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TS viz improvements #135

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TS viz improvements #135

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086d179
TS viz improvements
Oct 23, 2023
a22bc35
adding unit testing
P-Oveisi Oct 30, 2023
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44 changes: 35 additions & 9 deletions examples/eg004r__fitting_JR_example.py
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Original file line number Diff line number Diff line change
Expand Up @@ -26,6 +26,8 @@
from whobpyt.models.JansenRit import RNNJANSEN, ParamsJR
from whobpyt.optimization.custom_cost_JR import CostsJR
from whobpyt.run import Model_fitting
import mne


# array and pd stuff
import numpy as np
Expand Down Expand Up @@ -160,12 +162,36 @@
plt.show()

# %%
# Plot the EEG
fig, ax = plt.subplots(1,3, figsize=(12,8))
ax[0].plot(F.lastRec['P'].npTS().T)
ax[0].set_title('Test: sourced EEG')
ax[1].plot(F.lastRec["eeg"].npTS().T)
ax[1].set_title('Test')
ax[2].plot(eeg_data.T)
ax[2].set_title('empirical')
plt.show()
# Plotting the timeseries of the fitted model and the empirical data using mne package

# import a 64 channel montage for visualization
montage = mne.channels.make_standard_montage('biosemi64')
# create info object based on the montage
info = mne.create_info(ch_names = montage.ch_names, sfreq = 1000, ch_types = 'eeg')

# create mne.evoked objects
evoked_emp = mne.EvokedArray(eeg_data, info)
evoked_emp.set_montage(montage)
evoked_fit = mne.EvokedArray(F.lastRec["eeg"].npTS()/1e6, info)
evoked_fit.set_montage(montage)

plt.plot(F.lastRec['P'].npTS().T, linewidth = 0.5)
# resize plot
plt.rcParams["figure.figsize"] = (8, 3)
plt.title('Simulated - Source Activity (200 Parcels)');
plt.ylim(-.3, .3);
# add x label
plt.xlabel('Time (ms)');
# add bar between 110 and 120 for stimulus
plt.axvspan(110, 120, alpha=0.5, color='red');
# add label for stimulus
plt.text(115, .25, 'Stimulation ON', color='black', fontsize=8, horizontalalignment='right', verticalalignment='center');

fig = evoked_emp.plot(gfp=True, time_unit='s', show=False, titles='Empirical - Channel EEG')
for ax in fig.axes:
ax.axvspan(0.11, 0.12, alpha=0.5, color='red');


fig = evoked_fit.plot(gfp=True, time_unit='s', show=False, titles='Simulated - Channel EEG');
for ax in fig.axes:
ax.axvspan(0.11, 0.12, alpha=0.5, color='red');
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