qfc 0.3.2

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QFC - Quantized Fourier Compression of Timeseries Data with Application to Electrophysiology

Overview

With the increasing sizes of data for extracellular electrophysiology, it is crucial to develop efficient methods for compressing multi-channel time series data. While lossless methods are desirable for perfectly preserving the original signal, the compression ratios for these methods usually range only from 2-4x. What is needed are ratios on the order of 10-30x, leading us to consider lossy methods.

Here, we implement a simple lossy compression method, inspired by the Discrete Cosine Transform (DCT) and the quantization steps of JPEG compression for images. The method comprises the following steps:

• Compute the Discrete Fourier Transform (DFT) of the time series data in the time domain.
• Quantize the Fourier coefficients to achieve a target entropy (the entropy determines the theoretically achievable compression ratio). This is done by multiplying by a normalization factor and then rounding to the nearest integer.
• Compress the reduced-entropy quantized Fourier coefficients using zlib or zstd (other methods could be used instead).

To decompress:

• Decompress the quantized Fourier coefficients.
• Divide by the normalization factor.
• Compute the Inverse Discrete Fourier Transform (IDFT) to obtain the reconstructed time series data.

This method is particularly well-suited for data that has been bandpass-filtered, as the suppressed Fourier coefficients yield an especially low entropy of the quantized signal.

For a comparison of various lossy and lossless compression schemes, see Compression strategies for large-scale electrophysiology data, Buccino et al..

Installation

pip install qfc

Example usage

# See examples/example1.py

from matplotlib import pyplot as plt
import numpy as np
from qfc import qfc_estimate_quant_scale_factor
from qfc.codecs import QFCCodec


def main():
    sampling_frequency = 30000
    duration = 2
    num_channels = 10
    num_samples = int(sampling_frequency * duration)
    y = np.random.randn(num_samples, num_channels) * 50
    y = lowpass_filter(y, sampling_frequency, 6000)
    y = np.ascontiguousarray(y)  # compressor requires C-order arrays
    y = y.astype(np.int16)
    target_residual_stdev = 5

    ############################################################
    quant_scale_factor = qfc_estimate_quant_scale_factor(
        y,
        target_residual_stdev=target_residual_stdev
    )
    codec = QFCCodec(
        quant_scale_factor=quant_scale_factor,
        dtype="int16",
        segment_length=10000,
        compression_method="zstd",
        zstd_level=3
    )
    compressed_bytes = codec.encode(y)
    y_reconstructed = codec.decode(compressed_bytes)
    ############################################################

    y_resid = y - y_reconstructed
    original_size = y.nbytes
    compressed_size = len(compressed_bytes)
    compression_ratio = original_size / compressed_size
    print(f"Original size: {original_size} bytes")
    print(f"Compressed size: {compressed_size} bytes")
    print(f"Actual compression ratio: {compression_ratio}")
    print(f'Target residual std. dev.: {target_residual_stdev:.2f}')
    print(f'Actual Std. dev. of residual: {np.std(y_resid):.2f}')

    xgrid = np.arange(y.shape[0]) / sampling_frequency
    ch = 3  # select a channel to plot
    n = 1000  # number of samples to plot
    plt.figure()
    plt.plot(xgrid[:n], y[:n, ch], label="Original")
    plt.plot(xgrid[:n], y_reconstructed[:n, ch], label="Decompressed")
    plt.plot(xgrid[:n], y_resid[:n, ch], label="Residual")
    plt.xlabel("Time")
    plt.title(f'QFC compression ratio: {compression_ratio:.2f}')
    plt.legend()
    plt.show()


def lowpass_filter(input_array, sampling_frequency, cutoff_frequency):
    F = np.fft.fft(input_array, axis=0)
    N = input_array.shape[0]
    freqs = np.fft.fftfreq(N, d=1 / sampling_frequency)
    sigma = cutoff_frequency / 3
    window = np.exp(-np.square(freqs) / (2 * sigma**2))
    F_filtered = F * window[:, None]
    filtered_array = np.fft.ifft(F_filtered, axis=0)
    return np.real(filtered_array)


if __name__ == "__main__":
    main()

Zarr example

See examples/zarr_example.py

Benchmarks

I have put together some preliminary systematic benchmarks on real and synthetic data. See ./benchmarks and ./benchmarks/results.

As can be seen:

• Quantizing in the Fourier domain (QFC) is a lot better than quantizing in the time domain (call it QTC) for real data or for bandpass-filtered data.
• The compression ratio is a lot better for bandpass-filtered data compared with unfiltered raw.
• For the lossless part of the method, zstd is better than zlib, both in terms of all three of these factors: compression ratio, compression speed, and decompression speed.
• Obviously, the compression ratio is going to depend heavily on the target residual std. dev.

License

This code is provided under the Apache License, Version 2.0.

Author

Jeremy Magland, Center for Computational Mathematics, Flatiron Institute