From 908a91667c437f659a41810608a1e1d77c0d9a25 Mon Sep 17 00:00:00 2001
From: Taylor Salo <tsalo006@fiu.edu>
Date: Sat, 20 Apr 2024 09:30:03 -0400
Subject: [PATCH] Modify things.

---
 .../02_Volume-wise_T2star_estimation_with_t2smap.md   |  2 +-
 content/Multi_Echo_Datasets.md                        |  2 +-
 content/TE_Dependence.md                              | 11 +++++++++--
 content/book_utils.py                                 |  3 ++-
 content/references.bib                                |  2 +-
 5 files changed, 14 insertions(+), 6 deletions(-)

diff --git a/content/02_Volume-wise_T2star_estimation_with_t2smap.md b/content/02_Volume-wise_T2star_estimation_with_t2smap.md
index 700ff30..126fba7 100644
--- a/content/02_Volume-wise_T2star_estimation_with_t2smap.md
+++ b/content/02_Volume-wise_T2star_estimation_with_t2smap.md
@@ -14,7 +14,7 @@ kernelspec:
 # Volume-wise T2*/S0 estimation with `t2smap`
 
 Use {py:func}`tedana.workflows.t2smap_workflow` {cite:p}`DuPre2021` to calculate volume-wise T2*/S0,
-as in {cite:t}`power2018ridding` and {cite:t}`HEUNIS2021118244`.
+as in {cite:t}`power2018ridding` and {cite:t}`heunis2021effects`.
 
 ```{code-cell} ipython3
 import os
diff --git a/content/Multi_Echo_Datasets.md b/content/Multi_Echo_Datasets.md
index 871dba8..7c02dae 100644
--- a/content/Multi_Echo_Datasets.md
+++ b/content/Multi_Echo_Datasets.md
@@ -80,7 +80,7 @@ Accessing this dataset requires a data access application.
 
 **Link**: https://doi.org/10.34894/R1TNL8
 
-**Citation**: {cite:t}`HEUNIS2021118244`
+**Citation**: {cite:t}`heunis2021effects`
 
 **Brief Description**: 28 participants, with one run of resting-state,
 one run of emotion-processing task, one run of imagined emotion-processing task,
diff --git a/content/TE_Dependence.md b/content/TE_Dependence.md
index c07ea29..1f25240 100644
--- a/content/TE_Dependence.md
+++ b/content/TE_Dependence.md
@@ -423,8 +423,15 @@ mean_s0 = 16000
 s0_std = mean_s0 * frac
 
 # simulate the T2*/S0 time series
-t2s_ts = np.random.normal(loc=mean_t2s, scale=t2s_std, size=(n_trs + 20,))
-t2s_ts = signal.convolve(t2s_ts, hrf)[20 : n_trs + 20]
+scales = np.random.random(5) * 3
+t2s_ts = []
+for section in range(10):
+    ts = np.hstack((np.zeros(10), np.ones(20), np.zeros(10)))
+    ts *= scales[section]
+    t2s_ts.append(ts)
+
+t2s_ts = np.hstack(t2s_ts)[:n_trs + 20]
+t2s_ts = signal.convolve(t2s_ts, hrf)[:n_trs]
 t2s_ts *= t2s_std / np.std(t2s_ts)
 t2s_ts += mean_t2s - np.mean(t2s_ts)
 
diff --git a/content/book_utils.py b/content/book_utils.py
index 17eb9bd..350354e 100644
--- a/content/book_utils.py
+++ b/content/book_utils.py
@@ -1,4 +1,5 @@
 """Utility functions for the JupyterBook."""
+
 import numpy as np
 from nilearn import image, masking
 
@@ -95,7 +96,7 @@ def predict_bold_signal(echo_times, s0, t2s):
         Echo times for which to predict data, in milliseconds.
     s0 : numpy.ndarray of shape (time,)
         S0 time series.
-    t2s : numpy.ndarray of shpae (time,)
+    t2s : numpy.ndarray of shape (time,)
         T2* time series.
 
     Returns
diff --git a/content/references.bib b/content/references.bib
index 542eae5..c55013b 100644
--- a/content/references.bib
+++ b/content/references.bib
@@ -12,7 +12,7 @@ @article{power2018ridding
 	publisher={National Acad Sciences}
 }
 
-@article{HEUNIS2021118244,
+@article{heunis2021effects,
 	title = {The effects of multi-echo fMRI combination and rapid T2*-mapping on offline and real-time BOLD sensitivity},
 	journal = {NeuroImage},
 	volume = {238},