More documentation and sphinx fixes

Framework/Muon/inc/MantidMuon/ApplyMuonDetectorGroupPairing.h

@@ -56,8 +56,7 @@ public:
   }
   /// See also
   const std::vector<std::string> seeAlso() const override {
-    return {"MuonProcess", "ApplyMuonDetectorGrouping",
-            "LoadAndApplyMuonDetectorGrouping"};
+    return {"MuonProcess", "ApplyMuonDetectorGrouping"};
   }
   /// Perform validation of inputs to the algorithm
   std::map<std::string, std::string> validateInputs() override;

Framework/PythonInterface/mantid/plots/helperfunctions.py

@@ -42,6 +42,7 @@ def get_distribution(workspace, **kwargs):
     """
     Determine whether or not the data is a distribution. The value in
     the kwargs wins. Applies to Matrix workspaces only
+
     :param workspace: :class:`mantid.api.MatrixWorkspace` to extract the data from
     """
     distribution = kwargs.pop('distribution', workspace.isDistribution())
@@ -53,6 +54,7 @@ def get_normalization(md_workspace, **kwargs):
     Gets the normalization flag of an MDHistoWorkspace. For workspaces
     derived similar to MSlice/Horace, one needs to average data, the so-called
     "number of events" normalization.
+
     :param md_workspace: :class:`mantid.api.IMDHistoWorkspace` to extract the data from
     """
     normalization = kwargs.pop('normalization', md_workspace.displayNormalizationHisto())
@@ -62,6 +64,7 @@ def get_normalization(md_workspace, **kwargs):
 def points_from_boundaries(input_array):
     """
     The function returns bin centers from bin boundaries
+
     :param input_array: a :class:`numpy.ndarray` of bin boundaries
     """
     assert isinstance(input_array, numpy.ndarray), 'Not a numpy array'
@@ -73,6 +76,7 @@ def points_from_boundaries(input_array):
 def _dim2array(d):
     """
     Create a numpy array containing bin centers along the dimension d
+
     :param d: an :class:`mantid.geometry.IMDDimension` object
 
     returns: bin boundaries for dimension d
@@ -138,12 +142,14 @@ def get_md_data1d(workspace, normalization):
 def get_md_data(workspace, normalization, withError=False):
     """
     Generic function to extract data from an MDHisto workspace
+
     :param workspace: :class:`mantid.api.IMDHistoWorkspace` containing data
     :param normalization: if :class:`mantid.api.MDNormalization.NumEventsNormalization`
-        it will divide intensity by the number of corresponding MDEvents
+    it will divide intensity by the number of corresponding MDEvents
+    :param withError: flag for if the error is calculated. If False, err is returned as None
+
     returns a tuple containing bin boundaries for each dimension, the (maybe normalized)
     signal and error arrays
-    :param withError: flag for if the error is calculated. If False, err is returned as None
     """
     dims = workspace.getNonIntegratedDimensions()
     dim_arrays = [_dim2array(d) for d in dims]
@@ -169,6 +175,7 @@ def get_md_data(workspace, normalization, withError=False):
 def get_spectrum(workspace, wkspIndex, distribution, withDy=False, withDx=False):
     """
     Extract a single spectrum and process the data into a frequency
+
     :param workspace: a Workspace2D or an EventWorkspace
     :param wkspIndex: workspace index
     :param distribution: flag to divide the data by bin width. It happens only
@@ -176,9 +183,11 @@ def get_spectrum(workspace, wkspIndex, distribution, withDy=False, withDx=False)
         the mantid configuration is set up to divide such workspaces by bin
         width. The same effect can be obtained by running the
         :ref:`algm-ConvertToDistribution` algorithm
+
     :param withDy: if True, it will return the error in the "counts", otherwise None
     :param with Dx: if True, and workspace has them, it will return errors
         in the x coordinate, otherwise None
+
     Note that for workspaces containing bin boundaries, this function will return
     the bin centers for x.
     To be used in 1D plots (plot, scatter, errorbar)
@@ -210,7 +219,8 @@ def get_md_data2d_bin_bounds(workspace, normalization):
     Function to transform data in an MDHisto workspace with exactly
     two non-integrated dimension into arrays of bin boundaries in each
     dimension, and data. To be used in 2D plots (pcolor, pcolorfast, pcolormesh)
-    Note return coordinates are 1d vectors. Use numpy.meshgrid to generate 2d versions
+
+    Note: return coordinates are 1d vectors. Use numpy.meshgrid to generate 2d versions
     """
     coordinate, data, _ = get_md_data(workspace, normalization, withError=False)
     assert len(coordinate) == 2, 'The workspace is not 2D'
@@ -223,7 +233,8 @@ def get_md_data2d_bin_centers(workspace, normalization):
     two non-integrated dimension into arrays of bin centers in each
     dimension, and data. To be used in 2D plots (contour, contourf,
     tricontour, tricontourf, tripcolor)
-    Note return coordinates are 1d vectors. Use numpy.meshgrid to generate 2d versions
+
+    Note: return coordinates are 1d vectors. Use numpy.meshgrid to generate 2d versions
     """
     x, y, data = get_md_data2d_bin_bounds(workspace, normalization)
     x = points_from_boundaries(x)
@@ -234,6 +245,7 @@ def get_md_data2d_bin_centers(workspace, normalization):
 def boundaries_from_points(input_array):
     """"
     The function tries to guess bin boundaries from bin centers
+
     :param input_array: a :class:`numpy.ndarray` of bin centers
     """
     assert isinstance(input_array, numpy.ndarray), 'Not a numpy array'
@@ -257,12 +269,15 @@ def get_matrix_2d_data(workspace, distribution, histogram2D=False):
     '''
     Get all data from a Matrix workspace that has the same number of bins
     in every spectrum. It is used for 2D plots
+
     :param workspace: Matrix workspace to extract the data from
     :param distribution: if False, and the workspace contains histogram data,
         the intensity will be divided by the x bin width
+
     :param histogram2D: flag that specifies if the coordinates in the output are
         -bin centers (such as for contour) for False, or
         -bin edges (such as for pcolor) for True.
+
     Returns x,y,z 2D arrays
     '''
     try:
@@ -305,9 +320,11 @@ def get_uneven_data(workspace, distribution):
     '''
     Function to get data for uneven workspace2Ds, such as
     that pcolor, pcolorfast, and pcolormesh will plot axis aligned rectangles
+
     :param workspace: a workspace2d
     :param distribution: if False, and the workspace contains histogram data,
         the intensity will be divided by the x bin width
+
     Returns three lists. Each element in the x list is an array of boundaries
     for a spectra. Each element in the y list is a 2 element array with the extents
     of a particular spectra. The z list contains arrays of intensities at bin centers
@@ -340,7 +357,9 @@ def get_data_uneven_flag(workspace, **kwargs):
     :meth:`matplotlib.axes.Axes.pcolorfast`, and :meth:`matplotlib.axes.Axes.pcolormesh`
     to plot rectangles parallel to the axes even if the data is not
     on a regular grid.
+
     :param workspace: a workspace2d
+
     if axisaligned keyword is available and True or if the workspace does
     not have a constant number of bins, it will return true, otherwise false
     '''
@@ -396,6 +415,7 @@ def get_axes_labels(workspace):
     Returns a tuple. The first element is the quantity label, such as "Intensity" or "Counts".
     All other elements in the tuple are labels for axes.
     Some of them are latex formatted already.
+
     :param workspace: :class:`mantid.api.MatrixWorkspace` or :class:`mantid.api.IMDHistoWorkspace`
     """
     if isinstance(workspace, MDHistoWorkspace):

docs/source/api/python/mantid/plots/index.rst

@@ -258,4 +258,11 @@ Functions to use when **mantid3d** projection is not available
 Helper functions
 ----------------
 .. automodule:: mantid.plots.helperfunctions
+   :members: get_distribution, get_normalization,
+             points_from_boundaries, boundaries_from_points,
+             get_wksp_index_dist_and_label, get_md_data, get_md_data1d,
+             get_md_data2d_bin_bounds, get_md_data2d_bin_centers,
+             get_spectrum, get_matrix_2d_data,get_uneven_data,
+             get_sample_log, get_axes_labels
+