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 Computing features on Signals for information on computing features on signals.

../../_images/s_operation.png

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 Sum

\(y_{M} = \sum_{k=0}^{M-1}{y_{k}}\)

average Average

\(y_{M} = \dfrac{1}{M}\sum_{k=0}^{M-1}{y_{k}}\)

difference Difference

\(y_{2} = y_{1} - y_{0}\)

product Product

\(y_{M} = \prod_{k=0}^{M-1}{y_{k}}\)

division 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

constant_add Addition

\(y_{k} = y_{k-1} + c\)

constant_substract Subtraction

\(y_{k} = y_{k-1} - c\)

constant_multiply Multiplication

\(y_{k} = y_{k-1} \times c\)

constant_divide Division

\(y_{k} = \dfrac{y_{k-1}}{c}\)

Real and imaginary parts#

Operation

Description

abs Absolute value

\(y_{k} = |y_{k-1}|\)

re Real part

\(y_{k} = \Re(y_{k-1})\)

im Imaginary part

\(y_{k} = \Im(y_{k-1})\)

Data type conversion#

The “Convert data type” convert_dtype 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

exp Exponential

\(y_{k} = \exp(y_{k-1})\)

log10 Logarithm (base 10)

\(y_{k} = \log_{10}(y_{k-1})\)

power Power

\(y_{k} = y_{k-1}^{n}\)

sqrt Square root

\(y_{k} = \sqrt{y_{k-1}}\)

Other mathematical operations#

Operation

Implementation

derivative Derivative

Based on numpy.gradient

integral Integral

Based on scipy.integrate.cumulative_trapezoid

convolution Convolution

Based on scipy.signal.convolve