Advanced Examples

More advanced usage examples and workflows.

Complete Experimental Modal Analysis

See the Modal Analysis page for a complete EMA workflow example.

Custom Signal Processing

import pydvma as dvma
import numpy as np
from scipy import signal

# Record data
settings = dvma.MySettings()
dataset = dvma.log_data(settings)
time_data = dataset.time_data_list[0]

# Design custom filter
sos = signal.butter(4, [50, 500], 'bandpass',
                    fs=settings.fs, output='sos')

# Apply filter
filtered = signal.sosfiltfilt(sos, time_data.time_data, axis=0)

# Analyze filtered data
import copy
time_data_filtered = copy.copy(time_data)
time_data_filtered.time_data = filtered

freq_data = dvma.calculate_fft(time_data_filtered)

Batch Processing Multiple Files

import pydvma as dvma
import os

# Process all measurements in a directory. Files are pydvma's native
# .npy format produced by dvma.save_data(...).
data_dir = 'measurements'
files = [f for f in os.listdir(data_dir) if f.endswith('.npy')]

results = []
for filename in files:
    # Load data (DataSet from .npy via the standard helper)
    dataset = dvma.load_data(filename=os.path.join(data_dir, filename))
    time_data = dataset.time_data_list[0]

    # Analyze
    freq_data = dvma.calculate_fft(time_data)
    tf_data = dvma.calculate_tf(time_data, ch_in=0)

    # Store results
    results.append({
        'filename': filename,
        'freq_data': freq_data,
        'tf_data': tf_data
    })

Integration with Other Tools

import pydvma as dvma
import pandas as pd

# Convert to pandas DataFrame for analysis
time_data = dataset.time_data_list[0]
df = pd.DataFrame(
    time_data.time_data,
    index=time_data.time_axis,
    columns=[f'Channel_{i}' for i in range(time_data.time_data.shape[1])]
)

# Use pandas tools
print(df.describe())
df.plot()