semvec/pypi-packages huggingface dataset:
badwords 1.2.4.0
a small pkg demos for you and everyone


```shell
pip install badwords
```
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schnell 0.2.0
schNell - Fast calculation of N_l for GW anisotropies

# schNell

schNell is a very lightweight python module that can be used to compute basic map-level noise properties for generic networks of gravitational wave interferometers. This includes primarily the noise power spectrum  "N_ell", but also other things, such as antenna patterns, overlap functions, inverse variance maps etc.

## Installation
You can install schnell simply by typing
```
pip install schnell [--user]
```
(use `--user` if you don't have admin privileges on your machine).
Or for development versions you can download the repository with git and install from there using `python setup.py install [--user]`.

## Documentation
Documentation can be found on [readthedocs](https://schnell.readthedocs.io/en/latest/).

This example [notebook](https://github.com/damonge/schNell/blob/master/examples/Nell_example.ipynb) on github also showcases the main functionalities of the module.

## License and credits
If you use schNell, we kindly ask you to cite its companion paper (link to appear here soon).

The code is available under the [BSD 3-Clause](https://opensource.org/licenses/BSD-3-Clause) license.

If you have a problem you've not been able to debug, or a feature request/suggestion, please open an issue on github to discuss it.
PyLMD 1.0.4
Jonathan S. Smith. The local mean decomposition and its application to EEG perception data. Journal of the Royal Society Interface, 2005, 2(5): 443-454
Requires Python: >=3.6

# PyLMD
Method of decomposing signal into Product Functions

This project implements the paper:

[Jonathan S. Smith. The local mean decomposition and its application to EEG perception data. Journal of the Royal Society Interface, 2005, 2(5):443-454](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1618495/)

## How to install?

```
pip install PyLMD
```

requirements:
1. numpy
2. scipy

## Examples
```python
>>> import numpy as np
>>> from PyLMD import LMD
>>> x = np.linspace(0, 100, 101)
>>> y = 2 / 3 * np.sin(x * 30) + 2 / 3 * np.sin(x * 17.5) + 4 / 5 * np.cos(x * 2)
>>> lmd = LMD()
>>> PFs, resdue = lmd.lmd(y)
>>> PFs.shape
(6, 101)
```

![Example](https://raw.githubusercontent.com/shownlin/PyLMD/master/simple_example.png)

## Parameters
* **INCLUDE_ENDPOINTS** : bool, (default: True)

    Whether to treat the endpoint of the signal as a pseudo-extreme point

* **max_smooth_iteration** : int, (default: 12)

    Maximum number of iterations of moving average algorithm.

* **max_envelope_iteration** : int, (default: 200)

    Maximum number of iterations when separating local envelope signals.

* **envelope_epsilon** : float, (default: 0.01)

    Terminate processing when obtaining pure FM signal.

* **convergence_epsilon** : float, (default: 0.01)

    Terminate processing when modulation signal converges.

* **max_num_pf** : int, (default: 8)

    The maximum number of PFs generated.

## Return
* **PFs** : numpy array

    The decompose functions arrange is arranged from high frequency to low frequency.

* **residue** : numpy array

    residual component

## Contact
Use GitHub Issues or the mailing list to post your comments or questions.


## License
PyLMD is a free Open Source software released under the MIT license.


Lin, Q., 2020. **Python Implementation Of Local Mean Decomposition Algorithm.**
(https://github.com/shownlin/PyLMD)
coloredlog 0.2.5
Colorize your console output.
Requires Python: >=3.6

Colorize your console output.  
Provides `ConsoleHandler()` for working with `logging` module to get colored logging message.

# Usage Example
## 1. Colorize your logging output
```python
# you can just do `from colorlog import *`
import logging, coloredlog
logging.basicConfig(
    format='%(asctime)s %(filename)s:%(lineno)d [%(levelname)s] %(message)s',
    handlers=[coloredlog.ConsoleHandler()],
    level=logging.DEBUG
)
logger = logging.getLogger(__name__)

logger.info('logging started')
logger.debug('debug message')
logger.info('informative')
logger.log(coloredlog.NOTIFY, 'notification')
logger.warning('a warning message')
logger.error('message on error occurred')
try:
    raise RuntimeError('An exception!')
except:
    logger.exception('Exception:')
logger.critical('THIS IS CRITICAL')

```
**Result on Windows 10**
![result](https://raw.githubusercontent.com/cangyin/coloredlog/master/snapshots/snapshot1.png)


## 2. Decorate your normal message
```python
from coloredlog.color import *

print(deco('Hello, ', 0x011, bold=True) + reset() + 'world!')
print(deco('Hello, ', reverse=True) + reset() + 'world!')
print(deco('Hello, ', FG_BLUE, bold=True) + reset() + 'world!')
print(deco('Hello, ', FG_YELLOW, BG_GREEN, bold=True) + reset() + 'world!')
print(deco('Hello, ', FG_MAGENTA, bold=True) + reset() + 'w...')

# Simple convenience functions
warning("emmm, seems there is a small proble...")
error('Unknown error!')
```
_For more information on parameter rules for deco( ), please refer to the docstring._

**Result on Windows 10**
![result](https://raw.githubusercontent.com/cangyin/coloredlog/master/snapshots/snapshot2.png)

## 3. For **intensive** use case 
Since a complete run-through of `deco()` can be a bit time-consuming under intensive use case, you may consider method below to speed up the operation of `deco()`
```python
from coloredlog.color import *

const_deco = deco('', FG_BLUE, BG_WHITE, bold=True)
# simulates intensive use case
for i in range(10):
    print(deco('Hello world for {} times'.format(i), const_deco=const_deco)) # `deco()` will return immediately

print(reset()) # reset to normal color
```
**Result on Windows 10**
![result](https://raw.githubusercontent.com/cangyin/coloredlog/master/snapshots/snapshot3.png)


# Tested on
- Windows 10 (python 3.6)
- Ubuntu 16.4.1 (python 3.5)
uraeus.nmbd.python 0.0.1.dev4
Numerical simulation environment of constrained multi-body systems in python.
Requires Python: >=3.6

# **URAEUS** | NMBD-Py

**Numerical Multi-Body Dynamics in Python** | A numerical simulation environment of constrained multi-body systems.

## Description

**uraeus.nmbd.python** is a numerical simulation environment developed in python, that generates numerical object-oriented code from symbolic models developed in **[uraeus.smbd]( https://github.com/khaledghobashy/uraeus-smbd )**, and provides various numerical solvers for multi-body systems.

*Please visit **[uraeus.smbd]( https://github.com/khaledghobashy/uraeus-smbd )** for more information about **audience** , **fields of applications** and **background**.*

## Features

*ToDo*

## Installation

*ToDo*

## Usage Examples & Tutorials

*ToDo*

### Detailed Example - Spatial Fourbar Mechanism

*ToDo*

## Roadmap

*To be discussed ...*

## Implementation Details

*The **Implementation Details** will be provided in a separate documentation and linked here*

## Theoretical Basis

*The **Theoretical Basis** will be provided in a separate documentation and linked here*

## Support

As the tool is developed and maintained by one developer for now, if you have any inquiries, do not hesitate to contact me at [khaled.ghobashy@live.com](mailto:khaled.ghobashy@live.com) or [kh.ghobashy@gmail.com](mailto:kh.ghobashy@gmail.com)

## References

*ToDo*

## License

*ToDo*