Integration.
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parent
c3be26fa51
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import inspect
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import inspect
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import logging
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import statistics
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import statistics
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from dataclasses import asdict
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from dataclasses import asdict
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from datetime import timedelta
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from datetime import timedelta
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@ -7,21 +8,24 @@ from typing import List
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import requests_cache
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import requests_cache
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from flask import Flask, jsonify
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from flask import Flask, jsonify
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from .strategies import dmi, steam, miloStrats
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from .strategies import miloStrats, iss, cars_in_traffic, tide_strat, upstairs_neighbour
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from .util import Context
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from .util import Context
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logger = logging.getLogger(__name__)
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app = Flask(__name__)
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app = Flask(__name__)
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requests_cache.install_cache("requests_cache.sqlite", expire_after=timedelta(minutes=10))
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requests_cache.install_cache("requests_cache", expire_after=timedelta(minutes=10))
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strategies = {
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strategies = {
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# name: (weight, probability function)
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# name: (weight, probability function)
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"dmi": (0.5, dmi.probability),
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"tv2news": miloStrats.tv2newsStrat,
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"steam": (1.0, steam.probability),
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"australia": miloStrats.australiaStrat,
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"australia": (1.0, miloStrats.australiaStrat),
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"camera": miloStrats.camImgStrat,
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"camera": (1.0, miloStrats.camImgStrat),
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"iss": iss.night_on_iss,
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"tv2news": (1.0, miloStrats.tv2newsStrat)
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"cars_in_traffic": cars_in_traffic.cars_in_traffic,
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"tide": tide_strat.is_tide,
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"upstairs_neighbour": upstairs_neighbour.check_games,
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}
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}
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@ -31,17 +35,18 @@ def probabilities():
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context = Context(**phone_data)
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context = Context(**phone_data)
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predictions: List[dict] = []
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predictions: List[dict] = []
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for name, (weight, strategy) in strategies.items():
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for name, strategy in strategies.items():
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try:
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try:
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prediction = strategy(context)
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prediction = strategy(context)
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except Exception as e:
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except Exception as e:
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print(f"Strategy {name} failed: {e}")
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logger.warning("Strategy %s failed: %s", name, e)
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logger.exception(e)
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continue
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continue
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predictions.append({
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predictions.append({
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"name": name,
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"name": name,
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"description": inspect.getdoc(strategy),
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"description": inspect.getdoc(strategy),
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"weight": weight,
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"weight": prediction.weight,
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"weighted_probability": prediction.probability * weight,
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"weighted_probability": prediction.probability * prediction.weight,
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"night": prediction.probability > 0.5,
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"night": prediction.probability > 0.5,
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**asdict(prediction),
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**asdict(prediction),
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})
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})
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from ..util import Context, Prediction
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def probability(context: Context) -> Prediction:
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"""
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The data from DMI.
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"""
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p = Prediction()
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p.probability = 0.7
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p.reasons.append("It is raining in Tønder")
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return p
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89
server/nightr/strategies/iss.py
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server/nightr/strategies/iss.py
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import itertools
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import logging
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from datetime import datetime
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from math import pi, sqrt, sin, cos, atan2
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import pytz
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import requests
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from timezonefinder import TimezoneFinder
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from ..util import Context, Prediction
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logger = logging.getLogger(__name__)
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tf = TimezoneFinder(in_memory=True)
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def night_on_iss(context: Context) -> Prediction:
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"""
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It is night if it is night on the ISS and it is currently orbiting above us.
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"""
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p = Prediction()
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if not context.flat_earth:
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iss_position = requests.get("http://api.open-notify.org/iss-now.json").json()["iss_position"]
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the_iss = "The ISS"
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iss_position_description = "on board the ISS"
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else:
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p.reasons.append("The ISS is (obviously) located in Hollywood")
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the_iss = "Hollywood"
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iss_position = {'latitude': 34.092808, 'longitude': -118.328659} # Hollywood
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iss_position_description = "in the Hollywood studio"
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phone_position = context.position
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# Calculate ratio: a number between 0 and 1 saying how close we are to the ISS
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distance = haversine(iss_position, phone_position)
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max_distance = 40075 / 2 # the furthest you can be from any position is half of the earth's circumference
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ratio = distance / max_distance
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# We're in the same "timezone" as the ISS if we're on the same half of the earth
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on_iss_time = ratio < 0.5
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side = "same" if on_iss_time else "other"
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p.reasons.append(f"{the_iss} is {int(distance)} km away, so we are on the {side} side of the earth.")
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for i in itertools.count(1):
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iss_tz = tf.closest_timezone_at(lng=float(iss_position["longitude"]),
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lat=float(iss_position["latitude"]),
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delta_degree=i)
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if iss_tz is not None:
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break
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iss_time = datetime.now(pytz.timezone(iss_tz))
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iss_night = 6 < iss_time.hour > 22
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# iss_night on_iss_time night
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# 0 0 1
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# 0 1 0
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# 1 0 0
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# 1 1 1
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night = iss_night == on_iss_time
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iss_time_description = "nighttime" if iss_night else "daytime"
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time_description = "nighttime" if night else "daytime"
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p.probability = float(night)
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p.reasons.append(f"It is {iss_time_description} {iss_position_description}.")
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p.reasons.append(f"Therefore, it must be {time_description} where we are.")
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return p
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def haversine(pos1, pos2):
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"""
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Distance between two GPS coordinates.
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https://stackoverflow.com/a/18144531
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"""
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lat1 = float(pos1["latitude"])
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long1 = float(pos1["longitude"])
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lat2 = float(pos2["latitude"])
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long2 = float(pos2["longitude"])
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degree_to_rad = float(pi / 180.0)
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d_lat = (lat2 - lat1) * degree_to_rad
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d_long = (long2 - long1) * degree_to_rad
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a = pow(sin(d_lat / 2), 2) + cos(lat1 * degree_to_rad) * cos(lat2 * degree_to_rad) * pow(sin(d_long / 2), 2)
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c = 2 * atan2(sqrt(a), sqrt(1 - a))
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km = 6367 * c
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return km
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from datetime import datetime
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from datetime import datetime
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from pathlib import Path
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import requests
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import requests
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import cv2
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import cv2
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from pytz import timezone
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from pytz import timezone
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from ..util import Context, Prediction
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from ..util import Context, Prediction
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#from server.nightr.util import Context, Prediction
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def camImgStrat(context : Context) -> Prediction:
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def camImgStrat(context : Context) -> Prediction:
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"""
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"""
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The contents of the camera image
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The contents of the camera image
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"""
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"""
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img = cv2.imread('night.jpg',0)
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img = cv2.imread(str(Path(__file__).parent.joinpath("night.jpg")), 0)
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average = img.mean(axis=0).mean(axis=0)
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average = img.mean(axis=0).mean(axis=0)
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print(average)
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print(average)
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p = Prediction()
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p = Prediction()
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from ..util import Context, Prediction
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def probability(context: Context) -> Prediction:
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"""
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How many players are currently online on Steam.
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"""
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p = Prediction()
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p.probability = 0.2
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p.reasons.append("CSGO has more than 10.000 online players")
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return p
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@ -15,4 +15,5 @@ class Context:
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@dataclass
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@dataclass
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class Prediction:
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class Prediction:
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probability: float = 0.5
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probability: float = 0.5
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weight: float = 1.0
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reasons: List[str] = field(default_factory=list)
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reasons: List[str] = field(default_factory=list)
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Flask==1.0.2
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Flask
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requests==2.21.0
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requests
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requests-cache==0.4.13
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requests-cache
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pytz
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pytz
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beautifulsoup4
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beautifulsoup4
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pandas
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pandas
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opencv-python
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opencv-python
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timezonefinder
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scikit-learn
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html5lib
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xlrd
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