def main():
# initialize input/ouput objects
ts=time_series()
kw=k_omega()
for i in range(averages):
# initialize time series
ts.reset()
# Simulate narrow band acoustic data
ts.nb_sim()
# Simulate colored gaussian noise
ts.noise_sim()
# Perform k-omega beamforming
kw(ts())
# Obtain the result of beamforming
gram=kw.get()
# rearrange to bring zero-frequency component at the center of spectrum
for i in range(gram.size(1)):
gram[:,i] = freqswap(gram[:,i])
# Post-process the beamformer output to make it suitable for charting
max, idx = reduce.maxval(gram)
avg = reduce.meanval(gram)
gram = clip(gram,0.0,max,avg/100000.0,max)
# plot log-magnitude of beamformed power spectrum
gram = elm.log10(gram)
min, idx = reduce.minval(gram)
gram -= min
# Normalize the log-magnitude plot
max, idx = reduce.maxval(gram)
gram *= 1.0/max
fig = pyplot.figure(1,figsize=(10,4))
ax = fig.add_axes([0.10,0.10,0.85,0.80])
ax.set_yticklabels(['0','0','30','60','90','120','150','180'])
ax.yaxis.set_ticks_position('right')
pyplot.imshow(gram)
# Labeling plot axis appropriately
pyplot.title(u'K-Ω Beamformer Output')
pyplot.xlabel('Frequency')
pyplot.ylabel(r'$\frac{cos(\theta)}{\lambda}$',fontsize=16,rotation='horizontal')
pyplot.colorbar()
# Display the plot
pyplot.show()
# # Copyright (c) 2014 Stefan Seefeld # All rights reserved. # # This file is part of OpenVSIP. It is made available under the # license contained in the accompanying LICENSE.BSD file. import numpy as np from numpy import array from vsip import vector, matrix from vsip.math import reductions as red a = np.arange(16, dtype=float) v = vector(array=a) assert array(red.meanval(v) == np.mean(a)).all() assert array(red.maxval(v)[0] == np.max(a)).all() assert array(red.minval(v)[0] == np.min(a)).all() assert array(red.sumval(v) == np.sum(a)).all()
from vsip.math import reductions as red from vsip.math.solvers import llsqsol #import matplotlib.pyplot as plt # Define 'A' to be a two-column matrix containing Y[X] and X A = matrix(float, 10, 2) X = gen.ramp(float, 0.1, 0.1, 10) A[:,0] = elm.exp(-X) A[:,1] = X c1,c2= 5.0,2.0 Y = c1*elm.exp(-X) + c2*X Z = matrix(float, 10, 1) Z[:,0] = Y + 0.05*red.maxval(Y)[0]*random.rand(float).randn(Y.length()) R = llsqsol(A, Z) c,resid,rank,sigma = linalg.lstsq(A, Z) # Compare results of llsqsol with results from linalg.lstsq assert np.isclose(R, c).all() #X2 = gen.ramp(float, 0.1, 0.9/100, 100) #Y2 = c[0]*elm.exp(-X2) + c[1]*X2 #R2 = elm.exp(-X2)*R[0,0] + X2*R[1,0] #plt.plot(X, Z, 'x', X2, R2) #plt.plot(X, Z,'x', X2, Y2) #plt.axis([0,1.1,3.0,5.5])
# # Copyright (c) 2014 Stefan Seefeld # All rights reserved. # # This file is part of OpenVSIP. It is made available under the # license contained in the accompanying LICENSE.BSD file. import numpy as np from numpy import array from vsip import vector, matrix from vsip.math import reductions as red a = np.arange(16, dtype=float) v = vector(array=a) assert array(red.meanval(v) == np.mean(a)).all() assert array(red.maxval(v)[0] == np.max(a)).all() assert array(red.minval(v)[0] == np.min(a)).all() assert array(red.sumval(v) == np.sum(a)).all()