Update quantum code from matgwinc, and vectorize

Before this change and !31 (merged):

%timeit gwinc.gwinc(freq, ifo)

519 ms ± 15.6 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

After both:

%timeit gwinc.gwinc(freq, ifo)

50.4 ms ± 1.97 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

%prun gwinc.gwinc(freq, ifo)

     5304 function calls (4870 primitive calls) in 0.102 seconds

Ordered by: internal time

ncalls  tottime  percall  cumtime  percall filename:lineno(function)
    74    0.033    0.000    0.034    0.000 coatingthermal.py:743(getCoatRefl)
     1    0.014    0.014    0.016    0.016 quantum.py:257(shotradSignalRecycled)
    10    0.008    0.001    0.008    0.001 suspension.py:30(tst_force_to_tst_displ)
     2    0.007    0.004    0.007    0.004 coatingthermal.py:446(getCoatTOPhase)
     1    0.006    0.006    0.016    0.016 suspension.py:51(suspQuad)
     7    0.006    0.001    0.006    0.001 {built-in method numpy.core.multiarray.matmul}

I think the biggest remaining chunk of run time goes to the coating design step in precompIFO (which can be skipped by setting CoatLayerOpticalThickness in the ifo struct).

Pretty nice. I'll have to incorporate this into the 2mode which I know is slower. I'll try to pull this and merge it soon.

added 1 commit

• 0f25554e - More quantum optimization (precomputed trig functions)

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I'm going to mark this as work-in-progress. I think I got a little carried away here, and we should probably not merge this yet. The first step should have been to understand the ~5% discrepancy with matgwinc's quantum curve -- which I now see is produced by code that's very different from what's in pygwinc now. If we end up adopting matgwinc's implementation, most of this branch will have to be thrown away.

marked as a Work In Progress

changed the description

added 1 commit

• 3c94c320 - quantum.py functions updated from matgwinc

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unmarked as a Work In Progress

added 6 commits

• 3c94c320...ea42d0c6 - 2 commits from branch master
• 354f5576 - Speed up quad sus tf calculation 10x, by pre-solving the system symbolically
• c92a94a6 - Speed up quantum calculation with monolithic matrix multiply
• df905a0f - More quantum optimization (precomputed trig functions)
• 2749df6d - quantum.py functions updated from matgwinc

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added noise label

changed title from Speed up quantum calculation with monolithic matrix multiply to Update quantum code from matgwinc, and vectorize

added 3 commits

• c7f60418 - Speed up quantum calculation with monolithic matrix multiply
• 579140c3 - More quantum optimization (precomputed trig functions)
• 7534900a - quantum.py functions updated from matgwinc

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added 6 commits

• 7534900a...e9aabda9 - 3 commits from branch master
• be4a88c5 - Speed up quantum calculation with monolithic matrix multiply
• be7ec75d - More quantum optimization (precomputed trig functions)
• 95b010d4 - quantum.py functions updated from matgwinc

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merged

mentioned in commit 49fc9560