Update app.py

app.py

@@ -1,5 +1,5 @@
 import streamlit as st
-import pyplot.express as px
+import plotly.express as px
 import openmeteo_requests
 import requests_cache
 import pandas as pd
@@ -41,92 +41,7 @@ end_date = start_date + timedelta(days=10)
 st.caption(f"Time Period: {parseday(start_date)} to {parseday(end_date)}")
 
 #Draw Tabs
-tab1, tab2 = st.tabs(['Overall', 'Daily'])
-
-#Min and Max temperature
-min_max_date_list = []
-min_max_min_list = []
-min_max_max_list = []
-for i in range(11):
-    cur_date = start_date + timedelta(days=i)
-    minidf = wxdata[(wxdata['Day'] == cur_date.day)&(wxdata['Month'] == cur_date.month)&(wxdata['Year'] == cur_date.year)]
-    min_max_date_list.append(cur_date)
-    min_max_min_list.append(minidf['temperature_2m'].min())
-    min_max_max_list.append(minidf['temperature_2m'].max())
-
-min_max_df = pd.DataFrame()
-min_max_df['Date'] = min_max_date_list
-min_max_df['Minimum Temperature'] = min_max_min_list
-min_max_df['Maximum Temperature'] = min_max_max_list
-
-mintempfig = px.line(min_max_df, x = 'Date', y = 'Minimum Temperature')
-maxtempfig = px.line(min_max_df, x = 'Date', y = 'Maximum Temperature')
-
-with tab1.expander("Minimum and Maximum Temperature"):
-    st.plotly_chart(mintempfig, use_container_width = True)
-    st.plotly_chart(maxtempfig, use_container_width = True)
-
-#temperature and inversion
-#Temperature plot
-tempfig = px.line(wxdata, x = 'Date_IST', y = 'temperature_2m', hover_data= 'temperature_925hPa',
-                    labels = {'Date_IST': 'Date and Time', 'temperature_2m': 'Dry Bulb Temp', 'temperature_925hPa': 'Temp at F/L025'})
-
-#Inversion
-invlist = []
-temp2mlist = wxdata['temperature_2m'].values
-temp025list = wxdata['temperature_925hPa'].values
-
-for t in range(len(temp2mlist)):
-    if((temp025list[t] - temp2mlist[t])>0):
-        invlist.append((temp025list[t] - temp2mlist[t]))
-    else:
-        invlist.append(0)
-
-wxdata['Inversion'] = invlist
-invfig = px.bar(wxdata, x='Date_IST', y='Inversion')
-
-
-with tab1.expander("Temperature and Inversion"):
-    st.plotly_chart(tempfig, use_container_width = True)
-    st.plotly_chart(invfig, use_container_width = True)
-
-#RH plot
-rhfig = px.line(wxdata, x = 'Date_IST', y = 'relative_humidity_2m', labels = {'newdate': 'Date and Time', 'relative_humidity_2m': 'RH(%age)'})
-
-with tab1.expander("Relative Humidity"):
-    st.plotly_chart(rhfig, use_container_width = True)
-
-#Cloud Plots
-cloudsfig = px.line(wxdata, x = 'Date_IST', y = 'cloud_cover', hover_data = ['cloud_cover_low', 'cloud_cover_mid', 'cloud_cover_high'],
-                labels = {'Date_IST': 'Date and Time', 'cloud_cover': 'Total Cloud Cover (%age)',
-                                                        'cloud_cover_low': 'Low Clouds',
-                                                        'cloud_cover_mid': 'Medium Clouds',
-                                                        'cloud_cover_high': 'High Clouds'})
-cloudsfig.update_layout(yaxis_range=[0,100])
-lowcloudsfig = px.line(wxdata, x = 'Date_IST', y = 'cloud_cover_low', hover_data = ['cloud_cover', 'cloud_cover_mid', 'cloud_cover_high'],
-                labels = {'Date_IST': 'Date and Time', 'cloud_cover': 'Total Cloud Cover (%age)',
-                                                        'cloud_cover_low': 'Low Clouds',
-                                                        'cloud_cover_mid': 'Medium Clouds',
-                                                        'cloud_cover_high': 'High Clouds'})
-midcloudsfig = px.line(wxdata, x = 'Date_IST', y = 'cloud_cover_mid', hover_data = ['cloud_cover', 'cloud_cover_low', 'cloud_cover_high'],
-                labels = {'Date_IST': 'Date and Time', 'cloud_cover': 'Total Cloud Cover (%age)',
-                                                        'cloud_cover_low': 'Low Clouds',
-                                                        'cloud_cover_mid': 'Medium Clouds',
-                                                        'cloud_cover_high': 'High Clouds'})
-highcloudsfig = px.line(wxdata, x = 'Date_IST', y = 'cloud_cover_high', hover_data = ['cloud_cover', 'cloud_cover_low', 'cloud_cover_mid'],
-                labels = {'Date_IST': 'Date and Time', 'cloud_cover': 'Total Cloud Cover (%age)',
-                                                        'cloud_cover_low': 'Low Clouds',
-                                                        'cloud_cover_mid': 'Medium Clouds',
-                                                        'cloud_cover_high': 'High Clouds'})
-lowcloudsfig.update_layout(yaxis_range=[0,100])
-midcloudsfig.update_layout(yaxis_range=[0,100])
-highcloudsfig.update_layout(yaxis_range=[0,100])
-
-with tab1.expander("Cloudiness"):
-    st.plotly_chart(cloudsfig, use_container_width = True)
-    st.plotly_chart(lowcloudsfig, use_container_width = True)
-    st.plotly_chart(midcloudsfig, use_container_width = True)
-    st.plotly_chart(highcloudsfig, use_container_width = True)
+tab1, tab2 = st.tabs(['PPTN', 'LOCAL FCST'])
 
 #precipitaion plot
 pptfig = px.bar(wxdata, x = 'Date_IST', y = 'precipitation', labels = {'Date_IST': 'Date and Time', 'precipitation': 'Precipitation'})
@@ -155,76 +70,6 @@ with tab2.expander("Current Weather Register"):
     st.table(makecwr(wxdata2))
 
 
-#temperature and inversion
-#Temperature plot
-tempfig = px.line(wxdata2, x = 'Date_IST', y = 'temperature_2m', hover_data= 'temperature_925hPa',
-                    labels = {'Date_IST': 'Date and Time', 'temperature_2m': 'Dry Bulb Temp', 'temperature_925hPa': 'Temp at F/L025'})
-
-#Inversion
-invlist = []
-temp2mlist = wxdata2['temperature_2m'].values
-temp025list = wxdata2['temperature_925hPa'].values
-
-for t in range(len(temp2mlist)):
-    if((temp025list[t] - temp2mlist[t])>0):
-        invlist.append((temp025list[t] - temp2mlist[t]))
-    else:
-        invlist.append(0)
-
-wxdata2['Inversion'] = invlist
-invfig = px.bar(wxdata2, x='Date_IST', y='Inversion')
-invfig.update_layout(yaxis_range=[0,5])
-
-with tab2.expander("Temperature and Inversion"):
-    st.plotly_chart(tempfig, use_container_width = True)
-    st.plotly_chart(invfig, use_container_width = True)
-
-#RH plot
-rhfig = px.line(wxdata2, x = 'Date_IST', y = 'relative_humidity_2m', labels = {'newdate': 'Date and Time', 'relative_humidity_2m': 'RH(%age)'})
-
-with tab2.expander("Relative Humidity"):
-    st.plotly_chart(rhfig, use_container_width = True)
-
-#Cloud Plots
-cloudsfig = px.line(wxdata2, x = 'Date_IST', y = 'cloud_cover', hover_data = ['cloud_cover_low', 'cloud_cover_mid', 'cloud_cover_high'],
-                labels = {'Date_IST': 'Date and Time', 'cloud_cover': 'Total Cloud Cover (%age)',
-                                                        'cloud_cover_low': 'Low Clouds',
-                                                        'cloud_cover_mid': 'Medium Clouds',
-                                                        'cloud_cover_high': 'High Clouds'})
-cloudsfig.update_layout(yaxis_range=[0,100])
-lowcloudsfig = px.line(wxdata2, x = 'Date_IST', y = 'cloud_cover_low', hover_data = ['cloud_cover', 'cloud_cover_mid', 'cloud_cover_high'],
-                labels = {'Date_IST': 'Date and Time', 'cloud_cover': 'Total Cloud Cover (%age)',
-                                                        'cloud_cover_low': 'Low Clouds',
-                                                        'cloud_cover_mid': 'Medium Clouds',
-                                                        'cloud_cover_high': 'High Clouds'})
-midcloudsfig = px.line(wxdata2, x = 'Date_IST', y = 'cloud_cover_mid', hover_data = ['cloud_cover', 'cloud_cover_low', 'cloud_cover_high'],
-                labels = {'Date_IST': 'Date and Time', 'cloud_cover': 'Total Cloud Cover (%age)',
-                                                        'cloud_cover_low': 'Low Clouds',
-                                                        'cloud_cover_mid': 'Medium Clouds',
-                                                        'cloud_cover_high': 'High Clouds'})
-highcloudsfig = px.line(wxdata2, x = 'Date_IST', y = 'cloud_cover_high', hover_data = ['cloud_cover', 'cloud_cover_low', 'cloud_cover_mid'],
-                labels = {'Date_IST': 'Date and Time', 'cloud_cover': 'Total Cloud Cover (%age)',
-                                                        'cloud_cover_low': 'Low Clouds',
-                                                        'cloud_cover_mid': 'Medium Clouds',
-                                                        'cloud_cover_high': 'High Clouds'})
-lowcloudsfig.update_layout(yaxis_range=[0,100])
-midcloudsfig.update_layout(yaxis_range=[0,100])
-highcloudsfig.update_layout(yaxis_range=[0,100])
-
-with tab2.expander("Cloudiness"):
-    st.plotly_chart(cloudsfig, use_container_width = True)
-    st.plotly_chart(lowcloudsfig, use_container_width = True)
-    st.plotly_chart(midcloudsfig, use_container_width = True)
-    st.plotly_chart(highcloudsfig, use_container_width = True)
-
-#pressure plot
-qnhfig = px.line(wxdata2, x = 'Date_IST', y = 'surface_pressure', labels = {'Date_IST': 'Date and Time', 'surface_pressure': 'QNH(hPa)'})
-qnhfig.update_layout(yaxis_range=[995,1018])
-
-
-with tab2.expander("Surface Pressure"):
-    st.plotly_chart(qnhfig, use_container_width = True)
-
 old_ht = [500,2500,5000,10000,18000,30000,35000,40000]
 new_ht = [1000, 2000, 3000, 5000, 7000, 9000, 15000, 18000, 25000, 30000]
 upper_air_df = pd.DataFrame()