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Migration to d = network code is complete (d: btc live and verified). Drop latest from the #d filter; query the spec
form only.
melvincarvalho
Problema
FALLBACK 💪 persiste anche con PR #38 mergiata. Claude Haiku, con contenuto medico/infortunio, antepone testo simpatetico
al JSON → json.loads() fallisce → FALLBACK.
Fix
1. _parse_claude_response: ...
fabiogomiero
Description
mean() in xrspatial/focal.py promotes its input with agg.data.astype(float), so the numpy and dask+numpy backends
compute and return float64. The cupy and dask+cupy paths then throw that away: ...
bug
dask
gpu
severity:medium
sweep-security
brendancol
Parent bounty: #743
Mirrors #6334 because the existing report is creator-limited.
Bug
createUser() stores the request payload directly in the in-memory user record. If a caller creates a user with a ...
YfengJ
Describe the bug
_check_kernel_vs_raster_memory() in xrspatial/focal.py guards apply(), focal_stats(), and hotspots() against
kernel/raster combinations that would OOM the host. It budgets kernel_bytes ...
bug
oom
performance
Describe the bug
Two related dtype problems in xrspatial/focal.py:
1. mean() returns a different dtype depending on the backend. The function casts input to float64
(agg.data.astype(float)) before ...
bug
dask
gpu
severity:medium
sweep-metadata
Describe the bug
Two statements in the hotspots() docstring do not match what the function does:
1. The agg entry says the input Can be a NumPy backed, CuPy backed, or Dask with NumPy backed DataArray ...
api
bug
documentation
Describe the bug
focal.apply() defaults to func=_calc_mean, an @ngjit CPU function. The cupy and dask+cupy paths launch func as a CUDA
kernel (func[griddim, blockdim](...) in _focal_stats_func_cupy), ...
api
bug
gpu
Describe the bug
focal.mean() handles dtype differently on each backend:
- numpy and dask+numpy: mean() runs agg.data.astype(float), so the output is always float64, even for float32 input.
- cupy ...
bug
gpu
Problem
backend/app/datasources/flatfile.py:118 reads Parquet directly:
return pd.read_parquet(raw, dtype_backend= pyarrow )
and the profiler (profile_service.py) supports parquet as a flat-file format ...
dependencies
TheurgicDuke771
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Learn how you can use GitHub Issues to plan and track your work.
Save views for sprints, backlogs, teams, or releases. Rank, sort, and filter issues to suit the occasion. The possibilities are endless.Learn more about GitHub IssuesProTip! Restrict your search to the title by using the in:title qualifier.