def fn(p):
all_params.update(zip(optim_params, p))
try:
p_stats = simulate(all_params, n=1000)
return stats_error(p_stats, old_stats)
except InvalidModelParam:
return np.inf
def breed(population, fitness_scores):
newPopulation = []
newFitnessScores = []
remaining = C.PopulationSize
while remaining > 0:
if C.Replacement:
p = select_pair(population, fitness_scores, C.PopulationSize)
else:
p, population = select_pair(population, fitness_scores, remaining)
p1 = p[0]
p2 = p[1]
r = random.randrange(1, C.statespace)
c1 = p1[:r] + p2[r:]
c2 = p2[:r] + p1[r:]
if random.random() < C.MutationPct:
g1 = random.randrange(C.statespace)
g2 = random.randrange(C.statespace)
c1[g1], c1[g2] = c1[g2], c1[g1]
if random.random() < C.MutationPct:
g1 = random.randrange(C.statespace)
g2 = random.randrange(C.statespace)
c2[g1], c2[g2] = c2[g2], c2[g1]
if C.Elitism:
p1score = fitness_scores[population.index(p[0])]
p2score = fitness_scores[population.index(p[1])]
c1score = main.simulate(main.newGameContainer(), c1)
c2score = main.simulate(main.newGameContainer(), c2)
contenderscores = [p1score, p2score, c1score, c2score]
contenders = [p1, p2, c1, c2]
m1 = contenderscores.index(max(contenderscores))
newFitnessScores.append(contenderscores[m1])
newPopulation.append(contenders.pop(m1))
del contenderscores[m1]
m2 = contenderscores.index(max(contenderscores))
newFitnessScores.append(contenderscores[m2])
newPopulation.append(contenders.pop(m2))
else:
newPopulation.append(c1)
newPopulation.append(c2)
remaining -= 2
print("remaining: ", remaining)
return newPopulation, newFitnessScores
def main(argv):
try:
opts, args = getopt.getopt(argv,"ht:a:s:p:",['type=', 'archi=', 'sim=', 'proc=']) # h for help
except getopt.GetoptError:
print('Usage : ')
print('main.py -t <type> -a <path_to_cfg_architecture> -s <path_to_cfg_simulation> -p <path_to_cfg_processing>')
sys.exit(2)
if(len(opts)==0):
print('Usage : ')
print('main.py -t <type> -a <path_to_cfg_architecture> -s <path_to_cfg_simulation> -p <path_to_cfg_processing>')
sys.exit()
if(opts[0][0] == '-h'):
print('Usage : ')
print('main.py -t <type> -a <path_to_cfg_architecture> -s <path_to_cfg_simulation> -p <path_to_cfg_processing>')
sys.exit()
elif opts[0][0] in ('-t','--type'):
# resolve allows to go in absolute path (and note relative anymore)
mod_path = Path(__file__).resolve().parents[0]
#print(opts)
if(opts[1][0] in ('-a','--archi') and \
opts[2][0] in ('-s','--sim') and \
opts[3][0] in ('-p','--proc')):
system = (mod_path/Path(opts[1][1])).resolve()
simu = (mod_path/Path(opts[2][1])).resolve()
proc = (mod_path/Path(opts[3][1])).resolve()
#print(system, simu, proc)
if(opts[0][1] == 'simulation'):
simulate(system, simu, proc)
elif(opts[0][1] == 'processing'):
processing_only(system, simu, proc)
else :
print('Please precise path to cfg files.')
print('Usage : ')
print('main.py -t <type> -a <path_to_cfg_architecture> -s <path_to_cfg_simulation> -p <path_to_cfg_processing>')
sys.exit()
else :
pass
def classify(castles,
trials=10000,
threshold=.95,
name="None",
use_print=False):
prob_distr, points_won, opponent_points, wins_by_three, opponents_win_by_three = m.simulate(
trials, c1=castles, print_result=False, print_castles=False)
print("Strategy Type" + "[" + name + "]" + ": " + str(wins_by_three))
if wins_by_three >= threshold and use_print == True:
print("Aggresive")
else:
if use_print == True:
print("Conservative")
return wins_by_three
def generate():
# obtain variables passed from html page
lat = float(request.form['lat'])
long = float(request.form['long'])
rate = float(request.form['rate'])
result = ast.literal_eval(request.form['affectedCities'])
explored = ast.literal_eval(request.form['savedLocs'])
# use inputted coordinates if this is the first execution
if result == []:
result = [(lat, long)]
# determine affected cities/routes in the next cycle
result, explored = main.simulate(result, rate, explored)
# separate affected routes and cities (for visualization)
cities_road = [list(city) for city in result if len(city) == 4]
cities_air = [list(city) for city in result if len(city) == 3]
# calculate affected population
population = sum([x[2] for x in cities_road])
return render_template('layout_map.html', **locals())
data_a = []
data_e0 = []
data_e1 = []
data_e2 = []
data_e3 = []
data_e4 = []
data_e5 = []
bx_s = [(float(i)/10000000000.0) for i in range(1000000500,1000005000)]
for bx in bx_s:
i += 1
# dx = L_so/L
if i%10 == 0:
print(i)
es = simulate(L=L, dx=dx, t=t, mu=mu, Bx=0.0, By=bx, delta=delta, alpha=alpha)
# min(e, key=func)
# print(e)
# print(min(map(lambda x: abs(x), e)))
# print("%9.f" %(min(e)))
# data.append((min(map(lambda x: abs(x), e)), alpha, delta))
# data_x.append(bx/E_so)
data_x.append(bx)
data_a.append(alpha)
# data_y.append(by)
# e = list(map(lambda x: abs(x), es))
# e.sort()
# eigen = min(map(lambda x: abs(x), es))
# print(len(es))
# data_e.append(eigen)
def test_int_to_hex(self):
X_14 = main.simulate(10, 100)
X_14_bind = X_14[0]
X_14_conc = X_14[1]
self.assertEqual(True, abs(X_14_bind - 0.0047378863875207665) < 0.01)
self.assertEqual(True, abs(X_14_conc - 0) < 0.00001)
import main
# Run main method
main.simulate("genomeSample.fa", 100, 4, 7, 10)
# Tests
# Test if sample genome is correctly extracted from FASTA file
valid_genome_file = open('genome.txt', 'r')
valid_genome = [line.rstrip('\n') for line in valid_genome_file.readlines()]
i = 0
#for sequence_name, sequence in main.genome.items():
#assert sequence == valid_genome[i]
#i += 1
# Test reverse read
read = "ACGTTTANAGAC"
reversed_read = main.reverse_read(read)
assert (reversed_read == "CAGANATTTGCA")
# Test complement read
complemented_read = main.complement_read(read)
assert (complemented_read == "TGCAAATNTCTG")
# Test get quality
avg_quality = 15
sigma = 5
read_length = 5
qualities = main.get_quality(avg_quality, sigma, read_length)
print(qualities)
for quality in qualities:
def performance_vs_aggressiveness(trials, castle):
for x in range(trials):
opponent = m.select_random_castles()
opponent_aggressiveness = cst.classify(opponent, 250, .95,
"Opponent " + str(x))
prob, castle_avg_score, opponent_avg_score, castle_wins_by_three, opponent_wins_by_three = m.simulate(
200, castle, c2=opponent, print_result=False, print_castles=False)
arr_prob.append(prob)
arr_castle_avg_score.append(castle_avg_score)
arr_opponent_avg_score.append(opponent_avg_score)
arr_castle_wins_by_three.append(castle_wins_by_three)
arr_opponent_wins_by_three.append(opponent_wins_by_three)
arr_opponent_aggressiveness.append(opponent_aggressiveness)
my_file.append(arr_prob)
my_file.append(arr_castle_avg_score)
#my_file.append(arr_opponent)
my_file.append(arr_opponent_avg_score)
my_file.append(arr_castle_wins_by_three)
my_file.append(arr_opponent_wins_by_three)
my_file.append(arr_opponent_aggressiveness)
return my_file
import os
import main
print("Testing started...")
# Run simulate method with simple sequence sample
main.simulate("genomeSample.fa", 60, 10, 4, 7)
# Tests
# Test if sample sequence is correctly extracted from FASTA file as one string
# Valid genome sequence should be in genomeSample.txt in one line
valid_genome_file = open('genomeSample.txt', 'r')
valid_genome = {}
genome_name = ''
lines = valid_genome_file.read().split()
for line in lines:
if line[0] == '>':
genome_name = line[1::]
valid_genome[genome_name] = ''
else:
valid_genome[genome_name] = line
for sequence_name, sequence in main.genome.items():
assert sequence == valid_genome[sequence_name]
# Sample reads
reads = ["ACCTAG", "ACACGGGTACC", "ACCCAGTTTTACGT", "ACGACG"]
# Test reverse read
reverse_reads = ["GATCCA", "CCATGGGCACA", "TGCATTTTGACCCA", "GCAGCA"]