semvec/pypi-packages huggingface dataset:
boto3-pydantic-outposts 1.0.2
Pydantic models for AWS Outposts
Requires Python: <4.0,>=3.9
Keywords: aws, boto3, pydantic, models, outposts

# boto3-pydantic-outposts

This package was automatically generated by aws-resource-validator repository.

For more information, visit:
- Package homepage: https://coreoxide.github.io/aws_resource_validator/
- GitHub repository: https://github.com/CoreOxide/aws_resource_validator
- PyPI project: https://pypi.org/project/aws-resource-validator/

Pydantic models for AWS Outposts.
pystockparameter 0.1.2
Python package to fetch stock data from a merged Excel file

# pystockparameter

**pystockparameter** is a lightweight Python package that lets you easily fetch and explore stock-specific data from a precompiled Excel file. Itâ€™s designed to be fast, simple, and ideal for data analysis or financial research based on quarterly and yearly financials.

---

## ðŸ“¦ Installation

Install the package directly from PyPI:

```bash
pip install pystockparameter
boto3-pydantic-osis 1.0.2
Pydantic models for AWS Osis
Requires Python: <4.0,>=3.9
Keywords: aws, boto3, pydantic, models, osis

# boto3-pydantic-osis

This package was automatically generated by aws-resource-validator repository.

For more information, visit:
- Package homepage: https://coreoxide.github.io/aws_resource_validator/
- GitHub repository: https://github.com/CoreOxide/aws_resource_validator
- PyPI project: https://pypi.org/project/aws-resource-validator/

Pydantic models for AWS Osis.
boto3-pydantic-organizations 1.0.2
Pydantic models for AWS Organizations
Requires Python: <4.0,>=3.9
Keywords: aws, boto3, pydantic, models, organizations

# boto3-pydantic-organizations

This package was automatically generated by aws-resource-validator repository.

For more information, visit:
- Package homepage: https://coreoxide.github.io/aws_resource_validator/
- GitHub repository: https://github.com/CoreOxide/aws_resource_validator
- PyPI project: https://pypi.org/project/aws-resource-validator/

Pydantic models for AWS Organizations.
endt 0.11.1
Enhanced Decision Tree
Requires Python: >=3.10

# Enhanced Decision Tree (EDT)

## What is Enhanced Decision Tree

TODO

## Install

There is two option to install EDT library: from PyPi (easy) or from source.

### PyPi

Use simple command:

```bash
pip install edt
```

That's it

### From source

TODO

## Get started

Here some simple example to use EDT

### Classical Decision Tree

Construct classical Decision Tree on syntatic dataset:

```python
import endt

x1 = endt.ContinuumData([1.,1.,2.,2.], name="x1")
x2 = endt.ContinuumData([100.,200.,100.,200.], name="x2")
y = endt.ContinuumData([10.,20.,30.,40])

tree = endt.Tree()
tree.fit([x1, x2], y)
print(tree)

res = tree.predict(1,2)
print("predict y(1,2) =", res)
```

### Simple Enhanced Decision Tree

Add LSQ of order 2 in previous code:

```python
import endt

x1 = endt.ContinuumData([1.,1.,2.,2.], name="x1")
x2 = endt.ContinuumData([100.,200.,100.,200.], name="x2")
y = endt.ContinuumData([10.,20.,30.,40])

funcs = endt.func.create_lsq_functions([x1, x2], order=2)

tree = endt.Tree(lsq_funcs=funcs)
tree.fit([x1, x2], y)
print(tree)

res = tree.predict(1,2)
print("predict y(1,2) =", res)
```

### EDT from CSV

Let our dataset save is in the CSV file data.csv, containing two collumn of feature parameters and third collumn of reults

Create EDT from CSV file:

```python
import endt
import numpy as np

data = np.loadtxt("data.csv").T
x = list(map(endt.ContinuumData, data[:-1]))
y = endt.ContinuumData(data[-1])
for i, xi in enumerate(x, start=1):
    xi.name = f"x{i}" # set name x1, x2, x3... for every feature parameters
funcs = endt.func.create_lsq_functions(x, order=2)
tree = endt.Tree(lsq_funcs=funcs)
tree.fit(x, y)
print(tree)

res = tree.predict(1,2)
print("predict y(1,2) =", res)
```

## Full documentation

Read full documentation [https://nanoworld_ml.gitlab.io/enhanceddecisiontree/](https://nanoworld_ml.gitlab.io/enhanceddecisiontree/)
boto3-pydantic-opsworkscm 1.0.2
Pydantic models for AWS Opsworkscm
Requires Python: <4.0,>=3.9
Keywords: aws, boto3, pydantic, models, opsworkscm

# boto3-pydantic-opsworkscm

This package was automatically generated by aws-resource-validator repository.

For more information, visit:
- Package homepage: https://coreoxide.github.io/aws_resource_validator/
- GitHub repository: https://github.com/CoreOxide/aws_resource_validator
- PyPI project: https://pypi.org/project/aws-resource-validator/

Pydantic models for AWS Opsworkscm.
boto3-pydantic-opsworks 1.0.2
Pydantic models for AWS Opsworks
Requires Python: <4.0,>=3.9
Keywords: aws, boto3, pydantic, models, opsworks

# boto3-pydantic-opsworks

This package was automatically generated by aws-resource-validator repository.

For more information, visit:
- Package homepage: https://coreoxide.github.io/aws_resource_validator/
- GitHub repository: https://github.com/CoreOxide/aws_resource_validator
- PyPI project: https://pypi.org/project/aws-resource-validator/

Pydantic models for AWS Opsworks.
boto3-pydantic-opensearchserverless 1.0.2
Pydantic models for AWS Opensearchserverless
Requires Python: <4.0,>=3.9
Keywords: aws, boto3, pydantic, models, opensearchserverless

# boto3-pydantic-opensearchserverless

This package was automatically generated by aws-resource-validator repository.

For more information, visit:
- Package homepage: https://coreoxide.github.io/aws_resource_validator/
- GitHub repository: https://github.com/CoreOxide/aws_resource_validator
- PyPI project: https://pypi.org/project/aws-resource-validator/

Pydantic models for AWS Opensearchserverless.
boto3-pydantic-opensearch 1.0.2
Pydantic models for AWS Opensearch
Requires Python: <4.0,>=3.9
Keywords: aws, boto3, pydantic, models, opensearch

# boto3-pydantic-opensearch

This package was automatically generated by aws-resource-validator repository.

For more information, visit:
- Package homepage: https://coreoxide.github.io/aws_resource_validator/
- GitHub repository: https://github.com/CoreOxide/aws_resource_validator
- PyPI project: https://pypi.org/project/aws-resource-validator/

Pydantic models for AWS Opensearch.
eks-speak 0.1.0
Natural language interface for Kubernetes and Amazon EKS
Requires Python: >=3.6
Keywords: kubernetes, eks, kubectl, cli, natural language

# EKS Speak

A natural language interface for Kubernetes (with Amazon EKS support)

## Overview

EKS Speak is a command-line tool that allows users to interact with Kubernetes clusters using plain English commands. It translates natural language queries into proper `kubectl` commands, making it easier for beginners to work with Kubernetes and for experienced users to speed up common operations.

## Features

- Translate plain English commands to `kubectl` commands
- Support for Amazon EKS clusters
- Context-aware command processing
- Support for common Kubernetes resources:
  - Pods
  - Services
  - Deployments
  - Nodes
  - ConfigMaps
  - Secrets
  - Namespaces
  - Ingresses

## Installation

```bash
# Clone the repository
git clone https://github.com/yourusername/eks-speak.git

# Navigate to the project directory
cd eks-speak

# Install the package
pip install -e .
```

## Prerequisites

- Python 3.6 or higher
- `kubectl` installed and in your PATH
- AWS CLI (for EKS-specific functionality)
- Valid Kubernetes configuration

## Usage

Simply run the `eks-speak` command to start the interactive shell:

```bash
eks-speak
```

For verbose output (helpful for debugging):

```bash
eks-speak -v
```

## Examples

Here are some example commands you can use:

```
eks-speak> show all pods in namespace kube-system
eks-speak> get services in production namespace
eks-speak> list all deployments in namespace default
eks-speak> describe pod named my-pod-xyz in namespace app
eks-speak> show nodes in cluster my-eks-cluster
eks-speak> get logs for pod web-server in namespace frontend
```

## How It Works

1. The tool parses your natural language input
2. It identifies the intent (get, describe, delete, etc.)
3. It extracts resource types, namespaces, and names
4. It constructs the appropriate kubectl command
5. It executes the command and returns the output

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.