def test_multiply_with_scalar_operator(self):
x = np.linspace(0, 10, 100)
spectrum1 = 2 * Pattern(x, np.sin(x))
spectrum2 = 2 * Pattern(x, np.sin(x))
self.assertTrue(np.array_equal(spectrum2.y, np.sin(x) * 2))
def test_background_out_of_range_throws_error(self):
x1 = np.linspace(0, 10)
x2 = np.linspace(-10, -1)
spec = Pattern(x1, x1)
background_spectrum = Pattern(x2, x2)
with self.assertRaises(BkgNotInRangeError):
spec.background_pattern = background_spectrum
def test_using_background_spectrum(self):
x = np.linspace(-5, 5, 100)
spec_y = x**2
bkg_y = x
spec = Pattern(x, spec_y)
background_spectrum = Pattern(x, bkg_y)
spec.background_pattern = background_spectrum
new_x, new_y = spec.data
self.array_almost_equal(new_x, x)
self.array_almost_equal(new_y, spec_y - bkg_y)
def test_using_background_spectrum_with_different_spacing(self):
x = np.linspace(-5, 5, 100)
spec_y = x**2
x_bkg = np.linspace(-5, 5, 99)
bkg_y = x_bkg
spec = Pattern(x, spec_y)
background_spectrum = Pattern(x_bkg, bkg_y)
spec.background_pattern = background_spectrum
new_x, new_y = spec.data
self.array_almost_equal(new_x, x)
self.array_almost_equal(new_y, spec_y - x)
def test_saving_a_file(self):
x = np.linspace(-5, 5, 100)
y = x**2
spec = Pattern(x, y)
filename = os.path.join(data_path, "test.dat")
spec.save(filename)
spec2 = Pattern()
spec2.load(filename)
spec2_x, spec2_y = spec2.data
self.array_almost_equal(spec2_x, x)
self.array_almost_equal(spec2_y, y)
os.remove(filename)
def test_automatic_background_subtraction_with_roi(self):
x = np.linspace(0, 24, 2500)
y = np.zeros(x.shape)
peaks = [
[10, 3, 0.1],
[12, 4, 0.1],
[12, 6, 0.1],
]
for peak in peaks:
y += gaussian(x, peak[0], peak[1], peak[2])
y_bkg = x * 0.4 + 5.0
y_measurement = y + y_bkg
roi = [1, 23]
pattern = Pattern(x, y_measurement)
auto_background_subtraction_parameters = [2, 50, 50]
pattern.set_auto_background_subtraction(
auto_background_subtraction_parameters, roi)
x_spec, y_spec = pattern.data
self.assertGreater(x_spec[0], roi[0])
self.assertLess(x_spec[-1], roi[1])
def add_overlay_file(self, filename):
"""
Reads a 2-column (x,y) text file and adds it as overlay to the list of overlays
:param filename: path of the file to be loaded
"""
self.overlays.append(Pattern())
self.overlays[-1].load(filename)
self.overlay_added.emit()
def add_overlay(self, x, y, name=''):
"""
Adds an overlay to the list of overlays
:param x: x-values
:param y: y-values
:param name: name of overlay to be used for displaying etc.
"""
self.overlays.append(Pattern(x, y, name))
self.overlay_added.emit()
def test_plus_and_minus_operators_with_different_shapes(self):
x = np.linspace(0, 10, 1000)
x2 = np.linspace(0, 12, 1300)
spectrum1 = Pattern(x, np.sin(x))
spectrum2 = Pattern(x2, np.sin(x2))
spectrum3 = spectrum1 + spectrum2
self.array_almost_equal(spectrum3.x, spectrum1._original_x)
self.array_almost_equal(spectrum3.y, spectrum1._original_y * 2, 2)
spectrum3 = spectrum1 + spectrum1
self.array_almost_equal(spectrum3.y, np.sin(x) * 2, 2)
spectrum3 = spectrum1 - spectrum2
self.array_almost_equal(spectrum3.y, np.sin(x) * 0, 2)
spectrum3 = spectrum1 - spectrum1
self.array_almost_equal(spectrum3.y, np.sin(x) * 0, 2)
def test_loading_chi_file(self):
spec = Pattern()
x, y = spec.data
spec.load(os.path.join(data_path, 'spectrum_001.chi'))
new_x, new_y = spec.data
self.assertNotEqual(len(x), len(new_x))
self.assertNotEqual(len(y), len(new_y))
def test_setting_new_data(self):
spec = Pattern()
x = np.linspace(0, 10)
y = np.sin(x)
spec.data = x, y
new_x, new_y = spec.data
self.array_almost_equal(new_x, x)
self.array_almost_equal(new_y, y)
def add_spectrum_as_overlay(self):
"""
Adds the current data spectrum as overlay to the list of overlays
"""
current_spectrum = deepcopy(self.pattern)
overlay_spectrum = Pattern(current_spectrum.x, current_spectrum.y,
current_spectrum.name)
self.overlays.append(overlay_spectrum)
self.overlay_added.emit()
def __init__(self):
super(PatternModel, self).__init__()
self.pattern = Pattern()
self.overlays = []
self.phases = []
self.file_iteration_mode = 'number'
self.file_name_iterator = FileNameIterator()
self.bkg_ind = -1
self.pattern_filename = ''
def test_plus_and_minus_operators(self):
x = np.linspace(0, 10, 100)
spectrum1 = Pattern(x, np.sin(x))
spectrum2 = Pattern(x, np.sin(x))
spectrum3 = spectrum1 + spectrum2
self.assertTrue(np.array_equal(spectrum3.y, np.sin(x) * 2))
self.assertTrue(np.array_equal(spectrum2.original_y, np.sin(x) * 1))
self.assertTrue(np.array_equal(spectrum1.original_y, np.sin(x) * 1))
spectrum3 = spectrum1 + spectrum1
self.assertTrue(np.array_equal(spectrum3.y, np.sin(x) * 2))
self.assertTrue(np.array_equal(spectrum1.original_y, np.sin(x) * 1))
self.assertTrue(np.array_equal(spectrum1.original_y, np.sin(x) * 1))
spectrum3 = spectrum2 - spectrum1
self.assertTrue(np.array_equal(spectrum3.y, np.sin(x) * 0))
self.assertTrue(np.array_equal(spectrum2.original_y, np.sin(x) * 1))
self.assertTrue(np.array_equal(spectrum1.original_y, np.sin(x) * 1))
spectrum3 = spectrum1 - spectrum1
self.assertTrue(np.array_equal(spectrum3.y, np.sin(x) * 0))
self.assertTrue(np.array_equal(spectrum1.original_y, np.sin(x) * 1))
self.assertTrue(np.array_equal(spectrum1.original_y, np.sin(x) * 1))
def test_automatic_background_subtraction(self):
x = np.linspace(0, 24, 2500)
y = np.zeros(x.shape)
peaks = [
[10, 3, 0.1],
[12, 4, 0.1],
[12, 6, 0.1],
]
for peak in peaks:
y += gaussian(x, peak[0], peak[1], peak[2])
y_bkg = x * 0.4 + 5.0
y_measurement = y + y_bkg
pattern = Pattern(x, y_measurement)
auto_background_subtraction_parameters = [2, 50, 50]
pattern.set_auto_background_subtraction(
auto_background_subtraction_parameters)
x_spec, y_spec = pattern.data
self.array_almost_equal(y_spec, y)
def test_loading_invalid_file(self):
spec = Pattern()
self.assertEqual(
-1, spec.load(os.path.join(data_path, 'wrong_file_format.txt')))
def test_using_len(self):
x = np.linspace(0, 10, 234)
y = x**2
spec = Pattern(x, y)
self.assertEqual(len(spec), 234)