DOC: Add some missing parameter headings in mlab.

QuLogic · QuLogic · commit 58f367e6c5dc · 2016-01-05T02:54:18.000-05:00
diff --git a/lib/matplotlib/mlab.py b/lib/matplotlib/mlab.py
@@ -232,10 +232,12 @@ def apply_window(x, window, axis=0, return_window=None):
     '''
     Apply the given window to the given 1D or 2D array along the given axis.
 
+    Parameters
+    ----------
       *x*: 1D or 2D array or sequence
         Array or sequence containing the data.
 
-      *winodw*: function or array.
+      *window*: function or array.
         Either a function to generate a window or an array with length
         *x*.shape[*axis*]
 
@@ -286,6 +288,8 @@ def detrend(x, key=None, axis=None):
     '''
     Return x with its trend removed.
 
+    Parameters
+    ----------
       *x*: array or sequence
         Array or sequence containing the data.
 
@@ -347,6 +351,8 @@ def demean(x, axis=0):
     '''
     Return x minus its mean along the specified axis.
 
+    Parameters
+    ----------
       *x*: array or sequence
         Array or sequence containing the data
         Can have any dimensionality
@@ -373,6 +379,8 @@ def detrend_mean(x, axis=None):
     '''
     Return x minus the mean(x).
 
+    Parameters
+    ----------
       *x*: array or sequence
         Array or sequence containing the data
         Can have any dimensionality
@@ -418,6 +426,8 @@ def detrend_none(x, axis=None):
     '''
     Return x: no detrending.
 
+    Parameters
+    ----------
       *x*: any object
         An object containing the data
 
@@ -447,6 +457,8 @@ def detrend_linear(y):
     '''
     Return x minus best fit line; 'linear' detrending.
 
+    Parameters
+    ----------
       *y*: 0-D or 1-D array or sequence
         Array or sequence containing the data
 
@@ -499,6 +511,8 @@ def stride_windows(x, n, noverlap=None, axis=0):
         elements may point to the same piece of memory,
         so modifying one value may change others.
 
+    Parameters
+    ----------
       *x*: 1D array or sequence
         Array or sequence containing the data.
 
@@ -564,6 +578,8 @@ def stride_repeat(x, n, axis=0):
         elements may point to the same piece of memory, so
         modifying one value may change others.
 
+    Parameters
+    ----------
       *x*: 1D array or sequence
         Array or sequence containing the data.
 
@@ -1581,37 +1597,47 @@ def __init__(self, a, standardize=True):
         compute the SVD of a and store data for PCA.  Use project to
         project the data onto a reduced set of dimensions
 
-        Inputs:
-
-          *a*: a numobservations x numdims array
-          *standardize*: True if input data are to be standardized. If False,
-          only centering will be carried out.
-
-        Attrs:
-
-          *a* a centered unit sigma version of input a
+        Parameters
+        ----------
+        a: np.ndarray
+            A numobservations x numdims array
+        standardize: bool
+            True if input data are to be standardized. If False, only centering
+            will be carried out.
 
-          *numrows*, *numcols*: the dimensions of a
+        Attributes
+        ----------
+        a
+            A centered unit sigma version of input ``a``.
 
-          *mu*: a numdims array of means of a. This is the vector that points
-          to the origin of PCA space.
+        numrows, numcols
+            The dimensions of ``a``.
 
-          *sigma*: a numdims array of standard deviation of a
+        mu
+            A numdims array of means of ``a``. This is the vector that points
+            to the origin of PCA space.
 
-          *fracs*: the proportion of variance of each of the principal
-          components
+        sigma
+            A numdims array of standard deviation of ``a``.
 
-          *s*: the actual eigenvalues of the decomposition
+        fracs
+            The proportion of variance of each of the principal components.
 
-          *Wt*: the weight vector for projecting a numdims point or array into
-          PCA space
+        s
+            The actual eigenvalues of the decomposition.
 
-          *Y*: a projected into PCA space
+        Wt
+            The weight vector for projecting a numdims point or array into
+            PCA space.
 
+        Y
+            A projected into PCA space.
 
-        The factor loadings are in the Wt factor, i.e., the factor
-        loadings for the 1st principal component are given by Wt[0].
-        This row is also the 1st eigenvector.
+        Note
+        ----
+        The factor loadings are in the ``Wt`` factor, i.e., the factor loadings
+        for the first principal component are given by ``Wt[0]``. This row is
+        also the first eigenvector.
 
         """
         n, m = a.shape
