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=[])