Few improvements.

plot-stem/src/commonMain/kotlin/org/jetbrains/letsPlot/core/spec/back/PlotConfigBackend.kt

@@ -71,8 +71,9 @@ open class PlotConfigBackend(
  // Clean-up data before sending it to the front-end.
  dropUnusedDataBeforeEncoding(layerConfigs)
 
- // Re-create the "natural order" existed before faceting.
- // if (facets.isDefined) {
+ // Re-create the "natural order" existed before faceting
+ // or apply the specified order
+ if (facets.isDefined || specifiedFactorLeversByLayers.any { it.isNotEmpty() }) {
  // When faceting, each layer' data was split to panels, then re-combined with loss of 'natural order'.
  layerConfigs.forEachIndexed { layerIndex, layerConfig ->
  val layerData = layerConfig.ownData
@@ -91,7 +92,7 @@ open class PlotConfigBackend(
  layerConfig.update(DATA_META, layerDataMetaUpdated)
  }
  }
- // }
+ }
  }
 
  private fun dropUnusedDataBeforeEncoding(layerConfigs: List<LayerConfig>) {

plot-stem/src/commonMain/kotlin/org/jetbrains/letsPlot/core/spec/config/DataConfigUtil.kt

@@ -179,10 +179,10 @@ internal object DataConfigUtil {
  if (clientSide) {
  val variables = rawCombinedData.variables()
  val orderSpecs = OrderOptionUtil.createOrderSpecs(orderOptions, variables, varBindings, aggregateOperation)
-
  val factorLevelsByVar = DataMetaUtil.getFactorLevelsByVariable(ownDataMeta)
  .mapKeys { (varName, _) -> variables.find { it.name == varName } }
  .filterNotNullKeys()
+
  this
  .addOrderSpecs(orderSpecs)
  .addFactorLevels(factorLevelsByVar)

python-package/test/plot/test_series_annotations.py

@@ -66,25 +66,28 @@ def assert_series_annotations(data, expected):
  'v2': [1, 2],
  # add pandas Categorical
 }
-factor_levels = ['foo', 'bar']
 
 
 def test_factor_levels():
- mapping = aes(x=as_discrete('v1', levels=factor_levels), y='v2')
+ mapping = aes(as_discrete('v1', levels=['foo', 'bar']))
  data_meta = get_data_meta(data_dict2, mapping)
  expected_factor_levels = [
  {'column': 'v1', 'factor_levels': ['foo', 'bar']}
  ]
  assert expected_factor_levels == data_meta['series_annotations']
 
 
-def test_factor_levels_with_ordering():
- x = as_discrete("v1", order=-1)
- fill = as_discrete("v1", levels=factor_levels)
- data_meta = get_data_meta(data_dict2, mapping=aes(x=x, y='v2', fill=fill))
+def test_skip_mapping_annotations_for_variable_with_levels():
+ mapping = aes(
+ as_discrete("v1", order=-1),
+ 'v2',
+ a=as_discrete("v1", levels=['foo', 'bar']),
+ b=as_discrete("v2", levels=[2, 1])
+ )
+ data_meta = get_data_meta(data_dict2, mapping)
  expected_factor_levels = [
- {'column': 'v1', 'factor_levels': ['foo', 'bar']}
+ {'column': 'v1', 'factor_levels': ['foo', 'bar']},
+ {'column': 'v2', 'factor_levels': [2, 1]}
  ]
  assert expected_factor_levels == data_meta['series_annotations']
- # skip creation of 'mapping_annotations'
  assert 'mapping_annotations' not in data_meta