semvec/pypi-packages huggingface dataset:
Archiy 1.0.1
Aerospace Mathematics
Keywords: python

# Archy

 This is a  python package is created to facilitate calculations in the field of aerospace Mathematics Engineering in particular and mathematics geometry in general
As it can calculate the area of shapes and their perimeter with several types of input

[Archiy](https://pypi.org/project/Archiy/1.0.0/)

## shapes:

- **Point** : accept arguments : a , b represented the point coordinates in the orthogonal and homogeneous parameters  
 
 This class contain ` __init__`, `__repr__` and 'distance' methods, where the distance method take a point object as parameter to calculate the distance between the points.

 Example :

```python
p1 = Point(1, 30)
p2 = Point(4, 80)

p1.distance(p2)

```

- **Square** : Square class have 2 types of declaration :

  - init \* args:
    height: height of the rectangle
    width: width

  - init \*\* kwargs:
    points: a, b, c, d

This class contain ` __init__` and `__repr__` and other methods :

- perimetre() : this method calculate Square perimetre.
- surface() : this method calculate Square surface.
- diagonal() : this method calculate Square diagonal.

Example :

```python
from archy import  Point, Square


p1 = Point(1, 30)
p2 = Point(4, 80)
p3 = Point(6, 20)
p4 = Point(8, 10)


### Square ###
S1 = Square(4)
S2 = Square(a=p1, b=p2, c=p3, d=p4)
print(f"Square Side > {S1} \n")
print(f"Square side Diagonal > {S1.diagonal()}\n")
print(f"Square side surface > {S1.surface()}\n")
print(f"Square side perimetre > {S1.perimetre()}\n")

```

- **Triangle** : Triangle class have 2 types of declaration :

  - init \* args:
      accept 3 params type(int) representing side1, side2, side3

      example:
      ```python
        T = Triangle(5, 2, 3)
      ```
      
  <br/>  
  
  - init \*\* kwargs:
      accept 3 params (a,b,c) type(Point) representing side1, side2, side3

      example:
      ```python
      from archiy import Point, Triangle
      p1 = Point(1, 30)
      p2 = Point(4, 80)
      p3 = Point(6, 20)

      T = Triangle(a=p1, b=p2, c=p3)

      ```
This class contain ` __init__` , `__repr__` and 'perimetre'.

  The perimetre method allow to calculate the triangle perimeter :


  ```python

      from archiy import Triangle
      T = Triangle(1,2,10)
      T1.perimetre() 

```

- **Rectangle** : Rectangle class have 2 types of declaration :
    - init \* args: accept 2 params : (height,width)
        
        example:
        ```python
        from archiy import
        R = Rectangle(5,2)
        ```
    - init \*\* kwargs: accept 4 params (a,b,c,d) type(Point)

        example:
        ```python
        from archiy import Point, Rectangle
        p1 = Point(1, 30)
        p2 = Point(4, 80)
        p3 = Point(6, 20)
        p4 = Point(8, 10)

        ### Rectangle ###
        R = Rectangle(5, 5)
        print(f"Rectangle > {R}")
        print(f"Rectangle Surface > {R.surface()} \n")
        
        ```
vanidl 0.0.8
VaniDL is an tool for analyzing I/O patterns and behavior with Deep Learning Applications.
Requires Python: >=3.6

# VaniDL

VaniDL is an tool for analyzing I/O patterns and behavior with Deep Learning Applications. It analyzes Darshan Extended traces to extract various I/O patterns with Deep Learning applications. The tool at it core uses vanidl which converts Darshan Profiler’s trace into knowledge for analysis. It is designed to provide low-level I/O behavior details to tensorflow applications in HPC. 

**VaniDL features include:**

-   Easy-to-use and understand high-level API for extracting I/O behavior of the applications.
-   Fast prototyping through highly modular data representation through pandas for easy plotting of graphs.
-   Full transparency over profiling data with access to internal data structures such as timeline of applications, aggregation functions, and drill up/down data views.
-   Powerful helper functions to build a visual understanding of how applications perform I/O such as request distributions, file access pattern, and extracting file specific summaries.
-   Easy to use File Summary and Job Summary extractors for understanding the data consumed by Deep Learning Applications
 

## Overview
```python
#Initialize class
profile = DLProfile()
#Load darshan file
status = profile.Load("./run1.darshan")
#Get Job Summary
summary = profile.GetSummary()
```

```python
#Application Timeline of data operations
tl = profile.CreateIOTimeline()
plt.figure(figsize=(20,4))
plt.grid()
plt.plot(tl['time_step'], tl['operation_count']);
```
![timeline](./images/IMG_0097.jpg)

More examples are [here](https://github.com/hariharan-devarajan/vanidl/tree/master/examples)

## Installation

### Requirements
- numpy==1.18.5
- pandas==1.0.4
- h5py==2.10.0
- tensorflow~=2.2.0

**VaniDL Installation**

To install VaniDL, the easiest way is to run

For the bleeding edge version (recommended):

```bash
pip install git+https://github.com/hariharan-devarajan/vanidl.git
```
For the latest stable version:
```bash
pip install vanidl
```
Otherwise, you can also install from source by running (from source folder):
```bash
python setup.py install
```
### On Theta
```bash
module load VaniDL
```
## Getting Started
See _[Getting Started with VaniDL](https://github.com/hariharan-devarajan/vanidl/wiki/Getting-Started)_ to learn about VaniDL basic functionalities or start browsing _[TFLearn APIs](https://github.com/hariharan-devarajan/vanidl/wiki/API)_.

### Examples
There are many examples of analysis available, see _[Examples](https://github.com/hariharan-devarajan/vanidl/tree/master/examples)_.

## Contributions
This is the first release of VaniDL, if you find any bug, please report it in the GitHub issues section.

Improvements and requests for new features are more than welcome! Do not hesitate to twist and tweak VaniDL, and send pull-requests.

For more info:  _[Contribute to VaniDL](https://github.com/hariharan-devarajan/vanidl/wiki/contributions)_.
## License

MIT License
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helloworld-lliang17 0.0.1
Say hello!

# Hello World

This is an example project demonstrating how to publish a python module to PyPI.

## Installation

Run the following to install:

```python
pip install helloworld
```

## Usage

```python
from helloworld import say_hello

# Generate "Hello, World!"
say_hello()

# Generate "Hello, Everybody!"
say_hello("Everybody")
```

# Developing Hello World

To install helloworld, along with the tools you need to develop and run tests, run the following in your virtualenv:

```bash
$ pip install -e .[dev]
```