piwavelet 2.0.0

Pure Python wavelet, cross-wavelet and wavelet coherence analysis

PIWavelet

A modern Python library for continuous wavelet analysis, cross-wavelet transforms, and wavelet coherence.

PIWavelet provides a modular and scientifically robust implementation of:

• Continuous Wavelet Transform (CWT)
• Cross-Wavelet Transform (XWT)
• Wavelet Coherence (WTC)
• Torrence & Webster smoothing operators
• Cone of Influence (COI)
• Multiple mother wavelets
• Publication-quality visualization tools

The library is designed for scientific computing, signal analysis, geophysics, astrophysics, climatology, and time-frequency analysis applications.

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Features

Continuous Wavelet Transform

• Complex and real wavelets
• FFT-based implementation
• Scale-frequency conversion
• Cone of Influence estimation
• Multi-scale time-frequency analysis

Cross-Wavelet Transform

• Cross-wavelet power
• Relative phase analysis
• Shared spectral energy detection

Wavelet Coherence

• Magnitude-squared wavelet coherence
• Torrence-Webster smoothing operators
• Scale-aware temporal smoothing
• Localized time-frequency coherence analysis
• Phase relationship visualization

Supported Wavelets

• Morlet
• Paul
• DOG (Derivative of Gaussian)
• Mexican Hat

Visualization

• Wavelet power spectrum plots
• Cross-wavelet plots
• Wavelet coherence plots
• Cone of Influence overlay
• Phase arrows
• Log-period axis formatting
• Publication-ready figures

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Installation

pip install piwavelet

Development installation:

git clone <repository-url>
cd piwavelet
pip install -e .

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Quick Start

Continuous Wavelet Transform

import numpy as np

from piwavelet.transforms import cwt
from piwavelet.wavelets import Morlet

# synthetic signal

dt = 0.25

time = np.arange(0, 512) * dt

signal = (
    np.sin(2 * np.pi * time / 32)
    + 0.5 * np.sin(2 * np.pi * time / 8)
)

result = cwt(
    signal=signal,
    dt=dt,
    wavelet=Morlet(),
)

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Wavelet Coherence

import numpy as np

from piwavelet.transforms import wavelet_coherence
from piwavelet.wavelets import Morlet

np.random.seed(42)

# ------------------------------------------------------------------
# synthetic signal
# ------------------------------------------------------------------

dt = 0.25

time = np.arange(0, 512) * dt

# localized coherent structure
period = 16

shared = np.sin(
    2 * np.pi * time / period
)

x = shared.copy()

x += 0.4 * np.sin(
    2 * np.pi * time / 6
)

x += 0.5 * np.random.randn(len(time))

window = np.exp(
    -0.5 * ((time - 64) / 5) ** 2
)

y = (
    window * shared
    + 0.8 * np.random.randn(len(time))
)

# ------------------------------------------------------------------
# wavelet coherence
# ------------------------------------------------------------------

result = wavelet_coherence(
    x,
    y,
    dt=dt,
    wavelet=Morlet(),
)

This example produces a localized coherence region centered approximately at:

• time ≈ 64
• period ≈ 16

which demonstrates proper time-frequency localization.

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Plotting

Wavelet Coherence Plot

from piwavelet.plotting import plot_wavelet_coherence

fig = plot_wavelet_coherence(
    result,
    title="Wavelet Coherence",
    show_phase=True,
)

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Scientific Background

PIWavelet follows the classical formulations presented in:

• Torrence, C. & Compo, G. P. (1998) "A Practical Guide to Wavelet Analysis"

• Torrence, C. & Webster, P. J. (1999) "Interdecadal Changes in the ENSO-Monsoon System"

• Grinsted, A., Moore, J. C., & Jevrejeva, S. (2004) "Application of the cross wavelet transform and wavelet coherence to geophysical time series"

Implemented features include:

• scale-normalized coherence
• Torrence-Webster smoothing
• scale-dependent temporal smoothing
• cone of influence estimation
• phase relationship analysis

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Architecture

The project is organized into modular components:

piwavelet/
├── transforms/
├── wavelets/
├── smoothing/
├── plotting/
├── significance/
└── utils/

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Development Goals

Planned and ongoing features:

• Significance testing
• Monte Carlo coherence significance
• Partial wavelet coherence
• Multivariate coherence
• GPU acceleration
• Streaming transforms
• Better statistical diagnostics
• Xarray integration
• Interactive plotting

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License

MIT License

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Contributing

Contributions, issues, and suggestions are welcome.

Please open an issue or submit a pull request.