def testAutoscaleRange(self):
nan = numpy.nan
data_std_inside = numpy.array([0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2])
data_std_inside_nan = numpy.array([0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, numpy.nan])
data = [
# Positive values
(Colormap.LINEAR, Colormap.MINMAX, numpy.array([10, 20, 50]), (10, 50)),
(Colormap.LOGARITHM, Colormap.MINMAX, numpy.array([10, 50, 100]), (10, 100)),
(Colormap.LINEAR, Colormap.STDDEV3, data_std_inside, (0.026671473215424735, 1.9733285267845753)),
(Colormap.LOGARITHM, Colormap.STDDEV3, data_std_inside, (1, 1.6733506885453602)),
(Colormap.LINEAR, Colormap.STDDEV3, numpy.array([10, 100]), (10, 100)),
(Colormap.LOGARITHM, Colormap.STDDEV3, numpy.array([10, 100]), (10, 100)),
# With nan
(Colormap.LINEAR, Colormap.MINMAX, numpy.array([10, 20, 50, nan]), (10, 50)),
(Colormap.LOGARITHM, Colormap.MINMAX, numpy.array([10, 50, 100, nan]), (10, 100)),
(Colormap.LINEAR, Colormap.STDDEV3, data_std_inside_nan, (0.026671473215424735, 1.9733285267845753)),
(Colormap.LOGARITHM, Colormap.STDDEV3, data_std_inside_nan, (1, 1.6733506885453602)),
# With negative
(Colormap.LOGARITHM, Colormap.MINMAX, numpy.array([10, 50, 100, -50]), (10, 100)),
(Colormap.LOGARITHM, Colormap.STDDEV3, numpy.array([10, 100, -10]), (10, 100)),
]
for norm, mode, array, expectedRange in data:
with self.subTest(norm=norm, mode=mode, array=array):
colormap = Colormap()
colormap.setNormalization(norm)
colormap.setAutoscaleMode(mode)
vRange = colormap._computeAutoscaleRange(array)
if vRange is None:
self.assertIsNone(expectedRange)
else:
self.assertAlmostEqual(vRange[0], expectedRange[0])
self.assertAlmostEqual(vRange[1], expectedRange[1])
def testAutoscaleRange(self):
nan = numpy.nan
data = [
# Positive values
(Colormap.LINEAR, Colormap.MINMAX, numpy.array([10, 20, 50]), (10, 50)),
(Colormap.LOGARITHM, Colormap.MINMAX, numpy.array([10, 50, 100]), (10, 100)),
(Colormap.LINEAR, Colormap.STDDEV3, numpy.array([10, 100]), (-80, 190)),
(Colormap.LOGARITHM, Colormap.STDDEV3, numpy.array([10, 100]), (1, 1000)),
# With nan
(Colormap.LINEAR, Colormap.MINMAX, numpy.array([10, 20, 50, nan]), (10, 50)),
(Colormap.LOGARITHM, Colormap.MINMAX, numpy.array([10, 50, 100, nan]), (10, 100)),
(Colormap.LINEAR, Colormap.STDDEV3, numpy.array([10, 100, nan]), (-80, 190)),
(Colormap.LOGARITHM, Colormap.STDDEV3, numpy.array([10, 100, nan]), (1, 1000)),
# With negative
(Colormap.LOGARITHM, Colormap.MINMAX, numpy.array([10, 50, 100, -50]), (10, 100)),
(Colormap.LOGARITHM, Colormap.STDDEV3, numpy.array([10, 100, -10]), (1, 1000)),
]
for norm, mode, array, expectedRange in data:
with self.subTest(norm=norm, mode=mode, array=array):
colormap = Colormap()
colormap.setNormalization(norm)
colormap.setAutoscaleMode(mode)
vRange = colormap._computeAutoscaleRange(array)
if vRange is None:
self.assertIsNone(expectedRange)
else:
self.assertEqual(vRange[0], expectedRange[0])
self.assertEqual(vRange[1], expectedRange[1])
def testAutoscaleMode(self):
colormap = Colormap(autoscaleMode=Colormap.STDDEV3)
self.assertEqual(colormap.getAutoscaleMode(), Colormap.STDDEV3)
colormap.setAutoscaleMode(Colormap.MINMAX)
self.assertEqual(colormap.getAutoscaleMode(), Colormap.MINMAX)
