Merged app
This commit is contained in:
commit
39b1a43a53
4
.gitignore
vendored
4
.gitignore
vendored
|
@ -519,3 +519,7 @@ tags
|
|||
.history
|
||||
|
||||
# End of https://www.gitignore.io/api/vim,emacs,android,pycharm+all,androidstudio,visualstudiocode,python,java,angular
|
||||
|
||||
|
||||
# Custom
|
||||
requests_cache.sqlite
|
||||
|
|
13
client/Nightr/package-lock.json
generated
13
client/Nightr/package-lock.json
generated
|
@ -3948,6 +3948,14 @@
|
|||
}
|
||||
}
|
||||
},
|
||||
"nativescript-geolocation": {
|
||||
"version": "5.0.0",
|
||||
"resolved": "https://registry.npmjs.org/nativescript-geolocation/-/nativescript-geolocation-5.0.0.tgz",
|
||||
"integrity": "sha512-olFTkG68Y0pkqtxyaPoHalZSHgXcg3iL9q+r9gcEY5c7QY8sCtfdO/T5FhHeQlDu0YrrZhx2Ke20dUczuePmUA==",
|
||||
"requires": {
|
||||
"nativescript-permissions": "~1.2.3"
|
||||
}
|
||||
},
|
||||
"nativescript-hook": {
|
||||
"version": "0.2.5",
|
||||
"resolved": "https://registry.npmjs.org/nativescript-hook/-/nativescript-hook-0.2.5.tgz",
|
||||
|
@ -3978,6 +3986,11 @@
|
|||
"resolved": "https://registry.npmjs.org/nativescript-intl/-/nativescript-intl-3.0.0.tgz",
|
||||
"integrity": "sha1-gu6b59N3Fys8QpVzRyMDdijhhqc="
|
||||
},
|
||||
"nativescript-permissions": {
|
||||
"version": "1.2.3",
|
||||
"resolved": "https://registry.npmjs.org/nativescript-permissions/-/nativescript-permissions-1.2.3.tgz",
|
||||
"integrity": "sha1-4+ZVRfmP5IjdVXj3/5DrrjCI5wA="
|
||||
},
|
||||
"nativescript-theme-core": {
|
||||
"version": "1.0.4",
|
||||
"resolved": "https://registry.npmjs.org/nativescript-theme-core/-/nativescript-theme-core-1.0.4.tgz",
|
||||
|
|
|
@ -22,6 +22,7 @@
|
|||
"@angular/platform-browser-dynamic": "~7.2.0",
|
||||
"@angular/router": "~7.2.0",
|
||||
"nativescript-angular": "~7.2.0",
|
||||
"nativescript-geolocation": "^5.0.0",
|
||||
"nativescript-theme-core": "~1.0.4",
|
||||
"reflect-metadata": "~0.1.12",
|
||||
"rxjs": "~6.3.0",
|
||||
|
|
|
@ -5,5 +5,7 @@
|
|||
<ns-my-button (tap)=onTap($event) text="Nightr"></ns-my-button>
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||||
</StackLayout>
|
||||
|
||||
<ns-locationButton></ns-locationButton>
|
||||
|
||||
</AbsoluteLayout>
|
||||
|
||||
|
|
|
@ -2,6 +2,7 @@ import { Component } from "@angular/core";
|
|||
import * as dialogs from "tns-core-modules/ui/dialogs";
|
||||
import { MyHttpPostService } from './services/my-http-post-service'
|
||||
import { TouchGestureEventData, GestureEventData } from 'tns-core-modules/ui/gestures'
|
||||
import { isEnabled, enableLocationRequest, getCurrentLocation, watchLocation, distance, clearWatch } from "nativescript-geolocation";
|
||||
|
||||
@Component({
|
||||
selector: "ns-app",
|
||||
|
|
|
@ -4,6 +4,7 @@ import { NativeScriptModule } from "nativescript-angular/nativescript.module";
|
|||
import { AppComponent } from "./app.component";
|
||||
import { MyButtonComponent } from './component/my-button/my-button.component';
|
||||
import { NativeScriptHttpClientModule } from "nativescript-angular/http-client";
|
||||
import { MyLocationButtonComponent } from './component/locationButton/locationButton.component';
|
||||
|
||||
// Uncomment and add to NgModule imports if you need to use two-way binding
|
||||
// import { NativeScriptFormsModule } from "nativescript-angular/forms";
|
||||
|
@ -21,6 +22,7 @@ import { NativeScriptHttpClientModule } from "nativescript-angular/http-client";
|
|||
],
|
||||
declarations: [
|
||||
AppComponent,
|
||||
MyLocationButtonComponent,
|
||||
MyButtonComponent,
|
||||
],
|
||||
providers: [],
|
||||
|
|
|
@ -0,0 +1 @@
|
|||
/* Add mobile styles for the component here. */
|
|
@ -0,0 +1,4 @@
|
|||
<StackLayout>
|
||||
<Button text="{{title}}" class="btn btn-primary" (tap)="onTap()"></Button>
|
||||
<Label text="{{lat}}"></Label>
|
||||
</StackLayout>
|
|
@ -0,0 +1,25 @@
|
|||
import { Component, OnInit } from '@angular/core';
|
||||
import { MyGeoLocationService} from '../../services/my-geo-location.service';
|
||||
@Component({
|
||||
selector: 'ns-locationButton',
|
||||
templateUrl: './locationButton.component.html',
|
||||
styleUrls: ['./locationButton.component.css'],
|
||||
moduleId: module.id,
|
||||
})
|
||||
export class MyLocationButtonComponent implements OnInit {
|
||||
title = "Click to get location!";
|
||||
lat = "start";
|
||||
geoLocationService = new MyGeoLocationService();
|
||||
|
||||
constructor() {
|
||||
}
|
||||
ngOnInit() {
|
||||
}
|
||||
|
||||
onTap() {
|
||||
this.geoLocationService.getLocation().then(location => {
|
||||
this.lat = ""+location.latitude;
|
||||
}).catch(error => {
|
||||
});
|
||||
}
|
||||
}
|
35
client/Nightr/src/app/services/my-geo-location.service.ts
Normal file
35
client/Nightr/src/app/services/my-geo-location.service.ts
Normal file
|
@ -0,0 +1,35 @@
|
|||
import { Injectable } from '@angular/core';
|
||||
import { isEnabled, enableLocationRequest, getCurrentLocation, watchLocation, distance, clearWatch, Location } from "nativescript-geolocation";
|
||||
import { stringify } from '@angular/core/src/render3/util';
|
||||
|
||||
@Injectable({
|
||||
providedIn: 'root'
|
||||
})
|
||||
export class MyGeoLocationService {
|
||||
loc: Location;
|
||||
constructor() {
|
||||
}
|
||||
|
||||
getLocation(): Promise<Location> {
|
||||
this.isLocationEnabled();
|
||||
var result = getCurrentLocation({
|
||||
desiredAccuracy: 3,
|
||||
timeout: 5000
|
||||
});
|
||||
return result;
|
||||
}
|
||||
|
||||
private isLocationEnabled() {
|
||||
isEnabled().then(function (isEnabled) {
|
||||
if (!isEnabled) {
|
||||
enableLocationRequest().then(function () {
|
||||
}, function (e) {
|
||||
alert("Error: " + (e.message || e));
|
||||
});
|
||||
}
|
||||
}, function (e) {
|
||||
alert("Error: " + (e.message || e));
|
||||
});
|
||||
|
||||
}
|
||||
}
|
|
@ -4,15 +4,13 @@ FIRST_RUN=$?
|
|||
# Create and enter virtual environment
|
||||
if (( $FIRST_RUN )); then
|
||||
echo Creating virtual environment
|
||||
python3 -m venv venv
|
||||
python3.7 -m venv venv
|
||||
fi
|
||||
source venv/bin/activate
|
||||
|
||||
# Install required python packages
|
||||
if (( $FIRST_RUN )); then
|
||||
echo Installing required Python packages
|
||||
pip install -Ur requirements.txt
|
||||
fi
|
||||
echo Installing required Python packages
|
||||
pip install -Ur requirements.txt
|
||||
|
||||
function run() {
|
||||
python -m nightr
|
||||
|
|
|
@ -1,24 +1,33 @@
|
|||
import inspect
|
||||
import statistics
|
||||
import timeit
|
||||
from dataclasses import asdict
|
||||
from datetime import timedelta
|
||||
from logging import DEBUG
|
||||
from typing import List
|
||||
|
||||
import requests_cache
|
||||
from flask import Flask, jsonify
|
||||
from flask import Flask, jsonify, logging
|
||||
|
||||
from server.nightr.strategies import dmi, steam
|
||||
from server.nightr.util import Context
|
||||
from .strategies import miloStrats, iss, cars_in_traffic, tide_strat, upstairs_neighbour
|
||||
from .util import Context
|
||||
|
||||
app = Flask(__name__)
|
||||
logger = logging.create_logger(app)
|
||||
logger.setLevel(DEBUG)
|
||||
|
||||
requests_cache.install_cache("requests_cache.sqlite", expire_after=timedelta(minutes=10))
|
||||
requests_cache.install_cache("requests_cache", expire_after=timedelta(minutes=10))
|
||||
|
||||
|
||||
strategies = {
|
||||
# name: (weight, probability function)
|
||||
"dmi": (0.5, dmi.probability),
|
||||
"steam": (1.0, steam.probability),
|
||||
"tv2news": miloStrats.tv2newsStrat,
|
||||
"australia": miloStrats.australiaStrat,
|
||||
"camera": miloStrats.camImgStrat,
|
||||
"iss": iss.night_on_iss,
|
||||
"cars_in_traffic": cars_in_traffic.cars_in_traffic,
|
||||
"tide": tide_strat.is_tide,
|
||||
"upstairs_neighbour": upstairs_neighbour.check_games,
|
||||
}
|
||||
|
||||
|
||||
|
@ -28,17 +37,22 @@ def probabilities():
|
|||
context = Context(**phone_data)
|
||||
|
||||
predictions: List[dict] = []
|
||||
for name, (weight, strategy) in strategies.items():
|
||||
for name, strategy in strategies.items():
|
||||
try:
|
||||
start = timeit.default_timer()
|
||||
prediction = strategy(context)
|
||||
stop = timeit.default_timer()
|
||||
logger.debug("Execution time for %s: %ss", name, stop - start)
|
||||
except Exception as e:
|
||||
print(f"Strategy {name} failed: {e}")
|
||||
logger.warning("Strategy '%s' failed:", name)
|
||||
logger.exception(e)
|
||||
continue
|
||||
|
||||
predictions.append({
|
||||
"name": name,
|
||||
"description": inspect.getdoc(strategy),
|
||||
"weight": weight,
|
||||
"weighted_probability": prediction.probability * weight,
|
||||
"weight": prediction.weight,
|
||||
"weighted_probability": prediction.probability * prediction.weight,
|
||||
"night": prediction.probability > 0.5,
|
||||
**asdict(prediction),
|
||||
})
|
||||
|
@ -47,6 +61,12 @@ def probabilities():
|
|||
median = statistics.median(p["weighted_probability"] for p in predictions)
|
||||
night = mean > 0.5
|
||||
|
||||
# Invert if we're in Australia
|
||||
if context.in_australia:
|
||||
night = not night
|
||||
for prediction in predictions:
|
||||
prediction["night"] = not prediction["night"]
|
||||
|
||||
# Calculate contributions of predictions
|
||||
consensus_weight_sum = sum(p["weight"] for p in predictions if p["night"] == night)
|
||||
for prediction in predictions:
|
||||
|
@ -65,7 +85,7 @@ def probabilities():
|
|||
|
||||
|
||||
def main():
|
||||
app.run(host='0.0.0.0')
|
||||
app.run(host='0.0.0.0', debug=True)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
|
|
@ -1,6 +1,8 @@
|
|||
import requests
|
||||
from datetime import datetime
|
||||
import time
|
||||
from datetime import datetime
|
||||
|
||||
import requests
|
||||
|
||||
|
||||
def main():
|
||||
filename = "dotaplayers " + str(datetime.now()) + ".csv"
|
||||
|
@ -16,5 +18,6 @@ def main():
|
|||
f.close()
|
||||
time.sleep(100)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
40
server/nightr/strategies/cars_in_traffic.py
Normal file
40
server/nightr/strategies/cars_in_traffic.py
Normal file
|
@ -0,0 +1,40 @@
|
|||
import requests
|
||||
|
||||
from ..util import Prediction, Context
|
||||
|
||||
|
||||
def cars_in_traffic(context: Context) -> Prediction:
|
||||
"""
|
||||
How many cars are currently driving around Aarhus?
|
||||
"""
|
||||
r = requests.get('https://portal.opendata.dk/api/3/action/datastore_search?resource_id=b3eeb0ff-c8a8-4824-99d6-e0a3747c8b0d')
|
||||
night_avr = 3.38
|
||||
day_avr = 6.98
|
||||
|
||||
p = Prediction()
|
||||
|
||||
data = r.json()
|
||||
sum = 0
|
||||
len = 0
|
||||
for lel in data['result']['records']:
|
||||
sum += lel['vehicleCount']
|
||||
len += 1
|
||||
if sum > 0:
|
||||
curr_avg = len / sum
|
||||
else:
|
||||
curr_avg = 0
|
||||
|
||||
diff = day_avr - night_avr
|
||||
|
||||
if curr_avg >= day_avr:
|
||||
p.reasons.append(f"Because {curr_avg} cars are driving around Aarhus right now and {day_avr} is the expected number for daytime")
|
||||
p.probability = 0.0
|
||||
elif curr_avg <= night_avr:
|
||||
p.reasons.append(f"Because {curr_avg} cars are driving around Aarhus right now and {night_avr} is the expected number for nighttime")
|
||||
p.probability = 1.0
|
||||
else:
|
||||
p.reasons.append(f"Because average for daytime is {day_avr} and average for nighttime is {night_avr}, but the current average is {curr_avg}")
|
||||
res = 1 - curr_avg / diff
|
||||
p.probability = res
|
||||
|
||||
return p
|
|
@ -1,12 +0,0 @@
|
|||
from server.nightr.util import Context, Prediction
|
||||
|
||||
|
||||
def probability(context: Context) -> Prediction:
|
||||
"""
|
||||
The data from DMI.
|
||||
"""
|
||||
p = Prediction()
|
||||
p.probability = 0.7
|
||||
p.reasons.append("It is raining in Tønder")
|
||||
|
||||
return p
|
88
server/nightr/strategies/iss.py
Normal file
88
server/nightr/strategies/iss.py
Normal file
|
@ -0,0 +1,88 @@
|
|||
import itertools
|
||||
import logging
|
||||
from datetime import datetime
|
||||
from math import pi, sqrt, sin, cos, atan2
|
||||
|
||||
import pytz
|
||||
import requests
|
||||
from timezonefinder import TimezoneFinder
|
||||
|
||||
from ..util import Context, Prediction
|
||||
|
||||
tf = TimezoneFinder(in_memory=True)
|
||||
|
||||
|
||||
def night_on_iss(context: Context) -> Prediction:
|
||||
"""
|
||||
It is night if it is night on the ISS and it is currently orbiting above us.
|
||||
"""
|
||||
p = Prediction()
|
||||
|
||||
if not context.flat_earth:
|
||||
iss_position = requests.get("http://api.open-notify.org/iss-now.json").json()["iss_position"]
|
||||
the_iss = "The ISS"
|
||||
iss_position_description = "on board the ISS"
|
||||
else:
|
||||
p.reasons.append("The ISS is (obviously) located in Hollywood")
|
||||
the_iss = "Hollywood"
|
||||
iss_position = {'latitude': 34.092808, 'longitude': -118.328659} # Hollywood
|
||||
iss_position_description = "in the Hollywood studio"
|
||||
|
||||
phone_position = context.position
|
||||
|
||||
# Calculate ratio: a number between 0 and 1 saying how close we are to the ISS
|
||||
distance = haversine(iss_position, phone_position)
|
||||
max_distance = 40075 / 2 # the furthest you can be from any position is half of the earth's circumference
|
||||
ratio = distance / max_distance
|
||||
|
||||
# We're in the same "timezone" as the ISS if we're on the same half of the earth
|
||||
on_iss_time = ratio < 0.5
|
||||
|
||||
side = "same" if on_iss_time else "other"
|
||||
p.reasons.append(f"{the_iss} is {int(distance)} km away, so we are on the {side} side of the earth.")
|
||||
for i in itertools.count(1):
|
||||
iss_tz = tf.closest_timezone_at(lng=float(iss_position["longitude"]),
|
||||
lat=float(iss_position["latitude"]),
|
||||
delta_degree=i)
|
||||
if iss_tz is not None:
|
||||
break
|
||||
iss_time = datetime.now(pytz.timezone(iss_tz))
|
||||
|
||||
iss_night = 6 < iss_time.hour > 22
|
||||
|
||||
# iss_night on_iss_time night
|
||||
# 0 0 1
|
||||
# 0 1 0
|
||||
# 1 0 0
|
||||
# 1 1 1
|
||||
night = iss_night == on_iss_time
|
||||
|
||||
iss_time_description = "nighttime" if iss_night else "daytime"
|
||||
time_description = "nighttime" if night else "daytime"
|
||||
p.probability = float(night)
|
||||
p.reasons.append(f"It is {iss_time_description} {iss_position_description}.")
|
||||
p.reasons.append(f"Therefore, it must be {time_description} where we are.")
|
||||
|
||||
return p
|
||||
|
||||
|
||||
def haversine(pos1, pos2):
|
||||
"""
|
||||
Distance between two GPS coordinates.
|
||||
https://stackoverflow.com/a/18144531
|
||||
"""
|
||||
lat1 = float(pos1["latitude"])
|
||||
long1 = float(pos1["longitude"])
|
||||
lat2 = float(pos2["latitude"])
|
||||
long2 = float(pos2["longitude"])
|
||||
|
||||
degree_to_rad = float(pi / 180.0)
|
||||
|
||||
d_lat = (lat2 - lat1) * degree_to_rad
|
||||
d_long = (long2 - long1) * degree_to_rad
|
||||
|
||||
a = pow(sin(d_lat / 2), 2) + cos(lat1 * degree_to_rad) * cos(lat2 * degree_to_rad) * pow(sin(d_long / 2), 2)
|
||||
c = 2 * atan2(sqrt(a), sqrt(1 - a))
|
||||
km = 6367 * c
|
||||
|
||||
return km
|
11
server/nightr/strategies/just_eat.py
Normal file
11
server/nightr/strategies/just_eat.py
Normal file
|
@ -0,0 +1,11 @@
|
|||
import requests
|
||||
from bs4 import BeautifulSoup
|
||||
|
||||
|
||||
def is_restaurant_open(name):
|
||||
r = requests.get("https://www.just-eat.dk/area/8000-%C3%A5rhusc")
|
||||
soup = BeautifulSoup(r.content, features='html5lib')
|
||||
|
||||
print(soup.find('div', {'data-test-id': 'listingGroupOpen'}))
|
||||
|
||||
is_restaurant_open("stop2shop")
|
|
@ -1,14 +1,22 @@
|
|||
from datetime import datetime
|
||||
from pathlib import Path
|
||||
import requests
|
||||
|
||||
import cv2
|
||||
from datetime import datetime, timedelta
|
||||
from pytz import timezone
|
||||
from server.nightr.util import Context, Prediction
|
||||
|
||||
from ..util import Context, Prediction
|
||||
|
||||
|
||||
def camImgStrat(context : Context) -> Prediction:
|
||||
img = cv2.imread('night.jpg',0)
|
||||
"""
|
||||
The contents of the camera image
|
||||
"""
|
||||
img = cv2.imread(str(Path(__file__).parent.joinpath("night.jpg")), 0)
|
||||
average = img.mean(axis=0).mean(axis=0)
|
||||
print(average)
|
||||
p = Prediction()
|
||||
p.weight = 0.7
|
||||
|
||||
if average < 100:
|
||||
p.probability = 1.0
|
||||
p.reasons.append('Image was dark')
|
||||
|
@ -17,15 +25,43 @@ def camImgStrat(context : Context) -> Prediction:
|
|||
p.probability = 0.0
|
||||
return p
|
||||
|
||||
|
||||
def australiaStrat(context : Context) -> Prediction:
|
||||
"""
|
||||
Using time in Australia
|
||||
"""
|
||||
australia = timezone('Australia/Melbourne')
|
||||
t = datetime.now().astimezone(australia)
|
||||
hour = t.hour
|
||||
p = Prediction()
|
||||
|
||||
if hour > 22 or hour < 6:
|
||||
p.probability = 1.0
|
||||
p.reasons.append('It\'s day-time in Australia')
|
||||
else:
|
||||
p.probability = 0.0
|
||||
p.reasons.append('It\'s night-time in Australia')
|
||||
else:
|
||||
p.probability = 1.0
|
||||
p.reasons.append('It\'s day-time in Australia')
|
||||
return p
|
||||
|
||||
def tv2newsStrat(context : Context) -> Prediction:
|
||||
r = requests.get('http://mpx.services.tv2.dk/api/latest')
|
||||
data = r.json()
|
||||
publish_dates = [(x['pubDate'])//1000 for x in data][:10]
|
||||
delta_times = []
|
||||
for i in range(len(publish_dates)):
|
||||
if i == 0 : continue
|
||||
delta_times.append(publish_dates[i-1] - publish_dates[i])
|
||||
|
||||
avg_delta = 0
|
||||
for d in delta_times:
|
||||
avg_delta += d
|
||||
avg_timestamp = avg_delta // len(delta_times) // 60
|
||||
p = Prediction()
|
||||
if avg_timestamp < 0:
|
||||
p.weight = 0.0
|
||||
else:
|
||||
p.weight = 0.7
|
||||
p.probability = 1.0 if avg_timestamp > 50 else 0.0
|
||||
p.reasons.append('There were ' + ('few' if avg_timestamp > 50 else 'many') + ' recent articles on TV2 News')
|
||||
return p
|
||||
|
||||
|
|
1
server/nightr/strategies/parking_aarhus_1430.json
Normal file
1
server/nightr/strategies/parking_aarhus_1430.json
Normal file
|
@ -0,0 +1 @@
|
|||
{"help": "https://portal.opendata.dk/api/3/action/help_show?name=datastore_search", "success": true, "result": {"include_total": true, "resource_id": "2a82a145-0195-4081-a13c-b0e587e9b89c", "fields": [{"type": "int", "id": "_id"}, {"type": "text", "id": "date"}, {"type": "text", "id": "garageCode"}, {"type": "int4", "id": "totalSpaces"}, {"type": "int4", "id": "vehicleCount"}], "records_format": "objects", "records": [{"_id": 1, "date": "2019/04/06 14:30:01", "garageCode": "NORREPORT", "totalSpaces": 80, "vehicleCount": 61}, {"_id": 2, "date": "2019/04/06 14:30:01", "garageCode": "SCANDCENTER", "totalSpaces": 1240, "vehicleCount": 1033}, {"_id": 6, "date": "2019/04/06 14:30:01", "garageCode": "SALLING", "totalSpaces": 700, "vehicleCount": 575}, {"_id": 7, "date": "2019/04/06 14:30:01", "garageCode": "DOKK1", "totalSpaces": 1000, "vehicleCount": 0}, {"_id": 8, "date": "2019/04/06 14:30:01", "garageCode": "Navitas", "totalSpaces": 449, "vehicleCount": 208}, {"_id": 9, "date": "2019/04/06 14:30:01", "garageCode": "NewBusgadehuset", "totalSpaces": 105, "vehicleCount": 101}, {"_id": 3, "date": "2019/04/06 14:30:01", "garageCode": "BRUUNS", "totalSpaces": 953, "vehicleCount": 598}, {"_id": 4, "date": "2019/04/06 14:30:01", "garageCode": "MAGASIN", "totalSpaces": 378, "vehicleCount": 361}, {"_id": 5, "date": "2019/04/06 14:30:01", "garageCode": "KALKVAERKSVEJ", "totalSpaces": 210, "vehicleCount": 278}, {"_id": 10, "date": "2019/04/06 14:30:01", "garageCode": "Urban Level 1", "totalSpaces": 319, "vehicleCount": 99}, {"_id": 11, "date": "2019/04/06 14:30:01", "garageCode": "Urban Level 2+3", "totalSpaces": 654, "vehicleCount": 170}], "_links": {"start": "/api/3/action/datastore_search?resource_id=2a82a145-0195-4081-a13c-b0e587e9b89c", "next": "/api/3/action/datastore_search?offset=100&resource_id=2a82a145-0195-4081-a13c-b0e587e9b89c"}, "total": 11}}
|
|
@ -1,63 +1,7 @@
|
|||
import requests
|
||||
from bs4 import BeautifulSoup
|
||||
import pandas as pd
|
||||
import urllib.request
|
||||
from datetime import datetime, timedelta
|
||||
import json
|
||||
|
||||
def determine_month():
|
||||
ds = pd.read_excel(urllib.request.urlopen('https://sundogbaelt.dk/wp-content/uploads/2019/04/trafiktal-maaned.xls'))
|
||||
|
||||
cur_year = 2019
|
||||
amount_of_cur_year = sum([x == cur_year for x in ds['År']])
|
||||
|
||||
cur_year_total = sum(ds['Total'][1:amount_of_cur_year+1])
|
||||
last_year_total = sum(ds['Total'][amount_of_cur_year+1:amount_of_cur_year+13])
|
||||
|
||||
return (12/(last_year_total//cur_year_total))+1
|
||||
|
||||
def is_tide():
|
||||
month = determine_month()
|
||||
tide_data = requests.get('https://www.dmi.dk/fileadmin/user_upload/Bruger_upload/Tidevand/2019/Aarhus.t.txt')
|
||||
lines = tide_data.text[570:].split('\n')
|
||||
tuples = [x.split('\t') for x in lines]
|
||||
lel = [[datetime.strptime(x[0], '%Y%m%d%H%M'), x[1]] for x in tuples[:-1]]
|
||||
|
||||
matches = [[x[0], int(x[1])] for x in lel if x[0].month == month]
|
||||
|
||||
all_the_data = requests.get('https://www.dmi.dk/NinJo2DmiDk/ninjo2dmidk?cmd=odj&stations=22331&datatype=obs')
|
||||
current_water_level = json.loads(all_the_data.content)[0]['values'][-1]['value']
|
||||
|
||||
# Generate average of when the water is high
|
||||
last_match = matches[0]
|
||||
moments = []
|
||||
for idx, water_level in enumerate(matches[1:]):
|
||||
#print(last_match[1], water_level[1])
|
||||
diff = abs(last_match[1]) + abs(water_level[1])
|
||||
time_diff = (water_level[0] - last_match[0]).seconds
|
||||
|
||||
average_inc = time_diff/diff
|
||||
average_delta = timedelta(seconds=average_inc)
|
||||
|
||||
if last_match[1] < 0: # Increasing
|
||||
time = last_match
|
||||
while time[1] != current_water_level:
|
||||
time[0] += average_delta
|
||||
time[1] += 1
|
||||
|
||||
|
||||
elif last_match[1] > 0: # Decreasing
|
||||
time = last_match
|
||||
while time[1] != current_water_level:
|
||||
time[0] += average_delta
|
||||
time[1] -= 1
|
||||
|
||||
last_match = water_level
|
||||
moments.append(time[0])
|
||||
|
||||
night = sum([1 for x in moments if 6 >= x.hour or x.hour >= 22])
|
||||
|
||||
return night / len(moments)
|
||||
import requests
|
||||
from bs4 import BeautifulSoup
|
||||
|
||||
|
||||
def tmp():
|
||||
|
@ -66,32 +10,13 @@ def tmp():
|
|||
json.dump(r.json(), f)
|
||||
|
||||
|
||||
def read_tmp():
|
||||
with open('traffic_data_13_23.json') as f:
|
||||
data = json.load(f)
|
||||
number = sum([cars['vehicleCount'] for cars in data['result']['records']])
|
||||
print(number / len(data['result']['records']))
|
||||
|
||||
|
||||
def scrape_traffic():
|
||||
r = requests.get('https://portal.opendata.dk/api/3/action/datastore_search?resource_id=b3eeb0ff-c8a8-4824-99d6-e0a3747c8b0d')
|
||||
night_avr = 3.38
|
||||
day_avr = None
|
||||
|
||||
data = r.json()
|
||||
sum = 0
|
||||
len = 0
|
||||
for lel in data['result']['records']:
|
||||
sum += lel['vehicleCount']
|
||||
len += 1
|
||||
curr_avg = len / sum
|
||||
|
||||
diff= day_avr - night_avr
|
||||
|
||||
if curr_avg >= day_avr:
|
||||
return 0.0
|
||||
elif curr_avg <= night_avr:
|
||||
return 1.0
|
||||
res = 1 - curr_avg / diff
|
||||
|
||||
assert(res < 1 and res > 0)
|
||||
|
||||
return res
|
||||
|
||||
|
||||
def scrape_weather():
|
||||
|
@ -112,3 +37,5 @@ def scrape_dmi_aarhus():
|
|||
return 0.0
|
||||
#adak_latest_time, adak_latest_temp_aarhus = max(adak_timeserie.items(), key= lambda x : x[0])
|
||||
|
||||
|
||||
read_tmp()
|
|
@ -1,12 +0,0 @@
|
|||
from server.nightr.util import Context, Prediction
|
||||
|
||||
|
||||
def probability(context: Context) -> Prediction:
|
||||
"""
|
||||
How many players are currently online on Steam.
|
||||
"""
|
||||
p = Prediction()
|
||||
p.probability = 0.2
|
||||
p.reasons.append("CSGO has more than 10.000 online players")
|
||||
|
||||
return p
|
22
server/nightr/strategies/strat_utils.py
Normal file
22
server/nightr/strategies/strat_utils.py
Normal file
|
@ -0,0 +1,22 @@
|
|||
import pandas as pd
|
||||
import urllib.request
|
||||
import json
|
||||
import requests
|
||||
|
||||
def determine_month():
|
||||
ds = pd.read_excel(urllib.request.urlopen('https://sundogbaelt.dk/wp-content/uploads/2019/04/trafiktal-maaned.xls'))
|
||||
|
||||
cur_year = 2019
|
||||
amount_of_cur_year = sum([x == cur_year for x in ds['År']])
|
||||
|
||||
cur_year_total = sum(ds['Total'][1:amount_of_cur_year+1])
|
||||
last_year_total = sum(ds['Total'][amount_of_cur_year+1:amount_of_cur_year+13])
|
||||
|
||||
return ((12/(last_year_total//cur_year_total))+1), cur_year_total, last_year_total
|
||||
|
||||
|
||||
|
||||
def write_json(url, data_name, time):
|
||||
r = requests.get(url)
|
||||
with open(f"{data_name}_{time}.json", 'w') as f:
|
||||
json.dump(r.json(), f)
|
44
server/nightr/strategies/svm_strat.py
Normal file
44
server/nightr/strategies/svm_strat.py
Normal file
|
@ -0,0 +1,44 @@
|
|||
from sklearn import svm
|
||||
from sklearn.externals import joblib
|
||||
import requests
|
||||
import glob
|
||||
import json
|
||||
import numpy as np
|
||||
|
||||
|
||||
from server.nightr.strategies.strat_utils import write_json
|
||||
|
||||
|
||||
def find_data(time):
|
||||
write_json("https://portal.opendata.dk/api/3/action/datastore_search?resource_id=2a82a145-0195-4081-a13c-b0e587e9b89c", "parking_aarhus", time)
|
||||
|
||||
def load_data():
|
||||
|
||||
X = []
|
||||
Y = []
|
||||
|
||||
for filename in glob.glob("parking_aarhus*"):
|
||||
p_class = '2330' in filename
|
||||
|
||||
with open(filename) as file:
|
||||
data = json.load(file)
|
||||
|
||||
records = data['result']['records']
|
||||
frequencies = [house['vehicleCount'] / house['totalSpaces'] for house in records]
|
||||
X.append(frequencies)
|
||||
Y.append(int(p_class))
|
||||
|
||||
return np.array(X), np.array(Y)
|
||||
|
||||
def train():
|
||||
X, Y = load_data()
|
||||
classifier = svm.SVC(C=10, gamma=0.01, probability=True)
|
||||
classifier.fit(X, Y)
|
||||
joblib.dump(classifier, "nightness_classifier.pkl")
|
||||
|
||||
def predict(X):
|
||||
classifier = joblib.load("nightness_classifier.pkl")
|
||||
prob = classifier.predict_proba(X)
|
||||
return prob[0, 1]
|
||||
|
||||
train()
|
86
server/nightr/strategies/tide_strat.py
Normal file
86
server/nightr/strategies/tide_strat.py
Normal file
|
@ -0,0 +1,86 @@
|
|||
import calendar
|
||||
from datetime import datetime, timedelta
|
||||
|
||||
import json
|
||||
|
||||
import requests
|
||||
|
||||
from .strat_utils import determine_month
|
||||
from ..util import Context, Prediction
|
||||
|
||||
|
||||
def is_tide(context: Context) -> Prediction:
|
||||
"""
|
||||
Determine whether or not it is night in Aarhus based no the current water level and which month we are in, based
|
||||
on number of cars driving across The Storbæltsbro.
|
||||
"""
|
||||
|
||||
p = Prediction()
|
||||
|
||||
month, cur_year_total_cars, last_year_total_cars = determine_month()
|
||||
month = int(month)
|
||||
p.reasons.append(f"Because the month is f{calendar.month_name[month]}")
|
||||
p.reasons.append(f"Because the number of cars having driven on the Storbæltsbro is f{cur_year_total_cars}")
|
||||
p.reasons.append(f"And because the number of cars having driven over it in the last year is f{last_year_total_cars}")
|
||||
|
||||
|
||||
|
||||
tide_data = requests.get('https://www.dmi.dk/fileadmin/user_upload/Bruger_upload/Tidevand/2019/Aarhus.t.txt')
|
||||
lines = tide_data.text[570:].split('\n')
|
||||
tuples = [x.split('\t') for x in lines]
|
||||
lel = [[datetime.strptime(x[0], '%Y%m%d%H%M'), x[1]] for x in tuples[:-1]]
|
||||
|
||||
matches = [[x[0], int(x[1])] for x in lel if x[0].month == month]
|
||||
|
||||
all_the_data = requests.get('https://www.dmi.dk/NinJo2DmiDk/ninjo2dmidk?cmd=odj&stations=22331&datatype=obs')
|
||||
current_water_level = int(json.loads(all_the_data.content)[0]['values'][-1]['value'])
|
||||
|
||||
# Generate average of when the water is high
|
||||
last_match = matches[0]
|
||||
moments = []
|
||||
for idx, water_level in enumerate(matches[1:]):
|
||||
#print(last_match[1], water_level[1])
|
||||
diff = abs(last_match[1]) + abs(water_level[1])
|
||||
time_diff = (water_level[0] - last_match[0]).seconds
|
||||
|
||||
average_inc = time_diff/diff
|
||||
average_delta = timedelta(seconds=average_inc)
|
||||
|
||||
|
||||
if last_match[1] < 0 and last_match[1] < current_water_level: # Increasing
|
||||
time = last_match
|
||||
while time[1] != current_water_level:
|
||||
|
||||
time[0] += average_delta
|
||||
time[1] += 1
|
||||
|
||||
elif last_match[1] < 0 and last_match[1] > current_water_level:
|
||||
time = last_match
|
||||
while time[1] != current_water_level:
|
||||
time[0] += average_delta
|
||||
time[1] -= 1
|
||||
|
||||
elif last_match[1] > 0 and last_match[1] > current_water_level: # Decreasing
|
||||
time = last_match
|
||||
while time[1] != current_water_level:
|
||||
|
||||
time[0] += average_delta
|
||||
time[1] -= 1
|
||||
|
||||
elif last_match[1] > 0 and last_match[1] < current_water_level:
|
||||
time = last_match
|
||||
while time[1] != current_water_level:
|
||||
|
||||
time[0] += average_delta
|
||||
time[1] += 1
|
||||
|
||||
last_match = water_level
|
||||
moments.append(time[0])
|
||||
|
||||
night = sum([1 for x in moments if 6 >= x.hour or x.hour >= 22])
|
||||
|
||||
p.reasons.append(f"And because the number of times the water is at the current level at nighttime is: {night}, compared to the total amount of times in {calendar.month_name[month]}, being {len(moments)}")
|
||||
|
||||
p.probability = night / len(moments)
|
||||
|
||||
return p
|
40
server/nightr/strategies/upstairs_neighbour.py
Normal file
40
server/nightr/strategies/upstairs_neighbour.py
Normal file
|
@ -0,0 +1,40 @@
|
|||
import requests
|
||||
from bs4 import BeautifulSoup
|
||||
from datetime import datetime
|
||||
|
||||
from ..util import Prediction, Context
|
||||
|
||||
|
||||
def update():
|
||||
requests.post('https://euw.op.gg/summoner/ajax/renew.json/', data={'summonerId': 34009256})
|
||||
|
||||
|
||||
def check_games(context: Context) -> Prediction:
|
||||
"""
|
||||
Is Alexanders upstairs neighbour currently playing League of Legends?
|
||||
"""
|
||||
update()
|
||||
r = requests.get('https://euw.op.gg/summoner/userName=Im+Eating+Pros')
|
||||
|
||||
#if not "is not in an active game" in str(r.content):
|
||||
# return 1.0
|
||||
|
||||
p = Prediction()
|
||||
|
||||
soup = BeautifulSoup(r.content, features='html5lib')
|
||||
|
||||
timestamp = int(soup.find('div', {'class': 'GameItemList'}).find('div', {'class': 'GameItem'})['data-game-time'])
|
||||
last_played_game = datetime.fromtimestamp(timestamp)
|
||||
|
||||
last_game_in_hours = (((datetime.now() - last_played_game).seconds)/60/60)
|
||||
|
||||
if last_game_in_hours < 2:
|
||||
p.reasons.append("Alexanders upstairs neighbour is currently playing league")
|
||||
p.probability = 0.8
|
||||
else:
|
||||
last_game_in_hours = min(24.0, last_game_in_hours)
|
||||
|
||||
p.reasons.append(f"Alexanders upstairs neighbour has not played league for {last_game_in_hours} hours!")
|
||||
p.probability = 1 - (last_game_in_hours / 24)
|
||||
|
||||
return p
|
|
@ -1,14 +1,19 @@
|
|||
from dataclasses import dataclass, field
|
||||
from typing import List, Tuple
|
||||
from typing import List, Dict
|
||||
|
||||
|
||||
@dataclass
|
||||
class Context:
|
||||
battery: float = 1.0
|
||||
coordinates: Tuple[float, float] = (0.0, 0.0)
|
||||
position: Dict[str, float] = field(default_factory=lambda: {'latitude': 53.0, 'longitude': 9.0})
|
||||
|
||||
# App settings
|
||||
in_australia: bool = False
|
||||
flat_earth: bool = False
|
||||
|
||||
|
||||
@dataclass
|
||||
class Prediction:
|
||||
probability: float = 0.5
|
||||
weight: float = 1.0
|
||||
reasons: List[str] = field(default_factory=list)
|
||||
|
|
|
@ -1,3 +1,11 @@
|
|||
Flask==1.0.2
|
||||
requests==2.21.0
|
||||
requests-cache==0.4.13
|
||||
Flask
|
||||
requests
|
||||
requests-cache
|
||||
pytz
|
||||
beautifulsoup4
|
||||
pandas
|
||||
opencv-python
|
||||
timezonefinder
|
||||
scikit-learn
|
||||
html5lib
|
||||
xlrd
|
||||
|
|
Loading…
Reference in New Issue
Block a user