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.

../../_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	$y_{M} = \sum_{k = 0}^{M - 1} y_{k}$
Average	$y_{M} = \frac{1}{M} \sum_{k = 0}^{M - 1} y_{k}$
Difference	$y_{2} = y_{1} - y_{0}$
Product	$y_{M} = \prod_{k = 0}^{M - 1} y_{k}$
Division	$y_{2} = \frac{y_{1}}{y_{0}}$
Inverse	$y_{2} = \frac{1}{y_{1}}$

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	$y_{k} = \frac{y_{k - 1}}{c}$

Absolute value, real and imaginary parts#

Operation	Description
Absolute value	$y_{k} = \| y_{k - 1} \|$
Real part	$y_{k} = ℜ (y_{k - 1})$
Imaginary part	$y_{k} = ℑ (y_{k - 1})$

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
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