def precompute_generic(configuration_name, receptor_dirs, tris):
script_dir = os.path.abspath(os.path.split(__file__)[0])
os.chdir(script_dir)
########################################################
# SAVE INFO (from save_sparse_weights.py)
########################################################
# receptor_dirs, tris = make_subdivided_unit_icosahedron(n_subdivides=3) # n_subidivisions
# receptors_by_phi = sort_receptors_by_phi(receptor_dirs,nbins = 32)
edges = find_edges( tris )
verts = receptor_dirs
rad2deg = 180/math.pi
v0 = verts[0]
a_degs = [v0.angle(v)*rad2deg for v in verts[1:]]
a_degs.sort()
delta_phi_deg = a_degs[0] # inter receptor angle, 6.848549293 when 3 subdivisions of icosahedron
print 'delta_phi_deg',delta_phi_deg
delta_phi = delta_phi_deg/rad2deg
delta_rho = delta_phi * 1.1 # rough approximation. follows from caption of Fig. 18, Buchner, 1984 (in Ali)
weight_maps_64 = make_receptor_sensitivities( receptor_dirs, delta_rho_q=delta_rho, res=64 )
print 'weight_maps calculated'
#####################################
clip_thresh=1e-5
floattype=numpy.float32
weights = flatten_cubemap( weight_maps_64[0] ) # get first one to take size
n_receptors = len(receptor_dirs)
len_wm = len(weights)
print 'allocating memory...'
bigmat_64 = numpy.zeros( (n_receptors, len_wm), dtype=floattype )
print 'done'
print 'flattening, clipping, casting...'
for i, weight_cubemap in enumerate(weight_maps_64):
weights = flatten_cubemap( weight_cubemap )
if clip_thresh is not None:
weights = numpy.choose(weights<clip_thresh,(weights,0))
bigmat_64[i,:] = weights.astype( bigmat_64.dtype )
print 'done'
print 'worst gain (should be unity)',min(numpy.sum( bigmat_64, axis=1))
print 'filling spmat_64...'
sys.stdout.flush()
spmat_64 = scipy.sparse.csc_matrix(bigmat_64)
print 'done'
M,N = bigmat_64.shape
print 'Compressed to %d of %d'%(len(spmat_64.data),M*N)
faces = pseudo_voronoi(receptor_dirs,tris)
##################################################
# Save matlab version
fd = open('precomputed_%s.m' % configuration_name,'w')
fd.write( 'receptor_dirs = [ ...')
for rdir in receptor_dirs:
fd.write( '\n %s %s %s;'%( repr(rdir[0]), repr(rdir[1]), repr(rdir[2]) ) )
fd.write( '];\n\n')
fd.write( 'edges = [ ...')
for e in edges:
fd.write( '\n %d %d;'%( e[0]+1, e[1]+1 )) # convert to 1-based indexing
fd.write( '];\n\n')
fd.close()
##################################################
receptor_dir_slicer = {None:slice(0,len(receptor_dirs),1)}
edge_slicer = {None:slice(0,len(edges),1)}
#
fd = open('precomputed_%s.py' % configuration_name,'wb')
fd.write( '# Automatically generated by %s\n'%os.path.split(__name__)[-1])
fd.write( 'import numpy\n')
fd.write( 'import scipy\n')
fd.write( 'import scipy.sparse\n')
fd.write( 'import scipy.io\n')
fd.write( 'import cgtypes # cgkit 1.x\n')
fd.write( 'from cgtypes import vec3, quat #cgkit 1.x\n')
fd.write( 'import os\n')
fd.write( 'datadir = os.path.split(__file__)[0]\n')
fd.write( 'cube_order = %s\n'%repr(cube_order) )
save_as_python(fd, receptor_dir_slicer, 'receptor_dir_slicer', fname_extra='_synthetic' )
save_as_python(fd, edge_slicer, 'edge_slicer', fname_extra='_synthetic' )
save_as_python(fd, spmat_64, 'receptor_weight_matrix_64', fname_extra='_synthetic' )
save_as_python(fd, map(make_repr_able,receptor_dirs), 'receptor_dirs', fname_extra='_synthetic' )
save_as_python(fd, tris, 'triangles')
save_as_python(fd, edges, 'edges')
save_as_python(fd, map(make_repr_able,faces), 'hex_faces')
# save_as_python(fd, receptors_by_phi, 'receptors_by_phi',fname_extra='_synthetic' )
fd.write( '\n')
fd.write( '\n')
fd.write( '\n')
extra = open('plot_receptors_vtk.py','r').read()
fd.write( extra )
fd.close()
a_degs.sort()
delta_phi_deg = a_degs[0] # inter receptor angle, 6.848549293 when 3 subdivisions of icosahedron
print 'delta_phi_deg',delta_phi_deg
delta_phi = delta_phi_deg/rad2deg
delta_rho = delta_phi * 1.1 # rough approximation. follows from caption of Fig. 18, Buchner, 1984 (in Ali)
weight_maps_64 = make_receptor_sensitivities( receptor_dirs, delta_rho_q=delta_rho, res=64 )
print 'weight_maps calculated'
#####################################
clip_thresh=1e-5
floattype=numpy.float32
weights = flatten_cubemap( weight_maps_64[0] ) # get first one to take size
n_receptors = len(receptor_dirs)
len_wm = len(weights)
print 'allocating memory...'
bigmat_64 = numpy.zeros( (n_receptors, len_wm), dtype=floattype )
print 'done'
print 'flattening, clipping, casting...'
for i, weight_cubemap in enumerate(weight_maps_64):
weights = flatten_cubemap( weight_cubemap )
if clip_thresh is not None:
weights = numpy.choose(weights<clip_thresh,(weights,0))
bigmat_64[i,:] = weights.astype( bigmat_64.dtype )
print 'done'
