From 100f08a9ab754864e4f3850e5de0d8d15b726fa7 Mon Sep 17 00:00:00 2001 From: Taylor Salo Date: Wed, 16 Aug 2023 16:47:03 -0400 Subject: [PATCH] Fix some things. --- content/TE_Dependence.md | 4 ++-- content/plot_approach_figures.md | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) 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)