Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

view: convert integer to floating to enable masking with nan #1289

Merged
merged 1 commit into from
Nov 11, 2024

Conversation

yunjunz
Copy link
Member

@yunjunz yunjunz commented Nov 11, 2024

Description of proposed changes

  • view.viewer.plot(): convert integer to floating to enable masking with nan for one subplot scenario

Reminders

  • Pass Pre-commit check (green)
  • Pass Codacy code review (green)
  • Pass Circle CI test (green)
  • Make sure that your code follows our style. Use the other functions/files as a basis.
  • If modifying functionality, describe changes to function behavior and arguments in a comment below the function declaration.

Summary by Sourcery

Convert integer data types to floating point in the plot function to enable masking with NaN values, ensuring proper handling of no-data values.

Bug Fixes:

  • Convert integer data types to floating point in the plot function to enable masking with NaN values.

Enhancements:

  • Add a check to convert integer data types to floating point before applying NaN masking in the plot function.

+ view.viewer.plot(): convert integer to floating to enable masking with nan for one subplot scenario
Copy link

sourcery-ai bot commented Nov 11, 2024

Reviewer's Guide by Sourcery

The PR modifies the data type conversion logic in the plotting functionality to properly handle no-data values by converting integer arrays to floating-point before applying NaN masking. This ensures correct visualization when masking is needed.

No diagrams generated as the changes look simple and do not need a visual representation.

File-Level Changes

Change Details Files
Added data type conversion from integer to float32 before masking with NaN values
  • Added type checking using np.issubdtype to detect integer arrays
  • Implemented conversion to float32 for integer arrays before NaN masking
  • Applied conversion in both self.no_data_value and no_data_val masking scenarios
src/mintpy/view.py
Updated byte order comment in binary file reading function
  • Modified comment to indicate 'big-endian' next to 'little-endian' specification
src/mintpy/utils/readfile.py

Tips and commands

Interacting with Sourcery

  • Trigger a new review: Comment @sourcery-ai review on the pull request.
  • Continue discussions: Reply directly to Sourcery's review comments.
  • Generate a GitHub issue from a review comment: Ask Sourcery to create an
    issue from a review comment by replying to it.
  • Generate a pull request title: Write @sourcery-ai anywhere in the pull
    request title to generate a title at any time.
  • Generate a pull request summary: Write @sourcery-ai summary anywhere in
    the pull request body to generate a PR summary at any time. You can also use
    this command to specify where the summary should be inserted.

Customizing Your Experience

Access your dashboard to:

  • Enable or disable review features such as the Sourcery-generated pull request
    summary, the reviewer's guide, and others.
  • Change the review language.
  • Add, remove or edit custom review instructions.
  • Adjust other review settings.

Getting Help

Copy link

codeautopilot bot commented Nov 11, 2024

PR summary

This Pull Request addresses an issue in the view.viewer.plot() function where integer data types are converted to floating-point types to enable masking with NaN values. This change is necessary because NaN values are not compatible with integer data types, and the conversion ensures that data can be properly masked when a NO_DATA_VALUE is specified. The modification is particularly relevant for scenarios involving a single subplot.

Suggestion

To improve the clarity of the code, consider adding a comment explaining why the conversion from integer to floating-point is necessary, especially for those who might not be familiar with the limitations of NaN handling in integer arrays. Additionally, ensure that the conversion does not introduce unintended side effects, such as increased memory usage, especially for large datasets. If performance is a concern, consider implementing a check to only convert data types when necessary.

Disclaimer: This comment was entirely generated using AI. Be aware that the information provided may be incorrect.

Current plan usage: 0.00%

Have feedback or need help?
Discord
Documentation
[email protected]

Copy link

@sourcery-ai sourcery-ai bot left a comment

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Hey @yunjunz - I've reviewed your changes - here's some feedback:

Overall Comments:

  • There appears to be an unrelated comment about byte order ('# big-endian') in readfile.py - was this intended to be included in this PR?
Here's what I looked at during the review
  • 🟡 General issues: 2 issues found
  • 🟢 Security: all looks good
  • 🟢 Testing: all looks good
  • 🟢 Complexity: all looks good
  • 🟢 Documentation: all looks good

Sourcery is free for open source - if you like our reviews please consider sharing them ✨
Help me be more useful! Please click 👍 or 👎 on each comment and I'll use the feedback to improve your reviews.

@yunjunz yunjunz merged commit 718027c into insarlab:main Nov 11, 2024
7 checks passed
@yunjunz yunjunz deleted the view branch November 11, 2024 05:38
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
None yet
Projects
None yet
Development

Successfully merging this pull request may close these issues.

1 participant