Develop a python implementation of Gaussian elimination using partial pivoting using attached code. (just need add partial pivoting)

Develop a python code reducing echelon matrices to their reduced echelon forms. (need convert echelon to reduced echelon after gauss elimination)

You can use the python functions in the attached image.

Finding the bugs and the shortcomings of the attached code.

import numpy as np from scipy import linalg def eliminateCol(A, k=0): # guarantee that A is float type if A.dtype.kind != 'f' and A.dtype.kind != 'c': return None # load dimensions dim0,dim1 = np.shape(A) # initialize outputs swap = np.identity(dim0,dtype=float) oper = np.identity(dim0,dtype=float) # k < dim1 k = k%dim1 # if the column k is zero column, return if A[:,k].any() == False: return swap,oper i0 = 0 if k != 0: # find a row i0 whose left most k-1 entries are all zero while i0 < dim0: if A[i0,0:k].any() == False: break i0+= 1 # find a nonzero element A[i,k] to use as a pivot i = i0; while i < dim0: if A[i,k] != 0: break i+= 1 if i == dim0: return swap,oper # swap the row i with the row i0 if i != i0: A[[i0,i],] = A[[i,i0],];swap[[i0,i],] = swap[[i,i0],] # row i0 is the pivot row and A[j,k] is zero for i0= 0: if A[i,j] != 0: quot = A[i,j] / A[k,j] A[i,:] = A[i,:] - quot*A[k,:] oper[i,k] = -quot i-=1 return oper def eliminateScale(A): # guarantee that A is float type if A.dtype.kind != 'f' and A.dtype.kind != 'c': return None # load dimensions dim0,dim1 = np.shape(A) # initialize outputs oper = np.identity(dim0,dtype=float) # find pivots i = 0 while i < dim0: j = 0 while j < dim1: if A[i,j] == 0: j+= 1; continue else: oper[i,i] = 1/A[i,j] A[i,j:] = A[i,j:]/A[i,j] break j+= 1 i+= 1 return oper