Create app.py

app.py

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+import streamlit as st
+import plotly.express as px
+import openmeteo_requests
+import requests_cache
+import pandas as pd
+from retry_requests import retry
+from datetime import datetime, timedelta
+from support_functions import *
+import numpy as np
+from scipy import interpolate
+
+#Page Configuration
+st.set_page_config(initial_sidebar_state="collapsed", page_title='local_fcst', menu_items={
+        'Get Help': None,
+        'Report a bug': None,
+        'About': "Designed by Meteorama"
+    })
+
+
+st.header("Meteorama")
+
+st.header("for local forecast")
+
+#Check for Lat Lon info and Get ECMWF Data
+#Get Initial Configuration
+url_params = st.query_params
+url_params_keys = dict(url_params).keys()
+if('lat' not in url_params_keys or 'lon' not in url_params_keys):
+    latvals = 22.47
+    lonvals = 70.05
+    st.info("Latitude and Longitude values not defined. Defaulting to Jamnagar...")
+else:
+    latvals = float(url_params['lat'])
+    lonvals = float(url_params['lon'])
+
+wxdata = get_ecmwf_data(latvals, lonvals)
+
+#Get Dates list
+start_date = datetime(wxdata['Year'].values[0], wxdata['Month'].values[0], wxdata['Day'].values[0])
+end_date = start_date + timedelta(days=10)
+st.caption(f"Time Period: {parseday(start_date)} to {parseday(end_date)}")
+
+#Draw Tabs
+tab2 = st.tabs(['Daily'])
+
+#Daily Data
+ds = tab2.slider(
+    "Forecast for ", start_date, end_date,
+    value=start_date,
+    format="DD MMM YY")
+
+wxdata2 = wxdata[(wxdata['Day'] == ds.day)&(wxdata['Month'] == ds.month)&(wxdata['Year'] == ds.year)]
+
+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()
+upper_air_df['Height(KM)'] = [0.3,0.6,0.9,1.5,2.1,3.0,4.5,6.0,7.5,9.0][::-1]
+#Upper Air Data
+sel_times = tab2.multiselect("Select Time for Upper Air Data (IST)", wxdata2['Hour'].values, [5,11,17,23])
+for sel_time in sel_times:
+    filter_df = wxdata2[wxdata2['Hour'] == sel_time]
+    wind_dir_list = []
+    wind_speed_list = []
+    temp_list = []
+    for level in ['200hPa', '250hPa', '300hPa', '500hPa', '700hPa', '850hPa', '925hPa', '1000hPa'][::-1]:
+        wind_dir_list.append(filter_df[f'winddirection_{level}'].values[0])
+        wind_speed_list.append(filter_df[f'windspeed_{level}'].values[0])
+        temp_list.append(int(filter_df[f'temperature_{level}'].values[0]))
+
+    new_wind_dir = interp_for_levels(old_ht, wind_dir_list, new_ht, is_wind_dir = True)[::-1]
+    new_wind_speed = interp_for_levels(old_ht, wind_speed_list, new_ht)[::-1]
+    new_temp = interp_for_levels(old_ht, temp_list, new_ht, round_to = 1)[::-1]
+
+    text_to_be_shown = []
+    for y in range(len(new_wind_dir)):
+        while(new_wind_dir[y]>360):
+            new_wind_dir[y] = new_wind_dir[y]-360
+        wwww = f"{new_wind_dir[y]:03d}/{new_wind_speed[y]:02d}({new_temp[y]:02d})"
+        text_to_be_shown.append(wwww)
+
+    upper_air_df[f"{sel_time:02d}:30Hr"] = text_to_be_shown
+
+tab2.dataframe(upper_air_df, use_container_width=True)
+
+st.success("Made by Meteo Rama..!!")