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 "metadata": {
  "author": "",
  "description": "# qe-lib \u2014 Python bindings for libqe\n\nnanobind bindings that expose the libqe modules as the `qe` Python package\n(install `qe-lib`, `import qe`),\nbuilt with CMake + scikit-build-core.\n\n## API reference\n\nAll matrix arguments are 2-D `numpy.ndarray` with `dtype=float64`, C-contiguous.\nAll vector arguments are 1-D `numpy.ndarray` with `dtype=float64`.\nReturn values are always freshly allocated numpy arrays owned by Python.\n\nThe ENA model code (rotations, node positions, `ena_correlation`, CCD) is in\n[qe-ena](https://qe-libs.org/py/project/qe-ena/) (`import ena`) and all\naccumulation in [qe-tma](https://qe-libs.org/py/project/qe-tma/)\n(`import tma`); the `qe.accumulation`, `qe.rotation` and `qe.ccd` submodules\nwere removed in 0.2.0.\n\n### `qe.adjacency`\n\n| Function | Returns | Notes |\n|----------|---------|-------|\n| `choose_two(n)` | `int` | n \u00d7 (n-1) / 2 |\n| `connection_indices(len, row=-1)` | `2 \u00d7 n_pairs ndarray[int64]` | Upper-tri (i, j) pairs. `row=-1` both rows, `0` row-only, `1` col-only |\n| `code_connections(v)` | `1-D ndarray` | Pairwise products of a code vector \u2192 connection vector |\n| `fold_directed_network(v)` | `1-D ndarray` | n\u00b2 directed vector \u2192 upper-tri (symmetric pairs summed) |\n| `network_to_vector(x, full=True)` | `1-D ndarray` | Matrix \u2192 flat vector. `full=True` \u2192 n\u00b2, `False` \u2192 upper-tri |\n| `connection_names(names)` | `list[str]` | `\"A & B\"` pair labels for every upper-tri position |\n\n### `qe.normalization`\n\n| Function | Returns | Notes |\n|----------|---------|-------|\n| `normalize_networks(m)` | `2-D ndarray` | Row-wise L2 normalization. Zero rows left unchanged. |\n| `scale_networks(m)` | `2-D ndarray` | Max-norm scaling: divide all rows by the largest row L2 norm. |\n\n### `qe.modeling`\n\n| Function | Returns | Notes |\n|----------|---------|-------|\n| `center_points(values)` | `2-D ndarray` | Subtract column means. |\n| `mean_ci(points, conf_level=0.95)` | `n_dims \u00d7 3 ndarray` | `[mean, ci_lower, ci_upper]` \u2014 matches rENA `t.test` exactly. |\n| `outlier_ci(points, iqr_factor=1.5)` | `n_dims \u00d7 2 ndarray` | `[lower, upper]` symmetric around 0 \u2014 matches rENA IQR formula. |\n| `group_stats(g1, g2)` | `GroupStatsResult` | Per-dimension Welch t-test + Wilcoxon rank-sum. See fields below. |\n\n#### `GroupStatsResult` fields\n\n| Field | Type | Notes |\n|-------|------|-------|\n| `n1`, `n2` | `int` | Sample sizes |\n| `t` | `ndarray (n_dims,)` | Welch t-statistic per dimension |\n| `df` | `ndarray (n_dims,)` | Welch\u2013Satterthwaite degrees of freedom |\n| `pvalue_t` | `ndarray (n_dims,)` | Two-tailed p-values (t-test) |\n| `cohens_d` | `ndarray (n_dims,)` | Cohen's d (pooled SD, group1 \u2212 group2) |\n| `means` | `2 \u00d7 n_dims ndarray` | Row 0 = group1, row 1 = group2 |\n| `sds` | `2 \u00d7 n_dims ndarray` | Sample standard deviations |\n| `U` | `ndarray (n_dims,)` | Wilcoxon U for group 1 (= R's W) |\n| `pvalue_u` | `ndarray (n_dims,)` | Two-tailed p-values (normal approx, tie + continuity correction) |\n| `effect_r` | `ndarray (n_dims,)` | Rank-biserial: 1 \u2212 2\u00b7U / (n1\u00b7n2) |\n| `medians` | `2 \u00d7 n_dims ndarray` | Row 0 = group1, row 1 = group2 |\n\n### `qe.door`\n\n| Function | Returns | Notes |\n|----------|---------|-------|\n| `lookback_block(block, lookback_size=20, aggregate_mean=False, weighting_linear=False, segment_ids=[])` | `2-D ndarray` | Lookback pooling over one unit block; matches ETM missing-value behavior. |\n| `ema_block(block, alpha=0.1, segment_ids=[])` | `2-D ndarray` | EMA smoothing over one unit block; missing current values carry the prior smoothed value. |\n\n### `qe.trajectory`\n\n| Function | Returns | Notes |\n|----------|---------|-------|\n| `fit_poly(points, t=None, max_degree=3, fixed_degree=0, criterion=\"loocv\", basis=\"orthogonal\")` | `dict` | Fit a 2D polynomial trajectory; default basis matches R `stats::poly` predictions. |\n| `eval_curve(coeffs_x, coeffs_y, t_eval)` | `2-D ndarray` | Evaluate fitted trajectory coordinates. |\n| `eval_derivatives(coeffs_x, coeffs_y, t_eval)` | `dict` | Velocity, acceleration, speed, heading rate, and curvature. |\n| `integrated_distance(...)` | `float` | Integrated Euclidean distance between curves. |\n| `lagged_distance(...)` | `float` | Normalized follower/leader lagged curve distance. |\n| `signed_turn_lag(...)` / `sweep_signed_turn_lags(...)` | `dict` | Discrete signed turn-lag distance metrics. |\n| `dist_dist_correlation(X, Y)` | `float` | Correlation between pairwise distance structures. |\n\n## Build dependencies\n\n- Python \u2265 3.9\n- numpy\n- armadillo (system or conda-forge / homebrew)\n- nanobind \u2265 1.9 (`pip install nanobind` or `brew install nanobind`)\n- scikit-build-core \u2265 0.4 (`pip install scikit-build-core`)\n- cmake \u2265 3.18\n\n## Install (development)\n\n```bash\ncd libqe/python\npip install -e \".[dev]\"\n```\n\nOr build a wheel:\n\n```bash\npip wheel .\n```\n\n## Run tests\n\n```bash\ncd libqe/python\npytest tests/\n```\n\n## Usage example\n\n```python\nimport numpy as np\nfrom qe import adjacency, normalization, modeling\n\n# Pair names for 3 codes\nprint(adjacency.connection_names([\"X\", \"Y\", \"Z\"]))\n# ['X & Y', 'X & Z', 'Y & Z']\n\n# Sphere normalization\nlw = np.array([[1,1,0],[1,0,1],[0,1,1]], dtype=float)\nnormed = normalization.normalize_networks(lw)\n\n# Two-group comparison\ng1 = np.random.randn(20, 2)\ng2 = np.random.randn(18, 2) + 0.5\nr = modeling.group_stats(g1, g2)\nprint(r.pvalue_t)   # per-dimension two-tailed p-values\n```",
  "description_content_type": "text/markdown",
  "license": "",
  "name": "qe-lib",
  "project_urls": [
   [
    "Homepage",
    "https://github.com/epistemic-analytics/libqe"
   ],
   [
    "Documentation",
    "https://qe-libs.org/py/"
   ],
   [
    "Repository",
    "https://github.com/epistemic-analytics/libqe"
   ],
   [
    "Bug Tracker",
    "https://github.com/epistemic-analytics/libqe/issues"
   ]
  ],
  "requires_dist": [
   "numpy",
   "pytest; extra == \"dev\"",
   "numpy; extra == \"dev\""
  ],
  "requires_python": ">=3.9",
  "summary": "Python bindings for libqe \u2014 generic QE numerics: adjacency, normalization, statistics, door, and trajectory kernels",
  "version": "0.2.0"
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