def test_td_sub_timedelta64(self):
td = Timedelta(10, unit='d')
expected = Timedelta(0, unit='ns')
result = td - td.to_timedelta64()
assert isinstance(result, Timedelta)
assert result == expected
result = td.to_timedelta64() - td
assert isinstance(result, Timedelta)
assert result == expected
def _triple_barriers(data: pd.Series,
pt: pd.Series,
sl: pd.Series,
vb_delta: pd.Timedelta,
seed: pd.Series = None,
side: pd.Series = None):
"""
Triple barrier labeling for single index series
:param data: data to be labeled
:param pt: profit taking barrier
:param sl: stop loss barrier
:param vb_delta: vertical barrier delta
:param seed: True/False array. If True, the given moment will be used to seed one triple barrier. If false, the given moment will be omitted
:param side: if side is specified, then meta-labeling is enabled
:return dataframe with first timestamp of threshold crossing, type of threshold crossing and return
"""
result = data.to_frame()
result['interval_end'] = data.index.copy()
result['interval_end'].values.fill(np.datetime64('NaT'))
result['returns'] = data.copy()
result['returns'].values.fill(np.nan)
result.drop(data.name, axis=1, inplace=True)
seed = seed.values if seed is not None else np.full(data.size, True)
if side is None:
result['side'] = np.zeros(data.shape, dtype=np.int8)
__triple_barriers_side_jit(data=data.values,
timestamps=data.index.values,
seed=seed,
vb_delta=vb_delta.to_timedelta64(),
pt=pt.values,
sl=sl.values,
interval_end=result['interval_end'].values,
side=result['side'].values,
returns=result['returns'].values)
else:
result['size'] = np.zeros(data.shape, dtype=np.int8)
__triple_barriers_size_jit(data=data.values,
timestamps=data.index.values,
seed=seed,
vb_delta=vb_delta.to_timedelta64(),
pt=pt.values,
sl=sl.values,
interval_end=result['interval_end'].values,
side=side.values,
returns=result['returns'].values,
size=result['size'].values)
return result
def test_rfloordiv(self):
# GH#18846
td = Timedelta(hours=3, minutes=3)
scalar = Timedelta(hours=3, minutes=4)
# scalar others
# x // Timedelta is defined only for timedelta-like x. int-like,
# float-like, and date-like, in particular, should all either
# a) raise TypeError directly or
# b) return NotImplemented, following which the reversed
# operation will raise TypeError.
assert td.__rfloordiv__(scalar) == 1
assert (-td).__rfloordiv__(scalar.to_pytimedelta()) == -2
assert (2 * td).__rfloordiv__(scalar.to_timedelta64()) == 0
assert np.isnan(td.__rfloordiv__(pd.NaT))
assert np.isnan(td.__rfloordiv__(np.timedelta64('NaT')))
dt64 = np.datetime64('2016-01-01', dtype='datetime64[us]')
with pytest.raises(TypeError):
td.__rfloordiv__(dt64)
assert td.__rfloordiv__(np.nan) is NotImplemented
assert td.__rfloordiv__(3.5) is NotImplemented
assert td.__rfloordiv__(2) is NotImplemented
with pytest.raises(TypeError):
td.__rfloordiv__(np.float64(2.0))
with pytest.raises(TypeError):
td.__rfloordiv__(np.int32(2.0))
with pytest.raises(TypeError):
td.__rfloordiv__(np.uint8(9))
# Array-like others
assert td.__rfloordiv__(np.array(scalar.to_timedelta64())) == 1
res = td.__rfloordiv__(np.array([(3 * scalar).to_timedelta64()]))
expected = np.array([3], dtype=np.int64)
tm.assert_numpy_array_equal(res, expected)
arr = np.array([(10 * scalar).to_timedelta64(),
np.timedelta64('NaT')])
res = td.__rfloordiv__(arr)
expected = np.array([10, np.nan])
tm.assert_numpy_array_equal(res, expected)
ser = pd.Series([1], dtype=np.int64)
res = td.__rfloordiv__(ser)
assert res is NotImplemented
with pytest.raises(TypeError):
ser // td
def test_rfloordiv(self):
# GH#18846
td = Timedelta(hours=3, minutes=3)
scalar = Timedelta(hours=3, minutes=4)
# scalar others
# x // Timedelta is defined only for timedelta-like x. int-like,
# float-like, and date-like, in particular, should all either
# a) raise TypeError directly or
# b) return NotImplemented, following which the reversed
# operation will raise TypeError.
assert td.__rfloordiv__(scalar) == 1
assert (-td).__rfloordiv__(scalar.to_pytimedelta()) == -2
assert (2 * td).__rfloordiv__(scalar.to_timedelta64()) == 0
assert np.isnan(td.__rfloordiv__(pd.NaT))
assert np.isnan(td.__rfloordiv__(np.timedelta64('NaT')))
dt64 = np.datetime64('2016-01-01', dtype='datetime64[us]')
with pytest.raises(TypeError):
td.__rfloordiv__(dt64)
assert td.__rfloordiv__(np.nan) is NotImplemented
assert td.__rfloordiv__(3.5) is NotImplemented
assert td.__rfloordiv__(2) is NotImplemented
with pytest.raises(TypeError):
td.__rfloordiv__(np.float64(2.0))
with pytest.raises(TypeError):
td.__rfloordiv__(np.int32(2.0))
with pytest.raises(TypeError):
td.__rfloordiv__(np.uint8(9))
# Array-like others
assert td.__rfloordiv__(np.array(scalar.to_timedelta64())) == 1
res = td.__rfloordiv__(np.array([(3 * scalar).to_timedelta64()]))
expected = np.array([3], dtype=np.int64)
tm.assert_numpy_array_equal(res, expected)
arr = np.array([(10 * scalar).to_timedelta64(), np.timedelta64('NaT')])
res = td.__rfloordiv__(arr)
expected = np.array([10, np.nan])
tm.assert_numpy_array_equal(res, expected)
ser = pd.Series([1], dtype=np.int64)
res = td.__rfloordiv__(ser)
assert res is NotImplemented
with pytest.raises(TypeError):
ser // td
def test_td_floordiv_timedeltalike_array(self):
# GH#18846
td = Timedelta(hours=3, minutes=4)
scalar = Timedelta(hours=3, minutes=3)
# Array-like others
assert td // np.array(scalar.to_timedelta64()) == 1
res = (3 * td) // np.array([scalar.to_timedelta64()])
expected = np.array([3], dtype=np.int64)
tm.assert_numpy_array_equal(res, expected)
res = (10 * td) // np.array([scalar.to_timedelta64(), np.timedelta64("NaT")])
expected = np.array([10, np.nan])
tm.assert_numpy_array_equal(res, expected)
def test_td_floordiv_timedeltalike_array(self):
# GH#18846
td = Timedelta(hours=3, minutes=4)
scalar = Timedelta(hours=3, minutes=3)
# Array-like others
assert td // np.array(scalar.to_timedelta64()) == 1
res = (3 * td) // np.array([scalar.to_timedelta64()])
expected = np.array([3], dtype=np.int64)
tm.assert_numpy_array_equal(res, expected)
res = (10 * td) // np.array([scalar.to_timedelta64(),
np.timedelta64('NaT')])
expected = np.array([10, np.nan])
tm.assert_numpy_array_equal(res, expected)
def test_td_sub_timedelta64(self):
td = Timedelta(10, unit='d')
expected = Timedelta(0, unit='ns')
result = td - td.to_timedelta64()
assert isinstance(result, Timedelta)
# comparison fails even if we comment out the isinstance assertion
assert result == expected
def test_td_rsub_timedelta64(self):
td = Timedelta(10, unit='d')
expected = Timedelta(0, unit='ns')
result = td.to_timedelta64() - td
assert isinstance(result, Timedelta)
assert result == expected
def test_td_floordiv_timedeltalike_scalar(self):
# GH#18846
td = Timedelta(hours=3, minutes=4)
scalar = Timedelta(hours=3, minutes=3)
assert td // scalar == 1
assert -td // scalar.to_pytimedelta() == -2
assert (2 * td) // scalar.to_timedelta64() == 2
def test_td_floordiv_timedeltalike_scalar(self):
# GH#18846
td = Timedelta(hours=3, minutes=4)
scalar = Timedelta(hours=3, minutes=3)
assert td // scalar == 1
assert -td // scalar.to_pytimedelta() == -2
assert (2 * td) // scalar.to_timedelta64() == 2
def test_to_numpy_alias(self):
# GH 24653: alias .to_numpy() for scalars
td = Timedelta("10m7s")
assert td.to_timedelta64() == td.to_numpy()
# GH#44460
msg = "dtype and copy arguments are ignored"
with pytest.raises(ValueError, match=msg):
td.to_numpy("m8[s]")
with pytest.raises(ValueError, match=msg):
td.to_numpy(copy=True)
def test_tick_addition(kls, expected):
offset = kls(3)
td = Timedelta(hours=2)
for other in [td, td.to_pytimedelta(), td.to_timedelta64()]:
result = offset + other
assert isinstance(result, Timedelta)
assert result == expected
result = other + offset
assert isinstance(result, Timedelta)
assert result == expected
def test_floordiv(self):
# GH#18846
td = Timedelta(hours=3, minutes=4)
scalar = Timedelta(hours=3, minutes=3)
# scalar others
assert td // scalar == 1
assert -td // scalar.to_pytimedelta() == -2
assert (2 * td) // scalar.to_timedelta64() == 2
assert td // np.nan is pd.NaT
assert np.isnan(td // pd.NaT)
assert np.isnan(td // np.timedelta64('NaT'))
with pytest.raises(TypeError):
td // np.datetime64('2016-01-01', dtype='datetime64[us]')
expected = Timedelta(hours=1, minutes=32)
assert td // 2 == expected
assert td // 2.0 == expected
assert td // np.float64(2.0) == expected
assert td // np.int32(2.0) == expected
assert td // np.uint8(2.0) == expected
# Array-like others
assert td // np.array(scalar.to_timedelta64()) == 1
res = (3 * td) // np.array([scalar.to_timedelta64()])
expected = np.array([3], dtype=np.int64)
tm.assert_numpy_array_equal(res, expected)
res = (10 * td) // np.array(
[scalar.to_timedelta64(),
np.timedelta64('NaT')])
expected = np.array([10, np.nan])
tm.assert_numpy_array_equal(res, expected)
ser = pd.Series([1], dtype=np.int64)
res = td // ser
assert res.dtype.kind == 'm'
def test_floordiv(self):
# GH#18846
td = Timedelta(hours=3, minutes=4)
scalar = Timedelta(hours=3, minutes=3)
# scalar others
assert td // scalar == 1
assert -td // scalar.to_pytimedelta() == -2
assert (2 * td) // scalar.to_timedelta64() == 2
assert td // np.nan is pd.NaT
assert np.isnan(td // pd.NaT)
assert np.isnan(td // np.timedelta64('NaT'))
with pytest.raises(TypeError):
td // np.datetime64('2016-01-01', dtype='datetime64[us]')
expected = Timedelta(hours=1, minutes=32)
assert td // 2 == expected
assert td // 2.0 == expected
assert td // np.float64(2.0) == expected
assert td // np.int32(2.0) == expected
assert td // np.uint8(2.0) == expected
# Array-like others
assert td // np.array(scalar.to_timedelta64()) == 1
res = (3 * td) // np.array([scalar.to_timedelta64()])
expected = np.array([3], dtype=np.int64)
tm.assert_numpy_array_equal(res, expected)
res = (10 * td) // np.array([scalar.to_timedelta64(),
np.timedelta64('NaT')])
expected = np.array([10, np.nan])
tm.assert_numpy_array_equal(res, expected)
ser = pd.Series([1], dtype=np.int64)
res = td // ser
assert res.dtype.kind == 'm'
def test_td_rfloordiv_timedeltalike_scalar(self):
# GH#18846
td = Timedelta(hours=3, minutes=3)
scalar = Timedelta(hours=3, minutes=4)
# scalar others
# x // Timedelta is defined only for timedelta-like x. int-like,
# float-like, and date-like, in particular, should all either
# a) raise TypeError directly or
# b) return NotImplemented, following which the reversed
# operation will raise TypeError.
assert td.__rfloordiv__(scalar) == 1
assert (-td).__rfloordiv__(scalar.to_pytimedelta()) == -2
assert (2 * td).__rfloordiv__(scalar.to_timedelta64()) == 0
def test_td_rfloordiv_timedeltalike_scalar(self):
# GH#18846
td = Timedelta(hours=3, minutes=3)
scalar = Timedelta(hours=3, minutes=4)
# scalar others
# x // Timedelta is defined only for timedelta-like x. int-like,
# float-like, and date-like, in particular, should all either
# a) raise TypeError directly or
# b) return NotImplemented, following which the reversed
# operation will raise TypeError.
assert td.__rfloordiv__(scalar) == 1
assert (-td).__rfloordiv__(scalar.to_pytimedelta()) == -2
assert (2 * td).__rfloordiv__(scalar.to_timedelta64()) == 0
def test_invert(self):
td = Timedelta(10, unit="d")
msg = "bad operand type for unary ~"
with pytest.raises(TypeError, match=msg):
~td
# check this matches pytimedelta and timedelta64
with pytest.raises(TypeError, match=msg):
~(td.to_pytimedelta())
umsg = "ufunc 'invert' not supported for the input types"
with pytest.raises(TypeError, match=umsg):
~(td.to_timedelta64())
def test_td_add_sub_td64_ndarray(self):
td = Timedelta("1 day")
other = np.array([td.to_timedelta64()])
expected = np.array([Timedelta("2 Days").to_timedelta64()])
result = td + other
tm.assert_numpy_array_equal(result, expected)
result = other + td
tm.assert_numpy_array_equal(result, expected)
result = td - other
tm.assert_numpy_array_equal(result, expected * 0)
result = other - td
tm.assert_numpy_array_equal(result, expected * 0)
def test_td_rfloordiv_timedeltalike_array(self):
# GH#18846
td = Timedelta(hours=3, minutes=3)
scalar = Timedelta(hours=3, minutes=4)
# Array-like others
assert td.__rfloordiv__(np.array(scalar.to_timedelta64())) == 1
res = td.__rfloordiv__(np.array([(3 * scalar).to_timedelta64()]))
expected = np.array([3], dtype=np.int64)
tm.assert_numpy_array_equal(res, expected)
arr = np.array([(10 * scalar).to_timedelta64(), np.timedelta64('NaT')])
res = td.__rfloordiv__(arr)
expected = np.array([10, np.nan])
tm.assert_numpy_array_equal(res, expected)
def test_td_rfloordiv_timedeltalike_array(self):
# GH#18846
td = Timedelta(hours=3, minutes=3)
scalar = Timedelta(hours=3, minutes=4)
# Array-like others
assert td.__rfloordiv__(np.array(scalar.to_timedelta64())) == 1
res = td.__rfloordiv__(np.array([(3 * scalar).to_timedelta64()]))
expected = np.array([3], dtype=np.int64)
tm.assert_numpy_array_equal(res, expected)
arr = np.array([(10 * scalar).to_timedelta64(),
np.timedelta64('NaT')])
res = td.__rfloordiv__(arr)
expected = np.array([10, np.nan])
tm.assert_numpy_array_equal(res, expected)
def test_to_numpy_alias(self):
# GH 24653: alias .to_numpy() for scalars
td = Timedelta('10m7s')
assert td.to_timedelta64() == td.to_numpy()
def test_to_numpy_alias(self):
# GH 24653: alias .to_numpy() for scalars
td = Timedelta('10m7s')
assert td.to_timedelta64() == td.to_numpy()
