semvec/pypi-packages huggingface dataset:
rdcpy 0.1.0
Пакет для работы на платформе НЦСЭД.
Requires Python: >=2.7

---
id: 01JF8KXT59GBZAVMJMQB1FD8N4
---
# rdcpy

Пакет, создаваемый для работы с данными на платформе Национального центра социально-экономических данных (НЦСЭД).

### Features

- Препроцессинг данных.
- Исследовательский анализ данных.

### Usage

```
import rdcpy

# Коррекция названий переменных в соответствии с PEP8.
rdcpy.dt_rename_pep(df)  # returns dataframe 

```
aiosqs 1.0.6
Python asynchronous and lightweight SQS client.
Requires Python: >=3.8
Keywords: aws, sqs

# aiosqs

[![pypi](https://img.shields.io/pypi/v/aiosqs.svg)](https://pypi.org/project/aiosqs/)
![PyPI - Downloads](https://img.shields.io/pypi/dm/aiosqs)

### About package

Python asynchronous and lightweight SQS client. The goal of this library is to provide fast and optimal access to SQS 
for Python projects, e.g. when you need a high-load queue consumer or high-load queue producer written in Python.

Supports Python versions 3.8, 3.9, 3.10, 3.11, 3.12.

Supported and tested Amazon-like SQS providers: Amazon, VK Cloud.


### Why aiosqs?

Main problem of `botocore` and `aiobotocore` is huge memory and CPU consumption.
Also `aiobotocore` itself is a transition of `botocore` to async interface without any optimizations.

Related issues:
- https://github.com/boto/boto3/issues/1670
- https://github.com/aio-libs/aiobotocore/issues/940
- https://github.com/aio-libs/aiobotocore/issues/970


### Installation

```shell
pip install aiosqs
```


### Usage

Create a client:
```python
from aiosqs import SQSClient

client = SQSClient(
    aws_access_key_id="access_key_id",
    aws_secret_access_key="secret_access_key",
    region_name="us-west-2",
    host="sqs.us-west-2.amazonaws.com",
)
```

Receive the queue url by queue name:
```python
response = await client.get_queue_url(queue_name=queue_name)
queue_url = response["QueueUrl"]
```

Send a message to the queue:
```python
response = await client.send_message(
    queue_url=queue_url,
    message_body=json.dumps({"demo": 1, "key": "value"}),
    delay_seconds=0,
)
print(response)
```

Receive a message from the queue:
```python
response = await client.receive_message(
    queue_url=queue_url,
    max_number_of_messages=1,
    visibility_timeout=30,
)
if response:
    print(response)
    receipt_handle = response[0]["ReceiptHandle"]
```

Delete the message from the queue:
```python
await client.delete_message(
    queue_url=queue_url,
    receipt_handle=receipt_handle,
)
```

Close the client at the end:
```python
await client.close()
```

Another option is to use `SQSClient` as an async context manager. No need to call `close` manually in this case. Example:
```python
from aiosqs import SQSClient

async with SQSClient(
    aws_access_key_id="access_key_id",
    aws_secret_access_key="secret_access_key",
    region_name="us-west-2",
    host="sqs.us-west-2.amazonaws.com",
) as client:
    response = await client.get_queue_url(queue_name="dev_orders")
    queue_url = response["QueueUrl"]
    print(queue_url)
```
torch-mdct 0.4.1
A PyTorch implementation of the Modified Discrete Cosine Transform (MDCT) and its inverse for audio processing.
Requires Python: >=3.8
Keywords: audio, imdct, mdct, pytorch, signal processing

# torch_mdct

A PyTorch implementation of the Modified Discrete Cosine Transform (MDCT) and its inverse for audio processing.

## Installation 

```bash
pip install torch_mdct
```

## Usage

```python
import torchaudio
from torch_mdct import IMDCT, MDCT, kaiser_bessel_derived, vorbis

# Load a sample waveform 
waveform, sample_rate = torchaudio.load("/path/to/audio.file")

# Initialize the mdct and imdct transforms
mdct = MDCT(win_length=1024, window_fn=vorbis, window_kwargs=None, center=True)
imdct = IMDCT(win_length=1024, window_fn=vorbis, window_kwargs=None, center=True)

# Transform waveform into mdct spectrogram
spectrogram = mdct(waveform)

# Transform spectrogram back to audio 
reconst_waveform = imdct(spectrogram)

# Compute the differences
print(f"L1: {(waveform - reconst_waveform).abs().mean()}")
```

## References 
[[1]](https://github.com/zafarrafii/Zaf-Python) Zaf-Python: Zafar's Audio Functions in **Python** for audio signal analysis.

[[2]](https://github.com/nils-werner/mdct) MDCT: A fast MDCT implementation using SciPy and FFTs.
subrip-sync 0.1.2
Sync SubRip subtitle with audio
Requires Python: >=3.6

# Sync SubRip subtitle with audio
```
> subrip-sync -h
usage: subrip-sync [-h] [--backup | --no-backup] filename lag

Sync SubRip subtitle with audio

positional arguments:
  filename              SubRip subtitle file (.srt). UFT-8 encoding
  lag                   lag in milliseconds. eg: 220, -150, +350

options:
  -h, --help            show this help message and exit
  --backup, --no-backup
                        create a backup file (.bak), default
```

## run test using uv
```
uv run python -m unittest discover -s src
```

## run program using uv
```
uv run subrip-sync -h
```

## build program using uv
```
> uv build
Building source distribution...
Building wheel from source distribution...
Successfully built dist\subrip_sync-0.1.0.tar.gz
Successfully built dist\subrip_sync-0.1.0-py3-none-any.whl
```

see https://docs.astral.sh/uv/