Adjusted tolerance level for jacobian tests

Owner
There could be various reasons. For example, some FFTs in older versions of
scipy
were really inaccurate. It could also be a difference in the initial random seed. 
Author Developer
I think the SEED is fixed in all cases to the same value, because I used the same test_energy_gradients.py file with SEED defined on line 33.
Here in the ift.extra.check_jacobian_consistency I think most operations are just multiplications, additions and divisions so I was surprised that the difference already with these operations could be seen. Anyways, it was just interesting and I want to check whether it is not too strange to have this kind of difference. I will leave the tolerance then as it is with this push.
Thanks!

Owner
I can't really tell for which operator you are running
check_jacobian_consistency
, but typically these operators are highly nontrivial and involve some kind of harmonic transform. If that is the case, I'd not be too worried about the change in tolerance. On the other hand, if you are sure that the tested operator only consists of really trivial operations, it might be worth investigating the reason for the lowered accuracy. 
Author Developer
The operator is in the 'operators/energy_operators' file and it doesn't involve a HarmonicTransform. Maybe if you have time you can take a look and see whether this is okay.

Maintainer
There is at least a logarithm involved, so no wonder we get a bit of precision loss.

Author Developer
Correct. Okay, if you don't worry about this neither will I!
Thanks for the comments!

Owner
At some point I need to take the time to analyze why we are only seeing accuracies in the 1e6 to 1e7 range, instead of something below 1e12 ... probably due to the quadratic nature of the problem. @reimar, if you have a few minutes to discuss this, perhaps during coffee, that would be great!