npx 0.0.12

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.

dot

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).)

np.solve

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.

sum_at/add_at

npx.sum_at(a, idx, minlength=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:

Corresponding issue report

unique_rows

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.

Corresponding issue report

isin_rows

npx.isin_rows(a, b)

Returns a boolean array of length len(a) specifying if the rows a[k] appear in b. Similar to NumPy's own np.isin which only works for scalars.

SciPy Krylov methods

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, info = npx.bicg(A, b, tol=1.0e-10)
sol, info = npx.bicgstab(A, b, tol=1.0e-10)
sol, info = npx.cgs(A, b, tol=1.0e-10)
sol, info = npx.qmr(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.

Relevant issues:

• inconsistent number of callback calls between cg, minres

SciPy minimization

def f(x):
    return (x ** 2 - 2) ** 2


x0 = 1.5
out = npx.minimize(f, x0)

In SciPy, the result from a minimization out.x will always have shape (n,), no matter the input vector. npx changes this to respect the input vector shape.

Corresponding issue report

License

npx is published under the MIT license.