triples-sigfast 1.1.1

An enterprise-grade, JIT-compiled time-series and nuclear physics analysis engine stress-tested on 100M+ row datasets.

⚡ triples-sigfast

An enterprise-grade, JIT-compiled time-series engine stress-tested on 100M+ row datasets.

triples-sigfast uses Numba to compile Python functions to machine code at runtime, delivering C-level performance for time-series analysis — with zero C code required.

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Features

• Rolling Average — JIT-compiled sliding window mean
• EMA — Exponential Moving Average at C-speed
• Anomaly Detection — Z-score based outlier detection
• EMA Crossover Strategy — Trading signal generation (Buy/Sell/Hold)
• Pandas + NumPy compatible — Works with arrays, lists, and Series
• Cross-platform — Tested on Ubuntu, macOS, and Windows

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Installation

pip install triples-sigfast

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Benchmark

Tested on real datasets using JIT-compiled engine:

Dataset Size	RAM Size	Execution Time	Peak RAM
1,000,000 rows	8 MB	0.339s	192 MB
10,000,000 rows	80 MB	0.281s	404 MB
50,000,000 rows	400 MB	0.747s	940 MB
100,000,000 rows	800 MB	1.225s	1,596 MB

Survived the 100M row Crucible Test — processing 800MB of data in just over 1 second.

Usage

Rolling Average

import numpy as np
from triples_sigfast import rolling_average

data = np.random.randn(1_000_000)
result = rolling_average(data, window_size=50)
print(result[:5])

Exponential Moving Average (EMA)

from triples_sigfast import ema

prices = np.random.randn(1_000_000).cumsum() + 100
smoothed = ema(prices, span=20)
print(smoothed[:5])

Anomaly Detection

from triples_sigfast import detect_anomalies

data = np.random.randn(1_000_000)
data[500_000] = 999.0  # inject a spike

anomalies = detect_anomalies(data, threshold=3.0)
print(f"Anomalies found: {anomalies.sum()}")

EMA Crossover Strategy (Trading Signals)

from triples_sigfast import ema_crossover_strategy

prices = np.random.randn(1_000_000).cumsum() + 100
fast_ema, slow_ema, signals = ema_crossover_strategy(prices, fast_span=12, slow_span=26)

# signals: 1 = Buy, -1 = Sell, 0 = Hold
print(f"Buy signals:  {(signals == 1).sum()}")
print(f"Sell signals: {(signals == -1).sum()}")

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Running Tests

pip install triples-sigfast[dev]
pytest --cov=triples_sigfast

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Requirements

• Python >= 3.10
• NumPy >= 1.20.0
• Numba >= 0.55.0
• Pandas >= 1.3.0

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Contributing

Contributions are welcome! Please read CONTRIBUTING.md first.

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License

MIT License — see LICENSE for details.

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Author

TripleS Studio

• PyPI: triples-sigfast
• GitHub: SamdaniSayam/triples-sigfast

☢️ Nuclear Physics Features

Built for radiation shielding research with Geant4 + ROOT workflows:

• Savitzky-Golay Filter — Smooths Monte Carlo energy spectra while preserving peak shape
• Peak Finder — Detects gamma ray lines and neutron capture peaks
• Flux-to-Dose Converter — ICRP 74 standard conversion (particles/cm²/s → μSv/hr)
• Shielding Attenuation — Beer-Lambert law for 9 built-in materials (lead, polyethylene, borated_poly, polysulfone, etc.)

Quick Example

import uproot
from triples_sigfast import savitzky_golay, find_peaks, flux_to_dose, attenuation

# Read Geant4 ROOT output
file = uproot.open("simulation.root")
counts = file["neutron_spectrum"].values()

# Smooth and analyze
smoothed = savitzky_golay(counts, window=11, polyorder=3)
peaks = find_peaks(smoothed, min_height=50, min_distance=10)

# Dose and shielding
dose = flux_to_dose(flux=1e6, energy_mev=2.35, particle="neutron")
T = attenuation(thickness_cm=10, material="lead")
print(f"Dose: {dose:.4f} uSv/hr | Lead transmission: {T*100:.2f}%")