myoverse.datatypes.EMGData.plot

EMGData.plot(representation, nr_of_grids=None, nr_of_electrodes_per_grid=None, scaling_factor=20.0, use_grid_layouts=True)

Plots the data for a specific representation.

Parameters:

• representation (str) – The representation to plot.

• nr_of_grids (Optional[int], optional) – The number of electrode grids to plot. If None and grid_layouts is provided, will use the number of grids in grid_layouts. Default is None.

• nr_of_electrodes_per_grid (Optional[int], optional) – The number of electrodes per grid to plot. If None, will be determined from data shape or grid_layouts if available. Default is None.

• scaling_factor (Union[float, List[float]], optional) – The scaling factor for the data. The default is 20.0. If a list is provided, the scaling factor for each grid is used.

• use_grid_layouts (bool, optional) – Whether to use the grid_layouts for plotting. Default is True. If False, will use the nr_of_grids and nr_of_electrodes_per_grid parameters.

Examples

>>> import numpy as np
>>> from myoverse.datatypes import EMGData, create_grid_layout
>>>
>>> # Create sample EMG data (64 channels, 1000 samples)
>>> emg_data = np.random.randn(64, 1000)
>>>
>>> # Create EMGData with two 4×8 grids (32 electrodes each)
>>> grid1 = create_grid_layout(4, 8, 32, fill_pattern='row')
>>> grid2 = create_grid_layout(4, 8, 32, fill_pattern='row')
>>>
>>> # Adjust indices for second grid
>>> grid2[grid2 >= 0] += 32
>>>
>>> emg = EMGData(emg_data, 2000, grid_layouts=[grid1, grid2])
>>>
>>> # Plot the raw data using the grid layouts
>>> emg.plot('Input')
>>>
>>> # Adjust scaling for better visualization
>>> emg.plot('Input', scaling_factor=[15.0, 25.0])
>>>
>>> # Plot without using grid layouts (specify manual grid configuration)
>>> emg.plot('Input', nr_of_grids=2, nr_of_electrodes_per_grid=32,
...         use_grid_layouts=False)