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Tykayn
tykayn
commit
996524bc6d
107 changed files with 1295536 additions and 0 deletions
82
wip/chargemap/chargemap.ts
Normal file
82
wip/chargemap/chargemap.ts
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import fs from "fs";
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let sourceFilePath: string = './chargemap_data/hurepoix.json'
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/**
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makes a geojson from chargemap layer data
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*/
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function convertChargemapFile(sourceFilePath: string) {
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fs.readFile(sourceFilePath, 'utf8', function (err, data) {
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let data_transformed: any = JSON.parse(data)
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let base_dataset: any = {type: 'FeatureCollection', features: []}
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let base_point: any = {
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type: 'Feature',
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geometry: {
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type: 'Point',
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coordinates: []
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},
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properties: {
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amenity: "charging_station"
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}
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}
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console.log('data_transformed.items.length', data_transformed.items.length)
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data_transformed.items.forEach((item: any) => {
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let new_point: any = {...base_point}
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if (item.type == 'point') {
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new_point.geometry.coordinates = [
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item.properties?.lat,
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item.properties?.lng,
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]
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} else if (item.type == 'pool') {
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new_point.geometry.coordinates = [
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item.pool.gps_coordinates.lon,
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item.pool.gps_coordinates.lat,
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]
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console.log('new_point.geometry.coordinates', new_point.geometry.coordinates)
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}
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base_dataset.features.push(new_point)
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})
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console.log('base_dataset.features.length', base_dataset.features.length)
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if (base_dataset) {
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writeFile('hurepoix_geojson.json', JSON.stringify(base_dataset, null, 2))
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}
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})
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}
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/**
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* crée un fichier dans le dossier par défaut, output
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* @param fileName
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* @param fileContent
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*/
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function writeFile(fileName: string, fileContent: any) {
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let output_folder = 'output';
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return fs.writeFile(
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`./${output_folder}/${fileName}`,
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fileContent,
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'utf8',
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(err) => {
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if (err) {
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console.log(`Error writing file: ${err}`)
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} else {
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console.log(`File ${fileName} is written successfully!`)
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}
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}
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)
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}
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convertChargemapFile(sourceFilePath)
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9594
wip/chargemap/hurepoix.json
Normal file
9594
wip/chargemap/hurepoix.json
Normal file
File diff suppressed because it is too large
Load diff
384
wip/chargemap/normandie.json
Normal file
384
wip/chargemap/normandie.json
Normal file
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@ -0,0 +1,384 @@
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{
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"count": 18,
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"items": [
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{
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"lat": 50.0743484,
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"lng": 1.537268,
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"icon": "cluster.png",
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"type": "cluster",
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"count": 33
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},
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{
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"lat": 49.6465569,
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"lng": 0.595749,
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"icon": "cluster.png",
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"type": "cluster",
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"count": 31
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},
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{
|
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"lat": 49.9614983,
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"lng": 1.1935092,
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"icon": "cluster.png",
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"type": "cluster",
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"count": 26
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},
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{
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"lat": 50.1074982,
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"lng": 1.8426746,
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"icon": "cluster.png",
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"type": "cluster",
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"count": 24
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},
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{
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"lat": 50.2155533,
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"lng": 1.628114,
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"icon": "cluster.png",
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"type": "cluster",
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"count": 22
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},
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{
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"lat": 49.6115227,
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"lng": 0.7733023,
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"icon": "cluster.png",
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"type": "cluster",
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"count": 21
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},
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{
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"lat": 49.9128304,
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"lng": 1.0857821,
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"icon": "cluster.png",
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"type": "cluster",
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"count": 20
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},
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{
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"lat": 49.8144035,
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"lng": 0.657026,
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"icon": "cluster.png",
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"type": "cluster",
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"count": 16
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},
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{
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"lat": 49.8280449,
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"lng": 0.911511,
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"icon": "cluster.png",
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"type": "cluster",
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"count": 15
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||||
},
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||||
{
|
||||
"lat": 49.5984917,
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||||
"lng": 1.1111569,
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"icon": "cluster.png",
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"type": "cluster",
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"count": 14
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||||
},
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{
|
||||
"lat": 49.6525497,
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"lng": 1.6158921,
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"icon": "cluster.png",
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"type": "cluster",
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"count": 11
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||||
},
|
||||
{
|
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"lat": 49.7663574,
|
||||
"lng": 1.7457,
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"icon": "cluster.png",
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"type": "cluster",
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"count": 9
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},
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{
|
||||
"lat": 49.9557304,
|
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"lng": 1.767372,
|
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"icon": "cluster.png",
|
||||
"type": "cluster",
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"count": 7
|
||||
},
|
||||
{
|
||||
"lat": 49.8437424,
|
||||
"lng": 1.7790869,
|
||||
"icon": "cluster.png",
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||||
"type": "cluster",
|
||||
"count": 3
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||||
},
|
||||
{
|
||||
"lat": 49.6544685,
|
||||
"lng": 1.832823,
|
||||
"icon": "cluster.png",
|
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"type": "cluster",
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||||
"count": 2
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||||
},
|
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{
|
||||
"lat": 49.5701447,
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||||
"lng": 0.4898517,
|
||||
"icon": "cluster.png",
|
||||
"type": "cluster",
|
||||
"count": 2
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||||
},
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||||
{
|
||||
"lat": 49.5702896,
|
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"lng": 1.609848,
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||||
"icon": "icon-accelerated_on.png",
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"type": "pool",
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"pool": {
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"amenities": [],
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"city": "Saumont-la-Poterie",
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"real_time_available": true,
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"rating": null,
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||||
"i18n_country_id": 67,
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"emsps": [],
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||||
"gps_coordinates": {
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||||
"lon": 1.609848,
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||||
"lat": 49.5702896
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||||
},
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"street_name": "358 Route de Paris ",
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"speed": {
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"icon": "accelerated.svg",
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"self": "charging_speeds/3.json",
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"id": 3,
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"map_icon": "accelerated.png"
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||||
},
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"should_check_prices": true,
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"number": null,
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"schedules": [],
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"object_state_id": 2,
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"is_indoor": false,
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"id": 285402,
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"slug": "eco-pi-saumont-la-poterie-358-route-de-paris",
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"can_remote_start_charge": false,
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"statistic": {
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"global_note_average": null,
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"price_note_average": null,
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||||
"location_note_average": null,
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"security_note_average": null,
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"comments_count": 0,
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"reports_count": 0,
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"checkins_count": 0,
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"material_note_average": null,
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"creation_date": "2023-07-29T02:04:10+00:00",
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"ratings_count": 1
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},
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"can_update_charging_pool": false,
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"is_always_open": true,
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"can_charge_with_chargemap": true,
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"location_type_id": 21,
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"network_id": 2340,
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"evse_emi3_ids": [
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"FR*EPI*E11734505*1"
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],
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"charging_connectors": [
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{
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"count": 5,
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"available_count": 3,
|
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"evse_ids": [
|
||||
701375,
|
||||
701376,
|
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701379,
|
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701377,
|
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701378
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],
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||||
"type": "MENNEKES_TYPE_2",
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"connector_type": {
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"id": 14,
|
||||
"icon": "type2"
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||||
},
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||||
"power_max": 22
|
||||
}
|
||||
],
|
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"is_free": false,
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"name": "Eco-PI - Saumont-la-Poterie - 358 Route de Paris ",
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"location_type_slug": "unknown",
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"charging_speed_id": 3,
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"postal_code": "76440",
|
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"country_code": "FR",
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"is_tesla": false,
|
||||
"evses": [
|
||||
{
|
||||
"id": 701375,
|
||||
"is_available": true,
|
||||
"realtime_state": "AVAILABLE"
|
||||
},
|
||||
{
|
||||
"id": 701376,
|
||||
"is_available": true,
|
||||
"realtime_state": "AVAILABLE"
|
||||
},
|
||||
{
|
||||
"id": 701379,
|
||||
"is_available": true,
|
||||
"realtime_state": "AVAILABLE"
|
||||
},
|
||||
{
|
||||
"id": 701377,
|
||||
"is_available": false,
|
||||
"realtime_state": "OUT_OF_ORDER"
|
||||
},
|
||||
{
|
||||
"id": 701378,
|
||||
"is_available": false,
|
||||
"realtime_state": "OUT_OF_ORDER"
|
||||
}
|
||||
]
|
||||
}
|
||||
},
|
||||
{
|
||||
"lat": 49.5693893,
|
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"lng": 0.953997,
|
||||
"icon": "icon-accelerated_on.png",
|
||||
"type": "pool",
|
||||
"pool": {
|
||||
"amenities": [
|
||||
"drinks",
|
||||
"restoration",
|
||||
"shop",
|
||||
"restroom"
|
||||
],
|
||||
"city": "Pavilly",
|
||||
"real_time_available": true,
|
||||
"rating": 4,
|
||||
"i18n_country_id": 67,
|
||||
"emsps": [
|
||||
10,
|
||||
75,
|
||||
77,
|
||||
107,
|
||||
108,
|
||||
112
|
||||
],
|
||||
"gps_coordinates": {
|
||||
"lon": 0.953997,
|
||||
"lat": 49.5693893
|
||||
},
|
||||
"street_name": "Place du Président d'Esneval",
|
||||
"speed": {
|
||||
"icon": "accelerated.svg",
|
||||
"self": "charging_speeds/3.json",
|
||||
"id": 3,
|
||||
"map_icon": "accelerated.png"
|
||||
},
|
||||
"should_check_prices": true,
|
||||
"number": "4",
|
||||
"schedules": [],
|
||||
"object_state_id": 2,
|
||||
"is_indoor": false,
|
||||
"id": 105727,
|
||||
"slug": "sde76-place-du-president-desneval-4-pavilly",
|
||||
"can_remote_start_charge": false,
|
||||
"statistic": {
|
||||
"global_note_average": 4,
|
||||
"price_note_average": 5,
|
||||
"location_note_average": 2,
|
||||
"security_note_average": 5,
|
||||
"comments_count": 0,
|
||||
"reports_count": 0,
|
||||
"checkins_count": 25,
|
||||
"material_note_average": 2,
|
||||
"creation_date": "2023-07-29T02:01:25+00:00",
|
||||
"ratings_count": 1
|
||||
},
|
||||
"can_update_charging_pool": false,
|
||||
"is_always_open": true,
|
||||
"can_charge_with_chargemap": true,
|
||||
"location_type_id": 4,
|
||||
"network_id": 404,
|
||||
"evse_emi3_ids": [
|
||||
"FR*S76*E100*1*1"
|
||||
],
|
||||
"charging_connectors": [
|
||||
{
|
||||
"count": 4,
|
||||
"available_count": 2,
|
||||
"evse_ids": [
|
||||
25003,
|
||||
25004,
|
||||
390745,
|
||||
390747
|
||||
],
|
||||
"type": "DOMESTIC_TYPE_F",
|
||||
"connector_type": {
|
||||
"id": 6,
|
||||
"icon": "schuko"
|
||||
},
|
||||
"power_max": 3
|
||||
},
|
||||
{
|
||||
"count": 7,
|
||||
"available_count": 4,
|
||||
"evse_ids": [
|
||||
25003,
|
||||
25004,
|
||||
390745,
|
||||
390747,
|
||||
390662,
|
||||
390663,
|
||||
390664
|
||||
],
|
||||
"type": "MENNEKES_TYPE_2",
|
||||
"connector_type": {
|
||||
"id": 14,
|
||||
"icon": "type2"
|
||||
},
|
||||
"power_max": 22
|
||||
},
|
||||
{
|
||||
"count": 3,
|
||||
"available_count": 2,
|
||||
"evse_ids": [
|
||||
390662,
|
||||
390663,
|
||||
390664
|
||||
],
|
||||
"type": "DOMESTIC_TYPE_F",
|
||||
"connector_type": {
|
||||
"id": 6,
|
||||
"icon": "schuko"
|
||||
},
|
||||
"power_max": 2
|
||||
}
|
||||
],
|
||||
"is_free": false,
|
||||
"name": "SDE76 - Place du Président d'Esneval, 4 - Pavilly",
|
||||
"location_type_slug": "parking",
|
||||
"charging_speed_id": 3,
|
||||
"postal_code": "76570",
|
||||
"country_code": "FR",
|
||||
"is_tesla": false,
|
||||
"evses": [
|
||||
{
|
||||
"id": 25003,
|
||||
"is_available": true,
|
||||
"realtime_state": "AVAILABLE"
|
||||
},
|
||||
{
|
||||
"id": 25004,
|
||||
"is_available": true,
|
||||
"realtime_state": "AVAILABLE"
|
||||
},
|
||||
{
|
||||
"id": 390745,
|
||||
"is_available": false,
|
||||
"realtime_state": "OUT_OF_ORDER"
|
||||
},
|
||||
{
|
||||
"id": 390747,
|
||||
"is_available": false,
|
||||
"realtime_state": "OUT_OF_ORDER"
|
||||
},
|
||||
{
|
||||
"id": 390662,
|
||||
"is_available": false,
|
||||
"realtime_state": "OUT_OF_ORDER"
|
||||
},
|
||||
{
|
||||
"id": 390663,
|
||||
"is_available": true,
|
||||
"realtime_state": "AVAILABLE"
|
||||
},
|
||||
{
|
||||
"id": 390664,
|
||||
"is_available": true,
|
||||
"realtime_state": "AVAILABLE"
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
]
|
||||
}
|
||||
184
wip/group_irve_stats.py
Normal file
184
wip/group_irve_stats.py
Normal file
|
|
@ -0,0 +1,184 @@
|
|||
import pandas as pd
|
||||
|
||||
import folium
|
||||
import plotly.graph_objects as go
|
||||
import io
|
||||
|
||||
# Chargement du fichier JSON
|
||||
with open('/home/poule/encrypted/stockage-syncable/www/development/html/mapping-geojson-osm/etalab_data/irve_bornes_recharge/latest.csv', 'r') as file:
|
||||
data = pd.read_csv(file)
|
||||
|
||||
|
||||
def format_puissance_nominale(value):
|
||||
"""Formate la valeur de la puissance nominale"""
|
||||
if value >= 1000:
|
||||
return "{:,.1f} kW".format(value / 1000)
|
||||
else:
|
||||
return "{:,.0f} W".format(value)
|
||||
|
||||
|
||||
|
||||
|
||||
# Création du DataFrame pandas
|
||||
dataframe_irve = pd.DataFrame(data)
|
||||
print("Nombre total de lignes avant suppression de duplicatas: ", len(dataframe_irve))
|
||||
|
||||
|
||||
# enlever les duplicatas selon l'id du point de charge
|
||||
dataframe_irve = dataframe_irve.drop_duplicates(subset=["id_pdc_itinerance"])
|
||||
# enlever les duplicatas de placement
|
||||
dataframe_irve = dataframe_irve.drop_duplicates(subset=["coordonneesXY"])
|
||||
# enlever les lignes où on ne connait pas l'opérateur
|
||||
dataframe_irve = dataframe_irve[(dataframe_irve["nom_operateur"].notnull()) & (dataframe_irve["nom_amenageur"].notnull())]
|
||||
dataframe_irve = dataframe_irve[(dataframe_irve["coordonneesXY"].notnull())]
|
||||
|
||||
# Regroupement par opérateur
|
||||
groups = dataframe_irve.groupby("nom_operateur")
|
||||
|
||||
dataframe_irve_sorted = dataframe_irve.sort_values(by="nom_operateur")
|
||||
|
||||
# Affichage du résultat
|
||||
# print(dataframe_irve_sorted)
|
||||
|
||||
|
||||
# Filtres pour identifier les lignes incorrectes
|
||||
filtre_non_numerique = dataframe_irve["puissance_nominale"] % 1 > 0
|
||||
filtre_incorrect = filtre_non_numerique
|
||||
|
||||
# Lignes incorrectes
|
||||
erreurs_puissance_nominale = dataframe_irve[filtre_incorrect][[ "puissance_nominale", "id_station_itinerance"]]
|
||||
|
||||
groups_erreur = erreurs_puissance_nominale
|
||||
print('groups en erreur: \n',)
|
||||
groups_erreur.head()
|
||||
|
||||
# Minimum de puissance_nominale
|
||||
puissance_nominale_min = dataframe_irve["puissance_nominale"].min()
|
||||
|
||||
# Maximum de puissance_nominale
|
||||
puissance_nominale_max = dataframe_irve["puissance_nominale"].max()
|
||||
|
||||
print("\nMinimum de puissance_nominale : {}".format(puissance_nominale_min))
|
||||
print("Maximum de puissance_nominale : {}\n".format(puissance_nominale_max))
|
||||
|
||||
# Impression du nombre de stations restantes
|
||||
print("Nombre total de lignes : ", len(dataframe_irve))
|
||||
|
||||
|
||||
# Application de la fonction de formatage à la colonne "puissance_nominale"
|
||||
dataframe_irve["puissance_nominale_formatted"] = dataframe_irve["puissance_nominale"].apply(format_puissance_nominale)
|
||||
|
||||
# Remplacement de la colonne originale par celle nouvellement formatée
|
||||
dataframe_irve["puissance_nominale"] = dataframe_irve["puissance_nominale_formatted"]
|
||||
del dataframe_irve["puissance_nominale_formatted"]
|
||||
|
||||
|
||||
|
||||
# Compte le nombre d'anomalies pour chaque opérateur
|
||||
def count_errors_for_each_operator():
|
||||
# Initialisation du dictionnaire vide servant au stockage temporaire
|
||||
temp_dict = dict()
|
||||
|
||||
for _, row in dataframe_irve.iterrows():
|
||||
operator = str(row["nom_operateur"])
|
||||
|
||||
# Test uniquement les entrées textuelles présentes dans la colonne "puissance_nominale"
|
||||
power_entry = str(row["puissance_nominale"]).replace('W', '').replace(' ', '')
|
||||
if 'k' in power_entry or power_entry[-2:] == ". " or int(power_entry) > 6000 or int(power_entry) < 0:
|
||||
if operator not in temp_dict:
|
||||
temp_dict[operator] = {"error_count": 1}
|
||||
else:
|
||||
current_error_count = temp_dict[operator]["error_count"] + 1
|
||||
temp_dict[operator] = {"error_count": current_error_count}
|
||||
|
||||
# Transformation du dictionnaire local en liste de tuples
|
||||
return sorted([(key, val["error_count"]) for key, val in temp_dict.items()], key=lambda x: -x[1])
|
||||
|
||||
# Passage du DataFrame à la fonction
|
||||
countland = count_errors_for_each_operator()
|
||||
|
||||
print("\nListe des opérateurs classés par nombre d'anomalies :'" , countland)
|
||||
|
||||
# Affichage des résultats
|
||||
# print("\nListe des opérateurs classés par nombre d'anomalies : ")
|
||||
for i, item in enumerate(countland):
|
||||
print("{} - Opérateur : {}, Anomalies : {}".format(i+1, item[0], item[1]))
|
||||
|
||||
# ============== graphique ============================
|
||||
import plotly.express as px
|
||||
import plotly.io as pio
|
||||
pio.renderers.default = "svg"
|
||||
# Spécification du nom et du path du fichier de sortie
|
||||
output_filename = "puissances_repartition.svg"
|
||||
|
||||
# Génération du graphe
|
||||
# fig = px.histogram(
|
||||
# dataframe_irve,
|
||||
# x="puissance_nominale",
|
||||
# title="Répartition des Puissances",
|
||||
# labels={'puissance_nominale': 'Puissance'},
|
||||
# color_discrete_sequence=px.colors.sequential.Plasma_r,
|
||||
# opacity=0.7,
|
||||
# hover_name="nom_operateur",
|
||||
# category_orders={'puissance_nominale': ['<1 kW', '1 kW', '2 kW', '3 kW', '5 kW', '6 kW', '8 kW', '11 kW', '12 kW', '15 kW', '22 kW', '30 kW', '43 kW', '50 kW', '150 kW']},
|
||||
# )
|
||||
|
||||
# Visualisation du graphe
|
||||
# fig.show()
|
||||
|
||||
|
||||
# Sauvegarde du graphe Plotly sous forme de fichier SVG
|
||||
# fig.write_image(output_filename, engine='kaleido', scale=1.0)
|
||||
#
|
||||
# # Display the generated filename
|
||||
# print("\nGraph saved to:", output_filename)
|
||||
|
||||
dataframe_irve.to_csv("output/cleaned_irve_from_cipherbliss.csv", index=False)
|
||||
|
||||
|
||||
# ============ carte svg
|
||||
|
||||
|
||||
# Colonne "coordonneesXY" est un string, transformation en tuple
|
||||
dataframe_irve[['latitude','longitude']] = dataframe_irve['coordonneesXY'].str.findall(r'(-?[\d]+(\.[0-9]*)?)').apply(pd.Series)
|
||||
|
||||
dataframe_irve['latitude'].head()
|
||||
|
||||
# Centrer la map sur la france
|
||||
france_center = [47.5, 2.5]
|
||||
# map_folium = folium.Map(location=france_center, zoom_start=5)
|
||||
#
|
||||
# # Itération sur chaque point géographique pour l'ajouter à la map Folium
|
||||
# for index, row in dataframe_irve.iterrows():
|
||||
# location = [row['latitude'], row['longitude']]
|
||||
# icon = folium.Icon(color='blue', icon='info-sign')
|
||||
# tooltip = "<strong>{}</strong>{}".format(row['id_station_itinerance'], row['adresse_station'])
|
||||
# popup = '<div style="width:10rem;">{}</div>'.format(row['nom_station'])
|
||||
# marker = folium.Marker(location, popup=popup, tooltip=tooltip, icon=icon)
|
||||
# marker.add_to(map_folium)
|
||||
#
|
||||
# # Objet graphique Plotly contenant la carte Folium encodée
|
||||
# plotly_chart = go.Figure(go.Scattergeo(
|
||||
# lon=[row['longitude'] for index, row in dataframe_irve.iterrows()],
|
||||
# lat=[row['latitude'] for index, row in dataframe_irve.iterrows()]
|
||||
# ))
|
||||
#
|
||||
# # Configuration de l'apparence visuelle
|
||||
# plotly_chart.update_geos(fitbounds="locations", visible=True)
|
||||
# plotly_chart.update_layout(
|
||||
# geo_scope="world",
|
||||
# margin={"r":0,"t":0,"l":0,"b":0},
|
||||
# paper_bgcolor="#FFFFFF",
|
||||
# plot_bgcolor="#FFFFFF",
|
||||
# showlegend=False
|
||||
# )
|
||||
|
||||
|
||||
|
||||
# Encapsulation de la carte Plotly dans un objet BytesIO
|
||||
# buffer = io.BytesIO()
|
||||
# plotly_chart.write_html(buffer, auto_open=False)
|
||||
# buffer.seek(0)
|
||||
|
||||
# Affichage de la carte Plotly
|
||||
# plotly.io.write_image(plotly_chart, "carte_stations.svg", format="svg", engine="kaleido")
|
||||
35
wip/irve_split_by_operators_from_json.ts
Normal file
35
wip/irve_split_by_operators_from_json.ts
Normal file
|
|
@ -0,0 +1,35 @@
|
|||
const stations = [
|
||||
{
|
||||
id: 1,
|
||||
name: 'Tesla Supercharger',
|
||||
address: '123 Main St',
|
||||
lat: 37.7749,
|
||||
lon: -122.4194,
|
||||
network_operator: 'Tesla'
|
||||
},
|
||||
{
|
||||
id: 2,
|
||||
name: 'Electrify America',
|
||||
address: '456 Maple Ave',
|
||||
lat: 40.7128,
|
||||
lon: -74.0060,
|
||||
network_operator: 'Electrify America'
|
||||
},
|
||||
{id: 3, name: 'ChargePoint', address: '789 Oak St', lat: 34.0522, lon: -118.2437, network_operator: 'ChargePoint'},
|
||||
];
|
||||
|
||||
interface Station {
|
||||
[key: string]: string
|
||||
|
||||
}
|
||||
|
||||
function groupStationsByNetworkOperator(stations: Station[]) {
|
||||
|
||||
}
|
||||
|
||||
|
||||
function groupStationsByNetworkOperatorAndWriteToFiles(stations: any[], outputDir: string) {
|
||||
|
||||
}
|
||||
|
||||
groupStationsByNetworkOperatorAndWriteToFiles(stations, "output")
|
||||
Loading…
Add table
Add a link
Reference in a new issue