def rootpy(tree):
return np.unique(tree_to_ndarray(tree, branches=["a_x"]))
# define the model
class Event(TreeModel):
x = FloatCol()
y = FloatCol()
z = FloatCol()
i = IntCol()
tree = Tree("test", model=Event)
# fill the tree
for i in xrange(100000):
tree.x = gauss(.5, 1.)
tree.y = gauss(.3, 2.)
tree.z = gauss(13., 42.)
tree.i = i
tree.fill()
tree.write()
# convert tree into a numpy record array
from rootpy.root2array import tree_to_recarray, tree_to_ndarray
array = tree_to_recarray(tree)
print array
print array.x
print array.i
print tree_to_ndarray(tree)
f.close()
'include_weight=True)')
print "time without profiler overhead:"
t1 = time.time()
arr2 = tree_to_recarray(tree, branches=branches,
include_weight=True)
t2 = time.time()
print "%f seconds" % (t2 - t1)
print '=' * 40
print "Comparison of output:"
print
print "Python result:"
print arr1
print arr1['a_x']
print arr1['weight']
print
print "C result:"
print arr2
print arr2['a_x']
print arr2['weight']
arr3 = tree_to_ndarray(tree, branches=branches,
include_weight=True)
print
print "C result as ndarray:"
print arr3
print "Using compiled C extension..."
cProfile.run('arr2 = tree_to_recarray(tree, branches=branches,'
'include_weight=True)')
print "time without profiler overhead:"
t1 = time.time()
arr2 = tree_to_recarray(tree, branches=branches, include_weight=True)
t2 = time.time()
print "%f seconds" % (t2 - t1)
print '=' * 40
print "Comparison of output:"
print
print "Python result:"
print arr1
print arr1['a_x']
print arr1['weight']
print
print "C result:"
print arr2
print arr2['a_x']
print arr2['weight']
arr3 = tree_to_ndarray(tree, branches=branches, include_weight=True)
print
print "C result as ndarray:"
print arr3
