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| #standard imports | |
| import pandas as pd | |
| import numpy as np | |
| import streamlit as st | |
| import os | |
| import glob | |
| #imports for finding the nearest lat long using haversine distance | |
| from math import radians, cos, sin, asin, sqrt | |
| import math | |
| #visualization libraries to visualize different plots | |
| import plotly.express as px | |
| import plotly.graph_objects as go | |
| import altair as alt | |
| #for Logo plotting | |
| from PIL import Image | |
| #for Folium Map Creation | |
| from typing import Dict, List, Optional | |
| import folium | |
| from streamlit_folium import st_folium | |
| from branca.element import Figure | |
| from precipitation_function import lat_long_type | |
| #disabling warnings | |
| import warnings | |
| warnings.filterwarnings("ignore") | |
| #For parallel processing | |
| from pandarallel import pandarallel | |
| pandarallel.initialize(progress_bar=True) | |
| fig = Figure(width = 550,height = 350) | |
| #function for calculating annual average | |
| def annual_avg(df,year_lst): | |
| annual_avg_df_lst = list() | |
| for i in year_lst: | |
| d = df.get_group((i)) | |
| d['Annual avg'] = (d['daily_avg'].sum())/len(d) | |
| annual_avg_df_lst.append(d) | |
| return annual_avg_df_lst | |
| #function for splitting the date column values into year, month and day | |
| def df_date_split(df,lat_long_list): | |
| df_date_list = list() | |
| for i in lat_long_list: | |
| grouped_df = df.get_group(i) | |
| grouped_df[['Year','Month','Day']] = grouped_df['date'].str.split('-',expand = True) | |
| df_date_list.append(grouped_df) | |
| date_split_df = pd.concat(df_date_list) | |
| return date_split_df | |
| #function for creating 2 separate columns one for country code with 'et' values and 2nd with lat long concated values | |
| def lat_long_process(df): | |
| df['lat_long'] = df.loc[:,'lat'].astype(str)+','+df.loc[:,'long'].astype(str) | |
| df.drop_duplicates(inplace = True) | |
| return df | |
| #Creating a separate dataframe for lat long values and returning a list of lat_long values | |
| def lat_long_list_creation(df): | |
| lat_long_df = df[['lat','long']] | |
| lat_long_df['lat_long'] = lat_long_df.loc[:,'lat'].astype(str)+','+lat_long_df.loc[:,'long'].astype(str) | |
| lat_long_df.drop_duplicates(inplace=True) | |
| lat_long_list = lat_long_df['lat_long'] | |
| return lat_long_list | |
| #function for grouping the data on lat long and returning only the et lat long | |
| def group_df(df,lat_long_lst): | |
| in_names = df.groupby(df['lat_long']) | |
| temperaturedf_new = list() | |
| for i in lat_long_lst: | |
| df1 = in_names.get_group(i) | |
| temperaturedf_new.append(df1) | |
| result = pd.concat(temperaturedf_new) | |
| return result | |
| #function for calculating daily average | |
| def daily_avg(x,y): | |
| return (x+y)/2 | |
| #creating avg dataframe | |
| def annual_avg_plot(annual_avg_df,lat_long_option,lat_long_list): | |
| annual_avg_df = annual_avg_df.groupby('lat_long') | |
| annual_avg_df = df_date_split(annual_avg_df,lat_long_list) | |
| annual_avg_df = annual_avg_df.groupby('lat_long') | |
| annual_avg_df = annual_avg_df.get_group(lat_long_option) | |
| #returns the list of unique year values | |
| year_lst = annual_avg_df['Year'].unique() | |
| annual_avg_dataframe = annual_avg_df.groupby('Year') | |
| annual_avg_result = annual_avg(annual_avg_dataframe,year_lst) | |
| annual_average = pd.concat(annual_avg_result,axis = 0) | |
| return annual_average | |
| #creating average temp plotly chart | |
| def avg_temp_plot(annual_temp,lat_long_val): | |
| #CODE FOR PLOTTING ANNUAL AVERAGE TEMPERATURE | |
| fig_avg = px.line(annual_temp, x= 'Year',y='Annual avg') | |
| fig_avg.update_layout( | |
| yaxis = dict(tickfont = dict(size=15)), | |
| xaxis = dict(tickfont = dict(size=15)), | |
| plot_bgcolor = 'rgba(0,0,0,0)') | |
| fig_avg.update_traces(line_color ='dimgray') | |
| fig_avg.update_xaxes(gridcolor='whitesmoke') | |
| fig_avg.update_yaxes(gridcolor = 'whitesmoke') | |
| fig_avg.update_yaxes(title = 'Annual Average Temperature (C)') | |
| # fig_avg.update_layout(title = "Annual Average Temperature: "+str(lat_long_val)) | |
| return fig_avg | |
| def annual_min_plot(Annual_temp_min,option_annual_min_temp,lat_long_list): | |
| Annual_temp_min = Annual_temp_min.groupby('lat_long') | |
| Annual_temp_min = df_date_split(Annual_temp_min,lat_long_list) | |
| Annual_temp_min = Annual_temp_min.groupby(['lat_long','Year'])[['tmin']].min() | |
| Annual_temp_min.rename(columns = {'tmin':'Yearly_minimum_temp'},inplace = True) | |
| Annual_temp_min.reset_index(inplace = True) | |
| Annual_temp_min = Annual_temp_min.groupby('lat_long') | |
| df2 = Annual_temp_min.get_group(option_annual_min_temp) | |
| return df2 | |
| def annual_max_plot(Annual_temp,option_annual_temp,lat_long_list): | |
| Annual_temp = Annual_temp.groupby('lat_long') | |
| Annual_temp = df_date_split(Annual_temp,lat_long_list) | |
| Annual_temp = Annual_temp.groupby(['lat_long','Year'])[['tmax']].max() | |
| Annual_temp.rename(columns = {'tmax':'Yearly_maximum_temp'},inplace = True) | |
| Annual_temp.reset_index(inplace = True) | |
| Annual_temp = Annual_temp.groupby('lat_long') | |
| df1 = Annual_temp.get_group(option_annual_temp) | |
| return df1 | |
| def daily_avg_calc(result,option,start,end): | |
| grouped_temperature_df = result.groupby('lat_long') | |
| data_frame = grouped_temperature_df.get_group(option) | |
| data_frame.set_index('date',inplace = True) | |
| data_frame_start_end = data_frame.loc[str(start):str(end)] | |
| data_frame_start_end = data_frame_start_end.reset_index() | |
| return data_frame_start_end | |
| def daily_avg_plot(data_frame_start_end,lat_long_val): | |
| #Plotting the line chart of the daily average | |
| fig = px.line(data_frame_start_end, x = 'date',y='daily_avg',title = 'Daily Average Temperature') | |
| fig.update_traces(line_color = 'blue') | |
| fig.update_xaxes(title_text = 'Year',gridcolor = 'whitesmoke') | |
| fig.update_yaxes(ticklabelposition="inside top", title= 'Daily Average Temperature (C)',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 Average Temperature: "+str(lat_long_val)) | |
| return fig | |
| def monthly_mean_calc(temperature_monthly_df,lat_long_list): | |
| #monthly mean calculation | |
| temperature_monthly_df = temperature_monthly_df.groupby('lat_long') | |
| date_split_df = df_date_split(temperature_monthly_df,lat_long_list) | |
| monthly_avg_temp = date_split_df.groupby(['lat_long','Year','Month'])[['daily_avg']].mean() | |
| monthly_avg_temp.rename(columns = {'daily_avg':'Monthly mean temperature'},inplace = True) | |
| return monthly_avg_temp | |
| def selecting_mean(monthly_avg_temp,option_mean,start_year,end_year): | |
| monthly_avg_temp = monthly_avg_temp.reset_index().set_index('Year').groupby('lat_long') | |
| grouped_monthly_mean = monthly_avg_temp.get_group(option_mean) | |
| df = grouped_monthly_mean.loc[start_year:end_year] | |
| df = df.reset_index() | |
| return df | |
| def plot_mean_data(df,lat_long_val): | |
| title_text = "Monthly Mean Temperature: "+str(lat_long_val) | |
| 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('Monthly mean temperature:Q',scale = alt.Scale(domain =[int(df['Monthly mean temperature'].min()),int(df['Monthly mean temperature'].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('Monthly mean temperature:Q', title='Mean temp') | |
| ]).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=450).interactive() | |
| return mean_chart | |
| def max_temp_plot(df_max,lat_long_val): | |
| fig_max = px.line(df_max,x = 'Year',y='Yearly_maximum_temp') | |
| fig_max.update_traces(line_color = 'maroon') | |
| fig_max.update_layout( | |
| yaxis = dict(tickfont = dict(size=15)), | |
| xaxis = dict(tickfont = dict(size=15)), | |
| plot_bgcolor = 'rgba(0,0,0,0)') | |
| fig_max.update_xaxes(gridcolor='whitesmoke') | |
| fig_max.update_yaxes(gridcolor = 'whitesmoke') | |
| fig_max.update_yaxes(title = "Annual Maximum Temperature (C)") | |
| # fig_max.update_layout(title = "Yearly Maximum Temperature: "+str(lat_long_val)) | |
| return fig_max | |
| def min_temp_plot(df_min,lat_long_val): | |
| fig_min = px.line(df_min, x= 'Year',y = 'Yearly_minimum_temp') | |
| fig_min.update_traces(line_color ='blue') | |
| fig_min.update_layout( | |
| yaxis = dict(tickfont = dict(size=15)), | |
| xaxis = dict(tickfont = dict(size=15)), | |
| plot_bgcolor = 'rgba(0,0,0,0)') | |
| fig_min.update_xaxes(gridcolor='whitesmoke') | |
| fig_min.update_yaxes(gridcolor = 'whitesmoke') | |
| fig_min.update_yaxes(title = "Annual Minimum Temperature (C)") | |
| # fig_min.update_layout(title = "Yearly Minimum Temperature: "+str(lat_long_val)) | |
| return fig_min | |
| #code for downloading Data as a CSV File | |
| def convert_df(df): | |
| return df.to_csv().encode('utf-8') | |
| # a = st.sidebar.empty() | |
| #result dataframe contains the daily average value as well. | |
| #Function for creating folium map and returning the latitude and Longitude of the clicked location | |
| def map_creation(lat,long,clicked_lat,clicked_long): | |
| with st.sidebar: | |
| m = folium.Map(location = [9.14,40],zoom_start =7) | |
| fig.add_child(m) | |
| tile = folium.TileLayer( | |
| tiles = 'https://server.arcgisonline.com/ArcGIS/rest/services/World_Imagery/MapServer/tile/{z}/{y}/{x}', | |
| attr = 'Esri', | |
| name = 'Esri Satellite', | |
| overlay = False, | |
| control = True).add_to(m) | |
| folium.TileLayer('Stamen Terrain').add_to(m) | |
| folium.TileLayer('Stamen Water Color').add_to(m) | |
| folium.LayerControl().add_to(m) | |
| if (clicked_lat == 0) and (clicked_long == 0): | |
| folium.Marker([lat,long]).add_to(m) | |
| else: | |
| folium.Marker([lat,long]).add_to(m) | |
| folium.Marker([clicked_lat,clicked_long]).add_to(m) | |
| st_data = st_folium(m,key = 'map_fig_1') | |
| return st_data | |
| #df is the initial df that contains only et data | |
| def search_func(latitude,longitude,lt_lng_lst,df): | |
| df['lat_radian'],df['long_radian'] = np.radians(df['lat']),np.radians(df['long']) | |
| df['dLON'] = df['long_radian'] - math.radians(longitude) | |
| df['dLAT'] = df['lat_radian'] - math.radians(latitude) | |
| df['distance'] = 6371 * 2 * np.arcsin(np.sqrt(np.sin(df['dLAT']/2)**2 + math.cos(math.radians(longitude)) * np.cos(df['lat_radian']) * np.sin(df['dLON']/2)**2)) | |
| a = df['distance'].idxmin() | |
| nearest_neighbor = df._get_value(a,'lat_long') | |
| nearest_neighbor = lat_long_type(nearest_neighbor) | |
| # st.write("**Nearest Latitude and Longitude is :**",nearest_neighbor) | |
| return nearest_neighbor | |