meteo_functions.py

import pandas as pd
import requests
import numpy as np
import streamlit as st
import json
from datetime import date, timedelta

lat = 49.13114
lon = 15.18067

def get_meteo_data(start_date, end_date):
    url = f"https://archive-api.open-meteo.com/v1/archive?latitude={lat}&longitude={lon}&start_date={start_date}&end_date={end_date}&hourly=temperature_2m,relative_humidity_2m,surface_pressure,cloudcover,windspeed_10m,wind_direction_10m,direct_normal_irradiance,diffuse_radiation,shortwave_radiation&timezone=UTC"

    response = requests.get(url)
    if response.status_code == 200:
        weather_data = response.json()

        if "hourly" in weather_data:
            df_weather = pd.DataFrame({
                "DT": pd.to_datetime(weather_data["hourly"]["time"]),
                "Temperature_2m": weather_data["hourly"].get("temperature_2m", []),
                "Relative_Humidity_2m": weather_data["hourly"].get("relative_humidity_2m", []),
                "Surface_Pressure": weather_data["hourly"].get("surface_pressure", []),
                "Cloud_Cover": weather_data["hourly"].get("cloudcover", []),
                "Wind_Speed_10m": weather_data["hourly"].get("windspeed_10m", []),
                "Wind_Direction_10m": weather_data["hourly"].get("wind_direction_10m", []),
                "RAD": weather_data["hourly"].get("shortwave_radiation", [])
            })

            df_weather["DT"] = df_weather["DT"].dt.tz_localize(None)

            df_weather["wind_u"] = df_weather["Wind_Speed_10m"] * np.sin(np.radians(df_weather["Wind_Direction_10m"]))
            df_weather["wind_v"] = df_weather["Wind_Speed_10m"] * np.cos(np.radians(df_weather["Wind_Direction_10m"]))

            df_weather.drop(columns=["Wind_Speed_10m", "Wind_Direction_10m"], inplace=True)
        else:
            print("Chyba: Odpověď neobsahuje klíč 'hourly'.")

    else:
        print(f"Chyba při stahování dat: {response.status_code}, odpověď: {response.text}")

    return df_weather

def get_air_quality_data(start_date, end_date):

    url = f"https://air-quality-api.open-meteo.com/v1/air-quality?latitude={lat}&longitude={lon}&start_date={start_date}&end_date={end_date}&hourly=pm10,ozone&timezone=UTC"

    response = requests.get(url)
    if response.status_code == 200:
        air_quality_data = response.json()

        if "hourly" in air_quality_data:
            df_air_quality = pd.DataFrame({
                "DT": pd.to_datetime(air_quality_data["hourly"]["time"]),
                "PM10": air_quality_data["hourly"].get("pm10", []),
                "Ozone": air_quality_data["hourly"].get("ozone", [])
            })
            df_air_quality["DT"] = df_air_quality["DT"].dt.tz_localize(None)
        else:
            print("Chyba: Odpověď neobsahuje klíč 'hourly'.")
    else:
        print(f"Chyba při stahování dat: {response.status_code}, odpověď: {response.text}")
    
    return df_air_quality

def create_time_cycles(weather_dataset):
    weather_dataset["hour"] = weather_dataset["DT"].dt.hour
    weather_dataset["day_of_year"] = weather_dataset["DT"].dt.dayofyear
    weather_dataset["sin_hour"] = np.sin(2 * np.pi * weather_dataset["hour"] / 24)
    weather_dataset["cos_hour"] = np.cos(2 * np.pi * weather_dataset["hour"] / 24)
    weather_dataset["sin_day_of_year"] = np.sin(2 * np.pi * weather_dataset["day_of_year"] / 365)
    weather_dataset["cos_day_of_year"] = np.cos(2 * np.pi * weather_dataset["day_of_year"] / 365)
    weather_dataset.drop(columns=["hour", "day_of_year"], inplace=True)
    return weather_dataset

def get_forecast_meteo_data(start_date, end_date):
    url = f"https://api.open-meteo.com/v1/forecast?latitude={lat}&longitude={lon}&start_date={start_date}&end_date={end_date}&hourly=temperature_2m,relative_humidity_2m,surface_pressure,cloudcover,windspeed_10m,wind_direction_10m,direct_normal_irradiance,diffuse_radiation,shortwave_radiation&timezone=UTC"

    # Odeslání požadavku
    response = requests.get(url)
    if response.status_code == 200:
        weather_data = response.json()

        if "hourly" in weather_data:
            df_weather = pd.DataFrame({
                "DT": pd.to_datetime(weather_data["hourly"]["time"]),
                "Temperature_2m": weather_data["hourly"].get("temperature_2m", []),
                "Relative_Humidity_2m": weather_data["hourly"].get("relative_humidity_2m", []),
                "Surface_Pressure": weather_data["hourly"].get("surface_pressure", []),
                "Cloud_Cover": weather_data["hourly"].get("cloudcover", []),
                "Wind_Speed_10m": weather_data["hourly"].get("windspeed_10m", []),
                "Wind_Direction_10m": weather_data["hourly"].get("wind_direction_10m", []),
                "RAD": weather_data["hourly"].get("shortwave_radiation", [])
            })

            df_weather["DT"] = df_weather["DT"].dt.tz_localize(None)

            df_weather["wind_u"] = df_weather["Wind_Speed_10m"] * np.sin(np.radians(df_weather["Wind_Direction_10m"]))
            df_weather["wind_v"] = df_weather["Wind_Speed_10m"] * np.cos(np.radians(df_weather["Wind_Direction_10m"]))

            df_weather.drop(columns=["Wind_Speed_10m", "Wind_Direction_10m"], inplace=True)
        else:
            print("Chyba: Odpověď neobsahuje klíč 'hourly'.")
    else:
        print(f"Chyba při stahování dat: {response.status_code}, odpověď: {response.text}")

    return df_weather

def get_air_quality_forecast(start_date, end_date):
    url = f"https://air-quality-api.open-meteo.com/v1/air-quality?latitude={lat}&longitude={lon}&start_date={start_date}&end_date={end_date}&hourly=pm10,ozone&timezone=UTC"

    response = requests.get(url)
    if response.status_code == 200:
        air_quality_data = response.json()

        if "hourly" in air_quality_data:
            times = air_quality_data["hourly"].get("time", [])
            pm10 = air_quality_data["hourly"].get("pm10", [])
            ozone = air_quality_data["hourly"].get("ozone", [])

            if len(times) == len(pm10) == len(ozone):
                df_air_quality = pd.DataFrame({
                    "DT": pd.to_datetime(times),
                    "PM10": pm10,
                    "Ozone": ozone
                })
                df_air_quality["DT"] = df_air_quality["DT"].dt.tz_localize(None)  
            else:
                print("Chyba: Pola mají různé délky!")
                print(f"Počet časových údajů: {len(times)}, Počet PM10: {len(pm10)}, Počet Ozone: {len(ozone)}")
                df_air_quality = pd.DataFrame() 
        else:
            print("Chyba: Odpověď neobsahuje klíč 'hourly'.")
            df_air_quality = pd.DataFrame()  
    else:
        print(f"Chyba při stahování dat: {response.status_code}, odpověď: {response.text}")
        df_air_quality = pd.DataFrame()  
    
    return df_air_quality

def get_data_for_day(day: date, username: str, password: str) -> list[dict]:
    """
    Stáhne data z API pomocí POST požadavku pro daný den.

    :param day: Datum typu `datetime.date`
    :param username: Uživatelské jméno pro API
    :param password: Heslo pro API
    :return: Seznam měření (dictů) pro daný den
    """
    url = f"https://aba.solarmon.eu/rest-server/?q=getDataPredMod&date={day.strftime('%Y-%m-%d')}"
    payload = {
        'username': username,
        'password': password
    }

    try:
        response = requests.post(url, data=payload)
        response.raise_for_status()
        data = response.json()
        data = json.loads(data)
        return data.get("data", [])
    except (requests.RequestException, ValueError) as e:
        st.error(f"Chyba při načítání dat pro {day}: {e}")
        return []

def load_and_check_data_solarmon(start_date: date, end_date: date, username: str, password: str) -> pd.DataFrame:
    current_date = start_date
    all_data = []

    while current_date <= end_date + timedelta(days=1):# pridame dalsi den kvuli poslednim 2 hodinam
        daily_data = pd.DataFrame(get_data_for_day(current_date, username, password))
        all_data.append(daily_data)
        current_date += timedelta(days=1)

    solarmon_meteo = pd.concat(all_data).sort_index()  

    solarmon_meteo = pd.DataFrame(solarmon_meteo)
    solarmon_meteo["time"] = pd.to_datetime(solarmon_meteo["time"])  # + pd.Timedelta(minutes=5) # experimentalne zjistit zda je potreba
    solarmon_meteo["time"] = solarmon_meteo["time"].dt.tz_localize("Europe/Prague").dt.tz_convert("UTC")
    solarmon_meteo = solarmon_meteo.set_index("time")

    # převody typů a kontrola
    solarmon_meteo["int_sol_irr"] = pd.to_numeric(solarmon_meteo["int_sol_irr"], errors='coerce')
    solarmon_meteo["tmp_module"] = pd.to_numeric(solarmon_meteo["tmp_module"], errors='coerce')
    solarmon_meteo["energy"] = pd.to_numeric(solarmon_meteo["energy"], errors='coerce')

    # Resample na hodinové intervaly
    solarmon_meteo = solarmon_meteo.resample('1h').agg({
        'int_sol_irr': 'mean',
        'wind_vel': 'mean',
        'tmp_amb': 'mean',
        'tmp_module': 'mean',
        'energy': 'sum'
    })
    solarmon_meteo['energy'] = solarmon_meteo['energy'] / 1000 

    solarmon_meteo.rename_axis("DT", inplace=True)
    solarmon_meteo.rename(
    columns={
        'int_sol_irr': 'Me',
        'tmp_module': 'tp',
        'energy': 'output',
        'tmp_amb': 'to'
    },
    inplace=True
    )
    solarmon_meteo.drop(['wind_vel'], axis=1, inplace=True)

    solarmon_meteo.index = pd.to_datetime(solarmon_meteo.index)
    start_datetime = pd.to_datetime(start_date).tz_localize('UTC')
    end_datetime = pd.to_datetime(end_date).tz_localize('UTC') + pd.Timedelta(days=1)
    solarmon_meteo = solarmon_meteo[(solarmon_meteo.index >= start_datetime) & (solarmon_meteo.index < end_datetime)]

    # solarmon_meteo.isnull().values.any() kontroluje zda neni NaN hodnota
    solarmon_meteo = solarmon_meteo.reset_index(drop=False) # resetovani indexu

    # df_meteo["DT"] = df_meteo["DT"].dt.tz_localize("UTC")
    #df_meteo = get_meteo_data(previous_date, selected_date)
    #df_air_quality = get_air_quality_data(previous_date, selected_date)
    #df_meteo = df_meteo.merge(df_air_quality, on="DT", how="inner")

    
    
    #df = df.merge(df_meteo, on="DT", how="inner")
    #df = create_time_cycles(df)
    return solarmon_meteo
    # return df[2:26]