"""Plots for periodic fits and parameters."""
from itertools import cycle
import numpy as np
from fooof.sim import gen_freqs
from fooof.core.funcs import gaussian_function
from fooof.core.modutils import safe_import, check_dependency
from fooof.plts.settings import PLT_FIGSIZES
from fooof.plts.style import style_param_plot, style_plot
from fooof.plts.utils import check_ax, recursive_plot, savefig, check_plot_kwargs
plt = safe_import('.pyplot', 'matplotlib')
###################################################################################################
###################################################################################################
[docs]@savefig
@style_plot
@check_dependency(plt, 'matplotlib')
def plot_peak_params(peaks, freq_range=None, colors=None, labels=None, ax=None, **plot_kwargs):
"""Plot peak parameters as dots representing center frequency, power and bandwidth.
Parameters
----------
peaks : 2d array or list of 2d array
Peak data. Each row is a peak, as [CF, PW, BW].
freq_range : list of [float, float] , optional
The frequency range to plot the peak parameters across, as [f_min, f_max].
colors : str or list of str, optional
Color(s) to plot data.
labels : list of str, optional
Label(s) for plotted data, to be added in a legend.
ax : matplotlib.Axes, optional
Figure axes upon which to plot.
**plot_kwargs
Additional plot related keyword arguments, with styling options managed by ``style_plot``.
"""
ax = check_ax(ax, plot_kwargs.pop('figsize', PLT_FIGSIZES['params']))
# If there is a list, use recurse function to loop across arrays of data and plot them
if isinstance(peaks, list):
recursive_plot(peaks, plot_peak_params, ax, colors=colors, labels=labels)
# Otherwise, plot the array of data
else:
# Unpack data: CF as x; PW as y; BW as size
xs, ys = peaks[:, 0], peaks[:, 1]
sizes = peaks[:, 2] * plot_kwargs.pop('s', 150)
# Create the plot
plot_kwargs = check_plot_kwargs(plot_kwargs, {'alpha' : 0.7})
ax.scatter(xs, ys, sizes, c=colors, label=labels, **plot_kwargs)
# Add axis labels
ax.set_xlabel('Center Frequency')
ax.set_ylabel('Power')
# Set plot limits
if freq_range:
ax.set_xlim(freq_range)
ax.set_ylim([0, ax.get_ylim()[1]])
style_param_plot(ax)
[docs]@savefig
@style_plot
def plot_peak_fits(peaks, freq_range=None, colors=None, labels=None, ax=None, **plot_kwargs):
"""Plot reconstructions of model peak fits.
Parameters
----------
peaks : 2d array
Peak data. Each row is a peak, as [CF, PW, BW].
freq_range : list of [float, float] , optional
The frequency range to plot the peak fits across, as [f_min, f_max].
If not provided, defaults to +/- 4 around given peak center frequencies.
colors : str or list of str, optional
Color(s) to plot data.
labels : list of str, optional
Label(s) for plotted data, to be added in a legend.
ax : matplotlib.Axes, optional
Figure axes upon which to plot.
**plot_kwargs
Additional plot related keyword arguments, with styling options managed by ``style_plot``.
"""
ax = check_ax(ax, plot_kwargs.pop('figsize', PLT_FIGSIZES['params']))
if isinstance(peaks, list):
if not colors:
colors = cycle(plt.rcParams['axes.prop_cycle'].by_key()['color'])
recursive_plot(peaks, plot_function=plot_peak_fits, ax=ax,
freq_range=tuple(freq_range) if freq_range else freq_range,
colors=colors, labels=labels, **plot_kwargs)
else:
if not freq_range:
# Extract all the CF values, excluding any NaNs
cfs = peaks[~np.isnan(peaks[:, 0]), 0]
# Define the frequency range as +/- buffer around the data range
# This also doesn't let the plot range drop below 0
f_buffer = 4
freq_range = [cfs.min() - f_buffer if cfs.min() - f_buffer > 0 else 0,
cfs.max() + f_buffer]
# Create the frequency axis, which will be the plot x-axis
freqs = gen_freqs(freq_range, 0.1)
colors = colors[0] if isinstance(colors, list) else colors
avg_vals = np.zeros(shape=[len(freqs)])
for peak_params in peaks:
# Create & plot the peak model from parameters
peak_vals = gaussian_function(freqs, *peak_params)
ax.plot(freqs, peak_vals, color=colors, alpha=0.35, linewidth=1.25)
# Collect a running average average peaks
avg_vals = np.nansum(np.vstack([avg_vals, peak_vals]), axis=0)
# Plot the average across all components
avg = avg_vals / peaks.shape[0]
avg_color = 'black' if not colors else colors
ax.plot(freqs, avg, color=avg_color, linewidth=3.75, label=labels)
# Add axis labels
ax.set_xlabel('Frequency')
ax.set_ylabel('log(Power)')
# Set plot limits
ax.set_xlim(freq_range)
ax.set_ylim([0, ax.get_ylim()[1]])
# Apply plot style
style_param_plot(ax)