semvec/pypi-packages huggingface dataset:
databricks-tooling 0.4
A library for databricks jobs api
License: MIT

# Introduction 
TODO: Give a short introduction of your project. Let this section explain the objectives or the motivation behind this project. 

# Getting Started
TODO: Guide users through getting your code up and running on their own system. In this section you can talk about:
1.	Installation process
2.	Software dependencies
3.	Latest releases
4.	API references

# Build and Test
TODO: Describe and show how to build your code and run the tests. 

# Contribute
TODO: Explain how other users and developers can contribute to make your code better. 

If you want to learn more about creating good readme files then refer the following [guidelines](https://docs.microsoft.com/en-us/azure/devops/repos/git/create-a-readme?view=azure-devops). You can also seek inspiration from the below readme files:
- [ASP.NET Core](https://github.com/aspnet/Home)
- [Visual Studio Code](https://github.com/Microsoft/vscode)
- [Chakra Core](https://github.com/Microsoft/ChakraCore)
dpmmpythonStreaming 0.1.1
Python wrapper for DPMMSubClustersStreaming julia package
License: MIT
Keywords: julia python

## DPMMSubClustersStreaming

This package is a Python wrapper for the [DPMMSubClustersStreaming.jl](https://github.com/BGU-CS-VIL/DPMMSubClustersStreaming.jl) Julia package.<br>
(for our paper [Sampling in Dirichlet Process Mixture Models for Clustering Streaming Data](https://dinarior.github.io/papers/Dinari_AISTATS_streaming.pdf), AISTATS 2022.).

<br>
<p align="center">
<img src="appended.gif" alt="Streaming DPGMM">
</p>


### Installation

1. Install Julia from: https://julialang.org/downloads/platform
2. Add our DPMMSubClusterStreaming package from within a Julia terminal via Julia package manager:
```
] add DPMMSubClustersStreaming
```
3. Add our dpmmpythonStreaming package in python: pip install dpmmpythonStreaming
4. Add Environment Variables:
	#### On Linux:
	1. Add to the "PATH" environment variable the path to the Julia executable (e.g., in .bashrc add: export PATH =$PATH:$HOME/julia/julia-1.6.0/bin).
	#### On Windows:
	1. Add to the "PATH" environment variable the path to the Julia executable (e.g., C:\Users\<USER>\AppData\Local\Programs\Julia\Julia-1.6.0\bin).
5. Install PyJulia from within a Python terminal:
```
	import julia;julia.install();
```

### Usage Example:

```
from julia.api import Julia
jl = Julia(compiled_modules=False)
from dpmmpythonStreaming.dpmmwrapper import DPMMPython
from dpmmpythonStreaming.priors import niw
import numpy as np
data,gt = DPMMPython.generate_gaussian_data(10000, 2, 10, 100.0)
batch1 = data[:,0:5000]
batch2 = data[:,5000:]
prior = DPMMPython.create_prior(2, 0, 1, 1, 1)
model= DPMMPython.fit_init(batch1,100.0,prior = prior,verbose = True, burnout = 5, gt = None, epsilon = 0.0000001)
labels = DPMMPython.get_labels(model)
model = DPMMPython.fit_partial(model,1, 2, batch2)
labels = DPMMPython.get_labels(model)
print(labels)
```
### Misc

For any questions: dinari@post.bgu.ac.il

Contributions, feature requests, suggestion etc.. are welcomed.

If you use this code for your work, please cite the following:

```
@inproceedings{dinari2022streaming,
  title={Sampling in Dirichlet Process Mixture Models for Clustering Streaming Data},
  author={Dinari, Or and  Freifeld, Oren},
  booktitle={International Conference on Artificial Intelligence and Statistics},
  year={2022}
}
```
workers-kv.py 1.2.2
An api wrapper of Cloudflare Workers KV for Python
License: MIT
Requires Python: >=3.8

# What is this?
This is the api wrapper of Cloudflare Workers KV.  
This [repo](https://github.com/maguayo/python-workers-kv) wasn't compatible with bulk import, so I made my own wrapper instead.  
You can use this to store and retrieve data from your Cloudflare Workers KV much easier.

## Usage
```python
import workers_kv

# get these values from your Cloudflare account
# Create namespace
namespace = workers_kv.Namespace(account_id="WORKERS_KV_ACCOUNT",
                                 namespace_id="WORKERS_KV_NAMESPACE",
                                 api_key="TOKEN")

# List all keys
keys = namespace.list_keys()

# Get value with key
value = namespace.read("keyhere")

# Set value with key
# automatically use bulk import if you have more than 2 key-value pairs
namespace.write({"key1": "value1", "key2": "value2"})

# Delete single key
namespace.delete_one("key1")

# Delete multiple keys (bulk delete)
namespace.delete_many(["key1", "key2"])
```


## Installation
`pip install -U git+https://github.com/Alpaca131/workers-kv-py.git`