def plot_param_ratio_corr_exp(
exp,
title="Ratio vs. Spectral Features",
cbar=False,
show=False,
save_fig=False,
file_path=fp.eeg_corrs,
file_name="Spectral_Features_Ratio_corr",
):
"""Same as `plot_param_ratio_corr`, but for the exponent."""
fig, ax1 = plt.subplots(figsize=(2.5, 1.75))
ax1 = sns.heatmap(exp[0].reshape((1, 1)),
cmap="bwr",
annot=True,
cbar=cbar,
ax=ax1,
vmin=-1,
vmax=1,
annot_kws={"size": 30})
plt.tick_params(axis='both',
which='both',
bottom=False,
top=False,
labelbottom=False,
left=False,
labelleft=False)
if save_fig:
plt.savefig(fp.make_file_path(file_path, file_name + '_exp', 'pdf'))
if not show:
plt.close()
def plot_interacting_sims(data,
param1,
param2,
plot_log=False,
show=False,
save_fig=False,
file_path=fp.sims_interacting,
file_name='InteractingSims'):
"""Plots heatmaps for interacting parameter simulations.
Parameters
----------
data : list of lists
List of power spectra.
param1 : string
Param used in simulation.
param2 : string
Param used in simulation.
plot_log : boolean
Whether to log values before plotting the heatmap.
show : boolean
Whether to display to plot.
save_fig : boolean
Whether to save out the figure.
file_path : string
Path to save the plot to.
file_name : string
File name to save the plot with.
"""
# Calculate ratios
ratios = calc_interacting_param_ratios(data)
if plot_log:
ratios = np.log10(ratios)
vmin, vmax = 0, 1.6
else:
vmin, vmax = None, None
fig, ax = plt.subplots()
sns.heatmap(ratios,
vmin,
vmax,
xticklabels=PARAMS[param2],
yticklabels=PARAMS[param1])
plt.yticks(rotation=0)
ax.invert_yaxis()
plt.xlabel(param2, {'fontsize': 14})
plt.ylabel(param1, {'fontsize': 14})
plt.tight_layout()
if save_fig:
plt.savefig(fp.make_file_path(file_path, file_name, 'pdf'))
if not show:
plt.close()
def plot_param_ratio_corr(data,
title="Ratio vs. Spectral Features",
y_labels=["Theta", "Alpha", "Beta"],
show=True,
save_fig=False,
file_path=fp.eeg_corrs,
file_name="Spectral_Features_Ratio_corr"):
"""Plot correlations between BandRatio measures and spectral features.
Parameters
----------
data: 2x3 ndarray
Correlations of BandRatios to Spectral Features.
title: string
Title of plot.
y_labels: list of strings
Labels of slow and fast wave to use on y-axis.
show : boolean
Whether to display to plot.
save_fig: boolean
If True - save plot.
file_path : string
Path to save the plot to.
file_name : string
File name to save the plot with.
"""
if not np.all(data):
raise RuntimeError("No data - cannot proceed.")
fig, ax2 = plt.subplots()
ax2 = sns.heatmap(data,
cmap="bwr",
yticklabels=y_labels,
xticklabels=FEATURE_LABELS,
annot=True,
ax=ax2,
vmin=-1,
vmax=1,
annot_kws={"size": 20})
plt.yticks(rotation=45, verticalalignment='center')
plt.tight_layout()
if save_fig:
plt.savefig(fp.make_file_path(file_path, file_name, 'pdf'))
if not show:
plt.close()
def plot_param_topo(data, raw, label='', save=False):
"""Plots the topography of a spectral parameter."""
fig, ax = plt.subplots()
mne.viz.plot_topomap(data,
raw.info,
vmin=min(data),
vmax=max(data),
cmap=cm.viridis,
contours=0,
axes=ax)
ax.set_title(label)
if save:
fig.savefig(fp.make_file_path(fp.eeg_topos, label + '-topo', 'pdf'))
def main():
## Load data
cf_theta = np.load(dp.make_file_path(dp.sims_single, 'cf_theta', 'npy'))
cf_alpha = np.load(dp.make_file_path(dp.sims_single, 'cf_alpha', 'npy'))
cf_beta = np.load(dp.make_file_path(dp.sims_single, 'cf_beta', 'npy'))
pw_theta = np.load(dp.make_file_path(dp.sims_single, 'pw_theta', 'npy'))
pw_alpha = np.load(dp.make_file_path(dp.sims_single, 'pw_alpha', 'npy'))
pw_beta = np.load(dp.make_file_path(dp.sims_single, 'pw_beta', 'npy'))
bw_theta = np.load(dp.make_file_path(dp.sims_single, 'bw_theta', 'npy'))
bw_alpha = np.load(dp.make_file_path(dp.sims_single, 'bw_alpha', 'npy'))
bw_beta = np.load(dp.make_file_path(dp.sims_single, 'bw_beta', 'npy'))
f_data = np.load(dp.make_file_path(dp.sims_single, 'exp_data', 'npy'))
offset = np.load(dp.make_file_path(dp.sims_single, 'offset_data', 'npy'))
exp = np.load(dp.make_file_path(dp.sims_single, 'exp_data', 'npy'))
a_shift = np.load(
dp.make_file_path(dp.sims_single, 'shifting_alpha', 'npy'))
cf_theta_df = prep_single_sims(cf_theta, "CF")
cf_alpha_df = prep_single_sims(cf_alpha, "CF")
cf_beta_df = prep_single_sims(cf_beta, "CF")
pw_theta_df = prep_single_sims(pw_theta, "PW")
pw_alpha_df = prep_single_sims(pw_alpha, "PW")
pw_beta_df = prep_single_sims(pw_beta, "PW")
bw_theta_df = prep_single_sims(bw_theta, "BW")
bw_alpha_df = prep_single_sims(bw_alpha, "BW")
bw_beta_df = prep_single_sims(bw_beta, "BW")
for ratio in ["TAR", "TBR", "ABR"]:
fig = plt.figure(figsize=PE_FIG_SIZE)
# low cf
ax = fig.add_subplot(331)
ax.set_xlabel("CF")
ax.set_ylabel(ratio)
ax.plot(cf_theta_df.iloc[:, 3], cf_theta_df[ratio], linewidth=LW)
# low pw
ax = fig.add_subplot(332)
ax.set_xlabel("PW")
ax.set_ylabel(ratio)
ax.plot(pw_theta_df.iloc[:, 3], pw_theta_df[ratio], linewidth=LW)
if max(pw_theta_df[ratio]) - min(pw_theta_df[ratio]) < .5:
maxx = np.max(pw_theta_df[ratio])
ax.set_ylim([maxx - .3, maxx + .1])
# low bw
ax = fig.add_subplot(333)
ax.set_xlabel("BW")
ax.set_ylabel(ratio)
ax.plot(bw_theta_df.iloc[:, 3], bw_theta_df[ratio], linewidth=LW)
if max(bw_theta_df[ratio]) - min(bw_theta_df[ratio]) < .3:
maxx = np.max(bw_theta_df[ratio])
ax.set_ylim([maxx - .3, maxx + .1])
# middle cf
ax = fig.add_subplot(334)
ax.set_xlabel("CF")
ax.set_ylabel(ratio)
ax.plot(cf_alpha_df.iloc[:, 3], cf_alpha_df[ratio], linewidth=LW)
# middle pw
ax = fig.add_subplot(335)
ax.set_xlabel("PW")
ax.set_ylabel(ratio)
ax.plot(pw_alpha_df.iloc[:, 3], pw_alpha_df[ratio], linewidth=LW)
if max(pw_alpha_df[ratio]) - min(pw_alpha_df[ratio]) < .3:
maxx = np.max(pw_alpha_df[ratio])
ax.set_ylim([maxx - .3, maxx + .1])
# middle bw
ax = fig.add_subplot(336)
ax.set_xlabel("BW")
ax.set_ylabel(ratio)
ax.plot(bw_alpha_df.iloc[:, 3], bw_alpha_df[ratio], linewidth=LW)
if max(bw_alpha_df[ratio]) - min(bw_alpha_df[ratio]) < .3:
maxx = np.max(bw_alpha_df[ratio])
ax.set_ylim([maxx - .3, maxx + .1])
# high cf
ax = fig.add_subplot(337)
ax.set_xlabel("CF")
ax.set_ylabel(ratio)
ax.plot(cf_beta_df.iloc[:, 3], cf_beta_df[ratio], linewidth=LW)
# high pw
ax = fig.add_subplot(338)
ax.set_xlabel("PW")
ax.set_ylabel(ratio)
ax.plot(pw_beta_df.iloc[:, 3], pw_beta_df[ratio], linewidth=LW)
if max(pw_beta_df[ratio]) - min(pw_beta_df[ratio]) < .3:
maxx = np.max(pw_beta_df[ratio])
ax.set_ylim([maxx - .3, maxx + .1])
# high bw
ax = fig.add_subplot(339)
ax.set_xlabel("BW")
ax.set_ylabel(ratio)
ax.plot(bw_beta_df.iloc[:, 3], bw_beta_df[ratio], linewidth=LW)
if max(bw_beta_df[ratio]) - min(bw_beta_df[ratio]) < .3:
maxx = np.max(bw_beta_df[ratio])
ax.set_ylim([maxx - .3, maxx + .1])
plt.tight_layout()
plt.savefig(
fp.make_file_path(fp.sims_single, 'periodic_' + ratio,
SAVE_FORMAT))
plt.clf()
################################################
f_df = prep_single_sims(f_data, "EXP", periodic_param=0)
offset_df = prep_single_sims(offset, "OFF", periodic_param=0)
exp_df = prep_single_sims(exp, "EXP", periodic_param=0)
a_shift_df = prep_single_sims(a_shift, "Alpha CF")
fig = plt.figure(figsize=AP_FIG_SIZE)
# offset
ax = fig.add_subplot(211)
ax.set_xlabel("Offset")
ax.set_ylabel(ratio)
ax.plot(offset_df.iloc[:, 3], offset_df[ratio], linewidth=LW)
ax.locator_params(axis='y', nbins=4)
# exponent
ax = fig.add_subplot(212)
ax.set_xlabel("Exponent")
ax.set_ylabel(ratio)
ax.plot(exp_df.iloc[:, 3], exp_df[ratio], linewidth=LW)
fig.subplots_adjust(left=0.2, hspace=0.6)
plt.savefig(
fp.make_file_path(fp.sims_single, 'aperiodic_' + ratio,
SAVE_FORMAT))