predictr 0.1.2

Weibull Analysis Utilities

predictr

Weibull Analysis Utilities

Installation

Use the package manager pip to install predictr.

pip install predictr

Usage

Import predictr in python

from predictr import Analysis

Default Parameter values

df: list = None -> failures in seconds, days, bo. of cycles etc., e.g. df = [100, 120, 80, 300]
ds: list = None -> suspensions (right-censored) in seconds, days, bo. of cycles etc., e.g. ds = [300, 400, 400]
show: bool = False -> If True, the Weibull probability plot will be plotted.
plot_style = 'ggplot' -> Choose a style according to your needs. See https://matplotlib.org/3.1.0/gallery/style_sheets/style_sheets_reference.html for styles.
bounds=None -> Use following table to configure everything related to confidence bounds, e.g. if you want to use Monte-Carlo pivotal bounds for the Median Rank Regression: bounds = 'mcpb'.

confidence bounds	mle()	mrr()	uncensored data	censored data	type	argument value
Beta-Binomial Bounds	-	x	x	x	'2s', '1sl', '1su'	'bbb'
Monte-Carlo Pivotal Bounds	-	x	x	x	'2s', '1sl', '1su'	'mcpb'
Non-Parametric Bootstrap Bounds	x	x	x	-	'2s', '1sl', '1su'	'npbb'
Parametric Bootstrap Bounds	x	x	x	-	'2s', '1sl', '1su'	'pbb'
Fisher Bounds	x	-	x	x	'2s', '1sl', '1su'	'fisher'
Likelihood Ratio Bounds	x	-	x	x	'2s', '1sl', '1su'	'lrb'

bounds_type = '2s' -> '2s': two-sided confidence bounds, '1su': upper confidence bounds, '1sl': lower confidence bounds. E.g. bounds_type = '1sl'.
cl=0.9 -> configure the confidence level in the intervall (0, 1.0)
bcm=None -> Define the bias-correction method when the MLE is being used. Bootstrap bias-corrections are dependent on the number of bootstrap replication and the chosen statistic, e.g. if bcm = 'np_bs': bs_size = 5000 and est_type = 'median'.
bs_size = 5000 -> Resampling/Bootstrap sample size (number of replication). bs_size should be greater than or equal to 2000 for accurate results. The higher the nuber of replication, the longer it takes to compute the bias-correction.
est_type = 'median' -> When using bootstrap bias-corrections, this argument decides which statistic to compute from the bootstrap samples.
The following table provides possible configurations. Bias-corrections for mrr() are not supported, yet.

Bias-correction method	mle()	mrr()	argument value	config.	statistic
C4	x	-	'c4'	-	-
hrbu	x	-	'hrbu'	-	-
non-parametric Bootstrap correction	x	-	'np_bs'	bs_size	'mean', 'median', 'trimmed_mean'
Parametric Bootstrap correction	x	-	'p_bs'	bs_size	'mean', 'median', 'trimmed_mean'

unit = '-' -> Unit of the elements in df and ds, e.g. unit = 'seconds', unit = 'days', unit = 'ms' etc.

How to use the Maximum Likelihood Estimation (MLE)

Just add '.mle()' after Analysis() object = Analysis().mle()

Uncensored sample

Example:

failures = [0.4508831,  0.68564703, 0.76826143, 0.88231395, 1.48287253, 1.62876357]
prototype_a = Analysis(df=failures, bounds='fisher',show=True).mle()

Censored sample

Example:

failures = [0.4508831,  0.68564703, 0.76826143, 0.88231395, 1.48287253, 1.62876357]
suspensions = [1.9, 2.0, 2.0]
prototype_a = Analysis(df=uncen_sample, bounds='lrb',show=True).mle()

How to use the Maximum Rank Regression (MRR)

Just add '.mrr()' after Analysis() object = Analysis().mrr()

Uncensored sample

Example:

failures = [0.4508831,  0.68564703, 0.76826143, 0.88231395, 1.48287253, 1.62876357]
prototype_a = Analysis(df=failures, bounds='bbb',show=True).mrr()

Censored sample

Example:

failures = [0.4508831,  0.68564703, 0.76826143, 0.88231395, 1.48287253, 1.62876357]
suspensions = [1.9, 2.0, 2.0]
prototype_a = Analysis(df=failures, ds=suspensions, bounds='mcpb',show=True).mrr()

To Do

I will add a homepage with more detailed examples and guidelines for non-experts in the field of reliability engineering.