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 collate():
collation = Collate(*Collate.from_log(path),
activity=[1,2,3,4,5,6,7,8,9])
collation.extract_filelist('AllORPrefs', ['*_t{tid}_*/*Orientation_Preference.png'])
# Partition these files by topo.sim.time()
collation.partition_by_filename('AllORPrefs', 'ORPrefs', '*__{time:0>9.2f}_*', time=[1.0,5.0,10.0])
collation.extract_filelist('ORSel',
['*_t{tid}_*/*_{time:0>9.2f}_*Orientation_Selectivity.png'],
key_type={'time':float})
collation.extract_filelist('ColKey',
['*_t{tid}_*/*_{time:0>9.2f}_*Color_Key.png'],
key_type={'time':float})
collation.show()
return collation
