def fixed_fid_sum():
time_axis = numpy.arange(0, 0.512, 5e-4)
fid = basis.gaussian(time_axis, 0, 0,
50.0) + 0.00001 * (numpy.random.rand(1024) - 0.5)
fid2 = basis.gaussian(time_axis, 200, 0, 50.0)
return fid + fid2
def test_gaussian():
time_axis = numpy.arange(0, 0.512, 5e-4)
fid = basis.gaussian(time_axis, 0, 0, 10. / 1024 * 2000)
spectrum = numpy.fft.fft(fid)
numpy.testing.assert_almost_equal(numpy.amax(spectrum), 0.048099188669422144)
numpy.testing.assert_allclose(spectrum[5].real, 0.5 * numpy.amax(spectrum), rtol=0.05)
numpy.testing.assert_allclose(numpy.sum(spectrum), 0.512)
def test_gaussian():
time_axis = numpy.arange(0, 0.512, 5e-4)
fid = basis.gaussian(time_axis, 0, 0, 10. / 1024 * 2000)
spectrum = numpy.fft.fft(fid)
numpy.testing.assert_almost_equal(numpy.amax(spectrum),
0.048099188669422144)
numpy.testing.assert_allclose(spectrum[5].real,
0.5 * numpy.amax(spectrum),
rtol=0.05)
numpy.testing.assert_allclose(numpy.sum(spectrum), 0.512)
def test_gaussian():
time_axis = numpy.arange(0, 0.512, 5e-4)
fid = basis.gaussian(time_axis, 0, 0, 50.0)
data = MRSData(fid, 5e-4, 123)
model = singlet.Model({
"g1": {
"amplitude": 1.0,
"fwhm": 50.0,
"phase": "0",
"frequency": 0.0
}
})
fitting_result = model.fit(data)
numpy.testing.assert_allclose(fitting_result["params"]["g1"]["fwhm"], 50.0)
numpy.testing.assert_allclose(fitting_result["params"]["g1"]["amplitude"], 1.0)
numpy.testing.assert_allclose(fitting_result["params"]["g1"]["frequency"], 0.0, atol=1e-7)
numpy.testing.assert_allclose(fitting_result["fit"], fid)
def fixed_fid_sum():
time_axis = numpy.arange(0, 0.512, 5e-4)
fid = basis.gaussian(time_axis, 0, 0, 50.0) + 0.00001 * (numpy.random.rand(1024) - 0.5)
fid2 = basis.gaussian(time_axis, 200, 0, 50.0)
return fid + fid2
def correlated_peaks(single_peak):
fid2 = 0.67 * basis.gaussian(single_peak.time_axis(), 200, 0, 15.0)
return single_peak + fid2
def single_peak():
time_axis = np.arange(0, 0.512, 5e-4)
fid = basis.gaussian(time_axis, 0, 0,
10.0) + 0.00001 * (np.random.rand(1024) - 0.5)
return fid
