03_perform_simple.py

"""
3. Example: Performance for flattened matrices
----------------------------------------------

Here we compare all algorithms for solving pentadiagonal systems provided
by pentapy (except umf).

To use this script you need to have the following packages installed:

    * scipy
    * perfplot
    * matplotlib
"""

import numpy as np
import perfplot

from pentapy import solve, tools


def get_les(size):
    mat = (np.random.random((5, size)) - 0.5) * 1e-5
    V = np.array(np.random.random(size) * 1e5)
    return mat, V


def solve_1(in_val):
    """PTRANS-I"""
    mat, V = in_val
    return solve(mat, V, is_flat=True, index_row_wise=False, solver=1)


def solve_2(in_val):
    """PTRANS-II"""
    mat, V = in_val
    return solve(mat, V, is_flat=True, index_row_wise=False, solver=2)


def solve_3(in_val):
    mat, V = in_val
    return solve(mat, V, is_flat=True, index_row_wise=False, solver=3)


def solve_4(in_val):
    mat, V = in_val
    return solve(mat, V, is_flat=True, index_row_wise=False, solver=4)


def solve_5(in_val):
    mat, V = in_val
    M = tools.create_full(mat)
    return np.linalg.solve(M, V)


perfplot.show(
    setup=get_les,
    kernels=[solve_1, solve_2, solve_3, solve_4, solve_5],
    labels=[
        "PTRANS-I",
        "PTRANS-II",
        "scipy.linalg.solve_banded",
        "scipy.sparse.linalg.spsolve",
        "numpy.linalg.solve",
    ],
    n_range=[2**k for k in range(2, 13)],
    xlabel="Size [n]",
    logy=True,
)