semvec/pypi-packages huggingface dataset:
galcierestore 0.1
a tool for restore objects from aws glacier

# The tool for restore objects from AWS S3 to AWS Glacier
There is no way to restre objects under one folder to AWS S3, this tool can help you do this

## prerequisite
1. 配置 AWS CLI 以及 AK, SK。请参考[这里](https://docs.aws.amazon.com/zh_cn/cli/latest/userguide/cli-configure-quickstart.html#cli-configure-quickstart-config)

## install
```
galcierestore
```
根据命令行提示输入相关信息即可
lexica-arts 0.1.1
A module for generating image in Lexica.arts

Модуль `lexica_arts` предоставляет класс `Image`, который используется для генерации изображений с помощью API-сервиса Lexica. Для использования модуля необходимо установить библиотеки aiohttp и Pillow (библиотека для работы с изображениями).

Класс Image содержит метод generate, который принимает два параметра: `prompt` и `negative`. Параметр `prompt` содержит описание изображения, которое необходимо сгенерировать, а параметр `negative` содержит негативное описание (необязательно).
ByeByeLogger 3.0.0
A simple, yet powerful Python logging library that makes you say goodbye to your standard logger.

# ByeByeLogger

### Oh, great! Another logging package, because obviously, we need more of those, right? Well, let me introduce you to ByeByeLogger, the one that's going to make you say goodbye to your current logger (whether you like it or not).


# Installation
To get this totally groundbreaking package, just do the usual `pip` dance:
```bash
pip install byebyelogger
```
# Features
- Create simple loggers with just a few lines of code
- Customize log output with colors, formats, and callables
- Easily integrate with existing codebases

# Example Usage
## Simple Logger
Create a simple logger with a custom color and level:
```python
from byebyelogger import single_logger
from byebyelogger.style.color import RED
from byebyelogger.style.format import SQUARE_BRACKETS

logger = single_logger(RED, "INFO", SQUARE_BRACKETS)
logger.log("This is an example message.")
```
Output:

```bash
[INFO] This is an example message.
```
## Nested Logger
```python
from byebyelogger import nested_logger
from byebyelogger.style.color import LIGHTCYAN_EX
from byebyelogger.style.format import SQUARE_BRACKETS
from datetime import datetime

def timestamp_func():
    return datetime.now().strftime("%Y-%m-%d %H:%M:%S")

components = [
    (timestamp_func, SQUARE_BRACKETS),
    ("SQLITE", SQUARE_BRACKETS),
    (LIGHTCYAN_EX("INFO"), None),
]

logger = nested_logger(components)
logger.log("This is an example message.")
```
Output:
```bash
[2023-01-01 01:00:00] [SQLITE] INFO This is an example message.
```
# Documentation
For more detailed information on how to use `ByeByeLogger`, please refer to the inline documentation in the package files:

- `logger.py`
- `core/configuration.py`
- `core/stack_info.py`
- `style/color.py`
- `style/format.py`
# License
ByeByeLogger is released under the MIT License.

# Contributing
We welcome contributions to ByeByeLogger! If you'd like to contribute, please fork the repository and submit a pull request. We'll review your changes and merge them in if everything looks good.
marieSimulator 1.1.8
A Python Package to simulate a MARIE architecture on any computer
Requires Python: >=3.6


# MarieSimulator

Simulator for MARIE (Machine Architecture that is Really Intuitive and Easy)

## Code Format

```Python

from marieSimulator import Marie, MarieReader as mr # import the marie and marie reader modules

marie = Marie(mr('trial')) # trial is the file containing Marie instructions
marie.run() # run the program
marie.show() # show memory in hex after running

```

## Trial File

```Text

        Load        X
        Add         One
        Store       X
        Halt
X,      HEX         0000
One,    HEX         0001

```

## GUI Mode

```Python
from marieSimulator import MarieGUI, app
import sys

window = MarieGUI()
window.show()
sys.exit(app.exec())
```

### Supported Opcodes (Instructions)

1. Load X
2. Store X
3. Add X
4. Subt X
5. Input
6. Output
7. Halt
8. Skipcond X
9. Jump X
10. Clear
11. AddI X
12. JumpI X
13. LoadI X
14. StoreI X
15. JnS X
16. HEX X

---

Download the GUI application [here](https://www.aaryandehade.live/Marie%20Simulator.dmg)
Or copy: [https://www.aaryandehade.live/Marie%20Simulator.dmg](https://www.aaryandehade.live/Marie%20Simulator.dmg)
multisimil 0.0.1.1
A python package to find similarity between two images at multiple scales.
Keywords: python,multisimil,land use validation,categorical similarity,multi-scale similarity

## Multisimil
This is a Python package to calculate similarity between two classified images at multiple scales.

The main objective of this package is to enable comparision of actual and predicted land use maps at multiple scales so a more relaxed assesment of accuracy can be done which is not possible with pixel to pixel comarision. It allows for the simulated map to miss the exact location of particular land use by a few pixels (specified by the user), and still will be considered a good prediction.

The module can be installed by:
```Python
! pip install multisimil
```
It is important to understand that in case of pixel to pixel comarision, even if when the entire map is shifted by 1 pixel, it will drastically reduce the accuracy. However, with comparing the frequecy of occurance of different land use classes in a neighborhood, a better comparision can be made.

```Python
import multisimil
from osgeo import gdal

image1 = "path/to/image1.tif"
image2 = "path/to/image2.tif"

rband1 = gdal.Open(image1).GetRasterBand(1)
rband2 = gdal.Open(image2).GetRasterBand(1)

array1 = rband1.ReadAsArray()
array2 = rbdand2.ReadAsArray()

nodata1 = rband1.GetNoDataValue()
nodata2 = rband2.GetNoDataValue()

array1[array1 == nodata1] = 0
array2[array2 == nodata2] = 0

# TO use 9*9 neighborhood, set f = 9
similarity = multisimil.calculate_similarity(array1, array2, f = 9)

```

Similarly, we can calculate similarity for multiple neighborhood filter sizes.

```Python
# This will calculate similarity for neighborhood sizes from 1*1 to 91*91
find_multiresolution_similarity(array1, array2, min_size = 1, max_size = 100, steps = 10)
```
More features to come soon.