Better access to logical operand predicates

[jakevdp]

Vega-Lite has a number of logical operands; e.g.

• alt.LogicalAndPredicate
• alt.LogicalOrPredicate
• alt.LogicalNotPredicate

These come up, for example, in constructing filter transforms. It would be useful to make these available to Altair users via Python's logical operators, &, |, and ~, respectively.

There are two challenges:

1. How to implement them effectively... maybe create some sort of PredicateMixin for these classes? If we were constructing the wrapper classes by hand, it would make sense for alt.Predicate to be the base class of each of these logical predicates... perhaps we should change the code generation framework so that this kind of class hierarchy is (optionally) inferred in generated classes? Sounds hard to do well in a general way, though.

2. The interaction with selection operands (e.g. alt.SelectionOr, alt.SelectionAnd, alt.SelectionNot, and alt.NamedSelection) makes this a bit tricky, because NamedSelection objects serve two purposes in Altair (they act both as a alt.SelectionDef and a alt.SelectionPredicate depending on the context).

[kanitw]

I think using &, |, ~ is definitely way more natural.

We have to do {"and/or": [...]} in Vega-Lite because we're limited to be just a JSON format.

[pagpires]

A question regarding the combination of multiple operands: can we do that in altair now?
I met an error when I wanted to combine 'or' and 'not':

pop = vega_datasets.data.population()
alt.Chart(pop).mark_line().encode(
    x='age:O',
    y='sum(people):Q',
    color='year:O'
).properties(
    width=600, height=200
).transform_filter(
    {'or': [alt.FieldRangePredicate(field='year', range=[1850, 1880]), 
            alt.FieldRangePredicate(field='year', range=[1960, 2000])],
     # if I remove the following line and only keep 'or', it will work
    'not': alt.FieldRangePredicate(field='year', range=[1870, 1880])
    }    
)

[jakevdp]

Yes, you can do them in Altair. But no, you can't have or and not as keys within the same object... I'm not quite sure how to interpret your intent here.

Is this what you were intending to do?

{'and': [
        {'or': [
            alt.FieldRangePredicate(field='year', range=[1850, 1880]), 
            alt.FieldRangePredicate(field='year', range=[1960, 2000])
        ]},
        {'not': alt.FieldRangePredicate(field='year', range=[1870, 1880])}
    ]
} 

[pagpires]

Oh, I see... Yes this is exactly what I'm asking. Thanks!

[arrrobase]

Do logical operands work within "alt.condition"? I have a bar chart with a selection_single, and a scatter plot with a selection_interval, and I'm trying to set opacity of the scatter to be conditional on both selections. I've tried the following and other variations but keep getting schema errors.

The API says alt.condition should be able to take an operand, but I'm not sure about the syntax, which I adapted from the examples from transform_filter.

cond= {'and': [
    single_bar,
    interval_scatter
]}

opacity=alt.condition(cond, alt.value(0.5), alt.value(0.1))

Here's a minimal example of what I'm trying to do. It works if I set the opacity condition to just a single selector.

single_bar = alt.selection_single(encodings=['y'])
interval_scatter = alt.selection_interval(encodings=['x', 'y'])

cond = {'and': [
    single_bar,
    interval_scatter
]}

scatter= alt.Chart(df).mark_circle().encode(
    x='scan_quality:O',
    y='SSI:Q',,
    opacity=alt.condition(cond, alt.value(0.5), alt.value(0.1)),
).properties(
    selection=interval_scatter
)

bar = alt.Chart(df).mark_bar().encode(
    color='qc:N',
    x='count(qc)',
    y='scan_size:O',
).transform_filter(
    {'and': [alt.FieldRangePredicate(field='scan_quality', range=[1, 11]),
             interval_scatter.ref()]
    }
).properties(
    selection=single_bar
)

scatter & bar

[jakevdp]

For selections, logical operands like & and | work (this issue is about transforms, not selections). So instead of {'and': [single_bar, interval_scatter]}, you should use single_bar & interval_scatter.

[arrrobase]

That did it, thanks!

[gh-owestesson]

Sorry in advance if this is addressed by the above, but is it possible to combine a logical operand with a selection? I'd like to display all points not selected, like this:

import random
import numpy as np
data = pd.DataFrame( {col: np.random.randn(50) for col in 'xyz'})

selector = alt.selection_interval()
chart = alt.Chart( data )
xy = chart.mark_point().encode(
     x = 'x', 
     y = 'y').add_selection( selector )

xz = chart.mark_point().encode(
    x = 'x',
    y = 'z').transform_filter( ~selector)
xy | xz

[jakevdp]

Hi @breadbaron – I think this should work, but there is a bug in the way ~selection is being serialized.

Here's a workaround until this is fixed:

import numpy as np
data = pd.DataFrame( {col: np.random.randn(50) for col in 'xyz'})

selector = alt.selection_interval()
chart = alt.Chart( data )
xy = chart.mark_point().encode(
     x = 'x', 
     y = 'y'
).add_selection( selector )

xz = chart.mark_point().encode(
    x = 'x',
    y = 'z'
).transform_filter( {'not': selector.ref()})
xy | xz

[gh-owestesson]

@jakevdp thank you!!

[jakevdp]

@breadbaron fixed in #1075, which will make it into the 2.2 release 😄