Linear Regression Traces#
The linear regression trace collection is used to store the resulting
signal traces produced by the moving linear regression over the samples of a
signal trace.
Create the Trace Collection#
You can create a linear regression trace collection without
samples by calling the LinearRegressionTraces class.
>>> # create an empty linear regression trace collection
>>> traces = LinearRegressionTraces()
>>> traces
LinearRegressionTraces(level=Trace(label='Trace', samples=[]),
slope=Trace(label='Trace', samples=[]),
intercept=Trace(label='Trace', samples=[]),
mean=Trace(label='Trace', samples=[]),
median=Trace(label='Trace', samples=[]),
minimum=Trace(label='Trace', samples=[]),
maximum=Trace(label='Trace', samples=[]),
range=Trace(label='Trace', samples=[]),
error=Trace(label='Trace', samples=[]),
negative_error=Trace(label='Trace', samples=[]),
positive_error=Trace(label='Trace', samples=[]),
absolute_error=Trace(label='Trace', samples=[]),
variance=Trace(label='Trace', samples=[]),
deviation=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 LinearRegressionTraces
collection with the built-in function len().
>>> # number of traces in the collection
>>> len(traces)
16
Names of the Traces#
You can get the list with the names of the traces in the
LinearRegressionTraces collection.
A list with the key names of the traces is returned.
>>> # key names of the traces in the collection
>>> list(traces)
['level',
'slope',
'intercept',
'mean',
'median',
'minimum',
'maximum',
'range',
'error',
'negative_error',
'positive_error',
'absolute_error',
'variance',
'deviation',
'skew',
'kurtosis']
>>> # key names of the traces in the collection
>>> list(traces.keys())
['level',
'slope',
'intercept',
'mean',
'median',
'minimum',
'maximum',
'range',
'error',
'negative_error',
'positive_error',
'absolute_error',
'variance',
'deviation',
'skew',
'kurtosis']
Level Trace#
You can get the level Trace for the approximated lines with the
level attribute from the
LinearRegressionTraces collection.
The Trace with the level samples is returned.
>>> # level trace by attribute
>>> traces.level
Trace(label='Trace', samples=[])
>>> # level trace by key
>>> traces['level']
Trace(label='Trace', samples=[])
Slope Trace#
You can get the slope Trace for the approximated lines with the
slope attribute from the
LinearRegressionTraces collection.
The Trace with the slope samples is returned.
>>> # slope trace by attribute
>>> traces.slope
Trace(label='Trace', samples=[])
>>> # slope trace by key
>>> traces['slope']
Trace(label='Trace', samples=[])
Intercept Trace#
You can get the intercept Trace for the approximated lines with the
intercept attribute from the
LinearRegressionTraces collection.
The Trace with the intercept samples is returned.
>>> # intercept trace by attribute
>>> traces.intercept
Trace(label='Trace', samples=[])
>>> # intercept trace by key
>>> traces['intercept']
Trace(label='Trace', samples=[])
Mean Trace#
You can get the mean Trace for the approximated lines with the
mean attribute from the
LinearRegressionTraces collection.
The Trace with the mean samples is returned.
>>> # mean trace by attribute
>>> traces.mean
Trace(label='Trace', samples=[])
>>> # mean trace by key
>>> traces['mean']
Trace(label='Trace', samples=[])
Median Trace#
You can get the median Trace for the approximated lines with the
median attribute from the
LinearRegressionTraces 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=[])
Minimum Trace#
You can get the minimum Trace for the approximated lines with the
minimum attribute from the
LinearRegressionTraces 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 for the approximated lines with the
maximum attribute from the
LinearRegressionTraces 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 for the approximated lines with the
range attribute from the
LinearRegressionTraces 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=[])
Error Trace#
You can get the error Trace for the approximated lines with
the error attribute from the
LinearRegressionTraces collection.
The Trace with the error samples is returned.
>>> # error trace by attribute
>>> traces.error
Trace(label='Trace', samples=[])
>>> # error trace by key
>>> traces['error']
Trace(label='Trace', samples=[])
Maximal Negative Error Trace#
You can get the maximal negative error Trace for the approximated
lines with the error attribute from the
LinearRegressionTraces collection.
The Trace with the negative error samples is returned.
>>> # maximal negative error trace by attribute
>>> traces.negative_error
Trace(label='Trace', samples=[])
>>> # maximal negative error trace by key
>>> traces['negative_error']
Trace(label='Trace', samples=[])
Maximal Positive Error Trace#
You can get the maximal positive error Trace for the approximated
lines with the error attribute from the
LinearRegressionTraces collection.
The Trace with the positive error samples is returned.
>>> # maximal positive error trace by attribute
>>> traces.positive_error
Trace(label='Trace', samples=[])
>>> # maximal positive error trace by key
>>> traces['positive_error']
Trace(label='Trace', samples=[])
Absolute Error Trace#
You can get the absolute error Trace for the approximated lines with
the absolute_error attribute from the
LinearRegressionTraces 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 for the approximated lines with the
variance attribute from the
LinearRegressionTraces 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 for the approximated lines
with the deviation attribute from the
LinearRegressionTraces 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=[])
Skew Trace#
You can get the skew Trace for the approximated lines with the
skew attribute from the
LinearRegressionTraces 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 for the approximated lines with the
kurtosis attribute from the
LinearRegressionTraces 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=[])