Set Traces

The set trace collection is used to store the resulting signal traces produced by the signal statistics analysis for each combined set of the provided sets of signal samples.

Create the Trace Collection

You can create a set trace collection from multiple traces, iterables, iterators and/or numbers by calling the class method from_traces() with the sets of signal samples which shall be combined and statistically analyzed.

>>> # create a set trace collection from traces, iterables, iterators, numbers
>>> traces = SetTraces.from_traces(
...   Trace('Signal', [1, 2, 3]),
...   Trace('Signal', [-3, -2, -1]),
...   [1, 0, -1],
...   range(3),
...   0)
>>> traces
SetTraces(mean=Trace(label='SetTrace:mean',
                     samples=[-0.2, 0.2, 0.6]),
  weighted_mean=Trace(label='SetTrace:weighted_mean',
                      samples=[-0.13333333333333333, 0.13333333333333333, 0.4]),
  median=Trace(label='SetTrace:median',
               samples=[0, 0, 0]),
  mode=Trace(label='SetTrace:mode',
             samples=[1, 0, -1]),
  rms=Trace(label='SetTrace:rms',
            samples=[1.4832396974191326, 1.3416407864998738, 1.7320508075688772]),
  minimum=Trace(label='SetTrace:minimum',
                samples=[-3, -2, -1]),
  maximum=Trace(label='SetTrace:maximum',
                samples=[1, 2, 3]),
  range=Trace(label='SetTrace:range',
              samples=[4, 4, 4]),
  midrange=Trace(label='SetTrace:midrange',
                  samples=[-1.0, 0.0, 1.0]),
  absolute_error=Trace(label='SetTrace:absolute_error',
                       samples=[5.6000000000000005, 5.2, 7.6]),
  variance=Trace(label='SetTrace:variance',
                 samples=[2.1599999999999993, 1.7600000000000002, 2.6399999999999997]),
  deviation=Trace(label='SetTrace:deviation',
                  samples=[1.4696938456699067, 1.32664991614216, 1.624807680927192]),
  coefficient=Trace(label='SetTrace:coefficient',
                    samples=[-7.348469228349533, 6.6332495807108005, 2.7080128015453204]),
  skew=Trace(label='SetTrace:skew',
             samples=[-1.1642636431747204, -0.37003665016361925, 0.3804646084815739]),
  kurtosis=Trace(label='SetTrace:kurtosis',
           samples=[2.8127572016460904, 2.21694214876033, 1.4421487603305787]))

Important

All iterables should have the same length, otherwise only a subset is returned!

Note

The linear weighted mean is computed with the order of the provided traces to combine. The lowest weight has the first trace the highest weight has the last trace.

Empty Collection

You can create a set trace collection without samples by calling the SetTraces class.

>>> # create an empty set trace collection
>>> traces = SetTraces()
>>> traces
SetTraces(mean=Trace(label='Trace', samples=[]),
          weighted_mean=Trace(label='Trace', samples=[]),
          median=Trace(label='Trace', samples=[]),
          mode=Trace(label='Trace', samples=[]),
          rms=Trace(label='Trace', samples=[]),
          minimum=Trace(label='Trace', samples=[]),
          maximum=Trace(label='Trace', samples=[]),
          range=Trace(label='Trace', samples=[]),
          midrange=Trace(label='Trace', samples=[]),
          absolute_error=Trace(label='Trace', samples=[]),
          variance=Trace(label='Trace', samples=[]),
          deviation=Trace(label='Trace', samples=[]),
          coefficient=Trace(label='Trace', samples=[]),
          skew=Trace(label='Trace', samples=[]),
          kurtosis=Trace(label='Trace', samples=[]))

Number of Traces

You can get the number of the traces in the SetTraces collection with the built-in function len().

>>> # number of traces in the collection
>>> len(traces)
15

Names of the Traces

You can get the list with the names of the traces in the SetTraces collection.

A list with the key names of the traces is returned.

>>> # key names of the traces in the collection
>>> list(traces)
['mean',
 'weighted_mean',
 'median',
 'mode',
 'rms',
 'minimum',
 'maximum',
 'range',
 'midrange',
 'absolute_error',
 'variance',
 'deviation',
 'coefficient',
 'skew',
 'kurtosis']

>>> # key names of the traces in the collection
>>> list(traces.keys())
['mean',
 'weighted_mean',
 'median',
 'mode',
 'rms',
 'minimum',
 'maximum',
 'range',
 'midrange',
 'absolute_error',
 'variance',
 'deviation',
 'coefficient',
 'skew',
 'kurtosis']

Mean Trace

You can get the arithmetic mean Trace with the mean attribute from the SetTraces collection.

The Trace with the arithmetic mean samples is returned.

>>> # mean trace by attribute
>>> traces.mean
Trace(label='Trace', samples=[])
>>> # mean trace by key
>>> traces['mean']
Trace(label='Trace', samples=[])

Weighted Mean Trace

You can get the linear weighted mean Trace with the weighted_mean attribute from the SetTraces collection.

The Trace with the linear weighted mean samples is returned.

>>> # linear weighted mean trace by attribute
>>> traces.weighted_mean
Trace(label='Trace', samples=[])
>>> # linear weighted mean trace by key
>>> traces['weighted_mean']
Trace(label='Trace', samples=[])

Median Trace

You can get the median Trace with the median attribute from the SetTraces collection.

The Trace with the median samples is returned.

>>> # median trace by attribute
>>> traces.median
Trace(label='Trace', samples=[])
>>> # median trace by key
>>> traces['median']
Trace(label='Trace', samples=[])

Mode Trace

You can get the mode Trace with the mode attribute from the SetTraces collection.

The Trace with the mode samples is returned.

>>> # mode trace by attribute
>>> traces.mode
Trace(label='Trace', samples=[])
>>> # mode trace by key
>>> traces['mode']
Trace(label='Trace', samples=[])

Root Mean Square Trace

You can get the root mean square Trace with the rms attribute from the SetTraces collection.

The Trace with the root mean square samples is returned.

>>> # root mean square (rms) trace by attribute
>>> traces.rms
Trace(label='Trace', samples=[])
>>> # root mean square (rms) trace by key
>>> traces['rms']
Trace(label='Trace', samples=[])

Minimum Trace

You can get the minimum Trace with the minimum attribute from the SetTraces collection.

The Trace with the minimum samples is returned.

>>> # minimum trace by attribute
>>> traces.minimum
Trace(label='Trace', samples=[])
>>> # minimum trace by key
>>> traces['minimum']
Trace(label='Trace', samples=[])

Maximum Trace

You can get the maximum Trace with the maximum attribute from the SetTraces collection.

The Trace with the maximum samples is returned.

>>> # maximum trace by attribute
>>> traces.maximum
Trace(label='Trace', samples=[])
>>> # maximum trace by key
>>> traces['maximum']
Trace(label='Trace', samples=[])

Range Trace

You can get the range Trace with the maximum attribute from the SetTraces collection.

The Trace with the range samples is returned.

>>> # range trace by attribute
>>> traces.range
Trace(label='Trace', samples=[])
>>> # range trace by key
>>> traces['range']
Trace(label='Trace', samples=[])

Mid-Range Trace

You can get the midrange Trace with the midrange attribute from the SetTraces collection.

The Trace with the midrange samples is returned.

>>> # mid-range trace by attribute
>>> traces.midrange
Trace(label='Trace', samples=[])
>>> # mid-range trace by key
>>> traces['midrange']
Trace(label='Trace', samples=[])

Absolute Error Trace

You can get the absolute error Trace with the absolute_error attribute from the SetTraces collection.

The Trace with the absolute error samples is returned.

>>> # absolute error trace by attribute
>>> traces.absolute_error
Trace(label='Trace', samples=[])
>>> # absolute error trace by key
>>> traces['absolute_error']
Trace(label='Trace', samples=[])

Variance Trace

You can get the variance Trace with the variance attribute from the SetTraces collection.

The Trace with the variance samples is returned.

>>> # variance trace by attribute
>>> traces.variance
Trace(label='Trace', samples=[])
>>> # variance trace by key
>>> traces['variance']
Trace(label='Trace', samples=[])

Standard Deviation Trace

You can get the standard deviation Trace with the deviation attribute from the SetTraces collection.

The Trace with the standard deviation samples is returned.

>>> # standard deviation trace by attribute
>>> traces.deviation
Trace(label='Trace', samples=[])
>>> # standard deviation trace by key
>>> traces['deviation']
Trace(label='Trace', samples=[])

Coefficient of Variation Trace

You can get the coefficient of variation Trace with the coefficient attribute from the SetTraces collection.

The Trace with the coefficient of variation samples is returned.

>>> # coefficient of variation trace by attribute
>>> traces.coefficient
Trace(label='Trace', samples=[])
>>> # coefficient of variation trace by key
>>> traces['coefficient']
Trace(label='Trace', samples=[])

Skew Trace

You can get the skew Trace with the skew attribute from the SetTraces collection.

The Trace with the skew samples is returned.

>>> # biased skew trace by attribute
>>> traces.skew
Trace(label='Trace', samples=[])
>>> # biased skew trace by key
>>> traces['skew']
Trace(label='Trace', samples=[])

Kurtosis Trace

You can get the kurtosis Trace with the kurtosis attribute from the SetTraces collection.

The Trace with the kurtosis samples is returned.

>>> # biased kurtosis trace by attribute
>>> traces.kurtosis
Trace(label='Trace', samples=[])
>>> # biased kurtosis trace by key
>>> traces['kurtosis']
Trace(label='Trace', samples=[])

Figure

You can visualize the trace collection with a figure containing the subplots of the traces in the collection by calling the method figure().

>>> figure = SetTraces.from_traces(
...   Trace('Signal', [1, 2, 3]),
...   Trace('Signal', [-3, -2, -1]),
...   [1, 0, -1],
...   range(3),
...   0).figure()

(Source code, html)