From 140a9eb574c099f9eef8b73c63396818dd5f3a29 Mon Sep 17 00:00:00 2001 From: Alexander Munch-Hansen Date: Sat, 6 Apr 2019 13:38:39 +0200 Subject: [PATCH] More shit --- server/nightr/scrape.py | 78 +++++++++++++++++++++++++-- server/nightr/traffic_data_13_23.json | 1 + 2 files changed, 76 insertions(+), 3 deletions(-) create mode 100644 server/nightr/traffic_data_13_23.json diff --git a/server/nightr/scrape.py b/server/nightr/scrape.py index 559bcce..092c613 100644 --- a/server/nightr/scrape.py +++ b/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() \ No newline at end of file +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]) + + + + diff --git a/server/nightr/traffic_data_13_23.json b/server/nightr/traffic_data_13_23.json new file mode 100644 index 0000000..341e118 --- /dev/null +++ b/server/nightr/traffic_data_13_23.json @@ -0,0 +1 @@ +{"help": "https://portal.opendata.dk/api/3/action/help_show?name=datastore_search", "success": true, "result": {"include_total": true, "resource_id": "b3eeb0ff-c8a8-4824-99d6-e0a3747c8b0d", "fields": [{"type": "int", "id": "_id"}, {"type": "int4", "id": "REPORT_ID"}, {"type": "timestamp", "id": "TIMESTAMP"}, {"type": "text", "id": "status"}, {"type": "int4", "id": "avgMeasuredTime"}, {"type": "int4", "id": "medianMeasuredTime"}, {"type": "int4", "id": "vehicleCount"}, {"type": "int4", "id": "avgSpeed"}], "records_format": "objects", "records": [{"_id": 300, "REPORT_ID": 187695, "TIMESTAMP": 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