Another try to fix docs

doc/conf.py

@@ -75,7 +75,7 @@ def __getattr__(cls, name):
 # coming with Sphinx (named 'sphinx.ext.*') or your custom ones.
 extensions = [
 	'sphinx.ext.autodoc',
-	'numpydoc',
+#	'numpydoc',
 #	'sphinxcontrib.napoleon',
 	'sphinx.ext.napoleon',
 #	'sphinx.ext.viewcode',

doc/openmatrix.rst

@@ -17,88 +17,11 @@ fun. :sup:`[citation needed]`
 Importing Data
 --------------
 
-.. np:method:: OMX.import_datatable(filepath, one_based=True, chunksize=10000, column_map=None, default_atom='float32')
+.. automethod:: OMX.import_datatable
 
-	Import a table in r,c,x,x,x... format into the matrix.
-
-	The r and c columns need to be either 0-based or 1-based index values
-	(this may be relaxed in the future). The matrix must already be set up
-	with the correct size before importing the datatable.
-
-	Parameters
-	----------
-	filepath : str or buffer
-		This argument will be fed directly to the :func:`pandas.read_csv` function.
-	one_based : bool
-		If True (the default) it is assumed that zones are indexed sequentially starting with 1 
-		(as is typical for travel demand forecasting applications).
-		Otherwise, it is assumed that zones are indexed sequentially starting with 0 (typical for other c and python applications).
-	chunksize : int
-		The number of rows of the source file to read as a chunk.  Reading a giant file in moderate sized
-		chunks can be much faster and less memory intensive than reading the entire file.
-	column_map : dict or None
-		If given, this dict maps columns of the input file to OMX tables, with the keys as
-		the columns in the input and the values as the tables in the output.
-	default_atom : str or dtype
-		The default atomic type for imported data when the table does not already exist in this
-		openmatrix.
+.. automethod:: OMX.import_datatable_3d
 
-
-
-.. np:method:: OMX.import_datatable_3d(filepath, one_based=True, chunksize=10000, default_atom='float32')
-
-	Import a table in r,c,x,x,x... format into the matrix.
-
-	The r and c columns need to be either 0-based or 1-based index values
-	(this may be relaxed in the future). The matrix must already be set up
-	with the correct size before importing the datatable.
-
-	This method is functionally the same as :meth:`import_datatable` but uses a different implementation.
-	It is much more memory intensive but also much faster than the non-3d version.
-
-	Parameters
-	----------
-	filepath : str or buffer
-		This argument will be fed directly to the :func:`pandas.read_csv` function.
-	one_based : bool
-		If True (the default) it is assumed that zones are indexed sequentially starting with 1 
-		(as is typical for travel demand forecasting applications).
-		Otherwise, it is assumed that zones are indexed sequentially starting with 0 (typical for other c and python applications).
-	chunksize : int
-		The number of rows of the source file to read as a chunk.  Reading a giant file in moderate sized
-		chunks can be much faster and less memory intensive than reading the entire file.
-	default_atom : str or dtype
-		The default atomic type for imported data when the table does not already exist in this
-		openmatrix.
-
-
-.. np:method:: OMX.import_datatable_as_lookups(filepath, chunksize=10000, column_map=None, log=None, n_rows=None, zone_ix=None, zone_ix1=1, drop_zone=None)
-
-	Import a table in r_or_c,x,x,x... format into the matrix.
-
-	The r_or_c column needs to be either 0-based or 1-based index values
-	(this may be relaxed in the future). The matrix must already be set up
-	with the correct shape before importing the datatable.
-
-	Parameters
-	----------
-	filepath : str or buffer
-		This argument will be fed directly to the :func:`pandas.read_csv` function.
-	chunksize : int
-		The number of rows of the source file to read as a chunk.  Reading a giant file in moderate sized
-		chunks can be much faster and less memory intensive than reading the entire file.
-	column_map : dict or None
-		If given, this dict maps columns of the input file to OMX tables, with the keys as
-		the columns in the input and the values as the tables in the output.
-	n_rows : int or None
-		If given, this is the number of rows in the source file.  It can be omitted and will 
-		be discovered automatically, but only for source files with consecutive zone numbering.
-	zone_ix : str or None
-		If given, this is the column name in the source file that gives the zone numbers.
-	zone_ix1 : 1 or 0
-		The smallest zone number.  Defaults to 1
-	drop_zone : int or None
-		If given, zones with this number (typically 0 or -1) will be ignored.
+.. automethod:: OMX.import_datatable_as_lookups
 
 
 .. |idca| replace:: :ref:`idca <idca>`

py/model_reporter/xhtml.py

@@ -211,6 +211,7 @@ def xhtml_report_v0(self, cats=None, raw_xml=False, throw_exceptions=False, file
 			The report content. You need to save it to a file on your own,
 			if desired.
 
+
 		>>> m = larch.Model.Example(1, pre=True)
 		>>> from larch.util.temporaryfile import TemporaryHtml
 		>>> html = m.xhtml_report_v0()
@@ -1299,7 +1300,8 @@ def xhtml_artparams(self, groups=None, display_inital=False, display_id=False, *
 		-------
 		larch.util.xhtml.Elem
 			A div containing the model parameters.
-		
+
+	
 		>>> from larch.util.categorize import Categorizer, Renamer
 		>>> m = larch.Model.Example(1, pre=True)
 		>>> param_groups = [
@@ -1333,7 +1335,8 @@ def xhtml_ll(self,**format):
 		-------
 		larch.util.xhtml.Elem
 			A div containing the model parameters.
-		
+
+
 		>>> from larch.util.xhtml import XHTML
 		>>> m = larch.Model.Example(1, pre=True)
 		>>> m.xhtml('title', 'll')
@@ -1581,7 +1584,8 @@ def xhtml_ch_av(self,max_alts=50,**format):
 		-------
 		larch.util.xhtml.Elem
 			A div containing the summary statistics for choice and availability.
-		
+
+
 		>>> from larch.util.xhtml import XHTML
 		>>> m = larch.Model.Example(1, pre=True)
 		>>> m.df = larch.DT.Example('MTC')
@@ -1714,7 +1718,8 @@ def xhtml_utilitydata(self,**format):
 		-------
 		larch.util.xhtml.Elem
 			A div containing the summary statistics for choice and availability.
-		
+
+
 		>>> from larch.util.xhtml import XHTML
 		>>> m = larch.Model.Example(1, pre=True)
 		>>> m.df = larch.DT.Example('MTC')