semvec/pypi-packages huggingface dataset:
codeseg 1.0.3
CoDeSEG Community Detection Package
Requires Python: >=3.8
Keywords: community detection, graph clustering


# Community Detection in Large-Scale Complex Networks via Structural Entropy Game (CoDeSEG)

CoDeSEG supports undirected, directed, weighted, unweighted, overlapping, non-overlapping, and dynamic community detection. The relevant parameter descriptions are as follows:

| Parameter      | Description                                    | Type    | Default  | Required |
|----------------|------------------------------------------------|---------|----------|----------|
| `in_path`      | Input file of graph edge list                  | file    | None     | Yes      |
| `out_path`     | Output file of communities                     | file    | None     | Yes      |
| `ground_truth` | Ground truth file                              | file    | No       | No       |
| `weighted`     | Weighted graph                                 | bool    | false    | No       |
| `directed`     | directed graph                                 | bool    | false    | No       |
| `dynamic`      | dynamic graph                                  | bool    | false    | No       |
| `overlap`      | Overlapping communities                        | bool    | false    | No       |
| `gamma`        | Overlapping detecting factor                   | float   | 1.0      | No       |
| `tau`          | Non-overlapping entropy threshold              | float   | 0.3      | No       |
| `r`            | Stable round threshold for dynamic detection   | int     | 2        | No       |
| `it`           | Maximum number of iterations                   | int     | 10       | No       |
| `parallel`     | Number of threads                              | int     | 1        | No       |
| `verbose`      | Print detection iteration messages             | bool    | false    | No       |


## Note
### Build Requirements
- CMake >= 3.22
- python >= 3.8

### data format
The format of the input edge list is as follows:
```text
   1 \t 2 \n
   1 \t 3 \n
   2 \t 3 \n
``` 
For dynamic graphs, the input should be a file containing edge lists of multiple network snapshots, stored in the `/data/ntwk` directory. The file structure is as follows:
```text
data(your dataset)/ 
├── ntwk 
    ├── 1.txt
    ├── 2.txt
    ├── 3.txt
    ...
```
windscangeo 2025.1
Easy extraction and processing of matching GOES geostationary satellite and polar orbiting scatterometer data for model training.
Requires Python: >=3.8

# Windscangeo

**WindScanGEO** is a deep learning framework developped to detect wind speeds from the images of the  *Geostationary Operational Environmental Satellite (GOES)* .
It is able to deliver high frequency wind speed predictions (every 10 minutes) at a 25x25km spacial resolution over the ocean. This is useful
for any research that looks into daily and small-scale spacial variability and evolution of wind speeds. It is based on a ResNet50
model that is trained daily on scatterometer data which allows inference of the model on the extent of an entire GOES image.

This framework is open-source and has been developped by Y.Loo, Dr.
J.Sun, Dr. G.George at the Geoscience and Remote Sensing department and
the Intelligent Systems department of the TU Delft.

The framework is distributed as a **Python package** that is easily usable to train and infer models at any time and location desired by the user. For more information, go to [windscangeo.github.io ](https://windscangeo.github.io)To directly use the package yourself, go to the *"Installation"* page.

For any questions or inquiries, please contact [yloo@tudelft.nl](mailto:yloo@tudelft.nl)
otocp 1.0.1
A MCP server written in python to add, list, complete and delete tasks in Obsidian.
Requires Python: >=3.13


# Otocp

A MCP server written in python to add, list, complete and delete tasks in Obsidian.

### Tools available:

- add_task - Add tasks based on user input.
- read_tasks - Lists all the task.
- complete_task - Completes a specified task (doesn't have to be exact.)
- clear_tasks - Deletes everything

### Installation

Install using pip:

``` pip install otocp```

or use uv

Install uv first.

MacOS/Linux:

```curl -LsSf https://astral.sh/uv/install.sh | sh```

Windows:

```powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"```

Now run:

```uvx otocp```

### MCP Config

```json 
{
  "mcpServers": {
    "otocp": {
      "command": "otocp",
      "args": [],
      "env": {
        "OTOCP_TODO_PATH": "path to your todo.md"
      }
    }
  }
}

```
OTOCP_TODO_PATH example:

```
"OTOCP_TODO_PATH": "/home/crystal/Desktop/example/todo.md"
```

If you get any errors (most likely because of path of otocp):

On Linux/MacOS run ``` which otocp``` on terminal and put the full path in command instead of just otocp like: 
``` 
      "command": "/home/crystal/.local/bin/otocp",

```

On Windows you can use ```where otocp``` and do the same thing.

### For Development:

Clone the repo: ``` https://github.com/lattisse/otocp.git ```

``` 
cd otocp
uv venv

```

To run mcp through use this MCP Config:

``` json
{
  "mcpServers": {
    "otocp": {
      "command": "uv",
      "args": [
        "run",
        "--with",
        "mcp[cli]",
        "mcp",
        "run",
        "/ABSOLUTE/PATH/TO//otocp/src/otocp/todo.py"
      ]
    }
  }
}
```