API#

This page is the reference for the functions in the EMD package. Further details can be found on the page for each individual function.

Sift Functions#

Primary user-level functions for running the sift.

`emd.sift.sift`(X[, sift_thresh, ...])	Compute Intrinsic Mode Functions from an input data vector.
`emd.sift.ensemble_sift`(X[, nensembles, ...])	Compute Intrinsic Mode Functions with the ensemble EMD.
`emd.sift.complete_ensemble_sift`(X[, ...])	Compute Intrinsic Mode Functions with complete ensemble EMD.
`emd.sift.mask_sift`(X[, mask_amp, ...])	Compute Intrinsic Mode Functions using a mask sift.
`emd.sift.iterated_mask_sift`(X[, mask_0, ...])	Compute Intrinsic Mode Functions using an iterated mask sift.
`emd.sift.sift_second_layer`(IA[, sift_func, ...])	Compute second layer intrinsic mode functions.
`emd.sift.mask_sift_second_layer`(IA, mask_freqs)	Compute second layer IMFs using a mask sift.

Low-level utility functions used by the sift routines.

`emd.sift.get_config`([siftname])	Return a SiftConfig with default options for a specified sift variant.
`emd.sift.get_next_imf`(X[, env_step_size, ...])	Compute the next IMF from a data set.
`emd.sift.get_next_imf_mask`(X, z, amp[, ...])	Compute the next IMF from a data set a mask sift.
`emd.sift.interp_envelope`(X[, mode, ...])	Interpolate the amplitude envelope of a signal.
`emd.sift.get_padded_extrema`(X[, pad_width, ...])	Identify and pad the extrema in a signal.
`emd.sift.stop_imf_fixed_iter`(niters, max_iters)	Compute the fixed-iteraiton sift stopping metric.
`emd.sift.stop_imf_sd`(proto_imf, prev_imf[, ...])	Compute the sd sift stopping metric.
`emd.sift.stop_imf_rilling`(upper_env, lower_env)	Compute the Rilling et al 2003 sift stopping metric.
`emd.sift.stop_imf_energy`(imf, residue[, ...])	Compute energy change in IMF during a sift.

Computing frequency transforms from narrow band oscillations (IMFs).

`emd.spectra.frequency_transform`(imf, ...[, ...])	Compute instantaneous phase, frequency and amplitude from a set of IMFs.
`emd.spectra.phase_from_complex_signal`(...[, ...])	Compute the instantaneous phase from a complex signal.
`emd.spectra.freq_from_phase`(iphase, sample_rate)	Compute the instantaneous frequency from the instantaneous phase.
`emd.spectra.phase_from_freq`(ifrequency, ...)	Compute the instantaneous phase of a signal from its instantaneous frequency.

Compute Hilbert-Huang and Holospectra from instantaneous frequency data.

`emd.spectra.hilberthuang`(IF, IA[, edges, ...])	Compute a Hilbert-Huang transform (HHT).
`emd.spectra.holospectrum`(IF, IF2, IA2[, ...])	Compute a Holospectrum.
`emd.spectra.hilbertmarginal`(IF, IA[, order, ...])	Compute a generalised Hilbert marginal spectrum.

Low-level helper functions for spectrum computations.

`emd.spectra.define_hist_bins`(data_min, ...)	Define the bin edges and centre values for use in a histogram.
`emd.spectra.define_hist_bins_from_data`(X[, ...])	Find the bin edges and centre frequencies for use in a histogram.

Assess and analyse IMFs and their derivatives.

`emd.imftools.amplitude_normalise`(X[, ...])	Normalise the amplitude envelope of an IMF to be 1.
`emd.imftools.wrap_phase`(IP[, ncycles, mode])	Wrap a phase time-course.
`emd.imftools.zero_crossing_count`(X)	Count the number of zero-crossings within a time-course.
`emd.imftools.is_imf`(imf[, avg_tol, ...])	Determine whether a signal is a 'true IMF'.
`emd.imftools.est_orthogonality`(imf)	Compute the index of orthogonality from a set of IMFs.
`emd.imftools.check_decreasing_freq`(IF[, mode])	Similar to method 1 in http://dx.doi.org/10.11601/ijates.v5i1.139.
`emd.imftools.pseudo_mode_mixing_index`(imf)	Compute the Pseudo Mode Mixing Index from a set of IMFs.
`emd.imftools.assess_harmonic_criteria`(IP, IF, IA)	Assess IMFs for potential harmonic relationships.
`emd.imftools.assess_joint_if`(imf[, ...])	Assess whether two signals have a well formed joint instantaneous frequency.
`emd.imftools.apply_epochs`(X, trls)	Apply a set of epochs to a continuous dataset.
`emd.imftools.find_extrema_locked_epochs`(X, ...)	Define epochs around peaks or troughs within the data.

Create artificial oscillations.

`emd.simulate.ar_oscillator`(freq, ...[, r, ...])	Create a simulated oscillation using an autoregressive filter.
`emd.simulate.abreu2010`(f, nonlin_deg, ...)	Simulate a non-linear waveform using equation 7 in [R441f6af671e2-1].
`emd.simulate.compute_joint_if`(freq, amp, phase)	Compute joint instantaneous frequency from a set of oscillations.

Identify and analyse single cycles of an oscillation.

`emd.cycles.Cycles`(IP[, phase_step, ...])	Find, store and analyse single cycles [R9208c59e353f-1].
`emd.cycles.get_cycle_vector`(phase[, ...])	Identify cycles within a instantaneous phase time-course.
`emd.cycles.get_cycle_stat`(cycles, values[, ...])	Compute the average of a set of observations for each cycle.
`emd.cycles.get_control_points`(x, cycles[, ...])	Identify sets of control points from identified cycles.
`emd.cycles.phase_align`(ip, x[, cycles, ...])	Align a vector of observations to a template phase time-course.
`emd.cycles.normalised_waveform`(infreq)	Compute the time-domain waveform of an phase-aligned IF profile.
`emd.cycles.bin_by_phase`(ip, x[, nbins, ...])	Compute distribution of x by phase-bins in the Instantaneous Frequency.
`emd.cycles.mean_vector`(IP, X[, mask])	Compute the mean vector of a set of values wrapped around the unit circle.
`emd.cycles.kdt_match`(x, y[, K, ...])	Find unique nearest-neighbours between two n-dimensional feature sets.

Routines related to python, logging and installation.

`emd.support.get_install_dir`()	Get directory path of currently installed & imported emd.
`emd.support.get_installed_version`()	Read version of currently installed & imported emd.
`emd.logger.set_up`([prefix, log_file, level, ...])	Initialise the EMD module logger.