update doctests

doc/source/quickstart.rst

@@ -132,7 +132,7 @@ Once the AR and/or MA parameters are found, the :func:`~spectrum.arma.arma2psd`
     :width: 80%
 
     from spectrum import arma_estimate, arma2psd, marple_data
-    from pylab import *
+    from pylab import plot, axis, xlabel, ylabel, grid
     ar, ma, rho = arma_estimate(marple_data, 15, 15, 30)
     psd = arma2psd(ar, ma, rho=rho, NFFT=4096)
     plot(10*log10(psd/max(psd)))

doc/source/ref_others.rst

@@ -19,8 +19,7 @@ Classes
 Correlation
 -------------
 
-.. .. module:: correlation
-    :synopsis: Correlation methods
+.. module:: correlation
 .. automodule:: spectrum.correlation
     :members:
     :undoc-members:

src/spectrum/datasets.py

@@ -110,7 +110,10 @@ def data_cosine(N=1024, A=0.1, sampling=1024., freq=200):
 
     where w[t] is a white noise of variance 1.
 
-    >>> a = data_cosine(N=1024, sampling=1024, A=0.5, freq=100)
+    .. doctest::
+
+        >>> from spectrum import data_cosine
+        >>> a = data_cosine(N=1024, sampling=1024, A=0.5, freq=100)
 
     """
     t = arange(0, float(N)/sampling, 1./sampling)
@@ -132,6 +135,7 @@ class TimeSeries():
 
     .. doctest::
 
+        >>> from spectrum import TimeSeries
         >>> data = [1, 2, 3, 4, 3, 2, 1, 0 ]
         >>> ts = TimeSeries(data, sampling=1)
         >>> ts.plot()

src/spectrum/linalg.py

@@ -1,15 +1,15 @@
 """
-.. topic:: Linear algebra tools  
+.. topic:: Linear algebra tools
 
     .. autosummary::
-       
+
         csvd
         corrmtx
         pascal
-    
+
     .. codeauthor:: Thomas Cokelaer 2011
- 
-   
+
+
 """
 import numpy
 
@@ -20,16 +20,19 @@
 
 def pascal(n):
     """Return Pascal matrix
-    
+
     :param int n: size of the matrix
-    
-    >>> pascal(6)
-    array([[   1.,    1.,    1.,    1.,    1.,    1.],
-           [   1.,    2.,    3.,    4.,    5.,    6.],
-           [   1.,    3.,    6.,   10.,   15.,   21.],
-           [   1.,    4.,   10.,   20.,   35.,   56.],
-           [   1.,    5.,   15.,   35.,   70.,  126.],
-           [   1.,    6.,   21.,   56.,  126.,  252.]])
+
+    .. doctest::
+
+        >>> from spectrum import pascal
+        >>> pascal(6)
+        array([[   1.,    1.,    1.,    1.,    1.,    1.],
+               [   1.,    2.,    3.,    4.,    5.,    6.],
+               [   1.,    3.,    6.,   10.,   15.,   21.],
+               [   1.,    4.,   10.,   20.,   35.,   56.],
+               [   1.,    5.,   15.,   35.,   70.,  126.],
+               [   1.,    6.,   21.,   56.,  126.,  252.]])
 
     .. todo:: use the symmetric property to improve computational time if needed
     """
@@ -44,108 +47,108 @@ def pascal(n):
         for i in range(1, n):
             for j in range(1, n):
                 result[i, j] = result[i-1, j] + result[i, j-1]
-    return result      
+    return result
 
 
 
 def corrmtx(x_input, m, method='autocorrelation'):
     r"""Correlation matrix
-    
+
     This function is used by PSD estimator functions. It generates
     the correlation matrix from a correlation data set and a maximum lag.
-    
+
     :param array x: autocorrelation samples (1D)
     :param int m: the maximum lag
-    
-    Depending on the choice of the method, the correlation matrix has different 
-    sizes, but the number of rows is always m+1.  
+
+    Depending on the choice of the method, the correlation matrix has different
+    sizes, but the number of rows is always m+1.
 
     Method can be :
-    
-    * 'autocorrelation': (default) X is the (n+m)-by-(m+1) rectangular Toeplitz 
+
+    * 'autocorrelation': (default) X is the (n+m)-by-(m+1) rectangular Toeplitz
       matrix derived using prewindowed and postwindowed data.
     * 'prewindowed': X is the n-by-(m+1) rectangular Toeplitz matrix derived
       using prewindowed data only.
-    * 'postwindowed': X is the n-by-(m+1) rectangular Toeplitz matrix that 
+    * 'postwindowed': X is the n-by-(m+1) rectangular Toeplitz matrix that
       derived using postwindowed data only.
-    * 'covariance': X is the (n-m)-by-(m+1) rectangular Toeplitz matrix       
+    * 'covariance': X is the (n-m)-by-(m+1) rectangular Toeplitz matrix
       derived using nonwindowed data.
     * 'modified': X is the 2(n-m)-by-(m+1) modified rectangular Toeplitz
-      matrix that generates an autocorrelation estimate for the length n data 
+      matrix that generates an autocorrelation estimate for the length n data
       vector x, derived using forward and backward prediction error estimates.
-      
 
-    :return: 
+
+    :return:
         * the autocorrelation matrix
         * R, the (m+1)-by-(m+1) autocorrelation matrix estimate ``R= X'*X``.
-    
+
     .. rubric:: Algorithm details:
-    
+
     The **autocorrelation** matrix is a :math:`(N+p) \times (p+1)` rectangular Toeplilz
     data matrix:
-    
+
     .. math:: X_p = \begin{pmatrix}L_p\\T_p\\Up\end{pmatrix}
-    
-    where the lower triangular :math:`p \times (p+1)` matrix :math:`L_p` is  
-    
-    .. math:: L_p = 
+
+    where the lower triangular :math:`p \times (p+1)` matrix :math:`L_p` is
+
+    .. math:: L_p =
         \begin{pmatrix}
         x[1]     &  \cdots     & 0     & 0        \\
         \vdots    &  \ddots     & \vdots & \vdots    \\
-        x[p]     &  \cdots     & x[1]  & 0 
-        \end{pmatrix}    
-        
+        x[p]     &  \cdots     & x[1]  & 0
+        \end{pmatrix}
+
     where the rectangular :math:`(N-p) \times (p+1)` matrix :math:`T_p` is
-    
-    .. math:: T_p = 
+
+    .. math:: T_p =
         \begin{pmatrix}
         x[p+1]     &  \cdots    & x[1]        \\
         \vdots    &  \ddots     & \vdots    \\
         x[N-p]     &  \cdots    & x[p+1]        \\
         \vdots    &  \ddots     & \vdots    \\
-        x[N]     &  \cdots      & x[N-p] 
+        x[N]     &  \cdots      & x[N-p]
         \end{pmatrix}
-     
+
     and where the upper triangular :math:`p \times (p+1)` matrix :math:`U_p` is
-       
-    .. math:: U_p = 
+
+    .. math:: U_p =
         \begin{pmatrix}
         0         &  x[N]      & \cdots     & x[N-p+1]        \\
         \vdots    &  \vdots    & \ddots & \vdots    \\
-        0         &  0         & \cdots  & x[N] 
+        0         &  0         & \cdots  & x[N]
         \end{pmatrix}
 
-    From this definition, the prewindowed matrix is 
-    
+    From this definition, the prewindowed matrix is
+
     .. math:: X_p = \begin{pmatrix}L_p\\T_p\end{pmatrix}
-    
-    the postwindowed matrix is 
-    
+
+    the postwindowed matrix is
+
     .. math:: X_p = \begin{pmatrix}T_p\\U_p\end{pmatrix}
-    
+
     the covariance matrix is:
-    
+
     .. math:: X_p = \begin{pmatrix}T_p\end{pmatrix}
-    
+
     and the modified covariance matrix is:
-    
+
     .. math:: X_p = \begin{pmatrix}T_p\\T_p^*\end{pmatrix}
-    
-    
+
+
     """
-    
-    valid_methods = ['autocorrelation', 'prewindowed', 'postwindowed', 
+
+    valid_methods = ['autocorrelation', 'prewindowed', 'postwindowed',
                       'covariance', 'modified']
     if method not in valid_methods:
         raise ValueError("Invalid method. Try one of %s" % valid_methods)
 
     from scipy.linalg import toeplitz
-    
+
     # create the relevant matrices that will be useful to create
     # the correlation matrices
     N = len(x_input)
 
-    # FIXME:do we need a copy ? 
+    # FIXME:do we need a copy ?
     if isinstance(x_input, list):
         x = numpy.array(x_input)
     else:
@@ -156,18 +159,18 @@ def corrmtx(x_input, m, method='autocorrelation'):
         complex_type = True
     else:
         complex_type = False
-    
+
     # Compute the Lp, Up and Tp matrices according to the requested method
     if method in ['autocorrelation', 'prewindowed']:
         Lp = toeplitz(x[0:m], [0]*(m+1))
     Tp = toeplitz(x[m:N], x[m::-1])
     if method in ['autocorrelation', 'postwindowed']:
         Up = toeplitz([0]*(m+1), numpy.insert(x[N:N-m-1:-1],0,0))
-    
-    
-    
+
+
+
     # Create the output matrix
-     
+
     if method == 'autocorrelation':
         if complex_type == True:
             C = numpy.zeros((N+m, m+1), dtype=complex)
@@ -184,7 +187,7 @@ def corrmtx(x_input, m, method='autocorrelation'):
             C = numpy.zeros((N, m+1), dtype=complex)
         else:
             C = numpy.zeros((N, m+1))
-            
+
         for i in range(0, m):
             C[i] = Lp[i]
         for i in range(m, N):
@@ -210,28 +213,28 @@ def corrmtx(x_input, m, method='autocorrelation'):
         Tp = numpy.fliplr(Tp.conj())
         for i in range(N-m, 2*(N-m)):
             C[i] = Tp[i-N+m]
-        
-        
-        
+
+
+
     return C
     #autocorrelation n+m by m+1 matrix (Lp, Tp, Up)
-    
+
     #Rx = toeplitz(r)
 
-    
+
 
 def csvd(A):
     """SVD decomposition using numpy.linalg.svd
 
     :param A: a M by N matrix
-    
-    :return: 
+
+    :return:
         * U, a M by M matrix
         * S the N eigen values
         * V a N by N matrix
-        
+
     See :func:`numpy.linalg.svd` for a detailed documentation.
-        
+
     Should return the same as in [Marple]_ , CSVD routine.
 
     ::