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app.py

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+# -*- coding: utf-8 -*-
+"""
+Created on Tue Dec  6 09:56:29 2022
+
+@author: mritchey
+"""
+#streamlit run "C:\Users\mritchey\.spyder-py3\Python Scripts\streamlit projects\mrms\mrms_hail2 buffer.py"
+
+import plotly.express as px
+import os
+from PIL import Image
+from joblib import Parallel, delayed
+import pandas as pd
+import streamlit as st
+from geopy.extra.rate_limiter import RateLimiter
+from geopy.geocoders import Nominatim
+import folium
+from streamlit_folium import st_folium
+import math
+import geopandas as gpd
+from skimage.io import imread
+from streamlit_plotly_events import plotly_events
+import requests
+from requests.packages.urllib3.exceptions import InsecureRequestWarning
+import rasterio
+import rioxarray
+import numpy as np
+
+
+@st.cache
+def geocode(address, buffer_size):
+    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
+
+    df = pd.DataFrame({'Lat': [lat], 'Lon': [lon]})
+    gdf = gpd.GeoDataFrame(
+        df, geometry=gpd.points_from_xy(df.Lon, df.Lat, crs=4326))
+    gdf['buffer'] = gdf['geometry'].to_crs(
+        3857).buffer(buffer_size/2*2580).to_crs(4326)
+    return gdf
+
+
+@st.cache
+def get_pngs(date):
+    year, month, day = date[:4], date[4:6], date[6:]
+    url = f'https://mrms.nssl.noaa.gov/qvs/product_viewer/local/render_multi_domain_product_layer.php?mode=run&cpp_exec_dir=/home/metop/web/specific/opv/&web_resources_dir=/var/www/html/qvs/product_viewer/resources/&prod_root={prod_root}&qperate_pal_option=0&qpe_pal_option=0&year={year}&month={month}&day={day}&hour={hour}&minute={minute}&clon={lon}&clat={lat}&zoom={zoom}&width=920&height=630'
+    data = imread(url)[:, :, :3]
+    data2 = data.reshape(630*920, 3)
+    data2_df = pd.DataFrame(data2, columns=['R', 'G', 'B'])
+    data2_df2 = pd.merge(data2_df, lut[['R', 'G', 'B', 'Hail Scale', 'Hail Scale In']], on=['R', 'G', 'B'],
+                         how='left')[['Hail Scale', 'Hail Scale In']]
+    data2_df2['Date'] = date
+    return data2_df2.reset_index()
+
+
+@st.cache
+def get_pngs_parallel(dates):
+    results1 = Parallel(n_jobs=32, prefer="threads")(
+        delayed(get_pngs)(i) for i in dates)
+    return results1
+
+
+@st.cache
+def png_data(date):
+    year, month, day = date[:4], date[4:6], date[6:]
+    url = f'https://mrms.nssl.noaa.gov/qvs/product_viewer/local/render_multi_domain_product_layer.php?mode=run&cpp_exec_dir=/home/metop/web/specific/opv/&web_resources_dir=/var/www/html/qvs/product_viewer/resources/&prod_root={prod_root}&qperate_pal_option=0&qpe_pal_option=0&year={year}&month={month}&day={day}&hour={hour}&minute={minute}&clon={lon}&clat={lat}&zoom={zoom}&width=920&height=630'
+    data = imread(url)
+    return data
+
+
+@st.cache(allow_output_mutation=True)
+def map_folium(data, gdf):
+    m = folium.Map(location=[lat, lon],  zoom_start=zoom, height=300)
+    folium.Marker(
+        location=[lat, lon],
+        popup=address).add_to(m)
+
+    folium.GeoJson(gdf['buffer']).add_to(m)
+    folium.raster_layers.ImageOverlay(
+        data, opacity=0.8, bounds=bounds).add_to(m)
+    return m
+
+
+def to_radians(degrees):
+  return degrees * math.pi / 180
+
+
+def lat_lon_to_bounds(lat, lng, zoom, width, height):
+    earth_cir_m = 40075016.686
+    degreesPerMeter = 360 / earth_cir_m
+    m_pixel_ew = earth_cir_m / math.pow(2, zoom + 8)
+    m_pixel_ns = earth_cir_m / \
+        math.pow(2, zoom + 8) * math.cos(to_radians(lat))
+
+    shift_m_ew = width/2 * m_pixel_ew
+    shift_m_ns = height/2 * m_pixel_ns
+
+    shift_deg_ew = shift_m_ew * degreesPerMeter
+    shift_deg_ns = shift_m_ns * degreesPerMeter
+
+    return [[lat-shift_deg_ns, lng-shift_deg_ew], [lat+shift_deg_ns, lng+shift_deg_ew]]
+
+
+def image_to_geotiff(bounds, input_file_path, output_file_path='template.tiff'):
+    south, west, north, east = tuple(
+        [item for sublist in bounds for item in sublist])
+    dataset = rasterio.open(input_file_path, 'r')
+    bands = [1, 2, 3]
+    data = dataset.read(bands)
+    transform = rasterio.transform.from_bounds(west, south, east, north,
+                                               height=data.shape[1],
+                                               width=data.shape[2])
+    crs = {'init': 'epsg:4326'}
+
+    with rasterio.open(output_file_path, 'w', driver='GTiff',
+                       height=data.shape[1],
+                       width=data.shape[2],
+                       count=3, dtype=data.dtype, nodata=0,
+                       transform=transform, crs=crs,
+                       compress='lzw') as dst:
+        dst.write(data, indexes=bands)
+
+
+def get_mask(bounds, buffer_size):
+    year, month, day = date[:4], date[4:6], date[6:]
+    url = f'https://mrms.nssl.noaa.gov/qvs/product_viewer/local/render_multi_domain_product_layer.php?mode=run&cpp_exec_dir=/home/metop/web/specific/opv/&web_resources_dir=/var/www/html/qvs/product_viewer/resources/&prod_root={prod_root}&qperate_pal_option=0&qpe_pal_option=0&year={year}&month={month}&day={day}&hour={hour}&minute={minute}&clon={lon}&clat={lat}&zoom={zoom}&width=920&height=630'
+    img_data = requests.get(url, verify=False).content
+    input_file_path = f'image_name_{date}_{var}.png'
+    output_file_path = 'template.tiff'
+    with open(input_file_path, 'wb') as handler:
+        handler.write(img_data)
+
+    image_to_geotiff(bounds, input_file_path, output_file_path)
+    rds = rioxarray.open_rasterio(output_file_path)
+    # rds.plot.imshow()
+
+    rds = rds.assign_coords(distance=(haversine(rds.x, rds.y, lon, lat)))
+    mask = rds['distance'].values <= buffer_size
+    mask = np.transpose(np.stack([mask, mask, mask]), (1, 2, 0))
+    return mask
+
+
+def haversine(lon1, lat1, lon2, lat2):
+    # convert decimal degrees to radians
+    lon1 = np.deg2rad(lon1)
+    lon2 = np.deg2rad(lon2)
+    lat1 = np.deg2rad(lat1)
+    lat2 = np.deg2rad(lat2)
+
+    # haversine formula
+    dlon = lon2 - lon1
+    dlat = lat2 - lat1
+    a = np.sin(dlat/2)**2 + np.cos(lat1) * np.cos(lat2) * np.sin(dlon/2)**2
+    c = 2 * np.arcsin(np.sqrt(a))
+    r = 6371
+    return c * r
+
+
+#Set Columns
+st.set_page_config(layout="wide")
+col1, col2, col3 = st.columns((3))
+col1, col2, col3 = st.columns((3, 3, 1))
+
+#Input Data
+zoom = 10
+address = st.sidebar.text_input(
+    "Address", "123 Main Street, Cincinnati, OH 43215")
+
+date = st.sidebar.date_input("Date",  pd.Timestamp(
+    2022, 7, 6), key='date').strftime('%Y%m%d')
+d = pd.Timestamp(date)
+days_within = st.sidebar.selectbox('Within Days:', (5, 30, 60))
+var = 'Hail'
+var_input = 'hails&product=MESHMAX1440M'
+mask_select = st.sidebar.radio('Only Show Buffer Data:', ("No", "Yes"))
+buffer_size = st.sidebar.radio('Buffer Size (miles):', (5, 10, 15))
+
+year, month, day = date[:4], date[4:6], date[6:]
+hour = 23
+minute = 30
+
+prod_root = var_input[var_input.find('=')+1:]
+
+#Geocode
+gdf = geocode(address, buffer_size)
+lat, lon = tuple(gdf[['Lat', 'Lon']].values[0])
+
+#Get Value
+url = 'https://mrms.nssl.noaa.gov/qvs/product_viewer/local/get_multi_domain_rect_binary_value.php?mode=run&cpp_exec_dir=/home/metop/web/specific/opv/&web_resources_dir=/var/www/html/qvs/product_viewer/resources/'\
+    + f'&prod_root={prod_root}&lon={lon}&lat={lat}&year={year}&month={month}&day={day}&hour={hour}&minute={minute}'
+
+response = requests.get(url, verify=False).json()
+qvs_values = pd.DataFrame(response, index=[0])[
+    ['qvs_value', 'qvs_units']].values[0]
+qvs_value = qvs_values[0]
+qvs_unit = qvs_values[1]
+
+#Get PNG Focus
+data = png_data(date)
+
+#Legend
+legend = Image.open('hail scale2.png')
+
+#Get PNG Max
+start_date, end_date = d - \
+    pd.Timedelta(days=days_within), d+pd.Timedelta(days=days_within)
+dates = pd.date_range(start_date,
+                      end_date).strftime('%Y%m%d')
+lut = pd.read_csv('hail scale.csv')
+bounds = lat_lon_to_bounds(lat, lon, zoom, 920, 630)
+
+
+results1 = get_pngs_parallel(dates)
+# results1 = Parallel(n_jobs=32, prefer="threads")(delayed(get_pngs)(i) for i in dates)
+results = pd.concat(results1)
+max_data = results.groupby('index')[['Hail Scale']].max()
+
+max_data2 = pd.merge(max_data,
+                     lut[['R', 'G', 'B', 'Hail Scale']],
+                     on=['Hail Scale'],
+                     how='left')[['R', 'G', 'B']]
+
+data_max = max_data2.values.reshape(630, 920, 3)
+
+#Masked Data
+if mask_select == "Yes":
+    mask = get_mask(bounds, buffer_size)
+    mask1 = mask[:, :, 0].reshape(630*920)
+    results = pd.concat([i[mask1] for i in results1])
+    data_max = data_max*mask
+else:
+    pass
+
+
+#Bar
+bar = results.query("`Hail Scale`>4").groupby(
+    ['Date', 'Hail Scale In'])['index'].count().reset_index()
+bar['Date'] = pd.to_datetime(bar['Date'])
+
+bar = bar.reset_index()
+bar.columns = ['level_0', 'Date', 'Hail Scale In', 'count']
+bar['Hail Scale In'] = bar['Hail Scale In'].astype(str)
+bar = bar.sort_values('Hail Scale In', ascending=True)
+
+color_discrete_map = lut[['Hail Scale In', 'c_code']].sort_values(
+    'Hail Scale In', ascending=True).astype(str)
+color_discrete_map = color_discrete_map.set_index(
+    'Hail Scale In').to_dict()['c_code']
+
+fig = px.bar(bar, x="Date", y="count", color="Hail Scale In",
+             barmode='stack',
+             color_discrete_map=color_discrete_map)
+
+#Submit Url to New Tab
+url = f'https://mrms.nssl.noaa.gov/qvs/product_viewer/index.php?web_exec_mode=run&menu=menu_config.txt&year={year}&month={month}&day={day}&hour=23&minute=30&time_mode=static&zoom=9&clon={lon}&clat={lat}&base=0&overlays=1&mping_mode=0&product_type={var_input}&qpe_pal_option=0&opacity=.75&looping_active=off&num_frames=6&frame_step=200&seconds_step=600'
+
+
+#Map Focus
+m = map_folium(data, gdf)
+#Map Max
+m_max = map_folium(data_max, gdf)
+
+with st.container():
+    col1, col2, col3 = st.columns((1, 2, 2))
+    with col1:
+        link = f'[Go To MRMS Site]({url})'
+        st.markdown(link, unsafe_allow_html=True)
+        st.image(legend)
+    with col2:
+        st.header(f'{var} on {pd.Timestamp(date).strftime("%D")}')
+        st_folium(m, height=300)
+    with col3:
+        st.header(
+            f'Max from {start_date.strftime("%D")} to {end_date.strftime("%D")}')
+        st_folium(m_max, height=300)
+
+try:
+    selected_points = plotly_events(fig, click_event=True, hover_event=False)
+    date2 = pd.Timestamp(selected_points[0]['x']).strftime('%Y%m%d')
+    data2 = png_data(date2)
+    m3 = map_folium(data2, gdf)
+    st.header(f'{var} on {pd.Timestamp(date2).strftime("%D")}')
+    st_folium(m3, height=300)
+except:
+    pass
+
+
+st.markdown(""" <style>
+#MainMenu {visibility: hidden;}
+footer {visibility: hidden;}
+</style> """, unsafe_allow_html=True)

hail scale.csv

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+c_code,occurence,RGB,R,G,B,Hail Scale,Hail Scale In,R+G
+#01A0F6,408,"(1, 160, 246)",1,160,246,2,0.08,78546
+#BE55DC,408,"(190, 85, 220)",190,85,220,100,3.94,3545724
+#FF9000,408,"(255, 144, 0)",255,144,0,30,1.18,36608
+#00C800,408,"(0, 200, 0)",0,200,0,8,0.31,199
+#FF00FF,396,"(255, 0, 255)",255,0,255,75,2.95,-65536
+#C00000,396,"(192, 0, 0)",192,0,0,50,1.97,-193
+#FF0000,396,"(255, 0, 0)",255,0,0,40,1.57,-256
+#E7C000,396,"(231, 192, 0)",231,192,0,20,0.79,44312
+#FFFF00,396,"(255, 255, 0)",255,255,0,15,0.59,65024
+#009000,396,"(0, 144, 0)",0,144,0,10,0.39,143
+#00FF00,396,"(0, 255, 0)",0,255,0,6,0.24,254
+#0000F6,396,"(0, 0, 246)",0,0,246,4,0.16,-247
+#00ECEC,336,"(0, 236, 236)",0,236,236,1,0.04,55695
+#7E32A7,60,"(126, 50, 167)",126,50,167,200,7.87,1045464
+#7F7F7F,,"(127, 127, 127)",127,127,127,0,0,2064384

requirements.txt

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+folium==0.12.1
+geopandas==0.10.2
+geopy==2.2.0
+joblib==1.1.0
+numpy==1.21.5
+pandas==1.4.2
+Pillow==9.4.0
+plotly==5.7.0
+rasterio==1.2.10
+requests==2.27.1
+rioxarray==0.12.2
+scikit_image==0.19.2
+streamlit==1.4.0
+streamlit_folium==0.6.15
+streamlit_plotly_events==0.0.6