def test_Fit_Exponential_2P(): dist = Exponential_Distribution(Lambda=5, gamma=500) rawdata = dist.random_samples(20, seed=5) data = make_right_censored_data(data=rawdata, threshold=dist.mean) MLE = Fit_Exponential_2P(failures=data.failures, right_censored=data.right_censored, method='MLE', show_probability_plot=False, print_results=False) assert_allclose(MLE.Lambda, 7.062867654421206, rtol=rtol, atol=atol) assert_allclose(MLE.gamma, 500.016737532126, rtol=rtol, atol=atol) assert_allclose(MLE.AICc, -23.939665128347745, rtol=rtol, atol=atol) assert_allclose(MLE.BIC, -22.65408293418094, rtol=rtol, atol=atol) assert_allclose(MLE.loglik, 14.322773740644461, rtol=rtol, atol=atol) assert_allclose(MLE.AD, 29.413655089419287, rtol=rtol, atol=atol) LS = Fit_Exponential_2P(failures=data.failures, right_censored=data.right_censored, method='LS', show_probability_plot=False, print_results=False) assert_allclose(LS.Lambda, 6.4445633542175, rtol=rtol, atol=atol) assert_allclose(LS.gamma, 500.01368943066706, rtol=rtol, atol=atol) assert_allclose(LS.AICc, -23.031777273560103, rtol=rtol, atol=atol) assert_allclose(LS.BIC, -21.7461950793933, rtol=rtol, atol=atol) assert_allclose(LS.loglik, 13.86882981325064, rtol=rtol, atol=atol) assert_allclose(LS.AD, 29.33840933641424, rtol=rtol, atol=atol)
def test_Fit_Exponential_1P(): dist = Exponential_Distribution(Lambda=5) rawdata = dist.random_samples(20, seed=5) data = make_right_censored_data(data=rawdata, threshold=dist.mean) MLE = Fit_Exponential_1P(failures=data.failures, right_censored=data.right_censored, method='MLE', show_probability_plot=False, print_results=False) assert_allclose(MLE.Lambda, 6.101198944227536, rtol=rtol, atol=atol) assert_allclose(MLE.AICc, -22.032339191099148, rtol=rtol, atol=atol) assert_allclose(MLE.BIC, -21.25882913976738, rtol=rtol, atol=atol) assert_allclose(MLE.loglik, 12.127280706660684, rtol=rtol, atol=atol) assert_allclose(MLE.AD, 29.59913306667145, rtol=rtol, atol=atol) LS = Fit_Exponential_1P(failures=data.failures, right_censored=data.right_censored, method='LS', show_probability_plot=False, print_results=False) assert_allclose(LS.Lambda, 5.776959885774546, rtol=rtol, atol=atol) assert_allclose(LS.AICc, -21.988412212242917, rtol=rtol, atol=atol) assert_allclose(LS.BIC, -21.214902160911148, rtol=rtol, atol=atol) assert_allclose(LS.loglik, 12.10531721723257, rtol=rtol, atol=atol) assert_allclose(LS.AD, 29.52124203457833, rtol=rtol, atol=atol)
def test_Fit_Exponential_2P(): dist = Exponential_Distribution(Lambda=5, gamma=500) rawdata = dist.random_samples(20, seed=5) data = make_right_censored_data(data=rawdata, threshold=dist.mean) fit = Fit_Exponential_2P(failures=data.failures, right_censored=data.right_censored, show_probability_plot=False, print_results=False) assert_allclose(fit.Lambda, 7.00351280734533,rtol=rtol,atol=atol) assert_allclose(fit.gamma, 500.015837532126,rtol=rtol,atol=atol) assert_allclose(fit.AICc, -23.686473231109936,rtol=rtol,atol=atol) assert_allclose(fit.loglik, 14.196177792025557,rtol=rtol,atol=atol)
def test_Fit_Exponential_1P(): dist = Exponential_Distribution(Lambda=5) rawdata = dist.random_samples(20, seed=5) data = make_right_censored_data(data=rawdata, threshold=dist.mean) fit = Fit_Exponential_1P(failures=data.failures, right_censored=data.right_censored, show_probability_plot=False, print_results=False) assert_allclose(fit.Lambda, 6.101199434421275,rtol=rtol,atol=atol) assert_allclose(fit.gamma, 0,rtol=rtol,atol=atol) assert_allclose(fit.AICc, -22.032339191099254,rtol=rtol,atol=atol) assert_allclose(fit.loglik, 12.127280706660738,rtol=rtol,atol=atol)
def __update_params(_, self): value1 = self.s0.val value2 = self.s1.val value3 = self.s2.val if self.name == 'Weibull': dist = Weibull_Distribution(alpha=value1, beta=value2, gamma=value3) elif self.name == 'Loglogistic': dist = Loglogistic_Distribution(alpha=value1, beta=value2, gamma=value3) elif self.name == 'Gamma': dist = Gamma_Distribution(alpha=value1, beta=value2, gamma=value3) elif self.name == 'Loglogistic': dist = Loglogistic_Distribution(alpha=value1, beta=value2, gamma=value3) elif self.name == 'Lognormal': dist = Lognormal_Distribution(mu=value1, sigma=value2, gamma=value3) elif self.name == 'Beta': dist = Beta_Distribution(alpha=value1, beta=value2) elif self.name == 'Normal': dist = Normal_Distribution(mu=value1, sigma=value2) elif self.name == 'Exponential': dist = Exponential_Distribution(Lambda=value1, gamma=value2) else: raise ValueError(str(self.name + ' is an unknown distribution name')) plt.sca(self.ax_pdf) plt.cla() dist.PDF() plt.title('PDF') plt.xlabel('') plt.ylabel('') plt.sca(self.ax_cdf) plt.cla() dist.CDF() plt.title('CDF') plt.xlabel('') plt.ylabel('') plt.sca(self.ax_sf) plt.cla() dist.SF() plt.title('SF') plt.xlabel('') plt.ylabel('') plt.sca(self.ax_hf) plt.cla() dist.HF() plt.title('HF') plt.xlabel('') plt.ylabel('') plt.sca(self.ax_chf) plt.cla() dist.CHF() plt.title('CHF') plt.xlabel('') plt.ylabel('') plt.subplots_adjust(left=0.07, right=0.98, top=0.9, bottom=0.25, wspace=0.18, hspace=0.30) plt.suptitle(dist.param_title_long, fontsize=15) plt.draw()
def __init__(self, distribution, include_location_shifted=True, show_plot=True, print_results=True, number_of_distributions_to_show=3): # ensure the input is a distribution object if type(distribution) not in [ Weibull_Distribution, Normal_Distribution, Lognormal_Distribution, Exponential_Distribution, Gamma_Distribution, Beta_Distribution ]: raise ValueError( 'distribution must be a probability distribution object from the reliability.Distributions module. First define the distribution using Reliability.Distributions.___' ) # sample the CDF from 0.001 to 0.999. These samples will be used to fit all other distributions. RVS = distribution.quantile( np.linspace(0.001, 0.999, 698) ) # 698 samples is the ideal number for the points to align. Evidenced using plot_points. # filter out negative values RVS_filtered = [] negative_values_error = False for item in RVS: if item > 0: RVS_filtered.append(item) else: negative_values_error = True if negative_values_error is True: print( 'WARNING: The input distribution has non-negligible area for x<0. Samples from this region have been discarded to enable other distributions to be fitted.' ) fitted_results = Fit_Everything( failures=RVS_filtered, print_results=False, show_probability_plot=False, show_histogram_plot=False, show_PP_plot=False ) # fit all distributions to the filtered samples ranked_distributions = list(fitted_results.results.index.values) ranked_distributions.remove( distribution.name2 ) # removes the fitted version of the original distribution ranked_distributions_objects = [] ranked_distributions_labels = [] sigfig = 2 for dist_name in ranked_distributions: if dist_name == 'Weibull_2P': ranked_distributions_objects.append( Weibull_Distribution(alpha=fitted_results.Weibull_2P_alpha, beta=fitted_results.Weibull_2P_beta)) ranked_distributions_labels.append( str('Weibull_2P (α=' + str(round(fitted_results.Weibull_2P_alpha, sigfig)) + ',β=' + str(round(fitted_results.Weibull_2P_beta, sigfig)) + ')')) elif dist_name == 'Gamma_2P': ranked_distributions_objects.append( Gamma_Distribution(alpha=fitted_results.Gamma_2P_alpha, beta=fitted_results.Gamma_2P_beta)) ranked_distributions_labels.append( str('Gamma_2P (α=' + str(round(fitted_results.Gamma_2P_alpha, sigfig)) + ',β=' + str(round(fitted_results.Gamma_2P_beta, sigfig)) + ')')) elif dist_name == 'Normal_2P': ranked_distributions_objects.append( Normal_Distribution(mu=fitted_results.Normal_2P_mu, sigma=fitted_results.Normal_2P_sigma)) ranked_distributions_labels.append( str('Normal_2P (μ=' + str(round(fitted_results.Normal_2P_mu, sigfig)) + ',σ=' + str(round(fitted_results.Normal_2P_sigma, sigfig)) + ')')) elif dist_name == 'Lognormal_2P': ranked_distributions_objects.append( Lognormal_Distribution( mu=fitted_results.Lognormal_2P_mu, sigma=fitted_results.Lognormal_2P_sigma)) ranked_distributions_labels.append( str('Lognormal_2P (μ=' + str(round(fitted_results.Lognormal_2P_mu, sigfig)) + ',σ=' + str(round(fitted_results.Lognormal_2P_sigma, sigfig)) + ')')) elif dist_name == 'Exponential_1P': ranked_distributions_objects.append( Exponential_Distribution( Lambda=fitted_results.Expon_1P_lambda)) ranked_distributions_labels.append( str('Exponential_1P (lambda=' + str(round(fitted_results.Expon_1P_lambda, sigfig)) + ')')) elif dist_name == 'Beta_2P': ranked_distributions_objects.append( Beta_Distribution(alpha=fitted_results.Beta_2P_alpha, beta=fitted_results.Beta_2P_beta)) ranked_distributions_labels.append( str('Beta_2P (α=' + str(round(fitted_results.Beta_2P_alpha, sigfig)) + ',β=' + str(round(fitted_results.Beta_2P_beta, sigfig)) + ')')) if include_location_shifted is True: if dist_name == 'Weibull_3P': ranked_distributions_objects.append( Weibull_Distribution( alpha=fitted_results.Weibull_3P_alpha, beta=fitted_results.Weibull_3P_beta, gamma=fitted_results.Weibull_3P_gamma)) ranked_distributions_labels.append( str('Weibull_3P (α=' + str( round(fitted_results.Weibull_3P_alpha, sigfig)) + ',β=' + str(round(fitted_results.Weibull_3P_beta, sigfig)) + ',γ=' + str(round(fitted_results.Weibull_3P_gamma, sigfig)) + ')')) elif dist_name == 'Gamma_3P': ranked_distributions_objects.append( Gamma_Distribution( alpha=fitted_results.Gamma_3P_alpha, beta=fitted_results.Gamma_3P_beta, gamma=fitted_results.Gamma_3P_gamma)) ranked_distributions_labels.append( str('Gamma_3P (α=' + str(round(fitted_results.Gamma_3P_alpha, sigfig)) + ',β=' + str(round(fitted_results.Gamma_3P_beta, sigfig)) + ',γ=' + str(round(fitted_results.Gamma_3P_gamma, sigfig)) + ')')) elif dist_name == 'Lognormal_3P': ranked_distributions_objects.append( Lognormal_Distribution( mu=fitted_results.Lognormal_3P_mu, sigma=fitted_results.Lognormal_3P_sigma, gamma=fitted_results.Lognormal_3P_gamma)) ranked_distributions_labels.append( str('Lognormal_3P (μ=' + str( round(fitted_results.Lognormal_3P_mu, sigfig)) + ',σ=' + str( round(fitted_results.Lognormal_3P_sigma, sigfig)) + ',γ=' + str( round(fitted_results.Lognormal_3P_gamma, sigfig)) + ')')) elif dist_name == 'Exponential_2P': ranked_distributions_objects.append( Exponential_Distribution( Lambda=fitted_results.Expon_1P_lambda, gamma=fitted_results.Expon_2P_gamma)) ranked_distributions_labels.append( str('Exponential_1P (lambda=' + str( round(fitted_results.Expon_1P_lambda, sigfig)) + ',γ=' + str(round(fitted_results.Expon_2P_gamma, sigfig)) + ')')) number_of_distributions_fitted = len(ranked_distributions_objects) self.results = ranked_distributions_objects self.most_similar_distribution = ranked_distributions_objects[0] if print_results is True: print('The input distribution was:') print(distribution.param_title_long) if number_of_distributions_fitted < number_of_distributions_to_show: number_of_distributions_to_show = number_of_distributions_fitted print('\nThe top', number_of_distributions_to_show, 'most similar distributions are:') counter = 0 while counter < number_of_distributions_to_show and counter < number_of_distributions_fitted: dist = ranked_distributions_objects[counter] print(dist.param_title_long) counter += 1 if show_plot is True: plt.figure(figsize=(14, 6)) plt.suptitle( str('Plot of similar distributions to ' + distribution.param_title_long)) counter = 0 xlower = distribution.quantile(0.001) xupper = distribution.quantile(0.999) x_delta = xupper - xlower plt.subplot(121) distribution.PDF(label=str('Input distribution [' + distribution.name2 + ']'), linestyle='--') while counter < number_of_distributions_to_show and counter < number_of_distributions_fitted: ranked_distributions_objects[counter].PDF( label=ranked_distributions_labels[counter]) counter += 1 plt.xlim([xlower - x_delta * 0.1, xupper + x_delta * 0.1]) plt.legend() plt.title('PDF') counter = 0 plt.subplot(122) distribution.CDF(label=str('Input distribution [' + distribution.name2 + ']'), linestyle='--') while counter < number_of_distributions_to_show and counter < number_of_distributions_fitted: ranked_distributions_objects[counter].CDF( label=ranked_distributions_labels[counter]) counter += 1 plt.xlim([xlower - x_delta * 0.1, xupper + x_delta * 0.1]) plt.legend() plt.title('CDF') plt.subplots_adjust(left=0.08, right=0.95) plt.show()
def __update_distribution(name, self): self.name = name if self.name == 'Weibull': dist = Weibull_Distribution(alpha=100, beta=2, gamma=0) param_names = ['Alpha', 'Beta', 'Gamma'] plt.sca(self.ax0) plt.cla() self.s0 = Slider(self.ax0, param_names[0], valmin=0.1, valmax=500, valinit=dist.alpha) plt.sca(self.ax1) plt.cla() self.s1 = Slider(self.ax1, param_names[1], valmin=0.2, valmax=25, valinit=dist.beta) try: # clear the slider axis if it exists plt.sca(self.ax2) plt.cla() except ValueError: # if the slider axis does no exist (because it was destroyed by a 2P distribution) then recreate it self.ax2 = plt.axes([0.1, 0.05, 0.8, 0.03], facecolor=self.background_color) self.s2 = Slider(self.ax2, param_names[2], valmin=0, valmax=500, valinit=dist.gamma) elif self.name == 'Gamma': dist = Gamma_Distribution(alpha=100, beta=5, gamma=0) param_names = ['Alpha', 'Beta', 'Gamma'] plt.sca(self.ax0) plt.cla() self.s0 = Slider(self.ax0, param_names[0], valmin=0.1, valmax=500, valinit=dist.alpha) plt.sca(self.ax1) plt.cla() self.s1 = Slider(self.ax1, param_names[1], valmin=0.2, valmax=25, valinit=dist.beta) try: # clear the slider axis if it exists plt.sca(self.ax2) plt.cla() except ValueError: # if the slider axis does no exist (because it was destroyed by a 2P distribution) then recreate it self.ax2 = plt.axes([0.1, 0.05, 0.8, 0.03], facecolor=self.background_color) self.s2 = Slider(self.ax2, param_names[2], valmin=0, valmax=500, valinit=dist.gamma) elif self.name == 'Loglogistic': dist = Loglogistic_Distribution(alpha=100, beta=8, gamma=0) param_names = ['Alpha', 'Beta', 'Gamma'] plt.sca(self.ax0) plt.cla() self.s0 = Slider(self.ax0, param_names[0], valmin=0.1, valmax=500, valinit=dist.alpha) plt.sca(self.ax1) plt.cla() self.s1 = Slider(self.ax1, param_names[1], valmin=0.2, valmax=50, valinit=dist.beta) try: # clear the slider axis if it exists plt.sca(self.ax2) plt.cla() except ValueError: # if the slider axis does no exist (because it was destroyed by a 2P distribution) then recreate it self.ax2 = plt.axes([0.1, 0.05, 0.8, 0.03], facecolor=self.background_color) self.s2 = Slider(self.ax2, param_names[2], valmin=0, valmax=500, valinit=dist.gamma) elif self.name == 'Lognormal': dist = Lognormal_Distribution(mu=2.5, sigma=0.5, gamma=0) param_names = ['Mu', 'Sigma', 'Gamma'] plt.sca(self.ax0) plt.cla() self.s0 = Slider(self.ax0, param_names[0], valmin=0, valmax=5, valinit=dist.mu) plt.sca(self.ax1) plt.cla() self.s1 = Slider(self.ax1, param_names[1], valmin=0.01, valmax=2, valinit=dist.sigma) try: # clear the slider axis if it exists plt.sca(self.ax2) plt.cla() except ValueError: # if the slider axis does no exist (because it was destroyed by a 2P distribution) then recreate it self.ax2 = plt.axes([0.1, 0.05, 0.8, 0.03], facecolor=self.background_color) self.s2 = Slider(self.ax2, param_names[2], valmin=0, valmax=500, valinit=dist.gamma) elif self.name == 'Normal': dist = Normal_Distribution(mu=0, sigma=10) param_names = ['Mu', 'Sigma', ''] plt.sca(self.ax0) plt.cla() self.s0 = Slider(self.ax0, param_names[0], valmin=-100, valmax=100, valinit=dist.mu) plt.sca(self.ax1) plt.cla() self.s1 = Slider(self.ax1, param_names[1], valmin=0.01, valmax=20, valinit=dist.sigma) try: # clear the slider axis if it exists self.ax2.remove() # this will destroy the axes except KeyError: pass elif self.name == 'Exponential': dist = Exponential_Distribution(Lambda=1, gamma=0) param_names = ['Lambda', 'Gamma', ''] plt.sca(self.ax0) plt.cla() self.s0 = Slider(self.ax0, param_names[0], valmin=0.001, valmax=5, valinit=dist.Lambda) plt.sca(self.ax1) plt.cla() self.s1 = Slider(self.ax1, param_names[1], valmin=0, valmax=500, valinit=dist.gamma) try: # clear the slider axis if it exists self.ax2.remove() # this will destroy the axes except KeyError: pass elif self.name == 'Beta': dist = Beta_Distribution(alpha=2, beta=2) param_names = ['Alpha', 'Beta', ''] plt.sca(self.ax0) plt.cla() self.s0 = Slider(self.ax0, param_names[0], valmin=0.01, valmax=5, valinit=dist.alpha) plt.sca(self.ax1) plt.cla() self.s1 = Slider(self.ax1, param_names[1], valmin=0.01, valmax=5, valinit=dist.beta) try: # clear the slider axis if it exists self.ax2.remove() # this will destroy the axes except KeyError: pass else: raise ValueError(str(self.name + ' is an unknown distribution name')) plt.suptitle(dist.param_title_long, fontsize=15) distribution_explorer.__update_params(None, self) distribution_explorer.__interactive(self) plt.draw()
def __init__(self, distribution=None, include_location_shifted=True, show_plot=True, print_results=True, monte_carlo_trials=1000, number_of_distributions_to_show=3): if type(distribution) not in [ Weibull_Distribution, Normal_Distribution, Lognormal_Distribution, Exponential_Distribution, Gamma_Distribution, Beta_Distribution ]: raise ValueError( 'distribution must be a probability distribution object from the reliability.Distributions module. First define the distribution using Reliability.Distributions.___' ) if monte_carlo_trials < 100: print( 'WARNING: Using less than 100 monte carlo trials will lead to extremely inaccurate results. The number of monte carlo trials has been changed to 100 to ensure accuracy.' ) monte_carlo_trials = 100 elif monte_carlo_trials >= 100 and monte_carlo_trials < 1000: print( 'WARNING: Using less than 1000 monte carlo trials will lead to inaccurate results.' ) if monte_carlo_trials > 10000: print( 'The recommended number of monte carlo trials is 1000. Using over 10000 may take a long time to calculate.' ) RVS = distribution.random_samples( number_of_samples=monte_carlo_trials ) # draw random samples from the original distribution # filter out negative values RVS_filtered = [] negative_values_error = False for item in RVS: if item > 0: RVS_filtered.append(item) else: negative_values_error = True if negative_values_error is True: print( 'WARNING: The input distribution has non-negligible area for x<0. Monte carlo samples from this region have been discarded to enable other distributions to be fitted.' ) fitted_results = Fit_Everything( failures=RVS_filtered, print_results=False, show_probability_plot=False, show_histogram_plot=False, show_PP_plot=False ) # fit all distributions to the filtered samples ranked_distributions = list(fitted_results.results.index.values) ranked_distributions.remove( distribution.name2 ) # removes the fitted version of the original distribution ranked_distributions_objects = [] ranked_distributions_labels = [] sigfig = 2 for dist_name in ranked_distributions: if dist_name == 'Weibull_2P': ranked_distributions_objects.append( Weibull_Distribution(alpha=fitted_results.Weibull_2P_alpha, beta=fitted_results.Weibull_2P_beta)) ranked_distributions_labels.append( str('Weibull_2P (α=' + str(round(fitted_results.Weibull_2P_alpha, sigfig)) + ',β=' + str(round(fitted_results.Weibull_2P_beta, sigfig)) + ')')) elif dist_name == 'Gamma_2P': ranked_distributions_objects.append( Gamma_Distribution(alpha=fitted_results.Gamma_2P_alpha, beta=fitted_results.Gamma_2P_beta)) ranked_distributions_labels.append( str('Gamma_2P (α=' + str(round(fitted_results.Gamma_2P_alpha, sigfig)) + ',β=' + str(round(fitted_results.Gamma_2P_beta, sigfig)) + ')')) elif dist_name == 'Normal_2P': ranked_distributions_objects.append( Normal_Distribution(mu=fitted_results.Normal_2P_mu, sigma=fitted_results.Normal_2P_sigma)) ranked_distributions_labels.append( str('Normal_2P (μ=' + str(round(fitted_results.Normal_2P_mu, sigfig)) + ',σ=' + str(round(fitted_results.Normal_2P_sigma, sigfig)) + ')')) elif dist_name == 'Lognormal_2P': ranked_distributions_objects.append( Lognormal_Distribution( mu=fitted_results.Lognormal_2P_mu, sigma=fitted_results.Lognormal_2P_sigma)) ranked_distributions_labels.append( str('Lognormal_2P (μ=' + str(round(fitted_results.Lognormal_2P_mu, sigfig)) + ',σ=' + str(round(fitted_results.Lognormal_2P_sigma, sigfig)) + ')')) elif dist_name == 'Exponential_1P': ranked_distributions_objects.append( Exponential_Distribution( Lambda=fitted_results.Expon_1P_lambda)) ranked_distributions_labels.append( str('Exponential_1P (lambda=' + str(round(fitted_results.Expon_1P_lambda, sigfig)) + ')')) elif dist_name == 'Beta_2P': ranked_distributions_objects.append( Beta_Distribution(alpha=fitted_results.Beta_2P_alpha, beta=fitted_results.Beta_2P_beta)) ranked_distributions_labels.append( str('Beta_2P (α=' + str(round(fitted_results.Beta_2P_alpha, sigfig)) + ',β=' + str(round(fitted_results.Beta_2P_beta, sigfig)) + ')')) if include_location_shifted is True: if dist_name == 'Weibull_3P': ranked_distributions_objects.append( Weibull_Distribution( alpha=fitted_results.Weibull_3P_alpha, beta=fitted_results.Weibull_3P_beta, gamma=fitted_results.Weibull_3P_gamma)) ranked_distributions_labels.append( str('Weibull_3P (α=' + str( round(fitted_results.Weibull_3P_alpha, sigfig)) + ',β=' + str(round(fitted_results.Weibull_3P_beta, sigfig)) + ',γ=' + str(round(fitted_results.Weibull_3P_gamma, sigfig)) + ')')) elif dist_name == 'Gamma_3P': ranked_distributions_objects.append( Gamma_Distribution( alpha=fitted_results.Gamma_3P_alpha, beta=fitted_results.Gamma_3P_beta, gamma=fitted_results.Gamma_3P_gamma)) ranked_distributions_labels.append( str('Gamma_3P (α=' + str(round(fitted_results.Gamma_3P_alpha, sigfig)) + ',β=' + str(round(fitted_results.Gamma_3P_beta, sigfig)) + ',γ=' + str(round(fitted_results.Gamma_3P_gamma, sigfig)) + ')')) elif dist_name == 'Lognormal_3P': ranked_distributions_objects.append( Lognormal_Distribution( mu=fitted_results.Lognormal_3P_mu, sigma=fitted_results.Lognormal_3P_sigma, gamma=fitted_results.Lognormal_3P_gamma)) ranked_distributions_labels.append( str('Lognormal_3P (μ=' + str( round(fitted_results.Lognormal_3P_mu, sigfig)) + ',σ=' + str( round(fitted_results.Lognormal_3P_sigma, sigfig)) + ',γ=' + str( round(fitted_results.Lognormal_3P_gamma, sigfig)) + ')')) elif dist_name == 'Exponential_2P': ranked_distributions_objects.append( Exponential_Distribution( Lambda=fitted_results.Expon_1P_lambda, gamma=fitted_results.Expon_2P_gamma)) ranked_distributions_labels.append( str('Exponential_1P (lambda=' + str( round(fitted_results.Expon_1P_lambda, sigfig)) + ',γ=' + str(round(fitted_results.Expon_2P_gamma, sigfig)) + ')')) number_of_distributions_fitted = len(ranked_distributions_objects) self.results = ranked_distributions_objects self.most_similar_distribution = ranked_distributions_objects[0] if print_results is True: print('The input distribution was:') print(distribution.param_title_long) if number_of_distributions_fitted < number_of_distributions_to_show: number_of_distributions_to_show = number_of_distributions_fitted print('\nThe top', number_of_distributions_to_show, 'most similar distributions are:') counter = 0 while counter < number_of_distributions_to_show and counter < number_of_distributions_fitted: dist = ranked_distributions_objects[counter] print(dist.param_title_long) counter += 1 if show_plot is True: plt.figure(figsize=(14, 6)) plt.suptitle( str('Plot of similar distributions to ' + distribution.param_title_long)) counter = 0 xlower = distribution.quantile(0.001) xupper = distribution.quantile(0.999) x_delta = xupper - xlower plt.subplot(121) distribution.PDF(label='Input distribution', linestyle='--') while counter < number_of_distributions_to_show and counter < number_of_distributions_fitted: ranked_distributions_objects[counter].PDF( label=ranked_distributions_labels[counter]) counter += 1 plt.xlim([xlower - x_delta * 0.1, xupper + x_delta * 0.1]) plt.legend() plt.title('PDF') counter = 0 plt.subplot(122) distribution.CDF(label='Input distribution', linestyle='--') while counter < number_of_distributions_to_show and counter < number_of_distributions_fitted: ranked_distributions_objects[counter].CDF( label=ranked_distributions_labels[counter]) counter += 1 plt.xlim([xlower - x_delta * 0.1, xupper + x_delta * 0.1]) plt.legend() plt.title('CDF') plt.subplots_adjust(left=0.08, right=0.95) plt.show()
def test_Exponential_Distribution(): dist = Exponential_Distribution(Lambda=0.2, gamma=10) assert_allclose(dist.mean, 15, rtol=rtol, atol=atol) assert_allclose(dist.standard_deviation, 5, rtol=rtol, atol=atol) assert_allclose(dist.variance, 25, rtol=rtol, atol=atol) assert_allclose(dist.skewness, 2, rtol=rtol, atol=atol) assert_allclose(dist.kurtosis, 9, rtol=rtol, atol=atol) assert dist.param_title_long == 'Exponential Distribution (λ=0.2,γ=10)' assert_allclose(dist.quantile(0.2), 11.11571775657105, rtol=rtol, atol=atol) assert_allclose(dist.inverse_SF(q=0.7), 11.783374719693661, rtol=rtol, atol=atol) assert_allclose(dist.mean_residual_life(20), 5, rtol=rtol, atol=atol) xvals = [dist.gamma - 1, dist.quantile(0.001), dist.quantile(0.01), dist.quantile(0.1), dist.quantile(0.9), dist.quantile(0.99), dist.quantile(0.999)] assert_allclose(dist.PDF(xvals=xvals, show_plot=False), [0.0, 0.19980000000000003, 0.198, 0.18, 0.019999999999999997, 0.002000000000000001, 0.0002000000000000004], rtol=rtol, atol=atol) assert_allclose(dist.CDF(xvals=xvals, show_plot=False), [0.0, 0.001, 0.01, 0.1, 0.9, 0.99, 0.999], rtol=rtol, atol=atol) assert_allclose(dist.SF(xvals=xvals, show_plot=False), [1.0, 0.999, 0.99, 0.9, 0.1, 0.01, 0.001], rtol=rtol, atol=atol) assert_allclose(dist.HF(xvals=xvals, show_plot=False), [0.0, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2], rtol=rtol, atol=atol) assert_allclose(dist.CHF(xvals=xvals, show_plot=False), [0.0, 0.0010005003335834318, 0.01005033585350148, 0.10536051565782643, 2.3025850929940463, 4.605170185988091, 6.907755278982136], rtol=rtol, atol=atol)