numpydoc

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

@@ -17,11 +17,88 @@ fun. :sup:`[citation needed]`
 Importing Data
 --------------
 
-.. automethod:: OMX.import_datatable
+.. np:method:: OMX.import_datatable(filepath, one_based=True, chunksize=10000, column_map=None, default_atom='float32')
 
-.. automethod:: OMX.import_datatable_3d
+	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_as_lookups
+
+
+.. 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.
 
 
 .. |idca| replace:: :ref:`idca <idca>`