def plot_all_models(path_to_data, init_guess = 0.000001):
data = dp.read_data(path_to_data)
time_passed = data[-1] - data[-2]
jm_data = data[:]
times_of_falls = dp.convert_time_between_errors_to_time_of_errors(data)[:-1]
time_of_last_error = dp.time_of_last_error(data)
m = Musa(time_passed, times_of_falls, init_guess)
mo = MusaOkumoto(time_passed, times_of_falls, init_guess)
j = JelinskiMoranda(jm_data)
plot.add_errors_plot(data, {'label': 'Real errors'}) # label = 'Real error'
plot.add_func_plot(m._mu, 0, 1*time_of_last_error, 100, {'label': "Musa"}) # style : '--', label = "Musa"
plot.add_func_plot(mo._mu, 0, 1*time_of_last_error, 100, {'label': "Musa-Okumoto"}) # style : '-.', label = "Musa - Okumoto"
plot.add_func_plot(j.func_n, 0, 1*time_of_last_error, 100, {'label': 'Jelinsky-Moranda'}) # style : ':', label = "Jelinski - Moranda"
plot.legend()
# with open("G:/dynamic_models.csv", 'w+') as f:
# f.write('tau\treal_error\tmusa\tmusa_okumoto\tjm\n')
# err = 1
# for i in tau:
# f.write("{0:.2f}\t{1}\t{2:.2f}\t{3:.2f}\t{4:.2f}\n".format(i, err, m._mu(i)[0], mo._mu(i)[0], j.func_n(i)[0]))
# err += 1
plot.xlabel("Time")
plot.ylabel("Number of erros")
plot.grid()
plot.show()
def get_musa_okumoto_data(path_to_data):
data = dp.read_data(path_to_data)
time_passed = data[-1] - data[-2]
errors = dp.convert_time_between_errors_to_time_of_errors(data)
times_of_falls = errors[:-1]
time_of_last_error = times_of_falls[-1]
init_guess = 0.00000001
m = MusaOkumoto(time_passed, times_of_falls, init_guess)
x = [int(i) for i in np.linspace(1, 1.2 * time_of_last_error, 20)]
# Mean time of errors to the time of work
y_mu = [int(m.func_mu(i)) for i in x]
# Failure raite aka Intensivnost otkazov
y_lambda = [round(float(m.func_lambd(i)), 4) for i in x]
# Reliability function aka Funkciya nadezhnosti
prediction_time_of_work = 100
y_r = [round(float(m.func_r(i, prediction_time_of_work)), 4) for i in x]
return {
'mu': {
'x': x,
'y': y_mu
},
'lambda': {
'x': x,
'y': y_lambda
},
'r': {
'x': x,
'y': y_r
},
'errors_time': data
}
def get_jelinski_moranda_data(path_to_data):
data = dp.read_data(path_to_data)
times_of_falls = data[:]
j = JelinskiMoranda(times_of_falls)
time_of_last_error = dp.time_of_last_error(times_of_falls)
x = [int(i) for i in np.linspace(1, 1.2 * time_of_last_error, 20)]
y_n = [round(float(j.func_n(i)), 4) for i in x] # Mean value of errors
# TODO: Test this plots
x_numbers_of_errors = [i for i in range(len(times_of_falls))]
y_mttf = [int(j.func_MTTF(i)) for i in x_numbers_of_errors]
y_lambda = [
round(float(j.func_lambda(i)), 4) for i in x_numbers_of_errors
] # Intensity of errors - Step chart
y_r = [round(float(j.func_R(i)), 4)
for i in x_numbers_of_errors] # Reliability - Step chart
return {
'n': {
'x': x,
'y': y_n
},
'mttf': {
'x': x_numbers_of_errors,
'y': y_mttf
},
'lambda': {
'x': x_numbers_of_errors,
'y': y_lambda
},
'r': {
'x': x_numbers_of_errors,
'y': y_r
},
'errors_time': times_of_falls
}
def plot_all_models(path_to_data, init_guess=0.000001):
data = dp.read_data(path_to_data)
time_passed = data[-1] - data[-2]
jm_data = data[:]
times_of_falls = dp.convert_time_between_errors_to_time_of_errors(
data)[:-1]
time_of_last_error = dp.time_of_last_error(data)
m = Musa(time_passed, times_of_falls, init_guess)
mo = MusaOkumoto(time_passed, times_of_falls, init_guess)
j = JelinskiMoranda(jm_data)
plot.add_errors_plot(data,
{'label': 'Real errors'}) # label = 'Real error'
plot.add_func_plot(m._mu, 0, 1 * time_of_last_error, 100,
{'label': "Musa"}) # style : '--', label = "Musa"
plot.add_func_plot(
mo._mu, 0, 1 * time_of_last_error, 100,
{'label': "Musa-Okumoto"}) # style : '-.', label = "Musa - Okumoto"
plot.add_func_plot(j.func_n, 0, 1 * time_of_last_error, 100,
{'label': 'Jelinsky-Moranda'
}) # style : ':', label = "Jelinski - Moranda"
plot.legend()
# with open("G:/dynamic_models.csv", 'w+') as f:
# f.write('tau\treal_error\tmusa\tmusa_okumoto\tjm\n')
# err = 1
# for i in tau:
# f.write("{0:.2f}\t{1}\t{2:.2f}\t{3:.2f}\t{4:.2f}\n".format(i, err, m._mu(i)[0], mo._mu(i)[0], j.func_n(i)[0]))
# err += 1
plot.xlabel("Time")
plot.ylabel("Number of erros")
plot.grid()
plot.show()
def musa_okumoto_plot(path_to_data):
data = dp.read_data(path_to_data)
time_passed = data[-1] - data[-2]
errors = dp.convert_time_between_errors_to_time_of_errors(data)
times_of_falls = errors[:-1]
init_guess = 0.00000001
m = MusaOkumoto(time_passed, times_of_falls, init_guess)
plot.add_errors_plot(data)
plot.add_func_plot(m._mu, 0, 1.2 * m.t[-1], 1000)
plot.grid()
plot.show()
def jelinski_moranda_plot_errors(path_to_data):
'''Plot Errors for Jelinsky-moranda model'''
data = dp.read_data(path_to_data)
times_of_falls = data[:]
j = JelinskiMoranda(times_of_falls)
plot.add_errors_plot(times_of_falls)
plot.add_func_plot(j.func_n, 0, dp.time_of_last_error(times_of_falls), 1000)
plot.xlabel("Time")
plot.ylabel("Errors")
plot.grid()
plot.show()
def musa_okumoto_plot(path_to_data):
data = dp.read_data(path_to_data)
time_passed = data[-1] - data[-2]
errors = dp.convert_time_between_errors_to_time_of_errors(data)
times_of_falls = errors[:-1]
init_guess = 0.00000001
m = MusaOkumoto(time_passed, times_of_falls, init_guess)
plot.add_errors_plot(data)
plot.add_func_plot(m._mu, 0, 1.2 * m.t[-1], 1000)
plot.grid()
plot.show()
def jelinski_moranda_plot_errors(path_to_data):
'''Plot Errors for Jelinsky-moranda model'''
data = dp.read_data(path_to_data)
times_of_falls = data[:]
j = JelinskiMoranda(times_of_falls)
plot.add_errors_plot(times_of_falls)
plot.add_func_plot(j.func_n, 0, dp.time_of_last_error(times_of_falls),
1000)
plot.xlabel("Time")
plot.ylabel("Errors")
plot.grid()
plot.show()
def plot_musa_and_musa_okumoto(path_to_data):
data =dp.read_data(path_to_data)
time_passed = data[-1] - data[-2]
times_of_errors = dp.convert_time_between_errors_to_time_of_errors(data)[:-1]
init_guess = 0.00000001
time_of_last_error = dp.time_of_last_error(data)
m = Musa(time_passed, times_of_errors, init_guess)
mo = MusaOkumoto(time_passed, times_of_errors, init_guess)
plot.add_errors_plot(data)
plot.add_func_plot(m._mu, 0, 1.2 * time_of_last_error, 1000)
plot.add_func_plot(mo._mu, 0, 1.2 * time_of_last_error, 1000)
plot.xlabel("Time")
plot.ylabel("Errors")
plot.grid()
plot.show()
def plot_musa_and_musa_okumoto(path_to_data):
data = dp.read_data(path_to_data)
time_passed = data[-1] - data[-2]
times_of_errors = dp.convert_time_between_errors_to_time_of_errors(
data)[:-1]
init_guess = 0.00000001
time_of_last_error = dp.time_of_last_error(data)
m = Musa(time_passed, times_of_errors, init_guess)
mo = MusaOkumoto(time_passed, times_of_errors, init_guess)
plot.add_errors_plot(data)
plot.add_func_plot(m._mu, 0, 1.2 * time_of_last_error, 1000)
plot.add_func_plot(mo._mu, 0, 1.2 * time_of_last_error, 1000)
plot.xlabel("Time")
plot.ylabel("Errors")
plot.grid()
plot.show()
def get_jelinski_moranda_data(path_to_data):
data = dp.read_data(path_to_data)
times_of_falls = data[:]
j = JelinskiMoranda(times_of_falls)
time_of_last_error = dp.time_of_last_error(times_of_falls)
x = [int(i) for i in np.linspace(1, 1.2 * time_of_last_error, 20)]
y_n = [round(float(j.func_n(i)), 4) for i in x] # Mean value of errors
# TODO: Test this plots
x_numbers_of_errors = [i for i in range(len(times_of_falls))]
y_mttf = [int(j.func_MTTF(i)) for i in x_numbers_of_errors]
y_lambda = [round(float(j.func_lambda(i)), 4) for i in x_numbers_of_errors] # Intensity of errors - Step chart
y_r = [round(float(j.func_R(i)), 4) for i in x_numbers_of_errors] # Reliability - Step chart
return {'n': {'x': x, 'y': y_n},
'mttf': {'x': x_numbers_of_errors, 'y': y_mttf},
'lambda': {'x': x_numbers_of_errors, 'y': y_lambda},
'r': {'x': x_numbers_of_errors, 'y': y_r},
'errors_time': times_of_falls}
def jelinski_moranda_MTTF(path_to_data, file_to_write_data = ''):
data = dp.read_data(path_to_data)
times_of_falls = data[:]
j = JelinskiMoranda(times_of_falls)
x = [i for i in range(len(times_of_falls))]
y = [j.func_MTTF(i) for i in x]
if file_to_write_data:
with open(file_to_write_data, 'w+') as f:
f.write("i\treal_falls\tmttf\n")
for i in range(len(times_of_falls)):
f.write("{0}\t{1}\t{2:.2f}\n".format(i, times_of_falls[i], y[i][0]))
plot.add_xy_plot(x, y) # style '--', label = 'JM-model error'
plot.add_xy_plot(x, times_of_falls) # style label = 'Real error'
plot.xlabel("Error")
plot.ylabel("Mean time error")
plot.grid()
plot.show()
def jelinski_moranda_MTTF(path_to_data, file_to_write_data=''):
data = dp.read_data(path_to_data)
times_of_falls = data[:]
j = JelinskiMoranda(times_of_falls)
x = [i for i in range(len(times_of_falls))]
y = [j.func_MTTF(i) for i in x]
if file_to_write_data:
with open(file_to_write_data, 'w+') as f:
f.write("i\treal_falls\tmttf\n")
for i in range(len(times_of_falls)):
f.write("{0}\t{1}\t{2:.2f}\n".format(i, times_of_falls[i],
y[i][0]))
plot.add_xy_plot(x, y) # style '--', label = 'JM-model error'
plot.add_xy_plot(x, times_of_falls) # style label = 'Real error'
plot.xlabel("Error")
plot.ylabel("Mean time error")
plot.grid()
plot.show()
def get_musa_data(path_to_data):
data = dp.read_data(path_to_data)
time_passed = data[-1] - data[-2]
errors = dp.convert_time_between_errors_to_time_of_errors(data)
times_of_falls = errors[:-1]
time_of_last_error = times_of_falls[-1]
init_guess = 0.000001
m = Musa(time_passed, times_of_falls, init_guess)
x = [int(i) for i in np.linspace(1, 1.2 * time_of_last_error, 20)]
# Mean number of errors to the time of work
y_mu = [int(m.func_mu(i)) for i in x]
# Failure raite aka Intensivnost otkazov
y_lambda = [round(float(m.func_lambd(i)),4) for i in x]
# Reliability function aka Funkciya nadezhnosti
prediction_time_of_work = 100
y_r = [round(float(m.func_r(i, prediction_time_of_work)),4) for i in x]
return {
'mu': {'x': x, 'y': y_mu},
'lambda': {'x': x, 'y': y_lambda},
'r': {'x': x, 'y': y_r},
'errors_time': data}