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 testIterativBuildRinv(self):
M = self.sonardata_n
L = 24
Yavg = 5
d = 0 # diagonal loading as a function of matrix trace is not supported. Use the squared sum of channel data (i.e. the total energy)
invR = wb.iterativeBuildRinv(self.sonardata_ar, M, L, Yavg, d)
invRa = mynp.dot(invR, self.sonardata_a)
self.assertMatrixAlmosteEqual(self.sonardata_Ria, invRa, 7)
x = mynp.ones(M, dtype=complex)
invR = wb.iterativeBuildUpRinv(invR, x, M, L)
invR = wb.iterativeBuildDownRinv(invR, x, M, L)
invRa = mynp.dot(invR, self.sonardata_a)
self.assertMatrixAlmosteEqual(self.sonardata_Ria, invRa, 7)
def testComplexCholeskySolver(self):
N = 32
x = np.random.normal(loc=0.0, scale=1.0, size=(N,)) + 1j * np.random.normal(loc=0.0, scale=1.0, size=(N,))
A = np.outer(x, x.conj()).astype("complex128")
A = A + 0.5 * np.eye(N)
b = np.ones(N, dtype=np.complex128)
# A = np.array([[3+0j,5+1j],[5-1j,14+0j]])
# b = np.array([1+0j,1+0j])
yi = mklcSolveCholeskyC(A, b)
print yi
print "Numpy reference:"
C = np.linalg.solve(A, b)
print C
print "hello"
def runTradoffAnalysis():
datatag = 'motion phantom 16x 3.4 MHz 20dB30dB'
#filename = 'motion_phantom_16x'
#datatag.append('Vingmed data liver 2STB 1')
#datatag.append('Vingmed data liver 2STB 2')
#datatag.append('Vingmed data cardiac 2STB 1')
#datatag.append('Vingmed data cardiac 4MLA 1')
multiFile = True
capon = CaponProcessor(datatag, multiFile)
# set parameters for capon processor
capon.image_idx.updateValue(0)
capon.sliceRangeStart.updateValue(120) #(100)
capon.sliceAngleStart.updateValue(195) #(350)#200
capon.sliceRangeEnd.updateValue(145) #(120)#0
capon.sliceAngleEnd.updateValue(780) #(600)
capon.Ky.updateValue(4) # incoherent interpolation in range
capon.Kx.updateValue(4) # in azimuth
capon.Nb.updateValue(0)
#capon.L.updateValue()
capon.K.updateValue(2)
capon.minDynRange.updateValue(-20)
capon.maxDynRange.updateValue(35)
capon.minDynRangeCapon.updateValue(-20)
capon.maxDynRangeCapon.updateValue(35)
#capon.apod.updateValue(0) #'Apodization: 0=uniform, 1=hamming'
# position profile
#capon.profilePos = [0.0, 93.0, 0.0]
#Nb_L_range = range(32, 2, -1)
Nb_L_range = range(31, 0, -2)
fps = 25.0
p_depth = 90.0 #93.0 #mm
#p_depth += 0.2148 # add 1/4 of the pulse length
p_depth += 0.1388 #compensate for extra travel distance in elevation
frames = range(7, 30)
res_limit = 6.0
fig = pl.figure()
ax = fig.add_subplot(1, 1, 1)
for b in [True, False]:
resolution = []
if not b:
capon.L.resetValue()
capon.d.updateValue(-1) #1)
else:
capon.d.updateValue(-1) #1)
for v in Nb_L_range:
print 'Value is: ', v, ' b is:', b
if b:
capon.L.updateValue(v)
else:
capon.Nb.updateValue(v)
#capon.d.updateValue((1.0/30.0)*v - 61.0/30.0) # make d go from 0.1 at Nb=31 to 0.01 at Nb=1
resolution.append(
findResolution(capon,
frames,
res_limit,
p_depth,
fps,
highres=True))
if b:
ax.plot(Nb_L_range,
resolution,
c=(0.0, 0.0, 0.0),
ls='-',
marker='o',
lw=2.0,
label='Reducing subarrays')
else:
ax.plot(Nb_L_range,
resolution,
c=(0.2, 0.2, 0.2),
ls='-',
marker='s',
lw=2.0,
label='Reducing beamspace')
das_res = findResolution(capon,
frames,
res_limit,
p_depth,
fps,
highres=False)
ax.plot(Nb_L_range,
das_res * np.ones(len(Nb_L_range)),
c=(0.4, 0.4, 0.4),
ls='--',
lw=2.0,
label='Delay-and-sum')
#ax.set_ylim(ax.get_ylim()[0]-0.1, ax.get_ylim()[1]+0.1)
ax.set_ylim(ax.get_ylim()[0], ax.get_ylim()[1] + 0.1)
ax.set_xlim([Nb_L_range[0], Nb_L_range[-1]])
ax.set_title('Trading resolution for speed', fontsize='x-large')
ax.set_xlabel('$L$ or $N_b$', fontsize='large')
ax.set_ylabel('Resolution [mm]', fontsize='large')
ax.legend(loc=2, markerscale=0.5)
fig.savefig('speed_res_trade.png',
dpi=300,
bbox_inches='tight',
pad_inches=0.5)
def runTradoffAnalysis():
datatag = 'motion phantom 16x 3.4 MHz 20dB30dB'
#filename = 'motion_phantom_16x'
#datatag.append('Vingmed data liver 2STB 1')
#datatag.append('Vingmed data liver 2STB 2')
#datatag.append('Vingmed data cardiac 2STB 1')
#datatag.append('Vingmed data cardiac 4MLA 1')
multiFile = True
capon = CaponProcessor(datatag, multiFile)
# set parameters for capon processor
capon.image_idx.updateValue(0)
capon.sliceRangeStart.updateValue(120)#(100)
capon.sliceAngleStart.updateValue(195)#(350)#200
capon.sliceRangeEnd.updateValue(145)#(120)#0
capon.sliceAngleEnd.updateValue(780)#(600)
capon.Ky.updateValue(4) # incoherent interpolation in range
capon.Kx.updateValue(4) # in azimuth
capon.Nb.updateValue(0)
#capon.L.updateValue()
capon.K.updateValue(2)
capon.minDynRange.updateValue(-20)
capon.maxDynRange.updateValue(35)
capon.minDynRangeCapon.updateValue(-20)
capon.maxDynRangeCapon.updateValue(35)
#capon.apod.updateValue(0) #'Apodization: 0=uniform, 1=hamming'
# position profile
#capon.profilePos = [0.0, 93.0, 0.0]
#Nb_L_range = range(32, 2, -1)
Nb_L_range = range(31, 0, -2)
fps = 25.0
p_depth = 90.0#93.0 #mm
#p_depth += 0.2148 # add 1/4 of the pulse length
p_depth += 0.1388#compensate for extra travel distance in elevation
frames = range(7, 30)
res_limit = 6.0
fig = pl.figure()
ax = fig.add_subplot(1,1,1)
for b in [True, False]:
resolution = []
if not b:
capon.L.resetValue()
capon.d.updateValue(-1)#1)
else:
capon.d.updateValue(-1)#1)
for v in Nb_L_range:
print 'Value is: ', v, ' b is:', b
if b:
capon.L.updateValue(v)
else:
capon.Nb.updateValue(v)
#capon.d.updateValue((1.0/30.0)*v - 61.0/30.0) # make d go from 0.1 at Nb=31 to 0.01 at Nb=1
resolution.append(findResolution(capon, frames, res_limit, p_depth, fps, highres=True))
if b:
ax.plot(Nb_L_range, resolution, c=(0.0,0.0,0.0), ls='-', marker='o', lw=2.0, label='Reducing subarrays')
else:
ax.plot(Nb_L_range, resolution, c=(0.2,0.2,0.2), ls='-', marker='s', lw=2.0, label='Reducing beamspace')
das_res = findResolution(capon, frames, res_limit, p_depth, fps, highres=False)
ax.plot(Nb_L_range, das_res*np.ones(len(Nb_L_range)), c=(0.4,0.4,0.4), ls='--', lw=2.0, label='Delay-and-sum')
#ax.set_ylim(ax.get_ylim()[0]-0.1, ax.get_ylim()[1]+0.1)
ax.set_ylim(ax.get_ylim()[0], ax.get_ylim()[1]+0.1)
ax.set_xlim([Nb_L_range[0], Nb_L_range[-1]])
ax.set_title('Trading resolution for speed', fontsize='x-large')
ax.set_xlabel('$L$ or $N_b$', fontsize='large')
ax.set_ylabel('Resolution [mm]', fontsize='large')
ax.legend(loc=2, markerscale=0.5)
fig.savefig('speed_res_trade.png', dpi=300, bbox_inches='tight', pad_inches=0.5)