InverseFastFourierTransform¶
Calculate a time-domain signal from its frequency representation using the inverse Fast Fourier Transform (IFFT).
This node will replace the original (e.g., frequency) axis of the data by a time axis, carrying over the axis label, if any. More Info... Version 1.0.0
Ports/Properties¶
data¶
Data to process.
axis¶
Axis along which the IFFT will be calculated. You can also enter the axis label here as in frequency.mylabel. If the data is not a packet, any named axis will translate to -1.
n¶
Length of the output axis, in samples. Note that if this is given and differs from the size of the input, the input is either cropped or zero-padded. In the latter case, this can give unexpected results; please see the documentation of the numpy FFT algorithm for details.
normalization¶
Normalization to apply to the FFT. Backward means that the forward FFT is unscaled and the corresponding inverse FFT is scaled by 1/n. Ortho means that both the forward and inverse FFT are scaled by 1/sqrt(n). Forward means that the forward FFT is scaled by 1/n and the corresponding inverse FFT is unscaled.
onesided¶
Assume one-sided spectrum. If this is set, the input must be a one-sided (and complex-valued) spectrum, and the output will be a real-valued signal. Otherwise the output will be a complex-valued signal. This is equivalent to the RFFT / IRFFT distinction in numerical subroutines, e.g., numpy.
partition_axis¶
Axis along which to partition the computation. This can be used to reduce the memory requirements on large data.
num_partitions¶
Number of partitions to use. The memory usage is divided by this amount. Requires that partition_axis is set to something other than disabled.
set_breakpoint¶
Set a breakpoint on this node. If this is enabled, your debugger (if one is attached) will trigger a breakpoint.
metadata¶
User-definable meta-data associated with the node. Usually reserved for technical purposes.