Duplicate from mattritchey/wildfires

.gitattributes

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README.md

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+---
+title: Wildfires
+emoji: 🏢
+colorFrom: blue
+colorTo: yellow
+sdk: streamlit
+sdk_version: 1.21.0
+app_file: app.py
+pinned: false
+duplicated_from: mattritchey/wildfires
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

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
+import geopandas as gpd
+from vincenty import vincenty
+
+st.set_page_config(layout="wide")
+
+@st.cache_resource
+def get_perimeters(refresh=False):
+    if refresh:
+        gdf_perimeters = gpd.read_file(
+            'https://opendata.arcgis.com/api/v3/datasets/5e72b1699bf74eefb3f3aff6f4ba5511_0/downloads/data?format=shp&spatialRefId=4326&where=1%3D1')  # .to_crs(epsg=epsg_input)
+        gdf_perimeters = gdf_perimeters[['OBJECTID', 'poly_Incid', 'attr_Fir_7', 'poly_Creat',
+                                         'poly_DateC', 'poly_Polyg', 'poly_Acres', 'attr_Estim', 'geometry']].copy()
+        gdf_perimeters.columns = ['OBJECTID', 'Incident', 'DiscoveryDate', 'poly_Creat',
+                                  'LastUpdate', 'poly_Polyg', 'Size_acres', 'CurrentEstCost', 'geometry']
+        gdf_perimeters['Lat_centroid'] = gdf_perimeters.centroid.y
+        gdf_perimeters['Lon_centroid'] = gdf_perimeters.centroid.x
+        gdf_perimeters['DiscoveryDate'] = pd.to_datetime(
+            gdf_perimeters['DiscoveryDate'])
+
+    else:
+        gdf_perimeters = gpd.read_parquet(
+            "wildfire_perimeters.parquet").query("geometry==geometry")
+    return gdf_perimeters
+
+
+def map_perimeters(_gdf_data, address):
+    geojson_data = _gdf_data[['OBJECTID', 'Incident', 'DiscoveryDate',
+                              'Miles to Fire Centroid', 'geometry']].to_json()
+
+    m = folium.Map(location=[lat, lon],
+
+                   zoom_start=6,
+                   height=500)
+    folium.Marker(
+        location=[lat, lon],
+        tooltip=f'Address: {address}',
+    ).add_to(m)
+
+    folium.GeoJson(geojson_data,
+                   tooltip=folium.GeoJsonTooltip(fields=["Incident",
+                                                         "DiscoveryDate",
+                                                         'Miles to Fire Centroid']),
+                   ).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
+
+def extract_vertices(gdf):
+    g = [i for i in gdf.geometry]
+    all_data = []
+    for i in range(len(g)):
+        try:
+            try:
+                x, y = g[i].exterior.coords.xy
+            except:
+                x, y = g[i].coords.xy
+            df = pd.DataFrame({'Lat': y, 'Lon': x})
+        except:
+            all_data2 = []
+            try:
+                for j in range(len(g[i])):
+                    try:
+                        x, y = g[i][j].exterior.coords.xy
+                    except:
+                        x, y = g[i][j].coords.xy
+                    all_data2.append(pd.DataFrame({'Lat': y, 'Lon': x}))
+                df = pd.concat(all_data2)
+            except:
+                for i in g.geometry:
+                    x=np.concatenate([poly.exterior.coords.xy[0] for poly in i.geoms])
+                    y=np.concatenate([poly.exterior.coords.xy[1] for poly in i.geoms])
+                df = pd.DataFrame({'Lat': y,
+                                   'Lon': x, })
+        df['index_gdf'] = i
+        all_data.append(df)
+    return pd.concat(all_data).query('Lat==Lat').reset_index(drop=1).drop(columns='index_gdf')
+
+
+#Side Bar
+address = st.sidebar.text_input(
+    "Address", "Sacramento, CA")
+date = st.sidebar.date_input("Date",  pd.Timestamp(2021, 7, 14), key='date')
+number_days_range = st.sidebar.selectbox(
+    'Within Day Range:', (5, 10, 30, 90, 180))
+
+refresh = st.sidebar.radio(
+    'Refresh Data (as of 6/7/23): Will Take Time ', (False, True))
+miles_range = st.sidebar.selectbox(
+    'Find Fires within Range (Miles):', (None, 50, 100, 250, 500))
+
+size = st.sidebar.radio(
+    'Greater than 100 Acres', ("Yes", "No"))
+
+#Get Data
+gdf = get_perimeters(refresh)
+
+# Geocode Addreses
+lat, lon = geocode(address)
+
+#Filter Data
+start_date, end_date = date - \
+    pd.Timedelta(days=number_days_range), date + \
+    pd.Timedelta(days=number_days_range+1)
+start_date_str, end_date_str = start_date.strftime(
+    '%Y-%m-%d'), end_date.strftime('%Y-%m-%d')
+gdf_cut = gdf.query(f"'{start_date_str}'<=DiscoveryDate<='{end_date_str}'")
+gdf_cut['DiscoveryDate'] = gdf_cut['DiscoveryDate'].dt.strftime('%Y-%m-%d')
+
+
+#Distance to Fire
+gdf_cut["Lat_address"] = lat
+gdf_cut["Lon_address"] = lon
+gdf_cut['Miles to Fire Centroid'] = [
+    distance(i) for i in gdf_cut[gdf_cut.columns[-4:]].values]
+gdf_cut['Miles to Fire Centroid'] = gdf_cut['Miles to Fire Centroid'].round(2)
+if miles_range is not None:
+    gdf_cut = gdf_cut.query(f"`Miles to Fire Centroid`<={miles_range}")
+
+if size == 'Yes':
+    gdf_cut = gdf_cut.query("Size_acres>100")
+
+gdf_cut = gdf_cut.sort_values('Miles to Fire Centroid').reset_index(drop=1)
+gdf_cut.index = gdf_cut.index+1
+
+#Map Data
+m = map_perimeters(gdf_cut, address)
+
+#Incident Edge
+indicents = list(gdf_cut['Incident'].values)
+incident_edge = st.sidebar.selectbox(
+    'Find Distance to Closest Edge:', indicents)
+vertices = extract_vertices(gdf_cut[gdf_cut['Incident']==incident_edge])
+vertices["Lat_address"] = lat
+vertices["Lon_address"] = lon
+vertices['Distance'] = [
+    distance(i) for i in vertices.values]
+closest_edge = vertices[vertices['Distance']
+                        == vertices['Distance'].min()]
+
+try:
+    lon_point, lat_point = closest_edge[['Lon', 'Lat']].values[0]
+    distance_edge = closest_edge['Distance'].round(2).values[0]
+    folium.PolyLine([[lat, lon],
+                     [lat_point, lon_point]],
+                    color='black',
+                    tooltip=f'Distance: {distance_edge} Miles'
+                    ).add_to(m)
+except:
+    pass
+
+#Display
+col1, col2 = st.columns((2, 3))
+with col1:
+    st.header('Fire Perimeters')
+    st_folium(m, height=600)
+with col2:
+    st.header('Fires')
+    gdf_cut[['Incident', 'DiscoveryDate', 'Size_acres','Miles to Fire Centroid']]
+    
+    
+    

requirements.txt

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+folium
+geopandas
+geopy
+numpy
+pandas
+streamlit
+streamlit_folium
+vincenty
+Shapely==1.8.1.post1

wildfire_perimeters.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6e1b2b8347d7116e8a10a31db26905f48a67e3100eda50ebf7c371bce952eb7d
+size 145374232