Stripes facets
daspi.plotlib.facets.StripesFacets(target, *, target_on_y, single_axes, stripes=[], mean=False, median=False, control_limits=False, spec_limits=SpecLimits(), confidence=None, strategy='norm', agreement=6, **kwds)
¶
A class for creating location and spread width lines, specification limits and/or confidence interval areas as stripes on each Axes. The location and spread (and their confidence bands) represent the data per axes.
| PARAMETER | DESCRIPTION |
|---|---|
target
|
The target data for the estimation.
TYPE:
|
target_on_y
|
Whether the target data is on the y-axis or not.
TYPE:
|
single_axes
|
Whether a single axis is used for the chart or whether the stripes are used on multiple axes. This affects how the predefined legend labels are handled. If True, the corresponding position values are added to the label.
TYPE:
|
stripes
|
Additional non-predefined stripes to be added to the chart, by default [].
TYPE:
|
mean
|
Whether to include a mean line on the chart, by default False.
TYPE:
|
median
|
Whether to include a median line on the chart, by default False.
TYPE:
|
control_limits
|
Whether to include control limits on the chart, by default False.
TYPE:
|
spec_limits
|
The specification limits for the chart, by default SpecLimits().
TYPE:
|
confidence
|
The confidence level for the confidence intervals, by default None.
TYPE:
|
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:
|
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 agreementsigma, e.g. agreement = 6 -> 6sigma ~ 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 sigma) by default 6
TYPE:
|
**kwds
|
Additional keyword arguments to pass to the ProcessEstimator.
TYPE:
|
single_axes = single_axes
instance-attribute
¶
Whether a single axis is used for the chart or whether the stripes are used on multiple axes. This affects how the predefined legend labels are handled. If True, the corresponding position values are added to the label.
estimation = ProcessEstimator(samples=target, spec_limits=spec_limits, strategy=strategy, agreement=agreement, **kwds)
instance-attribute
¶
The process estimator object.
target_on_y = target_on_y
instance-attribute
¶
Whether the target is on the y-axis or the x-axis.
stripes = {(s.identity): s for s in stripes}
instance-attribute
¶
The stripes to plot on the chart.
confidence
property
¶
Get the confidence level for the confidence intervals (read-only).
orientation
property
¶
The orientation of the chart.
add_mean_stripes(add_confidence_span)
¶
Add mean stripes to the plot.
This method adds a stripe representing the mean of the target
data, using the StripeLine class. If add_confidence_span is
True, additional stripes representing the confidence interval
for the mean will also be added using the StripeSpan class.
| PARAMETER | DESCRIPTION |
|---|---|
add_confidence_span
|
Whether to add confidence stripes for the mean.
TYPE:
|
add_median_stripes(add_confidence_span)
¶
Add median stripes to the plot.
This method adds a stripe representing the median of the target
data, using the StripeLine class. If add_confidence_span is
True, additional stripes representing the confidence interval
for the median will also be added using the StripeSpan class.
| PARAMETER | DESCRIPTION |
|---|---|
add_confidence_span
|
Whether to add confidence stripes for the median.
TYPE:
|
add_control_limit_stripes(add_confidence_span)
¶
Add control limit stripes to the plot.
This method adds stripes representing the lower and upper control limits for the target data.
The stripes are added using the StripeLine class, with the
label and position set based on the control limits from the
estimation attribute. If add_confidence_span is True,
additional stripes representing the confidence interval for the
control limits will also be added using the StripeSpan class.
| PARAMETER | DESCRIPTION |
|---|---|
add_confidence_span
|
Whether to add confidence stripes for the control limits.
TYPE:
|
add_specification_limit_stripes()
¶
Add specification limit stripes to the plot.
This method adds stripes representing the lower and upper
specification limits to the plot. If a specification limit is
None, it will be skipped.
The stripes are added using the StripeLine class, with the
label and position set based on the specification limits from
the estimation attribute.
handles_labels()
¶
Get the legend handles and labels for the plot.
This method processes the self.stripes dictionary to extract
the legend handles and labels for the plot. It separates the
handles and labels for line stripes and span stripes, and
returns them as a tuple of handles and a tuple of labels.
| RETURNS | DESCRIPTION |
|---|---|
LegendHandlesLabels
|
A tuple containing the legend handles and labels.
TYPE:
|
draw(ax)
¶
Draw the stripes on the specified Axes.
| PARAMETER | DESCRIPTION |
|---|---|
ax
|
The Axes on which to draw the stripes.
TYPE:
|