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test_records.py
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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