def test_flipping_analysis(self):
# this test is based on an experiment with a known
# added detuning in the amplitude. The test tests that the analysis
# works for a range of known scale factors.
# 20% detuning only works for coarse
self._check_scaling('20170726_164507', 0.8, 1)
self._check_scaling('20170726_164536', 0.9, 1)
self._check_scaling('20170726_164550', 0.9, 1)
self._check_scaling('20170726_164605', 0.95, 2)
self._check_scaling('20170726_164619', 0.95, 2)
self._check_scaling('20170726_164635', 0.99, 2)
self._check_scaling('20170726_164649', 0.99, 2)
self._check_scaling('20170726_164704', 1, 2)
self._check_scaling('20170726_164718', 1, 2)
self._check_scaling('20170726_164733', 1.01, 2)
self._check_scaling('20170726_164747', 1.01, 2)
self._check_scaling('20170726_164802', 1.05, 1)
self._check_scaling('20170726_164816', 1.05, 1)
self._check_scaling('20170726_164831', 1.1, 1)
self._check_scaling('20170726_164845', 1.1, 1)
# 20% detuning only works for coarse
self._check_scaling('20170726_164901', 1.2, 1)
# Test running it once with showing the initial fit
ma.FlippingAnalysis(t_start='20170726_164901',
options_dict={'plot_init': True})
def _check_scaling(self, timestamp, known_detuning, places):
a = ma.FlippingAnalysis(t_start=timestamp)
s = a.get_scale_factor()
self.assertAlmostEqual(s * known_detuning, 1, places=places)
print('Scale factor {:.4f} known detuning {:.4f}'.format(
s, known_detuning))