semvec/pypi-packages huggingface dataset:
kwwutils 0.1.17
Add your description here
Requires Python: >=3.11

cat kwwutils.egg-info/PKG-INFO

from setuptools import setup, find_packages

setup(
    name='kwwutils',
    version='0.1.14',
    packages=find_packages(include=['kwwutils', 'tests']),
    # other options like install_requires, author, etc.
)


mv setup.cfg setup_old.cfg  # Move any existing setup.cfg file
nano setup.cfg


[metadata]
name = kwwutils
version = 0.1.14


rm -rf build dist kwwutils.egg-info
python setup.py sdist bdist_wheel
uv pip install --force-reinstall --no-cache-dir -e .


- debugging
from setuptools import setup, find_packages

version = '0.1.14'
print(f"Building version {version}")

setup(
    name='kwwutils',
    version=version,
    packages=find_packages(include=['kwwutils', 'tests']),
    # other options like install_requires, author, etc.
)
dependent-bterms 1.0.2
An extension to SageMath's module for computations with asymptotic expansions. Provides a special AsymptoticRing that allows to specify a secondary, dependent (monomially bounded) variable.
Requires Python: ~=3.9

# dependent_bterms

An extension to SageMath's module for computations with asymptotic expansions.
Provides a special AsymptoticRing that allows to specify a secondary,
dependent (monomially bounded) variable.

## Quickstart and Summary

The package can be made available to your SageMath installation by running
```sh
$ sage -pip install dependent_bterms
```
from your terminal, or just
```
!pip install dependent_bterms
```
from within a SageMath Jupyter notebook. The package can then be used as
follows:
```py
sage: import dependent_bterms as dbt
sage: AR, n, k = dbt.AsymptoticRingWithDependentVariable(  # create a special AsymptoticRing
....:     'n^QQ',  # in one asymptotic (monomial) variable n with rational powers
....:     'k', 0, 1/2,  # with a symbolic variable k assumed to be in the range n^0 <= k <= n^(1/2)
....:     bterm_round_to=2  # and explicit error terms should be rounded to two decimal places
....: )
sage: k*n^2 + O(n^(3/2)) + k^3*n  # summands are ordered w.r.t. their highest potential growth
k^3*n  + k*n^2 + O(n^(3/2))
sage: asy = 1/n + AR.B(k/n^2, valid_from=10)
sage: asy_exp = dbt.taylor_with_explicit_error(lambda t: exp(t), asy, order=3, valid_from=10)
sage: asy_exp
1 + n^(-1) + B((abs(28/25*k + 73/100))*n^(-2), n >= 10)
sage: dbt.simplify_expansion(asy_exp, simplify_bterm_growth=True)
1 + n^(-1) + B(34/25*n^(-3/2), n >= 10)
```

One-line descriptions of the top-level members exported with this module
are given below. A description of their respective input arguments and
several examples are provided in the respective docstrings.

- `AsymptoticRingWithDependentVariables` -- A special (univariate) `AsymptoticRing`
   that is aware of a monomially bounded symbolic variable.

- `evaluate` -- Evaluate a symbolic expression without necessarily returning a
  result in the symbolic ring.

- `simplify_expansion` -- Simplify an asymptotic expansion by allowing error
  terms to try and absorb parts of exact terms.

- `round_bterm_coefficients` -- Round the coefficients of all B-terms in the
  given expansion to the next integer (or rational with respect to the provided
  precision).

- `set_bterm_valid_from` -- Changes the point from which a B-term bound is valid
  such that the term remains valid.

- `expansion_upper_bound` -- Returns an upper bound for the given asymptotic
  expansion by turning all B-term instances into exact terms

- `taylor_with_explicit_error` -- Determines the series expansion with explicit
  error bounds of a given function `f` at a specified asymptotic term.


## Demo

A worksheet containing a comprehensive introduction to the capabilities of
this package can be found here: [`toolbox_demo.ipynb`](toolbox_demo.ipynb).
kos-sdk 0.0.1
Software Development Kit for KOS
Requires Python: >=3.11

# KOS SDK 
Software Development Kit (SDK) for KOS robots that bundles together libraries, tools, APIs for developers to build, test, and deploy Z-Bot and K-Bot applications. 

## Overview
Pre-built modules for sensors, policies, motion planning, perception, simulation, and tests for rapid prototyping, acceptance testing, and problem diagnosis. 

## Features
- Tests: Comprehensive unit tests for all robot components such as IMU, servos, camera, speaker, microhpone, led.
- Simulation: KOS-SIM compatbility. https://github.com/kscalelabs/kos-sim
- Locomotion: PPO based locomotion. 
- Manipulation: ACT and IK based manipulation. 
- Perception: Object detection, human pose estimation, etc.
- Algorithms: Motion planning, ZMP, balance control, etc.
- Tools: Keyboard teleoperation, telemtry data logging.

## Requirements
- Milk-V Image with kos version 0.6.1
- Python 3.11 with pykos version 0.7.1


## Getting Started
1. Set up Git LFS and pull large files

```bash
# Install Git LFS
git lfs install

# Pull large files (URDF models, neural networks, etc.)
git lfs pull
```

2. Clone the repository

```bash
git clone git@github.com:kscalelabs/kos-sdk.git
```

3. Make sure you're using Python 3.11 or greater

```bash
python --version  # Should show Python 3.11 or greater
```

4. Install dependencies

```bash
python -m venv .venv
source .venv/bin/activate
pip install -e .
```

5. Run the tests

```bash
make test
```

### Additional Tests

Check that the URDF and MJCF models are realistic:

```bash
# To check the URDF model:
ks robots urdf pybullet zbot-v2 --fixed-base

# To check the MJCF model:
ks robots urdf mujoco zbot-v2
```