npx 0.0.4

Some useful extensions for NumPy

npx

NumPy and SciPy are large libraries used everywhere in scientific computing. That's why breaking backwards-compatibility comes as a significant cost and is almost always avoided, even if the API of some methods is arguably lacking. This package provides drop-in wrappers "fixing" those.

If you have a fix for a NumPy method that can't go upstream for some reason, feel free to PR here.

• npx.dot(a, b)
  

  Forms the dot product between the last axis of a and the first axis of b.

  (Not the second-last axis of b as numpy.dot(a, b).)

• npx.solve(A, b)
  

  Solves a linear equation system with a matrix of shape (n, n) and an array of shape (n, ...). The output has the same shape as the second argument.

• npx.sum_at(a, idx, minlength: int = 0)
  npx.add_at(out, idx, a)
  

  Returns an array with entries of a summed up at indices idx with a minumum length of minlength. idx can have any shape as long as it's matching a. The output shape is (minlength,...).

  The numpy equivalent numpy.add.at is much slower:

  

  (See also this numpy bug.)

• npx.unique_rows(a, return_inverse=False, return_counts=False)
  

  Returns the unique rows of the integer array a. The numpy alternative np.unique(a, axis=0) is slow.

• sol, info = npx.cg(A, b, tol=1.0e-10)
  sol, info = npx.minres(A, b, tol=1.0e-10)
  sol, info = npx.gmres(A, b, tol=1.0e-10)
  

  sol is the solution of the linear system A @ x = b (or None if no convergence), and info contains some useful data, e.g., info.resnorms. The methods are wrappers around SciPy's iterative solvers.

License

npx is published under the MIT license.