我正在使用NumPy的array2string来编写ASCII文件。它在循环中或使用map执行Python字符串格式化：

aa = np.array2string(array.flatten(), precision=precision, separator=' ', max_line_width=(precision + 4) * ncolumns, prefix='         ', floatmode='fixed')
aa =  '         ' + aa[1:-1] + '\n'

我注意到当元素的数量少于几千个时，会出现奇怪的结果。使用map和join的性能比较符合我的预期（随着数组变大而变慢，由于NumPy函数的开销，对于小数组来说更快）：

numpy.array2string峰值的原因是什么？（100，3）数组比（50000，3）数组慢。NumPy是我的数据大小（>1000）的最佳选择，但峰值似乎很奇怪。完整代码：

import numpy as np
import perfplot

precision = 16
ncolumns = 6

# numpy method
def numpystring(array, precision, ncolumns):
    indent = '          '
    aa = np.array2string(array.flatten(), precision=precision, separator=' ', max_line_width=(precision + 6) * ncolumns,
                     prefix='         ', floatmode='fixed')
    return indent + aa[1:-1] + '\n'

# native python string creation
def nativepython_string(array, precision, ncolumns):
    fmt = '{' + f":.{precision}f" + '}'
    data_str = ''

    # calculate number of full rows
    if array.size <= ncolumns:
        nrows = 1
    else:
        nrows = int(array.size / ncolumns)

    # write full rows
    for row in range(nrows):
        shift = row * ncolumns
        data_str += '          ' + ' '.join(
            map(lambda x: fmt.format(x), array.flatten()[0 + shift:ncolumns + shift])) + '\n'

    # write any remaining data in last non-full row
    if array.size > ncolumns and array.size % ncolumns != 0:
        data_str += '          ' + ' '.join(
            map(lambda x: fmt.format(x), array.flatten()[ncolumns + shift::])) + '\n'

    return data_str

# Benchmark methods
out = perfplot.bench(
    setup=lambda n: np.random.random([n,3]),  # setup random nx3 array
    kernels=[
        lambda a: nativepython_string(a, precision, ncolumns),
        lambda a: numpystring(a, precision, ncolumns)
    ],
    equality_check=None,
    labels=["Native", "NumPy"],
    n_range=[2**k for k in range(16)],
    xlabel="Number of vectors [Nr.]",
    title="String Conversion Performance"

)

out.show(
    time_unit="us",  # set to one of ("auto", "s", "ms", "us", or "ns") to force plot units
)
out.save("perf.png", transparent=True, bbox_inches="tight")