semvec/pypi-packages huggingface dataset:
non-normal 0.1.2
Generate non-normal distributions with known mean, variance, skewness and kurtosis
License: MIT
Requires Python: >=3.9,<4.0
Keywords: non-normal,fleishman,distribution,statistics

# non-normal
Generate a non-normal distributions with given a mean, variance, skewness and kurtosis using
the [Fleishman Method](https://link.springer.com/article/10.1007/BF02293811),
essentially a cubic transformation on a standard normal [X~N(0, 1)]

$$
Y =a +bX +cX^2 +dX^3
$$

where the coefficients ($a, b, c, d$) are tuned to create a distribution
with the desired statistic

![Non-Normal Distribution](https://raw.githubusercontent.com/amanchokshi/non-normal/main/docs/imgs/banner.png)
Figure 1. A non-normal field generated in the `usage` section below. The title
shows the input parameters, and the emperically measured statistics of the 
generated distribution

### Installation

Installs cleanly with a single invocation of the standard Python package tool:

```
$ pip install non-normal
```

### Usage

```
from non_normal import fleishman

# Input parameters for non-normal field
mean = 0
var = 1
skew = 1
ekurt = 2
size = 2**20

# Create an instance of the Fleishman class
ff = fleishman.Fleishman(mean=mean, var=var, skew=skew, ekurt=ekurt, size=size)

# Generate the field
ff.gen_field()
non_normal_data = ff.field

# Measure the stats of the generated samples
ff.field_stats

>>> {'mean':    0.000203128504124, 
     'var':     1.001352686678266, 
     'skew':    1.005612915524984, 
     'ekurt':   2.052527629375554,}
```

### References

1. [A method for simulating non-normal distributions](https://link.springer.com/article/10.1007/BF02293811)
2. [Functions for Simulating Data by Using Fleishman’s Transformation](https://support.sas.com/content/dam/SAS/support/en/books/simulating-data-with-sas/65378_Appendix_D_Functions_for_Simulating_Data_by_Using_Fleishmans_Transformation.pdf)
3. [Generation of Non-normal Data – A Study of Fleishman’s Power Method](https://www.diva-portal.org/smash/get/diva2:407995/FULLTEXT01.pd)
4. [Computing the real solutions of Fleishman's equations for simulating non-normal data](https://pubmed.ncbi.nlm.nih.gov/34779511/)
5. [Simulating multivariate nonnormal distributions](https://link.springer.com/article/10.1007/BF02293687)
6. [https://gist.github.com/zeimusu/7432603b85dc6406c6ea](https://gist.github.com/zeimusu/7432603b85dc6406c6ea)
micropython-shtc3 0.1.0
MicroPython Driver for the Sensirion SHTC3 Temperature and Humidity Sensor
License: MIT
Keywords: sensor,micropython,shtc3,micropython,driver,sensor,shtc3,sensirion,humidity,temperature

Introduction
============


.. image:: https://readthedocs.org/projects/micropython-shtc3/badge/?version=latest
    :target: https://micropython-shtc3.readthedocs.io/en/latest/
    :alt: Documentation Status


.. image:: https://img.shields.io/badge/micropython-Ok-purple.svg
    :target: https://micropython.org
    :alt: micropython

.. image:: https://img.shields.io/pypi/v/micropython-shtc3.svg
    :alt: latest version on PyPI
    :target: https://pypi.python.org/pypi/micropython-shtc3

.. image:: https://static.pepy.tech/personalized-badge/micropython-shtc3?period=total&units=international_system&left_color=grey&right_color=blue&left_text=Pypi%20Downloads
    :alt: Total PyPI downloads
    :target: https://pepy.tech/project/micropython-shtc3

.. image:: https://img.shields.io/badge/code%20style-black-000000.svg
    :target: https://github.com/psf/black
    :alt: Code Style: Black

MicroPython Driver for the Sensirion SHTC3 Temperature and Humidity Sensor


Installing with mip
====================
To install using mpremote

.. code-block:: shell

    mpremote mip install github:jposada202020/MicroPython_SHTC3

To install directly using a WIFI capable board

.. code-block:: shell

    mip install github:jposada202020/MicroPython_SHTC3


Installing Library Examples
============================

If you want to install library examples:

.. code-block:: shell

    mpremote mip install github:jposada202020/MicroPython_SHTC3/examples.json

To install directly using a WIFI capable board

.. code-block:: shell

    mip install github:jposada202020/MicroPython_SHTC3/examples.json


Installing from PyPI
=====================

On supported GNU/Linux systems like the Raspberry Pi, you can install the driver locally `from
PyPI <https://pypi.org/project/micropython-shtc3/>`_.
To install for current user:

.. code-block:: shell

    pip3 install micropython-shtc3

To install system-wide (this may be required in some cases):

.. code-block:: shell

    sudo pip3 install micropython-shtc3

To install in a virtual environment in your current project:

.. code-block:: shell

    mkdir project-name && cd project-name
    python3 -m venv .venv
    source .env/bin/activate
    pip3 install micropython-shtc3


Usage Example
=============

Take a look at the examples directory

Documentation
=============
API documentation for this library can be found on `Read the Docs <https://micropython-shtc3.readthedocs.io/en/latest/>`_.
peinjecter 1.0.0
PEInjecter is a Python library that allows you to inject any executable file into the call lifecycle of an exe. This can be useful for a variety of purposes, such as adding custom functionality to an existing program or modifying its behavior.
License: MIT
Requires Python: >=3.10,<3.12

向可执行文件中注入python代码或者任意可执行文件

使用python解释器直接注入

```python
import tkinter.messagebox

import peinjecter


def hello():
    tkinter.messagebox.showinfo('Hello peinjecter!')


if __name__ == '__main__':
    peinjecter.inject('target.exe', before=hello)
```

打包后再进行注入

```python
import sys
import tkinter.messagebox

import peinjecter


def hello():
    tkinter.messagebox.showinfo('Hello peinjecter!')


if __name__ == '__main__':
    if getattr(sys, 'frozen', False):
        peinjecter.inject('target.exe', before=hello)
    else:
        peinjecter.build()
```

注入其他可执行文件

```python
import peinjecter

if __name__ == '__main__':
    peinjecter.inject('target.exe', before='test.exe')
```
image-processing-dan 0.0.1
Processamento de imagens/Image Processing Package using skimage
License: MIT
Requires Python: >=3.6

# Python Image Processing Packages

Description. 
The package package_name is used to:
	Processing:
		- Histogram matching
		- Structural similarity
		- Resize image

	Utils:
		- Read image
		- Save image
		- Plot image
		- Plot result
		- Plot histogram

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install package_name

```bash
pip install package_name
```

## Author
Danillo Souza Pereira, Karina Tiemi Kato

## License
[MIT](https://choosealicense.com/licenses/mit/)


py -m pip install --upgrade pip
py -m pip install --user twine
py -m pip install --user setuptools

py setup.py sdist bdist_wheel

pip install wheel