Shuffle CHOP

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The Shuffle CHOP reorganizes the samples in a set of channels.

It is useful for transforming data received by the SOP to CHOP and TOP to CHOPs into channels containing only one row or column. Data can be easily manipulated, then transformed back if needed.

PythonIcon.png shuffleCHOP_Class


Method - Chooses the operation "shuffle" performs:

  • Swap Channels and Samples - Performs a channel transpose operation, by storing all samples at the same index in the same channel. If 25 channels are in the CHOP with a length of 33 samples, 33 channels will be created with a length of 25.
  • Sequence Channels by Name - Sequence channels together that share the same alphabetic name, in the order of their number. (i.e. chan2, chan3 and chan1 would be sequenced in the order chan1, chan2, chan3).
  • Sequence All Channels - Sequence all channels in the CHOP resulting in one long channel.
  • Sequence N Channels - Sequence channels in groups of N together. For N=4, channels 0 to 3, 4 to 7, etc. will be sequenced.
  • Sequence Every Nth Channel - Sequence every Nth channel together. For N=4, channels 0,4,8,.., 1,5,9,..., etc. will be sequenced.
  • Split All Samples - Split every channel into channels of 1 sample, each containing a different sample from the original channel.
  • Split N Samples - Split each channel into segments of N samples (specified below).
  • Split Every Nth Sample - Take every Nth sample from the original to form N new channels.

For all splitting operations, channels will be numbered according to their segment number. (i.e. chan would be split into chan1, chan2, chan3, etc).

N Value - The value of N for Sequence: Every Nth Channel, Sequence N Channels, Split N Samples and Split Every Nth Sample.

Use First Sample Only - Just use the first sample of each channel.

Standard Options and Local Variables