def OR_analysis(row, template_dir, build_dir, name='OR_analysis',
selectivity=False, contours=False,
cfs=True, scalebox = False, size_factor=1.0,):
"""
Builds a pinwheel analysis of a single orientation map from a
template. Used in Figures 3 and 6-9. Displays an annotated map
with pinwheels next to the FFT spectrum and corresponding fit.
The selectivity channel of the map can be enabled as can pinwheel
contours and a column of central connection fields. The map can
have either a normal scalebar or a box showing the hypercolumn
area.
"""
template_path, output_dir = setup('OR_analysis', template_dir, build_dir, subdir=name)
savefig_opts = dict(transparent=True, format='svg', bbox_inches='tight', pad_inches=0)
pref = get_pref(row)
sel = get_sel(row) if selectivity else None
fname = os.path.join(output_dir, '%%s_%s.%%s' % name)
# Save the OR preference and selectivity raster
or_map = rasterplots.OR_map(pref, sel)
rasterplots.black_selectivity(or_map).save(fname % ('OR','png'))
# Save the FFT raster
rasterplots.greyscale(analysis.power_spectrum(pref)).save(fname % ('FFT','png'))
# Save the histogram SVG overlay
hist = vectorplots.FFT_histogram_overlay(row['amplitudes'], row['fit'])
hist.savefig(fname % ('HIST', 'svg'), **savefig_opts)
# Save the pinwheel contour SVG overlay
contours = (row['re_contours'], row['im_contours']) if contours else None
contour_fig = vectorplots.pinwheel_overlay(row['pinwheels'], contours=contours)
contour_fig.savefig(fname % ('OVERLAY','svg'), **savefig_opts)
# Save the CFs (if enabled)
if cfs and row['afferent_CFs'] is np.nan:
print "No afferent connection fields found in the given row"
elif cfs:
cf_block = rasterplots.cf_image(*row['afferent_CFs'],
border=5, width=1, height=8, pos=(4,1))
cf_block.save(fname % ('CFs', 'png'))
# Save the scale bar or scale box SVG overlay
scale_overlay = (vectorplots.scale_box_overlay if scalebox
else vectorplots.scale_bar_overlay)
scale = scale_overlay(row['units_per_hypercolumn']/float(pref.shape[0]))
scale.savefig(fname % ('SBOX' if scalebox else 'SBAR', 'svg'), **savefig_opts)
vectorplots.close([hist, contour_fig, scale])
layers = {'-scalebox':(not scalebox), '+scalebox':scalebox,
'-CFs':(not cfs), '+CFs':cfs}
svg_path = os.path.join(output_dir, '%s.svg' % name)
return compose.apply_template(template_path, svg_path,
mapping={'[LABEL]':name},
layers=layers,
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))
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 fig05(template_dir,
build_dir,
contrast=25,
seed=102,
times=[4000 * i for i in range(6)],
size_factor=1.0,
show_raster=False):
"""
Builds figure 5 showing the development of L model from an SVG
template.
"""
name = 'fig05'
if show_raster: return Display(snapshot=name, template_dir=template_dir)
template_path, output_dir = setup(name, template_dir, build_dir)
L_pattern = 'jn13_figures/output/Fig05_06/*/*/*.npz'
L_files = lancet.FilePattern('npz_file', L_pattern)
L_info = lancet.FileInfo(L_files, 'npz_file', lancet.NumpyFile())
filter_times = L_info.dframe['time'].map(lambda x: x in times)
selection = L_info.dframe[filter_times
& (L_info.dframe['contrast'] == contrast)
& (L_info.dframe['input_seed'] == seed)]
selected_rows = L_info.load(selection)
for (index, row) in selected_rows.iterrows():
fname = os.path.join(output_dir, '%%s_%.1f.png' % int(row['time']))
roi = row['roi']
(cfs, coords) = row['afferent_CFs']
# Create the combined preference/selectivity map
or_map = rasterplots.OR_map(get_pref(row), get_sel(row))
or_black = rasterplots.black_selectivity(or_map)
# Save the orientation map
or_black.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))
cf_block.save(fname % 'CF')
svg_path = os.path.join(output_dir, '%s.svg' % name)
(basepath, _) = os.path.split(svg_path)
svg = compose.SVGUtils.load(os.path.abspath(template_path))
svg = compose.SVGUtils.embed_rasters(svg, basepath)
svg = compose.SVGUtils.resize(svg, size_factor)
compose.SVGUtils.save(svg, svg_path)
return Display(compose.SVGUtils.string(svg))
def fig05(template_dir, build_dir, contrast=25, seed=102,
times=[4000*i for i in range(6)],
size_factor=1.0, show_raster=False):
"""
Builds figure 5 showing the development of L model from an SVG
template.
"""
name = 'fig05'
if show_raster: return Display(snapshot=name, template_dir=template_dir)
template_path, output_dir = setup(name, template_dir, build_dir)
L_pattern = 'jn13_figures/output/Fig05_06/*/*/*.npz'
L_files = lancet.FilePattern('npz_file', L_pattern)
L_info = lancet.FileInfo(L_files, 'npz_file', lancet.NumpyFile())
filter_times = L_info.dframe['time'].map(lambda x: x in times)
selection = L_info.dframe[filter_times
& (L_info.dframe['contrast']==contrast)
& (L_info.dframe['input_seed']==seed)]
selected_rows = L_info.load(selection)
for (index, row) in selected_rows.iterrows():
fname = os.path.join(output_dir, '%%s_%.1f.png' % int(row['time']))
roi=row['roi']
(cfs, coords) = row['afferent_CFs']
# Create the combined preference/selectivity map
or_map = rasterplots.OR_map(get_pref(row), get_sel(row))
or_black = rasterplots.black_selectivity(or_map)
# Save the orientation map
or_black.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))
cf_block.save(fname % 'CF')
svg_path = os.path.join(output_dir, '%s.svg' % name)
(basepath,_) = os.path.split(svg_path)
svg = compose.SVGUtils.load(os.path.abspath(template_path))
svg = compose.SVGUtils.embed_rasters(svg, basepath)
svg = compose.SVGUtils.resize(svg, size_factor)
compose.SVGUtils.save(svg, svg_path)
return Display(compose.SVGUtils.string(svg))
def OR_analysis(
row,
template_dir,
build_dir,
name='OR_analysis',
selectivity=False,
contours=False,
cfs=True,
scalebox=False,
size_factor=1.0,
):
"""
Builds a pinwheel analysis of a single orientation map from a
template. Used in Figures 3 and 6-9. Displays an annotated map
with pinwheels next to the FFT spectrum and corresponding fit.
The selectivity channel of the map can be enabled as can pinwheel
contours and a column of central connection fields. The map can
have either a normal scalebar or a box showing the hypercolumn
area.
"""
template_path, output_dir = setup('OR_analysis',
template_dir,
build_dir,
subdir=name)
savefig_opts = dict(transparent=True,
format='svg',
bbox_inches='tight',
pad_inches=0)
pref = get_pref(row)
sel = get_sel(row) if selectivity else None
fname = os.path.join(output_dir, '%%s_%s.%%s' % name)
# Save the OR preference and selectivity raster
or_map = rasterplots.OR_map(pref, sel)
rasterplots.black_selectivity(or_map).save(fname % ('OR', 'png'))
# Save the FFT raster
rasterplots.greyscale(analysis.power_spectrum(pref)).save(fname %
('FFT', 'png'))
# Save the histogram SVG overlay
hist = vectorplots.FFT_histogram_overlay(row['amplitudes'], row['fit'])
hist.savefig(fname % ('HIST', 'svg'), **savefig_opts)
# Save the pinwheel contour SVG overlay
contours = (row['re_contours'], row['im_contours']) if contours else None
contour_fig = vectorplots.pinwheel_overlay(row['pinwheels'],
contours=contours)
contour_fig.savefig(fname % ('OVERLAY', 'svg'), **savefig_opts)
# Save the CFs (if enabled)
if cfs and row['afferent_CFs'] is np.nan:
print "No afferent connection fields found in the given row"
elif cfs:
cf_block = rasterplots.cf_image(*row['afferent_CFs'],
border=5,
width=1,
height=8,
pos=(4, 1))
cf_block.save(fname % ('CFs', 'png'))
# Save the scale bar or scale box SVG overlay
scale_overlay = (vectorplots.scale_box_overlay
if scalebox else vectorplots.scale_bar_overlay)
scale = scale_overlay(row['units_per_hypercolumn'] / float(pref.shape[0]))
scale.savefig(fname % ('SBOX' if scalebox else 'SBAR', 'svg'),
**savefig_opts)
vectorplots.close([hist, contour_fig, scale])
layers = {
'-scalebox': (not scalebox),
'+scalebox': scalebox,
'-CFs': (not cfs),
'+CFs': cfs
}
svg_path = os.path.join(output_dir, '%s.svg' % name)
return compose.apply_template(template_path,
svg_path,
mapping={'[LABEL]': name},
layers=layers,
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))