T1 constant calculation #4290
T1 constant calculation #4290
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| bad_fit = cirq.experiments.T1DecayResult( | ||
| data=pd.DataFrame( | ||
| columns=['delay_ns', 'false_count', 'true_count'], | ||
| index=range(4), | ||
| data=[ | ||
| [100.0, 10, 0], | ||
| [400.0, 10, 0], | ||
| [700.0, 10, 0], | ||
| [1000.0, 10, 0], | ||
| ], | ||
| ) | ||
| ) | ||
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| try: | ||
| bad_fit.plot(include_fit=True) | ||
| assert False | ||
| except RuntimeWarning as warning: | ||
| assert warning is not None |
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It might be a good idea to split this error case off into a separate test method. You can also make use of
with pytest.raises(..., match='whatever the error string is'):
... something that breaks...to verify an error gets raised in the test.
| @@ -165,6 +167,72 @@ def test_all_off_results(): | |||
| ) | |||
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| def test_constant(): | |||
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It might be worth looking into parameterizing this test with @pytest.mark.parameterize so that you can remove some of the code duplication between the 100 and 400 cases, and just turn the relevant values between these two results into parameters for the test. Here's a quick example: https://github.com/quantumlib/Cirq/blob/master/cirq-core/cirq/ops/common_gates_test.py#L60
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Thanks for this suggestion! I've added this to the test, along with the explicitly exponential data from your other comment.
| good_fit.plot(include_fit=True) | ||
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| def test_curve_fit_plot_error(): |
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Might want to indicate this is a warn and not an error.
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thanks for catching this!
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Performs a curve fit to calculate t1 decay constant, includes option to plot curve fit Addresses Issue [quantumlib#4264](quantumlib#4264)
Performs a curve fit to calculate t1 decay constant, includes option to plot curve fit Addresses Issue [quantumlib#4264](quantumlib#4264)
Performs a curve fit to calculate t1 decay constant, includes option to plot curve fit
Addresses Issue #4264