app.py

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

@author: mritchey
"""

import gzip
import pickle
import h5py
import rasterio
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]],
        interactive=True
    ).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(row, col, radius):
    files = [
        "2023_hail.h5",
        "2022_hail.h5"]

    all_data = []
    all_dates = []
    for f in files:
        with h5py.File(f, 'r') as f:
            data = f['hail'][:, row-radius:row +
                             radius+1, col-radius:col+radius+1]
            dates = f['dates'][:]
            all_data.append(data)
            all_dates.append(dates)

    data_mat = np.concatenate(all_data)
    data_mat = np.where(data_mat < 0, 0, data_mat)*0.0393701
    dates_mat = np.concatenate(all_dates)

    data_actual = [i[radius, radius] for i in data_mat]
    data_max = np.max(data_mat, axis=(1, 2))
    data_max_2 = np.max(data_mat, axis=0)

    df = pd.DataFrame({'Date': dates_mat,
                       'Actual': data_actual,
                      'Max': data_max})

    df['Date'] = pd.to_datetime(df['Date'], format='%Y%m%d')

    return df, data_max_2


#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")
start_date = st.sidebar.date_input("Start Date",  pd.Timestamp(2022, 1, 1))
end_date = st.sidebar.date_input("End Date",  pd.Timestamp(2023, 12, 31))


circle_radius = st.sidebar.selectbox('Box Radius (Miles)', (5, 10, 25))

zoom_dic = {5: 12, 10: 11, 25: 10}
zoom = zoom_dic[circle_radius]

#Geocode and get Data
result = geocode(address)
lat, lon = result.values[0]


#Raster Data
extracted_file = 'hail_stage.grib2'
ds_stage = xr.open_dataarray(extracted_file,engine='rasterio')
transform = ds_stage.rio.transform()
row, col = rasterio.transform.rowcol(transform, lon, lat)


radius = int(np.ceil(circle_radius*1.6))
crop_coords = col-radius, row-radius, col+radius+1, row+radius+1

# Get Data
df_data, max_values = get_data(row, col, radius)

df_data=df_data.query(f"'{start_date}'<=Date<='{end_date}'")


# 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))


fig = px.bar(df_data, x="Date", y="Actual", color="Actual",
             # barmode="stack",
             # color='red',
             # color_discrete_map=color_discrete_map,

             )

# 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(df_data)
        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)