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new bda608431a feat: add a new ternary contour plot operator (#4193)
bda608431a is described below
commit bda608431ac943a442f3eabaa3b4eb7eeb7f749e
Author: Elliot Lin <[email protected]>
AuthorDate: Wed Apr 22 08:46:37 2026 -0700
feat: add a new ternary contour plot operator (#4193)
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### What changes were proposed in this PR?
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This change relates to the addition of a ternary contour plot operator,
which visualizes how a scalar value varies as a function of three
normalized components that sum to a constant (typically 1 or 100%).
In a ternary contour plot:
- Each vertex of the triangular plot represents 100% of one component.
- Any interior point represents a mixture of the three components.
- Contour lines or color gradients indicate equal values of the measured
quantity across different mixtures.
This visualization is useful for identifying regions where the output is
optimized or insensitive to changes in the component proportions, as
well as for understanding trade-offs between the three variables.
The operator takes in 4 inputs. The first three variables are the
components, and the fourth variable is the output that corresponds to
the proportion of the the three components.
### Any related issues, documentation, discussions?
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Needs python library scikit-image
Can be installed using: pip install scikit-image
### How was this PR tested?
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Tested with existing test cases
### Was this PR authored or co-authored using generative AI tooling?
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No
---------
Co-authored-by: Elliot <[email protected]>
Co-authored-by: Yicong Huang
<[email protected]>
Co-authored-by: Chen Li <[email protected]>
---
amber/operator-requirements.txt | 1 +
.../apache/texera/amber/operator/LogicalOp.scala | 2 +
.../ternaryContour/TernaryContourOpDesc.scala | 151 +++++++++++++++++++++
.../src/assets/operator_images/TernaryContour.png | Bin 0 -> 167675 bytes
4 files changed, 154 insertions(+)
diff --git a/amber/operator-requirements.txt b/amber/operator-requirements.txt
index f76f3bc014..2554373e0e 100644
--- a/amber/operator-requirements.txt
+++ b/amber/operator-requirements.txt
@@ -24,3 +24,4 @@ torch==2.8.0
scikit-learn==1.5.0
transformers==4.57.3
boto3==1.40.53
+scikit-image==0.25.2
diff --git
a/common/workflow-operator/src/main/scala/org/apache/texera/amber/operator/LogicalOp.scala
b/common/workflow-operator/src/main/scala/org/apache/texera/amber/operator/LogicalOp.scala
index d9a8a97407..4e9d6c6e2c 100644
---
a/common/workflow-operator/src/main/scala/org/apache/texera/amber/operator/LogicalOp.scala
+++
b/common/workflow-operator/src/main/scala/org/apache/texera/amber/operator/LogicalOp.scala
@@ -132,6 +132,7 @@ import
org.apache.texera.amber.operator.visualization.sankeyDiagram.SankeyDiagra
import
org.apache.texera.amber.operator.visualization.scatter3DChart.Scatter3dChartOpDesc
import
org.apache.texera.amber.operator.visualization.scatterplot.ScatterplotOpDesc
import
org.apache.texera.amber.operator.visualization.tablesChart.TablesPlotOpDesc
+import
org.apache.texera.amber.operator.visualization.ternaryContour.TernaryContourOpDesc
import
org.apache.texera.amber.operator.visualization.ternaryPlot.TernaryPlotOpDesc
import
org.apache.texera.amber.operator.visualization.parallelCoordinatesPlot.ParallelCoordinatesPlotOpDesc
import
org.apache.texera.amber.operator.visualization.polarChart.PolarChartOpDesc
@@ -260,6 +261,7 @@ trait StateTransferFunc
new Type(value = classOf[TablesPlotOpDesc], name = "TablesPlot"),
new Type(value = classOf[ContinuousErrorBandsOpDesc], name =
"ContinuousErrorBands"),
new Type(value = classOf[FigureFactoryTableOpDesc], name =
"FigureFactoryTable"),
+ new Type(value = classOf[TernaryContourOpDesc], name = "TernaryContour"),
new Type(value = classOf[TernaryPlotOpDesc], name = "TernaryPlot"),
new Type(value = classOf[DendrogramOpDesc], name = "Dendrogram"),
new Type(value = classOf[NestedTableOpDesc], name = "NestedTable"),
diff --git
a/common/workflow-operator/src/main/scala/org/apache/texera/amber/operator/visualization/ternaryContour/TernaryContourOpDesc.scala
b/common/workflow-operator/src/main/scala/org/apache/texera/amber/operator/visualization/ternaryContour/TernaryContourOpDesc.scala
new file mode 100644
index 0000000000..215e9ce0c9
--- /dev/null
+++
b/common/workflow-operator/src/main/scala/org/apache/texera/amber/operator/visualization/ternaryContour/TernaryContourOpDesc.scala
@@ -0,0 +1,151 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied. See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+package org.apache.texera.amber.operator.visualization.ternaryContour
+
+import com.fasterxml.jackson.annotation.{JsonProperty, JsonPropertyDescription}
+import com.kjetland.jackson.jsonSchema.annotations.JsonSchemaTitle
+import org.apache.texera.amber.core.tuple.{AttributeType, Schema}
+import org.apache.texera.amber.core.workflow.OutputPort.OutputMode
+import
org.apache.texera.amber.pybuilder.PythonTemplateBuilder.PythonTemplateBuilderStringContext
+import org.apache.texera.amber.pybuilder.PyStringTypes.EncodableString
+import org.apache.texera.amber.core.workflow.{InputPort, OutputPort,
PortIdentity}
+import org.apache.texera.amber.operator.PythonOperatorDescriptor
+import
org.apache.texera.amber.operator.metadata.annotations.AutofillAttributeName
+import org.apache.texera.amber.operator.metadata.{OperatorGroupConstants,
OperatorInfo}
+import org.apache.texera.amber.pybuilder.PythonTemplateBuilder
+
+/**
+ * Visualization Operator for Ternary Plots.
+ *
+ * This operator uses three data fields to construct a ternary plot.
+ * The points can optionally be color coded using a data field.
+ */
+
+class TernaryContourOpDesc extends PythonOperatorDescriptor {
+
+ // Add annotations for the first variable
+ @JsonProperty(value = "firstVariable", required = true)
+ @JsonSchemaTitle("Variable 1")
+ @JsonPropertyDescription("First variable data field")
+ @AutofillAttributeName var firstVariable: EncodableString = ""
+
+ // Add annotations for the second variable
+ @JsonProperty(value = "secondVariable", required = true)
+ @JsonSchemaTitle("Variable 2")
+ @JsonPropertyDescription("Second variable data field")
+ @AutofillAttributeName var secondVariable: EncodableString = ""
+
+ // Add annotations for the third variable
+ @JsonProperty(value = "thirdVariable", required = true)
+ @JsonSchemaTitle("Variable 3")
+ @JsonPropertyDescription("Third variable data field")
+ @AutofillAttributeName var thirdVariable: EncodableString = ""
+
+ // Add annotations for the fourth variable
+ @JsonProperty(value = "fourthVariable", required = true)
+ @JsonSchemaTitle("Measured Value")
+ @JsonPropertyDescription("Measured value data field")
+ @AutofillAttributeName var fourthVariable: EncodableString = ""
+
+ // OperatorInfo instance describing ternary plot
+ override def operatorInfo: OperatorInfo =
+ OperatorInfo(
+ userFriendlyName = "Ternary Contour",
+ operatorDescription =
+ "Shows how a measured value changes across all mixtures of three
components that sum to a constant",
+ operatorGroupName =
OperatorGroupConstants.VISUALIZATION_SCIENTIFIC_GROUP,
+ inputPorts = List(InputPort()),
+ outputPorts = List(OutputPort(mode = OutputMode.SINGLE_SNAPSHOT))
+ )
+
+ override def getOutputSchemas(
+ inputSchemas: Map[PortIdentity, Schema]
+ ): Map[PortIdentity, Schema] = {
+ val outputSchema = Schema()
+ .add("html-content", AttributeType.STRING)
+ Map(operatorInfo.outputPorts.head.id -> outputSchema)
+ }
+
+ /** Returns a Python string that drops any tuples with missing values */
+ def manipulateTable(): PythonTemplateBuilder = {
+ // Check for any empty data field names
+ assert(
+ firstVariable.nonEmpty && secondVariable.nonEmpty &&
thirdVariable.nonEmpty && fourthVariable.nonEmpty
+ )
+ pyb"""
+ | # Remove any tuples that contain missing values
+ | table.dropna(subset=[$firstVariable, $secondVariable,
$thirdVariable, $fourthVariable], inplace = True)
+ |
+ | #Remove rows where any of the first three variables are
negative
+ | table = table[(table[[$firstVariable, $secondVariable,
$thirdVariable]] >= 0).all(axis=1)]
+ |
+ | #Remove zero-sum rows
+ | s = table[$firstVariable] + table[$secondVariable] +
table[$thirdVariable]
+ | table = table[s > 0]
+ |"""
+ }
+
+ /** Returns a Python string that creates the ternary contour plot figure */
+ def createPlotlyFigure(): PythonTemplateBuilder = {
+ pyb"""
+ | A = table[$firstVariable].to_numpy()
+ | B = table[$secondVariable].to_numpy()
+ | C = table[$thirdVariable].to_numpy()
+ | Z = table[$fourthVariable].to_numpy()
+ | fig = ff.create_ternary_contour(np.array([A,B,C]), Z,
pole_labels=[$firstVariable, $secondVariable, $thirdVariable],
interp_mode='cartesian')
+ |"""
+ }
+
+ /** Returns a Python string that yields the html content of the ternary
contour plot */
+ override def generatePythonCode(): String = {
+ val finalCode =
+ pyb"""
+ |from pytexera import *
+ |
+ |import plotly.io
+ |import plotly.figure_factory as ff
+ |import numpy as np
+ |
+ |class ProcessTableOperator(UDFTableOperator):
+ |
+ | # Generate custom error message as html string
+ | def render_error(self, error_msg):
+ | return '''<h1>TernaryContour is not available.</h1>
+ | <p>Reasons are: {} </p>
+ | '''.format(error_msg)
+ |
+ | @overrides
+ | def process_table(self, table: Table, port: int) ->
Iterator[Optional[TableLike]]:
+ | if table.empty:
+ | yield {'html-content': self.render_error("Input table is
empty.")}
+ | return
+ | ${manipulateTable()}
+ | if table.empty:
+ | yield {'html-content': self.render_error("No valid rows
left (every row has at least 1 missing value).")}
+ | return
+ | ${createPlotlyFigure()}
+ | # Convert fig to html content
+ | html = plotly.io.to_html(fig, include_plotlyjs = 'cdn',
auto_play = False)
+ | yield {'html-content':html}
+ |"""
+ finalCode.encode
+ }
+
+}
diff --git a/frontend/src/assets/operator_images/TernaryContour.png
b/frontend/src/assets/operator_images/TernaryContour.png
new file mode 100644
index 0000000000..6526cb3bbd
Binary files /dev/null and
b/frontend/src/assets/operator_images/TernaryContour.png differ
