Blind Charging Demo

This package demonstrates the API of the Blind Charging Python package.

This package is for demonstration use only, to give a sense of how our package can fit into your existing software. This package does not contain our actual redaction algorithm.

Install

Our module runs on Python using version 3.7 or higher. However, for the purposes of this test module, you will need to use Python 3.9 or higher.

To install the Python module, you need to first create and then activate a virtual environment using the following code.

On MacOS or Unix:

python3 -m venv blind-charging-env
source blind-charging-env/bin/activate

On Windows:

python3 -m venv blind-charging-env
blind-charging-env\Scripts\activate.bat

Then, with this virtual environment activated, run in your terminal:

pip install blindcharging_demo-1.0.2-py3-none-any.whl

This will make the blindcharging demo module available to Python.

Testing

To verify that the blind charging demo module has successfully installed, try running the following from within the virtual environment (you can change the unredacted argument to any string):

python -m blindcharging --unredacted "This is a demo narrative."

Successful execution will produce the following output:

Unredacted narrative text: This is a demo narrative.
Redacted narrative text: [SECRET] is a demo narrative.
Annotations: [{'start': 0, 'end': 4, 'content': '[SECRET]', 'extent': 8, 'type': 'redaction', 'info': 'demo redaction'}]

Details

First, you need to define a Locale for your jurisdiction. The Locale supplies your jurisdiction's local context to the redaction algorithm, including local street and neighbordhood names as well as other custom indicators used in your police narratives.

Here's a Locale for the hypothetical jurisdiction of "Gallo Alto, California." A real Locale will have more information.

from blindcharging import Locale

gallo_alto_locale = Locale("Gallo Alto",
    police_districts=["Central", "Bayview", "University", "Southside"],
    street_names=[
        "Maple St",
        "Elm St",
        "College Ave",
        # Etc ... a real list would be very long, and probably best loaded
        # from a CSV or other data file.
        ],
    neighborhoods=["Pollo Park", "Doodle Hill", "Feather Lake", "The Beak"],
    indicators={
        "person": {
            "V": "Victim",
            "S": "Suspect",
            "W": "Witness",
            # There are probably many more abbreviations you use.
            },
        },
    indicator_position="prefix",
    excluded_names=["GAPD"],
    )

Now, you can use your Locale to redact a narrative.

from blindcharging import redact

demo_narrative = "This is a demo narrative."

result = redact(
    locale=gallo_alto_locale,
    narrative=demo_narrative,
    persons=[...],
    officers=[...],
    )

print("Unredacted narrative text:", demo_narrative)
print("Redacted narrative text:", result.redacted)
print("Annotations:", result.annotations)

Development (for Stanford reference only)

These are the steps to build a new wheel. (They are the same as many other Python projects.)

1. Create a virtual environment with python3 -m venv venv
2. Activate the environment with source venv/bin/activate
3. Install development packages with pip install -r requirements.txt
4. Build the wheel with python -m build

The new wheel (and tarball) will be available in dist/.