def setup_func():
global T, tv, nb_trees, tree_list, mtg_name, g
# build some random initial tree
stat_tool.plot.DISABLE_PLOT = True
inf_bound = 0
sup_bound = 3
distrib = stat_tool.Uniform(inf_bound, sup_bound)
max_depth = 3
max_size = 10
nbtrees = 40
# define a set of trees
tree_list = []
tv = [1., 0, 1, 2.] # trees.TreeValue([1., 0])
R = trees.TreeStructure(distrib, max_size, max_depth)
tmp_tree = trees.Tree(tv, R)
n = 1
tree_list.append(trees.Tree(tmp_tree))
while n < nbtrees:
n = n + 1
R.Simulate(distrib, max_size, max_depth)
tmp_tree = trees.Tree(tv, R)
tree_list.append(trees.Tree(tmp_tree))
distrib_list = []
for i in range(tmp_tree.NbInt()):
distrib_list.append(distrib)
for n in range(len(tree_list)):
tree_list[n].Simulate(distrib_list)
T = trees.Trees(tree_list)
nb_trees = nbtrees
mtg_name = "data/sample_mtg_forest.mtg"
g = build_mtg()
return T, tv, nb_trees, tree_list, mtg_name, g
def init():
"""Defines some constants"""
global distrib, max_depth, max_size, nbtrees, n
stat_tool.plot.DISABLE_PLOT = True
inf_bound = 1
sup_bound = 3
distrib = stat_tool.Uniform(inf_bound, sup_bound)
nbtrees = 40
max_depth = 3
max_size = 20
n = 5
# -*- coding: utf-8 -*- # comparison of the outputs for the Sequences and Trees classes import sys, os import openalea.stat_tool as stat_tool import openalea.tree_statistic.trees as trees import openalea.tree_statistic.hmt as hmt import openalea.sequence_analysis as aml nb_children=1 size=100 distrib = stat_tool.Uniform(1, 1) # name of hmotrefpath= "./hmot_np.hmt"; hmotinitpath= "./hmot_np_init.hmt"; hmcinitpath= "./hmc_init.hmc"; HMT=hmt.HiddenMarkovIndOutTree(hmotrefpath) HMTI=hmt.HiddenMarkovIndOutTree(hmotinitpath) HMCI=aml.HiddenSemiMarkov(hmcinitpath) T=HMT.Simulate(2, size, nb_children) # delete state variable T=T.SelectVariable(1, "Keep") # build sequence from T S=T.BuildPySequences() # S = S.markovian_sequences() # Print Sequences object print S # Print Trees object print T # Display sequence object print(S.display())
class MyThread(QThread):
def __init__(self):
QThread.__init__(self)
def doit(self, T):
app = QApplication([])
T.Plot()
#self.start()
#app.exec_()
stat_tools.plot.DISABLE_PLOT = True
inf_bound=0
sup_bound=3
distrib = stat_tools.Uniform(inf_bound, sup_bound)
print "Distribution used for the number of children and the tree attributes:"
print distrib
max_depth=3
max_size=10
nbtrees=40
# define a set of trees
tree_list=[]
tv=[1., 0, 1, 2.] # trees.TreeValue([1., 0])
R=trees.TreeStructure(distrib, max_size, max_depth)
tmp_tree=trees.Tree(tv, R)
n=1
tree_list.append(trees.Tree(tmp_tree))
while n < nbtrees:
n=n+1
R.Simulate(distrib, max_size, max_depth)
# -*- coding: utf-8 -*-
# a test for the class trees.Trees: tree manipulation (merge, cluster, etc.)
import sys, os
import openalea.stat_tool as stat_tool
import openalea.tree_statistic.trees as trees
inf_bound = 1
sup_bound = 3
probability = 0.6
children_distrib = stat_tool.Uniform(inf_bound, sup_bound)
# distribution of the children
attributes_distrib = stat_tool.Uniform(0, 10)
# distribution of the attributes
max_depth = 3
max_size = 10
nbtrees = 2
# defining a set of trees
tree_list = []
# simulation of the structure
R = trees.TreeStructure(children_distrib, max_size, max_depth)
tmp_tree = trees.Tree([1., 0], R)
n = 1
tree_list.append(trees.Tree(tmp_tree))
while n < nbtrees:
n = n+1
R.Simulate(children_distrib, max_size, max_depth)
tmp_tree = trees.Tree([1., 0], R)
tree_list.append(trees.Tree(tmp_tree))
distrib_list = []
# simulation of the labels
for i in range(tmp_tree.NbInt()):