def comparenflinl(ll, ecs, retina, pt_inl, pt_nfl, rsample, dolayer, engine='joblib', dojit=True, n_jobs=-1, tol=.05):
tm = ec2b.TemporalModel(lweight=ll)
inl_r = ec2b.pulse2percept(tm, ecs,r, pt_inl, rsample=rsample, dolayer='INL', dojit=False, engine='serial')
nfl_r = ec2b.pulse2percept(tm, ecs, r, pt_nfl, rsample=rsample, dolayer='NFL', dojit=False, engine='serial')
print(ll)
print(np.max(inl_r.data))
print(np.max(nfl_r.data))
return (np.max(10*inl_r.data)-np.max(10*nfl_r.data)) ** 2
rsample = int(np.round(
(1 / tm.tsample) / 30)) # resampling of the output to fps
pt = e2cm.Psycho2Pulsetrain(tsample=tm.tsample,
current_amplitude=100,
dur=.6,
pulse_dur=d / 1000.,
interphase_dur=.45 / 1000,
freq=2)
if modelver == 'Krishnan':
ca = tm.tsample * np.cumsum(np.maximum(0, pt.data))
pt.data = pt.data - ca
inl_r = ec2b.pulse2percept(temporal_model=tm,
ecs=ecs,
retina=r,
ptrain=[pt],
rsample=rsample,
dolayer='INL',
dojit=False,
engine='serial')
inl_out.append(np.max(inl_r.data) * scFac)
nfl_r = ec2b.pulse2percept(temporal_model=tm,
ecs=ecs,
retina=r,
ptrain=[pt],
rsample=rsample,
dolayer='NFL',
dojit=False,
engine='serial')
nfl_out.append(np.max(nfl_r.data) * scFac)
#plt.plot(pt.data)
del M, img
# create pulsetrain corresponding to the movie
pt = []
for rf in e_rf:
rflum = e2cm.retinalmovie2electrodtimeseries(rf, movie)
ptrain = e2cm.Movie2Pulsetrain(rflum)
# plt.plot(rflum) plt.plot(pt[ct].data) plt.plot(ptrain.data)
pt.append(ptrain)
del movie
rsample = (
1 / fps
) * pt[0].tsample # factor by which movies resampled for presentation
boom
brightness_movie = ec2b.pulse2percept(tm, ecs, r, pt, rsample)
# FILTERING BY ON OFF CELLS
# foveal vision is ~60 cpd. 293μm on the retina corresponds to 1 degree, so the smallest receptive field probably covers 293/60 ~=5μm,
# we set the largest as being 10 times bigger than that numbers roughly based on
# Field GD & Chichilnisky EJ (2007) Information processing in the primate retina: circuitry and coding. Annual Review of Neuroscience 30:1-30
# Chose 30 different sizes fairly randomly
retinasizes = np.unique(
np.ceil(np.array(np.linspace(5, 50, 15)) / r.sampling))
retinasizes = np.array([i for i in retinasizes if i >= 2])
[onmovie, offmovie] = ec2b.onoffFiltering(brightness_movie.data,
retinasizes)
[normalmovie, prostheticmovie] = ec2b.onoffRecombine(onmovie, offmovie)
# save the various movies
fps=30
pt=[]
inl_out=[]
nfl_out=[]
modelver='Krishnan'
tm = ec2b.TemporalModel(lweight= (1 / (3.16 * (10 ** 6))))
#for d in [.1, .2, .45, .75, 1., 2., 4., 8., 16., 32.]:
scFac = 2.41 * (10**3)
# at 0 off the retinal surface a 0.45 pulse in the nfl gives a response of 1
for d in [.1, .45, 1. ,2. ,4., 8., 16.]:
[ecs, cs] = r.electrode_ecs(e_all)
rsample=int(np.round((1/tm.tsample) / 30 )) # resampling of the output to fps
pt=e2cm.Psycho2Pulsetrain(tsample=tm.tsample, current_amplitude=100, dur=.6, pulse_dur=d/1000.,interphase_dur=.45/1000, freq=2)
if modelver=='Krishnan':
ca = tm.tsample * np.cumsum(np.maximum(0, pt.data))
pt.data = pt.data - ca
inl_r = ec2b.pulse2percept(temporal_model=tm, ecs=ecs, retina=r,
ptrain=[pt], rsample=rsample, dolayer='INL', dojit=False, engine='serial')
inl_out.append(np.max(inl_r.data) * scFac)
nfl_r = ec2b.pulse2percept(temporal_model=tm, ecs=ecs, retina=r,
ptrain=[pt], rsample=rsample, dolayer='NFL', dojit=False, engine='serial')
nfl_out.append(np.max(nfl_r.data) * scFac)
#plt.plot(pt.data)
#myout.append(tmp)
def findampval(amp, ecs, retina, rsample, whichlayer):
pt=e2cm.Psycho2Pulsetrain(tsample=tm.tsample, current_amplitude=amp,dur=.6, delay=10/1000,
pulse_dur=pd / 1000,interphase_dur=10/1000, freq=2)
resp = ec2b.pulse2percept(tm, ecs,r, [pt], rsample=rsample, dolayer=whichlayer, dojit=True, engine='serial')
return (( (np.max(resp.data)*1000) - 67.89) ** 2)
