acycle 0.6.7

AcyclePy: Advanced cyclostratigraphy and time series analysis toolkit

AcyclePy — Advanced Cyclostratigraphy and Time Series Analysis Toolkit

AcyclePy is the Python library companion to the Acycle desktop application for cyclostratigraphy, time series analysis, and paleoclimate research. It provides both a programmatic API for scripting and batch processing, and the full suite of Acycle desktop GUI tools.

Reference: Li, M., Hinnov, L., & Kump, L. (2019). Acycle: Time-series analysis software for paleoclimate research and education. Computers & Geosciences, 127, 12-22.

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Installation

ash pip install acycle

Requirements: Python >= 3.8, numpy, scipy, pandas, matplotlib, PySide6, scikit-image, scikit-learn, Pillow, qt-material, astropy.

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Quick Start — Programmatic API

`python import acycle as ac import numpy as np

Read time series data

s = ac.read_series("data.txt") # auto-detect whitespace/tab/comma s = ac.read_series("data.csv", delimiter=",") # explicit delimiter

Chain operations (fluent API)

s2 = s.clean(sort=True)
.interpolate(step=0.33)
.detrend(window=80)
.standardize()

Spectral analysis

from acycle import spectral freq, power = spectral._periodogram(s2.y) freq, power = spectral._mtm_spectrum(s2.y) # multitaper freq, power = spectral._lomb_scargle_spectrum(s2.x, s2.y) # uneven spacing

Wavelet analysis

from acycle import wavelet period, power, coi, significance = wavelet.cwt(s2.y, dt=s2.dt) coherence = wavelet.wavelet_coherence(s1.y, s2.y, dt=s1.dt)

Filtering

from acycle import filter as ft result = ft.apply_filter(y, dt=0.2, kind="bandpass", flow=0.01, fhigh=0.05)

Age modeling

from acycle import age model = age.build_age_model(depth, y, cycle_period=405) depth, age = age.tune(depth, y, model)

Load built-in datasets

lr04 = ac.load_example("lr04") # LR04 benthic stack cenogrid = ac.load_example("cenogrid_d18o") # CENOGRID d18O `

Result Objects

All analysis functions return structured result objects with a consistent interface:

`python psd = PSD(frequency=freq, power=power) psd.to_dataframe() # export to pandas DataFrame psd.save("my_spectrum") # save to CSV (my_spectrum_spectrum.csv) psd.plot() # matplotlib figure

Same interface for all result types:

EvolutiveSpectrum, WaveletResult, FilterResult, AgeModel, CocoResult, SedNoiseResult

`

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CLI Tools (Acycle Desktop GUI)

These commands launch the original Acycle desktop GUI tools bundled with the package:

ash acycle-imageprocessor # Image digitizing and data extraction acycle-plot # PlotPro — publication-quality plotting acycle-interpolation # Advanced interpolation with gap filling acycle-data-extractor # Extract data segments by range acycle-section-remover # Remove sections from time series acycle-gap-adder # Insert gaps into data acycle-data-clipper # Clip data by threshold acycle-image-analyzer # Advanced image analysis

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API Reference

Series Operations

Method	Description
Series.from_file(path)	Read from a delimited text file
.clean(sort, duplicate, dropna)	Sort, deduplicate, drop NaN
.interpolate(step, method)	Interpolate to uniform grid
.interpolate_pro(step, method)	Advanced interpolation with gap filling
.detrend(window, method)	Remove trend (lowess, loess, polynomial, savgol, moving_mean)
.standardize()	Z-score standardization
.log10(handle_nonpositive)	Base-10 logarithm
.derivative(order)	Numerical derivative
.prewhiten(method)	Prewhitening
.select(start, stop)	Sub-range selection
.moving_average(n)	Moving average smoothing
.gaussian_smooth(n, sigma)	Gaussian smoothing
.moving_median(n)	Moving median smoothing
.multiply(other_series)	Element-wise multiply with another series
.clip_by_threshold(threshold)	Clip or remove data by threshold
.to_dataframe()	Export to pandas DataFrame
.copy()	Deep copy

Spectral Analysis (cycle.spectral)

Function	Description
_periodogram(y, dt)	Classical periodogram
_mtm_spectrum(y, dt, nw)	Multitaper method
_lomb_scargle_spectrum(x, y)	Lomb-Scargle (uneven spacing)
_estimate_ar1_rho(y)	AR(1) lag-1 autocorrelation
_estimate_ar1_noise(y, dt)	AR(1) noise background
_ftest_mtm(y, dt, nw)	F-test for significant peaks

Wavelet Analysis (cycle.wavelet)

Function	Description
cwt(y, dt)	Continuous wavelet transform (Torrence & Compo 1998)
wavelet_coherence(y1, y2, dt)	Wavelet coherence and phase

Filtering (cycle.filter)

Function	Description
pply_filter(y, dt, kind)	Bandpass/lowpass/highpass (gaussian, butterworth, etc.)
dynamic_filter(x, y, window)	Sliding-window dynamic filtering
mplitude_modulation(x, y, flow, fhigh)	Envelope extraction

Age Modeling (cycle.age)

Function	Description
uild_age_model(x, y, cycle_period)	Age model from cycle counting
sedrate_to_age_model(depth, sedrate)	Convert sedimentation rate to age model
une(depth, y, age_model)	Depth-to-time conversion
stratigraphic_correlation(ref, target, tie_points)	Correlate two stratigraphic sections

Preprocessing (cycle.preprocess)

Function	Description
detrend(x, y, window, method)	Remove trend
clip_by_threshold(x, y, threshold)	Clip by value

| emove_sections(x, y, sections) | Remove data ranges | | dd_gaps(x, y, gaps) | Insert NaN-filled gaps | | emove_peaks(y, ymin, ymax) | Remove or cap peaks | | multiply_series(x, y1, y2) | Element-wise multiply two series | | merge_series(series_list) | Merge multiple series by x grid | | pca(data, n_components) | Principal component analysis | | changepoint(y, method) | Changepoint detection | | ransform_xy(x, y, a, b, c, d) | Affine coordinate transform | | ind_extreme(x, y, kind) | Find max or min in range |

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Citation

If you use AcyclePy in your research, please cite:

Li, M., Hinnov, L., & Kump, L. (2019). Acycle: Time-series analysis software for paleoclimate research and education. Computers & Geosciences, 127, 12–22. https://doi.org/10.1016/j.cageo.2019.02.011

License

MIT License — see LICENSE file for details.

Links

• PyPI
• Repository
• Documentation
• Issue Tracker