What do squared brackets do in this context in Python?-CodePudding

I'm trying to understand a script in Python for an assignment but I don't know what square brackets mean when used in a variable's name. This is the code:

import numpy as np

if __name__ == "__main__":
        
    epsilonff = 94.45
    sigmaff = 3.575
    delta = 0.01*sigmaff
    L = 10*sigmaff

    rhob = 0.004959169999999999/sigmaff**3
    kT = 77.4
    beta = 1.0/kT

    sigmasf, epssf, rhov, Delta = 3.494, 53.22, 0.114, 3.35

    x = np.arange(0,L,delta) 0.5*delta
    N = x.size

    def Vsteele(z,sigmaw,epsw,rhov,Delta):
        return 2*np.pi*rhov*epsw*sigmaw**2*Delta*((sigmaw/z)**10-(sigmaw/z)**4-sigmaw**4/(3*Delta*(z 0.61*Delta)**3))

    Vext = beta*Vsteele(x,sigmasf,epssf,rhov,Delta) beta*Vsteele(L-x,sigmasf,epssf,rhov,Delta)

    print(f'rhob is = {rhob}')

rhob is = 1.08537761e-04

I can understand that, but the following I don't get.

    n = rhob*np.ones(N,dtype=np.float32)
    nsig = int(0.5*sigmaff/delta)
    mask = Vext>beta*1e4
    n[mask] = 1.0e-16
    Vext[mask] = beta*1e4

Shouldn't the first line make an array of 1s with length N? yet this is my output: (I'd say is because of the squared brackets but I don't how do they affect)

[1.00000000e-16 1.00000000e-16 1.00000000e-16 1.00000000e-16
 1.00000000e-16 1.00000000e-16 1.00000000e-16 1.00000000e-16
 1.00000000e-16 1.00000000e-16 1.00000000e-16 1.00000000e-16
 1.00000000e-16 1.00000000e-16 1.00000000e-16 1.00000000e-16
 1.00000000e-16 1.00000000e-16 1.00000000e-16 1.00000000e-16
 1.00000000e-16 1.00000000e-16 1.00000000e-16 1.00000000e-16
 1.00000000e-16 1.00000000e-16 1.00000000e-16 1.00000000e-16
 1.00000000e-16 1.00000000e-16 1.00000000e-16 1.00000000e-16
 1.00000000e-16 1.00000000e-16 1.00000000e-16 1.00000000e-16
 1.00000000e-16 1.00000000e-16 1.00000000e-16 1.00000000e-16
 1.00000000e-16 1.00000000e-16 1.00000000e-16 1.00000000e-16
 1.00000000e-16 1.00000000e-16 1.00000000e-16 1.00000000e-16
 1.00000000e-16 1.00000000e-16 1.00000000e-16 1.00000000e-16
 1.00000000e-16 1.00000000e-16 1.00000000e-16 1.00000000e-16
 1.00000000e-16 1.00000000e-16 1.00000000e-16 1.00000000e-16
 1.00000000e-16 1.00000000e-16 1.00000000e-16 1.00000000e-16
 1.00000000e-16 1.00000000e-16 1.00000000e-16 1.00000000e-16
 1.00000000e-16 1.00000000e-16 1.00000000e-16 1.00000000e-16
 1.00000000e-16 1.00000000e-16 1.00000000e-16 1.00000000e-16
 1.00000000e-16 1.00000000e-16 1.00000000e-16 1.08537761e-04
 1.08537761e-04 1.08537761e-04 1.08537761e-04 1.08537761e-04
 1.08537761e-04 1.08537761e-04 1.08537761e-04 1.08537761e-04
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 1.08537761e-04 1.08537761e-04 1.08537761e-04 1.08537761e-04
 1.08537761e-04 1.08537761e-04 1.08537761e-04 1.08537761e-04
 1.08537761e-04 1.00000000e-16 1.00000000e-16 1.00000000e-16
 1.00000000e-16 1.00000000e-16 1.00000000e-16 1.00000000e-16
 1.00000000e-16 1.00000000e-16 1.00000000e-16 1.00000000e-16
 1.00000000e-16 1.00000000e-16 1.00000000e-16 1.00000000e-16
 1.00000000e-16 1.00000000e-16 1.00000000e-16 1.00000000e-16
 1.00000000e-16 1.00000000e-16 1.00000000e-16 1.00000000e-16
 1.00000000e-16 1.00000000e-16 1.00000000e-16 1.00000000e-16
 1.00000000e-16 1.00000000e-16 1.00000000e-16 1.00000000e-16
 1.00000000e-16 1.00000000e-16 1.00000000e-16 1.00000000e-16
 1.00000000e-16 1.00000000e-16 1.00000000e-16 1.00000000e-16
 1.00000000e-16 1.00000000e-16 1.00000000e-16 1.00000000e-16
 1.00000000e-16 1.00000000e-16 1.00000000e-16 1.00000000e-16
 1.00000000e-16 1.00000000e-16 1.00000000e-16 1.00000000e-16
 1.00000000e-16 1.00000000e-16 1.00000000e-16 1.00000000e-16
 1.00000000e-16 1.00000000e-16 1.00000000e-16 1.00000000e-16
 1.00000000e-16 1.00000000e-16 1.00000000e-16 1.00000000e-16
 1.00000000e-16 1.00000000e-16 1.00000000e-16 1.00000000e-16
 1.00000000e-16 1.00000000e-16 1.00000000e-16 1.00000000e-16
 1.00000000e-16 1.00000000e-16 1.00000000e-16 1.00000000e-16
 1.00000000e-16 1.00000000e-16 1.00000000e-16 1.00000000e-16]

CodePudding user response：

Square brackets are used in Python to index into or slice list-like objects. NumPy extends the base index functionality and allows boolean expressions to select subsets of its array objects. In your case, you are modifying every value in the array that satisfies the mask condition.

See the Python documentation: An Informal Introduction to Python - Lists

and the NumPy docs: Indexing on ndarrays