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# Licensed under a 3-clause BSD style license - see LICENSE.rst
import numpy as np
from ...tests.helper import pytest
from ...utils import NumpyRNGContext
from ..interval import (ManualInterval, MinMaxInterval, PercentileInterval,
AsymmetricPercentileInterval, ZScaleInterval)
class TestInterval(object):
data = np.linspace(-20., 60., 100)
def test_manual(self):
interval = ManualInterval(-10., +15.)
vmin, vmax = interval.get_limits(self.data)
np.testing.assert_allclose(vmin, -10.)
np.testing.assert_allclose(vmax, +15.)
def test_manual_defaults(self):
interval = ManualInterval(vmin=-10.)
vmin, vmax = interval.get_limits(self.data)
np.testing.assert_allclose(vmin, -10.)
np.testing.assert_allclose(vmax, np.max(self.data))
interval = ManualInterval(vmax=15.)
vmin, vmax = interval.get_limits(self.data)
np.testing.assert_allclose(vmin, np.min(self.data))
np.testing.assert_allclose(vmax, 15.)
def test_minmax(self):
interval = MinMaxInterval()
vmin, vmax = interval.get_limits(self.data)
np.testing.assert_allclose(vmin, -20.)
np.testing.assert_allclose(vmax, +60.)
def test_percentile(self):
interval = PercentileInterval(62.2)
vmin, vmax = interval.get_limits(self.data)
np.testing.assert_allclose(vmin, -4.88)
np.testing.assert_allclose(vmax, 44.88)
def test_asymmetric_percentile(self):
interval = AsymmetricPercentileInterval(10.5, 70.5)
vmin, vmax = interval.get_limits(self.data)
np.testing.assert_allclose(vmin, -11.6)
np.testing.assert_allclose(vmax, 36.4)
def test_asymmetric_percentile_nsamples(self):
with NumpyRNGContext(12345):
interval = AsymmetricPercentileInterval(10.5, 70.5, n_samples=20)
vmin, vmax = interval.get_limits(self.data)
np.testing.assert_allclose(vmin, -14.367676767676768)
np.testing.assert_allclose(vmax, 40.266666666666666)
class TestIntervalList(TestInterval):
# Make sure intervals work with lists
data = np.linspace(-20., 60., 100).tolist()
class TestInterval2D(TestInterval):
# Make sure intervals work with 2d arrays
data = np.linspace(-20., 60., 100).reshape(100, 1)
def test_zscale():
np.random.seed(42)
data = np.random.randn(100, 100) * 5 + 10
interval = ZScaleInterval()
vmin, vmax = interval.get_limits(data)
np.testing.assert_allclose(vmin, -9.6, atol=0.1)
np.testing.assert_allclose(vmax, 25.4, atol=0.1)
data = list(range(1000)) + [np.nan]
interval = ZScaleInterval()
vmin, vmax = interval.get_limits(data)
np.testing.assert_allclose(vmin, 0, atol=0.1)
np.testing.assert_allclose(vmax, 999, atol=0.1)
data = list(range(100))
interval = ZScaleInterval()
vmin, vmax = interval.get_limits(data)
np.testing.assert_allclose(vmin, 0, atol=0.1)
np.testing.assert_allclose(vmax, 99, atol=0.1)
def test_integers():
# Need to make sure integers get cast to float
interval = MinMaxInterval()
values = interval([1, 3, 4, 5, 6])
np.testing.assert_allclose(values, [0., 0.4, 0.6, 0.8, 1.0])
# Don't accept integer array in output
out = np.zeros(5, dtype=int)
with pytest.raises(TypeError) as exc:
values = interval([1, 3, 4, 5, 6], out=out)
assert exc.value.args[0] == ("Can only do in-place scaling for "
"floating-point arrays")
# But integer input and floating point output is fine
out = np.zeros(5, dtype=float)
interval([1, 3, 4, 5, 6], out=out)
np.testing.assert_allclose(out, [0., 0.4, 0.6, 0.8, 1.0])
def test_constant_data():
"""Test intervals with constant data (avoiding divide-by-zero)."""
shape = (10, 10)
data = np.ones(shape)
interval = MinMaxInterval()
limits = interval.get_limits(data)
values = interval(data)
np.testing.assert_allclose(limits, (1., 1.))
np.testing.assert_allclose(values, np.zeros(shape))
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