8000 Merge pull request #4872 from juliantaylor/backports-1.9 · numpy/numpy@029662b · GitHub
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charrischarris
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Merge pull request #4872 from juliantaylor/backports-1.9
Backports stuff from master to 1.9
2 parents b6903d7 + 2a87c3e commit 029662b

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6 files changed

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‎doc/source/reference/routines.polynomials.classes.rst

Lines changed: 3 additions & 3 deletions
Original file line numberDiff line numberDiff line change
@@ -211,7 +211,7 @@ constant are 0, but both can be specified.::
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In the first case the lower bound of the integration is set to -1 and the
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integration constant is 0. In the second the constant of integration is set
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to 1 as well. Differentiation is simpler since the only option is the
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number times the polynomial is differentiated::
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number of times the polynomial is differentiated::
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>>> p = P([1, 2, 3])
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>>> p.deriv(1)
@@ -270,7 +270,7 @@ polynomials up to degree 5 are plotted below.
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>>> import matplotlib.pyplot as plt
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>>> from numpy.polynomial import Chebyshev as T
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>>> x = np.linspace(-1, 1, 100)
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>>> for i in range(6): ax = plt.plot(x, T.basis(i)(x), lw=2, label="T_%d"%i)
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>>> for i in range(6): ax = plt.plot(x, T.basis(i)(x), lw=2, label="$T_%d$"%i)
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...
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>>> plt.legend(loc="upper left")
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<matplotlib.legend.Legend object at 0x3b3ee10>
@@ -284,7 +284,7 @@ The same plots over the range -2 <= `x` <= 2 look very different:
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>>> import matplotlib.pyplot as plt
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>>> from numpy.polynomial import Chebyshev as T
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>>> x = np.linspace(-2, 2, 100)
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>>> for i in range(6): ax = plt.plot(x, T.basis(i)(x), lw=2, label="T_%d"%i)
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>>> for i in range(6): ax = plt.plot(x, T.basis(i)(x), lw=2, label="$T_%d$"%i)
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...
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>>> plt.legend(loc="lower right")
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<matplotlib.legend.Legend object at 0x3b3ee10>

‎numpy/core/src/umath/simd.inc.src

Lines changed: 3 additions & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -37,7 +37,9 @@
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((abs(args[1] - args[0]) >= (vsize)) || ((abs(args[1] - args[0]) == 0))))
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#define IS_BLOCKABLE_REDUCE(esize, vsize) \
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(steps[1] == (esize) && abs(args[1] - args[0]) >= (vsize))
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(steps[1] == (esize) && abs(args[1] - args[0]) >= (vsize) && \
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npy_is_aligned(args[1], (esize)) && \
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npy_is_aligned(args[0], (esize)))
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#define IS_BLOCKABLE_BINARY(esize, vsize) \
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(steps[0] == steps[1] && steps[1] == steps[2] && steps[2] == (esize) && \

‎numpy/core/tests/test_scalarmath.py

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Original file line numberDiff line numberDiff line change
@@ -83,6 +83,18 @@ def test_blocked(self):
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np.add(1, inp2, out=out)
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assert_almost_equal(out, exp1, err_msg=msg)
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def test_lower_align(self):
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# check data that is not aligned to element size
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# i.e doubles are aligned to 4 bytes on i386
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d = np.zeros(23 * 8, dtype=np.int8)[4:-4].view(np.float64)
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o = np.zeros(23 * 8, dtype=np.int8)[4:-4].view(np.float64)
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assert_almost_equal(d + d, d * 2)
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np.add(d, d, out=o)
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np.add(np.ones_like(d), d, out=o)
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np.add(d, np.ones_like(d), out=o)
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np.add(np.ones_like(d), d)
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np.add(d, np.ones_like(d))
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class TestPower(TestCase):
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def test_small_types(self):

‎numpy/core/tests/test_umath.py

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Original file line numberDiff line numberDiff line change
@@ -753,6 +753,13 @@ def test_minmax_blocked(self):
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inp[i] = -1e10
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assert_equal(inp.min(), -1e10, err_msg=msg)
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def test_lower_align(self):
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# check data that is not aligned to element size
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# i.e doubles are aligned to 4 bytes on i386
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d = np.zeros(23 * 8, dtype=np.int8)[4:-4].view(np.float64)
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assert_equal(d.max(), d[0])
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assert_equal(d.min(), d[0])
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class TestAbsoluteNegative(TestCase):
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def test_abs_neg_blocked(self):
@@ -785,6 +792,17 @@ def test_abs_neg_blocked(self):
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np.negative(inp, out=out)
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assert_array_equal(out, -1*inp, err_msg=msg)
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795+
def test_lower_align(self):
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# check data that is not aligned to element size
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# i.e doubles are aligned to 4 bytes on i386
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d = np.zeros(23 * 8, dtype=np.int8)[4:-4].view(np.float64)
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assert_equal(np.abs(d), d)
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assert_equal(np.negative(d), -d)
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np.negative(d, out=d)
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np.negative(np.ones_like(d), out=d)
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np.abs(d, out=d)
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np.abs(np.ones_like(d), out=d)
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class TestSpecialMethods(TestCase):
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def test_wrap(self):

‎numpy/lib/tests/test_twodim_base.py

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Original file line numberDiff line numberDiff line change
@@ -311,6 +311,18 @@ def test_tril_triu_ndim3():
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yield assert_equal, a_triu_observed.dtype, a.dtype
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yield assert_equal, a_tril_observed.dtype, a.dtype
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314+
def test_tril_triu_with_inf():
315+
# Issue 4859
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arr = np.array([[1, 1, np.inf],
317+
[1, 1, 1],
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[np.inf, 1, 1]])
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out_tril = np.array([[1, 0, 0],
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[1, 1, 0],
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[np.inf, 1, 1]])
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out_triu = out_tril.T
323+
assert_array_equal(np.triu(arr), out_triu)
324+
assert_array_equal(np.tril(arr), out_tril)
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def test_mask_indices():
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# simple test without offset

‎numpy/lib/twodim_base.py

Lines changed: 6 additions & 3 deletions
Original file line numberDiff line numberDiff line change
@@ -387,7 +387,6 @@ def tri(N, M=None, k=0, dtype=float):
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dtype : dtype, optional
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Data type of the returned array. The default is float.
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Returns
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-------
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tri : ndarray of shape (N, M)
@@ -452,7 +451,9 @@ def tril(m, k=0):
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"""
454453
m = asanyarray(m)
455-
return multiply(tri(*m.shape[-2:], k=k, dtype=bool), m, dtype=m.dtype)
454+
mask = tri(*m.shape[-2:], k=k, dtype=bool)
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456+
return where(mask, m, 0)
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458459
def triu(m, k=0):
@@ -478,7 +479,9 @@ def triu(m, k=0):
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"""
480481
m = asanyarray(m)
481-
return multiply(~tri(*m.shape[-2:], k=k-1, dtype=bool), m, dtype=m.dtype)
482+
mask = tri(*m.shape[-2:], k=k-1, dtype=bool)
483+
484+
return where(mask, 0, m)
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# Originally borrowed from John Hunter and matplotlib

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