semvec/pypi-packages huggingface dataset:
webfscholar 0.0.3
Generate publications webpages from Google Scholar
Requires Python: >=3.5

![Mockup](https://user-images.githubusercontent.com/2068077/87393876-ac37e580-c563-11ea-8104-e9f8b43ffcef.jpg)

# webfscholar

Generate publications webpage from a google scholar in just two steps:

1. Convert Google Scholar to BibTeX
2. Convert BibTeX to webpage.

The default theme (pictured above) is mobile-friendly and searchable. You can view sample output at [r.aaalv.in](https://r.aaalv.in).

![webfscholar](https://user-images.githubusercontent.com/2068077/87399323-16548880-c56c-11ea-96d6-ad84da727a2f.gif)

## Installation

```
pip install webfscholar
```

# Why webfscholar

The intermediate bibtex allows you to plug-and-play with other
auto-publication-webpage services. Furthermore, unlike most Google Scholar
unofficial APIs, this library:

- Uses information pulled directly from the Google Scholar profile page, as
  opposed to un-configurable search results. This means `webfscholar` will
  respect any custom titles or removed publications.
- Supplants Google Scholar information using ArXiv.

The website-builder also supports:

- A default, mobile-friendly theme with a search bar
common-data 0.0.5
common_data
Requires Python: >=3.7

# Common Data gRPC API client

灵犀公共数据微服务接口

### 安装

```
pip install commondata
```

### 配置

settings配置指定server

```
COMMON_DATA_SERVER = 'host:port'

```

### 使用

```
# 导入查询接口
from commondata.apis import do_query

# 定义查询对象
query_obj = {
    'model': 'CompanyModify',		# model名（必须）
    'operate': {		# 操作（必须）
        'filter': "entid_id=363248",		# 方法、参数
        'order_by': "'-ALTDATE'",
    },
    'paging': '[0:5]'		# 分页（可选）
}

# 查询并接收结果

status, data = do_query(query_obj)

```
odi-contracts 0.0.1
Implementation of Mintable, Burnable, Capped, Pausable and Snapshot IRC2 token that can be used as standard token equivalent to ERC20 for ICON blockchain.
Requires Python: >=3.6

# ODIContracts

ODIContracts implements IRC2 standard token which is equivalent to [ERC20]() for [ICON](https://icon.foundation/?lang=en) blockchain. It helps to keep track of [fungible](https://en.wikipedia.org/wiki/Fungibility) tokens.

There are a few core contracts to implement IRC2 token.

-   [IIRC2](): Interface IRC2 methods should confirm into.
-   [IRC2](IRC2.md): The base implementation of IRC2 contract.

This has been extended to implement the following.

-   [IRC2Mintable](IRC2Mintable.md): To create token supply.
-   [IRC2Capped](IRC2Capped.md): Total supply cannot exceed the cap amount.
-   [IRC2Burnable](IRC2Burnable.md): To destroy the tokens.
-   [IRC2Pausable](IRC2Pausable.md): To pause token operation for all users.
stat-basic-dist 0.1
Gaussian and Bionomial distributions

# stat-basic-dist package

This a statistical package to calculate distribution mean, stdev and visualize data density function.

# installation

```
$ pip install stat-basic-dist
```

## Examples

```
from stat_basic_dist import Binomial, Gaussian
```

## TODOS

- Add more distributions
- Add flexibility to read more data format
- Add tests
- Add more visualization options
utoolbox-core 0.0.24.dev0
Core functions for uToolbox.
Requires Python: ~=3.7
Keywords: microscopy

# uToolbox [Core]

A Python image processing package for LLSM.

For more information, refer to [the documentation](https://utoolbox.readthedocs.io/en/latest/index.html).

## Installation
uToolbox is published on [PyPI](https://pypi.org/project/utoolbox/) and can be installed from there:

`pip install -U utoolbox`

Beta releases are also published:

`pip install -U --pre utoolbox`

If you wish to install uToolbox for development purposes, refer to [the developer guide](https://utoolbox.readthedocs.io/en/latest/intro/developer.html).

## Documentation
Documentation is available from [readthedocs.io](https://utoolbox.readthedocs.io/en/latest/index.html).

## Authors
- Liu, Yen-Ting

## License
This project is licensed under the Apache 2.0 License - see the [LICENSE.md](LICENSE.md) file for details

## Acknowledgments
- [LLSpy](https://github.com/tlambert03/LLSpy) by Talley Lambert.
janus-highvar 0.0.2
Dungeons and Dragons High Variance Die Roller
Requires Python: >=2.7

Janus provides Wyrmwood High Variance [dice](https://www.kickstarter.com/projects/wyrmwood/dice-by-wyrmwood) dice roller functions. These dice maintain the same average roll as "standard" Dungeons and Dragons dice, but they have individual values skewed towards higher or lower rolls. Janus utilizes a random selector to roll the dice.

![high variance dice](./high_variance_die.jpg)

These dice are intended for "do or die" situations, where the party will either succeed or fail in heroic fashion.

In order to use the functions:
    `janus [d4|d6|d8|d10|d12|d20|d100]`
cython-indicators 1.0.0
Indicator functions using cython
Requires Python: >=3.6

# C-Optimized Technical Indicator Functions
![PyPI](https://img.shields.io/pypi/v/cython-indicators)

## Authors
@epociask
@rjthompson2
@a-drain

## Installation

### Install from PyPi
```Shell
pip install cython-indicators
```

### Build from Source
1. Clone repository
1. `python setup.py build` to build
1. `python setup.py install --user` to install




## Currently Supported Indicators
- BB - [Bolinger Bands](https://www.investopedia.com/articles/technical/102201.asp)

- EMA -[Exponential Moving Average](https://www.investopedia.com/terms/e/ema.asp)

- FIB - [Upper Fibonacci Retracement Levels](https://www.investopedia.com/terms/f/fibonacciretracement.asp)

- FIB_BB - [Fibonacci Bolinger Bands](https://www.motivewave.com/studies/bollinger_bands_fib_ratios.htm)

- SMA - [Simple Moving Average](https://www.investopedia.com/terms/s/sma.asp)

- WMA - [Weighted Moving Average](https://www.fidelity.com/learning-center/trading-investing/technical-analysis/technical-indicator-guide/wma)

- SDV - [Standard Deviation](https://www.mathsisfun.com/data/standard-deviation-formulas.html)

- MOM - [Momentum](https://www.investopedia.com/investing/momentum-and-relative-strength-index/)

## How to Use

```python
import indicators
import numpy as np

closes = np.array([12.3, 11.5, 23.5, 24.2, 32.4])

print(indicators.SMA(closes, 4))

```
`> 22.9`


## Currently Supported and Tested on

-  MACOSX


## To deploy a new version
contact @a-drain or @epociask