Functions

Time dependent function objects.

ApodizedStep

class onscale.functions.ApodizedStep(amplitude: float, amplitude_before: float = 0.0, amplitude_after: float = 0.0, step_time1: float = 0.0, step_time2: float | None = None, exponent: float = 1.0, band: float = 0.0, time_delay: float = 0.0, step_type: str = 'cos')

Apodized step function.

Examples

>>> import onscale as on
>>> with on.Simulation("Static Solve") as sim:
...     on.functions.ApodizedStep(amplitude=1.0)
An apodization function is a function used to smoothly bring a

sampled signal down to zero at the edges of the sampled region.

Parameters:

  • amplitude – Required. Amplitude of step function for: step_time1 < time < step_time2

  • amplitude_before – Amplitude before the first step starts, time <= steptime1

  • amplitude_after – Amplitude before the second step ends, steptime2 < time

  • step_time1 – Time at which first step occurs.

  • step_time2 – Time at which second step occurs.

  • exponent – Exponent for cosine function.

  • band – Apodization band. For either side of the function

  • time_delay – The shift in time for all input time values. Positive time_delay indicates a later step.

  • step_type – Apodization function type. Function types: cos

static from_dict(data: Dict[str, Any], sim: Simulation | None = None) → Node

Construct this node from serialized dictionary data

to_json() → str

Convert this node into JSON representation

property uuid: str

Get the UUID associated with this node.

BlackmannHarrisWavelet

class onscale.functions.BlackmannHarrisWavelet(amplitude: float, drive_freq: float, time_delay: float = 0.0)

Blackmann Harris wavelet function.

Examples

>>> import onscale as on
>>> with on.Simulation("Static Solve") as sim:
...     on.functions.BlackmannHarrisWavelet(1.0, 1e-2)
The Blackman-Harris (BH) window family is a straightforward

generalization of the hamming family. The following parameters define the functional form of the 2nd derivative of the BH window. This function produces a wavelet in time whose frequency content is a Gaussian distribution centered at the center frequency.

Parameters:

  • amplitude – The absolute difference from the wave’s equilibrium to the maximum positive peak.

  • drive_freq – The initial value given to the frequency of the selected time function.

  • time_delay – Time delay of wavelet in periods.

static from_dict(data: Dict[str, Any], sim: Simulation | None = None) → Node

Construct this node from serialized dictionary data

to_json() → str

Convert this node into JSON representation

property uuid: str

Get the UUID associated with this node.

ChirpPulse

class onscale.functions.ChirpPulse(start_freq: float, stop_freq: float, amplitude: float, pulse_duration: float, ramp_up: float = 0.0, ramp_down: float = 0.0, time_delay: float = 0.0)

Chirp pulse function.

Examples

>>> import onscale as on
>>> with on.Simulation("Static Solve") as sim:
...     on.functions.ChirpPulse(1.0, 1e2, 10.0, 1e-2)
A chirp is a signal in which the frequency increase or decrease

with time, these can also be referred to respectively as up-chirp or down-chirp. The term chirp is used interchangeably with a sweep signal.

Parameters:

  • start_freq – Start frequency of the chirp function being defined.

  • stop_freq – Stop frequency of the chirp function being defined.

  • amplitude – The absolute difference from the wave’s equilibrium to the maximum positive peak.

  • pulse_duration – Time duration of the chirp function.

  • ramp_up – Time duration of the chirp function.

  • ramp_down – Time duration to ramp up to full amplitude at start of function.

  • time_delay – Time duration to ramp up to full amplitude at start of function.

static from_dict(data: Dict[str, Any], sim: Simulation | None = None) → Node

Construct this node from serialized dictionary data

to_json() → str

Convert this node into JSON representation

property uuid: str

Get the UUID associated with this node.

Custom

class onscale.functions.Custom(x: Iterable[float], y: Iterable[float])

Custom user-defined function.

Examples

>>> import onscale as on
>>> import numpy as np
>>> with on.Simulation("Static Solve") as sim:
...     x = np.arange(6)
...     y = np.sin(x) ** 2
...     on.functions.Custom(x,y)
Parameters:

  • x – Independent variable samples.

  • y – Dependent variable samples.

static from_dict(data: Dict[str, Any], sim: Simulation | None = None) → Node

Construct this node from serialized dictionary data

to_json() → str

Convert this node into JSON representation

property uuid: str

Get the UUID associated with this node.

Gaussian

class onscale.functions.Gaussian(amplitude: float, width: float = 0.5, time_delay: float = 0.0)

Gaussian time function.

Examples

>>> import onscale as on
>>> with on.Simulation("Static Solve") as sim:
...     on.functions.Gaussian(20.0)
Parameters:

  • amplitude – The absolute difference from the wave’s equilibrium to the maximum positive peak.

  • width – The time width between the positive and negative peaks of the two-sided Gaussian distribution. Approximately equal to one half the total pulse duration.

  • time_delay – Time delay of wavelet in periods.

static from_dict(data: Dict[str, Any], sim: Simulation | None = None) → Node

Construct this node from serialized dictionary data

to_json() → str

Convert this node into JSON representation

property uuid: str

Get the UUID associated with this node.

RickerWavelet

class onscale.functions.RickerWavelet(amplitude: float, drive_freq: float, phase_shift: float = 0.0, num_wavelets: float = 4.0, time_delay: float = 2.0)

Ricker wavelet function.

Examples

>>> import onscale as on
>>> with on.Simulation("Static Solve") as sim:
...     on.functions.RickerWavelet(15.0, 2e-4)
The Ricker Wavelet considers the effect of Newtonian viscosity and

is applicable to seismic waves propagated through viscoelastic homogeneous media. The Ricker Wavelet is defined in the time domain by a single parameter, which is the peak frequency in the amplitude spectrum.

Parameters:

  • amplitude – The absolute difference from the wave’s equilibrium to the maximum positive peak.

  • drive_freq – The initial value given to the frequency of the selected time function.

  • phase_shift – Defines a change in phase of a waveform. Specified as a numerical value and returned in degrees.

  • num_wavelets – The number of wavelets found at either side of the Ricker Wavelets central maxima.

  • time_delay – Time delay of wavelet in periods.

static from_dict(data: Dict[str, Any], sim: Simulation | None = None) → Node

Construct this node from serialized dictionary data

to_json() → str

Convert this node into JSON representation

property uuid: str

Get the UUID associated with this node.

SinusoidPulse

class onscale.functions.SinusoidPulse(drive_freq: float, amplitude: float, num_cycles: int = 1, ramped_cycles: int = 0, time_delay: float = 0.0, phase_shift: float = 0.0, offset: float = 0.0)

Sinusoidal pulse function.

Examples

>>> import onscale as on
>>> with on.Simulation("Static Solve") as sim:
...     on.functions.SinusoidPulse(14.0, 1e3)

A sinusoidal wave function.

Parameters:

  • drive_freq – User defined value specifying the maximum frequency at which the drive function will operate at.

  • amplitude – The absolute difference from the wave’s equilibrium to the maximum positive peak.

  • num_cycles – Length of time (periods) for the sinusoidal function defined. A period is the time needed for one complete cycle of vibration to pass.

  • ramped_cycles – The number of cycles over which the amplitude envelope is smoothly increased from 0 to 1.

  • time_delay – Time delay of wavelet in periods.

  • phase_shift – Defines a change in phase of a waveform. Specified as a numerical value and returned in degrees.

  • offset – Defines an offset in amplitude. Can be offset in the positive and negative direction.

static from_dict(data: Dict[str, Any], sim: Simulation | None = None) → Node

Construct this node from serialized dictionary data

to_json() → str

Convert this node into JSON representation

property uuid: str

Get the UUID associated with this node.

SquareWave

class onscale.functions.SquareWave(pulse_duration: float, amplitude: float, time_delay: float = 0.0)

Square wave function.

Examples

>>> import onscale as on
>>> with on.Simulation("Static Solve") as sim:
...     on.functions.SquareWave(5.0, 6.0)

A single pulse of a square wave.

Parameters:

  • pulse_duration – The duration of the square wave pulse.

  • amplitude – The height of the square pulse wave.

  • time_delay – Time delay before the pulse is sent.

static from_dict(data: Dict[str, Any], sim: Simulation | None = None) → Node

Construct this node from serialized dictionary data

to_json() → str

Convert this node into JSON representation

property uuid: str

Get the UUID associated with this node.