reliafy — push SurPyval models to Reliafy

Fit a model anywhere with SurPyval, then make it a shareable, trackable, citable artifact in Reliafy with one call. The notebook is the lab; Reliafy is the plant.

pip install reliafy-client

(The package installs as reliafy-client; you import it as reliafy.)

Quick start

import surpyval as sp
import reliafy

# Fit however you like — censored data, offsets, whatever.
model = sp.Weibull.fit(x=failures, c=censoring_flags)

reliafy.configure(token="rlf_...")            # from Reliafy → API access (Pro)
url = reliafy.push(model, name="Pump bearings — 2026 refit", unit="hours")
print(url)                                    # open it in Reliafy

push() uploads the model and its fitted data by default, so Reliafy shows the full probability plot with confidence bounds — and the model stays editable and refittable in the app, and can be cited as RCM evidence, used in RBD blocks, or tracked against a fleet.

Offset / limited-failure-population / zero-inflated fits are detected automatically from the model and reproduced.

Options

# Parameters only (no data upload): functions & life metrics, no plot.
reliafy.push(model, name="From a report", data=False)

# No SurPyval object — just numbers:
reliafy.push_params("weibull", [1200.0, 2.3], name="Handbook value", unit="hours")

# Self-hosted instance:
reliafy.configure(token="rlf_...", base_url="https://reliafy.mycompany.com")

Configuration also reads RELIAFY_TOKEN and RELIAFY_BASE_URL from the environment.

Notes

• The token is a personal API token, created on the API access page in Reliafy. On Reliafy Cloud this is a Pro feature; self-hosted instances have it unconditionally.
• Supported distributions: Weibull, Exponential, Normal, LogNormal, Gamma, LogLogistic, Exponentiated Weibull, Gumbel, Logistic.
• Pure standard library — no dependencies. You bring SurPyval to do the fit.