def _add_peaks_width(fm, plt_log, ax, **plot_kwargs):
"""Add a line across the width of peaks.
Parameters
----------
fm : FOOOF
FOOOF object containing results from fitting.
plt_log : boolean
Whether to plot the frequency values in log10 spacing.
ax : matplotlib.Axes
Figure axes upon which to plot.
**plot_kwargs
Keyword arguments to pass into the plot call.
Notes
-----
This line represents the bandwidth (width or gaussian standard deviation) of
the peak, though what is literally plotted is the full-width half-max.
"""
defaults = {
'color': PLT_COLORS['periodic'],
'alpha': 0.6,
'lw': 2.5,
'ms': 6
}
plot_kwargs = check_plot_kwargs(plot_kwargs, defaults)
for peak in fm.gaussian_params_:
peak_top = fm.power_spectrum[nearest_ind(fm.freqs, peak[0])]
bw_freqs = [
peak[0] - 0.5 * compute_fwhm(peak[2]),
peak[0] + 0.5 * compute_fwhm(peak[2])
]
if plt_log:
bw_freqs = np.log10(bw_freqs)
ax.plot(bw_freqs,
[peak_top - (0.5 * peak[1]), peak_top - (0.5 * peak[1])],
**plot_kwargs)
def plot_annotated_model(fm,
plt_log=False,
annotate_peaks=True,
annotate_aperiodic=True,
ax=None,
plot_style=style_spectrum_plot):
"""Plot a an annotated power spectrum and model, from a FOOOF object.
Parameters
----------
fm : FOOOF
FOOOF object, with model fit, data and settings available.
plt_log : boolean, optional, default: False
Whether to plot the frequency values in log10 spacing.
ax : matplotlib.Axes, optional
Figure axes upon which to plot.
plot_style : callable, optional, default: style_spectrum_plot
A function to call to apply styling & aesthetics to the plots.
Raises
------
NoModelError
If there are no model results available to plot.
"""
# Check that model is available
if not fm.has_model:
raise NoModelError("No model is available to plot, can not proceed.")
# Settings
fontsize = 15
lw1 = 4.0
lw2 = 3.0
ms1 = 12
# Create the baseline figure
ax = check_ax(ax, PLT_FIGSIZES['spectral'])
fm.plot(plot_peaks='dot-shade-width',
plt_log=plt_log,
ax=ax,
plot_style=None,
data_kwargs={
'lw': lw1,
'alpha': 0.6
},
aperiodic_kwargs={
'lw': lw1,
'zorder': 10
},
model_kwargs={
'lw': lw1,
'alpha': 0.5
},
peak_kwargs={
'dot': {
'color': PLT_COLORS['periodic'],
'ms': ms1,
'lw': lw2
},
'shade': {
'color': PLT_COLORS['periodic']
},
'width': {
'color': PLT_COLORS['periodic'],
'alpha': 0.75,
'lw': lw2
}
})
# Get freqs for plotting, and convert to log if needed
freqs = fm.freqs if not plt_log else np.log10(fm.freqs)
## Buffers: for spacing things out on the plot (scaled by plot values)
x_buff1 = max(freqs) * 0.1
x_buff2 = max(freqs) * 0.25
y_buff1 = 0.15 * np.ptp(ax.get_ylim())
shrink = 0.1
# There is a bug in annotations for some perpendicular lines, so add small offset
# See: https://github.com/matplotlib/matplotlib/issues/12820. Fixed in 3.2.1.
bug_buff = 0.000001
if annotate_peaks:
# Extract largest peak, to annotate, grabbing gaussian params
gauss = get_band_peak_fm(fm,
fm.freq_range,
attribute='gaussian_params')
peak_ctr, peak_hgt, peak_wid = gauss
bw_freqs = [
peak_ctr - 0.5 * compute_fwhm(peak_wid),
peak_ctr + 0.5 * compute_fwhm(peak_wid)
]
if plt_log:
peak_ctr = np.log10(peak_ctr)
bw_freqs = np.log10(bw_freqs)
peak_top = fm.power_spectrum[nearest_ind(freqs, peak_ctr)]
# Annotate Peak CF
ax.annotate('Center Frequency',
xy=(peak_ctr, peak_top),
xytext=(peak_ctr, peak_top + np.abs(0.6 * peak_hgt)),
verticalalignment='center',
horizontalalignment='center',
arrowprops=dict(facecolor=PLT_COLORS['periodic'],
shrink=shrink),
color=PLT_COLORS['periodic'],
fontsize=fontsize)
# Annotate Peak PW
ax.annotate('Power',
xy=(peak_ctr, peak_top - 0.3 * peak_hgt),
xytext=(peak_ctr + x_buff1, peak_top - 0.3 * peak_hgt),
verticalalignment='center',
arrowprops=dict(facecolor=PLT_COLORS['periodic'],
shrink=shrink),
color=PLT_COLORS['periodic'],
fontsize=fontsize)
# Annotate Peak BW
bw_buff = (peak_ctr - bw_freqs[0]) / 2
ax.annotate('Bandwidth',
xy=(peak_ctr - bw_buff + bug_buff,
peak_top - (0.5 * peak_hgt)),
xytext=(peak_ctr - bw_buff, peak_top - (1.5 * peak_hgt)),
verticalalignment='center',
horizontalalignment='right',
arrowprops=dict(facecolor=PLT_COLORS['periodic'],
shrink=shrink),
color=PLT_COLORS['periodic'],
fontsize=fontsize,
zorder=20)
if annotate_aperiodic:
# Annotate Aperiodic Offset
# Add a line to indicate offset, without adjusting plot limits below it
ax.set_autoscaley_on(False)
ax.plot([freqs[0], freqs[0]],
[ax.get_ylim()[0], fm.fooofed_spectrum_[0]],
color=PLT_COLORS['aperiodic'],
linewidth=lw2,
alpha=0.5)
ax.annotate('Offset',
xy=(freqs[0] + bug_buff, fm.power_spectrum[0] - y_buff1),
xytext=(freqs[0] - x_buff1,
fm.power_spectrum[0] - y_buff1),
verticalalignment='center',
horizontalalignment='center',
arrowprops=dict(facecolor=PLT_COLORS['aperiodic'],
shrink=shrink),
color=PLT_COLORS['aperiodic'],
fontsize=fontsize)
# Annotate Aperiodic Knee
if fm.aperiodic_mode == 'knee':
# Find the knee frequency point to annotate
knee_freq = compute_knee_frequency(
fm.get_params('aperiodic', 'knee'),
fm.get_params('aperiodic', 'exponent'))
knee_freq = np.log10(knee_freq) if plt_log else knee_freq
knee_pow = fm.power_spectrum[nearest_ind(freqs, knee_freq)]
# Add a dot to the plot indicating the knee frequency
ax.plot(knee_freq,
knee_pow,
'o',
color=PLT_COLORS['aperiodic'],
ms=ms1 * 1.5,
alpha=0.7)
ax.annotate('Knee',
xy=(knee_freq, knee_pow),
xytext=(knee_freq - x_buff2, knee_pow - y_buff1),
verticalalignment='center',
arrowprops=dict(facecolor=PLT_COLORS['aperiodic'],
shrink=shrink),
color=PLT_COLORS['aperiodic'],
fontsize=fontsize)
# Annotate Aperiodic Exponent
mid_ind = int(len(freqs) / 2)
ax.annotate('Exponent',
xy=(freqs[mid_ind], fm.power_spectrum[mid_ind]),
xytext=(freqs[mid_ind] - x_buff2,
fm.power_spectrum[mid_ind] - y_buff1),
verticalalignment='center',
arrowprops=dict(facecolor=PLT_COLORS['aperiodic'],
shrink=shrink),
color=PLT_COLORS['aperiodic'],
fontsize=fontsize)
# Apply style to plot & tune grid styling
check_n_style(plot_style, ax, plt_log, True)
ax.grid(True, alpha=0.5)
# Add labels to plot in the legend
da_patch = mpatches.Patch(color=PLT_COLORS['data'], label='Original Data')
ap_patch = mpatches.Patch(color=PLT_COLORS['aperiodic'],
label='Aperiodic Parameters')
pe_patch = mpatches.Patch(color=PLT_COLORS['periodic'],
label='Peak Parameters')
mo_patch = mpatches.Patch(color=PLT_COLORS['model'], label='Full Model')
handles = [
da_patch, ap_patch if annotate_aperiodic else None,
pe_patch if annotate_peaks else None, mo_patch
]
handles = [el for el in handles if el is not None]
ax.legend(handles=handles, handlelength=1, fontsize='x-large')
