semvec/pypi-packages huggingface dataset:
ai-nk-cce 0.1.5
This repository is used to train AI agents to predict good strategies in a social learning game based on a NK landscape.
Requires Python: <4.0,>=3.10

# human-ai-social-learning

[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black) [![PEP8](https://img.shields.io/badge/code%20style-pep8-orange.svg)](https://www.python.org/dev/peps/pep-0008/)

## Description

## Installation

This project uses Poetry for package management. If you haven't installed Poetry yet, please follow the instructions on the [official Poetry website](https://python-poetry.org/docs/#installation).

To install the project:

1. Clone the repository:

   ```bash
   git clone https://github.com/your-username/human-ai-social-learning.git
   cd human-ai-social-learning
   ```

2. Install dependencies with Poetry:

   ```bash
   poetry install
   ```

3. Activate the virtual environment:

   ```bash
   poetry shell
   ```

4. (Optional) Set up Jupyter kernel for notebooks:

   ```bash
   poetry run python -m ipykernel install --user --name nk-cce-kernel
   ```

Now your development environment is set up and ready to use.

## Contributing

We welcome contributions! Please note:

1. Please create a descriptive branch for each contribution (naming convention *feature_type/feature_name*)
2. Follow the project style (PEP 8 for Python).
3. Add tests and run all before commit. (At least one test per function or method.)
4. Write meaningful commit messages.
5. Keep in line with pre-commit linting.
6. Submit a Pull Request, to include your code into main.

### Pre-commit Hooks

We use pre-commit hooks. Installation:

```bash
poetry add pre-commit
pre-commit install
pre-commit run --all-files
```

### Running Tests

Tests need to be run in the virtual environment. You can use Poetry or Visual Studio Code settings to do so automatically.

To run all tests using Poetry run:

```bash
poetry run pytest
```

We included Visual Studio Code settings in the repository.
You can try to use them to run the tests within Visual Studio Code.

```text
.vscode/
├── settings.json
├── launch.json
```

## Documentation

For an overview of the repository structure, module organization, and
dependencies, see the [Repository Structure Guide](doc/repository_structure.md).

Additional documentation is available in the `doc/` directory:

- [How to compare hill climber vs AI](doc/how_to_compare_hill_climber_vs_ai.md)
- [How to find average hill climber landscape](doc/how_to_find_average_hill_climber_landscape.md)
- [Using MPCDF LLM inference](doc/use_mpcdf_llm_inference.md)
ibm-watsonx-data-intelligence-mcp-server 0.5.1
MCP Server implementation for IBM Data Intelligence
Requires Python: >=3.11
Keywords: mcp, model-context-protocol, server, ai, fastmcp, watsonx-data-intelligence, ibm, watsonx, data-intelligence

# Data Intelligence MCP Server

The IBM Data Intelligence MCP Server provides a modular and scalable implementation of the Model Context Protocol (MCP), purpose-built to integrate with IBM Data Intelligence services. It enables secure and extensible interaction between MCP clients and IBM's data intelligence capabilities.

For the list of tools supported in this version and sample prompts, refer to [Tools](https://github.com/IBM/data-intelligence-mcp-server/blob/main/TOOLS_PROMPTS.md).

Resources:
- [Integrating Claude + Data Intelligence](https://community.ibm.com/community/user/blogs/ramakanta-samal/2025/10/01/integrating-claude-with-watsonx-data-intelligence) A step-by-step guide showing how Claude Desktop connects to the Data Intelligence MCP Server.
- [IBM Bob + Data Intelligence](https://community.ibm.com/community/user/blogs/xixiang-chen/2025/10/15/integrating-bob-with-watsonx-data-intelligence) Blog post demonstrates how IBM Bob integrates with the MCP Server for automation.
- [Watsonx Orchestrate + Data Intelligence](https://community.ibm.com/community/user/blogs/ramakanta-samal/2025/09/25/data) Demonstrates how Watsonx Orchestrate integrates with the MCP Server for automation.
---
## Quick Install

### Prerequisites
- Python 3.11 or higher
- Data Intelligence SaaS or CPD 5.2.1

### Installation

```bash
pip install ibm-watsonx-data-intelligence-mcp-server
```

Or with uv:

```bash
uvx ibm-watsonx-data-intelligence-mcp-server
```

## Documentation

For complete documentation, including:
- Detailed setup instructions
- Client configuration guides
- Server modes (HTTP, HTTPS, stdio)
- Environment configuration
- SSL/TLS setup
- Available tools and sample prompts

Please visit the [GitHub repository](https://github.com/IBM/data-intelligence-mcp-server).

## Basic Usage

### Run in stdio mode (recommended for local setup)
```bash
uvx ibm-watsonx-data-intelligence-mcp-server --transport stdio
```

### Run in HTTPS mode
```bash
ibm-watsonx-data-intelligence-mcp-server --transport http --ssl-cert ./server.crt --ssl-key ./server.key
```

Check [SERVER_HTTPS.md](https://github.com/IBM/data-intelligence-mcp-server/blob/main/readme_guides/SERVER_HTTPS.md) for more information on setting up HTTPS.

### Run in HTTP mode
```bash
ibm-watsonx-data-intelligence-mcp-server --transport http --host 0.0.0.0 --port 3000
```

For more configuration options and detailed instructions, please refer to the [GitHub repository README](https://github.com/IBM/data-intelligence-mcp-server).

### CHANGELOG
Refer to [CHANGELOG.md](https://github.com/IBM/data-intelligence-mcp-server/blob/main/CHANGELOG.md)
configlite 0.1.8
Lightweight self-healing config manager
Requires Python: >=3.10
Keywords: config, configuration, yaml, defaults, self-healing, lightweight, python

# ConfigLite

A lightweight self-healing config handler.

## Quickstart

Subclass from the base `BaseConfig` object and add your variables and defaults.

You can then set import this from wherever is needed and access properties.


## Installation

`git clone` this repository, then use `pip install .` to install it in your current environment.


## Usage

### Creating a Config
Create a subclass of the base `BaseConfig` object, adding your parameters and their defaults.

For example:

```python
from configlite import BaseConfig


class MyConfig(BaseConfig):
    value: int = 10
    name: str = "test"
    pi: float = 3.14
```

### Access
To use your created config, there are two methods:

- "globally", where a single instance created.
- "locally", initialising the class where needed.

#### Global

To create a global config, set up the config as a parameter in the toplevel `__init__.py`:
```python
CONFIG = MyConfig("./path/to/config.yaml")
```

Then in the rest of your code you may import this object:

```python
from my_package import CONFIG

value = CONFIG.value
```

This is most useful if your code requires a single config file for everything.

#### Local
Or a local config, where you create an instance of your subclass wherever it is needed:

```python
from my_package import MyConfig

config = MyConfig("./path/to/config.yaml")

value = config.value
```

This can be useful if you are juggling multiple different config files dynamically.

## Paths

The `BaseConfig` object can take either a single path, or a list of paths.

If a list is passed, the config will search each one in order, using the last one in the list if none are found.

### Path Lists

Lets say you create a config this way:

```python
class Config(BaseConfig):
    ...

CONFIG = Config(
    paths=[
        "config.yaml",
        "~/.config/app/config.yaml",
    ]
)
```

In this case, `Config` will take these steps:

1) Search for `config.yaml` in the current working directory.
2) If not found, search for `~/.config/app/config.yaml`.
3) If still no file exists, create a default config at `~/.config/app/config.yaml`

If we get to step 3, but then create a new config at `./config.yaml`, this will take priority over the one found at `~/.config/app/config.yaml`.

## Example

See `usage.py` for an example.
torch-named-linops 0.6.0
A flexible linear operator abstraction in pytorch.
Requires Python: <3.13,>=3.10

# torch-named-linops

[![GitHub Actions Workflow Status](https://img.shields.io/github/actions/workflow/status/nishi951/torch-named-linops/test-python.yml)](https://github.com/nishi951/torch-named-linops/actions/workflows/test-python.yml)
[![Codecov](https://img.shields.io/codecov/c/github/nishi951/torch-named-linops)](https://app.codecov.io/gh/nishi951/torch-named-linops)
[![PyPI - Version](https://img.shields.io/pypi/v/torch-named-linops)](https://pypi.org/project/torch-named-linops/)
[![GitHub License](https://img.shields.io/github/license/nishi951/torch-named-linops)](https://www.apache.org/licenses/LICENSE-2.0)

A flexible linear operator abstraction implemented in PyTorch.

``` sh
$ pip install torch-named-linops
```

## Selected Feature List
- A dedicated abstraction for naming linear operator dimensions.
- A set of core linops, including:
  - `Dense`
  - `Diagonal`
  - `FFT`
  - `ArrayToBlocks`[^1] (similar to PyTorch's [unfold](https://pytorch.org/docs/stable/generated/torch.nn.Unfold.html) but in 1D/2D/3D/arbitrary dimensions)
    - Useful for local patch extraction
  - `Interpolate`[^1] (similar to SigPy's
    [interpolate/gridding](https://sigpy.readthedocs.io/en/latest/generated/sigpy.linop.Interpolate.html))
     - Comes with `kaiser_bessel` and first-order `spline` kernels.
- `.H` and `.N` properties for adjoint $A^H$ and normal $A^HA$ linop creation.
- `Chain` and `Add` for composing linops together.
- Splitting a single linop across multiple GPUs.
- Full support for complex numbers. Adjoint takes the conjugate transpose.
- Full support for `autograd`-based automatic differentiation.

[^1]: Includes a `functional` interface and [triton](https://github.com/triton-lang/triton) backend for 1D/2D/3D.


## Other Packages
This package was heavily inspired by a few other influential packages. In no particular order:
- [einops](https://einops.rocks): named dimensions/naming things in general.
- [sigpy](https://github.com/mikgroup/sigpy): the linop abstraction and the idea of having dedicated adjoint and normal properties. Also inspired the NUFFT, Interpolate, and ArrayToBlocks/BlocksToArray operators.
- [torch_linops](https://github.com/cvxgrp/torch_linops): another linop abstraction. Geared more towards optimization.
azure-mgmt-resource-databoundaries 1.0.0b1
Microsoft Azure Databoundaries Management Client Library for Python
Requires Python: >=3.9
Keywords: azure, azure sdk

# Microsoft Azure SDK for Python

This is the Microsoft Azure Resource Databoundaries Management Client Library.
This package has been tested with Python 3.9+.
For a more complete view of Azure libraries, see the [azure sdk python release](https://aka.ms/azsdk/python/all).

## _Disclaimer_

_Azure SDK Python packages support for Python 2.7 has ended 01 January 2022. For more information and questions, please refer to https://github.com/Azure/azure-sdk-for-python/issues/20691_

## Getting started

### Prerequisites

- Python 3.9+ is required to use this package.
- [Azure subscription](https://azure.microsoft.com/free/)

### Install the package

```bash
pip install azure-mgmt-resource-databoundaries
pip install azure-identity
```

### Authentication

By default, [Azure Active Directory](https://aka.ms/awps/aad) token authentication depends on correct configuration of the following environment variables.

- `AZURE_CLIENT_ID` for Azure client ID.
- `AZURE_TENANT_ID` for Azure tenant ID.
- `AZURE_CLIENT_SECRET` for Azure client secret.

With above configuration, client can be authenticated by following code:

```python
from azure.identity import DefaultAzureCredential
from azure.mgmt.resource.databoundaries import DataBoundaryMgmtClient

client = DataBoundaryMgmtClient(credential=DefaultAzureCredential())
```

## Examples

Code samples for this package can be found at:
- [Search Resource Databoundaries Management](https://docs.microsoft.com/samples/browse/?languages=python&term=Getting%20started%20-%20Managing&terms=Getting%20started%20-%20Managing) on docs.microsoft.com
- [Azure Python Mgmt SDK Samples Repo](https://aka.ms/azsdk/python/mgmt/samples)


## Troubleshooting

## Next steps

## Provide Feedback

If you encounter any bugs or have suggestions, please file an issue in the
[Issues](https://github.com/Azure/azure-sdk-for-python/issues)
section of the project. 

# Release History

## 1.0.0b1 (2026-02-04)

### Other Changes

  - Initial version
azure-mgmt-resource-changes 1.0.0b1
Microsoft Azure Changes Management Client Library for Python
Requires Python: >=3.9
Keywords: azure, azure sdk

# Microsoft Azure SDK for Python

This is the Microsoft Azure Resource Changes Management Client Library.
This package has been tested with Python 3.9+.
For a more complete view of Azure libraries, see the [azure sdk python release](https://aka.ms/azsdk/python/all).

## _Disclaimer_

_Azure SDK Python packages support for Python 2.7 has ended 01 January 2022. For more information and questions, please refer to https://github.com/Azure/azure-sdk-for-python/issues/20691_

## Getting started

### Prerequisites

- Python 3.9+ is required to use this package.
- [Azure subscription](https://azure.microsoft.com/free/)

### Install the package

```bash
pip install azure-mgmt-resource-changes
pip install azure-identity
```

### Authentication

By default, [Azure Active Directory](https://aka.ms/awps/aad) token authentication depends on correct configuration of the following environment variables.

- `AZURE_CLIENT_ID` for Azure client ID.
- `AZURE_TENANT_ID` for Azure tenant ID.
- `AZURE_CLIENT_SECRET` for Azure client secret.

In addition, Azure subscription ID can be configured via environment variable `AZURE_SUBSCRIPTION_ID`.

With above configuration, client can be authenticated by following code:

```python
from azure.identity import DefaultAzureCredential
from azure.mgmt.resource.changes import ChangesClient
import os

sub_id = os.getenv("AZURE_SUBSCRIPTION_ID")
client = ChangesClient(credential=DefaultAzureCredential(), subscription_id=sub_id)
```

## Examples

Code samples for this package can be found at:
- [Search Resource Changes Management](https://docs.microsoft.com/samples/browse/?languages=python&term=Getting%20started%20-%20Managing&terms=Getting%20started%20-%20Managing) on docs.microsoft.com
- [Azure Python Mgmt SDK Samples Repo](https://aka.ms/azsdk/python/mgmt/samples)


## Troubleshooting

## Next steps

## Provide Feedback

If you encounter any bugs or have suggestions, please file an issue in the
[Issues](https://github.com/Azure/azure-sdk-for-python/issues)
section of the project. 

# Release History

## 1.0.0b1 (2026-02-04)

### Other Changes

  - Initial version
azure-mgmt-resource-links 1.0.0b1
Microsoft Azure Links Management Client Library for Python
Requires Python: >=3.9
Keywords: azure, azure sdk

# Microsoft Azure SDK for Python

This is the Microsoft Azure Resource Links Management Client Library.
This package has been tested with Python 3.9+.
For a more complete view of Azure libraries, see the [azure sdk python release](https://aka.ms/azsdk/python/all).

## _Disclaimer_

_Azure SDK Python packages support for Python 2.7 has ended 01 January 2022. For more information and questions, please refer to https://github.com/Azure/azure-sdk-for-python/issues/20691_

## Getting started

### Prerequisites

- Python 3.9+ is required to use this package.
- [Azure subscription](https://azure.microsoft.com/free/)

### Install the package

```bash
pip install azure-mgmt-resource-links
pip install azure-identity
```

### Authentication

By default, [Azure Active Directory](https://aka.ms/awps/aad) token authentication depends on correct configuration of the following environment variables.

- `AZURE_CLIENT_ID` for Azure client ID.
- `AZURE_TENANT_ID` for Azure tenant ID.
- `AZURE_CLIENT_SECRET` for Azure client secret.

In addition, Azure subscription ID can be configured via environment variable `AZURE_SUBSCRIPTION_ID`.

With above configuration, client can be authenticated by following code:

```python
from azure.identity import DefaultAzureCredential
from azure.mgmt.resource.links import ManagementLinkClient
import os

sub_id = os.getenv("AZURE_SUBSCRIPTION_ID")
client = ManagementLinkClient(credential=DefaultAzureCredential(), subscription_id=sub_id)
```

## Examples

Code samples for this package can be found at:
- [Search Resource Links Management](https://docs.microsoft.com/samples/browse/?languages=python&term=Getting%20started%20-%20Managing&terms=Getting%20started%20-%20Managing) on docs.microsoft.com
- [Azure Python Mgmt SDK Samples Repo](https://aka.ms/azsdk/python/mgmt/samples)


## Troubleshooting

## Next steps

## Provide Feedback

If you encounter any bugs or have suggestions, please file an issue in the
[Issues](https://github.com/Azure/azure-sdk-for-python/issues)
section of the project. 

# Release History

## 1.0.0b1 (2026-02-04)

### Other Changes

  - Initial version
azure-mgmt-resource-features 1.0.0b1
Microsoft Azure Features Management Client Library for Python
Requires Python: >=3.9
Keywords: azure, azure sdk

# Microsoft Azure SDK for Python

This is the Microsoft Azure Resource Features Management Client Library.
This package has been tested with Python 3.9+.
For a more complete view of Azure libraries, see the [azure sdk python release](https://aka.ms/azsdk/python/all).

## _Disclaimer_

_Azure SDK Python packages support for Python 2.7 has ended 01 January 2022. For more information and questions, please refer to https://github.com/Azure/azure-sdk-for-python/issues/20691_

## Getting started

### Prerequisites

- Python 3.9+ is required to use this package.
- [Azure subscription](https://azure.microsoft.com/free/)

### Install the package

```bash
pip install azure-mgmt-resource-features
pip install azure-identity
```

### Authentication

By default, [Azure Active Directory](https://aka.ms/awps/aad) token authentication depends on correct configuration of the following environment variables.

- `AZURE_CLIENT_ID` for Azure client ID.
- `AZURE_TENANT_ID` for Azure tenant ID.
- `AZURE_CLIENT_SECRET` for Azure client secret.

In addition, Azure subscription ID can be configured via environment variable `AZURE_SUBSCRIPTION_ID`.

With above configuration, client can be authenticated by following code:

```python
from azure.identity import DefaultAzureCredential
from azure.mgmt.resource.features import FeatureClient
import os

sub_id = os.getenv("AZURE_SUBSCRIPTION_ID")
client = FeatureClient(credential=DefaultAzureCredential(), subscription_id=sub_id)
```

## Examples

Code samples for this package can be found at:
- [Search Resource Features Management](https://docs.microsoft.com/samples/browse/?languages=python&term=Getting%20started%20-%20Managing&terms=Getting%20started%20-%20Managing) on docs.microsoft.com
- [Azure Python Mgmt SDK Samples Repo](https://aka.ms/azsdk/python/mgmt/samples)


## Troubleshooting

## Next steps

## Provide Feedback

If you encounter any bugs or have suggestions, please file an issue in the
[Issues](https://github.com/Azure/azure-sdk-for-python/issues)
section of the project. 

# Release History

## 1.0.0b1 (2026-02-04)

### Other Changes

  - Initial version
azure-mgmt-resource-privatelinks 1.0.0b1
Microsoft Azure Privatelinks Management Client Library for Python
Requires Python: >=3.9
Keywords: azure, azure sdk

# Microsoft Azure SDK for Python

This is the Microsoft Azure Resource Privatelinks Management Client Library.
This package has been tested with Python 3.9+.
For a more complete view of Azure libraries, see the [azure sdk python release](https://aka.ms/azsdk/python/all).

## _Disclaimer_

_Azure SDK Python packages support for Python 2.7 has ended 01 January 2022. For more information and questions, please refer to https://github.com/Azure/azure-sdk-for-python/issues/20691_

## Getting started

### Prerequisites

- Python 3.9+ is required to use this package.
- [Azure subscription](https://azure.microsoft.com/free/)

### Install the package

```bash
pip install azure-mgmt-resource-privatelinks
pip install azure-identity
```

### Authentication

By default, [Azure Active Directory](https://aka.ms/awps/aad) token authentication depends on correct configuration of the following environment variables.

- `AZURE_CLIENT_ID` for Azure client ID.
- `AZURE_TENANT_ID` for Azure tenant ID.
- `AZURE_CLIENT_SECRET` for Azure client secret.

In addition, Azure subscription ID can be configured via environment variable `AZURE_SUBSCRIPTION_ID`.

With above configuration, client can be authenticated by following code:

```python
from azure.identity import DefaultAzureCredential
from azure.mgmt.resource.privatelinks import ResourcePrivateLinkClient
import os

sub_id = os.getenv("AZURE_SUBSCRIPTION_ID")
client = ResourcePrivateLinkClient(credential=DefaultAzureCredential(), subscription_id=sub_id)
```

## Examples

Code samples for this package can be found at:
- [Search Resource Privatelinks Management](https://docs.microsoft.com/samples/browse/?languages=python&term=Getting%20started%20-%20Managing&terms=Getting%20started%20-%20Managing) on docs.microsoft.com
- [Azure Python Mgmt SDK Samples Repo](https://aka.ms/azsdk/python/mgmt/samples)


## Troubleshooting

## Next steps

## Provide Feedback

If you encounter any bugs or have suggestions, please file an issue in the
[Issues](https://github.com/Azure/azure-sdk-for-python/issues)
section of the project. 

# Release History

## 1.0.0b1 (2026-02-04)

### Other Changes

  - Initial version
azure-mgmt-resource-locks 1.0.0b1
Microsoft Azure Locks Management Client Library for Python
Requires Python: >=3.9
Keywords: azure, azure sdk

# Microsoft Azure SDK for Python

This is the Microsoft Azure Resource Locks Management Client Library.
This package has been tested with Python 3.9+.
For a more complete view of Azure libraries, see the [azure sdk python release](https://aka.ms/azsdk/python/all).

## _Disclaimer_

_Azure SDK Python packages support for Python 2.7 has ended 01 January 2022. For more information and questions, please refer to https://github.com/Azure/azure-sdk-for-python/issues/20691_

## Getting started

### Prerequisites

- Python 3.9+ is required to use this package.
- [Azure subscription](https://azure.microsoft.com/free/)

### Install the package

```bash
pip install azure-mgmt-resource-locks
pip install azure-identity
```

### Authentication

By default, [Azure Active Directory](https://aka.ms/awps/aad) token authentication depends on correct configuration of the following environment variables.

- `AZURE_CLIENT_ID` for Azure client ID.
- `AZURE_TENANT_ID` for Azure tenant ID.
- `AZURE_CLIENT_SECRET` for Azure client secret.

In addition, Azure subscription ID can be configured via environment variable `AZURE_SUBSCRIPTION_ID`.

With above configuration, client can be authenticated by following code:

```python
from azure.identity import DefaultAzureCredential
from azure.mgmt.resource.locks import ManagementLockClient
import os

sub_id = os.getenv("AZURE_SUBSCRIPTION_ID")
client = ManagementLockClient(credential=DefaultAzureCredential(), subscription_id=sub_id)
```

## Examples

Code samples for this package can be found at:
- [Search Resource Locks Management](https://docs.microsoft.com/samples/browse/?languages=python&term=Getting%20started%20-%20Managing&terms=Getting%20started%20-%20Managing) on docs.microsoft.com
- [Azure Python Mgmt SDK Samples Repo](https://aka.ms/azsdk/python/mgmt/samples)


## Troubleshooting

## Next steps

## Provide Feedback

If you encounter any bugs or have suggestions, please file an issue in the
[Issues](https://github.com/Azure/azure-sdk-for-python/issues)
section of the project. 

# Release History

## 1.0.0b1 (2026-02-04)

### Other Changes

  - Initial version