def test_find_min_max_I_simple_1D_case(self):
"""
test _find_min_max_I for a 1D case, it finds the minimum
and maximum value to plot for a given McStasData set
"""
dummy_data = get_dummy_McStasData_1d()
found_min, found_max = _find_min_max_I(dummy_data)
# np.arange(20) + 5: min = 5, max = 5+19 = 24
self.assertEqual(found_min, 5)
self.assertEqual(found_max, 19 + 5)
def test_find_min_max_I_cut_min_1D_case(self):
"""
test _find_min_max_I for a 1D case, it finds the minimum
and maximum value to plot for a given McStasData set.
Here cut_min is used to limit the minimum plotted.
"""
dummy_data = get_dummy_McStasData_1d()
dummy_data.set_plot_options(cut_min=0.2)
found_min, found_max = _find_min_max_I(dummy_data)
# np.arange(20) + 5: min = 5, max = 5+19 = 24
self.assertEqual(found_min, 5 + (24 - 5) * 0.2)
self.assertEqual(found_max, 19 + 5)
def test_find_min_max_I_log_orders_of_mag_2D_case(self):
"""
test _find_min_max_I for a 1D case, it finds the minimum
and maximum value to plot for a given McStasData set.
Here orders_of_mag is used to limit the minimum plotted
while log mode is enabled.
"""
dummy_data = get_dummy_McStasData_2d()
dummy_data.Intensity[2, 2] = 10**6
dummy_data.set_plot_options(log=True, orders_of_mag=3)
found_min, found_max = _find_min_max_I(dummy_data)
self.assertAlmostEqual(found_min, 10**3)
self.assertAlmostEqual(found_max, 10**6)
def test_find_min_max_I_log_cut_max_2D_case(self):
"""
test _find_min_max_I for a 1D case, it finds the minimum
and maximum value to plot for a given McStasData set.
Here cut_max is used to limit the maximum plotted while
log mode is enabled.
"""
dummy_data = get_dummy_McStasData_2d()
dummy_data.set_plot_options(cut_max=0.8, log=True)
found_min, found_max = _find_min_max_I(dummy_data)
# np.arange(20) + 5: min = 5, max = 5+19 = 24
self.assertAlmostEqual(found_min, 5)
self.assertAlmostEqual(found_max, (19 + 5) * 0.8)
def test_find_min_max_I_fail_case(self):
"""
test _find_min_max_I for a 1D case, it finds the minimum
and maximum value to plot for a given McStasData set.
Here orders_of_mag is used to limit the minimum plotted
while log mode is enabled. A bin in the data contains
zero intensity, which should be ignored.
"""
dummy_data = get_dummy_McStasData_2d()
dummy_data.Intensity = np.zeros((5, 5))
dummy_data.set_plot_options(log=True, orders_of_mag=3)
found_min, found_max = _find_min_max_I(dummy_data)
self.assertEqual(found_min, 0)
self.assertEqual(found_max, 0)
def test_find_min_max_I_log_with_zero_2D_case(self):
"""
test _find_min_max_I for a 1D case, it finds the minimum
and maximum value to plot for a given McStasData set.
Here a bin contains zero intensity and log mode is enabled,
since log(0) is not allowed, this data point should be
ignored.
"""
dummy_data = get_dummy_McStasData_2d()
dummy_data.Intensity[2, 2] = 0
dummy_data.set_plot_options(log=True)
found_min, found_max = _find_min_max_I(dummy_data)
# np.arange(20) + 5: min = 5, max = 5+19 = 24
self.assertAlmostEqual(found_min, 5)
self.assertAlmostEqual(found_max, 19 + 5)
def test_find_min_max_I_log_orders_of_mag_1D_with_zero_case(self):
"""
test _find_min_max_I for a 1D case, it finds the minimum
and maximum value to plot for a given McStasData set.
Here orders_of_mag is used to limit the minimum plotted
while log mode is enabled. A bin in the data contains
zero intensity, which should be ignored.
"""
dummy_data = get_dummy_McStasData_1d()
dummy_data.Intensity[5] = 10**6
dummy_data.Intensity[6] = 0
dummy_data.set_plot_options(log=True, orders_of_mag=3)
found_min, found_max = _find_min_max_I(dummy_data)
self.assertAlmostEqual(found_min, 10**3)
self.assertAlmostEqual(found_max, 10**6)
