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Violin Plot: Docs draft
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Orange/widgets/visualize/owviolinplot.py

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@@ -41,9 +41,8 @@ class ViolinPlotViewBox(pg.ViewBox):
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sigSelectionChanged = Signal(QPointF, QPointF, bool)
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sigDeselect = Signal(bool)
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def __init__(self, parent):
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def __init__(self, _):
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super().__init__()
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self.graph: ViolinPlot = parent
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self.setMouseMode(self.RectMode)
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def mouseDragEvent(self, ev, axis=None):
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visual_settings = Setting({}, schema_only=True)
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graph_name = "graph.plotItem"
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buttons_area_orientation = None
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def __init__(self):
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super().__init__()

doc/visual-programming/source/index.rst

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:maxdepth: 1
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widgets/visualize/boxplot
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widgets/visualize/violinplot
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widgets/visualize/distributions
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widgets/visualize/heatmap
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widgets/visualize/scatterplot
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Violin Plot
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===========
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Visualize the distribution of feature values in a violin plot.
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**Inputs**
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- Data: input dataset
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**Outputs**
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- Selected Data: instances selected from the plot
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- Data: data with an additional column showing whether a point is selected
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The **Violin Plot** widget plays a similar role as a [Box Plot](boxplot.md). It shows the distribution of quantitative data across several levels of one (or more) categorical variables such that those distributions can be compared. Unlike the Box Plot, in which all of the plot components correspond to actual data points, the Violin Plot features a kernel density estimation of the underlying distribution.

doc/widgets.json

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"whisker"
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]
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},
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{
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"text": "Violin Plot",
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"doc": "visual-programming/source/widgets/visualize/violinplot.md",
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"icon": "../Orange/widgets/visualize/icons/ViolinPlot.svg",
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"background": "#FFB7B1",
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"keywords": [
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"kernel",
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"density"
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]
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},
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{
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"text": "Distributions",
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"doc": "visual-programming/source/widgets/visualize/distributions.md",

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