From 200ac98e0f1a8aca97433709b14e17adbe965d23 Mon Sep 17 00:00:00 2001
From: robfatland <rob5@uw.edu>
Date: Tue, 13 Aug 2024 10:28:16 -0700
Subject: [PATCH] Joe

---
 book/_toc.yml                    |  2 +-
 book/chapters/data.ipynb         | 80 ++++++++++++++++++--------------
 book/chapters/oceanscience.ipynb |  5 +-
 3 files changed, 47 insertions(+), 40 deletions(-)

diff --git a/book/_toc.yml b/book/_toc.yml
index 0ba8b27..b13845d 100644
--- a/book/_toc.yml
+++ b/book/_toc.yml
@@ -15,7 +15,6 @@ parts:
 - caption: Ocean Data Science
   chapters:
     - file: chapters/oceanscience
-    - file: chapters/data
     - file: chapters/epipelargosy
     - file: chapters/anomaly
     - file: chapters/annotation
@@ -24,6 +23,7 @@ parts:
     - file: chapters/modis
     - file: chapters/roms
     - file: chapters/spectrophotometer
+    - file: chapters/data
     - file: chapters/dataloader
     - file: chapters/technicalnotes
     - file: chapters/documentation
diff --git a/book/chapters/data.ipynb b/book/chapters/data.ipynb
index f974219..2eaaf98 100644
--- a/book/chapters/data.ipynb
+++ b/book/chapters/data.ipynb
@@ -10,23 +10,32 @@
     "# Data\n",
     "\n",
     "\n",
+    "This chapter describes the initial observational data we associate \n",
+    "with the upper water column: As provided by the regional cabled array \n",
+    "'shallow profiler' systems. The enthusiastic reader is encouraged to \n",
+    "skip ahead to the next chapter and retain this chapter as a reference\n",
+    "if and when it is needed later. \n",
+    "\n",
+    "\n",
     "## Data types\n",
     "\n",
     "\n",
-    "There are two central data concepts driving the initial work here:\n",
+    "There are two central data *concepts*:\n",
     "\n",
     "\n",
-    "- Platform metadata: Marks when a profiler is at rest / ascending / descending.\n",
-    "- Sensor data: The temperature as a function of depth for a given profile.\n",
+    "- Platform metadata: A record of when a profiler is at rest / ascending / descending\n",
+    "- Sensor data: Temperature as a function of depth for a given profile, and so on across salinity and the many other sensors\n",
     "\n",
     "\n",
     "### Profile metadata\n",
     "\n",
     "\n",
     "Profiles are not acquired instantaneously. One profile takes on the order of an hour\n",
-    "as the Science Pod rises with positive buoyancy from a depth of 200 meters to near the surface;\n",
-    "and then is winched back down to its cradle on the shallow profiler platform. \n",
-    "Typically nine profiles run per day. As a baseline we can view each profile \n",
+    "as the Science Pod rises due to positive buoyancy from a depth of 200 meters to near the surface.\n",
+    "Once it reaches the top of its ascent, ideally within 10 meters of the surface, the platform\n",
+    "winch engages and it is drawn back down to its cradle on the shallow profiler platform at\n",
+    "a depth of roughly 200 meters.\n",
+    "Typically nine of these profiles run per day. We can view each profile \n",
     "as an *observation* of the state of the epipelagic water column.\n",
     "Correspondingly, charts of sensor data do not feature a time axis. Rather the vertical \n",
     "axis is depth with the surface at the top. The horizontal axis is then the sensor \n",
@@ -266,7 +275,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {
     "tags": []
    },
@@ -275,19 +284,39 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "\n",
+      "Jupyter Notebook running Python 3\n",
       "\n",
       "Jupyter Notebook running Python 3\n"
      ]
     },
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "ename": "FileNotFoundError",
+     "evalue": "[Errno 2] No such file or directory: b'/home/kilroy/oceanography/book/chapters/data/rca/sensors/osb/ctd_jan22_conductivity.nc'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "File \u001b[0;32m~/miniconda3/envs/oceanography/lib/python3.12/site-packages/xarray/backends/file_manager.py:211\u001b[0m, in \u001b[0;36mCachingFileManager._acquire_with_cache_info\u001b[0;34m(self, needs_lock)\u001b[0m\n\u001b[1;32m    210\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 211\u001b[0m     file \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_cache[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_key]\n\u001b[1;32m    212\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m:\n",
+      "File \u001b[0;32m~/miniconda3/envs/oceanography/lib/python3.12/site-packages/xarray/backends/lru_cache.py:56\u001b[0m, in \u001b[0;36mLRUCache.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m     55\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock:\n\u001b[0;32m---> 56\u001b[0m     value \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_cache[key]\n\u001b[1;32m     57\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_cache\u001b[38;5;241m.\u001b[39mmove_to_end(key)\n",
+      "\u001b[0;31mKeyError\u001b[0m: [<class 'netCDF4._netCDF4.Dataset'>, ('/home/kilroy/oceanography/book/chapters/data/rca/sensors/osb/ctd_jan22_conductivity.nc',), 'r', (('clobber', True), ('diskless', False), ('format', 'NETCDF4'), ('persist', False)), 'aeba5f41-b198-417e-bfc2-04b4fa646b79']",
+      "\nDuring handling of the above exception, another exception occurred:\n",
+      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[1], line 8\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mcharts\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;241m*\u001b[39m\n\u001b[1;32m      6\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mJupyter Notebook running Python \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mformat(sys\u001b[38;5;241m.\u001b[39mversion_info[\u001b[38;5;241m0\u001b[39m]))\n\u001b[0;32m----> 8\u001b[0m ProfilerDepthChart(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m2022-01-01\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m2022-01-03\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m./data/rca/sensors/osb/ctd_jan22_conductivity.nc\u001b[39m\u001b[38;5;124m'\u001b[39m)  \u001b[38;5;66;03m# index [0] is arbitrary; any dataset will include z data\u001b[39;00m\n\u001b[1;32m     10\u001b[0m \u001b[38;5;66;03m# Enable this code for a more expansive view of shallow profiler depth history.\u001b[39;00m\n\u001b[1;32m     11\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m: VisualizeProfiles(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mjan22\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;241m31\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m2022\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m01\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mJanuary\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mOregon Slope Base\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mosb\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mctd_jan22_conductivity.nc\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
+      "File \u001b[0;32m~/oceanography/book/chapters/charts.py:25\u001b[0m, in \u001b[0;36mProfilerDepthChart\u001b[0;34m(t0, t1, fnm)\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mProfilerDepthChart\u001b[39m(t0, t1, fnm):\n\u001b[1;32m     21\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m'''\u001b[39;00m\n\u001b[1;32m     22\u001b[0m \u001b[38;5;124;03m    This is a very hardcoded function that generates a two-day span of profiles with some\u001b[39;00m\n\u001b[1;32m     23\u001b[0m \u001b[38;5;124;03m    annotations indicating what is going on, particularly with midnight / noon profiles.\u001b[39;00m\n\u001b[1;32m     24\u001b[0m \u001b[38;5;124;03m    '''\u001b[39;00m\n\u001b[0;32m---> 25\u001b[0m     ds \u001b[38;5;241m=\u001b[39m xr\u001b[38;5;241m.\u001b[39mopen_dataset(fnm)\u001b[38;5;241m.\u001b[39msel(time\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mslice\u001b[39m(dt64(t0), dt64(t1)))           \u001b[38;5;66;03m# this is not profiler metadata. It is actual sensor data.\u001b[39;00m\n\u001b[1;32m     26\u001b[0m     fig, axs \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39msubplots(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m12\u001b[39m,\u001b[38;5;241m4\u001b[39m), tight_layout\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m     27\u001b[0m     axs\u001b[38;5;241m.\u001b[39mplot(ds\u001b[38;5;241m.\u001b[39mtime, ds\u001b[38;5;241m.\u001b[39mz, marker\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m'\u001b[39m, ms\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m11.\u001b[39m, color\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mk\u001b[39m\u001b[38;5;124m'\u001b[39m, mfc\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124m'\u001b[39m, linewidth\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m.0001\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
+      "File \u001b[0;32m~/miniconda3/envs/oceanography/lib/python3.12/site-packages/xarray/backends/api.py:566\u001b[0m, in \u001b[0;36mopen_dataset\u001b[0;34m(filename_or_obj, engine, chunks, cache, decode_cf, mask_and_scale, decode_times, decode_timedelta, use_cftime, concat_characters, decode_coords, drop_variables, inline_array, chunked_array_type, from_array_kwargs, backend_kwargs, **kwargs)\u001b[0m\n\u001b[1;32m    554\u001b[0m decoders \u001b[38;5;241m=\u001b[39m _resolve_decoders_kwargs(\n\u001b[1;32m    555\u001b[0m     decode_cf,\n\u001b[1;32m    556\u001b[0m     open_backend_dataset_parameters\u001b[38;5;241m=\u001b[39mbackend\u001b[38;5;241m.\u001b[39mopen_dataset_parameters,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    562\u001b[0m     decode_coords\u001b[38;5;241m=\u001b[39mdecode_coords,\n\u001b[1;32m    563\u001b[0m )\n\u001b[1;32m    565\u001b[0m overwrite_encoded_chunks \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124moverwrite_encoded_chunks\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m--> 566\u001b[0m backend_ds \u001b[38;5;241m=\u001b[39m backend\u001b[38;5;241m.\u001b[39mopen_dataset(\n\u001b[1;32m    567\u001b[0m     filename_or_obj,\n\u001b[1;32m    568\u001b[0m     drop_variables\u001b[38;5;241m=\u001b[39mdrop_variables,\n\u001b[1;32m    569\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mdecoders,\n\u001b[1;32m    570\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m    571\u001b[0m )\n\u001b[1;32m    572\u001b[0m ds \u001b[38;5;241m=\u001b[39m _dataset_from_backend_dataset(\n\u001b[1;32m    573\u001b[0m     backend_ds,\n\u001b[1;32m    574\u001b[0m     filename_or_obj,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    584\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m    585\u001b[0m )\n\u001b[1;32m    586\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ds\n",
+      "File \u001b[0;32m~/miniconda3/envs/oceanography/lib/python3.12/site-packages/xarray/backends/netCDF4_.py:590\u001b[0m, in \u001b[0;36mNetCDF4BackendEntrypoint.open_dataset\u001b[0;34m(self, filename_or_obj, mask_and_scale, decode_times, concat_characters, decode_coords, drop_variables, use_cftime, decode_timedelta, group, mode, format, clobber, diskless, persist, lock, autoclose)\u001b[0m\n\u001b[1;32m    569\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mopen_dataset\u001b[39m(  \u001b[38;5;66;03m# type: ignore[override]  # allow LSP violation, not supporting **kwargs\u001b[39;00m\n\u001b[1;32m    570\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    571\u001b[0m     filename_or_obj: \u001b[38;5;28mstr\u001b[39m \u001b[38;5;241m|\u001b[39m os\u001b[38;5;241m.\u001b[39mPathLike[Any] \u001b[38;5;241m|\u001b[39m BufferedIOBase \u001b[38;5;241m|\u001b[39m AbstractDataStore,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    587\u001b[0m     autoclose\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m    588\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Dataset:\n\u001b[1;32m    589\u001b[0m     filename_or_obj \u001b[38;5;241m=\u001b[39m _normalize_path(filename_or_obj)\n\u001b[0;32m--> 590\u001b[0m     store \u001b[38;5;241m=\u001b[39m NetCDF4DataStore\u001b[38;5;241m.\u001b[39mopen(\n\u001b[1;32m    591\u001b[0m         filename_or_obj,\n\u001b[1;32m    592\u001b[0m         mode\u001b[38;5;241m=\u001b[39mmode,\n\u001b[1;32m    593\u001b[0m         \u001b[38;5;28mformat\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mformat\u001b[39m,\n\u001b[1;32m    594\u001b[0m         group\u001b[38;5;241m=\u001b[39mgroup,\n\u001b[1;32m    595\u001b[0m         clobber\u001b[38;5;241m=\u001b[39mclobber,\n\u001b[1;32m    596\u001b[0m         diskless\u001b[38;5;241m=\u001b[39mdiskless,\n\u001b[1;32m    597\u001b[0m         persist\u001b[38;5;241m=\u001b[39mpersist,\n\u001b[1;32m    598\u001b[0m         lock\u001b[38;5;241m=\u001b[39mlock,\n\u001b[1;32m    599\u001b[0m         autoclose\u001b[38;5;241m=\u001b[39mautoclose,\n\u001b[1;32m    600\u001b[0m     )\n\u001b[1;32m    602\u001b[0m     store_entrypoint \u001b[38;5;241m=\u001b[39m StoreBackendEntrypoint()\n\u001b[1;32m    603\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m close_on_error(store):\n",
+      "File \u001b[0;32m~/miniconda3/envs/oceanography/lib/python3.12/site-packages/xarray/backends/netCDF4_.py:391\u001b[0m, in \u001b[0;36mNetCDF4DataStore.open\u001b[0;34m(cls, filename, mode, format, group, clobber, diskless, persist, lock, lock_maker, autoclose)\u001b[0m\n\u001b[1;32m    385\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mdict\u001b[39m(\n\u001b[1;32m    386\u001b[0m     clobber\u001b[38;5;241m=\u001b[39mclobber, diskless\u001b[38;5;241m=\u001b[39mdiskless, persist\u001b[38;5;241m=\u001b[39mpersist, \u001b[38;5;28mformat\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mformat\u001b[39m\n\u001b[1;32m    387\u001b[0m )\n\u001b[1;32m    388\u001b[0m manager \u001b[38;5;241m=\u001b[39m CachingFileManager(\n\u001b[1;32m    389\u001b[0m     netCDF4\u001b[38;5;241m.\u001b[39mDataset, filename, mode\u001b[38;5;241m=\u001b[39mmode, kwargs\u001b[38;5;241m=\u001b[39mkwargs\n\u001b[1;32m    390\u001b[0m )\n\u001b[0;32m--> 391\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mcls\u001b[39m(manager, group\u001b[38;5;241m=\u001b[39mgroup, mode\u001b[38;5;241m=\u001b[39mmode, lock\u001b[38;5;241m=\u001b[39mlock, autoclose\u001b[38;5;241m=\u001b[39mautoclose)\n",
+      "File \u001b[0;32m~/miniconda3/envs/oceanography/lib/python3.12/site-packages/xarray/backends/netCDF4_.py:338\u001b[0m, in \u001b[0;36mNetCDF4DataStore.__init__\u001b[0;34m(self, manager, group, mode, lock, autoclose)\u001b[0m\n\u001b[1;32m    336\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_group \u001b[38;5;241m=\u001b[39m group\n\u001b[1;32m    337\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mode \u001b[38;5;241m=\u001b[39m mode\n\u001b[0;32m--> 338\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mformat \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mds\u001b[38;5;241m.\u001b[39mdata_model\n\u001b[1;32m    339\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_filename \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mds\u001b[38;5;241m.\u001b[39mfilepath()\n\u001b[1;32m    340\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mis_remote \u001b[38;5;241m=\u001b[39m is_remote_uri(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_filename)\n",
+      "File \u001b[0;32m~/miniconda3/envs/oceanography/lib/python3.12/site-packages/xarray/backends/netCDF4_.py:400\u001b[0m, in \u001b[0;36mNetCDF4DataStore.ds\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    398\u001b[0m \u001b[38;5;129m@property\u001b[39m\n\u001b[1;32m    399\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mds\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m--> 400\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_acquire()\n",
+      "File \u001b[0;32m~/miniconda3/envs/oceanography/lib/python3.12/site-packages/xarray/backends/netCDF4_.py:394\u001b[0m, in \u001b[0;36mNetCDF4DataStore._acquire\u001b[0;34m(self, needs_lock)\u001b[0m\n\u001b[1;32m    393\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_acquire\u001b[39m(\u001b[38;5;28mself\u001b[39m, needs_lock\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[0;32m--> 394\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_manager\u001b[38;5;241m.\u001b[39macquire_context(needs_lock) \u001b[38;5;28;01mas\u001b[39;00m root:\n\u001b[1;32m    395\u001b[0m         ds \u001b[38;5;241m=\u001b[39m _nc4_require_group(root, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_group, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mode)\n\u001b[1;32m    396\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m ds\n",
+      "File \u001b[0;32m~/miniconda3/envs/oceanography/lib/python3.12/contextlib.py:137\u001b[0m, in \u001b[0;36m_GeneratorContextManager.__enter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    135\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39margs, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkwds, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfunc\n\u001b[1;32m    136\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 137\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mnext\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgen)\n\u001b[1;32m    138\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mStopIteration\u001b[39;00m:\n\u001b[1;32m    139\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgenerator didn\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt yield\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n",
+      "File \u001b[0;32m~/miniconda3/envs/oceanography/lib/python3.12/site-packages/xarray/backends/file_manager.py:199\u001b[0m, in \u001b[0;36mCachingFileManager.acquire_context\u001b[0;34m(self, needs_lock)\u001b[0m\n\u001b[1;32m    196\u001b[0m \u001b[38;5;129m@contextlib\u001b[39m\u001b[38;5;241m.\u001b[39mcontextmanager\n\u001b[1;32m    197\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21macquire_context\u001b[39m(\u001b[38;5;28mself\u001b[39m, needs_lock\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[1;32m    198\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Context manager for acquiring a file.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 199\u001b[0m     file, cached \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_acquire_with_cache_info(needs_lock)\n\u001b[1;32m    200\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    201\u001b[0m         \u001b[38;5;28;01myield\u001b[39;00m file\n",
+      "File \u001b[0;32m~/miniconda3/envs/oceanography/lib/python3.12/site-packages/xarray/backends/file_manager.py:217\u001b[0m, in \u001b[0;36mCachingFileManager._acquire_with_cache_info\u001b[0;34m(self, needs_lock)\u001b[0m\n\u001b[1;32m    215\u001b[0m     kwargs \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mcopy()\n\u001b[1;32m    216\u001b[0m     kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmode\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mode\n\u001b[0;32m--> 217\u001b[0m file \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_opener(\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_args, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m    218\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mode \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mw\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m    219\u001b[0m     \u001b[38;5;66;03m# ensure file doesn't get overridden when opened again\u001b[39;00m\n\u001b[1;32m    220\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mode \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124ma\u001b[39m\u001b[38;5;124m\"\u001b[39m\n",
+      "File \u001b[0;32msrc/netCDF4/_netCDF4.pyx:2463\u001b[0m, in \u001b[0;36mnetCDF4._netCDF4.Dataset.__init__\u001b[0;34m()\u001b[0m\n",
+      "File \u001b[0;32msrc/netCDF4/_netCDF4.pyx:2026\u001b[0m, in \u001b[0;36mnetCDF4._netCDF4._ensure_nc_success\u001b[0;34m()\u001b[0m\n",
+      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: b'/home/kilroy/oceanography/book/chapters/data/rca/sensors/osb/ctd_jan22_conductivity.nc'"
+     ]
     }
    ],
    "source": [
@@ -993,27 +1022,6 @@
     "ds   = xr.Dataset(dict(zip(df.columns, vals)), attrs=ds.attrs)\n",
     "```"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
diff --git a/book/chapters/oceanscience.ipynb b/book/chapters/oceanscience.ipynb
index e832bb0..f8ee8b2 100644
--- a/book/chapters/oceanscience.ipynb
+++ b/book/chapters/oceanscience.ipynb
@@ -15,9 +15,7 @@
     "> But I now leave my cetological System standing thus unfinished, even as the great Cathedral of Cologne was left, with the crane still standing upon the top of the uncompleted tower. \\[For small monuments\\] may be finished by their first architects; grand ones, true ones, ever leave the copestone to posterity. God keep me from ever completing anything. This whole book is but a draught—nay, but the draught of a draught. Oh, Time, Strength, Cash, and Patience! <br><br> -Herman Melville\n",
     "\n",
     "\n",
-    "**Temporary note: I need to sort out markdown images working both for Jupyter Notebooks *and* \n",
-    "the Jupyter Book. Until then I resort to a double version which produces a broken link icon\n",
-    "in the Book.**\n",
+    "**Note: until images inline for both the Jupyter Notebook *and* the Jupyter Book: I will double up.**\n",
     "\n",
     "\n",
     "```{figure} ../img/revelle.jpg\n",
@@ -33,6 +31,7 @@
     "<div style=\"clear: left\">\n",
     "<BR>\n",
     "\n",
+    "\n",
     "## Science basis\n",
     "\n",
     "\n",