Flaky test `test/sample/test_sample.py::test_samples_cis`

See https://github.com/ICB-DCM/pyPESTO/runs/7522438577?check_suite_focus=true

_______________________________ test_samples_cis _______________________________

    def test_samples_cis():
        """
        Test whether :py:func:`pypesto.sample.calculate_ci_mcmc_sample` produces
        percentile-based credibility intervals correctly.
        """
        # load problem
        problem = gaussian_problem()
    
        # set a sampler
        sampler = sample.MetropolisSampler(
            options={
                'show_progress': False,
            },
        )
    
        # optimization
        result = optimize.minimize(
            problem=problem,
            n_starts=3,
            filename=None,
            progress_bar=False,
        )
    
        # sample
        result = sample.sample(
            problem=problem,
            sampler=sampler,
            n_samples=2000,
            result=result,
            filename=None,
        )
    
        # run geweke test
        sample.geweke_test(result)
    
        # get converged chain
        converged_chain = np.asarray(
            result.sample_result.trace_x[0, result.sample_result.burn_in :, :]
        )
    
        # set confidence levels
        alpha_values = [0.99, 0.95, 0.68]
    
        # loop over confidence levels
        for alpha in alpha_values:
            # calculate parameter samples confidence intervals
>           lb, ub = sample.calculate_ci_mcmc_sample(result, ci_level=alpha)

test/sample/test_sample.py:718: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
pypesto/sample/util.py:46: in calculate_ci_mcmc_sample
    lb, ub = calculate_ci(chain, ci_level=ci_level, axis=0)
pypesto/sample/util.py:96: in calculate_ci
    lb, ub = np.percentile(values, percentiles, **kwargs)
<__array_function__ internals>:180: in percentile
    ???
.tox/base/lib/python3.8/site-packages/numpy/lib/function_base.py:4134: in percentile
    return _quantile_unchecked(
.tox/base/lib/python3.8/site-packages/numpy/lib/function_base.py:4383: in _quantile_unchecked
    r, k = _ureduce(a,
.tox/base/lib/python3.8/site-packages/numpy/lib/function_base.py:3702: in _ureduce
    r = func(a, **kwargs)
.tox/base/lib/python3.8/site-packages/numpy/lib/function_base.py:4552: in _quantile_ureduce_func
    result = _quantile(arr,
.tox/base/lib/python3.8/site-packages/numpy/lib/function_base.py:4658: in _quantile
    take(arr, indices=-1, axis=DATA_AXIS)
<__array_function__ internals>:180: in take
    ???
.tox/base/lib/python3.8/site-packages/numpy/core/fromnumeric.py:190: in take
    return _wrapfunc(a, 'take', indices, axis=axis, out=out, mode=mode)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

obj = array([], shape=(0, 1), dtype=float64), method = 'take', args = (-1,)
kwds = {'axis': 0, 'mode': 'raise', 'out': None}
bound = <built-in method take of numpy.ndarray object at 0x7fdfbc362c90>

    def _wrapfunc(obj, method, *args, **kwds):
        bound = getattr(obj, method, None)
        if bound is None:
            return _wrapit(obj, method, *args, **kwds)
    
        try:
>           return bound(*args, **kwds)
E           IndexError: cannot do a non-empty take from an empty axes.

.tox/base/lib/python3.8/site-packages/numpy/core/fromnumeric.py:57: IndexError

[yannikschaelte]

@elbaraim it appears that this is a problem of auto-calculated burn-in, in case the entire array is found to be burn-in. I guess in this case an appropriate error should be thrown, and the test adjusted.

[dweindl]

Still happening… https://github.com/ICB-DCM/pyPESTO/actions/runs/3280972663/jobs/5402409945