semvec/pypi-packages huggingface dataset:
monitor-register 1.0.3
Register to monitor server
Requires Python: >3.6.0

# OVERVIEW

It will help you generate and register your monitor configuration to the specific monitor server.
here is the syntax of input api document: https://github.com/DaYeSquad/sakura-node-ts#doc

# Install

`pip install monitor-register`

# Develop

## Dependencies

- miniconda: download in https://conda.io/miniconda.html

- python dependency: execute `conda env create -f conda_env/monitor-register`

- pycharm setting: you need to change your interpreter to the existing conda environment: log-reigster

## Directory

```
.
├── README.md
├── bin
│   ├── ci_lint.sh
│   ├── lint.sh                     # local lint tool
│   ├── release_test.sh             # release to test pypi
│   └── test.sh                     # test tool, run all tests
├── conda_env
│   └── monitor-register.yml        # conda env
├── lint.rc
├── monitor_register
│   ├── __init__.py
│   ├── api_doc
│   │   ├── __init__.py
│   │   └── api_doc.py              # process api doc as monitor config
│   ├── monitor
│   │   ├── __init__.py
│   │   └── monitor_config.py       # main functions
│   ├── test
│   │   ├── __init__.py
│   │   ├── api_doc_test.py
│   │   ├── const.py
│   │   ├── file_util_test.py
│   │   └── monitor_config_test.py
│   └── util
│       ├── __init__.py
│       └── file_util.py            # file util
├── setup.py                        # setup script
└── test_data
    ├── apidoc.json
    ├── config.json
    └── test_saved_config.json

```

# Usage

## generate file

```python
import datetime
import os
from monitor_register.monitor.monitor_config import save_doc_as_monitor_config

input_file_path = os.getcwd() + '/test_data/config.json'
final_path = '/tmp/test.config.json'
host = 'http://mytest'
date = datetime.datetime(2018, 10, 29, 0, 0, 0, 0)
save_doc_as_monitor_config(input_file_path, final_path, host, 2, 3, 1, date)
```

## register api doc file to monitor server

```python
import datetime
import os
from monitor_register.monitor.monitor_config import register_to_monitor_server

input_file_path = os.getcwd() + '/test_data/config.json'
monitor_token = 'your_token';
url = 'https://monitor_server'
host = 'http://your_host'
date = datetime.datetime(2018, 10, 29, 0, 0, 0, 0)
res = register_to_monitor_server(input_file_path, url, monitor_token, host, 2, 3, 1, date)
if res['data']['code'] == 200:
    print("success")
else:
    print("failed")
```
force 0.1.4
A method for visualizing the receiptive fields of a neural network
Keywords: machine learning,logging,example

# Force: A package to visualize the receptive fields
Force is a Python package that allows you to visualize the receptive fields of a neural network.

## 1. How to Run

To run this algorithm in Python:
```
from force.receptive_field import visualize_neuron
from gdbn.dbn import buildDBN
import matplotlib.pyplot as plt

# Make a deep belief network
dbn = buildDBN([1, 100, 1])

# Get the 3rd neuron on the 1st layer. (With Linear averaging)
neuron = visualize_neuron(dbn, 0, 2)

# Plot!
plt.imshow(neuron)
plt.show()

```

All configurable options are in the SBB/config.py file, in the variable CONFIG.
txt2mobi3-app 0.2.1
A PyQt5 application for converting Chinese novel txt files into Kindle mobi files.
Requires Python: >=3
Keywords: txt mobi python3 pyqt5

txt2mobi3_app
=============

Create a simple PyQt5 application for converting Chinese txt files into
mobi files. It wraps
`txt2mobi3 <https://github.com/renweizhukov/txt2mobi3>`__. Since it
supports Chinese only, the documentation and the code comments are
written in Chinese.

1. 安装和运行
-------------

1.1. 从PyPI安装和运行：
~~~~~~~~~~~~~~~~~~~~~~~

.. code:: bash

   $ pip install txt2mobi3_app
   $ txt2mobi3_app

1.2. 从安装包：
~~~~~~~~~~~~~~~

(1) Linux

下面以Ubuntu 16.04LTS为例：

.. code:: bash

   $ sudo dpkg -i target/txt2mobi3_app.deb

(2) MacOS

将txt2mobi3_app.dmg移动到Application中。

(3) Windows

点击运行txt2mobi3_appSetup.exe。

2. 产生安装包
-------------

这里我们使用\ `fbs <https://github.com/mherrmann/fbs-tutorial>`__\ 来产生Linux、MacOS和Windows上的安装包。注意我们依赖的三个关键python包的版本如下：

::

   fbs==0.3.6
   PyQt5==5.11.3
   PyInstaller==3.4

2.1. Linux
~~~~~~~~~~

下面以Ubuntu 16.04LTS为例：

(1) `安装fbm <https://fpm.readthedocs.io/en/latest/installing.html>`__

.. code:: bash

   $ sudo apt install ruby ruby-dev rubygems build-essential
   $ sudo gem install --no-ri --no-rdoc fpm

(2) 产生安装包

.. code:: bash

   $ python -m fbs installer

生成的安装包\ ``txt2mobi3_app.deb``\ 会在\ ``target``\ 目录中。

如果想产生一个可以单独运行的可执行文件，那么就运行下面这个fbs命令。

.. code:: bash

   $ python -m fbs freeze

生成的可执行文件位于\ ``target/txt2mobi3_app``\ 目录中。将\ ``target/txt2mobi3_app``\ 目录复制到别的机器上后就可运行其中的可执行文件。

2.2. MacOS
~~~~~~~~~~

以macOS Sierra 10.12.6为例：

(1) 先产生一个可以单独执行的可执行文件。

.. code:: bash

   $ python -m fbs freeze

(2) 产生安装包。

.. code:: bash

   $ python -m fbs installer

2.3. Windows
~~~~~~~~~~~~

(1) 如果是Windows 10，你可能需要安装\ `Windows 10
    SDK <https://developer.microsoft.com/en-us/windows/downloads/windows-10-sdk>`__\ 。

(2) 安装\ `NSIS <http://nsis.sourceforge.net/Main_Page>`__\ 并将其安装目录添加到\ ``PATH``\ 环境变量中。

(3) 产生安装包

.. code:: bash

   $ python -m fbs installer

生成的安装包\ ``txt2mobi3_appSetup.exe.ext``\ 会在\ ``target``\ 目录中。

如果想产生一个可以单独运行的可执行文件，那么就运行下面这个fbs命令。

.. code:: bash

   $ python -m fbs freeze

3. README.rst
-------------

README.rst is generated from README.md via ``pandoc``.

.. code:: bash

   $ pandoc --from=markdown --to=rst --output=README.rst README.md
vx 0.1.3
Python implementation of the SBB genetic programming algorithm for classification tasks (library).
Keywords: machine learning,genetic programming,example

# SBBClassifier
VX: Python implementation of **Symbiotic Bid-Based (SBB)** framework for problem decomposition using Genetic Programming (GP). Algorithm developed by the NIMS laboratory, Dalhousie University, Canada. This implementation can be used as a library to apply GP to classification tasks.

## 1. References
**PhD Thesis**

Lichodzijewski, P. (2011) A Symbiotic Bid-Based (SBB) framework for problem decomposition using Genetic Programming, PhD Thesis ([link](http://web.cs.dal.ca/~mheywood/Thesis/PLichodzijewski.pdf))

## 2. How to Run

To run this algorithm in Python:
```
from vx.SBB.sbb import SBB
from vx.SBB.config import Config

if __name__ == "__main__":
    Config.check_parameters()
    SBB().run()
```

All configurable options are in the SBB/config.py file, in the variable CONFIG.
vortex 0.1.3
A Python API to predict the quality of an hackathon idea using machine learning.
Keywords: machine learning,naive bayes,example

This is an API to interface with an idea appraisal tool. This statisitcally predicts the quality of an idea that you may pursue. 

The API usage is as follows:

```
from vortex.app import predict


print(predict("A hackathon application that uses the Twilio API in a multiplayer fashion and plays Pokemon."))

```

You will recieve a scalar number from this function. 

Feel free to e-mail me at matkallada@gmail.com.
mask-plasmid 0.1.8
Configurable retrying.
Requires Python: >=3.6

## mask-plasmid: Create a quick BED file from an assembled genome

[![Build Status](https://travis-ci.org/andersgs/mask_plasmid.svg?branch=master)](https://travis-ci.org/andersgs/mask_plasmid)

## Background

When building phylogenetic trees with microbial genomic data, it is essential to
get as close as possible to the clonal frame.

A common technique for identifying the clonal frame is to map reads to a reference
genome, and then filter out any sites that are not present in all the samples of
interest.

In general, plasmids should not be part of the clonal frame. While theoretically
it is possible they are part of the clonal frame, with short read data it is
hard to say if the plasmids are all the same. More importantly, however, it is
quite impossible to say that the plasmid(s) and the chromosomes have been
vertically inherited from the most recent common ancestor of a sample.

Thus, it is generally recommended that plasmids be removed from the analyses.

The problem arises in small read data when it is not quite possible to say with
certainty if a read belongs in the plasmid or in the chromosomes. Sometimes plasmids
get inserted in chromosomes, sometimes reads should map to a plasmid but erroneously
map to the chromosome because the plasmid was not included in the analysis.
Thus, in the context of mapping reads to a reference to identify potential variants and
the clonal frame, the ambiguous reads (i.e., that could either map on the chromosome or
on a plasmid) should be removed from the pool of potential reads used to identify variant sites.

If one removes the plasmids from the reference dataset before attempting to map the
reads then it is not possible to identify ambiguous reads. Thus, a better strategy
might be to keep the plasmids in the reference dataset, map all the reads, identify
variable sites, and then mask the plasmid sites.

This can be achieved using [Snippy 4](https://github.com/tseemann/snippy) by using the
`--mask` option and giving it a BED file.

## What does this tool do?

This tool will produce a BED file with every locus in a Genbank file, which can
be easily edited and then used to `--mask` plasmids when using `Snippy 4`.

### Installation

```
pip install mask-plasmid
```

### Running

```
mask-plasmid <my_gb.gbk[.gz]> > plasmids.bed
```

## Development

### Pushing to Pypi

The following command will:

- run tests
- clean up the current branch
- bump the version
- generate the distributions
- clean up the current branch
- tag the commit with the version
- push to github
- push to pypi

```
git commit -a -m <message>
pipenv run inv deploy <new_version_number> [<patch|minor|major>]
```