snailz 3.2.0

Synthetic data generator for snail mutation survey

Snailz

snailz is a synthetic data generator that models a study of snails in the Pacific Northwest which are growing to unusual size as a result of exposure to pollution. The package can generate fully-reproducible datasets of varying sizes and with varying statistical properties, and is intended primarily for classroom use. For example, an instructor can give each learner a unique dataset to analyze, while learners can test their analysis pipelines using datasets they generate themselves.

The Story

Years ago, logging companies dumped toxic waste in a remote region of Vancouver Island. As the containers leaked and the pollution spread, some snails in the region began growing unusually large. Your team is now collecting and analyzing specimens from affected regions to determine if exposure to pollution is responsible.

snailz generates three related sets of data:

• Grids: the survey grids where pollution levels are measured.
• Persons: the scientists conducting the study.
• Samples: the snails collected from the survey sites.

Usage

1. pip install snailz (or the equivalent command for your Python environment).
2. snailz --help to see available commands.

To generate example data in a fresh directory:

# Create and activate Python virtual environment
$ uv venv
$ source .venv/bin/activate

# Install snailz and dependencies
$ uv pip install snailz

# Write default parameter values to the ./params.json file
$ snailz --defaults > params.json

# Generate all output files in the ./data directory
$ snailz --params params.json --outdir data

Parameters

snailz reads controlling parameters from a JSON file, and can generate a file with default parameter values as a starting point. The parameters, their meanings, and their properties are:

Name	Purpose	Default
clumsy_factor	personal effect on mass measurement	0.5
grid_size	width and height of (square) survey grids	11
locale	locale for person name generation	et_EE
num_grids	number of survey grids	3
num_persons	number of persons	5
num_samples	number of samples	20
pollution_factor	pollution effect on mass	0.3
precision	decimal places used to record masses	2
sample_date	min/max sample dates	(2025-01-01, 2025-01-01)
sample_mass	min/max sample mass	(0.5, 1.5)
seed	random number generation seed	123456

Data Dictionary

All of the generated data is stored in CSV files.

Grids

The pollution readings for each survey grid are stored in a file Gnnnn.csv (e.g., G0003.csv). These CSV files do not have column headers; instead, each contains a square integer matrix of pollution readings. A typical file is:

0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
0,0,0,0,0,0,0,0,0,1,1,0,0,0,0
0,0,0,0,0,0,0,0,1,2,1,0,0,0,0
0,0,0,0,0,0,0,0,2,1,0,0,0,0,0
0,0,0,0,0,0,0,1,2,0,0,0,0,0,0
0,0,0,0,0,0,0,1,2,1,0,0,0,0,0
0,0,0,0,0,0,0,0,1,2,0,0,0,0,0
0,0,0,0,0,0,0,2,2,1,0,0,0,0,0
0,0,0,0,0,0,0,1,3,0,0,0,0,0,0
0,0,0,0,0,0,0,1,3,1,0,0,0,0,0
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0

Persons

persons.csv stores the scientists performing the study in CSV format (with column headers):

id	personal	family
P06	Artur	Aasmäe
P07	Katrin	Kool
…	…	…

Its fields are:

Field	Purpose	Properties
id	identifier	text, unique, required
personal	personal name	text, required
family	family name	text, required

Samples

samples.csv stores information about sampled snails in CSV format (with column headers):

sample_id	grid_id	x	y	pollution	person	when	mass
S0001	G0001	9	8	0	P0004	2025-01-16	1.02
S0002	G0001	8	9	1	P0005	2025-03-30	2.39
…	…	…	…	…	…	…	…

Its fields are:

Field	Purpose	Properties
sample_id	specimen identifier	text, unique, required
grid_id	grid identifie	text, required
x	X coordinate in grid	integer, required
y	Y coordinate in grid	integer, required
pollution	pollution at that point	integer, required
person	who collected the sample	text, required
when	date sample collected	date, required
mass	sample weight in grams	real, required

The output directory also contains a file called changes.json that records parameters used to alter data, such as the daily growth rate of snails and the ID of the clumsy scientist whose measurements have systematic errors.

Colophon

snailz was inspired by the Palmer Penguins dataset and by conversations with Rohan Alexander about his book Telling Stories with Data.

The snail logo was created by sunar.ko.

My thanks to everyone who built the tools this project relies on, including:

• pydantic for storing and validating data (including parameters).
• pytest and faker for testing.
• ruff for checking the code.
• uv for managing packages and the virtual environment.