def fig10(template_dir, build_dir, bound_radius=0.6, selectivity_norm=0.07138,
size_factor=1.0, show_raster=False):
name = 'fig10'
if show_raster: return Display(snapshot=name, template_dir=template_dir)
times=[2000*i for i in range(6)]
template_path, output_dir = setup(name, template_dir, build_dir)
pattern = 'jn13_figures/output/Fig10_12/*/*/*.npz'
files = lancet.FilePattern('npz_file', pattern)
file_info = lancet.FileInfo(files, 'npz_file', lancet.NumpyFile())
filter_times = file_info.dframe['time'].map(lambda x: x in times)
selection = file_info.load(file_info.dframe[filter_times])
savefig_opts = dict(transparent=True, format='svg', bbox_inches='tight', pad_inches=0)
development_info = []
for (index, row) in selection.iterrows():
time =row['time']
fname = os.path.join(output_dir, '%%s_%.1f.png' % int(time))
bounds = imagen.boundingregion.BoundingBox(radius=0.75)
sheetcoords = imagen.SheetCoordinateSystem(bounds, xdensity=98)
roi = slice(*sheetcoords.sheet2matrixidx(bound_radius, bound_radius))
# Not applying ROI as new bounds will be used
pref = get_pref(row, roi=roi)
sel = get_sel(row, roi=roi)
(cfs, coords) = row['afferent_CFs']
roi = slice(*sheetcoords.sheet2matrixidx(bound_radius, bound_radius))
# Create the combined preference/selectivity map
or_map = rasterplots.OR_map(pref,sel)
or_black = rasterplots.black_selectivity(or_map)
(dim1,dim2) = pref.shape
or_black_resized = rasterplots.resize(or_black, (dim1*4,dim2*4))
# Save the orientation map
or_black_resized.save(fname % 'OR')
# Save the row of CF images
cf_block = rasterplots.cf_image(cfs, coords, border=5, width=8, height=1,
pos=(1,4), bg=(255,255,255))
cf_block.save(fname % 'CF')
# Save the FFT of the last map
if time == times[-1]:
normalized_spectrum = analysis.power_spectrum(pref[roi,roi])
fft_im = rasterplots.greyscale(normalized_spectrum)
fft_im.save(fname % 'FFT')
# Note: ROI not applied and the values of 3 are due to historical reasons
development_info.append((time,pref[3:-3, 3:-3], np.mean(sel[3:-3,3:-3])))
ts, prefs, mean_sels = zip(*sorted(development_info))
# We only need the stabilities up till 10000
fname = os.path.join(output_dir, 'model_development.svg')
development_fig = vectorplots.map_development_plot(analysis.stability_index(prefs),
mean_sels, selectivity_norm)
development_fig.savefig(fname, **savefig_opts)
vectorplots.close([development_fig])
svg_path = os.path.join(output_dir, '%s.svg' % name)
return Display(compose.apply_template(template_path, svg_path,
size_factor=size_factor))
def fig11(template_dir, build_dir, input_seed=102, size_factor=1.0, show_raster=False):
"""
Automatically generates Figure 11 showing the result of simulated
goggle rearing and comparing to the experimental data (Tanaka
2009).
"""
name = 'fig11'
if show_raster: return Display(snapshot=name, template_dir=template_dir)
resize_factor = 4
times = [ 6000, 9000, 12000, 20000]
template_path, output_dir = setup(name, template_dir, build_dir)
pattern = 'jn13_figures/output/Fig11/*seed102/*/*.npz'
files = lancet.FilePattern('npz_file', pattern)
file_info = lancet.FileInfo(files, 'npz_file', lancet.NumpyFile())
filter_times = file_info.dframe['time'].map(lambda x: x in times)
selection = file_info.load(file_info.dframe[filter_times])
roi = selection[selection['time']==times[-1]]['roi'].iloc[0]
savefig_opts = dict(transparent=True, format='svg', bbox_inches='tight', pad_inches=0)
for (index,row) in selection.iterrows():
fname = os.path.join(output_dir, 'OR_%d.png' % int(row['time']))
pref = get_pref(row)
sel = get_sel(row)
# Rotate the colour key to match the convention in Tanaka et el.
tanaka_pref = pref + (2.0 / 3.0) % 1
# Create the combined preference/selectivity map
or_map = rasterplots.OR_map(tanaka_pref,sel)
or_black = rasterplots.black_selectivity(or_map)
# Resize by factor
(dim1,dim2) = pref.shape
or_black_resized = rasterplots.resize(or_black, (dim1*resize_factor,
dim2*resize_factor))
# Save the orientation maps
or_black_resized.save(fname)
# Unbiased experimental histogram
unbiased_hist = vectorplots.tanaka_histogram('unbiased')
unbiased_hist.savefig(os.path.join(output_dir, 'unbiased_tanaka.svg'), **savefig_opts)
# Vertically biased experimental histogram
biased_hist = vectorplots.tanaka_histogram('biased')
biased_hist.savefig(os.path.join(output_dir, 'biased_tanaka.svg'), **savefig_opts)
# Approximation to vertical goggle rearing
fname = os.path.join(output_dir, 'VGR_%d.svg' % int(row['time']))
gcal_hist = vectorplots.tanaka_histogram(pref)
gcal_hist.savefig(fname, **savefig_opts)
vectorplots.close([biased_hist, gcal_hist])
svg_path = os.path.join(output_dir, '%s.svg' % name)
return Display(compose.apply_template(template_path, svg_path,
size_factor=size_factor))
def fig11(template_dir,
build_dir,
input_seed=102,
size_factor=1.0,
show_raster=False):
"""
Automatically generates Figure 11 showing the result of simulated
goggle rearing and comparing to the experimental data (Tanaka
2009).
"""
name = 'fig11'
if show_raster: return Display(snapshot=name, template_dir=template_dir)
resize_factor = 4
times = [6000, 9000, 12000, 20000]
template_path, output_dir = setup(name, template_dir, build_dir)
pattern = 'jn13_figures/output/Fig11/*seed102/*/*.npz'
files = lancet.FilePattern('npz_file', pattern)
file_info = lancet.FileInfo(files, 'npz_file', lancet.NumpyFile())
filter_times = file_info.dframe['time'].map(lambda x: x in times)
selection = file_info.load(file_info.dframe[filter_times])
roi = selection[selection['time'] == times[-1]]['roi'].iloc[0]
savefig_opts = dict(transparent=True,
format='svg',
bbox_inches='tight',
pad_inches=0)
for (index, row) in selection.iterrows():
fname = os.path.join(output_dir, 'OR_%d.png' % int(row['time']))
pref = get_pref(row)
sel = get_sel(row)
# Rotate the colour key to match the convention in Tanaka et el.
tanaka_pref = pref + (2.0 / 3.0) % 1
# Create the combined preference/selectivity map
or_map = rasterplots.OR_map(tanaka_pref, sel)
or_black = rasterplots.black_selectivity(or_map)
# Resize by factor
(dim1, dim2) = pref.shape
or_black_resized = rasterplots.resize(
or_black, (dim1 * resize_factor, dim2 * resize_factor))
# Save the orientation maps
or_black_resized.save(fname)
# Unbiased experimental histogram
unbiased_hist = vectorplots.tanaka_histogram('unbiased')
unbiased_hist.savefig(os.path.join(output_dir, 'unbiased_tanaka.svg'),
**savefig_opts)
# Vertically biased experimental histogram
biased_hist = vectorplots.tanaka_histogram('biased')
biased_hist.savefig(os.path.join(output_dir, 'biased_tanaka.svg'),
**savefig_opts)
# Approximation to vertical goggle rearing
fname = os.path.join(output_dir, 'VGR_%d.svg' % int(row['time']))
gcal_hist = vectorplots.tanaka_histogram(pref)
gcal_hist.savefig(fname, **savefig_opts)
vectorplots.close([biased_hist, gcal_hist])
svg_path = os.path.join(output_dir, '%s.svg' % name)
return Display(
compose.apply_template(template_path,
svg_path,
size_factor=size_factor))
def fig10(template_dir,
build_dir,
bound_radius=0.6,
selectivity_norm=0.07138,
size_factor=1.0,
show_raster=False):
name = 'fig10'
if show_raster: return Display(snapshot=name, template_dir=template_dir)
times = [2000 * i for i in range(6)]
template_path, output_dir = setup(name, template_dir, build_dir)
pattern = 'jn13_figures/output/Fig10_12/*/*/*.npz'
files = lancet.FilePattern('npz_file', pattern)
file_info = lancet.FileInfo(files, 'npz_file', lancet.NumpyFile())
filter_times = file_info.dframe['time'].map(lambda x: x in times)
selection = file_info.load(file_info.dframe[filter_times])
savefig_opts = dict(transparent=True,
format='svg',
bbox_inches='tight',
pad_inches=0)
development_info = []
for (index, row) in selection.iterrows():
time = row['time']
fname = os.path.join(output_dir, '%%s_%.1f.png' % int(time))
bounds = imagen.boundingregion.BoundingBox(radius=0.75)
sheetcoords = imagen.SheetCoordinateSystem(bounds, xdensity=98)
roi = slice(*sheetcoords.sheet2matrixidx(bound_radius, bound_radius))
# Not applying ROI as new bounds will be used
pref = get_pref(row, roi=roi)
sel = get_sel(row, roi=roi)
(cfs, coords) = row['afferent_CFs']
roi = slice(*sheetcoords.sheet2matrixidx(bound_radius, bound_radius))
# Create the combined preference/selectivity map
or_map = rasterplots.OR_map(pref, sel)
or_black = rasterplots.black_selectivity(or_map)
(dim1, dim2) = pref.shape
or_black_resized = rasterplots.resize(or_black, (dim1 * 4, dim2 * 4))
# Save the orientation map
or_black_resized.save(fname % 'OR')
# Save the row of CF images
cf_block = rasterplots.cf_image(cfs,
coords,
border=5,
width=8,
height=1,
pos=(1, 4),
bg=(255, 255, 255))
cf_block.save(fname % 'CF')
# Save the FFT of the last map
if time == times[-1]:
normalized_spectrum = analysis.power_spectrum(pref[roi, roi])
fft_im = rasterplots.greyscale(normalized_spectrum)
fft_im.save(fname % 'FFT')
# Note: ROI not applied and the values of 3 are due to historical reasons
development_info.append((time, pref[3:-3,
3:-3], np.mean(sel[3:-3, 3:-3])))
ts, prefs, mean_sels = zip(*sorted(development_info))
# We only need the stabilities up till 10000
fname = os.path.join(output_dir, 'model_development.svg')
development_fig = vectorplots.map_development_plot(
analysis.stability_index(prefs), mean_sels, selectivity_norm)
development_fig.savefig(fname, **savefig_opts)
vectorplots.close([development_fig])
svg_path = os.path.join(output_dir, '%s.svg' % name)
return Display(
compose.apply_template(template_path,
svg_path,
size_factor=size_factor))
