semvec/pypi-packages huggingface dataset:
PupilPathApi 1.0.0
The one and only PupilPath api wrapper

# PupilPath API

The only and only PupilPath API wrapper. This wrapper uses librarys ``Requests`` and ``BeautifulSoup``.

## Installation

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

```bash
pip install pupilpathapi
```

## Basic Usage

```python
import PupilPathApi as PupilPath


user = PupilPath.User({
    "Username" : "Username",
    "Password" : "Password"
})

print(f"{user.StudentName} is in {user.ClassCount} classes")
```

```python
user.StudentName # Returns the full name on the account
user.ClassCount # Returns the number of classes assigned on the account

user.GetClassData() # Returns a dictionary containing multiple lists e.g {"Title": [], "Teacher": [], "Department": [], "Average": []}
user.GetGradeData() # Returns a dictionary containing the average and overall e.g {"Average" : 97, "Overall" : "Honors"}

user.SendEmail(["bestfriend1@skedula.net", "bestfriend2@skedula.net"], {
    "Subject" : "Hello there!",
    
    "Content" : "This was sent from <b>Code</b>" # Supports rich text
})
```

## Contributing To The Development
I want to see cool things you can do with this! To get in contact with me send me a DM on discord `Dlark#2510`. It would mean the world to me!

## License
[MIT](https://choosealicense.com/licenses/mit/)
mpong 0.0.8
A simple pong game in Python
Requires Python: >=3.6

# mpong

It's Pong, by me.

                                                                           
```                                                                          
┃                                                                         ┃
┃                                                                         ┃
┃                                                                         ┃
┃                                                                         ┃
┃                                                                         ┃
┃                                                                         ┃
┃                                                                         ┃
                                                                           
                                                                           
                                                                           
                                                                           
                                                                           
                                                                           
               ●                                                           
                                                                           
                                                                           
                                 Misses: 2 
```
<hr>

## Installation
`python3 -m pip install mpong`

## Usage
`python3 -m mpong.mpong`

## Tutorial
Use `wasd` to control both paddles. Press `q` followed by any key to exit.

##### Contents of requirements.txt
`readchar==3.0.5`

<sub>Part of the M-Commands Line</sub>
nlp-emotion-analysis-core 0.0.3
Core NLP library for extracting and analysing emotion and group dynamics in a group setting.
Requires Python: >=3.6

# nlp-emotion-analysis-core
Core NLP library for extracting and analysing emotion and group dynamics in a group setting.

## Key functionalities

### 1. Emotion extraction
Emotion extraction is based the eight basic emotions model proposed by Plutchik[1]. The extraction is based on a vocabulary 
curated using wordembedding models. Online conversations related to health were used to train the word embedding model which capture the 
contextual relationships among terms. The trained model is then used to query for similar terms. Scripts to train the word embedding model,
query for similar terms and visualizations are included in core/emotions/ folder.
Emotion extraction is enriched with handling negation, intensifiers and is able to calculate the intensity of each emotion.

<img src="https://www.researchgate.net/profile/Gerardo_Maupome/publication/258313558/figure/fig1/AS:297350959517696@1447905401049/Plutchiks-wheel-of-emotions-with-basic-emotions-and-derivative-emotions-and-cone-like.png" data-canonical-src="https://www.researchgate.net/profile/Gerardo_Maupome/publication/258313558/figure/fig1/AS:297350959517696@1447905401049/Plutchiks-wheel-of-emotions-with-basic-emotions-and-derivative-emotions-and-cone-like.png" width="300" height="300" />
<h6>Image retrieved from [2].<h6>


### 2. User enagagement measures
User engagement in a group is measured based on two main components:
  i. Emotion enagagement
  ii. Participation based on number of posts etc.
  

### 3. Clinical information extraction
Clinical information can be extracted using a curated vocabulary of clinical terms. Physcial symptom extractor is added as a demonstration.


### 4. Keyphrase extraction
Keyphrase extraction is based on RAKE [3], which automatically extracts significant phrases from text.





Examples for emotion extraction, physical symptoms extraction and keyphrase extraction are included in the main.py







#### References:

[1] Plutchik, R. (1984). Emotions: A general psychoevolutionary theory. Approaches to emotion, 1984, 197-219.

[2] Retrieved from: https://www.researchgate.net/profile/Gerardo_Maupome/publication/258313558/figure/fig1/AS:297350959517696@1447905401049/Plutchiks-wheel-of-emotions-with-basic-emotions-and-derivative-emotions-and-cone-like.png

[3] Rose, S., Engel, D., Cramer, N., & Cowley, W. (2010). Automatic keyword extraction from individual documents. Text mining: applications and theory, 1, 1-20.
ansiblevarchecker 0.0.2
Variable checker for ansible playbooks
Requires Python: >=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*, !=3.6.*

# ansiblevarchecker

[![codecov](https://codecov.io/gh/KlutzyBubbles/ansible-var-checker/branch/main/graph/badge.svg?token=NKQROPA7NT)](https://codecov.io/gh/KlutzyBubbles/ansible-var-checker)

CLI to check what vars are defined / used to find undefined or extra vars not documented.

Based on source for ansible 2.9 and striped down and modified version of jinja2schema.

## Known Issues

- Sub attributes of a dictionary are marked as defined if a different sub attribute is set e.g

```yaml
# Setting
dict:
  sub: yay

# Will cause the use of the following undefined var to be marked as defined
{{ dict.undefined }}
```

- Setting variables in jinja2 templates are seen as variable usage and will be marked as undefined if they have not been registered outside of the jinja2 template

- Because of the removed scalar typing (to fix issues with filter discovery and other edge cases), infer-ing assumes all if statements evaluate to a boolean which can fail when actually run. This is out of scope of what avc is meant to do and is expected behavior (aka, test your code before pushing)

- Python 3.6.x isn't tested and isn't 100% supported due to https://www.python.org/dev/peps/pep-0538/ being introduced min 3.7. 3.6 can still be configured to work with UTF-8 encoding but this package is not tested against 3.6

- Tests can fail with `ImportError: cannot import name 'soft_unicode' from 'markupsafe'`. To overcome this a downgraded version of markupsafe must be used `pip install MarkupSafe==2.0.1`

- On my local machine `python -m` needs to come before the `ansiblevarchecker` otherwise a `ImportError: cannot import name 'main' from 'ansiblevarchecker'` occurs