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 plotSeries(cpd, diffract, Analysis, analysis_args, figPath='Figures/', save_plots=False):
'''
'''
for analysis in analysis_args:
ylabel = (analysis.replace('dist', '$log_{10}$ (mm)')
.replace('off_axis', 'angle (deg)')
.replace('pupil_size', 'pupil size (mm)')
.replace('focus', 'lens (D)'))
fig = plt.figure(figsize=(8,6))
ax = fig.add_subplot(111, projection='3d')
pf.AxisFormat()
keys = 0
for key in Analysis: keys += 1
samples = len(Analysis[0]['mtf'])
X = np.zeros((50, keys))
Y = np.zeros((50, keys))
Z = np.zeros((50, keys))
for key in Analysis:
# X is cpd of plot
X[:, key] = cpd[:50]
if analysis == 'dist':
Y[:, key] = np.ones(50) * np.log10(Analysis[key]['dist'])
else:
Y[:, key] = np.ones(50) * Analysis[key][analysis]
Z[:, key] = Analysis[key]['mtf'][:50]
ax.plot_wireframe(X, Y, Z)
ax.set_xlabel('cycles/degree')
ax.set_ylabel(ylabel)
plt.tight_layout()
if save_plots:
fig.show()
fig.savefig(self.figPath + 'MTFfamily.png')
plt.close()
else:
plt.show()
## Retina plot ##
#################
fig = plt.figure(figsize=(8,6))
ax = fig.add_subplot(111, projection='3d')
pf.AxisFormat()
for key in Analysis:
X[:, key] = cpd[:50]
if analysis == 'dist':
Y[:, key] = np.ones(50) * np.log10(Analysis[key]['dist'])
else:
Y[:, key] = np.ones(50) * Analysis[key][analysis]
contrast = (Analysis[key]['retina'][:50] /
np.max(diffract['retina'][:50]))
Z[:, key] = sig.decibels(contrast)
ax.plot_wireframe(X, Y, Z)
ax.set_xlabel('cycles/degree')
ax.set_ylabel(ylabel)
plt.tight_layout()
if save_plots:
fig.show()
fig.savefig(self.figPath + 'MTFfamily.png')
plt.close()
else:
plt.show()
def plotDoG(rec_field, min_dB=-20, figPath="Figures", save_plots=False):
"""
Currently this function outputs the following:
:param save_plots: decide whether to save plots (True) or not (False)
:type save_plots: bool
:returns: * Plot1: difference of gaussians receptive field \n
* Plot2: FFT spectrum of plot 1.
.. todo::
Get an analytical function from Curcio to find cone spacing
as a function of eccentricity.
.. figure:: ../../Figures/ConeRF.png
:height: 300px
:width: 400px
:align: center
**Fig 1:** A simple Diff of Gaussian receptive field
.. figure:: ../../Figures/ConeRF_FFT.png
:height: 300px
:width: 400px
:align: center
**Fig 2:** And converted into a probability density and Fourier
transformed
"""
fig = plt.figure(figsize=(8, 6))
ax = fig.add_subplot(111)
pf.AxisFormat()
pf.TufteAxis(ax, ["left", "bottom"], [5, 5])
for wv in rec_field:
if wv != "max":
ax.plot(rec_field[wv]["xvals"], rec_field[wv]["dog"], c=pf.wav2RGB(wv), linewidth=2.5)
plt.xlabel("distance (arcmin)")
plt.ylabel("amplitude")
plt.tight_layout()
if save_plots:
fig.show()
fig.savefig(figPath + "ConeRF" + loc + ".png")
plt.close()
else:
plt.show()
fig2 = plt.figure(figsize=(8, 6))
ax = fig2.add_subplot(111)
pf.AxisFormat()
pf.TufteAxis(ax, ["left", "bottom"], [5, 5])
for wv in rec_field:
if wv != "max":
length = rec_field[wv]["length"]
normFFT = rec_field[wv]["coneResponse"][length:] / rec_field["max"]
ax.semilogx(rec_field[wv]["xvals"][length:] * 60, sig.decibels(normFFT), c=pf.wav2RGB(wv), linewidth=2.5)
ax.get_xaxis().tick_bottom()
ax.get_yaxis().tick_left()
plt.ylim([-40, 0])
plt.xlim([0, 100])
plt.xlabel("spatial frequency (cycles / deg)")
plt.ylabel("contrast sensitivity (dB)")
plt.tight_layout()
if save_plots:
fig2.show()
fig2.savefig(figPath + "ConeRF_FFT" + loc + ".png")
plt.close()
else:
plt.show()
def plotActivity(cpd, Analysis, diffract, figPath='Figures/',
save_plots=False, legend=False):
"""Plot powerlaw amplitude spectrum after accounting for MTF and MTF \
+ receptive field
:param legend: turn legend on (True) or off (False). Default = True.
:type legend: bool
:param save_plots: decide whether to save a plot (True) or not (False)
:type save_plots: bool
:returns: Plot 1: power law times Modulation Transfer Function for
three conditions \n
Plot 2: same plot with difference of gaussians receptive
field included as well. This is the final plot in this \
series.
:rtype: plt.plot
.. note::
Eventually would like to introduce a heuristic to determine the
location of the power law text that is plotted. Currently hard \
coded.
**This function plots estimates of the photoreceptor activity**
.. figure:: ../../Figures/MTFfamilyMod.png
:height: 300px
:width: 400px
:align: center
**Fig 1:** Amplitude * MTF
.. figure:: ../../Figures/MTFfamilyModAmp.png
:height: 300px
:width: 400px
:align: center
**Fig2:** Amplitude * MTF * Receptive field
"""
fig = plt.figure(figsize=(8,6))
ax = fig.add_subplot(111)
pf.AxisFormat()
pf.TufteAxis(ax, ['left', 'bottom'], [5, 5])
for key in Analysis:
contrast = (Analysis[key]['preCone'] /
np.max(diffract['preCone']))
ax.semilogx(cpd[0:100], sig.decibels(contrast),
Analysis[key]['line']['style'],
c = Analysis[key]['line']['color'], label=key)
contrast = diffract['preCone'] / np.max(diffract['preCone'])
ax.plot(cpd[0:100], sig.decibels(contrast),
'k-', linewidth=2)
if legend:
ax.legend(loc='lower left')
ax.get_xaxis().tick_bottom()
ax.get_yaxis().tick_left()
plt.ylim([-20, 0])
plt.xlim([0, 60])
plt.xlabel('spatial frequency (cycles / deg)')
plt.ylabel('contrast sensitivity (dB)')
plt.tight_layout()
if save_plots:
fig.show()
fig.savefig(figPath + 'MTFfamilyMod.png')
plt.close()
else:
plt.show()
"""
plot 2
"""
if save_plots:
fig2 = plt.figure(figsize=(8,6))
ax = fig2.add_subplot(111)
else:
fig = plt.figure(figsize=(8,6))
ax = fig.add_subplot(111)
pf.AxisFormat()
pf.TufteAxis(ax, ['left', 'bottom'], [5, 5])
for key in Analysis:
contrast = (Analysis[key]['retina'] /
np.max(diffract['retina']))
ax.semilogx(cpd[0:100], sig.decibels(contrast),
Analysis[key]['line']['style'],
c = Analysis[key]['line']['color'], label=key)
contrast = diffract['retina'] / np.max(diffract['retina'])
ax.plot(cpd[0:100], sig.decibels(contrast),
'k-', linewidth=2)
if legend:
ax.legend(loc='upper right')
mi, ma = plt.ylim()
plt.ylim([-20, 0])
plt.xlim([0, 60])
plt.xlabel('spatial frequency (cycles / deg)')
plt.ylabel('contrast sensitivity (dB)')
plt.tight_layout()
if save_plots:
save_name = figPath + 'MTFfamilyModAmp.png'
fig2.show()
fig2.savefig(save_name)
plt.close()
else:
plt.show()