# -*- coding: utf-8 -*-
from warnings import warn
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from ..misc import NeuroKitWarning
from ..signal.signal_rate import _signal_rate_plot
from .ppg_peaks import _ppg_peaks_plot
from .ppg_segment import ppg_segment
[docs]
def ppg_plot(ppg_signals, info=None, static=True):
"""**Visualize photoplethysmogram (PPG) data**
Visualize the PPG signal processing.
Parameters
----------
ppg_signals : DataFrame
DataFrame obtained from :func:`.ppg_process`.
info : dict
The information Dict returned by ``ppg_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.
Returns
-------
See :func:`.ecg_plot` for details on how to access the figure, modify the size and save it.
See Also
--------
ppg_process
Examples
--------
.. ipython:: python
import neurokit2 as nk
# Simulate data
ppg = nk.ppg_simulate(duration=10, sampling_rate=100, heart_rate=70)
# Process signal
signals, info = nk.ppg_process(ppg, sampling_rate=100)
# Plot
@savefig p_ppg_plot1.png scale=100%
nk.ppg_plot(signals, info)
@suppress
plt.close()
"""
# Sanity-check input.
if not isinstance(ppg_signals, pd.DataFrame):
raise ValueError(
"NeuroKit error: The `ppg_signals` argument must"
" be the DataFrame returned by `ppg_process()`."
)
# Extract Peaks.
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}
# Extract Peaks (take those from df as it might have been cropped)
if "PPG_Peaks" in ppg_signals.columns:
info["PPG_Peaks"] = np.where(ppg_signals["PPG_Peaks"] == 1)[0]
if static:
# Prepare figure
gs = matplotlib.gridspec.GridSpec(2, 2, width_ratios=[2 / 3, 1 / 3])
fig = plt.figure(constrained_layout=False)
ax0 = fig.add_subplot(gs[0, :-1])
ax1 = fig.add_subplot(gs[1, :-1], sharex=ax0)
ax2 = fig.add_subplot(gs[:, -1])
fig.suptitle("Photoplethysmogram (PPG)", fontweight="bold")
# Plot cleaned and raw PPG
ax0 = _ppg_peaks_plot(
ppg_signals["PPG_Clean"].values,
info=info,
sampling_rate=info["sampling_rate"],
raw=ppg_signals["PPG_Raw"].values,
ax=ax0,
)
# Plot Heart Rate
ax1 = _signal_rate_plot(
ppg_signals["PPG_Rate"].values,
info["PPG_Peaks"],
sampling_rate=info["sampling_rate"],
title="Heart Rate",
ytitle="Beats per minute (bpm)",
color="#FB661C",
color_mean="#FBB41C",
color_points="#FF9800",
ax=ax1,
)
# Plot individual heart beats
ax2 = ppg_segment(
ppg_signals["PPG_Clean"].values,
info["PPG_Peaks"],
info["sampling_rate"],
show="return",
ax=ax2,
)
else:
try:
import plotly.graph_objects as go
from plotly.subplots import make_subplots
except ImportError as e:
raise ImportError(
"NeuroKit error: ppg_plot(): the 'plotly'",
" module is required when 'static' is False.",
" Please install it first (`pip install plotly`).",
) from e
# X-axis
x_axis = np.linspace(
0, len(ppg_signals) / info["sampling_rate"], len(ppg_signals)
)
fig = make_subplots(
rows=2,
cols=1,
shared_xaxes=True,
subplot_titles=("Raw and Cleaned Signal", "Rate"),
)
# Plot cleaned and raw PPG
fig.add_trace(
go.Scatter(x=x_axis, y=ppg_signals["PPG_Raw"], name="Raw"), row=1, col=1
)
fig.add_trace(
go.Scatter(
x=x_axis,
y=ppg_signals["PPG_Clean"],
name="Cleaned",
marker_color="#FB1CF0",
),
row=1,
col=1,
)
# Plot peaks
fig.add_trace(
go.Scatter(
x=x_axis[info["PPG_Peaks"]],
y=ppg_signals["PPG_Clean"][info["PPG_Peaks"]],
name="Peaks",
mode="markers",
marker_color="#D60574",
),
row=1,
col=1,
)
# Rate
ppg_rate_mean = ppg_signals["PPG_Rate"].mean()
fig.add_trace(
go.Scatter(
x=x_axis,
y=ppg_signals["PPG_Rate"],
name="Rate",
mode="lines",
marker_color="#FB661C",
),
row=2,
col=1,
)
fig.add_hline(
y=ppg_rate_mean,
line_dash="dash",
line_color="#FBB41C",
name="Mean",
row=2,
col=1,
)
fig.update_layout(title_text="Photoplethysmogram (PPG)", height=500, width=750)
if info["sampling_rate"] is not None:
fig.update_xaxes(title_text="Time (seconds)", row=1, col=1)
fig.update_xaxes(title_text="Time (seconds)", row=2, col=1)
elif info["sampling_rate"] is None:
fig.update_xaxes(title_text="Samples", row=1, col=1)
fig.update_xaxes(title_text="Samples", row=2, col=1)
return fig
