I have a function which takes two arguments, an integer x and a list l of floats. This function requires that len(l) == x.

I wish to write a unit test, with my function arguments x and l both being generated by hypothesis strategies. How can I force l to have the correct length? hypothesis.strategies.lists has min_size and max_size arguments, but in this case they need to depend on x, which is generated by another strategy.

At present, I am setting both min_size and max_size to be the largest possible value of x, then slicing the list with l[:x] when I actually call the function (see code below). However, this approach (a) is very slow and (b) typically fails hypothesis‘s health checks with

E hypothesis.errors.FailedHealthCheck: It looks like your
strategy is filtering out a lot of data. Health check found 50
filtered examples but only 9 good ones. This will make your tests much
slower, and also will probably distort the data generation quite a
lot. You should adapt your strategy to filter less. This can also be
caused by a low max_leaves parameter in recursive() calls

(At least, real examples are slow and fail the health check. The toy function in my minimal code below is OK.)

Is there a better approach to designing the strategies in this case?

Using Python 3.12.2 and hypothesis 6.102.4. My code:

from hypothesis import strategies, given

def my_function(x, l):
    """Dummy function requiring length of list `l` to match integer `x`"""
    if len(l) != x:
        raise ValueError

# Hypothesis strategies
max_int = 100
int_st = strategies.integers(min_value=1, max_value=max_int)
float_st = strategies.floats(min_value=0, max_value=1)
floats_list_st = strategies.lists(float_st, min_size=max_int, max_size=max_int)

@given(x=int_st, l=floats_list_st)
def test_my_function(x, l):
    """Test of my_function(), where length of `l` is limited to `x`"""

    # Make sure list has correct length by slicing it here
    my_function(x, l[:x])