def on_weibull(self, event):
"""Extreme value analysis - Maximum Weibull distribution"""
season = wx.GetSingleChoice("Select a season to analyze:",
"Select season",
["Winter", "Summer", "Spring", "Fall"])
if season:
ms = MONTHS[season]
str_coords = wx.GetTextFromUser("Coordinates (lon, lat):",
default_value="-74.85,11.11")
str_lon, str_lat = str_coords.split(",")
lon = float(str_lon)
lat = float(str_lat)
if lon < 0:
lon = 360 + lon
progress_dlg = wx.ProgressDialog(
"Read and analyze", "Reading and analyzing wave data...")
progress_dlg.Pulse()
fname = f"tmp/hs-peaks-{season}-{lon}-{lat}.tmp"
try:
peaks_hs = load_obj(fname)
except FileNotFoundError:
peaks_hs = weibull_data("hs", ms, lon, lat)
save_obj(peaks_hs, fname)
progress_dlg.Update(100)
analysis = weibull.Analysis(peaks_hs, unit="m")
analysis.fit()
analysis.probplot()
def test_censored_dataset():
current_run_time = 4200.0
fail_times = [current_run_time] * 10
fail_times[7] = 1034.5
fail_times[8] = 2550.9
fail_times[6] = 3043.4
suspended = [True, True, True, True, True, False, False, False, True, True]
analysis = weibull.Analysis(fail_times, suspended=suspended, unit='hour')
analysis.fit()
assert 1.5 < analysis.beta < 1.6
assert 6500.0 < analysis.eta < 6630
def test_non_fitted():
"""
Tests for proper fail condition if beta and eta do not exist
:return: None
"""
analysis = weibull.Analysis([0], [False])
assert analysis.beta is None
assert analysis.eta is None
with pytest.raises(weibull.ParameterError):
analysis.probplot()
with pytest.raises(weibull.ParameterError):
analysis.pdf()
with pytest.raises(weibull.ParameterError):
analysis.sf()
with pytest.raises(weibull.ParameterError):
analysis.hazard()
with pytest.raises(weibull.ParameterError):
analysis.cdf()
with pytest.raises(weibull.ParameterError):
analysis.fr()
with pytest.raises(weibull.ParameterError):
analysis.b(10)
with pytest.raises(weibull.ParameterError):
m = analysis.mean
with pytest.raises(weibull.ParameterError):
m = analysis.mttf
with pytest.raises(weibull.ParameterError):
m = analysis.median
with pytest.raises(weibull.ParameterError):
m = analysis.characteristic_life
def test_complete_dataset():
"""
Tests basic calculation of beta and eta for a complete data set
along with some misc. functionality based on the presence of
beta and eta.
:return:
"""
fail_times = [
9402.7, 6082.4, 13367.2, 10644.6, 8632.0, 3043.4, 12860.2, 1034.5,
2550.9, 3637.1
]
analysis = weibull.Analysis(fail_times)
analysis.fit()
assert 1.345 < analysis.beta < 1.355
assert 8100.0 < analysis.eta < 8160.0
assert 1500.0 < analysis.b(10) < 1600
assert 7400.0 < analysis.mttf < 7500
assert 8100.0 < analysis.eta < 8160.0
"""
Emulates the example in the book Weibull Analysis by Bryan Dodson
Data is sourced from page 22, table 2.5. The author's estimation was based
on a probability plot and resulted in a beta of 2.974 and eta of 203.3 vs. this
package's beta of 3.47 with an eta of 187.9 using linear regression and 2.97 and
203.3 using maximum likelihood estimation.
"""
import weibull
fail_times = [
42.1, 105.9, 151.3, 195.6, 77.8, 117.0, 157.3, 207.0, 83.3, 126.9, 163.8,
215.3, 88.7, 138.7, 177.2, 217.4, 101.8, 148.9, 194.3, 258.8
]
suspensions = [1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1]
# this is where the actual analysis and curve fitting occur
analysis = weibull.Analysis(fail_times, suspensions, unit='hour')
analysis.fit(method='mle', confidence_level=0.6)
print(analysis.stats)
analysis.probplot(file_name='gallery-probplot.png')
analysis.pdf(file_name='gallery-pdf.png')
analysis.hazard(file_name='gallery-hazard.png')
analysis.sf(file_name='gallery-survival.png')
analysis.fr(file_name='gallery-fr.png')
df = pd.read_csv(Archivo, index_col= False)
df["Fecha"] = pd.to_datetime(df["Fecha"])
for i in range(0,7):
# generando la mascara
mask = df.Fecha.dt.weekday == i
dfaux = df.loc[mask]
plt.subplots_adjust(hspace=0.9)
plt.subplot(2,1,1)
count, bins, ignored = plt.hist(dfaux["Viento - Velocidad (m/s)"],bins=range(0,23) )
# plt.close()
data=dfaux["Viento - Velocidad (m/s)"]
y,x,_=hist(data,range(0,23),label='data')
print(count," ",bins)
print(y, " " ,x)
x=(x[1:]+x[:-1])/2 # for len(x)==len(y)
analysis = weibull.Analysis(dfaux["Viento - Velocidad (m/s)"], unit = "m/s")
analysis.fit(method='mle')
# Capturando los parametros de weibull
forma = analysis.stats[3]
escala = analysis.stats[6]
xx = np.linspace(min(dfaux["Viento - Velocidad (m/s)"]),max(dfaux["Viento - Velocidad (m/s)"]),sum(count))
scale = count.max()/weib(xx,escala ,forma).max()
plt.plot(xx, weib(xx,escala,forma)*scale,'-b', label = 'Weibull')
# count, bins, ignored = plt.hist(dfaux["Viento - Velocidad (m/s)"],bins=range(0,int(dfaux["Viento - Velocidad (m/s)"].max()+2)))
# Parametros de Bimodal
params,cov=curve_fit(bimodal,x,y)
sigma=sqrt(diag(cov))
plot(xx,bimodal(xx,*params),color='green',lw=3,label='Bimodal')
plt.xlabel("Vel. Viento [m/s]")
plt.ylabel("Distribution")
dfauxs = dfs[mascara2]
aux = np.zeros(dfaux.shape[0])
auxt = np.zeros(df.shape[0])
plt.subplots_adjust(hspace=0.9)
plt.subplot(2, 1, 1)
count, bins, ignored = plt.hist(dfaux["Viento - Velocidad (m/s)"],
bins=range(0, 23),
density=True)
data = dfaux["Viento - Velocidad (m/s)"]
y, x, _ = hist(data, range(0, 23), label='Datos Observados', density=True)
print(count, " ", bins)
print(y, " ", x)
x = (x[1:] + x[:-1]) / 2 # for len(x)==len(y)
analysis = weibull.Analysis(dfaux["Viento - Velocidad (m/s)"], unit="m/s")
analysis.fit(method='mle')
# Capturando los parametros de weibull
forma = analysis.stats[3]
escala = analysis.stats[6]
xx = np.linspace(min(dfaux["Viento - Velocidad (m/s)"]),
max(dfaux["Viento - Velocidad (m/s)"]), sum(count))
print(x)
scale = count.max() / weib(x, escala, forma).max()
plt.plot(x, weib(x, escala, forma), '-b', label='Weibull')
#******************************************************************
# Datos simulados
#******************************************************************
# Archivo = 'df_marzoCorr.csv'
# dfs = pd.read_csv(Archivo, index_col= False)
import weibull
# the current run time of the test or the
# time that the test was suspended completely
current_run_time = 4200.0
fail_times = [current_run_time] * 10
fail_times[7] = 1034.5
fail_times[8] = 2550.9
fail_times[6] = 3043.4
suspended = [True, True, True, True, True,
False, False, False, True, True]
analysis = weibull.Analysis(fail_times, suspended=suspended, unit='hour')
analysis.fit()
analysis.probplot(show=False)
# beta and eta may be directly manipulated to try out different hypotheses
analysis.beta = 2.0
analysis.eta = 5000
analysis.probplot()
print(f'beta: {analysis.beta}\teta: {analysis.eta}')
print(f'B2 life: {analysis.b(2):.02f}\nB10 life: {analysis.b(10):.02f}\nB50 life: {analysis.b(50):.02f}')
print(f'median: {analysis.median}')
print(f'mean: {analysis.mean}')
print(f'mttf: {analysis.mttf}')
import weibull
# fail times include no censored data
fail_times = [
9402.7, 6082.4, 13367.2, 10644.6, 8632.0, 3043.4, 12860.2, 1034.5, 2550.9,
3637.1
]
# this is where the actual analysis and curve fitting occur
analysis = weibull.Analysis(fail_times, unit='hour')
analysis.fit()
analysis.probplot()
analysis.hazard(file_name='hazard.png')
print(analysis.stats)
""" This file being used to generate some of the plots for the documentation """ import weibull plotter = weibull.Analysis([0], [False]) # Linearized CDF comparison plotter.beta = 0.5 plotter.eta = 1.0 plotter.probplot(show=False) plotter.beta = 1.0 plotter.probplot(show=False) plotter.beta = 2.0 plotter.probplot(file_name='beta-effects-on-log-cdf.png') # PDF comparison plotter.beta = 0.5 plotter.pdf(show=False) plotter.beta = 1.0 plotter.pdf(show=False) plotter.beta = 2.0 plotter.pdf(file_name='beta-effects-on-pdf.png') # failure rate comparison plotter.beta = 0.5 plotter.fr(show=False)
