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import collections.abc
import textwrap
from io import BytesIO
from os import path
from pathlib import Path
import pytest
import numpy as np
from numpy.testing import (
assert_, assert_equal, assert_array_equal, assert_array_almost_equal,
assert_raises, temppath,
)
from numpy.compat import pickle
class TestFromrecords:
def test_fromrecords(self):
r = np.rec.fromrecords([[456, 'dbe', 1.2], [2, 'de', 1.3]],
names='col1,col2,col3')
assert_equal(r[0].item(), (456, 'dbe', 1.2))
assert_equal(r['col1'].dtype.kind, 'i')
assert_equal(r['col2'].dtype.kind, 'U')
assert_equal(r['col2'].dtype.itemsize, 12)
assert_equal(r['col3'].dtype.kind, 'f')
def test_fromrecords_0len(self):
""" Verify fromrecords works with a 0-length input """
dtype = [('a', float), ('b', float)]
r = np.rec.fromrecords([], dtype=dtype)
assert_equal(r.shape, (0,))
def test_fromrecords_2d(self):
data = [
[(1, 2), (3, 4), (5, 6)],
[(6, 5), (4, 3), (2, 1)]
]
expected_a = [[1, 3, 5], [6, 4, 2]]
expected_b = [[2, 4, 6], [5, 3, 1]]
# try with dtype
r1 = np.rec.fromrecords(data, dtype=[('a', int), ('b', int)])
assert_equal(r1['a'], expected_a)
assert_equal(r1['b'], expected_b)
# try with names
r2 = np.rec.fromrecords(data, names=['a', 'b'])
assert_equal(r2['a'], expected_a)
assert_equal(r2['b'], expected_b)
assert_equal(r1, r2)
def test_method_array(self):
r = np.rec.array(b'abcdefg' * 100, formats='i2,a3,i4', shape=3, byteorder='big')
assert_equal(r[1].item(), (25444, b'efg', 1633837924))
def test_method_array2(self):
r = np.rec.array([(1, 11, 'a'), (2, 22, 'b'), (3, 33, 'c'), (4, 44, 'd'), (5, 55, 'ex'),
(6, 66, 'f'), (7, 77, 'g')], formats='u1,f4,a1')
assert_equal(r[1].item(), (2, 22.0, b'b'))
def test_recarray_slices(self):
r = np.rec.array([(1, 11, 'a'), (2, 22, 'b'), (3, 33, 'c'), (4, 44, 'd'), (5, 55, 'ex'),
(6, 66, 'f'), (7, 77, 'g')], formats='u1,f4,a1')
assert_equal(r[1::2][1].item(), (4, 44.0, b'd'))
def test_recarray_fromarrays(self):
x1 = np.array([1, 2, 3, 4])
x2 = np.array(['a', 'dd', 'xyz', '12'])
x3 = np.array([1.1, 2, 3, 4])
r = np.rec.fromarrays([x1, x2, x3], names='a,b,c')
assert_equal(r[1].item(), (2, 'dd', 2.0))
x1[1] = 34
assert_equal(r.a, np.array([1, 2, 3, 4]))
def test_recarray_fromfile(self):
data_dir = path.join(path.dirname(__file__), 'data')
filename = path.join(data_dir, 'recarray_from_file.fits')
fd = open(filename, 'rb')
fd.seek(2880 * 2)
r1 = np.rec.fromfile(fd, formats='f8,i4,a5', shape=3, byteorder='big')
fd.seek(2880 * 2)
r2 = np.rec.array(fd, formats='f8,i4,a5', shape=3, byteorder='big')
fd.seek(2880 * 2)
bytes_array = BytesIO()
bytes_array.write(fd.read())
bytes_array.seek(0)
r3 = np.rec.fromfile(bytes_array, formats='f8,i4,a5', shape=3, byteorder='big')
fd.close()
assert_equal(r1, r2)
assert_equal(r2, r3)
def test_recarray_from_obj(self):
count = 10
a = np.zeros(count, dtype='O')
b = np.zeros(count, dtype='f8')
c = np.zeros(count, dtype='f8')
for i in range(len(a)):
a[i] = list(range(1, 10))
mine = np.rec.fromarrays([a, b, c], names='date,data1,data2')
for i in range(len(a)):
assert_((mine.date[i] == list(range(1, 10))))
assert_((mine.data1[i] == 0.0))
assert_((mine.data2[i] == 0.0))
def test_recarray_repr(self):
a = np.array([(1, 0.1), (2, 0.2)],
dtype=[('foo', '<i4'), ('bar', '<f8')])
a = np.rec.array(a)
assert_equal(
repr(a),
textwrap.dedent("""\
rec.array([(1, 0.1), (2, 0.2)],
dtype=[('foo', '<i4'), ('bar', '<f8')])""")
)
# make sure non-structured dtypes also show up as rec.array
a = np.array(np.ones(4, dtype='f8'))
assert_(repr(np.rec.array(a)).startswith('rec.array'))
# check that the 'np.record' part of the dtype isn't shown
a = np.rec.array(np.ones(3, dtype='i4,i4'))
assert_equal(repr(a).find('numpy.record'), -1)
a = np.rec.array(np.ones(3, dtype='i4'))
assert_(repr(a).find('dtype=int32') != -1)
def test_0d_recarray_repr(self):
arr_0d = np.rec.array((1, 2.0, '2003'), dtype='<i4,<f8,<M8[Y]')
assert_equal(repr(arr_0d), textwrap.dedent("""\
rec.array((1, 2., '2003'),
dtype=[('f0', '<i4'), ('f1', '<f8'), ('f2', '<M8[Y]')])"""))
record = arr_0d[()]
assert_equal(repr(record), "(1, 2., '2003')")
# 1.13 converted to python scalars before the repr
try:
np.set_printoptions(legacy='1.13')
assert_equal(repr(record), '(1, 2.0, datetime.date(2003, 1, 1))')
finally:
np.set_printoptions(legacy=False)
def test_recarray_from_repr(self):
a = np.array([(1,'ABC'), (2, "DEF")],
dtype=[('foo', int), ('bar', 'S4')])
recordarr = np.rec.array(a)
recarr = a.view(np.recarray)
recordview = a.view(np.dtype((np.record, a.dtype)))
recordarr_r = eval("numpy." + repr(recordarr), {'numpy': np})
recarr_r = eval("numpy." + repr(recarr), {'numpy': np})
recordview_r = eval("numpy." + repr(recordview), {'numpy': np})
assert_equal(type(recordarr_r), np.recarray)
assert_equal(recordarr_r.dtype.type, np.record)
assert_equal(recordarr, recordarr_r)
assert_equal(type(recarr_r), np.recarray)
assert_equal(recarr_r.dtype.type, np.record)
assert_equal(recarr, recarr_r)
assert_equal(type(recordview_r), np.ndarray)
assert_equal(recordview.dtype.type, np.record)
assert_equal(recordview, recordview_r)
def test_recarray_views(self):
a = np.array([(1,'ABC'), (2, "DEF")],
dtype=[('foo', int), ('bar', 'S4')])
b = np.array([1,2,3,4,5], dtype=np.int64)
#check that np.rec.array gives right dtypes
assert_equal(np.rec.array(a).dtype.type, np.record)
assert_equal(type(np.rec.array(a)), np.recarray)
assert_equal(np.rec.array(b).dtype.type, np.int64)
assert_equal(type(np.rec.array(b)), np.recarray)
#check that viewing as recarray does the same
assert_equal(a.view(np.recarray).dtype.type, np.record)
assert_equal(type(a.view(np.recarray)), np.recarray)
assert_equal(b.view(np.recarray).dtype.type, np.int64)
assert_equal(type(b.view(np.recarray)), np.recarray)
#check that view to non-structured dtype preserves type=np.recarray
r = np.rec.array(np.ones(4, dtype="f4,i4"))
rv = r.view('f8').view('f4,i4')
assert_equal(type(rv), np.recarray)
assert_equal(rv.dtype.type, np.record)
#check that getitem also preserves np.recarray and np.record
r = np.rec.array(np.ones(4, dtype=[('a', 'i4'), ('b', 'i4'),
('c', 'i4,i4')]))
assert_equal(r['c'].dtype.type, np.record)
assert_equal(type(r['c']), np.recarray)
#and that it preserves subclasses (gh-6949)
class C(np.recarray):
pass
c = r.view(C)
assert_equal(type(c['c']), C)
# check that accessing nested structures keep record type, but
# not for subarrays, non-void structures, non-structured voids
test_dtype = [('a', 'f4,f4'), ('b', 'V8'), ('c', ('f4',2)),
('d', ('i8', 'i4,i4'))]
r = np.rec.array([((1,1), b'11111111', [1,1], 1),
((1,1), b'11111111', [1,1], 1)], dtype=test_dtype)
assert_equal(r.a.dtype.type, np.record)
assert_equal(r.b.dtype.type, np.void)
assert_equal(r.c.dtype.type, np.float32)
assert_equal(r.d.dtype.type, np.int64)
# check the same, but for views
r = np.rec.array(np.ones(4, dtype='i4,i4'))
assert_equal(r.view('f4,f4').dtype.type, np.record)
assert_equal(r.view(('i4',2)).dtype.type, np.int32)
assert_equal(r.view('V8').dtype.type, np.void)
assert_equal(r.view(('i8', 'i4,i4')).dtype.type, np.int64)
#check that we can undo the view
arrs = [np.ones(4, dtype='f4,i4'), np.ones(4, dtype='f8')]
for arr in arrs:
rec = np.rec.array(arr)
# recommended way to view as an ndarray:
arr2 = rec.view(rec.dtype.fields or rec.dtype, np.ndarray)
assert_equal(arr2.dtype.type, arr.dtype.type)
assert_equal(type(arr2), type(arr))
def test_recarray_from_names(self):
ra = np.rec.array([
(1, 'abc', 3.7000002861022949, 0),
(2, 'xy', 6.6999998092651367, 1),
(0, ' ', 0.40000000596046448, 0)],
names='c1, c2, c3, c4')
pa = np.rec.fromrecords([
(1, 'abc', 3.7000002861022949, 0),
(2, 'xy', 6.6999998092651367, 1),
(0, ' ', 0.40000000596046448, 0)],
names='c1, c2, c3, c4')
assert_(ra.dtype == pa.dtype)
assert_(ra.shape == pa.shape)
for k in range(len(ra)):
assert_(ra[k].item() == pa[k].item())
def test_recarray_conflict_fields(self):
ra = np.rec.array([(1, 'abc', 2.3), (2, 'xyz', 4.2),
(3, 'wrs', 1.3)],
names='field, shape, mean')
ra.mean = [1.1, 2.2, 3.3]
assert_array_almost_equal(ra['mean'], [1.1, 2.2, 3.3])
assert_(type(ra.mean) is type(ra.var))
ra.shape = (1, 3)
assert_(ra.shape == (1, 3))
ra.shape = ['A', 'B', 'C']
assert_array_equal(ra['shape'], [['A', 'B', 'C']])
ra.field = 5
assert_array_equal(ra['field'], [[5, 5, 5]])
assert_(isinstance(ra.field, collections.abc.Callable))
def test_fromrecords_with_explicit_dtype(self):
a = np.rec.fromrecords([(1, 'a'), (2, 'bbb')],
dtype=[('a', int), ('b', object)])
assert_equal(a.a, [1, 2])
assert_equal(a[0].a, 1)
assert_equal(a.b, ['a', 'bbb'])
assert_equal(a[-1].b, 'bbb')
#
ndtype = np.dtype([('a', int), ('b', object)])
a = np.rec.fromrecords([(1, 'a'), (2, 'bbb')], dtype=ndtype)
assert_equal(a.a, [1, 2])
assert_equal(a[0].a, 1)
assert_equal(a.b, ['a', 'bbb'])
assert_equal(a[-1].b, 'bbb')
def test_recarray_stringtypes(self):
# Issue #3993
a = np.array([('abc ', 1), ('abc', 2)],
dtype=[('foo', 'S4'), ('bar', int)])
a = a.view(np.recarray)
assert_equal(a.foo[0] == a.foo[1], False)
def test_recarray_returntypes(self):
qux_fields = {'C': (np.dtype('S5'), 0), 'D': (np.dtype('S5'), 6)}
a = np.rec.array([('abc ', (1,1), 1, ('abcde', 'fgehi')),
('abc', (2,3), 1, ('abcde', 'jklmn'))],
dtype=[('foo', 'S4'),
('bar', [('A', int), ('B', int)]),
('baz', int), ('qux', qux_fields)])
assert_equal(type(a.foo), np.ndarray)
assert_equal(type(a['foo']), np.ndarray)
assert_equal(type(a.bar), np.recarray)
assert_equal(type(a['bar']), np.recarray)
assert_equal(a.bar.dtype.type, np.record)
assert_equal(type(a['qux']), np.recarray)
assert_equal(a.qux.dtype.type, np.record)
assert_equal(dict(a.qux.dtype.fields), qux_fields)
assert_equal(type(a.baz), np.ndarray)
assert_equal(type(a['baz']), np.ndarray)
assert_equal(type(a[0].bar), np.record)
assert_equal(type(a[0]['bar']), np.record)
assert_equal(a[0].bar.A, 1)
assert_equal(a[0].bar['A'], 1)
assert_equal(a[0]['bar'].A, 1)
assert_equal(a[0]['bar']['A'], 1)
assert_equal(a[0].qux.D, b'fgehi')
assert_equal(a[0].qux['D'], b'fgehi')
assert_equal(a[0]['qux'].D, b'fgehi')
assert_equal(a[0]['qux']['D'], b'fgehi')
def test_zero_width_strings(self):
# Test for #6430, based on the test case from #1901
cols = [['test'] * 3, [''] * 3]
rec = np.rec.fromarrays(cols)
assert_equal(rec['f0'], ['test', 'test', 'test'])
assert_equal(rec['f1'], ['', '', ''])
dt = np.dtype([('f0', '|S4'), ('f1', '|S')])
rec = np.rec.fromarrays(cols, dtype=dt)
assert_equal(rec.itemsize, 4)
assert_equal(rec['f0'], [b'test', b'test', b'test'])
assert_equal(rec['f1'], [b'', b'', b''])
class TestPathUsage:
# Test that pathlib.Path can be used
def test_tofile_fromfile(self):
with temppath(suffix='.bin') as path:
path = Path(path)
np.random.seed(123)
a = np.random.rand(10).astype('f8,i4,a5')
a[5] = (0.5,10,'abcde')
with path.open("wb") as fd:
a.tofile(fd)
x = np.core.records.fromfile(path,
formats='f8,i4,a5',
shape=10)
assert_array_equal(x, a)
class TestRecord:
def setup_method(self):
self.data = np.rec.fromrecords([(1, 2, 3), (4, 5, 6)],
dtype=[("col1", "<i4"),
("col2", "<i4"),
("col3", "<i4")])
def test_assignment1(self):
a = self.data
assert_equal(a.col1[0], 1)
a[0].col1 = 0
assert_equal(a.col1[0], 0)
def test_assignment2(self):
a = self.data
assert_equal(a.col1[0], 1)
a.col1[0] = 0
assert_equal(a.col1[0], 0)
def test_invalid_assignment(self):
a = self.data
def assign_invalid_column(x):
x[0].col5 = 1
assert_raises(AttributeError, assign_invalid_column, a)
def test_nonwriteable_setfield(self):
# gh-8171
r = np.rec.array([(0,), (1,)], dtype=[('f', 'i4')])
r.flags.writeable = False
with assert_raises(ValueError):
r.f = [2, 3]
with assert_raises(ValueError):
r.setfield([2,3], *r.dtype.fields['f'])
def test_out_of_order_fields(self):
# names in the same order, padding added to descr
x = self.data[['col1', 'col2']]
assert_equal(x.dtype.names, ('col1', 'col2'))
assert_equal(x.dtype.descr,
[('col1', '<i4'), ('col2', '<i4'), ('', '|V4')])
# names change order to match indexing, as of 1.14 - descr can't
# represent that
y = self.data[['col2', 'col1']]
assert_equal(y.dtype.names, ('col2', 'col1'))
assert_raises(ValueError, lambda: y.dtype.descr)
def test_pickle_1(self):
# Issue #1529
a = np.array([(1, [])], dtype=[('a', np.int32), ('b', np.int32, 0)])
for proto in range(2, pickle.HIGHEST_PROTOCOL + 1):
assert_equal(a, pickle.loads(pickle.dumps(a, protocol=proto)))
assert_equal(a[0], pickle.loads(pickle.dumps(a[0],
protocol=proto)))
def test_pickle_2(self):
a = self.data
for proto in range(2, pickle.HIGHEST_PROTOCOL + 1):
assert_equal(a, pickle.loads(pickle.dumps(a, protocol=proto)))
assert_equal(a[0], pickle.loads(pickle.dumps(a[0],
protocol=proto)))
def test_pickle_3(self):
# Issue #7140
a = self.data
for proto in range(2, pickle.HIGHEST_PROTOCOL + 1):
pa = pickle.loads(pickle.dumps(a[0], protocol=proto))
assert_(pa.flags.c_contiguous)
assert_(pa.flags.f_contiguous)
assert_(pa.flags.writeable)
assert_(pa.flags.aligned)
def test_pickle_void(self):
# issue gh-13593
dt = np.dtype([('obj', 'O'), ('int', 'i')])
a = np.empty(1, dtype=dt)
data = (bytearray(b'eman'),)
a['obj'] = data
a['int'] = 42
ctor, args = a[0].__reduce__()
# check the constructor is what we expect before interpreting the arguments
assert ctor is np.core.multiarray.scalar
dtype, obj = args
# make sure we did not pickle the address
assert not isinstance(obj, bytes)
assert_raises(RuntimeError, ctor, dtype, 13)
# Test roundtrip:
dump = pickle.dumps(a[0])
unpickled = pickle.loads(dump)
assert a[0] == unpickled
# Also check the similar (impossible) "object scalar" path:
with pytest.warns(DeprecationWarning):
assert ctor(np.dtype("O"), data) is data
def test_objview_record(self):
# https://github.com/numpy/numpy/issues/2599
dt = np.dtype([('foo', 'i8'), ('bar', 'O')])
r = np.zeros((1,3), dtype=dt).view(np.recarray)
r.foo = np.array([1, 2, 3]) # TypeError?
# https://github.com/numpy/numpy/issues/3256
ra = np.recarray((2,), dtype=[('x', object), ('y', float), ('z', int)])
ra[['x','y']] # TypeError?
def test_record_scalar_setitem(self):
# https://github.com/numpy/numpy/issues/3561
rec = np.recarray(1, dtype=[('x', float, 5)])
rec[0].x = 1
assert_equal(rec[0].x, np.ones(5))
def test_missing_field(self):
# https://github.com/numpy/numpy/issues/4806
arr = np.zeros((3,), dtype=[('x', int), ('y', int)])
assert_raises(KeyError, lambda: arr[['nofield']])
def test_fromarrays_nested_structured_arrays(self):
arrays = [
np.arange(10),
np.ones(10, dtype=[('a', '<u2'), ('b', '<f4')]),
]
arr = np.rec.fromarrays(arrays) # ValueError?
@pytest.mark.parametrize('nfields', [0, 1, 2])
def test_assign_dtype_attribute(self, nfields):
dt = np.dtype([('a', np.uint8), ('b', np.uint8), ('c', np.uint8)][:nfields])
data = np.zeros(3, dt).view(np.recarray)
# the original and resulting dtypes differ on whether they are records
assert data.dtype.type == np.record
assert dt.type != np.record
# ensure that the dtype remains a record even when assigned
data.dtype = dt
assert data.dtype.type == np.record
@pytest.mark.parametrize('nfields', [0, 1, 2])
def test_nested_fields_are_records(self, nfields):
""" Test that nested structured types are treated as records too """
dt = np.dtype([('a', np.uint8), ('b', np.uint8), ('c', np.uint8)][:nfields])
dt_outer = np.dtype([('inner', dt)])
data = np.zeros(3, dt_outer).view(np.recarray)
assert isinstance(data, np.recarray)
assert isinstance(data['inner'], np.recarray)
data0 = data[0]
assert isinstance(data0, np.record)
assert isinstance(data0['inner'], np.record)
def test_nested_dtype_padding(self):
""" test that trailing padding is preserved """
# construct a dtype with padding at the end
dt = np.dtype([('a', np.uint8), ('b', np.uint8), ('c', np.uint8)])
dt_padded_end = dt[['a', 'b']]
assert dt_padded_end.itemsize == dt.itemsize
dt_outer = np.dtype([('inner', dt_padded_end)])
data = np.zeros(3, dt_outer).view(np.recarray)
assert_equal(data['inner'].dtype, dt_padded_end)
data0 = data[0]
assert_equal(data0['inner'].dtype, dt_padded_end)
def test_find_duplicate():
l1 = [1, 2, 3, 4, 5, 6]
assert_(np.rec.find_duplicate(l1) == [])
l2 = [1, 2, 1, 4, 5, 6]
assert_(np.rec.find_duplicate(l2) == [1])
l3 = [1, 2, 1, 4, 1, 6, 2, 3]
assert_(np.rec.find_duplicate(l3) == [1, 2])
l3 = [2, 2, 1, 4, 1, 6, 2, 3]
assert_(np.rec.find_duplicate(l3) == [2, 1])
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