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0585eae d7b9aaa 0585eae b066138 0585eae f959bb1 9291f9c 0585eae 9291f9c 0585eae d7b9aaa 8ff9764 906b1b6 8ff9764 0585eae 8ff9764 0585eae 8ff9764 0585eae 8ff9764 0585eae 8ff9764 0585eae | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 | 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 forecast', menu_items={
'Get Help': None,
'Report a bug': None,
'About': "Designed by Meteorama"
})
st.header("FOR LOCAL FORECAST")
st.caption("winds & temp")
#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
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'})
with tab1.expander("Precipitation"):
st.plotly_chart(pptfig, use_container_width = True)
#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))
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..!!")
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