Minor fixes and updates to notebook

notebooks/tutorial.ipynb

@@ -112,7 +112,7 @@
    "source": [
     "### **Activate the DataJoint Pipeline**\n",
     "\n",
-    "This tutorial activates the `ephys-acute.py` module from `element-array-ephys`, along\n",
+    "This tutorial activates the `ephys_acute.py` module from `element-array-ephys`, along\n",
     "with upstream dependencies from `element-animal` and `element-session`. Please refer to the\n",
     "[`tutorial_pipeline.py`](./tutorial_pipeline.py) for the source code."
    ]
@@ -1065,7 +1065,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Every experimental session produces a set of data files. The purpose of the `SessionDirectory` table is to locate these files. It references a directory path relative to a root directory, defined in `dj.config[\\\"custom\\\"]`. More information about `dj.config` is provided in the [documentation](https://datajoint.com/docs/elements/user-guide/)."
+    "Every experimental session produces a set of data files. The purpose of the `SessionDirectory` table is to locate these files. It references a directory path relative to a root directory, defined in `dj.config[\"custom\"]`. More information about `dj.config` is provided in the [documentation](https://datajoint.com/docs/elements/user-guide/)."
    ]
   },
   {
@@ -1557,7 +1557,8 @@
    "source": [
     "### **Populate electrophysiology recording metadata**\n",
     "\n",
-    "In the upcoming cells, populate the `ephys.EphysRecording` table and its part table `ephys.EphysRecording.EphysFile` will extract and store the recording information from a given experimental session."
+    "In the upcoming cells, the `.populate()` method will automatically extract and store the\n",
+    "recording metadata for each experimental session in the `ephys.EphysRecording` table and its part table `ephys.EphysRecording.EphysFile`."
    ]
   },
   {
@@ -2194,8 +2195,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now that we've inserted kilosort parameters into the `ClusteringParamSet` table,\n",
-    "we're almost ready to sort our data. DataJoint uses a `ClusteringTask` table to\n",
+    "DataJoint uses a `ClusteringTask` table to\n",
     "manage which `EphysRecording` and `ClusteringParamSet` should be used during processing. \n",
     "\n",
     "This table is important for defining several important aspects of\n",
@@ -2235,18 +2235,12 @@
    "metadata": {},
    "source": [
     "The `ClusteringTask` table contains two important attributes: \n",
-    "+ `paramset_idx` \n",
-    "+ `task_mode` \n",
-    "\n",
-    "The `paramset_idx` attribute tracks\n",
-    "your kilosort parameter sets. You can choose the parameter set using which \n",
-    "you want spike sort ephys data. For example, `paramset_idx=0` may contain\n",
-    "default parameters for kilosort processing whereas `paramset_idx=1` contains your custom parameters for sorting. This\n",
-    "attribute tells the `Processing` table which set of parameters you are processing in a given `populate()`.\n",
-    "\n",
-    "The `task_mode` attribute can be set to either `load` or `trigger`. When set to `load`,\n",
-    "running the processing step initiates a search for exisiting kilosort output files. When set to `trigger`, the\n",
-    "processing step will run kilosort on the raw data. "
+    "+ `paramset_idx` - Allows the user to choose the parameter set with which you want to\n",
+    "  run spike sorting.\n",
+    "+ `task_mode` - Can be set to `load` or `trigger`. When set to `load`, running the\n",
+    "  Clustering step initiates a search for existing output files of the spike sorting\n",
+    "  algorithm defined in `ClusteringParamSet`. When set to `trigger`, the processing step\n",
+    "  will run spike sorting on the raw data."
    ]
   },
   {
@@ -2266,6 +2260,13 @@
     ")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's call populate on the `Clustering` table which checks for kilosort results since `task_mode=load`."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 28,