Vapor Python Binding: Fixing Annotation Axis Color Bug

Nov 17, 2025 by Alex Johnson 55 views

Introduction

In this article, we will address a specific bug encountered while using the Python bindings in VAPOR (Version 3.10.0). The issue arises when attempting to set the annotation axis color, which unexpectedly fails, unlike setting the background color, which functions correctly. This problem was observed within a micromamba environment using JupyterLab and Python 3.9.18. We'll delve into the details of the bug, provide a step-by-step guide to reproduce it, discuss the expected behavior, and offer potential solutions or workarounds.

Understanding the Bug

The core of the problem lies in the inability of the Python bindings to properly set the color of the annotation axis in VAPOR. When users try to modify the axis color, the changes are not reflected, leading to a discrepancy between the intended and actual visual representation. This can be particularly frustrating when aiming for specific aesthetic or informative visualizations. To fully grasp the impact, let's explore the context in which this bug manifests.

The Context: VAPOR, Python, and JupyterLab

VAPOR is a powerful tool utilized for scientific visualization, often employed to render complex datasets in a visually intuitive manner. Python, with its extensive libraries and bindings, serves as a versatile interface for interacting with VAPOR's functionalities. JupyterLab, an interactive development environment, further streamlines the process of writing and executing Python code, making it a popular choice for data exploration and visualization tasks. The combination of these tools provides a robust platform for scientific computing, but it also introduces potential points of failure, as highlighted by this specific bug.

Reproducing the Bug: A Step-by-Step Guide

To replicate the bug, follow these steps using the provided Python code snippet. This will help confirm the issue and provide a clear understanding of the problem.

Prerequisites

Install VAPOR: Ensure you have VAPOR version 3.10.0 installed.
Set up Python Environment: Use Python 3.9.18 within a micromamba environment. You can create one using:
```
micromamba create -n vapor-env python=3.9.18
micromamba activate vapor-env
```
Install Vapor Python Bindings: Install the necessary Python bindings for VAPOR.
Install JupyterLab: Make sure JupyterLab is installed and accessible.

Steps

Open JupyterLab: Launch JupyterLab in your micromamba environment.
Create a New Notebook: Create a new Python notebook.

Copy the Code: Paste the following Python code into the notebook:

from vapor import session, renderer, dataset, camera, widget, annotations
from vapor.animation import Animation

# create a VAPOR session
ses = session.Session()
ses.SetResolution(1024,1024)

# get default annotation axes
anno = ses.GetAxisAnnotations()

# following SUCCEEDS
backColorAxis = anno.GetAxisBackgroundColor()
print(backColorAxis)
backColorAxis = [0.,0.,0.,1.]
anno.SetAxisBackgroundColor(backColorAxis)
backColorAxis = anno.GetAxisBackgroundColor()
print(backColorAxis)

print()

# following FAILS to set values correctly
backColorAxis = anno.GetAxisColor()
print(backColorAxis)
backColorAxis = [0.5,0.5,0.5,1.]
anno.SetAxisColor(backColorAxis)
backColorAxis = anno.GetAxisColor()
print(backColorAxis)

Run the Code: Execute the code in the notebook.

Expected vs. Actual Behavior

Expected Behavior: The final two lines of the code should print the value [0.5, 0.5, 0.5, 1.0], indicating that the axis color has been successfully set.
Actual Behavior: The final two lines print the original axis color, demonstrating that the SetAxisColor function did not correctly update the axis color.

Analyzing the Code

The Python code is structured to interact with VAPOR's annotation axes. Let's break down each section to understand what it's doing:

Session Initialization

    ses = session.Session()
    ses.SetResolution(1024,1024)

This part initializes a VAPOR session and sets the resolution for the rendering window. The session.Session() creates a new session, and ses.SetResolution(1024,1024) configures the resolution to 1024x1024 pixels. This setup is fundamental for any VAPOR-based visualization.

Background Color Manipulation

    anno = ses.GetAxisAnnotations()

    # following SUCCEEDS
    backColorAxis = anno.GetAxisBackgroundColor()
    print(backColorAxis)
    backColorAxis = [0.,0.,0.,1.]
    anno.SetAxisBackgroundColor(backColorAxis)
    backColorAxis = anno.GetAxisBackgroundColor()
    print(backColorAxis)

This section retrieves the default annotation axes using ses.GetAxisAnnotations(). It then successfully sets the background color of the axis. First, the current background color is retrieved and printed using anno.GetAxisBackgroundColor(). Then, the background color is set to black [0.,0.,0.,1.] using anno.SetAxisBackgroundColor(backColorAxis). Finally, the updated background color is retrieved and printed to confirm the change. This part works as expected, demonstrating that the basic functionality of setting colors is operational.

The Failing Part: Axis Color Manipulation

    print()

    # following FAILS to set values correctly
    backColorAxis = anno.GetAxisColor()
    print(backColorAxis)
    backColorAxis = [0.5,0.5,0.5,1.]
    anno.SetAxisColor(backColorAxis)
    backColorAxis = anno.GetAxisColor()
    print(backColorAxis)

This is where the bug manifests. The code attempts to set the axis color to gray [0.5,0.5,0.5,1.]. Initially, the current axis color is retrieved and printed. Then, anno.SetAxisColor(backColorAxis) is called to set the new color. However, when the axis color is retrieved again and printed, it remains unchanged, indicating that SetAxisColor is not functioning correctly. This discrepancy highlights the specific bug related to setting the axis color.

Impact of the Bug

The impact of this bug can be categorized as follows:

Medium - User productivity partially degraded: While it doesn't completely halt the visualization process, it does require users to find workarounds or accept default axis colors, which may not be ideal for their specific needs.

Possible Causes and Workarounds

While the exact cause of this bug may require deeper investigation into the VAPOR source code, here are some potential reasons and workarounds:

Binding Issue: The Python binding for SetAxisColor might not be correctly implemented, causing it to fail silently.
Data Type Mismatch: There could be a mismatch in data types between the Python code and the underlying C++ implementation of VAPOR.
VAPOR Bug: The issue might stem from a bug within VAPOR itself, specifically in the handling of axis colors.

Workarounds

Direct Configuration Files: Modify VAPOR's configuration files directly to set the axis color. This bypasses the Python bindings and directly manipulates VAPOR's settings.
Alternative Visualization Techniques: If possible, explore alternative visualization techniques or libraries that offer more control over axis colors.
VAPOR API: If available, utilize other parts of the VAPOR API to achieve the desired visual effect indirectly.

Conclusion

In conclusion, the bug preventing the correct setting of the annotation axis color in VAPOR's Python bindings represents a significant inconvenience for users aiming for precise visual control. By following the steps outlined in this article, you can reproduce the bug and confirm its presence. While a definitive solution may require updates to VAPOR or its Python bindings, understanding the potential causes and exploring workarounds can help mitigate the impact on your visualization workflow. It's crucial to report such issues to the VAPOR development team to ensure they are addressed in future releases.

For more information on VAPOR and its capabilities, visit the NCAR VAPOR website.