semvec/pypi-packages huggingface dataset:
ixload 0.0.13
Python wrapper for IxLoad automation
Requires Python: !=3.0.*,!=3.1.*,!=3.2.*,>=2.7

# Package for IxLoad Automation
[![Python 2.7](https://img.shields.io/badge/Python-2.7-blue.svg)](#)
[![Python 3](https://img.shields.io/badge/Python-3-blue.svg)](#)

## To Install:
pip install ixload

## To import into project
from ixload import IxLoadUtils as IxLoadUtils \
from ixload import IxLoadTestSettings as TestSettings \
from ixload import IxRestUtils as IxRestUtils \
from ixload import IxRestPyUtils as IxRestPyUtils

##Information on IxLoad Modules visit github.com/OpenIxia
[OpenIxia](https://github.com/OpenIxia/IxLoad)
scora-notify 0.7.0
Scora Slack notification lib
Keywords: scora

Scora Notify
=============================
This package implements Slack notification and Mixpanel abstractions to be used in various projects.

This packages uses logging for console outputs.

The Slack notifier can:
* Start threads
* Update threads
* Finish threads

The event tracker can:
* Track events
* Time events 

To time an event, call the time_event method with the desired event_name, and later call track on that same event. The duration between calls will be saved as the "Duration" property.

Development
-----------------------------


Build, install, test locally
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

.. code-block:: bash

    # build from this source -- considering that dependencies are resolved
    rm -rf ./dist/* && python3 -m build

    # From another venv -- install 
    virtualenv -p python3
    . ./venv/bin/activate
    pip install <REPO_HOME>/dist/scora-notify-0.1.0.tar.gz 


    # Import and run
    from scora_notify import Notify 
    notify=Notify(slack_token="", slack_channel="", step="", tenant="", project="")

    # Import and run
    from scora_notify import EventTracker 
    tracker = src.EventTracker("",project="",tenant="")
    tracker.track("event",properties={"prop":1})
dcmrtstruct2nii 5
Convert DICOM RT-Struct to nii
Requires Python: >=3.6

[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.4037864.svg)](https://doi.org/10.5281/zenodo.4037864)
![unit tests](https://github.com/Sikerdebaard/dcmrtstruct2nii/workflows/Unit%20Tests/badge.svg)
  

# dcmrtstruct2nii
DICOM RT-Struct to nii-mask. This is a naïve approach to rasterizing rt-struct to masks in nii format. If there's holes in your RT-Struct then this approach will most likely not work. The RT-Struct needs to be within the bounds of the slices of the original DICOM. Rasterization is done on a slice-by-slice basis, interpolation between slices is currently unsupported.

# Interpolation
Interpolation of the mask between slices is currently unsupported. Send us an algorithm or a pull requests and we'll happly add it.

# Input file format
The DICOM and RT-Struct inputs need to be unzipped in a directory. Currently this is the only way to read the input files.

# CLI Tool
```
# install using pip and show tool help
pip install dcmrtstruct2nii
dcmrtstruct2nii --help

# list structures in DICOM RT Struct
dcmrtstruct2nii ls -r /path/to/rtstruct/file

# convert help output
dcmrtstruct2nii convert --help

# convert DICOM RT Structs to .nii.gz masks
dcmrtstruct2nii convert -r /path/to/rtstruct/file.dcm -d /path/to/original/extracted/dicom -o /output/path
```

# Python API
```
# install using pip and show tool help
pip install dcmrtstruct2nii
```

```
# lets test it
from dcmrtstruct2nii import dcmrtstruct2nii, list_rt_structs

print(list_rt_structs('/path/to/dicom/rtstruct/file.dcm'))

dcmrtstruct2nii('/path/to/dicom/rtstruct/file.dcm', '/path/to/original/extracted/dicom/files', '/output/path')
```

# License and academic use

The software is licensed [MIT License](https://github.com/Sikerdebaard/dcmrtstruct2nii/blob/master/LICENSE).

For academic use, use a presistent copy from [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.4037864.svg)](https://doi.org/10.5281/zenodo.4037864). 

Please cite:

```Thomas Phil, Thomas Albrecht, Skylar Gay, & Mathis Ersted Rasmussen. (2023). Sikerdebaard/dcmrtstruct2nii: dcmrtstruct2nii v5 (Version v5). Zenodo. https://doi.org/10.5281/zenodo.4037864```
iaccsept-perceptron 0.0.4
Perceptron paper code
Requires Python: >=3.7

# IACCSEPT python package
## How to use this package
'''python
from iaccsept.perceptron import Perceptron
model = Perceptron(eta=0.3,epochs=10)
'''


## Reference -
* [Official python docs](https://packaging.python.org/en/latest/tutorials/packaging-projects/)
* [github action file for PYPI](https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python#publishing-to-package-registries)
openai-python-cache 0.5.0
A thin wrapper around the OpenAI Python bindings for caching responses.
Requires Python: >=3.07,<4.0

# 🍰 openai-python-cache

A thin wrapper around the OpenAI Python bindings for caching responses.

## Motivation

I'm experimenting with a large-ish dataset locally that gets injected into GPT
prompts. Responses are not perfect, and occassionally I have to tweak some
of my data. This means that I'm making API calls for results that are okay,
because it's iterating over the entire dataset.

This solves the issue by cache-ing OpenAI responses in a local SQLite3 database.
**Only ChatCompletion is supported** at this time because it's the only API I use.

This is a quick and dirty solution. I'd go a level lower and inject this
behaviour directly in the requestor, but I don't have time to figure that
part out (yet?)!

## Installation

```sh
# Using pip:
$ pip install openai-python-cache

# Using poetry:
$ poetry add openai-python-cache
```

## Usage

```python
import os
import openai
from openai_python_cache.api import ChatCompletion
from openai_python_cache.provider import Sqlite3CacheProvider

openai.api_key = os.environ.get("OPENAI_API_KEY")

# Create a cache provider
cache_provider = Sqlite3CacheProvider()

# Use the ChatCompletion class from `openai_python_cache`
completion = ChatCompletion.create(
    model="gpt-3.5-turbo",
    messages=[
        {
            "role": "user",
            "content": "Tell the world about the ChatGPT API in the style of a pirate.",
        }
    ],
    # Inject the cache provider. Requests will NOT be cached if this is not
    # provided!
    cache_provider=cache_provider,
)

print(completion)
```

## Demo

```python
import os
import time
import openai
from openai_python_cache.api import ChatCompletion
from openai_python_cache.provider import Sqlite3CacheProvider

openai.api_key = os.environ.get("OPENAI_API_KEY")

cache_provider = Sqlite3CacheProvider()

params = {
    'model': "gpt-3.5-turbo",
    'messages': [
        {
            "role": "user",
            "content": "Testing cache!",
        }
    ]
}

# First request, cache miss. This will result in an API call to OpenAI, and
# the response will be saved to cache.
c0start = time.time()
ChatCompletion.create(cache_provider, **params)
c0end = time.time() - c0start
print(f"First request is a cache miss. It took {c0end} seconds!")
# >>> First request is a cache miss. It took 1.7009928226470947 seconds!

# Second request, cache hit. This will NOT result in an API call to OpenAI.
# The response will be served from cache.
c1start = time.time()
ChatCompletion.create(cache_provider, **params)
c1end = time.time() - c1start
print(f"Second request is a cache hit. It took {c1end} seconds!")
# >>> Second request is a cache hit. It took 0.00015616416931152344 seconds!
```