computeSpectrogram.py

This analysis script, computeSpectrogram.py, takes a history of field at a coordinate or cell index, and works out the spectrogram, writing it into a 2D dataset containing frequency vs time which may be viewed in VSimComposer.

-s <simname>, --simulationName=<simname>

(string, required)

<simname> is the name of the simulation to be analyzed. The file extension should NOT be included in this text field.

-H <histname>, --historyName=<histname>

(string, required)

<histname> is the name of the history dataset to analyze, for example, E1.

-f <size>, --fourierTransSize=<size>

(integer, required, default = 4096)

Replace <size> with the number of timesteps (or history points) to use for each Fourier transform. Determines lowest measurable frequency in the transform.

-n <overlap>, --noOverlap=<overlap>

(integer, required, default = 2048)

Replace <overlap> with the number of timesteps that will overlap between successive measurements of frequency. Set this to between zero and the size of the Fourier transform.

-F <max>, --maxFreq=<max>

(float, required, default = 0.0)

When preparing the data, discard frequencies above this maximum frequency <max>. FFTs show a maximum frequency corresponding to 2*dt. This is higher than any frequency you are reliably going to be able to calculate.

-W <windowfunc>, --windowType=<windowfunc>

(integer, required, default = 1)

Specify a windowing function for the spectrogram. Default is 1 - Hanning.

1. Hanning
2. Hamming
3. Blackmann
4. Bartlett

If you are unfamiliar with these functions, please consult numpy documentation.

-c <comp>, --component=<comp>

(integer, optional, default = 0)

<comp> is the component to select if history contains multicomponent data.

-w, --overwrite

(flag)

Whether a dataset or group should be overwritten if it already exists.