fusepoint 0.1.0

Fuse — find where your system breaks, before it does.

Fuse

Know your breaking point.

One function. Two columns of data. A publication-quality Fuse Report.

from fusepoint import analyze

card = analyze(x, y, current_x=0.01)
print(card.score)   # 87
card.save("fuse_report.png")

What it does

You have a parameter you turn and a result you measure. Fuse tells you:

• Where the tipping point is (and how sure it is — bootstrap CI)
• Whether it's real or noise (permutation test, not guessing)
• How sharp it is (k — is it a cliff or a gentle slope?)
• How safe you are (distance to the edge, as a percentage)
• A single Stability Score from 0 to 100

All of this in a beautiful Fuse Report you can screenshot, share, put in a presentation.

Install

pip install fusepoint

Dependencies: numpy, scipy, matplotlib, pandas. That's it.

Quick Start

Array mode

import numpy as np
from fusepoint import analyze

lr = np.linspace(1e-5, 0.1, 80)
loss = your_training_function(lr)

card = analyze(lr, loss, current_x=0.01,
               x_name="Learning Rate", y_name="Loss",
               label="Training Stability")
print(card.score)        # 87 — you're safe
print(card.critical_x)   # 0.035 — this is where it blows up
card.save("lr_report.png")

DataFrame mode — the natural API

import pandas as pd
from fusepoint import analyze

df = pd.read_csv("server_metrics.csv")
card = analyze(df, x="concurrent_requests", y="response_time_ms",
               current_x=5000, label="Production Server")
card.save("server_report.png")

Column names become axis labels automatically.

Scan mode — analyze everything at once

from fusepoint import scan

results = scan("data.csv")                # auto-detect x, analyze all y columns
results = scan(df, x="time", top_n=5)     # explicit x, top 5 results

for r in results:
    print(f"{r.y_name}: {r.score} ({r.grade})")
    r.save(f"{r.y_name}_report.png")

Accepts CSV, TSV, JSON (Plotly, Elasticsearch, Pandas formats), Excel, and Parquet.

Before / After — the comparison

from fusepoint import compare

delta = compare(x, y_before, x, y_after,
                current_x=0.2,
                label_before="Before Fix",
                label_after="After Fix")
print(delta.delta_score)  # +18 points
delta.save("improvement.png")

The Score

The Stability Score (0-100) is built from four independently validated statistical components:

Component	Weight	What it measures
Detection	40%	Is the tipping point real? (permutation p-value)
Clarity	20%	How sharp is it? (k = peak/mean ratio)
Precision	15%	How precisely located? (CI width / range)
Safety	25%	How far from the edge? (margin / range)

The score is self-calibrating: Detection is measured against your own data's null distribution, not against arbitrary thresholds.

What it's NOT

• Not a curve fitter (use scipy for that)
• Not an anomaly detector (use isolation forests for that)
• Not a time-series tool (use ruptures for changepoint detection)

Fuse finds parameter-space tipping points — the critical value of a knob where your system's behavior qualitatively changes. And it tells you how confident it is.

Built on

The mathematics behind Fuse come from sigmacore, a peer-reviewed universal criticality analysis framework published in AVS Quantum Science. Fuse is the simple door to that building.

License

Copyright (c) 2026 Forgotten Forge — forgottenforge.xyz

Dual-licensed: AGPL-3.0 for open-source use, commercial licenses available. Contact [e-mail] for commercial inquiries.