def compute_slope(data):
"""Compute the slope of the function relating changes in grating frequency
to changes in the most effective noise frequency."""
#compute slopes for observers
all_dips = np.full((3, 8), np.nan)
grating_freqs = [grating_frequencies[gf] for gf in
np.unique(data.grating_freq)]
grating_freqs.sort()
for i, vp_data in enumerate(data.by_field('vp')):
for j, freq_data in enumerate(vp_data.by_field('grating_freq')):
if freq_data.vp[0] == 'n4' and freq_data.grating_freq[0]==.2:
continue
fit = fit_gaussian(freq_data, constrained=True)
try:
all_dips[j, i] = fit.best_values['center']
except KeyError:
pass
all_dips = np.ma.MaskedArray(all_dips, np.isnan(all_dips))
slope_low = (all_dips[1] / all_dips[0]).mean() / (grating_freqs[1] /
grating_freqs[0])
slope_high = (all_dips[2] / all_dips[1]).mean() / (grating_freqs[2] /
grating_freqs[1])
# bootstrap slopes
slope_dist_low = np.full(10000, np.nan)
slope_dist_high = np.full(10000, np.nan)
for i in xrange(10000):
ratio_low = (all_dips[1] / all_dips[0]).compressed()
ratio_high = (all_dips[2] / all_dips[1]).compressed()
slope_dist_low[i] = np.random.choice(ratio_low, len(ratio_low)).mean()
slope_dist_high[i] = np.random.choice(ratio_high, len(ratio_high)).mean()
slope_dist_low /= (grating_freqs[1] / grating_freqs[0])
slope_dist_high /= (grating_freqs[2] / grating_freqs[1])
# compute slope for models
model_dips = np.full((2, 2), np.nan)
biwam = evaluate_models.get_model_data('biwam')
flodog = evaluate_models.get_model_data('flodog')
for i, model_data in enumerate([biwam, flodog]):
model_data = model_data[model_data.grating_freq!=.2]
for j, freq_data in enumerate(model_data.by_field('grating_freq')):
fit = evaluate_models.fit_gaussian(freq_data, constrained=True)
try:
model_dips[j, i] = fit.best_values['center']
except KeyError:
pass
slope_biwam = model_dips[1, 0] / model_dips[0, 0] / (grating_freqs[2] /
grating_freqs[1])
slope_flodog = model_dips[1, 1] / model_dips[0, 1] / (grating_freqs[2] /
grating_freqs[1])
return (slope_low, slope_high, slope_dist_low, slope_dist_high,
slope_biwam, slope_flodog)
def compute_slope(data):
"""Compute the slope of the function relating changes in grating frequency
to changes in the most effective noise frequency."""
#compute slopes for observers
all_dips = np.full((3, 8), np.nan)
grating_freqs = [
grating_frequencies[gf] for gf in np.unique(data.grating_freq)
]
grating_freqs.sort()
for i, vp_data in enumerate(data.by_field('vp')):
for j, freq_data in enumerate(vp_data.by_field('grating_freq')):
if freq_data.vp[0] == 'n4' and freq_data.grating_freq[0] == .2:
continue
fit = fit_gaussian(freq_data, constrained=True)
try:
all_dips[j, i] = fit.best_values['center']
except KeyError:
pass
all_dips = np.ma.MaskedArray(all_dips, np.isnan(all_dips))
slope_low = (all_dips[1] / all_dips[0]).mean() / (grating_freqs[1] /
grating_freqs[0])
slope_high = (all_dips[2] / all_dips[1]).mean() / (grating_freqs[2] /
grating_freqs[1])
# bootstrap slopes
slope_dist_low = np.full(10000, np.nan)
slope_dist_high = np.full(10000, np.nan)
for i in xrange(10000):
ratio_low = (all_dips[1] / all_dips[0]).compressed()
ratio_high = (all_dips[2] / all_dips[1]).compressed()
slope_dist_low[i] = np.random.choice(ratio_low, len(ratio_low)).mean()
slope_dist_high[i] = np.random.choice(ratio_high,
len(ratio_high)).mean()
slope_dist_low /= (grating_freqs[1] / grating_freqs[0])
slope_dist_high /= (grating_freqs[2] / grating_freqs[1])
# compute slope for models
model_dips = np.full((2, 2), np.nan)
biwam = evaluate_models.get_model_data('biwam')
flodog = evaluate_models.get_model_data('flodog')
for i, model_data in enumerate([biwam, flodog]):
model_data = model_data[model_data.grating_freq != .2]
for j, freq_data in enumerate(model_data.by_field('grating_freq')):
fit = evaluate_models.fit_gaussian(freq_data, constrained=True)
try:
model_dips[j, i] = fit.best_values['center']
except KeyError:
pass
slope_biwam = model_dips[1, 0] / model_dips[0, 0] / (grating_freqs[2] /
grating_freqs[1])
slope_flodog = model_dips[1, 1] / model_dips[0, 1] / (grating_freqs[2] /
grating_freqs[1])
return (slope_low, slope_high, slope_dist_low, slope_dist_high,
slope_biwam, slope_flodog)
def grating_freq_vs_dip_freq(data):
"""Plot the most effective noise frequency against grating frequency"""
#colors = ['#deebf7', '#c6dbef', '#9ecae1', '#6baed6', '#4292c6', '#2171b5',
# '#08519c', '#08306b']
colors = ['#9ecae1'] * 8
model_colors = ['#e41a1c', '#4daf4a']
data = data[data.noise_type=='global']
fig, ax = plt.subplots(figsize=(3,3))
all_dips = np.full((3, 8), np.nan)
for i, vp_data in enumerate(data.by_field('vp')):
grating_freqs = [grating_frequencies[gf] for gf in
np.unique(vp_data.grating_freq)]
grating_freqs.sort()
dip_frequencies = np.full(len(np.unique(vp_data.grating_freq)), np.nan)
for j, freq_data in enumerate(vp_data.by_field('grating_freq')):
fit = fit_gaussian(freq_data, constrained=True)
try:
dip_frequencies[j] = fit.best_values['center']
all_dips[j, i] = fit.best_values['center']
except KeyError:
pass
ax.plot(grating_freqs, dip_frequencies, marker='o', ms=3, ls='-',
lw=1, color=colors[i], mec='none', clip_on=False, zorder=100)
ax.plot(grating_freqs, np.nanmean(all_dips, 1), marker='o', ms=6, ls='-',
lw=2, color='#08519c', mec='none', clip_on=False, zorder=101)
ax.text(1, np.nanmean(all_dips[2,:]), 'observers',
fontname='Helvetica', fontsize=12, color='#08519c')
# include model results
biwam = evaluate_models.get_model_data('biwam')
flodog = evaluate_models.get_model_data('flodog')
for i, model_data in enumerate([biwam, flodog]):
model_data = model_data[model_data.grating_freq!=.2]
grating_freqs = [grating_frequencies[gf] for gf in
np.unique(model_data.grating_freq)]
grating_freqs.sort()
dip_frequencies = np.full(len(np.unique(model_data.grating_freq)), np.nan)
for j, freq_data in enumerate(model_data.by_field('grating_freq')):
fit = evaluate_models.fit_gaussian(freq_data, constrained=True)
try:
dip_frequencies[j] = fit.best_values['center']
except KeyError:
pass
ax.plot(grating_freqs, dip_frequencies, marker='o', ms=6, ls='-',
lw=2, color=model_colors[i], mec='none', clip_on=False, zorder=100)
ax.text(1, dip_frequencies[1], ['BIWAM', 'FLODOG'][i],
fontname='Helvetica', fontsize=12, color=model_colors[i])
# plot diagonal lines with constant spacing
for x in (.1 * 2 ** np.arange(0, 7)):
ax.plot((x, x * 100), (.1, 10.1), ls="-", c=".7", lw=.5)
ax.plot((.1, 10.1), (x, x * 100), ls="-", c=".7", lw=.5)
ax.set_ylim((.1, 10))
ax.set_xlim((.1, 10))
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.spines['bottom'].set_visible(False)
ax.spines['bottom'].set_position(('outward', 5))
ax.spines['left'].set_position(('outward', 5))
ax.get_xaxis().tick_bottom()
ax.get_yaxis().tick_left()
ax.set_xscale('log')
ax.set_xticklabels(['', '.1', '1', '10'], fontname='Helvetica')
ax.tick_params(axis='both', which='major', labelsize=10)
ax.set_yscale('log')
ax.set_yticklabels(['', '.1', '1', '10'], fontname='Helvetica')
ax.set_ylabel(r'Noise mask center SF ($cpd$)', fontname='Helvetica',
fontsize=12)
ax.set_xlabel(r'Grating spatial frequency ($cpd$)', fontname='Helvetica',
fontsize=12)
fig.savefig('../figures/grating_freq_vs_dip_freq.pdf',
bbox_inches='tight')
plt.close(fig)
def grating_freq_vs_dip_freq(data):
"""Plot the most effective noise frequency against grating frequency"""
#colors = ['#deebf7', '#c6dbef', '#9ecae1', '#6baed6', '#4292c6', '#2171b5',
# '#08519c', '#08306b']
colors = ['#9ecae1'] * 8
model_colors = ['#e41a1c', '#4daf4a']
data = data[data.noise_type == 'global']
fig, ax = plt.subplots(figsize=(3, 3))
all_dips = np.full((3, 8), np.nan)
for i, vp_data in enumerate(data.by_field('vp')):
grating_freqs = [
grating_frequencies[gf] for gf in np.unique(vp_data.grating_freq)
]
grating_freqs.sort()
dip_frequencies = np.full(len(np.unique(vp_data.grating_freq)), np.nan)
for j, freq_data in enumerate(vp_data.by_field('grating_freq')):
fit = fit_gaussian(freq_data, constrained=True)
try:
dip_frequencies[j] = fit.best_values['center']
all_dips[j, i] = fit.best_values['center']
except KeyError:
pass
ax.plot(grating_freqs,
dip_frequencies,
marker='o',
ms=3,
ls='-',
lw=1,
color=colors[i],
mec='none',
clip_on=False,
zorder=100)
ax.plot(grating_freqs,
np.nanmean(all_dips, 1),
marker='o',
ms=6,
ls='-',
lw=2,
color='#08519c',
mec='none',
clip_on=False,
zorder=101)
ax.text(1,
np.nanmean(all_dips[2, :]),
'observers',
fontname='Helvetica',
fontsize=12,
color='#08519c')
# include model results
biwam = evaluate_models.get_model_data('biwam')
flodog = evaluate_models.get_model_data('flodog')
for i, model_data in enumerate([biwam, flodog]):
model_data = model_data[model_data.grating_freq != .2]
grating_freqs = [
grating_frequencies[gf]
for gf in np.unique(model_data.grating_freq)
]
grating_freqs.sort()
dip_frequencies = np.full(len(np.unique(model_data.grating_freq)),
np.nan)
for j, freq_data in enumerate(model_data.by_field('grating_freq')):
fit = evaluate_models.fit_gaussian(freq_data, constrained=True)
try:
dip_frequencies[j] = fit.best_values['center']
except KeyError:
pass
ax.plot(grating_freqs,
dip_frequencies,
marker='o',
ms=6,
ls='-',
lw=2,
color=model_colors[i],
mec='none',
clip_on=False,
zorder=100)
ax.text(1,
dip_frequencies[1], ['BIWAM', 'FLODOG'][i],
fontname='Helvetica',
fontsize=12,
color=model_colors[i])
# plot diagonal lines with constant spacing
for x in (.1 * 2**np.arange(0, 7)):
ax.plot((x, x * 100), (.1, 10.1), ls="-", c=".7", lw=.5)
ax.plot((.1, 10.1), (x, x * 100), ls="-", c=".7", lw=.5)
ax.set_ylim((.1, 10))
ax.set_xlim((.1, 10))
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.spines['bottom'].set_visible(False)
ax.spines['bottom'].set_position(('outward', 5))
ax.spines['left'].set_position(('outward', 5))
ax.get_xaxis().tick_bottom()
ax.get_yaxis().tick_left()
ax.set_xscale('log')
ax.set_xticklabels(['', '.1', '1', '10'], fontname='Helvetica')
ax.tick_params(axis='both', which='major', labelsize=10)
ax.set_yscale('log')
ax.set_yticklabels(['', '.1', '1', '10'], fontname='Helvetica')
ax.set_ylabel(r'Noise mask center SF ($cpd$)',
fontname='Helvetica',
fontsize=12)
ax.set_xlabel(r'Grating spatial frequency ($cpd$)',
fontname='Helvetica',
fontsize=12)
fig.savefig('../figures/grating_freq_vs_dip_freq.pdf', bbox_inches='tight')
plt.close(fig)