semvec/pypi-packages huggingface dataset:
seven_wxapp_zxchj 1.0.18
seven wxapp zxchj
License: MIT

# seven_wxapp_zxchj

## 天志互联Python微信云应用在线抽盒机框架库

### 1.0.18 更新内容
* UseResetCardHandler更新prize_roster.goods_code = machine_prize.goods_code

### 1.0.17 更新内容
* 初始版本
lrengine 0.0.7
lrengine - classify by dates and names
License: MIT
Requires Python: >=3.6
Keywords: classify language recognition

# lrengine - classify by dates and names

[![https://thcasey3.github.io/lrengine/](https://img.shields.io/pypi/v/lrengine)](https://pypi.org/project/lrengine/)
[![Website](https://img.shields.io/website?down_message=offline&label=Documentation&up_message=online&url=https%3A%2F%2Fthcasey3.github.io%2Flrengine%2Findex.html)](https://thcasey3.github.io/lrengine/)

lrengine is an open-source Python package for correlating dates and specific language patterns in file and folder names with each other and with outputs of user-supplied functions. 

# Features

- Organize DataFrames using dates and specific language in file and folder names
- Find specific format date strings or look for possible date strings in file and folder names 
- Construct DataFrames from a function that processes files and folders of a parent directory
- Visualize correlations between dates and specific language in file and folder names and function outputs

# Requirements

  - Python 3.6 or Later
  - pandas, numpy, python-dateutil, seaborn

Install the required packages using pip:
```console
pip install pandas numpy python-dateutil seaborn
```

# Installation

Install lrengine using pip:

```console
pip install lrengine
```

# Documenation

- Online documentation: https://thcasey3.github.io/lrengine/

# Developement 

  - Clone the git repository from https://github.com/thcasey3/lrengine/tree/development
ramix 1.0.0
RaMix: A Python package for generating Raman Mixture Spectra
License: MIT
Requires Python: >=3
Keywords: raman spectra synthetic

RaMix generates synthetic Raman mixture spectra.These mixture spectra can be used to compare the predictive performance of different chemoinformatics algorithms, such as partial least squares (PLS) and 1D Convolution Neural Networks (1D-CNN).
uppaal-py 0.0.4
UPPAAL wrapper for Python.
License: MIT
Requires Python: >=3.8,<4.0

# uppaal-py
Python library for reading, writing, analyzing, and modifying UPPAAL timed automata files. Works with Python >= 3.8.

## Disclaimer
uppaal-py is a work-in-progress library. For bugs, missing features or documentation please create an issue or send me an email. API is subject to change.

## Dependencies
* [lxml](https://lxml.de/)
* [NetworkX](https://github.com/networkx/networkx)
* [ortools](https://developers.google.com/optimization)

## Installation
Via pip:
```
pip install uppaal-py
```

## License
[MIT](https://mit-license.org/)

## Features
- Reading and writing UPPAAL files.
- LP based path realizability analysis.
- Finding set of furthest reachable locations with respect to a set of target (unsafe) locations.
- Working with variables of type `int` and a subset of expressions involving `ints` for guards, invariants, and updates during transitions allowed in UPPAAL in addition to clocks during computations.

## TODO:
- [ ] Methods for calling UPPAAL/verifyta.
- [ ] Migrate to [libutap](https://github.com/MASKOR/libutap) for parsing files.
- [ ] Parameter synthesis for safety property.
- [ ] [lxml type annotations](https://github.com/lxml/lxml-stubs) and type annotations for the remaining functions.
- [ ] Auto-generated documentation.

## Non-features
- Analysis involving network of TA — product of two or more TA can be implemented in the future, though.
- Symbolic model checking, UPPAAL does that.
todo.sh-notion 0.1.4
Integration between todo.sh and Notion Task Lists!
License: MIT

# todo.sh-notion

Integration between todo.sh and Notion Task Lists!

![demonstration](examples/demonstration.gif)
![](../examples/demonstration.gif)


**Motivation**:

To helps the `todo.sh` users to sync your tasks with Notion. Without quiting the terminal or doing something extra effort. For me, who have focus problems, it helps a lot.

## Pre configuration

You need to provide two environment variables to allow the integration (put it in your .bashrc if you want):

    export NOTION_TOKEN_V2=''
    export NOTION_TASK_LIST_URL=''


To catch the `NOTION_TOKEN_V2` you need to go into https://notion.so page and enable the browser devtools,
find a token_v2 in cache and copy the value.

![token](examples/notion_token.png)
![](../examples/notion_token.png)

To catch the `NOTION_TASK_LIST_URL` it's quite simple, just navigate to your Task List and copy the URL.

## Installation

It's important that the `todonotion` command be available globally (like todo.sh)
so, we will install using the flag --user that installs the package in your personal directory for python dependencies.

    pip install todo.sh-notion --user

After that, we run the command:

    todonotion configure

This will create the `todo.sh` actions in `~/.todo.actions.d`:

- `nadd` like `add`, but **creates** the card in notion task list too.
- `ndo` like `done`, but **moves** the card in notion task list too.
- `ndoc` like `done`, but this allow you to perform actions in all cards by @context or any text part that contains in card titles, **moves** the cards in notion task list too.
- `ndel` like `del`, deletes by task ids, **delete** the card in notion task list too.
nemesis-daxfeliz 0.0.1
Python modules and scripts used for NEMESIS project.
License: MIT
Keywords: python,astronomy,photometry,TESS

# `NEMESIS`
exoplanet tra<span style="color:red">N</span>sit surv<span style="color:red">E</span>y of nearby <span style="color:red">M</span>-dwarfs in t<span style="color:red">ES</span>s full frame <span style="color:red">ImageS</span>

Description
----------

This repository contains documentation, code, and small chunks of data
supporting the NEMESIS project. 

To download data products from NEMESIS Planet Candidates detected in data from Sectors 1 to 5, visit our
[Filtergraph Portal](https://filtergraph.com/NEMESIS).

Author
----------
Dax Feliz

License
----------
MIT

Install
----------
`conda env create -f environment.yml -n nemesis`

Update env
----------
`conda env export --no-builds > environment.yml`