def processData(self):
if VERBOSE:
print 'Start processing image...'
if self.multiFile:
self.s = System(self.dataTag + ' %d' % (self.image_idx.value + 1))
self.Xd_i = self.s.data.Xd
else:
self.Xd_i = self.Xd[self.image_idx.value, :, :, :].copy()
self.Xd_i = self.Xd_i[self.sliceAngleStart.value:self.sliceAngleEnd.
value:self.sliceAngleStep.value,
self.sliceRangeStart.value:self.sliceRangeEnd.
value:self.sliceRangeStep.value,
self.sliceElementStart.value:self.sliceElementEnd
.value:self.sliceElementStep.value]
self.Nx = self.Xd_i.shape[0] # No. beams
self.Ny = self.Xd_i.shape[1] # No. range samples
self.Nm = self.Xd_i.shape[2] # No. channels
self.angles = self.s.data.angles.squeeze()
self.angles = self.angles[self.sliceAngleStart.value:self.sliceAngleEnd
.value:self.sliceAngleStep.value]
self.ranges = self.s.data.ranges.squeeze()
self.ranges = self.ranges[self.sliceRangeStart.value:self.sliceRangeEnd
.value:self.sliceRangeStep.value]
# Make delay and sum image
if self.apod.value is 1:
self.das_w = np.hamming(self.Nm)
self.das_w_sub = np.hamming(self.L.value)
else:
self.das_w = np.ones(self.Nm)
self.das_w_sub = np.ones(self.L.value)
self.img_das = np.dot(self.Xd_i, self.das_w) / self.Nm
if self.K.value > 0:
self.img_das = self.img_das[:, self.K.value:-self.K.
value] # truncate to get equal dim on das and capon image
# init sub/beam-space matrix
if self.Nb.value > 0:
self.B = np.ones((self.Nb.value, self.L.value))
else:
self.B = np.array([0])
# Make capon image
res_gpu = getCaponCUDA.getCaponCUDAPy(self.Xd_i, 10.0**self.d.value,
self.L.value, self.K.value,
self.B, False, False)
self.img_capon = res_gpu[0]
self.capon_weights = res_gpu[2]
#self.img_capon = self.img_das
if VERBOSE:
print 'getCaponCUDA return code: ', res_gpu[3]
print 'done.'
def processData(self):
if VERBOSE:
print 'Start processing image...'
if self.multiFile:
self.s = System(self.dataTag + ' %d'%(self.image_idx.value+1))
self.Xd_i = self.s.data.Xd
else:
self.Xd_i = self.Xd[self.image_idx.value, :, :, :].copy()
self.Xd_i = self.Xd_i[self.sliceAngleStart.value:self.sliceAngleEnd.value:self.sliceAngleStep.value,
self.sliceRangeStart.value:self.sliceRangeEnd.value:self.sliceRangeStep.value,
self.sliceElementStart.value:self.sliceElementEnd.value:self.sliceElementStep.value];
self.Nx = self.Xd_i.shape[0] # No. beams
self.Ny = self.Xd_i.shape[1] # No. range samples
self.Nm = self.Xd_i.shape[2] # No. channels
self.angles = self.s.data.angles.squeeze()
self.angles = self.angles[self.sliceAngleStart.value:self.sliceAngleEnd.value:self.sliceAngleStep.value]
self.ranges = self.s.data.ranges.squeeze()
self.ranges = self.ranges[self.sliceRangeStart.value:self.sliceRangeEnd.value:self.sliceRangeStep.value]
# Make delay and sum image
if self.apod.value is 1:
self.das_w = np.hamming(self.Nm)
self.das_w_sub = np.hamming(self.L.value)
else:
self.das_w = np.ones(self.Nm)
self.das_w_sub = np.ones(self.L.value)
self.img_das = np.dot(self.Xd_i, self.das_w) / self.Nm
if self.K.value > 0:
self.img_das = self.img_das[:,self.K.value:-self.K.value]# truncate to get equal dim on das and capon image
# init sub/beam-space matrix
if self.Nb.value > 0:
self.B = np.ones((self.Nb.value, self.L.value))
else:
self.B = np.array([0])
# Make capon image
res_gpu = getCaponCUDA.getCaponCUDAPy(self.Xd_i, 10.0**self.d.value, self.L.value, self.K.value, self.B, False, False)
self.img_capon = res_gpu[0]
self.capon_weights = res_gpu[2]
#self.img_capon = self.img_das
if VERBOSE:
print 'getCaponCUDA return code: ', res_gpu[3]
print 'done.'
def testMVDRKernelPerformance(M=32,L=16,K=0):
# c = 12345*6789
# print 'The result of 12345 x 6789 :', c
# return c
import framework.beamformer.capon.getCaponCUDA as getCaponCUDA
# import scipy.interpolate as interpolate
s = System('Holmengraa')
r = [3000,9000]
#Ny, Nx, M = s.data.Xd.shape
Xd = s.data.Xd[r[0]:r[1],:,0:M]
Nt = s.data.Nt[r[0]:r[1]]
ext=(0,62.76,Nt.max(),Nt.min())
fs = s.data.fs
Ny, Nx, M = Xd.shape
das = Xd.sum(2)
d = 0.01
# L = 16
# K = 0
V = np.array([])
# from time import sleep
# sleep(7)
Xd = Xd[:500,:100].copy()
print " Running getCaponCUDA.getCaponCUDAPy(Xd[100x10xM=%d], d, L=%d, K, V, False, False)"%(M,L)
mvdr1 = getCaponCUDA.getCaponCUDAPy(Xd, d, L, K, V, False, False)
