Jupyter Notebook Examples

EMGIO can be used effectively in Jupyter Notebooks for interactive data analysis and visualization. This page provides an overview of working with EMGIO in Jupyter environments.

Available Example Notebooks

The EMGIO repository includes several example Jupyter notebooks that demonstrate different aspects of working with EMG data:

1. Trigno Example Notebook: Working with Delsys Trigno data

2. Location: examples/trigno_example.ipynb

3. MATLAB Example Notebook: Working with MATLAB-exported data

4. Location: examples/matlab_example.ipynb

Running the Example Notebooks

To run the example notebooks:

1. Install EMGIO and Jupyter:

   pip install -e .  # Install EMGIO in development mode
   pip install jupyter
   

2. Start Jupyter:

   jupyter notebook
   

3. Navigate to the examples directory and open one of the notebooks.

Key Features for Jupyter Notebooks

When working with EMGIO in Jupyter notebooks, you can take advantage of these features:

Interactive Plotting

from emgio import EMG
import matplotlib.pyplot as plt
%matplotlib inline  # For displaying plots in the notebook

# Load EMG data
emg = EMG.from_file('data.csv', importer='trigno')

# Plot signals with customized appearance
emg.plot_signals(
    channels=['EMG1', 'EMG2'],
    time_range=(0, 5),
    title='EMG Signals',
    figsize=(12, 6),
    grid=True
)

Data Exploration

Jupyter notebooks are excellent for interactively exploring your EMG data:

# Load data
emg = EMG.from_file('data.otb+', importer='otb')

# Display channel information
channel_types = emg.get_channel_types()
for ch_type in channel_types:
    channels = emg.get_channels_by_type(ch_type)
    print(f"{ch_type} channels: {len(channels)}")

    # Show details of the first channel
    if channels:
        ch_name = channels[0]
        ch_info = emg.channels[ch_name]
        print(f"  Sample channel: {ch_name}")
        for key, value in ch_info.items():
            print(f"    {key}: {value}")

Creating Interactive Widgets

You can use ipywidgets to create interactive controls for exploring your data:

import ipywidgets as widgets
from IPython.display import display

# Load data
emg = EMG.from_file('data.set', importer='eeglab')

# Get all channels
all_channels = list(emg.channels.keys())

# Create widgets
channel_dropdown = widgets.Dropdown(
    options=all_channels,
    description='Channel:',
    disabled=False,
)

time_slider = widgets.FloatRangeSlider(
    value=[0, 5],
    min=0,
    max=emg.get_duration(),
    step=1,
    description='Time (s):',
    disabled=False,
    continuous_update=False,
    readout=True,
    readout_format='.1f',
)

# Define update function
def update_plot(channel, time_range):
    plt.figure(figsize=(12, 6))
    emg.plot_signals(
        channels=[channel],
        time_range=time_range,
        title=f'Channel: {channel}, Time: {time_range[0]}-{time_range[1]}s',
        grid=True
    )
    plt.show()

# Create interactive output
out = widgets.interactive_output(
    update_plot, 
    {'channel': channel_dropdown, 'time_range': time_slider}
)

# Display widgets and output
display(widgets.HBox([channel_dropdown, time_slider]), out)

Exporting Figures

You can easily export high-quality figures for publications:

# Create a figure
plt.figure(figsize=(10, 6), dpi=300)  # High DPI for publication quality
emg.plot_signals(
    channels=emg.get_channels_by_type('EMG')[:4],  # First 4 EMG channels
    time_range=(10, 15),
    title='EMG Signals During Movement',
    grid=True
)
plt.tight_layout()

# Save the figure in various formats
plt.savefig('emg_plot.png', dpi=300)  # PNG for presentations
plt.savefig('emg_plot.pdf')  # PDF for publications
plt.savefig('emg_plot.svg')  # SVG for editing

Tips for Jupyter Notebooks

1. Memory management: When working with large files, consider using:

   # Load only metadata first
   importer = EDFImporter('large_file.edf')
   signals, channels, metadata = importer.load(load_data=False)
   
   # Then load specific time segments as needed
   importer = EDFImporter('large_file.edf')
   signals, channels, metadata = importer.load(start_time=10, end_time=20)
   

2. Progress tracking: For long operations, use tqdm for progress bars:

   from tqdm.notebook import tqdm
   
   channel_stats = {}
   for channel in tqdm(emg.channels.keys()):
       # Perform analysis on each channel
       signal = emg.signals[channel].values
       channel_stats[channel] = {
           'mean': signal.mean(),
           'std': signal.std(),
           'max': signal.max(),
           'min': signal.min()
       }
   

3. Table display: Format channel information as a dataframe for better visualization:

   import pandas as pd
   
   # Convert channel info to DataFrame
   channel_df = pd.DataFrame()
   for ch_name, ch_info in emg.channels.items():
       ch_data = pd.Series(ch_info)
       ch_data.name = ch_name
       channel_df = channel_df.append(ch_data)
   
   # Display as an interactive table
   channel_df
   

These examples show how to leverage Jupyter notebooks for interactive exploration and analysis of EMG data with EMGIO.