help='Block B in A-B.'),
Argument(name='x column', type='int', default=0,
help="""
-Column of data block to differentiate with respect to.
+Column of data to return as x values.
""".strip()),
- Argument(name='f column', type='int', default=1,
+ Argument(name='y column', type='int', default=1,
help="""
-Column of data block to differentiate.
+Column of data block to difference.
""".strip()),
],
help=self.__doc__, plugin=plugin)
assert a[:,params['x column']] == b[:,params['x column']]
out = Data((a.shape[0],2), dtype=a.dtype)
out[:,0] = a[:,params['x column']]
- out[:,1] = a[:,params['f column']] - b[:,params['f column']]
+ out[:,1] = a[:,params['y column']] - b[:,params['y column']]
outqueue.put(out)
class DerivativeCommand (Command):
Data block to act on. For an approach/retract force curve, `0`
selects the approaching curve and `1` selects the retracting curve.
""".strip()),
- Argument(name='f column', type='int', default=1,
+ Argument(name='column', type='int', default=1,
help="""
-Column of data block to differentiate with respect to.
+Column of data block containing time-series data.
""".strip()),
Argument(name='freq', type='float', default=1.0,
help="""
def _run(self, hooke, inqueue, outqueue, params):
data = params['curve'].data[params['block']]
outqueue.put(unitary_avg_power_spectrum(
- data[:,params['f column']], freq=params['freq'],
+ data[:,params['column']], freq=params['freq'],
chunk_size=params['chunk size'],
overlap=params['overlap']))