如何在Python中将多个numpy文件附加到一个numpy文件中 [英] How to append many numpy files into one numpy file in python

问题描述

我试图放入许多numpy文件来获得一个大的numpy文件，我尝试遵循这两个链接 Python按照给定的顺序将多个文件附加到一个大文件中，这就是我所做的:

I am trying to put many numpy files to get one big numpy file, I tried to follow those two links Append multiple numpy files to one big numpy file in python and Python append multiple files in given order to one big file this is what I did:

import matplotlib.pyplot as plt 
import numpy as np
import glob
import os, sys
fpath ="/home/user/Desktop/OutFileTraces.npy"
npyfilespath ="/home/user/Desktop/test"   
os.chdir(npyfilespath)
with open(fpath,'wb') as f_handle:
    for npfile in glob.glob("*.npy"):
        # Find the path of the file
        filepath = os.path.join(npyfilespath, npfile)
        print filepath
        # Load file
        dataArray= np.load(filepath)
        print dataArray
        np.save(f_handle,dataArray)
        dataArray= np.load(fpath)
        print dataArray

我得到的结果的示例:

/home/user/Desktop/Trace=96
[[ 0.01518007  0.01499514  0.01479736 ..., -0.00392216 -0.0039761
  -0.00402747]]
[[-0.00824758 -0.0081808  -0.00811402 ..., -0.0077236  -0.00765425
  -0.00762086]]
/home/user/Desktop/Trace=97
[[ 0.00614908  0.00581004  0.00549154 ..., -0.00814741 -0.00813457
  -0.00809347]]
[[-0.00824758 -0.0081808  -0.00811402 ..., -0.0077236  -0.00765425
  -0.00762086]]
/home/user/Desktop/Trace=98
[[-0.00291786 -0.00309509 -0.00329287 ..., -0.00809861 -0.00797789
  -0.00784175]]
[[-0.00824758 -0.0081808  -0.00811402 ..., -0.0077236  -0.00765425
  -0.00762086]]
/home/user/Desktop/Trace=99
[[-0.00379887 -0.00410453 -0.00438963 ..., -0.03497837 -0.0353842
  -0.03575151]]
[[-0.00824758 -0.0081808  -0.00811402 ..., -0.0077236  -0.00765425
  -0.00762086]

此行代表第一条迹线:

[[-0.00824758 -0.0081808  -0.00811402 ..., -0.0077236  -0.00765425
      -0.00762086]]

一直重复.

两天前我问了第二个问题，起初我认为我的回答是最好的，但是在尝试建模以打印并打包最终文件'OutFileTraces.npy'之后，我发现了我的代码:

I asked the second question two days ago, at first I think that I had the best answer, but after trying to model to print and lot the final file 'OutFileTraces.npy' I found that my code:

1/不会按照其顺序(trace0，trace1，trace2，...)打印"test"文件夹中的numpy文件

1/ doesn't print numpy files from folder 'test' with respecting their order(trace0,trace1, trace2,...)

2/仅保存文件中的最后一条迹线，这意味着当打印或绘制OutFileTraces.npy时，我发现只有一条迹线，这是第一条迹线.

2/ saves only the last trace in the file, I mean by that when print or plot the OutFileTraces.npy, I found just one trace , it is the first one.

所以我需要更正我的代码，因为实际上我被阻止了.如果您能帮助我，我将不胜感激.

So I need to correct my code because really I am blocked. I would be very grateful if you could help me.

先谢谢了.

推荐答案

如

加载在追加模式下使用numpy.save保存的数组

可以多次保存到打开的文件，也可以多次加载.这没有记录，可能不是首选，但是可以. savez存档是保存多个阵列的首选方法.

it is possible to save multiple times to an open file, and it possible to load multiple times. That's not documented, and probably not preferred, but it works. savez archive is the preferred method for saving multiple arrays.

这是一个玩具示例:

In [777]: with open('multisave.npy','wb') as f:
     ...:     arr = np.arange(10)
     ...:     np.save(f, arr)
     ...:     arr = np.arange(20)
     ...:     np.save(f, arr)
     ...:     arr = np.ones((3,4))
     ...:     np.save(f, arr)
     ...:     
In [778]: ll multisave.npy
-rw-rw-r-- 1 paul 456 Feb 13 08:38 multisave.npy
In [779]: with open('multisave.npy','rb') as f:
     ...:     arr = np.load(f)
     ...:     print(arr)
     ...:     print(np.load(f))
     ...:     print(np.load(f))
     ...:     
[0 1 2 3 4 5 6 7 8 9]
[ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19]
[[ 1.  1.  1.  1.]
 [ 1.  1.  1.  1.]
 [ 1.  1.  1.  1.]]

---

这是保存相同形状的数组列表的简单示例

---

Here's a simple example of saving a list of arrays of the same shape

In [780]: traces = [np.arange(10),np.arange(10,20),np.arange(100,110)]
In [781]: traces
Out[781]: 
[array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]),
 array([10, 11, 12, 13, 14, 15, 16, 17, 18, 19]),
 array([100, 101, 102, 103, 104, 105, 106, 107, 108, 109])]
In [782]: arr = np.array(traces)
In [783]: arr
Out[783]: 
array([[  0,   1,   2,   3,   4,   5,   6,   7,   8,   9],
       [ 10,  11,  12,  13,  14,  15,  16,  17,  18,  19],
       [100, 101, 102, 103, 104, 105, 106, 107, 108, 109]])

In [785]: np.save('mult1.npy', arr)

In [786]: data = np.load('mult1.npy')
In [787]: data
Out[787]: 
array([[  0,   1,   2,   3,   4,   5,   6,   7,   8,   9],
       [ 10,  11,  12,  13,  14,  15,  16,  17,  18,  19],
       [100, 101, 102, 103, 104, 105, 106, 107, 108, 109]])
In [788]: list(data)
Out[788]: 
[array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]),
 array([10, 11, 12, 13, 14, 15, 16, 17, 18, 19]),
 array([100, 101, 102, 103, 104, 105, 106, 107, 108, 109])]

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