diff --git a/content/TE_Dependence.md b/content/TE_Dependence.md
index e0b667f..c07ea29 100644
--- a/content/TE_Dependence.md
+++ b/content/TE_Dependence.md
@@ -283,7 +283,7 @@ X2 = ((echo_times * mean_sig) / mean_t2s)[:, None]  # Model 2
 The predicted curve for the S0 model matches the real curve perfectly!
 
 ```{code-cell} ipython3
-:tags: [hide-cell]
+:tags: [hide-input]
 B = d_sig2[:, None]  # (E x S)
 alpha = (np.abs(B) ** 2).sum(axis=0)
 
@@ -321,7 +321,7 @@ It seems like the mismatch increases as the difference between the fluctuating v
 The fitted curve seems to actually match the mean signal, not the perturbed signal!
 
 ```{code-cell} ipython3
-:tags: [hide-cell]
+:tags: [hide-input]
 B = d_sig3[:, None]  # (E x S)
 alpha = (np.abs(B) ** 2).sum(axis=0)
 
diff --git a/content/plot_approach_figures.md b/content/plot_approach_figures.md
index f5d37a4..cc2960d 100644
--- a/content/plot_approach_figures.md
+++ b/content/plot_approach_figures.md
@@ -575,7 +575,7 @@ Scatter plot of voxel's signal for each echo with T2* estimate.
 ```{code-cell} ipython3
 :tags: [hide-cell]
 fig, ax = plt.subplots()
-sns.barplot(echo_times, alpha, ax=ax, palette=pal)
+sns.barplot(x=echo_times, y=alpha, ax=ax, palette=pal)
 ax.set_ylabel("Weight", fontsize=16)
 ax.set_xlabel("Echo Time (ms)", fontsize=16)
 ax.tick_params(axis="both", which="major", labelsize=14)