semvec/pypi-packages huggingface dataset:
choose-mcp-server 0.1.10
Add your description here
Requires Python: >=3.10.6

# Choose MCP Server Setup

1. Start by downloading the Claude Desktop Client: https://claude.ai/download

2. Install uv

```
brew install uv
```

3. Install the MCP server

Edit the `claude_desktop_config.json` file (located in `~/Library/Application\ Support/Claude`) and add the following to the mcpServers section:

```javascript
{
  "mcpServers": {
    "Choose MCP Server": {
      "command": "uvx",
      "args": ["choose-mcp-server"],
      "env": {
        "PROJECT_ID": YOUR_PROJECT_ID,
        "DATASETS": DATASET_1,DATASET_2,DATASET_3
        "DBT_MANIFEST_FILEPATH": YOUR_DBT_MANIFEST_FILEPATH
      }
    }
  }
}
```

N.B: the dbt manifest file path is optional.

4. Log into Google Cloud and update your Application Default Credentials (ADC)

```
gcloud auth login --update-adc
```

5. Open Claude Desktop and start asking questions!

## Troubleshooting

For Windows users, you may need to add the `APPDATA` environment variable to your Claude Desktop config file.

```javascript
"env": {
  "APPDATA": "C:\\Users\\YOUR_USERNAME\\AppData\\Roaming",
}
```
flet-staticserver 0.1.1
A simple static file server for Flet apps, perfect for mobile debugging.
Requires Python: >=3.7

```markdown
# **StaticServer Documentation** 🚀

The `StaticServer` class is designed to simplify debugging for mobile devices (iOS/Android) in Flet apps. It serves static files (like images, icons, or text files) from your computer, making them accessible to mobile devices during development. 📱💻

---

## **Why Use StaticServer?** 🤔
When developing for mobile:
- Direct file paths from your computer won’t work on devices. 🚫
- StaticServer hosts your files locally, providing a URL (`server.assets`) that devices can access. 🌐
- Perfect for debugging and testing assets on real devices. 🛠️

---

## **How to Use It** 🛠️

### **1. Installation**
pip install flet-staticserver
from flet_staticserver import StaticServer
```

---

### **2. Basic Setup**
Start the server and use it in your Flet app:

```python
import flet as ft
from serve_files import StaticServer

# Initialize the server 🎬
server = StaticServer()

def main(page: ft.Page):
    # Access files using server.assets 🖼️
    page.add(
        ft.Image(src=f"{server.assets}/icon.png"),
        ft.Text(f"Access files at: {server.assets}")
    )

# Run your Flet app 🚀
ft.app(main)
```

---

### **3. Customization Options** ⚙️
Customize the server to fit your needs:
- **Change the folder**: Serve files from a specific directory.
- **Change the port**: Use a different port if needed.

```python
server = StaticServer(
    directory="path/to/your/assets",  # 🗂️ Custom folder
    port=2222                        # 🚪 Custom port
)
```

---

### **4. Accessing Files** 📂
Once the server is running, access your files like this:
- **File in root**: `{server.assets}/file.txt`
- **File in subfolder**: `{server.assets}/images/photo.jpg`

Example:
```python
ft.Image(src=f"{server.assets}/images/icon.png")
```

---

## **How It Works** 🧙‍♂️
- The server runs in the background using **FastAPI** and **UVicorn**. ⚡
- It automatically detects your machine's IPv4 address, so devices on the same network can access the files. 🌐
- Files are served dynamically, making debugging seamless. 🛠️

---

## **Example Use Case** 🖼️
Display an image from your `assets` folder on a mobile device:

```python
import flet as ft
from serve_files import StaticServer

# Start the server 🎬
server = StaticServer()

def main(page: ft.Page):
    # Display an image from the server 🖼️
    page.add(
        ft.Image(src=f"{server.assets}/icon.png"),
        ft.Text("Your image is live!")
    )

# Run the app 🚀
ft.app(main)
```

---

## **Why It’s Useful** ❤️
- Solves the problem of inaccessible local files on mobile devices. 📱
- Simplifies debugging and testing during development. 🛠️
- Works seamlessly with Flet apps. 🎯

---

## **Final Notes** 📝
- The server stops automatically when your app closes.
- Use `server.assets` to get the base URL for your files.
- Happy debugging! 😄👨‍💻👩‍💻

---

**Enjoy building and debugging your Flet apps!** 🎉
claire-organizer 2.0.0
A command line file organizer made. My largest project so far.

# Claire Organizer - Python Edition
(Don't judge me, I'm outta names) <br/>
A command line file organizer made. My largest project as of the creation of this repository.

## Functionalities
Organize the content of a folder, with various option, these include:
1. Configure the file types of a category.
2. Add/Remove a category of file types.

## Installation
<ol type="1">
  <li>
    Install Python 3.x from <a href="https://www.python.org/downloads/">here</a>. (Ignore if you already have Python 3.x installed)
  </li>
  <li>
    Open terminal, run:
    
```powershell
pip install claire_organizer
```
  </li>
</ol>


## Usage
<ol type="1">
  <li>
    Open terminal, run: <!-- I hope the reference won't be too obvious -->

```
elford
```
  </li>
  <li>
    Alternatively, in a Python file (*.py), you can do:
    
```python
from claire_organizer import main
main()
```
  </li>
</ol>

## Changelog
### Update 2.0.0
1. Move to using Ziz's Ultilities functions.
2. Empty folders will now be deleted after sorting.
3. Added duplication handling.

## Requirement(s)
1.  Python 3.x

## Note(s)
1. Deleting the config folder/file could cause everything to revert back to the default setting the next time you boot up the progam.

## License
This project is licensed under the GNU General Public License 3.0, check [LICENSE](LICENSE) for more details.
agi-med-grpc-interceptors 3.2.0
Interceptors for grpc
Requires Python: >=3.11

# agi-med-grpc-interceptors

Стандартные middlewares для grpc

## Ответственный разработчик

@zhelvakov

## Общая информация

- LoggingInterceptor - работает на loguru. Принимает metadata, забирает все хедеры и пытается использовать как контекст, по-умолчанию дополнительно прописывает uuid в контекст. Обработка ошибок может быть переопределена с помощью метода exception_handler
- ExcHandlerInterceptor - добавляет подсчет ошибок с помощью prometheus: error_counter[path, status_code, error]. Обрабатывает ошибки, которые наследуются от BaseGrpcError (Пример ниже) 


### Примеры

1. Подключение interceptors

```python
import grpc
from concurrent import futures

from agi_med_grpc_interceptors import LoggingInterceptor, ExcHandlerInterceptor


server = grpc.server(
        futures.ThreadPoolExecutor(max_workers=10),
        interceptors=[LoggingInterceptor(), ExcHandlerInterceptor()],
    )
```

2. Реализация ошибки для сервиса

```python
#server
from agi_med_grpc_interceptors import BaseGrpcError, DefaultValue
from grpc import StatusCode

from .some_protos.Some_pb2_grpc import SomeServicer
from .Some_pb import GetSalaryResponse, GetSalaryRequest


class EmployerNotFound(BaseGrpcError[float]):
    def __init__(self, message: str) -> None:
        default_value = DefaultValue[float](value=0)
        super().__init__(StatusCode.ALREADY_EXISTS, message, default_value)

class Some(SomeServicer):
    def GetSalary(self, request: GetSalaryRequest) -> GetSalaryResponse:
        if request.Username == "bob":
            raise EmployerNotFound("Bob, go to home!")
        return GetSalaryResponse(salary=10_000)

#client
from agi_med_grpc_interceptors import BaseGrpcError, ErrorDetails
from grpc import RpcError

from .Some_pb import GetSalaryResponse, GetSalaryRequest
from .Some_pb2_grpc import SomeStub


stub = SomeStub("0.0.0.0:5000")

try:
    request = GetSalaryRequest(Username="bob")
    response: GetSalaryResponse = stub.DoSomething(request)
except RpcError as ex:
    error: ErrorDetails[float] | None = BaseGrpcError[float].extract_error(ex)
    default_value: float | None = BaseGrpcError[float].extract_default_value_from_error(ex)
    print(f"{error=}")
    print(f"{default_value=}")
```

### Линтеры

```shell
pip install black flake8-pyproject mypy
black .
flake8
mypy .
```
xpcsutilities 1.0.4
ESRF ID02 XPCSUtilities
Requires Python: >3.6

XPCSUtilities
=============

Display XPCS result files from ESRF ID02 beamline, perform basic
operations on results, basic fitting tools.

.. figure:: xpcsutilities/xpcsutilities-logo.svg
   :alt: XPCSUtilities logo

   XPCSUtilities logo

Installation
------------

**System requirements**

XPCSUtilities works only with python version >= 3.6. Pip will take care
of all dependencies. Dependency list can be showed in setup.py file.

It is **strongly** recommended to install XPCSUtilities in a dedicated
virtual environment (virtualenv) to prevent any library version mismatch
issue.

Stable version:

.. code:: bash

   pip install xpcsutilities

Development version:

.. code:: bash

   pip install https://gitlab.esrf.fr/id02/xpcsutilities/-/archive/main/xpcsutilities-main.zip

python package
--------------

This package can be used in your own analysis script to read data
directly from the hdf5/NxXPCS format.

.. code:: python


   from xpcsutilities.tools.result_file import XPCSResultFile

   with XPCSResultFile('my_filename.h5') as fd: # Always use into a with statement!
       
       print(fd.analysis_keys)
       
       lag, cf, std = fd.get_correlations('full') # Use here the right analysis key

       cf, lag, age = fd.get_ttcf('full', 3) # Return the TTCF of ROI#3 in the 'full' analysis

GUI
---

To launch the program:

.. code:: bash

   XPCSUtilities

If installed in an virtual environement, load the environement first.

On the left panel, you’ll find the files of current working folder. You
can select any files that you want to display on the right panel.
Current working folder is set by the “Set working folder” button. Files
are filtered according to the wildcard filter below the button. Multiple
filters can be set by using a comma-separated list if wildcards. New
files are automatically displayed on the list.

The plot button below display the selected file(s) in current tab.

SAXS 1D
~~~~~~~

This tab display the radial average of the time averaged 2D pattern.
This graphs also shows the correlation ranges.

SAXS 2D
~~~~~~~

This tab display the time averaged 2D pattern. Mask and q-mask can be
displayed as overlay.

-  Corrected: raw 2D pattern corrected by flatfield file
-  Raw: Time average of all 2D patterns recorded
-  Averaged: 2D pattern reconstructed from 1D curve

Correlations
~~~~~~~~~~~~

This tab display the correlation functions

TTCF
~~~~

This tab display the Two Time Correlation Functions

Fit
~~~

This tab provide basic fitting tools. Several models can be used
togethers to fit the data.