002143</td>\n",
-       "      <td>0.001942</td>\n",
+       "      <td>0.002029</td>\n",
+       "      <td>0.001880</td>\n",
        "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>11</td>\n",
-       "      <td>0.002180</td>\n",
-       "      <td>0.001937</td>\n",
+       "      <td>0.002024</td>\n",
+       "      <td>0.001987</td>\n",
        "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>12</td>\n",
-       "      <td>0.002070</td>\n",
-       "      <td>0.002380</td>\n",
+       "      <td>0.002015</td>\n",
+       "      <td>0.001850</td>\n",
        "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>13</td>\n",
-       "      <td>0.002155</td>\n",
-       "      <td>0.001971</td>\n",
+       "      <td>0.002048</td>\n",
+       "      <td>0.001914</td>\n",
        "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>14</td>\n",
-       "      <td>0.002038</td>\n",
-       "      <td>0.001825</td>\n",
+       "      <td>0.001991</td>\n",
+       "      <td>0.001904</td>\n",
        "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>15</td>\n",
-       "      <td>0.002060</td>\n",
-       "      <td>0.001911</td>\n",
+       "      <td>0.001982</td>\n",
+       "      <td>0.001887</td>\n",
        "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>16</td>\n",
-       "      <td>0.002039</td>\n",
-       "      <td>0.001885</td>\n",
+       "      <td>0.002081</td>\n",
+       "      <td>0.002071</td>\n",
        "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>17</td>\n",
-       "      <td>0.001926</td>\n",
-       "      <td>0.001842</td>\n",
+       "      <td>0.002040</td>\n",
+       "      <td>0.001817</td>\n",
        "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>18</td>\n",
-       "      <td>0.002069</td>\n",
-       "      <td>0.001873</td>\n",
+       "      <td>0.002070</td>\n",
+       "      <td>0.001904</td>\n",
        "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>19</td>\n",
-       "      <td>0.001889</td>\n",
-       "      <td>0.002042</td>\n",
+       "      <td>0.002146</td>\n",
+       "      <td>0.001811</td>\n",
        "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>20</td>\n",
-       "      <td>0.001960</td>\n",
        "      <td>0.001904</td>\n",
+       "      <td>0.001841</td>\n",
        "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>21</td>\n",
-       "      <td>0.001874</td>\n",
-       "      <td>0.001805</td>\n",
+       "      <td>0.001959</td>\n",
+       "      <td>0.001968</td>\n",
        "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>22</td>\n",
-       "      <td>0.001879</td>\n",
-       "      <td>0.001787</td>\n",
+       "      <td>0.002009</td>\n",
+       "      <td>0.001970</td>\n",
        "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>23</td>\n",
-       "      <td>0.001855</td>\n",
-       "      <td>0.001746</td>\n",
+       "      <td>0.002022</td>\n",
+       "      <td>0.002442</td>\n",
        "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>24</td>\n",
-       "      <td>0.001858</td>\n",
-       "      <td>0.001842</td>\n",
+       "      <td>0.001946</td>\n",
+       "      <td>0.001834</td>\n",
        "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>25</td>\n",
-       "      <td>0.001899</td>\n",
-       "      <td>0.001786</td>\n",
+       "      <td>0.001912</td>\n",
+       "      <td>0.001784</td>\n",
        "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>26</td>\n",
-       "      <td>0.001821</td>\n",
-       "      <td>0.001785</td>\n",
+       "      <td>0.001892</td>\n",
+       "      <td>0.001913</td>\n",
        "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>27</td>\n",
-       "      <td>0.001810</td>\n",
-       "      <td>0.001759</td>\n",
+       "      <td>0.001904</td>\n",
+       "      <td>0.001747</td>\n",
        "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>28</td>\n",
-       "      <td>0.001819</td>\n",
-       "      <td>0.001723</td>\n",
+       "      <td>0.001798</td>\n",
+       "      <td>0.001908</td>\n",
        "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>29</td>\n",
-       "      <td>0.001732</td>\n",
-       "      <td>0.001683</td>\n",
+       "      <td>0.001891</td>\n",
+       "      <td>0.001750</td>\n",
        "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>30</td>\n",
-       "      <td>0.001826</td>\n",
-       "      <td>0.001706</td>\n",
+       "      <td>0.001889</td>\n",
+       "      <td>0.001807</td>\n",
        "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>31</td>\n",
-       "      <td>0.001786</td>\n",
-       "      <td>0.001647</td>\n",
+       "      <td>0.001788</td>\n",
+       "      <td>0.001696</td>\n",
        "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>32</td>\n",
-       "      <td>0.001713</td>\n",
-       "      <td>0.001621</td>\n",
+       "      <td>0.001762</td>\n",
+       "      <td>0.001752</td>\n",
        "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>33</td>\n",
-       "      <td>0.001754</td>\n",
-       "      <td>0.001789</td>\n",
+       "      <td>0.001781</td>\n",
+       "      <td>0.001764</td>\n",
        "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>34</td>\n",
-       "      <td>0.001769</td>\n",
-       "      <td>0.001789</td>\n",
+       "      <td>0.001835</td>\n",
+       "      <td>0.001880</td>\n",
        "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>35</td>\n",
-       "      <td>0.001744</td>\n",
-       "      <td>0.001650</td>\n",
+       "      <td>0.001821</td>\n",
+       "      <td>0.001746</td>\n",
        "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>36</td>\n",
-       "      <td>0.001686</td>\n",
-       "      <td>0.001716</td>\n",
+       "      <td>0.001795</td>\n",
+       "      <td>0.001697</td>\n",
        "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>37</td>\n",
-       "      <td>0.001672</td>\n",
-       "      <td>0.001665</td>\n",
+       "      <td>0.001764</td>\n",
+       "      <td>0.001691</td>\n",
        "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>38</td>\n",
-       "      <td>0.001629</td>\n",
-       "      <td>0.001617</td>\n",
+       "      <td>0.001736</td>\n",
+       "      <td>0.001668</td>\n",
        "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>39</td>\n",
-       "      <td>0.001667</td>\n",
-       "      <td>0.001750</td>\n",
+       "      <td>0.001741</td>\n",
+       "      <td>0.001779</td>\n",
        "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>40</td>\n",
-       "      <td>0.001720</td>\n",
-       "      <td>0.001647</td>\n",
+       "      <td>0.001739</td>\n",
+       "      <td>0.001725</td>\n",
        "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>41</td>\n",
-       "      <td>0.001651</td>\n",
-       "      <td>0.001554</td>\n",
+       "      <td>0.001673</td>\n",
+       "      <td>0.001603</td>\n",
        "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>42</td>\n",
-       "      <td>0.001624</td>\n",
-       "      <td>0.001854</td>\n",
+       "      <td>0.001782</td>\n",
+       "      <td>0.001699</td>\n",
        "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>43</td>\n",
-       "      <td>0.001619</td>\n",
-       "      <td>0.001615</td>\n",
+       "      <td>0.001685</td>\n",
+       "      <td>0.001604</td>\n",
        "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>44</td>\n",
-       "      <td>0.001642</td>\n",
-       "      <td>0.001531</td>\n",
+       "      <td>0.001685</td>\n",
+       "      <td>0.001614</td>\n",
        "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>45</td>\n",
-       "      <td>0.001639</td>\n",
-       "      <td>0.001614</td>\n",
+       "      <td>0.001684</td>\n",
+       "      <td>0.001674</td>\n",
        "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>46</td>\n",
-       "      <td>0.001581</td>\n",
-       "      <td>0.001598</td>\n",
+       "      <td>0.001691</td>\n",
+       "      <td>0.001676</td>\n",
        "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>47</td>\n",
-       "      <td>0.001523</td>\n",
-       "      <td>0.001623</td>\n",
+       "      <td>0.001638</td>\n",
+       "      <td>0.001619</td>\n",
        "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>48</td>\n",
-       "      <td>0.001588</td>\n",
-       "      <td>0.001645</td>\n",
+       "      <td>0.001606</td>\n",
+       "      <td>0.001607</td>\n",
        "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>49</td>\n",
-       "      <td>0.001594</td>\n",
-       "      <td>0.001560</td>\n",
+       "      <td>0.001650</td>\n",
+       "      <td>0.001614</td>\n",
        "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>50</td>\n",
-       "      <td>0.001514</td>\n",
-       "      <td>0.001596</td>\n",
+       "      <td>0.001567</td>\n",
+       "      <td>0.001628</td>\n",
        "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>51</td>\n",
-       "      <td>0.001537</td>\n",
-       "      <td>0.001566</td>\n",
+       "      <td>0.001589</td>\n",
+       "      <td>0.001608</td>\n",
        "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>52</td>\n",
-       "      <td>0.001564</td>\n",
-       "      <td>0.001525</td>\n",
+       "      <td>0.001630</td>\n",
+       "      <td>0.001535</td>\n",
        "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>53</td>\n",
-       "      <td>0.001482</td>\n",
-       "      <td>0.001659</td>\n",
+       "      <td>0.001618</td>\n",
+       "      <td>0.001553</td>\n",
        "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>54</td>\n",
-       "      <td>0.001555</td>\n",
-       "      <td>0.001590</td>\n",
+       "      <td>0.001570</td>\n",
+       "      <td>0.001650</td>\n",
        "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>55</td>\n",
-       "      <td>0.001472</td>\n",
-       "      <td>0.001508</td>\n",
+       "      <td>0.001576</td>\n",
+       "      <td>0.001602</td>\n",
        "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>56</td>\n",
-       "      <td>0.001473</td>\n",
-       "      <td>0.001522</td>\n",
+       "      <td>0.001517</td>\n",
+       "      <td>0.001692</td>\n",
        "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>57</td>\n",
-       "      <td>0.001473</td>\n",
-       "      <td>0.001511</td>\n",
+       "      <td>0.001590</td>\n",
+       "      <td>0.001583</td>\n",
        "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>58</td>\n",
-       "      <td>0.001511</td>\n",
-       "      <td>0.001503</td>\n",
-       "      <td>00:01</td>\n",
+       "      <td>0.001536</td>\n",
+       "      <td>0.001549</td>\n",
+       "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>59</td>\n",
-       "      <td>0.001406</td>\n",
-       "      <td>0.001570</td>\n",
-       "      <td>00:01</td>\n",
+       "      <td>0.001552</td>\n",
+       "      <td>0.001539</td>\n",
+       "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>60</td>\n",
-       "      <td>0.001428</td>\n",
-       "      <td>0.001471</td>\n",
-       "      <td>00:01</td>\n",
+       "      <td>0.001490</td>\n",
+       "      <td>0.001507</td>\n",
+       "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>61</td>\n",
-       "      <td>0.001419</td>\n",
-       "      <td>0.001445</td>\n",
-       "      <td>00:01</td>\n",
+       "      <td>0.001539</td>\n",
+       "      <td>0.001529</td>\n",
+       "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>62</td>\n",
-       "      <td>0.001401</td>\n",
-       "      <td>0.001580</td>\n",
-       "      <td>00:01</td>\n",
+       "      <td>0.001535</td>\n",
+       "      <td>0.001516</td>\n",
+       "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>63</td>\n",
-       "      <td>0.001415</td>\n",
-       "      <td>0.001543</td>\n",
-       "      <td>00:01</td>\n",
+       "      <td>0.001519</td>\n",
+       "      <td>0.001523</td>\n",
+       "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>64</td>\n",
-       "      <td>0.001411</td>\n",
-       "      <td>0.001463</td>\n",
-       "      <td>00:01</td>\n",
+       "      <td>0.001490</td>\n",
+       "      <td>0.001523</td>\n",
+       "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>65</td>\n",
-       "      <td>0.001361</td>\n",
-       "      <td>0.001451</td>\n",
-       "      <td>00:01</td>\n",
+       "      <td>0.001423</td>\n",
+       "      <td>0.001511</td>\n",
+       "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>66</td>\n",
-       "      <td>0.001443</td>\n",
-       "      <td>0.001511</td>\n",
-       "      <td>00:01</td>\n",
+       "      <td>0.001533</td>\n",
+       "      <td>0.001707</td>\n",
+       "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>67</td>\n",
-       "      <td>0.001427</td>\n",
-       "      <td>0.001474</td>\n",
-       "      <td>00:01</td>\n",
+       "      <td>0.001460</td>\n",
+       "      <td>0.001521</td>\n",
+       "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>68</td>\n",
-       "      <td>0.001386</td>\n",
-       "      <td>0.001569</td>\n",
-       "      <td>00:01</td>\n",
+       "      <td>0.001451</td>\n",
+       "      <td>0.001519</td>\n",
+       "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>69</td>\n",
-       "      <td>0.001342</td>\n",
-       "      <td>0.001517</td>\n",
-       "      <td>00:01</td>\n",
+       "      <td>0.001492</td>\n",
+       "      <td>0.001487</td>\n",
+       "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>70</td>\n",
-       "      <td>0.001409</td>\n",
-       "      <td>0.001475</td>\n",
-       "      <td>00:01</td>\n",
+       "      <td>0.001487</td>\n",
+       "      <td>0.001496</td>\n",
+       "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>71</td>\n",
-       "      <td>0.001430</td>\n",
-       "      <td>0.001460</td>\n",
-       "      <td>00:01</td>\n",
+       "      <td>0.001455</td>\n",
+       "      <td>0.001467</td>\n",
+       "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>72</td>\n",
-       "      <td>0.001369</td>\n",
-       "      <td>0.001524</td>\n",
-       "      <td>00:01</td>\n",
+       "      <td>0.001456</td>\n",
+       "      <td>0.001469</td>\n",
+       "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>73</td>\n",
-       "      <td>0.001333</td>\n",
-       "      <td>0.001471</td>\n",
-       "      <td>00:01</td>\n",
+       "      <td>0.001427</td>\n",
+       "      <td>0.001491</td>\n",
+       "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>74</td>\n",
-       "      <td>0.001363</td>\n",
-       "      <td>0.001485</td>\n",
-       "      <td>00:01</td>\n",
+       "      <td>0.001451</td>\n",
+       "      <td>0.001449</td>\n",
+       "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>75</td>\n",
-       "      <td>0.001307</td>\n",
-       "      <td>0.001405</td>\n",
-       "      <td>00:01</td>\n",
+       "      <td>0.001385</td>\n",
+       "      <td>0.001458</td>\n",
+       "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>76</td>\n",
-       "      <td>0.001292</td>\n",
-       "      <td>0.001456</td>\n",
-       "      <td>00:01</td>\n",
+       "      <td>0.001434</td>\n",
+       "      <td>0.001466</td>\n",
+       "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>77</td>\n",
-       "      <td>0.001284</td>\n",
-       "      <td>0.001430</td>\n",
-       "      <td>00:01</td>\n",
+       "      <td>0.001484</td>\n",
+       "      <td>0.001487</td>\n",
+       "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>78</td>\n",
-       "      <td>0.001314</td>\n",
-       "      <td>0.001500</td>\n",
-       "      <td>00:01</td>\n",
+       "      <td>0.001406</td>\n",
+       "      <td>0.001484</td>\n",
+       "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>79</td>\n",
-       "      <td>0.001341</td>\n",
-       "      <td>0.001470</td>\n",
-       "      <td>00:01</td>\n",
+       "      <td>0.001442</td>\n",
+       "      <td>0.001484</td>\n",
+       "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>80</td>\n",
-       "      <td>0.001340</td>\n",
-       "      <td>0.001441</td>\n",
-       "      <td>00:01</td>\n",
+       "      <td>0.001355</td>\n",
+       "      <td>0.001475</td>\n",
+       "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>81</td>\n",
-       "      <td>0.001283</td>\n",
-       "      <td>0.001413</td>\n",
-       "      <td>00:01</td>\n",
+       "      <td>0.001411</td>\n",
+       "      <td>0.001499</td>\n",
+       "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>82</td>\n",
-       "      <td>0.001295</td>\n",
-       "      <td>0.001447</td>\n",
-       "      <td>00:01</td>\n",
+       "      <td>0.001367</td>\n",
+       "      <td>0.001466</td>\n",
+       "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>83</td>\n",
-       "      <td>0.001273</td>\n",
-       "      <td>0.001457</td>\n",
-       "      <td>00:01</td>\n",
+       "      <td>0.001446</td>\n",
+       "      <td>0.001466</td>\n",
+       "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>84</td>\n",
-       "      <td>0.001279</td>\n",
-       "      <td>0.001408</td>\n",
-       "      <td>00:01</td>\n",
+       "      <td>0.001396</td>\n",
+       "      <td>0.001467</td>\n",
+       "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>85</td>\n",
-       "      <td>0.001237</td>\n",
-       "      <td>0.001446</td>\n",
-       "      <td>00:01</td>\n",
+       "      <td>0.001399</td>\n",
+       "      <td>0.001462</td>\n",
+       "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>86</td>\n",
-       "      <td>0.001276</td>\n",
-       "      <td>0.001445</td>\n",
-       "      <td>00:01</td>\n",
+       "      <td>0.001330</td>\n",
+       "      <td>0.001451</td>\n",
+       "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>87</td>\n",
-       "      <td>0.001293</td>\n",
-       "      <td>0.001431</td>\n",
-       "      <td>00:01</td>\n",
+       "      <td>0.001401</td>\n",
+       "      <td>0.001473</td>\n",
+       "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>88</td>\n",
-       "      <td>0.001255</td>\n",
-       "      <td>0.001456</td>\n",
-       "      <td>00:01</td>\n",
+       "      <td>0.001393</td>\n",
+       "      <td>0.001444</td>\n",
+       "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>89</td>\n",
-       "      <td>0.001266</td>\n",
-       "      <td>0.001419</td>\n",
-       "      <td>00:01</td>\n",
+       "      <td>0.001313</td>\n",
+       "      <td>0.001443</td>\n",
+       "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>90</td>\n",
-       "      <td>0.001335</td>\n",
-       "      <td>0.001387</td>\n",
-       "      <td>00:01</td>\n",
+       "      <td>0.001350</td>\n",
+       "      <td>0.001472</td>\n",
+       "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>91</td>\n",
-       "      <td>0.001230</td>\n",
-       "      <td>0.001428</td>\n",
-       "      <td>00:01</td>\n",
+       "      <td>0.001339</td>\n",
+       "      <td>0.001466</td>\n",
+       "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>92</td>\n",
-       "      <td>0.001218</td>\n",
-       "      <td>0.001422</td>\n",
-       "      <td>00:01</td>\n",
+       "      <td>0.001339</td>\n",
+       "      <td>0.001468</td>\n",
+       "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>93</td>\n",
-       "      <td>0.001242</td>\n",
-       "      <td>0.001393</td>\n",
-       "      <td>00:01</td>\n",
+       "      <td>0.001404</td>\n",
+       "      <td>0.001437</td>\n",
+       "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>94</td>\n",
-       "      <td>0.001187</td>\n",
-       "      <td>0.001413</td>\n",
-       "      <td>00:01</td>\n",
+       "      <td>0.001313</td>\n",
+       "      <td>0.001497</td>\n",
+       "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>95</td>\n",
-       "      <td>0.001235</td>\n",
-       "      <td>0.001447</td>\n",
-       "      <td>00:01</td>\n",
+       "      <td>0.001341</td>\n",
+       "      <td>0.001470</td>\n",
+       "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>96</td>\n",
-       "      <td>0.001237</td>\n",
-       "      <td>0.001441</td>\n",
-       "      <td>00:01</td>\n",
+       "      <td>0.001346</td>\n",
+       "      <td>0.001494</td>\n",
+       "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>97</td>\n",
-       "      <td>0.001271</td>\n",
-       "      <td>0.001440</td>\n",
-       "      <td>00:01</td>\n",
+       "      <td>0.001317</td>\n",
+       "      <td>0.001455</td>\n",
+       "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>98</td>\n",
-       "      <td>0.001253</td>\n",
-       "      <td>0.001423</td>\n",
-       "      <td>00:01</td>\n",
+       "      <td>0.001334</td>\n",
+       "      <td>0.001459</td>\n",
+       "      <td>00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>99</td>\n",
-       "      <td>0.001230</td>\n",
-       "      <td>0.001416</td>\n",
-       "      <td>00:01</td>\n",
+       "      <td>0.001373</td>\n",
+       "      <td>0.001492</td>\n",
+       "      <td>00:00</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>"
@@ -2250,7 +2250,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -2354,7 +2354,7 @@
        "      <td>6.0</td>\n",
        "      <td>520</td>\n",
        "      <td>3.570236</td>\n",
-       "      <td>0.218953</td>\n",
+       "      <td>0.205817</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>158</th>\n",
@@ -2368,7 +2368,7 @@
        "      <td>-3.0</td>\n",
        "      <td>480</td>\n",
        "      <td>7.964495</td>\n",
-       "      <td>0.132769</td>\n",
+       "      <td>0.124888</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5927</th>\n",
@@ -2382,7 +2382,7 @@
        "      <td>6.5</td>\n",
        "      <td>560</td>\n",
        "      <td>3.339548</td>\n",
-       "      <td>0.243919</td>\n",
+       "      <td>0.243402</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7428</th>\n",
@@ -2396,7 +2396,7 @@
        "      <td>12.5</td>\n",
        "      <td>1360</td>\n",
        "      <td>6.224127</td>\n",
-       "      <td>0.101628</td>\n",
+       "      <td>0.111588</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8900</th>\n",
@@ -2410,7 +2410,7 @@
        "      <td>-3.0</td>\n",
        "      <td>480</td>\n",
        "      <td>7.412553</td>\n",
-       "      <td>0.006903</td>\n",
+       "      <td>0.001019</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -2432,11 +2432,11 @@
        "8900           0       -1.5       -3.0      480    7.412553   \n",
        "\n",
        "      prediction_results  \n",
-       "3150            0.218953  \n",
-       "158             0.132769  \n",
-       "5927            0.243919  \n",
-       "7428            0.101628  \n",
-       "8900            0.006903  "
+       "3150            0.205817  \n",
+       "158             0.124888  \n",
+       "5927            0.243402  \n",
+       "7428            0.111588  \n",
+       "8900            0.001019  "
       ]
      },
      "execution_count": 23,
@@ -2496,7 +2496,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "r_Square_fcn_test:  0.84667\n"
+      "r_Square_fcn_test:  0.84108\n"
      ]
     }
    ],
@@ -2574,16 +2574,16 @@
       "text/html": [
        "<div class=\"item_container\" style=\"height: auto; overflow: hidden; border: 1px solid #cfcfcf; border-radius: 2px; background: #f6fafa; line-height: 1.21429em; padding: 10px;\">\n",
        "                    <div class=\"item_left\" style=\"width: 210px; float: left;\">\n",
-       "                       <a href='https://geosaurus.maps.arcgis.com/home/item.html?id=0229e1ac47ee42c89f49c4eeac236825' target='_blank'>\n",
+       "                       <a href='https://geosaurus.maps.arcgis.com/home/item.html?id=5e9c7759eaaa492daa5c6ec0a493cf72' target='_blank'>\n",
        "                        <img src='http://static.arcgis.com/images/desktopapp.png' class=\"itemThumbnail\">\n",
        "                       </a>\n",
        "                    </div>\n",
        "\n",
        "                    <div class=\"item_right\"     style=\"float: none; width: auto; overflow: hidden;\">\n",
-       "                        <a href='https://geosaurus.maps.arcgis.com/home/item.html?id=0229e1ac47ee42c89f49c4eeac236825' target='_blank'><b>prediction_layer</b>\n",
+       "                        <a href='https://geosaurus.maps.arcgis.com/home/item.html?id=5e9c7759eaaa492daa5c6ec0a493cf72' target='_blank'><b>prediction_layer</b>\n",
        "                        </a>\n",
        "                        <br/><img src='https://geosaurus.maps.arcgis.com/home/js/jsapi/esri/css/images/item_type_icons/featureshosted16.png' style=\"vertical-align:middle;\" width=16 height=16>Feature Layer Collection by api_data_owner\n",
-       "                        <br/>Last Modified: July 24, 2024\n",
+       "                        <br/>Last Modified: July 29, 2024\n",
        "                        <br/>0 comments, 0 views\n",
        "                    </div>\n",
        "                </div>\n",
@@ -2658,15 +2658,15 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>1</td>\n",
-       "      <td>401.0</td>\n",
-       "      <td>2016-07-05</td>\n",
+       "      <td>1201.0</td>\n",
+       "      <td>2018-09-10</td>\n",
        "      <td>164440</td>\n",
        "      <td>Southland Leisure Centre</td>\n",
        "      <td>1100.0</td>\n",
        "      <td>50.962485</td>\n",
        "      <td>-114.108472</td>\n",
-       "      <td>3.787571</td>\n",
-       "      <td>58060.800781</td>\n",
+       "      <td>3.71462</td>\n",
+       "      <td>45619.199219</td>\n",
        "      <td>...</td>\n",
        "      <td>arcgis_python</td>\n",
        "      <td>2020-04-27</td>\n",
@@ -2676,21 +2676,21 @@
        "      <td>8512ccc3fffffff</td>\n",
        "      <td>8612ccd57ffffff</td>\n",
        "      <td>8712ccd52ffffff</td>\n",
-       "      <td>0.203452</td>\n",
+       "      <td>0.129638</td>\n",
        "      <td>{\"x\": -12702497.020502415, \"y\": 6614660.374377...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>2</td>\n",
-       "      <td>402.0</td>\n",
-       "      <td>2016-07-06</td>\n",
+       "      <td>1202.0</td>\n",
+       "      <td>2018-09-11</td>\n",
        "      <td>164440</td>\n",
        "      <td>Southland Leisure Centre</td>\n",
        "      <td>1100.0</td>\n",
        "      <td>50.962485</td>\n",
        "      <td>-114.108472</td>\n",
-       "      <td>3.30231</td>\n",
-       "      <td>58060.800781</td>\n",
+       "      <td>3.663262</td>\n",
+       "      <td>45619.199219</td>\n",
        "      <td>...</td>\n",
        "      <td>arcgis_python</td>\n",
        "      <td>2020-04-27</td>\n",
@@ -2700,21 +2700,21 @@
        "      <td>8512ccc3fffffff</td>\n",
        "      <td>8612ccd57ffffff</td>\n",
        "      <td>8712ccd52ffffff</td>\n",
-       "      <td>0.183459</td>\n",
+       "      <td>0.149016</td>\n",
        "      <td>{\"x\": -12702497.020502415, \"y\": 6614660.374377...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>3</td>\n",
-       "      <td>403.0</td>\n",
-       "      <td>2016-07-07</td>\n",
+       "      <td>1203.0</td>\n",
+       "      <td>2018-09-12</td>\n",
        "      <td>164440</td>\n",
        "      <td>Southland Leisure Centre</td>\n",
        "      <td>1100.0</td>\n",
        "      <td>50.962485</td>\n",
        "      <td>-114.108472</td>\n",
-       "      <td>3.923609</td>\n",
-       "      <td>58060.800781</td>\n",
+       "      <td>3.847847</td>\n",
+       "      <td>45273.601562</td>\n",
        "      <td>...</td>\n",
        "      <td>arcgis_python</td>\n",
        "      <td>2020-04-27</td>\n",
@@ -2724,21 +2724,21 @@
        "      <td>8512ccc3fffffff</td>\n",
        "      <td>8612ccd57ffffff</td>\n",
        "      <td>8712ccd52ffffff</td>\n",
-       "      <td>0.233764</td>\n",
+       "      <td>0.0457</td>\n",
        "      <td>{\"x\": -12702497.020502415, \"y\": 6614660.374377...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>4</td>\n",
-       "      <td>404.0</td>\n",
-       "      <td>2016-07-08</td>\n",
+       "      <td>1204.0</td>\n",
+       "      <td>2018-09-13</td>\n",
        "      <td>164440</td>\n",
        "      <td>Southland Leisure Centre</td>\n",
        "      <td>1100.0</td>\n",
        "      <td>50.962485</td>\n",
        "      <td>-114.108472</td>\n",
-       "      <td>4.37531</td>\n",
-       "      <td>57715.199219</td>\n",
+       "      <td>3.958236</td>\n",
+       "      <td>44928.0</td>\n",
        "      <td>...</td>\n",
        "      <td>arcgis_python</td>\n",
        "      <td>2020-04-27</td>\n",
@@ -2748,21 +2748,21 @@
        "      <td>8512ccc3fffffff</td>\n",
        "      <td>8612ccd57ffffff</td>\n",
        "      <td>8712ccd52ffffff</td>\n",
-       "      <td>0.224179</td>\n",
+       "      <td>0.04043</td>\n",
        "      <td>{\"x\": -12702497.020502415, \"y\": 6614660.374377...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>5</td>\n",
-       "      <td>405.0</td>\n",
-       "      <td>2016-07-09</td>\n",
+       "      <td>1205.0</td>\n",
+       "      <td>2018-09-14</td>\n",
        "      <td>164440</td>\n",
        "      <td>Southland Leisure Centre</td>\n",
        "      <td>1100.0</td>\n",
        "      <td>50.962485</td>\n",
        "      <td>-114.108472</td>\n",
-       "      <td>2.816725</td>\n",
-       "      <td>57715.199219</td>\n",
+       "      <td>4.275449</td>\n",
+       "      <td>44582.398438</td>\n",
        "      <td>...</td>\n",
        "      <td>arcgis_python</td>\n",
        "      <td>2020-04-27</td>\n",
@@ -2772,7 +2772,7 @@
        "      <td>8512ccc3fffffff</td>\n",
        "      <td>8612ccd57ffffff</td>\n",
        "      <td>8712ccd52ffffff</td>\n",
-       "      <td>0.177944</td>\n",
+       "      <td>0.046646</td>\n",
        "      <td>{\"x\": -12702497.020502415, \"y\": 6614660.374377...</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -2781,19 +2781,19 @@
        "</div>"
       ],
       "text/plain": [
-       "   FID  FID_1     Field1      ID                solar_plan  altitude_m  \\\n",
-       "0    1  401.0 2016-07-05  164440  Southland Leisure Centre      1100.0   \n",
-       "1    2  402.0 2016-07-06  164440  Southland Leisure Centre      1100.0   \n",
-       "2    3  403.0 2016-07-07  164440  Southland Leisure Centre      1100.0   \n",
-       "3    4  404.0 2016-07-08  164440  Southland Leisure Centre      1100.0   \n",
-       "4    5  405.0 2016-07-09  164440  Southland Leisure Centre      1100.0   \n",
+       "   FID   FID_1     Field1      ID                solar_plan  altitude_m  \\\n",
+       "0    1  1201.0 2018-09-10  164440  Southland Leisure Centre      1100.0   \n",
+       "1    2  1202.0 2018-09-11  164440  Southland Leisure Centre      1100.0   \n",
+       "2    3  1203.0 2018-09-12  164440  Southland Leisure Centre      1100.0   \n",
+       "3    4  1204.0 2018-09-13  164440  Southland Leisure Centre      1100.0   \n",
+       "4    5  1205.0 2018-09-14  164440  Southland Leisure Centre      1100.0   \n",
        "\n",
        "    latitude   longitude  wind_speed      dayl__s_  ...        Creator  \\\n",
-       "0  50.962485 -114.108472    3.787571  58060.800781  ...  arcgis_python   \n",
-       "1  50.962485 -114.108472     3.30231  58060.800781  ...  arcgis_python   \n",
-       "2  50.962485 -114.108472    3.923609  58060.800781  ...  arcgis_python   \n",
-       "3  50.962485 -114.108472     4.37531  57715.199219  ...  arcgis_python   \n",
-       "4  50.962485 -114.108472    2.816725  57715.199219  ...  arcgis_python   \n",
+       "0  50.962485 -114.108472     3.71462  45619.199219  ...  arcgis_python   \n",
+       "1  50.962485 -114.108472    3.663262  45619.199219  ...  arcgis_python   \n",
+       "2  50.962485 -114.108472    3.847847  45273.601562  ...  arcgis_python   \n",
+       "3  50.962485 -114.108472    3.958236       44928.0  ...  arcgis_python   \n",
+       "4  50.962485 -114.108472    4.275449  44582.398438  ...  arcgis_python   \n",
        "\n",
        "    EditDate         Editor         zone3_id         zone4_id  \\\n",
        "0 2020-04-27  arcgis_python  8312ccfffffffff  8412ccdffffffff   \n",
@@ -2803,11 +2803,11 @@
        "4 2020-04-27  arcgis_python  8312ccfffffffff  8412ccdffffffff   \n",
        "\n",
        "          zone5_id         zone6_id         zone7_id prediction  \\\n",
-       "0  8512ccc3fffffff  8612ccd57ffffff  8712ccd52ffffff   0.203452   \n",
-       "1  8512ccc3fffffff  8612ccd57ffffff  8712ccd52ffffff   0.183459   \n",
-       "2  8512ccc3fffffff  8612ccd57ffffff  8712ccd52ffffff   0.233764   \n",
-       "3  8512ccc3fffffff  8612ccd57ffffff  8712ccd52ffffff   0.224179   \n",
-       "4  8512ccc3fffffff  8612ccd57ffffff  8712ccd52ffffff   0.177944   \n",
+       "0  8512ccc3fffffff  8612ccd57ffffff  8712ccd52ffffff   0.129638   \n",
+       "1  8512ccc3fffffff  8612ccd57ffffff  8712ccd52ffffff   0.149016   \n",
+       "2  8512ccc3fffffff  8612ccd57ffffff  8712ccd52ffffff     0.0457   \n",
+       "3  8512ccc3fffffff  8612ccd57ffffff  8712ccd52ffffff    0.04043   \n",
+       "4  8512ccc3fffffff  8612ccd57ffffff  8712ccd52ffffff   0.046646   \n",
        "\n",
        "                                               SHAPE  \n",
        "0  {\"x\": -12702497.020502415, \"y\": 6614660.374377...  \n",
@@ -2890,6 +2890,21 @@
    "cell_type": "code",
    "execution_count": 30,
    "metadata": {},
+   "outputs": [],
+   "source": [
+    "optional_columns = ['prediction_results','prediction']\n",
+    "\n",
+    "pred_col = None \n",
+    "for opt_col in optional_columns:\n",
+    "    if opt_col in test_pred_layer_sdf.columns:\n",
+    "        pred_col = opt_col\n",
+    "        break"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -2925,27 +2940,27 @@
        "    <tr>\n",
        "      <th>2015-09-01</th>\n",
        "      <td>286.013</td>\n",
-       "      <td>681.206653</td>\n",
+       "      <td>645.847283</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2015-09-02</th>\n",
        "      <td>681.646</td>\n",
-       "      <td>599.791438</td>\n",
+       "      <td>573.81391</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2015-09-03</th>\n",
        "      <td>647.906</td>\n",
-       "      <td>663.318879</td>\n",
+       "      <td>550.952354</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2015-09-04</th>\n",
        "      <td>102.448</td>\n",
-       "      <td>269.727274</td>\n",
+       "      <td>188.131957</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2015-09-05</th>\n",
        "      <td>93.432</td>\n",
-       "      <td>174.089288</td>\n",
+       "      <td>105.872621</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -2955,27 +2970,27 @@
        "    <tr>\n",
        "      <th>2019-12-27</th>\n",
        "      <td>1.349</td>\n",
-       "      <td>50.156944</td>\n",
+       "      <td>40.980099</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2019-12-28</th>\n",
        "      <td>1.965</td>\n",
-       "      <td>36.086396</td>\n",
+       "      <td>22.588846</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2019-12-29</th>\n",
        "      <td>1.616</td>\n",
-       "      <td>83.125343</td>\n",
+       "      <td>74.11202</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2019-12-30</th>\n",
        "      <td>7.44</td>\n",
-       "      <td>116.609573</td>\n",
+       "      <td>110.594964</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2019-12-31</th>\n",
        "      <td>8.323</td>\n",
-       "      <td>85.097154</td>\n",
+       "      <td>57.998556</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -2985,35 +3000,35 @@
       "text/plain": [
        "            Actual_generation(KWh)  predicted_generation(KWh)\n",
        "date                                                         \n",
-       "2015-09-01                 286.013                 681.206653\n",
-       "2015-09-02                 681.646                 599.791438\n",
-       "2015-09-03                 647.906                 663.318879\n",
-       "2015-09-04                 102.448                 269.727274\n",
-       "2015-09-05                  93.432                 174.089288\n",
+       "2015-09-01                 286.013                 645.847283\n",
+       "2015-09-02                 681.646                  573.81391\n",
+       "2015-09-03                 647.906                 550.952354\n",
+       "2015-09-04                 102.448                 188.131957\n",
+       "2015-09-05                  93.432                 105.872621\n",
        "...                            ...                        ...\n",
-       "2019-12-27                   1.349                  50.156944\n",
-       "2019-12-28                   1.965                  36.086396\n",
-       "2019-12-29                   1.616                  83.125343\n",
-       "2019-12-30                    7.44                 116.609573\n",
-       "2019-12-31                   8.323                  85.097154\n",
+       "2019-12-27                   1.349                  40.980099\n",
+       "2019-12-28                   1.965                  22.588846\n",
+       "2019-12-29                   1.616                   74.11202\n",
+       "2019-12-30                    7.44                 110.594964\n",
+       "2019-12-31                   8.323                  57.998556\n",
        "\n",
        "[1590 rows x 2 columns]"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "# inverse scaling from capcacity factor to actual generation in KWh  - peak capcity of Southland Leisure Centre is 153KWp\n",
-    "test_pred_datetime = test_pred_layer_sdf[['Field1','capacity_f','prediction']].copy()\n",
+    "test_pred_datetime = test_pred_layer_sdf[['Field1','capacity_f',pred_col]].copy()\n",
     "test_pred_datetime = test_pred_datetime.rename(columns={'Field1':'date'})\n",
     "test_pred_datetime['date'] = pd.to_datetime(test_pred_datetime['date']) \n",
     "test_pred_datetime = test_pred_datetime.set_index(test_pred_datetime['date'])\n",
     "test_pred_datetime['Actual_generation(KWh)'] = test_pred_datetime['capacity_f']*24*153\n",
-    "test_pred_datetime['predicted_generation(KWh)'] = test_pred_datetime['prediction']*24*153\n",
-    "test_pred_datetime = test_pred_datetime.drop(['date','capacity_f','prediction'], axis=1).sort_index() \n",
+    "test_pred_datetime['predicted_generation(KWh)'] = test_pred_datetime[pred_col]*24*153\n",
+    "test_pred_datetime = test_pred_datetime.drop(['date','capacity_f',pred_col], axis=1).sort_index() \n",
     "test_pred_datetime"
    ]
   },
@@ -3026,7 +3041,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
@@ -3847,13 +3862,13 @@
    ],
    "source": [
     "# inverse scaling from capcacity factor to actual generation in KWh  - peak capcity of Southland Leisure Centre is 153KWp\n",
-    "valid_pred_datetime = valid_pred_layer_sdf[['Field1','capacity_f','prediction']].copy()\n",
+    "valid_pred_datetime = valid_pred_layer_sdf[['Field1','capacity_f',pred_col]].copy()\n",
     "valid_pred_datetime = valid_pred_datetime.rename(columns={'Field1':'date'})\n",
     "valid_pred_datetime['date'] = pd.to_datetime(valid_pred_datetime['date']) \n",
     "valid_pred_datetime = valid_pred_datetime.set_index(valid_pred_datetime['date'])\n",
     "valid_pred_datetime['Actual_generation(KWh)'] = valid_pred_datetime['capacity_f']*24*153\n",
-    "valid_pred_datetime['predicted_generation(KWh)'] = valid_pred_datetime['prediction']*24*153\n",
-    "valid_pred_datetime = valid_pred_datetime.drop(['date','capacity_f','prediction'], axis=1)\n",
+    "valid_pred_datetime['predicted_generation(KWh)'] = valid_pred_datetime[pred_col]*24*153\n",
+    "valid_pred_datetime = valid_pred_datetime.drop(['date','capacity_f',pred_col], axis=1)\n",
     "valid_pred_datetime = valid_pred_datetime.sort_index() \n",
     "valid_pred_datetime.head()"
    ]