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7ffff7d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 | # Licensed under a 3-clause BSD style license - see LICENSE.rst
import pytest
import numpy as np
from astropy.utils import NumpyRNGContext
from astropy.visualization.interval import (ManualInterval, MinMaxInterval, PercentileInterval,
AsymmetricPercentileInterval, ZScaleInterval)
class TestInterval:
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_manual_zero_limit(self):
# Regression test for a bug that caused ManualInterval to compute the
# limit (min or max) if it was set to zero.
interval = ManualInterval(vmin=0, vmax=0)
vmin, vmax = interval.get_limits(self.data)
np.testing.assert_allclose(vmin, 0)
np.testing.assert_allclose(vmax, 0)
def test_manual_defaults_with_nan(self):
interval = ManualInterval()
data = np.copy(self.data)
data[0] = np.nan
vmin, vmax = interval.get_limits(self.data)
np.testing.assert_allclose(vmin, -20)
np.testing.assert_allclose(vmax, +60)
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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