picasso-q-plugin
The q
plugin registers a q
dataset type that makes it a bit easier to extract data from a QIX hypercube. It also contains a brush helper that can be used to find appropriate selections in the underlying data engine.
Installation
npm install picasso-plugin-q
Register plugin
import picasso from 'picasso.js';
import picassoQ from 'picasso-plugin-q';
picasso.use(picassoQ);
q
dataset
This dataset type understands the QIX hypercube format and its internals, making it a bit easier to traverse and extract values from an otherwise complex structure.
const ds = picasso.data('q')({
key: 'qHyperCube',
data: layout.qHyperCube,
});
Dimensions, measures, attribute expressions and attribute dimensions are all recognized as fields and can be found using either the path or the title of the field:
const f = ds.field('Sales');
const ff = ds.field('qDimensionInfo/1/qAttrDimInfo/2');
Assuming we have a hypercube containing dimensions Year and Month, a measure Sales and an attribute expression on the first dimension containing color values:
{
qDimensionInfo: [
{ qFallbackTitle: 'Year', qAttrDimInfo: [{ qFallbackTitle: 'color' }, ], },
{ qFallbackTitle: 'Month', }
],
qMeasureInfo: [
{ qFallbackTitle: '# products', }
]
}
In a straight hypercube the qMatrix
might look like this:
[
[
{ "qText": "2011", "qNum": 2011, "qElemNumber": 0, "qState": "O", "qAttrExps": { "qValues": [{"qText": "red", "qNum": "NaN" }] } },
{ "qText": "Jan", "qNum": 1, "qElemNumber": 0, "qState": "S", "qAttrDims": { "qValues": [{ "qText": "Jan", "qElemNo": 0 }] } },
{ "qText": "61", "qNum": 61, "qElemNumber": 0, "qState": "L" }
],
[
{ "qText": "2011", "qNum": 2011, "qElemNumber": 0, "qState": "O", "qAttrExps": { "qValues": [{"qText": "blue", "qNum": "NaN" }] } },
{ "qText": "Feb", "qNum": 2, "qElemNumber": 1, "qState": "S", "qAttrDims": { "qValues": [{"qText": "Feb", "qElemNo": 1 }] } },
{ "qText": "62", "qNum": 62, "qElemNumber": 0, "qState": "L" }
],
[
{ "qText": "2012","qNum": 2012, "qElemNumber": 1, "qState": "O", "qAttrExps": { "qValues": [{"qText": "red", "qNum": "NaN" }] } },
{ "qText": "Jan", "qNum": 1, "qElemNumber": 0, "qState": "S", "qAttrDims": { "qValues": [{"qText": "Jan", "qElemNo": 0}] } },
{ "qText": "88", "qNum": 88, "qElemNumber": 0, "qState": "L" }
],
[
{ "qText": "2012", "qNum": 2012, "qElemNumber": 1, "qState": "O", "qAttrExps": { "qValues": [{"qText": "blue", "qNum": "NaN" }] } },
{ "qText": "Feb", "qNum": 2, "qElemNumber": 1, "qState": "S", "qAttrDims": { "qValues": [{"qText": "Feb","qElemNo": 1}] } },
{ "qText": "76", "qNum": 76, "qElemNumber": 0, "qState": "L" }
]
We can extract the unique Month values using:
ds.extract({
field: 'Month',
trackBy: (v) => v.qElemNumber,
});
[
{ value: 0, label: 'Jan', source: { key: 'qHyperCube', field: 'qDimensionInfo/1' } },
{ value: 1, label: 'Feb', source: { key: 'qHyperCube', field: 'qDimensionInfo/1' } },
];
and attach aggregated properties on each item using props
:
ds.extract({
field: 'Month',
trackBy: v => v.qElemNumber
props: {
years: { field: 'Year', value: v => v.qText, reduce: values => values.join(' - ') },
color: { field: 'color', value: v => v.qText },
products: { field: '# products', reduce: 'sum' }
}
});
[
{
value: 0, label: 'Jan', source: { key: 'qHyperCube', field: 'qDimensionInfo/1' },
years: { value: '2011 - 2012', source: { key: 'qHyperCube', field: 'qDimensionInfo/0' } }
color: { value: 'red', source: { key: 'qHyperCube', field: 'qDimensionInfo/0/qAttrExprInfo/0' } }
products: { value: 149, source: { key: 'qHyperCube', field: 'qMeasureInfo/0' } }
},
{
value: 1, label: 'Feb', source: { key: 'qHyperCube', field: 'qDimensionInfo/1' },
years: { value: '2011 - 2012', source: { key: 'qHyperCube', field: 'qDimensionInfo/0' } }
color: { value: 'blue', source: { key: 'qHyperCube', field: 'qDimensionInfo/0/qAttrExprInfo/0' } }
products: { value: 138, source: { key: 'qHyperCube', field: 'qMeasureInfo/0' } }
}
]
The default value
accessor for a field depends on the field type and the qMode
property of the hypercube:
- For measures and attribute expressions:
cell => cell.qNum
or cell => cell.qValue
- For dimensions and attribute dimensions:
cell => cell.qElemNumber
or cell => cell.qElemNo
The default reduce
function is avg
for measures and first
for dimensions.
QIX selections helper
The QIX selections helper provides a mapping from brushed data points to suitable QIX selections.
By dimension value
Brushing dimension values is done by adding the value of qElemNumber
to the brush, and providing the path to the relevant dimension:
const b = chart.brush('selection');
b.addValue('qHyperCube/qDimensionInfo/2', 4);
b.addValue('qHyperCube/qDimensionInfo/2', 7);
Calling picassoQ.selections
with the above instance generates relevant QIX methods and parameters to apply a selection to:
const selection = picassoQ.selections(b)[0];
The selection can then be applied to a QIX model:
model[selection.method](...selection.params);
By measure range
Brushing measure ranges:
const b = chart.brush('selection');
b.addRange('qHyperCube/qMeasureInfo/2', { min: 13, max: 35 });
const selection = picassoQ.selections(b)[0];
By dimension range
Brushing dimension ranges:
const b = chart.brush('selection');
b.addRange('qHyperCube/qDimensionInfo/1', { min: 13, max: 35 });
const selection = picassoQ.selections(b)[0];
By row indices
Brushing by table row index and column:
const b = chart.brush('selection');
b.addValue('qHyperCube/qDimensionInfo/1', 10);
b.addValue('qHyperCube/qDimensionInfo/1', 13);
b.addValue('qHyperCube/qDimensionInfo/0', 11);
b.addValue('qHyperCube/qDimensionInfo/0', 17);
In the above case, rows 10
and 13
have been brushed on dimension 1
, and rows 11
and 17
on dimension 0
.
To extract the relevant information, byCells
is enabled:
const selection = picassoQ.selections(b, { byCells: true })[0];
Row indices are used from the first dimension that adds a value to a brush, qDimensionInfo/1
, in the case above.
To use values from another dimension, primarySource
should be set:
const selection = picassoQ.selections(b, {
byCells: true,
primarySource: 'qHyperCube/qDimensionInfo/0',
})[0];
By attribute dimension
Brush on attribute dimension values:
const b = chart.brush('selection');
b.addValue('qHyperCube/qDimensionInfo/2/qAttrDimInfo/3', 6);
b.addValue('qHyperCube/qDimensionInfo/2/qAttrDimInfo/3', 9);
const selection = picassoQ.selections(b)[0];
By attribute expression range
Brush on attribute expression range:
const b = chart.brush('selection');
b.addRange('qHyperCube/qMeasureInfo/1/qAttrExprInfo/2', { min: 11, max: 21 });
QIX selections on attribute expressions are similar to selections on measure ranges. In this case however, the index of the measure
is derived from the number of measures and attribute expressions that exist in the hypercube. Therefore, to calculate
the index, layout
containing the hypercube needs to be provided as a parameter:
const selection = picassoQ.selections(b, {}, layout)[0];
Assuming a layout
of:
{
qHyperCube: {
qDimensionInfo: [
{ qAttrExprInfo: [{}] }
],
qMeasureInfo: [
{ qAttrExprInfo: [{}, {}] },
{ qAttrExprInfo: [{}, {}, { }] }
]
}
}
then qMeasureIx
is calculated as follows:
- number of measures:
2
- total number of attribute expressions in all dimensions:
1
- total number of attribute expressions in measures preceding the one specified:
2
(from first measure) - the actual index of the specified attribute expression:
2
which results in 2 + 1 + 2 + 2 = 7