semvec/pypi-packages huggingface dataset:
tldgen 0.0.1
Append TLDs to subdomains

# tldgen

Append TLD to words. Helps to perform domain enumeration.


## Example

Give a word to append TLDs:
```
$ tldgen contoso
contoso.aaa
contoso.aarp
contoso.abarth
contoso.abb
...
contoso.zippo
contoso.zm
contoso.zone
contoso.zuerich
contoso.zw
```

## Installation

```
pip install tldgen
```
flowlogger 0.2.2
This is a small library for logging while training ML. Current version is 0.2.2, its a feature-poor pre-alpha.
Requires Python: >=3.6

# flowlogger
flowlogger currently is small lib in pre-alpha version for helping with ML training logging.
# What is this?

flowlogger is a lib for ML training logging, currently is very few functions, currently will be improved.
*Currently in pre alpha*
# Installing
Through pip:
```python
pip install flowlogger
```
# Usage
**Calculating the function execution time**:

Using dict:
```python
options = {
        'class_name': 'Test'
        'save_path': 'test.log',
        'print_output': True
        'return_dict': True
    }
@fl.flclass(options=options)
def print_world():
    print('Hello World!')

print_world()
```
Using vars:
```python

@fl.flclass(class_name='Test', save_path='hello.log')
def five_plus_five():
    solution = 5 + 5
    print(solution)

five_plus_five()
```
mxene 0.0.16
MXenes scripts
Requires Python: >=3.6

# mxene
[![Python Versions](https://img.shields.io/pypi/pyversions/mxene.svg)](https://pypi.org/project/mxene/)
[![Version](https://img.shields.io/github/tag/boliqq07/mxene.svg)](https://github.com/boliqq07/releases/latest)
![pypi Versions](https://badge.fury.io/py/mxene.svg)
[![Documentation Status](https://readthedocs.org/projects/mxene/badge/?version=latest)](https://mxene.readthedocs.io/en/latest/?badge=latest)

mxene is one software to solving parallel problems for VASP calculation.

This software is optimized for 'MXene' material generation, 
calculation and analysis to achieve efficient data mining.

Using this software, you could:

1. Generate initial various kinds mxene structure and relevant VASP input.

2. Monitor VASP calculation processing.

3. Organize mxene data.

4. Extract structure features from crystal structure.

# Install

```bash
pip install mxene
```

# Initial configuration

Before first using, we recommend using the following methods 
to generate the configuration file.

```
>>> from mxene.conf_files import write_batch
>>> write_batch()
```

Support
----------------------
[![Jetbrains](jetbrains.svg)](https://jb.gg/OpenSource)
colab2pdf 1.0.0a14
Convert Your Colab Notebook to a PDF. One-Minute Install. Zero Configuration.
Requires Python: >=3.7
Keywords: ai,artificial-intelligence,colab,colab-notebook,colab-notebooks,data-science,deep-learning,google,google-colab,google-colaboratory,jupyter-notebook,jupyter-notebooks,machine-learning,ml,pdf,python,python3,pytorch,tensorflow,torch

<img src="https://drengskapur.com/colab2pdf.png" />

## Directions: Copy→Paste→Run [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1zqrIYC0iQ_CZkRqGXgZggrwjtt_4BmpL?usp=sharing)

```python
# Colab2PDF v1.0.0 by Drengskapur (github.com/drengskapur/colab2pdf) (License: GPL-3.0-or-later)
# @title {display-mode:"form"}
# @markdown ⬇️ Download PDF
def colab2pdf():
    ENABLE=True # @param {type:"boolean"}
    if ENABLE:
        import os, datetime, json, pathlib, urllib, requests, werkzeug, nbformat, google, yaml, warnings
        NAME = pathlib.Path(werkzeug.utils.secure_filename(urllib.parse.unquote(requests.get(f"http://{os.environ['COLAB_JUPYTER_IP']}:{os.environ['KMP_TARGET_PORT']}/api/sessions").json()[0]["name"])))
        TEMP = pathlib.Path("/content/pdfs") / f"{datetime.datetime.now().strftime('%Y%m%d_%H%M%S')}_{NAME.stem}"; TEMP.mkdir(parents=True, exist_ok=True)
        NB = [cell for cell in nbformat.reads(json.dumps(google.colab._message.blocking_request("get_ipynb", timeout_sec=30)["ipynb"]), as_version=4).cells if "--Colab2PDF" not in cell.source]
        warnings.filterwarnings('ignore', category=nbformat.validator.MissingIDFieldWarning)
        with (TEMP / f"{NAME.stem}.ipynb").open("w") as nb_copy: nbformat.write(nbformat.v4.new_notebook(cells=NB or [nbformat.v4.new_code_cell("#")]), nb_copy)
        if not pathlib.Path("/usr/local/bin/quarto").exists():
            !wget -q "https://quarto.org/download/latest/quarto-linux-amd64.deb" -P {TEMP} && dpkg -i {TEMP}/quarto-linux-amd64.deb > /dev/null && quarto install tinytex --update-path --quiet
        with (TEMP / "config.yml").open("w") as file: yaml.dump({'include-in-header': [{"text": r"\usepackage{fvextra}\DefineVerbatimEnvironment{Highlighting}{Verbatim}{breaksymbolleft={},showspaces=false,showtabs=false,breaklines,breakanywhere,commandchars=\\\{\}}"}],'include-before-body': [{"text": r"\DefineVerbatimEnvironment{verbatim}{Verbatim}{breaksymbolleft={},showspaces=false,showtabs=false,breaklines}"}]}, file)
        !quarto render {TEMP}/{NAME.stem}.ipynb --metadata-file={TEMP}/config.yml --to pdf -M latex-auto-install -M margin-top=1in -M margin-bottom=1in -M margin-left=1in -M margin-right=1in --quiet
        google.colab.files.download(str(TEMP / f"{NAME.stem}.pdf"))
colab2pdf()
```