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import http.client
from datetime import date, timedelta
import pandas as pd
from io import StringIO
import os
import re
import csv
def api_call():
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(7)).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)
combined_data.to_csv(f'{particle}_{today}.csv', index=False)
today = (date.today() - timedelta(days_today)).isoformat() + "T09:00:00Z"
yesterday = (date.today() - timedelta(days_yesterday)).isoformat() + "T09:00:00Z"
def delete_csv(csvs):
for csv in csvs:
if(os.path.exists(csv) and os.path.isfile(csv)):
os.remove(csv)
def clean_values():
particles = ["NO2", "O3"]
csvs = []
NO2 = []
O3 = []
today = date.today().isoformat() + "T09:00:00Z"
yesterday = (date.today() - timedelta(1)).isoformat() + "T09:00:00Z"
latest_date = (date.today() - timedelta(7)).isoformat() + "T09:00:00Z"
days_today = 0
while(today != latest_date):
for particle in particles:
name = f'{particle}_{today}.csv'
csvs.append(name)
days_today += 1
today = (date.today() - timedelta(days_today)).isoformat() + "T09:00:00Z"
for csv_file in csvs:
values = [] # Reset values for each CSV file
# Open the CSV file and read the values
with open(csv_file, 'r') as file:
reader = csv.reader(file)
for row in reader:
for value in row:
# Use regular expressions to extract numeric part
cleaned_value = re.findall(r"[-+]?\d*\.\d+|\d+", value)
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 "NO2" in csv_file:
NO2.append(avg)
else:
O3.append(avg)
delete_csv(csvs)
return NO2, O3
def add_columns():
file_path = 'weather_data.csv'
df = pd.read_csv(file_path)
df.insert(1, 'NO2', None)
df.insert(2, 'O3', None)
df.insert(10, 'weekday', None)
df.to_csv('combined_data.csv', index=False)
def scale():
file_path = 'combined_data.csv'
df = pd.read_csv(file_path)
columns = list(df.columns)
columns.insert(3, columns.pop(6))
df = df[columns]
columns.insert(5, columns.pop(9))
df = df[columns]
columns.insert(9, columns.pop(6))
df = df[columns]
df = df.rename(columns={
'datetime':'date',
'windspeed': 'wind_speed',
'temp': 'mean_temp',
'solarradiation':'global_radiation',
'precip':'percipitation',
'sealevelpressure':'pressure',
'visibility':'minimum_visibility'
})
df['date'] = pd.to_datetime(df['date'])
df['weekday'] = df['date'].dt.day_name()
df['wind_speed'] = (df['wind_speed'] / 3.6) * 10
df['mean_temp'] = df['mean_temp'] * 10
df['minimum_visibility'] = df['minimum_visibility'] * 10
df['percipitation'] = df['percipitation'] * 10
df['pressure'] = df['pressure'] * 10
df['wind_speed'] = df['wind_speed'].astype(int)
df['mean_temp'] = df['mean_temp'].astype(int)
df['minimum_visibility'] = df['minimum_visibility'].astype(int)
df['percipitation'] = df['percipitation'].astype(int)
df['pressure'] = df['pressure'].astype(int)
df['humidity'] = df['humidity'].astype(int)
df['global_radiation'] = df['global_radiation'].astype(int)
df.to_csv('recorded_data.csv', index=False)
def insert_pollution(NO2, O3):
file_path = 'recorded_data.csv'
df = pd.read_csv(file_path)
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
df.to_csv('recorded_data.csv', index=False)
api_call()
NO2, O3 = clean_values()
add_columns()
scale()
insert_pollution(NO2, O3)
os.remove('combined_data.csv')
os.remove('weather_data.csv')
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