def runtest_ufuncify(language, backend):
a, b, c = symbols('a b c')
fabc = ufuncify((a, b, c), a * b + c, backend=backend)
facb = ufuncify((a, c, b), a * b + c, backend=backend)
grid = numpy.linspace(-2, 2, 50)
b = numpy.linspace(-5, 4, 50)
c = numpy.linspace(-1, 1, 50)
expected = grid * b + c
numpy.testing.assert_allclose(fabc(grid, b, c), expected)
numpy.testing.assert_allclose(facb(grid, c, b), expected)
def test_wrap_twice_f95_f2py():
runtest_autowrap_twice('f95', 'f2py')
f = ufuncify(a, 2 * a, backend='f2py')
f2 = ufuncify((a, ), 2 * a, backend='f2py')
f3 = ufuncify((a, ), 2 * a, backend='f2py', language='f95')
grid = numpy.linspace(-2, 2, 50)
numpy.testing.assert_allclose(f(grid), f2(grid))
numpy.testing.assert_allclose(f(grid), f3(grid))
def runtest_ufuncify(language, backend):
a, b, c = symbols('a b c')
fabc = ufuncify((a, b, c), a*b + c, backend=backend)
facb = ufuncify((a, c, b), a*b + c, backend=backend)
grid = numpy.linspace(-2, 2, 50)
b = numpy.linspace(-5, 4, 50)
c = numpy.linspace(-1, 1, 50)
expected = grid*b + c
numpy.testing.assert_allclose(fabc(grid, b, c), expected)
numpy.testing.assert_allclose(facb(grid, c, b), expected)
def runtest_ufuncify(language, backend):
a, b, c = symbols('a b c')
if backend == 'numpy':
pytest.raises(ValueError, lambda: ufuncify((a, b), Eq(b, a + b), backend=backend))
pytest.raises(ValueError, lambda: ufuncify((a, b, c), [Eq(b, a), Eq(c, a**2)], backend=backend))
helpers = [('helper', a*b, (a, b)), ('spam', sin(a), (a,))]
fabc = ufuncify((a, b, c), a*b + c, backend=backend)
fabc_2 = ufuncify((a, b, c), a*b + c, backend=backend, helpers=helpers)
facb = ufuncify((a, c, b), a*b + c, backend=backend)
grid = numpy.linspace(-2, 2, 50)
b = numpy.linspace(-5, 4, 50)
c = numpy.linspace(-1, 1, 50)
expected = grid*b + c
numpy.testing.assert_allclose(fabc(grid, b, c), expected)
numpy.testing.assert_allclose(fabc_2(grid, b, c), expected)
numpy.testing.assert_allclose(facb(grid, c, b), expected)
def main():
print(__doc__)
x = symbols('x')
# a numpy array we can apply the ufuncs to
grid = np.linspace(-1, 1, 1000)
# set mpmath precision to 20 significant numbers for verification
mpmath.mp.dps = 20
print("Compiling legendre ufuncs and checking results:")
# Let's also plot the ufunc's we generate
for n in range(6):
# Setup the Diofant expression to ufuncify
expr = legendre(n, x)
print("The polynomial of degree %i is" % n)
pprint(expr)
# This is where the magic happens:
binary_poly = ufuncify(x, expr)
# It's now ready for use with numpy arrays
polyvector = binary_poly(grid)
# let's check the values against mpmath's legendre function
maxdiff = 0
for j in range(len(grid)):
precise_val = mpmath.legendre(n, grid[j])
diff = abs(polyvector[j] - precise_val)
if diff > maxdiff:
maxdiff = diff
print("The largest error in applied ufunc was %e" % maxdiff)
assert maxdiff < 1e-14
# We can also attach the autowrapped legendre polynomial to a diofant
# function and plot values as they are calculated by the binary function
plot1 = plt.pyplot.plot(grid, polyvector, hold=True)
print(
"Here's a plot with values calculated by the wrapped binary functions")
plt.pyplot.show()
