def graph(task):
lims = len(task) != 1
if lims:
fname = task[2]
else:
fname = task[0]
axis_name = fname
try:
fname_index = axis_name.rindex('\\') + 1
axis_name = axis_name[fname_index:]
except ValueError:
pass
try:
fname_index = axis_name.rindex('/') + 1
axis_name = axis_name[fname_index:]
except ValueError:
pass
try:
dot_index =axis_name.rindex('.')
axis_name = axis_name[:dot_index]
except ValueError:
pass
matrix_act = fs.load_matrix(fname)
if matrix_act is not None:
act = matrix_act[0]
result_name = "output/analyzer/graph_" + axis_name
plt.figure(figsize=(max(8, len(act)/50), 6))
plt.xlabel("time")
plt.ylabel(axis_name)
if lims:
plt.ylim(task[:2])
plt.plot(act)
plt.savefig(result_name + ".png")
plt.close()
stat = calculate_statistics_sequence(act)
output = "m = {0}\nD = {1}\na = {2}\nk = {3}".format(stat[0], stat[1], stat[2], stat[3])
fs.write_file(result_name + ".txt", output)
else:
print("cannot read file '" + fname + "'", flush=True)
def parameter(task):
id = task[0]
mn = task[1]
mx = task[2]
if len(task) == 4:
st = task[3]
else:
st = 1
val = mn
pool = [mn]
while mx - mn - st > EVALUATIONS_EPSILON:
mn += st
pool.append(mn)
pool.append(mx)
fname = "analyzer_settings.txt"
neurs = int(fs.load_parameter(fname, "NEURONS", 100))
elects = int(fs.load_parameter(fname, "ELECTRODES", 30))
cons = int(fs.load_parameter(fname, "CONNECTIONS", 2000))
elecons = int(fs.load_parameter(fname, "ELECONS", 150))
noize = int(fs.load_parameter(fname, "NOIZEVAL", 5))
testtime = int(fs.load_parameter(fname, "TESTTIME", 500))
tests = int(fs.load_parameter(fname, "TESTS", 10))
wind_size = int(fs.load_parameter(fname, "WINDOWSIZE", 7))
pstart = int(fs.load_parameter(fname, "PACKSTART", 79))
pend = int(fs.load_parameter(fname, "PACKEND", 21))
params = (wind_size, pstart, pend)
data = []
pool_burst_len = []
pool_noburst_len = []
pool_burst_percent = []
class Callback:
@staticmethod
def draw_info(info):
data.append(info)
for i in range(len(pool)):
sbl = 0
snbl = 0
sbp = 0
for j in range(tests):
data = []
callback = Callback()
ntw = Network(callback)
ntw.add_neurons(neurs)
ntw.add_electrodes(elects)
ntw.add_connections(True, cons, 0, neurs - 1, 0, neurs - 1)
ntw.add_connections(False, elecons, 0, cons - 1, 0, elects - 1)
ntw.setNoize(True, noize)
ntw.set_parameter(id, pool[i])
for k in range(testtime):
ntw.step()
bl, nbl, bp = analyze_activity(data, params)
sbl += bl
snbl +=nbl
sbp += bp
sbl /= tests
snbl /= tests
sbp /= tests
pool_burst_len.append(sbl)
pool_noburst_len.append(snbl)
pool_burst_percent.append(sbp)
lims = [0, max(max(pool_burst_len), max(pool_noburst_len))]
plt.xlabel("parameter " + str(id))
plt.ylabel("burst period length")
plt.ylim(lims)
plt.plot(pool, pool_burst_len)
plt.savefig("output/analyzer/id " + str(id) + " burst length" + ".png")
plt.close()
plt.xlabel("parameter " + str(id))
plt.ylabel("interburst period length")
plt.ylim(lims)
plt.plot(pool, pool_noburst_len)
plt.savefig("output/analyzer/id " + str(id) + " interburst length" + ".png")
plt.close()
plt.xlabel("parameter " + str(id))
plt.ylabel("burst percent")
plt.ylim([0, 1])
plt.plot(pool, pool_burst_percent)
plt.savefig("output/analyzer/id " + str(id) + " burst percent" + ".png")
plt.close()
output = "id {0}\nvalues: {1}\nburst length: {2}\ninterburst length: {3}\nburst percent {4}"\
.format(id, pool, pool_burst_len, pool_noburst_len, pool_burst_percent)
fs.write_file("output/analyzer/id " + str(id) + " testing results" + ".txt", output)
