semvec/pypi-packages huggingface dataset:
mkdocstrings-vba 0.0.10
MkDocstrings VBA handler
Requires Python: >=3.7

# mkdocstrings-vba

A VBA handler for [mkdocstrings](https://github.com/mkdocstrings/mkdocstrings).

Since there is no official way of documenting VBA functions, we have opted for
the [Google Docstring format](https://google.github.io/styleguide/pyguide.html#38-comments-and-docstrings) commonly used
in Python projects. This is conveniently parsed by the [`griffe`](https://mkdocstrings.github.io/griffe) library which
is also used by [`mkdocstrings[python]`](https://mkdocstrings.github.io/python/). The argument types and return types
are taken from the
VBA [Function](https://learn.microsoft.com/en-us/office/vba/language/reference/user-interface-help/function-statement)
or [Sub](https://learn.microsoft.com/en-us/office/vba/language/reference/user-interface-help/sub-statement) signatures,
which we parse using [regex](https://regular-expressions.info).

## Examples

See the [`examples`](https://github.com/mkdocstrings/vba/tree/master/examples) folder.

To build an example site:

1. `pip install mkdocstrings mkdocstrings-vba`
2. `cd examples/example1`
3. View the source code.
4. `mkdocs build`
5. cd `site/`
6. View the results.

## Running tests

```shell
pip install -r test-requirements.txt
python -m unittest
```

This will run all tests. This includes

- Unit tests from `tests/`.
- Doctests from `mkdocstrings_vba/`.
- Full builds from `examples/`.

## Linting

Fix code style using `black`:

```shell
black .
```

Check types using `mypy`:

```shell
mypy .
```
PyDbMixinLib 0.1
A mixin for working with databases in FastAPI using SQLAlchemy.
Requires Python: >=3.6

# DatabaseMixin

DatabaseMixin is a Python library for FastAPI and SQLAlchemy. It provides a set of helper methods for common database operations, including retrieving, creating, updating, and deleting records.

## Installation

To install DatabaseMixin, you can simply use pip:

```bash
pip install database_mixin
revit-extractor 0.1.2
A Library allow extract revit without open revit
Requires Python: >=3.9


![Platform](https://img.shields.io/badge/platform-Windows/MacOS/Linux-lightgray.svg) [![License: GNU v3](https://img.shields.io/badge/License-GNU-yellow.svg)](https://opensource.org/licenses/MIT)


![PyPI](https://img.shields.io/pypi/v/revit-extractor?label=pypi%20revit-extractor-python)
![PyPI - Downloads](https://img.shields.io/pypi/dm/revit-extractor?label=pipy-download)

<a href="https://twitter.com/intent/follow?screen_name=chuongmep">
<img src="https://img.shields.io/twitter/follow/chuongmep?style=social&logo=twitter"
alt="follow on Twitter"></a>

## Revit Extractor

Revit Extractor is a library that allows you to export native data from Revit files without needing to open Revit. It’s particularly useful for extracting data from Revit files and integrating it into other systems.

![](./samples/background.png)


## Requirements

- Make sure you have Revit installed on your machine.
- Make sure you have Python installed on your machine version `3.9` or later.
- Make sure you are running on Windows Platform. MacOS and Linux are not supported. 

## How to use

1. Install the library by using pip:
```bash
pip install revit-extractor --upgrade
```

2. Get Revit Version 

```
from revit_extract import RevitExtractor
rvt_path = r"D:\_WIP\Download\Sample Office Building Model V1.rvt"
version = RevitExtractor.get_version(rvt_path)
print(version)
```

3. Use the library in your code:

- Extract all categories from Revit file:

```python
from revit_extract import RevitExtractor
rvt_path = r"D:\_WIP\Download\Sample Office Building Model V1.rvt"
prodb = RevitExtractor(rvt_path).read_prob_data()
categories = prodb.get_all_categories()
for key in categories:
    print(key, categories[key])
```

- Extract data by categories and parameters from Revit file:
```python
from revit_extract import RevitExtractor
rvt_path = r"D:\_WIP\Download\Sample Office Building Model V1.rvt"
prodb = RevitExtractor(rvt_path).read_prob_data()
categories =["Walls", "Doors"]
params = ["Name", "Type", "Level"]
data_frame = prodb.get_data_by_categories_and_params(categories, params)
data_frame.to_excel("output.xlsx", index=False)
```

More ???

## Limitation

- Export Schedule table data is not supported with svf format.

- Geometry data is limit supported to read at the moment (But can export and use APS-Toolkit)

## Disclaimer

This is not an official Autodesk product to support for [Autodesk Platform Services](https://aps.autodesk.com/). Use it at your own risk.

I'm not responsible for any data loss or any other issues caused by using this library, some features need require cost for using. Please read carefully the Autodesk Forge and Autodesk Platform Services terms of use. I'm just doing, testing , maintaining in free time, so feel free to contribute and make it better.

## License

This project is licensed under the GNU v3 License - see the [LICENSE](LICENSE) file for details

## References

- [Use Revit Extractor](https://chuongmep.com/posts/2024-09-25-revit-extractor.html)
Subha-Package 0.1.0
Basic definitions
Requires Python: >=3.6

A brief description of what your project does.
Basic definitions

def data_analyst():
    """
    Prints the common tools used by a data analyst.

    This function outputs a list of popular tools used by data analysts, 
    which includes Excel, Power BI, and Python.

    Example:
        data_analyst()  # Output: "excel, powerbi, python"
    """
    print("excel, powerbi, python")

data_analyst()

def add(a, b):
    """
    Adds two numbers together.

    This function takes two arguments, `a` and `b`, and returns their sum.

    Args:
        a (int or float): The first number.
        b (int or float): The second number.

    Returns:
        int or float: The sum of `a` and `b`.

    Example:
        result = add(3, 5)  # result will be 8
    """
    return a + b
indexbinder 0.1.2
A library for storing and managing FAISS indexes on Google Cloud Storage. Fashionably serverless.
Requires Python: >=3.7

# IndexBinder

A very small library to help you store and retrieve faiss indexes from cloud storage

## Installation

You can install this library using pip:

```
pip install indexbinder
```

## Usage

Here's a basic example of how to use the image search functionality:

```python
from indexbinder import ImageSearch
from PIL import Image

# Create an ImageSearch instance
image_search = ImageSearch(
        "test_index", "google-project-name", "gcs-bucket", check_consistency=True
    )

image_search.add_image("path/to/your/image.jpg", {"example":  "metadata"})})


results = image_search.search(input_image, num_results=5)

# Process the results
for img, similarity in results:
    print(f"Similarity: {similarity}")
    img.show()  # Display the image
```

## Development

To set up the development environment:

1. Clone the repository
2. Create a virtual environment
3. Install the development dependencies:
   ```
   pip install -e ".[dev]"
   ```

## Testing

To run the tests:

```
pytest
```