Point to home for data operations.

content/01-pyafq.ipynb

@@ -41,20 +41,14 @@
     "import os\n",
     "import os.path as op\n",
     "\n",
+    "home = op.expanduser(\"~\")\n",
     "pwd = op.dirname(os.getcwd())\n",
     "\n",
-    "os.environ[\"TEMPLATEFLOW_HOME\"] = op.join(pwd, \"..\", \"data_\", \"tractometry\")\n",
-    "os.environ[\"DIPY_HOME\"] = op.join(pwd, \"..\", \"data_\", \"tractometry\")\n",
-    "os.environ[\"AFQ_HOME\"] = op.join(pwd, \"..\", \"data_\", \"tractometry\")\n"
+    "os.environ[\"TEMPLATEFLOW_HOME\"] = op.join(home, \"data_\", \"tractometry\")\n",
+    "os.environ[\"DIPY_HOME\"] = op.join(home, \"data_\", \"tractometry\")\n",
+    "os.environ[\"AFQ_HOME\"] = op.join(home, \"data_\", \"tractometry\")\n"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -129,10 +123,13 @@
     "\n",
     "```{alert}\n",
     "\n",
-    "Running the code below triggers the full pipeline of operations leading to the\n",
-    "computation of the tract profiles. Therefore, it takes a little while to run\n",
-    "(about 10 minutes, typically)\n",
-    "\n",
+    "For the purpose of the neurolibre preprint, the pipeline has been run in \n",
+    "advance and cached in the data folder (under `data/tractometry`). In \n",
+    "usual practice, the following line of code would trigger the full computational\n",
+    "pipeline, which takes about 10 minutes to run and requires more RAM than \n",
+    "is available through the neurolibre server. However, the caching behavior \n",
+    "is typical to pyAFQ. Once derivatives are computed, they are cached such that \n",
+    "they can be reused. \n",
     "```\n"
    ]
   },