semvec/pypi-packages huggingface dataset:
area-and-volume-calculator 0.0.1
Area and Volume Calculator

This python package is created to be used to compute the area ad volume of 2d and 3d shapes

The main purpose of the package is:
1) To help individual having not to remember the formula of area and volume for ever 2d and 3d shapes
2) To allow people to compute faster directly by feeding the data directly in the package
3) lastly to reduce the human error when calculating the areas and volume of various shapes.

Their are many shapes i have covered in the package to allow every individual compute without restricting the package to only basic shapes

check out the list of shapes i have covered in the package
FOR 2D SHAPES:
    1) Circle
    2) Rectangle
    3) Square
    4) Triangle
    5) Ellipe
    6) Rhombus
    7) Parellelogram
    8) Trapezium
    9) Octagon
    10) Pentagon
    11) Hexaggon

FOR 3D SHAPES:
    1) Cone
    2) Cylinder
    3) Sphere
    4) Hemisphere
    5) Regular Tetrahedron
    6) Octahedron
    7) Cube
    8) Cuboid
    9) Frustrum
    10) Hexagonal_Pyramid
    11) Right_Regular_Hexagonal_Prism
    12) Torus
    13) Right_Pentagonal_Pyramid
    14) Right_Regular_Pentagonal_Prism
    15) Triangular_Prism
    16) Right_Square_Pyramid
    17) Dodecahedron
    18) Icosahedron
library_template 0.1.0
Let's build a new CLI application.
Requires Python: >=3.8

# library_template

> My template for small libraries and command-line tools.

[![PyPI Version][pypi-image]][pypi-url]
[![Build Status][build-image]][build-url]
[![Code Coverage][coverage-image]][coverage-url]
[![Code Quality][quality-image]][quality-url]

## Installation

```sh
pip install library_template
```

<!-- Badges -->

[pypi-image]: https://img.shields.io/pypi/v/library_template
[pypi-url]: https://pypi.org/project/library_template/
[build-image]: https://github.com/0x29a/library_template/actions/workflows/build.yml/badge.svg
[build-url]: https://github.com/0x29a/library_template/actions/workflows/build.yml
[coverage-image]: https://codecov.io/gh/0x29a/library_template/branch/master/graph/badge.svg
[coverage-url]: https://codecov.io/gh/0x29a/library_template
[quality-image]: https://api.codeclimate.com/v1/badges/3af8e49ce0ce13dca358/maintainability
[quality-url]: https://codeclimate.com/github/0x29a/library_template
area-volume-calculator 0.0.2
Area and Volume Calculator

This python package is created to be used to compute the area ad volume of 2d and 3d shapes

The main purpose of the package is:
1) To help individual having not to remember the formula of area and volume for ever 2d and 3d shapes
2) To allow people to compute faster directly by feeding the data directly in the package
3) lastly to reduce the human error when calculating the areas and volume of various shapes.

Their are many shapes i have covered in the package to allow every individual compute without restricting the package to only basic shapes

check out the list of shapes i have covered in the package
FOR 2D SHAPES:
    1) Circle
    2) Rectangle
    3) Square
    4) Triangle
    5) Ellipe
    6) Rhombus
    7) Parellelogram
    8) Trapezium
    9) Octagon
    10) Pentagon
    11) Hexaggon

FOR 3D SHAPES:
    1) Cone
    2) Cylinder
    3) Sphere
    4) Hemisphere
    5) Regular Tetrahedron
    6) Octahedron
    7) Cube
    8) Cuboid
    9) Frustrum
    10) Hexagonal_Pyramid
    11) Right_Regular_Hexagonal_Prism
    12) Torus
    13) Right_Pentagonal_Pyramid
    14) Right_Regular_Pentagonal_Prism
    15) Triangular_Prism
    16) Right_Square_Pyramid
    17) Dodecahedron
    18) Icosahedron
area-and-volume-calculator-for-2d-and-3d-shapes 0.1
Area and Volume Calculator

This python package is created to be used to compute the area ad volume of 2d and 3d shapes

The main purpose of the package is:
1) To help individual having not to remember the formula of area and volume for ever 2d and 3d shapes
2) To allow people to compute faster directly by feeding the data directly in the package
3) lastly to reduce the human error when calculating the areas and volume of various shapes.

Their are many shapes i have covered in the package to allow every individual compute without restricting the package to only basic shapes

check out the list of shapes i have covered in the package
FOR 2D SHAPES:
    1) Circle
    2) Rectangle
    3) Square
    4) Triangle
    5) Ellipe
    6) Rhombus
    7) Parellelogram
    8) Trapezium
    9) Octagon
    10) Pentagon
    11) Hexaggon

FOR 3D SHAPES:
    1) Cone
    2) Cylinder
    3) Sphere
    4) Hemisphere
    5) Regular Tetrahedron
    6) Octahedron
    7) Cube
    8) Cuboid
    9) Frustrum
    10) Hexagonal_Pyramid
    11) Right_Regular_Hexagonal_Prism
    12) Torus
    13) Right_Pentagonal_Pyramid
    14) Right_Regular_Pentagonal_Prism
    15) Triangular_Prism
    16) Right_Square_Pyramid
    17) Dodecahedron
    18) Icosahedron
yapic 1.3.2
Yet another Pixel Classifier (based on deep learning)

[![Build Status](https://travis-ci.com/yapic/yapic.svg?branch=master)](https://travis-ci.com/yapic/yapic)
[![PyPI](https://img.shields.io/pypi/v/yapic.svg?color=green)](https://pypi.org/project/yapic/)

<!-- full url is needed for images to be rendered on pypi -->
![DZNE](https://raw.githubusercontent.com/yapic/yapic/master/docs/img/DZNE_CMYK_E.png)<!-- .element height="50%" width="50%" -->

YAPiC is developed by
[Image and Data Analysis Facility](https://www.dzne.de/forschung/core-facilities/image-and-data-analysisfacility/), [Core Reseach Facilities](https://www.dzne.de/forschung/core-facilities/), [DZNE](https://www.dzne.de/en) (German Center for Neurodegenerative Diseases).


# YAPiC - Yet Another Pixel Classifier (based on deep learning)

Check the [YAPiC Website](https://yapic.github.io/yapic/) for documentation,
examples and installation instructions.


## What is YAPiC for?

With YAPiC you can make your own customzied filter (we call it *model* or *classifier*) to enhance a certain structure of your choice.

We can, e.g train a model for detection of oak leafs in color images, and use this oak leaf model to filter out all image regions that are not covered by oak leaves:

![](https://raw.githubusercontent.com/yapic/yapic/master/docs/img/oak_example.png "oak leaf classifier example")

* Pixels that belong to other leaf types
  or to no leafs at all are mostly suppressed, they appear dark in the output image.
* Pixels that belong to oak leafs are enhanced, they appear bright in the output image.

The output image is also called a *probability map*, because the intensity of each pixel corresponds to the probability of the pixel belonging to an oak leave region.

You can train a model for almost any structure you are interested in, for example to detect a certain cell type in histological micrographs (here: purkinje cells of the human brain):

![](https://raw.githubusercontent.com/yapic/yapic/master/docs/img/histo_example.png "purkinje cell classifier example")
*Histology data provided by Oliver Kaut (University Clinic Bonn, Dept. of Neurology)*

We have used YAPiC for analyzing various microscopy image data. Our experiments are mainly related to neurobiology, cell biology, histopathology  and drug discovery (high content screening).
However, YAPiC is a very generally applicable tool and can be applied to very different domains. It could be used for detecting e.g. forest regions in satellite images, clouds in landscape photographs or fried eggs in food photography.