semvec/pypi-packages huggingface dataset:
secretmessage 0.0.2
For generating Messages Secretly.

# Secret-Message 
Create and share Messages in a  **Secret** way.

## Install
```
pip install secretmessage
```

## Import the package
```
import secretmessage

# ------OR------

#from secretmessage import create,show,history
```

## Create a Message:
```
import secretmessage as message 

message.create("<USERNAME>","<YOUR_MESSAGE>","<PASSWORD_FOR_THE_MESSAGE>")
```
For creating a message it takes only 3 parameters:

	1. Username - Just add your name as username(No registration stuff is required.)
	
	2. Text Message - You can add your message 
	
	3. Message Password = Create a password of your choice for the message you are creating 

• Output:
```
{
  "id": <YOUR_MESSAGE_ID>,
  "text_message": "<YOUR_MESSAGE>",
  "password": "<YOUR_MESSAGE_PASSWORD>",
  "web_msg_link": "<YOUR_MESSAGE_LINK_FOR_WEB>",
  "api_msg_link": "<YOUR_MESSAGE_LINK_FOR_API_CALL>"  
}
```
web_message_link - Using this link anyone will be able to access your message from web[Any web browser].

api_msg_link - Using this link anyone will be able to access your message by api call 

## Show/Read Message
```
import secretmessage as message

message.show("<USERNAME>","<MESSAGE_LINK>","<PASSWORD_OF_THE_MESSAGE>")
```
For showing or reading someone else's messages,it takes only 3 parameters:

	1. Username - Just add your name as username(No registration stuff is required.)
	
	2. Message Link - This link must the "api_msg_link" in order to read or view the message.
	
	3. Password - This must be the exact password for the message created by other user.
	
• Output:
```
{
  "text_message" : "<USER'S_MESSAGE>"
}
```

## Message History 
```
import secretmessage as message

message.history("<Username>")
```
For getting history of your messages,it takes only 1 parameter:

	1. Username - Just add your name as username(No registration stuff is required.)

• Output:
```
{
  "Messages": [
  	{
  	   "id": <YOUR_MESSAGE_ID>,
  	   "text_message": "<YOUR_MESSAGE>",
  	   "password": "<YOUR_MESSAGE_PASSWORD>",
  	   "web_msg_link": "<YOUR_MESSAGE_LINK_FOR_WEB>",
  	   "api_msg_link": "<YOUR_MESSAGE_LINK_FOR_API_CALL>" 
  	},
  	........
  ]
}
```
Outlyzer 0.0.4
Outlier detection
Keywords: outliers,outlier-detection,data-science,machine-learning,statistics,zscore,iqr,visualization

# Outlyzer -A Python package to detect outliers in a dataset


Outlyzer is a Python library that provides various methods for detecting outliers in a dataset. It includes implementation of Z-score, IQR, and Mahalanobis distance methods for identifying outliers, as well as visualization-based methods using scatter plots, box plots, and other types of visualizations.


## Installation
You can install Outlyzer using pip:
```
pip install outlyzer
```


Usage:

    - Import the desired method from the library, e.g.:
        from Outlyzer.zscore import detect_outliers_zscore        
        from Outlyzer.iqr import detect_outliers_iqr

    - Pass your dataset or data series to the respective function, e.g.:
        outliers_zscore = detect_outliers_zscore(data)
        outliers_iqr = detect_outliers_iqr(data)
    
    The functions will return a boolean array indicating whether each data point is an outlier (True) or not (False).


## 
<p align="center">
  <a href="https://star-history.com/#devparihar5/Outlyzer&Date">
    <img src="https://api.star-history.com/svg?repos=Devparihar5/Outlyzer&type=Date" alt="Star History Chart">
  </a>
</p>
yproject 1.0.2
Pipes for humans.

![y](https://avatars.githubusercontent.com/u/133489024?s=50)

---

This package brings Haskell's well-loved applicative functor interface to Python in the important special case of the _identity applicative_. This allows you to write code like the following:

```python
from yproject import y
from matplotlib import pyplot as plt
import numpy as np

y / np.random.random([1000, 1000]) /\
    np.average @ {'axis': 0} /\
    plt.hist @ {'bins': 100, 'range': (0.45, 0.55)} %\
    np.reshape * (4, -1) /\
    np.average @ {'axis': 0} /\
    np.histogram @ {'bins': 100, 'range': (0.45, 0.55)} /\
    (lambda x: x[0]) /\
    np.multiply * 4 /\
    (lambda y, x: plt.plot(x, y)) * np.linspace(0.45, 0.55, 100) %\
    ...
plt.show()
```

![demo](https://raw.githubusercontent.com/the-y-project/y/main/assets/demo.png)
sparta-auth0 1.0.0
Sparta Auth0 library
Requires Python: >=3.9

# sparta-auth0

Sparta auth0 library.

## Usage

```python
from sparta.auth0 import AuthError, TokenVerifier

tv = TokenVerifier(
    auth0_domain="your-domain.auth0.com",
    api_audience="https://your-domain.com/api",
    jwks_cache_ttl=60,  # optional
)
try:
    token_payload = tv.verify_auth_header(
        "bearer eyJhbGciOiJIUzI1NiIsInR5cCI6Ikp...."
    )
except AuthError as error:
    status_code = error.status_code  # suggested status code (401 or 403)
    code = error.code  # auth0 error code (example "invalid_header")
    description = error.description  # auth0 error description (example "Unable to parse authentication token")
    raise

your_claim = token_payload.get_required_claim("https://your-domain.com/your_claim")
```

```python
from sparta.auth0 import TokenProvider
from urllib.request import Request, urlopen

tp = TokenProvider(
    issuer="your-domain.auth0.com",
    audience="https://your-domain.com/api",
    client_id="1234",
    client_secret="secret"
)

# Machine to machine request
response = urlopen(Request(
    "https://other-machine.your-domain.com/api",
    headers={"authorization": tp.get_token().get_authorization()},
))
```