phasedm 1.1.1

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PhaseDM: Phase Dispersion Minimisation for Python

PhaseDM is a high-performance implementation of the Phase Dispersion Minimisation algorithm for Python, built with Rust. This package offers significant advantages over existing implementations like pdm-py, making it an ideal choice for time series analysis.

Features

• High Performance: Up to 100x faster than pure Python implementations and 10x than single threaded c implementions through parallelization with Rayon
• Better Compatibility: No Visual Studio development tools required
• Enhanced DateTime Support: Full support for datetime[ns] format (not available in pdm-py)
• Beta Statistic: Support for statistical analysis using Beta distribution

Prerequisites

• Python 3.8+

Option 1: Install from PyPI

pip install phasedm

Option 2: Install from source

Step 1: Install uv (fast Python package installer)

Follow the installation instructions at https://docs.astral.sh/uv/getting-started/installation/

Step 2: Create a virtual environment in the repository

uv venv

Step 3: Activate the virtual environment

# On Windows
.venv\Scripts\activate
# On macOS/Linux
source .venv/bin/activate

Step 4: Install dependencies

uv pip install maturin numpy matplotlib

Step 5: Build and install the package

maturin develop --release

Usage

from phasedm import pdm
import numpy as np
import pandas as pd
import time
import matplotlib.pyplot as plt
from phasedm import beta_test


resolution = int(1e4)

t = np.linspace(0, 20, resolution)

y = np.sin(t) + np.random.normal(0, 1, resolution)

sigma = np.zeros(resolution)

# t = pd.date_range(
#     start='2022-03-10 12:00:00',
#     end='2022-03-10 12:00:20',
#     periods=resolution
# ).values

plt.plot(t, y)

min_freq = 0.05
max_freq = 1
n_bins = 10
n_freqs = int(1e4)

sig_theta = beta_test(resolution, n_bins, 0.0001)
print(f"Significant theta {sig_theta}")

start = time.time()
freq, theta = rust_pdm(
    t, y, min_freq, max_freq, n_freqs, sigma=sigma, n_bins=n_bins, verbose=1
)
pydm_time = time.time() - start
print(f"pydm computed in {pydm_time}")

# Find the best period
best_freq = freq[np.argmin(theta)]
print(f"True period: {2*np.pi}, Detected period: {1/best_freq}")

# Plot results

plt.figure()
plt.plot(freq, theta)
plt.axvline(1 / (2 * np.pi), color="red", linestyle="--", label="True Frequency")
plt.axvline(best_freq, color="green", linestyle=":", label="Detected Period")
plt.axvline(best_freq / 2, color="red", linestyle=":", label="Harmonic Period")

plt.axhline(sig_theta, color="blue", linestyle="--", label="Significance Threshold")
plt.xlabel("Frequency")
plt.ylabel("PDM Statistic")
plt.title("Phase Dispersion Minimisation Results")
plt.legend()
plt.show()

Comparison with Other Implementations

Feature	phasedm	pdm-py
Performance	Up to 10x faster	Baseline
DateTime Support	✅	❌
Significance Testing	✅	❌
Dependencies	No VS dev tools	Requires Visual Studio tools on Windows
PDM2	Planned	❌

Technical Details

The main crates we use are

• Maturin: Builds and publishes Rust-based Python packages
• PyO3: Enables Rust to interact with Python code and objects
• NumPy: Efficient numerical operations in Python
• ndarray: Rust library for n-dimensional arrays
• Rayon: Provides data parallelism for Rust

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

References

• Stellingwerf https://www.stellingwerf.com/rfs-bin/index.cgi?action=PageView&id=34
• Schwarzenberg-Czerny https://iopscience.iop.org/article/10.1086/304832
• PY-PDM https://pypi.org/project/Py-PDM/

License

MIT Licence