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)