Meteorama/sixteendays_forecast

1import openmeteo_requests2import requests_cache3import pandas as pd4from retry_requests import retry5from datetime import datetime, timedelta6import numpy as np7from scipy import interpolate8 9def get_ecmwf_data(lat, lon):10 11    # Setup the Open-Meteo API client with cache and retry on error12    cache_session = requests_cache.CachedSession('.cache', expire_after = 3600)13    retry_session = retry(cache_session, retries = 5, backoff_factor = 0.2)14    openmeteo = openmeteo_requests.Client(session = retry_session)15 16    # Make sure all required weather variables are listed here17    # The order of variables in hourly or daily is important to assign them correctly below18    url = "https://api.open-meteo.com/v1/forecast"19    params = {20        "latitude": lat,21        "longitude": lon,22        "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"],23        "wind_speed_unit": "kn"24    }25    responses = openmeteo.weather_api(url, params=params)26 27    # Process first location. Add a for-loop for multiple locations or weather models28    response = responses[0]29 30    # Process hourly data. The order of variables needs to be the same as requested.31    hourly = response.Hourly()32    hourly_temperature_2m = hourly.Variables(0).ValuesAsNumpy()33    hourly_relative_humidity_2m = hourly.Variables(1).ValuesAsNumpy()34    hourly_dew_point_2m = hourly.Variables(2).ValuesAsNumpy()35    hourly_precipitation = hourly.Variables(3).ValuesAsNumpy()36    hourly_weather_code = hourly.Variables(4).ValuesAsNumpy()37    hourly_surface_pressure = hourly.Variables(5).ValuesAsNumpy()38    hourly_cloud_cover = hourly.Variables(6).ValuesAsNumpy()39    hourly_cloud_cover_low = hourly.Variables(7).ValuesAsNumpy()40    hourly_cloud_cover_mid = hourly.Variables(8).ValuesAsNumpy()41    hourly_cloud_cover_high = hourly.Variables(9).ValuesAsNumpy()42    hourly_wind_speed_10m = hourly.Variables(10).ValuesAsNumpy()43    hourly_wind_direction_10m = hourly.Variables(11).ValuesAsNumpy()44    hourly_wind_gusts_10m = hourly.Variables(12).ValuesAsNumpy()45    hourly_surface_temperature = hourly.Variables(13).ValuesAsNumpy()46    hourly_temperature_1000hPa = hourly.Variables(14).ValuesAsNumpy()47    hourly_temperature_925hPa = hourly.Variables(15).ValuesAsNumpy()48    hourly_temperature_850hPa = hourly.Variables(16).ValuesAsNumpy()49    hourly_temperature_700hPa = hourly.Variables(17).ValuesAsNumpy()50    hourly_temperature_500hPa = hourly.Variables(18).ValuesAsNumpy()51    hourly_temperature_300hPa = hourly.Variables(19).ValuesAsNumpy()52    hourly_temperature_250hPa = hourly.Variables(20).ValuesAsNumpy()53    hourly_temperature_200hPa = hourly.Variables(21).ValuesAsNumpy()54    #hourly_temperature_50hPa = hourly.Variables(22).ValuesAsNumpy()55    hourly_relative_humidity_1000hPa = hourly.Variables(23).ValuesAsNumpy()56    hourly_relative_humidity_925hPa = hourly.Variables(24).ValuesAsNumpy()57    hourly_relative_humidity_850hPa = hourly.Variables(25).ValuesAsNumpy()58    hourly_relative_humidity_700hPa = hourly.Variables(26).ValuesAsNumpy()59    hourly_relative_humidity_500hPa = hourly.Variables(27).ValuesAsNumpy()60    hourly_relative_humidity_300hPa = hourly.Variables(28).ValuesAsNumpy()61    hourly_relative_humidity_250hPa = hourly.Variables(29).ValuesAsNumpy()62    hourly_relative_humidity_200hPa = hourly.Variables(30).ValuesAsNumpy()63    #hourly_relative_humidity_50hPa = hourly.Variables(31).ValuesAsNumpy()64    hourly_windspeed_1000hPa = hourly.Variables(32).ValuesAsNumpy()65    hourly_windspeed_925hPa = hourly.Variables(33).ValuesAsNumpy()66    hourly_windspeed_850hPa = hourly.Variables(34).ValuesAsNumpy()67    hourly_windspeed_700hPa = hourly.Variables(35).ValuesAsNumpy()68    hourly_windspeed_500hPa = hourly.Variables(36).ValuesAsNumpy()69    hourly_windspeed_300hPa = hourly.Variables(37).ValuesAsNumpy()70    hourly_windspeed_250hPa = hourly.Variables(38).ValuesAsNumpy()71    hourly_windspeed_200hPa = hourly.Variables(39).ValuesAsNumpy()72    #hourly_windspeed_50hPa = hourly.Variables(40).ValuesAsNumpy()73    hourly_winddirection_1000hPa = hourly.Variables(41).ValuesAsNumpy()74    hourly_winddirection_925hPa = hourly.Variables(42).ValuesAsNumpy()75    hourly_winddirection_850hPa = hourly.Variables(43).ValuesAsNumpy()76    hourly_winddirection_700hPa = hourly.Variables(44).ValuesAsNumpy()77    hourly_winddirection_500hPa = hourly.Variables(45).ValuesAsNumpy()78    hourly_winddirection_300hPa = hourly.Variables(46).ValuesAsNumpy()79    hourly_winddirection_250hPa = hourly.Variables(47).ValuesAsNumpy()80    hourly_winddirection_200hPa = hourly.Variables(48).ValuesAsNumpy()81    #hourly_winddirection_50hPa = hourly.Variables(49).ValuesAsNumpy()82 83    hourly_data = {"date": pd.date_range(84        start = pd.to_datetime(hourly.Time(), unit = "s", utc = True),85        end = pd.to_datetime(hourly.TimeEnd(), unit = "s", utc = True),86        freq = pd.Timedelta(seconds = hourly.Interval()),87        inclusive = "left"88    )}89 90    d_vals, m_vals, y_vals, h_vals, date_vals = [],[],[],[], []91    for d in hourly_data['date'].values:92        cur_date = pd.Timestamp(d)93        cur_date_ist = cur_date + timedelta(hours=5, minutes=30)94        date_vals.append(cur_date_ist)95        d_vals.append(cur_date_ist.day)96        m_vals.append(cur_date_ist.month)97        y_vals.append(cur_date_ist.year)98        h_vals.append(cur_date_ist.hour)99 100    hourly_data['Date_IST'] = date_vals101    hourly_data['Day'] = d_vals102    hourly_data['Month'] = m_vals103    hourly_data['Year'] = y_vals104    hourly_data['Hour'] = h_vals105    hourly_data["temperature_2m"] = hourly_temperature_2m106    hourly_data["relative_humidity_2m"] = hourly_relative_humidity_2m107    hourly_data["dew_point_2m"] = hourly_dew_point_2m108    hourly_data["precipitation"] = hourly_precipitation109    hourly_data["weather_code"] = hourly_weather_code110    hourly_data["surface_pressure"] = hourly_surface_pressure111    hourly_data["cloud_cover"] = hourly_cloud_cover112    hourly_data["cloud_cover_low"] = hourly_cloud_cover_low113    hourly_data["cloud_cover_mid"] = hourly_cloud_cover_mid114    hourly_data["cloud_cover_high"] = hourly_cloud_cover_high115    hourly_data["wind_speed_10m"] = hourly_wind_speed_10m116    hourly_data["wind_direction_10m"] = hourly_wind_direction_10m117    hourly_data["wind_gusts_10m"] = hourly_wind_gusts_10m118    hourly_data["surface_temperature"] = hourly_surface_temperature119    hourly_data["temperature_1000hPa"] = hourly_temperature_1000hPa120    hourly_data["temperature_925hPa"] = hourly_temperature_925hPa121    hourly_data["temperature_850hPa"] = hourly_temperature_850hPa122    hourly_data["temperature_700hPa"] = hourly_temperature_700hPa123    hourly_data["temperature_500hPa"] = hourly_temperature_500hPa124    hourly_data["temperature_300hPa"] = hourly_temperature_300hPa125    hourly_data["temperature_250hPa"] = hourly_temperature_250hPa126    hourly_data["temperature_200hPa"] = hourly_temperature_200hPa127    #hourly_data["temperature_50hPa"] = hourly_temperature_50hPa128    hourly_data["relative_humidity_1000hPa"] = hourly_relative_humidity_1000hPa129    hourly_data["relative_humidity_925hPa"] = hourly_relative_humidity_925hPa130    hourly_data["relative_humidity_850hPa"] = hourly_relative_humidity_850hPa131    hourly_data["relative_humidity_700hPa"] = hourly_relative_humidity_700hPa132    hourly_data["relative_humidity_500hPa"] = hourly_relative_humidity_500hPa133    hourly_data["relative_humidity_300hPa"] = hourly_relative_humidity_300hPa134    hourly_data["relative_humidity_250hPa"] = hourly_relative_humidity_250hPa135    hourly_data["relative_humidity_200hPa"] = hourly_relative_humidity_200hPa136    #hourly_data["relative_humidity_50hPa"] = hourly_relative_humidity_50hPa137    hourly_data["windspeed_1000hPa"] = hourly_windspeed_1000hPa138    hourly_data["windspeed_925hPa"] = hourly_windspeed_925hPa139    hourly_data["windspeed_850hPa"] = hourly_windspeed_850hPa140    hourly_data["windspeed_700hPa"] = hourly_windspeed_700hPa141    hourly_data["windspeed_500hPa"] = hourly_windspeed_500hPa142    hourly_data["windspeed_300hPa"] = hourly_windspeed_300hPa143    hourly_data["windspeed_250hPa"] = hourly_windspeed_250hPa144    hourly_data["windspeed_200hPa"] = hourly_windspeed_200hPa145    #hourly_data["windspeed_50hPa"] = hourly_windspeed_50hPa146    hourly_data["winddirection_1000hPa"] = hourly_winddirection_1000hPa147    hourly_data["winddirection_925hPa"] = hourly_winddirection_925hPa148    hourly_data["winddirection_850hPa"] = hourly_winddirection_850hPa149    hourly_data["winddirection_700hPa"] = hourly_winddirection_700hPa150    hourly_data["winddirection_500hPa"] = hourly_winddirection_500hPa151    hourly_data["winddirection_300hPa"] = hourly_winddirection_300hPa152    hourly_data["winddirection_250hPa"] = hourly_winddirection_250hPa153    hourly_data["winddirection_200hPa"] = hourly_winddirection_200hPa154    #hourly_data["winddirection_50hPa"] = hourly_winddirection_50hPa155 156    hourly_dataframe = pd.DataFrame(data = hourly_data, index = None)157    hourly_dataframe = hourly_dataframe.drop('date', axis = 1)158    return hourly_dataframe159 160def wxcode_to_text(w):161    if(w==0): return "SKC"162    elif(w==1): return "Mainly Clear"163    elif(w==2): return "Partly Cloudy"164    elif(w==3): return "Overcast"165    elif(w==45): return "Fog"166    elif(w==48): return "Depositing Rime Fog"167    elif(w==51): return "Light Drizzle"168    elif(w==53): return "Moderate Drizzle"169    elif(w==55): return "Dense Drizzle"170    elif(w==56): return "Light Freezing Drizzle"171    elif(w==57): return "Dense Freezing Drizzle"172    elif(w==61): return "Slight Rain"173    elif(w==63): return "Moderate Rain"174    elif(w==65): return "Heavy Rain"175    elif(w==66): return "Light Freezing Rain"176    elif(w==67): return "Heavy Freezing Rain"177    elif(w==71): return "Slight Snowfall"178 179def extractdateforcwr(d):180    d = pd.Timestamp(d)181    day, month, year, hour = d.day, d.month, d.year, d.hour182    mtext = "Jan"183    if(month == 1): mtext = "Jan"184    elif(month == 2): mtext = "Feb"185    elif(month == 3): mtext = "Mar"186    elif(month == 4): mtext = "Apr"187    elif(month == 5): mtext = "May"188    elif(month == 6): mtext = "Jun"189    elif(month == 7): mtext = "Jul"190    elif(month == 8): mtext = "Aug"191    elif(month == 9): mtext = "Sep"192    elif(month == 10): mtext = "Oct"193    elif(month == 11): mtext = "Nov"194    elif(month == 12): mtext = "Dec"195    else: mtext = "NA"196 197    #GMT + 5:30198    new_time_local = datetime(year, month, day, hour)199    return f"{day:02d} {mtext} {str(year)[2:]} {new_time_local.hour:02d}{new_time_local.minute:02d}"200 201def parseday(d):202    d = pd.Timestamp(d)203    day, month, year, hour = d.day, d.month, d.year, d.hour204    mtext = "Jan"205    if(month == 1): mtext = "Jan"206    elif(month == 2): mtext = "Feb"207    elif(month == 3): mtext = "Mar"208    elif(month == 4): mtext = "Apr"209    elif(month == 5): mtext = "May"210    elif(month == 6): mtext = "Jun"211    elif(month == 7): mtext = "Jul"212    elif(month == 8): mtext = "Aug"213    elif(month == 9): mtext = "Sep"214    elif(month == 10): mtext = "Oct"215    elif(month == 11): mtext = "Nov"216    elif(month == 12): mtext = "Dec"217    else: mtext = "NA"218 219    #GMT + 5:30220    new_time_local = datetime(year, month, day, hour) + timedelta(hours = 5, minutes = 30)221    return f"{day:02d} {mtext} {str(year)[2:]}"222 223def makecwr(df):224    finaldf = pd.DataFrame()225    finaldf['Time'] = [extractdateforcwr(x) for x in df['Date_IST'].values]226    finaldf['DDD'] = [f"{int((x//10)*10):03d}" for x in df['wind_direction_10m'].values]227    finaldf['ff'] = [f"{int(x):02d}" for x in df['wind_speed_10m'].values]228    finaldf['Wx'] = [wxcode_to_text(x) for x in df['weather_code'].values]229    finaldf['DB'] = [round(float(x),2) for x in df['temperature_2m'].values]230    finaldf['DP'] = [round(x,2) for x in df['dew_point_2m'].values]231    finaldf['RH'] = [int(x) for x in df['relative_humidity_2m'].values]232    finaldf['Cloud Total'] = [int(x/12.5) for x in df['cloud_cover'].values]233    finaldf['QNH'] = [int(p) for p in df['surface_pressure'].values]234    return finaldf235 236def myround(x, base=5):237    x = int(x)238    return base * round(x/base)239 240def interp_for_levels(ht, vals, new_ht, is_wind_dir = False, round_to=5):241    if(is_wind_dir):242        wrapped_winds = np.unwrap(vals, period=360)243        f = interpolate.interp1d(ht, wrapped_winds ,kind="slinear")244        interpolated_vals = f(new_ht)245        actual_vals = []246        rounded_vals = []247        for w in interpolated_vals:248            if(w<0):249                actual_vals.append(w+360)250            else:251                actual_vals.append(w)252        for v in actual_vals:253            x = int(v)254            rounded_vals.append(10 * round(x/10))255        return rounded_vals256    else:257        f = interpolate.interp1d(ht, vals ,kind="slinear")258        interpolated_vals = f(new_ht)259        rounded_vals = []260        for v in interpolated_vals:261            x = int(v)262            rounded_vals.append(round_to * round(x/round_to))263        return rounded_vals264