semvec/pypi-packages huggingface dataset:
oracle-to-python 0.0.2
Oracle NULL functions equivalent in Oracle
Requires Python: >=3.6

# oracle_to_python

This package has equivalent NULL functions in Python as are there in Oracle

## This package oracle_to_python.nulls has below functions.

* nvl(value,return_if_value_None)
* nvl2(expr1, value_if_expr1_not_null, value_if_expr1_null) 
* nullif(value1, value2) returns None if value1 and value2 are same else value1
* coalesce(list_of_values) returns first not None value, if all values in list are False, then will return None

pip install oracle_to_python

## Module name

nulls
retronix.py-benfosterdev 1.1.3
A Python library for the interaction with the Retronix public API
Requires Python: >=3.7

# retronix.py
A Python library designed for interacting with the RetronixMC Public API.

This is the stable version. If you'd like to take a look at upcoming goodies that are in progress, then take a look at our `dev` branch!  *Be aware though, what you code with it might spook your program with a creepy-crawly code creature...*

### Documentation
The entirety of the Library is peppered in documentation internally, including new features that are in progress. Feel free to take a browse around the files and take a look at how stuff works to get a better understanding. Otherwise, basic guidance and detailing of the library is outlined in the Wiki section of the repository. If you need any help, feel free to get in touch with the dev team in the Discord.

### Bugs and code insects
In the event you find a bug, we ask that you open up an issue for further investigation. From there, a pull request can be made in an attempt to rectify the bug. Please follow the guidance and templates as stated in the issues tab on the proper procedure for filing an Issue. It makes your life much easier and keeps our sanity together.

Likewise for suggestions for what can be added to the library to make it more efficient or easier to use, open an issue with the suggestion label and follow the instructions from there. We'd love to hear your thoughts.
pyprofit 1.9.0
Libprofit wrapper for Python

pyprofit
########

.. image:: https://travis-ci.org/ICRAR/pyprofit.svg?branch=master
    :target: https://travis-ci.org/ICRAR/pyprofit

.. image:: https://img.shields.io/pypi/v/pyprofit.svg
    :target: https://pypi.python.org/pypi/pyprofit

.. image:: https://img.shields.io/pypi/pyversions/pyprofit.svg
    :target: https://pypi.python.org/pypi/pyprofit

*pyprofit* is a python wrapper for `libprofit <https://www.github.com/ICRAR/libprofit>`_.

Installing
==========

*pyprofit* is available in `PyPI <https://pypi.python.org/pypi/pyprofit>`_
and thus can be easily installed via::

 pip install pyprofit

Since version 1.8.1 pre-compiled binary versions of the package
are available for Linux distributions.
These are offered for convenience
and come with OpenMP and FFTW support,
but lack features that some users may want to use,
like OpenCL, threaded-FFTW
and some CPU-specific optimizations.

Compiling
---------

If you need to *compile* this package
(either because a binary is not available in your platform,
or because you want to get the last drop of performance)
you will need to compile **and install** *libprofit* first.
For instruction on how to compile and install *libprofit* please read
`libprofit's documentation <http://libprofit.readthedocs.io/en/latest/getting.html#compiling>`_.

If you need to point to a specific libprofit installation
set the ``LIBPROFIT_HOME`` environment variable to point to it,
e.g.::

 LIBPROFIT_HOME=/opt/software/libprofit/1.8.0/ pip install pyprofit

Note that most users will need to specify
a user-writable installation directory
using the ``-DCMAKE_INSTALL_PREFIX=/my/installation/directory`` option
when invoking ``cmake`` as part of libprofit's compilation.
This is the same directory that then needs to be specified to pyprofit
via its ``LIBPROFIT_HOME`` environment variable.

All in all, the process of compiling both libprofit and pyprofit
should work similar to this::

 $> git clone https://github.com/ICRAR/libprofit
 $> mkdir libprofit/build
 $> cd libprofit/build
 $> cmake .. -DCMAKE_INSTALL_PREFIX=/my/libprofit/installation/directory
 $> make
 $> make install
 $> LIBPROFIT_HOME=/my/libprofit/installation/directory pip install pyprofit


Troubleshooting
---------------

If you are experiencing problems with your installation
(specially if you are compiling this package)
try the following::

 DISTUTILS_DEBUG=1 PYPROFIT_NO_MUTE=1 python setup.py -v build
anki-kunren 1.4.0
Anki practice tool to drill japanese kanji stroke order and practice writing.
Requires Python: >=3.7
Keywords: japanese anki kanji stroke order writing

# Anki Kunren (暗記 訓練)
Anki Kunren is a program to drill japanese kanji stroke order and practice writing in sync with an Anki study session.

![screenshot](https://github.com/eshrh/anki-kunren/blob/master/2020-06-09-110417_581x590_scrot.png)

## Installation
1. install via pip: `pip install anki-kunren`
2. install [anki-connect](https://ankiweb.net/shared/info/2055492159) with code `2055492159`


## Usage
**Open Anki and begin a review. You can then use `kunren` as follows:**

usage: `kunren [-h] [-s S] [-d D] [--field FIELD] [--size SIZE]`

optional arguments:
+ `-h, --help` Show help message
+ `-s S` Start point size in px. defaults to 5px
+ `-d D` Stroke forgiveness in average px from actual. defaults to 25px
+ `--field FIELD` name of anki card field containing kanji. defaults to "Vocabulary-Kanji"
+ `--size SIZE` Length of a size of the square canvas in pixels. Defaults to 300.

While running, you can use the following controls:
+ `h`: hint the current stroke
+ `a`: animate the current stroke
+ `n`: next kanji in the expression
+ `esc`: quit
+ `c`: refresh the current card shown. This is done automatically when all characters have been drawn, but would slow the program to a crawl if checked for every frame.

### Notes
+ When the size parameter is changed, all tolerances get multiplied by the ratio of `size/109`.

## Other
This project uses KanjiVG stroke order data.
It is licensed by Ulrich Apel under the [Creative Commons Attribution Share-Alike 3.0](https://creativecommons.org/licenses/by-sa/3.0/) license.

The KanjiVG ascii filename code is taken from [Kanji Colorizer](https://github.com/cayennes/kanji-colorize) which was also the source of my initial inspiration.

## TODO
+ catch all indexoutofbounds
+ smoother kanji lines
+ different coloring for different parts of stroke based on how wrong it is.
milian-blockchain-sdk 1.0.1
链盟 Python SDK
Requires Python: >=3.7

# UN链盟 Python SDK
欢迎使用UN链盟 SDK. UN链盟 SDK 让您不用复杂编程即可部署、调用、查询合约等服务. 这里向您介绍如何获取UN链盟 Python SDK 并开始调用. 

如果您在使用 SDK 的过程中遇到任何问题, 欢迎通过我们 [UN链盟服务平台](http://chainun.milianblock.cn/technicalbase) 提问. 亦可在当前 GitHub 提交 Issues. 

## 环境准备

1. 要使用UN链盟 Python SDK, 您需要一个 APPID 和 一个 Public Key. 请在UN链盟控制台中的 [控制台](http://chainun.milianblock.cn/console/log) 上创建和查看项目的 APPID, 或者联系您的系统管理员. 
2. 要使用UN链盟 Python SDK 在某个区块链进行上链, 您需要在 [控制台](http://chainun.milianblock.cn/console/log) 的项目中添加该区块链平台. 

## SDK 获取和安装

### 使用 pip 安装
```bash
pip install milian-blockchain-sdk
```

## 开始调用
以下这个代码示例向您展示了调用UN链盟 SDK 的4个主要步骤: 

1. 创建请求类 LMChainRequest 实例 (根据使用情况实例其子类)
2. 为实例化的请求类的属性赋值
3. 创建服务类 LMChainService 实例
4. 根据使用情况调用服务类实例中相应的方法并处理结果

```python
# -- coding:utf-8 --
# 此处以在 `ETH` 上 `部署合约` 为例
import json
from lm_sdk import LMChainDeployRequest, LMChainService

# 创建部署合约请求类实例
lm_deployReq = LMChainDeployRequest()

# 为请求类实例属性赋值
lm_deployReq.appId = "UN链盟控制台中项目的 APPID"
lm_deployReq.chainType = "ETH"  # 要上的区块链的代码
lm_deployReq.hostUrl = "API域名"
lm_deployReq.publicKey = "UN链盟控制台中项目的 Public Key"
lm_deployReq.contractData = "要部署的合约的 Data"
lm_deployReq.inputParams = "合约中构造函数的入参"
lm_deployReq.ethAddress = "以太坊账户地址"
lm_deployReq.privateKey = "以太坊账户的私钥"

# 创建服务类 LMChainService 实例
lm_sevice = LMChainService()

# 调用部署函数, 并打印返回值
result = lm_sevice.chainDeploy(lm_deployReq)
print(json.loads(result))
```
alt-model-checkpoint 2.0.3
An adapter callback for Keras ModelCheckpoint that allows checkpointing an alternate model (often submodel of a multi-GPU model).
Requires Python: >=3

# alt-model-checkpoint

An adapter callback for Keras [ModelCheckpoint](https://keras.io/callbacks/#modelcheckpoint) that allows checkpointing
an alternate model (often submodel of a multi-GPU model).

## Installation

```bash
pip install alt-model-checkpoint
```

## Usage

*You must provide your own Keras or Tensorflow installation.* See `Pipfile` for preferred versions.


If using the Keras bundled in Tensorflow:

```python
from alt_model_checkpoint.tensorflow import AltModelCheckpoint
```

If using Keras standalone:

```python
from alt_model_checkpoint.keras import AltModelCheckpoint
```

Common usage involving multi-GPU models built with Keras `multi_gpu_model()`:

```python
from alt_model_checkpoint.keras import AltModelCheckpoint
from keras.models import Model
from keras.utils import multi_gpu_model

def compile_model(m):
    """Implement with your model compile logic; both base and GPU models should be compiled identically"""
    m.compile(...)

base_model = Model(...)
gpu_model = multi_gpu_model(base_model)
compile_model(base_model)
compile_model(gpu_model)

gpu_model.fit(..., callbacks=[
    AltModelCheckpoint('save/path/for/model.hdf5', base_model)
])
```

## Constructor args

### filepath

Model save file path; see [underlying ModelCheckpoint docs](https://keras.io/callbacks/#modelcheckpoint) for details.

### alternate_model

Keras model to save instead of the default. This is used especially when training multi-gpu models built with Keras
multi_gpu_model(). In that case, you would pass the original "template model" to be saved each checkpoint.

### inherit_optimizer

If TRUE (default), saves the optimizer of the base model (e.g. a multi-gpu model) with the alternate model. This is
necessary if you want to be able to resume training on a saved alternate model. If FALSE, the alternate model's
optimizer will be saved as-is.

### *args, **kwargs

These are passed as-is to the underlying [ModelCheckpoint](https://keras.io/callbacks/#modelcheckpoint) constructor.

## Dev environment setup

1. Install [pipenv](https://docs.pipenv.org/install/).
2. Run `make test` (runs `make test-build` automatically to ensure deps)
IPregexo 0.0.2
Validating and classifying IPv4 address
Requires Python: >=3.5

# IPregex
Validating and Classifying IPv4 Address


### Prerequisites:

* Linux OS 
* Python >=3.5 "pre-installed"
```
python3 --version
```


* Python pip3 "If not installed feel free to surf the web for installation guide based on your OS"
```
pip3 --version
```

### Installing:

* Download and install IPregexo python package
```
pip3 install IPregexo
```
Take care PyPi packages name are case sensitive

* Import IPrexego on every python script you want to use it on
```
from IPregexo import IPregexo
```

### Examples:
* 1. Check vaild IP format and print **message** if so.
<img src="https://github.com/omarthe95/Resources/blob/master/IPregexo/check_IP_message_1.PNG">
<img src="https://github.com/omarthe95/Resources/blob/master/IPregexo/check_IP_message_2.PNG">

* 2. Check vaild IP format and return **Boolean** result if so.
<img src="https://github.com/omarthe95/Resources/blob/master/IPregexo/check_IP_Boolean_1.PNG">
<img src="https://github.com/omarthe95/Resources/blob/master/IPregexo/check_IP_Boolean_2.PNG">


### Commands:
| Command | Description |
| --- | --- |
| `check_IP` | Vaildate IP *format* and print **Message** |
| `checkB_IP` | Vaildate IP *format* and return **Boolean** |
| `check_IP_Private` | Vaildate if *Private IP* and print **Message** |
| `checkB_IP_Private` | Vaildate if *Private IP* and return **Boolean** |
| `check_IP_Private_A` | Vaildate if *Private IP Class A* and print **Message** |
| `checkB_IP_Private_A` | Vaildate if *Private IP Class A* and return **Boolean** |
| `check_IP_Private_B` | Vaildate if *Private IP Class B* and print **Message** |
| `checkB_IP_Private_B` | Vaildate if *Private IP Class B* and return **Boolean** |
| `check_IP_Private_C` | Vaildate if *Private IP Class C* and print **Message** |
| `checkB_IP_Private_C` | Vaildate if *Private IP Class C* and return **Boolean** |

## Authors

* **Omar Adil** - *Network Engineer* - [Linkedin](https://www.linkedin.com/in/OmarAdil)



## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details