semvec/pypi-packages huggingface dataset:
ithinkyq 0.0.4
A package for graphing profit-loss option diagrams

# Options
This python project uses data from yahoo finance to graph option profit-loss diagrams

## Installing
Installing with pip:
```bash
pip install gops
```

## Usage
Simple program graphing a long call option:
```python
import options

contractName = 'AMD201218C00040000'
optionObj = options.scrapeCallOptions('AMD')

options.graphLongCall(optionObj, contractName)
```
unilever 1.0.5
A simple commandline app for automate UNILEVER Q&A report
Requires Python: >=2.7

# Unilever Q&A reporter
A simple commandline app for automate UNILEVER Q&A report
## Manual
```bash
  $ git clone https://github.com/carlosgeovany/unilever
  $ cd unilever
  $ python setup.py install
```
# Usage
```bash
$ unilever ber --file <filepath to ber file>

$ unilever tickets --file <filepath to closed_tickets file>
```
imgoingcrazyrjl 0.0.11
A package for graphing profit-loss option diagrams
Requires Python: >=3.6

# Options
This python project uses data from yahoo finance to graph option profit-loss diagrams

## Installing
Installing with pip:
```bash
pip install gops
```

## Usage
Simple program graphing a long call option:
```python
import options

contractName = 'AMD201218C00040000'
optionObj = options.scrapeCallOptions('AMD')

options.graphLongCall(optionObj, contractName)
```
Clumio-Tool-Deploy-CFT 0.0.5
Tool for deploying Clumio Cloud Formation Template at Scale

Deploy Clumio CFT at Scale using from your DEFAULT AWS account


	AWS Credentials (Make sure ./aws/credentials file is present)

        or

    AWS Role assumption approach ( Make sure ./aws/config file is present)

Make sure Config file is populated: ClumioToolsConfig.ini
---------------------------------
[AWS]
lambdacft = deployClumioCFT (Name of the Lambda which will be deployed in default account)

accounts_to_act_on = *      (If "*" it will act on all the accounts, otherwise provide profile names seperated by "," 
			     and it will act on those particular accounts)

[CLUMIO]

clumioapiurl = (Provide the Clumio API URL)

clumioapitoken = (Provide the API Token key)
ait-dsn 2.0.0
AIT DSN provides APIs for connecting to the Deep Space Network (DSN) via the Space Link Extension interfaces.

AMMOS Instrument Toolkit (AIT) Deep Space Network (DSN) Interface
=================================================================

AIT DSN provides APIS for connecting to spacecraft communication facilities via Consultative Committee for Space Data Systems (CCSDS) Space Link Extension (SLE) interfaces. AIT DSN supports the following interfaces:

- Return All Frames (RAF) interface (v4 and v5)
- Return Channel Frames (RCF) interface (v4 and v5)
- Forward Communications Link Transmission Unit (CLTU) interface (v4 and v5)
- CCSDS File Delivery Protocol Class 1 interface

Interface specifications are available in the `CCSDS Blue Books <https://public.ccsds.org/publications/BlueBooks.aspx>`__

Getting Started
---------------

You can read through the `Installation Page <http://ait-dsn.readthedocs.io/en/latest/installation.html>`__
for instruction on how to install AIT DSN.

Join the Community
------------------

The project's `User and Developer Mailing List <https://groups.google.com/forum/#!forum/ait-dev>`__ is the best way to communicate with the team, ask questions, brainstorm plans for future changes, and help contribute to the project.

This project exists thanks to the dedicated users, contributors, committers, and project management committee members. If you'd like to learn more about how the project is organized and how to become a part of the team please check out the `Project Structure and Governance <https://github.com/NASA-AMMOS/AIT-Core/wiki/Project-Structure-and-Governance>`__ documentation.

Contributing
------------

For information on how to contribute please see the `AIT Contributing
Guide <https://github.com/NASA-AMMOS/AIT-Core/wiki/Contributing>`__

--------------

|travis|
|docs|

.. |travis| image:: https://travis-ci.com/NASA-AMMOS/AIT-DSN.svg?branch=master
    :target: https://travis-ci.com/NASA-AMMOS/AIT-DSN

.. |docs| image:: https://readthedocs.org/projects/ait-dsn/badge/?version=master
    :alt: Documentation Status
    :scale: 100%
    :target: https://ait-dsn.readthedocs.io/en/latest/?badge=master
wacc 0.0.1
The Python micro framework for deriving cost of capital
Requires Python: >=3.6

![Alt text](docs/img/wacc-logo.png?raw=true "Title")
<p align="center">
    <em>WACC - The Python micro framework for deriving cost of capital</em>
</p>
<p align="center">
      <a href="https://codecov.io/gh/pax1a/wacc">
        <img src="https://codecov.io/gh/pax1a/wacc/branch/main/graph/badge.svg?token=6CVZ1XENIV"/>
      </a>
</p>

    
---

**Documentation**: <a href="https://py.wacc.io" target="_blank">https://py.wacc.io</a>

**Source Code**: <a href="https://github.com/pax1a/wacc" target="_blank">https://github.com/pax1a/wacc</a>

---
pastawrap 0.1.0
The Python wrapper for ratPASTA - R-based Awesome Toolkit for PASTA.
Requires Python: >=3.8

pastaWRAP
=========

**pastaWRAP** is a Python wrapper for **R-based Awesome Toolkit for PASTA**,
more commonly known as [**ratPASTA**](https://github.com/ikodvanj/ratPASTA) - an
R package used for processing and visualising data from startle experiments in
rodents or experiments measuring grip strength in rodents. Currently, pastaWRAP only
supports ratPASTA functionality for startle experiments, with plans for adding
griPASTA (grip strength test) functionality wrapping at a later date. The input data
for this package is created with a **PASTA** solution (**Platform for Acoustic STArtle**),
described in detail here:

*Virag, D., Homolak, J., Kodvanj, I., Babic Perhoc, A., Knezovic, A.,
Osmanovic Barilar, J., & Salkovic-Petrisic, M. (2020). Repurposing a
digital kitchen scale for neuroscience research: a complete hardware and
software cookbook for PASTA. BioRxiv, 2020.04.10.035766. 
https://doi.org/10.1101/2020.04.10.035766*

Using the same platform for measuring grip strength in rodents is
described here:

*Homolak, J., Virag, D., Kodvanj, I., Matak, I., Babic Perhoc, A.,
Knezovic, A., Osmanovic Barilar, J., Salkovic-Petrisic, M. (2020).
griPASTA: A hacked kitchen scale for quantification of grip strength in
rodents. BioRxiv, 2020.07.23.217737.
https://doi.org/10.1101/2020.07.23.217737*
scanoramaCT 1.2.0
An adapted version of the Scanorama package by Brian Hie, Bryan Bryson, and Bonnie Berger for application to CytoTRACE.

# Scanorma for CytoTRACE (ScanoramaCT)

`scanoramaCT.py` is an adapted version of the original <a href="https://github.com/brianhie/scanorama">Scanorama code</a> for application to CytoTRACE. The code has been modified to adjust the aligned gene count vectors using Gaussian kernel normalization. CytoTRACE is then applied to the Scanorama-adjusted gene counts and expression matrix. 

`scanoramaCT` is now available within the CytoTRACE codebase and web framework (<a href="https://github.com/gunsagargulati/CytoTRACE">CytoTRACE GitHub</a>).
affordable 0.0.1
Affordable is an abstraction layer to facilitate RL environment developing
Requires Python: >=3.6

# Affordable

An abstraction layer to facilitate RL environment developing.

Concepts
---------------

* Game
* World
* Agent
  * Affordable
    * Action
    * State
* Embedding


How to release
---------------

```bash
python3 setup.py sdist bdist_wheel
python3 -m twine upload dist/*

git tag va.b.c master
git push origin va.b.c
```
Deploy-Clumio-CFT-Tool 0.0.1
Tool for deploying Clumio Cloud Formation Template at Scale

Deploy Clumio CFT at Scale using from your DEFAULT AWS account


	AWS Credentials (Make sure ./aws/credentials file is present)

        or

    AWS Role assumption approach ( Make sure ./aws/config file is present)

Make sure Config file is populated: ClumioToolsConfig.ini
---------------------------------
[AWS]
lambdacft = deployClumioCFT (Name of the Lambda which will be deployed in default account)

accounts_to_act_on = *      (If "*" it will act on all the accounts, otherwise provide profile names seperated by "," 
			     and it will act on those particular accounts)

[CLUMIO]

clumioapiurl = (Provide the Clumio API URL)

clumioapitoken = (Provide the API Token key)
pycoffeece 0.0.1
Python useful classes for office work automation
Requires Python: >=3.6

# PyCofeece
Python useful classes for office work automation.
Currently working on Python 3.


## Popup
Tkinter popup functions to:
- show_info(title, message) -> shows a popup with the title and message provided.
- ask_file() -> popup asks for a file through file explorer. Returns path as string.
- ask_directory() -> popup asks for a directory through file explorer. Returns path as string.
- ask_yes_no(title, message) -> shows a popup with the title and message provided and ask yes-no. If yes returns 'True'.


## SysWalker
System navigation and recursion functions:
- list_files_by_type(src_directory, file_extension) -> Make a list of all the files with the given file extension in the given directory. Returns list.


## Why PyCofeece?
The main reason behind this little classes is that a huge part of the mechanical work at a job office can be automated through this kind of objects.
The other reason to use this is to make office scripts cleaner with an — even more — simplified dependency management.
bentobuild 0.0.5
Build BentoML Services into Images on K8S
Requires Python: >=3.6

# bentobuild

A helper which provides a simple api to launching pods to create docker images from BentoML Services in a Kubernetes clister, by making use of BentoMLs [YataiService](https://docs.bentoml.org/en/latest/concepts.html#customizing-model-repository).

This does not use the `bentoml containerize` command because it requires Docker daemon and build containers most commonly wont have a running a docker daemon. Instead, this package creates a custom Kubernetes Job, which uses` bentoml retrieve` alongside [`Kaniko executor`](https://github.com/GoogleContainerTools/kaniko) to build the BentoML context and artifacts into a docker image and push it to your desired docker image and tag.

The resulting image can be served via KFServing inferenceservice or via the bundled serving experiment.

The builder schedules a standard Kubernetes Job with several containers to build the image.

## YataiService Required

This project really only makes sense when using BentoML with a [`YataiService`](https://docs.bentoml.org/en/latest/concepts.html#customizing-model-repository), as multiple pods will need to access the model storage backend.

I would suggest running the `YataiService` so it is only open to local cluster traffic, and refer to it via its `cluster.local` domain name.

## Builder

In the example below, the builder will create a new Kubernetes Job, which will produce an image, tag it and push it the registry.


```
from bentobuild.job import BentoJobBuilder

builder = bentobuild.BentoJobBuilder()

# safe_build requires a BentoML Service ID, an image tag and your build namespace.
# This is the namespace where your docker credentials need to exist - see below.
builder.safe_build('FastaiDemo:20201105133154_04F821', 'iancoffey/mydemo:latest', 'test1')

# resources begin running

builder.status() # prints some output from the Job status

```

## Installation

`pip install bentobuild`

### Docker Credentials

It is important to configure Docker credentials for the builder to use to push to your desired repo.

To do this, the project supports mounting a configmap containting a Docker `config.json` into the build environment.

See the [Kaniko documentation](https://github.com/GoogleContainerTools/kaniko/blob/master/README.md#pushing-to-different-registries) for more info on how to format and create this secret.