#!/usr/bin/env python
-# -*- encoding: utf-8 -*-
+# -*- coding: utf-8 -*-
##############################################################################
#
-# OpenERP, Open Source Management Solution
-# Copyright (C) 2004-2009 Tiny SPRL (<http://tiny.be>). All Rights Reserved
-# $Id$
+# OpenERP, Open Source Management Solution
+# Copyright (C) 2004-2009 Tiny SPRL (<http://tiny.be>).
#
# This program is free software: you can redistribute it and/or modify
-# it under the terms of the GNU General Public License as published by
-# the Free Software Foundation, either version 3 of the License, or
-# (at your option) any later version.
+# it under the terms of the GNU Affero General Public License as
+# published by the Free Software Foundation, either version 3 of the
+# License, or (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-# GNU General Public License for more details.
+# GNU Affero General Public License for more details.
#
-# You should have received a copy of the GNU General Public License
+# You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
#
##############################################################################
+
import operator
import math
class graph(object):
def __init__(self, nodes, transitions, no_ancester=None):
"""Initailize graph's object
-
+
@param nodes: list of ids of nodes in the graph
@param transitions: list of edges in the graph in the form (source_node, destination_node)
- @param no_ancester: list of nodes with no incoming edges
+ @param no_ancester: list of nodes with no incoming edges
"""
-
+
self.nodes = nodes or []
self.edges = transitions or []
self.no_ancester = no_ancester or {}
trans = {}
-
+
for t in transitions:
trans.setdefault(t[0], [])
trans[t[0]].append(t[1])
self.transitions = trans
self.result = {}
-
-
+
+
def init_rank(self):
"""Computes rank of the nodes of the graph by finding initial feasible tree
"""
self.edge_wt = {}
for link in self.links:
self.edge_wt[link] = self.result[link[1]]['x'] - self.result[link[0]]['x']
-
+
tot_node = self.partial_order.__len__()
- #do until all the nodes in the component are searched
+ #do until all the nodes in the component are searched
while self.tight_tree()<tot_node:
list_node = []
list_edge = []
-
+
for node in self.nodes:
if node not in self.reachable_nodes:
list_node.append(node)
-
+
for edge in self.edge_wt:
if edge not in self.tree_edges:
list_edge.append(edge)
-
+
slack = 100
-
+
for edge in list_edge:
- if ((self.reachable_nodes.__contains__(edge[0]) and edge[1] not in self.reachable_nodes) or
+ 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)):
if(slack>self.edge_wt[edge]-1):
slack = self.edge_wt[edge]-1
new_edge = edge
-
+
if new_edge[0] not in self.reachable_nodes:
delta = -(self.edge_wt[new_edge]-1)
else:
delta = self.edge_wt[new_edge]-1
-
+
for node in self.result:
if node in self.reachable_nodes:
self.result[node]['x'] += delta
-
+
for edge in self.edge_wt:
- self.edge_wt[edge] = self.result[edge[1]]['x'] - self.result[edge[0]]['x']
-
- self.init_cutvalues()
-
-
+ self.edge_wt[edge] = self.result[edge[1]]['x'] - self.result[edge[0]]['x']
+
+ self.init_cutvalues()
+
+
def tight_tree(self):
self.reachable_nodes = []
self.tree_edges = []
- self.reachable_node(self.start)
+ self.reachable_node(self.start)
return self.reachable_nodes.__len__()
-
-
+
+
def reachable_node(self, node):
- """Find the nodes of the graph which are only 1 rank apart from each other
+ """Find the nodes of the graph which are only 1 rank apart from each other
"""
-
+
if node not in self.reachable_nodes:
self.reachable_nodes.append(node)
for edge in self.edge_wt:
if edge[1] not in self.reachable_nodes:
self.reachable_nodes.append(edge[1])
self.reachable_node(edge[1])
-
-
+
+
def init_cutvalues(self):
"""Initailize cut values of edges of the feasible tree.
- Edges with negative cut-values are removed from the tree to optimize rank assignment
+ Edges with negative cut-values are removed from the tree to optimize rank assignment
"""
self.cut_edges = {}
self.head_nodes = []
i=0;
-
+
for edge in self.tree_edges:
self.head_nodes = []
rest_edges = []
rest_edges += self.tree_edges
rest_edges.__delitem__(i)
- self.head_component(self.start, rest_edges)
+ self.head_component(self.start, rest_edges)
i+=1
positive = 0
negative = 0
for source_node in self.transitions:
- if source_node in self.head_nodes:
+ if source_node in self.head_nodes:
for dest_node in self.transitions[source_node]:
if dest_node not in self.head_nodes:
negative+=1
self.cut_edges[edge] = positive - negative
-
+
def head_component(self, node, rest_edges):
"""Find nodes which are reachable from the starting node, after removing an edge
"""
if node not in self.head_nodes:
self.head_nodes.append(node)
-
- for edge in rest_edges:
- if edge[0]==node:
- self.head_component(edge[1],rest_edges)
-
+
+ for edge in rest_edges:
+ if edge[0]==node:
+ self.head_component(edge[1],rest_edges)
+
def process_ranking(self, node, level=0):
"""Computes initial feasible ranking after making graph acyclic with depth-first search
"""
-
+
if node not in self.result:
self.result[node] = {'y': None, 'x':level, 'mark':0}
else:
if level > self.result[node]['x']:
self.result[node]['x'] = level
-
+
if self.result[node]['mark']==0:
self.result[node]['mark'] = 1
for sec_end in self.transitions.get(node, []):
self.process_ranking(sec_end, level+1)
-
-
+
+
def make_acyclic(self, parent, node, level, tree):
"""Computes Partial-order of the nodes with depth-first search
"""
-
+
if node not in self.partial_order:
self.partial_order[node] = {'level':level, 'mark':0}
if parent:
tree.append((parent, node))
-
+
if self.partial_order[node]['mark']==0:
self.partial_order[node]['mark'] = 1
for sec_end in self.transitions.get(node, []):
self.links.append((node, sec_end))
self.make_acyclic(node, sec_end, level+1, tree)
- return tree
+ return tree
+
-
def rev_edges(self, tree):
- """reverse the direction of the edges whose source-node-partail_order> destination-node-partail_order
- to make the graph acyclic
+ """reverse the direction of the edges whose source-node-partail_order> destination-node-partail_order
+ to make the graph acyclic
"""
Is_Cyclic = False
- i=0
+ i=0
for link in self.links:
src = link[0]
des = link[1]
- edge_len = self.partial_order[des]['level'] - self.partial_order[src]['level']
+ edge_len = self.partial_order[des]['level'] - self.partial_order[src]['level']
if edge_len < 0:
self.links.__delitem__(i)
self.links.insert(i, (des, src))
elif math.fabs(edge_len) > 1:
Is_Cyclic = True
i += 1
-
+
return Is_Cyclic
-
+
def exchange(self, e, f):
"""Exchange edges to make feasible-tree optimized
@param edge: edge with negative cut-value
self.tree_edges.__delitem__(self.tree_edges.index(e))
self.tree_edges.append(f)
self.init_cutvalues()
-
-
+
+
def enter_edge(self, edge):
- """Finds a new_edge with minimum slack value to replace an edge with negative cut-value
-
- @param edge: edge with negative cut-value
+ """Finds a new_edge with minimum slack value to replace an edge with negative cut-value
+
+ @param edge: edge with negative cut-value
"""
-
+
self.head_nodes = []
rest_edges = []
rest_edges += self.tree_edges
rest_edges.__delitem__(rest_edges.index(edge))
self.head_component(self.start, rest_edges)
-
+
if self.head_nodes.__contains__(edge[1]):
l = []
for node in self.result:
if not self.head_nodes.__contains__(node):
- l.append(node)
+ l.append(node)
self.head_nodes = l
-
+
slack = 100
new_edge = edge
for source_node in self.transitions:
- if source_node in self.head_nodes:
+ if source_node in self.head_nodes:
for dest_node in self.transitions[source_node]:
if dest_node not in self.head_nodes:
if(slack>(self.edge_wt[edge]-1)):
slack = self.edge_wt[edge]-1
new_edge = (source_node, dest_node)
-
- return new_edge
-
+
+ return new_edge
+
def leave_edge(self):
- """Returns the edge with negative cut_value(if exists)
+ """Returns the edge with negative cut_value(if exists)
"""
if self.critical_edges:
for edge in self.critical_edges:
self.cut_edges[edge] = 0
-
+
for edge in self.cut_edges:
if self.cut_edges[edge]<0:
return edge
-
- return None
-
-
+
+ return None
+
+
def finalize_rank(self, node, level):
self.result[node]['x'] = level
for destination in self.optimal_edges.get(node, []):
self.finalize_rank(destination, level+1)
-
-
+
+
def normalize(self):
"""The ranks are normalized by setting the least rank to zero.
"""
-
+
least_rank = min(map(lambda x: x['x'], self.result.values()))
-
+
if(least_rank!=0):
for node in self.result:
- self.result[node]['x']-=least_rank
-
-
- def make_chain(self):
- """Edges between nodes more than one rank apart are replaced by chains of unit
+ self.result[node]['x']-=least_rank
+
+
+ def make_chain(self):
+ """Edges between nodes more than one rank apart are replaced by chains of unit
length edges between temporary nodes.
"""
-
+
for edge in self.edge_wt:
if self.edge_wt[edge]>1:
self.transitions[edge[0]].remove(edge[1])
start = self.result[edge[0]]['x']
end = self.result[edge[1]]['x']
-
- for rank in range(start+1, end):
- if not self.result.get((rank, 'temp'), False):
+
+ for rank in range(start+1, end):
+ if not self.result.get((rank, 'temp'), False):
self.result[(rank, 'temp')] = {'y': None, 'x': rank, 'mark': 0}
-
+
for rank in range(start, end):
- if start==rank:
+ if start==rank:
self.transitions[edge[0]].append((rank+1, 'temp'))
elif rank==end-1:
self.transitions.setdefault((rank, 'temp'), []).append(edge[1])
else:
- self.transitions.setdefault((rank, 'temp'), []).append((rank+1, 'temp'))
-
-
+ self.transitions.setdefault((rank, 'temp'), []).append((rank+1, 'temp'))
+
+
def init_order(self, node, level):
"""Initialize orders the nodes in each rank with depth-first search
- """
- if not self.result[node]['y']:
+ """
+ if not self.result[node]['y']:
self.result[node]['y'] = self.order[level]
- self.order[level] = self.order[level]+1
-
+ self.order[level] = self.order[level]+1
+
for sec_end in self.transitions.get(node, []):
self.init_order(sec_end, self.result[sec_end]['x'])
-
-
+
+
def order_heuristic(self):
for i in range(12):
self.wmedian()
-
-
+
+
def wmedian(self):
"""Applies median heuristic to find optimzed order of the nodes with in their ranks
"""
- for level in self.levels:
-
+ for level in self.levels:
+
node_median = []
- nodes = self.levels[level]
- for node in nodes:
+ nodes = self.levels[level]
+ for node in nodes:
node_median.append((node, self.median_value(node, level-1)))
sort_list = sorted(node_median, key=operator.itemgetter(1))
new_list = [tuple[0] for tuple in sort_list]
-
+
self.levels[level] = new_list
order = 0
for node in nodes:
self.result[node]['y'] = order
- order +=1
+ 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
+ """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
+ return -1.0
elif l%2 == 1:
return adj_nodes[m]#median of the middle element
elif l==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)
-
-
+ 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
+
+ @param node: node to process
+ @param adj_rank: rank 1 less than the node's rank
"""
-
- pre_level_nodes = self.levels.get(adj_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]['y'])
-
- return adj_nodes
-
-
+
+ return adj_nodes
+
+
def preprocess_order(self):
levels = {}
-
+
for r in self.partial_order:
l = self.result[r]['x']
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
+
+
+ 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
+ mid_pos = 0.0
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]
+ factor = (max_level - no) * 0.10
+ list = self.levels[level]
list.reverse()
-
+
if no%2==0:
first_half = list[no/2:]
- factor = -factor
+ 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]]['y'] = mid_pos + (self.result[list[no/2]]['x']%2 * 0.5)
else:
self.result[list[no/2]]['y'] = mid_pos + factor
-
- last_half = list[:no/2]
-
+
+ last_half = list[:no/2]
+
i=1
for node in first_half:
self.result[node]['y'] = mid_pos - (i + factor)
i += 1
-
+
i=1
for node in last_half:
self.result[node]['y'] = mid_pos + (i + factor)
i += 1
- else:
+ else:
self.max_order += max_level+1
- mid_pos = self.result[self.start]['y']
-
+ mid_pos = self.result[self.start]['y']
+
def tree_order(self, node, last=0):
mid_pos = self.result[node]['y']
l = self.transitions.get(node, [])
l.reverse()
no = len(l)
-
+
if no%2==0:
- first_half = l[no/2:]
- factor = 1
+ first_half = l[no/2:]
+ factor = 1
else:
first_half = l[no/2+1:]
factor = 0
-
- last_half = l[:no/2]
-
+
+ last_half = l[:no/2]
+
i=1
for child in first_half:
self.result[child]['y'] = mid_pos - (i - (factor * 0.5))
i += 1
-
+
if self.transitions.get(child, False):
if last:
self.result[child]['y'] = 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]['y'] = mid_pos
-
+ self.result[mid_node]['y'] = mid_pos
+
if self.transitions.get((mid_node), False):
if last:
self.result[mid_node]['y'] = last + len(self.transitions[mid_node])/2 + 1
if last:
self.result[mid_node]['y'] = last + 1
self.result[node]['y'] = self.result[mid_node]['y']
- mid_pos = self.result[node]['y']
-
- i=1
+ mid_pos = self.result[node]['y']
+
+ i=1
last_child = None
- for child in last_half:
+ for child in last_half:
self.result[child]['y'] = mid_pos + (i - (factor * 0.5))
- last_child = child
+ last_child = child
i += 1
if self.transitions.get(child, False):
if last:
- self.result[child]['y'] = last + len(self.transitions[child])/2 + 1
+ self.result[child]['y'] = last + len(self.transitions[child])/2 + 1
last = self.tree_order(child, last)
-
+
if last_child:
last = self.result[last_child]['y']
-
- return last
-
-
- def process_order(self):
- """Finds actual-order of the nodes with respect to maximum number of nodes in a rank in component
+
+ return last
+
+
+ def process_order(self):
+ """Finds actual-order of the nodes with respect to maximum number of nodes in a rank in component
"""
-
+
if self.Is_Cyclic:
max_level = max(map(lambda x: len(x), self.levels.values()))
-
+
if max_level%2:
self.result[self.start]['y'] = (max_level+1)/2 + self.max_order + (self.max_order and 1)
else:
self.result[self.start]['y'] = (max_level)/2 + self.max_order + (self.max_order and 1)
-
+
self.graph_order()
-
- else:
- self.result[self.start]['y'] = 0
+
+ else:
+ self.result[self.start]['y'] = 0
self.tree_order(self.start, 0)
min_order = math.fabs(min(map(lambda x: x['y'], self.result.values())))
-
- index = self.start_nodes.index(self.start)
+
+ index = self.start_nodes.index(self.start)
same = False
-
+
roots = []
- if index>0:
+ if index>0:
for start in self.start_nodes[:index]:
- same = True
+ same = True
for edge in self.tree_list[start][1:]:
if self.tree_list[self.start].__contains__(edge):
continue
break
if same:
roots.append(start)
-
- if roots:
- min_order += self.max_order
+
+ if roots:
+ min_order += self.max_order
else:
min_order += self.max_order + 1
-
+
for level in self.levels:
for node in self.levels[level]:
self.result[node]['y'] += min_order
-
+
if roots:
roots.append(self.start)
one_level_el = self.tree_list[self.start][0][1]
base = self.result[one_level_el]['y']# * 2 / (index + 2)
-
-
+
+
no = len(roots)
first_half = roots[:no/2]
-
+
if no%2==0:
last_half = roots[no/2:]
else:
last_half = roots[no/2+1:]
-
+
factor = -math.floor(no/2)
for start in first_half:
- self.result[start]['y'] = base + factor
+ self.result[start]['y'] = base + factor
factor += 1
-
+
if no%2:
self.result[roots[no/2]]['y'] = base + factor
factor +=1
-
+
for start in last_half:
- self.result[start]['y'] = base + factor
+ self.result[start]['y'] = base + factor
factor += 1
-
+
self.max_order = max(map(lambda x: x['y'], self.result.values()))
-
- def find_starts(self):
+
+ def find_starts(self):
"""Finds other start nodes of the graph in the case when graph is disconneted
"""
rem_nodes = []
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 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.levels = {}
self.critical_edges = []
self.partial_order = {}
self.links = []
self.Is_Cyclic = False
-
+
self.tree_list[self.start] = self.make_acyclic(None, self.start, 0, [])
- self.Is_Cyclic = self.rev_edges(self.tree_list[self.start])
+ self.Is_Cyclic = self.rev_edges(self.tree_list[self.start])
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()
-
+ e = self.leave_edge()
+
while e :
f = self.enter_edge(e)
if e==f:
self.critical_edges.append(e)
else:
- self.exchange(e,f)
+ 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()
+ self.normalize()
for edge in self.edge_wt:
self.edge_wt[edge] = self.result[edge[1]]['x'] - self.result[edge[0]]['x']
-
+
def order_in_rank(self):
"""Finds optimized order of the nodes within their ranks using median heuristic
-
- @param start: starting node of the component
+
+ @param start: starting node of the component
"""
-
+
self.make_chain()
self.preprocess_order()
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]['x'])
-
+
for level in self.levels:
self.levels[level].sort(lambda x, y: cmp(self.result[x]['y'], self.result[y]['y']))
-
- self.order_heuristic()
+
+ 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
+
+ @param starting_node: node from where to start the graph search
"""
-
+
self.start_nodes = starting_node or []
- self.partial_order = {}
- self.links = []
- self.tree_list = {}
-
+ self.partial_order = {}
+ self.links = []
+ self.tree_list = {}
+
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 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
+ 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:
+ 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
-
+ self.order_in_rank() #Second step: ordering nodes within ranks
+
def __str__(self):
result = ''
for l in self.levels:
result += 'PosY: ' + str(l) + '\n'
for node in self.levels[l]:
- result += '\tPosX: '+ str(self.result[node]['y']) + ' - Node:' + node + "\n"
+ result += '\tPosX: '+ str(self.result[node]['y']) + ' - Node:' + str(node) + "\n"
return result
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]['x'] - self.result[src]['x'] == 0):
+ if (self.result[des]['x'] - self.result[src]['x'] == 0):
self.result[src]['x'] += 0.08
self.result[des]['x'] -= 0.08
-
+
factorX = maxx + nheight
- factorY = maxy + nwidth
-
+ factorY = maxy + nwidth
+
for node in self.result:
self.result[node]['y'] = (self.result[node]['y']) * factorX + margin
self.result[node]['x'] = (self.result[node]['x']) * factorY + margin
-
+
def result_get(self):
return self.result
img = Image.new("RGB", (800, 600), "#ffffff")
draw = ImageDraw.Draw(img)
- for name,node in g.result.items():
- draw.arc( (int(node['y']-radius), int(node['x']-radius),int(node['y']+radius), int(node['x']+radius) ), 0, 360, (128,128,128))
- draw.text( (int(node['y']), int(node['x'])), name, (128,128,128))
+ result = g.result_get()
+ node_res = {}
+ for node in nodes:
+ node_res[node] = result[node]
+ for name,node in node_res.items():
+
+ draw.arc( (int(node['y']-radius), int(node['x']-radius),int(node['y']+radius), int(node['x']+radius) ), 0, 360, (128,128,128))
+ draw.text( (int(node['y']), int(node['x'])), str(name), (128,128,128))
- for nodefrom in g.transitions:
- for nodeto in g.transitions[nodefrom]:
- draw.line( (int(g.result[nodefrom]['y']), int(g.result[nodefrom]['x']),int(g.result[nodeto]['y']),int(g.result[nodeto]['x'])),(128,128,128) )
+ for t in transitions:
+ draw.line( (int(node_res[t[0]]['y']), int(node_res[t[0]]['x']),int(node_res[t[1]]['y']),int(node_res[t[1]]['x'])),(128,128,128) )
img.save("graph.png", "PNG")
projects / odoo / odoo.git / blobdiff