semvec/pypi-packages huggingface dataset:
navicat-spock 0.0.4
Volcano Plot fitting tool
Requires Python: >=3.8
Keywords: compchem

spock: Volcano Plot Fitting Tool
==============================================

[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/rlaplaza/spock/HEAD?labpath=App.ipynb)
[![DOI](https://zenodo.org/badge/764582532.svg)](https://zenodo.org/doi/10.5281/zenodo.12804607)

![spock logo](./images/spock_logo.png)
[![PyPI version](https://badge.fury.io/py/navicat-spock.svg)](https://badge.fury.io/py/navicat-spock)

## Contents
* [About](#about-)
* [Install](#install-)
* [Examples](#examples-)
* [Citation](#citation-)

## About [↑](#about)

The code runs on pure python with the following dependencies: 
- `numpy`
- `scipy`
- `matplotlib`
- `pandas`
- `scikit-learn`
- `statsmodels`

## Install [↑](#install)

You can install spock using pip:

```python
pip install navicat_spock
```

Afterwards, you can call spock as:

```python
python -m navicat_spock [-h] [-version] -i [FILENAMES] [-wp WP] [-v VERB] [-pm PLOTMODE] [-rng SEED] [-fa FA] [-is IMPUTER_STRAT] [--plot_all PREFIT] [--save_fig SAVE_FIG] [--save_csv SAVE_CSV]
```
or simply
```python
navicat_spock [-h] [-version] -i [FILENAMES] [-wp WP] [-v VERB] [-pm PLOTMODE] [-rng SEED] [-fa FA] [-is IMPUTER_STRAT] [--plot_all PREFIT] [--save_fig SAVE_FIG] [--save_csv SAVE_CSV]
```

Alternatively, you can download the package and execute:

```python 
python setup.py install
```

Afterwards, you can call spock as above. Options can be consulted using the `-h` flag in either case. The help menu is quite detailed. 

Note that the volcano plot generation function is directly exposed in case you want to use it in your code.

## Examples [↑](#examples)

The `manuscript_examples` subdirectory contains several examples used in the manuscript. Any of the data files can be run as:

```python
python -m navicat_spock -i [FILENAME]
```

We also provide a jupyter notebook where the plots are generated one by one.

The input of spock is a `pandas` compatible dataframe, which includes plain .csv and .xls files. 

Regarding format, spock expects headers for all columns. The first column must contain names/identifiers. The catalytic performance metric must include the word "target" in its header. The rest of the columns will be interpreted as potential descriptors.

High verbosity levels (`-v 1`, `-v 2`, etc.) will force spock to print out information about every step. This can be useful for understanding what the code is doing and what can have possibly gone wrong. To be as automated as possible, reasonable default values are set for most choices. 

The plotmode (`-pm 1`, `-pm 2`) option can be used to modify the default look of the generated pngs, including more detail as the plotmode level increases. 


## Citation [↑](#citation)

Please cite the accompanying manuscript (to be submitted), which clarifies the details of volcano plot construction, and this repository through its DOI.


---
harmonyhub-cli 0.0.9
A CLI library for HarmonyHub
Requires Python: >=3.6

# HarmonyHub

## Overview
- This is a Visual API Orchestrator POC created for Rest API Calls.
- UI Generates YAML. Written in static HTML using WebComponents. 
- Python-based `CLI` is used to run YAML generated from UI. It can sequence and execute API Calls.

## Commands
- harmonyhub-cli init
  - harmonyhub-cli init --file workflow.yaml
- harmonyhub_cli run <<workflow.yaml>>
  - harmonyhub-cli run <<workflow.yaml>> --debug
  - harmonyhub-cli run <<workflow.yaml>> --toolsFile <<integration.yaml>> --debug
- harmonyhub-cli server
- harmonyhub-cli --help
- harmonyhub-cli --version

## Operator Supported
- Simple comparisons (==, !=, >, <, >=, <=)
- Logical operations (and, or, not)
- Basic arithmetic operations (+, -, *, /, %)

## Body Templating Engine
- Python [Jija2](http://jinja.quantprogramming.com/)

## Output Parser
- [JQ](https://www.devtoolsdaily.com/jq_playground/)

## System Defined Variables
- RestAPI
  - $today_datetime
  - $today_date
  - $today_time
  - $today_date_zero_time
  - $request_http_method
  - $request_url
  - $request_headers
  - $request_body
  - $response_http_status
  - $response_headers
  - $response_body
  - $random.randint(1000,9999)
  - $random.uniform(15.5, 80.5)
- Database
  - $response_body

## Roadmap
- Kafka
- GraphQL
- Grpc

## Demo
- Demo created [here](https://rahulkirangaddam.is-a.dev/HarmonyHub/)
  ```bash
  # https://pypi.org/project/harmonyhub-cli/
  pip install harmonyhub-cli/
  harmonyhub-cli serve
  ```
  ```bash
  # To run yaml generated standalone
  harmonyhub-cli <<file name.yaml>>
  ```

## Credits
- Below are projects used as inspiration
  - [Stepci](https://stepci.com/)
  - [Apache Karavan](https://github.com/apache/camel-karavan)
joao-marcionilo-cep-weather 0.1.1
Package to fetch data from an CEP API and scrape weather of a capital in Brazil
Requires Python: >=3.12

# CEP Weather

Package to fetch data from an CEP API and scrape weather of a capital in Brazil

## Installation

You can install it using pip:

```
py -m pip install joao_marcionilo_cep_weather
```

## Try it out

You can test the package with a local API running the following code

```
from joao_marcionilo_cep_weather import server

server.run()
```

[Click here](http://127.0.0.1:8000/docs) while running the code to access the doc of the API
pyserrf 0.2.2
Add your description here
Requires Python: >=3.12

# pySERRF
Python implementation of the Systematic Error Removal Using Random Forest (SERRF) algorithm.
SERRF is a qc-based sample normalization method designed for large-scale untargeted metabolomics data.
The method was developed by the Fan et al. in 2015 (see https://slfan2013.github.io/SERRF-online/).
This is simply an attempt to port its functionality from R to python.
The package structure is based on SKlearn's transformers, with fit and transform methods.

Documentation can be found at https://pyserrf.readthedocs.io


TODO: Implement cross-validation (almost done)
TODO: Verify if injection time is accounted for with current code 
TODO: Add documentation 
TODO: Add more tests 
TODO: Add CLI
matplotlib-layout 0.1.0
A package for laying out figures with geometric constraints
Requires Python: >=3.7

# MPLLayout

![Project logo](logo.svg)

## Summary

MPLLayout is a package to create precise figure layouts in [matplotlib](https://matplotlib.org/).
It works by modelling figure elements using geometric primitives (for example, text anchors are points, the figure is a quadrilateral, etc.), then constraining the sizes and positions of these elements using geometric constraints (for example, fixing the width of a figure, constraining axes sides to be collinear, constraining axes to lie on a grid, etc.).

Using this approach, MPLLayout can:

* align figure elements (axes, text label location, x and y axis, etc.),
* specify margins around axes,
* create templates for figures across different mediums (posters, manuscripts, slides, etc.),
* and more!

## Basic usage

The tutorial notebook in `examples/tutorial.ipynb` demonstrates the basic usage of the package and explains some of the commonly used geometric constraints.
Other examples are also given in the `examples` folder.
The notebook at `examples/ten_simple_rules_demo.ipynb` contains an interactive demo to recreate a figure from ["Ten Simple Rules For Better Figures"](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003833) (Rougier, Droettboom and Bourne 2014).

A summary of how to use the package is shown in the figure below.
The process resembles creating and constraining geometry in computer-aided design programs like AutoCAD, SolidWorks, etc.
![Project logo](doc/Summary.svg)

## Motivation

Matplotlib contains several strategies for creating figure layouts (for example, `GridSpec` and `subplots` for grid-based layouts).
While these approaches work well, greater control over figure element positions is sometimes desirable;
for example, when preparing figures for published documents, research papers, or slides.

## Installation

You can install the package from PyPI using

```bash
pip install matplotlib-layout
```

Alternateively, clone the repository into a local drive.
Navigate to the project directory and run

```bash
pip install .
```

The package requires `numpy`, `matplotlib`, and `jax`.

## Contributing

This project is a work in progress so there are likely bugs and missing features.
If you would like to contribute a bug fix, a feature, refactor etc. thank you!
All contributions are welcome.

## Motivation and Similar Projects

A similar project with a geometric constraint solver is [`pygeosolve`](https://github.com/SeanDS/pygeosolve).
There is also another project prototype for a constraint-based layout engine for `matplotlib` [`MplLayouter`](https://github.com/Tillsten/MplLayouter), although it doesn't seem active as of 2023.
apishare 0.2.0
A Python package for financial market data access
Requires Python: >=3.6

# APIShare

一个专业的金融数据接口 Python 包，提供股票、基金等金融市场数据的快速访问。
官网：https://www.apishare.cn

## 安装

```bash
pip install apishare
```

## 使用方法

```python
from apishare import APIShare

# 创建一个实例并传入你的 API 令牌
api = APIShare(token="your-api-token")

# 获取股票日线数据
daily_data = api.get_data(
    api_name="daily",          # 接口名称：日线数据(更多接口见官网)
    code="000001.SZ",         # 股票代码：平安银行
    start_date="20240101",    # 起始日期
    end_date="20241231"       # 结束日期
)
print(daily_data)
```

## 特性

- 覆盖全面的金融市场数据
- 简单直观的 API 设计
- 支持 DataFrame 格式输出
- 高效的数据获取和处理
- 详细的接口文档和示例

## 许可证

本项目采用 Apache License 2.0 许可证。
audiocencesored 0.2
Check your audio for profanity on steroids.

Audio profanity is a big headache and while it seems small but if you're working with age sensitive research or projects. You would want to rate your audio and know more about it. that's where this simple project come into play. it uses OpenAI whisper model to segment audio and let you know before it becomes a headache. 

### What can be done?
Honestly, tons! For starters I have written a simple substring based matching algorithm that can match and compare from a list of curse words released by CMU (Carnegie Mellon). Find more info: https://www.cs.cmu.edu/~biglou/resources/bad-words.txt 

1. Segment the audio and extract the transcriptions (not intended rather a byproduct)
2. Extract wordlist of your audio
3. and then do matching

I have more ideas in mind and gonna maintain this like a dedicated religion. Because I have a newfound interest in audio segmentation. 

### How it works?

Good question! 

```Python
from audiocencesored import *

# this func transcribes your audio. I didn't harcode file-name
transcribe_timestamps(audio_file, output_file)

# extracting the words from transcript
extract_words(json_file, output_file)

# let's download the CMU list
download_list(output_file="bad_words.txt")

# checking the score
check_profanity(word_list_file, bad_words_file, rating="R")
```

### Anything to keep in mind?
Certainly! Have your audio files in `.wav` format. 

Disclaimer: It's meant to be fun-project while providing support and feature is suppose to be religion for me. Drop a hi, on github if you have some features in mind. https://github.com/sleepingcat4/audio-profanity