def test_masked_1d_single(self):
data = ma.arange(11)
data[3:7] = ma.masked
actual = PERCENTILE.aggregate(data, axis=0, percent=50)
expected = 7
self.assertTupleEqual(actual.shape, ())
self.assertEqual(actual, expected)
def test_2d_single(self):
shape = (2, 11)
data = np.arange(np.prod(shape)).reshape(shape)
actual = PERCENTILE.aggregate(data, axis=0, percent=50)
self.assertTupleEqual(actual.shape, shape[-1:])
expected = np.arange(shape[-1]) + 5.5
self.assertArrayEqual(actual, expected)
def test_1d_multi(self):
data = np.arange(11)
percent = np.array([20, 50, 90])
actual = PERCENTILE.aggregate(data, axis=0, percent=percent)
expected = [2, 5, 9]
self.assertTupleEqual(actual.shape, percent.shape)
self.assertArrayEqual(actual, expected)
def test_masked_1d_single(self):
data = ma.arange(11)
data[3:7] = ma.masked
actual = PERCENTILE.aggregate(data, axis=0, percent=50)
expected = 7
self.assertTupleEqual(actual.shape, ())
self.assertEqual(actual, expected)
def test_1d_multi(self):
data = np.arange(11)
percent = np.array([20, 50, 90])
actual = PERCENTILE.aggregate(data, axis=0, percent=percent)
expected = [2, 5, 9]
self.assertTupleEqual(actual.shape, percent.shape)
self.assertArrayEqual(actual, expected)
def test_2d_single(self):
shape = (2, 11)
data = np.arange(np.prod(shape)).reshape(shape)
actual = PERCENTILE.aggregate(data, axis=0, percent=50)
self.assertTupleEqual(actual.shape, shape[-1:])
expected = np.arange(shape[-1]) + 5.5
self.assertArrayEqual(actual, expected)
def test_masked_1d_multi(self):
data = ma.arange(11)
data[3:9] = ma.masked
percent = np.array([25, 50, 75])
actual = PERCENTILE.aggregate(data, axis=0, percent=percent)
expected = [1, 2, 9]
self.assertTupleEqual(actual.shape, percent.shape)
self.assertArrayEqual(actual, expected)
def test_masked_1d_multi(self):
data = ma.arange(11)
data[3:9] = ma.masked
percent = np.array([25, 50, 75])
actual = PERCENTILE.aggregate(data, axis=0, percent=percent)
expected = [1, 2, 9]
self.assertTupleEqual(actual.shape, percent.shape)
self.assertArrayEqual(actual, expected)
def test_2d_multi(self):
shape = (2, 10)
data = np.arange(np.prod(shape)).reshape(shape)
percent = np.array([10, 50, 90, 100])
actual = PERCENTILE.aggregate(data, axis=0, percent=percent)
self.assertTupleEqual(actual.shape, (shape[-1], percent.size))
expected = np.tile(np.arange(shape[-1]), percent.size)
expected = expected.reshape(percent.size, shape[-1]).T + 1
expected = expected + (percent / 10 - 1)
self.assertArrayAlmostEqual(actual, expected)
def test_2d_multi(self):
shape = (2, 10)
data = np.arange(np.prod(shape)).reshape(shape)
percent = np.array([10, 50, 90, 100])
actual = PERCENTILE.aggregate(data, axis=0, percent=percent)
self.assertTupleEqual(actual.shape, (shape[-1], percent.size))
expected = np.tile(np.arange(shape[-1]), percent.size)
expected = expected.reshape(percent.size, shape[-1]).T + 1
expected = expected + (percent / 10 - 1)
self.assertArrayAlmostEqual(actual, expected)
def test_masked_2d_single(self):
shape = (2, 11)
data = ma.arange(np.prod(shape)).reshape(shape)
data[0, ::2] = ma.masked
data[1, 1::2] = ma.masked
actual = PERCENTILE.aggregate(data, axis=0, percent=50)
self.assertTupleEqual(actual.shape, shape[-1:])
expected = np.empty(shape[-1:])
expected[1::2] = data[0, 1::2]
expected[::2] = data[1, ::2]
self.assertArrayEqual(actual, expected)
def test_masked_2d_multi(self):
shape = (3, 10)
data = ma.arange(np.prod(shape)).reshape(shape)
data[1] = ma.masked
percent = np.array([10, 50, 70, 80])
actual = PERCENTILE.aggregate(data, axis=0, percent=percent)
self.assertTupleEqual(actual.shape, (shape[-1], percent.size))
expected = np.tile(np.arange(shape[-1]), percent.size)
expected = expected.reshape(percent.size, shape[-1]).T
expected = expected + (percent / 10 * 2)
self.assertArrayAlmostEqual(actual, expected)
def test_masked_2d_multi(self):
shape = (3, 10)
data = ma.arange(np.prod(shape)).reshape(shape)
data[1] = ma.masked
percent = np.array([10, 50, 70, 80])
actual = PERCENTILE.aggregate(data, axis=0, percent=percent)
self.assertTupleEqual(actual.shape, (shape[-1], percent.size))
expected = np.tile(np.arange(shape[-1]), percent.size)
expected = expected.reshape(percent.size, shape[-1]).T
expected = expected + (percent / 10 * 2)
self.assertArrayAlmostEqual(actual, expected)
def test_masked_2d_single(self):
shape = (2, 11)
data = ma.arange(np.prod(shape)).reshape(shape)
data[0, ::2] = ma.masked
data[1, 1::2] = ma.masked
actual = PERCENTILE.aggregate(data, axis=0, percent=50)
self.assertTupleEqual(actual.shape, shape[-1:])
expected = np.empty(shape[-1:])
expected[1::2] = data[0, 1::2]
expected[::2] = data[1, ::2]
self.assertArrayEqual(actual, expected)
def test_missing_mandatory_kwarg(self):
emsg = "percentile aggregator requires .* keyword argument 'percent'"
with self.assertRaisesRegexp(ValueError, emsg):
PERCENTILE.aggregate_shape()
with self.assertRaisesRegexp(ValueError, emsg):
kwargs = dict()
PERCENTILE.aggregate_shape(**kwargs)
with self.assertRaisesRegexp(ValueError, emsg):
kwargs = dict(point=10)
PERCENTILE.aggregate_shape(**kwargs)
def test_missing_mandatory_kwarg(self):
emsg = "percentile aggregator requires .* keyword argument 'percent'"
with self.assertRaisesRegexp(ValueError, emsg):
PERCENTILE.aggregate_shape()
with self.assertRaisesRegexp(ValueError, emsg):
kwargs = dict()
PERCENTILE.aggregate_shape(**kwargs)
with self.assertRaisesRegexp(ValueError, emsg):
kwargs = dict(point=10)
PERCENTILE.aggregate_shape(**kwargs)
def test_mandatory_kwarg_no_shape(self):
kwargs = dict(percent=50)
self.assertTupleEqual(PERCENTILE.aggregate_shape(**kwargs), ())
kwargs = dict(percent=[50])
self.assertTupleEqual(PERCENTILE.aggregate_shape(**kwargs), ())
def test_mandatory_kwarg_shape(self):
kwargs = dict(percent=(10, 20))
self.assertTupleEqual(PERCENTILE.aggregate_shape(**kwargs), (2,))
kwargs = dict(percent=list(range(13)))
self.assertTupleEqual(PERCENTILE.aggregate_shape(**kwargs), (13,))
def test_missing_mandatory_kwarg(self):
emsg = "percentile aggregator requires .* keyword argument 'percent'"
with self.assertRaisesRegexp(ValueError, emsg):
PERCENTILE.aggregate('dummy', axis=0)
def test_1d_single(self):
data = np.arange(11)
actual = PERCENTILE.aggregate(data, axis=0, percent=50)
expected = 5
self.assertTupleEqual(actual.shape, ())
self.assertEqual(actual, expected)
def test_missing_mandatory_kwarg(self):
emsg = "percentile aggregator requires .* keyword argument 'percent'"
with self.assertRaisesRegexp(ValueError, emsg):
PERCENTILE.aggregate("dummy", axis=0)
def test_mandatory_kwarg_shape(self):
kwargs = dict(percent=(10, 20))
self.assertTupleEqual(PERCENTILE.aggregate_shape(**kwargs), (2,))
kwargs = dict(percent=list(range(13)))
self.assertTupleEqual(PERCENTILE.aggregate_shape(**kwargs), (13,))
def test_mandatory_kwarg_no_shape(self):
kwargs = dict(percent=50)
self.assertTupleEqual(PERCENTILE.aggregate_shape(**kwargs), ())
kwargs = dict(percent=[50])
self.assertTupleEqual(PERCENTILE.aggregate_shape(**kwargs), ())
def test(self):
self.assertEqual(PERCENTILE.name(), "percentile")
def test(self):
self.assertEqual(PERCENTILE.name(), 'percentile')
def test_1d_single(self):
data = np.arange(11)
actual = PERCENTILE.aggregate(data, axis=0, percent=50)
expected = 5
self.assertTupleEqual(actual.shape, ())
self.assertEqual(actual, expected)
