semvec/pypi-packages huggingface dataset:
arifETS 0.1.1
A simple expense tracking package
Requires Python: >=3.6

# arifETS

arifETS is a Python package designed for tracking and analyzing expenses. It reads expense data from a CSV file (please provide path of your CSV file through terminal command or in your code)
Calculates total and average expenses, categorizes expenses by category, and plots the expenses using Matplotlib.

## Installation

You can install arifETS from PyPI using pip:

```bash
pip install arifETS
arifETS /path

Sample CSV Format
date,category,description,amount
2023-01-01,Food,Groceries,50.00
2023-01-02,Transport,Bus ticket,2.50
2023-01-03,Entertainment,Movie,12.00
2023-01-04,Food,Restaurant,30.00
2023-01-05,Bills,Electricity,100.00

(date,category,description,amount) these are main data headings please add enteries in your CSV according to it.

For reference CSV please visit 
https://github.com/Arifs75/expense/blob/main/expense.csv
torch-complex 0.4.4
A fugacious python class for PyTorch-ComplexTensor

# pytorch_complex

[![PyPI version](https://badge.fury.io/py/torch-complex.svg)](https://badge.fury.io/py/torch-complex)
[![Python Versions](https://img.shields.io/pypi/pyversions/torch-complex.svg)](https://pypi.org/project/torch-complex/)
[![Downloads](https://pepy.tech/badge/torch-complex)](https://pepy.tech/project/torch-complex)
[![Build Status](https://travis-ci.org/kamo-naoyuki/pytorch_complex.svg?branch=master)](https://travis-ci.org/kamo-naoyuki/pytorch_complex)
[![codecov](https://codecov.io/gh/kamo-naoyuki/pytorch_complex/branch/master/graph/badge.svg)](https://codecov.io/gh/kamo-naoyuki/pytorch_complex)

A temporal python class for PyTorch-ComplexTensor


## What is this?
A Python class to perform as `ComplexTensor` in PyTorch: Nothing except for the following,

```python
class ComplexTensor: 
    def __init__(self, ...):
        self.real = torch.Tensor(...)
        self.imag = torch.Tensor(...)
```

### Why?
PyTorch is great DNN Python library, except that it doesn't support `ComplexTensor` in Python level.

https://github.com/pytorch/pytorch/issues/755

I'm looking forward to the completion, but I need `ComplexTensor` for now.
 I created this cheap module for the temporal replacement of it. Thus, I'll throw away this project as soon as  `ComplexTensor` is completely supported!

## Requirements

```
Python>=3.6
PyTorch>=1.0
```

## Install

```
pip install torch_complex
```

## How to use

### Basic mathematical operation
```python
import numpy as np
from torch_complex.tensor import ComplexTensor

real = np.random.randn(3, 10, 10)
imag = np.random.randn(3, 10, 10)

x = ComplexTensor(real, imag)
x.numpy()

x + x
x * x
x - x
x / x
x ** 1.5
x @ x  # Batch-matmul
x.conj()
x.inverse() # Batch-inverse
```

All are implemented with combinations of computation of `RealTensor` in python level, thus the speed　is not good enough.


### Functional

```python
import torch_complex.functional as F
F.cat([x, x])
F.stack([x, x])
F.matmul(x, x)  # Same as x @ x
F.einsum('bij,bjk,bkl->bil', [x, x, x])
```

### For DNN
Almost all methods that `torch.Tensor` has are implemented. 

```python
x.cuda()
x.cpu()
(x + x).sum().backward()
```