semvec/pypi-packages huggingface dataset:
ravf 1.0.1
Raw Astro Video Format - File format designed to store raw frames from a camera in realtime primarily for Occultations
Requires Python: >=3.7

# Raw Astro Video Format (RAVF) Package

## Introduction

Python implementation of a reader/writer for the Raw Astro Video Format.

Specification: [RawAstroVideoFormat.md](https://github.com/ChasinSpin/ravf/blob/main/docs/RawAstroVideoFormat.md)

RAVF is loosely based on the [Astro Digital Video](http://www.hristopavlov.net/adv/index.html) specification with an emphasis on writing in a variety of raw formats natively on CPU-bound hardware, where the goal is to get the data written quickly at higher frame rates sometimes needed for occultations.

Install:

	pip install ravf

Upgrade:

	pip install --upgrade ravf
fastapi-framework 1.5.3.5
A FastAPI Framework for things like Database, Redis, Logging, JWT Authentication and Rate Limits
Requires Python: >=3.6

[![Codestyle](https://github.com/Tert0/fastapi-framework/actions/workflows/codestyle.yaml/badge.svg)](https://github.com/Tert0/fastapi-framework/actions/workflows/codestyle.yaml)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
[![Tests](https://github.com/Tert0/fastapi-framework/actions/workflows/tests.yaml/badge.svg)](https://github.com/Tert0/fastapi-framework/actions/workflows/tests.yaml)
[![codecov](https://codecov.io/gh/Tert0/fastapi-framework/branch/develop/graph/badge.svg?token=G7NH4651MB)](https://codecov.io/gh/Tert0/fastapi-framework)
[![PyPI version](https://badge.fury.io/py/fastapi-framework.svg)](https://badge.fury.io/py/fastapi-framework)
[![PyPI Downloads](https://img.shields.io/pypi/dm/fastapi-framework.svg)](https://pypi.org/project/fastapi-framework)


A FastAPI Framework for things like `Database`,
`Redis`, `Logging`, `JWT Authentication`, `Rate Limits` and `Sessions` 

# Install

You can install this Library with:

```shell
pip install fastapi-framework
```

# Documentation

For the Documentation click [here](https://tert0.github.io/fastapi-framework)

# Features
- ✅ Database
- ✅ Redis
- ✅ Redis Publish/Subscribe
- ✅ FastAPI JWT
- ❌ FastAPI Permission System (for routes)
- ✅ Rate Limits
- ✅ Config Parser
- ✅ Session System


# License

This Project is Licensed under the [MIT License](https://mit-license.org/)
NIDAQUSBDriver 0.11.0
Library for interfacing with NIDAQ devices

# NIDAQ-driver
 Python library for NI DAQ modules

To use, include NIDAQClient.py in your project.

# Example
```python
from NIDAQClient import NIDAQVoltage
from NIDAQClient import NIDAQThermo

# Create an instance of the client for thermocouple reading.
# This thermocouple module is installed in position 2 in the NI DAQ cage:
daq = NIDAQThermo(position=2, thermocouple_type='J')
daq.start()

# Here, use the `read_samples` method whenever you need to read from the channels.
data = daq.read_samples()

# Print the data here in the console
print("Received Thermocouple Data:")
print(data)

daq.stop()
daq.close()



# Now, read a voltage sensor installed in the NI cage in position 4:
daq = NIDAQVoltage(position=4)
daq.start()

# Here, use the `read_samples` method whenever you need to read from the channels.
data = daq.read_samples()

# Print the data here in the console
print("Received Voltage Data:")
print(data)

daq.stop()
daq.close()
```

The output will look something like this:
```
Received Thermocouple Data:
[1376.34 1376.34 1376.34 1376.34 1376.34 1376.34 1376.34 1376.34]
Received Voltage Data:
[3.27506 3.27454 3.31136 3.29971 3.32641 3.30917 3.33307 3.3026  3.16697
 3.15747 3.18815 3.15947 3.19679 3.17848 3.19905 3.16965 3.25606 3.25605
 3.29187 3.28341 3.30896 3.29355 3.31831 3.29019 3.16315 3.15645 3.18703
 3.16075 3.19613 3.17977 3.19904 3.1716 ]
 ```

 # Usage

 The NI DAQ 'cards' must be addressed by their position in the cage. The cage positions are represented in the ASCII art below:

 ```
 -----------------------------------------------
|             |       |       |       |       |
|  National   |   P   |   P   |   P   |   P   |
| Instruments |   O   |   O   |   O   |   O   |
|             |   S   |   S   |   S   |   S   |
|   NI cDAQ   |   I   |   I   |   I   |   I   |
|             |   T   |   T   |   T   |   T   |
|             |   I   |   I   |   I   |   I   |
|             |   O   |   O   |   O   |   O   |
|  []  USB    |   N   |   N   |   N   |   N   |
|  []  Power  |       |       |       |       |
|             |   1   |   2   |   3   |   4   |
|             |       |       |       |       |
-----------------------------------------------
```

For example if a thermocouple card is inserted in position 3, you must initalize the reader as
```python
daq = NIDAQThermo(position=2, thermocouple_type='J')
```

You can read the samples from the card with the following:
```python
data = daq.read_samples()
```

This code will return a list of values from the card. Thermocouple cards will return eight values, relating to thermocouple channels 0 through 7. Voltage cards will return thirty-two values, relating to channels 0 through 31. You can pull these values into the rest of your code to save or transmit to other parts of your code.
patternengine 0.0.6
patternengine

# patternengine

## tl;dr

A bullet hell pattern engine

Consult the embedded docs for class descriptions and usage.

Check out [the demo](https://github.com/dickerdackel/patternengine-demo).
Watch [the demo running](https://youtu.be/HtlNjl8-Zd0?si=HTU4GRDHWqJFArzt)

Look into src/patternengine/example.py

Preparation

```py
########################################################################
# Relevant part here
#
# A "ring" of 180 degrees aiming towards the top devided int 18 parts
# A heartbeat over 5 seconds with 4 emits and 4 gaps
# A BulletSource fetching 18 bullets at every heartbeat
#

ring = pe.Ring(100, 18, aim=-90, width=180)
heartbeat = pe.Heartbeat(5, '#.#.#.#.')
emitter = pe.BulletSource(18, ring, heartbeat)

#
########################################################################
```

Later in the game loop

```py

####################################################################
# Relevant part here
#
for bullet in next(emitter):
    Bullet(group,
	   position=bullet[0] + anchor,
	   momentum=bullet[1] * bullet_speed,
	   lifetime=10)
#
####################################################################
```

with Bullet being a simple subclass of pygame.sprite.Sprite

## Tutorial

Read TUTORIAL.md for exhaustive examples of all the parts.

Additionally, study the sources of the included tutorial scripts and the
demos, both in the package folder.
datastand 2.5.0
A python package to help Data Scientists, Machine Learning Engineers and Analysts better understand data. Gives quick insights about given data; general dataset statistics, shape of dataset, number of unique data types, number of numerical and non-numerical columns, missing data statistics, missing data heatmap and provides methodology to impute missing data.
Requires Python: >=3.9,<3.13

# datastand

-----------
![package logo](images/logo.jpg)
Why datastand? __Data + Understand__  
A python package to help Data Scientists, Machine Learning Engineers and Analysts better understand data. Gives quick insights about a given dataset.  


------------------

# Installation
Run the following command on the terminal to install the package:
```python
pip install datastand
```
### Usage :
Code:
```python
from datastand import datastand
import pandas as pd

df = pd.read_csv("path/to/target/dataframe")

datastand(df)

```
Output:
```python

General stats:
==================
Shape of DataFrame: (1202, 13)
Number of unique data types : {dtype('int64'), dtype('O')}
Number of numerical columns: 2
Number of non-numerical columns: 11


Missing data:
=======================
DataFrame contains 2670 missing values (17.09%) as follows column-wise:
-----------------------------------------------------------------------
Gender                 41
Car_Category          372
Subject_Car_Colour    697
Subject_Car_Make      248
LGA_Name              656
State                 656
dtype: int64
-----------------------------------------------------------------------

Do you wish to long-list missing data statistics?(y/n): y
.
.
.
```
Code:
```python
# This function is already available in the DataStand class and also available separately
# Here we're running it separately 
from datastand import plot_missing

plot_missing(df)

```
Output:

![missing data heatmap](images/missing_data_heatmap.png)

Code:
```python
from datastand import impute_missing

impute_missing(df)

```
Output:
```python
Imputing missing data...
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 80/80 [00:02<00:00, 30.52it/s]
Imputation complete.
```
## Author/Maintainer
**Vincent N.**
[[LinkedIn]](https://www.linkedin.com/in/vincent-njonge-528070178)  [[Twitter]](https://twitter.com/lyraxvincent)