def interpolateData(self):
import scipy.interpolate as interpolate
NyK = self.Ny - 2*self.K.value # The Capon image will be 2*K smaller in range
if VERBOSE:
print 'Start interpolating...'
# IQ-interpolation (in image domain, imag and real, hence coherent)
iq_interp_factor = 2
x_idx = np.arange(self.Nx)
y_idx = np.arange(NyK)
x_up_idx = np.linspace(0.25, self.Nx-1-0.25, (self.Nx-1)*iq_interp_factor)
y_up_idx = np.arange(NyK)
self.angles_intrp = interpolate.interp1d( x_idx, self.angles ) (x_up_idx)
self.ranges_intrp = self.ranges
if self.K.value > 0:
self.ranges_intrp = self.ranges_intrp[self.K.value:-self.K.value]
img_das_real = interpolate.RectBivariateSpline( x_idx, y_idx, self.img_das.real ) (x_up_idx, y_up_idx)
img_das_imag = interpolate.RectBivariateSpline( x_idx, y_idx, self.img_das.imag ) (x_up_idx, y_up_idx)
img_capon_real = interpolate.RectBivariateSpline( x_idx, y_idx, self.img_capon.real ) (x_up_idx, y_up_idx)
img_capon_imag = interpolate.RectBivariateSpline( x_idx, y_idx, self.img_capon.imag ) (x_up_idx, y_up_idx)
self.img_das_iq_intrp = img_das_real + 1j * img_das_imag
self.img_capon_iq_intrp = img_capon_real + 1j * img_capon_imag
# In-coherent interpolation
if self.Ky.value > 1 or self.Kx.value > 1:
NxK = (self.Nx-1) * iq_interp_factor
y_idx = np.arange(NyK)
x_idx = np.arange(NxK)
y_up_idx = np.linspace(0,NyK-1,NyK*self.Ky.value)
x_up_idx = np.linspace(0,NxK-1,NxK*self.Kx.value)
self.angles_intrp = interpolate.interp1d( x_idx, self.angles_intrp ) (x_up_idx)
if self.K.value > 0:
self.ranges_intrp = interpolate.interp1d( y_idx, self.ranges[self.K.value:-self.K.value].squeeze() ) (y_up_idx)
else:
self.ranges_intrp = interpolate.interp1d( y_idx, self.ranges ) (y_up_idx)
self.img_das_intrp = interpolate.RectBivariateSpline( x_idx, y_idx, abs(self.img_das_iq_intrp) ) (x_up_idx, y_up_idx)
self.img_capon_intrp = interpolate.RectBivariateSpline( x_idx, y_idx, abs(self.img_capon_iq_intrp) ) (x_up_idx, y_up_idx)
else: # do nothing
self.img_das_intrp = self.img_das_iq_intrp
self.img_capon_intrp = self.img_capon_iq_intrp
self.img_das_intrp = np.transpose(self.img_das_intrp)
self.img_capon_intrp = np.transpose(self.img_capon_intrp)
self.img_das_detected = self.logCompress(self.img_das_intrp, self.minDynRange.value, self.maxDynRange.value)
self.img_cap_detected = self.logCompress(self.img_capon_intrp, self.minDynRangeCapon.value, self.maxDynRangeCapon.value)
if VERBOSE:
print 'done'
def testComplexNonUniformUnivariateSpline(self):
x = np.arange(10)
y = x ** 2 + 1j * (x ** 2 + 1)
xi = np.linspace(0, 9, 91)
#
yi = mklcUnivariateSplineC(x, y, xi, 0, False)
print yi
##########
##
##app_name = "testEfficiency_mathcheck"
##math_run = run()
##events = "--events gld_request,gst_request,shared_load,shared_store"
##math_event_run = run()
#
#
#return info
##export PYTHONPATH="/home/me/Work/UiO/Phd/Code/Profile"
##python testEfficiency.py testMVDRKernelPerformance
#
##nvprof ./testEfficiency testEfficiency testMVDRKernelPerformance
M = 8
L_list = np.arange(M-1)+2
time = np.zeros((L_list.shape[0],6))
for l,L in enumerate(L_list):
time[l] = collectResults(M,L)
np.savetxt('time-M%d.txt'%M,time)
M = 16
L_list = np.arange(M-1)+2
time = np.zeros((L_list.shape[0],6))
for l,L in enumerate(L_list):
time[l] = collectResults(M,L)
np.savetxt('time-M%d.txt'%M,time)
M = 32
L_list = np.arange(M-1)+2
time = np.zeros((L_list.shape[0],6))
##########
##
##app_name = "testEfficiency_mathcheck"
##math_run = run()
##events = "--events gld_request,gst_request,shared_load,shared_store"
##math_event_run = run()
#
#
#return info
##export PYTHONPATH="/home/me/Work/UiO/Phd/Code/Profile"
##python testEfficiency.py testMVDRKernelPerformance
#
##nvprof ./testEfficiency testEfficiency testMVDRKernelPerformance
M = 8
L_list = np.arange(M - 1) + 2
time = np.zeros((L_list.shape[0], 6))
for l, L in enumerate(L_list):
time[l] = collectResults(M, L)
np.savetxt('time-M%d.txt' % M, time)
M = 16
L_list = np.arange(M - 1) + 2
time = np.zeros((L_list.shape[0], 6))
for l, L in enumerate(L_list):
time[l] = collectResults(M, L)
np.savetxt('time-M%d.txt' % M, time)
M = 32
L_list = np.arange(M - 1) + 2
time = np.zeros((L_list.shape[0], 6))
def interpolateData(self):
import scipy.interpolate as interpolate
NyK = self.Ny - 2 * self.K.value # The Capon image will be 2*K smaller in range
if VERBOSE:
print 'Start interpolating...'
# IQ-interpolation (in image domain, imag and real, hence coherent)
iq_interp_factor = 2
x_idx = np.arange(self.Nx)
y_idx = np.arange(NyK)
x_up_idx = np.linspace(0.25, self.Nx - 1 - 0.25,
(self.Nx - 1) * iq_interp_factor)
y_up_idx = np.arange(NyK)
self.angles_intrp = interpolate.interp1d(x_idx, self.angles)(x_up_idx)
self.ranges_intrp = self.ranges
if self.K.value > 0:
self.ranges_intrp = self.ranges_intrp[self.K.value:-self.K.value]
img_das_real = interpolate.RectBivariateSpline(
x_idx, y_idx, self.img_das.real)(x_up_idx, y_up_idx)
img_das_imag = interpolate.RectBivariateSpline(
x_idx, y_idx, self.img_das.imag)(x_up_idx, y_up_idx)
img_capon_real = interpolate.RectBivariateSpline(
x_idx, y_idx, self.img_capon.real)(x_up_idx, y_up_idx)
img_capon_imag = interpolate.RectBivariateSpline(
x_idx, y_idx, self.img_capon.imag)(x_up_idx, y_up_idx)
self.img_das_iq_intrp = img_das_real + 1j * img_das_imag
self.img_capon_iq_intrp = img_capon_real + 1j * img_capon_imag
# In-coherent interpolation
if self.Ky.value > 1 or self.Kx.value > 1:
NxK = (self.Nx - 1) * iq_interp_factor
y_idx = np.arange(NyK)
x_idx = np.arange(NxK)
y_up_idx = np.linspace(0, NyK - 1, NyK * self.Ky.value)
x_up_idx = np.linspace(0, NxK - 1, NxK * self.Kx.value)
self.angles_intrp = interpolate.interp1d(
x_idx, self.angles_intrp)(x_up_idx)
if self.K.value > 0:
self.ranges_intrp = interpolate.interp1d(
y_idx, self.ranges[self.K.value:-self.K.value].squeeze())(
y_up_idx)
else:
self.ranges_intrp = interpolate.interp1d(y_idx,
self.ranges)(y_up_idx)
self.img_das_intrp = interpolate.RectBivariateSpline(
x_idx, y_idx, abs(self.img_das_iq_intrp))(x_up_idx, y_up_idx)
self.img_capon_intrp = interpolate.RectBivariateSpline(
x_idx, y_idx, abs(self.img_capon_iq_intrp))(x_up_idx, y_up_idx)
else: # do nothing
self.img_das_intrp = self.img_das_iq_intrp
self.img_capon_intrp = self.img_capon_iq_intrp
self.img_das_intrp = np.transpose(self.img_das_intrp)
self.img_capon_intrp = np.transpose(self.img_capon_intrp)
self.img_das_detected = self.logCompress(self.img_das_intrp,
self.minDynRange.value,
self.maxDynRange.value)
self.img_cap_detected = self.logCompress(self.img_capon_intrp,
self.minDynRangeCapon.value,
self.maxDynRangeCapon.value)
if VERBOSE:
print 'done'
