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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_all buffer.py"
+
+import plotly.express as px
+
+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
+import rasterio
+import rioxarray
+import numpy as np
+import base64
+import re
+
+
+@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
+
+
+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', 'Value', ]], on=['R', 'G', 'B'],
+                         how='left')[['Value', ]]
+    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
+
+
+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
+
+
+def render_svg(svg):
+    """Renders the given svg string."""
+    b64 = base64.b64encode(svg.encode('utf-8')).decode("utf-8")
+    html = r'<img src="data:image/svg+xml;base64,%s"/>' % b64
+    st.write(html, unsafe_allow_html=True)
+
+
+def rgb_to_hex(rgb):
+    return '#'+'%02x%02x%02x' % rgb
+
+
+def get_legend_lut(prod_root):
+    url_legend = f'https://mrms.nssl.noaa.gov/qvs/product_viewer/shared/fetch_svg_legend_via_config.php?web_resources_dir=/var/www/html/qvs/product_viewer/resources/&config_name=title_and_legend_config.txt&product={prod_root}'
+    r = requests.get(url_legend)  # Get the webpage
+    svg = r.content.decode()  # Decoded response content with the svg string
+
+    if svg.find('size="16">mm</text>') > 0:
+        svg = svg.replace('size="16">mm</text>', 'size="16">in</text>')
+        beg_string = '"13">'
+        end_string = '</text>'
+        res = re.findall('%s(.*)%s' % (beg_string, end_string), svg)
+        for mm in res:
+            inc = round(float(mm)*0.0393701, 2)
+            svg = svg.replace(f'{beg_string}{mm}{end_string}',
+                              f'{beg_string}{str(inc)}{end_string}')
+
+    elif svg.find('font-size="12">') > 0:
+        beg_string = '"12">'
+        end_string = '</text>'
+
+    else:
+        beg_string = '"13">'
+        end_string = '</text>'
+
+    #Make LUT
+    values = re.findall('%s(.*)%s' % (beg_string, end_string), svg)
+
+    beg_string, end_string = 'fill="rgb(', ')" />'
+    rgb = re.findall('%s(.*)%s' % (beg_string, end_string), svg)
+    rgb = [eval(i[0]) for i in rgb]
+
+    beg_string, end_string = 'style="fill:rgb(', ');" />'
+    rgb2 = re.findall('%s(.*)%s' % (beg_string, end_string), svg)
+    rgb2 = [eval(i[0]) for i in rgb2]
+
+    rgb = rgb2+rgb
+
+    lut = pd.DataFrame({'Value': values,
+                       'RGB': rgb})
+    lut['R'], lut['G'], lut['B'] = lut['RGB'].str
+    lut[['R', 'G', 'B']] = lut[['R', 'G', 'B']].astype('uint8')
+    lut['Value'] = lut['Value'].astype(float)
+    lut['hex'] = lut['RGB'].apply(rgb_to_hex)
+    return svg, lut
+
+
+#Set Columns
+st.set_page_config(layout="wide")
+
+
+#Input Data
+zoom = 10
+address = st.sidebar.text_input(
+    "Address", "cape coral, fl")
+var = st.sidebar.selectbox(
+    'Product:', ('Hail', 'Flooding', 'Rain: Radar', 'Rain: Multi Sensor', 'Tornado'))
+
+date = st.sidebar.date_input("Date",  pd.Timestamp(
+    2022, 9, 28), key='date').strftime('%Y%m%d')
+d = pd.Timestamp(date)
+days_within = st.sidebar.selectbox('Within Days:', (5, 30, 60))
+
+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 = 0
+
+
+#Select Variable
+if var == 'Hail':
+    var_input = 'hails&product=MESHMAX1440M'
+elif var == 'Flooding':
+    var_input = 'flash&product=FL_ARI24H'
+elif var == 'Rain: Radar':
+    var_input = 'q3rads&product=Q3EVAP24H'
+elif var == 'Rain: Multi Sensor':
+    var_input = 'q3mss&product=P1_Q3MS24H'
+elif var == 'Tornado':
+    var_input = 'azsh&product=RT1440M'
+
+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)
+
+#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')
+#Get SVG and Lut
+svg, lut = get_legend_lut(prod_root)
+
+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).fillna(0)
+max_data = results.groupby('index')[['Value']].max()
+
+max_data2 = pd.merge(max_data,
+                     lut[['R', 'G', 'B', 'Value']],
+                     on=['Value'],
+                     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
+if var == 'Tornado':
+    bar = results.query("Value>.006").groupby(
+        ['Date', 'Value'])['index'].count().reset_index()
+else:
+    bar = results.query("Value>.2").groupby(['Date', 'Value'])[
+        'index'].count().reset_index()
+
+bar['Date'] = pd.to_datetime(bar['Date'])
+
+bar = bar.reset_index()
+bar.columns = ['level_0', 'Date', 'Value', 'count']
+bar = bar.sort_values('Value', ascending=True)
+bar['Value'] = bar['Value'].astype(str)
+
+
+color_discrete_map = lut[['Value', 'hex']].sort_values(
+    'Value', ascending=True).astype(str)
+color_discrete_map = color_discrete_map.set_index(
+    'Value').to_dict()['hex']
+
+fig = px.bar(bar, x="Date", y="count", color="Value",
+             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 = st.columns(2)
+
+    with col1:
+        st.header(f'{var} on {pd.Timestamp(date).strftime("%D")}')
+        st_folium(m, height=300)
+    with col2:
+        st.header(
+            f'Max from {start_date.strftime("%D")} to {end_date.strftime("%D")}')
+        st_folium(m_max, height=300)
+
+with st.container():
+    col1, col2, col3 = st.columns((1, 10, 6))
+    with col1:
+        render_svg(svg)
+    with col2:
+        link = f'[Go To MRMS Site]({url})'
+        st.markdown(link, unsafe_allow_html=True)
+        selected_points = plotly_events(
+            fig, click_event=True, hover_event=False)
+    with col3:
+        try:
+            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)

requirements.txt

@@ -0,0 +1,14 @@
+folium==0.12.1
+geopandas==0.10.2
+geopy==2.2.0
+joblib==1.1.0
+numpy==1.21.5
+pandas==1.4.2
+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