def run_parsimony_algorithms(current_tree, nodelist):
global START_TIME
global CURRENT_TIME
CURRENT_TIME = datetime.datetime.now().replace(microsecond=0)
print(
colored("---------------- Fitch1 parsimony ----------------", "green"))
fitch_MP_tree1 = deepcopy(current_tree)
fitch_MP_nodelist1 = deepcopy(nodelist)
fitch_parsimony(fitch_MP_tree1.clade, fitch_MP_nodelist1, 1)
CURRENT_TIME = Helpers.print_time(CURRENT_TIME)
print(
colored("---------------- Fitch2 parsimony ----------------", "green"))
fitch_MP_tree2 = deepcopy(current_tree)
fitch_MP_nodelist2 = deepcopy(nodelist)
fitch_parsimony(fitch_MP_tree2.clade, fitch_MP_nodelist2, 2)
CURRENT_TIME = Helpers.print_time(CURRENT_TIME)
print(
colored("---------------- Fitch3 parsimony ----------------", "green"))
fitch_MP_tree3 = deepcopy(current_tree)
fitch_MP_nodelist3 = deepcopy(nodelist)
fitch_parsimony(fitch_MP_tree3.clade, fitch_MP_nodelist3, 3)
CURRENT_TIME = Helpers.print_time(CURRENT_TIME)
print(
colored("---------------- Fitch4 parsimony ----------------", "green"))
fitch_MP_tree4 = deepcopy(current_tree)
fitch_MP_nodelist4 = deepcopy(nodelist)
fitch_parsimony(fitch_MP_tree4.clade, fitch_MP_nodelist4, 4)
CURRENT_TIME = Helpers.print_time(CURRENT_TIME)
# --------------------------------------------------------
print(colored("-------- evaluation --------", "green"))
differences = evaluation(nodelist, fitch_MP_nodelist1, fitch_MP_nodelist2,
fitch_MP_nodelist3, fitch_MP_nodelist4)
CURRENT_TIME = Helpers.print_time(CURRENT_TIME)
print(colored("--------------------------------", "green"))
return differences
def run_parsimony_algorithms(current_tree, nodelist):
global START_TIME
global CURRENT_TIME
CURRENT_TIME = datetime.datetime.now().replace(microsecond=0)
print(colored("---------------- Fitch parsimony ----------------",
"green"))
fitch_MP_tree = deepcopy(current_tree)
fitch_MP_nodelist = deepcopy(nodelist)
fitch_parsimony(fitch_MP_tree.clade, fitch_MP_nodelist)
CURRENT_TIME = Helpers.print_time(CURRENT_TIME)
print(colored("---------------- my parsimony ----------------", "green"))
my_MP_tree = deepcopy(current_tree)
my_MP_nodelist = deepcopy(nodelist)
my_parsimony(my_MP_tree.clade, my_MP_nodelist)
CURRENT_TIME = Helpers.print_time(CURRENT_TIME)
print(
colored("---------------- Sankoff parsimony ----------------",
"green"))
sankoff_MP_tree = deepcopy(current_tree)
sankoff_MP_nodelist = deepcopy(nodelist)
sankoff_parsimony(sankoff_MP_tree, sankoff_MP_nodelist)
CURRENT_TIME = Helpers.print_time(CURRENT_TIME)
# --------------------------------------------------------
print(colored("-------- evaluation --------", "green"))
differences = evaluation(nodelist, fitch_MP_nodelist, my_MP_nodelist,
sankoff_MP_nodelist)
CURRENT_TIME = Helpers.print_time(CURRENT_TIME)
print(colored("--------------------------------", "green"))
return differences
def get_random_tagged_tree(number_leafnodes, percentage_parasites,
percentage_unknown, beta_distribution_parameters):
"""build a random binary tree fully tagged with FL and P"""
# Arguments:
# number_leafnodes - needed for randomized function
# percentage_unknown - proportion of unknown leafnodes
# percentage_parasites
# beta_distribution_parameters - [A_FL, B_FL, A_P, B_P]
START_TIME = datetime.datetime.now().replace(microsecond=0)
CURRENT_TIME = datetime.datetime.now().replace(microsecond=0)
print("---- randomized tree ----")
current_percentage_parasites = 0
# randomized(cls, taxa, branch_length=1.0, branch_stdev=None)
# Create a randomized bifurcating tree given a list of taxa.
# https://github.com/biopython/biopython/blob/master/Bio/Phylo/BaseTree.py
randomized_tree = Phylo.BaseTree.Tree.randomized(number_leafnodes)
randomized_tree.clade.name = 'root'
boolean = True
CURRENT_TIME = Helpers.print_time(START_TIME)
print("---- tag tree ----")
while boolean:
current_tree = deepcopy(randomized_tree)
result = tag_tree(
current_tree.clade, [], 0, [0, 0], percentage_parasites,
percentage_unknown,
beta_distribution_parameters) # father_tag = 0 -> free living
nodelist = result[1]
leaf_distr = result[2]
# child_depth = child_depth + result[3]
# %P = #FL / (#P + #FL) * 100
current_percentage_parasites = leaf_distr[1] / (leaf_distr[0] +
leaf_distr[1])
print("tried", current_percentage_parasites * 100,
"% of parasites") # 40% parasites?
if (percentage_parasites -
permitted_deviation) < current_percentage_parasites < (
percentage_parasites + permitted_deviation):
boolean = False
print("----")
CURRENT_TIME = Helpers.print_time(CURRENT_TIME)
print("----")
# print(current_percentage_parasites, '% parasites,', 100 - current_percentage_parasites, '% free-living')
return [current_tree, nodelist]
def main():
"""Main method"""
global START_TIME
global CURRENT_TIME
print(
colored(
"------------------------ start simulation ------------------------",
"green"))
print(strftime("%Y-%m-%d %H:%M:%S", gmtime()))
CURRENT_TIME = Helpers.print_time(START_TIME)
print(colored("---------------- metadata ----------------", "green"))
metadata()
print(colored("---------------- parameters ----------------", "green"))
print("Simulate", colored(number_trees, 'blue'), "random trees with",
colored(number_leafnodes, 'blue'), "leafnodes",
colored(percentage_parasites * 100, 'blue'), "% parasites and",
colored(percentage_unknown * 100, 'blue'), "% unknown leafnodes.")
diffs = [["Fitch1", "Fitch2", "Fitch3", "Fitch4"]]
for i in range(1, number_trees + 1):
print("Tree", colored(i, 'red'))
print(
colored("---------------- get random tree ----------------",
"green"))
result = buildTree.get_random_tagged_tree(
number_leafnodes, percentage_parasites, percentage_unknown,
beta_distribution_parameters)
current_tree = result[0]
nodelist = result[1]
# CURRENT_TIME = Helpers.print_time(CURRENT_TIME)
print(
colored("---------------- multifurcate tree ----------------",
"green"))
buildTree.get_non_binary_tree(current_tree.clade, nodelist)
CURRENT_TIME = Helpers.print_time(CURRENT_TIME)
print(
colored(
"---------------- maximum parsimony algorithms ----------------",
"green"))
diff_percentage = run_parsimony_algorithms(current_tree, nodelist)
diffs.append(diff_percentage)
time_new = datetime.datetime.now().replace(microsecond=0)
print(strftime("%Y-%m-%d %H:%M:%S", gmtime()))
print("whole time needed:", time_new - START_TIME)
print(colored("--------------------------------", "red"))
f_dif1 = 0.0
f_dif2 = 0.0
f_dif3 = 0.0
f_dif4 = 0.0
for i in range(1, number_trees + 1):
f_dif1 += float(diffs[i][0])
f_dif2 += float(diffs[i][1])
f_dif3 += float(diffs[i][2])
f_dif4 += float(diffs[i][3])
f_dif1 = round(f_dif1 / number_trees, 2)
f_dif2 = round(f_dif2 / number_trees, 2)
f_dif3 = round(f_dif3 / number_trees, 2)
f_dif4 = round(f_dif4 / number_trees, 2)
row = [percentage_unknown, f_dif1, f_dif2, f_dif3, f_dif4]
csv_title = "evaluation/" + str(int(
percentage_parasites * 100)) + "-fitch-unknown_plot.csv"
fp = open(csv_title, 'a')
writer = csv.writer(fp)
writer.writerow((row))
fp.close()
print("saved in:")
print(csv_title)
print(colored("--------------------------------", "green"))
print(colored(number_trees, 'blue'), " trees simulated with",
colored(number_leafnodes, 'blue'), "leafnodes",
colored(percentage_parasites * 100, 'blue'), "% parasites and",
colored(percentage_unknown * 100, 'blue'), "% unknown leafnodes.")
print("correctly predicted (including already known leaf nodes):")
print("differences Fitch1 / Fitch2 / Fitch3 / Fitch4")
percentage_correctly_predicted = "| " + str(f_dif1) + " % | " + str(
f_dif2) + " % | " + str(f_dif3) + " % |" + str(f_dif4) + " % |"
print(colored(percentage_correctly_predicted, 'red'))
print(colored("--------------------------------", "green"))
return
def main():
"""Main method"""
global START_TIME
global CURRENT_TIME
print(
colored(
"------------------------ start simulation ------------------------",
"green"))
print(strftime("%Y-%m-%d %H:%M:%S", gmtime()))
CURRENT_TIME = Helpers.print_time(START_TIME)
print(colored("---------------- metadata ----------------", "green"))
metadata()
print(colored("---------------- parameters ----------------", "green"))
print("Simulate", colored(number_trees, 'blue'), "random trees with",
colored(number_leafnodes, 'blue'), "leafnodes",
colored(percentage_parasites * 100, 'blue'), "% parasites and",
colored(percentage_unknown * 100, 'blue'), "% unknown leafnodes.")
diffs = [["Fitch", "My", "Sankoff"]]
for i in range(1, number_trees + 1):
print("Tree", colored(i, 'red'))
print(
colored("---------------- get random tree ----------------",
"green"))
result = buildTree.get_random_tagged_tree(
number_leafnodes, percentage_parasites, percentage_unknown,
beta_distribution_parameters)
current_tree = result[0]
nodelist = result[1]
# CURRENT_TIME = Helpers.print_time(CURRENT_TIME)
print(
colored("---------------- multifurcate tree ----------------",
"green"))
buildTree.get_non_binary_tree(current_tree.clade, nodelist)
CURRENT_TIME = Helpers.print_time(CURRENT_TIME)
print(
colored(
"---------------- maximum parsimony algorithms ----------------",
"green"))
diff_percentage = run_parsimony_algorithms(current_tree, nodelist)
diffs.append(diff_percentage)
# ---------------- drawings ----------------
# do_some_drawings(current_tree, nodelist, parsimony_tree, parsimony_nodelist)
time_new = datetime.datetime.now().replace(microsecond=0)
print(strftime("%Y-%m-%d %H:%M:%S", gmtime()))
print("whole time needed:", time_new - START_TIME)
print(colored("--------------------------------", "red"))
# print("saved in:")
# csv_title = "evaluation/" + str(number_leafnodes) + " leafnodes - " + str(number_trees) + " trees - " + str(round(percentage_U * 100, 2)) + "% unknown.csv"
# print(csv_title)
# with open(csv_title, 'w', newline='') as csvfile:
# writer = csv.writer(csvfile)
# writer.writerows(diffs)
f_dif = 0.0
m_dif = 0.0
s_dif = 0.0
for i in range(1, number_trees + 1):
f_dif += float(diffs[i][0])
m_dif += float(diffs[i][1])
s_dif += float(diffs[i][2])
f_dif = round(f_dif / number_trees, 2)
m_dif = round(m_dif / number_trees, 2)
s_dif = round(s_dif / number_trees, 2)
row = [percentage_unknown, f_dif, m_dif, s_dif]
csv_title = "evaluation/" + str(int(
percentage_parasites * 100)) + "-unknown_plot.csv"
fp = open(csv_title, 'a')
writer = csv.writer(fp)
writer.writerow((row))
fp.close()
print("saved in:")
print(csv_title)
print(colored("--------------------------------", "green"))
print(colored(number_trees, 'blue'), " trees simulated with",
colored(number_leafnodes, 'blue'), "leafnodes",
colored(percentage_parasites * 100, 'blue'), "% parasites and",
colored(percentage_unknown * 100, 'blue'), "% unknown leafnodes.")
print("correctly predicted (including already known leaf nodes):")
print("differences Fitch / My / Sankoff")
percentage_correctly_predicted = "| " + str(f_dif) + " % | " + str(
m_dif) + " % | " + str(s_dif) + " % |"
print(colored(percentage_correctly_predicted, 'red'))
print(colored("--------------------------------", "green"))
return