我可以防止numpy.array将元素转换为numpy数组吗? [英] Can I prevent numpy.array from casting the elements as numpy arrays?

问题描述

我正在尝试将以下内容转换为间隔对象的2x2 numpy数组:

I am using trying to convert the following to a 2x2 numpy array of interval objects:

from interval import interval  # from the "pyinterval" package
import numpy as np

np.array([ 
    [interval([1.0, 2.0]), interval([1.0, 2.0])], 
    [interval([1.0, 2.0]), interval([1.0, 2.0])]
    ])

不幸的是，此操作将间隔强制转换为numpy数组，并给了我2x2x1x2矩阵.我有什么办法可以防止这种情况发生在numpy数组或矩阵上?

Unfortunately, this operation casts the intervals as numpy arrays, and gives me a 2x2x1x2 matrix. Is there any way I can prevent this from happening with numpy arrays or matrices?

我能够通过首先填充一个空数组来获得所需的结果，其中jac是间隔列表的列表:

I was able to get the desired result by populating an empty array first, where jac is a list of lists of intervals:

arr = np.empty(shape=(2,2), dtype=interval)

for i in range(len(arr)):
    for j in range(len(arr)):
        arr[i][j] = jac[i][j]

话虽如此，我怀疑还有一种更优雅的方法可以实现这一目标.有没有更多的"pythonic"方式来做到这一点?

That being said, I suspect there's a more elegant way to achieve this. Is there a more "pythonic" way to do this?

推荐答案

即使np.array用dtype=object调用，似乎numpy也会将Sequence之类的任何内容强制转换为新维度.您可以通过使用dtype=object直接创建一个空数组，然后手动填充它来解决此问题.

It seems like numpy will coerce anything Sequence-like into a new dimension, even if np.array is called with dtype=object. You can work around this by directly making an empty array with dtype=object and then filling it manually.

interval_list = get_intervals()
interval_array = np.ndarray(len(interval_list), dtype=object)

for i, interv in enumerate(interval_list):
   interval_array[i] = interv

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