python - 将嵌套列表转换为 numpy 数组的有效方法

Question

我有一个具有不同列表大小和类型的嵌套列表。

def read(f,tree,objects):

Event=[]
for o in objects:
    #find different features of one class 
    temp=[i.GetName() for i in tree.GetListOfBranches() if i.GetName().startswith(o)]
    tempList=[] #contains one class of objects
    for t in temp:
        #print t
        tempList.append(t)
        comp=np.asarray(getattr(tree,t))
        tempList.append(comp)
Event.append(tempList)

return Event



def main():
    path="path/to/file"
    objects= ['TauJet', 'Jet', 'Electron', 'Muon', 'Photon', 'Tracks', 'ETmis', 'CaloTower']

    f=ROOT.TFile(path)
    tree=f.Get("RecoTree")
    tree.GetEntry(100)
    event=read(f,tree,objects)

例如事件[0]的结果是

['TauJet', array(1), 'TauJet_E', array([ 31.24074173]), 'TauJet_Px', array([-28.27997971]), 'TauJet_Py', array([-13.18042469]), 'TauJet_Pz', array([-1.08304048]), 'TauJet_Eta', array([-0.03470514]), 'TauJet_Phi', array([-2.70545626]), 'TauJet_PT', array([ 31.20065498]), 'TauJet_Charge', array([ 1.]), 'TauJet_NTracks', array([3]), 'TauJet_EHoverEE', array([ 1745.89221191]), 'TauJet_size', array(1)]

如何将其转换为 numpy 数组？

注意 1:np.asarray(event, "object") 很慢。我正在寻找更好的方法。就我没有固定类型而言，np.fromiter() 也不适用

注意 2:我不知道我的事件的长度。

注意 3:如果它使事情变得更容易，我也可以使用名称。

Answer 1

您可以尝试这样的操作，但我不确定它的速度有多快。这会为第一行创建一个 numpy 记录数组。

data = event[0]
keys = data[0::2]
vals = data[1::2]
#there are some zero-rank arrays in there, so need to check for those, 
#but I think just recasting them to a np.float should work. 
temp = [np.float(v) for v in vals]
#you could also just create a np array from the line above with np.array(temp)
dtype={"names":keys, "formats":("f4")*len(vals)}
myArr = np.rec.fromarrays(temp, dtype=dtype)

#test it out
In [53]: data["TauJet_Pz"]
Out[53]: array(-1.0830404758453369, dtype=float32)


#alternatively, you could try something like this, which just creates a 2d numpy array
vals = np.array([[np.float(v) for v in row[1::2]] for row in event])
#now create a nice record array from that using the dtypes above
myRecordArray = np.rec.fromarrays(vals, dtype=dtype)