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app.py

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+# -*- coding: utf-8 -*-
+"""
+Created on Fri Oct 14 10:35:25 2022
+
+@author: mritchey
+"""
+# streamlit run "C:\Users\mritchey\.spyder-py3\Python Scripts\streamlit projects\hail\hail pngs 2.py"
+
+from tqdm import tqdm
+from joblib import Parallel, delayed
+import rasterio
+import glob
+from PIL import Image
+import streamlit as st
+import os
+import branca.colormap as cm
+import folium
+from streamlit_folium import st_folium
+import numpy as np
+import pandas as pd
+import plotly.express as px
+from geopy.extra.rate_limiter import RateLimiter
+from geopy.geocoders import Nominatim
+import rioxarray
+import xarray as xr
+import warnings
+warnings.filterwarnings("ignore")
+
+@st.cache_data
+def convert_df(df):
+    return df.to_csv(index=0).encode('utf-8')
+
+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 pd.DataFrame({'Lat': lat, 'Lon': lon}, index=[0])
+
+
+def map_folium(data, zoom=12):
+    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=[[bottom, left], [top, right]]).add_to(m)
+    return m
+
+@st.cache_data
+def crop_hail_jpg_filter(f, crop_coords, scaling_factor=255):
+    date = f[-19:-11]
+    image = Image.open(f)
+    cropped_image = image.crop(crop_coords)
+    image = (np.array(cropped_image)/scaling_factor)
+    if image.sum() > 0:
+        return date, image
+    
+@st.cache_data   
+def get_data(start_date,end_date):
+    files = glob.glob(
+        r'png\2022\**\*.png', recursive=True)
+    
+    files_dates = np.array([int(f[-19:-11]) for f in files])
+    mask = np.where((files_dates >= int(start_date)) & (
+        files_dates <= int(end_date)), True, False)
+    files = np.array(files)[mask]
+    
+    results = Parallel(n_jobs=96, prefer='threads')(
+        delayed(crop_hail_jpg_filter)(i, crop_coords) for i in tqdm(files))
+    results = [i for i in results if i is not None]
+
+    return results
+
+#Set up 2 Columns
+st.set_page_config(layout="wide")
+col1, col2 = st.columns((2))
+
+
+
+#Input Values
+address = st.sidebar.text_input("Address", "123 Main Street, Dallas, TX 75126")
+d = st.sidebar.date_input("Date",  pd.Timestamp(2022, 2, 1))
+days_within = st.sidebar.selectbox('Days Within:', ('30', '90', '180', 'Day Of',))
+circle_radius = st.sidebar.selectbox('Box Radius (Miles)', (5, 10, 25))
+
+zoom_dic = {5: 12, 10: 11, 25: 10}
+zoom = zoom_dic[circle_radius]
+
+try:
+    days_within = int(days_within)
+    start_date, end_date = d - \
+        pd.Timedelta(days=days_within), d+pd.Timedelta(days=days_within+1)
+    start_date, end_date = start_date.strftime(
+        '%Y%m%d'), end_date.strftime('%Y%m%d')
+except:
+    days_within = 0
+    start_date = end_date=d -pd.Timedelta(days=days_within)
+    start_date = end_date=start_date.strftime('%Y%m%d')
+#Geocode and get Data
+result = geocode(address)
+lat, lon = result.values[0]
+
+
+ds_stage = xr.open_rasterio('hail_stage.grib2')
+transform = ds_stage.rio.transform()
+
+
+row, col = rasterio.transform.rowcol(transform, lon, lat)
+
+# center=row,col
+radius = int(np.ceil(circle_radius*1.6))  
+crop_coords = col-radius, row-radius, col+radius+1, row+radius+1
+
+# Get Data
+results=get_data(start_date,end_date)
+        
+try:
+    max_values = np.max(np.array([i[1] for i in results]), axis=0)*0.0393701
+except:
+    max_values=np.zeros(shape=(2*radius+1,2*radius+1))
+
+# Bin Data
+bin_edges = [0, 0.1, 0.2, 0.4, 0.8, 1.2, 1.6, 2, 3, 4, np.inf]
+bin_names = ["<0.1", "0.1-0.2", "0.2-0.4", "0.4-0.8", "0.8-1.2", "1.2-1.6", "1.6-2",
+             "2-3", "3-4", ">4"]
+colors_values = ['#ffffff', '#ffff00', '#d1ab00', '#ff9b00', '#fe0000', '#cd0000', '#ff30ce',
+                 '#ff30cd', '#9a009b', '#4a4d4c']
+color_discrete_map = dict(zip(bin_names, colors_values))
+
+all_data = []
+for date, mat in results:
+    mat = mat*0.0393701
+    hist_values, _ = np.histogram(mat, bins=bin_edges)
+    df = pd.DataFrame({'bin_edges': bin_edges[:-1],
+                       'Bin': bin_names,
+                       'Values': hist_values},
+                      )
+    df['Date'] = pd.to_datetime(date)
+    all_data.append(df)
+
+try:
+    final = pd.concat(all_data)
+    final = final.query('bin_edges!=0')
+    fig = px.bar(final, x="Date", y="Values", color="Bin",
+                 barmode="stack",
+                 color_discrete_map=color_discrete_map)
+except:
+    pass
+
+
+# Crop the raster using the bounds
+cropped_data = ds_stage[0][row-radius:row+radius+1, col-radius:col+radius+1]
+cropped_data.values = max_values
+
+# Max Values Bin for RGB
+
+def hex_to_rgb(hex_code):
+    hex_code = hex_code.lstrip('#')  # Remove the '#' character if present
+    rgb = tuple(int(hex_code[i:i+2], 16) for i in (0, 2, 4))
+    return rgb
+
+def hex_to_rgba(hex_code, alpha=.8):
+    if hex_code == '#ffffff':
+        alpha = 0.0
+    alpha_scaled = int(alpha * 255)
+    rgb = hex_to_rgb(hex_code)
+    rgba = rgb + (alpha_scaled,)
+    return rgba
+
+
+bin_indices = np.digitize(np.flipud(max_values), bin_edges)-1
+bin_colors = np.take(colors_values, bin_indices)
+
+
+max_values_rgb = np.array([hex_to_rgba(i) for i in bin_colors.flatten()]).reshape(
+    max_values.shape[0], max_values.shape[0], 4)
+
+
+
+#Mapping
+img = max_values_rgb.astype('uint8')
+
+boundary = cropped_data.rio.bounds()
+left, bottom, right, top = boundary
+
+
+# img[img < 0.0] = np.nan
+clat = (bottom + top)/2
+clon = (left + right)/2
+
+vmin = np.floor(np.nanmin(img))
+vmax = np.ceil(np.nanmax(img))
+
+colormap = cm.StepColormap(colors=list(color_discrete_map.values()),
+                           index=bin_edges,
+                           # vmin=vmin, vmax=vmax
+                           )
+
+
+m = map_folium(img, zoom)
+
+
+with col1:
+    st.title('Hail Mesh')
+    st_folium(m, height=500)
+
+
+with col2:
+    st.title(f'Hail')
+    try:
+        st.plotly_chart(fig)
+        csv = convert_df(final)
+        st.download_button(
+            label="Download data as CSV",
+            data=csv,
+            file_name='data.csv',
+            mime='text/csv')
+    except:
+        pass
+
+# st.bokeh_chart(hv.render(nice_plot*points_lat_lon, backend='bokeh'),use_container_width=True)
+
+
+st.markdown(""" <style>
+#MainMenu {visibility: hidden;}
+footer {visibility: hidden;}
+</style> """, unsafe_allow_html=True)

requirements.txt

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+branca==0.5.0
+folium==0.12.1
+geopy==2.2.0
+joblib==1.2.0
+numpy==1.23.5
+pandas==1.4.2
+Pillow==10.0.0
+plotly==5.15.0
+rasterio==1.2.10
+rioxarray==0.12.2
+streamlit==1.24.0
+streamlit_folium==0.6.15
+tqdm==4.64.0
+xarray==2022.3.0
+~lotly==5.7.0
+~treamlit==1.20.0