# -*- coding: utf-8 -*-
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from ..events import events_plot
from ..stats import standardize as nk_standardize
[docs]
def signal_plot(
signal, sampling_rate=None, subplots=False, standardize=False, labels=None, **kwargs
):
"""**Plot signal with events as vertical lines**
Parameters
----------
signal : array or DataFrame
Signal array (can be a dataframe with many signals).
sampling_rate : int
The sampling frequency of the signal (in Hz, i.e., samples/second). Needs to be supplied if
the data should be plotted over time in seconds. Otherwise the data is plotted over samples.
Defaults to ``None``.
subplots : bool
If ``True``, each signal is plotted in a subplot.
standardize : bool
If ``True``, all signals will have the same scale (useful for visualisation).
labels : str or list
Defaults to ``None``.
**kwargs : optional
Arguments passed to matplotlib plotting.
See Also
--------
ecg_plot, rsp_plot, ppg_plot, emg_plot, eog_plot
Returns
-------
Though the function returns nothing, the figure can be retrieved and saved as follows:
.. code-block:: console
# To be run after signal_plot()
fig = plt.gcf()
fig.savefig("myfig.png")
Examples
----------
.. ipython:: python
import numpy as np
import pandas as pd
import neurokit2 as nk
signal = nk.signal_simulate(duration=10, sampling_rate=1000)
@savefig p_signal_plot1.png scale=100%
nk.signal_plot(signal, sampling_rate=1000, color="red")
@suppress
plt.close()
.. ipython:: python
# Simulate data
data = pd.DataFrame({"Signal2": np.cos(np.linspace(start=0, stop=20, num=1000)),
"Signal3": np.sin(np.linspace(start=0, stop=20, num=1000)),
"Signal4": nk.signal_binarize(np.cos(np.linspace(start=0, stop=40, num=1000)))})
# Process signal
@savefig p_signal_plot2.png scale=100%
nk.signal_plot(data, labels=['signal_1', 'signal_2', 'signal_3'], subplots=True)
nk.signal_plot([signal, data], standardize=True)
@suppress
plt.close()
"""
# Sanitize format
if isinstance(signal, list):
try:
for i in signal:
len(i)
except TypeError:
signal = np.array(signal)
if isinstance(signal, pd.DataFrame) is False:
# If list is passed
if isinstance(signal, list) or len(np.array(signal).shape) > 1:
out = pd.DataFrame()
for i, content in enumerate(signal):
if isinstance(content, pd.Series):
out = pd.concat(
[out, pd.DataFrame({content.name: content.values})],
axis=1,
sort=True,
)
elif isinstance(content, pd.DataFrame):
out = pd.concat([out, content], axis=1, sort=True)
else:
out = pd.concat(
[out, pd.DataFrame({"Signal" + str(i + 1): content})],
axis=1,
sort=True,
)
signal = out
# If vector is passed
else:
signal = pd.DataFrame({"Signal": signal})
# Copy signal
signal = signal.copy()
# Guess continuous and events columns
continuous_columns = list(signal.columns.values)
events_columns = []
for col in signal.columns:
vector = signal[col]
if vector.nunique() == 2:
indices = np.where(vector == np.max(vector.unique()))
if bool(np.any(np.diff(indices) == 1)) is False:
events_columns.append(col)
continuous_columns.remove(col)
# Adjust for sampling rate
if sampling_rate is not None:
signal.index = signal.index / sampling_rate
title_x = "Time (seconds)"
else:
title_x = "Time"
# x_axis = np.linspace(0, signal.shape[0] / sampling_rate, signal.shape[0])
# x_axis = pd.DataFrame(x_axis, columns=["Time (s)"])
# signal = pd.concat([signal, x_axis], axis=1)
# signal = signal.set_index("Time (s)")
# Plot accordingly
if len(events_columns) > 0:
events = []
for col in events_columns:
vector = signal[col]
events.append(np.where(vector == np.max(vector.unique()))[0])
events_plot(events, signal=signal[continuous_columns])
if sampling_rate is None and pd.api.types.is_integer_dtype(signal.index):
plt.gca().set_xlabel("Samples")
else:
plt.gca().set_xlabel(title_x)
else:
# Aesthetics
colors = [
"#1f77b4",
"#ff7f0e",
"#2ca02c",
"#d62728",
"#9467bd",
"#8c564b",
"#e377c2",
"#7f7f7f",
"#bcbd22",
"#17becf",
]
if len(continuous_columns) > len(colors):
colors = plt.cm.viridis(np.linspace(0, 1, len(continuous_columns)))
# Plot
if standardize is True:
signal[continuous_columns] = nk_standardize(signal[continuous_columns])
if subplots is True:
_, axes = plt.subplots(nrows=len(continuous_columns), ncols=1, sharex=True, **kwargs)
for ax, col, color in zip(axes, continuous_columns, colors):
ax.plot(signal[col], c=color, **kwargs)
else:
_ = signal[continuous_columns].plot(subplots=False, sharex=True, **kwargs)
if sampling_rate is None and pd.api.types.is_integer_dtype(signal.index):
plt.xlabel("Samples")
else:
plt.xlabel(title_x)
# Tidy legend locations and add labels
if labels is None:
labels = continuous_columns.copy()
if isinstance(labels, str):
n_labels = len([labels])
labels = [labels]
elif isinstance(labels, list):
n_labels = len(labels)
if len(signal[continuous_columns].columns) != n_labels:
raise ValueError(
"NeuroKit error: signal_plot(): number of labels does not equal the number of plotted signals."
)
if subplots is False:
plt.legend(labels, loc=1)
else:
for i, label in enumerate(labels):
axes[i].legend([label], loc=1)
