semvec/pypi-packages huggingface dataset:
physunits-pkg-pacosalces 0.0.2
Physical units as global variables for simple numerical propagation
Requires Python: >=3.6

# physunits

Simple package with common physical unit definitions. 

## Installation

## Usage

An example usage of this package is:
``from physunits import *``
which updates the globals dict with all ``physunits`` definitions. If only a set of units is desired, manually importing them as:
``from physunits import mm, nm, um``
is sufficient. 

## Contatct and support
trkir-rpi-usb-camera 0.57
Raspberry PI script for PT FLIR and IR cameras

**Raspberry PI script for PT Flir and IR cameras**


**Prerequirements**
- python3
- pip3

**Installation**
- `sudo pip3 install trkir-rpi-usb-camera`
- `sudo trkir-usb-install`

**Use**
- `trkir-usb-camera --show 1 --fps 1` to run camera script
- `trkir-camera-devices` to get available cameras and its usb ports
- `trkir-rpi-ip` to get raspberrypi ip
- `trkir-usb-reset` to reset usb hub (power cycle usb hub connection)

P.S 
- show - [number] 1 if show image from cameras, 0 or empty if don't, default is 0
- fps - [number] fps setting, default is 1
csvbase 0.2.0
A simple database with csv files

# csvbase

A simple database with csv files.

## Installation and Usage

Use `pip install csvbase` or `pip3 install csvbase` to install.

## Usage

Learn how to use this package with these [examples](https://github.com/bentoavb/csvbase/tree/master/examples).

## License

MIT License

Copyright (c) 2019 Anderson Vitor Bento
mango-coding-test-pl-sept20 2.0.0
Solutions to Mango's Python Coding Test

# mango_test

Package containing Peter Ling's solutions to the Mango Python coding test, September 2020.

## Installation

The package can be installed from PyPI via the command line by typing
`pip install mango_coding_test_pl_sept20`.

## Usage

The solutions can then be used in a Python 3 environment after using the import statement
`from mango_test import mango_test as mt`

#### Functions
The 'Functions' part of the exercise is called with the `random_draw(nsamples, dist, **params)` function, where `params` relate to the given distribution.

##### Examples
To return a 10 random samples (as a numpy array) from a Normal distribution with mean 100 and standard deviation 10:

    mt.random_draw(10, 'normal', mean=100, sd=10)

To return a 20 random samples from a Binomial distribution with _n_=100, _p_=0.5:

    mt.random_draw(20, 'binomial', n=100, p=0.5)

To return a 100 random samples from a Poisson distribution with $\lambda$=100:

    mt.random_draw(100, 'poisson', lam=100)

#### Object-oriented Programming
The 'Object-oriented Programming' part of the exercise is called with the `Sample` class.

##### Example
To create an instance of the `Sample` class, using the Normal distribution:

    s = mt.Sample('normal')

To set the parameters of `s` (mean and standard deviation):

    s.mean = 100
    s.sd = 100

To draw a 1000 samples from `s`, then summarise:

    s.draw(1000)
    s.summarise()