def test_filter_3():
t = Table()
t.a = np.random.rand(10)
t.b = pd.date_range('2000-01-01', freq='M', periods=10)
t.c = np.array([1, 2])
t.add_column('d', np.array([1, 2]), align='bottom')
t1 = t.filter(t.c > 0)
assert t1.c.values[0] == 1
assert np.all(t1.d.values == np.array([]))
def test_crop_2():
t = Table()
t.a = np.random.rand(10)
t.b = pd.date_range('2000-01-01', freq='D', periods=10)
t.c = np.array([1, 2])
t.add_column('d', np.array([1, 2]), align='bottom')
t1 = t.crop('d')
assert np.all(t1.c.values == np.array([]))
assert np.all(t1.d.values == np.array([1, 2]))
assert np.all(t1.a.values == t.a.values[-2:])
assert np.all(t1.b.values == t.b.values[-2:])
def test_sieve():
t = Table()
t.a = np.arange(10)
t.b = pd.date_range('2000-01-01', freq='D', periods=10)
t.c = np.array(['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j'])
t1 = t.sieve(np.array([0, 0, 0, 0, 0, 1, 1, 1, 0, 0], dtype=np.bool_))
print(t1.b.values)
assert t1.a.values[0] == 5
assert t1.b.values[0] == np.datetime64('2000-01-06')
assert t1.c.values[0] == 'f'
def test_filter_date():
t = Table()
t.a = np.random.rand(10)
t.b = pd.date_range('2000-01-01', freq='D', periods=10)
t.c = np.array([1, 2])
t.add_column('d', np.array([1, 2]), align='bottom')
thres1 = np.array(['2000-01-03'], dtype=np.datetime64)
thres2 = np.array(['2000-01-05'], dtype=np.datetime64)
t1 = t.filter(t.b >= thres1)
assert np.all(t1.c.values == np.array([]))
assert np.all(t1.d.values == np.array([1, 2]))
assert np.all(t1.a.values == t.a.values[2:])
t1 = t.filter((t.b >= thres1) & (t.b <= thres2))
assert np.all(t1.c.values == np.array([]))
assert np.all(t1.d.values == np.array([]))
assert np.all(t1.a.values == t.a.values[2:5])
t1 = t.filter(t.b.date_range(fr=thres1, to=thres2))
assert np.all(t1.c.values == np.array([]))
assert np.all(t1.d.values == np.array([]))
assert np.all(t1.a.values == t.a.values[2:5])
def test_add_array():
t = Table()
t.a = np.arange(10)
assert t.__repr__()[:13] == "<Table[ a[10]"