plotsim 0.4.0

Generate realistic multi-table datasets from behavioral archetypes. No real data needed.

plotsim

A Python synthetic data generator for realistic multi-table relational datasets — with no real data required.

plotsim is a synthetic data generator that produces multi-table CSV files with foreign keys, temporal patterns, and cross-table consistency. Unlike field-level fakers, every metric for an entity (engagement, revenue, churn) moves together over time because they derive from the same behavioral trajectory.

Use it to generate test data for dbt projects, analytics portfolios, dashboard prototypes, training courses, or any scenario where you need realistic relational data but have no access to production systems.

pip install plotsim

Usage

Generate from a template

plotsim template saas -o config.yaml
plotsim run config.yaml -o ./output --validate

Generate from Python

from plotsim import load_config, generate_tables, write_tables

config = load_config("config.yaml")
tables = generate_tables(config)
write_tables(tables, config)

Same config and seed produces identical output every run.

When to use plotsim

• Building an analytics portfolio and need realistic data to showcase dbt models, dashboards, or SQL analysis
• Teaching a data engineering course and need students to work with multi-table schemas that behave like production data
• Prototyping a dashboard or report before production data is available
• Writing integration tests for a data pipeline that expects relational input with referential integrity
• Practicing dbt, SQL, or data modeling with data that has temporal patterns, not just random values

What it generates

A single config produces a complete relational schema:

output/
├── dim_date.csv                # date spine
├── dim_company.csv             # entity attributes
├── dim_user.csv                # sub-entity attributes
├── dim_plan.csv                # reference lookup
├── fct_engagement.csv          # entity × period metrics
├── fct_revenue.csv             # entity × period metrics
├── fct_support_tickets.csv     # entity × period metrics
├── evt_login.csv               # behavioral events
├── evt_churn.csv               # threshold-triggered events
├── config.yaml                 # config that produced this output
└── validation_report.txt       # integrity checks

All foreign keys resolve. Event tables derive from fact values, not from independent random generation. If an entity's engagement declines, its login events decrease and churn events fire — across separate CSV files.

Templates

Five domain configs ship with the package:

Template	Domain	Entities	Tables
saas	B2B SaaS	accounts with users	10
hr	HR department	employees in departments	7
ecommerce	E-commerce	customer segments	8
education	University	student cohorts	7
healthcare	Clinic	patient groups	8

plotsim list-templates          # see all available
plotsim template hr -o hr.yaml  # export one to edit

Custom domains

The config file defines everything: entity types, metrics, behavioral archetypes, table schemas, correlations, and noise levels. Copy any template and modify it, or generate one with any LLM:

"Change this SaaS config to model a food delivery service with restaurants, orders, delivery times, and customer ratings."

Validate before generating:

plotsim validate my_config.yaml
plotsim run my_config.yaml -o ./output

How it works

Each entity is assigned an archetype — a trajectory curve composed of segments like sigmoid, exponential decay, step, plateau, or oscillation.

At each time step, the engine reads the entity's trajectory position (a value between 0 and 1) and derives every metric from it:

• Positive polarity metrics (engagement, revenue) rise when the trajectory rises.
• Negative polarity metrics (churn risk, support tickets) rise when the trajectory falls.

Distributions (lognormal, gamma, poisson, beta, normal, weibull) shape the raw values. Correlated noise is applied via Cholesky decomposition on the configured correlation matrix. Causal lag lets one metric trail another by N periods.

Dimension tables are generated first (dates, entities, reference lookups), then fact tables (trajectory-driven metrics per period), then event tables (derived from completed fact values, never from raw trajectories).

Config overview

A plotsim config has these sections:

• domain — name and entity label
• time_window — start, end, granularity (monthly / weekly / daily)
• seed — integer controlling all randomness
• metrics — name, distribution, polarity, optional causal lag
• archetypes — named trajectory shapes built from curve segments
• entities — instances assigned to archetypes
• tables — dim / fact / event schemas with typed columns
• correlations — optional metric-pair coefficients
• noise — gaussian sigma, outlier rate, missing data rate, temporal jitter
• stages — optional lifecycle sequence with enforceable ordering

Full schema with type annotations: plotsim/config.py

CLI reference

plotsim run <config>              Generate dataset from config
  -o, --output-dir <path>         Output directory (default: from config)
  -s, --seed <int>                Override seed
  -v, --validate                  Run validation after generation
  --strict                        Fail on validation warnings
  -q, --quiet                     Suppress output

plotsim validate <config>         Check config without generating
plotsim info <config>             Preview tables, rows, entities
plotsim list-templates            Show bundled templates
plotsim template <name>           Print template YAML to stdout
  -o, --output <path>             Write to file instead

Validation

The engine runs these checks after generation:

• FK integrity — every foreign key resolves to a parent row
• PK uniqueness — no duplicate primary keys
• Date spine — no gaps in the date dimension
• Causal coherence — lagged metrics inflect after their drivers
• Null policy — no unexpected nulls outside configured missing rates
• Correlation PSD — correlation matrix is positive semi-definite

plotsim run config.yaml --validate

The validation report is written alongside the CSVs as validation_report.txt.

Contributing

See CONTRIBUTING.md for dev setup, test commands, and how to add templates or curve types.

License

Apache-2.0 — see LICENSE and NOTICE.