semvec/pypi-packages huggingface dataset:
i-hate-papers 0.2.1
Create easily readable versions of academic papers via OpenAI
License: MIT
Requires Python: >=3.9,<4.0

# I "Hate" Papers

**Create easily readable versions of papers via OpenAI**

I often need to read a paper to provide background on a related topic. 
In these cases the technical depth of a paper can be a major obstacle.
So I created I Hate Papers to create easily digestible versions of 
academic research.

Currently works with:

* An arXiv paper ID
* A local `.tex` file
* A local `.md` file
* A local `.html` file (experimental)

# Installation

    pip install i-hate-papers

# Example use
    
    # First set your OpenAI API key
    ❱ export OPENAI_API_KEY=...
    
    # Summarise a arXiv paper ID
    ❱ i_hate_papers 2106.09685
    
    # Summarise a latex file
    ❱ i_hate_papers path/to/some-paper.tex
    
    # Summarise a html file
    ❱ i_hate_papers path/to/some-paper.html

# Example output

* [Example HTML](https://adamcharnock.github.io/i-hate-papers/examples/summary-2106.09685-d1-gpt-3.5-turbo-16k.html) (includes rendered math using MathJax)
* [Example Markdown](https://github.com/adamcharnock/i-hate-papers/blob/main/examples/summary-2106.09685-d1-gpt-3.5-turbo-16k.md)

# Reference

    ❱ i_hate_papers --help
    usage: i_hate_papers [-h] [--verbosity {0,1,2}] [--no-input] [--no-html] [--no-open] [--no-footer] 
                         [--no-glossary] [--detail-level {0,1,2}] [--model MODEL] INPUT
    
    Summarise an academic paper
    
    You must set the OPENAI_API_KEY environment variable using your OpenAi.com API key
    
    positional arguments:
      INPUT                 arXiv paper ID (example: 1234.56789) or path to a .tex/.html/.md file
    
    options:
      -h, --help            show this help message and exit
      --verbosity {0,1,2}   Set the logging verbosity (0 = quiet, 1 = info logging, 2 = debug logging). Default is 1
      --no-input            Don't prompt for file selection, just use the largest tex file
      --no-html             Skip HTML file generation
      --no-open             Don't open the HTML file when complete (macOS only)
      --no-footer           Don't include a footer containing metadata
      --no-glossary         Don't include a glossary
      --detail-level {0,1,2}
                            How detailed should the summary be? (0 = minimal detail, 1 = normal, 2 = more detail)
      --model MODEL         What model to use to generate the summaries

# Release process

For internal use:

    export VERSION=0.1.1
    poetry version $VERSION
    git ci -a -m "Releasing version $VERSION"
    git tag "v$VERSION"
    git push origin main refs/tags/v$VERSION
    poetry publish --build
elblog2dict 1.0.1
Utility for parsing and extracting data from ELB logs
License: MIT
Requires Python: >=3.6
Keywords: elb,log,parser,elblog2dict,regular expression

# ELBLOG2DICT

## Description

This module converts Amazon ELB traffic logs into a python dictionary format.

## Installation

```bash
pip install elblog2dict
```

## Usage

```python
from elblog2dict import parser
import gzip

file_path = 'log1.log.gz'
with gzip.open(file_path, 'rb') as f:
    data = f.read()

extracted_data = parser.parse(data)

for data in extracted_data:
    print(data)

```

## Output

```json
{
  "type": "http",
  "timestamp": "2023-06-19T01:24:36.560391Z",
  "elb": "app/crawling-ALB/df7b42c830cab31c",
  "client_ip": "185.254.196.173",
  "clent_port": "55646",
  "target_ip": "172.31.1.83",
  "target_port": "80",
  "request_processing_time": "0.012",
  "target_processing_time": "0.001",
  "response_processing_time": "0.000",
  "elb_status_code": "404",
  "target_status_code": "404",
  "received_bytes": "231",
  "sent_bytes": "3828",
  "request_method": "GET",
  "url": "http://15.165.214.35:80/.env",
  "http_version": "HTTP/1.1",
  "user_agent": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/81.0.4044.129 Safari/537.36",
  "ssl_cipher": "-",
  "ssl_protocol": "-",
  "target_group_arn": "arn:aws:elasticloadbalancing:ap-northeast-2:375350317796:targetgroup/crawling-TG/02bacd9ef5e3c0ab",
  "trace_id": "Root=1-648fae54-2521fdf43f8d31f87fc9061b",
  "domain_name": "-",
  "chosen_cert_arn": "-",
  "matched_rule_priority": "0",
  "request_creation_time": "2023-06-19T01:24:36.547000Z",
  "actions_executed": "waf,forward",
  "redirect_url": "-",
  "error_reason": "-",
  "target_port_list": "172.31.1.83:80",
  "target_status_code_list": "404",
  "classification": "-",
  "classification_reason": "-"
}

```

## License

MIT License

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.