Merged app

.gitignore

@@ -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

client/Nightr/package-lock.json

@@ -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",

client/Nightr/package.json

@@ -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",

client/Nightr/src/app/app.component.html

@@ -5,5 +5,7 @@
 <ns-my-button (tap)=onTap($event) text="Nightr"></ns-my-button>
 </StackLayout>
 
+<ns-locationButton></ns-locationButton>
+
 </AbsoluteLayout>
 

client/Nightr/src/app/app.component.ts

@@ -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",

client/Nightr/src/app/app.module.ts

@@ -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: [],

client/Nightr/src/app/component/locationButton/locationButton.component.css

@@ -0,0 +1 @@
+/* Add mobile styles for the component here.  */

client/Nightr/src/app/component/locationButton/locationButton.component.html

@@ -0,0 +1,4 @@
+<StackLayout>
+<Button text="{{title}}" class="btn btn-primary" (tap)="onTap()"></Button>
+<Label text="{{lat}}"></Label>
+</StackLayout>

client/Nightr/src/app/component/locationButton/locationButton.component.ts

@@ -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 => {
+    });
+  }
+}

client/Nightr/src/app/services/my-geo-location.service.ts

@@ -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));
+    });
+
+  }
+}

server/activate.sh

@@ -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
-    echo Installing required Python packages
+pip install -Ur requirements.txt
-    pip install -Ur requirements.txt
-fi
 
 function run() {
     python -m nightr

server/nightr/app.py

@@ -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 .strategies import miloStrats, iss, cars_in_traffic, tide_strat, upstairs_neighbour
-from server.nightr.util import Context
+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),
+    "tv2news": miloStrats.tv2newsStrat,
-    "steam": (1.0, steam.probability),
+    "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,
+            "weight": prediction.weight,
-            "weighted_probability": prediction.probability * 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__':

server/nightr/steamplayerhistory.py

@@ -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()

server/nightr/strategies/cars_in_traffic.py

@@ -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

server/nightr/strategies/dmi.py

@@ -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

server/nightr/strategies/iss.py

@@ -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

server/nightr/strategies/just_eat.py

@@ -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")

server/nightr/strategies/miloStrats.py

@@ -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
+

server/nightr/strategies/parking_aarhus_1430.json

@@ -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}}

server/nightr/strategies/scrape.py

@@ -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():
+import requests
-    ds = pd.read_excel(urllib.request.urlopen('https://sundogbaelt.dk/wp-content/uploads/2019/04/trafiktal-maaned.xls'))
+from bs4 import BeautifulSoup
-
-    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)
 
 
 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()

server/nightr/strategies/steam.py

@@ -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

server/nightr/strategies/strat_utils.py

@@ -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)

server/nightr/strategies/svm_strat.py

@@ -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()

server/nightr/strategies/tide_strat.py

@@ -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

server/nightr/strategies/upstairs_neighbour.py

@@ -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

server/nightr/util.py

@@ -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)

server/requirements.txt

@@ -1,3 +1,11 @@
-Flask==1.0.2
+Flask
-requests==2.21.0
+requests
-requests-cache==0.4.13
+requests-cache
+pytz
+beautifulsoup4
+pandas
+opencv-python
+timezonefinder
+scikit-learn
+html5lib
+xlrd