initial commit

README.md

+Demo help for user!
\ No newline at end of file

src/test_SDSC21.py

+# -*- coding: utf-8 -*-
+"""
+Created on Wed Mar 10 18:49:03 2021
+
+@author: Shub
+"""
+
+# -*- coding: utf-8 -*-
+"""
+Created on Sat Oct 31 16:54:15 2020
+
+@author: hp
+
+
+# -*- coding: utf-8 -*-
+
+Created on Sat Oct 31 16:28:11 2020
+
+@author: Shub
+"""
+
+import numpy as np
+import streamlit as st
+import pandas as pd
+#from shapely.geometry import Point,Polygon
+#import mplleaflet
+#import matplotlib.pyplot as plt
+import datetime
+import pydeck as pdk
+#import plotly.express as px
+#from windrose import WindroseAxes  
+import altair as alt                                                                                                                                                                                                                                                                                                                                                                                                                                                          
+#import plotly.graph_objects as go
+#import copy
+
+#import pandas as pd
+#import matplotlib.pyplot
+#from matplotlib.widgets import Cursor, MultiCursor
+
+#import altair as alt
+from statistics import mean # library for mathematical calulations
+#import streamlit as st
+#from streamlit_folium import folium_static
+#import folium
+#from vega_datasets import data
+#import numpy
+import streamlit_wide_mode
+import SessionState
+#import naming as n
+
+###@ function method @@@@@####################################################
+    
+
+streamlit_wide_mode._max_width_()
+
+### stacked chart function ####################################
+def bar_plot_method(filter_value2,parameter): 
+    #
+    brush = alt.selection(type='interval', encodings=['x'])
+    base = alt.Chart(filter_value2).mark_area().encode(
+    x="monthdate(time)",
+    y= parameter
+    ).properties(
+    width=1600,
+    height=200
+    )
+    upper = base.encode(alt.X("monthdate(time)", scale=alt.Scale(domain=brush))
+    #alt.Y("wind_speed", scale=alt.Scale(domain=brush))
+    )
+    lower = base.properties(
+        height=60
+        ).add_selection(brush)
+    upper&lower
+# end of method
+
+def format_coord(x, y):
+    
+    # Shree Radhakrishna
+    # https://stackoverflow.com/questions/46531243/how-to-control-mouseover-text-in-matplotlib
+    
+    try:
+        # z = # get value depending on x,y, e.g. via interpolation on grid
+        # I can't fill this because the kind of data is unknown here
+        # return "x: {}, y: {}, z: {}".format(x,y,z)
+        
+        return "x: {}, y: {}".format(x,y) ;
+
+    except:
+        # return "x: {}, y: {}".format(x,y)
+        
+        return "x: {}, y: {}".format(x,y)
+
+# ax.format_coord = format_coord
+
+
+
+###@ reding the data method @@@@@####################################################
+
+## reading the data completed
+#def testmethod(selectedsensor):
+#pathName_input =  'C:\\Users\\Dell\\Desktop\\didiji survey paper\\visualisation_codes\\more_codes\\updated_Sensordata\\updated_Sensordata\\'
+#pathName_input =  'F:/Spatial_interpolation/Sensors_data/updated_Sensordata/'#F:\stuttgart_wind_direction\final_str_Hbf_winddata
+pathName_input = 'C:/Users/Shub/streamlite_dashboard/data/updated_Sensordata/'
+#pathName_input = 'F:/Spatial_interpolation/Sensors_data/updated_Sensordata/'#F:\stuttgart_wind_direction\final_str_Hbf_winddata
+fileName1 = 'sensorA.csv'
+fileName2 = 'sensorB.csv'
+fileName3 = 'sensorC.csv'
+fileName4 = 'sensorD.csv'
+fileName5 = 'sensorE.csv'
+fileName6 = 'sensorF.csv'
+fileName7 = 'sensorG.csv'
+fileName8 = 'sensorH.csv'
+fileName9 = 'sensorI.csv'
+fileName10 = 'sensorJ.csv'
+
+
+#Stations = pd.concat([df1,df2,df3,df4],ignore_index=True)
+#Stations = pd.concat([df1,df2,df3,df4,df5,df6,df7,df8,df9,df10], axis=0).drop_duplicates(subset=['sensor'])
+
+
+#print('\n concatenated data is \n ' , Stations , '\n');
+
+#Stations['Windspeed'] = pd.to_numeric( Stations['Windspeed'] ) ;
+#Stations['PM10'] = pd.to_numeric( Stations['PM10']) ;
+#Stations['PM2_5'] = pd.to_numeric( Stations['PM2_5'] ) ;
+#Stations['Humd'] = pd.to_numeric( Stations['Humd'] ) ;
+
+@st.cache
+def load_data(nrows):
+    #df = pd.read_csv(( pathName_input +fileName ), sep = ",", nrows=nrows)
+    #data = pd.read_pickle(( pathName_input +fileName ), nrows=nrows)
+    #lowercase = lambda x: str(x).lower()
+    #Drop rows which contains any NaN or missing value
+    #newdf = df.dropna()
+    # Drop rows with NaN or missing value in all columns
+    #df = newdf.dropna(how='all')
+    #data.rename(lowercase, axis='columns', inplace=True)
+    #data['YEAR'] = pd.to_datetime(data['YEAR'])
+    # define eastern timezone
+    #eastern = timezone('US/Eastern')
+    # dti = dti.tz_localize('UTC')
+    #df_temp=copy.deepcopy(df)
+    sensorA = pd.read_csv(( pathName_input +fileName1 ), sep = ",", nrows=1000)
+    sensorB = pd.read_csv(( pathName_input +fileName2 ), sep = ",", nrows=1000)
+    sensorC = pd.read_csv(( pathName_input +fileName3 ), sep = ",", nrows=1000)
+    sensorD = pd.read_csv(( pathName_input +fileName4 ), sep = ",",nrows=1000)
+    sensorE = pd.read_csv(( pathName_input +fileName5 ), sep = ",", nrows=1000)
+    sensorF = pd.read_csv(( pathName_input +fileName6 ), sep = ",",nrows=1000)
+    sensorG = pd.read_csv(( pathName_input +fileName7 ), sep = ",",nrows=1000)
+    sensorH = pd.read_csv(( pathName_input +fileName8 ), sep = ",",nrows=1000)
+    sensorI = pd.read_csv(( pathName_input +fileName9 ), sep = ",",nrows=1000)
+    sensorJ = pd.read_csv(( pathName_input +fileName10 ), sep = ",",nrows=1000)
+    Stations = pd.concat([sensorA, sensorB, sensorC, sensorD,
+                        sensorE, sensorF,  sensorG,  sensorH, sensorI, sensorJ], axis=0)
+    Stations["time"] = pd.to_datetime(
+    Stations['YEAR'] + ' ' + Stations['time'])
+    Stations = Stations.drop(["YEAR"], axis=1)
+    # Kepler needs time as string, otherwise it will throw an error
+    #sensor_all["time"] = sensor_all["time"].apply(str)
+    Stations["time"] = pd.to_datetime(Stations["time"],utc=True, dayfirst=True)
+    ## removing neagtive 
+    #https://stackoverflow.com/questions/27759084/how-to-replace-negative-numbers-in-pandas-data-frame-by-zero
+    Stations['Windspeed'] [Stations['Windspeed'] < 0]=0
+    Stations['PM10'] [Stations['PM10'] < 0]=0
+    Stations['PM2_5'] [Stations['PM2_5'] < 0]=0
+    Stations['Humd'] [Stations['Humd'] < 0]=0
+    #df['timestamp'] = pd.to_datetime(df['date'], utc=True, dayfirst=True) #day saving problem recovered b the conversion to local time
+    #generate the datetime
+    #df['date'] = df['timestamp'].apply(lambda x: datetime.datetime(x.year,x.month,x.day, x.hour, x.minute, x.second))
+    #import time
+    #df['date']=time.strftime("%H:%M",  time.strptime(str(df['timestamp']), "%Y-%m-%d %H:%M:%S"))
+    #df['date'] = df['date'].astype('datetime64[ns]')
+    #df['date']=df['date'].to_pydatetime()
+    #df_temp['date']= pd.to_datetime(df['date'], dayfirst=True)
+    return Stations
+
+Stations = load_data(50) #458384
+
+print('\n concatenated data is \n ' , Stations , '\n');
+
+Stations['Windspeed'] = pd.to_numeric( Stations['Windspeed'] ) ;
+#Stations['Windspeed'] .clip_lower(0) ## 1st method removing the negative value with 0 in the normalised dataset
+#Stations['Windspeed'] [Stations['Windspeed'] < 0]=0
+ #df[df < 0] = 0 2 mthod of removing negative vlaues 
+#print ('wind speed data after removing the negative vlaues', Stations['Windspeed']  )
+Stations['PM10'] = pd.to_numeric( Stations['PM10']) ;
+#Stations['Windspeed'] .clip_lower(0) ## 1st method removing the negative value with 0 in the normalised dataset
+#Stations['PM10'] [Stations['Windspeed'] < 0]=0
+ #df[df < 0] = 0 2 mthod of removing negative vlaues 
+#print ('PM10 data after removing the negative vlaues', Stations['Windspeed']  )
+Stations['PM2_5'] = pd.to_numeric( Stations['PM2_5'] ) ;
+#Stations['Windspeed'] .clip_lower(0) ## 1st method removing the negative value with 0 in the normalised dataset
+#Stations['PM2_5'] [Stations['Windspeed'] < 0]=0
+ #df[df < 0] = 0 2 mthod of removing negative vlaues 
+#print ('PM2_5 data after removing the negative vlaues', Stations['Windspeed']  )
+Stations['Humd'] = pd.to_numeric( Stations['Humd'] ) ;
+#Stations['Windspeed'] .clip_lower(0) ## 1st method removing the negative value with 0 in the normalised dataset
+#Stations['Humd'] [Stations['Windspeed'] < 0]=0
+ #df[df < 0] = 0 2 mthod of removing negative vlaues 
+#print ('Humdiity removing the negative vlaues', Stations['Windspeed']  )
+
+
+
+#print('check this block')
+##plotting heading
+st.title("Environmental Dashboard For Stuttgart")#st.markdown('The dashboard will visualize the Wind and Air Quality Situation in Stuttgart')
+#st.markdown('Stuttgart in-depth analysis. The BW state capital Stuttgart started the new fine dust alarm season & calls on its citizens to use public transportation. This visual anlytics tool for interactively anlyse & explore historical+spatial+temporal+future of meterological & air qulity paremters')
+
+#print('check this block2')
+if st.checkbox('Show raw data'):
+    #st.subheader('Raw data')
+    st.write('Full Dataset', Stations)
+    selected_indices = st.multiselect('Select rows:', Stations.index)
+    selected_rows = Stations.loc[selected_indices]
+    st.write('Selected Rows', selected_rows)
+    print('check this block3')
+
+st.sidebar.markdown("### Measurements locations on map")
+#st.sidebar.markdown("Sensors different considered locations in Stuttgart")
+#if not st.sidebar.checkbox("Hide", True, key='1'):
+#st.map(Stations.query("Windspeed>=0.0")[["lat", "lon"]])
+st.sidebar.pydeck_chart(pdk.Deck(
+    map_style='mapbox://styles/mapbox/light-v9',
+    initial_view_state=pdk.ViewState(
+        longitude=9.183,
+        latitude=48.783,
+        zoom=13, #14 zoomed
+        pitch=50
+    ),
+        layers=[
+        pdk.Layer(
+            'ScatterplotLayer',
+            data= Stations,
+            get_position='[lon, lat]',
+            get_radius=55,
+           # radius=20,
+            auto_highlight=True,
+            #elevation_scale=100,
+            #elevation_range=[0, 5000],
+            get_fill_color=[220,20,60], #red # marron 128,0,0 #(220,20,60)
+            pickable=True,
+            extruded=True
+        )
+    ],
+    # layers=[
+    #     pdk.Layer(
+    #         'ColumnLayer',
+    #         data= Stations,
+    #         get_position='[lon, lat]',
+    #         get_elevation='[PM10]',
+    #         radius=20,
+    #         auto_highlight=True,
+    #         elevation_scale=100,
+    #         elevation_range=[0, 5000],
+    #         get_fill_color=[255, 140, 0],#orange
+    #         pickable=True,
+    #         extruded=True
+    #     )
+    # ],
+    tooltip={
+            'html': '<b>Sensor Name:</b> {sensor}',
+            'style': {
+            'color': 'white'
+        }}
+))
+##
+#if not st.sidebar.checkbox("Hide", True, key='2'):
+st.sidebar.title("Sensor data: filtering selector")
+#st.sidebar.markdown("### Wants to explore in-depth?: interact, select & visualise")
+st.sidebar.markdown("Perform spatial-temporal querry on different sensors and their nature comparision analysis")
+#
+# starting the data from 2016 to 2019
+
+today =  datetime.date(2019, 1,1)
+tomorrow  =  datetime.date(2019,1,7)
+start_date = st.sidebar.date_input('Start date', today)
+end_date = st.sidebar.date_input('End date', tomorrow)
+
+#if start_date < end_date:
+    #st.success()
+    #st.success('Start date: `%s`\n\nEnd date:`%s`' % (start_date, end_date))
+#else:
+    #st.error('Error: End date must fall after start date.')
+print(start_date)
+print(end_date)
+print("type(end_date)", type(end_date))
+print('type(time of Stations.iloc[1,0]) ', type(Stations.iloc[0,7]))
+print("date and month Stations",Stations.iloc[0,7].year,Stations.iloc[0,7].month, Stations.iloc[0,7].day)
+print("date and month displaying on the dashboard screen",start_date.year,start_date.month, start_date.day)
+#
+index4=[] 
+for i in range(0,Stations.shape[0],1):
+    df_manual_date = datetime.datetime(Stations.iloc[i,7].year , Stations.iloc[i,7].month , Stations.iloc[i,7].day )
+    start_manual_date = datetime.datetime(start_date.year , start_date.month , start_date.day )
+    end_manual_date = datetime.datetime(end_date.year , end_date.month , end_date.day )
+    if( df_manual_date >= start_manual_date and df_manual_date <= end_manual_date ):
+        index4.append(i)
+        #
+print("index4", index4)
+Stations=Stations.iloc[index4,:]
+#df.reset_index()# for indexing the data 
+Stations.reset_index(drop=True, inplace=True)
+print('Stations', Stations)
+# # Use the full page instead of a narrow central column
+# #st.beta_set_page_config(layout="wide")
+
+comp_data = Stations
+# temporal filtering
+comp_data['datetime']= pd.to_datetime(comp_data['time']).dt.strftime('%Y-%m-%dT%H:%M:%S')
+#start_date=datetime.date(2019, 1, 1)
+#end_date=datetime.date(2019, 1, 8)
+#d5 = st.date_input("date range without default", [start_date, end_date])
+#st.sidebar.write(d5)
+#print('type(time of Stations.iloc[1,0]) ', type(comp_data.iloc[0,7]))
+
+#Space out the maps so the first one is 2x the size of the other three
+#c1, c2, c3, c4 = st.beta_columns((2, 2, 1, 1))
+
+st.markdown("### Sensors Inspection: compare sensors together")
+#st.sidebar.markdown("Compare the nature of the selcted sensors and their temporal behaviour measurements in different considered Stuttgart's locations")
+#if not st.sidebar.checkbox("Expand with unchecking", True, key='3'):
+    #st.title("Air Quality")
+metrics = ['None','PM10','PM2_5','Windspeed','Humidity']
+cols = st.selectbox('Sensors metric to view', metrics)
+if (cols =='PM10'):
+    color = alt.Color('sensor:N')#
+    brush = alt.selection_interval(encodings=['x'])
+    click = alt.selection_multi(encodings=['color'])
+    highlight= alt.selection(type='single', on='mouseover',
+                             fields=['sensor'], nearest=True)
+    base=alt.Chart(comp_data).mark_line(point=True).encode(
+        x='datetime:T',
+        y='PM10:Q',
+        color='sensor:N').properties(
+            width=1500
+            )
+    points1 = base.mark_circle().encode(
+        opacity=alt.value(0)
+    ).add_selection(
+        highlight
+    ).properties(
+        width=1500
+    ),
+    color=alt.condition(brush, color, alt.value('lightgray')),    
+    #size=alt.Size('Windspeed:Q',scale=alt.Scale(range=[10, 30]))#size=alt.Size('mean(PM10):Q',scale=alt.Scale(range=[5, 50]))
+    #).properties(width=1250, height=650).add_selection(brush).transform_filter(click)
+    # Bottom panel is a bar chart of weather type
+    lines1 = base.mark_line().encode(
+       size=alt.condition(~highlight, alt.value(1), alt.value(3))
+       )
+    # Bottom panel is a bar chart of weather type
+    bars = alt.Chart().mark_bar().encode(
+        x='count(PM10)',
+        y='sensor:N',
+        color=alt.condition(click, color, alt.value('lightgray')),
+        ).transform_filter(
+            brush
+            ).properties(width=1500
+                
+                ).add_selection(
+                    click)
+    #st.altair_chart(alt.vconcat((points1 + lines1), bars))
+    st.altair_chart(alt.vconcat(points1, bars))
+if (cols =='PM2_5'):
+    compare_graph=alt.Chart(comp_data).mark_line(point=True).encode(
+        x='datetime:T',
+        y='PM2_5:Q',
+        color='sensor:N').properties(
+            width=1500
+            ) 
+    st.altair_chart(compare_graph)
+if (cols =='Windspeed'):
+    compare_graph=alt.Chart(comp_data).mark_line(point=True).encode(
+        x='datetime:T',
+        y='Windspeed:Q',
+        color='sensor:N').properties(
+            width=1500
+            ) 
+    st.altair_chart(compare_graph)
+if (cols =='Humidity'):
+    compare_graph=alt.Chart(comp_data).mark_line(point=True).encode(
+        x='datetime:T',
+        y='Humd:Q',
+        color='sensor:N').properties(
+            width=1500
+            ) 
+    st.altair_chart(compare_graph)
+        # compare_graph2=alt.Chart(comp_data).mark_area().encode(
+        #     x="datetime:T",
+        #     y=alt.Y("PM10:Q", stack="normalize"),
+        #     color="sensor:N"
+        #     )
+    #column_select=WS
+        # highlight= alt.selection(type='single', on='mouseover',
+        #                       fields=['sensor'], nearest=True)
+    
+        # base = alt.Chart(comp_data).encode(
+        #     x='day(datetime)',
+        #     y='mean(PM10)',
+        #     color='sensor'
+        # )
+        
+        # points1 = base.mark_circle().encode(
+        #     opacity=alt.value(0)
+        # ).add_selection(
+        #     highlight
+        # ).properties(
+        #     width=600
+        # )
+        
+        # lines1 = base.mark_line().encode(
+        #     size=alt.condition(~highlight, alt.value(1), alt.value(3))
+        # )
+        # #2b chart
+        # highlight = alt.selection(type='single', on='mouseover',
+        #                       fields=['sensor'], nearest=True)
+    
+        # base = alt.Chart(comp_data).encode(
+        #     x='datetime',
+        #     y='PM10',
+        #     color='sensor'
+        # )
+        
+        # points2 = base.mark_circle().encode(
+        #     opacity=alt.value(0)
+        # ).add_selection(
+        #     highlight
+        # ).properties(
+        #     width=600
+        # )
+        
+        # lines2 = base.mark_line().encode(
+        #     size=alt.condition(~highlight, alt.value(1), alt.value(3))
+        # )   
+        
+        # #points + lines 
+        # # to concatenate to chart horixonatally
+        # #c=alt.hconcat((points1 + lines1), (points2 + lines2))
+        # c=alt.vconcat((points1 + lines1), (points2 + lines2))
+ 
+       # st.altair_chart(compare_graph2)
+
+c1, c2 = st.beta_columns((1, 2))
+
+#comp_data = Stations
+#print(comp_data)
+#st.sidebar.markdown("a) Select sensors")
+#if not st.sidebar.checkbox("Uncheck to expand", True, key='18'):
+st.sidebar.markdown("Compare the nature of the each selcted sensors and their temporal behaviour measurements at different Stuttgart's locations")
+#if not st.sidebar.checkbox("Expand with unchecking", True, key='3'):
+parameter_select = st.sidebar.selectbox('Parameter', ['PM10', 'PM2_5','Windspeed','Humd'], key='19')
+sensors_select = st.sidebar.selectbox("Select sensors", ("None", "A", 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K'))
+#sensors_select = st.sidebar.selectbox("Select sensors", ( "A", 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K'))
+chart_placeholder2 = st.empty()
+#sensor A
+if sensors_select=='A':
+    comp_data=comp_data.loc[(comp_data['sensor']=='A')]
+    with c1:
+        st.map(Stations.query("sensor=='A'")[["lat", "lon"]])
+    with c2:
+        #comp_data['datetime']= pd.to_datetime(comp_data['time']).dt.strftime('%Y-%m-%dT%H:%M:%S')
+        #print('comp_data after filtering',comp_data)
+        c1 = alt.Chart(comp_data).mark_bar().encode(
+                    #alt.Y('monthdate(date):O', title='Month'),
+                    alt.X('day(datetime):O', title='week on avg'),
+                    #alt.X('datetime:O', title='tim'),
+                    #alt.X('day(date)', title='week'),
+                    # alt.X('date(date)', title='week')
+                    #alt.X('date(date)', axis=alt.Axis(values=[0, 5, 10, 15, 20, 30])),
+                    # alt.X('date(date)', title='date',axis=alt.Axis(
+                    #     tickCount=overview_df.shape[0],
+                    #     #tickOffset=2
+                    #     #tickCount="day",
+                    #     #tickCount=2,
+                    #     grid=False,
+                    #     #grid=True,
+                    #     #labelExpr="datum.value % 5 ? null : datum.label")
+                    #     #labelExpr="datum.label[0]")
+                    #     )),
+                    #x='date(datetime)',
+                    y=parameter_select,
+                    #x=alt.X('date(datetime):T', axis=alt.Axis(title='Date', format=("%b %d"),labelAngle=10, grid=True)),
+                        #x='hoursminutes(start)',
+                        #x2='hoursminutes(end)',
+                    
+                    # alt.X('date(date)'), #title='date',axis=alt.Axis(
+                        #tickCount=overview_df.shape[0],
+                        #tickOffset=2
+                        #tickCount="day",
+                        #tickCount=2,
+                        #grid=False,
+                        #grid=True,
+                        #labelExpr="datum.value % 5 ? null : datum.label")
+                        #labelExpr="datum.label[0]")
+                        #)),
+                    #alt.X(parameter_select, title=parameter_select),    
+                    color=alt.Color('day(datetime)', title='week', scale=alt.Scale(scheme='category10')), #set1
+                    tooltip=[ parameter_select]
+                    #parameter_select, #alt.Color('temp:Q', title='temperature (F)')
+            #         )
+            ).properties(
+            width=300,
+            height=300
+        ) 
+            # c1=alt.Chart(overview_df).mark_bar(opacity=0.7).encode(
+            #     x='date(date)',
+            #     #y=alt.Y('day(date)', stack=None),
+            #     y='day(date)',
+            #     color=parameter_select,
+            # )
+        c2 = alt.Chart(comp_data).mark_rect().encode(
+                    #alt.Y('monthdate(date):O', title='Month'),
+                    alt.Y('hoursminutes(datetime):O', title='hour of day'),
+                    #alt.X('datetime:O', title='tim'),
+                    #alt.X('day(date)', title='week'),
+                    # alt.X('date(date)', title='week')
+                    #alt.X('date(date)', axis=alt.Axis(values=[0, 5, 10, 15, 20, 30])),
+                    alt.X('date(datetime)', title='date',axis=alt.Axis(
+                        tickCount=comp_data.shape[0],
+                        #tickOffset=2,
+                        #tickCount="date",
+                        #tickCount=10,
+                        grid=False,
+                        #grid=True,
+                        #labelExpr="datum.value % 5 ? null : datum.label")
+                        #labelExpr="datum.label[0]")
+                        )),
+                    #alt.X(parameter_select, title=parameter_select),  #scale=alt.Scale(scheme='turbo') # lightgreyred ,#lightmulti, #lightgreyteal
+                    alt.Color(parameter_select, title=parameter_select, scale=alt.Scale(scheme='lightmulti')),#, scale=alt.Scale(scheme='brown'),
+                    #tooltip=['monthdate(datetime)' , parameter_select]
+                    #tooltip=['monthdate(datetime)']
+                    #parameter_select, #alt.Color('temp:Q', title='temperature (F)')
+            #         )
+            ).properties(
+            width=300,
+            height=300
+        ) 
+            
+            # alt.Chart(df).mark_circle().encode(
+            #     x='a',
+            #     y='b',
+            #     color=alt.value(color),
+            #     size='c'
+            #     )
+        #chart_placeholder2.altair_chart(alt.hconcat(c1,c2))
+        st.altair_chart(alt.hconcat(c1,c2))
+    #st.title("Environmental Temporal Filtering")
+    #bar_plot_method(comp_data,parameter_select)
+#     line = alt.Chart(comp_data).mark_line().encode(
+#     x='datetime',
+#     #y='mean(Windspeed)'
+#     y='Windspeed'
+# )
+
+#     band = alt.Chart(comp_data).mark_errorband(extent='ci').encode(
+#         x='datetime',
+#         y=alt.Y('Windspeed', title='m/s'),
+#     )
+    
+#     st.altair_chart(band + line)
+
+# sensor B
+if sensors_select=='B':
+    comp_data=comp_data.loc[(comp_data['sensor']=='B')]
+    with c1:
+        st.map(Stations.query("sensor=='B'")[["lat", "lon"]])
+    with c2:
+        #comp_data['datetime']= pd.to_datetime(comp_data['time']).dt.strftime('%Y-%m-%dT%H:%M:%S')
+        #print('comp_data after filtering',comp_data)
+        c1 = alt.Chart(comp_data).mark_bar().encode(
+                    #alt.Y('monthdate(date):O', title='Month'),
+                    alt.X('day(datetime):O', title='week on avg'),
+                    #alt.X('datetime:O', title='tim'),
+                    #alt.X('day(date)', title='week'),
+                    # alt.X('date(date)', title='week')
+                    #alt.X('date(date)', axis=alt.Axis(values=[0, 5, 10, 15, 20, 30])),
+                    # alt.X('date(date)', title='date',axis=alt.Axis(
+                    #     tickCount=overview_df.shape[0],
+                    #     #tickOffset=2
+                    #     #tickCount="day",
+                    #     #tickCount=2,
+                    #     grid=False,
+                    #     #grid=True,
+                    #     #labelExpr="datum.value % 5 ? null : datum.label")
+                    #     #labelExpr="datum.label[0]")
+                    #     )),
+                    #x='date(datetime)',
+                    y=parameter_select,
+                    #x=alt.X('date(datetime):T', axis=alt.Axis(title='Date', format=("%b %d"),labelAngle=10, grid=True)),
+                        #x='hoursminutes(start)',
+                        #x2='hoursminutes(end)',
+                    
+                    # alt.X('date(date)'), #title='date',axis=alt.Axis(
+                        #tickCount=overview_df.shape[0],
+                        #tickOffset=2
+                        #tickCount="day",
+                        #tickCount=2,
+                        #grid=False,
+                        #grid=True,
+                        #labelExpr="datum.value % 5 ? null : datum.label")
+                        #labelExpr="datum.label[0]")
+                        #)),
+                    #alt.X(parameter_select, title=parameter_select),    
+                    color=alt.Color('day(datetime)', title='week', scale=alt.Scale(scheme='category10')), #set1
+                    tooltip=[ parameter_select]
+                    #parameter_select, #alt.Color('temp:Q', title='temperature (F)')
+            #         )
+            ).properties(
+            width=300,
+            height=300
+        ) 
+            # c1=alt.Chart(overview_df).mark_bar(opacity=0.7).encode(
+            #     x='date(date)',
+            #     #y=alt.Y('day(date)', stack=None),
+            #     y='day(date)',
+            #     color=parameter_select,
+            # )
+        c2 = alt.Chart(comp_data).mark_rect().encode(
+                    #alt.Y('monthdate(date):O', title='Month'),
+                    alt.Y('hoursminutes(datetime):O', title='hour of day'),
+                    #alt.X('datetime:O', title='tim'),
+                    #alt.X('day(date)', title='week'),
+                    # alt.X('date(date)', title='week')
+                    #alt.X('date(date)', axis=alt.Axis(values=[0, 5, 10, 15, 20, 30])),
+                    alt.X('date(datetime)', title='date',axis=alt.Axis(
+                        tickCount=comp_data.shape[0],
+                        #tickOffset=2,
+                        #tickCount="date",
+                        #tickCount=10,
+                        grid=False,
+                        #grid=True,
+                        #labelExpr="datum.value % 5 ? null : datum.label")
+                        #labelExpr="datum.label[0]")
+                        )),
+                    #alt.X(parameter_select, title=parameter_select),  #scale=alt.Scale(scheme='turbo') # lightgreyred ,#lightmulti, #lightgreyteal
+                    alt.Color(parameter_select, title=parameter_select, scale=alt.Scale(scheme='lightmulti')),#, scale=alt.Scale(scheme='brown'),
+                    #tooltip=['monthdate(datetime)' , parameter_select]
+                    #tooltip=['monthdate(datetime)']
+                    #parameter_select, #alt.Color('temp:Q', title='temperature (F)')
+            #         )
+            ).properties(
+            width=300,
+            height=300
+        ) 
+            
+            # alt.Chart(df).mark_circle().encode(
+            #     x='a',
+            #     y='b',
+            #     color=alt.value(color),
+            #     size='c'
+            #     )
+        #chart_placeholder2.altair_chart(alt.hconcat(c1,c2))
+        st.altair_chart(alt.hconcat(c1,c2))
+
+## sensor C
+if sensors_select=='C':
+    comp_data=comp_data.loc[(comp_data['sensor']=='C')]
+    with c1:
+        st.map(Stations.query("sensor=='C'")[["lat", "lon"]])
+    with c2:
+        #comp_data['datetime']= pd.to_datetime(comp_data['time']).dt.strftime('%Y-%m-%dT%H:%M:%S')
+        #print('comp_data after filtering',comp_data)
+        c1 = alt.Chart(comp_data).mark_bar().encode(
+                    #alt.Y('monthdate(date):O', title='Month'),
+                    alt.X('day(datetime):O', title='week on avg'),
+                    #alt.X('datetime:O', title='tim'),
+                    #alt.X('day(date)', title='week'),
+                    # alt.X('date(date)', title='week')
+                    #alt.X('date(date)', axis=alt.Axis(values=[0, 5, 10, 15, 20, 30])),
+                    # alt.X('date(date)', title='date',axis=alt.Axis(
+                    #     tickCount=overview_df.shape[0],
+                    #     #tickOffset=2
+                    #     #tickCount="day",
+                    #     #tickCount=2,
+                    #     grid=False,
+                    #     #grid=True,
+                    #     #labelExpr="datum.value % 5 ? null : datum.label")
+                    #     #labelExpr="datum.label[0]")
+                    #     )),
+                    #x='date(datetime)',
+                    y=parameter_select,
+                    #x=alt.X('date(datetime):T', axis=alt.Axis(title='Date', format=("%b %d"),labelAngle=10, grid=True)),
+                        #x='hoursminutes(start)',
+                        #x2='hoursminutes(end)',
+                    
+                    # alt.X('date(date)'), #title='date',axis=alt.Axis(
+                        #tickCount=overview_df.shape[0],
+                        #tickOffset=2
+                        #tickCount="day",
+                        #tickCount=2,
+                        #grid=False,
+                        #grid=True,
+                        #labelExpr="datum.value % 5 ? null : datum.label")
+                        #labelExpr="datum.label[0]")
+                        #)),
+                    #alt.X(parameter_select, title=parameter_select),    
+                    color=alt.Color('day(datetime)', title='week', scale=alt.Scale(scheme='category10')), #set1
+                    tooltip=[ parameter_select]
+                    #parameter_select, #alt.Color('temp:Q', title='temperature (F)')
+            #         )
+            ).properties(
+            width=300,
+            height=300
+        ) 
+            # c1=alt.Chart(overview_df).mark_bar(opacity=0.7).encode(
+            #     x='date(date)',
+            #     #y=alt.Y('day(date)', stack=None),
+            #     y='day(date)',
+            #     color=parameter_select,
+            # )
+        c2 = alt.Chart(comp_data).mark_rect().encode(
+                    #alt.Y('monthdate(date):O', title='Month'),
+                    alt.Y('hoursminutes(datetime):O', title='hour of day'),
+                    #alt.X('datetime:O', title='tim'),
+                    #alt.X('day(date)', title='week'),
+                    # alt.X('date(date)', title='week')
+                    #alt.X('date(date)', axis=alt.Axis(values=[0, 5, 10, 15, 20, 30])),
+                    alt.X('date(datetime)', title='date',axis=alt.Axis(
+                        tickCount=comp_data.shape[0],
+                        #tickOffset=2,
+                        #tickCount="date",
+                        #tickCount=10,
+                        grid=False,
+                        #grid=True,
+                        #labelExpr="datum.value % 5 ? null : datum.label")
+                        #labelExpr="datum.label[0]")
+                        )),
+                    #alt.X(parameter_select, title=parameter_select),  #scale=alt.Scale(scheme='turbo') # lightgreyred ,#lightmulti, #lightgreyteal
+                    alt.Color(parameter_select, title=parameter_select, scale=alt.Scale(scheme='lightmulti')),#, scale=alt.Scale(scheme='brown'),
+                    #tooltip=['monthdate(datetime)' , parameter_select]
+                    #tooltip=['monthdate(datetime)']
+                    #parameter_select, #alt.Color('temp:Q', title='temperature (F)')
+            #         )
+            ).properties(
+            width=300,
+            height=300
+        ) 
+            
+            # alt.Chart(df).mark_circle().encode(
+            #     x='a',
+            #     y='b',
+            #     color=alt.value(color),
+            #     size='c'
+            #     )
+        #chart_placeholder2.altair_chart(alt.hconcat(c1,c2))
+        st.altair_chart(alt.hconcat(c1,c2))
+        
+# sensor D
+if sensors_select=='D':
+    comp_data=comp_data.loc[(comp_data['sensor']=='D')]
+    with c1:
+        st.map(Stations.query("sensor=='D'")[["lat", "lon"]])
+    with c2:
+        #comp_data['datetime']= pd.to_datetime(comp_data['time']).dt.strftime('%Y-%m-%dT%H:%M:%S')
+        #print('comp_data after filtering',comp_data)
+        c1 = alt.Chart(comp_data).mark_bar().encode(
+                    #alt.Y('monthdate(date):O', title='Month'),
+                    alt.X('day(datetime):O', title='week on avg'),
+                    #alt.X('datetime:O', title='tim'),
+                    #alt.X('day(date)', title='week'),
+                    # alt.X('date(date)', title='week')
+                    #alt.X('date(date)', axis=alt.Axis(values=[0, 5, 10, 15, 20, 30])),
+                    # alt.X('date(date)', title='date',axis=alt.Axis(
+                    #     tickCount=overview_df.shape[0],
+                    #     #tickOffset=2
+                    #     #tickCount="day",
+                    #     #tickCount=2,
+                    #     grid=False,
+                    #     #grid=True,
+                    #     #labelExpr="datum.value % 5 ? null : datum.label")
+                    #     #labelExpr="datum.label[0]")
+                    #     )),
+                    #x='date(datetime)',
+                    y=parameter_select,
+                    #x=alt.X('date(datetime):T', axis=alt.Axis(title='Date', format=("%b %d"),labelAngle=10, grid=True)),
+                        #x='hoursminutes(start)',
+                        #x2='hoursminutes(end)',
+                    
+                    # alt.X('date(date)'), #title='date',axis=alt.Axis(
+                        #tickCount=overview_df.shape[0],
+                        #tickOffset=2
+                        #tickCount="day",
+                        #tickCount=2,
+                        #grid=False,
+                        #grid=True,
+                        #labelExpr="datum.value % 5 ? null : datum.label")
+                        #labelExpr="datum.label[0]")
+                        #)),
+                    #alt.X(parameter_select, title=parameter_select),    
+                    color=alt.Color('day(datetime)', title='week', scale=alt.Scale(scheme='category10')), #set1
+                    tooltip=[ parameter_select]
+                    #parameter_select, #alt.Color('temp:Q', title='temperature (F)')
+            #         )
+            ).properties(
+            width=300,
+            height=300
+        ) 
+            # c1=alt.Chart(overview_df).mark_bar(opacity=0.7).encode(
+            #     x='date(date)',
+            #     #y=alt.Y('day(date)', stack=None),
+            #     y='day(date)',
+            #     color=parameter_select,
+            # )
+        c2 = alt.Chart(comp_data).mark_rect().encode(
+                    #alt.Y('monthdate(date):O', title='Month'),
+                    alt.Y('hoursminutes(datetime):O', title='hour of day'),
+                    #alt.X('datetime:O', title='tim'),
+                    #alt.X('day(date)', title='week'),
+                    # alt.X('date(date)', title='week')
+                    #alt.X('date(date)', axis=alt.Axis(values=[0, 5, 10, 15, 20, 30])),
+                    alt.X('date(datetime)', title='date',axis=alt.Axis(
+                        tickCount=comp_data.shape[0],
+                        #tickOffset=2,
+                        #tickCount="date",
+                        #tickCount=10,
+                        grid=False,
+                        #grid=True,
+                        #labelExpr="datum.value % 5 ? null : datum.label")
+                        #labelExpr="datum.label[0]")
+                        )),
+                    #alt.X(parameter_select, title=parameter_select),  #scale=alt.Scale(scheme='turbo') # lightgreyred ,#lightmulti, #lightgreyteal
+                    alt.Color(parameter_select, title=parameter_select, scale=alt.Scale(scheme='lightmulti')),#, scale=alt.Scale(scheme='brown'),
+                    #tooltip=['monthdate(datetime)' , parameter_select]
+                    #tooltip=['monthdate(datetime)']
+                    #parameter_select, #alt.Color('temp:Q', title='temperature (F)')
+            #         )
+            ).properties(
+            width=300,
+            height=300
+        ) 
+            
+            # alt.Chart(df).mark_circle().encode(
+            #     x='a',
+            #     y='b',
+            #     color=alt.value(color),
+            #     size='c'
+            #     )
+        #chart_placeholder2.altair_chart(alt.hconcat(c1,c2))
+        st.altair_chart(alt.hconcat(c1,c2))
+        
+ # sensor E   
+if sensors_select=='E':
+    comp_data=comp_data.loc[(comp_data['sensor']=='E')]
+    with c1:
+        st.map(Stations.query("sensor=='E'")[["lat", "lon"]])
+    with c2:
+        #comp_data['datetime']= pd.to_datetime(comp_data['time']).dt.strftime('%Y-%m-%dT%H:%M:%S')
+        #print('comp_data after filtering',comp_data)
+        c1 = alt.Chart(comp_data).mark_bar().encode(
+                    #alt.Y('monthdate(date):O', title='Month'),
+                    #alt.X('day(datetime):O', title='week on avg', axis=alt.Axis(tickMinStep=0.5)),
+                    alt.X('day(datetime):O', title='week on avg'),
+                    #alt.X('datetime:O', title='tim'),
+                    #alt.X('day(date)', title='week'),
+                    # alt.X('date(date)', title='week')
+                    #alt.X('date(date)', axis=alt.Axis(values=[0, 5, 10, 15, 20, 30])),
+                    # alt.X('date(date)', title='date',axis=alt.Axis(
+                    #     tickCount=overview_df.shape[0],
+                    #     #tickOffset=2
+                    #     #tickCount="day",
+                    #     #tickCount=2,
+                    #     grid=False,
+                    #     #grid=True,
+                    #     #labelExpr="datum.value % 5 ? null : datum.label")
+                    #     #labelExpr="datum.label[0]")
+                    #     )),
+                    #x='date(datetime)',
+                    y=parameter_select,
+                    #x=alt.X('date(datetime):T', axis=alt.Axis(title='Date', format=("%b %d"),labelAngle=10, grid=True)),
+                        #x='hoursminutes(start)',
+                        #x2='hoursminutes(end)',
+                    
+                    # alt.X('date(date)'), #title='date',axis=alt.Axis(
+                        #tickCount=overview_df.shape[0],
+                        #tickOffset=2
+                        #tickCount="day",
+                        #tickCount=2,
+                        #grid=False,
+                        #grid=True,
+                        #labelExpr="datum.value % 5 ? null : datum.label")
+                        #labelExpr="datum.label[0]")
+                        #)),
+                    #alt.X(parameter_select, title=parameter_select),    
+                    color=alt.Color('day(datetime)', title='week', scale=alt.Scale(scheme='category10')), #set1
+                    tooltip=[ parameter_select]
+                    #parameter_select, #alt.Color('temp:Q', title='temperature (F)')
+            #         )
+            ).properties(
+            width=300,
+            height=300
+        ) 
+            # c1=alt.Chart(overview_df).mark_bar(opacity=0.7).encode(
+            #     x='date(date)',
+            #     #y=alt.Y('day(date)', stack=None),
+            #     y='day(date)',
+            #     color=parameter_select,
+            # )
+        c2 = alt.Chart(comp_data).mark_rect().encode(
+                    #alt.Y('monthdate(date):O', title='Month'),
+                    alt.Y('hoursminutes(datetime):O', title='hour of day'),
+                    #alt.X('datetime:O', title='tim'),
+                    #alt.X('day(date)', title='week'),
+                    # alt.X('date(date)', title='week')
+                    #alt.X('date(date)', axis=alt.Axis(values=[0, 5, 10, 15, 20, 30])),
+                    alt.X('date(datetime)', title='date',axis=alt.Axis(
+                        tickCount=comp_data.shape[0],
+                        #tickOffset=2,
+                        #tickCount="date",
+                        #tickCount=10,
+                        grid=False,
+                        #grid=True,
+                        #labelExpr="datum.value % 5 ? null : datum.label")
+                        #labelExpr="datum.label[0]")
+                        )),
+                    #alt.X(parameter_select, title=parameter_select),  #scale=alt.Scale(scheme='turbo') # lightgreyred ,#lightmulti, #lightgreyteal
+                    alt.Color(parameter_select, title=parameter_select, scale=alt.Scale(scheme='lightmulti')),#, scale=alt.Scale(scheme='brown'),
+                    #tooltip=['monthdate(datetime)' , parameter_select]
+                    #tooltip=['monthdate(datetime)']
+                    #parameter_select, #alt.Color('temp:Q', title='temperature (F)')
+            #         )
+            ).properties(
+            width=300,
+            height=300
+        ) 
+            
+            # alt.Chart(df).mark_circle().encode(
+            #     x='a',
+            #     y='b',
+            #     color=alt.value(color),
+            #     size='c'
+            #     )
+        #chart_placeholder2.altair_chart(alt.hconcat(c1,c2))
+        st.altair_chart(alt.hconcat(c1,c2))   
+        
+# sensor F
+if sensors_select=='F':
+    comp_data=comp_data.loc[(comp_data['sensor']=='F')]
+    with c1:
+        st.map(Stations.query("sensor=='F'")[["lat", "lon"]])
+    with c2:
+        #comp_data['datetime']= pd.to_datetime(comp_data['time']).dt.strftime('%Y-%m-%dT%H:%M:%S')
+        #print('comp_data after filtering',comp_data)
+        c1 = alt.Chart(comp_data).mark_bar().encode(
+                    #alt.Y('monthdate(date):O', title='Month'),
+                    alt.X('day(datetime):O', title='week on avg'),
+                    #alt.X('datetime:O', title='tim'),
+                    #alt.X('day(date)', title='week'),
+                    # alt.X('date(date)', title='week')
+                    #alt.X('date(date)', axis=alt.Axis(values=[0, 5, 10, 15, 20, 30])),
+                    # alt.X('date(date)', title='date',axis=alt.Axis(
+                    #     tickCount=overview_df.shape[0],
+                    #     #tickOffset=2
+                    #     #tickCount="day",
+                    #     #tickCount=2,
+                    #     grid=False,
+                    #     #grid=True,
+                    #     #labelExpr="datum.value % 5 ? null : datum.label")
+                    #     #labelExpr="datum.label[0]")
+                    #     )),
+                    #x='date(datetime)',
+                    y=parameter_select,
+                    #x=alt.X('date(datetime):T', axis=alt.Axis(title='Date', format=("%b %d"),labelAngle=10, grid=True)),
+                        #x='hoursminutes(start)',
+                        #x2='hoursminutes(end)',
+                    
+                    # alt.X('date(date)'), #title='date',axis=alt.Axis(
+                        #tickCount=overview_df.shape[0],
+                        #tickOffset=2
+                        #tickCount="day",
+                        #tickCount=2,
+                        #grid=False,
+                        #grid=True,
+                        #labelExpr="datum.value % 5 ? null : datum.label")
+                        #labelExpr="datum.label[0]")
+                        #)),
+                    #alt.X(parameter_select, title=parameter_select),    
+                    color=alt.Color('day(datetime)', title='week', scale=alt.Scale(scheme='category10')), #set1
+                    tooltip=[ parameter_select]
+                    #parameter_select, #alt.Color('temp:Q', title='temperature (F)')
+            #         )
+            ).properties(
+            width=300,
+            height=300
+        ) 
+            # c1=alt.Chart(overview_df).mark_bar(opacity=0.7).encode(
+            #     x='date(date)',
+            #     #y=alt.Y('day(date)', stack=None),
+            #     y='day(date)',
+            #     color=parameter_select,
+            # )
+        c2 = alt.Chart(comp_data).mark_rect().encode(
+                    #alt.Y('monthdate(date):O', title='Month'),
+                    alt.Y('hoursminutes(datetime):O', title='hour of day'),
+                    #alt.X('datetime:O', title='tim'),
+                    #alt.X('day(date)', title='week'),
+                    # alt.X('date(date)', title='week')
+                    #alt.X('date(date)', axis=alt.Axis(values=[0, 5, 10, 15, 20, 30])),
+                    alt.X('date(datetime)', title='date',axis=alt.Axis(
+                        tickCount=comp_data.shape[0],
+                        #tickOffset=2,
+                        #tickCount="date",
+                        #tickCount=10,
+                        grid=False,
+                        #grid=True,
+                        #labelExpr="datum.value % 5 ? null : datum.label")
+                        #labelExpr="datum.label[0]")
+                        )),
+                    #alt.X(parameter_select, title=parameter_select),  #scale=alt.Scale(scheme='turbo') # lightgreyred ,#lightmulti, #lightgreyteal
+                    alt.Color(parameter_select, title=parameter_select, scale=alt.Scale(scheme='lightmulti')),#, scale=alt.Scale(scheme='brown'),
+                    #tooltip=['monthdate(datetime)' , parameter_select]
+                    #tooltip=['monthdate(datetime)']
+                    #parameter_select, #alt.Color('temp:Q', title='temperature (F)')
+            #         )
+            ).properties(
+            width=300,
+            height=300
+        ) 
+            
+            # alt.Chart(df).mark_circle().encode(
+            #     x='a',
+            #     y='b',
+            #     color=alt.value(color),
+            #     size='c'
+            #     )
+        #chart_placeholder2.altair_chart(alt.hconcat(c1,c2))
+        st.altair_chart(alt.hconcat(c1,c2))
+        
+#sensor G
+if sensors_select=='G':
+    comp_data=comp_data.loc[(comp_data['sensor']=='G')]
+    with c1:
+        st.map(Stations.query("sensor=='G'")[["lat", "lon"]])
+    with c2:
+        #comp_data['datetime']= pd.to_datetime(comp_data['time']).dt.strftime('%Y-%m-%dT%H:%M:%S')
+        #print('comp_data after filtering',comp_data)
+        c1 = alt.Chart(comp_data).mark_bar().encode(
+                    #alt.Y('monthdate(date):O', title='Month'),
+                    alt.X('day(datetime):O', title='week on avg'),
+                    #alt.X('datetime:O', title='tim'),
+                    #alt.X('day(date)', title='week'),
+                    # alt.X('date(date)', title='week')
+                    #alt.X('date(date)', axis=alt.Axis(values=[0, 5, 10, 15, 20, 30])),
+                    # alt.X('date(date)', title='date',axis=alt.Axis(
+                    #     tickCount=overview_df.shape[0],
+                    #     #tickOffset=2
+                    #     #tickCount="day",
+                    #     #tickCount=2,
+                    #     grid=False,
+                    #     #grid=True,
+                    #     #labelExpr="datum.value % 5 ? null : datum.label")
+                    #     #labelExpr="datum.label[0]")
+                    #     )),
+                    #x='date(datetime)',
+                    y=parameter_select,
+                    #x=alt.X('date(datetime):T', axis=alt.Axis(title='Date', format=("%b %d"),labelAngle=10, grid=True)),
+                        #x='hoursminutes(start)',
+                        #x2='hoursminutes(end)',
+                    
+                    # alt.X('date(date)'), #title='date',axis=alt.Axis(
+                        #tickCount=overview_df.shape[0],
+                        #tickOffset=2
+                        #tickCount="day",
+                        #tickCount=2,
+                        #grid=False,
+                        #grid=True,
+                        #labelExpr="datum.value % 5 ? null : datum.label")
+                        #labelExpr="datum.label[0]")
+                        #)),
+                    #alt.X(parameter_select, title=parameter_select),    
+                    color=alt.Color('day(datetime)', title='week', scale=alt.Scale(scheme='category10')), #set1
+                    tooltip=[ parameter_select]
+                    #parameter_select, #alt.Color('temp:Q', title='temperature (F)')
+            #         )
+            ).properties(
+            width=300,
+            height=300
+        ) 
+            # c1=alt.Chart(overview_df).mark_bar(opacity=0.7).encode(
+            #     x='date(date)',
+            #     #y=alt.Y('day(date)', stack=None),
+            #     y='day(date)',
+            #     color=parameter_select,
+            # )
+        c2 = alt.Chart(comp_data).mark_rect().encode(
+                    #alt.Y('monthdate(date):O', title='Month'),
+                    alt.Y('hoursminutes(datetime):O', title='hour of day'),
+                    #alt.X('datetime:O', title='tim'),
+                    #alt.X('day(date)', title='week'),
+                    # alt.X('date(date)', title='week')
+                    #alt.X('date(date)', axis=alt.Axis(values=[0, 5, 10, 15, 20, 30])),
+                    alt.X('date(datetime)', title='date',axis=alt.Axis(
+                        tickCount=comp_data.shape[0],
+                        #tickOffset=2,
+                        #tickCount="date",
+                        #tickCount=10,
+                        grid=False,
+                        #grid=True,
+                        #labelExpr="datum.value % 5 ? null : datum.label")
+                        #labelExpr="datum.label[0]")
+                        )),
+                    #alt.X(parameter_select, title=parameter_select),  #scale=alt.Scale(scheme='turbo') # lightgreyred ,#lightmulti, #lightgreyteal
+                    alt.Color(parameter_select, title=parameter_select, scale=alt.Scale(scheme='lightmulti')),#, scale=alt.Scale(scheme='brown'),
+                    #tooltip=['monthdate(datetime)' , parameter_select]
+                    #tooltip=['monthdate(datetime)']
+                    #parameter_select, #alt.Color('temp:Q', title='temperature (F)')
+            #         )
+            ).properties(
+            width=300,
+            height=300
+        ) 
+            
+            # alt.Chart(df).mark_circle().encode(
+            #     x='a',
+            #     y='b',
+            #     color=alt.value(color),
+            #     size='c'
+            #     )
+        #chart_placeholder2.altair_chart(alt.hconcat(c1,c2))
+        st.altair_chart(alt.hconcat(c1,c2))
+        
+# sensor H
+if sensors_select=='H':
+    comp_data=comp_data.loc[(comp_data['sensor']=='H')]
+    with c1:
+        st.map(Stations.query("sensor=='H'")[["lat", "lon"]])
+    with c2:
+        #comp_data['datetime']= pd.to_datetime(comp_data['time']).dt.strftime('%Y-%m-%dT%H:%M:%S')
+        #print('comp_data after filtering',comp_data)
+        c1 = alt.Chart(comp_data).mark_bar().encode(
+                    #alt.Y('monthdate(date):O', title='Month'),
+                    alt.X('day(datetime):O', title='week on avg'),
+                    #alt.X('datetime:O', title='tim'),
+                    #alt.X('day(date)', title='week'),
+                    # alt.X('date(date)', title='week')
+                    #alt.X('date(date)', axis=alt.Axis(values=[0, 5, 10, 15, 20, 30])),
+                    # alt.X('date(date)', title='date',axis=alt.Axis(
+                    #     tickCount=overview_df.shape[0],
+                    #     #tickOffset=2
+                    #     #tickCount="day",
+                    #     #tickCount=2,
+                    #     grid=False,
+                    #     #grid=True,
+                    #     #labelExpr="datum.value % 5 ? null : datum.label")
+                    #     #labelExpr="datum.label[0]")
+                    #     )),
+                    #x='date(datetime)',
+                    y=parameter_select,
+                    #x=alt.X('date(datetime):T', axis=alt.Axis(title='Date', format=("%b %d"),labelAngle=10, grid=True)),
+                        #x='hoursminutes(start)',
+                        #x2='hoursminutes(end)',
+                    
+                    # alt.X('date(date)'), #title='date',axis=alt.Axis(
+                        #tickCount=overview_df.shape[0],
+                        #tickOffset=2
+                        #tickCount="day",
+                        #tickCount=2,
+                        #grid=False,
+                        #grid=True,
+                        #labelExpr="datum.value % 5 ? null : datum.label")
+                        #labelExpr="datum.label[0]")
+                        #)),
+                    #alt.X(parameter_select, title=parameter_select),    
+                    color=alt.Color('day(datetime)', title='week', scale=alt.Scale(scheme='category10')), #set1
+                    tooltip=[ parameter_select]
+                    #parameter_select, #alt.Color('temp:Q', title='temperature (F)')
+            #         )
+            ).properties(
+            width=300,
+            height=300
+        ) 
+            # c1=alt.Chart(overview_df).mark_bar(opacity=0.7).encode(
+            #     x='date(date)',
+            #     #y=alt.Y('day(date)', stack=None),
+            #     y='day(date)',
+            #     color=parameter_select,
+            # )
+        c2 = alt.Chart(comp_data).mark_rect().encode(
+                    #alt.Y('monthdate(date):O', title='Month'),
+                    alt.Y('hoursminutes(datetime):O', title='hour of day'),
+                    #alt.X('datetime:O', title='tim'),
+                    #alt.X('day(date)', title='week'),
+                    # alt.X('date(date)', title='week')
+                    #alt.X('date(date)', axis=alt.Axis(values=[0, 5, 10, 15, 20, 30])),
+                    alt.X('date(datetime)', title='date',axis=alt.Axis(
+                        tickCount=comp_data.shape[0],
+                        #tickOffset=2,
+                        #tickCount="date",
+                        #tickCount=10,
+                        grid=False,
+                        #grid=True,
+                        #labelExpr="datum.value % 5 ? null : datum.label")
+                        #labelExpr="datum.label[0]")
+                        )),
+                    #alt.X(parameter_select, title=parameter_select),  #scale=alt.Scale(scheme='turbo') # lightgreyred ,#lightmulti, #lightgreyteal
+                    alt.Color(parameter_select, title=parameter_select, scale=alt.Scale(scheme='lightmulti')),#, scale=alt.Scale(scheme='brown'),
+                    #tooltip=['monthdate(datetime)' , parameter_select]
+                    #tooltip=['monthdate(datetime)']
+                    #parameter_select, #alt.Color('temp:Q', title='temperature (F)')
+            #         )
+            ).properties(
+            width=300,
+            height=300
+        ) 
+            
+            # alt.Chart(df).mark_circle().encode(
+            #     x='a',
+            #     y='b',
+            #     color=alt.value(color),
+            #     size='c'
+            #     )
+        #chart_placeholder2.altair_chart(alt.hconcat(c1,c2))
+        st.altair_chart(alt.hconcat(c1,c2))
+        
+#sensor I
+if sensors_select=='I':
+    comp_data=comp_data.loc[(comp_data['sensor']=='I')]
+    with c1:
+        st.map(Stations.query("sensor=='I'")[["lat", "lon"]])
+    with c2:
+        #comp_data['datetime']= pd.to_datetime(comp_data['time']).dt.strftime('%Y-%m-%dT%H:%M:%S')
+        #print('comp_data after filtering',comp_data)
+        c1 = alt.Chart(comp_data).mark_bar().encode(
+                    #alt.Y('monthdate(date):O', title='Month'),
+                    alt.X('day(datetime):O', title='week on avg'),
+                    #alt.X('datetime:O', title='tim'),
+                    #alt.X('day(date)', title='week'),
+                    # alt.X('date(date)', title='week')
+                    #alt.X('date(date)', axis=alt.Axis(values=[0, 5, 10, 15, 20, 30])),
+                    # alt.X('date(date)', title='date',axis=alt.Axis(
+                    #     tickCount=overview_df.shape[0],
+                    #     #tickOffset=2
+                    #     #tickCount="day",
+                    #     #tickCount=2,
+                    #     grid=False,
+                    #     #grid=True,
+                    #     #labelExpr="datum.value % 5 ? null : datum.label")
+                    #     #labelExpr="datum.label[0]")
+                    #     )),
+                    #x='date(datetime)',
+                    y=parameter_select,
+                    #x=alt.X('date(datetime):T', axis=alt.Axis(title='Date', format=("%b %d"),labelAngle=10, grid=True)),
+                        #x='hoursminutes(start)',
+                        #x2='hoursminutes(end)',
+                    
+                    # alt.X('date(date)'), #title='date',axis=alt.Axis(
+                        #tickCount=overview_df.shape[0],
+                        #tickOffset=2
+                        #tickCount="day",
+                        #tickCount=2,
+                        #grid=False,
+                        #grid=True,
+                        #labelExpr="datum.value % 5 ? null : datum.label")
+                        #labelExpr="datum.label[0]")
+                        #)),
+                    #alt.X(parameter_select, title=parameter_select),    
+                    color=alt.Color('day(datetime)', title='week', scale=alt.Scale(scheme='category10')), #set1
+                    tooltip=[ parameter_select]
+                    #parameter_select, #alt.Color('temp:Q', title='temperature (F)')
+            #         )
+            ).properties(
+            width=300,
+            height=300
+        ) 
+            # c1=alt.Chart(overview_df).mark_bar(opacity=0.7).encode(
+            #     x='date(date)',
+            #     #y=alt.Y('day(date)', stack=None),
+            #     y='day(date)',
+            #     color=parameter_select,
+            # )
+        c2 = alt.Chart(comp_data).mark_rect().encode(
+                    #alt.Y('monthdate(date):O', title='Month'),
+                    alt.Y('hoursminutes(datetime):O', title='hour of day'),
+                    #alt.X('datetime:O', title='tim'),
+                    #alt.X('day(date)', title='week'),
+                    # alt.X('date(date)', title='week')
+                    #alt.X('date(date)', axis=alt.Axis(values=[0, 5, 10, 15, 20, 30])),
+                    alt.X('date(datetime)', title='date',axis=alt.Axis(
+                        tickCount=comp_data.shape[0],
+                        #tickOffset=2,
+                        #tickCount="date",
+                        #tickCount=10,
+                        grid=False,
+                        #grid=True,
+                        #labelExpr="datum.value % 5 ? null : datum.label")
+                        #labelExpr="datum.label[0]")
+                        )),
+                    #alt.X(parameter_select, title=parameter_select),  #scale=alt.Scale(scheme='turbo') # lightgreyred ,#lightmulti, #lightgreyteal
+                    alt.Color(parameter_select, title=parameter_select, scale=alt.Scale(scheme='lightmulti')),#, scale=alt.Scale(scheme='brown'),
+                    #tooltip=['monthdate(datetime)' , parameter_select]
+                    #tooltip=['monthdate(datetime)']
+                    #parameter_select, #alt.Color('temp:Q', title='temperature (F)')
+            #         )
+            ).properties(
+            width=300,
+            height=300
+        ) 
+            
+            # alt.Chart(df).mark_circle().encode(
+            #     x='a',
+            #     y='b',
+            #     color=alt.value(color),
+            #     size='c'
+            #     )
+        #chart_placeholder2.altair_chart(alt.hconcat(c1,c2))
+        st.altair_chart(alt.hconcat(c1,c2))
+        
+#sensor J
+if sensors_select=='J':
+    comp_data=comp_data.loc[(comp_data['sensor']=='J')]
+    with c1:
+        st.map(Stations.query("sensor=='J'")[["lat", "lon"]])
+    with c2:
+        #comp_data['datetime']= pd.to_datetime(comp_data['time']).dt.strftime('%Y-%m-%dT%H:%M:%S')
+        #print('comp_data after filtering',comp_data)
+        c1 = alt.Chart(comp_data).mark_bar().encode(
+                    #alt.Y('monthdate(date):O', title='Month'),
+                    alt.X('day(datetime):O', title='week on avg'),
+                    #alt.X('datetime:O', title='tim'),
+                    #alt.X('day(date)', title='week'),
+                    # alt.X('date(date)', title='week')
+                    #alt.X('date(date)', axis=alt.Axis(values=[0, 5, 10, 15, 20, 30])),
+                    # alt.X('date(date)', title='date',axis=alt.Axis(
+                    #     tickCount=overview_df.shape[0],
+                    #     #tickOffset=2
+                    #     #tickCount="day",
+                    #     #tickCount=2,
+                    #     grid=False,
+                    #     #grid=True,
+                    #     #labelExpr="datum.value % 5 ? null : datum.label")
+                    #     #labelExpr="datum.label[0]")
+                    #     )),
+                    #x='date(datetime)',
+                    y=parameter_select,
+                    #x=alt.X('date(datetime):T', axis=alt.Axis(title='Date', format=("%b %d"),labelAngle=10, grid=True)),
+                        #x='hoursminutes(start)',
+                        #x2='hoursminutes(end)',
+                    
+                    # alt.X('date(date)'), #title='date',axis=alt.Axis(
+                        #tickCount=overview_df.shape[0],
+                        #tickOffset=2
+                        #tickCount="day",
+                        #tickCount=2,
+                        #grid=False,
+                        #grid=True,
+                        #labelExpr="datum.value % 5 ? null : datum.label")
+                        #labelExpr="datum.label[0]")
+                        #)),
+                    #alt.X(parameter_select, title=parameter_select),    
+                    color=alt.Color('day(datetime)', title='week', scale=alt.Scale(scheme='category10')), #set1
+                    tooltip=[ parameter_select]
+                    #parameter_select, #alt.Color('temp:Q', title='temperature (F)')
+            #         )
+            ).properties(
+            width=300,
+            height=300
+        ) 
+            # c1=alt.Chart(overview_df).mark_bar(opacity=0.7).encode(
+            #     x='date(date)',
+            #     #y=alt.Y('day(date)', stack=None),
+            #     y='day(date)',
+            #     color=parameter_select,
+            # )
+        c2 = alt.Chart(comp_data).mark_rect().encode(
+                    #alt.Y('monthdate(date):O', title='Month'),
+                    alt.Y('hoursminutes(datetime):O', title='hour of day'),
+                    #alt.X('datetime:O', title='tim'),
+                    #alt.X('day(date)', title='week'),
+                    # alt.X('date(date)', title='week')
+                    #alt.X('date(date)', axis=alt.Axis(values=[0, 5, 10, 15, 20, 30])),
+                    alt.X('date(datetime)', title='date',axis=alt.Axis(
+                        tickCount=comp_data.shape[0],
+                        #tickOffset=2,
+                        #tickCount="date",
+                        #tickCount=10,
+                        grid=False,
+                        #grid=True,
+                        #labelExpr="datum.value % 5 ? null : datum.label")
+                        #labelExpr="datum.label[0]")
+                        )),
+                    #alt.X(parameter_select, title=parameter_select),  #scale=alt.Scale(scheme='turbo') # lightgreyred ,#lightmulti, #lightgreyteal
+                    alt.Color(parameter_select, title=parameter_select, scale=alt.Scale(scheme='lightmulti')),#, scale=alt.Scale(scheme='brown'),
+                    #tooltip=['monthdate(datetime)' , parameter_select]
+                    #tooltip=['monthdate(datetime)']
+                    #parameter_select, #alt.Color('temp:Q', title='temperature (F)')
+            #         )
+            ).properties(
+            width=300,
+            height=300
+        ) 
+            
+            # alt.Chart(df).mark_circle().encode(
+            #     x='a',
+            #     y='b',
+            #     color=alt.value(color),
+            #     size='c'
+            #     )
+        #chart_placeholder2.altair_chart(alt.hconcat(c1,c2))
+        st.altair_chart(alt.hconcat(c1,c2))
+#         # st.altair_chart(alt.Chart(comp_data).mark_rect().encode(
+#         # #week1= alt.Chart(comp_data).mark_rect().encode(
+#         # alt.X('day(date):O', title='week (in selcted month)'),
+#         # alt.Y('hours(date):O', title='hour'),
+#         # alt.Color(parameter_select, title=parameter_select), #parameter_select, #alt.Color('temp:Q', title='temperature (F)'),
+#         # tooltip=[parameter_select]
+#         # ).properties(
+#         # width=1000,
+#         # height=200
+#         # )
+#         # )
+
+
+# # Inputs
+# #d = st.date_input("Selected day", date(today.year, today.month, today.day-1))
+# #opt = st.selectbox("Type of view", ['Map', 'Chart'], index=0)
+
+# # Display data according to selected type of view
+# #if opt == 'Map':
+# #   show_column_map(filtered_data)
+# #elif opt == 'Chart':
+#     #show_bar_chart(filtered_data)
+# # with c1:
+    
+# #     st.pydeck_chart(pdk.Deck(
+# #         map_style='mapbox://styles/mapbox/light-v9',
+# #         initial_view_state=pdk.ViewState(
+# #             longitude=9.183,
+# #             latitude=48.783,
+# #             zoom=13,
+# #             pitch=50
+# #         ),
+# #         layers=[
+# #             pdk.Layer(
+# #                 'ColumnLayer',
+# #                 data= Stations,
+# #                 get_position='[lon, lat]',
+# #                 get_elevation='[PM10]',
+# #                 radius=20,
+# #                 auto_highlight=True,
+# #                 elevation_scale=100,
+# #                 elevation_range=[0, 5000],
+# #                 pickable=True,
+# #                 extruded=True
+# #             )
+# #         ],
+# #         tooltip={
+# #                 'html': '<b>Sensor Name:</b> {sensor}',
+# #                 'style': {
+# #                 'color': 'white'
+# #             }}
+# #     ))
+
+# ##################################################################################################
+
+# ### plotting the sensors location on map#st.map(Stations.query("sensor== 'A' ")[["lat", "lon"]])   
+# #if (Stations.sensor[0]==selectedsensor):
+#     # plotting sensors as apoint in map
+
+# #print('sesonion sensor', selectedsensor)
+# #print('sesonion sensor', type(selectedsensor))
+# #print('sesonion sensor', type(Stations.sensor))
+
+
+# ## extracting the sensors data according to sensor data conditon selcted by the user
+
+# #if any("selectedsensor" in s for s in Stations.sensor):
+# # whatever
+# #print('check test')
+# #if selectedsensor=='A': 
+# #https://stackoverflow.com/questions/4843158/check-if-a-python-list-item-contains-a-string-inside-another-string
+# #matching = [s for s in selectedsensor if any(xs in s for xs in Stations.sensor)]
+# #print('check printing matching ', matching)
+# #print('matching type', type(matching)) 
+# #print('check printing matching ', matching[2])
+# #filter_value=[]
+# #for i in range(0,matching.shape[0], 1):
+#     #filter_value[i]= Stations.loc[(Stations['sensor']==(matching[i])) ] ;
+# #print('filter_value \n' , filter_value, '\n') ;
+# #filter_value1= Stations.loc[(Stations['sensor']==(matching[0])) ] ;
+# #filter_value2 = Stations.loc[(Stations['sensor']==(matching[1])) ] ;
+# #WS= filter_value1['Windspeed']
+# #filter_value3= Stations.loc[(Stations['sensor']==(matching[2])) ] ;
+# #filter_value4 = Stations.loc[(Stations['sensor']==(matching[3])) ] ;
+# #print('filter_value1 \n' , filter_value1, '\n') ;
+# #print('filter_value2 \n' , filter_value2, '\n') ;#source = data.stocks() #c=alt.Chart(Stations).mark_line().encode(x='day(time)', y='PM10',
+# #     """
+# #     #first altair chart
+# #     c=alt.Chart(Stations).mark_line().encode(x='time', y='PM10',
+# #     color='sensor',
+# #     strokeDash='sensor',
+# # )
+# #     st.altair_chart(c)
+# #     """
+#2 altair chart
+
+#https://elib.uni-stuttgart.de/simple-search?query=Weiskopf+Daniel&sort_by=score&order=desc&rpp=10&etal=0&start=10
+
+# sensor_list= ['A', 'B', 'C', 'D','E', 'F',  'G',  'H', 'I', 'J']
+# #sensor_list =pd.Series(['A', 'B', 'C', 'D','E', 'F',  'G',  'H', 'I', 'J'])
+# #listnames = show_names(name)
+# selectednames=st.sidebar.multiselect('Select sensor names', sensor_list)
+# print('selctname of sensors', selectednames)
+# print('type of selctname of sensors', type(selectednames))
+# #for name in selectednames:
+# #df_final = df[df['c'].isin(A)]  
+# print('isin function selctname of sensors', comp_data.sensor.isin(selectednames)) 
+# #st.map(comp_data.query("sensor"==[("sensor").isin(selectednames)]["lat", "lon"]))
+# #st.map(comp_data.query("sensor"==[comp_data.sensor.isin(selectednames)[0]]["lat", "lon"]))
+# #st.map(comp_data.query("sensor".isin(selectednames))["lat", "lon"])
+# #df_new.loc[ df_new['sensor'].isin(['A' , 'C'])  ]
+# value2 = comp_data.loc[comp_data['sensor'].isin([selectednames])  ][['lat', 'lon']] ;
+
+# print('shortlisted sensors', value2)
+
+# #for i in range(0, len(selectednames)):
+#  #   st.map(comp_data.query("sensor"=='A')[["lat", "lon"]])
+    
+# #st.map(Stations.query("sensor==name")[["lat", "lon"]])
+# st.map(value2)
+# c1, c2 = st.beta_columns((1, 1))
+
+#comp_data = Stations
+#print(comp_data)
+#st.sidebar.markdown("a) Select sensors")
+#if not st.sidebar.checkbox("Uncheck to expand", True, key='18'):
+# parameter_select = st.sidebar.selectbox('Parameter', ['Windspeed','PM10', 'PM2_5', 'Humd'], key='19')
+# sensors_select = st.sidebar.selectbox("Select sensors", ("None", "A", 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K'))
+# #sensors_select = st.sidebar.selectbox("Select sensors", ( "A", 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K'))
+# chart_placeholder2 = st.empty()
+# #sensor A
+# if sensors_select=='A':
+#     comp_data=comp_data.loc[(comp_data['sensor']=='A')]
+#     with c1:
+#         st.map(Stations.query("sensor=='A'")[["lat", "lon"]])
+#     with c2:
+
+
+# st.sidebar.markdown("### Compare sensors")
+# st.sidebar.markdown("Compare the nature of the selcted sensors and their temporal behaviour measurements in different considered Stuttgart's locations")
+# #if not st.sidebar.checkbox("Hide", True, key='3'):
+# #st.title("Air Quality")
+# metrics = ['PM10','PM2_5','Windspeed','Humd']
+# cols = st.selectbox('Sensors metric to view', metrics)
+# if (cols =='PM10'):
+# #column_select=WS
+#     highlight= alt.selection(type='single', on='mouseover',
+#                           fields=['sensor'], nearest=True)
+
+#     base = alt.Chart(comp_data).encode(
+#         x='day(time)',
+#         y='mean(PM10)',
+#         color='sensor'
+#     )
+    
+#     points1 = base.mark_circle().encode(
+#         opacity=alt.value(0)
+#     ).add_selection(
+#         highlight
+#     ).properties(
+#         width=600
+#     )
+    
+#     lines1 = base.mark_line().encode(
+#         size=alt.condition(~highlight, alt.value(1), alt.value(3))
+#     )
+#     #2b chart
+#     highlight = alt.selection(type='single', on='mouseover',
+#                           fields=['sensor'], nearest=True)
+
+#     base = alt.Chart(comp_data).encode(
+#         x='time',
+#         y='PM10',
+#         color='sensor'
+#     )
+    
+#     points2 = base.mark_circle().encode(
+#         opacity=alt.value(0)
+#     ).add_selection(
+#         highlight
+#     ).properties(
+#         width=600
+#     )
+    
+#     lines2 = base.mark_line().encode(
+#         size=alt.condition(~highlight, alt.value(1), alt.value(3))
+#     )   
+    
+#     #points + lines 
+#     # to concatenate to chart horixonatally
+#     #c=alt.hconcat((points1 + lines1), (points2 + lines2))
+#     c=alt.vconcat((points1 + lines1), (points2 + lines2))
+#     st.altair_chart(c)
+
+# def show_names(name):
+#     #if name == "sensors":
+#      #    list1 = ['A', 'B', 'C', 'D',
+#                      #'E', 'F',  'G',  'H', 'I', 'J']
+#     if name == "sensors":
+#          list1 = ['A', 'B', 'C', 'D',
+#                         'E', 'F',  'G',  'H', 'I', 'J']
+#     #elif name == "metrics":
+#        # list1 = ['Windflow','Humidity','Pm10','Pm2.5','NO']
+#     return list1
+
+
+# def inp_det(type):
+#     if type == 'source':
+#         st.sidebar.write('### Select (sensors/metrics)')
+#         st.sidebar.markdown("Select, interact and visualise: Explain and devise your hypothesis with visual analytics")
+#         name = st.sidebar.text_input('Source sensor name')
+#     elif type == 'destination':
+#         st.write('Select (sensors/metrics')
+#         name = st.sidebar.text_input('Destination name')
+#     return name
+# def main():
+#     #st.title("Environmental Dashboard")#st.markdown('The dashboard will visualize the Wind and Air Quality Situation in Stuttgart')
+#     #st.markdown("A visual anlytics tool for interactively anlysisng and exploring historical+spatial+temporal+future meterological and air qulity paremters")
+#     name = inp_det('source')
+#     session_state = SessionState.get(name="", button_sent=False)
+#     button_sent = st.sidebar.button("SUBMIT")
+#     if button_sent or session_state.button_sent: # <-- first time is button interaction, next time use state to go to multiselect
+#         session_state.button_sent = True
+#         #listnames = n.show_names(name)
+#         listnames = show_names(name)
+#         selectednames=st.sidebar.multiselect('Select sensor names',listnames)
+#         #st.write(selectednames)
+#         #sensors_on_map_with_parameters2.testmethod(selectednames)
+#         #sensors_on_map_with_parameters2_2.testmethod(selectednames)
+#         #Final_sensors_on_map_with_parameters2_2.testmethod(selectednames)
+# if __name__ == "__main__":
+#     main()
+
+
+#end of method
+        
+        
+
+
+#     c=alt.Chart(comp_data).mark_point().encode(
+#     alt.X(alt.repeat("column"), type='quantitative'),
+#     alt.Y(alt.repeat("row"), type='quantitative'),
+#     color='sensor'
+# ).properties(
+#     width=200,
+#     height=200
+# ).repeat(
+#     row=['time', 'time'],
+#     column=['PM10', 'PM2_5']
+# ).interactive()
+#     st.altair_chart(c)
+
+#     c=alt.Chart(Stations).mark_point().encode(
+#     x='time',
+#     y='PM10',
+#     color='sensor'
+# ).properties(
+#     width=180,
+#     height=180
+# ).facet(
+#     column='sensor'
+# )
+#     st.altair_chart(c)
+
+#     #3 altair chart
+#     selection = alt.selection_multi(fields=['sensor'], bind='legend')
+
+#     c=alt.Chart(comp_data).mark_area().encode(
+#         alt.X('time', axis=alt.Axis(domain=True, format='%d', tickSize=0)),
+#         alt.Y('PM10', stack='center', axis=None),
+#         alt.Color('sensor', scale=alt.Scale(scheme='category20b')),
+#         opacity=alt.condition(selection, alt.value(1), alt.value(0.2))
+#     ).add_selection(
+#         selection
+#     )
+#     st.altair_chart(c)
+
+#     print('last lines ')
+#     if not st.sidebar.checkbox("Hide", True, key='1'):
+#         metrics = ['PM10','PM2_5']
+#         cols = st.selectbox('Air quality metric to view', metrics)
+#         if (cols =='PM10'):
+#             concat_plot(Stations, cols)
+
+#st.map(Stations.query("Windspeed>=0.0")[["lat", "lon"]])
+#https://stackoverflow.com/questions/22591174/pandas-multiple-conditions-while-indexing-data-frame-unexpected-behavior
+#df_filtered = df.query('a == 4 & b != 2')
+# st.sidebar.markdown("### Which location measured highest today?: Pick date and see ")
+# st.sidebar.markdown("Perform the temporal querry to highlight and on-fly identify ( or ask the system) the location which is measuring highest(either, WS, PM10, PM2_5, Humidity) among different considered locations in Stuttgart")
+# if not st.sidebar.checkbox("Hide", True, key='14'):
+#     metrics = ['PM10','PM2_5','Windspeed','Humidity']
+#     cols = st.selectbox('Sensors metric to view', metrics)
+#     if (cols =='PM10'):
+#         st.map(Stations.query("PM10>=PM10.max()")[["lat", "lon", "sensor"]])
+#     if (cols =='PM2_5'):
+#         st.map(Stations.query("PM2_5>=PM2_5.max()")[["lat", "lon", "sensor"]])
+#     if (cols =='Windspeed'):
+#         st.map(Stations.query("Windspeed>=Windspeed.max()")[["lat", "lon", "sensor"]])
+#     if (cols =='Humidity'):
+#         st.map(Stations.query("Humd>=Humd.max()")[["lat", "lon", "sensor"]])
+#     # center on Liberty Bell
+
+#st.map(Stations.query("Windspeed>=0.0")[["lat", "lon"]])
+
+# layer = pdk.Layer(
+#     'HexagonLayer',
+#     Stations,
+#     get_position='[lon, lat]',
+#     auto_highlight=True,
+#     elevation_scale=50,
+#     pickable=True,
+#     elevation_range=[0, 3000],
+#     extruded=True,
+#     coverage=1)
+
+# # Set the viewport location
+# view_state = pdk.ViewState(
+#     longitude=9.183,
+#     latitude=48.783,
+#     zoom=6,
+#     min_zoom=5,
+#     max_zoom=15,
+#     pitch=40.5,
+#     bearing=-27.36)
+
+# # Combined all of it and render a viewport
+# r = pdk.Deck(
+#     layers=[layer],
+#     initial_view_state=view_state,
+#     tooltip={
+#         'html': '<b>Elevation Value:</b> {elevationValue}',
+#         'style': {
+#             'color': 'white'
+#         }
+#     }
+# )
+# r.to_html()
+#print('the sesnors data type', Stations)
+#d5 = st.sidebar.date_input("date range without default", [datetime.date(2019, 7, 6), datetime.date(2019, 7, 8)])
+#st.sidebar.write(d5)
+
+# alt.Chart(Stations).mark_geoshape()\
+#     .encode(color='sensors')\
+#     .add_selection(select_date)\
+#     .transform_filter(select_date)\
+#     .transform_lookup(
+#         lookup='id',
+#         from_=alt.LookupData(countries, key='id',
+#                               fields=["type", "properties", "geometry"])
+#     )\
+#     .project('equirectangular')\
+#     .properties(
+#         width=500,
+#         height=300,
+#         title='Title'
+#     )
+
+# =============================================================================
+# Transformer model sensors nature monitoring
+#data reading of all sensors predicted for 2020
+# =============================================================================
+pathName_input = 'C:\\Users\\Shub\\streamlite_dashboard\\data\\updated_Sensordata\\sensors_transformer_preditedoutputs\\'
+fileName1 = 'designed_Allsensors2020_predicted.csv' ;
+@st.cache
+def load_data(nrows):
+    #df = pd.read_csv(( pathName_input +fileName ), sep = ",", nrows=nrows)
+    #data = pd.read_pickle(( pathName_input +fileName ), nrows=nrows)
+    #lowercase = lambda x: str(x).lower()
+    #Drop rows which contains any NaN or missing value
+    #newdf = df.dropna()
+    # Drop rows with NaN or missing value in all columns
+    #df = newdf.dropna(how='all')
+    #data.rename(lowercase, axis='columns', inplace=True)
+    #data['YEAR'] = pd.to_datetime(data['YEAR'])
+    # define eastern timezone
+    #eastern = timezone('US/Eastern')
+    # dti = dti.tz_localize('UTC')
+    #df_temp=copy.deepcopy(df)
+    sensorA = pd.read_csv(( pathName_input +fileName1 ), sep = ",", nrows=1000)
+    #sensorB = pd.read_csv(( pathName_input +fileName2 ), sep = ",", nrows=1000)
+    #sensorC = pd.read_csv(( pathName_input +fileName3 ), sep = ",", nrows=1000)
+    #sensorD = pd.read_csv(( pathName_input +fileName4 ), sep = ",",nrows=1000)
+    #sensorE = pd.read_csv(( pathName_input +fileName5 ), sep = ",", nrows=1000)
+    #sensorF = pd.read_csv(( pathName_input +fileName6 ), sep = ",",nrows=1000)
+    #sensorG = pd.read_csv(( pathName_input +fileName7 ), sep = ",",nrows=1000)
+    #sensorH = pd.read_csv(( pathName_input +fileName8 ), sep = ",",nrows=1000)
+    #sensorI = pd.read_csv(( pathName_input +fileName9 ), sep = ",",nrows=1000)
+    #sensorJ = pd.read_csv(( pathName_input +fileName10 ), sep = ",",nrows=1000)
+    Stations_monitoring = pd.concat([sensorA], axis=0)
+    Stations_monitoring["time"] = pd.to_datetime(
+    Stations_monitoring['YEAR'] + ' ' + Stations_monitoring['time'])
+    Stations_monitoring = Stations_monitoring.drop(["YEAR"], axis=1)
+    # Kepler needs time as string, otherwise it will throw an error
+    #sensor_all["time"] = sensor_all["time"].apply(str)
+    Stations_monitoring["time"] = pd.to_datetime(Stations_monitoring["time"],utc=True, dayfirst=True)
+    #df['timestamp'] = pd.to_datetime(df['date'], utc=True, dayfirst=True) #day saving problem recovered b the conversion to local time
+    #generate the datetime
+    #df['date'] = df['timestamp'].apply(lambda x: datetime.datetime(x.year,x.month,x.day, x.hour, x.minute, x.second))
+    #import time
+    #df['date']=time.strftime("%H:%M",  time.strptime(str(df['timestamp']), "%Y-%m-%d %H:%M:%S"))
+    #df['date'] = df['date'].astype('datetime64[ns]')
+    #df['date']=df['date'].to_pydatetime()
+    #df_temp['date']= pd.to_datetime(df['date'], dayfirst=True)
+    return Stations_monitoring
+
+Stations_monitoring = load_data(4700) #458384
+
+print('\n concatenated data is \n ' , Stations_monitoring , '\n');
+
+Stations_monitoring['Windspeed_maximum_value'] = pd.to_numeric( Stations_monitoring['Windspeed_maximum_value'] ) ;
+Stations_monitoring['PM10_maximum_value'] = pd.to_numeric( Stations_monitoring['PM10_maximum_value']) ;
+Stations_monitoring['PM2_5_maximum_value'] = pd.to_numeric( Stations_monitoring['PM2_5_maximum_value'] ) ;
+Stations_monitoring['Humd_maximum_value'] = pd.to_numeric( Stations_monitoring['Humd_maximum_value'] ) ;
+print ("Stations_monitoring['Windspeed_maximum_value']", Stations_monitoring['Windspeed_maximum_value'])