Import and Export¶

Guide for importing and exporting data in various formats.

Export to Matlab¶

# Export entire dataset
dvma.export_to_matlab(dataset)

# This creates a .mat file with all data structures

The Matlab file contains: - Time data - Frequency data - Transfer function data - Settings and metadata

Export to CSV¶

# Export time data
dvma.export_to_csv(dataset.time_data_list)

# Export frequency data
dvma.export_to_csv(dataset.freq_data_list)

# Export transfer function data
dvma.export_to_csv(dataset.tf_data_list)

Import from Matlab¶

# Import data from JW Logger format
dataset = dvma.import_from_matlab_jwlogger()

Save and Load a DataSet (native format)¶

# Save dataset — opens a file-save dialog if filename is omitted
dvma.save_data(dataset, filename='dataset')

# Load dataset — opens a file-open dialog if filename is omitted
dataset = dvma.load_data(filename='dataset.dvma')

.dvma has been the native format since 1.5: a zip container of manifest.json plus pickle-free .npy arrays. Loading a .dvma file executes no code, so it's safe to share with others. If filename doesn't already end in .dvma or .npy, save_data appends .dvma for you. Format detection on load is by content (zip magic bytes), not extension, so a renamed file still loads correctly.

Legacy .npy pickle files from pydvma <= 1.4.0 still load forever. To keep writing that legacy pickle format, pass an explicit filename ending in .npy — an escape hatch for workflows that still need it, but note that unpickling can execute arbitrary code, so only load legacy .npy files you or your lab created. The helpers handle overwrite prompts and dialog fallback either way.

Export Plots¶

import matplotlib.pyplot as plt

# After creating a plot
plt.savefig('figure.png', dpi=300, bbox_inches='tight')
plt.savefig('figure.pdf', bbox_inches='tight')