def fenxi(path="param_file.txt"):
    with open(path, "r") as f:
        data = f.readlines()
        return data

if __name__ == '__main__':
    import numpy as np

    # np.set_printoptions(threshold=np.nan)
    np.set_printoptions(formatter={'float': '{: 0.3f}'.format})
    data_o=fenxi()

    biaotou=data_o[0].replace("\n","").split(";")

    no_show=[]
    for index,name in enumerate(biaotou[::-1]):
        if 'batch_norm' in name or 'bias' in name:
            biaotou.remove(name)
            no_show.append(index)


    print(len(biaotou),biaotou)

    max_list= []
    data_list=[]
    epoch_index=0
    for index, row in enumerate(data_o):
        if index>0:
            rows=row.split(";")
            row_1=rows[0].split(" ")
            rows[0]=row_1[3]
            rows= [float(i) for i in rows[:-1]]
            if int(row_1[0])==epoch_index:
                for index, name in enumerate(rows[::-1]):
                    if index+1 in no_show:
                        rows.remove(name)
                data_list.append(rows)
            else:
                max_list.append(np.max(np.asarray(data_list),axis=0).reshape(len(biaotou)-1))
                # print(epoch_index,np.max(np.asarray(data_list),axis=0),end='')
                epoch_index=int(row_1[0])
                data_list = []
    np.savetxt("dets1.txt", max_list, fmt='%.4f', delimiter=',')

    with open("dets1.txt", 'r+', encoding="utf-8") as  data:
        # data = open(fsb, 'r+')
        old = data.read()
        data.seek(0)
        data.write(",".join(biaotou)+"\n")
        data.write(old)