├── data ├── delete.txt └── iot_sensor_dataset.csv ├── doc └── images │ ├── delete.txt │ ├── pi_flow.png │ ├── create_wiot.png │ ├── ftp_data_pi.png │ ├── launch_wiot.png │ ├── iea_arch_flow.png │ ├── import_flow_pi.png │ ├── note_api_key.png │ ├── visit_app_url.png │ ├── DB2_credentials.png │ ├── create_notebook.png │ ├── dash_db_out_node.png │ ├── iea_aainiot_arch.png │ ├── iea_bmx2rpi_flow.png │ ├── iea_nodered_bmx.png │ ├── iea_rpi2bmx_flow.png │ ├── imported_pi_flow.png │ ├── table_definition.png │ ├── configure_api_key.png │ ├── deploy_nodered_flow.png │ ├── iea_bmx2rpi_results.png │ ├── iea_rpi2bmx_results.png │ ├── note_websocket_url.png │ ├── start_node_red_pi.png │ ├── click_watson_iot_node.png │ ├── create_db2_warehouse.png │ ├── enter_api_key_details.png │ ├── iea_nodered_bmx_blank.png │ ├── iea_nodered_bmx_form.png │ ├── iea_nodered_bmx_iotp.png │ ├── iea_nodered_bmx_plan.png │ ├── iea_nodered_bmx_serv.png │ ├── modify_db_credentials.png │ ├── bluemix_service_nodered.png │ ├── click_explore_schema_db.png │ ├── enter_device-credentials.png │ ├── ibm_cloud_node_red_flow.png │ ├── iea_nodered_bmx_editor.png │ ├── iea_nodered_bmx_edlaunch.png │ ├── iea_rpi2bmx_quickstartid.png │ ├── note_device_credentials.png │ ├── click_node_red_import_menu.png │ ├── click_sensor_trigger_event.png │ ├── select_role_data_processor.png │ ├── change_websocket_url_notebook.png │ ├── click_apps_generate_api_key.png │ ├── iea_bmxreceiveiottemp_results.png │ ├── simulate_shutdown-condition.png │ ├── click_watson_iot_commands_node.png │ ├── iea_bmxreceiveiottemp_bmx_devid.png │ └── iea_bmxreceiveiottemp_bmx_flow.png ├── notebooks ├── delete.txt └── watson_iotfailure_prediction_integrated.ipynb ├── DEBUGGING.md ├── configuration ├── RPi │ ├── LEDOnOff.json │ ├── RPi2BMX.json │ └── BMX2RPi.json └── BMX │ └── BMXReceiveProcessIoTTemp.json ├── CONTRIBUTING.md ├── node-red-flow ├── pi_flow.json └── orchestrate_dsx_workflow.json ├── MAINTAINERS.md ├── LICENSE └── README.md 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| 4 | Node-RED 5 | -------- 6 | 7 | * This Code Pattern uses [Raspberry Pi 3 Model B](https://www.raspberrypi.org/products/raspberry-pi-3-model-b/) 8 | * Whie installing Raspbian on Raspberry Pi make sure you installed the correct version. 9 | The version used in this IBMCode pattern can be found [here](http://downloads.raspberrypi.org/raspbian/images/raspbian-2017-08-17/) 10 | * You can refer to a complete list of Raspberry Pi OS archives [here](http://downloads.raspberrypi.org/raspbian/images/) 11 | * Make sure the import of Node-RED ran correctly. Ensure each flow works correctly in the 12 | sequence of data flow before moving on to the next. 13 | * All the flows depend on the outputs from previous flows and also the connectivity. 14 | If there is an issue in any stage stop and resolve the issue before moving further. 15 | Start from the beginning when troubleshooting. Examine the outputs in the debug window in 16 | Node-RED editor thorougly at each stage. 17 | * The flow relies on service credentials from IBM Cloud. 18 | Make sure to add your service credentials correctly. 19 | 20 | 21 | IBM IoT Platform 22 | ---------------- 23 | * Make sure data is received and transmitted correctly between Raspberry Pi 24 | and IBM IoT Platform on the cloud. 25 | * Specifically ensure the Region in which your Service is created in IBM Cloud 26 | is consistent 27 | * Also pay special attention to the unique ``Device ID`` you set on the Raspberry Pi 28 | Node-RED flow as well as the Node-RED flow in IBM Cloud. These must be same and unique 29 | to ensure seamless data transfer. 30 | -------------------------------------------------------------------------------- /configuration/RPi/LEDOnOff.json: -------------------------------------------------------------------------------- 1 | [ 2 | { 3 | "id": "e2527403.a3ad9", 4 | "type": "rpi-gpio out", 5 | "z": "45ff02b1.ba00fc", 6 | "name": "Red - LED", 7 | "pin": "11", 8 | "set": true, 9 | "level": "0", 10 | "out": "out", 11 | "x": 646.7666320800781, 12 | "y": 254.46665954589844, 13 | "wires": [] 14 | }, 15 | { 16 | "id": "38a89cec.276ac4", 17 | "type": "inject", 18 | "z": "45ff02b1.ba00fc", 19 | "name": "On", 20 | "topic": "", 21 | "payload": "1", 22 | "payloadType": "num", 23 | "repeat": "", 24 | "crontab": "", 25 | "once": false, 26 | "x": 177.76666259765625, 27 | "y": 192.01666259765625, 28 | "wires": [ 29 | [ 30 | "e2527403.a3ad9", 31 | "2e89b10b.e88696" 32 | ] 33 | ] 34 | }, 35 | { 36 | "id": "99d1fddc.642fb8", 37 | "type": "inject", 38 | "z": "45ff02b1.ba00fc", 39 | "name": "Off", 40 | "topic": "", 41 | "payload": "0", 42 | "payloadType": "num", 43 | "repeat": "", 44 | "crontab": "", 45 | "once": false, 46 | "x": 183.88333129882812, 47 | "y": 296.8833312988281, 48 | "wires": [ 49 | [ 50 | "e2527403.a3ad9", 51 | "2e89b10b.e88696" 52 | ] 53 | ] 54 | }, 55 | { 56 | "id": "2e89b10b.e88696", 57 | "type": "debug", 58 | "z": "45ff02b1.ba00fc", 59 | "name": "", 60 | "active": true, 61 | "console": "false", 62 | "complete": "false", 63 | "x": 633.7666320800781, 64 | "y": 132.5500030517578, 65 | "wires": [] 66 | } 67 | ] -------------------------------------------------------------------------------- /CONTRIBUTING.md: -------------------------------------------------------------------------------- 1 | ## Contributing In General 2 | 3 | Our project welcomes external contributions! If you have an itch, please 4 | feel free to scratch it. 5 | 6 | To contribute code or documentation, please submit a pull request to the [GitHub 7 | repository](https://github.com/IBM/iot-edge-predictive-models-dsx). 8 | 9 | A good way to familiarize yourself with the codebase and contribution process is 10 | to look for and tackle low-hanging fruit in the [issue 11 | tracker](https://github.com/IBM/iot-edge-predictive-models-dsx/issues). Before embarking on 12 | a more ambitious contribution, please quickly [get in touch](#communication) 13 | with us. 14 | 15 | **We appreciate your effort, and want to avoid a situation where a contribution 16 | requires extensive rework (by you or by us), sits in the queue for a long time, 17 | or cannot be accepted at all!** 18 | 19 | ### Proposing new features 20 | 21 | If you would like to implement a new feature, please [raise an 22 | issue](https://github.com/IBM/iot-edge-predictive-models-dsx/issues) before sending a pull 23 | request so the feature can be discussed. This is to avoid you spending your 24 | valuable time working on a feature that the project developers are not willing 25 | to accept into the code base. 26 | 27 | ### Fixing bugs 28 | 29 | If you would like to fix a bug, please [raise an 30 | issue](https://github.com/IBM/iot-edge-predictive-models-dsx/issues) before sending a pull 31 | request so it can be discussed. If the fix is trivial or non controversial then 32 | this is not usually necessary. 33 | 34 | ### Merge approval 35 | 36 | The project maintainers use LGTM (Looks Good To Me) in comments on the code 37 | review to indicate acceptance. A change requires LGTMs from two of the 38 | maintainers of each component affected. 39 | 40 | For more details, see the [MAINTAINERS](MAINTAINERS.md) page. 41 | 42 | ## Communication 43 | 44 | Please feel free to connect with us: [here](https://github.com/IBM/iot-edge-predictive-models-dsx/issues) 45 | 46 | ## Setup 47 | 48 | Please add any special setup instructions for your project to help the 49 | developer become productive quickly. 50 | 51 | ## Testing 52 | 53 | Please provide information that helps the developer test any changes they 54 | make before submitting. 55 | 56 | ## Coding style guidelines 57 | 58 | Beautiful code rocks! Please share any specific style guidelines you might 59 | have for your project. 60 | -------------------------------------------------------------------------------- /node-red-flow/pi_flow.json: -------------------------------------------------------------------------------- 1 | [{"id":"1bd9c8d5.6c2ca7","type":"wiotp in","z":"cb9d34cc.77518","authType":"d","deviceKey":"33a84934.5a651e","deviceType":"","deviceId":"","command":"+","commandType":"g","qos":0,"name":"","x":113,"y":329.5,"wires":[["d3572d25.05498"]]},{"id":"d3572d25.05498","type":"debug","z":"cb9d34cc.77518","name":"","active":true,"console":"false","complete":"false","x":361,"y":329.75,"wires":[]},{"id":"204757f1.448de8","type":"file in","z":"cb9d34cc.77518","name":"","filename":"/home/pi/iot_sensor_dataset.csv","format":"utf8","x":419,"y":96.5,"wires":[["beebaca6.c19b3"]]},{"id":"beebaca6.c19b3","type":"csv","z":"cb9d34cc.77518","name":"","sep":",","hdrin":true,"hdrout":"","multi":"one","ret":"\\n","temp":"","x":650,"y":94,"wires":[["2bd955c0.357b32","988060ba.46f6c8"]]},{"id":"95845415.d531d","type":"inject","z":"cb9d34cc.77518","name":"Sensor event trigger","topic":"","payload":"","payloadType":"date","repeat":"","crontab":"","once":false,"x":136.5,"y":96.5,"wires":[["204757f1.448de8"]]},{"id":"2bd955c0.357b32","type":"wiotp out","z":"cb9d34cc.77518","authType":"d","qs":"false","qsDeviceId":"","deviceKey":"33a84934.5a651e","deviceType":"","deviceId":"","event":"event","format":"json","qos":"","name":"","x":841,"y":93.75,"wires":[]},{"id":"a4b3638e.9538b8","type":"comment","z":"cb9d34cc.77518","name":"Send sensor events to Watson IoT Platform.","info":"","x":212,"y":41,"wires":[]},{"id":"574ded47.1eb7cc","type":"comment","z":"cb9d34cc.77518","name":"Recieve commands from Watson IoT platform","info":"","x":212,"y":284,"wires":[]},{"id":"f1bc890f.9c8f38","type":"inject","z":"cb9d34cc.77518","name":"Event - Running","topic":"","payload":"{\"footfall\":31,\"atemp\":2,\"selfLR\":7,\"ClinLR\":2,\"DoleLR\":6,\"PID\":6,\"outpressure\":19,\"inpressure\":4,\"temp\":3}","payloadType":"json","repeat":"","crontab":"","once":false,"x":578.5,"y":189.25,"wires":[["2bd955c0.357b32"]]},{"id":"22c60dbe.5a6a92","type":"inject","z":"cb9d34cc.77518","name":"Event - Failing","topic":"","payload":"{\"footfall\":31,\"atemp\":1,\"selfLR\":7,\"ClinLR\":2,\"DoleLR\":6,\"PID\":1,\"outpressure\":20,\"inpressure\":4,\"temp\":1}","payloadType":"json","repeat":"","crontab":"","once":false,"x":580.5,"y":241.25,"wires":[["2bd955c0.357b32"]]},{"id":"988060ba.46f6c8","type":"debug","z":"cb9d34cc.77518","name":"","active":true,"console":"false","complete":"false","x":778.5,"y":26.5,"wires":[]},{"id":"33a84934.5a651e","type":"wiotp-credentials","z":"","name":"","org":"","serverName":"","devType":"Equipment","devId":"Sensors","keepalive":"60","cleansession":true}] 2 | -------------------------------------------------------------------------------- /configuration/RPi/RPi2BMX.json: -------------------------------------------------------------------------------- 1 | [ 2 | { 3 | "id": "95b1e818.738bd8", 4 | "type": "inject", 5 | "z": "582b50ec.ce2268", 6 | "name": "", 7 | "topic": "", 8 | "payload": "", 9 | "payloadType": "date", 10 | "repeat": "5", 11 | "crontab": "", 12 | "once": true, 13 | "x": 109, 14 | "y": 130.5, 15 | "wires": [ 16 | [ 17 | "8e128d15.a06be8" 18 | ] 19 | ] 20 | }, 21 | { 22 | "id": "8e128d15.a06be8", 23 | "type": "exec", 24 | "z": "582b50ec.ce2268", 25 | "command": "vcgencmd", 26 | "addpay": false, 27 | "append": "measure_temp", 28 | "useSpawn": "", 29 | "timer": "", 30 | "name": "getCPUtemp", 31 | "x": 326, 32 | "y": 209, 33 | "wires": [ 34 | [ 35 | "483a2d3.28ae5d4", 36 | "e36fd413.601ef8" 37 | ], 38 | [], 39 | [] 40 | ] 41 | }, 42 | { 43 | "id": "483a2d3.28ae5d4", 44 | "type": "debug", 45 | "z": "582b50ec.ce2268", 46 | "name": "", 47 | "active": false, 48 | "console": "false", 49 | "complete": "payload", 50 | "x": 670, 51 | "y": 196, 52 | "wires": [] 53 | }, 54 | { 55 | "id": "e36fd413.601ef8", 56 | "type": "function", 57 | "z": "582b50ec.ce2268", 58 | "name": "Temperature", 59 | "func": "// Create MQTT message in JSON\nmsg = {\n payload: JSON.stringify(\n {\n d:{\n \"value\" : msg.payload.replace(\"temp=\", \"\").replace(\"\\'C\",\"\").replace(\"\\n\",\"\")}\n }\n )\n};\n\nreturn msg;\n", 60 | "outputs": 1, 61 | "noerr": 0, 62 | "x": 376, 63 | "y": 315.5, 64 | "wires": [ 65 | [ 66 | "483a2d3.28ae5d4", 67 | "47e56175.5c90c8" 68 | ] 69 | ] 70 | }, 71 | { 72 | "id": "47e56175.5c90c8", 73 | "type": "wiotp out", 74 | "z": "582b50ec.ce2268", 75 | "authType": "d", 76 | "qs": "true", 77 | "qsDeviceId": "kpedgetobmx20171207", 78 | "deviceKey": "", 79 | "deviceType": "", 80 | "deviceId": "", 81 | "event": "event", 82 | "format": "json", 83 | "qos": "", 84 | "name": "", 85 | "x": 659, 86 | "y": 314, 87 | "wires": [] 88 | }, 89 | { 90 | "id": "b0b5b4dd.2c35d8", 91 | "type": "comment", 92 | "z": "582b50ec.ce2268", 93 | "name": "IoT on Edge - Sensor data to Bluemix", 94 | "info": "Collect Sensor data and send to IBM Bluemix", 95 | "x": 163, 96 | "y": 69, 97 | "wires": [] 98 | } 99 | ] -------------------------------------------------------------------------------- /MAINTAINERS.md: -------------------------------------------------------------------------------- 1 | ## Maintainers Guide 2 | 3 | This guide is intended for maintainers — anybody with commit access to one or 4 | more Developer Journey repositories. 5 | 6 | ## Methodology: 7 | 8 | A master branch. This branch MUST be releasable at all times. Commits and 9 | merges against this branch MUST contain only bugfixes and/or security fixes. 10 | Maintenance releases are tagged against master. 11 | 12 | A develop branch. This branch contains your proposed changes. 13 | 14 | The remainder of this document details how to merge pull requests to the 15 | repositories. 16 | 17 | ## Merge approval 18 | 19 | The project maintainers use LGTM (Looks Good To Me) in comments on the code 20 | review to indicate acceptance. A change requires LGTMs from two of the members 21 | of the [cda-journey-dev-admins](https://github.com/orgs/IBM/teams/cda-journey-dev-admins) 22 | team. If the code is written by a member, the change only requires one more 23 | LGTM. 24 | 25 | ## Reviewing Pull Requests 26 | 27 | We recommend reviewing pull requests directly within GitHub. This allows a 28 | public commentary on changes, providing transparency for all users. When 29 | providing feedback be civil, courteous, and kind. Disagreement is fine, so 30 | long as the discourse is carried out politely. If we see a record of uncivil 31 | or abusive comments, we will revoke your commit privileges and invite you to 32 | leave the project. 33 | 34 | During your review, consider the following points: 35 | 36 | ### Does the change have impact? 37 | 38 | While fixing typos is nice as it adds to the overall quality of the project, 39 | merging a typo fix at a time can be a waste of effort. 40 | (Merging many typo fixes because somebody reviewed the entire component, 41 | however, is useful!) Other examples to be wary of: 42 | 43 | Changes in variable names. Ask whether or not the change will make 44 | understanding the code easier, or if it could simply a personal preference 45 | on the part of the author. 46 | 47 | Essentially: feel free to close issues that do not have impact. 48 | 49 | ### Do the changes make sense? 50 | 51 | If you do not understand what the changes are or what they accomplish, 52 | ask the author for clarification. Ask the author to add comments and/or 53 | clarify test case names to make the intentions clear. 54 | 55 | At times, such clarification will reveal that the author may not be using 56 | the code correctly, or is unaware of features that accommodate their needs. 57 | If you feel this is the case, work up a code sample that would address the 58 | issue for them, and feel free to close the issue once they confirm. 59 | 60 | ### Is this a new feature? If so: 61 | 62 | Does the issue contain narrative indicating the need for the feature? If not, 63 | ask them to provide that information. Since the issue will be linked in the 64 | changelog, this will often be a user's first introduction to it. 65 | 66 | Are new unit tests in place that test all new behaviors introduced? If not, do 67 | not merge the feature until they are! 68 | Is documentation in place for the new feature? (See the documentation 69 | guidelines). If not do not merge the feature until it is! 70 | Is the feature necessary for general use cases? Try and keep the scope of any 71 | given component narrow. If a proposed feature does not fit that scope, 72 | recommend to the user that they maintain the feature on their own, and close 73 | the request. You may also recommend that they see if the feature gains traction 74 | amongst other users, and suggest they re-submit when they can show such support. 75 | -------------------------------------------------------------------------------- /configuration/RPi/BMX2RPi.json: -------------------------------------------------------------------------------- 1 | [ 2 | { 3 | "id": "d4755d28.f93438", 4 | "type": "comment", 5 | "z": "a34917aa.b0ba6", 6 | "name": "IoT on Edge - Receive command from Bluemix", 7 | "info": "Receive command from Bluemix and \ninitiate Action", 8 | "x": 210, 9 | "y": 53, 10 | "wires": [] 11 | }, 12 | { 13 | "id": "121333e0.a71ef4", 14 | "type": "ibmiot in", 15 | "z": "a34917aa.b0ba6", 16 | "authentication": "quickstart", 17 | "apiKey": "325cb44c.cda34c", 18 | "inputType": "evt", 19 | "deviceId": "kpbmxtoedge20171207", 20 | "applicationId": "", 21 | "deviceType": "+", 22 | "eventType": "+", 23 | "commandType": "", 24 | "format": "json", 25 | "name": "Receive IBM IoT BMX command to Edge", 26 | "service": "quickstart", 27 | "allDevices": "", 28 | "allApplications": "", 29 | "allDeviceTypes": true, 30 | "allEvents": true, 31 | "allCommands": "", 32 | "allFormats": "", 33 | "qos": 0, 34 | "x": 205, 35 | "y": 126, 36 | "wires": [ 37 | [ 38 | "7d925507.4cd9fc", 39 | "f2672dcf.f28f08" 40 | ] 41 | ] 42 | }, 43 | { 44 | "id": "7d925507.4cd9fc", 45 | "type": "debug", 46 | "z": "a34917aa.b0ba6", 47 | "name": "", 48 | "active": false, 49 | "console": "false", 50 | "complete": "false", 51 | "x": 557, 52 | "y": 119, 53 | "wires": [] 54 | }, 55 | { 56 | "id": "f2672dcf.f28f08", 57 | "type": "function", 58 | "z": "a34917aa.b0ba6", 59 | "name": "getAction", 60 | "func": "return {payload:msg.payload.d.action};", 61 | "outputs": 1, 62 | "noerr": 0, 63 | "x": 139, 64 | "y": 217, 65 | "wires": [ 66 | [ 67 | "4b9c00bb.04c71", 68 | "d3b90e66.320688" 69 | ] 70 | ] 71 | }, 72 | { 73 | "id": "4b9c00bb.04c71", 74 | "type": "switch", 75 | "z": "a34917aa.b0ba6", 76 | "name": "Read Command", 77 | "property": "payload", 78 | "propertyType": "msg", 79 | "rules": [ 80 | { 81 | "t": "eq", 82 | "v": "fanon", 83 | "vt": "str" 84 | }, 85 | { 86 | "t": "else" 87 | } 88 | ], 89 | "checkall": "true", 90 | "outputs": 2, 91 | "x": 287.76666259765625, 92 | "y": 327.23333740234375, 93 | "wires": [ 94 | [ 95 | "16c8b586.568b72" 96 | ], 97 | [ 98 | "80a2f35b.90f9b" 99 | ] 100 | ] 101 | }, 102 | { 103 | "id": "16c8b586.568b72", 104 | "type": "template", 105 | "z": "a34917aa.b0ba6", 106 | "name": "On", 107 | "field": "payload", 108 | "fieldType": "msg", 109 | "format": "handlebars", 110 | "syntax": "mustache", 111 | "template": "1", 112 | "x": 510.7666320800781, 113 | "y": 292.3166809082031, 114 | "wires": [ 115 | [ 116 | "55a023b9.11de54", 117 | "ccabdca6.66545" 118 | ] 119 | ] 120 | }, 121 | { 122 | "id": "80a2f35b.90f9b", 123 | "type": "template", 124 | "z": "a34917aa.b0ba6", 125 | "name": "Off", 126 | "field": "payload", 127 | "fieldType": "msg", 128 | "format": "handlebars", 129 | "syntax": "mustache", 130 | "template": "0", 131 | "x": 512.8833312988281, 132 | "y": 351.8833312988281, 133 | "wires": [ 134 | [ 135 | "55a023b9.11de54", 136 | "ccabdca6.66545" 137 | ] 138 | ] 139 | }, 140 | { 141 | "id": "55a023b9.11de54", 142 | "type": "debug", 143 | "z": "a34917aa.b0ba6", 144 | "name": "", 145 | "active": true, 146 | "console": "false", 147 | "complete": "false", 148 | "x": 727.88330078125, 149 | "y": 276.8833312988281, 150 | "wires": [] 151 | }, 152 | { 153 | "id": "d3b90e66.320688", 154 | "type": "debug", 155 | "z": "a34917aa.b0ba6", 156 | "name": "", 157 | "active": true, 158 | "console": "false", 159 | "complete": "false", 160 | "x": 612.88330078125, 161 | "y": 200.88333129882812, 162 | "wires": [] 163 | }, 164 | { 165 | "id": "ccabdca6.66545", 166 | "type": "rpi-gpio out", 167 | "z": "a34917aa.b0ba6", 168 | "name": "Red - LED", 169 | "pin": "11", 170 | "set": true, 171 | "level": "0", 172 | "out": "out", 173 | "x": 755.88330078125, 174 | "y": 395.8833312988281, 175 | "wires": [] 176 | }, 177 | { 178 | "id": "325cb44c.cda34c", 179 | "type": "ibmiot", 180 | "z": "", 181 | "name": "CarKey", 182 | "keepalive": "60", 183 | "domain": "", 184 | "cleansession": true, 185 | "appId": "carsim", 186 | "shared": false 187 | } 188 | ] -------------------------------------------------------------------------------- /node-red-flow/orchestrate_dsx_workflow.json: -------------------------------------------------------------------------------- 1 | [{"id":"6e2a5dcb.d635dc","type":"dashDB out","z":"60cfbb27.7bd1d4","dashDB":"","service":"Db2 Warehouse-9z","table":"EQUIPMENT_DATA","name":"","x":930,"y":420,"wires":[]},{"id":"5d798f0b.b9bac8","type":"ibmiot in","z":"60cfbb27.7bd1d4","authentication":"quickstart","apiKey":"","inputType":"evt","logicalInterface":"","ruleId":"","deviceId":"","applicationId":"","deviceType":"+","eventType":"+","commandType":"","format":"json","name":"IBM IoT","service":"quickstart","allDevices":"","allApplications":"","allDeviceTypes":true,"allLogicalInterfaces":"","allEvents":true,"allCommands":"","allFormats":"","qos":0,"x":234,"y":300,"wires":[["4be54eb3.fe3dd"]]},{"id":"cb0b9fa7.d11e","type":"comment","z":"60cfbb27.7bd1d4","name":"Store data into DB2 Warehouse","info":"","x":950,"y":340,"wires":[]},{"id":"f1a3e54f.bad098","type":"debug","z":"60cfbb27.7bd1d4","name":"Equipment data","active":true,"tosidebar":true,"console":false,"tostatus":false,"complete":"payload","x":860,"y":280,"wires":[]},{"id":"723b895d.4b6558","type":"function","z":"60cfbb27.7bd1d4","name":"Prepare payload for storage","func":"var output = {};\nmsg.payload = msg.data;\noutput.payload =\n{\n FOOTFALL : msg.payload.d.footfall,\n ATEMP : msg.payload.d.atemp,\n SELFLR : msg.payload.d.selfLR,\n CLINLR : msg.payload.d.ClinLR,\n DOLELR : msg.payload.d.DoleLR,\n PID : msg.payload.d.PID,\n OUTPRESSURE : msg.payload.d.outpressure,\n INPRESSURE : msg.payload.d.inpressure,\n TEMP : msg.payload.d.temp,\n FAIL : msg.payload.d.fail\n}\nreturn output;\n","outputs":1,"noerr":0,"x":680,"y":380,"wires":[["f1a3e54f.bad098","6e2a5dcb.d635dc"]]},{"id":"96b17958.01e608","type":"dashDB in","z":"60cfbb27.7bd1d4","dashDB":"","service":"Db2 Warehouse-9z","query":"DELETE FROM EQUIPMENT_DATA","params":"","name":"EQUIPMENT_DATA","x":613,"y":618,"wires":[["b4935a29.af2e98"]]},{"id":"f710affd.dc8e58","type":"inject","z":"60cfbb27.7bd1d4","name":"Delete data in the EQUIPMENT_DATA table","topic":"","payload":"","payloadType":"date","repeat":"","crontab":"","once":false,"onceDelay":0.1,"x":383,"y":538,"wires":[["96b17958.01e608"]]},{"id":"b4935a29.af2e98","type":"debug","z":"60cfbb27.7bd1d4","name":"DB response","active":true,"tosidebar":true,"console":false,"tostatus":false,"complete":"payload","x":854,"y":620,"wires":[]},{"id":"b8077d5f.c314a","type":"comment","z":"60cfbb27.7bd1d4","name":"Delete Table data","info":"","x":304,"y":500,"wires":[]},{"id":"2122d832.7fb5b","type":"websocket in","z":"60cfbb27.7bd1d4","name":"","server":"7e580743.ca826","client":"","x":292,"y":782,"wires":[["b6da6c7b.c861b8","647c9ac0.dd1ddc"]]},{"id":"b6da6c7b.c861b8","type":"function","z":"60cfbb27.7bd1d4","name":"","func":"msg._session=\"\";\nreturn msg;","outputs":1,"noerr":0,"x":572,"y":782,"wires":[["e846ce9.a049d3"]]},{"id":"e846ce9.a049d3","type":"websocket out","z":"60cfbb27.7bd1d4","name":"","server":"7e580743.ca826","client":"","x":842,"y":782,"wires":[]},{"id":"1dfeb46b.246ee4","type":"comment","z":"60cfbb27.7bd1d4","name":"Flow that creates a Web socket server ","info":"This flow creates a web socket server","x":354,"y":720,"wires":[]},{"id":"7fba65bc.f9463c","type":"switch","z":"60cfbb27.7bd1d4","name":"switch","property":"payload","propertyType":"msg","rules":[{"t":"eq","v":"1","vt":"num"},{"t":"eq","v":"0","vt":"num"}],"checkall":"true","repair":false,"outputs":2,"x":650,"y":300,"wires":[["f6aaa516.404f3"],["723b895d.4b6558"]]},{"id":"2bc6ced2.1a6682","type":"websocket out","z":"60cfbb27.7bd1d4","name":"","server":"7e580743.ca826","client":"","x":960,"y":140,"wires":[]},{"id":"4be54eb3.fe3dd","type":"function","z":"60cfbb27.7bd1d4","name":"Backup data","func":"msg.data = msg.payload;\nvar prop = \"fail\";\nif (msg.data.d.hasOwnProperty(prop)) {\n msg.payload = 0;\n}\nelse {\n msg.payload = 1;\n}\nreturn msg;","outputs":1,"noerr":0,"x":434,"y":300,"wires":[["7fba65bc.f9463c"]]},{"id":"f6aaa516.404f3","type":"function","z":"60cfbb27.7bd1d4","name":"Construct message for invoking DSX","func":"var payload = {}\npayload = {\n 'cmd':'predict',\n 'atemp':msg.data.d.atemp,\n 'PID' : msg.data.d.PID,\n 'outpressure': msg.data.d.outpressure,\n 'inpressure' : msg.data.d.inpressure,\n 'temp' : msg.data.d.temp,\n}\nmsg.payload = payload;\nreturn msg;","outputs":1,"noerr":0,"x":790,"y":220,"wires":[["2bc6ced2.1a6682"]]},{"id":"647c9ac0.dd1ddc","type":"function","z":"60cfbb27.7bd1d4","name":"Assign prediction status","func":"var payload = msg.payload;\nvar jsonobj = JSON.parse(payload);\nvar cmd = jsonobj.cmd;\nvar status = \"Ignore\";\nif (cmd == 'response') {\n status = jsonobj.status;\n}\nmsg.status = status;\nreturn msg;","outputs":1,"noerr":0,"x":372,"y":882,"wires":[["e1c0a7ef.cf0c28"]]},{"id":"e5f018bc.747c9","type":"ibmiot out","z":"60cfbb27.7bd1d4","authentication":"quickstart","apiKey":"","outputType":"evt","deviceId":"Sensors","deviceType":"Equipment","eventCommandType":"shutdown","format":"json","data":"{\"cmd\":\"shutdown\"}","qos":0,"name":"IBM IoT","service":"quickstart","x":1042,"y":882,"wires":[]},{"id":"e1c0a7ef.cf0c28","type":"switch","z":"60cfbb27.7bd1d4","name":"","property":"status","propertyType":"msg","rules":[{"t":"eq","v":"0","vt":"str"},{"t":"eq","v":"Failing","vt":"str"},{"t":"eq","v":"Running","vt":"str"}],"checkall":"true","repair":false,"outputs":3,"x":612,"y":882,"wires":[["87527042.e7efc"],["5d1b88e5.62fe78"],["9d606002.fa491"]]},{"id":"5d1b88e5.62fe78","type":"function","z":"60cfbb27.7bd1d4","name":"Construct command","func":"var cmd = {}\ncmd = {\n \"cmd\":\"Shutdown\",\n }\nmsg.payload = cmd;\nreturn msg;","outputs":1,"noerr":0,"x":842,"y":882,"wires":[["e5f018bc.747c9","2a0e86dc.ebd882"]]},{"id":"87527042.e7efc","type":"debug","z":"60cfbb27.7bd1d4","name":"","active":true,"tosidebar":true,"console":false,"tostatus":false,"complete":"false","x":752,"y":842,"wires":[]},{"id":"9d606002.fa491","type":"debug","z":"60cfbb27.7bd1d4","name":"","active":true,"tosidebar":true,"console":false,"tostatus":false,"complete":"status","x":792,"y":942,"wires":[]},{"id":"2a0e86dc.ebd882","type":"debug","z":"60cfbb27.7bd1d4","name":"","active":true,"tosidebar":true,"console":false,"tostatus":false,"complete":"false","x":992,"y":942,"wires":[]},{"id":"81f40670.aa7f5","type":"comment","z":"60cfbb27.7bd1d4","name":"Store data into database / Invoke predctive analytics","info":"If database does not contain all the data, then the predictive analytics is not invoked.","x":374,"y":100,"wires":[]},{"id":"a747455c.71907","type":"ibmiot","z":"","name":"","keepalive":"60","serverName":"","cleansession":true,"appId":"","shared":false},{"id":"7e580743.ca826","type":"websocket-listener","z":"","path":"/ws/orchestrate","wholemsg":"false"}] 2 | -------------------------------------------------------------------------------- /configuration/BMX/BMXReceiveProcessIoTTemp.json: -------------------------------------------------------------------------------- 1 | [ 2 | { 3 | "id": "be58e26e.1faca", 4 | "type": "ibmiot in", 5 | "z": "a60b8a97.0bd088", 6 | "authentication": "quickstart", 7 | "apiKey": "", 8 | "inputType": "evt", 9 | "deviceId": "kpedgetobmx20171207", 10 | "applicationId": "", 11 | "deviceType": "+", 12 | "eventType": "+", 13 | "commandType": "", 14 | "format": "json", 15 | "name": "IBM IoT", 16 | "service": "quickstart", 17 | "allDevices": "", 18 | "allApplications": "", 19 | "allDeviceTypes": true, 20 | "allEvents": true, 21 | "allCommands": "", 22 | "allFormats": "", 23 | "qos": 0, 24 | "x": 90, 25 | "y": 100, 26 | "wires": [ 27 | [ 28 | "c69c0fc3.2a4bf", 29 | "ac0456dc.7855e" 30 | ] 31 | ] 32 | }, 33 | { 34 | "id": "c69c0fc3.2a4bf", 35 | "type": "debug", 36 | "z": "a60b8a97.0bd088", 37 | "name": "", 38 | "active": false, 39 | "console": "false", 40 | "complete": "payload", 41 | "x": 270, 42 | "y": 100, 43 | "wires": [] 44 | }, 45 | { 46 | "id": "ac0456dc.7855e", 47 | "type": "function", 48 | "z": "a60b8a97.0bd088", 49 | "name": "getTemperature", 50 | "func": "return {payload:parseFloat(msg.payload.d.value)};\n", 51 | "outputs": 1, 52 | "noerr": 0, 53 | "x": 280, 54 | "y": 180, 55 | "wires": [ 56 | [ 57 | "496324ba.c81eb4", 58 | "b38bf158.48135" 59 | ] 60 | ] 61 | }, 62 | { 63 | "id": "496324ba.c81eb4", 64 | "type": "debug", 65 | "z": "a60b8a97.0bd088", 66 | "name": "", 67 | "active": true, 68 | "console": "false", 69 | "complete": "false", 70 | "x": 510, 71 | "y": 180, 72 | "wires": [] 73 | }, 74 | { 75 | "id": "b38bf158.48135", 76 | "type": "switch", 77 | "z": "a60b8a97.0bd088", 78 | "name": "Classify", 79 | "property": "payload", 80 | "propertyType": "msg", 81 | "rules": [ 82 | { 83 | "t": "lte", 84 | "v": "30", 85 | "vt": "str" 86 | }, 87 | { 88 | "t": "lte", 89 | "v": "45", 90 | "vt": "str" 91 | }, 92 | { 93 | "t": "gt", 94 | "v": "45", 95 | "vt": "str" 96 | } 97 | ], 98 | "checkall": "true", 99 | "outputs": 3, 100 | "x": 280, 101 | "y": 300, 102 | "wires": [ 103 | [ 104 | "329f834.5f42efc", 105 | "73ad2d84.32d4b4" 106 | ], 107 | [ 108 | "5cbdc1b1.89d878", 109 | "cde85339.6e98f" 110 | ], 111 | [ 112 | "48715687.5744f", 113 | "1d2db6c8.fea829" 114 | ] 115 | ] 116 | }, 117 | { 118 | "id": "329f834.5f42efc", 119 | "type": "template", 120 | "z": "a60b8a97.0bd088", 121 | "name": "Green", 122 | "field": "payload", 123 | "fieldType": "msg", 124 | "format": "handlebars", 125 | "syntax": "mustache", 126 | "template": "Cool: {{payload}} !", 127 | "output": "str", 128 | "x": 470, 129 | "y": 260, 130 | "wires": [ 131 | [ 132 | "a8662c37.b077b8" 133 | ] 134 | ] 135 | }, 136 | { 137 | "id": "5cbdc1b1.89d878", 138 | "type": "template", 139 | "z": "a60b8a97.0bd088", 140 | "name": "Amber", 141 | "field": "payload", 142 | "fieldType": "msg", 143 | "format": "handlebars", 144 | "syntax": "mustache", 145 | "template": "Warm: {{payload}} !", 146 | "output": "str", 147 | "x": 471, 148 | "y": 298, 149 | "wires": [ 150 | [ 151 | "a8662c37.b077b8" 152 | ] 153 | ] 154 | }, 155 | { 156 | "id": "48715687.5744f", 157 | "type": "template", 158 | "z": "a60b8a97.0bd088", 159 | "name": "Red", 160 | "field": "payload", 161 | "fieldType": "msg", 162 | "format": "handlebars", 163 | "syntax": "mustache", 164 | "template": "Hot: {{payload}} !", 165 | "output": "str", 166 | "x": 471, 167 | "y": 338, 168 | "wires": [ 169 | [ 170 | "a8662c37.b077b8" 171 | ] 172 | ] 173 | }, 174 | { 175 | "id": "a8662c37.b077b8", 176 | "type": "debug", 177 | "z": "a60b8a97.0bd088", 178 | "name": "", 179 | "active": true, 180 | "console": "false", 181 | "complete": "false", 182 | "x": 670, 183 | "y": 300, 184 | "wires": [] 185 | }, 186 | { 187 | "id": "cb73afe4.516eb8", 188 | "type": "comment", 189 | "z": "a60b8a97.0bd088", 190 | "name": "IoT on IBM BMX - Analytics in Bluemix to Edge", 191 | "info": "Analyze data received from edge layer in \nBluemix and return a command of action", 192 | "x": 210, 193 | "y": 37, 194 | "wires": [] 195 | }, 196 | { 197 | "id": "73ad2d84.32d4b4", 198 | "type": "template", 199 | "z": "a60b8a97.0bd088", 200 | "name": "Green", 201 | "field": "payload", 202 | "fieldType": "msg", 203 | "format": "handlebars", 204 | "syntax": "mustache", 205 | "template": "Cool", 206 | "output": "str", 207 | "x": 470, 208 | "y": 400, 209 | "wires": [ 210 | [ 211 | "82404a7.f8acc38", 212 | "17420063.a494a8" 213 | ] 214 | ] 215 | }, 216 | { 217 | "id": "cde85339.6e98f", 218 | "type": "template", 219 | "z": "a60b8a97.0bd088", 220 | "name": "Amber", 221 | "field": "payload", 222 | "fieldType": "msg", 223 | "format": "handlebars", 224 | "syntax": "mustache", 225 | "template": "Warm", 226 | "output": "str", 227 | "x": 471, 228 | "y": 438, 229 | "wires": [ 230 | [ 231 | "82404a7.f8acc38", 232 | "17420063.a494a8" 233 | ] 234 | ] 235 | }, 236 | { 237 | "id": "1d2db6c8.fea829", 238 | "type": "template", 239 | "z": "a60b8a97.0bd088", 240 | "name": "Red", 241 | "field": "payload", 242 | "fieldType": "msg", 243 | "format": "handlebars", 244 | "syntax": "mustache", 245 | "template": "Hot", 246 | "output": "str", 247 | "x": 471, 248 | "y": 478, 249 | "wires": [ 250 | [ 251 | "82404a7.f8acc38", 252 | "17420063.a494a8" 253 | ] 254 | ] 255 | }, 256 | { 257 | "id": "82404a7.f8acc38", 258 | "type": "debug", 259 | "z": "a60b8a97.0bd088", 260 | "name": "", 261 | "active": true, 262 | "console": "false", 263 | "complete": "false", 264 | "x": 670, 265 | "y": 420, 266 | "wires": [] 267 | }, 268 | { 269 | "id": "17420063.a494a8", 270 | "type": "function", 271 | "z": "a60b8a97.0bd088", 272 | "name": "Action Comand to Edge", 273 | "func": "// Create MQTT message in JSON\nvar act;\nif (msg.payload ==\"Hot\")\n act = \"fanon\";\nelse\n act = \"fanoff\";\n\nmsg = {\n payload: JSON.stringify(\n {\n d:{\n \"action\" : act}\n }\n )\n};\n\nreturn msg;\n", 274 | "outputs": 1, 275 | "noerr": 0, 276 | "x": 731.36669921875, 277 | "y": 548, 278 | "wires": [ 279 | [ 280 | "6b6a2b30.265b34", 281 | "82849ad1.6e6be" 282 | ] 283 | ] 284 | }, 285 | { 286 | "id": "6b6a2b30.265b34", 287 | "type": "debug", 288 | "z": "a60b8a97.0bd088", 289 | "name": "", 290 | "active": true, 291 | "console": "false", 292 | "complete": "false", 293 | "x": 990, 294 | "y": 480, 295 | "wires": [] 296 | }, 297 | { 298 | "id": "82849ad1.6e6be", 299 | "type": "ibmiot out", 300 | "z": "a60b8a97.0bd088", 301 | "authentication": "quickstart", 302 | "apiKey": "", 303 | "outputType": "evt", 304 | "deviceId": "kpbmxtoedge20171207", 305 | "deviceType": "0.17.5", 306 | "eventCommandType": "action command", 307 | "format": "json", 308 | "data": "JSON", 309 | "qos": 0, 310 | "name": "IoT BMX Command to Edge", 311 | "service": "quickstart", 312 | "x": 1010, 313 | "y": 580, 314 | "wires": [] 315 | } 316 | ] -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. Definitions. 8 | 9 | "License" shall mean the terms and conditions for use, reproduction, 10 | and distribution as defined by Sections 1 through 9 of this document. 11 | 12 | "Licensor" shall mean the copyright owner or entity authorized by 13 | the copyright owner that is granting the License. 14 | 15 | "Legal Entity" shall mean the union of the acting entity and all 16 | other entities that control, are controlled by, or are under common 17 | control with that entity. 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In general application areas, IoT data collected by Sensors can be used for monitoring as well as predicting the outcomes. If any deviation from the norm is detected, corrective action can be prescribed either manually or by an automated process. Such actions may come out of rule based anomaly detection or a Statistical Change point detection or a Predictive model that predicts a faulty condition ahead of time. This approach goes a long way in implementing Predictive maintenance which is more prudent approach than Scheduled Preventive maintenance which is periodic in nature. 4 | 5 | The end to end process steps for applying Analytics on IoT data are listed below: 6 | 1. Collect IoT data from sensor 7 | 2. Change point detection using IoT Sensor data. Refer code pattern [Detect change points in IoT sensor data](https://developer.ibm.com/code/patterns/detect-change-points-in-iot-sensor-data/) for more details. 8 | 3. Predicting equipment failure using IoT Sensor data. Refer code pattern [Predict equipment failure using IoT sensor data](https://developer.ibm.com/code/patterns/predict-equipment-failure-using-iot-sensor-data/) for more details. 9 | 4. Sending decisions based on Analytics insights to the edge for automated action 10 | 11 | This IBM Code Pattern is a composite pattern that demostrates the building of complete IoT analytics solution. 12 | When you complete this code pattern, you will learn how to: 13 | * Send events from an edge device (we use Raspberry Pi for demonstration) to the Watson IoT Platform 14 | * Store the events in a DB2 database on IBM Cloud 15 | * Invoke a predictive model on Watson Studio for IoT events using the below code patterns: 16 | - [Predict equipment failure using IoT sensor data](https://developer.ibm.com/code/patterns/predict-equipment-failure-using-iot-sensor-data/) 17 | - [Orchestrate data science workflows using Node-RED](https://github.com/IBM/node-red-dsx-workflow) 18 | * Send a command back to the edge (we use Raspberry Pi for demonstration) based on the outcome of the predictive model 19 | 20 | This pattern uses a sample equipment sensors data. This data is sent to the Watson IoT platform and stored in a DB2 database. A predictive model is built using the data in the DB2 database. The predictive model then takes the sensor events from Watson IoT platform as input and returns the state of the equipment as `Running` or `Failing`. If the equipment is failing, then a shutdown command is sent back to the edge device which is a Raspberry Pi. 21 | 22 | This pattern uses [Node-RED](https://nodered.org/) at both device and cloud for building the solution: 23 | - Implementing device client on Raspberry Pi to send events to Watson IoT platform 24 | - Consuming events from Watson IoT platform on IBM Cloud and storing the events in a DB2 database 25 | - Invoke predictive model on Watson Studio and get a response back for an IoT event 26 | - Send a command back to the Raspberry Pi through the Watson IoT platform 27 | 28 | ## Prerequisites: 29 | * [Connect Raspberry Pi to the network and Note IP address for accessing the Pi](https://www.raspberrypi.org/documentation/) 30 | * [Running Node-RED on Raspberry Pi](https://nodered.org/docs/hardware/raspberrypi) 31 | 32 | ## Flow 33 | ![png](doc/images/iea_arch_flow.png) 34 | 35 | 1. The Raspberry Pi gets events from the sensors. In the absence of sensors, the sensor events are read from a file. 36 | 2. The Node-RED flows are invoked on the Raspberry Pi. 37 | 3. The sensor events are sent to the Watson IoT platform. 38 | 4. The Watson IoT platform receives the events and sends it to all subscribing applications. 39 | 5. The Node-RED flows on IBM Cloud are triggered. The sensor events are recieved and stored into a database. 40 | 6. The predictive model on Watson Studio is triggered. The outcome of the model execution is sent back to the Node-RED through websockets. 41 | 7. Based on the outcome, the Node-RED flow sends a command with the action to be taken to the edge device(Raspberry Pi) through the Watson IoT platform 42 | 8. The Node-RED flow on Raspberry Pi recieve the command 43 | 44 | ## Included Components 45 | * [IBM Cloud](https://console.bluemix.net/catalog/): IBM's innovative cloud computing platform or IBM Cloud (formerly Bluemix) combines 46 | platform as a service (PaaS) with infrastructure as a service (IaaS) and includes a rich catalog of 47 | cloud services that can be easily integrated with PaaS and IaaS to build business applications rapidly. 48 | * [IBM Watson IoT Platform](https://internetofthings.ibmcloud.com/): IBM Watson™ IoT Platform for IBM Cloud gives you a versatile 49 | toolkit that includes gateway devices, device management, and powerful application access. By using 50 | Watson IoT Platform, you can collect connected device data and perform analytics on real-time data 51 | from your organization. 52 | * [IBM Watson Studio](https://www.ibm.com/bs-en/marketplace/data-science-experience): Analyze data using Python, Jupyter Notebook 53 | and RStudio in a configured, collaborative environment that includes IBM value-adds, such as managed Spark. 54 | * [DB2 Warehouse](https://console.bluemix.net/catalog/services/db2-warehouse): IBM Db2 Warehouse on Cloud is a fully-managed, enterprise-class, cloud data warehouse service. 55 | 56 | ## Featured Technologies 57 | 58 | * [Analytics](https://developer.ibm.com/code/technologies/analytics?cm=IBMCode-_--_-featured_technologies-_-analytics): Finding patterns in data to derive information. 59 | * [Data Science](https://developer.ibm.com/code/technologies/data-science?cm=IBMCode-_--_-featured_technologies-_-data-science): Systems and scientific methods to analyze structured and unstructured data in 60 | order to extract knowledge and insights. 61 | * [Internet of Things](https://en.wikipedia.org/wiki/Internet_of_things) 62 | 63 | ## Watch the Video 64 | 65 | * [Video](https://youtu.be/2CJcqMPIFaY) 66 | 67 | ## Steps 68 | 69 | 1. [Create IBM Cloud services and configure](#1-create-ibm-cloud-services-and-configure) 70 | 2. [Configure Raspberry Pi](#2-configure-raspberry-pi) 71 | 3. [Trigger the Node-RED flow on Raspberry Pi](#3-trigger-the-node-red-flow-on-raspberry-pi) 72 | 4. [Run the notebook](#4-run-the-notebook) 73 | 5. [Analyze results](#5-analyze-results) 74 | 75 | ### 1. Create IBM Cloud services and configure 76 | 77 | #### Internet of Things Platform 78 | * Click on [Internet of Things Platform](https://console.bluemix.net/catalog/services/internet-of-things-platform) and create an instance of Internet of Things Platform. 79 | ![png](doc/images/create_wiot.png) 80 | 81 | * Click on `Launch` to launch the `Dashboard` 82 | ![png](doc/images/launch_wiot.png) 83 | 84 | * Create a device type `Equipment` and device `Sensors`. 85 | Refer [documentation](https://console.bluemix.net/docs/services/IoT/getting-started.html#getting-started-with-iotp) and [article](https://developer.ibm.com/recipes/tutorials/how-to-register-devices-in-ibm-iot-foundation/). 86 | ![png](doc/images/note_device_credentials.png) 87 | 88 | * Note down the device credentials. They cannot be retrieved later. 89 | > The device credentials will be used later to configure Node-RED. 90 | 91 | * Click on `Apps` on the menu. 92 | ![png](doc/images/click_apps_generate_api_key.png) 93 | 94 | * Click on `Generate API Key`. Click `Next`. 95 | * Select the role as `Data processor application`. 96 | ![png](doc/images/select_role_data_processor.png) 97 | 98 | * Make a note of the `API Key` and `Authentication Token`. This will be needed in the Node-RED flow configuration in the subsequent steps. 99 | ![png](doc/images/note_api_key.png) 100 | 101 | 102 | #### DB2 Warehouse 103 | * Create a [DB2 Warehouse](https://console.bluemix.net/catalog/services/db2-warehouse) instance. 104 | ![png](doc/images/create_db2_warehouse.png) 105 | > Make a note of the service name. This needs to be bound to Node-RED that is created in the next step. 106 | * Click on `Service Credentials`. Click on `New Credential`. Click on `View Credentials`. 107 | ![png](doc/images/DB2_credentials.png) 108 | > Make a note of the database credentials to be entered into Watson Studio notebook later. 109 | 110 | * Click on `Manage` 111 | * Click on `Open` to launch the `Dashboard` 112 | * Click on `Explore`. 113 | * Click on the schema starting with `DASH`. 114 | ![png](doc/images/click_explore_schema_db.png) 115 | > Make a note of the schema name to be configured later on Watson Studio. 116 | 117 | * Click on `New Table` and create a table `EQUIPMENT_DATA` with the configuration as shown. 118 | ![png](doc/images/table_definition.png) 119 | 120 | #### Node-RED on IBM Cloud 121 | * Create the [Node-RED Starter application](https://console.bluemix.net/catalog/starters/node-red-starter). 122 | * Choose an appropriate name for the Node-RED application - `App name:`. 123 | * Click on `Create`. 124 | 125 | ![png](doc/images/bluemix_service_nodered.png) 126 | 127 | * On the newly created Node-RED application page, click on `Connections`. 128 | * Click on `Create Connection` and select the DB2 Warehouse service that was created in the previous step. Click on `Connect`. This binds the DB2 Warehouse service to Node-RED. 129 | * Click on `Getting Started`. 130 | * Click on `Visit App URL` to launch the Node-RED editor once the application is in `Running` state. 131 | * On the `Welcome to your new Node-RED instance on IBM Cloud` screen, Click on `Next`. 132 | * On the `Secure your Node-RED editor` screen, enter a username and password to secure the Node-RED editor and click on `Next`. 133 | * On the `Browse available IBM Cloud nodes` screen, click on `Next`. 134 | * On the `Finish the install` screen, click on Finish. 135 | * Click on `Go to your Node-RED flow editor`. 136 | 137 | ##### Import the Node-RED flow 138 | 139 | * [Clone this repo](https://github.com/IBM/iot-edge-predictive-models-dsx). 140 | * Navigate to the [orchestrate_dsx_workflow.json](node-red-flow/orchestrate_dsx_workflow.json). 141 | * Open the file with a text editor and copy the contents to Clipboard. 142 | * On the Node-RED flow editor, click the Menu and select `Import` -> `Clipboard` and paste the contents. 143 | ![png](doc/images/click_node_red_import_menu.png) 144 | 145 | The imported Node-RED flow appears on the editor. 146 | ![png](doc/images/ibm_cloud_node_red_flow.png) 147 |
148 |
149 | * On the two DB2 nodes named `EQUIPMENT_DATA`. Select the DB2 Warehouse service. 150 | ![png](doc/images/dash_db_out_node.png) 151 | 152 | * Configure the two IoT nodes with the API Key and Authentication Token. Click on `Edit` icon shown in the image. 153 | ![png](doc/images/configure_api_key.png) 154 | 155 | * Enter the `API Key` and `Authentication Token` noted earlier. 156 | ![png](doc/images/enter_api_key_details.png) 157 | 158 | ##### Deploy the Node-RED flow by clicking on the `Deploy` button 159 | 160 | ![png](doc/images/deploy_nodered_flow.png) 161 | 162 | ##### Note the websocket URL 163 | 164 | ![png](doc/images/note_websocket_url.png) 165 | 166 | The websocket URL is ws://``/ws/orchestrate where the `NODERED_BASE_URL` is the marked portion of the URL in the above image. 167 | ### Note: 168 | An example websocket URL for a Node-RED app with name `myApp` is `ws://myApp.mybluemix.net/ws/orchestrate`, where `myApp.mybluemix.net` is the `NODERED_BASE_URL`. 169 | 170 | The `NODERED_BASE_URL` may have additional region information i.e. `eu-gb` for the UK region. In this case `NODERED_BASE_URL` would be: `myApp.eu-gb.mybluemix.net`. 171 | 172 | #### Watson Studio 173 | * Sign up for IBM's [Watson Studio](https://dataplatform.ibm.com/). 174 | * Create a project if necessary, provisioning an object storage service if required. 175 | * In the `Assets` tab, select the `Create notebook` option. 176 | * Select the `From URL` tab. 177 | * Enter a name for the notebook. 178 | * Optionally, enter a description for the notebook. 179 | * Enter this Notebook URL: https://github.com/IBM/iot-edge-predictive-models-dsx/blob/master/notebooks/watson_iotfailure_prediction_integrated.ipynb 180 | * Select the free runtime. 181 | * Click the `Create` button. 182 | ![png](doc/images/create_notebook.png) 183 | 184 | * In Section 7. of the notebook, enter the websocket URL noted earlier. 185 | ![png](doc/images/change_websocket_url_notebook.png) 186 | 187 | * In Section 4. of the notebook, enter the database credentials for DB2 Warehouse noted earlier. 188 | ![png](doc/images/modify_db_credentials.png) 189 | 190 | ### 2. Configure Raspberry Pi 191 | 192 | #### Copy the data file to Raspberry Pi and start Node-RED 193 | 194 | The data file can be found at the location - https://github.com/IBM/iot-edge-predictive-models-dsx/blob/master/data. Using ftp the file `iot_sensor_dataset.csv` is transferred to the Pi. The file is stored at the location `/home/pi`. After that Node-RED is started by running the command `node-red`. 195 | 196 | ##### Copy data file 197 | ![png](doc/images/ftp_data_pi.png) 198 | 199 | ##### Start Node-RED 200 | ![png](doc/images/start_node_red_pi.png) 201 | 202 | #### Configure Node-RED on the Raspberry Pi 203 | 204 | * Navigate to the [pi_flow.json](node-red-flow/pi_flow.json). 205 | * Open the file with a text editor and copy the contents to Clipboard. 206 | * Access Node-RED using the IP address of the RaspberryPi as shown below. 207 | * On the Node-RED flow editor, click the Menu and select `Import` -> `Clipboard` and paste the contents. 208 | ![png](doc/images/click_node_red_import_menu.png) 209 | 210 | The below flow will be imported into the Node-RED editor. 211 | ![png](doc/images/pi_flow.png) 212 | 213 | * Click on the `event` node. 214 | 215 | ![png](doc/images/click_watson_iot_node.png) 216 | 217 | * Configure the device credentials noted earlier. 218 | ![png](doc/images/enter_device-credentials.png) 219 | 220 | * Click on the `all commands` node. Select the credentials configured in the previous step. 221 | ![png](doc/images/click_watson_iot_commands_node.png) 222 | 223 | * Click on `Deploy` to deploy the Node-RED flow. 224 | 225 | ## 3. Trigger the Node-RED flow on Raspberry Pi 226 | Click on the inject node `Sensor event trigger`. This will send sensor events to the Watson IoT Platform. These events will get stored in the DB2 Warehouse. 227 | ![png](doc/images/click_sensor_trigger_event.png) 228 | 229 | ## 4. Run the notebook 230 | > Note: Only after the previous step (3. Trigger the Node-RED flow on Raspberry Pi) is complete and all events are stored into the DB2 Warehouse, run the cells in the notebook. The data in the DB2 Warehouse is then used for building the model. 231 | 232 | When a notebook is executed, what is actually happening is that each code cell in the notebook is executed, in order, from top to bottom. 233 | 234 | Each code cell is selectable and is preceded by a tag in the left margin. The tag 235 | format is `In [x]:`. Depending on the state of the notebook, the `x` can be: 236 | 237 | * A `blank`, this indicates that the cell has never been executed. 238 | * A `number`, this number represents the relative order this code step was executed. 239 | * A `*`, this indicates that the cell is currently executing. 240 | 241 | There are several ways to execute the code cells in your notebook: 242 | 243 | * One cell at a time. 244 | * Select the cell, and then press the `Play` button in the toolbar. 245 | * Batch mode, in sequential order. 246 | * From the `Cell` menu bar, there are several options available. For example, you 247 | can `Run All` cells in your notebook, or you can `Run All Below`, that will 248 | start executing from the first cell under the currently selected cell, and then 249 | continue executing all cells that follow. 250 | * At a scheduled time. 251 | * Press the `Schedule` button located in the top right section of your notebook 252 | panel. Here you can schedule your notebook to be executed once at some future 253 | time, or repeatedly at your specified interval. 254 | 255 | For this Notebook, you can simply `Run All` cells. 256 | The websocket client will be started when you run the cell under `7. Start websocket client`. This will start the communication between the UI and the Notebook. 257 | 258 | ## 5. Analyze results 259 | Go to the Node-RED flow on the Raspberry Pi. 260 | Click on the inject node `Event - Running`. This sends an event with values indicating a good health to the Watson IoT Platform. 261 | Click on the inject node `Event - Failing`. This sends an event with values indicating a failing health to the Watson IoT Platform. A shutdown command is received from the Watson IoT platform after running of the predictive model. 262 | ![png](doc/images/simulate_shutdown-condition.png) 263 | 264 | ## 6. Troubleshooting 265 | See [Debugging.md](https://github.com/IBM/iot-edge-predictive-models-dsx/blob/master/DEBUGGING.md) 266 | 267 | ## 7. License 268 | This code pattern is licensed under the Apache Software License, Version 2. Separate third party code objects invoked within this code pattern are licensed by their respective providers pursuant to their own separate licenses. Contributions are subject to the [Developer Certificate of Origin, Version 1.1 (DCO)](https://developercertificate.org/) and the [Apache Software License, Version 2](http://www.apache.org/licenses/LICENSE-2.0.txt). 269 | 270 | [Apache Software License (ASL) FAQ](http://www.apache.org/foundation/license-faq.html#WhatDoesItMEAN) 271 | 272 | -------------------------------------------------------------------------------- /notebooks/watson_iotfailure_prediction_integrated.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": { 6 | "collapsed": true 7 | }, 8 | "source": [ 9 | "# IoT Equipment Failure Prediction using Sensor data\n", 10 | "## 1 Environment Setup\n", 11 | "### 1.1 Install packages and Import dependent libraries" 12 | ] 13 | }, 14 | { 15 | "cell_type": "code", 16 | "execution_count": 1, 17 | "metadata": {}, 18 | "outputs": [ 19 | { 20 | "name": "stdout", 21 | "output_type": "stream", 22 | "text": [ 23 | "Collecting websocket-client\n", 24 | " Downloading https://files.pythonhosted.org/packages/8a/a1/72ef9aa26cfe1a75cee09fc1957e4723add9de098c15719416a1ee89386b/websocket_client-0.48.0-py2.py3-none-any.whl (198kB)\n", 25 | "\u001b[K 100% |████████████████████████████████| 204kB 3.8MB/s eta 0:00:01\n", 26 | "\u001b[?25hRequirement not upgraded as not directly required: six in /opt/conda/envs/DSX-Python35/lib/python3.5/site-packages (from websocket-client)\n", 27 | "Installing collected packages: websocket-client\n", 28 | "Successfully installed websocket-client-0.48.0\n" 29 | ] 30 | } 31 | ], 32 | "source": [ 33 | "!pip install websocket-client\n", 34 | "\n", 35 | "# Import libraries\n", 36 | "import pandas as pd\n", 37 | "import numpy as np\n", 38 | "import pdb\n", 39 | "import json\n", 40 | "import re\n", 41 | "import requests\n", 42 | "import sys\n", 43 | "import types\n", 44 | "import ibm_boto3\n", 45 | "import websocket" 46 | ] 47 | }, 48 | { 49 | "cell_type": "code", 50 | "execution_count": 2, 51 | "metadata": {}, 52 | "outputs": [ 53 | { 54 | "name": "stderr", 55 | "output_type": "stream", 56 | "text": [ 57 | "/opt/conda/envs/DSX-Python35/lib/python3.5/site-packages/sklearn/cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n", 58 | " \"This module will be removed in 0.20.\", DeprecationWarning)\n" 59 | ] 60 | } 61 | ], 62 | "source": [ 63 | "# Import libraries\n", 64 | "from io import StringIO\n", 65 | "from sklearn.cross_validation import train_test_split\n", 66 | "from sklearn.linear_model import LogisticRegression\n", 67 | "from sklearn import metrics\n", 68 | "from botocore.client import Config" 69 | ] 70 | }, 71 | { 72 | "cell_type": "markdown", 73 | "metadata": {}, 74 | "source": [ 75 | "## 2 Create IoT Predictive Analytics Functions" 76 | ] 77 | }, 78 | { 79 | "cell_type": "code", 80 | "execution_count": 3, 81 | "metadata": {}, 82 | "outputs": [], 83 | "source": [ 84 | "# Function to extract Column names of dataset\n", 85 | "def dataset_columns(dataset):\n", 86 | " return list(dataset.columns.values)\n", 87 | "\n", 88 | "# Function to train Logistic regression model\n", 89 | "def train_logistic_regression(x_vals, y_vals):\n", 90 | " logistic_regression_model = LogisticRegression()\n", 91 | " logistic_regression_model.fit(x_vals, y_vals)\n", 92 | " return logistic_regression_model\n", 93 | "\n", 94 | "# Function to return Predicted values\n", 95 | "def score_data(trained_model, x_vals):\n", 96 | " ypredict = trained_model.predict(x_vals)\n", 97 | " return ypredict\n", 98 | "\n", 99 | "# Function to calculate Prediction accuracy of model\n", 100 | "def model_accuracy(trained_model, variables, targets):\n", 101 | " accuracy_score = trained_model.score(variables, targets)\n", 102 | " return accuracy_score\n", 103 | "\n", 104 | "# Function to generate Confusion matrix\n", 105 | "def confusion_matrix(actfail, predictfail):\n", 106 | " # Compute Confusion matrix\n", 107 | " print(\"Actual, Predicted Observations: \",len(actfail), len(predictfail))\n", 108 | " # print(actfail, predictfail)\n", 109 | " anpn = 0\n", 110 | " anpy = 0\n", 111 | " aypn = 0\n", 112 | " aypy = 0\n", 113 | " \n", 114 | " for i in range(len(actfail)):\n", 115 | " if (actfail[i]==0 and predictfail[i]==0):\n", 116 | " anpn = anpn + 1\n", 117 | " elif (actfail[i]==0 and predictfail[i]==1):\n", 118 | " anpy = anpy + 1\n", 119 | " elif (actfail[i]==1 and predictfail[i]==0):\n", 120 | " aypn = aypn + 1\n", 121 | " else:\n", 122 | " aypy = aypy + 1\n", 123 | " # Confusoin matrix\n", 124 | " print (\"--------------------------------------------\")\n", 125 | " print (\"Confusion Matrix\")\n", 126 | " print (\"--------------------------------------------\")\n", 127 | " print (\" \", \"Predicted N\", \"Predicted Y\")\n", 128 | " print (\"Actual N \", anpn,\" \", anpy) \n", 129 | " print (\"Actual Y \", aypn,\" \", aypy)\n", 130 | " print (\"--------------------------------------------\")\n", 131 | " print (\"Total observations : \", anpn+anpy+aypn+aypy)\n", 132 | " print (\"False Positives : \", anpy)\n", 133 | " print (\"False Negatives : \", aypn)\n", 134 | " print (\"Overall Accuracy : \", round((float(anpn+aypy)/float(anpn+anpy+aypn+aypy))*100, 2), \"%\")\n", 135 | " print (\"Sensitivity/Recall : \", round((float(aypy)/float(aypn+aypy))*100, 2), \"%\")\n", 136 | " print (\"Specificity : \", round((float(anpn)/float(anpn+anpy))*100, 2), \"%\")\n", 137 | " print (\"Precision : \", round((float(aypy)/float(anpy+aypy))*100, 2), \"%\")\n", 138 | " print (\"--------------------------------------------\")\n" 139 | ] 140 | }, 141 | { 142 | "cell_type": "markdown", 143 | "metadata": {}, 144 | "source": [ 145 | "## 3. Read Configuration parametric values" 146 | ] 147 | }, 148 | { 149 | "cell_type": "code", 150 | "execution_count": 4, 151 | "metadata": {}, 152 | "outputs": [], 153 | "source": [ 154 | "# Function to Read json parametric values\n", 155 | "def f_getconfigval(injsonstr, invarname):\n", 156 | " # paramname, paramvalue\n", 157 | " # Unpack the json parameter values\n", 158 | " # This section requires regex\n", 159 | " for i in range(len(injsonstr)):\n", 160 | " pair = injsonstr[i]\n", 161 | " # Return parametric value\n", 162 | " if pair['paramname'] == invarname:\n", 163 | " return(pair['paramvalue'])" 164 | ] 165 | }, 166 | { 167 | "cell_type": "code", 168 | "execution_count": 5, 169 | "metadata": {}, 170 | "outputs": [ 171 | { 172 | "name": "stdout", 173 | "output_type": "stream", 174 | "text": [ 175 | "[{'paramname': 'features', 'paramvalue': \"['ATEMP', 'PID', 'OUTPRESSURE', 'INPRESSURE', 'TEMP']\"}, {'paramname': 'target', 'paramvalue': 'FAIL'}, {'paramname': 'data_size', 'paramvalue': '0.7'}]\n" 176 | ] 177 | } 178 | ], 179 | "source": [ 180 | "# Configuration parameters\n", 181 | "\n", 182 | "d = [{'paramvalue': \"['ATEMP', 'PID', 'OUTPRESSURE', 'INPRESSURE', 'TEMP']\", 'paramname': 'features'}, {'paramvalue': 'FAIL', 'paramname': 'target'}, {'paramvalue': '0.7', 'paramname': 'data_size'}]\n", 183 | "print(d)\n" 184 | ] 185 | }, 186 | { 187 | "cell_type": "code", 188 | "execution_count": 6, 189 | "metadata": {}, 190 | "outputs": [], 191 | "source": [ 192 | "# Read JSON configuration parametric values\n", 193 | "# Unpack the json parameter values\n", 194 | "# This section uses regex\n", 195 | "v_feature_list = eval(\"list(\"+ f_getconfigval(d, \"features\") +\")\")\n", 196 | "v_target = str(f_getconfigval(d, \"target\"))\n", 197 | "v_train_datasize = float(f_getconfigval(d, \"data_size\"))\n" 198 | ] 199 | }, 200 | { 201 | "cell_type": "code", 202 | "execution_count": 7, 203 | "metadata": {}, 204 | "outputs": [ 205 | { 206 | "name": "stdout", 207 | "output_type": "stream", 208 | "text": [ 209 | "['ATEMP', 'PID', 'OUTPRESSURE', 'INPRESSURE', 'TEMP'] FAIL 0.7\n" 210 | ] 211 | } 212 | ], 213 | "source": [ 214 | "# Verify configuration parametric values\n", 215 | "# print (feature_list, target, train_datasize)\n", 216 | "print (v_feature_list, v_target, v_train_datasize)" 217 | ] 218 | }, 219 | { 220 | "cell_type": "markdown", 221 | "metadata": {}, 222 | "source": [ 223 | "## 4 Read IoT Sensor data from database" 224 | ] 225 | }, 226 | { 227 | "cell_type": "code", 228 | "execution_count": 8, 229 | "metadata": {}, 230 | "outputs": [ 231 | { 232 | "name": "stdout", 233 | "output_type": "stream", 234 | "text": [ 235 | "Number of Observations : 540\n" 236 | ] 237 | }, 238 | { 239 | "data": { 240 | "text/html": [ 241 | "
\n", 242 | "\n", 255 | "\n", 256 | " \n", 257 | " \n", 258 | " \n", 259 | " \n", 260 | " \n", 261 | " \n", 262 | " \n", 263 | " \n", 264 | " \n", 265 | " \n", 266 | " \n", 267 | " \n", 268 | " \n", 269 | " \n", 270 | " \n", 271 | " \n", 272 | " \n", 273 | " \n", 274 | " \n", 275 | " \n", 276 | " \n", 277 | " \n", 278 | " \n", 279 | " \n", 280 | " \n", 281 | " \n", 282 | " \n", 283 | " \n", 284 | " \n", 285 | " \n", 286 | " \n", 287 | " \n", 288 | " \n", 289 | " \n", 290 | " \n", 291 | " \n", 292 | " \n", 293 | " \n", 294 | " \n", 295 | " \n", 296 | " \n", 297 | " \n", 298 | " \n", 299 | " \n", 300 | " \n", 301 | " \n", 302 | " \n", 303 | " \n", 304 | " \n", 305 | " \n", 306 | " \n", 307 | " \n", 308 | " \n", 309 | " \n", 310 | " \n", 311 | " \n", 312 | " \n", 313 | " \n", 314 | " \n", 315 | " \n", 316 | " \n", 317 | " \n", 318 | " \n", 319 | " \n", 320 | " \n", 321 | " \n", 322 | " \n", 323 | " \n", 324 | " \n", 325 | " \n", 326 | " \n", 327 | " \n", 328 | " \n", 329 | " \n", 330 | " \n", 331 | " \n", 332 | " \n", 333 | " \n", 334 | " \n", 335 | " \n", 336 | " \n", 337 | " \n", 338 | "
FOOTFALLATEMPSELFLRCLINLRDOLELRPIDOUTPRESSUREINPRESSURETEMPFAIL
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" 340 | ], 341 | "text/plain": [ 342 | " FOOTFALL ATEMP SELFLR CLINLR DOLELR PID OUTPRESSURE INPRESSURE \\\n", 343 | "0 0 7 7 1 6 6 36 3 \n", 344 | "1 190 1 3 3 5 1 20 4 \n", 345 | "2 110 3 3 4 6 1 21 4 \n", 346 | "3 100 7 5 6 4 1 77 4 \n", 347 | "4 31 1 5 4 5 4 21 4 \n", 348 | "\n", 349 | " TEMP FAIL \n", 350 | "0 1 1 \n", 351 | "1 1 0 \n", 352 | "2 1 0 \n", 353 | "3 1 0 \n", 354 | "4 1 0 " 355 | ] 356 | }, 357 | "execution_count": 8, 358 | "metadata": {}, 359 | "output_type": "execute_result" 360 | } 361 | ], 362 | "source": [ 363 | "# Read data from DB2 warehouse in BMX\n", 364 | "# -----------------------------------\n", 365 | "from ibmdbpy import IdaDataBase, IdaDataFrame\n", 366 | "\n", 367 | "# Call function to read data for specific sensor\n", 368 | "# @hidden_cell\n", 369 | "# The section below needs to be modified:\n", 370 | "# Insert your credentials to read data from your data sources and replace \n", 371 | "# the idaConnect() section below\n", 372 | "# This connection object is used to access your data and contains your credentials.\n", 373 | "idadb_d281f6cd34eb4bc98f0183a45598dbb9 = IdaDataBase(dsn='DASHDB;Database=BLUDB;Hostname=;Port=50000;PROTOCOL=TCPIP;UID=;PWD=')\n", 374 | "\n", 375 | "df_iotdata = IdaDataFrame(idadb_d281f6cd34eb4bc98f0183a45598dbb9, '.EQUIPMENT_DATA').as_dataframe()\n", 376 | "\n", 377 | "# Check Number of observations read for analysis\n", 378 | "print (\"Number of Observations :\", len(df_iotdata))\n", 379 | "# Inspect a few observations\n", 380 | "df_iotdata.head()\n" 381 | ] 382 | }, 383 | { 384 | "cell_type": "code", 385 | "execution_count": 9, 386 | "metadata": {}, 387 | "outputs": [ 388 | { 389 | "name": "stdout", 390 | "output_type": "stream", 391 | "text": [ 392 | "Data set columns : ['FOOTFALL', 'ATEMP', 'SELFLR', 'CLINLR', 'DOLELR', 'PID', 'OUTPRESSURE', 'INPRESSURE', 'TEMP', 'FAIL']\n" 393 | ] 394 | } 395 | ], 396 | "source": [ 397 | "# Print dataset column names\n", 398 | "datacolumns = dataset_columns(df_iotdata)\n", 399 | "print (\"Data set columns : \", list(datacolumns))" 400 | ] 401 | }, 402 | { 403 | "cell_type": "markdown", 404 | "metadata": {}, 405 | "source": [ 406 | "## 5 Run Failure Prediction algorithm on IoT data\n", 407 | "### 5.1 Split data into Training and Test data" 408 | ] 409 | }, 410 | { 411 | "cell_type": "code", 412 | "execution_count": 10, 413 | "metadata": {}, 414 | "outputs": [ 415 | { 416 | "name": "stdout", 417 | "output_type": "stream", 418 | "text": [ 419 | "Train x counts : 378 5\n", 420 | "Train y counts : 378\n", 421 | "Test x counts : 162 5\n", 422 | "Test y counts : 162\n" 423 | ] 424 | } 425 | ], 426 | "source": [ 427 | "# Split Training and Testing data\n", 428 | "train_x, test_x, train_y, test_y = train_test_split(df_iotdata[v_feature_list], df_iotdata[v_target], train_size=v_train_datasize)\n", 429 | "print (\"Train x counts : \", len(train_x), len(train_x.columns.values))\n", 430 | "print (\"Train y counts : \", len(train_y))\n", 431 | " \n", 432 | "print (\"Test x counts : \", len(test_x), len(test_x.columns.values))\n", 433 | "print (\"Test y counts : \", len(test_y))\n" 434 | ] 435 | }, 436 | { 437 | "cell_type": "markdown", 438 | "metadata": {}, 439 | "source": [ 440 | "### 5.2 Train the Predictive model" 441 | ] 442 | }, 443 | { 444 | "cell_type": "code", 445 | "execution_count": 11, 446 | "metadata": {}, 447 | "outputs": [ 448 | { 449 | "name": "stdout", 450 | "output_type": "stream", 451 | "text": [ 452 | "Training Accuracy : 92.06 %\n" 453 | ] 454 | } 455 | ], 456 | "source": [ 457 | "# Training Logistic regression model\n", 458 | "trained_logistic_regression_model = train_logistic_regression(train_x, train_y)\n", 459 | "\n", 460 | "train_accuracy = model_accuracy(trained_logistic_regression_model, train_x, train_y)\n", 461 | "\n", 462 | "# Testing the logistic regression model\n", 463 | "test_accuracy = model_accuracy(trained_logistic_regression_model, test_x, test_y)\n", 464 | "\n", 465 | "print (\"Training Accuracy : \", round(train_accuracy * 100, 2), \"%\")\n", 466 | "\n" 467 | ] 468 | }, 469 | { 470 | "cell_type": "markdown", 471 | "metadata": {}, 472 | "source": [ 473 | "### 5.3 Score the Test data using the Trained model" 474 | ] 475 | }, 476 | { 477 | "cell_type": "code", 478 | "execution_count": 12, 479 | "metadata": {}, 480 | "outputs": [], 481 | "source": [ 482 | "# Model accuracy: Score and construct Confusion matrix for Test data\n", 483 | "actfail = test_y.values\n", 484 | "predictfail = score_data(trained_logistic_regression_model, test_x)" 485 | ] 486 | }, 487 | { 488 | "cell_type": "markdown", 489 | "metadata": {}, 490 | "source": [ 491 | "## 6 Confusion matrix for deeper analysis of Prediction accuracy\n", 492 | "##### Confusion matrix outputs below can be used for calculating more customised Accuracy metrics" 493 | ] 494 | }, 495 | { 496 | "cell_type": "code", 497 | "execution_count": 13, 498 | "metadata": {}, 499 | "outputs": [ 500 | { 501 | "name": "stdout", 502 | "output_type": "stream", 503 | "text": [ 504 | "Actual, Predicted Observations: 162 162\n", 505 | "--------------------------------------------\n", 506 | "Confusion Matrix\n", 507 | "--------------------------------------------\n", 508 | " Predicted N Predicted Y\n", 509 | "Actual N 100 8\n", 510 | "Actual Y 5 49\n", 511 | "--------------------------------------------\n", 512 | "Total observations : 162\n", 513 | "False Positives : 8\n", 514 | "False Negatives : 5\n", 515 | "Overall Accuracy : 91.98 %\n", 516 | "Sensitivity/Recall : 90.74 %\n", 517 | "Specificity : 92.59 %\n", 518 | "Precision : 85.96 %\n", 519 | "--------------------------------------------\n" 520 | ] 521 | } 522 | ], 523 | "source": [ 524 | "# Print Count of Actual fails, Predicted fails\n", 525 | "# Print Confusion matrix\n", 526 | "confusion_matrix(actfail, predictfail)" 527 | ] 528 | }, 529 | { 530 | "cell_type": "markdown", 531 | "metadata": {}, 532 | "source": [ 533 | "## 7. Expose integration point with a websocket client" 534 | ] 535 | }, 536 | { 537 | "cell_type": "code", 538 | "execution_count": 14, 539 | "metadata": {}, 540 | "outputs": [], 541 | "source": [ 542 | "def on_message(ws, message):\n", 543 | " msg = json.loads(message)\n", 544 | " cmd = msg['cmd'];\n", 545 | " \n", 546 | " if(cmd == 'predict'):\n", 547 | " d = {'ATEMP': [msg['atemp']], 'PID': [msg['PID']], 'OUTPRESSURE':[msg['outpressure']], 'INPRESSURE':msg['inpressure'], 'TEMP':msg['temp']}\n", 548 | " df = pd.DataFrame(data=d)\n", 549 | " predict_fail = score_data(trained_logistic_regression_model, df)\n", 550 | " fail = predict_fail[0]\n", 551 | " status = \"Running\";\n", 552 | " if(fail == 0):\n", 553 | " status = \"Running\";\n", 554 | " else:\n", 555 | " status = \"Failing\";\n", 556 | " response = {}\n", 557 | " response['cmd'] = 'response';\n", 558 | " response['status'] = status;\n", 559 | " print(msg)\n", 560 | " print(response)\n", 561 | " ws.send(json.dumps(response));\n", 562 | " \n", 563 | "def on_error(ws, error):\n", 564 | " print(error)\n", 565 | "\n", 566 | "def on_close(ws):\n", 567 | " ws.send(\"DSX Listen End\")\n", 568 | "\n", 569 | "def on_open(ws):\n", 570 | " def run(*args):\n", 571 | " for i in range(10000):\n", 572 | " hbeat = '{\"cmd\":\"DSX HeartBeat\"}'\n", 573 | " ws.send(hbeat)\n", 574 | " time.sleep(100)\n", 575 | " \n", 576 | " _thread.start_new_thread(run, ())\n", 577 | "\n", 578 | "\n", 579 | "def start_websocket_listener():\n", 580 | " websocket.enableTrace(True)\n", 581 | " ws = websocket.WebSocketApp(\"ws:///ws/orchestrate\",\n", 582 | " on_message = on_message,\n", 583 | " on_error = on_error,\n", 584 | " on_close = on_close)\n", 585 | " ws.on_open = on_open\n", 586 | " ws.run_forever()" 587 | ] 588 | }, 589 | { 590 | "cell_type": "code", 591 | "execution_count": 15, 592 | "metadata": {}, 593 | "outputs": [ 594 | { 595 | "name": "stderr", 596 | "output_type": "stream", 597 | "text": [ 598 | " File \"/opt/conda/envs/DSX-Python35/lib/python3.5/site-packages/websocket/_app.py\", line 320, in _callback\n", 599 | " callback(self, *args)\n", 600 | " File \"\", line 35, in on_open\n", 601 | " _thread.start_new_thread(run, ())\n" 602 | ] 603 | }, 604 | { 605 | "name": "stdout", 606 | "output_type": "stream", 607 | "text": [ 608 | "\n" 609 | ] 610 | }, 611 | { 612 | "name": "stderr", 613 | "output_type": "stream", 614 | "text": [ 615 | " File \"/opt/conda/envs/DSX-Python35/lib/python3.5/site-packages/websocket/_app.py\", line 320, in _callback\n", 616 | " callback(self, *args)\n", 617 | " File \"\", line 26, in on_close\n", 618 | " ws.send(\"DSX Listen End\")\n", 619 | " File \"/opt/conda/envs/DSX-Python35/lib/python3.5/site-packages/websocket/_app.py\", line 151, in send\n", 620 | " if not self.sock or self.sock.send(data, opcode) == 0:\n", 621 | " File \"/opt/conda/envs/DSX-Python35/lib/python3.5/site-packages/websocket/_core.py\", line 240, in send\n", 622 | " return self.send_frame(frame)\n", 623 | " File \"/opt/conda/envs/DSX-Python35/lib/python3.5/site-packages/websocket/_core.py\", line 265, in send_frame\n", 624 | " l = self._send(data)\n", 625 | " File \"/opt/conda/envs/DSX-Python35/lib/python3.5/site-packages/websocket/_core.py\", line 430, in _send\n", 626 | " return send(self.sock, data)\n", 627 | " File \"/opt/conda/envs/DSX-Python35/lib/python3.5/site-packages/websocket/_socket.py\", line 114, in send\n", 628 | " raise WebSocketConnectionClosedException(\"socket is already closed.\")\n" 629 | ] 630 | } 631 | ], 632 | "source": [ 633 | "start_websocket_listener();" 634 | ] 635 | }, 636 | { 637 | "cell_type": "code", 638 | "execution_count": null, 639 | "metadata": {}, 640 | "outputs": [], 641 | "source": [] 642 | } 643 | ], 644 | "metadata": { 645 | "kernelspec": { 646 | "display_name": "Python 3.5", 647 | "language": "python", 648 | "name": "python3" 649 | }, 650 | "language_info": { 651 | "codemirror_mode": { 652 | "name": "ipython", 653 | "version": 3 654 | }, 655 | "file_extension": ".py", 656 | "mimetype": "text/x-python", 657 | "name": "python", 658 | "nbconvert_exporter": "python", 659 | "pygments_lexer": "ipython3", 660 | "version": "3.5.4" 661 | } 662 | }, 663 | "nbformat": 4, 664 | "nbformat_minor": 1 665 | } 666 | -------------------------------------------------------------------------------- /data/iot_sensor_dataset.csv: -------------------------------------------------------------------------------- 1 | footfall,atemp,selfLR,ClinLR,DoleLR,PID,outpressure,inpressure,temp,fail 2 | 0,7,7,1,6,6,36,3,1,1 3 | 190,1,3,3,5,1,20,4,1,0 4 | 31,7,2,2,6,1,24,6,1,0 5 | 83,4,3,4,5,1,28,6,1,0 6 | 640,7,5,6,4,0,68,6,1,0 7 | 110,3,3,4,6,1,21,4,1,0 8 | 100,7,5,6,4,1,77,4,1,0 9 | 31,1,5,4,5,4,21,4,1,0 10 | 180,7,4,6,3,3,31,4,1,0 11 | 2800,0,3,3,7,0,39,3,1,0 12 | 1600,0,3,2,4,4,26,2,1,0 13 | 330,5,4,3,6,1,31,4,1,0 14 | 190,2,5,4,6,5,22,4,1,1 15 | 100,7,4,4,6,0,42,5,1,0 16 | 1000,7,5,7,4,0,74,1,1,0 17 | 0,7,6,7,5,0,62,3,1,0 18 | 130,7,4,4,5,1,58,3,1,0 19 | 5,5,3,3,6,1,24,6,1,0 20 | 33,7,6,2,6,5,51,4,1,1 21 | 19,2,2,1,4,0,36,3,2,0 22 | 74,7,4,4,7,2,88,2,2,0 23 | 190,0,2,4,6,2,20,4,2,0 24 | 12,3,4,6,3,2,27,3,2,0 25 | 0,7,6,1,6,6,44,4,2,1 26 | 19,0,4,2,2,1,45,3,2,0 27 | 0,2,4,3,6,1,21,4,2,0 28 | 390,5,3,4,7,1,40,5,2,0 29 | 40,7,4,3,4,0,40,6,2,0 30 | 3,3,5,5,4,1,48,3,2,0 31 | 450,3,4,7,1,0,34,3,2,0 32 | 350,0,3,4,7,2,26,2,2,0 33 | 64,3,4,4,2,1,60,2,3,0 34 | 3,0,4,4,3,0,32,3,3,0 35 | 0,1,4,3,7,1,31,3,3,0 36 | 640,7,7,5,7,4,33,3,3,1 37 | 0,7,3,4,6,0,57,3,3,0 38 | 12,7,4,3,6,1,84,3,3,0 39 | 62,6,7,2,7,5,75,3,3,1 40 | 31,2,7,2,6,6,19,4,3,1 41 | 0,1,3,2,6,1,47,6,3,0 42 | 180,6,5,5,5,0,51,2,3,0 43 | 640,3,6,4,4,5,40,3,3,0 44 | 110,0,2,3,6,1,22,6,3,0 45 | 100,1,7,7,5,6,35,2,3,0 46 | 100,7,4,4,7,2,43,5,3,0 47 | 11,3,6,6,3,2,76,6,3,0 48 | 0,7,4,3,1,6,45,3,3,1 49 | 4,7,4,6,6,0,88,2,3,0 50 | 35,6,4,4,2,1,46,3,4,0 51 | 0,1,3,4,5,2,22,6,4,0 52 | 0,7,5,1,6,5,68,3,4,1 53 | 0,2,5,2,6,5,38,3,4,1 54 | 33,7,4,3,6,3,69,2,4,0 55 | 270,2,5,4,3,0,67,3,4,0 56 | 45,7,2,4,6,0,88,4,4,0 57 | 40,3,6,2,5,5,68,3,4,1 58 | 6,1,5,2,4,2,76,3,4,1 59 | 2,7,4,4,6,0,72,2,4,0 60 | 0,0,6,2,6,6,37,6,4,1 61 | 35,3,4,2,6,0,69,3,4,0 62 | 83,0,2,4,6,0,33,6,4,0 63 | 3500,7,2,2,6,0,34,4,4,0 64 | 100,2,4,4,7,2,30,3,4,0 65 | 350,2,3,3,6,1,19,3,4,0 66 | 100,3,4,6,2,0,44,3,4,0 67 | 67,1,4,4,7,1,64,3,4,0 68 | 30,5,7,7,2,0,37,4,4,0 69 | 0,7,6,3,5,4,31,5,5,1 70 | 0,0,6,1,5,4,88,4,5,1 71 | 6,7,6,2,6,6,77,4,5,1 72 | 350,1,4,5,6,5,30,6,5,0 73 | 400,1,2,3,7,1,32,4,5,0 74 | 15,7,6,2,6,6,59,1,5,1 75 | 0,0,4,4,4,3,47,4,5,0 76 | 3,2,4,6,5,1,22,3,5,0 77 | 22,5,4,2,6,2,55,3,5,0 78 | 64,2,2,1,3,0,24,2,5,0 79 | 32,5,3,7,4,1,65,1,5,0 80 | 390,7,3,6,2,2,24,3,5,0 81 | 0,7,3,4,5,3,30,3,5,0 82 | 0,7,4,5,2,3,73,3,5,0 83 | 59,5,3,3,5,1,73,5,5,0 84 | 0,6,4,3,6,2,91,1,5,0 85 | 35,7,3,2,5,0,71,2,5,0 86 | 0,2,6,4,5,4,34,4,5,1 87 | 170,7,4,3,2,0,48,2,6,0 88 | 12,1,6,2,6,5,42,4,6,1 89 | 40,4,6,5,4,0,72,2,6,0 90 | 31,2,3,4,6,6,20,4,6,1 91 | 31,7,2,2,7,0,22,4,6,0 92 | 1600,1,3,3,6,1,24,6,6,0 93 | 1,1,4,2,7,2,39,6,6,0 94 | 4,7,6,1,6,6,83,5,6,1 95 | 190,0,6,2,6,6,39,3,6,1 96 | 53,3,5,3,6,1,33,5,6,0 97 | 31,7,4,3,6,1,53,3,6,1 98 | 16,7,5,3,6,5,82,3,6,1 99 | 33,5,4,3,5,6,82,3,6,1 100 | 0,3,5,3,6,5,47,6,7,1 101 | 0,3,4,2,7,4,68,3,7,0 102 | 0,7,4,3,5,0,84,6,7,0 103 | 27,2,6,1,6,5,35,5,7,1 104 | 84,7,4,5,6,1,67,2,7,0 105 | 22,3,5,3,5,4,33,2,7,1 106 | 0,3,3,3,5,0,49,7,7,0 107 | 3500,0,4,3,7,0,91,1,7,0 108 | 390,7,4,5,3,1,43,3,7,0 109 | 0,7,4,3,2,6,65,4,7,0 110 | 16,7,5,6,3,0,69,3,7,0 111 | 200,0,5,5,4,1,56,4,8,0 112 | 640,0,2,3,5,0,24,6,8,0 113 | 0,7,4,4,5,0,77,3,8,0 114 | 45,7,6,3,7,0,74,3,8,0 115 | 12,0,7,3,6,6,25,6,8,1 116 | 20,7,6,2,5,4,85,1,8,1 117 | 7300,5,7,7,6,3,21,2,8,0 118 | 64,7,6,3,1,0,24,4,8,0 119 | 13,7,5,4,7,4,73,4,8,0 120 | 190,0,4,5,3,2,37,3,8,0 121 | 9,4,4,5,1,2,35,4,8,0 122 | 0,7,4,4,7,0,47,3,8,0 123 | 170,2,4,2,6,6,21,3,8,1 124 | 640,7,3,6,4,0,55,5,8,0 125 | 9,4,6,3,6,6,30,6,8,1 126 | 0,4,5,3,6,4,76,7,8,1 127 | 7300,5,3,4,3,3,36,4,8,0 128 | 2800,0,1,1,7,0,38,3,9,0 129 | 0,7,2,3,5,0,67,3,9,0 130 | 30,7,7,3,7,6,70,2,9,1 131 | 44,7,5,3,7,2,78,4,9,0 132 | 7300,1,2,2,7,3,27,6,9,0 133 | 330,4,3,5,6,1,51,4,9,0 134 | 3,0,6,7,3,5,33,4,9,0 135 | 51,2,6,1,5,6,80,6,9,1 136 | 29,5,4,1,6,1,79,1,9,0 137 | 630,2,6,4,5,4,66,1,9,1 138 | 170,0,4,1,6,0,32,3,10,0 139 | 33,7,4,5,7,0,70,2,10,0 140 | 0,3,2,3,6,3,42,3,10,0 141 | 9,5,5,4,5,5,73,4,10,1 142 | 22,4,4,4,6,0,87,2,10,0 143 | 100,0,7,5,1,1,30,5,10,0 144 | 2,2,4,4,5,3,52,3,10,0 145 | 0,6,5,3,6,1,62,4,10,0 146 | 50,7,6,3,4,0,67,3,10,0 147 | 15,4,6,3,4,4,37,6,10,0 148 | 3,4,3,5,7,0,37,4,10,0 149 | 720,5,1,5,6,1,64,6,10,0 150 | 640,7,1,1,5,0,34,3,10,0 151 | 5,7,4,4,7,0,70,3,10,0 152 | 24,2,6,2,6,6,31,5,10,1 153 | 22,7,2,2,6,0,29,6,11,0 154 | 55,7,4,5,4,1,71,2,11,0 155 | 0,2,4,4,4,0,67,1,11,0 156 | 1600,5,4,4,6,0,41,7,11,0 157 | 170,6,1,2,6,0,49,6,11,0 158 | 1000,7,4,4,5,0,42,5,11,0 159 | 63,0,6,3,2,0,78,2,11,0 160 | 110,0,4,1,6,1,24,3,11,0 161 | 16,7,4,6,6,1,29,3,11,0 162 | 100,3,4,2,6,5,39,5,11,1 163 | 7300,3,5,3,6,1,19,4,11,0 164 | 22,2,4,2,7,1,32,5,11,0 165 | 71,3,4,2,6,5,69,3,11,1 166 | 900,4,5,2,5,5,83,3,11,1 167 | 35,7,4,1,5,4,76,2,11,1 168 | 2,7,7,1,2,0,62,2,11,0 169 | 83,2,3,3,6,0,47,7,11,0 170 | 370,5,6,7,4,0,35,3,11,0 171 | 12,0,4,5,3,4,23,3,11,0 172 | 370,7,4,4,1,1,79,4,11,0 173 | 100,7,6,2,6,5,64,5,11,1 174 | 470,7,6,2,4,5,70,4,11,1 175 | 22,7,6,1,6,6,87,5,11,1 176 | 2800,0,3,6,1,0,28,2,12,0 177 | 47,5,3,5,7,1,58,3,12,0 178 | 900,5,4,4,6,1,85,2,12,0 179 | 330,7,3,6,4,0,62,3,12,0 180 | 84,0,3,2,7,1,26,6,12,0 181 | 0,0,6,2,5,5,28,3,12,1 182 | 33,3,6,1,7,6,88,2,12,1 183 | 53,7,2,3,6,0,57,6,12,0 184 | 8,7,2,2,6,0,78,3,12,0 185 | 2,7,4,4,2,0,56,3,12,0 186 | 0,0,4,6,3,3,46,5,12,0 187 | 0,2,4,4,3,5,20,3,12,1 188 | 0,0,5,6,4,1,24,4,12,0 189 | 0,7,5,2,6,2,72,4,12,0 190 | 15,7,2,4,7,1,51,4,12,0 191 | 900,0,6,2,5,6,34,6,12,1 192 | 30,2,4,2,6,1,21,4,12,0 193 | 0,7,4,4,6,2,74,7,12,0 194 | 170,3,4,4,6,1,48,1,12,0 195 | 900,2,3,3,7,5,28,3,12,0 196 | 0,6,7,1,7,5,38,2,12,1 197 | 1600,7,4,6,1,0,70,3,12,0 198 | 0,7,4,5,4,0,72,2,12,0 199 | 2800,0,4,5,6,0,41,3,12,0 200 | 110,5,3,4,5,1,50,7,12,0 201 | 1,7,6,2,5,5,73,3,12,1 202 | 3,5,5,2,4,0,79,6,12,0 203 | 0,4,5,1,4,5,76,2,12,1 204 | 22,0,5,3,5,5,62,5,12,1 205 | 63,3,6,2,6,6,30,6,12,1 206 | 290,0,6,3,6,5,35,4,12,1 207 | 2,7,1,2,7,1,66,4,12,0 208 | 40,0,2,4,6,0,35,4,12,0 209 | 67,0,6,1,5,6,57,6,12,1 210 | 0,5,4,5,4,5,37,5,12,1 211 | 470,7,5,5,2,1,61,3,13,0 212 | 0,7,6,2,6,6,56,3,13,1 213 | 4,6,3,4,5,1,53,3,13,0 214 | 20,0,4,5,3,2,24,6,13,1 215 | 2800,7,4,1,6,5,74,3,13,1 216 | 0,0,4,4,3,1,36,3,13,0 217 | 1,0,6,2,4,5,30,5,13,1 218 | 640,0,4,7,4,1,55,2,13,0 219 | 170,3,3,2,7,2,35,6,13,0 220 | 270,2,3,4,6,0,26,4,13,0 221 | 390,0,3,4,6,2,25,4,13,0 222 | 16,2,6,7,4,3,27,3,13,0 223 | 11,7,4,1,6,5,66,3,13,1 224 | 0,1,5,2,6,2,39,2,13,0 225 | 270,7,1,1,2,2,58,5,13,0 226 | 170,2,4,4,4,0,53,3,13,1 227 | 900,7,6,7,4,0,76,3,13,0 228 | 270,7,5,2,7,1,51,3,13,0 229 | 0,7,4,2,7,0,70,2,13,0 230 | 350,3,6,3,6,6,68,4,13,1 231 | 0,0,5,4,5,2,32,3,13,1 232 | 6,0,5,4,5,5,55,3,13,0 233 | 290,7,2,2,6,0,52,4,13,0 234 | 630,7,6,4,6,4,73,2,13,1 235 | 900,0,5,4,7,0,42,2,13,0 236 | 31,2,4,4,3,4,23,5,13,1 237 | 1600,5,2,3,6,0,30,7,14,0 238 | 71,7,2,2,7,0,68,4,14,0 239 | 200,7,5,2,3,2,68,3,14,0 240 | 0,0,6,4,7,3,68,6,14,0 241 | 30,5,2,3,6,0,38,5,14,0 242 | 10,1,4,3,6,2,74,3,14,0 243 | 0,7,5,6,3,0,59,2,14,0 244 | 900,2,5,2,5,2,73,2,14,0 245 | 71,7,2,3,6,0,79,3,14,0 246 | 22,3,7,1,6,5,28,4,14,1 247 | 0,7,6,2,6,6,50,3,14,1 248 | 0,4,6,3,1,1,36,4,14,0 249 | 0,3,6,2,6,6,50,3,14,1 250 | 0,1,6,2,6,6,61,3,14,1 251 | 7300,3,2,2,6,0,37,4,14,0 252 | 83,0,3,4,7,0,29,6,14,0 253 | 93,7,2,3,7,0,39,4,14,0 254 | 0,7,4,5,4,2,83,6,14,1 255 | 51,7,6,1,5,4,68,6,14,1 256 | 31,2,6,1,5,6,25,4,14,1 257 | 93,2,1,3,6,1,41,6,14,0 258 | 0,7,3,2,6,1,67,3,14,0 259 | 0,3,4,2,6,4,36,6,14,1 260 | 31,4,6,2,6,4,66,4,14,1 261 | 900,1,3,2,7,1,55,4,14,0 262 | 0,4,3,2,6,2,42,5,14,0 263 | 2,7,6,3,5,5,42,3,14,1 264 | 110,3,4,5,7,1,36,3,14,0 265 | 63,1,6,4,6,4,53,5,14,1 266 | 900,0,3,2,5,1,36,5,14,0 267 | 31,3,4,3,6,2,29,7,14,0 268 | 510,1,4,4,6,0,31,3,14,0 269 | 270,2,3,4,6,1,43,6,14,0 270 | 9,3,3,4,7,1,33,6,14,0 271 | 3,1,6,6,2,0,63,3,14,0 272 | 29,1,5,2,4,2,25,5,14,0 273 | 45,2,3,2,6,0,72,4,14,0 274 | 83,5,3,3,6,1,40,4,14,0 275 | 22,7,4,2,6,2,27,6,14,0 276 | 15,3,5,4,7,2,26,4,15,0 277 | 110,7,5,2,5,6,67,3,15,1 278 | 8,0,4,5,5,1,21,3,15,0 279 | 11,5,6,2,6,6,27,7,15,1 280 | 56,4,6,5,3,0,78,6,15,1 281 | 8,3,4,4,3,1,32,3,15,0 282 | 100,5,4,2,6,1,68,4,15,0 283 | 900,7,4,2,6,5,76,4,15,0 284 | 67,7,3,3,6,0,33,5,15,0 285 | 35,4,6,2,5,4,38,2,15,1 286 | 35,3,4,3,5,1,49,7,15,0 287 | 22,0,5,4,6,5,61,4,15,1 288 | 110,7,7,1,5,4,57,3,15,1 289 | 12,0,4,2,5,2,20,3,15,0 290 | 7300,7,4,2,4,4,63,6,15,1 291 | 0,0,4,1,5,4,53,3,15,1 292 | 19,5,4,2,6,5,35,4,15,1 293 | 470,3,4,4,7,0,39,3,15,0 294 | 4,3,3,5,3,4,48,3,15,1 295 | 640,4,6,2,5,4,62,3,15,1 296 | 640,2,4,2,3,1,30,5,15,0 297 | 200,7,7,1,4,6,26,6,15,1 298 | 0,7,3,2,4,5,74,6,15,1 299 | 29,4,3,4,6,2,37,5,15,0 300 | 330,2,4,5,5,1,43,5,15,0 301 | 19,5,5,1,4,6,68,3,15,1 302 | 1,7,5,3,4,5,73,5,15,1 303 | 110,5,6,1,6,6,60,7,15,1 304 | 0,7,5,4,6,1,35,3,15,0 305 | 350,4,3,4,6,0,29,6,15,0 306 | 2,5,3,3,5,1,25,6,15,0 307 | 0,7,2,2,6,0,25,7,15,0 308 | 7,4,2,4,5,0,70,6,15,0 309 | 71,1,6,2,5,6,41,3,15,1 310 | 53,0,4,2,6,1,37,6,15,0 311 | 0,0,6,1,6,6,39,5,15,1 312 | 2,6,3,4,6,0,35,4,15,0 313 | 190,4,2,3,6,0,62,7,15,0 314 | 31,0,3,2,6,1,30,7,15,0 315 | 16,7,4,2,6,0,74,4,15,0 316 | 22,7,3,3,4,5,47,3,15,0 317 | 3,4,4,5,3,1,43,6,15,0 318 | 0,6,5,3,6,5,64,2,15,1 319 | 0,7,4,3,5,2,75,4,15,1 320 | 67,3,4,4,7,1,27,6,15,0 321 | 40,7,4,4,6,0,21,3,15,0 322 | 74,4,2,2,6,1,70,2,15,0 323 | 3,6,5,2,6,5,67,3,15,1 324 | 140,7,6,4,5,6,82,5,15,0 325 | 14,0,2,2,6,0,40,6,15,0 326 | 110,0,5,3,6,5,26,4,15,1 327 | 35,3,4,3,5,1,29,6,15,0 328 | 0,1,4,5,6,2,28,6,15,0 329 | 310,7,6,4,3,5,65,3,15,0 330 | 900,2,6,5,3,1,25,3,15,0 331 | 0,7,3,2,7,1,65,2,15,0 332 | 11,4,6,2,6,5,38,5,15,1 333 | 0,2,4,3,5,5,72,7,15,1 334 | 270,7,3,2,7,1,67,3,15,0 335 | 51,7,3,1,7,0,74,3,15,0 336 | 11,5,2,4,4,2,71,6,15,0 337 | 2,0,4,6,4,0,47,3,15,0 338 | 20,7,5,4,6,1,69,1,15,0 339 | 31,3,3,3,6,1,29,6,15,0 340 | 2,0,6,2,5,6,34,3,15,1 341 | 5,3,4,3,6,0,43,4,15,0 342 | 22,7,2,1,7,0,30,3,15,0 343 | 0,7,5,5,4,0,76,2,15,0 344 | 27,0,2,4,6,1,26,5,16,0 345 | 7,7,4,3,6,2,76,5,16,0 346 | 0,0,1,4,6,1,42,7,16,0 347 | 0,4,3,1,4,5,33,3,16,1 348 | 0,1,6,2,5,5,25,3,16,1 349 | 2800,0,2,2,7,0,51,4,16,0 350 | 0,4,4,2,5,4,57,3,16,1 351 | 22,1,6,1,5,6,21,4,16,1 352 | 9,7,5,1,4,5,79,7,16,1 353 | 0,1,6,2,5,5,35,5,16,1 354 | 9,5,4,2,5,4,57,6,16,1 355 | 0,1,4,4,6,5,32,6,16,0 356 | 37,5,4,5,5,2,51,6,16,0 357 | 23,0,5,2,4,6,62,7,16,1 358 | 0,4,5,1,6,5,48,4,16,1 359 | 0,7,7,1,6,6,39,3,16,1 360 | 0,5,3,2,6,5,26,6,16,1 361 | 40,0,4,2,4,1,38,3,16,1 362 | 0,5,3,3,4,0,50,4,16,0 363 | 9,2,5,5,6,4,33,3,16,1 364 | 15,5,4,3,6,2,36,3,16,0 365 | 640,5,4,4,6,0,24,6,16,0 366 | 0,4,3,2,6,0,25,5,16,0 367 | 0,7,6,5,3,0,62,3,16,0 368 | 0,2,4,3,3,6,33,3,16,1 369 | 0,7,6,2,5,6,53,6,16,1 370 | 22,7,6,2,5,6,68,6,16,1 371 | 22,7,5,4,6,1,68,3,16,0 372 | 10,1,6,1,5,5,38,3,16,1 373 | 29,1,4,5,3,0,58,1,16,0 374 | 170,7,4,2,6,5,34,6,16,1 375 | 4,2,4,3,4,1,58,2,16,0 376 | 11,0,3,4,7,1,35,5,16,0 377 | 31,3,7,2,6,6,42,6,16,1 378 | 0,7,4,4,6,0,54,3,16,0 379 | 0,7,6,2,5,6,69,3,16,1 380 | 360,2,4,6,5,0,35,4,16,0 381 | 0,7,6,1,5,6,66,4,16,1 382 | 900,2,3,2,7,0,58,3,16,0 383 | 51,5,2,4,7,1,41,3,16,0 384 | 0,2,5,2,6,4,35,6,16,0 385 | 110,0,3,3,6,1,40,7,16,0 386 | 1,7,4,7,2,0,53,1,16,0 387 | 8,5,6,2,6,5,67,6,16,1 388 | 5,5,4,4,6,0,32,5,16,0 389 | 87,4,3,4,6,0,41,4,16,0 390 | 3,1,2,1,6,1,43,7,16,0 391 | 51,1,5,3,2,0,65,2,16,0 392 | 350,7,3,3,7,0,60,5,16,0 393 | 3,7,5,3,6,0,77,6,16,0 394 | 630,0,6,5,4,1,35,4,16,0 395 | 180,4,6,7,5,1,48,4,16,0 396 | 0,0,6,2,6,6,52,3,16,1 397 | 35,7,5,3,6,2,43,7,16,0 398 | 0,7,6,2,6,6,43,5,16,1 399 | 0,7,6,2,6,6,67,4,16,1 400 | 6,1,4,4,7,0,56,3,16,0 401 | 7300,2,3,3,4,0,62,4,16,0 402 | 2,1,7,7,5,0,62,3,16,0 403 | 35,3,3,2,6,1,22,6,16,0 404 | 0,0,2,2,6,1,21,5,16,0 405 | 45,3,6,1,6,6,34,3,16,1 406 | 0,7,4,3,5,0,70,3,16,0 407 | 5,1,3,2,6,2,50,3,16,0 408 | 35,5,6,1,6,6,42,4,16,1 409 | 900,5,6,2,6,6,73,3,16,1 410 | 35,2,3,1,6,0,57,7,16,0 411 | 0,7,6,1,5,6,40,6,16,1 412 | 0,2,6,1,6,6,58,6,16,1 413 | 11,7,4,6,2,1,62,3,16,0 414 | 40,1,3,5,3,1,44,3,17,0 415 | 0,3,5,3,7,6,30,6,17,0 416 | 23,7,3,3,7,0,76,5,17,0 417 | 270,3,4,3,3,1,50,4,17,0 418 | 9,1,6,2,5,5,41,4,17,1 419 | 0,6,6,3,6,6,77,3,17,1 420 | 0,3,6,3,5,6,35,6,17,1 421 | 0,7,4,3,4,3,39,4,17,0 422 | 2,0,4,2,4,4,72,3,17,1 423 | 0,2,6,2,5,6,42,7,17,1 424 | 16,7,5,2,6,5,85,2,17,1 425 | 7300,0,4,2,6,3,79,4,17,0 426 | 0,5,2,3,6,0,39,4,17,0 427 | 23,4,6,1,5,6,58,6,17,1 428 | 42,0,4,2,6,5,27,6,17,1 429 | 2,4,4,3,5,1,43,5,17,0 430 | 0,0,3,3,6,0,58,4,17,0 431 | 42,7,4,3,6,4,28,7,17,1 432 | 470,2,5,3,6,5,27,6,17,0 433 | 42,0,3,6,5,2,40,3,17,0 434 | 0,5,1,3,6,0,43,6,17,0 435 | 40,7,5,4,6,5,64,3,17,1 436 | 180,7,2,2,6,0,39,6,17,0 437 | 110,7,4,2,6,4,76,4,17,1 438 | 140,5,3,3,6,0,64,5,17,0 439 | 0,4,4,6,3,2,28,4,17,0 440 | 0,2,4,2,6,1,45,3,17,0 441 | 190,1,6,2,6,6,22,3,17,1 442 | 35,1,3,3,6,1,27,6,17,0 443 | 45,0,1,2,7,0,31,4,17,0 444 | 170,1,2,2,7,0,34,6,17,0 445 | 0,4,2,3,7,0,30,4,17,0 446 | 0,2,6,1,6,6,64,3,17,1 447 | 35,0,6,1,6,4,36,6,17,1 448 | 5,7,4,3,5,4,31,2,17,1 449 | 350,1,4,3,4,5,37,3,17,1 450 | 4,0,6,4,2,1,48,3,17,0 451 | 70,0,5,7,7,3,41,3,17,0 452 | 8,2,5,2,6,5,25,4,17,1 453 | 12,5,4,2,6,6,82,3,17,1 454 | 5,1,6,2,5,4,36,7,17,1 455 | 0,7,6,2,5,1,47,3,17,1 456 | 16,6,2,3,6,0,67,4,17,0 457 | 0,0,4,6,2,5,24,4,17,1 458 | 9,0,4,5,3,5,33,6,17,1 459 | 14,7,4,1,5,1,59,4,17,1 460 | 22,7,7,1,6,6,71,4,17,1 461 | 0,7,6,2,5,6,36,3,17,1 462 | 1,3,3,3,5,0,41,3,17,0 463 | 1600,5,6,2,6,6,38,6,17,1 464 | 7300,1,6,3,6,6,32,3,17,1 465 | 19,0,5,6,7,1,32,4,17,0 466 | 9,6,5,4,6,0,75,6,17,0 467 | 0,7,5,2,4,4,52,3,17,1 468 | 1600,5,2,3,6,0,29,7,17,0 469 | 12,7,3,4,6,0,71,3,17,0 470 | 1,0,6,2,5,2,33,6,17,0 471 | 0,3,1,2,7,2,67,7,17,0 472 | 0,2,6,1,6,6,49,4,17,1 473 | 0,0,2,3,5,1,31,7,17,0 474 | 9,7,3,4,6,1,53,2,17,0 475 | 0,3,2,3,7,0,35,7,17,0 476 | 170,7,6,2,6,6,49,7,18,1 477 | 3,7,6,4,4,0,74,3,18,0 478 | 14,0,6,1,5,6,29,4,18,1 479 | 1,5,2,3,6,0,27,6,18,0 480 | 23,5,4,4,6,5,70,4,18,0 481 | 0,0,6,1,6,6,43,6,18,1 482 | 1,5,2,3,6,2,47,7,18,0 483 | 0,4,4,4,6,0,42,7,18,0 484 | 9,0,6,2,5,6,41,6,18,1 485 | 130,2,6,4,6,6,37,4,18,1 486 | 5,5,5,5,4,1,61,3,18,0 487 | 4,7,4,2,6,0,84,4,18,0 488 | 2800,7,5,3,7,0,46,6,18,0 489 | 4,0,4,3,6,5,39,7,18,0 490 | 6,7,2,3,6,0,53,3,18,0 491 | 4,7,5,1,6,6,87,6,18,1 492 | 84,2,4,6,2,0,51,3,18,0 493 | 0,0,5,2,5,4,37,5,18,1 494 | 9,1,4,3,5,4,37,6,18,1 495 | 290,0,5,2,6,6,40,6,18,1 496 | 0,3,4,4,6,2,46,6,18,0 497 | 9,7,1,4,1,3,44,3,18,1 498 | 0,2,4,3,6,1,37,3,18,0 499 | 0,7,4,2,6,2,55,7,18,0 500 | 22,0,5,2,5,1,40,6,18,1 501 | 0,5,3,2,6,1,42,7,18,0 502 | 640,7,6,2,6,6,40,3,18,1 503 | 10,0,6,2,6,6,32,3,18,1 504 | 4,5,5,2,5,4,58,7,18,1 505 | 0,2,5,1,6,4,37,3,18,1 506 | 640,4,4,5,5,5,51,3,18,1 507 | 110,5,3,5,4,5,44,5,18,0 508 | 0,2,4,5,2,0,23,2,18,0 509 | 0,2,6,2,6,6,38,3,18,1 510 | 67,0,3,4,6,0,42,7,18,0 511 | 520,7,4,2,5,4,60,4,18,1 512 | 0,3,4,3,6,1,34,4,18,0 513 | 51,1,6,2,6,6,49,3,18,1 514 | 0,0,4,2,3,4,40,3,18,1 515 | 2800,7,2,2,7,0,23,4,18,0 516 | 110,2,2,3,6,0,47,3,18,0 517 | 7300,7,4,4,7,0,44,5,18,0 518 | 7300,5,5,2,6,1,37,7,18,1 519 | 0,2,6,1,6,5,26,5,18,1 520 | 16,7,7,6,5,2,51,4,18,0 521 | 140,4,4,4,6,0,69,3,18,0 522 | 54,2,2,2,7,0,36,6,18,0 523 | 2,2,2,3,6,2,28,4,18,0 524 | 470,4,5,2,6,5,40,4,19,1 525 | 0,1,6,1,6,5,45,4,19,1 526 | 75,3,5,2,5,5,75,6,19,1 527 | 11,2,6,2,5,5,32,6,19,1 528 | 0,0,3,2,6,1,27,3,19,0 529 | 2800,6,5,2,5,3,63,6,19,1 530 | 140,1,5,3,6,6,79,7,19,1 531 | 9,7,3,2,6,1,40,6,19,0 532 | 7300,2,4,3,6,1,57,6,19,0 533 | 45,3,3,4,5,1,44,6,19,0 534 | 1600,7,5,2,5,6,78,6,19,1 535 | 9,7,4,5,5,5,58,7,19,0 536 | 0,7,2,2,7,1,55,3,19,0 537 | 130,4,3,4,6,2,25,6,19,0 538 | 9,0,2,3,6,0,27,7,19,0 539 | 3500,3,4,2,7,2,21,3,19,0 540 | 0,3,6,2,6,5,37,4,19,1 541 | 0,0,7,3,4,5,31,3,19,1 542 | 35,7,2,4,6,0,85,7,19,0 543 | 900,1,4,2,6,5,33,3,19,0 544 | 27,7,3,7,4,3,63,2,19,1 545 | 900,7,4,4,6,1,51,3,19,0 546 | 0,2,3,5,2,0,46,3,19,0 547 | 64,2,5,2,6,6,26,5,19,1 548 | 4,7,3,3,6,0,57,6,19,0 549 | 0,3,4,2,6,1,48,3,19,1 550 | 0,2,7,1,5,5,55,3,19,1 551 | 53,0,3,6,4,1,37,5,19,0 552 | 290,2,6,2,6,6,39,3,19,1 553 | 15,2,2,3,4,1,24,3,19,1 554 | 0,0,6,3,6,6,30,6,19,1 555 | 35,0,3,4,6,1,36,7,19,0 556 | 0,0,5,4,6,4,45,3,19,0 557 | 470,1,5,2,6,2,29,3,19,0 558 | 16,7,6,3,6,5,41,4,19,1 559 | 40,1,5,3,5,4,28,5,19,1 560 | 290,7,2,2,7,0,35,4,19,0 561 | 4,7,2,3,6,0,46,4,19,0 562 | 1,7,1,2,7,1,55,4,19,0 563 | 140,3,2,3,6,6,65,4,19,0 564 | 110,1,4,2,6,1,22,4,19,0 565 | 0,1,4,4,6,1,41,4,19,0 566 | 900,1,2,3,6,2,43,7,19,0 567 | 0,2,4,3,6,0,49,4,19,0 568 | 0,4,4,2,5,4,35,7,19,1 569 | 51,7,3,3,5,1,80,3,19,0 570 | 71,2,4,6,2,0,57,4,19,0 571 | 900,4,3,3,6,0,48,3,19,0 572 | 83,4,1,4,6,2,43,6,19,0 573 | 360,3,4,4,7,0,34,4,19,0 574 | 180,7,2,2,4,0,44,5,19,0 575 | 1,3,6,1,6,4,49,6,20,1 576 | 22,7,5,2,7,2,43,7,20,0 577 | 1,0,4,3,5,6,39,4,20,1 578 | 2,2,4,3,7,1,27,3,20,0 579 | 34,4,6,2,6,6,30,6,20,1 580 | 33,2,6,2,6,4,33,6,20,1 581 | 10,1,2,3,7,0,32,5,20,0 582 | 290,7,5,2,6,6,31,6,20,1 583 | 0,2,5,3,7,2,27,5,20,0 584 | 76,3,2,2,6,0,36,4,20,0 585 | 20,7,3,2,6,2,45,5,20,0 586 | 0,5,4,6,5,2,51,3,20,0 587 | 0,0,6,2,3,5,36,4,20,1 588 | 0,7,6,4,6,4,47,7,20,1 589 | 470,4,4,4,6,1,42,6,20,0 590 | 9,6,6,1,5,6,70,6,20,1 591 | 0,0,3,3,6,3,40,4,20,0 592 | 0,7,2,5,7,0,38,3,20,0 593 | 8,0,5,1,6,4,29,3,20,1 594 | 1,0,4,2,5,6,58,3,20,1 595 | 1,7,4,6,1,2,28,3,20,0 596 | 7,1,6,3,6,5,30,4,20,1 597 | 0,3,4,3,6,2,48,5,20,0 598 | 170,0,6,2,6,6,35,4,20,1 599 | 270,3,6,7,6,0,38,3,20,0 600 | 0,0,4,2,5,4,56,7,20,1 601 | 13,4,5,3,6,5,62,4,20,1 602 | 10,7,6,1,6,5,56,4,20,1 603 | 22,0,6,2,5,6,32,3,20,1 604 | 640,5,4,5,7,1,50,5,20,0 605 | 900,3,3,4,6,1,45,6,20,0 606 | 22,7,3,3,4,0,36,6,20,0 607 | 62,1,6,2,6,6,49,4,20,1 608 | 110,2,7,1,6,6,31,6,20,1 609 | 84,4,2,3,6,2,33,7,20,0 610 | 0,1,6,2,6,6,35,4,20,1 611 | 13,2,4,2,5,4,73,4,20,1 612 | 20,7,6,1,5,6,54,5,20,1 613 | 0,3,4,2,7,1,48,3,20,0 614 | 16,3,6,2,6,4,65,4,20,1 615 | 6,7,5,6,5,6,46,4,20,0 616 | 12,0,2,2,6,0,45,7,20,0 617 | 0,7,6,2,5,6,65,7,20,1 618 | 0,0,6,2,6,6,45,6,20,1 619 | 170,7,4,1,5,4,89,7,20,1 620 | 100,0,5,3,6,4,32,4,20,0 621 | 5,4,6,4,4,1,38,4,20,0 622 | 3500,5,2,4,2,5,33,4,20,1 623 | 71,4,6,2,5,6,49,7,20,1 624 | 4,7,5,2,6,5,64,6,20,1 625 | 7300,5,5,4,7,1,55,4,20,0 626 | 7,0,3,3,5,1,56,7,20,0 627 | 290,4,4,5,3,5,30,6,20,0 628 | 0,0,5,4,6,5,41,7,20,1 629 | 0,1,6,2,5,6,39,5,20,1 630 | 520,7,4,4,6,4,34,3,20,0 631 | 430,5,4,5,7,1,53,3,20,0 632 | 9,2,3,3,6,2,40,6,20,0 633 | 40,0,3,3,6,1,48,6,20,0 634 | 2,7,6,5,5,5,59,4,20,0 635 | 75,7,6,2,4,4,54,4,20,1 636 | 170,0,3,5,6,1,41,6,20,0 637 | 170,1,2,3,6,0,41,6,20,0 638 | 640,5,5,3,6,4,63,7,20,0 639 | 2800,1,7,1,6,4,39,3,20,1 640 | 9,3,7,2,5,4,46,3,20,0 641 | 150,7,4,3,6,0,55,3,20,0 642 | 0,7,4,1,5,3,42,3,20,1 643 | 0,7,6,3,6,4,58,2,20,1 644 | 100,0,3,2,6,2,42,4,20,0 645 | 33,7,4,3,4,2,40,6,20,1 646 | 310,7,5,2,5,1,56,6,20,1 647 | 53,1,4,5,2,2,37,4,20,0 648 | 13,2,3,2,6,2,37,6,20,0 649 | 290,6,5,1,5,5,49,6,20,1 650 | 310,7,6,1,6,6,63,4,20,1 651 | 0,7,6,2,5,6,30,7,20,1 652 | 54,7,4,2,6,4,62,5,20,1 653 | 1600,0,4,3,6,1,30,6,20,0 654 | 14,0,6,3,6,6,34,6,20,1 655 | 25,7,6,3,6,5,41,4,20,1 656 | 45,0,4,5,4,6,43,3,20,1 657 | 20,2,5,2,6,4,33,5,20,0 658 | 18,6,4,3,5,5,67,7,20,1 659 | 740,7,5,1,6,0,55,5,20,1 660 | 9,6,2,2,6,0,33,5,20,0 661 | 5,0,6,1,5,5,61,4,20,1 662 | 7300,7,4,1,6,4,45,3,20,0 663 | 81,7,4,3,7,0,34,3,20,0 664 | 190,1,6,2,6,6,35,3,20,1 665 | 51,1,4,4,5,2,50,6,20,0 666 | 7300,7,3,4,2,0,38,4,20,0 667 | 350,0,6,2,6,5,56,3,20,1 668 | 27,2,4,1,6,5,31,3,20,1 669 | 33,5,6,1,4,5,40,7,20,1 670 | 50,4,3,2,6,0,44,3,20,0 671 | 1,7,6,5,2,2,39,4,20,0 672 | 11,1,4,3,6,1,45,3,20,0 673 | 51,5,3,3,6,0,72,6,20,0 674 | 160,7,2,3,5,1,44,7,20,0 675 | 16,7,1,3,6,0,61,7,21,0 676 | 110,6,6,2,6,6,34,7,21,1 677 | 110,4,5,2,5,5,61,6,21,1 678 | 13,6,3,3,7,1,67,4,21,0 679 | 220,7,4,1,4,6,38,6,21,1 680 | 470,7,4,2,6,1,50,6,21,0 681 | 22,6,6,1,6,6,62,4,21,1 682 | 9,2,6,3,6,5,36,6,21,1 683 | 22,3,6,3,5,4,50,6,21,1 684 | 190,2,4,2,7,1,30,6,21,0 685 | 100,5,6,2,6,6,59,5,21,1 686 | 14,7,3,3,6,0,62,6,21,0 687 | 0,7,4,3,6,3,40,4,21,0 688 | 180,1,6,1,6,6,30,4,21,1 689 | 3,6,3,2,6,2,47,3,21,0 690 | 51,2,5,2,6,1,41,6,21,0 691 | 9,2,6,1,6,6,35,7,21,1 692 | 0,7,5,2,6,6,45,4,21,1 693 | 0,2,4,2,6,4,34,6,21,1 694 | 71,2,5,7,2,2,55,3,21,0 695 | 290,1,5,3,6,2,37,4,21,0 696 | 45,3,5,2,4,4,61,7,21,1 697 | 0,3,4,5,4,1,62,2,21,0 698 | 26,1,5,2,6,6,54,6,21,1 699 | 87,3,2,3,6,1,33,6,21,0 700 | 0,0,2,3,6,1,50,6,21,0 701 | 630,0,3,4,7,2,37,3,21,0 702 | 50,5,5,3,6,3,44,3,21,1 703 | 35,7,4,3,4,3,78,3,21,0 704 | 180,7,6,2,4,6,56,3,21,0 705 | 32,0,2,3,4,2,29,6,21,0 706 | 0,7,3,2,5,4,52,6,21,0 707 | 51,1,6,4,5,1,31,3,21,0 708 | 40,0,6,3,6,6,34,7,21,1 709 | 0,2,5,3,6,5,31,7,21,1 710 | 0,7,4,3,5,2,43,6,21,0 711 | 0,1,4,4,3,5,31,3,21,1 712 | 1,6,6,1,6,6,63,7,21,1 713 | 7,2,2,3,7,4,38,4,21,0 714 | 0,2,6,2,6,6,31,5,21,1 715 | 71,2,2,1,7,0,64,3,21,0 716 | 75,2,3,2,5,5,55,7,21,0 717 | 55,1,2,2,6,0,41,3,21,0 718 | 290,4,3,4,5,5,38,4,21,1 719 | 88,4,6,3,6,5,28,6,21,1 720 | 0,7,4,3,5,5,42,5,21,1 721 | 16,7,2,4,3,1,43,4,21,0 722 | 75,1,5,1,6,5,37,4,21,1 723 | 220,1,4,2,6,3,47,5,21,0 724 | 3,5,5,1,6,5,52,7,21,1 725 | 130,0,5,2,6,5,32,4,21,1 726 | 0,5,4,4,3,2,29,3,21,0 727 | 110,2,6,2,5,4,56,3,21,1 728 | 12,7,4,3,5,5,63,7,21,1 729 | 180,3,3,3,6,1,35,5,21,0 730 | 93,7,4,3,5,3,36,4,21,1 731 | 170,7,4,2,7,0,75,5,21,0 732 | 31,5,3,3,6,1,48,6,21,0 733 | 62,4,7,2,6,6,36,5,21,1 734 | 30,4,4,3,6,2,34,6,21,0 735 | 66,7,4,3,7,1,35,5,21,0 736 | 3,3,5,2,5,6,50,4,21,1 737 | 18,3,5,2,5,6,39,7,21,1 738 | 350,5,5,4,6,5,70,7,21,1 739 | 71,7,4,2,5,6,76,3,21,1 740 | 3500,5,5,2,5,5,35,6,21,1 741 | 0,0,3,4,3,3,53,7,21,1 742 | 360,6,5,2,6,4,46,6,21,1 743 | 81,2,5,2,5,4,34,4,21,1 744 | 350,5,4,4,6,5,69,4,21,0 745 | 190,1,5,2,5,1,32,3,21,0 746 | 0,7,5,1,6,6,50,2,21,1 747 | 290,1,5,3,6,6,35,6,21,1 748 | 18,0,6,1,6,6,67,5,21,1 749 | 11,3,5,5,6,5,47,3,21,0 750 | 2,2,6,3,5,4,50,6,21,1 751 | 570,0,6,2,6,4,32,3,21,1 752 | 310,3,5,4,6,2,58,5,21,0 753 | 1,7,3,2,7,0,49,7,21,0 754 | 0,2,6,3,6,1,43,4,21,1 755 | 35,1,5,2,6,5,24,6,21,1 756 | 22,7,5,3,4,2,58,7,21,0 757 | 2,1,2,2,5,0,43,4,21,0 758 | 0,7,4,3,6,5,59,3,21,1 759 | 0,3,6,1,6,6,40,5,21,1 760 | 310,0,5,2,7,0,35,4,21,0 761 | 470,5,2,3,5,1,48,4,21,0 762 | 0,4,6,1,6,6,40,3,21,1 763 | 270,3,3,2,7,0,48,3,21,0 764 | 110,0,2,4,6,0,47,7,21,0 765 | 50,3,6,4,1,1,23,3,21,0 766 | 0,0,6,1,6,6,38,3,21,1 767 | 3,7,6,2,6,6,81,7,21,1 768 | 31,5,3,3,6,1,48,6,21,0 769 | 22,7,5,6,2,4,52,3,21,0 770 | 83,2,4,1,6,3,24,6,21,1 771 | 9,0,4,2,6,1,21,5,21,0 772 | 5,7,5,2,6,5,70,6,21,1 773 | 0,7,7,1,7,6,24,6,21,1 774 | 0,7,4,3,5,1,57,7,21,0 775 | 0,6,3,5,6,4,37,6,21,0 776 | 27,1,3,5,3,5,25,5,21,0 777 | 110,1,6,5,1,1,33,3,21,0 778 | 0,7,4,6,3,0,45,3,22,0 779 | 0,3,5,2,7,5,42,6,22,0 780 | 350,3,3,3,6,2,47,6,22,0 781 | 0,7,6,1,6,6,51,7,22,1 782 | 5,7,5,4,6,5,85,2,22,0 783 | 15,4,3,3,6,2,32,7,22,0 784 | 35,7,3,3,6,2,31,7,22,0 785 | 0,2,4,3,6,2,23,6,22,0 786 | 75,3,3,3,6,0,42,6,22,0 787 | 0,5,6,2,5,5,55,6,22,1 788 | 16,7,6,2,6,6,45,6,22,1 789 | 0,1,6,2,5,6,35,7,22,1 790 | 0,0,2,4,6,0,45,6,22,0 791 | 0,0,3,3,5,2,42,3,22,0 792 | 4,1,6,2,6,5,37,4,22,1 793 | 62,0,4,4,4,5,38,3,22,1 794 | 0,3,2,2,6,1,47,7,22,0 795 | 4,7,4,2,6,2,32,6,22,0 796 | 56,2,5,2,6,5,35,6,22,1 797 | 2,6,4,2,6,5,38,4,22,1 798 | 0,0,4,4,4,3,40,3,22,0 799 | 75,7,6,2,5,6,62,6,22,1 800 | 10,2,2,2,6,1,28,7,22,0 801 | 0,6,6,2,5,6,59,5,22,1 802 | 0,2,1,2,6,0,25,6,22,0 803 | 220,2,4,2,6,6,31,6,22,1 804 | 0,1,7,2,6,6,45,4,22,1 805 | 75,0,5,2,7,5,42,6,22,0 806 | 0,3,2,2,5,1,56,7,22,0 807 | 140,5,3,3,6,1,47,7,22,0 808 | 290,1,6,2,5,5,38,6,22,1 809 | 350,7,4,3,7,2,47,6,22,0 810 | 55,1,5,2,6,6,49,4,22,1 811 | 31,3,3,3,6,2,29,7,22,0 812 | 17,7,2,2,6,0,57,6,22,0 813 | 51,4,6,3,5,6,68,3,22,1 814 | 140,7,2,2,4,0,76,6,22,0 815 | 9,4,6,2,6,5,66,6,22,1 816 | 0,7,5,1,6,4,59,4,22,1 817 | 640,7,5,3,6,4,37,7,22,0 818 | 32,2,5,2,6,6,38,6,22,1 819 | 5,7,6,2,6,5,47,7,22,1 820 | 8,1,5,2,4,5,36,7,22,1 821 | 18,0,5,4,6,0,45,7,22,0 822 | 0,6,5,2,6,5,39,7,22,1 823 | 0,1,3,2,6,1,34,6,22,0 824 | 0,3,6,6,4,0,49,4,22,0 825 | 31,2,6,2,6,5,36,6,22,1 826 | 350,7,6,1,6,6,81,5,22,1 827 | 20,1,5,2,6,4,29,4,22,1 828 | 70,3,5,3,4,0,45,6,22,0 829 | 31,3,5,2,6,5,21,4,22,1 830 | 3,7,2,4,3,6,33,6,22,1 831 | 9,7,4,4,2,2,44,3,23,0 832 | 59,1,2,2,6,4,52,7,23,0 833 | 27,2,3,5,7,1,38,4,23,0 834 | 51,4,2,3,6,2,44,5,23,0 835 | 9,7,6,2,6,6,87,7,23,1 836 | 0,2,7,2,6,5,22,3,23,1 837 | 88,0,3,3,5,2,32,6,23,1 838 | 67,0,4,5,6,4,69,3,23,0 839 | 29,2,6,2,5,6,49,6,23,1 840 | 5,0,6,2,6,6,53,3,23,1 841 | 0,0,6,1,6,5,44,6,23,1 842 | 900,1,6,2,7,5,34,6,23,1 843 | 18,1,6,1,5,5,55,7,23,1 844 | 190,2,3,3,6,0,35,6,23,0 845 | 2,7,4,5,5,3,55,3,23,0 846 | 5,3,6,3,5,5,27,6,23,1 847 | 56,3,4,4,5,1,26,6,23,0 848 | 75,7,4,2,6,5,54,6,23,0 849 | 56,0,5,2,6,5,42,4,23,1 850 | 0,5,6,1,6,6,57,7,23,1 851 | 0,0,7,1,4,6,54,6,23,1 852 | 75,6,6,3,6,6,55,6,23,1 853 | 1600,7,5,3,6,6,50,7,23,1 854 | 15,0,5,5,6,1,57,7,23,0 855 | 19,3,5,3,7,3,46,4,23,0 856 | 16,7,6,2,6,6,53,6,23,1 857 | 42,2,3,3,5,0,32,7,23,0 858 | 18,5,5,2,5,3,53,5,23,1 859 | 0,3,3,3,6,1,39,6,23,0 860 | 310,1,5,2,5,4,47,6,23,1 861 | 1600,7,5,2,4,2,57,4,23,0 862 | 23,5,6,3,6,6,49,6,23,1 863 | 20,1,5,4,6,5,31,5,23,0 864 | 51,5,5,3,5,5,43,6,23,1 865 | 0,2,5,2,5,4,44,6,23,1 866 | 0,4,4,3,6,0,39,6,23,0 867 | 0,2,6,1,6,5,49,4,23,1 868 | 18,7,5,4,6,4,72,6,23,1 869 | 7300,7,5,2,6,5,50,6,23,1 870 | 110,1,5,2,6,6,28,4,23,1 871 | 0,0,5,2,7,3,48,7,23,1 872 | 3500,1,3,4,7,1,32,6,23,0 873 | 720,7,5,5,5,1,63,4,23,0 874 | 9,4,4,5,6,5,36,4,23,1 875 | 47,7,6,3,6,6,36,6,23,1 876 | 350,7,3,2,7,2,53,3,23,0 877 | 0,5,2,2,6,2,44,7,23,0 878 | 0,0,4,2,6,6,41,7,24,1 879 | 83,0,2,3,6,1,56,7,24,0 880 | 1,4,4,4,6,2,63,7,24,0 881 | 190,7,2,4,6,0,52,6,24,0 882 | 0,7,3,3,7,2,43,7,24,0 883 | 12,7,4,3,6,2,40,3,24,0 884 | 9,5,5,1,7,4,69,4,24,1 885 | 23,7,2,2,6,0,49,7,24,0 886 | 9,1,3,3,6,2,65,7,24,0 887 | 18,5,6,1,6,5,53,7,24,1 888 | 0,5,5,3,5,6,50,4,24,1 889 | 12,3,2,4,6,0,27,5,24,0 890 | 0,6,5,3,5,5,44,6,24,1 891 | 170,2,2,3,5,0,54,7,24,0 892 | 0,7,4,2,6,4,33,5,24,1 893 | 9,0,4,4,6,3,48,7,24,0 894 | 23,3,5,2,6,5,54,5,24,1 895 | 0,0,6,2,5,6,56,3,24,1 896 | 9,1,2,4,7,2,34,7,24,0 897 | 290,7,6,4,7,6,41,6,24,0 898 | 1,0,5,1,5,6,40,6,24,1 899 | 350,1,7,2,6,6,55,6,24,1 900 | 20,0,4,3,5,5,38,6,24,1 901 | 0,3,6,2,6,6,40,6,24,1 902 | 23,1,6,1,6,6,46,4,24,1 903 | 150,4,3,3,4,0,26,6,24,0 904 | 31,0,5,2,7,3,49,6,24,0 905 | 0,7,4,1,5,6,51,5,24,1 906 | 9,2,5,2,6,4,46,6,24,0 907 | 47,0,3,4,6,2,40,7,24,0 908 | 900,0,3,4,7,2,30,5,24,0 909 | 83,3,2,3,6,2,45,5,24,0 910 | 18,7,5,4,6,4,52,7,24,1 911 | 0,0,6,1,5,6,36,6,24,1 912 | 20,0,4,3,5,3,49,6,24,0 913 | 24,7,3,4,5,1,38,7,24,0 914 | 18,0,2,4,6,1,51,7,24,0 915 | 9,3,3,2,5,1,47,6,24,0 916 | 0,1,6,1,5,6,52,7,24,1 917 | 9,0,6,2,6,6,33,6,24,1 918 | 0,4,4,2,6,6,50,4,24,1 919 | 18,7,6,2,5,4,48,7,24,1 920 | 19,3,2,2,6,0,36,6,24,0 921 | 31,3,2,3,6,1,35,7,24,0 922 | 3500,7,7,3,5,4,34,7,24,0 923 | 0,7,2,4,5,2,53,6,24,0 924 | 33,0,4,3,6,2,33,7,24,0 925 | 0,1,6,3,6,6,52,6,24,1 926 | 18,3,4,3,6,4,44,7,24,0 927 | 0,0,3,4,4,0,48,6,24,0 928 | 31,3,5,2,6,5,20,4,24,1 929 | 0,5,3,2,4,6,45,6,24,1 930 | 59,7,4,2,6,2,70,3,24,0 931 | 0,0,3,3,4,2,39,3,24,0 932 | 7300,7,3,3,5,1,40,7,24,1 933 | 75,4,5,2,7,5,62,6,24,1 934 | 0,7,5,2,6,4,46,6,24,1 935 | 27,7,4,4,7,2,46,3,24,0 936 | 1600,7,4,2,5,6,56,7,24,1 937 | 0,7,6,3,6,6,55,7,24,1 938 | 0,7,6,2,6,6,41,4,24,1 939 | 7300,1,2,3,6,0,43,7,24,0 940 | 16,7,7,1,7,6,34,3,24,1 941 | 0,7,7,1,6,4,73,6,24,1 942 | 0,7,5,2,6,6,50,6,24,1 943 | 0,3,6,2,7,5,43,6,24,1 944 | 0,6,6,2,5,6,46,7,24,1 945 | 18,7,4,2,6,3,61,7,24,1 946 | --------------------------------------------------------------------------------