semvec/pypi-packages huggingface dataset:
npa 0.0.0
Normalized Pattern Analysis
Requires Python: >=3.9

Normalized Pattern Analysis
===========================

Normalized Pattern Analysis is currently under development.

Check out the official documentation: `https://npa.readthedocs.io/en/latest <https://npa.readthedocs.io/en/latest/>`_

Explore the open source code repository: `https://github.com/vitorinojoao/npa <https://github.com/vitorinojoao/npa>`_

Install it from the Python Package Index: `https://pypi.org/project/npa <https://pypi.org/project/npa/>`_
monarchopt 0.1.4
A Python implementation of Monarch Swarm Optimization algorithm
Requires Python: >=3.8
Keywords: optimization, swarm intelligence, metaheuristic, binary optimization

# MonarchOpt: Monarch Butterfly Swarm Optimization

A Python implementation of the Monarch Swarm Optimization algorithm, designed for solving binary optimization problems. The algorithm is inspired by the migration behavior of monarch butterflies and uses a novel approach combining swarm intelligence with gradient-based optimization.

## Features

- Binary optimization for various problem types
- Built-in command line interface
- Automatic result saving and history tracking
- Early stopping with known optimum
- Automatic progress reporting
- Built-in timeout mechanism
- Reproducible results with seed setting

## Installation

```bash
pip install monarchopt
```

## Quick Start

```python
from monarchopt import MSO
import numpy as np

def simple_fitness(solution):
    """Example fitness function: maximize sum of elements."""
    return np.sum(solution)

MSO.run(
    obj_func=simple_fitness,
    dim=100,
    pop_size=1000,
    max_iter=800,
    obj_type='max',
    neighbour_count=3,
    gradient_strength=0.8,
    base_learning_rate=0.1
)
```

## Test Examples and Data

To run the test examples (UFLP and DUF problems):

1. Clone the GitHub repository:
```bash
git clone https://github.com/gazioglue/monarchopt.git
cd monarchopt
```

2. Run UFLP solver:
```bash
python examples/solve_uflp.py examples/data/uflp/test_instances/cap71.txt
```

3. Run DUF solver:
```bash
python examples/solve_dufs.py duf1
```

### Available Command Line Options

For UFLP:
```bash
python solve_uflp.py cap71.txt --pop-size 2000 --max-iter 1000 --seed 42
```

For DUF:
```bash
python solve_dufs.py duf2 --dim 200 --pop-size 2000 --seed 42
```

## Documentation

For more detailed usage instructions and examples, see [USAGE.md](USAGE.md).

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
ibm-gdsc-sdk-software 3.6.1
IBM Guardium Data Security Center
Keywords: OpenAPI, IBM Guardium Data Security Center

# IBM Guardium Data Security Center Python client

## Requirements.

Python 3.7+

## Installation & Usage
### pip install

If the python package is hosted on a repository, you can install directly using:

```sh
pip install ibm-gdsc-sdk-software==3.6.1
```
(you may need to run `pip` with root permission: `sudo pip install ibm-gdsc-sdk-software==3.6.1`)

Then import the package:
```python
import ibm_gdsc_sdk_software
```

### Setuptools

Install via [Setuptools](http://pypi.python.org/pypi/setuptools).

```sh
python setup.py install --user
```
(or `sudo python setup.py install` to install the package for all users)

Then import the package:
```python
import ibm_gdsc_sdk_software
```

### Tests

Execute `pytest` to run the tests.

## Getting Started

Please follow the [installation procedure](#installation--usage) and then run the following:

```python

import os
import ibm_gdsc_sdk_software
from ibm_gdsc_sdk_software.models.tenantuserv3_get_users_response import Tenantuserv3GetUsersResponse
from ibm_gdsc_sdk_software.rest import ApiException
from pprint import pprint



# Configure HTTP basic authorization: BasicAuth
configuration = ibm_gdsc_sdk_software.Configuration(
    host = os.environ['URL'],
    username = os.environ['API_USERNAME'],
    password = os.environ['API_PASSWORD']
)

# Configure API key authorization: ApiKeyAuth
#configuration.api_key['ApiKeyAuth'] = os.environ['API_KEY']
configuration.verify_ssl = False
# Uncomment below to setup prefix (e.g. Bearer) for API key, if needed
#configuration.api_key_prefix['ApiKeyAuth'] = "Basic"
# Enter a context with an instance of the API client

with ibm_gdsc_sdk_software.ApiClient(configuration) as api_client:
    # Create an instance of the API class
    api_instance = ibm_gdsc_sdk_software.JumpboxServiceApi(api_client)
    uid = '' # str | Email. (optional)

    try:
        # Summary: Get users Description: Get all users base on a tenantID.
        api_response = api_instance.jumpbox_service_get_users(uid=uid)
        print("The response of JumpboxServiceApi->jumpbox_service_get_users:\n")
        pprint(api_response)
    except Exception as e:
        print("Exception when calling JumpboxServiceApi->jumpbox_service_get_users: %s\n" % e)

```