Getting started

pypret is a package to simulate and retrieve from measurements such as frequency-resolved optical gating (FROG), dispersion scan (d-scan), interferometric FROG (iFROG), time-domain ptychography (TDP) and even multiphoton intrapulse interference phase scan (MIIPS). These are all measurements used for ultrashort (sub-ps) laser pulse measurement. More generally the package can handle all kinds of parametrized nonlinear process spectra (PNPS) measurements.

A good place to start reading on the algorithms and the used notation is our paper [Geib2019] and its supplement. pypret can be thought to accompany this publication and can be used to reproduce most of the results shown there.

Basic Use

pypret can be used to simulate PNPS measurements. This is useful for designing experiments and necessary for retrieval, of course.

In a first step you have to set up the simulation grid in time and frequency:

ft = pypret.FourierTransform(256, dt=2.5e-15)

which generates a 256 elements grid with a temporal spacing of 2.5 fs centered around t=0. The frequency grid is chosen to match the reciprocity relation dt * dw = 2 * pi / N. Alternatively you can specify the frequency spacing. See the documentation at pypret.fourier module. Next you can instantiate a pypret.Pulse object:

pulse = pypret.Pulse(ft, 800e-9)

where we used a central wavelength of 800 nm. This class can already be used for small but useful calculations:

# generate pulse with Gaussian spectrum and field standard deviation
# of 20 nm
pulse.spectrum = pypret.lib.gaussian(pulse.wl, x0=800e-9, sigma=20e-9)
# print the accurate FWHM of the temporal intensity envelope
# propagate it through 1cm of BK7 (remove first ord)
phase = np.exp(1.0j * pypret.material.BK7.k(pulse.wl) * 0.01)
pulse.spectrum = pulse.spectrum * phase
# print the temporal FWHM again
# finally plot the pulse

You can now instantiate a PNPS class with that pulse object:

insertion = np.linspace(-0.025, 0.025, 128)  # insertion in m
pnps = pypret.PNPS(pulse, "dscan", "shg", material=pypret.material.BK7)
# calculate the measurement trace
pnps.calculate(pulse.spectrum, delay)
original_spectrum = pulse.spectrum
# and plot it

The PNPS constructor supports a lot of different PNPS measurements (see docs at pypret.pnps module). Furthermore, it is easy to implement your own.

Finally, you can use pypret for pulse retrieval by instantiating a Retriever object:

# do the retrieval
ret = pypret.Retriever(pnps, "copra", verbose=True, maxiter=300)
# start with a Gaussian spectrum with random phase as initial guess
pypret.random_gaussian(pulse, 50e-15, phase_max=0.0)
# now retrieve from the synthetic trace simulated above
ret.retrieve(pnps.trace, pulse.spectrum)
# and print the retrieval results

A lot of different retrieval algorithms besides the default, COPRA, are implemented (see docs at pypret.retrieval package). While COPRA should work for all PNPS measurements, you may try one of the others for verification.


The package subpackage supports saving almost arbitrary Python structures and all pypret classes to HDF5 files. You can either use the function or the save method on classes:

pnps.calculate(pulse.spectrum, insertion)"trace.hdf5")
# or, "trace.hdf5")
# load it with
trace = pypret.load("trace.hdf5")

This should make storing intermediate or final results almost effortless.

Experimental data

As this question is surely going to come: you can use pypret to retrieve pulses from experimental data, however, it currently has no pre-processing functions to make that convenient. The data fed to the retrieval functions has to be properly dark-subtracted and interpolated. Furthermore, some features that are very useful for retrieval from experimental data (e.g., handling non-calibrated traces) are not yet implemented. This is on the top of the ToDo-list, though.