Cleanup, test fixes and readme

Author: robinharms

Changelog

@@ -13,7 +13,7 @@ Enhancements:
     Make shortest_path (Dijkstra's algorithm) run in O(E log V) instead of O(E^2) (dkorduban)
     Make graph.add_edge run in O(1) instead of O(degree(V)) (dkorduban)
     Added implementation of Kruskal's Minimum Spanning Tree construction algorithm (goldragoon)
-
+    Repackaged and updated to python3 (robinharms)
 
 Release 1.8.2 [July 14, 2012]
 

Makefile Makefile.oldMakefile renamed to Makefile.old

@@ -1,5 +1,6 @@
 # python-graph
 # Makefile
+# Note! This file is only kept as a reference
 
 
 # Module directories -------------------------------------------------

README.rst

@@ -2,26 +2,11 @@
 python-graph
 ============
 
-.. image:: https://travis-ci.org/Shoobx/python-graph.png?branch=master
-   :target: https://travis-ci.org/Shoobx/python-graph
-
-.. image:: https://coveralls.io/repos/github/Shoobx/python-graph/badge.svg?branch=master
-   :target: https://coveralls.io/github/Shoobx/python-graph?branch=master
-
-.. image:: https://img.shields.io/pypi/v/python-graph-core.svg
-    :target: https://pypi.org/project/python-graph-core/
-
-.. image:: https://img.shields.io/pypi/pyversions/python-graph-core.svg
-    :target: https://pypi.org/project/python-graph-core/
-
-.. image:: https://api.codeclimate.com/v1/badges/8e78b3479160f2c5cdd0/maintainability
-   :target: https://codeclimate.com/github/Shoobx/python-graph/maintainability
-   :alt: Maintainability
 
 A library for working with graphs in Python
 -------------------------------------------
 
-This software provides a suitable data structure for representing graphs and a
+This software provides a suitable data structure for representing graphs and a
 whole set of important algorithms.
 
 
@@ -30,27 +15,17 @@ INSTALLING
 
 To install the core module, run:
 
-	make install-core
+	pip install python-graph
 
 To install the dot language support, run:
 
-	make install-dot
-
-Alternatively, if you don't have make, you can install the modules by running:
-
-	./setup.py install
-
-inside the module directory.
+	pip install python-graph[dot]
 
 
 DOCUMENTATION
 -------------
 
-To generate the API documentation for this package, run:
-
-	make docs
-
-You'll need epydoc installed in your system.
+FIXME: Module documentation isn't available
 
 
 WEBSITE
@@ -147,6 +122,9 @@ Daniel Merritt <dmerritt@gmail.com>
 Sandro Tosi <morph@debian.org>
 	* Some improvements to Makefile
 
+Robin Harms Oredsson <robin@betahaus.net>
+	* Repackaging and modernization to make it work with python3 and modern distribution.
+	* Typing
 
 LICENSE
 -------

poetry.lock

@@ -56,6 +56,6 @@
 >>> # Depth first search rooted on node X
 >>> st, pre, post = depth_first_search(gr, root='X')
 >>> # Print the spanning tree
->>> print st
-{'A': 'B', 'C': 'A', 'B': 'Y', 'Y': 'X', 'X': None, 'Z': 'X'}
+>>> {x:st[x] for x in sorted(st)}
+{'A': 'B', 'B': 'Y', 'C': 'A', 'X': None, 'Y': 'X', 'Z': 'X'}
 """

pygraph/__init__.py

@@ -149,7 +149,7 @@ def critical_path(graph):
     # find the critical node
     max = 0
     critical_node = None
-    for k, v in list(node_tuples.items()):
+    for k, v in node_tuples.items():
         if v[1] >= max:
             max = v[1]
             critical_node = k

pygraph/algorithms/critical.py

@@ -103,11 +103,11 @@ def generate_hypergraph(num_nodes, num_edges, r=0):
     random_graph = hypergraph()
 
     # Nodes
-    nodes = list(map(str, list(range(num_nodes))))
+    nodes = list(map(str, range(num_nodes)))
     random_graph.add_nodes(nodes)
 
     # Base edges
-    edges = list(map(str, list(range(num_nodes, num_nodes + num_edges))))
+    edges = list(map(str, range(num_nodes, num_nodes + num_edges)))
     random_graph.add_hyperedges(edges)
 
     # Connect the edges

pygraph/algorithms/generators.py

@@ -56,7 +56,7 @@ def optimize(self, graph):
         """
         for center in self.centers:
             shortest_routes = shortest_path(graph, center)[1]
-            for node, weight in list(shortest_routes.items()):
+            for node, weight in shortest_routes.items():
                 self.nodes.setdefault(node, []).append(weight)
 
     def __call__(self, start, end):
@@ -69,10 +69,10 @@ def __call__(self, start, end):
         @type  end: node
         @param end: End node.
         """
-        assert len(list(self.nodes.keys())) > 0, (
+        assert len(self.nodes.keys()) > 0, (
             "You need to optimize this heuristic for your graph before it can be used to estimate."
         )
 
-        cmp_sequence = list(zip(self.nodes[start], self.nodes[end]))
+        cmp_sequence = zip(self.nodes[start], self.nodes[end])
         chow_number = max(abs(a - b) for a, b in cmp_sequence)
         return chow_number

pygraph/algorithms/heuristics/chow.py

@@ -40,18 +40,20 @@ class euclidean:
         3. The C{optimize()} method should be called before the heuristic search.
 
     A small example for clarification:
-
+    >>> from pygraph.classes import graph
     >>> g = graph.graph()
     >>> g.add_nodes(['A','B','C'])
     >>> g.add_node_attribute('A', ('position',(0,0)))
     >>> g.add_node_attribute('B', ('position',(1,1)))
     >>> g.add_node_attribute('C', ('position',(0,2)))
-    >>> g.add_edge('A','B', wt=2)
-    >>> g.add_edge('B','C', wt=2)
-    >>> g.add_edge('A','C', wt=4)
-    >>> h = graph.heuristics.euclidean()
+    >>> g.add_edge(('A','B'), wt=2)
+    >>> g.add_edge(('B','C'), wt=2)
+    >>> g.add_edge(('A','C'), wt=4)
+    >>> h = euclidean()
     >>> h.optimize(g)
-    >>> g.heuristic_search('A', 'C', h)
+    >>> from pygraph.algorithms.minmax import heuristic_search
+    >>> heuristic_search(g, 'A', 'C', h)
+    ['A', 'C']
     """
 
     def __init__(self):
@@ -92,7 +94,7 @@ def __call__(self, start, end):
         @type  end: node
         @param end: End node.
         """
-        assert len(list(self.distances.keys())) > 0, (
+        assert len(self.distances.keys()) > 0, (
             "You need to optimize this heuristic for your graph before it can be used to estimate."
         )
 

pygraph/algorithms/heuristics/euclidean.py

@@ -64,7 +64,7 @@ def dfs(node):
         pre.append(node)
         # Explore recursively the connected component
         for each in graph[node]:
-            if each not in visited and list(filter(each, node)):
+            if each not in visited and filter(each, node):
                 spanning_tree[each] = node
                 dfs(each)
         post.append(node)
@@ -77,7 +77,7 @@ def dfs(node):
 
     # DFS from one node only
     if root is not None:
-        if list(filter(root, None)):
+        if filter(root, None):
             spanning_tree[root] = None
             dfs(root)
         setrecursionlimit(recursionlimit)
@@ -86,7 +86,7 @@ def dfs(node):
     # Algorithm loop
     for each in graph:
         # Select a non-visited node
-        if each not in visited and list(filter(each, None)):
+        if each not in visited and filter(each, None):
             spanning_tree[each] = None
             # Explore node's connected component
             dfs(each)
@@ -123,7 +123,7 @@ def bfs():
             node = queue.pop(0)
 
             for other in graph[node]:
-                if other not in spanning_tree and list(filter(other, node)):
+                if other not in spanning_tree and filter(other, node):
                     queue.append(other)
                     ordering.append(other)
                     spanning_tree[other] = node
@@ -135,7 +135,7 @@ def bfs():
 
     # BFS from one node only
     if root is not None:
-        if list(filter(root, None)):
+        if filter(root, None):
             queue.append(root)
             ordering.append(root)
             spanning_tree[root] = None
@@ -145,7 +145,7 @@ def bfs():
     # Algorithm
     for each in graph:
         if each not in spanning_tree:
-            if list(filter(each, None)):
+            if filter(each, None):
                 queue.append(each)
                 ordering.append(each)
                 spanning_tree[each] = None