import openmeteo_requests import requests_cache import pandas as pd from retry_requests import retry from datetime import datetime, timedelta import numpy as np from scipy import interpolate def get_ecmwf_data(lat, lon): # Setup the Open-Meteo API client with cache and retry on error cache_session = requests_cache.CachedSession('.cache', expire_after = 3600) retry_session = retry(cache_session, retries = 5, backoff_factor = 0.2) openmeteo = openmeteo_requests.Client(session = retry_session) # Make sure all required weather variables are listed here # The order of variables in hourly or daily is important to assign them correctly below url = "https://api.open-meteo.com/v1/ecmwf" params = { "latitude": lat, "longitude": lon, "hourly": ["temperature_2m", "relative_humidity_2m", "dew_point_2m", "precipitation", "weather_code", "surface_pressure", "cloud_cover", "cloud_cover_low", "cloud_cover_mid", "cloud_cover_high", "wind_speed_10m", "wind_direction_10m", "wind_gusts_10m", "surface_temperature", "temperature_1000hPa", "temperature_925hPa", "temperature_850hPa", "temperature_700hPa", "temperature_500hPa", "temperature_300hPa", "temperature_250hPa", "temperature_200hPa", "temperature_50hPa", "relative_humidity_1000hPa", "relative_humidity_925hPa", "relative_humidity_850hPa", "relative_humidity_700hPa", "relative_humidity_500hPa", "relative_humidity_300hPa", "relative_humidity_250hPa", "relative_humidity_200hPa", "relative_humidity_50hPa", "windspeed_1000hPa", "windspeed_925hPa", "windspeed_850hPa", "windspeed_700hPa", "windspeed_500hPa", "windspeed_300hPa", "windspeed_250hPa", "windspeed_200hPa", "windspeed_50hPa", "winddirection_1000hPa", "winddirection_925hPa", "winddirection_850hPa", "winddirection_700hPa", "winddirection_500hPa", "winddirection_300hPa", "winddirection_250hPa", "winddirection_200hPa", "winddirection_50hPa"], "wind_speed_unit": "kn" } responses = openmeteo.weather_api(url, params=params) # Process first location. Add a for-loop for multiple locations or weather models response = responses[0] # Process hourly data. The order of variables needs to be the same as requested. hourly = response.Hourly() hourly_temperature_2m = hourly.Variables(0).ValuesAsNumpy() hourly_relative_humidity_2m = hourly.Variables(1).ValuesAsNumpy() hourly_dew_point_2m = hourly.Variables(2).ValuesAsNumpy() hourly_precipitation = hourly.Variables(3).ValuesAsNumpy() hourly_weather_code = hourly.Variables(4).ValuesAsNumpy() hourly_surface_pressure = hourly.Variables(5).ValuesAsNumpy() hourly_cloud_cover = hourly.Variables(6).ValuesAsNumpy() hourly_cloud_cover_low = hourly.Variables(7).ValuesAsNumpy() hourly_cloud_cover_mid = hourly.Variables(8).ValuesAsNumpy() hourly_cloud_cover_high = hourly.Variables(9).ValuesAsNumpy() hourly_wind_speed_10m = hourly.Variables(10).ValuesAsNumpy() hourly_wind_direction_10m = hourly.Variables(11).ValuesAsNumpy() hourly_wind_gusts_10m = hourly.Variables(12).ValuesAsNumpy() hourly_surface_temperature = hourly.Variables(13).ValuesAsNumpy() hourly_temperature_1000hPa = hourly.Variables(14).ValuesAsNumpy() hourly_temperature_925hPa = hourly.Variables(15).ValuesAsNumpy() hourly_temperature_850hPa = hourly.Variables(16).ValuesAsNumpy() hourly_temperature_700hPa = hourly.Variables(17).ValuesAsNumpy() hourly_temperature_500hPa = hourly.Variables(18).ValuesAsNumpy() hourly_temperature_300hPa = hourly.Variables(19).ValuesAsNumpy() hourly_temperature_250hPa = hourly.Variables(20).ValuesAsNumpy() hourly_temperature_200hPa = hourly.Variables(21).ValuesAsNumpy() #hourly_temperature_50hPa = hourly.Variables(22).ValuesAsNumpy() hourly_relative_humidity_1000hPa = hourly.Variables(23).ValuesAsNumpy() hourly_relative_humidity_925hPa = hourly.Variables(24).ValuesAsNumpy() hourly_relative_humidity_850hPa = hourly.Variables(25).ValuesAsNumpy() hourly_relative_humidity_700hPa = hourly.Variables(26).ValuesAsNumpy() hourly_relative_humidity_500hPa = hourly.Variables(27).ValuesAsNumpy() hourly_relative_humidity_300hPa = hourly.Variables(28).ValuesAsNumpy() hourly_relative_humidity_250hPa = hourly.Variables(29).ValuesAsNumpy() hourly_relative_humidity_200hPa = hourly.Variables(30).ValuesAsNumpy() #hourly_relative_humidity_50hPa = hourly.Variables(31).ValuesAsNumpy() hourly_windspeed_1000hPa = hourly.Variables(32).ValuesAsNumpy() hourly_windspeed_925hPa = hourly.Variables(33).ValuesAsNumpy() hourly_windspeed_850hPa = hourly.Variables(34).ValuesAsNumpy() hourly_windspeed_700hPa = hourly.Variables(35).ValuesAsNumpy() hourly_windspeed_500hPa = hourly.Variables(36).ValuesAsNumpy() hourly_windspeed_300hPa = hourly.Variables(37).ValuesAsNumpy() hourly_windspeed_250hPa = hourly.Variables(38).ValuesAsNumpy() hourly_windspeed_200hPa = hourly.Variables(39).ValuesAsNumpy() #hourly_windspeed_50hPa = hourly.Variables(40).ValuesAsNumpy() hourly_winddirection_1000hPa = hourly.Variables(41).ValuesAsNumpy() hourly_winddirection_925hPa = hourly.Variables(42).ValuesAsNumpy() hourly_winddirection_850hPa = hourly.Variables(43).ValuesAsNumpy() hourly_winddirection_700hPa = hourly.Variables(44).ValuesAsNumpy() hourly_winddirection_500hPa = hourly.Variables(45).ValuesAsNumpy() hourly_winddirection_300hPa = hourly.Variables(46).ValuesAsNumpy() hourly_winddirection_250hPa = hourly.Variables(47).ValuesAsNumpy() hourly_winddirection_200hPa = hourly.Variables(48).ValuesAsNumpy() #hourly_winddirection_50hPa = hourly.Variables(49).ValuesAsNumpy() hourly_data = {"date": pd.date_range( start = pd.to_datetime(hourly.Time(), unit = "s", utc = True), end = pd.to_datetime(hourly.TimeEnd(), unit = "s", utc = True), freq = pd.Timedelta(seconds = hourly.Interval()), inclusive = "left" )} d_vals, m_vals, y_vals, h_vals, date_vals = [],[],[],[], [] for d in hourly_data['date'].values: cur_date = pd.Timestamp(d) cur_date_ist = cur_date + timedelta(hours=5, minutes=30) date_vals.append(cur_date_ist) d_vals.append(cur_date_ist.day) m_vals.append(cur_date_ist.month) y_vals.append(cur_date_ist.year) h_vals.append(cur_date_ist.hour) hourly_data['Date_IST'] = date_vals hourly_data['Day'] = d_vals hourly_data['Month'] = m_vals hourly_data['Year'] = y_vals hourly_data['Hour'] = h_vals hourly_data["temperature_2m"] = hourly_temperature_2m hourly_data["relative_humidity_2m"] = hourly_relative_humidity_2m hourly_data["dew_point_2m"] = hourly_dew_point_2m hourly_data["precipitation"] = hourly_precipitation hourly_data["weather_code"] = hourly_weather_code hourly_data["surface_pressure"] = hourly_surface_pressure hourly_data["cloud_cover"] = hourly_cloud_cover hourly_data["cloud_cover_low"] = hourly_cloud_cover_low hourly_data["cloud_cover_mid"] = hourly_cloud_cover_mid hourly_data["cloud_cover_high"] = hourly_cloud_cover_high hourly_data["wind_speed_10m"] = hourly_wind_speed_10m hourly_data["wind_direction_10m"] = hourly_wind_direction_10m hourly_data["wind_gusts_10m"] = hourly_wind_gusts_10m hourly_data["surface_temperature"] = hourly_surface_temperature hourly_data["temperature_1000hPa"] = hourly_temperature_1000hPa hourly_data["temperature_925hPa"] = hourly_temperature_925hPa hourly_data["temperature_850hPa"] = hourly_temperature_850hPa hourly_data["temperature_700hPa"] = hourly_temperature_700hPa hourly_data["temperature_500hPa"] = hourly_temperature_500hPa hourly_data["temperature_300hPa"] = hourly_temperature_300hPa hourly_data["temperature_250hPa"] = hourly_temperature_250hPa hourly_data["temperature_200hPa"] = hourly_temperature_200hPa #hourly_data["temperature_50hPa"] = hourly_temperature_50hPa hourly_data["relative_humidity_1000hPa"] = hourly_relative_humidity_1000hPa hourly_data["relative_humidity_925hPa"] = hourly_relative_humidity_925hPa hourly_data["relative_humidity_850hPa"] = hourly_relative_humidity_850hPa hourly_data["relative_humidity_700hPa"] = hourly_relative_humidity_700hPa hourly_data["relative_humidity_500hPa"] = hourly_relative_humidity_500hPa hourly_data["relative_humidity_300hPa"] = hourly_relative_humidity_300hPa hourly_data["relative_humidity_250hPa"] = hourly_relative_humidity_250hPa hourly_data["relative_humidity_200hPa"] = hourly_relative_humidity_200hPa #hourly_data["relative_humidity_50hPa"] = hourly_relative_humidity_50hPa hourly_data["windspeed_1000hPa"] = hourly_windspeed_1000hPa hourly_data["windspeed_925hPa"] = hourly_windspeed_925hPa hourly_data["windspeed_850hPa"] = hourly_windspeed_850hPa hourly_data["windspeed_700hPa"] = hourly_windspeed_700hPa hourly_data["windspeed_500hPa"] = hourly_windspeed_500hPa hourly_data["windspeed_300hPa"] = hourly_windspeed_300hPa hourly_data["windspeed_250hPa"] = hourly_windspeed_250hPa hourly_data["windspeed_200hPa"] = hourly_windspeed_200hPa #hourly_data["windspeed_50hPa"] = hourly_windspeed_50hPa hourly_data["winddirection_1000hPa"] = hourly_winddirection_1000hPa hourly_data["winddirection_925hPa"] = hourly_winddirection_925hPa hourly_data["winddirection_850hPa"] = hourly_winddirection_850hPa hourly_data["winddirection_700hPa"] = hourly_winddirection_700hPa hourly_data["winddirection_500hPa"] = hourly_winddirection_500hPa hourly_data["winddirection_300hPa"] = hourly_winddirection_300hPa hourly_data["winddirection_250hPa"] = hourly_winddirection_250hPa hourly_data["winddirection_200hPa"] = hourly_winddirection_200hPa #hourly_data["winddirection_50hPa"] = hourly_winddirection_50hPa hourly_dataframe = pd.DataFrame(data = hourly_data, index = None) hourly_dataframe = hourly_dataframe.drop('date', axis = 1) return hourly_dataframe def wxcode_to_text(w): if(w==0): return "SKC" elif(w==1): return "Mainly Clear" elif(w==2): return "Partly Cloudy" elif(w==3): return "Overcast" elif(w==45): return "Fog" elif(w==48): return "Depositing Rime Fog" elif(w==51): return "Light Drizzle" elif(w==53): return "Moderate Drizzle" elif(w==55): return "Dense Drizzle" elif(w==56): return "Light Freezing Drizzle" elif(w==57): return "Dense Freezing Drizzle" elif(w==61): return "Slight Rain" elif(w==63): return "Moderate Rain" elif(w==65): return "Heavy Rain" elif(w==66): return "Light Freezing Rain" elif(w==67): return "Heavy Freezing Rain" elif(w==71): return "Slight Snowfall" def extractdateforcwr(d): d = pd.Timestamp(d) day, month, year, hour = d.day, d.month, d.year, d.hour mtext = "Jan" if(month == 1): mtext = "Jan" elif(month == 2): mtext = "Feb" elif(month == 3): mtext = "Mar" elif(month == 4): mtext = "Apr" elif(month == 5): mtext = "May" elif(month == 6): mtext = "Jun" elif(month == 7): mtext = "Jul" elif(month == 8): mtext = "Aug" elif(month == 9): mtext = "Sep" elif(month == 10): mtext = "Oct" elif(month == 11): mtext = "Nov" elif(month == 12): mtext = "Dec" else: mtext = "NA" #GMT + 5:30 new_time_local = datetime(year, month, day, hour) return f"{day:02d} {mtext} {str(year)[2:]} {new_time_local.hour:02d}{new_time_local.minute:02d}" def parseday(d): d = pd.Timestamp(d) day, month, year, hour = d.day, d.month, d.year, d.hour mtext = "Jan" if(month == 1): mtext = "Jan" elif(month == 2): mtext = "Feb" elif(month == 3): mtext = "Mar" elif(month == 4): mtext = "Apr" elif(month == 5): mtext = "May" elif(month == 6): mtext = "Jun" elif(month == 7): mtext = "Jul" elif(month == 8): mtext = "Aug" elif(month == 9): mtext = "Sep" elif(month == 10): mtext = "Oct" elif(month == 11): mtext = "Nov" elif(month == 12): mtext = "Dec" else: mtext = "NA" #GMT + 5:30 new_time_local = datetime(year, month, day, hour) + timedelta(hours = 5, minutes = 30) return f"{day:02d} {mtext} {str(year)[2:]}" def makecwr(df): finaldf = pd.DataFrame() finaldf['Time'] = [extractdateforcwr(x) for x in df['Date_IST'].values] finaldf['DDD'] = [f"{int((x//10)*10):03d}" for x in df['wind_direction_10m'].values] finaldf['ff'] = [f"{int(x):02d}" for x in df['wind_speed_10m'].values] finaldf['Wx'] = [wxcode_to_text(x) for x in df['weather_code'].values] finaldf['DB'] = [round(float(x),2) for x in df['temperature_2m'].values] finaldf['DP'] = [round(x,2) for x in df['dew_point_2m'].values] finaldf['RH'] = [int(x) for x in df['relative_humidity_2m'].values] finaldf['Cloud Total'] = [int(x/12.5) for x in df['cloud_cover'].values] finaldf['QNH'] = [int(p) for p in df['surface_pressure'].values] return finaldf def myround(x, base=5): x = int(x) return base * round(x/base) def interp_for_levels(ht, vals, new_ht, is_wind_dir = False, round_to=5): if(is_wind_dir): wrapped_winds = np.unwrap(vals, period=360) f = interpolate.interp1d(ht, wrapped_winds ,kind="slinear") interpolated_vals = f(new_ht) actual_vals = [] rounded_vals = [] for w in interpolated_vals: if(w<0): actual_vals.append(w+360) else: actual_vals.append(w) for v in actual_vals: x = int(v) rounded_vals.append(10 * round(x/10)) return rounded_vals else: f = interpolate.interp1d(ht, vals ,kind="slinear") interpolated_vals = f(new_ht) rounded_vals = [] for v in interpolated_vals: x = int(v) rounded_vals.append(round_to * round(x/round_to)) return rounded_vals