pages/Weather API.py

import datetime
import glob
import os
import branca.colormap as cm
import folium
import numpy as np
import pandas as pd
import plotly.express as px
import streamlit as st
from geopy.extra.rate_limiter import RateLimiter
from geopy.geocoders import Nominatim
from matplotlib import colors as colors
import rioxarray
import xarray as xr
import cdsapi
import os



def geocode(address):
    try:
        address2 = address.replace(' ', '+').replace(',', '%2C')
        df = pd.read_json(
            f'https://geocoding.geo.census.gov/geocoder/locations/onelineaddress?address={address2}&benchmark=2020&format=json')
        results = df.iloc[:1, 0][0][0]['coordinates']
        lat, lon = results['y'], results['x']
    except:
        geolocator = Nominatim(user_agent="GTA Lookup")
        geocode = RateLimiter(geolocator.geocode, min_delay_seconds=1)
        location = geolocator.geocode(address)
        lat, lon = location.latitude, location.longitude
    return lat, lon




def get_weather_data(lat, lon, start_date, end_date):

    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,windgusts_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'],
                                           'windgusts_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


@st.cache
def convert_df(df):
    return df.to_csv(index=0).encode('utf-8')


st.set_page_config(layout="wide")
col1, col2 = st.columns((2))


address = st.sidebar.text_input(
    "Address", "1000 Main St, Cincinnati, OH 45202")
start_date = st.sidebar.date_input("Start Date",  pd.Timestamp(2022, 9, 28))
end_date = st.sidebar.date_input("End Date",  pd.Timestamp(2022, 9, 30))
type_var = st.sidebar.selectbox(
    'Type:', ('Gust', 'Wind', 'Temp', 'Precipitation'))
hourly_daily = st.sidebar.radio('Aggregate Data', ('Hourly', 'Daily'))


start_date = start_date.strftime("%Y-%m-%d")
end_date = end_date.strftime("%Y-%m-%d")

lat, lon = geocode(address)

df_all, df_all_agg = get_weather_data(lat, lon, start_date, end_date)

# Keys
var_key = {'Gust': 'i10fg', 'Wind': 'wind10',
           'Temp': 't2m', 'Precipitation': 'tp'}

variable = var_key[type_var]

unit_key = {'Gust': 'MPH', 'Wind': 'MPH',
            'Temp': 'F', 'Precipitation': 'In.'}
unit = unit_key[type_var]

cols_key = {'Gust': ['windgusts_10m'], 'Wind': ['windspeed_10m'], 'Temp': ['temperature_2m'],
            'Precipitation': ['precipitation']}

cols_key_agg = {'Gust': ['windgusts_10m_min', 'windgusts_10m_mean',
                         'windgusts_10m_max'],
                'Wind': ['windspeed_10m_min', 'windspeed_10m_mean',
                         'windspeed_10m_max'],
                'Temp': ['temperature_2m_min', 'temperature_2m_mean', 'temperature_2m_max'],
                'Precipitation': ['precipitation_sum']}

if hourly_daily == 'Hourly':
    cols = cols_key[type_var]
else:
    cols = cols_key_agg[type_var]

if hourly_daily == 'Hourly':
    fig = px.line(df_all, x="time", y=cols[0])
    df_downloald = df_all
else:
    fig = px.line(df_all_agg.reset_index(), x="date", y=cols[0])
    df_downloald = df_all_agg.reset_index()

with col1:
    st.title('Weather Data')
    st.plotly_chart(fig)

    csv = convert_df(df_downloald)

    st.download_button(
        label="Download data as CSV",
        data=csv,
        file_name=f'{start_date}.csv',
        mime='text/csv')


with col2:
    st.title('')


st.markdown(""" <style>
#MainMenu {visibility: hidden;}
footer {visibility: hidden;}
</style> """, unsafe_allow_html=True)