def test_intcolumn():
_test_numericcolumn(IntColumn)
_test_copying(IntColumn)
# Test automatic conversion to int
dm = DataMatrix(length=2)
dm.col = IntColumn
dm.col = 1.9, '2.9'
check_col(dm.col, [1, 2])
# Test setting invalid values
@raises(TypeError)
def _():
dm.col[0] = 'x'
_()
@raises(TypeError)
def _():
dm.col = 'x'
_()
@raises(TypeError)
def _():
dm.col[:-1] = 'x'
_()
# Check dtype
ok_(dm.col._seq.dtype == np.int64)
check_integrity(dm)
def check_mixedcolumn_sorting():
dm = DataMatrix(length=24)
dm.c = [
1, '1', 2, '2', 1.1, '1.1', 2.1, '2.1', INF, -INF, 'inf', '-inf', NAN,
NAN, 'nan', 'nan', None, None, None, None, 'alpha', 'beta', 'None', ''
]
dm.c = ops.shuffle(dm.c)
dm = ops.sort(dm, by=dm.c)
check_col(dm.c, [
-INF,
-INF,
1,
1,
1.1,
1.1,
2,
2,
2.1,
2.1,
INF,
INF,
'',
'None',
'alpha',
'beta',
None,
None,
None,
None,
NAN,
NAN,
NAN,
NAN,
])
def test_weight():
dm = DataMatrix(length=3)
dm.a = 'a', 'b', 'c'
dm.b = 1, 0, 2
dm = ops.weight(dm.b)
check_col(dm.a, ['a', 'c', 'c'])
check_col(dm.b, [1, 2, 2])
def check_str_operations(): dm = DataMatrix(length=2, default_col_type=MixedColumn) dm.col = 'a', 'b' check_col(dm.col, ['a', 'b']) dm.col += 'c', 'd' check_col(dm.col, ['ac', 'bd']) check_integrity(dm)
def test_weight(): dm = DataMatrix(length=3) dm.a = 'a', 'b', 'c' dm.b = 1, 0, 2 dm = ops.weight(dm.b) check_col(dm.a, ['a', 'c', 'c']) check_col(dm.b, [1, 2, 2])
def check_int_operations(): dm = DataMatrix(length=2, default_col_type=IntColumn) dm.col = 1.5, 2.5 check_col(dm.col, [1, 2]) dm.col *= 1.5 check_col(dm.col, [1, 3]) check_integrity(dm)
def check_str_operations(): dm = DataMatrix(length=2, default_col_type=MixedColumn) dm.col = 'a', 'b' check_col(dm.col, ['a', 'b']) dm.col += 'c', 'd' check_col(dm.col, ['ac', 'bd']) check_integrity(dm)
def test_fullfactorial():
dm = DataMatrix(length=3)
dm.a = 'a', 'b', ''
dm.b = 0, 1, 2
dm = ops.fullfactorial(dm)
check_col(dm.a, ['a', 'b', 'a', 'b', 'a', 'b'])
check_col(dm.b, [0, 0, 1, 1, 2, 2])
def test_fullfactorial(): dm = DataMatrix(length=3) dm.a = 'a', 'b', '' dm.b = 0, 1, 2 dm = ops.fullfactorial(dm) check_col(dm.a, ['a', 'b', 'a', 'b', 'a', 'b']) check_col(dm.b, [0, 0, 1, 1, 2, 2])
def test_reduce_():
dm = DataMatrix(length=2)
dm.series = SeriesColumn(depth=3)
dm.series[0] = 1, 2, 3
dm.series[1] = 2, 3, 4
dm.col = series.reduce_(dm.series)
check_col(dm.col, [2, 3])
check_integrity(dm)
def test_mixedcolumn():
_test_numericcolumn(MixedColumn)
_test_copying(MixedColumn)
dm = DataMatrix(length=4)
dm.col = '1.1', '1', 'x', None
check_col(dm.col, [1.1, 1, 'x', None])
dm.col[dm.col == {1, None}] = 'a', 'b'
check_col(dm.col, [1.1, 'a', 'x', 'b'])
def check_intcolumn_sorting(): dm = DataMatrix(length=8, default_col_type=IntColumn) dm.c = [ 1, '1', 2, '2', 1.1, '1.1', 2.1, '2.8', ] dm.c = ops.shuffle(dm.c) dm = ops.sort(dm, by=dm.c) check_col(dm.c, [ 1, 1, 1, 1, 2, 2, 2, 2 ])
def check_int_operations(): dm = DataMatrix(length=2, default_col_type=IntColumn) dm.col = 1.5, 2.5 check_col(dm.col, [1, 2]) dm.col *= 2.5 check_col(dm.col, [2, 4]) def _(): with pytest.raises(TypeError): dm.col *= 'x' _() check_integrity(dm)
def check_select(col_type): dm = DataMatrix(length=2, default_col_type=col_type) dm.col = 1, 2 dm_ = dm.col < 2 check_col(dm_.col, [1]) dm_ = dm.col == 2 check_col(dm_.col, [2]) dm_ = (dm.col == 1) | (dm.col == 2) check_col(dm_.col, [1,2]) dm_ = (dm.col == 1) & (dm.col == 2) check_col(dm_.col, []) dm_ = (dm.col == 1) ^ (dm.col == 2) check_col(dm_.col, [1,2]) check_integrity(dm)
def test_tuple_split(): dm = DataMatrix(length=4) dm.a = 'a', 'a', 'b', 'b' dm.b = 0, 1, 2, 3 dma, dmb = ops.tuple_split(dm.a, 'a', 'b') check_col(dma.a, ['a', 'a']) check_col(dma.b, [0, 1]) check_col(dmb.a, ['b', 'b']) check_col(dmb.b, [2, 3])
def test_sort(): dm = DataMatrix(length=2) dm.a = 'b', 'a' dm.b = 1, 0 dm.a = ops.sort(dm.a) check_col(dm.a, ['a', 'b']) check_col(dm.b, [1, 0]) dm = ops.sort(dm, by=dm.b) check_col(dm.a, ['b', 'a']) check_col(dm.b, [0, 1])
def test_sort():
dm = DataMatrix(length=2)
dm.a = 'b', 'a'
dm.b = 1, 0
dm.a = ops.sort(dm.a)
check_col(dm.a, ['a', 'b'])
check_col(dm.b, [1, 0])
dm = ops.sort(dm, by=dm.b)
check_col(dm.a, ['b', 'a'])
check_col(dm.b, [0, 1])
def test_replace():
dm = DataMatrix(length=3)
dm.a = 0, 1, 2
dm.c = FloatColumn
dm.c = np.nan, 1, 2
dm.s = SeriesColumn(depth=3)
dm.s[0] = 0, 1, 2
dm.s[1] = np.nan, 1, 2
dm.s[2] = np.nan, 1, 2
dm.a = ops.replace(dm.a, {0: 100, 2: 200})
dm.c = ops.replace(dm.c, {np.nan: 100, 2: np.nan})
dm.s = ops.replace(dm.s, {np.nan: 100, 2: np.nan})
check_col(dm.a, [100, 1, 200])
check_col(dm.c, [100, 1, np.nan])
check_series(dm.s, [
[0, 1, np.nan],
[100, 1, np.nan],
[100, 1, np.nan],
])
def test_split(): dm = DataMatrix(length=4) dm.a = 'a', 'a', 'b', 'b' dm.b = 0, 1, 2, 3 g = ops.split(dm.a) val, dm = g.next() eq_(val, 'a') check_col(dm.a, ['a', 'a']) check_col(dm.b, [0, 1]) val, dm = g.next() eq_(val, 'b') check_col(dm.a, ['b', 'b']) check_col(dm.b, [2, 3])
def test_intcolumn():
check_getrow(IntColumn)
check_select(IntColumn)
check_concat(IntColumn, invalid=0)
# Check selections with non-int types
dm = DataMatrix(length=2, default_col_type=IntColumn)
dm.col = 1, 2
dm2 = dm.col == '1.1' # Floored to 1
check_col(dm2.col, [1])
dm2 = dm.col == ''
check_col(dm2.col, [])
dm2 = dm.col != ''
check_col(dm2.col, [1, 2])
@raises(TypeError)
def _():
dm.col > ''
_()
# Check type selectors
dm = DataMatrix(length=2, default_col_type=IntColumn)
dm.col = 1, 2
eq_(len(dm.col == int), 2)
eq_(len(dm.col != int), 0)
eq_(len(dm.col == float), 0)
eq_(len(dm.col != float), 2)
eq_(len(dm.col == str), 0)
eq_(len(dm.col != str), 2)
def test_intcolumn():
dm = DataMatrix(length=2)
# Test assignment
dm.col = IntColumn
dm.col = 1
check_col(dm.col, [1, 1])
dm.col = 2, 3
check_col(dm.col, [2, 3])
dm.col[:-1] = 4
check_col(dm.col, [4, 3])
@raises(TypeError)
def _():
dm.col[0] = "test"
_()
@raises(TypeError)
def _():
dm.col[:] = "test"
_()
# Test shortening and lengthening
dm.length = 0
dm.length = 4
# Check uniqueness
dm.col = 1, 2, 1, 2
ok_(sorted(dm.col.unique) == [1, 2])
# Check dtype
ok_(dm.col._seq.dtype == np.int64)
check_integrity(dm)
def test_shuffle_horiz(): dm = DataMatrix(length=2) dm.a = 'a', 'b' dm.b = 0, 1 dm.c = '-', '-' while True: dm2 = ops.shuffle_horiz(dm) try: check_row(dm2[0], [0, '-', 'a']) break except: pass while True: dm2 = ops.shuffle_horiz(dm.a, dm.b) try: check_row(dm2[0], [0, 'a', '-']) break except: pass for i in range(1000): dm2 = ops.shuffle_horiz(dm.a, dm.b) check_col(dm.c, ['-', '-'])
def check_floatcolumn_sorting():
dm = DataMatrix(length=24, default_col_type=FloatColumn)
with pytest.warns(UserWarning):
dm.c = [
1, '1', 2, '2', 1.1, '1.1', 2.1, '2.1', INF, -INF, 'inf', '-inf',
NAN, NAN, 'nan', 'nan', None, None, None, None, 'alpha', 'beta',
'None', ''
]
dm.c = ops.shuffle(dm.c)
dm = ops.sort(dm, by=dm.c)
check_col(dm.c, [
-INF,
-INF,
1,
1,
1.1,
1.1,
2,
2,
2.1,
2.1,
INF,
INF,
NAN,
NAN,
NAN,
NAN,
NAN,
NAN,
NAN,
NAN,
NAN,
NAN,
NAN,
NAN,
])
def test_shuffle_horiz():
dm = DataMatrix(length=2)
dm.a = 'a', 'b'
dm.b = 0, 1
dm.c = '-', '-'
while True:
dm2 = ops.shuffle_horiz(dm)
try:
check_row(dm2[0], [0, '-', 'a'])
break
except:
pass
while True:
dm2 = ops.shuffle_horiz(dm.a, dm.b)
try:
check_row(dm2[0], [0, 'a', '-'])
break
except:
pass
for i in range(1000):
dm2 = ops.shuffle_horiz(dm.a, dm.b)
check_col(dm.c, ['-', '-'])
ops.shuffle_horiz(dm.a)
def check_float_operations(): dm = DataMatrix(length=2, default_col_type=FloatColumn) dm.col = 1, 2 check_col(dm.col, [1, 2]) dm.col *= 2.5 check_col(dm.col, [2.5, 5]) dm.col *= np.inf, np.nan check_col(dm.col, [np.inf, np.nan]) check_integrity(dm)
def check_concat(col_type, invalid):
dm1 = DataMatrix(length=2, default_col_type=col_type)
dm1.col1 = 1, 2
dm1.col_shared = 3, 4
dm2 = DataMatrix(length=2, default_col_type=col_type)
dm2.col2 = 5, 6
dm2.col_shared = 7, 8
dm3 = dm1 << dm2
check_col(dm3.col1, [1, 2, invalid, invalid])
check_col(dm3.col_shared, [3, 4, 7, 8])
check_col(dm3.col2, [invalid, invalid, 5, 6])
def check_concat(col_type, invalid): dm1 = DataMatrix(length=2, default_col_type=col_type) dm1.col1 = 1, 2 dm1.col_shared = 3, 4 dm2 = DataMatrix(length=2, default_col_type=col_type) dm2.col2 = 5, 6 dm2.col_shared = 7, 8 dm3 = dm1 << dm2 check_col(dm3.col1, [1,2,invalid,invalid]) check_col(dm3.col_shared, [3,4,7,8]) check_col(dm3.col2, [invalid,invalid,5,6])
def test_mixedcolumn():
dm = DataMatrix(length=2)
# Test assignment
dm.col = 1
check_col(dm.col, [1, 1])
dm.col = 2, 3
check_col(dm.col, [2, 3])
dm.col[:-1] = 4
check_col(dm.col, [4, 3])
dm.col[:] = "test"
check_col(dm.col, ["test", "test"])
# Test shortening and lengthening
dm.length = 0
dm.length = 4
# Check uniqueness
dm.col = 1, 2, 1, 2
ok_(sorted(dm.col.unique) == [1, 2])
check_integrity(dm)
def test_shuffle():
dm = DataMatrix(length=2)
dm.a = 'a', 'b'
dm.b = 0, 1
while True:
dm.a = ops.shuffle(dm.a)
check_col(dm.b, [0, 1])
try:
check_col(dm.a, ['b', 'a'])
break
except:
pass
dm = DataMatrix(length=2)
dm.a = 'a', 'b'
dm.b = 0, 1
while True:
dm = ops.shuffle(dm)
try:
check_col(dm.a, ['b', 'a'])
check_col(dm.b, [1, 0])
break
except:
pass
def test_shuffle(): dm = DataMatrix(length=2) dm.a = 'a', 'b' dm.b = 0, 1 while True: dm.a = ops.shuffle(dm.a) check_col(dm.b, [0, 1]) try: check_col(dm.a, ['b', 'a']) break except: pass dm = DataMatrix(length=2) dm.a = 'a', 'b' dm.b = 0, 1 while True: dm = ops.shuffle(dm) try: check_col(dm.a, ['b', 'a']) check_col(dm.b, [1, 0]) break except: pass
def test_floatcolumn():
dm = DataMatrix(length=2)
# Test assignment
dm.col = FloatColumn
dm.col = 1
check_col(dm.col, [1, 1])
dm.col = 2, 3
check_col(dm.col, [2, 3])
dm.col[:-1] = 4
check_col(dm.col, [4, 3])
dm.col[:] = "test"
for value in dm.col:
ok_(np.isnan(value))
# Test shortening and lengthening
dm.length = 0
dm.length = 4
# Check uniqueness
dm.col = 1, 2, 1, 2
ok_(sorted(dm.col.unique) == [1, 2])
# Check dtype
ok_(dm.col._seq.dtype == np.float64)
check_integrity(dm)
def _test_numericcolumn(cls):
# Test init and change by single value
dm = DataMatrix(length=2)
dm.col = cls
dm.col = 1
check_col(dm.col, [1, 1])
dm.col = 2
check_col(dm.col, [2, 2])
# Test init and change by sequence
dm = DataMatrix(length=2)
dm.col = cls
dm.col = 1, 2
check_col(dm.col, [1, 2])
dm.col = 3, 4
check_col(dm.col, [3, 4])
# Test setting by slice
dm = DataMatrix(length=3)
dm.col = cls
dm.col = 1
dm.col[1:] = 2
check_col(dm.col, [1, 2, 2])
dm.col[:-1] = 4, 3
check_col(dm.col, [4, 3, 2])
# Test setting by DataMatrix
dm = DataMatrix(length=10)
dm.x = range(10)
dm.y = FloatColumn
dm = dm.x != {3, 6}
dm.y[dm.x > 3] = 10
dm.y[dm.x >= 8] = 11
check_col(dm.y, [np.nan] * 3 + [10] * 3 + [11] * 2)
# Test shortening and lengthening
dm = DataMatrix(length=4)
dm.length = 0
dm.length = 4
# Check uniqueness
dm.col = 1, 2, 1, 2
assert sorted(dm.col.unique) == [1,2]
dm.col[dm.col == 2] = 0, 0
check_col(dm.col, [1, 0, 1, 0])
check_integrity(dm)
# Check if numericcolumns return right type
dm = DataMatrix(length=5)
dm.col = cls
dm.col = 1, 2, 3, 4, 5
# int -> float
val = dm.col[2]
assert isinstance(val, (int, float))
assert val == 3
# (int, int) -> FloatColumn
val = dm.col[1, 3]
assert isinstance(val, cls)
check_col(val, [2, 4])
# slice -> FloatColumn
val = dm.col[1:-1]
assert isinstance(val, cls)
check_col(val, [2, 3, 4])
# Check array setting and getting
if cls != MixedColumn:
a = dm.col.array
assert isinstance(a, np.ndarray)
assert a.shape == (5,)
assert all(a == [1, 2, 3, 4, 5])
def test_floatcolumn():
check_getrow(FloatColumn)
check_select(FloatColumn)
check_concat(FloatColumn, invalid=np.nan)
# Check selections with non-int types
dm = DataMatrix(length=4, default_col_type=FloatColumn)
dm.col = 1, 2, np.nan, np.inf
dm2 = dm.col == '1'
check_col(dm2.col, [1])
with pytest.warns(UserWarning):
dm2 = dm.col == ''
check_col(dm2.col, [np.nan])
with pytest.warns(UserWarning):
dm2 = dm.col != ''
check_col(dm2.col, [1, 2, np.inf])
dm2 = dm.col == np.nan
check_col(dm2.col, [np.nan])
dm2 = dm.col != np.nan
check_col(dm2.col, [1, 2, np.inf])
dm2 = dm.col == np.inf
check_col(dm2.col, [np.inf])
dm2 = dm.col != np.inf
check_col(dm2.col, [1, 2, np.nan])
with pytest.warns(UserWarning):
with pytest.raises(TypeError):
dm.col > ''
# Check type selectors
dm = DataMatrix(length=2, default_col_type=FloatColumn)
dm.col = 1, 2
assert len(dm.col == float) == 2
assert len(dm.col != float) == 0
assert len(dm.col == str) == 0
assert len(dm.col != str) == 2
assert len(dm.col == int) == 0
assert len(dm.col != int) == 2
def test_bin_split():
dm = DataMatrix(length=4)
dm.a = range(4)
dm = ops.shuffle(dm)
dm1, dm2 = ops.bin_split(dm.a, 2)
check_col(dm1.a, [0, 1])
check_col(dm2.a, [2, 3])
dm1, dm2, dm3 = ops.bin_split(dm.a, 3)
check_col(dm1.a, [0])
check_col(dm2.a, [1])
check_col(dm3.a, [2, 3])
dm1, = ops.bin_split(dm.a, 1)
check_col(dm1.a, [0, 1, 2, 3])
def _():
with pytest.raises(ValueError):
x, = ops.bin_split(dm.a, 5)
_()
def check_nan_sort():
dm = DataMatrix(length=3, default_col_type=FloatColumn)
dm.col1 = 2, np.nan, 1
dm.col2 = 1, 2, np.nan
dm = operations.sort(dm, by=dm.col1)
check_col(dm.col1, [1, 2, np.nan])
check_col(dm.col2, [np.nan, 1, 2])
dm = operations.sort(dm, by=dm.col2)
check_col(dm.col1, [2, np.nan, 1])
check_col(dm.col2, [1, 2, np.nan])
dm.col1 = operations.sort(dm.col1)
dm.col2 = operations.sort(dm.col2)
check_col(dm.col1, [1, 2, np.nan])
check_col(dm.col2, [1, 2, np.nan])
check_integrity(dm)
def check_select(col_type):
dm = DataMatrix(length=2, default_col_type=col_type)
dm.col = 1, 2
dm_ = dm.col < 2
check_col(dm_.col, [1])
dm_ = dm.col == 2
check_col(dm_.col, [2])
dm_ = (dm.col == 1) | (dm.col == 2) # or
check_col(dm_.col, [1, 2])
dm_ = (dm.col == 1) & (dm.col == 2) # and
check_col(dm_.col, [])
dm_ = (dm.col == 1) ^ (dm.col == 2) # xor
check_col(dm_.col, [1, 2])
# Pair-wise select by matching-length sequence
dm_ = dm.col == (1, 3)
check_col(dm_.col, [1])
# Check by set multimatching
dm_ = dm.col == {2, 3, 4}
check_col(dm_.col, [2])
dm_ = dm.col != {1, 3, 4}
check_col(dm_.col, [2])
# Check by lambda comparison
dm_ = dm.col == (lambda x: x == 2)
check_col(dm_.col, [2])
dm_ = dm.col != (lambda x: x == 2)
check_col(dm_.col, [1])
check_integrity(dm)
def _test_numericcolumn(cls):
# Test init and change by single value
dm = DataMatrix(length=2)
dm.col = cls
dm.col = 1
check_col(dm.col, [1, 1])
dm.col = 2
check_col(dm.col, [2, 2])
# Test init and change by sequence
dm = DataMatrix(length=2)
dm.col = cls
dm.col = 1, 2
check_col(dm.col, [1, 2])
dm.col = 3, 4
check_col(dm.col, [3, 4])
# Test setting by slice
dm = DataMatrix(length=3)
dm.col = cls
dm.col = 1
dm.col[1:] = 2
check_col(dm.col, [1, 2, 2])
dm.col[:-1] = 4, 3
check_col(dm.col, [4, 3, 2])
# Test shortening and lengthening
dm = DataMatrix(length=4)
dm.length = 0
dm.length = 4
# Check uniqueness
dm.col = 1, 2, 1, 2
ok_(sorted(dm.col.unique) == [1, 2])
dm.col[dm.col == 2] = 0, 0
check_col(dm.col, [1, 0, 1, 0])
check_integrity(dm)
# Check if numericcolumns return right type
dm = DataMatrix(length=5)
dm.col = cls
dm.col = 1, 2, 3, 4, 5
# int -> float
val = dm.col[2]
ok_(isinstance(val, (int, float)))
eq_(val, 3)
# (int, int) -> FloatColumn
val = dm.col[1, 3]
ok_(isinstance(val, cls))
check_col(val, [2, 4])
# slice -> FloatColumn
val = dm.col[1:-1]
ok_(isinstance(val, cls))
check_col(val, [2, 3, 4])
# Check array setting and getting
if cls != MixedColumn:
a = dm.col.array
ok_(isinstance(a, np.ndarray))
eq_(a.shape, (5, ))
ok_(all(a == [1, 2, 3, 4, 5]))
def test_floatcolumn():
check_getrow(FloatColumn)
check_select(FloatColumn)
check_concat(FloatColumn, invalid=np.nan)
# Check selections with non-int types
dm = DataMatrix(length=4, default_col_type=FloatColumn)
dm.col = 1, 2, np.nan, np.inf
dm2 = dm.col == '1'
check_col(dm2.col, [1])
dm2 = dm.col == ''
check_col(dm2.col, [np.nan])
dm2 = dm.col != ''
check_col(dm2.col, [1, 2, np.inf])
dm2 = dm.col == np.nan
check_col(dm2.col, [np.nan])
dm2 = dm.col != np.nan
check_col(dm2.col, [1, 2, np.inf])
dm2 = dm.col == np.inf
check_col(dm2.col, [np.inf])
dm2 = dm.col != np.inf
check_col(dm2.col, [1, 2, np.nan])
@raises(TypeError)
def _():
dm.col > ''
_()
# Check type selectors
dm = DataMatrix(length=2, default_col_type=FloatColumn)
dm.col = 1, 2
eq_(len(dm.col == float), 2)
eq_(len(dm.col != float), 0)
eq_(len(dm.col == str), 0)
eq_(len(dm.col != str), 2)
eq_(len(dm.col == int), 0)
eq_(len(dm.col != int), 2)
def test_split():
dm = DataMatrix(length=4)
dm.a = 'a', 'a', 'b', 'b'
dm.b = 0, 1, 2, 3
# Without values
g = ops.split(dm.a)
val, dm = next(g)
assert val == 'a'
check_col(dm.a, ['a', 'a'])
check_col(dm.b, [0, 1])
val, dm = next(g)
assert val == 'b'
check_col(dm.a, ['b', 'b'])
check_col(dm.b, [2, 3])
# With values
dm = DataMatrix(length=4)
dm.a = 'a', 'a', 'b', 'b'
dm.b = 0, 1, 2, 3
dma, dmb = ops.split(dm.a, 'a', 'b')
check_col(dma.a, ['a', 'a'])
check_col(dma.b, [0, 1])
check_col(dmb.a, ['b', 'b'])
check_col(dmb.b, [2, 3])
def check_nan_sort(): dm = DataMatrix(length=3, default_col_type=FloatColumn) dm.col1 = 2,np.nan,1 dm.col2 = 1,2,np.nan dm = operations.sort(dm, by=dm.col1) check_col(dm.col1, [1, 2, np.nan]) check_col(dm.col2, [np.nan, 1, 2]) dm = operations.sort(dm, by=dm.col2) check_col(dm.col1, [2, np.nan, 1]) check_col(dm.col2, [1, 2, np.nan]) dm.col1 = operations.sort(dm.col1) dm.col2 = operations.sort(dm.col2) check_col(dm.col1, [1, 2, np.nan]) check_col(dm.col2, [1, 2, np.nan]) check_integrity(dm)
def check_operations(col_type): dm = DataMatrix(length=2, default_col_type=col_type) dm.col = 1, '2' dm.col += 1 check_col(dm.col, [2, 3]) dm.col += 1, '2' check_col(dm.col, [3, 5]) dm.col -= 1 check_col(dm.col, [2, 4]) dm.col -= 1, '2' check_col(dm.col, [1, 2]) dm.col *= 2 check_col(dm.col, [2, 4]) dm.col *= 1, '2' check_col(dm.col, [2, 8]) dm.col /= 2 check_col(dm.col, [1, 4]) dm.col /= 1, '2' check_col(dm.col, [1, 2]) # Right-hand operations dm.col = 1 + dm.col check_col(dm.col, [2, 3]) dm.col = (1, 2) + dm.col check_col(dm.col, [3, 5]) dm.col = 5 - dm.col check_col(dm.col, [2, 0]) dm.col = (3, 1) - dm.col check_col(dm.col, [1, 1]) dm.col = 2 * dm.col check_col(dm.col, [2, 2]) dm.col = (1, 2) * dm.col check_col(dm.col, [2, 4]) dm.col = 4 / dm.col check_col(dm.col, [2, 1]) dm.col = (4, 2) / dm.col check_col(dm.col, [2, 2]) dm.col = 2 ** dm.col check_col(dm.col, [4, 4]) dm.col = (2, 4) ** dm.col check_col(dm.col, [16, 256]) dm.col = 17 % dm.col check_col(dm.col, [1, 17]) dm.col = (2, 16) % dm.col check_col(dm.col, [0, 16]) check_integrity(dm)
def test_bin_split(): dm = DataMatrix(length=4) dm.a = range(4) dm = ops.shuffle(dm) dm1, dm2 = ops.bin_split(dm.a, 2) check_col(dm1.a, [0,1]) check_col(dm2.a, [2,3]) dm1, dm2, dm3 = ops.bin_split(dm.a, 3) check_col(dm1.a, [0]) check_col(dm2.a, [1]) check_col(dm3.a, [2,3]) dm1, = ops.bin_split(dm.a, 1) check_col(dm1.a, [0,1,2,3]) @raises(ValueError) def _(): x, = ops.bin_split(dm.a, 5) _()
def check_sort(col_type): dm = DataMatrix(length=3, default_col_type=col_type) dm.col1 = 3,2,1 dm.col2 = 1,2,3 dm = operations.sort(dm, by=dm.col1) check_col(dm.col1, [1, 2, 3]) check_col(dm.col2, [3, 2, 1]) dm = operations.sort(dm, by=dm.col2) check_col(dm.col1, [3, 2, 1]) check_col(dm.col2, [1, 2, 3]) dm.col2 = operations.sort(dm.col2, by=dm.col1) check_col(dm.col2, [3, 2, 1]) dm.col1 = operations.sort(dm.col1) dm.col2 = operations.sort(dm.col2) check_col(dm.col1, [1, 2, 3]) check_col(dm.col2, [1, 2, 3]) check_integrity(dm)
def check_operations(col_type): dm = DataMatrix(length=2, default_col_type=col_type) dm.col = 1, 2 dm.col += 1 check_col(dm.col, [2, 3]) dm.col += 1, 2 check_col(dm.col, [3, 5]) dm.col -= 1 check_col(dm.col, [2, 4]) dm.col -= 1, 2 check_col(dm.col, [1, 2]) dm.col *= 2 check_col(dm.col, [2, 4]) dm.col *= 1.5, 3 check_col(dm.col, [3, 12]) dm.col /= 3 check_col(dm.col, [1, 4]) dm.col /= 1, 2 check_col(dm.col, [1, 2]) dm.col //= 1.5, 2.5 check_col(dm.col, [0, 0]) check_integrity(dm)
def test_split():
dm = DataMatrix(length=4)
dm.a = 'a', 'a', 'b', 'b'
dm.b = 0, 1, 2, 3
# Without values
g = ops.split(dm.a)
val, dm = next(g)
assert val == 'a'
check_col(dm.a, ['a', 'a'])
check_col(dm.b, [0, 1])
val, dm = next(g)
assert val == 'b'
check_col(dm.a, ['b', 'b'])
check_col(dm.b, [2, 3])
# With values
dm = DataMatrix(length=4)
dm.a = 'a', 'a', 'b', 'b'
dm.b = 0, 1, 2, 3
dma, dmb = ops.split(dm.a, 'a', 'b')
check_col(dma.a, ['a', 'a'])
check_col(dma.b, [0, 1])
check_col(dmb.a, ['b', 'b'])
check_col(dmb.b, [2, 3])
# With multiple columns
dm = DataMatrix(length=8)
dm.A = 0, 0, 1, 1, 0, 0, 1, 1
dm.B = 'a', 'b', 'a', 'b', 'a', 'b', 'a', 'b'
dm.C = 'x', 'x', 'x', 'x', 'y', 'y', 'y', 'y'
g = ops.split(dm.A, dm.B)
val1, val2, sdm = next(g)
assert val1 == 0
assert val2 == 'a'
assert (len(sdm) == 2)
val1, val2, sdm = next(g)
assert val1 == 0
assert val2 == 'b'
assert (len(sdm) == 2)
val1, val2, sdm = next(g)
assert val1 == 1
assert val2 == 'a'
assert (len(sdm) == 2)
val1, val2, sdm = next(g)
assert val1 == 1
assert val2 == 'b'
assert (len(sdm) == 2)
g = ops.split(dm.A, dm.B, dm.C)
val1, val2, val3, sdm = next(g)
assert val1 == 0
assert val2 == 'a'
assert val3 == 'x'
assert (len(sdm) == 1)
val1, val2, val3, sdm = next(g)
assert val1 == 0
assert val2 == 'a'
assert val3 == 'y'
assert (len(sdm) == 1)
val1, val2, val3, sdm = next(g)
assert val1 == 0
assert val2 == 'b'
assert val3 == 'x'
assert (len(sdm) == 1)
val1, val2, val3, sdm = next(g)
assert val1 == 0
assert val2 == 'b'
assert val3 == 'y'
assert (len(sdm) == 1)
val1, val2, val3, sdm = next(g)
assert val1 == 1
assert val2 == 'a'
assert val3 == 'x'
assert (len(sdm) == 1)
val1, val2, val3, sdm = next(g)
assert val1 == 1
assert val2 == 'a'
assert val3 == 'y'
assert (len(sdm) == 1)
val1, val2, val3, sdm = next(g)
assert val1 == 1
assert val2 == 'b'
assert val3 == 'x'
assert (len(sdm) == 1)
val1, val2, val3, sdm = next(g)
assert val1 == 1
assert val2 == 'b'
assert val3 == 'y'
assert (len(sdm) == 1)
def test_floatcolumn():
_test_numericcolumn(FloatColumn)
_test_copying(FloatColumn)
# Test automatic conversion to float
dm = DataMatrix(length=2)
dm.col = FloatColumn
dm.col = 1.9, '2.9'
check_col(dm.col, [1.9, 2.9])
# Test nans
dm.col = 'nan'
check_col(dm.col, [np.nan, np.nan])
dm.col = None
check_col(dm.col, [np.nan, np.nan])
dm.col = np.nan
check_col(dm.col, [np.nan, np.nan])
dm.col = 'x'
check_col(dm.col, [np.nan, np.nan])
# Test infs
dm.col = 'inf'
check_col(dm.col, [np.inf, np.inf])
dm.col = np.inf
check_col(dm.col, [np.inf, np.inf])
# Test nans and infs
dm.col = 'nan', 'inf'
check_col(dm.col, [np.nan, np.inf])
dm.col = np.inf, np.nan
check_col(dm.col, [np.inf, np.nan])
dm.col = 'x', None
check_col(dm.col, [np.nan, np.nan])
# Check dtype
ok_(dm.col._seq.dtype == np.float64)
check_integrity(dm)
def test_seriescolumn():
_test_copying(SeriesColumn(depth=1))
dm = DataMatrix(length=2)
dm.col = SeriesColumn(depth=3)
# Set all rows to a single value
dm.col = 1
check_series(dm.col, [[1, 1, 1], [1, 1, 1]])
# Set rows to different single values
dm.col = 2, 3
check_series(dm.col, [[2, 2, 2], [3, 3, 3]])
# Set one row to a single value
dm.col[0] = 4
check_series(dm.col, [[4, 4, 4], [3, 3, 3]])
# Set one row to different single values
dm.col[1] = 5, 6, 7
check_series(dm.col, [[4, 4, 4], [5, 6, 7]])
# Set all rows to different single values
dm.col.setallrows([8, 9, 10])
check_series(dm.col, [[8, 9, 10], [8, 9, 10]])
# Set the first value in all rows
dm.col[:, 0] = 1
check_series(dm.col, [[1, 9, 10], [1, 9, 10]])
# Set all values in the first row
dm.col[0, :] = 2
check_series(dm.col, [[2, 2, 2], [1, 9, 10]])
# Set all values
dm.col[:, :] = 3
check_series(dm.col, [[3, 3, 3], [3, 3, 3]])
# Test shortening and lengthening
dm.length = 0
check_series(dm.col, [])
dm.length = 3
dm.col = 1, 2, 3
dm.col.depth = 1
check_series(dm.col, [[1], [2], [3]])
dm.col.depth = 3
check_series(dm.col, [[1, NAN, NAN], [2, NAN, NAN], [3, NAN, NAN]])
check_integrity(dm)
# Test
dm = DataMatrix(length=2)
dm.col = SeriesColumn(depth=3)
dm.col = 1, 2
check_series(dm.col, [[1, 1, 1], [2, 2, 2]])
dm.col = 3, 4, 5
check_series(dm.col, [[3, 4, 5]] * 2)
dm.col.depth = 2
dm.col[:] = 1, 2
check_series(dm.col, [[1, 1], [2, 2]])
dm.col[:, :] = 3, 4
check_series(dm.col, [[3, 4], [3, 4]])
# Check if series return right type
dm = DataMatrix(length=4)
dm.col = SeriesColumn(depth=5)
dm.col = [[1, 2, 3, 4, 5], [6, 7, 8, 9, 10], [11, 12, 13, 14, 15],
[16, 17, 18, 19, 20]]
# (int, int) -> float
val = dm.col[2, 2]
eq_(val, 13)
eq_(type(val), float)
# (int) -> array
val = dm.col[2]
ok_(all(val == np.array([11, 12, 13, 14, 15])))
eq_(type(val), np.ndarray)
# (int, slice) -> array
val = dm.col[2, 1:-1]
ok_(all(val == np.array([12, 13, 14])))
eq_(type(val), np.ndarray)
# (int, (int, int)) -> array
val = dm.col[2, (1, 3)]
ok_(all(val == np.array([12, 14])))
eq_(type(val), np.ndarray)
# (slice) -> SeriesColumn
val = dm.col[1:-1]
check_series(val, [
[6, 7, 8, 9, 10],
[11, 12, 13, 14, 15],
])
# (slice, int) -> FloatColumn
val = dm.col[1:-1, 2]
ok_(isinstance(val, FloatColumn))
check_col(val, [8, 13])
# ((int, int), int) -> FloatColumn
val = dm.col[(1, 3), 2]
ok_(isinstance(val, FloatColumn))
check_col(val, [8, 18])
# (slice, slice) -> SeriesColumn
val = dm.col[1:-1, 1:-1]
ok_(isinstance(val, _SeriesColumn))
check_series(val, [
[7, 8, 9],
[12, 13, 14],
])
# ((int, int), slice) -> SeriesColumn
val = dm.col[(1, 3), 1:-1]
ok_(isinstance(val, _SeriesColumn))
check_series(val, [
[7, 8, 9],
[17, 18, 19],
])
# ((int, int), (int int)) -> SeriesColumn
val = dm.col[(1, 3), (1, 3)]
ok_(isinstance(val, _SeriesColumn))
check_series(val, [
[7, 9],
[17, 19],
])
def check_sort(col_type):
dm = DataMatrix(length=3, default_col_type=col_type)
dm.col1 = 3, 2, 1
dm.col2 = 1, 2, 3
dm = operations.sort(dm, by=dm.col1)
check_col(dm.col1, [1, 2, 3])
check_col(dm.col2, [3, 2, 1])
dm = operations.sort(dm, by=dm.col2)
check_col(dm.col1, [3, 2, 1])
check_col(dm.col2, [1, 2, 3])
dm.col2 = operations.sort(dm.col2, by=dm.col1)
check_col(dm.col2, [3, 2, 1])
dm.col1 = operations.sort(dm.col1)
dm.col2 = operations.sort(dm.col2)
check_col(dm.col1, [1, 2, 3])
check_col(dm.col2, [1, 2, 3])
check_integrity(dm)