semvec/pypi-packages huggingface dataset:
akasaka 0.1.6
Dynamic mutiprocess preprocessing task loader and dispatcher
Requires Python: >=3.6

# Akasaka

Dynamic mutiprocess preprocessing task loader and dispatcher.

To be brief, akasaka enables you to write a task class and run it in parallel with a simple command. It features

* Argument parsing through `argparse`
* Cache management, i.e. you can override `is_executed` to check if the task is already executed
* Automatic parallelization
* Some helper functions to make your life easier (to be added later)

## Install

```bash
pip install akasaka
```

## Usage

```bash
$ akasaka -H
usage: akasaka [--num_process NUM_PROCESS] [--chunksize CHUNKSIZE] [--devices DEVICES [DEVICES ...]] [-h] [-H] module_path

Dynamically load a Python class from a module.

positional arguments:
  module_path           The module path in the format "module.submodule.ClassName"

optional arguments:
  --num_process NUM_PROCESS
                        Number of processes to use, for normal task (default: number of CPU cores)
  --chunksize CHUNKSIZE
                        Number of tasks to be sent to a worker process at a time, for normal task (default: 1)
  --devices DEVICES [DEVICES ...]
                        The devices to run the task on, for torch task (default: [])
  -h, --help            Show help message for task and exit
  -H, --hel-akasaka     Show Akasaka help message and exit
```

Write a task class of the subclass of `AsakasaTask` and implement needed methods. Take a look at the examples in the [`examples` directory](./examples).

For PyTorch CUDA tasks, you would need to use `AsakasaTorchTask` as the base class. The number of processes is determined by the number of CUDA devices provided.

With akasaka installed, to run the examples, try following commands:

```bash
cd examples
# print.py
akasaka print.PrintTaskTest --test test_string
# directory_example/print.py
akasaka directory_example.print.PrintTaskTest --test test_string
```

## Development

To install from source, clone the repository and run

```bash
pip install -e .
```

## Known Issues

For torch (CUDA) task, you might need to wait for unexpected long time after model loaded before the task can start. Reason to be figured out.
gerritParse 0.6.0
This is a customized parser specific for code review information from gerrit.
Requires Python: >=3.5

Commands for installation:
    pip install gerritParse-*.tar.gz
    pip install gerritParse-*-py3-none-any.whl

Examples:
    from gerritParse.gerritdataparser import GerritDataParser
    ...
    parse_data.py
faye 0.1.11
Basic logging library with a customizable log message and progress bar!
Requires Python: >=3.6

<img src="https://raw.githubusercontent.com/battleoverflow/faye/main/assets/faye.jpg" />

Basic logging library with a customizable log message and progress bar!

The best way to utilize the Faye library is by installing via `pip`. You can install the library by running the following command:
```bash
pip install faye
```

## Usage
The library is currently limited due to it being updated on a necessity basis, but there are a few examples below to help with using it.

```python
# Import the Faye library
from faye import Faye

# Prints a "CRITICAL" log message to stdout
print(Faye.log(msg="Faye is awesome!", level="CRITICAL"))

# You can also use integer values for the level
print(Faye.log(msg="Faye is awesome!", level=5))

# Currently supported levels:
# - TRACE (0)
# - DEBUG (1)
# - INFO (2)
# - WARNING (3)
# - ERROR (4)
# - CRITICAL (5)

# You can add a splash of color to the logging as well
print(Faye.log(msg="Faye is awesome!", level="CRITICAL", color=True))

# Prints a basic progress bar to stdout
print(Faye.progress(total=100, description="Downloading..."))
```
polars-access-mdbtools 0.0.2
A library for reading tables from an Access database into Polars dataframes, using mdbtools
Requires Python: >=3.8
Keywords: access, database, dataframe, mdbtools, polars

# polars_access_mdbtools
A library for reading tables from an Access database into Polars dataframes, using mdbtools

## What is this?

A tiny, `subprocess`-based tool for reading a 
[MS Access](https://products.office.com/en-us/access) 
database (`.rdb`) as a [Python Polars Dataframe](https://docs.pola.rs).

## Installation

To read the database, this package thinly wraps 
[MDBTools](https://github.com/mdbtools/mdbtools).

If you are on `OSX`, install it via [Homebrew](http://brew.sh/):

```sh
$ brew install mdbtools
```

If you are on `Debian`, install it via apt:
```sh
$ sudo apt install mdbtools
```

If you are on `Windows`, it's a little tougher. Install `mdbtools` for [Windows](https://github.com/lsgunth/mdbtools-win). Manually add to PATH.
1. Download the mdb-tools files from Windows link above. Visit the Releases section, then downloadi the part that says "Source Code (zip)".
2. Extract that to somewhere like `C:/bin/mdbtools-win/mdbtools-win-1.0.0`.
3. Follow these instructions to [add that folder to your environment path](https://linuxhint.com/add-directory-to-path-environment-variables-windows/) (Method 1, but use the path to the mdbtools executable files).
4. Restart your computer or just close and re-open the program you're running it from. You can test that it works by opening a terminal and running `mdb-tables --help` and see that it doesn't fail.

Finally, on all OS's:
```sh
$ pip install polars_access_mdbtools
```

## Example Usage

```python
import polars as pl
import polars_access_mdbtools as pl_access

file_path = 'path_to_file.mdb'
pl_access.list_table_names(file_path)

df = pl_access.read_table(file_path, table_name='your_table_name')

```

## Acknowledgements

This code is based heavily on [jbn's `pandas_access` library](https://github.com/jbn/pandas_access).

## To Do

- [ ] unit tests
- [ ] better error messages when subsections (e.g., schema fetching) fail

## Contributing

Please Star this repo. 

Please submit bug reports as GitHub Issues. Feel free to submit a PR to fix an issue!
cdp-monitoring 0.2.0
cdp monitoring python sdk
Requires Python: >=3.8

# cdp-monitoring

fastapi prometheus + opentelemetry sdk 

## Install package
```shell  
VERSION=0.1.0
pip install git+https://github.daumkakao.com/cdpdev/cdp-monitoring.git#${VERSION}
```

## Usage  
### 1. Tempo url 을 환경변수로 설정  
설정되어 있지 않은 경우 모니터링도 비활성화  
```shell
export TEMPO_URL=""
```

### 2. 모니터링 대상 FastAPI 앱을 init_monitoring 함수의 인자로 전달
```python
# app 생성
from fastapi import FastAPI
app = FastAPI(title="My FastAPI App")

# 모니터링 설정 적용
from cdp_monitoring import init_monitoring
init_monitoring(app)
```

## Uninstall package  
```shell
pip uninstall cdp-monitoring
```