86 lines
3.2 KiB
Python
86 lines
3.2 KiB
Python
import calendar
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from datetime import datetime, timedelta
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import json
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import requests
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from .strat_utils import determine_month
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from ..util import Context, Prediction
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def is_tide(context: Context) -> Prediction:
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"""
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Determine whether or not it is night in Aarhus based on the current water level and which month we are in, based
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on number of cars driving across The Storbæltsbro.
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"""
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p = Prediction()
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month, cur_year_total_cars, last_year_total_cars = determine_month()
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month = int(month)
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p.reasons.append(f"The number of cars having driven on the Storbæltsbro is {cur_year_total_cars}, in the current year")
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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:.2f}")
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p.reasons.append(f"The month is therefore {calendar.month_name[month]}")
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tide_data = requests.get('https://www.dmi.dk/fileadmin/user_upload/Bruger_upload/Tidevand/2019/Aarhus.t.txt')
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lines = tide_data.text[570:].split('\n')
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tuples = [x.split('\t') for x in lines]
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lel = [[datetime.strptime(x[0], '%Y%m%d%H%M'), x[1]] for x in tuples[:-1]]
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matches = [[x[0], int(x[1])] for x in lel if x[0].month == month]
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all_the_data = requests.get('https://www.dmi.dk/NinJo2DmiDk/ninjo2dmidk?cmd=odj&stations=22331&datatype=obs')
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current_water_level = int(json.loads(all_the_data.content)[0]['values'][-1]['value'])
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# Generate average of when the water is high
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last_match = matches[0]
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moments = []
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for idx, water_level in enumerate(matches[1:]):
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#print(last_match[1], water_level[1])
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diff = abs(last_match[1]) + abs(water_level[1])
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time_diff = (water_level[0] - last_match[0]).seconds
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average_inc = time_diff/diff
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average_delta = timedelta(seconds=average_inc)
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if last_match[1] < 0 and last_match[1] <= current_water_level: # Increasing
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time = last_match
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while time[1] != current_water_level:
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time[0] += average_delta
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time[1] += 1
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elif last_match[1] < 0 and last_match[1] >= current_water_level:
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time = last_match
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while time[1] != current_water_level:
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time[0] += average_delta
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time[1] -= 1
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elif last_match[1] > 0 and last_match[1] >= current_water_level: # Decreasing
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time = last_match
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while time[1] != current_water_level:
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time[0] += average_delta
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time[1] -= 1
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elif last_match[1] > 0 and last_match[1] <= current_water_level:
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time = last_match
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while time[1] != current_water_level:
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time[0] += average_delta
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time[1] += 1
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last_match = water_level
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moments.append(time[0])
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night = sum([1 for x in moments if 6 >= x.hour or x.hour >= 22])
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p.reasons.append(f"The water level is currently at {current_water_level} in the Aarhus Bay")
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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)}")
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p.probability = 1 - (night / len(moments))
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return p
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