def calc_interacting_param_ratios(data,
low_band=BANDS["theta"],
high_band=BANDS["beta"]):
"""Calculates matrix of absolute ratios from interacting data.
Parameters
----------
data : custom object ndarray [apc value][frequency vector][psds][syn_params]
Outputs
-------
res : 2D matrix of ratios where each dimension is a varied parameter
------------------------
| | |
| r_11 | r_12 |
| | |
|-----------------------
| | |
| r_21 | r_22 |
| | |
------------------------
"""
fs = gen_freqs(FREQ_RANGE, FREQ_RES)
ratios = np.zeros(shape=(len(data), len(data[0])))
for rot_ind, rot_val in enumerate(data):
for del_ind, del_val in enumerate(data[0]):
psd = data[rot_ind, del_ind, :]
ratios[rot_ind, del_ind] = calc_band_ratio(fs, psd, low_band,
high_band)
return ratios
def return_fooof_regen(fg):
"""
Takes a fitted FOOOFGroup model and returns the fitted (modeled) PSDs in
linear power.
"""
f_regen = sim.gen_freqs(fg.freq_range, fg.freq_res)
n_psds = fg.get_params('error').shape[0]
psds_regen = 10**np.array([
fg.get_fooof(ind, regenerate=True).fooofed_spectrum_
for ind in range(n_psds)
])
return f_regen, psds_regen
def test_fooof_check_data():
"""Test FOOOF in with check data mode turned off, including with NaN data."""
tfm = FOOOF(verbose=False)
tfm.set_check_data_mode(False)
assert tfm._check_data is False
# Add data, with check data turned off
# In check data mode, adding data with NaN should run
freqs = gen_freqs([3, 50], 0.5)
powers = np.ones_like(freqs) * np.nan
tfm.add_data(freqs, powers)
assert tfm.has_data
# Model fitting should execute, but return a null model fit, given the NaNs, without failing
tfm.fit()
assert not tfm.has_model
def combine_fooofs(fooofs):
"""Combine a group of FOOOF and/or FOOOFGroup objects into a single FOOOFGroup object.
Parameters
----------
fooofs : list of FOOOF or FOOOFGroup
Objects to be concatenated into a FOOOFGroup.
Returns
-------
fg : FOOOFGroup
Resultant object from combining inputs.
Raises
------
IncompatibleSettingsError
If the input objects have incompatible settings for combining.
Examples
--------
Combine FOOOF objects together (where `fm1`, `fm2` & `fm3` are assumed to be defined and fit):
>>> fg = combine_fooofs([fm1, fm2, fm3]) # doctest:+SKIP
Combine FOOOFGroup objects together (where `fg1` & `fg2` are assumed to be defined and fit):
>>> fg = combine_fooofs([fg1, fg2]) # doctest:+SKIP
"""
# Compare settings
if not compare_info(fooofs, 'settings') or not compare_info(
fooofs, 'meta_data'):
raise IncompatibleSettingsError(
"These objects have incompatible settings "
"or meta data, and so cannot be combined.")
# Initialize FOOOFGroup object, with settings derived from input objects
fg = FOOOFGroup(*fooofs[0].get_settings(), verbose=fooofs[0].verbose)
# Use a temporary store to collect spectra, as we'll only add it if it is consistently present
# We check how many frequencies by accessing meta data, in case of no frequency vector
meta_data = fooofs[0].get_meta_data()
n_freqs = len(gen_freqs(meta_data.freq_range, meta_data.freq_res))
temp_power_spectra = np.empty([0, n_freqs])
# Add FOOOF results from each FOOOF object to group
for f_obj in fooofs:
# Add FOOOFGroup object
if isinstance(f_obj, FOOOFGroup):
fg.group_results.extend(f_obj.group_results)
if f_obj.power_spectra is not None:
temp_power_spectra = np.vstack(
[temp_power_spectra, f_obj.power_spectra])
# Add FOOOF object
else:
fg.group_results.append(f_obj.get_results())
if f_obj.power_spectrum is not None:
temp_power_spectra = np.vstack(
[temp_power_spectra, f_obj.power_spectrum])
# If the number of collected power spectra is consistent, then add them to object
if len(fg) == temp_power_spectra.shape[0]:
fg.power_spectra = temp_power_spectra
# Set the check data mode, as True if any of the inputs have it on, False otherwise
fg.set_check_data_mode(
any([getattr(f_obj, '_check_data') for f_obj in fooofs]))
# Add data information information
fg.add_meta_data(fooofs[0].get_meta_data())
return fg
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)
