# -*- coding: utf-8 -*-
from warnings import warn
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from ..misc import NeuroKitWarning
[docs]
def emg_plot(emg_signals, info=None, static=True):
"""**EMG Graph**
Visualize electromyography (EMG) data.
Parameters
----------
emg_signals : DataFrame
DataFrame obtained from ``emg_process()``.
info : dict
The information Dict returned by ``emg_process()``. Defaults to ``None``.
static : bool
If True, a static plot will be generated with matplotlib.
If False, an interactive plot will be generated with plotly.
Defaults to True.
See Also
--------
emg_process
Returns
-------
See :func:`.ecg_plot` for details on how to access the figure, modify the size and save it.
Examples
--------
.. ipython:: python
import neurokit2 as nk
# Simulate data
emg = nk.emg_simulate(duration=10, sampling_rate=1000, burst_number=3)
# Process signal
emg_signals, info = nk.emg_process(emg, sampling_rate=1000)
# Plot
@savefig p_emg_plot.png scale=100%
nk.emg_plot(emg_signals, info)
@suppress
plt.close()
"""
if info is None:
warn(
"'info' dict not provided. Some information might be missing."
+ " Sampling rate will be set to 1000 Hz.",
category=NeuroKitWarning,
)
info = {
"sampling_rate": 1000,
}
# Mark onsets, offsets, activity
onsets = np.where(emg_signals["EMG_Onsets"] == 1)[0]
offsets = np.where(emg_signals["EMG_Offsets"] == 1)[0]
# Sanity-check input.
if not isinstance(emg_signals, pd.DataFrame):
raise ValueError(
"NeuroKit error: The `emg_signals` argument must"
" be the DataFrame returned by `emg_process()`."
)
# Determine what to display on the x-axis, mark activity.
x_axis = np.linspace(
0, emg_signals.shape[0] / info["sampling_rate"], emg_signals.shape[0]
)
if static is True:
_emg_plot_static(emg_signals, x_axis, onsets, offsets, info["sampling_rate"])
else:
return _emg_plot_interactive(
emg_signals, x_axis, onsets, offsets, info["sampling_rate"]
)
# =============================================================================
# Internals
# =============================================================================
def _emg_plot_activity(emg_signals, onsets, offsets):
activity_signal = pd.Series(np.full(len(emg_signals), np.nan))
activity_signal[onsets] = emg_signals["EMG_Amplitude"][onsets].values
activity_signal[offsets] = emg_signals["EMG_Amplitude"][offsets].values
activity_signal = activity_signal.bfill()
if np.any(activity_signal.isna()):
index = np.min(np.where(activity_signal.isna())) - 1
value_to_fill = activity_signal[index]
activity_signal = activity_signal.fillna(value_to_fill)
return activity_signal
def _emg_plot_static(emg_signals, x_axis, onsets, offsets, sampling_rate):
# Prepare figure.
fig, (ax0, ax1) = plt.subplots(nrows=2, ncols=1, sharex=True)
if sampling_rate is not None:
ax1.set_xlabel("Time (seconds)")
elif sampling_rate is None:
ax1.set_xlabel("Samples")
fig.suptitle("Electromyography (EMG)", fontweight="bold")
plt.tight_layout(h_pad=0.2)
# Plot cleaned and raw EMG.
ax0.set_title("Raw and Cleaned Signal")
ax0.plot(x_axis, emg_signals["EMG_Raw"], color="#B0BEC5", label="Raw", zorder=1)
ax0.plot(
x_axis,
emg_signals["EMG_Clean"],
color="#FFC107",
label="Cleaned",
zorder=1,
linewidth=1.5,
)
ax0.legend(loc="upper right")
# Plot Amplitude.
ax1.set_title("Muscle Activation")
ax1.plot(
x_axis,
emg_signals["EMG_Amplitude"],
color="#FF9800",
label="Amplitude",
linewidth=1.5,
)
# Shade activity regions.
activity_signal = _emg_plot_activity(emg_signals, onsets, offsets)
ax1.fill_between(
x_axis,
emg_signals["EMG_Amplitude"],
activity_signal,
where=emg_signals["EMG_Amplitude"] > activity_signal,
color="#f7c568",
alpha=0.5,
label=None,
)
# Mark onsets and offsets.
ax1.scatter(
x_axis[onsets],
emg_signals["EMG_Amplitude"][onsets],
color="#f03e65",
label=None,
zorder=3,
)
ax1.scatter(
x_axis[offsets],
emg_signals["EMG_Amplitude"][offsets],
color="#f03e65",
label=None,
zorder=3,
)
if sampling_rate is not None:
onsets = onsets / sampling_rate
offsets = offsets / sampling_rate
for i, j in zip(list(onsets), list(offsets)):
ax1.axvline(i, color="#4a4a4a", linestyle="--", label=None, zorder=2)
ax1.axvline(j, color="#4a4a4a", linestyle="--", label=None, zorder=2)
ax1.legend(loc="upper right")
def _emg_plot_interactive(emg_signals, x_axis, onsets, offsets, sampling_rate):
try:
import plotly.graph_objects as go
from plotly.subplots import make_subplots
except ImportError:
raise ImportError(
"NeuroKit error: emg_plot(): the 'plotly' "
"module is required for this feature."
"Please install it first (`pip install plotly`)."
)
# Prepare figure.
fig = make_subplots(rows=2, cols=1, shared_xaxes=True)
fig.update_layout(title="Electromyography (EMG)", font=dict(size=18), height=600)
# Plot cleaned and raw EMG.
fig.add_trace(
go.Scatter(
x=x_axis,
y=emg_signals["EMG_Raw"],
mode="lines",
name="Raw",
line=dict(color="#B0BEC5"),
),
row=1,
col=1,
)
fig.add_trace(
go.Scatter(
x=x_axis,
y=emg_signals["EMG_Clean"],
mode="lines",
name="Cleaned",
line=dict(color="#FFC107"),
),
row=1,
col=1,
)
# Plot Amplitude.
fig.add_trace(
go.Scatter(
x=x_axis,
y=emg_signals["EMG_Amplitude"],
mode="lines",
name="Amplitude",
line=dict(color="#FF9800"),
),
row=2,
col=1,
)
# Mark onsets and offsets.
fig.add_trace(
go.Scatter(
x=x_axis[onsets],
y=emg_signals["EMG_Amplitude"][onsets],
mode="markers",
name="Onsets",
marker=dict(color="#f03e65", size=10),
),
row=2,
col=1,
)
fig.add_trace(
go.Scatter(
x=x_axis[offsets],
y=emg_signals["EMG_Amplitude"][offsets],
mode="markers",
name="Offsets",
marker=dict(color="#f03e65", size=10),
),
row=2,
col=1,
)
if sampling_rate is not None:
onsets = onsets / sampling_rate
offsets = offsets / sampling_rate
fig.update_xaxes(title_text="Time (seconds)", row=2, col=1)
elif sampling_rate is None:
fig.update_xaxes(title_text="Samples", row=2, col=1)
for i, j in zip(list(onsets), list(offsets)):
fig.add_shape(
type="line",
x0=i,
y0=0,
x1=i,
y1=1,
line=dict(color="#4a4a4a", width=2, dash="dash"),
row=2,
col=1,
)
fig.add_shape(
type="line",
x0=j,
y0=0,
x1=j,
y1=1,
line=dict(color="#4a4a4a", width=2, dash="dash"),
row=2,
col=1,
)
fig.update_yaxes(title_text="Amplitude", row=2, col=1)
return fig
