For testing and demonstrational purposes it is always good to work with synthetic data. Syncopy brings its own suite of synthetic data generators, but it is also possible to devise your own synthetic data and conveniently analyze it with Syncopy.
We can easily create custom synthetic datasets using basic NumPy functionality and Syncopy’s
To create a synthetic timeseries data set follow these steps:
write a function which returns a single trial as a 2d-
ndarraywith desired shape
collect all the trials into a Python
list, for example with a list comprehension or simply a for loop
AnalogDataobject by passing this list holding the trials as
dataand set the desired
In (pseudo-)Python code:
def generate_trial(nSamples, nChannels): trial = .. something fancy .. # These should evaluate to True isinstance(trial, np.ndarray) trial.shape == (nSamples, nChannels) return trial # collect the trials nSamples = 1000 nChannels = 2 nTrials = 100 trls =  for _ in range(nTrials): trial = generate_trial(Samples, nChannels) # manipulate further as needed, e.g. add a constant trial += 3 trls.append(trial) # instantiate syncopy data object my_fancy_data = spy.AnalogData(data=trls, samplerate=my_samplerate)
The same recipe can be used to generally instantiate Syncopy data objects from NumPy arrays.
Example: Noisy Harmonics#
Let’s create two harmonics and add some white noise to it:
import numpy as np import syncopy as spy def generate_noisy_harmonics(nSamples, nChannels, samplerate): f1, f2 = 20, 50 # the harmonic frequencies in Hz # the sampling times vector tvec = np.arange(nSamples) * 1 / samplerate # define the two harmonics ch1 = np.cos(2 * np.pi * f1 * tvec) ch2 = np.cos(2 * np.pi * f2 * tvec) # concatenate channels to to trial array trial = np.column_stack([ch1, ch2]) # add some white noise trial += 0.5 * np.random.randn(nSamples, nChannels) return trial nTrials = 50 nSamples = 1000 nChannels = 2 samplerate = 500 # in Hz # collect trials trials =  for _ in range(nTrials): trial = generate_noisy_harmonics(nSamples, nChannels, samplerate) trials.append(trial) synth_data = spy.AnalogData(trials, samplerate=samplerate)
Here we first defined the number of trials (
nTrials) and then the number of samples (
nSamples) and channels (
nChannels) per trial. With a sampling rate of 500Hz and 1000 samples this gives us a trial length of two seconds. The function
generate_noisy_harmonics adds a 20Hz harmonic on the 1st channel, a 50Hz harmonic on the 2nd channel and white noise to all channels, Every trial got collected into a Python
list, which at the last line was used to initialize our
synth_data. Note that data instantiated that way always has a default trigger offset of -1 seconds.
Now we can directly run a multi-tapered FFT analysis and plot the power spectra of all 2 channels:
spectrum = spy.freqanalysis(synth_data, foilim=[0,80], tapsmofrq=2, keeptrials=False) spectrum.singlepanelplot()
As constructed, we have two harmonic peaks at the respective frequencies (20Hz and 50Hz) and the white noise floor on all channels.
These generators return single-trial NumPy arrays, so to import them into Syncopy use the General Recipe described above.
A harmonic with frequency freq.
A linear trend on all channels from 0 to y_max in nSamples.
Linear (harmonic) phase evolution + a Brownian noise term inducing phase diffusion around the deterministic phase drift with slope
Simulation of a network of coupled AR(2) processes
Plain white noise with unity standard deviation.