Initial version: loads GeoJSON and generates SVG map
This commit is contained in:
Thomas Kolb
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[submodule "geo-countries"]
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path = geo-countries
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url = https://github.com/datasets/geo-countries
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Subproject commit cd9e0635901eac20294a57ee3b3ce0684d5e3f1a
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#!/usr/bin/env python3
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import svgwrite
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import numpy as np
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import matplotlib.pyplot as pp
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from matplotlib.colors import hsv_to_rgb
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import json
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import random
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REF_LATITUDE = 49.58666
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REF_LONGITUDE = 11.01250
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# REF_LATITUDE = 1
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# REF_LONGITUDE = 0
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def map_azimuthal_equidistant(lat, lon, ref_lat, ref_lon, R=1):
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""" Azimuthal equidistant projection.
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This function takes a point to map in latitude/longitude format as well as
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a reference point which becomes the "center" of the map.
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It then projects the point into the 2D plane such that the distance from
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the center is proportional to the distance on the Great Circle through the
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projected and the reference point. The angle represents the azimuthal
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direction of the projected point.
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Args:
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lat(numpy.array): Latitudes of the point to project.
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lon(numpy.array): Longitudes of the point to project.
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ref_lat(float): Latitude of the reference point.
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ref_lon(float): Longitude of the reference point.
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R(float): Radius (scale) of the map.
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Returns:
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x(numpy.array): The calculated x coordinates.
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y(numpy.array): The calculated y coordinates.
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"""
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dlon = lon - ref_lon
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rho_linear_norm = np.arccos(np.sin(ref_lat) * np.sin(lat)
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+ np.cos(ref_lat)
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* np.cos(lat) * np.cos(dlon)) / np.pi
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rho = R * rho_linear_norm
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theta = np.arctan2(np.cos(lat) * np.sin(dlon),
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(np.cos(ref_lat) * np.sin(lat)
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- np.sin(ref_lat) * np.cos(lat)
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* np.cos(dlon)))
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x = rho * np.sin(theta)
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y = -rho * np.cos(theta)
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return x, y
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def random_country_color():
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h = random.random()
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s = 0.7
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v = 0.8
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r, g, b = [int(255.99*x) for x in hsv_to_rgb([h, s, v])]
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return f"#{r:02x}{g:02x}{b:02x}"
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random.seed(0)
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""" Test code
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test_lat = [np.pi/2, np.pi/4, 0, -np.pi/4, -np.pi/2]
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test_lon = np.arange(-np.pi, np.pi, np.pi/4)
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for lat in test_lat:
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for lon in test_lon:
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x, y = map_azimuthal_equidistant(np.array([lat]), np.array([lon]), np.pi/2, 0)
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print(f"{lat*180/np.pi:6.3f}, {lon*180/np.pi:6.3f} => {x[0]:6.3f}, {y[0]:6.3f}")
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"""
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print("Loading Geodata…")
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with open('geo-countries/data/countries.geojson', 'r') as jfile:
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geojson = json.load(jfile)
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print("Finding boundaries…")
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# key: 3-letter country identifier
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# data: {full_name, numpy.array(coordinates), numpy.array(proj_coordinates)}.
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# coordinates is a list of 2xN arrays, where N is the number of points. Row 0
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# contains the longitude, Row 1 the latitude.
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# proj_coordinates is a list of 2xN arrays, where N is the number of points.
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# Row 0 contains the projected x, Row 1 the projected y.
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simplegeodata = {}
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features = geojson['features']
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for feature in features:
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name = feature['properties']['ADMIN']
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key = feature['properties']['ISO_A2']
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# handle duplicate keys (can happen for small countries)
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if key in simplegeodata.keys():
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key = name
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print(f"Preparing {key} ({name})…")
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multipoly = feature['geometry']['coordinates']
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conv_polys = []
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for poly in multipoly:
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for subpoly in poly:
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coords_list = [] # list of lists
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assert(len(subpoly[0]) == 2)
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coords_list += subpoly
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# convert coordinates to numpy array and radians
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coords = np.array(coords_list).T * np.pi / 180
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conv_polys.append(coords)
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simplegeodata[key] = {"name": name, "coordinates": conv_polys}
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ref_lat = REF_LATITUDE * np.pi / 180
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ref_lon = REF_LONGITUDE * np.pi / 180
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R = 500
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"""
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# Override data with test coordinate system
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coords = []
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N = 128
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# constant-latitude circles
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coords.append(np.array([np.linspace(-np.pi, np.pi, N),
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np.ones(N) * np.pi/4]))
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coords.append(np.array([np.linspace(-np.pi, np.pi, N),
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np.ones(N) * 0]))
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coords.append(np.array([np.linspace(-np.pi, np.pi, N),
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np.ones(N) * -np.pi/4]))
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# constant-longitude half-circles
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coords.append(np.array([np.ones(N) * -4*np.pi/4,
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np.linspace(-np.pi/2, np.pi/2, N)]))
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coords.append(np.array([np.ones(N) * -3*np.pi/4,
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np.linspace(-np.pi/2, np.pi/2, N)]))
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coords.append(np.array([np.ones(N) * -2*np.pi/4,
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np.linspace(-np.pi/2, np.pi/2, N)]))
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coords.append(np.array([np.ones(N) * -1*np.pi/4,
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np.linspace(-np.pi/2, np.pi/2, N)]))
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coords.append(np.array([np.ones(N) * 0*np.pi/4,
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np.linspace(-np.pi/2, np.pi/2, N)]))
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coords.append(np.array([np.ones(N) * 1*np.pi/4,
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np.linspace(-np.pi/2, np.pi/2, N)]))
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coords.append(np.array([np.ones(N) * 2*np.pi/4,
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np.linspace(-np.pi/2, np.pi/2, N)]))
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coords.append(np.array([np.ones(N) * 3*np.pi/4,
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np.linspace(-np.pi/2, np.pi/2, N)]))
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simplegeodata = {"XY": {'name': 'test', 'coordinates': coords}}
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"""
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# apply azimuthal equidistant projection
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for k, v in simplegeodata.items():
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proj_polys = []
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for poly in v['coordinates']:
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lat = poly[1, :]
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lon = poly[0, :]
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x, y = map_azimuthal_equidistant(lat, lon, ref_lat, ref_lon, R)
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coords = np.array([x, y])
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# remove any points that contain a NaN coordinate
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coords = coords[:, np.any(np.invert(np.isnan(coords)), axis=0)]
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proj_polys.append(coords)
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v['proj_coordinates'] = proj_polys
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#print(simplegeodata['AW']['proj_coordinates'])
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###print(simplegeodata['DE']['proj_coordinates'])
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#debug plot
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# generate the SVG
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doc = svgwrite.Drawing("/tmp/test.svg", size=(2*R, 2*R))
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doc.defs.add(doc.style("""
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.country {
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stroke: black;
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stroke-width: 0.01;
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}
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"""))
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doc.add(doc.circle(center=(R, R), r=R, fill='#ddeeff',
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stroke_width=1, stroke='black'))
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for k, v in simplegeodata.items():
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print(f"Exporting {k}…")
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color = random_country_color()
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group = doc.g()
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for poly in v['proj_coordinates']:
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points = poly.T + R # shift to the center of the drawing
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pgon = doc.polygon(points, **{
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'class': 'country',
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'fill': color})
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group.add(pgon)
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group.set_desc(title=v['name'])
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doc.add(group)
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# generate equidistant circles
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d_max = 40075/2
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for distance in [500, 1000, 2000, 3000, 4000, 5000, 6000, 8000, 10000, 12000,
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14000, 16000, 18000, 20000]:
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r = R * distance / d_max
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doc.add(doc.circle(center=(R, R), r=r, fill='none',
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stroke_width=0.1, stroke='black'))
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print(f"Saving {doc.filename}…")
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doc.save(pretty=True)
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exit(0)
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# Debug Plot
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for k, v in simplegeodata.items():
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for poly in v['proj_coordinates']:
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pp.plot(poly[0, :], poly[1, :])
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pp.plot([-1, 1], [0, 0], 'k', linewidth=0.5)
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pp.plot([0, 0], [-1, 1], 'k', linewidth=0.5)
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t = np.linspace(-np.pi, np.pi, 256)
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ct, st = np.cos(t), np.sin(t)
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pp.plot(ct, st, 'k', linewidth=0.5)
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U = 40075
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for distance in np.arange(0, U/2, 2000):
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f = distance / (U/2)
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pp.plot(f*ct, f*st, 'k', linewidth=0.2)
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pp.axis('equal')
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pp.show()
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