912 lines
38 KiB
Diff
912 lines
38 KiB
Diff
diff --git a/src/doc/en/faq/faq-usage.rst b/src/doc/en/faq/faq-usage.rst
|
|
index 2347a1190d..f5b0fe71a4 100644
|
|
--- a/src/doc/en/faq/faq-usage.rst
|
|
+++ b/src/doc/en/faq/faq-usage.rst
|
|
@@ -338,7 +338,7 @@ ints. For example::
|
|
sage: RealNumber = float; Integer = int
|
|
sage: from scipy import stats
|
|
sage: stats.ttest_ind(list([1,2,3,4,5]),list([2,3,4,5,.6]))
|
|
- Ttest_indResult(statistic=0.076752955645333687, pvalue=0.94070490247380478)
|
|
+ Ttest_indResult(statistic=0.0767529..., pvalue=0.940704...)
|
|
sage: stats.uniform(0,15).ppf([0.5,0.7])
|
|
array([ 7.5, 10.5])
|
|
|
|
diff --git a/src/doc/en/thematic_tutorials/numerical_sage/cvxopt.rst b/src/doc/en/thematic_tutorials/numerical_sage/cvxopt.rst
|
|
index 314811c42b..e5f54ec4c2 100644
|
|
--- a/src/doc/en/thematic_tutorials/numerical_sage/cvxopt.rst
|
|
+++ b/src/doc/en/thematic_tutorials/numerical_sage/cvxopt.rst
|
|
@@ -48,11 +48,13 @@ we could do the following.
|
|
sage: B = numpy.array([1.0]*5)
|
|
sage: B.shape=(5,1)
|
|
sage: print(B)
|
|
- [[ 1.]
|
|
- [ 1.]
|
|
- [ 1.]
|
|
- [ 1.]
|
|
- [ 1.]]
|
|
+ [[1.]
|
|
+ [1.]
|
|
+ [1.]
|
|
+ [1.]
|
|
+ [1.]]
|
|
+
|
|
+
|
|
sage: print(A)
|
|
[ 2.00e+00 3.00e+00 0 0 0 ]
|
|
[ 3.00e+00 0 4.00e+00 0 6.00e+00]
|
|
diff --git a/src/doc/en/thematic_tutorials/numerical_sage/numpy.rst b/src/doc/en/thematic_tutorials/numerical_sage/numpy.rst
|
|
index 5b89cd75ee..e50b2ea5d4 100644
|
|
--- a/src/doc/en/thematic_tutorials/numerical_sage/numpy.rst
|
|
+++ b/src/doc/en/thematic_tutorials/numerical_sage/numpy.rst
|
|
@@ -84,7 +84,7 @@ well as take slices
|
|
sage: l[3]
|
|
3.0
|
|
sage: l[3:6]
|
|
- array([ 3., 4., 5.])
|
|
+ array([3., 4., 5.])
|
|
|
|
You can do basic arithmetic operations
|
|
|
|
@@ -147,11 +147,11 @@ also do matrix vector multiplication, and matrix addition
|
|
sage: n = numpy.matrix([[1,2],[3,4]],dtype=float)
|
|
sage: v = numpy.array([[1],[2]],dtype=float)
|
|
sage: n*v
|
|
- matrix([[ 5.],
|
|
- [ 11.]])
|
|
+ matrix([[ 5.],
|
|
+ [11.]])
|
|
sage: n+n
|
|
- matrix([[ 2., 4.],
|
|
- [ 6., 8.]])
|
|
+ matrix([[2., 4.],
|
|
+ [6., 8.]])
|
|
|
|
If ``n`` was created with :meth:`numpy.array`, then to do matrix vector
|
|
multiplication, you would use ``numpy.dot(n,v)``.
|
|
@@ -170,11 +170,11 @@ to manipulate
|
|
22., 23., 24.])
|
|
sage: n.shape=(5,5)
|
|
sage: n
|
|
- array([[ 0., 1., 2., 3., 4.],
|
|
- [ 5., 6., 7., 8., 9.],
|
|
- [ 10., 11., 12., 13., 14.],
|
|
- [ 15., 16., 17., 18., 19.],
|
|
- [ 20., 21., 22., 23., 24.]])
|
|
+ array([[ 0., 1., 2., 3., 4.],
|
|
+ [ 5., 6., 7., 8., 9.],
|
|
+ [10., 11., 12., 13., 14.],
|
|
+ [15., 16., 17., 18., 19.],
|
|
+ [20., 21., 22., 23., 24.]])
|
|
|
|
This changes the one-dimensional array into a `5\times 5` array.
|
|
|
|
@@ -187,8 +187,8 @@ NumPy arrays can be sliced as well
|
|
sage: n=numpy.array(range(25),dtype=float)
|
|
sage: n.shape=(5,5)
|
|
sage: n[2:4,1:3]
|
|
- array([[ 11., 12.],
|
|
- [ 16., 17.]])
|
|
+ array([[11., 12.],
|
|
+ [16., 17.]])
|
|
|
|
It is important to note that the sliced matrices are references to
|
|
the original
|
|
@@ -224,8 +224,8 @@ Some particularly useful commands are
|
|
|
|
sage: x=numpy.arange(0,2,.1,dtype=float)
|
|
sage: x
|
|
- array([ 0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. ,
|
|
- 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9])
|
|
+ array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. , 1.1, 1.2,
|
|
+ 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9])
|
|
|
|
You can see that :meth:`numpy.arange` creates an array of floats increasing by 0.1
|
|
from 0 to 2. There is a useful command :meth:`numpy.r_` that is best explained by example
|
|
@@ -240,10 +240,11 @@ from 0 to 2. There is a useful command :meth:`numpy.r_` that is best explained b
|
|
sage: Integer=int
|
|
sage: n=r_[0.0:5.0]
|
|
sage: n
|
|
- array([ 0., 1., 2., 3., 4.])
|
|
+ array([0., 1., 2., 3., 4.])
|
|
sage: n=r_[0.0:5.0, [0.0]*5]
|
|
sage: n
|
|
- array([ 0., 1., 2., 3., 4., 0., 0., 0., 0., 0.])
|
|
+ array([0., 1., 2., 3., 4., 0., 0., 0., 0., 0.])
|
|
+
|
|
|
|
:meth:`numpy.r_` provides a shorthand for constructing NumPy arrays efficiently.
|
|
Note in the above ``0.0:5.0`` was shorthand for ``0.0, 1.0, 2.0, 3.0, 4.0``.
|
|
@@ -255,7 +256,7 @@ intervals. We can do this as follows
|
|
::
|
|
|
|
sage: r_[0.0:5.0:11*j]
|
|
- array([ 0. , 0.5, 1. , 1.5, 2. , 2.5, 3. , 3.5, 4. , 4.5, 5. ])
|
|
+ array([0. , 0.5, 1. , 1.5, 2. , 2.5, 3. , 3.5, 4. , 4.5, 5. ])
|
|
|
|
The notation ``0.0:5.0:11*j`` expands to a list of 11 equally space
|
|
points between 0 and 5 including both endpoints. Note that ``j`` is the
|
|
@@ -287,23 +288,23 @@ an equally spaced grid with `\Delta x = \Delta y = .25` for
|
|
sage: y=numpy.r_[0.0:1.0:5*j]
|
|
sage: xx,yy= meshgrid(x,y)
|
|
sage: xx
|
|
- array([[ 0. , 0.25, 0.5 , 0.75, 1. ],
|
|
- [ 0. , 0.25, 0.5 , 0.75, 1. ],
|
|
- [ 0. , 0.25, 0.5 , 0.75, 1. ],
|
|
- [ 0. , 0.25, 0.5 , 0.75, 1. ],
|
|
- [ 0. , 0.25, 0.5 , 0.75, 1. ]])
|
|
+ array([[0. , 0.25, 0.5 , 0.75, 1. ],
|
|
+ [0. , 0.25, 0.5 , 0.75, 1. ],
|
|
+ [0. , 0.25, 0.5 , 0.75, 1. ],
|
|
+ [0. , 0.25, 0.5 , 0.75, 1. ],
|
|
+ [0. , 0.25, 0.5 , 0.75, 1. ]])
|
|
sage: yy
|
|
- array([[ 0. , 0. , 0. , 0. , 0. ],
|
|
- [ 0.25, 0.25, 0.25, 0.25, 0.25],
|
|
- [ 0.5 , 0.5 , 0.5 , 0.5 , 0.5 ],
|
|
- [ 0.75, 0.75, 0.75, 0.75, 0.75],
|
|
- [ 1. , 1. , 1. , 1. , 1. ]])
|
|
+ array([[0. , 0. , 0. , 0. , 0. ],
|
|
+ [0.25, 0.25, 0.25, 0.25, 0.25],
|
|
+ [0.5 , 0.5 , 0.5 , 0.5 , 0.5 ],
|
|
+ [0.75, 0.75, 0.75, 0.75, 0.75],
|
|
+ [1. , 1. , 1. , 1. , 1. ]])
|
|
sage: f(xx,yy)
|
|
- array([[ 0. , 0.0625, 0.25 , 0.5625, 1. ],
|
|
- [ 0.0625, 0.125 , 0.3125, 0.625 , 1.0625],
|
|
- [ 0.25 , 0.3125, 0.5 , 0.8125, 1.25 ],
|
|
- [ 0.5625, 0.625 , 0.8125, 1.125 , 1.5625],
|
|
- [ 1. , 1.0625, 1.25 , 1.5625, 2. ]])
|
|
+ array([[0. , 0.0625, 0.25 , 0.5625, 1. ],
|
|
+ [0.0625, 0.125 , 0.3125, 0.625 , 1.0625],
|
|
+ [0.25 , 0.3125, 0.5 , 0.8125, 1.25 ],
|
|
+ [0.5625, 0.625 , 0.8125, 1.125 , 1.5625],
|
|
+ [1. , 1.0625, 1.25 , 1.5625, 2. ]])
|
|
|
|
You can see that :meth:`numpy.meshgrid` produces a pair of matrices, here denoted
|
|
`xx` and `yy`, such that `(xx[i,j],yy[i,j])` has coordinates
|
|
@@ -324,7 +325,7 @@ equation `Ax=b` do
|
|
sage: b=numpy.array(range(1,6))
|
|
sage: x=linalg.solve(A,b)
|
|
sage: numpy.dot(A,x)
|
|
- array([ 1., 2., 3., 4., 5.])
|
|
+ array([1., 2., 3., 4., 5.])
|
|
|
|
This creates a random 5x5 matrix ``A``, and solves `Ax=b` where
|
|
``b=[0.0,1.0,2.0,3.0,4.0]``. There are many other routines in the :mod:`numpy.linalg`
|
|
diff --git a/src/sage/calculus/riemann.pyx b/src/sage/calculus/riemann.pyx
|
|
index 60f37f7557..4ac3dedf1d 100644
|
|
--- a/src/sage/calculus/riemann.pyx
|
|
+++ b/src/sage/calculus/riemann.pyx
|
|
@@ -1191,30 +1191,30 @@ cpdef complex_to_spiderweb(np.ndarray[COMPLEX_T, ndim = 2] z_values,
|
|
sage: zval = numpy.array([[0, 1, 1000],[.2+.3j,1,-.3j],[0,0,0]],dtype = numpy.complex128)
|
|
sage: deriv = numpy.array([[.1]],dtype = numpy.float64)
|
|
sage: complex_to_spiderweb(zval, deriv,deriv, 4,4,[0,0,0],1,False,0.001)
|
|
- array([[[ 1., 1., 1.],
|
|
- [ 1., 1., 1.],
|
|
- [ 1., 1., 1.]],
|
|
+ array([[[1., 1., 1.],
|
|
+ [1., 1., 1.],
|
|
+ [1., 1., 1.]],
|
|
<BLANKLINE>
|
|
- [[ 1., 1., 1.],
|
|
- [ 0., 0., 0.],
|
|
- [ 1., 1., 1.]],
|
|
+ [[1., 1., 1.],
|
|
+ [0., 0., 0.],
|
|
+ [1., 1., 1.]],
|
|
<BLANKLINE>
|
|
- [[ 1., 1., 1.],
|
|
- [ 1., 1., 1.],
|
|
- [ 1., 1., 1.]]])
|
|
+ [[1., 1., 1.],
|
|
+ [1., 1., 1.],
|
|
+ [1., 1., 1.]]])
|
|
|
|
sage: complex_to_spiderweb(zval, deriv,deriv, 4,4,[0,0,0],1,True,0.001)
|
|
- array([[[ 1. , 1. , 1. ],
|
|
- [ 1. , 0.05558355, 0.05558355],
|
|
- [ 0.17301243, 0. , 0. ]],
|
|
+ array([[[1. , 1. , 1. ],
|
|
+ [1. , 0.05558355, 0.05558355],
|
|
+ [0.17301243, 0. , 0. ]],
|
|
<BLANKLINE>
|
|
- [[ 1. , 0.96804683, 0.48044583],
|
|
- [ 0. , 0. , 0. ],
|
|
- [ 0.77351965, 0.5470393 , 1. ]],
|
|
+ [[1. , 0.96804683, 0.48044583],
|
|
+ [0. , 0. , 0. ],
|
|
+ [0.77351965, 0.5470393 , 1. ]],
|
|
<BLANKLINE>
|
|
- [[ 1. , 1. , 1. ],
|
|
- [ 1. , 1. , 1. ],
|
|
- [ 1. , 1. , 1. ]]])
|
|
+ [[1. , 1. , 1. ],
|
|
+ [1. , 1. , 1. ],
|
|
+ [1. , 1. , 1. ]]])
|
|
"""
|
|
cdef Py_ssize_t i, j, imax, jmax
|
|
cdef FLOAT_T x, y, mag, arg, width, target, precision, dmag, darg
|
|
@@ -1279,14 +1279,14 @@ cpdef complex_to_rgb(np.ndarray[COMPLEX_T, ndim = 2] z_values):
|
|
sage: from sage.calculus.riemann import complex_to_rgb
|
|
sage: import numpy
|
|
sage: complex_to_rgb(numpy.array([[0, 1, 1000]], dtype = numpy.complex128))
|
|
- array([[[ 1. , 1. , 1. ],
|
|
- [ 1. , 0.05558355, 0.05558355],
|
|
- [ 0.17301243, 0. , 0. ]]])
|
|
+ array([[[1. , 1. , 1. ],
|
|
+ [1. , 0.05558355, 0.05558355],
|
|
+ [0.17301243, 0. , 0. ]]])
|
|
|
|
sage: complex_to_rgb(numpy.array([[0, 1j, 1000j]], dtype = numpy.complex128))
|
|
- array([[[ 1. , 1. , 1. ],
|
|
- [ 0.52779177, 1. , 0.05558355],
|
|
- [ 0.08650622, 0.17301243, 0. ]]])
|
|
+ array([[[1. , 1. , 1. ],
|
|
+ [0.52779177, 1. , 0.05558355],
|
|
+ [0.08650622, 0.17301243, 0. ]]])
|
|
|
|
|
|
TESTS::
|
|
diff --git a/src/sage/combinat/fully_packed_loop.py b/src/sage/combinat/fully_packed_loop.py
|
|
index 0a9bd61267..d2193cc2d6 100644
|
|
--- a/src/sage/combinat/fully_packed_loop.py
|
|
+++ b/src/sage/combinat/fully_packed_loop.py
|
|
@@ -72,11 +72,11 @@ def _make_color_list(n, colors=None, color_map=None, randomize=False):
|
|
sage: _make_color_list(5, ['blue', 'red'])
|
|
['blue', 'red', 'blue', 'red', 'blue']
|
|
sage: _make_color_list(5, color_map='summer')
|
|
- [(0.0, 0.5, 0.40000000000000002),
|
|
- (0.25098039215686274, 0.62549019607843137, 0.40000000000000002),
|
|
- (0.50196078431372548, 0.75098039215686274, 0.40000000000000002),
|
|
- (0.75294117647058822, 0.87647058823529411, 0.40000000000000002),
|
|
- (1.0, 1.0, 0.40000000000000002)]
|
|
+ [(0.0, 0.5, 0.4),
|
|
+ (0.25098039215686274, 0.6254901960784314, 0.4),
|
|
+ (0.5019607843137255, 0.7509803921568627, 0.4),
|
|
+ (0.7529411764705882, 0.8764705882352941, 0.4),
|
|
+ (1.0, 1.0, 0.4)]
|
|
sage: _make_color_list(8, ['blue', 'red'], randomize=True)
|
|
['blue', 'blue', 'red', 'blue', 'red', 'red', 'red', 'blue']
|
|
"""
|
|
diff --git a/src/sage/finance/time_series.pyx b/src/sage/finance/time_series.pyx
|
|
index 28779365df..3ab0282861 100644
|
|
--- a/src/sage/finance/time_series.pyx
|
|
+++ b/src/sage/finance/time_series.pyx
|
|
@@ -111,8 +111,8 @@ cdef class TimeSeries:
|
|
|
|
sage: import numpy
|
|
sage: v = numpy.array([[1,2], [3,4]], dtype=float); v
|
|
- array([[ 1., 2.],
|
|
- [ 3., 4.]])
|
|
+ array([[1., 2.],
|
|
+ [3., 4.]])
|
|
sage: finance.TimeSeries(v)
|
|
[1.0000, 2.0000, 3.0000, 4.0000]
|
|
sage: finance.TimeSeries(v[:,0])
|
|
@@ -2100,14 +2100,14 @@ cdef class TimeSeries:
|
|
|
|
sage: w[0] = 20
|
|
sage: w
|
|
- array([ 20. , -3. , 4.5, -2. ])
|
|
+ array([20. , -3. , 4.5, -2. ])
|
|
sage: v
|
|
[20.0000, -3.0000, 4.5000, -2.0000]
|
|
|
|
If you want a separate copy do not give the ``copy=False`` option. ::
|
|
|
|
sage: z = v.numpy(); z
|
|
- array([ 20. , -3. , 4.5, -2. ])
|
|
+ array([20. , -3. , 4.5, -2. ])
|
|
sage: z[0] = -10
|
|
sage: v
|
|
[20.0000, -3.0000, 4.5000, -2.0000]
|
|
diff --git a/src/sage/functions/hyperbolic.py b/src/sage/functions/hyperbolic.py
|
|
index aff552f450..7a6df931e7 100644
|
|
--- a/src/sage/functions/hyperbolic.py
|
|
+++ b/src/sage/functions/hyperbolic.py
|
|
@@ -214,7 +214,7 @@ class Function_coth(GinacFunction):
|
|
sage: import numpy
|
|
sage: a = numpy.arange(2, 5)
|
|
sage: coth(a)
|
|
- array([ 1.03731472, 1.00496982, 1.00067115])
|
|
+ array([1.03731472, 1.00496982, 1.00067115])
|
|
"""
|
|
return 1.0 / tanh(x)
|
|
|
|
@@ -267,7 +267,7 @@ class Function_sech(GinacFunction):
|
|
sage: import numpy
|
|
sage: a = numpy.arange(2, 5)
|
|
sage: sech(a)
|
|
- array([ 0.26580223, 0.09932793, 0.03661899])
|
|
+ array([0.26580223, 0.09932793, 0.03661899])
|
|
"""
|
|
return 1.0 / cosh(x)
|
|
|
|
@@ -318,7 +318,7 @@ class Function_csch(GinacFunction):
|
|
sage: import numpy
|
|
sage: a = numpy.arange(2, 5)
|
|
sage: csch(a)
|
|
- array([ 0.27572056, 0.09982157, 0.03664357])
|
|
+ array([0.27572056, 0.09982157, 0.03664357])
|
|
"""
|
|
return 1.0 / sinh(x)
|
|
|
|
@@ -586,7 +586,7 @@ class Function_arccoth(GinacFunction):
|
|
sage: import numpy
|
|
sage: a = numpy.arange(2,5)
|
|
sage: acoth(a)
|
|
- array([ 0.54930614, 0.34657359, 0.25541281])
|
|
+ array([0.54930614, 0.34657359, 0.25541281])
|
|
"""
|
|
return arctanh(1.0 / x)
|
|
|
|
diff --git a/src/sage/functions/orthogonal_polys.py b/src/sage/functions/orthogonal_polys.py
|
|
index ed6365bef4..99b8b04dad 100644
|
|
--- a/src/sage/functions/orthogonal_polys.py
|
|
+++ b/src/sage/functions/orthogonal_polys.py
|
|
@@ -810,12 +810,12 @@ class Func_chebyshev_T(ChebyshevFunction):
|
|
sage: z2 = numpy.array([[1,2],[1,2]])
|
|
sage: z3 = numpy.array([1,2,3.])
|
|
sage: chebyshev_T(1,z)
|
|
- array([ 1., 2.])
|
|
+ array([1., 2.])
|
|
sage: chebyshev_T(1,z2)
|
|
- array([[ 1., 2.],
|
|
- [ 1., 2.]])
|
|
+ array([[1., 2.],
|
|
+ [1., 2.]])
|
|
sage: chebyshev_T(1,z3)
|
|
- array([ 1., 2., 3.])
|
|
+ array([1., 2., 3.])
|
|
sage: chebyshev_T(z,0.1)
|
|
array([ 0.1 , -0.98])
|
|
"""
|
|
@@ -1095,12 +1095,12 @@ class Func_chebyshev_U(ChebyshevFunction):
|
|
sage: z2 = numpy.array([[1,2],[1,2]])
|
|
sage: z3 = numpy.array([1,2,3.])
|
|
sage: chebyshev_U(1,z)
|
|
- array([ 2., 4.])
|
|
+ array([2., 4.])
|
|
sage: chebyshev_U(1,z2)
|
|
- array([[ 2., 4.],
|
|
- [ 2., 4.]])
|
|
+ array([[2., 4.],
|
|
+ [2., 4.]])
|
|
sage: chebyshev_U(1,z3)
|
|
- array([ 2., 4., 6.])
|
|
+ array([2., 4., 6.])
|
|
sage: chebyshev_U(z,0.1)
|
|
array([ 0.2 , -0.96])
|
|
"""
|
|
diff --git a/src/sage/functions/other.py b/src/sage/functions/other.py
|
|
index 1883daa3e6..9885222817 100644
|
|
--- a/src/sage/functions/other.py
|
|
+++ b/src/sage/functions/other.py
|
|
@@ -389,7 +389,7 @@ class Function_ceil(BuiltinFunction):
|
|
sage: import numpy
|
|
sage: a = numpy.linspace(0,2,6)
|
|
sage: ceil(a)
|
|
- array([ 0., 1., 1., 2., 2., 2.])
|
|
+ array([0., 1., 1., 2., 2., 2.])
|
|
|
|
Test pickling::
|
|
|
|
@@ -553,7 +553,7 @@ class Function_floor(BuiltinFunction):
|
|
sage: import numpy
|
|
sage: a = numpy.linspace(0,2,6)
|
|
sage: floor(a)
|
|
- array([ 0., 0., 0., 1., 1., 2.])
|
|
+ array([0., 0., 0., 1., 1., 2.])
|
|
sage: floor(x)._sympy_()
|
|
floor(x)
|
|
|
|
@@ -869,7 +869,7 @@ def sqrt(x, *args, **kwds):
|
|
sage: import numpy
|
|
sage: a = numpy.arange(2,5)
|
|
sage: sqrt(a)
|
|
- array([ 1.41421356, 1.73205081, 2. ])
|
|
+ array([1.41421356, 1.73205081, 2. ])
|
|
"""
|
|
if isinstance(x, float):
|
|
return math.sqrt(x)
|
|
diff --git a/src/sage/functions/spike_function.py b/src/sage/functions/spike_function.py
|
|
index 1e021de3fe..56635ca98f 100644
|
|
--- a/src/sage/functions/spike_function.py
|
|
+++ b/src/sage/functions/spike_function.py
|
|
@@ -157,7 +157,7 @@ class SpikeFunction:
|
|
sage: S = spike_function([(-3,4),(-1,1),(2,3)]); S
|
|
A spike function with spikes at [-3.0, -1.0, 2.0]
|
|
sage: P = S.plot_fft_abs(8)
|
|
- sage: p = P[0]; p.ydata
|
|
+ sage: p = P[0]; p.ydata # abs tol 1e-8
|
|
[5.0, 5.0, 3.367958691924177, 3.367958691924177, 4.123105625617661, 4.123105625617661, 4.759921664218055, 4.759921664218055]
|
|
"""
|
|
w = self.vector(samples = samples, xmin=xmin, xmax=xmax)
|
|
@@ -176,8 +176,8 @@ class SpikeFunction:
|
|
sage: S = spike_function([(-3,4),(-1,1),(2,3)]); S
|
|
A spike function with spikes at [-3.0, -1.0, 2.0]
|
|
sage: P = S.plot_fft_arg(8)
|
|
- sage: p = P[0]; p.ydata
|
|
- [0.0, 0.0, -0.211524990023434..., -0.211524990023434..., 0.244978663126864..., 0.244978663126864..., -0.149106180027477..., -0.149106180027477...]
|
|
+ sage: p = P[0]; p.ydata # abs tol 1e-8
|
|
+ [0.0, 0.0, -0.211524990023434, -0.211524990023434, 0.244978663126864, 0.244978663126864, -0.149106180027477, -0.149106180027477]
|
|
"""
|
|
w = self.vector(samples = samples, xmin=xmin, xmax=xmax)
|
|
xmin, xmax = self._ranges(xmin, xmax)
|
|
diff --git a/src/sage/functions/trig.py b/src/sage/functions/trig.py
|
|
index 501e7ff6b6..5f760912f0 100644
|
|
--- a/src/sage/functions/trig.py
|
|
+++ b/src/sage/functions/trig.py
|
|
@@ -724,7 +724,7 @@ class Function_arccot(GinacFunction):
|
|
sage: import numpy
|
|
sage: a = numpy.arange(2, 5)
|
|
sage: arccot(a)
|
|
- array([ 0.46364761, 0.32175055, 0.24497866])
|
|
+ array([0.46364761, 0.32175055, 0.24497866])
|
|
"""
|
|
return math.pi/2 - arctan(x)
|
|
|
|
@@ -780,7 +780,7 @@ class Function_arccsc(GinacFunction):
|
|
sage: import numpy
|
|
sage: a = numpy.arange(2, 5)
|
|
sage: arccsc(a)
|
|
- array([ 0.52359878, 0.33983691, 0.25268026])
|
|
+ array([0.52359878, 0.33983691, 0.25268026])
|
|
"""
|
|
return arcsin(1.0/x)
|
|
|
|
@@ -838,7 +838,7 @@ class Function_arcsec(GinacFunction):
|
|
sage: import numpy
|
|
sage: a = numpy.arange(2, 5)
|
|
sage: arcsec(a)
|
|
- array([ 1.04719755, 1.23095942, 1.31811607])
|
|
+ array([1.04719755, 1.23095942, 1.31811607])
|
|
"""
|
|
return arccos(1.0/x)
|
|
|
|
@@ -913,13 +913,13 @@ class Function_arctan2(GinacFunction):
|
|
sage: a = numpy.linspace(1, 3, 3)
|
|
sage: b = numpy.linspace(3, 6, 3)
|
|
sage: atan2(a, b)
|
|
- array([ 0.32175055, 0.41822433, 0.46364761])
|
|
+ array([0.32175055, 0.41822433, 0.46364761])
|
|
|
|
sage: atan2(1,a)
|
|
- array([ 0.78539816, 0.46364761, 0.32175055])
|
|
+ array([0.78539816, 0.46364761, 0.32175055])
|
|
|
|
sage: atan2(a, 1)
|
|
- array([ 0.78539816, 1.10714872, 1.24904577])
|
|
+ array([0.78539816, 1.10714872, 1.24904577])
|
|
|
|
TESTS::
|
|
|
|
diff --git a/src/sage/matrix/constructor.pyx b/src/sage/matrix/constructor.pyx
|
|
index 12136f1773..491bf22e62 100644
|
|
--- a/src/sage/matrix/constructor.pyx
|
|
+++ b/src/sage/matrix/constructor.pyx
|
|
@@ -503,8 +503,8 @@ def matrix(*args, **kwds):
|
|
[7 8 9]
|
|
Full MatrixSpace of 3 by 3 dense matrices over Integer Ring
|
|
sage: n = matrix(QQ, 2, 2, [1, 1/2, 1/3, 1/4]).numpy(); n
|
|
- array([[ 1. , 0.5 ],
|
|
- [ 0.33333333, 0.25 ]])
|
|
+ array([[1. , 0.5 ],
|
|
+ [0.33333333, 0.25 ]])
|
|
sage: matrix(QQ, n)
|
|
[ 1 1/2]
|
|
[1/3 1/4]
|
|
diff --git a/src/sage/matrix/matrix_double_dense.pyx b/src/sage/matrix/matrix_double_dense.pyx
|
|
index 66e54a79a4..0498334f4b 100644
|
|
--- a/src/sage/matrix/matrix_double_dense.pyx
|
|
+++ b/src/sage/matrix/matrix_double_dense.pyx
|
|
@@ -606,6 +606,9 @@ cdef class Matrix_double_dense(Matrix_dense):
|
|
[ 3.0 + 9.0*I 4.0 + 16.0*I 5.0 + 25.0*I]
|
|
[6.0 + 36.0*I 7.0 + 49.0*I 8.0 + 64.0*I]
|
|
sage: B.condition()
|
|
+ doctest:warning
|
|
+ ...
|
|
+ ComplexWarning: Casting complex values to real discards the imaginary part
|
|
203.851798...
|
|
sage: B.condition(p='frob')
|
|
203.851798...
|
|
@@ -654,9 +657,7 @@ cdef class Matrix_double_dense(Matrix_dense):
|
|
True
|
|
sage: B = A.change_ring(CDF)
|
|
sage: B.condition()
|
|
- Traceback (most recent call last):
|
|
- ...
|
|
- LinAlgError: Singular matrix
|
|
+ +Infinity
|
|
|
|
Improper values of ``p`` are caught. ::
|
|
|
|
@@ -2519,7 +2520,7 @@ cdef class Matrix_double_dense(Matrix_dense):
|
|
sage: P.is_unitary(algorithm='orthonormal')
|
|
Traceback (most recent call last):
|
|
...
|
|
- ValueError: failed to create intent(cache|hide)|optional array-- must have defined dimensions but got (0,)
|
|
+ error: ((lwork==-1)||(lwork >= MAX(1,2*n))) failed for 3rd keyword lwork: zgees:lwork=0
|
|
|
|
TESTS::
|
|
|
|
@@ -3635,8 +3636,8 @@ cdef class Matrix_double_dense(Matrix_dense):
|
|
[0.0 1.0 2.0]
|
|
[3.0 4.0 5.0]
|
|
sage: m.numpy()
|
|
- array([[ 0., 1., 2.],
|
|
- [ 3., 4., 5.]])
|
|
+ array([[0., 1., 2.],
|
|
+ [3., 4., 5.]])
|
|
|
|
Alternatively, numpy automatically calls this function (via
|
|
the magic :meth:`__array__` method) to convert Sage matrices
|
|
@@ -3647,16 +3648,16 @@ cdef class Matrix_double_dense(Matrix_dense):
|
|
[0.0 1.0 2.0]
|
|
[3.0 4.0 5.0]
|
|
sage: numpy.array(m)
|
|
- array([[ 0., 1., 2.],
|
|
- [ 3., 4., 5.]])
|
|
+ array([[0., 1., 2.],
|
|
+ [3., 4., 5.]])
|
|
sage: numpy.array(m).dtype
|
|
dtype('float64')
|
|
sage: m = matrix(CDF, 2, range(6)); m
|
|
[0.0 1.0 2.0]
|
|
[3.0 4.0 5.0]
|
|
sage: numpy.array(m)
|
|
- array([[ 0.+0.j, 1.+0.j, 2.+0.j],
|
|
- [ 3.+0.j, 4.+0.j, 5.+0.j]])
|
|
+ array([[0.+0.j, 1.+0.j, 2.+0.j],
|
|
+ [3.+0.j, 4.+0.j, 5.+0.j]])
|
|
sage: numpy.array(m).dtype
|
|
dtype('complex128')
|
|
|
|
diff --git a/src/sage/matrix/special.py b/src/sage/matrix/special.py
|
|
index ccbd208810..c3f9a65093 100644
|
|
--- a/src/sage/matrix/special.py
|
|
+++ b/src/sage/matrix/special.py
|
|
@@ -706,7 +706,7 @@ def diagonal_matrix(arg0=None, arg1=None, arg2=None, sparse=True):
|
|
|
|
sage: import numpy
|
|
sage: entries = numpy.array([1.2, 5.6]); entries
|
|
- array([ 1.2, 5.6])
|
|
+ array([1.2, 5.6])
|
|
sage: A = diagonal_matrix(3, entries); A
|
|
[1.2 0.0 0.0]
|
|
[0.0 5.6 0.0]
|
|
@@ -716,7 +716,7 @@ def diagonal_matrix(arg0=None, arg1=None, arg2=None, sparse=True):
|
|
|
|
sage: j = numpy.complex(0,1)
|
|
sage: entries = numpy.array([2.0+j, 8.1, 3.4+2.6*j]); entries
|
|
- array([ 2.0+1.j , 8.1+0.j , 3.4+2.6j])
|
|
+ array([2. +1.j , 8.1+0.j , 3.4+2.6j])
|
|
sage: A = diagonal_matrix(entries); A
|
|
[2.0 + 1.0*I 0.0 0.0]
|
|
[ 0.0 8.1 0.0]
|
|
diff --git a/src/sage/modules/free_module_element.pyx b/src/sage/modules/free_module_element.pyx
|
|
index 37d92c1282..955d083b34 100644
|
|
--- a/src/sage/modules/free_module_element.pyx
|
|
+++ b/src/sage/modules/free_module_element.pyx
|
|
@@ -988,7 +988,7 @@ cdef class FreeModuleElement(Vector): # abstract base class
|
|
sage: v.numpy()
|
|
array([1, 2, 5/6], dtype=object)
|
|
sage: v.numpy(dtype=float)
|
|
- array([ 1. , 2. , 0.83333333])
|
|
+ array([1. , 2. , 0.83333333])
|
|
sage: v.numpy(dtype=int)
|
|
array([1, 2, 0])
|
|
sage: import numpy
|
|
@@ -999,7 +999,7 @@ cdef class FreeModuleElement(Vector): # abstract base class
|
|
be more efficient but may have unintended consequences::
|
|
|
|
sage: v.numpy(dtype=None)
|
|
- array([ 1. , 2. , 0.83333333])
|
|
+ array([1. , 2. , 0.83333333])
|
|
|
|
sage: w = vector(ZZ, [0, 1, 2^63 -1]); w
|
|
(0, 1, 9223372036854775807)
|
|
diff --git a/src/sage/modules/vector_double_dense.pyx b/src/sage/modules/vector_double_dense.pyx
|
|
index 39fc2970de..2badf98284 100644
|
|
--- a/src/sage/modules/vector_double_dense.pyx
|
|
+++ b/src/sage/modules/vector_double_dense.pyx
|
|
@@ -807,13 +807,13 @@ cdef class Vector_double_dense(FreeModuleElement):
|
|
|
|
sage: v = vector(CDF,4,range(4))
|
|
sage: v.numpy()
|
|
- array([ 0.+0.j, 1.+0.j, 2.+0.j, 3.+0.j])
|
|
+ array([0.+0.j, 1.+0.j, 2.+0.j, 3.+0.j])
|
|
sage: v = vector(CDF,0)
|
|
sage: v.numpy()
|
|
array([], dtype=complex128)
|
|
sage: v = vector(RDF,4,range(4))
|
|
sage: v.numpy()
|
|
- array([ 0., 1., 2., 3.])
|
|
+ array([0., 1., 2., 3.])
|
|
sage: v = vector(RDF,0)
|
|
sage: v.numpy()
|
|
array([], dtype=float64)
|
|
@@ -823,11 +823,11 @@ cdef class Vector_double_dense(FreeModuleElement):
|
|
sage: import numpy
|
|
sage: v = vector(CDF, 3, range(3))
|
|
sage: v.numpy()
|
|
- array([ 0.+0.j, 1.+0.j, 2.+0.j])
|
|
+ array([0.+0.j, 1.+0.j, 2.+0.j])
|
|
sage: v.numpy(dtype=numpy.float64)
|
|
- array([ 0., 1., 2.])
|
|
+ array([0., 1., 2.])
|
|
sage: v.numpy(dtype=numpy.float32)
|
|
- array([ 0., 1., 2.], dtype=float32)
|
|
+ array([0., 1., 2.], dtype=float32)
|
|
"""
|
|
if dtype is None or dtype is self._vector_numpy.dtype:
|
|
from copy import copy
|
|
diff --git a/src/sage/plot/complex_plot.pyx b/src/sage/plot/complex_plot.pyx
|
|
index ad9693da62..758fb709b7 100644
|
|
--- a/src/sage/plot/complex_plot.pyx
|
|
+++ b/src/sage/plot/complex_plot.pyx
|
|
@@ -61,9 +61,9 @@ cdef inline double mag_to_lightness(double r):
|
|
|
|
sage: from sage.plot.complex_plot import complex_to_rgb
|
|
sage: complex_to_rgb([[0, 1, 10]])
|
|
- array([[[ 0. , 0. , 0. ],
|
|
- [ 0.77172568, 0. , 0. ],
|
|
- [ 1. , 0.22134776, 0.22134776]]])
|
|
+ array([[[0. , 0. , 0. ],
|
|
+ [0.77172568, 0. , 0. ],
|
|
+ [1. , 0.22134776, 0.22134776]]])
|
|
"""
|
|
return atan(log(sqrt(r)+1)) * (4/PI) - 1
|
|
|
|
@@ -82,13 +82,13 @@ def complex_to_rgb(z_values):
|
|
|
|
sage: from sage.plot.complex_plot import complex_to_rgb
|
|
sage: complex_to_rgb([[0, 1, 1000]])
|
|
- array([[[ 0. , 0. , 0. ],
|
|
- [ 0.77172568, 0. , 0. ],
|
|
- [ 1. , 0.64421177, 0.64421177]]])
|
|
+ array([[[0. , 0. , 0. ],
|
|
+ [0.77172568, 0. , 0. ],
|
|
+ [1. , 0.64421177, 0.64421177]]])
|
|
sage: complex_to_rgb([[0, 1j, 1000j]])
|
|
- array([[[ 0. , 0. , 0. ],
|
|
- [ 0.38586284, 0.77172568, 0. ],
|
|
- [ 0.82210588, 1. , 0.64421177]]])
|
|
+ array([[[0. , 0. , 0. ],
|
|
+ [0.38586284, 0.77172568, 0. ],
|
|
+ [0.82210588, 1. , 0.64421177]]])
|
|
"""
|
|
import numpy
|
|
cdef unsigned int i, j, imax, jmax
|
|
diff --git a/src/sage/plot/histogram.py b/src/sage/plot/histogram.py
|
|
index 5d28473731..fc4b2046c0 100644
|
|
--- a/src/sage/plot/histogram.py
|
|
+++ b/src/sage/plot/histogram.py
|
|
@@ -53,10 +53,17 @@ class Histogram(GraphicPrimitive):
|
|
"""
|
|
import numpy as np
|
|
self.datalist=np.asarray(datalist,dtype=float)
|
|
+ if 'normed' in options:
|
|
+ from sage.misc.superseded import deprecation
|
|
+ deprecation(25260, "the 'normed' option is deprecated. Use 'density' instead.")
|
|
if 'linestyle' in options:
|
|
from sage.plot.misc import get_matplotlib_linestyle
|
|
options['linestyle'] = get_matplotlib_linestyle(
|
|
options['linestyle'], return_type='long')
|
|
+ if options.get('range', None):
|
|
+ # numpy.histogram performs type checks on "range" so this must be
|
|
+ # actual floats
|
|
+ options['range'] = [float(x) for x in options['range']]
|
|
GraphicPrimitive.__init__(self, options)
|
|
|
|
def get_minmax_data(self):
|
|
@@ -80,10 +87,14 @@ class Histogram(GraphicPrimitive):
|
|
{'xmax': 4.0, 'xmin': 0, 'ymax': 2, 'ymin': 0}
|
|
|
|
TESTS::
|
|
-
|
|
sage: h = histogram([10,3,5], normed=True)[0]
|
|
- sage: h.get_minmax_data() # rel tol 1e-15
|
|
- {'xmax': 10.0, 'xmin': 3.0, 'ymax': 0.4761904761904765, 'ymin': 0}
|
|
+ doctest:warning...:
|
|
+ DeprecationWarning: the 'normed' option is deprecated. Use 'density' instead.
|
|
+ See https://trac.sagemath.org/25260 for details.
|
|
+ sage: h.get_minmax_data()
|
|
+ doctest:warning ...:
|
|
+ VisibleDeprecationWarning: Passing `normed=True` on non-uniform bins has always been broken, and computes neither the probability density function nor the probability mass function. The result is only correct if the bins are uniform, when density=True will produce the same result anyway. The argument will be removed in a future version of numpy.
|
|
+ {'xmax': 10.0, 'xmin': 3.0, 'ymax': 0.476190476190..., 'ymin': 0}
|
|
"""
|
|
import numpy
|
|
|
|
@@ -152,7 +163,7 @@ class Histogram(GraphicPrimitive):
|
|
'rwidth': 'The relative width of the bars as a fraction of the bin width',
|
|
'cumulative': '(True or False) If True, then a histogram is computed in which each bin gives the counts in that bin plus all bins for smaller values. Negative values give a reversed direction of accumulation.',
|
|
'range': 'A list [min, max] which define the range of the histogram. Values outside of this range are treated as outliers and omitted from counts.',
|
|
- 'normed': 'Deprecated alias for density',
|
|
+ 'normed': 'Deprecated. Use density instead.',
|
|
'density': '(True or False) If True, the counts are normalized to form a probability density. (n/(len(x)*dbin)',
|
|
'weights': 'A sequence of weights the same length as the data list. If supplied, then each value contributes its associated weight to the bin count.',
|
|
'stacked': '(True or False) If True, multiple data are stacked on top of each other.',
|
|
@@ -199,7 +210,7 @@ class Histogram(GraphicPrimitive):
|
|
subplot.hist(self.datalist.transpose(), **options)
|
|
|
|
|
|
-@options(aspect_ratio='automatic',align='mid', weights=None, range=None, bins=10, edgecolor='black')
|
|
+@options(aspect_ratio='automatic', align='mid', weights=None, range=None, bins=10, edgecolor='black')
|
|
def histogram(datalist, **options):
|
|
"""
|
|
Computes and draws the histogram for list(s) of numerical data.
|
|
@@ -231,8 +242,9 @@ def histogram(datalist, **options):
|
|
- ``linewidth`` -- (float) width of the lines defining the bars
|
|
- ``linestyle`` -- (default: 'solid') Style of the line. One of 'solid'
|
|
or '-', 'dashed' or '--', 'dotted' or ':', 'dashdot' or '-.'
|
|
- - ``density`` -- (boolean - default: False) If True, the counts are
|
|
- normalized to form a probability density.
|
|
+ - ``density`` -- (boolean - default: False) If True, the result is the
|
|
+ value of the probability density function at the bin, normalized such
|
|
+ that the integral over the range is 1.
|
|
- ``range`` -- A list [min, max] which define the range of the
|
|
histogram. Values outside of this range are treated as outliers and
|
|
omitted from counts
|
|
diff --git a/src/sage/plot/line.py b/src/sage/plot/line.py
|
|
index 23f5e61446..3b1b51d7cf 100644
|
|
--- a/src/sage/plot/line.py
|
|
+++ b/src/sage/plot/line.py
|
|
@@ -502,14 +502,12 @@ def line2d(points, **options):
|
|
from sage.plot.all import Graphics
|
|
from sage.plot.plot import xydata_from_point_list
|
|
from sage.rings.all import CC, CDF
|
|
+ points = list(points) # make sure points is a python list
|
|
if points in CC or points in CDF:
|
|
pass
|
|
else:
|
|
- try:
|
|
- if not points:
|
|
- return Graphics()
|
|
- except ValueError: # numpy raises a ValueError if not empty
|
|
- pass
|
|
+ if len(points) == 0:
|
|
+ return Graphics()
|
|
xdata, ydata = xydata_from_point_list(points)
|
|
g = Graphics()
|
|
g._set_extra_kwds(Graphics._extract_kwds_for_show(options))
|
|
diff --git a/src/sage/plot/plot_field.py b/src/sage/plot/plot_field.py
|
|
index 0025098a8d..23c80902f3 100644
|
|
--- a/src/sage/plot/plot_field.py
|
|
+++ b/src/sage/plot/plot_field.py
|
|
@@ -49,9 +49,10 @@ class PlotField(GraphicPrimitive):
|
|
sage: r.xpos_array
|
|
[0.0, 0.0, 1.0, 1.0]
|
|
sage: r.yvec_array
|
|
- masked_array(data = [0.0 0.70710678118... 0.70710678118... 0.89442719...],
|
|
- mask = [False False False False],
|
|
- fill_value = 1e+20)
|
|
+ masked_array(data=[0.0, 0.70710678118..., 0.70710678118...,
|
|
+ 0.89442719...],
|
|
+ mask=[False, False, False, False],
|
|
+ fill_value=1e+20)
|
|
|
|
TESTS:
|
|
|
|
diff --git a/src/sage/plot/streamline_plot.py b/src/sage/plot/streamline_plot.py
|
|
index f3da57c370..3806f4b32f 100644
|
|
--- a/src/sage/plot/streamline_plot.py
|
|
+++ b/src/sage/plot/streamline_plot.py
|
|
@@ -38,16 +38,14 @@ class StreamlinePlot(GraphicPrimitive):
|
|
sage: r.options()['plot_points']
|
|
2
|
|
sage: r.xpos_array
|
|
- array([ 0., 1.])
|
|
+ array([0., 1.])
|
|
sage: r.yvec_array
|
|
- masked_array(data =
|
|
- [[1.0 1.0]
|
|
- [0.5403023058681398 0.5403023058681398]],
|
|
- mask =
|
|
- [[False False]
|
|
- [False False]],
|
|
- fill_value = 1e+20)
|
|
- <BLANKLINE>
|
|
+ masked_array(
|
|
+ data=[[1.0, 1.0],
|
|
+ [0.5403023058681398, 0.5403023058681398]],
|
|
+ mask=[[False, False],
|
|
+ [False, False]],
|
|
+ fill_value=1e+20)
|
|
|
|
TESTS:
|
|
|
|
diff --git a/src/sage/probability/probability_distribution.pyx b/src/sage/probability/probability_distribution.pyx
|
|
index 1b119e323f..3290b00695 100644
|
|
--- a/src/sage/probability/probability_distribution.pyx
|
|
+++ b/src/sage/probability/probability_distribution.pyx
|
|
@@ -130,7 +130,17 @@ cdef class ProbabilityDistribution:
|
|
0.0,
|
|
1.4650000000000003]
|
|
sage: b
|
|
- [0.0, 0.20000000000000001, 0.40000000000000002, 0.60000000000000009, 0.80000000000000004, 1.0, 1.2000000000000002, 1.4000000000000001, 1.6000000000000001, 1.8, 2.0]
|
|
+ [0.0,
|
|
+ 0.2,
|
|
+ 0.4,
|
|
+ 0.6000000000000001,
|
|
+ 0.8,
|
|
+ 1.0,
|
|
+ 1.2000000000000002,
|
|
+ 1.4000000000000001,
|
|
+ 1.6,
|
|
+ 1.8,
|
|
+ 2.0]
|
|
"""
|
|
import pylab
|
|
l = [float(self.get_random_element()) for _ in range(num_samples)]
|
|
diff --git a/src/sage/rings/rational.pyx b/src/sage/rings/rational.pyx
|
|
index 12ca1b222b..9bad7dae0c 100644
|
|
--- a/src/sage/rings/rational.pyx
|
|
+++ b/src/sage/rings/rational.pyx
|
|
@@ -1041,7 +1041,7 @@ cdef class Rational(sage.structure.element.FieldElement):
|
|
dtype('O')
|
|
|
|
sage: numpy.array([1, 1/2, 3/4])
|
|
- array([ 1. , 0.5 , 0.75])
|
|
+ array([1. , 0.5 , 0.75])
|
|
"""
|
|
if mpz_cmp_ui(mpq_denref(self.value), 1) == 0:
|
|
if mpz_fits_slong_p(mpq_numref(self.value)):
|
|
diff --git a/src/sage/rings/real_mpfr.pyx b/src/sage/rings/real_mpfr.pyx
|
|
index 9b90c8833e..1ce05b937d 100644
|
|
--- a/src/sage/rings/real_mpfr.pyx
|
|
+++ b/src/sage/rings/real_mpfr.pyx
|
|
@@ -1439,7 +1439,7 @@ cdef class RealNumber(sage.structure.element.RingElement):
|
|
|
|
sage: import numpy
|
|
sage: numpy.arange(10.0)
|
|
- array([ 0., 1., 2., 3., 4., 5., 6., 7., 8., 9.])
|
|
+ array([0., 1., 2., 3., 4., 5., 6., 7., 8., 9.])
|
|
sage: numpy.array([1.0, 1.1, 1.2]).dtype
|
|
dtype('float64')
|
|
sage: numpy.array([1.000000000000000000000000000000000000]).dtype
|
|
diff --git a/src/sage/schemes/elliptic_curves/height.py b/src/sage/schemes/elliptic_curves/height.py
|
|
index de31fe9883..7a33ea6f5b 100644
|
|
--- a/src/sage/schemes/elliptic_curves/height.py
|
|
+++ b/src/sage/schemes/elliptic_curves/height.py
|
|
@@ -1627,18 +1627,18 @@ class EllipticCurveCanonicalHeight:
|
|
even::
|
|
|
|
sage: H.wp_on_grid(v,4)
|
|
- array([[ 25.43920182, 5.28760943, 5.28760943, 25.43920182],
|
|
- [ 6.05099485, 1.83757786, 1.83757786, 6.05099485],
|
|
- [ 6.05099485, 1.83757786, 1.83757786, 6.05099485],
|
|
- [ 25.43920182, 5.28760943, 5.28760943, 25.43920182]])
|
|
+ array([[25.43920182, 5.28760943, 5.28760943, 25.43920182],
|
|
+ [ 6.05099485, 1.83757786, 1.83757786, 6.05099485],
|
|
+ [ 6.05099485, 1.83757786, 1.83757786, 6.05099485],
|
|
+ [25.43920182, 5.28760943, 5.28760943, 25.43920182]])
|
|
|
|
The array of values on the half-grid::
|
|
|
|
sage: H.wp_on_grid(v,4,True)
|
|
- array([[ 25.43920182, 5.28760943],
|
|
- [ 6.05099485, 1.83757786],
|
|
- [ 6.05099485, 1.83757786],
|
|
- [ 25.43920182, 5.28760943]])
|
|
+ array([[25.43920182, 5.28760943],
|
|
+ [ 6.05099485, 1.83757786],
|
|
+ [ 6.05099485, 1.83757786],
|
|
+ [25.43920182, 5.28760943]])
|
|
"""
|
|
tau = self.tau(v)
|
|
fk, err = self.fk_intervals(v, 15, CDF)
|
|
diff --git a/src/sage/symbolic/ring.pyx b/src/sage/symbolic/ring.pyx
|
|
index 9da38002e8..d61e74bf82 100644
|
|
--- a/src/sage/symbolic/ring.pyx
|
|
+++ b/src/sage/symbolic/ring.pyx
|
|
@@ -1136,7 +1136,7 @@ cdef class NumpyToSRMorphism(Morphism):
|
|
sage: cos(numpy.int('2'))
|
|
cos(2)
|
|
sage: numpy.cos(numpy.int('2'))
|
|
- -0.41614683654714241
|
|
+ -0.4161468365471424
|
|
"""
|
|
cdef _intermediate_ring
|
|
|