>           assert np.allclose(mt1[i].distribution_args, mt2[i].distribution_args)
E           AssertionError: assert False
E            +  where False = <function allclose at 0x7fb9680175e0>([-0.00971, 0.131], [-0.0257, 0.131])
E            +    where <function allclose at 0x7fb9680175e0> = np.allclose
E            +    and   [-0.00971, 0.131] = MutationType(dominance_coeff=None, distribution_type='g', distribution_args=[-0.00971, 0.131], convert_to_substitution=True, dominance_coeff_list=[0.0, 0.01, 0.1, 0.4], dominance_coeff_breaks=[-0.1, -0.01, -0.001]).distribution_args
E            +    and   [-0.0257, 0.131] = MutationType(dominance_coeff=None, distribution_type='g', distribution_args=[-0.0257, 0.131], convert_to_substitution=True, dominance_coeff_list=[0.0, 0.01, 0.1, 0.4], dominance_coeff_breaks=[-0.1, -0.01, -0.001]).distribution_args

The gamma shape was taken directly from the reported estimate.
The gamma mean was taken from the value reported in simulations. 
This was derived from the inferred shape and scale + the estimated Na, 
but since the estimated Na is not reported, I couldn't confirm the reported mean.

Selection coefficients (s) for deleterious mutations were drawn from a gamma
distribution estimated using the genomic data (see Inference of the distribution of selection
coefficients) with mean = -0.0257 and shape parameter = 0.1314. 

For sensitivity analyses
varying the mutation rate, we used the parameters inferred under the respective mutation rate
(mean = -0.00971 and shape = 0.1316 for μ = 2.2x10-9 and mean = -0.0476 and shape = 0.1315
for μ = 1.08x10-8; fig. S13).