semvec/pypi-packages huggingface dataset:
napari-stereoTis 0.3.0
Used to mask cells or tissues for spatial transcriptomes
Requires Python: >=3.10

# napari-stereoTis

[![PyPI](https://img.shields.io/pypi/v/napari-stereoTis.svg?color=green)](https://pypi.org/project/napari-stereoTis)
[![napari hub](https://img.shields.io/endpoint?url=https://api.napari-hub.org/shields/napari-stereoTis)](https://napari-hub.org/plugins/napari-stereoTis)

## Description

A napari plugin for spatial transcriptomics data analysis and visualization. This plugin provides tools for processing and analyzing spatial transcriptomics data using bio-photos, with a focus on cell aggregation detection, tissue segmentation, and data visualization.

## Features

- **Cell Aggregation Analysis**: Detect and analyze cell aggregates using OPTICS clustering algorithm
- **Tissue Segmentation**: Visualize gene signature scores across spatial coordinates and tissue segmentation
- **Data Visualization**: Interactive visualization of spatial transcriptomics data
- **Data Export**: Export processed data into multi-columns csv for further merging with anndata or seurat objects

## Installation

You can install `napari-stereoTis` via [pip]:

```bash
pip install napari-stereoTis
```

## Usage

1. Start napari:
   ```bash
   napari
   ```

2. Load the plugin from the Plugins menu


## Input Data Format

The plugin accepts CSV files with the following format:
- For cell aggregation: CSV with x,y coordinates (with header)
- For signature scoring: CSV with x,y coordinates and score values (with header)

## Requirements

- napari[all]
- numpy
- scikit-image
- opencv-python
- pandas
- matplotlib

## Contributing

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

## License

Distributed under the terms of the [Apache License 2.0],
"napari-stereoTis" is free and open source software.

## Issues

If you encounter any problems, please [file an issue] along with a detailed description.

[napari]: https://github.com/napari/napari
[Apache License 2.0]: https://www.apache.org/licenses/LICENSE-2.0
[file an issue]: https://github.com/secretloong/napari-stereoTis/issues
bilibili-video-info-mcp 0.2.0
An MCP server to fetch Bilibili video information like subtitles, comments, and danmaku.
Requires Python: >=3.10

# MCP Server for Bilibili Video Info

[![English](https://img.shields.io/badge/language-English-blue.svg)](./README.md) [![中文](https://img.shields.io/badge/language-中文-red.svg)](./README.zh.md)

A Bilibili MCP Server that can retrieve subtitles, danmaku (bullet comments), and comments information from videos using the video URL.

## Usage

This MCP server supports three transport methods:
1. **stdio** 
```json
{
    "mcpServers": {
        "bilibili-video-info-mcp": {
            "command": "uvx",
            "args": [
                "bilibili-video-info-mcp"
            ],
            "env": {
                "SESSDATA": "your valid sessdata"
            }
        }
    }
}
```

2. **sse** (Server-Sent Events)
run bilibili-video-info-mcp in sse mode
``` bash
cp .env.example .env
uvx run --env .env bilibili-video-info-mcp sse
```
then config your mcp client
```json
{
    "mcpServers": {
        "bilibili-video-info-mcp": {
            "url": "http://{your.ip.address}:$PORT$/sse"
        }
    }
}
```

3. **streamable-http** (HTTP Streaming)
run bilibili-video-info-mcp in streamable-http mode
``` bash
cp .env.example .env
uvx run --env .env bilibili-video-info-mcp streamable-http
```
then config your mcp client
```json
{
    "mcpServers": {
        "bilibili-video-info-mcp": {
            "url": "http://{your.ip.address}:$PORT$/mcp"
            }
        }
    }
}
```

## MCP Tools List

### 1. Get Video Subtitles

```json
{
  "name": "get_subtitles",
  "arguments": {
    "url": "https://www.bilibili.com/video/BV1x341177NN"
  }
}
```

### 2. Get Video Danmaku (Bullet Comments)

```json
{
  "name": "get_danmaku",
  "arguments": {
    "url": "https://www.bilibili.com/video/BV1x341177NN"
  }
}
```

### 3. Get Video Comments

```json
{
  "name": "get_comments",
  "arguments": {
    "url": "https://www.bilibili.com/video/BV1x341177NN"
  }
}
```

## FAQ

### 1. How to find SESSDATA?

1. Log in to the Bilibili website
2. Open browser developer tools (F12)
3. Go to Application/Storage -> Cookies
4. Find the value corresponding to SESSDATA

### 2. Error "SESSDATA environment variable is required"

Make sure you have set the environment variable:

```bash
export SESSDATA="your SESSDATA value"
```

### 3. What video link formats are supported?

Standard Bilibili video links are supported, such as:
- https://www.bilibili.com/video/BV1x341177NN
- https://b23.tv/xxxxx (short links)
- Any link containing a BV number
valmix 1.0.0
Adjust numerical values from a terminal user interface.
Requires Python: >=3.8
Keywords: multiprocessing, value, tui, user, interface

# Valmix

<img align="right" width=400 src="https://github.com/stephane-caron/valmix/assets/1189580/280c02b9-46a4-4a61-bb42-3befd1c59879">

[![Build](https://img.shields.io/github/actions/workflow/status/stephane-caron/valmix/ci.yml?branch=main)](https://github.com/stephane-caron/valmix/actions)
[![Documentation](https://img.shields.io/github/actions/workflow/status/stephane-caron/valmix/docs.yml?branch=main&label=docs)](https://stephane-caron.github.io/valmix/)
[![Coverage](https://coveralls.io/repos/github/stephane-caron/valmix/badge.svg?branch=main)](https://coveralls.io/github/stephane-caron/valmix?branch=main)
[![PyPI version](https://img.shields.io/pypi/v/valmix)](https://pypi.org/project/valmix/)

Valmix ("value mixer") gives a systematic way to tune Python program parameters from your terminal (similar to `alsamixer` for Linux users familiar with it). Wrap your parameters in `multiprocessing` values, pass them to both your program and `valmix.run()`, and a terminal user interface will appear 🪔 allowing you to modify parameters in real time while your program is running.

Code is shorter than words in [Usage](#usage) below :wink:

## Installation

### From conda-forge

```console
conda install -c conda-forge valmix
```

### From PyPI

```console
pip install valmix
```

## Usage

Suppose you have a Python program with parameters you want to tune:

```py
def main(kp: float, kd: float):
    pass  # your code here
```

Valmix gives a systematic way to tune these parameters from the command line. First, wrap your parameters in `multiprocessing.Value`s:

```py
import multiprocessing as mp

kp = mp.Value("f", 10.0)
kd = mp.Value("f", 1.0)
```

Next, update your program to read from the multiprocessing values. For example:

```py
import numpy as np
import time

def main(kp: mp.Value, kd: mp.Value):
    with open("/tmp/output", "w") as output:
        for _ in range(100):
            u = np.clip(kp.value * 1.0 + kd.value * 0.1, 5.0, 20.0)
            output.write(f"{u}\n")
            output.flush()
            time.sleep(1.0)

```

Finally, run your program and Valmix together, specifying the tuning range for each value:

```py
main_process = mp.Process(target=main, args=(kp, kd))
main_process.start()

valmix.run(
    {
        "kp": (kp, np.arange(0.0, 20.0, 0.5)),
        "kd": (kd, np.arange(0.0, 10.0, 0.5)),
    }
)
```

This will fire up a terminal user interface (TUI) where you can tune `kp` and `kd` while the program runs in the background:

![image](https://github.com/stephane-caron/valmix/assets/1189580/1d50ccf5-9bb2-4a73-95e3-9f3345a91311)

Useful for instance to [tune robot behaviors](https://github.com/upkie/upkie/blob/main/examples/wheeled_balancing.py) in real-time 😉

## See also

Related software:

- [Textual](https://textual.textualize.io/): terminal user interface (TUI) framework for Python, used to build this tool.
threedtool 0.0.8
This module is needed to work in geometric primitives
Requires Python: >=3.10

# ThreeDTool


This module is needed to work with mathematical structures such as plane, line, 
triangles and other polygons, polyhedron.

Этот модуль необходим для работы с математическими структурами, такими как плоскость, линия, 
треугольники и другие многоугольники, многогранники.
eoq3pyecorempl 2.9.4
A model persistance layer for EOQ3 based on pyecore
Requires Python: >=3.10

# eoq3pyecorempl - A model persistance layer for EOQ3 based on pyecore 

This is a model persitancy layer (MPL) based on pyecore. 
This MPL behaves similar to a WorkspaceDomain in EOQ2. 
A workspace directory containing M2 and M1 models in 

* <modelname>.ecore and <user model>.<modelname>
* *.xml

format is parsed and uploaded to a domain as a workspace model.
		
## Usage

Imports:

    from eoq3pyecorempl import PyEcoreWorkspaceMpl

Init and connect the MPL:

    mpl = PyEcoreWorkspaceMpl(WORKSPACE_PATH)
	
Load the workspace directory:

	mpl.Load()
	
Connect to the domain and upload the workspace model:

    mpl.Connect(domain)
	
	
Accessing a M2 model:

    res = domain.Do( Get(Cls("workspacemdbmodel__Resource").Sel(Pth("name").Equ("oaam.ecore"))) )
		
Accessing a M2 model:

	res = domain.Do( Get(Cls("workspacemdbmodel__Resource").Sel(Pth("name").Equ("MinimalFlightControl.oaam"))) )
	
Accessing the model content:

    res = domain.Do( Get(Cls("workspacemdbmodel__Resource").Sel(Pth("name").Equ("MinimalFlightControl.oaam")).Idx(0).Pth("content")) )
	
See also pyeoq/Test/Eoq3/test_pyecorempl.py for working code.

## Workspace

A valid WORKSPACE_PATH could look like

* Meta

  * oaam.ecore

* MinimalFlightControl.oaam
* Library.xml

Subfolders are not mandatory.

Such a workspace directory can be found at pyeoq/Test/Eoq3/testdata/Workspace.

## Documentation

For more information see EOQ3 documentation: https://eoq.gitlab.io/doc/eoq3/

## Author

2024 Bjoern Annighoefer
pysbml 0.3.0
Add your description here
Requires Python: >=3.12

# pySBML

[![pypi](https://img.shields.io/pypi/v/pysbml.svg)](https://pypi.python.org/pypi/pysbml)
[![docs][docs-badge]][docs]
![License](https://img.shields.io/badge/license-MIT-blue?style=flat-square)
![Coverage](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fgist.githubusercontent.com%2Fmarvinvanaalst%2Fb518d017c83b8938be27036ee04df0e3%2Fraw%2F77de2288523103b449f89df15b627431bbfdf403%2Fcoverage.json&query=%24.message&label=Coverage&color=%24.color&suffix=%20%25)
[![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)
[![security: bandit](https://img.shields.io/badge/security-bandit-yellow.svg)](https://github.com/PyCQA/bandit)
[![PyPI Downloads](https://static.pepy.tech/badge/pysbml)](https://pepy.tech/projects/pysbml)

[docs-badge]: https://img.shields.io/badge/docs-main-green.svg?style=flat-square
[docs]: https://computational-biology-aachen.github.io/pysbml/


`pySBML` takes SBML models and makes them simpler :heart:

## Installation

Installation is as easy as `pip install pysbml`.


## Transform this

**Compartments**

| name | size | is_constant |
| ---- | ---- | ----------- |
| C    | 1.0  | True        |

**Variables**

| name | amount | conc    | constant | substance_units | compartment | only_substance_units | boundary_condition |
| ---- | ------ | ------- | -------- | --------------- | ----------- | -------------------- | ------------------ |
| S1   | None   | 0.00015 | False    | substance       | C           | False                | False              |
| S2   | None   | 0.0     | False    | substance       | C           | False                | False              |

**Parameters**

| name | value | is_constant | unit |
| ---- | ----- | ----------- | ---- |
| k1   | 1.0   | True        |      |

**Reactions**

| name      | body        | args        | stoichiometry           | local pars |
| --------- | ----------- | ----------- | ----------------------- | ---------- |
| reaction1 | C * k1 * S1 | [C, k1, S1] | {'S1': -1.0, 'S2': 1.0} | {}         |

## Into this


**Parameters**

| name | value | unit |
| ---- | ----- | ---- |
| k1   | 1.0   | None |
| C    | 1.0   | None |

**Variables**

| name | value   | unit |
| ---- | ------- | ---- |
| S1   | 0.00015 | None |
| S2   | 0.0     | None |

**Reactions**

| name      | fn    | stoichiometry           |
| --------- | ----- | ----------------------- |
| reaction1 | S1*k1 | {'S1': -1.0, 'S2': 1.0} |



## Development setup

We recommend using `uv`

### uv

- Install `uv` as described in [the docs](https://docs.astral.sh/uv/getting-started/installation/).
- Run `uv sync --all-extras --all-groups` to install dependencies locally
salt-kafka 0.2.0
Manage Kafka clusters with Salt
Requires Python: >=3.10
Keywords: kafka, salt

# salt-kafka

Kafka tools for Salt

[![PyPI - Version](https://img.shields.io/pypi/v/salt-kafka)](https://pypi.org/project/salt-kafka/)
[![Gitea Stars](https://img.shields.io/gitea/stars/kfdm/salt-kafka?gitea_url=https://codeberg.org)](https://codeberg.org/kfdm/salt-kafka)

## Installation

Can be installed via `pip` or the Pip version distributed in Salt onedir installs.
Salt extensions need to be installed to any `salt-minion` where they may run.

```shell
# Example for Salt onedir
/opt/saltstack/salt/salt-pip install salt-kafka

# Can be installed via Salt commands
salt '*' pip.install salt-kafka
```

or Via a salt state

```yaml
salt-minion:
  service.running:
    - enable: true
  # Install any OS requirements
  pkg.installed:
    - aggregate: True
    - pkgs:
        - python3-docker
        - python3-setproctitle
  # Install any pip packages
  pip.installed:
    - pkgs:
        - salt-kafka
```

## Engine

`kafka_consumer` can be used to map Kafka topics to a Salt topic to be used in automation.

```yaml
# From Salt Master config

engines:
  - kafka_consumer:
      broker: kafka.example.com:9094
      subscribe:
        # Format is <kafka-topic> / <salt-topic>
        push-repo: kafka/repo
        push-package: kafka/container
```

## States

```yaml
# Example minion config
kafka.bootstrap.servers: "kafka.example.com:9094"
```

```yaml
# Example kafka.sls
my-topic:
    kafka.present:
        - config:
              cleanup.policy: delete
              retention.ms: {{salt['kafka.timedelta_ms']('2d')}}
              retention.bytes: {{ "1GB" | human_to_bytes }}

missing-topic:
    kafka.absent: []
```

See the Upstream Kafka documentation at <http://kafka.apache.org/documentation.html#topicconfigs> for valid config options.