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"""Perform a wiener filter on an N-dimensional array.
"""Perform a Wiener filter on an N-dimensional array.
def wiener(im,mysize=None,noise=None): """Perform a wiener filter on an N-dimensional array. Description: Apply a wiener filter to the N-dimensional array in. Inputs: in -- an N-dimensional array. kernel_size -- A scalar or an N-length list giving the size of the median filter window in each dimension. Elements of kernel_size should be odd. If kernel_size is a scalar, then this scalar is used as the size in each dimension. noise -- The noise-power to use. If None, then noise is estimated as the average of the local variance of the input. Outputs: (out,) out -- Wiener filtered result with the same shape as in. """ im = asarray(im) if mysize is None: mysize = [3] * len(im.shape) mysize = asarray(mysize); # Estimate the local mean lMean = correlate(im,Numeric.ones(mysize),1) / Numeric.product(mysize) # Estimate the local variance lVar = correlate(im**2,Numeric.ones(mysize),1) / Numeric.product(mysize) - lMean**2 # Estimate the noise power if needed. if noise==None: noise = mean(Numeric.ravel(lVar)) res = (im - lMean) res *= (1-noise / lVar) res += lMean out = where(lVar < noise, lMean, res) return out
936da8591e6b92539aeda1c0c884ca93d37a2754 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/936da8591e6b92539aeda1c0c884ca93d37a2754/signaltools.py
Apply a wiener filter to the N-dimensional array in.
Apply a Wiener filter to the N-dimensional array in.
def wiener(im,mysize=None,noise=None): """Perform a wiener filter on an N-dimensional array. Description: Apply a wiener filter to the N-dimensional array in. Inputs: in -- an N-dimensional array. kernel_size -- A scalar or an N-length list giving the size of the median filter window in each dimension. Elements of kernel_size should be odd. If kernel_size is a scalar, then this scalar is used as the size in each dimension. noise -- The noise-power to use. If None, then noise is estimated as the average of the local variance of the input. Outputs: (out,) out -- Wiener filtered result with the same shape as in. """ im = asarray(im) if mysize is None: mysize = [3] * len(im.shape) mysize = asarray(mysize); # Estimate the local mean lMean = correlate(im,Numeric.ones(mysize),1) / Numeric.product(mysize) # Estimate the local variance lVar = correlate(im**2,Numeric.ones(mysize),1) / Numeric.product(mysize) - lMean**2 # Estimate the noise power if needed. if noise==None: noise = mean(Numeric.ravel(lVar)) res = (im - lMean) res *= (1-noise / lVar) res += lMean out = where(lVar < noise, lMean, res) return out
936da8591e6b92539aeda1c0c884ca93d37a2754 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/936da8591e6b92539aeda1c0c884ca93d37a2754/signaltools.py
lMean = correlate(im,Numeric.ones(mysize),1) / Numeric.product(mysize)
lMean = correlate(im,ones(mysize),1) / product(mysize)
def wiener(im,mysize=None,noise=None): """Perform a wiener filter on an N-dimensional array. Description: Apply a wiener filter to the N-dimensional array in. Inputs: in -- an N-dimensional array. kernel_size -- A scalar or an N-length list giving the size of the median filter window in each dimension. Elements of kernel_size should be odd. If kernel_size is a scalar, then this scalar is used as the size in each dimension. noise -- The noise-power to use. If None, then noise is estimated as the average of the local variance of the input. Outputs: (out,) out -- Wiener filtered result with the same shape as in. """ im = asarray(im) if mysize is None: mysize = [3] * len(im.shape) mysize = asarray(mysize); # Estimate the local mean lMean = correlate(im,Numeric.ones(mysize),1) / Numeric.product(mysize) # Estimate the local variance lVar = correlate(im**2,Numeric.ones(mysize),1) / Numeric.product(mysize) - lMean**2 # Estimate the noise power if needed. if noise==None: noise = mean(Numeric.ravel(lVar)) res = (im - lMean) res *= (1-noise / lVar) res += lMean out = where(lVar < noise, lMean, res) return out
936da8591e6b92539aeda1c0c884ca93d37a2754 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/936da8591e6b92539aeda1c0c884ca93d37a2754/signaltools.py
lVar = correlate(im**2,Numeric.ones(mysize),1) / Numeric.product(mysize) - lMean**2
lVar = correlate(im**2,ones(mysize),1) / product(mysize) - lMean**2
def wiener(im,mysize=None,noise=None): """Perform a wiener filter on an N-dimensional array. Description: Apply a wiener filter to the N-dimensional array in. Inputs: in -- an N-dimensional array. kernel_size -- A scalar or an N-length list giving the size of the median filter window in each dimension. Elements of kernel_size should be odd. If kernel_size is a scalar, then this scalar is used as the size in each dimension. noise -- The noise-power to use. If None, then noise is estimated as the average of the local variance of the input. Outputs: (out,) out -- Wiener filtered result with the same shape as in. """ im = asarray(im) if mysize is None: mysize = [3] * len(im.shape) mysize = asarray(mysize); # Estimate the local mean lMean = correlate(im,Numeric.ones(mysize),1) / Numeric.product(mysize) # Estimate the local variance lVar = correlate(im**2,Numeric.ones(mysize),1) / Numeric.product(mysize) - lMean**2 # Estimate the noise power if needed. if noise==None: noise = mean(Numeric.ravel(lVar)) res = (im - lMean) res *= (1-noise / lVar) res += lMean out = where(lVar < noise, lMean, res) return out
936da8591e6b92539aeda1c0c884ca93d37a2754 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/936da8591e6b92539aeda1c0c884ca93d37a2754/signaltools.py
noise = mean(Numeric.ravel(lVar))
noise = mean(ravel(lVar))
def wiener(im,mysize=None,noise=None): """Perform a wiener filter on an N-dimensional array. Description: Apply a wiener filter to the N-dimensional array in. Inputs: in -- an N-dimensional array. kernel_size -- A scalar or an N-length list giving the size of the median filter window in each dimension. Elements of kernel_size should be odd. If kernel_size is a scalar, then this scalar is used as the size in each dimension. noise -- The noise-power to use. If None, then noise is estimated as the average of the local variance of the input. Outputs: (out,) out -- Wiener filtered result with the same shape as in. """ im = asarray(im) if mysize is None: mysize = [3] * len(im.shape) mysize = asarray(mysize); # Estimate the local mean lMean = correlate(im,Numeric.ones(mysize),1) / Numeric.product(mysize) # Estimate the local variance lVar = correlate(im**2,Numeric.ones(mysize),1) / Numeric.product(mysize) - lMean**2 # Estimate the noise power if needed. if noise==None: noise = mean(Numeric.ravel(lVar)) res = (im - lMean) res *= (1-noise / lVar) res += lMean out = where(lVar < noise, lMean, res) return out
936da8591e6b92539aeda1c0c884ca93d37a2754 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/936da8591e6b92539aeda1c0c884ca93d37a2754/signaltools.py
N = Numeric.size(a)-1 M = Numeric.size(b)-1
N = size(a)-1 M = size(b)-1
def lfiltic(b,a,y,x=None): """Given a linear filter (b,a) and initial conditions on the output y and the input x, return the inital conditions on the state vector zi which is used by lfilter to generate the output given the input. If M=len(b)-1 and N=len(a)-1. Then, the initial conditions are given in the vectors x and y as x = {x[-1],x[-2],...,x[-M]} y = {y[-1],y[-2],...,y[-N]} If x is not given, its inital conditions are assumed zero. If either vector is too short, then zeros are added to achieve the proper length. The output vector zi contains zi = {z_0[-1], z_1[-1], ..., z_K-1[-1]} where K=max(M,N). """ N = Numeric.size(a)-1 M = Numeric.size(b)-1 K = max(M,N) y = asarray(y) zi = zeros(K,y.dtype.char) if x is None: x = zeros(M,y.dtype.char) else: x = asarray(x) L = Numeric.size(x) if L < M: x = r_[x,zeros(M-L)] L = Numeric.size(y) if L < N: y = r_[y,zeros(N-L)] for m in range(M): zi[m] = Numeric.sum(b[m+1:]*x[:M-m]) for m in range(N): zi[m] -= Numeric.sum(a[m+1:]*y[:N-m]) return zi
936da8591e6b92539aeda1c0c884ca93d37a2754 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/936da8591e6b92539aeda1c0c884ca93d37a2754/signaltools.py
L = Numeric.size(x)
L = size(x)
def lfiltic(b,a,y,x=None): """Given a linear filter (b,a) and initial conditions on the output y and the input x, return the inital conditions on the state vector zi which is used by lfilter to generate the output given the input. If M=len(b)-1 and N=len(a)-1. Then, the initial conditions are given in the vectors x and y as x = {x[-1],x[-2],...,x[-M]} y = {y[-1],y[-2],...,y[-N]} If x is not given, its inital conditions are assumed zero. If either vector is too short, then zeros are added to achieve the proper length. The output vector zi contains zi = {z_0[-1], z_1[-1], ..., z_K-1[-1]} where K=max(M,N). """ N = Numeric.size(a)-1 M = Numeric.size(b)-1 K = max(M,N) y = asarray(y) zi = zeros(K,y.dtype.char) if x is None: x = zeros(M,y.dtype.char) else: x = asarray(x) L = Numeric.size(x) if L < M: x = r_[x,zeros(M-L)] L = Numeric.size(y) if L < N: y = r_[y,zeros(N-L)] for m in range(M): zi[m] = Numeric.sum(b[m+1:]*x[:M-m]) for m in range(N): zi[m] -= Numeric.sum(a[m+1:]*y[:N-m]) return zi
936da8591e6b92539aeda1c0c884ca93d37a2754 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/936da8591e6b92539aeda1c0c884ca93d37a2754/signaltools.py
L = Numeric.size(y)
L = size(y)
def lfiltic(b,a,y,x=None): """Given a linear filter (b,a) and initial conditions on the output y and the input x, return the inital conditions on the state vector zi which is used by lfilter to generate the output given the input. If M=len(b)-1 and N=len(a)-1. Then, the initial conditions are given in the vectors x and y as x = {x[-1],x[-2],...,x[-M]} y = {y[-1],y[-2],...,y[-N]} If x is not given, its inital conditions are assumed zero. If either vector is too short, then zeros are added to achieve the proper length. The output vector zi contains zi = {z_0[-1], z_1[-1], ..., z_K-1[-1]} where K=max(M,N). """ N = Numeric.size(a)-1 M = Numeric.size(b)-1 K = max(M,N) y = asarray(y) zi = zeros(K,y.dtype.char) if x is None: x = zeros(M,y.dtype.char) else: x = asarray(x) L = Numeric.size(x) if L < M: x = r_[x,zeros(M-L)] L = Numeric.size(y) if L < N: y = r_[y,zeros(N-L)] for m in range(M): zi[m] = Numeric.sum(b[m+1:]*x[:M-m]) for m in range(N): zi[m] -= Numeric.sum(a[m+1:]*y[:N-m]) return zi
936da8591e6b92539aeda1c0c884ca93d37a2754 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/936da8591e6b92539aeda1c0c884ca93d37a2754/signaltools.py
zi[m] = Numeric.sum(b[m+1:]*x[:M-m])
zi[m] = sum(b[m+1:]*x[:M-m])
def lfiltic(b,a,y,x=None): """Given a linear filter (b,a) and initial conditions on the output y and the input x, return the inital conditions on the state vector zi which is used by lfilter to generate the output given the input. If M=len(b)-1 and N=len(a)-1. Then, the initial conditions are given in the vectors x and y as x = {x[-1],x[-2],...,x[-M]} y = {y[-1],y[-2],...,y[-N]} If x is not given, its inital conditions are assumed zero. If either vector is too short, then zeros are added to achieve the proper length. The output vector zi contains zi = {z_0[-1], z_1[-1], ..., z_K-1[-1]} where K=max(M,N). """ N = Numeric.size(a)-1 M = Numeric.size(b)-1 K = max(M,N) y = asarray(y) zi = zeros(K,y.dtype.char) if x is None: x = zeros(M,y.dtype.char) else: x = asarray(x) L = Numeric.size(x) if L < M: x = r_[x,zeros(M-L)] L = Numeric.size(y) if L < N: y = r_[y,zeros(N-L)] for m in range(M): zi[m] = Numeric.sum(b[m+1:]*x[:M-m]) for m in range(N): zi[m] -= Numeric.sum(a[m+1:]*y[:N-m]) return zi
936da8591e6b92539aeda1c0c884ca93d37a2754 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/936da8591e6b92539aeda1c0c884ca93d37a2754/signaltools.py
zi[m] -= Numeric.sum(a[m+1:]*y[:N-m])
zi[m] -= sum(a[m+1:]*y[:N-m])
def lfiltic(b,a,y,x=None): """Given a linear filter (b,a) and initial conditions on the output y and the input x, return the inital conditions on the state vector zi which is used by lfilter to generate the output given the input. If M=len(b)-1 and N=len(a)-1. Then, the initial conditions are given in the vectors x and y as x = {x[-1],x[-2],...,x[-M]} y = {y[-1],y[-2],...,y[-N]} If x is not given, its inital conditions are assumed zero. If either vector is too short, then zeros are added to achieve the proper length. The output vector zi contains zi = {z_0[-1], z_1[-1], ..., z_K-1[-1]} where K=max(M,N). """ N = Numeric.size(a)-1 M = Numeric.size(b)-1 K = max(M,N) y = asarray(y) zi = zeros(K,y.dtype.char) if x is None: x = zeros(M,y.dtype.char) else: x = asarray(x) L = Numeric.size(x) if L < M: x = r_[x,zeros(M-L)] L = Numeric.size(y) if L < N: y = r_[y,zeros(N-L)] for m in range(M): zi[m] = Numeric.sum(b[m+1:]*x[:M-m]) for m in range(N): zi[m] -= Numeric.sum(a[m+1:]*y[:N-m]) return zi
936da8591e6b92539aeda1c0c884ca93d37a2754 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/936da8591e6b92539aeda1c0c884ca93d37a2754/signaltools.py
return Numeric.ones(M,Numeric.Float)
return ones(M, float)
def boxcar(M,sym=1): """The M-point boxcar window. """ return Numeric.ones(M,Numeric.Float)
936da8591e6b92539aeda1c0c884ca93d37a2754 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/936da8591e6b92539aeda1c0c884ca93d37a2754/signaltools.py
return Numeric.array([])
return array([])
def triang(M,sym=1): """The M-point triangular window. """ if M < 1: return Numeric.array([]) if M == 1: return Numeric.ones(1,'d') odd = M % 2 if not sym and not odd: M = M + 1 n = arange(1,int((M+1)/2)+1) if M % 2 == 0: w = (2*n-1.0)/M w = numpy.r_[w, w[::-1]] else: w = 2*n/(M+1.0) w = numpy.r_[w, w[-2::-1]] if not sym and not odd: w = w[:-1] return w
936da8591e6b92539aeda1c0c884ca93d37a2754 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/936da8591e6b92539aeda1c0c884ca93d37a2754/signaltools.py
return Numeric.ones(1,'d')
return ones(1,'d')
def triang(M,sym=1): """The M-point triangular window. """ if M < 1: return Numeric.array([]) if M == 1: return Numeric.ones(1,'d') odd = M % 2 if not sym and not odd: M = M + 1 n = arange(1,int((M+1)/2)+1) if M % 2 == 0: w = (2*n-1.0)/M w = numpy.r_[w, w[::-1]] else: w = 2*n/(M+1.0) w = numpy.r_[w, w[-2::-1]] if not sym and not odd: w = w[:-1] return w
936da8591e6b92539aeda1c0c884ca93d37a2754 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/936da8591e6b92539aeda1c0c884ca93d37a2754/signaltools.py
w = numpy.r_[w, w[::-1]]
w = r_[w, w[::-1]]
def triang(M,sym=1): """The M-point triangular window. """ if M < 1: return Numeric.array([]) if M == 1: return Numeric.ones(1,'d') odd = M % 2 if not sym and not odd: M = M + 1 n = arange(1,int((M+1)/2)+1) if M % 2 == 0: w = (2*n-1.0)/M w = numpy.r_[w, w[::-1]] else: w = 2*n/(M+1.0) w = numpy.r_[w, w[-2::-1]] if not sym and not odd: w = w[:-1] return w
936da8591e6b92539aeda1c0c884ca93d37a2754 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/936da8591e6b92539aeda1c0c884ca93d37a2754/signaltools.py
w = numpy.r_[w, w[-2::-1]]
w = r_[w, w[-2::-1]]
def triang(M,sym=1): """The M-point triangular window. """ if M < 1: return Numeric.array([]) if M == 1: return Numeric.ones(1,'d') odd = M % 2 if not sym and not odd: M = M + 1 n = arange(1,int((M+1)/2)+1) if M % 2 == 0: w = (2*n-1.0)/M w = numpy.r_[w, w[::-1]] else: w = 2*n/(M+1.0) w = numpy.r_[w, w[-2::-1]] if not sym and not odd: w = w[:-1] return w
936da8591e6b92539aeda1c0c884ca93d37a2754 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/936da8591e6b92539aeda1c0c884ca93d37a2754/signaltools.py
return Numeric.array([])
return array([])
def parzen(M,sym=1): """The M-point Parzen window """ if M < 1: return Numeric.array([]) if M == 1: return Numeric.ones(1,'d') odd = M % 2 if not sym and not odd: M = M+1 n = Numeric.arange(-(M-1)/2.0,(M-1)/2.0+0.5,1.0) na = extract(n < -(M-1)/4.0, n) nb = extract(abs(n) <= (M-1)/4.0, n) wa = 2*(1-abs(na)/(M/2.0))**3.0 wb = 1-6*(abs(nb)/(M/2.0))**2.0 + 6*(abs(nb)/(M/2.0))**3.0 w = numpy.r_[wa,wb,wa[::-1]] if not sym and not odd: w = w[:-1] return w
936da8591e6b92539aeda1c0c884ca93d37a2754 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/936da8591e6b92539aeda1c0c884ca93d37a2754/signaltools.py
return Numeric.ones(1,'d')
return ones(1,'d')
def parzen(M,sym=1): """The M-point Parzen window """ if M < 1: return Numeric.array([]) if M == 1: return Numeric.ones(1,'d') odd = M % 2 if not sym and not odd: M = M+1 n = Numeric.arange(-(M-1)/2.0,(M-1)/2.0+0.5,1.0) na = extract(n < -(M-1)/4.0, n) nb = extract(abs(n) <= (M-1)/4.0, n) wa = 2*(1-abs(na)/(M/2.0))**3.0 wb = 1-6*(abs(nb)/(M/2.0))**2.0 + 6*(abs(nb)/(M/2.0))**3.0 w = numpy.r_[wa,wb,wa[::-1]] if not sym and not odd: w = w[:-1] return w
936da8591e6b92539aeda1c0c884ca93d37a2754 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/936da8591e6b92539aeda1c0c884ca93d37a2754/signaltools.py
n = Numeric.arange(-(M-1)/2.0,(M-1)/2.0+0.5,1.0)
n = arange(-(M-1)/2.0,(M-1)/2.0+0.5,1.0)
def parzen(M,sym=1): """The M-point Parzen window """ if M < 1: return Numeric.array([]) if M == 1: return Numeric.ones(1,'d') odd = M % 2 if not sym and not odd: M = M+1 n = Numeric.arange(-(M-1)/2.0,(M-1)/2.0+0.5,1.0) na = extract(n < -(M-1)/4.0, n) nb = extract(abs(n) <= (M-1)/4.0, n) wa = 2*(1-abs(na)/(M/2.0))**3.0 wb = 1-6*(abs(nb)/(M/2.0))**2.0 + 6*(abs(nb)/(M/2.0))**3.0 w = numpy.r_[wa,wb,wa[::-1]] if not sym and not odd: w = w[:-1] return w
936da8591e6b92539aeda1c0c884ca93d37a2754 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/936da8591e6b92539aeda1c0c884ca93d37a2754/signaltools.py
w = numpy.r_[wa,wb,wa[::-1]]
w = r_[wa,wb,wa[::-1]]
def parzen(M,sym=1): """The M-point Parzen window """ if M < 1: return Numeric.array([]) if M == 1: return Numeric.ones(1,'d') odd = M % 2 if not sym and not odd: M = M+1 n = Numeric.arange(-(M-1)/2.0,(M-1)/2.0+0.5,1.0) na = extract(n < -(M-1)/4.0, n) nb = extract(abs(n) <= (M-1)/4.0, n) wa = 2*(1-abs(na)/(M/2.0))**3.0 wb = 1-6*(abs(nb)/(M/2.0))**2.0 + 6*(abs(nb)/(M/2.0))**3.0 w = numpy.r_[wa,wb,wa[::-1]] if not sym and not odd: w = w[:-1] return w
936da8591e6b92539aeda1c0c884ca93d37a2754 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/936da8591e6b92539aeda1c0c884ca93d37a2754/signaltools.py
return Numeric.array([])
return array([])
def bohman(M,sym=1): """The M-point Bohman window """ if M < 1: return Numeric.array([]) if M == 1: return Numeric.ones(1,'d') odd = M % 2 if not sym and not odd: M = M+1 fac = abs(linspace(-1,1,M)[1:-1]) w = (1 - fac)* cos(pi*fac) + 1.0/pi*sin(pi*fac) w = numpy.r_[0,w,0] if not sym and not odd: w = w[:-1] return w
936da8591e6b92539aeda1c0c884ca93d37a2754 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/936da8591e6b92539aeda1c0c884ca93d37a2754/signaltools.py
return Numeric.ones(1,'d')
return ones(1,'d')
def bohman(M,sym=1): """The M-point Bohman window """ if M < 1: return Numeric.array([]) if M == 1: return Numeric.ones(1,'d') odd = M % 2 if not sym and not odd: M = M+1 fac = abs(linspace(-1,1,M)[1:-1]) w = (1 - fac)* cos(pi*fac) + 1.0/pi*sin(pi*fac) w = numpy.r_[0,w,0] if not sym and not odd: w = w[:-1] return w
936da8591e6b92539aeda1c0c884ca93d37a2754 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/936da8591e6b92539aeda1c0c884ca93d37a2754/signaltools.py
w = numpy.r_[0,w,0]
w = r_[0,w,0]
def bohman(M,sym=1): """The M-point Bohman window """ if M < 1: return Numeric.array([]) if M == 1: return Numeric.ones(1,'d') odd = M % 2 if not sym and not odd: M = M+1 fac = abs(linspace(-1,1,M)[1:-1]) w = (1 - fac)* cos(pi*fac) + 1.0/pi*sin(pi*fac) w = numpy.r_[0,w,0] if not sym and not odd: w = w[:-1] return w
936da8591e6b92539aeda1c0c884ca93d37a2754 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/936da8591e6b92539aeda1c0c884ca93d37a2754/signaltools.py
return Numeric.array([])
return array([])
def blackman(M,sym=1): """The M-point Blackman window. """ if M < 1: return Numeric.array([]) if M == 1: return Numeric.ones(1,'d') odd = M % 2 if not sym and not odd: M = M+1 n = arange(0,M) w = 0.42-0.5*cos(2.0*pi*n/(M-1)) + 0.08*cos(4.0*pi*n/(M-1)) if not sym and not odd: w = w[:-1] return w
936da8591e6b92539aeda1c0c884ca93d37a2754 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/936da8591e6b92539aeda1c0c884ca93d37a2754/signaltools.py
return Numeric.ones(1,'d')
return ones(1,'d')
def blackman(M,sym=1): """The M-point Blackman window. """ if M < 1: return Numeric.array([]) if M == 1: return Numeric.ones(1,'d') odd = M % 2 if not sym and not odd: M = M+1 n = arange(0,M) w = 0.42-0.5*cos(2.0*pi*n/(M-1)) + 0.08*cos(4.0*pi*n/(M-1)) if not sym and not odd: w = w[:-1] return w
936da8591e6b92539aeda1c0c884ca93d37a2754 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/936da8591e6b92539aeda1c0c884ca93d37a2754/signaltools.py
return Numeric.array([])
return array([])
def nuttall(M,sym=1): """A minimum 4-term Blackman-Harris window according to Nuttall. """ if M < 1: return Numeric.array([]) if M == 1: return Numeric.ones(1,'d') odd = M % 2 if not sym and not odd: M = M+1 a = [0.3635819, 0.4891775, 0.1365995, 0.0106411] n = arange(0,M) fac = n*2*pi/(M-1.0) w = a[0] - a[1]*cos(fac) + a[2]*cos(2*fac) - a[3]*cos(3*fac) if not sym and not odd: w = w[:-1] return w
936da8591e6b92539aeda1c0c884ca93d37a2754 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/936da8591e6b92539aeda1c0c884ca93d37a2754/signaltools.py
return Numeric.ones(1,'d')
return ones(1,'d')
def nuttall(M,sym=1): """A minimum 4-term Blackman-Harris window according to Nuttall. """ if M < 1: return Numeric.array([]) if M == 1: return Numeric.ones(1,'d') odd = M % 2 if not sym and not odd: M = M+1 a = [0.3635819, 0.4891775, 0.1365995, 0.0106411] n = arange(0,M) fac = n*2*pi/(M-1.0) w = a[0] - a[1]*cos(fac) + a[2]*cos(2*fac) - a[3]*cos(3*fac) if not sym and not odd: w = w[:-1] return w
936da8591e6b92539aeda1c0c884ca93d37a2754 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/936da8591e6b92539aeda1c0c884ca93d37a2754/signaltools.py
return Numeric.array([])
return array([])
def blackmanharris(M,sym=1): """The M-point minimum 4-term Blackman-Harris window. """ if M < 1: return Numeric.array([]) if M == 1: return Numeric.ones(1,'d') odd = M % 2 if not sym and not odd: M = M+1 a = [0.35875, 0.48829, 0.14128, 0.01168]; n = arange(0,M) fac = n*2*pi/(M-1.0) w = a[0] - a[1]*cos(fac) + a[2]*cos(2*fac) - a[3]*cos(3*fac) if not sym and not odd: w = w[:-1] return w
936da8591e6b92539aeda1c0c884ca93d37a2754 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/936da8591e6b92539aeda1c0c884ca93d37a2754/signaltools.py
return Numeric.ones(1,'d')
return ones(1,'d')
def blackmanharris(M,sym=1): """The M-point minimum 4-term Blackman-Harris window. """ if M < 1: return Numeric.array([]) if M == 1: return Numeric.ones(1,'d') odd = M % 2 if not sym and not odd: M = M+1 a = [0.35875, 0.48829, 0.14128, 0.01168]; n = arange(0,M) fac = n*2*pi/(M-1.0) w = a[0] - a[1]*cos(fac) + a[2]*cos(2*fac) - a[3]*cos(3*fac) if not sym and not odd: w = w[:-1] return w
936da8591e6b92539aeda1c0c884ca93d37a2754 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/936da8591e6b92539aeda1c0c884ca93d37a2754/signaltools.py
return Numeric.array([])
return array([])
def bartlett(M,sym=1): """The M-point Bartlett window. """ if M < 1: return Numeric.array([]) if M == 1: return Numeric.ones(1,'d') odd = M % 2 if not sym and not odd: M = M+1 n = arange(0,M) w = where(Numeric.less_equal(n,(M-1)/2.0),2.0*n/(M-1),2.0-2.0*n/(M-1)) if not sym and not odd: w = w[:-1] return w
936da8591e6b92539aeda1c0c884ca93d37a2754 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/936da8591e6b92539aeda1c0c884ca93d37a2754/signaltools.py
return Numeric.ones(1,'d')
return ones(1,'d')
def bartlett(M,sym=1): """The M-point Bartlett window. """ if M < 1: return Numeric.array([]) if M == 1: return Numeric.ones(1,'d') odd = M % 2 if not sym and not odd: M = M+1 n = arange(0,M) w = where(Numeric.less_equal(n,(M-1)/2.0),2.0*n/(M-1),2.0-2.0*n/(M-1)) if not sym and not odd: w = w[:-1] return w
936da8591e6b92539aeda1c0c884ca93d37a2754 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/936da8591e6b92539aeda1c0c884ca93d37a2754/signaltools.py
w = where(Numeric.less_equal(n,(M-1)/2.0),2.0*n/(M-1),2.0-2.0*n/(M-1))
w = where(less_equal(n,(M-1)/2.0),2.0*n/(M-1),2.0-2.0*n/(M-1))
def bartlett(M,sym=1): """The M-point Bartlett window. """ if M < 1: return Numeric.array([]) if M == 1: return Numeric.ones(1,'d') odd = M % 2 if not sym and not odd: M = M+1 n = arange(0,M) w = where(Numeric.less_equal(n,(M-1)/2.0),2.0*n/(M-1),2.0-2.0*n/(M-1)) if not sym and not odd: w = w[:-1] return w
936da8591e6b92539aeda1c0c884ca93d37a2754 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/936da8591e6b92539aeda1c0c884ca93d37a2754/signaltools.py
return Numeric.array([])
return array([])
def hanning(M,sym=1): """The M-point Hanning window. """ if M < 1: return Numeric.array([]) if M == 1: return Numeric.ones(1,'d') odd = M % 2 if not sym and not odd: M = M+1 n = arange(0,M) w = 0.5-0.5*cos(2.0*pi*n/(M-1)) if not sym and not odd: w = w[:-1] return w
936da8591e6b92539aeda1c0c884ca93d37a2754 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/936da8591e6b92539aeda1c0c884ca93d37a2754/signaltools.py
return Numeric.ones(1,'d')
return ones(1,'d')
def hanning(M,sym=1): """The M-point Hanning window. """ if M < 1: return Numeric.array([]) if M == 1: return Numeric.ones(1,'d') odd = M % 2 if not sym and not odd: M = M+1 n = arange(0,M) w = 0.5-0.5*cos(2.0*pi*n/(M-1)) if not sym and not odd: w = w[:-1] return w
936da8591e6b92539aeda1c0c884ca93d37a2754 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/936da8591e6b92539aeda1c0c884ca93d37a2754/signaltools.py
return Numeric.array([])
return array([])
def barthann(M,sym=1): """Return the M-point modified Bartlett-Hann window. """ if M < 1: return Numeric.array([]) if M == 1: return Numeric.ones(1,'d') odd = M % 2 if not sym and not odd: M = M+1 n = arange(0,M) fac = abs(n/(M-1.0)-0.5) w = 0.62 - 0.48*fac + 0.38*cos(2*pi*fac) if not sym and not odd: w = w[:-1] return w
936da8591e6b92539aeda1c0c884ca93d37a2754 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/936da8591e6b92539aeda1c0c884ca93d37a2754/signaltools.py
return Numeric.ones(1,'d')
return ones(1,'d')
def barthann(M,sym=1): """Return the M-point modified Bartlett-Hann window. """ if M < 1: return Numeric.array([]) if M == 1: return Numeric.ones(1,'d') odd = M % 2 if not sym and not odd: M = M+1 n = arange(0,M) fac = abs(n/(M-1.0)-0.5) w = 0.62 - 0.48*fac + 0.38*cos(2*pi*fac) if not sym and not odd: w = w[:-1] return w
936da8591e6b92539aeda1c0c884ca93d37a2754 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/936da8591e6b92539aeda1c0c884ca93d37a2754/signaltools.py
return Numeric.array([])
return array([])
def hamming(M,sym=1): """The M-point Hamming window. """ if M < 1: return Numeric.array([]) if M == 1: return Numeric.ones(1,'d') odd = M % 2 if not sym and not odd: M = M+1 n = arange(0,M) w = 0.54-0.46*cos(2.0*pi*n/(M-1)) if not sym and not odd: w = w[:-1] return w
936da8591e6b92539aeda1c0c884ca93d37a2754 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/936da8591e6b92539aeda1c0c884ca93d37a2754/signaltools.py
return Numeric.ones(1,'d')
return ones(1,'d')
def hamming(M,sym=1): """The M-point Hamming window. """ if M < 1: return Numeric.array([]) if M == 1: return Numeric.ones(1,'d') odd = M % 2 if not sym and not odd: M = M+1 n = arange(0,M) w = 0.54-0.46*cos(2.0*pi*n/(M-1)) if not sym and not odd: w = w[:-1] return w
936da8591e6b92539aeda1c0c884ca93d37a2754 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/936da8591e6b92539aeda1c0c884ca93d37a2754/signaltools.py
return Numeric.array([])
return array([])
def kaiser(M,beta,sym=1): """Returns a Kaiser window of length M with shape parameter beta. """ if M < 1: return Numeric.array([]) if M == 1: return Numeric.ones(1,'d') odd = M % 2 if not sym and not odd: M = M+1 n = arange(0,M) alpha = (M-1)/2.0 w = special.i0(beta * sqrt(1-((n-alpha)/alpha)**2.0))/special.i0(beta) if not sym and not odd: w = w[:-1] return w
936da8591e6b92539aeda1c0c884ca93d37a2754 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/936da8591e6b92539aeda1c0c884ca93d37a2754/signaltools.py
return Numeric.ones(1,'d')
return ones(1,'d')
def kaiser(M,beta,sym=1): """Returns a Kaiser window of length M with shape parameter beta. """ if M < 1: return Numeric.array([]) if M == 1: return Numeric.ones(1,'d') odd = M % 2 if not sym and not odd: M = M+1 n = arange(0,M) alpha = (M-1)/2.0 w = special.i0(beta * sqrt(1-((n-alpha)/alpha)**2.0))/special.i0(beta) if not sym and not odd: w = w[:-1] return w
936da8591e6b92539aeda1c0c884ca93d37a2754 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/936da8591e6b92539aeda1c0c884ca93d37a2754/signaltools.py
return Numeric.array([])
return array([])
def gaussian(M,std,sym=1): """Returns a Gaussian window of length M with standard-deviation std. """ if M < 1: return Numeric.array([]) if M == 1: return Numeric.ones(1,'d') odd = M % 2 if not sym and not odd: M = M + 1 n = arange(0,M)-(M-1.0)/2.0 sig2 = 2*std*std w = exp(-n**2 / sig2) if not sym and not odd: w = w[:-1] return w
936da8591e6b92539aeda1c0c884ca93d37a2754 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/936da8591e6b92539aeda1c0c884ca93d37a2754/signaltools.py
return Numeric.ones(1,'d')
return ones(1,'d')
def gaussian(M,std,sym=1): """Returns a Gaussian window of length M with standard-deviation std. """ if M < 1: return Numeric.array([]) if M == 1: return Numeric.ones(1,'d') odd = M % 2 if not sym and not odd: M = M + 1 n = arange(0,M)-(M-1.0)/2.0 sig2 = 2*std*std w = exp(-n**2 / sig2) if not sym and not odd: w = w[:-1] return w
936da8591e6b92539aeda1c0c884ca93d37a2754 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/936da8591e6b92539aeda1c0c884ca93d37a2754/signaltools.py
return Numeric.array([])
return array([])
def general_gaussian(M,p,sig,sym=1): """Returns a window with a generalized Gaussian shape. exp(-0.5*(x/sig)**(2*p)) half power point is at (2*log(2)))**(1/(2*p))*sig """ if M < 1: return Numeric.array([]) if M == 1: return Numeric.ones(1,'d') odd = M % 2 if not sym and not odd: M = M+1 n = arange(0,M)-(M-1.0)/2.0 w = exp(-0.5*(n/sig)**(2*p)) if not sym and not odd: w = w[:-1] return w
936da8591e6b92539aeda1c0c884ca93d37a2754 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/936da8591e6b92539aeda1c0c884ca93d37a2754/signaltools.py
return Numeric.ones(1,'d')
return ones(1,'d')
def general_gaussian(M,p,sig,sym=1): """Returns a window with a generalized Gaussian shape. exp(-0.5*(x/sig)**(2*p)) half power point is at (2*log(2)))**(1/(2*p))*sig """ if M < 1: return Numeric.array([]) if M == 1: return Numeric.ones(1,'d') odd = M % 2 if not sym and not odd: M = M+1 n = arange(0,M)-(M-1.0)/2.0 w = exp(-0.5*(n/sig)**(2*p)) if not sym and not odd: w = w[:-1] return w
936da8591e6b92539aeda1c0c884ca93d37a2754 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/936da8591e6b92539aeda1c0c884ca93d37a2754/signaltools.py
return Numeric.array([])
return array([])
def slepian(M,width,sym=1): if (M*width > 27.38): raise ValueError, "Cannot reliably obtain slepian sequences for"\ " M*width > 27.38." if M < 1: return Numeric.array([]) if M == 1: return Numeric.ones(1,'d') odd = M % 2 if not sym and not odd: M = M+1 twoF = width/2.0 alpha = (M-1)/2.0 m = arange(0,M)-alpha n = m[:,NewAxis] k = m[NewAxis,:] AF = twoF*special.sinc(twoF*(n-k)) [lam,vec] = linalg.eig(AF) ind = argmax(abs(lam)) w = abs(vec[:,ind]) w = w / max(w) if not sym and not odd: w = w[:-1] return w
936da8591e6b92539aeda1c0c884ca93d37a2754 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/936da8591e6b92539aeda1c0c884ca93d37a2754/signaltools.py
return Numeric.ones(1,'d')
return ones(1,'d')
def slepian(M,width,sym=1): if (M*width > 27.38): raise ValueError, "Cannot reliably obtain slepian sequences for"\ " M*width > 27.38." if M < 1: return Numeric.array([]) if M == 1: return Numeric.ones(1,'d') odd = M % 2 if not sym and not odd: M = M+1 twoF = width/2.0 alpha = (M-1)/2.0 m = arange(0,M)-alpha n = m[:,NewAxis] k = m[NewAxis,:] AF = twoF*special.sinc(twoF*(n-k)) [lam,vec] = linalg.eig(AF) ind = argmax(abs(lam)) w = abs(vec[:,ind]) w = w / max(w) if not sym and not odd: w = w[:-1] return w
936da8591e6b92539aeda1c0c884ca93d37a2754 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/936da8591e6b92539aeda1c0c884ca93d37a2754/signaltools.py
if numpy.iscomplexobj(x):
if iscomplexobj(x):
def hilbert(x, N=None): """Return the hilbert transform of x of length N. """ x = asarray(x) if N is None: N = len(x) if N <=0: raise ValueError, "N must be positive." if numpy.iscomplexobj(x): print "Warning: imaginary part of x ignored." x = numpy.real(x) Xf = fft(x,N,axis=0) h = Numeric.zeros(N) if N % 2 == 0: h[0] = h[N/2] = 1 h[1:N/2] = 2 else: h[0] = 1 h[1:(N+1)/2] = 2 if len(x.shape) > 1: h = h[:,Numeric.NewAxis] x = ifft(Xf*h) return x
936da8591e6b92539aeda1c0c884ca93d37a2754 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/936da8591e6b92539aeda1c0c884ca93d37a2754/signaltools.py
x = numpy.real(x)
x = real(x)
def hilbert(x, N=None): """Return the hilbert transform of x of length N. """ x = asarray(x) if N is None: N = len(x) if N <=0: raise ValueError, "N must be positive." if numpy.iscomplexobj(x): print "Warning: imaginary part of x ignored." x = numpy.real(x) Xf = fft(x,N,axis=0) h = Numeric.zeros(N) if N % 2 == 0: h[0] = h[N/2] = 1 h[1:N/2] = 2 else: h[0] = 1 h[1:(N+1)/2] = 2 if len(x.shape) > 1: h = h[:,Numeric.NewAxis] x = ifft(Xf*h) return x
936da8591e6b92539aeda1c0c884ca93d37a2754 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/936da8591e6b92539aeda1c0c884ca93d37a2754/signaltools.py
h = Numeric.zeros(N)
h = zeros(N)
def hilbert(x, N=None): """Return the hilbert transform of x of length N. """ x = asarray(x) if N is None: N = len(x) if N <=0: raise ValueError, "N must be positive." if numpy.iscomplexobj(x): print "Warning: imaginary part of x ignored." x = numpy.real(x) Xf = fft(x,N,axis=0) h = Numeric.zeros(N) if N % 2 == 0: h[0] = h[N/2] = 1 h[1:N/2] = 2 else: h[0] = 1 h[1:(N+1)/2] = 2 if len(x.shape) > 1: h = h[:,Numeric.NewAxis] x = ifft(Xf*h) return x
936da8591e6b92539aeda1c0c884ca93d37a2754 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/936da8591e6b92539aeda1c0c884ca93d37a2754/signaltools.py
h = h[:,Numeric.NewAxis]
h = h[:, NewAxis]
def hilbert(x, N=None): """Return the hilbert transform of x of length N. """ x = asarray(x) if N is None: N = len(x) if N <=0: raise ValueError, "N must be positive." if numpy.iscomplexobj(x): print "Warning: imaginary part of x ignored." x = numpy.real(x) Xf = fft(x,N,axis=0) h = Numeric.zeros(N) if N % 2 == 0: h[0] = h[N/2] = 1 h[1:N/2] = 2 else: h[0] = 1 h[1:(N+1)/2] = 2 if len(x.shape) > 1: h = h[:,Numeric.NewAxis] x = ifft(Xf*h) return x
936da8591e6b92539aeda1c0c884ca93d37a2754 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/936da8591e6b92539aeda1c0c884ca93d37a2754/signaltools.py
if numpy.iscomplexobj(x):
if iscomplexobj(x):
def hilbert2(x,N=None): """Return the '2-D' hilbert transform of x of length N. """ x = asarray(x) x = asarray(x) if N is None: N = x.shape if len(N) < 2: if N <=0: raise ValueError, "N must be positive." N = (N,N) if numpy.iscomplexobj(x): print "Warning: imaginary part of x ignored." x = numpy.real(x) print N Xf = fft2(x,N,axes=(0,1)) h1 = Numeric.zeros(N[0],'d') h2 = Numeric.zeros(N[1],'d') for p in range(2): h = eval("h%d"%(p+1)) N1 = N[p] if N1 % 2 == 0: h[0] = h[N1/2] = 1 h[1:N1/2] = 2 else: h[0] = 1 h[1:(N1+1)/2] = 2 exec("h%d = h" % (p+1), globals(), locals()) h = h1[:,NewAxis] * h2[NewAxis,:] k = len(x.shape) while k > 2: h = h[:,Numeric.NewAxis] k -= 1 x = ifft2(Xf*h,axes=(0,1)) return x
936da8591e6b92539aeda1c0c884ca93d37a2754 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/936da8591e6b92539aeda1c0c884ca93d37a2754/signaltools.py
x = numpy.real(x)
x = real(x)
def hilbert2(x,N=None): """Return the '2-D' hilbert transform of x of length N. """ x = asarray(x) x = asarray(x) if N is None: N = x.shape if len(N) < 2: if N <=0: raise ValueError, "N must be positive." N = (N,N) if numpy.iscomplexobj(x): print "Warning: imaginary part of x ignored." x = numpy.real(x) print N Xf = fft2(x,N,axes=(0,1)) h1 = Numeric.zeros(N[0],'d') h2 = Numeric.zeros(N[1],'d') for p in range(2): h = eval("h%d"%(p+1)) N1 = N[p] if N1 % 2 == 0: h[0] = h[N1/2] = 1 h[1:N1/2] = 2 else: h[0] = 1 h[1:(N1+1)/2] = 2 exec("h%d = h" % (p+1), globals(), locals()) h = h1[:,NewAxis] * h2[NewAxis,:] k = len(x.shape) while k > 2: h = h[:,Numeric.NewAxis] k -= 1 x = ifft2(Xf*h,axes=(0,1)) return x
936da8591e6b92539aeda1c0c884ca93d37a2754 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/936da8591e6b92539aeda1c0c884ca93d37a2754/signaltools.py
h1 = Numeric.zeros(N[0],'d') h2 = Numeric.zeros(N[1],'d')
h1 = zeros(N[0],'d') h2 = zeros(N[1],'d')
def hilbert2(x,N=None): """Return the '2-D' hilbert transform of x of length N. """ x = asarray(x) x = asarray(x) if N is None: N = x.shape if len(N) < 2: if N <=0: raise ValueError, "N must be positive." N = (N,N) if numpy.iscomplexobj(x): print "Warning: imaginary part of x ignored." x = numpy.real(x) print N Xf = fft2(x,N,axes=(0,1)) h1 = Numeric.zeros(N[0],'d') h2 = Numeric.zeros(N[1],'d') for p in range(2): h = eval("h%d"%(p+1)) N1 = N[p] if N1 % 2 == 0: h[0] = h[N1/2] = 1 h[1:N1/2] = 2 else: h[0] = 1 h[1:(N1+1)/2] = 2 exec("h%d = h" % (p+1), globals(), locals()) h = h1[:,NewAxis] * h2[NewAxis,:] k = len(x.shape) while k > 2: h = h[:,Numeric.NewAxis] k -= 1 x = ifft2(Xf*h,axes=(0,1)) return x
936da8591e6b92539aeda1c0c884ca93d37a2754 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/936da8591e6b92539aeda1c0c884ca93d37a2754/signaltools.py
h = h[:,Numeric.NewAxis]
h = h[:, NewAxis]
def hilbert2(x,N=None): """Return the '2-D' hilbert transform of x of length N. """ x = asarray(x) x = asarray(x) if N is None: N = x.shape if len(N) < 2: if N <=0: raise ValueError, "N must be positive." N = (N,N) if numpy.iscomplexobj(x): print "Warning: imaginary part of x ignored." x = numpy.real(x) print N Xf = fft2(x,N,axes=(0,1)) h1 = Numeric.zeros(N[0],'d') h2 = Numeric.zeros(N[1],'d') for p in range(2): h = eval("h%d"%(p+1)) N1 = N[p] if N1 % 2 == 0: h[0] = h[N1/2] = 1 h[1:N1/2] = 2 else: h[0] = 1 h[1:(N1+1)/2] = 2 exec("h%d = h" % (p+1), globals(), locals()) h = h1[:,NewAxis] * h2[NewAxis,:] k = len(x.shape) while k > 2: h = h[:,Numeric.NewAxis] k -= 1 x = ifft2(Xf*h,axes=(0,1)) return x
936da8591e6b92539aeda1c0c884ca93d37a2754 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/936da8591e6b92539aeda1c0c884ca93d37a2754/signaltools.py
if numpy.iscomplexobj(p): indx = Numeric.argsort(abs(p))
if iscomplexobj(p): indx = argsort(abs(p))
def cmplx_sort(p): "sort roots based on magnitude." p = asarray(p) if numpy.iscomplexobj(p): indx = Numeric.argsort(abs(p)) else: indx = Numeric.argsort(p) return Numeric.take(p,indx), indx
936da8591e6b92539aeda1c0c884ca93d37a2754 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/936da8591e6b92539aeda1c0c884ca93d37a2754/signaltools.py
indx = Numeric.argsort(p) return Numeric.take(p,indx), indx
indx = argsort(p) return take(p,indx), indx
def cmplx_sort(p): "sort roots based on magnitude." p = asarray(p) if numpy.iscomplexobj(p): indx = Numeric.argsort(abs(p)) else: indx = Numeric.argsort(p) return Numeric.take(p,indx), indx
936da8591e6b92539aeda1c0c884ca93d37a2754 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/936da8591e6b92539aeda1c0c884ca93d37a2754/signaltools.py
from numpy import real_if_close, atleast_1d
def unique_roots(p,tol=1e-3,rtype='min'): """Determine the unique roots and their multiplicities in two lists Inputs: p -- The list of roots tol --- The tolerance for two roots to be considered equal. rtype --- How to determine the returned root from the close ones: 'max': pick the maximum 'min': pick the minimum 'avg': average roots Outputs: (pout, mult) pout -- The list of sorted roots mult -- The multiplicity of each root """ if rtype in ['max','maximum']: comproot = numpy.maximum elif rtype in ['min','minimum']: comproot = numpy.minimum elif rtype in ['avg','mean']: comproot = numpy.mean p = asarray(p)*1.0 tol = abs(tol) p, indx = cmplx_sort(p) pout = [] mult = [] indx = -1 curp = p[0] + 5*tol sameroots = [] for k in range(len(p)): tr = p[k] if abs(tr-curp) < tol: sameroots.append(tr) curp = comproot(sameroots) pout[indx] = curp mult[indx] += 1 else: pout.append(tr) curp = tr sameroots = [tr] indx += 1 mult.append(1) return array(pout), array(mult)
936da8591e6b92539aeda1c0c884ca93d37a2754 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/936da8591e6b92539aeda1c0c884ca93d37a2754/signaltools.py
r = Numeric.take(r,indx)
r = take(r,indx)
def invres(r,p,k,tol=1e-3,rtype='avg'): """Compute b(s) and a(s) from partial fraction expansion: r,p,k If M = len(b) and N = len(a) b(s) b[0] x**(M-1) + b[1] x**(M-2) + ... + b[M-1] H(s) = ------ = ---------------------------------------------- a(s) a[0] x**(N-1) + a[1] x**(N-2) + ... + a[N-1] r[0] r[1] r[-1] = -------- + -------- + ... + --------- + k(s) (s-p[0]) (s-p[1]) (s-p[-1]) If there are any repeated roots (closer than tol), then the partial fraction expansion has terms like r[i] r[i+1] r[i+n-1] -------- + ----------- + ... + ----------- (s-p[i]) (s-p[i])**2 (s-p[i])**n See also: residue, poly, polyval, unique_roots """ extra = k p, indx = cmplx_sort(p) r = Numeric.take(r,indx) pout, mult = unique_roots(p,tol=tol,rtype=rtype) p = [] for k in range(len(pout)): p.extend([pout[k]]*mult[k]) a = atleast_1d(poly(p)) if len(extra) > 0: b = polymul(extra,a) else: b = [0] indx = 0 for k in range(len(pout)): temp = [] for l in range(len(pout)): if l != k: temp.extend([pout[l]]*mult[l]) for m in range(mult[k]): t2 = temp[:] t2.extend([pout[k]]*(mult[k]-m-1)) b = polyadd(b,r[indx]*poly(t2)) indx += 1 b = real_if_close(b) while Numeric.allclose(b[0], 0, rtol=1e-14) and (b.shape[-1] > 1): b = b[1:] return b, a
936da8591e6b92539aeda1c0c884ca93d37a2754 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/936da8591e6b92539aeda1c0c884ca93d37a2754/signaltools.py
while Numeric.allclose(b[0], 0, rtol=1e-14) and (b.shape[-1] > 1):
while allclose(b[0], 0, rtol=1e-14) and (b.shape[-1] > 1):
def invres(r,p,k,tol=1e-3,rtype='avg'): """Compute b(s) and a(s) from partial fraction expansion: r,p,k If M = len(b) and N = len(a) b(s) b[0] x**(M-1) + b[1] x**(M-2) + ... + b[M-1] H(s) = ------ = ---------------------------------------------- a(s) a[0] x**(N-1) + a[1] x**(N-2) + ... + a[N-1] r[0] r[1] r[-1] = -------- + -------- + ... + --------- + k(s) (s-p[0]) (s-p[1]) (s-p[-1]) If there are any repeated roots (closer than tol), then the partial fraction expansion has terms like r[i] r[i+1] r[i+n-1] -------- + ----------- + ... + ----------- (s-p[i]) (s-p[i])**2 (s-p[i])**n See also: residue, poly, polyval, unique_roots """ extra = k p, indx = cmplx_sort(p) r = Numeric.take(r,indx) pout, mult = unique_roots(p,tol=tol,rtype=rtype) p = [] for k in range(len(pout)): p.extend([pout[k]]*mult[k]) a = atleast_1d(poly(p)) if len(extra) > 0: b = polymul(extra,a) else: b = [0] indx = 0 for k in range(len(pout)): temp = [] for l in range(len(pout)): if l != k: temp.extend([pout[l]]*mult[l]) for m in range(mult[k]): t2 = temp[:] t2.extend([pout[k]]*(mult[k]-m-1)) b = polyadd(b,r[indx]*poly(t2)) indx += 1 b = real_if_close(b) while Numeric.allclose(b[0], 0, rtol=1e-14) and (b.shape[-1] > 1): b = b[1:] return b, a
936da8591e6b92539aeda1c0c884ca93d37a2754 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/936da8591e6b92539aeda1c0c884ca93d37a2754/signaltools.py
r = Numeric.take(r,indx)
r = take(r,indx)
def invresz(r,p,k,tol=1e-3,rtype='avg'): """Compute b(z) and a(z) from partial fraction expansion: r,p,k If M = len(b) and N = len(a) b(z) b[0] + b[1] z**(-1) + ... + b[M-1] z**(-M+1) H(z) = ------ = ---------------------------------------------- a(z) a[0] + a[1] z**(-1) + ... + a[N-1] z**(-N+1) r[0] r[-1] = --------------- + ... + ---------------- + k[0] + k[1]z**(-1) ... (1-p[0]z**(-1)) (1-p[-1]z**(-1)) If there are any repeated roots (closer than tol), then the partial fraction expansion has terms like r[i] r[i+1] r[i+n-1] -------------- + ------------------ + ... + ------------------ (1-p[i]z**(-1)) (1-p[i]z**(-1))**2 (1-p[i]z**(-1))**n See also: residuez, poly, polyval, unique_roots """ extra = asarray(k) p, indx = cmplx_sort(p) r = Numeric.take(r,indx) pout, mult = unique_roots(p,tol=tol,rtype=rtype) p = [] for k in range(len(pout)): p.extend([pout[k]]*mult[k]) a = atleast_1d(poly(p)) if len(extra) > 0: b = polymul(extra,a) else: b = [0] indx = 0 brev = asarray(b)[::-1] for k in range(len(pout)): temp = [] # Construct polynomial which does not include any of this root for l in range(len(pout)): if l != k: temp.extend([pout[l]]*mult[l]) for m in range(mult[k]): t2 = temp[:] t2.extend([pout[k]]*(mult[k]-m-1)) brev = polyadd(brev,(r[indx]*poly(t2))[::-1]) indx += 1 b = real_if_close(brev[::-1]) return b, a
936da8591e6b92539aeda1c0c884ca93d37a2754 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/936da8591e6b92539aeda1c0c884ca93d37a2754/signaltools.py
N = int(Numeric.minimum(num,Nx)) Y = Numeric.zeros(newshape,'D')
N = int(numpy.minimum(num,Nx)) Y = zeros(newshape,'D')
def resample(x,num,t=None,axis=0,window=None): """Resample to num samples using Fourier method along the given axis. The resampled signal starts at the same value of x but is sampled with a spacing of len(x) / num * (spacing of x). Because a Fourier method is used, the signal is assumed periodic. Window controls a Fourier-domain window that tapers the Fourier spectrum before zero-padding to aleviate ringing in the resampled values for sampled signals you didn't intend to be interpreted as band-limited. If window is a string then use the named window. If window is a float, then it represents a value of beta for a kaiser window. If window is a tuple, then the first component is a string representing the window, and the next arguments are parameters for that window. Possible windows are: 'blackman' ('black', 'blk') 'hamming' ('hamm', 'ham') 'bartlett' ('bart', 'brt') 'hanning' ('hann', 'han') 'kaiser' ('ksr') # requires parameter (beta) 'gaussian' ('gauss', 'gss') # requires parameter (std.) 'general gauss' ('general', 'ggs') # requires two parameters (power, width) The first sample of the returned vector is the same as the first sample of the input vector, the spacing between samples is changed from dx to dx * len(x) / num If t is not None, then it represents the old sample positions, and the new sample positions will be returned as well as the new samples. """ x = asarray(x) X = fft(x,axis=axis) Nx = x.shape[axis] if window is not None: W = ifftshift(get_window(window,Nx)) newshape = ones(len(x.shape)) newshape[axis] = len(W) W=W.reshape(newshape) X = X*W sl = [slice(None)]*len(x.shape) newshape = list(x.shape) newshape[axis] = num N = int(Numeric.minimum(num,Nx)) Y = Numeric.zeros(newshape,'D') sl[axis] = slice(0,(N+1)/2) Y[sl] = X[sl] sl[axis] = slice(-(N-1)/2,None) Y[sl] = X[sl] y = ifft(Y,axis=axis)*(float(num)/float(Nx)) if x.dtype.char not in ['F','D']: y = y.real if t is None: return y else: new_t = arange(0,num)*(t[1]-t[0])* Nx / float(num) + t[0] return y, new_t
936da8591e6b92539aeda1c0c884ca93d37a2754 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/936da8591e6b92539aeda1c0c884ca93d37a2754/signaltools.py
from numpy import expand_dims, unique, prod, sort, zeros, ones, \ reshape, r_, any, c_, transpose, take, dot import scipy.linalg as linalg
def resample(x,num,t=None,axis=0,window=None): """Resample to num samples using Fourier method along the given axis. The resampled signal starts at the same value of x but is sampled with a spacing of len(x) / num * (spacing of x). Because a Fourier method is used, the signal is assumed periodic. Window controls a Fourier-domain window that tapers the Fourier spectrum before zero-padding to aleviate ringing in the resampled values for sampled signals you didn't intend to be interpreted as band-limited. If window is a string then use the named window. If window is a float, then it represents a value of beta for a kaiser window. If window is a tuple, then the first component is a string representing the window, and the next arguments are parameters for that window. Possible windows are: 'blackman' ('black', 'blk') 'hamming' ('hamm', 'ham') 'bartlett' ('bart', 'brt') 'hanning' ('hann', 'han') 'kaiser' ('ksr') # requires parameter (beta) 'gaussian' ('gauss', 'gss') # requires parameter (std.) 'general gauss' ('general', 'ggs') # requires two parameters (power, width) The first sample of the returned vector is the same as the first sample of the input vector, the spacing between samples is changed from dx to dx * len(x) / num If t is not None, then it represents the old sample positions, and the new sample positions will be returned as well as the new samples. """ x = asarray(x) X = fft(x,axis=axis) Nx = x.shape[axis] if window is not None: W = ifftshift(get_window(window,Nx)) newshape = ones(len(x.shape)) newshape[axis] = len(W) W=W.reshape(newshape) X = X*W sl = [slice(None)]*len(x.shape) newshape = list(x.shape) newshape[axis] = num N = int(Numeric.minimum(num,Nx)) Y = Numeric.zeros(newshape,'D') sl[axis] = slice(0,(N+1)/2) Y[sl] = X[sl] sl[axis] = slice(-(N-1)/2,None) Y[sl] = X[sl] y = ifft(Y,axis=axis)*(float(num)/float(Nx)) if x.dtype.char not in ['F','D']: y = y.real if t is None: return y else: new_t = arange(0,num)*(t[1]-t[0])* Nx / float(num) + t[0] return y, new_t
936da8591e6b92539aeda1c0c884ca93d37a2754 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/936da8591e6b92539aeda1c0c884ca93d37a2754/signaltools.py
if mode == 'F': image = Image.fromstring(mode,shape,data.astype('f').tostring()) elif mode == 'I':
if mode == 'I':
def toimage(arr,high=255,low=0,cmin=None,cmax=None,pal=None, mode=None,channel_axis=None): """Takes a Numeric array and returns a PIL image. The mode of the PIL image depends on the array shape, the pal keyword, and the mode keyword. For 2-D arrays, if pal is a valid (N,3) byte-array giving the RGB values (from 0 to 255) then mode='P', otherwise mode='L', unless mode is given as 'F' or 'I' in which case a float and/or integer array is made For 3-D arrays, the channel_axis argument tells which dimension of the array holds the channel data. For 3-D arrays if one of the dimensions is 3, the mode is 'RGB' by default or 'YCbCr' if selected. if the The Numeric array must be either 2 dimensional or 3 dimensional. """ data = Numeric.asarray(arr) shape = list(data.shape) valid = len(shape)==2 or ((len(shape)==3) and \ ((3 in shape) or (4 in shape))) assert valid, "Not a suitable array shape for any mode." if len(shape) == 2: shape = (shape[1],shape[0]) # columns show up first if mode in [None, 'L', 'P']: bytedata = bytescale(data,high=high,low=low,cmin=cmin,cmax=cmax) image = Image.fromstring('L',shape,bytedata.tostring()) if pal is not None: image.putpalette(asarray(pal,typecode=_UInt8).tostring()) # Becomes a mode='P' automagically. elif mode == 'P': # default gray-scale pal = arange(0,256,1,typecode='b')[:,NewAxis] * \ ones((3,),typecode='b')[NewAxis,:] image.putpalette(asarray(pal,typecode=_UInt8).tostring()) return image if mode == '1': # high input gives threshold for 1 bytedata = ((data > high)*255).astype('b') image = Image.fromstring('L',shape,bytedata.tostring()) image = image.convert(mode='1') return image if cmin is None: cmin = amin(ravel(data)) if cmax is None: cmax = amax(ravel(data)) data = (data*1.0 - cmin)*(high-low)/(cmax-cmin) + low if mode == 'F': image = Image.fromstring(mode,shape,data.astype('f').tostring()) elif mode == 'I': image = Image.fromstring(mode,shape,data.astype('i').tostring()) else: raise ValueError, _errstr return image # if here then 3-d array with a 3 or a 4 in the shape length. # Check for 3 in datacube shape --- 'RGB' or 'YCbCr' if channel_axis is None: if (3 in shape): ca = Numeric.nonzero(asarray(shape) == 3)[0] else: ca = Numeric.nonzero(asarray(shape) == 4) if len(ca): ca = ca[0] else: raise ValueError, "Could not find channel dimension." else: ca = channel_axis numch = shape[ca] if numch not in [3,4]: raise ValueError, "Channel axis dimension is not valid." bytedata = bytescale(data,high=high,low=low,cmin=cmin,cmax=cmax) if ca == 2: strdata = bytedata.tostring() shape = (shape[1],shape[0]) elif ca == 1: strdata = transpose(bytedata,(0,2,1)).tostring() shape = (shape[2],shape[0]) elif ca == 0: strdata = transpose(bytedata,(1,2,0)).tostring() shape = (shape[2],shape[1]) if mode is None: if numch == 3: mode = 'RGB' else: mode = 'RGBA' if mode not in ['RGB','RGBA','YCbCr','CMYK']: raise ValueError, _errstr if mode in ['RGB', 'YCbCr']: assert numch == 3, "Invalid array shape for mode." if mode in ['RGBA', 'CMYK']: assert numch == 4, "Invalid array shape for mode." # Here we know data and mode is coorect image = Image.fromstring(mode, shape, strdata) return image
42178e27612a7fe5b5734ae22063456e6338a414 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/42178e27612a7fe5b5734ae22063456e6338a414/pilutil.py
if isinstance(other, dok_matrix):
if isinstance(other, spmatrix):
def __mul__(self, other): if isinstance(other, dok_matrix): return self.matmat(other) other = asarray(other) if rank(other) > 0: return self.matvec(other) res = dok_matrix() for key in self.keys(): res[key] = other * self[key] return res
1e1de936eca9d78cf7f56312b3f8a5c1badf7b93 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/1e1de936eca9d78cf7f56312b3f8a5c1badf7b93/Sparse.py
current_row = ikey0 row_ptr[ikey0] = k
N = ikey1-current_col row_ptr[current_row+1:ikey0+1] = [k]*N current_row = ikey0
def tocsr(self): # Return Compressed Sparse Row format arrays for this matrix keys = self.keys() keys.sort() nnz = len(keys) data = [0]*nnz colind = [0]*nnz row_ptr = [0]*(self.shape[0]+1) current_row = -1 k = 0 for key in keys: ikey0 = int(key[0]) ikey1 = int(key[1]) if ikey0 != current_row: current_row = ikey0 row_ptr[ikey0] = k data[k] = self[key] colind[k] = ikey1 k += 1 row_ptr[-1] = nnz data = array(data) colind = array(colind) row_ptr = array(row_ptr) return csr_matrix(data,(colind, row_ptr))
1e1de936eca9d78cf7f56312b3f8a5c1badf7b93 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/1e1de936eca9d78cf7f56312b3f8a5c1badf7b93/Sparse.py
data = [None]*nnz colind = [None]*nnz col_ptr = [None]*(self.shape[1]+1) current_col = -1
data = [0]*nnz rowind = [0]*nnz col_ptr = [0]*(self.shape[1]+1) current_col = 0
def tocsc(self): # Return Compressed Sparse Column format arrays for this matrix keys = self.keys() keys.sort(csc_cmp) nnz = len(keys) data = [None]*nnz colind = [None]*nnz col_ptr = [None]*(self.shape[1]+1) current_col = -1 k = 0 for key in keys: ikey0 = int(key[0]) ikey1 = int(key[1]) if ikey1 != current_col: current_col = ikey1 col_ptr[ikey1] = k data[k] = self[key] colind[k] = ikey0 k += 1 col_ptr[-1] = nnz data = array(data) colind = array(colind) col_ptr = array(col_ptr) return csc_matrix(data, (colind, col_ptr))
1e1de936eca9d78cf7f56312b3f8a5c1badf7b93 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/1e1de936eca9d78cf7f56312b3f8a5c1badf7b93/Sparse.py
col_ptr[ikey1] = k
def tocsc(self): # Return Compressed Sparse Column format arrays for this matrix keys = self.keys() keys.sort(csc_cmp) nnz = len(keys) data = [None]*nnz colind = [None]*nnz col_ptr = [None]*(self.shape[1]+1) current_col = -1 k = 0 for key in keys: ikey0 = int(key[0]) ikey1 = int(key[1]) if ikey1 != current_col: current_col = ikey1 col_ptr[ikey1] = k data[k] = self[key] colind[k] = ikey0 k += 1 col_ptr[-1] = nnz data = array(data) colind = array(colind) col_ptr = array(col_ptr) return csc_matrix(data, (colind, col_ptr))
1e1de936eca9d78cf7f56312b3f8a5c1badf7b93 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/1e1de936eca9d78cf7f56312b3f8a5c1badf7b93/Sparse.py
colind[k] = ikey0
rowind[k] = ikey0
def tocsc(self): # Return Compressed Sparse Column format arrays for this matrix keys = self.keys() keys.sort(csc_cmp) nnz = len(keys) data = [None]*nnz colind = [None]*nnz col_ptr = [None]*(self.shape[1]+1) current_col = -1 k = 0 for key in keys: ikey0 = int(key[0]) ikey1 = int(key[1]) if ikey1 != current_col: current_col = ikey1 col_ptr[ikey1] = k data[k] = self[key] colind[k] = ikey0 k += 1 col_ptr[-1] = nnz data = array(data) colind = array(colind) col_ptr = array(col_ptr) return csc_matrix(data, (colind, col_ptr))
1e1de936eca9d78cf7f56312b3f8a5c1badf7b93 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/1e1de936eca9d78cf7f56312b3f8a5c1badf7b93/Sparse.py
colind = array(colind)
rowind = array(rowind)
def tocsc(self): # Return Compressed Sparse Column format arrays for this matrix keys = self.keys() keys.sort(csc_cmp) nnz = len(keys) data = [None]*nnz colind = [None]*nnz col_ptr = [None]*(self.shape[1]+1) current_col = -1 k = 0 for key in keys: ikey0 = int(key[0]) ikey1 = int(key[1]) if ikey1 != current_col: current_col = ikey1 col_ptr[ikey1] = k data[k] = self[key] colind[k] = ikey0 k += 1 col_ptr[-1] = nnz data = array(data) colind = array(colind) col_ptr = array(col_ptr) return csc_matrix(data, (colind, col_ptr))
1e1de936eca9d78cf7f56312b3f8a5c1badf7b93 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/1e1de936eca9d78cf7f56312b3f8a5c1badf7b93/Sparse.py
return csc_matrix(data, (colind, col_ptr))
return csc_matrix(data, (rowind, col_ptr))
def tocsc(self): # Return Compressed Sparse Column format arrays for this matrix keys = self.keys() keys.sort(csc_cmp) nnz = len(keys) data = [None]*nnz colind = [None]*nnz col_ptr = [None]*(self.shape[1]+1) current_col = -1 k = 0 for key in keys: ikey0 = int(key[0]) ikey1 = int(key[1]) if ikey1 != current_col: current_col = ikey1 col_ptr[ikey1] = k data[k] = self[key] colind[k] = ikey0 k += 1 col_ptr[-1] = nnz data = array(data) colind = array(colind) col_ptr = array(col_ptr) return csc_matrix(data, (colind, col_ptr))
1e1de936eca9d78cf7f56312b3f8a5c1badf7b93 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/1e1de936eca9d78cf7f56312b3f8a5c1badf7b93/Sparse.py
mask = (a < threshmin)
mask |= (a < threshmin)
def threshold(a, threshmin=None, threshmax=None, newval=0): """Clip array to a given value.
105f9006d6823d54ababf218e8d61d356e54e700 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/105f9006d6823d54ababf218e8d61d356e54e700/stats.py
aux = numpy.take( ar, perm 0,axis=0)
aux = numpy.take( ar, perm, axis=0)
def unique1d( ar1, retIndx = False ): """Unique elements of 1D array. When retIndx is True, return also the indices indx such that ar1[indx] is the resulting array of unique elements.""" ar = numpy.array( ar1 ).ravel() if retIndx: perm = numpy.argsort( ar ) aux = numpy.take( ar, perm 0,axis=0) flag = ediff1d( aux, 1 ) != 0 return numpy.compress( flag, perm ,axis=-1), numpy.compress( flag, aux ,axis=-1) else: aux = numpy.sort( ar ) return numpy.compress( ediff1d( aux, 1 ) != 0, aux ,axis=-1)
a9cc2a04d5448e5b6e7f01cc330879aa9029ee04 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/a9cc2a04d5448e5b6e7f01cc330879aa9029ee04/arraysetops.py
def ppcc_max(x, dist='tukeylambda'):
def ppcc_max(x, brack=(0.0,1.0), dist='tukeylambda'):
def ppcc_max(x, dist='tukeylambda'): """Returns the shape parameter that maximizes the probability plot correlation coefficient for the given data to a one-parameter family of distributions. See also ppcc_plot """ try: ppf_func = eval('distributions.%sppf'%dist) except AttributError: raise dist, "is not a valid distribution with a ppf." res = inspect.getargspec(ppf_func) if not ('loc' == res[0][-2] and 'scale' == res[0][-1] and \ 0.0==res[-1][-2] and 1.0==res[-1][-1]): raise ValueError, "Function has does not have default location", \ "and scale parameters\n that are 0.0 and 1.0 respectively." if (1 < len(res[0])-len(res[-1])-1) or \ (1 > len(res[0])-3): raise ValueError, "Must be a one-parameter family." N = len(x) Ui = zeros(N)*1.0 Ui[-1] = 0.5**(1.0/N) Ui[0] = 1-Ui[-1] i = arange(2,N) Ui[1:-1] = (i-0.3175)/(N+0.365) osr = sort(x) def tempfunc(shape, mi, yvals, func): xvals = func(mi, shape) slope, intercept, r, prob, sterrest = stats.linregress(xvals, yvals) return 1-r return optimize.brent(tempfunc, args=(Ui, osr, ppf_func))
871e698215989e20feab368164bfb8ffbba0114e /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/871e698215989e20feab368164bfb8ffbba0114e/morestats.py
return optimize.brent(tempfunc, args=(Ui, osr, ppf_func))
return optimize.brent(tempfunc, brack=brack, args=(Ui, osr, ppf_func))
def tempfunc(shape, mi, yvals, func): xvals = func(mi, shape) slope, intercept, r, prob, sterrest = stats.linregress(xvals, yvals) return 1-r
871e698215989e20feab368164bfb8ffbba0114e /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/871e698215989e20feab368164bfb8ffbba0114e/morestats.py
""" Later have log(0) raise warning, not error
""" log(0) should print warning, but succeed.
def check_log_0(self): """ Later have log(0) raise warning, not error """ try: val = logn(3,0) assert(0) except OverflowError: pass
c9316d1d2ba5e114019481f968becf25226dff17 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/c9316d1d2ba5e114019481f968becf25226dff17/test_misc.py
assert(0)
except: assert(0) def check_log_neg(self): """ log(-1) should print warning, but still raises error. """ try: val = logn(3,-1)
def check_log_0(self): """ Later have log(0) raise warning, not error """ try: val = logn(3,0) assert(0) except OverflowError: pass
c9316d1d2ba5e114019481f968becf25226dff17 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/c9316d1d2ba5e114019481f968becf25226dff17/test_misc.py
pass def check_log_neg(self): """ Later have log(-1) raise warning, not error """ try: val = logn(3,-1) assert(0) except ValueError:
def check_log_0(self): """ Later have log(0) raise warning, not error """ try: val = logn(3,0) assert(0) except OverflowError: pass
c9316d1d2ba5e114019481f968becf25226dff17 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/c9316d1d2ba5e114019481f968becf25226dff17/test_misc.py
""" Later have log(0) raise warning, not error
""" log(0) should print warning, but succeed.
def check_log_0(self): """ Later have log(0) raise warning, not error """ try: val = logn(3,0) assert(0) except OverflowError: pass
c9316d1d2ba5e114019481f968becf25226dff17 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/c9316d1d2ba5e114019481f968becf25226dff17/test_misc.py
val = logn(3,0) assert(0) except OverflowError: pass
val = log2(0) except: assert(0)
def check_log_0(self): """ Later have log(0) raise warning, not error """ try: val = logn(3,0) assert(0) except OverflowError: pass
c9316d1d2ba5e114019481f968becf25226dff17 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/c9316d1d2ba5e114019481f968becf25226dff17/test_misc.py
val = logn(3,-1) assert(0) except ValueError:
val = log2(-1) assert(0) except OverflowError:
def check_log_neg(self): """ Later have log(-1) raise warning, not error """ try: val = logn(3,-1) assert(0) except ValueError: pass
c9316d1d2ba5e114019481f968becf25226dff17 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/c9316d1d2ba5e114019481f968becf25226dff17/test_misc.py
ftype, lastel, data, index0, index1 = \ mat.ftype, mat.nnz, mat.data, mat.rowind, mat.indptr
if csc: index0 = mat.rowind else: index0 = mat.colind ftype, lastel, data, index1 = mat.ftype, mat.nnz, mat.data, mat.indptr
def spsolve(A, b, permc_spec=2): if not hasattr(A, 'tocsr') and not hasattr(A, 'tocsc'): raise ValueError, "sparse matrix must be able to return CSC format--"\ "A.tocsc()--or CSR format--A.tocsr()" if not hasattr(A, 'shape'): raise ValueError, "sparse matrix must be able to return shape" \ " (rows, cols) = A.shape" M, N = A.shape if (M != N): raise ValueError, "matrix must be square" if isUmfpack and useUmfpack: mat = _toCS_umfpack( A ) if mat.dtype.char not in 'dD': raise ValueError, "convert matrix data to double, please, using"\ " .astype(), or set linsolve.useUmfpack = False" family = {'d' : 'di', 'D' : 'zi'} umf = umfpack.UmfpackContext( family[mat.dtype.char] ) return umf.linsolve( umfpack.UMFPACK_A, mat, b, autoTranspose = True ) else: mat, csc = _toCS_superLU( A ) ftype, lastel, data, index0, index1 = \ mat.ftype, mat.nnz, mat.data, mat.rowind, mat.indptr gssv = eval('_superlu.' + ftype + 'gssv') print "data-ftype: %s compared to data %s" % (ftype, data.dtype.char) print "Calling _superlu.%sgssv" % ftype return gssv(N, lastel, data, index0, index1, b, csc, permc_spec)[0]
d181697e0f9d3cb08cc1f181833d2136acb2a3c5 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/d181697e0f9d3cb08cc1f181833d2136acb2a3c5/linsolve.py
res = csc + other return res
return csc + other
def __add__(self, other): csc = self.tocsc() res = csc + other return res
bdabf856b614694a8ef20c73a7d82bda3a7cf386 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/bdabf856b614694a8ef20c73a7d82bda3a7cf386/sparse.py
res = csc - other return res
return csc - other
def __sub__(self, other): csc = self.tocsc() res = csc - other return res
bdabf856b614694a8ef20c73a7d82bda3a7cf386 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/bdabf856b614694a8ef20c73a7d82bda3a7cf386/sparse.py
res = csc.__rsub__(other) return res
return csc.__rsub__(other)
def __rsub__(self, other): # other - self csc = self.tocsc() res = csc.__rsub__(other) return res
bdabf856b614694a8ef20c73a7d82bda3a7cf386 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/bdabf856b614694a8ef20c73a7d82bda3a7cf386/sparse.py
res = csc * other return res
return csc * other
def __mul__(self, other): csc = self.tocsc() res = csc * other return res
bdabf856b614694a8ef20c73a7d82bda3a7cf386 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/bdabf856b614694a8ef20c73a7d82bda3a7cf386/sparse.py
res = csc.__rmul__(other) return res
return csc.__rmul__(other)
def __rmul__(self, other): csc = self.tocsc() res = csc.__rmul__(other) return res
bdabf856b614694a8ef20c73a7d82bda3a7cf386 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/bdabf856b614694a8ef20c73a7d82bda3a7cf386/sparse.py
res = -csc return res
return -csc
def __neg__(self): csc = self.tocsc() res = -csc return res
bdabf856b614694a8ef20c73a7d82bda3a7cf386 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/bdabf856b614694a8ef20c73a7d82bda3a7cf386/sparse.py
res = csc.transpose() return res
return csc.transpose()
def transpose(self): csc = self.tocsc() res = csc.transpose() return res
bdabf856b614694a8ef20c73a7d82bda3a7cf386 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/bdabf856b614694a8ef20c73a7d82bda3a7cf386/sparse.py
res = csc.matrixmultiply(other) return res
return csc.matrixmultiply(other)
def matrixmultiply(self, other): """ A generic interface for matrix-matrix or matrix-vector multiplication. """ csc = self.tocsc() res = csc.matrixmultiply(other) return res
bdabf856b614694a8ef20c73a7d82bda3a7cf386 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/bdabf856b614694a8ef20c73a7d82bda3a7cf386/sparse.py
res = csc.matmat(other) return res
return csc.matmat(other)
def matmat(self, other): csc = self.tocsc() res = csc.matmat(other) return res
bdabf856b614694a8ef20c73a7d82bda3a7cf386 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/bdabf856b614694a8ef20c73a7d82bda3a7cf386/sparse.py
res = csc.matvec(vec) return res
return csc.matvec(vec)
def matvec(self, vec): csc = self.tocsc() res = csc.matvec(vec) return res
bdabf856b614694a8ef20c73a7d82bda3a7cf386 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/bdabf856b614694a8ef20c73a7d82bda3a7cf386/sparse.py
res = csc.rmatvec(vec, conj=conj) return res
return csc.rmatvec(vec, conj=conj)
def rmatvec(self, vec, conj=1): csc = self.tocsc() res = csc.rmatvec(vec, conj=conj) return res
bdabf856b614694a8ef20c73a7d82bda3a7cf386 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/bdabf856b614694a8ef20c73a7d82bda3a7cf386/sparse.py
ocs = csc_matrix(other) if (ocs.shape != self.shape): raise ValueError, "Inconsistent shapes." dtypechar = _coerce_rules[(self._dtypechar,other._dtypechar)] nnz1, nnz2 = self.nnz, other.nnz data1, data2 = _convert_data(self.data[:nnz1], ocs.data[:nnz2], dtypechar) func = getattr(sparsetools,_transtabl[dtypechar]+'cscadd') c,rowc,ptrc,ierr = func(data1,self.rowind[:nnz1],self.indptr,data2,ocs.rowind[:nnz2],ocs.indptr) if ierr: raise ValueError, "Ran out of space (but shouldn't have happened)." M, N = self.shape return csc_matrix.Construct(c,(rowc,ptrc),M=M,N=N)
if isscalar(other): raise NotImplementedError('adding a scalar to a sparse matrix is not yet supported') elif isspmatrix(other): ocs = other.tocsc() if (ocs.shape != self.shape): raise ValueError, "Inconsistent shapes." dtypechar = _coerce_rules[(self._dtypechar, ocs._dtypechar)] nnz1, nnz2 = self.nnz, other.nnz data1, data2 = _convert_data(self.data[:nnz1], ocs.data[:nnz2], dtypechar) func = getattr(sparsetools,_transtabl[dtypechar]+'cscadd') c,rowc,ptrc,ierr = func(data1,self.rowind[:nnz1],self.indptr,data2,ocs.rowind[:nnz2],ocs.indptr) if ierr: raise ValueError, "Ran out of space (but shouldn't have happened)." M, N = self.shape return csc_matrix.Construct(c,(rowc,ptrc),M=M,N=N)
def __add__(self, other): ocs = csc_matrix(other) if (ocs.shape != self.shape): raise ValueError, "Inconsistent shapes." dtypechar = _coerce_rules[(self._dtypechar,other._dtypechar)] nnz1, nnz2 = self.nnz, other.nnz data1, data2 = _convert_data(self.data[:nnz1], ocs.data[:nnz2], dtypechar) func = getattr(sparsetools,_transtabl[dtypechar]+'cscadd') c,rowc,ptrc,ierr = func(data1,self.rowind[:nnz1],self.indptr,data2,ocs.rowind[:nnz2],ocs.indptr) if ierr: raise ValueError, "Ran out of space (but shouldn't have happened)." M, N = self.shape return csc_matrix.Construct(c,(rowc,ptrc),M=M,N=N)
bdabf856b614694a8ef20c73a7d82bda3a7cf386 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/bdabf856b614694a8ef20c73a7d82bda3a7cf386/sparse.py
ocs = csc_matrix(other)
ocs = other.tocsc()
def __rmul__(self, other): # other * self if isspmatrix(other): ocs = csc_matrix(other) return ocs.matmat(self) elif isscalar(other): new = self.copy() new.data = other * new.data new._dtypechar = new.data.dtypechar new.ftype = _transtabl[new._dtypechar] return new else: return transpose(self.rmatvec(transpose(other),conj=0))
bdabf856b614694a8ef20c73a7d82bda3a7cf386 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/bdabf856b614694a8ef20c73a7d82bda3a7cf386/sparse.py
new.data = -new.data
new.data *= -1
def __neg__(self): new = self.copy() new.data = -new.data return new
bdabf856b614694a8ef20c73a7d82bda3a7cf386 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/bdabf856b614694a8ef20c73a7d82bda3a7cf386/sparse.py
ocs = csc_matrix(other) if (ocs.shape != self.shape): raise ValueError, "Inconsistent shapes." dtypechar = _coerce_rules[(self._dtypechar,ocs._dtypechar)] data1, data2 = _convert_data(self.data, ocs.data, dtypechar) func = getattr(sparsetools,_transtabl[dtypechar]+'cscadd') c,rowc,ptrc,ierr = func(data1,self.rowind,self.indptr,-data2,ocs.rowind,ocs.indptr) if ierr: raise ValueError, "Ran out of space (but shouldn't have happened)." M, N = self.shape return csc_matrix.Construct(c,(rowc,ptrc),M=M,N=N)
if isscalar(other): raise NotImplementedError('adding a scalar to a sparse matrix is not yet supported') elif isspmatrix(other): ocs = other.tocsc() if (ocs.shape != self.shape): raise ValueError, "Inconsistent shapes." dtypechar = _coerce_rules[(self._dtypechar,ocs._dtypechar)] data1, data2 = _convert_data(self.data, ocs.data, dtypechar) func = getattr(sparsetools,_transtabl[dtypechar]+'cscadd') c,rowc,ptrc,ierr = func(data1,self.rowind,self.indptr,-data2,ocs.rowind,ocs.indptr) if ierr: raise ValueError, "Ran out of space (but shouldn't have happened)." M, N = self.shape return csc_matrix.Construct(c,(rowc,ptrc),M=M,N=N)
def __sub__(self, other): ocs = csc_matrix(other) if (ocs.shape != self.shape): raise ValueError, "Inconsistent shapes." dtypechar = _coerce_rules[(self._dtypechar,ocs._dtypechar)] data1, data2 = _convert_data(self.data, ocs.data, dtypechar) func = getattr(sparsetools,_transtabl[dtypechar]+'cscadd') c,rowc,ptrc,ierr = func(data1,self.rowind,self.indptr,-data2,ocs.rowind,ocs.indptr) if ierr: raise ValueError, "Ran out of space (but shouldn't have happened)." M, N = self.shape return csc_matrix.Construct(c,(rowc,ptrc),M=M,N=N)
bdabf856b614694a8ef20c73a7d82bda3a7cf386 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/bdabf856b614694a8ef20c73a7d82bda3a7cf386/sparse.py
ocs = csc_matrix(other) if (ocs.shape != self.shape): raise ValueError, "Inconsistent shapes." dtypechar = _coerce_rules[(self._dtypechar,ocs._dtypechar)] data1, data2 = _convert_data(self.data, ocs.data, dtypechar) func = getattr(sparsetools,_transtabl[dtypechar]+'cscadd') c,rowc,ptrc,ierr = func(-data1,self.rowind,self.indptr,data2,ocs.rowind,ocs.indptr) if ierr: raise ValueError, "Ran out of space (but shouldn't have happened)." M, N = self.shape return csc_matrix.Construct(c,(rowc,ptrc),M=M,N=N)
if isscalar(other): raise NotImplementedError('adding a scalar to a sparse matrix is not yet supported') elif isspmatrix(other): ocs = other.tocsc() if (ocs.shape != self.shape): raise ValueError, "Inconsistent shapes." dtypechar = _coerce_rules[(self._dtypechar,ocs._dtypechar)] data1, data2 = _convert_data(self.data, ocs.data, dtypechar) func = getattr(sparsetools,_transtabl[dtypechar]+'cscadd') c,rowc,ptrc,ierr = func(-data1,self.rowind,self.indptr,data2,ocs.rowind,ocs.indptr) if ierr: raise ValueError, "Ran out of space (but shouldn't have happened)." M, N = self.shape return csc_matrix.Construct(c,(rowc,ptrc),M=M,N=N)
def __rsub__(self, other): # implement other - self ocs = csc_matrix(other) if (ocs.shape != self.shape): raise ValueError, "Inconsistent shapes." dtypechar = _coerce_rules[(self._dtypechar,ocs._dtypechar)] data1, data2 = _convert_data(self.data, ocs.data, dtypechar) func = getattr(sparsetools,_transtabl[dtypechar]+'cscadd') c,rowc,ptrc,ierr = func(-data1,self.rowind,self.indptr,data2,ocs.rowind,ocs.indptr) if ierr: raise ValueError, "Ran out of space (but shouldn't have happened)." M, N = self.shape return csc_matrix.Construct(c,(rowc,ptrc),M=M,N=N)
bdabf856b614694a8ef20c73a7d82bda3a7cf386 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/bdabf856b614694a8ef20c73a7d82bda3a7cf386/sparse.py
ocs = csc_matrix(other)
ocs = other.tocsc()
def __pow__(self, other): """ Element-by-element power (unless other is a scalar, in which case return the matrix power.) """ if isscalar(other): new = self.copy() new.data = new.data ** other new._dtypechar = new.data.dtypechar new.ftype = _transtabl[new._dtypechar] return new else: ocs = csc_matrix(other) if (ocs.shape != self.shape): raise ValueError, "Inconsistent shapes." dtypechar = _coerce_rules[(self._dtypechar,ocs._dtypechar)] nnz1, nnz2 = self.nnz, ocs.nnz data1, data2 = _convert_data(self.data[:nnz1], ocs.data[:nnz2], dtypechar) func = getattr(sparsetools,_transtabl[dtypechar]+'cscmul') c,rowc,ptrc,ierr = func(data1,self.rowind[:nnz1],self.indptr,data2,ocs.rowind[:nnz2],ocs.indptr) if ierr: raise ValueError, "Ran out of space (but shouldn't have happened)." M, N = self.shape return csc_matrix.Construct(c,(rowc,ptrc),M=M,N=N)
bdabf856b614694a8ef20c73a7d82bda3a7cf386 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/bdabf856b614694a8ef20c73a7d82bda3a7cf386/sparse.py
try: m,n = other.shape except AttributeError:
if isscalar(other):
def __add__(self, other): # First check if argument is a scalar try: m,n = other.shape except AttributeError: # Okay, assume it's scalar # Now we would add this scalar to every element. raise NotImplementedError('adding a scalar to a sparse matrix is not yet supported') other_csr = other.tocsr() #ocs = csr_matrix(other) if (other_csr.shape != self.shape): raise ValueError, "inconsistent shapes."
bdabf856b614694a8ef20c73a7d82bda3a7cf386 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/bdabf856b614694a8ef20c73a7d82bda3a7cf386/sparse.py
other_csr = other.tocsr() if (other_csr.shape != self.shape):
ocs = other.tocsr() if (ocs.shape != self.shape):
def __add__(self, other): # First check if argument is a scalar try: m,n = other.shape except AttributeError: # Okay, assume it's scalar # Now we would add this scalar to every element. raise NotImplementedError('adding a scalar to a sparse matrix is not yet supported') other_csr = other.tocsr() #ocs = csr_matrix(other) if (other_csr.shape != self.shape): raise ValueError, "inconsistent shapes."
bdabf856b614694a8ef20c73a7d82bda3a7cf386 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/bdabf856b614694a8ef20c73a7d82bda3a7cf386/sparse.py
dtypechar = _coerce_rules[(self._dtypechar,other._dtypechar)] data1, data2 = _convert_data(self.data, other.data, dtypechar)
dtypechar = _coerce_rules[(self._dtypechar, ocs._dtypechar)] data1, data2 = _convert_data(self.data, ocs.data, dtypechar)
def __add__(self, other): # First check if argument is a scalar try: m,n = other.shape except AttributeError: # Okay, assume it's scalar # Now we would add this scalar to every element. raise NotImplementedError('adding a scalar to a sparse matrix is not yet supported') other_csr = other.tocsr() #ocs = csr_matrix(other) if (other_csr.shape != self.shape): raise ValueError, "inconsistent shapes."
bdabf856b614694a8ef20c73a7d82bda3a7cf386 /local1/tlutelli/issta_data/temp/all_python//python/2006_temp/2006/12971/bdabf856b614694a8ef20c73a7d82bda3a7cf386/sparse.py