Operations on Signals

This section describes the operations that can be performed on signals.

See also

Processing Signals for more information on signal processing features, or Analysis features on Signals for information on analysis features on signals.

Screenshot of the “Operations” menu.

When the “Signal Panel” is selected, the menus and toolbars are updated to provide signal-related actions.

The “Operations” menu allows you to perform various operations on the selected signals, such as arithmetic operations, peak detection, or convolution.

Basic arithmetic operations

Operation	Description
Sum	\(y_{M} = \sum_{k=0}^{M-1}{y_{k}}\)
Average	\(y_{M} = \dfrac{1}{M}\sum_{k=0}^{M-1}{y_{k}}\)
Difference	\(y_{2} = y_{1} - y_{0}\)
Product	\(y_{M} = \prod_{k=0}^{M-1}{y_{k}}\)
Division	\(y_{2} = \dfrac{y_{1}}{y_{0}}\)

Operations with a constant

Create a new signal which is the result of a constant operation on each selected signal:

Operation	Description
Addition	\(y_{k} = y_{k-1} + c\)
Subtraction	\(y_{k} = y_{k-1} - c\)
Multiplication	\(y_{k} = y_{k-1} \times c\)
Division	\(y_{k} = \dfrac{y_{k-1}}{c}\)

Real and imaginary parts

Operation	Description
Absolute value	\(y_{k} = |y_{k-1}|\)
Real part	\(y_{k} = \Re(y_{k-1})\)
Imaginary part	\(y_{k} = \Im(y_{k-1})\)

Data type conversion

The “Convert data type” action allows you to convert the data type of the selected signals.

Note

Data type conversion relies on numpy.ndarray.astype() function with the default parameters (casting=’unsafe’).

Basic mathematical functions

Function	Description
Exponential	\(y_{k} = \exp(y_{k-1})\)
Logarithm (base 10)	\(y_{k} = \log_{10}(y_{k-1})\)
Power	\(y_{k} = y_{k-1}^{n}\)
Square root	\(y_{k} = \sqrt{y_{k-1}}\)

Other mathematical operations

Operation	Implementation
Derivative	Based on numpy.gradient
Integral	Based on scipy.integrate.cumulative_trapezoid
Convolution	Based on scipy.signal.convolve