ecliseutils 0.1.1

Shared general-purpose research utilities (tensor/module batching, linear algebra, ARE/ODE solvers, labeled arrays, plotting) used across multiple projects.

ecliseutils

Shared general-purpose research utilities used across multiple projects (e.g. KF_RNN, event_camera). This package is the single source of truth for code that previously lived as diverged copies inside each project's infrastructure/utils.py.

What's here

Module	Contents
settings	configure(device, dtype, precision, seed, debug), default_dtype, safe-global registration. No hardcoded paths; no import-time torch mutation.
types	ModelPair = tuple[nn.Module, TensorDict] and shared aliases.
labeled_array	LabeledArray / LabeledDataset (in-house replacement for dimarray).
modules	Module/tensor/TensorDict batching: stack_module_arr, run_module_arr, multi_vmap, td_items, ...
arrays	NumPy object-array comprehensions: multi_map, multi_zip, broadcast_dim_arrays, ...
linalg	pow_series, batch_trace, kl_div, sqrtm, hadamard_conjugation*, inverse, eig_some, ...
are	solve_discrete_are (torch Schur) and solve_continuous_are (scipy ordqz fallback) + residual tests.
ode	batch_odeint, linspace, geomspace (lazy torchdiffeq).
ensemble	EnsembleModule chunked vmap wrapper.
fast_conv_scan	conv_scan exponential cumulative scan autograd op.
recursive	Dotted-path rgetattr / rsetattr / rgetitem / rsetitem.
dicts	flatten_nested_dict, map_dict, nested_type, call_func_with_kwargs.
io	torch_load, empty_cache, reset_seed, model_size.
memory	get_tensors_in_memory, track_tensor_diff, ...
timing	Timer, print_disabled, print_enabled, track_calls.
plotting	color, confidence_ellipse.

Install

From PyPI (once published):

pip install ecliseutils
# with ODE support:
pip install "ecliseutils[ode]"

From GitHub (works on any device, no clone needed):

pip install "git+https://github.com/JophiArcana/ecliseutils.git"
# pin to a release:
pip install "git+https://github.com/JophiArcana/ecliseutils.git@v0.1.0"
# with ODE support:
pip install "ecliseutils[ode] @ git+https://github.com/JophiArcana/ecliseutils.git"

Editable (for local development):

pip install -e /path/to/ecliseutils
# with ODE support:
pip install -e "/path/to/ecliseutils[ode]"

Usage

import torch
import ecliseutils as eu

# Configure once at startup (typically from your project's settings module).
eu.configure(device="cpu", dtype=torch.float64, precision=8, seed=1212)

P = eu.solve_discrete_are(A, B, Q, R)

torchdiffeq is only required if you call ecliseutils.ode.batch_odeint; it is imported lazily.