semvec/pypi-packages huggingface dataset:
task_5_SYRKIN 0.0.1
This package provides functionality to count unique symbols in a string or file.
Requires Python: >=3.12

# Unique Character Counter

## Overview

This Python package provides a tool for counting unique characters in a string or file. The tool can be useful for text
analysis, file parsing, and general string processing tasks.

## Features

- Count unique characters in a string.
- Count unique characters in a file.
- Customizable options for ignoring case sensitivity or specific characters.

## Installation

Clone the repository and install the required dependencies.

```bash
git clone https://git.foxminded.ua/andrii-syrkin-py_ment/task_5.git
cd unique-character-counter
pip install -r requirements.txt
lgbm-to-code 0.2.1
Convert a trained LGBM instance into conditionals that return the same output as a predict function. Supports javascript, python and C++.
Requires Python: >=3.6

# lgbm-to-code

This package provides functionality to convert trained LightGBM models into native code for different programming languages. This allows you to deploy your models in environments where Python or LightGBM dependencies might not be readily available.

## Installation

```
pip install lgbm-to-code
```

## Usage

```python
import lightgbm as lgb
from lgbm_to_code import lgbm_to_code

# Train your LightGBM model...
# For example:
from sklearn.datasets import load_diabetes
from sklearn.model_selection import train_test_split

diabetes = load_diabetes()
X = diabetes.data
y = diabetes.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
model = lgb.LGBMRegressor(random_state=42)
model.fit(X_train, y_train)

# Convert to desired language
languages = ["python", "cpp", "javascript"]
for language in languages:
    code = lgbm_to_code.parse_lgbm_model(model._Booster, language)
    with open(f"lgbm_model_{language}.{'py' if language == 'python' else language}", "w") as f:
        f.write(code)
```

## Supported Languages

- Python
- C++
- JavaScript 

## Example

```python
import lightgbm as lgb
from sklearn.datasets import load_diabetes
from sklearn.model_selection import train_test_split
from lgbm_to_code import lgbm_to_code

# Load dataset and train model
diabetes = load_diabetes()
X = diabetes.data
y = diabetes.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
model = lgb.LGBMRegressor(random_state=42)
model.fit(X_train, y_train)

# Generate Python code
python_code = lgbm_to_code.parse_lgbm_model(model._Booster, "python")

# Save the code to a file
with open("lgbm_model.py", "w") as f:
    f.write(python_code)

# Now you can use this code in a separate Python environment
```

## Limitations

- Currently, the code generation only supports numerical features. 
- The generated code is not optimized for performance.

## License

[MIT](https://choosealicense.com/licenses/mit/)
lasp_opensearch_data_center 1.0.0rc1
Construct library for creating a CSV ingest pipeline into OpenSearch with a front end website.
Requires Python: <4.0,>=3.9

# LASP OpenSearch Data Center CDK Constructs

A construct library for implementing an OpenSearch data center. This library contains the following constructs:
* BackendStorageConstruct
* BackupVault
* Certificate
* FrontendStorageConstruct
* NetworkingComponents
* Frontend
* OpenSearch
* CloudWatchAlarmConstruct
* Ingest
* DynamoQuery

Example usage: 

```
domain_name = "example.com"
self.frontendStorage = FrontendStorage(
    self, domain_name, construct_id, environment, **kwargs
)

account_type = "dev"
self.backendStorage = BackendStorage(
    self,
    construct_id,
    dropbox_bucket_name=f"{account_type}-example-dropbox",
    ingest_bucket_name=f"{account_type}-example-ingest",
    opensearch_snapshot_bucket_name=f"{account_type}-example-opensearch-manual-snapshot",
)

frontend_bucket = self.frontendStorage.frontend_bucket
self.frontend = FrontEndConstruct(
    self,
    construct_id=construct_id,
    account_type=account_type,
    domain_name=domain_name,
    frontend_bucket=frontend_bucket,
    waf_ip_range="1.1.1.0/24",  # Example IP range
    environment=environment,
)

self.networking = NetworkingComponentsConstruct(self, construct_id)

self.certificate = CertificateConstruct(
    self, "CertificateConstruct", domain_name
)

# TODO: add example usage for constructs as they are completed
```

### Networking Construct
L3 Construct containing a custom VPC with specific subnet configurations.

### Back End Storage Construct
L3 Construct containing the necessary storage infrastructure that supports the back end ingest pipeline and 
Opensearch.

### Ingest Construct
Not yet implemented as a L3 Construct

# Installation

The package is available on PyPI:

```shell
pip install lasp-opensearch-data-center
```

# Roadmap and Plans

- Incorporate the last L3 construct for OpenSearch and data ingest
- Add static code analysis and automated security testing (e.g. Bandit) using Github actions
- Write user documentation for putting these lego blocks together, including example code for Stacks (e.g. an example CDK app)
    - Will include detailed instructions on customizing ingest handler code
- Clean up the docstrings and improve overall code polish
- Write developer documentation for developing further
gdextension-cli 0.0.4
A command line interface for Godot's GDExtension
Requires Python: >=3.8

[![PyPI version](https://badge.fury.io/py/gdextension-cli.svg)](https://badge.fury.io/py/gdextension-cli)

# gdextension-cli

A command line interface for Godot's GDExtension.

My intention with this tool was to create an automated way to create a C++ based GDExtension
project. The process behind `gdextension-cli new <NAME>` is
based [Godot's GDExtension documentation](https://docs.godotengine.org/en/stable/tutorials/scripting/gdextension/gdextension_cpp_example.html).

Therefore, this tool implements a templating mechanism to create projects from a git repository or
a local directory. By default, the `new` command uses https://github.com/3LK3/gdextension-template
as the template. You can also [use your own repository](#from-a-custom-git-repository).

## Requirements

- Python >= 3.8
- Git
- SCons
    - you will most likely need SCons to build your extension anyway. But since you have python
      installed, you can install SCons via `pip install scons`

## Installation

`pip install gdextension-cli`

## Usage

### Create a new project

`gdextension-cli new <NAME>`

This will create a project called <NAME> in a new directory relative to your current path.

By default, https://github.com/3LK3/gdextension-template is used as the project template.

In order to install the latest version of Godot (godot-cpp) with the default template you can use:

`gdextension-cli new <NAME> --godot-version master --template-version main`

First we are using

- *godot-version* **master** because this is the main branch
  of [godot-cpp](https://github.com/godotengine/godot-cpp).
- *template-version* **main** is the main branch of the template repository.

#### From a custom git repository

You can also use your own template repository to create a new project.

`gdextension-cli new <NAME> --from-git https://github.com/<you>/<your-custom-template>`

#### From a local directory

If you have a template on your local file system you can also create a project from there.

`gdextension-cli new <NAME> --from-local /home/you/your_template`

## Technical insides

Uses the following python libraries:

- [Jinja2](https://pypi.org/project/Jinja2/) used for templating new projects
Cheminfoedu 1.0.7
chemie education cooperate with ai
Requires Python: >=3.6

The python part of cheminformatics-EDU, use: !pip install gressling

Example notebook here:  [gressling.ipynb](https://colab.research.google.com/drive/1KDKH9iVO2niYitIt1kFzE4RHw3O6ppaR#scrollTo=B7hP9SoJ2fRh)