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.
snailz
can also be used to teach good software development practices:
it is well structured,
well tested,
and uses modern Python tools.
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 of the tree snails in the region began growing unusually large. Your team is now collecting and analyzing specimens from affected regions to determine if a mutant gene makes snails more susceptible to the pollution.
snailz
generates five related sets of data:
- Persons
- The scientists conducting the study. Persons are included in the dataset to simulate operator bias, i.e., the tendency for different people to perform experiments in slightly different ways.
- Machines
- The equipment used to analyze the samples. Like persons, they are included to enable simulation of bias.
- Surveys
- The locations where specimens are collected. Each survey site is represented as a square grid of pollution readings.
- Specimens
- The snails collected from the sites. The data records where the snail was found, the date it was collected, its mass, and a short fragment of its genome.
- Assays
- The chemical analysis of the snails' genomes. Each assay is performed by putting samples of a snail's tissue into some small wells in a plastic microplate. An inert material is placed in other wells as a control; the wells are then treated with chemicals and photographed, and the brightness of each well shows how reactive the material was. Each assay is stored in two files: a design file showing which wells contain samples and controls, and a readings file with the measured responses. The images that the readings are taken from are also stored.
Usage
pip install snailz
(or the equivalent command for your Python environment).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 ./params.json file
$ snailz params --output params.json
# Generate all output files in ./data directory
$ snailz data --params params.json --output 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:
Group | Name | Purpose | Default | Notes |
---|---|---|---|---|
overall | seed |
random number generation seed | 7493418 | non-negative integer |
assay |
baseline |
assay reading for non-mutant specimens | 1.0 | non-negative real |
degrade |
reading degradation per day between sample collection and assay | 0.05 | non-negative real | |
delay |
maximum days of delay between sample collection and assay | 5 | non-negative integer | |
mutant |
assay reading for mutant specimens | 10.0 | non-negative real, greater than baseline |
|
reading_noise |
random noise for readings | 0.1 | non-negative real | |
plate_size |
number of rows and columns in assay plate | 4 | non-negative integer | |
image_noise |
noise to add to assay images | 32 | scale is 0-255 | |
p_duplicate_assay |
probability of duplicate assay | 0.05 | probability | |
machine |
variation |
systematic variation in readings | 0.05 | percentage |
number |
number of machines | 5 | non-negative integer | |
person |
locale |
locale for random name generation | et_EE (Estonia) |
valid ISO locale |
number |
number of persons | 5 | non-negative integer | |
specimen |
length |
genome length in bases | 20 | non-negative integer |
start_date |
date of first assay | 2024-03-01 | copied from surveys for convenience | |
max_mass |
maximum unmutated snail mass | 10.0 | non-negative real | |
mut_mass_scale |
scaling factor for mutated snails | 2.0 | real greater or equal to 1.0 | |
num_mutations |
maximum number of mutations in genome | 5 | non-negative integer | |
spacing |
space between snail specimens | 3.75 | non-negative real | |
daily_growth |
percentage increase in mass per day | 0.01 | non-negative real | |
p_missing_location |
probability that sample location is unknown | 0.05 | probability | |
survey |
number |
number of survey sites | 3 | non-negative integer |
size |
survey grid size | 15 | non-negative integer | |
start_date |
overall survey start date | 2024-03-01 | ISO date | |
max_interval |
maximum number of days between specimen samples | 7 | non-negative integer |
Notes:
-
The pollution values in survey grids are generated by performing a random walk of the grid, adding one to each cell's value each time it is visited. The random walk starts when the polluted region reaches the boundary of the survey grid.
-
Survey sites are sampled sequentially, i.e., all of the samples from one site are collected before any samples are collected from the next.
-
Some specimens' grid locations are missing from the final data. Their XY coordinates are missing in
specimens.csv
andnull
insnailz.db
. -
All snail genomes are the same length, and are generated by mutating the bases at a few randomly-chosen locations. One of those locations and one of the variant bases is selected at random; a snail with that mutant base in that location is a mutant that can grow to unusual size.
-
The masses of all snails are scaled up by an amount that depends on how polluted their collection location is. This scaling uses a sigmoid function rather than a linear function.
-
A few specimens are assayed twice instead of once.
-
The actual readings for mutated and unmutated snails are randomly generated by adding uniform noise to
assay.baseline
andassay.mutant
. The readings for control wells are just noise. -
Assay readings for both mutated and unmutated snails are lowered by an amount that depends on the number of days between the sample being collected and the assay being performed.
Data Dictionary
All of the generated data is stored in a single JSON file called data.json
.
It can be read and analyzed directly,
but it is more realistic to use the data described below.
Persons
persons.csv
contains information about the scientists performing the study.
The file looks like this:
ident | personal | family |
---|---|---|
aa1942 | Artur | Aasmäe |
kk0085 | Katrin | Kool |
… | … | … |
and its fields are:
Field | Purpose | Properties |
---|---|---|
ident |
identifier | text, unique, required |
personal |
personal name | text, required |
family |
family name | text, required |
Machines
machines.csv
contains information about the machines used in the study.
The file looks like this:
ident | name |
---|---|
M0001 | AeroProbe |
M0002 | NanoCounter Plus |
… | … |
and its fields are:
Field | Purpose | Properties |
---|---|---|
ident |
identifier | text, unique, required |
name |
machine name | text, required |
Note that the systematic variation in readings introduced by different machines is not stored in the generated data.
Surveys
The surveys
directory contains one CSV file for each survey site.
Each file's name has the form Snnn.csv
(e.g., S165.csv
),
where Snnn
is the survey site's unique identifier.
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
Specimens
specimens.csv
holds information about individual snails in CSV format (with column headers).
The file looks like this:
ident | survey | x | y | collected | genome | mass |
---|---|---|---|---|---|---|
KHNKDL | S165 | 11 | 4 | 2024-03-01 | GCAACCGGACCGCCGTAAGG | 3.82 |
DZYIPY | S165 | 3 | 7 | 2024-03-01 | TCATACGGACCGCCGTAAGG | 3.53 |
… | … | … | … | … | … | … |
and its fields are:
Field | Purpose | Properties |
---|---|---|
ident |
specimen identifier | text, unique, required |
survey |
survey identifier | text, required |
x |
collection X coordinate within survey grid | integer, required |
y |
collection Y coordinate within survey grid | integer, required |
collected |
collection date | ISO date, required |
genome |
base sequence | text, required |
mass |
snail weight in grams | real, required |
Assays
Summary information about all assays is stored in assays.csv
.
The file looks like this:
ident | specimen | person | performed |
---|---|---|---|
386915 | KHNKDL | km3478 | 2024-03-05 |
508199 | DZYIPY | mt8294 | 2024-03-01 |
… | … | … | … |
and its fields are:
Field | Purpose | Properties |
---|---|---|
ident |
assay identifier | text, required |
specimen |
specimen identifier | text, required |
person |
scientist identifier | text, required |
performed |
assay date | ISO date, required |
The assays
directory contains three files for each assay:
-
a design file
nnnnnn_treatments.csv
showing whether specimen samples or control material was placed in each well of the assay plate; -
a readings file
nnnnnn_readings.csv
with the reading from each well; and -
an image file
nnnn.png
showing the image that the readings were taken from. (In actualitysnailz
generates the readings first and then the image.)
Each CSV file contains a multi-line header with metadata followed by a table of well values with row and column labels. A typical design file is:
id,037356,,,
specimen,AMEMRZ,,,
date,2024-03-11,,,
by,pv8677,,,
machine,M0002,,,
,A,B,C,D
1,S,C,C,S
2,C,C,S,C
3,S,C,S,S
4,C,S,C,S
while a typical readings file is:
id,037356,,,
specimen,AMEMRZ,,,
date,2024-03-11,,,
by,pv8677,,,
machine,M0002,,,
,A,B,C,D
1,1.09,0.08,0.02,1.1
2,0.02,0.02,1.0,0.07
3,1.03,0.03,1.1,1.07
4,0.09,1.02,0.04,1.01
The first five rows of each file are:
Field | Purpose | Properties |
---|---|---|
id |
assay identifier | text, required |
specimen |
specimen identifier | text, required |
date |
assay date | ISO date, required |
by |
scientist identifier | text |
machine |
machine identifier | text |
The assays
directory also contains files called nnnnnn_raw.csv
.
Each of these files contains the same data as the assay's readings file,
but has some deliberate errors:
header rows may be missing or out of order,
data may be indented,
and so on.
These files are provided so that people can learn how to deal with messy real-world data.
Database
All of the data about people, specimens, and assays is also stored in
a SQLite database called snailz.db
,
whose structure is shown below.
Data File Structure
data
├── params.json
├── data.json
├── persons.csv
├── machines.csv
├── surveys
│ ├── S165.csv
│ ├── S410.csv
│ └── …
├── specimens.csv
├── assays.csv
├── assays
│ ├── 037356_treatments.csv
│ ├── 037356.png
│ ├── 037356_raw.csv
│ ├── 037356_readings.csv
│ ├── 092025_treatments.csv
│ ├── 092025.png
│ ├── 092025_raw.csv
│ ├── 092025_readings.csv
│ └── …
└── snailz.db
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: