def plot_fg(fg, save_fig=False, file_name='FOOOF_group_fit', file_path=None):
"""Plots a figure with subplots covering the group results from a FOOOFGroup object.
Parameters
----------
fg : FOOOFGroup() object
FOOOFGroup object, containing results from fitting a group of power spectra.
save_fig : boolean, optional, default: False
Whether to save out a copy of the plot.
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.
"""
if not fg.group_results:
raise RuntimeError('No data available to plot - can not proceed.')
fig = plt.figure(figsize=(14, 10))
gs = gridspec.GridSpec(2, 2, wspace=0.35, hspace=0.25, height_ratios=[1, 1.2])
# Aperiodic parameters plot
ax0 = plt.subplot(gs[0, 0])
plot_fg_bg(fg, ax0)
# Goodness of fit plot
ax1 = plt.subplot(gs[0, 1])
plot_fg_gf(fg, ax1)
# Center frequencies plot
ax2 = plt.subplot(gs[1, :])
plot_fg_peak_cens(fg, ax2)
if save_fig:
plt.savefig(fpath(file_path, fname(file_name, 'png')))
def plot_fg(fg, save_fig=False, file_name=None, file_path=None):
"""Plot a figure with subplots visualizing the parameters from a FOOOFGroup object.
Parameters
----------
fg : FOOOFGroup
Object containing results from fitting a group of power spectra.
save_fig : bool, optional, default: False
Whether to save out a copy of the plot.
file_name : str, optional
Name to give the saved out file.
file_path : str, optional
Path to directory to save to. If None, saves to current directory.
Raises
------
NoModelError
If the FOOOF object does not have model fit data available to plot.
"""
if not fg.has_model:
raise NoModelError(
"No model fit results are available, can not proceed.")
fig = plt.figure(figsize=PLT_FIGSIZES['group'])
gs = gridspec.GridSpec(2,
2,
wspace=0.4,
hspace=0.25,
height_ratios=[1, 1.2])
# Aperiodic parameters plot
ax0 = plt.subplot(gs[0, 0])
plot_fg_ap(fg, ax0)
# Goodness of fit plot
ax1 = plt.subplot(gs[0, 1])
plot_fg_gf(fg, ax1)
# Center frequencies plot
ax2 = plt.subplot(gs[1, :])
plot_fg_peak_cens(fg, ax2)
if save_fig:
if not file_name:
raise ValueError(
"Input 'file_name' is required to save out the plot.")
plt.savefig(fpath(file_path, fname(file_name, 'png')))
def save_report_fg(fg, file_name, file_path=None):
"""Generate and save out as a PDF a report for a FOOOFGroup object.
Parameters
----------
fg : FOOOFGroup() object
FOOOFGroup object, containing results from fitting a group of power spectra.
file_name : str
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.
"""
font = _report_settings()
# Initialize figure
fig = plt.figure(figsize=(16, 20))
gs = gridspec.GridSpec(3,
2,
wspace=0.35,
hspace=0.25,
height_ratios=[0.8, 1.0, 1.0])
# First / top: text results
ax0 = plt.subplot(gs[0, :])
results_str = gen_results_fg_str(fg)
ax0.text(0.5, 0.7, results_str, font, ha='center', va='center')
ax0.set_frame_on(False)
ax0.set_xticks([])
ax0.set_yticks([])
# Aperiodic parameters plot
ax1 = plt.subplot(gs[1, 0])
plot_fg_ap(fg, ax1)
# Goodness of fit plot
ax2 = plt.subplot(gs[1, 1])
plot_fg_gf(fg, ax2)
# Peak center frequencies plot
ax3 = plt.subplot(gs[2, :])
plot_fg_peak_cens(fg, ax3)
# Save out the report
plt.savefig(fpath(file_path, fname(file_name, 'pdf')))
plt.close()
def decorated(*args, **kwargs):
# Grab file name and path arguments, if they are in kwargs
file_name = kwargs.pop('file_name', None)
file_path = kwargs.pop('file_path', None)
# Check for an explicit argument for whether to save figure or not
# Defaults to saving when file name given (since bool(str)->True; bool(None)->False)
save_fig = kwargs.pop('save_fig', bool(file_name))
func(*args, **kwargs)
if save_fig:
if not file_name:
raise ValueError(
"Input 'file_name' is required to save out the plot.")
plt.savefig(fpath(file_path, fname(file_name, 'png')))
def save_report_fm(fm, file_name, file_path=None, plt_log=False):
"""Generate and save out a as PDF a report for a FOOOF object.
Parameters
----------
fm : FOOOF() object
FOOOF object, containing results from fitting a power spectrum.
file_name : str
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.
plt_log : bool, optional, default: False
Whether or not to plot the frequency axis in log space.
"""
font = _report_settings()
# Set up outline figure, using gridspec
fig = plt.figure(figsize=(16, 20))
grid = gridspec.GridSpec(3, 1, height_ratios=[0.45, 1.0, 0.25])
# First - text results
ax0 = plt.subplot(grid[0])
results_str = gen_results_fm_str(fm)
ax0.text(0.5, 0.7, results_str, font, ha='center', va='center')
ax0.set_frame_on(False)
ax0.set_xticks([])
ax0.set_yticks([])
# Second - data plot
ax1 = plt.subplot(grid[1])
fm.plot(plt_log=plt_log, ax=ax1)
# Third - FOOOF settings
ax2 = plt.subplot(grid[2])
settings_str = gen_settings_str(fm, False)
ax2.text(0.5, 0.1, settings_str, font, ha='center', va='center')
ax2.set_frame_on(False)
ax2.set_xticks([])
ax2.set_yticks([])
# Save out the report
plt.savefig(fpath(file_path, fname(file_name, 'pdf')))
plt.close()
def save_report_fg(fg, file_name, file_path=None):
"""Generate and save out a PDF report for a group of power spectrum models.
Parameters
----------
fg : FOOOFGroup
Object with results from fitting a group of power spectra.
file_name : str
Name to give the saved out file.
file_path : str, optional
Path to directory to save to. If None, saves to current directory.
"""
# Initialize figure
_ = plt.figure(figsize=REPORT_FIGSIZE)
grid = gridspec.GridSpec(3,
2,
wspace=0.4,
hspace=0.25,
height_ratios=[0.8, 1.0, 1.0])
# First / top: text results
ax0 = plt.subplot(grid[0, :])
results_str = gen_results_fg_str(fg)
ax0.text(0.5, 0.7, results_str, REPORT_FONT, ha='center', va='center')
ax0.set_frame_on(False)
ax0.set_xticks([])
ax0.set_yticks([])
# Aperiodic parameters plot
ax1 = plt.subplot(grid[1, 0])
plot_fg_ap(fg, ax1)
# Goodness of fit plot
ax2 = plt.subplot(grid[1, 1])
plot_fg_gf(fg, ax2)
# Peak center frequencies plot
ax3 = plt.subplot(grid[2, :])
plot_fg_peak_cens(fg, ax3)
# Save out the report
plt.savefig(fpath(file_path, fname(file_name, SAVE_FORMAT)))
plt.close()
def save_report_fm(fm, file_name, file_path=None, plt_log=False):
"""Generate and save out a PDF report for a power spectrum model fit.
Parameters
----------
fm : FOOOF
Object with results from fitting a power spectrum.
file_name : str
Name to give the saved out file.
file_path : str, optional
Path to directory to save to. If None, saves to current directory.
plt_log : bool, optional, default: False
Whether or not to plot the frequency axis in log space.
"""
# Set up outline figure, using gridspec
_ = plt.figure(figsize=REPORT_FIGSIZE)
grid = gridspec.GridSpec(3, 1, height_ratios=[0.45, 1.0, 0.25])
# First - text results
ax0 = plt.subplot(grid[0])
results_str = gen_results_fm_str(fm)
ax0.text(0.5, 0.7, results_str, REPORT_FONT, ha='center', va='center')
ax0.set_frame_on(False)
ax0.set_xticks([])
ax0.set_yticks([])
# Second - data plot
ax1 = plt.subplot(grid[1])
fm.plot(plt_log=plt_log, ax=ax1)
# Third - FOOOF settings
ax2 = plt.subplot(grid[2])
settings_str = gen_settings_str(fm, False)
ax2.text(0.5, 0.1, settings_str, REPORT_FONT, ha='center', va='center')
ax2.set_frame_on(False)
ax2.set_xticks([])
ax2.set_yticks([])
# Save out the report
plt.savefig(fpath(file_path, fname(file_name, SAVE_FORMAT)))
plt.close()
def plot_fm(fm, plt_log=False, save_fig=False, file_name='FOOOF_fit', file_path=None, 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 None, 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.')
ax = check_ax(ax)
# Log Plot Settings - note that power values in FOOOF objects are already logged
log_freqs = plt_log
log_powers = False
# Create the plot, adding data as is available
if np.any(fm.power_spectrum):
plot_spectrum(fm.freqs, fm.power_spectrum, log_freqs, log_powers, ax,
color='k', linewidth=1.25, label='Original Spectrum')
if np.any(fm.fooofed_spectrum_):
plot_spectrum(fm.freqs, fm.fooofed_spectrum_, log_freqs, log_powers, ax,
color='r', linewidth=3.0, alpha=0.5, label='Full Model Fit')
plot_spectrum(fm.freqs, fm._ap_fit, log_freqs, log_powers, ax,
color='b', linestyle='dashed', linewidth=3.0,
alpha=0.5, label='Aperiodic Fit')
# Save out figure, if requested
if save_fig:
plt.savefig(fpath(file_path, fname(file_name, 'png')))
def plot_fm(fm,
plot_peaks=None,
plot_aperiodic=True,
plt_log=False,
add_legend=True,
save_fig=False,
file_name=None,
file_path=None,
ax=None,
plot_style=style_spectrum_plot,
data_kwargs=None,
model_kwargs=None,
aperiodic_kwargs=None,
peak_kwargs=None):
"""Plot the power spectrum and model fit results from a FOOOF object.
Parameters
----------
fm : FOOOF
Object containing a power spectrum and (optionally) results from fitting.
plot_peaks : None or {'shade', 'dot', 'outline', 'line'}, optional
What kind of approach to take to plot peaks. If None, peaks are not specifically plotted.
Can also be a combination of approaches, separated by '-', for example: 'shade-line'.
plot_aperiodic : boolean, optional, default: True
Whether to plot the aperiodic component of the model fit.
plt_log : boolean, optional, default: False
Whether to plot the frequency values in log10 spacing.
add_legend : boolean, optional, default: False
Whether to add a legend describing the plot components.
save_fig : bool, optional, default: False
Whether to save out a copy of the plot.
file_name : str, optional
Name to give the saved out file.
file_path : str, optional
Path to directory to save to. If None, saves to current directory.
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 plot.
data_kwargs, model_kwargs, aperiodic_kwargs, peak_kwargs : None or dict, optional
Keyword arguments to pass into the plot call for each plot element.
Notes
-----
Since FOOOF objects store power values in log spacing,
the y-axis (power) is plotted in log spacing by default.
"""
ax = check_ax(ax, PLT_FIGSIZES['spectral'])
# Log settings - note that power values in FOOOF objects are already logged
log_freqs = plt_log
log_powers = False
# Plot the data, if available
if fm.has_data:
data_kwargs = check_plot_kwargs(data_kwargs, \
{'color' : PLT_COLORS['data'], 'linewidth' : 2.0,
'label' : 'Original Spectrum' if add_legend else None})
plot_spectrum(fm.freqs,
fm.power_spectrum,
log_freqs,
log_powers,
ax=ax,
plot_style=None,
**data_kwargs)
# Add the full model fit, and components (if requested)
if fm.has_model:
model_kwargs = check_plot_kwargs(model_kwargs, \
{'color' : PLT_COLORS['model'], 'linewidth' : 3.0, 'alpha' : 0.5,
'label' : 'Full Model Fit' if add_legend else None})
plot_spectrum(fm.freqs,
fm.fooofed_spectrum_,
log_freqs,
log_powers,
ax=ax,
plot_style=None,
**model_kwargs)
# Plot the aperiodic component of the model fit
if plot_aperiodic:
aperiodic_kwargs = check_plot_kwargs(aperiodic_kwargs, \
{'color' : PLT_COLORS['aperiodic'], 'linewidth' : 3.0, 'alpha' : 0.5,
'linestyle' : 'dashed', 'label' : 'Aperiodic Fit' if add_legend else None})
plot_spectrum(fm.freqs,
fm._ap_fit,
log_freqs,
log_powers,
ax=ax,
plot_style=None,
**aperiodic_kwargs)
# Plot the periodic components of the model fit
if plot_peaks:
_add_peaks(fm, plot_peaks, plt_log, ax=ax, peak_kwargs=peak_kwargs)
# Apply style to plot
check_n_style(plot_style, ax, log_freqs, True)
# Save out figure, if requested
if save_fig:
if not file_name:
raise ValueError(
"Input 'file_name' is required to save out the plot.")
plt.savefig(fpath(file_path, fname(file_name, 'png')))