semvec/pypi-packages huggingface dataset:
kitkot 0.0.1
Node package for interacting with our API
Requires Python: >=3.7


<p align="center"><img src="https://kitkot.io/img/logo.png?width=128&height=128" alt="kitkot_image"/></p>

---
<h3 align="center">
	<p>
	Python Package For The <a url="https://kitkot.io">KitKot</a> API
	</p>
</h3>

`pip install kitkot`

📘 For more examples please check the tests folder [here](), documentation can also be found [here](https://docs.kitkot.io/)
```python
from KitKot import KitKot

kitkot  =  KitKot(api_key="my-api-key")
params  =  {
	"id":  "tiktok"
}
user_ids  =  kitkot.user.get_user_ids(params=params)
print(user_ids)
```
---
<h3 align="center">
	<p>
		⚙️ Library Methods
	</p>
</h3>

- [x] Follow Users
- [x] Like Videos
- [x] Post Comments
- [x] Scrape Information (User, Video, etc)
- [x] Join lives & talk/fetch live messages
kitkot-kitkot 0.0.1
Node package for interacting with our API
Requires Python: >=3.7


<p align="center"><img src="https://kitkot.io/img/logo.png?width=128&height=128" alt="kitkot_image"/></p>

---
<h3 align="center">
	<p>
	Python Package For The <a url="https://kitkot.io">KitKot</a> API
	</p>
</h3>

`pip install kitkot`

📘 For more examples please check the tests folder [here](), documentation can also be found [here](https://docs.kitkot.io/)
```python
from KitKot import KitKot

kitkot  =  KitKot(api_key="my-api-key")
params  =  {
	"id":  "tiktok"
}
user_ids  =  kitkot.user.get_user_ids(params=params)
print(user_ids)
```
---
<h3 align="center">
	<p>
		⚙️ Library Methods
	</p>
</h3>

- [x] Follow Users
- [x] Like Videos
- [x] Post Comments
- [x] Scrape Information (User, Video, etc)
- [x] Join lives & talk/fetch live messages
propsim 0.0.5
A simple and intuitive tool for simulating different types of aircraft engines.

# propsim
A simple and intuitive tool for simulating different types of aircraft engines.

##  Overview
This library aims to bring a simple and intuitive way to perform computational calculations for the design and validation of the main models of aeronautical engines.

Through a core `AircraftEngines` class the user is able to switch between the following engine types and evaluation methodologies:

Engine Model  | Functionality                                              | Implemented function
------------- | -------------                                              | -------------
Turbojet      | Ideal<br />Non-ideal on design<br />Non-ideal off design   | `ideal_turbojet`<br />`real_turbojet`<br />`real_turbojet_off_design` 
Turbofan      | Ideal<br />Non-ideal on design<br />Non-ideal off design   | `ideal_turbofan`<br />`real_turbofan`<br />`real_turbofan_off_design` 
Turboprop     | Ideal<br />Non-ideal on design                             | `ideal_turboprop`<br />`real_turboprop`
Ramjet        | Ideal                                                      | `ideal_ramjet`

##  Setup process

First, one must install the library using the following command in an environment containing Python 3.6 or higher.

```
pip install propsim
```
This command will install the library and its dependencies.

##  Usage cases

Once the library is properly installed, we have the following use cases:

1. The first one consists of carrying out a single point analysis with a fixed $\pi_c$ value. In this specific example an ideal turbofan type engine is shown.

    ```
    from propsim import AircraftEngines

    engines = AircraftEngines(12500)

    engines.ideal_turbofan(M0=0.7, gamma=1.4, cp=1004, hpr=42.8*10**6, Tt4=1850, pi_c=10, pi_f=2, alpha=5)
    ```

    The expected result for this test case is the following data set:

    ```
    {
    'pi_c': [10],
    'F_m0': [279.62], 
    'f': [0.03], 
    'S': [1.94e-05], 
    'eta_T': [0.52], 
    'eta_P': [0.46], 
    'eta_Total': [0.24], 
    'FR': [4.46]
    }
    ```


2. The second one consists of carrying out a bach analysis were $\pi_c$ values vary and so the output data can be plotted for carrying out the appropriate analysis. This feature is currently available only for the ideal turbofan, ideal turbojet and real turbojet. In this specific example an ideal turbofan type engine is also shown.

    ```
    from propsim import AircraftEngines

    engines = AircraftEngines(12500)

    engines.ideal_turbofan(M0=0.7, gamma=1.4, cp=1004, hpr=42.8*10**6, Tt4=1850, pi_c=10, pi_f=2, alpha=5, batch_size=3, min_pi_c=7, max_pi_c=15)
    ```

    The expected result for this test case is the following data set:

    ```
    {
    'pi_c': [7, 9.66, 12.33, 14.99], 
    'F_m0': [271.16, 278.94, 283.34, 286.05], 
    'f': [0.031, 0.032, 0.0312, 0.031],
    'S': [2.06e-05, 1.95e-05, 1.87e-05, 1.82e-05],
    'eta_T': [0.47, 0.52, 0.55, 0.57],
    'eta_P': [0.48, 0.47, 0.46, 0.45],
    'eta_Total': [0.23, 0.24, 0.25, 0.26],
    'FR': [4.17, 4.43, 4.58, 4.67]
    }
    ```
kilroy-face-client-py-sdk 0.8.0
SDK for kilroy face clients in Python 🧰
Requires Python: >=3.10,<4.0

<h1 align="center">kilroy-face-client-py-sdk</h1>

<div align="center">

SDK for kilroy face clients in Python 🧰

[![Lint](https://github.com/kilroybot/kilroy-face-client-py-sdk/actions/workflows/lint.yaml/badge.svg)](https://github.com/kilroybot/kilroy-face-client-py-sdk/actions/workflows/lint.yaml)
[![Tests](https://github.com/kilroybot/kilroy-face-client-py-sdk/actions/workflows/test-multiplatform.yaml/badge.svg)](https://github.com/kilroybot/kilroy-face-client-py-sdk/actions/workflows/test-multiplatform.yaml)
[![Docs](https://github.com/kilroybot/kilroy-face-client-py-sdk/actions/workflows/docs.yaml/badge.svg)](https://github.com/kilroybot/kilroy-face-client-py-sdk/actions/workflows/docs.yaml)

</div>

---

## Installing

Using `pip`:

```sh
pip install kilroy-face-client-py-sdk
```

## Usage

```python
from kilroy_face_client_py_sdk import FaceService

service = FaceService(host="localhost", port=10000)

await service.get_metadata()
```
kilroy-face-py-shared 0.6.0
shared code for kilroy face SDKs in Python 🤝
Requires Python: >=3.10,<4.0

<h1 align="center">kilroy-face-py-shared</h1>

<div align="center">

shared code for kilroy face SDKs in Python 🤝

[![Lint](https://github.com/kilroybot/kilroy-face-py-shared/actions/workflows/lint.yaml/badge.svg)](https://github.com/kilroybot/kilroy-face-py-shared/actions/workflows/lint.yaml)
[![Tests](https://github.com/kilroybot/kilroy-face-py-shared/actions/workflows/test-multiplatform.yaml/badge.svg)](https://github.com/kilroybot/kilroy-face-py-shared/actions/workflows/test-multiplatform.yaml)
[![Docs](https://github.com/kilroybot/kilroy-face-py-shared/actions/workflows/docs.yaml/badge.svg)](https://github.com/kilroybot/kilroy-face-py-shared/actions/workflows/docs.yaml)

</div>

---

## Overview

This package contains code shared by SDKs related to faces.
Mostly it's just a bunch of utilities and dataclasses.

## Installing

Using `pip`:

```sh
pip install kilroy-face-py-shared
```

## Messages

Messages are dataclasses that are used in the APIs.
They are automatically generated from the protobuf definitions.

## Posts

Posts are `pydantic` models that are used to represent various types of posts.
There are definitions for:

- `TextOnlyPost`
- `ImageOnlyPost`
- `TextAndImagePost`
- `TextOrImagePost`
- `TextWithOptionalImagePost`
- `ImageWithOptionalTextPost`

## Models

One useful thing this package provides is a `SerializableModel` class.
It's a base class for `pydantic` models
that can be serialized to and from JSON
with a proper case convention.
aws-container-launcher 4.1.0
AWS Container Launcher makes it easy to programmatically run a Docker container in AWS using the Python package for Batch processing, ML jobs, or fast Web server running.
Requires Python: >3.7.0

# Prerequisites

* Python 3.7
* AWS Credentials

# AWS Container Launcher (aws-container-launcher)

AWS Container Launcher makes it easy to programmatically run a Docker container in AWS using the Python package for Batch processing, ML jobs, or fast Web server running.

| WARNING: After processing a job, you are in charge of destroying the infrastructure. Do not forget to call the "destroy" method; otherwise, the instance will continue to operate and you will be charged by Amazon. |
|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|


# Instance type determination

You can specify the instance type explicitly or set the CPU and memory, and the package will automatically select the optimal instance type for you.

# Usage

To better understand, familiarize yourself with the available methods and the [start_container](https://github.com/kankou-aliaksei/aws-container-launcher/blob/main/doc/method/START_CONTAINER.MD) method in particular.

# Client

* [Client configuration](https://github.com/kankou-aliaksei/aws-container-launcher/blob/main/doc/client/CONFIGURATION.MD)

# Methods

* [start_container](https://github.com/kankou-aliaksei/aws-container-launcher/blob/main/doc/method/START_CONTAINER.MD)
* [get_container_status](https://github.com/kankou-aliaksei/aws-container-launcher/blob/main/doc/method/GET_CONTAINER_STATUS.MD)
* [get_container_record](https://github.com/kankou-aliaksei/aws-container-launcher/blob/main/doc/method/GET_CONTAINER_RECORD.MD)
* [destroy](https://github.com/kankou-aliaksei/aws-container-launcher/blob/main/doc/method/DESTROY.MD)
* [delete_monitoring_table](https://github.com/kankou-aliaksei/aws-container-launcher/blob/main/doc/method/DELETE_MONITORING_TABLE.MD)

# Storage

* [SSD instance store](https://github.com/kankou-aliaksei/aws-container-launcher/blob/main/doc/storage/SSD.MD)
* [EBS](https://github.com/kankou-aliaksei/aws-container-launcher/blob/main/doc/storage/EBS.MD)
* [Amazon FSx for Lustre](https://github.com/kankou-aliaksei/aws-container-launcher/blob/main/doc/storage/FSX.MD)
* [EFS](https://github.com/kankou-aliaksei/aws-container-launcher/blob/main/doc/storage/EFS.MD)

# Spot instance

* [Using spot instances](https://github.com/kankou-aliaksei/aws-container-launcher/blob/main/doc/SPOT_INSTANCE.MD)

# Scaling options

* [Scaling in](https://github.com/kankou-aliaksei/aws-container-launcher/blob/main/doc/scaling/SCALING_IN.MD)