def solveWithRes(A, U, R):
A = todia(A)
d = foldMatFor(A)
diagonalize = compose(todia, d.dot)
undiagonalize = compose(lambda x: x.A[0], d.todense().I.dot)
diaA = diagonalize(A)
fp(diaA)
ret = diaA.todense().I.dot(d.dot(U-R)).A[0]
return ret
def test_scale_and_combine_FGG_01(self):
self.FGG.scale_and_combine_operators()
ref = self.F.operators[(0,1)].copy()
tst = self.FGG.operators[(0,1)].immigrate()
npt.assert_array_almost_equal(tst.D.data, ref.D.data, decimal=14)
tst.D *= 0
ref.D *= 0
npt.assert_equal(tst, ref)
dom = np.random.random(self.FGG.grid.shape)
ref = self.FGG.operators[(0,1)].apply(dom.copy())
tst = self.F.operators[(0,1)].apply(dom)
fp(ref - tst, 'e')
npt.assert_array_almost_equal(tst, ref, decimal=14)
def test_to_csr(self):
A = self.F.operators[0].copy()
A.R = None
A.axis = 1
A.dirichlet = (None, None)
A.is_mixed_derivative = True
B = BOG.BandedOperator(A)
B = B.immigrate()
B.D = B.D.tocsr()
ref = A.apply(self.v2)
tst = B.apply(self.v2)
fp(ref - tst, 'e')
npt.assert_array_almost_equal(ref, tst, decimal=12)
B = BOG.BandedOperator(A)
tst = B.apply(self.v2)
fp(tst)
fp(ref)
fp(tst - ref, 'e')
npt.assert_array_almost_equal(ref, tst, decimal=12)
def test_scale_and_combine_FGG_01(self):
self.FGG.scale_and_combine_operators()
ref = self.F.operators[(0, 1)].copy()
tst = self.FGG.operators[(0, 1)].immigrate()
npt.assert_array_almost_equal(tst.D.data, ref.D.data, decimal=14)
tst.D *= 0
ref.D *= 0
npt.assert_equal(tst, ref)
dom = np.random.random(self.FGG.grid.shape)
ref = self.FGG.operators[(0, 1)].apply(dom.copy())
tst = self.F.operators[(0, 1)].apply(dom)
fp(ref - tst, 'e')
npt.assert_array_almost_equal(tst, ref, decimal=14)
def test_derivative_solve_0(self):
x = np.linspace(0,1,500)
B = BO.for_vector(x)
B.D.data[1,:] += 1
B.D.data[1,0] = B.D.data[1,-1] = 2
BG = BOG.BandedOperator(B)
ref = np.e**x
tst = BG.apply(ref) / 2
fp(ref - tst, 'e')
npt.assert_allclose(tst, ref, rtol=1e-4, atol=1e-6,
err_msg="d/dx (apply) not accurate")
# fp(B.D.data)
tst = BG.solve(ref) * 2
npt.assert_allclose(ref, tst, rtol=1e-4, atol=1e-6,
err_msg="integral (solve) not accurate")
def test_axis_1_derivative_2(self):
B = self.F.simple_operators[(1,1)]
BG = BOG.for_vector(self.F.grid.mesh[1],
self.F.grid.shape[0], 2, 1, 0).immigrate()
fp(B.D.data)
print
fp(BG.D.data)
print
fp(B.D.data - BG.D.data)
print B.D.offsets
fp(B.D)
print BG.D.offsets
fp(BG.D)
npt.assert_array_equal(B.D.data, BG.D.data)
npt.assert_equal(B, BG)
def test_combine_operators_1(self):
ref = self.F.operators[1]
npt.assert_array_equal([1, 0, -1, -2], ref.D.offsets)
npt.assert_equal(ref.bottom_fold_status, "CAN_FOLD")
fst = self.FG.simple_operators[(1, )]
snd = self.FG.simple_operators[(1, 1)]
tst = (fst + snd) + 0.5
tst = tst.immigrate()
npt.assert_array_equal([1, 0, -1, -2], tst.D.offsets)
npt.assert_equal(tst.bottom_fold_status, "CAN_FOLD")
tst.derivative = ref.derivative
fp(tst.D.data - ref.D.data, 'e')
tstD = tst.D.data
refD = ref.D.data
npt.assert_array_almost_equal(tstD, refD)
tst.D.data *= 0
ref.D.data *= 0
npt.assert_equal(tst, ref)
def test_combine_operators_1(self):
ref = self.F.operators[1]
npt.assert_array_equal([1, 0, -1, -2], ref.D.offsets)
npt.assert_equal(ref.bottom_fold_status, "CAN_FOLD")
fst = self.FG.simple_operators[(1,)]
snd = self.FG.simple_operators[(1,1)]
tst = (fst + snd) + 0.5
tst = tst.immigrate()
npt.assert_array_equal([1, 0, -1, -2], tst.D.offsets)
npt.assert_equal(tst.bottom_fold_status, "CAN_FOLD")
tst.derivative = ref.derivative
fp(tst.D.data - ref.D.data, 'e')
tstD = tst.D.data
refD = ref.D.data
npt.assert_array_almost_equal(tstD, refD)
tst.D.data *= 0
ref.D.data *= 0
npt.assert_equal(tst, ref)
def test_csr_solve(self):
raise unittest.SkipTest
A = self.F.operators[1].copy() + 1
Tri = BOG.BandedOperator(A)
A.diagonalize()
Tri.diagonalize()
# ref = A.apply(self.F.grid.domain[-1])
# tst = Tri.apply(self.FG.grid.domain[-1])
# fp(tst - ref, 'e')
# npt.assert_array_almost_equal(ref, tst, decimal=11)
A.is_mixed_derivative = True
Csr = BOG.BandedOperator(A)
# ref = Tri.immigrate()
# tst = Csr.immigrate()
# fp(tst.D.data)
# fp(tst.D.indices)
# fp(tst.D.indptr)
# tst.is_mixed_derivative = False
# npt.assert_equal(tst, ref)
# domT = self.FG.gpugrid.copy(True)
# domC = self.FG.gpugrid.copy(True)
# domT = SizedArrayPtr(self.F.grid.domain[-1])
# domC = SizedArrayPtr(self.F.grid.domain[-1])
# Csr.solve_(domC, True)
# tst = Csr.immigrate().solve(domC.to_numpy())
# Tri.solve_(domT, True)
tst = domC.to_numpy()
ref = domT.to_numpy()
print "Ref"
fp(ref)
print "Test"
fp(tst)
print "Diff"
fp(tst - ref)
npt.assert_array_almost_equal(ref, tst, decimal=11)
assert False
def test_csr_solve(self):
raise unittest.SkipTest
A = self.F.operators[1].copy() + 1
Tri = BOG.BandedOperator(A)
A.diagonalize()
Tri.diagonalize()
# ref = A.apply(self.F.grid.domain[-1])
# tst = Tri.apply(self.FG.grid.domain[-1])
# fp(tst - ref, 'e')
# npt.assert_array_almost_equal(ref, tst, decimal=11)
A.is_mixed_derivative = True
Csr = BOG.BandedOperator(A)
# ref = Tri.immigrate()
# tst = Csr.immigrate()
# fp(tst.D.data)
# fp(tst.D.indices)
# fp(tst.D.indptr)
# tst.is_mixed_derivative = False
# npt.assert_equal(tst, ref)
# domT = self.FG.gpugrid.copy(True)
# domC = self.FG.gpugrid.copy(True)
# domT = SizedArrayPtr(self.F.grid.domain[-1])
# domC = SizedArrayPtr(self.F.grid.domain[-1])
# Csr.solve_(domC, True)
# tst = Csr.immigrate().solve(domC.to_numpy())
# Tri.solve_(domT, True)
tst = domC.to_numpy()
ref = domT.to_numpy()
print "Ref"
fp(ref)
print "Test"
fp(tst)
print "Diff"
fp(tst - ref)
npt.assert_array_almost_equal(ref, tst, decimal=11)
assert False
def test_diagonalize(self):
B = self.B.copy()
print "ref pre"
fp(self.B.D)
fp(self.B.D.data)
print "tst"
fp(B.D)
fp(B.D.data)
B.diagonalize()
print "tst mid"
fp(B.D)
fp(B.D.data)
B.undiagonalize()
npt.assert_(not B.is_tridiagonal())
print "ref after"
fp(self.B.D)
fp(self.B.D.data)
print "tst"
fp(B.D)
fp(B.D.data)
npt.assert_array_equal(self.B.D.data, B.D.data, err_msg="Diagonalize roundtrip doesn't preserve operator matrix.")
npt.assert_(B == self.B, msg="Diagonalize roundtrip doesn't preserve operator.")
def test_numpy_vs_operators(self):
spots = np.linspace(0, 1, 4)
vars = np.linspace(0, 10, 4)
# spots = self.G.mesh[0]
# vars = self.G.mesh[1]
G = Grid.Grid((spots, vars), initializer=lambda x0, x1: x1 * 1)
coeffs = {
(): lambda t: 0,
(0, ): lambda t, *dim: dim[0],
(0, 0): lambda t, *dim: dim[0],
(1, ): lambda t, *dim: 0 * dim[1],
(1, 1): lambda t, *dim: dim[1],
(0, 1): lambda t, *dim: dim[0] * dim[1]
}
bounds = {
(0, ): ((None, lambda *x: None), (None, lambda *x: 3)),
(0, 0): ((None, lambda *x: None), (None, lambda *x: 3)),
(1, ): ((None, lambda *x: None), (None, lambda *x: 1)),
(1, 1): ((None, lambda *x: None), (None, lambda *x: 1)),
}
F = FD.FiniteDifferenceEngineADI(G,
coefficients=coeffs,
boundaries=bounds,
force_bandwidth=None)
F.init()
cb = utils.clear_boundary
g = F.grid.domain[-1]
op_ = {'delta': {}, 'grid_delta': {}, 'derivative': {}}
np_ = {'delta': {}, 'grid_delta': {}, 'derivative': {}}
dims = coeffs.keys()
dims.remove(())
for dim in dims:
op_['grid_delta'][dim] = F.simple_operators[dim].copy()
op_['grid_delta'][dim].R = None
op_['delta'][(0, )] = op_['grid_delta'][(0, )].deltas[:, np.newaxis]
op_['delta'][(1, )] = op_['grid_delta'][(1, )].deltas
x = F.grid.mesh[0]
y = F.grid.mesh[1]
np_['delta'][(0, )] = np.gradient(x)[:, np.newaxis]
np_['delta'][(1, )] = np.gradient(y)
np_['delta'][(0, )][0, 0] = np.nan
np_['delta'][(1, )][0] = np.nan
for f in (op_, np_):
f['delta'][(0, 0)] = f['delta'][(0, )]**2
f['delta'][(1, 1)] = f['delta'][(1, )]**2
f['delta'][(0, 1)] = f['delta'][(1, )] * f['delta'][(0, )]
f['delta'][(1, 0)] = f['delta'][(1, )] * f['delta'][(0, )]
np_['grid_delta'][(0, )], np_['grid_delta'][(1, )] = np.gradient(g)
for fst in [0, 1]:
np_['grid_delta'][(fst,
0)], np_['grid_delta'][(fst, 1)] = np.gradient(
np_['grid_delta'][(fst, )])
Y, X = np.meshgrid(y, x)
for dim in dims:
op_['derivative'][dim] = cb(op_['grid_delta'][dim].apply(g),
inplace=True)
npt.assert_(op_['derivative'][dim].shape == g.shape)
np_['derivative'][dim] = cb(np_['grid_delta'][dim] /
np_['delta'][dim],
inplace=True)
np_['derivative'][dim] *= F.coefficients[dim](0, X, Y)
op_['derivative'][dim] *= F.coefficients[dim](0, X, Y)
np_['derivative'][(1, 0)] = cb(np_['grid_delta'][(1, 0)] /
np_['delta'][(1, 0)],
inplace=True)
np_['derivative'][(1, 0)] *= F.coefficients[(0, 1)](0, X, Y)
for dim in dims:
for x in ('delta', 'derivative'):
msg = "Comparing %s in dimension %s" % (x, dim)
try:
npt.assert_array_almost_equal(op_[x][dim],
np_[x][dim],
decimal=7,
err_msg=msg)
except AssertionError:
print msg, op_['grid_delta'][dim].axis
fp((op_['grid_delta'][dim]).D.data)
fp((op_[x][dim]), fmt='f')
fp((np_[x][dim]), fmt='f')
fp((op_[x][dim] - np_[x][dim]), fmt='f')
# npt.assert_allclose(op_[x][dim], np_[x][dim], err_msg=msg)
npt.assert_array_almost_equal(op_[x][dim],
np_[x][dim],
decimal=7,
err_msg=msg)
npt.assert_array_almost_equal(np_['derivative'][(0, 1)],
np_['derivative'][(1, 0)],
err_msg=msg)
def test_numpy_vs_operators(self):
spots = np.linspace(0, 1, 4)
vars = np.linspace(0, 10, 4)
# spots = self.G.mesh[0]
# vars = self.G.mesh[1]
G = Grid.Grid((spots, vars), initializer=lambda x0, x1: x1 * 1)
coeffs = {
(): lambda t: 0,
(0,): lambda t, *dim: dim[0],
(0, 0): lambda t, *dim: dim[0],
(1,): lambda t, *dim: 0 * dim[1],
(1, 1): lambda t, *dim: dim[1],
(0, 1): lambda t, *dim: dim[0] * dim[1],
}
bounds = {
(0,): ((None, lambda *x: None), (None, lambda *x: 3)),
(0, 0): ((None, lambda *x: None), (None, lambda *x: 3)),
(1,): ((None, lambda *x: None), (None, lambda *x: 1)),
(1, 1): ((None, lambda *x: None), (None, lambda *x: 1)),
}
F = FD.FiniteDifferenceEngineADI(G, coefficients=coeffs, boundaries=bounds, force_bandwidth=None)
F.init()
cb = utils.clear_boundary
g = F.grid.domain[-1]
op_ = {"delta": {}, "grid_delta": {}, "derivative": {}}
np_ = {"delta": {}, "grid_delta": {}, "derivative": {}}
dims = coeffs.keys()
dims.remove(())
for dim in dims:
op_["grid_delta"][dim] = F.simple_operators[dim].copy()
op_["grid_delta"][dim].R = None
op_["delta"][(0,)] = op_["grid_delta"][(0,)].deltas[:, np.newaxis]
op_["delta"][(1,)] = op_["grid_delta"][(1,)].deltas
x = F.grid.mesh[0]
y = F.grid.mesh[1]
np_["delta"][(0,)] = np.gradient(x)[:, np.newaxis]
np_["delta"][(1,)] = np.gradient(y)
np_["delta"][(0,)][0, 0] = np.nan
np_["delta"][(1,)][0] = np.nan
for f in (op_, np_):
f["delta"][(0, 0)] = f["delta"][(0,)] ** 2
f["delta"][(1, 1)] = f["delta"][(1,)] ** 2
f["delta"][(0, 1)] = f["delta"][(1,)] * f["delta"][(0,)]
f["delta"][(1, 0)] = f["delta"][(1,)] * f["delta"][(0,)]
np_["grid_delta"][(0,)], np_["grid_delta"][(1,)] = np.gradient(g)
for fst in [0, 1]:
np_["grid_delta"][(fst, 0)], np_["grid_delta"][(fst, 1)] = np.gradient(np_["grid_delta"][(fst,)])
Y, X = np.meshgrid(y, x)
for dim in dims:
op_["derivative"][dim] = cb(op_["grid_delta"][dim].apply(g), inplace=True)
npt.assert_(op_["derivative"][dim].shape == g.shape)
np_["derivative"][dim] = cb(np_["grid_delta"][dim] / np_["delta"][dim], inplace=True)
np_["derivative"][dim] *= F.coefficients[dim](0, X, Y)
op_["derivative"][dim] *= F.coefficients[dim](0, X, Y)
np_["derivative"][(1, 0)] = cb(np_["grid_delta"][(1, 0)] / np_["delta"][(1, 0)], inplace=True)
np_["derivative"][(1, 0)] *= F.coefficients[(0, 1)](0, X, Y)
for dim in dims:
for x in ("delta", "derivative"):
msg = "Comparing %s in dimension %s" % (x, dim)
try:
npt.assert_array_almost_equal(op_[x][dim], np_[x][dim], decimal=7, err_msg=msg)
except AssertionError:
print msg, op_["grid_delta"][dim].axis
fp((op_["grid_delta"][dim]).D.data)
fp((op_[x][dim]), fmt="f")
fp((np_[x][dim]), fmt="f")
fp((op_[x][dim] - np_[x][dim]), fmt="f")
# npt.assert_allclose(op_[x][dim], np_[x][dim], err_msg=msg)
npt.assert_array_almost_equal(op_[x][dim], np_[x][dim], decimal=7, err_msg=msg)
npt.assert_array_almost_equal(np_["derivative"][(0, 1)], np_["derivative"][(1, 0)], err_msg=msg)
