# Skip remaining header f.seek(256) # Read frequency vector (float64) freqs = np.fromfile(f, dtype=np.float64, count=n_freq) # Read FRF data shape: (n_freq, n_resp, n_ref) # Stored column-major: each FRF (complex) as two floats frf_data = np.fromfile(f, dtype=np.float64) # Reshape & convert to complex if needed # ... (depends on exact layout) return freqs, frf_data ⚠️ Without the exact spec from VW, the above is illustrative only. If you need to exchange data outside the VW ecosystem, convert to:
import numpy as np import struct def read_vw_frf(filename): with open(filename, 'rb') as f: # Read header (example: 256 bytes) magic = f.read(5).decode() # should be 'VWFRF' version = struct.unpack('B', f.read(1))[0] n_freq = struct.unpack('I', f.read(4))[0] n_ref = struct.unpack('I', f.read(4))[0] n_resp = struct.unpack('I', f.read(4))[0] data_format = struct.unpack('B', f.read(1))[0] # 0=complex,1=magnitude vw frf files
| Target format | Tool/method | |---------------|--------------| | | Write a custom converter (MATLAB/Python) | | Siemens .lms | Use Testlab with VW plugin | | HDF5 | Recommended for archiving + metadata | | CSV | Lossy (no complex native), only for inspection | Example conversion to UFF (ASCII, universal): UFF uses 58 (FRF data set). A minimal UFF FRF block looks like: # Skip remaining header f