semvec/pypi-packages huggingface dataset:
loganalyst 1.0.14
Analyse some log files
License: MIT
Requires Python: >=3.8

# Log analyst

## Features

- parse log files
- filters by date / timestamps
- correlates log lines (start and end of some processing)
   - show total duration
- friendly colored output
- able to output short summaries
- "folding" lines
   - keeps lines not matching an iso timestamp attached to the matching ones
- supports gzipped files

## Usage

```
usage: loga [--extra] [--summary] [--nolog] [--max] [-b DATE] [-e DATE] [-h]
            TOML_FILE LOG_FILE

positional arguments:
  TOML_FILE             (Path, default=None) correlation rules to use
  LOG_FILE              (Path, default=None) (possibly gzipped) log file

options:
  --extra               (bool, default=False) show extra log lines (not
                        matched by iso_regex)
  --summary             (bool, default=False) show summary
  --nolog               (bool, default=False) don't show log
  --max                 (bool, default=False) show max durations
  -b DATE, --begin DATE
                        (Optional[str], default=None) start from a date
  -e DATE, --end DATE   (Optional[str], default=None) stop to a date
  -h, --help            show this help message and exit
```

For instance, with systemd logs:

```
journalctl -b 5 -o short-iso | loga --summary --nolog correlators/sample.toml -
```

## Sample correlation


*Note*: the "loganalyst" section is a configuration, which is optional, use only in case overriding values is needed.

Use the documented correlation file in [correlators/sample.toml](https://github.com/fdev31/loganalyst/blob/main/correlators/sample.toml). You can also [download the file](https://raw.githubusercontent.com/fdev31/loganalyst/main/correlators/sample.toml).
nonebot-plugin-phlogo 0.0.3
生成ph风格logo
License: MIT
Requires Python: >=3.7.3

# nonebot-plugin-phlogo

生成ph风格logo

![nb](docs/png.png)
# 安装

直接使用 `pip install nonebot-plugin-phlogo` 进行安装

然后在 `bot.py` 中 写入 `nonebot.load_plugin("nonebot_plugin_phlogo")`

# 指令

`phlogo/pornhub/ph图标 [text1] [text2]`

# 配置

## 字体文件 可选 环境配置

```
PHLOGO_FONT = "./data/font.ttf"
```

- 使用 truetype 字体
- 建议使用微软雅黑
photonai-graph 0.2.5
PHOTON Graph - Graph machine learning with photonai.
License: MIT
Keywords: machine learning,deep learning,graph convolutional neural networks,graphs

[![Python application](https://github.com/wwu-mmll/photonai_graph/actions/workflows/lintandtest.yml/badge.svg)](https://github.com/wwu-mmll/photonai_graph/actions/workflows/lintandtest.yml)
[![Coverage Status](https://coveralls.io/repos/github/wwu-mmll/photonai_graph/badge.svg?branch=master)](https://coveralls.io/github/wwu-mmll/photonai_graph?branch=master)
[![Quality Gate Status](https://sonarcloud.io/api/project_badges/measure?project=wwu-mmll_photonai_graph&metric=alert_status)](https://sonarcloud.io/summary/new_code?id=wwu-mmll_photonai_graph)
![GitHub](https://img.shields.io/github/license/wwu-mmll/photonai_graph)
[![Twitter URL](https://img.shields.io/twitter/url?style=social&url=https%3A%2F%2Ftwitter.com%2Fwwu_mmll)](https://twitter.com/wwu_mmll)

![PHOTONAI Graph](https://raw.githubusercontent.com/wwu-mmll/photonai_graph/master/documentation/docs/assets/img/photonai-02.png)

# photonai-graph

Photon Graph is an extension for the PHOTON framework that allows for the use of machine learning based on graphs. Furthermore, the Graph Utilities contain a wide variety of functions that allow for the visualization and converting of graphs.

# Documentation

You can find a detailed documentation here: <https://wwu-mmll.github.io/photonai_graph/>

# Installation

To install photonai-graph create a dedicated conda/python environment and activate it. Then install photonai-graph via

```bash
pip install photonai-graph
```

To be able to use all modules of the toolbox you will still need to install tensorflow, dgl, pytorch and grakel according to your system configuration, for example with

```bash
pip install tensorflow
pip install torch
pip install dgl
pip install grakel
```

For graph embeddings the gem python package is needed, along with tensorflow. Please install tensorflow according to your system.

```bash
pip install nxt-gem
pip install tensorflow
```

For graph kernels the grakel package needs to be installed. You can install grakel via pip.

```bash
pip install git+https://github.com/ysig/GraKeL.git@cfd14e0543075308d201327ac778a48643f81095'
```

For graph neural networks pytorch and deep graph library are required. You can install them via pip

```bash
pip install torch
pip install dgl
```