def test_nan(self):
# bail out early if the input data contains nans
x = np.arange(10, dtype=float)
y = x**3
w = np.ones_like(x)
# also test LSQUnivariateSpline [which needs explicit knots]
spl = UnivariateSpline(x, y, check_finite=True)
t = spl.get_knots()[3:4] # interior knots w/ default k=3
y_end = y[-1]
for z in [np.nan, np.inf, -np.inf]:
y[-1] = z
assert_raises(ValueError, UnivariateSpline,
**dict(x=x, y=y, check_finite=True))
assert_raises(ValueError, InterpolatedUnivariateSpline,
**dict(x=x, y=y, check_finite=True))
assert_raises(ValueError, LSQUnivariateSpline,
**dict(x=x, y=y, t=t, check_finite=True))
y[-1] = y_end # check valid y but invalid w
w[-1] = z
assert_raises(ValueError, UnivariateSpline,
**dict(x=x, y=y, w=w, check_finite=True))
assert_raises(ValueError, InterpolatedUnivariateSpline,
**dict(x=x, y=y, w=w, check_finite=True))
assert_raises(ValueError, LSQUnivariateSpline,
**dict(x=x, y=y, t=t, w=w, check_finite=True))
def test_linear_1d(self):
x = [1,2,3]
y = [0,2,4]
lut = UnivariateSpline(x,y,k=1)
assert_array_almost_equal(lut.get_knots(),[1,3])
assert_array_almost_equal(lut.get_coeffs(),[0,4])
assert_almost_equal(lut.get_residual(),0.0)
assert_array_almost_equal(lut([1,1.5,2]),[0,1,2])
def test_increasing_x(self):
xx = np.arange(10, dtype=float)
yy = xx**3
x = np.arange(10, dtype=float)
x[1] = x[0]
y = x**3
w = np.ones_like(x)
# also test LSQUnivariateSpline [which needs explicit knots]
spl = UnivariateSpline(xx, yy, check_finite=True)
t = spl.get_knots()[3:4] # interior knots w/ default k=3
assert_raises(ValueError, UnivariateSpline,
**dict(x=x, y=y, check_finite=True))
assert_raises(ValueError, InterpolatedUnivariateSpline,
**dict(x=x, y=y, check_finite=True))
assert_raises(ValueError, LSQUnivariateSpline,
**dict(x=x, y=y, t=t, w=w, check_finite=True))
def test_increasing_x(self):
xx = np.arange(10, dtype=float)
yy = xx**3
x = np.arange(10, dtype=float)
x[1] = x[0]
y = x**3
w = np.ones_like(x)
# also test LSQUnivariateSpline [which needs explicit knots]
spl = UnivariateSpline(xx, yy, check_finite=True)
t = spl.get_knots()[3:4] # interior knots w/ default k=3
assert_raises(ValueError, UnivariateSpline,
**dict(x=x, y=y, check_finite=True))
assert_raises(ValueError, InterpolatedUnivariateSpline,
**dict(x=x, y=y, check_finite=True))
assert_raises(ValueError, LSQUnivariateSpline,
**dict(x=x, y=y, t=t, w=w, check_finite=True))
def test_strictly_increasing_x(self):
# Test the x is required to be strictly increasing for
# UnivariateSpline if s=0 and for InterpolatedUnivariateSpline,
# but merely increasing for UnivariateSpline if s>0
# and for LSQUnivariateSpline; see gh-8535
xx = np.arange(10, dtype=float)
yy = xx**3
x = np.arange(10, dtype=float)
x[1] = x[0]
y = x**3
w = np.ones_like(x)
# also test LSQUnivariateSpline [which needs explicit knots]
spl = UnivariateSpline(xx, yy, check_finite=True)
t = spl.get_knots()[3:4] # interior knots w/ default k=3
UnivariateSpline(x=x, y=y, w=w, s=1, check_finite=True)
LSQUnivariateSpline(x=x, y=y, t=t, w=w, check_finite=True)
assert_raises(ValueError, UnivariateSpline,
**dict(x=x, y=y, s=0, check_finite=True))
assert_raises(ValueError, InterpolatedUnivariateSpline,
**dict(x=x, y=y, check_finite=True))
