def test_matrix_formats(self):
# Do dense, csr, bsr and csc versions of A all yield the same solver
A = poisson((7, 7), format='csr')
cases = [A.tobsr(blocksize=(1, 1))]
cases.append(A.tocsc())
cases.append(A.toarray())
rs_old = ruge_stuben_solver(A, max_coarse=10)
for AA in cases:
rs_new = ruge_stuben_solver(AA, max_coarse=10)
assert(np.abs(np.ravel(rs_old.levels[-1].A.toarray() - rs_new.levels[-1].A.toarray())).max() < 0.01)
rs_old = rs_new
def test_poisson(self):
cases = []
cases.append((500,))
cases.append((250, 250))
cases.append((25, 25, 25))
for case in cases:
A = poisson(case, format='csr')
np.random.seed(0) # make tests repeatable
x = np.random.rand(A.shape[0])
b = A*np.random.rand(A.shape[0]) # zeros_like(x)
ml = ruge_stuben_solver(A, max_coarse=50)
res = []
x_sol = ml.solve(b, x0=x, maxiter=20, tol=1e-12,
residuals=res)
del x_sol
avg_convergence_ratio = (res[-1]/res[0])**(1.0/len(res))
assert(avg_convergence_ratio < 0.20)
def test_poisson(self):
cases = []
cases.append((500,))
cases.append((250, 250))
cases.append((25, 25, 25))
for case in cases:
A = poisson(case, format='csr')
np.random.seed(0) # make tests repeatable
x = sp.rand(A.shape[0])
b = A*sp.rand(A.shape[0]) # zeros_like(x)
ml = ruge_stuben_solver(A, max_coarse=50)
res = []
x_sol = ml.solve(b, x0=x, maxiter=20, tol=1e-12,
residuals=res)
del x_sol
avg_convergence_ratio = (res[-1]/res[0])**(1.0/len(res))
assert(avg_convergence_ratio < 0.20)
def run_cases(self, opts):
for A in self.cases:
ml = ruge_stuben_solver(A, max_coarse=10)
np.random.seed(0) # make tests repeatable
x = sp.rand(A.shape[0])
b = A*sp.rand(A.shape[0]) # zeros_like(x)
res = []
x_sol = ml.solve(b, x0=x, maxiter=20, tol=1e-12,
residuals=res)
del x_sol
avg_convergence_ratio = (res[-1]/res[0])**(1.0/len(res))
assert(avg_convergence_ratio < 0.20)
import scipy
from pyamg.gallery import stencil_grid
from pyamg.gallery.diffusion import diffusion_stencil_2d
from pyamg.strength import classical_strength_of_connection
from pyamg.classical.classical import ruge_stuben_solver
from convergence_tools import print_cycle_history
if __name__ == '__main__':
n = 100
nx = n
ny = n
# Rotated Anisotropic Diffusion
stencil = diffusion_stencil_2d(type='FE',epsilon=0.001,theta=scipy.pi/3)
A = stencil_grid(stencil, (nx,ny), format='csr')
S = classical_strength_of_connection(A, 0.0)
numpy.random.seed(625)
x = scipy.rand(A.shape[0])
b = A*scipy.rand(A.shape[0])
ml = ruge_stuben_solver(A, max_coarse=10)
resvec = []
x = ml.solve(b, x0=x, maxiter=20, tol=1e-14, residuals=resvec)
print_cycle_history(resvec, ml, verbose=True, plotting=True)
from biofilm.classify import flat from biofilm.model import media from solver_diagnostics import solver_diagnostics import numpy as np cells = flat.make_image() boundary = media.make_boundary_layer(cells, 5).flatten() M = media.make_matrix(cells, boundary, 1.0) rhs = np.zeros(cells.size) rhs[boundary] = 1 from pyamg.classical.classical import ruge_stuben_solver import scipy from convergence_tools import print_cycle_history ml = ruge_stuben_solver(M, max_coarse=500) x0 = np.zeros_like(rhs)#np.linspace(0, 1, cells.shape[0])[:, np.newaxis].repeat(cells.shape[1], axis=1) resvec = [] x = ml.solve(rhs, x0=x0, maxiter=20, tol=1e-8, residuals=resvec) print_cycle_history(resvec, ml, verbose=True, plotting=True) #solver_diagnostics(M) #from solver_diagnostic import solver_diagnostic #solver_diagnostic(M)
