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-rw-r--r--numpy/lib/tests/test__datasource.py15
-rw-r--r--numpy/lib/tests/test__iotools.py3
-rw-r--r--numpy/lib/tests/test_arraypad.py86
-rw-r--r--numpy/lib/tests/test_arraysetops.py1
-rw-r--r--numpy/lib/tests/test_format.py26
-rw-r--r--numpy/lib/tests/test_function_base.py8
-rw-r--r--numpy/lib/tests/test_histograms.py71
-rw-r--r--numpy/lib/tests/test_io.py14
-rw-r--r--numpy/lib/tests/test_mixins.py11
-rw-r--r--numpy/lib/tests/test_polynomial.py50
-rw-r--r--numpy/lib/tests/test_recfunctions.py74
-rw-r--r--numpy/lib/tests/test_shape_base.py4
12 files changed, 320 insertions, 43 deletions
diff --git a/numpy/lib/tests/test__datasource.py b/numpy/lib/tests/test__datasource.py
index 1df8bebf6..8eac16b58 100644
--- a/numpy/lib/tests/test__datasource.py
+++ b/numpy/lib/tests/test__datasource.py
@@ -361,3 +361,18 @@ class TestOpenFunc(object):
fp = datasource.open(local_file)
assert_(fp)
fp.close()
+
+def test_del_attr_handling():
+ # DataSource __del__ can be called
+ # even if __init__ fails when the
+ # Exception object is caught by the
+ # caller as happens in refguide_check
+ # is_deprecated() function
+
+ ds = datasource.DataSource()
+ # simulate failed __init__ by removing key attribute
+ # produced within __init__ and expected by __del__
+ del ds._istmpdest
+ # should not raise an AttributeError if __del__
+ # gracefully handles failed __init__:
+ ds.__del__()
diff --git a/numpy/lib/tests/test__iotools.py b/numpy/lib/tests/test__iotools.py
index b4888f1bd..e04fdc808 100644
--- a/numpy/lib/tests/test__iotools.py
+++ b/numpy/lib/tests/test__iotools.py
@@ -1,6 +1,5 @@
from __future__ import division, absolute_import, print_function
-import sys
import time
from datetime import date
@@ -246,7 +245,7 @@ class TestStringConverter(object):
converter = StringConverter(int, default=0,
missing_values="N/A")
assert_equal(
- converter.missing_values, set(['', 'N/A']))
+ converter.missing_values, {'', 'N/A'})
def test_int64_dtype(self):
"Check that int64 integer types can be specified"
diff --git a/numpy/lib/tests/test_arraypad.py b/numpy/lib/tests/test_arraypad.py
index e62fccaa0..20f6e4a1b 100644
--- a/numpy/lib/tests/test_arraypad.py
+++ b/numpy/lib/tests/test_arraypad.py
@@ -9,6 +9,91 @@ import numpy as np
from numpy.testing import (assert_array_equal, assert_raises, assert_allclose,
assert_equal)
from numpy.lib import pad
+from numpy.lib.arraypad import _as_pairs
+
+
+class TestAsPairs(object):
+
+ def test_single_value(self):
+ """Test casting for a single value."""
+ expected = np.array([[3, 3]] * 10)
+ for x in (3, [3], [[3]]):
+ result = _as_pairs(x, 10)
+ assert_equal(result, expected)
+ # Test with dtype=object
+ obj = object()
+ assert_equal(
+ _as_pairs(obj, 10),
+ np.array([[obj, obj]] * 10)
+ )
+
+ def test_two_values(self):
+ """Test proper casting for two different values."""
+ # Broadcasting in the first dimension with numbers
+ expected = np.array([[3, 4]] * 10)
+ for x in ([3, 4], [[3, 4]]):
+ result = _as_pairs(x, 10)
+ assert_equal(result, expected)
+ # and with dtype=object
+ obj = object()
+ assert_equal(
+ _as_pairs(["a", obj], 10),
+ np.array([["a", obj]] * 10)
+ )
+
+ # Broadcasting in the second / last dimension with numbers
+ assert_equal(
+ _as_pairs([[3], [4]], 2),
+ np.array([[3, 3], [4, 4]])
+ )
+ # and with dtype=object
+ assert_equal(
+ _as_pairs([["a"], [obj]], 2),
+ np.array([["a", "a"], [obj, obj]])
+ )
+
+ def test_with_none(self):
+ expected = ((None, None), (None, None), (None, None))
+ assert_equal(
+ _as_pairs(None, 3, as_index=False),
+ expected
+ )
+ assert_equal(
+ _as_pairs(None, 3, as_index=True),
+ expected
+ )
+
+ def test_pass_through(self):
+ """Test if `x` already matching desired output are passed through."""
+ expected = np.arange(12).reshape((6, 2))
+ assert_equal(
+ _as_pairs(expected, 6),
+ expected
+ )
+
+ def test_as_index(self):
+ """Test results if `as_index=True`."""
+ assert_equal(
+ _as_pairs([2.6, 3.3], 10, as_index=True),
+ np.array([[3, 3]] * 10, dtype=np.intp)
+ )
+ assert_equal(
+ _as_pairs([2.6, 4.49], 10, as_index=True),
+ np.array([[3, 4]] * 10, dtype=np.intp)
+ )
+ for x in (-3, [-3], [[-3]], [-3, 4], [3, -4], [[-3, 4]], [[4, -3]],
+ [[1, 2]] * 9 + [[1, -2]]):
+ with pytest.raises(ValueError, match="negative values"):
+ _as_pairs(x, 10, as_index=True)
+
+ def test_exceptions(self):
+ """Ensure faulty usage is discovered."""
+ with pytest.raises(ValueError, match="more dimensions than allowed"):
+ _as_pairs([[[3]]], 10)
+ with pytest.raises(ValueError, match="could not be broadcast"):
+ _as_pairs([[1, 2], [3, 4]], 3)
+ with pytest.raises(ValueError, match="could not be broadcast"):
+ _as_pairs(np.ones((2, 3)), 3)
class TestConditionalShortcuts(object):
@@ -535,6 +620,7 @@ class TestConstant(object):
assert_array_equal(arr, expected)
+
class TestLinearRamp(object):
def test_check_simple(self):
a = np.arange(100).astype('f')
diff --git a/numpy/lib/tests/test_arraysetops.py b/numpy/lib/tests/test_arraysetops.py
index fef06ba53..a17fc66e5 100644
--- a/numpy/lib/tests/test_arraysetops.py
+++ b/numpy/lib/tests/test_arraysetops.py
@@ -4,7 +4,6 @@
from __future__ import division, absolute_import, print_function
import numpy as np
-import sys
from numpy.testing import (assert_array_equal, assert_equal,
assert_raises, assert_raises_regex)
diff --git a/numpy/lib/tests/test_format.py b/numpy/lib/tests/test_format.py
index 3185e32ac..077507082 100644
--- a/numpy/lib/tests/test_format.py
+++ b/numpy/lib/tests/test_format.py
@@ -287,7 +287,6 @@ from io import BytesIO
import numpy as np
from numpy.testing import (
assert_, assert_array_equal, assert_raises, assert_raises_regex,
- raises
)
from numpy.lib import format
@@ -524,6 +523,30 @@ def test_compressed_roundtrip():
assert_array_equal(arr, arr1)
+# aligned
+dt1 = np.dtype('i1, i4, i1', align=True)
+# non-aligned, explicit offsets
+dt2 = np.dtype({'names': ['a', 'b'], 'formats': ['i4', 'i4'],
+ 'offsets': [1, 6]})
+# nested struct-in-struct
+dt3 = np.dtype({'names': ['c', 'd'], 'formats': ['i4', dt2]})
+# field with '' name
+dt4 = np.dtype({'names': ['a', '', 'b'], 'formats': ['i4']*3})
+# titles
+dt5 = np.dtype({'names': ['a', 'b'], 'formats': ['i4', 'i4'],
+ 'offsets': [1, 6], 'titles': ['aa', 'bb']})
+
+@pytest.mark.parametrize("dt", [dt1, dt2, dt3, dt4, dt5])
+def test_load_padded_dtype(dt):
+ arr = np.zeros(3, dt)
+ for i in range(3):
+ arr[i] = i + 5
+ npz_file = os.path.join(tempdir, 'aligned.npz')
+ np.savez(npz_file, arr=arr)
+ arr1 = np.load(npz_file)['arr']
+ assert_array_equal(arr, arr1)
+
+
def test_python2_python3_interoperability():
if sys.version_info[0] >= 3:
fname = 'win64python2.npy'
@@ -533,7 +556,6 @@ def test_python2_python3_interoperability():
data = np.load(path)
assert_array_equal(data, np.ones(2))
-
def test_pickle_python2_python3():
# Test that loading object arrays saved on Python 2 works both on
# Python 2 and Python 3 and vice versa
diff --git a/numpy/lib/tests/test_function_base.py b/numpy/lib/tests/test_function_base.py
index 0c789e012..3d4b0e3b2 100644
--- a/numpy/lib/tests/test_function_base.py
+++ b/numpy/lib/tests/test_function_base.py
@@ -11,17 +11,15 @@ from numpy import ma
from numpy.testing import (
assert_, assert_equal, assert_array_equal, assert_almost_equal,
assert_array_almost_equal, assert_raises, assert_allclose,
- assert_array_max_ulp, assert_warns, assert_raises_regex, suppress_warnings,
- HAS_REFCOUNT,
+ assert_warns, assert_raises_regex, suppress_warnings, HAS_REFCOUNT,
)
import numpy.lib.function_base as nfb
from numpy.random import rand
from numpy.lib import (
add_newdoc_ufunc, angle, average, bartlett, blackman, corrcoef, cov,
delete, diff, digitize, extract, flipud, gradient, hamming, hanning,
- histogram, histogramdd, i0, insert, interp, kaiser, meshgrid, msort,
- piecewise, place, rot90, select, setxor1d, sinc, split, trapz, trim_zeros,
- unwrap, unique, vectorize
+ i0, insert, interp, kaiser, meshgrid, msort, piecewise, place, rot90,
+ select, setxor1d, sinc, trapz, trim_zeros, unwrap, unique, vectorize
)
from numpy.compat import long
diff --git a/numpy/lib/tests/test_histograms.py b/numpy/lib/tests/test_histograms.py
index 1b5a71d0e..c96b01d42 100644
--- a/numpy/lib/tests/test_histograms.py
+++ b/numpy/lib/tests/test_histograms.py
@@ -6,7 +6,7 @@ from numpy.lib.histograms import histogram, histogramdd, histogram_bin_edges
from numpy.testing import (
assert_, assert_equal, assert_array_equal, assert_almost_equal,
assert_array_almost_equal, assert_raises, assert_allclose,
- assert_array_max_ulp, assert_warns, assert_raises_regex, suppress_warnings,
+ assert_array_max_ulp, assert_raises_regex, suppress_warnings,
)
@@ -289,13 +289,13 @@ class TestHistogram(object):
def test_object_array_of_0d(self):
# gh-7864
assert_raises(ValueError,
- histogram, [np.array([0.4]) for i in range(10)] + [-np.inf])
+ histogram, [np.array(0.4) for i in range(10)] + [-np.inf])
assert_raises(ValueError,
- histogram, [np.array([0.4]) for i in range(10)] + [np.inf])
+ histogram, [np.array(0.4) for i in range(10)] + [np.inf])
# these should not crash
- np.histogram([np.array([0.5]) for i in range(10)] + [.500000000000001])
- np.histogram([np.array([0.5]) for i in range(10)] + [.5])
+ np.histogram([np.array(0.5) for i in range(10)] + [.500000000000001])
+ np.histogram([np.array(0.5) for i in range(10)] + [.5])
def test_some_nan_values(self):
# gh-7503
@@ -431,7 +431,7 @@ class TestHistogramOptimBinNums(object):
def test_empty(self):
estimator_list = ['fd', 'scott', 'rice', 'sturges',
- 'doane', 'sqrt', 'auto']
+ 'doane', 'sqrt', 'auto', 'stone']
# check it can deal with empty data
for estimator in estimator_list:
a, b = histogram([], bins=estimator)
@@ -447,11 +447,11 @@ class TestHistogramOptimBinNums(object):
# Some basic sanity checking, with some fixed data.
# Checking for the correct number of bins
basic_test = {50: {'fd': 4, 'scott': 4, 'rice': 8, 'sturges': 7,
- 'doane': 8, 'sqrt': 8, 'auto': 7},
+ 'doane': 8, 'sqrt': 8, 'auto': 7, 'stone': 2},
500: {'fd': 8, 'scott': 8, 'rice': 16, 'sturges': 10,
- 'doane': 12, 'sqrt': 23, 'auto': 10},
+ 'doane': 12, 'sqrt': 23, 'auto': 10, 'stone': 9},
5000: {'fd': 17, 'scott': 17, 'rice': 35, 'sturges': 14,
- 'doane': 17, 'sqrt': 71, 'auto': 17}}
+ 'doane': 17, 'sqrt': 71, 'auto': 17, 'stone': 20}}
for testlen, expectedResults in basic_test.items():
# Create some sort of non uniform data to test with
@@ -471,11 +471,11 @@ class TestHistogramOptimBinNums(object):
precalculated.
"""
small_dat = {1: {'fd': 1, 'scott': 1, 'rice': 1, 'sturges': 1,
- 'doane': 1, 'sqrt': 1},
+ 'doane': 1, 'sqrt': 1, 'stone': 1},
2: {'fd': 2, 'scott': 1, 'rice': 3, 'sturges': 2,
- 'doane': 1, 'sqrt': 2},
+ 'doane': 1, 'sqrt': 2, 'stone': 1},
3: {'fd': 2, 'scott': 2, 'rice': 3, 'sturges': 3,
- 'doane': 3, 'sqrt': 2}}
+ 'doane': 3, 'sqrt': 2, 'stone': 1}}
for testlen, expectedResults in small_dat.items():
testdat = np.arange(testlen)
@@ -499,7 +499,7 @@ class TestHistogramOptimBinNums(object):
"""
novar_dataset = np.ones(100)
novar_resultdict = {'fd': 1, 'scott': 1, 'rice': 1, 'sturges': 1,
- 'doane': 1, 'sqrt': 1, 'auto': 1}
+ 'doane': 1, 'sqrt': 1, 'auto': 1, 'stone': 1}
for estimator, numbins in novar_resultdict.items():
a, b = np.histogram(novar_dataset, estimator)
@@ -538,12 +538,32 @@ class TestHistogramOptimBinNums(object):
xcenter = np.linspace(-10, 10, 50)
outlier_dataset = np.hstack((np.linspace(-110, -100, 5), xcenter))
- outlier_resultdict = {'fd': 21, 'scott': 5, 'doane': 11}
+ outlier_resultdict = {'fd': 21, 'scott': 5, 'doane': 11, 'stone': 6}
for estimator, numbins in outlier_resultdict.items():
a, b = np.histogram(outlier_dataset, estimator)
assert_equal(len(a), numbins)
+ def test_scott_vs_stone(self):
+ """Verify that Scott's rule and Stone's rule converges for normally distributed data"""
+
+ def nbins_ratio(seed, size):
+ rng = np.random.RandomState(seed)
+ x = rng.normal(loc=0, scale=2, size=size)
+ a, b = len(np.histogram(x, 'stone')[0]), len(np.histogram(x, 'scott')[0])
+ return a / (a + b)
+
+ ll = [[nbins_ratio(seed, size) for size in np.geomspace(start=10, stop=100, num=4).round().astype(int)]
+ for seed in range(256)]
+
+ # the average difference between the two methods decreases as the dataset size increases.
+ assert_almost_equal(abs(np.mean(ll, axis=0) - 0.5),
+ [0.1065248,
+ 0.0968844,
+ 0.0331818,
+ 0.0178057],
+ decimal=3)
+
def test_simple_range(self):
"""
Straightforward testing with a mixture of linspace data (for
@@ -555,11 +575,11 @@ class TestHistogramOptimBinNums(object):
# Checking for the correct number of bins
basic_test = {
50: {'fd': 8, 'scott': 8, 'rice': 15,
- 'sturges': 14, 'auto': 14},
+ 'sturges': 14, 'auto': 14, 'stone': 8},
500: {'fd': 15, 'scott': 16, 'rice': 32,
- 'sturges': 20, 'auto': 20},
+ 'sturges': 20, 'auto': 20, 'stone': 80},
5000: {'fd': 33, 'scott': 33, 'rice': 69,
- 'sturges': 27, 'auto': 33}
+ 'sturges': 27, 'auto': 33, 'stone': 80}
}
for testlen, expectedResults in basic_test.items():
@@ -794,3 +814,20 @@ class TestHistogramdd(object):
hist_dd, edges_dd = histogramdd((v,), (bins,), density=True)
assert_equal(hist, hist_dd)
assert_equal(edges, edges_dd[0])
+
+ def test_density_via_normed(self):
+ # normed should simply alias to density argument
+ v = np.arange(10)
+ bins = np.array([0, 1, 3, 6, 10])
+ hist, edges = histogram(v, bins, density=True)
+ hist_dd, edges_dd = histogramdd((v,), (bins,), normed=True)
+ assert_equal(hist, hist_dd)
+ assert_equal(edges, edges_dd[0])
+
+ def test_density_normed_redundancy(self):
+ v = np.arange(10)
+ bins = np.array([0, 1, 3, 6, 10])
+ with assert_raises_regex(TypeError, "Cannot specify both"):
+ hist_dd, edges_dd = histogramdd((v,), (bins,),
+ density=True,
+ normed=True)
diff --git a/numpy/lib/tests/test_io.py b/numpy/lib/tests/test_io.py
index b746937b9..7ef25538b 100644
--- a/numpy/lib/tests/test_io.py
+++ b/numpy/lib/tests/test_io.py
@@ -6,7 +6,6 @@ import os
import threading
import time
import warnings
-import gc
import io
import re
import pytest
@@ -18,7 +17,7 @@ import locale
import numpy as np
import numpy.ma as ma
from numpy.lib._iotools import ConverterError, ConversionWarning
-from numpy.compat import asbytes, bytes, unicode, Path
+from numpy.compat import asbytes, bytes, Path
from numpy.ma.testutils import assert_equal
from numpy.testing import (
assert_warns, assert_, assert_raises_regex, assert_raises,
@@ -355,6 +354,16 @@ class TestSaveTxt(object):
c.seek(0)
assert_equal(c.readlines(), [b'1 2\n', b'3 4\n'])
+ @pytest.mark.skipif(Path is None, reason="No pathlib.Path")
+ def test_multifield_view(self):
+ a = np.ones(1, dtype=[('x', 'i4'), ('y', 'i4'), ('z', 'f4')])
+ v = a[['x', 'z']]
+ with temppath(suffix='.npy') as path:
+ path = Path(path)
+ np.save(path, v)
+ data = np.load(path)
+ assert_array_equal(data, v)
+
def test_delimiter(self):
a = np.array([[1., 2.], [3., 4.]])
c = BytesIO()
@@ -2049,7 +2058,6 @@ M 33 21.99
def test_utf8_file(self):
utf8 = b"\xcf\x96"
- latin1 = b"\xf6\xfc\xf6"
with temppath() as path:
with open(path, "wb") as f:
f.write((b"test1,testNonethe" + utf8 + b",test3\n") * 2)
diff --git a/numpy/lib/tests/test_mixins.py b/numpy/lib/tests/test_mixins.py
index f2d915502..3dd5346b6 100644
--- a/numpy/lib/tests/test_mixins.py
+++ b/numpy/lib/tests/test_mixins.py
@@ -199,6 +199,17 @@ class TestNDArrayOperatorsMixin(object):
err_msg = 'failed for operator {}'.format(op)
_assert_equal_type_and_value(expected, actual, err_msg=err_msg)
+ def test_matmul(self):
+ array = np.array([1, 2], dtype=np.float64)
+ array_like = ArrayLike(array)
+ expected = ArrayLike(np.float64(5))
+ _assert_equal_type_and_value(expected, np.matmul(array_like, array))
+ if not PY2:
+ _assert_equal_type_and_value(
+ expected, operator.matmul(array_like, array))
+ _assert_equal_type_and_value(
+ expected, operator.matmul(array, array_like))
+
def test_ufunc_at(self):
array = ArrayLike(np.array([1, 2, 3, 4]))
assert_(np.negative.at(array, np.array([0, 1])) is None)
diff --git a/numpy/lib/tests/test_polynomial.py b/numpy/lib/tests/test_polynomial.py
index 9f7c117a2..77414ba7c 100644
--- a/numpy/lib/tests/test_polynomial.py
+++ b/numpy/lib/tests/test_polynomial.py
@@ -3,7 +3,7 @@ from __future__ import division, absolute_import, print_function
import numpy as np
from numpy.testing import (
assert_, assert_equal, assert_array_equal, assert_almost_equal,
- assert_array_almost_equal, assert_raises
+ assert_array_almost_equal, assert_raises, assert_allclose
)
@@ -122,27 +122,34 @@ class TestPolynomial(object):
weights = np.arange(8, 1, -1)**2/7.0
# Check exception when too few points for variance estimate. Note that
- # the Bayesian estimate requires the number of data points to exceed
- # degree + 3.
+ # the estimate requires the number of data points to exceed
+ # degree + 1
assert_raises(ValueError, np.polyfit,
- [0, 1, 3], [0, 1, 3], deg=0, cov=True)
+ [1], [1], deg=0, cov=True)
# check 1D case
m, cov = np.polyfit(x, y+err, 2, cov=True)
est = [3.8571, 0.2857, 1.619]
assert_almost_equal(est, m, decimal=4)
- val0 = [[2.9388, -5.8776, 1.6327],
- [-5.8776, 12.7347, -4.2449],
- [1.6327, -4.2449, 2.3220]]
+ val0 = [[ 1.4694, -2.9388, 0.8163],
+ [-2.9388, 6.3673, -2.1224],
+ [ 0.8163, -2.1224, 1.161 ]]
assert_almost_equal(val0, cov, decimal=4)
m2, cov2 = np.polyfit(x, y+err, 2, w=weights, cov=True)
assert_almost_equal([4.8927, -1.0177, 1.7768], m2, decimal=4)
- val = [[8.7929, -10.0103, 0.9756],
- [-10.0103, 13.6134, -1.8178],
- [0.9756, -1.8178, 0.6674]]
+ val = [[ 4.3964, -5.0052, 0.4878],
+ [-5.0052, 6.8067, -0.9089],
+ [ 0.4878, -0.9089, 0.3337]]
assert_almost_equal(val, cov2, decimal=4)
+ m3, cov3 = np.polyfit(x, y+err, 2, w=weights, cov="unscaled")
+ assert_almost_equal([4.8927, -1.0177, 1.7768], m3, decimal=4)
+ val = [[ 0.1473, -0.1677, 0.0163],
+ [-0.1677, 0.228 , -0.0304],
+ [ 0.0163, -0.0304, 0.0112]]
+ assert_almost_equal(val, cov3, decimal=4)
+
# check 2D (n,1) case
y = y[:, np.newaxis]
c = c[:, np.newaxis]
@@ -158,6 +165,29 @@ class TestPolynomial(object):
assert_almost_equal(val0, cov[:, :, 0], decimal=4)
assert_almost_equal(val0, cov[:, :, 1], decimal=4)
+ # check order 1 (deg=0) case, were the analytic results are simple
+ np.random.seed(123)
+ y = np.random.normal(size=(4, 10000))
+ mean, cov = np.polyfit(np.zeros(y.shape[0]), y, deg=0, cov=True)
+ # Should get sigma_mean = sigma/sqrt(N) = 1./sqrt(4) = 0.5.
+ assert_allclose(mean.std(), 0.5, atol=0.01)
+ assert_allclose(np.sqrt(cov.mean()), 0.5, atol=0.01)
+ # Without scaling, since reduced chi2 is 1, the result should be the same.
+ mean, cov = np.polyfit(np.zeros(y.shape[0]), y, w=np.ones(y.shape[0]),
+ deg=0, cov="unscaled")
+ assert_allclose(mean.std(), 0.5, atol=0.01)
+ assert_almost_equal(np.sqrt(cov.mean()), 0.5)
+ # If we estimate our errors wrong, no change with scaling:
+ w = np.full(y.shape[0], 1./0.5)
+ mean, cov = np.polyfit(np.zeros(y.shape[0]), y, w=w, deg=0, cov=True)
+ assert_allclose(mean.std(), 0.5, atol=0.01)
+ assert_allclose(np.sqrt(cov.mean()), 0.5, atol=0.01)
+ # But if we do not scale, our estimate for the error in the mean will
+ # differ.
+ mean, cov = np.polyfit(np.zeros(y.shape[0]), y, w=w, deg=0, cov="unscaled")
+ assert_allclose(mean.std(), 0.5, atol=0.01)
+ assert_almost_equal(np.sqrt(cov.mean()), 0.25)
+
def test_objects(self):
from decimal import Decimal
p = np.poly1d([Decimal('4.0'), Decimal('3.0'), Decimal('2.0')])
diff --git a/numpy/lib/tests/test_recfunctions.py b/numpy/lib/tests/test_recfunctions.py
index 5585a95f9..11f8a5afa 100644
--- a/numpy/lib/tests/test_recfunctions.py
+++ b/numpy/lib/tests/test_recfunctions.py
@@ -10,7 +10,8 @@ from numpy.testing import assert_, assert_raises
from numpy.lib.recfunctions import (
drop_fields, rename_fields, get_fieldstructure, recursive_fill_fields,
find_duplicates, merge_arrays, append_fields, stack_arrays, join_by,
- repack_fields)
+ repack_fields, unstructured_to_structured, structured_to_unstructured,
+ apply_along_fields, require_fields, assign_fields_by_name)
get_names = np.lib.recfunctions.get_names
get_names_flat = np.lib.recfunctions.get_names_flat
zip_descr = np.lib.recfunctions.zip_descr
@@ -204,6 +205,77 @@ class TestRecFunctions(object):
dt = np.dtype((np.record, dt))
assert_(repack_fields(dt).type is np.record)
+ def test_structured_to_unstructured(self):
+ a = np.zeros(4, dtype=[('a', 'i4'), ('b', 'f4,u2'), ('c', 'f4', 2)])
+ out = structured_to_unstructured(a)
+ assert_equal(out, np.zeros((4,5), dtype='f8'))
+
+ b = np.array([(1, 2, 5), (4, 5, 7), (7, 8 ,11), (10, 11, 12)],
+ dtype=[('x', 'i4'), ('y', 'f4'), ('z', 'f8')])
+ out = np.mean(structured_to_unstructured(b[['x', 'z']]), axis=-1)
+ assert_equal(out, np.array([ 3. , 5.5, 9. , 11. ]))
+
+ c = np.arange(20).reshape((4,5))
+ out = unstructured_to_structured(c, a.dtype)
+ want = np.array([( 0, ( 1., 2), [ 3., 4.]),
+ ( 5, ( 6., 7), [ 8., 9.]),
+ (10, (11., 12), [13., 14.]),
+ (15, (16., 17), [18., 19.])],
+ dtype=[('a', '<i4'),
+ ('b', [('f0', '<f4'), ('f1', '<u2')]),
+ ('c', '<f4', (2,))])
+ assert_equal(out, want)
+
+ d = np.array([(1, 2, 5), (4, 5, 7), (7, 8 ,11), (10, 11, 12)],
+ dtype=[('x', 'i4'), ('y', 'f4'), ('z', 'f8')])
+ assert_equal(apply_along_fields(np.mean, d),
+ np.array([ 8.0/3, 16.0/3, 26.0/3, 11. ]))
+ assert_equal(apply_along_fields(np.mean, d[['x', 'z']]),
+ np.array([ 3. , 5.5, 9. , 11. ]))
+
+ # check that for uniform field dtypes we get a view, not a copy:
+ d = np.array([(1, 2, 5), (4, 5, 7), (7, 8 ,11), (10, 11, 12)],
+ dtype=[('x', 'i4'), ('y', 'i4'), ('z', 'i4')])
+ dd = structured_to_unstructured(d)
+ ddd = unstructured_to_structured(dd, d.dtype)
+ assert_(dd.base is d)
+ assert_(ddd.base is d)
+
+ # test that nested fields with identical names don't break anything
+ point = np.dtype([('x', int), ('y', int)])
+ triangle = np.dtype([('a', point), ('b', point), ('c', point)])
+ arr = np.zeros(10, triangle)
+ res = structured_to_unstructured(arr, dtype=int)
+ assert_equal(res, np.zeros((10, 6), dtype=int))
+
+
+ def test_field_assignment_by_name(self):
+ a = np.ones(2, dtype=[('a', 'i4'), ('b', 'f8'), ('c', 'u1')])
+ newdt = [('b', 'f4'), ('c', 'u1')]
+
+ assert_equal(require_fields(a, newdt), np.ones(2, newdt))
+
+ b = np.array([(1,2), (3,4)], dtype=newdt)
+ assign_fields_by_name(a, b, zero_unassigned=False)
+ assert_equal(a, np.array([(1,1,2),(1,3,4)], dtype=a.dtype))
+ assign_fields_by_name(a, b)
+ assert_equal(a, np.array([(0,1,2),(0,3,4)], dtype=a.dtype))
+
+ # test nested fields
+ a = np.ones(2, dtype=[('a', [('b', 'f8'), ('c', 'u1')])])
+ newdt = [('a', [('c', 'u1')])]
+ assert_equal(require_fields(a, newdt), np.ones(2, newdt))
+ b = np.array([((2,),), ((3,),)], dtype=newdt)
+ assign_fields_by_name(a, b, zero_unassigned=False)
+ assert_equal(a, np.array([((1,2),), ((1,3),)], dtype=a.dtype))
+ assign_fields_by_name(a, b)
+ assert_equal(a, np.array([((0,2),), ((0,3),)], dtype=a.dtype))
+
+ # test unstructured code path for 0d arrays
+ a, b = np.array(3), np.array(0)
+ assign_fields_by_name(b, a)
+ assert_equal(b[()], 3)
+
class TestRecursiveFillFields(object):
# Test recursive_fill_fields.
diff --git a/numpy/lib/tests/test_shape_base.py b/numpy/lib/tests/test_shape_base.py
index e338467f9..01ea028bb 100644
--- a/numpy/lib/tests/test_shape_base.py
+++ b/numpy/lib/tests/test_shape_base.py
@@ -260,8 +260,8 @@ class TestApplyAlongAxis(object):
def test_with_iterable_object(self):
# from issue 5248
d = np.array([
- [set([1, 11]), set([2, 22]), set([3, 33])],
- [set([4, 44]), set([5, 55]), set([6, 66])]
+ [{1, 11}, {2, 22}, {3, 33}],
+ [{4, 44}, {5, 55}, {6, 66}]
])
actual = np.apply_along_axis(lambda a: set.union(*a), 0, d)
expected = np.array([{1, 11, 4, 44}, {2, 22, 5, 55}, {3, 33, 6, 66}])
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