def arbitrary_colours(_, atlas_name):
"""
Non-meaningful colour selection.
"""
from palettable.colorbrewer.qualitative import Paired_12
cmap = Paired_12.get_mpl_colormap()
regions = atlas.get_atlas_labels(atlas_name)
x = np.linspace(0, 1, len(regions))
data = {r: cmap(x[i])[:3] for i, r in enumerate(regions)}
print data
return 0, 1, data
def groups(filename, atlas_name):
"""
Returns a dictionary of colours for the brain surfaces. Input values are
assumed to be ordinal values of clusters or groups of regions, starting
with zero.
"""
regions = atlas.get_atlas_labels(atlas_name)
data = [x.rstrip().split(',') for x in open(filename).readlines()]
data = [(x, int(y)) for x, y in data if x in regions]
n = max(y for x, y in data)
import seaborn
cmap = seaborn.color_palette(n_colors=n+1)
data = {x: cmap[y] for x, y in data}
return 0, n, data
def make_fig(filename, hemi, surf, atlas_name, colour_func, output_dir, missing=(0.5, 0.5, 0.5)):
"""
Make a brain figure.
filename : path to the csv that contains the brain region names and the
associated values.
hemi : 'lh' or 'rh'.
surf : 'inflated' or 'pial'
atlas_name : 'Destrieux' or 'DSK'.
colour_func : function object that creates a colour dictionary based on the
data inside the csv.
output_dir : name of output directory (put inside 'outputs').
"""
if colour_func is not None:
min, max, cd = colour_func(filename, atlas_name)
else:
cd = {}
regions = atlas.get_atlas_labels(atlas_name)
for region in regions:
if region in cd:
c = cd[region]
load.load_surface(hemi, surf, atlas_name, region, c)
else:
if missing is not None:
c = missing
print "Warning: '%s' region missing (%s atlas)." % (region, atlas_name)
load.load_surface(hemi, surf, atlas_name, region, c)
# reposition the camera
angles = [[180, -90, -70], [180, 90, 70], [180, 0, 0], [0, 180, 0]]
fpath = paths.pj(paths.OUTPUTS_DIR, output_dir)
if not os.path.exists(fpath):
os.makedirs(fpath)
for a, e, r in angles:
f = paths.pj(fpath, "%i_%i_%i.png" % (a, e, r))
mlab.view(a, e, roll=r)
mlab.savefig(f, magnification=3)
def minuslog10pvals(filename, atlas_name):
"""
Returns a dictionary of colours for the brain surfaces. Input values are
assumed to be p-values and colours are based on -log10 of those values.
"""
cmap = plt.cm.get_cmap('afmhot_r')
regions = atlas.get_atlas_labels(atlas_name)
f = lambda x: (
x.rstrip().split(',')[0],
-np.log10(float(x.rstrip().split(',')[1]))
)
data = [f(x) for x in open(filename).readlines()]
extra_regions = [x for x, y in data if x not in regions]
print 'Warning: Extra regions:', extra_regions
data = [x for x in data if x[0] in regions]
vmin, vmax = min(x[1] for x in data), max(x[1] for x in data)
data = {r: cmap(v / vmax)[:3] for r, v in data}
print data
return vmin, vmax, data
