Merge in latest develop branch

build/pkgs/numpy/checksums.ini

@@ -1,4 +1,4 @@
-tarball=numpy-VERSION.zip
-sha1=3d39f6768fa861c6b7000bb33182eb2599954d45
-md5=300a6f0528122128ac07c6deb5c95917
-cksum=4207303903
+tarball=numpy-VERSION.tar.gz
+sha1=e4917f6848f4065a70788b954332f1eb87b81989
+md5=5cf4daff88042326334266f80ad38884
+cksum=3849172272

build/pkgs/numpy/package-version.txt

@@ -1 +1 @@
-1.13.3.p2
+1.15.0

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])
 

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]

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`

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::

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']
     """

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]

src/sage/functions/hyperbolic.py

@@ -214,7 +214,7 @@ def _eval_numpy_(self, x):
             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 @@ def _eval_numpy_(self, x):
             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 @@ def _eval_numpy_(self, x):
             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 @@ def _eval_numpy_(self, x):
             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)
 

src/sage/functions/orthogonal_polys.py

@@ -810,12 +810,12 @@ def _eval_numpy_(self, n, x):
             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 @@ def _eval_numpy_(self, n, x):
             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])
         """