Tutorials
Incident-based walkthroughs with runnable notebooks.
These tutorials are designed to be practical, reproducible, and physics
transparent. Each one links to a notebook that can be run locally with
the same API and constants system used in production integrations.
Quick Start
From installation to first consequence result
Install the package, load materials, select a release scenario, run a
source model, then feed that output to dispersion and effect models.
pip install deepsafety
from deepsafety import DeepSafetyClient
client = DeepSafetyClient("http://127.0.0.1:8000")
materials = client.list_materials("gasoline")
scenario = client.select_release_scenario({
"scenarioType": "worst_case",
"equipmentType": "tank",
"inventoryMass": 5000,
"siteTopography": "urban"
})
Scenario Logic
Realistic vs worst-case vs conservative runs
Deep Safety keeps scenario mode explicit so teams can compare
outcomes instead of embedding assumptions inside an opaque
controller.
| Mode |
Intent |
Typical use |
realistic_case |
Best-estimate operating conditions |
Operational planning and day-to-day risk decisions |
worst_case |
Bounding assumptions |
Regulatory screening and envelope checks |
conservative-analysis |
Intentional overestimation |
Safety margin and sensitivity studies |
Incident Walkthrough
Buncefield-style vapor cloud workflow
This notebook shows how to configure a tank-overfill style release,
build source and dispersion screening outputs, and evaluate blast
proxy metrics with explicit assumptions and constants.
Source -> dispersion -> VCE screening
W_TNT = m_fuel * DeltaH_c * eta / DeltaH_TNT
Z = R / W_TNT^(1/3)
Incident Walkthrough
CSB-style toxic release and effect workflow
This notebook demonstrates a toxic release chain using source,
Gaussian dispersion, toxic criteria lookup, and probit-based effect
estimation for population screening.
Toxic load and response
L_toxic = C^n * t
Y = a + b * ln(L_toxic)
Pr = 0.5 * [1 + erf((Y - 5) / sqrt(2))]
Verification
Change a constant and measure the impact
The tutorial notebooks include a sensitivity section where one
constant is overridden per request to show how outputs shift in a
controlled and auditable way.
{
"constants": {
"fire.default_radiative_fraction": 0.4
}
}
This pattern is useful for design-margin studies and assumption
reviews without forking model code.
Endpoint Workbook
One notebook tour across the full API endpoint surface
A single realistic workbook that walks through materials, scenarios,
source terms, dispersion, fire and explosion, health and hygiene,
prevention/reactivity/relief, hazard evaluation, and GIS/sign
integration in sequence.
Notebook Bundle
Notebook bundle
Start with explorer, then run incident-focused notebooks.