Anomaly Detection.json

{"paragraphs":[{"text":"%md\nAnomaly detection detects data points in data that does not fit well with the rest of data. In this notebook we demonstrate how to do unsupervised anomaly detection using recurrent nueral network (RNN) model.\n\nWe used one of the dataset in Numenta Anomaly Benchmark (NAB) (https://github.com/numenta/NAB) for demo, i.e. NYC taxi passengers dataset, which contains 10320 records, each indicating the total number of taxi passengers in NYC at a corresonponding time spot. We use RNN to learn from 50 previous values, and predict just the 1 next value. The data points whose actual values are distant from predicted values are considered anomalies (distance threshold can be adjusted as needed).\n\nReferences:\n\nUnsupervised real-time anomaly detection for streaming data (https://www.sciencedirect.com/science/article/pii/S0925231217309864).","user":"anonymous","dateUpdated":"2019-06-17T20:45:15+0800","config":{"colWidth":12,"fontSize":9,"enabled":true,"results":{},"editorSetting":{"language":"markdown","editOnDblClick":true,"completionKey":"TAB","completionSupport":false},"editorMode":"ace/mode/markdown","editorHide":true,"tableHide":false},"settings":{"params":{},"forms":{}},"apps":[],"jobName":"paragraph_1560774847388_-1565391231","id":"20190617-203407_2036110693","dateCreated":"2019-06-17T20:34:07+0800","status":"FINISHED","progressUpdateIntervalMs":500,"focus":true,"$$hashKey":"object:4278","dateFinished":"2019-06-17T20:45:16+0800","dateStarted":"2019-06-17T20:45:16+0800","results":{"code":"SUCCESS","msg":[{"type":"HTML","data":"<div class=\"markdown-body\">\n<p>Anomaly detection detects data points in data that does not fit well with the rest of data. In this notebook we demonstrate how to do unsupervised anomaly detection using recurrent nueral network (RNN) model.</p>\n<p>We used one of the dataset in Numenta Anomaly Benchmark (NAB) (<a href=\"https://github.com/numenta/NAB\">https://github.com/numenta/NAB</a>) for demo, i.e. NYC taxi passengers dataset, which contains 10320 records, each indicating the total number of taxi passengers in NYC at a corresonponding time spot. We use RNN to learn from 50 previous values, and predict just the 1 next value. The data points whose actual values are distant from predicted values are considered anomalies (distance threshold can be adjusted as needed).</p>\n<p>References:</p>\n<p>Unsupervised real-time anomaly detection for streaming data (<a href=\"https://www.sciencedirect.com/science/article/pii/S0925231217309864)\">https://www.sciencedirect.com/science/article/pii/S0925231217309864)</a>.</p>\n</div>"}]}},{"text":"%pyspark\n\nfrom qcloud_cos import CosConfig\nfrom qcloud_cos import CosS3Client\nimport sys\n\nsecret_id = 'AKIDy0ra5NkDUYNwLWqfRxxkKvFBUxF33Tc4'      # 替换为用户的 secretId\nsecret_key = 'mDQgtyuaz9tLn21kDWcWGwPfiDakmZ5R'      # 替换为用户的 secretKey\nregion = 'ap-shanghai'     # 替换为用户的 Region\ntoken = None                # 使用临时密钥需要传入 Token，默认为空，可不填\nscheme = 'https'            # 指定使用 http/https 协议来访问 COS，默认为 https，可不填\nconfig = CosConfig(Region=region, SecretId=secret_id, SecretKey=secret_key, Token=token, Scheme=scheme)\n# 2. 获取客户端对象\nclient = CosS3Client(config)","user":"anonymous","dateUpdated":"2019-06-17T20:45:16+0800","config":{"editorSetting":{"language":"python","editOnDblClick":false,"completionKey":"TAB","completionSupport":true},"colWidth":12,"editorMode":"ace/mode/python","fontSize":9,"results":{},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[]},"apps":[],"jobName":"paragraph_1560774712319_1984410787","id":"20190611-161926_159021905","dateCreated":"2019-06-17T20:31:52+0800","status":"FINISHED","progressUpdateIntervalMs":500,"focus":true,"$$hashKey":"object:2300","dateFinished":"2019-06-17T20:45:16+0800","dateStarted":"2019-06-17T20:45:16+0800"},{"text":"%pyspark\nresponse = client.get_object(\n    Bucket='demo-sh-1258997244',\n    Key='/nyc_taxi.csv'\n)\nprint(type(response['Body']))\nresponse['Body'].get_stream_to_file('/tmp/nyc_taxi.csv')","user":"anonymous","dateUpdated":"2019-06-17T20:45:17+0800","config":{"editorSetting":{"language":"python","editOnDblClick":false,"completionKey":"TAB","completionSupport":true},"colWidth":12,"editorMode":"ace/mode/python","fontSize":9,"results":{},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"TEXT","data":"<class 'qcloud_cos.streambody.StreamBody'>\n"}]},"apps":[],"jobName":"paragraph_1560774712319_2104175129","id":"20190611-162428_1336195074","dateCreated":"2019-06-17T20:31:52+0800","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:2301","dateFinished":"2019-06-17T20:45:17+0800","dateStarted":"2019-06-17T20:45:17+0800"},{"text":"%pyspark\nimport os\nimport pandas as pd\nimport numpy as np\nfrom sklearn import preprocessing\nimport matplotlib\nimport matplotlib.dates as md\nfrom matplotlib import pyplot as plt\nmatplotlib.use('Agg')\n\nfrom zoo.common.nncontext import init_nncontext\nfrom zoo.pipeline.api.keras.layers import Dense, Dropout, LSTM\nfrom zoo.pipeline.api.keras.models import Sequential\n\nsc = init_nncontext(\"Anomaly Detection Example\")","user":"anonymous","dateUpdated":"2019-06-17T20:45:17+0800","config":{"editorSetting":{"language":"python","editOnDblClick":false,"completionKey":"TAB","completionSupport":true},"colWidth":12,"editorMode":"ace/mode/python","fontSize":9,"results":{},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[]},"apps":[],"jobName":"paragraph_1560774712319_1882770741","id":"20190611-162506_1480817536","dateCreated":"2019-06-17T20:31:52+0800","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:2303","dateFinished":"2019-06-17T20:45:19+0800","dateStarted":"2019-06-17T20:45:18+0800"},{"text":"%md\nEach record is in format of (timestamp, value). Timestamps range between 2014-07-01 and 2015-01-31.","user":"anonymous","dateUpdated":"2019-06-17T20:45:19+0800","config":{"colWidth":12,"fontSize":9,"enabled":true,"results":{},"editorSetting":{"language":"markdown","editOnDblClick":true,"completionKey":"TAB","completionSupport":false},"editorMode":"ace/mode/markdown","editorHide":true,"tableHide":false},"settings":{"params":{},"forms":{}},"apps":[],"jobName":"paragraph_1560774917115_894749170","id":"20190617-203517_758553812","dateCreated":"2019-06-17T20:35:17+0800","status":"FINISHED","progressUpdateIntervalMs":500,"focus":true,"$$hashKey":"object:4376","dateFinished":"2019-06-17T20:45:19+0800","dateStarted":"2019-06-17T20:45:19+0800","results":{"code":"SUCCESS","msg":[{"type":"HTML","data":"<div class=\"markdown-body\">\n<p>Each record is in format of (timestamp, value). Timestamps range between 2014-07-01 and 2015-01-31.</p>\n</div>"}]}},{"text":"%pyspark\ndf = pd.read_csv('/tmp/nyc_taxi.csv')\nprint(df.info())","user":"anonymous","dateUpdated":"2019-06-17T20:45:19+0800","config":{"editorSetting":{"language":"python","editOnDblClick":false,"completionKey":"TAB","completionSupport":true},"colWidth":12,"editorMode":"ace/mode/python","fontSize":9,"results":{},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"TEXT","data":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 10320 entries, 0 to 10319\nData columns (total 2 columns):\ntimestamp    10320 non-null object\nvalue        10320 non-null int64\ndtypes: int64(1), object(1)\nmemory usage: 161.3+ KB\nNone\n"}]},"apps":[],"jobName":"paragraph_1560774712319_1141842780","id":"20190611-171058_1906718983","dateCreated":"2019-06-17T20:31:52+0800","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:2305","dateFinished":"2019-06-17T20:45:19+0800","dateStarted":"2019-06-17T20:45:19+0800"},{"text":"%pyspark\nprint(df['timestamp'].head(10))","user":"anonymous","dateUpdated":"2019-06-17T20:45:19+0800","config":{"editorSetting":{"language":"python","editOnDblClick":false,"completionKey":"TAB","completionSupport":true},"colWidth":12,"editorMode":"ace/mode/python","fontSize":9,"results":{},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"TEXT","data":"0    2014-07-01 00:00:00\n1    2014-07-01 00:30:00\n2    2014-07-01 01:00:00\n3    2014-07-01 01:30:00\n4    2014-07-01 02:00:00\n5    2014-07-01 02:30:00\n6    2014-07-01 03:00:00\n7    2014-07-01 03:30:00\n8    2014-07-01 04:00:00\n9    2014-07-01 04:30:00\nName: timestamp, dtype: object\n"}]},"apps":[],"jobName":"paragraph_1560774712319_-1865623221","id":"20190611-171121_1467246163","dateCreated":"2019-06-17T20:31:52+0800","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:2306","dateFinished":"2019-06-17T20:45:19+0800","dateStarted":"2019-06-17T20:45:19+0800"},{"text":"%pyspark\nprint(df['value'].mean())","user":"anonymous","dateUpdated":"2019-06-17T20:45:19+0800","config":{"editorSetting":{"language":"python","editOnDblClick":false,"completionKey":"TAB","completionSupport":true},"colWidth":12,"editorMode":"ace/mode/python","fontSize":9,"results":{},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"TEXT","data":"15137.569379844961\n"}]},"apps":[],"jobName":"paragraph_1560774712319_802968777","id":"20190611-171138_1643173142","dateCreated":"2019-06-17T20:31:52+0800","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:2307","dateFinished":"2019-06-17T20:45:20+0800","dateStarted":"2019-06-17T20:45:20+0800"},{"text":"%pyspark\nimport io\ndef show(p):\n    img = io.StringIO()\n    p.savefig(img, format='svg')\n    img.seek(0)\n    print(\"%html <div style='width:600px'>\" + img.getvalue() + \"</div>\")\n\nfrom pandas.plotting import register_matplotlib_converters\nregister_matplotlib_converters()\n# change the type of timestamp column for plotting\ndf['datetime'] = pd.to_datetime(df['timestamp'])\n\n# visualisation of anomaly throughout time (viz 1)\nfig, ax = plt.subplots(figsize=(12, 5))\n\nax.plot(df['datetime'], df['value'], color='blue', linewidth=0.6)\nax.set_title('NYC taxi passengers throughout time')\n\nplt.xlabel('datetime')\nplt.xticks(rotation=45) \nplt.ylabel('The Number of NYC taxi passengers')\nplt.legend(loc='upper left')\nshow(plt)","user":"anonymous","dateUpdated":"2019-06-17T20:45:20+0800","config":{"editorSetting":{"language":"python","editOnDblClick":false,"completionKey":"TAB","completionSupport":true},"colWidth":12,"editorMode":"ace/mode/python","fontSize":9,"results":{},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"HTML","data":"<div style='width:600px'><?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Created with matplotlib (https://matplotlib.org/) -->\n<svg height=\"360pt\" version=\"1.1\" viewBox=\"0 0 864 360\" width=\"864pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n <defs>\n  <style type=\"text/css\">\n*{stroke-linecap:butt;stroke-linejoin:round;}\n  </style>\n </defs>\n <g id=\"figure_1\">\n  <g id=\"patch_1\">\n   <path d=\"M 0 360 \nL 864 360 \nL 864 0 \nL 0 0 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200.341989 \nL 244.443042 200.052622 \nL 244.502032 200.87571 \nL 244.561023 190.78645 \nL 244.620014 191.886045 \nL 244.737996 198.727964 \nL 244.855978 190.625691 \nL 244.914969 191.63526 \nL 245.032951 205.788517 \nL 245.150933 222.063799 \nL 245.445887 147.239945 \nL 245.504878 148.718931 \nL 245.563869 154.197612 \nL 245.62286 152.538575 \nL 245.681851 148.51959 \nL 245.740842 150.095032 \nL 245.799833 150.448702 \nL 245.858824 151.895537 \nL 245.976805 165.386466 \nL 246.389742 270.535972 \nL 246.566715 284.695659 \nL 246.625705 284.425584 \nL 246.743687 265.095874 \nL 246.979651 200.264824 \nL 247.038642 188.754452 \nL 247.097633 192.593386 \nL 247.333596 206.830238 \nL 247.451578 198.277838 \nL 247.510569 198.547914 \nL 247.56956 201.595912 \nL 247.746533 193.930904 \nL 247.864515 207.595453 \nL 247.982497 222.874026 \nL 248.277451 148.989007 \nL 248.572406 166.453908 \nL 248.808369 152.339233 \nL 248.86736 154.493409 \nL 249.221306 221.028508 \nL 249.51626 285.351558 \nL 249.575251 284.296976 \nL 249.929197 243.772747 \nL 250.10617 211.65302 \nL 250.16516 210.141882 \nL 250.342133 200.438444 \nL 250.460115 195.589941 \nL 250.519106 199.50604 \nL 250.578097 199.306698 \nL 250.637088 198.850142 \nL 250.696079 199.010901 \nL 250.75507 202.547608 \nL 250.814061 202.046038 \nL 251.109015 166.531072 \nL 251.226997 184.651872 \nL 251.285988 180.52357 \nL 251.698924 155.445104 \nL 252.111861 231.857261 \nL 252.170852 240.338927 \nL 252.347824 285.499456 \nL 252.406815 286.894848 \nL 252.524797 284.123356 \nL 253.173697 203.242088 \nL 253.35067 199.274546 \nL 253.527643 207.89125 \nL 253.586634 207.267504 \nL 253.645625 207.981275 \nL 253.881588 190.27202 \nL 254.471497 231.966577 \nL 254.766452 286.881987 \nL 254.943425 294.45054 \nL 255.120398 290.309378 \nL 255.29737 242.531685 \nL 255.533334 194.297436 \nL 255.651316 200.547761 \nL 255.769298 215.427651 \nL 255.887279 210.032565 \nL 255.94627 204.798239 \nL 256.005261 208.013427 \nL 256.064252 207.074592 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267.50849 194.985486 \nL 267.567481 196.213688 \nL 267.803445 219.215142 \nL 268.098399 151.471132 \nL 268.393354 165.707984 \nL 268.570327 153.155891 \nL 268.629317 153.541713 \nL 268.80629 177.700635 \nL 269.337209 284.136217 \nL 269.396199 283.345281 \nL 270.10409 204.862543 \nL 270.163081 203.389987 \nL 270.281063 197.28756 \nL 270.340054 198.580066 \nL 270.399045 196.695966 \nL 270.458036 197.834142 \nL 270.517027 197.79556 \nL 270.635009 205.055454 \nL 270.929963 164.762719 \nL 271.106936 179.623318 \nL 271.165927 179.584736 \nL 271.460882 158.11371 \nL 271.519872 158.261609 \nL 271.755836 189.950501 \nL 272.168773 285.820975 \nL 272.227763 286.200367 \nL 272.404736 274.632121 \nL 272.876664 208.997275 \nL 273.171618 195.030498 \nL 273.230609 195.114093 \nL 273.407582 203.480012 \nL 273.466573 203.312822 \nL 273.702536 190.329894 \nL 273.761527 191.911766 \nL 273.879509 204.926847 \nL 273.9385 201.467305 \nL 273.997491 202.431861 \nL 274.056482 201.962444 \nL 274.351436 244.351481 \nL 274.5874 283.692521 \nL 274.764373 295.30578 \nL 275.000337 280.387308 \nL 275.354282 191.80888 \nL 275.472264 202.824114 \nL 275.649237 217.453219 \nL 275.826209 208.977984 \nL 275.8852 211.897374 \nL 276.062173 201.872418 \nL 276.121164 202.23895 \nL 276.298137 214.836056 \nL 276.593091 169.450463 \nL 276.770064 181.121595 \nL 276.829055 180.394963 \nL 276.947037 191.506652 \nL 277.536946 293.466692 \nL 277.595937 294.849223 \nL 277.654928 292.977984 \nL 277.713919 295.427957 \nL 277.77291 293.704616 \nL 278.067864 213.222032 \nL 278.185846 185.99582 \nL 278.244837 192.336171 \nL 278.303828 193.500069 \nL 278.480801 210.077578 \nL 278.657773 196.708826 \nL 278.716764 203.981582 \nL 278.834746 191.095108 \nL 278.893737 193.017791 \nL 279.129701 218.411345 \nL 279.424655 155.509408 \nL 279.601628 167.051933 \nL 279.660619 167.109806 \nL 279.71961 167.379882 \nL 279.778601 170.009906 \nL 280.014564 229.606629 \nL 280.250528 277.673689 \nL 280.427501 291.216061 \nL 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289.344821 \nL 286.385584 268.870504 \nL 286.680538 196.020776 \nL 286.975493 213.164158 \nL 287.034484 208.727199 \nL 287.152465 204.12305 \nL 287.211456 204.592467 \nL 287.270447 204.868973 \nL 287.388429 199.454597 \nL 287.44742 201.184368 \nL 287.624393 219.395193 \nL 287.919347 162.222721 \nL 287.978338 168.093654 \nL 288.037329 170.279982 \nL 288.09632 177.906407 \nL 288.155311 177.128332 \nL 288.214302 177.822813 \nL 288.450266 160.904494 \nL 288.509257 161.714721 \nL 289.158157 286.001026 \nL 289.217148 284.168369 \nL 289.512102 253.855577 \nL 289.866048 207.910541 \nL 289.925039 208.527857 \nL 290.102011 201.550899 \nL 290.161002 206.785225 \nL 290.219993 205.196923 \nL 290.278984 205.814239 \nL 290.396966 203.679354 \nL 290.455957 206.392973 \nL 290.514948 203.242088 \nL 290.750911 171.025905 \nL 290.868893 178.877394 \nL 290.927884 157.014116 \nL 290.986875 186.973237 \nL 291.045866 183.185746 \nL 291.163848 170.07421 \nL 291.340821 162.100544 \nL 291.753757 230.937717 \nL 291.989721 288.894695 \nL 292.107702 285.312976 \nL 292.166693 283.33885 \nL 292.874584 205.602036 \nL 292.933575 204.958999 \nL 292.992566 201.801684 \nL 293.051557 203.897987 \nL 293.22853 211.13859 \nL 293.287521 210.862084 \nL 293.405503 195.905029 \nL 293.464494 196.683105 \nL 293.582475 197.705535 \nL 293.641466 204.3031 \nL 293.700457 201.846697 \nL 293.87743 210.572717 \nL 294.113394 231.336401 \nL 294.408348 286.206798 \nL 294.585321 294.932818 \nL 294.762294 292.097022 \nL 294.880276 269.700023 \nL 295.17523 199.653939 \nL 295.234221 200.792115 \nL 295.470185 222.346735 \nL 295.647157 213.890791 \nL 295.706148 212.591855 \nL 295.883121 206.071454 \nL 296.119085 214.405221 \nL 296.414039 174.35041 \nL 296.591012 186.947516 \nL 296.650003 187.545541 \nL 297.357894 294.347654 \nL 297.534867 296.013121 \nL 297.593858 295.575855 \nL 297.829821 235.040297 \nL 298.006794 190.124122 \nL 298.065785 197.551206 \nL 298.124776 200.316267 \nL 298.301749 213.691449 \nL 298.419731 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303.728913 192.805588 \nL 303.964877 210.411957 \nL 304.023868 202.136064 \nL 304.082859 202.23895 \nL 304.14185 204.367404 \nL 304.20084 203.820822 \nL 304.377813 195.017638 \nL 304.613777 220.822736 \nL 304.908731 160.853051 \nL 305.026713 162.72429 \nL 305.144695 166.859022 \nL 305.203686 163.103682 \nL 305.262677 164.132542 \nL 305.380659 172.852132 \nL 306.029559 285.930292 \nL 306.08855 288.772518 \nL 306.442495 221.099242 \nL 306.501486 202.283962 \nL 306.560477 205.569884 \nL 306.678459 215.466233 \nL 306.73745 213.53712 \nL 306.796441 213.080563 \nL 306.914423 202.75981 \nL 306.973414 205.557024 \nL 307.209377 194.162398 \nL 307.268368 195.352017 \nL 307.445341 214.148006 \nL 307.740295 165.888035 \nL 307.976259 183.648733 \nL 308.03525 183.565138 \nL 308.094241 181.430253 \nL 308.153232 169.797703 \nL 308.212223 173.758815 \nL 308.271214 169.90702 \nL 308.448186 195.712118 \nL 308.68415 238.557713 \nL 308.979105 287.029886 \nL 309.097087 280.104371 \nL 309.392041 247.212999 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288.598898 \nL 315.173151 244.949506 \nL 315.409115 217.568966 \nL 315.645078 201.319406 \nL 315.76306 195.91146 \nL 315.940033 206.084314 \nL 316.058015 193.52579 \nL 316.117006 194.818296 \nL 316.175997 193.538651 \nL 316.234988 193.622246 \nL 316.647924 221.832305 \nL 317.001869 284.663508 \nL 317.237833 298.649575 \nL 317.414806 292.116313 \nL 317.65077 212.84907 \nL 317.827742 186.870351 \nL 317.886733 184.253188 \nL 317.945724 188.072832 \nL 318.122697 202.303253 \nL 318.29967 190.419919 \nL 318.358661 194.046651 \nL 318.417651 186.555263 \nL 318.476642 190.599969 \nL 318.535633 188.799464 \nL 318.594624 189.764021 \nL 318.653615 192.413335 \nL 318.771597 214.694588 \nL 319.007561 159.695583 \nL 319.125542 164.415479 \nL 319.361506 180.163469 \nL 319.951415 290.322238 \nL 320.187379 295.614438 \nL 320.24637 293.370237 \nL 320.423343 232.101615 \nL 320.659306 177.539876 \nL 320.836279 191.410197 \nL 320.89527 190.336324 \nL 320.954261 196.60594 \nL 321.013252 188.04711 \nL 321.072243 188.362199 \nL 321.190225 192.754145 \nL 321.249215 185.777187 \nL 321.308206 188.947363 \nL 321.367197 185.211314 \nL 321.426188 187.648427 \nL 321.603161 214.752461 \nL 321.957106 150.455133 \nL 322.016097 152.017714 \nL 322.370043 190.43921 \nL 322.782979 281.28756 \nL 322.900961 292.354237 \nL 322.959952 289.820669 \nL 323.018943 292.932971 \nL 323.077934 292.219199 \nL 323.195916 265.19876 \nL 323.431879 175.430713 \nL 323.608852 185.507112 \nL 323.785825 191.841032 \nL 323.903807 181.224481 \nL 323.962798 184.561846 \nL 324.021789 193.236423 \nL 324.198761 177.186205 \nL 324.316743 196.020776 \nL 324.434725 220.083243 \nL 324.847661 140.372303 \nL 325.024634 149.001868 \nL 325.083625 147.973008 \nL 325.319589 185.918656 \nL 325.614543 266.877088 \nL 325.850507 287.306392 \nL 325.909498 289.344821 \nL 326.02748 260.948281 \nL 326.263443 176.883978 \nL 326.440416 183.854505 \nL 326.617389 192.117538 \nL 326.67638 183.584429 \nL 326.735371 185.64858 \nL 326.853353 191.918196 \nL 326.912344 191.738146 \nL 327.030325 178.877394 \nL 327.148307 199.1588 \nL 327.266289 219.568813 \nL 327.561244 132.064258 \nL 327.797207 141.182531 \nL 327.856198 141.317569 \nL 328.033171 134.874332 \nL 328.092162 135.755294 \nL 328.269135 165.624389 \nL 328.62308 242.184445 \nL 328.800053 284.682799 \nL 328.859044 283.538192 \nL 329.095007 253.997045 \nL 329.389962 187.500528 \nL 329.448953 187.461946 \nL 329.625926 171.996892 \nL 329.684917 171.148082 \nL 329.743908 166.730414 \nL 329.802898 168.601654 \nL 329.861889 167.476338 \nL 329.92088 160.685861 \nL 330.097853 197.956319 \nL 330.392808 140.815999 \nL 330.451799 142.172809 \nL 330.628771 158.602419 \nL 330.687762 156.821205 \nL 330.746753 150.210779 \nL 330.864735 112.541637 \nL 331.690608 287.145633 \nL 331.749599 281.101079 \nL 331.80859 279.91146 \nL 332.45749 165.206415 \nL 332.634462 178.543015 \nL 332.693453 177.970711 \nL 332.752444 186.490959 \nL 332.811435 185.410656 \nL 332.870426 183.777341 \nL 332.988408 186.632427 \nL 333.10639 168.247983 \nL 333.165381 173.488739 \nL 333.224372 174.35041 \nL 333.75529 229.10506 \nL 334.050244 283.235964 \nL 334.227217 292.881528 \nL 334.40419 289.196923 \nL 334.640154 213.549981 \nL 334.758135 188.240021 \nL 334.935108 192.213994 \nL 335.112081 204.901125 \nL 335.289054 192.683411 \nL 335.348045 201.332267 \nL 335.525017 181.912532 \nL 335.584008 183.59729 \nL 335.760981 204.920416 \nL 336.055936 150.017867 \nL 336.35089 172.196234 \nL 336.409881 169.193248 \nL 336.763827 268.876935 \nL 337.058781 292.650034 \nL 337.176763 293.145173 \nL 337.235754 292.135604 \nL 337.353736 265.018709 \nL 337.530709 179.224634 \nL 337.589699 179.526862 \nL 337.64869 179.906254 \nL 337.825663 197.023915 \nL 337.884654 194.001638 \nL 337.943645 197.96275 \nL 338.120618 188.252882 \nL 338.179609 193.937334 \nL 338.356581 175.662206 \nL 338.474563 199.692521 \nL 338.592545 219.86461 \nL 338.8875 153.078726 \nL 338.946491 147.522881 \nL 339.005482 147.555033 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264.909393 \nL 345.671455 285.775963 \nL 345.730446 287.557177 \nL 345.848428 264.112026 \nL 346.084392 175.630054 \nL 346.261364 186.298048 \nL 346.438337 198.380724 \nL 346.556319 189.847615 \nL 346.674301 197.904876 \nL 346.851273 183.410809 \nL 347.087237 223.304861 \nL 347.441183 133.652561 \nL 347.618155 146.185363 \nL 347.677146 143.90901 \nL 347.854119 131.794182 \nL 347.91311 132.405068 \nL 348.149074 170.92945 \nL 348.385037 224.693822 \nL 348.679992 284.239103 \nL 349.033937 227.90901 \nL 349.21091 188.715869 \nL 349.269901 187.249744 \nL 349.387883 172.684942 \nL 349.446874 174.53689 \nL 349.564856 169.598362 \nL 349.623847 176.330965 \nL 349.741828 139.697114 \nL 349.918801 195.184827 \nL 349.977792 184.79334 \nL 350.213756 134.745725 \nL 350.331738 135.948205 \nL 350.449719 164.344745 \nL 350.50871 162.647125 \nL 350.744674 130.746031 \nL 350.803665 132.173574 \nL 351.275592 233.156197 \nL 351.452565 284.907862 \nL 351.74752 258.176789 \nL 352.160456 177.874256 \nL 352.219447 176.607471 \nL 352.278438 167.784996 \nL 352.337429 175.167067 \nL 352.455411 180.697191 \nL 352.514402 178.80023 \nL 352.750365 190.278451 \nL 352.927338 169.251121 \nL 352.986329 169.354008 \nL 353.281283 194.760423 \nL 353.340274 194.432473 \nL 353.871193 287.711506 \nL 354.048165 295.65945 \nL 354.225138 289.987859 \nL 354.402111 231.182071 \nL 354.579084 186.407364 \nL 354.638075 187.53268 \nL 354.933029 208.264212 \nL 355.051011 199.866141 \nL 355.110002 200.560622 \nL 355.168993 206.097175 \nL 355.345966 188.883059 \nL 355.404956 191.185134 \nL 355.581929 205.749935 \nL 355.817893 150.455133 \nL 355.876884 151.033867 \nL 355.935875 152.28136 \nL 355.994866 151.027436 \nL 356.879729 295.82021 \nL 357.056702 292.842946 \nL 357.174684 266.279063 \nL 357.410648 162.467075 \nL 357.646611 191.686703 \nL 357.764593 191.390906 \nL 358.000557 202.367557 \nL 358.23652 187.732022 \nL 358.413493 217.98694 \nL 358.708448 149.471285 \nL 358.767439 145.587338 \nL 358.82643 148.127337 \nL 359.121384 176.601041 \nL 359.593312 287.537886 \nL 359.711293 292.759351 \nL 359.770284 292.502136 \nL 359.829275 293.743198 \nL 359.888266 291.910541 \nL 360.065239 228.687086 \nL 360.242212 176.92256 \nL 360.301203 178.137901 \nL 360.537166 196.470903 \nL 360.596157 199.358141 \nL 360.655148 190.207716 \nL 360.714139 190.349185 \nL 360.832121 197.924167 \nL 361.009094 188.169287 \nL 361.068084 191.152982 \nL 361.245057 221.465774 \nL 361.540012 141.703391 \nL 361.599003 143.90258 \nL 361.952948 163.463783 \nL 362.188912 238.602725 \nL 362.424876 283.094496 \nL 362.542857 291.235352 \nL 362.601848 290.039302 \nL 362.660839 291.923402 \nL 362.71983 290.630896 \nL 362.837812 260.961142 \nL 363.073776 174.787675 \nL 363.427721 196.978902 \nL 363.486712 190.310602 \nL 363.545703 191.345893 \nL 363.663685 194.451765 \nL 363.840658 187.693439 \nL 363.899648 191.243007 \nL 364.076621 226.256404 \nL 364.371576 152.712195 \nL 364.489558 140.642379 \nL 364.548549 146.018173 \nL 364.725521 142.19853 \nL 364.784512 145.728807 \nL 365.020476 206.540871 \nL 365.31543 272.625844 \nL 365.551394 286.830544 \nL 365.669376 260.369548 \nL 365.90534 181.353089 \nL 366.082312 191.783159 \nL 366.259285 197.519054 \nL 366.318276 190.722147 \nL 366.377267 190.979362 \nL 366.495249 198.027054 \nL 366.55424 196.689535 \nL 366.672222 190.259159 \nL 366.731213 193.506499 \nL 366.908185 225.323999 \nL 367.262131 130.752461 \nL 367.439104 140.076506 \nL 367.498094 139.073368 \nL 367.616076 129.852209 \nL 367.793049 145.478022 \nL 368.441949 285.763102 \nL 368.50094 283.486749 \nL 368.618922 273.24959 \nL 369.031858 189.339616 \nL 369.090849 188.612983 \nL 369.267822 175.37927 \nL 369.385804 171.643221 \nL 369.503786 176.176636 \nL 369.562777 175.03846 \nL 369.621767 176.761801 \nL 369.739749 200.52847 \nL 370.034704 141.84486 \nL 370.152686 149.066172 \nL 370.270668 154.67989 \nL 370.565622 136.687698 \nL 370.624613 137.594381 \nL 370.860577 174.504739 \nL 371.09654 232.01802 \nL 371.273513 285.512317 \nL 371.568468 263.951267 \nL 371.863422 198.078497 \nL 372.099386 189.873337 \nL 372.453331 219.35018 \nL 372.571313 231.072755 \nL 372.866268 194.753992 \nL 372.98425 191.564526 \nL 373.515168 265.758203 \nL 373.692141 287.679354 \nL 373.869113 295.254337 \nL 374.046086 289.132619 \nL 374.223059 231.915134 \nL 374.400032 194.876169 \nL 374.694986 213.53069 \nL 374.753977 214.881069 \nL 374.871959 207.929832 \nL 375.048932 210.026135 \nL 375.225905 202.438291 \nL 375.343886 219.401623 \nL 375.402877 218.083396 \nL 375.697832 167.489198 \nL 375.756823 161.078114 \nL 375.815814 162.222721 \nL 376.051777 177.707066 \nL 376.641687 294.341223 \nL 376.700677 295.845931 \nL 376.87765 292.463554 \nL 376.995632 260.92899 \nL 377.231596 190.207716 \nL 377.408568 203.428569 \nL 377.585541 216.745878 \nL 377.703523 209.511705 \nL 377.762514 211.582286 \nL 377.821505 215.517676 \nL 377.880496 214.701018 \nL 377.939487 214.797474 \nL 377.998478 211.910235 \nL 378.116459 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383.484633 197.017484 \nL 383.543624 197.763408 \nL 383.779588 215.118993 \nL 383.897569 225.414024 \nL 384.192524 158.178014 \nL 384.251515 155.303636 \nL 384.428488 168.530919 \nL 384.60546 156.126724 \nL 384.782433 203.113481 \nL 385.077388 269.783618 \nL 385.313351 287.518595 \nL 385.372342 288.952568 \nL 385.490324 265.237342 \nL 385.726288 201.422292 \nL 385.785279 194.89546 \nL 385.84427 199.570344 \nL 385.903261 200.425584 \nL 386.080233 209.344515 \nL 386.198215 200.341989 \nL 386.316197 206.264365 \nL 386.49317 195.699257 \nL 386.611152 209.633882 \nL 386.729133 221.800153 \nL 387.083079 144.307693 \nL 387.14207 145.490883 \nL 387.260052 151.387537 \nL 387.319043 150.480854 \nL 387.496015 137.58152 \nL 387.731979 160.068545 \nL 388.262897 285.100773 \nL 388.321888 284.001179 \nL 389.029779 178.356534 \nL 389.08877 179.449698 \nL 389.147761 176.601041 \nL 389.206752 177.205496 \nL 389.265743 179.694052 \nL 389.324734 176.015877 \nL 389.442716 184.008834 \nL 389.560697 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395.51878 161.515379 \nL 395.636762 168.685248 \nL 395.990707 219.613825 \nL 396.344653 286.766241 \nL 396.521626 295.935957 \nL 396.698598 292.328516 \nL 396.81658 266.22762 \nL 397.052544 179.616887 \nL 397.111535 180.054153 \nL 397.347498 202.200367 \nL 397.406489 204.740366 \nL 397.46548 195.364878 \nL 397.524471 196.618801 \nL 397.642453 205.184062 \nL 397.819426 189.133844 \nL 397.878417 189.783312 \nL 398.055389 217.028814 \nL 398.350344 150.770221 \nL 398.409335 150.422981 \nL 398.527317 156.654015 \nL 398.645299 155.87594 \nL 398.881262 208.077731 \nL 399.235208 287.042747 \nL 399.471171 295.241476 \nL 399.530162 291.916972 \nL 399.707135 231.220654 \nL 399.943099 172.81998 \nL 400.179062 197.634801 \nL 400.238053 199.994749 \nL 400.297044 183.121442 \nL 400.356035 187.622705 \nL 400.474017 196.400168 \nL 400.533008 194.773283 \nL 400.65099 186.690301 \nL 400.709981 188.355768 \nL 400.886953 216.533675 \nL 401.240899 144.429871 \nL 401.417872 151.927689 \nL 401.476863 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407.906873 246.499227 \nL 408.083845 287.081329 \nL 408.142836 285.827406 \nL 408.3788 263.475419 \nL 408.673755 190.529235 \nL 408.732745 190.625691 \nL 408.850727 188.625844 \nL 408.968709 192.053234 \nL 409.0277 187.063263 \nL 409.086691 193.982347 \nL 409.145682 192.117538 \nL 409.204673 191.892475 \nL 409.263664 193.062803 \nL 409.322655 198.239256 \nL 409.381646 196.985333 \nL 409.735591 137.510786 \nL 409.853573 157.483534 \nL 409.912564 156.641154 \nL 410.148527 141.877011 \nL 410.207518 141.645518 \nL 410.266509 145.651642 \nL 410.915409 286.894848 \nL 410.9744 286.779101 \nL 411.210364 261.494863 \nL 411.6233 186.355921 \nL 411.682291 188.857338 \nL 411.741282 184.214606 \nL 411.918255 195.409891 \nL 411.977246 193.287866 \nL 412.095228 199.364572 \nL 412.21321 210.206185 \nL 412.331191 190.0984 \nL 412.508164 177.970711 \nL 412.567155 180.594305 \nL 412.744128 201.853127 \nL 412.803119 200.940014 \nL 413.393028 293.749629 \nL 413.51101 296.720462 \nL 413.687982 289.929985 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267.487974 \nL 515.388323 292.855806 \nL 515.565296 294.733476 \nL 515.624287 293.203047 \nL 515.80126 246.319176 \nL 516.037223 193.242854 \nL 516.096214 194.323157 \nL 516.332178 208.553579 \nL 516.391169 204.521733 \nL 516.45016 205.184062 \nL 516.568142 208.926541 \nL 516.745114 202.798392 \nL 516.804105 202.515456 \nL 516.981078 229.317262 \nL 517.276033 160.563684 \nL 517.453005 167.739983 \nL 517.570987 163.07153 \nL 517.74796 197.666952 \nL 518.042915 277.320018 \nL 518.278878 293.196616 \nL 518.337869 292.720769 \nL 518.39686 294.077578 \nL 518.455851 291.518288 \nL 518.573833 263.848381 \nL 518.809797 180.446406 \nL 518.868787 181.668177 \nL 519.04576 201.415862 \nL 519.222733 191.429488 \nL 519.281724 193.879461 \nL 519.340715 202.862696 \nL 519.399706 201.576621 \nL 519.576679 190.497083 \nL 519.69466 203.113481 \nL 519.812642 224.82243 \nL 520.107597 154.429105 \nL 520.166588 148.706071 \nL 520.28457 154.712042 \nL 520.34356 149.696348 \nL 520.402551 150.345816 \nL 520.461542 152.152752 \nL 520.579524 174.196081 \nL 520.93347 275.371614 \nL 521.110442 291.679048 \nL 521.169433 289.743505 \nL 521.228424 293.395958 \nL 521.287415 290.997428 \nL 521.405397 262.440129 \nL 521.641361 177.95785 \nL 521.759342 185.47496 \nL 521.877324 196.966041 \nL 521.995306 196.695966 \nL 522.054297 186.085846 \nL 522.113288 188.786603 \nL 522.23127 196.329434 \nL 522.349252 184.407517 \nL 522.408243 184.343214 \nL 522.526224 202.451152 \nL 522.644206 225.497619 \nL 522.939161 152.11417 \nL 523.116134 140.700253 \nL 523.175124 137.935191 \nL 523.470079 174.684789 \nL 523.883015 275.551665 \nL 524.059988 288.64391 \nL 524.118979 287.981582 \nL 524.236961 262.182914 \nL 524.472925 176.466003 \nL 524.531916 174.517599 \nL 524.708888 192.15612 \nL 524.767879 193.564373 \nL 524.82687 193.480778 \nL 524.944852 189.821894 \nL 525.062834 197.866294 \nL 525.239807 182.201898 \nL 525.357788 203.27424 \nL 525.47577 221.214989 \nL 525.770725 132.970941 \nL 525.947698 141.439746 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296.50826 \nL 532.613671 288.81753 \nL 533.026608 186.818908 \nL 533.085598 192.15612 \nL 533.321562 209.807502 \nL 533.498535 197.216826 \nL 533.616517 190.889336 \nL 533.675508 183.623012 \nL 533.734499 187.236883 \nL 533.970462 220.957774 \nL 534.265417 172.30555 \nL 534.324408 170.029197 \nL 534.737344 211.492261 \nL 535.032299 280.226548 \nL 535.209272 294.386236 \nL 535.268262 296.077425 \nL 535.327253 295.074286 \nL 535.386244 295.402235 \nL 535.445235 292.765781 \nL 535.563217 263.256786 \nL 535.799181 174.684789 \nL 535.858172 173.475878 \nL 536.153126 201.370849 \nL 536.271108 191.480931 \nL 536.38909 197.866294 \nL 536.566063 184.407517 \nL 536.625054 185.674301 \nL 536.802026 219.581673 \nL 537.096981 153.085157 \nL 537.214963 143.465314 \nL 537.273954 145.162933 \nL 537.332945 143.857567 \nL 537.509917 162.261303 \nL 537.981845 286.380418 \nL 538.099826 292.965123 \nL 538.158817 292.019858 \nL 538.217808 294.495552 \nL 538.276799 292.109883 \nL 538.453772 224.880303 \nL 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284.232672 \nL 563.583903 289.364112 \nL 563.642894 289.312669 \nL 563.760875 295.620868 \nL 563.878857 289.396264 \nL 564.350785 231.32997 \nL 564.763721 189.294603 \nL 564.822712 193.538651 \nL 564.999685 185.378504 \nL 565.117666 201.165077 \nL 565.294639 175.21851 \nL 565.412621 161.779025 \nL 565.648585 188.985945 \nL 565.707576 189.429641 \nL 565.766567 180.491419 \nL 565.825557 181.359519 \nL 565.884548 184.169594 \nL 566.415467 254.209247 \nL 566.710421 293.170895 \nL 566.769412 287.859404 \nL 566.828403 286.116773 \nL 567.182349 227.208099 \nL 567.418312 188.89592 \nL 567.477303 187.732022 \nL 567.595285 180.337089 \nL 567.654276 184.471821 \nL 567.831249 175.3857 \nL 567.94923 200.734242 \nL 568.008221 187.7063 \nL 568.244185 153.168751 \nL 568.303176 157.264901 \nL 568.480149 182.034709 \nL 568.53914 183.925239 \nL 568.657121 168.575932 \nL 568.716112 171.823272 \nL 568.775103 171.720386 \nL 569.070058 211.614438 \nL 569.541985 287.897987 \nL 569.83694 260.125193 \nL 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156.956243 \nL 577.033832 157.464243 \nL 577.092823 160.640848 \nL 577.328786 227.098783 \nL 577.56475 281.834142 \nL 577.741723 293.466692 \nL 577.800714 292.521427 \nL 577.859705 294.28978 \nL 577.918695 292.39925 \nL 578.036677 265.867519 \nL 578.272641 173.295828 \nL 578.331632 173.514461 \nL 578.626586 202.226089 \nL 578.744568 184.45253 \nL 578.803559 185.950808 \nL 578.86255 190.252729 \nL 578.921541 186.68387 \nL 578.980532 185.65501 \nL 579.157505 210.733476 \nL 579.275487 231.992299 \nL 579.51145 169.05178 \nL 579.570441 168.383021 \nL 579.747414 165.643681 \nL 579.983378 170.382868 \nL 580.101359 190.972931 \nL 580.455305 283.416015 \nL 580.573287 290.675909 \nL 580.632278 289.569884 \nL 580.691269 291.929832 \nL 580.750259 290.682339 \nL 580.868241 263.861242 \nL 581.104205 173.090056 \nL 581.45815 196.503054 \nL 581.517141 186.85106 \nL 581.576132 189.281742 \nL 581.694114 197.158953 \nL 581.871087 184.124581 \nL 581.930078 185.237036 \nL 582.107051 225.143949 \nL 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137.903039 \nL 588.596051 130.141575 \nL 588.832015 164.78201 \nL 589.067979 217.98051 \nL 589.362933 287.422139 \nL 589.598897 266.163316 \nL 590.011833 163.894618 \nL 590.129815 158.904647 \nL 590.247797 165.566516 \nL 590.306788 164.859175 \nL 590.365779 165.142111 \nL 590.601743 196.895307 \nL 590.660734 189.738299 \nL 590.778715 170.170665 \nL 590.837706 170.376437 \nL 590.896697 168.087223 \nL 591.132661 196.387308 \nL 591.191652 195.956472 \nL 591.250643 198.406446 \nL 591.663579 284.599204 \nL 591.899543 296.353931 \nL 592.076516 290.315808 \nL 592.253488 230.789819 \nL 592.489452 180.774355 \nL 592.607434 187.256174 \nL 592.784407 199.461027 \nL 592.902388 191.924627 \nL 593.02037 194.175258 \nL 593.197343 185.429947 \nL 593.256334 185.687162 \nL 593.433307 215.086841 \nL 593.728261 157.567129 \nL 593.787252 157.747179 \nL 593.905234 164.4412 \nL 593.964225 161.804746 \nL 594.082207 166.518212 \nL 594.613125 288.29024 \nL 594.790098 293.627452 \nL 594.849089 294.122591 \nL 594.90808 291.183909 \nL 595.026061 256.369854 \nL 595.262025 175.192789 \nL 595.321016 171.418158 \nL 595.497989 193.307158 \nL 595.55698 193.17855 \nL 595.615971 197.480472 \nL 595.674961 190.940779 \nL 595.733952 195.435612 \nL 595.792943 207.048871 \nL 595.851934 204.849682 \nL 596.028907 194.059512 \nL 596.146889 216.61084 \nL 596.264871 229.066478 \nL 596.559825 164.132542 \nL 596.618816 165.244997 \nL 596.677807 160.010671 \nL 596.736798 165.560086 \nL 596.795789 158.62171 \nL 596.85478 159.367634 \nL 596.913771 162.422062 \nL 597.149734 216.205726 \nL 597.385698 276.014652 \nL 597.562671 292.5793 \nL 597.621662 291.563301 \nL 597.680653 292.270642 \nL 597.739644 291.698339 \nL 597.857625 264.632887 \nL 598.093589 170.575779 \nL 598.15258 171.739677 \nL 598.447535 195.435612 \nL 598.506525 187.121136 \nL 598.565516 189.847615 \nL 598.742489 195.326296 \nL 598.80148 194.946903 \nL 598.860471 191.654551 \nL 598.919462 195.821435 \nL 599.096435 235.998423 \nL 599.45038 164.846314 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226.661517 \nL 605.054517 144.455592 \nL 605.113508 143.439593 \nL 605.172499 147.812248 \nL 605.23149 145.870275 \nL 605.290481 144.841415 \nL 605.467454 135.703851 \nL 605.762408 161.071683 \nL 606.116354 239.689459 \nL 606.293327 285.827406 \nL 606.352317 285.505887 \nL 606.824245 184.394657 \nL 607.001218 157.046268 \nL 607.17819 151.156044 \nL 607.414154 169.669096 \nL 607.591127 223.356304 \nL 607.650118 211.13216 \nL 607.945072 151.522575 \nL 608.004063 151.464702 \nL 608.063054 156.962673 \nL 608.122045 152.718625 \nL 608.358009 137.56866 \nL 608.417 138.243849 \nL 608.534981 142.301416 \nL 608.947918 232.063033 \nL 609.124891 283.879002 \nL 609.183881 284.631356 \nL 609.301863 281.236117 \nL 609.537827 241.24561 \nL 609.773791 177.938559 \nL 609.950763 163.438062 \nL 610.3637 195.924321 \nL 610.422691 209.518135 \nL 610.481682 202.116773 \nL 610.717645 182.150455 \nL 610.953609 204.920416 \nL 611.0126 202.75338 \nL 611.071591 205.524872 \nL 611.6615 294.424818 \nL 611.720491 294.585578 \nL 611.956455 277.017791 \nL 612.3104 187.326908 \nL 612.369391 192.15612 \nL 612.605355 208.669326 \nL 612.664346 200.933583 \nL 612.723336 201.853127 \nL 612.782327 200.86928 \nL 612.841318 204.097328 \nL 612.900309 201.666646 \nL 613.018291 190.619261 \nL 613.077282 193.892322 \nL 613.254255 222.784001 \nL 613.549209 169.759121 \nL 613.6082 162.852897 \nL 613.667191 166.871882 \nL 613.726182 171.97117 \nL 613.785173 170.286412 \nL 613.844164 169.103223 \nL 613.903155 172.691373 \nL 614.493064 289.942846 \nL 614.552055 293.511705 \nL 614.611046 292.527857 \nL 614.670037 293.666034 \nL 614.729028 292.06487 \nL 614.906 232.718931 \nL 615.141964 175.8937 \nL 615.436919 203.357835 \nL 615.5549 193.275006 \nL 615.672882 202.24538 \nL 615.731873 198.104218 \nL 615.849855 190.979362 \nL 615.908846 192.123968 \nL 616.085819 227.484605 \nL 616.380773 161.251734 \nL 616.439764 151.213917 \nL 616.498755 155.53513 \nL 616.616737 154.493409 \nL 616.675728 149.111184 \nL 616.852701 178.060736 \nL 617.383619 289.83996 \nL 617.44261 289.171201 \nL 617.501601 291.106744 \nL 617.560592 290.913833 \nL 617.678574 265.346659 \nL 617.914537 177.694205 \nL 617.973528 177.179775 \nL 618.268483 197.750547 \nL 618.327474 187.301187 \nL 618.386465 187.551971 \nL 618.504446 193.538651 \nL 618.563437 193.236423 \nL 618.681419 188.89592 \nL 618.74041 192.085386 \nL 618.917383 229.323693 \nL 619.212337 159.431938 \nL 619.271328 161.335329 \nL 619.448301 146.622629 \nL 619.566283 147.876552 \nL 619.861237 212.50826 \nL 620.097201 274.490653 \nL 620.274174 285.89814 \nL 620.333165 290.611605 \nL 620.392156 288.052316 \nL 620.510138 262.652331 \nL 620.746101 176.131624 \nL 620.864083 180.838659 \nL 621.100047 195.712118 \nL 621.218029 182.555569 \nL 621.33601 189.378198 \nL 621.395001 188.285034 \nL 621.512983 182.22762 \nL 621.571974 184.844783 \nL 621.748947 227.857567 \nL 622.102892 147.696502 \nL 622.279865 140.243696 \nL 622.397847 142.346429 \nL 622.633811 174.755523 \nL 622.751792 199.178091 \nL 623.046747 271.500528 \nL 623.105738 272.998806 \nL 623.22372 284.174799 \nL 623.341702 263.822659 \nL 623.577665 174.202511 \nL 623.636656 174.337549 \nL 623.695647 172.620638 \nL 623.813629 188.915211 \nL 623.87262 186.960377 \nL 623.931611 187.18544 \nL 623.990602 177.899977 \nL 624.049593 181.275924 \nL 624.167574 192.374753 \nL 624.344547 177.700635 \nL 624.52152 212.308918 \nL 624.580511 226.282125 \nL 624.934456 134.900054 \nL 624.993447 135.543091 \nL 625.052438 140.732405 \nL 625.111429 138.861165 \nL 625.17042 138.070229 \nL 625.229411 134.84218 \nL 625.288402 136.012509 \nL 625.347393 141.902733 \nL 625.406384 137.883748 \nL 625.465375 140.815999 \nL 625.937302 238.261915 \nL 626.114275 282.1428 \nL 626.173266 281.615509 \nL 626.527211 232.963286 \nL 626.763175 180.446406 \nL 626.940147 161.553962 \nL 626.999138 161.598974 \nL 627.176111 166.987629 \nL 627.235102 168.02935 \nL 627.294093 171.701095 \nL 627.412075 209.08087 \nL 627.471066 195.512777 \nL 627.707029 142.687239 \nL 627.76602 145.471592 \nL 627.942993 163.020087 \nL 628.237948 141.536201 \nL 628.296939 143.683947 \nL 628.532902 180.909393 \nL 628.945839 284.219812 \nL 629.00483 282.297129 \nL 629.122811 282.631509 \nL 629.358775 248.454061 \nL 629.594739 189.436071 \nL 629.771711 171.302411 \nL 630.007675 178.645901 \nL 630.125657 184.426809 \nL 630.243639 207.216061 \nL 630.30263 198.168522 \nL 630.420612 181.250203 \nL 630.479602 182.709898 \nL 630.538593 185.044125 \nL 630.774557 205.074745 \nL 630.833548 203.010595 \nL 630.95153 220.057521 \nL 631.423457 290.579453 \nL 631.541439 294.476261 \nL 631.60043 291.38325 \nL 631.659421 292.174187 \nL 631.718412 291.460415 \nL 631.954375 237.245916 \nL 632.131348 201.493026 \nL 632.190339 202.142494 \nL 632.24933 200.586343 \nL 632.308321 207.132466 \nL 632.367312 204.142341 \nL 632.426303 202.541177 \nL 632.603276 193.114246 \nL 632.662266 196.740978 \nL 632.839239 187.834908 \nL 632.89823 189.352476 \nL 633.075203 224.526632 \nL 633.370157 174.099625 \nL 633.488139 185.796479 \nL 633.54713 181.816076 \nL 633.606121 181.353089 \nL 633.665112 176.935421 \nL 633.783094 195.384169 \nL 634.078048 260.633193 \nL 634.314012 289.608467 \nL 634.373003 290.952415 \nL 634.431994 289.749935 \nL 634.490985 291.621174 \nL 634.549976 290.17434 \nL 634.785939 238.371232 \nL 634.962912 199.088066 \nL 635.021903 199.737533 \nL 635.080894 196.882447 \nL 635.257867 202.817684 \nL 635.43484 188.072832 \nL 635.49383 189.571109 \nL 635.552821 182.272633 \nL 635.611812 187.18544 \nL 635.670803 183.918809 \nL 635.788785 202.226089 \nL 635.906767 229.587338 \nL 636.142731 176.819674 \nL 636.201721 178.825951 \nL 636.437685 196.001485 \nL 636.496676 189.378198 \nL 637.027594 272.780173 \nL 637.204567 286.624772 \nL 637.263558 285.480165 \nL 637.38154 290.405833 \nL 637.735485 243.663431 \nL 638.089431 183.867366 \nL 638.207413 188.516528 \nL 638.325394 169.636944 \nL 638.561358 195.004777 \nL 638.620349 196.046498 \nL 638.738331 209.5953 \nL 638.797322 199.133078 \nL 638.856313 199.68609 \nL 638.915304 197.917737 \nL 638.974295 199.628217 \nL 639.269249 227.773972 \nL 639.32824 224.873873 \nL 639.387231 227.066631 \nL 639.446222 224.803139 \nL 639.800167 261.494863 \nL 640.213104 296.270336 \nL 640.272095 296.559703 \nL 640.508058 288.373834 \nL 640.567049 289.036163 \nL 640.744022 273.06311 \nL 641.097968 238.126877 \nL 641.156958 239.573712 \nL 641.333931 229.285111 \nL 641.392922 230.436148 \nL 641.510904 235.303942 \nL 641.569895 241.104141 \nL 641.628886 239.419383 \nL 641.687877 238.480548 \nL 641.746868 238.493409 \nL 641.805859 234.332956 \nL 641.864849 235.43255 \nL 641.982831 244.660139 \nL 642.159804 235.323234 \nL 642.277786 239.547991 \nL 642.51375 273.693286 \nL 642.867695 297.382791 \nL 642.926686 295.235046 \nL 643.044668 298.469525 \nL 643.280631 279.712118 \nL 643.81155 223.581367 \nL 643.988522 214.231601 \nL 644.047513 217.421067 \nL 644.106504 214.572411 \nL 644.224486 201.055761 \nL 644.401459 214.668866 \nL 644.578432 200.393432 \nL 644.637423 200.412723 \nL 644.696414 191.378045 \nL 644.932377 212.701171 \nL 644.991368 213.041981 \nL 645.10935 203.859404 \nL 645.581277 256.395575 \nL 645.75825 269.732175 \nL 645.935223 292.977984 \nL 645.994214 292.476414 \nL 646.230177 278.8826 \nL 646.702105 210.559856 \nL 646.761096 209.177325 \nL 646.820087 198.277838 \nL 646.879077 200.567052 \nL 646.938068 203.09419 \nL 647.05605 193.00493 \nL 647.115041 195.660675 \nL 647.233023 211.402235 \nL 647.292014 207.730491 \nL 647.468987 191.255868 \nL 647.527978 188.072832 \nL 647.70495 206.206492 \nL 647.763941 204.187354 \nL 647.881923 191.493792 \nL 647.940914 194.496777 \nL 647.999905 195.872878 \nL 648.058896 194.71541 \nL 648.648805 274.233438 \nL 648.825778 290.077884 \nL 649.00275 280.200827 \nL 649.179723 259.713649 \nL 649.356696 222.456051 \nL 649.415687 223.028355 \nL 649.651651 205.537733 \nL 649.710641 211.382944 \nL 649.769632 210.739907 \nL 649.946605 200.817837 \nL 650.005596 201.981735 \nL 650.064587 209.543857 \nL 650.123578 205.042594 \nL 650.182569 201.055761 \nL 650.24156 201.39014 \nL 650.300551 200.88214 \nL 650.359542 202.097481 \nL 650.595505 219.787445 \nL 650.654496 215.183296 \nL 650.772478 224.552354 \nL 651.185414 284.406293 \nL 651.362387 294.315502 \nL 651.421378 293.25449 \nL 651.480369 294.778489 \nL 651.53936 294.199755 \nL 651.657342 280.528776 \nL 651.952296 229.61306 \nL 652.070278 223.922177 \nL 652.778169 192.175411 \nL 652.896151 209.010135 \nL 653.132115 178.260078 \nL 653.191106 176.903269 \nL 653.368078 200.605634 \nL 653.427069 195.171967 \nL 653.48606 195.982194 \nL 653.781015 236.847233 \nL 654.075969 282.567205 \nL 654.311933 292.424971 \nL 654.370924 294.405527 \nL 654.606888 262.671622 \nL 654.901842 215.774891 \nL 654.960833 215.041828 \nL 655.314779 183.487974 \nL 655.491751 187.545541 \nL 655.550742 186.278757 \nL 655.668724 202.264671 \nL 655.727715 210.9714 \nL 656.02267 162.852897 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208.830085 \nL 663.75048 199.094496 \nL 663.809471 203.634341 \nL 663.986443 192.83131 \nL 664.045434 192.516221 \nL 664.104425 190.43921 \nL 664.222407 208.019858 \nL 664.517362 166.396035 \nL 664.753325 196.181536 \nL 664.812316 195.204119 \nL 664.930298 181.044431 \nL 665.579198 257.032183 \nL 665.756171 289.891403 \nL 665.815162 289.782087 \nL 666.169107 249.142111 \nL 666.582044 178.768078 \nL 666.700026 169.816995 \nL 666.759016 170.447171 \nL 666.818007 168.344438 \nL 666.876998 169.411881 \nL 667.053971 198.1428 \nL 667.112962 187.757743 \nL 667.348926 154.802067 \nL 667.525898 192.072525 \nL 667.64388 186.375212 \nL 667.702871 177.578458 \nL 667.761862 178.890255 \nL 667.820853 180.613596 \nL 667.879844 172.845702 \nL 668.410762 263.976988 \nL 668.587735 291.460415 \nL 668.646726 291.100314 \nL 668.882689 276.361892 \nL 669.295626 214.977524 \nL 669.472599 202.849835 \nL 669.53159 205.66634 \nL 669.59058 205.222644 \nL 669.649571 202.547608 \nL 669.708562 209.447401 \nL 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0)).astype(int)","user":"anonymous","dateUpdated":"2019-06-17T20:45:20+0800","config":{"editorSetting":{"language":"python","editOnDblClick":false,"completionKey":"TAB","completionSupport":true},"colWidth":12,"editorMode":"ace/mode/python","fontSize":9,"results":{},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[]},"apps":[],"jobName":"paragraph_1560774712321_-1559494480","id":"20190611-171158_1369871678","dateCreated":"2019-06-17T20:31:52+0800","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:2309","dateFinished":"2019-06-17T20:45:20+0800","dateStarted":"2019-06-17T20:45:20+0800"},{"text":"%pyspark\n# creation of 2 distinct categories that seem useful (sleeping time and awake time)\ndf['categories'] = df['awake']\n\na = df.loc[df['categories'] == 0, 'value']\nb = df.loc[df['categories'] == 1, 'value']\n\nfig, ax = plt.subplots()\na_heights, a_bins = np.histogram(a)\nb_heights, b_bins = np.histogram(b, bins=a_bins)\n\nwidth = (a_bins[1] - a_bins[0])/6\n\nax.bar(a_bins[:-1], a_heights*100/a.count(), width=width, facecolor='yellow', label='Sleeping time')\nax.bar(b_bins[:-1]+width, (b_heights*100/b.count()), width=width, facecolor='red', label ='Awake time')\nax.set_title('Histogram of NYC taxi passengers in different categories')\n\nplt.xlabel('The number of NYC taxi passengers')\nplt.ylabel('Record counts')\nplt.legend()\nshow(plt)","user":"anonymous","dateUpdated":"2019-06-17T20:45:20+0800","config":{"editorSetting":{"language":"python","editOnDblClick":false,"completionKey":"TAB","completionSupport":true},"colWidth":12,"editorMode":"ace/mode/python","fontSize":9,"results":{},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"HTML","data":"<div style='width:600px'><?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Created with matplotlib 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</defs>\n</svg>\n</div>\n"}]},"apps":[],"jobName":"paragraph_1560774712321_1309221859","id":"20190611-174741_1353534671","dateCreated":"2019-06-17T20:31:52+0800","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:2310","dateFinished":"2019-06-17T20:45:21+0800","dateStarted":"2019-06-17T20:45:20+0800"},{"text":"%pyspark\ndf['awake'].head(4)","user":"anonymous","dateUpdated":"2019-06-17T20:45:21+0800","config":{"editorSetting":{"language":"python","editOnDblClick":false,"completionKey":"TAB","completionSupport":true},"colWidth":12,"editorMode":"ace/mode/python","fontSize":9,"results":{},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"TEXT","data":"0    1\n1    1\n2    0\n3    0\nName: awake, dtype: int64\n"}]},"apps":[],"jobName":"paragraph_1560774712322_-1143745618","id":"20190611-174757_1943443200","dateCreated":"2019-06-17T20:31:52+0800","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:2311","dateFinished":"2019-06-17T20:45:21+0800","dateStarted":"2019-06-17T20:45:21+0800"},{"text":"%pyspark\ndf['timestamp'].head(4)","user":"anonymous","dateUpdated":"2019-06-17T20:45:21+0800","config":{"editorSetting":{"language":"python","editOnDblClick":false,"completionKey":"TAB","completionSupport":true},"colWidth":12,"editorMode":"ace/mode/python","fontSize":9,"results":{},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"TEXT","data":"0    2014-07-01 00:00:00\n1    2014-07-01 00:30:00\n2    2014-07-01 01:00:00\n3    2014-07-01 01:30:00\nName: timestamp, dtype: object\n"}]},"apps":[],"jobName":"paragraph_1560774712322_-583698566","id":"20190611-174809_1855730646","dateCreated":"2019-06-17T20:31:52+0800","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:2312","dateFinished":"2019-06-17T20:45:21+0800","dateStarted":"2019-06-17T20:45:21+0800"},{"text":"%md\nFrom the above result, we can conclude that more people take taxi when they are awake.\n\nWe then standardize the data and split them into the train data and the test data.","user":"anonymous","dateUpdated":"2019-06-17T20:45:21+0800","config":{"colWidth":12,"fontSize":9,"enabled":true,"results":{},"editorSetting":{"language":"markdown","editOnDblClick":true,"completionKey":"TAB","completionSupport":false},"editorMode":"ace/mode/markdown","editorHide":true,"tableHide":false},"settings":{"params":{},"forms":{}},"apps":[],"jobName":"paragraph_1560774990378_-187823249","id":"20190617-203630_571504583","dateCreated":"2019-06-17T20:36:30+0800","status":"FINISHED","progressUpdateIntervalMs":500,"focus":true,"$$hashKey":"object:4562","dateFinished":"2019-06-17T20:45:21+0800","dateStarted":"2019-06-17T20:45:21+0800","results":{"code":"SUCCESS","msg":[{"type":"HTML","data":"<div class=\"markdown-body\">\n<p>From the above result, we can conclude that more people take taxi when they are awake.</p>\n<p>We then standardize the data and split them into the train data and the test data.</p>\n</div>"}]}},{"text":"%pyspark\n#select and standardize data\ndata_n = df[['value', 'hours', 'awake']]\nmin_max_scaler = preprocessing.StandardScaler()\nnp_scaled = min_max_scaler.fit_transform(data_n)\ndata_n = pd.DataFrame(np_scaled)\n\n#important parameters and train/test size\nprediction_time = 1 \ntestdatasize = 1000\nunroll_length = 50\ntestdatacut = testdatasize + unroll_length  + 1\n\n#train data\nx_train = data_n[0:-prediction_time-testdatacut].as_matrix()\ny_train = data_n[prediction_time:-testdatacut  ][0].as_matrix()\n\n#test data\nx_test = data_n[0-testdatacut:-prediction_time].as_matrix()\ny_test = data_n[prediction_time-testdatacut:  ][0].as_matrix()","user":"anonymous","dateUpdated":"2019-06-17T20:45:21+0800","config":{"editorSetting":{"language":"python","editOnDblClick":false,"completionKey":"TAB","completionSupport":true},"colWidth":12,"editorMode":"ace/mode/python","fontSize":9,"results":{},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"TEXT","data":"<string>:14: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n<string>:15: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n<string>:18: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n<string>:19: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n"}]},"apps":[],"jobName":"paragraph_1560774712322_-387023358","id":"20190611-174817_2088453312","dateCreated":"2019-06-17T20:31:52+0800","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:2313","dateFinished":"2019-06-17T20:45:22+0800","dateStarted":"2019-06-17T20:45:22+0800"},{"text":"%md\nUnroll data: for the data point at index i, create a sequence from i to (i+unroll_length)\nfor example, if unroll_length=5\n\n[[1]\n\n[2]\n\n[3]\n\n[4]\n\n[5]\n\n[6]\n\n[7]\n\n[8]\n\n[9]\n\n[10]\n\n...\n\n]\n\nwill be unrolled to create following sequences\n\n[[ 1, 2, 3, 4, 5]\n\n[ 2, 3, 4, 5, 6]\n\n[ 3, 4, 5, 6, 7]\n\n[ 4, 5, 6, 7, 8]\n\n[ 5, 6, 7, 8, 9]\n\n[ 6, 7, 8, 9, 10]\n\n...\n\n]","user":"anonymous","dateUpdated":"2019-06-17T20:45:22+0800","config":{"colWidth":12,"fontSize":9,"enabled":true,"results":{},"editorSetting":{"language":"markdown","editOnDblClick":true,"completionKey":"TAB","completionSupport":false},"editorMode":"ace/mode/markdown","editorHide":true,"tableHide":false},"settings":{"params":{},"forms":{}},"apps":[],"jobName":"paragraph_1560775052803_899600071","id":"20190617-203732_705611230","dateCreated":"2019-06-17T20:37:32+0800","status":"FINISHED","progressUpdateIntervalMs":500,"focus":true,"$$hashKey":"object:4681","dateFinished":"2019-06-17T20:45:22+0800","dateStarted":"2019-06-17T20:45:22+0800","results":{"code":"SUCCESS","msg":[{"type":"HTML","data":"<div class=\"markdown-body\">\n<p>Unroll data: for the data point at index i, create a sequence from i to (i+unroll_length)<br/>for example, if unroll_length=5</p>\n<p>[[1]</p>\n<p>[2]</p>\n<p>[3]</p>\n<p>[4]</p>\n<p>[5]</p>\n<p>[6]</p>\n<p>[7]</p>\n<p>[8]</p>\n<p>[9]</p>\n<p>[10]</p>\n<p>&hellip;</p>\n<p>]</p>\n<p>will be unrolled to create following sequences</p>\n<p>[[ 1, 2, 3, 4, 5]</p>\n<p>[ 2, 3, 4, 5, 6]</p>\n<p>[ 3, 4, 5, 6, 7]</p>\n<p>[ 4, 5, 6, 7, 8]</p>\n<p>[ 5, 6, 7, 8, 9]</p>\n<p>[ 6, 7, 8, 9, 10]</p>\n<p>&hellip;</p>\n<p>]</p>\n</div>"}]}},{"text":"%pyspark\n#unroll: create sequence of 50 previous data points for each data points\ndef unroll(data,sequence_length=24):\n    result = []\n    for index in range(len(data) - sequence_length):\n        result.append(data[index: index + sequence_length])\n    return np.asarray(result)\n\n# adapt the datasets for the sequence data shape\nx_train = unroll(x_train,unroll_length)\nx_test  = unroll(x_test,unroll_length)\ny_train = y_train[-x_train.shape[0]:]\ny_test  = y_test[-x_test.shape[0]:]\n\n# see the shape\nprint(\"x_train\", x_train.shape)\nprint(\"y_train\", y_train.shape)\nprint(\"x_test\", x_test.shape)\nprint(\"y_test\", y_test.shape)","user":"anonymous","dateUpdated":"2019-06-17T20:45:22+0800","config":{"editorSetting":{"language":"python","editOnDblClick":false,"completionKey":"TAB","completionSupport":true},"colWidth":12,"editorMode":"ace/mode/python","fontSize":9,"results":{},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"TEXT","data":"x_train (9218, 50, 3)\ny_train (9218,)\nx_test (1000, 50, 3)\ny_test (1000,)\n"}]},"apps":[],"jobName":"paragraph_1560774712322_965496085","id":"20190611-174833_1561067818","dateCreated":"2019-06-17T20:31:52+0800","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:2314","dateFinished":"2019-06-17T20:45:22+0800","dateStarted":"2019-06-17T20:45:22+0800"},{"text":"%md\nHere we show an example of building a RNN network using Analytics Zoo Keras-Style API. There are three LSTM layers and one Dense layer.","user":"anonymous","dateUpdated":"2019-06-17T20:45:22+0800","config":{"colWidth":12,"fontSize":9,"enabled":true,"results":{},"editorSetting":{"language":"markdown","editOnDblClick":true,"completionKey":"TAB","completionSupport":false},"editorMode":"ace/mode/markdown","editorHide":true,"tableHide":false},"settings":{"params":{},"forms":{}},"apps":[],"jobName":"paragraph_1560775071915_-1227981408","id":"20190617-203751_1514094630","dateCreated":"2019-06-17T20:37:51+0800","status":"FINISHED","progressUpdateIntervalMs":500,"focus":true,"$$hashKey":"object:4774","dateFinished":"2019-06-17T20:45:22+0800","dateStarted":"2019-06-17T20:45:22+0800","results":{"code":"SUCCESS","msg":[{"type":"HTML","data":"<div class=\"markdown-body\">\n<p>Here we show an example of building a RNN network using Analytics Zoo Keras-Style API. There are three LSTM layers and one Dense layer.</p>\n</div>"}]}},{"text":"%pyspark\n# Build the model\nmodel = Sequential()\n\nmodel.add(LSTM(\n    input_shape=(x_train.shape[1], x_train.shape[-1]),\n    output_dim=20,\n    return_sequences=True))\nmodel.add(Dropout(0.2))\n\nmodel.add(LSTM(\n    10,\n    return_sequences=False))\nmodel.add(Dropout(0.2))\n\nmodel.add(Dense(\n    output_dim=1))\n\nmodel.compile(loss='mse', optimizer='rmsprop')","user":"anonymous","dateUpdated":"2019-06-17T20:45:22+0800","config":{"editorSetting":{"language":"python","editOnDblClick":false,"completionKey":"TAB","completionSupport":true},"colWidth":12,"editorMode":"ace/mode/python","fontSize":9,"results":{},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"TEXT","data":"creating: createZooKerasSequential\ncreating: createZooKerasLSTM\ncreating: createZooKerasDropout\ncreating: createZooKerasLSTM\ncreating: createZooKerasDropout\ncreating: createZooKerasDense\ncreating: createRMSprop\ncreating: createZooKerasMeanSquaredError\n"}]},"apps":[],"jobName":"paragraph_1560774712322_1800077959","id":"20190611-174920_1415353897","dateCreated":"2019-06-17T20:31:52+0800","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:2315","dateFinished":"2019-06-17T20:45:23+0800","dateStarted":"2019-06-17T20:45:22+0800"},{"text":"%pyspark\n# Train the model\nprint(\"Training begins.\")\nmodel.fit(\n    x_train,\n    y_train,\n    batch_size=1024,\n    nb_epoch=20)\nprint(\"Training completed.\")","user":"anonymous","dateUpdated":"2019-06-17T20:45:23+0800","config":{"editorSetting":{"language":"python","editOnDblClick":false,"completionKey":"TAB","completionSupport":true},"colWidth":12,"editorMode":"ace/mode/python","fontSize":9,"results":{},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"TEXT","data":"Training begins.\nTraining completed.\n"}]},"apps":[],"jobName":"paragraph_1560774712322_2051990478","id":"20190611-174930_1633960373","dateCreated":"2019-06-17T20:31:52+0800","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:2316","dateFinished":"2019-06-17T20:46:37+0800","dateStarted":"2019-06-17T20:45:23+0800","runtimeInfos":{"jobUrl":{"propertyName":"jobUrl","label":"SPARK JOB","tooltip":"View in Spark web 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create the list of difference between prediction and test data\ndiff=[]\nratio=[]\npredictions = model.predict(x_test)\np = predictions.collect()\nfor u in range(len(y_test)):\n    pr = p[u][0]\n    ratio.append((y_test[u]/pr)-1)\n    diff.append(abs(y_test[u]- pr))","user":"anonymous","dateUpdated":"2019-06-17T20:46:37+0800","config":{"editorSetting":{"language":"python","editOnDblClick":false,"completionKey":"TAB","completionSupport":true},"colWidth":12,"editorMode":"ace/mode/python","fontSize":9,"results":{},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[]},"apps":[],"jobName":"paragraph_1560774712322_-477231145","id":"20190611-174944_1803306901","dateCreated":"2019-06-17T20:31:52+0800","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:2317","dateFinished":"2019-06-17T20:46:38+0800","dateStarted":"2019-06-17T20:46:37+0800","runtimeInfos":{"jobUrl":{"propertyName":"jobUrl","label":"SPARK JOB","tooltip":"View in Spark web UI","group":"spark","values":["http://10.0.96.85:42034/jobs"],"interpreterSettingId":"spark"}}},{"text":"%md\nWe plot the prediction and the reality.","user":"anonymous","dateUpdated":"2019-06-17T20:46:38+0800","config":{"colWidth":12,"fontSize":9,"enabled":true,"results":{},"editorSetting":{"language":"markdown","editOnDblClick":true,"completionKey":"TAB","completionSupport":false},"editorMode":"ace/mode/markdown","editorHide":true,"tableHide":false},"settings":{"params":{},"forms":{}},"apps":[],"jobName":"paragraph_1560775278235_1264915308","id":"20190617-204118_239164043","dateCreated":"2019-06-17T20:41:18+0800","status":"FINISHED","progressUpdateIntervalMs":500,"focus":true,"$$hashKey":"object:4870","dateFinished":"2019-06-17T20:46:38+0800","dateStarted":"2019-06-17T20:46:38+0800","results":{"code":"SUCCESS","msg":[{"type":"HTML","data":"<div class=\"markdown-body\">\n<p>We plot the prediction and the reality.</p>\n</div>"}]}},{"text":"%pyspark\n# plot the predicted values and actual values (for the test data)\nfig, axs = plt.subplots()\n\naxs.plot(p,color='red', label='predicted values')\naxs.plot(y_test,color='blue', label='actual values')\naxs.set_title('the predicted values and actual values (for the test data)')\n\nplt.xlabel('test data index')\nplt.ylabel('number of taxi passengers after min_max_scalar')\nplt.legend(loc='upper left')\nshow(plt)","user":"anonymous","dateUpdated":"2019-06-17T20:46:38+0800","config":{"editorSetting":{"language":"python","editOnDblClick":false,"completionKey":"TAB","completionSupport":true},"colWidth":12,"editorMode":"ace/mode/python","fontSize":9,"results":{},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"HTML","data":"<div style='width:600px'><?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Created with matplotlib (https://matplotlib.org/) -->\n<svg 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There're many ways to select this threshold. Here we set the expected proportion of anomalies among the entire set. Then we set the threshold as the minimum value of top N distances (here N is the total number of anomalies, i.e. anomaly fraction * total no. of samples)","user":"anonymous","dateUpdated":"2019-06-17T20:46:38+0800","config":{"colWidth":12,"fontSize":9,"enabled":true,"results":{},"editorSetting":{"language":"markdown","editOnDblClick":true,"completionKey":"TAB","completionSupport":false},"editorMode":"ace/mode/markdown","editorHide":true,"tableHide":false},"settings":{"params":{},"forms":{}},"apps":[],"jobName":"paragraph_1560775305968_145293443","id":"20190617-204145_1701409098","dateCreated":"2019-06-17T20:41:45+0800","status":"FINISHED","progressUpdateIntervalMs":500,"focus":true,"$$hashKey":"object:4963","dateFinished":"2019-06-17T20:46:39+0800","dateStarted":"2019-06-17T20:46:39+0800","results":{"code":"SUCCESS","msg":[{"type":"HTML","data":"<div class=\"markdown-body\">\n<p>Set the distance thresold for anomalies. There&rsquo;re many ways to select this threshold. Here we set the expected proportion of anomalies among the entire set. Then we set the threshold as the minimum value of top N distances (here N is the total number of anomalies, i.e. anomaly fraction * total no. of samples)</p>\n</div>"}]}},{"text":"%pyspark\n# An estimation of anomly population of the dataset\noutliers_fraction = 0.01\n# select the most distant prediction/reality data points as anomalies\ndiff = pd.Series(diff)\nnumber_of_outliers = int(outliers_fraction*len(diff))\nthreshold = diff.nlargest(number_of_outliers).min()","user":"anonymous","dateUpdated":"2019-06-17T20:46:39+0800","config":{"editorSetting":{"language":"python","editOnDblClick":false,"completionKey":"TAB","completionSupport":true},"colWidth":12,"editorMode":"ace/mode/python","fontSize":9,"results":{},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[]},"apps":[],"jobName":"paragraph_1560774712323_-1971009101","id":"20190611-175310_110268588","dateCreated":"2019-06-17T20:31:52+0800","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:2319","dateFinished":"2019-06-17T20:46:39+0800","dateStarted":"2019-06-17T20:46:39+0800"},{"text":"%md\nWe then plot anomalies in the test data throughout time.","user":"anonymous","dateUpdated":"2019-06-17T20:46:39+0800","config":{"colWidth":12,"fontSize":9,"enabled":true,"results":{},"editorSetting":{"language":"markdown","editOnDblClick":true,"completionKey":"TAB","completionSupport":false},"editorMode":"ace/mode/markdown","editorHide":true,"tableHide":false},"settings":{"params":{},"forms":{}},"apps":[],"jobName":"paragraph_1560775329764_-1189116369","id":"20190617-204209_910603534","dateCreated":"2019-06-17T20:42:09+0800","status":"FINISHED","progressUpdateIntervalMs":500,"focus":true,"$$hashKey":"object:5085","dateFinished":"2019-06-17T20:46:40+0800","dateStarted":"2019-06-17T20:46:40+0800","results":{"code":"SUCCESS","msg":[{"type":"HTML","data":"<div class=\"markdown-body\">\n<p>We then plot anomalies in the test data throughout time.</p>\n</div>"}]}},{"text":"%pyspark\n# plot the difference and the threshold (for the test data)\nfig, axs = plt.subplots()\n\naxs.plot(diff,color='blue', label='diff')\naxs.set_title('the difference 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269.869452 \nL 141.685527 281.005106 \nL 142.010182 249.02541 \nL 142.334836 289.969224 \nL 142.659491 286.986587 \nL 142.984145 273.804523 \nL 143.3088 282.350001 \nL 143.633455 255.473797 \nL 143.958109 195.823264 \nL 144.282764 162.68514 \nL 144.607418 250.064459 \nL 144.932073 220.381082 \nL 145.256727 138.55509 \nL 145.581382 133.823413 \nL 145.906036 160.922557 \nL 146.230691 215.876531 \nL 146.88 284.645076 \nL 147.204655 268.013923 \nL 147.529309 270.514653 \nL 147.853964 268.166792 \nL 148.178618 287.710278 \nL 148.503273 269.72808 \nL 149.152582 161.046452 \nL 149.477236 151.677768 \nL 149.801891 237.903587 \nL 150.126545 272.405855 \nL 150.4512 270.408455 \nL 150.775855 259.956231 \nL 151.100509 262.912701 \nL 151.425164 284.826711 \nL 151.749818 283.073518 \nL 152.074473 282.165111 \nL 152.399127 256.120682 \nL 153.048436 285.743171 \nL 153.373091 222.581867 \nL 153.697745 237.497926 \nL 154.0224 221.509371 \nL 154.347055 241.20985 \nL 154.671709 252.659823 \nL 154.996364 287.577459 \nL 155.321018 281.938915 \nL 155.645673 243.043649 \nL 155.970327 242.977086 \nL 156.294982 228.962392 \nL 156.619636 294.33619 \nL 156.944291 284.946269 \nL 157.268945 285.491722 \nL 157.5936 262.740726 \nL 157.918255 293.603059 \nL 158.242909 261.840019 \nL 158.567564 251.606908 \nL 158.892218 278.947082 \nL 159.216873 247.470692 \nL 159.541527 198.619962 \nL 159.866182 179.600287 \nL 160.190836 261.357324 \nL 160.840145 113.923486 \nL 161.1648 99.213128 \nL 161.489455 113.270805 \nL 161.814109 156.096177 \nL 162.463418 279.978174 \nL 162.788073 294.359917 \nL 163.112727 282.798309 \nL 163.762036 231.016715 \nL 164.736 250.69807 \nL 165.060655 280.310764 \nL 165.385309 288.611747 \nL 166.034618 282.449071 \nL 166.359273 266.504934 \nL 166.683927 263.73245 \nL 167.008582 257.178788 \nL 167.333236 248.011979 \nL 167.657891 279.74232 \nL 167.982545 260.841782 \nL 168.3072 278.459256 \nL 168.631855 284.746207 \nL 168.956509 251.501448 \nL 169.281164 285.726433 \nL 169.605818 290.320987 \nL 169.930473 254.946501 \nL 170.255127 281.980556 \nL 170.579782 267.544628 \nL 170.904436 292.645808 \nL 171.229091 281.951688 \nL 171.553745 277.129464 \nL 171.8784 277.529601 \nL 172.203055 235.118931 \nL 172.527709 232.10678 \nL 172.852364 230.226682 \nL 173.177018 239.507905 \nL 174.150982 294.216347 \nL 174.475636 267.898354 \nL 174.800291 258.623309 \nL 175.124945 287.334194 \nL 175.4496 265.803117 \nL 175.774255 294.439804 \nL 176.098909 211.197679 \nL 176.423564 199.869004 \nL 176.748218 173.60032 \nL 177.072873 159.376362 \nL 177.397527 232.133871 \nL 177.722182 247.55135 \nL 178.046836 215.4618 \nL 178.371491 233.598482 \nL 178.696145 293.117914 \nL 179.345455 169.960809 \nL 179.670109 156.226086 \nL 179.994764 160.106294 \nL 180.319418 157.220894 \nL 180.644073 195.121089 \nL 180.968727 194.122946 \nL 181.293382 223.986161 \nL 181.618036 203.87111 \nL 181.942691 216.305321 \nL 182.267345 250.891821 \nL 182.592 254.047777 \nL 182.916655 268.335529 \nL 183.241309 246.203519 \nL 183.565964 235.871389 \nL 183.890618 255.821877 \nL 184.215273 282.921875 \nL 184.539927 286.453272 \nL 184.864582 295.108816 \nL 185.189236 293.712746 \nL 185.513891 293.997934 \nL 185.838545 287.909243 \nL 186.1632 294.676203 \nL 186.487855 248.858576 \nL 186.812509 245.33138 \nL 187.137164 184.539509 \nL 187.461818 183.839549 \nL 187.786473 162.098717 \nL 188.111127 180.827398 \nL 188.435782 212.48777 \nL 188.760436 223.960141 \nL 189.409745 268.707433 \nL 189.7344 220.358307 \nL 190.059055 268.370226 \nL 190.383709 245.243874 \nL 190.708364 204.875185 \nL 191.033018 220.251037 \nL 191.357673 225.967088 \nL 191.682327 272.041009 \nL 192.006982 287.336218 \nL 192.331636 242.894293 \nL 192.656291 237.314845 \nL 192.980945 155.795933 \nL 193.3056 164.412497 \nL 193.630255 232.722828 \nL 194.279564 287.903946 \nL 194.604218 240.563586 \nL 194.928873 267.346969 \nL 195.253527 271.071551 \nL 195.902836 251.672071 \nL 196.227491 224.68413 \nL 196.552145 283.759477 \nL 196.8768 295.176682 \nL 197.201455 271.707968 \nL 197.526109 271.992787 \nL 197.850764 265.398511 \nL 198.175418 269.290031 \nL 198.500073 271.407693 \nL 198.824727 293.86959 \nL 199.149382 274.862518 \nL 199.798691 258.014183 \nL 200.123345 269.67225 \nL 200.448 191.198481 \nL 200.772655 161.788823 \nL 201.097309 176.082221 \nL 201.421964 251.450001 \nL 201.746618 266.54106 \nL 202.071273 289.153641 \nL 202.395927 259.420616 \nL 202.720582 280.715082 \nL 203.045236 261.822087 \nL 203.369891 288.786531 \nL 203.694545 286.205839 \nL 204.0192 274.076853 \nL 204.343855 293.287302 \nL 204.668509 290.671772 \nL 204.993164 282.265794 \nL 205.317818 295.393628 \nL 205.642473 283.242893 \nL 205.967127 287.654645 \nL 206.291782 260.558156 \nL 206.616436 244.859045 \nL 206.941091 258.859678 \nL 207.265745 292.638025 \nL 207.5904 271.952106 \nL 207.915055 271.421726 \nL 208.239709 279.008321 \nL 208.564364 233.701007 \nL 208.889018 206.655248 \nL 209.213673 207.169099 \nL 209.538327 218.863398 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287.872874 \nL 224.472436 277.344793 \nL 224.797091 260.862483 \nL 225.121745 291.606733 \nL 225.4464 274.31941 \nL 225.771055 284.072511 \nL 226.420364 201.499332 \nL 226.745018 176.107376 \nL 227.069673 181.180964 \nL 227.394327 179.04076 \nL 227.718982 252.984526 \nL 228.368291 291.151825 \nL 228.692945 292.693013 \nL 229.0176 292.92778 \nL 229.342255 295.488 \nL 229.666909 293.76651 \nL 229.991564 284.02115 \nL 230.316218 269.346092 \nL 230.640873 293.693637 \nL 230.965527 278.889714 \nL 231.290182 196.460412 \nL 231.614836 208.915112 \nL 231.939491 208.778097 \nL 232.264145 228.747783 \nL 232.5888 229.328985 \nL 232.913455 277.863706 \nL 233.238109 276.962371 \nL 233.562764 242.061235 \nL 233.887418 262.549485 \nL 234.212073 232.399403 \nL 234.536727 269.443077 \nL 234.861382 293.058155 \nL 235.186036 271.693774 \nL 235.510691 240.57278 \nL 235.835345 288.504185 \nL 236.16 285.647967 \nL 236.484655 275.52358 \nL 236.809309 281.441802 \nL 237.458618 214.904739 \nL 237.783273 185.674977 \nL 238.107927 268.107399 \nL 238.757236 130.078758 \nL 239.081891 155.550087 \nL 239.406545 199.027156 \nL 239.7312 262.579786 \nL 240.055855 285.743095 \nL 240.380509 267.072572 \nL 240.705164 295.19956 \nL 241.029818 291.774584 \nL 241.354473 291.828968 \nL 241.679127 274.962301 \nL 242.328436 154.58323 \nL 242.653091 161.94342 \nL 242.977745 175.160563 \nL 243.3024 258.191302 \nL 243.951709 292.100319 \nL 244.276364 293.854839 \nL 244.601018 289.345472 \nL 244.925673 290.284897 \nL 245.250327 292.297396 \nL 245.899636 266.69339 \nL 246.224291 286.230458 \nL 246.548945 294.919293 \nL 246.8736 204.40982 \nL 247.198255 227.780245 \nL 247.522909 198.191935 \nL 248.172218 241.886056 \nL 248.496873 291.853279 \nL 248.821527 270.080703 \nL 249.146182 220.465354 \nL 249.470836 239.739876 \nL 249.795491 221.138488 \nL 250.120145 292.877267 \nL 250.4448 273.181895 \nL 250.769455 286.535186 \nL 251.094109 238.971294 \nL 251.418764 280.83507 \nL 251.743418 294.393839 \nL 252.068073 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266.352873 282.032196 \nL 266.677527 267.220362 \nL 267.002182 293.876096 \nL 267.326836 270.789022 \nL 267.651491 256.94971 \nL 267.976145 283.057098 \nL 268.625455 195.519356 \nL 268.950109 192.219636 \nL 269.274764 283.546347 \nL 269.599418 192.501985 \nL 269.924073 133.788232 \nL 270.248727 129.249314 \nL 270.573382 127.413906 \nL 271.222691 263.401243 \nL 271.547345 280.047695 \nL 271.872 272.84478 \nL 272.196655 295.132958 \nL 272.521309 238.740573 \nL 272.845964 220.948573 \nL 273.170618 217.778664 \nL 273.495273 226.635568 \nL 273.819927 227.569545 \nL 274.144582 280.480741 \nL 274.469236 247.553496 \nL 274.793891 255.110174 \nL 275.118545 291.97145 \nL 275.4432 276.81549 \nL 275.767855 291.404348 \nL 276.092509 265.07126 \nL 276.417164 253.368088 \nL 276.741818 292.845855 \nL 277.066473 278.624428 \nL 277.391127 282.65303 \nL 277.715782 289.557639 \nL 278.040436 254.303459 \nL 278.365091 280.026409 \nL 278.689745 282.012359 \nL 279.0144 243.452456 \nL 279.339055 255.999392 \nL 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295.571782 275.499565 \nL 295.896436 275.656538 \nL 296.221091 221.663734 \nL 296.545745 253.12924 \nL 296.8704 239.070885 \nL 297.195055 240.986484 \nL 297.519709 247.604173 \nL 297.844364 268.149977 \nL 298.169018 272.942796 \nL 298.493673 283.079735 \nL 298.818327 285.877492 \nL 299.142982 255.659698 \nL 299.467636 264.852876 \nL 299.792291 260.084753 \nL 300.116945 265.875801 \nL 300.4416 268.660406 \nL 300.766255 285.474464 \nL 301.090909 268.466502 \nL 301.415564 290.429861 \nL 302.064873 200.368013 \nL 302.389527 178.811799 \nL 302.714182 208.019039 \nL 303.038836 224.158566 \nL 303.363491 273.954354 \nL 303.688145 262.59057 \nL 304.0128 231.169218 \nL 304.337455 231.235435 \nL 305.311418 154.000471 \nL 305.636073 212.384936 \nL 306.934691 289.441095 \nL 307.259345 295.317456 \nL 307.584 288.472778 \nL 307.908655 294.598168 \nL 308.233309 294.990108 \nL 308.557964 267.607258 \nL 308.882618 253.331679 \nL 309.207273 176.012569 \nL 309.531927 230.859621 \nL 309.856582 236.700105 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pd.Series(0, index=np.arange(len(data_n)-testdatasize))\nlast_train_data= (df['datetime'].tolist())[-testdatasize]\n# add the data to the main\ndf['anomaly27'] = complement.append(test, ignore_index='True')","user":"anonymous","dateUpdated":"2019-06-17T20:46:40+0800","config":{"editorSetting":{"language":"python","editOnDblClick":false,"completionKey":"TAB","completionSupport":true},"colWidth":12,"editorMode":"ace/mode/python","fontSize":9,"results":{},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[]},"apps":[],"jobName":"paragraph_1560774712324_-344361868","id":"20190611-175338_1346021530","dateCreated":"2019-06-17T20:31:52+0800","status":"FINISHED","progressUpdateIntervalMs":500,"$$hashKey":"object:2321","dateFinished":"2019-06-17T20:46:40+0800","dateStarted":"2019-06-17T20:46:40+0800"},{"text":"%pyspark\n# visualisation of anomaly throughout time (viz 1)\nfig, ax = plt.subplots(figsize=(12, 5))\n\na = df.loc[df['anomaly27'] == 1, ['datetime', 'value']] #anomaly\nax.plot(df['datetime'], df['value'], color='blue', label='no anomaly value', linewidth=0.6)\nax.scatter(a['datetime'].tolist(),a['value'], color='red', label='anomalies value')\nax.set_title('the number of nyc taxi value throughout time (with anomalies scattered)')\n\nmax_value = df['value'].max()\nmin_value = df['value'].min()\nplt.vlines(last_train_data, min_value, max_value, color='black', linestyles = \"dashed\", label='test begins')\nplt.xlabel('datetime')\nplt.xticks(rotation=45)\nplt.ylabel('the number of nyc taxi value')\nplt.legend(loc='upper left')\nshow(plt)","user":"anonymous","dateUpdated":"2019-06-17T20:46:40+0800","config":{"editorSetting":{"language":"python","editOnDblClick":false,"completionKey":"TAB","completionSupport":true},"colWidth":12,"editorMode":"ace/mode/python","fontSize":9,"results":{},"enabled":true},"settings":{"params":{},"forms":{}},"results":{"code":"SUCCESS","msg":[{"type":"HTML","data":"<div style='width:600px'><?xml 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148.001574 \nL 188.99158 164.193537 \nL 189.05057 152.964554 \nL 189.109561 154.222865 \nL 189.168552 153.794911 \nL 189.227543 139.838525 \nL 189.286534 140.969088 \nL 189.345525 141.045736 \nL 189.404516 139.455283 \nL 189.64048 187.845939 \nL 190.053416 283.726634 \nL 190.112407 283.126222 \nL 190.93828 187.635157 \nL 190.997271 188.708233 \nL 191.115253 194.348274 \nL 191.174243 193.875609 \nL 191.233234 191.020459 \nL 191.292225 197.842161 \nL 191.351216 197.158714 \nL 191.646171 156.107137 \nL 191.705162 162.098482 \nL 191.764153 177.249306 \nL 191.823144 175.601366 \nL 192.177089 141.039349 \nL 192.354062 158.885639 \nL 192.943971 284.371758 \nL 193.179935 270.389822 \nL 193.710853 193.664826 \nL 193.769844 194.776227 \nL 193.887826 187.328563 \nL 193.946817 190.471145 \nL 194.123789 200.607889 \nL 194.18278 197.861323 \nL 194.241771 195.146695 \nL 194.300762 183.323687 \nL 194.359753 183.99436 \nL 194.418744 182.225061 \nL 194.536726 189.576915 \nL 194.595717 200.793123 \nL 194.654708 199.617848 \nL 194.772689 193.275197 \nL 195.067644 245.52382 \nL 195.303608 284.640027 \nL 195.48058 294.323268 \nL 195.716544 277.166813 \nL 196.07049 187.124168 \nL 196.12948 187.271077 \nL 196.365444 209.281927 \nL 196.542417 201.96201 \nL 196.601408 203.954867 \nL 196.778381 195.651296 \nL 197.014344 206.92499 \nL 197.309299 159.63296 \nL 197.486272 185.648687 \nL 197.545262 176.917163 \nL 197.663244 173.621284 \nL 198.253153 290.969903 \nL 198.312144 292.662554 \nL 198.371135 291.908845 \nL 198.430126 292.81585 \nL 198.489117 289.992636 \nL 198.725081 219.073753 \nL 198.902054 178.073275 \nL 198.961044 181.854594 \nL 199.020035 184.703357 \nL 199.079026 195.35109 \nL 199.138017 193.211324 \nL 199.197008 194.948686 \nL 199.31499 187.047519 \nL 199.373981 178.609814 \nL 199.432972 183.732478 \nL 199.550954 181.848206 \nL 199.727926 196.245321 \nL 199.845908 211.338658 \nL 200.140863 138.54189 \nL 200.317836 164.698139 \nL 200.376827 149.1002 \nL 200.435817 151.795667 \nL 200.553799 157.518744 \nL 200.966736 276.074574 \nL 201.202699 289.270864 \nL 201.26169 290.765507 \nL 201.320681 289.558296 \nL 201.497654 230.871211 \nL 201.733618 174.560226 \nL 201.792609 177.690034 \nL 202.028572 189.161736 \nL 202.205545 180.883715 \nL 202.264536 182.921283 \nL 202.382518 177.651709 \nL 202.55949 192.911118 \nL 202.677472 209.639619 \nL 202.972427 136.721492 \nL 203.031418 139.953497 \nL 203.090409 157.640103 \nL 203.1494 142.233786 \nL 203.208391 136.261602 \nL 203.975272 289.979862 \nL 204.034263 288.357472 \nL 204.093254 290.15232 \nL 204.152245 289.104793 \nL 204.270227 260.412762 \nL 204.506191 183.183165 \nL 204.565182 175.109539 \nL 204.624173 178.399031 \nL 204.683163 180.704868 \nL 204.860136 197.739964 \nL 204.978118 187.647931 \nL 205.037109 188.919016 \nL 205.0961 193.217711 \nL 205.214082 187.277464 \nL 205.273073 187.692643 \nL 205.450045 211.747449 \nL 205.509036 219.207888 \nL 205.803991 143.453772 \nL 205.862982 146.577192 \nL 205.980964 162.334815 \nL 206.098945 140.917989 \nL 206.334909 158.246903 \nL 206.806836 274.528832 \nL 206.983809 283.707472 \nL 207.160782 236.626225 \nL 207.396746 183.662217 \nL 207.455737 186.051091 \nL 207.6917 202.434675 \nL 207.809682 193.60734 \nL 207.986655 199.962766 \nL 208.104637 193.14745 \nL 208.163628 193.358233 \nL 208.3406 220.108506 \nL 208.635555 145.229459 \nL 208.753537 163.36318 \nL 208.812528 162.705282 \nL 208.9895 149.732549 \nL 209.107482 135.699514 \nL 209.166473 138.624926 \nL 209.225464 138.177811 \nL 209.874364 281.554931 \nL 209.933355 279.875055 \nL 210.582255 186.747313 \nL 210.759228 189.877121 \nL 210.818219 194.182203 \nL 210.87721 193.383783 \nL 211.054182 190.720252 \nL 211.113173 198.091268 \nL 211.467119 158.623757 \nL 211.585101 177.715583 \nL 211.644092 176.751091 \nL 211.880055 148.608373 \nL 211.998037 141.167096 \nL 212.057028 142.27211 \nL 212.17501 162.117644 \nL 212.764919 284.135425 \nL 212.882901 281.829587 \nL 213.590792 190.087904 \nL 213.649783 193.422107 \nL 213.708774 186.734538 \nL 213.767765 189.532203 \nL 213.826756 190.036805 \nL 213.944737 196.111186 \nL 214.239692 175.173413 \nL 214.416665 200.039414 \nL 214.475656 197.778288 \nL 214.534647 189.583302 \nL 215.301529 292.458158 \nL 215.41951 291.027389 \nL 215.478501 287.603763 \nL 215.950429 208.828424 \nL 216.00942 210.827669 \nL 216.186392 216.084468 \nL 216.363365 205.513383 \nL 216.422356 212.571419 \nL 216.599329 201.847037 \nL 216.835292 219.22705 \nL 217.012265 190.100678 \nL 217.130247 175.435295 \nL 217.189238 176.738317 \nL 217.30722 189.602464 \nL 217.366211 188.503838 \nL 217.602174 206.688658 \nL 218.015111 288.625741 \nL 218.133093 293.773955 \nL 218.192083 292.969147 \nL 218.251074 293.096894 \nL 218.310065 291.506441 \nL 218.723002 187.865102 \nL 218.781993 191.090719 \nL 219.017956 205.602806 \nL 219.135938 195.299991 \nL 219.25392 198.876914 \nL 219.371902 194.06723 \nL 219.430893 195.434126 \nL 219.489884 195.389414 \nL 219.666856 215.912009 \nL 219.961811 147.618332 \nL 220.020802 149.355695 \nL 220.079793 160.782686 \nL 220.138784 158.49601 \nL 220.256766 153.807686 \nL 220.433738 176.942712 \nL 220.846675 282.736593 \nL 220.964657 289.705205 \nL 221.023647 288.459669 \nL 221.141629 289.526359 \nL 221.259611 262.01599 \nL 221.495575 188.682684 \nL 221.554566 179.714827 \nL 221.613557 184.160431 \nL 221.672548 185.514552 \nL 221.84952 202.575197 \nL 221.967502 191.569772 \nL 222.085484 198.35315 \nL 222.144475 192.898343 \nL 222.203466 193.830898 \nL 222.262457 193.977807 \nL 222.380439 205.724166 \nL 222.49842 224.241129 \nL 222.852366 146.877398 \nL 222.970348 153.175337 \nL 223.08833 145.599926 \nL 223.14732 146.660228 \nL 223.324293 181.228632 \nL 223.619248 268.16702 \nL 223.796221 285.304312 \nL 223.855211 285.297925 \nL 223.973193 289.801015 \nL 224.209157 217.617435 \nL 224.38613 177.670871 \nL 224.681084 202.766818 \nL 224.799066 192.157409 \nL 224.858057 192.815307 \nL 224.917048 196.251708 \nL 225.03503 190.349785 \nL 225.094021 191.071557 \nL 225.329984 222.197174 \nL 225.68393 145.293332 \nL 225.742921 151.64237 \nL 225.801912 149.00439 \nL 225.919894 140.292028 \nL 226.096866 150.850337 \nL 226.214848 177.536737 \nL 226.627785 274.790714 \nL 226.686775 276.476978 \nL 226.804757 284.94662 \nL 226.98173 235.182681 \nL 227.217694 181.854594 \nL 227.276685 187.48186 \nL 227.335676 190.407272 \nL 227.512648 203.910155 \nL 227.63063 197.139552 \nL 227.807603 200.262972 \nL 227.866594 194.980623 \nL 227.925585 197.343947 \nL 227.984576 199.809469 \nL 228.161548 224.11977 \nL 228.456503 152.568538 \nL 228.515494 154.497521 \nL 228.574485 162.960776 \nL 228.633476 157.595392 \nL 228.692467 156.356244 \nL 228.751458 160.393057 \nL 228.92843 140.407 \nL 228.987421 143.421835 \nL 229.046412 144.986739 \nL 229.695312 283.100672 \nL 229.754303 281.229175 \nL 230.403203 201.649029 \nL 230.462194 200.844222 \nL 230.580176 191.480349 \nL 230.639167 193.02609 \nL 230.698158 194.910362 \nL 230.875131 190.994909 \nL 230.993112 196.51359 \nL 231.347058 159.288043 \nL 231.406049 172.695116 \nL 231.46504 169.405625 \nL 231.524031 170.287081 \nL 231.642013 166.461051 \nL 231.818985 146.583579 \nL 231.877976 148.953291 \nL 232.172931 193.173 \nL 232.526876 282.851565 \nL 232.585867 283.420041 \nL 232.76284 269.495591 \nL 233.057795 230.724302 \nL 233.293758 199.400678 \nL 233.352749 193.415719 \nL 233.41174 195.280829 \nL 233.470731 196.51359 \nL 233.529722 187.705418 \nL 233.588713 191.946626 \nL 233.824676 201.118878 \nL 234.06064 180.832616 \nL 234.355595 198.921626 \nL 234.414586 197.535568 \nL 234.650549 236.511252 \nL 235.004495 290.037348 \nL 235.122477 292.662554 \nL 235.181468 292.036592 \nL 235.299449 290.312004 \nL 235.712386 188.523 \nL 235.830368 196.155898 \nL 235.889359 210.16977 \nL 235.948349 206.433164 \nL 236.00734 210.188933 \nL 236.066331 202.88179 \nL 236.125322 203.258645 \nL 236.184313 203.654661 \nL 236.243304 203.431103 \nL 236.420277 196.238933 \nL 236.65624 211.185361 \nL 236.951195 162.015447 \nL 237.128168 170.255144 \nL 237.187159 169.820803 \nL 237.423122 205.564482 \nL 237.777068 282.730205 \nL 237.954041 293.320452 \nL 238.013032 292.272925 \nL 238.072022 292.700878 \nL 238.131013 291.781098 \nL 238.366977 221.277393 \nL 238.54395 179.076091 \nL 238.838904 203.456653 \nL 239.015877 190.420046 \nL 239.074868 194.52712 \nL 239.133859 187.258302 \nL 239.19285 187.513797 \nL 239.251841 188.222794 \nL 239.487804 220.153218 \nL 239.782759 154.261189 \nL 239.900741 163.082136 \nL 240.077714 152.574925 \nL 240.313677 201.374373 \nL 240.667623 281.414409 \nL 240.785605 289.079244 \nL 240.844596 288.261661 \nL 240.903586 291.065713 \nL 240.962577 289.545521 \nL 241.13955 231.04367 \nL 241.375514 180.098069 \nL 241.670468 204.114551 \nL 241.78845 191.454799 \nL 241.906432 196.321969 \nL 241.965423 191.518673 \nL 242.024414 191.716681 \nL 242.201387 201.878974 \nL 242.319369 220.740855 \nL 242.614323 149.796423 \nL 242.791296 155.71112 \nL 242.909278 148.403978 \nL 243.08625 169.156518 \nL 243.499187 275.42945 \nL 243.617169 286.313516 \nL 243.67616 285.732266 \nL 243.735151 289.290026 \nL 243.794141 288.89401 \nL 244.207078 182.25061 \nL 244.32506 189.423618 \nL 244.384051 199.375128 \nL 244.443042 199.087697 \nL 244.502032 199.905279 \nL 244.561023 189.883508 \nL 244.620014 190.975747 \nL 244.737996 197.7719 \nL 244.855978 189.723824 \nL 244.914969 190.72664 \nL 245.032951 204.785224 \nL 245.150933 220.951638 \nL 245.445887 146.628291 \nL 245.504878 148.097384 \nL 245.563869 153.539417 \nL 245.62286 151.891477 \nL 245.681851 147.899376 \nL 245.740842 149.46428 \nL 245.799833 149.815585 \nL 245.858824 151.252741 \nL 245.976805 164.653427 \nL 246.389742 269.099574 \nL 246.566715 283.164546 \nL 246.625705 282.896277 \nL 246.743687 263.695866 \nL 246.979651 199.29848 \nL 247.038642 187.865102 \nL 247.097633 191.678357 \nL 247.333596 205.819977 \nL 247.451578 197.324785 \nL 247.510569 197.593054 \nL 247.56956 200.620664 \nL 247.746533 193.006928 \nL 247.864515 206.580073 \nL 247.982497 221.756446 \nL 248.277451 148.365654 \nL 248.572406 165.713729 \nL 248.808369 151.693469 \nL 248.86736 153.833236 \nL 249.221306 219.923273 \nL 249.51626 283.816057 \nL 249.575251 282.76853 \nL 249.929197 242.515373 \nL 250.10617 210.610498 \nL 250.16516 209.109468 \nL 250.342133 199.470939 \nL 250.460115 194.654868 \nL 250.519106 198.544771 \nL 250.578097 198.346763 \nL 250.637088 197.89326 \nL 250.696079 198.052944 \nL 250.75507 201.565994 \nL 250.814061 201.067779 \nL 251.109015 165.790378 \nL 251.226997 183.789964 \nL 251.285988 179.689278 \nL 251.698924 154.778565 \nL 252.111861 230.679591 \nL 252.170852 239.104521 \nL 252.347824 283.962966 \nL 252.406815 285.349024 \nL 252.524797 282.596071 \nL 253.173697 202.255829 \nL 253.35067 198.314826 \nL 253.527643 206.873892 \nL 253.586634 206.254317 \nL 253.645625 206.963315 \nL 253.881588 189.372519 \nL 254.471497 230.788176 \nL 254.766452 285.336249 \nL 254.943425 292.854175 \nL 255.120398 288.740713 \nL 255.29737 241.282612 \nL 255.533334 193.371008 \nL 255.651316 199.579524 \nL 255.769298 214.35988 \nL 255.887279 209.000883 \nL 255.94627 203.80157 \nL 256.005261 206.995251 \nL 256.064252 206.062697 \nL 256.123243 201.291337 \nL 256.182234 203.437491 \nL 256.300216 200.550403 \nL 256.359207 202.383576 \nL 256.477189 215.458506 \nL 256.772143 168.658303 \nL 257.008107 181.797107 \nL 257.126089 188.906242 \nL 257.715998 290.280068 \nL 257.892971 292.860562 \nL 257.951962 290.503625 \nL 258.187925 222.912558 \nL 258.364898 180.027808 \nL 258.659853 209.946213 \nL 258.836825 195.504387 \nL 258.895816 201.732065 \nL 259.072789 184.57561 \nL 259.13178 187.686255 \nL 259.308753 221.871418 \nL 259.485725 168.875474 \nL 259.662698 139.698003 \nL 259.78068 174.694361 \nL 259.898662 161.146765 \nL 260.370589 266.142226 \nL 260.606553 291.263721 \nL 260.665544 290.401428 \nL 260.724535 293.013859 \nL 260.783526 290.765507 \nL 260.901507 264.545385 \nL 261.137471 190.675541 \nL 261.196462 180.9923 \nL 261.255453 184.505349 \nL 261.314444 184.422313 \nL 261.491417 208.700677 \nL 261.668389 196.17506 \nL 261.72738 198.01462 \nL 261.786371 194.290788 \nL 261.845362 195.759881 \nL 261.904353 194.597381 \nL 262.140317 216.365512 \nL 262.435271 147.292577 \nL 262.553253 160.393057 \nL 262.612244 160.33557 \nL 262.789217 156.024101 \nL 262.907199 173.927877 \nL 263.379126 283.5414 \nL 263.556099 289.622169 \nL 263.61509 288.881235 \nL 263.733071 266.327459 \nL 263.969035 192.776983 \nL 264.028026 180.685706 \nL 264.087017 188.331379 \nL 264.322981 206.484262 \nL 264.440962 194.143879 \nL 264.558944 198.576708 \nL 264.735917 192.093535 \nL 264.853899 203.328906 \nL 264.971881 219.10569 \nL 265.266835 152.645186 \nL 265.325826 152.511052 \nL 265.384817 152.95178 \nL 265.443808 151.022796 \nL 265.502799 152.012837 \nL 265.56179 151.968126 \nL 265.620781 148.646697 \nL 265.738763 155.353428 \nL 266.328672 276.368393 \nL 266.446654 285.962211 \nL 266.564635 265.631237 \nL 266.800599 201.910911 \nL 266.85959 186.325747 \nL 266.918581 189.142574 \nL 267.036563 207.186872 \nL 267.154545 206.784468 \nL 267.272526 195.344703 \nL 267.390508 200.499304 \nL 267.449499 199.157958 \nL 267.50849 194.054456 \nL 267.567481 195.274442 \nL 267.803445 218.122036 \nL 268.098399 150.831175 \nL 268.393354 164.972795 \nL 268.570327 152.504664 \nL 268.629317 152.887906 \nL 268.80629 176.885226 \nL 269.337209 282.608846 \nL 269.396199 281.8232 \nL 270.10409 203.865444 \nL 270.163081 202.402738 \nL 270.281063 196.341131 \nL 270.340054 197.624991 \nL 270.399045 195.753494 \nL 270.458036 196.884057 \nL 270.517027 196.845733 \nL 270.635009 204.057065 \nL 270.929963 164.033853 \nL 271.106936 178.795047 \nL 271.165927 178.756723 \nL 271.460882 157.429321 \nL 271.519872 157.57623 \nL 271.755836 189.053151 \nL 272.168773 284.282334 \nL 272.227763 284.659189 \nL 272.404736 273.168324 \nL 272.876664 207.972518 \nL 273.171618 194.099167 \nL 273.230609 194.182203 \nL 273.407582 202.492161 \nL 273.466573 202.32609 \nL 273.702536 189.430005 \nL 273.761527 191.001296 \nL 273.879509 203.929318 \nL 273.9385 200.492917 \nL 273.997491 201.451021 \nL 274.056482 200.984744 \nL 274.351436 243.090235 \nL 274.5874 282.168118 \nL 274.764373 293.703694 \nL 275.000337 278.885013 \nL 275.354282 190.899099 \nL 275.472264 201.84065 \nL 275.649237 216.371899 \nL 275.826209 207.953356 \nL 275.8852 210.853218 \nL 276.062173 200.895321 \nL 276.121164 201.2594 \nL 276.298137 213.772243 \nL 276.593091 168.69024 \nL 276.770064 180.283303 \nL 276.829055 179.561531 \nL 276.947037 190.598893 \nL 277.536946 291.876908 \nL 277.595937 293.250191 \nL 277.654928 291.391469 \nL 277.713919 293.825054 \nL 277.77291 292.113241 \nL 278.067864 212.169015 \nL 278.185846 185.124923 \nL 278.244837 191.422862 \nL 278.303828 192.578975 \nL 278.480801 209.045595 \nL 278.657773 195.766269 \nL 278.716764 202.990375 \nL 278.834746 190.190101 \nL 278.893737 192.099923 \nL 279.129701 217.323616 \nL 279.424655 154.842439 \nL 279.601628 166.307754 \nL 279.660619 166.36524 \nL 279.71961 166.633509 \nL 279.778601 169.245941 \nL 280.014564 228.444014 \nL 280.250528 276.189547 \nL 280.427501 289.641331 \nL 280.486492 288.708777 \nL 280.545483 292.011043 \nL 280.604474 291.544765 \nL 280.781446 241.953285 \nL 281.01741 182.231448 \nL 281.076401 188.075885 \nL 281.312365 204.459469 \nL 281.489337 194.884813 \nL 281.548328 195.070046 \nL 281.725301 193.204936 \nL 281.961265 218.562764 \nL 282.256219 151.929802 \nL 282.31521 157.339897 \nL 282.374201 157.742301 \nL 282.551174 161.070117 \nL 282.610165 160.290859 \nL 282.787138 197.318398 \nL 283.200074 282.513035 \nL 283.377047 288.248886 \nL 283.436038 286.575398 \nL 283.55402 267.355825 \nL 283.848974 182.684951 \nL 284.143929 205.69223 \nL 284.261911 195.261667 \nL 284.320901 199.445389 \nL 284.379892 198.800266 \nL 284.497874 190.522244 \nL 284.556865 184.581997 \nL 284.792829 221.520113 \nL 285.087783 147.778016 \nL 285.205765 151.88509 \nL 285.264756 149.668675 \nL 285.441729 153.060365 \nL 285.50072 140.419775 \nL 285.559711 145.88097 \nL 285.677693 174.892369 \nL 285.972647 266.340234 \nL 286.208611 285.789752 \nL 286.267602 287.782609 \nL 286.385584 267.445248 \nL 286.680538 195.082821 \nL 286.975493 212.111529 \nL 287.034484 207.704249 \nL 287.152465 203.130897 \nL 287.211456 203.597175 \nL 287.270447 203.871831 \nL 287.388429 198.493672 \nL 287.44742 200.211873 \nL 287.624393 218.300883 \nL 287.919347 161.510845 \nL 287.978338 167.342507 \nL 288.037329 169.51421 \nL 288.09632 177.089621 \nL 288.155311 176.316751 \nL 288.214302 177.006586 \nL 288.450266 160.201436 \nL 288.509257 161.006243 \nL 289.158157 284.461181 \nL 289.217148 282.640782 \nL 289.512102 252.530757 \nL 289.866048 206.893054 \nL 289.925039 207.50624 \nL 290.102011 200.575952 \nL 290.161002 205.775265 \nL 290.219993 204.197587 \nL 290.278984 204.810774 \nL 290.396966 202.690169 \nL 290.455957 205.385636 \nL 290.514948 202.255829 \nL 290.750911 170.255144 \nL 290.868893 178.054113 \nL 290.927884 156.337082 \nL 290.986875 186.095802 \nL 291.045866 182.333646 \nL 291.163848 169.309814 \nL 291.340821 161.389485 \nL 291.753757 229.766198 \nL 291.989721 287.335494 \nL 292.107702 283.777733 \nL 292.166693 281.816813 \nL 292.874584 204.599991 \nL 292.933575 203.961254 \nL 292.992566 200.82506 \nL 293.051557 202.90734 \nL 293.22853 210.099509 \nL 293.287521 209.824853 \nL 293.405503 194.967848 \nL 293.464494 195.740719 \nL 293.582475 196.75631 \nL 293.641466 203.309743 \nL 293.700457 200.869771 \nL 293.87743 209.537422 \nL 294.113394 230.162214 \nL 294.408348 284.665576 \nL 294.585321 293.333227 \nL 294.762294 290.5164 \nL 294.880276 268.269217 \nL 295.17523 198.691681 \nL 295.234221 199.822244 \nL 295.470185 221.232682 \nL 295.647157 212.833301 \nL 295.706148 211.543053 \nL 295.883121 205.066268 \nL 296.119085 213.34429 \nL 296.414039 173.55741 \nL 296.591012 186.070253 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157.35906 \nL 302.195149 156.107137 \nL 302.372122 163.490927 \nL 302.431113 150.01998 \nL 302.785058 251.534328 \nL 303.021022 285.898337 \nL 303.197995 291.832197 \nL 303.256986 291.397856 \nL 303.492949 233.509192 \nL 303.669922 185.85947 \nL 303.728913 191.88914 \nL 303.964877 209.377738 \nL 304.023868 201.157202 \nL 304.082859 201.2594 \nL 304.14185 203.373617 \nL 304.20084 202.830691 \nL 304.377813 194.086392 \nL 304.613777 219.718877 \nL 304.908731 160.150337 \nL 305.026713 162.009059 \nL 305.144695 166.116133 \nL 305.203686 162.385914 \nL 305.262677 163.407892 \nL 305.380659 172.069155 \nL 306.029559 284.39092 \nL 306.08855 287.214134 \nL 306.442495 219.993534 \nL 306.501486 201.304112 \nL 306.560477 204.568054 \nL 306.678459 214.398204 \nL 306.73745 212.481996 \nL 306.796441 212.028493 \nL 306.914423 201.776777 \nL 306.973414 204.555279 \nL 307.209377 193.236873 \nL 307.268368 194.418535 \nL 307.445341 213.088795 \nL 307.740295 165.151641 \nL 307.976259 182.793536 \nL 308.03525 182.7105 \nL 308.094241 180.589896 \nL 308.153232 169.035158 \nL 308.212223 172.969773 \nL 308.271214 169.143743 \nL 308.448186 194.776227 \nL 308.68415 237.335222 \nL 308.979105 285.483159 \nL 309.097087 278.603969 \nL 309.392041 245.932612 \nL 309.569014 220.619495 \nL 309.628005 218.160361 \nL 309.804978 205.404798 \nL 310.040941 199.809469 \nL 310.099932 200.729249 \nL 310.158923 198.423411 \nL 310.217914 206.560911 \nL 310.276905 203.731309 \nL 310.571859 180.053358 \nL 310.63085 181.203083 \nL 310.689841 194.246076 \nL 310.748832 192.911118 \nL 310.807823 195.25528 \nL 310.866814 194.495184 \nL 311.102778 179.331586 \nL 311.279751 199.585911 \nL 311.810669 288.389408 \nL 311.86966 289.641331 \nL 311.987642 285.936661 \nL 312.341587 241.320936 \nL 312.51856 211.919908 \nL 312.695533 199.803082 \nL 312.813514 194.43131 \nL 312.872505 195.894016 \nL 312.931496 200.480142 \nL 312.990487 197.005417 \nL 313.049478 195.683233 \nL 313.16746 200.940032 \nL 313.344433 193.811736 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179.331586 \nL 319.951415 288.753488 \nL 320.187379 294.010287 \nL 320.24637 291.781098 \nL 320.423343 230.92231 \nL 320.659306 176.725542 \nL 320.836279 190.503082 \nL 320.89527 189.436393 \nL 320.954261 195.664071 \nL 321.013252 187.162492 \nL 321.072243 187.475472 \nL 321.190225 191.838041 \nL 321.249215 184.907753 \nL 321.308206 188.056722 \nL 321.367197 184.345665 \nL 321.426188 186.766475 \nL 321.603161 213.689207 \nL 321.957106 149.821972 \nL 322.016097 151.374101 \nL 322.370043 189.53859 \nL 322.782979 279.779244 \nL 322.900961 290.771895 \nL 322.959952 288.255274 \nL 323.018943 291.346757 \nL 323.077934 290.63776 \nL 323.195916 263.798064 \nL 323.431879 174.630487 \nL 323.608852 184.639484 \nL 323.785825 190.931035 \nL 323.903807 180.3855 \nL 323.962798 183.700541 \nL 324.021789 192.317093 \nL 324.198761 176.374237 \nL 324.316743 195.082821 \nL 324.434725 218.98433 \nL 324.847661 139.806588 \nL 325.024634 148.378428 \nL 325.083625 147.35645 \nL 325.319589 185.048275 \nL 325.614543 265.465165 \nL 325.850507 285.757815 \nL 325.909498 287.782609 \nL 326.02748 259.576017 \nL 326.263443 176.074031 \nL 326.440416 182.997932 \nL 326.617389 191.205692 \nL 326.67638 182.729662 \nL 326.735371 184.780006 \nL 326.853353 191.007684 \nL 326.912344 190.828838 \nL 327.030325 178.054113 \nL 327.148307 198.199854 \nL 327.266289 218.473341 \nL 327.561244 131.554116 \nL 327.797207 140.611396 \nL 327.856198 140.74553 \nL 328.033171 134.345393 \nL 328.092162 135.220462 \nL 328.269135 164.88976 \nL 328.62308 240.937694 \nL 328.800053 283.151771 \nL 328.859044 282.014821 \nL 329.095007 252.671279 \nL 329.389962 186.619566 \nL 329.448953 186.581242 \nL 329.625926 171.219636 \nL 329.684917 170.376504 \nL 329.743908 165.988386 \nL 329.802898 167.847108 \nL 329.861889 166.72932 \nL 329.92088 159.984265 \nL 330.097853 197.005417 \nL 330.392808 140.247316 \nL 330.451799 141.59505 \nL 330.628771 157.91476 \nL 330.687762 156.145461 \nL 330.746753 149.579252 \nL 330.864735 112.162084 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337.943645 197.011804 \nL 338.120618 187.366887 \nL 338.179609 193.013316 \nL 338.356581 174.860432 \nL 338.474563 198.730005 \nL 338.592545 218.76716 \nL 338.8875 152.428016 \nL 338.946491 146.909335 \nL 339.005482 146.941272 \nL 339.241445 158.272452 \nL 339.890345 287.929518 \nL 339.949336 287.961455 \nL 340.008327 291.084875 \nL 340.067318 289.079244 \nL 340.1853 262.297034 \nL 340.421264 173.646833 \nL 340.657227 195.466063 \nL 340.775209 194.201365 \nL 340.8342 185.105761 \nL 340.893191 187.571283 \nL 341.011173 194.955074 \nL 341.188145 181.305281 \nL 341.247136 184.21153 \nL 341.424109 217.413039 \nL 341.719064 141.882481 \nL 341.837046 139.212563 \nL 342.014018 151.20803 \nL 342.073009 148.908579 \nL 342.308973 210.744633 \nL 342.544937 270.019355 \nL 342.839891 289.583845 \nL 342.898882 288.568255 \nL 343.016864 261.683847 \nL 343.252828 169.782479 \nL 343.606773 200.24381 \nL 343.724755 185.469841 \nL 343.783746 187.290239 \nL 343.842737 193.351846 \nL 344.019709 178.437355 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135.412083 \nL 350.449719 163.618675 \nL 350.50871 161.932411 \nL 350.744674 130.244707 \nL 350.803665 131.662701 \nL 351.275592 231.969838 \nL 351.452565 283.375329 \nL 351.74752 256.823064 \nL 352.160456 177.057685 \nL 352.219447 175.799374 \nL 352.278438 167.035913 \nL 352.337429 174.368605 \nL 352.455411 179.861737 \nL 352.514402 177.977465 \nL 352.750365 189.378906 \nL 352.927338 168.492232 \nL 352.986329 168.59443 \nL 353.281283 193.830898 \nL 353.340274 193.505142 \nL 353.871193 286.160219 \nL 354.048165 294.054999 \nL 354.225138 288.421345 \nL 354.402111 230.008918 \nL 354.579084 185.533714 \nL 354.638075 186.651503 \nL 354.933029 207.244359 \nL 355.051011 198.902464 \nL 355.110002 199.592299 \nL 355.168993 205.091818 \nL 355.345966 187.992849 \nL 355.404956 190.279524 \nL 355.581929 204.7469 \nL 355.817893 149.821972 \nL 355.876884 150.396835 \nL 355.935875 151.635983 \nL 355.994866 150.390447 \nL 356.879729 294.214683 \nL 357.056702 291.257334 \nL 357.174684 264.871141 \nL 357.410648 161.753565 \nL 357.646611 190.777739 \nL 357.764593 190.48392 \nL 358.000557 201.387147 \nL 358.23652 186.849511 \nL 358.413493 216.90205 \nL 358.708448 148.844706 \nL 358.767439 144.986739 \nL 358.82643 147.509747 \nL 359.121384 175.792987 \nL 359.593312 285.98776 \nL 359.711293 291.174298 \nL 359.770284 290.918804 \nL 359.829275 292.151565 \nL 359.888266 290.331167 \nL 360.065239 227.530621 \nL 360.242212 176.112355 \nL 360.301203 177.319567 \nL 360.537166 195.529936 \nL 360.596157 198.397862 \nL 360.655148 189.308645 \nL 360.714139 189.449167 \nL 360.832121 196.97348 \nL 361.009094 187.283852 \nL 361.068084 190.247588 \nL 361.245057 220.357613 \nL 361.540012 141.128772 \nL 361.599003 143.31325 \nL 361.952948 162.743606 \nL 362.188912 237.379934 \nL 362.424876 281.574093 \nL 362.542857 289.660493 \nL 362.601848 288.472444 \nL 362.660839 290.343941 \nL 362.71983 289.060081 \nL 362.837812 259.588792 \nL 363.073776 173.991751 \nL 363.427721 196.034538 \nL 363.486712 189.410843 \nL 363.545703 190.439209 \nL 363.663685 193.524304 \nL 363.840658 186.811187 \nL 363.899648 190.337011 \nL 364.076621 225.116198 \nL 364.371576 152.063936 \nL 364.489558 140.074857 \nL 364.548549 145.414692 \nL 364.725521 141.620599 \nL 364.784512 145.127261 \nL 365.020476 205.532546 \nL 365.31543 271.175467 \nL 365.551394 285.28515 \nL 365.669376 259.001155 \nL 365.90534 180.513248 \nL 366.082312 190.873549 \nL 366.259285 196.571076 \nL 366.318276 189.819634 \nL 366.377267 190.075129 \nL 366.495249 197.075678 \nL 366.55424 195.747107 \nL 366.672222 189.359744 \nL 366.731213 192.585362 \nL 366.908185 224.190031 \nL 367.262131 130.251094 \nL 367.439104 139.512769 \nL 367.498094 138.516341 \nL 367.616076 129.356863 \nL 367.793049 144.878154 \nL 368.441949 284.224848 \nL 368.50094 281.963722 \nL 368.618922 271.795041 \nL 369.031858 188.446352 \nL 369.090849 187.72458 \nL 369.267822 174.579388 \nL 369.385804 170.868331 \nL 369.503786 175.371421 \nL 369.562777 174.240858 \nL 369.621767 175.952671 \nL 369.739749 199.560362 \nL 370.034704 141.269294 \nL 370.152686 148.442302 \nL 370.270668 154.018469 \nL 370.565622 136.14663 \nL 370.624613 137.047248 \nL 370.860577 173.710707 \nL 371.09654 230.839275 \nL 371.273513 283.975741 \nL 371.568468 262.558916 \nL 371.863422 197.126777 \nL 372.099386 188.976503 \nL 372.453331 218.256171 \nL 372.571313 229.900332 \nL 372.866268 193.824511 \nL 372.98425 190.656379 \nL 373.515168 264.353764 \nL 373.692141 286.128282 \nL 373.869113 293.652595 \nL 374.046086 287.571826 \nL 374.223059 230.737077 \nL 374.400032 193.94587 \nL 374.694986 212.475608 \nL 374.753977 213.816954 \nL 374.871959 206.912216 \nL 375.048932 208.994496 \nL 375.225905 201.457408 \nL 375.343886 218.30727 \nL 375.402877 216.997861 \nL 375.697832 166.742095 \nL 375.756823 160.373895 \nL 375.815814 161.510845 \nL 376.051777 176.891613 \nL 376.641687 292.745589 \nL 376.700677 294.240232 \nL 376.87765 290.88048 \nL 376.995632 259.556855 \nL 377.231596 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382.953715 189.551365 \nL 383.012706 195.185019 \nL 383.071697 190.688316 \nL 383.130687 191.818879 \nL 383.189678 191.614483 \nL 383.248669 201.981172 \nL 383.30766 201.029455 \nL 383.366651 195.13392 \nL 383.425642 198.021008 \nL 383.484633 196.072862 \nL 383.543624 196.813796 \nL 383.779588 214.053287 \nL 383.897569 224.279454 \nL 384.192524 157.493194 \nL 384.251515 154.638043 \nL 384.428488 167.776847 \nL 384.60546 155.455626 \nL 384.782433 202.128081 \nL 385.077388 268.352253 \nL 385.313351 285.968598 \nL 385.372342 287.39298 \nL 385.490324 263.836388 \nL 385.726288 200.448205 \nL 385.785279 193.965032 \nL 385.84427 198.608645 \nL 385.903261 199.458164 \nL 386.080233 208.317435 \nL 386.198215 199.375128 \nL 386.316197 205.257889 \nL 386.49317 194.763453 \nL 386.611152 208.604867 \nL 386.729133 220.689756 \nL 387.083079 143.715654 \nL 387.14207 144.890928 \nL 387.260052 150.74814 \nL 387.319043 149.847522 \nL 387.496015 137.034473 \nL 387.731979 159.371079 \nL 388.262897 283.56695 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212.418122 \nL 394.456944 210.770183 \nL 394.692907 201.527669 \nL 394.810889 206.624784 \nL 394.987862 188.989277 \nL 395.046853 191.167368 \nL 395.223825 211.996556 \nL 395.400798 158.489623 \nL 395.459789 161.06373 \nL 395.51878 160.808235 \nL 395.636762 167.930144 \nL 395.990707 218.518053 \nL 396.344653 285.221277 \nL 396.521626 294.329655 \nL 396.698598 290.746345 \nL 396.81658 264.820042 \nL 397.052544 178.78866 \nL 397.111535 179.223 \nL 397.347498 201.221076 \nL 397.406489 203.744084 \nL 397.46548 194.43131 \nL 397.524471 195.676846 \nL 397.642453 204.184812 \nL 397.819426 188.241956 \nL 397.878417 188.88708 \nL 398.055389 215.950333 \nL 398.350344 150.134953 \nL 398.409335 149.790035 \nL 398.527317 155.979389 \nL 398.645299 155.206518 \nL 398.881262 207.059125 \nL 399.235208 285.495933 \nL 399.471171 293.63982 \nL 399.530162 290.337554 \nL 399.707135 230.047242 \nL 399.943099 172.037218 \nL 400.179062 196.686049 \nL 400.238053 199.030211 \nL 400.297044 182.269772 \nL 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188.350541 \nL 496.983157 187.641544 \nL 497.16013 220.121281 \nL 497.455085 161.64498 \nL 497.514076 159.741546 \nL 497.573066 160.195048 \nL 497.632057 163.133235 \nL 497.691048 158.694018 \nL 497.868021 178.941957 \nL 498.398939 288.619353 \nL 498.45793 291.749161 \nL 498.516921 290.85493 \nL 498.575912 292.081304 \nL 498.634903 289.398612 \nL 498.752885 261.40919 \nL 498.988848 178.820597 \nL 499.047839 176.508372 \nL 499.342794 198.557546 \nL 499.401785 188.919016 \nL 499.460776 190.400884 \nL 499.578758 196.155898 \nL 499.814721 185.361256 \nL 499.991694 222.165237 \nL 500.286649 154.753016 \nL 500.40463 154.216477 \nL 500.463621 156.771422 \nL 500.581603 151.872315 \nL 500.699585 160.514416 \nL 500.876558 217.72602 \nL 501.112521 273.95397 \nL 501.289494 288.549092 \nL 501.348485 286.224093 \nL 501.407476 289.398612 \nL 501.466467 288.536318 \nL 501.584449 254.932405 \nL 501.820412 169.871902 \nL 501.879403 170.235982 \nL 502.056376 184.581997 \nL 502.115367 183.815514 \nL 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144.565173 \nL 537.332945 143.268538 \nL 537.509917 161.549169 \nL 537.981845 284.838035 \nL 538.099826 291.378694 \nL 538.158817 290.439752 \nL 538.217808 292.898886 \nL 538.276799 290.529175 \nL 538.453772 223.749303 \nL 538.630745 166.710158 \nL 538.689736 169.137355 \nL 538.925699 191.473961 \nL 538.98469 194.533508 \nL 539.043681 187.424374 \nL 539.102672 188.580486 \nL 539.220654 197.899648 \nL 539.279645 192.304318 \nL 539.456618 187.584058 \nL 539.63359 222.433506 \nL 539.928545 153.577741 \nL 540.046527 149.668675 \nL 540.223499 152.664348 \nL 540.400472 172.260776 \nL 540.8724 287.105549 \nL 541.049372 291.77471 \nL 541.108363 290.267293 \nL 541.226345 261.364479 \nL 541.462309 175.626916 \nL 541.698272 188.088659 \nL 541.816254 199.368741 \nL 541.875245 187.552121 \nL 541.934236 189.219222 \nL 542.052218 199.502876 \nL 542.229191 187.449923 \nL 542.288182 190.694703 \nL 542.465154 231.177805 \nL 542.760109 148.889417 \nL 542.8191 149.266272 \nL 542.878091 144.533236 \nL 542.937082 146.232275 \nL 542.996073 144.239417 \nL 543.350018 179.452945 \nL 543.703964 273.647376 \nL 543.939927 287.450466 \nL 544.057909 259.857061 \nL 544.293873 174.240858 \nL 544.352864 171.021627 \nL 544.529836 188.98289 \nL 544.588827 187.073069 \nL 544.647818 191.28234 \nL 544.706809 184.35844 \nL 544.7658 185.437904 \nL 544.883782 195.606585 \nL 545.060755 184.147657 \nL 545.178736 204.318947 \nL 545.296718 223.180827 \nL 545.650664 135.769775 \nL 545.709655 138.707962 \nL 545.768646 138.248072 \nL 545.827637 137.730695 \nL 545.886627 137.896767 \nL 546.0636 130.072248 \nL 546.181582 138.6888 \nL 546.830482 283.451977 \nL 546.889473 282.845178 \nL 547.243419 218.882133 \nL 547.420391 176.508372 \nL 547.479382 176.789415 \nL 547.656355 156.420117 \nL 547.833328 171.008853 \nL 547.95131 159.211395 \nL 548.128282 203.788796 \nL 548.423237 131.298622 \nL 548.659201 161.153153 \nL 548.718192 158.757892 \nL 548.895164 126.514487 \nL 548.954155 124.30446 \nL 549.190119 161.561944 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192.195733 \nL 556.151047 192.048824 \nL 556.210038 194.080005 \nL 556.32802 187.507409 \nL 556.387011 187.960912 \nL 556.446002 186.77925 \nL 556.622974 230.008918 \nL 556.917929 173.4616 \nL 556.97692 172.407685 \nL 557.271874 188.286667 \nL 557.389856 194.635706 \nL 557.566829 236.044975 \nL 557.861784 283.745796 \nL 557.920775 285.412898 \nL 557.979765 282.544972 \nL 558.038756 283.273131 \nL 558.097747 283.030411 \nL 558.215729 260.093394 \nL 558.510684 181.247794 \nL 558.687656 189.711049 \nL 558.92362 176.968262 \nL 558.982611 180.960363 \nL 559.041602 175.505556 \nL 559.159584 183.809127 \nL 559.218575 181.151984 \nL 559.336557 194.099167 \nL 559.454538 216.078081 \nL 559.690502 168.32616 \nL 559.749493 162.4434 \nL 559.926466 190.899099 \nL 559.985457 189.276709 \nL 560.044448 187.756516 \nL 560.693348 267.726292 \nL 560.811329 272.369904 \nL 560.929311 283.554175 \nL 560.988302 282.436387 \nL 561.401239 252.735152 \nL 561.578211 233.419769 \nL 561.696193 220.574784 \nL 561.755184 221.328492 \nL 562.168121 208.726227 \nL 562.286102 222.363245 \nL 562.345093 221.385979 \nL 562.404084 220.996349 \nL 562.463075 223.576844 \nL 562.640048 216.065306 \nL 562.699039 216.180278 \nL 562.935003 206.177669 \nL 563.465921 282.704656 \nL 563.583903 287.801771 \nL 563.642894 287.750672 \nL 563.760875 294.016675 \nL 563.878857 287.833708 \nL 564.350785 230.155827 \nL 564.763721 188.40164 \nL 564.822712 192.617299 \nL 564.999685 184.511736 \nL 565.117666 200.192711 \nL 565.294639 174.419704 \nL 565.412621 161.070117 \nL 565.648585 188.095047 \nL 565.707576 188.535775 \nL 565.766567 179.657341 \nL 565.825557 180.519635 \nL 565.884548 183.310912 \nL 566.415467 252.882062 \nL 566.710421 291.58309 \nL 566.769412 286.307128 \nL 566.828403 284.576153 \nL 567.182349 226.061528 \nL 567.418312 188.005624 \nL 567.477303 186.849511 \nL 567.595285 179.504044 \nL 567.654276 183.611118 \nL 567.831249 174.585775 \nL 567.94923 199.764757 \nL 568.008221 186.823962 \nL 568.244185 152.517439 \nL 568.303176 156.586189 \nL 568.480149 181.190308 \nL 568.53914 183.068193 \nL 568.657121 167.821559 \nL 568.716112 171.047177 \nL 568.775103 170.944979 \nL 569.070058 210.572174 \nL 569.541985 286.345452 \nL 569.83694 258.758435 \nL 570.308867 189.046764 \nL 570.426849 192.208508 \nL 570.544831 183.809127 \nL 570.780794 204.568054 \nL 571.016758 190.598893 \nL 571.252722 208.170526 \nL 571.311713 217.885704 \nL 571.370704 216.212215 \nL 571.429695 218.569152 \nL 571.960613 292.164339 \nL 572.078595 295.696551 \nL 572.255567 287.584601 \nL 572.373549 259.952872 \nL 572.609513 189.238384 \nL 572.668504 186.427945 \nL 572.963458 213.34429 \nL 573.08144 202.524098 \nL 573.199422 206.260705 \nL 573.435386 190.356173 \nL 573.612358 213.848891 \nL 573.848322 164.257411 \nL 573.966304 168.607204 \nL 574.025295 162.187905 \nL 574.438231 228.871967 \nL 574.792177 291.723611 \nL 574.910159 295.1664 \nL 575.087131 291.500054 \nL 575.264104 230.14944 \nL 575.500068 171.705075 \nL 575.795022 202.485774 \nL 575.971995 183.75164 \nL 576.030986 185.342093 \nL 576.089977 181.337217 \nL 576.443923 224.11977 \nL 576.738877 157.084403 \nL 576.797868 158.732342 \nL 576.856859 154.66998 \nL 576.91585 159.664897 \nL 576.974841 156.279595 \nL 577.033832 156.784197 \nL 577.092823 159.939554 \nL 577.328786 225.952943 \nL 577.56475 280.32217 \nL 577.741723 291.876908 \nL 577.800714 290.937966 \nL 577.859705 292.694491 \nL 577.918695 290.816606 \nL 578.036677 264.462349 \nL 578.272641 172.509883 \nL 578.331632 172.727053 \nL 578.626586 201.246625 \nL 578.744568 183.591956 \nL 578.803559 185.080212 \nL 578.86255 189.353357 \nL 578.921541 185.808371 \nL 578.980532 184.786393 \nL 579.157505 209.697106 \nL 579.275487 230.813725 \nL 579.51145 168.294224 \nL 579.570441 167.629938 \nL 579.747414 164.908922 \nL 579.983378 169.616408 \nL 580.101359 190.068742 \nL 580.455305 281.893461 \nL 580.573287 289.104793 \nL 580.632278 288.006167 \nL 580.691269 290.350329 \nL 580.750259 289.11118 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138.66325 \nL 587.416233 153.986532 \nL 587.475224 151.367714 \nL 587.534215 152.862357 \nL 587.593206 153.654389 \nL 587.770179 206.075471 \nL 587.829169 188.567711 \nL 588.065133 141.384267 \nL 588.301097 154.33145 \nL 588.47807 137.353841 \nL 588.596051 129.644295 \nL 588.832015 164.053015 \nL 589.067979 216.895663 \nL 589.362933 285.872788 \nL 589.598897 264.756168 \nL 590.011833 163.171559 \nL 590.129815 158.214966 \nL 590.247797 164.832273 \nL 590.306788 164.129663 \nL 590.365779 164.410707 \nL 590.601743 195.951502 \nL 590.660734 188.842368 \nL 590.778715 169.405625 \nL 590.837706 169.61002 \nL 590.896697 167.336119 \nL 591.132661 195.446901 \nL 591.191652 195.018947 \nL 591.250643 197.452532 \nL 591.663579 283.068736 \nL 591.899543 294.744834 \nL 592.076516 288.747101 \nL 592.253488 229.619288 \nL 592.489452 179.938385 \nL 592.607434 186.376846 \nL 592.784407 198.50006 \nL 592.902388 191.014071 \nL 593.02037 193.249648 \nL 593.197343 184.562835 \nL 593.256334 184.81833 \nL 593.433307 214.02135 \nL 593.728261 156.886395 \nL 593.787252 157.065241 \nL 593.905234 163.714485 \nL 593.964225 161.095666 \nL 594.082207 165.777603 \nL 594.613125 286.735082 \nL 594.790098 292.036592 \nL 594.849089 292.528419 \nL 594.90808 289.609395 \nL 595.026061 255.028215 \nL 595.262025 174.394155 \nL 595.321016 170.644773 \nL 595.497989 192.387354 \nL 595.55698 192.259607 \nL 595.615971 196.532752 \nL 595.674961 190.036805 \nL 595.733952 194.501571 \nL 595.792943 206.037147 \nL 595.851934 203.852669 \nL 596.028907 193.134675 \nL 596.146889 215.535155 \nL 596.264871 227.907475 \nL 596.559825 163.407892 \nL 596.618816 164.512905 \nL 596.677807 159.313592 \nL 596.736798 164.825886 \nL 596.795789 157.933922 \nL 596.85478 158.674856 \nL 596.913771 161.708853 \nL 597.149734 215.132751 \nL 597.385698 274.541607 \nL 597.562671 290.995452 \nL 597.621662 289.986249 \nL 597.680653 290.688859 \nL 597.739644 290.120384 \nL 597.857625 263.235976 \nL 598.093589 169.808029 \nL 598.15258 170.964141 \nL 598.447535 194.501571 \nL 598.506525 186.242712 \nL 598.565516 188.950953 \nL 598.742489 194.392986 \nL 598.80148 194.016131 \nL 598.860471 190.745802 \nL 598.919462 194.884813 \nL 599.096435 234.793052 \nL 599.45038 164.116889 \nL 599.509371 164.564004 \nL 599.627353 150.294637 \nL 599.686344 151.52101 \nL 599.922307 180.161943 \nL 600.394235 287.820933 \nL 600.453226 287.935906 \nL 600.512217 290.695246 \nL 600.571208 289.698818 \nL 600.689189 263.548957 \nL 600.925153 172.54182 \nL 600.984144 174.170597 \nL 601.279099 193.613728 \nL 601.338089 184.843879 \nL 601.39708 185.795596 \nL 601.456071 191.595321 \nL 601.515062 191.358989 \nL 601.692035 176.987424 \nL 601.810017 202.913727 \nL 601.927999 227.9458 \nL 602.281944 152.919843 \nL 602.399926 155.545049 \nL 602.517908 152.038387 \nL 602.576899 152.696285 \nL 602.753871 165.330488 \nL 602.930844 202.741268 \nL 603.225799 271.820591 \nL 603.402772 285.674779 \nL 603.520753 262.092638 \nL 603.756717 175.294773 \nL 603.815708 173.091133 \nL 603.992681 191.103494 \nL 604.051672 187.590445 \nL 604.110663 189.679112 \nL 604.169654 185.342093 \nL 604.287635 198.538384 \nL 604.346626 192.182958 \nL 604.523599 183.987973 \nL 604.58259 189.359744 \nL 604.759563 225.518602 \nL 605.054517 143.862563 \nL 605.113508 142.85336 \nL 605.172499 147.196766 \nL 605.23149 145.267783 \nL 605.290481 144.245805 \nL 605.467454 135.169363 \nL 605.762408 160.367507 \nL 606.116354 238.459398 \nL 606.293327 284.288722 \nL 606.352317 283.969354 \nL 606.824245 183.53447 \nL 607.001218 156.369018 \nL 607.17819 150.518195 \nL 607.414154 168.90741 \nL 607.591127 222.235498 \nL 607.650118 210.093122 \nL 607.945072 150.882274 \nL 608.004063 150.824788 \nL 608.063054 156.285983 \nL 608.122045 152.070324 \nL 608.358009 137.021698 \nL 608.417 137.692371 \nL 608.534981 141.722797 \nL 608.947918 230.883986 \nL 609.124891 282.353351 \nL 609.183881 283.100672 \nL 609.301863 279.728145 \nL 609.537827 240.005139 \nL 609.773791 177.121558 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615.731873 197.152326 \nL 615.849855 190.075129 \nL 615.908846 191.212079 \nL 616.085819 226.336184 \nL 616.380773 160.546353 \nL 616.439764 150.575681 \nL 616.498755 154.867988 \nL 616.616737 153.833236 \nL 616.675728 148.487013 \nL 616.852701 177.242918 \nL 617.383619 288.274436 \nL 617.44261 287.61015 \nL 617.501601 289.532746 \nL 617.560592 289.341125 \nL 617.678574 263.944973 \nL 617.914537 176.878839 \nL 617.973528 176.36785 \nL 618.268483 196.801021 \nL 618.327474 186.421558 \nL 618.386465 186.670665 \nL 618.504446 192.617299 \nL 618.563437 192.317093 \nL 618.681419 188.005624 \nL 618.74041 191.173755 \nL 618.917383 228.16297 \nL 619.212337 158.73873 \nL 619.271328 160.629389 \nL 619.448301 146.015104 \nL 619.566283 147.26064 \nL 619.861237 211.460018 \nL 620.097201 273.027802 \nL 620.274174 284.358983 \nL 620.333165 289.040919 \nL 620.392156 286.498749 \nL 620.510138 261.268668 \nL 620.746101 175.326709 \nL 620.864083 180.002259 \nL 621.100047 194.776227 \nL 621.218029 181.707684 \nL 621.33601 188.484676 \nL 621.395001 187.398824 \nL 621.512983 181.381929 \nL 621.571974 183.981585 \nL 621.748947 226.706651 \nL 622.102892 147.081794 \nL 622.279865 139.678841 \nL 622.397847 141.767508 \nL 622.633811 173.959814 \nL 622.751792 198.219016 \nL 623.046747 270.057679 \nL 623.105738 271.545934 \nL 623.22372 282.64717 \nL 623.341702 262.431168 \nL 623.577665 173.410501 \nL 623.636656 173.544635 \nL 623.695647 171.83921 \nL 623.813629 188.024786 \nL 623.87262 186.083027 \nL 623.931611 186.306585 \nL 623.990602 177.083234 \nL 624.049593 180.436599 \nL 624.167574 191.461186 \nL 624.344547 176.885226 \nL 624.52152 211.262009 \nL 624.580511 225.141748 \nL 624.934456 134.370943 \nL 624.993447 135.009679 \nL 625.052438 140.16428 \nL 625.111429 138.305558 \nL 625.17042 137.519912 \nL 625.229411 134.313457 \nL 625.288402 135.475957 \nL 625.347393 141.32678 \nL 625.406384 137.334679 \nL 625.465375 140.247316 \nL 625.937302 237.041403 \nL 626.114275 280.628763 \nL 626.173266 280.105 \nL 626.527211 231.778217 \nL 626.763175 179.61263 \nL 626.940147 160.846559 \nL 626.999138 160.891271 \nL 627.176111 166.24388 \nL 627.235102 167.278633 \nL 627.294093 170.925817 \nL 627.412075 208.055554 \nL 627.471066 194.578219 \nL 627.707029 142.106039 \nL 627.76602 144.871766 \nL 627.942993 162.302878 \nL 628.237948 140.962701 \nL 628.296939 143.09608 \nL 628.532902 180.07252 \nL 628.945839 282.691881 \nL 629.00483 280.78206 \nL 629.122811 281.114203 \nL 629.358775 247.165373 \nL 629.594739 188.542162 \nL 629.771711 170.5298 \nL 630.007675 177.824168 \nL 630.125657 183.566407 \nL 630.243639 206.203219 \nL 630.30263 197.2162 \nL 630.420612 180.41105 \nL 630.479602 181.860981 \nL 630.538593 184.179594 \nL 630.774557 204.076227 \nL 630.833548 202.025884 \nL 630.95153 218.958781 \nL 631.423457 289.008983 \nL 631.541439 292.879724 \nL 631.60043 289.807403 \nL 631.659421 290.593048 \nL 631.718412 289.884051 \nL 631.954375 236.0322 \nL 632.131348 200.518466 \nL 632.190339 201.16359 \nL 632.24933 199.617848 \nL 632.308321 206.120183 \nL 632.367312 203.150059 \nL 632.426303 201.559606 \nL 632.603276 192.195733 \nL 632.662266 195.798205 \nL 632.839239 186.951709 \nL 632.89823 188.459126 \nL 633.075203 223.397998 \nL 633.370157 173.308303 \nL 633.488139 184.926915 \nL 633.54713 180.973138 \nL 633.606121 180.513248 \nL 633.665112 176.12513 \nL 633.783094 194.450472 \nL 634.078048 259.263037 \nL 634.314012 288.044491 \nL 634.373003 289.37945 \nL 634.431994 288.185013 \nL 634.490985 290.043735 \nL 634.549976 288.606579 \nL 634.785939 237.149989 \nL 634.962912 198.129593 \nL 635.021903 198.774716 \nL 635.080894 195.938727 \nL 635.257867 201.834263 \nL 635.43484 187.188041 \nL 635.49383 188.676297 \nL 635.552821 181.42664 \nL 635.611812 186.306585 \nL 635.670803 183.061805 \nL 635.788785 201.246625 \nL 635.906767 228.424852 \nL 636.142731 176.010157 \nL 636.201721 178.003014 \nL 636.437685 195.063659 \nL 636.496676 188.484676 \nL 637.027594 271.328764 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272.235769 \nL 642.867695 295.766812 \nL 642.926686 293.633433 \nL 643.044668 296.846276 \nL 643.280631 278.21434 \nL 643.81155 222.459055 \nL 643.988522 213.171831 \nL 644.047513 216.339962 \nL 644.106504 213.510361 \nL 644.224486 200.084126 \nL 644.401459 213.606171 \nL 644.578432 199.426227 \nL 644.637423 199.445389 \nL 644.696414 190.471145 \nL 644.932377 211.651639 \nL 644.991368 211.990169 \nL 645.10935 202.869015 \nL 645.581277 255.053765 \nL 645.75825 268.301154 \nL 645.935223 291.391469 \nL 645.994214 290.893254 \nL 646.230177 277.390371 \nL 646.702105 209.524647 \nL 646.761096 208.151364 \nL 646.820087 197.324785 \nL 646.879077 199.598686 \nL 646.938068 202.108919 \nL 647.05605 192.087148 \nL 647.115041 194.725129 \nL 647.233023 210.361391 \nL 647.292014 206.714208 \nL 647.468987 190.349785 \nL 647.527978 187.188041 \nL 647.70495 205.200403 \nL 647.763941 203.194771 \nL 647.881923 190.586118 \nL 647.940914 193.569016 \nL 647.999905 194.935912 \nL 648.058896 193.786186 \nL 648.648805 272.772308 \nL 648.825778 288.510768 \nL 649.00275 278.69978 \nL 649.179723 258.349644 \nL 649.356696 221.341267 \nL 649.415687 221.909742 \nL 649.651651 204.536117 \nL 649.710641 210.342229 \nL 649.769632 209.703493 \nL 649.946605 199.847793 \nL 650.005596 201.003906 \nL 650.064587 208.515444 \nL 650.123578 204.04429 \nL 650.182569 200.084126 \nL 650.24156 200.416268 \nL 650.300551 199.911667 \nL 650.359542 201.118878 \nL 650.595505 218.690512 \nL 650.654496 214.11716 \nL 650.772478 223.423547 \nL 651.185414 282.877115 \nL 651.362387 292.72004 \nL 651.421378 291.666125 \nL 651.480369 293.17993 \nL 651.53936 292.605067 \nL 651.657342 279.025535 \nL 651.952296 228.450401 \nL 652.070278 222.797586 \nL 652.778169 191.263178 \nL 652.896151 207.985293 \nL 653.132115 177.440926 \nL 653.191106 176.093193 \nL 653.368078 199.63701 \nL 653.427069 194.239689 \nL 653.48606 195.044497 \nL 653.781015 235.636184 \nL 654.075969 281.050329 \nL 654.311933 290.842156 \nL 654.370924 292.809463 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207.0719 \nL 661.626807 200.454593 \nL 661.921761 221.047448 \nL 661.980752 219.552806 \nL 662.393689 265.758984 \nL 662.570661 286.198543 \nL 662.865616 293.678144 \nL 662.983598 282.059532 \nL 663.396534 230.602942 \nL 663.632498 207.806446 \nL 663.75048 198.13598 \nL 663.809471 202.645458 \nL 663.986443 191.914689 \nL 664.045434 191.601708 \nL 664.104425 189.53859 \nL 664.222407 207.001639 \nL 664.517362 165.656243 \nL 664.753325 195.242505 \nL 664.812316 194.271626 \nL 664.930298 180.206654 \nL 665.579198 255.686114 \nL 665.756171 288.325535 \nL 665.815162 288.21695 \nL 666.169107 247.84882 \nL 666.582044 177.945528 \nL 666.700026 169.05432 \nL 666.759016 169.680281 \nL 666.818007 167.591614 \nL 666.876998 168.651916 \nL 667.053971 197.19065 \nL 667.112962 186.87506 \nL 667.348926 154.139829 \nL 667.525898 191.16098 \nL 667.64388 185.501778 \nL 667.702871 176.763866 \nL 667.761862 178.066888 \nL 667.820853 179.778701 \nL 667.879844 172.062767 \nL 668.410762 262.584465 \nL 668.587735 289.884051 \nL 668.646726 289.526359 \nL 668.882689 274.886525 \nL 669.295626 213.912765 \nL 669.472599 201.8662 \nL 669.53159 204.663864 \nL 669.59058 204.223136 \nL 669.649571 201.565994 \nL 669.708562 208.419633 \nL 669.767553 202.236667 \nL 669.826544 204.778837 \nL 669.944526 207.346556 \nL 670.121499 202.575197 \nL 670.18049 216.148342 \nL 670.239481 192.559813 \nL 670.298471 203.46304 \nL 670.593426 242.259878 \nL 671.065353 291.940782 \nL 671.124344 295.888172 \nL 671.183335 295.326084 \nL 671.360308 290.350329 \nL 671.47829 265.088311 \nL 671.714253 192.725884 \nL 671.773244 191.026846 \nL 671.950217 222.682613 \nL 672.068199 221.737283 \nL 672.186181 214.321556 \nL 672.245172 216.020594 \nL 672.304163 216.857339 \nL 672.422144 208.643191 \nL 672.540126 194.878425 \nL 672.717099 212.628905 \nL 672.953063 166.24388 \nL 673.012054 165.681793 \nL 674.014899 295.932883 \nL 674.191872 292.637004 \nL 674.368845 234.831376 \nL 674.604808 177.766682 \nL 674.899763 219.22705 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280.667087 \nL 682.627573 292.266537 \nL 682.686564 290.618598 \nL 682.804546 267.502734 \nL 682.981519 210.284743 \nL 683.04051 209.562971 \nL 683.217482 202.236667 \nL 683.394455 212.884399 \nL 683.512437 204.210362 \nL 683.571428 204.299785 \nL 683.748401 195.172244 \nL 683.807391 185.71256 \nL 683.866382 189.033989 \nL 684.043355 209.914276 \nL 684.33831 130.877056 \nL 684.397301 129.3888 \nL 684.515282 154.395323 \nL 684.574273 153.622453 \nL 684.633264 153.366958 \nL 684.751246 143.670942 \nL 684.810237 144.526849 \nL 684.869228 145.631862 \nL 684.98721 163.535639 \nL 685.518128 284.51228 \nL 685.577119 287.61015 \nL 685.63611 285.3107 \nL 685.872074 258.739273 \nL 686.167028 185.95528 \nL 686.402992 153.788524 \nL 686.461983 158.789829 \nL 686.579965 169.884677 \nL 686.638956 163.273757 \nL 686.756937 153.245598 \nL 686.874919 177.613385 \nL 686.93391 171.142987 \nL 687.110883 134.300682 \nL 687.169874 127.044638 \nL 687.228865 134.645599 \nL 687.287856 127.42788 \nL 687.405837 155.449238 \nL 687.700792 124.757963 \nL 688.23171 243.652323 \nL 688.408683 286.313516 \nL 688.467674 287.750672 \nL 688.703638 266.991745 \nL 689.116574 178.322382 \nL 689.175565 179.733989 \nL 689.234556 167.502191 \nL 689.293547 170.140171 \nL 689.52951 189.56414 \nL 689.706483 181.969566 \nL 689.824465 179.504044 \nL 689.883456 179.427396 \nL 689.942447 181.062561 \nL 690.591347 260.51496 \nL 690.827311 289.539134 \nL 691.004283 295.441056 \nL 691.181256 291.455342 \nL 691.41722 215.848136 \nL 691.594193 180.775129 \nL 691.889147 209.697106 \nL 692.007129 194.182203 \nL 692.125111 203.514139 \nL 692.361074 182.333646 \nL 692.538047 212.303149 \nL 692.774011 164.685364 \nL 692.833002 168.313386 \nL 693.009975 187.794841 \nL 693.068965 186.07664 \nL 693.186947 195.676846 \nL 693.540893 273.321621 \nL 693.776856 294.668185 \nL 693.835847 295.038652 \nL 693.894838 294.023062 \nL 693.953829 295.428282 \nL 694.01282 293.160768 \nL 694.248784 210.808507 \nL 694.366766 170.146559 \nL 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