可以说我有
lags = [0,30,60,90,120,150,180,np.inf]
和
list = [[500,800,1000,200,1500],[220,450,350,1070,1780],[900,1780,1450,100],[340,670,830,1370,1420],[850,630,1230,1670,910]] angle = [[50,80,100,20,150],[22,45,35,107,178],[90,178,145,10],[34,67,83,137,142],[85,63,123,167,91]]
我想将每个元素放在列表中,并根据其值将其存储在不同的单独数组中;
for all list.values where angles.value is less than 30 list1 = [200,220,100] for all list.values where angles.value is between 30 and 60 list2 = [500,340] for all list.values where angles.value is between 60 and 90 list3 = [800,850,630]
等等..
我做了这样的事情:
sortlist = defaultdict(list) ulist = np.unique(list) uangle = np.unique(angle) for lag in lags: count += 1 for k,dummy_val in enumerate(uangle): if lag <= uangle[k] < lag + 1: sortlist[count].append(ulist[k])
解决方法
这是一个矢量化的方法 –
an = angle.ravel() sidx = an.argsort() cut_idx = np.searchsorted(an[sidx],lags) out = np.split(list1.ravel()[sidx],cut_idx[1:-1])
样本输入,输出 –
In [97]: lags = np.array([0,np.inf]) ...: ...: list1 = np.array([[500,\ ...: [220,\ ...: [900,...: [340,\ ...: [850,910]]) ...: ...: angle = np.array([[50,\ ...: [22,\ ...: [90,...: [34,\ ...: [85,91]]) ...: In [99]: out Out[99]: [array([100,220]),# <----- 0 to 30 array([340,500]),# <----- 30 to 60 array([630,850]),# <----- 60 to 90 array([ 900,910,1070]),# <----- 90 to 120 array([1230,1420,1450]),# <----- 120 to 150 array([1500,1780]),# <----- 150 to 180 array([],dtype=int64)] # <----- 180 to Inf
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