discrete-fourier 0.3.2

Calculate discrete Fourier coefficients, detect periods, and validate periodic patterns

Discrete Fourier

A Python library for computing Discrete Fourier Series coefficients and reconstructing signals from discrete data. This implementation provides efficient methods for Fourier analysis, signal reconstruction, and derivative calculations.

Features

• Fourier Coefficient Calculation: Compute real Fourier series coefficients (a_k, b_k) from discrete data
• Signal Reconstruction: Evaluate the Fourier series at any position (interpolation and extrapolation)
• First Derivative: Calculate the rate of change of the reconstructed signal
• Second Derivative: Compute the curvature/acceleration of the signal
• Period Detection: Find dominant periods and cyclical patterns in data
• Period Validation: Verify if detected periods are real patterns with confidence scores
• NumPy-based: Efficient vectorized computations for fast performance

Why Not Just Use NumPy FFT?

While NumPy and SciPy provide excellent FFT implementations, this library offers distinct advantages for working with real Fourier series:

Real Coefficients vs Complex Numbers

NumPy FFT:

import numpy as np

fft_result = np.fft.fft(data)
# Returns: [c0, c1, c2, ...] - complex numbers
# Example: [(2.5+0j), (1.2+0.8j), (-0.5-1.2j), ...]
# Requires understanding of complex arithmetic

This Library:

from discrete_fourier import DiscreteFourier

coefs = DiscreteFourier.calculate_fourier_coefs(data)
# Returns: (a_k, b_k) - separate real arrays
# a_k: [2.5, 1.2, -0.5, ...]  (cosine coefficients)
# b_k: [0.0, 0.8, -1.2, ...]  (sine coefficients)
# More interpretable, no complex numbers needed

Point Evaluation

NumPy FFT:

# To evaluate at a single point, you must:
# 1. Compute full inverse FFT
reconstructed = np.fft.ifft(fft_result).real
value_at_50 = reconstructed[50]  # Only works for original indices

# 2. For arbitrary positions (like t=50.5), need custom interpolation

This Library:

# Evaluate at any position directly
value = DiscreteFourier.calculate_fourier_value(coefs, 50.5)
# Works for any t, including beyond original data range

Analytical Derivatives

NumPy FFT:

# No built-in derivative calculation
# Must write custom code to differentiate Fourier series
# Requires understanding of complex derivative formulas

This Library:

# Built-in analytical derivatives
first_deriv = DiscreteFourier.calculate_fourier_derivative_value(coefs, t)
second_deriv = DiscreteFourier.calculate_fourier_double_derivative_value(coefs, t)
# Ready to use for trend analysis, extrema detection, etc.

Comparison Table

Feature	NumPy/SciPy FFT	discrete_fourier
Coefficient format	Complex numbers	Real (a_k, b_k)
Learning curve	Requires complex math	Simple real arithmetic
Point evaluation	Reconstruct full signal	Direct evaluation at any t
Derivatives	Manual implementation	Built-in 1st & 2nd derivatives
Extrapolation	Not straightforward	Natural (periodic)
Use case	General FFT operations	Real Fourier series focus

When to Use This Library

✅ Use discrete_fourier when:

• Working with real-valued signals (not complex)
• Need to evaluate series at arbitrary positions
• Require derivative calculations
• Want interpretable cosine/sine coefficients
• Teaching or learning Fourier series concepts
• Prefer simple API over FFT theory

❌ Use NumPy/SciPy FFT when:

• Need full FFT/IFFT transformations
• Working with complex signals
• Require 2D/3D transforms
• Need specialized FFT algorithms (Bluestein, Rader, etc.)
• Performance critical applications with large datasets

Installation

pip install discrete_fourier

Quick Start

from discrete_fourier import DiscreteFourier

# Sample data
data = [1.0, 2.5, 4.0, 3.5, 2.0, 1.5]

# Calculate Fourier coefficients
coefs = DiscreteFourier.calculate_fourier_coefs(data)

# Reconstruct values at original positions
for i in range(len(data)):
    value = DiscreteFourier.calculate_fourier_value(coefs, i + 1)
    print(f"Position {i+1}: Original={data[i]:.2f}, Reconstructed={value:.2f}")

# Predict future values (extrapolation)
future_value = DiscreteFourier.calculate_fourier_value(coefs, len(data) + 1)
print(f"Next value: {future_value:.2f}")

# Calculate derivatives
derivative = DiscreteFourier.calculate_fourier_derivative_value(coefs, 3)
second_deriv = DiscreteFourier.calculate_fourier_double_derivative_value(coefs, 3)
print(f"At position 3: f'={derivative:.2f}, f''={second_deriv:.2f}")

# Find dominant period
dominant = DiscreteFourier.find_dominant_period(data)
print(f"Dominant period: {dominant['period']:.2f} points")

# Validate the period
validation = DiscreteFourier.validate_period(data, dominant['period'])
print(f"Period is valid: {validation['valid']}")
print(f"Confidence: {validation['confidence']:.2f}")

# Find top 3 periods
top_periods = DiscreteFourier.find_top_periods(data, n_periods=3)
for i, p in enumerate(top_periods, 1):
    print(f"{i}. Period: {p['period']:.1f}, k={p['k']}, {p['percent']:.1f}% of signal")

API Reference

DiscreteFourier.calculate_fourier_coefs(data_in)

Calculate Fourier series coefficients from discrete data.

Parameters:

• data_in (list or array-like): Input data sequence

Returns:

• tuple: (a_k, b_k) where:
  • a_k: Cosine coefficients (a_k[0] is the mean)
  • b_k: Sine coefficients (b_k[0] is always 0)

Notes:

• If input has odd length, the first element is removed to ensure even length
• Uses real Fourier series representation: f(t) = a_0 + Σ[a_k*cos(2πkt/N) + b_k*sin(2πkt/N)]

DiscreteFourier.calculate_fourier_value(fourier_coefs, t)

Reconstruct the signal value at position t.

Parameters:

• fourier_coefs (tuple): (a_k, b_k) from calculate_fourier_coefs()
• t (int or float): Position to evaluate (can be beyond original data range)

Returns:

• float: Reconstructed value at position t

Notes:

• Due to periodicity: f(t) = f(t + N) where N is the original data length
• Can be used for interpolation (within data range) or extrapolation (beyond data range)

DiscreteFourier.calculate_fourier_derivative_value(fourier_coefs, t)

Calculate the first derivative (slope) at position t.

Parameters:

• fourier_coefs (tuple): (a_k, b_k) from calculate_fourier_coefs()
• t (int or float): Position to evaluate

Returns:

• float: First derivative df/dt at position t

Use cases:

• Trend detection (positive = increasing, negative = decreasing)
• Finding local maxima/minima (where f'(t) = 0)
• Velocity calculation from position data

DiscreteFourier.calculate_fourier_double_derivative_value(fourier_coefs, t)

Calculate the second derivative (curvature) at position t.

Parameters:

• fourier_coefs (tuple): (a_k, b_k) from calculate_fourier_coefs()
• t (int or float): Position to evaluate

Returns:

• float: Second derivative d²f/dt² at position t

Use cases:

• Concavity detection (positive = concave up, negative = concave down)
• Finding inflection points (where f''(t) = 0)
• Acceleration calculation from position data

DiscreteFourier.find_dominant_period(data_in=None, fourier_coefs=None)

Find the dominant (most prominent) period in the data.

Parameters:

• data_in (list or array-like, optional): Input data sequence
• fourier_coefs (tuple, optional): Pre-calculated (a_k, b_k) coefficients
  • Either data_in or fourier_coefs must be provided

Returns:

• dict: Dictionary containing:
  • 'period' (float): Dominant period in data points
  • 'k' (int): Frequency index with highest magnitude
  • 'magnitude' (float): Magnitude of the dominant component
  • 'data_length' (int): Original data length N

Use cases:

• Detecting the main cyclical pattern in time series
• Identifying the most important frequency component
• Understanding periodicity in seasonal data

Important notes:

• Maximum detectable period is N (the data length)
• To detect periods longer than N, you need more data
• The DC component (mean) is excluded from analysis

Example:

data = [1, 2, 3, 4, 3, 2, 1, 2, 3, 4, 3, 2]  # Period of 6
result = DiscreteFourier.find_dominant_period(data)
print(f"Period: {result['period']:.1f} points")  # Should be close to 6

DiscreteFourier.find_top_periods(data_in=None, fourier_coefs=None, n_periods=3)

Find the top N dominant periods ranked by magnitude.

Parameters:

• data_in (list or array-like, optional): Input data sequence
• fourier_coefs (tuple, optional): Pre-calculated (a_k, b_k) coefficients
  • Either data_in or fourier_coefs must be provided
• n_periods (int, default=3): Number of top periods to return

Returns:

• list of dict: List sorted by magnitude (highest first), each containing:
  • 'period' (float): Period in data points (N/k)
  • 'k' (int): Frequency index
  • 'magnitude' (float): Magnitude of this component
  • 'percent' (float): Percentage of total magnitude

Use cases:

• Identifying multiple cyclical patterns
• Understanding complex periodic behavior
• Comparing relative importance of different periods

Example:

import numpy as np

# Signal with multiple periods
t = np.linspace(0, 100, 200)
signal = np.sin(2*np.pi*t/20) + 0.5*np.sin(2*np.pi*t/10)

top = DiscreteFourier.find_top_periods(signal.tolist(), n_periods=5)
for i, p in enumerate(top, 1):
    print(f"{i}. Period: {p['period']:.1f}, {p['percent']:.1f}%")

DiscreteFourier.validate_period(data_in, period, window_size=None, method='all')

Validate if a detected period actually repeats in the data.

Parameters:

• data_in (list or array-like): Input data sequence
• period (float): Period to validate (in data points)
• window_size (int, optional): Size of comparison window. If None, uses min(period/2, 50)
• method (str, default='all'): Validation method
  • 'correlation': Returns only correlation coefficient
  • 'rmse': Returns only normalized RMSE
  • 'cosine': Returns only cosine similarity
  • 'all': Returns full validation metrics (recommended)

Returns:

• dict (when method='all'): Dictionary containing:
  • 'valid' (bool): True if period appears valid
  • 'confidence' (float): Overall confidence score (0-1)
  • 'correlation' (float): Pearson correlation (-1 to 1)
  • 'rmse' (float): Normalized RMSE (0-1, lower is better)
  • 'cosine_similarity' (float): Cosine similarity (0-1)
  • 'period' (float): The period being validated
  • 'window_size' (int): Size of comparison window used
• float (when method is specific metric): The requested metric value

Use cases:

• Verifying that detected periods are real patterns, not artifacts
• Filtering false positives in noisy data
• Assessing confidence before using period for predictions

Interpretation:

• Correlation > 0.7: Strong periodic pattern (high confidence)
• Correlation 0.5-0.7: Moderate periodicity
• Correlation < 0.3: Weak or no periodicity (likely false positive)
• Confidence > 0.8: Very reliable period
• Confidence < 0.4: Period questionable, use with caution

Example:

# Find and validate dominant period
data = numpy.sin(numpy.linspace(0, 4*numpy.pi, 100))
dominant = DiscreteFourier.find_dominant_period(data)
validation = DiscreteFourier.validate_period(data, dominant['period'])

if validation['valid']:
    print(f"Period {validation['period']:.1f} is valid!")
    print(f"Confidence: {validation['confidence']:.2f}")
    print(f"Correlation: {validation['correlation']:.2f}")
else:
    print("Period may not be reliable")

# Validate all top periods
top_periods = DiscreteFourier.find_top_periods(data, n_periods=5)
for p in top_periods:
    val = DiscreteFourier.validate_period(data, p['period'])
    print(f"Period {p['period']:.1f}: confidence={val['confidence']:.2f}")

Mathematical Background

The Discrete Fourier Series represents a periodic signal as a sum of sinusoids:

f(t) = a₀ + Σ[aₖ·cos(2πkt/N) + bₖ·sin(2πkt/N)]

Where:

• N is the number of data points
• k ranges from 1 to N/2
• a₀ is the mean (DC component)

The coefficients are computed using:

a₀ = mean(data)
aₖ = (2/N)·Σ[data[i]·cos(2πki/N)]  for k = 1..N/2
bₖ = (2/N)·Σ[data[i]·sin(2πki/N)]  for k = 1..N/2-1

Use Cases

• Signal Processing: Analyze periodic signals and extract frequency components
• Time Series Analysis: Smooth noisy data and identify cyclical patterns
• Data Interpolation: Fill missing values in periodic sequences
• Trend Analysis: Calculate derivatives to detect trends and turning points
• Financial Analysis: Model cyclical patterns in market data
• Scientific Computing: Represent periodic phenomena mathematically
• Education: Teaching Fourier series with clear, interpretable real coefficients
• Physics: Modeling oscillatory systems (springs, pendulums, waves)
• Economics: Analyzing seasonal patterns and business cycles

Limitations

• Periodicity Assumption: Fourier series assumes the signal is periodic. Extrapolation beyond the original data will repeat the pattern.
• Even Length: Input data is adjusted to even length (first element removed if odd).
• Discontinuities: Sharp jumps in data may cause Gibbs phenomenon (ringing artifacts).

Example: Complete Workflow

import numpy as np
from discrete_fourier import DiscreteFourier

# Create sample data (sine wave with noise)
N = 100
t = np.linspace(0, 4*np.pi, N)
data = np.sin(t) + 0.1 * np.random.randn(N)

# Step 1: Calculate Fourier coefficients
coefs = DiscreteFourier.calculate_fourier_coefs(data.tolist())

# Step 2: Reconstruct the smoothed signal
reconstructed = [
    DiscreteFourier.calculate_fourier_value(coefs, i+1) 
    for i in range(N)
]

# Step 3: Find local maxima (where derivative changes from + to -)
derivatives = [
    DiscreteFourier.calculate_fourier_derivative_value(coefs, i+1)
    for i in range(N)
]

# Step 4: Predict next 10 values
predictions = [
    DiscreteFourier.calculate_fourier_value(coefs, N + i + 1)
    for i in range(10)
]

print(f"Predicted next values: {predictions}")

Requirements

• Python 3.8+
• NumPy

License

See LICENSE file for details.

Author

Ricardo Marcelo Alvarez
Date: 2025-12-19

Contributing

Contributions are welcome! Please feel free to submit issues or pull requests.