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| import pandas as pd | |
| import numpy as np | |
| import streamlit as st | |
| import folium | |
| import folium.plugins | |
| from streamlit_folium import st_folium | |
| from branca.element import Figure | |
| from precipitation_function import date_split,lat_long_process_precp,load_data | |
| from precipitation_function import concat_func, drop_dup_funct,lat_long_type | |
| from precipitation_function import daily_precp_plot,start_end_date_ui,lat_long_ui,year_selection_ui | |
| from precipitation_function import monthly_mean_plot, annual_max_precip_plot,daily_precp_data,cumulative_plot | |
| from precipitation_function import annual_max_precip_plot,annual_min_precip_plot,annual_avg_plot,max_precip,min_precip,avg_precip,cumulative,main_concat | |
| from temperature_functions import convert_df, map_creation, search_func | |
| def run(): | |
| fig = Figure(width = 550,height = 350) | |
| nearest_lat_long = 0 | |
| nn = 0 | |
| start = pd.to_datetime('2001/01/01') | |
| end = pd.to_datetime('2019/12/31') | |
| p1 = 'historicalData/precip1.zip' | |
| p2 = 'historicalData/precip2.zip' | |
| # p3 = 'historicalData/precip3.zip' | |
| # p4 = 'historicalData/precip4.zip' | |
| # p5 = 'historicalData/precip5.zip' | |
| # p6 = 'historicalData/precip6.zip' | |
| # p7 = 'historicalData/precip7.zip' | |
| # p8 = 'historicalData/precip8.zip' | |
| precipitation_1_og = load_data(p1) | |
| precipitation_2_og = load_data(p2) | |
| # precipitation_3_og = load_data(p3) | |
| # precipitation_4_og = load_data(p4) | |
| # precipitation_5_og = load_data(p5) | |
| # precipitation_6_og = load_data(p6) | |
| # precipitation_7_og = load_data(p7) | |
| # precipitation_8_og = load_data(p8) | |
| lat_long_precipitation_1 = precipitation_1_og['lat_long'] | |
| lat_long_precipitation_2 = precipitation_2_og['lat_long'] | |
| # lat_long_precipitation_3 = precipitation_3_og['lat_long'] | |
| # lat_long_precipitation_4 = precipitation_4_og['lat_long'] | |
| # lat_long_precipitation_5 = precipitation_5_og['lat_long'] | |
| # lat_long_precipitation_6 = precipitation_6_og['lat_long'] | |
| # lat_long_precipitation_7 = precipitation_7_og['lat_long'] | |
| # lat_long_precipitation_8 = precipitation_8_og['lat_long'] | |
| # lat_long_precipitation_list_1 = concat_func(lat_long_precipitation_1,lat_long_precipitation_2,lat_long_precipitation_3,lat_long_precipitation_4) | |
| lat_long_precipitation_list = concat_func(lat_long_precipitation_1,lat_long_precipitation_2) | |
| # lat_long_precipitation_list_2 = concat_func(lat_long_precipitation_5,lat_long_precipitation_6,lat_long_precipitation_7,lat_long_precipitation_8) | |
| # lat_long_precipitation_list = main_concat(lat_long_precipitation_list_1,lat_long_precipitation_list_2) | |
| # precipitation_conc_1 = concat_func(precipitation_1_og,precipitation_2_og,precipitation_3_og,precipitation_4_og) | |
| precipitation = concat_func(precipitation_1_og,precipitation_2_og) | |
| # precipitation_conc_2 = concat_func(precipitation_5_og,precipitation_6_og,precipitation_7_og,precipitation_8_og) | |
| # precipitation = main_concat(precipitation_conc_1,precipitation_conc_2) | |
| # precipitation = precipitation.drop([Unnamed:0.2,Unnamed:0.1,Unnamed:0],axis =1) | |
| precipitation.drop(precipitation.filter(regex="Unnamed"),axis=1, inplace=True) | |
| precipitation_temp = precipitation.copy() | |
| precipitation_temp = precipitation_temp.groupby('lat_long') | |
| with st.sidebar: | |
| data_type = st.radio("Select Data Type to View", | |
| ('Daily Precipitation','Cumulative Monthly Precipitation','Monthly Average Precipitation', | |
| 'Annual Maximum Precipitation','Annual Minimum Precipitation', | |
| 'Annual Average Precipitation','Annual Max, Min, & Average Precipitation')) | |
| if data_type == "Cumulative Monthly Precipitation": | |
| col1,col2 = st.columns(2) | |
| with col1: | |
| # start_year,end_year = year_selection_ui(99,100) | |
| st.markdown('**Select the Start Year**') | |
| start_year = st.selectbox('', | |
| ('2001','2002','2003','2004','2005','2006','2007','2008','2009', | |
| '2010','2011','2012','2013','2014','2015','2016','2017','2018','2019'),key = 99) | |
| st.markdown('**Select the End Year**') | |
| end_year = st.selectbox('', | |
| ('2001','2002','2003','2004','2005','2006','2007','2008','2009', | |
| '2010','2011','2012','2013','2014','2015','2016','2017','2018','2019'),key =100) | |
| with col2: | |
| #calling the ui method to create a lat long input UI | |
| # latitude_input,longitude_input = lat_long_ui(300,400) | |
| st.markdown('**Enter the latitude**') | |
| latitude_input = st.text_input('','12.55',key = 1) | |
| st.markdown('**Enter the longitude**') | |
| longitude_input = st.text_input('','42.45',key = 2) | |
| latitude_input,longitude_input= float(latitude_input),float(longitude_input) | |
| nearest_lat_long = search_func(latitude_input,longitude_input,lat_long_precipitation_list,precipitation) | |
| option = lat_long_type(nearest_lat_long) | |
| with col1: | |
| st.write("**Nearest latitude and longitude from the entered latitude longitude is :**",option) | |
| df_cumulative = precipitation_temp.get_group(option) | |
| df_cumulative = date_split(df_cumulative) | |
| lat = float(option.split(',')[0]) | |
| long = float(option.split(',')[1]) | |
| m = map_creation(lat,long,0,0) | |
| last_click = m['last_clicked'] | |
| with col2: | |
| if last_click is not None: | |
| clicked_lat = last_click['lat'] | |
| clicked_long = last_click['lng'] | |
| st.markdown("**Last Clicked Latitude Longitude point is:**") | |
| st.write("{:0.2f},{:0.2f}".format(clicked_lat,clicked_long)) | |
| nn = search_func(clicked_lat,clicked_long,lat_long_precipitation_list,precipitation) | |
| nn = lat_long_type(nn) | |
| st.write("**Nearest Latitude and Longitude is:**",nn) | |
| st.markdown(" ") | |
| else: | |
| st.markdown("**Click on the map to fetch the Latitude and Longitude**") | |
| if last_click is not None: | |
| clicked_lat = last_click['lat'] | |
| clicked_long = last_click['lng'] | |
| map_creation(lat,long,clicked_lat,clicked_long) | |
| df_c = cumulative(df_cumulative,start_year,end_year) | |
| fig_cumulative = cumulative_plot(df_c) | |
| st.plotly_chart(fig_cumulative,use_container_width=True) | |
| c1,c2,c3,c4,c5 = st.columns(5) | |
| with c3: | |
| st.download_button("Download Data",data = convert_df(df_c), | |
| file_name='cumulative_precipitation.csv', | |
| mime='text/csv',) | |
| elif data_type == "Daily Precipitation": | |
| col1,col2 = st.columns(2) | |
| with col1: | |
| #calling the ui method to create a start end date input UI | |
| # start,end = start_end_date_ui(start,end,11,22) | |
| st.markdown('**Enter Start Date**') | |
| start = st.date_input("",value = start,key = 9000) | |
| if start < pd.to_datetime('2001/01/01'): | |
| st.write('Start date should not be less than 2001/01/01') | |
| st.markdown('**Enter End Date**') | |
| end = st.date_input("",value = end, key = 900) | |
| if end > pd.to_datetime('2019/12/31'): | |
| st.write('End date should not be greater than 2019/12/31') | |
| with col2: | |
| # latitude_input,longitude_input = lat_long_ui(1,2) | |
| st.markdown('**Enter the latitude**') | |
| latitude_input = st.text_input('','12.55',key = 132) | |
| st.markdown('**Enter the longitude**') | |
| longitude_input = st.text_input('','42.45',key = 223) | |
| latitude_input,longitude_input= float(latitude_input),float(longitude_input) | |
| nearest_lat_long = search_func(latitude_input,longitude_input,lat_long_precipitation_list,precipitation) | |
| option = lat_long_type(nearest_lat_long) | |
| with col1: | |
| st.write("**Nearest latitude and longitude from the entered latitude longitude is :**",option) | |
| lat = float(option.split(',')[0]) | |
| long = float(option.split(',')[1]) | |
| m = map_creation(lat,long,0,0) | |
| last_click = m['last_clicked'] | |
| with col2: | |
| if last_click is not None: | |
| clicked_lat = last_click['lat'] | |
| clicked_long = last_click['lng'] | |
| st.markdown("**Last Clicked Latitude Longitude point is:**") | |
| st.write("{:0.2f},{:0.2f}".format(clicked_lat,clicked_long)) | |
| nn = search_func(clicked_lat,clicked_long,lat_long_precipitation_list,precipitation) | |
| nn = lat_long_type(nn) | |
| st.write("**Nearest Latitude and Longitude is:**",nn) | |
| st.markdown(" ") | |
| else: | |
| st.markdown("**Click on the map to fetch the Latitude and Longitude**") | |
| if last_click is not None: | |
| clicked_lat = last_click['lat'] | |
| clicked_long = last_click['lng'] | |
| map_creation(lat,long,clicked_lat,clicked_long) | |
| daily_precp_df = daily_precp_data(precipitation_temp,start,end,option) | |
| # st.write(daily_precp_df.head()) | |
| daily_precp_df.drop_duplicates(inplace = True) | |
| daily_df = daily_precp_df.loc[str(start):str(end)] | |
| # st.write(daily_df) | |
| fig_daily_precp = daily_precp_plot(daily_df) | |
| st.plotly_chart(fig_daily_precp,use_container_width = True) | |
| c1,c2,c3,c4,c5 = st.columns(5) | |
| with c3: | |
| st.download_button("Download Data",data = convert_df(daily_precp_df), | |
| file_name='daily_precipitation.csv', | |
| mime='text/csv',) | |
| elif data_type == 'Monthly Average Precipitation': | |
| col1,col2 = st.columns(2) | |
| with col1: | |
| # start_year,end_year = year_selection_ui(9,10) | |
| st.markdown('**Select the Start Year**') | |
| start_year = st.selectbox('', | |
| ('2001','2002','2003','2004','2005','2006','2007','2008','2009', | |
| '2010','2011','2012','2013','2014','2015','2016','2017','2018','2019'),key = 9) | |
| st.markdown('**Select the End Year**') | |
| end_year = st.selectbox('', | |
| ('2001','2002','2003','2004','2005','2006','2007','2008','2009', | |
| '2010','2011','2012','2013','2014','2015','2016','2017','2018','2019'),key = 10) | |
| with col2: | |
| #calling the ui method to create a lat long input UI | |
| # latitude_input,longitude_input = lat_long_ui(3,4) | |
| st.markdown('**Enter the latitude**') | |
| latitude_input = st.text_input('','12.55',key = 134) | |
| st.markdown('**Enter the longitude**') | |
| longitude_input = st.text_input('','42.45',key = 256) | |
| latitude_input,longitude_input= float(latitude_input),float(longitude_input) | |
| nearest_lat_long = search_func(latitude_input,longitude_input,lat_long_precipitation_list,precipitation) | |
| option = lat_long_type(nearest_lat_long) | |
| lat = float(option.split(',')[0]) | |
| long = float(option.split(',')[1]) | |
| m = map_creation(lat,long,0,0) | |
| last_click = m['last_clicked'] | |
| with col1: | |
| st.write("**Nearest latitude and longitude from the entered latitude longitude is :**",option) | |
| with col2: | |
| if last_click is not None: | |
| clicked_lat = last_click['lat'] | |
| clicked_long = last_click['lng'] | |
| st.markdown("**Last Clicked Latitude Longitude point is:**") | |
| st.write("{:0.2f},{:0.2f}".format(clicked_lat,clicked_long)) | |
| nn = search_func(clicked_lat,clicked_long,lat_long_precipitation_list,precipitation) | |
| nn = lat_long_type(nn) | |
| st.write("**Nearest Latitude and Longitude is:**",nn) | |
| st.markdown(" ") | |
| else: | |
| st.markdown("**Click on the map to fetch the Latitude and Longitude**") | |
| if last_click is not None: | |
| clicked_lat = last_click['lat'] | |
| clicked_long = last_click['lng'] | |
| map_creation(lat,long,clicked_lat,clicked_long) | |
| precipitation_monthly_avg = precipitation_temp.get_group(option) | |
| precipitation_monthly_avg = date_split(precipitation_monthly_avg) | |
| precipt_monthly_avg_df = precipitation_monthly_avg.groupby(['Year','Month'],as_index=False)['precip'].mean() | |
| precipt_monthly_avg_df = precipt_monthly_avg_df.set_index('Year') | |
| precipitation_monthly_avg_df = precipt_monthly_avg_df.loc[str(start_year):str(end_year)] | |
| precipitation_monthly_avg_df.reset_index(inplace = True) | |
| mean_chart_plot = monthly_mean_plot(precipitation_monthly_avg_df) | |
| st.altair_chart(mean_chart_plot) | |
| c1,c2,c3,c4,c5 = st.columns(5) | |
| with c3: | |
| st.download_button("Download Data",data = convert_df(precipitation_monthly_avg_df), | |
| file_name='Monthly_avg_precipitation.csv', | |
| mime='text/csv',) | |
| elif data_type == 'Annual Maximum Precipitation': | |
| col1,col2 = st.columns(2) | |
| with col1: | |
| # start_year,end_year = year_selection_ui(11,12) | |
| st.markdown('**Select the Start Year**') | |
| start_year = st.selectbox('', | |
| ('2001','2002','2003','2004','2005','2006','2007','2008','2009', | |
| '2010','2011','2012','2013','2014','2015','2016','2017','2018','2019'),key = 111) | |
| st.markdown('**Select the End Year**') | |
| end_year = st.selectbox('', | |
| ('2001','2002','2003','2004','2005','2006','2007','2008','2009', | |
| '2010','2011','2012','2013','2014','2015','2016','2017','2018','2019'),key =222) | |
| with col2: | |
| #calling the ui method to create a lat long input UI | |
| # latitude_input,longitude_input = lat_long_ui(5,6) | |
| st.markdown('**Enter the latitude**') | |
| latitude_input = st.text_input('','12.55',key = 145) | |
| st.markdown('**Enter the longitude**') | |
| longitude_input = st.text_input('','42.45',key = 267) | |
| latitude_input,longitude_input= float(latitude_input),float(longitude_input) | |
| nearest_lat_long = search_func(latitude_input,longitude_input,lat_long_precipitation_list,precipitation) | |
| option = lat_long_type(nearest_lat_long) | |
| lat = float(option.split(',')[0]) | |
| long = float(option.split(',')[1]) | |
| m = map_creation(lat,long,0,0) | |
| last_click = m['last_clicked'] | |
| with col1: | |
| st.write("**Nearest latitude and longitude from the entered latitude longitude is :**",option) | |
| with col2: | |
| if last_click is not None: | |
| clicked_lat = last_click['lat'] | |
| clicked_long = last_click['lng'] | |
| st.markdown("**Last Clicked Latitude Longitude point is:**") | |
| st.write("{:0.2f},{:0.2f}".format(clicked_lat,clicked_long)) | |
| nn = search_func(clicked_lat,clicked_long,lat_long_precipitation_list,precipitation) | |
| nn = lat_long_type(nn) | |
| st.write("**Nearest Latitude and Longitude is:**",nn) | |
| st.markdown(" ") | |
| else: | |
| st.markdown("**Click on the map to fetch the Latitude and Longitude**") | |
| if last_click is not None: | |
| clicked_lat = last_click['lat'] | |
| clicked_long = last_click['lng'] | |
| map_creation(lat,long,clicked_lat,clicked_long) | |
| Annual_max_precip = max_precip(precipitation_temp,option,start_year,end_year) | |
| fig_max = annual_max_precip_plot(Annual_max_precip) | |
| st.plotly_chart(fig_max,use_container_width=True) | |
| c1,c2,c3,c4,c5 = st.columns(5) | |
| with c3: | |
| st.download_button("Download Data",data = convert_df(Annual_max_precip), | |
| file_name='Annual_max_precipitation.csv', | |
| mime='text/csv',) | |
| elif data_type == 'Annual Minimum Precipitation': | |
| col1,col2 = st.columns(2) | |
| with col1: | |
| # start_year,end_year = year_selection_ui(13,14) | |
| st.markdown('**Select the Start Year**') | |
| start_year = st.selectbox('', | |
| ('2001','2002','2003','2004','2005','2006','2007','2008','2009', | |
| '2010','2011','2012','2013','2014','2015','2016','2017','2018','2019'),key = 13) | |
| st.markdown('**Select the End Year**') | |
| end_year = st.selectbox('', | |
| ('2001','2002','2003','2004','2005','2006','2007','2008','2009', | |
| '2010','2011','2012','2013','2014','2015','2016','2017','2018','2019'),key = 14) | |
| with col2: | |
| #calling the ui method to create a lat long input UI | |
| # latitude_input,longitude_input = lat_long_ui(7,8) | |
| st.markdown('**Enter the latitude**') | |
| latitude_input = st.text_input('','12.55',key = 109) | |
| st.markdown('**Enter the longitude**') | |
| longitude_input = st.text_input('','42.45',key = 209) | |
| latitude_input,longitude_input= float(latitude_input),float(longitude_input) | |
| nearest_lat_long = search_func(latitude_input,longitude_input,lat_long_precipitation_list,precipitation) | |
| option = lat_long_type(nearest_lat_long) | |
| lat = float(option.split(',')[0]) | |
| long = float(option.split(',')[1]) | |
| m = map_creation(lat,long,0,0) | |
| last_click = m['last_clicked'] | |
| with col1: | |
| st.write("**Nearest latitude and longitude from the entered latitude longitude is :**",option) | |
| with col2: | |
| if last_click is not None: | |
| clicked_lat = last_click['lat'] | |
| clicked_long = last_click['lng'] | |
| st.markdown("**Last Clicked Latitude Longitude point is:**") | |
| st.write("{:0.2f},{:0.2f}".format(clicked_lat,clicked_long)) | |
| nn = search_func(clicked_lat,clicked_long,lat_long_precipitation_list,precipitation) | |
| nn = lat_long_type(nn) | |
| st.write("**Nearest Latitude and Longitude is:**",nn) | |
| st.markdown(" ") | |
| else: | |
| st.markdown("**Click on the map to fetch the Latitude and Longitude**") | |
| if last_click is not None: | |
| clicked_lat = last_click['lat'] | |
| clicked_long = last_click['lng'] | |
| map_creation(lat,long,clicked_lat,clicked_long) | |
| minimum_precip_df = min_precip(precipitation_temp,option,start_year,end_year) | |
| fig_min = annual_min_precip_plot(minimum_precip_df) | |
| st.plotly_chart(fig_min,use_container_width = True) | |
| c1,c2,c3,c4,c5 = st.columns(5) | |
| with c3: | |
| st.download_button("Download Data",data = convert_df(minimum_precip_df), | |
| file_name='Annual_minimum_precipitation.csv', | |
| mime='text/csv',) | |
| elif data_type == 'Annual Average Precipitation': | |
| col1,col2 = st.columns(2) | |
| with col1: | |
| # start_year,end_year = year_selection_ui(15,16) | |
| st.markdown('**Select the Start Year**') | |
| start_year = st.selectbox('', | |
| ('2001','2002','2003','2004','2005','2006','2007','2008','2009', | |
| '2010','2011','2012','2013','2014','2015','2016','2017','2018','2019'),key = 15) | |
| st.markdown('**Select the End Year**') | |
| end_year = st.selectbox('', | |
| ('2001','2002','2003','2004','2005','2006','2007','2008','2009', | |
| '2010','2011','2012','2013','2014','2015','2016','2017','2018','2019'),key = 26) | |
| with col2: | |
| #calling the ui method to create a lat long input UI | |
| # latitude_input,longitude_input = lat_long_ui(9,10) | |
| st.markdown('**Enter the latitude**') | |
| latitude_input = st.text_input('','12.55',key = 198) | |
| st.markdown('**Enter the longitude**') | |
| longitude_input = st.text_input('','42.45',key =298) | |
| latitude_input,longitude_input= float(latitude_input),float(longitude_input) | |
| nearest_lat_long = search_func(latitude_input,longitude_input,lat_long_precipitation_list,precipitation) | |
| option = lat_long_type(nearest_lat_long) | |
| lat = float(option.split(',')[0]) | |
| long = float(option.split(',')[1]) | |
| m = map_creation(lat,long,0,0) | |
| last_click = m['last_clicked'] | |
| with col1: | |
| st.write("**Nearest latitude and longitude from the entered latitude longitude is :**",option) | |
| with col2: | |
| if last_click is not None: | |
| clicked_lat = last_click['lat'] | |
| clicked_long = last_click['lng'] | |
| st.markdown("**Last Clicked Latitude Longitude point is:**") | |
| st.write("{:0.2f},{:0.2f}".format(clicked_lat,clicked_long)) | |
| nn = search_func(clicked_lat,clicked_long,lat_long_precipitation_list,precipitation) | |
| nn = lat_long_type(nn) | |
| st.write("**Nearest Latitude and Longitude is:**",nn) | |
| st.markdown(" ") | |
| else: | |
| st.markdown("**Click on the map to fetch the Latitude and Longitude**") | |
| if last_click is not None: | |
| clicked_lat = last_click['lat'] | |
| clicked_long = last_click['lng'] | |
| map_creation(lat,long,clicked_lat,clicked_long) | |
| avg_precip_df_se = avg_precip(precipitation_temp,option,start_year,end_year) | |
| fig_avg = annual_avg_plot(avg_precip_df_se) | |
| st.plotly_chart(fig_avg,use_container_width = True) | |
| c1,c2,c3,c4,c5 = st.columns(5) | |
| with c3: | |
| st.download_button("Download Data",data = convert_df(avg_precip_df_se), | |
| file_name='Annual_avg_precipitation.csv', | |
| mime='text/csv',) | |
| elif data_type == 'Annual Max, Min, & Average Precipitation': | |
| col1,col2 = st.columns(2) | |
| with col1: | |
| # start_year,end_year = year_selection_ui(17,18) | |
| st.markdown('**Select the Start Year**') | |
| start_year = st.selectbox('', | |
| ('2001','2002','2003','2004','2005','2006','2007','2008','2009', | |
| '2010','2011','2012','2013','2014','2015','2016','2017','2018','2019'),key = 1111) | |
| st.markdown('**Select the End Year**') | |
| end_year = st.selectbox('', | |
| ('2001','2002','2003','2004','2005','2006','2007','2008','2009', | |
| '2010','2011','2012','2013','2014','2015','2016','2017','2018','2019'),key = 2222) | |
| with col2: | |
| # latitude_input,longitude_input = lat_long_ui(11,12) | |
| st.markdown('**Enter the latitude**') | |
| latitude_input = st.text_input('','12.55',key = 186) | |
| st.markdown('**Enter the longitude**') | |
| longitude_input = st.text_input('','42.45',key = 2543) | |
| latitude_input,longitude_input= float(latitude_input),float(longitude_input) | |
| nearest_lat_long = search_func(latitude_input,longitude_input,lat_long_precipitation_list,precipitation) | |
| option = lat_long_type(nearest_lat_long) | |
| lat = float(option.split(',')[0]) | |
| long = float(option.split(',')[1]) | |
| m = map_creation(lat,long,0,0) | |
| last_click = m['last_clicked'] | |
| with col1: | |
| st.write("**Nearest latitude and longitude from the entered latitude longitude is :**",option) | |
| with col2: | |
| if last_click is not None: | |
| clicked_lat = last_click['lat'] | |
| clicked_long = last_click['lng'] | |
| st.markdown("**Last Clicked Latitude Longitude point is:**") | |
| st.write("{:0.2f},{:0.2f}".format(clicked_lat,clicked_long)) | |
| nn = search_func(clicked_lat,clicked_long,lat_long_precipitation_list,precipitation) | |
| nn = lat_long_type(nn) | |
| st.write("**Nearest Latitude and Longitude is:**",nn) | |
| st.markdown(" ") | |
| else: | |
| st.markdown("**Click on the map to fetch the Latitude and Longitude**") | |
| if last_click is not None: | |
| clicked_lat = last_click['lat'] | |
| clicked_long = last_click['lng'] | |
| map_creation(lat,long,clicked_lat,clicked_long) | |
| col11,col22,col33 = st.columns(3) | |
| with col11: | |
| annual_max_data = max_precip(precipitation_temp,option,start_year,end_year) | |
| fig_max_2 = annual_max_precip_plot(annual_max_data) | |
| st.plotly_chart(fig_max_2,use_container_width=True) | |
| st.download_button("Download Data",data = convert_df(annual_max_data), | |
| file_name='Annual_max_precipitation_data.csv', | |
| mime='text/csv',) | |
| with col22: | |
| annual_min_data = min_precip(precipitation_temp,option,start_year,end_year) | |
| fig_min_2 = annual_min_precip_plot(annual_min_data) | |
| st.plotly_chart(fig_min_2,use_container_width = True) | |
| st.download_button("Download Data",data = convert_df(annual_min_data), | |
| file_name='Annual_min_precipitation_data.csv', | |
| mime='text/csv',) | |
| with col33: | |
| annual_avg_data = avg_precip(precipitation_temp,option,start_year,end_year) | |
| fig_avg_2 = annual_avg_plot(annual_avg_data) | |
| st.plotly_chart(fig_avg_2, use_container_width = True) | |
| st.download_button("Download Data",data = convert_df(annual_avg_data), | |
| file_name='Annual_avg_precipitation_data.csv', | |
| mime='text/csv',) | |
| if __name__ == 'main': | |
| run() | |