def HybridNormalGPDCDF(xs, u, mu, sigma, shape, loc, scale):
'''
Params:
xs: unsorted list of datat to fit semi-parametric CDF to.
u: threshold to move from Gaussian CDF Fit in center to GPD tail fitting.
mu: mean of the data.
sigma: standard deviation of the data.
shape: gpd least squares estimated shape parameter.
loc: gpd least squares estimated location parameter.
scale: gpd least squares estimated scale parameter.
Returns:
an array that would result from xs.apply(semiparametric_fittedfunction) or F_n(xs) where F_n is the CDF fit.
'''
out = list()
l = (mu - abs(u - mu))
h = (mu + abs(u - mu))
#print('u = %.10f,l = %.10f,h = %.10f'%(u,l,h))
for x in xs:
if x < l:
nrm = norm.cdf(l, mu, sigma)
out.append(nrm *
(1 - genpareto.cdf(l - x, shape, loc=loc, scale=scale)))
elif x >= h:
nrm = norm.cdf(h, mu, sigma)
out.append((1 - nrm) *
genpareto.cdf(x - h, shape, loc=loc, scale=scale) + nrm)
else:
out.append(norm.cdf(x, mu, sigma))
return out
def get_pvalue(sorted_scores, stat, n):
# approximate the gpd tail
n_exceed = 250
is_gpd_fitted = False
while n_exceed >= 10:
exceedances = sorted_scores[:n_exceed]
# check if the n_exceed largest permutation values follow GPD
# with Anderson-Darling goodness-of-fit test
try:
ad = eva.gpdAd(FloatVector(exceedances))
ad_pval = ad.rx2('p.value')[0]
except:
n_exceed -= 10
continue
# H0 = exceedances come from a GPD
if ad_pval > 0.05:
is_gpd_fitted = True
break
n_exceed -= 10
if not is_gpd_fitted:
#print('GPD good fit is never reached - use ECDF instead...')
return (None)
# compute the exceedance threshold t
t = float((sorted_scores[n_exceed] + sorted_scores[n_exceed - 1]) / 2)
# estimate shape and scale params with maximum likelihood
gpd_fit = eva.gpdFit(FloatVector(sorted_scores),
threshold=t,
method='mle')
scale, shape = gpd_fit.rx2('par.ests')[0], gpd_fit.rx2('par.ests')[1]
# compute GPD p-value
f_gpd = genpareto.cdf(x=gt_score - t, c=shape, scale=scale)
return (n_exceed / n * (1 - f_gpd))
def gpd_ad(x, tp):
u, y = get_excesses(x, tp)
xi, sigma = gpd_fit(y)
z = genpareto.cdf(y, xi, 0, sigma)
z = np.sort(z)
n = len(z)
i = np.linspace(1, n, n)
stat = -n - (1 / n) * np.sum(
(2 * i - 1) * (np.log(z) + np.log1p(-z[::-1])))
return u, stat, xi, sigma
def _margin_tail_cdf(self, x, i):
# CDF of GP approximation (no need to weight it by p, that's done elsewhere)
# i = component index
if self.shapes[i] != 0:
return gp.cdf(x,
c=self.shapes[i],
loc=self.u[i],
scale=self.scales[i])
else:
return expdist.cdf(x, loc=self.u[i], scale=self.scales[i])
def EstimaProbabilidade(self, Magnitude, Parametros):
if self.tipoSerie == 'Parcial':
probabilidade = genpareto.cdf(Magnitude, Parametros[0],
loc = Parametros[1],
scale = Parametros[2])
elif self.tipoSerie == 'Anual':
probabilidade = genextreme.cdf(Magnitude, Parametros[0],
loc = Parametros[1],
scale = Parametros[2])
return probabilidade
def survival_function(sample, threshold, fit_method,
alpha): #Plot the survival function, (1 - cdf)
[shape, scale, sample, sample_excess,
sample_over_thresh] = gpdfit(sample, threshold, fit_method)
n = len(sample_over_thresh)
y_surv = 1 - np.arange(1, n + 1) / n
i_initial = 0
n = len(sample)
for i in range(0, n):
if sample[i] > threshold + 0.0001:
i_initial = i
break
#Computing confidence interval with the Dvoretzky–Kiefer–Wolfowitz
F1 = []
F2 = []
for i in range(i_initial, len(sample)):
e = (((mt.log(2 / alpha)) / (2 * len(sample_over_thresh)))**0.5)
F1.append(y_surv[i - i_initial] - e)
F2.append(y_surv[i - i_initial] + e)
x_points = np.arange(0, max(sample), 0.001)
surv_func = 1 - genpareto.cdf(x_points, shape, loc=threshold, scale=scale)
#Plotting survival function
plt.figure(9)
plt.plot(x_points,
surv_func,
color='black',
label='Theoretical Survival Function')
plt.xlabel('Data')
plt.ylabel('Survival Function')
plt.title('Data Survival Function Plot')
plt.scatter(sorted(sample_over_thresh),
y_surv,
label='Empirical Survival Function')
plt.plot(sorted(sample_over_thresh),
F1,
linestyle='--',
color='red',
alpha=0.8,
lw=0.9,
label='Dvoretzky–Kiefer–Wolfowitz Confidence Bands')
plt.plot(sorted(sample_over_thresh),
F2,
linestyle='--',
color='red',
alpha=0.8,
lw=0.9)
plt.legend()
plt.show()
def GeneralizedPareto_CDF(x):
'''
Generalized Pareto fit
Returns cumulative probability function at x.
'''
# fit a generalized pareto and get params
shape, _, scale = genpareto.fit(x)
# get generalized pareto CDF
cdf = genpareto.cdf(x, shape, scale=scale)
return cdf
def HybridSemiParametricGPDCDF(xs, u, ydata, shape, loc, scale):
'''
Params:
xs: unsorted list of datat to fit semi-parametric CDF to.
u: threshold to move from Gaussian Kernel estimation to GPD tail fitting.
mu: mean of the data.
sigma: standard deviation of the data.
shape: gpd least squares estimated shape parameter.
loc: gpd least squares estimated location parameter.
scale: gpd least squares estimated scale parameter.
Returns:
an array that would result from xs.apply(semiparametric_fittedfunction) or F_n(xs) where F_n is the CDF fit.
'''
#print("Starting Canonical Maximum Likelihood")
out = list()
mu = mean(ydata)
l = (mu - abs(u - mu))
h = (mu + abs(u - mu))
#print('u = %.10f,l = %.10f,h = %.10f'%(u,l,h))
srtdxs = sorted(list(xs) + [l, h])
bandwidth = 0.2
cdf_smoother, bandwidth = kde_statsmodels_m_cdf_output(ydata,
srtdxs,
bandwidth=bandwidth)
d = dict(zip(srtdxs, cdf_smoother))
for x in xs:
if x < l:
nrm = d[l]
out.append(nrm *
(1 - genpareto.cdf(l - x, shape, loc=loc, scale=scale)))
elif x >= h:
nrm = d[h]
out.append((1 - nrm) *
genpareto.cdf(x - h, shape, loc=loc, scale=scale) + nrm)
else:
out.append(d[x])
return xs, out, srtdxs, cdf_smoother, bandwidth
def gpdcdf(sample, threshold, fit_method,
alpha): #plot gpd cdf with empirical points
[shape, scale, sample, sample_excess,
sample_over_thresh] = gpdfit(sample, threshold, fit_method) #fit the data
n = len(sample_over_thresh)
y = np.arange(1, n + 1) / n #empirical probabilities
i_initial = 0
n = len(sample)
for i in range(0, n):
if sample[i] > threshold + 0.0001:
i_initial = i
break
#Computing confidence interval with the Dvoretzky–Kiefer–Wolfowitz method based on the empirical points
F1 = []
F2 = []
for i in range(i_initial, len(sample)):
e = (((mt.log(2 / alpha)) / (2 * len(sample_over_thresh)))**0.5)
F1.append(y[i - i_initial] - e)
F2.append(y[i - i_initial] + e)
x_points = np.arange(0, max(sample),
0.001) #generating points to apply in the cdf
cdf = genpareto.cdf(x_points, shape, loc=threshold,
scale=scale) #getting theoretical cdf
#Plotting cdf
plt.figure(7)
plt.plot(x_points, cdf, color='black', label='Theoretical CDF')
plt.xlabel('Data')
plt.ylabel('CDF')
plt.title('Data Comulative Distribution Function')
plt.scatter(sorted(sample_over_thresh), y, label='Empirical CDF')
plt.plot(sorted(sample_over_thresh),
F1,
linestyle='--',
color='red',
alpha=0.8,
lw=0.9,
label='Dvoretzky–Kiefer–Wolfowitz Confidence Bands')
plt.plot(sorted(sample_over_thresh),
F2,
linestyle='--',
color='red',
alpha=0.8,
lw=0.9)
plt.legend()
plt.show()
def ppplot(sample, threshold, fit_method,
alpha): #probability-probability plot to diagnostic the model
[shape, scale, sample, sample_excess,
sample_over_thresh] = gpdfit(sample, threshold, fit_method) #fit the data
n = len(sample_over_thresh)
#Getting empirical probabilities
y = np.arange(1, n + 1) / n
#Getting theoretical probabilities
cdf_pp = genpareto.cdf(sample_over_thresh,
shape,
loc=threshold,
scale=scale)
#Getting Confidence Intervals using the Dvoretzky–Kiefer–Wolfowitz method
i_initial = 0
n = len(sample)
for i in range(0, n):
if sample[i] > threshold + 0.0001:
i_initial = i
break
F1 = []
F2 = []
for i in range(i_initial, len(sample)):
e = (((mt.log(2 / alpha)) / (2 * len(sample_over_thresh)))**0.5)
F1.append(y[i - i_initial] - e)
F2.append(y[i - i_initial] + e)
#Plotting PP
plt.figure(6)
sns.regplot(y,
cdf_pp,
ci=None,
line_kws={
'color': 'black',
'label': 'Regression Line'
})
plt.plot(y,
F1,
linestyle='--',
color='red',
alpha=0.5,
lw=0.8,
label='Dvoretzky–Kiefer–Wolfowitz Confidence Bands')
plt.plot(y, F2, linestyle='--', color='red', alpha=0.5, lw=0.8)
plt.legend()
plt.title('P-P Plot')
plt.xlabel('Empirical Probability')
plt.ylabel('Theoritical Probability')
plt.show()
def EstimaFrequencias(self, Parametros):
if self.tipoSerie == 'Parcial':
limite = lp.LimiteParcial(self.dadoSerie).AchaLimite(2)
Parciais = se.Series(self.dadoSerie).serieMaxParcial(limite)
datasP, PicosParciais = se.Series(Parciais).separaDados()
PicosParciais.sort(reverse = True)
print(PicosParciais)
frequencias = genpareto.cdf(PicosParciais, Parametros[0],
loc = Parametros[1],
scale = Parametros[2])
elif self.tipoSerie == 'Anual':
Anuais = se.Series(self.dadoSerie).serieMaxAnual()
datasA, PicosAnuais = se.Series(Anuais).separaDados()
PicosAnuais.sort(reverse = True)
print(PicosAnuais)
frequencias = genextreme.cdf(PicosAnuais, Parametros[0],
loc = Parametros[1],
scale = Parametros[2])
return frequencias
def extremal_distribution_fit(data,
var_name,
sample,
threshold,
fit_type,
x_min,
x_max,
n_points,
loc=None,
scale=None,
cumulative=True):
# Initialization of the output variables
param = None
x = None
y = None
y_rp = None
if fit_type == 'gpd':
# Fit the exceedances over threshold to Generalized Pareto distribution
param = generalized_pareto_distribution_fit(sample, threshold, loc,
scale)
# Calculate the pdf and/or cdf
x = np.linspace(x_min, x_max, n_points)
if cumulative:
y = genpareto.cdf(x, param[0], param[1], param[2])
# Calculate the number of extreme peaks per year
n_peaks_year = len(sample) / len(
data[var_name].index.year.unique())
y_rp = return_period_curve(n_peaks_year, y)
else:
y = genpareto.pdf(x, param[0], param[1], param[2])
elif fit_type == 'coles':
# Fit the exceedances over threshold to Generalized Pareto distribution
param = generalized_pareto_distribution_fit(sample, threshold, loc,
scale)
x = np.arange(1, 501)
u = param[1]
sigma = param[2]
xi = param[0]
# Mean number of data in a year (numero medio de datos en un año)
n_y = len(data[var_name]) / len(data[var_name].index.year.unique())
# Total number of POT / number of years
z_u = len(sample) / len(data[var_name])
# n_y*z_u is the number of POT / number of years -- > numer of POT per year
y_rp = u + (sigma / xi) * (((x * n_y * z_u)**xi) - 1)
elif fit_type == 'gev':
param = generalized_extreme_value_distribution_fit(sample, loc, scale)
# Calculate the pdf and/or cdf
x = np.linspace(x_min, x_max, n_points)
if cumulative:
y = genextreme.cdf(x, param[0], param[1], param[2])
# Calculate the number of extreme peaks per year
n_peaks_year = 1
y_rp = return_period_curve(n_peaks_year, y)
else:
y = genpareto.pdf(x, param[0], param[1], param[2])
elif fit_type == 'poisson':
# Calculate the pdf and/or cdf
x = np.linspace(x_min, x_max, n_points)
# Fit the exceedances over threshold to Generalized Pareto distribution
gpd_param = generalized_pareto_distribution_fit(
sample, threshold, loc, scale)
# Poisson parameter (número de eventos extraños al año)
poisspareto_param = len(sample) / len(
data[var_name].index.year.unique())
# Poisson pareto parameters
poisspareto_param = [
poisspareto_param, gpd_param[0], gpd_param[2], gpd_param[1]
]
# Equivalent gev parameters
param = [0, 0, 0]
param[0] = -poisspareto_param[1]
param[1] = poisspareto_param[2] * (poisspareto_param[0]**
poisspareto_param[1])
param[2] = poisspareto_param[3] + (
(poisspareto_param[2] / poisspareto_param[1]) *
((poisspareto_param[0]**poisspareto_param[1]) - 1))
if cumulative:
y = genextreme.cdf(x, param[0], param[2], param[1])
# Calculate the number of extreme peaks per year
n_peaks_year = 1
y_rp = return_period_curve(n_peaks_year, y)
else:
y = genextreme.pdf(x, param[0], param[2], param[1])
return param, x, y, y_rp
fit = genpareto.fit(poted_values, floc=[poted_values[-1]])
fit = genpareto.fit(poted_values,
floc=fit[1],
fscale=fit[2])
if j == 0:
#print(fit[2])
mu_check.append(poted_values[-1])
gamma = fit[0]
mu = fit[1]
sigma = fit[2]
#gpd_params_dict[str(j + 1)]["gamma"].append(gamma)
#gpd_params_dict[str(j + 1)]["mu"].append(mu[0])
#gpd_params_dict[str(j + 1)]["sigma"].insert(sigma)
if dw[i, j] >= fit[1]:
hpp[i - gev_window, j] = 1 - genpareto.cdf(
dw[i, j], fit[0], fit[1], fit[2]) + 1e-50
#gpd_params[j].append(fit)
totalhpp1 = -np.log10(np.prod(hpp, axis=1))
min_index = np.argmax(totalhpp1)
le = pa.detection.learning_entropy(w, m=1200, order=1)
snr[seed_counter] = 10 * np.log10(
(np.std(desired_output[gev_window:])**2) / (noise_sigma**2))
# print(Fore.RED + "experiment number: " + str(seed_counter))
# print(Fore.GREEN + "SNR: " + (str(snr[seed_counter])))
# print(Fore.BLACK + "min_index GPD: " + str(min_index))
if min_index > 199 and min_index < 211:
gpd_result[seed_counter] = 1
max_index_elbnd = np.argmax(elbnd[-400:])
def genpareto_gradient_cdf(x, c, scale):
"""Gradient of the Generalized Pareto Distribution function w.r.t. to the scale and shape parameter
:param x: array_like
quantiles
:param c: positive number
shape parameter
:param scale:positive number
scale parameter (default=1)
:return: (2 X n)-matrix where n is equal to the size of x
The first row corresponds to the gradient of the cdf w.r.t. the shape parameter evaluated at x
The second row corresponds to the gradient of the cdf w.r.t. the scale parameter evaluated at x
"""
output = np.zeros(shape=(2, x.size))
cond = 0 < (1+c*x/scale)
output[0] = np.where(cond, (-1/c**2*log(1 + c*x/scale) + x/(c*(scale + c * x)))*(1 - genpareto.cdf(x, c, scale)), 0)
output[1] = -x/scale*genpareto.pdf(x, c, scale)
return output
import numpy as np from scipy.stats import genpareto import matplotlib.pyplot as plt fig, ax = plt.subplots(1, 1) c = 0.1 mean, var, skew, kurt = genpareto.stats(c, moments='mvsk') x = np.linspace(genpareto.ppf(0.01, c),genpareto.ppf(0.99, c), 100) ax.plot(x, genpareto.pdf(x, c),'r-', lw=5, alpha=0.6, label='genpareto pdf') rv = genpareto(c) ax.plot(x, rv.pdf(x), 'k-', lw=2, label='frozen pdf') vals = genpareto.ppf([0.001, 0.5, 0.999], c) np.allclose([0.001, 0.5, 0.999], genpareto.cdf(vals, c)) r = genpareto.rvs(c, size=1000) ax.hist(r, normed=True, histtype='stepfilled', alpha=0.2) ax.legend(loc='best', frameon=False) plt.show()
padx=4)
quantileroot.mainloop()
#Probability Plot
line2 = [.01 * x for x in range(100)]
plt.title('Probability Plot')
plt.ylabel('Model')
plt.xlabel('Empirical')
plt.scatter(listofpercents, [
genparetodist(x, Threshold, Maxsigma, Maxxis)
for x in xvalues['x' + str(threshposition)]
],
s=.5)
plt.scatter(listofpercents, [
genpareto.cdf(y, Maxxis, loc=0, scale=Maxsigma)
for y in yvalues['y' + str(threshposition)]
],
s=.5,
label='Simulation Data')
plt.axis([0, 1, 0, 1])
plt.plot(line2, line2, 'b', label='Best Model Fit')
plt.legend(bbox_to_anchor=(0., 1.1, 1., .102),
loc=3,
ncol=1,
mode="expand",
borderaxespad=0.)
plt.show()
print 'The Dotted line (Real Data), should reasonably agree with the model to make a Linear fit.'
print '\n'
import numpy as np from scipy.stats import genpareto import matplotlib.pyplot as plt fig, ax = plt.subplots(1, 1) c = 0.1 mean, var, skew, kurt = genpareto.stats(c, moments='mvsk') x = np.linspace(genpareto.ppf(0.01, c), genpareto.ppf(0.99, c), 100) ax.plot(x, genpareto.pdf(x, c), 'r-', lw=5, alpha=0.6, label='genpareto pdf') rv = genpareto(c) ax.plot(x, rv.pdf(x), 'k-', lw=2, label='frozen pdf') vals = genpareto.ppf([0.001, 0.5, 0.999], c) np.allclose([0.001, 0.5, 0.999], genpareto.cdf(vals, c)) r = genpareto.rvs(c, size=1000) ax.hist(r, normed=True, histtype='stepfilled', alpha=0.2) ax.legend(loc='best', frameon=False) plt.show()
gamma_k = lamda * mean_X - gamma_alpha / gamma_beta
sort_comp_poisson_rnd = compound_poisson_distribution(
lamda, num_values, mu, sigma)
sort_comp_poisson_rnd.sort()
alpha_low = .99
alpha_high = .99999
low_val = sort_comp_poisson_rnd[math.floor(num_values * alpha_low)]
high_val = sort_comp_poisson_rnd[math.floor(num_values * alpha_high)]
mu_gp = low_val
sample_data = compound_poisson_distribution(lamda, num_values, mu, sigma)
data_gp = sample_data[sample_data > mu_gp] - mu_gp
gpd_value = gpd.fit(data_gp)
cdf_values = np.arange(low_val, high_val + (high_val - low_val) / 1000,
(high_val - low_val) / 1000)
norm_cdf_tail = 1 - norm.cdf(cdf_values, mean_SN, (var_SN)**(1 / 2))
gamma_cdf_tail = 1 - gamma.cdf(
cdf_values - gamma_k, gamma_alpha, scale=1 / gamma_beta)
GP_cdf_tail = 1 - (genpareto.cdf(
cdf_values - low_val, gpd_value[0], scale=gpd_value[2]) * 0.01 + 0.99)
emp_cdf_tails = emp_cdf_tail(sample_data, cdf_values)
plt.loglog(cdf_values, norm_cdf_tail, label='CLT')
plt.loglog(cdf_values, gamma_cdf_tail, label='GAMMA')
plt.loglog(cdf_values, GP_cdf_tail, label='GP')
plt.loglog(cdf_values, emp_cdf_tails, label='EMP')
plt.title('LOG-LOG plot of 1-F_SN vs x')
plt.legend()
plt.show()