semvec/pypi-packages huggingface dataset:
cepclient 0.8.3
Chameleon Experiment Precis
License: MIT

======================================
Chameleon Experiment Precis Client
======================================

Users can use this command line tool to generate a "history" of openration events performed on Chameleon Testbed corresponding to a user session in json format.
An experiment precis is defined as a series of real-time events a user performed under a lease of a project. Corresponding hardware and metric information is also included. 

_______________
Install
_______________

   .. code-block:: shell

      pip install cepclient

_______________
Run
_______________

Before requesting the experiment precis using CEP client, user needs to provide Chameleon authentication information. 
You can either ``source`` `the OpenStack RC file <https://chameleoncloud.readthedocs.io/en/latest/technical/cli.html#the-openstack-rc-script>`_
or pass the authentication information as command line arguments. 
Use ``cep --help`` for authentication options and usage details. 

To list all experiment precis, use command:

   .. code-block:: shell

      cep list

      # use help for more information
      cep list --help

To get an experiment precis, use command:

   .. code-block::shell

      cep print <ep name or id>

      # use help for more information
      cep print --help

You can apply different filters to print experiment precis. For example, use ``--exclude-hardware`` to hide hardware information 
or use ``--exclude-metric`` to screen out metrics from the printed experiment precis. 
In addition, you can prettyprint the experiment precis by passing ``--pretty`` argument. 
To save the experiment precis to a file, specify output location using ``--output`` argument. 
For more options of printing an experiment precis, use ``cep print --help``.

To rename an experiment precis, use command:

   .. code-block:: shell

      cep rename --name <new name> <ep name or id>

      # use help for more information
      cep rename --help
huginn 0.1.4
Tools to Detect Anomalous Events and News Related to Entities
License: MIT
Requires Python: >=3.5, <3.7

# huginn
Named for one of the Norse god Odin's ravens who would gather information for him. A package to facilitate the investigation of anomalous spikes in public interest in an entity, then fetch and summarize news stories relevant to those spikes.

# Installation and Setup

Requires Python=3.6

`huginn` is [available on PyPi](<https://pypi.org/project/huginn/>) and installable via pip:

```pip install huginn``` 

## Spacy models

Download English spacy models by running the following in the command line: 

```
$ python3 -m spacy download en
```

## NYT API Key

This project requires the usage of the New York Times article search API.  Request a key at [developer.nytimes.com](developer.nytimes.com). You'll need to set your NYT API key as a system environment variable. Open the file `~/.bashrc` (or `~/.bash_profile` if the `.bashrc` doesn't exist) in any Unix based system (Mac, Ubuntu, etc), and add the following two lines to the end of the file: 

```
# Setting NYT API Key as an environment variable
export NYT_API_KEY="YOUR_KEY_HERE"
```

Where `YOUR_KEY_HERE`is your NYT API key, enclosed in quotes.

This  is necessary for requesting relevant links for a potential entity's anomalies.

# Usage

huginn is based around the `Huginn` object. A user only need provide the entity that they are interested in.

```python
from huginn import Huginn

point72 = Huginn('Point72')
```

At this point huginn fetches Google Trends search interest on this entity looking back to 2003.  We can then find the most anomalous dates and plot the Google trends search interest with the anomalies:

```python
point72.get_anomalies()
point72.plot_interest_with_anomalies()
```

![](img/plot_interest_with_anomalies.png)We then fetch news articles and relevant information related to our anomalies:

```
point72.get_articles_info()
```

Now we can get summaries, either specific to each anomaly or globally:

```
point72.get_global_summary()
point72.get_articles_info_and_summary_after_LDA()
```

These require downloading some rather large summarization models to run.
mphyspy 0.2.3
Python3 library for calculating college-level modern physics
License: MIT

# MphysPy

[![PyPI version](https://badge.fury.io/py/mphyspy.svg)](https://badge.fury.io/py/mphyspy)
[![Run on Repl.it](https://repl.it/badge/github/eunchan1001/mphyspy)](https://repl.it/github/eunchan1001/mphyspy)

MphyPy is a Python3 library for calculating college level modern physics problems

## Requirements

To use mphyspy, these packages are required

- [sympy](https://www.sympy.org/en/index.html)
- [numpy](https://numpy.org)

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install mphyspy.

```bash
    $pip install mphyspy
```
## Usage
```bash
    $python
```
```python
    >>>from mphyspy import *
    modules loaded successfully
    >>>ct.g
    #finestructure constant
    2.002319
```
## Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.

## License

[MIT](https://choosealicense.com/licenses/mit/)