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import codecs
import csv
import http.client
import os
import re
import sys
import urllib.request
from datetime import date, timedelta
from io import StringIO
import pandas as pd
WEATHER_DATA_FILE = "weather_data.csv"
POLLUTION_DATA_FILE = "pollution_data.csv"
def update_weather_data():
today = date.today().isoformat()
if os.path.exists(WEATHER_DATA_FILE):
df = pd.read_csv(WEATHER_DATA_FILE)
last_date = pd.to_datetime(df["date"]).max()
start_date = (last_date + timedelta(1)).isoformat()
else:
df = pd.DataFrame()
start_date = (date.today() - timedelta(7)).isoformat()
try:
ResultBytes = urllib.request.urlopen(
f"https://weather.visualcrossing.com/VisualCrossingWebServices/rest/services/timeline/Utrecht/{start_date}/{today}?unitGroup=metric&elements=datetime%2Cwindspeed%2Ctemp%2Csolarradiation%2Cprecip%2Cpressure%2Cvisibility%2Chumidity&include=days&key=7Y6AY56M6RWVNHQ3SAVHNJWFS&maxStations=1&contentType=csv"
)
CSVText = csv.reader(codecs.iterdecode(ResultBytes, "utf-8"))
new_data = pd.DataFrame(list(CSVText))
new_data.columns = new_data.iloc[0]
new_data = new_data[1:]
new_data = new_data.rename(columns={"datetime": "date"})
updated_df = pd.concat([df, new_data], ignore_index=True)
updated_df.drop_duplicates(subset="date", keep="last", inplace=True)
updated_df.to_csv(WEATHER_DATA_FILE, index=False)
except urllib.error.HTTPError as e:
ErrorInfo = e.read().decode()
print("Error code: ", e.code, ErrorInfo)
sys.exit()
except urllib.error.URLError as e:
ErrorInfo = e.read().decode()
print("Error code: ", e.code, ErrorInfo)
sys.exit()
def update_pollution_data():
O3 = []
NO2 = []
particles = ["NO2", "O3"]
stations = ["NL10636", "NL10639", "NL10643"]
all_dataframes = []
today = date.today().isoformat() + "T09:00:00Z"
yesterday = (date.today() - timedelta(1)).isoformat() + "T09:00:00Z"
latest_date = (date.today() - timedelta(8)).isoformat() + "T09:00:00Z"
days_today = 0
days_yesterday = 1
while today != latest_date:
days_today += 1
days_yesterday += 1
for particle in particles:
for station in stations:
conn = http.client.HTTPSConnection("api.luchtmeetnet.nl")
payload = ""
headers = {}
conn.request(
"GET",
f"/open_api/measurements?station_number={station}&formula={particle}&page=1&order_by=timestamp_measured&order_direction=desc&end={today}&start={yesterday}",
payload,
headers,
)
res = conn.getresponse()
data = res.read()
decoded_data = data.decode("utf-8")
df = pd.read_csv(StringIO(decoded_data))
df = df.filter(like="value")
all_dataframes.append(df)
combined_data = pd.concat(all_dataframes, ignore_index=True)
values = []
for row in combined_data:
cleaned_value = re.findall(r"[-+]?\d*\.\d+|\d+", row)
if cleaned_value: # If we successfully extract a number
values.append(
float(cleaned_value[0])
) # Convert the first match to float
# Compute the average if the values list is not empty
if values:
avg = sum(values) / len(values)
if particle == "NO2":
NO2.append(avg)
else:
O3.append(avg)
today = (date.today() - timedelta(days_today)).isoformat() + "T09:00:00Z"
yesterday = (
date.today() - timedelta(days_yesterday)
).isoformat() + "T09:00:00Z"
avg_combined_data = pd.DataFrame(
{
"date": pd.date_range(end=date.today(), periods=len(NO2)),
"NO2": NO2,
"O3": O3,
}
)
avg_combined_data = reverse_pollution(NO2, O3, avg_combined_data)
if os.path.exists(POLLUTION_DATA_FILE):
existing_data = pd.read_csv(POLLUTION_DATA_FILE)
last_date = pd.to_datetime(existing_data["date"]).max()
new_data = avg_combined_data[avg_combined_data["date"] > last_date]
updated_data = pd.concat([existing_data, new_data], ignore_index=True)
updated_data.drop_duplicates(subset="date", keep="last", inplace=True)
else:
updated_data = avg_combined_data
updated_data.to_csv(POLLUTION_DATA_FILE, index=False)
def reverse_pollution(NO2, O3, data):
df = data
start_index = 0
while NO2:
df.loc[start_index, "NO2"] = NO2.pop()
start_index += 1
start_index = 0
while O3:
df.loc[start_index, "O3"] = O3.pop()
start_index += 1
return df
def get_combined_data():
update_weather_data()
update_pollution_data()
weather_df = pd.read_csv(WEATHER_DATA_FILE)
weather_df.insert(1, "NO2", None)
weather_df.insert(2, "O3", None)
weather_df.insert(10, "weekday", None)
columns = list(weather_df.columns)
columns.insert(3, columns.pop(6))
weather_df = weather_df[columns]
columns.insert(5, columns.pop(9))
weather_df = weather_df[columns]
columns.insert(9, columns.pop(6))
weather_df = weather_df[columns]
combined_df = weather_df
# Apply scaling and renaming similar to the scale function from previous code
combined_df = combined_df.rename(
columns={
"date": "date",
"windspeed": "wind_speed",
"temp": "mean_temp",
"solarradiation": "global_radiation",
"precip": "percipitation",
"sealevelpressure": "pressure",
"visibility": "minimum_visibility",
}
)
combined_df["date"] = pd.to_datetime(combined_df["date"])
combined_df["weekday"] = combined_df["date"].dt.day_name()
combined_df["wind_speed"] = (combined_df["wind_speed"] / 3.6) * 10
combined_df["mean_temp"] = combined_df["mean_temp"] * 10
combined_df["minimum_visibility"] = combined_df["minimum_visibility"] * 10
combined_df["percipitation"] = combined_df["percipitation"] * 10
combined_df["pressure"] = combined_df["pressure"] * 10
combined_df["wind_speed"] = combined_df["wind_speed"].astype(int)
combined_df["mean_temp"] = combined_df["mean_temp"].astype(int)
combined_df["minimum_visibility"] = combined_df["minimum_visibility"].astype(int)
combined_df["percipitation"] = combined_df["percipitation"].astype(int)
combined_df["pressure"] = combined_df["pressure"].astype(int)
combined_df["humidity"] = combined_df["humidity"].astype(int)
combined_df["global_radiation"] = combined_df["global_radiation"].astype(int)
pollution_df = pd.read_csv(POLLUTION_DATA_FILE)
combined_df["NO2"] = pollution_df["NO2"]
combined_df["O3"] = pollution_df["O3"]
return combined_df
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