João Paulo Figueira joaofig

Data Scientist, Algorithm Developer, Optimization Freak

joaofig / show_density_map.py

Created May 18, 2024 11:52

Creates and displays the VED density map on a Jupyter Notebook

	import folium
	from folium.raster_layers import TileLayer

	html_map = folium.Map(prefer_canvas=True,
	tiles="cartodbpositron",
	location=(42.274569, -83.733228),
	zoom_start=13)
	tile_layer = TileLayer(tiles="http://127.0.0.1:8000/density/{x}/{y}/{z}", overlay=True,
	attr="(C) JPF")
	tile_layer.add_to(html_map)

joaofig / paint_tile.rs

Created May 17, 2024 18:00

Paints a single tile

	fn paint_tile(pixels: Vec<TilePixel>,
	color_grad: Gradient,
	range: LevelRange) -> RgbaImage {
	let mut bitmap = make_rgba_tile(0, 0, 0, 0);

	if range.width() > 0.0 {
	for pixel in pixels {
	let intensity = (pixel.get_c() - range.min()).ln() / range.width().ln();
	let rgba = color_grad.at(intensity);
	let pix = Rgba::from(rgba.to_rgba8());

joaofig / get_tile_file_name.rs

Last active May 12, 2024 14:54

Computes the tile file name and generates its contents if the file does not exist.

	async fn get_tile_file_name(x: i32, y: i32, z:i32) -> String {
	let quad_key = Quadkey::tile_to_quadkey(x, y, z as usize);
	let mut file_name = format!("./tiles/{}/{}.png", z, quad_key);
	let path = Path::new(&file_name);

	if !path.exists() {
	let tile_quad_keys = get_tile_quad_keys(&quad_key).await;

	if tile_quad_keys.is_empty() {
	file_name = get_default_filename();

joaofig / get_density_tile.rs

Created May 9, 2024 17:00

The main entry point to the density tile server

	#[get("/density/<x>/<y>/<z>")]
	async fn get_density_tile(x: i32, y: i32, z: i32) -> Result<NamedFile> {
	let file_name = if !(1..=18).contains(&z) {
	get_default_filename()
	} else {
	get_tile_file_name(x, y, z).await
	};
	NamedFile::open(PathBuf::from(file_name)).await
	}

joaofig / make_rgba_tile.rs

Created May 6, 2024 13:04

Creates a 256x256 tile with a solid color.

	fn make_rgba_tile(r: u8, g: u8, b: u8, a: u8) -> RgbaImage {
	ImageBuffer::from_pixel(256, 256, Rgba([r, g, b, a]))
	}

joaofig / sqlx-sample.rs

Created May 5, 2024 10:21

sqlx sample code

	use std::result::Result;
	use sqlx::{SqlitePool, FromRow};

	#[derive(FromRow, Debug, PartialEq, Clone)]
	struct LevelRange {
	level_num: i64,
	level_min: f64,
	level_max: f64,
	}

joaofig / CompoundTrajectory_final.py

Last active January 15, 2024 15:53

Final methods of the compound trajectory class

	@classmethod
	def _trim_array(cls, array: np.ndarray) -> np.ndarray:
	if array.shape[0] > 2:
	return array[1:-1]
	else:
	return array

	def to_trajectory(self) -> Trajectory:
	lat = np.concatenate([self._trim_array(segment.lat) for segment in self.segments])
	lon = np.concatenate([self._trim_array(segment.lon) for segment in self.segments])

joaofig / segment_to_trajectory.py

Created January 9, 2024 19:58

Converts a trajectory segment to a node-based trajectory, recomputing traversal times.

	def segment_to_trajectory(segment: list[LatLon],
	dt: float,
	t0: float) -> Trajectory:
	lat = np.array([point.lat for point in segment])
	lon = np.array([point.lon for point in segment])
	time = np.zeros_like(lat)

	seg_lens = vec_haversine(lat[1:], lon[1:], lat[:-1], lon[:-1])
	avg_speed = np.sum(seg_lens) / dt
	time[0] = t0

joaofig / merge_trajectory.py

Created January 9, 2024 19:45

Merges the map-matched trajectory to the corresponding map nodes

	def merge_trajectory(trajectory: Trajectory,
	map_lat: np.ndarray,
	map_lon: np.ndarray) -> list[list[LatLon]]:
	segments: list[list[LatLon]] = []
	j = 0
	for i in range(trajectory.lat.shape[0]-1):
	pt0 = LatLon(float(trajectory.lat[i]), float(trajectory.lon[i]))
	pt1 = LatLon(float(trajectory.lat[i+1]), float(trajectory.lon[i+1]))
	seg_len = pt0.haversine(*pt1.to_tuple())

joaofig / CompoundTrajectory_init.py

Created January 9, 2024 19:14

The compound trajectory constructor

	class CompoundTrajectory:
	def __init__(self, trajectory: Trajectory,
	map_lat: np.ndarray,
	map_lon: np.ndarray):
	self.segments: list[Trajectory] = []

	t0 = 0
	merged = merge_trajectory(trajectory, map_lat, map_lon)
	for i, segment in enumerate(merged):
	dt = trajectory.dt[i]