def aggregate_group_loop(*args, **kwargs):
"""wraps func in lambda which prevents aggregate_numpy from
recognising and optimising it. Instead it groups and loops."""
func = kwargs['func']
del kwargs['func']
return aggregate_np(*args, func=lambda x: func(x), **kwargs)
def aggregate_group_loop(*args, **kwargs):
"""wraps func in lambda which prevents aggregate_numpy from
recognising and optimising it. Instead it groups and loops."""
func = kwargs['func']
del kwargs['func']
return aggregate_np(*args, func=lambda x: func(x), **kwargs)
"""wraps func in lambda which prevents aggregate_numpy from
recognising and optimising it. Instead it groups and loops."""
func = kwargs['func']
del kwargs['func']
return aggregate_np(*args, func=lambda x: func(x), **kwargs)
print "TODO: use more extensive tests in test_accumarray.py"
print ""
print "-----simple examples----------"
test_a = np.array([12.0, 3.2, -15, 88, 12.9])
test_group_idx = np.array([1, 0, 1, 4, 1])
print "test_a: ", test_a
print "test_group_idx: ", test_group_idx
print "accumarray(test_group_idx, test_a):"
print aggregate_np(test_group_idx, test_a) # group vals by idx and sum
# array([3.2, 9.9, 0., 0., 88.])
print "accumarray(test_group_idx, test_a, sz=8, func='min', fill_value=np.nan):"
print aggregate_np(test_group_idx,
test_a,
size=8,
func='min',
fill_value=np.nan)
# array([3.2, -15., nan, 88., nan, nan, nan, nan])
print "accumarray(test_group_idx, test_a, sz=5, func=lambda x: ' + '.join(str(xx) for xx in x),fill_value='')"
print aggregate_np(test_group_idx,
test_a,
size=5,
func=lambda x: ' + '.join(str(xx) for xx in x),
fill_value='')
"""wraps func in lambda which prevents aggregate_numpy from
recognising and optimising it. Instead it groups and loops."""
func = kwargs['func']
del kwargs['func']
return aggregate_np(*args, func=lambda x: func(x), **kwargs)
print "TODO: use more extensive tests in test_accumarray.py"
print ""
print "-----simple examples----------"
test_a = np.array([12.0, 3.2, -15, 88, 12.9])
test_group_idx = np.array([1, 0, 1, 4, 1 ])
print "test_a: ", test_a
print "test_group_idx: ", test_group_idx
print "accumarray(test_group_idx, test_a):"
print aggregate_np(test_group_idx, test_a) # group vals by idx and sum
# array([3.2, 9.9, 0., 0., 88.])
print "accumarray(test_group_idx, test_a, sz=8, func='min', fill_value=np.nan):"
print aggregate_np(test_group_idx, test_a, size=8, func='min', fill_value=np.nan)
# array([3.2, -15., nan, 88., nan, nan, nan, nan])
print "accumarray(test_group_idx, test_a, sz=5, func=lambda x: ' + '.join(str(xx) for xx in x),fill_value='')"
print aggregate_np(test_group_idx, test_a, size=5, func=lambda x: ' + '.join(str(xx) for xx in x), fill_value='')
print ""
print "---------testing--------------"
print "compare against group-and-loop with numpy"
testable_funcs = {aliasing[f]: f for f in (np.sum, np.prod, np.any, np.all, np.min, np.max, np.std, np.var, np.mean)}
test_group_idx = np.random.randint(0, 1e3, 1e5)
test_a = np.random.rand(1e5) * 100 - 50
test_a[test_a > 25] = 0 # for use with bool functions