semvec/pypi-packages huggingface dataset:
LinearProgramOptimizer 1.0.2
Optimization models for solving Linear Programming Problems
Keywords: python,linear programming,operation research,optimization,simplex


# LinearProgramOptimizer
Models for optimizing linear programming problems


For now, the only model available in the Revised Simplex model which is capable of solving Linear Programs. 

You are required to provide an array for each of the following:
The objective function
The LHS of the constraints
The RHS of the constraints
authit 0.0.7
An identity platform

# authit

`authit` is the simple and light weight *Identity platform*

## Installation

Install directly from PIP registry

```
pip install authit
```

## Usage

Import package and initialize

```python
import authit
auth = authit.connect()
```

To create user

```python
auth.createUser(
    userName="user",
    password="password"
)
```

To authenticate user

```python
validUser = auth.authenticate(
    userName="user",
    password="password"
)
rgbt 1.0.1
RGB-T toolkit for GTOT, RGBT210, RGBT234, LasHeR
Requires Python: >=3.6


# RGBT toolkit for GTOT, RGBT210, RGBT234, LasHeR

This project was created for the convenience of RGBT Tracking researchers. By utilizing this tool, you will be able to easily implement the following features:

- Use the same tool to evaluate your tracking results on different RGBT datasets.
- The test results of each attribute are available and a radar chart is obtained. You can also test an attribute individually.
- Precision plot and other plot are available.
- Supports GTOT, RGBT210, RGBT234, LasHeR datasets.

> 经测试，由于计算精度的差异，部分情况下会与原始工具箱产生`0.1%`的误差。**所有数据集都经过论文中给定结果的校准。** <br>
> It was tested to produce `0.1%` error from the original toolbox in some cases due to differences in computational accuracy. **All datasets are calibrated for the given results in the paper.**


## Install

```
pip install rgbt
```

## Evaluate and Visualize

We provide the ground truth file, so you can directly call it.

```python
from rgbt import RGBT234

rgbt234 = RGBT234()

# Register your tracker
rgbt234(
    tracker_name="APFNet", 
    result_path="./result/RGBT234/APFNet", 
    bbox_type="corner")

rgbt234(
    tracker_name="TFNet", 
    result_path="./result/RGBT234/TFNet", 
    bbox_type="corner",
    prefix="TFNet_")

# Evaluate multiple trackers
pr_dict = rgbt234.MPR()
print(pr_dict["APFNet"][0])

# Evaluate single tracker
apf_pr,_ = rgbt234.MPR("APFNet")
print(apf_pr)

# Evaluate single challenge
pr_tc_dict = rgbt234.MPR(seqs=rgbt234.TC)
sr_tc_dict = rgbt234.MSR(seqs=rgbt234.TC)

# Draw a radar chart of all challenge attributes
rgbt234.draw_attributeRadar(metric_fun=rgbt234.MPR, filename="RGBT234_MPR_radar.png")
rgbt234.draw_attributeRadar(metric_fun=rgbt234.MSR)     # this is ok

# Draw a curve plot.
rgbt234.draw_plot(metric_fun=rgbt234.MPR)
rgbt234.draw_plot(metric_fun=rgbt234.MSR)
```

Any operation requires only one line of code.

![image0](RGBT234_MSR_radar.png)

![image1](RGBT234_MSR_plot.png)


## 其他

对于最早的GTOT，后来的RGBT210,234，以及最新的LasHeR，在各种指标上计算方式有比较多的不同，导致大概2个点的误差。
经过仔细比对，发现这些差异并不会导致相对性能的变化。为了与历史跟踪器进行公平比较，我们对于这些差异做了兼容。

这使得在测试GTOT时，需要在测试前额外调用`RGBT_start()`。

```python
from rgbt import GTOT
from rgbt.utils import RGBT_start,RGBT_end

RGBT_start()
gtot = GTOT()

# Register your tracker
gtot(
    tracker_name="APFNet",
    result_path="./result/GTOT/APFNet", 
    bbox_type="corner",
    prefix="")

pr_dict = gtot.MPR()

gtot.draw_plot(metric_fun=gtot.MPR)
gtot.draw_plot(metric_fun=gtot.MSR)

RGBT_end()
```
pynostr-cli 0.1.2
CLI tool for interacting with the Nostr Protocol
Requires Python: >=3.11,<4.0

# pynostr-cli

Very early work in progress of a cli tool that will interface with the nostr protocol

Currently supported functionality:

- generates public and private key pair in either hex or pem format, with an option to output to a file

For more information, visit https://github.com/nostr-protocol/nostr

# Get started

`pip install pynostr-cli`

`pynostr --help`
netbox-plugin-machine-tracking 0.0.5
A Netbox plugin tracking failures and other state changes in devices.
Requires Python: >=3.7

# Machine failures tracking system

A Netbox plugin for keeping track of failures and other state changes for individual devices.


## Installation
The plugin is available as a Python package in pypi and can be installed with pip

```
pip install netbox-plugin-machine-tracking
```

Once installed, the plugin needs to be added to the configuration.py file.

```python
PLUGINS = ['machine_tracking']

```


## Usage

The plugin shows a log of all state changes under the 'Events' page and log of all replacements under 'Replacements'.

For each device, a panel has been added to show the number of failures in the past 60 days, average time between failures and a link to see all events (state changes) related to that device.