Create app.py

app.py

@@ -0,0 +1,207 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Fri Oct 14 10:35:25 2022
+@author: mritchey
+"""
+
+import datetime
+import glob
+import os
+import urllib.request
+import branca.colormap as cm
+import folium
+import numpy as np
+import pandas as pd
+import plotly.express as px
+import rasterio
+import rioxarray
+import streamlit as st
+from geopy.extra.rate_limiter import RateLimiter
+from geopy.geocoders import Nominatim
+from joblib import Parallel, delayed
+from matplotlib import colors as colors
+from streamlit_folium import st_folium
+from threading import Thread
+
+def download_file_get_data(url,rows, columns):
+    file = urllib.request.urlretrieve(url, url[-23:])[0]
+    rds = rioxarray.open_rasterio(file)
+    wind_mph = rds.rio.reproject("EPSG:4326")[0, rows, columns].values*2.23694
+    time = url[-15:-11]
+    return [wind_mph, time]
+
+def threading(df_input,func_input):
+    starttime=time.time()
+    tasks_thread=df_input
+    results_thread=[]
+    def thread_func(value_input):
+        response=func_input(value_input)
+        results_thread.append(response)
+        return True
+    
+    threads = []
+    for i in range(len(tasks_thread)):
+        process = Thread(target=thread_func, args=[tasks_thread[i]])
+        process.start()
+        threads.append(process)   
+        
+    for process in threads:
+        process.join()
+    print(f'Time: {str(round((time.time()-starttime)/60,5))} Minutes') 
+    return results_thread
+
+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:
+        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
+
+@st.cache
+def get_grib_data(url, d, t):
+    file = urllib.request.urlretrieve(url, f'{d}{t}{type_wind}.grib2')[0]
+    return file
+
+@st.cache
+def graph_entire_day(d,rows, columns):
+    year, month, day = d[:4], d[4:6], d[6:8]
+    times = [f'0{str(i)}'[-2:] for i in range(0, 24)]
+    urls = [
+        f'https://mtarchive.geol.iastate.edu/{year}/{month}/{day}/grib2/ncep/RTMA/{d}{t}00_{type_wind.upper()}.grib2' for t in times]
+    
+    results = Parallel(n_jobs=4)(
+        delayed(download_file_get_data)(i,rows, columns) for i in urls)
+
+    df_all = pd.DataFrame(results, columns=['MPH', 'Time'])
+    df_all['MPH'] = df_all['MPH'].round(2)
+    df_all['Time'] = pd.to_datetime(d+df_all['Time'], format='%Y%m%d%H%M')
+    return df_all
+
+
+@st.cache
+def convert_df(df):
+    return df.to_csv(index=0).encode('utf-8')
+
+
+try:
+    for i in glob.glob('*.grib2'):
+        os.remove(i)
+except:
+    pass
+
+st.set_page_config(layout="wide")
+col1, col2 = st.columns((2))
+
+address = st.sidebar.text_input(
+    "Address", "123 Main Street, Columbus, OH 43215")
+d = st.sidebar.date_input(
+    "Date",  pd.Timestamp(2022, 9, 28)).strftime('%Y%m%d')
+
+time = st.sidebar.selectbox('Time:', ('12 AM', '6 AM', '12 PM', '6 PM',))
+type_wind = st.sidebar.selectbox('Type:', ('Gust', 'Wind'))
+entire_day = st.sidebar.radio(
+    'Graph Entire Day (Takes a Bit):', ('No', 'Yes'))
+
+if time[-2:] == 'PM' and int(time[:2].strip()) < 12:
+    t = datetime.time(int(time[:2].strip())+12, 00).strftime('%H')+'00'
+elif time[-2:] == 'AM' and int(time[:2].strip()) == 12:
+    t = '0000'
+else:
+    t = datetime.time(int(time[:2].strip()), 00).strftime('%H')+'00'
+
+year, month, day = d[:4], d[4:6], d[6:8]
+
+url = f'https://mtarchive.geol.iastate.edu/{year}/{month}/{day}/grib2/ncep/RTMA/{d}{t}_{type_wind.upper()}.grib2'
+file = get_grib_data(url, d, t)
+
+lat, lon = geocode(address)
+
+rds = rioxarray.open_rasterio(file)
+projected = rds.rio.reproject("EPSG:4326")
+wind_mph = projected.sel(x=lon, y=lat, method="nearest").values*2.23694
+
+affine = projected.rio.transform()
+
+rows, columns = rasterio.transform.rowcol(affine, lon, lat)
+
+size = 40
+
+projected2 = projected[0, rows-size:rows+size, columns-size:columns+size]
+
+img = projected2.values*2.23694
+boundary = projected2.rio.bounds()
+left, bottom, right, top = boundary
+
+img[img < 0.0] = np.nan
+
+clat = (bottom + top)/2
+clon = (left + right)/2
+
+vmin = np.floor(np.nanmin(img))
+vmax = np.ceil(np.nanmax(img))
+
+colormap = cm.LinearColormap(
+    colors=['blue', 'lightblue', 'red'], vmin=vmin, vmax=vmax)
+
+m = folium.Map(location=[lat, lon],  zoom_start=9, height=500)
+
+folium.Marker(
+    location=[lat, lon],
+    popup=f"{wind_mph[0].round(2)} MPH").add_to(m)
+
+folium.raster_layers.ImageOverlay(
+    image=img,
+    name='Wind Speed Map',
+    opacity=.8,
+    bounds=[[bottom, left], [top, right]],
+    colormap=lambda value: mapvalue2color(value, colormap)
+).add_to(m)
+
+
+folium.LayerControl().add_to(m)
+colormap.caption = 'Wind Speed: MPH'
+m.add_child(colormap)
+
+with col1:
+    st.title('RTMA Model')
+    st.write(
+        f"{type_wind.title()} Speed: {wind_mph[0].round(2)} MPH at {time} UTC")
+    st_folium(m, height=500)
+
+
+if entire_day == 'Yes':
+    df_all = graph_entire_day(d,rows, columns)
+    fig = px.line(df_all, x="Time", y="MPH")
+    with col2:
+        st.title('Analysis')
+        st.plotly_chart(fig)
+
+        csv = convert_df(df_all)
+
+        st.download_button(
+            label="Download data as CSV",
+            data=csv,
+            file_name=f'{d}.csv',
+            mime='text/csv')
+else:
+    pass
+
+st.markdown(""" <style>
+#MainMenu {visibility: hidden;}
+footer {visibility: hidden;}
+</style> """, unsafe_allow_html=True)