semvec/pypi-packages huggingface dataset:
neuralmap-client 0.0.0
Search and recommandation engines.
License: MIT
Requires Python: >=3.10

# Neuralmap API

Neuralmap is designed by Tensor AI to streamline and enhance every aspect of search and recommandation engines.

## Getting started

To get started with the Neuralmap API client, you'll need to sign up for an API key on the Neuralmap platform. Once you have your API key, you can initialize the API client in your application and start making requests to the Neuralmap API.

### Install the API Client
You can install the Neuralmap API client for Python using the following pip command:

```bash
pip install neuralmap-client
```

This command will download and install the Neuralmap API client along with its dependencies from the Python Package Index (PyPI). Once installed, you can start using the API client to interact with the Neuralmap API and process candidate data programmatically.

### Test your installation
To ensure that the installation of the Neuralmap API client for Python was successful, you can perform a simple test to verify its functionality. Here's a basic example of how you can test the installation:

```python
from neuralmap import BaseClient

client = BaseClient(api_key='your_api_key_here')
client.hello()
```

The full and official documentation can be found [here](https://docs.neuralmap.io).
neuralmap-python 0.0.1
Search and recommandation engines.
License: MIT
Requires Python: >=3.10

# Neuralmap API

Neuralmap is designed by Tensor AI to streamline and enhance every aspect of search and recommandation engines.

## Getting started

To get started with the Neuralmap API client, you'll need to sign up for an API key on the Neuralmap platform. Once you have your API key, you can initialize the API client in your application and start making requests to the Neuralmap API.

### Install the API Client
You can install the Neuralmap API client for Python using the following pip command:

```bash
pip install neuralmap
```

This command will download and install the Neuralmap API client along with its dependencies from the Python Package Index (PyPI). Once installed, you can start using the API client to interact with the Neuralmap API and process candidate data programmatically.

### Test your installation
To ensure that the installation of the Neuralmap API client for Python was successful, you can perform a simple test to verify its functionality. Here's a basic example of how you can test the installation:

```python
from neuralmap import BaseClient

client = BaseClient(api_key='your_api_key_here')
client.hello()
```

The full and official documentation can be found [here](https://docs.neuralmap.io).
feather-extract 0.1.16
A package for extracting handwritten data from PDF documents, and returning an Excel workbook output.
License: MIT
Requires Python: >=3.8

# feather-extract

`feather-extract` is a Python package designed to extract handwritten data from PDF documents, format the extracted data, and save it as an Excel workbook. This package is particularly useful for businesses in the bar and restaurant industry that need to manage inventory.

[video-tutorial](https://www.youtube.com/watch?v=s1YWwMr3lRk)
[documentation](https://pypi.org/project/feather-extract/0.1.16/)

## Installation

To install `feather-extract`, run the following command:
`pip install feather-extract`

## Extracting Text from Documents

feather_extract is trained on a standarized form, to download this form simply run the `get_form()` function

by setting `get_form(filled_out=True)` you can also download an already filled out form for testing and practice.

`from feather_extract import get_form`

`form = get_form()`

To extract text from a document, use the `extract_text_from_document` function:

`from feather_extract import extract_text_from_document`

`extracted_text = extract_text_from_document('path/to/document.pdf')`

This function uses the Azure Form Recognizer service to extract text from the document. You'll need to provide your Azure Form Recognizer API key and endpoint when prompted.

## Formatting Extracted Text

The format_extracted_text function takes the extracted text and formats it into a list of rows, each containing an item, quantity, and bar designation:
`from feather_extract import format_extracted_text`

`formatted_data = format_extracted_text(extracted_text)`

The formatted_data variable will contain a list of lists, where each inner list represents a row with the following format: [item, quantity, bar_designation].
Saving Data to Excel

To save the formatted data to an Excel workbook, use the save_to_excel function:

`from feather_extract import save_to_excel`

`save_to_excel(headers, formatted_data, 'output.xlsx')`

This function creates a new Excel workbook, writes the headers and formatted data rows to the active worksheet, and saves the workbook to the specified file name ('output.xlsx' in this example).

## Dependencies

`feather-extract` relies on the following dependencies:

`azure-ai-formrecognizer` - For extracting text from PDF documents using the Azure Form Recognizer service.

`openpyxl` - For creating and writing data to Excel workbooks.

These dependencies will be automatically installed when you install feather-extract using pip.

## Contributing
Contributions to feather_extract are welcome! 

If you encounter any issues or have suggestions for improvements, please open an issue on the [GitHub repository.](https://github.com/hofmannj0n/feather-extract)

To contribute code changes, follow these steps:

- Fork the repository

- Create a new branch for your changes

- Make your changes and commit them with descriptive commit messages

- Push your changes to your forked repository

- Open a pull request against the main repository

## License
feather-extract is released under the MIT License.
hikconnect 1.2.1
Communicate with Hikvision smart doorbells via Hik-Connect cloud.
License: MIT
Requires Python: <4.0,>=3.8

# Usage

```python
from hikconnect.api import HikConnect

async with HikConnect() as api:

    await api.login("foo", "bar")

    devices = [device async for device in api.get_devices()]
    print(devices)
    # [{
    #   'id': 'DS-XXXXXX-YYYYYYYYYYYYYYYYYYYYYYYYY',
    #   'name': 'DS-XXXXXX-Y(ZZZZZZZZZ)',
    #   'serial': 'ZZZZZZZZZ',
    #   'type': 'DS-XXXXXX-Y',
    #   'version': 'V1.2.3 build 123456',
    #   'locks': {1: 2, 2: 0, 3: 1}
    # }]
    # locks data means (guessing): <channel number>: <number of locks connected>

    my_device_serial = devices[0]["serial"]

    cameras = [camera async for camera in api.get_cameras(my_device_serial)]
    print(cameras)
    # [
    #   {'id': '4203fd7c5f89ce96f8ff0adfdbe8b731', 'name': 'foo', 'channel_number': 1, 'signal_status': 1, 'is_shown': 0},
    #   {'id': 'cd72bc923956952194468738123b7a5e', 'name': 'bar', 'channel_number': 2, 'signal_status': 1, 'is_shown': 1},
    #   {'id': 'd2a2057d853438d9a5b4954baec136e3', 'name': 'baz', 'channel_number': 3, 'signal_status': 0, 'is_shown': 0}
    # ]

    call_status = await api.get_call_status(my_device_serial)
    print(call_status)
    # {
    #   'status': 'idle',
    #   'info': {
    #     'building_number': 0,
    #     'floor_number': 0,
    #     'zone_number': 0,
    #     'unit_number': 0,
    #     'device_number': 0,
    #     'device_type': 0,
    #     'lock_number': 0
    #   }
    # }
    # can be "idle" / "ringing" / "call in progress" - see hikconnect/api.py:45
    
    # Unlock device
    await api.unlock(my_device_serial, 1)
    
    # Cancel call for device
    await api.cancel_call(my_device_serial)

    # call this periodically at least once per 30 mins!
    if api.is_refresh_login_needed():
        await api.refresh_login()
```

If you are new to `async` Python, you simply need to wrap your code in a construction like this:

```python
import asyncio

async def main():
    # put the original code containing `async` keywords here

asyncio.run(main())
```

More info [in the `async` docs](https://docs.python.org/3/library/asyncio.html).