def plotMovement():
'''
'''
temp = np.arange(1, 80)
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, brownian_motion(spat, temp), '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 plotAmpSpec(imageData, figPath='Figures/', _brownian=True, save_plots=False):
"""Plot the amplitude spec of image.
:param save_plots: decide whether to save plots (True) or not (False)
:type save_plots: bool
**This function produces:**
.. figure:: ../../Figures/ampSpec.png
:height: 300px
:width: 400px
:align: center
**Fig 1:** An amplitude spectrum of a series of images.
"""
fig = plt.figure(figsize=(8,6))
ax = fig.add_subplot(111)
pf.AxisFormat()
pf.TufteAxis(ax, ['left', 'bottom'], [5, 3])
plaw = imageData['powerlaw'](imageData['imagexval'][10:300])
if _brownian:
from emmetrop.eye.movement import brownian_motion
temp = np.arange(1, 80, 1)
spat = imageData['imagexval'][10:300]
movement_filter = brownian_motion(spat, temp)
#ax.semilogx(imageData['imagexval'][10:300], movement_filter,
# 'g', linewidth = 2.5)
whiteStim = plaw * movement_filter
whiteStim = sig.decibels(whiteStim)
ax.semilogx(imageData['imagexval'][10:300], whiteStim,
'k', linewidth = 2.5)
plaw = sig.decibels(plaw)
ax.semilogx(imageData['imagexval'][10:300],
imageData['decibels'][10:300],
'r.', markersize = 10, markeredgewidth = 0)
ax.semilogx(imageData['imagexval'][10:300],
plaw,
'k', linewidth = 2.5)
ax.set_xlim([0.45, 15])
#ax.text(1, 10**-2.4, r'$\frac{1}{\mathit{f}}$', size = 35)
plt.xlabel('spatial frequency (cycles / deg)')
plt.ylabel('spectral density (dB)')
plt.tight_layout()
if save_plots:
fig.show()
fig.savefig(self.figPath + 'ampSpec.png')
plt.close()
else:
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
