def _get_weibull_dist(self, qty, mean=None, std=None, scale=1.0, shape=5.0):
x_line = np.arange(mean - std * 4.0, mean + std * 5.0, 1 * std)
if self.weibull_dist_method == 'double':
_data = dweibull(shape, loc=mean, scale=std)
y_line = _data.pdf(x_line) * qty
if self.weibull_dist_method == 'inverted':
_data = invweibull(shape, loc=mean, scale=std)
y_line = _data.pdf(x_line) * qty
if self.weibull_dist_method == 'exponential':
_data = exponweib(scale, shape, loc=mean, scale=std)
y_line = _data.pdf(x_line) * qty
if self.weibull_dist_method == 'min':
_data = weibull_min(shape, loc=mean, scale=std)
y_line = _data.pdf(x_line) * qty
if self.weibull_dist_method == 'max':
_data = weibull_max(shape, loc=mean, scale=std)
y_line = _data.pdf(x_line) * qty
line = Line(width=1280, height=600)
line.add(u'{0}'.format(self.spc_target), x_line, y_line, xaxis_name=u'{0}'.format(self.spc_target),
yaxis_name=u'数量(Quantity)',
line_color='rgba(0 ,255 ,127,0.5)', legend_pos='center',
is_smooth=True, line_width=2,
tooltip_tragger='axis', is_fill=True, area_color='#20B2AA', area_opacity=0.4)
pyecharts.configure(force_js_embed=True)
return line.render_embed()
def weibullRelease(self, period):
"""
Returns the rate from a user-shaped weibull distribution function
at a specific period.
"""
par = self.parameters
if len(par) == 2: # scale = 1
c = par[0]
loc = par[1]
frozenWeib = st.dweibull(c, loc)
rate = frozenWeib.pdf(period)
return rate
elif len(self.parameters) == 3: # scale defined by user
c = par[0]
loc = par[1]
scale = par[2]
frozenWeib = st.dweibull(c, loc, scale)
rate = frozenWeib.pdf(period)
return rate
else:
print('ERROR:')
print('Too few or too many arguments for weibull release function.')
print('Enter two or three arguments for weibull release function.')
ax1 = fig.add_subplot(1, 2, 1)
sm.qqplot(logeados,
stats.dgamma(parametros_dgamma[0],
loc=parametros_dgamma[1],
scale=parametros_dgamma[2]),
line="45",
ax=ax1)
ax1.set_title('dgamma', size=11.0)
ax1.set_xlabel("")
ax1.set_ylabel("")
ax2 = fig.add_subplot(1, 2, 2)
sm.qqplot(logeados,
stats.dweibull(parametros_dweibull[0],
loc=parametros_dweibull[1],
scale=parametros_dweibull[2]),
line="45",
ax=ax2)
ax2.set_title('dweibull', size=11.0)
ax2.set_xlabel("")
ax2.set_ylabel("")
fig.tight_layout(pad=0.7)
fig.text(0.5, 0, 'Cuantiles teóricos', ha='center', va='center')
fig.text(0.,
0.5,
'Cuantiles observados',
ha='center',
va='center',
import numpy as np
from scipy.stats import dweibull
from matplotlib import pyplot as plt
#------------------------------------------------------------
# Define the distribution parameters to be plotted
k_values = [0.5, 1, 2, 2]
lam_values = [1, 1, 1, 2]
linestyles = ['-', '--', ':', '-.', '--']
mu = 0
x = np.linspace(-10, 10, 1000)
#------------------------------------------------------------
# plot the distributions
for (k, lam, ls) in zip(k_values, lam_values, linestyles):
dist = dweibull(k, mu, lam)
plt.plot(x,
dist.pdf(x),
ls=ls,
c='black',
label=r'$k=%.1f,\ \lambda=%i$' % (k, lam))
plt.xlim(0, 5)
plt.ylim(0, 1.0)
plt.xlabel('$x$', fontsize=14)
plt.ylabel(r'$P(x|k,\lambda)$', fontsize=14)
plt.title('Weibull Distribution')
plt.legend()
plt.show()
# "Statistics, Data Mining, and Machine Learning in Astronomy" (2013)
# For more information, see http://astroML.github.com
import numpy as np
from scipy.stats import dweibull
from matplotlib import pyplot as plt
#------------------------------------------------------------
# Define the distribution parameters to be plotted
k_values = [0.5, 1, 2, 2]
lam_values = [1, 1, 1, 2]
linestyles = ['-', '--', ':', '-.', '--']
mu = 0
x = np.linspace(-10, 10, 1000)
#------------------------------------------------------------
# plot the distributions
for (k, lam, ls) in zip(k_values, lam_values, linestyles):
dist = dweibull(k, mu, lam)
plt.plot(x, dist.pdf(x), ls=ls, c='black',
label=r'$k=%.1f,\ \lambda=%i$' % (k, lam))
plt.xlim(0, 5)
plt.ylim(0, 1.0)
plt.xlabel('$x$', fontsize=14)
plt.ylabel(r'$p(x|k,\lambda)$', fontsize=14)
plt.title('Weibull Distribution')
plt.legend()
plt.show()
def dweibull(n = 100, c = 2.07, loc = 0, scale = 1):
'''
double weibull distribution
n - розмір вибірки
loc - середина
scale - відхилення
c - параметр c
Повертає список вигляду [згенерована вибірка, список x, список y, середнє, мода, медіана,
розмах, девіація, варіанса, стандарт, варіація, асиметрія, ексцес]
'''
distribution = st.dweibull(loc=loc, c=c, scale=scale)
sample = list(distribution.rvs(size = n))
for i in range(len(sample)):
sample[i] = round(sample[i], 2)
var = list(sample)
var.sort()
x = list(set(sample))
y = list()
x.sort()
freq_table = dict()
for num in x:
freq_table[num] = sample.count(num)
int_len = ((max(sample) - min(sample)) / r(sample))
int_bounds = list()
next = min(sample)
for i in range(r(sample)):
int_bounds.append(round(next, 2))
next += int_len
int_bounds.append(max(sample))
freq_table = dict()
int_list = list()
for i in range(len(int_bounds)-1):
int_list.append([int_bounds[i], int_bounds[i+1]])
for i in range(len(int_list)):
if i != len(int_list)-1:
freq_table["[" + str(int_list[i][0]) + "; " + str(int_list[i][1]) + ")"] = 0
else:
freq_table["[" + str(int_list[i][0]) + "; " + str(int_list[i][1]) + "]"] = 0
for i in range(len(sample)):
for j in range(len(int_list)):
if sample[i] >= int_list[j][0] and sample[i] < int_list[j][1] and j != len(int_list)-1:
freq_table["[" + str(int_list[j][0]) + "; " + str(int_list[j][1]) + ")"] += 1
elif sample[i] >= int_list[j][0] and sample[i] <= int_list[j][1] and j == len(int_list)-1:
freq_table["[" + str(int_list[j][0]) + "; " + str(int_list[j][1]) + "]"] += 1
int_list_values = list()
for key in freq_table:
int_list_values.append(int(freq_table[key]))
intr = list(freq_table.keys())
centered_int = list()
for intr in int_list:
centered_int.append(round(((intr[0] + intr[1])/2), 3))
freq_table_disc = dict()
x = list(set(sample))
for num in x:
freq_table_disc[num] = sample.count(num)
result = list()
result.append(sample)
start = distribution.ppf(0.01)
end = distribution.ppf(0.99)
x = list(np.linspace(start, end, n))
y = list(distribution.pdf(x))
result.append(x)
result.append(y)
mean = np.mean(sample)
result.append(mean)
moda = list(mode(freq_table_disc).keys())
result.append(moda)
med = statistics.median(sample)
result.append(med)
ro = max(sample) - min(sample)
result.append(ro)
deviation = dev(freq_table_disc)
result.append(deviation)
variansa = dev(freq_table_disc) / (len(sample)-1)
result.append(variansa)
standart = math.sqrt(variansa)
result.append(standart)
variation = standart / np.mean(sample)
asym = st.skew(sample)
result.append(asym)
ex = st.kurtosis(sample)
result.append(ex)
return result
c = 2.07 mean, var, skew, kurt = dweibull.stats(c, moments='mvsk') # Display the probability density function (``pdf``): x = np.linspace(dweibull.ppf(0.01, c), dweibull.ppf(0.99, c), 100) ax.plot(x, dweibull.pdf(x, c), 'r-', lw=5, alpha=0.6, label='dweibull pdf') # Alternatively, the distribution object can be called (as a function) # to fix the shape, location and scale parameters. This returns a "frozen" # RV object holding the given parameters fixed. # Freeze the distribution and display the frozen ``pdf``: rv = dweibull(c) ax.plot(x, rv.pdf(x), 'k-', lw=2, label='frozen pdf') # Check accuracy of ``cdf`` and ``ppf``: vals = dweibull.ppf([0.001, 0.5, 0.999], c) np.allclose([0.001, 0.5, 0.999], dweibull.cdf(vals, c)) # True # Generate random numbers: r = dweibull.rvs(c, size=1000) # And compare the histogram: ax.hist(r, normed=True, histtype='stepfilled', alpha=0.2)
import numpy as np
from scipy.stats import dweibull
from matplotlib import pyplot as plt
plt.ion()
Weibull = dweibull(2, 0, 6) #k=2, lamda=6
x_axis = np.asarray([i + 0.5 for i in range(20)])
y_axis = np.asarray([
2.75, 7.80, 11.64, 13.79, 14.20, 13.15, 11.14, 8.72, 6.34, 4.30, 2.73,
1.62, 0.91, 0.48, 0.24, 0.11, 0.05, 0.02, 0.01, 0.00
]) * 100
y, c = [], 0
for i in range(len(y_axis)):
y = np.concatenate((y, np.linspace(c, c + 1, y_axis[i])), axis=0)
c += 1
plt.hist(y, bins=30, normed=True, label=' Data Histogram')
x = np.linspace(0, 20, 10e3)
plt.plot(x, Weibull.pdf(x) * 2, '-k', label='Weibull k=2,$ \lambda=6$')
plt.xlabel('x')
plt.ylabel('p(x|k,$\lambda)$')
plt.title('Weibull Distribution')
plt.legend()
plt.show()
def all_dists():
# dists param were taken from scipy.stats official
# documentaion examples
# Total - 89
return {
"alpha":
stats.alpha(a=3.57, loc=0.0, scale=1.0),
"anglit":
stats.anglit(loc=0.0, scale=1.0),
"arcsine":
stats.arcsine(loc=0.0, scale=1.0),
"beta":
stats.beta(a=2.31, b=0.627, loc=0.0, scale=1.0),
"betaprime":
stats.betaprime(a=5, b=6, loc=0.0, scale=1.0),
"bradford":
stats.bradford(c=0.299, loc=0.0, scale=1.0),
"burr":
stats.burr(c=10.5, d=4.3, loc=0.0, scale=1.0),
"cauchy":
stats.cauchy(loc=0.0, scale=1.0),
"chi":
stats.chi(df=78, loc=0.0, scale=1.0),
"chi2":
stats.chi2(df=55, loc=0.0, scale=1.0),
"cosine":
stats.cosine(loc=0.0, scale=1.0),
"dgamma":
stats.dgamma(a=1.1, loc=0.0, scale=1.0),
"dweibull":
stats.dweibull(c=2.07, loc=0.0, scale=1.0),
"erlang":
stats.erlang(a=2, loc=0.0, scale=1.0),
"expon":
stats.expon(loc=0.0, scale=1.0),
"exponnorm":
stats.exponnorm(K=1.5, loc=0.0, scale=1.0),
"exponweib":
stats.exponweib(a=2.89, c=1.95, loc=0.0, scale=1.0),
"exponpow":
stats.exponpow(b=2.7, loc=0.0, scale=1.0),
"f":
stats.f(dfn=29, dfd=18, loc=0.0, scale=1.0),
"fatiguelife":
stats.fatiguelife(c=29, loc=0.0, scale=1.0),
"fisk":
stats.fisk(c=3.09, loc=0.0, scale=1.0),
"foldcauchy":
stats.foldcauchy(c=4.72, loc=0.0, scale=1.0),
"foldnorm":
stats.foldnorm(c=1.95, loc=0.0, scale=1.0),
# "frechet_r": stats.frechet_r(c=1.89, loc=0.0, scale=1.0),
# "frechet_l": stats.frechet_l(c=3.63, loc=0.0, scale=1.0),
"genlogistic":
stats.genlogistic(c=0.412, loc=0.0, scale=1.0),
"genpareto":
stats.genpareto(c=0.1, loc=0.0, scale=1.0),
"gennorm":
stats.gennorm(beta=1.3, loc=0.0, scale=1.0),
"genexpon":
stats.genexpon(a=9.13, b=16.2, c=3.28, loc=0.0, scale=1.0),
"genextreme":
stats.genextreme(c=-0.1, loc=0.0, scale=1.0),
"gausshyper":
stats.gausshyper(a=13.8, b=3.12, c=2.51, z=5.18, loc=0.0, scale=1.0),
"gamma":
stats.gamma(a=1.99, loc=0.0, scale=1.0),
"gengamma":
stats.gengamma(a=4.42, c=-3.12, loc=0.0, scale=1.0),
"genhalflogistic":
stats.genhalflogistic(c=0.773, loc=0.0, scale=1.0),
"gilbrat":
stats.gilbrat(loc=0.0, scale=1.0),
"gompertz":
stats.gompertz(c=0.947, loc=0.0, scale=1.0),
"gumbel_r":
stats.gumbel_r(loc=0.0, scale=1.0),
"gumbel_l":
stats.gumbel_l(loc=0.0, scale=1.0),
"halfcauchy":
stats.halfcauchy(loc=0.0, scale=1.0),
"halflogistic":
stats.halflogistic(loc=0.0, scale=1.0),
"halfnorm":
stats.halfnorm(loc=0.0, scale=1.0),
"halfgennorm":
stats.halfgennorm(beta=0.675, loc=0.0, scale=1.0),
"hypsecant":
stats.hypsecant(loc=0.0, scale=1.0),
"invgamma":
stats.invgamma(a=4.07, loc=0.0, scale=1.0),
"invgauss":
stats.invgauss(mu=0.145, loc=0.0, scale=1.0),
"invweibull":
stats.invweibull(c=10.6, loc=0.0, scale=1.0),
"johnsonsb":
stats.johnsonsb(a=4.32, b=3.18, loc=0.0, scale=1.0),
"johnsonsu":
stats.johnsonsu(a=2.55, b=2.25, loc=0.0, scale=1.0),
"ksone":
stats.ksone(n=1e03, loc=0.0, scale=1.0),
"kstwobign":
stats.kstwobign(loc=0.0, scale=1.0),
"laplace":
stats.laplace(loc=0.0, scale=1.0),
"levy":
stats.levy(loc=0.0, scale=1.0),
"levy_l":
stats.levy_l(loc=0.0, scale=1.0),
"levy_stable":
stats.levy_stable(alpha=0.357, beta=-0.675, loc=0.0, scale=1.0),
"logistic":
stats.logistic(loc=0.0, scale=1.0),
"loggamma":
stats.loggamma(c=0.414, loc=0.0, scale=1.0),
"loglaplace":
stats.loglaplace(c=3.25, loc=0.0, scale=1.0),
"lognorm":
stats.lognorm(s=0.954, loc=0.0, scale=1.0),
"lomax":
stats.lomax(c=1.88, loc=0.0, scale=1.0),
"maxwell":
stats.maxwell(loc=0.0, scale=1.0),
"mielke":
stats.mielke(k=10.4, s=3.6, loc=0.0, scale=1.0),
"nakagami":
stats.nakagami(nu=4.97, loc=0.0, scale=1.0),
"ncx2":
stats.ncx2(df=21, nc=1.06, loc=0.0, scale=1.0),
"ncf":
stats.ncf(dfn=27, dfd=27, nc=0.416, loc=0.0, scale=1.0),
"nct":
stats.nct(df=14, nc=0.24, loc=0.0, scale=1.0),
"norm":
stats.norm(loc=0.0, scale=1.0),
"pareto":
stats.pareto(b=2.62, loc=0.0, scale=1.0),
"pearson3":
stats.pearson3(skew=0.1, loc=0.0, scale=1.0),
"powerlaw":
stats.powerlaw(a=1.66, loc=0.0, scale=1.0),
"powerlognorm":
stats.powerlognorm(c=2.14, s=0.446, loc=0.0, scale=1.0),
"powernorm":
stats.powernorm(c=4.45, loc=0.0, scale=1.0),
"rdist":
stats.rdist(c=0.9, loc=0.0, scale=1.0),
"reciprocal":
stats.reciprocal(a=0.00623, b=1.01, loc=0.0, scale=1.0),
"rayleigh":
stats.rayleigh(loc=0.0, scale=1.0),
"rice":
stats.rice(b=0.775, loc=0.0, scale=1.0),
"recipinvgauss":
stats.recipinvgauss(mu=0.63, loc=0.0, scale=1.0),
"semicircular":
stats.semicircular(loc=0.0, scale=1.0),
"t":
stats.t(df=2.74, loc=0.0, scale=1.0),
"triang":
stats.triang(c=0.158, loc=0.0, scale=1.0),
"truncexpon":
stats.truncexpon(b=4.69, loc=0.0, scale=1.0),
"truncnorm":
stats.truncnorm(a=0.1, b=2, loc=0.0, scale=1.0),
"tukeylambda":
stats.tukeylambda(lam=3.13, loc=0.0, scale=1.0),
"uniform":
stats.uniform(loc=0.0, scale=1.0),
"vonmises":
stats.vonmises(kappa=3.99, loc=0.0, scale=1.0),
"vonmises_line":
stats.vonmises_line(kappa=3.99, loc=0.0, scale=1.0),
"wald":
stats.wald(loc=0.0, scale=1.0),
"weibull_min":
stats.weibull_min(c=1.79, loc=0.0, scale=1.0),
"weibull_max":
stats.weibull_max(c=2.87, loc=0.0, scale=1.0),
"wrapcauchy":
stats.wrapcauchy(c=0.0311, loc=0.0, scale=1.0),
}
pp_plot(logeados, stats.gennorm(beta = parametros_normal[0],
loc = parametros_normal[1],
scale=parametros_normal[2]),
line = True, ax=ax1)
ax1.set_title('Normal generalizada', fontsize=11)
pp_plot(logeados, stats.genpareto(c = parametros_pareto[0],
loc = parametros_pareto[1],
scale=parametros_pareto[2]),
line = True,ax=ax2)
ax2.set_title('Pareto generalizada', fontsize=11)
pp_plot(logeados, stats.dweibull(c = parametros_weibull[0],
loc = parametros_weibull[1],
scale=parametros_weibull[2]),
line = True,ax=ax3)
ax3.set_title('Weibull doble', fontsize=11)
pp_plot(logeados, stats.gamma(a = parametros_gamma[0],
loc = parametros_gamma[1],
scale=parametros_gamma[2]),
line = True,ax=ax4)
ax4.set_title('Gamma', fontsize=11)
fig.tight_layout(pad=0.7)
fig.text(0.5, 0, 'Probabilidades teóricas', ha='center', va='center')
fig.text(0., 0.5, 'Probabilidades observadas', ha='center', va='center', rotation='vertical')