Add CLAUDE.md for Claude Code guidance

CLAUDE.md

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+# CLAUDE.md
+
+This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
+
+## Project Overview
+
+**xskillscore** is a Python package for computing forecast verification metrics using xarray. It provides both deterministic and probabilistic forecast verification metrics designed to work with multi-dimensional labeled arrays, with support for Dask parallel computing.
+
+Originally developed to parallelize forecast metrics for multi-model-multi-ensemble forecasts in the SubX project.
+
+## Development Commands
+
+### Testing
+
+Run full test suite:
+```bash
+pytest -n 4 --cov=xskillscore --cov-report=xml --verbose
+```
+
+Run tests for a single file:
+```bash
+pytest xskillscore/tests/test_deterministic.py
+```
+
+Run a specific test:
+```bash
+pytest xskillscore/tests/test_deterministic.py::test_pearson_r -v
+```
+
+Run tests with specific markers:
+```bash
+pytest -m "not slow"  # Skip slow tests
+pytest -m "not network"  # Skip tests requiring network
+```
+
+### Doctests
+
+Run doctests on all modules:
+```bash
+python -m pytest --doctest-modules xskillscore --ignore xskillscore/tests
+```
+
+### Code Quality
+
+Run pre-commit checks:
+```bash
+pre-commit run --all-files
+```
+
+Linting and formatting (via ruff):
+```bash
+ruff check --fix .
+ruff format .
+```
+
+Type checking:
+```bash
+mypy xskillscore
+```
+
+### Documentation
+
+Build documentation:
+```bash
+cd docs
+make html
+```
+
+Test notebooks in documentation:
+```bash
+cd docs
+nbstripout source/*.ipynb
+make -j4 html
+```
+
+### Installation
+
+Install in development mode:
+```bash
+pip install -e .
+```
+
+Install with test dependencies:
+```bash
+pip install -e ".[test]"
+```
+
+Install with all dependencies:
+```bash
+pip install -e ".[complete]"
+```
+
+## Architecture
+
+### Core Module Structure
+
+The `xskillscore/core/` directory contains the main implementation:
+
+- **deterministic.py**: Deterministic forecast metrics (pearson_r, rmse, mae, mse, etc.)
+- **probabilistic.py**: Probabilistic metrics (crps_*, brier_score, rps, rank_histogram, etc.)
+- **comparative.py**: Comparative tests (sign_test, halfwidth_ci_test)
+- **stattests.py**: Statistical tests (multipletests)
+- **contingency.py**: Contingency table class and categorical metrics
+- **resampling.py**: Resampling and bootstrapping utilities
+- **accessor.py**: xarray accessor (`ds.xs.metric()`) for convenient API
+- **utils.py**: Shared utilities for preprocessing dimensions, weights, and broadcasting
+- **np_deterministic.py**: NumPy implementations of deterministic metrics
+- **np_probabilistic.py**: NumPy implementations of probabilistic metrics
+- **types.py**: Type definitions
+
+### Key Design Patterns
+
+1. **xarray.apply_ufunc Pattern**: All metrics use `xr.apply_ufunc` to:
+   - Apply NumPy implementations to xarray objects
+   - Handle broadcasting automatically
+   - Enable Dask parallelization with `dask="parallelized"`
+   - Preserve attributes with `keep_attrs` parameter
+
+2. **Dimension Preprocessing**: Metrics follow this pattern:
+   ```python
+   dim, axis = _preprocess_dims(dim, a)  # Convert dim to list and axis tuple
+   a, b = xr.broadcast(a, b, exclude=dim)  # Broadcast arrays
+   a, b, new_dim, weights = _stack_input_if_needed(a, b, dim, weights)  # Stack multi-dims
+   weights = _preprocess_weights(a, dim, new_dim, weights)  # Normalize weights
+   ```
+
+3. **Separation of xarray and NumPy logic**:
+   - High-level functions in `deterministic.py`/`probabilistic.py` handle xarray objects
+   - Low-level functions in `np_deterministic.py`/`np_probabilistic.py` contain pure NumPy logic
+   - This enables easier testing and reuse
+
+4. **Optional Weights**: Most metrics support optional `weights` parameter matching the dimensions being reduced.
+
+5. **Member Dimension Convention**: Probabilistic metrics use `member_dim="member"` by default for ensemble dimensions.
+
+### xarray Accessor
+
+Users can access metrics via the `.xs` accessor on xarray Datasets:
+```python
+ds = xr.Dataset({"a": a_dataarray, "b": b_dataarray})
+result = ds.xs.pearson_r("a", "b", dim="time")
+```
+
+The accessor handles converting string variable names to actual DataArrays.
+
+### Testing Infrastructure
+
+- **conftest.py**: Centralized pytest fixtures for test data (times, lats, lons, members, etc.)
+- Test fixtures provide consistent test data across test modules
+- Fixtures include regular data, NaN-masked data, dask-chunked data, and 1D timeseries
+- Use `np.random.seed(42)` in doctests for deterministic examples
+
+## Important Considerations
+
+### Temporal Metrics
+
+Some metrics are specifically designed for temporal dimensions:
+- `effective_sample_size()`, `pearson_r_eff_p_value()`, `spearman_r_eff_p_value()`
+- These raise warnings if applied to non-"time" dimensions
+- They account for autocorrelation and should only be used on time series
+
+### NumPy Version Compatibility
+
+The codebase supports both numpy<2.0 and numpy>=2.0. When using NumPy functions:
+- Use try/except for imports that changed between versions
+- Example: `trapezoid` (new) vs `trapz` (old)
+
+### Dimension Handling
+
+- `dim=None` means reduce over all dimensions
+- `dim` can be a string or list of strings
+- When multiple dimensions are provided, they are stacked into a single dimension internally
+- The `member` dimension in probabilistic forecasts is special and should not be included in `dim`
+
+### NaN Handling
+
+- Most metrics support `skipna` parameter (default: False)
+- Probabilistic metrics use `_keep_nans_masked()` to preserve NaN patterns from inputs
+
+### Dask Support
+
+All metrics support Dask arrays via `dask="parallelized"` in `xr.apply_ufunc`. No special handling needed when adding new metrics.
+
+## Python Support
+
+- Minimum Python version: 3.9
+- Supported versions: 3.9, 3.10, 3.11, 3.12, 3.13
+
+## Key Dependencies
+
+- xarray >= 2023.4.0 (core data structure)
+- numpy >= 1.24
+- scipy >= 1.10
+- dask[array] >= 2023.4.0 (parallel computing)
+- properscoring (probabilistic metrics)
+- xhistogram >= 0.3.2 (histogram computations)
+- statsmodels (statistical tests)
+
+Optional acceleration:
+- bottleneck (faster NaN operations)
+- numba >= 0.57 (JIT compilation)
+
+## Contributing Workflow
+
+1. Create a new branch for your feature
+2. Make changes and add tests in `xskillscore/tests/`
+3. Add docstring examples (they are tested via doctest)
+4. Run `pre-commit run --all-files` before committing
+5. Ensure tests pass: `pytest -n 4`
+6. Ensure doctests pass: `python -m pytest --doctest-modules xskillscore --ignore xskillscore/tests`
+7. Update CHANGELOG.rst if appropriate
+8. Submit PR to main branch
+
+Note: CI includes tests on multiple Python versions, doctest validation, and notebook execution in docs.

xskillscore/core/stattests.py

@@ -182,10 +182,7 @@ def multipletests(
     ret = tuple(r.unstack("s").transpose(*p.dims, ...) for r in ret)
 
     def _add_kwargs_as_coords(r: XArray):
-        return r.assign_coords(
-            multipletests_method=method,
-            multipletests_alpha=alpha
-        )
+        return r.assign_coords(multipletests_method=method, multipletests_alpha=alpha)
 
     ret = tuple(_add_kwargs_as_coords(r) for r in ret)