def madd(A,B,C):
assert 'pyJvsip' in repr(A) and '__View' in repr(A),'Input parameters must be views of type pyJvsip.__View.'
assert type(A) == type(B) and type(A) == type(C),'Input paramteters must be the same type'
assert 'mview' in A.type,'Only matrix views are supported for madd.'
L = A.rowlength
a = A.colview
b = B.colview
c = C.colview
for i in range(L):
pjv.add(a(i),b(i),c(i))
return C
def noiseGen(t,alpha,Mp,Nn,Ns):
"""
Usage:
data = noiseGen(t,alpha,Mp,Nn,Ns)
where:
data is a matrix of type t and size Mp by Ns)
alpha is a linear array constant
Mp is the number of sensors in the linear array
Nn is the size of the simulated noise vector
Ns is the size of the output noise field for each sensor
Note the noise field is returned in data
"""
# program parameters
from math import pi as M_PI
kaiser=9
Nfilter=10 # Kernel length for noise filter
Nnoise=64 # number of Simulated Noise Directions
cnst1 = M_PI/Nnoise
offset0 = int(alpha * Mp + 1)
nv = pv.create('vview_d',2 * Nn)
noise=pv.create('mview_d',Mp,Nn)
kernel = pv.create('vview_d',Nfilter).kaiser(kaiser)
fir = pv.FIR('fir_d',kernel,'NONE',2 * Nn,2,'YES')
data = pv.create('mview_d',Mp,Ns).fill(0.0)
state = pv.Rand('PRNG',15)
for j in range(Nnoise):
noise_j=noise.rowview(j)
state.randn(nv)
fir.flt(nv,noise_j)
noise_j *= 12.0/float(Nnoise)
#view_store(noise,'noiseData');
noise.putrowlength(Ns)
for i in range(Mp):
data_v = data.rowview(i)
for j in range(Nnoise):
noise_j=noise.rowview(j)
noise_j.putoffset(offset0 +(int)( i * alpha * cos(j * cnst1)))
pv.add(noise_j,data_v,data_v)
#view_store(data,'Data');
return data
def noiseGen(t, alpha, Mp, Nn, Ns):
"""
Usage:
data = noiseGen(t,alpha,Mp,Nn,Ns)
where:
data is a matrix of type t and size Mp by Ns)
alpha is a linear array constant
Mp is the number of sensors in the linear array
Nn is the size of the simulated noise vector
Ns is the size of the output noise field for each sensor
Note the noise field is returned in data
"""
# program parameters
from math import pi as M_PI
kaiser = 9
Nfilter = 10 # Kernel length for noise filter
Nnoise = 64 # number of Simulated Noise Directions
cnst1 = M_PI / Nnoise
offset0 = int(alpha * Mp + 1)
nv = pv.create('vview_d', 2 * Nn)
noise = pv.create('mview_d', Mp, Nn)
kernel = pv.create('vview_d', Nfilter).kaiser(kaiser)
fir = pv.FIR('fir_d', kernel, 'NONE', 2 * Nn, 2, 'YES')
data = pv.create('mview_d', Mp, Ns).fill(0.0)
state = pv.Rand('PRNG', 15)
for j in range(Nnoise):
noise_j = noise.rowview(j)
state.randn(nv)
fir.flt(nv, noise_j)
noise_j *= 12.0 / float(Nnoise)
#view_store(noise,'noiseData');
noise.putrowlength(Ns)
for i in range(Mp):
data_v = data.rowview(i)
for j in range(Nnoise):
noise_j = noise.rowview(j)
noise_j.putoffset(offset0 + (int)(i * alpha * cos(j * cnst1)))
pv.add(noise_j, data_v, data_v)
#view_store(data,'Data');
return data
dataOut = pv.create('vview_f',N)
spect_avg = pv.create('vview_f',int(N/2) + 1)
spect_new = pv.create('vview_f',int(N/2) + 1)
state = pv.Rand('NPRNG',N)
fft=pv.FFT('rcfftop_f',(N,1,0,0))
b = [0.0234, -0.0094, -0.0180, -0.0129, 0.0037,
0.0110, -0.0026, -0.0195, -0.0136, 0.0122,
0.0232, -0.0007, -0.0314, -0.0223, 0.0250,
0.0483, -0.0002, -0.0746, -0.0619, 0.0930,
0.3023, 0.3999, 0.3023, 0.0930, -0.0619,
-0.0746, -0.0002, 0.0483, 0.0250, -0.0223,
-0.0314, -0.0007, 0.0232, 0.0122, -0.0136,
-0.0195, -0.0026, 0.0110, 0.0037, -0.0129,
-0.0180 ,-0.0094, 0.0234]
fir = pv.FIR('fir_f',pv.listToJv('vview_f',b),'NONE',D*N,D,'NO')
spect_avg.fill(0.0)
for i in range(avg):
state.randu(dataIn)
pv.add(-.5,dataIn,dataIn)
fir.flt(dataIn,dataOut)
fft.dft(dataOut,dataFFT)
pv.cmagsq(dataFFT,spect_new)
pv.add(spect_new,spect_avg,spect_avg)
pv.mul(1.0/avg,spect_avg,spect_avg);
#print("spect_avg =");spect_avg.mprint('%.4f')
x=spect_avg.empty.ramp(0,1.0/(spect_avg.length-1))
pyplot.plot(x.list,spect_avg.list)
pyplot.title('Decimation 2')
pyplot.ylabel('Not Normalized')
pyplot.show()
import pyJvsip as pv
N=6
A = pv.create('cvview_d',N).randn(7)
B = A.empty.fill(5.0)
C = A.empty.fill(0.0)
print('A = '+A.mstring('%+.2f'))
print('B = '+B.mstring('%+.2f'))
pv.add(A,B,C)
print('C = A+B')
print('C = '+C.mstring('%+.2f'))
""" OUTPUT
A = [+0.16+0.50i -0.21-0.75i -0.56-0.09i \
+1.15+0.45i +0.10+0.43i +0.63-1.05i]
B = [+5.00+0.00i +5.00+0.00i +5.00+0.00i \
+5.00+0.00i +5.00+0.00i +5.00+0.00i]
C = A+B
C = [+5.16+0.50i +4.79-0.75i +4.44-0.09i \
+6.15+0.45i +5.10+0.43i +5.63-1.05i]
"""
#* Created RJudd */
#* pyJvsip version of c_VSIP_example example1
import pyJvsip as pv
N = 8
A = pv.create('vview_f', N).ramp(0, 1)
B = A.empty.fill(5.0)
C = A.empty.fill(0.0)
print('A = ')
A.mprint('%2.0f')
print('B = ')
B.mprint('%2.0f')
pv.add(A, B, C)
print('C = A+B')
print('C = ')
C.mprint('%2.0f')
