fig, ax, plt = poly(poly_coords, color = df.gdp_per_capita, colormap = :viridis, axis = (; aspect = DataAspect()))
using Proj4 wgs84 = Proj4.Proj("+proj=longlat +datum=WGS84") webmerc = Proj4.Proj("+proj=merc +a=6378137 +b=6378137 +lat_ts=0.0 +lon_0=0.0 +x_0=0.0 +y_0=0 +k=1.0 +units=m") Transform a point x_merc, y_merc = Proj4.transform(wgs84, webmerc, -74.006, 40.7128) # NYC
using GLMakie, Random Random.seed!(42) lats = 60.17 .+ randn(10_000_000) * 0.01 lons = 24.94 .+ randn(10_000_000) * 0.01
Imagine: an optimization that adjusts the projection parameters to minimize visual distortion for your specific data distribution . Or a neural field that learns the optimal color mapping for a colorblind audience. With Zygote.jl or Enzyme.jl , this becomes a one-liner.
Colorbar(fig[1, 2], plt) fig
In the golden age of Python’s pandas and R’s tidyverse, why would a data scientist reach for Julia? The answer lies not in syntax prettiness, but in a more fundamental cartographic principle: the map is not the territory, but a well-crafted map reveals hidden valleys, unseen ridges, and the true flow of information.
using GeoArrays, ArchGDAL ga = GeoArray("landsat_band4.tif") roi = ga[100:200, 100:200] Apply a filter (e.g., NDVI calculation) ndvi = (ga.band4 - ga.band3) / (ga.band4 + ga.band3) Write back with preserved georeferencing GeoArrays.write("ndvi_map.tif", ndvi)
NBT Editor
Explore the potential of vanilla Minecraft. Change world settings, customize entities & items, remove corruption, peek inside ender chest inventories, enable achievements and much more.