Remove isce dep

[mirzaees]

This would remove isce2 dependency for tops data when loading them. There is still work to be done for stripmap
It also adds the correct number of looks for snaphu unwrapping

Summary by Sourcery

Remove ISCE dependency for TOPS data.

Enhancements:

• Use meta_utils instead of isce_utils for TOPS data metadata extraction.

Tests:

• Update tests to reflect the changes in metadata extraction and processing.

[sourcery-ai]

Reviewer's Guide by Sourcery

This pull request removes the isce2 dependency for loading TOPS data. It also adds the number of looks to the snaphu command.

Sequence diagram for TOPS metadata extraction process

sequenceDiagram
    participant C as Client
    participant M as MetaUtils
    participant X as XML Parser
    participant U as Utils
    C->>M: extract_tops_metadata(xml_file)
    M->>X: parse XML file
    X-->>M: return parsed data
    M->>U: interpolateHermite(state_vectors)
    U-->>M: return positions & velocities
    M->>U: xyz_to_llh(positions)
    U-->>M: return coordinates
    M->>U: enubasis(coordinates)
    U-->>M: return transformation matrices
    M-->>C: return metadata & frame info

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Class diagram showing metadata extraction changes

classDiagram
    class MetaUtils {
        +extract_tops_metadata(xml_file)
        +extract_stripmap_metadata(xml_file)
        +extract_alosStack_metadata(xml_file)
    }
    class AstronomicalHandbook {
        +PlanetsData
        +SatellitesData
        +Constants
    }
    class Utils {
        +xyz_to_llh()
        +enubasis()
        +radiusOfCurvature()
        +interpolateHermite()
    }
    MetaUtils --> AstronomicalHandbook
    MetaUtils --> Utils
    note for MetaUtils "Replaces ISCE2 dependency"

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File-Level Changes

Change	Details	Files
Removed the isce2 dependency for loading TOPS data.	Commented out the load_product function. Modified extract_isce_metadata to use meta_utils.extract_tops_metadata instead of isce_utils.extract_tops_metadata for TOPS data. The dependency on isce is only removed for tops, others need to be worked on	src/miaplpy/prep_slc_isce.py
Added the number of looks to the snaphu command.	Added nlooks to the run_unwrap function in miaplpyApp.py. Added nlooks to the __init__ function in unwrap_ifgram.py. Added NCORRLOOKS to the config file in unwrap_ifgram.py. Added the -n argument to the unwrap_parser in arg_parser.py.	src/miaplpy/miaplpyApp.py src/miaplpy/unwrap_ifgram.py src/miaplpy/objects/arg_parser.py
Changed the data type of the date dataset to bytes.	Changed the dsDataType of the date dataset to np.bytes_ in crop_geo.py. Changed the dsDataType of the date dataset to np.bytes_ in slcStack.py. Changed the dtype of the dates array to np.bytes_ in ifgram_inversion_L1L2.py. Changed the dtype of the data array to np.bytes_ in invert.pyx. Changed the dsDataType of the date dataset to np.bytes_ in geometryStack.py. Changed the dtype of the dates array to np.bytes_ in utils.py.	src/miaplpy/objects/crop_geo.py src/miaplpy/objects/slcStack.py src/miaplpy/dev/ifgram_inversion_L1L2.py src/miaplpy/lib/invert.pyx src/miaplpy/objects/geometryStack.py src/miaplpy/objects/utils.py
Added a new file for astronomical constants and a new io utils file.	Added a new file AstronomicalHandBook.py containing astronomical constants. Added a new file utils.py containing io utilities.	src/miaplpy/io/AstronomicalHandBook.py src/miaplpy/io/utils.py

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[sourcery-ai]

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

Overall Comments:

• Consider adding tests to verify the new TOPS metadata extraction produces identical results to the original ISCE2 version

Here's what I looked at during the review

• 🟡 General issues: 1 issue found
• 🟢 Security: all looks good
• 🟢 Testing: all looks good
• 🟢 Complexity: all looks good
• 🟢 Documentation: all looks good

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[sourcery-ai]

suggestion (bug_risk): Add validation for the nlooks parameter to ensure it's within reasonable bounds.

SNAPHU's correlation looks parameter should be positive and reasonably sized. Consider adding bounds checking to prevent invalid values.

Suggested implementation:

        def validate_nlooks(value):
            try:
                nlooks = int(value)
                if nlooks <= 0:
                    raise argparse.ArgumentTypeError("nlooks must be positive")
                if nlooks > 10000:  # reasonable upper bound for SNAPHU
                    raise argparse.ArgumentTypeError("nlooks value is unreasonably large (>10000)")
                return nlooks
            except ValueError:
                raise argparse.ArgumentTypeError("nlooks must be an integer")

        parser.add_argument('-n', '--nlooks', dest='nlooks', type=validate_nlooks, default=200,
                            help='The equivalent number of independent looks (positive integer, max 10000)')

You'll need to ensure that argparse is imported at the top of the file. If it's not already there, add:

import argparse