ENH improve figures

Author: TomDLT

tutorials/movies_3T/01_plot_explainable_variance.py

@@ -17,7 +17,7 @@
 performances. The explainable variance is also sometimes called the *noise
 ceiling*.
 """
-# sphinx_gallery_thumbnail_number = 2
+# sphinx_gallery_thumbnail_number = 1
 ###############################################################################
 # Path of the data directory
 import os
@@ -63,23 +63,27 @@
 import matplotlib.pyplot as plt
 
 voxel_1 = np.argmax(ev)
-plt.figure(figsize=(10, 4))
+plt.figure(figsize=(10, 3))
 plt.plot(Y_test[:, :, voxel_1].T, color='C0', alpha=0.5)
 plt.plot(Y_test[:, :, voxel_1].mean(0), color='C1', label='average')
 plt.xlabel("Time points")
 plt.title("Voxel with large explainable variance (%.2f)" % ev[voxel_1])
+plt.yticks([])
 plt.legend()
+plt.tight_layout()
 plt.show()
 
 ###############################################################################
 # ... and of a voxel with low explainable variance.
 voxel_2 = np.argmin(ev)
-plt.figure(figsize=(10, 4))
+plt.figure(figsize=(10, 3))
 plt.plot(Y_test[:, :, voxel_2].T, color='C0', alpha=0.5)
 plt.plot(Y_test[:, :, voxel_2].mean(0), color='C1', label='average')
 plt.xlabel("Time points")
 plt.title("Voxel with low explainable variance (%.2f)" % ev[voxel_2])
+plt.yticks([])
 plt.legend()
+plt.tight_layout()
 plt.show()
 
 ###############################################################################

tutorials/notebooks/movies_3T/01_plot_explainable_variance.ipynb

@@ -127,7 +127,7 @@
       },
       "outputs": [],
       "source": [
-        "import matplotlib.pyplot as plt\n\nvoxel_1 = np.argmax(ev)\nplt.figure(figsize=(10, 4))\nplt.plot(Y_test[:, :, voxel_1].T, color='C0', alpha=0.5)\nplt.plot(Y_test[:, :, voxel_1].mean(0), color='C1', label='average')\nplt.xlabel(\"Time points\")\nplt.title(\"Voxel with large explainable variance (%.2f)\" % ev[voxel_1])\nplt.legend()\nplt.show()"
+        "import matplotlib.pyplot as plt\n\nvoxel_1 = np.argmax(ev)\nplt.figure(figsize=(10, 3))\nplt.plot(Y_test[:, :, voxel_1].T, color='C0', alpha=0.5)\nplt.plot(Y_test[:, :, voxel_1].mean(0), color='C1', label='average')\nplt.xlabel(\"Time points\")\nplt.title(\"Voxel with large explainable variance (%.2f)\" % ev[voxel_1])\nplt.yticks([])\nplt.legend()\nplt.tight_layout()\nplt.show()"
       ]
     },
     {
@@ -145,7 +145,7 @@
       },
       "outputs": [],
       "source": [
-        "voxel_2 = np.argmin(ev)\nplt.figure(figsize=(10, 4))\nplt.plot(Y_test[:, :, voxel_2].T, color='C0', alpha=0.5)\nplt.plot(Y_test[:, :, voxel_2].mean(0), color='C1', label='average')\nplt.xlabel(\"Time points\")\nplt.title(\"Voxel with low explainable variance (%.2f)\" % ev[voxel_2])\nplt.legend()\nplt.show()"
+        "voxel_2 = np.argmin(ev)\nplt.figure(figsize=(10, 3))\nplt.plot(Y_test[:, :, voxel_2].T, color='C0', alpha=0.5)\nplt.plot(Y_test[:, :, voxel_2].mean(0), color='C1', label='average')\nplt.xlabel(\"Time points\")\nplt.title(\"Voxel with low explainable variance (%.2f)\" % ev[voxel_2])\nplt.yticks([])\nplt.legend()\nplt.tight_layout()\nplt.show()"
       ]
     },
     {