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The way Hypothesis handles this is with the recursive() function which you pass in a base case and a function that, given a strategy for your data type, returns a new strategy for it. So for example:

That is, we start with our leaf data and then we augment it by allowing lists and dictionaries of anything we can generate as JSON data.

The size control of this works by limiting the maximum number of values that can be drawn from the base strategy. So for example if we wanted to only generate really small JSON we could do this as:

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decorator lets you combine other strategies in more or less arbitrary ways. It’s probably the main thing you’ll want to use for complicated custom strategies.

The composite decorator works by converting a function that returns one example into a function that returns a strategy that produces such examples - which you can pass to Naturalizer Geonna Womens 0cLjl
, modify with .map or .filter , and generally use like any other strategy.

It does this by giving you a special function draw as the first argument, which can be used just like the corresponding method of the Clarks Sillian Bella Womens 0p38L1U
strategy within a test. In fact, the implementation is almost the same - but defining a strategy with @composite makes code reuse easier, and usually improves the display of failing examples.

For example, the following gives you a list and an index into it:

draw(s) is a function that should be thought of as returning s.example() , except that the result is reproducible and will minimize correctly. The decorated function has the initial argument removed from the list, but will accept all the others in the expected order. Defaults are preserved.

Note that the repr will work exactly like it does for all the built-in strategies: it will be a function that you can call to get the strategy in question, with values provided only if they do not match the defaults.

You can use assume inside composite functions:

This works as assume normally would, filtering out any examples for which the passed in argument is falsey.

There is also the data() strategy, which gives you a means of using strategies interactively. Rather than having to specify everything up front in @given you can draw from strategies in the body of your test:

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Research Article
Accepted Manuscript
Weakly electric fish distinguish between envelope stimuli arising from different behavioral contexts
Rhalena A. Thomas , Michael G. Metzen , Maurice J. Chacron
Journal of Experimental Biology 2018 : jeb.178244 doi:10.1242/jeb.178244 Published 28 June 2018
Rhalena A. Thomas
Department of Physiology, McGill University, Canada
Michael G. Metzen
Department of Physiology, McGill University, Canada
Maurice J. Chacron
Department of Physiology, McGill University, Canada
Rocky Western Technoram 209 Mens Qaotrdv

Understanding how sensory information is processed by the brain in order to give rise to behavior remains poorly understood in general. Here we investigated the behavioral responses of the weakly electric fish Apteronotus albifrons to stimuli arising from different contexts, by measuring changes in the electric organ discharge (EOD) frequency. Specifically, we focused on envelopes, which can arise either due to movement (i.e., motion envelopes) or because of interactions between the electric fields of three of more fish (i.e., social envelopes). Overall, we found that the animal's EOD frequency effectively tracked the detailed timecourse of both motion and social envelopes. In general, behavioral sensitivity (i.e., gain) decreased while phase lag increased with increasing envelope and carrier frequency. However, changes in gain and phase lag as a function of changes in carrier frequency were more prominent for motion than for social envelopes in general. Importantly, we compared behavioral responses to motion and social envelopes with similar characteristics. While behavioral sensitivities were similar, we observed an increased response lag for social envelopes primarily for low carrier frequencies. Thus, our results imply that the organism can, based on behavioral responses, distinguish envelope stimuli resulting from movement from those that instead result from social interactions. We discuss the implications of our results for neural coding of envelopes and propose that behavioral responses to motion and social envelopes are mediated by different neural circuits in the brain.

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