class TestUnit(TestCase): @given(sfst.get_array_1d2d()) # type: ignore def test_shape_filter(self, shape: np.ndarray) -> None: self.assertTrue(len(TypeBlocks.shape_filter(shape)), 2) @given(sfst.get_type_blocks()) # type: ignore def test_basic_attributes(self, tb: TypeBlocks) -> None: self.assertTrue(len(tb.dtypes), len(tb)) self.assertTrue(len(tb.shapes), len(tb.mloc)) @given(sfst.get_type_blocks()) # type: ignore def test_values(self, tb: TypeBlocks) -> None: values = tb.values self.assertEqual(values.shape, tb.shape) self.assertEqual(values.dtype, tb._row_dtype) @given(sfst.get_type_blocks()) # type: ignore def test_element_items(self, tb: TypeBlocks) -> None: # NOTE: this found a flaw in _extract_iloc where we tried to optimize selection with a unified array count = 0 for k, v in tb.element_items(): count += 1 v_extract = tb.iloc[k] self.assertEqualWithNaN(v, v_extract) self.assertEqual(count, tb.size) @given(sfst.get_type_blocks_aligned_array()) # type: ignore def test_append( self, tb_aligned_array: tp.Tuple[TypeBlocks, np.ndarray]) -> None: tb, aa = tb_aligned_array shape_original = tb.shape tb.append(aa) if aa.ndim == 1: self.assertEqual(tb.shape[1], shape_original[1] + 1) else: self.assertEqual(tb.shape[1], shape_original[1] + aa.shape[1]) @given(sfst.get_type_blocks_aligned_type_blocks(min_size=2, max_size=2) ) # type: ignore def test_extend(self, tbs: tp.Sequence[TypeBlocks]) -> None: front = tbs[0] back = tbs[1] shape_original = front.shape # extend with type blocks front.extend(back) self.assertEqual( front.shape, (shape_original[0], shape_original[1] + back.shape[1])) # extend with iterable of arrays front.extend(back._blocks) self.assertEqual( front.shape, (shape_original[0], shape_original[1] + back.shape[1] * 2))
class TestUnit(TestCase): @given(get_array_1d2d()) def test_mloc(self, array): x = util.mloc(array) self.assertTrue(isinstance(x, int)) @given(get_dtype_pairs()) def test_resolve_dtype(self, dtype_pair): x = util.resolve_dtype(*dtype_pair) self.assertTrue(isinstance(x, np.dtype)) @given(get_dtypes(min_size=1)) def test_resolve_dtype_iter(self, dtypes): x = util.resolve_dtype_iter(dtypes) self.assertTrue(isinstance(x, np.dtype)) @given(get_labels(min_size=1)) def test_resolve_type_object_iter(self, objects): x = util.resolve_type_object_iter(objects) self.assertTrue(x in (None, str, object)) @given(get_arrays_2d_aligned_columns()) def test_concat_resolved_axis_0(self, arrays): array = util.concat_resolved(arrays, axis=0) self.assertEqual(array.ndim, 2) self.assertEqual(array.dtype, util.resolve_dtype_iter((x.dtype for x in arrays))) @given(get_arrays_2d_aligned_rows()) def test_concat_resolved_axis_1(self, arrays): array = util.concat_resolved(arrays, axis=1) self.assertEqual(array.ndim, 2) self.assertEqual(array.dtype, util.resolve_dtype_iter((x.dtype for x in arrays))) @given(get_dtype(), get_shape_1d2d(), get_value()) def test_full_or_fill(self, dtype, shape, value): array = util.full_for_fill(dtype, shape, fill_value=value) self.assertTrue(array.shape == shape) if isinstance(value, (float, complex)) and np.isnan(value): pass else: self.assertTrue(value in array)
class TestUnit(TestCase): @given(sfst.get_labels()) # type: ignore def test_get_labels(self, values: tp.Iterable[tp.Hashable]) -> None: for value in values: self.assertTrue(isinstance(hash(value), int)) @given(sfst.get_dtypes()) # type: ignore def test_get_dtypes(self, dtypes: tp.Iterable[np.dtype]) -> None: for dt in dtypes: self.assertTrue(isinstance(dt, np.dtype)) @given(sfst.get_spacing(10)) # type: ignore def test_get_spacing_10(self, spacing: tp.Iterable[int]) -> None: self.assertEqual(sum(spacing), 10) @hypo_settings(max_examples=10) # type: ignore @given(sfst.get_shape_1d2d()) # type: ignore def test_get_shape_1d2d(self, shape: tp.Tuple[int, ...]) -> None: self.assertTrue(isinstance(shape, tuple)) self.assertTrue(len(shape) in (1, 2)) @hypo_settings(max_examples=10) # type: ignore @given(sfst.get_array_1d2d()) # type: ignore def test_get_array_1d2d(self, array: np.ndarray) -> None: self.assertTrue(isinstance(array, np.ndarray)) self.assertTrue(array.ndim in (1, 2)) @hypo_settings(max_examples=10) # type: ignore @given(sfst.get_arrays_2d_aligned_columns(min_size=2)) # type: ignore def test_get_arrays_2s_aligned_columns(self, arrays: tp.Iterable[np.ndarray]) -> None: array_iter = iter(arrays) a1 = next(array_iter) match = a1.shape[1] for array in array_iter: self.assertEqual(array.shape[1], match) @given(sfst.get_arrays_2d_aligned_rows(min_size=2)) # type: ignore def test_get_arrays_2s_aligned_rows(self, arrays: tp.Iterable[np.ndarray]) -> None: array_iter = iter(arrays) a1 = next(array_iter) match = a1.shape[0] for array in array_iter: self.assertEqual(array.shape[0], match) @hypo_settings(max_examples=10) # type: ignore @given(sfst.get_blocks()) # type: ignore def test_get_blocks(self, blocks: tp.Tuple[np.ndarray]) -> None: self.assertTrue(isinstance(blocks, tuple)) for b in blocks: self.assertTrue(isinstance(b, np.ndarray)) self.assertTrue(b.ndim in (1, 2)) @hypo_settings(max_examples=10) # type: ignore @given(sfst.get_type_blocks()) # type: ignore def test_get_type_blocks(self, tb: TypeBlocks) -> None: self.assertTrue(isinstance(tb, TypeBlocks)) rows, cols = tb.shape col_count = 0 for b in tb._blocks: if b.ndim == 1: self.assertEqual(len(b), rows) col_count += 1 else: self.assertEqual(b.ndim, 2) self.assertEqual(b.shape[0], rows) col_count += b.shape[1] self.assertEqual(col_count, cols) @hypo_settings(max_examples=10) # type: ignore @given(sfst.get_index()) # type: ignore def test_get_index(self, idx: Index) -> None: self.assertTrue(isinstance(idx, Index)) self.assertEqual(len(idx), len(idx.values)) @hypo_settings(max_examples=10) # type: ignore @given(sfst.get_index_hierarchy()) # type: ignore def test_get_index_hierarchy(self, idx: IndexHierarchy) -> None: self.assertTrue(isinstance(idx, IndexHierarchy)) self.assertTrue(idx.depth > 1) self.assertEqual(len(idx), len(idx.values)) @hypo_settings(max_examples=10) # type: ignore @given(sfst.get_series()) # type: ignore def test_get_series(self, series: Series) -> None: self.assertTrue(isinstance(series, Series)) self.assertEqual(len(series), len(series.values)) @hypo_settings(max_examples=10) # type: ignore @given(sfst.get_frame()) # type: ignore def test_get_frame(self, frame: Frame) -> None: self.assertTrue(isinstance(frame, Frame)) self.assertEqual(frame.shape, frame.values.shape) @hypo_settings(max_examples=10) # type: ignore @given(sfst.get_frame(index_cls=IndexHierarchy, columns_cls=IndexHierarchy)) # type: ignore def test_get_frame_hierarchy(self, frame: Frame) -> None: self.assertTrue(isinstance(frame, Frame)) self.assertTrue(frame.index.depth > 1) self.assertTrue(frame.columns.depth > 1) self.assertEqual(frame.shape, frame.values.shape)
class TestUnit(TestCase): @given(get_array_1d2d()) # type: ignore def test_mloc(self, array: np.ndarray) -> None: x = util.mloc(array) self.assertTrue(isinstance(x, int)) @given(get_array_1d2d()) # type: ignore def test_shape_filter(self, shape: np.ndarray) -> None: self.assertTrue(len(util.shape_filter(shape)), 2) @given(get_dtype_pairs()) # type: ignore def test_resolve_dtype(self, dtype_pair: tp.Tuple[np.dtype, np.dtype]) -> None: x = util.resolve_dtype(*dtype_pair) self.assertTrue(isinstance(x, np.dtype)) @given(get_dtypes(min_size=1)) # type: ignore def test_resolve_dtype_iter(self, dtypes: tp.Iterable[np.dtype]) -> None: x = util.resolve_dtype_iter(dtypes) self.assertTrue(isinstance(x, np.dtype)) @given(get_labels(min_size=1)) # type: ignore def test_resolve_type_iter(self, objects: tp.Iterable[object]) -> None: known_types = set( (None, type(None), bool, str, object, int, float, complex, datetime.date, datetime.datetime, fractions.Fraction)) resolved, has_tuple, values_post = util.resolve_type_iter(objects) self.assertTrue(resolved in known_types) @given(get_arrays_2d_aligned_columns()) # type: ignore def test_concat_resolved_axis_0(self, arrays: tp.List[np.ndarray]) -> None: array = util.concat_resolved(arrays, axis=0) self.assertEqual(array.ndim, 2) self.assertEqual(array.dtype, util.resolve_dtype_iter((x.dtype for x in arrays))) @given(get_arrays_2d_aligned_rows()) # type: ignore def test_concat_resolved_axis_1(self, arrays: tp.List[np.ndarray]) -> None: array = util.concat_resolved(arrays, axis=1) self.assertEqual(array.ndim, 2) self.assertEqual(array.dtype, util.resolve_dtype_iter((x.dtype for x in arrays))) @given(get_dtype(), get_shape_1d2d(), get_value()) # type: ignore def test_full_or_fill(self, dtype: np.dtype, shape: tp.Union[tp.Tuple[int], tp.Tuple[int, int]], value: object) -> None: array = util.full_for_fill(dtype, shape, fill_value=value) self.assertTrue(array.shape == shape) if isinstance(value, (float, complex)) and np.isnan(value): pass else: self.assertTrue(value in array) @given(get_dtype()) # type: ignore def test_dtype_to_na(self, dtype: util.DtypeSpecifier) -> None: post = util.dtype_to_na(dtype) self.assertTrue(post in {0, False, None, '', np.nan, util.NAT}) @given(get_array_1d2d(dtype_group=DTGroup.NUMERIC)) # type: ignore def test_ufunc_axis_skipna(self, array: np.ndarray) -> None: has_na = util.isna_array(array).any() for nt in UFUNC_AXIS_SKIPNA.values(): ufunc = nt.ufunc ufunc_skipna = nt.ufunc_skipna # dtypes = nt.dtypes # composable = nt.composable # doc = nt.doc_header # size_one_unity = nt.size_one_unity with np.errstate(over='ignore', under='ignore', divide='ignore'): post = util.ufunc_axis_skipna(array=array, skipna=True, axis=0, ufunc=ufunc, ufunc_skipna=ufunc_skipna) if array.ndim == 2: self.assertTrue(post.ndim == 1) @given(get_array_1d2d()) # type: ignore def test_ufunc_unique(self, array: np.ndarray) -> None: post = util.ufunc_unique(array, axis=0) self.assertTrue(len(post) <= array.shape[0]) @given(get_array_1d(min_size=1), st.integers()) # type: ignore def test_roll_1d(self, array: np.ndarray, shift: int) -> None: post = util.roll_1d(array, shift) self.assertEqual(len(post), len(array)) self.assertEqualWithNaN(array[-(shift % len(array))], post[0]) @given(get_array_2d(min_rows=1, min_columns=1), st.integers()) # type: ignore def test_roll_2d(self, array: np.ndarray, shift: int) -> None: for axis in (0, 1): post = util.roll_2d(array, shift=shift, axis=axis) self.assertEqual(post.shape, array.shape) start = -(shift % array.shape[axis]) if axis == 0: a = array[start] b = post[0] else: a = array[:, start] b = post[:, 0] self.assertAlmostEqualValues(a, b) @given(get_array_1d(dtype_group=DTGroup.OBJECT)) # type: ignore def test_iterable_to_array_a(self, array: np.ndarray) -> None: values = array.tolist() post, _ = util.iterable_to_array(values) self.assertAlmostEqualValues(post, values) # explicitly giving object dtype post, _ = util.iterable_to_array(values, dtype=util.DTYPE_OBJECT) self.assertAlmostEqualValues(post, values) @given(get_labels()) # type: ignore def test_iterable_to_array_b(self, labels: tp.Iterable[tp.Any]) -> None: post, _ = util.iterable_to_array(labels) self.assertAlmostEqualValues(post, labels) self.assertTrue(isinstance(post, np.ndarray)) @given(st.slices(10)) # type: ignore #pylint: disable=E1120 def test_slice_to_ascending_slice(self, key: slice) -> None: post_key = util.slice_to_ascending_slice(key, size=10) self.assertEqual(set(range(*key.indices(10))), set(range(*post_key.indices(10)))) # to_datetime64 # to_timedelta64 # key_to_datetime_key @given(get_array_1d2d()) # type: ignore def test_array_to_groups_and_locations(self, array: np.ndarray) -> None: groups, locations = util.array_to_groups_and_locations(array, 0) if len(array) > 0: self.assertTrue(len(groups) >= 1) # always 1dm locations self.assertTrue(locations.ndim == 1) self.assertTrue(len(np.unique(locations)) == len(groups)) @given(get_array_1d2d()) # type: ignore def test_isna_array(self, array: np.ndarray) -> None: post = util.isna_array(array) self.assertTrue(post.dtype == bool) values = np.ravel(array) count_na = sum(util.isna_element(x) for x in values) self.assertTrue(np.ravel(post).sum() == count_na) @given(get_array_1d(dtype_group=DTGroup.BOOL)) # type: ignore def test_binary_transition(self, array: np.ndarray) -> None: post = util.binary_transition(array) # could be 32 via result of np.nonzero self.assertTrue(post.dtype in (np.int32, np.int64)) # if no True in original array, result will be empty if array.sum() == 0: self.assertTrue(len(post) == 0) # if all True, result is empty elif array.sum() == len(array): self.assertTrue(len(post) == 0) else: # the post selection shold always be indices that are false self.assertTrue(array[post].sum() == 0) @given(get_array_1d2d()) # type: ignore def test_array_to_duplicated(self, array: np.ndarray) -> None: if array.ndim == 2: for axis in (0, 1): post = util.array_to_duplicated(array, axis=axis) if axis == 0: unique_count = len(set(tuple(x) for x in array)) else: unique_count = len( set(tuple(array[:, i]) for i in range(array.shape[1]))) if unique_count < array.shape[axis]: self.assertTrue(post.sum() > 0) else: post = util.array_to_duplicated(array) # if not all value are unique, we must have some duplicated if len(set(array)) < len(array): self.assertTrue(post.sum() > 0) self.assertTrue(post.dtype == bool) @given(get_array_1d2d()) # type: ignore def test_array_shift(self, array: np.ndarray) -> None: for shift in (-1, 1): for wrap in (True, False): tests = [] post1 = util.array_shift(array=array, shift=shift, axis=0, wrap=wrap) tests.append(post1) if array.ndim == 2: post2 = util.array_shift(array=array, shift=shift, axis=1, wrap=wrap) tests.append(post2) for post in tests: self.assertTrue(array.shape == post.shape) # type is only always maintained if we are wrapping if wrap: self.assertTrue(array.dtype == post.dtype) @given(st.lists(get_array_1d(), min_size=2, max_size=2)) # type: ignore def test_union1d(self, arrays: tp.Sequence[np.ndarray]) -> None: post = util.union1d(arrays[0], arrays[1], assume_unique=False) self.assertTrue(post.ndim == 1) # nan values in complex numbers make direct comparison tricky self.assertTrue(len(post) == len(set(arrays[0]) | set(arrays[1]))) # complex results are tricky to compare after forming sets if (post.dtype.kind not in ('O', 'M', 'm', 'c', 'f') and not np.isnan(post).any()): self.assertTrue(set(post) == (set(arrays[0]) | set(arrays[1]))) @given(st.lists(get_array_1d(), min_size=2, max_size=2)) # type: ignore def test_intersect1d(self, arrays: tp.Sequence[np.ndarray]) -> None: post = util.intersect1d(arrays[0], arrays[1], assume_unique=False) self.assertTrue(post.ndim == 1) # nan values in complex numbers make direct comparison tricky self.assertTrue(len(post) == len(set(arrays[0]) & set(arrays[1]))) if (post.dtype.kind not in ('O', 'M', 'm', 'c', 'f') and not np.isnan(post).any()): self.assertTrue(set(post) == (set(arrays[0]) & set(arrays[1]))) @given(get_arrays_2d_aligned_columns(min_size=2, max_size=2)) # type: ignore def test_union2d(self, arrays: tp.Sequence[np.ndarray]) -> None: post = util.union2d(arrays[0], arrays[1], assume_unique=False) if post.dtype == object: self.assertTrue(post.ndim == 1) else: self.assertTrue(post.ndim == 2) self.assertTrue( len(post) == len( set(util.array2d_to_tuples(arrays[0])) | set(util.array2d_to_tuples(arrays[1])))) @given(get_arrays_2d_aligned_columns(min_size=2, max_size=2)) # type: ignore def test_intersect2d(self, arrays: tp.Sequence[np.ndarray]) -> None: post = util.intersect2d(arrays[0], arrays[1], assume_unique=False) if post.dtype == object: self.assertTrue(post.ndim == 1) else: self.assertTrue(post.ndim == 2) self.assertTrue( len(post) == len( set(util.array2d_to_tuples(arrays[0])) & set(util.array2d_to_tuples(arrays[1])))) @given(get_arrays_2d_aligned_columns()) # type: ignore def test_array_set_ufunc_many(self, arrays: tp.Sequence[np.ndarray]) -> None: for union in (True, False): post = util.ufunc_set_iter(arrays, union=union) if post.dtype == object: # returned object arrays might be 2D or 1D of tuples self.assertTrue(post.ndim in (1, 2)) else: self.assertTrue(post.ndim == 2)
class TestUnit(TestCase): @given(get_array_1d2d()) # type: ignore def test_mloc(self, array: np.ndarray) -> None: x = util.mloc(array) self.assertTrue(isinstance(x, int)) @given(get_dtype_pairs()) # type: ignore def test_resolve_dtype(self, dtype_pair: tp.Tuple[np.dtype, np.dtype]) -> None: x = util.resolve_dtype(*dtype_pair) self.assertTrue(isinstance(x, np.dtype)) @given(get_dtypes(min_size=1)) # type: ignore def test_resolve_dtype_iter(self, dtypes: tp.Iterable[np.dtype]) -> None: x = util.resolve_dtype_iter(dtypes) self.assertTrue(isinstance(x, np.dtype)) @given(get_labels(min_size=1)) # type: ignore def test_resolve_type_iter(self, objects: tp.Iterable[object]) -> None: known_types = set(( None, type(None), bool, str, object, int, float, complex, datetime.date, datetime.datetime, fractions.Fraction )) resolved, has_tuple, values_post = util.resolve_type_iter(objects) self.assertTrue(resolved in known_types) @given(get_arrays_2d_aligned_columns()) # type: ignore def test_concat_resolved_axis_0(self, arrays: tp.List[np.ndarray]) -> None: array = util.concat_resolved(arrays, axis=0) self.assertEqual(array.ndim, 2) self.assertEqual(array.dtype, util.resolve_dtype_iter((x.dtype for x in arrays))) @given(get_arrays_2d_aligned_rows()) # type: ignore def test_concat_resolved_axis_1(self, arrays: tp.List[np.ndarray]) -> None: array = util.concat_resolved(arrays, axis=1) self.assertEqual(array.ndim, 2) self.assertEqual(array.dtype, util.resolve_dtype_iter((x.dtype for x in arrays))) @given(get_dtype(), get_shape_1d2d(), get_value()) # type: ignore def test_full_or_fill(self, dtype: np.dtype, shape: tp.Union[tp.Tuple[int], tp.Tuple[int, int]], value: object) -> None: array = util.full_for_fill(dtype, shape, fill_value=value) self.assertTrue(array.shape == shape) if isinstance(value, (float, complex)) and np.isnan(value): pass else: self.assertTrue(value in array) @given(get_dtype()) # type: ignore def test_dtype_to_na(self, dtype: util.DtypeSpecifier) -> None: post = util.dtype_to_na(dtype) self.assertTrue(post in {0, False, None, '', np.nan, util.NAT}) @given(get_array_1d(min_size=1, dtype_group=DTGroup.NUMERIC)) # type: ignore def test_ufunc_skipna_1d(self, array: np.ndarray) -> None: has_na = util.isna_array(array).any() for ufunc, ufunc_skipna, dtype in UFUNC_AXIS_SKIPNA.values(): with np.errstate(over='ignore', under='ignore'): v1 = ufunc_skipna(array) # this should return a single value self.assertFalse(isinstance(v1, np.ndarray)) if has_na: v2 = ufunc(array) self.assertFalse(isinstance(v2, np.ndarray)) @given(get_array_1d2d()) # type: ignore def test_ufunc_unique(self, array: np.ndarray) -> None: post = util.ufunc_unique(array, axis=0) self.assertTrue(len(post) <= array.shape[0]) @given(get_array_1d(min_size=1), st.integers()) # type: ignore def test_roll_1d(self, array: np.ndarray, shift: int) -> None: post = util.roll_1d(array, shift) self.assertEqual(len(post), len(array)) self.assertEqualWithNaN(array[-(shift % len(array))], post[0]) @given(get_array_2d(min_rows=1, min_columns=1), st.integers()) # type: ignore def test_roll_2d(self, array: np.ndarray, shift: int) -> None: for axis in (0, 1): post = util.roll_2d(array, shift=shift, axis=axis) self.assertEqual(post.shape, array.shape) start = -(shift % array.shape[axis]) if axis == 0: a = array[start] b = post[0] else: a = array[:, start] b = post[:, 0] self.assertAlmostEqualValues(a, b) @given(get_array_1d(dtype_group=DTGroup.OBJECT)) # type: ignore def test_iterable_to_array_a(self, array: np.ndarray) -> None: values = array.tolist() post, _ = util.iterable_to_array(values) self.assertAlmostEqualValues(post, values) # explicitly giving object dtype post, _ = util.iterable_to_array(values, dtype=util.DTYPE_OBJECT) self.assertAlmostEqualValues(post, values) @given(get_labels()) # type: ignore def test_iterable_to_array_b(self, labels: tp.Iterable[tp.Any]) -> None: post, _ = util.iterable_to_array(labels) self.assertAlmostEqualValues(post, labels) self.assertTrue(isinstance(post, np.ndarray)) @given(st.slices(10)) # type: ignore def test_slice_to_ascending_slice(self, key: slice) -> None: post_key = util.slice_to_ascending_slice(key, size=10) self.assertEqual( set(range(*key.indices(10))), set(range(*post_key.indices(10))) ) # to_datetime64 # to_timedelta64 # key_to_datetime_key @given(get_array_1d2d()) # type: ignore def test_array_to_groups_and_locations(self, array: np.ndarray) -> None: groups, locations = util.array_to_groups_and_locations(array, 0) if len(array) > 0: self.assertTrue(len(groups) >= 1) # always 1dm locations self.assertTrue(locations.ndim == 1) self.assertTrue(len(np.unique(locations)) == len(groups)) @given(get_array_1d2d()) # type: ignore def test_isna_array(self, array: np.ndarray) -> None: post = util.isna_array(array) self.assertTrue(post.dtype == bool) values = np.ravel(array) count_na = sum(util.isna_element(x) for x in values) self.assertTrue(np.ravel(post).sum() == count_na) @given(get_array_1d(dtype_group=DTGroup.BOOL)) # type: ignore def test_binary_transition(self, array: np.ndarray) -> None: post = util.binary_transition(array) # could be 32 via result of np.nonzero self.assertTrue(post.dtype in (np.int32, np.int64)) # if no True in original array, result will be empty if array.sum() == 0: self.assertTrue(len(post) == 0) # if all True, result is empty elif array.sum() == len(array): self.assertTrue(len(post) == 0) else: # the post selection shold always be indices that are false self.assertTrue(array[post].sum() == 0)
class TestUnit(TestCase): @given( sfst.get_frame( dtype_group=sfst.DTGroup.ALL_NO_OBJECT, index_dtype_group=sfst.DTGroup.BASIC, columns_dtype_group=sfst.DTGroup.BASIC, )) def test_frame_to_npz_a(self, f1: Frame) -> None: # if f1.columns.dtype.kind != 'O' and f1.index.dtype.kind != 'O': with temp_file('.npz') as fp: f1.to_npz(fp) f2 = Frame.from_npz(fp) self.assertTrue( f1.equals(f2, compare_name=True, compare_dtype=True, compare_class=True)) @given( sfst.get_frame( dtype_group=sfst.DTGroup.ALL_NO_OBJECT, index_cls=IndexDate, index_dtype_group=sfst.DTGroup.DATE, columns_cls=IndexDate, columns_dtype_group=sfst.DTGroup.DATE, )) def test_frame_to_npz_b(self, f1: Frame) -> None: # if f1.columns.dtype.kind != 'O' and f1.index.dtype.kind != 'O': with temp_file('.npz') as fp: f1.to_npz(fp) f2 = Frame.from_npz(fp) self.assertTrue( f1.equals(f2, compare_name=True, compare_dtype=True, compare_class=True)) @given(sfst.get_array_1d2d(dtype_group=sfst.DTGroup.ALL_NO_OBJECT)) def test_frame_to_npy_a(self, a1: Frame) -> None: header_decode_cache: HeaderDecodeCacheType = {} with temp_file('.npy') as fp: with open(fp, 'wb') as f: NPYConverter.to_npy(f, a1) # check compatibility with built-in NPY reading a2 = np.load(fp) if a2.dtype.kind in DTYPE_INEXACT_KINDS: self.assertAlmostEqualArray(a1, a2) else: self.assertTrue((a1 == a2).all()) self.assertTrue(a1.shape == a2.shape) with open(fp, 'rb') as f: a3, _ = NPYConverter.from_npy(f, header_decode_cache) if a3.dtype.kind in DTYPE_INEXACT_KINDS: self.assertAlmostEqualArray(a1, a3) else: self.assertTrue((a1 == a3).all()) self.assertTrue(a1.shape == a3.shape) @given(sfst.get_array_1d2d(dtype_group=sfst.DTGroup.ALL_NO_OBJECT)) def test_frame_to_npy_b(self, a1: Frame) -> None: header_decode_cache: HeaderDecodeCacheType = {} with temp_file('.npy') as fp: with open(fp, 'wb') as f: NPYConverter.to_npy(f, a1) with open(fp, 'rb') as f: a2, mm = NPYConverter.from_npy( f, header_decode_cache, memory_map=True, ) if a2.dtype.kind in DTYPE_INEXACT_KINDS: self.assertAlmostEqualArray(a1, a2) else: self.assertTrue((a1 == a2).all()) self.assertTrue(a1.shape == a2.shape)