Extract independent cleanup fixes

[nabobalis]

Summary by Sourcery

Improve cosmic ray removal, WCS generation, and visualization slider labeling while adding dask support and updating dependencies.

New Features:

• Support Dask-backed data and masks in astroscrappy-based cosmic ray removal.
• Automatically shorten WCS-derived slider labels for IRIS visualizations based on axis semantics.

Bug Fixes:

• Handle NaN values and in-mask combination correctly per-frame in astroscrappy cosmic ray detection.
• Ensure velocity-space WCS construction is robust by building it directly from array shape and velocity grid instead of modifying an existing WCS header.
• Reject use of the rsliding cosmic-ray removal method on Dask-backed cubes that cannot be fully loaded into memory.

Enhancements:

• Relax type annotations in rsliding cosmic ray removal helpers to work with non-NumPy array backends.
• Standardize default raster sequence slider labels to sentence case for consistency.

Build:

• Add runtime dependencies on dask[array] and gwcs and remove the docs-only dependency on dask[distributed].

Tests:

• Add tests covering Dask-backed astroscrappy cosmic ray removal and validation of rsliding behavior with Dask cubes.
• Add visualization tests to assert shortened default slider labels and respect for custom slider labels.

[sourcery-ai]

Reviewer's Guide

Refactors cosmic ray removal to be robust for NaNs and dask-backed cubes, simplifies velocity WCS construction for red/blue profile cubes, standardizes/shortens IRIS visualization slider labels, and adjusts runtime dependencies and tests accordingly.

File-Level Changes

Change	Details	Files
Make astroscrappy-based cosmic ray removal robust to NaNs and support dask-backed cubes while rejecting incompatible rsliding usage.	Generalize _remove_cosmic_rays_rsliding input types to accept non-NumPy array data. Rewrite _remove_cosmic_rays_astroscrappy to iterate over leading dimensions, build per-frame masks including NaNs and optional inmask, and zero out masked pixels before calling detect_cosmics. Detect dask-backed cubes in remove_cosmic_rays, forbid rsliding for such cubes with a clear error, and avoid global mask materialization by passing through the cube mask directly for astroscrappy. Adjust working mask construction to be skipped for dask-backed data and retain NaN-handling only in the in-memory path.	irispy/utils/cosmic_rays.py
Extend tests to cover dask-backed cosmic ray removal behavior and validation of unsupported methods.	Introduce a simple FakeCube helper used to wrap data/mask for tests and to_nddata conversion. Add tests that validate astroscrappy cosmic ray removal on dask-backed cubes, including NaN handling and inmask propagation. Add a test ensuring rsliding raises a ValueError when used with dask-backed cubes. Keep existing tests for missing optional dependencies intact while integrating with new helper.	irispy/utils/tests/test_cosmic_rays.py
Construct a self-contained velocity WCS for red/blue profile cubes without relying on the base WCS.	Replace FITS-header-based _make_velocity_wcs implementation with one that creates a new astropy.wcs.WCS with specified naxis and velocity axis properties only. Compute the velocity axis index in WCS order, set VELO CTYPE, km/s units, and basic linear WCS parameters from the velocity grid, and attach array_shape. Update _make_profile_cube to call the new _make_velocity_wcs signature that no longer takes the base cube WCS.	irispy/utils/red_blue.py
Standardize and shorten IRIS visualization slider labels based on WCS coordinate types, and tweak default sequence labels.	Extend LAT_LABELS and LON_LABELS lists to include lowercase helioprojective labels and introduce TIME_LABELS for time axes. Add _shorten_slider_label helper that collapses wavelength/time/scan-related composite labels to simple canonical forms. Override IRISArrayAnimatorWCS._compute_slider_labels_from_wcs to post-process parent labels through _shorten_slider_label. Adjust IRISSequenceAnimator defaults to use sentence case slider labels ("Raster step", "Scan number").	irispy/visualization.py
Add visualization tests to ensure slider label shortening and user overrides behave as expected.	Add an SJI plotting test asserting the default slider labels collapse to ["Time"] for the test cube. Add an SJI plotting test asserting custom slider_labels passed into plot are respected unchanged and still close figures to avoid leakage.	irispy/tests/test_sji.py
Update project dependencies to include required runtime packages for dask-backed operations and gwcs, and simplify docs extras.	Declare dask[array]>=2024.6.0 and gwcs>=1.0.0 as core runtime dependencies. Remove dask[distributed] from the docs extras group, keeping other documentation dependencies unchanged.	pyproject.toml

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[sourcery-ai]

Hey - I've found 1 issue, and left some high level feedback:

• The astroscrappy code path eagerly converts each dask chunk to NumPy and stores results in preallocated NumPy arrays, so the output cube is no longer dask-backed; if preserving laziness is desired, consider returning a dask array (e.g. via map_blocks) or at least documenting that this operation materializes the full cube in memory.
• In _shorten_slider_label, set(LON_LABELS + LAT_LABELS) is recomputed on every call; consider creating a module-level SCAN_LABELS set instead to avoid repeated allocation on frequently updated sliders.
• The new _make_velocity_wcs creates a fresh WCS with only the velocity axis configured, dropping any contextual WCS information from the original cube; if some non-velocity axes metadata should be preserved (e.g. observer or coordinate frames), consider explicitly copying over the relevant parts from the input cube WCS.

Prompt for AI Agents

Please address the comments from this code review:

## Overall Comments
- The astroscrappy code path eagerly converts each dask chunk to NumPy and stores results in preallocated NumPy arrays, so the output cube is no longer dask-backed; if preserving laziness is desired, consider returning a dask array (e.g. via map_blocks) or at least documenting that this operation materializes the full cube in memory.
- In `_shorten_slider_label`, `set(LON_LABELS + LAT_LABELS)` is recomputed on every call; consider creating a module-level `SCAN_LABELS` set instead to avoid repeated allocation on frequently updated sliders.
- The new `_make_velocity_wcs` creates a fresh WCS with only the velocity axis configured, dropping any contextual WCS information from the original cube; if some non-velocity axes metadata should be preserved (e.g. observer or coordinate frames), consider explicitly copying over the relevant parts from the input cube WCS.

## Individual Comments

### Comment 1
<location path="irispy/utils/cosmic_rays.py" line_range="65-74" />
<code_context>
+        frame_mask = np.zeros(frame.shape, dtype=bool) if mask is None else np.asarray(mask[index], dtype=bool)
+        if np.issubdtype(frame.dtype, np.floating):
+            frame_mask = frame_mask | np.isnan(frame)
+        if inmask is not None:
+            frame_mask = frame_mask | np.asarray(inmask[index], dtype=bool)
+        frame[frame_mask] = 0.0
+        detected_mask, cleaned_frame = astroscrappy.detect_cosmics(frame, inmask=frame_mask, **method_kwargs)
</code_context>
<issue_to_address>
**issue (bug_risk):** Indexing `inmask` per frame changes semantics for 2D masks on >2D data and may break previous broadcasting behavior.

Previously, a 2D `inmask` was combined with `mask` without indexing, effectively applying the same mask to every frame in a higher‑dimensional cube. By indexing `inmask[index]`, a 2D `inmask` for an n>2D cube will now either fail or (worse) mask a different set of pixels than before. If you want to preserve the old behavior, consider checking `inmask.ndim`/shape and only index when it matches `data`’s leading dimensions, otherwise broadcast it explicitly across frames.
</issue_to_address>

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