save numpy array in append mode

问题:

Is it possible to save a numpy array appending it to an already existing npy-file --- something like np.save(filename,arr,mode='a')?

I have several functions that have to iterate over the rows of a large array. I cannot create the array at once because of memory constrains. To avoid to create the rows over and over again, I wanted to create each row once and save it to file appending it to the previous row in the file. Later I could load the npy-file in mmap_mode, accessing the slices when needed.

回答1:

The build-in .npy file format is perfectly fine for working with small datasets, without relying on external modules other then numpy.

However, when you start having large amounts of data, the use of a file format, such as HDF5, designed to handle such datasets, is to be preferred [1].

For instance, below is a solution to save numpy arrays in HDF5 with PyTables,

Step 1: Create an extendable EArray storage

import tables
import numpy as np

filename = 'outarray.h5'
ROW_SIZE = 100
NUM_COLUMNS = 200

f = tables.open_file(filename, mode='w')
atom = tables.Float64Atom()

array_c = f.create_earray(f.root, 'data', atom, (0, ROW_SIZE))

for idx in range(NUM_COLUMNS):
    x = np.random.rand(1, ROW_SIZE)
    array_c.append(x)
f.close()

Step 2: Append rows to an existing dataset (if needed)

f = tables.open_file(filename, mode='a')
f.root.data.append(x)

Step 3: Read back a subset of the data

f = tables.open_file(filename, mode='r')
print(f.root.data[1:10,2:20]) # e.g. read from disk only this part of the dataset

回答2:

For appending data to an already existing file using numpy.save, we should use:

f_handle = file(filename, 'a')
numpy.save(f_handle, arr)
f_handle.close()

I have checked that it works in python 2.7 and numpy 1.10.4

I have adapted the code from here, which talks about savetxt method.

回答3:

.npy files contain header which has shape and dtype of the array in it. If you know what your resulting array looks like, you can write header yourself and then data in chunks. E.g., here is the code for concatenating 2d matrices:

import numpy as np
import numpy.lib.format as fmt

def get_header(fnames):
    dtype = None
    shape_0 = 0
    shape_1 = None
    for i, fname in enumerate(fnames):
        m = np.load(fname, mmap_mode='r') # mmap so we read only header really fast
        if i == 0:
            dtype = m.dtype
            shape_1 = m.shape[1]
        else:
            assert m.dtype == dtype
            assert m.shape[1] == shape_1
        shape_0 += m.shape[0]
    return {'descr': fmt.dtype_to_descr(dtype), 'fortran_order': False, 'shape': (shape_0, shape_1)}

def concatenate(res_fname, input_fnames):
    header = get_header(input_fnames)
    with open(res_fname, 'wb') as f:
        fmt.write_array_header_2_0(f, header)
        for fname in input_fnames:
            m = np.load(fname)
            f.write(m.tostring('C'))

If you need a more general solution (edit header in place while appending) you'll have to resort to fseek tricks like in [1].

Inspired by
[1]: https://mail.scipy.org/pipermail/numpy-discussion/2009-August/044570.html (doesn't work out of the box)
[2]: https://docs.scipy.org/doc/numpy/neps/npy-format.html
[3]: https://github.com/numpy/numpy/blob/master/numpy/lib/format.py

回答4:

This is an expansion on Mohit Pandey's answer showing a full save / load example. It was tested using Python 3.6 and Numpy 1.11.3.

from pathlib import Path
import numpy as np
import os

p = Path('temp.npy')
with p.open('ab') as f:
    np.save(f, np.zeros(2))
    np.save(f, np.ones(2))

with p.open('rb') as f:
    fsz = os.fstat(f.fileno()).st_size
    out = np.load(f)
    while f.tell() < fsz:
        out = np.vstack((out, np.load(f)))

out = array([[ 0., 0.], [ 1., 1.]])

回答5:

you can try something like reading the file then add new data

import numpy as np
import os.path

x = np.arange(10) #[0 1 2 3 4 5 6 7 8 9]

y = np.load("save.npy") if os.path.isfile("save.npy") else [] #get data if exist
np.save("save.npy",np.append(y,x)) #save the new

after 2 operation:

print(np.load("save.npy")) #[0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9]