def retina_reduce(info, activity_retina, collation):
collation.insert_columns(retina_activity=activity_retina)
s = Select(collation)
s.view(int_keys=['time'],
float_keys=['retina_density', 'cortex_density', 'V1_activity', 'retina_activity'])
condition = (s.retina_density > 2.0) & (s.time == 1)
result = np.mean(s[condition].retina_activity)
print "Average std. of retina activity vs cortex density for retina density of 2.0 (@time=1): %f" % result
return collation
def select():
spec = Collate(*Collate.from_log(path))
sel = Select(spec, float_keys=['cortex_density', 'retina_density'])
data = np.random.rand(9)
selection = (sel.retina_density > 3.0) & (sel.cortex_density > 5)
assert set(sel[selection].tid) == set([5, 8])
assert repr(sel.select(selection, data)) == repr(data[selection])
list_data = [None, 3, False, object, 3.4, '32', np.int64, unicode, lambda x: x]
list_selection = [True, True, False, True] + [False]*5
assert repr(sel.select(list_selection, list_data)) == repr([None, 3,object])
new_sel = Select(sel.tolist(selection), float_keys=['cortex_density', 'retina_density'])
return new_sel
def V1_reduce(info, activity_V1, accumulator):
import pylab
collation = Collate(*info, V1_activity=activity_V1)
s = Select(collation)
s.view(float_keys=['time', 'retina_density', 'cortex_density', 'V1_activity'])
condition = (s.cortex_density > 0.5) & (s.retina_density == 2.0)
pylab.plot(s[condition].V1_activity, s[condition].cortex_density)
pylab.title('Mean activity (cortex) against cortex density at time = 1.0 for retina density=2')
[root_directory, _] = info
figure_path = os.path.join(root_directory, 'cluster_figures')
try: os.mkdir(figure_path)
except :pass
pylab.savefig(os.path.join(figure_path, 'rd2_mean_activity.png'))
print "Saved figure rd2_mean_activity.png in folder cluster_figures"
return collation
def pytable_save(collation):
loader = PILImageLoader(['.png'], transform = lambda im: im.rotate(180))
(images, metadata, imlabels) = collation.extract_files(loader, 'ColKey','ColKeyLabels')
sel = Select(collation,
float_keys=['cortex_density', 'retina_density'],
int_keys=['time', 'activity'],
visible_keys = ['cortex_density', 'retina_density', 'activity', 'ColKeyLabels'])
condition = (sel.retina_density > 3.0)
image_sel, iminfo_sel, imlabel_sel = sel.select(condition, images, metadata, imlabels)
with PyTableUtils('utils-test.h5', 'util_test', 'Testing purposes', title='Title') as pytable:
pytable.declare_group('images', 'Storing images')
pytable.declare_group('selector', 'The Select object')
for (ims, iminfos, labels) in zip(image_sel, iminfo_sel, imlabel_sel):
for (im, label, iminfo) in zip(ims, labels, iminfos):
pytable.store_array('images', np.array(im), label, iminfo=iminfo)
pytable.store_selector('selector', sel.subselector(condition), 'subselector')
