Process capability analysis charts

daspi.plotlib.precast.ProcessCapabilityAnalysisCharts(source, target, *, spec_limits, hue='', dist='norm', error_values=(), strategy='norm', agreement=6, possible_dists=DIST.COMMON, stretch_figsize=False)

Bases: JointChart

A class for creating process capability analysis charts.

This class extends the JointChart class and provides methods for creating process capability analysis charts. It allows you to visualize process capability analysis data and perform various analysis tasks.

PARAMETER	DESCRIPTION
source	The source data. TYPE: DataFrame
target	The target (dependant) variable. TYPE: str
spec_limits	The specification limits for the process capability analysis. TYPE: SpecLimits
hue	The hue variable for the chart, by default ''. TYPE: str DEFAULT: ''
dist	The probability distribution use for creating feature data (the theoretical values). Default is 'norm'. TYPE: scipy stats rv_continuous DEFAULT: 'norm'
error_values	If the process data may contain coded values for measurement errors or similar, they can be specified here, by default []. TYPE: tuple of float DEFAULT: ()
strategy	Which strategy should be used to determine the control limits (process spread): - eval: The strategy is determined according to the given evaluate function. If none is given, the internal evaluate method is used. - fit: First, the distribution that best represents the process data is searched for and then the agreed process spread is calculated - norm: it is assumed that the data is subject to normal distribution. The variation tolerance is then calculated as agreement * standard deviation - data: The quantiles for the process variation tolerance are read directly from the data. by default 'norm' TYPE: (eval, fit, norm, data) DEFAULT: 'eval'
agreement	Specify the tolerated process variation for which the control limits are to be calculated. - If int, the spread is determined using the normal distribution agreementσ, e.g. agreement = 6 -> 6σ ~ covers 99.75 % of the data. The upper and lower permissible quantiles are then calculated from this. - If float, the value must be between 0 and 1.This value is then interpreted as the acceptable proportion for the spread, e.g. 0.9973 (which corresponds to ~ 6 σ) by default 6 TYPE: float or int DEFAULT: 6
possible_dists	Distributions to which the data may be subject. Only continuous distributions of scipy.stats are allowed, by default DIST.COMMON TYPE: tuple of strings or rv_continous DEFAULT: COMMON
stretch_figsize	If True, the height and width of the figure are stretched based on the number rows and columns in the axes grid. If a float is provided, the figure size is stretched by the given factor. If a tuple of two floats is provided, the figure size is stretched by the given factors for the x and y axis, respectively. by default False. TYPE: bool | float | Tuple[float, float] DEFAULT: False

Examples:

import daspi as dsp

df = dsp.load_dataset('drop_card')
target = 'distance'

chart = dsp.ProcessCapabilityAnalysisCharts(
        source=df,
        target=target,
        spec_limits=dsp.SpecLimits(0, float(df.loc[0, 'usl'])),
        hue='method'
    ).plot(
    ).stripes(
    ).label(
        fig_title='Process Capability Analysis',
        sub_title='Drop Card Experiment',
        target_label='Distance (cm)',
        info=True)

spec_limits = spec_limits instance-attribute

The specification limits for the process capability analysis.

dist = dist instance-attribute

The probability distribution use for creating feature data (the theoretical values).

kw_estim = dict(error_values=error_values, strategy=strategy, agreement=agreement, possible_dists=possible_dists) instance-attribute

Keyword arguments for the ProcessEstimator instances used for calculating the capability indices.

n_groups = source.groupby(hue).ngroups if hue else 1 instance-attribute

The number of hue groups in the source data.

processes()

Get a dictionary of ProcessEstimator instances that are used for calculating the capability indices.

RETURNS	DESCRIPTION
Dict[str, ProcessEstimator]	A dictionary where: - Keys: The hue categories or the target name if no hue is used. - Values: The corresponding ProcessEstimator instances.

process_descriptions(exclude=())

Get a DataFrame containing the descriptions of each process.

This method generates a DataFrame that contains the descriptions of each process, excluding any specified columns.

PARAMETER	DESCRIPTION
exclude	Attributes to exclude from the summary statistics, by default (). TYPE: Tuple[str, ...] DEFAULT: ()

RETURNS	DESCRIPTION
DataFrame	A DataFrame containing the descriptions of each process.

plot(**kwds_cpi)

Plot the process capability analysis charts.

This method plots the process capability analysis charts, including scatter plots, probability density functions, Gaussian kernel density estimation, and capability confidence intervals.

PARAMETER	DESCRIPTION
kwds_cpi	Additional keyword arguments for the capability confidence interval plot. TYPE: dict DEFAULT: {}

RETURNS	DESCRIPTION
Self	The ProcessCapabilityAnalysisCharts instance, for method chaining.

stripes(*, mean=True, median=True, control_limits=True)

This method adds stripes to the process capability analysis charts, including mean, median, control limits and specification limits.

PARAMETER	DESCRIPTION
mean	Whether to add a line for the mean, by default True. TYPE: bool DEFAULT: True
median	Whether to add a line for the median (if applicable), by default True. TYPE: bool DEFAULT: True
control_limits	Whether to add lines for the control limits (if applicable), by default True. TYPE: bool DEFAULT: True

RETURNS	DESCRIPTION
Self	The ProcessCapabilityAnalysisCharts instance, for method chaining.

label(*, fig_title='', sub_title='', feature_label=(), target_label='', info=False)

Label the process capability analysis charts.

This method labels the process capability analysis charts with custom labels and titles.

PARAMETER	DESCRIPTION
fig_title	The title of the figure, by default ''. TYPE: str DEFAULT: ''
sub_title	The subtitle of the figure, by default ''. TYPE: str DEFAULT: ''
feature_label	The labels for the features. If not provided, default labels will be used. The tuple should contain five elements. By default, the labels are: ('Observation order', '', 'Quantiles of standard normal distribution', '', '') TYPE: Tuple[str, ...] DEFAULT: ()
target_label	The label for the target variable, by default ''. TYPE: str DEFAULT: ''
info	Whether to display information about the chart. If True, the information will be displayed. If a string is provided, it will be used as the title of the information box. By default, the information is not displayed. TYPE: bool | str DEFAULT: False

RETURNS	DESCRIPTION
Self	The ProcessCapabilityAnalysisCharts instance, for method chaining.