Updated docs to NumPy style

I installed numpydoc, and switched docstrings to NumPy
formats.Getting a lot of warnings during build, but the
HTML is okay (still needs some cleaning up).

docs/conf.py

@@ -49,11 +49,11 @@
 extensions = [
     'sphinx.ext.autodoc',
     'sphinx.ext.doctest',
+    'numpydoc',
     'sphinx.ext.intersphinx',
     'sphinx.ext.mathjax',
     'sphinx.ext.viewcode',
-    'sphinx.ext.autodoc',
-    'sphinx.ext.napoleon'
+    'sphinx.ext.autodoc'
 ]
 
 # Add any paths that contain templates here, relative to this directory.
@@ -70,7 +70,7 @@
 
 # General information about the project.
 project = 'FilterPy'
-copyright = '2015, Roger R. Labbe'
+copyright = '2014-2016, Roger R. Labbe'
 
 # The version info for the project you're documenting, acts as replacement for
 # |version| and |release|, also used in various other places throughout the
@@ -97,10 +97,10 @@
 
 # The reST default role (used for this markup: `text`) to use for all
 # documents.
-#default_role = None
+#default_role = "autolink"
 
 # If true, '()' will be appended to :func: etc. cross-reference text.
-#add_function_parentheses = True
+add_function_parentheses = False
 
 # If true, the current module name will be prepended to all description
 # unit titles (such as .. function::).
@@ -185,13 +185,13 @@
 #html_split_index = False
 
 # If true, links to the reST sources are added to the pages.
-#html_show_sourcelink = True
+html_show_sourcelink = True
 
 # If true, "Created using Sphinx" is shown in the HTML footer. Default is True.
-#html_show_sphinx = True
+html_show_sphinx = True
 
 # If true, "(C) Copyright ..." is shown in the HTML footer. Default is True.
-#html_show_copyright = True
+html_show_copyright = True
 
 # If true, an OpenSearch description file will be output, and all pages will
 # contain a <link> tag referring to it.  The value of this option must be the

docs/index.rst

@@ -165,7 +165,7 @@ filterpy.kalman
 
 The classes in this submodule implement the various Kalman filters. There is
 also support for smoother functions. The smoothers are methods of the classes.
-For example, the KalmanFilter class contains rts_smoother. to perform 
+For example, the KalmanFilter class contains rts_smoother to perform 
 Rauch-Tung-Striebal smoothing. 
 
 
@@ -202,9 +202,7 @@ These modules are used to implement the Unscented Kalman filter.
 
    kalman/UnscentedKalmanFilter
    kalman/unscented_transform
-   kalman/MerweScaledSigmaPoints
-   kalman/JulierSigmaPoints
-
+
 Ensemble Kalman Filter
 +++++++++++++++++++++++
 .. toctree::

docs/kalman/UnscentedKalmanFilter.rst

@@ -27,7 +27,6 @@ This implements the unscented Kalman filter.
     .. automethod:: __init__
 
 
-
 --------
 
 

filterpy/__init__.py

@@ -14,4 +14,4 @@
 for more information.
 """
 
-__version__ = "0.1.0"
+__version__ = "0.1.1"

filterpy/changelog.txt

@@ -1,3 +1,16 @@
+Version 0.1.10
+==============
+
+* Added discrete_bayes module, which supports discrete Bayesian filtering.
+
+* Brought docstrings (mostly) into compliance with NumPy documentation style.
+This requires installation of numpy doc with
+    pip install numpydoc
+
+docs\conf.py has been modified to use numpydoc. 
+
+
+
 Version 0.1.0
 ==============
 

filterpy/common/discretization.py

@@ -30,10 +30,11 @@ def Q_discrete_white_noise(dim, dt=1., var=1.):
     variance in the noise.
 
     Q is computed as the G * G^T * variance, where G is the process noise per
-    time step. In other words, G = [[.5dt^2][dt]]^T for the constant velocity 
+    time step. In other words, G = [[.5dt^2][dt]]^T for the constant velocity
     model.
 
-    **Paramaeters**
+    Parameters
+    -----------
 
     dim : int (2 or 3)
         dimension for Q, where the final dimension is (dim x dim)
@@ -62,7 +63,8 @@ def Q_continuous_white_noise(dim, dt=1., spectral_density=1.):
     Model. dim may be either 2 or 3, dt is the time step, and sigma is the
     variance in the noise.
 
-    **Paramaeters**
+    Parameters
+    ----------
 
     dim : int (2 or 3)
         dimension for Q, where the final dimension is (dim x dim)
@@ -99,7 +101,8 @@ def van_loan_discretization(F, G, dt):
     that discretizes that equation.
 
 
-    **Example**::
+    Examples
+    --------
 
         Given y'' + y = 2u(t), we create the continuous state model of
 
@@ -124,7 +127,8 @@ def van_loan_discretization(F, G, dt):
         (example taken from Brown[2])
 
 
-    **References**
+    References
+    ----------
 
     [1] C. F. van Loan. "Computing Integrals Involving the Matrix Exponential."
         IEEE Trans. Automomatic Control, AC-23 (3): 395-404 (June 1978)

filterpy/common/helpers.py

@@ -37,7 +37,8 @@ def dotn(*args):
 def runge_kutta4(y, x, dx, f):
     """computes 4th order Runge-Kutta for dy/dx.
 
-    **Parameters**
+    Parameters
+    ----------
 
     y : scalar
         Initial/current value for y

filterpy/discrete_bayes/discrete_bayes.py

@@ -70,9 +70,9 @@ def update(likelihood, prior):
         Returns array representing the posterior.
 
 
-    Example
-    -------
-    .. code::
+    Examples
+    --------
+    .. code-block:: Python
 
         # self driving car. Sensor returns values that can be equated to positions
         # on the road. A real likelihood compuation would be much more complicated
@@ -113,9 +113,9 @@ def predict(pdf, offset, kernel, mode='wrap', cval=0.):
     If `mode='constant'`, or any other value the pdf is shifted, with `cval`
     used to fill in missing elements.
 
-    Example
-    -------
-    .. code::
+    Examples
+    --------
+    .. code-block:: Python
 
         belief = [.05, .05, .05, .05, .55, .05, .05, .05, .05, .05]
         prior = predict(belief, offset=2, kernel=[.1, .8, .1])