Note

Go to the end to download the full example code

# Code speed and efficiency#

EMD analysis can be time-consuming. This tutorial outlines some basic information about how long different computations may take and what features can be used to speed this up.

## Sift Speed#

The sift can be time-consuming for two reasons. Firstly, it is an iterative process which can vary in how long it takes to converge. Though many signals can be sifted in a handful of iterations some may take tens or hundreds of iterations before an IMF is identified - unfortunately we can’t tell before the process is running. Secondly, the sift is sequential in that we can’t compute the second IMF until the first IMF has been identified.

The default settings in the sift are selected to operate reasonably well and reasonable quickly on a signal. Here we include some a very rough, order of magnitude illustration of timings based on running speeds on a modern computer (the readthedocs server generating this website).

```
import emd
import time
import numpy as np
# ---- Ten thousand sample example
x = np.random.randn(10000,)
t = time.process_time()
imf = emd.sift.sift(x)
elapsed = 1000 * (time.process_time() - t)
print('{0} samples sifted in {1} milliseconds'.format(10000, elapsed))
# ---- Five thousand samples example
x = np.random.randn(5000,)
t = time.process_time()
imf = emd.sift.sift(x)
elapsed = 1000 * (time.process_time() - t)
print('{0} samples sifted in {1} milliseconds'.format(5000, elapsed))
# ---- Five hundred samples example
x = np.random.randn(500,)
t = time.process_time()
imf = emd.sift.sift(x)
elapsed = 1000 * (time.process_time() - t)
print('{0} samples sifted in {1} milliseconds'.format(500, elapsed))
```

```
10000 samples sifted in 71.20085999999048 milliseconds
5000 samples sifted in 41.42034399998806 milliseconds
500 samples sifted in 15.109744000000092 milliseconds
```

The sift executes in well less than a second for all examples. Computation time increases with input array size linearly for relatively short input but exponentially but larger ones (>1 million samples, not computed here…).

Some options can noticeably slow down the sift. For example, the imf option
`imf_opts/stop_method='rilling'`

is tends to use more iterations than the
default `imf_opts/stop_method='sd'`

. Similarly changing the thresholds for
either stopping method can increase the number of iterations computed. the
envelope interpolation method `envelope_opes/interp_method='mono_pchip'`

is
much slower than the default `envelope_opes/interp_method='splrep'`

## Sift Variants#

Compared to the classic sift, the ensemble and mask sift are slower but have
more options for speeding up computation. The computation speed of
`emd.sift.ensemble_sift`

and `emd.sift.complete_ensemble_sift`

is most
strongly determined by the number of ensembles that are computed - however,
these can be parallelised by setting the `nprocesses`

option to be greater
than 1.

```
# Run an ensemble sift with 24 ensembles
imf = emd.sift.ensemble_sift(x, nensembles=24, max_imfs=6)
# Run an ensemble sift with the 24 ensembles splits across 6 parallel threads
imf = emd.sift.ensemble_sift(x, nensembles=24, max_imfs=6, nprocesses=6)
```

Similarly, the timing of `emd.sift.mask_sift`

is strongly determined by the
number of separate masks applied to each IMF - specified by `nphases`

.
Again this can be parallelised by setting `nprocesses`

to speed up
computation time.

```
# Compute a mask sift, applying four masks per IMF
imf = emd.sift.mask_sift(x, nphases=4)
# Compute a mask sift, applying four masks per IMF split across 4 parallel processes
imf = emd.sift.mask_sift(x, nphases=4, nprocesses=4)
```

## Sparse Time-Frequency Transforms.#

Another potentially slow computation during an EMD analysis is generating Hilbert-Huang and Holospectrum arrays. Both of these algorithms make use of nested looping to form the output. As this can be very slow, these operations are accelerated internally by using sparse arrays. This allows the Hilbert-Huang transform and Holospectrum arrays to be formed in one shot without looping.

By default, these outputs are cast to normal numpy arrays before being
returned to the user. If you are working with a very large transform, it is
far more memory and computationally efficient to work with the sparse form of
these arrays. These can be returned by specifying `return_sparse=True`

in
the options in either `emd.spectra.hilberthuang`

or
`emd.spectra.holospectrum`

.

```
IP, IF, IA = emd.spectra.frequency_transform(imf, 1, 'hilbert')
freq_edges, freq_bins = emd.spectra.define_hist_bins(0, .5, 75)
msg = 'Output is a {0} of size {1} using {2}Kb of memory'
f, hht = emd.spectra.hilberthuang(IF, IA, freq_edges)
print(msg.format(type(hht), hht.shape, hht.nbytes/1024))
f, hht = emd.spectra.hilberthuang(IF, IA, freq_edges, return_sparse=True)
print(msg.format(type(hht), hht.shape, hht.data.nbytes/1024))
```

```
Output is a <class 'numpy.ndarray'> of size (75,) using 0.5859375Kb of memory
Output is a <class 'numpy.ndarray'> of size (75,) using 0.5859375Kb of memory
```

**Total running time of the script:** ( 0 minutes 1.904 seconds)