# Generate sample FRF data frequencies = np.linspace(0, 100, 1000) frf_data = np.random.rand(1000) + 1j * np.random.rand(1000)
FRF data is usually obtained through experimental measurements, where a system is excited with a range of frequencies, and its response is recorded. The resulting data is a set of complex values representing the system's frequency response, which can be visualized as a frequency response curve. frf to bin
Before diving into the conversion process, it's essential to understand the nature of FRF data. The Frequency Response Function (FRF) is a measure of how a system responds to different frequencies of input signals. It's typically represented as a complex-valued function, which describes the magnitude and phase of the system's response at various frequencies. FRF data is commonly used in fields like mechanical engineering, aerospace, and civil engineering, where it helps in analyzing and characterizing the dynamic behavior of structures, mechanical systems, and other types of systems. # Generate sample FRF data frequencies = np
# One-hot encoding binary_data = np.eye(len(bin_boundaries))(binned_data) The Frequency Response Function (FRF) is a measure
import numpy as np import matplotlib.pyplot as plt
