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)
