Merge branch 'master' of https://gitfub.space/caspervk/nightr

server/nightr/app.py

@@ -10,7 +10,7 @@ from typing import List
 import requests_cache
 from flask import Flask, jsonify, logging, request
 
-from .strategies import miloStrats, iss, cars_in_traffic, tide_strat, upstairs_neighbour, bing
+from .strategies import miloStrats, iss, cars_in_traffic, tide_strat, upstairs_neighbour, bing, battery
 from .util import Context
 
 app = Flask(__name__)
@@ -30,6 +30,7 @@ strategies = {
     "tide": tide_strat.is_tide,
     "upstairs_neighbour": upstairs_neighbour.check_games,
     "bing": bing.clock,
+    "battery_level": battery.battery_level,
 }
 
 
@@ -78,14 +79,13 @@ def probabilities():
             prediction["night"] = not prediction["night"]
 
     # Calculate contributions of predictions
-    consensus_weight_sum = sum(p["weight"] for p in predictions if p["night"] == night)
+    weight_sum = sum(p["weight"] for p in predictions)
     for prediction in predictions:
-        # If this prediction agrees with the consensus it contributed
+        prediction["contribution"] = prediction["weight"] / weight_sum
-        if prediction["night"] == night:
-            prediction["contribution"] = prediction["weight"] / consensus_weight_sum
-        else:
-            prediction["contribution"] = 0.0
 
+        # If this prediction disagrees with the consensus it contributed negatively
+        if prediction["night"] != night:
+            prediction["contribution"] *= -1
     return jsonify({
         "predictions": predictions,
         "weighted_probabilities_mean": mean,

server/nightr/strategies/battery.py

@@ -0,0 +1,21 @@
+from ..util import Context, Prediction
+
+
+def battery_level(context: Context) -> Prediction:
+    """
+    If the battery is low, it's probably bedtime soon.
+    """
+    p = Prediction()
+
+    if context.battery > 60:
+        p.reasons.append("Battery level's good, so it's probably still early in the day.")
+    elif context.battery > 30:
+        p.reasons.append("Battery level's getting low, so it's probably around dinnertime.")
+    elif context.battery > 10:
+        p.reasons.append("Your phone is dying, so it's bedtime soon?")
+    else:
+        p.reasons.append("Your phone's practically dead, so it's probably around four in the morning.")
+
+    p.probability = 1 - (context.battery / 100)  # night is inverse proportional to battery level
+
+    return p

server/nightr/strategies/bing.py

@@ -11,7 +11,7 @@ def clock(context: Context) -> Prediction:
     It's nighttime if Bing says it's daytime.
     """
     p = Prediction()
-    p.weight = 0.5
+    p.weight = 0.02
 
     headers = {
         'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/35.0.1916.47 Safari/537.36'}
@@ -22,12 +22,12 @@ def clock(context: Context) -> Prediction:
     time = datetime.strptime(time_str, "%H:%M")
     night = time.hour < 6 or time.hour >= 22
 
-    time_description = "" if night else "daytime"
+    time_description = "nighttime" if night else "daytime"
     time_description_oppersite = "daytime" if night else "nighttime"
 
     p.reasons.append(f"Bing says its {time_description}.")
-    p.reasons.append(f"We don't really trust it.")
+    p.reasons.append(f"But we don't really trust it (who does?).")
-    p.reasons.append(f"Let's guess its {time_description_oppersite}.")
+    p.reasons.append(f"Let's guess it's {time_description_oppersite}.")
 
     p.probability = 1 - p.probability
 

server/nightr/strategies/cars_in_traffic.py

@@ -27,13 +27,13 @@ def cars_in_traffic(context: Context) -> Prediction:
     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.reasons.append(f"Because {curr_avg:.1f} cars are driving around Aarhus right now and {day_avr:.1f} 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.reasons.append(f"Because {curr_avg:.1f} cars are driving around Aarhus right now and {night_avr:.1f} 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}")
+        p.reasons.append(f"Because average for daytime is {day_avr:.1f} and average for nighttime is {night_avr:.1f}, but the current average is {curr_avg:.1f}")
         res = 1 - curr_avg / diff
         p.probability = res
 

server/nightr/strategies/iss.py

@@ -1,5 +1,4 @@
 import itertools
-import logging
 from datetime import datetime
 from math import pi, sqrt, sin, cos, atan2
 
@@ -14,7 +13,7 @@ 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.
+    It is night if it is night on the ISS and it is currently orbiting above us. http://www.isstracker.com/
     """
     p = Prediction()
 

server/nightr/strategies/just_eat.py

@@ -17,7 +17,7 @@ def is_restaurant_open(name, open, close) -> Prediction:
     soup = BeautifulSoup(r.content, features='html5lib')
     listing_groups = soup.find_all('div', {'class': 'listing-group'})
 
-    p.reasons.append("Hopefully we are not banned from Just-eat ..")
+    #p.reasons.append("Hopefully we are not banned from Just-eat ..")
 
     nice_group = None
     for x in listing_groups:
@@ -32,10 +32,12 @@ def is_restaurant_open(name, open, close) -> Prediction:
     all_listings = nice_group.find_all('a', {'class': 'mediaElement'})
 
     if any(name in x['href'] for x in all_listings):
-        p.reasons.append(f"{name} is currently open. We conclude from this, that there is {1 / 11}% chance of it being night outside!")
+        p.reasons.append(f"Our favorite pizza place, {name}, is currently open.")
+        p.reasons.append(f"We conclude from this, that there is {1 / 11}% chance of it being night outside")
         p.probability = 1 / 11
     else:
-        p.reasons.append(f"{name} is not open. We can conclude from this, that there is {1 - (1/11)}% chance of it currently being night outside! ")
+        p.reasons.append(f"Our favorite pizza place, {name}, is closed.")
+        p.reasons.append(f"We can conclude from this, that there is {1 - (1/11)}% chance of it currently being night outside!")
         p.probability = 1 - (1 / 11)
 
     return p

server/nightr/strategies/miloStrats.py

@@ -11,15 +11,18 @@ def camImgStrat(context : Context) -> Prediction:
     The contents of the camera image
     """
     img = context.image
-    average = img.mean()
+    average = float(img.mean())
     p = Prediction()
-    p.weight = 0.7
+    p.weight = 1.0
-    if average < 100:
+
-        p.probability = 1.0
+    p.probability = 1 - round((average/255),3)
-        p.reasons.append('Image was dark')
+    if average < 128:
+        p.weight = round(1 - (average/255), 3)
+        p.reasons.append('Camera image was dark, so the sun has probably set.')
+
     else:
-        p.reasons.append('Image was light')
+        p.weight = round(average / 255, 3)
-        p.probability = 0.0
+        p.reasons.append('Camera image was light, so the sun is still shining.')
     return p
 
 
@@ -34,10 +37,10 @@ def australiaStrat(context : Context) -> Prediction:
 
     if hour > 22 or hour < 6:
         p.probability = 0.0
-        p.reasons.append('It\'s night-time in Australia')
+        p.reasons.append('It\'s night-time in Australia, so it must be day-time here.')
     else:
         p.probability = 1.0
-        p.reasons.append('It\'s day-time in Australia')
+        p.reasons.append('It\'s day-time in Australia, so it must be night-time here.')
     return p
 
 
@@ -46,6 +49,7 @@ def tv2newsStrat(context : Context) -> Prediction:
     The number of articles releases in the last few hours on TV2.dk
     """
     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 = []

server/nightr/strategies/svm_strat.py

@@ -6,10 +6,11 @@ import json
 import numpy as np
 
 
-from server.nightr.strategies.strat_utils import write_json
+from .strat_utils import write_json
+from ..util import Context, Prediction
 
 
-def find_data(time):
+def write_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():
@@ -18,7 +19,7 @@ def load_data():
     Y = []
 
     for filename in glob.glob("parking_aarhus*"):
-        p_class = '2330' in filename
+        p_class = '2235' in filename
 
         with open(filename) as file:
             data = json.load(file)
@@ -32,13 +33,26 @@ def load_data():
 
 def train():
     X, Y = load_data()
-    classifier = svm.SVC(C=10, gamma=0.01, probability=True)
+    classifier = svm.SVC(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)
+    prob = classifier.predict_proba(np.array(X).reshape(1, -1))
     return prob[0, 1]
 
-train()
+
+def perform_svm_pred(context: Context) -> Prediction:
+    p = Prediction()
+    data = requests.get('https://portal.opendata.dk/api/3/action/datastore_search?resource_id=2a82a145-0195-4081-a13c-b0e587e9b89c')
+
+    records = data.json()['result']['records']
+    X = [house['vehicleCount'] / house['totalSpaces'] for house in records]
+    X = [min(x, 1) for x in X]
+    p.reasons.append("We only have two data points")
+    p.reasons.append("Our only two data points have 11 dimensions")
+    p.reasons.append("We are using a SVM")
+
+    p.probability = predict(X)
+    return p

server/nightr/strategies/tide_strat.py

@@ -19,10 +19,9 @@ def is_tide(context: Context) -> 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"The month is {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"The number of cars having driven on the Storbæltsbro is {cur_year_total_cars}, in the current year")
-    p.reasons.append(f"And because the number of cars having driven over it in the last year is f{last_year_total_cars}")
+    p.reasons.append(f"The number of cars having driven over it in the last year is {last_year_total_cars}, thus the frequency is: {last_year_total_cars / cur_year_total_cars}")
-
 
 
     tide_data = requests.get('https://www.dmi.dk/fileadmin/user_upload/Bruger_upload/Tidevand/2019/Aarhus.t.txt')
@@ -47,27 +46,27 @@ def is_tide(context: Context) -> Prediction:
         average_delta = timedelta(seconds=average_inc)
 
 
-        if last_match[1] < 0 and last_match[1] < current_water_level: # Increasing
+        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:
+        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
+        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:
+        elif last_match[1] > 0 and last_match[1] <= current_water_level:
             time = last_match
             while time[1] != current_water_level:
 
@@ -78,9 +77,9 @@ def is_tide(context: Context) -> Prediction:
         moments.append(time[0])
 
     night = sum([1 for x in moments if 6 >= x.hour or x.hour >= 22])
+    p.reasons.append(f"The water level is currently at {current_water_level}")
+    p.reasons.append(f"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.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 = 1 - (night / len(moments))
-
-    p.probability = night / len(moments)
 
     return p

server/nightr/strategies/upstairs_neighbour.py

@@ -1,12 +1,17 @@
 import requests
 from bs4 import BeautifulSoup
-from datetime import datetime
+from datetime import datetime, timedelta
 
 from ..util import Prediction, Context
+last_update = datetime.min
 
 
 def update():
+    global last_update
+    now = datetime.utcnow()
+    if (now - timedelta(minutes=5)) > last_update:
         requests.post('https://euw.op.gg/summoner/ajax/renew.json/', data={'summonerId': 34009256})
+        last_update = now
 
 
 def check_games(context: Context) -> Prediction:
@@ -29,12 +34,12 @@ def check_games(context: Context) -> Prediction:
     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.reasons.append("Alexander's 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.reasons.append(f"Alexanders upstairs neighbour has not played league for {last_game_in_hours:.2f} hours!")
         p.probability = 1 - (last_game_in_hours / 24)
 
     return p

server/nightr/util.py

@@ -9,8 +9,8 @@ import numpy as np
 
 @dataclass
 class Context:
-    battery: int = 100
+    battery: int = 55
-    position: Dict[str, float] = field(default_factory=lambda: {'latitude': 53.0, 'longitude': 9.0})
+    position: Dict[str, float] = field(default_factory=lambda: {'latitude': 53.0, 'longitude': 9.0})  # Denmark somewhere
     image: np.ndarray = None
 
     # App settings