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app.py.py

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+# -*- coding: utf-8 -*-
+"""
+Created on Fri Oct 14 10:35:25 2022
+
+@author: mritchey
+"""
+# streamlit run "C:\Users\mritchey\.spyder-py3\Python Scripts\streamlit projects\hrrr\windtrial2_streamlit_hrrr 2.py"
+import base64
+import datetime
+import itertools
+
+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 s3fs
+
+import streamlit as st
+import xarray as xr
+from geogif import gif
+from geopy.extra.rate_limiter import RateLimiter
+from geopy.geocoders import Nominatim
+from joblib import Parallel, delayed
+from matplotlib import colors as colors
+from pyproj import CRS, Transformer
+from streamlit_folium import st_folium
+
+
+@st.cache
+def convert_df(df):
+    # IMPORTANT: Cache the conversion to prevent computation on every rerun
+    return df.to_csv(index=0).encode('utf-8')
+
+@st.cache
+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_data_date_time(date_time, lat, lon):
+    date, time = date_time
+    path = f"hrrrzarr/sfc/{date}/{date}_{time[-2:]}z_{analysis_forecast}.zarr/{vertical_level}/{variable}"
+    try:
+        ds = xr.open_mfdataset([lookup(path), lookup(f"{path}/{vertical_level}")],
+                               engine="zarr")
+        result = ds.sel(projection_x_coordinate=lon, projection_y_coordinate=lat, method="nearest")[
+            variable].values*2.23694
+    except:
+        result = None
+    date2 = pd.to_datetime(str(date)+str(time), format='%Y%m%d%H')
+    df = pd.DataFrame({'Date': date2, 'MPH': result}, index=[0])
+    return df
+
+def mapvalue2color(value, cmap):
+
+    if np.isnan(value):
+        return (1, 0, 0, 0)
+    else:
+        return colors.to_rgba(cmap(value), 0.7)
+
+
+def lookup(path):
+    return s3fs.S3Map(path, s3=s3)
+
+
+
+s3 = s3fs.S3FileSystem(anon=True)
+
+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'))
+analysis_forecast1 = st.sidebar.radio(
+    'Analysis or Forecast:', ('Analysis', 'Forecast'))
+entire_day = st.sidebar.radio(
+    'Graph Entire Day/Forecast (Takes a Bit):', ('No', 'Yes'))
+animate_forecast = st.sidebar.radio(
+    'Animate Forecast:', ('No', 'Yes'))
+
+
+if analysis_forecast1 == 'Analysis':
+    analysis_forecast = 'anl'
+else:
+    analysis_forecast = 'fcst'
+
+
+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]
+
+if type_wind == 'Gust':
+    variable = type_wind.upper()
+    vertical_level = 'surface'
+else:
+    vertical_level = '10m_above_ground'
+    if analysis_forecast == 'anl':
+        variable = 'WIND_max_fcst'
+    else:
+        variable = 'WIND_1hr_max_fcst'
+
+path = f"hrrrzarr/sfc/{d}/{d}_{t[:2]}z_{analysis_forecast}.zarr/{vertical_level}/{variable}"
+ds = xr.open_mfdataset([lookup(path), lookup(f"{path}/{vertical_level}")],
+                       engine="zarr")
+
+lat, lon = geocode(address)
+
+ds = ds.rename(projection_x_coordinate="x", projection_y_coordinate="y")
+crs = CRS.from_cf({"grid_mapping_name": "lambert_conformal_conic",
+                   "longitude_of_central_meridian": -97.5,
+                   "latitude_of_projection_origin": 38.5,
+                   "standard_parallel": 38.5})
+ds = ds.rio.write_crs(crs, inplace=True)
+
+ds[variable] = ds[variable].astype("float64")
+projected = ds.rio.reproject("EPSG:4326")
+
+if analysis_forecast == 'fcst':
+    projected_org = projected
+    projected = projected.sel(time=projected.time.values[0])
+else:
+    pass
+
+wind_mph = projected.sel(x=lon, y=lat, method="nearest")[
+    variable].values*2.23694
+
+
+affine = projected.rio.transform()
+
+rows, columns = rasterio.transform.rowcol(affine, lon, lat)
+
+size = 40
+
+projected2 = projected[variable][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.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('HRRR Model')
+    st.write(f"{type_wind.title()} Speed: {wind_mph.round(2)} MPH at {time} UTC")
+    st_folium(m, height=500)
+
+
+proj = Transformer.from_crs(4326, crs, always_xy=True)
+lon2, lat2 = proj.transform(lon, lat)
+
+
+if entire_day == 'Yes':
+    if analysis_forecast == 'anl':
+        times = [f'0{str(i)}'[-2:] for i in range(0, 24)]
+        dates_times = list(itertools.product([d], times))
+        
+        results = Parallel(n_jobs=-1, prefer="threads")(
+            delayed(get_data_date_time)(i, lat2, lon2) for i in dates_times)
+
+        df_all = pd.concat(results)
+        df_all['MPH'] = df_all['MPH'].round(2)
+
+    else:
+        result = ds[variable].sel(x=lon2, y=lat2, method="nearest")*2.23694
+        df_all = result.to_dataframe()[variable].reset_index()
+        df_all.columns = ['Date', 'MPH']
+        df_all['MPH'] = df_all['MPH'].round(2)
+        if animate_forecast == 'Yes':
+            # Not working
+            gif(projected_org[variable], to='ds.gif',
+                date_format='%m-%d-%Y: %I%p', cmap="RdBu_r", vmax=35)
+            st.download_button(
+                label="Download Gif",
+                data='ds.gif',
+                file_name='ds.gif',
+            )
+            file_ = open("ds.gif", "rb")
+            contents = file_.read()
+            data_url = base64.b64encode(contents).decode("utf-8")
+            file_.close()
+
+            st.markdown(
+                f'<img src="data:image/gif;base64,{data_url}" alt="ds gif" width="1200">',
+                unsafe_allow_html=True,
+            )
+
+    fig = px.line(df_all, x="Date", y="MPH")
+    with col2:
+        st.title(f'{analysis_forecast1}')
+        st.plotly_chart(fig)
+
+        csv = convert_df(df_all)
+
+        st.download_button(
+            label="Download data as CSV",
+            data=csv,
+            file_name='data.csv',
+            mime='text/csv')
+
+else:
+    pass
+
+st.markdown(""" <style>
+#MainMenu {visibility: hidden;}
+footer {visibility: hidden;}
+</style> """, unsafe_allow_html=True)

requirements.txt

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+ecmwflibs
+zarr
+cfgrib
+branca==0.5.0
+folium==0.12.1
+geopy==2.2.0
+geogif==0.1.3
+joblib==1.1.0
+matplotlib==3.5.1
+numpy==1.21.5
+pandas==1.4.2
+plotly==5.7.0
+pyproj==3.3.1
+rasterio==1.2.10
+rioxarray==0.12.2
+s3fs==2022.10.0
+streamlit==1.4.0
+streamlit_folium==0.6.15
+xarray[complete]
+geogif==0.1.3