Source code for fooof.plts.aperiodic

"""Plots for aperiodic fits and parameters."""

from itertools import cycle

import numpy as np
import matplotlib.pyplot as plt

from fooof.sim.gen import gen_freqs, gen_aperiodic
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')

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[docs]@savefig @style_plot @check_dependency(plt, 'matplotlib') def plot_aperiodic_params(aps, colors=None, labels=None, ax=None, **plot_kwargs): """Plot aperiodic parameters as dots representing offset and exponent value. Parameters ---------- aps : 2d array or list of 2d array Aperiodic parameters. Each row is a parameter set, as [Off, Exp] or [Off, Knee, Exp]. 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(aps, list): recursive_plot(aps, plot_aperiodic_params, ax, colors=colors, labels=labels) else: # Unpack data: offset as x; exponent as y xs, ys = aps[:, 0], aps[:, -1] sizes = 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('Offset') ax.set_ylabel('Exponent') style_param_plot(ax)
[docs]@savefig @style_plot @check_dependency(plt, 'matplotlib') def plot_aperiodic_fits(aps, freq_range, control_offset=False, log_freqs=False, colors=None, labels=None, ax=None, **plot_kwargs): """Plot reconstructions of model aperiodic fits. Parameters ---------- aps : 2d array Aperiodic parameters. Each row is a parameter set, as [Off, Exp] or [Off, Knee, Exp]. freq_range : list of [float, float] The frequency range to plot the peak fits across, as [f_min, f_max]. control_offset : boolean, optional, default: False Whether to control for the offset, by setting it to zero. log_freqs : boolean, optional, default: False Whether to plot the x-axis in log space. 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(aps, list): if not colors: colors = cycle(plt.rcParams['axes.prop_cycle'].by_key()['color']) recursive_plot(aps, plot_aperiodic_fits, ax=ax, freq_range=tuple(freq_range), control_offset=control_offset, log_freqs=log_freqs, colors=colors, labels=labels, **plot_kwargs) else: freqs = gen_freqs(freq_range, 0.1) plt_freqs = np.log10(freqs) if log_freqs else freqs colors = colors[0] if isinstance(colors, list) else colors avg_vals = np.zeros(shape=[len(freqs)]) for ap_params in aps: if control_offset: # Copy the object to not overwrite any data ap_params = ap_params.copy() ap_params[0] = 0 # Recreate & plot the aperiodic component from parameters ap_vals = gen_aperiodic(freqs, ap_params) ax.plot(plt_freqs, ap_vals, color=colors, alpha=0.35, linewidth=1.25) # Collect a running average across components avg_vals = np.nansum(np.vstack([avg_vals, ap_vals]), axis=0) # Plot the average component avg = avg_vals / aps.shape[0] avg_color = 'black' if not colors else colors ax.plot(plt_freqs, avg, linewidth=3.75, color=avg_color, label=labels) # Add axis labels ax.set_xlabel('log(Frequency)' if log_freqs else 'Frequency') ax.set_ylabel('log(Power)') # Set plot limit ax.set_xlim(np.log10(freq_range) if log_freqs else freq_range) style_param_plot(ax)