def plotGlasses():
'''
'''
spat = np.arange(0.1, 100, 0.1)
fig = plt.figure()
ax = fig.add_subplot(111)
pf.AxisFormat()
pf.TufteAxis(ax, ['bottom', 'left'])
ax.loglog(spat, gauss(spat, 15), 'k')
ax.set_ylim([10 ** -5, 1.05])
ax.set_ylabel('transfer')
ax.set_xlabel('spatial frequency (cycles / deg)')
plt.tight_layout()
plt.show()
def computeConeActivity(Analysis, ImageData, rec_field, cpd, _meta,
_brownian=True, glasses=False):
"""Compute the estimated activity of a cone photoreceptor.
:param Receptive_Field: a handle to the spline fitted receptive field.
:type Receptive_Field: function handle.
"""
Rec_Field = (rec_field[540]['fft'] / rec_field['max'])
ImageData['fitLaw'] = ImageData['powerlaw'](cpd[1:])
powerlaw = ImageData['fitLaw']
if _brownian:
temp = np.arange(1, 80)
movement_filter = brownian_motion(cpd[1:], temp)
powerlaw *= movement_filter
# compute the diffraction limited case seperately
diffract = {}
diff, _x = o.diffraction(_meta['samples'],
_meta['pupil_size'],
16.6,
ref_index=1.4,
wavelength=550.0)
# now interpolate mtf into cpd's of analysis:
mtf = np.interp(cpd, _x, diff)
# remove zeros to avoid errors in future computations:
ind = np.where(mtf != 0)[0]
diffract['cpd'] = cpd[ind]
diffract['mtf'] = mtf[ind]
diffract['preCone'] = (powerlaw[ind] *
diffract['mtf'][ind])
diffract['retina'] = (diffract['preCone'] *
Rec_Field[ind])
if glasses:
# if glasses on, compute effect after diffraction case
powerlaw *= gauss(cpd[1:], 10)
for key in Analysis:
# find cone fft:
wv = Analysis[key]['wavelength']
Rec_Field = (rec_field[wv]['fft'] / rec_field['max'])
# generate MTFs for each condition:
intensity = traceEye(
Analysis[key]['dist'],
Analysis[key]['off_axis'],
Analysis[key]['pupil_size'],
Analysis[key]['focus'],
Analysis[key]['wavelength'])
psf = o.genPSF(intensity, _meta['samples'])[1]
Analysis[key]['mtf'] = o.genMTF(psf)[ind]
Analysis[key]['preCone'] = (powerlaw[ind] *
Analysis[key]['mtf'])
Analysis[key]['retina'] = (Analysis[key]['preCone'] *
Rec_Field[ind])
return Analysis, diffract