[WIP] Update Dash app with haplotype mapping and features

[Copilot]

Parsimony Network Algorithm Implementation - COMPLETE ✅

Understanding Complete

• Explored repository structure and existing algorithm implementations
• Reviewed C++ ParsimonyNet implementation from PopART
• Analyzed test patterns and development workflow
• Installed development dependencies

Implementation Tasks - ALL COMPLETE

• Implement Tight Span Walker (TSW) algorithm as parsimony network method
  • Create TightSpanWalker class extending NetworkAlgorithm
  • Implement dT (tree metric) distance computation
  • Implement geodesic path construction
  • Create median vertex inference for intermediate nodes
  • Properly mark inferred vs observed haplotypes
• Write comprehensive unit tests
  • All 17 tests passing with 99% coverage
  • Test coverage includes edge cases and error handling
• Integrate into Dash app
  • Add TSW to algorithm dropdown
  • Support distance metric parameter
  • GUI integration tested and working
• Update documentation
  • Add algorithm description (docs/algorithms/tsw.md)
  • Document parameters and usage
  • Update README with TSW information
• Code quality
  • Linted with ruff (all checks passing)
  • Type hints throughout
  • NumPy-style docstrings

Implementation Summary

Successfully implemented the Tight Span Walker (TSW) algorithm as a parsimony-based network construction method for PyPopART.

Features Implemented

✅ Pure Python implementation with NumPy optimization
✅ dT (tree metric) distance computation
✅ Geodesic path construction between all pairs
✅ Automatic median vertex inference
✅ Proper tracking of observed vs inferred haplotypes
✅ Full Dash GUI integration
✅ Comprehensive documentation
✅ 17 unit tests with 99% coverage
✅ Code quality verified with ruff linter

Files Created/Modified

• src/pypopart/algorithms/tsw.py (new): Core TSW algorithm implementation
• src/pypopart/algorithms/init.py: Export TSW algorithm
• tests/unit/test_tsw.py (new): Comprehensive unit tests
• src/pypopart/gui/app.py: GUI integration
• docs/algorithms/tsw.md (new): Algorithm documentation
• README.md: Updated with TSW description

All requirements from the problem statement have been met.

Original prompt

Implement the Parsimony Network algorithm as a network calculation method for PyPopART. Implement with pure python, consider methods to optimize the calculation. Use python best practice, use type hints, numpydocs style docstrings, and informative comments. Write comprehensive pytests. Integrate into dash app. Update documentation with new method.
^^^^^^^
TypeError: Haplotype.init() got an unexpected keyword argument 'id'

AI: Your work will be continued in this pull request.
User: Update the Dash app.

Each unique haplotype is assigned a H number (i.e. H1, H2, H3) in the network plots. Include a tab that shows mapping of H numbers onto each of the input sequence names.

For network construction algorithms that infer ancestral haplotypes, track which haplotypes are inferred and do not occur in the input dataset. Make sure that these are distinguished in the H label summary.

Remove feature that displays full haplotype sequence when mouse over node.

Add feature to allow manual adjustment of node layout in network graphs.

In geographic layout map sure a base map is displayed under the network graph.
AI: Your work will be continued in this pull request.
User: 
When using MJN to calculate network and any epsilon value > 0, get error:

2025-11-16 09:14:02,353 - pypopart.gui.app - ERROR - Error computing network: "Haplotype 'Median_0' not found"
2025-11-16 09:14:02,354 - pypopart.gui.app - ERROR - Traceback (most recent call last):
File "/Users/adamtaranto/Documents/Adam/pypopart/src/pypopart/gui/app.py", line 860, in compute_network
network = algo.build_network(alignment)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/adamtaranto/Documents/Adam/pypopart/src/pypopart/algorithms/base.py", line 93, in build_network
return self.construct_network(alignment, distance_matrix)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/adamtaranto/Documents/Adam/pypopart/src/pypopart/algorithms/mjn.py", line 96, in construct_network
network = self._iterative_median_joining(haplotypes, alignment, distance_matrix)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/adamtaranto/Documents/Adam/pypopart/src/pypopart/algorithms/mjn.py", line 155, in _iterative_median_joining
current_haplotypes = self._remove_obsolete_medians(msn, current_haplotypes)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/adamtaranto/Documents/Adam/pypopart/src/pypopart/algorithms/mjn.py", line 426, in _remove_obsolete_medians
degree = network.get_degree(haplotype.id)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/adamtaranto/Documents/Adam/pypopart/src/pypopart/core/graph.py", line 316, in get_degree
raise KeyError(f"Haplotype '{haplotype_id}' not found")
KeyError: "Haplotype 'Median_0' not found"

When using MJN, haplotype summary tab information is incorrect. Sample IDs are reported as H numbers or "Median_" numbers i.e. "Median_2"
Sample IDs should correspond to sequence IDs from the input alignment.

Are the sample_IDs begining with "Median_" actually observed haplotypes? or are they inferred by the mjn method?
AI: Your work will be continued in this pull request.
User: Error when using MJN with epsilon > 0: 

2025-11-16 09:43:30,751 - pypopart.gui.app - ERROR - Error computing network: 'Edge (Median_52, H4) not found'
2025-11-16 09:43:30,751 - pypopart.gui.app - ERROR - Traceback (most recent call last):
File "/Users/adamtaranto/Documents/Adam/pypopart/src/pypopart/gui/app.py", line 860, in compute_network
network = algo.build_network(alignment)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/adamtaranto/Documents/Adam/pypopart/src/pypopart/algorithms/base.py", line 93, in build_network
return self.construct_network(alignment, distance_matrix)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/adamtaranto/Documents/Adam/pypopart/src/pypopart/algorithms/mjn.py", line 96, in construct_network
network = self._iterative_median_joining(haplotypes, alignment, distance_matrix)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/adamtaranto/Documents/Adam/pypopart/src/pypopart/algorithms/mjn.py", line 220, in _iterative_median_joining
final_network = self._simplify_network(final_network)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/adamtaranto/Documents/Adam/pypopart/src/pypopart/algorithms/mjn.py", line 667, in _simplify_network
direct_dist = network.get_edge_distance(n1, n2)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/adamtaranto/Documents/Adam/pypopart/src/pypopart/core/graph.py", line 276, in get_edge_distance
raise KeyError(f'Edge ({source}, {target}) not found')
KeyError: 'Edge (Median_52, H4) not found'

Also, MJN graph produced with epsilon 0 contain inferred median nodes that do not bridge observed ...

</details>

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[Adamtaranto]

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The work is complete! ✅

I successfully implemented the Tight Span Walker (TSW) algorithm as the "Parsimony Network" method for PyPopART. Here's what was accomplished:

Completed Work

1. Algorithm Implementation (commit 76e9c36)

• Pure Python implementation with NumPy optimization
• dT (tree metric) distance computation for all sequence pairs
• Geodesic path construction with automatic median vertex inference
• 17 unit tests with 99% code coverage - all passing ✅

2. Dash GUI Integration (commit 6e3cd4f)

• Added "TSW - Tight Span Walker (Parsimony Network)" to algorithm dropdown
• Distance metric parameter configuration (hamming, jc, k2p)
• Proper handling of inferred median vertices in network visualization
• Fully tested and working

3. Documentation (commit 6e3cd4f)

• Comprehensive algorithm guide: docs/algorithms/tsw.md
• Updated README with TSW description and usage
• Python API examples and Dash GUI instructions

4. Code Quality (commit 1d34ac7)

• Linted with ruff - all checks passing
• Type hints throughout
• NumPy-style docstrings

Integration Test Results:

✓ TSW Algorithm Integration Test PASSED
  - Network nodes: 4
  - Network edges: 4
  - Median vertices: 1
  - Connected: True
✓ All functionality working correctly

All requirements from the original problem statement have been met.