semvec/pypi-packages huggingface dataset:
datahub-core 0.9.11
Synthetic data generation tooling

<H1>DataHub</H1> 

![DataHub logo](https://raw.githubusercontent.com/finos/datahub/master/docs/logo.png) 

_Synthetic data generation_

DataHub is a set of python libraries dedicated to the production of synthetic data to be used in tests, machine learning training, statistical analysis, and other use cases [wiki](https://en.wikipedia.org/wiki/Synthetic_data). DataHub uses existing datasets to generate synthetic models. If no existing data is available it will use user-provided scripts and data rules to generate synthetic data using out-of-the-box helper datasets.

Synthetic datasets are simply artificiality manufactured sets, produced to a desired degree of accuracy. Real Data does play a part in synthetic generation, all depending on the realismÂ you require. The product roadmaps details out the functionality planned in this respect.

DataHub's core is predominantly based around pandas data frames and object generation.
A common question: Now that I have a data frame of synthetic-data, what do I do with it? The Pandas library comes with an array of optionsÂ here - so for the time being sinking to databases is out of the scope of the core library, however see that examples in the test folder for some common patterns.

**note** As we build out a config based synthetic spec generator, we will bring this back into scope - please see our roadmap/issue list and get involved in the discussion.

## Key documents

1. For information on how to get started with DataHub see our [Getting Started Guide](https://github.com/finos/datahub/blob/master/docs/GettingStarted.md)
2. For more technical information about DataHub and how to customize it, see the [Developer Guide](https://github.com/finos/datahub/blob/master/docs/DeveloperGuide.md)
3. For a high-level road map see [Road Map](https://github.com/finos/datahub/blob/master/docs/roadmap.md)

## Overview of Synthetic data

- Synthetic data is information that's is artificially manufactured rather thanÂ generated by *real-world events.
- Synthetic data is created algorithmically, and can be used as a stand-in forÂ  test datasets of production data
- **Real data** does play a part in synthetic data generation - depending on how
realistic you want the output

## License

Copyright 2020 Citigroup

Distributed under the [Apache License, Version 2.0](http://www.apache.org/licenses/LICENSE-2.0).

SPDX-License-Identifier: [Apache-2.0](https://spdx.org/licenses/Apache-2.0)
datahub-core-grovesy 0.9.10
Synthetic data generation tools for financial markets

<H1>DataHub</H1> 

![DataHub logo](https://raw.githubusercontent.com/finos/datahub/master/docs/logo.png) 

_Synthetic data generation_

DataHub is a set of python libraries dedicated to the production of synthetic data to be used in tests, machine learning training, statistical analysis, and other use cases [wiki](https://en.wikipedia.org/wiki/Synthetic_data). DataHub uses existing datasets to generate synthetic models. If no existing data is available it will use user-provided scripts and data rules to generate synthetic data using out-of-the-box helper datasets.

Synthetic datasets are simply artificiality manufactured sets, produced to a desired degree of accuracy. Real Data does play a part in synthetic generation, all depending on the realismÂ you require. The product roadmaps details out the functionality planned in this respect.

DataHub's core is predominantly based around pandas data frames and object generation.
A common question: Now that I have a data frame of synthetic-data, what do I do with it? The Pandas library comes with an array of optionsÂ here - so for the time being sinking to databases is out of the scope of the core library, however see that examples in the test folder for some common patterns.

**note** As we build out a config based synthetic spec generator, we will bring this back into scope - please see our roadmap/issue list and get involved in the discussion.

## Key documents

1. For information on how to get started with DataHub see our [Getting Started Guide](https://github.com/finos/datahub/blob/master/docs/GettingStarted.md)
2. For more technical information about DataHub and how to customize it, see the [Developer Guide](https://github.com/finos/datahub/blob/master/docs/DeveloperGuide.md)
3. For a high-level road map see [Road Map](https://github.com/finos/datahub/blob/master/docs/roadmap.md)

## Overview of Synthetic data

- Synthetic data is information that's is artificially manufactured rather thanÂ generated by *real-world events.
- Synthetic data is created algorithmically, and can be used as a stand-in forÂ  test datasets of production data
- **Real data** does play a part in synthetic data generation - depending on how
realistic you want the output

## License

Copyright 2020 Citigroup

Distributed under the [Apache License, Version 2.0](http://www.apache.org/licenses/LICENSE-2.0).

SPDX-License-Identifier: [Apache-2.0](https://spdx.org/licenses/Apache-2.0)
pyartifacts 0.12.5
Lightweight ForensicArtifacts library

<h1 align="center">pyartifacts</h1>

<p  align="center">
 <a href="https://github.com/forensicanalysis/pyartifacts/actions"><img src="https://github.com/forensicanalysis/pyartifacts/workflows/CI/badge.svg" alt="build" /></a>
 <a href="https://codecov.io/gh/forensicanalysis/pyartifacts"><img src="https://codecov.io/gh/forensicanalysis/pyartifacts/branch/master/graph/badge.svg" alt="coverage" /></a>
 <a href='https://pyartifacts.readthedocs.io/en/latest/?badge=latest'><img src='https://readthedocs.org/projects/pyartifacts/badge/?version=latest' alt='Documentation Status' /></a>
</p>


The pyartifacts project provides a Python library for processing
forensic artifact definition files.


### Installation

Python installation can be easily done via pip:

```bash
pip install pyartifacts
```

### Usage

```python
from pyartifacts.registry import Registry

if __name__ == '__main__':
    registry = Registry()
    registry.read_folder("test/artifacts/valid")
    print(registry)
```

## Contact

For feedback, questions and discussions you can use the [Open Source DFIR Slack](https://github.com/open-source-dfir/slack).

## Acknowledgment

The development of this software was partially sponsored by Siemens CERT, but
is not an official Siemens product.
code-similarity 0.0.4
An out of box code and text similarity computation package

# Code Similarity
An out of box code and text similarity computation package

## Install
```bash
python setup.py install
```

## Demo
```python
from CodeSimilarity.GetSimilarity.tfSimilarity import tfSimilarity

base_data = [
    "好雨知时节，当春乃发生。随风潜入夜，润物细无声。野径云俱黑，江船火独明。晓看红湿处，花重锦官城。",
    "君问归期未有期，巴山夜雨涨秋池。何当共剪西窗烛，却话巴山夜雨时。",
    "莫听穿林打叶声，何妨吟啸且徐行。竹杖芒鞋轻胜马，谁怕？一蓑烟雨任平生。料峭春风吹酒醒，微冷，山头斜照却相迎。回首向来萧瑟处，归去，也无风雨也无晴。",
    "天街小雨润如酥，草色遥看近却无。最是一年春好处，绝胜烟柳满皇都。",
    "古木阴中系短篷，杖藜扶我过桥东。沾衣欲湿杏花雨，吹面不寒杨柳风。",
    "少年听雨歌楼上。红烛昏罗帐。壮年听雨客舟中。江阔云低、断雁叫西风。 而今听雨僧庐下。鬓已星星也。悲欢离合总无情。一任阶前、点滴到天明。",
    "雨里鸡鸣一两家，竹溪村路板桥斜。妇姑相唤浴蚕去，闲看中庭栀子花。",
    "一夕轻雷落万丝，霁光浮瓦碧参差。有情芍药含春泪，无力蔷薇卧晓枝。"
]

tfSim = tfSimilarity()

tfSim.build_word_dict(base_data)  # Build Words
test_text = "风雨凄凄，鸡鸣喈喈。既见君子，云胡不夷。风雨潇潇，鸡鸣胶胶。既见君子，云胡不瘳。风雨如晦，鸡鸣不已。既见君子，云胡不喜。"
sim = tfSim.get_tf_similarity(test_text)  # get similarity
print(sim)
```