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fixed typos in lcc and gml #28

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4 changes: 1 addition & 3 deletions src/zen/algorithms/clustering.pyx
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Original file line number Diff line number Diff line change
Expand Up @@ -248,8 +248,6 @@ cpdef np.ndarray[np.float_t, ndim=1] __lcc_undirected(Graph G):
else:
C[ni] = <float> num_tris / <float> num_vees

idx += 1

return C

cpdef float ncc(G) except -1:
Expand All @@ -259,4 +257,4 @@ cpdef float ncc(G) except -1:
"""
cdef np.ndarray[np.float_t, ndim=1] C = lcc_(G)

return <float> np.ma.masked_equal(C,-1.0).mean()
return <float> np.ma.masked_equal(C,-1.0).mean()
136 changes: 68 additions & 68 deletions src/zen/io/gml.py
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Original file line number Diff line number Diff line change
@@ -1,19 +1,19 @@
"""
The ``zen.io.gml`` module (available as ``zen.gml``) supports the reading and
writing network data in the `Graph Modeling Language (GML)
<http://en.wikipedia.org/wiki/Graph_Modelling_Language>`_. At present only
The ``zen.io.gml`` module (available as ``zen.gml``) supports the reading and
writing network data in the `Graph Modeling Language (GML)
<http://en.wikipedia.org/wiki/Graph_Modelling_Language>`_. At present only
reading GML is supported.

GML is a flexible language for specifying the structure of a network (nodes
and edges) that can be annotated with arbitrary attributes and information.
This module provides a full implementation of the GML file format as specified
in the `technical report
GML is a flexible language for specifying the structure of a network (nodes
and edges) that can be annotated with arbitrary attributes and information.
This module provides a full implementation of the GML file format as specified
in the `technical report
<http://www.fim.uni-passau.de/fileadmin/files/lehrstuhl/brandenburg/projekte/gml/gml-technical-report.pdf>`_:

.. note::
Source: Michael Himsolt. *GML: A portable Graph File Format*. Technical
Source: Michael Himsolt. *GML: A portable Graph File Format*. Technical
Report, Universitat Passau.

Functions
---------

Expand Down Expand Up @@ -41,36 +41,36 @@

def write(G, filename, **kwargs):
"""
Writes graph to file using Graph Modeling Language (gml). Node / Edge /
Graph objects, if not None, are stored in the `name` attribute, and are
restricted to numeric (but not complex), string, and boolean data types,
otherwise an exception is raised.
Writes graph to file using Graph Modeling Language (gml). Node / Edge /
Graph objects, if not None, are stored in the `name` attribute, and are
restricted to numeric (but not complex), string, and boolean data types,
otherwise an exception is raised.

Node / Edge / Graph data, if not None, are stored in a zen_data attribute
Node / Edge / Graph data, if not None, are stored in a zen_data attribute
and are similarly restricted. Support may be added later for serialization
of arbitrary objects / data and associated to zen graphs.
see
http://www.fim.uni-passau.de/fileadmin/files/lehrstuhl/brandenburg/projekte/gml/gml-technical-report.pdf

see
http://www.fim.uni-passau.de/fileadmin/files/lehrstuhl/brandenburg/projekte/gml/gml-technical-report.pdf
for more info about gml.

**Args**
* ``G`` (:py:class:`zen.Graph`, :py:class:'zen.Digraph`,
:py:class:`zen.BipartiteGraph): Graph object to be written to
* ``G`` (:py:class:`zen.Graph`, :py:class:'zen.Digraph`,
:py:class:`zen.BipartiteGraph): Graph object to be written to
file. Hypergraphs are not supported.

* ``filename`` (str): Absolute path for the file to be written.

**KwArgs**
* ``write-data`` (bool | (bool, bool)): If 2-tuple of booleans
supplied, first indicates whether to write out node data second
whether to write out edge data. If bool provided, it is applied
* ``write-data`` (bool | (bool, bool)): If 2-tuple of booleans
supplied, first indicates whether to write out node data second
whether to write out edge data. If bool provided, it is applied
for both node and edge data.

* ``use-zen-data`` (bool | (bool, bool)): Indicates whether to write
out If 2-tuple of booleans supplied, first indicates whether to
* ``use-zen-data`` (bool | (bool, bool)): Indicates whether to write
out If 2-tuple of booleans supplied, first indicates whether to
write out node data second whether to write out edge data. If bool
provided, it is applied for both node and edge data.
provided, it is applied for both node and edge data.

**Returns**:
* None
Expand All @@ -79,7 +79,7 @@ def write(G, filename, **kwargs):
# Determine the write mode. There are various options the user can specify
# which are reconsiled and validated here
write_node_data, write_edge_data, use_node_zen_data, use_edge_zen_data = resolve_write_mode(kwargs)

# Get the encoder to use. This call resolves the various ways the user can
# specify encoding, and does basic checks that the encoder is valid
enc = resolve_codec(kwargs)
Expand All @@ -88,7 +88,7 @@ def write(G, filename, **kwargs):
fh.write('# This is a graph object in gml file format\n')
fh.write('# produced by the zen graph library\n\n')

# Describe the encoding method used to generate the file.
# Describe the encoding method used to generate the file.
fh.write('ZenCodec "%s"\n' % enc.__name__)
fh.write('ZenStringEncoder "%s"\n\n' % enc.encode_str.__name__)

Expand All @@ -108,7 +108,7 @@ def write(G, filename, **kwargs):

else:
is_bipartite = False
fh.write('\tbipartite 0\n')
fh.write('\tbipartite 0\n')

# iterate over nodes, writing them to the new gml file
for nidx, nobj, ndata in G.nodes_iter_(obj=True, data=True):
Expand Down Expand Up @@ -136,7 +136,7 @@ def write(G, filename, **kwargs):
fh.write('\tedge [\n')

# for digraphs, assumes endpoints order [source, target]
fh.write('\t\tsource ' + str(G.endpoints_(eidx)[0]) + '\n')
fh.write('\t\tsource ' + str(G.endpoints_(eidx)[0]) + '\n')
fh.write('\t\ttarget ' + str(G.endpoints_(eidx)[1]) + '\n')
fh.write('\t\tweight ' + str(weight) + '\n')

Expand All @@ -153,25 +153,25 @@ def write(G, filename, **kwargs):
fh.write(']\n')
fh.close()


def format_zen_data(keyname, data, tab_depth, encoder, strict=True):
"""
Reformats supplied data to use gml. Enforces restrictions on the types of
Reformats supplied data to use gml. Enforces restrictions on the types of
data that can be written to gml.

**Args**
* data (bool | int | long | float | str | dict | list): object to be
* data (bool | int | long | float | str | dict | list): object to be
written in gml
* key (str): key to be used in gml. Needed here because in gml lists
* key (str): key to be used in gml. Needed here because in gml lists
are made by repeating the key in front of each value.
* tab_depth (int): number of tab characters to add to the beginning
* tab_depth (int): number of tab characters to add to the beginning
of each line for nice formatting.

**Returns**
* formatted_data (str): gml representation of data
"""


# Validation: key names must be strictly alphanumeric
if re.search('[^a-zA-Z0-9]', keyname) and strict:
raise ZenException(
Expand Down Expand Up @@ -224,7 +224,7 @@ def make_tree(fname, **kwargs):
# resolve the codec. The user can specify the codec in various ways.
codec = resolve_codec(kwargs)

# read the file
# read the file
fh = open(fname,'r')
gml_str = fh.read()
fh.close()
Expand All @@ -243,27 +243,27 @@ def make_tree(fname, **kwargs):
def read(fname,**kwargs):
"""
Read GML-formatted network data stored in file named ``fname``.
The node's ``id`` attribute is used to specify the node index. The node's
``name`` attribute is preferably used as the node object.

The node's ``id`` attribute is used to specify the node index. The node's
``name`` attribute is preferably used as the node object.

However, if the ``name`` attribute is missing and the ``label`` is present,
then the node's ``label`` attribute will be used as the node object. If
then the node's ``label`` attribute will be used as the node object. If
both are missing, then the node id will be used as the node object.
.. note::
Currently graph attributes are not supported by the reader. If

.. note::
Currently graph attributes are not supported by the reader. If
encountered, they will simply be skipped over and not added to the
final graph. This is simply because graph objects don't support
final graph. This is simply because graph objects don't support
arbitrary data yet.

**KwArgs**:

* ``weight_fxn [=None]``: derive weight assignments from edge data. If
specified, this function is called with one parameter: the full
specified, this function is called with one parameter: the full
set of attributes that were specified for the edge.
"""

# extract keyword arguments
weight_fxn = kwargs.pop('weight_fxn',None)

Expand All @@ -278,7 +278,7 @@ def read(fname,**kwargs):
graph_tree = gml_tree[0]
print 'Warning: multiple graphs stored in this file. Use '\
'gml.read_all(fname, [...]) to get list of all graphs'

return build_graph(graph_tree, weight_fxn)

else:
Expand All @@ -303,7 +303,7 @@ def read_all(fname, **kwargs):
graph_tree = gml_tree[0]
print 'Warning: multiple graphs stored in this file. Use '\
'gml.read_all(fname, [...]) to get list of all graphs'

return build_graph(graph_tree, weight_fxn)

else:
Expand Down Expand Up @@ -337,7 +337,7 @@ def build_graph(graph_tree, weight_fxn):
nodes = [ nodes ]

# Build each node and add to the graph
for node in nodes:
for node in nodes:

# Does the node have an id?
has_id = True
Expand All @@ -354,7 +354,7 @@ def build_graph(graph_tree, weight_fxn):
# Got a valid node id
node_idx = node['id']

# For bipartite graphs determine which node set this belongs to
# For bipartite graphs determine which node set this belongs to
if is_bipartite:
is_in_U = node['isInU']

Expand All @@ -370,15 +370,15 @@ def build_graph(graph_tree, weight_fxn):
node_obj = val

# give preference to 'name' as source of node_obj
elif key == 'label' and node_obj is None:
elif key == 'label' and node_obj is None:
node_obj = val

elif key == 'zenData':
zen_data = val

# node_data is dict of all other attributes
else:
node_data[key] = val
else:
node_data[key] = val

# _set_ to node_data else _append_
if zen_data is not None:
Expand All @@ -402,7 +402,7 @@ def build_graph(graph_tree, weight_fxn):
node_obj = tuple(node_obj)


# For bipartite graph, this insertion method does not guarantee
# For bipartite graph, this insertion method does not guarantee
# that indices will be unchanged after a read-write cycle
if is_bipartite:
G.add_node_by_class(is_in_U, node_obj, node_data)
Expand All @@ -413,7 +413,7 @@ def build_graph(graph_tree, weight_fxn):
G.edge_list_capacity, node_obj,node_data)

else:
G.add_node_x(node_idx, G.edge_list_capacity, node_obj,
G.add_node_x(node_idx, G.edge_list_capacity, node_obj,
node_data)

else:
Expand Down Expand Up @@ -477,14 +477,14 @@ def build_graph(graph_tree, weight_fxn):
zen_data = val

# edge_data is dict of all other attributes
else:
edge_data[key] = val
else:
edge_data[key] = val

# give precedence to a weight-getting function if provided
if weight_fxn != None:
weight = weight_fxn(edge)

# if zenData is only other attribute aside from those handled above
# if zenData is only other attribute aside from those handled above
# _set_ to edge_data else _append_
if zen_data is not None:
if len(edge_data) == 0:
Expand All @@ -501,7 +501,7 @@ def build_graph(graph_tree, weight_fxn):
G.add_edge_(source,target,edge_data,weight)

return G

def resolve_write_mode(kwargs):

# Decide whether to write node and edge data
Expand All @@ -519,7 +519,7 @@ def resolve_write_mode(kwargs):

#validation:
if(not isinstance(write_node_data, bool) or not
isintstance(write_edge_data, bool)):
isinstance(write_edge_data, bool)):
raise zenException('write_data keyword argument takes bool or'\
' 2-tuple of bools. Found: %s (%s)' %(
write_data, type(write_data)))
Expand All @@ -536,12 +536,12 @@ def resolve_write_mode(kwargs):
use_node_zen_data = use_edge_zen_data = kwargs['use_zen_data']

#validation:
if(not isinstance(write_node_data, bool) or
not isintstance(write_edge_data, bool)):
if(not isinstance(write_node_data, bool) or
not isinstance(write_edge_data, bool)):
raise zenException('write_data keyword argument takes bool or'\
' 2-tuple of bools. Found: %s (%s)' %(
write_data, type(write_data)))

return write_node_data, write_edge_data, use_node_zen_data, use_edge_zen_data


Expand Down