def test_empty_wavelengths(self):
spectrum = Spectrum(np.array([0.2, 0.3, 0.5]))
assert_array_equal(spectrum.wavelengths, [0, 1, 2])
spectrum = Spectrum(np.array([0.2, 0.3, 0.5]),
first_wavelength=5,
dispersion=2)
assert_array_equal(spectrum.wavelengths, [5, 7, 9])
def test_calibrate(self):
spectrum = Spectrum(np.array([0.2, 0.3, 0.5]), np.array([3, 4, 5]))
spectrum.calibrate(points=[{'x': 1, 'wavelength': 2}], dispersion=4)
assert_array_equal(spectrum.wavelengths, [-2., 2., 6.])
spectrum = Spectrum(np.array([0.2, 0.3, 0.5, 0.7]), np.array([3, 4,
5]))
spectrum.calibrate(points=[{
'x': 1,
'wavelength': 3.
}, {
'x': 2,
'wavelength': 9.
}])
assert_array_almost_equal(spectrum.wavelengths, [-3., 3., 9., 15.])
def execute_operation(self):
max_wavelengths = lambda operands: np.arange(max([o[0].wavelengths[0] for o in operands]), min([o[0].wavelengths[-1] for o in operands]))
datasets = lambda operands, wavelengths: [PlotsMath.__data(o[1], wavelengths) for o in operands]
operands = [(self.operands_model.item(a).data(PlotsMath.FITS_SPECTRUM), self.operands_model.item(a).data(PlotsMath.F_X)) for a in np.arange(self.operands_model.rowCount())]
def divide(operands):
if len(operands) > 2:
print("Division supports only 2 operands, truncating")
wavelengths = max_wavelengths(operands[0:2])
datas = datasets(operands[0:2], wavelengths)
return (wavelengths, datas[0]/datas[1])
def mean(operands):
wavelengths = max_wavelengths(operands)
mean_data = np.zeros(wavelengths.shape)
for data in datasets(operands, wavelengths):
mean_data += data
return (wavelengths, mean_data/len(wavelengths))
operations = { 0: divide, 1: mean }
try:
wavelengths, data = operations[self.ui.operation_type.currentIndex()](operands)
self.spectrum = Spectrum(data, wavelengths)
self.spectrum.normalize_to_max()
self.undo.set_spectrum(self.spectrum)
self.ui.spline_degrees.setValue(5)
self.ui.spline_factor.setValue(0)
self.ui.spline_factor_auto.setChecked(False)
self.draw()
except IndexError:
QMessageBox.warning(None, "Error", "Datasets are not compatible. Maybe you need to calibrate better, or use a different reference file")
def test_get_index_from_wavelength(self):
spectrum = Spectrum(np.array([0.2, 0.3, 0.5, 0.7]),
np.array([3, 6, 9, 12]))
self.assertEqual(1, spectrum.wavelength_index(6))
self.assertEqual(0, spectrum.wavelength_index(5))
self.assertEqual(0, spectrum.wavelength_index(3))
self.assertEqual(2, spectrum.wavelength_index(9))
self.assertEqual(3, spectrum.wavelength_index(12))
def test_cut(self):
spectrum = Spectrum(np.array([0.2, 0.3, 0.5, 0.7]),
np.array([3, 6, 9, 12]))
spectrum.cut(start=1)
assert_array_equal(spectrum.wavelengths, [6, 9, 12])
assert_array_equal(spectrum.fluxes, [0.3, 0.5, 0.7])
spectrum = Spectrum(np.array([0.2, 0.3, 0.5, 0.7]),
np.array([3, 6, 9, 12]))
spectrum.cut(end=2)
assert_array_equal(spectrum.wavelengths, [3, 6, 9])
assert_array_equal(spectrum.fluxes, [0.2, 0.3, 0.5])
spectrum = Spectrum(np.array([0.2, 0.3, 0.5, 0.7]),
np.array([3, 6, 9, 12]))
spectrum.cut(start=1, end=2)
assert_array_equal(spectrum.wavelengths, [6, 9])
assert_array_equal(spectrum.fluxes, [0.3, 0.5])
def test_calc_dispersion(self):
spectrum = Spectrum(np.array([0.2, 0.3, 0.5]), np.array([0, 1, 2]))
self.assertEqual(spectrum.dispersion(), 1)
spectrum = Spectrum(np.array([0.2, 0.3, 0.5]), np.array([0, 2.5, 5]))
self.assertEqual(spectrum.dispersion(), 2.5)
