uv add qe-lib --index qe-libs=https://qe-libs.org/py/simple/

pip install --extra-index-url https://qe-libs.org/py/simple/ qe-lib

--extra-index-url https://qe-libs.org/py/simple/
qe-lib

pip install --extra-index-url https://qe-libs.org/py/dev/simple/ qe-lib

uv add qe-lib --index qe-libs-dev=https://qe-libs.org/py/dev/simple/

qe-lib — Python bindings for libqe

nanobind bindings that expose the libqe modules as the qe Python package (install qe-lib, import qe), built with CMake + scikit-build-core.

API reference

All matrix arguments are 2-D numpy.ndarray with dtype=float64, C-contiguous. All vector arguments are 1-D numpy.ndarray with dtype=float64. Return values are always freshly allocated numpy arrays owned by Python.

The ENA model code (rotations, node positions, ena_correlation, CCD) is in qe-ena (import ena) and all accumulation in qe-tma (import tma); the qe.accumulation, qe.rotation and qe.ccd submodules were removed in 0.2.0.

qe.adjacency

Function	Returns	Notes
choose_two(n)	int	n × (n-1) / 2
connection_indices(len, row=-1)	2 × n_pairs ndarray[int64]	Upper-tri (i, j) pairs. row=-1 both rows, 0 row-only, 1 col-only
code_connections(v)	1-D ndarray	Pairwise products of a code vector → connection vector
fold_directed_network(v)	1-D ndarray	n² directed vector → upper-tri (symmetric pairs summed)
network_to_vector(x, full=True)	1-D ndarray	Matrix → flat vector. full=True → n², False → upper-tri
connection_names(names)	list[str]	"A & B" pair labels for every upper-tri position

qe.normalization

Function	Returns	Notes
normalize_networks(m)	2-D ndarray	Row-wise L2 normalization. Zero rows left unchanged.
scale_networks(m)	2-D ndarray	Max-norm scaling: divide all rows by the largest row L2 norm.

qe.modeling

Function	Returns	Notes
center_points(values)	2-D ndarray	Subtract column means.
mean_ci(points, conf_level=0.95)	n_dims × 3 ndarray	[mean, ci_lower, ci_upper] — matches rENA t.test exactly.
outlier_ci(points, iqr_factor=1.5)	n_dims × 2 ndarray	[lower, upper] symmetric around 0 — matches rENA IQR formula.
group_stats(g1, g2)	GroupStatsResult	Per-dimension Welch t-test + Wilcoxon rank-sum. See fields below.

GroupStatsResult fields

Field	Type	Notes
n1, n2	int	Sample sizes
t	ndarray (n_dims,)	Welch t-statistic per dimension
df	ndarray (n_dims,)	Welch–Satterthwaite degrees of freedom
pvalue_t	ndarray (n_dims,)	Two-tailed p-values (t-test)
cohens_d	ndarray (n_dims,)	Cohen's d (pooled SD, group1 − group2)
means	2 × n_dims ndarray	Row 0 = group1, row 1 = group2
sds	2 × n_dims ndarray	Sample standard deviations
U	ndarray (n_dims,)	Wilcoxon U for group 1 (= R's W)
pvalue_u	ndarray (n_dims,)	Two-tailed p-values (normal approx, tie + continuity correction)
effect_r	ndarray (n_dims,)	Rank-biserial: 1 − 2·U / (n1·n2)
medians	2 × n_dims ndarray	Row 0 = group1, row 1 = group2

qe.door

Function	Returns	Notes
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.
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.

qe.trajectory

Function	Returns	Notes
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.
eval_curve(coeffs_x, coeffs_y, t_eval)	2-D ndarray	Evaluate fitted trajectory coordinates.
eval_derivatives(coeffs_x, coeffs_y, t_eval)	dict	Velocity, acceleration, speed, heading rate, and curvature.
integrated_distance(...)	float	Integrated Euclidean distance between curves.
lagged_distance(...)	float	Normalized follower/leader lagged curve distance.
signed_turn_lag(...) / sweep_signed_turn_lags(...)	dict	Discrete signed turn-lag distance metrics.
dist_dist_correlation(X, Y)	float	Correlation between pairwise distance structures.

Build dependencies

• Python ≥ 3.9
• numpy
• armadillo (system or conda-forge / homebrew)
• nanobind ≥ 1.9 (pip install nanobind or brew install nanobind)
• scikit-build-core ≥ 0.4 (pip install scikit-build-core)
• cmake ≥ 3.18

Install (development)

cd libqe/python
pip install -e ".[dev]"

Or build a wheel:

pip wheel .

Run tests

cd libqe/python
pytest tests/

Usage example

import numpy as np
from qe import adjacency, normalization, modeling

# Pair names for 3 codes
print(adjacency.connection_names(["X", "Y", "Z"]))
# ['X & Y', 'X & Z', 'Y & Z']

# Sphere normalization
lw = np.array([[1,1,0],[1,0,1],[0,1,1]], dtype=float)
normed = normalization.normalize_networks(lw)

# Two-group comparison
g1 = np.random.randn(20, 2)
g2 = np.random.randn(18, 2) + 0.5
r = modeling.group_stats(g1, g2)
print(r.pvalue_t)   # per-dimension two-tailed p-values