diff --git a/.DS_Store b/.DS_Store
index 9f9e4d112d4764c1914730f9083ff6f46eac5872..6be50f2b0fb9dd24f3ed303669512d55d8da3476 100644
Binary files a/.DS_Store and b/.DS_Store differ
diff --git a/Tavara- raskaan liikenne/.DS_Store b/Tavara- raskaan liikenne/.DS_Store
index e85420dcb6bab8e6e59d205eaea7d59b538ac611..87c75be43d7b7354483c7032dae37670735692c9 100644
Binary files a/Tavara- raskaan liikenne/.DS_Store and b/Tavara- raskaan liikenne/.DS_Store differ
diff --git a/Tavara- raskaan liikenne/backend/index.js b/Tavara- raskaan liikenne/backend/index.js
index e3806cc4a4406b77fd4d55a9d5b26c3157bdbee2..5273f20fb417882fc4fc02c27815b0e0147793a1 100644
--- a/Tavara- raskaan liikenne/backend/index.js
+++ b/Tavara- raskaan liikenne/backend/index.js
@@ -2,52 +2,18 @@
var express = require('express');
var cors = require('cors');
var app = express();
-var port = 3030;
-app.listen(port, function () {
- console.log(`server running on http://localhost:${port}`);
-});
-
-app.use(cors());
-
-//Part2: Express Get Request for Covid-19 Time Series data
-app.get('/',function (req, res) {
-
- console.log(req.query.data)
- var spawn = require('child_process').spawn;
- var childProcess = spawn('python', ['../python/test.py',
- req.query.data],{shell: true});
-
- // childProcess.stdout.on('data', function (data) {
- //
- // res.send(data.toString());
- //
- // });
- var uint8arrayToString = function(data){
- return String.fromCharCode.apply(null, data);
- };
-
- // Handle normal output
- childProcess.stdout.on('data', (data) => {
- console.log(uint8arrayToString(data));
- res.send(data);
- console.log(typeof data)
- });
-
- // Handle error output
- childProcess.stderr.on('data', (data) => {
- // As said before, convert the Uint8Array to a readable string.
- console.log(uint8arrayToString(data));
- });
-
- childProcess.on('exit', (code) => {
- console.log("Process quit with code : " + code);
- });
+app.use(cors());
+app.use("/",require("./routes/test.js"))
+app.use("/api/vehicle/", require('./routes/api/vehicles/vehicle'));
-});
+var port = 3030;
+app.listen(port, function () {
+ console.log(`server running on http://localhost:${port}`);
+});
diff --git a/Tavara- raskaan liikenne/backend/package-lock.json b/Tavara- raskaan liikenne/backend/package-lock.json
index c53e47d2f377cc2fe0052ad61c14b150dc0ff56f..3a115bfabfacefdf495f301e2a5501bbbd376dc7 100644
--- a/Tavara- raskaan liikenne/backend/package-lock.json
+++ b/Tavara- raskaan liikenne/backend/package-lock.json
@@ -84,6 +84,14 @@
"resolved": "https://registry.npmjs.org/array-flatten/-/array-flatten-1.1.1.tgz",
"integrity": "sha1-ml9pkFGx5wczKPKgCJaLZOopVdI="
},
+ "axios": {
+ "version": "0.19.2",
+ "resolved": "https://registry.npmjs.org/axios/-/axios-0.19.2.tgz",
+ "integrity": "sha512-fjgm5MvRHLhx+osE2xoekY70AhARk3a6hkN+3Io1jc00jtquGvxYlKlsFUhmUET0V5te6CcZI7lcv2Ym61mjHA==",
+ "requires": {
+ "follow-redirects": "1.5.10"
+ }
+ },
"balanced-match": {
"version": "1.0.0",
"resolved": "https://registry.npmjs.org/balanced-match/-/balanced-match-1.0.0.tgz",
@@ -453,6 +461,24 @@
"unpipe": "~1.0.0"
}
},
+ "follow-redirects": {
+ "version": "1.5.10",
+ "resolved": "https://registry.npmjs.org/follow-redirects/-/follow-redirects-1.5.10.tgz",
+ "integrity": "sha512-0V5l4Cizzvqt5D44aTXbFZz+FtyXV1vrDN6qrelxtfYQKW0KO0W2T/hkE8xvGa/540LkZlkaUjO4ailYTFtHVQ==",
+ "requires": {
+ "debug": "=3.1.0"
+ },
+ "dependencies": {
+ "debug": {
+ "version": "3.1.0",
+ "resolved": "https://registry.npmjs.org/debug/-/debug-3.1.0.tgz",
+ "integrity": "sha512-OX8XqP7/1a9cqkxYw2yXss15f26NKWBpDXQd0/uK/KPqdQhxbPa994hnzjcE2VqQpDslf55723cKPUOGSmMY3g==",
+ "requires": {
+ "ms": "2.0.0"
+ }
+ }
+ }
+ },
"forwarded": {
"version": "0.1.2",
"resolved": "https://registry.npmjs.org/forwarded/-/forwarded-0.1.2.tgz",
@@ -970,6 +996,32 @@
"lowercase-keys": "^1.0.0"
}
},
+ "router": {
+ "version": "1.3.5",
+ "resolved": "https://registry.npmjs.org/router/-/router-1.3.5.tgz",
+ "integrity": "sha512-kozCJZUhuSJ5VcLhSb3F8fsmGXy+8HaDbKCAerR1G6tq3mnMZFMuSohbFvGv1c5oMFipijDjRZuuN/Sq5nMf3g==",
+ "requires": {
+ "array-flatten": "3.0.0",
+ "debug": "2.6.9",
+ "methods": "~1.1.2",
+ "parseurl": "~1.3.3",
+ "path-to-regexp": "0.1.7",
+ "setprototypeof": "1.2.0",
+ "utils-merge": "1.0.1"
+ },
+ "dependencies": {
+ "array-flatten": {
+ "version": "3.0.0",
+ "resolved": "https://registry.npmjs.org/array-flatten/-/array-flatten-3.0.0.tgz",
+ "integrity": "sha512-zPMVc3ZYlGLNk4mpK1NzP2wg0ml9t7fUgDsayR5Y5rSzxQilzR9FGu/EH2jQOcKSAeAfWeylyW8juy3OkWRvNA=="
+ },
+ "setprototypeof": {
+ "version": "1.2.0",
+ "resolved": "https://registry.npmjs.org/setprototypeof/-/setprototypeof-1.2.0.tgz",
+ "integrity": "sha512-E5LDX7Wrp85Kil5bhZv46j8jOeboKq5JMmYM3gVGdGH8xFpPWXUMsNrlODCrkoxMEeNi/XZIwuRvY4XNwYMJpw=="
+ }
+ }
+ },
"safe-buffer": {
"version": "5.1.2",
"resolved": "https://registry.npmjs.org/safe-buffer/-/safe-buffer-5.1.2.tgz",
diff --git a/Tavara- raskaan liikenne/backend/package.json b/Tavara- raskaan liikenne/backend/package.json
index 887107a8055f153d30957a4a81ca0545bb20f6df..16e5a30715da465d30207c186e3404bf97a789e9 100644
--- a/Tavara- raskaan liikenne/backend/package.json
+++ b/Tavara- raskaan liikenne/backend/package.json
@@ -9,9 +9,11 @@
"author": "",
"license": "ISC",
"dependencies": {
+ "axios": "^0.19.2",
"cors": "^2.8.5",
"express": "^4.17.1",
"nodemon": "^2.0.4",
- "python-shell": "^2.0.1"
+ "python-shell": "^2.0.1",
+ "router": "^1.3.5"
}
}
diff --git a/Tavara- raskaan liikenne/backend/routes/api/vehicles/vehicle.js b/Tavara- raskaan liikenne/backend/routes/api/vehicles/vehicle.js
new file mode 100644
index 0000000000000000000000000000000000000000..c12f687b5f47b8fe2e535be8d14a9bce140ba43b
--- /dev/null
+++ b/Tavara- raskaan liikenne/backend/routes/api/vehicles/vehicle.js
@@ -0,0 +1,63 @@
+const express = require('express');
+const router = express.Router();
+const axios = require('axios');
+
+
+//Part2: Express Get Request for Covid-19 Time Series data
+router.get('/',function (req, res) {
+
+ var year = req.query.year;
+ var areaID = req.query.areaID;
+ var lamID = req.query.lamID;
+ var startDayNumber = req.query.startDayNumber;
+ var endDayNumber = req.query.endDayNumber;
+
+
+ //var vehicleClass =req.query.vehicleClass;
+
+ console.log(req.query);
+
+
+ if(endDayNumber== undefined | endDayNumber =="" | endDayNumber ==NaN)endDayNumber=NaN;
+
+ var spawn = require('child_process').spawn;
+ var childProcess = spawn('python', ['../python/cars/lamData.py',
+ year,
+ areaID,
+ lamID,
+ startDayNumber,
+ endDayNumber
+ //,vehicleClass,
+ ]
+ ,{shell: true});
+
+ // childProcess.stdout.on('data', function (data) {
+ //
+ // res.send(data.toString());
+ //
+ // });
+ var uint8arrayToString = function(data){
+ return String.fromCharCode.apply(null, data);
+ };
+
+ // Handle normal output
+ childProcess.stdout.on('data', (data) => {
+ console.log(uint8arrayToString(data));
+ res.send(data);
+ console.log(typeof data)
+ });
+
+ // Handle error output
+ childProcess.stderr.on('data', (data) => {
+ //As said before, convert the Uint8Array to a readable string.
+ console.log(uint8arrayToString(data));
+ //res.send(data)
+ });
+
+ childProcess.on('exit', (code) => {
+ console.log("Process quit with code : " + code);
+ });
+
+});
+
+module.exports = router
\ No newline at end of file
diff --git a/Tavara- raskaan liikenne/backend/routes/test.js b/Tavara- raskaan liikenne/backend/routes/test.js
new file mode 100644
index 0000000000000000000000000000000000000000..1fb6802969f06d571f9a66e29657583d29edf7a5
--- /dev/null
+++ b/Tavara- raskaan liikenne/backend/routes/test.js
@@ -0,0 +1,52 @@
+const express = require('express');
+const router = express.Router();
+const axios = require('axios');
+
+
+
+
+
+
+//Part2: Express Get Request for Covid-19 Time Series data
+router.get('/',function (req, res) {
+
+ var data = req.query.data;
+
+
+ console.log(req.query)
+ var spawn = require('child_process').spawn;
+ var childProcess = spawn('python', ['../python/test.py',
+ data,
+
+ ]
+ ,{shell: true});
+
+ // childProcess.stdout.on('data', function (data) {
+ //
+ // res.send(data.toString());
+ //
+ // });
+ var uint8arrayToString = function(data){
+ return String.fromCharCode.apply(null, data);
+ };
+
+ // Handle normal output
+ childProcess.stdout.on('data', (data) => {
+ console.log(uint8arrayToString(data));
+ res.send(data);
+ console.log(typeof data)
+ });
+
+ // Handle error output
+ childProcess.stderr.on('data', (data) => {
+ // As said before, convert the Uint8Array to a readable string.
+ console.log(uint8arrayToString(data));
+ });
+
+ childProcess.on('exit', (code) => {
+ console.log("Process quit with code : " + code);
+ });
+
+});
+
+module.exports = router
\ No newline at end of file
diff --git a/Tavara- raskaan liikenne/frontend/.DS_Store b/Tavara- raskaan liikenne/frontend/.DS_Store
new file mode 100644
index 0000000000000000000000000000000000000000..7d9703c3e1f3c722797ecc2725817d086695036c
Binary files /dev/null and b/Tavara- raskaan liikenne/frontend/.DS_Store differ
diff --git a/Tavara- raskaan liikenne/frontend/traffic/package-lock.json b/Tavara- raskaan liikenne/frontend/traffic/package-lock.json
index 8f6133c4f87148417269484da7706c100d56caf6..b4514ad5273402a5c853a4ed4299034dd65b2c91 100644
--- a/Tavara- raskaan liikenne/frontend/traffic/package-lock.json
+++ b/Tavara- raskaan liikenne/frontend/traffic/package-lock.json
@@ -1356,6 +1356,25 @@
"resolved": "https://registry.npmjs.org/@nodelib/fs.stat/-/fs.stat-1.1.3.tgz",
"integrity": "sha512-shAmDyaQC4H92APFoIaVDHCx5bStIocgvbwQyxPRrbUY20V1EYTbSDchWbuwlMG3V17cprZhA6+78JfB+3DTPw=="
},
+ "@popperjs/core": {
+ "version": "2.4.0",
+ "resolved": "https://registry.npmjs.org/@popperjs/core/-/core-2.4.0.tgz",
+ "integrity": "sha512-NMrDy6EWh9TPdSRiHmHH2ye1v5U0gBD7pRYwSwJvomx7Bm4GG04vu63dYiVzebLOx2obPpJugew06xVP0Nk7hA=="
+ },
+ "@restart/context": {
+ "version": "2.1.4",
+ "resolved": "https://registry.npmjs.org/@restart/context/-/context-2.1.4.tgz",
+ "integrity": "sha512-INJYZQJP7g+IoDUh/475NlGiTeMfwTXUEr3tmRneckHIxNolGOW9CTq83S8cxq0CgJwwcMzMJFchxvlwe7Rk8Q=="
+ },
+ "@restart/hooks": {
+ "version": "0.3.25",
+ "resolved": "https://registry.npmjs.org/@restart/hooks/-/hooks-0.3.25.tgz",
+ "integrity": "sha512-m2v3N5pxTsIiSH74/sb1yW8D9RxkJidGW+5Mfwn/lHb2QzhZNlaU1su7abSyT9EGf0xS/0waLjrf7/XxQHUk7w==",
+ "requires": {
+ "lodash": "^4.17.15",
+ "lodash-es": "^4.17.15"
+ }
+ },
"@sheerun/mutationobserver-shim": {
"version": "0.3.3",
"resolved": "https://registry.npmjs.org/@sheerun/mutationobserver-shim/-/mutationobserver-shim-0.3.3.tgz",
@@ -1830,6 +1849,11 @@
}
}
},
+ "@types/warning": {
+ "version": "3.0.0",
+ "resolved": "https://registry.npmjs.org/@types/warning/-/warning-3.0.0.tgz",
+ "integrity": "sha1-DSUBJorY+ZYrdA04fEZU9fjiPlI="
+ },
"@types/yargs": {
"version": "13.0.9",
"resolved": "https://registry.npmjs.org/@types/yargs/-/yargs-13.0.9.tgz",
@@ -3581,6 +3605,11 @@
}
}
},
+ "classnames": {
+ "version": "2.2.6",
+ "resolved": "https://registry.npmjs.org/classnames/-/classnames-2.2.6.tgz",
+ "integrity": "sha512-JR/iSQOSt+LQIWwrwEzJ9uk0xfN3mTVYMwt1Ir5mUcSN6pU+V4zQFFaJsclJbPuAUQH+yfWef6tm7l1quW3C8Q=="
+ },
"clean-css": {
"version": "4.2.3",
"resolved": "https://registry.npmjs.org/clean-css/-/clean-css-4.2.3.tgz",
@@ -4616,6 +4645,15 @@
"utila": "~0.4"
}
},
+ "dom-helpers": {
+ "version": "5.1.4",
+ "resolved": "https://registry.npmjs.org/dom-helpers/-/dom-helpers-5.1.4.tgz",
+ "integrity": "sha512-TjMyeVUvNEnOnhzs6uAn9Ya47GmMo3qq7m+Lr/3ON0Rs5kHvb8I+SQYjLUSYn7qhEm0QjW0yrBkvz9yOrwwz1A==",
+ "requires": {
+ "@babel/runtime": "^7.8.7",
+ "csstype": "^2.6.7"
+ }
+ },
"dom-serializer": {
"version": "0.2.2",
"resolved": "https://registry.npmjs.org/dom-serializer/-/dom-serializer-0.2.2.tgz",
@@ -7937,6 +7975,11 @@
"resolved": "https://registry.npmjs.org/lodash/-/lodash-4.17.15.tgz",
"integrity": "sha512-8xOcRHvCjnocdS5cpwXQXVzmmh5e5+saE2QGoeQmbKmRS6J3VQppPOIt0MnmE+4xlZoumy0GPG0D0MVIQbNA1A=="
},
+ "lodash-es": {
+ "version": "4.17.15",
+ "resolved": "https://registry.npmjs.org/lodash-es/-/lodash-es-4.17.15.tgz",
+ "integrity": "sha512-rlrc3yU3+JNOpZ9zj5pQtxnx2THmvRykwL4Xlxoa8I9lHBlVbbyPhgyPMioxVZ4NqyxaVVtaJnzsyOidQIhyyQ=="
+ },
"lodash._reinterpolate": {
"version": "3.0.0",
"resolved": "https://registry.npmjs.org/lodash._reinterpolate/-/lodash._reinterpolate-3.0.0.tgz",
@@ -10288,6 +10331,15 @@
"react-is": "^16.8.1"
}
},
+ "prop-types-extra": {
+ "version": "1.1.1",
+ "resolved": "https://registry.npmjs.org/prop-types-extra/-/prop-types-extra-1.1.1.tgz",
+ "integrity": "sha512-59+AHNnHYCdiC+vMwY52WmvP5dM3QLeoumYuEyceQDi9aEhtwN9zIQ2ZNo25sMyXnbh32h+P1ezDsUpUH3JAew==",
+ "requires": {
+ "react-is": "^16.3.2",
+ "warning": "^4.0.0"
+ }
+ },
"proxy-addr": {
"version": "2.0.6",
"resolved": "https://registry.npmjs.org/proxy-addr/-/proxy-addr-2.0.6.tgz",
@@ -10475,6 +10527,26 @@
"whatwg-fetch": "^3.0.0"
}
},
+ "react-bootstrap": {
+ "version": "1.0.1",
+ "resolved": "https://registry.npmjs.org/react-bootstrap/-/react-bootstrap-1.0.1.tgz",
+ "integrity": "sha512-xMHwsvDN7sIv26P9wWiosWjITZije2dRCjEJHVfV2KFoSJY+8uv2zttEw0XMB7xviQcW3zuIGLJXuj8vf6lYEg==",
+ "requires": {
+ "@babel/runtime": "^7.4.2",
+ "@restart/context": "^2.1.4",
+ "@restart/hooks": "^0.3.21",
+ "@types/react": "^16.9.23",
+ "classnames": "^2.2.6",
+ "dom-helpers": "^5.1.2",
+ "invariant": "^2.2.4",
+ "prop-types": "^15.7.2",
+ "prop-types-extra": "^1.1.0",
+ "react-overlays": "^3.1.2",
+ "react-transition-group": "^4.0.0",
+ "uncontrollable": "^7.0.0",
+ "warning": "^4.0.3"
+ }
+ },
"react-chartjs-2": {
"version": "2.9.0",
"resolved": "https://registry.npmjs.org/react-chartjs-2/-/react-chartjs-2-2.9.0.tgz",
@@ -10751,6 +10823,26 @@
"resolved": "https://registry.npmjs.org/react-is/-/react-is-16.13.1.tgz",
"integrity": "sha512-24e6ynE2H+OKt4kqsOvNd8kBpV65zoxbA4BVsEOB3ARVWQki/DHzaUoC5KuON/BiccDaCCTZBuOcfZs70kR8bQ=="
},
+ "react-lifecycles-compat": {
+ "version": "3.0.4",
+ "resolved": "https://registry.npmjs.org/react-lifecycles-compat/-/react-lifecycles-compat-3.0.4.tgz",
+ "integrity": "sha512-fBASbA6LnOU9dOU2eW7aQ8xmYBSXUIWr+UmF9b1efZBazGNO+rcXT/icdKnYm2pTwcRylVUYwW7H1PHfLekVzA=="
+ },
+ "react-overlays": {
+ "version": "3.2.0",
+ "resolved": "https://registry.npmjs.org/react-overlays/-/react-overlays-3.2.0.tgz",
+ "integrity": "sha512-YTgCmw6l4uBOYylSnc3V8WLX+A0EoGnzDrqkYz0K7MUKbMBZFpaxLXH4EF9eZbspd+syZHQ5XAABI7n/zak1EA==",
+ "requires": {
+ "@babel/runtime": "^7.4.5",
+ "@popperjs/core": "^2.0.0",
+ "@restart/hooks": "^0.3.12",
+ "@types/warning": "^3.0.0",
+ "dom-helpers": "^5.1.0",
+ "prop-types": "^15.7.2",
+ "uncontrollable": "^7.0.0",
+ "warning": "^4.0.3"
+ }
+ },
"react-scripts": {
"version": "3.4.1",
"resolved": "https://registry.npmjs.org/react-scripts/-/react-scripts-3.4.1.tgz",
@@ -10811,6 +10903,17 @@
"workbox-webpack-plugin": "4.3.1"
}
},
+ "react-transition-group": {
+ "version": "4.4.1",
+ "resolved": "https://registry.npmjs.org/react-transition-group/-/react-transition-group-4.4.1.tgz",
+ "integrity": "sha512-Djqr7OQ2aPUiYurhPalTrVy9ddmFCCzwhqQmtN+J3+3DzLO209Fdr70QrN8Z3DsglWql6iY1lDWAfpFiBtuKGw==",
+ "requires": {
+ "@babel/runtime": "^7.5.5",
+ "dom-helpers": "^5.0.1",
+ "loose-envify": "^1.4.0",
+ "prop-types": "^15.6.2"
+ }
+ },
"read-pkg": {
"version": "3.0.0",
"resolved": "https://registry.npmjs.org/read-pkg/-/read-pkg-3.0.0.tgz",
@@ -12826,6 +12929,17 @@
"resolved": "https://registry.npmjs.org/typedarray/-/typedarray-0.0.6.tgz",
"integrity": "sha1-hnrHTjhkGHsdPUfZlqeOxciDB3c="
},
+ "uncontrollable": {
+ "version": "7.1.1",
+ "resolved": "https://registry.npmjs.org/uncontrollable/-/uncontrollable-7.1.1.tgz",
+ "integrity": "sha512-EcPYhot3uWTS3w00R32R2+vS8Vr53tttrvMj/yA1uYRhf8hbTG2GyugGqWDY0qIskxn0uTTojVd6wPYW9ZEf8Q==",
+ "requires": {
+ "@babel/runtime": "^7.6.3",
+ "@types/react": "^16.9.11",
+ "invariant": "^2.2.4",
+ "react-lifecycles-compat": "^3.0.4"
+ }
+ },
"unicode-canonical-property-names-ecmascript": {
"version": "1.0.4",
"resolved": "https://registry.npmjs.org/unicode-canonical-property-names-ecmascript/-/unicode-canonical-property-names-ecmascript-1.0.4.tgz",
@@ -13112,6 +13226,14 @@
"makeerror": "1.0.x"
}
},
+ "warning": {
+ "version": "4.0.3",
+ "resolved": "https://registry.npmjs.org/warning/-/warning-4.0.3.tgz",
+ "integrity": "sha512-rpJyN222KWIvHJ/F53XSZv0Zl/accqHR8et1kpaMTD/fLCRxtV8iX8czMzY7sVZupTI3zcUTg8eycS2kNF9l6w==",
+ "requires": {
+ "loose-envify": "^1.0.0"
+ }
+ },
"watchpack": {
"version": "1.7.2",
"resolved": "https://registry.npmjs.org/watchpack/-/watchpack-1.7.2.tgz",
diff --git a/Tavara- raskaan liikenne/frontend/traffic/package.json b/Tavara- raskaan liikenne/frontend/traffic/package.json
index 73684d38f220ac88c8d3e18f3404331cef87672a..6a0e608030c9df3131448d22a9015397d98fcd96 100644
--- a/Tavara- raskaan liikenne/frontend/traffic/package.json
+++ b/Tavara- raskaan liikenne/frontend/traffic/package.json
@@ -12,6 +12,7 @@
"chart.js": "^2.9.3",
"react": "^16.13.1",
"react-apexcharts": "^1.3.7",
+ "react-bootstrap": "^1.0.1",
"react-chartjs-2": "^2.9.0",
"react-dom": "^16.13.1",
"react-scripts": "3.4.1"
diff --git a/Tavara- raskaan liikenne/frontend/traffic/src/App.css b/Tavara- raskaan liikenne/frontend/traffic/src/App.css
index 74b5e053450a48a6bdb4d71aad648e7af821975c..2496b4fa6d3f3a5b3f6a9c2dc4aa7a2c1ab1c530 100644
--- a/Tavara- raskaan liikenne/frontend/traffic/src/App.css
+++ b/Tavara- raskaan liikenne/frontend/traffic/src/App.css
@@ -7,6 +7,12 @@
pointer-events: none;
}
+.Cars_graph{
+ max-width: 443px;
+ margin: 30px;
+}
+
+
@media (prefers-reduced-motion: no-preference) {
.App-logo {
animation: App-logo-spin infinite 20s linear;
diff --git a/Tavara- raskaan liikenne/frontend/traffic/src/App.js b/Tavara- raskaan liikenne/frontend/traffic/src/App.js
index 1a40f819380ad6dba745274d76e1b656285c7316..5d972204a037ad136b9598b4ea3449e5aa18deb2 100644
--- a/Tavara- raskaan liikenne/frontend/traffic/src/App.js
+++ b/Tavara- raskaan liikenne/frontend/traffic/src/App.js
@@ -1,12 +1,14 @@
import React from 'react';
-import logo from './logo.svg';
-import Linechart from './components/Linechart'
+import Linechart from './components/Linechart';
+import CarsGraph from './components/CarsGraph';
import './App.css';
-
+import 'bootstrap/dist/css/bootstrap.min.css';
+//<Linechart />
function App() {
return (
<div className="App">
- <Linechart />
+
+ <CarsGraph />
</div>
);
}
diff --git a/Tavara- raskaan liikenne/frontend/traffic/src/components/CarsGraph.js b/Tavara- raskaan liikenne/frontend/traffic/src/components/CarsGraph.js
new file mode 100644
index 0000000000000000000000000000000000000000..bd77fecb6c9865912283024a94232bb21bb049f4
--- /dev/null
+++ b/Tavara- raskaan liikenne/frontend/traffic/src/components/CarsGraph.js
@@ -0,0 +1,576 @@
+import React, { Component } from "react";
+import axios from "axios";
+import Chart from "react-apexcharts";
+import Form from "react-bootstrap/Form";
+import Col from "react-bootstrap/Col";
+import Button from "react-bootstrap/Button";
+import Row from "react-bootstrap/Row";
+
+var year = "";
+var fromDate;
+var toDate = "";
+var startDayNumber;
+var endDayNumber;
+
+var VehicleNumberDataObjectForOneDay;
+var AvgSpeedDataObjectForOneDay;
+var DataOjbectForMultipleDays;
+
+export class CarsGraph extends Component {
+ constructor(props) {
+ super(props);
+ this.state = {
+ options: {},
+ series: [],
+
+ options2: {},
+ series2: [],
+
+ year: "",
+ areaID: "",
+ lamID: "",
+ startDayNumber: "",
+ endDayNumber: "",
+ vehicleClass: '8',//8 means all classes
+
+ error: "",
+ message:"",
+ toggleDisabled:true,
+ isloaded: false,
+ };
+ this.yearHandler = this.yearHandler.bind(this);
+ this.lamIDHandler = this.lamIDHandler.bind(this);
+ this.areaIDHandler = this.areaIDHandler.bind(this);
+ this.startDayNumberHandler = this.startDayNumberHandler.bind(this);
+ this.endDayNumberHandler = this.endDayNumberHandler.bind(this);
+ this.vehicleClassHandler = this.vehicleClassHandler.bind(this);
+ }
+
+ getData() {
+ var year_ = this.state.year;
+ var areaID = this.state.areaID;
+ var lamID = this.state.lamID;
+ var startDayNumber_ = this.state.startDayNumber;
+ var endDayNumber_ = this.state.endDayNumber;
+ var vehicleClass = this.state.vehicleClass;
+
+ var url = `http://localhost:3030/api/vehicle/?year=${year_}&areaID=${areaID}&lamID=${lamID}&startDayNumber=${startDayNumber_}&endDayNumber=${endDayNumber_}`;//&vehicleClass=${vehicleClass}
+ axios
+ .get(url)
+ .then((res) => {
+ console.log(res.data);
+ console.log(res.status);
+ //if the user has put only one day
+ if (res.status === 200) {
+
+ if(isNaN(endDayNumber)){
+ //getting the vehicle number in each our based on vehicle class from response of the request
+ var vehicle_number = res.data.vehicle_number;
+ //filling the missing values of vehicle number to zero
+ let final_Vehicle_number = this.fillMissingValues(vehicle_number);
+ //console.log(final)
+ VehicleNumberDataObjectForOneDay = this.filterDataForOneDay(final_Vehicle_number);
+ //console.log(VehicleNumberDataObjectForOneDay);
+
+ //getting the cars avarage speed based in each our based on car category
+ var avg_speed = res.data.avg_speed_km_h;
+ //filling the missing values of avarage speed to zero
+ let final_avg_speed = this.fillMissingValues(avg_speed);
+ AvgSpeedDataObjectForOneDay = this.filterDataForOneDay(final_avg_speed);
+ //getting the avarage speed of all cars in each hour
+ for(let x=0; x<24; x++){
+ let count=0
+ Object.keys(AvgSpeedDataObjectForOneDay).map(key=>{
+ let value = AvgSpeedDataObjectForOneDay[key];
+ if(value[x] === undefined | value[x] ===0 | value[x] === NaN | value[x] === null){
+ count+=1;
+ }
+ });
+ let temp = (AvgSpeedDataObjectForOneDay['8'][x]/(7-count)).toFixed(1);
+ AvgSpeedDataObjectForOneDay['8'][x] = parseFloat(temp);
+ }
+ console.log({AvgSpeedDataObjectForOneDay})
+
+ //using setDataForGraph function to set the filtered and sorted data as graph's data set
+ this.setDataForGraph(VehicleNumberDataObjectForOneDay,AvgSpeedDataObjectForOneDay);
+ this.setState({toggleDisabled: false});
+
+
+
+ }
+ //if user has a start date and end date (multiple days)
+ else{
+ console.log("time iterval!!!!")
+ }
+
+
+ }
+ })
+ .catch((er) => {
+ console.log(er);
+ });
+ }
+
+ setDataForGraph = (Vnumber,Vspeed)=>{
+ //data for graph
+ var TIME=[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23];
+ var VCLASS = this.state.vehicleClass;
+ //Setting the fetched data as graph's input data
+ this.setState({
+ //Data for vehcile number graph
+ options:{
+ chart: {
+ id: "basic-bar"
+ },
+ dataLabels: {
+ enabled: false
+ },
+ xaxis: {
+ categories: TIME,
+ title:{
+ text : "Hour"
+ }
+ },
+ yaxis: {
+ title: {
+ text: 'Vehicle Number'
+ }
+ },
+
+
+ title: {
+ text: 'Number of vehicles in each hour',
+ align: 'center'
+ },
+ legend: {
+ position: 'top',
+ horizontalAlign: 'right',
+ floating: true,
+ offsetY: -25,
+ offsetX: -5
+ }
+
+ },
+ series:[{
+ name: "Vehicle Number",
+ data: Vnumber[VCLASS]
+ }],
+ //data for avarage speed garaph
+ options2:{
+ chart: {
+ id: "basic-bar"
+ },
+ dataLabels: {
+ enabled: false
+ },
+ xaxis: {
+ categories: TIME,
+ title:{
+ text : "Hour"
+ }
+ },
+ yaxis: {
+ title: {
+ text: 'Avrage speed km/h'
+ }
+
+ },
+ colors: ['#cc3b31'],
+
+ title: {
+ text: 'Avrage Speed in each hour',
+ align: 'center'
+ },
+ legend: {
+ position: 'top',
+ horizontalAlign: 'right',
+ floating: true,
+ offsetY: -25,
+ offsetX: -5
+ }
+ },
+ series2:[{
+ name: "Avarage Speed km/h",
+ data: Vspeed[VCLASS]
+ }],
+
+ });
+ }
+
+ filterVehicleNumberDataForMultipleDay= (input)=>{
+ var dataObj = {
+ 1: [],
+ 2: [],
+ 3: [],
+ 4: [],
+ 5: [],
+ 6: [],
+ 7: [],
+ 8: [],
+ };
+
+ Object.keys(input).forEach((key) => {
+ let value = input[key];
+ let keyList = key
+ .toString()
+ .replace("[", "")
+ .replace("]", "")
+ .split(",");
+ let hour = parseInt(keyList[0]);
+ let categ = parseInt(keyList[1]);
+
+ for (let x = 1; x < 8; x++) {
+ if (categ === x) {
+ dataObj[x][hour] = value;
+ }
+ }
+ if (dataObj[8][hour]) {
+ dataObj[8][hour] += value;
+ } else {
+ dataObj[8][hour] = value;
+ }
+ });
+ //console.log({ dataObj });
+ return dataObj;
+ }
+
+
+//function for filling the missing values like vehicle category and its number
+fillMissingValues=(input)=>{
+ let final = {};
+ for (let i = 0; i <= 23; i++) {
+ for (let j = 1; j <= 7; j++) {
+ const key = `[${i},${j}]`;
+ final[key] = input[key];
+ if (input[key] === undefined) {
+ final[key] = 0;
+ }
+ }
+ }
+ return final;
+}
+
+
+ //function for filtering data from one day to one object based on each car category
+ filterDataForOneDay = (input)=>{
+ var dataObj = {
+ 1: [],
+ 2: [],
+ 3: [],
+ 4: [],
+ 5: [],
+ 6: [],
+ 7: [],
+ 8: [],
+ };
+ Object.keys(input).forEach((key) => {
+ let value = input[key];
+ let keyList = key
+ .toString()
+ .replace("[", "")
+ .replace("]", "")
+ .split(",");
+ let hour = parseInt(keyList[0]);
+ let categ = parseInt(keyList[1]);
+
+ for (let x = 1; x < 8; x++) {
+ if (categ === x) {
+ dataObj[x][hour] = value;
+ }
+ }
+ if (dataObj[8][hour]) {
+ dataObj[8][hour] += value;
+ } else {
+ dataObj[8][hour] = value;
+ }
+ });
+ //console.log({ dataObj });
+ return dataObj;
+ }
+
+
+//function for filling the missing values like vehicle category and its number
+ fillMissingValues=(input)=>{
+ let final = {};
+ for (let i = 0; i <= 23; i++) {
+ for (let j = 1; j <= 7; j++) {
+ const key = `[${i},${j}]`;
+ final[key] = input[key];
+ if (input[key] === undefined) {
+ final[key] = 0;
+ }
+ }
+ }
+ return final;
+ }
+
+ dateTodaynumber = (year, md) => {
+ var x = new Date(year, 0, 0);
+ x = x.getTime();
+
+ var d = md.split(".");
+ d = new Date(year, parseInt(d[0]) - 1, d[1]);
+ d = d.getTime();
+ var sec = d - x;
+ var secToDay = sec / 1000 / 60 / 60 / 24;
+ secToDay = Math.floor(secToDay);
+ return secToDay;
+ }
+
+ yearHandler(e) {
+ year = e.target.value;
+ this.setState({ year });
+ }
+ areaIDHandler(e) {
+ var areaID = e.target.value;
+ this.setState({ areaID });
+ }
+ lamIDHandler(e) {
+ var lamID = e.target.value;
+ this.setState({ lamID });
+ }
+ startDayNumberHandler(e) {
+ fromDate = e.target.value;
+ startDayNumber = this.dateTodaynumber(year, e.target.value);
+ this.setState({ startDayNumber });
+ }
+ endDayNumberHandler(e) {
+ toDate = e.target.value;
+ endDayNumber = this.dateTodaynumber(year, e.target.value);
+ this.setState({ endDayNumber });
+
+ }
+ vehicleClassHandler(e) {
+ var vehicleClass = e.target.value;
+ this.setState({ vehicleClass },()=>{
+ //using setDataForGraph function to set the filtered and sorted data as graph's data set
+ this.setDataForGraph(VehicleNumberDataObjectForOneDay,AvgSpeedDataObjectForOneDay);
+ });
+
+
+
+ }
+
+ buttonHandler = () => {
+ //checking for all input if they are fed with the right data
+ var patt = /^(0[1-9]|1[0-2])\.([0-2][0-9]|3[0-1])$/;
+ var matchPattern = patt.test(fromDate);
+ //console.log(matchPattern)
+
+ if (!matchPattern) {
+ this.setState({ error: "Please insert a valid date mm.dd!" });
+ } else {
+ var matchPattern2 =true;
+ if (toDate.length === 0) {
+ this.setState({ error: "" });
+ } else {
+ matchPattern2 = patt.test(toDate);
+ if (!matchPattern2) this.setState({ error: "Please insert a valid date mm.dd!" });
+ else {
+ var dateCheck = endDayNumber - startDayNumber;
+ console.log(endDayNumber, startDayNumber, dateCheck);
+ if ((dateCheck === 0) | (dateCheck < 0)) {
+ this.setState({
+ error: "End date is smaller than the start date!",
+ });
+ } else this.setState({ error: "" });
+ }
+ }
+ }
+ var inputs = [
+ this.state.year,
+ this.state.areaID,
+ this.state.lamID,
+ this.state.vehicleClass,
+ ];
+ if (
+ (inputs[0] === "") |
+ (inputs[1] === "") |
+ (inputs[2] === "") |
+ !matchPattern |
+ !matchPattern2
+
+ ) {
+ if(this.state.error !==""){
+ this.setState({error:
+ "Please make sure that you have shosen right value for Year, Area, LAM point ID fields!",
+ });
+ }
+
+ } else {
+ this.setState({message : "Please wait while the data is beeing processed! Depending on your inputs This might take a while!"})
+ this.getData();
+ console.log("Sending request to server!")
+ //console.log(this.state)
+ }
+ };
+
+ render() {
+
+
+ return (
+ <div className="Cars_graph">
+ <div style={{ color: "red" }}>{this.state.error}</div>
+ <Form>
+ <Form.Row>
+ <Form.Label
+ className="my-1 mr-2"
+ htmlFor="inlineFormCustomSelectPref"
+ >
+ Year
+ </Form.Label>
+ <Form.Control
+ as="select"
+ className="my-1 mr-sm-2"
+ id="year"
+ custom
+ onChange={this.yearHandler}
+ >
+ <option value="0">Choose...</option>
+ <option value="2020">2020</option>
+ <option value="2019">2019</option>
+ <option value="2018">2018</option>
+ <option value="2017">2017</option>
+ <option value="2016">2016</option>
+ <option value="2015">2015</option>
+ <option value="2014">2014</option>
+ <option value="2013">2013</option>
+ <option value="2012">2012</option>
+ <option value="2011">2011</option>
+ <option value="2010">2010</option>
+ <option value="2009">2009</option>
+ </Form.Control>
+
+ <Form.Label
+ className="my-1 mr-2"
+ htmlFor="inlineFormCustomSelectPref"
+ >
+ Month and day (mm.dd)
+ </Form.Label>
+ <Row>
+ <Col>
+ <Form.Control
+ id="dateFrom"
+ placeholder="From"
+ onChange={this.startDayNumberHandler}
+ pattern="(0[1-9]|1[0-2])\.([0-2]d|3[0-1])"
+ title="the right format is: mm.dd "
+ />
+ </Col>
+ <Col>
+ <Form.Control
+ id="dateTo"
+ placeholder="To"
+ onChange={this.endDayNumberHandler}
+ />
+ </Col>
+ </Row>
+
+
+ <Form.Label
+ className="my-1 mr-2"
+ htmlFor="inlineFormCustomSelectPref"
+ >
+ Choose an Area
+ </Form.Label>
+ <Form.Control
+ as="select"
+ className="my-1 mr-sm-2"
+ id="areaID"
+ custom
+ onChange={this.areaIDHandler}
+ >
+ <option value="">Choose...</option>
+ <option value="01">Uusimaa, Häme</option>
+ <option value="02">Varsinais-Suomi, Satakunta</option>
+ <option value="03">Kaakkois-Suomi</option>
+ <option value="04">Pirkanmaa</option>
+ <option value="08">
+ Pohjois-Savo, Etelä-Savo, Pohjois-Karjala)
+ </option>
+ <option value="09">Keski-Suomi</option>
+ <option value="10">Etelä-Pohjanmaa, Pohjanmaa</option>
+ <option value="12">Pohjois-Pohjanmaa, Kainuu</option>
+ <option value="14">Lappi</option>
+ </Form.Control>
+
+ <Form.Label
+ className="my-1 mr-2"
+ htmlFor="inlineFormCustomSelectPref"
+ >
+ LAM point ID
+ </Form.Label>
+ <Form.Control
+ as="select"
+ className="my-1 mr-sm-2"
+ id="lamID"
+ custom
+ onChange={this.lamIDHandler}
+ >
+ <option value="">Choose...</option>
+ <option value="101">101</option>
+ </Form.Control>
+
+
+ <Button
+ type="button"
+ className="my-1"
+ id="drawGraph"
+ onClick={this.buttonHandler}
+ >
+ Draw graph
+ </Button>
+ </Form.Row>
+ </Form>
+ <div>
+ <br/>
+ <div style={{ color: "blue" }}>
+ {this.state.message}
+ </div><br/>
+
+ <Form.Label
+ className="my-1 mr-2"
+ htmlFor="inlineFormCustomSelectPref"
+ >
+ vehicle Category
+ </Form.Label>
+ <Form.Control
+ as="select"
+ className="my-1 mr-sm-2"
+ id="vehicleClass"
+ custom
+ onChange={this.vehicleClassHandler}
+ disabled={this.state.toggleDisabled}
+ >
+ <option value="8">All</option>
+ <option value="1">1 HA-PA (henkilö- tai pakettiauto)</option>
+ <option value="2">2 KAIP (kuorma-auto ilman perävaunua)</option>
+ <option value="3">3 Linja-autot</option>
+ <option value="4">4 KAPP (kuorma-auto ja puoliperävaunu)</option>
+ <option value="5">5 KATP (kuorma-auto ja täysperävaunu)</option>
+ <option value="6">6 HA + PK (henkilöauto ja peräkärry)</option>
+ <option value="7">7 HA + AV (henkilöauto ja asuntovaunu)</option>
+ </Form.Control>
+
+
+ <Chart
+ options={this.state.options}
+ series={this.state.series}
+ type="bar"
+ width="500"
+ />
+ <Chart
+ options={this.state.options2}
+ series={this.state.series2}
+ type="bar"
+ width="500"
+ />
+
+ </div>
+
+ </div>
+ );
+ }
+}
+
+export default CarsGraph;
diff --git a/Tavara- raskaan liikenne/frontend/traffic/src/components/Linechart.js b/Tavara- raskaan liikenne/frontend/traffic/src/components/Linechart.js
index 15d14a76885a494c1424c60543de752b85fcc23c..e2682c5e058d4ebadf256fa6fb1e5af7fb284749 100644
--- a/Tavara- raskaan liikenne/frontend/traffic/src/components/Linechart.js
+++ b/Tavara- raskaan liikenne/frontend/traffic/src/components/Linechart.js
@@ -2,7 +2,6 @@ import React, { Component } from 'react'
import axios from 'axios';
-import { Line } from 'react-chartjs-2';
import Chart from "react-apexcharts";
export class Linechart extends Component {
diff --git a/Tavara- raskaan liikenne/python/.ipynb_checkpoints/LAM_rawData_analysis-checkpoint.ipynb b/Tavara- raskaan liikenne/python/.ipynb_checkpoints/LAM_rawData_analysis-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..93ac740cd7155e39cd52be5d3dacb9d1ab757594
--- /dev/null
+++ b/Tavara- raskaan liikenne/python/.ipynb_checkpoints/LAM_rawData_analysis-checkpoint.ipynb
@@ -0,0 +1,582 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " point_d Year day_number hour minute second 100th_of_a_second \\\n",
+ "28 101 17 32 0 3 39 84 \n",
+ "79 101 17 32 0 10 47 86 \n",
+ "97 101 17 32 0 13 52 99 \n",
+ "124 101 17 32 0 18 53 89 \n",
+ "140 101 17 32 0 23 44 99 \n",
+ "... ... ... ... ... ... ... ... \n",
+ "71536 101 17 32 23 24 41 99 \n",
+ "71554 101 17 32 23 27 38 13 \n",
+ "71621 101 17 32 23 37 30 71 \n",
+ "71729 101 17 32 23 51 39 99 \n",
+ "71745 101 17 32 23 53 43 99 \n",
+ "\n",
+ " length_m lane direction vehicle_class speed_km/h \\\n",
+ "28 14.0 5 2 4 80 \n",
+ "79 13.2 2 1 4 80 \n",
+ "97 14.4 5 2 4 84 \n",
+ "124 13.8 5 2 4 76 \n",
+ "140 15.6 5 2 4 87 \n",
+ "... ... ... ... ... ... \n",
+ "71536 13.4 1 1 4 76 \n",
+ "71554 14.0 5 2 4 86 \n",
+ "71621 16.6 5 2 4 78 \n",
+ "71729 14.2 5 2 4 82 \n",
+ "71745 15.4 5 2 4 89 \n",
+ "\n",
+ " faulty_0=valid_1=incorrect total_time interval jonoalku \n",
+ "28 0 21984 4125 0 \n",
+ "79 0 64786 8189 0 \n",
+ "97 0 83299 1684 0 \n",
+ "124 0 113389 2774 0 \n",
+ "140 0 142499 3643 0 \n",
+ "... ... ... ... ... \n",
+ "71536 0 8428199 2702 0 \n",
+ "71554 0 8445813 4749 0 \n",
+ "71621 0 8505071 5957 0 \n",
+ "71729 0 8589999 3669 0 \n",
+ "71745 0 8602399 2756 0 \n",
+ "\n",
+ "[829 rows x 16 columns]\n",
+ " vehicle_class\n",
+ "hour \n",
+ "0 7\n",
+ "1 7\n",
+ "2 2\n",
+ "3 5\n",
+ "4 8\n",
+ "5 13\n",
+ "6 38\n",
+ "7 33\n",
+ "8 36\n",
+ "9 75\n",
+ "10 31\n",
+ "11 26\n",
+ "12 36\n",
+ "13 75\n",
+ "14 73\n",
+ "15 76\n",
+ "16 54\n",
+ "17 40\n",
+ "18 51\n",
+ "19 34\n",
+ "20 25\n",
+ "21 69\n",
+ "22 9\n",
+ "23 6\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "year = 2020\n",
+ "areaID = \"01\"\n",
+ "lamID = 101\n",
+ "\n",
+ "\n",
+ "\n",
+ "#print(\"\\n-----------------------------------------\\n\")\n",
+ "\n",
+ "names = [\"point_d\", \"Year\", \"day_number\", \"hour\", \"minute\", \"second\", \"100th_of_a_second\", \"length_m\",\"lane\", \"direction\", \"vehicle_class\", \"speed_km/h\", \"faulty_0=valid_1=incorrect\", \"total_time\", \"interval\",\"jonoalku\"]\n",
+ "\n",
+ "\n",
+ "df = pd.read_csv(\"https://aineistot.vayla.fi/lam/rawdata/2017/01/lamraw_101_17_32.csv\", names=names, sep = \";\")\n",
+ "\n",
+ "#print(df.head())\n",
+ "\n",
+ "#print(\"\\n-----------------------------------------\\n\")\n",
+ "\n",
+ "\n",
+ "df1 = df[df[\"vehicle_class\"]==4]\n",
+ "print(df1)\n",
+ "\n",
+ "df2 = df1.groupby(\"hour\")[\"vehicle_class\"].count()\n",
+ "\n",
+ "df3= pd.DataFrame(df2)\n",
+ "print(df3)\n",
+ "#print(\"\\n-----------------------------------------\\n\")\n",
+ "\n",
+ "\n",
+ "df3.plot()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "hour vehicle_class\n",
+ "0 1 178\n",
+ " 2 4\n",
+ " 3 26\n",
+ " 4 3\n",
+ " 5 0\n",
+ " 6 0\n",
+ " 7 0\n",
+ "1 1 130\n",
+ " 2 9\n",
+ " 3 17\n",
+ " 4 7\n",
+ " 5 0\n",
+ " 6 0\n",
+ " 7 0\n",
+ "2 1 73\n",
+ " 2 8\n",
+ " 3 2\n",
+ " 4 0\n",
+ " 5 0\n",
+ " 6 0\n",
+ " 7 0\n",
+ "3 1 53\n",
+ " 2 3\n",
+ " 3 0\n",
+ " 4 4\n",
+ "dtype: int64\n",
+ "{\"[0,1]\":178,\"[0,2]\":4,\"[0,3]\":26,\"[0,4]\":3,\"[0,5]\":0,\"[0,6]\":0,\"[0,7]\":0,\"[1,1]\":130,\"[1,2]\":9,\"[1,3]\":17,\"[1,4]\":7,\"[1,5]\":0,\"[1,6]\":0,\"[1,7]\":0,\"[2,1]\":73,\"[2,2]\":8,\"[2,3]\":2,\"[2,4]\":0,\"[2,5]\":0,\"[2,6]\":0,\"[2,7]\":0,\"[3,1]\":53,\"[3,2]\":3,\"[3,3]\":0,\"[3,4]\":4,\"[3,5]\":0,\"[3,6]\":1,\"[3,7]\":0,\"[4,1]\":44,\"[4,2]\":3,\"[4,3]\":1,\"[4,4]\":0,\"[4,5]\":0,\"[4,6]\":0,\"[4,7]\":0,\"[5,1]\":118,\"[5,2]\":5,\"[5,3]\":1,\"[5,4]\":1,\"[5,5]\":2,\"[5,6]\":0,\"[5,7]\":0,\"[6,1]\":347,\"[6,2]\":8,\"[6,3]\":5,\"[6,4]\":20,\"[6,5]\":4,\"[6,6]\":2,\"[6,7]\":1,\"[7,1]\":389,\"[7,2]\":4,\"[7,3]\":8,\"[7,4]\":60,\"[7,5]\":6,\"[7,6]\":3,\"[7,7]\":1,\"[8,1]\":526,\"[8,2]\":12,\"[8,3]\":5,\"[8,4]\":44,\"[8,5]\":8,\"[8,6]\":0,\"[8,7]\":2,\"[9,1]\":978,\"[9,2]\":9,\"[9,3]\":4,\"[9,4]\":28,\"[9,5]\":5,\"[9,6]\":5,\"[9,7]\":1,\"[10,1]\":1602,\"[10,2]\":6,\"[10,3]\":4,\"[10,4]\":8,\"[10,5]\":2,\"[10,6]\":6,\"[10,7]\":2,\"[11,1]\":2039,\"[11,2]\":13,\"[11,3]\":3,\"[11,4]\":18,\"[11,5]\":5,\"[11,6]\":10,\"[11,7]\":0,\"[12,1]\":2493,\"[12,2]\":11,\"[12,3]\":5,\"[12,4]\":11,\"[12,5]\":4,\"[12,6]\":8,\"[12,7]\":2,\"[13,1]\":2589,\"[13,2]\":8,\"[13,3]\":4,\"[13,4]\":23,\"[13,5]\":7,\"[13,6]\":6,\"[13,7]\":0,\"[14,1]\":2721,\"[14,2]\":8,\"[14,3]\":7,\"[14,4]\":21,\"[14,5]\":5,\"[14,6]\":10,\"[14,7]\":0,\"[15,1]\":2680,\"[15,2]\":7,\"[15,3]\":5,\"[15,4]\":15,\"[15,5]\":2,\"[15,6]\":9,\"[15,7]\":3,\"[16,1]\":2462,\"[16,2]\":5,\"[16,3]\":3,\"[16,4]\":4,\"[16,5]\":2,\"[16,6]\":11,\"[16,7]\":0,\"[17,1]\":2259,\"[17,2]\":10,\"[17,3]\":6,\"[17,4]\":2,\"[17,5]\":1,\"[17,6]\":10,\"[17,7]\":2,\"[18,1]\":1677,\"[18,2]\":7,\"[18,3]\":6,\"[18,4]\":2,\"[18,5]\":1,\"[18,6]\":6,\"[18,7]\":0,\"[19,1]\":1177,\"[19,2]\":7,\"[19,3]\":3,\"[19,4]\":2,\"[19,5]\":1,\"[19,6]\":5,\"[19,7]\":0,\"[20,1]\":982,\"[20,2]\":6,\"[20,3]\":9,\"[20,4]\":2,\"[20,5]\":2,\"[20,6]\":3,\"[20,7]\":0,\"[21,1]\":720,\"[21,2]\":3,\"[21,3]\":6,\"[21,4]\":3,\"[21,5]\":0,\"[21,6]\":1,\"[21,7]\":0,\"[22,1]\":446,\"[22,2]\":0,\"[22,3]\":5,\"[22,4]\":4,\"[22,5]\":1,\"[22,6]\":1,\"[22,7]\":0,\"[23,1]\":338,\"[23,2]\":3,\"[23,3]\":12,\"[23,4]\":11,\"[23,5]\":1,\"[23,6]\":3,\"[23,7]\":0}\n",
+ "11.04.2020\n"
+ ]
+ }
+ ],
+ "source": [
+ "#%%time\n",
+ "\n",
+ "import datetime\n",
+ "\n",
+ "year = \"2020\"\n",
+ "areaID = \"01\"\n",
+ "lamID = \"101\"\n",
+ "shortYear = year[-2:]\n",
+ "startDayNumber = \"101\"\n",
+ "endDayNumber = \"\"\n",
+ "vehicle_class =[4]\n",
+ "\n",
+ "output = pd.DataFrame()\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "if(len(endDayNumber)!=0):\n",
+ " start = int(startDayNumber)\n",
+ " end = int(endDayNumber)\n",
+ " duration = end - start\n",
+ " print(duration)\n",
+ " \n",
+ " \n",
+ " \n",
+ " vehicleTotalNumberInOneDay = []\n",
+ " date = []\n",
+ " \n",
+ " for x in range(duration):\n",
+ " url = \"https://aineistot.vayla.fi/lam/rawdata/{year}/{areaID}/lamraw_{lamID}_{shortYear}_{start}.csv\".format(year=year,areaID=areaID,lamID=lamID,shortYear = shortYear, start=start)\n",
+ " print(url)\n",
+ " \n",
+ " csv = pd.read_csv(url, names=names, sep = \";\")\n",
+ " df = csv[[\"vehicle_class\"]==vehicle_class[0]]\n",
+ " vehicleTotalNumberInOneDay.append(df[\"vehicle_class\"].count())\n",
+ " \n",
+ " print(vehicleTotalNumberInOneDay)\n",
+ " \n",
+ " dt = datetime.datetime(int(year),1,1)\n",
+ " dtdelta = datetime.timedelta(days=start)\n",
+ " dt = dt + dtdelta\n",
+ " dt=dt.strftime('%d.%m.%Y')\n",
+ " date.append(dt)\n",
+ " print(date)\n",
+ " \n",
+ " start +=1\n",
+ "\n",
+ "\n",
+ " output[\"vehicle_number\"] = vehicleTotalNumberInOneDay\n",
+ " output[\"time\"] = date\n",
+ " output.set_index(\"time\")\n",
+ "\n",
+ "else:\n",
+ " url = \"https://aineistot.vayla.fi/lam/rawdata/{year}/{areaID}/lamraw_{lamID}_{shortYear}_{startDayNumber}.csv\".format(year=year,areaID=areaID,lamID=lamID,shortYear = shortYear, startDayNumber=startDayNumber)\n",
+ " \n",
+ " allData = pd.read_csv(url, names=names, sep = \";\")\n",
+ " #df = allData[allData[\"vehicle_class\"]==vehicle_class[0]]\n",
+ " \n",
+ " df = allData.groupby([\"hour\",\"vehicle_class\"])[\"vehicle_class\"].count().unstack(fill_value=0).stack()\n",
+ " print(df.head(25))\n",
+ " \n",
+ " outjson = df.to_json()\n",
+ " print(outjson)\n",
+ "\n",
+ " #df1 = df.groupby(df[\"hour\"])[\"vehicle_class\"].count()\n",
+ "\n",
+ " #output[\"vehicle_number\"]= df1[\"vehicle_class\"]\n",
+ " #output[\"time\"] = df2.index.values\n",
+ " \n",
+ " #print(output)\n",
+ " # getting date from day number\n",
+ " dt = datetime.datetime(int(year),1,1)\n",
+ " dtdelta = datetime.timedelta(days=int(startDayNumber))\n",
+ " dt = dt + dtdelta\n",
+ " dt=dt.strftime('%d.%m.%Y')\n",
+ " print(dt)\n",
+ " \n",
+ " \n",
+ "\n",
+ "\n",
+ "\n",
+ "#print(\"\\n-----------------------------------------\\n\")\n",
+ "\n",
+ "\n",
+ "#print(\"\\n-----------------------------------------\\n\")\n",
+ "\n",
+ "\n",
+ "#output.plot()\n",
+ "#plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "in else!\n",
+ "11.04.2020\n",
+ " vehicle_number avg_speed_km_h\n",
+ "hour vehicle_class direction \n",
+ "0 1 1 107 84.373832\n",
+ " 2 71 83.943662\n",
+ " 2 1 2 85.000000\n",
+ " 2 2 81.500000\n",
+ " 3 1 12 77.833333\n",
+ " 2 14 79.642857\n",
+ " 4 1 3 82.000000\n",
+ "1 1 1 76 88.026316\n",
+ " 2 54 85.000000\n",
+ " 2 1 5 78.200000\n",
+ " 2 4 78.000000\n",
+ " 3 1 11 78.000000\n",
+ " 2 6 78.000000\n",
+ " 4 1 4 76.750000\n",
+ " 2 3 81.666667\n",
+ "2 1 1 31 82.129032\n",
+ " 2 42 86.642857\n",
+ " 2 1 5 70.800000\n",
+ " 2 3 76.000000\n",
+ " 3 1 2 71.500000\n",
+ "3 1 1 32 77.343750\n",
+ " 2 21 80.238095\n",
+ " 2 1 2 21.500000\n",
+ " 2 1 82.000000\n",
+ " 4 1 2 26.500000\n",
+ 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+ ]
+ }
+ ],
+ "source": [
+ "\n",
+ "# getting count number for each vehicle class, avarage speed for each class and direction\n",
+ "\n",
+ "import datetime\n",
+ "\n",
+ "\n",
+ "year = \"2020\"\n",
+ "areaID = \"01\"\n",
+ "lamID = \"101\"\n",
+ "shortYear = year[-2:]\n",
+ "startDayNumber = \"101\"\n",
+ "endDayNumber = \"\"\n",
+ "vehicle_class =[4]\n",
+ "\n",
+ "output = pd.DataFrame()\n",
+ "\n",
+ "\n",
+ "\n",
+ "if(len(endDayNumber)!=0):\n",
+ " start = int(startDayNumber)\n",
+ " end = int(endDayNumber)\n",
+ " duration = (end+1) - start\n",
+ " print(duration)\n",
+ " \n",
+ " \n",
+ " \n",
+ " vehicleTotalNumberInOneDay = pd.DataFrame()\n",
+ " date = []\n",
+ " \n",
+ " for x in range(duration):\n",
+ " url = \"https://aineistot.vayla.fi/lam/rawdata/{year}/{areaID}/lamraw_{lamID}_{shortYear}_{start}.csv\".format(year=year,areaID=areaID,lamID=lamID,shortYear = shortYear, start=start)\n",
+ " csv = pd.read_csv(url, names=names, sep = \";\")\n",
+ " \n",
+ " #getting the day from sequectial day number\n",
+ " dt = datetime.datetime(int(year),1,1)\n",
+ " dtdelta = datetime.timedelta(days=start)\n",
+ " dt = dt + dtdelta\n",
+ " dt=dt.strftime('%d.%m.%Y')\n",
+ " date.append(dt)\n",
+ " print(date)\n",
+ " \n",
+ " \n",
+ " #temp = csv.groupby([\"vehicle_class\"])[\"vehicle_class\"].count()\n",
+ " temp = csv.groupby([\"vehicle_class\",\"direction\"]).agg({\"vehicle_class\":\"count\", \"speed_km/h\":\"mean\"})\\\n",
+ " .rename(columns={'vehicle_class':'vehicle_number','speed_km/h':'avg_speed_km_h'})\n",
+ " \n",
+ " #changing indexes to create uniqe indexs containing date and vehicle class\n",
+ " indexv = temp.index.values.tolist() \n",
+ " newindex = []\n",
+ " for i in range(len(indexv)):\n",
+ " a= list(indexv[i])\n",
+ " a[0] = dt+\"_\"+str(a[0])\n",
+ " newindex.append(tuple(a))\n",
+ " \n",
+ " #print(newindex)\n",
+ " \n",
+ " \n",
+ " #setting the new index to dataframe\n",
+ " temp[\"date_vehicleclass_direction\"]= newindex\n",
+ " temp.set_index(\"date_vehicleclass_direction\", inplace= True)\n",
+ " \n",
+ " vehicleTotalNumberInOneDay= vehicleTotalNumberInOneDay.append(temp)\n",
+ " \n",
+ " #print(vehicleTotalNumberInOneDay)\n",
+ " \n",
+ "\n",
+ " \n",
+ " start +=1\n",
+ " \n",
+ " \n",
+ " output= vehicleTotalNumberInOneDay\n",
+ " #output[\"date\"] = date\n",
+ " #output.set_index(\"date\", inplace = True)\n",
+ " print(output)\n",
+ " outputJson = output.to_json()\n",
+ " print(outputJson)\n",
+ "\n",
+ "else:\n",
+ " print(\"in else!\")\n",
+ " url = \"https://aineistot.vayla.fi/lam/rawdata/{year}/{areaID}/lamraw_{lamID}_{shortYear}_{startDayNumber}.csv\".format(year=year,areaID=areaID,lamID=lamID,shortYear = shortYear, startDayNumber=startDayNumber)\n",
+ " \n",
+ " allData = pd.read_csv(url, names=names, sep = \";\")\n",
+ " #df = allData[allData[\"vehicle_class\"]==vehicle_class[0]]\n",
+ " \n",
+ " # getting date from day number\n",
+ " dt = datetime.datetime(int(year),1,1)\n",
+ " dtdelta = datetime.timedelta(days=int(startDayNumber))\n",
+ " dt = dt + dtdelta\n",
+ " dt=dt.strftime('%d.%m.%Y')\n",
+ " print(dt)\n",
+ " \n",
+ " df = allData.groupby([\"hour\",\"vehicle_class\",\"direction\"]).agg({\"vehicle_class\": \"count\", \"speed_km/h\":\"mean\"})\\\n",
+ " .rename(columns={'vehicle_class':'vehicle_number','speed_km/h':'avg_speed_km_h'})\n",
+ " \n",
+ " print(df.head(25))\n",
+ " \n",
+ " outjson = df.to_json()\n",
+ " print(outjson)\n",
+ " \n",
+ "\n",
+ "#print(\"\\n-----------------------------------------\\n\")\n",
+ "\n",
+ "#output.plot()\n",
+ "#plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "in else!\n",
+ "09.01.2020\n",
+ " vehicle_number avg_speed_km_h\n",
+ "hour vehicle_class direction \n",
+ "0 1 1 103 82.83\n",
+ " 2 81 81.14\n",
+ " 2 1 3 69.00\n",
+ " 2 3 83.33\n",
+ " 3 1 9 72.44\n",
+ " 2 13 79.69\n",
+ " 4 1 5 84.00\n",
+ " 5 1 1 77.00\n",
+ "1 1 1 58 82.60\n",
+ " 2 40 84.45\n",
+ " 2 1 3 74.33\n",
+ " 2 4 79.75\n",
+ " 3 1 11 79.82\n",
+ " 2 8 80.00\n",
+ " 4 1 3 77.00\n",
+ " 2 2 80.00\n",
+ "2 1 1 35 83.09\n",
+ " 2 27 82.04\n",
+ " 2 1 5 80.20\n",
+ " 2 2 86.50\n",
+ " 3 1 1 73.00\n",
+ " 4 2 1 80.00\n",
+ " 5 1 1 82.00\n",
+ " 2 2 83.00\n",
+ "3 1 1 35 82.80\n",
+ 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+ ]
+ }
+ ],
+ "source": [
+ "# getting count number for each vehicle class, avarage speed for each class and direction\n",
+ "\n",
+ "import datetime\n",
+ "\n",
+ "\n",
+ "year = \"2020\"\n",
+ "areaID = \"01\"\n",
+ "lamID = \"101\"\n",
+ "shortYear = year[-2:]\n",
+ "startDayNumber = \"8\"\n",
+ "endDayNumber = \"\"\n",
+ "vehicle_class =[4]\n",
+ "\n",
+ "output = pd.DataFrame()\n",
+ "\n",
+ "\n",
+ "\n",
+ "if(len(endDayNumber)!=0):\n",
+ " start = int(startDayNumber)\n",
+ " end = int(endDayNumber)\n",
+ " duration = (end+1) - start\n",
+ " print(duration)\n",
+ " \n",
+ " \n",
+ " \n",
+ " vehicleTotalNumberInOneDay = pd.DataFrame()\n",
+ " date = []\n",
+ " \n",
+ " for x in range(duration):\n",
+ " print(start)\n",
+ " url = \"https://aineistot.vayla.fi/lam/rawdata/{year}/{areaID}/lamraw_{lamID}_{shortYear}_{start}.csv\".format(year=year,areaID=areaID,lamID=lamID,shortYear = shortYear, start=start)\n",
+ " csv = pd.read_csv(url, names=names, sep = \";\")\n",
+ " \n",
+ " #getting the day from sequectial day number\n",
+ " dt = datetime.datetime(int(year),1,1)\n",
+ " dtdelta = datetime.timedelta(days=(start-1))\n",
+ " dt = dt + dtdelta\n",
+ " dt=dt.strftime('%d.%m.%Y')\n",
+ " date.append(dt)\n",
+ " print(date)\n",
+ " \n",
+ " \n",
+ " #temp = csv.groupby([\"vehicle_class\"])[\"vehicle_class\"].count()\n",
+ " temp = csv.groupby([\"vehicle_class\",\"direction\"]).agg({\"vehicle_class\":\"count\", \"speed_km/h\":\"mean\"})\\\n",
+ " .rename(columns={'vehicle_class':'vehicle_number','speed_km/h':'avg_speed_km_h'})\n",
+ " \n",
+ " #changing indexes to create uniqe indexs containing date and vehicle class\n",
+ " indexv = temp.index.values.tolist() \n",
+ " newindex = []\n",
+ " for i in range(len(indexv)):\n",
+ " a= list(indexv[i])\n",
+ " a[0] = dt+\"_\"+str(a[0])\n",
+ " newindex.append(tuple(a))\n",
+ " \n",
+ " #print(newindex)\n",
+ " \n",
+ " \n",
+ " #setting the new index to dataframe\n",
+ " temp[\"date_vehicleclass_direction\"]= newindex\n",
+ " temp.set_index(\"date_vehicleclass_direction\", inplace= True)\n",
+ " \n",
+ " vehicleTotalNumberInOneDay= vehicleTotalNumberInOneDay.append(temp)\n",
+ " \n",
+ " #print(vehicleTotalNumberInOneDay)\n",
+ " \n",
+ "\n",
+ " \n",
+ " start +=1\n",
+ " \n",
+ " \n",
+ " output= vehicleTotalNumberInOneDay\n",
+ " #output[\"date\"] = date\n",
+ " #output.set_index(\"date\", inplace = True)\n",
+ " print(output)\n",
+ " outputJson = output.to_json()\n",
+ " print(outputJson)\n",
+ "\n",
+ "else:\n",
+ " print(\"in else!\")\n",
+ " url = \"https://aineistot.vayla.fi/lam/rawdata/{year}/{areaID}/lamraw_{lamID}_{shortYear}_{startDayNumber}.csv\".format(year=year,areaID=areaID,lamID=lamID,shortYear = shortYear, startDayNumber=startDayNumber)\n",
+ " \n",
+ " allData = pd.read_csv(url, names=names, sep = \";\")\n",
+ " #df = allData[allData[\"vehicle_class\"]==vehicle_class[0]]\n",
+ " \n",
+ " # getting date from day number\n",
+ " dt = datetime.datetime(int(year),1,1)\n",
+ " dtdelta = datetime.timedelta(days=int(startDayNumber))\n",
+ " dt = dt + dtdelta\n",
+ " dt=dt.strftime('%d.%m.%Y')\n",
+ " print(dt)\n",
+ " \n",
+ " df = allData.groupby([\"hour\",\"vehicle_class\",\"direction\"]).agg({\"vehicle_class\": \"count\", \"speed_km/h\":\"mean\"})\\\n",
+ " .rename(columns={'vehicle_class':'vehicle_number','speed_km/h':'avg_speed_km_h'})\n",
+ " df['avg_speed_km_h'] = df['avg_speed_km_h'].astype(float).round(1)\n",
+ " print(df.head(25))\n",
+ " \n",
+ " outjson = df.to_json()\n",
+ " print(outjson)\n",
+ " \n",
+ "\n",
+ "#print(\"\\n-----------------------------------------\\n\")\n",
+ "\n",
+ "#output.plot()\n",
+ "#plt.show()"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.4"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Tavara- raskaan liikenne/python/LAM_rawData_analysis.ipynb b/Tavara- raskaan liikenne/python/LAM_rawData_analysis.ipynb
index e7f88c55b4a81100be98b90810cc30f410c8c26b..df1db26ef64e34216757c1e5c0ac3165277fef48 100644
--- a/Tavara- raskaan liikenne/python/LAM_rawData_analysis.ipynb
+++ b/Tavara- raskaan liikenne/python/LAM_rawData_analysis.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 142,
+ "execution_count": 2,
"metadata": {},
"outputs": [
{
@@ -79,14 +79,11 @@
},
{
"data": {
- "image/png": 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\n",
"text/plain": [
- "<Figure size 432x288 with 1 Axes>"
+ "<Figure size 640x480 with 1 Axes>"
]
},
- "metadata": {
- "needs_background": "light"
- },
+ "metadata": {},
"output_type": "display_data"
}
],
@@ -130,41 +127,13 @@
},
{
"cell_type": "code",
- "execution_count": 111,
+ "execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "hour vehicle_class\n",
- "0 1 178\n",
- " 2 4\n",
- " 3 26\n",
- " 4 3\n",
- "1 1 130\n",
- " 2 9\n",
- " 3 17\n",
- " 4 7\n",
- "2 1 73\n",
- " 2 8\n",
- " 3 2\n",
- "3 1 53\n",
- " 2 3\n",
- " 4 4\n",
- " 6 1\n",
- "4 1 44\n",
- " 2 3\n",
- " 3 1\n",
- "5 1 118\n",
- " 2 5\n",
- " 3 1\n",
- " 4 1\n",
- " 5 2\n",
- "6 1 347\n",
- " 2 8\n",
- "Name: vehicle_class, dtype: int64\n",
- "{\"[0,1]\":178,\"[0,2]\":4,\"[0,3]\":26,\"[0,4]\":3,\"[1,1]\":130,\"[1,2]\":9,\"[1,3]\":17,\"[1,4]\":7,\"[2,1]\":73,\"[2,2]\":8,\"[2,3]\":2,\"[3,1]\":53,\"[3,2]\":3,\"[3,4]\":4,\"[3,6]\":1,\"[4,1]\":44,\"[4,2]\":3,\"[4,3]\":1,\"[5,1]\":118,\"[5,2]\":5,\"[5,3]\":1,\"[5,4]\":1,\"[5,5]\":2,\"[6,1]\":347,\"[6,2]\":8,\"[6,3]\":5,\"[6,4]\":20,\"[6,5]\":4,\"[6,6]\":2,\"[6,7]\":1,\"[7,1]\":389,\"[7,2]\":4,\"[7,3]\":8,\"[7,4]\":60,\"[7,5]\":6,\"[7,6]\":3,\"[7,7]\":1,\"[8,1]\":526,\"[8,2]\":12,\"[8,3]\":5,\"[8,4]\":44,\"[8,5]\":8,\"[8,7]\":2,\"[9,1]\":978,\"[9,2]\":9,\"[9,3]\":4,\"[9,4]\":28,\"[9,5]\":5,\"[9,6]\":5,\"[9,7]\":1,\"[10,1]\":1602,\"[10,2]\":6,\"[10,3]\":4,\"[10,4]\":8,\"[10,5]\":2,\"[10,6]\":6,\"[10,7]\":2,\"[11,1]\":2039,\"[11,2]\":13,\"[11,3]\":3,\"[11,4]\":18,\"[11,5]\":5,\"[11,6]\":10,\"[12,1]\":2493,\"[12,2]\":11,\"[12,3]\":5,\"[12,4]\":11,\"[12,5]\":4,\"[12,6]\":8,\"[12,7]\":2,\"[13,1]\":2589,\"[13,2]\":8,\"[13,3]\":4,\"[13,4]\":23,\"[13,5]\":7,\"[13,6]\":6,\"[14,1]\":2721,\"[14,2]\":8,\"[14,3]\":7,\"[14,4]\":21,\"[14,5]\":5,\"[14,6]\":10,\"[15,1]\":2680,\"[15,2]\":7,\"[15,3]\":5,\"[15,4]\":15,\"[15,5]\":2,\"[15,6]\":9,\"[15,7]\":3,\"[16,1]\":2462,\"[16,2]\":5,\"[16,3]\":3,\"[16,4]\":4,\"[16,5]\":2,\"[16,6]\":11,\"[17,1]\":2259,\"[17,2]\":10,\"[17,3]\":6,\"[17,4]\":2,\"[17,5]\":1,\"[17,6]\":10,\"[17,7]\":2,\"[18,1]\":1677,\"[18,2]\":7,\"[18,3]\":6,\"[18,4]\":2,\"[18,5]\":1,\"[18,6]\":6,\"[19,1]\":1177,\"[19,2]\":7,\"[19,3]\":3,\"[19,4]\":2,\"[19,5]\":1,\"[19,6]\":5,\"[20,1]\":982,\"[20,2]\":6,\"[20,3]\":9,\"[20,4]\":2,\"[20,5]\":2,\"[20,6]\":3,\"[21,1]\":720,\"[21,2]\":3,\"[21,3]\":6,\"[21,4]\":3,\"[21,6]\":1,\"[22,1]\":446,\"[22,3]\":5,\"[22,4]\":4,\"[22,5]\":1,\"[22,6]\":1,\"[23,1]\":338,\"[23,2]\":3,\"[23,3]\":12,\"[23,4]\":11,\"[23,5]\":1,\"[23,6]\":3}\n",
"hour vehicle_class\n",
"0 1 178\n",
" 2 4\n",
@@ -192,6 +161,7 @@
" 3 0\n",
" 4 4\n",
"dtype: int64\n",
+ "{\"[0,1]\":178,\"[0,2]\":4,\"[0,3]\":26,\"[0,4]\":3,\"[0,5]\":0,\"[0,6]\":0,\"[0,7]\":0,\"[1,1]\":130,\"[1,2]\":9,\"[1,3]\":17,\"[1,4]\":7,\"[1,5]\":0,\"[1,6]\":0,\"[1,7]\":0,\"[2,1]\":73,\"[2,2]\":8,\"[2,3]\":2,\"[2,4]\":0,\"[2,5]\":0,\"[2,6]\":0,\"[2,7]\":0,\"[3,1]\":53,\"[3,2]\":3,\"[3,3]\":0,\"[3,4]\":4,\"[3,5]\":0,\"[3,6]\":1,\"[3,7]\":0,\"[4,1]\":44,\"[4,2]\":3,\"[4,3]\":1,\"[4,4]\":0,\"[4,5]\":0,\"[4,6]\":0,\"[4,7]\":0,\"[5,1]\":118,\"[5,2]\":5,\"[5,3]\":1,\"[5,4]\":1,\"[5,5]\":2,\"[5,6]\":0,\"[5,7]\":0,\"[6,1]\":347,\"[6,2]\":8,\"[6,3]\":5,\"[6,4]\":20,\"[6,5]\":4,\"[6,6]\":2,\"[6,7]\":1,\"[7,1]\":389,\"[7,2]\":4,\"[7,3]\":8,\"[7,4]\":60,\"[7,5]\":6,\"[7,6]\":3,\"[7,7]\":1,\"[8,1]\":526,\"[8,2]\":12,\"[8,3]\":5,\"[8,4]\":44,\"[8,5]\":8,\"[8,6]\":0,\"[8,7]\":2,\"[9,1]\":978,\"[9,2]\":9,\"[9,3]\":4,\"[9,4]\":28,\"[9,5]\":5,\"[9,6]\":5,\"[9,7]\":1,\"[10,1]\":1602,\"[10,2]\":6,\"[10,3]\":4,\"[10,4]\":8,\"[10,5]\":2,\"[10,6]\":6,\"[10,7]\":2,\"[11,1]\":2039,\"[11,2]\":13,\"[11,3]\":3,\"[11,4]\":18,\"[11,5]\":5,\"[11,6]\":10,\"[11,7]\":0,\"[12,1]\":2493,\"[12,2]\":11,\"[12,3]\":5,\"[12,4]\":11,\"[12,5]\":4,\"[12,6]\":8,\"[12,7]\":2,\"[13,1]\":2589,\"[13,2]\":8,\"[13,3]\":4,\"[13,4]\":23,\"[13,5]\":7,\"[13,6]\":6,\"[13,7]\":0,\"[14,1]\":2721,\"[14,2]\":8,\"[14,3]\":7,\"[14,4]\":21,\"[14,5]\":5,\"[14,6]\":10,\"[14,7]\":0,\"[15,1]\":2680,\"[15,2]\":7,\"[15,3]\":5,\"[15,4]\":15,\"[15,5]\":2,\"[15,6]\":9,\"[15,7]\":3,\"[16,1]\":2462,\"[16,2]\":5,\"[16,3]\":3,\"[16,4]\":4,\"[16,5]\":2,\"[16,6]\":11,\"[16,7]\":0,\"[17,1]\":2259,\"[17,2]\":10,\"[17,3]\":6,\"[17,4]\":2,\"[17,5]\":1,\"[17,6]\":10,\"[17,7]\":2,\"[18,1]\":1677,\"[18,2]\":7,\"[18,3]\":6,\"[18,4]\":2,\"[18,5]\":1,\"[18,6]\":6,\"[18,7]\":0,\"[19,1]\":1177,\"[19,2]\":7,\"[19,3]\":3,\"[19,4]\":2,\"[19,5]\":1,\"[19,6]\":5,\"[19,7]\":0,\"[20,1]\":982,\"[20,2]\":6,\"[20,3]\":9,\"[20,4]\":2,\"[20,5]\":2,\"[20,6]\":3,\"[20,7]\":0,\"[21,1]\":720,\"[21,2]\":3,\"[21,3]\":6,\"[21,4]\":3,\"[21,5]\":0,\"[21,6]\":1,\"[21,7]\":0,\"[22,1]\":446,\"[22,2]\":0,\"[22,3]\":5,\"[22,4]\":4,\"[22,5]\":1,\"[22,6]\":1,\"[22,7]\":0,\"[23,1]\":338,\"[23,2]\":3,\"[23,3]\":12,\"[23,4]\":11,\"[23,5]\":1,\"[23,6]\":3,\"[23,7]\":0}\n",
"11.04.2020\n"
]
}
@@ -279,10 +249,6 @@
"\n",
"\n",
"\n",
- "\n",
- "\n",
- "\n",
- "\n",
"#print(\"\\n-----------------------------------------\\n\")\n",
"\n",
"\n",
@@ -295,13 +261,14 @@
},
{
"cell_type": "code",
- "execution_count": 173,
+ "execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
+ "in else!\n",
"11.04.2020\n",
" vehicle_number avg_speed_km_h\n",
"hour vehicle_class direction \n",
@@ -353,8 +320,6 @@
"\n",
"\n",
"\n",
- "\n",
- "\n",
"if(len(endDayNumber)!=0):\n",
" start = int(startDayNumber)\n",
" end = int(endDayNumber)\n",
@@ -415,6 +380,7 @@
" print(outputJson)\n",
"\n",
"else:\n",
+ " print(\"in else!\")\n",
" url = \"https://aineistot.vayla.fi/lam/rawdata/{year}/{areaID}/lamraw_{lamID}_{shortYear}_{startDayNumber}.csv\".format(year=year,areaID=areaID,lamID=lamID,shortYear = shortYear, startDayNumber=startDayNumber)\n",
" \n",
" allData = pd.read_csv(url, names=names, sep = \";\")\n",
@@ -436,16 +402,156 @@
" print(outjson)\n",
" \n",
"\n",
+ "#print(\"\\n-----------------------------------------\\n\")\n",
+ "\n",
+ "#output.plot()\n",
+ "#plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "in else!\n",
+ "09.01.2020\n",
+ " vehicle_number avg_speed_km_h\n",
+ "hour vehicle_class direction \n",
+ "0 1 1 103 82.8\n",
+ " 2 81 81.1\n",
+ " 2 1 3 69.0\n",
+ " 2 3 83.3\n",
+ " 3 1 9 72.4\n",
+ " 2 13 79.7\n",
+ " 4 1 5 84.0\n",
+ " 5 1 1 77.0\n",
+ "1 1 1 58 82.6\n",
+ " 2 40 84.4\n",
+ " 2 1 3 74.3\n",
+ " 2 4 79.8\n",
+ " 3 1 11 79.8\n",
+ " 2 8 80.0\n",
+ " 4 1 3 77.0\n",
+ " 2 2 80.0\n",
+ "2 1 1 35 83.1\n",
+ " 2 27 82.0\n",
+ " 2 1 5 80.2\n",
+ " 2 2 86.5\n",
+ " 3 1 1 73.0\n",
+ " 4 2 1 80.0\n",
+ " 5 1 1 82.0\n",
+ " 2 2 83.0\n",
+ "3 1 1 35 82.8\n",
+ 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+ ]
+ }
+ ],
+ "source": [
+ "# getting count number for each vehicle class, avarage speed for each class and direction\n",
+ "\n",
+ "import datetime\n",
"\n",
- " \n",
- " \n",
"\n",
+ "year = \"2020\"\n",
+ "areaID = \"01\"\n",
+ "lamID = \"101\"\n",
+ "shortYear = year[-2:]\n",
+ "startDayNumber = \"8\"\n",
+ "endDayNumber = \"\"\n",
+ "vehicle_class =[4]\n",
"\n",
+ "output = pd.DataFrame()\n",
"\n",
"\n",
"\n",
- "#print(\"\\n-----------------------------------------\\n\")\n",
+ "if(len(endDayNumber)!=0):\n",
+ " start = int(startDayNumber)\n",
+ " end = int(endDayNumber)\n",
+ " duration = (end+1) - start\n",
+ " print(duration)\n",
+ " \n",
+ " \n",
+ " \n",
+ " vehicleTotalNumberInOneDay = pd.DataFrame()\n",
+ " date = []\n",
+ " \n",
+ " for x in range(duration):\n",
+ " print(start)\n",
+ " url = \"https://aineistot.vayla.fi/lam/rawdata/{year}/{areaID}/lamraw_{lamID}_{shortYear}_{start}.csv\".format(year=year,areaID=areaID,lamID=lamID,shortYear = shortYear, start=start)\n",
+ " csv = pd.read_csv(url, names=names, sep = \";\")\n",
+ " \n",
+ " #getting the day from sequectial day number\n",
+ " dt = datetime.datetime(int(year),1,1)\n",
+ " dtdelta = datetime.timedelta(days=(start-1))\n",
+ " dt = dt + dtdelta\n",
+ " dt=dt.strftime('%d.%m.%Y')\n",
+ " date.append(dt)\n",
+ " print(date)\n",
+ " \n",
+ " \n",
+ " #temp = csv.groupby([\"vehicle_class\"])[\"vehicle_class\"].count()\n",
+ " temp = csv.groupby([\"vehicle_class\",\"direction\"]).agg({\"vehicle_class\":\"count\", \"speed_km/h\":\"mean\"})\\\n",
+ " .rename(columns={'vehicle_class':'vehicle_number','speed_km/h':'avg_speed_km_h'})\n",
+ " \n",
+ " #changing indexes to create uniqe indexs containing date and vehicle class\n",
+ " indexv = temp.index.values.tolist() \n",
+ " newindex = []\n",
+ " for i in range(len(indexv)):\n",
+ " a= list(indexv[i])\n",
+ " a[0] = dt+\"_\"+str(a[0])\n",
+ " newindex.append(tuple(a))\n",
+ " \n",
+ " #print(newindex)\n",
+ " \n",
+ " \n",
+ " #setting the new index to dataframe\n",
+ " temp[\"date_vehicleclass_direction\"]= newindex\n",
+ " temp.set_index(\"date_vehicleclass_direction\", inplace= True)\n",
+ " \n",
+ " vehicleTotalNumberInOneDay= vehicleTotalNumberInOneDay.append(temp)\n",
+ " \n",
+ " #print(vehicleTotalNumberInOneDay)\n",
+ " \n",
+ "\n",
+ " \n",
+ " start +=1\n",
+ " \n",
+ " \n",
+ " output= vehicleTotalNumberInOneDay\n",
+ " #output[\"date\"] = date\n",
+ " #output.set_index(\"date\", inplace = True)\n",
+ " print(output)\n",
+ " outputJson = output.to_json()\n",
+ " print(outputJson)\n",
+ "\n",
+ "else:\n",
+ " print(\"in else!\")\n",
+ " url = \"https://aineistot.vayla.fi/lam/rawdata/{year}/{areaID}/lamraw_{lamID}_{shortYear}_{startDayNumber}.csv\".format(year=year,areaID=areaID,lamID=lamID,shortYear = shortYear, startDayNumber=startDayNumber)\n",
+ " \n",
+ " allData = pd.read_csv(url, names=names, sep = \";\")\n",
+ " #df = allData[allData[\"vehicle_class\"]==vehicle_class[0]]\n",
+ " \n",
+ " # getting date from day number\n",
+ " dt = datetime.datetime(int(year),1,1)\n",
+ " dtdelta = datetime.timedelta(days=int(startDayNumber))\n",
+ " dt = dt + dtdelta\n",
+ " dt=dt.strftime('%d.%m.%Y')\n",
+ " print(dt)\n",
+ " \n",
+ " df = allData.groupby([\"hour\",\"vehicle_class\",\"direction\"]).agg({\"vehicle_class\": \"count\", \"speed_km/h\":\"mean\"})\\\n",
+ " .rename(columns={'vehicle_class':'vehicle_number','speed_km/h':'avg_speed_km_h'})\n",
+ " df['avg_speed_km_h'] = df['avg_speed_km_h'].astype(float).round(1)\n",
+ " print(df.head(25))\n",
+ " \n",
+ " outjson = df.to_json()\n",
+ " print(outjson)\n",
+ " \n",
"\n",
+ "#print(\"\\n-----------------------------------------\\n\")\n",
"\n",
"#output.plot()\n",
"#plt.show()"
diff --git a/Tavara- raskaan liikenne/python/cars/lamData.py b/Tavara- raskaan liikenne/python/cars/lamData.py
new file mode 100644
index 0000000000000000000000000000000000000000..d35a70c43074fc17552444b2f91f0c2a6f947b83
--- /dev/null
+++ b/Tavara- raskaan liikenne/python/cars/lamData.py
@@ -0,0 +1,88 @@
+import datetime
+import pandas as pd
+import numpy as np
+import sys
+import math
+
+#argument should be given in following sequence
+year = sys.argv[1]
+areaID = sys.argv[2]
+lamID = sys.argv[3]
+startDayNumber = sys.argv[4]
+endDayNumber = sys.argv[5]
+#vehicle_class =sys.argv[6]
+
+names = ["point_d", "Year", "day_number", "hour", "minute", "second", "100th_of_a_second", "length_m","lane", "direction", "vehicle_class", "speed_km/h", "faulty_0=valid_1=incorrect", "total_time", "interval","jonoalku"]
+shortYear = year[-2:]
+output = pd.DataFrame()
+
+
+check = math.isnan(float(endDayNumber))
+if(check == False):
+
+ start = int(startDayNumber)
+ end = int(endDayNumber)
+ duration = (end+1) - start
+
+ vehicleTotalNumberInOneDay = pd.DataFrame()
+ date = []
+ day = start
+ for x in range(duration):
+
+ url = "https://aineistot.vayla.fi/lam/rawdata/{year}/{areaID}/lamraw_{lamID}_{shortYear}_{day}.csv".format(year=year,areaID=areaID,lamID=lamID,shortYear = shortYear, day=day)
+ csv = pd.read_csv(url, names=names, sep = ";",error_bad_lines=False)
+
+ #getting the day from sequectial day number
+ dt = datetime.datetime(int(year),1,1)
+ dtdelta = datetime.timedelta(days=(day-1))
+ dt = dt + dtdelta
+ dt=dt.strftime('%d.%m.%Y')
+ date.append(dt)
+
+ #temp = csv.groupby(["vehicle_class"])["vehicle_class"].count()
+ temp = csv.groupby(["vehicle_class","direction"]).agg({"vehicle_class":"count", "speed_km/h":"mean"})\
+ .rename(columns={'vehicle_class':'vehicle_number','speed_km/h':'avg_speed_km_h'})
+
+ #changing indexes to create uniqe indexs containing date and vehicle class
+ indexv = temp.index.values.tolist()
+ newindex = []
+ for i in range(len(indexv)):
+ a= list(indexv[i])
+ ind = dt+"_"+str(a[0])+"_"+str(a[1])
+ newindex.append(ind)
+
+ #setting the new index to dataframe
+ temp["date_vehicleclass_direction"]= newindex
+ temp.set_index("date_vehicleclass_direction", inplace= True)
+ vehicleTotalNumberInOneDay= vehicleTotalNumberInOneDay.append(temp)
+
+ day +=1
+
+
+ output= vehicleTotalNumberInOneDay
+ outputJson = output.to_json()
+ print(outputJson)
+
+#data for only one day
+else:
+ url = "https://aineistot.vayla.fi/lam/rawdata/{year}/{areaID}/lamraw_{lamID}_{shortYear}_{startDayNumber}.csv"\
+ .format(year=year,areaID=areaID,lamID=lamID,shortYear = shortYear, startDayNumber=startDayNumber)
+
+ allData = pd.read_csv(url, names=names, sep = ";",error_bad_lines=False)
+
+ # getting date from day number
+ dt = datetime.datetime(int(year),1,1)
+ dtdelta = datetime.timedelta(days=int(startDayNumber))
+ dt = dt + dtdelta
+ dt=dt.strftime('%d.%m.%Y')
+
+ # df = allData.groupby(["hour","vehicle_class","direction"]).agg({"vehicle_class": "count", "speed_km/h":"mean"})\
+ # .rename(columns={'vehicle_class':'vehicle_number','speed_km/h':'avg_speed_km_h'})
+
+ df = allData.groupby(["hour","vehicle_class"]).agg({"vehicle_class": "count", "speed_km/h":"mean"})\
+ .rename(columns={'vehicle_class':'vehicle_number','speed_km/h':'avg_speed_km_h'})
+ df['avg_speed_km_h'] = df['avg_speed_km_h'].astype(float).round(1)
+ outjson = df.to_json()
+ print(outjson)
+
+sys.stdout.flush()
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