本文整理汇总了Python中scipy.interpolate.BPoly.derivative方法的典型用法代码示例。如果您正苦于以下问题：Python BPoly.derivative方法的具体用法？Python BPoly.derivative怎么用？Python BPoly.derivative使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类scipy.interpolate.BPoly的用法示例。
在下文中一共展示了BPoly.derivative方法的7个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_derivative
# 需要导入模块: from scipy.interpolate import BPoly [as 别名]
# 或者: from scipy.interpolate.BPoly import derivative [as 别名]
def test_derivative(self):
x = [0, 1, 3]
c = [[3, 0], [0, 0], [0, 2]]
bp = BPoly(c, x) # [3*(1-x)**2, 2*((x-1)/2)**2]
bp_der = bp.derivative()
assert_allclose(bp_der(0.4), -6*(0.6))
assert_allclose(bp_der(1.7), 0.7)示例2: test_make_poly_12
# 需要导入模块: from scipy.interpolate import BPoly [as 别名]
# 或者: from scipy.interpolate.BPoly import derivative [as 别名]
def test_make_poly_12(self):
np.random.seed(12345)
ya = np.r_[0, np.random.random(5)]
yb = np.r_[0, np.random.random(5)]
c = BPoly._construct_from_derivatives(0, 1, ya, yb)
pp = BPoly(c[:, None], [0, 1])
for j in range(6):
assert_allclose([pp(0.), pp(1.)], [ya[j], yb[j]])
pp = pp.derivative()示例3: test_deriv_inplace
# 需要导入模块: from scipy.interpolate import BPoly [as 别名]
# 或者: from scipy.interpolate.BPoly import derivative [as 别名]
def test_deriv_inplace(self):
np.random.seed(1234)
m, k = 5, 8 # number of intervals, order
x = np.sort(np.random.random(m))
c = np.random.random((k, m-1))
bp = BPoly(c, x)
xp = np.linspace(x[0], x[-1], 21)
for i in range(k):
assert_allclose(bp(xp, i), bp.derivative(i)(xp))示例4: test_multi_shape
# 需要导入模块: from scipy.interpolate import BPoly [as 别名]
# 或者: from scipy.interpolate.BPoly import derivative [as 别名]
def test_multi_shape(self):
c = np.random.rand(6, 2, 1, 2, 3)
x = np.array([0, 0.5, 1])
p = BPoly(c, x)
assert_equal(p.x.shape, x.shape)
assert_equal(p.c.shape, c.shape)
assert_equal(p(0.3).shape, c.shape[2:])
assert_equal(p(np.random.rand(5,6)).shape,
(5,6)+c.shape[2:])
dp = p.derivative()
assert_equal(dp.c.shape, (5, 2, 1, 2, 3))示例5: test_derivative
# 需要导入模块: from scipy.interpolate import BPoly [as 别名]
# 或者: from scipy.interpolate.BPoly import derivative [as 别名]
def test_derivative(self):
x = [0, 1, 3]
c = [[3, 0], [0, 0], [0, 2]]
bp = BPoly(c, x) # [3*(1-x)**2, 2*((x-1)/2)**2]
bp_der = bp.derivative()
assert_allclose(bp_der(0.4), -6*(0.6))
assert_allclose(bp_der(1.7), 0.7)
# derivatives in-place
assert_allclose([bp(0.4, nu=1), bp(0.4, nu=2), bp(0.4, nu=3)],
[-6*(1-0.4), 6., 0.])
assert_allclose([bp(1.7, nu=1), bp(1.7, nu=2), bp(1.7, nu=3)],
[0.7, 1., 0])示例6: test_derivative_ppoly
# 需要导入模块: from scipy.interpolate import BPoly [as 别名]
# 或者: from scipy.interpolate.BPoly import derivative [as 别名]
def test_derivative_ppoly(self):
# make sure it's consistent w/ power basis
np.random.seed(1234)
m, k = 5, 8 # number of intervals, order
x = np.sort(np.random.random(m))
c = np.random.random((k, m-1))
bp = BPoly(c, x)
pp = PPoly.from_bernstein_basis(bp)
for d in range(k):
bp = bp.derivative()
pp = pp.derivative()
xp = np.linspace(x[0], x[-1], 21)
assert_allclose(bp(xp), pp(xp))示例7: test_extrapolate_attr
# 需要导入模块: from scipy.interpolate import BPoly [as 别名]
# 或者: from scipy.interpolate.BPoly import derivative [as 别名]
def test_extrapolate_attr(self):
x = [0, 2]
c = [[3], [1], [4]]
bp = BPoly(c, x)
for extrapolate in (True, False, None):
bp = BPoly(c, x, extrapolate=extrapolate)
bp_d = bp.derivative()
if extrapolate is False:
assert_(np.isnan(bp([-0.1, 2.1])).all())
assert_(np.isnan(bp_d([-0.1, 2.1])).all())
else:
assert_(not np.isnan(bp([-0.1, 2.1])).any())
assert_(not np.isnan(bp_d([-0.1, 2.1])).any())