Removes np.nan in output of spatial_filter.py

[bjmarfito]

Description of proposed changes

Removes np.nan when using spatial_filter.py. This is useful to generate velocity using timeseries2velocity.py.

Reminders

• Fix #xxxx
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Summary by Sourcery

Bug Fixes:

• Remove np.nan values from the output of spatial_filter.py to ensure compatibility with timeseries2velocity.py.

[sourcery-ai]

Reviewer's Guide by Sourcery

The PR modifies the spatial filtering functionality to handle NaN values by zeroing them out after filtering. This change is implemented by adding a single line of code that multiplies the filtered data with a boolean mask where NaN values are False.

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

File-Level Changes

Change	Details	Files
Modified spatial filtering to handle NaN values by converting them to zeros	Added multiplication with boolean mask (~np.isnan(data)) to convert NaN values to zeros after filtering Ensures compatibility with timeseries2velocity.py processing	src/mintpy/spatial_filter.py

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PR summary

This Pull Request addresses the issue of np.nan values in the output of the spatial_filter.py script. By removing these np.nan values, the script's output becomes more suitable for subsequent processing, such as generating velocity using timeseries2velocity.py. The change involves a simple modification where the data is multiplied by the negation of its np.isnan result, effectively setting np.nan values to zero.

Suggestion

To ensure robustness, consider adding a test case to verify that the removal of np.nan values does not inadvertently alter valid data. Additionally, it might be beneficial to include a comment in the code explaining the purpose of the data *= ~np.isnan(data) line for future maintainers.

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

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[sourcery-ai]

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

Overall Comments:

• The NaN handling could be more explicit - consider using np.nan_to_num() or np.where() instead of boolean multiplication. This would make the intention clearer.
• Please document this behavior change in the function's docstring, explaining what happens to NaN values and why this change was needed.

Here's what I looked at during the review

• 🟢 General issues: all looks good
• 🟢 Security: all looks good
• 🟢 Testing: all looks good
• 🟢 Complexity: all looks good
• 🟢 Documentation: all looks good

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[yunjunz]

I don't get the intention here, this line of change won't do anything, from my simple testing. Could you clarify? and add some inline comment or a new option instead?

[bjmarfito]

Hi @yunjunz, after looking at the code, I found out that adding this line of code does not solve the issue of generating velocity using timeseries2velocity.py after using spatial_filter.py. Need to look in-depth regarding this problem.

Closing this PR.