Geocode 4D dataset

[bjmarfito]

Description of the desired feature

Request to geocode 4D dataset such as timeseriesDecorCov.h5 since geocode.py can only geocode 3D dataset.

Is your feature request related to a problem? Please describe

The error is generated by geocode.py when I try to geocode timeseriesDecorCov.h5.

reading timeseries in block (2032, 6116, 6465, 6605) from timeseriesDecorCov.h5 ...
nearest resampling with pyresample.kd_tree using 1 CPU cores in 1 segments ...
Traceback (most recent call last):
  File "/home/bryanjim001/tools/MintPy/src/mintpy/cli/geocode.py", line 269, in <module>
    main(sys.argv[1:])
  File "/home/bryanjim001/tools/MintPy/src/mintpy/cli/geocode.py", line 264, in main
    run_geocode(inps)
  File "/home/bryanjim001/tools/MintPy/src/mintpy/geocode.py", line 121, in run_geocode
    data = res_obj.run_resample(src_data=data, box_ind=i)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/bryanjim001/tools/MintPy/src/mintpy/objects/resample.py", line 214, in run_resample
    dest_data = self.run_pyresample(
                ^^^^^^^^^^^^^^^^^^^^
  File "/home/bryanjim001/tools/MintPy/src/mintpy/objects/resample.py", line 724, in run_pyresample
    dest_data = pr.kd_tree.resample_nearest(
                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/bryanjim001/.conda/envs/isce2expe/lib/python3.11/site-packages/pyresample/kd_tree.py", line 107, in resample_nearest
    return _resample(source_geo_def, data, target_geo_def, 'nn',
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/bryanjim001/.conda/envs/isce2expe/lib/python3.11/site-packages/pyresample/kd_tree.py", line 270, in _resample
    return get_sample_from_neighbour_info(resample_type,
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/bryanjim001/.conda/envs/isce2expe/lib/python3.11/site-packages/pyresample/kd_tree.py", line 613, in get_sample_from_neighbour_info
    raise ValueError('Mismatch between geometry and dataset')
ValueError: Mismatch between geometry and dataset

Describe the solution you'd like

Describe alternatives you have considered

Additional context

Are you willing to help implement and maintain this feature?

• Yes
• No

[codeautopilot]

Potential solution

The task is to modify the existing geocoding script to handle 4D datasets, as the current implementation only supports 3D datasets. The error message indicates a mismatch between the geometry and dataset, likely due to the additional dimension in the 4D dataset. The solution involves updating the script to recognize and process this extra dimension, ensuring that the geocoding and resampling logic can handle each slice of the 4D data appropriately.

How to implement

1. Identify the Additional Dimension: Determine what the fourth dimension represents (e.g., time, depth) and how it should be processed. This understanding is crucial for implementing the correct logic.

2. Update Argument Parsing: Modify the script to accept inputs that specify how to handle the fourth dimension. This might involve adding new command-line arguments or modifying existing ones.

3. Modify Data Handling in geocode.py:

   • Update the run_geocode function to handle 4D data. Check the shape of the input data to determine if it is 4D.
   • Iterate over the additional dimension and apply the geocoding process to each slice of the data.
   • Ensure that the output retains the 4D structure.

   def run_geocode(inps):
       data_shape = inps.data.shape
       if len(data_shape) == 4:
           for i in range(data_shape[0]):
               slice_data = inps.data[i]
               # Apply existing geocoding logic to each 3D slice
               geocoded_slice = res_obj.run_resample(src_data=slice_data, box_ind=i)
               # Store the geocoded slice back into the 4D structure
               inps.data[i] = geocoded_slice
       else:
           # Existing 3D geocoding logic
           data = res_obj.run_resample(src_data=inps.data, box_ind=i)

4. Adjust Resampling Logic in resample.py:

   • Modify the run_resample method to check if the input data is 4D. If it is, iterate over the additional dimension and apply the resampling logic to each 3D subset.
   • Ensure that the output is reconstructed into a 4D array after processing each slice.

   def run_resample(self, src_data, box_ind):
       if src_data.ndim == 4:
           dest_data = np.empty_like(src_data)
           for i in range(src_data.shape[0]):
               dest_data[i] = self.run_pyresample(src_data[i], box_ind)
           return dest_data
       else:
           return self.run_pyresample(src_data, box_ind)

5. Testing: Thoroughly test the script with 4D datasets to ensure that it behaves as expected and produces correct results.

6. Documentation: Update the script's documentation and examples to reflect the new capability to handle 4D datasets.

By implementing these changes, the geocode.py script and its associated resampling logic will be able to handle 4D datasets, processing each slice of the additional dimension correctly.

Click here to create a Pull Request with the proposed solution

Files used for this task:

Changes on src/mintpy/cli/geocode.py

To modify the geocode.py script to support 4D datasets, you need to add logic that can handle the additional dimension. Here's a practical proposal for how you might approach this:

1. Identify the Additional Dimension: Determine what the fourth dimension represents (e.g., time, depth, etc.) and how it should be processed. This understanding is crucial for implementing the correct logic.

2. Update Argument Parsing: Ensure that the script can accept inputs that specify how to handle the fourth dimension. This might involve adding new command-line arguments or modifying existing ones.

3. Modify Data Handling: In the run_geocode function, update the logic to handle 4D data. This could involve:

   • Checking the shape of the input data to determine if it is 4D.
   • Iterating over the additional dimension and applying the geocoding process to each slice of the data.
   • Ensuring that the output retains the 4D structure.

4. Adjust Resampling Logic: If the resampling logic in resample.py is not already capable of handling 4D data, you will need to modify it to ensure that it can process each slice of the 4D data correctly.

5. Testing: After implementing the changes, thoroughly test the script with 4D datasets to ensure that it behaves as expected and produces correct results.

6. Documentation: Update the script's documentation and examples to reflect the new capability to handle 4D datasets.

By following these steps, you can extend the functionality of geocode.py to support 4D datasets.

Changes on src/mintpy/objects/resample.py

To update the resample.py file to accommodate 4D datasets, you need to modify the resampling methods to handle an additional dimension. Here's a practical proposal to achieve this:

1. Identify 4D Data Handling:

   • Determine how the additional dimension (e.g., time or another spatial dimension) should be handled during resampling. This could involve iterating over the additional dimension and applying the existing 3D resampling logic to each slice.

2. Modify run_resample Method:

   • Update the run_resample method to check if the input data is 4D. If it is, iterate over the additional dimension and apply the resampling logic to each 3D subset.
   • Ensure that the output is reconstructed into a 4D array after processing each slice.

3. Adjust Geometry Preparation:

   • Ensure that the geometry preparation methods (prepare_geometry_definition_radar, prepare_geometry_definition_geo, etc.) can handle the additional dimension if necessary. This might involve ensuring that any metadata or auxiliary data structures are compatible with 4D data.

4. Update Documentation and Examples:

   • Update the class and method documentation to reflect the new capability to handle 4D datasets.
   • Provide examples of how to use the updated class with 4D data.

5. Testing:

   • Implement tests to verify that the resampling works correctly with 4D datasets. This includes checking that the output dimensions are correct and that the data integrity is maintained.

By following these steps, you can extend the functionality of the resample.py file to support 4D datasets, ensuring that the additional dimension is correctly processed during resampling.

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

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