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The ordering of traces in the CC dataset for the increasing runtime is a mess. To be fixed: right now the plots don't match their titles...

Change one (probably bound) to something else (side?) to make it less confusing in the doc

This might be an issue in TriScale, or me misunderstanding a use-case.

TL;DR: analysis_kpi() returns a valid value when too few data-points supplied - if the "unintuitive" bound is selected (upper for percentile < 50, and vice versa).

Background: The intuitive way to calculate a KPI is to specify a bound which gives us the "worst case" (upper when percentile > 50, and vice versa). This allows us to make the "performance is at least X"-statements. However, I was thinking there was information in the other bound as well. This would show the width of the CI, and we could learn if the given metric varies a lot between runs. The first example coming to mind is industrial scenarios, where not only the maximum latency is interesting, but also its variability.

With this background I was routinely calling analysis_kpi() twice, once with bound set to upper and another with lower. Doing this I noticed I would be getting a valid value when the "unintuitive" bound was selected (upper for percentile < 50, and vice versa), even if I had too little data.

Example with too little data:

import triscale as triscale
import numpy as np

data = np.random.randint(0, 10, size=(5))

settings = {"bound": "lower", "percentile": 99,
            "confidence": 95, "bounds": [min(data), max(data)]}

independent, kpi = triscale.analysis_kpi(
                    data,
                    settings,
                    verbose=False)
print("KPI: " + str(kpi))

With bound set to "upper", the KPI correctly returns NaN. With bound set to "lower", a number is returned.

The comments for default tolerance value says consistently 1 %, while code is using both 5 % and 1%, see e.g. https://github.com/TriScale-Anon/triscale/blob/9627caa7a5e6ec32551c38335fde881fe9530134/triscale.py#L516

In analysis_metric(), if all elements in "y-axis" data are the same (i.e. min and max are identical), it leads to a division by zero in the convergence test, see https://github.com/romain-jacob/triscale/blob/master/helpers.py#L66 and two lines above.

The issue can easily be reproduced:

x = np.arange(0, 100)
y_same_value = np.full(len(x), 100)
df = pd.DataFrame(
    {'x': x,
     'y': y_same_value})

triscale.analysis_metric( 
    df,
    metric = {'measure': 50},
    convergence = {'expected': True})

>  triscale/helpers.py:66: RuntimeWarning: invalid value encountered in true_divide

An intuitive solution is to simply state that the data is converged in such cases with identical elements, but perhaps I am missing something about the statistics so I'll leave the PR to someone else 😅