本文整理汇总了Python中scipy.interpolate.fitpack2.LSQBivariateSpline.get_knots方法的典型用法代码示例。如果您正苦于以下问题：Python LSQBivariateSpline.get_knots方法的具体用法？Python LSQBivariateSpline.get_knots怎么用？Python LSQBivariateSpline.get_knots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.LSQBivariateSpline的用法示例。
在下文中一共展示了LSQBivariateSpline.get_knots方法的6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_bilinearity
# 需要导入模块: from scipy.interpolate.fitpack2 import LSQBivariateSpline [as 别名]
# 或者: from scipy.interpolate.fitpack2.LSQBivariateSpline import get_knots [as 别名]
def test_bilinearity(self):
x = [1,1,1,2,2,2,3,3,3]
y = [1,2,3,1,2,3,1,2,3]
z = [0,7,8,3,4,7,1,3,4]
s = 0.1
tx = [1+s,3-s]
ty = [1+s,3-s]
warn_ctx = WarningManager()
warn_ctx.__enter__()
try:
# This seems to fail (ier=1, see ticket 1642).
warnings.simplefilter('ignore', UserWarning)
lut = LSQBivariateSpline(x,y,z,tx,ty,kx=1,ky=1)
finally:
warn_ctx.__exit__()
tx, ty = lut.get_knots()
for xa, xb in zip(tx[:-1], tx[1:]):
for ya, yb in zip(ty[:-1], ty[1:]):
for t in [0.1, 0.5, 0.9]:
for s in [0.3, 0.4, 0.7]:
xp = xa*(1-t) + xb*t
yp = ya*(1-s) + yb*s
zp = (+ lut(xa, ya)*(1-t)*(1-s)
+ lut(xb, ya)*t*(1-s)
+ lut(xa, yb)*(1-t)*s
+ lut(xb, yb)*t*s)
assert_almost_equal(lut(xp,yp), zp)示例2: test_integral
# 需要导入模块: from scipy.interpolate.fitpack2 import LSQBivariateSpline [as 别名]
# 或者: from scipy.interpolate.fitpack2.LSQBivariateSpline import get_knots [as 别名]
def test_integral(self):
x = [1,1,1,2,2,2,8,8,8]
y = [1,2,3,1,2,3,1,2,3]
z = array([0,7,8,3,4,7,1,3,4])
s = 0.1
tx = [1+s,3-s]
ty = [1+s,3-s]
lut = LSQBivariateSpline(x,y,z,tx,ty,kx=1,ky=1)
tx, ty = lut.get_knots()
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)示例3: test_integral
# 需要导入模块: from scipy.interpolate.fitpack2 import LSQBivariateSpline [as 别名]
# 或者: from scipy.interpolate.fitpack2.LSQBivariateSpline import get_knots [as 别名]
def test_integral(self):
x = [1, 1, 1, 2, 2, 2, 8, 8, 8]
y = [1, 2, 3, 1, 2, 3, 1, 2, 3]
z = array([0, 7, 8, 3, 4, 7, 1, 3, 4])
s = 0.1
tx = [1 + s, 3 - s]
ty = [1 + s, 3 - s]
with warnings.catch_warnings(record=True): # coefficients of the ...
lut = LSQBivariateSpline(x, y, z, tx, ty, kx=1, ky=1)
tx, ty = lut.get_knots()
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)示例4: test_integral
# 需要导入模块: from scipy.interpolate.fitpack2 import LSQBivariateSpline [as 别名]
# 或者: from scipy.interpolate.fitpack2.LSQBivariateSpline import get_knots [as 别名]
def test_integral(self):
x = [1,1,1,2,2,2,8,8,8]
y = [1,2,3,1,2,3,1,2,3]
z = array([0,7,8,3,4,7,1,3,4])
s = 0.1
tx = [1+s,3-s]
ty = [1+s,3-s]
with suppress_warnings() as sup:
r = sup.record(UserWarning, "\nThe coefficients of the spline")
lut = LSQBivariateSpline(x, y, z, tx, ty, kx=1, ky=1)
assert_equal(len(r), 1)
tx, ty = lut.get_knots()
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)示例5: test_bilinearity
# 需要导入模块: from scipy.interpolate.fitpack2 import LSQBivariateSpline [as 别名]
# 或者: from scipy.interpolate.fitpack2.LSQBivariateSpline import get_knots [as 别名]
def test_bilinearity(self):
x = [1,1,1,2,2,2,3,3,3]
y = [1,2,3,1,2,3,1,2,3]
z = [0,7,8,3,4,7,1,3,4]
s = 0.1
tx = [1+s,3-s]
ty = [1+s,3-s]
lut = LSQBivariateSpline(x,y,z,tx,ty,kx=1,ky=1)
tx, ty = lut.get_knots()
for xa, xb in zip(tx[:-1], tx[1:]):
for ya, yb in zip(ty[:-1], ty[1:]):
for t in [0.1, 0.5, 0.9]:
for s in [0.3, 0.4, 0.7]:
xp = xa*(1-t) + xb*t
yp = ya*(1-s) + yb*s
zp = (+ lut(xa, ya)*(1-t)*(1-s)
+ lut(xb, ya)*t*(1-s)
+ lut(xa, yb)*(1-t)*s
+ lut(xb, yb)*t*s)
assert_almost_equal(lut(xp,yp), zp)示例6: test_bilinearity
# 需要导入模块: from scipy.interpolate.fitpack2 import LSQBivariateSpline [as 别名]
# 或者: from scipy.interpolate.fitpack2.LSQBivariateSpline import get_knots [as 别名]
def test_bilinearity(self):
x = [1,1,1,2,2,2,3,3,3]
y = [1,2,3,1,2,3,1,2,3]
z = [0,7,8,3,4,7,1,3,4]
s = 0.1
tx = [1+s,3-s]
ty = [1+s,3-s]
with suppress_warnings() as sup:
# This seems to fail (ier=1, see ticket 1642).
sup.filter(UserWarning, "\nThe coefficients of the spline")
lut = LSQBivariateSpline(x,y,z,tx,ty,kx=1,ky=1)
tx, ty = lut.get_knots()
for xa, xb in zip(tx[:-1], tx[1:]):
for ya, yb in zip(ty[:-1], ty[1:]):
for t in [0.1, 0.5, 0.9]:
for s in [0.3, 0.4, 0.7]:
xp = xa*(1-t) + xb*t
yp = ya*(1-s) + yb*s
zp = (+ lut(xa, ya)*(1-t)*(1-s)
+ lut(xb, ya)*t*(1-s)
+ lut(xa, yb)*(1-t)*s
+ lut(xb, yb)*t*s)
assert_almost_equal(lut(xp,yp), zp)