def test120degE(self):
rectGridBld = grdBld(minX=1,
minY=10,
dx=1,
dy=1,
nx=3,
ny=3,
minXYIsCentroid=False)
hrmtx = self.getMockHiResAlphaMtxAndCstCellDet()
coastalCellDetector = self.getMockHiResAlphaMtxAndCstCellDet()
grd = rectGridBld.buildGrid(hrmtx, coastalCellDetector)
cell = grd.cells[4] # the central cell
neighTotPoly = grd.cells[0]
for p in grd.cells[1:]:
if p != cell:
neighTotPoly = neighTotPoly.union(p)
theta = np.pi * 4. / 6.
upl = upe.getUpstreamPoly(cell, neighTotPoly, theta)
uplcrds = upl.boundary.coords[:]
expuplcrds = [(2.577350269189625, 10.0), (2.0, 11.0), (3.0, 11.0),\
(3.0, 12.0), (4.0, 10.26794919243112), (4.0, 10.0), (3.0, 10.0),\
(2.577350269189625, 10.0)]
self.assertEqual(len(expuplcrds), len(uplcrds))
for ecrd in expuplcrds:
self._findClosePoint(ecrd, uplcrds)
def testLocal(self):
abSingleCellAlphaEstimator.defaultKShape = 1
abSingleCellBetaEstimator.defaultKShape = 1
abClEstMod.defaultShadowAlphaAlleviationParam = 1
rectGridBld = grdBld(minX=1,
minY=10,
dx=1,
dy=1,
nx=3,
ny=2,
minXYIsCentroid=False)
hiresxs = np.arange(0, 4, .1)
hiresys = np.arange(9, 13, .1)
alphas = np.ones([len(hiresys), len(hiresxs)])
xobstr = np.array([25])
yobstr = np.arange(0, len(hiresys), 2)
xyobstr = np.meshgrid(xobstr, yobstr)
alphas[xyobstr[1], xyobstr[0]] = 0
hrmtx = hiResAMtx(hiresxs, hiresys, alphas)
coastalCellDetector = self.getMockHiResAlphaMtxAndCstCellDet()
grd = rectGridBld.buildGrid(hrmtx, coastalCellDetector)
dirs = [0., np.pi / 2., np.pi, 3. * np.pi / 2.]
freqs = [.1, .2, .3]
cellEst = abCellsEstimator(grd, hrmtx, dirs, freqs, None)
cellEstP = abCellsEstimatorParallel(cellEst, 4, None)
loccrd, locgeocrd, localpha, locbeta, sizes = cellEstP.computeLocalAlphaBeta(
)
self.assertEqual(2, len(loccrd))
self.assertTrue((np.array([1, 0]) == loccrd[0]).all())
self.assertTrue((np.array([1, 1]) == loccrd[1]).all())
self.assertEqual(2, len(locgeocrd))
self.assertEqual([(2.5, 10.5), (2.5, 11.5)], locgeocrd)
self.assertEqual(2, len(localpha))
a0 = localpha[0]
a00 = a0[0]
a01 = a0[-1]
self.assertTrue((a00 == a01).all())
self.assertTrue(np.allclose(a00, np.array([.5, .9, .5, .9])))
self.assertTrue(np.allclose(localpha[0], localpha[1]))
self.assertEqual(2, len(locbeta))
b0 = locbeta[0]
b00 = b0[0]
b01 = b0[-1]
self.assertTrue((b00 == b01).all())
self.assertTrue(
np.allclose(b00, np.array([.75, .9, .7, .91]), rtol=.05))
self.assertTrue(np.allclose(locbeta[0], locbeta[1]))
def testShadow(self):
abSingleCellAlphaEstimator.defaultKShape = 1
abSingleCellBetaEstimator.defaultKShape = 1
abClEstMod.defaultShadowAlphaAlleviationParam = 1
rectGridBld = grdBld(minX = 1, minY = 10, dx = 1, dy = 1, nx = 3, ny = 2, minXYIsCentroid = False)
hiResAlphaMtx = self.getMockHiResAlphaMtxAndCstCellDet()
coastalCellDetector = self.getMockHiResAlphaMtxAndCstCellDet()
grd = rectGridBld.buildGrid(hiResAlphaMtx, coastalCellDetector)
hiresxs = np.arange(0, 4, .1)
hiresys = np.arange(9, 13, .1)
alphas = np.ones([len(hiresys), len(hiresxs)])
xobstr = np.array([25])
yobstr = np.arange(0, len(hiresys), 2)
xyobstr = np.meshgrid(xobstr, yobstr)
alphas[xyobstr[1], xyobstr[0]] = 0
hrmtx = hiResAMtx(hiresxs, hiresys, alphas)
dirs = [0., np.pi/2., np.pi, 3.*np.pi/2.]
freqs = [.1, .2, .3]
cellEst = abCellsEstimator(grd, hrmtx, dirs, freqs, abOptions(shadRecalibFactor=1))
cellEst.computeLocalAlphaBeta()
shdcrd, shdgeocrd, shdalpha, shdbeta, sizes = cellEst.computeShadowAlphaBeta()
self.assertEqual(6, len(shdcrd))
self.assertTrue((np.array([0, 0]) == shdcrd[0]).all())
self.assertTrue((np.array([0, 1]) == shdcrd[1]).all())
self.assertTrue((np.array([1, 0]) == shdcrd[2]).all())
self.assertTrue((np.array([1, 1]) == shdcrd[3]).all())
self.assertTrue((np.array([2, 0]) == shdcrd[4]).all())
self.assertTrue((np.array([2, 1]) == shdcrd[5]).all())
self.assertEqual(6, len(shdgeocrd))
self.assertEqual([(1.5, 10.5), (1.5, 11.5), (2.5, 10.5), (2.5, 11.5), (3.5, 10.5), (3.5, 11.5)], shdgeocrd)
expshdalpha = np.array(\
[np.array([[ 1. , 1. , 0.5, 1. ],\
[ 1. , 1. , 0.5, 1. ],\
[ 1. , 1. , 0.5, 1. ]]),\
np.array([[ 1. , 1. , 0.5, 1. ],\
[ 1. , 1. , 0.5, 1. ],\
[ 1. , 1. , 0.5, 1. ]]),\
np.array([[ 1. , 1. , 1. , 0.9],\
[ 1. , 1. , 1. , 0.9],\
[ 1. , 1. , 1. , 0.9]]),\
np.array([[ 1. , 0.9, 1. , 1. ],\
[ 1. , 0.9, 1. , 1. ],\
[ 1. , 0.9, 1. , 1. ]]),\
np.array([[ 0.5, 1. , 1. , 1. ],\
[ 0.5, 1. , 1. , 1. ],\
[ 0.5, 1. , 1. , 1. ]]),\
np.array([[ 0.5, 1. , 1. , 1. ],\
[ 0.5, 1. , 1. , 1. ],\
[ 0.5, 1. , 1. , 1. ]])])
self.assertTrue(np.allclose(expshdalpha, np.array(shdalpha)))
expshdbeta = np.array(\
[np.array([[ 1. , 1. , 0.7, 1. ],
[ 1. , 1. , 0.7, 1. ],\
[ 1. , 1. , 0.7, 1. ]]),\
np.array([[ 1. , 1. , 0.7, 1. ],\
[ 1. , 1. , 0.7, 1. ],\
[ 1. , 1. , 0.7, 1. ]]),\
np.array([[ 1. , 1. , 1. , 0.91],\
[ 1. , 1. , 1. , 0.91],\
[ 1. , 1. , 1. , 0.91]]),\
np.array([[ 1. , 0.9, 1. , 1. ],
[ 1. , 0.9, 1. , 1. ],\
[ 1. , 0.9, 1. , 1. ]]),\
np.array([[ 0.75, 1. , 1. , 1. ],\
[ 0.75, 1. , 1. , 1. ],\
[ 0.75, 1. , 1. , 1. ]]),\
np.array([[ 0.75, 1. , 1. , 1. ],
[ 0.75, 1. , 1. , 1. ],\
[ 0.75, 1. , 1. , 1. ]])])
self.assertTrue(np.allclose(expshdbeta, np.array(shdbeta), rtol = .05))