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| import pandas as pd | |
| import numpy as np | |
| import streamlit as st | |
| #imports for finding the nearest lat long using haversine distance | |
| #visualization libraries to visualize different plots | |
| import plotly.express as px | |
| import plotly.graph_objects as go | |
| import altair as alt | |
| import io | |
| #for Logo plotting | |
| from PIL import Image | |
| #disabling warnings | |
| import warnings | |
| warnings.filterwarnings("ignore") | |
| #For parallel processing | |
| from pandarallel import pandarallel | |
| pandarallel.initialize(progress_bar=True) | |
| from google.oauth2 import service_account | |
| from google.cloud import storage | |
| # Create API client. | |
| # credentials = service_account.Credentials.from_service_account_info( | |
| # st.secrets["gcp_service_account"] | |
| # ) | |
| # client = storage.Client(credentials=credentials) | |
| # | |
| # @st.cache(allow_output_mutation = True) | |
| # def read_file(bucket_name, file_path): | |
| # bucket = client.bucket(bucket_name) | |
| # data = bucket.blob(file_path).download_as_bytes() | |
| # df = pd.read_csv(io.BytesIO(data),compression='zip') | |
| # return df | |
| def date_split(df): | |
| df[['Year','Month','Day']] = df['date'].str.split('-',expand = True) | |
| return df | |
| def lat_long_process_precp(df): | |
| df['lat_long'] = df['lat'].astype(str)+','+df['long'].astype(str) | |
| return df | |
| def drop_dup_funct(x): | |
| x.drop_duplicates(inplace = True) | |
| return x | |
| def concat_func(x,y,a,b): | |
| z = pd.concat([x,y,a,b],ignore_index = True) | |
| return z | |
| def lat_long_type(nn_value): | |
| if isinstance(nn_value,str): | |
| return nn_value | |
| else: | |
| return nn_value.item((0)) | |
| def cumulative(df,start,end): | |
| df1 = df.groupby(['Year','Month'])['precip'].sum() | |
| df1 = df1.reset_index() | |
| df1 = df1.set_index('Year') | |
| df1 = df1.loc[str(start):str(end)] | |
| return df1 | |
| def cumulative_plot(df): | |
| fig = px.line(df, y='precip',title = 'Monthly Cumulative Precipitation') | |
| fig.update_traces(line_color = 'blue') | |
| fig.update_xaxes(title_text = 'Year',gridcolor = 'whitesmoke') | |
| fig.update_yaxes(ticklabelposition="inside top", title= 'Monthly Cumulative Precipitation in mm',gridcolor = 'whitesmoke') | |
| fig.update_layout(margin = dict(l=25,r=25,t=25,b=25)) | |
| fig.update_layout(plot_bgcolor = 'rgba(0,0,0,0)') | |
| fig.update_layout(title = "Monthly Cumulative Precipitation") | |
| return fig | |
| def daily_precp_data(precipitation_temp,start,end,option): | |
| df_daily = precipitation_temp.get_group(option) | |
| df_daily.set_index('date',inplace = True) | |
| # df_2 = df_daily.loc[str(start):str(end)] | |
| # df_3=df_2.reset_index() | |
| return df_daily | |
| def daily_precp_plot(df): | |
| fig = px.line(df,y='precip',title = 'Daily Precipitation') | |
| fig.update_traces(line_color = 'blue') | |
| fig.update_xaxes(title_text = 'Year',gridcolor = 'whitesmoke') | |
| fig.update_yaxes(ticklabelposition="inside top", title= 'Daily Precipitation in mm',gridcolor = 'whitesmoke') | |
| fig.update_layout(margin = dict(l=25,r=25,t=25,b=25)) | |
| fig.update_layout(plot_bgcolor = 'rgba(0,0,0,0)') | |
| fig.update_layout(title = "Daily Precipitation") | |
| return fig | |
| def start_end_date_ui(start,end,key1,key2): | |
| st.markdown('**Enter Start Date**') | |
| start = st.date_input("",value = start,key = key1) | |
| 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 = key2) | |
| if end > pd.to_datetime('2019/12/31'): | |
| st.write('End date should not be greater than 2019/12/31') | |
| return start,end | |
| def lat_long_ui(key1,key2): | |
| st.markdown('**Enter the latitude**') | |
| latitude_input = st.text_input('','12.55',key = key1) | |
| st.markdown('**Enter the longitude**') | |
| longitude_input = st.text_input('','42.45',key = key2) | |
| return latitude_input,longitude_input | |
| def year_selection_ui(key1,key2): | |
| 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 = key1) | |
| 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 = key2) | |
| return start_year,end_year | |
| def monthly_mean_plot(df): | |
| title_text = "Monthly Mean Precipitation" | |
| highlight = alt.selection( | |
| type='single', on='mouseover', fields=['Year'], nearest=True) | |
| base = alt.Chart(df,title = title_text).encode( | |
| x = alt.X('Month:Q',scale = alt.Scale(domain=[1,12]),axis=alt.Axis(tickMinStep=1)), | |
| y = alt.Y('precip:Q',scale = alt.Scale(domain=[df['precip'].min(),df['precip'].max()])), | |
| color = alt.Color('Year:O',scale = alt.Scale(scheme = 'magma')) | |
| ) | |
| points = base.mark_circle().encode( | |
| opacity=alt.value(0), | |
| tooltip=[ | |
| alt.Tooltip('Year:O', title='Year'), | |
| alt.Tooltip('Month:Q', title='Month'), | |
| alt.Tooltip('precip:Q', title='Monthly Mean Precipitation') | |
| ]).add_selection(highlight) | |
| lines = base.mark_line().encode( | |
| size=alt.condition(~highlight, alt.value(1), alt.value(3))) | |
| mean_chart = (points + lines).properties(width=1000, height=400).interactive() | |
| return mean_chart | |
| def annual_max_precip_plot(df): | |
| fig_max = px.line(df, x = 'Year',y='precip',title = 'Annual Maximum Precipitation') | |
| fig_max.update_traces(line_color = 'maroon') | |
| fig_max.update_xaxes(title_text = 'Year',gridcolor = 'whitesmoke') | |
| fig_max.update_yaxes(ticklabelposition="inside top", title= 'Annual Maximum Precipitation in mm',gridcolor = 'whitesmoke') | |
| fig_max.update_layout(margin = dict(l=25,r=25,t=25,b=25)) | |
| fig_max.update_layout(plot_bgcolor = 'rgba(0,0,0,0)') | |
| fig_max.update_layout(title = "Annual Maximum Precipitation") | |
| return fig_max | |
| def annual_min_precip_plot(df): | |
| fig_min = px.line(df, x = 'Year',y='precip',title = 'Annual Minimum Precipitation') | |
| fig_min.update_traces(line_color = 'blue') | |
| fig_min.update_xaxes(title_text = 'Year',gridcolor = 'whitesmoke') | |
| fig_min.update_yaxes(ticklabelposition="inside top", title= 'Annual Minimum Precipitation in mm',gridcolor = 'whitesmoke') | |
| fig_min.update_layout(margin = dict(l=25,r=25,t=25,b=25)) | |
| fig_min.update_layout(plot_bgcolor = 'rgba(0,0,0,0)') | |
| fig_min.update_layout(title = "Annual Minimum Precipitation") | |
| return fig_min | |
| def annual_avg_plot(df): | |
| fig_avg = px.line(df, x = 'Year',y='precip',title = 'Annual Average Precipitation') | |
| fig_avg.update_traces(line_color = 'dimgray') | |
| fig_avg.update_xaxes(title_text = 'Year',gridcolor = 'whitesmoke') | |
| fig_avg.update_yaxes(ticklabelposition="inside top", title= 'Annual Average Precipitation in mm',gridcolor = 'whitesmoke') | |
| fig_avg.update_layout(margin = dict(l=25,r=25,t=25,b=25)) | |
| fig_avg.update_layout(plot_bgcolor = 'rgba(0,0,0,0)') | |
| fig_avg.update_layout(title = "Annual Average Precipitation") | |
| return fig_avg | |
| def max_precip(precipitation_temp,option,start_year,end_year): | |
| maximum_precip_df = precipitation_temp.get_group(option) | |
| maximum_precip_df = date_split(maximum_precip_df) | |
| Annual_max_precip = maximum_precip_df.groupby('Year')['precip'].max() | |
| Annual_max_precip = Annual_max_precip.loc[str(start_year):str(end_year)] | |
| Annual_max_precip = Annual_max_precip.reset_index() | |
| return Annual_max_precip | |
| def min_precip(precipitation_temp,option,start_year,end_year): | |
| minimum_precip_df = precipitation_temp.get_group(option) | |
| minimum_precip_df = date_split(minimum_precip_df) | |
| minimum_precip_df = minimum_precip_df.where(minimum_precip_df['precip']>0) | |
| minimum_precip_df = minimum_precip_df.groupby('Year')['precip'].min() | |
| minimum_precip_df = minimum_precip_df.loc[str(start_year):str(end_year)] | |
| minimum_precip_df = minimum_precip_df.reset_index() | |
| return minimum_precip_df | |
| def avg_precip(precipitation_temp,option,start_year,end_year): | |
| avg_precip_df = precipitation_temp.get_group(option) | |
| avg_precip_df = date_split(avg_precip_df) | |
| avg_precip_df = avg_precip_df.groupby('Year')['precip'].mean() | |
| avg_precip_df_s_e = avg_precip_df.loc[str(start_year):str(end_year)] | |
| avg_precip_df_s_e = avg_precip_df_s_e.reset_index() | |
| return avg_precip_df_s_e | |