def HistogramPLOT_wbm(data, month, year):
#Initiate
Situation = []
mon = [
'January', 'Febuary', 'March', 'April', 'May', 'June', 'July',
'August', 'September', 'October', 'November', 'December'
]
data01 = data[['DateTime', 'WS95']].copy()
data01.dropna(how='any', inplace=True)
logicY = (data01["DateTime"].apply(lambda x: x.year) == (year))
data01 = data01[logicY].copy()
fig = plt.figure(figsize=(20, 32), facecolor='w', edgecolor='r')
#Plotting 12 graph
xvals = np.linspace(0, 30, 1000)
for i in range(month):
ax = plt.subplot2grid((4, 3), (int(np.floor(i / 3)), int(i % 3)))
logic = (data01["DateTime"].apply(lambda x: x.month)) == (i + 1)
ws = data01['WS95'][logic]
ws = ws + 0.0001
failures = []
censored = []
threshold = 30
for item in ws:
if item > threshold:
censored.append(threshold)
else:
failures.append(item)
xvals = np.linspace(0, 30, 1000)
if (np.sum(logic) != 0):
ax.hist(ws, bins=30, density=True)
hist, edge = np.histogram(np.array(ws),
bins=1000,
range=(0, 30),
density=True)
wbm = Fit_Weibull_Mixture(failures=failures,
right_censored=censored,
show_plot=False,
print_results=False)
part1_pdf = Weibull_Distribution(alpha=wbm.alpha_1,
beta=wbm.beta_1).PDF(
xvals=xvals, show_plot=False)
part2_pdf = Weibull_Distribution(alpha=wbm.alpha_2,
beta=wbm.beta_2).PDF(
xvals=xvals, show_plot=False)
Mixture_PDF = part1_pdf * wbm.proportion_1 + part2_pdf * wbm.proportion_2
ax.plot(xvals, Mixture_PDF, label='Weibull_Mixture')
ax.legend()
ax.set_ylim(0, 0.18)
ax.set_xlim(0, 30)
ax.set_xticks([0, 5, 10, 15, 20, 25, 30])
ax.tick_params(axis="x", labelsize=30)
ax.tick_params(axis="y", labelsize=26)
ax.set_title('{}'.format(mon[i]), fontweight='bold', size=30)
plt.tight_layout()
plt.show()
def test_Fit_Weibull_Mixture():
group_1 = Weibull_Distribution(alpha=10, beta=3).random_samples(40, seed=2)
group_2 = Weibull_Distribution(alpha=40, beta=4).random_samples(60, seed=2)
raw_data = np.hstack([group_1, group_2])
data = make_right_censored_data(data=raw_data, threshold=40)
fit = Fit_Weibull_Mixture(failures=data.failures, right_censored=data.right_censored, print_results=False, show_probability_plot=False)
assert_allclose(fit.alpha_1, 8.711417800323375,rtol=rtol,atol=atol)
assert_allclose(fit.alpha_2, 36.96927163816745, rtol=rtol, atol=atol)
assert_allclose(fit.beta_1, 3.8780149153215278, rtol=rtol, atol=atol)
assert_allclose(fit.beta_2, 4.901762153365169, rtol=rtol, atol=atol)
assert_allclose(fit.AICc, 678.8135631521253, rtol=rtol, atol=atol)
assert_allclose(fit.loglik, -334.08763263989243, rtol=rtol, atol=atol)
def test_Fit_Weibull_Mixture():
d1 = Weibull_Distribution(alpha=10, beta=3)
d2 = Weibull_Distribution(alpha=40, beta=4)
dist = Mixture_Model(distributions=[d1, d2], proportions=[0.2, 0.8])
raw_data = dist.random_samples(100, seed=2)
data = make_right_censored_data(data=raw_data, threshold=dist.mean)
MLE = Fit_Weibull_Mixture(failures=data.failures, right_censored=data.right_censored, show_probability_plot=False, print_results=False)
assert_allclose(MLE.alpha_1, 11.06604639424718, rtol=rtol, atol=atol)
assert_allclose(MLE.beta_1, 2.735078296796997, rtol=rtol, atol=atol)
assert_allclose(MLE.alpha_2, 34.325433665495346, rtol=rtol, atol=atol)
assert_allclose(MLE.beta_2, 7.60238532821206, rtol=rtol, atol=atol)
assert_allclose(MLE.proportion_1, 0.23640116719132157, rtol=rtol, atol=atol)
assert_allclose(MLE.proportion_2, 0.7635988328086785, rtol=rtol, atol=atol)
assert_allclose(MLE.AICc, 471.97390405380236, rtol=rtol, atol=atol)
assert_allclose(MLE.BIC, 484.3614571114024, rtol=rtol, atol=atol)
assert_allclose(MLE.loglik, -230.66780309073096, rtol=rtol, atol=atol)
assert_allclose(MLE.AD, 320.1963544647712, rtol=rtol, atol=atol)
def HistogramPLOT_all(data, month, year):
#Initiate
Situation = []
mon = [
'January', 'Febuary', 'March', 'April', 'May', 'June', 'July',
'August', 'September', 'October', 'November', 'December'
]
#Get just Full day data
logicF = (data["isFULL"].apply(lambda x: x) == (1))
data01 = data[logicF].copy()
data01.fillna(method='ffill', inplace=True)
logicY = (data01["DateTime"].apply(lambda x: x.year) == (year))
data01 = data01[logicY].copy()
fig = plt.figure(figsize=(24, 18), dpi=80, facecolor='w', edgecolor='r')
#Plotting 12 graph
xvals = np.linspace(0, 30, 1000)
for i in range(month):
ax = plt.subplot2grid((4, 3), (int(np.floor(i / 3)), int(i % 3)))
logic = (data01["DateTime"].apply(lambda x: x.month)) == (i + 1)
ws = data01['WS95'][logic]
ws = ws + 0.0001
failures = []
censored = []
threshold = 30
for item in ws:
if item > threshold:
censored.append(threshold)
else:
failures.append(item)
xvals = np.linspace(0, 30, 1000)
print(ws.shape)
if (np.sum(logic) != 0):
ax.hist(ws, bins=30, normed=True)
hist, edge = np.histogram(np.array(ws),
bins=1000,
range=(0, 30),
normed=True)
wb2 = Fit_Weibull_2P(failures=failures,
show_probability_plot=False,
print_results=False)
wb3 = Fit_Weibull_3P(failures=failures,
show_probability_plot=False,
print_results=False)
gm2 = Fit_Gamma_2P(failures=failures,
show_probability_plot=False,
print_results=False)
gm3 = Fit_Gamma_3P(failures=failures,
show_probability_plot=False,
print_results=False)
ln2 = Fit_Lognormal_2P(failures=failures,
show_probability_plot=False,
print_results=False)
wbm = Fit_Weibull_Mixture(failures=failures,
right_censored=censored,
show_plot=False,
print_results=False)
wb2_pdf = Weibull_Distribution(alpha=wb2.alpha, beta=wb2.beta).PDF(
xvals=xvals, show_plot=True, label='Weibull_2P')
wb3_pdf = Weibull_Distribution(alpha=wb3.alpha,
beta=wb3.beta,
gamma=wb3.gamma).PDF(
xvals=xvals,
show_plot=True,
label='Weibull_3P')
gm2_pdf = Gamma_Distribution(alpha=gm2.alpha,
beta=gm2.beta).PDF(xvals=xvals,
show_plot=True,
label='Gamma_2P')
gm3_pdf = Gamma_Distribution(alpha=gm3.alpha,
beta=gm3.beta,
gamma=gm3.gamma).PDF(xvals=xvals,
show_plot=True,
label='Gamma_3P')
ln2_pdf = Lognormal_Distribution(mu=ln2.mu, sigma=ln2.sigma).PDF(
xvals=xvals, show_plot=True, label='Lognormal_2P')
part1_pdf = Weibull_Distribution(alpha=wbm.alpha_1,
beta=wbm.beta_1).PDF(
xvals=xvals, show_plot=False)
part2_pdf = Weibull_Distribution(alpha=wbm.alpha_2,
beta=wbm.beta_2).PDF(
xvals=xvals, show_plot=False)
Mixture_PDF = part1_pdf * wbm.proportion_1 + part2_pdf * wbm.proportion_2
ax.plot(xvals, Mixture_PDF, label='Weibull_Mixture')
ax.legend()
ax.set_ylim(0, 0.16)
ax.set_xlim(0, 30)
ax.set_xticks([0, 5, 10, 15, 20, 25, 30])
ax.tick_params(axis="x", labelsize=20)
ax.tick_params(axis="y", labelsize=20)
ax.set_title('{}'.format(mon[i]), fontweight='bold', size=20)
plt.tight_layout()
plt.show()