def plot_peak_params(peaks, freq_range=None, colors=None, labels=None, ax=None, plot_style=style_param_plot, **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_style : callable, optional, default: style_param_plot A function to call to apply styling & aesthetics to the plot. **plot_kwargs Keyword arguments to pass into the plot call. """ 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, plot_style=plot_style, **plot_kwargs) # 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]]) check_n_style(plot_style, ax)
def plot_aperiodic_params(aps, colors=None, labels=None, ax=None, plot_style=style_param_plot, **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_style : callable, optional, default: style_param_plot A function to call to apply styling & aesthetics to the plot. **plot_kwargs Keyword arguments to pass into the plot call. """ 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, plot_style=plot_style, **plot_kwargs) else: # Unpack data: offset as x; exponent as y xs, ys = aps[:, 0], aps[:, -1] sizes = plot_kwargs.pop('s', 150) 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') check_n_style(plot_style, ax)
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 Keyword arguments to pass into the ``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)
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 Keyword arguments to pass into the plot call. """ 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)