semvec/pypi-packages huggingface dataset:
pymplog 0.0.6
Serial terminal logger with fancy colours
Requires Python: >=3.7

# pymplog

[![PyPI - Version](https://img.shields.io/pypi/v/pymplog.svg)](https://pypi.org/project/pymplog)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/pymplog.svg)](https://pypi.org/project/pymplog)

-----

**Table of Contents**

- [Installation](#installation)
- [License](#license)

## Installation

```console
pip install pymplog
```

## License

`pymplog` is distributed under the terms of the [MIT](https://spdx.org/licenses/MIT.html) license.
api-master 1.11
Utilize several market-data APIs in production-ready format for real-time and simulated market analysis.
Keywords: trading,stocks,options,crypto,forex,indices,discord

## ```pip install api_master```



A terminal is being built: [polygonsdks](https://polygonsdks.herokuapp.com/)

# Welcome to the Polygon.io toolkit

This is a tool-kit designed for polygon.io users as well as anyone looking to integrate real-time data feeds into their personal Discord servers.

I'll be automating the creation process over-time, such as webhook and channel creation, as well as incorporating a stream-lined way to recieve

Discord feeds based upon your personal preference.


To view more in-depth README - please see the *read-me* folder.


Any questions can be sent to chuckdustin12@gmail.com and I'll do my best to reply promptly.


Here's a general walkthrough:


### STEP 1:
---
Visit https://www.polygon.io if you're wanting to work with their robust real-time data. Use code FUDSTOP at checkout to recieve a 10% discount.


Once you have your API key, you can place it in "cfg.py" after downloading or cloning the repo. Replace "YOUR POLYGON API KEY GOES HERE" with your API KEY.

---


### STEP 2:
---
You'll see five files in the root directory -

- 1. get_latest_crypto_data.py
- 2. get_latest_forex_data.py
- 3. get_latest_indices_data.py
- 4. get_latest_options_data.py
- 5. get_latest_ticker_data.py

Once you have your key, and depending on the service you chose - you can select the corresponding file and run it to generate a fresh spreadsheet of market-wide data.

For example - if you chose the stocks subscription - you would select *get_latest_ticker_data.py* and run it after placing your APIKEY into "cfg.py".

Once you have the file - you can then immediately utilize the *simulated_markets* sub-folder found in the *examples* folder to run a simulated options market or stock market data-stream using the latest data.

You can find real-time market stream examples in the *realtime_markets* folder.

If interested in the Discord aspect of this project - please see the discord readme inside of the *read-me* folder.
mactemperatures 0.0.2
Python Package for Obtaining Temperature Sensor Readings on M1 Macs
Requires Python: >=3.7

# Mac Temperatures

This is a simple program to obtain readings from thermal sensors on M1 Macs. I adapted the Objective-C [code](https://github.com/fermion-star/apple_sensors) written by fermion-star as a C program and wrote Python bindings to make the code more flexible and easier to use.

## Install

```shell
pip install mactemperatures
```

## Usage

```python
>>> from mactemperatures import get_thermal_readings
>>> get_thermal_readings()
```
dww-test 0.0.6
Hatch test project
Requires Python: >=3.7

# dww_test

[![PyPI - Version](https://img.shields.io/pypi/v/dww-test.svg)](https://pypi.org/project/dww-test)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/dww-test.svg)](https://pypi.org/project/dww-test)

-----

**Table of Contents**

- [Installation](#installation)
- [License](#license)

## Installation

```console
pip install dww-test
```

## License

`dww-test` is distributed under the terms of the [MIT](https://spdx.org/licenses/MIT.html) license.
smolnet 0.1.0
smolnet, a tiny MLP module, implemented from scratch using numpy
Requires Python: >=3.10,<4.0

Implementation of Multi Layer Perceptron from scratch using only `numpy`. Broadly based on [Michael Nielson's book](http://neuralnetworksanddeeplearning.com/) and inspired by Karpathy's [micrograd](https://github.com/karpathy/micrograd).
miley 1.0.2
TBD
Requires Python: >=3.9,<4.0

# Miley - a proof of concept malware scanner for containers

Miley is a proof of concept malware scanner built for scanning container images, as well as local files.

Miley works by taking file hashes from the container/directory, and querying the hashes against a malware databse, [Malware Bazaar](https://bazaar.abuse.ch/).

This tool was created to satisfy classwork for Dakota State University's CSC-842 Security Tool Development.

Demo video: [here](coming soon!)

## Prerequisites

- Python 3 interpreter
- Pip3
- Docker for container scanning

## Usage

```bash
pip install miley

miley --help
```

**Scan a hash (MD5, SHA-1, or SHA-256)**

```bash
miley --hash f617abd6a9ccb98e34a6e32184004d5a08ea11d198fca9fed88b04b9dfc96de2
```

**Scan a directory for malware**

```bash
miley --path /path/to/directory/
```

**Scan a remote container image**

```bash
miley --image alpine:latest
```

## Future Work

If I were to maintain this program going forward, I would:

- speed up the program
- support additional malware databases, such as Virus Total
- add a GUI

## Why is it named Miley?

Miley is my beloved family dog, who passed away in 2023 due to old age.

Miley was always good at detecting and alerting to potentially dangerous situations, despite being blind.

I wrote this tool with her memory at the forefront of my mind.
tech-ninja-tistory-api 1.0.4
Tistory API Wrapper
Requires Python: >=3.6

# Tistory API Wrapper

## Overview
Tistory API Wrapper

### Install
```
$ pip install tech-ninja-tistory-api
```

### Usage  
```
from tech_ninja_tistory_api import TistoryAPI

blog_name = 'yscho03'
tistory_api = TistoryAPI(blog_name)

tistory_client_id = 'xxxxxxxxxx'
tistory_redirect_uri = 'http://localhost/tistory/auth/callback'

oauth_url = tistory_api.authorize(tistory_client_id, tistory_redirect_uri)
print('>>> Generated Oauth URL')
print(oauth_url)

"""
https://www.tistory.com/oauth/authorize?client_id=xxxxxxxxxx&redirect_uri=http://localhost/tistory/auth/callback&response_type=code&state=
"""

# access_token
tistory.access_token = '<YOUR_ACCESS_TOKEN>'
```
semremover 1.0.0
semantic object removal made easy.
Requires Python: >=3.10

# Semantic Object Removal
Using semantic segmentation and in-painting to remove objects based on labels. Inspired by [Inpaint Anything](https://github.com/geekyutao/Inpaint-Anything) by  Tao Yu et al. Using [MaskFormer](https://github.com/facebookresearch/MaskFormer) for semantic segmentation to select areas to remove using [LaMa](https://github.com/advimman/lama) for in-painting.

## Installation
Install the package.
```
python -m pip install semremover
```

Use the `SemanticObjectRemover` in your code.
``` python
from semremover import SemanticObjectRemover

sem_obj_remover = SemanticObjectRemover()
labels = ['car', 'minibike', 'van']
inpainted_image = sem_obj_remover.remove_objects_from_image("example.jpg", labels)
```

## Development

### Installation
Install the Python requirements.
```
python -m pip install -r requirements.txt
```

### Usage
To use the script you can call it with various options. The first positional argument is the input path, which can point to either an image or a directory of images. To remove objects from a picture add them to the labels option when running the script. The default labels can be found in `./semremover/models/config/ade20k_labels.json`.

### Example
**input**
```
python -m semremover example/paris.jpg --labels car minibike van
```

![A picture of a street lined with cars in Paris.](https://github.com/Jessseee/semantic-object-removal/blob/main/example/paris.jpg?raw=true)

**Output**

![A picture of the same street in paris with the cars digitally removed.](https://github.com/Jessseee/semantic-object-removal/blob/main/example/paris-inpainted.jpg?raw=true)
ctd-python 0.2.1
Python interface to access data from The Comparative Toxicogenomics Database (CTD)

## CTD Python

Pyhton interface to access data from The Comparative Toxicogenomics Database (CTD)

### install

```
pip3 install ctd-python
```

### import the package in order to pull the data

```
from ctd import get_data
```

## The package offers the following resources as DataFrames

- GeneInteractionTypes
- ChemicalPathwaysEnriched
- GeneDisease
- GenePathways
- DiseasePathways
- ChemocalPhenoTypeInteractions
- Chemicals
- Genes
- ChemicalGeneInteractions
- Chemicals
- Diseases

### Get the Gene Information for TNF
```
gene_df = get_data('Genes')
tnf_df = gene_df[gene_df['GeneSymbol'] == 'TNF']
```

### Get Chemical Interactions for TNF
```
chem_gene_df = get_data('ChemicalGeneInteractions')
tnf_chem_df = chem_gene_df[chem_gene_df['GeneSymbol'] == 'TNF']
```

### Get Disease Hierarchy for Parkinson Disease
```
child_diseases = get_disease_hierarchy('MESH:D010300')
```
OnlinePVT 0.0.11
Python SDK for accessing OnlinePVT API
Requires Python: >=3.9

# onlinepvt: Python SDK for accessing OnlinePVT API

[![PyPi](https://badge.fury.io/py/onlinepvt.svg)](https://pypi.org/project/onlinepvt/)

## Table of Contents

- [Overview](#overview)
- [Installation](#installation)
- [Supported Python Versions](#supported-python-versions)
- [License and Terms](#license-and-terms)

## Overview

[OnlinePVT](https://onlinepvt.com/) is the cloud version of VLXE.
It supports all the calculations and systems from the Excel version of VLXE but also much more.
The OnlinePVT Python SDK enables access to OnlinePVT services from privileged environments
(such as servers or cloud) in Python.

For more information, visit the [OnlinePVT product guide](https://onlinepvt.com/productguide).

## Installation

OnlinePvt Python SDK releases are available as wheel packages for macOS, Windows and Linux on PyPI. Install it using `pip <https://pypi.org/project/pip/>`\_:

```
pip install onlinepvt
pip install aiohttp
pip install matplotlib
pip install pytest-asyncio
```

## Supported Python Versions

We currently support Python 3.6+. OnlinePVT
Python SDK is also tested on PyPy environment.

## License and Terms

OnlinePVT Python SDK is licensed under the
[Apache License, version 2.0](http://www.apache.org/licenses/LICENSE-2.0).

Your use of OnlinePVT is governed by the
[Terms of Service for OnlinePVT](https://onlinepvt.com/).
termuxgui 0.1.6
Python bindings for the Termux:GUI plugin.
Requires Python: >=3.10

# Termux:GUI Python Bindings
A Python library to interact with the Termux:GUI plugin.  
  
See [here](https://github.com/tareksander/termux-gui-python-bindings/blob/main/TUTORIAL.md) for a tutorial
and [here](https://github.com/tareksander/termux-gui-python-bindings/blob/main/TUTORIAL_OOP.md) for using the more object-oriented subpackage.  
  
There is also [documentation](https://tareksander.github.io/termux-gui-python-bindings/index.html) generated from the docstrings.   


### Installing and updating:  
Just use `pip install termuxgui` to install from pypi.


### License

The license is the [Mozilla Public License 2.0](https://www.mozilla.org/en-US/MPL/2.0/).  
TL;DR: You can use this library in your own projects, regardless of the license you choose for it. Modifications to this library have to be published under the MPL 2.0 (or a GNU license in some cases) though.
rvtools 0.1.6
Top-level package for Probability distribution and random variable tools.
Requires Python: >=3.9,<3.13

A collection of tools for conveniently working with SciPy probability distributions.

# Installation
```shell
pip install rvtools  # or `poetry add rvtools`
```
# [Documentation](https://tadamcz.com/rvtools/)

# Sample usage
```python
from rvtools.construct import lognorm, uniform, loguniform, CopulaJoint, beta, pert, certainty
# PERT distribution: (min, mode, max) like triangular, but smoother shape
pert(1, 2, 4)

lognorm(mu=1, sigma=2) # SciPy equivalent: lognorm(scale=np.exp(mu), s=sigma). Hard to remember.
lognorm(mean=3, sd=4)  # Would need explicit calculation in SciPy
lognorm(p50=1, p95=2)  # Percentiles. Would need explicit calculation in SciPy

uniform(1, 2)  # SciPy equivalent: uniform(1, scale=2-1). Can trip you up.
uniform(2, 1)  # Not possible to invert the order in SciPy even though it's mathematically equivalent

loguniform(p10=42, p90=100)  # Percentiles. Would need explicit calculation in SciPy

certainty(42)  # 100% probability of 42, but as a _continuous_ distribution

# Arbitrary quantiles (not just integer percentiles)
lognorm(quantiles={0.0123: 1, 999/1000: 2})

# Copula
# Specify a joint probability distribution from arbitrary marginal distributions
# and pairwise rank correlations.
marginals = {
    "consumption elasticity": uniform(0.75, 3),
    "market return": lognorm(p50=0.05, p95=0.15),
    "risk of war": beta(2, 4),
}
tau = {
    # Missing pairs are assumed to be independent
    ("risk of war", "market return"): -0.5,
}
CopulaJoint(marginals, kendall_tau=tau)
```

# TODO
## Truncated lognormal
## Add https://github.com/tadamcz/metalogistic once quality is good enough