def test_idiv(self):
x = UTPS(numpy.array([1.,2.,3.]), P = 1)
x2 = x.copy()
y = UTPS(numpy.array([5.,7.,11.]), P = 1)
x /= y
assert_array_almost_equal(div(x2,y,x2).data,x.data)
def test_idiv_vectorized(self):
try:
import sympy
except:
return
x = UTPS(numpy.array([1.,2.,3, 4.,6.]),P = 2)
y = UTPS(numpy.array([5.,7.,11, 1.,2.]),P = 2)
t = sympy.symbols('t')
sx1 = x.data[0] + x.data[1]*t + x.data[2]*t**2
sy1 = y.data[0] + y.data[1]*t + y.data[2]*t**2
sz1 = sx1/sy1
sx2 = x.data[0] + x.data[3]*t + x.data[4]*t**2
sy2 = y.data[0] + y.data[3]*t + y.data[4]*t**2
sz2 = sx2/sy2
correct1 = numpy.array([ x.data[0]/y.data[0],
sz1.series(t).coeff(t).evalf(),
sz1.series(t).coeff(t**2).evalf()])
correct2 = numpy.array([ x.data[0]/y.data[0],
sz2.series(t).coeff(t).evalf(),
sz2.series(t).coeff(t**2).evalf()])
x = div(x,y,x)
assert_array_almost_equal(correct1, x.data[[0,1,2]])
assert_array_almost_equal(correct2, x.data[[0,3,4]])
def test_compare_pushforward_pullback_derivatives2(self):
x = UTPS(numpy.array([1.,0.,0.,0.]), P = 1)
y = UTPS(numpy.array([2.,1.,0.,0.]), P = 1)
z = div(x,y)
zbar = UTPS([1.,0.,0., 0.], P = 1)
xbar = UTPS(numpy.zeros(4), P = 1)
ybar = UTPS(numpy.zeros(4), P = 1)
epb_div(x,y,z, zbar, xbar, ybar)
facs = numpy.array([1.,1.,2.,6.])
assert_array_almost_equal((z.data*facs)[1:], (ybar.data*facs)[:-1])
def test_div_epb(self):
x = UTPS(numpy.array([1.,2.,3.]), P = 1)
y = UTPS(numpy.array([5.,7.,11.]), P = 1)
z = div(x,y)
zbar = UTPS(numpy.random.rand(3), P = 1)
xbar = UTPS(numpy.zeros(3), P = 1)
ybar = UTPS(numpy.zeros(3), P = 1)
xbar2 = zbar/y
ybar2 = - zbar*z/y
epb_div(x,y,z, zbar, xbar, ybar)
assert_array_almost_equal(xbar2.data, xbar.data)
assert_array_almost_equal(ybar2.data, ybar.data)
def test_idiv(self):
try:
import sympy
except:
return
x = UTPS(numpy.array([1.,4.,3.]), P = 1)
x2 = UTPS(numpy.array([1.,4.,3.]), P = 1)
y = UTPS(numpy.array([5.,1, 7.]), P = 1)
x = div(x,y,x)
assert_array_almost_equal([5.,1, 7.], y.data)
t = sympy.symbols('t')
sx = x2.data[0] + x2.data[1]*t + x2.data[2]*t**2
sy = y.data[0] + y.data[1]*t + y.data[2]*t**2
sz = sx/sy
correct = numpy.array([ x2.data[0]/y.data[0],
sz.series(t).coeff(t).evalf(),
sz.series(t).coeff(t**2).evalf()])
assert_array_almost_equal(correct, x.data)
def test_div(self):
x = UTPS(numpy.array([1.,2.,3.]), P = 1)
y = UTPS(numpy.array([5.,7.,11.]), P = 1)
z = x / y
assert_array_almost_equal(div(x,y).data,z.data)
