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index_updated.html
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<!DOCTYPE html>
<html land="en">
<head>
<link rel="stylesheet" href="style.css">
<title>WoFS U-Net Tornado Viewer</title>
<script src="https://d3js.org/d3.v7.min.js"></script>
<script src="https://cdn.plot.ly/plotly-2.24.1.min.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/proj4js/2.8.0/proj4.js"></script>
<script src="https://unpkg.com/@msgpack/[email protected]/dist.es5+umd/msgpack.min.js"></script>
<!--<script src="https://cdn.jsdelivr.net/gh/nicolaspanel/[email protected]/dist/numjs.min.js"></script>-->
<script>
async function init() {
let run_dates = await read_valid_dates();
let date_sel = document.getElementById("model_run");
const formatTimeValue = d3.utcFormat("%Y%m%d");
const formatTimeLabel = d3.utcFormat("%Y %b %d");
run_dates.forEach(function (rd) {
let default_selected = false;
let opt = new Option(formatTimeLabel(rd), formatTimeValue(rd), default_selected, default_selected);
date_sel.add(opt);
})
let msg_file_len = get_ens_file_strings("wofs_sparse_prob_", 5, "ML_PREDICTED_TOR").length;
let json = {};
let progress_bar = document.getElementById('load_prog');
let progress_buff = 10;
progress_bar.max = msg_file_len * 2 + progress_buff;
await load_data_parallel("wofs_sparse_prob_", "ML_PREDICTED_TOR", 0, 1, json);
let wofs_x_length = 300;
let wofs_y_length = 300;
let resolution = 3000;
let radius = resolution / 2;
let orig_proj = "WGS84";
let base_proj = "+proj=lcc +lat_0=34.321392 +lon_0=-98.0134 +lat_1=30 +lat_2=60 +a=6370000 +b=6370000 +ellps=WGS84";
let base_transformer = proj4(base_proj, orig_proj);
let base_coord = base_transformer.inverse(json['fm_0']['se_coords']);
let wofs_proj = derive_new_proj(base_transformer, base_coord);
let transformer = proj4(wofs_proj, orig_proj);
let coord = transformer.inverse(json['fm_0']['se_coords']);
let spaghetti_traces = [];
let cell_i, cell_j;
let plot_d = {}
build_data_object(0, 1, json, plot_d);
let total_grid_cells = json['fm_0']['MEM_median']['rows'].length;
let lon_array_m = create_coord_array(coord[0], wofs_x_length, resolution);
let lat_array_m = create_coord_array(coord[1], wofs_y_length, resolution);
let domain = get_wofs_domain_geom(lon_array_m, lat_array_m);
let plot_data = plot_d['0_median'];
let plot_geom = plot_data[0];
let plot_coords = plot_data[1];
let refl_data, total_grid_cells_r, plot_geom_r, plot_coords_r;
let config = {mapboxAccessToken: "pk.eyJ1IjoiYnBldHprZSIsImEiOiJjbGtsY2I1cTAwNnR1M21wY3kxZnk3NG0xIn0.VBcAZDXsltnUxPWsj6TJPA"};
let fcst_dates = get_fcst_date_range(5);
console.log("Forecast Dates");
console.log(fcst_dates);
let map_data = {
type: "choroplethmapbox",
locations: d3.range(total_grid_cells),
marker: {
line: {width: 0},
opacity: 0.7
},
z: json['fm_0']['MEM_median']['values'],
zmin: 0, zmax: 0.75,
colorbar: {x: -0.05, thickness: 20},
hoverinfo: "z",
customdata: plot_coords,
colorscale: 'YlGnBu',
// hovertemplate: "%{customdata}",
geojson: plot_geom
};
let init_trace = {
x: [fcst_dates[Math.floor(msg_file_len / 2)]],
y: [0.25],
text: ['Click on a probability grid cell to display a spaghetti plot of all ensemble members.'],
textfont: {size: 16},
mode: 'text',
xaxis: 'x2',
yaxis: 'y2',
type: 'scatter',
showlegend: false
};
let wofs_domain = {
type: "scattermapbox",
showlegend: false,
mode: 'lines',
line: {color: 'grey', width: 2},
lon: domain[0],
lat: domain[1],
};
let cell_domain = {
type: "scattermapbox",
showlegend: false,
mode: 'lines',
line: {color: 'black', width: 2},
lon: null,
lat: null
};
let all_traces = [map_data, init_trace, wofs_domain, cell_domain].flat();
let layout = {
title: {text: get_title_timestamp(), x: 0.05, font: {size: 22}},
mapbox: {
style: "dark",
layers: [
{
sourcetype: "geojson",
source: "data_geojson/geojson-counties-fips.json",
type: "line",
color: "#BA9DD5",
line: {"width": 0.25},
below: "traces"
},
{
sourcetype: "geojson",
source: "data_geojson/cnty_warn_bnds.json",
type: "line",
color: "yellow",
line: {"width": 0.4, opacity: 0.0},
below: "traces"
}
],
center: {lon: domain[2][0], lat: domain[2][1]},
zoom: 5
},
showlegend: true,
grid: {rows: 1, columns: 2, pattern: 'independent'},
height: 800,
yaxis2: {range: [0, 1.0], title: {text: 'Probability of Tornado', font: {size: 20}}},
xaxis2: {
range: [fcst_dates[0], fcst_dates[fcst_dates.length - 1]],
title: {text: 'Forecast Date/Time', font: {size: 20}},
tickformat: '%m-%d %H:%M',
tickangle: 35
},
// xaxis2: {range: get_timestamp(5), title: {text:'Forecast Minute', font: {size: 20}}, tickformat: '%H:%M:%S'},
shapes: [{
type: 'line',
x0: fcst_dates[0],
y0: 0,
x1: fcst_dates[0],
y1: 1.0,
opacity: 0.3,
line: {
color: 'rgba(0,128,26,0.68)',
width: 10,
opacity: 0.5
}
}],
legend: {
y: 1,
x: 0.95,
xaxis: 'x2',
yaxis: 'y2',
font: {size: 18},
},
annotations: [
{
y: 1,
yshift: 20,
text: 'Press . key to advance forward in time and , key to advance back in time',
showarrow: false,
align: "center",
valigh: "bottom",
}
]
};
Plotly.newPlot(document.getElementById("mapview"), all_traces, layout, config);
await load_data_parallel("wofs_sparse_prob_", "ML_PREDICTED_TOR", 1, msg_file_len, json);
build_data_object(1, msg_file_len, json, plot_d);
let fms = Object.keys(json);
let max_fm_str = fms[fms.length - 1];
let max_minutes = parseInt(max_fm_str.substring(3, max_fm_str.length));
let reflectivity = {};
await load_data_parallel("wofs_sparse_prob_", "COMPOSITE_REFL_10CM", 0, msg_file_len, reflectivity);
plot_r = {};
document.getElementById('refl').addEventListener('change', async function () {
build_data_object(0, msg_file_len, reflectivity, plot_r);
update();
});
document.getElementById('refl_alpha').addEventListener('change', update);
document.getElementById('mapview').on('plotly_click', get_spaghetti);
document.getElementById('ens_mem').addEventListener('change', update_mem);
document.getElementById('forecast_minute').addEventListener('change', update);
document.getElementById('model_run').addEventListener('change', update_init_time);
document.getElementById('init_time').addEventListener('change', update_init_time);
document.addEventListener('keypress', function (event) {
let code = event.code;
if (code === "Period") {
update_time("forward");
}
if (code === "Comma") {
update_time("backward");
}
},
false);
progress_bar.value += progress_buff;
//Sleep
await new Promise(r => setTimeout(r, 500));
//Hide progress bar
document.getElementById('loading').style.display = "none";
async function update() {
let trace = select_trace(json, plot_d)
plot_geom = trace[1][0]
plot_coords = trace[1][1]
let trace_values = trace[0]
total_grid_cells = trace_values['rows'].length
let minuteIdx = document.getElementById("forecast_minute").selectedIndex;
let refl_alpha = document.getElementById("refl_alpha").value / 100
map_data = {
type: "choroplethmapbox",
locations: d3.range(total_grid_cells),
marker: {
line: {width: 0},
opacity: 0.7
},
z: trace_values['values'],
zmin: 0, zmax: 0.75,
colorbar: {x: -0.05, thickness: 20},
hoverinfo: "z",
colorscale: 'YlGnBu',
customdata: plot_coords,
geojson: plot_geom
};
wofs_domain = {
type: "scattermapbox",
showlegend: false,
mode: 'lines',
line: {color: 'grey', width: 2},
lon: domain[0],
lat: domain[1]
};
fcst_dates = get_fcst_date_range(5);
layout['xaxis2']['range'] = [fcst_dates[0], fcst_dates[fcst_dates.length - 1]];
layout["title"]["text"] = get_title_timestamp()
layout["shapes"][0]["x0"] = fcst_dates[minuteIdx];
layout["shapes"][0]["x1"] = fcst_dates[minuteIdx];
if (document.getElementById("refl").checked) {
let trace_r = select_trace(reflectivity, plot_r)
plot_geom_r = trace_r[1][0]
plot_coords_r = trace_r[1][1]
let trace_values_r = trace_r[0]
total_grid_cells_r = trace_values_r['rows'].length
refl_data = {
type: "choroplethmapbox",
locations: d3.range(total_grid_cells_r),
marker: {
line: {width: 0},
opacity: refl_alpha
},
z: trace_values_r['values'],
zmin: 0, zmax: 80,
colorbar: {x: 0.22, y: -0.1, orientation: 'h', thickness: 15, len: 0.45},
hoverinfo: "skip",
customdata: plot_coords_r,
colorscale: 'Jet',
geojson: plot_geom_r
};
all_traces = [map_data, refl_data, spaghetti_traces, wofs_domain, cell_domain].flat();
} else {
all_traces = [map_data, spaghetti_traces, wofs_domain, cell_domain].flat();
}
Plotly.react(document.getElementById("mapview"), all_traces, layout);
}
function derive_new_proj(base_transformer, coord) {
let center_proj_x = coord[1] + (3000 * 150) + 1500
let center_proj_y = coord[0] + (3000 * 150) + 1500
let center_lonlat = base_transformer.forward([center_proj_y, center_proj_x])
let proj = "+proj=lcc +lat_0=" + center_lonlat[1] + " +lon_0=" + center_lonlat[0] + " +lat_1=30 +lat_2=60 +a=6370000 +b=6370000 +ellps=WGS84";
return proj
}
function get_selected_cell_geom(i, j, lon_array_m, lat_array_m) {
let geom = create_geom(i, j, lon_array_m, lat_array_m)
let lons = geom.map(item => item[0])
let lats = geom.map(item => item[1])
cell_domain = {
type: "scattermapbox",
showlegend: false,
mode: 'lines',
line: {color: 'red', width: 2},
lon: lons,
lat: lats,
hoverinfo: "skip"
};
return cell_domain
}
function get_wofs_domain_geom(lon_array_m, lat_array_m) {
let se = transformer.forward([lon_array_m[0], lat_array_m[0]]);
let sw = transformer.forward([lon_array_m[wofs_x_length - 1], lat_array_m[0]]);
let nw = transformer.forward([lon_array_m[wofs_x_length - 1], lat_array_m[wofs_y_length - 1]]);
let ne = transformer.forward([lon_array_m[0], lat_array_m[wofs_y_length - 1]]);
let center = transformer.forward([lon_array_m[Math.floor(wofs_x_length / 2)], lat_array_m[Math.floor(wofs_y_length / 2)]])
let lons = [se[0], sw[0], nw[0], ne[0], se[0]]
let lats = [se[1], sw[1], nw[1], ne[1], se[1]]
return [lons, lats, center]
}
function select_trace(data, geom) {
let member = document.getElementById("ens_mem").value
let time = document.getElementById("forecast_minute").value
let p = data["fm_" + String(parseInt(time))]['MEM_' + member]
let geo = geom[String(parseInt(time)) + "_" + member]
return [p, geo]
}
function build_traces(trace_data) {
let all_traces = []
let forecast_minutes = get_fcst_date_range(5);
for (let i = 1; i <= trace_data.length; i++) {
var trace = {
x: forecast_minutes,
y: trace_data[i],
xaxis: 'x2',
yaxis: 'y2',
type: 'scatter',
showlegend: false,
line: {color: 'lightgrey', width: 1}
};
all_traces.push(trace)
}
var mean = {
name: "Mean",
x: forecast_minutes,
y: trace_data["ens_mean"],
xaxis: 'x2',
yaxis: 'y2',
type: 'scatter',
line: {color: 'black', width: 4}
};
all_traces.push(mean)
var median = {
name: 'Median',
x: forecast_minutes,
y: trace_data["ens_median"],
xaxis: 'x2',
yaxis: 'y2',
type: 'scatter',
line: {color: 'black', width: 2, dash: 'dot'}
};
all_traces.push(median)
return all_traces
}
async function get_spaghetti(point_data) {
let pt = (point_data.points || [])[0]
let index = pt.customdata
cell_i = index[0]
cell_j = index[1]
let spaghetti_data = await spaghetti(cell_i, cell_j)
spaghetti_traces = build_traces(spaghetti_data)
cell_domain = get_selected_cell_geom(index[0], index[1], lon_array_m, lat_array_m)
if (document.getElementById("refl").checked) {
all_traces = [map_data, refl_data, spaghetti_traces, wofs_domain, cell_domain].flat();
} else {
all_traces = [map_data, spaghetti_traces, wofs_domain, cell_domain].flat();
}
Plotly.react(document.getElementById("mapview"), all_traces, layout);
}
async function spaghetti(i, j) {
const calc_mean = array => array.reduce((a, b) => a + b) / array.length
const calc_median = (arr) => {
return arr.slice().sort((a, b) => a - b)[Math.floor(arr.length / 2)];
};
let d = {'length': 18, 'ens_mean': [], 'ens_median': []}
for (let m of d3.range(0, max_minutes + 5, 5)) {
let fm = "fm_" + m
let f = json[fm]
let ens_all_ts = []
for (mem = 1; mem < 19; mem++) {
if (m === 0) {
d[mem] = []
}
var member = "MEM_" + String(mem)
var found = false
let row_indices = f[member]['rows'].flatMap((x, z) => x === i ? z : [])
for (index = 0; index < row_indices.length; index++) {
if (f[member]['columns'][row_indices[index]] === j) {
d[mem].push(f[member]['values'][row_indices[index]])
found = true
}
}
if (found === false) {
d[mem].push(0)
}
ens_all_ts.push(d[mem][m / 5])
}
let mean_ens = calc_mean(ens_all_ts)
let median_ens = calc_median(ens_all_ts)
d['ens_mean'].push(mean_ens)
d['ens_median'].push(median_ens)
}
return d
}
function get_ens_file_strings(file_prefix, interval, variable) {
const formatTime = d3.timeFormat("%Y%m%d%H%M00");
let datetime = document.getElementById("model_run").value;
let inittime = document.getElementById("init_time").value;
let year = datetime.substring(0, 4);
let month = parseInt(datetime.substring(4, 6)) - 1;
let day = datetime.substring(6, 8);
let start_hour = inittime.substring(0, 2);
let start_min = inittime.substring(2, 4);
let start_hour_int = parseInt(start_hour);
if (start_hour_int <= 4) {
var start_date = new Date(year, month, day, start_hour, start_min);
start_date = new Date(start_date.getTime() + 86400000);
} else {
start_date = new Date(year, month, day, start_hour, start_min);
}
end_date = new Date(start_date.getTime() + 3 * 3600000 + 5 * 60000)
console.log("Start and End dates");
console.log(start_date);
console.log(end_date);
var end_hour = end_date.getHours();
var date_range = d3.timeMinutes(start_date,
end_date, interval);
console.log("Date Range")
console.log(date_range)
let file_list = [];
date_range.forEach(function (x) {
file_list.push("https://wofsdltornado.blob.core.windows.net/wofs-dl-preds-updated/"
+ datetime + "/" + start_hour + start_min + "/" + file_prefix + formatTime(x) + "_" + variable + ".msgpk")
});
console.log(file_list);
return file_list;
}
// function get_timestamp(interval) {
//
// const formatTime = d3.timeFormat("%H%M")
// var datetime = document.getElementById("model_run").value
// var year = datetime.substring(0, 4)
// var month = parseInt(datetime.substring(4, 6)) - 1
// var day = datetime.substring(6, 8)
// var start_hour = datetime.substring(8, 10)
// var end_hour = parseInt(start_hour) + 3
// var date_range = d3.timeMinutes(new Date(year, month, day, start_hour, 0),
// new Date(year, month, day, end_hour, 5), interval)
// return date_range;
// }
async function read_valid_dates(valid_init_hours = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23]) {
let date_filename = "https://wofsdltornado.blob.core.windows.net/wofs-dl-preds-updated/available_dates.csv";
console.log(date_filename)
let date_resp = await fetch(date_filename);
let date_str = await date_resp.text();
let run_date_strs = date_str.split("\n");
let dates = [];
for (let i = 0; i < run_date_strs.length; i++) {
dates.push(new Date(Date.UTC(parseInt(run_date_strs[i].substring(0, 4)),
parseInt(run_date_strs[i].substring(4, 6)) - 1,
parseInt(run_date_strs[i].substring(6, 8))))
);
}
dates = dates.filter(function (d) {
return valid_init_hours.includes(d.getUTCHours());
});
dates = dates.reverse();
return dates;
}
async function load_data_parallel(file_prefix, variable, start, end, json_out) {
let progress_bar = document.getElementById('load_prog');
var file_list = get_ens_file_strings(file_prefix, 5, variable).slice(start, end)
return Promise.all(file_list.map(url => fetch(url)))
.then(responses => Promise.all(responses.map(response => MessagePack.decodeAsync(response.body))))
.then(messages => {
j = start
for (let i = 0; i < messages.length; i++) {
progress_bar.value += 1
let key = "fm_" + String(j * 5)
json_out[key] = messages[i]
j += 1
}
});
}
function build_data_object(start, end, data, obj_dict_out) {
let member = document.getElementById("ens_mem").value
let coord = transformer.inverse(data['fm_0']['se_coords'])
let lon_array_m = create_coord_array(coord[0], wofs_x_length, resolution)
let lat_array_m = create_coord_array(coord[1], wofs_y_length, resolution)
for (let i of d3.range(start, end)) {
let minutes = i * 5
let subset = data["fm_" + minutes]["MEM_" + member]
let plot_data = create_geom_object(subset["rows"], subset["columns"], lon_array_m, lat_array_m)
obj_dict_out[minutes + "_" + member] = plot_data
}
}
function update_mem() {
plot_d = {};
build_data_object(0, msg_file_len, json, plot_d);
if (document.getElementById("refl").checked) {
plot_r = {};
build_data_object(0, msg_file_len, reflectivity, plot_r);
}
update();
}
async function update_init_time() {
document.getElementById("refl").checked = false;
msg_file_len = get_ens_file_strings("wofs_sparse_prob_", 5, "ML_PREDICTED_TOR").length;
json = {};
await load_data_parallel("wofs_sparse_prob_", "ML_PREDICTED_TOR", 0, msg_file_len, json);
reflectivity = {};
await load_data_parallel("wofs_sparse_prob_", "COMPOSITE_REFL_10CM", 0, msg_file_len, reflectivity);
base_coord = base_transformer.inverse(json['fm_0']['se_coords']);
wofs_proj = derive_new_proj(base_transformer, base_coord);
transformer = proj4(wofs_proj, orig_proj);
coord = transformer.inverse(json['fm_0']['se_coords']);
lon_array_m = create_coord_array(coord[0], wofs_x_length, resolution);
lat_array_m = create_coord_array(coord[1], wofs_y_length, resolution);
domain = get_wofs_domain_geom(lon_array_m, lat_array_m);
layout["mapbox"]["center"] = {lon: domain[2][0], lat: domain[2][1]};
layout["mapbox"]["zoom"] = 5
fms = Object.keys(json);
max_fm_str = fms[fms.length - 1];
max_minutes = parseInt(max_fm_str.substring(3, max_fm_str.length));
plot_d = {};
build_data_object(0, msg_file_len, json, plot_d);
let spaghetti_data = await spaghetti(cell_i, cell_j)
spaghetti_traces = build_traces(spaghetti_data)
await update();
}
function create_coord_array(coord, len, resolution) {
let array = new Array(len);
for (let i = 0; i < len; i++) {
array[i] = coord + (resolution * i);
}
return array
}
function get_title_timestamp() {
var datetime = document.getElementById("model_run").value
var inittime = document.getElementById("init_time").value
var year = datetime.substring(0, 4)
var month = parseInt(datetime.substring(4, 6)) - 1
var day = datetime.substring(6, 8)
var hour = inittime.substring(0, 2)
var minute = inittime.substring(2, 4)
var forecast_minutes = document.getElementById("forecast_minute").value
var time_ms = new Date(Date.UTC(year, month, day, hour, minute)).getTime()
var forecast_mins_in_ms = forecast_minutes * 60 * 1000
var date_string = new Date(time_ms + forecast_mins_in_ms).toUTCString()
return "Probability of Tornado: " + date_string
}
function create_geom(i, j, lons, lats) {
let south_lat_m = lats[i] - radius
let north_lat_m = lats[i] + radius
let west_lon_m = lons[j] - radius
let east_lon_m = lons[j] + radius
let se = transformer.forward([east_lon_m, south_lat_m])
let ne = transformer.forward([east_lon_m, north_lat_m])
let sw = transformer.forward([west_lon_m, south_lat_m])
let nw = transformer.forward([west_lon_m, north_lat_m])
return [sw, nw, ne, se, sw]
}
function create_geom_object(i_indices, j_indices, lons, lats) {
let coords = new Array(i_indices.length)
let grid_obj = {type: "FeatureCollection", features: new Array(i_indices.length)}
for (index of d3.range(i_indices.length)) {
coords[index] = [i_indices[index], j_indices[index]]
let geom = create_geom(i_indices[index], j_indices[index], lons, lats)
let grid_cell_obj = {
type: "Feature",
id: index,
geometry: {type: "Polygon", coordinates: [geom]}
}
grid_obj["features"][index] = grid_cell_obj
}
return [grid_obj, coords]
}
function update_time(direction) {
let selectElement = document.querySelectorAll('[name=forecast_minute]');
let valid_times = [...selectElement[0].options].map(o => o.value)
let current = document.getElementById("forecast_minute").value;
let current_int = parseInt(current);
var new_time;
if (direction == "forward") {
new_time = String(current_int + 5).padStart(2, '0');
if (!valid_times.includes(new_time)) {
new_time = valid_times[0]
}
}
if (direction == "backward") {
new_time = String(current_int - 5).padStart(2, '0');
if (!valid_times.includes(new_time)) {
new_time = valid_times[valid_times.length - 1]
}
}
document.getElementById("forecast_minute").value = new_time;
update();
}}
function get_fcst_date_range(interval) {
let datetime = document.getElementById("model_run").value;
let inittime = document.getElementById("init_time").value;
let year = datetime.substring(0, 4);
let month = parseInt(datetime.substring(4, 6)) - 1;
let day = datetime.substring(6, 8);
let start_hour = inittime.substring(0, 2);
let start_min = inittime.substring(2, 4);
let start_hour_int = parseInt(start_hour);
let start_date = new Date(year, month, day, start_hour, start_min);
if (start_hour_int <= 4) {
start_date = new Date(start_date.getTime() + 86400000);
}
end_date = new Date(start_date.getTime() + 3 * 3600000 + 5 * 60000);
let date_range = d3.timeMinutes(start_date,
end_date, interval);
return date_range;
}
</script>
<style>
progress{
width: 300px;
height: 20px;
accent-color: light-purple;
}
</style>
<body onload="init()">
<div id="menubar">
<label for="model_run">Run Date: </label>
<select id="model_run" name="model_run">
</select>
<label for="init_time">Run Init Time (UTC): </label>
<select id="init_time" name="init_time">
<option value="1700">17:00</option>
<option value="1730">17:30</option>
<option value="1800">18:00</option>
<option value="1830">18:30</option>
<option value="1900">19:00</option>
<option value="1930">19:30</option>
<option value="2000">20:00</option>
<option value="2030">20:30</option>
<option value="2100">21:00</option>
<option value="2130">21:30</option>
<option value="2200">22:00</option>
<option value="2230">22:30</option>
<option value="2300">23:00</option>
<option value="2330">23:30</option>
<option value="0030">00:30</option>
<option value="0100">01:00</option>
<option value="0130">01:30</option>
<option value="0200">02:00</option>
<option value="0230">02:30</option>
<option value="0300">03:00</option>
</select>
<label for="ens_mem">Ensemble Member: </label>
<select id="ens_mem" name="ens_mem">
<option value="median" selected>Median</option>
<option value="mean">Mean</option>
<option value="max">Max</option>
<option value="1">Member 1</option>
<option value="2" >Member 2</option>
<option value="3">Member 3</option>
<option value="4">Member 4</option>
<option value="5">Member 5</option>
<option value="6">Member 6</option>
<option value="7">Member 7</option>
<option value="8">Member 8</option>
<option value="9">Member 9</option>
<option value="10">Member 10</option>
<option value="11">Member 11</option>
<option value="12">Member 12</option>
<option value="13">Member 13</option>
<option value="14">Member 14</option>
<option value="15">Member 15</option>
<option value="16">Member 16</option>
<option value="17">Member 17</option>
<option value="18">Member 18</option>
</select>
<label for="forecast_minute">Forecast Hour: </label>
<select id="forecast_minute" name="forecast_minute">
<option value="00" selected>00:00</option>
<option value="05">00:05</option>
<option value="10">00:10</option>
<option value="15">00:15</option>
<option value="20">00:20</option>
<option value="25">00:25</option>
<option value="30">00:30</option>
<option value="35">00:35</option>
<option value="40">00:40</option>
<option value="45">00:45</option>
<option value="50">00:50</option>
<option value="55">00:55</option>
<option value="60">01:00</option>
<option value="65">01:05</option>
<option value="70">01:10</option>
<option value="75">01:15</option>
<option value="80">01:20</option>
<option value="85">01:25</option>
<option value="90">01:30</option>
<option value="95">01:35</option>
<option value="100">01:40</option>
<option value="105">01:45</option>
<option value="110">01:50</option>
<option value="115">01:55</option>
<option value="120">02:00</option>
<option value="125">02:05</option>
<option value="130">02:10</option>
<option value="135">02:15</option>
<option value="140">02:20</option>
<option value="145">02:25</option>
<option value="150">02:30</option>
<option value="155">02:35</option>
<option value="160">02:40</option>
<option value="165">02:45</option>
<option value="170">02:50</option>
<option value="175">02:55</option>
<option value="180">03:00</option>
</select>
<input type="checkbox", id="refl">
<label for="refl">Reflectivity Overlay</label>
<input type="range" min="0" max="100" value="10" class="slider" id="refl_alpha">
</div>
<div id="loading" style="display: flex; justify-content: flex-end">
<label>Loading: </label>
<progress id="load_prog" value="0"></progress>
</div>
<div id="mapview">
</div>
</div>
<div id="spaghetti">
</div>
<div id="dvals">
</div>
</body>
</html>