Doc edits

CHANGELOG.rst

@@ -36,8 +36,7 @@ New features
 - Added ability to write out the full set of pair-wise results in the write commands, and to
   read them back in.  This allows correlation objects to still be able to correctly calculate
   the various patch-based covariance matrix estimates after round-tripping through a file.
-  To use this feature, use ``corr.write(..., write_patch_results=True)``.
-  (#141)
+  To use this feature, use ``corr.write(..., write_patch_results=True)``. (#141)
 - Allow None as a configuration parameter to be equivalent to not specifying a parameter.
   E.g. ``bin_size=0.1, min_sep=1., max_sep=100., nbins=None`` is allowed now. (#142)
 

README.rst

@@ -72,7 +72,7 @@ system, you might need to use one of the following variants for pip installation
     sudo pip install treecorr
     pip install treecorr --user
 
-The latter installs the Python module into ``~/.local/lib/python3.7/site-packages``,
+The latter installs the Python module into ``~/.local/lib/python3.X/site-packages``,
 which is normally already in your PYTHONPATH, but it puts the executables
 ``corr2`` and ``corr3`` into ``~/.local/bin`` which is probably not in your PATH.
 To use these scripts, you should add this directory to your PATH.  If you would
@@ -81,7 +81,7 @@ rather install into a different prefix rather than ~/.local, you can use::
     pip install treecorr --install-option="--prefix=PREFIX"
 
 This would install the executables into ``PREFIX/bin`` and the Python module
-into ``PREFIX/lib/python3.7/site-packages``.
+into ``PREFIX/lib/python3.X/site-packages``.
 
 
 If you would rather download the tarball and install TreeCorr yourself,

docs/correlation2.rst

@@ -23,3 +23,5 @@ features in common about how they are constructed.  The common features are docu
 
 .. autofunction:: treecorr.estimate_multi_cov
 
+.. autofunction:: treecorr.build_multi_cov_design_matrix
+

treecorr/binnedcorr2.py

@@ -1449,7 +1449,7 @@ def build_multi_cov_design_matrix(corrs, method, *, func=None, comm=None):
         - 'marked_bootstrap': The data vector for a random resampling of patches in the first
           catalog, using all patches for the second catalog.  Based on the algorithm in Loh(2008).
 
-    See `estimate_mult_cov` for more details.
+    See `estimate_multi_cov` for more details.
 
     The return value includes both the design matrix and a vector of weights (the total weight
     array in the computed correlation functions).  The weights are used for the sample method

treecorr/catalog.py

@@ -169,7 +169,7 @@ class Catalog(object):
                             file.  Any optional kwargs may be given here in the config dict if
                             desired.  Invalid keys in the config dict are ignored. (default: None)
 
-    Keyword-only Arguments:
+    Keyword Arguments:
 
         num (int):          Which number catalog are we reading.  e.g. for NG correlations the
                             catalog for the N has num=0, the one for G has num=1.  This is only