semvec/pypi-packages huggingface dataset:
c99-api 0.1.1
A Python wrapper and handler for the c99.nl API

<p align="center">
<br><br><br>
<a https://api.c99.nl/"><img src="https://api.c99.nl/assets/images/logo.png" width="100px"></a>
<br><br><br>
</p>

<p align="center">
<b>Seamlessly integrate the c99.nl API into your Python projects</b>
</p>

<p align=center>
<a href="https://pypi.org/project/c99-api/"><img src="https://badgen.net/pypi/v/c99-api/"></a>
<a href="https://github.com/Haste171/c99-api/releases"><img src="https://badgen.net/github/release/Haste171/c99-api">
<a href="https://gitHub.com/Haste171/c99-api/graphs/commit-activity"><img src="https://img.shields.io/badge/Maintained%3F-yes-green.svg">
<a href="https://github.com/Haste171/c99-api/blob/master/LICENSE"><img src="https://img.shields.io/github/license/Haste171/c99-api">
<a href="http://makeapullrequest.com"><img src="https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square">
<!-- <a href="https://pypistats.org/packages/c99api"><img src="https://pepy.tech/badge/c99api"></a> -->
<a href="https://discord.gg/rR6xkP6rJu"><img src="https://img.shields.io/badge/Discord-700-blueviolet?logo=discord&amp;logoColor=white&style=round">

# Installation

```
pip install c99api
```

# Usage

```python
# Import the package
from c99api import EndpointClient

api = EndpointClient

# Define your API key
api.key = 'your_key'
```

```python 
# Access list of different methods available
response = api.list_methods()
print(response)
```

```python
# Example Methods

# Phone lookup
response = api.phonelookup(number='1234567890', json=True)
print(response)

# Email validation
response = api.emailvalidator(email='example@example.com', json=True)
print(response)

# Port scanner
response = api.portscanner(host='example.com', json=True)
print(response)
```

## Other Docs

[API Overview](https://api.c99.nl/dashboard/api_overview)

## Warning

**To use the c99.nl API you need to [purchase](https://api.c99.nl/dashboard/shop) an API key**
okxpy 0.1.7
A pip package for a Python wrapper that provides an interface to the OKX API
Requires Python: >=3.8

# okxpy

[![Downloads](https://pepy.tech/badge/okxpy)](https://pepy.tech/project/okxpy)

# Description

Okxpy is a Python package that provides a convenient wrapper for making API requests to the OKX exchange. With this package, users can easily access the various services offered by the OKX exchange, such as trading, account management, and market data retrieval.

Using Okxpy, developers can quickly integrate OKX functionality into their Python applications, without the need for manual HTTP requests and parsing JSON responses. The package abstracts away the details of HTTP communication and handles error responses gracefully, so users can focus on their application logic.

The package includes a variety of modules that correspond to the different services provided by the OKX API. These modules include methods for making requests to the OKX API endpoints, as well as for parsing and processing the responses. The package also includes a variety of utility functions for common tasks, such as generating authentication headers.

To use Okxpy, users simply need to install the package using pip and provide their OKX API credentials. The package supports both public and private API calls, with authentication handled automatically for private calls.

Overall, Okxpy provides a simple, convenient, and reliable way to access the OKX exchange API from Python.


## Source code

https://github.com/EnkhAmar/okxpy


## Reference

[OKX API Documentation](https://www.okx.com/docs-v5/en/)

[How to upload package to pypi?](https://www.freecodecamp.org/news/how-to-create-and-upload-your-first-python-package-to-pypi/)


## Push update to pypi

```shell
python -m build
twine upload --skip-existing dist/*  
```
pylathedb 1.0.9
Keyword Search over Relational Databases
Requires Python: >=3.6

# PyLatheDB

**A Python library for Relational Keyword Search with Support to Schema References.**

PyLatheDB is based on Lathe, an R-KwS framework that generalizes the well-known concepts of Query Matches (QMs) and Candidate Joining Networks (CJNs) to handle keywords referring to schema elements and introduces new algorithms to generate them, along with a novel approach to automatically select the CJNs that are more likely to represent the user intent when issuing a keyword query.

We showcase PyLatheDB through a Jupyter Notebook. We recommend running it using [Google Colab](https://colab.research.google.com/github/pr3martins/PyLatheDB/blob/main/demo/PyLatheDB_DEMO.ipynb). But its static version is also available in our repository [here](https://github.com/pr3martins/PyLatheDB/blob/main/demo/PyLatheDB_DEMO.ipynb).

The database source files are available [here](https://drive.google.com/drive/folders/1Xz3mPN0NC7xdIETKZlFDTcqcU6lsVUYD?usp=sharing)
doodaw 0.1.1
A Discord bot

This is a Discord bot that will do stuff.
Right now it only responds to '$hello' in the general channel of https://discord.gg/JPyHw558

Author: The kids
Requires-Python: >=3.9,<4.0
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.9
pytorch-check 1.0.3
Check PyTorch models for unsafe code
Requires Python: >=3.7

# pytorch-check

Check PyTorch models for (overtly) unsafe code

Depends on fickling, the Python pickling decompiler and static analyzer.

By default, this checks for common patterns used to inject malicious code
into Python bytecode, but is not a 100% guarantee against any malicious
code embedded into models.

The -t flag can be used to safely trace the execution of the bytecode to
see what's actually going on, and the -d flag can be used to print the
disassembled bytecode, for manual analysis.

This script handles the TorchScript serialization format that is used for
PyTorch models (generally using the .pt or .pth extension), since that is
not handled directly by the CLI tool that comes with the fickling package.

The format is defined here:
https://github.com/pytorch/pytorch/blob/master/torch/csrc/jit/docs/serialization.md

Basically .pkl/.debug_pkl files are embedded into a Zip archive along with
data files as well as optional Python source code.

For those of you who are unaware, pkl is serialized Python bytecode, that
is executed when the model is loaded, allowing for execution of arbitrary
code. Trojanizing PyTorch model files is trivial, including by using the
fickling tool by TrailOfBits to inject code into one of the embedded .pkl
or .debug_pkl files in a model.

Install with:
```bash
python3 -m pip install --user pytorch-check
```

Or from this directory:
```bash
python3 -m pip install --user .
```

Then use the command line tool like this:
```bash
pytorch-check /path/to/file-or-directory...
```

This will scan all of the specified files, treating .pkl/.pickle as files
containing pickled Python bytecode directly, and any other files like they
are PyTorch model files (i.e. Zip archives with embedded .pkl files).

If a directory is specified, it will be scanned for files with the .ckpt,
.pt, .pth, .pkl or .pickle file extensions where the .ckpt, .pt and .pth
file extensions will be treated as PyTorch model files.

Note that the pickled code embedded into PyTorch model files in general will
have some embedded imports from the pytorch package itself. This will emit a
warning since it's technically executing code, but is generally benign. Read
the warnings to see what is actually going on.

Stay safe!
dsdn-distrib-20230330-gk 0.1
Gaussian and binomial distribution

# dsdn-distrib-20230330-gk package

This is the first package I created as part of the Data Science Nanodegree course I am currently studying at Udacity

There is a parent class called Distibution from which another two classes (Gaussian and Binomial) inherit a few attributes and methods.

For Binomial and Gaussian classes there are some methods for calculating mean and st. dev. as well as plotting some bar charts

#Files

Generaldistribution.py: Contains the parent class (Distribution)
Gaussiandistribution.py: Contains the class for gaussian distribution. Methods for mean and st.dev available 
Binomialdistribution.py: Contains the class for binomial distribution. Methods for mean, st.dev and bar charts available

#Installation

The package is available in pypi so you can just use pip install dsdn-distrib-20230330-gk

#Licensing

You can find details of the licensing in file license.txt
ledger-reader 0.7
A simple parser for hledger journal files.
Requires Python: >=3.7

# Ledger Reader

A simple parser for hledger journal files. It's always fun to play with rply and use a simple grammar. PR's welcome :).

## Install

```
pip install ledger-reader
```

## Example usage


### Read a journal

```python
from ledger_reader import LedgerReader

reader = LedgerReader()
journal = reader.parse_file('bank.journal')

for entry in journal:
    print(entry.date, entry.description)
    for transaction in entry.transactions:
        print(f'{transaction.account} {transaction.currency} {transaction.amount}')

    print()

```

### Append to a journal


```python
from datetime import date
from ledger_reader import Entry, Transaction

entry = Entry(
    date(2022, 3, 5),
    'Cash Transfer',
    transactions=[
        Transaction('assets:bank:boa:checking', '$', -5000),
        Transaction('assets:bank:boa:savings')
    ]
)

with open('bank.journal', 'a') as fp:
    fp.write(f'\n{entry}')
```