Tutorial 1. Generate a fake data file

Aims of tutorial

This tutorial will take you through installing Headfake, creating a Headfake configuration file and using it to generate a data file.

Install Headfake

You will need at least Python 3.6+ installed in order to use Headfake. The easiest way to install it is through pip. In the terminal enter:

pip install headfake

Check it is installed correctly by running:

headfake --help

You should get some information on the usage of the tool.

Creating a template file

Headfake is powered through a plain text template file in YAML format. The easiest way to demonstrate its functionality is to try out an example.

Open a file tutorial.yml in your home directory, copy and paste the information below into it and save it:

fieldset:
  class: headfake.Fieldset
  fields:
    - name: main_pat_id
      class: headfake.field.IdField
      prefix: S
      generator:
        class: headfake.field.IncrementIdGenerator
        length: 7
        min_value: 1000000

    - name: gender
      class: headfake.field.GenderField
      male_value: "M"
      female_value: "F"
      male_probability: 0.3

    - name: last_name
      class: headfake.field.LastNameField
      gender_field: gender

    - name: dob
      class: headfake.field.DateOfBirthField
      min: 0
      max: 105
      mean: 45
      sd: 13
      distribution: scipy.stats.norm
      date_format: "%Y-%m-%d"

Generating fake data

Now on the command-line run:

headfake /path/to/tutorial1.yml -o /path/to/tutorial1.txt -n100

Here you are specifying the template to use, and then an output file (/path/to/tutorial1.txt) and number of rows to generate (100)

If you now open the output file you will see 1000 rows of generated data.

You can also run without the -o option and the data will be output to the screen:

headfake /path/to/tutorial1.yml  -n100

This latter approach is often better when building templates in Headfake as it more immediate.

Analysis

This is a simple example of what Headfake can do. It is also possible to use YAML to provide your fields as a dictionary, or to embed it directly as Python data structures and/or code. There is additional information on different ways for initialising Headfake.

The template you used defines a set of fields containing autogenerated IDs, gender, a last name and a date of birth following a normal distribution.

You can see from this tutorial how straightforward it would be to add additional fields to the template and to generate more/less rows of data.

In the next tutorial we will take a closer look at the setup of the fields in the YAML file and modify them to create different data and formats.