semvec/pypi-packages huggingface dataset:
topology-mcp 0.1.10
A command-line interface for accessing topology data
Requires Python: >=3.8

# Topology MCP Server

A Model Context Protocol (MCP) server for accessing topology data from a topology service.

## Installation

### Via uvx (recommended)

```bash
uvx install topology-mcp
```

### Via pip

```bash
pip install topology-mcp
```

## Usage

### Running the MCP Server

```bash
# Start the MCP server with stdio transport (for MCP clients)
topology-mcp --transport stdio

# Start the MCP server with HTTP transport
topology-mcp --transport streamable-http --port 8001

# Start the MCP server with SSE transport
topology-mcp --transport sse --port 8002
```

### Advanced Usage

```bash
# Use a different topology service URL
export TOPOLOGY_URL=http://my-topology-service:8080
topology-mcp --transport stdio

# Use a different port
topology-mcp --transport streamable-http --port 9000

# Use environment variable for topology service
export TOPOLOGY_URL=http://my-service:9000
topology-mcp --transport sse
```

### Environment Variables

- `TOPOLOGY_URL`: Base URL for the topology service (defaults to `http://localhost:8000`)
- `PORT`: Server port (defaults to 8000)
- `HOST`: Server host (defaults to 0.0.0.0)
- `TRANSPORT`: Transport method (sse, stdio, streamable-http)

## Examples

```bash
# Start MCP server for use with Claude Desktop
topology-mcp --transport stdio

# Start MCP server on custom port
topology-mcp --transport streamable-http --port 9000

# Connect to a remote topology service
export TOPOLOGY_URL=https://topology.example.com
topology-mcp --transport sse
```

## Development

### Setup

```bash
# Clone the repository
git clone https://github.com/yourusername/topology-mcp.git
cd topology-mcp

# Install in development mode with uv
uv sync --dev

# Or with pip
pip install -e ".[dev]"
```

### Running Tests

```bash
pytest
```

### Code Formatting

```bash
black src/
```

## License

MIT License - see LICENSE file for details.
colbertfinance 0.4.9
Librería paso-a-paso para enseñar Markowitz
Requires Python: >=3.9

# colbertfinance

Pequeña librería pedagógica para mostrar **paso a paso** el modelo de
portafolio de Markowitz (media-varianza).

Cada función calcula un paso y, además, guarda automáticamente un
Excel con los resultados para que los alumnos verifiquen los números.
vit-keras 0.2.0
Keras implementation of ViT (Vision Transformer)
Requires Python: <3.12,>=3.10

# vit-keras
This is a Keras implementation of the models described in [An Image is Worth 16x16 Words:
Transformes For Image Recognition at Scale](https://arxiv.org/pdf/2010.11929.pdf). It is based on an earlier implementation from [tuvovan](https://github.com/tuvovan/Vision_Transformer_Keras), modified to match the Flax implementation in the [official repository](https://github.com/google-research/vision_transformer).

The weights here are ported over from the weights provided in the official repository. See `utils.load_weights_numpy` to see how this is done (it's not pretty, but it does the job).

## Usage
Install this package using `pip install vit-keras`

You can use the model out-of-the-box with ImageNet 2012 classes using
something like the following. The weights will be downloaded automatically.

```python
from vit_keras import vit, utils

image_size = 384
classes = utils.get_imagenet_classes()
model = vit.vit_b16(
    image_size=image_size,
    activation='sigmoid',
    pretrained=True,
    include_top=True,
    pretrained_top=True
)
url = 'https://upload.wikimedia.org/wikipedia/commons/d/d7/Granny_smith_and_cross_section.jpg'
image = utils.read(url, image_size)
X = vit.preprocess_inputs(image).reshape(1, image_size, image_size, 3)
y = model.predict(X)
print(classes[y[0].argmax()]) # Granny smith
```

You can fine-tune using a model loaded as follows.

```python
image_size = 224
model = vit.vit_l32(
    image_size=image_size,
    activation='sigmoid',
    pretrained=True,
    include_top=True,
    pretrained_top=False,
    classes=200
)
# Train this model on your data as desired.
```

## Visualizing Attention Maps
There's some functionality for plotting attention maps for a given image and model. See example below. I'm not sure I'm doing this correctly (the official repository didn't have example code). Feedback /corrections welcome!

```python
import numpy as np
import matplotlib.pyplot as plt
from vit_keras import vit, utils, visualize

# Load a model
image_size = 384
classes = utils.get_imagenet_classes()
model = vit.vit_b16(
    image_size=image_size,
    activation='sigmoid',
    pretrained=True,
    include_top=True,
    pretrained_top=True
)
classes = utils.get_imagenet_classes()

# Get an image and compute the attention map
url = 'https://upload.wikimedia.org/wikipedia/commons/b/bc/Free%21_%283987584939%29.jpg'
image = utils.read(url, image_size)
attention_map = visualize.attention_map(model=model, image=image)
print('Prediction:', classes[
    model.predict(vit.preprocess_inputs(image)[np.newaxis])[0].argmax()]
)  # Prediction: Eskimo dog, husky

# Plot results
fig, (ax1, ax2) = plt.subplots(ncols=2)
ax1.axis('off')
ax2.axis('off')
ax1.set_title('Original')
ax2.set_title('Attention Map')
_ = ax1.imshow(image)
_ = ax2.imshow(attention_map)
```

![example of attention map](https://raw.githubusercontent.com/faustomorales/vit-keras/master/docs/attention_map_example.jpg)