app.py

# -*- coding: utf-8 -*-
"""
Created on Fri Oct 14 10:35:25 2022

@author: mritchey
"""
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,crop_coords):
    files = glob.glob(f'png/**/*.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, 7, 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,crop_coords)
    

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(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)