ENH add explainable variance plot for two voxels

Author: TomDLT

tutorials/movies_3T/01_plot_explainable_variance.py

@@ -57,10 +57,34 @@
 print("(n_voxels,) =", ev.shape)
 
 ###############################################################################
-# We can plot the distribution of explainable variance over voxels.
+# To better understand the explainable variance, we can plot the time-courses
+# of a voxel with large explainable variance...
 
 import matplotlib.pyplot as plt
 
+voxel_1 = np.argmax(ev)
+plt.figure(figsize=(10, 4))
+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.legend()
+plt.show()
+
+###############################################################################
+# ... and of a voxel with low explainable variance.
+voxel_2 = np.argmin(ev)
+plt.figure(figsize=(10, 4))
+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.legend()
+plt.show()
+
+###############################################################################
+# We can also plot the distribution of explainable variance over voxels.
+
 plt.hist(ev, bins=np.linspace(0, 1, 100), log=True, histtype='step')
 plt.xlabel("Explainable variance")
 plt.ylabel("Number of voxels")
@@ -133,7 +157,7 @@
 
 import cortex
 
-surface = "fsaverage_pycortex"  # ("fsaverage" outside the Gallant lab)
+surface = "fsaverage"
 
 if not hasattr(cortex.db, surface):
     cortex.utils.download_subject(subject_id=surface)

tutorials/notebooks/movies_3T/01_plot_explainable_variance.ipynb

@@ -116,7 +116,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "We can plot the distribution of explainable variance over voxels.\n\n"
+        "To better understand the explainable variance, we can plot the time-courses\nof a voxel with large explainable variance...\n\n"
       ]
     },
     {
@@ -127,7 +127,43 @@
       },
       "outputs": [],
       "source": [
-        "import matplotlib.pyplot as plt\n\nplt.hist(ev, bins=np.linspace(0, 1, 100), log=True, histtype='step')\nplt.xlabel(\"Explainable variance\")\nplt.ylabel(\"Number of voxels\")\nplt.title('Histogram of explainable variance')\nplt.grid('on')\nplt.show()"
+        "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()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "... and of a voxel with low explainable variance.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "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()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We can also plot the distribution of explainable variance over voxels.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "plt.hist(ev, bins=np.linspace(0, 1, 100), log=True, histtype='step')\nplt.xlabel(\"Explainable variance\")\nplt.ylabel(\"Number of voxels\")\nplt.title('Histogram of explainable variance')\nplt.grid('on')\nplt.show()"
       ]
     },
     {
@@ -184,7 +220,7 @@
       },
       "outputs": [],
       "source": [
-        "import cortex\n\nsurface = \"fsaverage_pycortex\"  # (\"fsaverage\" outside the Gallant lab)\n\nif not hasattr(cortex.db, surface):\n    cortex.utils.download_subject(subject_id=surface)"
+        "import cortex\n\nsurface = \"fsaverage\"\n\nif not hasattr(cortex.db, surface):\n    cortex.utils.download_subject(subject_id=surface)"
       ]
     },
     {