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betolink/plate_boundaries.py

Created August 9, 2023 16:40
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plate_boundaries.py
from collections import namedtuple
from functools import partial
from shapely.affinity import translate
from shapely.geometry.multipolygon import MultiPolygon
from shapely.geometry.point import Point
from shapely.geometry.polygon import Polygon
from shapely.ops import transform
# For some plates, points that cross the meridian need to be adjusted
# to be negative for coordinate continuity. This allows us to test for
# point inclusion correctly with Shapely.
def _shift_large_longitudes(threshold, x, y):
if x > threshold:
return (x - 360, y)
else:
return (x, y)
ANTA = Polygon([
(359.70, -54.50),
(359.30, -54.80),
(358.90, -54.80),
(358.60, -55.10),
(358.40, -55.30),
(358.40, -55.60),
(355.30, -55.80),
(353.00, -57.80),
(343.80, -58.30),
(343.80, -59.00),
(342.00, -59.10),
(342.00, -59.60),
(341.50, -59.60),
(341.50, -60.20),
(341.10, -60.20),
(341.00, -60.50),
(340.70, -60.50),
(340.60, -60.80),
(340.10, -60.90),
(339.50, -60.90),
(338.80, -61.00),
(338.10, -61.00),
(337.40, -61.00),
(336.70, -61.00),
(336.10, -61.00),
(335.60, -60.90),
(335.10, -60.80),
(334.40, -60.70),
(326.70, -60.00),
(322.50, -61.00),
(312.50, -60.00),
(304.00, -60.70),
(289.20, -55.40),
(288.00, -54.90),
(286.90, -54.40),
(285.70, -53.60),
(284.80, -53.00),
(284.70, -51.90),
(284.60, -50.70),
(284.60, -49.60),
(284.30, -48.80),
(284.10, -47.70),
(284.00, -46.90),
(283.80, -45.80),
(283.80, -45.30),
(283.40, -45.60),
(282.70, -45.60),
(281.50, -45.20),
(280.40, -45.00),
(279.40, -44.90),
(277.70, -44.80),
(277.30, -44.30),
(277.10, -43.30),
(276.90, -41.90),
(276.10, -41.80),
(275.30, -41.70),
(273.60, -41.50),
(271.30, -41.30),
(268.90, -41.20),
(268.30, -40.30),
(268.20, -39.10),
(267.50, -38.80),
(266.30, -38.40),
(266.40, -37.40),
(265.40, -37.20),
(263.70, -37.10),
(263.70, -36.50),
(263.00, -36.40),
(261.60, -36.30),
(259.70, -36.10),
(258.20, -35.90),
(256.30, -35.80),
(254.70, -35.70),
(253.40, -35.50),
(252.30, -35.30),
(251.10, -35.20),
(249.80, -35.20),
(249.10, -35.10),
(248.00, -34.90),
(248.30, -38.40),
(247.80, -40.70),
(247.30, -42.70),
(246.70, -45.20),
(245.90, -48.30),
(245.00, -50.00),
(243.60, -49.60),
(243.00, -50.80),
(241.60, -53.20),
(242.00, -53.30),
(241.00, -54.50),
(239.50, -54.30),
(237.70, -55.80),
(233.60, -54.70),
(233.00, -55.30),
(231.30, -54.90),
(227.60, -54.10),
(227.00, -54.90),
(223.10, -54.00),
(222.40, -55.00),
(222.10, -54.90),
(220.80, -56.20),
(220.10, -56.00),
(219.20, -56.80),
(215.30, -55.70),
(213.40, -57.30),
(212.90, -57.10),
(210.70, -58.70),
(210.90, -58.80),
(209.20, -60.00),
(207.90, -59.60),
(202.50, -63.10),
(198.70, -61.70),
(195.60, -63.30),
(194.10, -62.80),
(189.20, -65.10),
(188.10, -64.70),
(182.30, -66.70),
(174.80, -63.70),
(173.40, -64.10),
(170.50, -62.50),
(168.40, -63.00),
(167.40, -62.60),
(166.40, -62.90),
(165.00, -62.00),
(163.80, -62.30),
(163.20, -61.80),
(155.90, -63.00),
(155.70, -62.90),
(153.80, -60.10),
(150.40, -60.50),
(148.80, -57.50),
(147.50, -57.60),
(147.20, -55.60),
(146.20, -55.70),
(146.10, -54.70),
(145.00, -54.70),
(144.50, -54.30),
(143.90, -54.40),
(143.60, -54.10),
(141.20, -54.20),
(140.80, -52.70),
(140.00, -52.80),
(139.60, -51.90),
(138.60, -52.00),
(137.70, -50.60),
(136.40, -50.80),
(136.00, -50.40),
(134.40, -50.40),
(134.20, -50.30),
(132.30, -50.30),
(132.30, -50.10),
(130.90, -50.10),
(130.80, -50.30),
(127.50, -49.90),
(127.70, -49.00),
(126.90, -48.90),
(126.60, -49.70),
(125.70, -49.60),
(125.80, -49.40),
(125.40, -49.30),
(125.40, -49.20),
(125.00, -49.10),
(124.80, -49.40),
(124.50, -49.40),
(124.50, -49.70),
(123.90, -49.60),
(123.70, -50.00),
(122.70, -49.80),
(122.90, -49.30),
(122.30, -49.20),
(121.70, -50.20),
(118.80, -49.70),
(118.60, -49.90),
(118.10, -49.80),
(118.00, -50.10),
(116.40, -49.80),
(115.90, -50.70),
(114.80, -50.40),
(114.30, -51.20),
(100.40, -47.60),
(99.70, -48.20),
(95.10, -46.30),
(95.50, -46.00),
(88.30, -42.00),
(87.20, -43.00),
(85.30, -41.80),
(83.50, -43.20),
(81.10, -41.20),
(79.60, -42.20),
(75.90, -39.10),
(79.00, -36.30),
(78.30, -35.80),
(79.10, -35.10),
(77.40, -33.70),
(78.10, -32.90),
(74.80, -30.20),
(75.50, -29.50),
(74.10, -28.40),
(74.20, -28.20),
(73.00, -27.40),
(72.80, -27.50),
(71.20, -26.20),
(70.80, -26.30),
(70.05, -25.51),
(70.05, -25.51),
(69.30, -25.80),
(68.90, -26.00),
(67.80, -26.60),
(66.10, -27.70),
(63.20, -28.10),
(61.00, -29.00),
(60.80, -30.70),
(59.00, -30.70),
(58.30, -31.80),
(57.00, -31.90),
(57.00, -33.80),
(55.10, -34.70),
(54.20, -34.70),
(54.10, -35.40),
(53.40, -35.30),
(53.30, -36.10),
(52.30, -36.10),
(52.20, -37.50),
(49.00, -37.80),
(46.20, -38.80),
(46.00, -40.10),
(42.70, -40.90),
(42.40, -42.80),
(39.30, -43.70),
(35.70, -44.80),
(34.80, -47.20),
(32.50, -47.00),
(30.50, -49.80),
(29.50, -50.20),
(27.80, -52.80),
(26.20, -52.50),
(25.50, -53.80),
(22.50, -53.00),
(20.00, -52.80),
(18.50, -52.70),
(15.90, -51.70),
(15.10, -52.20),
(13.90, -51.80),
(12.70, -52.80),
(11.50, -52.20),
(8.30, -54.00),
(7.60, -53.60),
(5.00, -54.80),
(4.00, -54.20),
(3.00, -53.60),
(0.80, -54.90),
(0.00, -54.90), # Synthetic
(0.00, -90.00), # Synthetic
(360.00, -90.00), # Synthetic
(360.00, -54.50), # Synthetic
(359.70, -54.50), # Synthetic
])
ANTA_FALSE_EASTING = 0.0
ARAB = Polygon([
(65.80, 25.20),
(65.00, 25.20),
(64.00, 25.20),
(63.00, 25.00),
(62.00, 24.90),
(61.00, 25.00),
(60.00, 25.20),
(59.80, 25.20),
(59.00, 25.40),
(58.70, 25.40),
(58.10, 25.60),
(57.00, 25.80),
(56.00, 26.00),
(55.10, 26.20),
(54.30, 26.30),
(53.30, 26.50),
(52.40, 27.30),
(51.80, 28.00),
(51.20, 28.70),
(50.40, 29.70),
(49.80, 30.40),
(49.30, 31.30),
(48.70, 32.10),
(48.10, 32.80),
(47.70, 33.50),
(47.10, 34.10),
(46.40, 35.00),
(45.90, 35.50),
(45.40, 36.00),
(44.90, 36.40),
(44.30, 37.20),
(44.00, 37.60),
(43.60, 38.00),
(43.60, 38.00),
(43.00, 38.30),
(42.30, 38.90),
(41.60, 39.20),
(40.70, 39.20),
(40.10, 39.00),
(39.50, 38.70),
(39.00, 38.40),
(38.50, 38.20),
(37.90, 37.90),
(37.40, 37.50),
(36.80, 37.20),
(36.30, 36.70),
(35.90, 36.40),
(35.60, 35.90),
(35.80, 35.70),
(35.70, 34.90),
(35.50, 34.00),
(35.20, 32.90),
(35.00, 32.10),
(35.00, 31.30),
(34.90, 30.80),
(34.90, 30.00),
(34.90, 29.50),
(34.70, 29.00),
(34.50, 28.40),
(34.20, 27.90),
(34.20, 27.10),
(34.60, 26.60),
(35.30, 25.30),
(36.20, 23.80),
(37.20, 22.00),
(38.50, 19.80),
(39.40, 18.10),
(40.50, 16.00),
(41.60, 14.30),
(42.50, 13.20),
(43.50, 12.20),
(43.70, 11.90),
(46.30, 12.20),
(46.50, 12.50),
(48.20, 12.60),
(48.80, 13.00),
(49.80, 13.10),
(50.20, 13.40),
(51.20, 13.10),
(52.20, 14.60),
(52.80, 14.40),
(53.00, 14.60),
(53.80, 14.30),
(54.50, 15.20),
(56.10, 14.30),
(56.30, 14.50),
(57.50, 13.50),
(58.30, 12.70),
(58.60, 13.00),
(58.90, 13.50),
(59.10, 14.00),
(59.50, 14.50),
(59.80, 15.00),
(59.90, 15.50),
(60.00, 16.00),
(60.00, 16.50),
(60.10, 17.00),
(60.20, 17.50),
(60.30, 18.00),
(60.45, 18.50),
(60.80, 19.00),
(61.20, 20.00),
(61.60, 20.50),
(62.00, 21.00),
(62.25, 21.50),
(62.50, 22.00),
(63.00, 22.50),
(63.50, 23.00),
(63.80, 23.20),
(64.80, 23.30),
(65.20, 24.00),
(65.70, 25.00),
(65.80, 25.20),
])
ARAB_FALSE_EASTING = 0.0
AUST = Polygon([
(91.80, 9.30),
(90.00, 5.00),
(89.50, 0.00),
(89.00, -5.00),
(80.00, -5.00),
(68.73, -4.51),
(68.36, -4.90),
(68.60, -5.10),
(68.78, -5.24),
(67.98, -6.08),
(68.50, -6.56),
(67.92, -7.12),
(68.25, -7.40),
(67.86, -7.75),
(68.28, -8.20),
(66.54, -9.64),
(66.84, -9.88),
(66.28, -10.40),
(66.79, -10.78),
(66.20, -11.30),
(66.32, -11.56),
(65.75, -12.00),
(65.56, -12.14),
(66.44, -12.88),
(66.49, -13.12),
(66.70, -13.34),
(65.86, -13.99),
(66.38, -14.80),
(66.72, -14.90),
(67.24, -15.44),
(67.08, -15.68),
(67.38, -16.00),
(66.47, -16.61),
(66.82, -16.96),
(65.02, -17.98),
(66.23, -20.25),
(66.82, -19.95),
(67.24, -20.38),
(67.64, -20.13),
(67.91, -20.64),
(68.40, -20.46),
(69.22, -22.13),
(69.08, -22.20),
(69.35, -22.69),
(69.26, -22.74),
(69.35, -22.91),
(69.26, -22.96),
(69.35, -23.13),
(69.19, -23.25),
(69.74, -24.22),
(69.61, -24.30),
(69.81, -24.62),
(69.77, -24.65),
(70.07, -25.19),
(69.95, -25.32),
(70.05, -25.51),
(70.80, -26.30),
(71.20, -26.20),
(72.80, -27.50),
(73.00, -27.40),
(74.20, -28.20),
(74.10, -28.40),
(75.50, -29.50),
(74.80, -30.20),
(78.10, -32.90),
(77.40, -33.70),
(79.10, -35.10),
(78.30, -35.80),
(79.00, -36.30),
(75.90, -39.10),
(79.60, -42.20),
(81.10, -41.20),
(83.50, -43.20),
(85.30, -41.80),
(87.20, -43.00),
(88.30, -42.00),
(95.50, -46.00),
(95.10, -46.30),
(99.70, -48.20),
(100.40, -47.60),
(114.30, -51.20),
(114.80, -50.40),
(115.90, -50.70),
(116.40, -49.80),
(118.00, -50.10),
(118.10, -49.80),
(118.60, -49.90),
(118.80, -49.70),
(121.70, -50.20),
(122.30, -49.20),
(122.90, -49.30),
(122.70, -49.80),
(123.70, -50.00),
(123.90, -49.60),
(124.50, -49.70),
(124.50, -49.40),
(124.80, -49.40),
(125.00, -49.10),
(125.40, -49.20),
(125.40, -49.30),
(125.80, -49.40),
(125.70, -49.60),
(126.60, -49.70),
(126.90, -48.90),
(127.70, -49.00),
(127.50, -49.90),
(130.80, -50.30),
(130.90, -50.10),
(132.30, -50.10),
(132.30, -50.30),
(134.20, -50.30),
(134.40, -50.40),
(136.00, -50.40),
(136.40, -50.80),
(137.70, -50.60),
(138.60, -52.00),
(139.60, -51.90),
(140.00, -52.80),
(140.80, -52.70),
(141.20, -54.20),
(143.60, -54.10),
(143.90, -54.40),
(144.50, -54.30),
(145.00, -54.70),
(146.10, -54.70),
(146.20, -55.70),
(147.20, -55.60),
(147.50, -57.60),
(148.80, -57.50),
(150.40, -60.50),
(153.80, -60.10),
(155.70, -62.90),
(155.90, -63.00),
(161.20, -61.80),
(160.90, -61.60),
(158.80, -59.70),
(158.80, -59.60),
(158.60, -59.50),
(158.50, -59.40),
(158.50, -59.30),
(158.40, -59.20),
(158.30, -59.10),
(158.20, -59.00),
(158.10, -58.90),
(158.00, -58.80),
(157.90, -58.80),
(157.80, -58.70),
(157.80, -58.60),
(157.70, -58.50),
(157.60, -58.40),
(157.50, -58.30),
(157.30, -58.10),
(157.30, -57.90),
(157.30, -57.80),
(157.40, -57.70),
(157.40, -57.50),
(157.50, -57.40),
(157.60, -57.30),
(157.70, -57.20),
(157.70, -57.10),
(157.70, -56.90),
(157.90, -56.80),
(158.00, -56.60),
(158.10, -56.50),
(158.30, -56.40),
(158.40, -56.10),
(158.60, -55.90),
(158.50, -55.80),
(158.60, -55.60),
(158.70, -55.40),
(158.70, -55.30),
(158.80, -55.10),
(158.90, -54.90),
(159.10, -54.70),
(159.20, -54.40),
(159.30, -54.20),
(159.40, -54.10),
(159.50, -53.90),
(159.70, -53.70),
(159.90, -53.50),
(160.60, -52.70),
(161.10, -52.10),
(162.40, -50.90),
(163.30, -50.30),
(164.10, -49.70),
(164.30, -49.10),
(164.70, -48.60),
(165.30, -47.90),
(165.90, -47.00),
(166.50, -46.00),
(167.30, -45.20),
(167.80, -44.70),
(169.00, -44.10),
(169.90, -43.70),
(171.00, -43.20),
(172.20, -42.60),
(173.30, -42.00),
(174.40, -41.70),
(175.30, -41.60),
(176.40, -41.10),
(176.90, -40.70),
(178.20, -39.40),
(178.80, -38.70),
(179.60, -37.40),
(180.80, -35.80),
(181.50, -34.40),
(182.00, -33.30),
(182.90, -31.70),
(183.10, -31.10),
(183.80, -29.80),
(184.30, -28.60),
(184.90, -27.20),
(185.50, -25.40),
(186.20, -23.60),
(186.50, -22.60),
(187.10, -21.40),
(187.90, -19.70),
(188.60, -18.10),
(188.90, -16.80),
(188.90, -15.40),
(188.40, -14.50),
(188.40, -14.50),
(187.20, -14.50),
(186.10, -14.50),
(185.10, -14.50),
(183.80, -14.60),
(182.30, -14.70),
(181.10, -15.10),
(180.70, -15.20),
(179.70, -15.50),
(178.30, -16.10),
(177.20, -17.20),
(176.20, -18.30),
(175.60, -19.30),
(174.30, -21.10),
(173.50, -22.00),
(172.50, -22.90),
(171.50, -23.70),
(170.60, -23.70),
(169.90, -23.10),
(169.10, -22.10),
(168.40, -21.00),
(168.00, -20.00),
(167.40, -19.00),
(166.90, -17.80),
(166.40, -16.80),
(165.80, -15.00),
(165.40, -13.60),
(164.80, -12.20),
(163.80, -11.80),
(162.90, -11.70),
(161.70, -11.60),
(160.50, -11.00),
(159.50, -10.40),
(158.50, -10.00),
(157.70, -9.60),
(156.60, -8.90),
(155.30, -8.00),
(154.20, -7.20),
(153.20, -6.70),
(152.40, -6.70),
(151.30, -6.90),
(150.50, -7.10),
(149.70, -7.30),
(148.60, -7.50),
(147.50, -7.30),
(147.00, -6.70),
(146.60, -5.90),
(146.10, -5.20),
(145.50, -4.40),
(144.60, -3.60),
(143.80, -3.00),
(142.70, -2.30),
(141.40, -1.90),
(140.10, -1.70),
(139.10, -1.60),
(137.80, -1.40),
(136.80, -1.40),
(136.50, -1.20),
(136.20, -1.00),
(135.90, -0.70),
(135.70, -0.50),
(135.40, -0.20),
(135.10, 0.00),
(134.30, 0.20),
(133.40, 0.50),
(132.60, 0.70),
(131.80, 1.00),
(130.90, 1.10),
(130.70, -0.30),
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AUST_FALSE_EASTING = 0.0
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EURA = transform(partial(_shift_large_longitudes, 270.0), EURA)
EURA_FALSE_EASTING = abs(min(EURA.exterior.xy[0]))
EURA = translate(EURA, xoff=EURA_FALSE_EASTING)
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INDI_FALSE_EASTING = 0.0
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(246.40, 30.10),
(246.10, 30.50),
(245.80, 30.80),
(245.70, 31.00),
(245.60, 31.30),
(245.50, 31.60),
(245.30, 31.80),
(245.30, 31.90),
(245.10, 32.10),
(244.90, 32.30),
(244.80, 32.50),
(244.50, 32.90),
(244.40, 33.30),
(243.45, 34.00),
(242.70, 34.15),
(241.50, 34.70),
(240.65, 34.90),
(239.95, 35.50),
(239.45, 36.00),
(238.90, 36.50),
(238.30, 37.00),
(237.70, 37.50),
(237.25, 38.00),
(236.80, 38.50),
(236.30, 39.00),
(236.10, 39.50),
(236.00, 40.00),
(235.60, 40.20),
(235.00, 40.40),
(232.50, 40.40),
(232.60, 41.60),
(232.80, 41.60),
(233.10, 42.30),
(233.50, 43.00),
(232.20, 43.50),
(230.80, 44.00),
(229.50, 44.50),
(229.90, 45.50),
(230.40, 46.50),
(230.80, 47.50),
(231.20, 48.20),
(231.00, 48.50),
(229.50, 49.20),
(229.60, 49.50),
(230.30, 50.10),
(230.80, 50.50),
(230.10, 51.00),
(229.30, 51.50),
(228.70, 52.00),
(227.20, 53.00),
(226.10, 54.00),
(225.50, 55.00),
(224.50, 56.00),
(223.90, 57.00),
(223.50, 58.00),
(223.00, 58.50),
(222.00, 59.00),
(221.00, 59.50),
(220.00, 59.90),
(219.00, 60.10),
(218.00, 59.80),
(217.00, 59.40),
(216.00, 59.30),
(215.00, 59.20),
(214.00, 59.00),
(213.00, 58.30),
(212.00, 57.80),
(210.00, 56.80),
(208.00, 56.00),
(206.00, 55.20),
(204.00, 54.70),
(202.00, 54.00),
(200.00, 53.70),
(198.00, 53.20),
(196.00, 52.80),
(194.00, 52.20),
(192.00, 51.70),
(190.00, 51.30),
(188.00, 51.00),
(186.00, 50.80),
(184.00, 50.50),
(182.00, 50.40),
(180.00, 50.50),
(178.00, 50.50),
(176.00, 50.80),
(174.00, 51.30),
(172.00, 51.90),
(170.00, 52.60),
(168.00, 53.60),
(166.00, 54.30),
(164.00, 55.50),
(163.00, 54.00),
(162.20, 53.00),
(161.00, 52.00),
(160.50, 51.00),
(159.50, 50.00),
(157.80, 49.00),
(156.20, 48.00),
(155.00, 47.00),
(153.80, 46.00),
(152.00, 45.00),
(150.00, 44.00),
(148.50, 43.00),
(147.00, 42.00),
(144.80, 41.00),
])
PCFC_FALSE_EASTING = 0.0
SOAM = Polygon([
(359.30, -54.80),
(359.00, -54.60),
(358.50, -54.00),
(357.60, -54.30),
(357.00, -53.50),
(355.20, -52.20),
(353.00, -50.10),
(352.00, -49.30),
(350.20, -48.20),
(349.30, -46.90),
(346.70, -47.40),
(346.20, -46.20),
(344.80, -45.20),
(344.00, -44.10),
(343.50, -41.70),
(343.20, -40.30),
(344.10, -40.10),
(344.00, -39.10),
(342.50, -37.30),
(342.20, -35.50),
(344.70, -35.30),
(344.80, -34.30),
(345.40, -33.90),
(345.50, -33.00),
(345.50, -32.50),
(346.70, -32.30),
(346.60, -31.70),
(346.60, -31.10),
(346.30, -29.20),
(347.10, -29.00),
(346.80, -27.30),
(346.20, -26.90),
(346.30, -25.10),
(346.60, -24.70),
(346.50, -23.10),
(347.50, -22.60),
(348.40, -21.10),
(348.00, -19.20),
(347.00, -17.80),
(345.90, -17.90),
(346.00, -17.00),
(345.70, -16.20),
(346.80, -16.10),
(346.50, -15.00),
(346.40, -14.00),
(345.70, -14.10),
(345.50, -13.50),
(345.10, -11.90),
(347.10, -11.50),
(347.00, -10.50),
(346.50, -8.00),
(346.60, -7.20),
(348.50, -6.80),
(347.20, -0.90),
(344.20, -1.40),
(343.70, 0.10),
(335.40, -1.20),
(335.00, 0.50),
(333.80, 0.60),
(333.50, 0.90),
(329.60, 0.90),
(328.60, 3.90),
(327.40, 4.00),
(327.40, 5.20),
(326.00, 7.20),
(322.60, 7.60),
(322.20, 8.10),
(320.80, 8.10),
(320.60, 8.70),
(319.80, 8.80),
(319.30, 9.50),
(319.10, 10.70),
(316.30, 10.80),
(316.30, 12.10),
(315.90, 12.10),
(315.90, 12.60),
(315.20, 12.60),
(315.20, 13.50),
(315.00, 13.70),
(315.00, 15.30),
(313.30, 15.40),
(313.40, 16.80),
(313.60, 18.50),
(313.90, 19.00),
(314.20, 19.80),
(314.30, 20.30),
(314.30, 20.30),
(299.00, 18.00),
(299.70, 17.40),
(300.20, 16.90),
(300.60, 16.30),
(300.70, 15.60),
(300.70, 14.90),
(300.60, 14.50),
(300.40, 14.10),
(300.30, 13.60),
(300.10, 13.10),
(298.30, 10.40),
(297.40, 10.40),
(296.00, 10.40),
(294.80, 10.40),
(293.40, 10.50),
(291.90, 10.40),
(290.70, 10.30),
(289.90, 9.90),
(289.30, 9.20),
(288.60, 8.40),
(288.00, 7.70),
(287.30, 7.00),
(286.50, 6.20),
(285.60, 5.50),
(284.60, 4.90),
(283.60, 4.30),
(282.50, 3.80),
(281.90, 3.60),
(281.70, 2.60),
(281.20, 2.20),
(280.90, 1.90),
(280.20, 1.00),
(279.70, 0.30),
(279.10, -1.20),
(279.00, -2.50),
(278.70, -4.10),
(278.70, -5.40),
(278.70, -6.50),
(279.00, -7.80),
(279.40, -9.40),
(279.90, -10.50),
(280.50, -11.70),
(281.30, -12.80),
(282.10, -13.90),
(283.20, -15.10),
(284.20, -15.90),
(285.00, -16.60),
(286.00, -17.40),
(286.90, -18.10),
(287.80, -19.50),
(288.40, -20.40),
(288.80, -21.20),
(289.20, -22.20),
(289.40, -23.40),
(289.20, -24.90),
(288.80, -26.60),
(288.40, -28.50),
(287.90, -30.20),
(287.40, -32.00),
(287.30, -33.70),
(286.90, -35.10),
(286.50, -36.40),
(285.70, -37.60),
(284.90, -38.90),
(284.40, -40.50),
(284.30, -41.80),
(284.10, -43.00),
(283.80, -43.90),
(283.80, -44.80),
(283.80, -45.30),
(283.80, -45.80),
(284.00, -46.90),
(284.10, -47.70),
(284.30, -48.80),
(284.60, -49.60),
(284.60, -50.70),
(284.70, -51.90),
(284.80, -53.00),
(285.70, -53.60),
(286.90, -54.40),
(288.00, -54.90),
(289.20, -55.40),
(291.00, -55.60),
(293.10, -55.60),
(294.30, -55.50),
(295.20, -55.60),
(296.60, -55.70),
(297.70, -55.80),
(299.90, -56.00),
(300.00, -56.00),
(302.50, -55.90),
(305.00, -55.70),
(307.50, -55.60),
(310.00, -55.50),
(311.80, -55.40),
(313.50, -55.20),
(315.30, -55.10),
(317.00, -55.00),
(317.80, -55.00),
(318.50, -54.90),
(319.30, -54.80),
(320.00, -54.80),
(322.50, -54.80),
(325.00, -54.80),
(327.50, -54.80),
(330.00, -54.80),
(330.70, -54.80),
(331.50, -54.90),
(333.00, -55.00),
(333.50, -55.20),
(334.00, -55.40),
(334.50, -55.50),
(335.00, -55.70),
(335.20, -55.80),
(335.50, -55.90),
(335.80, -56.00),
(336.00, -56.10),
(336.30, -56.60),
(336.60, -57.00),
(336.90, -57.50),
(337.20, -58.00),
(336.90, -58.40),
(336.60, -58.80),
(336.30, -59.20),
(336.00, -59.60),
(335.10, -60.70),
(334.40, -60.70),
(335.10, -60.80),
(335.60, -60.90),
(336.10, -61.00),
(336.70, -61.00),
(337.40, -61.00),
(338.10, -61.00),
(338.80, -61.00),
(339.50, -60.90),
(340.10, -60.90),
(340.60, -60.80),
(340.70, -60.50),
(341.00, -60.50),
(341.10, -60.20),
(341.50, -60.20),
(341.50, -59.60),
(342.00, -59.60),
(342.00, -59.10),
(343.80, -59.00),
(343.80, -58.30),
(353.00, -57.80),
(355.30, -55.80),
(358.40, -55.60),
(358.40, -55.30),
(358.60, -55.10),
(358.90, -54.80),
(359.30, -54.80),
])
SOAM_FALSE_EASTING = 0.0
def shift_lon(x, y):
if x < 0.0:
return (360.0 + x, y)
else:
return (x, y)
Plate = namedtuple('Plate', 'polygon, false_easting name')
PLATES = [
Plate(NOAM, NOAM_FALSE_EASTING, 'NOAM'),
Plate(EURA, EURA_FALSE_EASTING, 'EURA'),
Plate(ANTA, ANTA_FALSE_EASTING, 'ANTA'),
Plate(SOAM, SOAM_FALSE_EASTING, 'SOAM'),
Plate(PCFC, PCFC_FALSE_EASTING, 'PCFC'),
Plate(ARAB, ARAB_FALSE_EASTING, 'ARAB'),
Plate(AUST, AUST_FALSE_EASTING, 'AUST'),
Plate(INDI, INDI_FALSE_EASTING, 'INDI'),
Plate(NAZC, NAZC_FALSE_EASTING, 'NAZC'),
]
def plate_name(point):
"""Determine the Tectonic plate name based on the lon/lat of the given
point. The point's longitude (x) should be in the range [-180, 180],
and latitude (y) in the range [-90, 90].
"""
x, y = point.coords.xy
for polygon, fe, name in PLATES:
p = Point(*shift_lon(x[0] + fe, y[0]))
if polygon.intersects(p):
return name
Raw
transform.py
import collections
import datetime as dt
import logging
import os
import time
import arrow
import pandas as pd
from pyproj import Transformer
from shapely.geometry.point import Point
from valkyrie.itrf.plate_boundaries import plate_name
DF_CHUNKSIZE = 250000
GranuleItrfRange = collections.namedtuple('GranuleItrfRange', 'start end itrf')
ATM1B_GRANULE_ITRFS = [
GranuleItrfRange(arrow.Arrow(1993, 6, 23), arrow.Arrow(1996, 6, 5), "ITRF93"),
GranuleItrfRange(arrow.Arrow(1997, 4, 25), arrow.Arrow(1997, 5, 28), "ITRF94"),
GranuleItrfRange(arrow.Arrow(1998, 6, 27), arrow.Arrow(1999, 5, 25), "ITRF96"),
GranuleItrfRange(arrow.Arrow(2000, 5, 12), arrow.Arrow(2001, 5, 27), "ITRF97"),
GranuleItrfRange(arrow.Arrow(2001, 12, 18), arrow.Arrow(2002, 11, 21), "ITRF2000"),
GranuleItrfRange(arrow.Arrow(2002, 11, 22), arrow.Arrow(2002, 11, 22), "ITRF97"),
GranuleItrfRange(arrow.Arrow(2002, 11, 23), arrow.Arrow(2002, 12, 13), "ITRF2000"),
GranuleItrfRange(arrow.Arrow(2002, 12, 14), arrow.Arrow(2002, 12, 14), "ITRF97"),
GranuleItrfRange(arrow.Arrow(2002, 12, 15), arrow.Arrow(2007, 5, 11), "ITRF2000"),
GranuleItrfRange(arrow.Arrow(2007, 9, 10), arrow.Arrow(2011, 5, 16), "ITRF2005"),
GranuleItrfRange(arrow.Arrow(2011, 10, 12), arrow.Arrow(2018, 5, 1), "ITRF2008"),
]
def convert_itrf(order_id, dataset_name, csv_filename, target_itrf=None, target_epoch=None):
target_csv_filename = csv_filename
root, ext = os.path.splitext(csv_filename)
src_csv_filename = f"{root}.src{ext}"
os.rename(csv_filename, src_csv_filename)
df_reader = _df_reader_from_csv(src_csv_filename, DF_CHUNKSIZE)
for chunk in df_reader:
chunk = _rename_columns(chunk)
if target_epoch is not None:
groups = _itrf_plate_groups(chunk)
else:
groups = _itrf_groups(chunk)
logging.info(f"Number of groups: {len(groups)}")
for grouping, part in groups:
if target_epoch is not None:
source_itrf, plate = grouping
logging.info(f"Converting part of size {len(part)} "
f"from {source_itrf} to {target_itrf} "
f"with plate name {plate} "
f"and epoch {target_epoch}")
lons, lats, elevs, _ = _convert_df(part, source_itrf, target_itrf, plate, target_epoch)
else:
source_itrf = grouping
logging.info(f"Converting part of size {len(part)} "
f"from {source_itrf} to {target_itrf}")
lons, lats, elevs, _ = _convert_df(part, source_itrf, target_itrf)
new_part = part.copy()
new_part["latitude"] = lats
new_part["longitude"] = lons
new_part["elevation"] = elevs
del new_part["itrf"]
if "plate" in new_part.columns:
del new_part["plate"]
new_part.to_csv(csv_filename, mode="a", header=False, index=False)
# Cleanup
# os.remove(src_csv_filename)
return target_csv_filename
def _df_reader_from_csv(csv_filepath, df_chunksize):
"""Copies data from the database to file, and returns a reader to a pandas
dataframe of the data.
"""
return pd.read_csv(
csv_filepath,
parse_dates=[0],
header=None,
chunksize=df_chunksize,
)
def _rename_columns(df):
"""Renames the first four columns to useful names since we only use
these and don't need the others to be named. This also allows us to
work with any of the data products since all have the same first four
columns."""
return df.rename(columns={
0: 'utc_datetime',
1: 'longitude',
2: 'latitude',
3: 'elevation'
})
def _itrf_plate_groups(chunk):
logging.info("Adding source ITRF")
chunk['itrf'] = chunk['utc_datetime'].apply(_granule_itrf)
logging.info("Adding source plate name")
chunk['plate'] = chunk[['longitude', 'latitude']].apply(lambda lonlat: plate_name(Point(*lonlat)), axis=1)
logging.info("Grouping by source ITRF and plate")
return chunk.groupby(['itrf', 'plate'], sort=False)
def _itrf_groups(chunk):
logging.info("Adding source ITRF")
chunk['itrf'] = chunk['utc_datetime'].apply(_granule_itrf)
logging.info("Grouping by source ITRF")
return chunk.groupby('itrf', sort=False)
def _granule_itrf(granule_datetime):
def _find(gdt, i, lower, upper):
# Binary search for the correct ITRF to make this fast. If we
# do a naive linear search, it can run up to 10x longer.
g = ATM1B_GRANULE_ITRFS[i]
if gdt < g.start:
if i > lower:
return _find(gdt, (i - lower) // 2, lower, i)
else:
return None
elif gdt <= g.end:
return g.itrf
else:
if i < (upper - 1):
return _find(gdt, (upper + i) // 2, i, upper)
else:
return None
gdt = arrow.get(granule_datetime)
lower, upper = (0, len(ATM1B_GRANULE_ITRFS))
i = (upper - lower) // 2
return _find(gdt, i, lower, upper)
def _convert_df(chunk, source_itrf, target_itrf, plate=None, target_epoch=None):
"""Converts a chunk of a dataframe from a source ITRF to a target ITRF"""
decimalyears = chunk.utc_datetime.apply(_datetime_to_decimal_year).to_numpy()
# TODO: Should we create a new decimalyears when doing an epoch
# propagation since PROJ doesn't do this?
return _transformer(source_itrf, target_itrf, plate, target_epoch).transform(
chunk.longitude.to_numpy(),
chunk.latitude.to_numpy(),
chunk.elevation.to_numpy(),
decimalyears,
)
def _datetime_to_decimal_year(date):
"""Stolen from
https://stackoverflow.com/questions/6451655/python-how-to-convert-datetime-dates-to-decimal-years
"""
def sinceEpoch(date):
# returns seconds since epoch
return time.mktime(date.timetuple())
s = sinceEpoch
year = date.year
startOfThisYear = dt.datetime(year=year, month=1, day=1)
startOfNextYear = dt.datetime(year=year + 1, month=1, day=1)
yearElapsed = s(date) - s(startOfThisYear)
yearDuration = s(startOfNextYear) - s(startOfThisYear)
fraction = yearElapsed / yearDuration
return date.year + fraction
def _transformer(source_itrf, target_itrf, plate=None, target_epoch=None):
"""Pipeline string for proj to transform from the source to the target
ITRF frame and, optionally, epoch.
"""
plate_model_step = ""
if plate and target_epoch:
plate_model_step = (
f"+step +inv +init={target_itrf}:{plate} +t_epoch={target_epoch} "
)
pipeline = (
f"+proj=pipeline +ellps=WGS84 "
f"+step +proj=unitconvert +xy_in=deg +xy_out=rad "
f"+step +proj=latlon "
f"+step +proj=cart "
f"+step +inv +init={target_itrf}:{source_itrf} "
f"{plate_model_step}"
f"+step +inv +proj=cart "
f"+step +proj=unitconvert +xy_in=rad +xy_out=deg"
)
return Transformer.from_pipeline(pipeline)
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