def test_select_by_index(eng):
data = fromlist([arange(12)], index=[0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2], engine=eng)
result = data.select_by_index(1)
assert allclose(result.toarray(), array([4, 5, 6, 7]))
assert allclose(result.index, array([1, 1, 1, 1]))
result = data.select_by_index(1, squeeze=True)
assert allclose(result.index, array([0, 1, 2, 3]))
index = [
[0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1],
[0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1],
[0, 1, 0, 1, 2, 3, 0, 1, 0, 1, 2, 3]
]
data.index = array(index).T
result, mask = data.select_by_index(0, level=2, return_mask=True)
assert allclose(result.toarray(), array([0, 2, 6, 8]))
assert allclose(result.index, array([[0, 0, 0], [0, 1, 0], [1, 0, 0], [1, 1, 0]]))
assert allclose(mask, array([1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0]))
result = data.select_by_index(0, level=2, squeeze=True)
assert allclose(result.toarray(), array([0, 2, 6, 8]))
assert allclose(result.index, array([[0, 0], [0, 1], [1, 0], [1, 1]]))
result = data.select_by_index([1, 0], level=[0, 1])
assert allclose(result.toarray(), array([6, 7]))
assert allclose(result.index, array([[1, 0, 0], [1, 0, 1]]))
result = data.select_by_index(val=[0, [2,3]], level=[0, 2])
assert allclose(result.toarray(), array([4, 5]))
assert allclose(result.index, array([[0, 1, 2], [0, 1, 3]]))
result = data.select_by_index(1, level=1, filter=True)
assert allclose(result.toarray(), array([0, 1, 6, 7]))
assert allclose(result.index, array([[0, 0, 0], [0, 0, 1], [1, 0, 0], [1, 0, 1]]))
def test_mean_by_panel(eng):
data = fromlist([arange(8)], engine=eng)
test1 = data.mean_by_panel(4)
assert allclose(test1.index, array([0, 1, 2, 3]))
assert allclose(test1.toarray(), [[2, 3, 4, 5]])
test2 = data.mean_by_panel(2)
assert allclose(test2.index, array([0, 1]))
assert allclose(test2.toarray(), [[3, 4]])
def test_times_vector(eng):
mat1raw = asarray([[1, 2, 3], [4, 5, 6]])
mat2 = [7, 8, 9]
mat1 = fromlist(mat1raw, engine=eng)
truth = dot(mat1raw, mat2)
result = mat1.times(mat2)
assert allclose(result.toarray(), truth)
assert allclose(result.index, [0])
def test_times_array_alt(eng):
mat1raw = asarray([[1, 2, 3], [4, 5, 6]])
mat2 = asarray([[7, 8, 7, 8], [9, 10, 9, 10], [11, 12, 11, 12]])
mat1 = fromlist(mat1raw, engine=eng)
truth = dot(mat1raw, mat2)
result = mat1.times(mat2)
assert allclose(result.toarray(), truth)
assert allclose(result.index, range(0, 4))
def test_standardize_axis0(eng):
data = fromlist([array([1, 2]), array([3, 4])], engine=eng)
centered = data.center(0)
standardized = data.standardize(0)
zscored = data.zscore(0)
assert allclose(centered.toarray(), array([[-1, -1], [1, 1]]), atol=1e-3)
assert allclose(standardized.toarray(), array([[1, 2], [3, 4]]), atol=1e-3)
assert allclose(zscored.toarray(), array([[-1, -1], [1, 1]]), atol=1e-3)
def test_times_scalar(eng):
mat1raw = asarray([[1, 2, 3], [4, 5, 6]])
mat2 = 5
mat1 = fromlist(mat1raw, engine=eng)
truth = mat1raw * mat2
result = mat1.times(mat2)
assert allclose(result.toarray(), truth)
assert allclose(result.index, range(0, 3))
def test_squelch(eng):
data = fromlist([array([1, 2]), array([3, 4])], engine=eng)
squelched = data.squelch(5)
assert allclose(squelched.toarray(), [[0, 0], [0, 0]])
squelched = data.squelch(3)
assert allclose(squelched.toarray(), [[0, 0], [3, 4]])
squelched = data.squelch(1)
assert allclose(squelched.toarray(), [[1, 2], [3, 4]])
def test_correlate(eng):
data = fromlist([array([1, 2, 3, 4, 5])], engine=eng)
sig = [4, 5, 6, 7, 8]
corr = data.correlate(sig).toarray()
assert allclose(corr, 1)
sigs = [[4, 5, 6, 7, 8], [8, 7, 6, 5, 4]]
corrs = data.correlate(sigs).toarray()
assert allclose(corrs, [1, -1])
def test_index_setting(eng):
data = fromlist([array([1, 2, 3]), array([2, 2, 4]), array([4, 2, 1])], engine=eng)
assert allclose(data.index, array([0, 1, 2]))
data.index = [3, 2, 1]
assert allclose(data.index, [3, 2, 1])
with pytest.raises(ValueError):
data.index = 5
with pytest.raises(ValueError):
data.index = [1, 2]
def test_crosscorr(eng):
local = array([1.0, 2.0, -4.0, 5.0, 8.0, 3.0, 4.1, 0.9, 2.3])
data = fromlist([local], engine=eng)
sig = array([1.5, 2.1, -4.2, 5.6, 8.1, 3.9, 4.2, 0.3, 2.1])
betas = data.crosscorr(signal=sig, lag=0)
assert allclose(betas.toarray(), corrcoef(local, sig)[0, 1])
betas = data.crosscorr(signal=sig, lag=2)
truth = array([-0.18511, 0.03817, 0.99221, 0.06567, -0.25750])
assert allclose(betas.toarray(), truth, atol=1E-5)
def test_select(eng):
index = ['label1', 'label2', 'label3', 'label4']
data = fromlist([array([4, 5, 6, 7]), array([8, 9, 10, 11])], engine=eng, index=index)
assert data.select('label1').shape == (2, 1)
assert allclose(data.select('label1').toarray(), [4, 8])
assert allclose(data.select(['label1']).toarray(), [4, 8])
assert allclose(data.select(['label1', 'label2']).toarray(), array([[4, 5], [8, 9]]))
assert data.select('label1').index == ['label1']
assert data.select(['label1']).index == ['label1']
def test_correlate_multiindex(eng):
index = [[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]]
data = fromlist([array([1, 2, 3, 4, 5])], index=asarray(index).T, engine=eng)
sig = [4, 5, 6, 7, 8]
corr = data.correlate(sig).toarray()
assert allclose(corr, 1)
sigs = [[4, 5, 6, 7, 8], [8, 7, 6, 5, 4]]
corrs = data.correlate(sigs).toarray()
assert allclose(corrs, [1, -1])
def test_mean_by_window(eng):
data = fromlist([array([0, 1, 2, 3, 4, 5, 6])], engine=eng)
test1 = data.mean_by_window(indices=[3, 5], window=2).toarray()
assert allclose(test1, [3, 4])
test2 = data.mean_by_window(indices=[3, 5], window=3).toarray()
assert allclose(test2, [3, 4, 5])
test3 = data.mean_by_window(indices=[3, 5], window=4).toarray()
assert allclose(test3, [2, 3, 4, 5])
test4 = data.mean_by_window(indices=[3], window=4).toarray()
assert allclose(test4, [1, 2, 3, 4])
def test_standardize_axis1(eng):
data = fromlist([array([1, 2, 3, 4, 5])], engine=eng)
centered = data.center(1)
standardized = data.standardize(1)
zscored = data.zscore(1)
assert allclose(centered.toarray(), array([-2, -1, 0, 1, 2]), atol=1e-3)
assert allclose(standardized.toarray(),
array([0.70710, 1.41421, 2.12132, 2.82842, 3.53553]), atol=1e-3)
assert allclose(zscored.toarray(),
array([-1.41421, -0.70710, 0, 0.70710, 1.41421]), atol=1e-3)
def test_normalize_window(eng):
y = array([1, 2, 3, 4, 5])
data = fromlist([y], engine=eng)
vals = data.normalize('window', window=2).toarray()
b = array([1, 1, 2, 3, 4])
result_true = (y - b) / (b + 0.1)
assert allclose(vals, result_true, atol=1e-3)
vals = data.normalize('window', window=5).toarray()
b = array([1, 1, 2, 3, 4])
result_true = (y - b) / (b + 0.1)
assert allclose(vals, result_true, atol=1e-3)
def test_normalize_window_exact(eng):
y = array([1, 2, 3, 4, 5])
data = fromlist([y], engine=eng)
vals = data.normalize('window-exact', window=2).toarray()
b = array([1.2, 1.4, 2.4, 3.4, 4.2])
result_true = (y - b) / (b + 0.1)
assert allclose(vals, result_true, atol=1e-3)
vals = data.normalize('window-exact', window=6).toarray()
b = array([1.6, 1.8, 1.8, 1.8, 2.6])
result_true = (y - b) / (b + 0.1)
assert allclose(vals, result_true, atol=1e-3)
def test_stat_by_index_multi(eng):
index = [
[0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1],
[0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1],
[0, 1, 0, 1, 2, 3, 0, 1, 0, 1, 2, 3]
]
data = fromlist([arange(12)], index=array(index).T, engine=eng)
result = data.stat_by_index('sum', level=[0, 1])
assert allclose(result.toarray(), array([1, 14, 13, 38]))
assert allclose(result.index, array([[0, 0], [0, 1], [1, 0], [1, 1]]))
result = data.sum_by_index(level=[0, 1])
assert allclose(result.toarray(), array([1, 14, 13, 38]))
assert allclose(result.index, array([[0, 0], [0, 1], [1, 0], [1, 1]]))
def test_aggregate_by_index(eng):
data = fromlist([arange(12)], index=[0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2], engine=eng)
result = data.aggregate_by_index(sum)
assert allclose(result.toarray(), array([6, 22, 38]))
assert allclose(result.index, array([0, 1, 2]))
index = [
[0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1],
[0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1],
[0, 1, 0, 1, 2, 3, 0, 1, 0, 1, 2, 3]
]
data.index = array(index).T
result = data.aggregate_by_index(sum, level=[0, 1])
assert allclose(result.toarray(), array([1, 14, 13, 38]))
assert allclose(result.index, array([[0, 0], [0, 1], [1, 0], [1, 1]]))
def test_stat_by_index(eng):
data = fromlist([arange(12)], index=[0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2], engine=eng)
assert allclose(data.stat_by_index('sum').toarray(), array([6, 22, 38]))
assert allclose(data.stat_by_index('mean').toarray(), array([1.5, 5.5, 9.5]))
assert allclose(data.stat_by_index('min').toarray(), array([0, 4, 8]))
assert allclose(data.stat_by_index('max').toarray(), array([3, 7, 11]))
assert allclose(data.stat_by_index('count').toarray(), array([4, 4, 4]))
assert allclose(data.stat_by_index('median').toarray(), array([1.5, 5.5, 9.5]))
assert allclose(data.sum_by_index().toarray(), array([6, 22, 38]))
assert allclose(data.mean_by_index().toarray(), array([1.5, 5.5, 9.5]))
assert allclose(data.min_by_index().toarray(), array([0, 4, 8]))
assert allclose(data.max_by_index().toarray(), array([3, 7, 11]))
assert allclose(data.count_by_index().toarray(), array([4, 4, 4]))
assert allclose(data.median_by_index().toarray(), array([1.5, 5.5, 9.5]))
def test_labels(eng):
x = [array([0, 1]), array([2, 3]), array([4, 5]), array([6, 7])]
data = fromlist(x, labels=[0, 1, 2, 3], engine=eng)
assert allclose(data.filter(lambda x: x[0]>2).labels, array([2, 3]))
assert allclose(data[2:].labels, array([2, 3]))
assert allclose(data[1].labels, array([1]))
assert allclose(data[1, :].labels, array([1]))
assert allclose(data[[0, 2]].labels, array([0, 2]))
assert allclose(data.flatten().labels, array([0, 1, 2, 3]))
x = [array([[0, 1],[2, 3]]), array([[4, 5], [6, 7]])]
data = img_fromlist(x, engine=eng).toseries()
data.labels = [[0, 1], [2, 3]]
assert allclose(data.filter(lambda x: x[0]>1).labels, array([2, 3]))
assert allclose(data[0].labels, array([[0, 1]]))
assert allclose(data[:, 0].labels, array([[0], [2]]))
assert allclose(data.flatten().labels, array([0, 1, 2, 3]))
def test_map(eng):
data = fromlist([array([1, 2]), array([3, 4])], engine=eng)
assert allclose(data.map(lambda x: x + 1).toarray(), [[2, 3], [4, 5]])
assert data.map(lambda x: 1.0*x, dtype=float64).dtype == float64
assert data.map(lambda x: 1.0*x).dtype == float64
def test_labels_setting(eng):
x = [array([0, 1]), array([2, 3]), array([4, 5]), array([6, 7])]
data = fromlist(x, engine=eng)
with pytest.raises(ValueError):
data.labels = [0, 1, 2]
def test_cov(eng):
mat1raw = asarray([[1, 2, 3], [4, 5, 6]])
mat1 = fromlist(mat1raw, engine=eng)
result = mat1.cov()
truth = cov(mat1raw.T)
assert allclose(result.toarray(), truth)
def test_fourier(eng):
data = fromlist([array([1.0, 2.0, -4.0, 5.0, 8.0, 3.0, 4.1, 0.9, 2.3])],
engine=eng)
vals = data.fourier(freq=2)
assert allclose(vals.select('coherence').toarray(), 0.578664)
assert allclose(vals.select('phase').toarray(), 4.102501)
def test_convolve(eng):
data = fromlist([array([1, 2, 3, 4, 5])], engine=eng)
sig = array([1, 2, 3])
betas = data.convolve(sig, mode='same')
assert allclose(betas.toarray(), array([4, 10, 16, 22, 22]))
def test_detrend(eng):
data = fromlist([array([1, 2, 3, 4, 5])], engine=eng)
out = data.detrend('linear')
assert (allclose(out.toarray(), array([1, 1, 1, 1, 1])))
def test_detrend(eng):
data = fromlist([array([1, 2, 3, 4, 5])], engine=eng)
out = data.detrend('linear')
assert(allclose(out.toarray(), array([1, 1, 1, 1, 1])))
def test_normalize_percentile(eng):
data = fromlist([array([1, 2, 3, 4, 5])], engine=eng)
out = data.normalize('percentile', perc=20)
vals = out.toarray()
assert str(vals.dtype) == 'float64'
assert allclose(vals, array([-0.42105, 0.10526, 0.63157, 1.15789, 1.68421]), atol=1e-3)
def test_map_singletons(eng):
data = fromlist([array([4, 5, 6, 7]), array([8, 9, 10, 11])], engine=eng)
mapped = data.map(lambda x: x.mean())
assert mapped.shape == (2, 1)
def test_std(eng):
data = fromlist([arange(8), arange(8)], engine=eng)
val = data.std().toarray()
expected = data.toarray().std(axis=0)
assert allclose(val, expected)
assert str(val.dtype) == 'float64'
def test_filter(eng):
data = fromlist([array([1, 2]), array([3, 4])], engine=eng)
assert allclose(data.filter(lambda x: x.sum() > 3).toarray(), [3, 4])
def test_clip(eng):
data = fromlist([array([1, 2, 3, 4, 5])], engine=eng)
assert allclose(data.clip(2).toarray(), [2, 2, 3, 4, 5])
assert allclose(data.clip(2, 3).toarray(), [2, 2, 3, 3, 3])
def test_between(eng):
data = fromlist([array([4, 5, 6, 7]), array([8, 9, 10, 11])], engine=eng)
val = data.between(0, 2)
assert allclose(val.index, array([0, 1]))
assert allclose(val.toarray(), array([[4, 5], [8, 9]]))
def test_map(eng):
data = fromlist([array([1, 2]), array([3, 4])], engine=eng)
assert allclose(data.map(lambda x: x + 1).toarray(), [[2, 3], [4, 5]])
assert data.map(lambda x: 1.0 * x, dtype=float64).dtype == float64
assert data.map(lambda x: 1.0 * x).dtype == float64
def test_normalize_percentile(eng):
data = fromlist([array([1, 2, 3, 4, 5])], engine=eng)
out = data.normalize('percentile', perc=20)
vals = out.toarray()
assert str(vals.dtype) == 'float64'
assert allclose(vals, array([-0.42105, 0.10526, 0.63157, 1.15789, 1.68421]), atol=1e-3)
def test_first(eng):
data = fromlist([array([4, 5, 6, 7]), array([8, 9, 10, 11])], engine=eng)
assert allclose(data.first(), [4, 5, 6, 7])
def test_between(eng):
data = fromlist([array([4, 5, 6, 7]), array([8, 9, 10, 11])], engine=eng)
val = data.between(0, 2)
assert allclose(val.index, array([0, 1]))
assert allclose(val.toarray(), array([[4, 5], [8, 9]]))
def test_normalize_mean(eng):
data = fromlist([array([1, 2, 3, 4, 5])], engine=eng)
vals = data.normalize('mean').toarray()
assert allclose(vals,
array([-0.64516, -0.32258, 0.0, 0.32258, 0.64516]),
atol=1e-3)
def test_normalize_mean(eng):
data = fromlist([array([1, 2, 3, 4, 5])], engine=eng)
vals = data.normalize('mean').toarray()
assert allclose(vals, array([-0.64516, -0.32258, 0.0, 0.32258, 0.64516]), atol=1e-3)
def test_gramian(eng):
mat1raw = asarray([[1, 2, 3], [4, 5, 6]])
mat1 = fromlist(mat1raw, engine=eng)
result = mat1.gramian()
truth = dot(mat1raw.T, mat1raw)
assert allclose(result.toarray(), truth)
def test_first(eng):
data = fromlist([array([4, 5, 6, 7]), array([8, 9, 10, 11])], engine=eng)
assert allclose(data.first(), [4, 5, 6, 7])
def test_cov(eng):
mat1raw = asarray([[1, 2, 3], [4, 5, 6]])
mat1 = fromlist(mat1raw, engine=eng)
result = mat1.cov()
truth = cov(mat1raw.T)
assert allclose(result.toarray(), truth)
def test_gramian(eng):
mat1raw = asarray([[1, 2, 3], [4, 5, 6]])
mat1 = fromlist(mat1raw, engine=eng)
result = mat1.gramian()
truth = dot(mat1raw.T, mat1raw)
assert allclose(result.toarray(), truth)
def test_fourier(eng):
data = fromlist([array([1.0, 2.0, -4.0, 5.0, 8.0, 3.0, 4.1, 0.9, 2.3])], engine=eng)
vals = data.fourier(freq=2)
assert allclose(vals.select('coherence').toarray(), 0.578664)
assert allclose(vals.select('phase').toarray(), 4.102501)
def test_downsample_uneven(eng):
data = fromlist([arange(9)], engine=eng)
vals = data.downsample(2).toarray()
assert allclose(vals, [0.5, 2.5, 4.5, 6.5])
def test_convolve(eng):
data = fromlist([array([1, 2, 3, 4, 5])], engine=eng)
sig = array([1, 2, 3])
betas = data.convolve(sig, mode='same')
assert allclose(betas.toarray(), array([4, 10, 16, 22, 22]))
def test_sample(eng):
data = fromlist([array([1, 5]), array([1, 10]), array([1, 15])], engine=eng)
assert allclose(data.sample(3).shape, (3, 2))
assert allclose(data.filter(lambda x: x.max() > 10).sample(1).toarray(), [1, 15])
def test_min(eng):
data = fromlist([arange(8), arange(8)], engine=eng)
val = data.min().toarray()
expected = data.toarray().min(axis=0)
assert allclose(val, expected)
