pycsamt.tdem.waveform#

Transmitter waveform models for TEM systems.

Classes

`CustomWaveform`(t_waveform, i_waveform, *[, ...])	User-defined waveform supplied as paired time/current arrays.
`HalfSineWaveform`([base_frequency, verbose, ...])	Half-sine transmitter current (used by some CSEM / airborne systems).
`RampWaveform`([base_frequency, ramp_off, ...])	Transmitter current with a finite linear ramp on switch-off.
`SquareWaveform`([base_frequency, duty_cycle, ...])	Ideal square-wave transmitter current (zero ramp time).

class pycsamt.tdem.waveform.SquareWaveform(base_frequency=25.0, duty_cycle=0.5, verbose=0, logger=None)[source]#

Bases: _WaveformBase

Ideal square-wave transmitter current (zero ramp time).

The waveform alternates between +I and −I at base_frequency Hz. For late-time TEM processing the waveform is treated as an ideal step turn-off, so this is the appropriate choice when ramp effects are negligible.

Parameters:

base_frequency (float) – Fundamental repetition rate in Hz (e.g. 25 Hz for ZongeGDP).
duty_cycle (float, optional) – Fraction of the half-period spent at full current. Default 0.5 (50 % duty cycle → symmetric square wave).
verbose (int)
logger (object | None)

Examples

>>> from pycsamt.tdem.waveform import SquareWaveform
>>> wf = SquareWaveform(base_frequency=25.0)
>>> wf.half_period
0.02

base_frequency: float = 25.0#

duty_cycle: float = 0.5#

verbose: int = 0#

logger: object | None = None#

current_at(t)[source]#

Return the normalised transmitter current (0–1) at times t.

Parameters:: t (array-like) – Times in seconds relative to the switch-off event at t = 0. Negative values are on-time; positive values are off-time.
Returns:: Current envelope (dimensionless, in [0, 1]).
Return type:: np.ndarray

class pycsamt.tdem.waveform.RampWaveform(base_frequency=25.0, ramp_off=0.0001, ramp_on=0.0, duty_cycle=0.5, verbose=0, logger=None)[source]#

Bases: _WaveformBase

Transmitter current with a finite linear ramp on switch-off.

The current decays linearly from 1 to 0 over ramp_off seconds starting at t = 0 (the nominal switch-off instant). This is the waveform required for accurate early-time TEM processing where the ramp duration is comparable to the first measurement gates.

Parameters:

base_frequency (float) – Fundamental repetition rate in Hz.
ramp_off (float) – Duration of the current turn-off ramp in seconds.
ramp_on (float, optional) – Duration of the current turn-on ramp in seconds. Rarely needed; default 0.0 (ideal step turn-on).
duty_cycle (float, optional) – Fraction of the half-period at full current (before ramp). Default 0.5.
verbose (int)
logger (object | None)

Examples

>>> from pycsamt.tdem.waveform import RampWaveform
>>> wf = RampWaveform(base_frequency=25.0, ramp_off=1e-4)
>>> wf.ramp_off
0.0001

base_frequency: float = 25.0#

ramp_off: float = 0.0001#

ramp_on: float = 0.0#

duty_cycle: float = 0.5#

verbose: int = 0#

logger: object | None = None#

current_at(t)[source]#

Return the normalised transmitter current (0–1) at times t.

Parameters:: t (array-like) – Times in seconds relative to the switch-off event at t = 0. Negative values are on-time; positive values are off-time.
Returns:: Current envelope (dimensionless, in [0, 1]).
Return type:: np.ndarray

class pycsamt.tdem.waveform.HalfSineWaveform(base_frequency=30.0, verbose=0, logger=None)[source]#

Bases: _WaveformBase

Half-sine transmitter current (used by some CSEM / airborne systems).

Parameters:

base_frequency (float) – Fundamental repetition rate in Hz.
verbose (int)
logger (object | None)

Examples

>>> from pycsamt.tdem.waveform import HalfSineWaveform
>>> wf = HalfSineWaveform(base_frequency=30.0)
>>> round(wf.half_period, 6)
0.016667

base_frequency: float = 30.0#

verbose: int = 0#

logger: object | None = None#

current_at(t)[source]#

Return the normalised transmitter current (0–1) at times t.

Parameters:: t (array-like) – Times in seconds relative to the switch-off event at t = 0. Negative values are on-time; positive values are off-time.
Returns:: Current envelope (dimensionless, in [0, 1]).
Return type:: np.ndarray

class pycsamt.tdem.waveform.CustomWaveform(t_waveform, i_waveform, *, base_frequency=25.0, verbose=0, logger=None)[source]#

Bases: _WaveformBase

User-defined waveform supplied as paired time/current arrays.

Parameters:

t_waveform (array-like of float) – Time samples in seconds (relative to switch-off at t = 0).
i_waveform (array-like of float) – Normalised current values (0–1) at each t_waveform sample.
base_frequency (float) – Fundamental repetition rate in Hz.
verbose (int)
logger (object | None)

Examples

>>> import numpy as np
>>> from pycsamt.tdem.waveform import CustomWaveform
>>> t = np.linspace(-0.02, 0.02, 200)
>>> I = np.where(t < 0, 1.0, 0.0)
>>> wf = CustomWaveform(t, I, base_frequency=25.0)

property base_frequency: float[source]#

current_at(t)[source]#

Return the normalised transmitter current (0–1) at times t.

Parameters:: t (array-like) – Times in seconds relative to the switch-off event at t = 0. Negative values are on-time; positive values are off-time.
Returns:: Current envelope (dimensionless, in [0, 1]).
Return type:: np.ndarray