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Style: fix flake8/isort findings in cost_distance.py (#3339) (#3350)
Categories addressed: - Cat 3 (F401): remove unused `from functools import partial` (refactor leftover; not re-exported) - Cat 5 (mutable default): public `cost_distance(target_values=[])` -> `None` sentinel normalized to `[]` in the body, mirroring proximity.py (#2725). Reassigned via np.asarray before any mutation, so behaviour is preserved. Adds regression tests covering default reuse and None. - Cat 1 (E302): two blank lines before `@ngjit _heap_push` - Cat 1 (E127): fix continuation-line over-indent in _cost_distance_dask_cupy and _cost_distance_dask_iterative signatures - Cat 4 (isort): dataset_support before utils; utils from-import reflowed to 100 cols No behavioural change for the Cat 1/3/4 fixes. The Cat 5 change preserves behaviour. flake8 and isort --check-only are clean on the module; 57 cost_distance tests pass.
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.claude/sweep-style-state.csv

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module,last_inspected,issue,severity_max,categories_found,notes
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aspect,2026-05-29,2683,MEDIUM,1,E402+E305 line 38: from xrspatial.geodesic import block sat below _geodesic_cuda_dims; moved up with top-of-file imports. E501 lines 219/263: wrapped two _run_gpu_geodesic_aspect kernel-launch calls (101/109 chars). Cat 4 isort reviewed but NOT applied: slope.py/curvature.py use one-import-per-line for xrspatial.utils so raw isort would make aspect inconsistent. Cat 2/3/5 grep clean. PR #2740. 82 aspect+geodesic tests pass.
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contour,2026-05-29,2698,HIGH,3,"F821 line 557: contours() return annotation ""gpd.GeoDataFrame"" referenced gpd not bound at module scope (only imported inside _to_geopandas). Fixed via TYPE_CHECKING-guarded import geopandas as gpd, matching polygonize.py. No runtime change; geopandas stays optional. isort clean. Cat 1/2/4/5 clean. 24 contour tests pass. PR open."
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cost_distance,2026-06-15,3339,HIGH,1;3;4;5,"Cat 3 F401: removed unused 'from functools import partial' (L32, refactor leftover, not re-exported). Cat 5 mutable default target_values: list = [] was found here too, but the parallel api-consistency PR #3348 fixed it first (same None-sentinel normalization, merged to main); on reconciliation this PR's duplicate source change and its two target_values tests were dropped, so PR #3350 is now lint-only. Cat 1: E302 L63 (1->2 blank lines before @ngjit _heap_push; placed blanks between comment divider and decorator to satisfy both E302 and isort) + E127 x5 (_cost_distance_dask_cupy L461-462, _cost_distance_dask_iterative L1005-1007 signature continuation over-indent). Cat 4 isort: dataset_support before utils, utils from-import reflowed to 100 cols. Cat 2 grep clean. flake8+isort clean on module after fix; cost_distance tests pass (CUDA available). Pre-existing test-file lint (F401 L314/L504, E201 L449, isort drift) left untouched - out of module scope. PR open."
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focal,2026-05-29,2731,HIGH,3;4;5,"F401 not_implemented_func (import line 36, unused, not re-exported). isort: stdlib reorder (import math before from-imports), dropped stray blank lines in import groups, alphabetised+rewrapped convolution/utils from-imports, moved dataset_support import into order. Cat 5: mutable default excludes=[np.nan] in mean() (line 238) -> None sentinel, resolved to [np.nan] in body; never mutated so behaviour preserved; regression test test_mean_default_excludes_does_not_leak added. Cat 1/2 clean. 115 focal tests pass. PR pending."
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geotiff,2026-06-14,3329,MEDIUM,4,"Re-sweep after #3259: flake8 baseline 0 across all 33 production files (flake8 7.3.0). Cat 4 only: isort (line_length=100) drift in _writers/eager.py (the from .._validation import block wrapped at ~80 cols, not 100); rewrapped, no behavioural change. Cat 1/2/3/5 clean: no E/W/F codes; grep hits for == False (comment), ': dict'/': list' annotations, and type=getaddrinfo kwarg are all false positives, not shadowed builtins or comparisons. isort+flake8 clean after fix; import smoke test ok. PR via #3329."
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hydro-d8,2026-05-29,2705,HIGH,1;3;4,"flake8+isort over the 13 D8 files only (dinf/mfd out of scope). Cat 3 HIGH: F401 x2 (flow_length_d8 function-local _compute_accum_seeds never called; snap_pour_point_d8 module-level cuda_args unused) - both confirmed dead, no re-export. Cat 1: E127/E128 continuation-indent x90 (mostly multi-line def signatures); E302/E303 blank-line cluster in watershed_d8; E501 x4 (flow_path_d8 + snap_pour_point_d8, wrapped ternaries). Cat 4: isort import-block reordering on all 13 files. No Cat 2 (W-codes), no Cat 5 (grep clean: no bare except, mutable defaults, ==None/==True, or shadowed builtins). flake8+isort clean after fix; 385 D8 tests pass. flow_direction_d8 needed manual blank-line placement to satisfy both isort and E302."

xrspatial/cost_distance.py

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from __future__ import annotations
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import math as _math
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from functools import partial
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from math import sqrt
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import numpy as np
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class cupy: # type: ignore[no-redef]
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ndarray = False
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from xrspatial.utils import (
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_dask_task_name_kwargs,
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_validate_raster,
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cuda_args, get_dataarray_resolution, ngjit,
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has_cuda_and_cupy, is_cupy_array, is_dask_cupy,
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)
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from xrspatial.dataset_support import supports_dataset
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from xrspatial.utils import (_dask_task_name_kwargs, _validate_raster, cuda_args,
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get_dataarray_resolution, has_cuda_and_cupy, is_cupy_array,
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is_dask_cupy, ngjit)
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# ---------------------------------------------------------------------------
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# Numba binary min-heap (three parallel arrays: keys, rows, cols)
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# ---------------------------------------------------------------------------
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@ngjit
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def _heap_push(keys, rows, cols, size, key, row, col):
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"""Push (key, row, col) onto the heap. Returns new size."""
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def _cost_distance_dask_cupy(source_da, friction_da,
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cellsize_x, cellsize_y, max_cost,
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target_values, dy, dx, dd):
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cellsize_x, cellsize_y, max_cost,
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target_values, dy, dx, dd):
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"""Dask+CuPy cost distance.
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Bounded max_cost: ``da.map_overlap`` with per-chunk GPU relaxation.
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def _cost_distance_dask_iterative(source_da, friction_da,
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cellsize_x, cellsize_y,
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max_cost, target_values,
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dy, dx, dd):
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cellsize_x, cellsize_y,
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max_cost, target_values,
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dy, dx, dd):
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"""Iterative boundary-only Dijkstra for arbitrarily large dask arrays.
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Memory usage is O(sqrt(N)) for inter-iteration storage.

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