twodsi

Submodules

twodsi.classic_algorithms_2dsi module

class twodsi.classic_algorithms_2dsi.DirectReconstruction(delay, frequency, measured_trace, nonlinear_method, spectral_filter1, spectral_filter2, **kwargs)

Bases: ClassicAlgorithmsBASE, RetrievePulses2DSI

Reconstructs a 2DSI trace non-iteratively by extracting the relative phase of the fringes for each frequency.

[1] J. R. Birge et al., Opt. Lett. 31, 2063-2065, 10.1364/OL.31.002063 (2006)

integration_method

the integration method for the group delay. Has to be one of cumsum or euler_maclaurin_k

Type:

str

integration_order

the euler_maclaurin order, infered from integration_method

Type:

None, int

windowing

windowing type for the FFT. Can be one of False, hamming or hann.

Type:

bool, str

cut_off_intensity_for_GD

a cut-off which sets the GD to zero for smaller intensities. (Needed for stable numerics)

Type:

float

apply_windowing(a0, signal, axis=-1)

Applies a windowing on a signal along a given axis.

interpolate_group_delay_onto_spectral_amplitude(spectral_phase, measurement_info)

The group delay is obtained on the wrong position on the frequency axis. This is solved by shifted/interpolation.

reconstruct_2dsi_1dfft(descent_state, measurement_info, descent_info)

Performs the standard 2DSI reconstruction by integrating over the group delay. Which is obtained as the phase of an fft of the measured trace along the delay axis.

calc_error_of_reconstruction(descent_state, measurement_info, descent_info)

Calculates the error of the reconstruction.

step(descent_state, measurement_info, descent_info)

Performs the reconstruction in one interation.

Parameters:

• descent_state – Pytree,

• measurement_info – Pytree,

• descent_info – Pytree,

Returns:

tuple[Pytree, jnp.array], the updated descent state and the current errors

initialize_run(population)

Prepares all provided data and parameters for the reconstruction. Here the final shape/structure of descent_state, measurement_info and descent_info are determined.

Parameters:

population – Pytree, the initial guess as created by self.create_initial_population()

Returns:

tuple[Pytree, Callable], the initial descent state and the step-function of the algorithm.

class twodsi.classic_algorithms_2dsi.GeneralizedProjection(delay, frequency, measured_trace, nonlinear_method, spectral_filter1, spectral_filter2, cross_correlation=False, **kwargs)

Bases: GeneralizedProjectionBASE, RetrievePulses2DSI

Implements the Generalized Projection Algorithm.

11. 23. DeLong et al., Opt. Lett. 19, 2152-2154 (1994)

no_steps_descent

the numer of descent steps per iteration

Type:

int

optimize_spectral_phase_directly

Type:

bool

calculate_Z_gradient_individual(signal_t, signal_t_new, tau_arr, measurement_info, pulse_or_gate)

Calculates the Z-error gradient for an individual.

calculate_Z_newton_direction(grad, signal_t_new, signal_t, tau_arr, descent_state, measurement_info, descent_info, full_or_diagonal, pulse_or_gate)

Calculates the Z-error newton direction for a population.

class twodsi.classic_algorithms_2dsi.PtychographicIterativeEngine(delay, frequency, measured_trace, nonlinear_method, spectral_filter1, spectral_filter2, cross_correlation=False, **kwargs)

Bases: PtychographicIterativeEngineBASE, RetrievePulses2DSI

Implements a version of the Ptychographic Iterative Engine (PIE).

A. Maiden et al., Optica 4, 736-745 (2017) T. Schweizer, “Time-Domain Ptychography and its Applications in Ultrafast Science”, PhD Thesis, Bern (2021)

alpha

a regularization parameter

Type:

float

pie_method

specifies the PIE variant. Can be one of None, PIE, ePIE, rPIE. Where None indicates that the pure gradient is used.

Type:

None, str

optimize_spectral_phase_directly

Type:

bool

calculate_PIE_descent_direction_m(signal_t, signal_t_new, tau, pie_method, measurement_info, descent_info, pulse_or_gate)

Calculates the PIE direction for a given shift.

calculate_PIE_newton_direction(grad, signal_t, tau_arr, measured_trace, local_or_global_state, measurement_info, descent_info, pulse_or_gate, local_or_global)

Calculates the PIE newton direction for a population.

class twodsi.classic_algorithms_2dsi.COPRA(delay, frequency, measured_trace, nonlinear_method, spectral_filter1, spectral_filter2, cross_correlation=False, **kwargs)

Bases: COPRABASE, RetrievePulses2DSI

Implements a version of the Common Pulse Retrieval Algorithm (COPRA).

14. 3. Geib et al., Optica 6, 495-505 (2019)

get_Z_gradient_individual(signal_t, signal_t_new, tau_arr, measurement_info, pulse_or_gate)

Calculates the Z-error gradient for an individual.

get_Z_newton_direction(grad, signal_t, signal_t_new, tau_arr, local_or_global_state, measurement_info, descent_info, full_or_diagonal, pulse_or_gate)

Calculates the Z-error newton direction for a population.

class twodsi.classic_algorithms_2dsi.LSF(delay, frequency, measured_trace, nonlinear_method, spectral_filter1, spectral_filter2, cross_correlation=False, **kwargs)

Bases: LSFBASE, RetrievePulses2DSI

Implements a version of the Linesearch FROG Algorithm (LSF). Despite its name the algorithm is NOT restricted to FROG.

3. 15. Krook and V. Pasiskevicius, Opt. Express 33, 33258-33269 (2025)

no_sections

number of sections to split the search line into

Type:

int

number_of_disection_iterations

as the name says

Type:

int

direction_mode

can be random or continuous

Type:

str

ratio_points_for_continuous

smaller value means more randomness/eratic

Type:

int

only_allow_improvements

if true, only steps that decrease the error will be accepted

Type:

bool

twodsi.general_algorithms_2dsi module

class twodsi.general_algorithms_2dsi.DifferentialEvolution(delay, frequency, measured_trace, nonlinear_method, spectral_filter1, spectral_filter2, cross_correlation=False, strategy='best1_bin', selection_mechanism='greedy', mutation_rate=0.5, crossover_rate=0.7, **kwargs)

Bases: DifferentialEvolutionBASE, RetrievePulses2DSI

Implements a Differential-Evolution Algorithm. Based on Qiang, J., Mitchell, C., A Unified Differential Evolution Algorithm for Global Optimization, 2014, https://www.osti.gov/servlets/purl/1163659

strategy

the mutation and selection strategy, analogous to scipy’s differential evolution.

Type:

str

mutation_rate

the mutation rate

Type:

float

crossover_rate

the crossover rate

Type:

float

selection_mechanism

the selection mechanism, can be greedy or global, defined in select_population().

Type:

str

temperature

a temperature value for the global selection mechanism

Type:

float

class twodsi.general_algorithms_2dsi.Evosax(delay, frequency, measured_trace, nonlinear_method, spectral_filter1, spectral_filter2, cross_correlation=False, solver=None, **kwargs)

Bases: EvosaxBASE, RetrievePulses2DSI

Employs the evosax package to perform the optimization.

Robert Tjarko Lange, evosax: JAX-based Evolution Strategies, arXiv preprint arXiv:2212.04180 (2022)

solver

any evosax-solver should work

Type:

evosax-solver

solver_params

user defined parameters for the evosax-solver, if None the default params set in evosax are used

Type:

any

solver_kwargs

some evosax-solver require additional input arguments. These can be supplied via this aattribute.

Type:

dict

class twodsi.general_algorithms_2dsi.AutoDiff(delay, frequency, measured_trace, nonlinear_method, spectral_filter1, spectral_filter2, cross_correlation=False, solver=None, **kwargs)

Bases: AutoDiffBASE, RetrievePulses2DSI

Employs the optimistix package to perform the optimization via Automatic-Differentiation.

J. Rader, T. Lyons and P.Kidger, Optimistix: modular optimisation in JAX and Equinox, arXiv:2402.09983 (2024) DeepMind et al., The DeepMind JAX Ecosystem, http://github.com/google-deepmind (2020)

solver

solvers need to be initialized

Type:

optimistix-solver, optax-solver

alternating_optimization

if true, the optimizer alternates between amplitude and phase

Type:

bool

optimize_group_delay

if true, the group delay will be optimized instead of the spectral phase

Type:

bool

Module contents