More strats

server/nightr/strategies/cars_in_traffic.py

@@ -0,0 +1,34 @@
+import requests
+
+from server.nightr.util import Prediction, Context
+
+
+def scrape_traffic(context: Context) -> Prediction:
+    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
+    curr_avg = len / sum
+
+    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/scrape.py

@@ -4,60 +4,10 @@ import pandas as pd
 import urllib.request
 from datetime import datetime, timedelta
 import json
-
+from server.nightr.strategies.strat_utils import determine_month
-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)
 
 
 def tmp():
@@ -66,32 +16,13 @@ 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 +43,5 @@ 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/strat_utils.py

@@ -0,0 +1,14 @@
+import pandas as pd
+import urllib.request
+
+
+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

server/nightr/strategies/tide_strat.py

@@ -0,0 +1,69 @@
+import datetime
+import json
+import calendar
+import requests
+
+from server.nightr.strategies.strat_utils import determine_month
+from server.nightr.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()
+
+    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 = 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 = datetime.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])
+
+    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