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sage: 8.4 -> 8.5.beta0

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This commit is contained in:
Timo Kaufmann 2018-11-05 11:02:56 +01:00
parent fe551d1d53
commit 6138f5d5eb
2 changed files with 2 additions and 916 deletions
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911
pkgs/applications/science/math/sage/patches/numpy-1.15.1.patch
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@ -1,911 +0,0 @@
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

7
pkgs/applications/science/math/sage/sage-src.nix
View File

@ -9,14 +9,14 @@
# all get the same sources with the same patches applied.
stdenv.mkDerivation rec {
version = "8.4";
version = "8.5.beta0";
name = "sage-src-${version}";
src = fetchFromGitHub {
owner = "sagemath";
repo = "sage";
rev = version;
sha256 = "0gips1hagiz9m7s21bg5as8hrrm2x5k47h1bsq0pc46iplfwmv2d";
sha256 = "0kc8fqwz97dwyf4hxz5yr9sjwh5q0jr7a9g1yrdaz66m5805r859";
};
# Patches needed because of particularities of nix or the way this is packaged.
@ -76,9 +76,6 @@ stdenv.mkDerivation rec {
stripLen = 1;
})
# https://trac.sagemath.org/ticket/25260
./patches/numpy-1.15.1.patch
# needed for ntl update
# https://trac.sagemath.org/ticket/25532
(fetchpatch {