.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "gallery/plot_13_globe.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code. .. rst-class:: sphx-glr-example-title .. _sphx_glr_gallery_plot_13_globe.py: The world as a globe ==================== An orthographic projection turns the flat world into a sphere seen from space. The trick is to clip the data to the visible hemisphere before projecting (distant points would otherwise fold onto invalid coordinates), then draw on a dark background: * an ocean disc shaded by a radial gradient, for the spherical volume, plus a soft outer glow for the atmosphere; * the land masses clipped to the same hemisphere; * a graticule from :class:`~mappyng.GraticuleLayer`, whose meridians and parallels now bend with the sphere; * great-circle air routes from Paris to African cities, whose width is proportional to the real 2024 passenger traffic on each route. .. GENERATED FROM PYTHON SOURCE LINES 18-131 .. raw:: html .. code-block:: Python import math from pathlib import Path import geopandas as gpd import pandas as pd from pyproj import Geod from shapely.geometry import LineString, Point from mappyng import Map, GraticuleLayer, VectorLayer URL = ("https://raw.githubusercontent.com/nvkelso/natural-earth-vector/" "master/geojson/ne_110m_admin_0_countries.geojson") R = 6378137.0 # Earth radius (metres) in the orthographic CRS LON0, LAT0 = 0, 12 # centre of the visible hemisphere ORTHO = f"+proj=ortho +lat_0={LAT0} +lon_0={LON0} +ellps=WGS84 +units=m +no_defs" AEQD = f"+proj=aeqd +lat_0={LAT0} +lon_0={LON0} +ellps=WGS84 +units=m +no_defs" # Visible hemisphere as a polygon in lon/lat: a disc of radius 90 degrees # (just under, to avoid the degenerate limb) built in an azimuthal # equidistant CRS, then expressed in WGS84. cap = gpd.GeoDataFrame( geometry=[Point(0, 0).buffer(R * math.pi / 2 * 0.985, resolution=180)], crs=AEQD).to_crs(4326) cap["geometry"] = cap.make_valid() # Land: clip to the hemisphere first, then project. make_valid() after the # projection repairs the few polygons that touch the limb. world = gpd.read_file(URL) world["geometry"] = world.make_valid() land = gpd.clip(world, cap) land = land[~land.is_empty].to_crs(ORTHO) land["geometry"] = land.make_valid() land = land[~land.is_empty] # Ocean sphere. disc = gpd.GeoDataFrame(geometry=[Point(0, 0).buffer(R, resolution=256)], crs=ORTHO) # Air routes: great circles from Paris to African cities. The line width # encodes the real 2024 passenger traffic (Eurostat). Geod.npts samples the # geodesic, so the densified line bends correctly once projected. PARIS = (2.35, 48.85) traffic = pd.read_csv(Path("data") / "paris_africa_air_traffic.csv") geod = Geod(ellps="WGS84") lines = [LineString([PARIS] + geod.npts(PARIS[0], PARIS[1], lon, lat, 80) + [(lon, lat)]) for lon, lat in zip(traffic["lon"], traffic["lat"])] routes = gpd.GeoDataFrame({"pax": traffic["passengers_2024"]}, geometry=lines, crs=4326) routes = gpd.clip(routes, cap) routes = routes[~routes.is_empty].to_crs(ORTHO) pmin, pmax = routes["pax"].min(), routes["pax"].max() # City markers as small discs (point fills are not drawn, so buffer them). dots = gpd.GeoDataFrame( geometry=[Point(xy) for xy in zip(traffic["lon"], traffic["lat"])], crs=4326).to_crs(ORTHO) dots["geometry"] = dots.buffer(55000) paris = gpd.GeoDataFrame(geometry=[Point(PARIS)], crs=4326).to_crs(ORTHO) paris["geometry"] = paris.buffer(80000) # Widen the frame beyond the disc so the atmosphere glow is not clipped by # the viewport edge (the bbox is the map's clipping window). margin = 0.18 * R m = Map(disc, bbox=[-R - margin, -R - margin, R + margin, R + margin], width=860, height=860, padding=24, style={"base": "classic", "title_color": "#e8edf4"}, background="#070b18") # Atmosphere: a centred, blurred glow of the disc shape. m.add_shadow(disc, {"dx": 0, "dy": 0, "stdDeviation": 22, "color": "#48cae4", "opacity": 0.85, "fill": "#48cae4"}) # Ocean shaded as a sphere: the focal point sits up and to the left, so the # highlight reads as a light source and the colour deepens towards the limb. ocean = m.add_radial_gradient( [(0.0, "#3d8fc0"), (0.55, "#1c5d86"), (1.0, "#0a3553")], fx=0.36, fy=0.34, r=0.62) # Stacking with explicit z values: atmosphere glow (z=1) < ocean (2) < # graticule (3) < land (5, the default background depth). The grid therefore # shows over the sea and is hidden behind the continents. m.add(VectorLayer(disc, fill=ocean, stroke="#0b3a57", stroke_width=1.2, z=2)) m.add(GraticuleLayer(step=20, stroke="#bcdcef", opacity=0.55, stroke_width=0.6, z=3)) m.add(VectorLayer(land, fill="#e9edc9", stroke="#b9c79a", stroke_width=0.3)) # Limb darkening on top of everything: transparent at the highlight, dark at # the edge, so land and ocean share the same spherical shading. shade = m.add_radial_gradient( [(0.0, "#06121f", 0.0), (0.6, "#06121f", 0.0), (1.0, "#06121f", 0.6)], fx=0.36, fy=0.34) m.add(VectorLayer(disc, fill=shade, stroke="none", position="top")) # Air routes and city markers, drawn above the shading so they stay bright. # Line WIDTH is proportional to 2024 passenger traffic, all in the same red. # A wide, faint red stroke under each route makes it glow. m.add(VectorLayer(routes, fill="none", stroke="#e63946", stroke_width=4.0, stroke_opacity=0.16, stroke_linecap="round", position="top")) for _, route in routes.iterrows(): t = (route["pax"] - pmin) / (pmax - pmin) line = gpd.GeoDataFrame(geometry=[route.geometry], crs=routes.crs) m.add(VectorLayer(line, fill="none", stroke="#e63946", stroke_width=0.6 + 2.6 * t, stroke_linecap="round", position="top")) m.add(VectorLayer(dots, fill="#e63946", stroke="#ffffff", stroke_width=0.4, position="top")) m.add(VectorLayer(paris, fill="#ffffff", stroke="#e63946", stroke_width=0.8, position="top")) m.title("Air routes from Paris", subtitle="Line width: 2024 passenger traffic") m.source("Traffic: Eurostat (avia_par_fr, 2024) | Borders: Natural Earth") .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 0.516 seconds) .. _sphx_glr_download_gallery_plot_13_globe.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_13_globe.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_13_globe.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_13_globe.zip ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_