import streamlit as st import plotly.express as px import openmeteo_requests import requests_cache import pandas as pd from retry_requests import retry from datetime import datetime, timedelta from support_functions import * import numpy as np from scipy import interpolate #Page Configuration st.set_page_config(initial_sidebar_state="collapsed", page_title='10 Days Forecast', menu_items={ 'Get Help': None, 'Report a bug': None, 'About': "Designed by Meteorama" }) st.header("Meteorama") st.header("10 Days Weather Forecast") #Check for Lat Lon info and Get ECMWF Data #Get Initial Configuration url_params = st.query_params url_params_keys = dict(url_params).keys() if('lat' not in url_params_keys or 'lon' not in url_params_keys): latvals = 22.47 lonvals = 70.05 st.info("Latitude and Longitude values not defined. Defaulting to Jamnagar...") else: latvals = float(url_params['lat']) lonvals = float(url_params['lon']) wxdata = get_ecmwf_data(latvals, lonvals) #Get Dates list start_date = datetime(wxdata['Year'].values[0], wxdata['Month'].values[0], wxdata['Day'].values[0]) end_date = start_date + timedelta(days=10) st.caption(f"Time Period: {parseday(start_date)} to {parseday(end_date)}") #Draw Tabs tab1, tab2 = st.tabs(['Overall', 'Daily']) #Min and Max temperature min_max_date_list = [] min_max_min_list = [] min_max_max_list = [] for i in range(11): cur_date = start_date + timedelta(days=i) minidf = wxdata[(wxdata['Day'] == cur_date.day)&(wxdata['Month'] == cur_date.month)&(wxdata['Year'] == cur_date.year)] min_max_date_list.append(cur_date) min_max_min_list.append(minidf['temperature_2m'].min()) min_max_max_list.append(minidf['temperature_2m'].max()) min_max_df = pd.DataFrame() min_max_df['Date'] = min_max_date_list min_max_df['Minimum Temperature'] = min_max_min_list min_max_df['Maximum Temperature'] = min_max_max_list mintempfig = px.line(min_max_df, x = 'Date', y = 'Minimum Temperature') maxtempfig = px.line(min_max_df, x = 'Date', y = 'Maximum Temperature') with tab1.expander("Minimum and Maximum Temperature"): st.plotly_chart(mintempfig, use_container_width = True) st.plotly_chart(maxtempfig, use_container_width = True) #temperature and inversion #Temperature plot tempfig = px.line(wxdata, x = 'Date_IST', y = 'temperature_2m', hover_data= 'temperature_925hPa', labels = {'Date_IST': 'Date and Time', 'temperature_2m': 'Dry Bulb Temp', 'temperature_925hPa': 'Temp at F/L025'}) #Inversion invlist = [] temp2mlist = wxdata['temperature_2m'].values temp025list = wxdata['temperature_925hPa'].values for t in range(len(temp2mlist)): if((temp025list[t] - temp2mlist[t])>0): invlist.append((temp025list[t] - temp2mlist[t])) else: invlist.append(0) wxdata['Inversion'] = invlist invfig = px.bar(wxdata, x='Date_IST', y='Inversion') with tab1.expander("Temperature and Inversion"): st.plotly_chart(tempfig, use_container_width = True) st.plotly_chart(invfig, use_container_width = True) #RH plot rhfig = px.line(wxdata, x = 'Date_IST', y = 'relative_humidity_2m', labels = {'newdate': 'Date and Time', 'relative_humidity_2m': 'RH(%age)'}) with tab1.expander("Relative Humidity"): st.plotly_chart(rhfig, use_container_width = True) #Cloud Plots cloudsfig = px.line(wxdata, x = 'Date_IST', y = 'cloud_cover', hover_data = ['cloud_cover_low', 'cloud_cover_mid', 'cloud_cover_high'], labels = {'Date_IST': 'Date and Time', 'cloud_cover': 'Total Cloud Cover (%age)', 'cloud_cover_low': 'Low Clouds', 'cloud_cover_mid': 'Medium Clouds', 'cloud_cover_high': 'High Clouds'}) cloudsfig.update_layout(yaxis_range=[0,100]) lowcloudsfig = px.line(wxdata, x = 'Date_IST', y = 'cloud_cover_low', hover_data = ['cloud_cover', 'cloud_cover_mid', 'cloud_cover_high'], labels = {'Date_IST': 'Date and Time', 'cloud_cover': 'Total Cloud Cover (%age)', 'cloud_cover_low': 'Low Clouds', 'cloud_cover_mid': 'Medium Clouds', 'cloud_cover_high': 'High Clouds'}) midcloudsfig = px.line(wxdata, x = 'Date_IST', y = 'cloud_cover_mid', hover_data = ['cloud_cover', 'cloud_cover_low', 'cloud_cover_high'], labels = {'Date_IST': 'Date and Time', 'cloud_cover': 'Total Cloud Cover (%age)', 'cloud_cover_low': 'Low Clouds', 'cloud_cover_mid': 'Medium Clouds', 'cloud_cover_high': 'High Clouds'}) highcloudsfig = px.line(wxdata, x = 'Date_IST', y = 'cloud_cover_high', hover_data = ['cloud_cover', 'cloud_cover_low', 'cloud_cover_mid'], labels = {'Date_IST': 'Date and Time', 'cloud_cover': 'Total Cloud Cover (%age)', 'cloud_cover_low': 'Low Clouds', 'cloud_cover_mid': 'Medium Clouds', 'cloud_cover_high': 'High Clouds'}) lowcloudsfig.update_layout(yaxis_range=[0,100]) midcloudsfig.update_layout(yaxis_range=[0,100]) highcloudsfig.update_layout(yaxis_range=[0,100]) with tab1.expander("Cloudiness"): st.plotly_chart(cloudsfig, use_container_width = True) st.plotly_chart(lowcloudsfig, use_container_width = True) st.plotly_chart(midcloudsfig, use_container_width = True) st.plotly_chart(highcloudsfig, use_container_width = True) #precipitaion plot pptfig = px.bar(wxdata, x = 'Date_IST', y = 'precipitation', labels = {'Date_IST': 'Date and Time', 'precipitation': 'Precipitation'}) with tab1.expander("Precipitation"): st.plotly_chart(pptfig, use_container_width = True) #pressure plot qnhfig = px.line(wxdata, x = 'Date_IST', y = 'surface_pressure', labels = {'Date_IST': 'Date and Time', 'surface_pressure': 'QNH(hPa)'}) qnhfig.update_layout(yaxis_range=[995,1018]) with tab1.expander("Surface Pressure"): st.plotly_chart(qnhfig, use_container_width = True) #Daily Data ds = tab2.slider( "Forecast for ", start_date, end_date, value=start_date, format="DD MMM YY") wxdata2 = wxdata[(wxdata['Day'] == ds.day)&(wxdata['Month'] == ds.month)&(wxdata['Year'] == ds.year)] with tab2.expander("Current Weather Register"): st.table(makecwr(wxdata2)) #temperature and inversion #Temperature plot tempfig = px.line(wxdata2, x = 'Date_IST', y = 'temperature_2m', hover_data= 'temperature_925hPa', labels = {'Date_IST': 'Date and Time', 'temperature_2m': 'Dry Bulb Temp', 'temperature_925hPa': 'Temp at F/L025'}) #Inversion invlist = [] temp2mlist = wxdata2['temperature_2m'].values temp025list = wxdata2['temperature_925hPa'].values for t in range(len(temp2mlist)): if((temp025list[t] - temp2mlist[t])>0): invlist.append((temp025list[t] - temp2mlist[t])) else: invlist.append(0) wxdata2['Inversion'] = invlist invfig = px.bar(wxdata2, x='Date_IST', y='Inversion') invfig.update_layout(yaxis_range=[0,5]) with tab2.expander("Temperature and Inversion"): st.plotly_chart(tempfig, use_container_width = True) st.plotly_chart(invfig, use_container_width = True) #RH plot rhfig = px.line(wxdata2, x = 'Date_IST', y = 'relative_humidity_2m', labels = {'newdate': 'Date and Time', 'relative_humidity_2m': 'RH(%age)'}) with tab2.expander("Relative Humidity"): st.plotly_chart(rhfig, use_container_width = True) #Cloud Plots cloudsfig = px.line(wxdata2, x = 'Date_IST', y = 'cloud_cover', hover_data = ['cloud_cover_low', 'cloud_cover_mid', 'cloud_cover_high'], labels = {'Date_IST': 'Date and Time', 'cloud_cover': 'Total Cloud Cover (%age)', 'cloud_cover_low': 'Low Clouds', 'cloud_cover_mid': 'Medium Clouds', 'cloud_cover_high': 'High Clouds'}) cloudsfig.update_layout(yaxis_range=[0,100]) lowcloudsfig = px.line(wxdata2, x = 'Date_IST', y = 'cloud_cover_low', hover_data = ['cloud_cover', 'cloud_cover_mid', 'cloud_cover_high'], labels = {'Date_IST': 'Date and Time', 'cloud_cover': 'Total Cloud Cover (%age)', 'cloud_cover_low': 'Low Clouds', 'cloud_cover_mid': 'Medium Clouds', 'cloud_cover_high': 'High Clouds'}) midcloudsfig = px.line(wxdata2, x = 'Date_IST', y = 'cloud_cover_mid', hover_data = ['cloud_cover', 'cloud_cover_low', 'cloud_cover_high'], labels = {'Date_IST': 'Date and Time', 'cloud_cover': 'Total Cloud Cover (%age)', 'cloud_cover_low': 'Low Clouds', 'cloud_cover_mid': 'Medium Clouds', 'cloud_cover_high': 'High Clouds'}) highcloudsfig = px.line(wxdata2, x = 'Date_IST', y = 'cloud_cover_high', hover_data = ['cloud_cover', 'cloud_cover_low', 'cloud_cover_mid'], labels = {'Date_IST': 'Date and Time', 'cloud_cover': 'Total Cloud Cover (%age)', 'cloud_cover_low': 'Low Clouds', 'cloud_cover_mid': 'Medium Clouds', 'cloud_cover_high': 'High Clouds'}) lowcloudsfig.update_layout(yaxis_range=[0,100]) midcloudsfig.update_layout(yaxis_range=[0,100]) highcloudsfig.update_layout(yaxis_range=[0,100]) with tab2.expander("Cloudiness"): st.plotly_chart(cloudsfig, use_container_width = True) st.plotly_chart(lowcloudsfig, use_container_width = True) st.plotly_chart(midcloudsfig, use_container_width = True) st.plotly_chart(highcloudsfig, use_container_width = True) #pressure plot qnhfig = px.line(wxdata2, x = 'Date_IST', y = 'surface_pressure', labels = {'Date_IST': 'Date and Time', 'surface_pressure': 'QNH(hPa)'}) qnhfig.update_layout(yaxis_range=[995,1018]) with tab2.expander("Surface Pressure"): st.plotly_chart(qnhfig, use_container_width = True) old_ht = [500,2500,5000,10000,18000,30000,35000,40000] new_ht = [1000, 2000, 3000, 5000, 7000, 9000, 15000, 18000, 25000, 30000] upper_air_df = pd.DataFrame() upper_air_df['Height(KM)'] = [0.3,0.6,0.9,1.5,2.1,3.0,4.5,6.0,7.5,9.0][::-1] #Upper Air Data sel_times = tab2.multiselect("Select Time for Upper Air Data (IST)", wxdata2['Hour'].values, [5,11,17,23]) for sel_time in sel_times: filter_df = wxdata2[wxdata2['Hour'] == sel_time] wind_dir_list = [] wind_speed_list = [] temp_list = [] for level in ['200hPa', '250hPa', '300hPa', '500hPa', '700hPa', '850hPa', '925hPa', '1000hPa'][::-1]: wind_dir_list.append(filter_df[f'winddirection_{level}'].values[0]) wind_speed_list.append(filter_df[f'windspeed_{level}'].values[0]) temp_list.append(int(filter_df[f'temperature_{level}'].values[0])) new_wind_dir = interp_for_levels(old_ht, wind_dir_list, new_ht, is_wind_dir = True)[::-1] new_wind_speed = interp_for_levels(old_ht, wind_speed_list, new_ht)[::-1] new_temp = interp_for_levels(old_ht, temp_list, new_ht, round_to = 1)[::-1] text_to_be_shown = [] for y in range(len(new_wind_dir)): while(new_wind_dir[y]>360): new_wind_dir[y] = new_wind_dir[y]-360 wwww = f"{new_wind_dir[y]:03d}/{new_wind_speed[y]:02d}({new_temp[y]:02d})" text_to_be_shown.append(wwww) upper_air_df[f"{sel_time:02d}:30Hr"] = text_to_be_shown tab2.dataframe(upper_air_df, use_container_width=True) st.success("Made by Meteo Rama..!!")