def extract_transform(chart: alt.Chart) -> alt.Chart:
"""Extract transforms from encodings
This takes a chart with transforms specified within encodings, and returns
an equivalent chart with transforms specified separately in the ``transform``
field.
Parameters
----------
chart : alt.Chart
Input chart, which will not be modified
Returns
-------
chart : alt.Chart
A copy of the input chart with any encoding-specified transforms moved
to the transforms-attribute
Example
-------
>>> chart = alt.Chart('data.csv').mark_bar().encode(x='mean(x):Q', y='y:N')
>>> new_chart = extract_transform(chart)
>>> new_chart.transform
[AggregateTransform({
aggregate: [AggregatedFieldDef({
as: FieldName('mean_x'),
field: FieldName('x'),
op: AggregateOp('mean')
})],
groupby: [FieldName('y')]
})]
>>> new_chart.encoding
FacetedEncoding({
x: PositionFieldDef({
field: FieldName('mean_x'),
title: 'Mean of x',
type: StandardType('quantitative')
}),
y: PositionFieldDef({
field: FieldName('y'),
type: StandardType('nominal')
})
})
"""
chart = chart.copy()
encoding_dict = chart.encoding.copy().to_dict(context={"data": chart.data})
encoding, transform = _encoding_to_transform(encoding_dict)
if transform:
chart.encoding = alt.FacetedEncoding.from_dict(encoding)
if chart.transform is alt.Undefined:
chart.transform = []
chart.transform.extend(alt.Transform.from_dict(t) for t in transform)
return chart
def extract_transform(chart: alt.Chart) -> alt.Chart:
"""Extract transforms within a chart specification."""
chart = chart.copy()
encoding_dict = chart.encoding.copy().to_dict(context={"data": chart.data})
encoding, transform = _encoding_to_transform(encoding_dict)
if transform:
chart.encoding = alt.FacetedEncoding.from_dict(encoding)
if chart.transform is alt.Undefined:
chart.transform = []
chart.transform.extend(alt.Transform.from_dict(t) for t in transform)
return chart
