phasedm 1.0.0

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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 astronomical time series analysis.

Features

• High Performance: Up to 10x faster than pure Python implementations through Rust 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)
• Coverage Analysis: Support for statistical coverage analysis (Coming Soon)
• Future Development: F-scoring and PDM2 implementations planned

Technical Details

PyDM leverages several technologies to achieve its performance and functionality:

• 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

Installation

Prerequisites

• Python 3.7+
• Rust compiler (automatically installed by many package managers)

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 as rust_pdm
from pdmpy import pdm as c_pdm
import numpy as np
import pandas as pd
import time
import matplotlib.pyplot as plt

resolution = int(1e4)
t = np.linspace(0, 20, resolution)

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

min_freq = 0.1
max_freq = 1
n_bins = 10
n_freqs = int(1e4)

start = time.time()
freq, theta = rust_pdm(t,y,min_freq,max_freq, n_freqs, 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.xlabel('Frequency')
plt.ylabel('PDM Statistic')
plt.title('Phase Dispersion Minimisation Results')
plt.legend()
plt.show()
plt.savefig('theta_rust.png')

freq_step = (max_freq-min_freq)/n_freqs
start = time.time()
freq, theta = c_pdm(t, y, f_min = min_freq, f_max = max_freq, delf = freq_step, nbin = n_bins)
pdmpy_time = time.time()-start
print(f"py-pdm computed in {pdmpy_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.xlabel('Frequency')
plt.ylabel('PDM Statistic')
plt.title('Phase Dispersion Minimisation Results')
plt.legend()
plt.show()
plt.savefig('theta_c.png')

print(f"{pdmpy_time/pydm_time} x speed-up" )

Takes obvious inspiration from PY-PDM https://pypi.org/project/Py-PDM/ and Stellingwerfs orginal version https://www.stellingwerf.com/rfs-bin/index.cgi?action=PageView&id=34

Comparison with Other Implementations

Feature	PyDM	pdm-py
Performance	Up to 10x faster	Baseline
DateTime Support	✅	❌
Coverage Analysis	Coming Soon	❌
Dependencies	No VS dev tools	Requires Visual Studio tools on Windows
F-scoring	Planned	❌
PDM2	Planned	❌

Contributing

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

License

[Insert license information here]