Update pages/Weather API.py

pages/Weather API.py

@@ -10,19 +10,12 @@ import streamlit as st
 from geopy.extra.rate_limiter import RateLimiter
 from geopy.geocoders import Nominatim
 from matplotlib import colors as colors
-from streamlit_folium import st_folium
 import rioxarray
 import xarray as xr
 import cdsapi
 import os
 
 
-def mapvalue2color(value, cmap):
-    if np.isnan(value):
-        return (1, 0, 0, 0)
-    else:
-        return colors.to_rgba(cmap(value), 0.7)
-
 
 def geocode(address):
     try:
@@ -39,30 +32,6 @@ def geocode(address):
     return lat, lon
 
 
-def graph_within_date_range(d, number_days_range):
-    year, month, day = d[:4], d[4:6], d[6:8]
-    date = pd.Timestamp(d)
-    start_date, end_date = date - \
-        pd.Timedelta(days=number_days_range), date + \
-        pd.Timedelta(days=number_days_range+1)
-    start_date = start_date.strftime("%Y-%m-%d")
-    end_date = end_date.strftime("%Y-%m-%d")
-    url = f'https://archive-api.open-meteo.com/v1/archive?latitude={lat}&longitude={lon}&start_date={start_date}&end_date={end_date}&hourly=temperature_2m,precipitation,windspeed_10m,wind_gusts_10m&models=best_match&temperature_unit=fahrenheit&windspeed_unit=mph&precipitation_unit=inch'
-    df = pd.read_json(url).reset_index()
-    data = pd.DataFrame({c['index']: c['hourly'] for r, c in df.iterrows()})
-    data['time'] = pd.to_datetime(data['time'])
-    data['date'] = pd.to_datetime(data['time'].dt.date)
-    data = data.query("temperature_2m==temperature_2m")
-
-    data_agg = data.groupby(['date']).agg({'temperature_2m': ['min', 'mean', 'max'],
-                                           'precipitation': ['sum'],
-                                           'windspeed_10m': ['min', 'mean', 'max'],
-                                           'wind_gusts_10m': ['min', 'mean', 'max']
-                                           })
-    data_agg.columns = data_agg.columns.to_series().str.join('_')
-    data_agg = data_agg.query("temperature_2m_min==temperature_2m_min")
-    return data.drop(columns=['date']), data_agg
-
 
 
 def get_weather_data(lat, lon, start_date, end_date):