本文整理汇总了Python中scipy.interpolate.fitpack2.SmoothBivariateSpline.integral方法的典型用法代码示例。如果您正苦于以下问题：Python SmoothBivariateSpline.integral方法的具体用法？Python SmoothBivariateSpline.integral怎么用？Python SmoothBivariateSpline.integral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.fitpack2.SmoothBivariateSpline的用法示例。
在下文中一共展示了SmoothBivariateSpline.integral方法的3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_integral
# 需要导入模块: from scipy.interpolate.fitpack2 import SmoothBivariateSpline [as 别名]
# 或者: from scipy.interpolate.fitpack2.SmoothBivariateSpline import integral [as 别名]
def test_integral(self):
x = [1,1,1,2,2,2,4,4,4]
y = [1,2,3,1,2,3,1,2,3]
z = array([0,7,8,3,4,7,1,3,4])
with suppress_warnings() as sup:
# This seems to fail (ier=1, see ticket 1642).
sup.filter(UserWarning, "\nThe required storage space")
lut = SmoothBivariateSpline(x, y, z, kx=1, ky=1, s=0)
tx = [1,2,4]
ty = [1,2,3]
tz = lut(tx, ty)
trpz = .25*(diff(tx)[:,None]*diff(ty)[None,:]
* (tz[:-1,:-1]+tz[1:,:-1]+tz[:-1,1:]+tz[1:,1:])).sum()
assert_almost_equal(lut.integral(tx[0], tx[-1], ty[0], ty[-1]), trpz)
lut2 = SmoothBivariateSpline(x, y, z, kx=2, ky=2, s=0)
assert_almost_equal(lut2.integral(tx[0], tx[-1], ty[0], ty[-1]), trpz,
decimal=0) # the quadratures give 23.75 and 23.85
tz = lut(tx[:-1], ty[:-1])
trpz = .25*(diff(tx[:-1])[:,None]*diff(ty[:-1])[None,:]
* (tz[:-1,:-1]+tz[1:,:-1]+tz[:-1,1:]+tz[1:,1:])).sum()
assert_almost_equal(lut.integral(tx[0], tx[-2], ty[0], ty[-2]), trpz)示例2: test_integral
# 需要导入模块: from scipy.interpolate.fitpack2 import SmoothBivariateSpline [as 别名]
# 或者: from scipy.interpolate.fitpack2.SmoothBivariateSpline import integral [as 别名]
def test_integral(self):
x = [1,1,1,2,2,2,4,4,4]
y = [1,2,3,1,2,3,1,2,3]
z = array([0,7,8,3,4,7,1,3,4])
warn_ctx = WarningManager()
warn_ctx.__enter__()
try:
# This seems to fail (ier=1, see ticket 1642).
warnings.simplefilter('ignore', UserWarning)
lut = SmoothBivariateSpline(x, y, z, kx=1, ky=1, s=0)
finally:
warn_ctx.__exit__()
tx = [1,2,4]
ty = [1,2,3]
tz = lut(tx, ty)
trpz = .25*(diff(tx)[:,None]*diff(ty)[None,:]
* (tz[:-1,:-1]+tz[1:,:-1]+tz[:-1,1:]+tz[1:,1:])).sum()
assert_almost_equal(lut.integral(tx[0], tx[-1], ty[0], ty[-1]), trpz)
lut2 = SmoothBivariateSpline(x, y, z, kx=2, ky=2, s=0)
assert_almost_equal(lut2.integral(tx[0], tx[-1], ty[0], ty[-1]), trpz,
decimal=0) # the quadratures give 23.75 and 23.85
tz = lut(tx[:-1], ty[:-1])
trpz = .25*(diff(tx[:-1])[:,None]*diff(ty[:-1])[None,:]
* (tz[:-1,:-1]+tz[1:,:-1]+tz[:-1,1:]+tz[1:,1:])).sum()
assert_almost_equal(lut.integral(tx[0], tx[-2], ty[0], ty[-2]), trpz)示例3: test_integral
# 需要导入模块: from scipy.interpolate.fitpack2 import SmoothBivariateSpline [as 别名]
# 或者: from scipy.interpolate.fitpack2.SmoothBivariateSpline import integral [as 别名]
def test_integral(self):
x = [1, 1, 1, 2, 2, 2, 4, 4, 4]
y = [1, 2, 3, 1, 2, 3, 1, 2, 3]
z = array([0, 7, 8, 3, 4, 7, 1, 3, 4])
lut = SmoothBivariateSpline(x, y, z, kx=1, ky=1, s=0)
tx = [1, 2, 4]
ty = [1, 2, 3]
tz = lut(tx, ty)
trpz = (
0.25
* (diff(tx)[:, None] * diff(ty)[None, :] * (tz[:-1, :-1] + tz[1:, :-1] + tz[:-1, 1:] + tz[1:, 1:])).sum()
)
assert_almost_equal(lut.integral(tx[0], tx[-1], ty[0], ty[-1]), trpz)
lut2 = SmoothBivariateSpline(x, y, z, kx=2, ky=2, s=0)
assert_almost_equal(
lut2.integral(tx[0], tx[-1], ty[0], ty[-1]), trpz, decimal=0
) # the quadratures give 23.75 and 23.85
tz = lut(tx[:-1], ty[:-1])
trpz = (
0.25
* (
diff(tx[:-1])[:, None]
* diff(ty[:-1])[None, :]
* (tz[:-1, :-1] + tz[1:, :-1] + tz[:-1, 1:] + tz[1:, 1:])
).sum()
)
assert_almost_equal(lut.integral(tx[0], tx[-2], ty[0], ty[-2]), trpz)