plotsim 0.7.0

Generate multi-table relational datasets with behavioral trajectories, correlations, and causal lags. Config-driven, deterministic, no real data required

Python library to generate synthetic multi-table relational datasets — star schema, correlated metrics, dbt-seed-ready CSV & Parquet. Config-driven, deterministic, no real data required

pip install plotsim

plotsim generates multi-table relational datasets from a behavioral description. You define metrics, segments, and how entities behave over time — the engine produces a star schema where every value traces back to one trajectory. shape: every entity follows a behavioral trajectory, and every metric across every table reads from the same trajectory position. When engagement rises, revenue follows. When it declines, churn fires.

Quick start

from plotsim import create, generate_tables

cfg = create(
    about="Subscription customers",
    unit="customer",
    window=("2024-01", "2024-12", "monthly"),
    metrics=[
        {"name": "engagement", "type": "score", "polarity": "positive"},
        {"name": "payments",   "type": "count", "polarity": "positive"},
    ],
    segments=[
        {"name": "active",   "count": 50, "archetype": "growth"},
        {"name": "inactive", "count": 30, "archetype": "decline"},
    ],
)
tables = generate_tables(cfg)
for name, df in tables.items():
    print(f"{name}: {len(df)} rows")
# dim_date: 12 rows
# dim_customer: 80 rows
# fct_customer: 960 rows

Generate multi-table synthetic data

Top panel: one trajectory for one customer, 24 months. Bottom panel: four metrics on the same x-axis. Engagement and MRR rise with the trajectory; support tickets and churn risk fall as it rises. Every value reads from that one curve.

The same idea in tables — one company, twelve months, the same SaaS schema generated two ways:

Random columns (Faker-style) — every column is independent. The numbers don't agree.

month	engagement	mrr	tickets	churn_risk
2024-01	0.842	$483	7	0.611
2024-02	0.117	$4,201	0	0.043
2024-03	0.674	$1,089	11	0.892
2024-04	0.298	$112	2	0.355
2024-05	0.951	$7,733	4	0.018
2024-06	0.024	$964	9	0.477
2024-07	0.560	$2,154	1	0.802
2024-08	0.405	$328	6	0.220
2024-09	0.789	$617	0	0.998
2024-10	0.131	$5,440	8	0.156
2024-11	0.847	$192	3	0.501
2024-12	0.334	$3,876	12	0.063

Engagement at 0.95 with churn risk near zero, then 0.79 at the highest churn risk in the table. No story — only fields filled.

plotsim (trajectory-correlated) — plotsim run saas. Same dim_company row, twelve monthly rows from fct_engagement, fct_revenue, fct_support_tickets.

month	engagement	mrr	tickets	churn_risk
2024-01	0.587	$1,191	0	0.261
2024-02	0.807	$1,265	1	0.189
2024-03	1.000	$3,532	2	0.129
2024-04	0.593	$818	0	0.171
2024-05	0.904	$3,567	2	0.237
2024-06	0.956	$4,264	1	0.257
2024-07	1.000	$302	2	0.000
2024-08	0.917	$1,507	0	0.000
2024-09	1.000	$890	1	0.000
2024-10	0.783	$512	1	0.264
2024-11	0.956	$837	0	0.000
2024-12	0.827	$351	1	0.248

Engagement is climbing toward its plateau. MRR moves with it. Support tickets stay low. Churn risk stays near zero. All four columns read from the same underlying trajectory position — not from four independent random generators.

The contrast is the entire product.

Star schema output

A plotsim run produces a complete star schema in the chosen output directory:

output/
├── dim_date.csv                # complete date spine
├── dim_company.csv             # entity attributes (with SCD2 plan_tier)
├── 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               # proportional events
├── evt_churn.csv               # threshold-triggered events
├── config.yaml                 # frozen copy of the input config
└── validation_report.txt       # FK + PK + spine integrity checks

If a company's engagement trajectory declines, its login rows decrease in evt_login.csv and churn events appear in evt_churn.csv — both event tables read from the same trajectory the fact tables do.

Same config + same seed produces byte-identical output every time. CSV is the default; Parquet is one config flag away. See the output guide for format details and the manifest schema.

Who is this for

Educators and students who need realistic datasets for SQL courses, data modeling workshops, analytics training, or portfolio projects — six bundled templates ready to go, same seed produces the same data every time.

Data engineers who need test fixtures that behave like production data — with FK integrity, realistic distributions, and configurable corruption — without copying production or hand-rolling three-row CSVs.

Data scientists who need labeled training data with known ground truth — archetype labels, trajectory positions, and temporal holdout splits — to validate models before touching real data.

Analytics engineers who need a star schema to build dbt models, test transformations, or demonstrate a pipeline end-to-end without waiting for upstream data.

BI and analytics teams who need a populated star schema to build dashboards, test reports, or demo a new tool to stakeholders — dims, facts, events, and SCD versioning out of the box.

Demo builders who need a convincing dataset for a conference talk, a product walkthrough, or a proof of concept — correlated metrics that tell a realistic story, not random noise.

How it works

Every entity in the dataset follows a behavioral trajectory — a curve shape like growth, decline, seasonal, or spike-then-crash. At each time period, the entity's position on that curve determines every metric value across every table. Revenue, engagement, churn risk, and support tickets all read from the same position, so they move together the way real business metrics do.

Metric relationships are enforced through a Gaussian copula — declare engagement opposes churn_risk and the engine delivers the configured correlation coefficient regardless of whether one metric is beta-distributed and the other is Poisson. Causal lags compose: if A → B (lag 2) → C (lag 3), then C reflects A from 5 periods ago.

Output is deterministic. Every random draw flows through a single seeded numpy.Generator. Same config + same seed = byte-identical tables within the same Python and dependency versions. The manifest records every generation decision — archetype assignments, trajectory positions, correlation adjustments, quality injections — so any cell value can be traced back to its origin.

Config-time validation catches problems before generation starts: circular causal chains, non-positive-definite correlation matrices, broken foreign key references, duplicate metric names, and SQL-unsafe identifiers all surface as parse errors with fix suggestions.

See the docs site for the full pipeline.

Docs

mohossam01.github.io/plotsim — quickstart, user guide, tutorials, API reference, cookbooks.

Contributing

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

License

Apache-2.0 — see LICENSE and NOTICE.