title: Is it possible to have Segmentation Fault using numpy eigenvectors calculation in numpy version 1.7.1
body: <p>I'm using a cluster which has a very old  version of Numpy (1.7.1). I'm using python 3.8.5 (but in 2.7 the problem is the same). I have a code which involves calculating the eigenvector of a large hermitian matrix and the do some calculations with those vectors.</p>
  <p>In my computer, with numpy 1.21.0 I can go basically any size that my RAM can but in The cluster it work until N (being the  linear size of the matrix. [the total is N^2]) equals 2*35^2 but beyond it gives segmentantion fault (core dumped).</p>
  <p>I Know that this is a thing in C/C++ but in Python I don't know how to solve it.</p>
  <p>Since the only difference between my PC and the Server is the numpy version that's it the problem or maybe I have a undercover bug in the code?</p>
  <p>I give the GitHub Link for the code: <a href="https://github.com/nic677/Chern_Number/blob/main/Chern_Wfixo_Bvariable.py" rel="nofollow noreferrer">https://github.com/nic677/Chern_Number/blob/main/Chern_Wfixo_Bvariable.py</a></p>
  <p><strong>atention</strong>: The function <em>Hamil</em> it's just creating the matrix, the code itself it's under it.</p>
  <p><strong>Aditional Info</strong>: I Submit the program to cluster by slurm</p>