feat: stalign with a fit apply pattern. #1195
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…leyen/squidpy into feat/spatial_neighbours
timtreis
reviewed
Jun 16, 2026
timtreis
reviewed
Jun 16, 2026
| # element we register a transform on so `output_mode="copy"` leaves the input untouched. | ||
| element = sd_deepcopy(element) | ||
| getattr(out, etype)[name] = element | ||
| set_transformation(element, sd_affine, to_coordinate_system=target_cs) |
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breaks if the element already has a transformation, you need to Sequence them
existing = get_transformation(element, to_coordinate_system=moving_cs)
set_transformation(element, Sequence([existing, sd_affine]), to_coordinate_system=target_cs)
timtreis
reviewed
Jun 16, 2026
| radius = int(np.ceil(float(np.max(blur_values)) * 4.0)) | ||
| denom = 2.0 * (dx * blur_values * 2.0) ** 2 | ||
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| for x_i, y_i in zip(x, y, strict=False): |
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How many points do we have? Should probably rasterize
timtreis
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Jun 16, 2026
| p: float = 2.0, | ||
| expand: float = 2.0, | ||
| nt: int = 3, | ||
| niter: int = 5000, |
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Can we do some form of early stopping or do we always have to train for 5k steps? Might be too much/too little depending on data
timtreis
reviewed
Jun 16, 2026
| return data_ref.tables[ref_key], sdata_query.tables[query_key], sdata_query, query_key | ||
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| def get_coords(adata: AnnData, spatial_key: str) -> np.ndarray: |
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Claude:
get_coords materializes coords up to 3× per align() (_io.py:71; called at _api.py:83,84 and again _io.py:97). np.asarray(..., dtype=float) forces a float64 copy each time. Thread the already-computed query_xy into writeback_obs and avoid the forced upcast when the array is already suitable.
timtreis
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Jun 16, 2026
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Continuation of #1162