def test_display_limits_are_reasonable_when_using_log_scale(self):
test_ranges = (((-0.1, 11.0), (0.0, 100.0)), ((1000.0, 1100.0),
(998.85, 1122.02)),
((1.0, 2.0), (0.99, 2.0)), ((3.0, 4.0), (2.98, 5.01)),
((2.0, 2.1), (2.0, 2.14)), ((0.0, 5.0), (0.05, 5.0)))
for data_in, data_out in test_ranges:
with self.subTest(data_in=data_in, data_out=data_out):
data = numpy.linspace(data_in[0],
data_in[1],
100,
endpoint=False)
data_style = "log"
calibrated_data_min, calibrated_data_max, y_ticker = LineGraphCanvasItem.calculate_y_axis(
[data], None, None, None, data_style)
axes = LineGraphCanvasItem.LineGraphAxes(1.0,
calibrated_data_min,
calibrated_data_max,
data_style=data_style,
y_ticker=y_ticker)
self.assertAlmostEqual(axes.uncalibrated_data_min,
data_out[0],
places=1)
self.assertAlmostEqual(axes.uncalibrated_data_max,
data_out[1],
places=1)
calibrated_data = axes.calculate_calibrated_xdata(
DataAndMetadata.new_data_and_metadata(data)).data
data[data <= 0] = numpy.nan
self.assertAlmostEqual(numpy.nanmin(calibrated_data),
math.log10(numpy.nanmin(data)))
self.assertAlmostEqual(numpy.nanmax(calibrated_data),
math.log10(numpy.nanmax(data)))
def test_display_limits_are_reasonable_when_using_log_scale(self):
data = numpy.linspace(-0.1, 10.0, 10)
data_style = "log"
calibrated_data_min, calibrated_data_max, y_ticker = LineGraphCanvasItem.calculate_y_axis(
[data], None, None, None, data_style)
axes = LineGraphCanvasItem.LineGraphAxes(1.0,
calibrated_data_min,
calibrated_data_max,
data_style=data_style,
y_ticker=y_ticker)
self.assertAlmostEqual(axes.uncalibrated_data_min, 1.0)
self.assertAlmostEqual(axes.uncalibrated_data_max, 10.0)
calibrated_data = axes.calculate_calibrated_xdata(
DataAndMetadata.new_data_and_metadata(data)).data
self.assertAlmostEqual(numpy.amin(calibrated_data), math.log10(1.0))
self.assertAlmostEqual(numpy.amax(calibrated_data), math.log10(10.0))
def test_data_values_give_pretty_limits_when_auto(self):
test_ranges = (
((7.46, 85.36), (0.0, 100.0)),
((7.67, 12.95), (0.0, 15.0)),
((6.67, 11.95), (0.0, 15.0)),
((0.00, 0.00), (-1.0, 1.0))
)
for data_in, data_out in test_ranges:
data_min, data_max = data_in
expected_uncalibrated_data_min, expected_uncalibrated_data_max = data_out
data = numpy.zeros((16, 16), dtype=numpy.float64)
irow, icol = numpy.ogrid[0:16, 0:16]
data[:] = data_min + (data_max - data_min) * (irow / 15.0)
# auto on min/max
calibrated_data_min, calibrated_data_max, y_ticker = LineGraphCanvasItem.calculate_y_axis([data], None, None, None, None)
axes = LineGraphCanvasItem.LineGraphAxes(1.0, calibrated_data_min, calibrated_data_max, y_ticker=y_ticker)
self.assertEqual(axes.uncalibrated_data_min, expected_uncalibrated_data_min)
self.assertEqual(axes.uncalibrated_data_max, expected_uncalibrated_data_max)
def test_display_limits_are_reasonable_when_using_calibrated_log_scale(
self):
intensity_calibration = Calibration.Calibration(-5, 2)
data = numpy.linspace(-0.1, 10.0, 10)
data_style = "log"
calibrated_data_min, calibrated_data_max, y_ticker = LineGraphCanvasItem.calculate_y_axis(
[data], None, None, intensity_calibration, data_style)
axes = LineGraphCanvasItem.LineGraphAxes(
1.0,
calibrated_data_min,
calibrated_data_max,
y_calibration=intensity_calibration,
data_style=data_style,
y_ticker=y_ticker)
self.assertAlmostEqual(axes.calibrated_data_min, 0.0)
self.assertAlmostEqual(axes.calibrated_data_max,
1.5) # empirically mesaured
calibrated_data = axes.calculate_calibrated_xdata(
DataAndMetadata.new_data_and_metadata(data)).data
self.assertAlmostEqual(numpy.amin(calibrated_data), math.log10(1.0))
self.assertAlmostEqual(numpy.amax(calibrated_data), math.log10(15.0))
