semvec/pypi-packages huggingface dataset:
curricularanalytics 0.2.0
A toolbox for studying and analyzing academic program curricula.
Requires Python: >=3.8

# CurricularAnalytics.py

**CurricularAnalytics.py** is a toolbox for studying and analyzing academic program curricula. The toolbox represents curricula as graphs, allowing various graph-theoretic measures to be applied in order to quantify the complexity of curricula. In addition to analyzing curricular complexity, the toolbox supports the ability to visualize curricula, to compare and contrast curricula, to create optimal degree plans for completing curricula that satisfy particular constraints, and to simulate the impact of various events on student progression through a curriculum.

CurricularAnalytics.py is a Python port of the original Julia package [CurricularAnalytics.jl](https://github.com/CurricularAnalytics/CurricularAnalytics.jl).

- [PyPI listing](https://pypi.org/project/curricularanalytics/)
- [Documentation](https://sheeptester-forks.github.io/CurricularAnalytics.py/)
- [GitHub repo](https://github.com/SheepTester-forks/CurricularAnalytics.py)
- [Original Julia version](https://github.com/CurricularAnalytics/CurricularAnalytics.jl)

## Documentation

Full documentation is available at [GitHub Pages](https://sheeptester-forks.github.io/CurricularAnalytics.py/).
Documentation for functions in this toolbox is also available via the Julia REPL help system.
Additional tutorials can be found at the [Curricular Analytics Notebooks](https://github.com/CurricularAnalytics/CA-Notebooks) site.

# Installation

Installation is straightforward. First, ensure you have Python 3.8 or higher installed. Then, run the following command.

```sh
# Linux/macOS
python3 -m pip install -U curricularanalytics

# Windows
py -3 -m pip install -U curricularanalytics
```

# Contributing and Reporting Bugs

We welcome contributions and bug reports! Please see [CONTRIBUTING.md](./CONTRIBUTING.md)
for guidance on development and bug reporting.

# Development

To build a distribution for upload to [PyPI](https://pypi.org/), run the following command.

```sh
rm -r dist
python -m build
# To Test PyPI
python3 -m twine upload --repository testpypi dist/*
# To real PyPI
twine upload dist/*
```

Learn more about [build and setuptools](https://setuptools.pypa.io/en/latest/userguide/quickstart.html).
SOSPysp 0.1.10
Python version of MATLAB SOSTOOLS, a toolbox for sum-of-squares optimization
Requires Python: >=3.10
Keywords: Sum of Square Optimization,SOSTOOLS

This package is a Python version of MATLAB TOOLBOX -- SOSTOOLS. 

This package contains functions based on **sympy** package. However, in case of a large number of decision variables, **sympy** may take a long time to parse the data.

Demos are in the folder [SOSPy_demos](https://github.com/zm2404/SOSPy/tree/main/SOSPy_demos/Sympy_demos).

### Updates in version 0.1.10:
- Print latex form of the solution when calling **sosgetsol()**.
- Adjust the logic of changing solvers when one encounters failure.

### Updates in version 0.1.9:

- Fixed some bugs
- When creating an SOS program, the code searches for and prints out all installed and available solvers.
- We have added **cvxpy** as a solver. As a result, users can now utilize other solvers through **cvxpy**.
- The solvers we currently support are **mosek** and **cvxpy**.
- It's worth noting that since **cvxpy** acts as a parser, certain results cannot be read after executing with **cvxpy**.
This includes details like the **iteration number**, **primal infeasible**, and **dual infeasible**.



Contributors: 
- James Anderson, email: james.anderson@columbia.edu
- Leonardo Felipe Toso, email: lt2879@columbia.edu
- Zhe Mo, email: zm2404@columbia.edu
mimesis-ai 0.1
Agents
Requires Python: >=3.7

# Example Package

This is a simple example package. You can use
[Github-flavored Markdown](https://guides.github.com/features/mastering-markdown/)
to write your content.# Example Package

This is a simple example package. You can use
[Github-flavored Markdown](https://guides.github.com/features/mastering-markdown/)
to write your content.