Re-uploaded as plain files. Put attached files in /tto folder at same level as /pyinstaller-develop folder. Create .pyd with cythonize.py. Generate --onedir executable using command line options in 'make.txt' from within /tto folder. Run executable from /dist.
The sample example is from http://docs.scipy.org/doc/scipy/reference/tutorial/optimize.html#still-too-slow-preconditioning. Works in python interpreter; works with pyd files; doesn't work with pyinstaller - see run-time "ImportError cannot import name Random" message. Note that source files do not directly use random or random.Random. Environment: windows XP/7, python 2.7.4, numpy 1.7.1, scipy 0.12, cython 0.18, pyinstaller 2.0, with and without virtualenv. -- You received this message because you are subscribed to the Google Groups "PyInstaller" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. To post to this group, send email to [email protected]. Visit this group at http://groups.google.com/group/pyinstaller?hl=en. For more options, visit https://groups.google.com/groups/opt_out.
python ..//pyinstaller-develop//pyinstaller.py --noconfirm --log-level=ERROR --onedir --console --nowindowed tto.spec
tto.spec
Description: Binary data
import other
if __name__ == '__main__':
other.solve()
## usage: python setup.py build_ext --inplace
import numpy
from distutils.core import setup
# from distutils.extension import Extension
from Cython.Distutils.extension import Extension
from Cython.Distutils import build_ext
ext_modules = [Extension("other", ["other.pyx"])]
setup(
cmdclass = {'build_ext': build_ext},
ext_modules = ext_modules,
include_dirs=[numpy.get_include()]
)
# http://docs.scipy.org/doc/scipy/reference/tutorial/optimize.html import numpy as np from scipy.optimize import root from scipy.sparse import spdiags, kron from scipy.sparse.linalg import spilu, LinearOperator from numpy import cosh, zeros_like, mgrid, zeros, eye # parameters nx, ny = 75, 75 hx, hy = 1./(nx-1), 1./(ny-1) P_left, P_right = 0, 0 P_top, P_bottom = 1, 0 def get_preconditioner(): """Compute the preconditioner M""" diags_x = zeros((3, nx)) diags_x[0,:] = 1/hx/hx diags_x[1,:] = -2/hx/hx diags_x[2,:] = 1/hx/hx Lx = spdiags(diags_x, [-1,0,1], nx, nx) diags_y = zeros((3, ny)) diags_y[0,:] = 1/hy/hy diags_y[1,:] = -2/hy/hy diags_y[2,:] = 1/hy/hy Ly = spdiags(diags_y, [-1,0,1], ny, ny) J1 = kron(Lx, eye(ny)) + kron(eye(nx), Ly) # Now we have the matrix `J_1`. We need to find its inverse `M` -- # however, since an approximate inverse is enough, we can use # the *incomplete LU* decomposition J1_ilu = spilu(J1) # This returns an object with a method .solve() that evaluates # the corresponding matrix-vector product. We need to wrap it into # a LinearOperator before it can be passed to the Krylov methods: M = LinearOperator(shape=(nx*ny, nx*ny), matvec=J1_ilu.solve) return M def solve(preconditioning=True): """Compute the solution""" count = [0] def residual(P): count[0] += 1 d2x = zeros_like(P) d2y = zeros_like(P) d2x[1:-1] = (P[2:] - 2*P[1:-1] + P[:-2])/hx/hx d2x[0] = (P[1] - 2*P[0] + P_left)/hx/hx d2x[-1] = (P_right - 2*P[-1] + P[-2])/hx/hx d2y[:,1:-1] = (P[:,2:] - 2*P[:,1:-1] + P[:,:-2])/hy/hy d2y[:,0] = (P[:,1] - 2*P[:,0] + P_bottom)/hy/hy d2y[:,-1] = (P_top - 2*P[:,-1] + P[:,-2])/hy/hy return d2x + d2y + 5*cosh(P).mean()**2 # preconditioner if preconditioning: M = get_preconditioner() else: M = None # solve guess = zeros((nx, ny), float) sol = root(residual, guess, method='krylov', options={'disp': True, 'jac_options': {'inner_M': M}}) print 'Residual', abs(residual(sol.x)).max() print 'Evaluations', count[0] return sol.x def solve(): sol = solve(preconditioning=True)
other.pyx
Description: Binary data
