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 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 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_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_seriescolumn():
dm1 = DataMatrix(length=2)
dm1.col1 = SeriesColumn(2)
dm1.col1 = 1, 2
dm1.col_shared = SeriesColumn(2)
dm1.col_shared = 3, 4
dm2 = DataMatrix(length=2)
dm2.col2 = SeriesColumn(2)
dm2.col2 = 5, 6
dm2.col_shared = SeriesColumn(2)
dm2.col_shared = 7, 8
dm3 = dm1 << dm2
check_series(dm3.col1,
[[1, 1], [2, 2], [np.nan, np.nan], [np.nan, np.nan]])
check_series(dm3.col_shared, [[3, 3], [4, 4], [7, 7], [8, 8]])
check_series(dm3.col2,
[[np.nan, np.nan], [np.nan, np.nan], [5, 5], [6, 6]])
dm3.i = [4, 0, 2, 1]
dm4 = dm3.i <= 2
dm5 = (dm3.i <= 2) | (dm3.i >= 3)
check_integrity(dm1)
check_integrity(dm2)
check_integrity(dm3)
check_integrity(dm4)
check_integrity(dm5)
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 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_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_window():
dm = DataMatrix(length=2)
dm.series = SeriesColumn(depth=4)
dm.series[0] = 0, 1, 1, 0
dm.series[1] = 0, 2, 2, 0
dm.window = series.window(dm.series, 1, 3)
check_series(dm.window, [[1, 1], [2, 2]])
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 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_shuffle(col_type): dm = DataMatrix(length=3, default_col_type=col_type) dm.col1 = 11,12,13 dm.col2 = 1,2,3 dm = operations.shuffle(dm) for row in dm: ok_(row.col1 == row.col2+10) dm.col1 = operations.shuffle(dm.col1) dm.col2 = operations.shuffle(dm.col2) check_integrity(dm)
def test_downsample():
dm = DataMatrix(length=2)
dm.series = SeriesColumn(depth=10)
dm.series[0] = range(10)
dm.series[1] = [0, 1] * 5
dm.d3 = series.downsample(dm.series, 3)
dm.d5 = series.downsample(dm.series, 5)
check_series(dm.d3, [[1, 4, 7], [1. / 3, 2. / 3, 1. / 3]])
check_series(dm.d5, [[2, 7], [.4, .6]])
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 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_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 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_seriescolumn():
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, 0, 0], [2, 0, 0], [3, 0, 0]])
check_integrity(dm)
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 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_seriescolumn(): dm1 = DataMatrix(length=2) dm1.col1 = SeriesColumn(2) dm1.col1 = 1, 2 dm1.col_shared = SeriesColumn(2) dm1.col_shared = 3, 4 dm2 = DataMatrix(length=2) dm2.col2 = SeriesColumn(2) dm2.col2 = 5, 6 dm2.col_shared = SeriesColumn(2) dm2.col_shared = 7, 8 dm3 = dm1 << dm2 check_series(dm3.col1, [[1,1],[2,2],[0,0],[0,0]]) check_series(dm3.col_shared, [[3,3],[4,4],[7,7],[8,8]]) check_series(dm3.col2, [[0,0],[0,0],[5,5],[6,6]]) dm3.i = [4,0,2,1] dm4 = dm3.i <= 2 dm5 = (dm3.i <= 2) | (dm3.i >= 3) check_integrity(dm1) check_integrity(dm2) check_integrity(dm3) check_integrity(dm4) check_integrity(dm5)
