Create app.py

app.py

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+
+import plotly.graph_objects as go
+from folium.raster_layers import ImageOverlay
+import re
+import glob
+import pickle
+import h5py
+import rasterio
+import streamlit as st
+import os
+import branca.colormap as cm
+import folium
+import numpy as np
+import pandas as pd
+from geopy.extra.rate_limiter import RateLimiter
+from geopy.geocoders import Nominatim
+from streamlit_plotly_events import plotly_events
+import warnings
+from folium import plugins
+warnings.filterwarnings("ignore")
+
+
+@st.cache_data
+def convert_df(df):
+    return df.to_csv(index=0).encode('utf-8')
+
+
+@st.cache_data
+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 pd.DataFrame({'Lat': lat, 'Lon': lon}, index=[0])
+
+
+@st.cache_data
+def get_data(row, col, radius):
+    files = [
+        "data/GUST_hrrr_2021_all_max.h5",
+        "data/GUST_hrrr_2022_all_max.h5",
+        "data/mritchey/GUST_hrrr_2023_all_max.h5",
+        "data/GUST_hrrr_202401_202409_max.h5",
+    ]
+
+    all_data = []
+    all_dates = []
+    for f in files:
+        with h5py.File(f, 'r') as f:
+            data = f['GUST'][:, row-radius:row +
+                             radius+1, col-radius:col+radius+1]
+            dates = f['dates'][:]
+            all_data.append(data)
+            all_dates.append(dates)
+
+    data_mat = np.concatenate(all_data)
+    data_mat = np.where(data_mat < 0, 0, data_mat)*2.23694
+
+    dates_mat = np.concatenate(all_dates)
+
+    data_actual = np.array([i[radius, radius] for i in data_mat])
+
+    data_max = np.max(data_mat, axis=(1, 2))
+    data_max_2 = np.max(data_mat, axis=0)
+    data_max_2 = data_max_2
+
+    df = pd.DataFrame({'Date': dates_mat,
+                       'Actual': data_actual,
+                      'Max': data_max})
+
+    df['Date'] = pd.to_datetime(df['Date'], format='%Y%m%d')
+    # df['Date']=df['Date']+pd.Timedelta(days=1)
+
+    return df, data_max_2
+
+
+def lat_lon_to_row_col(lat, lon):
+    crs_dic = pickle.load(open('data/hrrr_crs.pkl', 'rb'))
+    transform = crs_dic['affine']
+    trans_rtma = crs_dic['proj_4326']
+    lon_rtma, lat_rtma = trans_rtma.transform(lon, lat)
+
+    row, col = rasterio.transform.rowcol(transform, lon_rtma, lat_rtma)
+    row, col = int(row), int(col)
+    return row, col
+
+
+def map_folium(lat, lon, files_dates_selected, actual_point, max_point):
+    popup_content = f"""
+    <div style="font-size: 7pt; width: 100px; height: 100px;">
+        <b>{address}</b><br>
+        <b>Gust: {actual_point:,.2f} MPH</b><br>
+        <b>Max: {max_point:,.2f} MPH</b><br>
+    </div>
+    """
+    # Create a base map
+    m = folium.Map(location=[lat, lon], zoom_start=6)
+    folium.Marker(location=[lat, lon],
+
+                  popup=folium.Popup(popup_content, max_width=400)
+
+
+                  ).add_to(m)
+
+    # Define the image bounds (SW and NE corners)
+    image_bounds = [[21.081227294744885, -134.02385805744265],
+                    [52.62502506520796, -60.98015065638055]]
+
+    # Add ImageOverlays for each image
+
+    overlay = ImageOverlay(image=files_dates_selected, bounds=image_bounds,
+                           opacity=.75,
+                           mercator_project=False)
+
+    overlay.add_to(m)
+    colormap_hail = cm.LinearColormap(
+        colors=['blue', 'lightblue', 'pink', 'red'], vmin=0, vmax=75)
+    # Add the color legend to the map
+    colormap_hail.caption = 'Legend: Wind Gust (MPH)'
+    colormap_hail.add_to(m)
+
+    plugins.Fullscreen().add_to(m)
+
+    return m
+
+
+def get_all_data(lat, lon, radius, start_date, end_date):
+    #Geocode and get Data
+
+    row, col = lat_lon_to_row_col(lat, lon)
+
+    df_data, max_values = get_data(row, col, radius)
+
+    df_data = df_data.query(f"'{start_date}'<=Date<='{end_date}'")
+    df_data['Actual'] = df_data['Actual'].astype(float).round(2)
+    df_data['Max'] = df_data['Max'].astype(float).round(2)
+
+    fig = go.Figure()
+
+    # Add bars for actual values
+    fig.add_trace(go.Bar(
+        x=df_data['Date'],
+        y=df_data['Actual'],
+        name='Actual Value',
+        marker_color='#2D5986',
+        hoverinfo='text',  # Show text information only
+        text=df_data.apply(
+            lambda row: f'Date: {row["Date"].date()}<br>Gust: {row["Actual"]}<br>Max: {row["Max"]}', axis=1)
+
+    ))
+
+    # Update layout
+    fig.update_layout(
+        title='',
+        xaxis_title='Date',
+        yaxis_title='Gust (MPH)',
+        barmode='group'
+    )
+
+    return fig, df_data
+
+
+#Set up 2 Columns
+st.set_page_config(layout="wide")
+col1, col2 = st.columns((2))
+
+os.chdir(r'C:\Users\mritchey')
+
+#Input Values
+address = st.sidebar.text_input("Address", "123 Main Street, Dallas, TX 75126")
+date_focus = st.sidebar.date_input("Date",  pd.Timestamp(2021, 7, 1))
+within_days = st.sidebar.selectbox('Days Within', (90, 180, 365))
+circle_radius = st.sidebar.selectbox('Box Radius (Miles)', (5, 10, 25))
+interactive_map = st.sidebar.radio(
+    'Interactive Map (Lower Res)', (False, True))
+
+
+start_date = date_focus+pd.Timedelta(days=-within_days)
+end_date = date_focus+pd.Timedelta(days=within_days)
+
+date_range = pd.date_range(start=start_date, end=end_date).strftime('%Y%m%d')
+
+result = geocode(address)
+lat, lon = result.values[0]
+radius = int(np.ceil(circle_radius*1.6/2.5))
+
+fig, df_data = get_all_data(lat, lon, radius, start_date, end_date)
+
+_, actual_point, max_point = df_data.query(f"Date=='{date_focus}'").values[0]
+
+files = glob.glob(
+    r'data\**\*.webp', recursive=True)
+
+with col1:
+    st.title('Gust')
+    try:
+
+        selected_points = plotly_events(fig)
+        csv = convert_df(df_data)
+        st.download_button(
+            label="Download data as CSV",
+            data=csv,
+            file_name='data.csv',
+            mime='text/csv')
+    except:
+        pass
+
+
+with col2:
+    st.title('GUST MAP')
+
+    if selected_points:
+        # Extract the details of the first selected point
+        selected_index = selected_points[0]['pointIndex']
+        selected_data = df_data.iloc[selected_index]
+
+   
+        _, actual_point, max_point = df_data.query(
+            f"Date=='{selected_data['Date']}'").values[0]
+        files_dates_selected = [i for i in files if selected_data['Date'].strftime(
+            '%Y%m%d') in re.search(r'(\d{8})', i).group()][0]
+        m = map_folium(lat, lon, files_dates_selected, actual_point, max_point)
+        m.save("map_new.html")
+
+        st.write('Date: ' + selected_data['Date'].strftime('%m-%d-%Y'))
+        st.components.v1.html(
+            open("map_new.html", 'r').read(), height=500, width=500)
+
+    else:
+        files_dates_selected = [i for i in files if date_focus.strftime(
+            '%Y%m%d') in re.search(r'(\d{8})', i).group()][0]
+        st.write('Date: ' + date_focus.strftime('%m-%d-%Y'))
+        m = map_folium(lat, lon, files_dates_selected, actual_point, max_point)
+        m.save("map_new.html")
+        st.components.v1.html(
+            open("map_new.html", 'r').read(), height=500, width=500)
+
+
+st.markdown(""" <style>
+#MainMenu {visibility: hidden;}
+footer {visibility: hidden;}
+</style> """, unsafe_allow_html=True)