def test_lagfit(self):
def f(x):
return x * (x - 1) * (x - 2)
# Test exceptions
assert_raises(ValueError, lag.lagfit, [1], [1], -1)
assert_raises(TypeError, lag.lagfit, [[1]], [1], 0)
assert_raises(TypeError, lag.lagfit, [], [1], 0)
assert_raises(TypeError, lag.lagfit, [1], [[[1]]], 0)
assert_raises(TypeError, lag.lagfit, [1, 2], [1], 0)
assert_raises(TypeError, lag.lagfit, [1], [1, 2], 0)
assert_raises(TypeError, lag.lagfit, [1], [1], 0, w=[[1]])
assert_raises(TypeError, lag.lagfit, [1], [1], 0, w=[1, 1])
assert_raises(ValueError, lag.lagfit, [1], [1], [
-1,
])
assert_raises(ValueError, lag.lagfit, [1], [1], [2, -1, 6])
assert_raises(TypeError, lag.lagfit, [1], [1], [])
# Test fit
x = np.linspace(0, 2)
y = f(x)
#
coef3 = lag.lagfit(x, y, 3)
assert_equal(len(coef3), 4)
assert_almost_equal(lag.lagval(x, coef3), y)
coef3 = lag.lagfit(x, y, [0, 1, 2, 3])
assert_equal(len(coef3), 4)
assert_almost_equal(lag.lagval(x, coef3), y)
#
coef4 = lag.lagfit(x, y, 4)
assert_equal(len(coef4), 5)
assert_almost_equal(lag.lagval(x, coef4), y)
coef4 = lag.lagfit(x, y, [0, 1, 2, 3, 4])
assert_equal(len(coef4), 5)
assert_almost_equal(lag.lagval(x, coef4), y)
#
coef2d = lag.lagfit(x, np.array([y, y]).T, 3)
assert_almost_equal(coef2d, np.array([coef3, coef3]).T)
coef2d = lag.lagfit(x, np.array([y, y]).T, [0, 1, 2, 3])
assert_almost_equal(coef2d, np.array([coef3, coef3]).T)
# test weighting
w = np.zeros_like(x)
yw = y.copy()
w[1::2] = 1
y[0::2] = 0
wcoef3 = lag.lagfit(x, yw, 3, w=w)
assert_almost_equal(wcoef3, coef3)
wcoef3 = lag.lagfit(x, yw, [0, 1, 2, 3], w=w)
assert_almost_equal(wcoef3, coef3)
#
wcoef2d = lag.lagfit(x, np.array([yw, yw]).T, 3, w=w)
assert_almost_equal(wcoef2d, np.array([coef3, coef3]).T)
wcoef2d = lag.lagfit(x, np.array([yw, yw]).T, [0, 1, 2, 3], w=w)
assert_almost_equal(wcoef2d, np.array([coef3, coef3]).T)
# test scaling with complex values x points whose square
# is zero when summed.
x = [1, 1j, -1, -1j]
assert_almost_equal(lag.lagfit(x, x, 1), [1, -1])
assert_almost_equal(lag.lagfit(x, x, [0, 1]), [1, -1])
def test_boolean(self):
a = np.array(5)
assert_equal(a[np.array(True)], 5)
a[np.array(True)] = 1
assert_equal(a, 1)
# NOTE: This is different from normal broadcasting, as
# arr[boolean_array] works like in a multi index. Which means
# it is aligned to the left. This is probably correct for
# consistency with arr[boolean_array,] also no broadcasting
# is done at all
self._check_multi_index(
self.a, (np.zeros_like(self.a, dtype=bool),))
self._check_multi_index(
self.a, (np.zeros_like(self.a, dtype=bool)[..., 0],))
self._check_multi_index(
self.a, (np.zeros_like(self.a, dtype=bool)[None, ...],))
def test_blocked(self):
# test alignments offsets for simd instructions
# alignments for vz + 2 * (vs - 1) + 1
for dt, sz in [(np.float32, 11), (np.float64, 7), (np.int32, 11)]:
for out, inp1, inp2, msg in _gen_alignment_data(dtype=dt,
type='binary',
max_size=sz):
exp1 = np.ones_like(inp1)
inp1[...] = np.ones_like(inp1)
inp2[...] = np.zeros_like(inp2)
assert_almost_equal(np.add(inp1, inp2), exp1, err_msg=msg)
assert_almost_equal(np.add(inp1, 2), exp1 + 2, err_msg=msg)
assert_almost_equal(np.add(1, inp2), exp1, err_msg=msg)
np.add(inp1, inp2, out=out)
assert_almost_equal(out, exp1, err_msg=msg)
inp2[...] += np.arange(inp2.size, dtype=dt) + 1
assert_almost_equal(np.square(inp2),
np.multiply(inp2, inp2),
err_msg=msg)
# skip true divide for ints
if dt != np.int32 or (sys.version_info.major < 3
and not sys.py3kwarning):
assert_almost_equal(np.reciprocal(inp2),
np.divide(1, inp2),
err_msg=msg)
inp1[...] = np.ones_like(inp1)
np.add(inp1, 2, out=out)
assert_almost_equal(out, exp1 + 2, err_msg=msg)
inp2[...] = np.ones_like(inp2)
np.add(2, inp2, out=out)
assert_almost_equal(out, exp1 + 2, err_msg=msg)
def test_fit(Poly):
def f(x):
return x * (x - 1) * (x - 2)
x = np.linspace(0, 3)
y = f(x)
# check default value of domain and window
p = Poly.fit(x, y, 3)
assert_almost_equal(p.domain, [0, 3])
assert_almost_equal(p(x), y)
assert_equal(p.degree(), 3)
# check with given domains and window
d = Poly.domain + random((2, )) * .25
w = Poly.window + random((2, )) * .25
p = Poly.fit(x, y, 3, domain=d, window=w)
assert_almost_equal(p(x), y)
assert_almost_equal(p.domain, d)
assert_almost_equal(p.window, w)
p = Poly.fit(x, y, [0, 1, 2, 3], domain=d, window=w)
assert_almost_equal(p(x), y)
assert_almost_equal(p.domain, d)
assert_almost_equal(p.window, w)
# check with class domain default
p = Poly.fit(x, y, 3, [])
assert_equal(p.domain, Poly.domain)
assert_equal(p.window, Poly.window)
p = Poly.fit(x, y, [0, 1, 2, 3], [])
assert_equal(p.domain, Poly.domain)
assert_equal(p.window, Poly.window)
# check that fit accepts weights.
w = np.zeros_like(x)
z = y + random(y.shape) * .25
w[::2] = 1
p1 = Poly.fit(x[::2], z[::2], 3)
p2 = Poly.fit(x, z, 3, w=w)
p3 = Poly.fit(x, z, [0, 1, 2, 3], w=w)
assert_almost_equal(p1(x), p2(x))
assert_almost_equal(p2(x), p3(x))
def test_chebfit(self):
def f(x):
return x*(x - 1)*(x - 2)
def f2(x):
return x**4 + x**2 + 1
# Test exceptions
assert_raises(ValueError, cheb.chebfit, [1], [1], -1)
assert_raises(TypeError, cheb.chebfit, [[1]], [1], 0)
assert_raises(TypeError, cheb.chebfit, [], [1], 0)
assert_raises(TypeError, cheb.chebfit, [1], [[[1]]], 0)
assert_raises(TypeError, cheb.chebfit, [1, 2], [1], 0)
assert_raises(TypeError, cheb.chebfit, [1], [1, 2], 0)
assert_raises(TypeError, cheb.chebfit, [1], [1], 0, w=[[1]])
assert_raises(TypeError, cheb.chebfit, [1], [1], 0, w=[1, 1])
assert_raises(ValueError, cheb.chebfit, [1], [1], [-1,])
assert_raises(ValueError, cheb.chebfit, [1], [1], [2, -1, 6])
assert_raises(TypeError, cheb.chebfit, [1], [1], [])
# Test fit
x = np.linspace(0, 2)
y = f(x)
#
coef3 = cheb.chebfit(x, y, 3)
assert_equal(len(coef3), 4)
assert_almost_equal(cheb.chebval(x, coef3), y)
coef3 = cheb.chebfit(x, y, [0, 1, 2, 3])
assert_equal(len(coef3), 4)
assert_almost_equal(cheb.chebval(x, coef3), y)
#
coef4 = cheb.chebfit(x, y, 4)
assert_equal(len(coef4), 5)
assert_almost_equal(cheb.chebval(x, coef4), y)
coef4 = cheb.chebfit(x, y, [0, 1, 2, 3, 4])
assert_equal(len(coef4), 5)
assert_almost_equal(cheb.chebval(x, coef4), y)
# check things still work if deg is not in strict increasing
coef4 = cheb.chebfit(x, y, [2, 3, 4, 1, 0])
assert_equal(len(coef4), 5)
assert_almost_equal(cheb.chebval(x, coef4), y)
#
coef2d = cheb.chebfit(x, np.array([y, y]).T, 3)
assert_almost_equal(coef2d, np.array([coef3, coef3]).T)
coef2d = cheb.chebfit(x, np.array([y, y]).T, [0, 1, 2, 3])
assert_almost_equal(coef2d, np.array([coef3, coef3]).T)
# test weighting
w = np.zeros_like(x)
yw = y.copy()
w[1::2] = 1
y[0::2] = 0
wcoef3 = cheb.chebfit(x, yw, 3, w=w)
assert_almost_equal(wcoef3, coef3)
wcoef3 = cheb.chebfit(x, yw, [0, 1, 2, 3], w=w)
assert_almost_equal(wcoef3, coef3)
#
wcoef2d = cheb.chebfit(x, np.array([yw, yw]).T, 3, w=w)
assert_almost_equal(wcoef2d, np.array([coef3, coef3]).T)
wcoef2d = cheb.chebfit(x, np.array([yw, yw]).T, [0, 1, 2, 3], w=w)
assert_almost_equal(wcoef2d, np.array([coef3, coef3]).T)
# test scaling with complex values x points whose square
# is zero when summed.
x = [1, 1j, -1, -1j]
assert_almost_equal(cheb.chebfit(x, x, 1), [0, 1])
assert_almost_equal(cheb.chebfit(x, x, [0, 1]), [0, 1])
# test fitting only even polynomials
x = np.linspace(-1, 1)
y = f2(x)
coef1 = cheb.chebfit(x, y, 4)
assert_almost_equal(cheb.chebval(x, coef1), y)
coef2 = cheb.chebfit(x, y, [0, 2, 4])
assert_almost_equal(cheb.chebval(x, coef2), y)
assert_almost_equal(coef1, coef2)