from __future__ import division
import numpy as np
import scipy
class Filter(object):
def __init__(self, nelx, nely, rmin):
"""
Filter: Build (and assemble) the index+data vectors for the coo matrix
format.
"""
nfilter = int(nelx * nely * ((2 * (np.ceil(rmin) - 1) + 1)**2))
iH = np.zeros(nfilter)
jH = np.zeros(nfilter)
sH = np.zeros(nfilter)
cc = 0
for i in range(nelx):
for j in range(nely):
row = i * nely + j
kk1 = int(np.maximum(i - (np.ceil(rmin) - 1), 0))
kk2 = int(np.minimum(i + np.ceil(rmin), nelx))
ll1 = int(np.maximum(j - (np.ceil(rmin) - 1), 0))
ll2 = int(np.minimum(j + np.ceil(rmin), nely))
for k in range(kk1, kk2):
for l in range(ll1, ll2):
col = k * nely + l
fac = rmin - np.sqrt(
((i - k) * (i - k) + (j - l) * (j - l)))
iH[cc] = row
jH[cc] = col
sH[cc] = np.maximum(0.0, fac)
cc = cc + 1
# Finalize assembly and convert to csc format
self.H = scipy.sparse.coo_matrix((sH, (iH, jH)),
shape=(nelx * nely, nelx * nely)).tocsc()
self.Hs = self.H.sum(1)
def filter_variables(self, x, xPhys, ft):
if ft == 0:
xPhys[:] = x
elif ft == 1:
xPhys[:] = np.asarray(self.H * x[np.newaxis].T / self.Hs)[:, 0]
def filter_compliance_sensitivities(self, xPhys, dc, ft):
if ft == 0:
dc[:] = (np.asarray((self.H * (xPhys * dc))[np.newaxis].T /
self.Hs)[:, 0] / np.maximum(0.001, xPhys))
elif ft == 1:
dc[:] = np.asarray(self.H * (dc[np.newaxis].T / self.Hs))[:, 0]
def filter_volume_sensitivities(self, _xPhys, dv, ft):
if ft == 0:
pass
elif ft == 1:
dv[:] = np.asarray(self.H * (dv[np.newaxis].T / self.Hs))[:, 0]
