Integration.

server/nightr/app.py

@@ -1,4 +1,5 @@
 import inspect
+import logging
 import statistics
 from dataclasses import asdict
 from datetime import timedelta
@@ -7,21 +8,24 @@ from typing import List
 import requests_cache
 from flask import Flask, jsonify
 
-from .strategies import dmi, steam, miloStrats
+from .strategies import miloStrats, iss, cars_in_traffic, tide_strat, upstairs_neighbour
 from .util import Context
 
+logger = logging.getLogger(__name__)
 app = Flask(__name__)
 
-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),
-    "australia": (1.0, miloStrats.australiaStrat),
-    "camera": (1.0, miloStrats.camImgStrat),
-    "tv2news": (1.0, miloStrats.tv2newsStrat)
+    "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,
 }
 
 
@@ -31,17 +35,18 @@ def probabilities():
     context = Context(**phone_data)
 
     predictions: List[dict] = []
-    for name, (weight, strategy) in strategies.items():
+    for name, strategy in strategies.items():
         try:
             prediction = strategy(context)
         except Exception as e:
-            print(f"Strategy {name} failed: {e}")
+            logger.warning("Strategy %s failed: %s", name, e)
+            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),
         })

server/nightr/strategies/dmi.py

@@ -1,12 +0,0 @@
-from ..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 +1,89 @@
+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
+
+logger = logging.getLogger(__name__)
+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/miloStrats.py

@@ -1,18 +1,18 @@
 from datetime import datetime
+from pathlib import Path
 import requests
 
 import cv2
 from pytz import timezone
 
 from ..util import Context, Prediction
-#from server.nightr.util import Context, Prediction
 
 
 def camImgStrat(context : Context) -> Prediction:
     """
     The contents of the camera image
     """
-    img = cv2.imread('night.jpg',0)
+    img = cv2.imread(str(Path(__file__).parent.joinpath("night.jpg")), 0)
     average = img.mean(axis=0).mean(axis=0)
     print(average)
     p = Prediction()

server/nightr/strategies/steam.py

@@ -1,12 +0,0 @@
-from ..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/util.py

@@ -15,4 +15,5 @@ class Context:
 @dataclass
 class Prediction:
     probability: float = 0.5
+    weight: float = 1.0
     reasons: List[str] = field(default_factory=list)

server/requirements.txt

@@ -1,7 +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