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-rw-r--r--numpy/core/src/umath/loops.c.src20
-rw-r--r--numpy/core/tests/test_umath_complex.py134
-rw-r--r--numpy/core/umath_tests.py15
-rw-r--r--numpy/lib/histograms.py55
-rw-r--r--numpy/lib/tests/test_index_tricks.py136
-rw-r--r--numpy/lib/tests/test_twodim_base.py57
-rw-r--r--numpy/polynomial/tests/test_classes.py114
-rw-r--r--numpy/testing/decorators.py8
-rw-r--r--numpy/testing/noseclasses.py9
-rw-r--r--numpy/testing/nosetester.py8
-rw-r--r--numpy/testing/utils.py8
11 files changed, 296 insertions, 268 deletions
diff --git a/numpy/core/src/umath/loops.c.src b/numpy/core/src/umath/loops.c.src
index c1dfe15da..d196a8d4e 100644
--- a/numpy/core/src/umath/loops.c.src
+++ b/numpy/core/src/umath/loops.c.src
@@ -30,6 +30,16 @@
*/
#define PW_BLOCKSIZE 128
+
+/*
+ * largest simd vector size in bytes numpy supports
+ * it is currently a extremely large value as it is only used for memory
+ * overlap checks
+ */
+#ifndef NPY_MAX_SIMD_SIZE
+#define NPY_MAX_SIMD_SIZE 1024
+#endif
+
/*
* include vectorized functions and dispatchers
* this file is safe to include also for generic builds
@@ -180,10 +190,12 @@
do { \
/* condition allows compiler to optimize the generic macro */ \
if (IS_BINARY_CONT(tin, tout)) { \
- if (args[2] == args[0]) { \
+ if (abs_ptrdiff(args[2], args[0]) == 0 && \
+ abs_ptrdiff(args[2], args[1]) >= NPY_MAX_SIMD_SIZE) { \
BASE_BINARY_LOOP_INP(tin, tout, op) \
} \
- else if (args[2] == args[1]) { \
+ else if (abs_ptrdiff(args[2], args[1]) == 0 && \
+ abs_ptrdiff(args[2], args[0]) >= NPY_MAX_SIMD_SIZE) { \
BASE_BINARY_LOOP_INP(tin, tout, op) \
} \
else { \
@@ -191,7 +203,7 @@
} \
} \
else if (IS_BINARY_CONT_S1(tin, tout)) { \
- if (args[1] == args[2]) { \
+ if (abs_ptrdiff(args[2], args[1]) == 0) { \
BASE_BINARY_LOOP_S_INP(tin, tout, in1, args[0], in2, ip2, op) \
} \
else { \
@@ -199,7 +211,7 @@
} \
} \
else if (IS_BINARY_CONT_S2(tin, tout)) { \
- if (args[0] == args[2]) { \
+ if (abs_ptrdiff(args[2], args[0]) == 0) { \
BASE_BINARY_LOOP_S_INP(tin, tout, in2, args[1], in1, ip1, op) \
} \
else { \
diff --git a/numpy/core/tests/test_umath_complex.py b/numpy/core/tests/test_umath_complex.py
index 659a8a09e..785ae8c57 100644
--- a/numpy/core/tests/test_umath_complex.py
+++ b/numpy/core/tests/test_umath_complex.py
@@ -35,11 +35,11 @@ class TestCexp(object):
check = check_complex_value
f = np.exp
- yield check, f, 1, 0, np.exp(1), 0, False
- yield check, f, 0, 1, np.cos(1), np.sin(1), False
+ check(f, 1, 0, np.exp(1), 0, False)
+ check(f, 0, 1, np.cos(1), np.sin(1), False)
ref = np.exp(1) * complex(np.cos(1), np.sin(1))
- yield check, f, 1, 1, ref.real, ref.imag, False
+ check(f, 1, 1, ref.real, ref.imag, False)
@platform_skip
def test_special_values(self):
@@ -49,25 +49,25 @@ class TestCexp(object):
f = np.exp
# cexp(+-0 + 0i) is 1 + 0i
- yield check, f, np.PZERO, 0, 1, 0, False
- yield check, f, np.NZERO, 0, 1, 0, False
+ check(f, np.PZERO, 0, 1, 0, False)
+ check(f, np.NZERO, 0, 1, 0, False)
# cexp(x + infi) is nan + nani for finite x and raises 'invalid' FPU
# exception
- yield check, f, 1, np.inf, np.nan, np.nan
- yield check, f, -1, np.inf, np.nan, np.nan
- yield check, f, 0, np.inf, np.nan, np.nan
+ check(f, 1, np.inf, np.nan, np.nan)
+ check(f, -1, np.inf, np.nan, np.nan)
+ check(f, 0, np.inf, np.nan, np.nan)
# cexp(inf + 0i) is inf + 0i
- yield check, f, np.inf, 0, np.inf, 0
+ check(f, np.inf, 0, np.inf, 0)
# cexp(-inf + yi) is +0 * (cos(y) + i sin(y)) for finite y
- yield check, f, -np.inf, 1, np.PZERO, np.PZERO
- yield check, f, -np.inf, 0.75 * np.pi, np.NZERO, np.PZERO
+ check(f, -np.inf, 1, np.PZERO, np.PZERO)
+ check(f, -np.inf, 0.75 * np.pi, np.NZERO, np.PZERO)
# cexp(inf + yi) is +inf * (cos(y) + i sin(y)) for finite y
- yield check, f, np.inf, 1, np.inf, np.inf
- yield check, f, np.inf, 0.75 * np.pi, -np.inf, np.inf
+ check(f, np.inf, 1, np.inf, np.inf)
+ check(f, np.inf, 0.75 * np.pi, -np.inf, np.inf)
# cexp(-inf + inf i) is +-0 +- 0i (signs unspecified)
def _check_ninf_inf(dummy):
@@ -77,7 +77,7 @@ class TestCexp(object):
if z.real != 0 or z.imag != 0:
raise AssertionError(msgform % (z.real, z.imag))
- yield _check_ninf_inf, None
+ _check_ninf_inf(None)
# cexp(inf + inf i) is +-inf + NaNi and raised invalid FPU ex.
def _check_inf_inf(dummy):
@@ -87,7 +87,7 @@ class TestCexp(object):
if not np.isinf(z.real) or not np.isnan(z.imag):
raise AssertionError(msgform % (z.real, z.imag))
- yield _check_inf_inf, None
+ _check_inf_inf(None)
# cexp(-inf + nan i) is +-0 +- 0i
def _check_ninf_nan(dummy):
@@ -97,7 +97,7 @@ class TestCexp(object):
if z.real != 0 or z.imag != 0:
raise AssertionError(msgform % (z.real, z.imag))
- yield _check_ninf_nan, None
+ _check_ninf_nan(None)
# cexp(inf + nan i) is +-inf + nan
def _check_inf_nan(dummy):
@@ -107,18 +107,18 @@ class TestCexp(object):
if not np.isinf(z.real) or not np.isnan(z.imag):
raise AssertionError(msgform % (z.real, z.imag))
- yield _check_inf_nan, None
+ _check_inf_nan(None)
# cexp(nan + yi) is nan + nani for y != 0 (optional: raises invalid FPU
# ex)
- yield check, f, np.nan, 1, np.nan, np.nan
- yield check, f, np.nan, -1, np.nan, np.nan
+ check(f, np.nan, 1, np.nan, np.nan)
+ check(f, np.nan, -1, np.nan, np.nan)
- yield check, f, np.nan, np.inf, np.nan, np.nan
- yield check, f, np.nan, -np.inf, np.nan, np.nan
+ check(f, np.nan, np.inf, np.nan, np.nan)
+ check(f, np.nan, -np.inf, np.nan, np.nan)
# cexp(nan + nani) is nan + nani
- yield check, f, np.nan, np.nan, np.nan, np.nan
+ check(f, np.nan, np.nan, np.nan, np.nan)
# TODO This can be xfail when the generator functions are got rid of.
@pytest.mark.skip(reason="cexp(nan + 0I) is wrong on most platforms")
@@ -274,24 +274,28 @@ class TestClog(object):
for i in range(len(xa)):
assert_almost_equal(np.log(xa[i].conj()), ya[i].conj())
+
class TestCsqrt(object):
def test_simple(self):
# sqrt(1)
- yield check_complex_value, np.sqrt, 1, 0, 1, 0
+ check_complex_value(np.sqrt, 1, 0, 1, 0)
# sqrt(1i)
- yield check_complex_value, np.sqrt, 0, 1, 0.5*np.sqrt(2), 0.5*np.sqrt(2), False
+ rres = 0.5*np.sqrt(2)
+ ires = rres
+ check_complex_value(np.sqrt, 0, 1, rres, ires, False)
# sqrt(-1)
- yield check_complex_value, np.sqrt, -1, 0, 0, 1
+ check_complex_value(np.sqrt, -1, 0, 0, 1)
def test_simple_conjugate(self):
ref = np.conj(np.sqrt(complex(1, 1)))
def f(z):
return np.sqrt(np.conj(z))
- yield check_complex_value, f, 1, 1, ref.real, ref.imag, False
+
+ check_complex_value(f, 1, 1, ref.real, ref.imag, False)
#def test_branch_cut(self):
# _check_branch_cut(f, -1, 0, 1, -1)
@@ -304,29 +308,29 @@ class TestCsqrt(object):
f = np.sqrt
# csqrt(+-0 + 0i) is 0 + 0i
- yield check, f, np.PZERO, 0, 0, 0
- yield check, f, np.NZERO, 0, 0, 0
+ check(f, np.PZERO, 0, 0, 0)
+ check(f, np.NZERO, 0, 0, 0)
# csqrt(x + infi) is inf + infi for any x (including NaN)
- yield check, f, 1, np.inf, np.inf, np.inf
- yield check, f, -1, np.inf, np.inf, np.inf
+ check(f, 1, np.inf, np.inf, np.inf)
+ check(f, -1, np.inf, np.inf, np.inf)
- yield check, f, np.PZERO, np.inf, np.inf, np.inf
- yield check, f, np.NZERO, np.inf, np.inf, np.inf
- yield check, f, np.inf, np.inf, np.inf, np.inf
- yield check, f, -np.inf, np.inf, np.inf, np.inf
- yield check, f, -np.nan, np.inf, np.inf, np.inf
+ check(f, np.PZERO, np.inf, np.inf, np.inf)
+ check(f, np.NZERO, np.inf, np.inf, np.inf)
+ check(f, np.inf, np.inf, np.inf, np.inf)
+ check(f, -np.inf, np.inf, np.inf, np.inf)
+ check(f, -np.nan, np.inf, np.inf, np.inf)
# csqrt(x + nani) is nan + nani for any finite x
- yield check, f, 1, np.nan, np.nan, np.nan
- yield check, f, -1, np.nan, np.nan, np.nan
- yield check, f, 0, np.nan, np.nan, np.nan
+ check(f, 1, np.nan, np.nan, np.nan)
+ check(f, -1, np.nan, np.nan, np.nan)
+ check(f, 0, np.nan, np.nan, np.nan)
# csqrt(-inf + yi) is +0 + infi for any finite y > 0
- yield check, f, -np.inf, 1, np.PZERO, np.inf
+ check(f, -np.inf, 1, np.PZERO, np.inf)
# csqrt(inf + yi) is +inf + 0i for any finite y > 0
- yield check, f, np.inf, 1, np.inf, np.PZERO
+ check(f, np.inf, 1, np.inf, np.PZERO)
# csqrt(-inf + nani) is nan +- infi (both +i infi are valid)
def _check_ninf_nan(dummy):
@@ -337,16 +341,16 @@ class TestCsqrt(object):
if not (np.isnan(z.real) and np.isinf(z.imag)):
raise AssertionError(msgform % (z.real, z.imag))
- yield _check_ninf_nan, None
+ _check_ninf_nan(None)
# csqrt(+inf + nani) is inf + nani
- yield check, f, np.inf, np.nan, np.inf, np.nan
+ check(f, np.inf, np.nan, np.inf, np.nan)
# csqrt(nan + yi) is nan + nani for any finite y (infinite handled in x
# + nani)
- yield check, f, np.nan, 0, np.nan, np.nan
- yield check, f, np.nan, 1, np.nan, np.nan
- yield check, f, np.nan, np.nan, np.nan, np.nan
+ check(f, np.nan, 0, np.nan, np.nan)
+ check(f, np.nan, 1, np.nan, np.nan)
+ check(f, np.nan, np.nan, np.nan, np.nan)
# XXX: check for conj(csqrt(z)) == csqrt(conj(z)) (need to fix branch
# cuts first)
@@ -425,21 +429,21 @@ class TestCabs(object):
# cabs(+-nan + nani) returns nan
x.append(np.nan)
y.append(np.nan)
- yield check_real_value, np.abs, np.nan, np.nan, np.nan
+ check_real_value(np.abs, np.nan, np.nan, np.nan)
x.append(np.nan)
y.append(-np.nan)
- yield check_real_value, np.abs, -np.nan, np.nan, np.nan
+ check_real_value(np.abs, -np.nan, np.nan, np.nan)
# According to C99 standard, if exactly one of the real/part is inf and
# the other nan, then cabs should return inf
x.append(np.inf)
y.append(np.nan)
- yield check_real_value, np.abs, np.inf, np.nan, np.inf
+ check_real_value(np.abs, np.inf, np.nan, np.inf)
x.append(-np.inf)
y.append(np.nan)
- yield check_real_value, np.abs, -np.inf, np.nan, np.inf
+ check_real_value(np.abs, -np.inf, np.nan, np.inf)
# cabs(conj(z)) == conj(cabs(z)) (= cabs(z))
def f(a):
@@ -451,7 +455,7 @@ class TestCabs(object):
xa = np.array(x, dtype=complex)
for i in range(len(xa)):
ref = g(x[i], y[i])
- yield check_real_value, f, x[i], y[i], ref
+ check_real_value(f, x[i], y[i], ref)
class TestCarg(object):
def test_simple(self):
@@ -494,31 +498,32 @@ class TestCarg(object):
def test_special_values(self):
# carg(-np.inf +- yi) returns +-pi for finite y > 0
- yield check_real_value, ncu._arg, -np.inf, 1, np.pi, False
- yield check_real_value, ncu._arg, -np.inf, -1, -np.pi, False
+ check_real_value(ncu._arg, -np.inf, 1, np.pi, False)
+ check_real_value(ncu._arg, -np.inf, -1, -np.pi, False)
# carg(np.inf +- yi) returns +-0 for finite y > 0
- yield check_real_value, ncu._arg, np.inf, 1, np.PZERO, False
- yield check_real_value, ncu._arg, np.inf, -1, np.NZERO, False
+ check_real_value(ncu._arg, np.inf, 1, np.PZERO, False)
+ check_real_value(ncu._arg, np.inf, -1, np.NZERO, False)
# carg(x +- np.infi) returns +-pi/2 for finite x
- yield check_real_value, ncu._arg, 1, np.inf, 0.5 * np.pi, False
- yield check_real_value, ncu._arg, 1, -np.inf, -0.5 * np.pi, False
+ check_real_value(ncu._arg, 1, np.inf, 0.5 * np.pi, False)
+ check_real_value(ncu._arg, 1, -np.inf, -0.5 * np.pi, False)
# carg(-np.inf +- np.infi) returns +-3pi/4
- yield check_real_value, ncu._arg, -np.inf, np.inf, 0.75 * np.pi, False
- yield check_real_value, ncu._arg, -np.inf, -np.inf, -0.75 * np.pi, False
+ check_real_value(ncu._arg, -np.inf, np.inf, 0.75 * np.pi, False)
+ check_real_value(ncu._arg, -np.inf, -np.inf, -0.75 * np.pi, False)
# carg(np.inf +- np.infi) returns +-pi/4
- yield check_real_value, ncu._arg, np.inf, np.inf, 0.25 * np.pi, False
- yield check_real_value, ncu._arg, np.inf, -np.inf, -0.25 * np.pi, False
+ check_real_value(ncu._arg, np.inf, np.inf, 0.25 * np.pi, False)
+ check_real_value(ncu._arg, np.inf, -np.inf, -0.25 * np.pi, False)
# carg(x + yi) returns np.nan if x or y is nan
- yield check_real_value, ncu._arg, np.nan, 0, np.nan, False
- yield check_real_value, ncu._arg, 0, np.nan, np.nan, False
+ check_real_value(ncu._arg, np.nan, 0, np.nan, False)
+ check_real_value(ncu._arg, 0, np.nan, np.nan, False)
+
+ check_real_value(ncu._arg, np.nan, np.inf, np.nan, False)
+ check_real_value(ncu._arg, np.inf, np.nan, np.nan, False)
- yield check_real_value, ncu._arg, np.nan, np.inf, np.nan, False
- yield check_real_value, ncu._arg, np.inf, np.nan, np.nan, False
def check_real_value(f, x1, y1, x, exact=True):
z1 = np.array([complex(x1, y1)])
@@ -527,6 +532,7 @@ def check_real_value(f, x1, y1, x, exact=True):
else:
assert_almost_equal(f(z1), x)
+
def check_complex_value(f, x1, y1, x2, y2, exact=True):
z1 = np.array([complex(x1, y1)])
z2 = complex(x2, y2)
diff --git a/numpy/core/umath_tests.py b/numpy/core/umath_tests.py
new file mode 100644
index 000000000..28e325b98
--- /dev/null
+++ b/numpy/core/umath_tests.py
@@ -0,0 +1,15 @@
+"""
+Shim for _umath_tests to allow a deprecation period for the new name.
+
+"""
+from __future__ import division, absolute_import, print_function
+
+import warnings
+
+# 2018-04-04, numpy 1.15.0
+warnings.warn(("numpy.core.umath_tests is an internal NumPy "
+ "module and should not be imported. It will "
+ "be removed in a future NumPy release."),
+ category=DeprecationWarning, stacklevel=2)
+
+from ._umath_tests import *
diff --git a/numpy/lib/histograms.py b/numpy/lib/histograms.py
index 66e2ccda1..a0346f6c5 100644
--- a/numpy/lib/histograms.py
+++ b/numpy/lib/histograms.py
@@ -10,6 +10,10 @@ from numpy.compat.py3k import basestring
__all__ = ['histogram', 'histogramdd', 'histogram_bin_edges']
+# range is a keyword argument to many functions, so save the builtin so they can
+# use it.
+_range = range
+
def _hist_bin_sqrt(x):
"""
@@ -693,7 +697,7 @@ def histogram(a, bins=10, range=None, normed=False, weights=None,
# large arrays, it is actually faster (for example for a 10^8 array it
# is 2x as fast) and it results in a memory footprint 3x lower in the
# limit of large arrays.
- for i in np.arange(0, len(a), BLOCK):
+ for i in _range(0, len(a), BLOCK):
tmp_a = a[i:i+BLOCK]
if weights is None:
tmp_w = None
@@ -741,12 +745,12 @@ def histogram(a, bins=10, range=None, normed=False, weights=None,
# Compute via cumulative histogram
cum_n = np.zeros(bin_edges.shape, ntype)
if weights is None:
- for i in np.arange(0, len(a), BLOCK):
+ for i in _range(0, len(a), BLOCK):
sa = np.sort(a[i:i+BLOCK])
cum_n += _search_sorted_inclusive(sa, bin_edges)
else:
zero = np.zeros(1, dtype=ntype)
- for i in np.arange(0, len(a), BLOCK):
+ for i in _range(0, len(a), BLOCK):
tmp_a = a[i:i+BLOCK]
tmp_w = weights[i:i+BLOCK]
sorting_index = np.argsort(tmp_a)
@@ -873,7 +877,7 @@ def histogramdd(sample, bins=10, range=None, normed=False, weights=None):
raise ValueError('range argument must have one entry per dimension')
# Create edge arrays
- for i in np.arange(D):
+ for i in _range(D):
if np.ndim(bins[i]) == 0:
if bins[i] < 1:
raise ValueError(
@@ -894,21 +898,20 @@ def histogramdd(sample, bins=10, range=None, normed=False, weights=None):
nbin[i] = len(edges[i]) + 1 # includes an outlier on each end
dedges[i] = np.diff(edges[i])
- nbin = np.asarray(nbin)
-
# Handle empty input.
if N == 0:
return np.zeros(nbin-2), edges
# Compute the bin number each sample falls into.
- Ncount = {}
- for i in np.arange(D):
- Ncount[i] = np.digitize(sample[:, i], edges[i])
+ Ncount = tuple(
+ np.digitize(sample[:, i], edges[i])
+ for i in _range(D)
+ )
# Using digitize, values that fall on an edge are put in the right bin.
# For the rightmost bin, we want values equal to the right edge to be
# counted in the last bin, and not as an outlier.
- for i in np.arange(D):
+ for i in _range(D):
# Rounding precision
mindiff = dedges[i].min()
if not np.isinf(mindiff):
@@ -918,35 +921,21 @@ def histogramdd(sample, bins=10, range=None, normed=False, weights=None):
on_edge = (np.around(sample[:, i], decimal) ==
np.around(edges[i][-1], decimal))
# Shift these points one bin to the left.
- Ncount[i][np.nonzero(on_edge & not_smaller_than_edge)[0]] -= 1
-
- # Flattened histogram matrix (1D)
- # Reshape is used so that overlarge arrays
- # will raise an error.
- hist = np.zeros(nbin, float).reshape(-1)
+ Ncount[i][on_edge & not_smaller_than_edge] -= 1
# Compute the sample indices in the flattened histogram matrix.
- ni = nbin.argsort()
- xy = np.zeros(N, np.intp)
- for i in np.arange(0, D-1):
- xy += Ncount[ni[i]] * nbin[ni[i+1:]].prod()
- xy += Ncount[ni[-1]]
+ # This raises an error if the array is too large.
+ xy = np.ravel_multi_index(Ncount, nbin)
# Compute the number of repetitions in xy and assign it to the
# flattened histmat.
- if len(xy) == 0:
- return np.zeros(nbin-2, int), edges
-
- flatcount = np.bincount(xy, weights)
- a = np.arange(len(flatcount))
- hist[a] = flatcount
+ hist = np.bincount(xy, weights, minlength=nbin.prod())
# Shape into a proper matrix
- hist = hist.reshape(np.sort(nbin))
- for i in np.arange(nbin.size):
- j = ni.argsort()[i]
- hist = hist.swapaxes(i, j)
- ni[i], ni[j] = ni[j], ni[i]
+ hist = hist.reshape(nbin)
+
+ # This preserves the (bad) behavior observed in gh-7845, for now.
+ hist = hist.astype(float, casting='safe')
# Remove outliers (indices 0 and -1 for each dimension).
core = D*(slice(1, -1),)
@@ -955,7 +944,7 @@ def histogramdd(sample, bins=10, range=None, normed=False, weights=None):
# Normalize if normed is True
if normed:
s = hist.sum()
- for i in np.arange(D):
+ for i in _range(D):
shape = np.ones(D, int)
shape[i] = nbin[i] - 2
hist = hist / dedges[i].reshape(shape)
diff --git a/numpy/lib/tests/test_index_tricks.py b/numpy/lib/tests/test_index_tricks.py
index a19f3f1be..f934e952a 100644
--- a/numpy/lib/tests/test_index_tricks.py
+++ b/numpy/lib/tests/test_index_tricks.py
@@ -284,71 +284,77 @@ def test_c_():
assert_equal(a, [[1, 2, 3, 0, 0, 4, 5, 6]])
-def test_fill_diagonal():
- a = np.zeros((3, 3), int)
- fill_diagonal(a, 5)
- yield (assert_array_equal, a,
- np.array([[5, 0, 0],
- [0, 5, 0],
- [0, 0, 5]]))
-
- #Test tall matrix
- a = np.zeros((10, 3), int)
- fill_diagonal(a, 5)
- yield (assert_array_equal, a,
- np.array([[5, 0, 0],
- [0, 5, 0],
- [0, 0, 5],
- [0, 0, 0],
- [0, 0, 0],
- [0, 0, 0],
- [0, 0, 0],
- [0, 0, 0],
- [0, 0, 0],
- [0, 0, 0]]))
-
- #Test tall matrix wrap
- a = np.zeros((10, 3), int)
- fill_diagonal(a, 5, True)
- yield (assert_array_equal, a,
- np.array([[5, 0, 0],
- [0, 5, 0],
- [0, 0, 5],
- [0, 0, 0],
- [5, 0, 0],
- [0, 5, 0],
- [0, 0, 5],
- [0, 0, 0],
- [5, 0, 0],
- [0, 5, 0]]))
-
- #Test wide matrix
- a = np.zeros((3, 10), int)
- fill_diagonal(a, 5)
- yield (assert_array_equal, a,
- np.array([[5, 0, 0, 0, 0, 0, 0, 0, 0, 0],
- [0, 5, 0, 0, 0, 0, 0, 0, 0, 0],
- [0, 0, 5, 0, 0, 0, 0, 0, 0, 0]]))
-
- # The same function can operate on a 4-d array:
- a = np.zeros((3, 3, 3, 3), int)
- fill_diagonal(a, 4)
- i = np.array([0, 1, 2])
- yield (assert_equal, np.where(a != 0), (i, i, i, i))
+class TestFillDiagonal(object):
+ def test_basic(self):
+ a = np.zeros((3, 3), int)
+ fill_diagonal(a, 5)
+ assert_array_equal(
+ a, np.array([[5, 0, 0],
+ [0, 5, 0],
+ [0, 0, 5]])
+ )
+
+ def test_tall_matrix(self):
+ a = np.zeros((10, 3), int)
+ fill_diagonal(a, 5)
+ assert_array_equal(
+ a, np.array([[5, 0, 0],
+ [0, 5, 0],
+ [0, 0, 5],
+ [0, 0, 0],
+ [0, 0, 0],
+ [0, 0, 0],
+ [0, 0, 0],
+ [0, 0, 0],
+ [0, 0, 0],
+ [0, 0, 0]])
+ )
+
+ def test_tall_matrix_wrap(self):
+ a = np.zeros((10, 3), int)
+ fill_diagonal(a, 5, True)
+ assert_array_equal(
+ a, np.array([[5, 0, 0],
+ [0, 5, 0],
+ [0, 0, 5],
+ [0, 0, 0],
+ [5, 0, 0],
+ [0, 5, 0],
+ [0, 0, 5],
+ [0, 0, 0],
+ [5, 0, 0],
+ [0, 5, 0]])
+ )
+
+ def test_wide_matrix(self):
+ a = np.zeros((3, 10), int)
+ fill_diagonal(a, 5)
+ assert_array_equal(
+ a, np.array([[5, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+ [0, 5, 0, 0, 0, 0, 0, 0, 0, 0],
+ [0, 0, 5, 0, 0, 0, 0, 0, 0, 0]])
+ )
+
+ def test_operate_4d_array(self):
+ a = np.zeros((3, 3, 3, 3), int)
+ fill_diagonal(a, 4)
+ i = np.array([0, 1, 2])
+ assert_equal(np.where(a != 0), (i, i, i, i))
def test_diag_indices():
di = diag_indices(4)
a = np.array([[1, 2, 3, 4],
- [5, 6, 7, 8],
- [9, 10, 11, 12],
- [13, 14, 15, 16]])
+ [5, 6, 7, 8],
+ [9, 10, 11, 12],
+ [13, 14, 15, 16]])
a[di] = 100
- yield (assert_array_equal, a,
- np.array([[100, 2, 3, 4],
- [5, 100, 7, 8],
- [9, 10, 100, 12],
- [13, 14, 15, 100]]))
+ assert_array_equal(
+ a, np.array([[100, 2, 3, 4],
+ [5, 100, 7, 8],
+ [9, 10, 100, 12],
+ [13, 14, 15, 100]])
+ )
# Now, we create indices to manipulate a 3-d array:
d3 = diag_indices(2, 3)
@@ -356,12 +362,12 @@ def test_diag_indices():
# And use it to set the diagonal of a zeros array to 1:
a = np.zeros((2, 2, 2), int)
a[d3] = 1
- yield (assert_array_equal, a,
- np.array([[[1, 0],
- [0, 0]],
-
- [[0, 0],
- [0, 1]]]))
+ assert_array_equal(
+ a, np.array([[[1, 0],
+ [0, 0]],
+ [[0, 0],
+ [0, 1]]])
+ )
def test_diag_indices_from():
diff --git a/numpy/lib/tests/test_twodim_base.py b/numpy/lib/tests/test_twodim_base.py
index 6e0fe0654..d3a072af3 100644
--- a/numpy/lib/tests/test_twodim_base.py
+++ b/numpy/lib/tests/test_twodim_base.py
@@ -244,32 +244,32 @@ class TestHistogram2d(object):
def test_binparameter_combination(self):
x = array(
- [0, 0.09207008, 0.64575234, 0.12875982, 0.47390599,
+ [0, 0.09207008, 0.64575234, 0.12875982, 0.47390599,
0.59944483, 1])
y = array(
- [0, 0.14344267, 0.48988575, 0.30558665, 0.44700682,
+ [0, 0.14344267, 0.48988575, 0.30558665, 0.44700682,
0.15886423, 1])
edges = (0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1)
H, xe, ye = histogram2d(x, y, (edges, 4))
answer = array(
- [[ 2., 0., 0., 0.],
- [ 0., 1., 0., 0.],
- [ 0., 0., 0., 0.],
- [ 0., 0., 0., 0.],
- [ 0., 1., 0., 0.],
- [ 1., 0., 0., 0.],
- [ 0., 1., 0., 0.],
- [ 0., 0., 0., 0.],
- [ 0., 0., 0., 0.],
- [ 0., 0., 0., 1.]])
+ [[2., 0., 0., 0.],
+ [0., 1., 0., 0.],
+ [0., 0., 0., 0.],
+ [0., 0., 0., 0.],
+ [0., 1., 0., 0.],
+ [1., 0., 0., 0.],
+ [0., 1., 0., 0.],
+ [0., 0., 0., 0.],
+ [0., 0., 0., 0.],
+ [0., 0., 0., 1.]])
assert_array_equal(H, answer)
assert_array_equal(ye, array([0., 0.25, 0.5, 0.75, 1]))
H, xe, ye = histogram2d(x, y, (4, edges))
answer = array(
- [[ 1., 1., 0., 1., 0., 0., 0., 0., 0., 0.],
- [ 0., 0., 0., 0., 1., 0., 0., 0., 0., 0.],
- [ 0., 1., 0., 0., 1., 0., 0., 0., 0., 0.],
- [ 0., 0., 0., 0., 0., 0., 0., 0., 0., 1.]])
+ [[1., 1., 0., 1., 0., 0., 0., 0., 0., 0.],
+ [0., 0., 0., 0., 1., 0., 0., 0., 0., 0.],
+ [0., 1., 0., 0., 1., 0., 0., 0., 0., 0.],
+ [0., 0., 0., 0., 0., 0., 0., 0., 0., 1.]])
assert_array_equal(H, answer)
assert_array_equal(xe, array([0., 0.25, 0.5, 0.75, 1]))
@@ -288,11 +288,11 @@ def test_tril_triu_ndim2():
a = np.ones((2, 2), dtype=dtype)
b = np.tril(a)
c = np.triu(a)
- yield assert_array_equal, b, [[1, 0], [1, 1]]
- yield assert_array_equal, c, b.T
+ assert_array_equal(b, [[1, 0], [1, 1]])
+ assert_array_equal(c, b.T)
# should return the same dtype as the original array
- yield assert_equal, b.dtype, a.dtype
- yield assert_equal, c.dtype, a.dtype
+ assert_equal(b.dtype, a.dtype)
+ assert_equal(c.dtype, a.dtype)
def test_tril_triu_ndim3():
@@ -314,10 +314,11 @@ def test_tril_triu_ndim3():
], dtype=dtype)
a_triu_observed = np.triu(a)
a_tril_observed = np.tril(a)
- yield assert_array_equal, a_triu_observed, a_triu_desired
- yield assert_array_equal, a_tril_observed, a_tril_desired
- yield assert_equal, a_triu_observed.dtype, a.dtype
- yield assert_equal, a_tril_observed.dtype, a.dtype
+ assert_array_equal(a_triu_observed, a_triu_desired)
+ assert_array_equal(a_tril_observed, a_tril_desired)
+ assert_equal(a_triu_observed.dtype, a.dtype)
+ assert_equal(a_tril_observed.dtype, a.dtype)
+
def test_tril_triu_with_inf():
# Issue 4859
@@ -484,12 +485,12 @@ class TestVander(object):
[16, -8, 4, -2, 1],
[81, 27, 9, 3, 1]])
# Check default value of N:
- yield (assert_array_equal, v, powers[:, 1:])
+ assert_array_equal(v, powers[:, 1:])
# Check a range of N values, including 0 and 5 (greater than default)
m = powers.shape[1]
for n in range(6):
v = vander(c, N=n)
- yield (assert_array_equal, v, powers[:, m-n:m])
+ assert_array_equal(v, powers[:, m-n:m])
def test_dtypes(self):
c = array([11, -12, 13], dtype=np.int8)
@@ -497,7 +498,7 @@ class TestVander(object):
expected = np.array([[121, 11, 1],
[144, -12, 1],
[169, 13, 1]])
- yield (assert_array_equal, v, expected)
+ assert_array_equal(v, expected)
c = array([1.0+1j, 1.0-1j])
v = vander(c, N=3)
@@ -506,4 +507,4 @@ class TestVander(object):
# The data is floating point, but the values are small integers,
# so assert_array_equal *should* be safe here (rather than, say,
# assert_array_almost_equal).
- yield (assert_array_equal, v, expected)
+ assert_array_equal(v, expected)
diff --git a/numpy/polynomial/tests/test_classes.py b/numpy/polynomial/tests/test_classes.py
index 262de5fe2..738741668 100644
--- a/numpy/polynomial/tests/test_classes.py
+++ b/numpy/polynomial/tests/test_classes.py
@@ -8,6 +8,7 @@ from __future__ import division, absolute_import, print_function
import operator as op
from numbers import Number
+import pytest
import numpy as np
from numpy.polynomial import (
Polynomial, Legendre, Chebyshev, Laguerre, Hermite, HermiteE)
@@ -17,44 +18,19 @@ from numpy.testing import (
from numpy.compat import long
+#
+# fixtures
+#
+
classes = (
Polynomial, Legendre, Chebyshev, Laguerre,
- Hermite, HermiteE)
-
-
-def test_class_methods():
- for Poly1 in classes:
- for Poly2 in classes:
- yield check_conversion, Poly1, Poly2
- yield check_cast, Poly1, Poly2
- for Poly in classes:
- yield check_call, Poly
- yield check_identity, Poly
- yield check_basis, Poly
- yield check_fromroots, Poly
- yield check_fit, Poly
- yield check_equal, Poly
- yield check_not_equal, Poly
- yield check_add, Poly
- yield check_sub, Poly
- yield check_mul, Poly
- yield check_floordiv, Poly
- yield check_truediv, Poly
- yield check_mod, Poly
- yield check_divmod, Poly
- yield check_pow, Poly
- yield check_integ, Poly
- yield check_deriv, Poly
- yield check_roots, Poly
- yield check_linspace, Poly
- yield check_mapparms, Poly
- yield check_degree, Poly
- yield check_copy, Poly
- yield check_cutdeg, Poly
- yield check_truncate, Poly
- yield check_trim, Poly
- yield check_ufunc_override, Poly
+ Hermite, HermiteE
+ )
+classids = tuple(cls.__name__ for cls in classes)
+@pytest.fixture(params=classes, ids=classids)
+def Poly(request):
+ return request.param
#
# helper functions
@@ -73,11 +49,14 @@ def assert_poly_almost_equal(p1, p2, msg=""):
#
-# conversion methods that depend on two classes
+# Test conversion methods that depend on combinations of two classes.
#
+Poly1 = Poly
+Poly2 = Poly
+
-def check_conversion(Poly1, Poly2):
+def test_conversion(Poly1, Poly2):
x = np.linspace(0, 1, 10)
coef = random((3,))
@@ -94,7 +73,7 @@ def check_conversion(Poly1, Poly2):
assert_almost_equal(p2(x), p1(x))
-def check_cast(Poly1, Poly2):
+def test_cast(Poly1, Poly2):
x = np.linspace(0, 1, 10)
coef = random((3,))
@@ -112,11 +91,11 @@ def check_cast(Poly1, Poly2):
#
-# methods that depend on one class
+# test methods that depend on one class
#
-def check_identity(Poly):
+def test_identity(Poly):
d = Poly.domain + random((2,))*.25
w = Poly.window + random((2,))*.25
x = np.linspace(d[0], d[1], 11)
@@ -126,7 +105,7 @@ def check_identity(Poly):
assert_almost_equal(p(x), x)
-def check_basis(Poly):
+def test_basis(Poly):
d = Poly.domain + random((2,))*.25
w = Poly.window + random((2,))*.25
p = Poly.basis(5, domain=d, window=w)
@@ -135,7 +114,7 @@ def check_basis(Poly):
assert_equal(p.coef, [0]*5 + [1])
-def check_fromroots(Poly):
+def test_fromroots(Poly):
# check that requested roots are zeros of a polynomial
# of correct degree, domain, and window.
d = Poly.domain + random((2,))*.25
@@ -154,7 +133,7 @@ def check_fromroots(Poly):
assert_almost_equal(p2.coef[-1], 1)
-def check_fit(Poly):
+def test_fit(Poly):
def f(x):
return x*(x - 1)*(x - 2)
@@ -198,7 +177,7 @@ def check_fit(Poly):
assert_almost_equal(p2(x), p3(x))
-def check_equal(Poly):
+def test_equal(Poly):
p1 = Poly([1, 2, 3], domain=[0, 1], window=[2, 3])
p2 = Poly([1, 1, 1], domain=[0, 1], window=[2, 3])
p3 = Poly([1, 2, 3], domain=[1, 2], window=[2, 3])
@@ -209,7 +188,7 @@ def check_equal(Poly):
assert_(not p1 == p4)
-def check_not_equal(Poly):
+def test_not_equal(Poly):
p1 = Poly([1, 2, 3], domain=[0, 1], window=[2, 3])
p2 = Poly([1, 1, 1], domain=[0, 1], window=[2, 3])
p3 = Poly([1, 2, 3], domain=[1, 2], window=[2, 3])
@@ -220,7 +199,7 @@ def check_not_equal(Poly):
assert_(p1 != p4)
-def check_add(Poly):
+def test_add(Poly):
# This checks commutation, not numerical correctness
c1 = list(random((4,)) + .5)
c2 = list(random((3,)) + .5)
@@ -242,7 +221,7 @@ def check_add(Poly):
assert_raises(TypeError, op.add, p1, Polynomial([0]))
-def check_sub(Poly):
+def test_sub(Poly):
# This checks commutation, not numerical correctness
c1 = list(random((4,)) + .5)
c2 = list(random((3,)) + .5)
@@ -264,7 +243,7 @@ def check_sub(Poly):
assert_raises(TypeError, op.sub, p1, Polynomial([0]))
-def check_mul(Poly):
+def test_mul(Poly):
c1 = list(random((4,)) + .5)
c2 = list(random((3,)) + .5)
p1 = Poly(c1)
@@ -287,7 +266,7 @@ def check_mul(Poly):
assert_raises(TypeError, op.mul, p1, Polynomial([0]))
-def check_floordiv(Poly):
+def test_floordiv(Poly):
c1 = list(random((4,)) + .5)
c2 = list(random((3,)) + .5)
c3 = list(random((2,)) + .5)
@@ -315,7 +294,7 @@ def check_floordiv(Poly):
assert_raises(TypeError, op.floordiv, p1, Polynomial([0]))
-def check_truediv(Poly):
+def test_truediv(Poly):
# true division is valid only if the denominator is a Number and
# not a python bool.
p1 = Poly([1,2,3])
@@ -342,7 +321,7 @@ def check_truediv(Poly):
assert_raises(TypeError, op.truediv, p2, ptype(1))
-def check_mod(Poly):
+def test_mod(Poly):
# This checks commutation, not numerical correctness
c1 = list(random((4,)) + .5)
c2 = list(random((3,)) + .5)
@@ -369,7 +348,7 @@ def check_mod(Poly):
assert_raises(TypeError, op.mod, p1, Polynomial([0]))
-def check_divmod(Poly):
+def test_divmod(Poly):
# This checks commutation, not numerical correctness
c1 = list(random((4,)) + .5)
c2 = list(random((3,)) + .5)
@@ -414,7 +393,7 @@ def check_divmod(Poly):
assert_raises(TypeError, divmod, p1, Polynomial([0]))
-def check_roots(Poly):
+def test_roots(Poly):
d = Poly.domain * 1.25 + .25
w = Poly.window
tgt = np.linspace(d[0], d[1], 5)
@@ -425,12 +404,12 @@ def check_roots(Poly):
assert_almost_equal(res, tgt)
-def check_degree(Poly):
+def test_degree(Poly):
p = Poly.basis(5)
assert_equal(p.degree(), 5)
-def check_copy(Poly):
+def test_copy(Poly):
p1 = Poly.basis(5)
p2 = p1.copy()
assert_(p1 == p2)
@@ -440,7 +419,7 @@ def check_copy(Poly):
assert_(p1.window is not p2.window)
-def check_integ(Poly):
+def test_integ(Poly):
P = Polynomial
# Check defaults
p0 = Poly.cast(P([1*2, 2*3, 3*4]))
@@ -469,7 +448,7 @@ def check_integ(Poly):
assert_poly_almost_equal(p2, P([0, 0, 1, 1, 1]))
-def check_deriv(Poly):
+def test_deriv(Poly):
# Check that the derivative is the inverse of integration. It is
# assumes that the integration has been checked elsewhere.
d = Poly.domain + random((2,))*.25
@@ -487,7 +466,7 @@ def check_deriv(Poly):
assert_almost_equal(p2.deriv(2).coef, p1.coef)
-def check_linspace(Poly):
+def test_linspace(Poly):
d = Poly.domain + random((2,))*.25
w = Poly.window + random((2,))*.25
p = Poly([1, 2, 3], domain=d, window=w)
@@ -505,7 +484,7 @@ def check_linspace(Poly):
assert_almost_equal(yres, ytgt)
-def check_pow(Poly):
+def test_pow(Poly):
d = Poly.domain + random((2,))*.25
w = Poly.window + random((2,))*.25
tgt = Poly([1], domain=d, window=w)
@@ -524,7 +503,7 @@ def check_pow(Poly):
assert_raises(ValueError, op.pow, tgt, -1)
-def check_call(Poly):
+def test_call(Poly):
P = Polynomial
d = Poly.domain
x = np.linspace(d[0], d[1], 11)
@@ -536,7 +515,7 @@ def check_call(Poly):
assert_almost_equal(res, tgt)
-def check_cutdeg(Poly):
+def test_cutdeg(Poly):
p = Poly([1, 2, 3])
assert_raises(ValueError, p.cutdeg, .5)
assert_raises(ValueError, p.cutdeg, -1)
@@ -546,7 +525,7 @@ def check_cutdeg(Poly):
assert_equal(len(p.cutdeg(0)), 1)
-def check_truncate(Poly):
+def test_truncate(Poly):
p = Poly([1, 2, 3])
assert_raises(ValueError, p.truncate, .5)
assert_raises(ValueError, p.truncate, 0)
@@ -556,7 +535,7 @@ def check_truncate(Poly):
assert_equal(len(p.truncate(1)), 1)
-def check_trim(Poly):
+def test_trim(Poly):
c = [1, 1e-6, 1e-12, 0]
p = Poly(c)
assert_equal(p.trim().coef, c[:3])
@@ -564,7 +543,7 @@ def check_trim(Poly):
assert_equal(p.trim(1e-5).coef, c[:1])
-def check_mapparms(Poly):
+def test_mapparms(Poly):
# check with defaults. Should be identity.
d = Poly.domain
w = Poly.window
@@ -576,13 +555,18 @@ def check_mapparms(Poly):
assert_almost_equal([1, 2], p.mapparms())
-def check_ufunc_override(Poly):
+def test_ufunc_override(Poly):
p = Poly([1, 2, 3])
x = np.ones(3)
assert_raises(TypeError, np.add, p, x)
assert_raises(TypeError, np.add, x, p)
+#
+# Test class method that only exists for some classes
+#
+
+
class TestInterpolate(object):
def f(self, x):
diff --git a/numpy/testing/decorators.py b/numpy/testing/decorators.py
index 1988afd29..68c1554b5 100644
--- a/numpy/testing/decorators.py
+++ b/numpy/testing/decorators.py
@@ -3,9 +3,13 @@ Back compatibility decorators module. It will import the appropriate
set of tools
"""
+from __future__ import division, absolute_import, print_function
+
import warnings
-warnings.warn("Import from numpy.testing, not numpy.testing.decorators",
- ImportWarning)
+# 2018-04-04, numpy 1.15.0
+warnings.warn("Importing from numpy.testing.decorators is deprecated, "
+ "import from numpy.testing instead.",
+ DeprecationWarning, stacklevel=2)
from ._private.decorators import *
diff --git a/numpy/testing/noseclasses.py b/numpy/testing/noseclasses.py
index dde62e825..e0e728a32 100644
--- a/numpy/testing/noseclasses.py
+++ b/numpy/testing/noseclasses.py
@@ -2,10 +2,13 @@
Back compatibility noseclasses module. It will import the appropriate
set of tools
"""
+from __future__ import division, absolute_import, print_function
+
import warnings
-warnings.warn("Import from numpy.testing, not numpy.testing.noseclasses",
- ImportWarning)
+# 2018-04-04, numpy 1.15.0
+warnings.warn("Importing from numpy.testing.noseclasses is deprecated, "
+ "import from numpy.testing instead",
+ DeprecationWarning, stacklevel=2)
from ._private.noseclasses import *
-
diff --git a/numpy/testing/nosetester.py b/numpy/testing/nosetester.py
index 772bff305..c8c7d6e68 100644
--- a/numpy/testing/nosetester.py
+++ b/numpy/testing/nosetester.py
@@ -3,10 +3,14 @@ Back compatibility nosetester module. It will import the appropriate
set of tools
"""
+from __future__ import division, absolute_import, print_function
+
import warnings
-warnings.warn("Import from numpy.testing, not numpy.testing.nosetester",
- ImportWarning)
+# 2018-04-04, numpy 1.15.0
+warnings.warn("Importing from numpy.testing.nosetester is deprecated, "
+ "import from numpy.testing instead.",
+ DeprecationWarning, stacklevel=2)
from ._private.nosetester import *
diff --git a/numpy/testing/utils.py b/numpy/testing/utils.py
index 3cd89e639..78cf405cf 100644
--- a/numpy/testing/utils.py
+++ b/numpy/testing/utils.py
@@ -3,10 +3,14 @@ Back compatibility utils module. It will import the appropriate
set of tools
"""
+from __future__ import division, absolute_import, print_function
+
import warnings
-warnings.warn("Import from numpy.testing, not numpy.testing.utils",
- ImportWarning)
+# 2018-04-04, numpy 1.15.0
+warnings.warn("Importing from numpy.testing.utils is deprecated, "
+ "import from numpy.testing instead.",
+ ImportWarning, stacklevel=2)
from ._private.utils import *
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