adding words to the nb

docs/examples/ytnapari_scene_04_timeseries.ipynb

@@ -7,9 +7,9 @@
    "source": [
     "## Timeseries analysis in yt_napari \n",
     "\n",
-    "Intro\n",
+    "`yt_napari` includes a number of ways to load in timeseries data. From a jupyter notebook, you can use `yt_napari.timeseries.add_to_viewer` to specify a set of files to sequentially load and sample and add to an existing `napari.Viewer`. \n",
     "\n",
-    "Create a selection object that gets applied to a series of files:"
+    "Unlike `yt` proper, you first instantiate a selection object **separate** from a dataset object. Right now, the otpions are a `Slice` or 3D `Region`:\n"
    ]
   },
   {
@@ -96,10 +96,11 @@
    "id": "edd2babf-5aae-4d2f-8079-96a68b594b22",
    "metadata": {},
    "source": [
-    "in order to allow for reading delayed arrays, yt_napari does not at present rely on yt's existing `DataSeries` object. \n",
+    "Once you create a `Slice` or `Region`, you can pass that to `add_to_viewer` and it will be used to sample each timestep specified. \n",
     "\n",
+    "## Slices through a timeseries\n",
     "\n",
-    "## Slices through a timeseries"
+    "To start, let's initialize a `napari` viewer"
    ]
   },
   {
@@ -122,6 +123,14 @@
     "v = napari.Viewer()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "f3ee393c-1bbe-40fc-b401-4d7862f899a8",
+   "metadata": {},
+   "source": [
+    "and let's build the `Slice` object that will get applied to each timestep. To do so, you need to at least specify the field to sample and the normal axis for the slice:"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 6,
@@ -914,7 +923,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.16"
+   "version": "3.9.0"
   }
  },
  "nbformat": 4,