Add ssar

[ehavazli]

Description of proposed changes

This PR adds SSAR support to the NISAR loading workflow while keeping the current LSAR behavior as the default.

It introduces a new band option for NISAR loading through both the CLI and the MintPy template:

• --band in prep_nisar.py
• mintpy.load.band in smallbaselineApp.cfg

The selected band is passed through load_data.py into prep_nisar.py, where the HDF5 dataset and metadata paths are now resolved dynamically from the NISAR product family instead of being hardcoded for LSAR.

As part of this update, the NISAR preparation logic is made band-aware across:

• dataset lookup
• metadata extraction
• subset reading
• geometry generation
• water-mask handling
• date-pair parsing
• stack preparation

This allows the same workflow to ingest both LSAR and SSAR GUNW products without manual path edits or separate code paths.

Defaults remain backward compatible:

• band=auto resolves to LSAR
• frequency=auto continues to resolve to A

These changes keep existing LSAR processing unchanged while extending MintPy support to SSAR NISAR products.

Reminders

• Fix #xxxx
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Summary by Sourcery

Add band-aware NISAR loading to support both LSAR and SSAR products while keeping LSAR as the default.

New Features:

• Introduce a configurable NISAR SAR band option (LSAR or SSAR) exposed via the CLI and MintPy configuration.
• Enable band-aware handling of NISAR GUNW datasets across metadata extraction, subset reading, geometry generation, date-pair parsing, and stack preparation.

Enhancements:

• Generalize hardcoded LSAR HDF5 paths into reusable helpers that resolve dataset and metadata locations based on the selected SAR band.
• Improve error messages for missing NISAR datasets by including the selected product family in the diagnostics.

Documentation:

• Document the new mintpy.load.band configuration option in the default smallbaselineApp.cfg template.

[sourcery-ai]

Reviewer's Guide

Adds a band-aware NISAR loading workflow that introduces a configurable SAR product family (LSAR/SSAR) option, threads it through CLI and MintPy config, and refactors all NISAR HDF5 path resolution and preparation utilities to derive datasets dynamically from the selected band while preserving LSAR as the default.

Flow diagram for band-aware NISAR loading pipeline

flowchart LR
    subgraph User_Input
        A1[CLI prep_nisar.py --band]
        A2[smallbaselineApp.cfg mintpy.load.band]
    end

    A1 --> B[prep_nisar.load_nisar]
    A2 --> C[load_data.prepare_metadata]

    C -->|builds args with --band| D[CLI prep_nisar.py]
    D --> B

    B --> E[_normalize_sar_band]
    E -->|sar_band| F[_science_root]
    E -->|sar_band| G[_processinfo]

    B -->|sar_band, frequency, polarization| H[_datasets_for_pol]
    B -->|sar_band| I[_get_date_pairs]
    B -->|sar_band| J[extract_metadata]
    B -->|sar_band| K[prepare_geometry]
    B -->|sar_band| L[prepare_water_mask]
    B -->|sar_band| M["prepare_stack (ifgram/ion/tropo/set)"]

    F --> H
    G --> J
    G --> I
    G --> K
    G --> L
    G --> M

Loading

File-Level Changes

Change	Details	Files
Introduce SAR band abstraction and dynamic HDF5 path resolution for NISAR products instead of LSAR-only hardcoded paths.	Define DEFAULT_SAR_BAND/VALID_SAR_BANDS and a _normalize_sar_band helper with validation and auto->LSAR behavior. Replace global IDENTIFICATION/PROCESSINFO constants with helpers (_science_root, _identification_root, _radargrid_root, _processinfo) parameterized by sar_band. Update helpers that build dataset paths (_dataset_root_unw, _parameters_root, _center_frequency_path, _datasets_for_pol) to take sar_band and construct /science/{sar_band}/... paths.	src/mintpy/prep_nisar.py
Make NISAR loading, masking, geometry, troposphere/SET interpolation, and stack preparation band-aware by threading sar_band through the workflow.	Extend frequency resolution, dataset existence checks, and error messages to include sar_band context in _resolve_frequency. Add sar_band parameters (with DEFAULT_SAR_BAND default) to mask readers, target-grid readers, radar-grid readers, interpolation setup, and geometry/tropo/SET interpolation functions and use _processinfo(sar_band) and band-aware dataset maps internally. Propagate sar_band through stack-related utilities (_required_paths_for_stack_type, _missing_required_paths, _read_stack_observation, prepare_stack) and through geometry/water-mask helpers (extract_metadata, get_raster_corners, common_raster_bound, read_subset, prepare_geometry, prepare_water_mask). Update date-pair parsing in _get_date_pairs to use band-aware identification paths (reference/secondaryZeroDopplerStartTime from _processinfo).	src/mintpy/prep_nisar.py
Update load_nisar entry point to accept and normalize SAR band and drive a fully band-aware NISAR ingestion path.	Normalize inps.sar_band once via _normalize_sar_band in load_nisar and store back to the inps object for downstream use. Pass sar_band into all downstream calls from load_nisar (frequency resolution, metadata extraction, mask computation, geometry preparation, water-mask generation, and stack preparation for ifgram/ion/tropo/set). Adjust logging to print the selected sar_band alongside the resolved NISAR frequency.	src/mintpy/prep_nisar.py
Expose the SAR band configuration through the prep_nisar CLI and thread it from MintPy configuration into the prep_nisar invocation.	Add a new -ba/--band CLI option (dest='sar_band', choices auto/LSAR/SSAR, default auto) to prep_nisar’s argument parser, documenting that auto defaults to LSAR. Read mintpy.load.band (default auto) from the configuration in load_data.prepare_metadata and include it in the constructed iargs when invoking prep_nisar. Extend smallbaselineApp.cfg defaults with mintpy.load.band = auto and brief documentation indicating valid values and that it is NISAR-only.	src/mintpy/cli/prep_nisar.py src/mintpy/load_data.py src/mintpy/defaults/smallbaselineApp.cfg

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[sourcery-ai]

Hey - I've left some high level feedback:

• Consider defining a module-level constant for the default SAR band (e.g., DEFAULT_SAR_BAND = "LSAR") and using it consistently instead of repeating the literal "LSAR" in many function signatures and messages, which will reduce the risk of future mismatches.
• Several public helpers now accept a sar_band argument and internally normalize it; you could normalize sar_band once at the top-level (e.g., in load_nisar/CLI) and pass the normalized value through to avoid repeated normalization and make error messages and logging more predictable.

Prompt for AI Agents

Please address the comments from this code review:

## Overall Comments
- Consider defining a module-level constant for the default SAR band (e.g., `DEFAULT_SAR_BAND = "LSAR"`) and using it consistently instead of repeating the literal "LSAR" in many function signatures and messages, which will reduce the risk of future mismatches.
- Several public helpers now accept a `sar_band` argument and internally normalize it; you could normalize `sar_band` once at the top-level (e.g., in `load_nisar`/CLI) and pass the normalized value through to avoid repeated normalization and make error messages and logging more predictable.

---

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[ehavazli]

@sourcery-ai review

[sourcery-ai]

Hey - I've left some high level feedback:

• Now that sar_band is threaded through most helpers, consider introducing a small context object (e.g., carrying sar_band, science_root, and processinfo) and passing that instead of the raw sar_band parameter to reduce repeated root/path computation and make future band-specific extensions easier to maintain.

Prompt for AI Agents

Please address the comments from this code review:

## Overall Comments
- Now that `sar_band` is threaded through most helpers, consider introducing a small context object (e.g., carrying `sar_band`, `science_root`, and `processinfo`) and passing that instead of the raw `sar_band` parameter to reduce repeated root/path computation and make future band-specific extensions easier to maintain.

---

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• Facebook

_{Help me be more useful! Please click 👍 or 👎 on each comment and I'll use the feedback to improve your reviews.}

[ehavazli]

@yunjunz
Codacy is flagging both D203 and D211 here, but those rules conflict. One requires a blank line before the class docstring; the other forbids it. Could we confirm which convention this repo wants and disable the other rule?