Does StatsModels' power.tt_ind_solve_power assume a single standard deviation despite two different means? Does StatsModels' power.tt_ind_solve_power (link) assume a single standard deviation despite two different means? I think so. Why is this a reasonable assumption?
I come from a primarily Bayesian background, so if I wanted to pool variances, I'd probably draw $\sigma_1$ and $\sigma_2$ from the same prior distribution.
I don't follow the logic that the means would be the different but the variances would be the same.
There is a ratio parameter (what % is population 1?) Perhaps this is used to decouple the variances?

Yes, this function use's Cohen's d for the effect size which relies on a pooled standard deviation. Obviously, for certain distributions such as a binomial distribution where the variance is given by the mean, this doesn't make a lot of sense, but for a t-test, it's not unlike the pooled variance version of the t-test. The ratio parameter is not very directly related to this but rather to the fact that for optimal power, in most cases, both sample should be of equal size.
