BUG: get design space working for magnitude effect models

[drbenvincent]

So far we do not have multiple RB values when choosing magnitude effect models

In fact, there is a bug which will crash things when we enter multiple RB values. Need to fix this in DARC_Designs.__innit__()

• [x] ask for better designs in chooser.py
• [x] set up a failing test in test_designs.py
• [ ] Make a solution happen in DARC_Designs.__innit__().
• [ ] test passes?
• [ ] manual human experiment test working via PsychoPy expt and chooser.py

This relates to #21 in that we need robust validation and checking of desired design space specs etc.

• [ ] break this out of the __innit__() and have a separate validator?

Update

This is happening because this Python implementation encodes the design space directly (as in you ask for combinations of RA, DA, PA, RB, DB, PB), but in the Matlab implementation we had RA_over_RB instead of specifying RA directly. This new approach is a move forward, but the RA_over_RB does make sense for magnitude effect models. Why? Because if we have RB values of 10, 100, 1000 etc, it makes most sense for RA to be uniformly spaced as a proportion RB. Small increments make sense when RB is 10, but makes no sense when RB is 10000.

What to do? Allow class to accept an optional RA_over_RB list.

• [x] update the way we create the DARCDesign for magnitude effect in the tests.
• [x] update DARCDesign.__init__ to deal with RA_over_RB being provided as an argument.
• [x] deal with the presence of RA_over_RB in how we generate the initial set of designs.

[drbenvincent]

I think this is mostly resolved now. We can get design spaces appropriate for the magnitude effect. It also runs being called from PsychoPy. The only niggle is that it's not utilising the full range of DB values. I think this might be a case of design selection, and the still to be completed completion of the heuristics (see #27)

[drbenvincent]

Yes, the point of this issue (that there was a bug) is now resolved. Some other issue will ensure we have sensible heuristics to guide the design optimisation (#27)