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# Copyright (c) 2020-2024, Manfred Moitzi
# License: MIT License
import time
import random
import csv
import pathlib
from ezdxf.math.linalg import (
Matrix,
BandedMatrixLU,
banded_matrix,
NumpySolver,
)
CWD = pathlib.Path("~/Desktop/Outbox").expanduser()
if not CWD.exists():
CWD = pathlib.Path(".")
def random_values(n, spread=1.0):
s = spread / 2.0
return [s - random.random() * s for _ in range(n)]
def random_matrix(shape, m1: int, m2: int):
m = Matrix(shape=shape)
for i in range(-m1, m2):
m.set_diag(-i, random_values(m.nrows))
return m
def profile_numpy_matrix_solver(count: int, A: Matrix, B: Matrix):
for _ in range(count):
lu = NumpySolver(A.matrix)
lu.solve_matrix(B.matrix)
def profile_banded_matrix_solver(count, A: Matrix, B: Matrix):
for _ in range(count):
lu = BandedMatrixLU(*banded_matrix(A, check_all=False))
lu.solve_matrix(B.matrix)
def profile(func, *args):
t0 = time.perf_counter()
func(*args)
t1 = time.perf_counter()
delta = t1 - t0
return delta
REPEAT = 5
with open(CWD / "profiling_banded_matrix.csv", mode="wt", newline="") as f:
writer = csv.writer(f, dialect="excel")
writer.writerow(["Parameters", "Standard LU", "Banded LU", "Factor"])
for size in range(10, 101, 5):
for m1, m2 in [
(1, 1),
(2, 1),
(1, 2),
(2, 2),
(2, 3),
(3, 2),
(3, 3),
(3, 4),
(4, 3),
(4, 4),
]:
A = random_matrix((size, size), m1, m2)
B = Matrix(
list(
zip(
random_values(size),
random_values(size),
random_values(size),
)
)
)
t0 = profile(profile_numpy_matrix_solver, REPEAT, A, B)
t1 = profile(profile_banded_matrix_solver, REPEAT, A, B)
factor = t0 / t1
print(
f"Matrix {size}x{size}, m1={m1}, m2={m2}, {REPEAT}x: Numpy {t0:0.3f}s Banded LU {t1:0.3}s factor: x{factor:.1f}"
)
writer.writerow(
[
f"N={size}, m1+m2+1={m1 + m2 + 1}",
round(t0, 3),
round(t1, 3),
round(factor, 1),
]
)
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