semvec/pypi-packages huggingface dataset:
gotya-text-anonymizer 1.1.8
A text anonymizer to redact sensitive information

# Gotya Text Anonymizer

Gotya Text Anonymizer is a Python package that helps you to anonymize text by detecting and redacting personally identifiable information (PII) such as names, addresses, phone numbers, credit card numbers, email addresses, and more.

## Installation

To install the Gotya Text Anonymizer package, run the following command:

```bash
pip install gotya-text-anonymizer
```


## Usage

Here's an example of how to use the Gotya Text Anonymizer package:

```python
from gotya_text_anonymizer.anonymizer import load_model, anonymize

load_model()

text = """My name is gotya tech and I live in Berlin, credit card 1234567890123456, 
Phone Number: +441234567890, Address: 123 Main St, Anytown, UK, 
Email: johndoe@example.com, PIN: 8888, CVV: 123"""

redacted_text = anonymize(text, threshold=0.3)

print(redacted_text)



This will print the redacted text with PII replaced by tags. You can customize the threshold value to control the confidence level of the detected entities.

```


## Contributing

Contributions are welcome! If you would like to contribute to the Gotya Text Anonymizer package, please submit a pull request or open an issue on GitHub.

## License

The Gotya Text Anonymizer package is licensed under the MIT License.
mustlink 0.3.2
A python wrapper for the ESA mustlink API
Requires Python: >=3.0
Keywords: telemetry,MUST,ESA

# mustlink
A python wrapper for the WebMUST API (mustlink)

This is a simple wrapper in python for the [WebMUST](https://www.esa.int/Enabling_Support/Operations/WebMUST_br_A_web-based_client_for_MUST) API.

## Dependencies

The following dependencies must be met:
- python 3
- matplotlib
- pandas
- pyyaml
- requests

## Installation

### pip

```pip install mustlink```

should do the job, although creating a dedicated environment is recommended (see below).

### conda

First, clone this repository. If you are using conda, the dependencies can be installed in a new environment using the provided environment file:

```conda env create -f environment.yml```

The newly created environment can be activated with:

```conda activate mustlink```

Otherwise, please make sure the dependencies are installed with your system package manager, or a tool like `pip`. Use of a conda environment or virtualenv is recommended!

The package can then be installed with:

```python setup.py install```


## URL

The URL for the WebMUST instance in use can be specified when instantiating the Must class. If none is given, a default URL is used. For example:

```python
must = mustlink.Must(url='https://mustinstance.com/mustlink')
```

## Authentication

Access to WebMUST needs authentication. This is controlled by a config file which can be pointed to by the `config_file` parameter when instantiating the Must class, for example:

```python
must = mustlink.Must(config_file='path_to/config.file')
```

If nothing is specified, a file `mustlink.yml` is looked for in paths pointed to by the environment variables `APPDATA`, `XDG_CONFIG_HOME` or in the `.config` folder in the user's home directory.

The configuration file should be in YAML format and contain the username and password as follows:

```yaml
user:
    login: "userone"
    password: "blah"
```

## Example

The Jupyter notebook included with this repository shows an example of how to use the code. Note that not all API functions are wrapped by this library, but only those that are commonly used. To view the notebook, click [here](https://nbviewer.jupyter.org/github/msbentley/mustlink/blob/master/mustlink_example.ipynb).