Update volume.py

[Poisoned]

change the default alpha gradient for volume to be more even

[coderabbitai]

Walkthrough

The changes in this pull request involve updates to the Volume class in the vedo/volume.py file. The constructor's default alpha values for volume rendering have been modified from [0.0, 0.0, 0.2, 0.4, 0.8, 1.0] to [0.0, 0.2, 0.4, 0.6, 0.8, 1.0], affecting transparency in rendering. Additionally, minor adjustments were made to comments and documentation to align with the new functionality, while the overall structure and logic of the class remain unchanged.

Changes

File	Change Summary
vedo/volume.py	Updated default alpha values in the Volume class constructor; adjusted comments and documentation.

Sequence Diagram(s)

sequenceDiagram
    participant User
    participant Volume
    User->>Volume: Create Volume instance
    Volume->>Volume: Set default alpha values [0.0, 0.2, 0.4, 0.6, 0.8, 1.0]
    User->>Volume: Call alpha method
    Volume->>Volume: Update alpha values if provided

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📥 Commits

Reviewing files that changed from the base of the PR and between 2e5fd11 and d790c39.

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• vedo/volume.py (1 hunks)

🔇 Additional comments (1)

vedo/volume.py (1)

193-193: LGTM! The new alpha gradient provides better visual consistency.

The change from [0.0, 0.0, 0.2, 0.4, 0.8, 1.0] to [0.0, 0.2, 0.4, 0.6, 0.8, 1.0] improves the alpha gradient by:

• Removing the redundant 0.0 value
• Creating uniform 0.2 increments between values
• Providing a more linear progression in transparency

This should result in better depth perception and more intuitive volume visualization.

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[marcomusy]

I think we need to revert this! most of the times for typical volumes there is a very large peak at zero and this needs to be removed.
So for the moment I put

                # make asigmoidal transfer function by default
                # xvalues = np.linspace(0, 1, 11)
                # sigmoid = np.clip(1/(1+np.exp(-20*(xvalues-0.5))), 0, 1)
                # print("Volume: setting sigmoidal transfer function", xvalues, sigmoid)
                # self.alpha(sigmoid)
                self.alpha([0.0, 0.001, 0.3, 0.5, 0.7, 0.8, 1.0])

we might have some refinement by looking at the histogram of the scalars to make some more sensible automatic choice...