Exemplo n.º 1
0
def test_piecewise_linear():
    from larch.util.data_expansion import piecewise_linear

    func = piecewise_linear(X.DataName, P.ParamName, [3, 5, 7])
    assert func[0] == P('ParamName ① up to 3') * X('piece(DataName,None,3)')
    assert func[1] == P('ParamName ② 3 to 5') * X('piece(DataName,3,5)')
    assert func[2] == P('ParamName ③ 5 to 7') * X('piece(DataName,5,7)')
    assert func[3] == P('ParamName ④ over 7') * X('piece(DataName,7,None)')
    assert len(func) == 4

    func = piecewise_linear(X.DataName, breaks=[3, 5, 7])
    assert func[0] == P('DataName ① up to 3') * X('piece(DataName,None,3)')
    assert func[1] == P('DataName ② 3 to 5') * X('piece(DataName,3,5)')
    assert func[2] == P('DataName ③ 5 to 7') * X('piece(DataName,5,7)')
    assert func[3] == P('DataName ④ over 7') * X('piece(DataName,7,None)')
    assert len(func) == 4

    func = piecewise_linear('GenName', breaks=[3, 5, 7])
    assert func[0] == P('GenName ① up to 3') * X('piece(GenName,None,3)')
    assert func[1] == P('GenName ② 3 to 5') * X('piece(GenName,3,5)')
    assert func[2] == P('GenName ③ 5 to 7') * X('piece(GenName,5,7)')
    assert func[3] == P('GenName ④ over 7') * X('piece(GenName,7,None)')
    assert len(func) == 4

    with pytest.raises(ValueError):
        func = piecewise_linear('GenName', [3, 5, 7])
Exemplo n.º 2
0
def test_piecewise_expansion():
    import pandas, io, numpy
    from larch.util.data_expansion import piecewise_expansion, piecewise_linear

    df = pandas.DataFrame(
        numpy.linspace(0, 10, 25),
        columns=['DataName'],
    )

    expanded = piecewise_expansion(df, [3, 5, 7])

    s = '''
	    piece(DataName,None,3)  piece(DataName,3,5)  piece(DataName,5,7)  piece(DataName,7,None)
	0                 0.000000             0.000000             0.000000                0.000000
	1                 0.416667             0.000000             0.000000                0.000000
	2                 0.833333             0.000000             0.000000                0.000000
	3                 1.250000             0.000000             0.000000                0.000000
	4                 1.666667             0.000000             0.000000                0.000000
	5                 2.083333             0.000000             0.000000                0.000000
	6                 2.500000             0.000000             0.000000                0.000000
	7                 2.916667             0.000000             0.000000                0.000000
	8                 3.000000             0.333333             0.000000                0.000000
	9                 3.000000             0.750000             0.000000                0.000000
	10                3.000000             1.166667             0.000000                0.000000
	11                3.000000             1.583333             0.000000                0.000000
	12                3.000000             2.000000             0.000000                0.000000
	13                3.000000             2.000000             0.416667                0.000000
	14                3.000000             2.000000             0.833333                0.000000
	15                3.000000             2.000000             1.250000                0.000000
	16                3.000000             2.000000             1.666667                0.000000
	17                3.000000             2.000000             2.000000                0.083333
	18                3.000000             2.000000             2.000000                0.500000
	19                3.000000             2.000000             2.000000                0.916667
	20                3.000000             2.000000             2.000000                1.333333
	21                3.000000             2.000000             2.000000                1.750000
	22                3.000000             2.000000             2.000000                2.166667
	23                3.000000             2.000000             2.000000                2.583333
	24                3.000000             2.000000             2.000000                3.000000
	'''

    correct = pandas.read_csv(io.StringIO(s), sep='\s+')

    pandas.testing.assert_frame_equal(expanded, correct)

    func = piecewise_linear(X.DataName, P.ParamName, [3, 5, 7])
    expanded2 = piecewise_expansion(df, func)
    pandas.testing.assert_frame_equal(expanded2, correct)