semvec/pypi-packages huggingface dataset:
action-graph 1.3.5
Autonomous agent for task/action planning and execution
Requires Python: >=3.7,<4.0

# ActionGraph
ActionGraph is a symbolic AI agent for generating action plans based on preconditions and effects. This is loosely based on STRIPS approach (https://en.wikipedia.org/wiki/Stanford_Research_Institute_Problem_Solver). State variables are modeled as nodes; the actions represent edges/transitions from one state to another. Dijikstra's shortest path algorithm (A* but without the heuristic cost estimate) is used to generate a feasible, lowest cost plan.

Source: https://github.com/bharathra/ACTION_GRAPH


## Usage:

```

from action_graph.agent import Agent
from action_graph.action import Action


class Drive(Action):
    effects = {"driving": True}
    preconditions = {"has_drivers_license": True, "tank_has_gas": True}


class FillGas(Action):
    effects = {"tank_has_gas": True}
    preconditions = {"has_car": True}


class RentCar(Action):
    effects = {"has_car": True}

    cost = 100  # dollars


class BuyCar(Action):
    effects = {"has_car": True}
    preconditions = {}

    cost = 10_000  # dollars


if __name__ == "__main__":
    world_state = {"has_car": False, "has_drivers_license": True}
    goal_state = {"driving": True}

    ai = Agent()

    actions = [a(ai) for a in Action.__subclasses__()]
    ai.load_actions(actions)

    print("Initial State:", world_state)
    ai.update_state(world_state)

    print("Goal State:   ", goal_state)
    plan = ai.get_plan(goal_state)

    # ai.execute_plan(plan)

```
spork-tda 0.0.4
Spork - the teaspoon companion
Requires Python: >=3.6

Description
==============

This is a companion package to [Teaspoon](https://teaspoontda.github.io/teaspoon/), and includes the modules of teaspoon that use zigzag persistence, and rely on [dionysus](https://pypi.org/project/dionysus/).  Many Teaspoon users did not need the full install that included additional system dependencies.

Full documentation of this package is available [here](https://teaspoontda.github.io/spork/). The full documentation includes information about installation, module documentation with examples, contributing, the license, and citing spork.

The code is a compilation of work done by [Elizabeth Munch](http://www.elizabethmunch.com) and [Firas Khasawneh](http://www.firaskhasawneh.com/) along with their students and collaborators.  People who have contributed to spork include:

- [Audun Myers](https://www.audunmyers.com)
- [Melih Yesilli](https://www.melihcanyesilli.com)
- [Sarah Tymochko](https://www.egr.msu.edu/~tymochko/)
- [Danielle Barnes](https://github.com/barnesd8)
- [Sunia Tanweer](https://stanweer1.github.io/)
- [Max Chumley](https://www.maxchumley.com/)
- [Ismail Guzel](https://ismailguzel.github.io/)

We gratefully acknowledge the support of the National Science Foundation, which has helped make this work possible.

Installation
=============
To install this package, both boost and CMake must be installed as system dependencies.  For boost see for [unix](https://www.boost.org/doc/libs/1_66_0/more/getting_started/unix-variants.html) and [windows](https://www.boost.org/doc/libs/1_62_0/more/getting_started/windows.html).  For mac, you can run ``brew install boost`` if using homebrew as a package manager.  For CMake see [here](https://cmake.org/install/).

The spork package is available through pip install with version details found [here](https://pypi.org/project/spork/).
The package can be installed using the following pip installation:

	``pip install spork``

To install the most up-to-date version of the code, you can clone the repo and then run::

  ``pip install .``

from the main directory.  Note that the master branch will correspond to the version available in pypi, and the test_release branch may have new features.

Please reference the requirements page in the [documentation](https://teaspoontda.github.io/spork/) for more details on other required installations.

Contacts
=============
* Liz Munch: [muncheli@msu.edu](mailto:muncheli@msu.edu).
* Firas Khasawneh: [khasawn3@egr.msu.edu](mailto:khasawn3@egr.msu.edu).
python-univer 1.5.0
Данная библиотека содержит SqlAlchemy ORM для системы Univer
Requires Python: >=3.6

# Python Univer

Содержит маппинг данных из Univer для SqlAlchemy

## Установка

```sh
$ pip install python_univer
```

## Конфигурация
```python
from python_univer.config import Config, DRIVER
from python_univer.orm import get_session


config = Config(host='адрес', user='пользователь', password='пароль', db='имя базы данных', driver=DRIVER.FREETDS)
Session = get_session(config)
```

## Использование
```python
from python_univer.models import Student


session = Session()

students = session.query(Student).filter(Student.course == 2, Student.status == 1)

for student in students:
    print(student)
```