def plot_peak_iter(fm):
"""Plots a series of plots illustrating the peak search from a flattened spectrum.
Parameters
----------
fm : FOOOF() object
FOOOF object, with model fit and data and settings available.
"""
flatspec = fm._spectrum_flat
n_gauss = fm._gaussian_params.shape[0]
ylims = [
min(flatspec) - 0.1 * np.abs(min(flatspec)),
max(flatspec) + 0.1 * max(flatspec)
]
for ind in range(n_gauss + 1):
_, ax = plt.subplots(figsize=(12, 10))
plot_spectrum(fm.freqs,
flatspec,
linewidth=2.0,
label='Flattened Spectrum',
ax=ax)
plot_spectrum(fm.freqs,
[fm.peak_threshold * np.std(flatspec)] * len(fm.freqs),
color='orange',
linestyle='dashed',
label='Relative Threshold',
ax=ax)
plot_spectrum(fm.freqs, [fm.min_peak_amplitude] * len(fm.freqs),
color='red',
linestyle='dashed',
label='Absolute Threshold',
ax=ax)
maxi = np.argmax(flatspec)
ax.plot(fm.freqs[maxi], flatspec[maxi], '.', markersize=24)
ax.set_ylim(ylims)
ax.set_title('Iteration #' + str(ind + 1), fontsize=16)
if ind < n_gauss:
gauss = gaussian_function(fm.freqs, *fm._gaussian_params[ind, :])
plot_spectrum(fm.freqs,
gauss,
label='Gaussian Fit',
linestyle=':',
linewidth=2.0,
ax=ax)
flatspec = flatspec - gauss
def get_flattened_plots(spectrum, frequencies, number_of_patients, fm, fg,
patient_group, brain_region, params_save_path):
freqs = frequencies
doge = spectrum
plt_log = False
for n in range(0, number_of_patients):
spectrum = doge[n]
fm.add_data(freqs, spectrum, [0, 60])
print(fm._spectrum_flat)
plot_spectrum(freqs, spectrum, plt_log, label='Flattened Spectrum1')
fm.fit(freqs, spectrum, [0, 60])
plot_spectrum(fm.freqs,
fm._spectrum_flat,
plt_log,
label='Flattened Spectrum')
joiner = [
params_save_path, patient_group, 'FlatSpecs_', brain_region, '/',
patient_group, 'FlatSpec_', brain_region
]
to_string = [''.join(joiner), n + 1]
string_to_name = ''.join(str(e) for e in to_string)
plt.savefig(string_to_name)
plt.close(string_to_name)
def plot_fm(fm,
plt_log=False,
save_fig=False,
file_name='FOOOF_fit',
file_path='',
ax=None):
"""Plot the original power spectrum, and full model fit from FOOOF object.
Parameters
----------
fm : FOOOF() object
FOOOF object, containing a power spectrum and (optionally) results from fitting.
plt_log : boolean, optional
Whether or not to plot the frequency axis in log space. default: False
save_fig : boolean, optional
Whether to save out a copy of the plot. default : False
file_name : str, optional
Name to give the saved out file.
file_path : str, optional
Path to directory in which to save. If not provided, saves to current directory.
ax : matplotlib.Axes, optional
Figure axes upon which to plot.
"""
if not np.all(fm.freqs):
raise RuntimeError('No data available to plot - can not proceed.')
if not ax:
fig, ax = plt.subplots(figsize=(12, 10))
# Create the plot, adding data as is available
if np.all(fm.power_spectrum):
plot_spectrum(fm.freqs,
fm.power_spectrum,
plt_log,
ax,
color='k',
linewidth=1.25,
label='Original Spectrum')
if np.all(fm.fooofed_spectrum_):
plot_spectrum(fm.freqs,
fm.fooofed_spectrum_,
plt_log,
ax,
color='r',
linewidth=3.0,
alpha=0.5,
label='Full Model Fit')
plot_spectrum(fm.freqs,
fm._bg_fit,
plt_log,
ax,
color='b',
linestyle='dashed',
linewidth=3.0,
alpha=0.5,
label='Background Fit')
# Save out figure, if requested
if save_fig:
plt.savefig(os.path.join(file_path, file_name + '.png'))