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app.py

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+# -*- coding: utf-8 -*-
+"""
+Created on Thu Jun  8 03:39:02 2023
+
+@author: mritchey
+"""
+
+import pandas as pd
+import numpy as np
+import streamlit as st
+from geopy.extra.rate_limiter import RateLimiter
+from geopy.geocoders import Nominatim
+import folium
+from streamlit_folium import st_folium
+from vincenty import vincenty
+
+st.set_page_config(layout="wide")
+
+@st.cache_data
+def convert_df(df):
+    return df.to_csv(index=0).encode('utf-8')
+
+@st.cache_data
+def get_data(file='hail2010-20230920_significant_bulk_all.parquet'):
+    return pd.read_parquet(file) 
+
+
+def map_perimeters(address,lat ,lon):
+
+    m = folium.Map(location=[lat, lon],
+
+                    zoom_start=6,
+                    height=400)
+    folium.Marker(
+        location=[lat, lon],
+        tooltip=f'Address: {address}',
+    ).add_to(m)
+
+    return m
+
+def distance(x):
+    left_coords = (x[0], x[1])
+    right_coords = (x[2], x[3])
+    return vincenty(left_coords, right_coords, miles=True)
+
+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
+
+
+
+
+
+#Side Bar
+address = st.sidebar.text_input(
+    "Address", "Dallas, TX")
+date = st.sidebar.date_input("Loss Date",  pd.Timestamp(2021, 7, 14), key='date')
+df_hail=get_data()
+
+
+#Geocode Addreses
+lat, lon = geocode(address)
+
+#Filter DAta
+df_hail_cut=df_hail.query(f"{lat}-1<=LAT<={lat}+1 and {lon}-1<=LON<={lon}+1 ")
+df_hail_cut=df_hail_cut.query("Date_est<=@date")
+
+
+df_hail_cut["Lat_address"] = lat
+df_hail_cut["Lon_address"] = lon
+df_hail_cut['Miles to Hail'] = [
+    distance(i) for i in df_hail_cut[['LAT','LON','Lat_address','Lon_address']].values]
+df_hail_cut['MAXSIZE'] = df_hail_cut['MAXSIZE'].round(2)
+
+df_hail_cut=df_hail_cut.query("`Miles to Hail`<10")
+df_hail_cut['Category']=np.where(df_hail_cut['Miles to Hail']<.1,"At Location",
+                                 np.where(df_hail_cut['Miles to Hail']<1,"Within 1 Mile",
+                                  np.where(df_hail_cut['Miles to Hail']<3,"Within 3 Miles",       
+                                    np.where(df_hail_cut['Miles to Hail']<10,"Within 10 Miles",'Other'))))
+
+df_hail_cut_group=pd.pivot_table(df_hail_cut,index='Date_est',
+                                 columns='Category',
+                                 values='MAXSIZE',
+                                 aggfunc='max')
+
+cols=df_hail_cut_group.columns
+cols_focus=['At Location',"Within 1 Mile","Within 3 Miles","Within 10 Miles"]
+
+missing_cols=set(cols_focus)-set(cols)
+for c in missing_cols:
+    df_hail_cut_group[c]=np.nan
+ 
+df_hail_cut_group2=df_hail_cut_group[cols_focus]  
+
+for i in range(3):
+    df_hail_cut_group2[cols_focus[i+1]]=np.where(df_hail_cut_group2[cols_focus[i+1]]<
+                                                 df_hail_cut_group2[cols_focus[i]],
+                                                 df_hail_cut_group2[cols_focus[i]],
+                                                 df_hail_cut_group2[cols_focus[i+1]])
+
+
+df_hail_cut_group2=df_hail_cut_group2.sort_index(ascending=False)
+
+#Map Data
+m = map_perimeters(address,lat, lon)
+
+#Display
+col1, col2 = st.columns((3, 2))
+with col1:
+    st.header('Estimated Maximum Hail Size')
+    st.write('Data from 2010 to 2023-09-20')
+    df_hail_cut_group2
+    csv2 = convert_df(df_hail_cut_group2.reset_index())
+    st.download_button(
+        label="Download data as CSV",
+        data=csv2,
+        file_name=f'{address}_{date}.csv',
+        mime='text/csv')
+with col2:
+
+    st.header('Map')
+    st_folium(m, height=400)

hail2010-20230920_significant_bulk_all.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:86c86463c9f9180d9fff30973d31228b2f96bec03f460da9caaa7645fe00831b
+size 75828421

requirements.txt

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+folium==0.12.1.post1
+geopy==2.2.0
+numpy==1.19.5
+pandas==1.5.2
+streamlit==1.20.0
+streamlit_folium==0.6.15
+vincenty==0.1.4