示例#1
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    def __init__(self):
        # length of narrow band simulated noise signal
        L = int(2 * rate/(sensors * spacing/speed) + nts + 1)
        # Kaiser window kernel to reject out of band noise
	kernel = window.kaiser(float, 6, 1)
	# Window-based FIR filter design using above kernel
	self.fir = fir(kernel, symmetry.none, 2*L, 2, obj_state.save, 0, alg_hint.time)
	# band-limited noise vectors
	self.noise = vsip.vector(float, 2*L)
        self.bl_noise = vsip.vector(float, L)
	# Generate white gaussian noise
	self.rand  = vsip.random.rand(float, 7, True)
	# time series indices
	self.t = generation.ramp(float, 0.,1./rate, nts)
        self.t_dt = vsip.vector(float, nts)
	# simulated data matrix in which each row corresponds to a sensor
	self.data = vsip.matrix(float, sensors, nts)
        self.d_t = spacing/speed
示例#2
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#
# Copyright (c) 2013 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.

from vsip import vector 
from vsip import matrix
from vsip.selgen.generation import ramp
from vsip.math.matvec import dot, prod
import numpy as np
from numpy import array as A


v1 = ramp(float, 0, 1, 8)
v2 = ramp(float, 1, 2, 8)
d = dot(v1, v2)
assert d == np.dot(A(v1), A(v2))

v1 = vector(array=np.arange(4, dtype=float))
m1 = matrix(array=np.arange(16, dtype=float).reshape(4,4))
m2 = matrix(array=np.arange(16, dtype=float).reshape(4,4))
# vector * matrix
v3 = prod(v1, m1)
assert (A(v3) == np.tensordot(A(v1), A(m1), (0, 0))).all()
# matrix * vector
v3 = prod(m1, v1)
assert (A(v3) == np.tensordot(A(m1), A(v1), (1, 0))).all()
# matrix * matrix
m3 = prod(m1, m2)
示例#3
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# Copyright (c) 2013 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.

from vsip import vector 
from vsip import matrix
from vsip.selgen.generation import ramp
from vsip.math import elementwise as elm
from vsip.signal import *
from vsip.signal.fftm import *
import numpy as np
#from matplotlib.pyplot import *

v1 = ramp(float, 0, 0.1, 1024)
v1 = elm.sin(v1)
m1 = matrix(float, 16, 1024)
for r in range(16):
    m1[r,:] = ramp(float, r, 0.1, 1024)
fwd_fftm = fftm(float, fwd, 16, 1024, 1., 0, 1, alg_hint.time)
inv_fftm = fftm(float, inv, 16, 1024, 1./1024, 0, 1, alg_hint.time)
m2 = matrix(complex, 16, 513)
fwd_fftm(m1, m2)
m3 = matrix(float, 16, 1024)
inv_fftm(m2, m3)

assert np.isclose(m1, m3).all()

#plot(v1)
#plot(v3)
示例#4
0
# license contained in the accompanying LICENSE.BSD file.

import numpy as np
from scipy import linalg
from vsip import vector, matrix
from vsip import random
from vsip.selgen import generation as gen
from vsip.math import elementwise as elm
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()
示例#5
0
文件: fft.py 项目: bradhowes/openvsip
#
# Copyright (c) 2013 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.

from vsip import vector 
from vsip import matrix
from vsip.selgen.generation import ramp
from vsip.math import elementwise as elm
from vsip.signal import *
from vsip.signal.fft import *
import numpy as np
#from matplotlib.pyplot import *

v1 = ramp(float, 0, 0.1, 1024)
v1 = elm.sin(v1)
fwd_fft = fft(float, fwd, 1024, 1., 1, alg_hint.time)
inv_fft = fft(float, inv, 1024, 1./1024, 1, alg_hint.time)
v2 = vector(complex, 513)
fwd_fft(v1, v2)
v3 = vector(float, 1024)
inv_fft(v2, v3)

assert np.isclose(v1, v3).all()

#plot(v1)
#plot(v3)
#show()