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Update pages/Hail.py
Browse files- pages/Hail.py +1 -92
pages/Hail.py
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# -*- coding: utf-8 -*-
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"""
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Created on Tue Dec 6 09:56:29 2022
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@author: mritchey
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"""
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#streamlit run "C:\Users\mritchey\.spyder-py3\Python Scripts\streamlit projects\mrms\mrms_hail2 buffer.py"
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import plotly.express as px
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import os
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from PIL import Image
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from joblib import Parallel, delayed
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import pandas as pd
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import streamlit as st
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from geopy.extra.rate_limiter import RateLimiter
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from geopy.geocoders import Nominatim
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import folium
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from streamlit_folium import st_folium
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import math
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import geopandas as gpd
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from skimage.io import imread
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from streamlit_plotly_events import plotly_events
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import requests
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from requests.packages.urllib3.exceptions import InsecureRequestWarning
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import rasterio
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import rioxarray
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import numpy as np
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@st.cache(allow_output_mutation=True)
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def geocode(address, buffer_size):
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try:
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address2 = address.replace(' ', '+').replace(',', '%2C')
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df = pd.read_json(
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f'https://geocoding.geo.census.gov/geocoder/locations/onelineaddress?address={address2}&benchmark=2020&format=json')
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results = df.iloc[:1, 0][0][0]['coordinates']
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lat, lon = results['y'], results['x']
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except:
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geolocator = Nominatim(user_agent="GTA Lookup")
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geocode = RateLimiter(geolocator.geocode, min_delay_seconds=1)
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location = geolocator.geocode(address)
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lat, lon = location.latitude, location.longitude
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df = pd.DataFrame({'Lat': [lat], 'Lon': [lon]})
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gdf = gpd.GeoDataFrame(
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df, geometry=gpd.points_from_xy(df.Lon, df.Lat, crs=4326))
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gdf['buffer'] = gdf['geometry'].to_crs(
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3857).buffer(buffer_size/2*2580).to_crs(4326)
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return gdf
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@st.cache(allow_output_mutation=True)
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def get_pngs(date):
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year, month, day = date[:4], date[4:6], date[6:]
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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'
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data = imread(url)[:, :, :3]
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data2 = data.reshape(630*920, 3)
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data2_df = pd.DataFrame(data2, columns=['R', 'G', 'B'])
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data2_df2 = pd.merge(data2_df, lut[['R', 'G', 'B', 'Hail Scale', 'Hail Scale In']], on=['R', 'G', 'B'],
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how='left')[['Hail Scale', 'Hail Scale In']]
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data2_df2['Date'] = date
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return data2_df2.reset_index()
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@st.cache(allow_output_mutation=True)
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def get_pngs_parallel(dates):
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results1 = Parallel(n_jobs=32, prefer="threads")(
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delayed(get_pngs)(i) for i in dates)
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return results1
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@st.cache(allow_output_mutation=True)
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def png_data(date):
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year, month, day = date[:4], date[4:6], date[6:]
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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'
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data = imread(url)
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return data
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@st.cache(allow_output_mutation=True)
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def map_folium(data, gdf):
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m = folium.Map(location=[lat, lon], zoom_start=zoom, height=300)
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folium.Marker(
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location=[lat, lon],
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popup=address).add_to(m)
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folium.GeoJson(gdf['buffer']).add_to(m)
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folium.raster_layers.ImageOverlay(
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data, opacity=0.8, bounds=bounds).add_to(m)
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return m
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st.markdown(""" <style>
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#MainMenu {visibility: hidden;}
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footer {visibility: hidden;}
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</style> """, unsafe_allow_html=True)
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folium.raster_layers.ImageOverlay(
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data, opacity=0.8, bounds=bounds).add_to(m)
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return m
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st.markdown(""" <style>
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#MainMenu {visibility: hidden;}
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footer {visibility: hidden;}
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</style> """, unsafe_allow_html=True)
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