def plot_error_var():
input = "../../src/results/"
export = "../../export/testfile"
configs = ["ErrorDependentExponent", "RegularExponent", "InverseExponent"]
markers = {"ErrorDependentExponent": 's', "RegularExponent": '^', "InverseExponent": 'o'}
colors = {"ErrorDependentExponent": 'r', "RegularExponent": 'b', "InverseExponent": 'g'}
metric = "errVar"
plt.figure()
ax = plt.gca()
for i, config in enumerate(configs):
# see all the result files
files = [f for f in listdir(input) if (isfile(join(input, f)) and (config in f) and ('.sca' in f))]
print(files)
files.sort(key=lambda x: int(re.findall('-(.+?).sca', x)[0]))
print(files)
# --- number of subsystems --- #
n_min = 10
n_max = 42
step = 4
n_s = [n_min + step * x for x in range(1, int((n_max - n_min) / step + 1))]
df = pd.DataFrame()
for f in files:
df = df.append(parse_sca(filename=input+f))
print(df)
means = df.groupby('num_subsystems').mean()
print(means)
cis = df.groupby('num_subsystems').apply(lambda x: getCI(x, confidence=0.95))
print(cis)
# means.plot(kind="line", yerr=cis.value, ax=ax, label=config, color=colors[config], marker=markers[config])
means.plot(kind="line", ax=ax, label=config, color=colors[config], marker=markers[config])
plt.grid(True)
legend = plt.legend(loc=0)
for i, config in enumerate(configs):
legend.get_texts()[i].set_text(config)
# plt.yscale('log')
plt.show()
return
for i_s in n_s:
# every number of subsystems
thr = []
acc = []
col = []
for i_rep in range(n_rep):
# every replication
thr.append(float(lines[i]))
acc.append(float(lines1[i]))
col.append(float(lines2[i]))
i += 1
throughput_box.append(list(thr))
throughput.append(np.mean(thr))
throughput_ci.append(ci.getCI(thr))
access.append(np.mean(acc))
access_box.append(list(acc))
access_ci.append(ci.getCI(acc))
collisions.append(np.mean(col))
collisions_box.append(list(col))
collisions_ci.append(ci.getCI(col))
# --- cleanup --- #
f.close()
f1.close()
f2.close()
# remove_simdata()
for lmb_v in lmb:
# every lambda value
var = []
for i_rep in range(n_rep):
# every replication
var_temp = []
for s in range(i_s):
# every subsystem in the replication
var_temp.append(float(lines[i]))
i += 1
var.append(np.mean(var_temp))
results[n_s.index(i_s)].append(np.mean(var))
results_box[n_s.index(i_s)].append(list(var))
results_ci[n_s.index(i_s)].append(ci.getCI(var))
f.close()
# remove_simdata()
# --- plotting --- #
fig, ax = plt.subplots(figsize=(8.5, 5))
p = []
market_style = ['^', 'o', 's', 'x']
for i_s in n_s:
if i_s == 20:
p.append(ax.plot(lmb[13:], results[n_s.index(i_s)][13:], market_style[n_s.index(i_s)]+'-'))
var_nab_temp = []
for s in range(i_s):
# every subsystem in the replication
var_temp.append(float(lines[i]))
var_na_temp.append(float(lines1[i]))
var_nab_temp.append(float(lines2[i]))
i += 1
var.append(np.mean(var_temp))
var_na.append(np.mean(var_na_temp))
var_nab.append(np.mean(var_nab_temp))
# if i_s==60:
# p1 = ax.plot([x for x in range(20)], var, 'bs-')
variance_box.append(list(var))
variance_mean.append(np.mean(var))
variance_ci.append(ci.getCI(var))
variance_na_box.append(list(var_na))
variance_na_mean.append(np.mean(var_na))
variance_na_ci.append(ci.getCI(var_na))
variance_nab_box.append(list(var_nab))
variance_nab_mean.append(np.mean(var_nab))
variance_nab_ci.append(ci.getCI(var_nab))
# --- cleanup --- #
f.close()
f1.close()
f2.close()
remove_simdata()
