More shit

server/nightr/scrape.py

@@ -1,6 +1,71 @@
 import requests
+from bs4 import BeautifulSoup
+import pandas as pd
+import urllib.request
+from datetime import datetime, timedelta
 import json
-import os
+
+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():
+    r = requests.get('https://portal.opendata.dk/api/3/action/datastore_search?resource_id=b3eeb0ff-c8a8-4824-99d6-e0a3747c8b0d')
+    with open('traffic_data_13_23.json', 'w') as f:
+        json.dump(r.json(), f)
+
+
 
 
 def scrape_traffic():
@@ -23,10 +88,17 @@ def scrape_traffic():
     elif curr_avg <= night_avr:
         return 1.0
     res = 1 - curr_avg / diff
-    
+
     assert(res < 1 and res > 0)
 
     return res
 
 
-scrape_traffic()
+def scrape_weather():
+    r = requests.get('https://weather.com/weather/hourbyhour/l/99546:4:US')
+    soup = BeautifulSoup(r.content)
+    print(soup.find_all('td', {'class': 'temp'})[0])
+
+
+
+