Merged workflow branch
bzr revid: ame@tinyerp.com-20080910054235-5teuucitb7gjz7cz
This commit is contained in:
Amit Mendapara
commit
46865e5b92
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@ -54,34 +54,45 @@ class workflow(osv.osv):
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return super(workflow, self).write(cr, user, ids, vals, context=context)
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#
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# scale = [stepx, stepy, posx, posy ]
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# scale = (vertical-distance, horizontal-distance, min-node-width(optional), min-node-height(optional), margin(default=20))
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#
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def graph_get(self, cr, uid, id, scale, context={}):
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nodes= []
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nodes_name = []
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transitions = []
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start = []
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tres = {}
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no_ancester = []
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workflow = self.browse(cr, uid, id, context)
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for a in workflow.activities:
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nodes.append((a.id,a.name))
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nodes_name.append((a.id,a.name))
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nodes.append(a.id)
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if a.flow_start:
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start.append((a.id,a.name))
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start.append(a.id)
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else:
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if not a.in_transitions:
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no_ancester.append(a.id)
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for t in a.out_transitions:
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transitions.append( ((a.id,a.name), (t.act_to.id,t.act_to.name)) )
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tres[t.id] = (a.id,t.act_to.id)
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g = graph(nodes, transitions)
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transitions.append((a.id, t.act_to.id))
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tres[t.id] = (a.id, t.act_to.id)
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g = graph(nodes, transitions, no_ancester)
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g.process(start)
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g.scale(*scale)
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result = g.result_get()
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results = {}
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for r in result.items():
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r[1]['name'] = r[0][1]
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results[str(r[0][0])] = r[1]
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return {'node': results, 'transition': tres}
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for node in nodes_name:
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results[str(node[0])] = result[node[0]]
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results[str(node[0])]['name'] = node[1]
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return {'nodes': results, 'transitions': tres}
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def create(self, cr, user, vals, context=None):
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if not context:
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@ -27,102 +27,111 @@
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# along with this program; if not, write to the Free Software
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# Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA.
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###############################################################################
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import operator
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import math
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class graph(object):
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def __init__(self, nodes, transitions):
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self.nodes = nodes
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self.links = transitions
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def __init__(self, nodes, transitions, no_ancester=None):
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"""Initailize graph's object
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@param nodes: list of ids of nodes in the graph
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@param transitions: list of edges in the graph in the form (source_node, destination_node)
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@param no_ancester: list of nodes with no incoming edges
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"""
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self.nodes = nodes or []
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self.edges = transitions or []
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self.no_ancester = no_ancester or {}
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trans = {}
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for t in transitions:
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trans.setdefault(t[0], [])
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trans[t[0]].append(t[1])
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self.transitions = trans
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self.result = {}
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self.levels = {}
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def get_parent(self,node):
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count = 0
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for item in self.transitions:
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if self.transitions[item].__contains__(node):
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count +=1
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return count
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def init_rank(self):
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self.temp = {}
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"""Computes rank of the nodes of the graph by finding initial feasible tree
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"""
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self.edge_wt = {}
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for link in self.links:
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self.temp[link] = self.result[link[1]]['y'] - self.result[link[0]]['y']
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self.edge_wt[link] = self.result[link[1]]['y'] - self.result[link[0]]['y']
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cnt = 0
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list_node = []
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list_edge = []
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while self.tight_tree()<self.result.__len__():
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cnt+=1
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tot_node = self.partial_order.__len__()
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#do until all the nodes in the component are searched
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while self.tight_tree()<tot_node:
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list_node = []
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list_edge = []
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for node in self.nodes:
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if node not in self.reachable_nodes:
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list_node.append(node)
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list_edge = []
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for link in self.temp:
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if link not in self.tree_edges:
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list_edge.append(link)
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for edge in self.edge_wt:
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if edge not in self.tree_edges:
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list_edge.append(edge)
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slack = 100
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for edge in list_edge:
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if (self.reachable_nodes.__contains__(edge[0]) and edge[1] not in self.reachable_nodes) or ( self.reachable_nodes.__contains__(edge[1]) and edge[0] not in self.reachable_nodes):
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if(slack>self.temp[edge]-1):
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slack = self.temp[edge]-1
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if ((self.reachable_nodes.__contains__(edge[0]) and edge[1] not in self.reachable_nodes) or
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(self.reachable_nodes.__contains__(edge[1]) and edge[0] not in self.reachable_nodes)):
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if(slack>self.edge_wt[edge]-1):
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slack = self.edge_wt[edge]-1
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new_edge = edge
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if new_edge[0] not in self.reachable_nodes:
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delta = -(self.temp[new_edge]-1)
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delta = -(self.edge_wt[new_edge]-1)
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else:
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delta = self.temp[new_edge]-1
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delta = self.edge_wt[new_edge]-1
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for node in self.result:
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if node in self.reachable_nodes:
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self.result[node]['y'] += delta
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for link in self.temp:
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self.temp[link] = self.result[link[1]]['y'] - self.result[link[0]]['y']
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for edge in self.edge_wt:
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self.edge_wt[edge] = self.result[edge[1]]['y'] - self.result[edge[0]]['y']
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self.init_cutvalues()
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self.init_cutvalues()
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def tight_tree(self,):
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def tight_tree(self):
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self.reachable_nodes = []
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self.tree_edges = []
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self.reachable_node(self.start)
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return self.reachable_nodes.__len__()
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def reachable_node(self,node):
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def reachable_node(self, node):
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"""Find the nodes of the graph which are only 1 rank apart from each other
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"""
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if node not in self.reachable_nodes:
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self.reachable_nodes.append(node)
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for link in self.temp:
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if link[0]==node:
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# print link[0]
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if self.temp[link]==1:
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self.tree_edges.append(link)
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if link[1] not in self.reachable_nodes:
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self.reachable_nodes.append(link[1])
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self.reachable_node(link[1])
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for edge in self.edge_wt:
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if edge[0]==node:
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if self.edge_wt[edge]==1:
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self.tree_edges.append(edge)
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if edge[1] not in self.reachable_nodes:
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self.reachable_nodes.append(edge[1])
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self.reachable_node(edge[1])
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def init_cutvalues(self):
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"""Initailize cut values of edges of the feasible tree.
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Edges with negative cut-values are removed from the tree to optimize rank assignment
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"""
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self.cut_edges = {}
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self.head_nodes = []
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i=0;
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for edge in self.tree_edges:
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self.head_nodes = []
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rest_edges = []
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rest_edges += self.tree_edges
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rest_edges.__delitem__(i)
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self.head_component(self.start,rest_edges)
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self.head_component(self.start, rest_edges)
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i+=1
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positive = 0
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negative = 0
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@ -137,129 +146,527 @@ class graph(object):
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positive+=1
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self.cut_edges[edge] = positive - negative
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def head_component(self, node, rest_edges):
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"""Find nodes which are reachable from the starting node, after removing an edge
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"""
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if node not in self.head_nodes:
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self.head_nodes.append(node)
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for link in rest_edges:
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if link[0]==node:
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self.head_component(link[1],rest_edges)
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for edge in rest_edges:
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if edge[0]==node:
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self.head_component(edge[1],rest_edges)
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def process_ranking(self, node, level=0):
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"""Computes initial feasible ranking after making graph acyclic with depth-first search
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"""
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if node not in self.result:
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self.result[node] = {'x': None, 'y':level, 'mark':0}
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else:
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if level > self.result[node]['y']:
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self.result[node]['y'] = level
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if self.result[node]['mark']==0:
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self.result[node]['mark'] = 1
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for t in self.transitions.get(node, []):
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self.process_ranking(t, level+1)
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for sec_end in self.transitions.get(node, []):
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self.process_ranking(sec_end, level+1)
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def make_acyclic(self, parent, node, level, tree):
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"""Computes Partial-order of the nodes with depth-first search
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"""
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if node not in self.partial_order:
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self.partial_order[node] = {'level':level, 'mark':0}
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if parent:
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tree.append((parent, node))
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if self.partial_order[node]['mark']==0:
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self.partial_order[node]['mark'] = 1
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for sec_end in self.transitions.get(node, []):
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self.links.append((node, sec_end))
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self.make_acyclic(node, sec_end, level+1, tree)
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def preprocess_order(self):
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levels = {}
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for r in self.result:
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l = self.result[r]['y']
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levels.setdefault(l,[])
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levels[l].append(r)
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self.levels = levels
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return tree
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def process_order(self, level):
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self.levels[level].sort(lambda x,y: cmp(self.result[x]['x'], self.result[y]['x']))
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for nodepos in range(len(self.levels[level])):
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node = self.levels[level][nodepos]
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if nodepos == 0:
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left = self.result[node]['x']- 0.5
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else:
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left = (self.result[node]['x'] + self.result[self.levels[level][nodepos-1]]['x']) / 2.0
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if nodepos == (len(self.levels[level])-1):
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right = self.result[node]['x'] + 0.5
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else:
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right = (self.result[node]['x'] + self.result[self.levels[level][nodepos+1]]['x']) / 2.0
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if self.transitions.get(node, False):
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if len(self.transitions[node])==1:
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pos = (left+right)/2.0
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step = 0
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else:
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pos = left
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step = (-left+right) / (len(self.transitions[node])-1)
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for n2 in self.transitions[node]:
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self.result[n2]['x'] = pos
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pos += step
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def exchange(self,e,f):
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def rev_edges(self, tree):
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"""reverse the direction of the edges whose source-node-partail_order> destination-node-partail_order
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to make the graph acyclic
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"""
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Is_Cyclic = False
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i=0
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for link in self.links:
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src = link[0]
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des = link[1]
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edge_len = self.partial_order[des]['level'] - self.partial_order[src]['level']
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if edge_len < 0:
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self.links.__delitem__(i)
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self.links.insert(i, (des, src))
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self.transitions[src].remove(des)
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self.transitions.setdefault(des, []).append(src)
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Is_Cyclic = True
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elif math.fabs(edge_len) > 1:
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Is_Cyclic = True
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i += 1
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return Is_Cyclic
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def exchange(self, e, f):
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"""Exchange edges to make feasible-tree optimized
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@param edge: edge with negative cut-value
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@param edge: new edge with minimum slack-value
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"""
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self.tree_edges.__delitem__(self.tree_edges.index(e))
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self.tree_edges.append(f)
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self.init_cutvalues()
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def enter_edge(self,edge):
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def enter_edge(self, edge):
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"""Finds a new_edge with minimum slack value to replace an edge with negative cut-value
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@param edge: edge with negative cut-value
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"""
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self.head_nodes = []
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rest_edges = []
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rest_edges += self.tree_edges
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rest_edges.__delitem__(rest_edges.index(edge))
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self.head_component(self.start,rest_edges)
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self.head_component(self.start, rest_edges)
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if self.head_nodes.__contains__(edge[1]):
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l = []
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for node in self.result:
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if not self.head_nodes.__contains__(node):
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l.append(node)
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self.head_nodes = l
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slack = 100
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new_edge = edge
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for source_node in self.transitions:
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if source_node in self.head_nodes:
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for dest_node in self.transitions[source_node]:
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if dest_node not in self.head_nodes:
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if(slack>(self.temp[edge]-1)):
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slack = self.temp[edge]-1
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new_edge = (source_node,dest_node)
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if(slack>(self.edge_wt[edge]-1)):
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slack = self.edge_wt[edge]-1
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new_edge = (source_node, dest_node)
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return new_edge
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def leave_edge(self):
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"""Returns the edge with negative cut_value(if exists)
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"""
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if self.critical_edges:
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for edge in self.critical_edges:
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self.cut_edges[edge] = 0
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for edge in self.cut_edges:
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if self.cut_edges[edge]<0:
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return edge
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return ()
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def process(self, starting_node):
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pos = (len(starting_node) - 1.0)/2.0
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self.start = starting_node[0]
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for s in starting_node:
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self.process_ranking(s)
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self.result[s]['x'] = pos
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pos += 1.0
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self.init_rank()
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#normalize
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return None
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def finalize_rank(self, node, level):
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self.result[node]['y'] = level
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for destination in self.optimal_edges.get(node, []):
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self.finalize_rank(destination, level+1)
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def normalize(self):
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"""The ranks are normalized by setting the least rank to zero.
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"""
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least_rank=100
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#normalization
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for node in self.result:
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if least_rank>self.result[node]['y']:
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least_rank = self.result[node]['y']
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if(least_rank!=0):
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diff = least_rank
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for node in self.result:
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self.result[node]['y']-=least_rank
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self.result[node]['y']-=least_rank
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e = self.leave_edge()
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#while e:
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f = self.enter_edge(e)
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self.exchange(e,f)
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e = self.leave_edge()
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def make_chain(self):
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"""Edges between nodes more than one rank apart are replaced by chains of unit
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length edges between temporary nodes.
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"""
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for edge in self.edge_wt:
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if self.edge_wt[edge]>1:
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self.transitions[edge[0]].remove(edge[1])
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start = self.result[edge[0]]['y']
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end = self.result[edge[1]]['y']
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for rank in range(start+1, end):
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if not self.result.get((rank, 'temp'), False):
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self.result[(rank, 'temp')] = {'x': None, 'y': rank, 'mark': 0}
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for rank in range(start, end):
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if start==rank:
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self.transitions[edge[0]].append((rank+1, 'temp'))
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elif rank==end-1:
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self.transitions.setdefault((rank, 'temp'), []).append(edge[1])
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else:
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self.transitions.setdefault((rank, 'temp'), []).append((rank+1, 'temp'))
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def init_order(self, node, level):
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"""Initialize orders the nodes in each rank with depth-first search
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"""
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if not self.result[node]['x']:
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self.result[node]['x'] = self.order[level]
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self.order[level] = self.order[level]+1
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for sec_end in self.transitions.get(node, []):
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self.init_order(sec_end, self.result[sec_end]['y'])
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def order_heuristic(self):
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for i in range(12):
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self.wmedian()
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def wmedian(self):
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"""Applies median heuristic to find optimzed order of the nodes with in their ranks
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"""
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for level in self.levels:
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node_median = []
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nodes = self.levels[level]
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for node in nodes:
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node_median.append((node, self.median_value(node, level-1)))
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sort_list = sorted(node_median, key=operator.itemgetter(1))
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||||
new_list = [tuple[0] for tuple in sort_list]
|
||||
|
||||
self.levels[level] = new_list
|
||||
order = 0
|
||||
for node in nodes:
|
||||
self.result[node]['x'] = order
|
||||
order +=1
|
||||
|
||||
|
||||
|
||||
|
||||
def median_value(self, node, adj_rank):
|
||||
"""Returns median value of a vertex , defined as the median position of the adjacent vertices
|
||||
|
||||
@param node: node to process
|
||||
@param adj_rank: rank 1 less than the node's rank
|
||||
"""
|
||||
adj_nodes = self.adj_position(node, adj_rank)
|
||||
l = len(adj_nodes)
|
||||
m = l/2
|
||||
|
||||
if l==0:
|
||||
return -1.0
|
||||
elif l%2 == 1:
|
||||
return adj_nodes[m]#median of the middle element
|
||||
elif l==2:
|
||||
return (adj_nodes[0]+adj_nodes[1])/2
|
||||
else:
|
||||
left = adj_nodes[m-1] - adj_nodes[0]
|
||||
right = adj_nodes[l-1] - adj_nodes[m]
|
||||
return ((adj_nodes[m-1]*right) + (adj_nodes[m]*left))/(left+right)
|
||||
|
||||
|
||||
def adj_position(self, node, adj_rank):
|
||||
"""Returns list of the present positions of the nodes adjacent to node in the given adjacent rank.
|
||||
|
||||
@param node: node to process
|
||||
@param adj_rank: rank 1 less than the node's rank
|
||||
"""
|
||||
|
||||
pre_level_nodes = self.levels.get(adj_rank, [])
|
||||
adj_nodes = []
|
||||
|
||||
if pre_level_nodes:
|
||||
for src in pre_level_nodes:
|
||||
if (self.transitions.get(src) and self.transitions[src].__contains__(node)):
|
||||
adj_nodes.append(self.result[src]['x'])
|
||||
|
||||
return adj_nodes
|
||||
|
||||
|
||||
def preprocess_order(self):
|
||||
levels = {}
|
||||
|
||||
for r in self.partial_order:
|
||||
l = self.result[r]['y']
|
||||
levels.setdefault(l,[])
|
||||
levels[l].append(r)
|
||||
|
||||
self.levels = levels
|
||||
|
||||
|
||||
def graph_order(self):
|
||||
"""Finds actual-order of the nodes with respect to maximum number of nodes in a rank in component
|
||||
"""
|
||||
mid_pos = None
|
||||
max_level = max(map(lambda x: len(x), self.levels.values()))
|
||||
|
||||
for level in self.levels:
|
||||
if level:
|
||||
no = len(self.levels[level])
|
||||
factor = (max_level - no) * 0.10
|
||||
list = self.levels[level]
|
||||
list.reverse()
|
||||
|
||||
if no%2==0:
|
||||
first_half = list[no/2:]
|
||||
factor = -factor
|
||||
else:
|
||||
first_half = list[no/2+1:]
|
||||
if max_level==1:#for the case when horizontal graph is there
|
||||
self.result[list[no/2]]['x'] = mid_pos + (self.result[list[no/2]]['y']%2 * 0.5)
|
||||
else:
|
||||
self.result[list[no/2]]['x'] = mid_pos + factor
|
||||
|
||||
last_half = list[:no/2]
|
||||
|
||||
i=1
|
||||
for node in first_half:
|
||||
self.result[node]['x'] = mid_pos - (i + factor)
|
||||
i += 1
|
||||
|
||||
i=1
|
||||
for node in last_half:
|
||||
self.result[node]['x'] = mid_pos + (i + factor)
|
||||
i += 1
|
||||
else:
|
||||
self.max_order += max_level+1
|
||||
mid_pos = self.result[self.start]['x']
|
||||
|
||||
|
||||
def tree_order(self, node, last=0):
|
||||
mid_pos = self.result[node]['x']
|
||||
l = self.transitions.get(node, [])
|
||||
l.reverse()
|
||||
no = len(l)
|
||||
|
||||
if no%2==0:
|
||||
first_half = l[no/2:]
|
||||
factor = 1
|
||||
else:
|
||||
first_half = l[no/2+1:]
|
||||
factor = 0
|
||||
|
||||
last_half = l[:no/2]
|
||||
|
||||
i=1
|
||||
for child in first_half:
|
||||
self.result[child]['x'] = mid_pos - (i - (factor * 0.5))
|
||||
i += 1
|
||||
|
||||
if self.transitions.get(child, False):
|
||||
if last:
|
||||
self.result[child]['x'] = last + len(self.transitions[child])/2 + 1
|
||||
last = self.tree_order(child, last)
|
||||
|
||||
if no%2:
|
||||
mid_node = l[no/2]
|
||||
self.result[mid_node]['x'] = mid_pos
|
||||
|
||||
if self.transitions.get((mid_node), False):
|
||||
if last:
|
||||
self.result[mid_node]['x'] = last + len(self.transitions[mid_node])/2 + 1
|
||||
last = self.tree_order(mid_node)
|
||||
else:
|
||||
if last:
|
||||
self.result[mid_node]['x'] = last + 1
|
||||
self.result[node]['x'] = self.result[mid_node]['x']
|
||||
mid_pos = self.result[node]['x']
|
||||
|
||||
i=1
|
||||
last_child = None
|
||||
for child in last_half:
|
||||
self.result[child]['x'] = mid_pos + (i - (factor * 0.5))
|
||||
last_child = child
|
||||
i += 1
|
||||
if self.transitions.get(child, False):
|
||||
if last:
|
||||
self.result[child]['x'] = last + len(self.transitions[child])/2 + 1
|
||||
last = self.tree_order(child, last)
|
||||
|
||||
if last_child:
|
||||
last = self.result[last_child]['x']
|
||||
|
||||
return last
|
||||
|
||||
|
||||
def process_order(self):
|
||||
"""Finds actual-order of the nodes with respect to maximum number of nodes in a rank in component
|
||||
"""
|
||||
max_level = max(map(lambda x: len(x), self.levels.values()))
|
||||
|
||||
if max_level%2:
|
||||
self.result[self.start]['x'] = (max_level+1)/2 + self.max_order +1
|
||||
else:
|
||||
self.result[self.start]['x'] = (max_level)/2 + self.max_order + 1
|
||||
|
||||
if self.Is_Cyclic:
|
||||
self.graph_order()
|
||||
|
||||
else:
|
||||
self.result[self.start]['x'] = 0
|
||||
self.tree_order(self.start, 0)
|
||||
min_order = math.fabs(min(map(lambda x: x['x'], self.result.values())))
|
||||
min_order += self.max_order + 1
|
||||
|
||||
for level in self.levels:
|
||||
for node in self.levels[level]:
|
||||
self.result[node]['x'] += min_order
|
||||
|
||||
self.max_order = max(map(lambda x: x['x'], self.result.values()))
|
||||
|
||||
def find_starts(self):
|
||||
"""Finds other start nodes of the graph in the case when graph is disconneted
|
||||
"""
|
||||
rem_nodes = []
|
||||
for node in self.nodes:
|
||||
if not self.partial_order.get(node):
|
||||
rem_nodes.append(node)
|
||||
cnt = 0
|
||||
while True:
|
||||
if len(rem_nodes)==1:
|
||||
self.start_nodes.append(rem_nodes[0])
|
||||
break
|
||||
else:
|
||||
count = 0
|
||||
new_start = rem_nodes[0]
|
||||
largest_tree = []
|
||||
|
||||
for node in rem_nodes:
|
||||
self.partial_order = {}
|
||||
tree = self.make_acyclic(None, node, 0, [])
|
||||
if len(tree)+1 > count:
|
||||
count = len(tree) + 1
|
||||
new_start = node
|
||||
largest_tree = tree
|
||||
else:
|
||||
if not largest_tree:
|
||||
new_start = rem_nodes[0]
|
||||
rem_nodes.remove(new_start)
|
||||
|
||||
self.start_nodes.append(new_start)
|
||||
|
||||
|
||||
for edge in largest_tree:
|
||||
if rem_nodes.__contains__(edge[0]):
|
||||
rem_nodes.remove(edge[0])
|
||||
if rem_nodes.__contains__(edge[1]):
|
||||
rem_nodes.remove(edge[1])
|
||||
|
||||
if not rem_nodes:
|
||||
break
|
||||
|
||||
|
||||
def rank(self):
|
||||
"""Finds the optimized rank of the nodes using Network-simplex algorithm
|
||||
|
||||
@param start: starting node of the component
|
||||
"""
|
||||
self.levels = {}
|
||||
self.critical_edges = []
|
||||
self.partial_order = {}
|
||||
self.links = []
|
||||
self.Is_Cyclic = False
|
||||
|
||||
tree = self.make_acyclic(None, self.start, 0, [])
|
||||
self.Is_Cyclic = self.rev_edges(tree)
|
||||
self.process_ranking(self.start)
|
||||
self.init_rank()
|
||||
|
||||
#make cut values of all tree edges to 0 to optimize feasible tree
|
||||
e = self.leave_edge()
|
||||
|
||||
while e :
|
||||
f = self.enter_edge(e)
|
||||
if e==f:
|
||||
self.critical_edges.append(e)
|
||||
else:
|
||||
self.exchange(e,f)
|
||||
e = self.leave_edge()
|
||||
|
||||
#finalize rank using optimum feasible tree
|
||||
# self.optimal_edges = {}
|
||||
# for edge in self.tree_edges:
|
||||
# source = self.optimal_edges.setdefault(edge[0], [])
|
||||
# source.append(edge[1])
|
||||
|
||||
# self.finalize_rank(self.start, 0)
|
||||
|
||||
#normalization
|
||||
self.normalize()
|
||||
for edge in self.edge_wt:
|
||||
self.edge_wt[edge] = self.result[edge[1]]['y'] - self.result[edge[0]]['y']
|
||||
|
||||
def order_in_rank(self):
|
||||
"""Finds optimized order of the nodes within their ranks using median heuristic
|
||||
|
||||
@param start: starting node of the component
|
||||
"""
|
||||
|
||||
self.make_chain()
|
||||
self.preprocess_order()
|
||||
for n in self.levels:
|
||||
self.process_order(n)
|
||||
|
||||
self.order = {}
|
||||
max_rank = max(map(lambda x: x, self.levels.keys()))
|
||||
|
||||
for i in range(max_rank+1):
|
||||
self.order[i] = 0
|
||||
|
||||
self.init_order(self.start, self.result[self.start]['y'])
|
||||
|
||||
for level in self.levels:
|
||||
self.levels[level].sort(lambda x, y: cmp(self.result[x]['x'], self.result[y]['x']))
|
||||
|
||||
self.order_heuristic()
|
||||
self.process_order()
|
||||
|
||||
def process(self, starting_node):
|
||||
"""Process the graph to find ranks and order of the nodes
|
||||
|
||||
@param starting_node: node from where to start the graph search
|
||||
"""
|
||||
|
||||
self.start_nodes = starting_node or []
|
||||
self.partial_order = {}
|
||||
self.links = []
|
||||
|
||||
if self.nodes:
|
||||
if self.start_nodes:
|
||||
#add dummy edges to the nodes which does not have any incoming edges
|
||||
tree = self.make_acyclic(None, self.start_nodes[0], 0, [])
|
||||
|
||||
for node in self.no_ancester:
|
||||
for sec_node in self.transitions.get(node, []):
|
||||
if sec_node in self.partial_order.keys():
|
||||
self.transitions[self.start_nodes[0]].append(node)
|
||||
break
|
||||
|
||||
self.partial_order = {}
|
||||
tree = self.make_acyclic(None, self.start_nodes[0], 0, [])
|
||||
|
||||
|
||||
# if graph is disconnected or no start-node is given
|
||||
#than to find starting_node for each component of the node
|
||||
if len(self.nodes) > len(self.partial_order):
|
||||
self.find_starts()
|
||||
|
||||
self.max_order = 0
|
||||
#for each component of the graph find ranks and order of the nodes
|
||||
for s in self.start_nodes:
|
||||
self.start = s
|
||||
self.rank() # First step:Netwoek simplex algorithm
|
||||
self.order_in_rank() #Second step: ordering nodes within ranks
|
||||
|
||||
|
||||
def __str__(self):
|
||||
result = ''
|
||||
|
|
@ -269,23 +676,25 @@ class graph(object):
|
|||
result += '\tPosX: '+ str(self.result[node]['x']) + ' - Node:' + node + "\n"
|
||||
return result
|
||||
|
||||
def scale(self, maxx, maxy, plusx2=0, plusy2=0):
|
||||
plusx = - min(map(lambda x: x['x'],self.result.values()))
|
||||
plusy = - min(map(lambda x: x['y'],self.result.values()))
|
||||
|
||||
maxcurrent = 1.0
|
||||
diff = 1.0
|
||||
for l in self.levels:
|
||||
for n in range(1, len(self.levels[l])):
|
||||
n1 = self.levels[l][n]
|
||||
n2 = self.levels[l][n-1]
|
||||
diff = abs(self.result[n2]['x']-self.result[n1]['x'])
|
||||
if diff<maxcurrent:
|
||||
maxcurrent=diff
|
||||
factor = maxx / diff
|
||||
for r in self.result:
|
||||
self.result[r]['x'] = (self.result[r]['x']+plusx) * factor + plusx2
|
||||
self.result[r]['y'] = (self.result[r]['y']+plusy) * maxy + plusy2
|
||||
def scale(self, maxx, maxy, nwidth=0, nheight=0, margin=20):
|
||||
"""Computes actual co-ordiantes of the nodes
|
||||
"""
|
||||
|
||||
#for flat edges ie. source an destination nodes are on the same rank
|
||||
for src in self.transitions:
|
||||
for des in self.transitions[src]:
|
||||
if (self.result[des]['y'] - self.result[src]['y'] == 0):
|
||||
self.result[src]['y'] += 0.08
|
||||
self.result[des]['y'] -= 0.08
|
||||
|
||||
factorX = maxx + nheight
|
||||
factorY = maxy + nwidth
|
||||
|
||||
for node in self.result:
|
||||
self.result[node]['x'] = (self.result[node]['x']) * factorX + margin
|
||||
self.result[node]['y'] = (self.result[node]['y']) * factorY + margin
|
||||
|
||||
|
||||
def result_get(self):
|
||||
return self.result
|
||||
|
|
|
|||
Loading…
Reference in New Issue