nyctaxi (ai42).ipynb

{"cells":[{"cell_type":"markdown","source":["download parquet files from this web site : https://www1.nyc.gov/site/tlc/about/tlc-trip-record-data.page"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"e9d19066-83fd-4f5d-9569-f48bb8b853f7"}}},{"cell_type":"markdown","source":["# Get data from Data Lake"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"edaec3e5-f3c2-4638-9b21-cfbb0a685c39"}}},{"cell_type":"code","source":["years_path = [\n            \"/mnt/delta/nyctaxi/parquet/2019/**\",\n            \"/mnt/delta/nyctaxi/parquet/2020/**\",\n            \"/mnt/delta/nyctaxi/parquet/2021/**\",\n            \"/mnt/delta/nyctaxi/parquet/2022/**\"\n]"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"9244050e-84d8-47a3-816d-5bd2c228e60f"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\"></div>","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\"></div>"]}}],"execution_count":0},{"cell_type":"code","source":["sdf = spark.read.parquet(*years_path)"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"cd23aadd-890f-4416-877a-41f2ea1d0f8b"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\"></div>","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\"></div>"]}}],"execution_count":0},{"cell_type":"code","source":["sdf.count()\n# 2019 - 2022Q1 : 149 223 088\n# 2018 - 2022Q1 : 252 094 475"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"04128ecc-db38-4f0a-a191-e0acaaa00437"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">Out[3]: 149223088</div>","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">Out[3]: 149223088</div>"]}}],"execution_count":0},{"cell_type":"code","source":["display(sdf)"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"19c50e99-0e60-4fb1-9fe5-b468620c8e8b"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"overflow":true,"datasetInfos":[],"data":[[1,"2019-03-01T00:24:41.000+0000","2019-03-01T00:25:31.000+0000",1.0,0.0,1.0,"N",145,145,2,2.5,0.5,0.5,0.0,0.0,0.3,3.8,0.0,null],[1,"2019-03-01T00:25:27.000+0000","2019-03-01T00:36:37.000+0000",2.0,3.7,1.0,"N",95,130,1,13.0,0.5,0.5,0.7,0.0,0.3,15.0,0.0,null],[1,"2019-03-01T00:05:21.000+0000","2019-03-01T00:38:23.000+0000",1.0,14.1,1.0,"N",249,28,1,41.0,3.0,0.5,10.1,5.76,0.3,60.66,2.5,null],[1,"2019-03-01T00:48:55.000+0000","2019-03-01T01:06:03.000+0000",1.0,9.6,1.0,"N",138,98,2,27.0,0.5,0.5,0.0,0.0,0.3,28.3,0.0,null],[1,"2019-03-01T00:11:42.000+0000","2019-03-01T00:16:40.000+0000",1.0,0.8,1.0,"N",48,48,1,5.5,3.0,0.5,3.0,0.0,0.3,12.3,2.5,null],[1,"2019-03-01T00:45:03.000+0000","2019-03-01T00:49:38.000+0000",1.0,1.2,1.0,"N",246,48,2,6.0,3.0,0.5,0.0,0.0,0.3,9.8,2.5,null],[1,"2019-03-01T00:02:37.000+0000","2019-03-01T00:07:30.000+0000",1.0,0.6,1.0,"Y",239,238,2,5.5,3.0,0.5,0.0,0.0,0.3,9.3,2.5,null],[2,"2019-02-28T19:52:45.000+0000","2019-02-28T20:01:54.000+0000",1.0,5.65,1.0,"N",132,197,2,17.0,0.5,0.5,0.0,0.0,0.3,18.3,0.0,null],[2,"2019-03-01T00:14:23.000+0000","2019-03-01T00:19:17.000+0000",1.0,1.16,1.0,"N",229,170,1,6.0,0.5,0.5,2.45,0.0,0.3,12.25,2.5,null],[2,"2019-03-01T00:19:09.000+0000","2019-03-01T00:23:30.000+0000",1.0,0.71,1.0,"N",137,234,1,5.0,0.5,0.5,1.0,0.0,0.3,9.8,2.5,null],[2,"2019-03-01T00:06:46.000+0000","2019-03-01T00:17:09.000+0000",5.0,2.63,1.0,"N",231,246,1,10.5,0.5,0.5,2.86,0.0,0.3,17.16,2.5,null],[1,"2019-03-01T00:00:46.000+0000","2019-03-01T00:26:41.000+0000",1.0,5.0,1.0,"N",230,7,3,20.5,3.0,0.5,0.0,0.0,0.3,24.3,2.5,null],[2,"2019-03-01T00:00:40.000+0000","2019-03-01T00:49:26.000+0000",2.0,14.04,1.0,"N",170,123,1,45.5,0.5,0.5,14.79,0.0,0.3,64.09,2.5,null],[1,"2019-03-01T00:21:46.000+0000","2019-03-01T00:30:25.000+0000",1.0,2.0,1.0,"N",143,263,1,9.0,3.0,0.5,2.55,0.0,0.3,15.35,2.5,null],[2,"2019-03-01T00:25:16.000+0000","2019-03-01T00:31:39.000+0000",5.0,2.11,1.0,"N",229,236,1,8.0,0.5,0.5,2.0,0.0,0.3,13.8,2.5,null],[1,"2019-03-01T00:03:09.000+0000","2019-03-01T00:49:16.000+0000",1.0,6.8,1.0,"N",162,49,2,31.5,3.0,0.5,0.0,0.0,0.3,35.3,2.5,null],[2,"2019-03-01T00:55:49.000+0000","2019-03-01T01:11:06.000+0000",1.0,5.97,1.0,"N",152,153,2,19.0,0.5,0.5,0.0,0.0,0.3,20.3,0.0,null],[1,"2019-03-01T00:35:42.000+0000","2019-03-01T00:47:06.000+0000",1.0,3.4,1.0,"N",162,74,1,12.0,3.0,0.5,1.5,0.0,0.3,17.3,2.5,null],[2,"2019-03-01T00:45:03.000+0000","2019-03-01T01:14:25.000+0000",1.0,16.89,2.0,"N",132,229,1,52.0,0.0,0.5,11.06,0.0,0.3,66.36,2.5,null],[1,"2019-03-01T00:42:38.000+0000","2019-03-01T01:02:58.000+0000",1.0,5.4,1.0,"N",25,246,1,19.5,3.0,0.5,3.5,0.0,0.3,26.8,2.5,null],[1,"2019-03-01T00:26:45.000+0000","2019-03-01T00:45:42.000+0000",0.0,10.6,1.0,"N",138,144,1,30.0,3.0,0.5,6.75,0.0,0.3,40.55,2.5,null],[1,"2019-03-01T00:54:07.000+0000","2019-03-01T01:01:53.000+0000",0.0,1.8,1.0,"N",144,90,1,8.5,3.0,0.5,2.45,0.0,0.3,14.75,2.5,null],[1,"2019-03-01T00:25:52.000+0000","2019-03-01T00:31:29.000+0000",1.0,1.0,1.0,"N",249,125,1,6.0,3.0,0.5,1.95,0.0,0.3,11.75,2.5,null],[1,"2019-03-01T00:17:10.000+0000","2019-03-01T00:41:03.000+0000",1.0,6.1,1.0,"N",137,7,2,21.5,3.0,0.5,0.0,0.0,0.3,25.3,2.5,null],[2,"2019-03-01T00:40:30.000+0000","2019-03-01T01:01:37.000+0000",3.0,4.49,1.0,"N",107,25,1,18.0,0.5,0.5,4.36,0.0,0.3,26.16,2.5,null],[2,"2019-03-01T00:12:19.000+0000","2019-03-01T00:53:34.000+0000",4.0,18.66,2.0,"N",132,68,1,52.0,0.0,0.5,15.26,5.76,0.3,76.32,2.5,null],[2,"2019-03-01T00:13:43.000+0000","2019-03-01T00:21:39.000+0000",2.0,1.67,1.0,"N",107,161,1,8.0,0.5,0.5,2.36,0.0,0.3,14.16,2.5,null],[2,"2019-03-01T00:10:51.000+0000","2019-03-01T00:28:23.000+0000",1.0,3.35,1.0,"N",162,231,1,14.0,0.5,0.5,2.67,0.0,0.3,20.47,2.5,null],[2,"2019-03-01T00:46:39.000+0000","2019-03-01T00:55:10.000+0000",1.0,1.62,1.0,"N",231,88,1,7.5,0.5,0.5,2.26,0.0,0.3,13.56,2.5,null],[1,"2019-03-01T00:11:09.000+0000","2019-03-01T00:12:40.000+0000",1.0,0.1,1.0,"N",161,161,2,3.0,3.0,0.5,0.0,0.0,0.3,6.8,2.5,null],[1,"2019-03-01T00:42:29.000+0000","2019-03-01T00:58:55.000+0000",1.0,4.0,1.0,"N",137,143,1,14.5,3.0,0.5,3.0,0.0,0.3,21.3,2.5,null],[1,"2019-03-01T00:18:31.000+0000","2019-03-01T00:21:14.000+0000",2.0,0.5,1.0,"N",148,79,1,4.0,3.0,0.5,1.5,0.0,0.3,9.3,2.5,null],[1,"2019-03-01T00:31:33.000+0000","2019-03-01T00:49:14.000+0000",0.0,2.5,1.0,"N",68,144,1,12.5,3.0,0.5,1.0,0.0,0.3,17.3,2.5,null],[1,"2019-03-01T00:04:03.000+0000","2019-03-01T00:12:11.000+0000",1.0,1.5,1.0,"N",164,68,2,8.0,3.0,0.5,0.0,0.0,0.3,11.8,2.5,null],[1,"2019-03-01T00:54:39.000+0000","2019-03-01T01:00:48.000+0000",1.0,1.5,1.0,"N",48,237,1,7.0,3.0,0.5,2.15,0.0,0.3,12.95,2.5,null],[1,"2019-03-01T00:04:37.000+0000","20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3-01T00:41:43.000+0000","2019-03-01T00:46:54.000+0000",1.0,0.86,1.0,"N",234,90,1,5.5,0.5,0.5,1.86,0.0,0.3,11.16,2.5,null],[2,"2019-02-28T23:55:18.000+0000","2019-03-01T00:13:32.000+0000",1.0,4.2,1.0,"N",107,236,1,15.5,0.5,0.5,3.86,0.0,0.3,23.16,2.5,null],[2,"2019-03-01T00:46:40.000+0000","2019-03-01T00:53:43.000+0000",1.0,1.43,1.0,"N",234,162,1,7.0,0.5,0.5,2.16,0.0,0.3,12.96,2.5,null],[2,"2019-03-01T00:04:39.000+0000","2019-03-01T00:11:56.000+0000",1.0,1.34,1.0,"N",144,158,1,7.0,0.5,0.5,2.7,0.0,0.3,13.5,2.5,null],[2,"2019-03-01T00:13:19.000+0000","2019-03-01T00:22:45.000+0000",1.0,2.02,1.0,"N",158,13,1,9.0,0.5,0.5,1.0,0.0,0.3,13.8,2.5,null],[2,"2019-03-01T00:25:37.000+0000","2019-03-01T00:39:16.000+0000",1.0,4.1,1.0,"N",13,48,1,14.0,0.5,0.5,3.56,0.0,0.3,21.36,2.5,null],[2,"2019-03-01T00:20:12.000+0000","2019-03-01T00:28:28.000+0000",1.0,0.43,1.0,"N",211,148,2,6.5,0.5,0.5,0.0,0.0,0.3,10.3,2.5,null],[2,"2019-03-01T00:09:43.000+0000","2019-03-01T00:21:30.000+0000",1.0,2.06,1.0,"N",230,249,1,10.0,0.5,0.5,2.76,0.0,0.3,16.56,2.5,null],[2,"2019-03-01T00:09:36.000+0000","2019-03-01T00:16:36.000+0000",1.0,1.64,1.0,"N",164,48,1,7.0,0.5,0.5,2.16,0.0,0.3,12.96,2.5,null],[2,"2019-03-01T00:35:06.000+0000","2019-03-01T00:44:18.000+0000",1.0,1.78,1.0,"N",238,236,1,8.5,0.5,0.5,2.46,0.0,0.3,14.76,2.5,null],[2,"2019-03-01T00:09:11.000+0000","2019-03-01T00:15:59.000+0000",5.0,1.25,1.0,"N",161,48,1,7.0,0.5,0.5,2.16,0.0,0.3,12.96,2.5,null],[2,"2019-03-01T00:36:02.000+0000","2019-03-01T00:42:36.000+0000",5.0,1.36,1.0,"N",100,229,1,7.0,0.5,0.5,2.16,0.0,0.3,12.96,2.5,null],[2,"2019-03-01T00:54:43.000+0000","2019-03-01T01:02:27.000+0000",5.0,1.84,1.0,"N",100,68,1,8.0,0.5,0.5,2.36,0.0,0.3,14.16,2.5,null],[1,"2019-03-01T00:01:29.000+0000","2019-03-01T00:10:14.000+0000",1.0,1.7,1.0,"N",142,68,1,8.0,3.0,0.5,1.5,0.0,0.3,13.3,2.5,null]],"plotOptions":{"displayType":"table","customPlotOptions":{},"pivotColumns":null,"pivotAggregation":null,"xColumns":null,"yColumns":null},"columnCustomDisplayInfos":{},"aggType":"","isJsonSchema":true,"removedWidgets":[],"aggSchema":[],"schema":[{"name":"VendorID","type":"\"long\"","metadata":"{}"},{"name":"tpep_pickup_datetime","type":"\"timestamp\"","metadata":"{}"},{"name":"tpep_dropoff_datetime","type":"\"timestamp\"","metadata":"{}"},{"name":"passenger_count","type":"\"double\"","metadata":"{}"},{"name":"trip_distance","type":"\"double\"","metadata":"{}"},{"name":"RatecodeID","type":"\"double\"","metadata":"{}"},{"name":"store_and_fwd_flag","type":"\"string\"","metadata":"{}"},{"name":"PULocationID","type":"\"long\"","metadata":"{}"},{"name":"DOLocationID","type":"\"long\"","metadata":"{}"},{"name":"payment_type","type":"\"long\"","metadata":"{}"},{"name":"fare_amount","type":"\"double\"","metadata":"{}"},{"name":"extra","type":"\"double\"","metadata":"{}"},{"name":"mta_tax","type":"\"double\"","metadata":"{}"},{"name":"tip_amount","type":"\"double\"","metadata":"{}"},{"name":"tolls_amount","type":"\"double\"","metadata":"{}"},{"name":"improvement_surcharge","type":"\"double\"","metadata":"{}"},{"name":"total_amount","type":"\"double\"","metadata":"{}"},{"name":"congestion_surcharge","type":"\"double\"","metadata":"{}"},{"name":"airport_fee","type":"\"integer\"","metadata":"{}"}],"aggError":"","aggData":[],"addedWidgets":{},"metadata":{},"dbfsResultPath":null,"type":"table","aggOverflow":false,"aggSeriesLimitReached":false,"arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .table-result-container {\n    max-height: 300px;\n    overflow: auto;\n  }\n  table, th, td {\n    border: 1px solid black;\n    border-collapse: collapse;\n  }\n  th, td {\n    padding: 5px;\n  }\n  th {\n    text-align: left;\n  }\n</style><div class='table-result-container'><table class='table-result'><thead style='background-color: white'><tr><th>VendorID</th><th>tpep_pickup_datetime</th><th>tpep_dropoff_datetime</th><th>passenger_count</th><th>trip_distance</th><th>RatecodeID</th><th>store_and_fwd_flag</th><th>PULocationID</th><th>DOLocationID</th><th>payment_type</th><th>fare_amount</th><th>extra</th><th>mta_tax</th><th>tip_amount</th><th>tolls_amount</th><th>improvement_surcharge</th><th>total_amount</th><th>congestion_surcharge</th><th>airport_fee</th></tr></thead><tbody><tr><td>1</td><td>2019-03-01T00:24:41.000+0000</td><td>2019-03-01T00:25:31.000+0000</td><td>1.0</td><td>0.0</td><td>1.0</td><td>N</td><td>145</td><td>145</td><td>2</td><td>2.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>3.8</td><td>0.0</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:25:27.000+0000</td><td>2019-03-01T00:36:37.000+0000</td><td>2.0</td><td>3.7</td><td>1.0</td><td>N</td><td>95</td><td>130</td><td>1</td><td>13.0</td><td>0.5</td><td>0.5</td><td>0.7</td><td>0.0</td><td>0.3</td><td>15.0</td><td>0.0</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:05:21.000+0000</td><td>2019-03-01T00:38:23.000+0000</td><td>1.0</td><td>14.1</td><td>1.0</td><td>N</td><td>249</td><td>28</td><td>1</td><td>41.0</td><td>3.0</td><td>0.5</td><td>10.1</td><td>5.76</td><td>0.3</td><td>60.66</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:48:55.000+0000</td><td>2019-03-01T01:06:03.000+0000</td><td>1.0</td><td>9.6</td><td>1.0</td><td>N</td><td>138</td><td>98</td><td>2</td><td>27.0</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>28.3</td><td>0.0</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:11:42.000+0000</td><td>2019-03-01T00:16:40.000+0000</td><td>1.0</td><td>0.8</td><td>1.0</td><td>N</td><td>48</td><td>48</td><td>1</td><td>5.5</td><td>3.0</td><td>0.5</td><td>3.0</td><td>0.0</td><td>0.3</td><td>12.3</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:45:03.000+0000</td><td>2019-03-01T00:49:38.000+0000</td><td>1.0</td><td>1.2</td><td>1.0</td><td>N</td><td>246</td><td>48</td><td>2</td><td>6.0</td><td>3.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>9.8</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:02:37.000+0000</td><td>2019-03-01T00:07:30.000+0000</td><td>1.0</td><td>0.6</td><td>1.0</td><td>Y</td><td>239</td><td>238</td><td>2</td><td>5.5</td><td>3.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>9.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-02-28T19:52:45.000+0000</td><td>2019-02-28T20:01:54.000+0000</td><td>1.0</td><td>5.65</td><td>1.0</td><td>N</td><td>132</td><td>197</td><td>2</td><td>17.0</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>18.3</td><td>0.0</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:14:23.000+0000</td><td>2019-03-01T00:19:17.000+0000</td><td>1.0</td><td>1.16</td><td>1.0</td><td>N</td><td>229</td><td>170</td><td>1</td><td>6.0</td><td>0.5</td><td>0.5</td><td>2.45</td><td>0.0</td><td>0.3</td><td>12.25</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:19:09.000+0000</td><td>2019-03-01T00:23:30.000+0000</td><td>1.0</td><td>0.71</td><td>1.0</td><td>N</td><td>137</td><td>234</td><td>1</td><td>5.0</td><td>0.5</td><td>0.5</td><td>1.0</td><td>0.0</td><td>0.3</td><td>9.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:06:46.000+0000</td><td>2019-03-01T00:17:09.000+0000</td><td>5.0</td><td>2.63</td><td>1.0</td><td>N</td><td>231</td><td>246</td><td>1</td><td>10.5</td><td>0.5</td><td>0.5</td><td>2.86</td><td>0.0</td><td>0.3</td><td>17.16</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:00:46.000+0000</td><td>2019-03-01T00:26:41.000+0000</td><td>1.0</td><td>5.0</td><td>1.0</td><td>N</td><td>230</td><td>7</td><td>3</td><td>20.5</td><td>3.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>24.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:00:40.000+0000</td><td>2019-03-01T00:49:26.000+0000</td><td>2.0</td><td>14.04</td><td>1.0</td><td>N</td><td>170</td><td>123</td><td>1</td><td>45.5</td><td>0.5</td><td>0.5</td><td>14.79</td><td>0.0</td><td>0.3</td><td>64.09</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:21:46.000+0000</td><td>2019-03-01T00:30:25.000+0000</td><td>1.0</td><td>2.0</td><td>1.0</td><td>N</td><td>143</td><td>263</td><td>1</td><td>9.0</td><td>3.0</td><td>0.5</td><td>2.55</td><td>0.0</td><td>0.3</td><td>15.35</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:25:16.000+0000</td><td>2019-03-01T00:31:39.000+0000</td><td>5.0</td><td>2.11</td><td>1.0</td><td>N</td><td>229</td><td>236</td><td>1</td><td>8.0</td><td>0.5</td><td>0.5</td><td>2.0</td><td>0.0</td><td>0.3</td><td>13.8</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:03:09.000+0000</td><td>2019-03-01T00:49:16.000+0000</td><td>1.0</td><td>6.8</td><td>1.0</td><td>N</td><td>162</td><td>49</td><td>2</td><td>31.5</td><td>3.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>35.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:55:49.000+0000</td><td>2019-03-01T01:11:06.000+0000</td><td>1.0</td><td>5.97</td><td>1.0</td><td>N</td><td>152</td><td>153</td><td>2</td><td>19.0</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>20.3</td><td>0.0</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:35:42.000+0000</td><td>2019-03-01T00:47:06.000+0000</td><td>1.0</td><td>3.4</td><td>1.0</td><td>N</td><td>162</td><td>74</td><td>1</td><td>12.0</td><td>3.0</td><td>0.5</td><td>1.5</td><td>0.0</td><td>0.3</td><td>17.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:45:03.000+0000</td><td>2019-03-01T01:14:25.000+0000</td><td>1.0</td><td>16.89</td><td>2.0</td><td>N</td><td>132</td><td>229</td><td>1</td><td>52.0</td><td>0.0</td><td>0.5</td><td>11.06</td><td>0.0</td><td>0.3</td><td>66.36</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:42:38.000+0000</td><td>2019-03-01T01:02:58.000+0000</td><td>1.0</td><td>5.4</td><td>1.0</td><td>N</td><td>25</td><td>246</td><td>1</td><td>19.5</td><td>3.0</td><td>0.5</td><td>3.5</td><td>0.0</td><td>0.3</td><td>26.8</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:26:45.000+0000</td><td>2019-03-01T00:45:42.000+0000</td><td>0.0</td><td>10.6</td><td>1.0</td><td>N</td><td>138</td><td>144</td><td>1</td><td>30.0</td><td>3.0</td><td>0.5</td><td>6.75</td><td>0.0</td><td>0.3</td><td>40.55</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:54:07.000+0000</td><td>2019-03-01T01:01:53.000+0000</td><td>0.0</td><td>1.8</td><td>1.0</td><td>N</td><td>144</td><td>90</td><td>1</td><td>8.5</td><td>3.0</td><td>0.5</td><td>2.45</td><td>0.0</td><td>0.3</td><td>14.75</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:25:52.000+0000</td><td>2019-03-01T00:31:29.000+0000</td><td>1.0</td><td>1.0</td><td>1.0</td><td>N</td><td>249</td><td>125</td><td>1</td><td>6.0</td><td>3.0</td><td>0.5</td><td>1.95</td><td>0.0</td><td>0.3</td><td>11.75</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:17:10.000+0000</td><td>2019-03-01T00:41:03.000+0000</td><td>1.0</td><td>6.1</td><td>1.0</td><td>N</td><td>137</td><td>7</td><td>2</td><td>21.5</td><td>3.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>25.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:40:30.000+0000</td><td>2019-03-01T01:01:37.000+0000</td><td>3.0</td><td>4.49</td><td>1.0</td><td>N</td><td>107</td><td>25</td><td>1</td><td>18.0</td><td>0.5</td><td>0.5</td><td>4.36</td><td>0.0</td><td>0.3</td><td>26.16</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:12:19.000+0000</td><td>2019-03-01T00:53:34.000+0000</td><td>4.0</td><td>18.66</td><td>2.0</td><td>N</td><td>132</td><td>68</td><td>1</td><td>52.0</td><td>0.0</td><td>0.5</td><td>15.26</td><td>5.76</td><td>0.3</td><td>76.32</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:13:43.000+0000</td><td>2019-03-01T00:21:39.000+0000</td><td>2.0</td><td>1.67</td><td>1.0</td><td>N</td><td>107</td><td>161</td><td>1</td><td>8.0</td><td>0.5</td><td>0.5</td><td>2.36</td><td>0.0</td><td>0.3</td><td>14.16</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:10:51.000+0000</td><td>2019-03-01T00:28:23.000+0000</td><td>1.0</td><td>3.35</td><td>1.0</td><td>N</td><td>162</td><td>231</td><td>1</td><td>14.0</td><td>0.5</td><td>0.5</td><td>2.67</td><td>0.0</td><td>0.3</td><td>20.47</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:46:39.000+0000</td><td>2019-03-01T00:55:10.000+0000</td><td>1.0</td><td>1.62</td><td>1.0</td><td>N</td><td>231</td><td>88</td><td>1</td><td>7.5</td><td>0.5</td><td>0.5</td><td>2.26</td><td>0.0</td><td>0.3</td><td>13.56</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:11:09.000+0000</td><td>2019-03-01T00:12:40.000+0000</td><td>1.0</td><td>0.1</td><td>1.0</td><td>N</td><td>161</td><td>161</td><td>2</td><td>3.0</td><td>3.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>6.8</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:42:29.000+0000</td><td>2019-03-01T00:58:55.000+0000</td><td>1.0</td><td>4.0</td><td>1.0</td><td>N</td><td>137</td><td>143</td><td>1</td><td>14.5</td><td>3.0</td><td>0.5</td><td>3.0</td><td>0.0</td><td>0.3</td><td>21.3</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:18:31.000+0000</td><td>2019-03-01T00:21:14.000+0000</td><td>2.0</td><td>0.5</td><td>1.0</td><td>N</td><td>148</td><td>79</td><td>1</td><td>4.0</td><td>3.0</td><td>0.5</td><td>1.5</td><td>0.0</td><td>0.3</td><td>9.3</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:31:33.000+0000</td><td>2019-03-01T00:49:14.000+0000</td><td>0.0</td><td>2.5</td><td>1.0</td><td>N</td><td>68</td><td>144</td><td>1</td><td>12.5</td><td>3.0</td><td>0.5</td><td>1.0</td><td>0.0</td><td>0.3</td><td>17.3</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:04:03.000+0000</td><td>2019-03-01T00:12:11.000+0000</td><td>1.0</td><td>1.5</td><td>1.0</td><td>N</td><td>164</td><td>68</td><td>2</td><td>8.0</td><td>3.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>11.8</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:54:39.000+0000</td><td>2019-03-01T01:00:48.000+0000</td><td>1.0</td><td>1.5</td><td>1.0</td><td>N</td><td>48</td><td>237</td><td>1</td><td>7.0</td><td>3.0</td><td>0.5</td><td>2.15</td><td>0.0</td><td>0.3</td><td>12.95</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:04:37.000+0000</td><td>2019-03-01T00:57:05.000+0000</td><td>1.0</td><td>6.4</td><td>1.0</td><td>N</td><td>142</td><td>256</td><td>1</td><td>33.5</td><td>3.0</td><td>0.5</td><td>9.3</td><td>0.0</td><td>0.3</td><td>46.6</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:03:34.000+0000</td><td>2019-03-01T00:29:47.000+0000</td><td>1.0</td><td>10.8</td><td>1.0</td><td>N</td><td>132</td><td>225</td><td>1</td><td>32.0</td><td>0.5</td><td>0.5</td><td>6.65</td><td>0.0</td><td>0.3</td><td>39.95</td><td>0.0</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:35:05.000+0000</td><td>2019-03-01T00:37:30.000+0000</td><td>1.0</td><td>0.5</td><td>1.0</td><td>N</td><td>61</td><td>61</td><td>2</td><td>4.0</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>5.3</td><td>0.0</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:07:06.000+0000</td><td>2019-03-01T00:28:01.000+0000</td><td>0.0</td><td>8.7</td><td>1.0</td><td>N</td><td>144</td><td>89</td><td>1</td><td>26.5</td><td>3.0</td><td>0.5</td><td>4.7</td><td>0.0</td><td>0.3</td><td>35.0</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:13:24.000+0000</td><td>2019-03-01T00:18:10.000+0000</td><td>1.0</td><td>1.1</td><td>1.0</td><td>N</td><td>140</td><td>162</td><td>1</td><td>5.5</td><td>3.0</td><td>0.5</td><td>1.86</td><td>0.0</td><td>0.3</td><td>11.16</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:47:57.000+0000</td><td>2019-03-01T01:04:20.000+0000</td><td>1.0</td><td>8.7</td><td>1.0</td><td>N</td><td>87</td><td>14</td><td>1</td><td>25.5</td><td>3.0</td><td>0.5</td><td>5.0</td><td>0.0</td><td>0.3</td><td>34.3</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:19:02.000+0000</td><td>2019-03-01T00:39:47.000+0000</td><td>1.0</td><td>5.8</td><td>1.0</td><td>N</td><td>237</td><td>244</td><td>2</td><td>19.0</td><td>3.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>22.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-02-28T23:59:46.000+0000</td><td>2019-03-01T00:03:57.000+0000</td><td>1.0</td><td>0.82</td><td>1.0</td><td>N</td><td>234</td><td>170</td><td>1</td><td>5.0</td><td>0.5</td><td>0.5</td><td>2.0</td><td>0.0</td><td>0.3</td><td>10.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:18:07.000+0000</td><td>2019-03-01T00:26:39.000+0000</td><td>1.0</td><td>0.98</td><td>1.0</td><td>N</td><td>107</td><td>114</td><td>1</td><td>7.0</td><td>0.5</td><td>0.5</td><td>2.16</td><td>0.0</td><td>0.3</td><td>12.96</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:43:53.000+0000</td><td>2019-03-01T00:54:13.000+0000</td><td>1.0</td><td>3.68</td><td>1.0</td><td>N</td><td>79</td><td>75</td><td>1</td><td>13.0</td><td>0.5</td><td>0.5</td><td>3.36</td><td>0.0</td><td>0.3</td><td>20.16</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:44:26.000+0000</td><td>2019-03-01T01:18:20.000+0000</td><td>1.0</td><td>19.6</td><td>2.0</td><td>N</td><td>132</td><td>249</td><td>1</td><td>52.0</td><td>2.5</td><td>0.5</td><td>12.2</td><td>5.76</td><td>0.3</td><td>73.26</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:27:35.000+0000</td><td>2019-03-01T00:50:42.000+0000</td><td>1.0</td><td>9.9</td><td>1.0</td><td>N</td><td>132</td><td>177</td><td>1</td><td>29.0</td><td>0.5</td><td>0.5</td><td>6.05</td><td>0.0</td><td>0.3</td><td>36.35</td><td>0.0</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:06:49.000+0000</td><td>2019-03-01T00:15:10.000+0000</td><td>1.0</td><td>2.5</td><td>1.0</td><td>N</td><td>41</td><td>143</td><td>1</td><td>9.5</td><td>0.5</td><td>0.5</td><td>2.66</td><td>0.0</td><td>0.3</td><td>15.96</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:32:35.000+0000</td><td>2019-03-01T00:42:22.000+0000</td><td>1.0</td><td>1.5</td><td>1.0</td><td>N</td><td>50</td><td>230</td><td>1</td><td>8.5</td><td>0.5</td><td>0.5</td><td>3.08</td><td>0.0</td><td>0.3</td><td>15.38</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:12:24.000+0000</td><td>2019-03-01T00:34:52.000+0000</td><td>1.0</td><td>3.9</td><td>1.0</td><td>N</td><td>186</td><td>88</td><td>1</td><td>16.5</td><td>3.0</td><td>0.5</td><td>4.05</td><td>0.0</td><td>0.3</td><td>24.35</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:04:21.000+0000</td><td>2019-03-01T00:27:06.000+0000</td><td>1.0</td><td>4.7</td><td>1.0</td><td>N</td><td>237</td><td>209</td><td>1</td><td>18.0</td><td>3.0</td><td>0.5</td><td>5.45</td><td>0.0</td><td>0.3</td><td>27.25</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:56:40.000+0000</td><td>2019-03-01T01:08:23.000+0000</td><td>1.0</td><td>5.4</td><td>1.0</td><td>N</td><td>238</td><td>243</td><td>1</td><td>17.5</td><td>3.0</td><td>0.5</td><td>4.25</td><td>0.0</td><td>0.3</td><td>25.55</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:12:26.000+0000</td><td>2019-03-01T00:21:44.000+0000</td><td>2.0</td><td>2.21</td><td>1.0</td><td>N</td><td>246</td><td>50</td><td>1</td><td>9.5</td><td>0.5</td><td>0.5</td><td>3.32</td><td>0.0</td><td>0.3</td><td>16.62</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:28:58.000+0000</td><td>2019-03-01T00:46:57.000+0000</td><td>2.0</td><td>4.43</td><td>1.0</td><td>N</td><td>48</td><td>145</td><td>1</td><td>16.0</td><td>0.5</td><td>0.5</td><td>5.94</td><td>0.0</td><td>0.3</td><td>25.74</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-02-28T23:59:29.000+0000</td><td>2019-03-01T00:04:47.000+0000</td><td>1.0</td><td>1.95</td><td>1.0</td><td>N</td><td>142</td><td>151</td><td>1</td><td>7.5</td><td>0.5</td><td>0.5</td><td>2.26</td><td>0.0</td><td>0.3</td><td>13.56</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:14:57.000+0000</td><td>2019-03-01T00:24:23.000+0000</td><td>1.0</td><td>2.62</td><td>1.0</td><td>N</td><td>239</td><td>74</td><td>1</td><td>10.0</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>13.8</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:25:34.000+0000</td><td>2019-03-01T00:31:19.000+0000</td><td>1.0</td><td>1.4</td><td>1.0</td><td>N</td><td>100</td><td>107</td><td>1</td><td>6.0</td><td>3.0</td><td>0.5</td><td>1.2</td><td>0.0</td><td>0.3</td><td>11.0</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:46:37.000+0000</td><td>2019-03-01T00:49:33.000+0000</td><td>1.0</td><td>0.56</td><td>1.0</td><td>N</td><td>90</td><td>107</td><td>1</td><td>4.0</td><td>0.5</td><td>0.5</td><td>1.95</td><td>0.0</td><td>0.3</td><td>9.75</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:08:33.000+0000</td><td>2019-03-01T00:22:31.000+0000</td><td>1.0</td><td>2.57</td><td>1.0</td><td>N</td><td>107</td><td>163</td><td>2</td><td>11.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>15.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:32:44.000+0000</td><td>2019-03-01T00:39:20.000+0000</td><td>1.0</td><td>1.48</td><td>1.0</td><td>N</td><td>48</td><td>246</td><td>1</td><td>6.5</td><td>0.5</td><td>0.5</td><td>0.52</td><td>0.0</td><td>0.3</td><td>10.82</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:34:22.000+0000</td><td>2019-03-01T00:38:35.000+0000</td><td>1.0</td><td>1.2</td><td>1.0</td><td>N</td><td>158</td><td>186</td><td>1</td><td>5.5</td><td>3.0</td><td>0.5</td><td>2.3</td><td>0.0</td><td>0.3</td><td>11.6</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:49:35.000+0000</td><td>2019-03-01T00:54:13.000+0000</td><td>2.0</td><td>1.1</td><td>1.0</td><td>N</td><td>162</td><td>140</td><td>1</td><td>6.0</td><td>3.0</td><td>0.5</td><td>1.5</td><td>0.0</td><td>0.3</td><td>11.3</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:00:13.000+0000</td><td>2019-03-01T00:16:03.000+0000</td><td>3.0</td><td>2.4</td><td>1.0</td><td>N</td><td>45</td><td>158</td><td>1</td><td>12.0</td><td>3.0</td><td>0.5</td><td>0.47</td><td>0.0</td><td>0.3</td><td>16.27</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:45:06.000+0000</td><td>2019-03-01T01:03:38.000+0000</td><td>1.0</td><td>3.9</td><td>1.0</td><td>N</td><td>232</td><td>189</td><td>1</td><td>16.0</td><td>3.0</td><td>0.5</td><td>3.95</td><td>0.0</td><td>0.3</td><td>23.75</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:13:46.000+0000</td><td>2019-03-01T00:15:56.000+0000</td><td>1.0</td><td>0.67</td><td>1.0</td><td>N</td><td>141</td><td>236</td><td>1</td><td>4.0</td><td>0.5</td><td>0.5</td><td>2.34</td><td>0.0</td><td>0.3</td><td>10.14</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:52:52.000+0000</td><td>2019-03-01T01:11:06.000+0000</td><td>1.0</td><td>6.61</td><td>1.0</td><td>N</td><td>79</td><td>41</td><td>1</td><td>19.5</td><td>0.5</td><td>0.5</td><td>2.0</td><td>0.0</td><td>0.3</td><td>25.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:02:09.000+0000</td><td>2019-03-01T00:21:35.000+0000</td><td>5.0</td><td>2.99</td><td>1.0</td><td>N</td><td>48</td><td>224</td><td>1</td><td>14.5</td><td>0.5</td><td>0.5</td><td>3.66</td><td>0.0</td><td>0.3</td><td>21.96</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:44:20.000+0000</td><td>2019-03-01T01:02:42.000+0000</td><td>1.0</td><td>8.5</td><td>1.0</td><td>N</td><td>138</td><td>141</td><td>1</td><td>25.0</td><td>3.0</td><td>0.5</td><td>5.0</td><td>5.76</td><td>0.3</td><td>39.56</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:00:08.000+0000</td><td>2019-03-01T00:08:26.000+0000</td><td>4.0</td><td>1.7</td><td>1.0</td><td>N</td><td>50</td><td>162</td><td>1</td><td>7.5</td><td>3.0</td><td>0.5</td><td>2.25</td><td>0.0</td><td>0.3</td><td>13.55</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:39:28.000+0000</td><td>2019-03-01T01:00:17.000+0000</td><td>1.0</td><td>4.9</td><td>1.0</td><td>N</td><td>50</td><td>112</td><td>1</td><td>18.5</td><td>3.0</td><td>0.5</td><td>4.0</td><td>0.0</td><td>0.3</td><td>26.3</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:05:06.000+0000</td><td>2019-03-01T00:09:17.000+0000</td><td>1.0</td><td>0.5</td><td>1.0</td><td>N</td><td>158</td><td>114</td><td>2</td><td>4.5</td><td>3.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>8.3</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:12:01.000+0000</td><td>2019-03-01T00:26:40.000+0000</td><td>1.0</td><td>2.6</td><td>1.0</td><td>N</td><td>211</td><td>233</td><td>1</td><td>12.0</td><td>3.0</td><td>0.5</td><td>2.0</td><td>0.0</td><td>0.3</td><td>17.8</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:26:07.000+0000</td><td>2019-03-01T00:58:26.000+0000</td><td>3.0</td><td>10.1</td><td>1.0</td><td>N</td><td>97</td><td>43</td><td>1</td><td>34.0</td><td>3.0</td><td>0.5</td><td>9.4</td><td>0.0</td><td>0.3</td><td>47.2</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:15:51.000+0000</td><td>2019-03-01T00:15:59.000+0000</td><td>1.0</td><td>0.02</td><td>1.0</td><td>N</td><td>193</td><td>193</td><td>3</td><td>-2.5</td><td>-0.5</td><td>-0.5</td><td>0.0</td><td>0.0</td><td>-0.3</td><td>-3.8</td><td>0.0</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:15:51.000+0000</td><td>2019-03-01T00:15:59.000+0000</td><td>1.0</td><td>0.02</td><td>1.0</td><td>N</td><td>193</td><td>193</td><td>2</td><td>2.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>3.8</td><td>0.0</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:52:50.000+0000</td><td>2019-03-01T01:11:51.000+0000</td><td>1.0</td><td>4.7</td><td>1.0</td><td>N</td><td>90</td><td>262</td><td>1</td><td>16.5</td><td>3.0</td><td>0.5</td><td>4.05</td><td>0.0</td><td>0.3</td><td>24.35</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:14:27.000+0000</td><td>2019-03-01T00:27:40.000+0000</td><td>1.0</td><td>2.8</td><td>1.0</td><td>N</td><td>100</td><td>239</td><td>1</td><td>12.0</td><td>3.0</td><td>0.5</td><td>3.15</td><td>0.0</td><td>0.3</td><td>18.95</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:27:15.000+0000</td><td>2019-03-01T00:29:09.000+0000</td><td>1.0</td><td>0.6</td><td>1.0</td><td>N</td><td>90</td><td>186</td><td>2</td><td>4.0</td><td>3.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>7.8</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:40:41.000+0000</td><td>2019-03-01T00:45:45.000+0000</td><td>1.0</td><td>1.4</td><td>1.0</td><td>N</td><td>163</td><td>246</td><td>1</td><td>6.0</td><td>3.0</td><td>0.5</td><td>0.98</td><td>0.0</td><td>0.3</td><td>10.78</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:48:00.000+0000</td><td>2019-03-01T01:27:24.000+0000</td><td>1.0</td><td>5.18</td><td>1.0</td><td>N</td><td>161</td><td>87</td><td>1</td><td>26.0</td><td>0.5</td><td>0.5</td><td>5.96</td><td>0.0</td><td>0.3</td><td>35.76</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-02-28T21:22:31.000+0000</td><td>2019-02-28T21:51:35.000+0000</td><td>1.0</td><td>19.47</td><td>2.0</td><td>N</td><td>132</td><td>114</td><td>1</td><td>52.0</td><td>0.0</td><td>0.5</td><td>10.0</td><td>0.0</td><td>0.3</td><td>65.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:02:11.000+0000</td><td>2019-03-01T00:38:43.000+0000</td><td>1.0</td><td>4.64</td><td>1.0</td><td>N</td><td>231</td><td>37</td><td>1</td><td>25.0</td><td>0.5</td><td>0.5</td><td>5.76</td><td>0.0</td><td>0.3</td><td>34.56</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:17:26.000+0000</td><td>2019-03-01T00:23:58.000+0000</td><td>1.0</td><td>0.89</td><td>1.0</td><td>N</td><td>162</td><td>164</td><td>2</td><td>6.0</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>9.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:27:20.000+0000</td><td>2019-03-01T00:37:24.000+0000</td><td>1.0</td><td>1.88</td><td>1.0</td><td>N</td><td>164</td><td>114</td><td>1</td><td>9.0</td><td>0.5</td><td>0.5</td><td>2.06</td><td>0.0</td><td>0.3</td><td>14.86</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:44:33.000+0000</td><td>2019-03-01T01:01:57.000+0000</td><td>1.0</td><td>3.7</td><td>1.0</td><td>N</td><td>107</td><td>262</td><td>2</td><td>15.0</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>18.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:01:06.000+0000</td><td>2019-03-01T00:14:04.000+0000</td><td>2.0</td><td>2.07</td><td>1.0</td><td>N</td><td>170</td><td>114</td><td>1</td><td>10.5</td><td>0.5</td><td>0.5</td><td>2.86</td><td>0.0</td><td>0.3</td><td>17.16</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:22:10.000+0000</td><td>2019-03-01T00:30:41.000+0000</td><td>2.0</td><td>1.28</td><td>1.0</td><td>N</td><td>144</td><td>79</td><td>1</td><td>7.5</td><td>0.5</td><td>0.5</td><td>1.5</td><td>0.0</td><td>0.3</td><td>12.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:17:28.000+0000</td><td>2019-03-01T00:40:34.000+0000</td><td>1.0</td><td>4.44</td><td>1.0</td><td>N</td><td>229</td><td>145</td><td>2</td><td>18.0</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>21.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:26:01.000+0000</td><td>2019-03-01T00:42:27.000+0000</td><td>4.0</td><td>7.29</td><td>1.0</td><td>N</td><td>138</td><td>255</td><td>1</td><td>22.5</td><td>0.5</td><td>0.5</td><td>4.76</td><td>0.0</td><td>0.3</td><td>28.56</td><td>0.0</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:32:23.000+0000</td><td>2019-03-01T00:35:10.000+0000</td><td>1.0</td><td>0.39</td><td>1.0</td><td>N</td><td>231</td><td>125</td><td>1</td><td>4.0</td><td>0.5</td><td>0.5</td><td>2.34</td><td>0.0</td><td>0.3</td><td>10.14</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:58:10.000+0000</td><td>2019-03-01T01:27:21.000+0000</td><td>1.0</td><td>7.65</td><td>1.0</td><td>N</td><td>163</td><td>198</td><td>1</td><td>26.0</td><td>0.5</td><td>0.5</td><td>2.0</td><td>0.0</td><td>0.3</td><td>31.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:19:03.000+0000</td><td>2019-03-01T00:26:23.000+0000</td><td>5.0</td><td>0.99</td><td>1.0</td><td>N</td><td>161</td><td>229</td><td>1</td><td>6.5</td><td>0.5</td><td>0.5</td><td>2.06</td><td>0.0</td><td>0.3</td><td>12.36</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:09:50.000+0000</td><td>2019-03-01T00:40:47.000+0000</td><td>1.0</td><td>18.3</td><td>2.0</td><td>N</td><td>132</td><td>246</td><td>1</td><td>52.0</td><td>2.5</td><td>0.5</td><td>12.2</td><td>5.76</td><td>0.3</td><td>73.26</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:05:53.000+0000</td><td>2019-03-01T00:18:17.000+0000</td><td>0.0</td><td>2.8</td><td>1.0</td><td>N</td><td>142</td><td>249</td><td>2</td><td>11.0</td><td>3.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>14.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:02:30.000+0000</td><td>2019-03-01T00:05:19.000+0000</td><td>1.0</td><td>0.32</td><td>1.0</td><td>N</td><td>229</td><td>162</td><td>2</td><td>4.0</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>7.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:27:09.000+0000</td><td>2019-03-01T00:43:21.000+0000</td><td>1.0</td><td>3.84</td><td>1.0</td><td>N</td><td>161</td><td>151</td><td>1</td><td>14.5</td><td>0.5</td><td>0.5</td><td>3.66</td><td>0.0</td><td>0.3</td><td>21.96</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:56:02.000+0000</td><td>2019-03-01T01:29:57.000+0000</td><td>1.0</td><td>17.8</td><td>2.0</td><td>N</td><td>132</td><td>42</td><td>2</td><td>52.0</td><td>0.0</td><td>0.5</td><td>0.0</td><td>5.76</td><td>0.3</td><td>58.56</td><td>0.0</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:38:20.000+0000</td><td>2019-03-01T00:57:43.000+0000</td><td>1.0</td><td>14.84</td><td>1.0</td><td>N</td><td>132</td><td>179</td><td>1</td><td>40.0</td><td>0.5</td><td>0.5</td><td>8.26</td><td>0.0</td><td>0.3</td><td>49.56</td><td>0.0</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:22:44.000+0000</td><td>2019-03-01T00:31:02.000+0000</td><td>1.0</td><td>2.2</td><td>1.0</td><td>N</td><td>238</td><td>152</td><td>2</td><td>9.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>10.8</td><td>0.0</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:32:56.000+0000</td><td>2019-03-01T00:41:25.000+0000</td><td>1.0</td><td>2.0</td><td>1.0</td><td>N</td><td>116</td><td>244</td><td>2</td><td>9.0</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>10.3</td><td>0.0</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:59:13.000+0000</td><td>2019-03-01T01:30:23.000+0000</td><td>2.0</td><td>10.1</td><td>1.0</td><td>N</td><td>238</td><td>241</td><td>1</td><td>33.0</td><td>0.5</td><td>0.5</td><td>6.85</td><td>0.0</td><td>0.3</td><td>41.15</td><td>0.0</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:17:20.000+0000</td><td>2019-03-01T00:24:15.000+0000</td><td>1.0</td><td>1.1</td><td>1.0</td><td>N</td><td>113</td><td>158</td><td>1</td><td>6.5</td><td>3.0</td><td>0.5</td><td>2.0</td><td>0.0</td><td>0.3</td><td>12.3</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:19:33.000+0000</td><td>2019-03-01T00:34:37.000+0000</td><td>1.0</td><td>3.5</td><td>1.0</td><td>N</td><td>163</td><td>211</td><td>1</td><td>13.0</td><td>3.0</td><td>0.5</td><td>3.36</td><td>0.0</td><td>0.3</td><td>20.16</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:36:55.000+0000</td><td>2019-03-01T00:45:52.000+0000</td><td>2.0</td><td>2.4</td><td>1.0</td><td>N</td><td>211</td><td>164</td><td>1</td><td>9.5</td><td>3.0</td><td>0.5</td><td>3.3</td><td>0.0</td><td>0.3</td><td>16.6</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:54:22.000+0000</td><td>2019-03-01T00:58:23.000+0000</td><td>1.0</td><td>0.8</td><td>1.0</td><td>N</td><td>233</td><td>230</td><td>2</td><td>5.0</td><td>3.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>8.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:35:16.000+0000</td><td>2019-03-01T00:42:26.000+0000</td><td>2.0</td><td>1.34</td><td>1.0</td><td>N</td><td>113</td><td>186</td><td>1</td><td>7.0</td><td>0.5</td><td>0.5</td><td>1.0</td><td>0.0</td><td>0.3</td><td>11.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:18:31.000+0000</td><td>2019-03-01T00:28:04.000+0000</td><td>1.0</td><td>2.3</td><td>1.0</td><td>N</td><td>238</td><td>75</td><td>2</td><td>9.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>13.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:26:18.000+0000</td><td>2019-03-01T00:41:30.000+0000</td><td>3.0</td><td>7.35</td><td>1.0</td><td>N</td><td>132</td><td>205</td><td>2</td><td>22.0</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>23.3</td><td>0.0</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:02:39.000+0000</td><td>2019-03-01T00:19:15.000+0000</td><td>2.0</td><td>4.45</td><td>1.0</td><td>N</td><td>246</td><td>262</td><td>1</td><td>16.0</td><td>0.5</td><td>0.5</td><td>3.96</td><td>0.0</td><td>0.3</td><td>23.76</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:33:31.000+0000</td><td>2019-03-01T00:35:49.000+0000</td><td>2.0</td><td>0.61</td><td>1.0</td><td>N</td><td>141</td><td>263</td><td>1</td><td>4.0</td><td>0.5</td><td>0.5</td><td>1.56</td><td>0.0</td><td>0.3</td><td>9.36</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:07:12.000+0000</td><td>2019-03-01T00:35:56.000+0000</td><td>1.0</td><td>19.0</td><td>2.0</td><td>N</td><td>132</td><td>141</td><td>1</td><td>52.0</td><td>0.0</td><td>0.5</td><td>7.5</td><td>5.76</td><td>0.3</td><td>68.56</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:00:29.000+0000</td><td>2019-03-01T00:01:55.000+0000</td><td>1.0</td><td>0.5</td><td>1.0</td><td>N</td><td>162</td><td>141</td><td>1</td><td>3.5</td><td>3.0</td><td>0.5</td><td>0.73</td><td>0.0</td><td>0.3</td><td>8.03</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:11:28.000+0000</td><td>2019-03-01T00:17:29.000+0000</td><td>1.0</td><td>1.9</td><td>1.0</td><td>N</td><td>162</td><td>236</td><td>1</td><td>7.5</td><td>3.0</td><td>0.5</td><td>2.25</td><td>0.0</td><td>0.3</td><td>13.55</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:32:36.000+0000</td><td>2019-03-01T00:46:39.000+0000</td><td>2.0</td><td>2.6</td><td>1.0</td><td>N</td><td>161</td><td>79</td><td>1</td><td>11.5</td><td>3.0</td><td>0.5</td><td>3.05</td><td>0.0</td><td>0.3</td><td>18.35</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:55:41.000+0000</td><td>2019-03-01T00:58:23.000+0000</td><td>2.0</td><td>0.6</td><td>1.0</td><td>N</td><td>114</td><td>79</td><td>1</td><td>4.0</td><td>3.0</td><td>0.5</td><td>1.55</td><td>0.0</td><td>0.3</td><td>9.35</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:25:54.000+0000</td><td>2019-03-01T00:29:24.000+0000</td><td>1.0</td><td>0.49</td><td>1.0</td><td>N</td><td>79</td><td>79</td><td>2</td><td>4.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>8.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:51:13.000+0000</td><td>2019-03-01T01:00:41.000+0000</td><td>2.0</td><td>1.5</td><td>1.0</td><td>N</td><td>170</td><td>50</td><td>2</td><td>8.0</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>11.8</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:04:47.000+0000</td><td>2019-03-01T00:39:13.000+0000</td><td>1.0</td><td>17.6</td><td>2.0</td><td>N</td><td>132</td><td>100</td><td>1</td><td>52.0</td><td>2.5</td><td>0.5</td><td>5.0</td><td>5.76</td><td>0.3</td><td>66.06</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:48:45.000+0000</td><td>2019-03-01T01:05:12.000+0000</td><td>1.0</td><td>4.5</td><td>1.0</td><td>N</td><td>230</td><td>202</td><td>1</td><td>16.0</td><td>3.0</td><td>0.5</td><td>3.95</td><td>0.0</td><td>0.3</td><td>23.75</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:19:12.000+0000</td><td>2019-03-01T00:22:19.000+0000</td><td>1.0</td><td>0.8</td><td>1.0</td><td>N</td><td>68</td><td>246</td><td>1</td><td>4.5</td><td>3.0</td><td>0.5</td><td>2.05</td><td>0.0</td><td>0.3</td><td>10.35</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:30:34.000+0000</td><td>2019-03-01T00:38:12.000+0000</td><td>1.0</td><td>1.8</td><td>1.0</td><td>N</td><td>90</td><td>144</td><td>2</td><td>8.0</td><td>3.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>11.8</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:12:19.000+0000</td><td>2019-03-01T00:31:39.000+0000</td><td>0.0</td><td>3.1</td><td>1.0</td><td>N</td><td>107</td><td>87</td><td>1</td><td>14.0</td><td>3.0</td><td>0.5</td><td>3.55</td><td>0.0</td><td>0.3</td><td>21.35</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:38:28.000+0000</td><td>2019-03-01T00:52:27.000+0000</td><td>0.0</td><td>4.1</td><td>1.0</td><td>N</td><td>231</td><td>48</td><td>1</td><td>14.0</td><td>3.0</td><td>0.5</td><td>2.0</td><td>0.0</td><td>0.3</td><td>19.8</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:19:25.000+0000</td><td>2019-03-01T00:48:57.000+0000</td><td>1.0</td><td>5.4</td><td>1.0</td><td>N</td><td>158</td><td>80</td><td>1</td><td>23.0</td><td>3.0</td><td>0.5</td><td>5.35</td><td>0.0</td><td>0.3</td><td>32.15</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:32:33.000+0000</td><td>2019-03-01T01:01:32.000+0000</td><td>1.0</td><td>17.3</td><td>2.0</td><td>N</td><td>132</td><td>141</td><td>1</td><td>52.0</td><td>0.0</td><td>0.5</td><td>11.06</td><td>0.0</td><td>0.3</td><td>66.36</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:03:38.000+0000</td><td>2019-03-01T00:09:15.000+0000</td><td>1.0</td><td>1.16</td><td>1.0</td><td>N</td><td>231</td><td>261</td><td>1</td><td>6.0</td><td>0.5</td><td>0.5</td><td>2.45</td><td>0.0</td><td>0.3</td><td>12.25</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:20:46.000+0000</td><td>2019-03-01T00:36:13.000+0000</td><td>1.0</td><td>3.21</td><td>1.0</td><td>N</td><td>114</td><td>162</td><td>1</td><td>13.0</td><td>0.5</td><td>0.5</td><td>3.36</td><td>0.0</td><td>0.3</td><td>20.16</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:13:45.000+0000</td><td>2019-03-01T00:33:19.000+0000</td><td>1.0</td><td>7.7</td><td>1.0</td><td>N</td><td>138</td><td>236</td><td>1</td><td>24.5</td><td>3.0</td><td>0.5</td><td>4.5</td><td>5.76</td><td>0.3</td><td>38.56</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:05:52.000+0000</td><td>2019-03-01T00:17:20.000+0000</td><td>2.0</td><td>1.8</td><td>1.0</td><td>N</td><td>144</td><td>13</td><td>1</td><td>9.5</td><td>3.0</td><td>0.5</td><td>2.0</td><td>0.0</td><td>0.3</td><td>15.3</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:32:56.000+0000</td><td>2019-03-01T00:36:04.000+0000</td><td>1.0</td><td>0.5</td><td>1.0</td><td>N</td><td>181</td><td>106</td><td>2</td><td>4.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>5.8</td><td>0.0</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:36:58.000+0000</td><td>2019-03-01T00:48:03.000+0000</td><td>1.0</td><td>4.1</td><td>1.0</td><td>N</td><td>106</td><td>14</td><td>2</td><td>14.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>15.8</td><td>0.0</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:05:19.000+0000</td><td>2019-03-01T00:23:58.000+0000</td><td>2.0</td><td>1.9</td><td>1.0</td><td>N</td><td>234</td><td>148</td><td>1</td><td>12.5</td><td>3.0</td><td>0.5</td><td>3.25</td><td>0.0</td><td>0.3</td><td>19.55</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:41:15.000+0000</td><td>2019-03-01T01:04:25.000+0000</td><td>1.0</td><td>6.1</td><td>1.0</td><td>N</td><td>87</td><td>61</td><td>1</td><td>21.0</td><td>3.0</td><td>0.5</td><td>3.0</td><td>0.0</td><td>0.3</td><td>27.8</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:19:38.000+0000</td><td>2019-03-01T00:26:54.000+0000</td><td>1.0</td><td>1.9</td><td>1.0</td><td>N</td><td>164</td><td>229</td><td>1</td><td>8.0</td><td>3.0</td><td>0.5</td><td>2.35</td><td>0.0</td><td>0.3</td><td>14.15</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:05:32.000+0000</td><td>2019-03-01T00:14:11.000+0000</td><td>1.0</td><td>1.59</td><td>1.0</td><td>N</td><td>100</td><td>48</td><td>1</td><td>8.0</td><td>0.5</td><td>0.5</td><td>1.0</td><td>0.0</td><td>0.3</td><td>12.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:11:54.000+0000</td><td>2019-03-01T00:18:46.000+0000</td><td>1.0</td><td>1.06</td><td>1.0</td><td>N</td><td>88</td><td>261</td><td>1</td><td>6.5</td><td>0.5</td><td>0.5</td><td>2.06</td><td>0.0</td><td>0.3</td><td>12.36</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:27:55.000+0000</td><td>2019-03-01T00:36:49.000+0000</td><td>1.0</td><td>1.78</td><td>1.0</td><td>N</td><td>211</td><td>107</td><td>1</td><td>8.5</td><td>0.5</td><td>0.5</td><td>2.46</td><td>0.0</td><td>0.3</td><td>14.76</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:55:37.000+0000</td><td>2019-03-01T00:59:02.000+0000</td><td>1.0</td><td>0.89</td><td>1.0</td><td>N</td><td>164</td><td>234</td><td>1</td><td>4.5</td><td>0.5</td><td>0.5</td><td>1.66</td><td>0.0</td><td>0.3</td><td>9.96</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:26:50.000+0000</td><td>2019-03-01T00:49:00.000+0000</td><td>1.0</td><td>5.29</td><td>1.0</td><td>N</td><td>246</td><td>211</td><td>2</td><td>18.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>22.3</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:01:24.000+0000</td><td>2019-03-01T00:04:58.000+0000</td><td>0.0</td><td>0.0</td><td>1.0</td><td>N</td><td>234</td><td>234</td><td>1</td><td>4.0</td><td>3.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>7.8</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:09:02.000+0000</td><td>2019-03-01T00:18:26.000+0000</td><td>1.0</td><td>1.1</td><td>1.0</td><td>N</td><td>234</td><td>68</td><td>1</td><td>8.0</td><td>3.0</td><td>0.5</td><td>1.0</td><td>0.0</td><td>0.3</td><td>12.8</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:23:05.000+0000</td><td>2019-03-01T00:31:31.000+0000</td><td>1.0</td><td>2.2</td><td>1.0</td><td>N</td><td>158</td><td>48</td><td>1</td><td>9.0</td><td>3.0</td><td>0.5</td><td>2.55</td><td>0.0</td><td>0.3</td><td>15.35</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:39:46.000+0000</td><td>2019-03-01T00:46:18.000+0000</td><td>1.0</td><td>1.5</td><td>1.0</td><td>N</td><td>142</td><td>239</td><td>1</td><td>7.5</td><td>3.0</td><td>0.5</td><td>2.25</td><td>0.0</td><td>0.3</td><td>13.55</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:18:48.000+0000</td><td>2019-03-01T00:33:53.000+0000</td><td>1.0</td><td>3.0</td><td>1.0</td><td>N</td><td>230</td><td>140</td><td>2</td><td>13.0</td><td>3.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>16.8</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:04:03.000+0000</td><td>2019-03-01T00:12:37.000+0000</td><td>1.0</td><td>1.4</td><td>1.0</td><td>N</td><td>148</td><td>107</td><td>1</td><td>7.5</td><td>3.0</td><td>0.5</td><td>1.0</td><td>0.0</td><td>0.3</td><td>12.3</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:19:41.000+0000</td><td>2019-03-01T00:25:59.000+0000</td><td>1.0</td><td>0.7</td><td>1.0</td><td>N</td><td>79</td><td>79</td><td>1</td><td>6.0</td><td>3.0</td><td>0.5</td><td>2.45</td><td>0.0</td><td>0.3</td><td>12.25</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:34:02.000+0000</td><td>2019-03-01T00:43:03.000+0000</td><td>1.0</td><td>1.5</td><td>1.0</td><td>N</td><td>211</td><td>13</td><td>1</td><td>8.0</td><td>3.0</td><td>0.5</td><td>3.0</td><td>0.0</td><td>0.3</td><td>14.8</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:58:01.000+0000</td><td>2019-03-01T01:10:27.000+0000</td><td>1.0</td><td>3.2</td><td>1.0</td><td>N</td><td>249</td><td>239</td><td>1</td><td>12.0</td><td>3.0</td><td>0.5</td><td>1.2</td><td>0.0</td><td>0.3</td><td>17.0</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:04:10.000+0000</td><td>2019-03-01T00:17:38.000+0000</td><td>1.0</td><td>6.24</td><td>1.0</td><td>N</td><td>132</td><td>134</td><td>1</td><td>19.0</td><td>0.5</td><td>0.5</td><td>4.06</td><td>0.0</td><td>0.3</td><td>24.36</td><td>0.0</td><td>null</td></tr><tr><td>2</td><td>2019-02-28T23:56:59.000+0000</td><td>2019-02-28T23:59:56.000+0000</td><td>1.0</td><td>0.53</td><td>1.0</td><td>N</td><td>170</td><td>164</td><td>1</td><td>4.0</td><td>0.5</td><td>0.5</td><td>1.0</td><td>0.0</td><td>0.3</td><td>8.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:00:39.000+0000</td><td>2019-03-01T00:15:57.000+0000</td><td>1.0</td><td>3.49</td><td>1.0</td><td>N</td><td>164</td><td>236</td><td>1</td><td>14.0</td><td>0.5</td><td>0.5</td><td>4.45</td><td>0.0</td><td>0.3</td><td>22.25</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:40:25.000+0000</td><td>2019-03-01T00:49:02.000+0000</td><td>1.0</td><td>2.04</td><td>1.0</td><td>N</td><td>163</td><td>90</td><td>1</td><td>8.5</td><td>0.5</td><td>0.5</td><td>1.84</td><td>0.0</td><td>0.3</td><td>14.14</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:07:23.000+0000</td><td>2019-03-01T00:41:57.000+0000</td><td>1.0</td><td>14.0</td><td>2.0</td><td>N</td><td>130</td><td>152</td><td>1</td><td>52.0</td><td>0.0</td><td>0.5</td><td>14.64</td><td>5.76</td><td>0.3</td><td>73.2</td><td>0.0</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:30:34.000+0000</td><td>2019-03-01T00:50:39.000+0000</td><td>2.0</td><td>4.9</td><td>1.0</td><td>N</td><td>170</td><td>179</td><td>2</td><td>18.0</td><td>3.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>21.8</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:20:11.000+0000</td><td>2019-03-01T00:35:13.000+0000</td><td>2.0</td><td>4.4</td><td>1.0</td><td>N</td><td>249</td><td>263</td><td>1</td><td>15.0</td><td>3.0</td><td>0.5</td><td>2.5</td><td>0.0</td><td>0.3</td><td>21.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:28:02.000+0000</td><td>2019-03-01T00:35:21.000+0000</td><td>1.0</td><td>1.21</td><td>1.0</td><td>N</td><td>161</td><td>229</td><td>2</td><td>6.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>10.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:45:54.000+0000</td><td>2019-03-01T00:51:19.000+0000</td><td>1.0</td><td>1.14</td><td>1.0</td><td>N</td><td>196</td><td>95</td><td>2</td><td>6.0</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>7.3</td><td>0.0</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:24:23.000+0000</td><td>2019-03-01T00:44:23.000+0000</td><td>1.0</td><td>10.3</td><td>1.0</td><td>N</td><td>132</td><td>39</td><td>2</td><td>30.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>31.8</td><td>0.0</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:13:53.000+0000</td><td>2019-03-01T00:27:55.000+0000</td><td>2.0</td><td>2.0</td><td>1.0</td><td>N</td><td>107</td><td>68</td><td>1</td><td>11.0</td><td>3.0</td><td>0.5</td><td>2.95</td><td>0.0</td><td>0.3</td><td>17.75</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:04:50.000+0000</td><td>2019-03-01T00:12:11.000+0000</td><td>1.0</td><td>2.02</td><td>1.0</td><td>N</td><td>255</td><td>232</td><td>1</td><td>8.5</td><td>0.5</td><td>0.5</td><td>3.08</td><td>0.0</td><td>0.3</td><td>15.38</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:20:51.000+0000</td><td>2019-03-01T00:26:38.000+0000</td><td>1.0</td><td>1.25</td><td>1.0</td><td>N</td><td>79</td><td>137</td><td>1</td><td>6.0</td><td>0.5</td><td>0.5</td><td>1.0</td><td>0.0</td><td>0.3</td><td>10.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:41:35.000+0000</td><td>2019-03-01T01:10:18.000+0000</td><td>1.0</td><td>6.95</td><td>1.0</td><td>N</td><td>48</td><td>82</td><td>1</td><td>25.0</td><td>0.5</td><td>0.5</td><td>8.64</td><td>0.0</td><td>0.3</td><td>37.44</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:24:37.000+0000</td><td>2019-03-01T01:08:59.000+0000</td><td>1.0</td><td>5.8</td><td>1.0</td><td>N</td><td>264</td><td>87</td><td>2</td><td>28.5</td><td>3.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>32.3</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:30:37.000+0000</td><td>2019-03-01T00:34:22.000+0000</td><td>1.0</td><td>0.5</td><td>1.0</td><td>N</td><td>162</td><td>229</td><td>1</td><td>4.5</td><td>3.0</td><td>0.5</td><td>2.45</td><td>0.0</td><td>0.3</td><td>10.75</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:39:01.000+0000</td><td>2019-03-01T00:50:43.000+0000</td><td>1.0</td><td>2.5</td><td>1.0</td><td>N</td><td>229</td><td>113</td><td>1</td><td>10.5</td><td>3.0</td><td>0.5</td><td>2.85</td><td>0.0</td><td>0.3</td><td>17.15</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:31:22.000+0000</td><td>2019-03-01T00:37:51.000+0000</td><td>1.0</td><td>0.8</td><td>1.0</td><td>N</td><td>162</td><td>229</td><td>1</td><td>6.0</td><td>3.0</td><td>0.5</td><td>1.95</td><td>0.0</td><td>0.3</td><td>11.75</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:47:56.000+0000</td><td>2019-03-01T00:52:44.000+0000</td><td>1.0</td><td>1.0</td><td>1.0</td><td>N</td><td>249</td><td>231</td><td>2</td><td>5.5</td><td>3.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>9.3</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:44:33.000+0000</td><td>2019-03-01T01:10:14.000+0000</td><td>1.0</td><td>18.7</td><td>2.0</td><td>N</td><td>132</td><td>263</td><td>1</td><td>52.0</td><td>2.5</td><td>0.5</td><td>15.25</td><td>5.76</td><td>0.3</td><td>76.31</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:28:25.000+0000</td><td>2019-03-01T00:34:05.000+0000</td><td>1.0</td><td>1.49</td><td>1.0</td><td>N</td><td>246</td><td>230</td><td>1</td><td>6.5</td><td>0.5</td><td>0.5</td><td>1.03</td><td>0.0</td><td>0.3</td><td>11.33</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:48:31.000+0000</td><td>2019-03-01T01:00:34.000+0000</td><td>1.0</td><td>2.78</td><td>1.0</td><td>N</td><td>90</td><td>143</td><td>1</td><td>11.0</td><td>0.5</td><td>0.5</td><td>3.7</td><td>0.0</td><td>0.3</td><td>18.5</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:05:09.000+0000</td><td>2019-03-01T00:40:39.000+0000</td><td>1.0</td><td>19.4</td><td>2.0</td><td>N</td><td>132</td><td>238</td><td>2</td><td>52.0</td><td>2.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>55.3</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:59:16.000+0000</td><td>2019-03-01T01:02:42.000+0000</td><td>1.0</td><td>0.9</td><td>1.0</td><td>N</td><td>48</td><td>50</td><td>2</td><td>5.0</td><td>3.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>8.8</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:01:42.000+0000</td><td>2019-03-01T00:28:49.000+0000</td><td>1.0</td><td>8.6</td><td>1.0</td><td>N</td><td>230</td><td>126</td><td>1</td><td>28.5</td><td>3.0</td><td>0.5</td><td>0.01</td><td>0.0</td><td>0.3</td><td>32.31</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:28:12.000+0000</td><td>2019-03-01T00:42:18.000+0000</td><td>0.0</td><td>3.4</td><td>1.0</td><td>N</td><td>186</td><td>141</td><td>1</td><td>13.0</td><td>3.0</td><td>0.5</td><td>2.0</td><td>0.0</td><td>0.3</td><td>18.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-02-28T23:58:34.000+0000</td><td>2019-03-01T00:08:47.000+0000</td><td>2.0</td><td>1.8</td><td>1.0</td><td>N</td><td>249</td><td>13</td><td>1</td><td>9.0</td><td>0.5</td><td>0.5</td><td>5.0</td><td>0.0</td><td>0.3</td><td>17.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:12:40.000+0000</td><td>2019-03-01T00:20:26.000+0000</td><td>1.0</td><td>1.07</td><td>1.0</td><td>N</td><td>13</td><td>87</td><td>1</td><td>6.5</td><td>0.5</td><td>0.5</td><td>2.06</td><td>0.0</td><td>0.3</td><td>12.36</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:51:29.000+0000</td><td>2019-03-01T01:05:02.000+0000</td><td>2.0</td><td>2.82</td><td>1.0</td><td>N</td><td>90</td><td>142</td><td>2</td><td>12.0</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>15.8</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:06:06.000+0000</td><td>2019-03-01T00:10:51.000+0000</td><td>1.0</td><td>1.3</td><td>1.0</td><td>N</td><td>236</td><td>239</td><td>1</td><td>6.5</td><td>3.0</td><td>0.5</td><td>3.05</td><td>0.0</td><td>0.3</td><td>13.35</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:05:47.000+0000</td><td>2019-03-01T00:38:06.000+0000</td><td>1.0</td><td>18.53</td><td>2.0</td><td>N</td><td>132</td><td>90</td><td>1</td><td>52.0</td><td>0.0</td><td>0.5</td><td>12.21</td><td>5.76</td><td>0.3</td><td>73.27</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:16:33.000+0000</td><td>2019-03-01T00:18:25.000+0000</td><td>1.0</td><td>0.34</td><td>1.0</td><td>N</td><td>262</td><td>236</td><td>1</td><td>3.5</td><td>0.5</td><td>0.5</td><td>0.7</td><td>0.0</td><td>0.3</td><td>8.0</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:20:42.000+0000</td><td>2019-03-01T00:22:26.000+0000</td><td>1.0</td><td>0.36</td><td>1.0</td><td>N</td><td>236</td><td>263</td><td>2</td><td>3.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>7.3</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:27:59.000+0000</td><td>2019-03-01T00:31:02.000+0000</td><td>1.0</td><td>0.8</td><td>1.0</td><td>N</td><td>263</td><td>140</td><td>1</td><td>5.0</td><td>3.0</td><td>0.5</td><td>2.0</td><td>0.0</td><td>0.3</td><td>10.8</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:14:35.000+0000</td><td>2019-03-01T00:43:53.000+0000</td><td>1.0</td><td>7.0</td><td>1.0</td><td>N</td><td>87</td><td>188</td><td>1</td><td>25.5</td><td>3.0</td><td>0.5</td><td>5.85</td><td>0.0</td><td>0.3</td><td>35.15</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:01:04.000+0000</td><td>2019-03-01T00:27:45.000+0000</td><td>2.0</td><td>5.6</td><td>1.0</td><td>N</td><td>209</td><td>80</td><td>1</td><td>22.5</td><td>3.0</td><td>0.5</td><td>5.25</td><td>0.0</td><td>0.3</td><td>31.55</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:42:04.000+0000</td><td>2019-03-01T01:10:16.000+0000</td><td>1.0</td><td>13.9</td><td>1.0</td><td>N</td><td>255</td><td>243</td><td>1</td><td>39.5</td><td>3.0</td><td>0.5</td><td>8.65</td><td>0.0</td><td>0.3</td><td>51.95</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:16:13.000+0000</td><td>2019-03-01T00:25:42.000+0000</td><td>4.0</td><td>1.3</td><td>1.0</td><td>N</td><td>170</td><td>230</td><td>1</td><td>8.0</td><td>3.0</td><td>0.5</td><td>3.5</td><td>0.0</td><td>0.3</td><td>15.3</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:46:28.000+0000</td><td>2019-03-01T00:52:21.000+0000</td><td>2.0</td><td>0.6</td><td>1.0</td><td>N</td><td>114</td><td>113</td><td>1</td><td>5.5</td><td>3.0</td><td>0.5</td><td>2.75</td><td>0.0</td><td>0.3</td><td>12.05</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:16:18.000+0000</td><td>2019-03-01T00:22:16.000+0000</td><td>1.0</td><td>0.9</td><td>1.0</td><td>N</td><td>116</td><td>166</td><td>3</td><td>6.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>7.8</td><td>0.0</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:30:13.000+0000</td><td>2019-03-01T00:34:54.000+0000</td><td>1.0</td><td>0.9</td><td>1.0</td><td>N</td><td>43</td><td>236</td><td>3</td><td>5.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>6.8</td><td>0.0</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:07:00.000+0000</td><td>2019-03-01T00:46:46.000+0000</td><td>1.0</td><td>4.09</td><td>1.0</td><td>N</td><td>107</td><td>255</td><td>2</td><td>25.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>29.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:26:10.000+0000</td><td>2019-03-01T00:54:55.000+0000</td><td>1.0</td><td>7.05</td><td>1.0</td><td>N</td><td>230</td><td>83</td><td>1</td><td>25.0</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>28.8</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:24:51.000+0000</td><td>2019-03-01T00:54:07.000+0000</td><td>2.0</td><td>18.4</td><td>2.0</td><td>N</td><td>132</td><td>264</td><td>2</td><td>52.0</td><td>2.5</td><td>0.5</td><td>0.0</td><td>5.76</td><td>0.3</td><td>61.06</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:17:10.000+0000</td><td>2019-03-01T00:23:44.000+0000</td><td>1.0</td><td>1.0</td><td>1.0</td><td>N</td><td>113</td><td>90</td><td>2</td><td>6.0</td><td>3.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>9.8</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:57:18.000+0000</td><td>2019-03-01T01:34:40.000+0000</td><td>1.0</td><td>6.7</td><td>1.0</td><td>N</td><td>158</td><td>17</td><td>1</td><td>27.0</td><td>3.0</td><td>0.5</td><td>6.15</td><td>0.0</td><td>0.3</td><td>36.95</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:05:03.000+0000</td><td>2019-03-01T00:23:25.000+0000</td><td>6.0</td><td>2.4</td><td>1.0</td><td>N</td><td>246</td><td>79</td><td>1</td><td>13.0</td><td>0.5</td><td>0.5</td><td>3.0</td><td>0.0</td><td>0.3</td><td>19.8</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:10:48.000+0000</td><td>2019-03-01T00:33:38.000+0000</td><td>1.0</td><td>5.1</td><td>1.0</td><td>N</td><td>100</td><td>260</td><td>1</td><td>18.5</td><td>3.0</td><td>0.5</td><td>3.7</td><td>0.0</td><td>0.3</td><td>26.0</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:29:43.000+0000</td><td>2019-03-01T00:35:25.000+0000</td><td>1.0</td><td>1.2</td><td>1.0</td><td>N</td><td>148</td><td>107</td><td>1</td><td>6.5</td><td>3.0</td><td>0.5</td><td>2.05</td><td>0.0</td><td>0.3</td><td>12.35</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:56:05.000+0000</td><td>2019-03-01T01:14:32.000+0000</td><td>3.0</td><td>3.4</td><td>1.0</td><td>N</td><td>114</td><td>100</td><td>2</td><td>15.0</td><td>3.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>18.8</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:24:51.000+0000</td><td>2019-03-01T00:34:34.000+0000</td><td>1.0</td><td>2.5</td><td>1.0</td><td>N</td><td>68</td><td>143</td><td>1</td><td>10.0</td><td>3.0</td><td>0.5</td><td>2.07</td><td>0.0</td><td>0.3</td><td>15.87</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:39:41.000+0000</td><td>2019-03-01T00:41:33.000+0000</td><td>1.0</td><td>0.58</td><td>1.0</td><td>N</td><td>238</td><td>24</td><td>1</td><td>4.0</td><td>0.5</td><td>0.5</td><td>1.06</td><td>0.0</td><td>0.3</td><td>6.36</td><td>0.0</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:47:13.000+0000</td><td>2019-03-01T00:58:11.000+0000</td><td>1.0</td><td>2.63</td><td>1.0</td><td>N</td><td>238</td><td>116</td><td>1</td><td>11.0</td><td>0.5</td><td>0.5</td><td>2.46</td><td>0.0</td><td>0.3</td><td>14.76</td><td>0.0</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:40:11.000+0000</td><td>2019-03-01T00:45:41.000+0000</td><td>1.0</td><td>1.36</td><td>1.0</td><td>N</td><td>239</td><td>151</td><td>1</td><td>6.5</td><td>0.5</td><td>0.5</td><td>2.06</td><td>0.0</td><td>0.3</td><td>12.36</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:19:18.000+0000</td><td>2019-03-01T00:26:23.000+0000</td><td>2.0</td><td>1.08</td><td>1.0</td><td>N</td><td>112</td><td>255</td><td>1</td><td>6.5</td><td>0.5</td><td>0.5</td><td>1.56</td><td>0.0</td><td>0.3</td><td>9.36</td><td>0.0</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:31:46.000+0000</td><td>2019-03-01T00:50:09.000+0000</td><td>2.0</td><td>2.65</td><td>1.0</td><td>N</td><td>256</td><td>49</td><td>1</td><td>12.5</td><td>0.5</td><td>0.5</td><td>3.0</td><td>0.0</td><td>0.3</td><td>16.8</td><td>0.0</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:08:56.000+0000</td><td>2019-03-01T00:13:54.000+0000</td><td>1.0</td><td>0.84</td><td>1.0</td><td>N</td><td>164</td><td>170</td><td>1</td><td>5.5</td><td>0.5</td><td>0.5</td><td>1.0</td><td>0.0</td><td>0.3</td><td>10.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:19:31.000+0000</td><td>2019-03-01T00:24:34.000+0000</td><td>2.0</td><td>0.89</td><td>1.0</td><td>N</td><td>233</td><td>161</td><td>2</td><td>5.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>9.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:38:34.000+0000</td><td>2019-03-01T00:43:36.000+0000</td><td>2.0</td><td>0.84</td><td>1.0</td><td>N</td><td>107</td><td>233</td><td>2</td><td>5.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>9.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:39:24.000+0000</td><td>2019-03-01T01:04:30.000+0000</td><td>1.0</td><td>3.17</td><td>1.0</td><td>N</td><td>162</td><td>79</td><td>1</td><td>17.0</td><td>0.5</td><td>0.5</td><td>4.16</td><td>0.0</td><td>0.3</td><td>24.96</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:55:57.000+0000</td><td>2019-03-01T01:18:57.000+0000</td><td>1.0</td><td>17.0</td><td>1.0</td><td>N</td><td>132</td><td>178</td><td>1</td><td>45.5</td><td>0.5</td><td>0.5</td><td>9.35</td><td>0.0</td><td>0.3</td><td>56.15</td><td>0.0</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:21:10.000+0000</td><td>2019-03-01T00:49:05.000+0000</td><td>2.0</td><td>16.4</td><td>2.0</td><td>N</td><td>132</td><td>162</td><td>1</td><td>52.0</td><td>2.5</td><td>0.5</td><td>10.0</td><td>5.76</td><td>0.3</td><td>71.06</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:16:41.000+0000</td><td>2019-03-01T00:23:09.000+0000</td><td>2.0</td><td>1.2</td><td>1.0</td><td>N</td><td>162</td><td>48</td><td>1</td><td>7.0</td><td>0.5</td><td>0.5</td><td>2.16</td><td>0.0</td><td>0.3</td><td>12.96</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:44:06.000+0000</td><td>2019-03-01T01:01:12.000+0000</td><td>4.0</td><td>3.42</td><td>1.0</td><td>N</td><td>229</td><td>249</td><td>1</td><td>14.0</td><td>0.5</td><td>0.5</td><td>3.56</td><td>0.0</td><td>0.3</td><td>21.36</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:09:40.000+0000</td><td>2019-03-01T00:42:24.000+0000</td><td>4.0</td><td>5.2</td><td>1.0</td><td>N</td><td>234</td><td>80</td><td>1</td><td>24.5</td><td>3.0</td><td>0.5</td><td>4.0</td><td>0.0</td><td>0.3</td><td>32.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:06:00.000+0000</td><td>2019-03-01T00:07:06.000+0000</td><td>1.0</td><td>0.45</td><td>1.0</td><td>N</td><td>50</td><td>143</td><td>1</td><td>3.5</td><td>0.5</td><td>0.5</td><td>1.46</td><td>0.0</td><td>0.3</td><td>8.76</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:14:11.000+0000</td><td>2019-03-01T00:21:27.000+0000</td><td>1.0</td><td>1.8</td><td>1.0</td><td>N</td><td>230</td><td>239</td><td>1</td><td>8.0</td><td>0.5</td><td>0.5</td><td>2.36</td><td>0.0</td><td>0.3</td><td>14.16</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:31:18.000+0000</td><td>2019-03-01T00:43:00.000+0000</td><td>1.0</td><td>3.28</td><td>1.0</td><td>N</td><td>48</td><td>146</td><td>1</td><td>12.0</td><td>0.5</td><td>0.5</td><td>3.16</td><td>0.0</td><td>0.3</td><td>18.96</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:53:44.000+0000</td><td>2019-03-01T01:23:54.000+0000</td><td>1.0</td><td>17.98</td><td>2.0</td><td>N</td><td>132</td><td>48</td><td>2</td><td>52.0</td><td>0.0</td><td>0.5</td><td>0.0</td><td>5.76</td><td>0.3</td><td>61.06</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:34:38.000+0000</td><td>2019-03-01T00:41:23.000+0000</td><td>1.0</td><td>1.38</td><td>1.0</td><td>N</td><td>144</td><td>4</td><td>1</td><td>7.0</td><td>0.5</td><td>0.5</td><td>2.16</td><td>0.0</td><td>0.3</td><td>12.96</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:45:38.000+0000</td><td>2019-03-01T01:08:11.000+0000</td><td>1.0</td><td>3.86</td><td>1.0</td><td>N</td><td>79</td><td>256</td><td>1</td><td>18.0</td><td>0.5</td><td>0.5</td><td>2.18</td><td>0.0</td><td>0.3</td><td>23.98</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:28:04.000+0000</td><td>2019-03-01T00:35:56.000+0000</td><td>1.0</td><td>1.79</td><td>1.0</td><td>N</td><td>113</td><td>137</td><td>2</td><td>8.0</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>11.8</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:12:13.000+0000</td><td>2019-03-01T00:49:26.000+0000</td><td>1.0</td><td>18.6</td><td>1.0</td><td>N</td><td>132</td><td>97</td><td>1</td><td>52.0</td><td>0.5</td><td>0.5</td><td>10.65</td><td>0.0</td><td>0.3</td><td>63.95</td><td>0.0</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:04:28.000+0000</td><td>2019-03-01T00:34:03.000+0000</td><td>1.0</td><td>19.5</td><td>2.0</td><td>N</td><td>132</td><td>41</td><td>1</td><td>52.0</td><td>0.0</td><td>0.5</td><td>11.7</td><td>5.76</td><td>0.3</td><td>70.26</td><td>0.0</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:27:30.000+0000</td><td>2019-03-01T00:41:17.000+0000</td><td>1.0</td><td>2.5</td><td>1.0</td><td>N</td><td>239</td><td>166</td><td>1</td><td>12.0</td><td>3.0</td><td>0.5</td><td>0.03</td><td>0.0</td><td>0.3</td><td>15.83</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:48:01.000+0000</td><td>2019-03-01T00:57:25.000+0000</td><td>2.0</td><td>3.6</td><td>1.0</td><td>N</td><td>166</td><td>244</td><td>2</td><td>12.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>13.8</td><td>0.0</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:27:08.000+0000</td><td>2019-03-01T00:48:03.000+0000</td><td>1.0</td><td>6.5</td><td>1.0</td><td>N</td><td>158</td><td>75</td><td>1</td><td>22.5</td><td>3.0</td><td>0.5</td><td>2.0</td><td>0.0</td><td>0.3</td><td>28.3</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:07:48.000+0000</td><td>2019-03-01T00:15:17.000+0000</td><td>1.0</td><td>1.2</td><td>1.0</td><td>N</td><td>100</td><td>234</td><td>1</td><td>7.0</td><td>3.0</td><td>0.5</td><td>1.0</td><td>0.0</td><td>0.3</td><td>11.8</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:49:23.000+0000</td><td>2019-03-01T00:53:35.000+0000</td><td>3.0</td><td>0.7</td><td>1.0</td><td>N</td><td>234</td><td>107</td><td>1</td><td>5.0</td><td>3.0</td><td>0.5</td><td>1.0</td><td>0.0</td><td>0.3</td><td>9.8</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:04:10.000+0000</td><td>2019-03-01T00:14:41.000+0000</td><td>1.0</td><td>1.0</td><td>1.0</td><td>N</td><td>232</td><td>144</td><td>1</td><td>8.0</td><td>3.0</td><td>0.5</td><td>2.36</td><td>0.0</td><td>0.3</td><td>14.16</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:24:54.000+0000</td><td>2019-03-01T00:38:41.000+0000</td><td>1.0</td><td>3.6</td><td>1.0</td><td>N</td><td>144</td><td>40</td><td>1</td><td>14.0</td><td>3.0</td><td>0.5</td><td>3.55</td><td>0.0</td><td>0.3</td><td>21.35</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:41:39.000+0000</td><td>2019-03-01T00:44:48.000+0000</td><td>1.0</td><td>0.8</td><td>1.0</td><td>N</td><td>25</td><td>54</td><td>1</td><td>4.5</td><td>0.5</td><td>0.5</td><td>1.15</td><td>0.0</td><td>0.3</td><td>6.95</td><td>0.0</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:53:13.000+0000</td><td>2019-03-01T00:57:20.000+0000</td><td>1.0</td><td>0.8</td><td>1.0</td><td>N</td><td>65</td><td>97</td><td>2</td><td>5.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>6.8</td><td>0.0</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:57:40.000+0000</td><td>2019-03-01T01:08:20.000+0000</td><td>1.0</td><td>3.67</td><td>1.0</td><td>N</td><td>148</td><td>79</td><td>2</td><td>12.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>16.3</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:43:51.000+0000</td><td>2019-03-01T01:04:08.000+0000</td><td>1.0</td><td>7.5</td><td>1.0</td><td>N</td><td>138</td><td>229</td><td>1</td><td>23.5</td><td>3.0</td><td>0.5</td><td>6.8</td><td>0.0</td><td>0.3</td><td>34.1</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:03:28.000+0000</td><td>2019-03-01T00:23:52.000+0000</td><td>1.0</td><td>8.7</td><td>1.0</td><td>N</td><td>45</td><td>74</td><td>2</td><td>26.0</td><td>3.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>29.8</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:39:52.000+0000</td><td>2019-03-01T00:48:18.000+0000</td><td>1.0</td><td>3.8</td><td>1.0</td><td>N</td><td>238</td><td>244</td><td>1</td><td>12.5</td><td>3.0</td><td>0.5</td><td>4.85</td><td>0.0</td><td>0.3</td><td>21.15</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:52:44.000+0000</td><td>2019-03-01T01:04:13.000+0000</td><td>1.0</td><td>1.9</td><td>1.0</td><td>N</td><td>116</td><td>244</td><td>2</td><td>10.0</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>11.3</td><td>0.0</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:31:23.000+0000</td><td>2019-03-01T00:43:40.000+0000</td><td>1.0</td><td>7.69</td><td>1.0</td><td>N</td><td>138</td><td>75</td><td>1</td><td>22.0</td><td>0.5</td><td>0.5</td><td>5.81</td><td>5.76</td><td>0.3</td><td>34.87</td><td>0.0</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:08:39.000+0000</td><td>2019-03-01T00:24:02.000+0000</td><td>1.0</td><td>9.87</td><td>1.0</td><td>N</td><td>261</td><td>14</td><td>1</td><td>27.5</td><td>0.5</td><td>0.5</td><td>9.26</td><td>5.76</td><td>0.3</td><td>46.32</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:50:28.000+0000</td><td>2019-03-01T01:05:22.000+0000</td><td>1.0</td><td>4.95</td><td>1.0</td><td>N</td><td>88</td><td>112</td><td>1</td><td>17.0</td><td>0.5</td><td>0.5</td><td>4.16</td><td>0.0</td><td>0.3</td><td>24.96</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:03:48.000+0000</td><td>2019-03-01T00:24:59.000+0000</td><td>5.0</td><td>3.15</td><td>1.0</td><td>N</td><td>90</td><td>68</td><td>2</td><td>14.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>18.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-02-28T23:56:46.000+0000</td><td>2019-02-28T23:59:33.000+0000</td><td>1.0</td><td>0.41</td><td>1.0</td><td>N</td><td>24</td><td>24</td><td>1</td><td>4.0</td><td>0.5</td><td>0.5</td><td>1.06</td><td>0.0</td><td>0.3</td><td>6.36</td><td>0.0</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:16:45.000+0000</td><td>2019-03-01T00:20:58.000+0000</td><td>1.0</td><td>0.9</td><td>1.0</td><td>N</td><td>237</td><td>141</td><td>1</td><td>5.0</td><td>3.0</td><td>0.5</td><td>1.75</td><td>0.0</td><td>0.3</td><td>10.55</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:27:03.000+0000</td><td>2019-03-01T00:30:18.000+0000</td><td>2.0</td><td>0.6</td><td>1.0</td><td>N</td><td>163</td><td>163</td><td>2</td><td>4.5</td><td>3.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>8.3</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:34:29.000+0000</td><td>2019-03-01T00:44:05.000+0000</td><td>1.0</td><td>2.1</td><td>1.0</td><td>N</td><td>163</td><td>90</td><td>2</td><td>9.0</td><td>3.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>12.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:30:06.000+0000</td><td>2019-03-01T00:57:05.000+0000</td><td>1.0</td><td>6.2</td><td>1.0</td><td>N</td><td>140</td><td>36</td><td>1</td><td>22.5</td><td>0.5</td><td>0.5</td><td>3.0</td><td>0.0</td><td>0.3</td><td>29.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:13:37.000+0000</td><td>2019-03-01T00:31:54.000+0000</td><td>1.0</td><td>7.87</td><td>1.0</td><td>N</td><td>138</td><td>164</td><td>1</td><td>24.0</td><td>0.5</td><td>0.5</td><td>8.39</td><td>5.76</td><td>0.3</td><td>41.95</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:44:11.000+0000</td><td>2019-03-01T01:02:28.000+0000</td><td>1.0</td><td>4.5</td><td>1.0</td><td>N</td><td>234</td><td>263</td><td>1</td><td>16.5</td><td>0.5</td><td>0.5</td><td>2.0</td><td>0.0</td><td>0.3</td><td>22.3</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:20:08.000+0000</td><td>2019-03-01T01:32:29.000+0000</td><td>1.0</td><td>6.8</td><td>1.0</td><td>N</td><td>263</td><td>87</td><td>1</td><td>46.5</td><td>3.0</td><td>0.5</td><td>7.54</td><td>0.0</td><td>0.3</td><td>57.84</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:01:19.000+0000</td><td>2019-03-01T00:15:41.000+0000</td><td>1.0</td><td>2.0</td><td>1.0</td><td>N</td><td>158</td><td>148</td><td>1</td><td>11.0</td><td>3.0</td><td>0.5</td><td>1.0</td><td>0.0</td><td>0.3</td><td>15.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:02:41.000+0000</td><td>2019-03-01T00:15:08.000+0000</td><td>1.0</td><td>2.68</td><td>1.0</td><td>N</td><td>239</td><td>262</td><td>1</td><td>11.5</td><td>0.5</td><td>0.5</td><td>2.0</td><td>0.0</td><td>0.3</td><td>17.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:40:50.000+0000</td><td>2019-03-01T00:46:20.000+0000</td><td>1.0</td><td>1.04</td><td>1.0</td><td>N</td><td>166</td><td>41</td><td>2</td><td>6.0</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>7.3</td><td>0.0</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:52:40.000+0000</td><td>2019-03-01T01:03:21.000+0000</td><td>1.0</td><td>4.68</td><td>1.0</td><td>N</td><td>152</td><td>127</td><td>2</td><td>15.0</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>16.3</td><td>0.0</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:13:49.000+0000</td><td>2019-03-01T00:17:44.000+0000</td><td>1.0</td><td>1.2</td><td>1.0</td><td>N</td><td>79</td><td>170</td><td>1</td><td>5.5</td><td>0.5</td><td>0.5</td><td>1.0</td><td>0.0</td><td>0.3</td><td>10.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-02-28T23:53:59.000+0000</td><td>2019-03-01T00:11:48.000+0000</td><td>3.0</td><td>3.67</td><td>1.0</td><td>N</td><td>79</td><td>97</td><td>1</td><td>15.0</td><td>0.5</td><td>0.5</td><td>3.76</td><td>0.0</td><td>0.3</td><td>22.56</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:49:48.000+0000</td><td>2019-03-01T00:50:16.000+0000</td><td>1.0</td><td>0.0</td><td>5.0</td><td>N</td><td>79</td><td>79</td><td>1</td><td>61.0</td><td>0.0</td><td>0.0</td><td>12.25</td><td>0.0</td><td>0.3</td><td>73.55</td><td>0.0</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:11:07.000+0000</td><td>2019-03-01T00:45:43.000+0000</td><td>6.0</td><td>19.32</td><td>1.0</td><td>N</td><td>132</td><td>49</td><td>1</td><td>52.5</td><td>0.5</td><td>0.5</td><td>10.76</td><td>0.0</td><td>0.3</td><td>64.56</td><td>0.0</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:02:20.000+0000</td><td>2019-03-01T00:30:36.000+0000</td><td>1.0</td><td>4.89</td><td>1.0</td><td>N</td><td>230</td><td>7</td><td>1</td><td>20.5</td><td>0.5</td><td>0.5</td><td>4.86</td><td>0.0</td><td>0.3</td><td>29.16</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:14:59.000+0000</td><td>2019-03-01T00:30:22.000+0000</td><td>1.0</td><td>2.9</td><td>1.0</td><td>N</td><td>249</td><td>161</td><td>1</td><td>12.5</td><td>3.0</td><td>0.5</td><td>2.0</td><td>0.0</td><td>0.3</td><td>18.3</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:33:10.000+0000</td><td>2019-03-01T00:39:59.000+0000</td><td>1.0</td><td>1.1</td><td>1.0</td><td>N</td><td>161</td><td>48</td><td>1</td><td>6.5</td><td>3.0</td><td>0.5</td><td>2.05</td><td>0.0</td><td>0.3</td><td>12.35</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:55:43.000+0000</td><td>2019-03-01T01:01:44.000+0000</td><td>1.0</td><td>0.8</td><td>1.0</td><td>N</td><td>249</td><td>211</td><td>1</td><td>5.5</td><td>3.0</td><td>0.5</td><td>1.85</td><td>0.0</td><td>0.3</td><td>11.15</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:10:56.000+0000</td><td>2019-03-01T00:26:32.000+0000</td><td>1.0</td><td>3.3</td><td>1.0</td><td>N</td><td>140</td><td>146</td><td>1</td><td>14.0</td><td>3.0</td><td>0.5</td><td>3.55</td><td>0.0</td><td>0.3</td><td>21.35</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:30:44.000+0000</td><td>2019-03-01T00:43:47.000+0000</td><td>1.0</td><td>3.8</td><td>1.0</td><td>N</td><td>146</td><td>143</td><td>1</td><td>13.5</td><td>3.0</td><td>0.5</td><td>2.5</td><td>0.0</td><td>0.3</td><td>19.8</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:59:54.000+0000</td><td>2019-03-01T01:10:42.000+0000</td><td>1.0</td><td>3.1</td><td>1.0</td><td>N</td><td>142</td><td>75</td><td>2</td><td>11.5</td><td>3.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>15.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-02-28T23:57:49.000+0000</td><td>2019-03-01T00:31:21.000+0000</td><td>2.0</td><td>19.86</td><td>2.0</td><td>N</td><td>132</td><td>140</td><td>1</td><td>52.0</td><td>0.0</td><td>0.5</td><td>10.0</td><td>5.76</td><td>0.3</td><td>71.06</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:04:47.000+0000</td><td>2019-03-01T00:10:07.000+0000</td><td>5.0</td><td>1.77</td><td>1.0</td><td>N</td><td>246</td><td>50</td><td>1</td><td>7.0</td><td>0.5</td><td>0.5</td><td>2.16</td><td>0.0</td><td>0.3</td><td>12.96</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:11:59.000+0000</td><td>2019-03-01T00:29:11.000+0000</td><td>1.0</td><td>5.49</td><td>1.0</td><td>N</td><td>163</td><td>168</td><td>1</td><td>17.5</td><td>0.5</td><td>0.5</td><td>3.7</td><td>0.0</td><td>0.3</td><td>25.0</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:49:42.000+0000</td><td>2019-03-01T00:52:48.000+0000</td><td>1.0</td><td>0.41</td><td>1.0</td><td>N</td><td>237</td><td>237</td><td>3</td><td>-4.0</td><td>-0.5</td><td>-0.5</td><td>0.0</td><td>0.0</td><td>-0.3</td><td>-7.8</td><td>-2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:49:42.000+0000</td><td>2019-03-01T00:52:48.000+0000</td><td>1.0</td><td>0.41</td><td>1.0</td><td>N</td><td>237</td><td>237</td><td>2</td><td>4.0</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>7.8</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:01:33.000+0000</td><td>2019-03-01T00:11:21.000+0000</td><td>1.0</td><td>1.4</td><td>1.0</td><td>N</td><td>264</td><td>264</td><td>1</td><td>8.5</td><td>0.5</td><td>0.5</td><td>1.95</td><td>0.0</td><td>0.3</td><td>11.75</td><td>0.0</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:19:36.000+0000</td><td>2019-03-01T00:44:00.000+0000</td><td>1.0</td><td>5.6</td><td>1.0</td><td>N</td><td>264</td><td>264</td><td>1</td><td>21.0</td><td>0.5</td><td>0.5</td><td>4.45</td><td>0.0</td><td>0.3</td><td>26.75</td><td>0.0</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:08:38.000+0000</td><td>2019-03-01T00:21:01.000+0000</td><td>1.0</td><td>2.3</td><td>1.0</td><td>N</td><td>232</td><td>90</td><td>2</td><td>10.5</td><td>3.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>14.3</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:46:02.000+0000</td><td>2019-03-01T00:52:06.000+0000</td><td>1.0</td><td>1.3</td><td>1.0</td><td>N</td><td>246</td><td>234</td><td>1</td><td>6.5</td><td>3.0</td><td>0.5</td><td>1.0</td><td>0.0</td><td>0.3</td><td>11.3</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:09:26.000+0000</td><td>2019-03-01T00:25:15.000+0000</td><td>3.0</td><td>2.2</td><td>1.0</td><td>N</td><td>100</td><td>79</td><td>1</td><td>11.5</td><td>3.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>15.3</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:36:49.000+0000</td><td>2019-03-01T00:46:08.000+0000</td><td>2.0</td><td>1.2</td><td>1.0</td><td>N</td><td>234</td><td>107</td><td>1</td><td>8.0</td><td>3.0</td><td>0.5</td><td>1.18</td><td>0.0</td><td>0.3</td><td>12.98</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:47:16.000+0000</td><td>2019-03-01T01:02:23.000+0000</td><td>1.0</td><td>4.6</td><td>1.0</td><td>N</td><td>107</td><td>239</td><td>1</td><td>15.0</td><td>3.0</td><td>0.5</td><td>1.5</td><td>0.0</td><td>0.3</td><td>20.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:43:07.000+0000</td><td>2019-03-01T00:53:49.000+0000</td><td>2.0</td><td>1.46</td><td>1.0</td><td>N</td><td>107</td><td>249</td><td>1</td><td>9.0</td><td>0.5</td><td>0.5</td><td>4.0</td><td>0.0</td><td>0.3</td><td>16.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:04:30.000+0000</td><td>2019-03-01T00:21:08.000+0000</td><td>1.0</td><td>2.68</td><td>1.0</td><td>N</td><td>249</td><td>107</td><td>2</td><td>12.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>16.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:27:23.000+0000</td><td>2019-03-01T00:33:20.000+0000</td><td>1.0</td><td>1.17</td><td>1.0</td><td>N</td><td>137</td><td>229</td><td>1</td><td>6.5</td><td>0.5</td><td>0.5</td><td>2.06</td><td>0.0</td><td>0.3</td><td>12.36</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:27:46.000+0000</td><td>2019-03-01T00:38:10.000+0000</td><td>1.0</td><td>1.3</td><td>1.0</td><td>N</td><td>114</td><td>79</td><td>1</td><td>8.5</td><td>3.0</td><td>0.5</td><td>1.48</td><td>0.0</td><td>0.3</td><td>13.78</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:31:57.000+0000</td><td>2019-03-01T00:37:10.000+0000</td><td>1.0</td><td>0.93</td><td>1.0</td><td>N</td><td>161</td><td>233</td><td>1</td><td>5.5</td><td>0.5</td><td>0.5</td><td>1.86</td><td>0.0</td><td>0.3</td><td>11.16</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:39:17.000+0000</td><td>2019-03-01T00:42:43.000+0000</td><td>1.0</td><td>1.19</td><td>1.0</td><td>N</td><td>233</td><td>237</td><td>1</td><td>5.5</td><td>0.5</td><td>0.5</td><td>1.4</td><td>0.0</td><td>0.3</td><td>10.7</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:47:10.000+0000</td><td>2019-03-01T00:53:43.000+0000</td><td>1.0</td><td>1.11</td><td>1.0</td><td>N</td><td>237</td><td>237</td><td>2</td><td>6.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>10.3</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:02:37.000+0000</td><td>2019-03-01T00:11:01.000+0000</td><td>1.0</td><td>1.4</td><td>1.0</td><td>N</td><td>161</td><td>100</td><td>1</td><td>7.5</td><td>3.0</td><td>0.5</td><td>1.5</td><td>0.0</td><td>0.3</td><td>12.8</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:16:23.000+0000</td><td>2019-03-01T00:34:23.000+0000</td><td>1.0</td><td>6.5</td><td>1.0</td><td>N</td><td>90</td><td>166</td><td>1</td><td>21.5</td><td>3.0</td><td>0.5</td><td>2.0</td><td>0.0</td><td>0.3</td><td>27.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:16:53.000+0000</td><td>2019-03-01T00:32:37.000+0000</td><td>1.0</td><td>2.84</td><td>1.0</td><td>N</td><td>161</td><td>249</td><td>1</td><td>12.0</td><td>0.5</td><td>0.5</td><td>4.74</td><td>0.0</td><td>0.3</td><td>20.54</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:55:25.000+0000</td><td>2019-03-01T01:07:23.000+0000</td><td>1.0</td><td>3.53</td><td>1.0</td><td>N</td><td>79</td><td>88</td><td>1</td><td>13.0</td><td>0.5</td><td>0.5</td><td>3.36</td><td>0.0</td><td>0.3</td><td>20.16</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:07:37.000+0000</td><td>2019-03-01T00:12:54.000+0000</td><td>1.0</td><td>0.9</td><td>1.0</td><td>N</td><td>237</td><td>237</td><td>2</td><td>4.5</td><td>3.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>8.3</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:16:11.000+0000</td><td>2019-03-01T00:31:26.000+0000</td><td>1.0</td><td>3.3</td><td>1.0</td><td>N</td><td>237</td><td>113</td><td>1</td><td>13.0</td><td>3.0</td><td>0.5</td><td>5.0</td><td>0.0</td><td>0.3</td><td>21.8</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:52:48.000+0000</td><td>2019-03-01T00:55:10.000+0000</td><td>1.0</td><td>0.1</td><td>1.0</td><td>N</td><td>170</td><td>170</td><td>1</td><td>3.5</td><td>3.0</td><td>0.5</td><td>2.0</td><td>0.0</td><td>0.3</td><td>9.3</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:48:03.000+0000</td><td>2019-03-01T00:52:45.000+0000</td><td>1.0</td><td>0.8</td><td>1.0</td><td>N</td><td>114</td><td>79</td><td>2</td><td>5.5</td><td>3.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>9.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:42:14.000+0000</td><td>2019-03-01T00:46:33.000+0000</td><td>4.0</td><td>1.12</td><td>1.0</td><td>N</td><td>50</td><td>50</td><td>1</td><td>5.5</td><td>0.5</td><td>0.5</td><td>2.0</td><td>0.0</td><td>0.3</td><td>11.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:07:19.000+0000</td><td>2019-03-01T00:15:03.000+0000</td><td>6.0</td><td>1.48</td><td>1.0</td><td>N</td><td>68</td><td>234</td><td>1</td><td>7.0</td><td>0.5</td><td>0.5</td><td>1.88</td><td>0.0</td><td>0.3</td><td>12.68</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:20:30.000+0000</td><td>2019-03-01T00:39:17.000+0000</td><td>6.0</td><td>2.95</td><td>1.0</td><td>N</td><td>164</td><td>261</td><td>1</td><td>14.0</td><td>0.5</td><td>0.5</td><td>3.56</td><td>0.0</td><td>0.3</td><td>21.36</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:54:22.000+0000</td><td>2019-03-01T00:59:20.000+0000</td><td>6.0</td><td>1.11</td><td>1.0</td><td>N</td><td>234</td><td>230</td><td>1</td><td>6.0</td><td>0.5</td><td>0.5</td><td>1.0</td><td>0.0</td><td>0.3</td><td>10.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:09:16.000+0000</td><td>2019-03-01T00:29:57.000+0000</td><td>1.0</td><td>13.09</td><td>2.0</td><td>N</td><td>93</td><td>238</td><td>1</td><td>52.0</td><td>0.0</td><td>0.5</td><td>9.0</td><td>5.76</td><td>0.3</td><td>70.06</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:42:32.000+0000</td><td>2019-03-01T01:00:00.000+0000</td><td>1.0</td><td>4.66</td><td>1.0</td><td>N</td><td>230</td><td>88</td><td>1</td><td>15.5</td><td>0.5</td><td>0.5</td><td>3.86</td><td>0.0</td><td>0.3</td><td>23.16</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:10:11.000+0000</td><td>2019-03-01T00:37:23.000+0000</td><td>3.0</td><td>9.0</td><td>1.0</td><td>N</td><td>138</td><td>230</td><td>1</td><td>29.0</td><td>3.0</td><td>0.5</td><td>7.7</td><td>5.76</td><td>0.3</td><td>46.26</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-02-28T23:59:38.000+0000</td><td>2019-03-01T00:07:47.000+0000</td><td>2.0</td><td>1.02</td><td>1.0</td><td>N</td><td>114</td><td>148</td><td>2</td><td>7.0</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>10.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:29:40.000+0000</td><td>2019-03-01T00:39:55.000+0000</td><td>2.0</td><td>1.53</td><td>1.0</td><td>N</td><td>114</td><td>79</td><td>1</td><td>8.0</td><td>0.5</td><td>0.5</td><td>2.36</td><td>0.0</td><td>0.3</td><td>14.16</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:10:12.000+0000</td><td>2019-03-01T00:18:59.000+0000</td><td>1.0</td><td>2.1</td><td>1.0</td><td>N</td><td>79</td><td>186</td><td>1</td><td>9.0</td><td>3.0</td><td>0.5</td><td>2.55</td><td>0.0</td><td>0.3</td><td>15.35</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:13:18.000+0000</td><td>2019-03-01T00:18:59.000+0000</td><td>1.0</td><td>0.83</td><td>1.0</td><td>N</td><td>166</td><td>41</td><td>2</td><td>6.0</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>7.3</td><td>0.0</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:38:44.000+0000</td><td>2019-03-01T00:55:40.000+0000</td><td>1.0</td><td>2.92</td><td>1.0</td><td>N</td><td>151</td><td>163</td><td>2</td><td>14.0</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>17.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-02-28T23:56:31.000+0000</td><td>2019-03-01T00:11:00.000+0000</td><td>1.0</td><td>2.05</td><td>1.0</td><td>N</td><td>211</td><td>87</td><td>1</td><td>10.5</td><td>0.5</td><td>0.5</td><td>2.86</td><td>0.0</td><td>0.3</td><td>17.16</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:33:41.000+0000</td><td>2019-03-01T00:39:51.000+0000</td><td>1.0</td><td>0.97</td><td>1.0</td><td>N</td><td>144</td><td>249</td><td>1</td><td>6.0</td><td>0.5</td><td>0.5</td><td>1.96</td><td>0.0</td><td>0.3</td><td>11.76</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:54:25.000+0000</td><td>2019-03-01T01:15:41.000+0000</td><td>1.0</td><td>5.37</td><td>1.0</td><td>N</td><td>90</td><td>40</td><td>1</td><td>18.5</td><td>0.5</td><td>0.5</td><td>5.58</td><td>0.0</td><td>0.3</td><td>27.88</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:23:51.000+0000</td><td>2019-03-01T00:29:11.000+0000</td><td>1.0</td><td>2.9</td><td>1.0</td><td>N</td><td>132</td><td>10</td><td>2</td><td>10.0</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>11.3</td><td>0.0</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:06:59.000+0000</td><td>2019-03-01T01:15:34.000+0000</td><td>1.0</td><td>5.7</td><td>1.0</td><td>N</td><td>170</td><td>25</td><td>1</td><td>42.0</td><td>3.0</td><td>0.5</td><td>11.4</td><td>0.0</td><td>0.3</td><td>57.2</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:48:08.000+0000</td><td>2019-03-01T01:01:13.000+0000</td><td>1.0</td><td>2.4</td><td>1.0</td><td>N</td><td>4</td><td>164</td><td>1</td><td>11.0</td><td>3.0</td><td>0.5</td><td>2.0</td><td>0.0</td><td>0.3</td><td>16.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:16:47.000+0000</td><td>2019-03-01T00:18:38.000+0000</td><td>1.0</td><td>1.08</td><td>2.0</td><td>N</td><td>132</td><td>132</td><td>2</td><td>52.0</td><td>0.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>52.8</td><td>0.0</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:17:37.000+0000</td><td>2019-03-01T00:27:58.000+0000</td><td>1.0</td><td>1.6</td><td>1.0</td><td>N</td><td>68</td><td>125</td><td>1</td><td>8.5</td><td>3.0</td><td>0.5</td><td>2.45</td><td>0.0</td><td>0.3</td><td>14.75</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:34:40.000+0000</td><td>2019-03-01T00:51:43.000+0000</td><td>1.0</td><td>4.3</td><td>1.0</td><td>N</td><td>144</td><td>262</td><td>1</td><td>15.5</td><td>3.0</td><td>0.5</td><td>5.0</td><td>0.0</td><td>0.3</td><td>24.3</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:21:14.000+0000</td><td>2019-03-01T00:27:35.000+0000</td><td>1.0</td><td>1.1</td><td>1.0</td><td>N</td><td>161</td><td>100</td><td>1</td><td>6.0</td><td>3.0</td><td>0.5</td><td>2.45</td><td>0.0</td><td>0.3</td><td>12.25</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:31:49.000+0000</td><td>2019-03-01T00:38:46.000+0000</td><td>1.0</td><td>4.02</td><td>1.0</td><td>N</td><td>132</td><td>219</td><td>2</td><td>13.0</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>14.3</td><td>0.0</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:15:56.000+0000</td><td>2019-03-01T00:37:44.000+0000</td><td>1.0</td><td>18.0</td><td>2.0</td><td>N</td><td>132</td><td>74</td><td>1</td><td>52.0</td><td>0.0</td><td>0.5</td><td>8.0</td><td>5.76</td><td>0.3</td><td>66.56</td><td>0.0</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:28:42.000+0000</td><td>2019-03-01T00:38:48.000+0000</td><td>1.0</td><td>2.3</td><td>1.0</td><td>N</td><td>161</td><td>262</td><td>1</td><td>10.0</td><td>3.0</td><td>0.5</td><td>2.75</td><td>0.0</td><td>0.3</td><td>16.55</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:48:21.000+0000</td><td>2019-03-01T00:49:36.000+0000</td><td>1.0</td><td>0.0</td><td>1.0</td><td>N</td><td>237</td><td>237</td><td>2</td><td>3.0</td><td>3.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>6.8</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:51:45.000+0000</td><td>2019-03-01T01:11:52.000+0000</td><td>4.0</td><td>4.0</td><td>1.0</td><td>N</td><td>237</td><td>79</td><td>1</td><td>16.0</td><td>3.0</td><td>0.5</td><td>3.0</td><td>0.0</td><td>0.3</td><td>22.8</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:04:53.000+0000</td><td>2019-03-01T00:48:27.000+0000</td><td>1.0</td><td>21.5</td><td>2.0</td><td>N</td><td>132</td><td>48</td><td>2</td><td>52.0</td><td>2.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>55.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:16:00.000+0000</td><td>2019-03-01T00:24:18.000+0000</td><td>1.0</td><td>0.79</td><td>1.0</td><td>N</td><td>249</td><td>249</td><td>1</td><td>7.0</td><td>0.5</td><td>0.5</td><td>1.62</td><td>0.0</td><td>0.3</td><td>12.42</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:40:57.000+0000</td><td>2019-03-01T01:06:12.000+0000</td><td>1.0</td><td>5.84</td><td>1.0</td><td>N</td><td>249</td><td>188</td><td>2</td><td>21.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>25.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:06:26.000+0000</td><td>2019-03-01T00:38:03.000+0000</td><td>1.0</td><td>18.36</td><td>2.0</td><td>N</td><td>132</td><td>230</td><td>1</td><td>52.0</td><td>0.0</td><td>0.5</td><td>12.21</td><td>5.76</td><td>0.3</td><td>73.27</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:14:48.000+0000</td><td>2019-03-01T00:20:06.000+0000</td><td>1.0</td><td>0.9</td><td>1.0</td><td>N</td><td>162</td><td>141</td><td>1</td><td>6.0</td><td>3.0</td><td>0.5</td><td>1.47</td><td>0.0</td><td>0.3</td><td>11.27</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:47:52.000+0000</td><td>2019-03-01T01:01:03.000+0000</td><td>1.0</td><td>2.6</td><td>1.0</td><td>N</td><td>100</td><td>140</td><td>1</td><td>12.0</td><td>3.0</td><td>0.5</td><td>3.16</td><td>0.0</td><td>0.3</td><td>18.96</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:08:17.000+0000</td><td>2019-03-01T00:15:59.000+0000</td><td>0.0</td><td>1.5</td><td>1.0</td><td>N</td><td>239</td><td>236</td><td>1</td><td>8.0</td><td>3.0</td><td>0.5</td><td>1.18</td><td>0.0</td><td>0.3</td><td>12.98</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:41:42.000+0000</td><td>2019-03-01T00:48:25.000+0000</td><td>0.0</td><td>1.2</td><td>1.0</td><td>N</td><td>239</td><td>143</td><td>2</td><td>7.0</td><td>3.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>10.8</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:56:32.000+0000</td><td>2019-03-01T01:07:19.000+0000</td><td>0.0</td><td>4.6</td><td>1.0</td><td>N</td><td>239</td><td>244</td><td>1</td><td>15.5</td><td>3.0</td><td>0.5</td><td>3.85</td><td>0.0</td><td>0.3</td><td>23.15</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:27:56.000+0000</td><td>2019-03-01T00:39:08.000+0000</td><td>1.0</td><td>2.66</td><td>1.0</td><td>N</td><td>170</td><td>141</td><td>2</td><td>10.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>14.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:16:54.000+0000</td><td>2019-03-01T00:28:17.000+0000</td><td>1.0</td><td>3.42</td><td>1.0</td><td>N</td><td>79</td><td>141</td><td>1</td><td>12.0</td><td>0.5</td><td>0.5</td><td>3.95</td><td>0.0</td><td>0.3</td><td>19.75</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:41:46.000+0000</td><td>2019-03-01T00:49:30.000+0000</td><td>1.0</td><td>2.26</td><td>1.0</td><td>N</td><td>237</td><td>238</td><td>1</td><td>9.0</td><td>0.5</td><td>0.5</td><td>2.56</td><td>0.0</td><td>0.3</td><td>15.36</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:13:37.000+0000</td><td>2019-03-01T00:22:30.000+0000</td><td>1.0</td><td>1.2</td><td>1.0</td><td>N</td><td>148</td><td>211</td><td>4</td><td>8.0</td><td>3.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>11.8</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:23:03.000+0000</td><td>2019-03-01T00:32:26.000+0000</td><td>1.0</td><td>1.5</td><td>1.0</td><td>N</td><td>211</td><td>90</td><td>4</td><td>8.0</td><td>3.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>11.8</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:06:19.000+0000</td><td>2019-03-01T00:12:08.000+0000</td><td>1.0</td><td>1.1</td><td>1.0</td><td>N</td><td>229</td><td>230</td><td>1</td><td>6.0</td><td>3.0</td><td>0.5</td><td>1.95</td><td>0.0</td><td>0.3</td><td>11.75</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:19:08.000+0000</td><td>2019-03-01T00:31:29.000+0000</td><td>1.0</td><td>3.8</td><td>1.0</td><td>N</td><td>48</td><td>262</td><td>1</td><td>13.5</td><td>3.0</td><td>0.5</td><td>3.45</td><td>0.0</td><td>0.3</td><td>20.75</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:12:32.000+0000</td><td>2019-03-01T00:24:42.000+0000</td><td>1.0</td><td>2.7</td><td>1.0</td><td>N</td><td>113</td><td>162</td><td>1</td><td>11.0</td><td>3.0</td><td>0.5</td><td>2.95</td><td>0.0</td><td>0.3</td><td>17.75</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:11:44.000+0000</td><td>2019-03-01T00:13:55.000+0000</td><td>1.0</td><td>0.5</td><td>1.0</td><td>N</td><td>79</td><td>79</td><td>1</td><td>4.0</td><td>0.5</td><td>0.5</td><td>2.34</td><td>0.0</td><td>0.3</td><td>10.14</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:16:39.000+0000</td><td>2019-03-01T00:27:03.000+0000</td><td>1.0</td><td>0.83</td><td>1.0</td><td>N</td><td>79</td><td>148</td><td>1</td><td>7.5</td><td>0.5</td><td>0.5</td><td>2.26</td><td>0.0</td><td>0.3</td><td>13.56</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:38:12.000+0000</td><td>2019-03-01T00:43:23.000+0000</td><td>1.0</td><td>2.04</td><td>1.0</td><td>N</td><td>107</td><td>237</td><td>1</td><td>7.5</td><td>0.5</td><td>0.5</td><td>2.26</td><td>0.0</td><td>0.3</td><td>13.56</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:54:18.000+0000</td><td>2019-03-01T01:13:48.000+0000</td><td>1.0</td><td>8.81</td><td>1.0</td><td>N</td><td>230</td><td>243</td><td>2</td><td>27.0</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>30.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:19:11.000+0000</td><td>2019-03-01T00:23:29.000+0000</td><td>1.0</td><td>1.69</td><td>1.0</td><td>N</td><td>79</td><td>233</td><td>1</td><td>6.5</td><td>0.5</td><td>0.5</td><td>2.06</td><td>0.0</td><td>0.3</td><td>12.36</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:35:39.000+0000</td><td>2019-03-01T00:42:52.000+0000</td><td>5.0</td><td>1.08</td><td>1.0</td><td>N</td><td>45</td><td>79</td><td>1</td><td>6.5</td><td>0.5</td><td>0.5</td><td>2.06</td><td>0.0</td><td>0.3</td><td>12.36</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:51:18.000+0000</td><td>2019-03-01T01:01:19.000+0000</td><td>1.0</td><td>1.8</td><td>1.0</td><td>N</td><td>41</td><td>42</td><td>2</td><td>9.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>10.8</td><td>0.0</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:10:40.000+0000</td><td>2019-03-01T00:21:11.000+0000</td><td>2.0</td><td>1.6</td><td>1.0</td><td>N</td><td>249</td><td>148</td><td>2</td><td>8.5</td><td>3.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>12.3</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:42:41.000+0000</td><td>2019-03-01T00:53:20.000+0000</td><td>2.0</td><td>1.5</td><td>1.0</td><td>N</td><td>148</td><td>125</td><td>1</td><td>9.0</td><td>3.0</td><td>0.5</td><td>1.0</td><td>0.0</td><td>0.3</td><td>13.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-02-28T23:57:16.000+0000</td><td>2019-03-01T00:03:44.000+0000</td><td>1.0</td><td>0.7</td><td>1.0</td><td>N</td><td>107</td><td>107</td><td>1</td><td>6.0</td><td>0.5</td><td>0.5</td><td>1.0</td><td>0.0</td><td>0.3</td><td>10.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:17:21.000+0000</td><td>2019-03-01T00:30:03.000+0000</td><td>1.0</td><td>2.55</td><td>1.0</td><td>N</td><td>144</td><td>164</td><td>1</td><td>11.0</td><td>0.5</td><td>0.5</td><td>2.96</td><td>0.0</td><td>0.3</td><td>17.76</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:20:35.000+0000</td><td>2019-03-01T00:25:45.000+0000</td><td>3.0</td><td>0.76</td><td>1.0</td><td>N</td><td>79</td><td>4</td><td>1</td><td>5.0</td><td>0.5</td><td>0.5</td><td>1.0</td><td>0.0</td><td>0.3</td><td>9.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:38:56.000+0000</td><td>2019-03-01T00:44:02.000+0000</td><td>3.0</td><td>0.55</td><td>1.0</td><td>N</td><td>79</td><td>148</td><td>1</td><td>5.0</td><td>0.5</td><td>0.5</td><td>1.76</td><td>0.0</td><td>0.3</td><td>10.56</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:22:52.000+0000</td><td>2019-03-01T00:29:19.000+0000</td><td>1.0</td><td>0.99</td><td>1.0</td><td>N</td><td>161</td><td>161</td><td>1</td><td>6.5</td><td>0.5</td><td>0.5</td><td>2.06</td><td>0.0</td><td>0.3</td><td>12.36</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:38:57.000+0000</td><td>2019-03-01T01:03:05.000+0000</td><td>1.0</td><td>3.91</td><td>1.0</td><td>N</td><td>170</td><td>256</td><td>1</td><td>18.0</td><td>0.5</td><td>0.5</td><td>4.36</td><td>0.0</td><td>0.3</td><td>26.16</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:10:49.000+0000</td><td>2019-03-01T00:15:59.000+0000</td><td>1.0</td><td>0.7</td><td>1.0</td><td>N</td><td>162</td><td>100</td><td>2</td><td>5.5</td><td>3.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>9.3</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:25:18.000+0000</td><td>2019-03-01T01:07:57.000+0000</td><td>1.0</td><td>5.8</td><td>1.0</td><td>N</td><td>162</td><td>25</td><td>1</td><td>29.0</td><td>3.0</td><td>0.5</td><td>9.8</td><td>0.0</td><td>0.3</td><td>42.6</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:04:06.000+0000</td><td>2019-03-01T00:37:57.000+0000</td><td>5.0</td><td>6.79</td><td>1.0</td><td>N</td><td>87</td><td>188</td><td>1</td><td>27.0</td><td>0.5</td><td>0.5</td><td>6.16</td><td>0.0</td><td>0.3</td><td>36.96</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:16:49.000+0000</td><td>2019-03-01T00:29:05.000+0000</td><td>1.0</td><td>2.8</td><td>1.0</td><td>N</td><td>170</td><td>262</td><td>2</td><td>11.0</td><td>3.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>14.8</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:37:03.000+0000</td><td>2019-03-01T00:48:36.000+0000</td><td>1.0</td><td>3.6</td><td>1.0</td><td>N</td><td>140</td><td>74</td><td>2</td><td>12.5</td><td>3.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>16.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:06:12.000+0000</td><td>2019-03-01T00:15:43.000+0000</td><td>1.0</td><td>2.53</td><td>1.0</td><td>N</td><td>146</td><td>262</td><td>2</td><td>10.0</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>13.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:19:42.000+0000</td><td>2019-03-01T00:22:08.000+0000</td><td>1.0</td><td>0.76</td><td>1.0</td><td>N</td><td>263</td><td>141</td><td>2</td><td>4.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>8.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:43:13.000+0000</td><td>2019-03-01T01:01:11.000+0000</td><td>1.0</td><td>4.14</td><td>1.0</td><td>N</td><td>229</td><td>7</td><td>2</td><td>15.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>19.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:21:56.000+0000</td><td>2019-03-01T00:32:22.000+0000</td><td>6.0</td><td>1.61</td><td>1.0</td><td>N</td><td>148</td><td>79</td><td>1</td><td>8.5</td><td>0.5</td><td>0.5</td><td>1.0</td><td>0.0</td><td>0.3</td><td>13.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:33:42.000+0000</td><td>2019-03-01T00:44:11.000+0000</td><td>6.0</td><td>1.21</td><td>1.0</td><td>N</td><td>79</td><td>107</td><td>1</td><td>8.5</td><td>0.5</td><td>0.5</td><td>2.0</td><td>0.0</td><td>0.3</td><td>14.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:00:23.000+0000</td><td>2019-03-01T00:23:16.000+0000</td><td>1.0</td><td>16.94</td><td>2.0</td><td>N</td><td>132</td><td>170</td><td>1</td><td>52.0</td><td>0.0</td><td>0.5</td><td>12.21</td><td>5.76</td><td>0.3</td><td>73.27</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:26:16.000+0000</td><td>2019-03-01T00:43:25.000+0000</td><td>1.0</td><td>2.13</td><td>1.0</td><td>N</td><td>107</td><td>148</td><td>2</td><td>12.0</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>15.8</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:31:27.000+0000</td><td>2019-03-01T00:37:41.000+0000</td><td>2.0</td><td>1.1</td><td>1.0</td><td>N</td><td>230</td><td>100</td><td>1</td><td>6.0</td><td>3.0</td><td>0.5</td><td>1.95</td><td>0.0</td><td>0.3</td><td>11.75</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:52:34.000+0000</td><td>2019-03-01T01:02:22.000+0000</td><td>2.0</td><td>1.6</td><td>1.0</td><td>N</td><td>142</td><td>230</td><td>1</td><td>8.5</td><td>3.0</td><td>0.5</td><td>3.65</td><td>0.0</td><td>0.3</td><td>15.95</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:07:14.000+0000</td><td>2019-03-01T00:22:56.000+0000</td><td>3.0</td><td>3.4</td><td>1.0</td><td>N</td><td>264</td><td>264</td><td>1</td><td>13.5</td><td>0.5</td><td>0.5</td><td>2.0</td><td>0.0</td><td>0.3</td><td>16.8</td><td>0.0</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:28:31.000+0000</td><td>2019-03-01T00:56:53.000+0000</td><td>2.0</td><td>6.3</td><td>1.0</td><td>N</td><td>264</td><td>264</td><td>1</td><td>24.0</td><td>0.5</td><td>0.5</td><td>5.05</td><td>0.0</td><td>0.3</td><td>30.35</td><td>0.0</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:51:43.000+0000</td><td>2019-03-01T00:58:24.000+0000</td><td>4.0</td><td>0.0</td><td>1.0</td><td>N</td><td>251</td><td>251</td><td>1</td><td>5.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>6.8</td><td>0.0</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:04:49.000+0000</td><td>2019-03-01T00:07:40.000+0000</td><td>1.0</td><td>0.5</td><td>1.0</td><td>N</td><td>230</td><td>186</td><td>2</td><td>4.0</td><td>3.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>7.8</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:16:51.000+0000</td><td>2019-03-01T00:27:19.000+0000</td><td>1.0</td><td>2.9</td><td>1.0</td><td>N</td><td>249</td><td>142</td><td>1</td><td>10.5</td><td>3.0</td><td>0.5</td><td>2.85</td><td>0.0</td><td>0.3</td><td>17.15</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:58:11.000+0000</td><td>2019-03-01T01:06:50.000+0000</td><td>1.0</td><td>1.6</td><td>1.0</td><td>N</td><td>231</td><td>79</td><td>1</td><td>8.0</td><td>3.0</td><td>0.5</td><td>2.36</td><td>0.0</td><td>0.3</td><td>14.16</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:02:07.000+0000</td><td>2019-03-01T00:25:51.000+0000</td><td>5.0</td><td>12.42</td><td>1.0</td><td>N</td><td>231</td><td>167</td><td>1</td><td>36.0</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>39.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:01:52.000+0000</td><td>2019-03-01T00:08:17.000+0000</td><td>1.0</td><td>0.98</td><td>1.0</td><td>N</td><td>239</td><td>239</td><td>1</td><td>6.0</td><td>0.5</td><td>0.5</td><td>1.96</td><td>0.0</td><td>0.3</td><td>11.76</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:24:16.000+0000</td><td>2019-03-01T00:34:38.000+0000</td><td>1.0</td><td>2.09</td><td>1.0</td><td>N</td><td>48</td><td>186</td><td>1</td><td>9.0</td><td>0.5</td><td>0.5</td><td>2.56</td><td>0.0</td><td>0.3</td><td>15.36</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:41:02.000+0000</td><td>2019-03-01T01:18:24.000+0000</td><td>1.0</td><td>6.79</td><td>1.0</td><td>N</td><td>90</td><td>37</td><td>1</td><td>27.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>31.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:42:11.000+0000</td><td>2019-03-01T00:54:20.000+0000</td><td>1.0</td><td>3.39</td><td>1.0</td><td>N</td><td>244</td><td>244</td><td>1</td><td>12.5</td><td>0.5</td><td>0.5</td><td>2.76</td><td>0.0</td><td>0.3</td><td>18.51</td><td>0.0</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:02:59.000+0000</td><td>2019-03-01T00:11:58.000+0000</td><td>1.0</td><td>1.19</td><td>1.0</td><td>N</td><td>162</td><td>48</td><td>1</td><td>7.5</td><td>0.5</td><td>0.5</td><td>5.0</td><td>0.0</td><td>0.3</td><td>16.3</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:00:25.000+0000</td><td>2019-03-01T00:12:47.000+0000</td><td>1.0</td><td>2.2</td><td>1.0</td><td>N</td><td>162</td><td>79</td><td>1</td><td>10.0</td><td>3.0</td><td>0.5</td><td>2.75</td><td>0.0</td><td>0.3</td><td>16.55</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:41:50.000+0000</td><td>2019-03-01T00:51:18.000+0000</td><td>1.0</td><td>1.1</td><td>1.0</td><td>N</td><td>148</td><td>79</td><td>1</td><td>7.5</td><td>3.0</td><td>0.5</td><td>2.25</td><td>0.0</td><td>0.3</td><td>13.55</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:00:55.000+0000</td><td>2019-03-01T00:06:17.000+0000</td><td>0.0</td><td>0.9</td><td>1.0</td><td>N</td><td>161</td><td>100</td><td>1</td><td>5.5</td><td>3.0</td><td>0.5</td><td>0.7</td><td>0.0</td><td>0.3</td><td>10.0</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-02-28T23:59:49.000+0000</td><td>2019-03-01T00:27:06.000+0000</td><td>1.0</td><td>3.57</td><td>1.0</td><td>N</td><td>211</td><td>255</td><td>1</td><td>19.5</td><td>0.5</td><td>0.5</td><td>4.66</td><td>0.0</td><td>0.3</td><td>27.96</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:15:57.000+0000</td><td>2019-03-01T00:23:35.000+0000</td><td>1.0</td><td>1.4</td><td>1.0</td><td>N</td><td>229</td><td>237</td><td>2</td><td>7.5</td><td>3.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>11.3</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:30:12.000+0000</td><td>2019-03-01T00:41:19.000+0000</td><td>3.0</td><td>2.1</td><td>1.0</td><td>N</td><td>163</td><td>107</td><td>1</td><td>10.0</td><td>3.0</td><td>0.5</td><td>2.75</td><td>0.0</td><td>0.3</td><td>16.55</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:44:13.000+0000</td><td>2019-03-01T00:47:45.000+0000</td><td>1.0</td><td>0.8</td><td>1.0</td><td>N</td><td>234</td><td>90</td><td>1</td><td>4.5</td><td>3.0</td><td>0.5</td><td>1.65</td><td>0.0</td><td>0.3</td><td>9.95</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:04:38.000+0000</td><td>2019-03-01T00:13:09.000+0000</td><td>0.0</td><td>2.3</td><td>1.0</td><td>N</td><td>68</td><td>13</td><td>1</td><td>9.0</td><td>3.0</td><td>0.5</td><td>2.55</td><td>0.0</td><td>0.3</td><td>15.35</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:07:46.000+0000</td><td>2019-03-01T00:24:18.000+0000</td><td>1.0</td><td>9.7</td><td>1.0</td><td>N</td><td>138</td><td>141</td><td>1</td><td>27.0</td><td>3.0</td><td>0.5</td><td>7.3</td><td>5.76</td><td>0.3</td><td>43.86</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:34:41.000+0000</td><td>2019-03-01T00:40:07.000+0000</td><td>2.0</td><td>1.1</td><td>1.0</td><td>N</td><td>146</td><td>7</td><td>2</td><td>6.0</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>7.3</td><td>0.0</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:18:28.000+0000</td><td>2019-03-01T00:31:22.000+0000</td><td>1.0</td><td>2.4</td><td>1.0</td><td>N</td><td>144</td><td>68</td><td>2</td><td>11.0</td><td>3.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>14.8</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:34:45.000+0000</td><td>2019-03-01T01:03:37.000+0000</td><td>1.0</td><td>7.1</td><td>1.0</td><td>N</td><td>68</td><td>61</td><td>1</td><td>25.0</td><td>3.0</td><td>0.5</td><td>5.75</td><td>0.0</td><td>0.3</td><td>34.55</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:41:26.000+0000</td><td>2019-03-01T00:51:37.000+0000</td><td>1.0</td><td>1.8</td><td>1.0</td><td>N</td><td>264</td><td>37</td><td>2</td><td>9.0</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>10.3</td><td>0.0</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:12:38.000+0000</td><td>2019-03-01T00:29:38.000+0000</td><td>3.0</td><td>4.2</td><td>1.0</td><td>N</td><td>211</td><td>141</td><td>1</td><td>15.5</td><td>0.5</td><td>0.5</td><td>3.86</td><td>0.0</td><td>0.3</td><td>23.16</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:32:19.000+0000</td><td>2019-03-01T00:35:21.000+0000</td><td>3.0</td><td>0.36</td><td>1.0</td><td>N</td><td>141</td><td>237</td><td>2</td><td>4.0</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>7.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:36:39.000+0000</td><td>2019-03-01T00:52:33.000+0000</td><td>4.0</td><td>4.32</td><td>1.0</td><td>N</td><td>237</td><td>7</td><td>2</td><td>15.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>19.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:33:52.000+0000</td><td>2019-03-01T00:37:44.000+0000</td><td>6.0</td><td>0.48</td><td>1.0</td><td>N</td><td>114</td><td>148</td><td>1</td><td>4.5</td><td>0.5</td><td>0.5</td><td>1.66</td><td>0.0</td><td>0.3</td><td>9.96</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:50:40.000+0000</td><td>2019-03-01T01:08:11.000+0000</td><td>4.0</td><td>1.98</td><td>1.0</td><td>N</td><td>68</td><td>79</td><td>1</td><td>11.0</td><td>0.5</td><td>0.5</td><td>2.22</td><td>0.0</td><td>0.3</td><td>17.02</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:16:45.000+0000</td><td>2019-03-01T00:22:27.000+0000</td><td>1.0</td><td>1.65</td><td>1.0</td><td>N</td><td>68</td><td>48</td><td>1</td><td>7.0</td><td>0.5</td><td>0.5</td><td>1.2</td><td>0.0</td><td>0.3</td><td>12.0</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:15:16.000+0000</td><td>2019-03-01T00:23:26.000+0000</td><td>1.0</td><td>2.12</td><td>1.0</td><td>N</td><td>249</td><td>163</td><td>1</td><td>8.5</td><td>0.5</td><td>0.5</td><td>1.84</td><td>0.0</td><td>0.3</td><td>14.14</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:51:26.000+0000</td><td>2019-03-01T01:06:49.000+0000</td><td>4.0</td><td>4.67</td><td>1.0</td><td>N</td><td>79</td><td>112</td><td>1</td><td>16.5</td><td>0.5</td><td>0.5</td><td>5.08</td><td>0.0</td><td>0.3</td><td>25.38</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:10:03.000+0000</td><td>2019-03-01T00:31:28.000+0000</td><td>1.0</td><td>5.77</td><td>1.0</td><td>N</td><td>231</td><td>239</td><td>1</td><td>20.5</td><td>0.5</td><td>0.5</td><td>4.86</td><td>0.0</td><td>0.3</td><td>29.16</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:41:39.000+0000</td><td>2019-03-01T00:57:44.000+0000</td><td>1.0</td><td>4.44</td><td>1.0</td><td>N</td><td>239</td><td>145</td><td>1</td><td>16.0</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>19.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:19:19.000+0000</td><td>2019-03-01T00:23:37.000+0000</td><td>1.0</td><td>0.88</td><td>1.0</td><td>N</td><td>48</td><td>48</td><td>1</td><td>5.0</td><td>0.5</td><td>0.5</td><td>1.76</td><td>0.0</td><td>0.3</td><td>10.56</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:07:24.000+0000</td><td>2019-03-01T00:24:19.000+0000</td><td>1.0</td><td>7.12</td><td>1.0</td><td>N</td><td>138</td><td>119</td><td>1</td><td>22.0</td><td>0.5</td><td>0.5</td><td>5.81</td><td>5.76</td><td>0.3</td><td>34.87</td><td>0.0</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:06:05.000+0000</td><td>2019-03-01T00:51:09.000+0000</td><td>2.0</td><td>5.5</td><td>1.0</td><td>N</td><td>107</td><td>37</td><td>1</td><td>30.0</td><td>3.0</td><td>0.5</td><td>6.75</td><td>0.0</td><td>0.3</td><td>40.55</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-02-28T23:53:17.000+0000</td><td>2019-02-28T23:59:41.000+0000</td><td>1.0</td><td>1.57</td><td>1.0</td><td>N</td><td>75</td><td>41</td><td>2</td><td>7.0</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>8.3</td><td>0.0</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:10:01.000+0000</td><td>2019-03-01T00:15:02.000+0000</td><td>1.0</td><td>1.1</td><td>1.0</td><td>N</td><td>163</td><td>237</td><td>1</td><td>6.0</td><td>0.5</td><td>0.5</td><td>2.0</td><td>0.0</td><td>0.3</td><td>11.8</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:12:11.000+0000</td><td>2019-03-01T00:28:31.000+0000</td><td>2.0</td><td>3.8</td><td>1.0</td><td>N</td><td>249</td><td>239</td><td>1</td><td>14.5</td><td>3.0</td><td>0.5</td><td>4.55</td><td>0.0</td><td>0.3</td><td>22.85</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:09:04.000+0000</td><td>2019-03-01T00:22:19.000+0000</td><td>1.0</td><td>3.26</td><td>1.0</td><td>N</td><td>97</td><td>257</td><td>1</td><td>12.5</td><td>0.5</td><td>0.5</td><td>2.25</td><td>0.0</td><td>0.3</td><td>16.05</td><td>0.0</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:12:06.000+0000</td><td>2019-03-01T00:32:25.000+0000</td><td>5.0</td><td>4.04</td><td>1.0</td><td>N</td><td>234</td><td>262</td><td>1</td><td>16.5</td><td>0.5</td><td>0.5</td><td>3.0</td><td>0.0</td><td>0.3</td><td>23.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:51:20.000+0000</td><td>2019-03-01T00:53:11.000+0000</td><td>5.0</td><td>0.46</td><td>1.0</td><td>N</td><td>140</td><td>229</td><td>2</td><td>4.0</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>7.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:16:20.000+0000</td><td>2019-03-01T00:24:10.000+0000</td><td>2.0</td><td>1.56</td><td>1.0</td><td>N</td><td>249</td><td>107</td><td>1</td><td>7.5</td><td>0.5</td><td>0.5</td><td>2.26</td><td>0.0</td><td>0.3</td><td>13.56</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:59:15.000+0000</td><td>2019-03-01T01:15:32.000+0000</td><td>2.0</td><td>4.05</td><td>1.0</td><td>N</td><td>186</td><td>263</td><td>1</td><td>15.0</td><td>0.5</td><td>0.5</td><td>3.76</td><td>0.0</td><td>0.3</td><td>22.56</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:01:55.000+0000</td><td>2019-03-01T00:09:46.000+0000</td><td>1.0</td><td>1.9</td><td>1.0</td><td>N</td><td>161</td><td>141</td><td>1</td><td>8.0</td><td>3.0</td><td>0.5</td><td>3.5</td><td>0.0</td><td>0.3</td><td>15.3</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:46:11.000+0000</td><td>2019-03-01T00:53:26.000+0000</td><td>1.0</td><td>1.1</td><td>1.0</td><td>N</td><td>230</td><td>229</td><td>1</td><td>6.5</td><td>3.0</td><td>0.5</td><td>1.0</td><td>0.0</td><td>0.3</td><td>11.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:23:26.000+0000</td><td>2019-03-01T00:40:00.000+0000</td><td>1.0</td><td>9.62</td><td>1.0</td><td>N</td><td>138</td><td>148</td><td>1</td><td>27.5</td><td>0.5</td><td>0.5</td><td>4.0</td><td>0.0</td><td>0.3</td><td>35.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:50:36.000+0000</td><td>2019-03-01T00:55:06.000+0000</td><td>1.0</td><td>0.95</td><td>1.0</td><td>N</td><td>148</td><td>114</td><td>2</td><td>5.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>9.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:34:26.000+0000</td><td>2019-03-01T00:40:59.000+0000</td><td>1.0</td><td>1.56</td><td>1.0</td><td>N</td><td>129</td><td>260</td><td>2</td><td>7.0</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>8.3</td><td>0.0</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:52:34.000+0000</td><td>2019-03-01T01:02:34.000+0000</td><td>1.0</td><td>2.7</td><td>1.0</td><td>N</td><td>151</td><td>116</td><td>1</td><td>10.5</td><td>0.5</td><td>0.5</td><td>1.5</td><td>0.0</td><td>0.3</td><td>13.3</td><td>0.0</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:23:41.000+0000</td><td>2019-03-01T00:36:18.000+0000</td><td>1.0</td><td>3.8</td><td>1.0</td><td>N</td><td>230</td><td>74</td><td>1</td><td>13.0</td><td>3.0</td><td>0.5</td><td>2.0</td><td>0.0</td><td>0.3</td><td>18.8</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:53:02.000+0000</td><td>2019-03-01T01:05:50.000+0000</td><td>2.0</td><td>3.5</td><td>1.0</td><td>N</td><td>229</td><td>7</td><td>3</td><td>12.5</td><td>3.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>16.3</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:30:06.000+0000</td><td>2019-03-01T00:37:03.000+0000</td><td>2.0</td><td>0.4</td><td>1.0</td><td>N</td><td>211</td><td>148</td><td>1</td><td>6.0</td><td>3.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>9.8</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:41:44.000+0000</td><td>2019-03-01T01:03:38.000+0000</td><td>1.0</td><td>7.9</td><td>1.0</td><td>N</td><td>148</td><td>89</td><td>1</td><td>25.0</td><td>3.0</td><td>0.5</td><td>5.75</td><td>0.0</td><td>0.3</td><td>34.55</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:27:30.000+0000</td><td>2019-03-01T01:00:34.000+0000</td><td>1.0</td><td>23.66</td><td>1.0</td><td>N</td><td>132</td><td>181</td><td>1</td><td>63.0</td><td>0.5</td><td>0.5</td><td>12.86</td><td>0.0</td><td>0.3</td><td>77.16</td><td>0.0</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:00:29.000+0000</td><td>2019-03-01T00:21:58.000+0000</td><td>1.0</td><td>5.4</td><td>1.0</td><td>N</td><td>24</td><td>233</td><td>1</td><td>19.0</td><td>3.0</td><td>0.5</td><td>3.0</td><td>0.0</td><td>0.3</td><td>25.8</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:27:34.000+0000</td><td>2019-03-01T00:51:48.000+0000</td><td>1.0</td><td>3.7</td><td>1.0</td><td>N</td><td>137</td><td>66</td><td>2</td><td>17.5</td><td>3.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>21.3</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:59:04.000+0000</td><td>2019-03-01T01:05:30.000+0000</td><td>1.0</td><td>1.2</td><td>1.0</td><td>N</td><td>148</td><td>79</td><td>1</td><td>6.5</td><td>3.0</td><td>0.5</td><td>1.0</td><td>0.0</td><td>0.3</td><td>11.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:29:46.000+0000</td><td>2019-03-01T00:37:05.000+0000</td><td>1.0</td><td>1.37</td><td>1.0</td><td>N</td><td>48</td><td>161</td><td>2</td><td>7.0</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>10.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:28:00.000+0000</td><td>2019-03-01T00:38:33.000+0000</td><td>2.0</td><td>1.99</td><td>1.0</td><td>N</td><td>246</td><td>161</td><td>1</td><td>9.5</td><td>0.5</td><td>0.5</td><td>2.66</td><td>0.0</td><td>0.3</td><td>15.96</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:49:11.000+0000</td><td>2019-03-01T01:03:06.000+0000</td><td>2.0</td><td>2.25</td><td>1.0</td><td>N</td><td>229</td><td>186</td><td>2</td><td>11.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>15.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:07:49.000+0000</td><td>2019-03-01T00:47:04.000+0000</td><td>1.0</td><td>6.71</td><td>5.0</td><td>N</td><td>137</td><td>17</td><td>1</td><td>29.95</td><td>0.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>30.75</td><td>0.0</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:08:52.000+0000</td><td>2019-03-01T00:10:03.000+0000</td><td>4.0</td><td>0.34</td><td>1.0</td><td>N</td><td>142</td><td>142</td><td>1</td><td>3.0</td><td>0.5</td><td>0.5</td><td>2.04</td><td>0.0</td><td>0.3</td><td>8.84</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:17:27.000+0000</td><td>2019-03-01T00:27:04.000+0000</td><td>3.0</td><td>1.95</td><td>1.0</td><td>N</td><td>48</td><td>158</td><td>1</td><td>9.0</td><td>0.5</td><td>0.5</td><td>2.56</td><td>0.0</td><td>0.3</td><td>15.36</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:29:22.000+0000</td><td>2019-03-01T00:32:48.000+0000</td><td>4.0</td><td>0.74</td><td>1.0</td><td>N</td><td>158</td><td>158</td><td>1</td><td>4.5</td><td>0.5</td><td>0.5</td><td>1.0</td><td>0.0</td><td>0.3</td><td>9.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:53:25.000+0000</td><td>2019-03-01T01:11:17.000+0000</td><td>3.0</td><td>4.81</td><td>1.0</td><td>N</td><td>249</td><td>112</td><td>1</td><td>17.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>21.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-02-28T23:57:30.000+0000</td><td>2019-03-01T00:07:34.000+0000</td><td>1.0</td><td>1.44</td><td>1.0</td><td>N</td><td>229</td><td>230</td><td>2</td><td>8.0</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>11.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:17:22.000+0000</td><td>2019-03-01T00:25:12.000+0000</td><td>1.0</td><td>0.7</td><td>1.0</td><td>N</td><td>161</td><td>230</td><td>1</td><td>6.5</td><td>0.5</td><td>0.5</td><td>1.0</td><td>0.0</td><td>0.3</td><td>11.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:44:15.000+0000</td><td>2019-03-01T00:52:48.000+0000</td><td>1.0</td><td>1.53</td><td>1.0</td><td>N</td><td>114</td><td>90</td><td>1</td><td>7.5</td><td>0.5</td><td>0.5</td><td>2.5</td><td>0.0</td><td>0.3</td><td>13.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-02-28T20:43:33.000+0000</td><td>2019-02-28T21:03:06.000+0000</td><td>1.0</td><td>5.72</td><td>1.0</td><td>N</td><td>61</td><td>67</td><td>2</td><td>19.0</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>20.3</td><td>0.0</td><td>null</td></tr><tr><td>2</td><td>2019-02-28T21:34:06.000+0000</td><td>2019-03-01T20:41:16.000+0000</td><td>1.0</td><td>1.79</td><td>1.0</td><td>N</td><td>65</td><td>61</td><td>2</td><td>7.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>8.8</td><td>0.0</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:04:50.000+0000</td><td>2019-03-01T00:10:24.000+0000</td><td>1.0</td><td>1.61</td><td>1.0</td><td>N</td><td>238</td><td>263</td><td>1</td><td>7.0</td><td>0.5</td><td>0.5</td><td>1.5</td><td>0.0</td><td>0.3</td><td>12.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:28:32.000+0000</td><td>2019-03-01T00:44:07.000+0000</td><td>1.0</td><td>2.65</td><td>1.0</td><td>N</td><td>144</td><td>255</td><td>1</td><td>13.0</td><td>0.5</td><td>0.5</td><td>3.36</td><td>0.0</td><td>0.3</td><td>20.16</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:23:45.000+0000</td><td>2019-03-01T00:26:11.000+0000</td><td>1.0</td><td>0.6</td><td>1.0</td><td>N</td><td>263</td><td>262</td><td>1</td><td>4.0</td><td>3.0</td><td>0.5</td><td>1.55</td><td>0.0</td><td>0.3</td><td>9.35</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:35:28.000+0000</td><td>2019-03-01T00:42:09.000+0000</td><td>1.0</td><td>1.3</td><td>1.0</td><td>N</td><td>229</td><td>233</td><td>1</td><td>7.0</td><td>3.0</td><td>0.5</td><td>2.15</td><td>0.0</td><td>0.3</td><td>12.95</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:17:59.000+0000</td><td>2019-03-01T00:29:05.000+0000</td><td>1.0</td><td>1.52</td><td>1.0</td><td>N</td><td>48</td><td>233</td><td>2</td><td>9.0</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>12.8</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:04:24.000+0000</td><td>2019-03-01T00:19:46.000+0000</td><td>1.0</td><td>3.0</td><td>1.0</td><td>N</td><td>114</td><td>230</td><td>2</td><td>12.0</td><td>3.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>15.8</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:23:22.000+0000</td><td>2019-03-01T00:30:58.000+0000</td><td>1.0</td><td>1.0</td><td>1.0</td><td>N</td><td>48</td><td>164</td><td>1</td><td>7.0</td><td>3.0</td><td>0.5</td><td>1.0</td><td>0.0</td><td>0.3</td><td>11.8</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:34:53.000+0000</td><td>2019-03-01T00:49:12.000+0000</td><td>1.0</td><td>2.1</td><td>1.0</td><td>N</td><td>164</td><td>79</td><td>1</td><td>11.0</td><td>3.0</td><td>0.5</td><td>2.95</td><td>0.0</td><td>0.3</td><td>17.75</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:01:48.000+0000</td><td>2019-03-01T00:06:32.000+0000</td><td>1.0</td><td>1.1</td><td>1.0</td><td>N</td><td>142</td><td>163</td><td>1</td><td>5.5</td><td>3.0</td><td>0.5</td><td>1.0</td><td>0.0</td><td>0.3</td><td>10.3</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:08:57.000+0000</td><td>2019-03-01T00:40:08.000+0000</td><td>1.0</td><td>9.9</td><td>1.0</td><td>N</td><td>163</td><td>92</td><td>3</td><td>31.5</td><td>3.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>35.3</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:05:59.000+0000</td><td>2019-03-01T00:17:40.000+0000</td><td>1.0</td><td>1.8</td><td>1.0</td><td>N</td><td>107</td><td>144</td><td>1</td><td>9.5</td><td>3.0</td><td>0.5</td><td>1.5</td><td>0.0</td><td>0.3</td><td>14.8</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:02:28.000+0000</td><td>2019-03-01T00:37:36.000+0000</td><td>2.0</td><td>4.8</td><td>1.0</td><td>N</td><td>48</td><td>148</td><td>1</td><td>23.0</td><td>3.0</td><td>0.5</td><td>5.35</td><td>0.0</td><td>0.3</td><td>32.15</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:01:03.000+0000</td><td>2019-03-01T00:10:43.000+0000</td><td>1.0</td><td>1.79</td><td>1.0</td><td>N</td><td>79</td><td>90</td><td>1</td><td>8.5</td><td>0.5</td><td>0.5</td><td>2.0</td><td>0.0</td><td>0.3</td><td>14.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:23:06.000+0000</td><td>2019-03-01T00:34:23.000+0000</td><td>1.0</td><td>1.76</td><td>1.0</td><td>N</td><td>249</td><td>231</td><td>2</td><td>9.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>13.3</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:02:23.000+0000</td><td>2019-03-01T00:13:08.000+0000</td><td>1.0</td><td>4.3</td><td>1.0</td><td>Y</td><td>151</td><td>244</td><td>4</td><td>14.0</td><td>3.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>17.8</td><td>0.0</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:31:53.000+0000</td><td>2019-03-01T00:46:04.000+0000</td><td>1.0</td><td>3.3</td><td>1.0</td><td>N</td><td>142</td><td>146</td><td>1</td><td>12.5</td><td>3.0</td><td>0.5</td><td>2.6</td><td>0.0</td><td>0.3</td><td>18.9</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:26:50.000+0000</td><td>2019-03-01T00:49:56.000+0000</td><td>3.0</td><td>3.9</td><td>1.0</td><td>N</td><td>230</td><td>4</td><td>1</td><td>17.5</td><td>3.0</td><td>0.5</td><td>4.25</td><td>0.0</td><td>0.3</td><td>25.55</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-02-28T23:58:19.000+0000</td><td>2019-03-01T00:01:07.000+0000</td><td>1.0</td><td>0.46</td><td>1.0</td><td>N</td><td>162</td><td>170</td><td>1</td><td>4.0</td><td>0.5</td><td>0.5</td><td>1.56</td><td>0.0</td><td>0.3</td><td>9.36</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:26:14.000+0000</td><td>2019-03-01T00:33:54.000+0000</td><td>1.0</td><td>1.63</td><td>1.0</td><td>N</td><td>170</td><td>68</td><td>1</td><td>7.5</td><td>0.5</td><td>0.5</td><td>2.26</td><td>0.0</td><td>0.3</td><td>13.56</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:23:20.000+0000</td><td>2019-03-01T00:28:26.000+0000</td><td>1.0</td><td>0.63</td><td>1.0</td><td>N</td><td>230</td><td>163</td><td>1</td><td>5.0</td><td>0.5</td><td>0.5</td><td>1.76</td><td>0.0</td><td>0.3</td><td>10.56</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:05:23.000+0000</td><td>2019-03-01T00:23:10.000+0000</td><td>1.0</td><td>9.46</td><td>1.0</td><td>N</td><td>138</td><td>107</td><td>1</td><td>27.0</td><td>0.5</td><td>0.5</td><td>10.97</td><td>5.76</td><td>0.3</td><td>47.53</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:47:42.000+0000</td><td>2019-03-01T00:49:41.000+0000</td><td>2.0</td><td>0.4</td><td>1.0</td><td>N</td><td>239</td><td>238</td><td>1</td><td>3.5</td><td>3.0</td><td>0.5</td><td>1.45</td><td>0.0</td><td>0.3</td><td>8.75</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:15:48.000+0000</td><td>2019-03-01T00:34:47.000+0000</td><td>1.0</td><td>4.8</td><td>1.0</td><td>N</td><td>231</td><td>181</td><td>1</td><td>18.0</td><td>3.0</td><td>0.5</td><td>5.5</td><td>5.76</td><td>0.3</td><td>33.06</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:14:30.000+0000</td><td>2019-03-01T00:20:05.000+0000</td><td>1.0</td><td>0.7</td><td>1.0</td><td>N</td><td>79</td><td>4</td><td>2</td><td>5.5</td><td>3.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>9.3</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:34:26.000+0000</td><td>2019-03-01T00:57:07.000+0000</td><td>1.0</td><td>6.3</td><td>1.0</td><td>N</td><td>113</td><td>7</td><td>1</td><td>21.0</td><td>3.0</td><td>0.5</td><td>4.96</td><td>0.0</td><td>0.3</td><td>29.76</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:29:03.000+0000</td><td>2019-03-01T01:05:08.000+0000</td><td>2.0</td><td>7.23</td><td>1.0</td><td>N</td><td>68</td><td>225</td><td>1</td><td>27.5</td><td>0.5</td><td>0.5</td><td>7.82</td><td>0.0</td><td>0.3</td><td>39.12</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:01:57.000+0000</td><td>2019-03-01T00:26:06.000+0000</td><td>1.0</td><td>9.97</td><td>1.0</td><td>N</td><td>142</td><td>212</td><td>2</td><td>28.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>32.3</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:15:30.000+0000</td><td>2019-03-01T00:28:46.000+0000</td><td>1.0</td><td>1.4</td><td>1.0</td><td>N</td><td>163</td><td>170</td><td>1</td><td>10.0</td><td>3.0</td><td>0.5</td><td>1.0</td><td>0.0</td><td>0.3</td><td>14.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:39:07.000+0000</td><td>2019-03-01T00:58:55.000+0000</td><td>2.0</td><td>4.96</td><td>1.0</td><td>N</td><td>48</td><td>42</td><td>1</td><td>18.5</td><td>0.5</td><td>0.5</td><td>4.46</td><td>0.0</td><td>0.3</td><td>26.76</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:17:56.000+0000</td><td>2019-03-01T00:38:46.000+0000</td><td>2.0</td><td>5.2</td><td>1.0</td><td>N</td><td>230</td><td>179</td><td>1</td><td>19.0</td><td>0.5</td><td>0.5</td><td>2.0</td><td>0.0</td><td>0.3</td><td>24.8</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:18:40.000+0000</td><td>2019-03-01T00:41:02.000+0000</td><td>1.0</td><td>6.2</td><td>1.0</td><td>N</td><td>114</td><td>151</td><td>2</td><td>21.0</td><td>3.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>24.8</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:49:57.000+0000</td><td>2019-03-01T00:58:40.000+0000</td><td>1.0</td><td>1.3</td><td>1.0</td><td>N</td><td>239</td><td>143</td><td>1</td><td>7.5</td><td>3.0</td><td>0.5</td><td>2.25</td><td>0.0</td><td>0.3</td><td>13.55</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:48:11.000+0000</td><td>2019-03-01T01:06:10.000+0000</td><td>1.0</td><td>8.0</td><td>1.0</td><td>N</td><td>138</td><td>164</td><td>2</td><td>24.0</td><td>3.0</td><td>0.5</td><td>0.0</td><td>5.76</td><td>0.3</td><td>33.56</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:28:54.000+0000</td><td>2019-03-01T00:33:04.000+0000</td><td>1.0</td><td>0.61</td><td>1.0</td><td>N</td><td>230</td><td>186</td><td>2</td><td>4.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>8.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:45:51.000+0000</td><td>2019-03-01T00:47:48.000+0000</td><td>1.0</td><td>0.31</td><td>1.0</td><td>N</td><td>158</td><td>158</td><td>1</td><td>3.5</td><td>0.5</td><td>0.5</td><td>1.0</td><td>0.0</td><td>0.3</td><td>8.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:49:54.000+0000</td><td>2019-03-01T01:00:10.000+0000</td><td>1.0</td><td>2.21</td><td>1.0</td><td>N</td><td>158</td><td>148</td><td>1</td><td>9.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>13.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:41:15.000+0000</td><td>2019-03-01T00:46:04.000+0000</td><td>1.0</td><td>1.15</td><td>1.0</td><td>N</td><td>255</td><td>112</td><td>1</td><td>6.0</td><td>0.5</td><td>0.5</td><td>1.82</td><td>0.0</td><td>0.3</td><td>9.12</td><td>0.0</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:22:06.000+0000</td><td>2019-03-01T00:31:35.000+0000</td><td>1.0</td><td>1.3</td><td>1.0</td><td>N</td><td>48</td><td>162</td><td>1</td><td>8.0</td><td>3.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>11.8</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:33:14.000+0000</td><td>2019-03-01T00:49:54.000+0000</td><td>1.0</td><td>3.2</td><td>1.0</td><td>N</td><td>162</td><td>158</td><td>1</td><td>13.5</td><td>3.0</td><td>0.5</td><td>3.45</td><td>0.0</td><td>0.3</td><td>20.75</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:56:36.000+0000</td><td>2019-03-01T01:04:09.000+0000</td><td>1.0</td><td>1.7</td><td>1.0</td><td>N</td><td>249</td><td>164</td><td>1</td><td>8.0</td><td>3.0</td><td>0.5</td><td>2.35</td><td>0.0</td><td>0.3</td><td>14.15</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:00:37.000+0000</td><td>2019-03-01T00:06:35.000+0000</td><td>1.0</td><td>2.06</td><td>1.0</td><td>N</td><td>148</td><td>137</td><td>1</td><td>7.5</td><td>0.5</td><td>0.5</td><td>1.5</td><td>0.0</td><td>0.3</td><td>12.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:23:06.000+0000</td><td>2019-03-01T00:35:30.000+0000</td><td>1.0</td><td>1.92</td><td>1.0</td><td>N</td><td>170</td><td>50</td><td>1</td><td>10.5</td><td>0.5</td><td>0.5</td><td>3.58</td><td>0.0</td><td>0.3</td><td>17.88</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:40:50.000+0000</td><td>2019-03-01T01:07:07.000+0000</td><td>1.0</td><td>7.14</td><td>1.0</td><td>N</td><td>246</td><td>49</td><td>1</td><td>24.5</td><td>0.5</td><td>0.5</td><td>4.24</td><td>0.0</td><td>0.3</td><td>32.54</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:35:50.000+0000</td><td>2019-03-01T00:39:58.000+0000</td><td>3.0</td><td>0.84</td><td>1.0</td><td>N</td><td>79</td><td>79</td><td>2</td><td>5.0</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>8.8</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:13:46.000+0000</td><td>2019-03-01T00:22:56.000+0000</td><td>4.0</td><td>1.9</td><td>1.0</td><td>N</td><td>79</td><td>164</td><td>1</td><td>8.5</td><td>3.0</td><td>0.5</td><td>1.0</td><td>0.0</td><td>0.3</td><td>13.3</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:46:51.000+0000</td><td>2019-03-01T00:57:42.000+0000</td><td>1.0</td><td>2.0</td><td>1.0</td><td>N</td><td>48</td><td>249</td><td>2</td><td>9.5</td><td>3.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>13.3</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:12:11.000+0000</td><td>2019-03-01T00:20:30.000+0000</td><td>1.0</td><td>2.8</td><td>1.0</td><td>N</td><td>162</td><td>238</td><td>1</td><td>10.0</td><td>3.0</td><td>0.5</td><td>2.75</td><td>0.0</td><td>0.3</td><td>16.55</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:30:03.000+0000</td><td>2019-03-01T00:33:23.000+0000</td><td>1.0</td><td>0.8</td><td>1.0</td><td>N</td><td>162</td><td>162</td><td>1</td><td>4.5</td><td>3.0</td><td>0.5</td><td>1.65</td><td>0.0</td><td>0.3</td><td>9.95</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:48:21.000+0000</td><td>2019-03-01T01:03:37.000+0000</td><td>2.0</td><td>3.3</td><td>1.0</td><td>N</td><td>7</td><td>129</td><td>2</td><td>13.0</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>14.3</td><td>0.0</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:20:53.000+0000</td><td>2019-03-01T00:23:28.000+0000</td><td>1.0</td><td>0.73</td><td>1.0</td><td>N</td><td>239</td><td>142</td><td>1</td><td>4.5</td><td>0.5</td><td>0.5</td><td>1.08</td><td>0.0</td><td>0.3</td><td>9.38</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:51:54.000+0000</td><td>2019-03-01T00:56:12.000+0000</td><td>1.0</td><td>0.6</td><td>1.0</td><td>N</td><td>125</td><td>114</td><td>3</td><td>5.0</td><td>3.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>8.8</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:09:02.000+0000</td><td>2019-03-01T00:48:06.000+0000</td><td>1.0</td><td>5.1</td><td>1.0</td><td>N</td><td>100</td><td>256</td><td>1</td><td>26.5</td><td>3.0</td><td>0.5</td><td>1.52</td><td>0.0</td><td>0.3</td><td>31.82</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:50:57.000+0000</td><td>2019-03-01T00:54:46.000+0000</td><td>1.0</td><td>1.7</td><td>1.0</td><td>N</td><td>256</td><td>148</td><td>1</td><td>6.5</td><td>3.0</td><td>0.5</td><td>1.0</td><td>0.0</td><td>0.3</td><td>11.3</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:06:19.000+0000</td><td>2019-03-01T00:33:10.000+0000</td><td>1.0</td><td>16.1</td><td>1.0</td><td>N</td><td>132</td><td>145</td><td>1</td><td>44.5</td><td>0.5</td><td>0.5</td><td>8.0</td><td>0.0</td><td>0.3</td><td>53.8</td><td>0.0</td><td>null</td></tr><tr><td>2</td><td>2019-02-28T23:58:08.000+0000</td><td>2019-03-01T00:07:01.000+0000</td><td>1.0</td><td>1.43</td><td>1.0</td><td>N</td><td>229</td><td>48</td><td>1</td><td>8.0</td><td>0.5</td><td>0.5</td><td>2.36</td><td>0.0</td><td>0.3</td><td>14.16</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:08:00.000+0000</td><td>2019-03-01T00:12:58.000+0000</td><td>1.0</td><td>1.14</td><td>1.0</td><td>N</td><td>48</td><td>50</td><td>1</td><td>6.0</td><td>0.5</td><td>0.5</td><td>1.96</td><td>0.0</td><td>0.3</td><td>11.76</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:24:09.000+0000</td><td>2019-03-01T00:56:29.000+0000</td><td>1.0</td><td>15.1</td><td>1.0</td><td>N</td><td>161</td><td>16</td><td>1</td><td>44.0</td><td>0.5</td><td>0.5</td><td>9.56</td><td>0.0</td><td>0.3</td><td>57.36</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:24:33.000+0000</td><td>2019-03-01T00:32:01.000+0000</td><td>3.0</td><td>1.3</td><td>1.0</td><td>N</td><td>114</td><td>79</td><td>1</td><td>7.5</td><td>3.0</td><td>0.5</td><td>2.25</td><td>0.0</td><td>0.3</td><td>13.55</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:36:21.000+0000</td><td>2019-03-01T00:45:18.000+0000</td><td>1.0</td><td>1.9</td><td>1.0</td><td>N</td><td>79</td><td>68</td><td>1</td><td>9.0</td><td>3.0</td><td>0.5</td><td>1.0</td><td>0.0</td><td>0.3</td><td>13.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:23:55.000+0000</td><td>2019-03-01T00:46:20.000+0000</td><td>2.0</td><td>6.4</td><td>1.0</td><td>N</td><td>239</td><td>260</td><td>1</td><td>21.0</td><td>0.5</td><td>0.5</td><td>3.0</td><td>0.0</td><td>0.3</td><td>27.8</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:46:42.000+0000</td><td>2019-03-01T00:51:05.000+0000</td><td>1.0</td><td>1.7</td><td>1.0</td><td>N</td><td>229</td><td>263</td><td>1</td><td>6.5</td><td>3.0</td><td>0.5</td><td>2.05</td><td>0.0</td><td>0.3</td><td>12.35</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-02-28T23:59:10.000+0000</td><td>2019-03-01T00:07:11.000+0000</td><td>1.0</td><td>1.0</td><td>1.0</td><td>N</td><td>107</td><td>79</td><td>1</td><td>7.0</td><td>0.5</td><td>0.5</td><td>2.16</td><td>0.0</td><td>0.3</td><td>12.96</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:12:16.000+0000</td><td>2019-03-01T00:18:43.000+0000</td><td>1.0</td><td>1.0</td><td>1.0</td><td>N</td><td>79</td><td>211</td><td>1</td><td>6.0</td><td>3.0</td><td>0.5</td><td>1.95</td><td>0.0</td><td>0.3</td><td>11.75</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:21:20.000+0000</td><td>2019-03-01T00:32:31.000+0000</td><td>1.0</td><td>1.6</td><td>1.0</td><td>N</td><td>211</td><td>232</td><td>1</td><td>9.0</td><td>3.0</td><td>0.5</td><td>0.9</td><td>0.0</td><td>0.3</td><td>13.7</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:56:09.000+0000</td><td>2019-03-01T01:07:21.000+0000</td><td>1.0</td><td>2.0</td><td>1.0</td><td>N</td><td>164</td><td>144</td><td>1</td><td>9.5</td><td>3.0</td><td>0.5</td><td>1.0</td><td>0.0</td><td>0.3</td><td>14.3</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:05:39.000+0000</td><td>2019-03-01T00:17:20.000+0000</td><td>1.0</td><td>2.0</td><td>1.0</td><td>N</td><td>144</td><td>137</td><td>1</td><td>9.5</td><td>3.0</td><td>0.5</td><td>2.65</td><td>0.0</td><td>0.3</td><td>15.95</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:28:52.000+0000</td><td>2019-03-01T00:42:02.000+0000</td><td>1.0</td><td>3.3</td><td>1.0</td><td>N</td><td>90</td><td>239</td><td>1</td><td>12.5</td><td>3.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>16.3</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:43:08.000+0000</td><td>2019-03-01T00:49:42.000+0000</td><td>1.0</td><td>1.9</td><td>1.0</td><td>N</td><td>88</td><td>125</td><td>1</td><td>8.0</td><td>3.0</td><td>0.5</td><td>3.5</td><td>0.0</td><td>0.3</td><td>15.3</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:56:10.000+0000</td><td>2019-03-01T01:07:05.000+0000</td><td>1.0</td><td>3.2</td><td>1.0</td><td>N</td><td>231</td><td>50</td><td>1</td><td>12.0</td><td>3.0</td><td>0.5</td><td>2.0</td><td>0.0</td><td>0.3</td><td>17.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:10:37.000+0000</td><td>2019-03-01T00:17:31.000+0000</td><td>1.0</td><td>1.01</td><td>1.0</td><td>N</td><td>137</td><td>234</td><td>2</td><td>6.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>10.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:27:46.000+0000</td><td>2019-03-01T00:34:05.000+0000</td><td>1.0</td><td>1.21</td><td>1.0</td><td>N</td><td>249</td><td>231</td><td>2</td><td>6.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>10.3</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:21:34.000+0000</td><td>2019-03-01T00:24:38.000+0000</td><td>0.0</td><td>1.4</td><td>1.0</td><td>N</td><td>262</td><td>74</td><td>2</td><td>5.5</td><td>3.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>9.3</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:38:51.000+0000</td><td>2019-03-01T00:40:12.000+0000</td><td>1.0</td><td>0.6</td><td>1.0</td><td>N</td><td>75</td><td>75</td><td>2</td><td>3.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>4.8</td><td>0.0</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:54:10.000+0000</td><td>2019-03-01T01:18:07.000+0000</td><td>0.0</td><td>7.8</td><td>1.0</td><td>N</td><td>75</td><td>83</td><td>1</td><td>24.5</td><td>3.0</td><td>0.5</td><td>0.1</td><td>0.0</td><td>0.3</td><td>28.4</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:43:29.000+0000</td><td>2019-03-01T00:53:31.000+0000</td><td>2.0</td><td>1.8</td><td>1.0</td><td>N</td><td>144</td><td>107</td><td>1</td><td>9.0</td><td>3.0</td><td>0.5</td><td>2.0</td><td>0.0</td><td>0.3</td><td>14.8</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:44:28.000+0000</td><td>2019-03-01T01:06:36.000+0000</td><td>1.0</td><td>16.6</td><td>1.0</td><td>N</td><td>132</td><td>34</td><td>1</td><td>44.5</td><td>0.5</td><td>0.5</td><td>6.0</td><td>0.0</td><td>0.3</td><td>51.8</td><td>0.0</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:05:57.000+0000</td><td>2019-03-01T00:13:41.000+0000</td><td>1.0</td><td>1.32</td><td>1.0</td><td>N</td><td>237</td><td>237</td><td>2</td><td>7.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>11.3</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:51:05.000+0000</td><td>2019-03-01T01:09:57.000+0000</td><td>2.0</td><td>2.9</td><td>1.0</td><td>N</td><td>233</td><td>249</td><td>1</td><td>14.5</td><td>3.0</td><td>0.5</td><td>1.7</td><td>0.0</td><td>0.3</td><td>20.0</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:17:46.000+0000</td><td>2019-03-01T00:28:15.000+0000</td><td>1.0</td><td>2.2</td><td>1.0</td><td>N</td><td>246</td><td>43</td><td>2</td><td>9.5</td><td>3.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>13.3</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:50:40.000+0000</td><td>2019-03-01T01:07:08.000+0000</td><td>1.0</td><td>3.4</td><td>1.0</td><td>N</td><td>151</td><td>162</td><td>1</td><td>14.0</td><td>3.0</td><td>0.5</td><td>3.55</td><td>0.0</td><td>0.3</td><td>21.35</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:50:03.000+0000</td><td>2019-03-01T00:58:34.000+0000</td><td>2.0</td><td>2.27</td><td>1.0</td><td>N</td><td>142</td><td>151</td><td>1</td><td>9.5</td><td>0.5</td><td>0.5</td><td>2.66</td><td>0.0</td><td>0.3</td><td>15.96</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-02-28T22:04:35.000+0000</td><td>2019-02-28T22:15:03.000+0000</td><td>1.0</td><td>0.97</td><td>1.0</td><td>N</td><td>79</td><td>232</td><td>2</td><td>8.0</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>11.8</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:01:02.000+0000</td><td>2019-03-01T00:06:38.000+0000</td><td>1.0</td><td>1.3</td><td>1.0</td><td>N</td><td>48</td><td>246</td><td>2</td><td>6.5</td><td>3.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>10.3</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:11:22.000+0000</td><td>2019-03-01T00:22:00.000+0000</td><td>1.0</td><td>3.0</td><td>1.0</td><td>N</td><td>68</td><td>141</td><td>1</td><td>11.5</td><td>3.0</td><td>0.5</td><td>3.8</td><td>0.0</td><td>0.3</td><td>19.1</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:49:13.000+0000</td><td>2019-03-01T00:54:02.000+0000</td><td>1.0</td><td>0.6</td><td>1.0</td><td>N</td><td>230</td><td>164</td><td>1</td><td>5.0</td><td>3.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>8.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:11:04.000+0000</td><td>2019-03-01T00:18:11.000+0000</td><td>1.0</td><td>0.83</td><td>1.0</td><td>N</td><td>148</td><td>148</td><td>1</td><td>6.0</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>9.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:28:40.000+0000</td><td>2019-03-01T00:50:22.000+0000</td><td>1.0</td><td>5.64</td><td>1.0</td><td>N</td><td>107</td><td>40</td><td>1</td><td>19.0</td><td>0.5</td><td>0.5</td><td>4.56</td><td>0.0</td><td>0.3</td><td>27.36</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:10:16.000+0000</td><td>2019-03-01T00:42:05.000+0000</td><td>1.0</td><td>18.5</td><td>2.0</td><td>N</td><td>132</td><td>90</td><td>1</td><td>52.0</td><td>2.5</td><td>0.5</td><td>12.2</td><td>5.76</td><td>0.3</td><td>73.26</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:26:46.000+0000</td><td>2019-03-01T00:46:45.000+0000</td><td>1.0</td><td>4.92</td><td>1.0</td><td>N</td><td>164</td><td>33</td><td>1</td><td>17.5</td><td>0.5</td><td>0.5</td><td>3.0</td><td>0.0</td><td>0.3</td><td>24.3</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:22:43.000+0000</td><td>2019-03-01T01:00:31.000+0000</td><td>1.0</td><td>6.0</td><td>1.0</td><td>N</td><td>234</td><td>36</td><td>1</td><td>28.0</td><td>3.0</td><td>0.5</td><td>6.35</td><td>0.0</td><td>0.3</td><td>38.15</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:34:34.000+0000</td><td>2019-03-01T00:53:00.000+0000</td><td>1.0</td><td>7.4</td><td>1.0</td><td>N</td><td>138</td><td>42</td><td>3</td><td>22.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>5.76</td><td>0.3</td><td>29.56</td><td>0.0</td><td>null</td></tr><tr><td>2</td><td>2019-02-28T23:59:00.000+0000</td><td>2019-03-01T00:06:01.000+0000</td><td>1.0</td><td>1.03</td><td>1.0</td><td>N</td><td>164</td><td>137</td><td>1</td><td>6.5</td><td>0.5</td><td>0.5</td><td>2.06</td><td>0.0</td><td>0.3</td><td>12.36</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:43:07.000+0000</td><td>2019-03-01T00:46:40.000+0000</td><td>1.0</td><td>0.5</td><td>1.0</td><td>N</td><td>234</td><td>170</td><td>1</td><td>4.5</td><td>3.0</td><td>0.5</td><td>1.66</td><td>0.0</td><td>0.3</td><td>9.96</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:07:19.000+0000</td><td>2019-03-01T00:33:05.000+0000</td><td>4.0</td><td>4.98</td><td>1.0</td><td>N</td><td>164</td><td>256</td><td>1</td><td>20.5</td><td>0.5</td><td>0.5</td><td>4.86</td><td>0.0</td><td>0.3</td><td>29.16</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:00:48.000+0000</td><td>2019-03-01T00:14:18.000+0000</td><td>1.0</td><td>2.2</td><td>1.0</td><td>N</td><td>161</td><td>114</td><td>1</td><td>11.0</td><td>3.0</td><td>0.5</td><td>2.0</td><td>0.0</td><td>0.3</td><td>16.8</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:29:41.000+0000</td><td>2019-03-01T00:39:21.000+0000</td><td>1.0</td><td>1.6</td><td>1.0</td><td>N</td><td>90</td><td>230</td><td>2</td><td>8.0</td><td>3.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>11.8</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:44:47.000+0000</td><td>2019-03-01T01:07:28.000+0000</td><td>1.0</td><td>6.8</td><td>1.0</td><td>N</td><td>48</td><td>244</td><td>1</td><td>22.0</td><td>3.0</td><td>0.5</td><td>5.15</td><td>0.0</td><td>0.3</td><td>30.95</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:08:56.000+0000</td><td>2019-03-01T00:20:23.000+0000</td><td>4.0</td><td>3.56</td><td>1.0</td><td>N</td><td>107</td><td>263</td><td>1</td><td>12.0</td><td>0.5</td><td>0.5</td><td>3.16</td><td>0.0</td><td>0.3</td><td>18.96</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:50:33.000+0000</td><td>2019-03-01T00:54:35.000+0000</td><td>3.0</td><td>1.08</td><td>1.0</td><td>N</td><td>48</td><td>68</td><td>1</td><td>5.5</td><td>0.5</td><td>0.5</td><td>1.86</td><td>0.0</td><td>0.3</td><td>11.16</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:28:24.000+0000</td><td>2019-03-01T00:59:33.000+0000</td><td>1.0</td><td>11.63</td><td>1.0</td><td>N</td><td>132</td><td>61</td><td>2</td><td>34.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>35.8</td><td>0.0</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:26:39.000+0000</td><td>2019-03-01T00:31:15.000+0000</td><td>1.0</td><td>0.85</td><td>1.0</td><td>N</td><td>107</td><td>170</td><td>1</td><td>5.0</td><td>0.5</td><td>0.5</td><td>1.0</td><td>0.0</td><td>0.3</td><td>9.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:41:24.000+0000</td><td>2019-03-01T00:53:35.000+0000</td><td>1.0</td><td>3.74</td><td>1.0</td><td>N</td><td>164</td><td>13</td><td>2</td><td>12.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>16.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:28:40.000+0000</td><td>2019-03-01T00:32:05.000+0000</td><td>1.0</td><td>0.64</td><td>1.0</td><td>N</td><td>261</td><td>13</td><td>2</td><td>4.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>8.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:41:00.000+0000</td><td>2019-03-01T00:58:40.000+0000</td><td>1.0</td><td>3.4</td><td>1.0</td><td>N</td><td>249</td><td>65</td><td>1</td><td>15.0</td><td>0.5</td><td>0.5</td><td>3.76</td><td>0.0</td><td>0.3</td><td>22.56</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:05:43.000+0000</td><td>2019-03-01T00:14:00.000+0000</td><td>2.0</td><td>1.5</td><td>1.0</td><td>N</td><td>100</td><td>249</td><td>1</td><td>7.5</td><td>3.0</td><td>0.5</td><td>2.25</td><td>0.0</td><td>0.3</td><td>13.55</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:30:33.000+0000</td><td>2019-03-01T00:36:09.000+0000</td><td>1.0</td><td>1.8</td><td>1.0</td><td>N</td><td>246</td><td>143</td><td>1</td><td>7.5</td><td>3.0</td><td>0.5</td><td>1.5</td><td>0.0</td><td>0.3</td><td>12.8</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:20:43.000+0000</td><td>2019-03-01T00:25:46.000+0000</td><td>1.0</td><td>0.9</td><td>1.0</td><td>N</td><td>137</td><td>233</td><td>1</td><td>5.5</td><td>3.0</td><td>0.5</td><td>2.3</td><td>0.0</td><td>0.3</td><td>11.6</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:42:24.000+0000</td><td>2019-03-01T00:49:38.000+0000</td><td>2.0</td><td>1.2</td><td>1.0</td><td>N</td><td>264</td><td>107</td><td>2</td><td>7.0</td><td>3.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>10.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:13:00.000+0000</td><td>2019-03-01T00:45:58.000+0000</td><td>3.0</td><td>15.93</td><td>2.0</td><td>N</td><td>10</td><td>50</td><td>1</td><td>52.0</td><td>0.0</td><td>0.5</td><td>18.32</td><td>5.76</td><td>0.3</td><td>79.38</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:05:17.000+0000</td><td>2019-03-01T00:32:22.000+0000</td><td>1.0</td><td>17.39</td><td>1.0</td><td>N</td><td>138</td><td>67</td><td>2</td><td>46.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>47.8</td><td>0.0</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:05:05.000+0000</td><td>2019-03-01T00:08:42.000+0000</td><td>1.0</td><td>1.0</td><td>1.0</td><td>N</td><td>236</td><td>75</td><td>1</td><td>5.0</td><td>3.0</td><td>0.5</td><td>1.75</td><td>0.0</td><td>0.3</td><td>10.55</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:04:53.000+0000</td><td>2019-03-01T00:09:03.000+0000</td><td>1.0</td><td>0.57</td><td>1.0</td><td>N</td><td>230</td><td>50</td><td>1</td><td>4.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>8.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:15:18.000+0000</td><td>2019-03-01T00:23:58.000+0000</td><td>1.0</td><td>2.83</td><td>1.0</td><td>N</td><td>48</td><td>238</td><td>1</td><td>10.0</td><td>0.5</td><td>0.5</td><td>2.76</td><td>0.0</td><td>0.3</td><td>16.56</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:29:45.000+0000</td><td>2019-03-01T00:32:52.000+0000</td><td>1.0</td><td>0.97</td><td>1.0</td><td>N</td><td>142</td><td>43</td><td>2</td><td>5.0</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>8.8</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:07:58.000+0000</td><td>2019-03-01T00:38:55.000+0000</td><td>1.0</td><td>17.2</td><td>2.0</td><td>N</td><td>132</td><td>140</td><td>1</td><td>52.0</td><td>2.5</td><td>0.5</td><td>2.5</td><td>0.0</td><td>0.3</td><td>57.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:19:32.000+0000</td><td>2019-03-01T00:34:00.000+0000</td><td>1.0</td><td>3.87</td><td>1.0</td><td>N</td><td>50</td><td>263</td><td>1</td><td>14.0</td><td>0.5</td><td>0.5</td><td>3.56</td><td>0.0</td><td>0.3</td><td>21.36</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:55:03.000+0000</td><td>2019-03-01T01:17:07.000+0000</td><td>1.0</td><td>5.14</td><td>1.0</td><td>N</td><td>48</td><td>231</td><td>1</td><td>19.5</td><td>0.5</td><td>0.5</td><td>6.99</td><td>0.0</td><td>0.3</td><td>30.29</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:50:31.000+0000</td><td>2019-03-01T01:03:17.000+0000</td><td>1.0</td><td>1.7</td><td>1.0</td><td>N</td><td>186</td><td>234</td><td>1</td><td>10.0</td><td>3.0</td><td>0.5</td><td>2.75</td><td>0.0</td><td>0.3</td><td>16.55</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:03:08.000+0000</td><td>2019-03-01T00:10:31.000+0000</td><td>2.0</td><td>1.3</td><td>1.0</td><td>N</td><td>164</td><td>230</td><td>1</td><td>7.0</td><td>3.0</td><td>0.5</td><td>2.15</td><td>0.0</td><td>0.3</td><td>12.95</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:14:59.000+0000</td><td>2019-03-01T00:39:21.000+0000</td><td>2.0</td><td>5.6</td><td>1.0</td><td>N</td><td>48</td><td>87</td><td>1</td><td>20.5</td><td>3.0</td><td>0.5</td><td>3.5</td><td>0.0</td><td>0.3</td><td>27.8</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:42:10.000+0000</td><td>2019-03-01T00:59:30.000+0000</td><td>1.0</td><td>6.9</td><td>1.0</td><td>N</td><td>87</td><td>142</td><td>1</td><td>21.5</td><td>3.0</td><td>0.5</td><td>5.05</td><td>0.0</td><td>0.3</td><td>30.35</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:34:20.000+0000</td><td>2019-03-01T00:41:55.000+0000</td><td>1.0</td><td>1.28</td><td>1.0</td><td>N</td><td>148</td><td>125</td><td>1</td><td>7.0</td><td>0.5</td><td>0.5</td><td>2.16</td><td>0.0</td><td>0.3</td><td>12.96</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:26:08.000+0000</td><td>2019-03-01T00:32:31.000+0000</td><td>1.0</td><td>1.2</td><td>1.0</td><td>N</td><td>48</td><td>186</td><td>2</td><td>6.0</td><td>3.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>9.8</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:11:32.000+0000</td><td>2019-03-01T00:42:07.000+0000</td><td>1.0</td><td>17.6</td><td>2.0</td><td>N</td><td>132</td><td>79</td><td>1</td><td>52.0</td><td>2.5</td><td>0.5</td><td>11.05</td><td>0.0</td><td>0.3</td><td>66.35</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:45:06.000+0000</td><td>2019-03-01T00:57:57.000+0000</td><td>1.0</td><td>1.0</td><td>1.0</td><td>N</td><td>79</td><td>45</td><td>1</td><td>9.0</td><td>3.0</td><td>0.5</td><td>2.55</td><td>0.0</td><td>0.3</td><td>15.35</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:07:02.000+0000</td><td>2019-03-01T00:29:09.000+0000</td><td>1.0</td><td>5.73</td><td>1.0</td><td>N</td><td>237</td><td>13</td><td>1</td><td>20.0</td><td>0.5</td><td>0.5</td><td>4.76</td><td>0.0</td><td>0.3</td><td>28.56</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:10:30.000+0000</td><td>2019-03-01T00:33:40.000+0000</td><td>1.0</td><td>6.89</td><td>1.0</td><td>N</td><td>236</td><td>231</td><td>1</td><td>23.0</td><td>0.5</td><td>0.5</td><td>5.36</td><td>0.0</td><td>0.3</td><td>32.16</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:50:10.000+0000</td><td>2019-03-01T01:04:25.000+0000</td><td>1.0</td><td>2.92</td><td>1.0</td><td>N</td><td>144</td><td>80</td><td>1</td><td>12.5</td><td>0.5</td><td>0.5</td><td>3.26</td><td>0.0</td><td>0.3</td><td>19.56</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:19:16.000+0000</td><td>2019-03-01T00:32:01.000+0000</td><td>1.0</td><td>2.04</td><td>1.0</td><td>N</td><td>249</td><td>209</td><td>1</td><td>10.0</td><td>0.5</td><td>0.5</td><td>2.76</td><td>0.0</td><td>0.3</td><td>16.56</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:44:29.000+0000</td><td>2019-03-01T01:02:35.000+0000</td><td>1.0</td><td>3.62</td><td>1.0</td><td>N</td><td>249</td><td>256</td><td>1</td><td>15.0</td><td>0.5</td><td>0.5</td><td>3.76</td><td>0.0</td><td>0.3</td><td>22.56</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:20:14.000+0000</td><td>2019-03-01T00:52:11.000+0000</td><td>2.0</td><td>6.69</td><td>1.0</td><td>N</td><td>230</td><td>238</td><td>1</td><td>25.5</td><td>0.5</td><td>0.5</td><td>5.86</td><td>0.0</td><td>0.3</td><td>35.16</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:33:55.000+0000</td><td>2019-03-01T00:47:29.000+0000</td><td>1.0</td><td>3.52</td><td>1.0</td><td>N</td><td>148</td><td>112</td><td>1</td><td>14.0</td><td>0.5</td><td>0.5</td><td>3.56</td><td>0.0</td><td>0.3</td><td>21.36</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:44:25.000+0000</td><td>2019-03-01T00:51:46.000+0000</td><td>1.0</td><td>2.1</td><td>1.0</td><td>N</td><td>162</td><td>263</td><td>1</td><td>8.5</td><td>3.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>12.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:28:41.000+0000</td><td>2019-03-01T00:57:54.000+0000</td><td>1.0</td><td>20.39</td><td>1.0</td><td>N</td><td>132</td><td>52</td><td>1</td><td>55.0</td><td>0.5</td><td>0.5</td><td>11.26</td><td>0.0</td><td>0.3</td><td>67.56</td><td>0.0</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:04:10.000+0000</td><td>2019-03-01T00:13:59.000+0000</td><td>0.0</td><td>2.8</td><td>1.0</td><td>N</td><td>231</td><td>186</td><td>1</td><td>10.0</td><td>3.0</td><td>0.5</td><td>1.5</td><td>0.0</td><td>0.3</td><td>15.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:05:15.000+0000</td><td>2019-03-01T00:09:39.000+0000</td><td>2.0</td><td>0.93</td><td>1.0</td><td>N</td><td>144</td><td>231</td><td>1</td><td>5.0</td><td>0.5</td><td>0.5</td><td>1.76</td><td>0.0</td><td>0.3</td><td>10.56</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:11:21.000+0000</td><td>2019-03-01T00:14:56.000+0000</td><td>1.0</td><td>1.35</td><td>1.0</td><td>N</td><td>231</td><td>114</td><td>2</td><td>5.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>9.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:16:40.000+0000</td><td>2019-03-01T00:23:56.000+0000</td><td>2.0</td><td>1.66</td><td>1.0</td><td>N</td><td>114</td><td>164</td><td>1</td><td>7.5</td><td>0.5</td><td>0.5</td><td>2.26</td><td>0.0</td><td>0.3</td><td>13.56</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:26:41.000+0000</td><td>2019-03-01T00:34:56.000+0000</td><td>1.0</td><td>1.32</td><td>1.0</td><td>N</td><td>164</td><td>48</td><td>1</td><td>7.5</td><td>0.5</td><td>0.5</td><td>2.82</td><td>0.0</td><td>0.3</td><td>14.12</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:44:34.000+0000</td><td>2019-03-01T01:00:34.000+0000</td><td>1.0</td><td>5.19</td><td>1.0</td><td>N</td><td>164</td><td>74</td><td>1</td><td>17.0</td><td>0.5</td><td>0.5</td><td>4.16</td><td>0.0</td><td>0.3</td><td>24.96</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:19:45.000+0000</td><td>2019-03-01T00:23:33.000+0000</td><td>1.0</td><td>0.8</td><td>1.0</td><td>N</td><td>231</td><td>13</td><td>1</td><td>5.0</td><td>3.0</td><td>0.5</td><td>1.75</td><td>0.0</td><td>0.3</td><td>10.55</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:43:44.000+0000</td><td>2019-03-01T00:58:41.000+0000</td><td>1.0</td><td>4.0</td><td>1.0</td><td>N</td><td>231</td><td>230</td><td>1</td><td>15.0</td><td>3.0</td><td>0.5</td><td>3.0</td><td>0.0</td><td>0.3</td><td>21.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:34:29.000+0000</td><td>2019-03-01T01:01:26.000+0000</td><td>2.0</td><td>20.11</td><td>2.0</td><td>N</td><td>132</td><td>87</td><td>1</td><td>52.0</td><td>0.0</td><td>0.5</td><td>11.06</td><td>0.0</td><td>0.3</td><td>66.36</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:21:06.000+0000</td><td>2019-03-01T00:27:12.000+0000</td><td>1.0</td><td>2.2</td><td>1.0</td><td>N</td><td>141</td><td>170</td><td>1</td><td>8.0</td><td>3.0</td><td>0.5</td><td>2.36</td><td>0.0</td><td>0.3</td><td>14.16</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:36:03.000+0000</td><td>2019-03-01T01:27:06.000+0000</td><td>1.0</td><td>16.4</td><td>1.0</td><td>N</td><td>234</td><td>76</td><td>4</td><td>51.5</td><td>3.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>55.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:12:59.000+0000</td><td>2019-03-01T00:15:35.000+0000</td><td>1.0</td><td>0.69</td><td>1.0</td><td>N</td><td>68</td><td>48</td><td>2</td><td>4.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>8.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:16:24.000+0000</td><td>2019-03-01T00:19:59.000+0000</td><td>1.0</td><td>0.76</td><td>1.0</td><td>N</td><td>48</td><td>230</td><td>2</td><td>4.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>8.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:28:47.000+0000</td><td>2019-03-01T00:35:22.000+0000</td><td>1.0</td><td>1.12</td><td>1.0</td><td>N</td><td>230</td><td>229</td><td>1</td><td>6.0</td><td>0.5</td><td>0.5</td><td>2.45</td><td>0.0</td><td>0.3</td><td>12.25</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:08:42.000+0000</td><td>2019-03-01T00:44:36.000+0000</td><td>1.0</td><td>21.6</td><td>2.0</td><td>N</td><td>132</td><td>161</td><td>2</td><td>52.0</td><td>2.5</td><td>0.5</td><td>0.0</td><td>5.76</td><td>0.3</td><td>61.06</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:58:51.000+0000</td><td>2019-03-01T01:11:59.000+0000</td><td>0.0</td><td>2.6</td><td>1.0</td><td>N</td><td>68</td><td>229</td><td>1</td><td>11.0</td><td>3.0</td><td>0.5</td><td>1.0</td><td>0.0</td><td>0.3</td><td>15.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:03:39.000+0000</td><td>2019-03-01T00:18:50.000+0000</td><td>1.0</td><td>2.79</td><td>1.0</td><td>N</td><td>148</td><td>233</td><td>1</td><td>12.0</td><td>0.5</td><td>0.5</td><td>2.0</td><td>0.0</td><td>0.3</td><td>17.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:24:10.000+0000</td><td>2019-03-01T01:00:41.000+0000</td><td>1.0</td><td>10.12</td><td>1.0</td><td>N</td><td>233</td><td>135</td><td>1</td><td>36.5</td><td>0.5</td><td>0.5</td><td>7.0</td><td>5.76</td><td>0.3</td><td>53.06</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:29:57.000+0000</td><td>2019-03-01T00:41:42.000+0000</td><td>1.0</td><td>2.4</td><td>1.0</td><td>N</td><td>48</td><td>158</td><td>1</td><td>10.0</td><td>0.5</td><td>0.5</td><td>2.76</td><td>0.0</td><td>0.3</td><td>16.56</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:49:01.000+0000</td><td>2019-03-01T00:54:52.000+0000</td><td>1.0</td><td>1.08</td><td>1.0</td><td>N</td><td>249</td><td>107</td><td>2</td><td>6.0</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>9.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:00:12.000+0000</td><td>2019-03-01T00:33:07.000+0000</td><td>1.0</td><td>4.5</td><td>1.0</td><td>N</td><td>106</td><td>256</td><td>1</td><td>22.0</td><td>0.5</td><td>0.5</td><td>5.82</td><td>0.0</td><td>0.3</td><td>29.12</td><td>0.0</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:13:02.000+0000</td><td>2019-03-01T00:31:27.000+0000</td><td>1.0</td><td>3.75</td><td>1.0</td><td>N</td><td>158</td><td>229</td><td>1</td><td>15.0</td><td>0.5</td><td>0.5</td><td>2.0</td><td>0.0</td><td>0.3</td><td>20.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:58:07.000+0000</td><td>2019-03-01T01:07:44.000+0000</td><td>1.0</td><td>1.89</td><td>1.0</td><td>N</td><td>162</td><td>68</td><td>1</td><td>9.0</td><td>0.5</td><td>0.5</td><td>2.56</td><td>0.0</td><td>0.3</td><td>15.36</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:34:43.000+0000</td><td>2019-03-01T01:10:31.000+0000</td><td>1.0</td><td>21.9</td><td>2.0</td><td>N</td><td>132</td><td>48</td><td>1</td><td>52.0</td><td>2.5</td><td>0.5</td><td>9.16</td><td>5.76</td><td>0.3</td><td>70.22</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:45:37.000+0000</td><td>2019-03-01T00:50:28.000+0000</td><td>1.0</td><td>1.34</td><td>1.0</td><td>N</td><td>100</td><td>68</td><td>1</td><td>6.0</td><td>0.5</td><td>0.5</td><td>1.96</td><td>0.0</td><td>0.3</td><td>11.76</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:55:40.000+0000</td><td>2019-03-01T01:11:43.000+0000</td><td>1.0</td><td>2.34</td><td>1.0</td><td>N</td><td>158</td><td>232</td><td>1</td><td>12.0</td><td>0.5</td><td>0.5</td><td>1.2</td><td>0.0</td><td>0.3</td><td>17.0</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:54:13.000+0000</td><td>2019-03-01T01:09:23.000+0000</td><td>1.0</td><td>7.41</td><td>1.0</td><td>N</td><td>138</td><td>236</td><td>1</td><td>22.0</td><td>0.5</td><td>0.5</td><td>6.31</td><td>5.76</td><td>0.3</td><td>37.87</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:25:51.000+0000</td><td>2019-03-01T00:30:20.000+0000</td><td>1.0</td><td>0.92</td><td>1.0</td><td>N</td><td>209</td><td>232</td><td>2</td><td>5.0</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>8.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:31:17.000+0000</td><td>2019-03-01T00:36:49.000+0000</td><td>1.0</td><td>0.82</td><td>1.0</td><td>N</td><td>232</td><td>148</td><td>2</td><td>5.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>9.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:40:00.000+0000</td><td>2019-03-01T00:47:40.000+0000</td><td>1.0</td><td>1.78</td><td>1.0</td><td>N</td><td>79</td><td>170</td><td>1</td><td>8.0</td><td>0.5</td><td>0.5</td><td>2.36</td><td>0.0</td><td>0.3</td><td>14.16</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:55:04.000+0000</td><td>2019-03-01T01:02:02.000+0000</td><td>1.0</td><td>1.44</td><td>1.0</td><td>N</td><td>161</td><td>50</td><td>1</td><td>7.0</td><td>0.5</td><td>0.5</td><td>2.16</td><td>0.0</td><td>0.3</td><td>12.96</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:08:49.000+0000</td><td>2019-03-01T00:15:00.000+0000</td><td>2.0</td><td>0.83</td><td>1.0</td><td>N</td><td>48</td><td>100</td><td>1</td><td>6.0</td><td>0.5</td><td>0.5</td><td>1.96</td><td>0.0</td><td>0.3</td><td>11.76</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:20:57.000+0000</td><td>2019-03-01T00:26:47.000+0000</td><td>2.0</td><td>0.73</td><td>1.0</td><td>N</td><td>164</td><td>170</td><td>1</td><td>5.5</td><td>0.5</td><td>0.5</td><td>2.79</td><td>0.0</td><td>0.3</td><td>12.09</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:04:50.000+0000</td><td>2019-03-01T00:40:01.000+0000</td><td>2.0</td><td>11.0</td><td>1.0</td><td>N</td><td>234</td><td>127</td><td>1</td><td>35.5</td><td>3.0</td><td>0.5</td><td>6.0</td><td>0.0</td><td>0.3</td><td>45.3</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:17:36.000+0000</td><td>2019-03-01T00:19:50.000+0000</td><td>1.0</td><td>0.4</td><td>1.0</td><td>N</td><td>68</td><td>68</td><td>1</td><td>3.5</td><td>3.0</td><td>0.5</td><td>1.0</td><td>0.0</td><td>0.3</td><td>8.3</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:27:25.000+0000</td><td>2019-03-01T00:38:42.000+0000</td><td>1.0</td><td>1.8</td><td>1.0</td><td>N</td><td>68</td><td>233</td><td>1</td><td>9.5</td><td>3.0</td><td>0.5</td><td>2.66</td><td>0.0</td><td>0.3</td><td>15.96</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:15:47.000+0000</td><td>2019-03-01T00:18:27.000+0000</td><td>2.0</td><td>0.61</td><td>1.0</td><td>N</td><td>231</td><td>125</td><td>1</td><td>4.5</td><td>0.5</td><td>0.5</td><td>1.0</td><td>0.0</td><td>0.3</td><td>9.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:05:05.000+0000</td><td>2019-03-01T00:24:09.000+0000</td><td>1.0</td><td>9.34</td><td>1.0</td><td>N</td><td>132</td><td>95</td><td>2</td><td>27.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>28.8</td><td>0.0</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:33:51.000+0000</td><td>2019-03-01T00:57:05.000+0000</td><td>2.0</td><td>4.7</td><td>1.0</td><td>N</td><td>230</td><td>7</td><td>1</td><td>18.5</td><td>3.0</td><td>0.5</td><td>5.55</td><td>0.0</td><td>0.3</td><td>27.85</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:13:58.000+0000</td><td>2019-03-01T00:42:09.000+0000</td><td>1.0</td><td>18.1</td><td>2.0</td><td>N</td><td>132</td><td>4</td><td>1</td><td>52.0</td><td>2.5</td><td>0.5</td><td>11.05</td><td>0.0</td><td>0.3</td><td>66.35</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:43:47.000+0000</td><td>2019-03-01T01:20:31.000+0000</td><td>1.0</td><td>19.41</td><td>5.0</td><td>N</td><td>230</td><td>265</td><td>1</td><td>80.0</td><td>0.0</td><td>0.0</td><td>18.66</td><td>10.5</td><td>0.3</td><td>111.96</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:14:24.000+0000</td><td>2019-03-01T00:52:31.000+0000</td><td>1.0</td><td>17.6</td><td>2.0</td><td>N</td><td>132</td><td>48</td><td>2</td><td>52.0</td><td>2.5</td><td>0.5</td><td>0.0</td><td>5.76</td><td>0.3</td><td>61.06</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:12:54.000+0000</td><td>2019-03-01T00:18:33.000+0000</td><td>1.0</td><td>2.0</td><td>1.0</td><td>N</td><td>263</td><td>233</td><td>2</td><td>7.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>11.3</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:18:42.000+0000</td><td>2019-03-01T00:40:54.000+0000</td><td>1.0</td><td>7.5</td><td>1.0</td><td>N</td><td>138</td><td>229</td><td>1</td><td>24.0</td><td>3.0</td><td>0.5</td><td>8.3</td><td>0.0</td><td>0.3</td><td>36.1</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:25:21.000+0000</td><td>2019-03-01T00:36:30.000+0000</td><td>2.0</td><td>1.5</td><td>1.0</td><td>N</td><td>45</td><td>148</td><td>2</td><td>9.0</td><td>3.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>12.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:01:48.000+0000</td><td>2019-03-01T00:11:25.000+0000</td><td>2.0</td><td>2.05</td><td>1.0</td><td>N</td><td>48</td><td>236</td><td>2</td><td>9.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>13.3</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:04:07.000+0000</td><td>2019-03-01T00:08:29.000+0000</td><td>1.0</td><td>0.6</td><td>1.0</td><td>N</td><td>49</td><td>189</td><td>2</td><td>5.0</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>6.3</td><td>0.0</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:00:21.000+0000</td><td>2019-03-01T00:20:04.000+0000</td><td>1.0</td><td>3.0</td><td>1.0</td><td>N</td><td>170</td><td>231</td><td>1</td><td>14.0</td><td>3.0</td><td>0.5</td><td>3.55</td><td>0.0</td><td>0.3</td><td>21.35</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:30:14.000+0000</td><td>2019-03-01T00:43:21.000+0000</td><td>1.0</td><td>2.9</td><td>1.0</td><td>N</td><td>261</td><td>224</td><td>1</td><td>12.0</td><td>3.0</td><td>0.5</td><td>3.15</td><td>0.0</td><td>0.3</td><td>18.95</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:57:02.000+0000</td><td>2019-03-01T01:06:23.000+0000</td><td>1.0</td><td>2.48</td><td>1.0</td><td>N</td><td>66</td><td>234</td><td>1</td><td>9.5</td><td>0.5</td><td>0.5</td><td>2.39</td><td>0.0</td><td>0.3</td><td>15.69</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:23:12.000+0000</td><td>2019-03-01T00:48:34.000+0000</td><td>3.0</td><td>18.99</td><td>2.0</td><td>N</td><td>132</td><td>114</td><td>1</td><td>52.0</td><td>0.0</td><td>0.5</td><td>11.06</td><td>0.0</td><td>0.3</td><td>66.36</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:00:51.000+0000</td><td>2019-03-01T00:04:34.000+0000</td><td>2.0</td><td>0.36</td><td>1.0</td><td>N</td><td>161</td><td>162</td><td>1</td><td>4.0</td><td>0.5</td><td>0.5</td><td>1.56</td><td>0.0</td><td>0.3</td><td>9.36</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:10:35.000+0000</td><td>2019-03-01T00:20:55.000+0000</td><td>2.0</td><td>2.56</td><td>1.0</td><td>N</td><td>162</td><td>263</td><td>1</td><td>10.0</td><td>0.5</td><td>0.5</td><td>2.76</td><td>0.0</td><td>0.3</td><td>16.56</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:45:36.000+0000</td><td>2019-03-01T01:10:45.000+0000</td><td>1.0</td><td>18.36</td><td>2.0</td><td>N</td><td>132</td><td>236</td><td>1</td><td>52.0</td><td>0.0</td><td>0.5</td><td>12.21</td><td>5.76</td><td>0.3</td><td>73.27</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:10:44.000+0000</td><td>2019-03-01T00:33:19.000+0000</td><td>1.0</td><td>10.1</td><td>1.0</td><td>N</td><td>138</td><td>246</td><td>1</td><td>29.0</td><td>3.0</td><td>0.5</td><td>3.0</td><td>5.76</td><td>0.3</td><td>41.56</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:19:41.000+0000</td><td>2019-03-01T00:25:04.000+0000</td><td>3.0</td><td>1.0</td><td>1.0</td><td>N</td><td>179</td><td>7</td><td>2</td><td>6.0</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>7.3</td><td>0.0</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:03:00.000+0000</td><td>2019-03-01T00:16:13.000+0000</td><td>1.0</td><td>4.0</td><td>1.0</td><td>N</td><td>261</td><td>79</td><td>2</td><td>14.5</td><td>3.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>18.3</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:21:08.000+0000</td><td>2019-03-01T00:41:04.000+0000</td><td>1.0</td><td>4.1</td><td>1.0</td><td>N</td><td>79</td><td>143</td><td>1</td><td>16.5</td><td>3.0</td><td>0.5</td><td>4.05</td><td>0.0</td><td>0.3</td><td>24.35</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:11:01.000+0000</td><td>2019-03-01T00:21:06.000+0000</td><td>2.0</td><td>1.06</td><td>1.0</td><td>N</td><td>113</td><td>148</td><td>1</td><td>7.5</td><td>0.5</td><td>0.5</td><td>2.26</td><td>0.0</td><td>0.3</td><td>13.56</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:22:50.000+0000</td><td>2019-03-01T00:32:09.000+0000</td><td>2.0</td><td>1.73</td><td>1.0</td><td>N</td><td>148</td><td>87</td><td>1</td><td>8.5</td><td>0.5</td><td>0.5</td><td>1.75</td><td>0.0</td><td>0.3</td><td>14.05</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:42:27.000+0000</td><td>2019-03-01T00:49:04.000+0000</td><td>2.0</td><td>1.1</td><td>1.0</td><td>N</td><td>211</td><td>79</td><td>1</td><td>6.5</td><td>0.5</td><td>0.5</td><td>2.06</td><td>0.0</td><td>0.3</td><td>12.36</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:54:04.000+0000</td><td>2019-03-01T01:02:44.000+0000</td><td>2.0</td><td>1.5</td><td>1.0</td><td>N</td><td>79</td><td>249</td><td>2</td><td>7.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>11.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:14:07.000+0000</td><td>2019-03-01T00:19:07.000+0000</td><td>1.0</td><td>0.6</td><td>1.0</td><td>N</td><td>164</td><td>170</td><td>1</td><td>5.0</td><td>0.5</td><td>0.5</td><td>1.0</td><td>0.0</td><td>0.3</td><td>9.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:19:21.000+0000</td><td>2019-03-01T00:30:02.000+0000</td><td>1.0</td><td>2.36</td><td>1.0</td><td>N</td><td>231</td><td>66</td><td>1</td><td>10.5</td><td>0.5</td><td>0.5</td><td>2.86</td><td>0.0</td><td>0.3</td><td>17.16</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:55:06.000+0000</td><td>2019-03-01T01:03:22.000+0000</td><td>1.0</td><td>2.88</td><td>1.0</td><td>N</td><td>48</td><td>238</td><td>1</td><td>10.0</td><td>0.5</td><td>0.5</td><td>2.76</td><td>0.0</td><td>0.3</td><td>16.56</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:28:00.000+0000</td><td>2019-03-01T00:37:04.000+0000</td><td>1.0</td><td>1.55</td><td>1.0</td><td>N</td><td>48</td><td>186</td><td>1</td><td>7.5</td><td>0.5</td><td>0.5</td><td>1.7</td><td>0.0</td><td>0.3</td><td>13.0</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:00:51.000+0000</td><td>2019-03-01T00:06:40.000+0000</td><td>2.0</td><td>0.81</td><td>1.0</td><td>N</td><td>43</td><td>75</td><td>1</td><td>5.5</td><td>0.5</td><td>0.5</td><td>2.04</td><td>0.0</td><td>0.3</td><td>8.84</td><td>0.0</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:12:34.000+0000</td><td>2019-03-01T00:32:52.000+0000</td><td>2.0</td><td>7.28</td><td>1.0</td><td>N</td><td>236</td><td>244</td><td>1</td><td>23.0</td><td>0.5</td><td>0.5</td><td>8.04</td><td>0.0</td><td>0.3</td><td>34.84</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:42:59.000+0000</td><td>2019-03-01T00:53:44.000+0000</td><td>2.0</td><td>2.33</td><td>1.0</td><td>N</td><td>116</td><td>238</td><td>2</td><td>10.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>11.8</td><td>0.0</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:32:53.000+0000</td><td>2019-03-01T00:51:36.000+0000</td><td>1.0</td><td>9.51</td><td>1.0</td><td>N</td><td>138</td><td>234</td><td>1</td><td>27.5</td><td>0.5</td><td>0.5</td><td>7.41</td><td>5.76</td><td>0.3</td><td>44.47</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:59:06.000+0000</td><td>2019-03-01T01:09:46.000+0000</td><td>1.0</td><td>2.56</td><td>1.0</td><td>N</td><td>234</td><td>231</td><td>1</td><td>10.0</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>13.8</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:34:58.000+0000</td><td>2019-03-01T00:42:18.000+0000</td><td>1.0</td><td>2.2</td><td>1.0</td><td>N</td><td>114</td><td>68</td><td>1</td><td>8.5</td><td>3.0</td><td>0.5</td><td>2.45</td><td>0.0</td><td>0.3</td><td>14.75</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:44:27.000+0000</td><td>2019-03-01T00:47:42.000+0000</td><td>1.0</td><td>0.7</td><td>1.0</td><td>N</td><td>50</td><td>68</td><td>1</td><td>4.5</td><td>3.0</td><td>0.5</td><td>1.0</td><td>0.0</td><td>0.3</td><td>9.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:05:54.000+0000</td><td>2019-03-01T00:37:23.000+0000</td><td>1.0</td><td>18.58</td><td>2.0</td><td>N</td><td>132</td><td>236</td><td>1</td><td>52.0</td><td>0.0</td><td>0.5</td><td>5.0</td><td>5.76</td><td>0.3</td><td>66.06</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:09:51.000+0000</td><td>2019-03-01T00:16:54.000+0000</td><td>1.0</td><td>0.7</td><td>1.0</td><td>N</td><td>114</td><td>114</td><td>2</td><td>6.0</td><td>3.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>9.8</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:20:21.000+0000</td><td>2019-03-01T00:25:00.000+0000</td><td>1.0</td><td>0.7</td><td>1.0</td><td>Y</td><td>114</td><td>249</td><td>1</td><td>5.0</td><td>3.0</td><td>0.5</td><td>1.75</td><td>0.0</td><td>0.3</td><td>10.55</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:33:03.000+0000</td><td>2019-03-01T00:49:05.000+0000</td><td>1.0</td><td>4.2</td><td>1.0</td><td>N</td><td>90</td><td>263</td><td>1</td><td>14.5</td><td>3.0</td><td>0.5</td><td>2.0</td><td>0.0</td><td>0.3</td><td>20.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:33:01.000+0000</td><td>2019-03-01T00:58:20.000+0000</td><td>1.0</td><td>3.72</td><td>1.0</td><td>N</td><td>79</td><td>255</td><td>1</td><td>19.0</td><td>0.5</td><td>0.5</td><td>4.56</td><td>0.0</td><td>0.3</td><td>27.36</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:14:40.000+0000</td><td>2019-03-01T00:37:26.000+0000</td><td>1.0</td><td>5.32</td><td>1.0</td><td>N</td><td>79</td><td>226</td><td>2</td><td>19.0</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>22.8</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:03:16.000+0000</td><td>2019-03-01T00:12:22.000+0000</td><td>0.0</td><td>1.4</td><td>1.0</td><td>N</td><td>161</td><td>186</td><td>1</td><td>8.5</td><td>3.0</td><td>0.5</td><td>2.45</td><td>0.0</td><td>0.3</td><td>14.75</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:14:10.000+0000</td><td>2019-03-01T00:29:43.000+0000</td><td>1.0</td><td>3.2</td><td>1.0</td><td>N</td><td>100</td><td>238</td><td>1</td><td>13.5</td><td>3.0</td><td>0.5</td><td>2.0</td><td>0.0</td><td>0.3</td><td>19.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:15:51.000+0000</td><td>2019-03-01T00:22:42.000+0000</td><td>2.0</td><td>1.47</td><td>1.0</td><td>N</td><td>88</td><td>231</td><td>1</td><td>6.5</td><td>0.5</td><td>0.5</td><td>1.5</td><td>0.0</td><td>0.3</td><td>11.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:38:02.000+0000</td><td>2019-03-01T00:50:17.000+0000</td><td>3.0</td><td>6.35</td><td>1.0</td><td>N</td><td>13</td><td>233</td><td>1</td><td>18.5</td><td>0.5</td><td>0.5</td><td>5.58</td><td>0.0</td><td>0.3</td><td>27.88</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:01:33.000+0000</td><td>2019-03-01T00:14:16.000+0000</td><td>1.0</td><td>2.1</td><td>1.0</td><td>N</td><td>68</td><td>233</td><td>1</td><td>10.5</td><td>0.5</td><td>0.5</td><td>2.86</td><td>0.0</td><td>0.3</td><td>17.16</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:18:25.000+0000</td><td>2019-03-01T00:23:52.000+0000</td><td>1.0</td><td>1.44</td><td>1.0</td><td>N</td><td>161</td><td>90</td><td>1</td><td>6.5</td><td>0.5</td><td>0.5</td><td>1.0</td><td>0.0</td><td>0.3</td><td>11.3</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:50:22.000+0000</td><td>2019-03-01T00:52:35.000+0000</td><td>1.0</td><td>0.5</td><td>1.0</td><td>N</td><td>163</td><td>50</td><td>2</td><td>4.0</td><td>3.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>7.8</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:13:30.000+0000</td><td>2019-03-01T00:34:02.000+0000</td><td>1.0</td><td>9.4</td><td>1.0</td><td>N</td><td>138</td><td>249</td><td>1</td><td>27.5</td><td>3.0</td><td>0.5</td><td>7.4</td><td>5.76</td><td>0.3</td><td>44.46</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:04:11.000+0000</td><td>2019-03-01T00:16:19.000+0000</td><td>1.0</td><td>1.8</td><td>1.0</td><td>N</td><td>229</td><td>48</td><td>2</td><td>9.5</td><td>3.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>13.3</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:31:30.000+0000</td><td>2019-03-01T00:41:10.000+0000</td><td>1.0</td><td>1.8</td><td>1.0</td><td>N</td><td>162</td><td>100</td><td>1</td><td>8.5</td><td>3.0</td><td>0.5</td><td>0.15</td><td>0.0</td><td>0.3</td><td>12.45</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:45:48.000+0000</td><td>2019-03-01T00:53:26.000+0000</td><td>1.0</td><td>1.5</td><td>1.0</td><td>N</td><td>100</td><td>162</td><td>1</td><td>7.5</td><td>3.0</td><td>0.5</td><td>2.25</td><td>0.0</td><td>0.3</td><td>13.55</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-02-28T23:59:37.000+0000</td><td>2019-03-01T00:04:37.000+0000</td><td>5.0</td><td>0.84</td><td>1.0</td><td>N</td><td>230</td><td>161</td><td>2</td><td>5.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>9.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:09:24.000+0000</td><td>2019-03-01T00:16:31.000+0000</td><td>6.0</td><td>1.06</td><td>1.0</td><td>N</td><td>161</td><td>233</td><td>1</td><td>6.5</td><td>0.5</td><td>0.5</td><td>2.06</td><td>0.0</td><td>0.3</td><td>12.36</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:16:21.000+0000</td><td>2019-03-01T00:49:49.000+0000</td><td>2.0</td><td>19.02</td><td>2.0</td><td>N</td><td>132</td><td>234</td><td>1</td><td>52.0</td><td>0.0</td><td>0.5</td><td>12.21</td><td>5.76</td><td>0.3</td><td>73.27</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:45:41.000+0000</td><td>2019-03-01T00:58:01.000+0000</td><td>1.0</td><td>3.15</td><td>1.0</td><td>N</td><td>146</td><td>230</td><td>2</td><td>12.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>16.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:58:54.000+0000</td><td>2019-03-01T01:08:40.000+0000</td><td>1.0</td><td>2.12</td><td>1.0</td><td>N</td><td>230</td><td>141</td><td>2</td><td>9.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>13.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:22:22.000+0000</td><td>2019-03-01T00:27:51.000+0000</td><td>1.0</td><td>1.13</td><td>1.0</td><td>N</td><td>100</td><td>170</td><td>1</td><td>6.0</td><td>0.5</td><td>0.5</td><td>1.96</td><td>0.0</td><td>0.3</td><td>11.76</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:55:04.000+0000</td><td>2019-03-01T01:03:57.000+0000</td><td>1.0</td><td>1.96</td><td>1.0</td><td>N</td><td>148</td><td>107</td><td>1</td><td>9.0</td><td>0.5</td><td>0.5</td><td>2.56</td><td>0.0</td><td>0.3</td><td>15.36</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:16:40.000+0000</td><td>2019-03-01T00:32:30.000+0000</td><td>1.0</td><td>3.56</td><td>1.0</td><td>N</td><td>100</td><td>263</td><td>1</td><td>13.5</td><td>0.5</td><td>0.5</td><td>1.2</td><td>0.0</td><td>0.3</td><td>18.5</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:35:56.000+0000</td><td>2019-03-01T00:55:36.000+0000</td><td>2.0</td><td>4.43</td><td>1.0</td><td>N</td><td>263</td><td>186</td><td>1</td><td>16.5</td><td>0.5</td><td>0.5</td><td>4.06</td><td>0.0</td><td>0.3</td><td>24.36</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:18:51.000+0000</td><td>2019-03-01T00:35:24.000+0000</td><td>1.0</td><td>1.6</td><td>1.0</td><td>N</td><td>148</td><td>87</td><td>1</td><td>11.5</td><td>0.5</td><td>0.5</td><td>3.06</td><td>0.0</td><td>0.3</td><td>18.36</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:14:44.000+0000</td><td>2019-03-01T00:26:54.000+0000</td><td>1.0</td><td>2.55</td><td>1.0</td><td>N</td><td>90</td><td>87</td><td>1</td><td>10.5</td><td>0.5</td><td>0.5</td><td>2.86</td><td>0.0</td><td>0.3</td><td>17.16</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:01:46.000+0000</td><td>2019-03-01T00:34:24.000+0000</td><td>1.0</td><td>12.93</td><td>1.0</td><td>N</td><td>97</td><td>57</td><td>2</td><td>40.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>41.8</td><td>0.0</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:39:06.000+0000</td><td>2019-03-01T01:11:59.000+0000</td><td>1.0</td><td>18.2</td><td>2.0</td><td>N</td><td>132</td><td>142</td><td>1</td><td>52.0</td><td>2.5</td><td>0.5</td><td>11.05</td><td>0.0</td><td>0.3</td><td>66.35</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:34:20.000+0000</td><td>2019-03-01T00:38:02.000+0000</td><td>1.0</td><td>0.5</td><td>1.0</td><td>N</td><td>231</td><td>125</td><td>1</td><td>4.5</td><td>3.0</td><td>0.5</td><td>1.0</td><td>0.0</td><td>0.3</td><td>9.3</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:42:25.000+0000</td><td>2019-03-01T00:46:52.000+0000</td><td>1.0</td><td>1.1</td><td>1.0</td><td>N</td><td>231</td><td>87</td><td>1</td><td>5.5</td><td>3.0</td><td>0.5</td><td>2.75</td><td>0.0</td><td>0.3</td><td>12.05</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:07:48.000+0000</td><td>2019-03-01T00:37:13.000+0000</td><td>1.0</td><td>8.6</td><td>1.0</td><td>N</td><td>113</td><td>116</td><td>1</td><td>29.0</td><td>3.0</td><td>0.5</td><td>6.55</td><td>0.0</td><td>0.3</td><td>39.35</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:36:11.000+0000</td><td>2019-03-01T00:40:11.000+0000</td><td>1.0</td><td>1.53</td><td>1.0</td><td>N</td><td>263</td><td>75</td><td>1</td><td>6.0</td><td>0.5</td><td>0.5</td><td>2.0</td><td>0.0</td><td>0.3</td><td>11.8</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:24:29.000+0000</td><td>2019-03-01T00:29:37.000+0000</td><td>1.0</td><td>1.0</td><td>1.0</td><td>N</td><td>41</td><td>42</td><td>2</td><td>6.0</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>7.3</td><td>0.0</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:23:47.000+0000</td><td>2019-03-01T00:30:40.000+0000</td><td>1.0</td><td>1.89</td><td>1.0</td><td>N</td><td>141</td><td>233</td><td>1</td><td>8.0</td><td>0.5</td><td>0.5</td><td>2.36</td><td>0.0</td><td>0.3</td><td>14.16</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:32:26.000+0000</td><td>2019-03-01T00:39:45.000+0000</td><td>1.0</td><td>1.01</td><td>1.0</td><td>N</td><td>137</td><td>186</td><td>1</td><td>6.5</td><td>0.5</td><td>0.5</td><td>2.06</td><td>0.0</td><td>0.3</td><td>12.36</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:18:31.000+0000</td><td>2019-03-01T00:26:54.000+0000</td><td>4.0</td><td>1.2</td><td>1.0</td><td>N</td><td>162</td><td>43</td><td>2</td><td>7.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>11.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:39:47.000+0000</td><td>2019-03-01T00:45:43.000+0000</td><td>4.0</td><td>0.92</td><td>1.0</td><td>N</td><td>163</td><td>163</td><td>1</td><td>6.0</td><td>0.5</td><td>0.5</td><td>1.96</td><td>0.0</td><td>0.3</td><td>11.76</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:44:21.000+0000</td><td>2019-03-01T01:15:26.000+0000</td><td>1.0</td><td>7.73</td><td>1.0</td><td>N</td><td>249</td><td>36</td><td>1</td><td>27.0</td><td>0.5</td><td>0.5</td><td>6.16</td><td>0.0</td><td>0.3</td><td>36.96</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:19:26.000+0000</td><td>2019-03-01T00:49:23.000+0000</td><td>2.0</td><td>15.46</td><td>1.0</td><td>N</td><td>132</td><td>89</td><td>1</td><td>44.0</td><td>0.5</td><td>0.5</td><td>9.06</td><td>0.0</td><td>0.3</td><td>54.36</td><td>0.0</td><td>null</td></tr><tr><td>2</td><td>2019-02-28T23:52:48.000+0000</td><td>2019-02-28T23:54:35.000+0000</td><td>3.0</td><td>0.22</td><td>1.0</td><td>N</td><td>249</td><td>249</td><td>2</td><td>3.0</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>6.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-02-28T23:56:12.000+0000</td><td>2019-03-01T00:10:37.000+0000</td><td>3.0</td><td>3.22</td><td>1.0</td><td>N</td><td>249</td><td>142</td><td>2</td><td>12.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>16.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:14:37.000+0000</td><td>2019-03-01T00:33:59.000+0000</td><td>3.0</td><td>4.56</td><td>1.0</td><td>N</td><td>48</td><td>7</td><td>2</td><td>17.0</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>20.8</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:03:46.000+0000</td><td>2019-03-01T00:30:49.000+0000</td><td>2.0</td><td>18.0</td><td>2.0</td><td>N</td><td>132</td><td>148</td><td>1</td><td>52.0</td><td>2.5</td><td>0.5</td><td>6.0</td><td>0.0</td><td>0.3</td><td>61.3</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:40:33.000+0000</td><td>2019-03-01T00:49:33.000+0000</td><td>1.0</td><td>2.4</td><td>1.0</td><td>N</td><td>142</td><td>236</td><td>1</td><td>10.0</td><td>3.0</td><td>0.5</td><td>3.45</td><td>0.0</td><td>0.3</td><td>17.25</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:16:28.000+0000</td><td>2019-03-01T00:30:26.000+0000</td><td>2.0</td><td>3.1</td><td>1.0</td><td>N</td><td>234</td><td>141</td><td>1</td><td>12.0</td><td>3.0</td><td>0.5</td><td>3.15</td><td>0.0</td><td>0.3</td><td>18.95</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:57:55.000+0000</td><td>2019-03-01T01:10:28.000+0000</td><td>1.0</td><td>2.2</td><td>1.0</td><td>N</td><td>161</td><td>224</td><td>1</td><td>10.0</td><td>3.0</td><td>0.5</td><td>2.75</td><td>0.0</td><td>0.3</td><td>16.55</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:13:56.000+0000</td><td>2019-03-01T00:33:59.000+0000</td><td>1.0</td><td>4.5</td><td>1.0</td><td>N</td><td>100</td><td>75</td><td>1</td><td>16.5</td><td>3.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>20.3</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:30:33.000+0000</td><td>2019-03-01T00:36:14.000+0000</td><td>1.0</td><td>1.0</td><td>1.0</td><td>N</td><td>137</td><td>186</td><td>1</td><td>6.0</td><td>3.0</td><td>0.5</td><td>1.95</td><td>0.0</td><td>0.3</td><td>11.75</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:08:56.000+0000</td><td>2019-03-01T00:18:18.000+0000</td><td>1.0</td><td>2.1</td><td>1.0</td><td>N</td><td>158</td><td>170</td><td>1</td><td>9.0</td><td>3.0</td><td>0.5</td><td>3.8</td><td>0.0</td><td>0.3</td><td>16.6</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:26:49.000+0000</td><td>2019-03-01T00:51:12.000+0000</td><td>5.0</td><td>4.24</td><td>1.0</td><td>N</td><td>137</td><td>87</td><td>1</td><td>17.5</td><td>0.5</td><td>0.5</td><td>4.26</td><td>0.0</td><td>0.3</td><td>25.56</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:18:18.000+0000</td><td>2019-03-01T00:51:47.000+0000</td><td>1.0</td><td>8.29</td><td>1.0</td><td>N</td><td>209</td><td>77</td><td>2</td><td>28.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>32.3</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:23:28.000+0000</td><td>2019-03-01T00:36:32.000+0000</td><td>1.0</td><td>2.6</td><td>1.0</td><td>N</td><td>181</td><td>89</td><td>1</td><td>11.5</td><td>0.5</td><td>0.5</td><td>2.56</td><td>0.0</td><td>0.3</td><td>15.36</td><td>0.0</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:09:20.000+0000</td><td>2019-03-01T00:26:03.000+0000</td><td>1.0</td><td>3.2</td><td>1.0</td><td>N</td><td>231</td><td>137</td><td>1</td><td>14.0</td><td>3.0</td><td>0.5</td><td>3.55</td><td>0.0</td><td>0.3</td><td>21.35</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:06:16.000+0000</td><td>2019-03-01T00:12:18.000+0000</td><td>1.0</td><td>1.64</td><td>1.0</td><td>N</td><td>161</td><td>237</td><td>2</td><td>7.0</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>10.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:18:54.000+0000</td><td>2019-03-01T00:24:57.000+0000</td><td>1.0</td><td>1.64</td><td>1.0</td><td>N</td><td>237</td><td>263</td><td>1</td><td>7.5</td><td>0.5</td><td>0.5</td><td>1.0</td><td>0.0</td><td>0.3</td><td>12.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-02-28T23:59:33.000+0000</td><td>2019-03-01T00:05:46.000+0000</td><td>1.0</td><td>0.93</td><td>1.0</td><td>N</td><td>230</td><td>186</td><td>1</td><td>6.0</td><td>0.5</td><td>0.5</td><td>1.96</td><td>0.0</td><td>0.3</td><td>11.76</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:13:08.000+0000</td><td>2019-03-01T00:37:43.000+0000</td><td>1.0</td><td>5.82</td><td>1.0</td><td>N</td><td>249</td><td>226</td><td>1</td><td>21.0</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>24.8</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:30:15.000+0000</td><td>2019-03-01T00:33:29.000+0000</td><td>1.0</td><td>0.6</td><td>1.0</td><td>N</td><td>142</td><td>239</td><td>1</td><td>4.5</td><td>3.0</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.3</td><td>8.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:12:24.000+0000</td><td>2019-03-01T00:20:44.000+0000</td><td>4.0</td><td>2.56</td><td>1.0</td><td>N</td><td>161</td><td>75</td><td>1</td><td>9.5</td><td>0.5</td><td>0.5</td><td>3.99</td><td>0.0</td><td>0.3</td><td>17.29</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:08:20.000+0000</td><td>2019-03-01T00:09:59.000+0000</td><td>2.0</td><td>0.71</td><td>1.0</td><td>N</td><td>140</td><td>262</td><td>1</td><td>4.0</td><td>0.5</td><td>0.5</td><td>1.56</td><td>0.0</td><td>0.3</td><td>9.36</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:31:27.000+0000</td><td>2019-03-01T00:41:41.000+0000</td><td>2.0</td><td>1.93</td><td>1.0</td><td>N</td><td>233</td><td>79</td><td>1</td><td>9.0</td><td>0.5</td><td>0.5</td><td>2.56</td><td>0.0</td><td>0.3</td><td>15.36</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:44:34.000+0000</td><td>2019-03-01T00:52:23.000+0000</td><td>2.0</td><td>3.29</td><td>1.0</td><td>N</td><td>79</td><td>141</td><td>2</td><td>10.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>14.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:12:33.000+0000</td><td>2019-03-01T00:17:42.000+0000</td><td>1.0</td><td>0.35</td><td>1.0</td><td>N</td><td>162</td><td>229</td><td>2</td><td>5.0</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>8.8</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:23:14.000+0000</td><td>2019-03-01T00:53:48.000+0000</td><td>1.0</td><td>6.4</td><td>1.0</td><td>N</td><td>231</td><td>36</td><td>1</td><td>25.5</td><td>3.0</td><td>0.5</td><td>5.85</td><td>0.0</td><td>0.3</td><td>35.15</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:57:51.000+0000</td><td>2019-03-01T01:19:03.000+0000</td><td>1.0</td><td>3.98</td><td>1.0</td><td>N</td><td>140</td><td>114</td><td>1</td><td>16.5</td><td>0.5</td><td>0.5</td><td>4.06</td><td>0.0</td><td>0.3</td><td>24.36</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:33:02.000+0000</td><td>2019-03-01T00:56:28.000+0000</td><td>3.0</td><td>7.8</td><td>1.0</td><td>N</td><td>79</td><td>239</td><td>1</td><td>25.5</td><td>3.0</td><td>0.5</td><td>5.85</td><td>0.0</td><td>0.3</td><td>35.15</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:35:37.000+0000</td><td>2019-03-01T00:57:49.000+0000</td><td>1.0</td><td>11.29</td><td>1.0</td><td>N</td><td>138</td><td>230</td><td>1</td><td>32.5</td><td>0.5</td><td>0.5</td><td>8.41</td><td>5.76</td><td>0.3</td><td>50.47</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:09:38.000+0000</td><td>2019-03-01T00:19:25.000+0000</td><td>1.0</td><td>2.3</td><td>1.0</td><td>N</td><td>144</td><td>170</td><td>1</td><td>9.5</td><td>3.0</td><td>0.5</td><td>2.65</td><td>0.0</td><td>0.3</td><td>15.95</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:23:42.000+0000</td><td>2019-03-01T00:36:18.000+0000</td><td>2.0</td><td>3.9</td><td>1.0</td><td>N</td><td>170</td><td>239</td><td>1</td><td>13.0</td><td>3.0</td><td>0.5</td><td>3.35</td><td>0.0</td><td>0.3</td><td>20.15</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:36:01.000+0000</td><td>2019-03-01T00:46:36.000+0000</td><td>2.0</td><td>1.87</td><td>1.0</td><td>N</td><td>161</td><td>107</td><td>1</td><td>9.5</td><td>0.5</td><td>0.5</td><td>3.99</td><td>0.0</td><td>0.3</td><td>17.29</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:52:40.000+0000</td><td>2019-03-01T01:03:15.000+0000</td><td>2.0</td><td>1.66</td><td>1.0</td><td>N</td><td>107</td><td>114</td><td>2</td><td>8.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>12.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-02-28T23:56:43.000+0000</td><td>2019-03-01T00:12:47.000+0000</td><td>1.0</td><td>3.08</td><td>1.0</td><td>N</td><td>50</td><td>107</td><td>1</td><td>13.5</td><td>0.5</td><td>0.5</td><td>3.46</td><td>0.0</td><td>0.3</td><td>20.76</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:33:35.000+0000</td><td>2019-03-01T00:46:12.000+0000</td><td>1.0</td><td>3.05</td><td>1.0</td><td>N</td><td>249</td><td>50</td><td>1</td><td>12.0</td><td>0.5</td><td>0.5</td><td>3.16</td><td>0.0</td><td>0.3</td><td>18.96</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:36:38.000+0000</td><td>2019-03-01T00:47:41.000+0000</td><td>1.0</td><td>1.6</td><td>1.0</td><td>N</td><td>79</td><td>211</td><td>1</td><td>9.0</td><td>3.0</td><td>0.5</td><td>2.55</td><td>0.0</td><td>0.3</td><td>15.35</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:52:01.000+0000</td><td>2019-03-01T00:58:55.000+0000</td><td>1.0</td><td>1.9</td><td>1.0</td><td>N</td><td>211</td><td>68</td><td>2</td><td>8.0</td><td>3.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>11.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:10:43.000+0000</td><td>2019-03-01T00:17:38.000+0000</td><td>1.0</td><td>0.3</td><td>1.0</td><td>N</td><td>107</td><td>107</td><td>1</td><td>6.0</td><td>0.5</td><td>0.5</td><td>2.94</td><td>0.0</td><td>0.3</td><td>12.74</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:20:43.000+0000</td><td>2019-03-01T00:50:17.000+0000</td><td>1.0</td><td>9.79</td><td>1.0</td><td>N</td><td>137</td><td>235</td><td>2</td><td>32.0</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>35.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:18:02.000+0000</td><td>2019-03-01T00:32:06.000+0000</td><td>1.0</td><td>5.26</td><td>1.0</td><td>N</td><td>4</td><td>52</td><td>2</td><td>17.0</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>20.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:28:43.000+0000</td><td>2019-03-01T01:08:27.000+0000</td><td>3.0</td><td>7.42</td><td>1.0</td><td>N</td><td>68</td><td>225</td><td>2</td><td>29.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>33.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-02-28T23:56:25.000+0000</td><td>2019-03-01T00:10:31.000+0000</td><td>1.0</td><td>1.75</td><td>1.0</td><td>N</td><td>137</td><td>114</td><td>1</td><td>10.5</td><td>0.5</td><td>0.5</td><td>1.0</td><td>0.0</td><td>0.3</td><td>15.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:12:54.000+0000</td><td>2019-03-01T00:22:05.000+0000</td><td>3.0</td><td>2.3</td><td>1.0</td><td>N</td><td>114</td><td>48</td><td>2</td><td>9.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>13.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:25:06.000+0000</td><td>2019-03-01T00:32:37.000+0000</td><td>2.0</td><td>1.72</td><td>1.0</td><td>N</td><td>48</td><td>164</td><td>1</td><td>7.5</td><td>0.5</td><td>0.5</td><td>2.26</td><td>0.0</td><td>0.3</td><td>13.56</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:08:39.000+0000</td><td>2019-03-01T00:18:01.000+0000</td><td>5.0</td><td>1.96</td><td>1.0</td><td>N</td><td>142</td><td>237</td><td>1</td><td>9.0</td><td>0.5</td><td>0.5</td><td>2.56</td><td>0.0</td><td>0.3</td><td>15.36</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:40:30.000+0000</td><td>2019-03-01T00:54:33.000+0000</td><td>5.0</td><td>3.1</td><td>1.0</td><td>N</td><td>238</td><td>244</td><td>1</td><td>13.5</td><td>0.5</td><td>0.5</td><td>2.96</td><td>0.0</td><td>0.3</td><td>17.76</td><td>0.0</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:09:41.000+0000</td><td>2019-03-01T00:30:57.000+0000</td><td>1.0</td><td>3.69</td><td>1.0</td><td>N</td><td>230</td><td>144</td><td>1</td><td>16.0</td><td>0.5</td><td>0.5</td><td>5.94</td><td>0.0</td><td>0.3</td><td>25.74</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:42:23.000+0000</td><td>2019-03-01T01:03:54.000+0000</td><td>1.0</td><td>6.11</td><td>1.0</td><td>N</td><td>231</td><td>263</td><td>1</td><td>20.0</td><td>0.5</td><td>0.5</td><td>4.2</td><td>0.0</td><td>0.3</td><td>28.0</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:16:02.000+0000</td><td>2019-03-01T00:24:09.000+0000</td><td>5.0</td><td>1.16</td><td>1.0</td><td>N</td><td>161</td><td>48</td><td>1</td><td>7.0</td><td>0.5</td><td>0.5</td><td>1.62</td><td>0.0</td><td>0.3</td><td>12.42</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:25:17.000+0000</td><td>2019-03-01T00:50:05.000+0000</td><td>5.0</td><td>6.34</td><td>1.0</td><td>N</td><td>48</td><td>83</td><td>1</td><td>22.5</td><td>0.5</td><td>0.5</td><td>4.0</td><td>0.0</td><td>0.3</td><td>30.3</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:02:27.000+0000</td><td>2019-03-01T00:06:02.000+0000</td><td>1.0</td><td>0.4</td><td>1.0</td><td>N</td><td>79</td><td>4</td><td>2</td><td>4.5</td><td>3.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>8.3</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:08:43.000+0000</td><td>2019-03-01T00:20:37.000+0000</td><td>1.0</td><td>1.8</td><td>1.0</td><td>N</td><td>4</td><td>249</td><td>1</td><td>9.5</td><td>3.0</td><td>0.5</td><td>2.65</td><td>0.0</td><td>0.3</td><td>15.95</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:48:14.000+0000</td><td>2019-03-01T01:31:12.000+0000</td><td>1.0</td><td>6.0</td><td>1.0</td><td>N</td><td>79</td><td>17</td><td>2</td><td>30.0</td><td>3.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>33.8</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:02:44.000+0000</td><td>2019-03-01T00:10:09.000+0000</td><td>1.0</td><td>1.3</td><td>1.0</td><td>N</td><td>162</td><td>107</td><td>1</td><td>7.0</td><td>3.0</td><td>0.5</td><td>1.62</td><td>0.0</td><td>0.3</td><td>12.42</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:22:30.000+0000</td><td>2019-03-01T00:37:53.000+0000</td><td>2.0</td><td>3.6</td><td>1.0</td><td>N</td><td>234</td><td>263</td><td>1</td><td>14.0</td><td>3.0</td><td>0.5</td><td>3.55</td><td>0.0</td><td>0.3</td><td>21.35</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:13:05.000+0000</td><td>2019-03-01T00:20:24.000+0000</td><td>1.0</td><td>1.93</td><td>1.0</td><td>N</td><td>163</td><td>141</td><td>1</td><td>8.0</td><td>0.5</td><td>0.5</td><td>2.36</td><td>0.0</td><td>0.3</td><td>14.16</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:39:31.000+0000</td><td>2019-03-01T00:43:46.000+0000</td><td>1.0</td><td>0.61</td><td>1.0</td><td>N</td><td>41</td><td>41</td><td>2</td><td>5.0</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>6.3</td><td>0.0</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:48:37.000+0000</td><td>2019-03-01T00:52:50.000+0000</td><td>1.0</td><td>0.91</td><td>1.0</td><td>N</td><td>42</td><td>41</td><td>2</td><td>5.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>6.8</td><td>0.0</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:54:59.000+0000</td><td>2019-03-01T00:56:53.000+0000</td><td>1.0</td><td>0.45</td><td>1.0</td><td>N</td><td>41</td><td>41</td><td>1</td><td>3.5</td><td>0.5</td><td>0.5</td><td>0.96</td><td>0.0</td><td>0.3</td><td>5.76</td><td>0.0</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:11:07.000+0000</td><td>2019-03-01T00:15:51.000+0000</td><td>1.0</td><td>1.01</td><td>1.0</td><td>N</td><td>237</td><td>263</td><td>1</td><td>5.5</td><td>0.5</td><td>0.5</td><td>1.86</td><td>0.0</td><td>0.3</td><td>11.16</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:26:18.000+0000</td><td>2019-03-01T00:36:57.000+0000</td><td>1.0</td><td>2.07</td><td>1.0</td><td>N</td><td>236</td><td>229</td><td>1</td><td>9.5</td><td>0.5</td><td>0.5</td><td>2.66</td><td>0.0</td><td>0.3</td><td>15.96</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:25:47.000+0000</td><td>2019-03-01T00:33:02.000+0000</td><td>2.0</td><td>2.04</td><td>1.0</td><td>N</td><td>239</td><td>263</td><td>1</td><td>8.5</td><td>0.5</td><td>0.5</td><td>3.08</td><td>0.0</td><td>0.3</td><td>15.38</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:35:20.000+0000</td><td>2019-03-01T00:53:27.000+0000</td><td>2.0</td><td>3.7</td><td>1.0</td><td>N</td><td>141</td><td>90</td><td>1</td><td>15.0</td><td>0.5</td><td>0.5</td><td>1.88</td><td>0.0</td><td>0.3</td><td>20.68</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:54:41.000+0000</td><td>2019-03-01T01:15:48.000+0000</td><td>2.0</td><td>5.02</td><td>1.0</td><td>N</td><td>90</td><td>256</td><td>1</td><td>18.0</td><td>0.5</td><td>0.5</td><td>6.54</td><td>0.0</td><td>0.3</td><td>28.34</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:43:33.000+0000</td><td>2019-03-01T00:52:37.000+0000</td><td>2.0</td><td>1.94</td><td>1.0</td><td>N</td><td>170</td><td>246</td><td>1</td><td>8.5</td><td>0.5</td><td>0.5</td><td>2.46</td><td>0.0</td><td>0.3</td><td>14.76</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:58:00.000+0000</td><td>2019-03-01T01:21:28.000+0000</td><td>2.0</td><td>5.91</td><td>1.0</td><td>N</td><td>48</td><td>87</td><td>2</td><td>20.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>24.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:22:59.000+0000</td><td>2019-03-01T00:27:25.000+0000</td><td>1.0</td><td>0.55</td><td>1.0</td><td>N</td><td>211</td><td>211</td><td>1</td><td>4.5</td><td>0.5</td><td>0.5</td><td>1.66</td><td>0.0</td><td>0.3</td><td>9.96</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:32:07.000+0000</td><td>2019-03-01T00:53:29.000+0000</td><td>1.0</td><td>4.93</td><td>1.0</td><td>N</td><td>211</td><td>188</td><td>1</td><td>19.0</td><td>0.5</td><td>0.5</td><td>4.56</td><td>0.0</td><td>0.3</td><td>27.36</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:04:08.000+0000</td><td>2019-03-01T00:36:34.000+0000</td><td>1.0</td><td>4.56</td><td>1.0</td><td>N</td><td>249</td><td>256</td><td>1</td><td>22.5</td><td>0.5</td><td>0.5</td><td>5.26</td><td>0.0</td><td>0.3</td><td>31.56</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:25:36.000+0000</td><td>2019-03-01T00:35:55.000+0000</td><td>1.0</td><td>3.84</td><td>1.0</td><td>N</td><td>255</td><td>66</td><td>1</td><td>13.0</td><td>0.5</td><td>0.5</td><td>2.86</td><td>0.0</td><td>0.3</td><td>17.16</td><td>0.0</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:49:59.000+0000</td><td>2019-03-01T01:01:16.000+0000</td><td>1.0</td><td>1.85</td><td>1.0</td><td>N</td><td>88</td><td>232</td><td>1</td><td>9.5</td><td>0.5</td><td>0.5</td><td>1.0</td><td>0.0</td><td>0.3</td><td>14.3</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:15:44.000+0000</td><td>2019-03-01T00:25:45.000+0000</td><td>1.0</td><td>1.6</td><td>1.0</td><td>N</td><td>114</td><td>148</td><td>1</td><td>8.5</td><td>3.0</td><td>0.5</td><td>2.45</td><td>0.0</td><td>0.3</td><td>14.75</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:27:09.000+0000</td><td>2019-03-01T00:33:54.000+0000</td><td>1.0</td><td>1.3</td><td>1.0</td><td>N</td><td>148</td><td>87</td><td>2</td><td>7.0</td><td>3.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>10.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:43:59.000+0000</td><td>2019-03-01T00:59:44.000+0000</td><td>5.0</td><td>2.8</td><td>1.0</td><td>N</td><td>255</td><td>17</td><td>1</td><td>12.5</td><td>0.5</td><td>0.5</td><td>2.76</td><td>0.0</td><td>0.3</td><td>16.56</td><td>0.0</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:28:55.000+0000</td><td>2019-03-01T00:36:53.000+0000</td><td>2.0</td><td>1.2</td><td>1.0</td><td>N</td><td>234</td><td>79</td><td>1</td><td>7.0</td><td>3.0</td><td>0.5</td><td>2.15</td><td>0.0</td><td>0.3</td><td>12.95</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:41:06.000+0000</td><td>2019-03-01T00:45:38.000+0000</td><td>1.0</td><td>1.2</td><td>1.0</td><td>N</td><td>65</td><td>49</td><td>1</td><td>6.0</td><td>0.5</td><td>0.5</td><td>1.45</td><td>0.0</td><td>0.3</td><td>8.75</td><td>0.0</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:46:48.000+0000</td><td>2019-03-01T00:56:34.000+0000</td><td>1.0</td><td>2.39</td><td>1.0</td><td>N</td><td>163</td><td>75</td><td>2</td><td>9.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>13.3</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:45:23.000+0000</td><td>2019-03-01T01:24:30.000+0000</td><td>1.0</td><td>21.3</td><td>2.0</td><td>N</td><td>132</td><td>239</td><td>2</td><td>52.0</td><td>2.5</td><td>0.5</td><td>0.0</td><td>5.76</td><td>0.3</td><td>61.06</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:05:12.000+0000</td><td>2019-03-01T00:35:26.000+0000</td><td>2.0</td><td>5.0</td><td>1.0</td><td>N</td><td>79</td><td>61</td><td>1</td><td>22.5</td><td>3.0</td><td>0.5</td><td>5.25</td><td>0.0</td><td>0.3</td><td>31.55</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:07:40.000+0000</td><td>2019-03-01T00:14:41.000+0000</td><td>1.0</td><td>1.7</td><td>1.0</td><td>N</td><td>162</td><td>263</td><td>1</td><td>7.5</td><td>3.0</td><td>0.5</td><td>1.13</td><td>0.0</td><td>0.3</td><td>12.43</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:16:07.000+0000</td><td>2019-03-01T00:18:36.000+0000</td><td>1.0</td><td>0.7</td><td>1.0</td><td>N</td><td>141</td><td>236</td><td>1</td><td>4.5</td><td>3.0</td><td>0.5</td><td>1.65</td><td>0.0</td><td>0.3</td><td>9.95</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:10:57.000+0000</td><td>2019-03-01T00:24:18.000+0000</td><td>1.0</td><td>2.77</td><td>1.0</td><td>N</td><td>163</td><td>140</td><td>1</td><td>11.5</td><td>0.5</td><td>0.5</td><td>3.06</td><td>0.0</td><td>0.3</td><td>18.36</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:49:12.000+0000</td><td>2019-03-01T01:02:36.000+0000</td><td>1.0</td><td>5.53</td><td>1.0</td><td>N</td><td>48</td><td>116</td><td>1</td><td>17.5</td><td>0.5</td><td>0.5</td><td>4.26</td><td>0.0</td><td>0.3</td><td>25.56</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:01:12.000+0000</td><td>2019-03-01T00:36:23.000+0000</td><td>2.0</td><td>7.2</td><td>1.0</td><td>N</td><td>230</td><td>49</td><td>1</td><td>27.0</td><td>3.0</td><td>0.5</td><td>7.7</td><td>0.0</td><td>0.3</td><td>38.5</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:25:16.000+0000</td><td>2019-03-01T00:44:05.000+0000</td><td>1.0</td><td>5.2</td><td>1.0</td><td>N</td><td>236</td><td>255</td><td>1</td><td>18.0</td><td>3.0</td><td>0.5</td><td>0.01</td><td>0.0</td><td>0.3</td><td>21.81</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:20:38.000+0000</td><td>2019-03-01T00:20:38.000+0000</td><td>1.0</td><td>0.0</td><td>1.0</td><td>Y</td><td>107</td><td>264</td><td>2</td><td>2.5</td><td>3.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>6.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:29:41.000+0000</td><td>2019-03-01T00:38:21.000+0000</td><td>1.0</td><td>2.31</td><td>1.0</td><td>N</td><td>100</td><td>125</td><td>1</td><td>8.5</td><td>0.5</td><td>0.5</td><td>2.46</td><td>0.0</td><td>0.3</td><td>14.76</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:39:08.000+0000</td><td>2019-03-01T00:52:48.000+0000</td><td>1.0</td><td>3.02</td><td>1.0</td><td>N</td><td>125</td><td>48</td><td>2</td><td>12.0</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>15.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-02-28T23:51:08.000+0000</td><td>2019-03-01T00:04:47.000+0000</td><td>2.0</td><td>2.5</td><td>1.0</td><td>N</td><td>43</td><td>229</td><td>2</td><td>11.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>15.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:07:59.000+0000</td><td>2019-03-01T00:12:20.000+0000</td><td>2.0</td><td>0.51</td><td>1.0</td><td>N</td><td>229</td><td>162</td><td>2</td><td>5.0</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>8.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:42:31.000+0000</td><td>2019-03-01T00:49:03.000+0000</td><td>2.0</td><td>1.19</td><td>1.0</td><td>N</td><td>231</td><td>114</td><td>2</td><td>6.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>10.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:08:03.000+0000</td><td>2019-03-01T00:24:35.000+0000</td><td>1.0</td><td>8.79</td><td>1.0</td><td>N</td><td>138</td><td>140</td><td>1</td><td>25.5</td><td>0.5</td><td>0.5</td><td>5.26</td><td>5.76</td><td>0.3</td><td>40.32</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:32:47.000+0000</td><td>2019-03-01T00:37:19.000+0000</td><td>1.0</td><td>0.88</td><td>1.0</td><td>N</td><td>237</td><td>229</td><td>1</td><td>5.5</td><td>0.5</td><td>0.5</td><td>2.5</td><td>0.0</td><td>0.3</td><td>11.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:02:50.000+0000</td><td>2019-03-01T00:09:07.000+0000</td><td>2.0</td><td>0.73</td><td>1.0</td><td>N</td><td>234</td><td>68</td><td>2</td><td>6.0</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>9.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:11:00.000+0000</td><td>2019-03-01T00:22:21.000+0000</td><td>2.0</td><td>2.03</td><td>1.0</td><td>N</td><td>68</td><td>170</td><td>2</td><td>10.0</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>13.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:54:37.000+0000</td><td>2019-03-01T01:04:00.000+0000</td><td>2.0</td><td>1.71</td><td>1.0</td><td>N</td><td>148</td><td>87</td><td>1</td><td>8.0</td><td>0.5</td><td>0.5</td><td>1.0</td><td>0.0</td><td>0.3</td><td>12.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:09:02.000+0000</td><td>2019-03-01T00:10:47.000+0000</td><td>1.0</td><td>0.38</td><td>1.0</td><td>N</td><td>235</td><td>235</td><td>1</td><td>3.5</td><td>0.5</td><td>0.5</td><td>1.44</td><td>0.0</td><td>0.3</td><td>6.24</td><td>0.0</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:10:01.000+0000</td><td>2019-03-01T00:28:22.000+0000</td><td>1.0</td><td>4.7</td><td>1.0</td><td>N</td><td>48</td><td>226</td><td>2</td><td>16.5</td><td>3.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>20.3</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:39:09.000+0000</td><td>2019-03-01T00:46:19.000+0000</td><td>1.0</td><td>1.9</td><td>1.0</td><td>N</td><td>141</td><td>137</td><td>1</td><td>8.0</td><td>3.0</td><td>0.5</td><td>2.35</td><td>0.0</td><td>0.3</td><td>14.15</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:54:14.000+0000</td><td>2019-03-01T00:57:20.000+0000</td><td>1.0</td><td>0.8</td><td>1.0</td><td>N</td><td>264</td><td>140</td><td>1</td><td>4.5</td><td>3.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>8.3</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:10:11.000+0000</td><td>2019-03-01T00:24:35.000+0000</td><td>1.0</td><td>2.6</td><td>1.0</td><td>N</td><td>114</td><td>246</td><td>1</td><td>12.0</td><td>3.0</td><td>0.5</td><td>1.58</td><td>0.0</td><td>0.3</td><td>17.38</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:31:07.000+0000</td><td>2019-03-01T00:35:52.000+0000</td><td>1.0</td><td>1.7</td><td>1.0</td><td>N</td><td>246</td><td>143</td><td>1</td><td>7.0</td><td>3.0</td><td>0.5</td><td>1.2</td><td>0.0</td><td>0.3</td><td>12.0</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:17:29.000+0000</td><td>2019-03-01T00:34:24.000+0000</td><td>2.0</td><td>2.16</td><td>1.0</td><td>N</td><td>186</td><td>79</td><td>2</td><td>12.0</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>15.8</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:30:29.000+0000</td><td>2019-03-01T00:57:45.000+0000</td><td>1.0</td><td>15.4</td><td>1.0</td><td>N</td><td>132</td><td>7</td><td>1</td><td>42.5</td><td>0.0</td><td>0.5</td><td>8.65</td><td>0.0</td><td>0.3</td><td>51.95</td><td>0.0</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:22:15.000+0000</td><td>2019-03-01T00:32:06.000+0000</td><td>1.0</td><td>1.4</td><td>1.0</td><td>N</td><td>264</td><td>107</td><td>1</td><td>8.0</td><td>3.0</td><td>0.5</td><td>1.0</td><td>0.0</td><td>0.3</td><td>12.8</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:43:25.000+0000</td><td>2019-03-01T00:45:54.000+0000</td><td>2.0</td><td>0.3</td><td>1.0</td><td>N</td><td>79</td><td>79</td><td>1</td><td>3.5</td><td>3.0</td><td>0.5</td><td>1.45</td><td>0.0</td><td>0.3</td><td>8.75</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:49:11.000+0000</td><td>2019-03-01T00:51:33.000+0000</td><td>1.0</td><td>0.3</td><td>1.0</td><td>N</td><td>79</td><td>79</td><td>1</td><td>3.5</td><td>3.0</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.3</td><td>7.8</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:53:33.000+0000</td><td>2019-03-01T00:55:53.000+0000</td><td>1.0</td><td>0.6</td><td>1.0</td><td>N</td><td>79</td><td>137</td><td>1</td><td>4.0</td><td>3.0</td><td>0.5</td><td>0.7</td><td>0.0</td><td>0.3</td><td>8.5</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:21:02.000+0000</td><td>2019-03-01T00:29:45.000+0000</td><td>1.0</td><td>1.08</td><td>1.0</td><td>N</td><td>79</td><td>144</td><td>1</td><td>7.5</td><td>0.5</td><td>0.5</td><td>1.0</td><td>0.0</td><td>0.3</td><td>12.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:36:40.000+0000</td><td>2019-03-01T00:52:27.000+0000</td><td>1.0</td><td>3.93</td><td>1.0</td><td>N</td><td>114</td><td>142</td><td>1</td><td>14.5</td><td>0.5</td><td>0.5</td><td>3.0</td><td>0.0</td><td>0.3</td><td>21.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:09:14.000+0000</td><td>2019-03-01T00:28:18.000+0000</td><td>1.0</td><td>5.25</td><td>1.0</td><td>N</td><td>264</td><td>264</td><td>1</td><td>18.0</td><td>0.5</td><td>0.5</td><td>4.36</td><td>0.0</td><td>0.3</td><td>26.16</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:12:20.000+0000</td><td>2019-03-01T00:16:23.000+0000</td><td>1.0</td><td>0.94</td><td>1.0</td><td>N</td><td>140</td><td>140</td><td>1</td><td>5.0</td><td>0.5</td><td>0.5</td><td>1.76</td><td>0.0</td><td>0.3</td><td>10.56</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:06:34.000+0000</td><td>2019-03-01T00:15:16.000+0000</td><td>1.0</td><td>1.56</td><td>1.0</td><td>N</td><td>79</td><td>158</td><td>1</td><td>7.5</td><td>0.5</td><td>0.5</td><td>2.26</td><td>0.0</td><td>0.3</td><td>13.56</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:32:57.000+0000</td><td>2019-03-01T00:58:23.000+0000</td><td>1.0</td><td>5.92</td><td>1.0</td><td>N</td><td>79</td><td>7</td><td>2</td><td>22.0</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>25.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:12:54.000+0000</td><td>2019-03-01T00:27:47.000+0000</td><td>2.0</td><td>1.69</td><td>1.0</td><td>N</td><td>148</td><td>249</td><td>1</td><td>11.0</td><td>0.5</td><td>0.5</td><td>1.0</td><td>0.0</td><td>0.3</td><td>15.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:29:57.000+0000</td><td>2019-03-01T00:51:23.000+0000</td><td>2.0</td><td>5.24</td><td>1.0</td><td>N</td><td>249</td><td>112</td><td>1</td><td>19.5</td><td>0.5</td><td>0.5</td><td>3.0</td><td>0.0</td><td>0.3</td><td>26.3</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:02:12.000+0000</td><td>2019-03-01T00:09:47.000+0000</td><td>2.0</td><td>1.7</td><td>1.0</td><td>N</td><td>230</td><td>143</td><td>2</td><td>8.0</td><td>3.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>11.8</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:32:38.000+0000</td><td>2019-03-01T00:42:28.000+0000</td><td>1.0</td><td>1.6</td><td>1.0</td><td>N</td><td>114</td><td>209</td><td>2</td><td>8.5</td><td>3.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>12.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:02:10.000+0000</td><td>2019-03-01T00:33:58.000+0000</td><td>1.0</td><td>7.26</td><td>1.0</td><td>N</td><td>125</td><td>37</td><td>1</td><td>26.0</td><td>0.5</td><td>0.5</td><td>5.96</td><td>0.0</td><td>0.3</td><td>35.76</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:24:38.000+0000</td><td>2019-03-01T00:32:42.000+0000</td><td>1.0</td><td>1.74</td><td>1.0</td><td>N</td><td>237</td><td>151</td><td>1</td><td>8.0</td><td>0.5</td><td>0.5</td><td>1.0</td><td>0.0</td><td>0.3</td><td>12.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:49:43.000+0000</td><td>2019-03-01T00:54:26.000+0000</td><td>1.0</td><td>0.83</td><td>1.0</td><td>N</td><td>246</td><td>68</td><td>2</td><td>5.0</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>8.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:42:19.000+0000</td><td>2019-03-01T00:53:10.000+0000</td><td>1.0</td><td>2.04</td><td>1.0</td><td>N</td><td>162</td><td>246</td><td>1</td><td>9.5</td><td>0.5</td><td>0.5</td><td>2.66</td><td>0.0</td><td>0.3</td><td>15.96</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:10:58.000+0000</td><td>2019-03-01T00:20:48.000+0000</td><td>3.0</td><td>1.9</td><td>1.0</td><td>N</td><td>230</td><td>107</td><td>1</td><td>9.0</td><td>3.0</td><td>0.5</td><td>2.55</td><td>0.0</td><td>0.3</td><td>15.35</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:50:43.000+0000</td><td>2019-03-01T01:05:55.000+0000</td><td>1.0</td><td>3.1</td><td>1.0</td><td>N</td><td>162</td><td>79</td><td>1</td><td>12.5</td><td>3.0</td><td>0.5</td><td>3.26</td><td>0.0</td><td>0.3</td><td>19.56</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:04:34.000+0000</td><td>2019-03-01T00:12:13.000+0000</td><td>5.0</td><td>1.16</td><td>1.0</td><td>N</td><td>211</td><td>158</td><td>1</td><td>7.0</td><td>0.5</td><td>0.5</td><td>2.0</td><td>0.0</td><td>0.3</td><td>12.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:20:40.000+0000</td><td>2019-03-01T00:33:04.000+0000</td><td>2.0</td><td>2.2</td><td>1.0</td><td>N</td><td>114</td><td>261</td><td>2</td><td>10.0</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>13.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:01:33.000+0000</td><td>2019-03-01T00:32:03.000+0000</td><td>1.0</td><td>18.93</td><td>1.0</td><td>N</td><td>132</td><td>25</td><td>1</td><td>51.5</td><td>0.5</td><td>0.5</td><td>13.2</td><td>0.0</td><td>0.3</td><td>66.0</td><td>0.0</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:46:07.000+0000</td><td>2019-03-01T00:46:42.000+0000</td><td>1.0</td><td>0.07</td><td>1.0</td><td>N</td><td>106</td><td>106</td><td>2</td><td>2.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>3.8</td><td>0.0</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:08:15.000+0000</td><td>2019-03-01T00:36:57.000+0000</td><td>1.0</td><td>10.7</td><td>1.0</td><td>N</td><td>138</td><td>36</td><td>1</td><td>31.5</td><td>0.5</td><td>0.5</td><td>6.55</td><td>0.0</td><td>0.3</td><td>39.35</td><td>0.0</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:17:56.000+0000</td><td>2019-03-01T00:26:41.000+0000</td><td>2.0</td><td>1.84</td><td>1.0</td><td>N</td><td>230</td><td>140</td><td>1</td><td>8.5</td><td>0.5</td><td>0.5</td><td>2.46</td><td>0.0</td><td>0.3</td><td>14.76</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:39:27.000+0000</td><td>2019-03-01T00:50:22.000+0000</td><td>2.0</td><td>2.21</td><td>1.0</td><td>N</td><td>230</td><td>114</td><td>1</td><td>9.5</td><td>0.5</td><td>0.5</td><td>1.11</td><td>0.0</td><td>0.3</td><td>14.41</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:26:43.000+0000</td><td>2019-03-01T00:31:03.000+0000</td><td>1.0</td><td>0.0</td><td>5.0</td><td>N</td><td>265</td><td>265</td><td>3</td><td>0.01</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.3</td><td>0.31</td><td>0.0</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:01:04.000+0000</td><td>2019-03-01T00:09:11.000+0000</td><td>1.0</td><td>1.0</td><td>1.0</td><td>N</td><td>231</td><td>209</td><td>1</td><td>6.5</td><td>3.0</td><td>0.5</td><td>1.5</td><td>0.0</td><td>0.3</td><td>11.8</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:22:10.000+0000</td><td>2019-03-01T00:35:56.000+0000</td><td>1.0</td><td>2.9</td><td>1.0</td><td>N</td><td>231</td><td>25</td><td>1</td><td>12.5</td><td>3.0</td><td>0.5</td><td>3.26</td><td>0.0</td><td>0.3</td><td>19.56</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:46:30.000+0000</td><td>2019-03-01T01:16:28.000+0000</td><td>1.0</td><td>6.7</td><td>1.0</td><td>N</td><td>231</td><td>80</td><td>1</td><td>25.0</td><td>3.0</td><td>0.5</td><td>5.75</td><td>0.0</td><td>0.3</td><td>34.55</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:06:36.000+0000</td><td>2019-03-01T00:12:54.000+0000</td><td>2.0</td><td>1.2</td><td>1.0</td><td>N</td><td>79</td><td>249</td><td>1</td><td>6.5</td><td>3.0</td><td>0.5</td><td>2.05</td><td>0.0</td><td>0.3</td><td>12.35</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:21:23.000+0000</td><td>2019-03-01T00:31:02.000+0000</td><td>1.0</td><td>1.4</td><td>1.0</td><td>N</td><td>211</td><td>87</td><td>1</td><td>8.0</td><td>3.0</td><td>0.5</td><td>2.35</td><td>0.0</td><td>0.3</td><td>14.15</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:36:24.000+0000</td><td>2019-03-01T00:45:53.000+0000</td><td>1.0</td><td>1.9</td><td>1.0</td><td>N</td><td>261</td><td>148</td><td>1</td><td>9.0</td><td>3.0</td><td>0.5</td><td>2.55</td><td>0.0</td><td>0.3</td><td>15.35</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:23:03.000+0000</td><td>2019-03-01T00:32:00.000+0000</td><td>3.0</td><td>1.53</td><td>1.0</td><td>N</td><td>161</td><td>90</td><td>1</td><td>8.0</td><td>0.5</td><td>0.5</td><td>2.36</td><td>0.0</td><td>0.3</td><td>14.16</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:46:47.000+0000</td><td>2019-03-01T00:51:44.000+0000</td><td>2.0</td><td>1.54</td><td>1.0</td><td>N</td><td>163</td><td>68</td><td>1</td><td>6.5</td><td>0.5</td><td>0.5</td><td>2.0</td><td>0.0</td><td>0.3</td><td>12.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-02-28T23:59:46.000+0000</td><td>2019-03-01T00:08:44.000+0000</td><td>1.0</td><td>1.42</td><td>1.0</td><td>N</td><td>148</td><td>231</td><td>2</td><td>7.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>11.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:22:49.000+0000</td><td>2019-03-01T01:02:02.000+0000</td><td>3.0</td><td>12.31</td><td>1.0</td><td>N</td><td>48</td><td>174</td><td>2</td><td>39.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>43.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:01:56.000+0000</td><td>2019-03-01T00:14:21.000+0000</td><td>1.0</td><td>2.03</td><td>1.0</td><td>N</td><td>163</td><td>186</td><td>1</td><td>10.0</td><td>0.5</td><td>0.5</td><td>3.45</td><td>0.0</td><td>0.3</td><td>17.25</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:22:31.000+0000</td><td>2019-03-01T00:41:23.000+0000</td><td>1.0</td><td>3.1</td><td>1.0</td><td>N</td><td>246</td><td>148</td><td>2</td><td>14.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>18.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:43:47.000+0000</td><td>2019-03-01T00:50:30.000+0000</td><td>1.0</td><td>1.22</td><td>1.0</td><td>N</td><td>79</td><td>148</td><td>1</td><td>6.5</td><td>0.5</td><td>0.5</td><td>2.06</td><td>0.0</td><td>0.3</td><td>12.36</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:07:50.000+0000</td><td>2019-03-01T00:39:36.000+0000</td><td>2.0</td><td>10.3</td><td>1.0</td><td>N</td><td>107</td><td>14</td><td>1</td><td>33.0</td><td>3.0</td><td>0.5</td><td>7.35</td><td>0.0</td><td>0.3</td><td>44.15</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:16:48.000+0000</td><td>2019-03-01T00:42:53.000+0000</td><td>1.0</td><td>5.65</td><td>1.0</td><td>N</td><td>141</td><td>80</td><td>1</td><td>21.0</td><td>0.5</td><td>0.5</td><td>4.96</td><td>0.0</td><td>0.3</td><td>29.76</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:44:17.000+0000</td><td>2019-03-01T00:52:14.000+0000</td><td>1.0</td><td>1.13</td><td>1.0</td><td>N</td><td>80</td><td>225</td><td>2</td><td>7.0</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>8.3</td><td>0.0</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:01:31.000+0000</td><td>2019-03-01T00:15:59.000+0000</td><td>1.0</td><td>3.7</td><td>1.0</td><td>N</td><td>75</td><td>48</td><td>1</td><td>14.0</td><td>3.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>17.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-02-28T23:59:55.000+0000</td><td>2019-03-01T00:03:18.000+0000</td><td>1.0</td><td>0.81</td><td>1.0</td><td>N</td><td>170</td><td>164</td><td>1</td><td>4.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>8.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:58:52.000+0000</td><td>2019-03-01T01:06:13.000+0000</td><td>1.0</td><td>1.16</td><td>1.0</td><td>N</td><td>164</td><td>48</td><td>1</td><td>6.5</td><td>0.5</td><td>0.5</td><td>2.06</td><td>0.0</td><td>0.3</td><td>12.36</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:06:32.000+0000</td><td>2019-03-01T00:15:07.000+0000</td><td>2.0</td><td>1.21</td><td>1.0</td><td>N</td><td>79</td><td>90</td><td>1</td><td>7.5</td><td>0.5</td><td>0.5</td><td>2.26</td><td>0.0</td><td>0.3</td><td>13.56</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:20:37.000+0000</td><td>2019-03-01T00:27:02.000+0000</td><td>2.0</td><td>1.42</td><td>1.0</td><td>N</td><td>68</td><td>164</td><td>1</td><td>7.0</td><td>0.5</td><td>0.5</td><td>1.0</td><td>0.0</td><td>0.3</td><td>11.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:07:05.000+0000</td><td>2019-03-01T00:21:56.000+0000</td><td>2.0</td><td>3.44</td><td>1.0</td><td>N</td><td>79</td><td>50</td><td>1</td><td>13.5</td><td>0.5</td><td>0.5</td><td>3.46</td><td>0.0</td><td>0.3</td><td>20.76</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:48:05.000+0000</td><td>2019-03-01T01:34:58.000+0000</td><td>2.0</td><td>10.56</td><td>1.0</td><td>N</td><td>229</td><td>181</td><td>2</td><td>39.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>43.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:19:04.000+0000</td><td>2019-03-01T00:40:18.000+0000</td><td>2.0</td><td>4.88</td><td>1.0</td><td>N</td><td>142</td><td>7</td><td>1</td><td>18.0</td><td>0.5</td><td>0.5</td><td>4.36</td><td>0.0</td><td>0.3</td><td>26.16</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:12:54.000+0000</td><td>2019-03-01T00:17:33.000+0000</td><td>1.0</td><td>0.8</td><td>1.0</td><td>N</td><td>162</td><td>170</td><td>1</td><td>5.0</td><td>3.0</td><td>0.5</td><td>1.75</td><td>0.0</td><td>0.3</td><td>10.55</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:01:19.000+0000</td><td>2019-03-01T00:16:16.000+0000</td><td>2.0</td><td>2.9</td><td>1.0</td><td>N</td><td>68</td><td>162</td><td>1</td><td>12.5</td><td>3.0</td><td>0.5</td><td>4.05</td><td>0.0</td><td>0.3</td><td>20.35</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:25:23.000+0000</td><td>2019-03-01T00:53:38.000+0000</td><td>1.0</td><td>5.0</td><td>1.0</td><td>N</td><td>107</td><td>181</td><td>1</td><td>22.0</td><td>3.0</td><td>0.5</td><td>6.45</td><td>0.0</td><td>0.3</td><td>32.25</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:07:57.000+0000</td><td>2019-03-01T00:12:00.000+0000</td><td>1.0</td><td>0.84</td><td>1.0</td><td>N</td><td>161</td><td>229</td><td>2</td><td>5.0</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>8.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:20:22.000+0000</td><td>2019-03-01T00:34:01.000+0000</td><td>1.0</td><td>1.72</td><td>1.0</td><td>N</td><td>137</td><td>90</td><td>1</td><td>10.0</td><td>0.5</td><td>0.5</td><td>1.38</td><td>0.0</td><td>0.3</td><td>15.18</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:28:13.000+0000</td><td>2019-03-01T00:36:21.000+0000</td><td>2.0</td><td>1.56</td><td>1.0</td><td>N</td><td>164</td><td>224</td><td>1</td><td>8.0</td><td>0.5</td><td>0.5</td><td>2.36</td><td>0.0</td><td>0.3</td><td>14.16</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:10:03.000+0000</td><td>2019-03-01T00:27:31.000+0000</td><td>1.0</td><td>3.3</td><td>1.0</td><td>N</td><td>234</td><td>88</td><td>1</td><td>14.0</td><td>3.0</td><td>0.5</td><td>3.55</td><td>0.0</td><td>0.3</td><td>21.35</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:15:05.000+0000</td><td>2019-03-01T00:19:07.000+0000</td><td>1.0</td><td>0.97</td><td>1.0</td><td>N</td><td>114</td><td>107</td><td>1</td><td>5.5</td><td>0.5</td><td>0.5</td><td>1.86</td><td>0.0</td><td>0.3</td><td>11.16</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:38:24.000+0000</td><td>2019-03-01T00:59:17.000+0000</td><td>2.0</td><td>4.6</td><td>1.0</td><td>N</td><td>79</td><td>181</td><td>1</td><td>17.5</td><td>0.5</td><td>0.5</td><td>4.26</td><td>0.0</td><td>0.3</td><td>25.56</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:24:53.000+0000</td><td>2019-03-01T00:27:15.000+0000</td><td>1.0</td><td>0.9</td><td>1.0</td><td>N</td><td>41</td><td>166</td><td>1</td><td>4.5</td><td>0.5</td><td>0.5</td><td>1.16</td><td>0.0</td><td>0.3</td><td>6.96</td><td>0.0</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:37:18.000+0000</td><td>2019-03-01T00:51:09.000+0000</td><td>1.0</td><td>3.53</td><td>1.0</td><td>N</td><td>24</td><td>244</td><td>2</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:53:37.000+0000</td><td>2019-03-01T00:53:41.000+0000</td><td>1.0</td><td>0.0</td><td>5.0</td><td>N</td><td>244</td><td>244</td><td>1</td><td>14.8</td><td>0.0</td><td>0.0</td><td>3.02</td><td>0.0</td><td>0.3</td><td>18.12</td><td>0.0</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:07:11.000+0000</td><td>2019-03-01T00:21:18.000+0000</td><td>3.0</td><td>2.53</td><td>1.0</td><td>N</td><td>234</td><td>230</td><td>1</td><td>11.5</td><td>0.5</td><td>0.5</td><td>3.06</td><td>0.0</td><td>0.3</td><td>18.36</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:34:27.000+0000</td><td>2019-03-01T00:47:36.000+0000</td><td>2.0</td><td>3.73</td><td>1.0</td><td>N</td><td>164</td><td>263</td><td>2</td><td>13.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>17.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:51:06.000+0000</td><td>2019-03-01T00:51:33.000+0000</td><td>2.0</td><td>0.07</td><td>1.0</td><td>N</td><td>263</td><td>263</td><td>1</td><td>2.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>6.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:08:07.000+0000</td><td>2019-03-01T00:11:07.000+0000</td><td>2.0</td><td>0.83</td><td>1.0</td><td>N</td><td>244</td><td>116</td><td>1</td><td>5.0</td><td>0.5</td><td>0.5</td><td>1.0</td><td>0.0</td><td>0.3</td><td>7.3</td><td>0.0</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:54:35.000+0000</td><td>2019-03-01T01:04:20.000+0000</td><td>2.0</td><td>3.12</td><td>1.0</td><td>N</td><td>239</td><td>74</td><td>1</td><td>11.0</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>14.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:08:16.000+0000</td><td>2019-03-01T00:12:30.000+0000</td><td>5.0</td><td>0.52</td><td>1.0</td><td>N</td><td>113</td><td>79</td><td>1</td><td>4.5</td><td>0.5</td><td>0.5</td><td>1.66</td><td>0.0</td><td>0.3</td><td>9.96</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-02-28T23:56:01.000+0000</td><td>2019-03-01T00:13:11.000+0000</td><td>2.0</td><td>4.02</td><td>1.0</td><td>N</td><td>234</td><td>263</td><td>1</td><td>15.0</td><td>0.5</td><td>0.5</td><td>3.76</td><td>0.0</td><td>0.3</td><td>22.56</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:27:38.000+0000</td><td>2019-03-01T00:31:44.000+0000</td><td>3.0</td><td>0.67</td><td>1.0</td><td>N</td><td>237</td><td>229</td><td>2</td><td>5.0</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>8.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:45:24.000+0000</td><td>2019-03-01T00:51:14.000+0000</td><td>3.0</td><td>1.19</td><td>1.0</td><td>N</td><td>234</td><td>164</td><td>1</td><td>6.0</td><td>0.5</td><td>0.5</td><td>1.08</td><td>0.0</td><td>0.3</td><td>10.88</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:53:28.000+0000</td><td>2019-03-01T01:00:41.000+0000</td><td>3.0</td><td>1.35</td><td>1.0</td><td>N</td><td>234</td><td>114</td><td>1</td><td>7.0</td><td>0.5</td><td>0.5</td><td>2.16</td><td>0.0</td><td>0.3</td><td>12.96</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:07:49.000+0000</td><td>2019-03-01T00:37:48.000+0000</td><td>2.0</td><td>8.22</td><td>1.0</td><td>N</td><td>138</td><td>230</td><td>1</td><td>28.5</td><td>0.5</td><td>0.5</td><td>6.46</td><td>0.0</td><td>0.3</td><td>38.76</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:56:51.000+0000</td><td>2019-03-01T01:22:19.000+0000</td><td>2.0</td><td>20.44</td><td>2.0</td><td>N</td><td>132</td><td>209</td><td>1</td><td>52.0</td><td>0.0</td><td>0.5</td><td>11.06</td><td>0.0</td><td>0.3</td><td>66.36</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:15:15.000+0000</td><td>2019-03-01T00:17:37.000+0000</td><td>2.0</td><td>0.44</td><td>1.0</td><td>N</td><td>143</td><td>43</td><td>2</td><td>3.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>7.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:10:01.000+0000</td><td>2019-03-01T00:14:37.000+0000</td><td>6.0</td><td>1.79</td><td>1.0</td><td>N</td><td>239</td><td>24</td><td>2</td><td>7.0</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>10.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:17:09.000+0000</td><td>2019-03-01T00:21:10.000+0000</td><td>6.0</td><td>0.72</td><td>1.0</td><td>N</td><td>24</td><td>151</td><td>2</td><td>5.0</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>6.3</td><td>0.0</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:29:44.000+0000</td><td>2019-03-01T00:38:57.000+0000</td><td>6.0</td><td>1.8</td><td>1.0</td><td>N</td><td>239</td><td>263</td><td>1</td><td>8.5</td><td>0.5</td><td>0.5</td><td>2.46</td><td>0.0</td><td>0.3</td><td>14.76</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:08:41.000+0000</td><td>2019-03-01T00:28:47.000+0000</td><td>5.0</td><td>5.32</td><td>1.0</td><td>N</td><td>48</td><td>7</td><td>1</td><td>19.0</td><td>0.5</td><td>0.5</td><td>4.56</td><td>0.0</td><td>0.3</td><td>27.36</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:04:23.000+0000</td><td>2019-03-01T00:15:39.000+0000</td><td>2.0</td><td>1.11</td><td>1.0</td><td>N</td><td>125</td><td>148</td><td>1</td><td>7.5</td><td>0.5</td><td>0.5</td><td>0.01</td><td>0.0</td><td>0.3</td><td>11.31</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-02-28T21:06:29.000+0000</td><td>2019-02-28T21:12:14.000+0000</td><td>2.0</td><td>0.87</td><td>1.0</td><td>N</td><td>100</td><td>230</td><td>1</td><td>6.0</td><td>0.5</td><td>0.5</td><td>1.96</td><td>0.0</td><td>0.3</td><td>11.76</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:49:53.000+0000</td><td>2019-03-01T00:56:09.000+0000</td><td>1.0</td><td>1.62</td><td>1.0</td><td>N</td><td>113</td><td>164</td><td>2</td><td>7.0</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>10.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:57:39.000+0000</td><td>2019-03-01T01:00:23.000+0000</td><td>2.0</td><td>0.51</td><td>1.0</td><td>N</td><td>170</td><td>170</td><td>1</td><td>4.0</td><td>0.5</td><td>0.5</td><td>1.56</td><td>0.0</td><td>0.3</td><td>9.36</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:02:25.000+0000</td><td>2019-03-01T00:43:09.000+0000</td><td>1.0</td><td>21.25</td><td>2.0</td><td>N</td><td>132</td><td>239</td><td>1</td><td>52.0</td><td>0.0</td><td>0.5</td><td>15.26</td><td>5.76</td><td>0.3</td><td>76.32</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:58:31.000+0000</td><td>2019-03-01T01:06:46.000+0000</td><td>1.0</td><td>2.0</td><td>1.0</td><td>N</td><td>79</td><td>100</td><td>2</td><td>8.5</td><td>3.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>12.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:08:29.000+0000</td><td>2019-03-01T00:17:24.000+0000</td><td>1.0</td><td>1.66</td><td>1.0</td><td>N</td><td>144</td><td>231</td><td>1</td><td>8.0</td><td>0.5</td><td>0.5</td><td>2.36</td><td>0.0</td><td>0.3</td><td>14.16</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:19:57.000+0000</td><td>2019-03-01T00:27:41.000+0000</td><td>1.0</td><td>0.95</td><td>1.0</td><td>N</td><td>231</td><td>87</td><td>1</td><td>6.5</td><td>0.5</td><td>0.5</td><td>2.06</td><td>0.0</td><td>0.3</td><td>12.36</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:42:05.000+0000</td><td>2019-03-01T01:00:24.000+0000</td><td>1.0</td><td>4.63</td><td>1.0</td><td>N</td><td>148</td><td>263</td><td>1</td><td>16.5</td><td>0.5</td><td>0.5</td><td>5.08</td><td>0.0</td><td>0.3</td><td>25.38</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:13:54.000+0000</td><td>2019-03-01T00:22:19.000+0000</td><td>5.0</td><td>1.58</td><td>1.0</td><td>N</td><td>163</td><td>234</td><td>1</td><td>7.5</td><td>0.5</td><td>0.5</td><td>2.26</td><td>0.0</td><td>0.3</td><td>13.56</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-02-28T23:57:30.000+0000</td><td>2019-03-01T00:06:39.000+0000</td><td>1.0</td><td>2.65</td><td>1.0</td><td>N</td><td>230</td><td>238</td><td>1</td><td>10.0</td><td>0.5</td><td>0.5</td><td>3.5</td><td>0.0</td><td>0.3</td><td>17.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:37:13.000+0000</td><td>2019-03-01T00:59:11.000+0000</td><td>3.0</td><td>5.76</td><td>1.0</td><td>N</td><td>48</td><td>116</td><td>2</td><td>19.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>23.3</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:33:41.000+0000</td><td>2019-03-01T00:47:10.000+0000</td><td>1.0</td><td>3.6</td><td>1.0</td><td>N</td><td>170</td><td>7</td><td>1</td><td>13.5</td><td>3.0</td><td>0.5</td><td>3.0</td><td>0.0</td><td>0.3</td><td>20.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:03:59.000+0000</td><td>2019-03-01T00:16:19.000+0000</td><td>1.0</td><td>3.58</td><td>1.0</td><td>N</td><td>230</td><td>145</td><td>1</td><td>13.0</td><td>0.5</td><td>0.5</td><td>3.36</td><td>0.0</td><td>0.3</td><td>20.16</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:20:12.000+0000</td><td>2019-03-01T00:27:36.000+0000</td><td>2.0</td><td>1.51</td><td>1.0</td><td>N</td><td>145</td><td>112</td><td>1</td><td>7.5</td><td>0.5</td><td>0.5</td><td>1.76</td><td>0.0</td><td>0.3</td><td>10.56</td><td>0.0</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:49:35.000+0000</td><td>2019-03-02T00:27:36.000+0000</td><td>2.0</td><td>0.35</td><td>1.0</td><td>N</td><td>79</td><td>79</td><td>2</td><td>5.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>9.3</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:17:30.000+0000</td><td>2019-03-01T00:45:27.000+0000</td><td>0.0</td><td>12.1</td><td>1.0</td><td>N</td><td>132</td><td>196</td><td>2</td><td>37.0</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>38.3</td><td>0.0</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:28:38.000+0000</td><td>2019-03-01T00:32:47.000+0000</td><td>5.0</td><td>0.69</td><td>1.0</td><td>N</td><td>234</td><td>224</td><td>1</td><td>5.0</td><td>0.5</td><td>0.5</td><td>1.76</td><td>0.0</td><td>0.3</td><td>10.56</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:21:02.000+0000</td><td>2019-03-01T00:25:12.000+0000</td><td>1.0</td><td>0.6</td><td>1.0</td><td>N</td><td>249</td><td>211</td><td>1</td><td>4.5</td><td>3.0</td><td>0.5</td><td>1.65</td><td>0.0</td><td>0.3</td><td>9.95</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:13:21.000+0000</td><td>2019-03-01T00:26:59.000+0000</td><td>1.0</td><td>2.4</td><td>1.0</td><td>N</td><td>237</td><td>113</td><td>1</td><td>11.5</td><td>3.0</td><td>0.5</td><td>3.05</td><td>0.0</td><td>0.3</td><td>18.35</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:50:47.000+0000</td><td>2019-03-01T00:56:29.000+0000</td><td>1.0</td><td>1.0</td><td>1.0</td><td>N</td><td>164</td><td>233</td><td>1</td><td>6.0</td><td>3.0</td><td>0.5</td><td>1.0</td><td>0.0</td><td>0.3</td><td>10.8</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:45:27.000+0000</td><td>2019-03-01T01:01:42.000+0000</td><td>1.0</td><td>7.2</td><td>1.0</td><td>N</td><td>138</td><td>255</td><td>1</td><td>22.0</td><td>0.5</td><td>0.5</td><td>4.65</td><td>0.0</td><td>0.3</td><td>27.95</td><td>0.0</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:12:34.000+0000</td><td>2019-03-01T00:20:36.000+0000</td><td>1.0</td><td>1.5</td><td>1.0</td><td>N</td><td>141</td><td>170</td><td>1</td><td>7.5</td><td>3.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>11.3</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:23:52.000+0000</td><td>2019-03-01T00:57:47.000+0000</td><td>1.0</td><td>5.8</td><td>1.0</td><td>N</td><td>170</td><td>25</td><td>1</td><td>24.0</td><td>3.0</td><td>0.5</td><td>5.0</td><td>0.0</td><td>0.3</td><td>32.8</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:49:34.000+0000</td><td>2019-03-01T00:57:49.000+0000</td><td>1.0</td><td>2.5</td><td>1.0</td><td>N</td><td>164</td><td>236</td><td>1</td><td>9.0</td><td>3.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>12.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:25:38.000+0000</td><td>2019-03-01T00:37:01.000+0000</td><td>3.0</td><td>1.75</td><td>1.0</td><td>N</td><td>234</td><td>68</td><td>1</td><td>9.5</td><td>0.5</td><td>0.5</td><td>3.33</td><td>0.0</td><td>0.3</td><td>16.63</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:49:25.000+0000</td><td>2019-03-01T00:52:15.000+0000</td><td>2.0</td><td>1.03</td><td>1.0</td><td>N</td><td>249</td><td>90</td><td>1</td><td>5.0</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>8.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:16:49.000+0000</td><td>2019-03-01T00:19:52.000+0000</td><td>2.0</td><td>0.91</td><td>1.0</td><td>N</td><td>142</td><td>48</td><td>1</td><td>4.5</td><td>0.5</td><td>0.5</td><td>1.66</td><td>0.0</td><td>0.3</td><td>9.96</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:27:23.000+0000</td><td>2019-03-01T00:41:58.000+0000</td><td>2.0</td><td>3.24</td><td>1.0</td><td>N</td><td>100</td><td>148</td><td>1</td><td>13.0</td><td>0.5</td><td>0.5</td><td>3.36</td><td>0.0</td><td>0.3</td><td>20.16</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:51:31.000+0000</td><td>2019-03-01T00:58:27.000+0000</td><td>2.0</td><td>1.49</td><td>1.0</td><td>N</td><td>148</td><td>107</td><td>1</td><td>7.0</td><td>0.5</td><td>0.5</td><td>2.16</td><td>0.0</td><td>0.3</td><td>12.96</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:12:06.000+0000</td><td>2019-03-01T00:57:41.000+0000</td><td>2.0</td><td>3.53</td><td>1.0</td><td>N</td><td>170</td><td>87</td><td>1</td><td>27.0</td><td>0.5</td><td>0.5</td><td>2.0</td><td>0.0</td><td>0.3</td><td>32.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:39:18.000+0000</td><td>2019-03-01T00:56:02.000+0000</td><td>1.0</td><td>9.91</td><td>1.0</td><td>N</td><td>138</td><td>4</td><td>1</td><td>28.0</td><td>0.5</td><td>0.5</td><td>7.95</td><td>0.0</td><td>0.3</td><td>39.75</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:09:45.000+0000</td><td>2019-03-01T00:18:45.000+0000</td><td>1.0</td><td>3.7</td><td>1.0</td><td>N</td><td>137</td><td>75</td><td>1</td><td>12.0</td><td>3.0</td><td>0.5</td><td>2.37</td><td>0.0</td><td>0.3</td><td>18.17</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:05:49.000+0000</td><td>2019-03-01T00:16:56.000+0000</td><td>1.0</td><td>1.6</td><td>1.0</td><td>N</td><td>158</td><td>79</td><td>1</td><td>9.0</td><td>3.0</td><td>0.5</td><td>1.0</td><td>0.0</td><td>0.3</td><td>13.8</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:25:50.000+0000</td><td>2019-03-01T00:29:35.000+0000</td><td>1.0</td><td>0.7</td><td>1.0</td><td>N</td><td>148</td><td>148</td><td>4</td><td>4.5</td><td>3.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>8.3</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:13:35.000+0000</td><td>2019-03-01T00:19:39.000+0000</td><td>1.0</td><td>0.4</td><td>1.0</td><td>N</td><td>79</td><td>79</td><td>1</td><td>5.5</td><td>3.0</td><td>0.5</td><td>1.85</td><td>0.0</td><td>0.3</td><td>11.15</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:00:08.000+0000</td><td>2019-03-01T00:07:30.000+0000</td><td>1.0</td><td>0.8</td><td>1.0</td><td>N</td><td>162</td><td>170</td><td>2</td><td>6.0</td><td>3.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>9.8</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:10:09.000+0000</td><td>2019-03-01T00:18:10.000+0000</td><td>0.0</td><td>1.6</td><td>1.0</td><td>N</td><td>107</td><td>158</td><td>1</td><td>7.5</td><td>3.0</td><td>0.5</td><td>2.25</td><td>0.0</td><td>0.3</td><td>13.55</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:29:13.000+0000</td><td>2019-03-01T00:52:25.000+0000</td><td>1.0</td><td>4.7</td><td>1.0</td><td>N</td><td>48</td><td>226</td><td>1</td><td>16.5</td><td>3.0</td><td>0.5</td><td>2.0</td><td>0.0</td><td>0.3</td><td>22.3</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:07:22.000+0000</td><td>2019-03-01T00:24:51.000+0000</td><td>1.0</td><td>9.6</td><td>1.0</td><td>N</td><td>138</td><td>33</td><td>1</td><td>27.5</td><td>0.5</td><td>0.5</td><td>5.75</td><td>0.0</td><td>0.3</td><td>34.55</td><td>0.0</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:40:19.000+0000</td><td>2019-03-01T00:52:46.000+0000</td><td>1.0</td><td>3.1</td><td>1.0</td><td>N</td><td>231</td><td>162</td><td>1</td><td>11.5</td><td>3.0</td><td>0.5</td><td>1.0</td><td>0.0</td><td>0.3</td><td>16.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:30:57.000+0000</td><td>2019-03-01T00:44:16.000+0000</td><td>3.0</td><td>0.77</td><td>1.0</td><td>N</td><td>79</td><td>148</td><td>2</td><td>9.0</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>12.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:45:13.000+0000</td><td>2019-03-01T01:08:47.000+0000</td><td>2.0</td><td>10.6</td><td>1.0</td><td>N</td><td>148</td><td>14</td><td>2</td><td>31.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>35.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-02-28T23:56:36.000+0000</td><td>2019-03-01T00:23:08.000+0000</td><td>1.0</td><td>5.74</td><td>1.0</td><td>N</td><td>230</td><td>83</td><td>2</td><td>22.0</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>25.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:40:46.000+0000</td><td>2019-03-01T00:47:55.000+0000</td><td>1.0</td><td>1.57</td><td>1.0</td><td>N</td><td>87</td><td>144</td><td>1</td><td>7.0</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>10.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:56:58.000+0000</td><td>2019-03-01T01:00:23.000+0000</td><td>1.0</td><td>0.4</td><td>1.0</td><td>N</td><td>79</td><td>79</td><td>2</td><td>4.0</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>7.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:09:57.000+0000</td><td>2019-03-01T00:15:38.000+0000</td><td>2.0</td><td>1.22</td><td>1.0</td><td>N</td><td>107</td><td>79</td><td>1</td><td>6.5</td><td>0.5</td><td>0.5</td><td>2.06</td><td>0.0</td><td>0.3</td><td>12.36</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:25:53.000+0000</td><td>2019-03-01T00:37:45.000+0000</td><td>2.0</td><td>1.85</td><td>1.0</td><td>N</td><td>79</td><td>158</td><td>1</td><td>10.0</td><td>0.5</td><td>0.5</td><td>2.76</td><td>0.0</td><td>0.3</td><td>16.56</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:56:11.000+0000</td><td>2019-03-01T01:20:01.000+0000</td><td>3.0</td><td>5.62</td><td>1.0</td><td>N</td><td>114</td><td>61</td><td>1</td><td>21.0</td><td>0.5</td><td>0.5</td><td>4.96</td><td>0.0</td><td>0.3</td><td>29.76</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:09:10.000+0000</td><td>2019-03-01T00:20:41.000+0000</td><td>2.0</td><td>2.99</td><td>1.0</td><td>N</td><td>237</td><td>113</td><td>1</td><td>11.0</td><td>0.5</td><td>0.5</td><td>1.5</td><td>0.0</td><td>0.3</td><td>16.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:48:57.000+0000</td><td>2019-03-01T00:56:24.000+0000</td><td>1.0</td><td>2.16</td><td>1.0</td><td>N</td><td>148</td><td>256</td><td>1</td><td>9.0</td><td>0.5</td><td>0.5</td><td>1.0</td><td>0.0</td><td>0.3</td><td>13.8</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:07:49.000+0000</td><td>2019-03-01T00:14:30.000+0000</td><td>1.0</td><td>2.1</td><td>1.0</td><td>N</td><td>246</td><td>142</td><td>1</td><td>8.0</td><td>3.0</td><td>0.5</td><td>2.0</td><td>0.0</td><td>0.3</td><td>13.8</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:35:16.000+0000</td><td>2019-03-01T01:01:58.000+0000</td><td>2.0</td><td>5.1</td><td>1.0</td><td>N</td><td>50</td><td>232</td><td>1</td><td>21.0</td><td>3.0</td><td>0.5</td><td>4.95</td><td>0.0</td><td>0.3</td><td>29.75</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:05:13.000+0000</td><td>2019-03-01T00:07:30.000+0000</td><td>3.0</td><td>0.49</td><td>1.0</td><td>N</td><td>164</td><td>186</td><td>1</td><td>4.0</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>7.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:22:15.000+0000</td><td>2019-03-01T00:29:06.000+0000</td><td>4.0</td><td>1.15</td><td>1.0</td><td>N</td><td>100</td><td>234</td><td>1</td><td>6.5</td><td>0.5</td><td>0.5</td><td>2.06</td><td>0.0</td><td>0.3</td><td>12.36</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:33:31.000+0000</td><td>2019-03-01T00:39:55.000+0000</td><td>2.0</td><td>1.51</td><td>1.0</td><td>N</td><td>164</td><td>249</td><td>1</td><td>7.0</td><td>0.5</td><td>0.5</td><td>2.16</td><td>0.0</td><td>0.3</td><td>12.96</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:44:21.000+0000</td><td>2019-03-01T01:10:31.000+0000</td><td>2.0</td><td>9.36</td><td>1.0</td><td>N</td><td>249</td><td>244</td><td>1</td><td>29.5</td><td>0.5</td><td>0.5</td><td>6.66</td><td>0.0</td><td>0.3</td><td>39.96</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:33:43.000+0000</td><td>2019-03-01T00:42:21.000+0000</td><td>1.0</td><td>1.5</td><td>1.0</td><td>N</td><td>164</td><td>233</td><td>1</td><td>8.0</td><td>3.0</td><td>0.5</td><td>2.35</td><td>0.0</td><td>0.3</td><td>14.15</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:05:50.000+0000</td><td>2019-03-01T00:13:57.000+0000</td><td>1.0</td><td>1.56</td><td>1.0</td><td>N</td><td>164</td><td>162</td><td>1</td><td>7.5</td><td>0.5</td><td>0.5</td><td>2.26</td><td>0.0</td><td>0.3</td><td>13.56</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:26:48.000+0000</td><td>2019-03-01T00:39:03.000+0000</td><td>1.0</td><td>2.47</td><td>1.0</td><td>N</td><td>161</td><td>79</td><td>1</td><td>11.0</td><td>0.5</td><td>0.5</td><td>3.7</td><td>0.0</td><td>0.3</td><td>18.5</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:25:19.000+0000</td><td>2019-03-01T00:46:09.000+0000</td><td>1.0</td><td>4.01</td><td>1.0</td><td>N</td><td>79</td><td>80</td><td>2</td><td>17.0</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>20.8</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:39:19.000+0000</td><td>2019-03-01T00:45:34.000+0000</td><td>1.0</td><td>1.5</td><td>1.0</td><td>N</td><td>229</td><td>164</td><td>1</td><td>7.0</td><td>3.0</td><td>0.5</td><td>1.62</td><td>0.0</td><td>0.3</td><td>12.42</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:06:59.000+0000</td><td>2019-03-01T00:33:06.000+0000</td><td>1.0</td><td>7.02</td><td>1.0</td><td>N</td><td>231</td><td>24</td><td>1</td><td>25.0</td><td>0.5</td><td>0.5</td><td>5.76</td><td>0.0</td><td>0.3</td><td>34.56</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:08:42.000+0000</td><td>2019-03-01T00:24:02.000+0000</td><td>1.0</td><td>4.3</td><td>1.0</td><td>N</td><td>138</td><td>226</td><td>1</td><td>15.5</td><td>0.5</td><td>0.5</td><td>3.35</td><td>0.0</td><td>0.3</td><td>20.15</td><td>0.0</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:14:50.000+0000</td><td>2019-03-01T00:29:27.000+0000</td><td>1.0</td><td>3.61</td><td>1.0</td><td>N</td><td>114</td><td>50</td><td>1</td><td>14.0</td><td>0.5</td><td>0.5</td><td>1.78</td><td>0.0</td><td>0.3</td><td>19.58</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:45:46.000+0000</td><td>2019-03-01T00:51:13.000+0000</td><td>1.0</td><td>0.61</td><td>1.0</td><td>N</td><td>125</td><td>144</td><td>2</td><td>5.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>9.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:22:50.000+0000</td><td>2019-03-01T00:46:57.000+0000</td><td>1.0</td><td>5.83</td><td>1.0</td><td>N</td><td>231</td><td>61</td><td>1</td><td>21.5</td><td>0.5</td><td>0.5</td><td>5.06</td><td>0.0</td><td>0.3</td><td>30.36</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:49:46.000+0000</td><td>2019-03-01T00:53:11.000+0000</td><td>1.0</td><td>0.67</td><td>1.0</td><td>N</td><td>17</td><td>17</td><td>2</td><td>4.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>5.8</td><td>0.0</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:41:11.000+0000</td><td>2019-03-01T00:48:02.000+0000</td><td>0.0</td><td>0.8</td><td>1.0</td><td>N</td><td>90</td><td>68</td><td>1</td><td>6.0</td><td>3.0</td><td>0.5</td><td>1.95</td><td>0.0</td><td>0.3</td><td>11.75</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:50:07.000+0000</td><td>2019-03-01T00:53:44.000+0000</td><td>4.0</td><td>1.1</td><td>1.0</td><td>N</td><td>68</td><td>48</td><td>1</td><td>5.5</td><td>3.0</td><td>0.5</td><td>1.5</td><td>0.0</td><td>0.3</td><td>10.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:28:38.000+0000</td><td>2019-03-01T00:36:11.000+0000</td><td>2.0</td><td>1.42</td><td>1.0</td><td>N</td><td>161</td><td>234</td><td>1</td><td>7.0</td><td>0.5</td><td>0.5</td><td>2.16</td><td>0.0</td><td>0.3</td><td>12.96</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:39:28.000+0000</td><td>2019-03-01T00:43:56.000+0000</td><td>2.0</td><td>0.79</td><td>1.0</td><td>N</td><td>100</td><td>107</td><td>2</td><td>5.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>9.3</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:02:10.000+0000</td><td>2019-03-01T00:12:29.000+0000</td><td>1.0</td><td>2.0</td><td>1.0</td><td>N</td><td>186</td><td>107</td><td>1</td><td>9.0</td><td>3.0</td><td>0.5</td><td>2.55</td><td>0.0</td><td>0.3</td><td>15.35</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:27:22.000+0000</td><td>2019-03-01T00:39:16.000+0000</td><td>1.0</td><td>2.8</td><td>1.0</td><td>N</td><td>234</td><td>239</td><td>2</td><td>11.5</td><td>3.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>15.3</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:59:31.000+0000</td><td>2019-03-01T01:07:15.000+0000</td><td>1.0</td><td>1.8</td><td>1.0</td><td>N</td><td>239</td><td>166</td><td>1</td><td>8.5</td><td>3.0</td><td>0.5</td><td>2.21</td><td>0.0</td><td>0.3</td><td>14.51</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-02-28T23:58:57.000+0000</td><td>2019-03-01T00:12:34.000+0000</td><td>1.0</td><td>2.37</td><td>1.0</td><td>N</td><td>162</td><td>224</td><td>1</td><td>10.5</td><td>0.5</td><td>0.5</td><td>2.86</td><td>0.0</td><td>0.3</td><td>17.16</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:26:07.000+0000</td><td>2019-03-01T00:36:56.000+0000</td><td>5.0</td><td>1.65</td><td>1.0</td><td>N</td><td>140</td><td>163</td><td>2</td><td>9.0</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>12.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:40:29.000+0000</td><td>2019-03-01T01:01:06.000+0000</td><td>5.0</td><td>3.47</td><td>1.0</td><td>N</td><td>230</td><td>148</td><td>1</td><td>15.5</td><td>0.5</td><td>0.5</td><td>3.86</td><td>0.0</td><td>0.3</td><td>23.16</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:29:31.000+0000</td><td>2019-03-01T00:33:12.000+0000</td><td>1.0</td><td>1.38</td><td>1.0</td><td>N</td><td>231</td><td>211</td><td>1</td><td>6.0</td><td>0.5</td><td>0.5</td><td>0.78</td><td>0.0</td><td>0.3</td><td>10.58</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:42:19.000+0000</td><td>2019-03-01T00:48:01.000+0000</td><td>1.0</td><td>0.9</td><td>1.0</td><td>N</td><td>114</td><td>158</td><td>1</td><td>5.5</td><td>0.5</td><td>0.5</td><td>1.86</td><td>0.0</td><td>0.3</td><td>11.16</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:12:48.000+0000</td><td>2019-03-01T00:16:32.000+0000</td><td>3.0</td><td>0.64</td><td>1.0</td><td>N</td><td>140</td><td>229</td><td>2</td><td>4.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>8.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:34:06.000+0000</td><td>2019-03-01T01:18:12.000+0000</td><td>2.0</td><td>7.28</td><td>1.0</td><td>N</td><td>79</td><td>36</td><td>1</td><td>30.0</td><td>0.5</td><td>0.5</td><td>10.14</td><td>0.0</td><td>0.3</td><td>43.94</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:25:43.000+0000</td><td>2019-03-01T00:31:26.000+0000</td><td>2.0</td><td>0.69</td><td>1.0</td><td>N</td><td>107</td><td>113</td><td>2</td><td>5.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>9.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:37:42.000+0000</td><td>2019-03-01T00:43:10.000+0000</td><td>2.0</td><td>1.37</td><td>1.0</td><td>N</td><td>211</td><td>13</td><td>1</td><td>6.5</td><td>0.5</td><td>0.5</td><td>1.0</td><td>0.0</td><td>0.3</td><td>11.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:50:05.000+0000</td><td>2019-03-01T00:54:20.000+0000</td><td>2.0</td><td>0.77</td><td>1.0</td><td>N</td><td>13</td><td>231</td><td>1</td><td>5.0</td><td>0.5</td><td>0.5</td><td>2.2</td><td>0.0</td><td>0.3</td><td>11.0</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:03:34.000+0000</td><td>2019-03-01T00:12:27.000+0000</td><td>1.0</td><td>1.5</td><td>1.0</td><td>N</td><td>230</td><td>68</td><td>1</td><td>8.0</td><td>3.0</td><td>0.5</td><td>2.95</td><td>0.0</td><td>0.3</td><td>14.75</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:50:43.000+0000</td><td>2019-03-01T01:00:26.000+0000</td><td>1.0</td><td>2.5</td><td>1.0</td><td>N</td><td>186</td><td>144</td><td>2</td><td>9.5</td><td>3.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>13.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:12:40.000+0000</td><td>2019-03-01T00:16:48.000+0000</td><td>1.0</td><td>0.66</td><td>1.0</td><td>N</td><td>230</td><td>162</td><td>2</td><td>5.0</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>8.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:36:20.000+0000</td><td>2019-03-01T00:41:32.000+0000</td><td>1.0</td><td>0.88</td><td>1.0</td><td>N</td><td>249</td><td>90</td><td>2</td><td>5.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>9.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:47:15.000+0000</td><td>2019-03-01T00:51:11.000+0000</td><td>1.0</td><td>0.65</td><td>1.0</td><td>N</td><td>249</td><td>90</td><td>1</td><td>4.5</td><td>0.5</td><td>0.5</td><td>1.24</td><td>0.0</td><td>0.3</td><td>9.54</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:55:25.000+0000</td><td>2019-03-01T01:24:15.000+0000</td><td>1.0</td><td>6.51</td><td>1.0</td><td>N</td><td>249</td><td>231</td><td>1</td><td>24.5</td><td>0.5</td><td>0.5</td><td>5.66</td><td>0.0</td><td>0.3</td><td>33.96</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:31:04.000+0000</td><td>2019-03-01T00:51:38.000+0000</td><td>1.0</td><td>4.7</td><td>1.0</td><td>N</td><td>230</td><td>7</td><td>1</td><td>18.0</td><td>3.0</td><td>0.5</td><td>4.36</td><td>0.0</td><td>0.3</td><td>26.16</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:00:49.000+0000</td><td>2019-03-01T00:05:37.000+0000</td><td>2.0</td><td>0.94</td><td>1.0</td><td>N</td><td>164</td><td>163</td><td>1</td><td>5.5</td><td>0.5</td><td>0.5</td><td>2.32</td><td>0.0</td><td>0.3</td><td>11.62</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:30:35.000+0000</td><td>2019-03-01T00:35:52.000+0000</td><td>2.0</td><td>1.69</td><td>1.0</td><td>N</td><td>50</td><td>68</td><td>2</td><td>7.0</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>10.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:14:13.000+0000</td><td>2019-03-01T00:23:07.000+0000</td><td>2.0</td><td>2.32</td><td>1.0</td><td>N</td><td>255</td><td>232</td><td>1</td><td>10.0</td><td>0.5</td><td>0.5</td><td>2.76</td><td>0.0</td><td>0.3</td><td>16.56</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:34:11.000+0000</td><td>2019-03-01T00:47:24.000+0000</td><td>2.0</td><td>2.99</td><td>1.0</td><td>N</td><td>4</td><td>161</td><td>1</td><td>12.0</td><td>0.5</td><td>0.5</td><td>3.16</td><td>0.0</td><td>0.3</td><td>18.96</td><td>2.5</td><td>null</td></tr><tr><td>4</td><td>2019-03-01T00:35:38.000+0000</td><td>2019-03-01T00:48:45.000+0000</td><td>1.0</td><td>1.58</td><td>1.0</td><td>N</td><td>158</td><td>79</td><td>1</td><td>9.5</td><td>0.5</td><td>0.5</td><td>2.66</td><td>0.0</td><td>0.3</td><td>15.96</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-02-28T23:54:50.000+0000</td><td>2019-03-01T00:14:04.000+0000</td><td>2.0</td><td>5.17</td><td>1.0</td><td>N</td><td>249</td><td>263</td><td>1</td><td>17.5</td><td>0.5</td><td>0.5</td><td>4.26</td><td>0.0</td><td>0.3</td><td>25.56</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:31:03.000+0000</td><td>2019-03-01T00:42:40.000+0000</td><td>1.0</td><td>2.83</td><td>1.0</td><td>N</td><td>237</td><td>238</td><td>2</td><td>11.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>15.3</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:27:39.000+0000</td><td>2019-03-01T00:41:48.000+0000</td><td>1.0</td><td>2.8</td><td>1.0</td><td>N</td><td>100</td><td>140</td><td>1</td><td>12.0</td><td>3.0</td><td>0.5</td><td>1.0</td><td>0.0</td><td>0.3</td><td>16.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:02:21.000+0000</td><td>2019-03-01T00:05:46.000+0000</td><td>1.0</td><td>0.56</td><td>1.0</td><td>N</td><td>162</td><td>237</td><td>1</td><td>4.5</td><td>0.5</td><td>0.5</td><td>1.0</td><td>0.0</td><td>0.3</td><td>9.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:19:18.000+0000</td><td>2019-03-01T00:36:38.000+0000</td><td>2.0</td><td>1.6</td><td>1.0</td><td>N</td><td>163</td><td>48</td><td>1</td><td>12.0</td><td>0.5</td><td>0.5</td><td>3.16</td><td>0.0</td><td>0.3</td><td>18.96</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:40:37.000+0000</td><td>2019-03-01T00:50:48.000+0000</td><td>2.0</td><td>2.85</td><td>1.0</td><td>N</td><td>48</td><td>236</td><td>1</td><td>10.5</td><td>0.5</td><td>0.5</td><td>3.58</td><td>0.0</td><td>0.3</td><td>17.88</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:11:41.000+0000</td><td>2019-03-01T00:22:37.000+0000</td><td>6.0</td><td>3.48</td><td>1.0</td><td>N</td><td>144</td><td>54</td><td>1</td><td>12.0</td><td>0.5</td><td>0.5</td><td>3.16</td><td>0.0</td><td>0.3</td><td>18.96</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:11:41.000+0000</td><td>2019-03-01T00:13:25.000+0000</td><td>6.0</td><td>0.38</td><td>1.0</td><td>N</td><td>164</td><td>186</td><td>2</td><td>3.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>7.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:23:05.000+0000</td><td>2019-03-01T00:33:42.000+0000</td><td>6.0</td><td>1.74</td><td>1.0</td><td>N</td><td>186</td><td>107</td><td>1</td><td>9.0</td><td>0.5</td><td>0.5</td><td>2.56</td><td>0.0</td><td>0.3</td><td>15.36</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:25:59.000+0000</td><td>2019-03-01T01:19:28.000+0000</td><td>2.0</td><td>0.0</td><td>1.0</td><td>N</td><td>17</td><td>17</td><td>2</td><td>0.0</td><td>0.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>0.8</td><td>0.0</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:25:59.000+0000</td><td>2019-03-01T01:29:31.000+0000</td><td>1.0</td><td>1.72</td><td>1.0</td><td>N</td><td>17</td><td>68</td><td>1</td><td>9.0</td><td>0.5</td><td>0.5</td><td>2.0</td><td>0.0</td><td>0.3</td><td>14.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-02-28T23:59:35.000+0000</td><td>2019-03-01T00:26:23.000+0000</td><td>1.0</td><td>8.3</td><td>1.0</td><td>N</td><td>100</td><td>147</td><td>1</td><td>28.0</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>31.8</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:02:10.000+0000</td><td>2019-03-01T00:05:18.000+0000</td><td>1.0</td><td>0.7</td><td>1.0</td><td>N</td><td>50</td><td>143</td><td>1</td><td>5.0</td><td>3.0</td><td>0.5</td><td>1.75</td><td>0.0</td><td>0.3</td><td>10.55</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:20:37.000+0000</td><td>2019-03-01T00:27:29.000+0000</td><td>1.0</td><td>1.1</td><td>1.0</td><td>N</td><td>163</td><td>100</td><td>2</td><td>6.5</td><td>3.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>10.3</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:34:21.000+0000</td><td>2019-03-01T00:38:39.000+0000</td><td>1.0</td><td>0.6</td><td>1.0</td><td>N</td><td>90</td><td>249</td><td>2</td><td>4.5</td><td>3.0</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>8.3</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:49:19.000+0000</td><td>2019-03-01T01:00:42.000+0000</td><td>1.0</td><td>1.6</td><td>1.0</td><td>N</td><td>114</td><td>209</td><td>1</td><td>9.0</td><td>3.0</td><td>0.5</td><td>3.8</td><td>0.0</td><td>0.3</td><td>16.6</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:54:28.000+0000</td><td>2019-03-01T01:04:24.000+0000</td><td>2.0</td><td>2.2</td><td>1.0</td><td>N</td><td>211</td><td>164</td><td>1</td><td>9.5</td><td>3.0</td><td>0.5</td><td>2.0</td><td>0.0</td><td>0.3</td><td>15.3</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:01:00.000+0000</td><td>2019-03-01T00:01:05.000+0000</td><td>1.0</td><td>0.0</td><td>1.0</td><td>N</td><td>132</td><td>132</td><td>3</td><td>2.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>3.8</td><td>0.0</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:01:26.000+0000</td><td>2019-03-01T00:44:44.000+0000</td><td>1.0</td><td>23.7</td><td>1.0</td><td>N</td><td>132</td><td>241</td><td>1</td><td>66.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>5.76</td><td>0.3</td><td>73.56</td><td>0.0</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:29:38.000+0000</td><td>2019-03-01T00:35:36.000+0000</td><td>1.0</td><td>1.35</td><td>1.0</td><td>N</td><td>246</td><td>90</td><td>1</td><td>6.5</td><td>0.5</td><td>0.5</td><td>2.58</td><td>0.0</td><td>0.3</td><td>12.88</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:38:45.000+0000</td><td>2019-03-01T00:47:16.000+0000</td><td>1.0</td><td>1.54</td><td>1.0</td><td>N</td><td>234</td><td>170</td><td>1</td><td>8.0</td><td>0.5</td><td>0.5</td><td>2.36</td><td>0.0</td><td>0.3</td><td>14.16</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:41:43.000+0000</td><td>2019-03-01T00:46:54.000+0000</td><td>1.0</td><td>0.86</td><td>1.0</td><td>N</td><td>234</td><td>90</td><td>1</td><td>5.5</td><td>0.5</td><td>0.5</td><td>1.86</td><td>0.0</td><td>0.3</td><td>11.16</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-02-28T23:55:18.000+0000</td><td>2019-03-01T00:13:32.000+0000</td><td>1.0</td><td>4.2</td><td>1.0</td><td>N</td><td>107</td><td>236</td><td>1</td><td>15.5</td><td>0.5</td><td>0.5</td><td>3.86</td><td>0.0</td><td>0.3</td><td>23.16</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:46:40.000+0000</td><td>2019-03-01T00:53:43.000+0000</td><td>1.0</td><td>1.43</td><td>1.0</td><td>N</td><td>234</td><td>162</td><td>1</td><td>7.0</td><td>0.5</td><td>0.5</td><td>2.16</td><td>0.0</td><td>0.3</td><td>12.96</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:04:39.000+0000</td><td>2019-03-01T00:11:56.000+0000</td><td>1.0</td><td>1.34</td><td>1.0</td><td>N</td><td>144</td><td>158</td><td>1</td><td>7.0</td><td>0.5</td><td>0.5</td><td>2.7</td><td>0.0</td><td>0.3</td><td>13.5</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:13:19.000+0000</td><td>2019-03-01T00:22:45.000+0000</td><td>1.0</td><td>2.02</td><td>1.0</td><td>N</td><td>158</td><td>13</td><td>1</td><td>9.0</td><td>0.5</td><td>0.5</td><td>1.0</td><td>0.0</td><td>0.3</td><td>13.8</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:25:37.000+0000</td><td>2019-03-01T00:39:16.000+0000</td><td>1.0</td><td>4.1</td><td>1.0</td><td>N</td><td>13</td><td>48</td><td>1</td><td>14.0</td><td>0.5</td><td>0.5</td><td>3.56</td><td>0.0</td><td>0.3</td><td>21.36</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:20:12.000+0000</td><td>2019-03-01T00:28:28.000+0000</td><td>1.0</td><td>0.43</td><td>1.0</td><td>N</td><td>211</td><td>148</td><td>2</td><td>6.5</td><td>0.5</td><td>0.5</td><td>0.0</td><td>0.0</td><td>0.3</td><td>10.3</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:09:43.000+0000</td><td>2019-03-01T00:21:30.000+0000</td><td>1.0</td><td>2.06</td><td>1.0</td><td>N</td><td>230</td><td>249</td><td>1</td><td>10.0</td><td>0.5</td><td>0.5</td><td>2.76</td><td>0.0</td><td>0.3</td><td>16.56</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:09:36.000+0000</td><td>2019-03-01T00:16:36.000+0000</td><td>1.0</td><td>1.64</td><td>1.0</td><td>N</td><td>164</td><td>48</td><td>1</td><td>7.0</td><td>0.5</td><td>0.5</td><td>2.16</td><td>0.0</td><td>0.3</td><td>12.96</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:35:06.000+0000</td><td>2019-03-01T00:44:18.000+0000</td><td>1.0</td><td>1.78</td><td>1.0</td><td>N</td><td>238</td><td>236</td><td>1</td><td>8.5</td><td>0.5</td><td>0.5</td><td>2.46</td><td>0.0</td><td>0.3</td><td>14.76</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:09:11.000+0000</td><td>2019-03-01T00:15:59.000+0000</td><td>5.0</td><td>1.25</td><td>1.0</td><td>N</td><td>161</td><td>48</td><td>1</td><td>7.0</td><td>0.5</td><td>0.5</td><td>2.16</td><td>0.0</td><td>0.3</td><td>12.96</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:36:02.000+0000</td><td>2019-03-01T00:42:36.000+0000</td><td>5.0</td><td>1.36</td><td>1.0</td><td>N</td><td>100</td><td>229</td><td>1</td><td>7.0</td><td>0.5</td><td>0.5</td><td>2.16</td><td>0.0</td><td>0.3</td><td>12.96</td><td>2.5</td><td>null</td></tr><tr><td>2</td><td>2019-03-01T00:54:43.000+0000</td><td>2019-03-01T01:02:27.000+0000</td><td>5.0</td><td>1.84</td><td>1.0</td><td>N</td><td>100</td><td>68</td><td>1</td><td>8.0</td><td>0.5</td><td>0.5</td><td>2.36</td><td>0.0</td><td>0.3</td><td>14.16</td><td>2.5</td><td>null</td></tr><tr><td>1</td><td>2019-03-01T00:01:29.000+0000</td><td>2019-03-01T00:10:14.000+0000</td><td>1.0</td><td>1.7</td><td>1.0</td><td>N</td><td>142</td><td>68</td><td>1</td><td>8.0</td><td>3.0</td><td>0.5</td><td>1.5</td><td>0.0</td><td>0.3</td><td>13.3</td><td>2.5</td><td>null</td></tr></tbody></table>Showing the first 1000 rows.</div>"]}}],"execution_count":0},{"cell_type":"code","source":["sdf.printSchema()"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"96f06277-8601-4a9b-8082-d8da4d609853"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">root\n |-- VendorID: long (nullable = true)\n |-- tpep_pickup_datetime: timestamp (nullable = true)\n |-- tpep_dropoff_datetime: timestamp (nullable = true)\n |-- passenger_count: double (nullable = true)\n |-- trip_distance: double (nullable = true)\n |-- RatecodeID: double (nullable = true)\n |-- store_and_fwd_flag: string (nullable = true)\n |-- PULocationID: long (nullable = true)\n |-- DOLocationID: long (nullable = true)\n |-- payment_type: long (nullable = true)\n |-- fare_amount: double (nullable = true)\n |-- extra: double (nullable = true)\n |-- mta_tax: double (nullable = true)\n |-- tip_amount: double (nullable = true)\n |-- tolls_amount: double (nullable = true)\n |-- improvement_surcharge: double (nullable = true)\n |-- total_amount: double (nullable = true)\n |-- congestion_surcharge: double (nullable = true)\n |-- airport_fee: integer (nullable = true)\n\n</div>","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">root\n-- VendorID: long (nullable = true)\n-- tpep_pickup_datetime: timestamp (nullable = true)\n-- tpep_dropoff_datetime: timestamp (nullable = true)\n-- passenger_count: double (nullable = true)\n-- trip_distance: double (nullable = true)\n-- RatecodeID: double (nullable = true)\n-- store_and_fwd_flag: string (nullable = true)\n-- PULocationID: long (nullable = true)\n-- DOLocationID: long (nullable = true)\n-- payment_type: long (nullable = true)\n-- fare_amount: double (nullable = true)\n-- extra: double (nullable = true)\n-- mta_tax: double (nullable = true)\n-- tip_amount: double (nullable = true)\n-- tolls_amount: double (nullable = true)\n-- improvement_surcharge: double (nullable = true)\n-- total_amount: double (nullable = true)\n-- congestion_surcharge: double (nullable = true)\n-- airport_fee: integer (nullable = true)\n\n</div>"]}}],"execution_count":0},{"cell_type":"markdown","source":["### clean data"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"16143d0e-b8d4-41bd-8bc4-055377687012"}}},{"cell_type":"code","source":["from pyspark.sql import functions as F\n\nsdf_clean = sdf.select(\"VendorID\", \"trip_distance\", \"passenger_count\", \"RatecodeID\", \"payment_type\", \"PULocationID\", \"DOLocationID\", \"store_and_fwd_flag\", \"fare_amount\") \\\n    .filter(F.col(\"trip_distance\")>0) \\\n    .filter(F.col(\"passenger_count\").isNotNull()) \\\n    .filter(F.col(\"fare_amount\")>0) \\\n    .filter(F.col(\"fare_amount\")<50000)\n\ndisplay(sdf_clean)"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"9428364b-61bf-42e3-8b90-277972d249b2"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"overflow":true,"datasetInfos":[],"data":[[3.7,2.0,1.0,1,95,130,13.0],[14.1,1.0,1.0,1,249,28,41.0],[9.6,1.0,1.0,2,138,98,27.0],[0.8,1.0,1.0,1,48,48,5.5],[1.2,1.0,1.0,2,246,48,6.0],[0.6,1.0,1.0,2,239,238,5.5],[5.65,1.0,1.0,2,132,197,17.0],[1.16,1.0,1.0,1,229,170,6.0],[0.71,1.0,1.0,1,137,234,5.0],[2.63,5.0,1.0,1,231,246,10.5],[5.0,1.0,1.0,3,230,7,20.5],[14.04,2.0,1.0,1,170,123,45.5],[2.0,1.0,1.0,1,143,263,9.0],[2.11,5.0,1.0,1,229,236,8.0],[6.8,1.0,1.0,2,162,49,31.5],[5.97,1.0,1.0,2,152,153,19.0],[3.4,1.0,1.0,1,162,74,12.0],[16.89,1.0,2.0,1,132,229,52.0],[5.4,1.0,1.0,1,25,246,19.5],[10.6,0.0,1.0,1,138,144,30.0],[1.8,0.0,1.0,1,144,90,8.5],[1.0,1.0,1.0,1,249,125,6.0],[6.1,1.0,1.0,2,137,7,21.5],[4.49,3.0,1.0,1,107,25,18.0],[18.66,4.0,2.0,1,132,68,52.0],[1.67,2.0,1.0,1,107,161,8.0],[3.35,1.0,1.0,1,162,231,14.0],[1.62,1.0,1.0,1,231,88,7.5],[0.1,1.0,1.0,2,161,161,3.0],[4.0,1.0,1.0,1,137,143,14.5],[0.5,2.0,1.0,1,148,79,4.0],[2.5,0.0,1.0,1,68,144,12.5],[1.5,1.0,1.0,2,164,68,8.0],[1.5,1.0,1.0,1,48,237,7.0],[6.4,1.0,1.0,1,142,256,33.5],[10.8,1.0,1.0,1,132,225,32.0],[0.5,1.0,1.0,2,61,61,4.0],[8.7,0.0,1.0,1,144,89,26.5],[1.1,1.0,1.0,1,140,162,5.5],[8.7,1.0,1.0,1,87,14,25.5],[5.8,1.0,1.0,2,237,244,19.0],[0.82,1.0,1.0,1,234,170,5.0],[0.98,1.0,1.0,1,107,114,7.0],[3.68,1.0,1.0,1,79,75,13.0],[19.6,1.0,2.0,1,132,249,52.0],[9.9,1.0,1.0,1,132,177,29.0],[2.5,1.0,1.0,1,41,143,9.5],[1.5,1.0,1.0,1,50,230,8.5],[3.9,1.0,1.0,1,186,88,16.5],[4.7,1.0,1.0,1,237,209,18.0],[5.4,1.0,1.0,1,238,243,17.5],[2.21,2.0,1.0,1,246,50,9.5],[4.43,2.0,1.0,1,48,145,16.0],[1.95,1.0,1.0,1,142,151,7.5],[2.62,1.0,1.0,1,239,74,10.0],[1.4,1.0,1.0,1,100,107,6.0],[0.56,1.0,1.0,1,90,107,4.0],[2.57,1.0,1.0,2,107,163,11.5],[1.48,1.0,1.0,1,48,246,6.5],[1.2,1.0,1.0,1,158,186,5.5],[1.1,2.0,1.0,1,162,140,6.0],[2.4,3.0,1.0,1,45,158,12.0],[3.9,1.0,1.0,1,232,189,16.0],[0.67,1.0,1.0,1,141,236,4.0],[6.61,1.0,1.0,1,79,41,19.5],[2.99,5.0,1.0,1,48,224,14.5],[8.5,1.0,1.0,1,138,141,25.0],[1.7,4.0,1.0,1,50,162,7.5],[4.9,1.0,1.0,1,50,112,18.5],[0.5,1.0,1.0,2,158,114,4.5],[2.6,1.0,1.0,1,211,233,12.0],[10.1,3.0,1.0,1,97,43,34.0],[0.02,1.0,1.0,2,193,193,2.5],[4.7,1.0,1.0,1,90,262,16.5],[2.8,1.0,1.0,1,100,239,12.0],[0.6,1.0,1.0,2,90,186,4.0],[1.4,1.0,1.0,1,163,246,6.0],[5.18,1.0,1.0,1,161,87,26.0],[19.47,1.0,2.0,1,132,114,52.0],[4.64,1.0,1.0,1,231,37,25.0],[0.89,1.0,1.0,2,162,164,6.0],[1.88,1.0,1.0,1,164,114,9.0],[3.7,1.0,1.0,2,107,262,15.0],[2.07,2.0,1.0,1,170,114,10.5],[1.28,2.0,1.0,1,144,79,7.5],[4.44,1.0,1.0,2,229,145,18.0],[7.29,4.0,1.0,1,138,255,22.5],[0.39,1.0,1.0,1,231,125,4.0],[7.65,1.0,1.0,1,163,198,26.0],[0.99,5.0,1.0,1,161,229,6.5],[18.3,1.0,2.0,1,132,246,52.0],[2.8,0.0,1.0,2,142,249,11.0],[0.32,1.0,1.0,2,229,162,4.0],[3.84,1.0,1.0,1,161,151,14.5],[17.8,1.0,2.0,2,132,42,52.0],[14.84,1.0,1.0,1,132,179,40.0],[2.2,1.0,1.0,2,238,152,9.5],[2.0,1.0,1.0,2,116,244,9.0],[10.1,2.0,1.0,1,238,241,33.0],[1.1,1.0,1.0,1,113,158,6.5],[3.5,1.0,1.0,1,163,211,13.0],[2.4,2.0,1.0,1,211,164,9.5],[0.8,1.0,1.0,2,233,230,5.0],[1.34,2.0,1.0,1,113,186,7.0],[2.3,1.0,1.0,2,238,75,9.5],[7.35,3.0,1.0,2,132,205,22.0],[4.45,2.0,1.0,1,246,262,16.0],[0.61,2.0,1.0,1,141,263,4.0],[19.0,1.0,2.0,1,132,141,52.0],[0.5,1.0,1.0,1,162,141,3.5],[1.9,1.0,1.0,1,162,236,7.5],[2.6,2.0,1.0,1,161,79,11.5],[0.6,2.0,1.0,1,114,79,4.0],[0.49,1.0,1.0,2,79,79,4.5],[1.5,2.0,1.0,2,170,50,8.0],[17.6,1.0,2.0,1,132,100,52.0],[4.5,1.0,1.0,1,230,202,16.0],[0.8,1.0,1.0,1,68,246,4.5],[1.8,1.0,1.0,2,90,144,8.0],[3.1,0.0,1.0,1,107,87,14.0],[4.1,0.0,1.0,1,231,48,14.0],[5.4,1.0,1.0,1,158,80,23.0],[17.3,1.0,2.0,1,132,141,52.0],[1.16,1.0,1.0,1,231,261,6.0],[3.21,1.0,1.0,1,114,162,13.0],[7.7,1.0,1.0,1,138,236,24.5],[1.8,2.0,1.0,1,144,13,9.5],[0.5,1.0,1.0,2,181,106,4.5],[4.1,1.0,1.0,2,106,14,14.5],[1.9,2.0,1.0,1,234,148,12.5],[6.1,1.0,1.0,1,87,61,21.0],[1.9,1.0,1.0,1,164,229,8.0],[1.59,1.0,1.0,1,100,48,8.0],[1.06,1.0,1.0,1,88,261,6.5],[1.78,1.0,1.0,1,211,107,8.5],[0.89,1.0,1.0,1,164,234,4.5],[5.29,1.0,1.0,2,246,211,18.5],[1.1,1.0,1.0,1,234,68,8.0],[2.2,1.0,1.0,1,158,48,9.0],[1.5,1.0,1.0,1,142,239,7.5],[3.0,1.0,1.0,2,230,140,13.0],[1.4,1.0,1.0,1,148,107,7.5],[0.7,1.0,1.0,1,79,79,6.0],[1.5,1.0,1.0,1,211,13,8.0],[3.2,1.0,1.0,1,249,239,12.0],[6.24,1.0,1.0,1,132,134,19.0],[0.53,1.0,1.0,1,170,164,4.0],[3.49,1.0,1.0,1,164,236,14.0],[2.04,1.0,1.0,1,163,90,8.5],[14.0,1.0,2.0,1,130,152,52.0],[4.9,2.0,1.0,2,170,179,18.0],[4.4,2.0,1.0,1,249,263,15.0],[1.21,1.0,1.0,2,161,229,6.5],[1.14,1.0,1.0,2,196,95,6.0],[10.3,1.0,1.0,2,132,39,30.5],[2.0,2.0,1.0,1,107,68,11.0],[2.02,1.0,1.0,1,255,232,8.5],[1.25,1.0,1.0,1,79,137,6.0],[6.95,1.0,1.0,1,48,82,25.0],[5.8,1.0,1.0,2,264,87,28.5],[0.5,1.0,1.0,1,162,229,4.5],[2.5,1.0,1.0,1,229,113,10.5],[0.8,1.0,1.0,1,162,229,6.0],[1.0,1.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scoped>\n  .table-result-container {\n    max-height: 300px;\n    overflow: auto;\n  }\n  table, th, td {\n    border: 1px solid black;\n    border-collapse: collapse;\n  }\n  th, td {\n    padding: 5px;\n  }\n  th {\n    text-align: left;\n  }\n</style><div class='table-result-container'><table class='table-result'><thead style='background-color: white'><tr><th>trip_distance</th><th>passenger_count</th><th>RatecodeID</th><th>payment_type</th><th>PULocationID</th><th>DOLocationID</th><th>fare_amount</th></tr></thead><tbody><tr><td>3.7</td><td>2.0</td><td>1.0</td><td>1</td><td>95</td><td>130</td><td>13.0</td></tr><tr><td>14.1</td><td>1.0</td><td>1.0</td><td>1</td><td>249</td><td>28</td><td>41.0</td></tr><tr><td>9.6</td><td>1.0</td><td>1.0</td><td>2</td><td>138</td><td>98</td><td>27.0</td></tr><tr><td>0.8</td><td>1.0</td><td>1.0</td><td>1</td><td>48</td><td>48</td><td>5.5</td></tr><tr><td>1.2</td><td>1.0</td><td>1.0</td><td>2</td><td>246</td><td>48</td><td>6.0</td></tr><tr><td>0.6</td><td>1.0</td><td>1.0</td><td>2</td><td>239</td><td>238</td><td>5.5</td></tr><tr><td>5.65</td><td>1.0</td><td>1.0</td><td>2</td><td>132</td><td>197</td><td>17.0</td></tr><tr><td>1.16</td><td>1.0</td><td>1.0</td><td>1</td><td>229</td><td>170</td><td>6.0</td></tr><tr><td>0.71</td><td>1.0</td><td>1.0</td><td>1</td><td>137</td><td>234</td><td>5.0</td></tr><tr><td>2.63</td><td>5.0</td><td>1.0</td><td>1</td><td>231</td><td>246</td><td>10.5</td></tr><tr><td>5.0</td><td>1.0</td><td>1.0</td><td>3</td><td>230</td><td>7</td><td>20.5</td></tr><tr><td>14.04</td><td>2.0</td><td>1.0</td><td>1</td><td>170</td><td>123</td><td>45.5</td></tr><tr><td>2.0</td><td>1.0</td><td>1.0</td><td>1</td><td>143</td><td>263</td><td>9.0</td></tr><tr><td>2.11</td><td>5.0</td><td>1.0</td><td>1</td><td>229</td><td>236</td><td>8.0</td></tr><tr><td>6.8</td><td>1.0</td><td>1.0</td><td>2</td><td>162</td><td>49</td><td>31.5</td></tr><tr><td>5.97</td><td>1.0</td><td>1.0</td><td>2</td><td>152</td><td>153</td><td>19.0</td></tr><tr><td>3.4</td><td>1.0</td><td>1.0</td><td>1</td><td>162</td><td>74</td><td>12.0</td></tr><tr><td>16.89</td><td>1.0</td><td>2.0</td><td>1</td><td>132</td><td>229</td><td>52.0</td></tr><tr><td>5.4</td><td>1.0</td><td>1.0</td><td>1</td><td>25</td><td>246</td><td>19.5</td></tr><tr><td>10.6</td><td>0.0</td><td>1.0</td><td>1</td><td>138</td><td>144</td><td>30.0</td></tr><tr><td>1.8</td><td>0.0</td><td>1.0</td><td>1</td><td>144</td><td>90</td><td>8.5</td></tr><tr><td>1.0</td><td>1.0</td><td>1.0</td><td>1</td><td>249</td><td>125</td><td>6.0</td></tr><tr><td>6.1</td><td>1.0</td><td>1.0</td><td>2</td><td>137</td><td>7</td><td>21.5</td></tr><tr><td>4.49</td><td>3.0</td><td>1.0</td><td>1</td><td>107</td><td>25</td><td>18.0</td></tr><tr><td>18.66</td><td>4.0</td><td>2.0</td><td>1</td><td>132</td><td>68</td><td>52.0</td></tr><tr><td>1.67</td><td>2.0</td><td>1.0</td><td>1</td><td>107</td><td>161</td><td>8.0</td></tr><tr><td>3.35</td><td>1.0</td><td>1.0</td><td>1</td><td>162</td><td>231</td><td>14.0</td></tr><tr><td>1.62</td><td>1.0</td><td>1.0</td><td>1</td><td>231</td><td>88</td><td>7.5</td></tr><tr><td>0.1</td><td>1.0</td><td>1.0</td><td>2</td><td>161</td><td>161</td><td>3.0</td></tr><tr><td>4.0</td><td>1.0</td><td>1.0</td><td>1</td><td>137</td><td>143</td><td>14.5</td></tr><tr><td>0.5</td><td>2.0</td><td>1.0</td><td>1</td><td>148</td><td>79</td><td>4.0</td></tr><tr><td>2.5</td><td>0.0</td><td>1.0</td><td>1</td><td>68</td><td>144</td><td>12.5</td></tr><tr><td>1.5</td><td>1.0</td><td>1.0</td><td>2</td><td>164</td><td>68</td><td>8.0</td></tr><tr><td>1.5</td><td>1.0</td><td>1.0</td><td>1</td><td>48</td><td>237</td><td>7.0</td></tr><tr><td>6.4</td><td>1.0</td><td>1.0</td><td>1</td><td>142</td><td>256</td><td>33.5</td></tr><tr><td>10.8</td><td>1.0</td><td>1.0</td><td>1</td><td>132</td><td>225</td><td>32.0</td></tr><tr><td>0.5</td><td>1.0</td><td>1.0</td><td>2</td><td>61</td><td>61</td><td>4.0</td></tr><tr><td>8.7</td><td>0.0</td><td>1.0</td><td>1</td><td>144</td><td>89</td><td>26.5</td></tr><tr><td>1.1</td><td>1.0</td><td>1.0</td><td>1</td><td>140</td><td>162</td><td>5.5</td></tr><tr><td>8.7</td><td>1.0</td><td>1.0</td><td>1</td><td>87</td><td>14</td><td>25.5</td></tr><tr><td>5.8</td><td>1.0</td><td>1.0</td><td>2</td><td>237</td><td>244</td><td>19.0</td></tr><tr><td>0.82</td><td>1.0</td><td>1.0</td><td>1</td><td>234</td><td>170</td><td>5.0</td></tr><tr><td>0.98</td><td>1.0</td><td>1.0</td><td>1</td><td>107</td><td>114</td><td>7.0</td></tr><tr><td>3.68</td><td>1.0</td><td>1.0</td><td>1</td><td>79</td><td>75</td><td>13.0</td></tr><tr><td>19.6</td><td>1.0</td><td>2.0</td><td>1</td><td>132</td><td>249</td><td>52.0</td></tr><tr><td>9.9</td><td>1.0</td><td>1.0</td><td>1</td><td>132</td><td>177</td><td>29.0</td></tr><tr><td>2.5</td><td>1.0</td><td>1.0</td><td>1</td><td>41</td><td>143</td><td>9.5</td></tr><tr><td>1.5</td><td>1.0</td><td>1.0</td><td>1</td><td>50</td><td>230</td><td>8.5</td></tr><tr><td>3.9</td><td>1.0</td><td>1.0</td><td>1</td><td>186</td><td>88</td><td>16.5</td></tr><tr><td>4.7</td><td>1.0</td><td>1.0</td><td>1</td><td>237</td><td>209</td><td>18.0</td></tr><tr><td>5.4</td><td>1.0</td><td>1.0</td><td>1</td><td>238</td><td>243</td><td>17.5</td></tr><tr><td>2.21</td><td>2.0</td><td>1.0</td><td>1</td><td>246</td><td>50</td><td>9.5</td></tr><tr><td>4.43</td><td>2.0</td><td>1.0</td><td>1</td><td>48</td><td>145</td><td>16.0</td></tr><tr><td>1.95</td><td>1.0</td><td>1.0</td><td>1</td><td>142</td><td>151</td><td>7.5</td></tr><tr><td>2.62</td><td>1.0</td><td>1.0</td><td>1</td><td>239</td><td>74</td><td>10.0</td></tr><tr><td>1.4</td><td>1.0</td><td>1.0</td><td>1</td><td>100</td><td>107</td><td>6.0</td></tr><tr><td>0.56</td><td>1.0</td><td>1.0</td><td>1</td><td>90</td><td>107</td><td>4.0</td></tr><tr><td>2.57</td><td>1.0</td><td>1.0</td><td>2</td><td>107</td><td>163</td><td>11.5</td></tr><tr><td>1.48</td><td>1.0</td><td>1.0</td><td>1</td><td>48</td><td>246</td><td>6.5</td></tr><tr><td>1.2</td><td>1.0</td><td>1.0</td><td>1</td><td>158</td><td>186</td><td>5.5</td></tr><tr><td>1.1</td><td>2.0</td><td>1.0</td><td>1</td><td>162</td><td>140</td><td>6.0</td></tr><tr><td>2.4</td><td>3.0</td><td>1.0</td><td>1</td><td>45</td><td>158</td><td>12.0</td></tr><tr><td>3.9</td><td>1.0</td><td>1.0</td><td>1</td><td>232</td><td>189</td><td>16.0</td></tr><tr><td>0.67</td><td>1.0</td><td>1.0</td><td>1</td><td>141</td><td>236</td><td>4.0</td></tr><tr><td>6.61</td><td>1.0</td><td>1.0</td><td>1</td><td>79</td><td>41</td><td>19.5</td></tr><tr><td>2.99</td><td>5.0</td><td>1.0</td><td>1</td><td>48</td><td>224</td><td>14.5</td></tr><tr><td>8.5</td><td>1.0</td><td>1.0</td><td>1</td><td>138</td><td>141</td><td>25.0</td></tr><tr><td>1.7</td><td>4.0</td><td>1.0</td><td>1</td><td>50</td><td>162</td><td>7.5</td></tr><tr><td>4.9</td><td>1.0</td><td>1.0</td><td>1</td><td>50</td><td>112</td><td>18.5</td></tr><tr><td>0.5</td><td>1.0</td><td>1.0</td><td>2</td><td>158</td><td>114</td><td>4.5</td></tr><tr><td>2.6</td><td>1.0</td><td>1.0</td><td>1</td><td>211</td><td>233</td><td>12.0</td></tr><tr><td>10.1</td><td>3.0</td><td>1.0</td><td>1</td><td>97</td><td>43</td><td>34.0</td></tr><tr><td>0.02</td><td>1.0</td><td>1.0</td><td>2</td><td>193</td><td>193</td><td>2.5</td></tr><tr><td>4.7</td><td>1.0</td><td>1.0</td><td>1</td><td>90</td><td>262</td><td>16.5</td></tr><tr><td>2.8</td><td>1.0</td><td>1.0</td><td>1</td><td>100</td><td>239</td><td>12.0</td></tr><tr><td>0.6</td><td>1.0</td><td>1.0</td><td>2</td><td>90</td><td>186</td><td>4.0</td></tr><tr><td>1.4</td><td>1.0</td><td>1.0</td><td>1</td><td>163</td><td>246</td><td>6.0</td></tr><tr><td>5.18</td><td>1.0</td><td>1.0</td><td>1</td><td>161</td><td>87</td><td>26.0</td></tr><tr><td>19.47</td><td>1.0</td><td>2.0</td><td>1</td><td>132</td><td>114</td><td>52.0</td></tr><tr><td>4.64</td><td>1.0</td><td>1.0</td><td>1</td><td>231</td><td>37</td><td>25.0</td></tr><tr><td>0.89</td><td>1.0</td><td>1.0</td><td>2</td><td>162</td><td>164</td><td>6.0</td></tr><tr><td>1.88</td><td>1.0</td><td>1.0</td><td>1</td><td>164</td><td>114</td><td>9.0</td></tr><tr><td>3.7</td><td>1.0</td><td>1.0</td><td>2</td><td>107</td><td>262</td><td>15.0</td></tr><tr><td>2.07</td><td>2.0</td><td>1.0</td><td>1</td><td>170</td><td>114</td><td>10.5</td></tr><tr><td>1.28</td><td>2.0</td><td>1.0</td><td>1</td><td>144</td><td>79</td><td>7.5</td></tr><tr><td>4.44</td><td>1.0</td><td>1.0</td><td>2</td><td>229</td><td>145</td><td>18.0</td></tr><tr><td>7.29</td><td>4.0</td><td>1.0</td><td>1</td><td>138</td><td>255</td><td>22.5</td></tr><tr><td>0.39</td><td>1.0</td><td>1.0</td><td>1</td><td>231</td><td>125</td><td>4.0</td></tr><tr><td>7.65</td><td>1.0</td><td>1.0</td><td>1</td><td>163</td><td>198</td><td>26.0</td></tr><tr><td>0.99</td><td>5.0</td><td>1.0</td><td>1</td><td>161</td><td>229</td><td>6.5</td></tr><tr><td>18.3</td><td>1.0</td><td>2.0</td><td>1</td><td>132</td><td>246</td><td>52.0</td></tr><tr><td>2.8</td><td>0.0</td><td>1.0</td><td>2</td><td>142</td><td>249</td><td>11.0</td></tr><tr><td>0.32</td><td>1.0</td><td>1.0</td><td>2</td><td>229</td><td>162</td><td>4.0</td></tr><tr><td>3.84</td><td>1.0</td><td>1.0</td><td>1</td><td>161</td><td>151</td><td>14.5</td></tr><tr><td>17.8</td><td>1.0</td><td>2.0</td><td>2</td><td>132</td><td>42</td><td>52.0</td></tr><tr><td>14.84</td><td>1.0</td><td>1.0</td><td>1</td><td>132</td><td>179</td><td>40.0</td></tr><tr><td>2.2</td><td>1.0</td><td>1.0</td><td>2</td><td>238</td><td>152</td><td>9.5</td></tr><tr><td>2.0</td><td>1.0</td><td>1.0</td><td>2</td><td>116</td><td>244</td><td>9.0</td></tr><tr><td>10.1</td><td>2.0</td><td>1.0</td><td>1</td><td>238</td><td>241</td><td>33.0</td></tr><tr><td>1.1</td><td>1.0</td><td>1.0</td><td>1</td><td>113</td><td>158</td><td>6.5</td></tr><tr><td>3.5</td><td>1.0</td><td>1.0</td><td>1</td><td>163</td><td>211</td><td>13.0</td></tr><tr><td>2.4</td><td>2.0</td><td>1.0</td><td>1</td><td>211</td><td>164</td><td>9.5</td></tr><tr><td>0.8</td><td>1.0</td><td>1.0</td><td>2</td><td>233</td><td>230</td><td>5.0</td></tr><tr><td>1.34</td><td>2.0</td><td>1.0</td><td>1</td><td>113</td><td>186</td><td>7.0</td></tr><tr><td>2.3</td><td>1.0</td><td>1.0</td><td>2</td><td>238</td><td>75</td><td>9.5</td></tr><tr><td>7.35</td><td>3.0</td><td>1.0</td><td>2</td><td>132</td><td>205</td><td>22.0</td></tr><tr><td>4.45</td><td>2.0</td><td>1.0</td><td>1</td><td>246</td><td>262</td><td>16.0</td></tr><tr><td>0.61</td><td>2.0</td><td>1.0</td><td>1</td><td>141</td><td>263</td><td>4.0</td></tr><tr><td>19.0</td><td>1.0</td><td>2.0</td><td>1</td><td>132</td><td>141</td><td>52.0</td></tr><tr><td>0.5</td><td>1.0</td><td>1.0</td><td>1</td><td>162</td><td>141</td><td>3.5</td></tr><tr><td>1.9</td><td>1.0</td><td>1.0</td><td>1</td><td>162</td><td>236</td><td>7.5</td></tr><tr><td>2.6</td><td>2.0</td><td>1.0</td><td>1</td><td>161</td><td>79</td><td>11.5</td></tr><tr><td>0.6</td><td>2.0</td><td>1.0</td><td>1</td><td>114</td><td>79</td><td>4.0</td></tr><tr><td>0.49</td><td>1.0</td><td>1.0</td><td>2</td><td>79</td><td>79</td><td>4.5</td></tr><tr><td>1.5</td><td>2.0</td><td>1.0</td><td>2</td><td>170</td><td>50</td><td>8.0</td></tr><tr><td>17.6</td><td>1.0</td><td>2.0</td><td>1</td><td>132</td><td>100</td><td>52.0</td></tr><tr><td>4.5</td><td>1.0</td><td>1.0</td><td>1</td><td>230</td><td>202</td><td>16.0</td></tr><tr><td>0.8</td><td>1.0</td><td>1.0</td><td>1</td><td>68</td><td>246</td><td>4.5</td></tr><tr><td>1.8</td><td>1.0</td><td>1.0</td><td>2</td><td>90</td><td>144</td><td>8.0</td></tr><tr><td>3.1</td><td>0.0</td><td>1.0</td><td>1</td><td>107</td><td>87</td><td>14.0</td></tr><tr><td>4.1</td><td>0.0</td><td>1.0</td><td>1</td><td>231</td><td>48</td><td>14.0</td></tr><tr><td>5.4</td><td>1.0</td><td>1.0</td><td>1</td><td>158</td><td>80</td><td>23.0</td></tr><tr><td>17.3</td><td>1.0</td><td>2.0</td><td>1</td><td>132</td><td>141</td><td>52.0</td></tr><tr><td>1.16</td><td>1.0</td><td>1.0</td><td>1</td><td>231</td><td>261</td><td>6.0</td></tr><tr><td>3.21</td><td>1.0</td><td>1.0</td><td>1</td><td>114</td><td>162</td><td>13.0</td></tr><tr><td>7.7</td><td>1.0</td><td>1.0</td><td>1</td><td>138</td><td>236</td><td>24.5</td></tr><tr><td>1.8</td><td>2.0</td><td>1.0</td><td>1</td><td>144</td><td>13</td><td>9.5</td></tr><tr><td>0.5</td><td>1.0</td><td>1.0</td><td>2</td><td>181</td><td>106</td><td>4.5</td></tr><tr><td>4.1</td><td>1.0</td><td>1.0</td><td>2</td><td>106</td><td>14</td><td>14.5</td></tr><tr><td>1.9</td><td>2.0</td><td>1.0</td><td>1</td><td>234</td><td>148</td><td>12.5</td></tr><tr><td>6.1</td><td>1.0</td><td>1.0</td><td>1</td><td>87</td><td>61</td><td>21.0</td></tr><tr><td>1.9</td><td>1.0</td><td>1.0</td><td>1</td><td>164</td><td>229</td><td>8.0</td></tr><tr><td>1.59</td><td>1.0</td><td>1.0</td><td>1</td><td>100</td><td>48</td><td>8.0</td></tr><tr><td>1.06</td><td>1.0</td><td>1.0</td><td>1</td><td>88</td><td>261</td><td>6.5</td></tr><tr><td>1.78</td><td>1.0</td><td>1.0</td><td>1</td><td>211</td><td>107</td><td>8.5</td></tr><tr><td>0.89</td><td>1.0</td><td>1.0</td><td>1</td><td>164</td><td>234</td><td>4.5</td></tr><tr><td>5.29</td><td>1.0</td><td>1.0</td><td>2</td><td>246</td><td>211</td><td>18.5</td></tr><tr><td>1.1</td><td>1.0</td><td>1.0</td><td>1</td><td>234</td><td>68</td><td>8.0</td></tr><tr><td>2.2</td><td>1.0</td><td>1.0</td><td>1</td><td>158</td><td>48</td><td>9.0</td></tr><tr><td>1.5</td><td>1.0</td><td>1.0</td><td>1</td><td>142</td><td>239</td><td>7.5</td></tr><tr><td>3.0</td><td>1.0</td><td>1.0</td><td>2</td><td>230</td><td>140</td><td>13.0</td></tr><tr><td>1.4</td><td>1.0</td><td>1.0</td><td>1</td><td>148</td><td>107</td><td>7.5</td></tr><tr><td>0.7</td><td>1.0</td><td>1.0</td><td>1</td><td>79</td><td>79</td><td>6.0</td></tr><tr><td>1.5</td><td>1.0</td><td>1.0</td><td>1</td><td>211</td><td>13</td><td>8.0</td></tr><tr><td>3.2</td><td>1.0</td><td>1.0</td><td>1</td><td>249</td><td>239</td><td>12.0</td></tr><tr><td>6.24</td><td>1.0</td><td>1.0</td><td>1</td><td>132</td><td>134</td><td>19.0</td></tr><tr><td>0.53</td><td>1.0</td><td>1.0</td><td>1</td><td>170</td><td>164</td><td>4.0</td></tr><tr><td>3.49</td><td>1.0</td><td>1.0</td><td>1</td><td>164</td><td>236</td><td>14.0</td></tr><tr><td>2.04</td><td>1.0</td><td>1.0</td><td>1</td><td>163</td><td>90</td><td>8.5</td></tr><tr><td>14.0</td><td>1.0</td><td>2.0</td><td>1</td><td>130</td><td>152</td><td>52.0</td></tr><tr><td>4.9</td><td>2.0</td><td>1.0</td><td>2</td><td>170</td><td>179</td><td>18.0</td></tr><tr><td>4.4</td><td>2.0</td><td>1.0</td><td>1</td><td>249</td><td>263</td><td>15.0</td></tr><tr><td>1.21</td><td>1.0</td><td>1.0</td><td>2</td><td>161</td><td>229</td><td>6.5</td></tr><tr><td>1.14</td><td>1.0</td><td>1.0</td><td>2</td><td>196</td><td>95</td><td>6.0</td></tr><tr><td>10.3</td><td>1.0</td><td>1.0</td><td>2</td><td>132</td><td>39</td><td>30.5</td></tr><tr><td>2.0</td><td>2.0</td><td>1.0</td><td>1</td><td>107</td><td>68</td><td>11.0</td></tr><tr><td>2.02</td><td>1.0</td><td>1.0</td><td>1</td><td>255</td><td>232</td><td>8.5</td></tr><tr><td>1.25</td><td>1.0</td><td>1.0</td><td>1</td><td>79</td><td>137</td><td>6.0</td></tr><tr><td>6.95</td><td>1.0</td><td>1.0</td><td>1</td><td>48</td><td>82</td><td>25.0</td></tr><tr><td>5.8</td><td>1.0</td><td>1.0</td><td>2</td><td>264</td><td>87</td><td>28.5</td></tr><tr><td>0.5</td><td>1.0</td><td>1.0</td><td>1</td><td>162</td><td>229</td><td>4.5</td></tr><tr><td>2.5</td><td>1.0</td><td>1.0</td><td>1</td><td>229</td><td>113</td><td>10.5</td></tr><tr><td>0.8</td><td>1.0</td><td>1.0</td><td>1</td><td>162</td><td>229</td><td>6.0</td></tr><tr><td>1.0</td><td>1.0</td><td>1.0</td><td>2</td><td>249</td><td>231</td><td>5.5</td></tr><tr><td>18.7</td><td>1.0</td><td>2.0</td><td>1</td><td>132</td><td>263</td><td>52.0</td></tr><tr><td>1.49</td><td>1.0</td><td>1.0</td><td>1</td><td>246</td><td>230</td><td>6.5</td></tr><tr><td>2.78</td><td>1.0</td><td>1.0</td><td>1</td><td>90</td><td>143</td><td>11.0</td></tr><tr><td>19.4</td><td>1.0</td><td>2.0</td><td>2</td><td>132</td><td>238</td><td>52.0</td></tr><tr><td>0.9</td><td>1.0</td><td>1.0</td><td>2</td><td>48</td><td>50</td><td>5.0</td></tr><tr><td>8.6</td><td>1.0</td><td>1.0</td><td>1</td><td>230</td><td>126</td><td>28.5</td></tr><tr><td>3.4</td><td>0.0</td><td>1.0</td><td>1</td><td>186</td><td>141</td><td>13.0</td></tr><tr><td>1.8</td><td>2.0</td><td>1.0</td><td>1</td><td>249</td><td>13</td><td>9.0</td></tr><tr><td>1.07</td><td>1.0</td><td>1.0</td><td>1</td><td>13</td><td>87</td><td>6.5</td></tr><tr><td>2.82</td><td>2.0</td><td>1.0</td><td>2</td><td>90</td><td>142</td><td>12.0</td></tr><tr><td>1.3</td><td>1.0</td><td>1.0</td><td>1</td><td>236</td><td>239</td><td>6.5</td></tr><tr><td>18.53</td><td>1.0</td><td>2.0</td><td>1</td><td>132</td><td>90</td><td>52.0</td></tr><tr><td>0.34</td><td>1.0</td><td>1.0</td><td>1</td><td>262</td><td>236</td><td>3.5</td></tr><tr><td>0.36</td><td>1.0</td><td>1.0</td><td>2</td><td>236</td><td>263</td><td>3.5</td></tr><tr><td>0.8</td><td>1.0</td><td>1.0</td><td>1</td><td>263</td><td>140</td><td>5.0</td></tr><tr><td>7.0</td><td>1.0</td><td>1.0</td><td>1</td><td>87</td><td>188</td><td>25.5</td></tr><tr><td>5.6</td><td>2.0</td><td>1.0</td><td>1</td><td>209</td><td>80</td><td>22.5</td></tr><tr><td>13.9</td><td>1.0</td><td>1.0</td><td>1</td><td>255</td><td>243</td><td>39.5</td></tr><tr><td>1.3</td><td>4.0</td><td>1.0</td><td>1</td><td>170</td><td>230</td><td>8.0</td></tr><tr><td>0.6</td><td>2.0</td><td>1.0</td><td>1</td><td>114</td><td>113</td><td>5.5</td></tr><tr><td>0.9</td><td>1.0</td><td>1.0</td><td>3</td><td>116</td><td>166</td><td>6.5</td></tr><tr><td>0.9</td><td>1.0</td><td>1.0</td><td>3</td><td>43</td><td>236</td><td>5.5</td></tr><tr><td>4.09</td><td>1.0</td><td>1.0</td><td>2</td><td>107</td><td>255</td><td>25.5</td></tr><tr><td>7.05</td><td>1.0</td><td>1.0</td><td>1</td><td>230</td><td>83</td><td>25.0</td></tr><tr><td>18.4</td><td>2.0</td><td>2.0</td><td>2</td><td>132</td><td>264</td><td>52.0</td></tr><tr><td>1.0</td><td>1.0</td><td>1.0</td><td>2</td><td>113</td><td>90</td><td>6.0</td></tr><tr><td>6.7</td><td>1.0</td><td>1.0</td><td>1</td><td>158</td><td>17</td><td>27.0</td></tr><tr><td>2.4</td><td>6.0</td><td>1.0</td><td>1</td><td>246</td><td>79</td><td>13.0</td></tr><tr><td>5.1</td><td>1.0</td><td>1.0</td><td>1</td><td>100</td><td>260</td><td>18.5</td></tr><tr><td>1.2</td><td>1.0</td><td>1.0</td><td>1</td><td>148</td><td>107</td><td>6.5</td></tr><tr><td>3.4</td><td>3.0</td><td>1.0</td><td>2</td><td>114</td><td>100</td><td>15.0</td></tr><tr><td>2.5</td><td>1.0</td><td>1.0</td><td>1</td><td>68</td><td>143</td><td>10.0</td></tr><tr><td>0.58</td><td>1.0</td><td>1.0</td><td>1</td><td>238</td><td>24</td><td>4.0</td></tr><tr><td>2.63</td><td>1.0</td><td>1.0</td><td>1</td><td>238</td><td>116</td><td>11.0</td></tr><tr><td>1.36</td><td>1.0</td><td>1.0</td><td>1</td><td>239</td><td>151</td><td>6.5</td></tr><tr><td>1.08</td><td>2.0</td><td>1.0</td><td>1</td><td>112</td><td>255</td><td>6.5</td></tr><tr><td>2.65</td><td>2.0</td><td>1.0</td><td>1</td><td>256</td><td>49</td><td>12.5</td></tr><tr><td>0.84</td><td>1.0</td><td>1.0</td><td>1</td><td>164</td><td>170</td><td>5.5</td></tr><tr><td>0.89</td><td>2.0</td><td>1.0</td><td>2</td><td>233</td><td>161</td><td>5.5</td></tr><tr><td>0.84</td><td>2.0</td><td>1.0</td><td>2</td><td>107</td><td>233</td><td>5.5</td></tr><tr><td>3.17</td><td>1.0</td><td>1.0</td><td>1</td><td>162</td><td>79</td><td>17.0</td></tr><tr><td>17.0</td><td>1.0</td><td>1.0</td><td>1</td><td>132</td><td>178</td><td>45.5</td></tr><tr><td>16.4</td><td>2.0</td><td>2.0</td><td>1</td><td>132</td><td>162</td><td>52.0</td></tr><tr><td>1.2</td><td>2.0</td><td>1.0</td><td>1</td><td>162</td><td>48</td><td>7.0</td></tr><tr><td>3.42</td><td>4.0</td><td>1.0</td><td>1</td><td>229</td><td>249</td><td>14.0</td></tr><tr><td>5.2</td><td>4.0</td><td>1.0</td><td>1</td><td>234</td><td>80</td><td>24.5</td></tr><tr><td>0.45</td><td>1.0</td><td>1.0</td><td>1</td><td>50</td><td>143</td><td>3.5</td></tr><tr><td>1.8</td><td>1.0</td><td>1.0</td><td>1</td><td>230</td><td>239</td><td>8.0</td></tr><tr><td>3.28</td><td>1.0</td><td>1.0</td><td>1</td><td>48</td><td>146</td><td>12.0</td></tr><tr><td>17.98</td><td>1.0</td><td>2.0</td><td>2</td><td>132</td><td>48</td><td>52.0</td></tr><tr><td>1.38</td><td>1.0</td><td>1.0</td><td>1</td><td>144</td><td>4</td><td>7.0</td></tr><tr><td>3.86</td><td>1.0</td><td>1.0</td><td>1</td><td>79</td><td>256</td><td>18.0</td></tr><tr><td>1.79</td><td>1.0</td><td>1.0</td><td>2</td><td>113</td><td>137</td><td>8.0</td></tr><tr><td>18.6</td><td>1.0</td><td>1.0</td><td>1</td><td>132</td><td>97</td><td>52.0</td></tr><tr><td>19.5</td><td>1.0</td><td>2.0</td><td>1</td><td>132</td><td>41</td><td>52.0</td></tr><tr><td>2.5</td><td>1.0</td><td>1.0</td><td>1</td><td>239</td><td>166</td><td>12.0</td></tr><tr><td>3.6</td><td>2.0</td><td>1.0</td><td>2</td><td>166</td><td>244</td><td>12.5</td></tr><tr><td>6.5</td><td>1.0</td><td>1.0</td><td>1</td><td>158</td><td>75</td><td>22.5</td></tr><tr><td>1.2</td><td>1.0</td><td>1.0</td><td>1</td><td>100</td><td>234</td><td>7.0</td></tr><tr><td>0.7</td><td>3.0</td><td>1.0</td><td>1</td><td>234</td><td>107</td><td>5.0</td></tr><tr><td>1.0</td><td>1.0</td><td>1.0</td><td>1</td><td>232</td><td>144</td><td>8.0</td></tr><tr><td>3.6</td><td>1.0</td><td>1.0</td><td>1</td><td>144</td><td>40</td><td>14.0</td></tr><tr><td>0.8</td><td>1.0</td><td>1.0</td><td>1</td><td>25</td><td>54</td><td>4.5</td></tr><tr><td>0.8</td><td>1.0</td><td>1.0</td><td>2</td><td>65</td><td>97</td><td>5.5</td></tr><tr><td>3.67</td><td>1.0</td><td>1.0</td><td>2</td><td>148</td><td>79</td><td>12.5</td></tr><tr><td>7.5</td><td>1.0</td><td>1.0</td><td>1</td><td>138</td><td>229</td><td>23.5</td></tr><tr><td>8.7</td><td>1.0</td><td>1.0</td><td>2</td><td>45</td><td>74</td><td>26.0</td></tr><tr><td>3.8</td><td>1.0</td><td>1.0</td><td>1</td><td>238</td><td>244</td><td>12.5</td></tr><tr><td>1.9</td><td>1.0</td><td>1.0</td><td>2</td><td>116</td><td>244</td><td>10.0</td></tr><tr><td>7.69</td><td>1.0</td><td>1.0</td><td>1</td><td>138</td><td>75</td><td>22.0</td></tr><tr><td>9.87</td><td>1.0</td><td>1.0</td><td>1</td><td>261</td><td>14</td><td>27.5</td></tr><tr><td>4.95</td><td>1.0</td><td>1.0</td><td>1</td><td>88</td><td>112</td><td>17.0</td></tr><tr><td>3.15</td><td>5.0</td><td>1.0</td><td>2</td><td>90</td><td>68</td><td>14.5</td></tr><tr><td>0.41</td><td>1.0</td><td>1.0</td><td>1</td><td>24</td><td>24</td><td>4.0</td></tr><tr><td>0.9</td><td>1.0</td><td>1.0</td><td>1</td><td>237</td><td>141</td><td>5.0</td></tr><tr><td>0.6</td><td>2.0</td><td>1.0</td><td>2</td><td>163</td><td>163</td><td>4.5</td></tr><tr><td>2.1</td><td>1.0</td><td>1.0</td><td>2</td><td>163</td><td>90</td><td>9.0</td></tr><tr><td>6.2</td><td>1.0</td><td>1.0</td><td>1</td><td>140</td><td>36</td><td>22.5</td></tr><tr><td>7.87</td><td>1.0</td><td>1.0</td><td>1</td><td>138</td><td>164</td><td>24.0</td></tr><tr><td>4.5</td><td>1.0</td><td>1.0</td><td>1</td><td>234</td><td>263</td><td>16.5</td></tr><tr><td>6.8</td><td>1.0</td><td>1.0</td><td>1</td><td>263</td><td>87</td><td>46.5</td></tr><tr><td>2.0</td><td>1.0</td><td>1.0</td><td>1</td><td>158</td><td>148</td><td>11.0</td></tr><tr><td>2.68</td><td>1.0</td><td>1.0</td><td>1</td><td>239</td><td>262</td><td>11.5</td></tr><tr><td>1.04</td><td>1.0</td><td>1.0</td><td>2</td><td>166</td><td>41</td><td>6.0</td></tr><tr><td>4.68</td><td>1.0</td><td>1.0</td><td>2</td><td>152</td><td>127</td><td>15.0</td></tr><tr><td>1.2</td><td>1.0</td><td>1.0</td><td>1</td><td>79</td><td>170</td><td>5.5</td></tr><tr><td>3.67</td><td>3.0</td><td>1.0</td><td>1</td><td>79</td><td>97</td><td>15.0</td></tr><tr><td>19.32</td><td>6.0</td><td>1.0</td><td>1</td><td>132</td><td>49</td><td>52.5</td></tr><tr><td>4.89</td><td>1.0</td><td>1.0</td><td>1</td><td>230</td><td>7</td><td>20.5</td></tr><tr><td>2.9</td><td>1.0</td><td>1.0</td><td>1</td><td>249</td><td>161</td><td>12.5</td></tr><tr><td>1.1</td><td>1.0</td><td>1.0</td><td>1</td><td>161</td><td>48</td><td>6.5</td></tr><tr><td>0.8</td><td>1.0</td><td>1.0</td><td>1</td><td>249</td><td>211</td><td>5.5</td></tr><tr><td>3.3</td><td>1.0</td><td>1.0</td><td>1</td><td>140</td><td>146</td><td>14.0</td></tr><tr><td>3.8</td><td>1.0</td><td>1.0</td><td>1</td><td>146</td><td>143</td><td>13.5</td></tr><tr><td>3.1</td><td>1.0</td><td>1.0</td><td>2</td><td>142</td><td>75</td><td>11.5</td></tr><tr><td>19.86</td><td>2.0</td><td>2.0</td><td>1</td><td>132</td><td>140</td><td>52.0</td></tr><tr><td>1.77</td><td>5.0</td><td>1.0</td><td>1</td><td>246</td><td>50</td><td>7.0</td></tr><tr><td>5.49</td><td>1.0</td><td>1.0</td><td>1</td><td>163</td><td>168</td><td>17.5</td></tr><tr><td>0.41</td><td>1.0</td><td>1.0</td><td>2</td><td>237</td><td>237</td><td>4.0</td></tr><tr><td>1.4</td><td>1.0</td><td>1.0</td><td>1</td><td>264</td><td>264</td><td>8.5</td></tr><tr><td>5.6</td><td>1.0</td><td>1.0</td><td>1</td><td>264</td><td>264</td><td>21.0</td></tr><tr><td>2.3</td><td>1.0</td><td>1.0</td><td>2</td><td>232</td><td>90</td><td>10.5</td></tr><tr><td>1.3</td><td>1.0</td><td>1.0</td><td>1</td><td>246</td><td>234</td><td>6.5</td></tr><tr><td>2.2</td><td>3.0</td><td>1.0</td><td>1</td><td>100</td><td>79</td><td>11.5</td></tr><tr><td>1.2</td><td>2.0</td><td>1.0</td><td>1</td><td>234</td><td>107</td><td>8.0</td></tr><tr><td>4.6</td><td>1.0</td><td>1.0</td><td>1</td><td>107</td><td>239</td><td>15.0</td></tr><tr><td>1.46</td><td>2.0</td><td>1.0</td><td>1</td><td>107</td><td>249</td><td>9.0</td></tr><tr><td>2.68</td><td>1.0</td><td>1.0</td><td>2</td><td>249</td><td>107</td><td>12.5</td></tr><tr><td>1.17</td><td>1.0</td><td>1.0</td><td>1</td><td>137</td><td>229</td><td>6.5</td></tr><tr><td>1.3</td><td>1.0</td><td>1.0</td><td>1</td><td>114</td><td>79</td><td>8.5</td></tr><tr><td>0.93</td><td>1.0</td><td>1.0</td><td>1</td><td>161</td><td>233</td><td>5.5</td></tr><tr><td>1.19</td><td>1.0</td><td>1.0</td><td>1</td><td>233</td><td>237</td><td>5.5</td></tr><tr><td>1.11</td><td>1.0</td><td>1.0</td><td>2</td><td>237</td><td>237</td><td>6.5</td></tr><tr><td>1.4</td><td>1.0</td><td>1.0</td><td>1</td><td>161</td><td>100</td><td>7.5</td></tr><tr><td>6.5</td><td>1.0</td><td>1.0</td><td>1</td><td>90</td><td>166</td><td>21.5</td></tr><tr><td>2.84</td><td>1.0</td><td>1.0</td><td>1</td><td>161</td><td>249</td><td>12.0</td></tr><tr><td>3.53</td><td>1.0</td><td>1.0</td><td>1</td><td>79</td><td>88</td><td>13.0</td></tr><tr><td>0.9</td><td>1.0</td><td>1.0</td><td>2</td><td>237</td><td>237</td><td>4.5</td></tr><tr><td>3.3</td><td>1.0</td><td>1.0</td><td>1</td><td>237</td><td>113</td><td>13.0</td></tr><tr><td>0.1</td><td>1.0</td><td>1.0</td><td>1</td><td>170</td><td>170</td><td>3.5</td></tr><tr><td>0.8</td><td>1.0</td><td>1.0</td><td>2</td><td>114</td><td>79</td><td>5.5</td></tr><tr><td>1.12</td><td>4.0</td><td>1.0</td><td>1</td><td>50</td><td>50</td><td>5.5</td></tr><tr><td>1.48</td><td>6.0</td><td>1.0</td><td>1</td><td>68</td><td>234</td><td>7.0</td></tr><tr><td>2.95</td><td>6.0</td><td>1.0</td><td>1</td><td>164</td><td>261</td><td>14.0</td></tr><tr><td>1.11</td><td>6.0</td><td>1.0</td><td>1</td><td>234</td><td>230</td><td>6.0</td></tr><tr><td>13.09</td><td>1.0</td><td>2.0</td><td>1</td><td>93</td><td>238</td><td>52.0</td></tr><tr><td>4.66</td><td>1.0</td><td>1.0</td><td>1</td><td>230</td><td>88</td><td>15.5</td></tr><tr><td>9.0</td><td>3.0</td><td>1.0</td><td>1</td><td>138</td><td>230</td><td>29.0</td></tr><tr><td>1.02</td><td>2.0</td><td>1.0</td><td>2</td><td>114</td><td>148</td><td>7.0</td></tr><tr><td>1.53</td><td>2.0</td><td>1.0</td><td>1</td><td>114</td><td>79</td><td>8.0</td></tr><tr><td>2.1</td><td>1.0</td><td>1.0</td><td>1</td><td>79</td><td>186</td><td>9.0</td></tr><tr><td>0.83</td><td>1.0</td><td>1.0</td><td>2</td><td>166</td><td>41</td><td>6.0</td></tr><tr><td>2.92</td><td>1.0</td><td>1.0</td><td>2</td><td>151</td><td>163</td><td>14.0</td></tr><tr><td>2.05</td><td>1.0</td><td>1.0</td><td>1</td><td>211</td><td>87</td><td>10.5</td></tr><tr><td>0.97</td><td>1.0</td><td>1.0</td><td>1</td><td>144</td><td>249</td><td>6.0</td></tr><tr><td>5.37</td><td>1.0</td><td>1.0</td><td>1</td><td>90</td><td>40</td><td>18.5</td></tr><tr><td>2.9</td><td>1.0</td><td>1.0</td><td>2</td><td>132</td><td>10</td><td>10.0</td></tr><tr><td>5.7</td><td>1.0</td><td>1.0</td><td>1</td><td>170</td><td>25</td><td>42.0</td></tr><tr><td>2.4</td><td>1.0</td><td>1.0</td><td>1</td><td>4</td><td>164</td><td>11.0</td></tr><tr><td>1.08</td><td>1.0</td><td>2.0</td><td>2</td><td>132</td><td>132</td><td>52.0</td></tr><tr><td>1.6</td><td>1.0</td><td>1.0</td><td>1</td><td>68</td><td>125</td><td>8.5</td></tr><tr><td>4.3</td><td>1.0</td><td>1.0</td><td>1</td><td>144</td><td>262</td><td>15.5</td></tr><tr><td>1.1</td><td>1.0</td><td>1.0</td><td>1</td><td>161</td><td>100</td><td>6.0</td></tr><tr><td>4.02</td><td>1.0</td><td>1.0</td><td>2</td><td>132</td><td>219</td><td>13.0</td></tr><tr><td>18.0</td><td>1.0</td><td>2.0</td><td>1</td><td>132</td><td>74</td><td>52.0</td></tr><tr><td>2.3</td><td>1.0</td><td>1.0</td><td>1</td><td>161</td><td>262</td><td>10.0</td></tr><tr><td>4.0</td><td>4.0</td><td>1.0</td><td>1</td><td>237</td><td>79</td><td>16.0</td></tr><tr><td>21.5</td><td>1.0</td><td>2.0</td><td>2</td><td>132</td><td>48</td><td>52.0</td></tr><tr><td>0.79</td><td>1.0</td><td>1.0</td><td>1</td><td>249</td><td>249</td><td>7.0</td></tr><tr><td>5.84</td><td>1.0</td><td>1.0</td><td>2</td><td>249</td><td>188</td><td>21.5</td></tr><tr><td>18.36</td><td>1.0</td><td>2.0</td><td>1</td><td>132</td><td>230</td><td>52.0</td></tr><tr><td>0.9</td><td>1.0</td><td>1.0</td><td>1</td><td>162</td><td>141</td><td>6.0</td></tr><tr><td>2.6</td><td>1.0</td><td>1.0</td><td>1</td><td>100</td><td>140</td><td>12.0</td></tr><tr><td>1.5</td><td>0.0</td><td>1.0</td><td>1</td><td>239</td><td>236</td><td>8.0</td></tr><tr><td>1.2</td><td>0.0</td><td>1.0</td><td>2</td><td>239</td><td>143</td><td>7.0</td></tr><tr><td>4.6</td><td>0.0</td><td>1.0</td><td>1</td><td>239</td><td>244</td><td>15.5</td></tr><tr><td>2.66</td><td>1.0</td><td>1.0</td><td>2</td><td>170</td><td>141</td><td>10.5</td></tr><tr><td>3.42</td><td>1.0</td><td>1.0</td><td>1</td><td>79</td><td>141</td><td>12.0</td></tr><tr><td>2.26</td><td>1.0</td><td>1.0</td><td>1</td><td>237</td><td>238</td><td>9.0</td></tr><tr><td>1.2</td><td>1.0</td><td>1.0</td><td>4</td><td>148</td><td>211</td><td>8.0</td></tr><tr><td>1.5</td><td>1.0</td><td>1.0</td><td>4</td><td>211</td><td>90</td><td>8.0</td></tr><tr><td>1.1</td><td>1.0</td><td>1.0</td><td>1</td><td>229</td><td>230</td><td>6.0</td></tr><tr><td>3.8</td><td>1.0</td><td>1.0</td><td>1</td><td>48</td><td>262</td><td>13.5</td></tr><tr><td>2.7</td><td>1.0</td><td>1.0</td><td>1</td><td>113</td><td>162</td><td>11.0</td></tr><tr><td>0.5</td><td>1.0</td><td>1.0</td><td>1</td><td>79</td><td>79</td><td>4.0</td></tr><tr><td>0.83</td><td>1.0</td><td>1.0</td><td>1</td><td>79</td><td>148</td><td>7.5</td></tr><tr><td>2.04</td><td>1.0</td><td>1.0</td><td>1</td><td>107</td><td>237</td><td>7.5</td></tr><tr><td>8.81</td><td>1.0</td><td>1.0</td><td>2</td><td>230</td><td>243</td><td>27.0</td></tr><tr><td>1.69</td><td>1.0</td><td>1.0</td><td>1</td><td>79</td><td>233</td><td>6.5</td></tr><tr><td>1.08</td><td>5.0</td><td>1.0</td><td>1</td><td>45</td><td>79</td><td>6.5</td></tr><tr><td>1.8</td><td>1.0</td><td>1.0</td><td>2</td><td>41</td><td>42</td><td>9.5</td></tr><tr><td>1.6</td><td>2.0</td><td>1.0</td><td>2</td><td>249</td><td>148</td><td>8.5</td></tr><tr><td>1.5</td><td>2.0</td><td>1.0</td><td>1</td><td>148</td><td>125</td><td>9.0</td></tr><tr><td>0.7</td><td>1.0</td><td>1.0</td><td>1</td><td>107</td><td>107</td><td>6.0</td></tr><tr><td>2.55</td><td>1.0</td><td>1.0</td><td>1</td><td>144</td><td>164</td><td>11.0</td></tr><tr><td>0.76</td><td>3.0</td><td>1.0</td><td>1</td><td>79</td><td>4</td><td>5.0</td></tr><tr><td>0.55</td><td>3.0</td><td>1.0</td><td>1</td><td>79</td><td>148</td><td>5.0</td></tr><tr><td>0.99</td><td>1.0</td><td>1.0</td><td>1</td><td>161</td><td>161</td><td>6.5</td></tr><tr><td>3.91</td><td>1.0</td><td>1.0</td><td>1</td><td>170</td><td>256</td><td>18.0</td></tr><tr><td>0.7</td><td>1.0</td><td>1.0</td><td>2</td><td>162</td><td>100</td><td>5.5</td></tr><tr><td>5.8</td><td>1.0</td><td>1.0</td><td>1</td><td>162</td><td>25</td><td>29.0</td></tr><tr><td>6.79</td><td>5.0</td><td>1.0</td><td>1</td><td>87</td><td>188</td><td>27.0</td></tr><tr><td>2.8</td><td>1.0</td><td>1.0</td><td>2</td><td>170</td><td>262</td><td>11.0</td></tr><tr><td>3.6</td><td>1.0</td><td>1.0</td><td>2</td><td>140</td><td>74</td><td>12.5</td></tr><tr><td>2.53</td><td>1.0</td><td>1.0</td><td>2</td><td>146</td><td>262</td><td>10.0</td></tr><tr><td>0.76</td><td>1.0</td><td>1.0</td><td>2</td><td>263</td><td>141</td><td>4.5</td></tr><tr><td>4.14</td><td>1.0</td><td>1.0</td><td>2</td><td>229</td><td>7</td><td>15.5</td></tr><tr><td>1.61</td><td>6.0</td><td>1.0</td><td>1</td><td>148</td><td>79</td><td>8.5</td></tr><tr><td>1.21</td><td>6.0</td><td>1.0</td><td>1</td><td>79</td><td>107</td><td>8.5</td></tr><tr><td>16.94</td><td>1.0</td><td>2.0</td><td>1</td><td>132</td><td>170</td><td>52.0</td></tr><tr><td>2.13</td><td>1.0</td><td>1.0</td><td>2</td><td>107</td><td>148</td><td>12.0</td></tr><tr><td>1.1</td><td>2.0</td><td>1.0</td><td>1</td><td>230</td><td>100</td><td>6.0</td></tr><tr><td>1.6</td><td>2.0</td><td>1.0</td><td>1</td><td>142</td><td>230</td><td>8.5</td></tr><tr><td>3.4</td><td>3.0</td><td>1.0</td><td>1</td><td>264</td><td>264</td><td>13.5</td></tr><tr><td>6.3</td><td>2.0</td><td>1.0</td><td>1</td><td>264</td><td>264</td><td>24.0</td></tr><tr><td>0.5</td><td>1.0</td><td>1.0</td><td>2</td><td>230</td><td>186</td><td>4.0</td></tr><tr><td>2.9</td><td>1.0</td><td>1.0</td><td>1</td><td>249</td><td>142</td><td>10.5</td></tr><tr><td>1.6</td><td>1.0</td><td>1.0</td><td>1</td><td>231</td><td>79</td><td>8.0</td></tr><tr><td>12.42</td><td>5.0</td><td>1.0</td><td>1</td><td>231</td><td>167</td><td>36.0</td></tr><tr><td>0.98</td><td>1.0</td><td>1.0</td><td>1</td><td>239</td><td>239</td><td>6.0</td></tr><tr><td>2.09</td><td>1.0</td><td>1.0</td><td>1</td><td>48</td><td>186</td><td>9.0</td></tr><tr><td>6.79</td><td>1.0</td><td>1.0</td><td>1</td><td>90</td><td>37</td><td>27.5</td></tr><tr><td>3.39</td><td>1.0</td><td>1.0</td><td>1</td><td>244</td><td>244</td><td>12.5</td></tr><tr><td>1.19</td><td>1.0</td><td>1.0</td><td>1</td><td>162</td><td>48</td><td>7.5</td></tr><tr><td>2.2</td><td>1.0</td><td>1.0</td><td>1</td><td>162</td><td>79</td><td>10.0</td></tr><tr><td>1.1</td><td>1.0</td><td>1.0</td><td>1</td><td>148</td><td>79</td><td>7.5</td></tr><tr><td>0.9</td><td>0.0</td><td>1.0</td><td>1</td><td>161</td><td>100</td><td>5.5</td></tr><tr><td>3.57</td><td>1.0</td><td>1.0</td><td>1</td><td>211</td><td>255</td><td>19.5</td></tr><tr><td>1.4</td><td>1.0</td><td>1.0</td><td>2</td><td>229</td><td>237</td><td>7.5</td></tr><tr><td>2.1</td><td>3.0</td><td>1.0</td><td>1</td><td>163</td><td>107</td><td>10.0</td></tr><tr><td>0.8</td><td>1.0</td><td>1.0</td><td>1</td><td>234</td><td>90</td><td>4.5</td></tr><tr><td>2.3</td><td>0.0</td><td>1.0</td><td>1</td><td>68</td><td>13</td><td>9.0</td></tr><tr><td>9.7</td><td>1.0</td><td>1.0</td><td>1</td><td>138</td><td>141</td><td>27.0</td></tr><tr><td>1.1</td><td>2.0</td><td>1.0</td><td>2</td><td>146</td><td>7</td><td>6.0</td></tr><tr><td>2.4</td><td>1.0</td><td>1.0</td><td>2</td><td>144</td><td>68</td><td>11.0</td></tr><tr><td>7.1</td><td>1.0</td><td>1.0</td><td>1</td><td>68</td><td>61</td><td>25.0</td></tr><tr><td>1.8</td><td>1.0</td><td>1.0</td><td>2</td><td>264</td><td>37</td><td>9.0</td></tr><tr><td>4.2</td><td>3.0</td><td>1.0</td><td>1</td><td>211</td><td>141</td><td>15.5</td></tr><tr><td>0.36</td><td>3.0</td><td>1.0</td><td>2</td><td>141</td><td>237</td><td>4.0</td></tr><tr><td>4.32</td><td>4.0</td><td>1.0</td><td>2</td><td>237</td><td>7</td><td>15.5</td></tr><tr><td>0.48</td><td>6.0</td><td>1.0</td><td>1</td><td>114</td><td>148</td><td>4.5</td></tr><tr><td>1.98</td><td>4.0</td><td>1.0</td><td>1</td><td>68</td><td>79</td><td>11.0</td></tr><tr><td>1.65</td><td>1.0</td><td>1.0</td><td>1</td><td>68</td><td>48</td><td>7.0</td></tr><tr><td>2.12</td><td>1.0</td><td>1.0</td><td>1</td><td>249</td><td>163</td><td>8.5</td></tr><tr><td>4.67</td><td>4.0</td><td>1.0</td><td>1</td><td>79</td><td>112</td><td>16.5</td></tr><tr><td>5.77</td><td>1.0</td><td>1.0</td><td>1</td><td>231</td><td>239</td><td>20.5</td></tr><tr><td>4.44</td><td>1.0</td><td>1.0</td><td>1</td><td>239</td><td>145</td><td>16.0</td></tr><tr><td>0.88</td><td>1.0</td><td>1.0</td><td>1</td><td>48</td><td>48</td><td>5.0</td></tr><tr><td>7.12</td><td>1.0</td><td>1.0</td><td>1</td><td>138</td><td>119</td><td>22.0</td></tr><tr><td>5.5</td><td>2.0</td><td>1.0</td><td>1</td><td>107</td><td>37</td><td>30.0</td></tr><tr><td>1.57</td><td>1.0</td><td>1.0</td><td>2</td><td>75</td><td>41</td><td>7.0</td></tr><tr><td>1.1</td><td>1.0</td><td>1.0</td><td>1</td><td>163</td><td>237</td><td>6.0</td></tr><tr><td>3.8</td><td>2.0</td><td>1.0</td><td>1</td><td>249</td><td>239</td><td>14.5</td></tr><tr><td>3.26</td><td>1.0</td><td>1.0</td><td>1</td><td>97</td><td>257</td><td>12.5</td></tr><tr><td>4.04</td><td>5.0</td><td>1.0</td><td>1</td><td>234</td><td>262</td><td>16.5</td></tr><tr><td>0.46</td><td>5.0</td><td>1.0</td><td>2</td><td>140</td><td>229</td><td>4.0</td></tr><tr><td>1.56</td><td>2.0</td><td>1.0</td><td>1</td><td>249</td><td>107</td><td>7.5</td></tr><tr><td>4.05</td><td>2.0</td><td>1.0</td><td>1</td><td>186</td><td>263</td><td>15.0</td></tr><tr><td>1.9</td><td>1.0</td><td>1.0</td><td>1</td><td>161</td><td>141</td><td>8.0</td></tr><tr><td>1.1</td><td>1.0</td><td>1.0</td><td>1</td><td>230</td><td>229</td><td>6.5</td></tr><tr><td>9.62</td><td>1.0</td><td>1.0</td><td>1</td><td>138</td><td>148</td><td>27.5</td></tr><tr><td>0.95</td><td>1.0</td><td>1.0</td><td>2</td><td>148</td><td>114</td><td>5.5</td></tr><tr><td>1.56</td><td>1.0</td><td>1.0</td><td>2</td><td>129</td><td>260</td><td>7.0</td></tr><tr><td>2.7</td><td>1.0</td><td>1.0</td><td>1</td><td>151</td><td>116</td><td>10.5</td></tr><tr><td>3.8</td><td>1.0</td><td>1.0</td><td>1</td><td>230</td><td>74</td><td>13.0</td></tr><tr><td>3.5</td><td>2.0</td><td>1.0</td><td>3</td><td>229</td><td>7</td><td>12.5</td></tr><tr><td>0.4</td><td>2.0</td><td>1.0</td><td>1</td><td>211</td><td>148</td><td>6.0</td></tr><tr><td>7.9</td><td>1.0</td><td>1.0</td><td>1</td><td>148</td><td>89</td><td>25.0</td></tr><tr><td>23.66</td><td>1.0</td><td>1.0</td><td>1</td><td>132</td><td>181</td><td>63.0</td></tr><tr><td>5.4</td><td>1.0</td><td>1.0</td><td>1</td><td>24</td><td>233</td><td>19.0</td></tr><tr><td>3.7</td><td>1.0</td><td>1.0</td><td>2</td><td>137</td><td>66</td><td>17.5</td></tr><tr><td>1.2</td><td>1.0</td><td>1.0</td><td>1</td><td>148</td><td>79</td><td>6.5</td></tr><tr><td>1.37</td><td>1.0</td><td>1.0</td><td>2</td><td>48</td><td>161</td><td>7.0</td></tr><tr><td>1.99</td><td>2.0</td><td>1.0</td><td>1</td><td>246</td><td>161</td><td>9.5</td></tr><tr><td>2.25</td><td>2.0</td><td>1.0</td><td>2</td><td>229</td><td>186</td><td>11.5</td></tr><tr><td>6.71</td><td>1.0</td><td>5.0</td><td>1</td><td>137</td><td>17</td><td>29.95</td></tr><tr><td>0.34</td><td>4.0</td><td>1.0</td><td>1</td><td>142</td><td>142</td><td>3.0</td></tr><tr><td>1.95</td><td>3.0</td><td>1.0</td><td>1</td><td>48</td><td>158</td><td>9.0</td></tr><tr><td>0.74</td><td>4.0</td><td>1.0</td><td>1</td><td>158</td><td>158</td><td>4.5</td></tr><tr><td>4.81</td><td>3.0</td><td>1.0</td><td>1</td><td>249</td><td>112</td><td>17.5</td></tr><tr><td>1.44</td><td>1.0</td><td>1.0</td><td>2</td><td>229</td><td>230</td><td>8.0</td></tr><tr><td>0.7</td><td>1.0</td><td>1.0</td><td>1</td><td>161</td><td>230</td><td>6.5</td></tr><tr><td>1.53</td><td>1.0</td><td>1.0</td><td>1</td><td>114</td><td>90</td><td>7.5</td></tr><tr><td>5.72</td><td>1.0</td><td>1.0</td><td>2</td><td>61</td><td>67</td><td>19.0</td></tr><tr><td>1.79</td><td>1.0</td><td>1.0</td><td>2</td><td>65</td><td>61</td><td>7.5</td></tr><tr><td>1.61</td><td>1.0</td><td>1.0</td><td>1</td><td>238</td><td>263</td><td>7.0</td></tr><tr><td>2.65</td><td>1.0</td><td>1.0</td><td>1</td><td>144</td><td>255</td><td>13.0</td></tr><tr><td>0.6</td><td>1.0</td><td>1.0</td><td>1</td><td>263</td><td>262</td><td>4.0</td></tr><tr><td>1.3</td><td>1.0</td><td>1.0</td><td>1</td><td>229</td><td>233</td><td>7.0</td></tr><tr><td>1.52</td><td>1.0</td><td>1.0</td><td>2</td><td>48</td><td>233</td><td>9.0</td></tr><tr><td>3.0</td><td>1.0</td><td>1.0</td><td>2</td><td>114</td><td>230</td><td>12.0</td></tr><tr><td>1.0</td><td>1.0</td><td>1.0</td><td>1</td><td>48</td><td>164</td><td>7.0</td></tr><tr><td>2.1</td><td>1.0</td><td>1.0</td><td>1</td><td>164</td><td>79</td><td>11.0</td></tr><tr><td>1.1</td><td>1.0</td><td>1.0</td><td>1</td><td>142</td><td>163</td><td>5.5</td></tr><tr><td>9.9</td><td>1.0</td><td>1.0</td><td>3</td><td>163</td><td>92</td><td>31.5</td></tr><tr><td>1.8</td><td>1.0</td><td>1.0</td><td>1</td><td>107</td><td>144</td><td>9.5</td></tr><tr><td>4.8</td><td>2.0</td><td>1.0</td><td>1</td><td>48</td><td>148</td><td>23.0</td></tr><tr><td>1.79</td><td>1.0</td><td>1.0</td><td>1</td><td>79</td><td>90</td><td>8.5</td></tr><tr><td>1.76</td><td>1.0</td><td>1.0</td><td>2</td><td>249</td><td>231</td><td>9.5</td></tr><tr><td>4.3</td><td>1.0</td><td>1.0</td><td>4</td><td>151</td><td>244</td><td>14.0</td></tr><tr><td>3.3</td><td>1.0</td><td>1.0</td><td>1</td><td>142</td><td>146</td><td>12.5</td></tr><tr><td>3.9</td><td>3.0</td><td>1.0</td><td>1</td><td>230</td><td>4</td><td>17.5</td></tr><tr><td>0.46</td><td>1.0</td><td>1.0</td><td>1</td><td>162</td><td>170</td><td>4.0</td></tr><tr><td>1.63</td><td>1.0</td><td>1.0</td><td>1</td><td>170</td><td>68</td><td>7.5</td></tr><tr><td>0.63</td><td>1.0</td><td>1.0</td><td>1</td><td>230</td><td>163</td><td>5.0</td></tr><tr><td>9.46</td><td>1.0</td><td>1.0</td><td>1</td><td>138</td><td>107</td><td>27.0</td></tr><tr><td>0.4</td><td>2.0</td><td>1.0</td><td>1</td><td>239</td><td>238</td><td>3.5</td></tr><tr><td>4.8</td><td>1.0</td><td>1.0</td><td>1</td><td>231</td><td>181</td><td>18.0</td></tr><tr><td>0.7</td><td>1.0</td><td>1.0</td><td>2</td><td>79</td><td>4</td><td>5.5</td></tr><tr><td>6.3</td><td>1.0</td><td>1.0</td><td>1</td><td>113</td><td>7</td><td>21.0</td></tr><tr><td>7.23</td><td>2.0</td><td>1.0</td><td>1</td><td>68</td><td>225</td><td>27.5</td></tr><tr><td>9.97</td><td>1.0</td><td>1.0</td><td>2</td><td>142</td><td>212</td><td>28.5</td></tr><tr><td>1.4</td><td>1.0</td><td>1.0</td><td>1</td><td>163</td><td>170</td><td>10.0</td></tr><tr><td>4.96</td><td>2.0</td><td>1.0</td><td>1</td><td>48</td><td>42</td><td>18.5</td></tr><tr><td>5.2</td><td>2.0</td><td>1.0</td><td>1</td><td>230</td><td>179</td><td>19.0</td></tr><tr><td>6.2</td><td>1.0</td><td>1.0</td><td>2</td><td>114</td><td>151</td><td>21.0</td></tr><tr><td>1.3</td><td>1.0</td><td>1.0</td><td>1</td><td>239</td><td>143</td><td>7.5</td></tr><tr><td>8.0</td><td>1.0</td><td>1.0</td><td>2</td><td>138</td><td>164</td><td>24.0</td></tr><tr><td>0.61</td><td>1.0</td><td>1.0</td><td>2</td><td>230</td><td>186</td><td>4.5</td></tr><tr><td>0.31</td><td>1.0</td><td>1.0</td><td>1</td><td>158</td><td>158</td><td>3.5</td></tr><tr><td>2.21</td><td>1.0</td><td>1.0</td><td>1</td><td>158</td><td>148</td><td>9.5</td></tr><tr><td>1.15</td><td>1.0</td><td>1.0</td><td>1</td><td>255</td><td>112</td><td>6.0</td></tr><tr><td>1.3</td><td>1.0</td><td>1.0</td><td>1</td><td>48</td><td>162</td><td>8.0</td></tr><tr><td>3.2</td><td>1.0</td><td>1.0</td><td>1</td><td>162</td><td>158</td><td>13.5</td></tr><tr><td>1.7</td><td>1.0</td><td>1.0</td><td>1</td><td>249</td><td>164</td><td>8.0</td></tr><tr><td>2.06</td><td>1.0</td><td>1.0</td><td>1</td><td>148</td><td>137</td><td>7.5</td></tr><tr><td>1.92</td><td>1.0</td><td>1.0</td><td>1</td><td>170</td><td>50</td><td>10.5</td></tr><tr><td>7.14</td><td>1.0</td><td>1.0</td><td>1</td><td>246</td><td>49</td><td>24.5</td></tr><tr><td>0.84</td><td>3.0</td><td>1.0</td><td>2</td><td>79</td><td>79</td><td>5.0</td></tr><tr><td>1.9</td><td>4.0</td><td>1.0</td><td>1</td><td>79</td><td>164</td><td>8.5</td></tr><tr><td>2.0</td><td>1.0</td><td>1.0</td><td>2</td><td>48</td><td>249</td><td>9.5</td></tr><tr><td>2.8</td><td>1.0</td><td>1.0</td><td>1</td><td>162</td><td>238</td><td>10.0</td></tr><tr><td>0.8</td><td>1.0</td><td>1.0</td><td>1</td><td>162</td><td>162</td><td>4.5</td></tr><tr><td>3.3</td><td>2.0</td><td>1.0</td><td>2</td><td>7</td><td>129</td><td>13.0</td></tr><tr><td>0.73</td><td>1.0</td><td>1.0</td><td>1</td><td>239</td><td>142</td><td>4.5</td></tr><tr><td>0.6</td><td>1.0</td><td>1.0</td><td>3</td><td>125</td><td>114</td><td>5.0</td></tr><tr><td>5.1</td><td>1.0</td><td>1.0</td><td>1</td><td>100</td><td>256</td><td>26.5</td></tr><tr><td>1.7</td><td>1.0</td><td>1.0</td><td>1</td><td>256</td><td>148</td><td>6.5</td></tr><tr><td>16.1</td><td>1.0</td><td>1.0</td><td>1</td><td>132</td><td>145</td><td>44.5</td></tr><tr><td>1.43</td><td>1.0</td><td>1.0</td><td>1</td><td>229</td><td>48</td><td>8.0</td></tr><tr><td>1.14</td><td>1.0</td><td>1.0</td><td>1</td><td>48</td><td>50</td><td>6.0</td></tr><tr><td>15.1</td><td>1.0</td><td>1.0</td><td>1</td><td>161</td><td>16</td><td>44.0</td></tr><tr><td>1.3</td><td>3.0</td><td>1.0</td><td>1</td><td>114</td><td>79</td><td>7.5</td></tr><tr><td>1.9</td><td>1.0</td><td>1.0</td><td>1</td><td>79</td><td>68</td><td>9.0</td></tr><tr><td>6.4</td><td>2.0</td><td>1.0</td><td>1</td><td>239</td><td>260</td><td>21.0</td></tr><tr><td>1.7</td><td>1.0</td><td>1.0</td><td>1</td><td>229</td><td>263</td><td>6.5</td></tr><tr><td>1.0</td><td>1.0</td><td>1.0</td><td>1</td><td>107</td><td>79</td><td>7.0</td></tr><tr><td>1.0</td><td>1.0</td><td>1.0</td><td>1</td><td>79</td><td>211</td><td>6.0</td></tr><tr><td>1.6</td><td>1.0</td><td>1.0</td><td>1</td><td>211</td><td>232</td><td>9.0</td></tr><tr><td>2.0</td><td>1.0</td><td>1.0</td><td>1</td><td>164</td><td>144</td><td>9.5</td></tr><tr><td>2.0</td><td>1.0</td><td>1.0</td><td>1</td><td>144</td><td>137</td><td>9.5</td></tr><tr><td>3.3</td><td>1.0</td><td>1.0</td><td>1</td><td>90</td><td>239</td><td>12.5</td></tr><tr><td>1.9</td><td>1.0</td><td>1.0</td><td>1</td><td>88</td><td>125</td><td>8.0</td></tr><tr><td>3.2</td><td>1.0</td><td>1.0</td><td>1</td><td>231</td><td>50</td><td>12.0</td></tr><tr><td>1.01</td><td>1.0</td><td>1.0</td><td>2</td><td>137</td><td>234</td><td>6.5</td></tr><tr><td>1.21</td><td>1.0</td><td>1.0</td><td>2</td><td>249</td><td>231</td><td>6.5</td></tr><tr><td>1.4</td><td>0.0</td><td>1.0</td><td>2</td><td>262</td><td>74</td><td>5.5</td></tr><tr><td>0.6</td><td>1.0</td><td>1.0</td><td>2</td><td>75</td><td>75</td><td>3.5</td></tr><tr><td>7.8</td><td>0.0</td><td>1.0</td><td>1</td><td>75</td><td>83</td><td>24.5</td></tr><tr><td>1.8</td><td>2.0</td><td>1.0</td><td>1</td><td>144</td><td>107</td><td>9.0</td></tr><tr><td>16.6</td><td>1.0</td><td>1.0</td><td>1</td><td>132</td><td>34</td><td>44.5</td></tr><tr><td>1.32</td><td>1.0</td><td>1.0</td><td>2</td><td>237</td><td>237</td><td>7.5</td></tr><tr><td>2.9</td><td>2.0</td><td>1.0</td><td>1</td><td>233</td><td>249</td><td>14.5</td></tr><tr><td>2.2</td><td>1.0</td><td>1.0</td><td>2</td><td>246</td><td>43</td><td>9.5</td></tr><tr><td>3.4</td><td>1.0</td><td>1.0</td><td>1</td><td>151</td><td>162</td><td>14.0</td></tr><tr><td>2.27</td><td>2.0</td><td>1.0</td><td>1</td><td>142</td><td>151</td><td>9.5</td></tr><tr><td>0.97</td><td>1.0</td><td>1.0</td><td>2</td><td>79</td><td>232</td><td>8.0</td></tr><tr><td>1.3</td><td>1.0</td><td>1.0</td><td>2</td><td>48</td><td>246</td><td>6.5</td></tr><tr><td>3.0</td><td>1.0</td><td>1.0</td><td>1</td><td>68</td><td>141</td><td>11.5</td></tr><tr><td>0.6</td><td>1.0</td><td>1.0</td><td>1</td><td>230</td><td>164</td><td>5.0</td></tr><tr><td>0.83</td><td>1.0</td><td>1.0</td><td>1</td><td>148</td><td>148</td><td>6.0</td></tr><tr><td>5.64</td><td>1.0</td><td>1.0</td><td>1</td><td>107</td><td>40</td><td>19.0</td></tr><tr><td>18.5</td><td>1.0</td><td>2.0</td><td>1</td><td>132</td><td>90</td><td>52.0</td></tr><tr><td>4.92</td><td>1.0</td><td>1.0</td><td>1</td><td>164</td><td>33</td><td>17.5</td></tr><tr><td>6.0</td><td>1.0</td><td>1.0</td><td>1</td><td>234</td><td>36</td><td>28.0</td></tr><tr><td>7.4</td><td>1.0</td><td>1.0</td><td>3</td><td>138</td><td>42</td><td>22.5</td></tr><tr><td>1.03</td><td>1.0</td><td>1.0</td><td>1</td><td>164</td><td>137</td><td>6.5</td></tr><tr><td>0.5</td><td>1.0</td><td>1.0</td><td>1</td><td>234</td><td>170</td><td>4.5</td></tr><tr><td>4.98</td><td>4.0</td><td>1.0</td><td>1</td><td>164</td><td>256</td><td>20.5</td></tr><tr><td>2.2</td><td>1.0</td><td>1.0</td><td>1</td><td>161</td><td>114</td><td>11.0</td></tr><tr><td>1.6</td><td>1.0</td><td>1.0</td><td>2</td><td>90</td><td>230</td><td>8.0</td></tr><tr><td>6.8</td><td>1.0</td><td>1.0</td><td>1</td><td>48</td><td>244</td><td>22.0</td></tr><tr><td>3.56</td><td>4.0</td><td>1.0</td><td>1</td><td>107</td><td>263</td><td>12.0</td></tr><tr><td>1.08</td><td>3.0</td><td>1.0</td><td>1</td><td>48</td><td>68</td><td>5.5</td></tr><tr><td>11.63</td><td>1.0</td><td>1.0</td><td>2</td><td>132</td><td>61</td><td>34.5</td></tr><tr><td>0.85</td><td>1.0</td><td>1.0</td><td>1</td><td>107</td><td>170</td><td>5.0</td></tr><tr><td>3.74</td><td>1.0</td><td>1.0</td><td>2</td><td>164</td><td>13</td><td>12.5</td></tr><tr><td>0.64</td><td>1.0</td><td>1.0</td><td>2</td><td>261</td><td>13</td><td>4.5</td></tr><tr><td>3.4</td><td>1.0</td><td>1.0</td><td>1</td><td>249</td><td>65</td><td>15.0</td></tr><tr><td>1.5</td><td>2.0</td><td>1.0</td><td>1</td><td>100</td><td>249</td><td>7.5</td></tr><tr><td>1.8</td><td>1.0</td><td>1.0</td><td>1</td><td>246</td><td>143</td><td>7.5</td></tr><tr><td>0.9</td><td>1.0</td><td>1.0</td><td>1</td><td>137</td><td>233</td><td>5.5</td></tr><tr><td>1.2</td><td>2.0</td><td>1.0</td><td>2</td><td>264</td><td>107</td><td>7.0</td></tr><tr><td>15.93</td><td>3.0</td><td>2.0</td><td>1</td><td>10</td><td>50</td><td>52.0</td></tr><tr><td>17.39</td><td>1.0</td><td>1.0</td><td>2</td><td>138</td><td>67</td><td>46.5</td></tr><tr><td>1.0</td><td>1.0</td><td>1.0</td><td>1</td><td>236</td><td>75</td><td>5.0</td></tr><tr><td>0.57</td><td>1.0</td><td>1.0</td><td>1</td><td>230</td><td>50</td><td>4.5</td></tr><tr><td>2.83</td><td>1.0</td><td>1.0</td><td>1</td><td>48</td><td>238</td><td>10.0</td></tr><tr><td>0.97</td><td>1.0</td><td>1.0</td><td>2</td><td>142</td><td>43</td><td>5.0</td></tr><tr><td>17.2</td><td>1.0</td><td>2.0</td><td>1</td><td>132</td><td>140</td><td>52.0</td></tr><tr><td>3.87</td><td>1.0</td><td>1.0</td><td>1</td><td>50</td><td>263</td><td>14.0</td></tr><tr><td>5.14</td><td>1.0</td><td>1.0</td><td>1</td><td>48</td><td>231</td><td>19.5</td></tr><tr><td>1.7</td><td>1.0</td><td>1.0</td><td>1</td><td>186</td><td>234</td><td>10.0</td></tr><tr><td>1.3</td><td>2.0</td><td>1.0</td><td>1</td><td>164</td><td>230</td><td>7.0</td></tr><tr><td>5.6</td><td>2.0</td><td>1.0</td><td>1</td><td>48</td><td>87</td><td>20.5</td></tr><tr><td>6.9</td><td>1.0</td><td>1.0</td><td>1</td><td>87</td><td>142</td><td>21.5</td></tr><tr><td>1.28</td><td>1.0</td><td>1.0</td><td>1</td><td>148</td><td>125</td><td>7.0</td></tr><tr><td>1.2</td><td>1.0</td><td>1.0</td><td>2</td><td>48</td><td>186</td><td>6.0</td></tr><tr><td>17.6</td><td>1.0</td><td>2.0</td><td>1</td><td>132</td><td>79</td><td>52.0</td></tr><tr><td>1.0</td><td>1.0</td><td>1.0</td><td>1</td><td>79</td><td>45</td><td>9.0</td></tr><tr><td>5.73</td><td>1.0</td><td>1.0</td><td>1</td><td>237</td><td>13</td><td>20.0</td></tr><tr><td>6.89</td><td>1.0</td><td>1.0</td><td>1</td><td>236</td><td>231</td><td>23.0</td></tr><tr><td>2.92</td><td>1.0</td><td>1.0</td><td>1</td><td>144</td><td>80</td><td>12.5</td></tr><tr><td>2.04</td><td>1.0</td><td>1.0</td><td>1</td><td>249</td><td>209</td><td>10.0</td></tr><tr><td>3.62</td><td>1.0</td><td>1.0</td><td>1</td><td>249</td><td>256</td><td>15.0</td></tr><tr><td>6.69</td><td>2.0</td><td>1.0</td><td>1</td><td>230</td><td>238</td><td>25.5</td></tr><tr><td>3.52</td><td>1.0</td><td>1.0</td><td>1</td><td>148</td><td>112</td><td>14.0</td></tr><tr><td>2.1</td><td>1.0</td><td>1.0</td><td>1</td><td>162</td><td>263</td><td>8.5</td></tr><tr><td>20.39</td><td>1.0</td><td>1.0</td><td>1</td><td>132</td><td>52</td><td>55.0</td></tr><tr><td>2.8</td><td>0.0</td><td>1.0</td><td>1</td><td>231</td><td>186</td><td>10.0</td></tr><tr><td>0.93</td><td>2.0</td><td>1.0</td><td>1</td><td>144</td><td>231</td><td>5.0</td></tr><tr><td>1.35</td><td>1.0</td><td>1.0</td><td>2</td><td>231</td><td>114</td><td>5.5</td></tr><tr><td>1.66</td><td>2.0</td><td>1.0</td><td>1</td><td>114</td><td>164</td><td>7.5</td></tr><tr><td>1.32</td><td>1.0</td><td>1.0</td><td>1</td><td>164</td><td>48</td><td>7.5</td></tr><tr><td>5.19</td><td>1.0</td><td>1.0</td><td>1</td><td>164</td><td>74</td><td>17.0</td></tr><tr><td>0.8</td><td>1.0</td><td>1.0</td><td>1</td><td>231</td><td>13</td><td>5.0</td></tr><tr><td>4.0</td><td>1.0</td><td>1.0</td><td>1</td><td>231</td><td>230</td><td>15.0</td></tr><tr><td>20.11</td><td>2.0</td><td>2.0</td><td>1</td><td>132</td><td>87</td><td>52.0</td></tr><tr><td>2.2</td><td>1.0</td><td>1.0</td><td>1</td><td>141</td><td>170</td><td>8.0</td></tr><tr><td>16.4</td><td>1.0</td><td>1.0</td><td>4</td><td>234</td><td>76</td><td>51.5</td></tr><tr><td>0.69</td><td>1.0</td><td>1.0</td><td>2</td><td>68</td><td>48</td><td>4.5</td></tr><tr><td>0.76</td><td>1.0</td><td>1.0</td><td>2</td><td>48</td><td>230</td><td>4.5</td></tr><tr><td>1.12</td><td>1.0</td><td>1.0</td><td>1</td><td>230</td><td>229</td><td>6.0</td></tr><tr><td>21.6</td><td>1.0</td><td>2.0</td><td>2</td><td>132</td><td>161</td><td>52.0</td></tr><tr><td>2.6</td><td>0.0</td><td>1.0</td><td>1</td><td>68</td><td>229</td><td>11.0</td></tr><tr><td>2.79</td><td>1.0</td><td>1.0</td><td>1</td><td>148</td><td>233</td><td>12.0</td></tr><tr><td>10.12</td><td>1.0</td><td>1.0</td><td>1</td><td>233</td><td>135</td><td>36.5</td></tr><tr><td>2.4</td><td>1.0</td><td>1.0</td><td>1</td><td>48</td><td>158</td><td>10.0</td></tr><tr><td>1.08</td><td>1.0</td><td>1.0</td><td>2</td><td>249</td><td>107</td><td>6.0</td></tr><tr><td>4.5</td><td>1.0</td><td>1.0</td><td>1</td><td>106</td><td>256</td><td>22.0</td></tr><tr><td>3.75</td><td>1.0</td><td>1.0</td><td>1</td><td>158</td><td>229</td><td>15.0</td></tr><tr><td>1.89</td><td>1.0</td><td>1.0</td><td>1</td><td>162</td><td>68</td><td>9.0</td></tr><tr><td>21.9</td><td>1.0</td><td>2.0</td><td>1</td><td>132</td><td>48</td><td>52.0</td></tr><tr><td>1.34</td><td>1.0</td><td>1.0</td><td>1</td><td>100</td><td>68</td><td>6.0</td></tr><tr><td>2.34</td><td>1.0</td><td>1.0</td><td>1</td><td>158</td><td>232</td><td>12.0</td></tr><tr><td>7.41</td><td>1.0</td><td>1.0</td><td>1</td><td>138</td><td>236</td><td>22.0</td></tr><tr><td>0.92</td><td>1.0</td><td>1.0</td><td>2</td><td>209</td><td>232</td><td>5.0</td></tr><tr><td>0.82</td><td>1.0</td><td>1.0</td><td>2</td><td>232</td><td>148</td><td>5.5</td></tr><tr><td>1.78</td><td>1.0</td><td>1.0</td><td>1</td><td>79</td><td>170</td><td>8.0</td></tr><tr><td>1.44</td><td>1.0</td><td>1.0</td><td>1</td><td>161</td><td>50</td><td>7.0</td></tr><tr><td>0.83</td><td>2.0</td><td>1.0</td><td>1</td><td>48</td><td>100</td><td>6.0</td></tr><tr><td>0.73</td><td>2.0</td><td>1.0</td><td>1</td><td>164</td><td>170</td><td>5.5</td></tr><tr><td>11.0</td><td>2.0</td><td>1.0</td><td>1</td><td>234</td><td>127</td><td>35.5</td></tr><tr><td>0.4</td><td>1.0</td><td>1.0</td><td>1</td><td>68</td><td>68</td><td>3.5</td></tr><tr><td>1.8</td><td>1.0</td><td>1.0</td><td>1</td><td>68</td><td>233</td><td>9.5</td></tr><tr><td>0.61</td><td>2.0</td><td>1.0</td><td>1</td><td>231</td><td>125</td><td>4.5</td></tr><tr><td>9.34</td><td>1.0</td><td>1.0</td><td>2</td><td>132</td><td>95</td><td>27.5</td></tr><tr><td>4.7</td><td>2.0</td><td>1.0</td><td>1</td><td>230</td><td>7</td><td>18.5</td></tr><tr><td>18.1</td><td>1.0</td><td>2.0</td><td>1</td><td>132</td><td>4</td><td>52.0</td></tr><tr><td>19.41</td><td>1.0</td><td>5.0</td><td>1</td><td>230</td><td>265</td><td>80.0</td></tr><tr><td>17.6</td><td>1.0</td><td>2.0</td><td>2</td><td>132</td><td>48</td><td>52.0</td></tr><tr><td>2.0</td><td>1.0</td><td>1.0</td><td>2</td><td>263</td><td>233</td><td>7.5</td></tr><tr><td>7.5</td><td>1.0</td><td>1.0</td><td>1</td><td>138</td><td>229</td><td>24.0</td></tr><tr><td>1.5</td><td>2.0</td><td>1.0</td><td>2</td><td>45</td><td>148</td><td>9.0</td></tr><tr><td>2.05</td><td>2.0</td><td>1.0</td><td>2</td><td>48</td><td>236</td><td>9.5</td></tr><tr><td>0.6</td><td>1.0</td><td>1.0</td><td>2</td><td>49</td><td>189</td><td>5.0</td></tr><tr><td>3.0</td><td>1.0</td><td>1.0</td><td>1</td><td>170</td><td>231</td><td>14.0</td></tr><tr><td>2.9</td><td>1.0</td><td>1.0</td><td>1</td><td>261</td><td>224</td><td>12.0</td></tr><tr><td>2.48</td><td>1.0</td><td>1.0</td><td>1</td><td>66</td><td>234</td><td>9.5</td></tr><tr><td>18.99</td><td>3.0</td><td>2.0</td><td>1</td><td>132</td><td>114</td><td>52.0</td></tr><tr><td>0.36</td><td>2.0</td><td>1.0</td><td>1</td><td>161</td><td>162</td><td>4.0</td></tr><tr><td>2.56</td><td>2.0</td><td>1.0</td><td>1</td><td>162</td><td>263</td><td>10.0</td></tr><tr><td>18.36</td><td>1.0</td><td>2.0</td><td>1</td><td>132</td><td>236</td><td>52.0</td></tr><tr><td>10.1</td><td>1.0</td><td>1.0</td><td>1</td><td>138</td><td>246</td><td>29.0</td></tr><tr><td>1.0</td><td>3.0</td><td>1.0</td><td>2</td><td>179</td><td>7</td><td>6.0</td></tr><tr><td>4.0</td><td>1.0</td><td>1.0</td><td>2</td><td>261</td><td>79</td><td>14.5</td></tr><tr><td>4.1</td><td>1.0</td><td>1.0</td><td>1</td><td>79</td><td>143</td><td>16.5</td></tr><tr><td>1.06</td><td>2.0</td><td>1.0</td><td>1</td><td>113</td><td>148</td><td>7.5</td></tr><tr><td>1.73</td><td>2.0</td><td>1.0</td><td>1</td><td>148</td><td>87</td><td>8.5</td></tr><tr><td>1.1</td><td>2.0</td><td>1.0</td><td>1</td><td>211</td><td>79</td><td>6.5</td></tr><tr><td>1.5</td><td>2.0</td><td>1.0</td><td>2</td><td>79</td><td>249</td><td>7.5</td></tr><tr><td>0.6</td><td>1.0</td><td>1.0</td><td>1</td><td>164</td><td>170</td><td>5.0</td></tr><tr><td>2.36</td><td>1.0</td><td>1.0</td><td>1</td><td>231</td><td>66</td><td>10.5</td></tr><tr><td>2.88</td><td>1.0</td><td>1.0</td><td>1</td><td>48</td><td>238</td><td>10.0</td></tr><tr><td>1.55</td><td>1.0</td><td>1.0</td><td>1</td><td>48</td><td>186</td><td>7.5</td></tr><tr><td>0.81</td><td>2.0</td><td>1.0</td><td>1</td><td>43</td><td>75</td><td>5.5</td></tr><tr><td>7.28</td><td>2.0</td><td>1.0</td><td>1</td><td>236</td><td>244</td><td>23.0</td></tr><tr><td>2.33</td><td>2.0</td><td>1.0</td><td>2</td><td>116</td><td>238</td><td>10.5</td></tr><tr><td>9.51</td><td>1.0</td><td>1.0</td><td>1</td><td>138</td><td>234</td><td>27.5</td></tr><tr><td>2.56</td><td>1.0</td><td>1.0</td><td>1</td><td>234</td><td>231</td><td>10.0</td></tr><tr><td>2.2</td><td>1.0</td><td>1.0</td><td>1</td><td>114</td><td>68</td><td>8.5</td></tr><tr><td>0.7</td><td>1.0</td><td>1.0</td><td>1</td><td>50</td><td>68</td><td>4.5</td></tr><tr><td>18.58</td><td>1.0</td><td>2.0</td><td>1</td><td>132</td><td>236</td><td>52.0</td></tr><tr><td>0.7</td><td>1.0</td><td>1.0</td><td>2</td><td>114</td><td>114</td><td>6.0</td></tr><tr><td>0.7</td><td>1.0</td><td>1.0</td><td>1</td><td>114</td><td>249</td><td>5.0</td></tr><tr><td>4.2</td><td>1.0</td><td>1.0</td><td>1</td><td>90</td><td>263</td><td>14.5</td></tr><tr><td>3.72</td><td>1.0</td><td>1.0</td><td>1</td><td>79</td><td>255</td><td>19.0</td></tr><tr><td>5.32</td><td>1.0</td><td>1.0</td><td>2</td><td>79</td><td>226</td><td>19.0</td></tr><tr><td>1.4</td><td>0.0</td><td>1.0</td><td>1</td><td>161</td><td>186</td><td>8.5</td></tr><tr><td>3.2</td><td>1.0</td><td>1.0</td><td>1</td><td>100</td><td>238</td><td>13.5</td></tr><tr><td>1.47</td><td>2.0</td><td>1.0</td><td>1</td><td>88</td><td>231</td><td>6.5</td></tr><tr><td>6.35</td><td>3.0</td><td>1.0</td><td>1</td><td>13</td><td>233</td><td>18.5</td></tr><tr><td>2.1</td><td>1.0</td><td>1.0</td><td>1</td><td>68</td><td>233</td><td>10.5</td></tr><tr><td>1.44</td><td>1.0</td><td>1.0</td><td>1</td><td>161</td><td>90</td><td>6.5</td></tr><tr><td>0.5</td><td>1.0</td><td>1.0</td><td>2</td><td>163</td><td>50</td><td>4.0</td></tr><tr><td>9.4</td><td>1.0</td><td>1.0</td><td>1</td><td>138</td><td>249</td><td>27.5</td></tr><tr><td>1.8</td><td>1.0</td><td>1.0</td><td>2</td><td>229</td><td>48</td><td>9.5</td></tr><tr><td>1.8</td><td>1.0</td><td>1.0</td><td>1</td><td>162</td><td>100</td><td>8.5</td></tr><tr><td>1.5</td><td>1.0</td><td>1.0</td><td>1</td><td>100</td><td>162</td><td>7.5</td></tr><tr><td>0.84</td><td>5.0</td><td>1.0</td><td>2</td><td>230</td><td>161</td><td>5.5</td></tr><tr><td>1.06</td><td>6.0</td><td>1.0</td><td>1</td><td>161</td><td>233</td><td>6.5</td></tr><tr><td>19.02</td><td>2.0</td><td>2.0</td><td>1</td><td>132</td><td>234</td><td>52.0</td></tr><tr><td>3.15</td><td>1.0</td><td>1.0</td><td>2</td><td>146</td><td>230</td><td>12.5</td></tr><tr><td>2.12</td><td>1.0</td><td>1.0</td><td>2</td><td>230</td><td>141</td><td>9.5</td></tr><tr><td>1.13</td><td>1.0</td><td>1.0</td><td>1</td><td>100</td><td>170</td><td>6.0</td></tr><tr><td>1.96</td><td>1.0</td><td>1.0</td><td>1</td><td>148</td><td>107</td><td>9.0</td></tr><tr><td>3.56</td><td>1.0</td><td>1.0</td><td>1</td><td>100</td><td>263</td><td>13.5</td></tr><tr><td>4.43</td><td>2.0</td><td>1.0</td><td>1</td><td>263</td><td>186</td><td>16.5</td></tr><tr><td>1.6</td><td>1.0</td><td>1.0</td><td>1</td><td>148</td><td>87</td><td>11.5</td></tr><tr><td>2.55</td><td>1.0</td><td>1.0</td><td>1</td><td>90</td><td>87</td><td>10.5</td></tr><tr><td>12.93</td><td>1.0</td><td>1.0</td><td>2</td><td>97</td><td>57</td><td>40.5</td></tr><tr><td>18.2</td><td>1.0</td><td>2.0</td><td>1</td><td>132</td><td>142</td><td>52.0</td></tr><tr><td>0.5</td><td>1.0</td><td>1.0</td><td>1</td><td>231</td><td>125</td><td>4.5</td></tr><tr><td>1.1</td><td>1.0</td><td>1.0</td><td>1</td><td>231</td><td>87</td><td>5.5</td></tr><tr><td>8.6</td><td>1.0</td><td>1.0</td><td>1</td><td>113</td><td>116</td><td>29.0</td></tr><tr><td>1.53</td><td>1.0</td><td>1.0</td><td>1</td><td>263</td><td>75</td><td>6.0</td></tr><tr><td>1.0</td><td>1.0</td><td>1.0</td><td>2</td><td>41</td><td>42</td><td>6.0</td></tr><tr><td>1.89</td><td>1.0</td><td>1.0</td><td>1</td><td>141</td><td>233</td><td>8.0</td></tr><tr><td>1.01</td><td>1.0</td><td>1.0</td><td>1</td><td>137</td><td>186</td><td>6.5</td></tr><tr><td>1.2</td><td>4.0</td><td>1.0</td><td>2</td><td>162</td><td>43</td><td>7.5</td></tr><tr><td>0.92</td><td>4.0</td><td>1.0</td><td>1</td><td>163</td><td>163</td><td>6.0</td></tr><tr><td>7.73</td><td>1.0</td><td>1.0</td><td>1</td><td>249</td><td>36</td><td>27.0</td></tr><tr><td>15.46</td><td>2.0</td><td>1.0</td><td>1</td><td>132</td><td>89</td><td>44.0</td></tr><tr><td>0.22</td><td>3.0</td><td>1.0</td><td>2</td><td>249</td><td>249</td><td>3.0</td></tr><tr><td>3.22</td><td>3.0</td><td>1.0</td><td>2</td><td>249</td><td>142</td><td>12.5</td></tr><tr><td>4.56</td><td>3.0</td><td>1.0</td><td>2</td><td>48</td><td>7</td><td>17.0</td></tr><tr><td>18.0</td><td>2.0</td><td>2.0</td><td>1</td><td>132</td><td>148</td><td>52.0</td></tr><tr><td>2.4</td><td>1.0</td><td>1.0</td><td>1</td><td>142</td><td>236</td><td>10.0</td></tr><tr><td>3.1</td><td>2.0</td><td>1.0</td><td>1</td><td>234</td><td>141</td><td>12.0</td></tr><tr><td>2.2</td><td>1.0</td><td>1.0</td><td>1</td><td>161</td><td>224</td><td>10.0</td></tr><tr><td>4.5</td><td>1.0</td><td>1.0</td><td>1</td><td>100</td><td>75</td><td>16.5</td></tr><tr><td>1.0</td><td>1.0</td><td>1.0</td><td>1</td><td>137</td><td>186</td><td>6.0</td></tr><tr><td>2.1</td><td>1.0</td><td>1.0</td><td>1</td><td>158</td><td>170</td><td>9.0</td></tr><tr><td>4.24</td><td>5.0</td><td>1.0</td><td>1</td><td>137</td><td>87</td><td>17.5</td></tr><tr><td>8.29</td><td>1.0</td><td>1.0</td><td>2</td><td>209</td><td>77</td><td>28.5</td></tr><tr><td>2.6</td><td>1.0</td><td>1.0</td><td>1</td><td>181</td><td>89</td><td>11.5</td></tr><tr><td>3.2</td><td>1.0</td><td>1.0</td><td>1</td><td>231</td><td>137</td><td>14.0</td></tr><tr><td>1.64</td><td>1.0</td><td>1.0</td><td>2</td><td>161</td><td>237</td><td>7.0</td></tr><tr><td>1.64</td><td>1.0</td><td>1.0</td><td>1</td><td>237</td><td>263</td><td>7.5</td></tr><tr><td>0.93</td><td>1.0</td><td>1.0</td><td>1</td><td>230</td><td>186</td><td>6.0</td></tr><tr><td>5.82</td><td>1.0</td><td>1.0</td><td>1</td><td>249</td><td>226</td><td>21.0</td></tr><tr><td>0.6</td><td>1.0</td><td>1.0</td><td>1</td><td>142</td><td>239</td><td>4.5</td></tr><tr><td>2.56</td><td>4.0</td><td>1.0</td><td>1</td><td>161</td><td>75</td><td>9.5</td></tr><tr><td>0.71</td><td>2.0</td><td>1.0</td><td>1</td><td>140</td><td>262</td><td>4.0</td></tr><tr><td>1.93</td><td>2.0</td><td>1.0</td><td>1</td><td>233</td><td>79</td><td>9.0</td></tr><tr><td>3.29</td><td>2.0</td><td>1.0</td><td>2</td><td>79</td><td>141</td><td>10.5</td></tr><tr><td>0.35</td><td>1.0</td><td>1.0</td><td>2</td><td>162</td><td>229</td><td>5.0</td></tr><tr><td>6.4</td><td>1.0</td><td>1.0</td><td>1</td><td>231</td><td>36</td><td>25.5</td></tr><tr><td>3.98</td><td>1.0</td><td>1.0</td><td>1</td><td>140</td><td>114</td><td>16.5</td></tr><tr><td>7.8</td><td>3.0</td><td>1.0</td><td>1</td><td>79</td><td>239</td><td>25.5</td></tr><tr><td>11.29</td><td>1.0</td><td>1.0</td><td>1</td><td>138</td><td>230</td><td>32.5</td></tr><tr><td>2.3</td><td>1.0</td><td>1.0</td><td>1</td><td>144</td><td>170</td><td>9.5</td></tr><tr><td>3.9</td><td>2.0</td><td>1.0</td><td>1</td><td>170</td><td>239</td><td>13.0</td></tr><tr><td>1.87</td><td>2.0</td><td>1.0</td><td>1</td><td>161</td><td>107</td><td>9.5</td></tr><tr><td>1.66</td><td>2.0</td><td>1.0</td><td>2</td><td>107</td><td>114</td><td>8.5</td></tr><tr><td>3.08</td><td>1.0</td><td>1.0</td><td>1</td><td>50</td><td>107</td><td>13.5</td></tr><tr><td>3.05</td><td>1.0</td><td>1.0</td><td>1</td><td>249</td><td>50</td><td>12.0</td></tr><tr><td>1.6</td><td>1.0</td><td>1.0</td><td>1</td><td>79</td><td>211</td><td>9.0</td></tr><tr><td>1.9</td><td>1.0</td><td>1.0</td><td>2</td><td>211</td><td>68</td><td>8.0</td></tr><tr><td>0.3</td><td>1.0</td><td>1.0</td><td>1</td><td>107</td><td>107</td><td>6.0</td></tr><tr><td>9.79</td><td>1.0</td><td>1.0</td><td>2</td><td>137</td><td>235</td><td>32.0</td></tr><tr><td>5.26</td><td>1.0</td><td>1.0</td><td>2</td><td>4</td><td>52</td><td>17.0</td></tr><tr><td>7.42</td><td>3.0</td><td>1.0</td><td>2</td><td>68</td><td>225</td><td>29.5</td></tr><tr><td>1.75</td><td>1.0</td><td>1.0</td><td>1</td><td>137</td><td>114</td><td>10.5</td></tr><tr><td>2.3</td><td>3.0</td><td>1.0</td><td>2</td><td>114</td><td>48</td><td>9.5</td></tr><tr><td>1.72</td><td>2.0</td><td>1.0</td><td>1</td><td>48</td><td>164</td><td>7.5</td></tr><tr><td>1.96</td><td>5.0</td><td>1.0</td><td>1</td><td>142</td><td>237</td><td>9.0</td></tr><tr><td>3.1</td><td>5.0</td><td>1.0</td><td>1</td><td>238</td><td>244</td><td>13.5</td></tr><tr><td>3.69</td><td>1.0</td><td>1.0</td><td>1</td><td>230</td><td>144</td><td>16.0</td></tr><tr><td>6.11</td><td>1.0</td><td>1.0</td><td>1</td><td>231</td><td>263</td><td>20.0</td></tr><tr><td>1.16</td><td>5.0</td><td>1.0</td><td>1</td><td>161</td><td>48</td><td>7.0</td></tr><tr><td>6.34</td><td>5.0</td><td>1.0</td><td>1</td><td>48</td><td>83</td><td>22.5</td></tr><tr><td>0.4</td><td>1.0</td><td>1.0</td><td>2</td><td>79</td><td>4</td><td>4.5</td></tr><tr><td>1.8</td><td>1.0</td><td>1.0</td><td>1</td><td>4</td><td>249</td><td>9.5</td></tr><tr><td>6.0</td><td>1.0</td><td>1.0</td><td>2</td><td>79</td><td>17</td><td>30.0</td></tr><tr><td>1.3</td><td>1.0</td><td>1.0</td><td>1</td><td>162</td><td>107</td><td>7.0</td></tr><tr><td>3.6</td><td>2.0</td><td>1.0</td><td>1</td><td>234</td><td>263</td><td>14.0</td></tr><tr><td>1.93</td><td>1.0</td><td>1.0</td><td>1</td><td>163</td><td>141</td><td>8.0</td></tr><tr><td>0.61</td><td>1.0</td><td>1.0</td><td>2</td><td>41</td><td>41</td><td>5.0</td></tr><tr><td>0.91</td><td>1.0</td><td>1.0</td><td>2</td><td>42</td><td>41</td><td>5.5</td></tr><tr><td>0.45</td><td>1.0</td><td>1.0</td><td>1</td><td>41</td><td>41</td><td>3.5</td></tr><tr><td>1.01</td><td>1.0</td><td>1.0</td><td>1</td><td>237</td><td>263</td><td>5.5</td></tr><tr><td>2.07</td><td>1.0</td><td>1.0</td><td>1</td><td>236</td><td>229</td><td>9.5</td></tr><tr><td>2.04</td><td>2.0</td><td>1.0</td><td>1</td><td>239</td><td>263</td><td>8.5</td></tr><tr><td>3.7</td><td>2.0</td><td>1.0</td><td>1</td><td>141</td><td>90</td><td>15.0</td></tr><tr><td>5.02</td><td>2.0</td><td>1.0</td><td>1</td><td>90</td><td>256</td><td>18.0</td></tr><tr><td>1.94</td><td>2.0</td><td>1.0</td><td>1</td><td>170</td><td>246</td><td>8.5</td></tr><tr><td>5.91</td><td>2.0</td><td>1.0</td><td>2</td><td>48</td><td>87</td><td>20.5</td></tr><tr><td>0.55</td><td>1.0</td><td>1.0</td><td>1</td><td>211</td><td>211</td><td>4.5</td></tr><tr><td>4.93</td><td>1.0</td><td>1.0</td><td>1</td><td>211</td><td>188</td><td>19.0</td></tr><tr><td>4.56</td><td>1.0</td><td>1.0</td><td>1</td><td>249</td><td>256</td><td>22.5</td></tr><tr><td>3.84</td><td>1.0</td><td>1.0</td><td>1</td><td>255</td><td>66</td><td>13.0</td></tr><tr><td>1.85</td><td>1.0</td><td>1.0</td><td>1</td><td>88</td><td>232</td><td>9.5</td></tr><tr><td>1.6</td><td>1.0</td><td>1.0</td><td>1</td><td>114</td><td>148</td><td>8.5</td></tr><tr><td>1.3</td><td>1.0</td><td>1.0</td><td>2</td><td>148</td><td>87</td><td>7.0</td></tr><tr><td>2.8</td><td>5.0</td><td>1.0</td><td>1</td><td>255</td><td>17</td><td>12.5</td></tr><tr><td>1.2</td><td>2.0</td><td>1.0</td><td>1</td><td>234</td><td>79</td><td>7.0</td></tr><tr><td>1.2</td><td>1.0</td><td>1.0</td><td>1</td><td>65</td><td>49</td><td>6.0</td></tr><tr><td>2.39</td><td>1.0</td><td>1.0</td><td>2</td><td>163</td><td>75</td><td>9.5</td></tr><tr><td>21.3</td><td>1.0</td><td>2.0</td><td>2</td><td>132</td><td>239</td><td>52.0</td></tr><tr><td>5.0</td><td>2.0</td><td>1.0</td><td>1</td><td>79</td><td>61</td><td>22.5</td></tr><tr><td>1.7</td><td>1.0</td><td>1.0</td><td>1</td><td>162</td><td>263</td><td>7.5</td></tr><tr><td>0.7</td><td>1.0</td><td>1.0</td><td>1</td><td>141</td><td>236</td><td>4.5</td></tr><tr><td>2.77</td><td>1.0</td><td>1.0</td><td>1</td><td>163</td><td>140</td><td>11.5</td></tr><tr><td>5.53</td><td>1.0</td><td>1.0</td><td>1</td><td>48</td><td>116</td><td>17.5</td></tr><tr><td>7.2</td><td>2.0</td><td>1.0</td><td>1</td><td>230</td><td>49</td><td>27.0</td></tr><tr><td>5.2</td><td>1.0</td><td>1.0</td><td>1</td><td>236</td><td>255</td><td>18.0</td></tr><tr><td>2.31</td><td>1.0</td><td>1.0</td><td>1</td><td>100</td><td>125</td><td>8.5</td></tr><tr><td>3.02</td><td>1.0</td><td>1.0</td><td>2</td><td>125</td><td>48</td><td>12.0</td></tr><tr><td>2.5</td><td>2.0</td><td>1.0</td><td>2</td><td>43</td><td>229</td><td>11.5</td></tr><tr><td>0.51</td><td>2.0</td><td>1.0</td><td>2</td><td>229</td><td>162</td><td>5.0</td></tr><tr><td>1.19</td><td>2.0</td><td>1.0</td><td>2</td><td>231</td><td>114</td><td>6.5</td></tr><tr><td>8.79</td><td>1.0</td><td>1.0</td><td>1</td><td>138</td><td>140</td><td>25.5</td></tr><tr><td>0.88</td><td>1.0</td><td>1.0</td><td>1</td><td>237</td><td>229</td><td>5.5</td></tr><tr><td>0.73</td><td>2.0</td><td>1.0</td><td>2</td><td>234</td><td>68</td><td>6.0</td></tr><tr><td>2.03</td><td>2.0</td><td>1.0</td><td>2</td><td>68</td><td>170</td><td>10.0</td></tr><tr><td>1.71</td><td>2.0</td><td>1.0</td><td>1</td><td>148</td><td>87</td><td>8.0</td></tr><tr><td>0.38</td><td>1.0</td><td>1.0</td><td>1</td><td>235</td><td>235</td><td>3.5</td></tr><tr><td>4.7</td><td>1.0</td><td>1.0</td><td>2</td><td>48</td><td>226</td><td>16.5</td></tr><tr><td>1.9</td><td>1.0</td><td>1.0</td><td>1</td><td>141</td><td>137</td><td>8.0</td></tr><tr><td>0.8</td><td>1.0</td><td>1.0</td><td>1</td><td>264</td><td>140</td><td>4.5</td></tr><tr><td>2.6</td><td>1.0</td><td>1.0</td><td>1</td><td>114</td><td>246</td><td>12.0</td></tr><tr><td>1.7</td><td>1.0</td><td>1.0</td><td>1</td><td>246</td><td>143</td><td>7.0</td></tr><tr><td>2.16</td><td>2.0</td><td>1.0</td><td>2</td><td>186</td><td>79</td><td>12.0</td></tr><tr><td>15.4</td><td>1.0</td><td>1.0</td><td>1</td><td>132</td><td>7</td><td>42.5</td></tr><tr><td>1.4</td><td>1.0</td><td>1.0</td><td>1</td><td>264</td><td>107</td><td>8.0</td></tr><tr><td>0.3</td><td>2.0</td><td>1.0</td><td>1</td><td>79</td><td>79</td><td>3.5</td></tr><tr><td>0.3</td><td>1.0</td><td>1.0</td><td>1</td><td>79</td><td>79</td><td>3.5</td></tr><tr><td>0.6</td><td>1.0</td><td>1.0</td><td>1</td><td>79</td><td>137</td><td>4.0</td></tr><tr><td>1.08</td><td>1.0</td><td>1.0</td><td>1</td><td>79</td><td>144</td><td>7.5</td></tr><tr><td>3.93</td><td>1.0</td><td>1.0</td><td>1</td><td>114</td><td>142</td><td>14.5</td></tr><tr><td>5.25</td><td>1.0</td><td>1.0</td><td>1</td><td>264</td><td>264</td><td>18.0</td></tr><tr><td>0.94</td><td>1.0</td><td>1.0</td><td>1</td><td>140</td><td>140</td><td>5.0</td></tr><tr><td>1.56</td><td>1.0</td><td>1.0</td><td>1</td><td>79</td><td>158</td><td>7.5</td></tr><tr><td>5.92</td><td>1.0</td><td>1.0</td><td>2</td><td>79</td><td>7</td><td>22.0</td></tr><tr><td>1.69</td><td>2.0</td><td>1.0</td><td>1</td><td>148</td><td>249</td><td>11.0</td></tr><tr><td>5.24</td><td>2.0</td><td>1.0</td><td>1</td><td>249</td><td>112</td><td>19.5</td></tr><tr><td>1.7</td><td>2.0</td><td>1.0</td><td>2</td><td>230</td><td>143</td><td>8.0</td></tr><tr><td>1.6</td><td>1.0</td><td>1.0</td><td>2</td><td>114</td><td>209</td><td>8.5</td></tr><tr><td>7.26</td><td>1.0</td><td>1.0</td><td>1</td><td>125</td><td>37</td><td>26.0</td></tr><tr><td>1.74</td><td>1.0</td><td>1.0</td><td>1</td><td>237</td><td>151</td><td>8.0</td></tr><tr><td>0.83</td><td>1.0</td><td>1.0</td><td>2</td><td>246</td><td>68</td><td>5.0</td></tr><tr><td>2.04</td><td>1.0</td><td>1.0</td><td>1</td><td>162</td><td>246</td><td>9.5</td></tr><tr><td>1.9</td><td>3.0</td><td>1.0</td><td>1</td><td>230</td><td>107</td><td>9.0</td></tr><tr><td>3.1</td><td>1.0</td><td>1.0</td><td>1</td><td>162</td><td>79</td><td>12.5</td></tr><tr><td>1.16</td><td>5.0</td><td>1.0</td><td>1</td><td>211</td><td>158</td><td>7.0</td></tr><tr><td>2.2</td><td>2.0</td><td>1.0</td><td>2</td><td>114</td><td>261</td><td>10.0</td></tr><tr><td>18.93</td><td>1.0</td><td>1.0</td><td>1</td><td>132</td><td>25</td><td>51.5</td></tr><tr><td>0.07</td><td>1.0</td><td>1.0</td><td>2</td><td>106</td><td>106</td><td>2.5</td></tr><tr><td>10.7</td><td>1.0</td><td>1.0</td><td>1</td><td>138</td><td>36</td><td>31.5</td></tr><tr><td>1.84</td><td>2.0</td><td>1.0</td><td>1</td><td>230</td><td>140</td><td>8.5</td></tr><tr><td>2.21</td><td>2.0</td><td>1.0</td><td>1</td><td>230</td><td>114</td><td>9.5</td></tr><tr><td>1.0</td><td>1.0</td><td>1.0</td><td>1</td><td>231</td><td>209</td><td>6.5</td></tr><tr><td>2.9</td><td>1.0</td><td>1.0</td><td>1</td><td>231</td><td>25</td><td>12.5</td></tr><tr><td>6.7</td><td>1.0</td><td>1.0</td><td>1</td><td>231</td><td>80</td><td>25.0</td></tr><tr><td>1.2</td><td>2.0</td><td>1.0</td><td>1</td><td>79</td><td>249</td><td>6.5</td></tr><tr><td>1.4</td><td>1.0</td><td>1.0</td><td>1</td><td>211</td><td>87</td><td>8.0</td></tr><tr><td>1.9</td><td>1.0</td><td>1.0</td><td>1</td><td>261</td><td>148</td><td>9.0</td></tr><tr><td>1.53</td><td>3.0</td><td>1.0</td><td>1</td><td>161</td><td>90</td><td>8.0</td></tr><tr><td>1.54</td><td>2.0</td><td>1.0</td><td>1</td><td>163</td><td>68</td><td>6.5</td></tr><tr><td>1.42</td><td>1.0</td><td>1.0</td><td>2</td><td>148</td><td>231</td><td>7.5</td></tr><tr><td>12.31</td><td>3.0</td><td>1.0</td><td>2</td><td>48</td><td>174</td><td>39.5</td></tr><tr><td>2.03</td><td>1.0</td><td>1.0</td><td>1</td><td>163</td><td>186</td><td>10.0</td></tr><tr><td>3.1</td><td>1.0</td><td>1.0</td><td>2</td><td>246</td><td>148</td><td>14.5</td></tr><tr><td>1.22</td><td>1.0</td><td>1.0</td><td>1</td><td>79</td><td>148</td><td>6.5</td></tr><tr><td>10.3</td><td>2.0</td><td>1.0</td><td>1</td><td>107</td><td>14</td><td>33.0</td></tr><tr><td>5.65</td><td>1.0</td><td>1.0</td><td>1</td><td>141</td><td>80</td><td>21.0</td></tr><tr><td>1.13</td><td>1.0</td><td>1.0</td><td>2</td><td>80</td><td>225</td><td>7.0</td></tr><tr><td>3.7</td><td>1.0</td><td>1.0</td><td>1</td><td>75</td><td>48</td><td>14.0</td></tr><tr><td>0.81</td><td>1.0</td><td>1.0</td><td>1</td><td>170</td><td>164</td><td>4.5</td></tr><tr><td>1.16</td><td>1.0</td><td>1.0</td><td>1</td><td>164</td><td>48</td><td>6.5</td></tr><tr><td>1.21</td><td>2.0</td><td>1.0</td><td>1</td><td>79</td><td>90</td><td>7.5</td></tr><tr><td>1.42</td><td>2.0</td><td>1.0</td><td>1</td><td>68</td><td>164</td><td>7.0</td></tr><tr><td>3.44</td><td>2.0</td><td>1.0</td><td>1</td><td>79</td><td>50</td><td>13.5</td></tr><tr><td>10.56</td><td>2.0</td><td>1.0</td><td>2</td><td>229</td><td>181</td><td>39.5</td></tr><tr><td>4.88</td><td>2.0</td><td>1.0</td><td>1</td><td>142</td><td>7</td><td>18.0</td></tr><tr><td>0.8</td><td>1.0</td><td>1.0</td><td>1</td><td>162</td><td>170</td><td>5.0</td></tr><tr><td>2.9</td><td>2.0</td><td>1.0</td><td>1</td><td>68</td><td>162</td><td>12.5</td></tr><tr><td>5.0</td><td>1.0</td><td>1.0</td><td>1</td><td>107</td><td>181</td><td>22.0</td></tr><tr><td>0.84</td><td>1.0</td><td>1.0</td><td>2</td><td>161</td><td>229</td><td>5.0</td></tr><tr><td>1.72</td><td>1.0</td><td>1.0</td><td>1</td><td>137</td><td>90</td><td>10.0</td></tr><tr><td>1.56</td><td>2.0</td><td>1.0</td><td>1</td><td>164</td><td>224</td><td>8.0</td></tr><tr><td>3.3</td><td>1.0</td><td>1.0</td><td>1</td><td>234</td><td>88</td><td>14.0</td></tr><tr><td>0.97</td><td>1.0</td><td>1.0</td><td>1</td><td>114</td><td>107</td><td>5.5</td></tr><tr><td>4.6</td><td>2.0</td><td>1.0</td><td>1</td><td>79</td><td>181</td><td>17.5</td></tr><tr><td>0.9</td><td>1.0</td><td>1.0</td><td>1</td><td>41</td><td>166</td><td>4.5</td></tr><tr><td>2.53</td><td>3.0</td><td>1.0</td><td>1</td><td>234</td><td>230</td><td>11.5</td></tr><tr><td>3.73</td><td>2.0</td><td>1.0</td><td>2</td><td>164</td><td>263</td><td>13.5</td></tr><tr><td>0.07</td><td>2.0</td><td>1.0</td><td>1</td><td>263</td><td>263</td><td>2.5</td></tr><tr><td>0.83</td><td>2.0</td><td>1.0</td><td>1</td><td>244</td><td>116</td><td>5.0</td></tr><tr><td>3.12</td><td>2.0</td><td>1.0</td><td>1</td><td>239</td><td>74</td><td>11.0</td></tr><tr><td>0.52</td><td>5.0</td><td>1.0</td><td>1</td><td>113</td><td>79</td><td>4.5</td></tr><tr><td>4.02</td><td>2.0</td><td>1.0</td><td>1</td><td>234</td><td>263</td><td>15.0</td></tr><tr><td>0.67</td><td>3.0</td><td>1.0</td><td>2</td><td>237</td><td>229</td><td>5.0</td></tr><tr><td>1.19</td><td>3.0</td><td>1.0</td><td>1</td><td>234</td><td>164</td><td>6.0</td></tr><tr><td>1.35</td><td>3.0</td><td>1.0</td><td>1</td><td>234</td><td>114</td><td>7.0</td></tr><tr><td>8.22</td><td>2.0</td><td>1.0</td><td>1</td><td>138</td><td>230</td><td>28.5</td></tr><tr><td>20.44</td><td>2.0</td><td>2.0</td><td>1</td><td>132</td><td>209</td><td>52.0</td></tr><tr><td>0.44</td><td>2.0</td><td>1.0</td><td>2</td><td>143</td><td>43</td><td>3.5</td></tr><tr><td>1.79</td><td>6.0</td><td>1.0</td><td>2</td><td>239</td><td>24</td><td>7.0</td></tr><tr><td>0.72</td><td>6.0</td><td>1.0</td><td>2</td><td>24</td><td>151</td><td>5.0</td></tr><tr><td>1.8</td><td>6.0</td><td>1.0</td><td>1</td><td>239</td><td>263</td><td>8.5</td></tr><tr><td>5.32</td><td>5.0</td><td>1.0</td><td>1</td><td>48</td><td>7</td><td>19.0</td></tr><tr><td>1.11</td><td>2.0</td><td>1.0</td><td>1</td><td>125</td><td>148</td><td>7.5</td></tr><tr><td>0.87</td><td>2.0</td><td>1.0</td><td>1</td><td>100</td><td>230</td><td>6.0</td></tr><tr><td>1.62</td><td>1.0</td><td>1.0</td><td>2</td><td>113</td><td>164</td><td>7.0</td></tr><tr><td>0.51</td><td>2.0</td><td>1.0</td><td>1</td><td>170</td><td>170</td><td>4.0</td></tr><tr><td>21.25</td><td>1.0</td><td>2.0</td><td>1</td><td>132</td><td>239</td><td>52.0</td></tr><tr><td>2.0</td><td>1.0</td><td>1.0</td><td>2</td><td>79</td><td>100</td><td>8.5</td></tr><tr><td>1.66</td><td>1.0</td><td>1.0</td><td>1</td><td>144</td><td>231</td><td>8.0</td></tr><tr><td>0.95</td><td>1.0</td><td>1.0</td><td>1</td><td>231</td><td>87</td><td>6.5</td></tr><tr><td>4.63</td><td>1.0</td><td>1.0</td><td>1</td><td>148</td><td>263</td><td>16.5</td></tr><tr><td>1.58</td><td>5.0</td><td>1.0</td><td>1</td><td>163</td><td>234</td><td>7.5</td></tr><tr><td>2.65</td><td>1.0</td><td>1.0</td><td>1</td><td>230</td><td>238</td><td>10.0</td></tr><tr><td>5.76</td><td>3.0</td><td>1.0</td><td>2</td><td>48</td><td>116</td><td>19.5</td></tr><tr><td>3.6</td><td>1.0</td><td>1.0</td><td>1</td><td>170</td><td>7</td><td>13.5</td></tr><tr><td>3.58</td><td>1.0</td><td>1.0</td><td>1</td><td>230</td><td>145</td><td>13.0</td></tr><tr><td>1.51</td><td>2.0</td><td>1.0</td><td>1</td><td>145</td><td>112</td><td>7.5</td></tr><tr><td>0.35</td><td>2.0</td><td>1.0</td><td>2</td><td>79</td><td>79</td><td>5.5</td></tr><tr><td>12.1</td><td>0.0</td><td>1.0</td><td>2</td><td>132</td><td>196</td><td>37.0</td></tr><tr><td>0.69</td><td>5.0</td><td>1.0</td><td>1</td><td>234</td><td>224</td><td>5.0</td></tr><tr><td>0.6</td><td>1.0</td><td>1.0</td><td>1</td><td>249</td><td>211</td><td>4.5</td></tr><tr><td>2.4</td><td>1.0</td><td>1.0</td><td>1</td><td>237</td><td>113</td><td>11.5</td></tr><tr><td>1.0</td><td>1.0</td><td>1.0</td><td>1</td><td>164</td><td>233</td><td>6.0</td></tr><tr><td>7.2</td><td>1.0</td><td>1.0</td><td>1</td><td>138</td><td>255</td><td>22.0</td></tr><tr><td>1.5</td><td>1.0</td><td>1.0</td><td>1</td><td>141</td><td>170</td><td>7.5</td></tr><tr><td>5.8</td><td>1.0</td><td>1.0</td><td>1</td><td>170</td><td>25</td><td>24.0</td></tr><tr><td>2.5</td><td>1.0</td><td>1.0</td><td>1</td><td>164</td><td>236</td><td>9.0</td></tr><tr><td>1.75</td><td>3.0</td><td>1.0</td><td>1</td><td>234</td><td>68</td><td>9.5</td></tr><tr><td>1.03</td><td>2.0</td><td>1.0</td><td>1</td><td>249</td><td>90</td><td>5.0</td></tr><tr><td>0.91</td><td>2.0</td><td>1.0</td><td>1</td><td>142</td><td>48</td><td>4.5</td></tr><tr><td>3.24</td><td>2.0</td><td>1.0</td><td>1</td><td>100</td><td>148</td><td>13.0</td></tr><tr><td>1.49</td><td>2.0</td><td>1.0</td><td>1</td><td>148</td><td>107</td><td>7.0</td></tr><tr><td>3.53</td><td>2.0</td><td>1.0</td><td>1</td><td>170</td><td>87</td><td>27.0</td></tr><tr><td>9.91</td><td>1.0</td><td>1.0</td><td>1</td><td>138</td><td>4</td><td>28.0</td></tr><tr><td>3.7</td><td>1.0</td><td>1.0</td><td>1</td><td>137</td><td>75</td><td>12.0</td></tr><tr><td>1.6</td><td>1.0</td><td>1.0</td><td>1</td><td>158</td><td>79</td><td>9.0</td></tr><tr><td>0.7</td><td>1.0</td><td>1.0</td><td>4</td><td>148</td><td>148</td><td>4.5</td></tr><tr><td>0.4</td><td>1.0</td><td>1.0</td><td>1</td><td>79</td><td>79</td><td>5.5</td></tr><tr><td>0.8</td><td>1.0</td><td>1.0</td><td>2</td><td>162</td><td>170</td><td>6.0</td></tr><tr><td>1.6</td><td>0.0</td><td>1.0</td><td>1</td><td>107</td><td>158</td><td>7.5</td></tr><tr><td>4.7</td><td>1.0</td><td>1.0</td><td>1</td><td>48</td><td>226</td><td>16.5</td></tr><tr><td>9.6</td><td>1.0</td><td>1.0</td><td>1</td><td>138</td><td>33</td><td>27.5</td></tr><tr><td>3.1</td><td>1.0</td><td>1.0</td><td>1</td><td>231</td><td>162</td><td>11.5</td></tr><tr><td>0.77</td><td>3.0</td><td>1.0</td><td>2</td><td>79</td><td>148</td><td>9.0</td></tr><tr><td>10.6</td><td>2.0</td><td>1.0</td><td>2</td><td>148</td><td>14</td><td>31.5</td></tr><tr><td>5.74</td><td>1.0</td><td>1.0</td><td>2</td><td>230</td><td>83</td><td>22.0</td></tr><tr><td>1.57</td><td>1.0</td><td>1.0</td><td>1</td><td>87</td><td>144</td><td>7.0</td></tr><tr><td>0.4</td><td>1.0</td><td>1.0</td><td>2</td><td>79</td><td>79</td><td>4.0</td></tr><tr><td>1.22</td><td>2.0</td><td>1.0</td><td>1</td><td>107</td><td>79</td><td>6.5</td></tr><tr><td>1.85</td><td>2.0</td><td>1.0</td><td>1</td><td>79</td><td>158</td><td>10.0</td></tr><tr><td>5.62</td><td>3.0</td><td>1.0</td><td>1</td><td>114</td><td>61</td><td>21.0</td></tr><tr><td>2.99</td><td>2.0</td><td>1.0</td><td>1</td><td>237</td><td>113</td><td>11.0</td></tr><tr><td>2.16</td><td>1.0</td><td>1.0</td><td>1</td><td>148</td><td>256</td><td>9.0</td></tr><tr><td>2.1</td><td>1.0</td><td>1.0</td><td>1</td><td>246</td><td>142</td><td>8.0</td></tr><tr><td>5.1</td><td>2.0</td><td>1.0</td><td>1</td><td>50</td><td>232</td><td>21.0</td></tr><tr><td>0.49</td><td>3.0</td><td>1.0</td><td>1</td><td>164</td><td>186</td><td>4.0</td></tr><tr><td>1.15</td><td>4.0</td><td>1.0</td><td>1</td><td>100</td><td>234</td><td>6.5</td></tr><tr><td>1.51</td><td>2.0</td><td>1.0</td><td>1</td><td>164</td><td>249</td><td>7.0</td></tr><tr><td>9.36</td><td>2.0</td><td>1.0</td><td>1</td><td>249</td><td>244</td><td>29.5</td></tr><tr><td>1.5</td><td>1.0</td><td>1.0</td><td>1</td><td>164</td><td>233</td><td>8.0</td></tr><tr><td>1.56</td><td>1.0</td><td>1.0</td><td>1</td><td>164</td><td>162</td><td>7.5</td></tr><tr><td>2.47</td><td>1.0</td><td>1.0</td><td>1</td><td>161</td><td>79</td><td>11.0</td></tr><tr><td>4.01</td><td>1.0</td><td>1.0</td><td>2</td><td>79</td><td>80</td><td>17.0</td></tr><tr><td>1.5</td><td>1.0</td><td>1.0</td><td>1</td><td>229</td><td>164</td><td>7.0</td></tr><tr><td>7.02</td><td>1.0</td><td>1.0</td><td>1</td><td>231</td><td>24</td><td>25.0</td></tr><tr><td>4.3</td><td>1.0</td><td>1.0</td><td>1</td><td>138</td><td>226</td><td>15.5</td></tr><tr><td>3.61</td><td>1.0</td><td>1.0</td><td>1</td><td>114</td><td>50</td><td>14.0</td></tr><tr><td>0.61</td><td>1.0</td><td>1.0</td><td>2</td><td>125</td><td>144</td><td>5.5</td></tr><tr><td>5.83</td><td>1.0</td><td>1.0</td><td>1</td><td>231</td><td>61</td><td>21.5</td></tr><tr><td>0.67</td><td>1.0</td><td>1.0</td><td>2</td><td>17</td><td>17</td><td>4.5</td></tr><tr><td>0.8</td><td>0.0</td><td>1.0</td><td>1</td><td>90</td><td>68</td><td>6.0</td></tr><tr><td>1.1</td><td>4.0</td><td>1.0</td><td>1</td><td>68</td><td>48</td><td>5.5</td></tr><tr><td>1.42</td><td>2.0</td><td>1.0</td><td>1</td><td>161</td><td>234</td><td>7.0</td></tr><tr><td>0.79</td><td>2.0</td><td>1.0</td><td>2</td><td>100</td><td>107</td><td>5.5</td></tr><tr><td>2.0</td><td>1.0</td><td>1.0</td><td>1</td><td>186</td><td>107</td><td>9.0</td></tr><tr><td>2.8</td><td>1.0</td><td>1.0</td><td>2</td><td>234</td><td>239</td><td>11.5</td></tr><tr><td>1.8</td><td>1.0</td><td>1.0</td><td>1</td><td>239</td><td>166</td><td>8.5</td></tr><tr><td>2.37</td><td>1.0</td><td>1.0</td><td>1</td><td>162</td><td>224</td><td>10.5</td></tr><tr><td>1.65</td><td>5.0</td><td>1.0</td><td>2</td><td>140</td><td>163</td><td>9.0</td></tr><tr><td>3.47</td><td>5.0</td><td>1.0</td><td>1</td><td>230</td><td>148</td><td>15.5</td></tr><tr><td>1.38</td><td>1.0</td><td>1.0</td><td>1</td><td>231</td><td>211</td><td>6.0</td></tr><tr><td>0.9</td><td>1.0</td><td>1.0</td><td>1</td><td>114</td><td>158</td><td>5.5</td></tr><tr><td>0.64</td><td>3.0</td><td>1.0</td><td>2</td><td>140</td><td>229</td><td>4.5</td></tr><tr><td>7.28</td><td>2.0</td><td>1.0</td><td>1</td><td>79</td><td>36</td><td>30.0</td></tr><tr><td>0.69</td><td>2.0</td><td>1.0</td><td>2</td><td>107</td><td>113</td><td>5.5</td></tr><tr><td>1.37</td><td>2.0</td><td>1.0</td><td>1</td><td>211</td><td>13</td><td>6.5</td></tr><tr><td>0.77</td><td>2.0</td><td>1.0</td><td>1</td><td>13</td><td>231</td><td>5.0</td></tr><tr><td>1.5</td><td>1.0</td><td>1.0</td><td>1</td><td>230</td><td>68</td><td>8.0</td></tr><tr><td>2.5</td><td>1.0</td><td>1.0</td><td>2</td><td>186</td><td>144</td><td>9.5</td></tr><tr><td>0.66</td><td>1.0</td><td>1.0</td><td>2</td><td>230</td><td>162</td><td>5.0</td></tr><tr><td>0.88</td><td>1.0</td><td>1.0</td><td>2</td><td>249</td><td>90</td><td>5.5</td></tr><tr><td>0.65</td><td>1.0</td><td>1.0</td><td>1</td><td>249</td><td>90</td><td>4.5</td></tr><tr><td>6.51</td><td>1.0</td><td>1.0</td><td>1</td><td>249</td><td>231</td><td>24.5</td></tr><tr><td>4.7</td><td>1.0</td><td>1.0</td><td>1</td><td>230</td><td>7</td><td>18.0</td></tr><tr><td>0.94</td><td>2.0</td><td>1.0</td><td>1</td><td>164</td><td>163</td><td>5.5</td></tr><tr><td>1.69</td><td>2.0</td><td>1.0</td><td>2</td><td>50</td><td>68</td><td>7.0</td></tr><tr><td>2.32</td><td>2.0</td><td>1.0</td><td>1</td><td>255</td><td>232</td><td>10.0</td></tr><tr><td>2.99</td><td>2.0</td><td>1.0</td><td>1</td><td>4</td><td>161</td><td>12.0</td></tr><tr><td>1.58</td><td>1.0</td><td>1.0</td><td>1</td><td>158</td><td>79</td><td>9.5</td></tr><tr><td>5.17</td><td>2.0</td><td>1.0</td><td>1</td><td>249</td><td>263</td><td>17.5</td></tr><tr><td>2.83</td><td>1.0</td><td>1.0</td><td>2</td><td>237</td><td>238</td><td>11.5</td></tr><tr><td>2.8</td><td>1.0</td><td>1.0</td><td>1</td><td>100</td><td>140</td><td>12.0</td></tr><tr><td>0.56</td><td>1.0</td><td>1.0</td><td>1</td><td>162</td><td>237</td><td>4.5</td></tr><tr><td>1.6</td><td>2.0</td><td>1.0</td><td>1</td><td>163</td><td>48</td><td>12.0</td></tr><tr><td>2.85</td><td>2.0</td><td>1.0</td><td>1</td><td>48</td><td>236</td><td>10.5</td></tr><tr><td>3.48</td><td>6.0</td><td>1.0</td><td>1</td><td>144</td><td>54</td><td>12.0</td></tr><tr><td>0.38</td><td>6.0</td><td>1.0</td><td>2</td><td>164</td><td>186</td><td>3.5</td></tr><tr><td>1.74</td><td>6.0</td><td>1.0</td><td>1</td><td>186</td><td>107</td><td>9.0</td></tr><tr><td>1.72</td><td>1.0</td><td>1.0</td><td>1</td><td>17</td><td>68</td><td>9.0</td></tr><tr><td>8.3</td><td>1.0</td><td>1.0</td><td>1</td><td>100</td><td>147</td><td>28.0</td></tr><tr><td>0.7</td><td>1.0</td><td>1.0</td><td>1</td><td>50</td><td>143</td><td>5.0</td></tr><tr><td>1.1</td><td>1.0</td><td>1.0</td><td>2</td><td>163</td><td>100</td><td>6.5</td></tr><tr><td>0.6</td><td>1.0</td><td>1.0</td><td>2</td><td>90</td><td>249</td><td>4.5</td></tr><tr><td>1.6</td><td>1.0</td><td>1.0</td><td>1</td><td>114</td><td>209</td><td>9.0</td></tr><tr><td>2.2</td><td>2.0</td><td>1.0</td><td>1</td><td>211</td><td>164</td><td>9.5</td></tr><tr><td>23.7</td><td>1.0</td><td>1.0</td><td>1</td><td>132</td><td>241</td><td>66.5</td></tr><tr><td>1.35</td><td>1.0</td><td>1.0</td><td>1</td><td>246</td><td>90</td><td>6.5</td></tr><tr><td>1.54</td><td>1.0</td><td>1.0</td><td>1</td><td>234</td><td>170</td><td>8.0</td></tr><tr><td>0.86</td><td>1.0</td><td>1.0</td><td>1</td><td>234</td><td>90</td><td>5.5</td></tr><tr><td>4.2</td><td>1.0</td><td>1.0</td><td>1</td><td>107</td><td>236</td><td>15.5</td></tr><tr><td>1.43</td><td>1.0</td><td>1.0</td><td>1</td><td>234</td><td>162</td><td>7.0</td></tr><tr><td>1.34</td><td>1.0</td><td>1.0</td><td>1</td><td>144</td><td>158</td><td>7.0</td></tr><tr><td>2.02</td><td>1.0</td><td>1.0</td><td>1</td><td>158</td><td>13</td><td>9.0</td></tr><tr><td>4.1</td><td>1.0</td><td>1.0</td><td>1</td><td>13</td><td>48</td><td>14.0</td></tr><tr><td>0.43</td><td>1.0</td><td>1.0</td><td>2</td><td>211</td><td>148</td><td>6.5</td></tr><tr><td>2.06</td><td>1.0</td><td>1.0</td><td>1</td><td>230</td><td>249</td><td>10.0</td></tr><tr><td>1.64</td><td>1.0</td><td>1.0</td><td>1</td><td>164</td><td>48</td><td>7.0</td></tr><tr><td>1.78</td><td>1.0</td><td>1.0</td><td>1</td><td>238</td><td>236</td><td>8.5</td></tr><tr><td>1.25</td><td>5.0</td><td>1.0</td><td>1</td><td>161</td><td>48</td><td>7.0</td></tr><tr><td>1.36</td><td>5.0</td><td>1.0</td><td>1</td><td>100</td><td>229</td><td>7.0</td></tr><tr><td>1.84</td><td>5.0</td><td>1.0</td><td>1</td><td>100</td><td>68</td><td>8.0</td></tr><tr><td>1.7</td><td>1.0</td><td>1.0</td><td>1</td><td>142</td><td>68</td><td>8.0</td></tr><tr><td>1.33</td><td>1.0</td><td>1.0</td><td>2</td><td>249</td><td>68</td><td>6.0</td></tr><tr><td>3.5</td><td>1.0</td><td>1.0</td><td>2</td><td>249</td><td>217</td><td>21.0</td></tr><tr><td>4.3</td><td>1.0</td><td>1.0</td><td>1</td><td>164</td><td>41</td><td>19.0</td></tr><tr><td>2.0</td><td>1.0</td><td>1.0</td><td>1</td><td>238</td><td>236</td><td>8.5</td></tr><tr><td>0.5</td><td>1.0</td><td>1.0</td><td>1</td><td>234</td><td>164</td><td>4.5</td></tr><tr><td>2.7</td><td>1.0</td><td>1.0</td><td>1</td><td>161</td><td>79</td><td>11.0</td></tr><tr><td>2.03</td><td>2.0</td><td>1.0</td><td>1</td><td>68</td><td>164</td><td>9.5</td></tr><tr><td>1.18</td><td>2.0</td><td>1.0</td><td>1</td><td>107</td><td>137</td><td>7.0</td></tr><tr><td>2.59</td><td>2.0</td><td>1.0</td><td>2</td><td>170</td><td>263</td><td>9.0</td></tr><tr><td>1.4</td><td>1.0</td><td>1.0</td><td>1</td><td>162</td><td>143</td><td>9.0</td></tr><tr><td>0.6</td><td>1.0</td><td>1.0</td><td>1</td><td>90</td><td>90</td><td>4.5</td></tr><tr><td>1.8</td><td>1.0</td><td>1.0</td><td>1</td><td>186</td><td>142</td><td>8.0</td></tr><tr><td>0.8</td><td>1.0</td><td>1.0</td><td>1</td><td>144</td><td>209</td><td>5.0</td></tr></tbody></table>Showing the first 1000 rows.</div>"]}}],"execution_count":0},{"cell_type":"code","source":["del sdf"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"65ef8322-e4f8-488b-a3bd-9e4ac8e5f180"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\"></div>","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\"></div>"]}}],"execution_count":0},{"cell_type":"code","source":["sdf_clean.count()\n# 2019 - 2022Q1 : 144 240 949"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"0b91348a-a0e2-46fa-a611-a5c2507e3ea4"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">Out[8]: 144240949</div>","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">Out[8]: 144240949</div>"]}}],"execution_count":0},{"cell_type":"markdown","source":["#SynapseML"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"47873c7b-4185-475a-bdf1-b66b5f735426"}}},{"cell_type":"code","source":["import synapse.ml"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"97168b45-e82b-4368-aa05-99c885e011b2"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\"></div>","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\"></div>"]}}],"execution_count":0},{"cell_type":"code","source":["labelCol = \"fare_amount\""],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"9cd1e500-969b-4dcd-b32a-e8e6c0eda3ab"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\"></div>","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\"></div>"]}}],"execution_count":0},{"cell_type":"markdown","source":["### train test split"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"784e545d-fd0c-47a8-a053-bf0fb7464505"}}},{"cell_type":"code","source":["train, test = sdf_clean.randomSplit([0.75, 0.25], seed=42)"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"1fccc4a3-62cd-43ff-a902-ac8f41cd2ade"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\"></div>","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\"></div>"]}}],"execution_count":0},{"cell_type":"markdown","source":["### convert columns to categorical"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"4b7a5b64-9b75-473b-89b1-59f7f06a2ec5"}}},{"cell_type":"code","source":["# catCols = [\"store_and_fwd_flag\"]\n# trainCat = train\n# testCat = test\n# for catCol in catCols:\n#     simodel = StringIndexer(inputCol=catCol, outputCol=catCol + \"Tmp\").fit(train)\n#     trainCat = simodel.transform(trainCat).drop(catCol).withColumnRenamed(catCol + \"Tmp\", catCol)\n#     testCat = simodel.transform(testCat).drop(catCol).withColumnRenamed(catCol + \"Tmp\", catCol)"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"47ef6569-27ec-4d15-8256-9605e2161ff5"}},"outputs":[],"execution_count":0},{"cell_type":"markdown","source":["## TrainedRegressorModel"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"c3edf485-7d91-45c7-81d7-d85387246df1"}}},{"cell_type":"code","source":["from synapse.ml.train import TrainRegressor, TrainedRegressorModel\nfrom pyspark.ml.regression import LinearRegression\nfrom pyspark.ml.feature import StringIndexer"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"b81d926d-ed91-4054-b24e-015a16b5ad69"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\"></div>","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\"></div>"]}}],"execution_count":0},{"cell_type":"code","source":["help(synapse.ml.train.TrainRegressor)"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"f25759c6-30e0-4c2f-867c-8f75428fbc63"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">Help on class TrainRegressor in module synapse.ml.train.TrainRegressor:\n\nclass TrainRegressor(synapse.ml.core.schema.Utils.ComplexParamsMixin, pyspark.ml.util.JavaMLReadable, pyspark.ml.util.JavaMLWritable, pyspark.ml.wrapper.JavaEstimator)\n |  TrainRegressor(java_obj=None, featuresCol=&#39;TrainRegressor_69b2b06ef6c3_features&#39;, labelCol=None, model=None, numFeatures=0)\n |  \n |  Args:\n |      featuresCol (object): The name of the features column\n |      labelCol (object): The name of the label column\n |      model (object): Regressor to run\n |      numFeatures (int): Number of features to hash to\n |  \n |  Method resolution order:\n |      TrainRegressor\n |      synapse.ml.core.schema.Utils.ComplexParamsMixin\n |      pyspark.ml.util.JavaMLReadable\n |      pyspark.ml.util.MLReadable\n |      pyspark.ml.util.JavaMLWritable\n |      pyspark.ml.util.MLWritable\n |      pyspark.ml.wrapper.JavaEstimator\n |      pyspark.ml.wrapper.JavaParams\n |      pyspark.ml.wrapper.JavaWrapper\n |      pyspark.ml.base.Estimator\n |      pyspark.ml.param.Params\n |      pyspark.ml.util.Identifiable\n |      builtins.object\n |  \n |  Methods defined here:\n |  \n |  __init__(self, java_obj=None, featuresCol=&#39;TrainRegressor_69b2b06ef6c3_features&#39;, labelCol=None, model=None, numFeatures=0)\n |      Initialize self.  See help(type(self)) for accurate signature.\n |  \n |  getFeaturesCol(self)\n |      Returns:\n |          featuresCol: The name of the features column\n |  \n |  getLabelCol(self)\n |      Returns:\n |          labelCol: The name of the label column\n |  \n |  getModel(self)\n |      Returns:\n |          model: Regressor to run\n |  \n |  getNumFeatures(self)\n |      Returns:\n |          numFeatures: Number of features to hash to\n |  \n |  setFeaturesCol(self, value)\n |      Args:\n |          featuresCol: The name of the features column\n |  \n |  setLabelCol(self, value)\n |      Args:\n |          labelCol: The name of the label column\n |  \n |  setModel(self, value)\n |      Args:\n |          model: Regressor to run\n |  \n |  setNumFeatures(self, value)\n |      Args:\n |          numFeatures: Number of features to hash to\n |  \n |  setParams(self, featuresCol=&#39;TrainRegressor_69b2b06ef6c3_features&#39;, labelCol=None, model=None, numFeatures=0)\n |      Set the (keyword only) parameters\n |  \n |  ----------------------------------------------------------------------\n |  Class methods defined here:\n |  \n |  read() from abc.ABCMeta\n |      Returns an MLReader instance for this class.\n |  \n |  ----------------------------------------------------------------------\n |  Static methods defined here:\n |  \n |  getJavaPackage()\n |      Returns package name String.\n |  \n |  ----------------------------------------------------------------------\n |  Data and other attributes defined here:\n |  \n |  __abstractmethods__ = frozenset()\n |  \n |  featuresCol = Param(parent=&#39;undefined&#39;, name=&#39;featuresCol&#39;, doc=&#39;The n...\n |  \n |  labelCol = Param(parent=&#39;undefined&#39;, name=&#39;labelCol&#39;, doc=&#39;The name of...\n |  \n |  model = Param(parent=&#39;undefined&#39;, name=&#39;model&#39;, doc=&#39;Regressor to run&#39;...\n |  \n |  numFeatures = Param(parent=&#39;undefined&#39;, name=&#39;numFeatures&#39;, doc=&#39;Numbe...\n |  \n |  ----------------------------------------------------------------------\n |  Class methods inherited from pyspark.ml.util.MLReadable:\n |  \n |  load(path) from abc.ABCMeta\n |      Reads an ML instance from the input path, a shortcut of `read().load(path)`.\n |  \n |  ----------------------------------------------------------------------\n |  Data descriptors inherited from pyspark.ml.util.MLReadable:\n |  \n |  __dict__\n |      dictionary for instance variables (if defined)\n |  \n |  __weakref__\n |      list of weak references to the object (if defined)\n |  \n |  ----------------------------------------------------------------------\n |  Methods inherited from pyspark.ml.util.JavaMLWritable:\n |  \n |  write(self)\n |      Returns an MLWriter instance for this ML instance.\n |  \n |  ----------------------------------------------------------------------\n |  Methods inherited from pyspark.ml.util.MLWritable:\n |  \n |  save(self, path)\n |      Save this ML instance to the given path, a shortcut of &#39;write().save(path)&#39;.\n |  \n |  ----------------------------------------------------------------------\n |  Methods inherited from pyspark.ml.wrapper.JavaParams:\n |  \n |  clear(self, param)\n |      Clears a param from the param map if it has been explicitly set.\n |  \n |  copy(self, extra=None)\n |      Creates a copy of this instance with the same uid and some\n |      extra params. This implementation first calls Params.copy and\n |      then make a copy of the companion Java pipeline component with\n |      extra params. So both the Python wrapper and the Java pipeline\n |      component get copied.\n |      \n |      Parameters\n |      ----------\n |      extra : dict, optional\n |          Extra parameters to copy to the new instance\n |      \n |      Returns\n |      -------\n |      :py:class:`JavaParams`\n |          Copy of this instance\n |  \n |  ----------------------------------------------------------------------\n |  Methods inherited from pyspark.ml.wrapper.JavaWrapper:\n |  \n |  __del__(self)\n |  \n |  ----------------------------------------------------------------------\n |  Methods inherited from pyspark.ml.base.Estimator:\n |  \n |  fit(self, dataset, params=None)\n |      Fits a model to the input dataset with optional parameters.\n |      \n |      .. versionadded:: 1.3.0\n |      \n |      Parameters\n |      ----------\n |      dataset : :py:class:`pyspark.sql.DataFrame`\n |          input dataset.\n |      params : dict or list or tuple, optional\n |          an optional param map that overrides embedded params. If a list/tuple of\n |          param maps is given, this calls fit on each param map and returns a list of\n |          models.\n |      \n |      Returns\n |      -------\n |      :py:class:`Transformer` or a list of :py:class:`Transformer`\n |          fitted model(s)\n |  \n |  fitMultiple(self, dataset, paramMaps)\n |      Fits a model to the input dataset for each param map in `paramMaps`.\n |      \n |      .. versionadded:: 2.3.0\n |      \n |      Parameters\n |      ----------\n |      dataset : :py:class:`pyspark.sql.DataFrame`\n |          input dataset.\n |      paramMaps : :py:class:`collections.abc.Sequence`\n |          A Sequence of param maps.\n |      \n |      Returns\n |      -------\n |      :py:class:`_FitMultipleIterator`\n |          A thread safe iterable which contains one model for each param map. Each\n |          call to `next(modelIterator)` will return `(index, model)` where model was fit\n |          using `paramMaps[index]`. `index` values may not be sequential.\n |  \n |  ----------------------------------------------------------------------\n |  Methods inherited from pyspark.ml.param.Params:\n |  \n |  explainParam(self, param)\n |      Explains a single param and returns its name, doc, and optional\n |      default value and user-supplied value in a string.\n |  \n |  explainParams(self)\n |      Returns the documentation of all params with their optionally\n |      default values and user-supplied values.\n |  \n |  extractParamMap(self, extra=None)\n |      Extracts the embedded default param values and user-supplied\n |      values, and then merges them with extra values from input into\n |      a flat param map, where the latter value is used if there exist\n |      conflicts, i.e., with ordering: default param values &lt;\n |      user-supplied values &lt; extra.\n |      \n |      Parameters\n |      ----------\n |      extra : dict, optional\n |          extra param values\n |      \n |      Returns\n |      -------\n |      dict\n |          merged param map\n |  \n |  getOrDefault(self, param)\n |      Gets the value of a param in the user-supplied param map or its\n |      default value. Raises an error if neither is set.\n |  \n |  getParam(self, paramName)\n |      Gets a param by its name.\n |  \n |  hasDefault(self, param)\n |      Checks whether a param has a default value.\n |  \n |  hasParam(self, paramName)\n |      Tests whether this instance contains a param with a given\n |      (string) name.\n |  \n |  isDefined(self, param)\n |      Checks whether a param is explicitly set by user or has\n |      a default value.\n |  \n |  isSet(self, param)\n |      Checks whether a param is explicitly set by user.\n |  \n |  set(self, param, value)\n |      Sets a parameter in the embedded param map.\n |  \n |  ----------------------------------------------------------------------\n |  Readonly properties inherited from pyspark.ml.param.Params:\n |  \n |  params\n |      Returns all params ordered by name. The default implementation\n |      uses :py:func:`dir` to get all attributes of type\n |      :py:class:`Param`.\n |  \n |  ----------------------------------------------------------------------\n |  Methods inherited from pyspark.ml.util.Identifiable:\n |  \n |  __repr__(self)\n |      Return repr(self).\n\n</div>","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">Help on class TrainRegressor in module synapse.ml.train.TrainRegressor:\n\nclass TrainRegressor(synapse.ml.core.schema.Utils.ComplexParamsMixin, pyspark.ml.util.JavaMLReadable, pyspark.ml.util.JavaMLWritable, pyspark.ml.wrapper.JavaEstimator)\n  TrainRegressor(java_obj=None, featuresCol=&#39;TrainRegressor_69b2b06ef6c3_features&#39;, labelCol=None, model=None, numFeatures=0)\n  \n  Args:\n      featuresCol (object): The name of the features column\n      labelCol (object): The name of the label column\n      model (object): Regressor to run\n      numFeatures (int): Number of features to hash to\n  \n  Method resolution order:\n      TrainRegressor\n      synapse.ml.core.schema.Utils.ComplexParamsMixin\n      pyspark.ml.util.JavaMLReadable\n      pyspark.ml.util.MLReadable\n      pyspark.ml.util.JavaMLWritable\n      pyspark.ml.util.MLWritable\n      pyspark.ml.wrapper.JavaEstimator\n      pyspark.ml.wrapper.JavaParams\n      pyspark.ml.wrapper.JavaWrapper\n      pyspark.ml.base.Estimator\n      pyspark.ml.param.Params\n      pyspark.ml.util.Identifiable\n      builtins.object\n  \n  Methods defined here:\n  \n  __init__(self, java_obj=None, featuresCol=&#39;TrainRegressor_69b2b06ef6c3_features&#39;, labelCol=None, model=None, numFeatures=0)\n      Initialize self.  See help(type(self)) for accurate signature.\n  \n  getFeaturesCol(self)\n      Returns:\n          featuresCol: The name of the features column\n  \n  getLabelCol(self)\n      Returns:\n          labelCol: The name of the label column\n  \n  getModel(self)\n      Returns:\n          model: Regressor to run\n  \n  getNumFeatures(self)\n      Returns:\n          numFeatures: Number of features to hash to\n  \n  setFeaturesCol(self, value)\n      Args:\n          featuresCol: The name of the features column\n  \n  setLabelCol(self, value)\n      Args:\n          labelCol: The name of the label column\n  \n  setModel(self, value)\n      Args:\n          model: Regressor to run\n  \n  setNumFeatures(self, value)\n      Args:\n          numFeatures: Number of features to hash to\n  \n  setParams(self, featuresCol=&#39;TrainRegressor_69b2b06ef6c3_features&#39;, labelCol=None, model=None, numFeatures=0)\n      Set the (keyword only) parameters\n  \n  ----------------------------------------------------------------------\n  Class methods defined here:\n  \n  read() from abc.ABCMeta\n      Returns an MLReader instance for this class.\n  \n  ----------------------------------------------------------------------\n  Static methods defined here:\n  \n  getJavaPackage()\n      Returns package name String.\n  \n  ----------------------------------------------------------------------\n  Data and other attributes defined here:\n  \n  __abstractmethods__ = frozenset()\n  \n  featuresCol = Param(parent=&#39;undefined&#39;, name=&#39;featuresCol&#39;, doc=&#39;The n...\n  \n  labelCol = Param(parent=&#39;undefined&#39;, name=&#39;labelCol&#39;, doc=&#39;The name of...\n  \n  model = Param(parent=&#39;undefined&#39;, name=&#39;model&#39;, doc=&#39;Regressor to run&#39;...\n  \n  numFeatures = Param(parent=&#39;undefined&#39;, name=&#39;numFeatures&#39;, doc=&#39;Numbe...\n  \n  ----------------------------------------------------------------------\n  Class methods inherited from pyspark.ml.util.MLReadable:\n  \n  load(path) from abc.ABCMeta\n      Reads an ML instance from the input path, a shortcut of `read().load(path)`.\n  \n  ----------------------------------------------------------------------\n  Data descriptors inherited from pyspark.ml.util.MLReadable:\n  \n  __dict__\n      dictionary for instance variables (if defined)\n  \n  __weakref__\n      list of weak references to the object (if defined)\n  \n  ----------------------------------------------------------------------\n  Methods inherited from pyspark.ml.util.JavaMLWritable:\n  \n  write(self)\n      Returns an MLWriter instance for this ML instance.\n  \n  ----------------------------------------------------------------------\n  Methods inherited from pyspark.ml.util.MLWritable:\n  \n  save(self, path)\n      Save this ML instance to the given path, a shortcut of &#39;write().save(path)&#39;.\n  \n  ----------------------------------------------------------------------\n  Methods inherited from pyspark.ml.wrapper.JavaParams:\n  \n  clear(self, param)\n      Clears a param from the param map if it has been explicitly set.\n  \n  copy(self, extra=None)\n      Creates a copy of this instance with the same uid and some\n      extra params. This implementation first calls Params.copy and\n      then make a copy of the companion Java pipeline component with\n      extra params. So both the Python wrapper and the Java pipeline\n      component get copied.\n      \n      Parameters\n      ----------\n      extra : dict, optional\n          Extra parameters to copy to the new instance\n      \n      Returns\n      -------\n      :py:class:`JavaParams`\n          Copy of this instance\n  \n  ----------------------------------------------------------------------\n  Methods inherited from pyspark.ml.wrapper.JavaWrapper:\n  \n  __del__(self)\n  \n  ----------------------------------------------------------------------\n  Methods inherited from pyspark.ml.base.Estimator:\n  \n  fit(self, dataset, params=None)\n      Fits a model to the input dataset with optional parameters.\n      \n      .. versionadded:: 1.3.0\n      \n      Parameters\n      ----------\n      dataset : :py:class:`pyspark.sql.DataFrame`\n          input dataset.\n      params : dict or list or tuple, optional\n          an optional param map that overrides embedded params. If a list/tuple of\n          param maps is given, this calls fit on each param map and returns a list of\n          models.\n      \n      Returns\n      -------\n      :py:class:`Transformer` or a list of :py:class:`Transformer`\n          fitted model(s)\n  \n  fitMultiple(self, dataset, paramMaps)\n      Fits a model to the input dataset for each param map in `paramMaps`.\n      \n      .. versionadded:: 2.3.0\n      \n      Parameters\n      ----------\n      dataset : :py:class:`pyspark.sql.DataFrame`\n          input dataset.\n      paramMaps : :py:class:`collections.abc.Sequence`\n          A Sequence of param maps.\n      \n      Returns\n      -------\n      :py:class:`_FitMultipleIterator`\n          A thread safe iterable which contains one model for each param map. Each\n          call to `next(modelIterator)` will return `(index, model)` where model was fit\n          using `paramMaps[index]`. `index` values may not be sequential.\n  \n  ----------------------------------------------------------------------\n  Methods inherited from pyspark.ml.param.Params:\n  \n  explainParam(self, param)\n      Explains a single param and returns its name, doc, and optional\n      default value and user-supplied value in a string.\n  \n  explainParams(self)\n      Returns the documentation of all params with their optionally\n      default values and user-supplied values.\n  \n  extractParamMap(self, extra=None)\n      Extracts the embedded default param values and user-supplied\n      values, and then merges them with extra values from input into\n      a flat param map, where the latter value is used if there exist\n      conflicts, i.e., with ordering: default param values &lt;\n      user-supplied values &lt; extra.\n      \n      Parameters\n      ----------\n      extra : dict, optional\n          extra param values\n      \n      Returns\n      -------\n      dict\n          merged param map\n  \n  getOrDefault(self, param)\n      Gets the value of a param in the user-supplied param map or its\n      default value. Raises an error if neither is set.\n  \n  getParam(self, paramName)\n      Gets a param by its name.\n  \n  hasDefault(self, param)\n      Checks whether a param has a default value.\n  \n  hasParam(self, paramName)\n      Tests whether this instance contains a param with a given\n      (string) name.\n  \n  isDefined(self, param)\n      Checks whether a param is explicitly set by user or has\n      a default value.\n  \n  isSet(self, param)\n      Checks whether a param is explicitly set by user.\n  \n  set(self, param, value)\n      Sets a parameter in the embedded param map.\n  \n  ----------------------------------------------------------------------\n  Readonly properties inherited from pyspark.ml.param.Params:\n  \n  params\n      Returns all params ordered by name. The default implementation\n      uses :py:func:`dir` to get all attributes of type\n      :py:class:`Param`.\n  \n  ----------------------------------------------------------------------\n  Methods inherited from pyspark.ml.util.Identifiable:\n  \n  __repr__(self)\n      Return repr(self).\n\n</div>"]}}],"execution_count":0},{"cell_type":"code","source":["lr = LinearRegression().setRegParam(0.1).setElasticNetParam(0.3)\nmodel = TrainRegressor(model=lr, labelCol=labelCol).fit(train)"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"68c447af-ea7f-4ced-b547-6fe75c646c76"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\"></div>","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\"></div>"]}}],"execution_count":0},{"cell_type":"code","source":["model_name = \"dbfs:/mnt/delta/nyctaxi/modelMML/nycTaxiModel.mml\"\nmodel.write().overwrite().save(model_name)"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"7f62d324-dc6c-42e1-874f-cca6c11de5db"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\"></div>","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\"></div>"]}}],"execution_count":0},{"cell_type":"markdown","source":["### score and evaluate"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"12df4eb8-fab5-4cac-a9fa-5cfccdbba305"}}},{"cell_type":"code","source":["nycTaxiModel = TrainedRegressorModel.load(model_name)\n\nscoredData = nycTaxiModel.transform(test)\ndisplay(scoredData.limit(10).toPandas())"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"1372b7a4-a302-4383-8d64-5016860b94e6"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"overflow":false,"datasetInfos":[],"data":[[0.01,0.0,5.0,1,1,1,95.0,20.3710189379381],[0.01,0.0,5.0,1,132,132,78.0,16.930258267872016],[0.01,0.0,5.0,1,138,138,40.0,16.772666176113265],[0.01,0.0,5.0,1,138,138,46.12,16.772666176113265],[0.01,0.0,5.0,1,142,142,10.0,16.66760478160743],[0.01,0.0,5.0,1,265,265,50.0,13.436966900553017],[0.01,0.0,5.0,2,246,246,13.3,13.483173041944982],[0.01,1.0,1.0,1,24,239,6.5,13.625418476394506],[0.01,1.0,1.0,1,36,36,3.0,16.083030855457064],[0.01,1.0,1.0,1,43,236,5.0,13.410104872848821]],"plotOptions":{"displayType":"table","customPlotOptions":{},"pivotColumns":null,"pivotAggregation":null,"xColumns":null,"yColumns":null},"columnCustomDisplayInfos":{},"aggType":"","isJsonSchema":true,"removedWidgets":[],"aggSchema":[],"schema":[{"name":"trip_distance","type":"\"double\"","metadata":"{}"},{"name":"passenger_count","type":"\"double\"","metadata":"{}"},{"name":"RatecodeID","type":"\"double\"","metadata":"{}"},{"name":"payment_type","type":"\"long\"","metadata":"{}"},{"name":"PULocationID","type":"\"long\"","metadata":"{}"},{"name":"DOLocationID","type":"\"long\"","metadata":"{}"},{"name":"fare_amount","type":"\"double\"","metadata":"{}"},{"name":"prediction","type":"\"double\"","metadata":"{}"}],"aggError":"","aggData":[],"addedWidgets":{},"metadata":{},"dbfsResultPath":null,"type":"table","aggOverflow":false,"aggSeriesLimitReached":false,"arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .table-result-container {\n    max-height: 300px;\n    overflow: auto;\n  }\n  table, th, td {\n    border: 1px solid black;\n    border-collapse: collapse;\n  }\n  th, td {\n    padding: 5px;\n  }\n  th {\n    text-align: left;\n  }\n</style><div class='table-result-container'><table class='table-result'><thead style='background-color: white'><tr><th>trip_distance</th><th>passenger_count</th><th>RatecodeID</th><th>payment_type</th><th>PULocationID</th><th>DOLocationID</th><th>fare_amount</th><th>prediction</th></tr></thead><tbody><tr><td>0.01</td><td>0.0</td><td>5.0</td><td>1</td><td>1</td><td>1</td><td>95.0</td><td>20.3710189379381</td></tr><tr><td>0.01</td><td>0.0</td><td>5.0</td><td>1</td><td>132</td><td>132</td><td>78.0</td><td>16.930258267872016</td></tr><tr><td>0.01</td><td>0.0</td><td>5.0</td><td>1</td><td>138</td><td>138</td><td>40.0</td><td>16.772666176113265</td></tr><tr><td>0.01</td><td>0.0</td><td>5.0</td><td>1</td><td>138</td><td>138</td><td>46.12</td><td>16.772666176113265</td></tr><tr><td>0.01</td><td>0.0</td><td>5.0</td><td>1</td><td>142</td><td>142</td><td>10.0</td><td>16.66760478160743</td></tr><tr><td>0.01</td><td>0.0</td><td>5.0</td><td>1</td><td>265</td><td>265</td><td>50.0</td><td>13.436966900553017</td></tr><tr><td>0.01</td><td>0.0</td><td>5.0</td><td>2</td><td>246</td><td>246</td><td>13.3</td><td>13.483173041944982</td></tr><tr><td>0.01</td><td>1.0</td><td>1.0</td><td>1</td><td>24</td><td>239</td><td>6.5</td><td>13.625418476394506</td></tr><tr><td>0.01</td><td>1.0</td><td>1.0</td><td>1</td><td>36</td><td>36</td><td>3.0</td><td>16.083030855457064</td></tr><tr><td>0.01</td><td>1.0</td><td>1.0</td><td>1</td><td>43</td><td>236</td><td>5.0</td><td>13.410104872848821</td></tr></tbody></table></div>"]}}],"execution_count":0},{"cell_type":"code","source":["from synapse.ml.train import ComputeModelStatistics\n\nmetrics = ComputeModelStatistics().transform(scoredData)\nmetrics.toPandas()"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"b4e0dab1-7da5-46ae-ba44-6b16562f708d"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">Out[18]: </div>","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">Out[18]: </div>"]}},{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>mean_squared_error</th>\n      <th>root_mean_squared_error</th>\n      <th>R^2</th>\n      <th>mean_absolute_error</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>129.144233</td>\n      <td>11.364164</td>\n      <td>0.050481</td>\n      <td>6.950638</td>\n    </tr>\n  </tbody>\n</table>\n</div>","textData":null,"removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"htmlSandbox","arguments":{}}},"output_type":"display_data","data":{"text/html":["<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>mean_squared_error</th>\n      <th>root_mean_squared_error</th>\n      <th>R^2</th>\n      <th>mean_absolute_error</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>129.144233</td>\n      <td>11.364164</td>\n      <td>0.050481</td>\n      <td>6.950638</td>\n    </tr>\n  </tbody>\n</table>\n</div>"]}}],"execution_count":0},{"cell_type":"code","source":["from synapse.ml.train import ComputePerInstanceStatistics\n\nevalPerInstance = ComputePerInstanceStatistics().transform(scoredData)\nevalPerInstance.select(\"fare_amount\", \"prediction\", \"L1_loss\", \"L2_loss\").limit(10).toPandas()"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"c0efaafd-bcdb-4e17-bec0-a8f37e93e380"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">Out[19]: </div>","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">Out[19]: </div>"]}},{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>fare_amount</th>\n      <th>prediction</th>\n      <th>L1_loss</th>\n      <th>L2_loss</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>65.0</td>\n      <td>15.227890</td>\n      <td>49.772110</td>\n      <td>2477.262919</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>11.5</td>\n      <td>14.356254</td>\n      <td>2.856254</td>\n      <td>8.158187</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>9.0</td>\n      <td>13.936009</td>\n      <td>4.936009</td>\n      <td>24.364180</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>15.3</td>\n      <td>13.936009</td>\n      <td>1.363991</td>\n      <td>1.860473</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>6.0</td>\n      <td>19.839387</td>\n      <td>13.839387</td>\n      <td>191.528644</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>5.5</td>\n      <td>16.700031</td>\n      <td>11.200031</td>\n      <td>125.440685</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>18.5</td>\n      <td>16.371950</td>\n      <td>2.128050</td>\n      <td>4.528598</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>2.5</td>\n      <td>15.767847</td>\n      <td>13.267847</td>\n      <td>176.035755</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>3.0</td>\n      <td>15.767847</td>\n      <td>12.767847</td>\n      <td>163.017909</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>2.5</td>\n      <td>15.452662</td>\n      <td>12.952662</td>\n      <td>167.771466</td>\n    </tr>\n  </tbody>\n</table>\n</div>","textData":null,"removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"htmlSandbox","arguments":{}}},"output_type":"display_data","data":{"text/html":["<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>fare_amount</th>\n      <th>prediction</th>\n      <th>L1_loss</th>\n      <th>L2_loss</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>65.0</td>\n      <td>15.227890</td>\n      <td>49.772110</td>\n      <td>2477.262919</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>11.5</td>\n      <td>14.356254</td>\n      <td>2.856254</td>\n      <td>8.158187</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>9.0</td>\n      <td>13.936009</td>\n      <td>4.936009</td>\n      <td>24.364180</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>15.3</td>\n      <td>13.936009</td>\n      <td>1.363991</td>\n      <td>1.860473</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>6.0</td>\n      <td>19.839387</td>\n      <td>13.839387</td>\n      <td>191.528644</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>5.5</td>\n      <td>16.700031</td>\n      <td>11.200031</td>\n      <td>125.440685</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>18.5</td>\n      <td>16.371950</td>\n      <td>2.128050</td>\n      <td>4.528598</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>2.5</td>\n      <td>15.767847</td>\n      <td>13.267847</td>\n      <td>176.035755</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>3.0</td>\n      <td>15.767847</td>\n      <td>12.767847</td>\n      <td>163.017909</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>2.5</td>\n      <td>15.452662</td>\n      <td>12.952662</td>\n      <td>167.771466</td>\n    </tr>\n  </tbody>\n</table>\n</div>"]}}],"execution_count":0},{"cell_type":"markdown","source":["##LightGMB"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"5540f1d5-eefc-41e5-9cd3-8fcdd895254f"}}},{"cell_type":"code","source":["from pyspark.ml.feature import VectorAssembler\nfrom synapse.ml.lightgbm import LightGBMRegressor"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"7ab7d6b3-3b56-4410-bd7e-ad3f29946eb6"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\"></div>","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\"></div>"]}}],"execution_count":0},{"cell_type":"code","source":["help(synapse.ml.lightgbm.LightGBMRegressor)"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"5e52518e-fcf7-4c07-a34a-4d6a080026d2"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">Help on class LightGBMRegressor in module synapse.ml.lightgbm.LightGBMRegressor:\n\nclass LightGBMRegressor(synapse.ml.core.schema.Utils.ComplexParamsMixin, pyspark.ml.util.JavaMLReadable, pyspark.ml.util.JavaMLWritable, pyspark.ml.wrapper.JavaEstimator)\n |  LightGBMRegressor(java_obj=None, alpha=0.9, baggingFraction=1.0, baggingFreq=0, baggingSeed=3, binSampleCount=200000, boostFromAverage=True, boostingType=&#39;gbdt&#39;, categoricalSlotIndexes=[], categoricalSlotNames=[], chunkSize=10000, defaultListenPort=12400, driverListenPort=0, dropRate=0.1, earlyStoppingRound=0, featureFraction=1.0, featuresCol=&#39;features&#39;, featuresShapCol=&#39;&#39;, fobj=None, improvementTolerance=0.0, initScoreCol=None, isProvideTrainingMetric=False, labelCol=&#39;label&#39;, lambdaL1=0.0, lambdaL2=0.0, leafPredictionCol=&#39;&#39;, learningRate=0.1, matrixType=&#39;auto&#39;, maxBin=255, maxBinByFeature=[], maxDeltaStep=0.0, maxDepth=-1, maxDrop=50, metric=&#39;&#39;, minDataInLeaf=20, minGainToSplit=0.0, minSumHessianInLeaf=0.001, modelString=&#39;&#39;, negBaggingFraction=1.0, numBatches=0, numIterations=100, numLeaves=31, numTasks=0, numThreads=0, objective=&#39;regression&#39;, parallelism=&#39;data_parallel&#39;, posBaggingFraction=1.0, predictDisableShapeCheck=False, predictionCol=&#39;prediction&#39;, repartitionByGroupingColumn=True, skipDrop=0.5, slotNames=[], timeout=1200.0, topK=20, tweedieVariancePower=1.5, uniformDrop=False, useBarrierExecutionMode=False, useSingleDatasetMode=True, validationIndicatorCol=None, verbosity=-1, weightCol=None, xgboostDartMode=False)\n |  \n |  Args:\n |      alpha (float): parameter for Huber loss and Quantile regression\n |      baggingFraction (float): Bagging fraction\n |      baggingFreq (int): Bagging frequency\n |      baggingSeed (int): Bagging seed\n |      binSampleCount (int): Number of samples considered at computing histogram bins\n |      boostFromAverage (bool): Adjusts initial score to the mean of labels for faster convergence\n |      boostingType (object): Default gbdt = traditional Gradient Boosting Decision Tree. Options are: gbdt, gbrt, rf (Random Forest), random_forest, dart (Dropouts meet Multiple Additive Regression Trees), goss (Gradient-based One-Side Sampling). \n |      categoricalSlotIndexes (list): List of categorical column indexes, the slot index in the features column\n |      categoricalSlotNames (list): List of categorical column slot names, the slot name in the features column\n |      chunkSize (int): Advanced parameter to specify the chunk size for copying Java data to native.  If set too high, memory may be wasted, but if set too low, performance may be reduced during data copy.If dataset size is known beforehand, set to the number of rows in the dataset.\n |      defaultListenPort (int): The default listen port on executors, used for testing\n |      driverListenPort (int): The listen port on a driver. Default value is 0 (random)\n |      dropRate (float): Dropout rate: a fraction of previous trees to drop during the dropout\n |      earlyStoppingRound (int): Early stopping round\n |      featureFraction (float): Feature fraction\n |      featuresCol (object): features column name\n |      featuresShapCol (object): Output SHAP vector column name after prediction containing the feature contribution values\n |      fobj (object): Customized objective function. Should accept two parameters: preds, train_data, and return (grad, hess).\n |      improvementTolerance (float): Tolerance to consider improvement in metric\n |      initScoreCol (object): The name of the initial score column, used for continued training\n |      isProvideTrainingMetric (bool): Whether output metric result over training dataset.\n |      labelCol (object): label column name\n |      lambdaL1 (float): L1 regularization\n |      lambdaL2 (float): L2 regularization\n |      leafPredictionCol (object): Predicted leaf indices&#39;s column name\n |      learningRate (float): Learning rate or shrinkage rate\n |      matrixType (object): Advanced parameter to specify whether the native lightgbm matrix constructed should be sparse or dense.  Values can be auto, sparse or dense. Default value is auto, which samples first ten rows to determine type.\n |      maxBin (int): Max bin\n |      maxBinByFeature (list): Max number of bins for each feature\n |      maxDeltaStep (float): Used to limit the max output of tree leaves\n |      maxDepth (int): Max depth\n |      maxDrop (int): Max number of dropped trees during one boosting iteration\n |      metric (object): Metrics to be evaluated on the evaluation data.  Options are: empty string or not specified means that metric corresponding to specified objective will be used (this is possible only for pre-defined objective functions, otherwise no evaluation metric will be added). None (string, not a None value) means that no metric will be registered, aliases: na, null, custom. l1, absolute loss, aliases: mean_absolute_error, mae, regression_l1. l2, square loss, aliases: mean_squared_error, mse, regression_l2, regression. rmse, root square loss, aliases: root_mean_squared_error, l2_root. quantile, Quantile regression. mape, MAPE loss, aliases: mean_absolute_percentage_error. huber, Huber loss. fair, Fair loss. poisson, negative log-likelihood for Poisson regression. gamma, negative log-likelihood for Gamma regression. gamma_deviance, residual deviance for Gamma regression. tweedie, negative log-likelihood for Tweedie regression. ndcg, NDCG, aliases: lambdarank. map, MAP, aliases: mean_average_precision. auc, AUC. binary_logloss, log loss, aliases: binary. binary_error, for one sample: 0 for correct classification, 1 for error classification. multi_logloss, log loss for multi-class classification, aliases: multiclass, softmax, multiclassova, multiclass_ova, ova, ovr. multi_error, error rate for multi-class classification. cross_entropy, cross-entropy (with optional linear weights), aliases: xentropy. cross_entropy_lambda, intensity-weighted cross-entropy, aliases: xentlambda. kullback_leibler, Kullback-Leibler divergence, aliases: kldiv. \n |      minDataInLeaf (int): Minimal number of data in one leaf. Can be used to deal with over-fitting.\n |      minGainToSplit (float): The minimal gain to perform split\n |      minSumHessianInLeaf (float): Minimal sum hessian in one leaf\n |      modelString (object): LightGBM model to retrain\n |      negBaggingFraction (float): Negative Bagging fraction\n |      numBatches (int): If greater than 0, splits data into separate batches during training\n |      numIterations (int): Number of iterations, LightGBM constructs num_class * num_iterations trees\n |      numLeaves (int): Number of leaves\n |      numTasks (int): Advanced parameter to specify the number of tasks.  SynapseML tries to guess this based on cluster configuration, but this parameter can be used to override.\n |      numThreads (int): Number of threads for LightGBM. For the best speed, set this to the number of real CPU cores.\n |      objective (object): The Objective. For regression applications, this can be: regression_l2, regression_l1, huber, fair, poisson, quantile, mape, gamma or tweedie. For classification applications, this can be: binary, multiclass, or multiclassova. \n |      parallelism (object): Tree learner parallelism, can be set to data_parallel or voting_parallel\n |      posBaggingFraction (float): Positive Bagging fraction\n |      predictDisableShapeCheck (bool): control whether or not LightGBM raises an error when you try to predict on data with a different number of features than the training data\n |      predictionCol (object): prediction column name\n |      repartitionByGroupingColumn (bool): Repartition training data according to grouping column, on by default.\n |      skipDrop (float): Probability of skipping the dropout procedure during a boosting iteration\n |      slotNames (list): List of slot names in the features column\n |      timeout (float): Timeout in seconds\n |      topK (int): The top_k value used in Voting parallel, set this to larger value for more accurate result, but it will slow down the training speed. It should be greater than 0\n |      tweedieVariancePower (float): control the variance of tweedie distribution, must be between 1 and 2\n |      uniformDrop (bool): Set this to true to use uniform drop in dart mode\n |      useBarrierExecutionMode (bool): Barrier execution mode which uses a barrier stage, off by default.\n |      useSingleDatasetMode (bool): Use single dataset execution mode to create a single native dataset per executor (singleton) to reduce memory and communication overhead. Note this is disabled when running spark in local mode.\n |      validationIndicatorCol (object): Indicates whether the row is for training or validation\n |      verbosity (int): Verbosity where lt 0 is Fatal, eq 0 is Error, eq 1 is Info, gt 1 is Debug\n |      weightCol (object): The name of the weight column\n |      xgboostDartMode (bool): Set this to true to use xgboost dart mode\n |  \n |  Method resolution order:\n |      LightGBMRegressor\n |      synapse.ml.core.schema.Utils.ComplexParamsMixin\n |      pyspark.ml.util.JavaMLReadable\n |      pyspark.ml.util.MLReadable\n |      pyspark.ml.util.JavaMLWritable\n |      pyspark.ml.util.MLWritable\n |      pyspark.ml.wrapper.JavaEstimator\n |      pyspark.ml.wrapper.JavaParams\n |      pyspark.ml.wrapper.JavaWrapper\n |      pyspark.ml.base.Estimator\n |      pyspark.ml.param.Params\n |      pyspark.ml.util.Identifiable\n |      builtins.object\n |  \n |  Methods defined here:\n |  \n |  __init__(self, java_obj=None, alpha=0.9, baggingFraction=1.0, baggingFreq=0, baggingSeed=3, binSampleCount=200000, boostFromAverage=True, boostingType=&#39;gbdt&#39;, categoricalSlotIndexes=[], categoricalSlotNames=[], chunkSize=10000, defaultListenPort=12400, driverListenPort=0, dropRate=0.1, earlyStoppingRound=0, featureFraction=1.0, featuresCol=&#39;features&#39;, featuresShapCol=&#39;&#39;, fobj=None, improvementTolerance=0.0, initScoreCol=None, isProvideTrainingMetric=False, labelCol=&#39;label&#39;, lambdaL1=0.0, lambdaL2=0.0, leafPredictionCol=&#39;&#39;, learningRate=0.1, matrixType=&#39;auto&#39;, maxBin=255, maxBinByFeature=[], maxDeltaStep=0.0, maxDepth=-1, maxDrop=50, metric=&#39;&#39;, minDataInLeaf=20, minGainToSplit=0.0, minSumHessianInLeaf=0.001, modelString=&#39;&#39;, negBaggingFraction=1.0, numBatches=0, numIterations=100, numLeaves=31, numTasks=0, numThreads=0, objective=&#39;regression&#39;, parallelism=&#39;data_parallel&#39;, posBaggingFraction=1.0, predictDisableShapeCheck=False, predictionCol=&#39;prediction&#39;, repartitionByGroupingColumn=True, skipDrop=0.5, slotNames=[], timeout=1200.0, topK=20, tweedieVariancePower=1.5, uniformDrop=False, useBarrierExecutionMode=False, useSingleDatasetMode=True, validationIndicatorCol=None, verbosity=-1, weightCol=None, xgboostDartMode=False)\n |      Initialize self.  See help(type(self)) for accurate signature.\n |  \n |  getAlpha(self)\n |      Returns:\n |          alpha: parameter for Huber loss and Quantile regression\n |  \n |  getBaggingFraction(self)\n |      Returns:\n |          baggingFraction: Bagging fraction\n |  \n |  getBaggingFreq(self)\n |      Returns:\n |          baggingFreq: Bagging frequency\n |  \n |  getBaggingSeed(self)\n |      Returns:\n |          baggingSeed: Bagging seed\n |  \n |  getBinSampleCount(self)\n |      Returns:\n |          binSampleCount: Number of samples considered at computing histogram bins\n |  \n |  getBoostFromAverage(self)\n |      Returns:\n |          boostFromAverage: Adjusts initial score to the mean of labels for faster convergence\n |  \n |  getBoostingType(self)\n |      Returns:\n |          boostingType: Default gbdt = traditional Gradient Boosting Decision Tree. Options are: gbdt, gbrt, rf (Random Forest), random_forest, dart (Dropouts meet Multiple Additive Regression Trees), goss (Gradient-based One-Side Sampling).\n |  \n |  getCategoricalSlotIndexes(self)\n |      Returns:\n |          categoricalSlotIndexes: List of categorical column indexes, the slot index in the features column\n |  \n |  getCategoricalSlotNames(self)\n |      Returns:\n |          categoricalSlotNames: List of categorical column slot names, the slot name in the features column\n |  \n |  getChunkSize(self)\n |      Returns:\n |          chunkSize: Advanced parameter to specify the chunk size for copying Java data to native.  If set too high, memory may be wasted, but if set too low, performance may be reduced during data copy.If dataset size is known beforehand, set to the number of rows in the dataset.\n |  \n |  getDefaultListenPort(self)\n |      Returns:\n |          defaultListenPort: The default listen port on executors, used for testing\n |  \n |  getDriverListenPort(self)\n |      Returns:\n |          driverListenPort: The listen port on a driver. Default value is 0 (random)\n |  \n |  getDropRate(self)\n |      Returns:\n |          dropRate: Dropout rate: a fraction of previous trees to drop during the dropout\n |  \n |  getEarlyStoppingRound(self)\n |      Returns:\n |          earlyStoppingRound: Early stopping round\n |  \n |  getFeatureFraction(self)\n |      Returns:\n |          featureFraction: Feature fraction\n |  \n |  getFeaturesCol(self)\n |      Returns:\n |          featuresCol: features column name\n |  \n |  getFeaturesShapCol(self)\n |      Returns:\n |          featuresShapCol: Output SHAP vector column name after prediction containing the feature contribution values\n |  \n |  getFobj(self)\n |      Returns:\n |          fobj: Customized objective function. Should accept two parameters: preds, train_data, and return (grad, hess).\n |  \n |  getImprovementTolerance(self)\n |      Returns:\n |          improvementTolerance: Tolerance to consider improvement in metric\n |  \n |  getInitScoreCol(self)\n |      Returns:\n |          initScoreCol: The name of the initial score column, used for continued training\n |  \n |  getIsProvideTrainingMetric(self)\n |      Returns:\n |          isProvideTrainingMetric: Whether output metric result over training dataset.\n |  \n |  getLabelCol(self)\n |      Returns:\n |          labelCol: label column name\n |  \n |  getLambdaL1(self)\n |      Returns:\n |          lambdaL1: L1 regularization\n |  \n |  getLambdaL2(self)\n |      Returns:\n |          lambdaL2: L2 regularization\n |  \n |  getLeafPredictionCol(self)\n |      Returns:\n |          leafPredictionCol: Predicted leaf indices&#39;s column name\n |  \n |  getLearningRate(self)\n |      Returns:\n |          learningRate: Learning rate or shrinkage rate\n |  \n |  getMatrixType(self)\n |      Returns:\n |          matrixType: Advanced parameter to specify whether the native lightgbm matrix constructed should be sparse or dense.  Values can be auto, sparse or dense. Default value is auto, which samples first ten rows to determine type.\n |  \n |  getMaxBin(self)\n |      Returns:\n |          maxBin: Max bin\n |  \n |  getMaxBinByFeature(self)\n |      Returns:\n |          maxBinByFeature: Max number of bins for each feature\n |  \n |  getMaxDeltaStep(self)\n |      Returns:\n |          maxDeltaStep: Used to limit the max output of tree leaves\n |  \n |  getMaxDepth(self)\n |      Returns:\n |          maxDepth: Max depth\n |  \n |  getMaxDrop(self)\n |      Returns:\n |          maxDrop: Max number of dropped trees during one boosting iteration\n |  \n |  getMetric(self)\n |      Returns:\n |          metric: Metrics to be evaluated on the evaluation data.  Options are: empty string or not specified means that metric corresponding to specified objective will be used (this is possible only for pre-defined objective functions, otherwise no evaluation metric will be added). None (string, not a None value) means that no metric will be registered, aliases: na, null, custom. l1, absolute loss, aliases: mean_absolute_error, mae, regression_l1. l2, square loss, aliases: mean_squared_error, mse, regression_l2, regression. rmse, root square loss, aliases: root_mean_squared_error, l2_root. quantile, Quantile regression. mape, MAPE loss, aliases: mean_absolute_percentage_error. huber, Huber loss. fair, Fair loss. poisson, negative log-likelihood for Poisson regression. gamma, negative log-likelihood for Gamma regression. gamma_deviance, residual deviance for Gamma regression. tweedie, negative log-likelihood for Tweedie regression. ndcg, NDCG, aliases: lambdarank. map, MAP, aliases: mean_average_precision. auc, AUC. binary_logloss, log loss, aliases: binary. binary_error, for one sample: 0 for correct classification, 1 for error classification. multi_logloss, log loss for multi-class classification, aliases: multiclass, softmax, multiclassova, multiclass_ova, ova, ovr. multi_error, error rate for multi-class classification. cross_entropy, cross-entropy (with optional linear weights), aliases: xentropy. cross_entropy_lambda, intensity-weighted cross-entropy, aliases: xentlambda. kullback_leibler, Kullback-Leibler divergence, aliases: kldiv.\n |  \n |  getMinDataInLeaf(self)\n |      Returns:\n |          minDataInLeaf: Minimal number of data in one leaf. Can be used to deal with over-fitting.\n |  \n |  getMinGainToSplit(self)\n |      Returns:\n |          minGainToSplit: The minimal gain to perform split\n |  \n |  getMinSumHessianInLeaf(self)\n |      Returns:\n |          minSumHessianInLeaf: Minimal sum hessian in one leaf\n |  \n |  getModelString(self)\n |      Returns:\n |          modelString: LightGBM model to retrain\n |  \n |  getNegBaggingFraction(self)\n |      Returns:\n |          negBaggingFraction: Negative Bagging fraction\n |  \n |  getNumBatches(self)\n |      Returns:\n |          numBatches: If greater than 0, splits data into separate batches during training\n |  \n |  getNumIterations(self)\n |      Returns:\n |          numIterations: Number of iterations, LightGBM constructs num_class * num_iterations trees\n |  \n |  getNumLeaves(self)\n |      Returns:\n |          numLeaves: Number of leaves\n |  \n |  getNumTasks(self)\n |      Returns:\n |          numTasks: Advanced parameter to specify the number of tasks.  SynapseML tries to guess this based on cluster configuration, but this parameter can be used to override.\n |  \n |  getNumThreads(self)\n |      Returns:\n |          numThreads: Number of threads for LightGBM. For the best speed, set this to the number of real CPU cores.\n |  \n |  getObjective(self)\n |      Returns:\n |          objective: The Objective. For regression applications, this can be: regression_l2, regression_l1, huber, fair, poisson, quantile, mape, gamma or tweedie. For classification applications, this can be: binary, multiclass, or multiclassova.\n |  \n |  getParallelism(self)\n |      Returns:\n |          parallelism: Tree learner parallelism, can be set to data_parallel or voting_parallel\n |  \n |  getPosBaggingFraction(self)\n |      Returns:\n |          posBaggingFraction: Positive Bagging fraction\n |  \n |  getPredictDisableShapeCheck(self)\n |      Returns:\n |          predictDisableShapeCheck: control whether or not LightGBM raises an error when you try to predict on data with a different number of features than the training data\n |  \n |  getPredictionCol(self)\n |      Returns:\n |          predictionCol: prediction column name\n |  \n |  getRepartitionByGroupingColumn(self)\n |      Returns:\n |          repartitionByGroupingColumn: Repartition training data according to grouping column, on by default.\n |  \n |  getSkipDrop(self)\n |      Returns:\n |          skipDrop: Probability of skipping the dropout procedure during a boosting iteration\n |  \n |  getSlotNames(self)\n |      Returns:\n |          slotNames: List of slot names in the features column\n |  \n |  getTimeout(self)\n |      Returns:\n |          timeout: Timeout in seconds\n |  \n |  getTopK(self)\n |      Returns:\n |          topK: The top_k value used in Voting parallel, set this to larger value for more accurate result, but it will slow down the training speed. It should be greater than 0\n |  \n |  getTweedieVariancePower(self)\n |      Returns:\n |          tweedieVariancePower: control the variance of tweedie distribution, must be between 1 and 2\n |  \n |  getUniformDrop(self)\n |      Returns:\n |          uniformDrop: Set this to true to use uniform drop in dart mode\n |  \n |  getUseBarrierExecutionMode(self)\n |      Returns:\n |          useBarrierExecutionMode: Barrier execution mode which uses a barrier stage, off by default.\n |  \n |  getUseSingleDatasetMode(self)\n |      Returns:\n |          useSingleDatasetMode: Use single dataset execution mode to create a single native dataset per executor (singleton) to reduce memory and communication overhead. Note this is disabled when running spark in local mode.\n |  \n |  getValidationIndicatorCol(self)\n |      Returns:\n |          validationIndicatorCol: Indicates whether the row is for training or validation\n |  \n |  getVerbosity(self)\n |      Returns:\n |          verbosity: Verbosity where lt 0 is Fatal, eq 0 is Error, eq 1 is Info, gt 1 is Debug\n |  \n |  getWeightCol(self)\n |      Returns:\n |          weightCol: The name of the weight column\n |  \n |  getXgboostDartMode(self)\n |      Returns:\n |          xgboostDartMode: Set this to true to use xgboost dart mode\n |  \n |  setAlpha(self, value)\n |      Args:\n |          alpha: parameter for Huber loss and Quantile regression\n |  \n |  setBaggingFraction(self, value)\n |      Args:\n |          baggingFraction: Bagging fraction\n |  \n |  setBaggingFreq(self, value)\n |      Args:\n |          baggingFreq: Bagging frequency\n |  \n |  setBaggingSeed(self, value)\n |      Args:\n |          baggingSeed: Bagging seed\n |  \n |  setBinSampleCount(self, value)\n |      Args:\n |          binSampleCount: Number of samples considered at computing histogram bins\n |  \n |  setBoostFromAverage(self, value)\n |      Args:\n |          boostFromAverage: Adjusts initial score to the mean of labels for faster convergence\n |  \n |  setBoostingType(self, value)\n |      Args:\n |          boostingType: Default gbdt = traditional Gradient Boosting Decision Tree. Options are: gbdt, gbrt, rf (Random Forest), random_forest, dart (Dropouts meet Multiple Additive Regression Trees), goss (Gradient-based One-Side Sampling).\n |  \n |  setCategoricalSlotIndexes(self, value)\n |      Args:\n |          categoricalSlotIndexes: List of categorical column indexes, the slot index in the features column\n |  \n |  setCategoricalSlotNames(self, value)\n |      Args:\n |          categoricalSlotNames: List of categorical column slot names, the slot name in the features column\n |  \n |  setChunkSize(self, value)\n |      Args:\n |          chunkSize: Advanced parameter to specify the chunk size for copying Java data to native.  If set too high, memory may be wasted, but if set too low, performance may be reduced during data copy.If dataset size is known beforehand, set to the number of rows in the dataset.\n |  \n |  setDefaultListenPort(self, value)\n |      Args:\n |          defaultListenPort: The default listen port on executors, used for testing\n |  \n |  setDriverListenPort(self, value)\n |      Args:\n |          driverListenPort: The listen port on a driver. Default value is 0 (random)\n |  \n |  setDropRate(self, value)\n |      Args:\n |          dropRate: Dropout rate: a fraction of previous trees to drop during the dropout\n |  \n |  setEarlyStoppingRound(self, value)\n |      Args:\n |          earlyStoppingRound: Early stopping round\n |  \n |  setFeatureFraction(self, value)\n |      Args:\n |          featureFraction: Feature fraction\n |  \n |  setFeaturesCol(self, value)\n |      Args:\n |          featuresCol: features column name\n |  \n |  setFeaturesShapCol(self, value)\n |      Args:\n |          featuresShapCol: Output SHAP vector column name after prediction containing the feature contribution values\n |  \n |  setFobj(self, value)\n |      Args:\n |          fobj: Customized objective function. Should accept two parameters: preds, train_data, and return (grad, hess).\n |  \n |  setImprovementTolerance(self, value)\n |      Args:\n |          improvementTolerance: Tolerance to consider improvement in metric\n |  \n |  setInitScoreCol(self, value)\n |      Args:\n |          initScoreCol: The name of the initial score column, used for continued training\n |  \n |  setIsProvideTrainingMetric(self, value)\n |      Args:\n |          isProvideTrainingMetric: Whether output metric result over training dataset.\n |  \n |  setLabelCol(self, value)\n |      Args:\n |          labelCol: label column name\n |  \n |  setLambdaL1(self, value)\n |      Args:\n |          lambdaL1: L1 regularization\n |  \n |  setLambdaL2(self, value)\n |      Args:\n |          lambdaL2: L2 regularization\n |  \n |  setLeafPredictionCol(self, value)\n |      Args:\n |          leafPredictionCol: Predicted leaf indices&#39;s column name\n |  \n |  setLearningRate(self, value)\n |      Args:\n |          learningRate: Learning rate or shrinkage rate\n |  \n |  setMatrixType(self, value)\n |      Args:\n |          matrixType: Advanced parameter to specify whether the native lightgbm matrix constructed should be sparse or dense.  Values can be auto, sparse or dense. Default value is auto, which samples first ten rows to determine type.\n |  \n |  setMaxBin(self, value)\n |      Args:\n |          maxBin: Max bin\n |  \n |  setMaxBinByFeature(self, value)\n |      Args:\n |          maxBinByFeature: Max number of bins for each feature\n |  \n |  setMaxDeltaStep(self, value)\n |      Args:\n |          maxDeltaStep: Used to limit the max output of tree leaves\n |  \n |  setMaxDepth(self, value)\n |      Args:\n |          maxDepth: Max depth\n |  \n |  setMaxDrop(self, value)\n |      Args:\n |          maxDrop: Max number of dropped trees during one boosting iteration\n |  \n |  setMetric(self, value)\n |      Args:\n |          metric: Metrics to be evaluated on the evaluation data.  Options are: empty string or not specified means that metric corresponding to specified objective will be used (this is possible only for pre-defined objective functions, otherwise no evaluation metric will be added). None (string, not a None value) means that no metric will be registered, aliases: na, null, custom. l1, absolute loss, aliases: mean_absolute_error, mae, regression_l1. l2, square loss, aliases: mean_squared_error, mse, regression_l2, regression. rmse, root square loss, aliases: root_mean_squared_error, l2_root. quantile, Quantile regression. mape, MAPE loss, aliases: mean_absolute_percentage_error. huber, Huber loss. fair, Fair loss. poisson, negative log-likelihood for Poisson regression. gamma, negative log-likelihood for Gamma regression. gamma_deviance, residual deviance for Gamma regression. tweedie, negative log-likelihood for Tweedie regression. ndcg, NDCG, aliases: lambdarank. map, MAP, aliases: mean_average_precision. auc, AUC. binary_logloss, log loss, aliases: binary. binary_error, for one sample: 0 for correct classification, 1 for error classification. multi_logloss, log loss for multi-class classification, aliases: multiclass, softmax, multiclassova, multiclass_ova, ova, ovr. multi_error, error rate for multi-class classification. cross_entropy, cross-entropy (with optional linear weights), aliases: xentropy. cross_entropy_lambda, intensity-weighted cross-entropy, aliases: xentlambda. kullback_leibler, Kullback-Leibler divergence, aliases: kldiv.\n |  \n |  setMinDataInLeaf(self, value)\n |      Args:\n |          minDataInLeaf: Minimal number of data in one leaf. Can be used to deal with over-fitting.\n |  \n |  setMinGainToSplit(self, value)\n |      Args:\n |          minGainToSplit: The minimal gain to perform split\n |  \n |  setMinSumHessianInLeaf(self, value)\n |      Args:\n |          minSumHessianInLeaf: Minimal sum hessian in one leaf\n |  \n |  setModelString(self, value)\n |      Args:\n |          modelString: LightGBM model to retrain\n |  \n |  setNegBaggingFraction(self, value)\n |      Args:\n |          negBaggingFraction: Negative Bagging fraction\n |  \n |  setNumBatches(self, value)\n |      Args:\n |          numBatches: If greater than 0, splits data into separate batches during training\n |  \n |  setNumIterations(self, value)\n |      Args:\n |          numIterations: Number of iterations, LightGBM constructs num_class * num_iterations trees\n |  \n |  setNumLeaves(self, value)\n |      Args:\n |          numLeaves: Number of leaves\n |  \n |  setNumTasks(self, value)\n |      Args:\n |          numTasks: Advanced parameter to specify the number of tasks.  SynapseML tries to guess this based on cluster configuration, but this parameter can be used to override.\n |  \n |  setNumThreads(self, value)\n |      Args:\n |          numThreads: Number of threads for LightGBM. For the best speed, set this to the number of real CPU cores.\n |  \n |  setObjective(self, value)\n |      Args:\n |          objective: The Objective. For regression applications, this can be: regression_l2, regression_l1, huber, fair, poisson, quantile, mape, gamma or tweedie. For classification applications, this can be: binary, multiclass, or multiclassova.\n |  \n |  setParallelism(self, value)\n |      Args:\n |          parallelism: Tree learner parallelism, can be set to data_parallel or voting_parallel\n |  \n |  setParams(self, alpha=0.9, baggingFraction=1.0, baggingFreq=0, baggingSeed=3, binSampleCount=200000, boostFromAverage=True, boostingType=&#39;gbdt&#39;, categoricalSlotIndexes=[], categoricalSlotNames=[], chunkSize=10000, defaultListenPort=12400, driverListenPort=0, dropRate=0.1, earlyStoppingRound=0, featureFraction=1.0, featuresCol=&#39;features&#39;, featuresShapCol=&#39;&#39;, fobj=None, improvementTolerance=0.0, initScoreCol=None, isProvideTrainingMetric=False, labelCol=&#39;label&#39;, lambdaL1=0.0, lambdaL2=0.0, leafPredictionCol=&#39;&#39;, learningRate=0.1, matrixType=&#39;auto&#39;, maxBin=255, maxBinByFeature=[], maxDeltaStep=0.0, maxDepth=-1, maxDrop=50, metric=&#39;&#39;, minDataInLeaf=20, minGainToSplit=0.0, minSumHessianInLeaf=0.001, modelString=&#39;&#39;, negBaggingFraction=1.0, numBatches=0, numIterations=100, numLeaves=31, numTasks=0, numThreads=0, objective=&#39;regression&#39;, parallelism=&#39;data_parallel&#39;, posBaggingFraction=1.0, predictDisableShapeCheck=False, predictionCol=&#39;prediction&#39;, repartitionByGroupingColumn=True, skipDrop=0.5, slotNames=[], timeout=1200.0, topK=20, tweedieVariancePower=1.5, uniformDrop=False, useBarrierExecutionMode=False, useSingleDatasetMode=True, validationIndicatorCol=None, verbosity=-1, weightCol=None, xgboostDartMode=False)\n |      Set the (keyword only) parameters\n |  \n |  setPosBaggingFraction(self, value)\n |      Args:\n |          posBaggingFraction: Positive Bagging fraction\n |  \n |  setPredictDisableShapeCheck(self, value)\n |      Args:\n |          predictDisableShapeCheck: control whether or not LightGBM raises an error when you try to predict on data with a different number of features than the training data\n |  \n |  setPredictionCol(self, value)\n |      Args:\n |          predictionCol: prediction column name\n |  \n |  setRepartitionByGroupingColumn(self, value)\n |      Args:\n |          repartitionByGroupingColumn: Repartition training data according to grouping column, on by default.\n |  \n |  setSkipDrop(self, value)\n |      Args:\n |          skipDrop: Probability of skipping the dropout procedure during a boosting iteration\n |  \n |  setSlotNames(self, value)\n |      Args:\n |          slotNames: List of slot names in the features column\n |  \n |  setTimeout(self, value)\n |      Args:\n |          timeout: Timeout in seconds\n |  \n |  setTopK(self, value)\n |      Args:\n |          topK: The top_k value used in Voting parallel, set this to larger value for more accurate result, but it will slow down the training speed. It should be greater than 0\n |  \n |  setTweedieVariancePower(self, value)\n |      Args:\n |          tweedieVariancePower: control the variance of tweedie distribution, must be between 1 and 2\n |  \n |  setUniformDrop(self, value)\n |      Args:\n |          uniformDrop: Set this to true to use uniform drop in dart mode\n |  \n |  setUseBarrierExecutionMode(self, value)\n |      Args:\n |          useBarrierExecutionMode: Barrier execution mode which uses a barrier stage, off by default.\n |  \n |  setUseSingleDatasetMode(self, value)\n |      Args:\n |          useSingleDatasetMode: Use single dataset execution mode to create a single native dataset per executor (singleton) to reduce memory and communication overhead. Note this is disabled when running spark in local mode.\n |  \n |  setValidationIndicatorCol(self, value)\n |      Args:\n |          validationIndicatorCol: Indicates whether the row is for training or validation\n |  \n |  setVerbosity(self, value)\n |      Args:\n |          verbosity: Verbosity where lt 0 is Fatal, eq 0 is Error, eq 1 is Info, gt 1 is Debug\n |  \n |  setWeightCol(self, value)\n |      Args:\n |          weightCol: The name of the weight column\n |  \n |  setXgboostDartMode(self, value)\n |      Args:\n |          xgboostDartMode: Set this to true to use xgboost dart mode\n |  \n |  ----------------------------------------------------------------------\n |  Class methods defined here:\n |  \n |  read() from abc.ABCMeta\n |      Returns an MLReader instance for this class.\n |  \n |  ----------------------------------------------------------------------\n |  Static methods defined here:\n |  \n |  getJavaPackage()\n |      Returns package name String.\n |  \n |  ----------------------------------------------------------------------\n |  Data and other attributes defined here:\n |  \n |  __abstractmethods__ = frozenset()\n |  \n |  alpha = Param(parent=&#39;undefined&#39;, name=&#39;alpha&#39;, doc=&#39;parameter for Hub...\n |  \n |  baggingFraction = Param(parent=&#39;undefined&#39;, name=&#39;baggingFraction&#39;, do...\n |  \n |  baggingFreq = Param(parent=&#39;undefined&#39;, name=&#39;baggingFreq&#39;, doc=&#39;Baggi...\n |  \n |  baggingSeed = Param(parent=&#39;undefined&#39;, name=&#39;baggingSeed&#39;, doc=&#39;Baggi...\n |  \n |  binSampleCount = Param(parent=&#39;undefined&#39;, name=&#39;binSampleCount&#39;,... s...\n |  \n |  boostFromAverage = Param(parent=&#39;undefined&#39;, name=&#39;boostFromAverage......\n |  \n |  boostingType = Param(parent=&#39;undefined&#39;, name=&#39;boostingType&#39;, d...ees)...\n |  \n |  categoricalSlotIndexes = Param(parent=&#39;undefined&#39;, name=&#39;categoricalSl...\n |  \n |  categoricalSlotNames = Param(parent=&#39;undefined&#39;, name=&#39;categoricalSlot...\n |  \n |  chunkSize = Param(parent=&#39;undefined&#39;, name=&#39;chunkSize&#39;, doc=...hand, s...\n |  \n |  defaultListenPort = Param(parent=&#39;undefined&#39;, name=&#39;defaultListenPor.....\n |  \n |  driverListenPort = Param(parent=&#39;undefined&#39;, name=&#39;driverListenPort......\n |  \n |  dropRate = Param(parent=&#39;undefined&#39;, name=&#39;dropRate&#39;, doc=&#39;...on of pr...\n |  \n |  earlyStoppingRound = Param(parent=&#39;undefined&#39;, name=&#39;earlyStoppingRoun...\n |  \n |  featureFraction = Param(parent=&#39;undefined&#39;, name=&#39;featureFraction&#39;, do...\n |  \n |  featuresCol = Param(parent=&#39;undefined&#39;, name=&#39;featuresCol&#39;, doc=&#39;featu...\n |  \n |  featuresShapCol = Param(parent=&#39;undefined&#39;, name=&#39;featuresShapCol&#39;...t...\n |  \n |  fobj = Param(parent=&#39;undefined&#39;, name=&#39;fobj&#39;, doc=&#39;Cust...rs: preds, t...\n |  \n |  improvementTolerance = Param(parent=&#39;undefined&#39;, name=&#39;improvementTole...\n |  \n |  initScoreCol = Param(parent=&#39;undefined&#39;, name=&#39;initScoreCol&#39;, d...itia...\n |  \n |  isProvideTrainingMetric = Param(parent=&#39;undefined&#39;, name=&#39;isProvideTra...\n |  \n |  labelCol = Param(parent=&#39;undefined&#39;, name=&#39;labelCol&#39;, doc=&#39;label colum...\n |  \n |  lambdaL1 = Param(parent=&#39;undefined&#39;, name=&#39;lambdaL1&#39;, doc=&#39;L1 regulari...\n |  \n |  lambdaL2 = Param(parent=&#39;undefined&#39;, name=&#39;lambdaL2&#39;, doc=&#39;L2 regulari...\n |  \n |  leafPredictionCol = Param(parent=&#39;undefined&#39;, name=&#39;leafPredictionCol&#39;...\n |  \n |  learningRate = Param(parent=&#39;undefined&#39;, name=&#39;learningRate&#39;, doc=&#39;Lea...\n |  \n |  matrixType = Param(parent=&#39;undefined&#39;, name=&#39;matrixType&#39;, doc...which ...\n |  \n |  maxBin = Param(parent=&#39;undefined&#39;, name=&#39;maxBin&#39;, doc=&#39;Max bin&#39;)\n |  \n |  maxBinByFeature = Param(parent=&#39;undefined&#39;, name=&#39;maxBinByFeature&#39;, do...\n |  \n |  maxDeltaStep = Param(parent=&#39;undefined&#39;, name=&#39;maxDeltaStep&#39;, doc=&#39;Use...\n |  \n |  maxDepth = Param(parent=&#39;undefined&#39;, name=&#39;maxDepth&#39;, doc=&#39;Max depth&#39;)\n |  \n |  maxDrop = Param(parent=&#39;undefined&#39;, name=&#39;maxDrop&#39;, doc=&#39;M... of dropp...\n |  \n |  metric = Param(parent=&#39;undefined&#39;, name=&#39;metric&#39;, doc=&#39;Me..., Kullback...\n |  \n |  minDataInLeaf = Param(parent=&#39;undefined&#39;, name=&#39;minDataInLeaf&#39;, ...ne ...\n |  \n |  minGainToSplit = Param(parent=&#39;undefined&#39;, name=&#39;minGainToSplit&#39;, doc=...\n |  \n |  minSumHessianInLeaf = Param(parent=&#39;undefined&#39;, name=&#39;minSumHessianInL...\n |  \n |  modelString = Param(parent=&#39;undefined&#39;, name=&#39;modelString&#39;, doc=&#39;Light...\n |  \n |  negBaggingFraction = Param(parent=&#39;undefined&#39;, name=&#39;negBaggingFractio...\n |  \n |  numBatches = Param(parent=&#39;undefined&#39;, name=&#39;numBatches&#39;, doc...lits d...\n |  \n |  numIterations = Param(parent=&#39;undefined&#39;, name=&#39;numIterations&#39;, ...GBM...\n |  \n |  numLeaves = Param(parent=&#39;undefined&#39;, name=&#39;numLeaves&#39;, doc=&#39;Number of...\n |  \n |  numTasks = Param(parent=&#39;undefined&#39;, name=&#39;numTasks&#39;, doc=&#39;...on, but ...\n |  \n |  numThreads = Param(parent=&#39;undefined&#39;, name=&#39;numThreads&#39;, doc...peed, ...\n |  \n |  objective = Param(parent=&#39;undefined&#39;, name=&#39;objective&#39;, doc=... can be...\n |  \n |  parallelism = Param(parent=&#39;undefined&#39;, name=&#39;parallelism&#39;, do... can ...\n |  \n |  posBaggingFraction = Param(parent=&#39;undefined&#39;, name=&#39;posBaggingFractio...\n |  \n |  predictDisableShapeCheck = Param(parent=&#39;undefined&#39;, name=&#39;predictDisa...\n |  \n |  predictionCol = Param(parent=&#39;undefined&#39;, name=&#39;predictionCol&#39;, doc=&#39;p...\n |  \n |  repartitionByGroupingColumn = Param(parent=&#39;undefined&#39;, name=&#39;repartit...\n |  \n |  skipDrop = Param(parent=&#39;undefined&#39;, name=&#39;skipDrop&#39;, doc=&#39;...e dropou...\n |  \n |  slotNames = Param(parent=&#39;undefined&#39;, name=&#39;slotNames&#39;, doc=&#39;List of s...\n |  \n |  timeout = Param(parent=&#39;undefined&#39;, name=&#39;timeout&#39;, doc=&#39;Timeout in se...\n |  \n |  topK = Param(parent=&#39;undefined&#39;, name=&#39;topK&#39;, doc=&#39;The ...the training...\n |  \n |  tweedieVariancePower = Param(parent=&#39;undefined&#39;, name=&#39;tweedieVariance...\n |  \n |  uniformDrop = Param(parent=&#39;undefined&#39;, name=&#39;uniformDrop&#39;, do...t thi...\n |  \n |  useBarrierExecutionMode = Param(parent=&#39;undefined&#39;, name=&#39;useBarrierEx...\n |  \n |  useSingleDatasetMode = Param(parent=&#39;undefined&#39;, name=&#39;useSingleDatase...\n |  \n |  validationIndicatorCol = Param(parent=&#39;undefined&#39;, name=&#39;validationInd...\n |  \n |  verbosity = Param(parent=&#39;undefined&#39;, name=&#39;verbosity&#39;, doc=...tal, eq...\n |  \n |  weightCol = Param(parent=&#39;undefined&#39;, name=&#39;weightCol&#39;, doc=&#39;The name ...\n |  \n |  xgboostDartMode = Param(parent=&#39;undefined&#39;, name=&#39;xgboostDartMode&#39;, do...\n |  \n |  ----------------------------------------------------------------------\n |  Class methods inherited from pyspark.ml.util.MLReadable:\n |  \n |  load(path) from abc.ABCMeta\n |      Reads an ML instance from the input path, a shortcut of `read().load(path)`.\n |  \n |  ----------------------------------------------------------------------\n |  Data descriptors inherited from pyspark.ml.util.MLReadable:\n |  \n |  __dict__\n |      dictionary for instance variables (if defined)\n |  \n |  __weakref__\n |      list of weak references to the object (if defined)\n |  \n |  ----------------------------------------------------------------------\n |  Methods inherited from pyspark.ml.util.JavaMLWritable:\n |  \n |  write(self)\n |      Returns an MLWriter instance for this ML instance.\n |  \n |  ----------------------------------------------------------------------\n |  Methods inherited from pyspark.ml.util.MLWritable:\n |  \n |  save(self, path)\n |      Save this ML instance to the given path, a shortcut of &#39;write().save(path)&#39;.\n |  \n |  ----------------------------------------------------------------------\n |  Methods inherited from pyspark.ml.wrapper.JavaParams:\n |  \n |  clear(self, param)\n |      Clears a param from the param map if it has been explicitly set.\n |  \n |  copy(self, extra=None)\n |      Creates a copy of this instance with the same uid and some\n |      extra params. This implementation first calls Params.copy and\n |      then make a copy of the companion Java pipeline component with\n |      extra params. So both the Python wrapper and the Java pipeline\n |      component get copied.\n |      \n |      Parameters\n |      ----------\n |      extra : dict, optional\n |          Extra parameters to copy to the new instance\n |      \n |      Returns\n |      -------\n |      :py:class:`JavaParams`\n |          Copy of this instance\n |  \n |  ----------------------------------------------------------------------\n |  Methods inherited from pyspark.ml.wrapper.JavaWrapper:\n |  \n |  __del__(self)\n |  \n |  ----------------------------------------------------------------------\n |  Methods inherited from pyspark.ml.base.Estimator:\n |  \n |  fit(self, dataset, params=None)\n |      Fits a model to the input dataset with optional parameters.\n |      \n |      .. versionadded:: 1.3.0\n |      \n |      Parameters\n |      ----------\n |      dataset : :py:class:`pyspark.sql.DataFrame`\n |          input dataset.\n |      params : dict or list or tuple, optional\n |          an optional param map that overrides embedded params. If a list/tuple of\n |          param maps is given, this calls fit on each param map and returns a list of\n |          models.\n |      \n |      Returns\n |      -------\n |      :py:class:`Transformer` or a list of :py:class:`Transformer`\n |          fitted model(s)\n |  \n |  fitMultiple(self, dataset, paramMaps)\n |      Fits a model to the input dataset for each param map in `paramMaps`.\n |      \n |      .. versionadded:: 2.3.0\n |      \n |      Parameters\n |      ----------\n |      dataset : :py:class:`pyspark.sql.DataFrame`\n |          input dataset.\n |      paramMaps : :py:class:`collections.abc.Sequence`\n |          A Sequence of param maps.\n |      \n |      Returns\n |      -------\n |      :py:class:`_FitMultipleIterator`\n |          A thread safe iterable which contains one model for each param map. Each\n |          call to `next(modelIterator)` will return `(index, model)` where model was fit\n |          using `paramMaps[index]`. `index` values may not be sequential.\n |  \n |  ----------------------------------------------------------------------\n |  Methods inherited from pyspark.ml.param.Params:\n |  \n |  explainParam(self, param)\n |      Explains a single param and returns its name, doc, and optional\n |      default value and user-supplied value in a string.\n |  \n |  explainParams(self)\n |      Returns the documentation of all params with their optionally\n |      default values and user-supplied values.\n |  \n |  extractParamMap(self, extra=None)\n |      Extracts the embedded default param values and user-supplied\n |      values, and then merges them with extra values from input into\n |      a flat param map, where the latter value is used if there exist\n |      conflicts, i.e., with ordering: default param values &lt;\n |      user-supplied values &lt; extra.\n |      \n |      Parameters\n |      ----------\n |      extra : dict, optional\n |          extra param values\n |      \n |      Returns\n |      -------\n |      dict\n |          merged param map\n |  \n |  getOrDefault(self, param)\n |      Gets the value of a param in the user-supplied param map or its\n |      default value. Raises an error if neither is set.\n |  \n |  getParam(self, paramName)\n |      Gets a param by its name.\n |  \n |  hasDefault(self, param)\n |      Checks whether a param has a default value.\n |  \n |  hasParam(self, paramName)\n |      Tests whether this instance contains a param with a given\n |      (string) name.\n |  \n |  isDefined(self, param)\n |      Checks whether a param is explicitly set by user or has\n |      a default value.\n |  \n |  isSet(self, param)\n |      Checks whether a param is explicitly set by user.\n |  \n |  set(self, param, value)\n |      Sets a parameter in the embedded param map.\n |  \n |  ----------------------------------------------------------------------\n |  Readonly properties inherited from pyspark.ml.param.Params:\n |  \n |  params\n |      Returns all params ordered by name. The default implementation\n |      uses :py:func:`dir` to get all attributes of type\n |      :py:class:`Param`.\n |  \n |  ----------------------------------------------------------------------\n |  Methods inherited from pyspark.ml.util.Identifiable:\n |  \n |  __repr__(self)\n |      Return repr(self).\n\n</div>","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">Help on class LightGBMRegressor in module synapse.ml.lightgbm.LightGBMRegressor:\n\nclass LightGBMRegressor(synapse.ml.core.schema.Utils.ComplexParamsMixin, pyspark.ml.util.JavaMLReadable, pyspark.ml.util.JavaMLWritable, pyspark.ml.wrapper.JavaEstimator)\n  LightGBMRegressor(java_obj=None, alpha=0.9, baggingFraction=1.0, baggingFreq=0, baggingSeed=3, binSampleCount=200000, boostFromAverage=True, boostingType=&#39;gbdt&#39;, categoricalSlotIndexes=[], categoricalSlotNames=[], chunkSize=10000, defaultListenPort=12400, driverListenPort=0, dropRate=0.1, earlyStoppingRound=0, featureFraction=1.0, featuresCol=&#39;features&#39;, featuresShapCol=&#39;&#39;, fobj=None, improvementTolerance=0.0, initScoreCol=None, isProvideTrainingMetric=False, labelCol=&#39;label&#39;, lambdaL1=0.0, lambdaL2=0.0, leafPredictionCol=&#39;&#39;, learningRate=0.1, matrixType=&#39;auto&#39;, maxBin=255, maxBinByFeature=[], maxDeltaStep=0.0, maxDepth=-1, maxDrop=50, metric=&#39;&#39;, minDataInLeaf=20, minGainToSplit=0.0, minSumHessianInLeaf=0.001, modelString=&#39;&#39;, negBaggingFraction=1.0, numBatches=0, numIterations=100, numLeaves=31, numTasks=0, numThreads=0, objective=&#39;regression&#39;, parallelism=&#39;data_parallel&#39;, posBaggingFraction=1.0, predictDisableShapeCheck=False, predictionCol=&#39;prediction&#39;, repartitionByGroupingColumn=True, skipDrop=0.5, slotNames=[], timeout=1200.0, topK=20, tweedieVariancePower=1.5, uniformDrop=False, useBarrierExecutionMode=False, useSingleDatasetMode=True, validationIndicatorCol=None, verbosity=-1, weightCol=None, xgboostDartMode=False)\n  \n  Args:\n      alpha (float): parameter for Huber loss and Quantile regression\n      baggingFraction (float): Bagging fraction\n      baggingFreq (int): Bagging frequency\n      baggingSeed (int): Bagging seed\n      binSampleCount (int): Number of samples considered at computing histogram bins\n      boostFromAverage (bool): Adjusts initial score to the mean of labels for faster convergence\n      boostingType (object): Default gbdt = traditional Gradient Boosting Decision Tree. Options are: gbdt, gbrt, rf (Random Forest), random_forest, dart (Dropouts meet Multiple Additive Regression Trees), goss (Gradient-based One-Side Sampling). \n      categoricalSlotIndexes (list): List of categorical column indexes, the slot index in the features column\n      categoricalSlotNames (list): List of categorical column slot names, the slot name in the features column\n      chunkSize (int): Advanced parameter to specify the chunk size for copying Java data to native.  If set too high, memory may be wasted, but if set too low, performance may be reduced during data copy.If dataset size is known beforehand, set to the number of rows in the dataset.\n      defaultListenPort (int): The default listen port on executors, used for testing\n      driverListenPort (int): The listen port on a driver. Default value is 0 (random)\n      dropRate (float): Dropout rate: a fraction of previous trees to drop during the dropout\n      earlyStoppingRound (int): Early stopping round\n      featureFraction (float): Feature fraction\n      featuresCol (object): features column name\n      featuresShapCol (object): Output SHAP vector column name after prediction containing the feature contribution values\n      fobj (object): Customized objective function. Should accept two parameters: preds, train_data, and return (grad, hess).\n      improvementTolerance (float): Tolerance to consider improvement in metric\n      initScoreCol (object): The name of the initial score column, used for continued training\n      isProvideTrainingMetric (bool): Whether output metric result over training dataset.\n      labelCol (object): label column name\n      lambdaL1 (float): L1 regularization\n      lambdaL2 (float): L2 regularization\n      leafPredictionCol (object): Predicted leaf indices&#39;s column name\n      learningRate (float): Learning rate or shrinkage rate\n      matrixType (object): Advanced parameter to specify whether the native lightgbm matrix constructed should be sparse or dense.  Values can be auto, sparse or dense. Default value is auto, which samples first ten rows to determine type.\n      maxBin (int): Max bin\n      maxBinByFeature (list): Max number of bins for each feature\n      maxDeltaStep (float): Used to limit the max output of tree leaves\n      maxDepth (int): Max depth\n      maxDrop (int): Max number of dropped trees during one boosting iteration\n      metric (object): Metrics to be evaluated on the evaluation data.  Options are: empty string or not specified means that metric corresponding to specified objective will be used (this is possible only for pre-defined objective functions, otherwise no evaluation metric will be added). None (string, not a None value) means that no metric will be registered, aliases: na, null, custom. l1, absolute loss, aliases: mean_absolute_error, mae, regression_l1. l2, square loss, aliases: mean_squared_error, mse, regression_l2, regression. rmse, root square loss, aliases: root_mean_squared_error, l2_root. quantile, Quantile regression. mape, MAPE loss, aliases: mean_absolute_percentage_error. huber, Huber loss. fair, Fair loss. poisson, negative log-likelihood for Poisson regression. gamma, negative log-likelihood for Gamma regression. gamma_deviance, residual deviance for Gamma regression. tweedie, negative log-likelihood for Tweedie regression. ndcg, NDCG, aliases: lambdarank. map, MAP, aliases: mean_average_precision. auc, AUC. binary_logloss, log loss, aliases: binary. binary_error, for one sample: 0 for correct classification, 1 for error classification. multi_logloss, log loss for multi-class classification, aliases: multiclass, softmax, multiclassova, multiclass_ova, ova, ovr. multi_error, error rate for multi-class classification. cross_entropy, cross-entropy (with optional linear weights), aliases: xentropy. cross_entropy_lambda, intensity-weighted cross-entropy, aliases: xentlambda. kullback_leibler, Kullback-Leibler divergence, aliases: kldiv. \n      minDataInLeaf (int): Minimal number of data in one leaf. Can be used to deal with over-fitting.\n      minGainToSplit (float): The minimal gain to perform split\n      minSumHessianInLeaf (float): Minimal sum hessian in one leaf\n      modelString (object): LightGBM model to retrain\n      negBaggingFraction (float): Negative Bagging fraction\n      numBatches (int): If greater than 0, splits data into separate batches during training\n      numIterations (int): Number of iterations, LightGBM constructs num_class * num_iterations trees\n      numLeaves (int): Number of leaves\n      numTasks (int): Advanced parameter to specify the number of tasks.  SynapseML tries to guess this based on cluster configuration, but this parameter can be used to override.\n      numThreads (int): Number of threads for LightGBM. For the best speed, set this to the number of real CPU cores.\n      objective (object): The Objective. For regression applications, this can be: regression_l2, regression_l1, huber, fair, poisson, quantile, mape, gamma or tweedie. For classification applications, this can be: binary, multiclass, or multiclassova. \n      parallelism (object): Tree learner parallelism, can be set to data_parallel or voting_parallel\n      posBaggingFraction (float): Positive Bagging fraction\n      predictDisableShapeCheck (bool): control whether or not LightGBM raises an error when you try to predict on data with a different number of features than the training data\n      predictionCol (object): prediction column name\n      repartitionByGroupingColumn (bool): Repartition training data according to grouping column, on by default.\n      skipDrop (float): Probability of skipping the dropout procedure during a boosting iteration\n      slotNames (list): List of slot names in the features column\n      timeout (float): Timeout in seconds\n      topK (int): The top_k value used in Voting parallel, set this to larger value for more accurate result, but it will slow down the training speed. It should be greater than 0\n      tweedieVariancePower (float): control the variance of tweedie distribution, must be between 1 and 2\n      uniformDrop (bool): Set this to true to use uniform drop in dart mode\n      useBarrierExecutionMode (bool): Barrier execution mode which uses a barrier stage, off by default.\n      useSingleDatasetMode (bool): Use single dataset execution mode to create a single native dataset per executor (singleton) to reduce memory and communication overhead. Note this is disabled when running spark in local mode.\n      validationIndicatorCol (object): Indicates whether the row is for training or validation\n      verbosity (int): Verbosity where lt 0 is Fatal, eq 0 is Error, eq 1 is Info, gt 1 is Debug\n      weightCol (object): The name of the weight column\n      xgboostDartMode (bool): Set this to true to use xgboost dart mode\n  \n  Method resolution order:\n      LightGBMRegressor\n      synapse.ml.core.schema.Utils.ComplexParamsMixin\n      pyspark.ml.util.JavaMLReadable\n      pyspark.ml.util.MLReadable\n      pyspark.ml.util.JavaMLWritable\n      pyspark.ml.util.MLWritable\n      pyspark.ml.wrapper.JavaEstimator\n      pyspark.ml.wrapper.JavaParams\n      pyspark.ml.wrapper.JavaWrapper\n      pyspark.ml.base.Estimator\n      pyspark.ml.param.Params\n      pyspark.ml.util.Identifiable\n      builtins.object\n  \n  Methods defined here:\n  \n  __init__(self, java_obj=None, alpha=0.9, baggingFraction=1.0, baggingFreq=0, baggingSeed=3, binSampleCount=200000, boostFromAverage=True, boostingType=&#39;gbdt&#39;, categoricalSlotIndexes=[], categoricalSlotNames=[], chunkSize=10000, defaultListenPort=12400, driverListenPort=0, dropRate=0.1, earlyStoppingRound=0, featureFraction=1.0, featuresCol=&#39;features&#39;, featuresShapCol=&#39;&#39;, fobj=None, improvementTolerance=0.0, initScoreCol=None, isProvideTrainingMetric=False, labelCol=&#39;label&#39;, lambdaL1=0.0, lambdaL2=0.0, leafPredictionCol=&#39;&#39;, learningRate=0.1, matrixType=&#39;auto&#39;, maxBin=255, maxBinByFeature=[], maxDeltaStep=0.0, maxDepth=-1, maxDrop=50, metric=&#39;&#39;, minDataInLeaf=20, minGainToSplit=0.0, minSumHessianInLeaf=0.001, modelString=&#39;&#39;, negBaggingFraction=1.0, numBatches=0, numIterations=100, numLeaves=31, numTasks=0, numThreads=0, objective=&#39;regression&#39;, parallelism=&#39;data_parallel&#39;, posBaggingFraction=1.0, predictDisableShapeCheck=False, predictionCol=&#39;prediction&#39;, repartitionByGroupingColumn=True, skipDrop=0.5, slotNames=[], timeout=1200.0, topK=20, tweedieVariancePower=1.5, uniformDrop=False, useBarrierExecutionMode=False, useSingleDatasetMode=True, validationIndicatorCol=None, verbosity=-1, weightCol=None, xgboostDartMode=False)\n      Initialize self.  See help(type(self)) for accurate signature.\n  \n  getAlpha(self)\n      Returns:\n          alpha: parameter for Huber loss and Quantile regression\n  \n  getBaggingFraction(self)\n      Returns:\n          baggingFraction: Bagging fraction\n  \n  getBaggingFreq(self)\n      Returns:\n          baggingFreq: Bagging frequency\n  \n  getBaggingSeed(self)\n      Returns:\n          baggingSeed: Bagging seed\n  \n  getBinSampleCount(self)\n      Returns:\n          binSampleCount: Number of samples considered at computing histogram bins\n  \n  getBoostFromAverage(self)\n      Returns:\n          boostFromAverage: Adjusts initial score to the mean of labels for faster convergence\n  \n  getBoostingType(self)\n      Returns:\n          boostingType: Default gbdt = traditional Gradient Boosting Decision Tree. Options are: gbdt, gbrt, rf (Random Forest), random_forest, dart (Dropouts meet Multiple Additive Regression Trees), goss (Gradient-based One-Side Sampling).\n  \n  getCategoricalSlotIndexes(self)\n      Returns:\n          categoricalSlotIndexes: List of categorical column indexes, the slot index in the features column\n  \n  getCategoricalSlotNames(self)\n      Returns:\n          categoricalSlotNames: List of categorical column slot names, the slot name in the features column\n  \n  getChunkSize(self)\n      Returns:\n          chunkSize: Advanced parameter to specify the chunk size for copying Java data to native.  If set too high, memory may be wasted, but if set too low, performance may be reduced during data copy.If dataset size is known beforehand, set to the number of rows in the dataset.\n  \n  getDefaultListenPort(self)\n      Returns:\n          defaultListenPort: The default listen port on executors, used for testing\n  \n  getDriverListenPort(self)\n      Returns:\n          driverListenPort: The listen port on a driver. Default value is 0 (random)\n  \n  getDropRate(self)\n      Returns:\n          dropRate: Dropout rate: a fraction of previous trees to drop during the dropout\n  \n  getEarlyStoppingRound(self)\n      Returns:\n          earlyStoppingRound: Early stopping round\n  \n  getFeatureFraction(self)\n      Returns:\n          featureFraction: Feature fraction\n  \n  getFeaturesCol(self)\n      Returns:\n          featuresCol: features column name\n  \n  getFeaturesShapCol(self)\n      Returns:\n          featuresShapCol: Output SHAP vector column name after prediction containing the feature contribution values\n  \n  getFobj(self)\n      Returns:\n          fobj: Customized objective function. Should accept two parameters: preds, train_data, and return (grad, hess).\n  \n  getImprovementTolerance(self)\n      Returns:\n          improvementTolerance: Tolerance to consider improvement in metric\n  \n  getInitScoreCol(self)\n      Returns:\n          initScoreCol: The name of the initial score column, used for continued training\n  \n  getIsProvideTrainingMetric(self)\n      Returns:\n          isProvideTrainingMetric: Whether output metric result over training dataset.\n  \n  getLabelCol(self)\n      Returns:\n          labelCol: label column name\n  \n  getLambdaL1(self)\n      Returns:\n          lambdaL1: L1 regularization\n  \n  getLambdaL2(self)\n      Returns:\n          lambdaL2: L2 regularization\n  \n  getLeafPredictionCol(self)\n      Returns:\n          leafPredictionCol: Predicted leaf indices&#39;s column name\n  \n  getLearningRate(self)\n      Returns:\n          learningRate: Learning rate or shrinkage rate\n  \n  getMatrixType(self)\n      Returns:\n          matrixType: Advanced parameter to specify whether the native lightgbm matrix constructed should be sparse or dense.  Values can be auto, sparse or dense. Default value is auto, which samples first ten rows to determine type.\n  \n  getMaxBin(self)\n      Returns:\n          maxBin: Max bin\n  \n  getMaxBinByFeature(self)\n      Returns:\n          maxBinByFeature: Max number of bins for each feature\n  \n  getMaxDeltaStep(self)\n      Returns:\n          maxDeltaStep: Used to limit the max output of tree leaves\n  \n  getMaxDepth(self)\n      Returns:\n          maxDepth: Max depth\n  \n  getMaxDrop(self)\n      Returns:\n          maxDrop: Max number of dropped trees during one boosting iteration\n  \n  getMetric(self)\n      Returns:\n          metric: Metrics to be evaluated on the evaluation data.  Options are: empty string or not specified means that metric corresponding to specified objective will be used (this is possible only for pre-defined objective functions, otherwise no evaluation metric will be added). None (string, not a None value) means that no metric will be registered, aliases: na, null, custom. l1, absolute loss, aliases: mean_absolute_error, mae, regression_l1. l2, square loss, aliases: mean_squared_error, mse, regression_l2, regression. rmse, root square loss, aliases: root_mean_squared_error, l2_root. quantile, Quantile regression. mape, MAPE loss, aliases: mean_absolute_percentage_error. huber, Huber loss. fair, Fair loss. poisson, negative log-likelihood for Poisson regression. gamma, negative log-likelihood for Gamma regression. gamma_deviance, residual deviance for Gamma regression. tweedie, negative log-likelihood for Tweedie regression. ndcg, NDCG, aliases: lambdarank. map, MAP, aliases: mean_average_precision. auc, AUC. binary_logloss, log loss, aliases: binary. binary_error, for one sample: 0 for correct classification, 1 for error classification. multi_logloss, log loss for multi-class classification, aliases: multiclass, softmax, multiclassova, multiclass_ova, ova, ovr. multi_error, error rate for multi-class classification. cross_entropy, cross-entropy (with optional linear weights), aliases: xentropy. cross_entropy_lambda, intensity-weighted cross-entropy, aliases: xentlambda. kullback_leibler, Kullback-Leibler divergence, aliases: kldiv.\n  \n  getMinDataInLeaf(self)\n      Returns:\n          minDataInLeaf: Minimal number of data in one leaf. Can be used to deal with over-fitting.\n  \n  getMinGainToSplit(self)\n      Returns:\n          minGainToSplit: The minimal gain to perform split\n  \n  getMinSumHessianInLeaf(self)\n      Returns:\n          minSumHessianInLeaf: Minimal sum hessian in one leaf\n  \n  getModelString(self)\n      Returns:\n          modelString: LightGBM model to retrain\n  \n  getNegBaggingFraction(self)\n      Returns:\n          negBaggingFraction: Negative Bagging fraction\n  \n  getNumBatches(self)\n      Returns:\n          numBatches: If greater than 0, splits data into separate batches during training\n  \n  getNumIterations(self)\n      Returns:\n          numIterations: Number of iterations, LightGBM constructs num_class * num_iterations trees\n  \n  getNumLeaves(self)\n      Returns:\n          numLeaves: Number of leaves\n  \n  getNumTasks(self)\n      Returns:\n          numTasks: Advanced parameter to specify the number of tasks.  SynapseML tries to guess this based on cluster configuration, but this parameter can be used to override.\n  \n  getNumThreads(self)\n      Returns:\n          numThreads: Number of threads for LightGBM. For the best speed, set this to the number of real CPU cores.\n  \n  getObjective(self)\n      Returns:\n          objective: The Objective. For regression applications, this can be: regression_l2, regression_l1, huber, fair, poisson, quantile, mape, gamma or tweedie. For classification applications, this can be: binary, multiclass, or multiclassova.\n  \n  getParallelism(self)\n      Returns:\n          parallelism: Tree learner parallelism, can be set to data_parallel or voting_parallel\n  \n  getPosBaggingFraction(self)\n      Returns:\n          posBaggingFraction: Positive Bagging fraction\n  \n  getPredictDisableShapeCheck(self)\n      Returns:\n          predictDisableShapeCheck: control whether or not LightGBM raises an error when you try to predict on data with a different number of features than the training data\n  \n  getPredictionCol(self)\n      Returns:\n          predictionCol: prediction column name\n  \n  getRepartitionByGroupingColumn(self)\n      Returns:\n          repartitionByGroupingColumn: Repartition training data according to grouping column, on by default.\n  \n  getSkipDrop(self)\n      Returns:\n          skipDrop: Probability of skipping the dropout procedure during a boosting iteration\n  \n  getSlotNames(self)\n      Returns:\n          slotNames: List of slot names in the features column\n  \n  getTimeout(self)\n      Returns:\n          timeout: Timeout in seconds\n  \n  getTopK(self)\n      Returns:\n          topK: The top_k value used in Voting parallel, set this to larger value for more accurate result, but it will slow down the training speed. It should be greater than 0\n  \n  getTweedieVariancePower(self)\n      Returns:\n          tweedieVariancePower: control the variance of tweedie distribution, must be between 1 and 2\n  \n  getUniformDrop(self)\n      Returns:\n          uniformDrop: Set this to true to use uniform drop in dart mode\n  \n  getUseBarrierExecutionMode(self)\n      Returns:\n          useBarrierExecutionMode: Barrier execution mode which uses a barrier stage, off by default.\n  \n  getUseSingleDatasetMode(self)\n      Returns:\n          useSingleDatasetMode: Use single dataset execution mode to create a single native dataset per executor (singleton) to reduce memory and communication overhead. Note this is disabled when running spark in local mode.\n  \n  getValidationIndicatorCol(self)\n      Returns:\n          validationIndicatorCol: Indicates whether the row is for training or validation\n  \n  getVerbosity(self)\n      Returns:\n          verbosity: Verbosity where lt 0 is Fatal, eq 0 is Error, eq 1 is Info, gt 1 is Debug\n  \n  getWeightCol(self)\n      Returns:\n          weightCol: The name of the weight column\n  \n  getXgboostDartMode(self)\n      Returns:\n          xgboostDartMode: Set this to true to use xgboost dart mode\n  \n  setAlpha(self, value)\n      Args:\n          alpha: parameter for Huber loss and Quantile regression\n  \n  setBaggingFraction(self, value)\n      Args:\n          baggingFraction: Bagging fraction\n  \n  setBaggingFreq(self, value)\n      Args:\n          baggingFreq: Bagging frequency\n  \n  setBaggingSeed(self, value)\n      Args:\n          baggingSeed: Bagging seed\n  \n  setBinSampleCount(self, value)\n      Args:\n          binSampleCount: Number of samples considered at computing histogram bins\n  \n  setBoostFromAverage(self, value)\n      Args:\n          boostFromAverage: Adjusts initial score to the mean of labels for faster convergence\n  \n  setBoostingType(self, value)\n      Args:\n          boostingType: Default gbdt = traditional Gradient Boosting Decision Tree. Options are: gbdt, gbrt, rf (Random Forest), random_forest, dart (Dropouts meet Multiple Additive Regression Trees), goss (Gradient-based One-Side Sampling).\n  \n  setCategoricalSlotIndexes(self, value)\n      Args:\n          categoricalSlotIndexes: List of categorical column indexes, the slot index in the features column\n  \n  setCategoricalSlotNames(self, value)\n      Args:\n          categoricalSlotNames: List of categorical column slot names, the slot name in the features column\n  \n  setChunkSize(self, value)\n      Args:\n          chunkSize: Advanced parameter to specify the chunk size for copying Java data to native.  If set too high, memory may be wasted, but if set too low, performance may be reduced during data copy.If dataset size is known beforehand, set to the number of rows in the dataset.\n  \n  setDefaultListenPort(self, value)\n      Args:\n          defaultListenPort: The default listen port on executors, used for testing\n  \n  setDriverListenPort(self, value)\n      Args:\n          driverListenPort: The listen port on a driver. Default value is 0 (random)\n  \n  setDropRate(self, value)\n      Args:\n          dropRate: Dropout rate: a fraction of previous trees to drop during the dropout\n  \n  setEarlyStoppingRound(self, value)\n      Args:\n          earlyStoppingRound: Early stopping round\n  \n  setFeatureFraction(self, value)\n      Args:\n          featureFraction: Feature fraction\n  \n  setFeaturesCol(self, value)\n      Args:\n          featuresCol: features column name\n  \n  setFeaturesShapCol(self, value)\n      Args:\n          featuresShapCol: Output SHAP vector column name after prediction containing the feature contribution values\n  \n  setFobj(self, value)\n      Args:\n          fobj: Customized objective function. Should accept two parameters: preds, train_data, and return (grad, hess).\n  \n  setImprovementTolerance(self, value)\n      Args:\n          improvementTolerance: Tolerance to consider improvement in metric\n  \n  setInitScoreCol(self, value)\n      Args:\n          initScoreCol: The name of the initial score column, used for continued training\n  \n  setIsProvideTrainingMetric(self, value)\n      Args:\n          isProvideTrainingMetric: Whether output metric result over training dataset.\n  \n  setLabelCol(self, value)\n      Args:\n          labelCol: label column name\n  \n  setLambdaL1(self, value)\n      Args:\n          lambdaL1: L1 regularization\n  \n  setLambdaL2(self, value)\n      Args:\n          lambdaL2: L2 regularization\n  \n  setLeafPredictionCol(self, value)\n      Args:\n          leafPredictionCol: Predicted leaf indices&#39;s column name\n  \n  setLearningRate(self, value)\n      Args:\n          learningRate: Learning rate or shrinkage rate\n  \n  setMatrixType(self, value)\n      Args:\n          matrixType: Advanced parameter to specify whether the native lightgbm matrix constructed should be sparse or dense.  Values can be auto, sparse or dense. Default value is auto, which samples first ten rows to determine type.\n  \n  setMaxBin(self, value)\n      Args:\n          maxBin: Max bin\n  \n  setMaxBinByFeature(self, value)\n      Args:\n          maxBinByFeature: Max number of bins for each feature\n  \n  setMaxDeltaStep(self, value)\n      Args:\n          maxDeltaStep: Used to limit the max output of tree leaves\n  \n  setMaxDepth(self, value)\n      Args:\n          maxDepth: Max depth\n  \n  setMaxDrop(self, value)\n      Args:\n          maxDrop: Max number of dropped trees during one boosting iteration\n  \n  setMetric(self, value)\n      Args:\n          metric: Metrics to be evaluated on the evaluation data.  Options are: empty string or not specified means that metric corresponding to specified objective will be used (this is possible only for pre-defined objective functions, otherwise no evaluation metric will be added). None (string, not a None value) means that no metric will be registered, aliases: na, null, custom. l1, absolute loss, aliases: mean_absolute_error, mae, regression_l1. l2, square loss, aliases: mean_squared_error, mse, regression_l2, regression. rmse, root square loss, aliases: root_mean_squared_error, l2_root. quantile, Quantile regression. mape, MAPE loss, aliases: mean_absolute_percentage_error. huber, Huber loss. fair, Fair loss. poisson, negative log-likelihood for Poisson regression. gamma, negative log-likelihood for Gamma regression. gamma_deviance, residual deviance for Gamma regression. tweedie, negative log-likelihood for Tweedie regression. ndcg, NDCG, aliases: lambdarank. map, MAP, aliases: mean_average_precision. auc, AUC. binary_logloss, log loss, aliases: binary. binary_error, for one sample: 0 for correct classification, 1 for error classification. multi_logloss, log loss for multi-class classification, aliases: multiclass, softmax, multiclassova, multiclass_ova, ova, ovr. multi_error, error rate for multi-class classification. cross_entropy, cross-entropy (with optional linear weights), aliases: xentropy. cross_entropy_lambda, intensity-weighted cross-entropy, aliases: xentlambda. kullback_leibler, Kullback-Leibler divergence, aliases: kldiv.\n  \n  setMinDataInLeaf(self, value)\n      Args:\n          minDataInLeaf: Minimal number of data in one leaf. Can be used to deal with over-fitting.\n  \n  setMinGainToSplit(self, value)\n      Args:\n          minGainToSplit: The minimal gain to perform split\n  \n  setMinSumHessianInLeaf(self, value)\n      Args:\n          minSumHessianInLeaf: Minimal sum hessian in one leaf\n  \n  setModelString(self, value)\n      Args:\n          modelString: LightGBM model to retrain\n  \n  setNegBaggingFraction(self, value)\n      Args:\n          negBaggingFraction: Negative Bagging fraction\n  \n  setNumBatches(self, value)\n      Args:\n          numBatches: If greater than 0, splits data into separate batches during training\n  \n  setNumIterations(self, value)\n      Args:\n          numIterations: Number of iterations, LightGBM constructs num_class * num_iterations trees\n  \n  setNumLeaves(self, value)\n      Args:\n          numLeaves: Number of leaves\n  \n  setNumTasks(self, value)\n      Args:\n          numTasks: Advanced parameter to specify the number of tasks.  SynapseML tries to guess this based on cluster configuration, but this parameter can be used to override.\n  \n  setNumThreads(self, value)\n      Args:\n          numThreads: Number of threads for LightGBM. For the best speed, set this to the number of real CPU cores.\n  \n  setObjective(self, value)\n      Args:\n          objective: The Objective. For regression applications, this can be: regression_l2, regression_l1, huber, fair, poisson, quantile, mape, gamma or tweedie. For classification applications, this can be: binary, multiclass, or multiclassova.\n  \n  setParallelism(self, value)\n      Args:\n          parallelism: Tree learner parallelism, can be set to data_parallel or voting_parallel\n  \n  setParams(self, alpha=0.9, baggingFraction=1.0, baggingFreq=0, baggingSeed=3, binSampleCount=200000, boostFromAverage=True, boostingType=&#39;gbdt&#39;, categoricalSlotIndexes=[], categoricalSlotNames=[], chunkSize=10000, defaultListenPort=12400, driverListenPort=0, dropRate=0.1, earlyStoppingRound=0, featureFraction=1.0, featuresCol=&#39;features&#39;, featuresShapCol=&#39;&#39;, fobj=None, improvementTolerance=0.0, initScoreCol=None, isProvideTrainingMetric=False, labelCol=&#39;label&#39;, lambdaL1=0.0, lambdaL2=0.0, leafPredictionCol=&#39;&#39;, learningRate=0.1, matrixType=&#39;auto&#39;, maxBin=255, maxBinByFeature=[], maxDeltaStep=0.0, maxDepth=-1, maxDrop=50, metric=&#39;&#39;, minDataInLeaf=20, minGainToSplit=0.0, minSumHessianInLeaf=0.001, modelString=&#39;&#39;, negBaggingFraction=1.0, numBatches=0, numIterations=100, numLeaves=31, numTasks=0, numThreads=0, objective=&#39;regression&#39;, parallelism=&#39;data_parallel&#39;, posBaggingFraction=1.0, predictDisableShapeCheck=False, predictionCol=&#39;prediction&#39;, repartitionByGroupingColumn=True, skipDrop=0.5, slotNames=[], timeout=1200.0, topK=20, tweedieVariancePower=1.5, uniformDrop=False, useBarrierExecutionMode=False, useSingleDatasetMode=True, validationIndicatorCol=None, verbosity=-1, weightCol=None, xgboostDartMode=False)\n      Set the (keyword only) parameters\n  \n  setPosBaggingFraction(self, value)\n      Args:\n          posBaggingFraction: Positive Bagging fraction\n  \n  setPredictDisableShapeCheck(self, value)\n      Args:\n          predictDisableShapeCheck: control whether or not LightGBM raises an error when you try to predict on data with a different number of features than the training data\n  \n  setPredictionCol(self, value)\n      Args:\n          predictionCol: prediction column name\n  \n  setRepartitionByGroupingColumn(self, value)\n      Args:\n          repartitionByGroupingColumn: Repartition training data according to grouping column, on by default.\n  \n  setSkipDrop(self, value)\n      Args:\n          skipDrop: Probability of skipping the dropout procedure during a boosting iteration\n  \n  setSlotNames(self, value)\n      Args:\n          slotNames: List of slot names in the features column\n  \n  setTimeout(self, value)\n      Args:\n          timeout: Timeout in seconds\n  \n  setTopK(self, value)\n      Args:\n          topK: The top_k value used in Voting parallel, set this to larger value for more accurate result, but it will slow down the training speed. It should be greater than 0\n  \n  setTweedieVariancePower(self, value)\n      Args:\n          tweedieVariancePower: control the variance of tweedie distribution, must be between 1 and 2\n  \n  setUniformDrop(self, value)\n      Args:\n          uniformDrop: Set this to true to use uniform drop in dart mode\n  \n  setUseBarrierExecutionMode(self, value)\n      Args:\n          useBarrierExecutionMode: Barrier execution mode which uses a barrier stage, off by default.\n  \n  setUseSingleDatasetMode(self, value)\n      Args:\n          useSingleDatasetMode: Use single dataset execution mode to create a single native dataset per executor (singleton) to reduce memory and communication overhead. Note this is disabled when running spark in local mode.\n  \n  setValidationIndicatorCol(self, value)\n      Args:\n          validationIndicatorCol: Indicates whether the row is for training or validation\n  \n  setVerbosity(self, value)\n      Args:\n          verbosity: Verbosity where lt 0 is Fatal, eq 0 is Error, eq 1 is Info, gt 1 is Debug\n  \n  setWeightCol(self, value)\n      Args:\n          weightCol: The name of the weight column\n  \n  setXgboostDartMode(self, value)\n      Args:\n          xgboostDartMode: Set this to true to use xgboost dart mode\n  \n  ----------------------------------------------------------------------\n  Class methods defined here:\n  \n  read() from abc.ABCMeta\n      Returns an MLReader instance for this class.\n  \n  ----------------------------------------------------------------------\n  Static methods defined here:\n  \n  getJavaPackage()\n      Returns package name String.\n  \n  ----------------------------------------------------------------------\n  Data and other attributes defined here:\n  \n  __abstractmethods__ = frozenset()\n  \n  alpha = Param(parent=&#39;undefined&#39;, name=&#39;alpha&#39;, doc=&#39;parameter for Hub...\n  \n  baggingFraction = Param(parent=&#39;undefined&#39;, name=&#39;baggingFraction&#39;, do...\n  \n  baggingFreq = Param(parent=&#39;undefined&#39;, name=&#39;baggingFreq&#39;, doc=&#39;Baggi...\n  \n  baggingSeed = Param(parent=&#39;undefined&#39;, name=&#39;baggingSeed&#39;, doc=&#39;Baggi...\n  \n  binSampleCount = Param(parent=&#39;undefined&#39;, name=&#39;binSampleCount&#39;,... s...\n  \n  boostFromAverage = Param(parent=&#39;undefined&#39;, name=&#39;boostFromAverage......\n  \n  boostingType = Param(parent=&#39;undefined&#39;, name=&#39;boostingType&#39;, d...ees)...\n  \n  categoricalSlotIndexes = Param(parent=&#39;undefined&#39;, name=&#39;categoricalSl...\n  \n  categoricalSlotNames = Param(parent=&#39;undefined&#39;, name=&#39;categoricalSlot...\n  \n  chunkSize = Param(parent=&#39;undefined&#39;, name=&#39;chunkSize&#39;, doc=...hand, s...\n  \n  defaultListenPort = Param(parent=&#39;undefined&#39;, name=&#39;defaultListenPor.....\n  \n  driverListenPort = Param(parent=&#39;undefined&#39;, name=&#39;driverListenPort......\n  \n  dropRate = Param(parent=&#39;undefined&#39;, name=&#39;dropRate&#39;, doc=&#39;...on of pr...\n  \n  earlyStoppingRound = Param(parent=&#39;undefined&#39;, name=&#39;earlyStoppingRoun...\n  \n  featureFraction = Param(parent=&#39;undefined&#39;, name=&#39;featureFraction&#39;, do...\n  \n  featuresCol = Param(parent=&#39;undefined&#39;, name=&#39;featuresCol&#39;, doc=&#39;featu...\n  \n  featuresShapCol = Param(parent=&#39;undefined&#39;, name=&#39;featuresShapCol&#39;...t...\n  \n  fobj = Param(parent=&#39;undefined&#39;, name=&#39;fobj&#39;, doc=&#39;Cust...rs: preds, t...\n  \n  improvementTolerance = Param(parent=&#39;undefined&#39;, name=&#39;improvementTole...\n  \n  initScoreCol = Param(parent=&#39;undefined&#39;, name=&#39;initScoreCol&#39;, d...itia...\n  \n  isProvideTrainingMetric = Param(parent=&#39;undefined&#39;, name=&#39;isProvideTra...\n  \n  labelCol = Param(parent=&#39;undefined&#39;, name=&#39;labelCol&#39;, doc=&#39;label colum...\n  \n  lambdaL1 = Param(parent=&#39;undefined&#39;, name=&#39;lambdaL1&#39;, doc=&#39;L1 regulari...\n  \n  lambdaL2 = Param(parent=&#39;undefined&#39;, name=&#39;lambdaL2&#39;, doc=&#39;L2 regulari...\n  \n  leafPredictionCol = Param(parent=&#39;undefined&#39;, name=&#39;leafPredictionCol&#39;...\n  \n  learningRate = Param(parent=&#39;undefined&#39;, name=&#39;learningRate&#39;, doc=&#39;Lea...\n  \n  matrixType = Param(parent=&#39;undefined&#39;, name=&#39;matrixType&#39;, doc...which ...\n  \n  maxBin = Param(parent=&#39;undefined&#39;, name=&#39;maxBin&#39;, doc=&#39;Max bin&#39;)\n  \n  maxBinByFeature = Param(parent=&#39;undefined&#39;, name=&#39;maxBinByFeature&#39;, do...\n  \n  maxDeltaStep = Param(parent=&#39;undefined&#39;, name=&#39;maxDeltaStep&#39;, doc=&#39;Use...\n  \n  maxDepth = Param(parent=&#39;undefined&#39;, name=&#39;maxDepth&#39;, doc=&#39;Max depth&#39;)\n  \n  maxDrop = Param(parent=&#39;undefined&#39;, name=&#39;maxDrop&#39;, doc=&#39;M... of dropp...\n  \n  metric = Param(parent=&#39;undefined&#39;, name=&#39;metric&#39;, doc=&#39;Me..., Kullback...\n  \n  minDataInLeaf = Param(parent=&#39;undefined&#39;, name=&#39;minDataInLeaf&#39;, ...ne ...\n  \n  minGainToSplit = Param(parent=&#39;undefined&#39;, name=&#39;minGainToSplit&#39;, doc=...\n  \n  minSumHessianInLeaf = Param(parent=&#39;undefined&#39;, name=&#39;minSumHessianInL...\n  \n  modelString = Param(parent=&#39;undefined&#39;, name=&#39;modelString&#39;, doc=&#39;Light...\n  \n  negBaggingFraction = Param(parent=&#39;undefined&#39;, name=&#39;negBaggingFractio...\n  \n  numBatches = Param(parent=&#39;undefined&#39;, name=&#39;numBatches&#39;, doc...lits d...\n  \n  numIterations = Param(parent=&#39;undefined&#39;, name=&#39;numIterations&#39;, ...GBM...\n  \n  numLeaves = Param(parent=&#39;undefined&#39;, name=&#39;numLeaves&#39;, doc=&#39;Number of...\n  \n  numTasks = Param(parent=&#39;undefined&#39;, name=&#39;numTasks&#39;, doc=&#39;...on, but ...\n  \n  numThreads = Param(parent=&#39;undefined&#39;, name=&#39;numThreads&#39;, doc...peed, ...\n  \n  objective = Param(parent=&#39;undefined&#39;, name=&#39;objective&#39;, doc=... can be...\n  \n  parallelism = Param(parent=&#39;undefined&#39;, name=&#39;parallelism&#39;, do... can ...\n  \n  posBaggingFraction = Param(parent=&#39;undefined&#39;, name=&#39;posBaggingFractio...\n  \n  predictDisableShapeCheck = Param(parent=&#39;undefined&#39;, name=&#39;predictDisa...\n  \n  predictionCol = Param(parent=&#39;undefined&#39;, name=&#39;predictionCol&#39;, doc=&#39;p...\n  \n  repartitionByGroupingColumn = Param(parent=&#39;undefined&#39;, name=&#39;repartit...\n  \n  skipDrop = Param(parent=&#39;undefined&#39;, name=&#39;skipDrop&#39;, doc=&#39;...e dropou...\n  \n  slotNames = Param(parent=&#39;undefined&#39;, name=&#39;slotNames&#39;, doc=&#39;List of s...\n  \n  timeout = Param(parent=&#39;undefined&#39;, name=&#39;timeout&#39;, doc=&#39;Timeout in se...\n  \n  topK = Param(parent=&#39;undefined&#39;, name=&#39;topK&#39;, doc=&#39;The ...the training...\n  \n  tweedieVariancePower = Param(parent=&#39;undefined&#39;, name=&#39;tweedieVariance...\n  \n  uniformDrop = Param(parent=&#39;undefined&#39;, name=&#39;uniformDrop&#39;, do...t thi...\n  \n  useBarrierExecutionMode = Param(parent=&#39;undefined&#39;, name=&#39;useBarrierEx...\n  \n  useSingleDatasetMode = Param(parent=&#39;undefined&#39;, name=&#39;useSingleDatase...\n  \n  validationIndicatorCol = Param(parent=&#39;undefined&#39;, name=&#39;validationInd...\n  \n  verbosity = Param(parent=&#39;undefined&#39;, name=&#39;verbosity&#39;, doc=...tal, eq...\n  \n  weightCol = Param(parent=&#39;undefined&#39;, name=&#39;weightCol&#39;, doc=&#39;The name ...\n  \n  xgboostDartMode = Param(parent=&#39;undefined&#39;, name=&#39;xgboostDartMode&#39;, do...\n  \n  ----------------------------------------------------------------------\n  Class methods inherited from pyspark.ml.util.MLReadable:\n  \n  load(path) from abc.ABCMeta\n      Reads an ML instance from the input path, a shortcut of `read().load(path)`.\n  \n  ----------------------------------------------------------------------\n  Data descriptors inherited from pyspark.ml.util.MLReadable:\n  \n  __dict__\n      dictionary for instance variables (if defined)\n  \n  __weakref__\n      list of weak references to the object (if defined)\n  \n  ----------------------------------------------------------------------\n  Methods inherited from pyspark.ml.util.JavaMLWritable:\n  \n  write(self)\n      Returns an MLWriter instance for this ML instance.\n  \n  ----------------------------------------------------------------------\n  Methods inherited from pyspark.ml.util.MLWritable:\n  \n  save(self, path)\n      Save this ML instance to the given path, a shortcut of &#39;write().save(path)&#39;.\n  \n  ----------------------------------------------------------------------\n  Methods inherited from pyspark.ml.wrapper.JavaParams:\n  \n  clear(self, param)\n      Clears a param from the param map if it has been explicitly set.\n  \n  copy(self, extra=None)\n      Creates a copy of this instance with the same uid and some\n      extra params. This implementation first calls Params.copy and\n      then make a copy of the companion Java pipeline component with\n      extra params. So both the Python wrapper and the Java pipeline\n      component get copied.\n      \n      Parameters\n      ----------\n      extra : dict, optional\n          Extra parameters to copy to the new instance\n      \n      Returns\n      -------\n      :py:class:`JavaParams`\n          Copy of this instance\n  \n  ----------------------------------------------------------------------\n  Methods inherited from pyspark.ml.wrapper.JavaWrapper:\n  \n  __del__(self)\n  \n  ----------------------------------------------------------------------\n  Methods inherited from pyspark.ml.base.Estimator:\n  \n  fit(self, dataset, params=None)\n      Fits a model to the input dataset with optional parameters.\n      \n      .. versionadded:: 1.3.0\n      \n      Parameters\n      ----------\n      dataset : :py:class:`pyspark.sql.DataFrame`\n          input dataset.\n      params : dict or list or tuple, optional\n          an optional param map that overrides embedded params. If a list/tuple of\n          param maps is given, this calls fit on each param map and returns a list of\n          models.\n      \n      Returns\n      -------\n      :py:class:`Transformer` or a list of :py:class:`Transformer`\n          fitted model(s)\n  \n  fitMultiple(self, dataset, paramMaps)\n      Fits a model to the input dataset for each param map in `paramMaps`.\n      \n      .. versionadded:: 2.3.0\n      \n      Parameters\n      ----------\n      dataset : :py:class:`pyspark.sql.DataFrame`\n          input dataset.\n      paramMaps : :py:class:`collections.abc.Sequence`\n          A Sequence of param maps.\n      \n      Returns\n      -------\n      :py:class:`_FitMultipleIterator`\n          A thread safe iterable which contains one model for each param map. Each\n          call to `next(modelIterator)` will return `(index, model)` where model was fit\n          using `paramMaps[index]`. `index` values may not be sequential.\n  \n  ----------------------------------------------------------------------\n  Methods inherited from pyspark.ml.param.Params:\n  \n  explainParam(self, param)\n      Explains a single param and returns its name, doc, and optional\n      default value and user-supplied value in a string.\n  \n  explainParams(self)\n      Returns the documentation of all params with their optionally\n      default values and user-supplied values.\n  \n  extractParamMap(self, extra=None)\n      Extracts the embedded default param values and user-supplied\n      values, and then merges them with extra values from input into\n      a flat param map, where the latter value is used if there exist\n      conflicts, i.e., with ordering: default param values &lt;\n      user-supplied values &lt; extra.\n      \n      Parameters\n      ----------\n      extra : dict, optional\n          extra param values\n      \n      Returns\n      -------\n      dict\n          merged param map\n  \n  getOrDefault(self, param)\n      Gets the value of a param in the user-supplied param map or its\n      default value. Raises an error if neither is set.\n  \n  getParam(self, paramName)\n      Gets a param by its name.\n  \n  hasDefault(self, param)\n      Checks whether a param has a default value.\n  \n  hasParam(self, paramName)\n      Tests whether this instance contains a param with a given\n      (string) name.\n  \n  isDefined(self, param)\n      Checks whether a param is explicitly set by user or has\n      a default value.\n  \n  isSet(self, param)\n      Checks whether a param is explicitly set by user.\n  \n  set(self, param, value)\n      Sets a parameter in the embedded param map.\n  \n  ----------------------------------------------------------------------\n  Readonly properties inherited from pyspark.ml.param.Params:\n  \n  params\n      Returns all params ordered by name. The default implementation\n      uses :py:func:`dir` to get all attributes of type\n      :py:class:`Param`.\n  \n  ----------------------------------------------------------------------\n  Methods inherited from pyspark.ml.util.Identifiable:\n  \n  __repr__(self)\n      Return repr(self).\n\n</div>"]}}],"execution_count":0},{"cell_type":"markdown","source":["### Train"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"abc2cbd0-3827-47fd-9d55-2e6c6c99a8cf"}}},{"cell_type":"code","source":["feature_cols = sdf_clean.columns[:-1]\nfeaturizer = VectorAssembler(\n    inputCols=feature_cols,\n    outputCol='features'\n)\ntrain = featurizer.transform(train)[labelCol, 'features']\ntest = featurizer.transform(test)[labelCol, 'features']"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"80f9cb19-5d77-46b1-9e8c-5b82a19b637d"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\"></div>","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\"></div>"]}}],"execution_count":0},{"cell_type":"code","source":["model = LightGBMRegressor(objective='regression',\n                          labelCol=labelCol,\n                          alpha=0.2,\n                          learningRate=0.3,\n                          numLeaves=31).fit(train)"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"3fd79aaa-548e-49c7-8c7c-cc45bcea8dbe"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\"></div>","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\"></div>"]}}],"execution_count":0},{"cell_type":"markdown","source":["### Feature importance"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"31b42fcc-ef0b-49a1-9ebe-a485b66695da"}}},{"cell_type":"code","source":["print(model.getFeatureImportances())"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"427054c1-fafc-47b1-a03f-5fb69184bad0"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">[591.0, 367.0, 660.0, 419.0, 600.0, 363.0]\n</div>","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">[591.0, 367.0, 660.0, 419.0, 600.0, 363.0]\n</div>"]}}],"execution_count":0},{"cell_type":"code","source":["import pandas as pd\nimport matplotlib.pyplot as plt\n\nfeature_importances = model.getFeatureImportances()\nfi = pd.Series(feature_importances,index = feature_cols)\nfi = fi.sort_values(ascending = True)\nf_index = fi.index\nf_values = fi.values\n \n# print feature importances \nprint ('f_index:',f_index)\nprint ('f_values:',f_values)\n\n# plot\nx_index = list(range(len(fi)))\nx_index = [x/len(fi) for x in x_index]\nplt.rcParams['figure.figsize'] = (20,5)\nplt.barh(x_index,f_values,height = 0.10 ,align=\"center\",color = 'tan',tick_label=f_index)\nplt.xlabel('importances')\nplt.ylabel('features')\nplt.show()"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"e1925c52-0bce-402e-91b0-398c3eeabaf3"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">f_index: Index([&#39;DOLocationID&#39;, &#39;passenger_count&#39;, &#39;payment_type&#39;, &#39;trip_distance&#39;,\n       &#39;PULocationID&#39;, &#39;RatecodeID&#39;],\n      dtype=&#39;object&#39;)\nf_values: [363. 367. 419. 591. 600. 660.]\n</div>","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">f_index: Index([&#39;DOLocationID&#39;, &#39;passenger_count&#39;, &#39;payment_type&#39;, &#39;trip_distance&#39;,\n       &#39;PULocationID&#39;, &#39;RatecodeID&#39;],\n      dtype=&#39;object&#39;)\nf_values: [363. 367. 419. 591. 600. 660.]\n</div>"]}},{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"data:image/png;base64,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"}}],"execution_count":0},{"cell_type":"code","source":["model_path = \"dbfs:/mnt/delta/nyctaxi/modelLightGBM/lgbmregressor.model\""],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"53f1633f-dc7b-4b3b-8f9f-eddcaa64a4b5"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\"></div>","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\"></div>"]}}],"execution_count":0},{"cell_type":"code","source":["model.saveNativeModel(model_path)"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"2d583866-9c46-405d-87a3-763ddb948c59"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\"></div>","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\"></div>"]}}],"execution_count":0},{"cell_type":"code","source":["from synapse.ml.lightgbm import LightGBMRegressionModel\n\nmodel = LightGBMRegressionModel.loadNativeModelFromFile(model_path)"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"7a751fc2-7cc2-441a-a3d8-cb76cb796cd1"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\"></div>","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\"></div>"]}}],"execution_count":0},{"cell_type":"code","source":["predictions = model.transform(test)\npredictions.limit(100).toPandas()"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"a2963652-cd6e-40aa-9c55-7909125866f8"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">/databricks/spark/python/pyspark/sql/pandas/conversion.py:92: UserWarning: toPandas attempted Arrow optimization because &#39;spark.sql.execution.arrow.pyspark.enabled&#39; is set to true; however, failed by the reason below:\n  Unable to convert the field features. If this column is not necessary, you may consider dropping it or converting to primitive type before the conversion.\nDirect cause: Unsupported type in conversion to Arrow: VectorUDT\nAttempting non-optimization as &#39;spark.sql.execution.arrow.pyspark.fallback.enabled&#39; is set to true.\n  warnings.warn(msg)\nOut[28]: </div>","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">/databricks/spark/python/pyspark/sql/pandas/conversion.py:92: UserWarning: toPandas attempted Arrow optimization because &#39;spark.sql.execution.arrow.pyspark.enabled&#39; is set to true; however, failed by the reason below:\n  Unable to convert the field features. If this column is not necessary, you may consider dropping it or converting to primitive type before the conversion.\nDirect cause: Unsupported type in conversion to Arrow: VectorUDT\nAttempting non-optimization as &#39;spark.sql.execution.arrow.pyspark.fallback.enabled&#39; is set to true.\n  warnings.warn(msg)\nOut[28]: </div>"]}},{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>fare_amount</th>\n      <th>features</th>\n      <th>prediction</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>65.0</td>\n      <td>[0.01, 0.0, 5.0, 1.0, 132.0, 264.0]</td>\n      <td>33.232095</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>11.5</td>\n      <td>[0.01, 0.0, 5.0, 1.0, 230.0, 230.0]</td>\n      <td>17.756373</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>9.0</td>\n      <td>[0.01, 0.0, 5.0, 1.0, 246.0, 246.0]</td>\n      <td>21.638038</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>15.3</td>\n      <td>[0.01, 0.0, 5.0, 1.0, 246.0, 246.0]</td>\n      <td>21.638038</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>6.0</td>\n      <td>[0.01, 0.0, 5.0, 2.0, 4.0, 4.0]</td>\n      <td>21.875035</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>95</th>\n      <td>2.5</td>\n      <td>[0.01, 1.0, 1.0, 2.0, 125.0, 264.0]</td>\n      <td>2.887829</td>\n    </tr>\n    <tr>\n      <th>96</th>\n      <td>2.5</td>\n      <td>[0.01, 1.0, 1.0, 2.0, 129.0, 129.0]</td>\n      <td>4.685873</td>\n    </tr>\n    <tr>\n      <th>97</th>\n      <td>2.5</td>\n      <td>[0.01, 1.0, 1.0, 2.0, 132.0, 132.0]</td>\n      <td>4.685873</td>\n    </tr>\n    <tr>\n      <th>98</th>\n      <td>2.5</td>\n      <td>[0.01, 1.0, 1.0, 2.0, 132.0, 132.0]</td>\n      <td>4.685873</td>\n    </tr>\n    <tr>\n      <th>99</th>\n      <td>2.5</td>\n      <td>[0.01, 1.0, 1.0, 2.0, 132.0, 132.0]</td>\n      <td>4.685873</td>\n    </tr>\n  </tbody>\n</table>\n<p>100 rows × 3 columns</p>\n</div>","textData":null,"removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"htmlSandbox","arguments":{}}},"output_type":"display_data","data":{"text/html":["<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>fare_amount</th>\n      <th>features</th>\n      <th>prediction</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>65.0</td>\n      <td>[0.01, 0.0, 5.0, 1.0, 132.0, 264.0]</td>\n      <td>33.232095</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>11.5</td>\n      <td>[0.01, 0.0, 5.0, 1.0, 230.0, 230.0]</td>\n      <td>17.756373</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>9.0</td>\n      <td>[0.01, 0.0, 5.0, 1.0, 246.0, 246.0]</td>\n      <td>21.638038</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>15.3</td>\n      <td>[0.01, 0.0, 5.0, 1.0, 246.0, 246.0]</td>\n      <td>21.638038</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>6.0</td>\n      <td>[0.01, 0.0, 5.0, 2.0, 4.0, 4.0]</td>\n      <td>21.875035</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>95</th>\n      <td>2.5</td>\n      <td>[0.01, 1.0, 1.0, 2.0, 125.0, 264.0]</td>\n      <td>2.887829</td>\n    </tr>\n    <tr>\n      <th>96</th>\n      <td>2.5</td>\n      <td>[0.01, 1.0, 1.0, 2.0, 129.0, 129.0]</td>\n      <td>4.685873</td>\n    </tr>\n    <tr>\n      <th>97</th>\n      <td>2.5</td>\n      <td>[0.01, 1.0, 1.0, 2.0, 132.0, 132.0]</td>\n      <td>4.685873</td>\n    </tr>\n    <tr>\n      <th>98</th>\n      <td>2.5</td>\n      <td>[0.01, 1.0, 1.0, 2.0, 132.0, 132.0]</td>\n      <td>4.685873</td>\n    </tr>\n    <tr>\n      <th>99</th>\n      <td>2.5</td>\n      <td>[0.01, 1.0, 1.0, 2.0, 132.0, 132.0]</td>\n      <td>4.685873</td>\n    </tr>\n  </tbody>\n</table>\n<p>100 rows × 3 columns</p>\n</div>"]}}],"execution_count":0},{"cell_type":"code","source":["from synapse.ml.train import ComputeModelStatistics\n\nmetrics = ComputeModelStatistics(evaluationMetric=\"regression\", labelCol=labelCol, scoredLabelsCol='prediction').transform(predictions)\nmetrics.toPandas()\n# metrics.select('root_mean_squared_error').show()"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"ba1fde37-f107-4eeb-92cc-bc149d6fbfc0"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\"></div>","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\"></div>"]}},{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"data":"<div class=\"ansiout\"><span class=\"ansi-red-fg\">---------------------------------------------------------------------------</span>\n<span class=\"ansi-red-fg\">Py4JJavaError</span>                             Traceback (most recent call last)\n<span class=\"ansi-green-fg\">&lt;command-3284417899613051&gt;</span> in <span class=\"ansi-cyan-fg\">&lt;module&gt;</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">      2</span> \n<span class=\"ansi-green-intense-fg ansi-bold\">      3</span> <span class=\"ansi-red-fg\"># metrics = ComputeModelStatistics(evaluationMetric=&#34;regression&#34;, labelCol=labelCol, scoredLabelsCol=&#39;prediction&#39;).transform(predictions)</span>\n<span class=\"ansi-green-fg\">----&gt; 4</span><span class=\"ansi-red-fg\"> </span>metrics <span class=\"ansi-blue-fg\">=</span> ComputeModelStatistics<span class=\"ansi-blue-fg\">(</span>evaluationMetric<span class=\"ansi-blue-fg\">=</span><span class=\"ansi-blue-fg\">&#34;regression&#34;</span><span class=\"ansi-blue-fg\">,</span> labelCol<span class=\"ansi-blue-fg\">=</span>labelCol<span class=\"ansi-blue-fg\">,</span> scoredLabelsCol<span class=\"ansi-blue-fg\">=</span><span class=\"ansi-blue-fg\">&#39;prediction&#39;</span><span class=\"ansi-blue-fg\">)</span><span class=\"ansi-blue-fg\">.</span>transform<span class=\"ansi-blue-fg\">(</span>predictions<span class=\"ansi-blue-fg\">)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">      5</span> metrics<span class=\"ansi-blue-fg\">.</span>toPandas<span class=\"ansi-blue-fg\">(</span><span class=\"ansi-blue-fg\">)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">      6</span> <span class=\"ansi-red-fg\"># metrics.select(&#39;root_mean_squared_error&#39;).show()</span>\n\n<span class=\"ansi-green-fg\">/databricks/spark/python/pyspark/ml/base.py</span> in <span class=\"ansi-cyan-fg\">transform</span><span class=\"ansi-blue-fg\">(self, dataset, params)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    215</span>                 <span class=\"ansi-green-fg\">return</span> self<span class=\"ansi-blue-fg\">.</span>copy<span class=\"ansi-blue-fg\">(</span>params<span class=\"ansi-blue-fg\">)</span><span class=\"ansi-blue-fg\">.</span>_transform<span class=\"ansi-blue-fg\">(</span>dataset<span class=\"ansi-blue-fg\">)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    216</span>             <span class=\"ansi-green-fg\">else</span><span class=\"ansi-blue-fg\">:</span>\n<span class=\"ansi-green-fg\">--&gt; 217</span><span class=\"ansi-red-fg\">                 </span><span class=\"ansi-green-fg\">return</span> self<span class=\"ansi-blue-fg\">.</span>_transform<span class=\"ansi-blue-fg\">(</span>dataset<span class=\"ansi-blue-fg\">)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    218</span>         <span class=\"ansi-green-fg\">else</span><span class=\"ansi-blue-fg\">:</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    219</span>             <span class=\"ansi-green-fg\">raise</span> TypeError<span class=\"ansi-blue-fg\">(</span><span class=\"ansi-blue-fg\">&#34;Params must be a param map but got %s.&#34;</span> <span class=\"ansi-blue-fg\">%</span> type<span class=\"ansi-blue-fg\">(</span>params<span class=\"ansi-blue-fg\">)</span><span class=\"ansi-blue-fg\">)</span>\n\n<span class=\"ansi-green-fg\">/databricks/spark/python/pyspark/ml/wrapper.py</span> in <span class=\"ansi-cyan-fg\">_transform</span><span class=\"ansi-blue-fg\">(self, dataset)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    348</span>     <span class=\"ansi-green-fg\">def</span> _transform<span class=\"ansi-blue-fg\">(</span>self<span class=\"ansi-blue-fg\">,</span> dataset<span class=\"ansi-blue-fg\">)</span><span class=\"ansi-blue-fg\">:</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    349</span>         self<span class=\"ansi-blue-fg\">.</span>_transfer_params_to_java<span class=\"ansi-blue-fg\">(</span><span class=\"ansi-blue-fg\">)</span>\n<span class=\"ansi-green-fg\">--&gt; 350</span><span class=\"ansi-red-fg\">         </span><span class=\"ansi-green-fg\">return</span> DataFrame<span class=\"ansi-blue-fg\">(</span>self<span class=\"ansi-blue-fg\">.</span>_java_obj<span class=\"ansi-blue-fg\">.</span>transform<span class=\"ansi-blue-fg\">(</span>dataset<span class=\"ansi-blue-fg\">.</span>_jdf<span class=\"ansi-blue-fg\">)</span><span class=\"ansi-blue-fg\">,</span> dataset<span class=\"ansi-blue-fg\">.</span>sql_ctx<span class=\"ansi-blue-fg\">)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    351</span> \n<span class=\"ansi-green-intense-fg ansi-bold\">    352</span> \n\n<span class=\"ansi-green-fg\">/databricks/spark/python/lib/py4j-0.10.9.1-src.zip/py4j/java_gateway.py</span> in <span class=\"ansi-cyan-fg\">__call__</span><span class=\"ansi-blue-fg\">(self, *args)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">   1302</span> \n<span class=\"ansi-green-intense-fg ansi-bold\">   1303</span>         answer <span class=\"ansi-blue-fg\">=</span> self<span class=\"ansi-blue-fg\">.</span>gateway_client<span class=\"ansi-blue-fg\">.</span>send_command<span class=\"ansi-blue-fg\">(</span>command<span class=\"ansi-blue-fg\">)</span>\n<span class=\"ansi-green-fg\">-&gt; 1304</span><span class=\"ansi-red-fg\">         return_value = get_return_value(\n</span><span class=\"ansi-green-intense-fg ansi-bold\">   1305</span>             answer, self.gateway_client, self.target_id, self.name)\n<span class=\"ansi-green-intense-fg ansi-bold\">   1306</span> \n\n<span class=\"ansi-green-fg\">/databricks/spark/python/pyspark/sql/utils.py</span> in <span class=\"ansi-cyan-fg\">deco</span><span class=\"ansi-blue-fg\">(*a, **kw)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    115</span>     <span class=\"ansi-green-fg\">def</span> deco<span class=\"ansi-blue-fg\">(</span><span class=\"ansi-blue-fg\">*</span>a<span class=\"ansi-blue-fg\">,</span> <span class=\"ansi-blue-fg\">**</span>kw<span class=\"ansi-blue-fg\">)</span><span class=\"ansi-blue-fg\">:</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    116</span>         <span class=\"ansi-green-fg\">try</span><span class=\"ansi-blue-fg\">:</span>\n<span class=\"ansi-green-fg\">--&gt; 117</span><span class=\"ansi-red-fg\">             </span><span class=\"ansi-green-fg\">return</span> f<span class=\"ansi-blue-fg\">(</span><span class=\"ansi-blue-fg\">*</span>a<span class=\"ansi-blue-fg\">,</span> <span class=\"ansi-blue-fg\">**</span>kw<span class=\"ansi-blue-fg\">)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    118</span>         <span class=\"ansi-green-fg\">except</span> py4j<span class=\"ansi-blue-fg\">.</span>protocol<span class=\"ansi-blue-fg\">.</span>Py4JJavaError <span class=\"ansi-green-fg\">as</span> e<span class=\"ansi-blue-fg\">:</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    119</span>             converted <span class=\"ansi-blue-fg\">=</span> convert_exception<span class=\"ansi-blue-fg\">(</span>e<span class=\"ansi-blue-fg\">.</span>java_exception<span class=\"ansi-blue-fg\">)</span>\n\n<span class=\"ansi-green-fg\">/databricks/spark/python/lib/py4j-0.10.9.1-src.zip/py4j/protocol.py</span> in <span class=\"ansi-cyan-fg\">get_return_value</span><span class=\"ansi-blue-fg\">(answer, gateway_client, target_id, name)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    324</span>             value <span class=\"ansi-blue-fg\">=</span> OUTPUT_CONVERTER<span class=\"ansi-blue-fg\">[</span>type<span class=\"ansi-blue-fg\">]</span><span class=\"ansi-blue-fg\">(</span>answer<span class=\"ansi-blue-fg\">[</span><span class=\"ansi-cyan-fg\">2</span><span class=\"ansi-blue-fg\">:</span><span class=\"ansi-blue-fg\">]</span><span class=\"ansi-blue-fg\">,</span> gateway_client<span class=\"ansi-blue-fg\">)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    325</span>             <span class=\"ansi-green-fg\">if</span> answer<span class=\"ansi-blue-fg\">[</span><span class=\"ansi-cyan-fg\">1</span><span class=\"ansi-blue-fg\">]</span> <span class=\"ansi-blue-fg\">==</span> REFERENCE_TYPE<span class=\"ansi-blue-fg\">:</span>\n<span class=\"ansi-green-fg\">--&gt; 326</span><span class=\"ansi-red-fg\">                 raise Py4JJavaError(\n</span><span class=\"ansi-green-intense-fg ansi-bold\">    327</span>                     <span class=\"ansi-blue-fg\">&#34;An error occurred while calling {0}{1}{2}.\\n&#34;</span><span class=\"ansi-blue-fg\">.</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    328</span>                     format(target_id, &#34;.&#34;, name), value)\n\n<span class=\"ansi-red-fg\">Py4JJavaError</span>: An error occurred while calling o3049.transform.\n: java.lang.NullPointerException\n\tat org.apache.spark.sql.Column.&lt;init&gt;(Column.scala:153)\n\tat org.apache.spark.sql.Column$.apply(Column.scala:39)\n\tat org.apache.spark.sql.functions$.col(functions.scala:99)\n\tat com.microsoft.azure.synapse.ml.train.ComputeModelStatistics.selectAndCastToDF(ComputeModelStatistics.scala:260)\n\tat com.microsoft.azure.synapse.ml.train.ComputeModelStatistics.selectAndCastToRDD(ComputeModelStatistics.scala:269)\n\tat com.microsoft.azure.synapse.ml.train.ComputeModelStatistics.$anonfun$transform$1(ComputeModelStatistics.scala:150)\n\tat com.microsoft.azure.synapse.ml.logging.BasicLogging.logVerb(BasicLogging.scala:63)\n\tat com.microsoft.azure.synapse.ml.logging.BasicLogging.logVerb$(BasicLogging.scala:60)\n\tat com.microsoft.azure.synapse.ml.train.ComputeModelStatistics.logVerb(ComputeModelStatistics.scala:59)\n\tat com.microsoft.azure.synapse.ml.logging.BasicLogging.logTransform(BasicLogging.scala:53)\n\tat com.microsoft.azure.synapse.ml.logging.BasicLogging.logTransform$(BasicLogging.scala:52)\n\tat com.microsoft.azure.synapse.ml.train.ComputeModelStatistics.logTransform(ComputeModelStatistics.scala:59)\n\tat com.microsoft.azure.synapse.ml.train.ComputeModelStatistics.transform(ComputeModelStatistics.scala:75)\n\tat sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\n\tat sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\n\tat sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\n\tat java.lang.reflect.Method.invoke(Method.java:498)\n\tat py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)\n\tat py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:380)\n\tat py4j.Gateway.invoke(Gateway.java:295)\n\tat py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)\n\tat py4j.commands.CallCommand.execute(CallCommand.java:79)\n\tat py4j.GatewayConnection.run(GatewayConnection.java:251)\n\tat java.lang.Thread.run(Thread.java:748)\n</div>","errorSummary":"java.lang.NullPointerException","metadata":{},"errorTraceType":"html","type":"ipynbError","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\"><span class=\"ansi-red-fg\">---------------------------------------------------------------------------</span>\n<span class=\"ansi-red-fg\">Py4JJavaError</span>                             Traceback (most recent call last)\n<span class=\"ansi-green-fg\">&lt;command-3284417899613051&gt;</span> in <span class=\"ansi-cyan-fg\">&lt;module&gt;</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">      2</span> \n<span class=\"ansi-green-intense-fg ansi-bold\">      3</span> <span class=\"ansi-red-fg\"># metrics = ComputeModelStatistics(evaluationMetric=&#34;regression&#34;, labelCol=labelCol, scoredLabelsCol=&#39;prediction&#39;).transform(predictions)</span>\n<span class=\"ansi-green-fg\">----&gt; 4</span><span class=\"ansi-red-fg\"> </span>metrics <span class=\"ansi-blue-fg\">=</span> ComputeModelStatistics<span class=\"ansi-blue-fg\">(</span>evaluationMetric<span class=\"ansi-blue-fg\">=</span><span class=\"ansi-blue-fg\">&#34;regression&#34;</span><span class=\"ansi-blue-fg\">,</span> labelCol<span class=\"ansi-blue-fg\">=</span>labelCol<span class=\"ansi-blue-fg\">,</span> scoredLabelsCol<span class=\"ansi-blue-fg\">=</span><span class=\"ansi-blue-fg\">&#39;prediction&#39;</span><span class=\"ansi-blue-fg\">)</span><span class=\"ansi-blue-fg\">.</span>transform<span class=\"ansi-blue-fg\">(</span>predictions<span class=\"ansi-blue-fg\">)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">      5</span> metrics<span class=\"ansi-blue-fg\">.</span>toPandas<span class=\"ansi-blue-fg\">(</span><span class=\"ansi-blue-fg\">)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">      6</span> <span class=\"ansi-red-fg\"># metrics.select(&#39;root_mean_squared_error&#39;).show()</span>\n\n<span class=\"ansi-green-fg\">/databricks/spark/python/pyspark/ml/base.py</span> in <span class=\"ansi-cyan-fg\">transform</span><span class=\"ansi-blue-fg\">(self, dataset, params)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    215</span>                 <span class=\"ansi-green-fg\">return</span> self<span class=\"ansi-blue-fg\">.</span>copy<span class=\"ansi-blue-fg\">(</span>params<span class=\"ansi-blue-fg\">)</span><span class=\"ansi-blue-fg\">.</span>_transform<span class=\"ansi-blue-fg\">(</span>dataset<span class=\"ansi-blue-fg\">)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    216</span>             <span class=\"ansi-green-fg\">else</span><span class=\"ansi-blue-fg\">:</span>\n<span class=\"ansi-green-fg\">--&gt; 217</span><span class=\"ansi-red-fg\">                 </span><span class=\"ansi-green-fg\">return</span> self<span class=\"ansi-blue-fg\">.</span>_transform<span class=\"ansi-blue-fg\">(</span>dataset<span class=\"ansi-blue-fg\">)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    218</span>         <span class=\"ansi-green-fg\">else</span><span class=\"ansi-blue-fg\">:</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    219</span>             <span class=\"ansi-green-fg\">raise</span> TypeError<span class=\"ansi-blue-fg\">(</span><span class=\"ansi-blue-fg\">&#34;Params must be a param map but got %s.&#34;</span> <span class=\"ansi-blue-fg\">%</span> type<span class=\"ansi-blue-fg\">(</span>params<span class=\"ansi-blue-fg\">)</span><span class=\"ansi-blue-fg\">)</span>\n\n<span class=\"ansi-green-fg\">/databricks/spark/python/pyspark/ml/wrapper.py</span> in <span class=\"ansi-cyan-fg\">_transform</span><span class=\"ansi-blue-fg\">(self, dataset)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    348</span>     <span class=\"ansi-green-fg\">def</span> _transform<span class=\"ansi-blue-fg\">(</span>self<span class=\"ansi-blue-fg\">,</span> dataset<span class=\"ansi-blue-fg\">)</span><span class=\"ansi-blue-fg\">:</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    349</span>         self<span class=\"ansi-blue-fg\">.</span>_transfer_params_to_java<span class=\"ansi-blue-fg\">(</span><span class=\"ansi-blue-fg\">)</span>\n<span class=\"ansi-green-fg\">--&gt; 350</span><span class=\"ansi-red-fg\">         </span><span class=\"ansi-green-fg\">return</span> DataFrame<span class=\"ansi-blue-fg\">(</span>self<span class=\"ansi-blue-fg\">.</span>_java_obj<span class=\"ansi-blue-fg\">.</span>transform<span class=\"ansi-blue-fg\">(</span>dataset<span class=\"ansi-blue-fg\">.</span>_jdf<span class=\"ansi-blue-fg\">)</span><span class=\"ansi-blue-fg\">,</span> dataset<span class=\"ansi-blue-fg\">.</span>sql_ctx<span class=\"ansi-blue-fg\">)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    351</span> \n<span class=\"ansi-green-intense-fg ansi-bold\">    352</span> \n\n<span class=\"ansi-green-fg\">/databricks/spark/python/lib/py4j-0.10.9.1-src.zip/py4j/java_gateway.py</span> in <span class=\"ansi-cyan-fg\">__call__</span><span class=\"ansi-blue-fg\">(self, *args)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">   1302</span> \n<span class=\"ansi-green-intense-fg ansi-bold\">   1303</span>         answer <span class=\"ansi-blue-fg\">=</span> self<span class=\"ansi-blue-fg\">.</span>gateway_client<span class=\"ansi-blue-fg\">.</span>send_command<span class=\"ansi-blue-fg\">(</span>command<span class=\"ansi-blue-fg\">)</span>\n<span class=\"ansi-green-fg\">-&gt; 1304</span><span class=\"ansi-red-fg\">         return_value = get_return_value(\n</span><span class=\"ansi-green-intense-fg ansi-bold\">   1305</span>             answer, self.gateway_client, self.target_id, self.name)\n<span class=\"ansi-green-intense-fg ansi-bold\">   1306</span> \n\n<span class=\"ansi-green-fg\">/databricks/spark/python/pyspark/sql/utils.py</span> in <span class=\"ansi-cyan-fg\">deco</span><span class=\"ansi-blue-fg\">(*a, **kw)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    115</span>     <span class=\"ansi-green-fg\">def</span> deco<span class=\"ansi-blue-fg\">(</span><span class=\"ansi-blue-fg\">*</span>a<span class=\"ansi-blue-fg\">,</span> <span class=\"ansi-blue-fg\">**</span>kw<span class=\"ansi-blue-fg\">)</span><span class=\"ansi-blue-fg\">:</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    116</span>         <span class=\"ansi-green-fg\">try</span><span class=\"ansi-blue-fg\">:</span>\n<span class=\"ansi-green-fg\">--&gt; 117</span><span class=\"ansi-red-fg\">             </span><span class=\"ansi-green-fg\">return</span> f<span class=\"ansi-blue-fg\">(</span><span class=\"ansi-blue-fg\">*</span>a<span class=\"ansi-blue-fg\">,</span> <span class=\"ansi-blue-fg\">**</span>kw<span class=\"ansi-blue-fg\">)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    118</span>         <span class=\"ansi-green-fg\">except</span> py4j<span class=\"ansi-blue-fg\">.</span>protocol<span class=\"ansi-blue-fg\">.</span>Py4JJavaError <span class=\"ansi-green-fg\">as</span> e<span class=\"ansi-blue-fg\">:</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    119</span>             converted <span class=\"ansi-blue-fg\">=</span> convert_exception<span class=\"ansi-blue-fg\">(</span>e<span class=\"ansi-blue-fg\">.</span>java_exception<span class=\"ansi-blue-fg\">)</span>\n\n<span class=\"ansi-green-fg\">/databricks/spark/python/lib/py4j-0.10.9.1-src.zip/py4j/protocol.py</span> in <span class=\"ansi-cyan-fg\">get_return_value</span><span class=\"ansi-blue-fg\">(answer, gateway_client, target_id, name)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    324</span>             value <span class=\"ansi-blue-fg\">=</span> OUTPUT_CONVERTER<span class=\"ansi-blue-fg\">[</span>type<span class=\"ansi-blue-fg\">]</span><span class=\"ansi-blue-fg\">(</span>answer<span class=\"ansi-blue-fg\">[</span><span class=\"ansi-cyan-fg\">2</span><span class=\"ansi-blue-fg\">:</span><span class=\"ansi-blue-fg\">]</span><span class=\"ansi-blue-fg\">,</span> gateway_client<span class=\"ansi-blue-fg\">)</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    325</span>             <span class=\"ansi-green-fg\">if</span> answer<span class=\"ansi-blue-fg\">[</span><span class=\"ansi-cyan-fg\">1</span><span class=\"ansi-blue-fg\">]</span> <span class=\"ansi-blue-fg\">==</span> REFERENCE_TYPE<span class=\"ansi-blue-fg\">:</span>\n<span class=\"ansi-green-fg\">--&gt; 326</span><span class=\"ansi-red-fg\">                 raise Py4JJavaError(\n</span><span class=\"ansi-green-intense-fg ansi-bold\">    327</span>                     <span class=\"ansi-blue-fg\">&#34;An error occurred while calling {0}{1}{2}.\\n&#34;</span><span class=\"ansi-blue-fg\">.</span>\n<span class=\"ansi-green-intense-fg ansi-bold\">    328</span>                     format(target_id, &#34;.&#34;, name), value)\n\n<span class=\"ansi-red-fg\">Py4JJavaError</span>: An error occurred while calling o3049.transform.\n: java.lang.NullPointerException\n\tat org.apache.spark.sql.Column.&lt;init&gt;(Column.scala:153)\n\tat org.apache.spark.sql.Column$.apply(Column.scala:39)\n\tat org.apache.spark.sql.functions$.col(functions.scala:99)\n\tat com.microsoft.azure.synapse.ml.train.ComputeModelStatistics.selectAndCastToDF(ComputeModelStatistics.scala:260)\n\tat com.microsoft.azure.synapse.ml.train.ComputeModelStatistics.selectAndCastToRDD(ComputeModelStatistics.scala:269)\n\tat com.microsoft.azure.synapse.ml.train.ComputeModelStatistics.$anonfun$transform$1(ComputeModelStatistics.scala:150)\n\tat com.microsoft.azure.synapse.ml.logging.BasicLogging.logVerb(BasicLogging.scala:63)\n\tat com.microsoft.azure.synapse.ml.logging.BasicLogging.logVerb$(BasicLogging.scala:60)\n\tat com.microsoft.azure.synapse.ml.train.ComputeModelStatistics.logVerb(ComputeModelStatistics.scala:59)\n\tat com.microsoft.azure.synapse.ml.logging.BasicLogging.logTransform(BasicLogging.scala:53)\n\tat com.microsoft.azure.synapse.ml.logging.BasicLogging.logTransform$(BasicLogging.scala:52)\n\tat com.microsoft.azure.synapse.ml.train.ComputeModelStatistics.logTransform(ComputeModelStatistics.scala:59)\n\tat com.microsoft.azure.synapse.ml.train.ComputeModelStatistics.transform(ComputeModelStatistics.scala:75)\n\tat sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\n\tat sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\n\tat sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\n\tat java.lang.reflect.Method.invoke(Method.java:498)\n\tat py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)\n\tat py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:380)\n\tat py4j.Gateway.invoke(Gateway.java:295)\n\tat py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)\n\tat py4j.commands.CallCommand.execute(CallCommand.java:79)\n\tat py4j.GatewayConnection.run(GatewayConnection.java:251)\n\tat java.lang.Thread.run(Thread.java:748)\n</div>"]}}],"execution_count":0},{"cell_type":"markdown","source":["### Convert to ONNX"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"685c1dd9-85f3-459b-a0d5-fb15eaf23651"}}},{"cell_type":"code","source":["import onnx\nimport onnxmltools\nfrom onnxconverter_common.data_types import FloatTensorType"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"2770d43a-53cd-4d40-bbad-19985e2ba3eb"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\"></div>","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\"></div>"]}}],"execution_count":0},{"cell_type":"code","source":["print(onnxmltools.__version__)"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"0e8a4fc8-9c3e-4b1f-be00-817e56c579ca"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">1.7.0\n</div>","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">1.7.0\n</div>"]}}],"execution_count":0},{"cell_type":"code","source":["import lightgbm as lgb\n\nbooster_model_str = model.getLightGBMBooster().modelStr().get()\nbooster = lgb.Booster(model_str=booster_model_str)"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"930e93bf-4c94-4aa7-8077-b115d8a9f881"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">Finished loading model, total used 100 iterations\n</div>","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">Finished loading model, total used 100 iterations\n</div>"]}}],"execution_count":0},{"cell_type":"code","source":["onnx_model = onnxmltools.convert_lightgbm(booster, initial_types=[['input', FloatTensorType([1, len(feature_cols)])]])"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"22caf9b8-3eda-4c67-9262-bed31bc5dc2c"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">The maximum opset needed by this model is only 1.\n</div>","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">The maximum opset needed by this model is only 1.\n</div>"]}}],"execution_count":0},{"cell_type":"code","source":["# Save as protobuf\noutput_onnx_model = \"/databricks/driver/lgbmregressor.onnx\"\nonnxmltools.utils.save_model(onnx_model, output_onnx_model)"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"d2340fb2-fd15-49a1-8452-2af2ce4b466b"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\"></div>","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\"></div>"]}}],"execution_count":0},{"cell_type":"code","source":["dbutils.fs.ls(\"file:/databricks/driver/\")"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"9f6f44c3-5ab8-40d7-9559-a45e5cf1bc0a"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">Out[36]: [FileInfo(path=&#39;file:/databricks/driver/conf/&#39;, name=&#39;conf/&#39;, size=4096, modificationTime=1654080394482),\n FileInfo(path=&#39;file:/databricks/driver/preload_class.lst&#39;, name=&#39;preload_class.lst&#39;, size=1307454, modificationTime=1654080395042),\n FileInfo(path=&#39;file:/databricks/driver/eventlogs/&#39;, name=&#39;eventlogs/&#39;, size=4096, modificationTime=1654080473219),\n FileInfo(path=&#39;file:/databricks/driver/lgbmregressor.onnx&#39;, name=&#39;lgbmregressor.onnx&#39;, size=223483, modificationTime=1654082085667),\n FileInfo(path=&#39;file:/databricks/driver/ganglia/&#39;, name=&#39;ganglia/&#39;, size=4096, modificationTime=1654081202820),\n FileInfo(path=&#39;file:/databricks/driver/logs/&#39;, name=&#39;logs/&#39;, size=4096, modificationTime=1654081329854)]</div>","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">Out[36]: [FileInfo(path=&#39;file:/databricks/driver/conf/&#39;, name=&#39;conf/&#39;, size=4096, modificationTime=1654080394482),\n FileInfo(path=&#39;file:/databricks/driver/preload_class.lst&#39;, name=&#39;preload_class.lst&#39;, size=1307454, modificationTime=1654080395042),\n FileInfo(path=&#39;file:/databricks/driver/eventlogs/&#39;, name=&#39;eventlogs/&#39;, size=4096, modificationTime=1654080473219),\n FileInfo(path=&#39;file:/databricks/driver/lgbmregressor.onnx&#39;, name=&#39;lgbmregressor.onnx&#39;, size=223483, modificationTime=1654082085667),\n FileInfo(path=&#39;file:/databricks/driver/ganglia/&#39;, name=&#39;ganglia/&#39;, size=4096, modificationTime=1654081202820),\n FileInfo(path=&#39;file:/databricks/driver/logs/&#39;, name=&#39;logs/&#39;, size=4096, modificationTime=1654081329854)]</div>"]}}],"execution_count":0},{"cell_type":"code","source":["dbutils.fs.cp(\"file:/databricks/driver/lgbmregressor.onnx\", 'dbfs:/mnt/delta/nyctaxi/modelONNX/lgbmregressor.onnx')"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"88c31879-8669-4fe1-b537-5a8ffea2e656"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">Out[37]: True</div>","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">Out[37]: True</div>"]}}],"execution_count":0},{"cell_type":"markdown","source":["https://github.com/lutzroeder/Netron"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"b2170e29-dd98-4726-aaa9-0941bef1bfaa"}}},{"cell_type":"code","source":["import netron\n\nnetron.start('/dbfs/mnt/delta/nyctaxi/modelONNX/lgbmregressor.onnx')"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"ba0d7461-8427-4c9c-a055-b5f17a888d3e"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"data":"","errorSummary":"Command skipped","metadata":{},"errorTraceType":"html","type":"ipynbError","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>"]}}],"execution_count":0},{"cell_type":"markdown","source":["### Register model on Azure ML"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"0a37a2af-dcb1-468c-ac00-0b77d9939e20"}}},{"cell_type":"code","source":["from azureml.core import Workspace\nfrom azureml.core.run import Run\nfrom azureml.core import Datastore, Dataset\nfrom azureml.core import Model\nfrom azureml.core.resource_configuration import ResourceConfiguration"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"a38d85a3-2183-47d8-8893-bef3f72d066f"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\"></div>","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\"></div>"]}}],"execution_count":0},{"cell_type":"code","source":["svc_pr_password = 'sXB8Q~2ohtO-XDlHQ9rShkUObb.rXC4ROoJ2rbtx'"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"fb1bcc95-08b9-479b-ae21-6c29679b19cf"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\"></div>","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\"></div>"]}}],"execution_count":0},{"cell_type":"code","source":["from azureml.core.authentication import ServicePrincipalAuthentication\n\ntenant_id = '8e2e7c2d-4702-496d-af6c-96e4bfc9f667'\nservice_principal_id = '598685ec-b180-4db0-a825-f1c8b15c3a30'\n\nsubscription_id = 'f80606e5-788f-4dc3-a9ea-2eb9a7836082'\nresource_group = 'rg-centralized-registry'\nworkspace_name = 'centralizedaml'\n\nsvc_pr = ServicePrincipalAuthentication(\n   tenant_id=tenant_id,\n   service_principal_id=service_principal_id,\n   service_principal_password=svc_pr_password)\n\n\nws = Workspace(\n   subscription_id=subscription_id,\n   resource_group=resource_group,\n   workspace_name=workspace_name,\n   auth=svc_pr\n   )\n\nprint(\"Found workspace {} at location {}\".format(ws.name, ws.location))"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"a730f1d2-7873-4767-81eb-dd8159437649"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">Found workspace centralizedaml at location eastus2\n</div>","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">Found workspace centralizedaml at location eastus2\n</div>"]}}],"execution_count":0},{"cell_type":"code","source":["from azureml.core import Model\n\nmodel = Model.register(workspace=ws,\n                       model_name='nyctaxi-regression-onnx',\n                       model_path='/dbfs/mnt/delta/nyctaxi/modelONNX/lgbmregressor.onnx',\n                       model_framework=Model.Framework.ONNX,\n                       model_framework_version='1.7.0',\n                       description='Onnx NYC Taxi fare amount regression model')\n\nprint('Name:', model.name)"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"81674dc7-ab1e-4f76-bd71-16a85fd4572e"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">Registering model nyctaxi-regression-onnx\nName: nyctaxi-regression-onnx\n</div>","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">Registering model nyctaxi-regression-onnx\nName: nyctaxi-regression-onnx\n</div>"]}}],"execution_count":0},{"cell_type":"markdown","source":["deploy model https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/deployment/onnx/onnx-model-register-and-deploy.ipynb"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"8191b89d-5e5e-4cd4-8906-14c7b553aa33"}}},{"cell_type":"markdown","source":["### Inference session"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"dc17967e-9193-488f-92ed-69fc1e81ae47"}}},{"cell_type":"code","source":["import onnx\n\n# onnx_model is an in-memory ModelProto\nonnx_model = onnx.load('/dbfs/mnt/delta/nyctaxi/modelONNX/lgbmregressor.onnx')"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"fe140e03-2019-4953-bbd0-253e86249462"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\"></div>","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\"></div>"]}}],"execution_count":0},{"cell_type":"code","source":["# onnx_model = (\n#   onnx_model\n#     .setDeviceType(\"CPU\")\n#     .setFeedDict({\"input\": \"features\"})\n#     .setFetchDict({\"probability\": \"probabilities\", \"prediction\": \"label\"})\n#     .setMiniBatchSize(5000)\n# )"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"0e4b2586-bd64-4a53-9122-21346ddd9cf6"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"data":"","errorSummary":"Command skipped","metadata":{},"errorTraceType":"html","type":"ipynbError","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>"]}}],"execution_count":0},{"cell_type":"code","source":["import onnxruntime\n\nsession = onnxruntime.InferenceSession('/dbfs/mnt/delta/nyctaxi/modelONNX/lgbmregressor.onnx')"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"dbf12864-f2ba-4e49-9d65-d2f613467f67"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\"></div>","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\"></div>"]}}],"execution_count":0},{"cell_type":"code","source":["session.get_modelmeta()\ninput_name = session.get_inputs()[0].name\nlabel_name = session.get_outputs()[0].name"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"66a0d017-dbcf-48c4-9fd6-3b12e6161e0f"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\"></div>","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\"></div>"]}}],"execution_count":0},{"cell_type":"code","source":["sess_input = session.get_inputs()\nsess_output = session.get_outputs()\nprint(f\"No. of inputs : {len(sess_input)}, No. of outputs : {len(sess_output)}\")   "],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"ca7f47f7-df58-4615-89a9-28e429d5639e"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">No. of inputs : 1, No. of outputs : 1\n</div>","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">No. of inputs : 1, No. of outputs : 1\n</div>"]}}],"execution_count":0},{"cell_type":"code","source":["sess_input = session.get_inputs()\nsess_output = session.get_outputs()\nprint(f\"No. of inputs : {len(sess_input)}, No. of outputs : {len(sess_output)}\")\n\nfor idx, input_ in enumerate(range(len(sess_input))):\n    input_name = sess_input[input_].name\n    input_shape = sess_input[input_].shape\n    input_type = sess_input[input_].type\n    print(f\"{idx} Input name : { input_name }, Input shape : {input_shape}, \\\n    Input type  : {input_type}\")  \n\nfor idx, output in enumerate(range(len(sess_output))):\n    output_name = sess_output[output].name\n    output_shape = sess_output[output].shape\n    output_type = sess_output[output].type\n    print(f\" {idx} Output name : {output_name}, Output shape : {output_shape}, \\\n    Output type  : {output_type}\")"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"61b1278d-119c-4d64-a487-0c15b30930c3"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">No. of inputs : 1, No. of outputs : 1\n0 Input name : input, Input shape : [1, 6],     Input type  : tensor(float)\n 0 Output name : variable, Output shape : [1, 1],     Output type  : tensor(float)\n</div>","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">No. of inputs : 1, No. of outputs : 1\n0 Input name : input, Input shape : [1, 6],     Input type  : tensor(float)\n 0 Output name : variable, Output shape : [1, 1],     Output type  : tensor(float)\n</div>"]}}],"execution_count":0},{"cell_type":"code","source":["import numpy as np\n\nX_pred = np.random.random((1, len(feature_cols)))\nX_pred = X_pred.astype(np.float32)\nX_pred"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"de22be49-b146-4423-9eae-250c6abdcb32"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">Out[47]: array([[0.43964374, 0.07839109, 0.68153435, 0.5391525 , 0.49421284,\n        0.09838344]], dtype=float32)</div>","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">Out[47]: array([[0.43964374, 0.07839109, 0.68153435, 0.5391525 , 0.49421284,\n        0.09838344]], dtype=float32)</div>"]}}],"execution_count":0},{"cell_type":"code","source":["pred_onnx = session.run([label_name], {input_name: X_pred})[0]"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"c5d7d887-a638-4532-a1ed-1454fad245e2"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\"></div>","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\"></div>"]}}],"execution_count":0},{"cell_type":"code","source":["pred_onnx"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"d55ead40-fa73-4501-bc35-23a8b1d542e0"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">Out[50]: array([[5.881787]], dtype=float32)</div>","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">Out[50]: array([[5.881787]], dtype=float32)</div>"]}}],"execution_count":0},{"cell_type":"markdown","source":["# MLFlow"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"45c1a919-69fe-4499-a279-82ec64cd5244"}}},{"cell_type":"code","source":["import mlflow"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"a51c1fa2-c2fd-4b11-9699-74c4e7dab5e7"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\"></div>","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\"></div>"]}}],"execution_count":0},{"cell_type":"code","source":["print(\"MLflow Version:\", mlflow.__version__)\nprint(\"MLflow Tracking URI:\", mlflow.get_tracking_uri())\nclient = mlflow.tracking.MlflowClient()"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"e6d6d373-0ed6-4a9b-b5f6-271533b5963f"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">MLflow Version: 1.26.1\nMLflow Tracking URI: databricks\n</div>","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">MLflow Version: 1.26.1\nMLflow Tracking URI: databricks\n</div>"]}}],"execution_count":0},{"cell_type":"markdown","source":["### Tracking URI: Databricks"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"76a85747-637c-44a9-8b1e-85ebec8c5585"}}},{"cell_type":"code","source":[" with mlflow.start_run() as run:\n        run_id = run.info.run_id\n        experiment_id = run.info.experiment_id\n        print(\"MLflow:\")\n        print(\"  run_id:\", run_id)\n        print(\"  experiment_id:\", experiment_id)\n        print(\"  experiment_name:\", client.get_experiment(experiment_id).name)\n        \n        model = LightGBMRegressor(objective='regression',\n                          labelCol=labelCol,\n                          alpha=0.2,\n                          learningRate=0.3,\n                          numLeaves=31).fit(train)\n        \n        mlflow.log_param(\"alpha\", 0.2)\n        mlflow.log_param(\"learningRate\", 0.3)\n        mlflow.log_param(\"numLeaves\", 31)\n        \n        \n#         metrics = ComputeModelStatistics(evaluationMetric=\"regression\", labelCol=labelCol, scoredLabelsCol='prediction').transform(predictions)\n        mlflow.log_metric(\"rmse\", 0.0)\n        mlflow.log_metric(\"r2\", 0.0)\n        mlflow.log_metric(\"mae\", 0.0)\n        \n        mlflow.set_tag(\"framework\", \"LightGBM\")\n        mlflow.set_tag(\"mlflow_version\", mlflow.__version__)\n        \n        booster_model_str = model.getLightGBMBooster().modelStr().get()\n        booster = lgb.Booster(model_str=booster_model_str)\n        \n        onnx_model = onnxmltools.convert_lightgbm(booster, initial_types=[['input', FloatTensorType([1, len(feature_cols)])]])\n        \n        mlflow.onnx.log_model(onnx_model, \"onnx-model\")"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"a0c19e0e-1d77-4325-85cc-7b72ae7f1ede"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">MLflow:\n  run_id: 1874d6c4899a4a45afb559c145ba7f16\n  experiment_id: 1767855574562193\n  experiment_name: /Shared/ai42/nyctaxi\nFinished loading model, total used 100 iterations\nThe maximum opset needed by this model is only 1.\n</div>","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">MLflow:\n  run_id: 1874d6c4899a4a45afb559c145ba7f16\n  experiment_id: 1767855574562193\n  experiment_name: /Shared/ai42/nyctaxi\nFinished loading model, total used 100 iterations\nThe maximum opset needed by this model is only 1.\n</div>"]}}],"execution_count":0},{"cell_type":"markdown","source":["### Tracking URI: Azure ML"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"5f6b590c-e7ab-42d0-a738-6162e8178fc7"}}},{"cell_type":"code","source":["azureml_tracking_uri = ws.get_mlflow_tracking_uri()\nprint(f\"Azure ML Tracking URI: {azureml_tracking_uri}\")\n\nmlflow.set_tracking_uri(azureml_tracking_uri)\nprint(\"MLflow Tracking URI:\", mlflow.get_tracking_uri())\n\nclient = mlflow.tracking.MlflowClient()"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"11e9b3ae-666a-47ad-b26f-711fb0eda2ba"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">Azure ML Tracking URI: azureml://eastus2.api.azureml.ms/mlflow/v1.0/subscriptions/f80606e5-788f-4dc3-a9ea-2eb9a7836082/resourceGroups/rg-centralized-registry/providers/Microsoft.MachineLearningServices/workspaces/centralizedaml?\nMLflow Tracking URI: azureml://eastus2.api.azureml.ms/mlflow/v1.0/subscriptions/f80606e5-788f-4dc3-a9ea-2eb9a7836082/resourceGroups/rg-centralized-registry/providers/Microsoft.MachineLearningServices/workspaces/centralizedaml?\n</div>","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">Azure ML Tracking URI: azureml://eastus2.api.azureml.ms/mlflow/v1.0/subscriptions/f80606e5-788f-4dc3-a9ea-2eb9a7836082/resourceGroups/rg-centralized-registry/providers/Microsoft.MachineLearningServices/workspaces/centralizedaml?\nMLflow Tracking URI: azureml://eastus2.api.azureml.ms/mlflow/v1.0/subscriptions/f80606e5-788f-4dc3-a9ea-2eb9a7836082/resourceGroups/rg-centralized-registry/providers/Microsoft.MachineLearningServices/workspaces/centralizedaml?\n</div>"]}}],"execution_count":0},{"cell_type":"code","source":["experiment_name = 'Yellow_Taxi_fromDBX_experiment'\nmlflow.set_experiment(experiment_name)"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"32e5b5b7-4beb-49f3-afee-b872ace21b05"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">Out[55]: &lt;Experiment: artifact_location=&#39;&#39;, experiment_id=&#39;d140cd27-08d8-4638-ab62-bf60ca444378&#39;, lifecycle_stage=&#39;active&#39;, name=&#39;Yellow_Taxi_fromDBX_experiment&#39;, tags={}&gt;</div>","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">Out[55]: &lt;Experiment: artifact_location=&#39;&#39;, experiment_id=&#39;d140cd27-08d8-4638-ab62-bf60ca444378&#39;, lifecycle_stage=&#39;active&#39;, name=&#39;Yellow_Taxi_fromDBX_experiment&#39;, tags={}&gt;</div>"]}}],"execution_count":0},{"cell_type":"code","source":[" with mlflow.start_run() as run:\n#         run_id = run.info.run_id\n#         experiment_id = run.info.experiment_id\n#         print(\"MLflow:\")\n#         print(\"  run_id:\", run_id)\n#         print(\"  experiment_id:\", experiment_id)\n#         print(\"  experiment_name:\", client.get_experiment(experiment_id).name)\n        \n        model = LightGBMRegressor(objective='regression',\n                          labelCol=labelCol,\n                          alpha=0.2,\n                          learningRate=0.3,\n                          numLeaves=31).fit(train)\n        \n        mlflow.log_param(\"alpha\", 0.2)\n        mlflow.log_param(\"learningRate\", 0.3)\n        mlflow.log_param(\"numLeaves\", 31)\n        \n#         metrics = ComputeModelStatistics(evaluationMetric=\"regression\", labelCol=labelCol, scoredLabelsCol='prediction').transform(predictions)\n        mlflow.log_metric(\"rmse\", 0.0)\n        mlflow.log_metric(\"r2\", 0.0)\n        mlflow.log_metric(\"mae\", 0.0)\n        \n        mlflow.set_tag(\"framework\", \"LightGBM\")\n        mlflow.set_tag(\"mlflow_version\", mlflow.__version__)\n        \n        booster_model_str = model.getLightGBMBooster().modelStr().get()\n        booster = lgb.Booster(model_str=booster_model_str)\n        \n        onnx_model = onnxmltools.convert_lightgbm(booster, initial_types=[['input', FloatTensorType([1, len(feature_cols)])]])\n        \n        mlflow.onnx.log_model(onnx_model, \"onnx-model\")"],"metadata":{"application/vnd.databricks.v1+cell":{"title":"","showTitle":false,"inputWidgets":{},"nuid":"72be87de-302d-4b4e-8bdd-2cbb19ca5998"}},"outputs":[{"output_type":"display_data","metadata":{"application/vnd.databricks.v1+output":{"datasetInfos":[],"data":"<div class=\"ansiout\">Finished loading model, total used 100 iterations\nThe maximum opset needed by this model is only 1.\n</div>","removedWidgets":[],"addedWidgets":{},"metadata":{},"type":"html","arguments":{}}},"output_type":"display_data","data":{"text/html":["<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\">Finished loading model, total used 100 iterations\nThe maximum opset needed by this model is only 1.\n</div>"]}}],"execution_count":0}],"metadata":{"application/vnd.databricks.v1+notebook":{"notebookName":"nyctaxi (ai42)","dashboards":[],"notebookMetadata":{"pythonIndentUnit":4},"language":"python","widgets":{},"notebookOrigID":2711731877881850}},"nbformat":4,"nbformat_minor":0}