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pep8

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Per A Brodtkorb 8 years ago
parent 07bcbde53b
commit 53a65e4f3d
2 changed files with 52 additions and 52 deletions
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3
wafo/covariance/estimation.py
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@ -143,7 +143,8 @@ class CovarianceEstimator(object):
nfft = 2 ** nextpow2(n) nfft = 2 ** nextpow2(n)
raw_periodogram = abs(fft(x, nfft)) ** 2 / Ncens raw_periodogram = abs(fft(x, nfft)) ** 2 / Ncens
auto_cov = np.real(fft(raw_periodogram)) / nfft # ifft = fft/nfft since raw_periodogram is real! # ifft = fft/nfft since raw_periodogram is real!
auto_cov = np.real(fft(raw_periodogram)) / nfft
if self.flag.startswith('unbiased'): if self.flag.startswith('unbiased'):
# unbiased result, i.e. divide by n-abs(lag) # unbiased result, i.e. divide by n-abs(lag)

97
wafo/objects.py
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@ -848,7 +848,7 @@ class CyclePairs(PlotData):
n = len(F) # Size of matrix n = len(F) # Size of matrix
N = np.sum(F) # Total number of cycles N = np.sum(F) # Total number of cycles
Fsmooth = np.zeros((n,n)) Fsmooth = np.zeros((n, n))
if method == 1 or method == 2: # Kernel estimator if method == 1 or method == 2: # Kernel estimator
@ -862,59 +862,59 @@ class CyclePairs(PlotData):
# therefore we choose a slightly smaller bandwidth # therefore we choose a slightly smaller bandwidth
if aut_h == 1: if aut_h == 1:
h_norm = smoothcmat_hnorm(F,NOsubzero); h_norm = smoothcmat_hnorm(F, NOsubzero)
h = 0.7*h_norm # Don't oversmooth h = 0.7 * h_norm # Don't oversmooth
#h0 = N^(-1/(d+4)); # h0 = N^(-1/(d+4));
#FF = F+F'; # FF = F+F';
#mean_F = sum(sum(FF).*(1:n))/N/2; # mean_F = sum(sum(FF).*(1:n))/N/2;
#s2 = sum(sum(FF).*((1:n)-mean_F).^2)/N/2; # s2 = sum(sum(FF).*((1:n)-mean_F).^2)/N/2;
#s = sqrt(s2); % Mean of std in each direction # s = sqrt(s2); % Mean of std in each direction
#h_norm = s*h0; % Optimal for Normal distr. # h_norm = s*h0; % Optimal for Normal distr.
#h = h_norm; % Test # h = h_norm; % Test
# endif # endif
# Calculating kernel estimate # Calculating kernel estimate
# Kernel: 2-dim normal density function # Kernel: 2-dim normal density function
for i in range(n-1): for i in range(n - 1):
for j in range(i+1,n): for j in range(i + 1, n):
if F(i,j) != 0: if F[i, j] != 0:
F1 = exp(-1/(2*h**2)*((I-i)**2+(J-j)**2)); # Gaussian kernel F1 = exp(-1 / (2 * h**2) * ((I - i)**2 + (J - j)**2)) # Gaussian kernel
F1 = F1+F1.T; # Mirror kernel in diagonal F1 = F1 + F1.T # Mirror kernel in diagonal
F1 = np.triu(F1,1+NOsubzero); # Set to zero below and on diagonal F1 = np.triu(F1, 1 + NOsubzero) # Set to zero below and on diagonal
F1 = F[i,j] * F1/np.sum(F1) # Normalize F1 = F[i, j] * F1 / np.sum(F1) # Normalize
Fsmooth = Fsmooth+F1; Fsmooth = Fsmooth + F1
# endif # endif
# endfor # endfor
# endfor # endfor
# endif method 1 or 2 # endif method 1 or 2
if method == 2: if method == 2:
Fpilot = Fsmooth/N; Fpilot = Fsmooth / N
Fsmooth = np.zeros(n,n); Fsmooth = np.zeros(n, n)
[I1,I2] = find(F>0); [I1, I2] = find(F > 0)
logg = 0; logg = 0
for i in range(len(I1)): # =1:length(I1): for i in range(len(I1)): # =1:length(I1):
logg = logg + F(I1(i),I2(i)) * log(Fpilot(I1(i),I2(i))); logg = logg + F(I1[i], I2[i]) * log(Fpilot(I1[i], I2[i]))
#endfor # endfor
g = np.exp(logg/N); g = np.exp(logg / N)
lamda = (Fpilot/g)**(-alpha); _lamda = (Fpilot / g)**(-alpha)
for i in range(n-1): # = 1:n-1 for i in range(n - 1): # = 1:n-1
for j in range(i+1, n): # = i+1:n for j in range(i + 1, n): # = i+1:n
if F[i,j] != 0: if F[i, j] != 0:
hi = h*lamda[i,j] hi = h * _lamda[i, j]
F1 = np.exp(-1/(2*hi**2)*((I-i)**2+(J-j)**2)); # Gaussian kernel F1 = np.exp(-1 / (2 * hi**2) * ((I - i)**2 + (J - j)**2)) # Gaussian kernel
F1 = F1+F1.T; # Mirror kernel in diagonal F1 = F1 + F1.T # Mirror kernel in diagonal
F1 = np.triu(F1,1+NOsubzero); # Set to zero below and on diagonal F1 = np.triu(F1, 1 + NOsubzero) # Set to zero below and on diagonal
F1 = F[i,j] * F1/np.sum(F1); # Normalize F1 = F[i, j] * F1 / np.sum(F1) # Normalize
Fsmooth = Fsmooth+F1; Fsmooth = Fsmooth + F1
# endif # endif
# endfor # endfor
# endfor # endfor
#endif method 2 # endif method 2
return Fsmooth,h return Fsmooth,h
def cycle_matrix(self, param=(), ddef=1, method=0, h=None, NOsubzero=0, alpha=0.5): def cycle_matrix(self, param=(), ddef=1, method=0, h=None, NOsubzero=0, alpha=0.5):
@ -1029,30 +1029,29 @@ class CyclePairs(PlotData):
cp1, cp2 = np.copy(self.args), np.copy(self.data) cp1, cp2 = np.copy(self.args), np.copy(self.data)
# Make so that minima is in first column # Make so that minima is in first column
ix = np.flatnonzero(cp1>cp2) ix = np.flatnonzero(cp1 > cp2)
if np.any(ix): if np.any(ix):
cp1[ix], cp2[ix] = cp2[ix], cp1[ix] cp1[ix], cp2[ix] = cp2[ix], cp1[ix]
# Make discretization # Make discretization
a, b, n = param a, b, n = param
delta = (b-a)/(n-1) # Discretization step delta = (b - a) / (n - 1) # Discretization step
cp1 = (cp1-a)/delta + 1 cp1 = (cp1 - a) / delta + 1
cp2 = (cp2-a)/delta + 1 cp2 = (cp2 - a) / delta + 1
if ddef == 0: if ddef == 0:
cp1 = np.clip(np.round(cp1), 0, n-1) cp1 = np.clip(np.round(cp1), 0, n - 1)
cp2 = np.clip(np.round(cp2), 0, n-1) cp2 = np.clip(np.round(cp2), 0, n - 1)
elif ddef == +1: elif ddef == +1:
cp1 = np.clip(np.floor(cp1), 0, n-2) cp1 = np.clip(np.floor(cp1), 0, n - 2)
cp2 = np.clip(np.ceil(cp2), 1, n-1) cp2 = np.clip(np.ceil(cp2), 1, n - 1)
elif ddef == -1: elif ddef == -1:
cp1 = np.clip(np.ceil(cp1), 1, n-1) cp1 = np.clip(np.ceil(cp1), 1, n - 1)
cp2 = np.clip(np.floor(cp2), 0, n-2) cp2 = np.clip(np.floor(cp2), 0, n - 2)
else: else:
raise ValueError('Undefined discretization definition, ddef = {}'.format(ddef)) raise ValueError('Undefined discretization definition, ddef = {}'.format(ddef))
if np.any(ix): if np.any(ix):
cp1[ix], cp2[ix] = cp2[ix], cp1[ix] cp1[ix], cp2[ix] = cp2[ix], cp1[ix]
return np.asarray(cp1, type=int), np.asarray(cp2, type=int) return np.asarray(cp1, type=int), np.asarray(cp2, type=int)

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