def findResolution(caponProcessor, srcFrames, resLimit, p_depth, fps=25.0, highres=True):
capon = caponProcessor
frames = srcFrames
for frame in frames:
capon.image_idx.updateValue(frame)
capon.processData()
capon.interpolateData()
p1 = 0.5 + (1+1.5)*frame/fps
p2 = -0.5 + (1-1.5)*frame/fps
pc = (p1 + p2) / 2
p = [np.array([p1, p_depth]), np.array([p2, p_depth]), np.array([pc, p_depth])]
amp_p = []
for i in range(len(p)):
p_dist = np.linalg.norm(p[i])
p_angle = np.arctan(p[i][0]/p[i][1])
idx_range = np.argmin(abs(1000*capon.ranges_intrp-p_dist))
idx_angle = np.argmin(abs(capon.angles_intrp-p_angle))
if highres:
amp_p.append(capon.img_cap_detected[idx_range][idx_angle])
else:
amp_p.append(capon.img_das_detected[idx_range][idx_angle])
res = (amp_p[0] + amp_p[1])/2 - amp_p[2]
if (res > resLimit):
print 'Resolution measured to ', p1-p2, ' from', amp_p, ' in frame ', frame
return p1 - p2
def testCholesky(self):
# cho_solve(cho_factor(R), a)
self.R = np.array([[3, 2], [2, 3]], dtype=complex)
self.a = np.array([7, 7], dtype=complex)
self.n = 2
Ria = np.linalg.solve(self.R, self.a)
U = md.cholesky(self.R, self.n)
UT = U.conjugate().transpose()
y = ls.forwardSolve(UT, self.a, self.n)
x = ls.backtrackSolve(U, y, self.n)
self.almostEqual(Ria, x)
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 testCholesky(self):
# cho_solve(cho_factor(R), a)
self.R = np.array([[3,2],[2,3]],dtype=complex)
self.a = np.array([7,7],dtype=complex)
self.n = 2
Ria = np.linalg.solve(self.R, self.a)
U = md.cholesky(self.R, self.n)
UT = U.conjugate().transpose()
y = ls.forwardSolve(UT, self.a, self.n)
x = ls.backtrackSolve(U, y, self.n)
self.almostEqual(Ria, x)
def findResolution(caponProcessor,
srcFrames,
resLimit,
p_depth,
fps=25.0,
highres=True):
capon = caponProcessor
frames = srcFrames
for frame in frames:
capon.image_idx.updateValue(frame)
capon.processData()
capon.interpolateData()
p1 = 0.5 + (1 + 1.5) * frame / fps
p2 = -0.5 + (1 - 1.5) * frame / fps
pc = (p1 + p2) / 2
p = [
np.array([p1, p_depth]),
np.array([p2, p_depth]),
np.array([pc, p_depth])
]
amp_p = []
for i in range(len(p)):
p_dist = np.linalg.norm(p[i])
p_angle = np.arctan(p[i][0] / p[i][1])
idx_range = np.argmin(abs(1000 * capon.ranges_intrp - p_dist))
idx_angle = np.argmin(abs(capon.angles_intrp - p_angle))
if highres:
amp_p.append(capon.img_cap_detected[idx_range][idx_angle])
else:
amp_p.append(capon.img_das_detected[idx_range][idx_angle])
res = (amp_p[0] + amp_p[1]) / 2 - amp_p[2]
if (res > resLimit):
print 'Resolution measured to ', p1 - p2, ' from', amp_p, ' in frame ', frame
return p1 - p2
def iterativeBuildUpRinv(Ainv, X, M, L):
'''
Update Ainv with the data in X, using sub-arrays of length L.
Remember to normalize X before calling this function.
'''
newAinv = mynp.array(Ainv)
K = M - L + 1;
for l in range(K):
newAinv = shermanMorrisonUp(X[l:l+L], newAinv, L)
return newAinv
def iterativeBuildUpRinv(Ainv, X, M, L):
'''
Update Ainv with the data in X, using sub-arrays of length L.
Remember to normalize X before calling this function.
'''
newAinv = mynp.array(Ainv)
K = M - L + 1
for l in range(K):
newAinv = shermanMorrisonUp(X[l:l + L], newAinv, L)
return newAinv
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 start(s):
PROFILE = False
Xd_i = Xd[0, :, :, :]
res = getCaponC.getCapon(Xd_i, regCoef, L, nTimeAverage, V,
doForwardBackwardAveraging, verbose)
img_capon_ampl = res[0]
# Steering angles:
M = s['M'][0]
d = s['d'][0]
c = s['c'][0]
fc = s['fc'][0]
D = M * d
lmda = float(c) / fc
# Get the relevant parameters. Todo: Make system.open() for this
Xd = s.data.Xd
Ni = Xd.shape[0] # No. realisations
N = Xd.shape[1] # No. time samples (y-coordinates)
Ny = N # No. y-coordinates in image
Nx = Xd.shape[2] # No. x-coordinates in image
Nm = Xd.shape[3] # No. channels
# Capon parameters
regCoef = 1.0 / 100 # Diagonal loading
L = 16 # Subarray size <= N/2 (e.g. 16)
nTimeAverage = 1 # Capon range-average window (+- this value)
V = np.array([]) # Subspace matrix
doForwardBackwardAveraging = False
verbose = False
Xd_i = Xd[0, :, :, :]
res = getCaponC.getCapon(Xd_i, regCoef, L, nTimeAverage, V,
doForwardBackwardAveraging, verbose)
img_capon_ampl = res[0]
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)
def __init__(self, dataTag, multiFile=False):
if multiFile:
self.s = System(dataTag + ' %d' % 1)
self.Xd = self.s.data.Xd
self.Ni = self.s.data.NFrames # No. frames
self.Nx = self.Xd.shape[0] # No. beams
self.Ny = self.Xd.shape[1] # No. range samples
self.Nm = self.Xd.shape[2] # No. channels
else:
self.s = System(dataTag)
self.Xd = self.s.data.Xd
self.Ni = self.Xd.shape[0] # No. frames
self.Ny = self.Xd.shape[1] # No. range samples
self.Nx = self.Xd.shape[2] # No. beams
self.Nm = self.Xd.shape[3] # No. channels
self.multiFile = multiFile
self.dataTag = dataTag
noReprocessing = False
# self.sliceRangeStart = Parameter('Range slice start', 0, 1, self.Ny)
# self.sliceAngleStart = Parameter('Angle slice start', 0, 1, self.Nx)
# self.sliceElementStart = Parameter('Element slice start', 0, 1, self.Nm)
# self.sliceRangeEnd = Parameter('Range slice end', self.Ny, 1, self.Ny)
# self.sliceAngleEnd = Parameter('Angle slice end', self.Nx, 1, self.Nx)
# self.sliceElementEnd = Parameter('Element slice end', self.Nm, 1, self.Nm)
# self.sliceRangeStep = Parameter('Range slice step', 1, 1, self.Ny/2)
# self.sliceAngleStep = Parameter('Angle slice step', 1, 1, self.Nx/2)
# self.sliceElementStep = Parameter('Element slice step', 1, 1, self.Nm/2)
self.sliceRangeStart = Parameter('Range slice start', self.Ny / 4, 1,
self.Ny)
self.sliceAngleStart = Parameter('Angle slice start', self.Nx / 5, 1,
self.Nx)
self.sliceElementStart = Parameter('Element slice start', 0, 1,
self.Nm)
self.sliceRangeEnd = Parameter('Range slice end', self.Ny * 3 / 4, 1,
self.Ny)
self.sliceAngleEnd = Parameter('Angle slice end', self.Nx * 4 / 5, 1,
self.Nx)
self.sliceElementEnd = Parameter('Element slice end', self.Nm, 1,
self.Nm)
self.sliceRangeStep = Parameter('Range slice step', 1, 1, self.Ny / 2)
self.sliceAngleStep = Parameter('Angle slice step', 1, 1, self.Nx / 2)
self.sliceElementStep = Parameter('Element slice step', 1, 1,
self.Nm / 2)
self.Ky = Parameter('Radial upsampling', 4, 1, 8, 1,
noReprocessing) # Up-sampling factors
self.Kx = Parameter('Lateral upsampling', 2, 1, 8, 1, noReprocessing)
self.interp_method = 0 # 0 imag and real interp, 1 angle and radian interp, 2 fft2 interp
self.Nb = Parameter('Beamspace dimension', 0, 0,
self.Nm) # Dim. beamspace
# Inital Capon parameters
self.d = Parameter('Diagonal loading (power of 10)', -1, -5,
5) # Diagonal loading in percent
self.L = Parameter('Subarray size', int(self.Nm / 2), 1,
self.Nm) # Subarray size <= Nm/2 (e.g. 16)
self.K = Parameter('Time averaging', 2, 0, (self.Ny - 1) /
2) # Capon range-average window (+- this value)
self.B = np.array([0]) # Subspace matrix
self.image_idx = Parameter('Frame no.', 0, 0, self.Ni - 1)
self.minDynRange = Parameter('Min dynamic range', -20, -60, 60, 1,
noReprocessing)
self.maxDynRange = Parameter('Max dynamic range', 20, -60, 60, 1,
noReprocessing)
self.minDynRangeCapon = Parameter('Min dynamic range Capon', -20, -60,
60, 1, noReprocessing)
self.maxDynRangeCapon = Parameter('Max dynamic range Capon', 20, -60,
60, 1, noReprocessing)
self.fps = Parameter('Video fps', 10, 1, 50, 1,
noReprocessing) # TODO: Move to gui
# self.profile_on_off = Parameter('Profile plots on (1) off (0)', 1, 0, 1, 1, noReprocessing)
self.save_single_plots = Parameter('Save single plots: on=1, off=0', 0,
0, 1, 1,
noReprocessing) # TODO: Move to gui
self.show_legends = Parameter('Show legends: on=1, off=0', 0, 0, 1, 1,
noReprocessing)
self.apod = Parameter('Apodization: 0=uniform, 1=hamming', 0, 0, 1)
#self.compress = Parameter('Number of logs: 1=log(), 2=log(log())', 1, 1, 2, 1, noReprocessing)
# profile position (Lateral and axial pattern is plotted, crossing at this position)
self.profilePos = [10.0, 80.0, 0.0]
def __init__(self, dataTag, multiFile=False):
if multiFile:
self.s = System(dataTag + ' %d'%1)
self.Xd = self.s.data.Xd
self.Ni = self.s.data.NFrames # No. frames
self.Nx = self.Xd.shape[0] # No. beams
self.Ny = self.Xd.shape[1] # No. range samples
self.Nm = self.Xd.shape[2] # No. channels
else:
self.s = System(dataTag)
self.Xd = self.s.data.Xd
self.Ni = self.Xd.shape[0] # No. frames
self.Ny = self.Xd.shape[1] # No. range samples
self.Nx = self.Xd.shape[2] # No. beams
self.Nm = self.Xd.shape[3] # No. channels
self.multiFile = multiFile
self.dataTag = dataTag
noReprocessing = False
# self.sliceRangeStart = Parameter('Range slice start', 0, 1, self.Ny)
# self.sliceAngleStart = Parameter('Angle slice start', 0, 1, self.Nx)
# self.sliceElementStart = Parameter('Element slice start', 0, 1, self.Nm)
# self.sliceRangeEnd = Parameter('Range slice end', self.Ny, 1, self.Ny)
# self.sliceAngleEnd = Parameter('Angle slice end', self.Nx, 1, self.Nx)
# self.sliceElementEnd = Parameter('Element slice end', self.Nm, 1, self.Nm)
# self.sliceRangeStep = Parameter('Range slice step', 1, 1, self.Ny/2)
# self.sliceAngleStep = Parameter('Angle slice step', 1, 1, self.Nx/2)
# self.sliceElementStep = Parameter('Element slice step', 1, 1, self.Nm/2)
self.sliceRangeStart = Parameter('Range slice start', self.Ny/4, 1, self.Ny)
self.sliceAngleStart = Parameter('Angle slice start', self.Nx/5, 1, self.Nx)
self.sliceElementStart = Parameter('Element slice start', 0, 1, self.Nm)
self.sliceRangeEnd = Parameter('Range slice end', self.Ny*3/4, 1, self.Ny)
self.sliceAngleEnd = Parameter('Angle slice end', self.Nx*4/5, 1, self.Nx)
self.sliceElementEnd = Parameter('Element slice end', self.Nm, 1, self.Nm)
self.sliceRangeStep = Parameter('Range slice step', 1, 1, self.Ny/2)
self.sliceAngleStep = Parameter('Angle slice step', 1, 1, self.Nx/2)
self.sliceElementStep = Parameter('Element slice step', 1, 1, self.Nm/2)
self.Ky = Parameter('Radial upsampling', 4, 1, 8, 1, noReprocessing) # Up-sampling factors
self.Kx = Parameter('Lateral upsampling', 2, 1, 8, 1, noReprocessing)
self.interp_method = 0 # 0 imag and real interp, 1 angle and radian interp, 2 fft2 interp
self.Nb = Parameter('Beamspace dimension', 0, 0, self.Nm) # Dim. beamspace
# Inital Capon parameters
self.d = Parameter('Diagonal loading (power of 10)', -1, -5, 5) # Diagonal loading in percent
self.L = Parameter('Subarray size', int(self.Nm/2), 1, self.Nm) # Subarray size <= Nm/2 (e.g. 16)
self.K = Parameter('Time averaging', 2, 0, (self.Ny-1)/2) # Capon range-average window (+- this value)
self.B = np.array([0]) # Subspace matrix
self.image_idx = Parameter('Frame no.', 0, 0, self.Ni-1)
self.minDynRange = Parameter('Min dynamic range', -20, -60, 60, 1, noReprocessing)
self.maxDynRange = Parameter('Max dynamic range', 20, -60, 60, 1, noReprocessing)
self.minDynRangeCapon = Parameter('Min dynamic range Capon', -20, -60, 60, 1, noReprocessing)
self.maxDynRangeCapon = Parameter('Max dynamic range Capon', 20, -60, 60, 1, noReprocessing)
self.fps = Parameter('Video fps', 10, 1, 50, 1, noReprocessing) # TODO: Move to gui
# self.profile_on_off = Parameter('Profile plots on (1) off (0)', 1, 0, 1, 1, noReprocessing)
self.save_single_plots = Parameter('Save single plots: on=1, off=0', 0, 0, 1, 1, noReprocessing) # TODO: Move to gui
self.show_legends = Parameter('Show legends: on=1, off=0', 0, 0, 1, 1, noReprocessing)
self.apod = Parameter('Apodization: 0=uniform, 1=hamming', 0, 0, 1)
#self.compress = Parameter('Number of logs: 1=log(), 2=log(log())', 1, 1, 2, 1, noReprocessing)
# profile position (Lateral and axial pattern is plotted, crossing at this position)
self.profilePos = [10.0, 80.0, 0.0]
def solveBiCG(A, b, x0, tol, itr):
''' Solves the complex linear system Ax = b using the complex-biconjugate-gradient method
Arguments:
A -- Coefficient matrix
b -- Right-side vector
x0 -- Initial solution (default: 0-vector)
tol -- Stop solver when error is less than tol (default: 0)
itr -- Stop solver after itr iterations -- default
'''
# Check arguments
m, n = len(A), len(A[0])
if m != n:
raise Exception('Coefficient matrix is not square')
if len(b) != n:
raise Exception('Dimension mismatch between A and b')
x = x0 # make origin as starting point if no x is passed
# Calculate initial residual r = b - Ax0 and search direction p
r0 = b - np.dot(A, x)
r = r0
normr0 = np.euclidnorm(r0)
if (np.euclidnorm(r) /
normr0) <= tol: # break if error is less-than tolerance.
# TODO: Should also test bir
return x
p = np.array(r)
# init biresidual and bidirection
bir = r.conjugate()
bip = p.conjugate()
numerator, denominator = 0, 0
alpha, betha = 0, 0
AH = np.conjugatetranspose(
A
) # Simplification: since A is Hermitian and semi-positive definite in our case, we can avoid this step...
if itr <= 0 or itr > m:
itr = m
for i in range(itr):
# Calculate common matrix-vector products
Ap = np.dot(A, p)
AHbip = np.dot(AH, bip)
# Calculate step-length parameter
numerator = np.vdot(bir, r)
denominator = np.vdot(bip, Ap)
alpha = numerator / denominator
# Obtain new solution
x = x + (alpha * p)
# Calculate new residual and biresidual
# r = r - (alpha * Ap)
for i in range(m):
r[i] = r[i] - alpha * Ap[
i] # Calculate new residual and biresidual
if np.euclidnorm(r) / normr0 <= tol:
# TODO: Should also test bir
return x
bir = bir - (alpha.conjugate() * AHbip)
# Calculate the biconjugacy coefficient
numerator = np.vdot(AHbip, r)
denominator = np.vdot(bip, Ap)
betha = -1.0 * (numerator / denominator)
# Obtain new search and bi-search-direction
p = r + (betha * p)
bip = bir + (betha.conjugate() * bip)
return x
def solveBiCG(A, b, x0, tol, itr):
''' Solves the complex linear system Ax = b using the complex-biconjugate-gradient method
Arguments:
A -- Coefficient matrix
b -- Right-side vector
x0 -- Initial solution (default: 0-vector)
tol -- Stop solver when error is less than tol (default: 0)
itr -- Stop solver after itr iterations -- default
'''
# Check arguments
m,n = len(A),len(A[0])
if m != n:
raise Exception('Coefficient matrix is not square')
if len(b) != n:
raise Exception('Dimension mismatch between A and b')
x = x0 # make origin as starting point if no x is passed
# Calculate initial residual r = b - Ax0 and search direction p
r0 = b - np.dot(A, x)
r = r0
normr0 = np.euclidnorm(r0)
if (np.euclidnorm(r) / normr0) <= tol: # break if error is less-than tolerance.
# TODO: Should also test bir
return x
p = np.array(r)
# init biresidual and bidirection
bir = r.conjugate()
bip = p.conjugate()
numerator,denominator = 0,0
alpha,betha = 0,0
AH = np.conjugatetranspose(A) # Simplification: since A is Hermitian and semi-positive definite in our case, we can avoid this step...
if itr <= 0 or itr > m:
itr = m
for i in range(itr):
# Calculate common matrix-vector products
Ap = np.dot(A, p)
AHbip = np.dot(AH, bip)
# Calculate step-length parameter
numerator = np.vdot(bir, r)
denominator = np.vdot(bip, Ap)
alpha = numerator / denominator
# Obtain new solution
x = x + (alpha * p)
# Calculate new residual and biresidual
# r = r - (alpha * Ap)
for i in range(m):
r[i] = r[i] - alpha * Ap[i] # Calculate new residual and biresidual
if np.euclidnorm(r) / normr0 <= tol:
# TODO: Should also test bir
return x
bir = bir - (alpha.conjugate() * AHbip)
# Calculate the biconjugacy coefficient
numerator = np.vdot(AHbip, r)
denominator = np.vdot(bip, Ap)
betha = -1.0 * (numerator / denominator)
# Obtain new search and bi-search-direction
p = r + (betha * p)
bip = bir + (betha.conjugate() * bip)
return x
def setUp(self):
self.n = 3
self.A = np.array([[2.0, -1.0, 0.0], [-1.0, 2.0, -1.0], [0.0, -1.0, 2.0]],dtype=complex)
self.A2 = np.array([[4.0, -2.0, 0.0], [-2.0, 4.0, -2.0], [0.0, -2.0, 4.0]],dtype=complex)
self.R = np.array([[1.414213562373095, -0.707106781186547, 0.0],
[0.0, 1.224744871391589, -0.816496580927726],
[0.0, 0.0, 1.154700538379252]],dtype=complex)
self.AA = np.array([[5, -4, 1], [-4, 6, -4], [1, -4, 5]],dtype=complex)
self.B = np.array([[1, 2, 3], [1, 2, 3], [1, 2, 3]],dtype=complex)
self.BT = np.array([[1, 1, 1], [2, 2, 2], [3, 3, 3]],dtype=complex)
self.b1 = np.array([1.0, 1.0, 1.0],dtype=complex)
self.x1 = np.array([3.0/2, 2.0, 3.0/2],dtype=complex)
self.b2 = np.array([1, 2, 3],dtype=complex)
self.x2 = np.array([5.0/2, 4, 7.0/2],dtype=complex)
self.C = np.array([[1, 2, 3], [0, 1, 1], [0, 0, 1]],dtype=complex)
self.b1c = np.array([1, 1, 1],dtype=complex)
self.x1c = np.array([-2, 0, 1],dtype=complex)
self.x1cT = np.array([1, -1, -1],dtype=complex)
self.x0_zero = np.zeros((3,), dtype=complex)
self.Ab2 = np.array([0, 0, 4],dtype=complex)
self.Ab1 = np.array([1, 0, 1],dtype=complex)
self.Ab1b1T = np.array([[3, 1, 3], [1, 6, 5], [3, 5, 11]],dtype=complex)
self.invA = np.array([[0.750, 0.50, 0.250], [0.50, 1.0, 0.50], [0.250, 0.50, 0.750]],dtype=complex)
self.invAb1b1T = np.array([[0.465909090909091, 0.045454545454545, -0.147727272727273],
[0.045454545454545, 0.272727272727273, -0.136363636363636],
[-0.147727272727273, -0.136363636363636, 0.193181818181818]],dtype=complex)
self.complexA = np.array([[2.0, 3.0 + 1.0j, 2.0 - 2.0j],
[3.0 - 1.0j, 9.0, -2.0j],
[2.0 + 2.0j, 2.0j, 14.0]], dtype=complex)
self.complexR = np.array([[1.414213562373095, 2.121320343559642 + 0.707106781186547j, 1.414213562373095 - 1.414213562373095j],
[0.0, 2.0, -1.0 + 1.0j],
[0.0, 0.0, 2.828427124746190]], dtype=complex)
self.complexb = np.array([1.0, 1.0 + 1.0j, 1.0 - 2.0j], dtype=complex)
self.complexy = np.array([0.707106781186547 - 0.0j,
-0.250 + 0.750j,
-0.353553390593273 - 0.883883476483184j], dtype=complex)
self.complexx = np.array([1.593749999999999 - 0.06250j,
-0.343750 + 0.281250j,
-0.1250 - 0.31250j], dtype=complex)
self.complexAb = np.array([2.0 - 2.0j, 8.0 + 6.0j, 14.0 - 24.0j], dtype=complex)
self.but4 = np.array([[0.5]*4, [0.5, -0.5j, -0.5, 0.5j], [0.5, -0.5]*2, [0.5, 0.5j, -0.5, -0.5j]], dtype=complex)
self.but3 = np.array([[0.577350269189626, 0.577350269189626, 0.577350269189626],
[0.577350269189626, -0.288675134594813 - 0.50j, -0.288675134594813 + 0.50j],
[0.577350269189626, -0.288675134594813 + 0.50j, -0.288675134594813 - 0.50j]], dtype=complex)
self.bsComplexb = np.array([1.732050807568878 - 0.577350269189626j, 1.50 + 0.288675134594813j], dtype=complex)
self.diag = 0.2
self.x = np.array([1.0, 1.0 + 1.0j, 1.0 - 2.0j, 2.0 + 1.0j], dtype=complex)
self.complexAbbH = np.array([[10.0, 11.0 + 1.0j, 10.0 - 2.0j],
[11.0 - 1.0j, 17.0, 8.0 - 2.0j],
[10.0 + 2.0j, 8.0 + 2.0j, 22.0]], dtype=complex)
self.complexInvAbbH = np.array([[1.067010309278351, -0.407216494845361 - 0.015463917525773j, -0.190721649484536 + 0.020618556701031j],
[-0.407216494845361 + 0.015463917525773j, 0.247422680412371, 0.077319587628866 - 0.015463917525773j],
[-0.190721649484536 - 0.020618556701031j, 0.077319587628866 + 0.015463917525773j, 0.087628865979381]], dtype=complex)
self.complexInvA = np.array([[1.906250, -0.593750 - 0.156250j, -0.250 + 0.18750j],
[-0.593750 + 0.156250j, 0.31250, 0.06250 - 0.06250j],
[-0.250 - 0.18750j, 0.06250 + 0.06250j, 0.1250]], dtype=complex)
self.complexA4x4 = np.array([[22.0, 8.0, 11.0 - 11.0j, 22.0 - 7.0j],
[8.0, 22.0, 17.0 - 2.0j, 11.0 - 7.0j],
[11.0 + 11.0j, 17.0 + 2.0j, 45.0, 23.0 - 5.0j],
[22.0 + 7.0j, 11.0 + 7.0j, 23.0 + 5.0j, 37.0]], dtype=complex)
self.U4x4 = np.array([[1.0000, 0.3636, 0.50 - 0.50j, 1.0 - 0.3182j],
[0.0, 1.0, 0.6810 + 0.1048j, 0.1571 - 0.2333j],
[0.0, 0.0, 1.0, 0.2776 - 0.3670j],
[0.0, 0.0, 0.0, 1.0]], dtype=complex)
self.D4x4 = np.array([22.0, 19.0909, 24.9381, 5.9806])
self.sonardata_R = np.array(np.load('./data/data_R.npy')) # created without diagonal loading
self.sonardata_a = np.array(np.load('./data/data_a.npy'))
self.sonardata_Ria = np.array(np.load('./data/data_Ria.npy'))
self.sonardata_ar = np.array(np.load('./data/data_ar.npy'))
self.sonardata_n = 32
# random data for testing
self.L = L = 24
self.d = d = 100
U = np.triu(np.random.randn(L,L) + np.random.randn(L,L)*1j) + np.eye(L)*d
self.randA = np.dot(U.conjugate().T, U)
self.randb = np.random.randn(L) + np.random.randn(L)*1j
def collectResults(M=32,L=16):
if QUIET:
print "Collecting results for M=%d, L=%d"%(M,L)
if not QUIET:
print "############"
print "## DEFAULT #"
print "############"
print ""
functions=['gauss_solve','buildR_kernel','amplitude_capon']
timings_default = getTimings(functions=functions,M=M,L=L)
memops_default = getMemoryOps(functions=functions,M=M,L=L)
instructions_default = getInstructions(functions=functions,M=M,L=L)
if not QUIET:
print 'Runtimes: %d %d %d'%tuple(timings_default)
print ''
print 'Memory instructions (gld_request gst_request shared_load shared_store):'
for i,key in enumerate(functions):
a = [functions[i]]
a.extend(memops_default[i])
print '%s: %d %d %d %d'%tuple(a)
print ''
print 'Instructions %d %d %d'%tuple(instructions_default)
#print "############"
#print "## DEFAULT #"
#print "############"
#print ""
#print "Runtimes: 40700 2510 742"
#print ""
#print "Memory instructions (gld_request gst_request shared_load shared_store):"
#print "gauss_solve: 160000 10000 7380000 2860000"
#print "buildR_kernel: 10000 85000 555000 95000"
#print "amplitude_capon: 15000 10000 245000 20000"
#print ""
#print "Instructions 65520464 7268280 2220345"
if not QUIET:
print ""
print "###########"
print "## MEMORY #"
print "###########"
print ""
timings_memory = getTimings(app_name='testEfficiency_memcheck', functions=functions,M=M,L=L)
memops_memory = getMemoryOps(app_name='testEfficiency_memcheck', functions=functions,M=M,L=L)
instructions_memory = getInstructions(app_name='testEfficiency_memcheck', functions=functions,M=M,L=L)
if not QUIET:
print 'Runtimes [ms]: %d %d %d'%tuple(timings_memory)
print ''
print 'Memory instructions diff (should be all zeros):'
for i,key in enumerate(functions):
a = [functions[i]]
a.extend(memops_memory[i]-memops_default[i])
print '%s: %d %d %d %d'%tuple(a)
print ''
print 'Instructions %d %d %d'%tuple(instructions_memory)
if not QUIET:
print ""
print "################"
print "## MATH GLOBAL #"
print "################"
print ""
timings_math = getTimings(app_name='testEfficiency_mathcheck_global', functions=functions,M=M,L=L)
memops_math = getMemoryOps(app_name='testEfficiency_mathcheck_global', functions=functions,M=M,L=L)
instructions_math = getInstructions(app_name='testEfficiency_mathcheck_global', functions=functions,M=M,L=L)
if not QUIET:
print 'Runtimes [ms]: %d %d %d'%tuple(timings_math)
print ''
print 'Memory instructions diff (should be all zeros):'
for i,key in enumerate(functions):
a = [functions[i]]
a.extend(memops_math[i]-memops_default[i])
print '%s: %d %d %d %d'%tuple(a)
print ''
print 'Instructions %d %d %d'%tuple(instructions_math)
print ""
print "################"
print "## MATH SHARED #"
print "################"
print ""
timings_math = getTimings(app_name='testEfficiency_mathcheck_shared', functions=functions,M=M,L=L)
memops_math = getMemoryOps(app_name='testEfficiency_mathcheck_shared', functions=functions,M=M,L=L)
instructions_math = getInstructions(app_name='testEfficiency_mathcheck_shared', functions=functions,M=M,L=L)
if not QUIET:
print 'Runtimes [ms]: %d %d %d'%tuple(timings_math)
print ''
print 'Memory instructions diff (should be all zeros):'
for i,key in enumerate(functions):
a = [functions[i]]
a.extend(memops_math[i]-memops_default[i])
print '%s: %d %d %d %d'%tuple(a)
print ''
print 'Instructions %d %d %d'%tuple(instructions_math)
if not QUIET:
print ""
print "#########"
print "## MATH #"
print "#########"
print ""
timings_math = getTimings(app_name='testEfficiency_mathcheck', functions=functions,M=M,L=L)
memops_math = getMemoryOps(app_name='testEfficiency_mathcheck', functions=functions,M=M,L=L)
instructions_math = getInstructions(app_name='testEfficiency_mathcheck', functions=functions,M=M,L=L)
if not QUIET:
print 'Runtimes [ms]: %d %d %d'%tuple(timings_math)
print ''
print 'Memory instructions diff (should be all zeros):'
for i,key in enumerate(functions):
a = [functions[i]]
a.extend(memops_math[i]-memops_default[i])
print '%s: %d %d %d %d'%tuple(a)
print ''
print 'Instructions %d %d %d'%tuple(instructions_math)
if not QUIET:
print ""
print "#########"
print "## TOTAL #"
print "#########"
print ""
# time_total = timings_default[0] + timings_default[1]
# time_math = timings_math[0] + timings_math[1]
# time_memory = timings_memory[0] + timings_memory[1]
return np.array([timings_default[0],timings_default[1],
timings_math[0], timings_math[1],
timings_memory[0], timings_memory[1]])
def collectResults(M=32, L=16):
if QUIET:
print "Collecting results for M=%d, L=%d" % (M, L)
if not QUIET:
print "############"
print "## DEFAULT #"
print "############"
print ""
functions = ['gauss_solve', 'buildR_kernel', 'amplitude_capon']
timings_default = getTimings(functions=functions, M=M, L=L)
memops_default = getMemoryOps(functions=functions, M=M, L=L)
instructions_default = getInstructions(functions=functions, M=M, L=L)
if not QUIET:
print 'Runtimes: %d %d %d' % tuple(timings_default)
print ''
print 'Memory instructions (gld_request gst_request shared_load shared_store):'
for i, key in enumerate(functions):
a = [functions[i]]
a.extend(memops_default[i])
print '%s: %d %d %d %d' % tuple(a)
print ''
print 'Instructions %d %d %d' % tuple(instructions_default)
#print "############"
#print "## DEFAULT #"
#print "############"
#print ""
#print "Runtimes: 40700 2510 742"
#print ""
#print "Memory instructions (gld_request gst_request shared_load shared_store):"
#print "gauss_solve: 160000 10000 7380000 2860000"
#print "buildR_kernel: 10000 85000 555000 95000"
#print "amplitude_capon: 15000 10000 245000 20000"
#print ""
#print "Instructions 65520464 7268280 2220345"
if not QUIET:
print ""
print "###########"
print "## MEMORY #"
print "###########"
print ""
timings_memory = getTimings(app_name='testEfficiency_memcheck',
functions=functions,
M=M,
L=L)
memops_memory = getMemoryOps(app_name='testEfficiency_memcheck',
functions=functions,
M=M,
L=L)
instructions_memory = getInstructions(app_name='testEfficiency_memcheck',
functions=functions,
M=M,
L=L)
if not QUIET:
print 'Runtimes [ms]: %d %d %d' % tuple(timings_memory)
print ''
print 'Memory instructions diff (should be all zeros):'
for i, key in enumerate(functions):
a = [functions[i]]
a.extend(memops_memory[i] - memops_default[i])
print '%s: %d %d %d %d' % tuple(a)
print ''
print 'Instructions %d %d %d' % tuple(instructions_memory)
if not QUIET:
print ""
print "################"
print "## MATH GLOBAL #"
print "################"
print ""
timings_math = getTimings(app_name='testEfficiency_mathcheck_global',
functions=functions,
M=M,
L=L)
memops_math = getMemoryOps(app_name='testEfficiency_mathcheck_global',
functions=functions,
M=M,
L=L)
instructions_math = getInstructions(
app_name='testEfficiency_mathcheck_global',
functions=functions,
M=M,
L=L)
if not QUIET:
print 'Runtimes [ms]: %d %d %d' % tuple(timings_math)
print ''
print 'Memory instructions diff (should be all zeros):'
for i, key in enumerate(functions):
a = [functions[i]]
a.extend(memops_math[i] - memops_default[i])
print '%s: %d %d %d %d' % tuple(a)
print ''
print 'Instructions %d %d %d' % tuple(instructions_math)
print ""
print "################"
print "## MATH SHARED #"
print "################"
print ""
timings_math = getTimings(app_name='testEfficiency_mathcheck_shared',
functions=functions,
M=M,
L=L)
memops_math = getMemoryOps(app_name='testEfficiency_mathcheck_shared',
functions=functions,
M=M,
L=L)
instructions_math = getInstructions(
app_name='testEfficiency_mathcheck_shared',
functions=functions,
M=M,
L=L)
if not QUIET:
print 'Runtimes [ms]: %d %d %d' % tuple(timings_math)
print ''
print 'Memory instructions diff (should be all zeros):'
for i, key in enumerate(functions):
a = [functions[i]]
a.extend(memops_math[i] - memops_default[i])
print '%s: %d %d %d %d' % tuple(a)
print ''
print 'Instructions %d %d %d' % tuple(instructions_math)
if not QUIET:
print ""
print "#########"
print "## MATH #"
print "#########"
print ""
timings_math = getTimings(app_name='testEfficiency_mathcheck',
functions=functions,
M=M,
L=L)
memops_math = getMemoryOps(app_name='testEfficiency_mathcheck',
functions=functions,
M=M,
L=L)
instructions_math = getInstructions(app_name='testEfficiency_mathcheck',
functions=functions,
M=M,
L=L)
if not QUIET:
print 'Runtimes [ms]: %d %d %d' % tuple(timings_math)
print ''
print 'Memory instructions diff (should be all zeros):'
for i, key in enumerate(functions):
a = [functions[i]]
a.extend(memops_math[i] - memops_default[i])
print '%s: %d %d %d %d' % tuple(a)
print ''
print 'Instructions %d %d %d' % tuple(instructions_math)
if not QUIET:
print ""
print "#########"
print "## TOTAL #"
print "#########"
print ""
# time_total = timings_default[0] + timings_default[1]
# time_math = timings_math[0] + timings_math[1]
# time_memory = timings_memory[0] + timings_memory[1]
return np.array([
timings_default[0], timings_default[1], timings_math[0],
timings_math[1], timings_memory[0], timings_memory[1]
])