diff --git a/book/_toc.yml b/book/_toc.yml
index 72c7030..3965032 100644
--- a/book/_toc.yml
+++ b/book/_toc.yml
@@ -16,12 +16,9 @@ parts:
   chapters:
     - file: chapters/ocean_science
     - file: chapters/data
-    - file: chapters/shallowprofiler
+    - file: chapters/epipelargosy
     - file: chapters/anomaly
-    - file: chapters/anomaly2
-    - file: chapters/anomaly3
     - file: chapters/annotation
-    - file: chapters/annotation2
     - file: chapters/argo
     - file: chapters/glodap
     - file: chapters/modis
diff --git a/book/chapters/anomaly.ipynb b/book/chapters/anomaly_and_coincidence.ipynb
similarity index 99%
rename from book/chapters/anomaly.ipynb
rename to book/chapters/anomaly_and_coincidence.ipynb
index 8865483..e99e957 100644
--- a/book/chapters/anomaly.ipynb
+++ b/book/chapters/anomaly_and_coincidence.ipynb
@@ -9,7 +9,7 @@
     "\n",
     "\n",
     "\n",
-    "# Anomaly\n",
+    "# Anomaly and Coincidence\n",
     "\n",
     "\n",
     "This notebook generates anomaly metadata for an OOI RCA shallow profiler, typically scanning a time series lasting\n",
@@ -822,7 +822,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.3"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,
diff --git a/book/chapters/data.ipynb b/book/chapters/data.ipynb
index 3453903..270a617 100644
--- a/book/chapters/data.ipynb
+++ b/book/chapters/data.ipynb
@@ -7,25 +7,27 @@
     "[Jupyter Book](https://geo-smart.github.io/oceanography/intro.html) and [GitHub repo](https://github.com/geo-smart/oceanography).\n",
     "\n",
     "\n",
+    "> Author's note: This chapter introduces the mechanics of data access: Profiles and sensors. \n",
+    "\n",
     "# Data\n",
     "\n",
     "\n",
     "## Data types\n",
     "\n",
     "\n",
-    "There are two central data type concepts in this work:\n",
+    "There are two central data concepts driving the initial work here:\n",
     "\n",
     "\n",
-    "- Platform metadata: When a profiler is ascending / descending / at rest?\n",
-    "- Sensor data: What is the temperature as a function of depth for a given profile?\n",
+    "- Platform metadata: When a profiler is at rest / ascending / descending.\n",
+    "- Sensor data: The temperature as a function of depth for a given profile.\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 about 180 meters to near the surface\n",
-    "and then is winched back down to the shallow profiler platform. \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 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",
@@ -33,17 +35,17 @@
     "parameter: Temperature, salinity, dissolved oxygen, fluorescence etcetera.\n",
     "\n",
     "\n",
-    "Isolating sensor data from a particular profile is a necessary task. This is done\n",
-    "in terms of time boundaries, typically start of ascent to end of ascent. This information\n",
-    "in stored as profile timestamp metadata. (Sensor data includes timestamps and depth but\n",
-    "no information on the state of the science pod.) \n",
-    "A single profiles is viewed as three consecutive stages: Rest, Ascent, and Descent.\n",
-    "The Rest stage consists of the Science Pod parked at the platform at a depth of 200 meters. \n",
+    "Isolating sensor time-series data to a particular profile time interval is a necessary task. \n",
+    "This is done using bounding times, typically start of ascent to end of ascent. This information\n",
+    "in stored as a table of profile metadata timestamps. (Sensor data includes timestamps and depth \n",
+    "but no information on the state of the science pod.) \n",
+    "A single profile is viewed in three consecutive stages: Rest, Ascent, and Descent.\n",
+    "The Rest stage consists of the Science Pod parked on the platform at a depth of 200 meters. \n",
     "\n",
     "\n",
-    "Profile stage metadata describing the timestamps is stored in a CSV file in this\n",
-    "repository. That information is read in to memory as a pandas DataFrame for reference \n",
-    "in time-boxing profiles.\n",
+    "Profile stage metadata describing the timestamps is stored as CSV files in this\n",
+    "repository, sorted by time range and location. That information is read in \n",
+    "as a pandas DataFrame for use in time-boxing profiles.\n",
     "\n",
     "\n",
     "### Sensor data\n",
@@ -77,16 +79,16 @@
     "        - Coastal Endurance \"Oregon Offshore\" in 500m depth, distal edge of the continental shelf\n",
     "        - RCA \"Oregon Slope Base\" at 3000m depth, bottom of the continental shelf break\n",
     "        - RCA \"Axial Base\" at 2100m depth, further out in the Pacific at the base of Axial Seamount\n",
-    "- Moderate-size datasets can be stored in this repository\n",
+    "- Moderate-size datasets can be stored in this repository (upper limit about 50MB)\n",
     "- Large datasets can be saved external to the repository\n",
-    "    - GitHub recommends a maximum file size of 50MB\n",
-    "- We can take advantage of virtual or *lazy* loading of data structures\n",
-    "    - The data are not literally read into memory from local storage\n",
-    "    - Rather a description of the data is read into memory from remote or external storage\n",
-    "        - This allows for virtual data reduction before actual data is read\n",
-    "        - There are two means of reducing data volume\n",
-    "            - Strip away excess data that is not of immediate interest\n",
-    "            - sub-sample profile data at different vertical resolutions"
+    "- We use virtual or *lazy* loading of data structures using the Python `xarray` library\n",
+    "    - When a data file is 'read' the data are literally *not* read into memory\n",
+    "        - Rather a description of the data is loaded into an XArray Dataset\n",
+    "        - Operations on the data cause it to be *actually* read into memory\n",
+    "            - Lazy loading facilitates data reduction:\n",
+    "                - Strip away excess data elements that are not of interest\n",
+    "                - Time-box a subset of the data\n",
+    "                - Sub-sample the data to lower temporal / spatial resolution"
    ]
   },
   {
@@ -118,7 +120,7 @@
     "```\n",
     "\n",
     "\n",
-    "### Data systems \n",
+    "### Data resources \n",
     "\n",
     "\n",
     "Shallow profiler data was originally pulled from the [OOI Data Explorer](https://dataexplorer.oceanobservatories.org/).\n",
@@ -239,7 +241,7 @@
     "## Profiles\n",
     "\n",
     "\n",
-    "This section describes loading profile metadata: Pre-generated and stored in this \n",
+    "This section describes profile metadata: Pre-generated and stored in this \n",
     "repository at relative path `./data/rca/profiles/<site-abbrev>`. \n",
     "\n",
     "\n",
@@ -254,7 +256,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 53,
    "metadata": {
     "tags": []
    },
@@ -263,22 +265,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\n",
-      "Jupyter Notebook running Python 3\n",
       "\n",
       "Jupyter Notebook running Python 3\n"
      ]
     },
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
     {
      "data": {
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",
@@ -299,7 +289,7 @@
     "\n",
     "\n",
     "def ProfilerDepthChart(t0, t1, fnm):\n",
-    "    ds = xr.open_dataset(fnm).sel(time=slice(dt64(t0), dt64(t1)))\n",
+    "    ds = xr.open_dataset(fnm).sel(time=slice(dt64(t0), dt64(t1)))           # this is not profiler metadata. It is actual sensor data.\n",
     "    fig, axs = plt.subplots(figsize=(12,4), tight_layout=True)\n",
     "    axs.plot(ds.time, ds.z, marker='.', ms=11., color='k', mfc='r', linewidth='.0001')\n",
     "    axs.set(ylim = (-210., 0.), title='Shallow profiler depth over two days', ylabel='depth (m)', xlabel='Hours (UTM)')\n",
@@ -314,7 +304,27 @@
     "    return True\n",
     "    \n",
     "\n",
-    "ProfilerDepthChart('2022-01-01', '2022-01-03', './data/rca/sensors/osb/ctd_jan22_conductivity.nc')  # index [0] is arbitrary; any dataset will include z data"
+    "ProfilerDepthChart('2022-01-01', '2022-01-03', './data/rca/sensors/osb/ctd_jan22_conductivity.nc')  # index [0] is arbitrary; any dataset will include z data\n",
+    "\n",
+    "\n",
+    "def VisualizeProfiles(date_id, n_days, year_id, month_id, month_name, site_name):\n",
+    "    ds = xr.open_dataset('./data/rca/sensors/osb/ctd_jan22_conductivity.nc')\n",
+    "    fig, axs = plt.subplots(n_days, 1, figsize=(15,n_days), tight_layout=True)\n",
+    "\n",
+    "    for i in range(n_days):\n",
+    "        daystring = str(i+1) if i > 8 else '0' + str(i+1)\n",
+    "        time0 = dt64(year_id + '-' + month_id + '-' + daystring + 'T00:00:00')\n",
+    "        time1 = dt64(year_id + '-' + month_id + '-' + daystring + 'T23:59:59')\n",
+    "        dsDay = ds.sel(time=slice(time0, time1))\n",
+    "        axs[i].plot(dsDay.time, dsDay.z, marker='.', markersize=3., color='k')\n",
+    "        axs[i].set(ylim = (-200., 0.))\n",
+    "\n",
+    "    print('...' + month_name + ' ' + str(year_id) + ' ' + site_name + ' daily profiles...')\n",
+    "    return True\n",
+    "\n",
+    "\n",
+    "# Enable this code for a more expansive view of the January 2022 Oregon Slope Base shallow profiler depth history.\n",
+    "if False: VisualizeProfiles('jan22', 31, '2022', '01', 'January', 'Oregon Slope Base')\n"
    ]
   },
   {
@@ -339,9 +349,305 @@
     "for examples.\n"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Comment on profile phase durations\n",
+    "\n",
+    "\n",
+    "```\n",
+    "  Ascents:   67.4 +-3.0  minutes\n",
+    "  Descents:  vary depending: midnight/noon descents are slow (70 minutes); otherwise quick (20 minutes)\n",
+    "  Rests:     vary with descent, 30 / 60 minutes\n",
+    "```\n",
+    "\n",
+    "### Example use of profile metadata\n",
+    "\n",
+    "\n",
+    "The following cell reads January 2022 Oregon Slope Base metadata into a pandas DataFrame 'profiles'.\n",
+    "In so doing it converts the information from text to pandas Timestamps. The rows of the resulting \n",
+    "DataFrame correspond to consecutive profiles. The columns correspond to (time depth)\n",
+    "pairs: r0t is rest start time. r0z is depth at that time. r1t is rest end time.\n",
+    "r1z is depth at that time (typically close to r0z). Following this are a0, a1 for \n",
+    "ascent and d0, d1 for descent. There is some degeneracy in this metadata as the \n",
+    "end of the ascent corresponds to the start of the descent.\n",
+    "\n",
+    "January has 31 days so there are a maximum of 279 possible profiles."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 54,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>r0t</th>\n",
+       "      <th>r0z</th>\n",
+       "      <th>r1t</th>\n",
+       "      <th>r1z</th>\n",
+       "      <th>a0t</th>\n",
+       "      <th>a0z</th>\n",
+       "      <th>a1t</th>\n",
+       "      <th>a1z</th>\n",
+       "      <th>d0t</th>\n",
+       "      <th>d0z</th>\n",
+       "      <th>d1t</th>\n",
+       "      <th>d1z</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2022-01-01 00:00:00</td>\n",
+       "      <td>-191.0</td>\n",
+       "      <td>2022-01-01 00:22:00</td>\n",
+       "      <td>-191.0</td>\n",
+       "      <td>2022-01-01 00:22:00</td>\n",
+       "      <td>-191.0</td>\n",
+       "      <td>2022-01-01 01:32:00</td>\n",
+       "      <td>-19.0</td>\n",
+       "      <td>2022-01-01 01:32:00</td>\n",
+       "      <td>-19.0</td>\n",
+       "      <td>2022-01-01 02:11:00</td>\n",
+       "      <td>-194.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2022-01-01 02:11:00</td>\n",
+       "      <td>-194.0</td>\n",
+       "      <td>2022-01-01 02:37:00</td>\n",
+       "      <td>-194.0</td>\n",
+       "      <td>2022-01-01 02:37:00</td>\n",
+       "      <td>-194.0</td>\n",
+       "      <td>2022-01-01 03:15:00</td>\n",
+       "      <td>-105.0</td>\n",
+       "      <td>2022-01-01 03:15:00</td>\n",
+       "      <td>-105.0</td>\n",
+       "      <td>2022-01-01 03:38:00</td>\n",
+       "      <td>-196.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2022-01-01 03:38:00</td>\n",
+       "      <td>-196.0</td>\n",
+       "      <td>2022-01-01 04:47:00</td>\n",
+       "      <td>-196.0</td>\n",
+       "      <td>2022-01-01 04:47:00</td>\n",
+       "      <td>-196.0</td>\n",
+       "      <td>2022-01-01 05:57:00</td>\n",
+       "      <td>-24.0</td>\n",
+       "      <td>2022-01-01 05:57:00</td>\n",
+       "      <td>-24.0</td>\n",
+       "      <td>2022-01-01 06:36:00</td>\n",
+       "      <td>-196.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2022-01-01 06:36:00</td>\n",
+       "      <td>-196.0</td>\n",
+       "      <td>2022-01-01 07:17:00</td>\n",
+       "      <td>-194.0</td>\n",
+       "      <td>2022-01-01 07:17:00</td>\n",
+       "      <td>-194.0</td>\n",
+       "      <td>2022-01-01 08:30:00</td>\n",
+       "      <td>-12.0</td>\n",
+       "      <td>2022-01-01 08:30:00</td>\n",
+       "      <td>-12.0</td>\n",
+       "      <td>2022-01-01 10:12:00</td>\n",
+       "      <td>-192.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2022-01-01 10:12:00</td>\n",
+       "      <td>-192.0</td>\n",
+       "      <td>2022-01-01 11:07:00</td>\n",
+       "      <td>-193.0</td>\n",
+       "      <td>2022-01-01 11:07:00</td>\n",
+       "      <td>-193.0</td>\n",
+       "      <td>2022-01-01 12:17:00</td>\n",
+       "      <td>-23.0</td>\n",
+       "      <td>2022-01-01 12:17:00</td>\n",
+       "      <td>-23.0</td>\n",
+       "      <td>2022-01-01 12:56:00</td>\n",
+       "      <td>-198.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>274</th>\n",
+       "      <td>2022-01-31 12:53:00</td>\n",
+       "      <td>-190.0</td>\n",
+       "      <td>2022-01-31 11:07:00</td>\n",
+       "      <td>-191.0</td>\n",
+       "      <td>2022-01-31 11:07:00</td>\n",
+       "      <td>-191.0</td>\n",
+       "      <td>2022-01-31 12:15:00</td>\n",
+       "      <td>-23.0</td>\n",
+       "      <td>2022-01-31 12:15:00</td>\n",
+       "      <td>-23.0</td>\n",
+       "      <td>2022-01-31 15:15:00</td>\n",
+       "      <td>-191.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>275</th>\n",
+       "      <td>2022-01-31 15:15:00</td>\n",
+       "      <td>-191.0</td>\n",
+       "      <td>2022-01-31 13:27:00</td>\n",
+       "      <td>-190.0</td>\n",
+       "      <td>2022-01-31 13:27:00</td>\n",
+       "      <td>-190.0</td>\n",
+       "      <td>2022-01-31 14:36:00</td>\n",
+       "      <td>-20.0</td>\n",
+       "      <td>2022-01-31 14:36:00</td>\n",
+       "      <td>-20.0</td>\n",
+       "      <td>2022-01-31 17:31:00</td>\n",
+       "      <td>-193.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>276</th>\n",
+       "      <td>2022-01-31 17:31:00</td>\n",
+       "      <td>-193.0</td>\n",
+       "      <td>2022-01-31 15:42:00</td>\n",
+       "      <td>-191.0</td>\n",
+       "      <td>2022-01-31 15:42:00</td>\n",
+       "      <td>-191.0</td>\n",
+       "      <td>2022-01-31 16:52:00</td>\n",
+       "      <td>-20.0</td>\n",
+       "      <td>2022-01-31 16:52:00</td>\n",
+       "      <td>-20.0</td>\n",
+       "      <td>2022-01-31 19:56:00</td>\n",
+       "      <td>-193.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>277</th>\n",
+       "      <td>2022-01-31 19:56:00</td>\n",
+       "      <td>-193.0</td>\n",
+       "      <td>2022-01-31 18:07:00</td>\n",
+       "      <td>-193.0</td>\n",
+       "      <td>2022-01-31 18:07:00</td>\n",
+       "      <td>-193.0</td>\n",
+       "      <td>2022-01-31 19:17:00</td>\n",
+       "      <td>-20.0</td>\n",
+       "      <td>2022-01-31 19:17:00</td>\n",
+       "      <td>-20.0</td>\n",
+       "      <td>2022-01-31 23:32:00</td>\n",
+       "      <td>-193.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>278</th>\n",
+       "      <td>2022-01-31 23:32:00</td>\n",
+       "      <td>-193.0</td>\n",
+       "      <td>2022-01-31 20:37:00</td>\n",
+       "      <td>-194.0</td>\n",
+       "      <td>2022-01-31 20:37:00</td>\n",
+       "      <td>-194.0</td>\n",
+       "      <td>2022-01-31 21:50:00</td>\n",
+       "      <td>-15.0</td>\n",
+       "      <td>2022-01-31 21:50:00</td>\n",
+       "      <td>-15.0</td>\n",
+       "      <td>2022-01-31 23:32:00</td>\n",
+       "      <td>-193.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>279 rows × 12 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                    r0t    r0z                 r1t    r1z                 a0t  \\\n",
+       "0   2022-01-01 00:00:00 -191.0 2022-01-01 00:22:00 -191.0 2022-01-01 00:22:00   \n",
+       "1   2022-01-01 02:11:00 -194.0 2022-01-01 02:37:00 -194.0 2022-01-01 02:37:00   \n",
+       "2   2022-01-01 03:38:00 -196.0 2022-01-01 04:47:00 -196.0 2022-01-01 04:47:00   \n",
+       "3   2022-01-01 06:36:00 -196.0 2022-01-01 07:17:00 -194.0 2022-01-01 07:17:00   \n",
+       "4   2022-01-01 10:12:00 -192.0 2022-01-01 11:07:00 -193.0 2022-01-01 11:07:00   \n",
+       "..                  ...    ...                 ...    ...                 ...   \n",
+       "274 2022-01-31 12:53:00 -190.0 2022-01-31 11:07:00 -191.0 2022-01-31 11:07:00   \n",
+       "275 2022-01-31 15:15:00 -191.0 2022-01-31 13:27:00 -190.0 2022-01-31 13:27:00   \n",
+       "276 2022-01-31 17:31:00 -193.0 2022-01-31 15:42:00 -191.0 2022-01-31 15:42:00   \n",
+       "277 2022-01-31 19:56:00 -193.0 2022-01-31 18:07:00 -193.0 2022-01-31 18:07:00   \n",
+       "278 2022-01-31 23:32:00 -193.0 2022-01-31 20:37:00 -194.0 2022-01-31 20:37:00   \n",
+       "\n",
+       "       a0z                 a1t    a1z                 d0t    d0z  \\\n",
+       "0   -191.0 2022-01-01 01:32:00  -19.0 2022-01-01 01:32:00  -19.0   \n",
+       "1   -194.0 2022-01-01 03:15:00 -105.0 2022-01-01 03:15:00 -105.0   \n",
+       "2   -196.0 2022-01-01 05:57:00  -24.0 2022-01-01 05:57:00  -24.0   \n",
+       "3   -194.0 2022-01-01 08:30:00  -12.0 2022-01-01 08:30:00  -12.0   \n",
+       "4   -193.0 2022-01-01 12:17:00  -23.0 2022-01-01 12:17:00  -23.0   \n",
+       "..     ...                 ...    ...                 ...    ...   \n",
+       "274 -191.0 2022-01-31 12:15:00  -23.0 2022-01-31 12:15:00  -23.0   \n",
+       "275 -190.0 2022-01-31 14:36:00  -20.0 2022-01-31 14:36:00  -20.0   \n",
+       "276 -191.0 2022-01-31 16:52:00  -20.0 2022-01-31 16:52:00  -20.0   \n",
+       "277 -193.0 2022-01-31 19:17:00  -20.0 2022-01-31 19:17:00  -20.0   \n",
+       "278 -194.0 2022-01-31 21:50:00  -15.0 2022-01-31 21:50:00  -15.0   \n",
+       "\n",
+       "                    d1t    d1z  \n",
+       "0   2022-01-01 02:11:00 -194.0  \n",
+       "1   2022-01-01 03:38:00 -196.0  \n",
+       "2   2022-01-01 06:36:00 -196.0  \n",
+       "3   2022-01-01 10:12:00 -192.0  \n",
+       "4   2022-01-01 12:56:00 -198.0  \n",
+       "..                  ...    ...  \n",
+       "274 2022-01-31 15:15:00 -191.0  \n",
+       "275 2022-01-31 17:31:00 -193.0  \n",
+       "276 2022-01-31 19:56:00 -193.0  \n",
+       "277 2022-01-31 23:32:00 -193.0  \n",
+       "278 2022-01-31 23:32:00 -193.0  \n",
+       "\n",
+       "[279 rows x 12 columns]"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from shallowprofiler import *\n",
+    "from charts import *\n",
+    "\n",
+    "profiles = ReadProfileMetadata()\n",
+    "profiles"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
    "metadata": {
     "tags": []
    },
@@ -350,24 +656,41 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "...January 2022 Oregon Slope Base daily profiles...\n"
+      "2022-01-01 00:22:00 2022-01-01 01:32:00 Time difference: 0 days 01:10:00\n",
+      "Type of these times: <class 'pandas._libs.tslibs.timestamps.Timestamp'>\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "# profile time access syntax:\n",
+    "profile_row_index = 0\n",
+    "tA0, tA1 = profiles['a0t'][profile_row_index], profiles['a1t'][profile_row_index]\n",
+    "print(tA0, tA1, 'Time difference:', tA1 - tA0)\n",
+    "print('Type of these times:', type(tA0))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
     {
      "data": {
       "text/plain": [
-       "True"
+       "[<matplotlib.lines.Line2D at 0x7f4b49e323d0>]"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 56,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 1500x300 with 3 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -375,41 +698,9 @@
     }
    ],
    "source": [
-    "#### visual on one month of profiles\n",
-    "# !!!!! hard coded flag\n",
-    "# !!!!! flag move to charts\n",
-    "\n",
-    "def VisualizeProfiles(date_id, n_days, year_id, month_id, month_name, site_name):\n",
-    "    ds = xr.open_dataset('./data/rca/sensors/osb/ctd_jan22_conductivity.nc')\n",
-    "    fig, axs = plt.subplots(n_days, 1, figsize=(15,n_days), tight_layout=True)\n",
-    "\n",
-    "    for i in range(n_days):\n",
-    "        daystring = str(i+1) if i > 8 else '0' + str(i+1)\n",
-    "        time0 = dt64(year_id + '-' + month_id + '-' + daystring + 'T00:00:00')\n",
-    "        time1 = dt64(year_id + '-' + month_id + '-' + daystring + 'T23:59:59')\n",
-    "        dsDay = ds.sel(time=slice(time0, time1))\n",
-    "        axs[i].plot(dsDay.time, dsDay.z, marker='.', markersize=3., color='k')\n",
-    "        axs[i].set(ylim = (-200., 0.))\n",
-    "\n",
-    "    print('...' + month_name + ' ' + str(year_id) + ' ' + site_name + ' daily profiles...')\n",
-    "    return True\n",
-    "\n",
-    "\n",
-    "VisualizeProfiles('jan22', 3, '2022', '01', 'January', 'Oregon Slope Base')\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Comment on profile phase durations\n",
-    "\n",
-    "\n",
-    "```\n",
-    "  Ascents:   67.4 +-3.0  minutes\n",
-    "  Descents:  vary depending: midnight/noon descents are slow (70 minutes); otherwise quick (20 minutes)\n",
-    "  Rests:     vary with descent, 30 / 60 minutes\n",
-    "```\n"
+    "ds = xr.open_dataset('./data/rca/sensors/osb/ctd_jan22_conductivity.nc')\n",
+    "ds_timebox = ds.sel(time=slice(tA0, tA1))\n",
+    "ds_timebox.conductivity.plot()"
    ]
   },
   {
@@ -419,14 +710,14 @@
     "## Sensor Data\n",
     "\n",
     "\n",
-    "This section connects to existing sensor datasets, data found within the repository corresponding to\n",
-    "the Oregon Slope Base ('osb') site during January 2022. \n",
+    "In the preceding section we accessed some conductivity data from Oregon Slope Base circa\n",
+    "January 2022. This section connects more formally to these existing sensor datasets.\n",
     "\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 57,
    "metadata": {
     "tags": []
    },
@@ -440,7 +731,6 @@
     }
    ],
    "source": [
-    "\n",
     "base_path = './data/rca/sensors/'\n",
     "date_id   = 'jan22'\n",
     "site_id   = 'osb'\n",
@@ -485,7 +775,135 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# This cell assembles a data dictionary \"d\" from the OOI datasets\n",
+    "\n",
+    "d = {}                 # empty Python dictionary\n",
+    "\n",
+    "relative_path = './data/rca/sensors/'\n",
+    "sitestring = 'osb'                    # available: osb = Oregon Slope Base\n",
+    "monthstring = 'jan22'                 # available: apr21, jul21, jan22\n",
+    "\n",
+    "def GetSensorTuple(s, f):\n",
+    "    '''\n",
+    "    s is the sensor identifier string like 'temperature'\n",
+    "        It is actually a 2-element list: [0] is the sensor, [1] is the instrument\n",
+    "    f is the source filename like './../data/osb_ctd_jan22_temperature.nc' \n",
+    "    '''\n",
+    "    df_sensor    = xr.open_dataset(f)[s]\n",
+    "    df_z         = xr.open_dataset(f)['z']\n",
+    "    range_lo     = ranges[s][0]\n",
+    "    range_hi     = ranges[s][1]\n",
+    "    sensor_color = colors[s]\n",
+    "    return (df_sensor, df_z, range_lo, range_hi, sensor_color)\n",
+    "\n",
+    "for sensor in sensors:          # sensor is a list of 2 strings [sensor_str, instrument_str]\n",
+    "    f = relative_path + sitestring + '/' + sensor[1] + '_' + monthstring + '_' + sensor[0] + '.nc'\n",
+    "    d[sensor[0]] = GetSensorTuple(sensor[0], f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Attempting 1 charts\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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F/vTc77//jre3N+3atSMxMdE01KxZE39//3R3ttSsWZOgoCDTdOXKlQGtaTv1tSHG+adOnUr3ni+99JLZdJcuXQBMTX2///47BoOBl19+2Swmf39/atSokS4mHx8fi9dqHD9+nC5duuDv74+9vT2Ojo5EREQA2nUBeeH55583m85uXdI6f/48BoMBX19fi8vbt2+Po6OjafrYsWMcPnzY9DdO/Z5PP/00Fy5c4N9//wW0v7exadjI3t6eTp065aTqFt25c4etW7fSoUMHPDw8zN6na9eunD171hRP8+bNuXfvHps3bwa0U24tWrTgySefNDUpG087PfnkkwBs3ryZ69ev061bN7O6Jicn07p1a7Zv357u1EzafZSZxo0bM3r0aCZNmsQbb7zByy+/TI8ePczKpH7fxMREs9OG06ZNo3bt2ri4uODg4ICjoyOrV69O9/krUaIECxYsYNeuXTRq1IiyZcsybdq0LOOrX78+M2fO5JNPPmHLli2ZnlotVaoU586dy3Kb0dHRtG/fnhIlSpiOm1deeYWkpCSOHDliVtbf35/69eubzatevbrZcb9mzRqqVKlCjRo1zMoZj/uslChRgg0bNrB9+3bGjh3LM888w5EjRxg6dCjVqlUzO2WUF8d8mzZtsLe3N6sfWP5us0ZoaCht27ZlypQpps/KTz/9xLVr1+jfv3+m6xpPn5UqVcps/ubNm4mNjaVv376ZXmPyxhtvAPD999+b5n399ddUq1aNxx9/3DTv8uXL9OnThzJlypg+t8HBwYDlv2P79u3NpqtXr879+/e5fPlypvXJDRl9tmJjY9m1a1e6+an/PsHBwTRq1Mj025Od70+j7HyfVKhQge+//54lS5bQtm1bmjRpku4GpMy+T3755RcaN26Mh4eHab9Mnz493T5xdnbm559/5tq1a9SuXRulFPPmzTP7HFtSv359/vrrL4YMGcLatWu5d+9ehmWt/T5Jq8AnTZcuXeLmzZs4OTnh6OhoNly8eDHdOezixYubTTs5OWU6//79+2bzHRwcKFGihNk8f39/QLt2wRiTUgo/P790MW3ZsiVdTAEBAenqdfv2bZo0acLWrVv55JNPWLt2Ldu3b2fx4sUAmX4YHkbaWLJbl7Tu3buHo6Njhh92S+8H2h0Oad+vb9++AKb3vHbtmulvn5qleTl148YNlFIW91FgYKApDsB0TdqqVas4duwYJ0+eNCVNW7du5fbt26xatYrQ0FBCQkLM6tuhQ4d09f3ss89QSnH9+nWz97UUS2ZeeuklnJyciIuL49133zVbdvLkyXTvu27dOgAmTpzIG2+8QYMGDVi0aBFbtmxh+/bttG7d2uLnr0GDBlSpUoX79+/zxhtv4O7unmVsCxYsoFu3bvzwww+Eh4dTvHhxXnnlFS5evJiurIuLS5af+9OnT9OkSRPOnTvHl19+aUpWjNf9pV0/7bEM2hd26nK59TmrW7cu77//Pr/88gvnz5/n7bff5uTJk6aLwfPqmE9bR+ONFg/zHTJgwACOHj1q+mfgm2++ITw8nNq1a2e6nvE9XVxczOYbrz/N6mJ0Pz8/OnXqxLfffktSUhJ79+5lw4YNZslacnIyLVu2ZPHixbz33nusXr2abdu2ma7jslTvvPgbWSuzz5bxuyWrsql/e8C670+j7H6ftGnTBj8/P+7fv8+gQYPSfbenfd9Zs2YBsHjxYjp27EhQUBBz5swhKiqK7du389prr6X7nQUtQWvSpAn379/npZdesirOr776ivfff58lS5bQrFkzihcvzrPPPsvRo0fTlbXm+8QSm797Liu+vr6UKFEiwzsxihUrlqvvl5iYyLVr18wOMuMXvHGer68vBoOBDRs2WLwTLO08S/9Z/fPPP5w/f561a9ea/tMEuHnzptWxGr+Y0l7Yl/ZAzCyW7NYlLV9fX+Lj47lz547FH1FL7wcwdOjQDO+2q1ixIqD9vS39uFqal1M+Pj7Y2dlZvMjR+F+zMWYnJycee+wxVq1aRenSpfH396datWqEhoYC2sWcq1evpm3btqZtGNedPHlyhneCpG5JA8ufl4wkJSXx0ksv4ePjg7OzMz169GDTpk2mfwoCAwPZvn272TrGv++cOXNo2rQpU6dONVue0QXQkZGR7Nu3jzp16jB8+HDatm1rqntGfH19mTRpEpMmTeL06dMsXbqUIUOGcPny5XTH9PXr1ylXrlym21uyZAl37txh8eLFppYFgN27d2e6Xmby4nPm6OhIZGQkX3zxBfv37wdy55jPL0888QRVq1bl66+/xsPDg127djFnzpws1zN+3tP+I1CyZEkAzp49m+U2BgwYwOzZs/ntt99Yvnw53t7eZq3/+/fvZ8+ePcycOZNu3bqZ5h87dsyquj2s1N+7qb8fM/oHM7PPVtpkLqOyqX97wLrvT6Ps3j1mvAmiSpUqvPXWWzRp0gQfHx/T8rTfJ8Z/EOfMmUNISAgLFiwwe8+MLjz/4Ycf+OOPP6hfvz5ff/01nTp1okGDBpnG5u7uzsiRIxk5ciSXLl0ytTq1a9eOw4cPm5W9fv16hmdAMlPgk6a2bdsyf/58kpKSsvyD5pa5c+fy1ltvmaZ/+uknANOdK23btmXs2LGcO3eOjh075ug9jB+qtEnJt99+m65s6v+KXF1dTfP9/PxwcXFh7969ZuXT3q2QmYetS6VKlQCtMzvjaYHMVKxYkUceeYQ9e/bw6aefZlq2WbNmLF26lEuXLpkSi6SkJBYsWJDtODPi7u5OgwYNWLx4MePHjzf9fZOTk5kzZw6lS5fm0UcfNZV/8sknGTp0KMWKFTOdgnN3d6dhw4ZMnjyZ8+fPm+aDdvrM29ubgwcPZnlqIyciIyPZsGEDK1aswN3dnccff5x3332XL7/8EtASvbp161pc12AwpPv87d27l6ioKMqUKWM2f+XKlYwZM4YPP/yQgQMHUrNmTTp16mSWoGWlbNmy9O/fn9WrV7Np0yazZYmJiZw5c4ann346021YOm6UUmanc7KrWbNmfP755+zZs8fsNIrxuM/KhQsXLP6XbDwlYWyxzM4xnx/Stril9dZbb9GnTx9iYmLw8/PjhRdeyHKbwcHBuLq6prsLslGjRnh5eTFt2jQ6d+6c6Q95nTp1aNSoEZ999hn79++nV69eZv+Q6f13NCb2e/fupV69eqb5ae8uNTpw4IDFz1axYsXStdzNmzePQYMGmep46tQpNm/ezCuvvAJk7/szJ3744QfmzJnDjz/+SEREBLVr1+bVV19lyZIlpjKZfZ84OTmZ7duLFy9a/D3at28fb731Fq+88grff/89jRo1olOnTkRHR5slaJnx8/Oje/fu7Nmzh0mTJqXrnuX48eNUrVrVypo/UOCTps6dOzN37lyefvppBgwYQP369XF0dOTs2bOsWbOGZ555hueeey7X3s/JyYkJEyZw+/Zt6tWrx+bNm/nkk0946qmneOyxxwDth7BXr168+uqr7Nixg8cffxx3d3cuXLjAxo0bqVatmuncfEYaNWqEj48Pffr0ITIyEkdHR+bOncuePXvSla1WrRoAn332GU899RT29vZUr14dJycnXn75ZX788UfKly9PjRo12LZtm9Vf9rlRF2MiuWXLFquSJtC+3J566ilatWpF9+7dCQoK4vr16xw6dIhdu3bxyy+/APDhhx+ydOlSnnjiCYYPH46bmxvffPONxduzZ86cyauvvsqMGTOy3VnfmDFjaNGiBc2aNWPw4ME4OTkxZcoU9u/fz7x588y+BJo3b05SUhKrV682NUuDlkxFRkZiMBjMrl/z8PBg8uTJdOvWjevXr9OhQwdKlSrFlStX2LNnD1euXEnX0mMtYyLz0Ucf0bx5c1NdBg8eTNOmTbM8Ltq2bcvHH39MZGQkERER/Pvvv4waNYqQkBCzLhQuXLjAyy+/TEREBJGRkdjZ2bFgwQIef/xx3nvvPSZNmmRx+zExMTRr1owuXbpQqVIlihUrxvbt21m+fHm6/5L37t3L3bt3adasWaYxt2jRAicnJ1588UXee+897t+/z9SpU7lx44YVfzHLBg4cyI8//kibNm345JNP8PPzY+7cuen+c81Iq1atKF26NO3ataNSpUokJyeze/duJkyYgIeHBwMGDACyd8znh2rVqjF//nwWLFhAaGgoLi4upu8agJdffpmhQ4eyfv16PvzwQ6uSYycnJ4tdHnh4eDBhwgR69uzJk08+yeuvv46fnx/Hjh1jz5496W4/HzBgAJ06dcJgMJhOOxlVqlSJ8uXLM2TIEJRSFC9enGXLlpndqp4TI0aMYOTIkaxZsybTnteffvppihcvTo8ePRg1ahQODg7MnDmTM2fOWCwfGBhI+/btGTFiBAEBAcyZM4eVK1fy2WefpeuD7fLlyzz33HO8/vrrxMTEEBkZiYuLC0OHDjWVsfb7M7uMiUy3bt1MnUJOnz6dDh06MGnSJAYOHJjp+m3btmXx4sX07duXDh06cObMGT7++GMCAgLMTp/duXOHjh07EhISwpQpU3BycuLnn3+2mKCl1aBBA9q2bUv16tXx8fHh0KFDzJ49m/DwcLO/5bVr1zh69Chvvvlm9v8Q2b50XGdp755TSrvldPz48apGjRrKxcVFeXh4qEqVKqnevXuro0ePmsoFBwerNm3apNsmoPr162c2z3jnSerbPo3vvXfvXtW0aVPl6uqqihcvrt544w11+/btdNv98ccfVYMGDZS7u7tydXVV5cuXV6+88orasWOHqUxERISqUqWKxbpu3rxZhYeHKzc3N1WyZEnVs2dPtWvXrnR3IMXFxamePXuqkiVLKoPBYHaHRkxMjOrZs6fy8/NT7u7uql27durkyZMZ3j135coVi7FYU5eMNGnSRD399NNm8yz9fVPbs2eP6tixoypVqpRydHRU/v7+6oknnlDTpk0zK7dp0ybVsGFD5ezsrPz9/dW7776rvvvuu3R3qUyePFkBavny5ZnGmtEdhxs2bFBPPPGEqf4NGzZUy5YtS7d+cnKy8vX1VYA6d+6cWZyk3P1iybp161SbNm1U8eLFlaOjowoKClJt2rRRv/zyi6lMVvsotfPnz6tSpUqpJ554wnS3jDG+du3aKW9v7yzvLoyLi1ODBw9WQUFBysXFRdWuXVstWbJEdevWTQUHByultDtgIiIilJ+fn7pw4YLZ+uPGjVOA+vXXX5VS6f+29+/fV3369FHVq1dXnp6eytXVVVWsWFFFRkaa3cmqlFIfffSR8vX1Nd2ZmJlly5aZvguCgoLUu+++q/766690d7pldOylrp/RwYMHVYsWLZSLi4sqXry46tGjh/rtt9+suntuwYIFqkuXLuqRRx5RHh4eytHRUZUtW1Z17dpVHTx40Kystcd8du6es3SMZXT8p3by5EnVsmVLVaxYMVNXF2l1795dOTg4mN2RnJXp06cre3t7df78+XTL/vzzTxUREaHc3d1N3Yl89tln6crFxcUpZ2dn1bp1a4vvYdxfxYoVUz4+PuqFF15Qp0+ftvp7z9Kdbu+8844yGAzq0KFDWdZx27ZtqlGjRsrd3V0FBQWpyMhI9cMPP1i8e65NmzZq4cKFqkqVKsrJyUmVK1dOTZw40Wx7xrvnZs+erd566y1VsmRJ5ezsrJo0aWLxO9ia709jHY239Wfm9u3bqlKlSiosLCzdsdmvXz/l6OiY7i5US8aOHavKlSunnJ2dVeXKldX333+f7rP38ssvKzc3N3XgwAGzdX/55RcFmN21mXZ/DhkyRNWtW1f5+PgoZ2dnFRoaqt5+++103WBMnz5dOTo6ZtlljiUFLmnSk6WETWRt4cKFyt7ePltfrLnthRdeUHXr1tXt/UXOJSYmqnLlyqlhw4bpHYpIJS4uTgUEBJj1j2SNe/fuqZIlS6brdiA7li5dqgD1xx9/5Hgb2VWvXj3VoUOHXN1mRv/Ip2VMmlL/EyVy7rHHHlNdunTJ0boF/u45Yfv+97//Ua9ePVPPvvlNKcXatWsZPXq0Lu8vHs6cOXO4fft2ujv/hD6uXLnCxo0beeONN7h06RJDhgzJ1vouLi6MHDmSiRMnWtXTeWoHDx7kr7/+MvUs/dRTT2Vr/ZyKjY1lz549jBo1Kl/eT+Sd9evXs337dj7++OMcrV/gr2kSts9gMPD999+zdOlSkpOT8/2hvQaDIV/6WxF5Izk5mblz5+Lt7a13KALtAcSvvvoqAQEBTJkyJctuBizp1asXN2/e5Pjx42bXSWWlb9++bNq0idq1azNr1qx8e0aap6dnhnd5iYLl2rVr/N///V+Wd/ZmxKBUqp6nhBBCCCGERXJ6TgghhBDCCpI0CSGEEEJYQZImIYQQQggrSNIkhBBCCGEFSZqEEEIIIaxg00nT1KlTqV69Op6ennh6ehIeHs5ff/0FQEJCAu+//z7VqlXD3d2dwMBAXnnlFdNDVDOzaNEiwsLCcHZ2JiwsjF9//TVdmSlTphASEoKLiwt16tRhw4YNuV6/zORF3b///nvTwxV9fHx48skn2bZtm1mZESNGYDAYzIbsPs39YeVF3WfOnJmuXgaDId3TtfXc73lR76ZNm1qsd5s2bUxlbH2fG2OsVKkS7u7ups/u1q1bs9xuQT/WIWd1LwzHujHG7Na9oB/rkLN6F5ZjPbXevXtjMBgyfBRTavl2rOdmL5u5benSpeqPP/5Q//77r/r333/VsGHDlKOjo9q/f7+6efOmevLJJ9WCBQvU4cOHVVRUlGrQoIGqU6dOptvcvHmzsre3V59++qk6dOiQ+vTTT5WDg4PasmWLqcz8+fOVo6Oj+v7779XBgwfVgAEDlLu7uzp16lReV9kkL+repUsX9c0336jo6Gh16NAh9eqrryovLy+znrojIyNVlSpV1IULF0zD5cuX87q6ZvKi7jNmzFCenp5m9Ur72A+993te1PvatWtm9d2/f7+yt7dP90gOW97nSik1d+5ctXLlSvXff/+p/fv3qx49eihPT89M4ywMx7pSOat7YTjWlcpZ3Qv6sa5UzupdWI51o19//VXVqFFDBQYGmj06xZL8PNZtOmmyxMfHR/3www8Wl23btk0Bmf4ROnbsmO55Ra1atVKdO3c2TdevX1/16dPHrEylSpXUkCFDHiLyh/ewdU8rMTFRFStWTM2aNcs0LzIyUtWoUeNhQ811D1v3GTNmKC8vr0zfwxb3e27v8y+++EIVK1bM7FmJBXGfx8TEKECtWrUqw/UL67FuTd3TKizHujV1L4zHek72eUE+1s+ePauCgoLU/v37VXBwcJZJU34e6zZ9ei61pKQk5s+fz507dwgPD7dYJiYmBoPBkGnPwVFRUbRs2dJsXqtWrdi8eTMA8fHx7Ny5M12Zli1bmsrkt9yqe1p3794lISGB4sWLm80/evQogYGBhISE0LlzZ44fP/4w4T+U3Kz77du3CQ4OpnTp0rRt25bo6GjTMlvb73m1z6dPn07nzp1xd3c3m1+Q9nl8fDzfffcdXl5e1KhRI8PtFMZj3dq6p1UYjvXs1L0wHes53ecF9VhPTk6ma9euvPvuu1SpUsWq7eTrsZ6tFEsHe/fuVe7u7sre3l55eXll+IDGe/fuqTp16qiXXnop0+05OjqquXPnms2bO3eucnJyUkopde7cOQWoTZs2mZUZPXq0evTRRx+iJtmX23VPq2/fvqp8+fLq3r17pnl//vmnWrhwodq7d69auXKl6Qn2aZ8Snddyu+5RUVFq9uzZavfu3Wr9+vXq+eefV66ururIkSNKKdvZ73m5z7du3aqAdE8jLyj7fNmyZcrd3V0ZDAYVGBiotm3blun2CtOxnt26p1WQj/Xs1r2wHOsPs88L8rH+6aefqhYtWqjk5GSllLKqpSk/j3WbT5ri4uLU0aNH1fbt29WQIUOUr6+vOnDggFmZ+Ph49cwzz6hatWqpmJiYTLfn6OiofvrpJ7N5c+bMUc7OzkqpB3/czZs3m5X55JNPVMWKFXOhRtbL7bqn9tlnnykfHx+1Z8+eTMvdvn1b+fn5qQkTJuSoDjmVl3VXSqmkpCRVo0YN9eabbyqlbGe/52W9e/XqpapWrZplOVvd57dv31ZHjx5VUVFR6rXXXlPlypVTly5dynB7helYz27dUyvox/rD1F2pgnusP0y9C+qxvmPHDuXn56fOnTtnKmtt0pRfx7rNJ01pNW/eXPXq1cs0HR8fr5599llVvXp1q7LlMmXKqIkTJ5rNmzhxoipbtqxSStuZ9vb2avHixWZl3nrrLfX444/nQg1y7mHrbjRu3Djl5eWltm/fblX5J598Mt254PyWW3VPrWfPnqbz4La633Or3nfu3FGenp5q0qRJVpW3xX2eVoUKFdSnn36a4fLCdKynlVXdjQrDsZ6WtXVPrSAe62lZW++CfKx/8cUXymAwKHt7e9MAKDs7OxUcHJzh+vl5rBeYa5qMlFKmp00nJCTQsWNHjh49yqpVqyhRokSW64eHh7Ny5UqzeStWrKBRo0YAODk5UadOnXRlVq5caSqjl4etO8C4ceP4+OOPWb58OXXr1s2yfFxcHIcOHSIgIOChYn9YuVH3tNvbvXu3qV62ut9zq94///wzcXFxvPzyy1mWtcV9npPlheVYz8lyKBzHek6WWypf0I71nCw3KsjHeteuXdm7dy+7d+82DYGBgbz77rv8/fffGa6fr8d6tlKsfDZ06FC1fv16deLECbV37141bNgwZWdnp1asWKESEhJU+/btVenSpdXu3bvNbqGMi4szbaNr165mV8dv2rRJ2dvbq7Fjx6pDhw6psWPHZnhr4vTp09XBgwfVwIEDlbu7uzp58mSBrvtnn32mnJyc1MKFC83WuXXrlqnMO++8o9auXauOHz+utmzZotq2bauKFStW4Os+YsQItXz5cvXff/+p6Oho9eqrryoHBwezc/567/e8qLfRY489pjp16mTxfW19n9++fVsNHTpURUVFqZMnT6qdO3eqHj16KGdnZ7PblAvjsZ7TuheGYz2ndS/ox3pO621UkI91SyydntPzWLfppOm1115TwcHBysnJSZUsWVI1b97c9Ic9ceKEAiwOa9asMW0jIiJCdevWzWy7v/zyi6pYsaJydHRUlSpVUosWLUr33t98843pvWvXrq3WrVuXl1VNJy/qHhwcbHGdyMhIU5lOnTqpgIAA5ejoqAIDA9X//ve/dNfU5LW8qPvAgQNV2bJlTdts2bJluvPbSum73/Pq8/7vv/8qIMMvJVvf5/fu3VPPPfecCgwMVE5OTiogIEC1b98+3YWxhfFYz2ndC8OxntO6F/Rj/WE+7wX9WLfEUtKk57FuUEqp7LVNCSGEEEIUPQXumiYhhBBCCD1I0iSEEEIIYQVJmoQQQgghrCBJkxBCCCGEFSRpEkIIIYSwgiRNQgghhBBWkKRJCCGEEMIKRSJpiouLY8SIEdnqer+wKKp1L6r1Bqm71F3qXlQU1XqDfnUvMJ1bTpkyhXHjxnHhwgWqVKnCpEmTaNKkiVXrxsbG4uXlRUxMDJ6ennkcqW0pqnUvqvUGqbvUXepeVBTVeoN+dS8QLU0LFixg4MCBfPDBB0RHR9OkSROeeuopTp8+rXdoQgghhCgiCkTSNHHiRHr06EHPnj2pXLkykyZNokyZMkydOlXv0IQQQghRRDjoHUBW4uPj2blzJ0OGDDGb37JlSzZv3mxxnbi4OLPznNevXwfgzJkzeHl55V2wNujWrVsAnDt3jtjYWJ2jyT9Ftd4gdQepu9S9aCiq9QaIiYkBIDExMX/fONuP+M1n586dU4DatGmT2fzRo0erRx991OI6kZGRGT4RXgYZZJBBBhlkKBzDhg0b8iMVMbH5liYjg8FgNq2USjfPaOjQoQwaNMg0febMGapWrcq2bdsICAjI0ziFEEIIkbcuXLhA/fr1KVu2bL6+r80nTb6+vtjb23Px4kWz+ZcvX8bPz8/iOs7Ozjg7O5umjafkAgICKF26dN4FK4QQQoh8Y2eXv5dm2/yF4E5OTtSpU4eVK1eazV+5ciWNGjXSKSohhBBCFDU239IEMGjQILp27UrdunUJDw/nu+++4/Tp0/Tp00fv0IQQQghRRNh8SxNAp06dmDRpEqNGjaJmzZqsX7+eP//8k+DgYL1DEw/h8uXL9O7dm7Jly+Ls7Iy/vz+tWrUiKirKVMZgMLBkyZJceb+TJ09iMBjYvXt3puXWrl2LwWDg5s2b6ZbVrFmTESNGmMpkNsycOROARYsW0bRpU7y8vPDw8KB69eqMGjXKdFenNRYvXkyLFi0oWbIknp6ehIeH8/fff6crt2jRIsLCwnB2diYsLIxff/3VbPmYMWOoV68exYoVo1SpUjz77LP8+++/puUJCQm8//77VKtWDXd3dwIDA3nllVc4f/58ljHeuHGDrl274uXlhZeXF127dk33NxwwYAB16tTB2dmZmjVrWl3/devWUadOHVxcXAgNDWXatGlmyw8cOMDzzz9PuXLlMBgMTJo0yart7tu3j4iICFxdXQkKCmLUqFGoNP39ZvXeaV2/fp0333yTihUr4ubmRtmyZXnrrbdMd/uA9lns0aMHISEhuLq6Ur58eSIjI4mPj88y5qz2MWidAYeEhODi4kKdOnXYsGGDVX8PIUTmCkTSBNC3b19OnjxJXFwcO3fu5PHHH9c7JPGQnn/+efbs2cOsWbM4cuQIS5cupWnTptlKJqxlzY9RdjRq1IgLFy6Yho4dO9K6dWuzeZ06deKDDz6gU6dO1KtXj7/++ov9+/czYcIE9uzZw+zZs61+v/Xr19OiRQv+/PNPdu7cSbNmzWjXrh3R0dGmMlFRUXTq1ImuXbuyZ88eunbtSseOHdm6daupzLp16+jXrx9btmxh5cqVJCYm0rJlS+7cuQPA3bt32bVrFx999BG7du1i8eLFHDlyhPbt22cZY5cuXdi9ezfLly9n+fLl7N69m65du5qVUUrx2muv0alTJ6vrfuLECZ5++mmaNGlCdHQ0w4YN46233mLRokWmMnfv3iU0NJSxY8fi7+9v1XZjY2Np0aIFgYGBbN++ncmTJzN+/HgmTpyYrfdO6/z585w/f57x48ezb98+Zs6cyfLly+nRo4epzOHDh0lOTubbb7/lwIEDfPHFF0ybNo1hw4ZlGrM1+1g6AxYiD+XrvXo6OXPmjALUmTNn9A5FpLhx44YC1Nq1azMsExwcbHZraXBwsFJKqWPHjqn27durUqVKKXd3d1W3bl21cuXKdOt+/PHHqlu3bsrT01O98sor6W5VjYiIsPi+a9asUYC6ceNGumU1atRQkZGR6eZ369ZNPfPMM2bztm7dqgA1adKkDP8GDyMsLEyNHDnSNN2xY0fVunVrszKtWrVSnTt3znAbly9fVoBat25dhmW2bdumAHXq1KkMyxw8eFABasuWLaZ5UVFRClCHDx9OVz4yMlLVqFEjw+2l9t5776lKlSqZzevdu7dq2LChxfLBwcHqiy++yHK7U6ZMUV5eXur+/fumeWPGjFGBgYEqOTk5R++dkZ9//lk5OTmphISEDMt8/vnnKiQkJNPtWLOP69evr/r06WNWplKlSmrIkCHZilkIW6bX73qBaWkShYuHhwceHh4sWbIkwwcubt++HYAZM2Zw4cIF0/Tt27d5+umnWbVqFdHR0bRq1Yp27dql+0963LhxVK1alZ07d/LRRx+xbds2AFatWsWFCxdYvHhxHtYQ5s6di4eHB3379rW43NvbG3hw2nDt2rVWbzs5OZlbt25RvHhx07yoqChatmxpVq5Vq1YZdgILDzqIS70dS2UMBoMpXkuioqLw8vKiQYMGpnkNGzbEy8sr0/e3Rkb12rFjBwkJCQ+13YiICLM7bVu1asX58+c5efKk1e9tPFVrXMcS4/OxHBwyvow0JiYm3X4oV64cI0aMMIs5s31s7Aw4bZnMOgMWQlhPkiahCwcHB2bOnMmsWbPw9vamcePGDBs2jL1795rKlCxZEtCSC39/f9N0jRo16N27N9WqVeORRx7hk08+ITQ0lKVLl5q9xxNPPMHgwYOpUKECFSpUMK1fokQJ/P39M00UcsPRo0cJDQ3F0dEx03KOjo6m61+sNWHCBO7cuUPHjh1N8y5evJiuGw4/P7903XUYKaUYNGgQjz32GFWrVrVY5v79+wwZMoQuXbpk+lDMixcvUqpUqXTzS5UqleH7WyujeiUmJnL16tVc365xmbXv7ebmRsWKFTPcz9euXePjjz+md+/eGcby33//MXny5HQ3t5QvXx5fX98sYzbGe/XqVZKSkrL1ORBCWE+SJqGb559/nvPnz7N06VJatWrF2rVrqV27tukC6ozcuXOH9957j7CwMLy9vfHw8ODw4cPpWprq1q2bh9FnTWXSAWtqQUFBHD58mPr161u13Xnz5jFixAgWLFiQLlHJTiew/fv3Z+/evcybN8/i8oSEBDp37kxycjJTpkwxze/Tp4+ppdDDwyPD987q/S1Jvd3UCYSlemX0ntlhzXazKlO/fn0OHz5MUFBQuu3HxsbSpk0bwsLCiIyMtBjD+fPnad26NS+88AI9e/Y0W7Z69Wr69++fZcxp52XncyCEsF6B6HJAFF4uLi60aNGCFi1aMHz4cHr27ElkZCTdu3fPcJ13332Xv//+m/Hjx1OhQgVcXV3p0KFDuou93d3dcxSTsUUlJiYm3SmpmzdvWv38wkcffZSNGzeSkJCQZWuTtRYsWECPHj345ZdfePLJJ82W+fv7W90J7JtvvsnSpUtZv369xQ5fExIS6NixIydOnOCff/4xa2UaNWoUgwcPTvfely5dSredK1euZNgJrSWp72w0vmdG9XJwcKBEiRJWbzutjLYLD1qcHua9b926RevWrfHw8ODXX3+1+Bk4f/48zZo1M3WlktOYjfHmpDNgIYT1pKVJ2JSwsDDTnVygnbpKSkoyK7Nhwwa6d+/Oc889R7Vq1fD398/0ehIjJycngHTbS+uRRx7Bzs7OdA2V0YULFzh37hwVK1a0qi5dunTh9u3bZq00qVnq0iAz8+bNo3v37vz000+0adMm3fLw8PB0ncCuWLHCrBNYpRT9+/dn8eLF/PPPP4SEhKTbjjFhOnr0KKtWrUqXHJQqVcp0yrNChQqm946JiTFdNwawdetWYmJistUJbertGlvRMqpX3bp1HyoZDQ8PZ/369WbJ9ooVKwgMDKRcuXIP9d6xsbG0bNkSJycnli5diouLS7oy586do2nTptSuXZsZM2ZY1bNxVvtYOgMWIo/l62XnOpG752zP1atXVbNmzdTs2bPVnj171PHjx9XPP/+s/Pz81GuvvWYq98gjj6g33nhDXbhwQV2/fl0ppdSzzz6ratasqaKjo9Xu3btVu3btVLFixdSAAQNM61m6gyohIUG5urqqTz75RF28eFHdvHkzw/jeeOMNVbZsWfXrr7+q48ePq40bN6qIiAhVrVo1i3dAWbp7Tint7it7e3v17rvvqs2bN6uTJ0+qVatWqQ4dOpjuqjt79qyqWLGi2rp1a4bx/PTTT8rBwUF988036sKFC6YhdR02bdqk7O3t1dixY9WhQ4fU2LFjlYODg9kdbW+88Yby8vJSa9euNdvO3bt3TX+j9u3bq9KlS6vdu3eblYmLi8swPqWUat26tapevbqKiopSUVFRqlq1aqpt27ZmZY4ePaqio6NV79691aOPPqqio6NVdHR0pts+fvy4cnNzU2+//bY6ePCgmj59unJ0dFQLFy40lYmLizNtKyAgQA0ePFhFR0ero0ePZrjdmzdvKj8/P/Xiiy+qffv2qcWLFytPT081fvz4bL331q1bVcWKFdXZs2eVUkrFxsaqBg0aqGrVqqljx46Z/Q0TExOVUtqDyCtUqKCeeOIJdfbsWbMyqT3xxBNq8uTJpmlr9vH8+fOVo6Ojmj59ujp48KAaOHCgcnd3VydPnszwbyFEQaPX77okTUIX9+/fV0OGDFG1a9dWXl5eys3NTVWsWFF9+OGHph9wpZRaunSpqlChgnJwcDB1OXDixAnVrFkz5erqqsqUKaO+/vprFRERkWXSpJRS33//vSpTpoyys7PLsMsBY3yjRo1SlStXVq6urio4OFh179493Y+aUUZJk1JKLViwQD3++OOqWLFiyt3dXVWvXl2NGjXK1OXAiRMnFKDWrFmTYTwREREWn/DdrVs3s3K//PKLqlixonJ0dFSVKlVSixYtMltuaRuAmjFjhlkslobM4lNKqWvXrqmXXnpJFStWTBUrVky99NJL6bpVyKgeJ06cyHTba9euVbVq1VJOTk6qXLlyaurUqWbLM4o7s32slFJ79+5VTZo0Uc7Ozsrf31+NGDHC1N2Ate9t7KLCWAfjdGb1nDFjRoZlUgsODk7XxUVW+1gppb755hsVHBysnJycVO3atTPtUkKIgkiv33WDUmm6vy2Ezp49S5kyZThz5ow8sFcIIYQo4PT6XZdrmoQQQgghrCBJkxBCCCGEFSRpEkIIIYSwgiRNQgghhBBWkKRJCCGEEMIKkjQJIYQQQlhBkiYhhBBCCCtI0iSEEEIIYQVJmoQQQgghrCBJkxBCCCGEFSRpEkIIIYSwgiRNRdhF4Hdgt85xCCGE7nbuhOXL4cYNvSMRNkySpiJsM9AO6Kt3IEIIobdvvoGnnoLx4/WORNgwSZqKsGsp4xK6RiGEEDZg61Zt3LChvnEImyZJUxEmSZMQQgAxMXDokPa6QQN9YxE2TZKmIsyYNPnqGoUQQuhs+3ZQCsqVg1Kl9I5G2DBJmoowaWkSQghgyxZtLKfmRBZ0S5pOnjxJjx49CAkJwdXVlfLlyxMZGUl8fLxZOYPBkG6YNm2aTlEXLndSxm66RiGEEDq7elUbe3joG4eweQ56vfHhw4dJTk7m22+/pUKFCuzfv5/XX3+dO3fuMD7N3QszZsygdevWpmkvL6/8DrdQKpYyvq1rFEIIobOnnoIvv4QlS2DKFHB01DsiYaN0S5pat25tlgiFhoby77//MnXq1HRJk7e3N/7+/vkdYqFnTD1jdI1CCCF01rw5+PpqLU6rV0Oq3yYhUrOpa5piYmIoXrx4uvn9+/fH19eXevXqMW3aNJKTk3WIrvAxnpa7q2sUQgihMwcHaNVKe71zp76xCJumW0tTWv/99x+TJ09mwoQJZvM//vhjmjdvjqurK6tXr+add97h6tWrfPjhhxluKy4ujri4ONP0rVu38izugiwxZSwN0UKIIs/4z7ibXOUpMpbrLU0jRoywePF26mHHjh1m65w/f57WrVvzwgsv0LNnT7NlH374IeHh4dSsWZN33nmHUaNGMW7cuExjGDNmDF5eXqYhLCwst6tZKCSkjCVpEkIUebGx2rhYsczLiSIt11ua+vfvT+fOnTMtU65cOdPr8+fP06xZM8LDw/nuu++y3H7Dhg2JjY3l0qVL+Pn5WSwzdOhQBg0aZJo+d+6cJE4WGO9TdNI1CiGEsAHGMxKSNIlM5HrS5Ovri6+vdd0lnjt3jmbNmlGnTh1mzJiBnV3WDV/R0dG4uLjg7e2dYRlnZ2ecnZ1N07HG/yCEGWlpEkKIFLdT7iOWpElkQrdrms6fP0/Tpk0pW7Ys48eP58qVK6Zlxjvlli1bxsWLFwkPD8fV1ZU1a9bwwQcf0KtXL7OkSAghhHgoDik/h4mJmZcTRZpuSdOKFSs4duwYx44do3Tp0mbLlFIAODo6MmXKFAYNGkRycjKhoaGMGjWKfv366RFyoWOfMpZ7EYUQRZ6npzaWG4dEJnRLmrp370737t0zLZO2LyeRu4wnQ5N0jUIIIWyAMWmKkZ7rRMZsqp8mkb+MLUz2mZYSQogiwGAwHwthgSRNRZixJyu5e04IUeTdv6+NXVz0jUPYNEmaijDpckAIIVJI0iSsIElTESZJkxBCpJCkSVhBkqYiTO6aE0KIFMbHqEiXAyITkjQVYcZHI1/XNQohhLABFSpo4yNH9I1D2DRJmoqwkinjK5mWEkKIIsD4qK2DB/WNQ9g0SZqKMEmahBAihTFpOnRI3ziETZOkqQiTpEkIIVKkTpqS5YpPYZkkTUIIIURAgDa+fx/u3dM3FmGzJGkqwowXgBfPtJQQQhQBTqk6X4mPz7icKNIkaSrCbqSMfXSNQgghbIBDqkexGvtsEiINSZqKMI+U8Y1MSwkhRBFgMEBQkPZauh0QGZCkqQhLueyRA7pGIYQQNqJhQ228ZYu+cQibJUlTEVYlZXyMBw/vFUKIIis8XBtL0iQyIElTEeaXMk4GbusZiBBC2AK/lG/FW7f0jUPYLEmairDUT1hyyLCUEEIUEVdSeq0rWTLzcqLIkqSpCEudNNnrFoUQQtgISZpEFiRpKsKkpUkIIVKJjdXGXl76xiFsliRNRVjqpMlRtyiEEMJGxKXcEuPiom8cwmZJ0lSEGZMmA3J6TgghTJ1aStIkMiBJUxGWkDKWU3NCCMGDliZnZ33jEDZLkqYizNgTuPxPJYQo8hISYO9e7XWxYvrGImyWJE1F2KqUcUNdoxBCCBvw5Zfw77/g6wvt2ukdjbBRkjQVYX+ljJ/SNQohhNDZ2bMwYoT2+vPPoXhxXcMRtkuSpiLqDrAu5bUkTUKIIm3QILhzBxo1gm7d9I5G2DBJmoqotUA8EAxU1DcUIYTQz8aN8MsvYGcHU6ZoYyEyoOuno1y5chgMBrNhyJAhZmVOnz5Nu3btcHd3x9fXl7feeov4+HidIi48/ksZ10frckAIIYqkbdu0cfv2UKOGvrEIm6f73eajRo3i9ddfN017eHiYXiclJdGmTRtKlizJxo0buXbtGt26dUMpxeTJk/UIt9BI6Y0EN12jEEIInSmljVP99giREd2TpmLFiuHv729x2YoVKzh48CBnzpwhMDAQgAkTJtC9e3dGjx6Np6dnfoZaqKT0RiI9gQshBMCFC5CUBPbS1a/ImO4nbz/77DNKlChBzZo1GT16tNmpt6ioKKpWrWpKmABatWpFXFwcO3fu1CPcQqNsyngxcFXPQIQQQk+1a2vj1auhbVu4fl3feIRN0zVpGjBgAPPnz2fNmjX079+fSZMm0bdvX9Pyixcv4ufnZ7aOj48PTk5OXLx4McPtxsXFERsbaxpu3bqVZ3UoqF4CqgPXgfd1jkUIIXTTrBnMng2urrB8OdStC7t36x2VsFG5njSNGDEi3cXdaYcdO3YA8PbbbxMREUH16tXp2bMn06ZNY/r06Vy7ds20PYMh/WXKSimL843GjBmDl5eXaQgLC8vtahZ4DsDUlNc/Ajt0jEUIIXT18suweTOEhMCJExAe/uACcSFSyfWkqX///hw6dCjToWrVqhbXbdhQ65v62LFjAPj7+6drUbpx4wYJCQnpWqBSGzp0KDExMabh4MGDuVS7wqUR0Crl9VY9AxFCCL3VrAk7dkC1atqDezdv1jsiYYNy/UJwX19ffH19c7RudHQ0AAEBAQCEh4czevRoLly4YJq3YsUKnJ2dqVOnTobbcXZ2xjnVAxdjY2NzFE9RYHzunFwQLoQo8ooXh6Ag2LcPvL31jkbYIN3unouKimLLli00a9YMLy8vtm/fzttvv0379u0pW1a7TLlly5aEhYXRtWtXxo0bx/Xr1xk8eDCvv/663DmXSxJSxpI0CSEEcCPlUeY+PvrGIWySbkmTs7MzCxYsYOTIkcTFxREcHMzrr7/Oe++9Zypjb2/PH3/8Qd++fWncuDGurq506dKF8ePH6xV2oSNJkxBCpCJJk8iEbklT7dq12bJlS5blypYty++//54PERVNkjQJIUQqMTHaWM5mCAt076dJ6Cs5ZSwfBCGEABxT/oVMTNQ3DmGT5LeyiFN6ByCEELbE3V0b372rbxzCJknSJAB5aK8QQgDglvJEzjt39I1D2CRJmoo4Y0uTJE1CCMGDliZJmoQFkjQVccVSxld0jUIIIWyEJE0iE5I0FXE1U8a79AxCCCFshfH03L17+sYhbJIkTUVcyvO92alrFEIIYSPsUn4W5UJwYYEkTUVcqZTxtUxLCSFEEfDVV7Bokfa6TBl9YxE2SZKmIm5fytjyI5SFEKIIUAqGD4cBA7TpAQPg+ef1jUnYJN16BBe2YU/KuIauUQghhE6Sk+HNN2HKFG3644/hgw/AIPcUi/QkaSriJGkSQhRZ8fHQrRvMn68lSd98A2+8oXdUwoZJ0lTEHUwZh+kahRBC6GDIEC1hcnSE2bOhUye9IxI2Tq5pKuL8U8ZndI1CCCHy2c6d8OWX2usFCyRhElaRpKmIa5QyjtI1CiGEyAeJiXDxIuzZA717a9czde4Mzz2nd2SigJDTc0VcOPB/wNdorU69kA+FEKIAuXcPLl2Cy5fNx5ZeX7um3Sln5OUFX3yhX+yiwJHfxyLuRWAa2gXh/YCpwCSguY4xCSGKMKUgJibjBCjtvFu3srd9gwF8faF0afj0U/D3z3odIVJI0lTEeQE7gO+Aj4D9wJPAM8B4oIJ+oQkhCoukJLh6NeuWIOM4Pj5723dyAj8/KFXKfGzpta8v2NvnTT1FoSdJk8AB6At0BkYC3wC/AX8BA4EPAE+9ghNC2Kb7961rCbp0SUuYUp8Ws0axYlknQMaxl5f0qyTyhSRNwqQ48CXQG3gbWAF8DsxEa30qm2oIThlLMiWEDVBKa525fx/i4rTB+Dqzedktf+fOg0QoNjZ7MRoMUKKE5aTH0jxX17z5WwnxECRpEumEAcuBP4BBwFHgpwzKemGeRKVNqgIAaQgXhVZi4sMlIbmV3MTF6VN/R0frWoL8/LTTYg7ykyMKNvkEC4sMQFugJVrydAw4nWo4BdwAYtCeX7fP8mZwAILIOKkqC3jkVSVE4aSUPomJpXlJSXr/NSxzcgJnZ3BxMR8/7Dw3N/NkyNtbTouJIkWSJpEpJyCjHkxuoXWKmTqRSp1YnQUSU+afyuQ9fMg8qfJHOhTTnVIPWlVyK+HI6bzsXiScX+zs0icauZ20WDPPyUmLRQiR6yRpEjlWDO1UXkaPYEkCLmCeSKVNrm6itVjdAHZnsB1HoAzmSZUxsaqU8rpIUErrkyY29sFw717+tbAkJ+v9F7AsbfJgbXKR20mLnHoSotCTo1zkGXugdMrQKIMysWitVWlbqYzJ1TkgATieMlhSFngcaJIyroh2etFmKAV375onOzEx5tPWzreV00EODnnTSpKTVhU5PSSEyCeSNAldeQJVUgZLEjFvrTqV5vWhlNdzUgaAkjxIoJoANcjhxehKaXcL5Uayk5utNAYDeHpqt2S7uub96R5Ly6SfGyFEESRJk7BpDmin5soAjS0svw1sATYA61NeXwEWpwwAnomJNLp5k8cvXKDJ6dPUPX4clxs3sk52bt3K3WTHzk5LdlIPXl7p52U239MT3N3lmhUhhNCBJE2iQPNA60PqyZTpOGAnWgK1QSk23rpFrKcny319We7rC9Wq4RgfT83du2m4ZYs2bN9OyIkTGZ/Ss7e3LpnJKgFyc5NTSUIIUYAZlMpuN625Y+3atTRr1szism3btlGvXj0ADBZ+ZKZOnUqfPn2sfq+zZ89SpkwZzpw5Q+nSpXMWsCiQkmrWZK/BwIaWLVn/1FNsqFGDyz4+6cqVvHePhjdv0vDOHRomJlLP3p5ixYppyY6rqyQ7QghhQ/T6XdctaYqPj+f69etm8z766CNWrVrF8ePHTcmSwWBgxowZtG7d2lTOy8sL12z0FitJUxE2Zw507QolS8LJkyg3N04CW9FO5W0BdqFdbJ6aAagKNEw1VEK6PhBCCFug1++6bqfnnJyc8E/1dOmEhASWLl1K//7907UueXt7m5UVwmqdO8NHH8HJkzB9OoY33yQECEF71h7AfbTuDrakGk7xoNPO71PKeQINeJBENQBK5FM1hBBC6E+3lqa0Fi1aRMeOHTl58iRlypQxzTcYDAQFBXH//n1CQkLo0aMHvXr1wi6TC2Hj4uKIS/VYgXPnzhEWFiYtTUXVtGnwxhtQpgwcOaLdAZaFC5i3Rm0H7loo9whQB63PqKBUQ2nAD7loUAgh8kKRa2lKa/r06bRq1cosYQL4+OOPad68Oa6urqxevZp33nmHq1ev8uGHH2a4rTFjxjBy5Mi8DlkUFN27w6hRcOYMvPQS/PxzlrfMBwDPpgygdX2wH/PWqH/Rnst3NINt2KH1Zl4a82Qq7Wu3HFZLCCFE/sr1lqYRI0ZkmbBs376dunXrmqbPnj1LcHAwP//8M88//3ym606YMIFRo0YRExOTYRlpaRLprF0LrVppj+Do1UtrfXrIi7tvANuAPWidcBqHs2gtVdZ2Q+lNxgmV8XUJbKzDTiGE0FGhaWnq378/nTt3zrRMuXLlzKZnzJhBiRIlaN++fZbbb9iwIbGxsVy6dAk/Pz+LZZydnXF2djZNx8bGZh24KNyaNoV58+CFF+C777S74saOfahOGn2AVilDWknAZbQEKm1Clfr1HbRHydwEDmTyXs5AIFoSVQaoBzQFqpPDjjuFEEJkW64nTb6+vvj6+lpdXinFjBkzeOWVV3B0dMyyfHR0NC4uLnh7ez9ElKJI+t//YOpU6N0bxo+HzZth1iyoUCHX38oe7RRfAFqCY4lCe4yMpYQq9fRltP6nTqQMAPNSxt5oPZ83RZIoIYTIa7pf0/TPP/9w4sQJevTokW7ZsmXLuHjxIuHh4bi6urJmzRo++OADevXqZdaSJITVevXS+l3q109LmmrUgAkTtEQqn/tiMgBeKUNGDz0GiAfO8yCZOgFsROvA8yawNGUASaKEECIv6X73XJcuXTh16hSbNm1Kt2z58uUMHTqUY8eOkZycTGhoKD179qRfv344ZOOJ4tJPk0jn1Cl49VVYs0abbtUKpk+HoCB948qGRLSuEtamDBvQWq5S80aSKCFE4VPkOrfMT5I0CYuSk2HyZBgyBO7fB29v+OYbeO45rTWqgMluEhUBVAYKXk2FEEWdJE15SJImkalDh+CVV2DHjgfzXF2hRInMh+LFzae9vR/qwvLcljaJWg/cslDOC61rBEtDQKrXvkgrlRDCNhSau+eEKHAqV9aub/r0Uxg3Du7cgXv34OxZbbCWwQA+PlknV2kHt7zpqckBqJsyDCbjlqiYlOHfLLZnB5TCckKVdiiGdJEghCh8pKVJiNSUgpgYuH4drl3LejCWu2WpDcdKLi7WJ1jGMsWLP3SrlvHuvYspw4VUr9MOl1PKW8sN61qvSgFOD1ULIURRJC1NQtgCg0E7zebtDaGh1q8XH595opXRssRE7Xqqc+e0ITu8vbN/CtHd3XSXYOq79ypm8VaJwBUyTqpSJ1y30B45czxlyEoJrGu9Ko60Xgkh9CVJkxC5wckJ/P21wVpKaS1U1iRXqQdjZ603b2rDf/9lL05LiVVgoJYkGoeAAEj1fEcHHvQ7lZU7wCWsa8FKBK6lDJl17gngSPpEqjLQCKiFtFgJIfKeJE1C6MVg0Hom9/SEkBDr10tIgBs3snf68No1rTUsPh4uXNCGzDg7azGlTqSMQ0gIeHhkuKo7EJoyZCYZuE7GCVXqhOs6kACcSRnSckG7dqtRyhCOdupPCCFykyRNQhQ0jo5QqpQ2WEsp7QL3jJKrs2fh+HFtOH0a4uLg8GFtsKRkScsJVWio1teVFddb2aHdkecLVM2ibBzadVWpE6pzwC5gM1pL1caUwagCD5KoRkCVlPcUQoickqRJiKLAYNBahzw8IDg487IJCXDmjJZAnTjxIJkyDtevw5Ur2rB1a/r1HR2hXLkHLVWlSmnXX3l5PbhezPjay0sbsniEkjPaM/fKWFimgKNoyZNxOAAcSxn+L6WcJ1oLVA205wZ6ofVb5ZVqME57INdPCSHSk6RJCGHO0fFBq5ElN29aTqaOH4eTJ7Wk6+hRbbCWm5vlhCqzZCtlbPD25lE3Nx41GOiesrkbwFYeJFFb0O4U/DtlyIodWpKVWWKV2TxvtE5DJfESonCRpEkIkT3e3lCrljaklZSknepLnVRdu6Z142C8cN34OiZGO2UIcPeuNpw/n7OYHBzMEikfb29ae3nROiXZSvT2Zn/58mwqX56jJUsS4+ZGjKsrN52diXF0JMbenhiDgZtoF6cnoz3X7yZwKmcR4UDOkq3U0y45fG8hRN6QpEkIkXvs7bXTf8HB0LRp1uUTErS7AS0lVKnHmS1LTta6bjBen2WBA1AzZciQhwfK25t7fn7EBAURExBATKlS3CxVipgSJYjx8SHGy4sYT09uenhoiZeLCzFOTtx0dCTGYNAGtKQr9Z2BOeVEzlu6jK8zP/EphMgOSZqEEPpxdHzQ9UFOKAW3b+cs2TK+vndP29bt2xhu38bt7Fncdu60qnuFdHXx9kZ5e3PH35+bQUHE+Plpg68vN42Jl6cnMcWKEePuzk03N2KcnYlxcjJr7bqFdq1WPNoF8Jdz9tcBtNOEOW3pMg7y+BwhNJI0CSEKLoMBihXThpz2Chwfn71kK22ZmBgteUtIgCtXMFy5gsfRo3gA2YrI1xe2bIHy5UlGS5xi0E4RxqQZrJl3O2Wz91KGizn526Bd31UGrQuJ8qkG47R3DrcrREEkSZMQomhzctK6UChZMmfrJydrrV3ZbeFKPY6Lg6tXYfly6NcPOx608pTNYbUSMX+2oKXEKu102nn30E41nkoZ1lh4n+KYJ1SpXwch3TyIwkWSJiGEeBh2dg86Kc2p997THhZ96FCuheWAltAUf4htxKNdk3UC+C/VcDxlfAmt49HrwA4L6zsBIaRPpoydn7o+RGxC6EGSJiGE0FtYmDaeP1+7A7FZM4iIAD8/XcNy4sHjcxpZWH6bjBOqk2hJ178pgyUBaElVqIVxIHItlbA9kjQJIYTeIiK0hylfuwbTpmkDQOXK2l2IxiE7vcDnAw+gWsqQViJwlvTJlHGIRXtMzgW0vrTScgTK8SCJSptY+eReNYSwmiRNQgiht5AQrRf2detg7Vpt2LNHO1136BBMnaqVCwt7kEBFRNhcEpWaA1rSUw5onmaZ4sFpvxNoCVXq8Sm0Zw0eTRks8UZLniy1VJVD60VeiNxmUEopvYPIa2fPnqVMmTKcOXOG0jm9w0YIIfLTtWuwYQOsWaMlUXv3pi9TpYp5EpXTi9ltTCLaswXTJlPG8aUs1jegnd6zdNovBO20oFygXrDp9bsuSZMQQhQE167B+vUPWqIyS6J69YLq1fM5wPxzB+2aqYySqjtZrO+M1hplTKIqAi8ChSPlLBokacpDkjQJIQqdq1fNk6h9+x4sq1kToqN1CkxfCriC5WTqOHAGSLKwngvQHRgEPJIfgYqHotfvulzTJIQQBZGvL/zvf9qQnAyvvgr/93/asv799Y1NRwagVMrQwMLyBLTEKXUytRKty4RpwLfAs8C7QHjehysKGDmtK4QQBVlSEvTurSVMBgN8/z306KF3VDbLEe20XHPgdeBTYBuwFmiL1lL1K1oXC42BJWgdfAoBkjQJIUTBlZiotTD98IPWyeasWdCzp95RFTgGIAJYBhwAXkPro2oz8BxQCa0F6p5eAQqbIUmTEEIUROfPwzPPwOzZYG8P8+ZB1656R1XghQHT0S40H4rWtcFRoA/aReNRegUmbIIkTUIIUZAopbUshYXBn3+CoyMsXAgdO+odWaESgHbq7gwwCQhG6+rgacDCfYuiiJCkSQghCopjx6B5c3j9de1hv/Xqwc6d8OyzekdWaHkAA9BO2zVCe6BxSzLudFMUbnmaNI0ePZpGjRrh5uaGt7e3xTKnT5+mXbt2uLu74+vry1tvvUV8fLxZmX379hEREYGrqytBQUGMGjWKItBTghBCaBITtQf6VqumdXbp6goTJkBUlDZP5Dl34A+gBlqLUwu0x8SIoiVPuxyIj4/nhRdeIDw8nOnTp6dbnpSURJs2bShZsiQbN27k2rVrdOvWDaUUkydPBiA2NpYWLVrQrFkztm/fzpEjR+jevTvu7u688847eRm+EELoKz4eNm6E997TWpRAa2n67jsIDdU3tiLIG/gbaILW0tQSWA/46hiTyF95mjSNHDkSgJkzZ1pcvmLFCg4ePMiZM2cIDAwEYMKECXTv3p3Ro0fj6enJ3LlzuX//PjNnzsTZ2ZmqVaty5MgRJk6cyKBBgzAYDHlZBSGEyF/Hj8Py5fD33/DPP3D7tjbf2xsmToTu3bWuBYQu/IDZQEPgEPANEKlrRCI/6XpNU1RUFFWrVjUlTACtWrUiLi6OnSn/VUVFRREREYGzs7NZmfPnz3Py5EmL242LiyM2NtY03Lp1K0/rIYQQOXb7NixbpnVI+cgjUL489OsHS5dqy0qW1LoROHRI615AEiZdRQMdUl77oHWEKYoOXXsEv3jxIn5+fmbzfHx8cHJy4uLFi6Yy5cqVMytjXOfixYuEhISk2+6YMWNMrVxCCGFTrlyBAwdg61atNWnjRkhIeLDcwQEaNYLWraFVK+2RKHZyz44tWAK8BNxF67tpGVBBz4BEvst20jRixIgsE5Lt27dTt25dq7Zn6fSaUspsftoyxovAMzo1N3ToUAYNGmSaPnfuHGFhYVbFI4QQucKYHB08qI2Nr69cSV82NFRLkFq1gmbNwNMz/+MVmYpDe6jvfbTexBeiXeMkipZsJ039+/enc+fOmZZJ2zKUEX9/f7Zu3Wo278aNGyQkJJhak/z9/U2tTkaXL18GSNdKZeTs7Gx2Oi82NtaqeIQQItuykxwZhYRA9erQooWWKFWQ9gpb54z2LLt1KdNeOsYi9JPtpMnX1xdf39y5VyA8PJzRo0dz4cIFAgICAO3icGdnZ+rUqWMqM2zYMOLj43FycjKVCQwMtDo5E0KIh3blSvrE6MCBrJOjKlW0ISxMG1eqBO7u+Re3yDU/ANWB1cB3QG99wxE6yNNrmk6fPs3169c5ffo0SUlJ7N69G4AKFSrg4eFBy5YtCQsLo2vXrowbN47r168zePBgXn/9dTxTmqe7dOnCyJEj6d69O8OGDePo0aN8+umnDB8+XO6cE0LkPkmORAYqAGOAgcBgoDVaT+Gi6MjTpGn48OHMmjXLNF2rVi0A1qxZQ9OmTbG3t+ePP/6gb9++NG7cGFdXV7p06cL48eNN63h5ebFy5Ur69etH3bp18fHxYdCgQWbXLAkhRI5cvAjbtj0Ydu+2LjkyJkaSHBU5b6Jdz7QR6AmsQHvgrygaDKoIdK199uxZypQpw5kzZyhdurTe4Qgh9HD7ttZBZOok6fRpy2UlORKZOIp2mu4+MAftjjqRv/T6Xde1ywEhhMgTiYkPbus3JkgHDkBysnk5g0FLiOrX14Y6daByZUmORKYeAT4CPgAGoT3E10fXiER+kaRJCFGwKQWnTpknSDt3wr176cuWLg0NGpgnScWK5X/MosAbjNbKdAgYBkzVNxyRTyRpEkIULAkJsG4dbN78IEmydB2Sp+eD5Kh+fahXD1I9fUCIh+EETAMigG+BbmiPVhGFmyRNQgjbl5wMUVEwdy78/DNcu2a+3NERatQwb0V69FHpSVvkqceB7sBMtFN1q/UMRuQLSZqEELZr/3746SdtOHXqwfxSpbSOIY1JUo0a4OKiX5yiyHofLWnaDCQAjrpGI/KaJE1CCNuhFPz7r/aw2rlzYe/eB8s8POB//4OXXoInntCe0SaEzh5F6x08BtgH1NY3HJHH5FtHCKGf+/dhxw7YtEkbNm82P/Xm6AhPPw1dukC7duDqql+sQlhgB9QHVgKjgJ8AN10jEnlJkiYhRP65fPlBcrRpk3aXW3y8eRkXF2jUCDp1gg4doHhxfWIVwkqDgLXAb2jXOf0GBOkZkMgzkjQJIfJGcjIcPvygFWnTJjh2LH05Pz9o3PjBUKsWpDxnUoiCoDXaReDPATvRWp6WAnX0DErkCUmahBC54+5d2L79QYIUFQU3bpiXMXYm2bix1prUuDGEhmrzhSjAmgDbgHbAwZTp/wM66BmUyHWSNAkhck4p7YLtr7/WTrUlJpovd3XV7nAztiKFh4O3ty6hCpHXQtHuousMLAdeACYD/fUMSuQqSZqEEDmzcye89ZZ2fZJRQID5qbaaNbWLuYUoIryAZWjXOU0GhgKvIReHFxaSNAkhsufKFfjgA/jhB62lyd0dhg3T7nALDpZTbaLIcwC+REueTqJd39RZz4BErpHucoUQ1klIgC+/hEcege+/1xKml17S+lUaNgzKlZOESYgUBuDllNdz9AxE5CpJmoQQWVu9WjvVNnAgxMRod7ht2ABz5kCQ3FwthCUvpYz/Biw8HVEUQJI0CSEyt3699siSgwfB1xe++067S+6xx/SOTAibVgmoCyQCU3WOReQOSZqEEJkzXrvUpg0cOQKvvw729npHJUSB8HbK+GNgl56BiFwhSZMQImP378Nvv2mvhw4FHx994xGigHkReB6ttekl4K6+4YiHJEmTEOIBpbTTcN98oz3CpHRpiI3VxuHhekcnRIFjAL4FAoDDQCfgjq4RiYchXQ4IUZQppZ1yW7MG1q7VhkuXzMu4ucHw4WAn/2MJkRMl0O6gawP8DjRF647AX8eYRM5I0iREUaIUHD+uJUnGROn8efMyLi5ax5TNmmlDvXrSQaUQD+kJtOfTPQPsABoCfwJhegYlsk2SJiEKu1OnHiRJa9bAmTPmy52ctFNvxiSpQQNwdtYnViEKsUZAFPA0cDRl+legmZ5BiWyRpEmIwujaNYiMhD//hBMnzJc5OGiJkTFJCg/XnhEnhMhzFdASp2eATUArYCbQRceYhPUkaRKisFm1Crp1e3Dazd5eO8XWtKmWJDVurD36RAihixLAKqAb8DPwCuCOlkgJ2yZJkxCFRXy89ky48eO16YoVYdw4LVkqVkzX0IQQ5lyAeWjJ0gy0u+qWo10kLmyXJE1CFAaHD2sPzI2O1qZ794YJE6RFSQgbZgd8B9wAlgDtgbVAbf1CElnI03uIR48eTaNGjXBzc8Pb2zvd8j179vDiiy9SpkwZXF1dqVy5Ml9++aVZmZMnT2IwGNINy5cvz8vQhSgYlIJvv4XatbWEqUQJ+PVXmDZNEiYhCgAHtBanpsAtoDVwRM+ARKbytKUpPj6eF154gfDwcKZPn55u+c6dOylZsiRz5syhTJkybN68mV69emFvb0///v3Nyq5atYoqVaqYposXL56XoQtRMLzzDnzxhfb6ySdh1iwIDNQ3JiFEtrgAv6HdRbcLqA+8DvQHgnWMS6SXp0nTyJEjAZg5c6bF5a+99prZdGhoKFFRUSxevDhd0lSiRAn8/aUrMCFMvv/+QcL0+edaAiUdUApRIHkCf6HdTbcbGA9MBJ4DBgKN0XoXF/qyuW/YmJgYi61I7du3p1SpUjRu3JiFCxfqEJkQNmTdOujbV3s9ahS8+64kTEIUcKWAnWi9hTcHkoFFQBOgLjAbiNMtOgE2ljRFRUXx888/07t3b9M8Dw8PJk6cyMKFC/nzzz9p3rw5nTp1Ys6cORluJy4ujtjYWNNw69at/AhfiPxx4gQ8/zwkJkKnTvDhh3pHJITIJXZAW7QuCfYCPdFO3+1C65qgHDAKuKxTfEVdtpOmESNGWLwwO/WwY8eObAdy4MABnnnmGYYPH06LFi1M8319fXn77bepX78+devWZdSoUfTt25fPP/88w22NGTMGLy8v0xAWJh3Vi0Lizh1o107rvLJOHfjxRzBIo70QhVE14HvgDDAaCAQuApFAGeA14KRewRVRBqWUys4KV69e5erVq5mWKVeuHC4uLqbpmTNnMnDgQG7evGmx/MGDB2nWrBk9e/Zk9OjRWcYwd+5cevbsyb179ywuj4uLIy7uQSPmuXPnCAsL48yZM5QuXTrL7Qths1asgFattNfbtmmdVgohioQEYCEwCdiWMs8ZeBd4H/DQJyxdnD17ljJlyuT773q2LwT39fXF19c31wI4cOAATzzxBN26dbMqYQKIjo4mICAgw+XOzs44p3p2Vmxs7EPHKYRNiIiAmjVh927tOqbVq7Uev4UQhZ4j8GLKEAV8AKwBPgF+BMYCL2Fj190UMnl699zp06e5fv06p0+fJikpid27dwNQoUIFPDw8OHDgAM2aNaNly5YMGjSIixcvAmBvb0/JkiUBmDVrFo6OjtSqVQs7OzuWLVvGV199xWeffZaXoQthm5ydYcEC7dTcunXw8ccwYoTeUQkh8lk4sBqtU8zBwHG0a56+QTulV023yAo5lYe6deumgHTDmjVrlFJKRUZGWlweHBxs2sbMmTNV5cqVlZubmypWrJiqU6eOmj17drbiOHPmjALUmTNncrF2Quho7lylQClHR6XOntU7GiGEju4ppcYopTyUUiil3JVSv+kaUd7T63c929c0FUR6nfsUIk81baq1Nr33HkjLqxBF3gWgK1oLlAH4DK0VqjDeKqLX77qc+hSioBo8WBtPmwZy3Z4QRV4AWgeZfdBO27yHdoed9O2UeyRpEqKgevppqFRJS5h++EHvaIQQNsARmAJMRvuBnwm0AK7oGFNhIkmTEAWVnR0MGqS9/vJL7eG9Qogiz4D23Lo/0R7PsgGoA/yuZ1CFhCRNQhRkJ05oY4c8vRFWCFEAtQK2AOXROshsB3QAzukZVAEnSZMQBdW//8L48drrL76QnsGFEOlUBvagdYBpj/Ysu8rA10CSjnEVVJI0CVEQKQVvvgkJCdq1Te3a6R2REMJGuQOfoz0MuAFwC3gTra+n3fqFVSBJ0iREQbR4MaxcqXV2+dVX0sokhMhSDWATWgeYnsB2oC7wDnBDx7gKEkmahCiIfvpJGyckwIABsGSJ9loIITJhD/QFDgEd0U7RTQTKASOBGN0iKxgkaRKiIPrwQ3j8cUhOhj/+gOeegzJltI4u//1X7+iEEDYuEFiAdoddNSAWGAGEAJ+incIT6UnSJERBVKuW1hv4v//C+++Dnx9cugTjxml9Nz3+OMyaBXfu6B2pEMKGPYV2XdMCtAvEb6A9CDgUGAfIN4g5SZqEKMgefRTGjoUzZ7RTdG3bav03bdgA3btDYCD06QM7dkg/TkIIi+zQTtXtA+YAjwBX0XoUDwUmAff0Cs7GSNIkRGHg6AjPPAPLlsHp0zB6NISGar2Ff/st1KsHL7ygd5RCCBtmD7wEHARmoJ2quwy8DVQAfkJ7PEtRJkmTEIVNUBAMGwZHj8I//0CXLtr8RYvgijxMQQiROQegO/Av8B1QBjiPllA1BfbqFZgNkKRJiMLKzg6aNYO5cyEsTJu3ebO+MQkhCgxH4HXgCPAx4AqsB2oDA4CbukWmH0mahCgKHntMG2/cqG8cQogCxwX4EK2bgufRuin4CngU7TResn6h5TtJmoQoCho31sabNukbhxCiwAoGFgIrgErAFeA1oBHao1qKAkmahCjM7t+H77+HTz7Rpg8e1DceIUSB1wItSRoHeABbgSbANj2DyieSNAlRGMXEwGefQUgI9OqlXRTu46PdVSeEEA/JCRiMdrH442idYbai8D/LzkHvAIQQuSQhAaKjtbvkpk3TuhsAKF0aBg2C118HDw99YxRCFCqBwB9oCdNm4ElgHVBFz6DykCRNQhRUd+/C1q1aR5YbNkBUlHkP4GFh2mNVXnwRnJz0i1MIUah5AL8D5YFraI9j+UXPgPKQJE1CFBQ3bmgXcq9fryVJO3emf0ivjw80aQI9e0KbNlq3A0IIkYcSgbfQHsFiD7ysbzh5SpImIWzVuXMPWpE2bID9+9M/CiUoSEuSHn9cG4eFSaIkhMg3CWidXv6CllDMA57RNaK8JUmTELZAKe1i7dRJ0vHj6cs9+uiDBKlJEyhXDgyGfA9XCCHigE7Ab2gdYf5C4U6YQJImIfShFPz3n/aYk3/+gbVr4dIl8zJ2dlCz5oME6bHHwM9Pj2iFEMKMAl5ES5icgV+Bp3SNKH9I0iREfjlz5kGStGaNNp2akxM0aPAgSWrUCDw99YlVCCEysRwtUXJCuwj8SX3DyTeSNAmRVy5d0pIjY5J07Jj5ckdHCA+HJ57QnhFXvz64uOgTqxBCWCkZGJry+i2KTsIEkjQJkXuuX4d16x4kSQcOmC+3s4N69bQk6YkntJYkNzd9YhVCiByaj9YjuCcwROdY8lueJk2jR4/mjz/+YPfu3Tg5OXHz5s10ZQwWLmKdOnUqffr0MU3v27eP/v37s23bNooXL07v3r356KOPLK4rRL5ISNAu1D58WHsI7j//aB1Lpr67zWCAGjUeJElNmsjpNiFEgRYPfJTy+n2ghI6x6CFPk6b4+HheeOEFwsPDmT59eoblZsyYQevWrU3TXl5eptexsbG0aNGCZs2asX37do4cOUL37t1xd3fnnXfeycvwRVGnFFy5Av/+m344fhwSE9OvU7nygyQpIgJKFLWvFCFEYfYDcBzwBwboHIse8jRpGjlyJAAzZ87MtJy3tzf+/v4Wl82dO5f79+8zc+ZMnJ2dqVq1KkeOHGHixIkMGjRIWpvEw7t/X7vd35gQHTny4LWF1lETNzeoWBHq1NGSpKZNISAgv6IWQoh8dQ8wPr3yQ8Bdx1j0YhPXNPXv35+ePXsSEhJCjx496NWrF3YpHfRFRUURERGBs7OzqXyrVq0YOnQoJ0+eJCQkRK+wRUGilNZZpKVWo1On0ncaaWQwQHCwlhylHYKCpI8kIUSRMQ04D5QFeuoci150T5o+/vhjmjdvjqurK6tXr+add97h6tWrfPjhhwBcvHiRcuXKma3jl9JXzcWLFy0mTXFxccTFxZmmb926lXcVELbl1i3zliJjy9GRI+bPZUvL21tLhB591DwxqlABXF3zLXwhhLBFt4GxKa+Ho/XNVBRlO2kaMWKE6bRbRrZv307dunWt2p4xOQKoWbMmAKNGjTKbn/YUnEppFcjo1NyYMWOyjFEUInfvwqBBsGwZnD+fcTkHBwgNtdxqVLKktBoJIUQGpgCX0R7K+4rOsegp20lT//796dy5c6Zl0rYMZUfDhg2JjY3l0qVL+Pn54e/vz8WLF83KXL58GXjQ4pTW0KFDGTRokGn63LlzhIWF5TgmYcPOn4f27bWH1xqVLGk5MQoN1fpGEkIIkS0rU8Zvoz0ypajKdtLk6+uLr69vXsQCQHR0NC4uLnh7ewMQHh7OsGHDiI+Px8nJCYAVK1YQGBiYYXLm7Oxsdg1UbGxsnsUrdLRrF7RrpyVOJUrAjBnao0Z8fPSOTAghCpWbKeNgPYOwAXl6TdPp06e5fv06p0+fJikpid27dwNQoUIFPDw8WLZsGRcvXiQ8PBxXV1fWrFnDBx98QK9evUxJT5cuXRg5ciTdu3dn2LBhHD16lE8//ZThw4fLnXNF2eLF0LWrdmouLEw7NRcaqndUQghRKBmbHrwyLVX45WnSNHz4cGbNmmWarlWrFgBr1qyhadOmODo6MmXKFAYNGkRycjKhoaGMGjWKfv36mdbx8vJi5cqV9OvXj7p16+Lj48OgQYPMTr+JIuTWLfjqKzBe89aqFSxYAF5F/VAWQoi8E5MyLurd8xqUyuhe68Lj7NmzlClThjNnzlC6dGm9wxHZcekSbNig9bq9YQPs3g3JydqyN9+EiRO1C7yFEELkiUtAGSABOIltnKLT63ddfm2E7VBKe6itMUHauFHrdDKt4GAYNgx69cr/GIUQoghRQF+0hKkmWh9NRZkkTUI/iYmwZ495knTpknkZgwGqVtWe2/bYY9pQpow+8QohRBHzC7AYLVmYART1K4klaRL55+5d2Lr1QZIUFQW3b5uXcXKCevUeJEmNGsndcEIIoYPLgPEK4w/QWpqKOkmaRN47eBB699YSpoQE82WentC48YMkqV49cHHRJ04hhBAmA4CrQHVgmM6x2ApJmkTeW7VKa10yCgnRevBu0kQ79WZvr19sQgghLNqUMn4USRaM7PQOQBQBffrAqFFQrJg2feIE/PorxMdLwiSEEDbqW8AeWAgM0TkWWyFJk8h7Tk7w0Ufw338wcKA2/c8/UL8+vPyydkG4EEIIm/IUMD3l9Ti0JKqok6RJ5J+SJeGLL+Dff8HYr8ZPP8GVK/rGJYQQwqJuaBeBA/yfnoHYCEmaRP5KToZx4+DsWW160iQICNA1JCGEEBkrlzKW+5jl2i6Rn5KStA4pf/xR63/p22/h9df1jkoIIUQmzqWMg3SNwjZI0iTyR0ICdOsG8+aBnR3MmqVdzySEEMKmGZOmQF2jsA2SNIn88dFHWsLk4KCNO3TQOyIhhBBWuJAy9tU1Ctsg1zSJvHf5Mnz1lfZ69mxJmIQQogCpnDJerGsUtkGSJpH3JkyAe/e03r47ddI7GiGEENnQH62/pn+AXTrHojdJmkTeunoVvvlGez18uHYBuBBCiAKjLGD8d3eCnoHYAEmaRN6aNAnu3NG6FWjQQO9ohBBC5MA7KeMFwDo9A9GZJE0ib+3cqY0vXAA/P+0U3QcfwLp12mNUhBBC2LzawJNAEtAUeBz4C1A6xqQHSZpE3vr2W3jnHahWDZSCHTvg00+haVMoXhzatYPJk7VewlVRO/yEEKLgmAP0AByBDcDTQC1gPloyVRQYlCr8v1Rnz56lTJkynDlzhtLGx3eI/Hf+PKxaBStWwMqV2l11qZUtCy1bakPz5lpSJYQQwqacAyaiPYvuTsq88sB7wCuASz7EoNfvuiRNQh/JybB3r5ZArVgBGzaYn64zGLRTecYkqlEjsLfXL14hhBBmrgNfA18B11Lm+QODgN6AZx6+tyRNeUiSpgLg7l1Yv/5BEnXggPnyZs3gr7/A2Vmf+IQQQlh0B/gBGA+kPFWUYGALWhKVF/T6XZdrmoRtcHOD1q1h4kTYv197oO+MGfDii+DuDmvWwFtv6R2lEEKINNyBAcB/wAy0LgpOAR2Awna7jyRNwjYFBUH37vDTT/DLL9rpuu++g2nT9I5MCCGEBU5Ad2Al4AVsQkumChNJmoTte+op7Y47gDff1K5/EkIIYZMeBX4CDMA04Dt9w8lVkjSJguH997VHsCQmas+uO3s263WEEELo4mlgdMrr/mitToWBJE2iYDAYYPp0qFFD66pg3Di9IxJCCJGJIWjXNSUA/wNO6xtOrpCkSRQc7u7wySfa64ULtW4LhBBC2CQDMBOoAVwGnuFBv04FVZ4mTaNHj6ZRo0a4ubnh7e2dbvnMmTMxGAwWh8spHR+ePHnS4vLly5fnZejCVrVoAZ6eWkeZmzfrHY0QQohMuAO/ASWB3WgXihfkf3fzNGmKj4/nhRde4I033rC4vFOnTly4cMFsaNWqFREREZQqVcqs7KpVq8zKPfHEE3kZurBVzs7w7LPa619+0TUUIYQQWQsGFqM9fmUh8LG+4TyUPE2aRo4cydtvv021atUsLnd1dcXf39802Nvb888//9CjR490ZUuUKGFW1snJKS9DF7asQwdtvGSJrmEIIYSwzmPA1JTXI4B/9AvlodjUNU3/93//h5ubGx2MP4qptG/fnlKlStG4cWMWLlyoQ3TCZuzbp409PPSNQwghhNV6pAygPbuuILKppOnHH3+kS5cuuLq6muZ5eHgwceJEFi5cyJ9//knz5s3p1KkTc+bMyXA7cXFxxMbGmoZbt27lR/giP9y4AZ9/rr0eNkzfWIQQQmTLuynjvyiYd9NlO2kaMWJEhhdvG4cdO3ZkO5CoqCgOHjyY7tScr68vb7/9NvXr16du3bqMGjWKvn378rnxh9OCMWPG4OXlZRrCwsKyHY+wUePHQ0wMVK0KnTvrHY0QQohsqAg0Q7sY/AedY8mJbD+w9+rVq1y9ejXTMuXKlcPFxcU0PXPmTAYOHMjNmzczXKdHjx7s2rWL6OjoLGOYO3cuPXv25N69exaXx8XFERcXZ5o+d+4cYWFh8sDegu78eXjkEe3hvkuWwDPP6B2REEKIbFoAdAYC0Z5R55CDbej1wN5sx+rr64uvr2+uBnH79m1+/vlnxowZY1X56OhoAgICMlzu7OyMs7OzaTo2NvahYxQ627kTnn9eS5jq14f27fWOSAghRA48h9YFwXm0u+o66htOtuQkwbPa6dOnuX79OqdPnyYpKYndu3cDUKFCBTxSXcS7YMECEhMTeemll9JtY9asWTg6OlKrVi3s7OxYtmwZX331FZ999llehi5syQ8/QL9+EB8P5cvDrFlaD+FCCCEKHCfgDWAUMB54Aa0jzIIgT5Om4cOHM2vWLNN0rVq1AFizZg1NmzY1zZ8+fTr/+9//8PHxsbidTz75hFOnTmFvb8+jjz7Kjz/+yMsvv5yXoQtbcO8e9O8PP/6oTbdvryVMFjpKFUIIUXD0Bz4HtgPrgQh9w7Fatq9pKoj0OvcpHsKJE9rpuOhosLPTHp/y/vvaayGEEAXeG8A0oA3wezbX1et3XX6BhO1ZvRrq1NESJl9fWLEChg6VhEkIIQqRQWin5f4Ajugci7XkV0jYlu+/h9attf6Y6teHXbugeXO9oxJCCJHLHgGMzws5pmcg2SBJk7ANSUkweDD06gWJidClC6xbB2XK6B2ZEEKIPGLsHKigPN9BkiahvwsXoG1bmDBBmx45EubMgVR9fQkhhCh8bqeMi+kahfXy9O45IbK0cCH06QPXroGzM8yYAS++qHdUQggh8sGNlLGXrlFYT5ImoY+bN7XuBObO1aZr1YLZs6FKFV3DEkIIkT/uAHdTXpfSM5BskNNzIv+tXg3VqmkJk50dfPghbNkiCZMQQhQhl1PGLoC7noFkg7Q0ifxz/77W19JXX2nTFSporUsNG+oblxBCiHx3JWVcCukRXAhzly7Bc89BVJQ2/cYbMG4cuBeU/y+EEELkJmPSVFLXKLJHkiaR9/buhXbt4PRp7REoc+fC00/rHZUQQggdxaSMC8pF4CBJk8hrS5dqfS7duQOPPAK//w6PPqp3VEIIIXR2K2XsqWsU2SMXgou8oRR8/jk8+6yWMDVvDlu3SsIkhBACgNiUcUHpowkkaRJ5ISEBevbULvpWSuuH6a+/wMdH78iEEELYCGNLU0FKmuT0nMhdd+7ACy9oSZKdHUyapPXHZCgo90YIIYTID9dSxgXp32lJmkTuuXJFexzKtm3g6goLFmgXgAshhBBpnEsZB+kaRfZI0iRyx/Hj0Lo1HD0KxYtrF3yHh+sdlRBCCBslSZMoOmJj4a23ICZG6z7go4+0vpiCg2H5cqhUSe8IhRBC2LAdKWNJmkThV68eHDmivV6yRBvXqAF//gmBgbqFJYQQwvapVK+3AXX0CiSb5O45kTNffPHgdatW0K8frFsnCZMQQogspb416CXdosg+aWkSOfP001p3AkIIIUQOFMRfEGlpEkIIIYSwgiRNQgghhBBWkKRJCCGEEMIKkjQJIYQQQlhBkiYhhBBCCCtI0iSEEEIIYQVJmoQQQgghrJBnSdPJkyfp0aMHISEhuLq6Ur58eSIjI4mPjzcrd/r0adq1a4e7uzu+vr689dZb6crs27ePiIgIXF1dCQoKYtSoUSjpI0gIIYQQ+SjPOrc8fPgwycnJfPvtt1SoUIH9+/fz+uuvc+fOHcaPHw9AUlISbdq0oWTJkmzcuJFr167RrVs3lFJMnjwZgNjYWFq0aEGzZs3Yvn07R44coXv37ri7u/POO+/kVfhCCCGEEGYMKh+bbMaNG8fUqVM5fvw4AH/99Rdt27blzJkzBKY8fmP+/Pl0796dy5cv4+npydSpUxk6dCiXLl3C2dkZgLFjxzJ58mTOnj2LwWDI8P2Mzp49S5kyZThz5gylS5fOuwoKIYQQIs/p9buer49RiYmJoXjx4qbpqKgoqlatakqYAFq1akVcXBw7d+6kWbNmREVFERERYUqYjGWGDh3KyZMnCQkJSfc+cXFxxMXFmb0vwIULF/KiWkIIIYTIR8bf8+Tk5Hx933xLmv777z8mT57MhAkTTPMuXryIn5+fWTkfHx+cnJy4ePGiqUy5cuXMyhjXuXjxosWkacyYMYwcOTLd/Pr16z9sNYQQQghhI86cOUPZsmXz7f2ynTSNGDHCYkKS2vbt26lbt65p+vz587Ru3ZoXXniBnj17mpW1dHpNKWU2P20Z4xnFjE7NDR06lEGDBpmmr1+/TkhICPv378fLyyvT2AubW7duERYWxsGDBylWrJje4eSbolpvkLpL3aXuRUVRrTdoZ5CqVq1K5cqV8/V9s5009e/fn86dO2daJnXL0Pnz52nWrBnh4eF89913ZuX8/f3ZunWr2bwbN26QkJBgak3y9/c3tToZXb58GSBdK5WRs7Oz2ek8ozJlyuDp6Zlp7IVNbGwsAEFBQUWq7kW13iB1B6m71L1oKKr1Bkz1dXDI16uMsp80+fr64uvra1XZc+fO0axZM+rUqcOMGTOwszPv4SA8PJzRo0dz4cIFAgICAFixYgXOzs7UqVPHVGbYsGHEx8fj5ORkKhMYGJjutJ0QQgghRF7Js36azp8/T9OmTSlTpgzjx4/nypUrXLx40azVqGXLloSFhdG1a1eio6NZvXo1gwcP5vXXXzdlkV26dMHZ2Znu3buzf/9+fv31Vz799FMGDRpk1Z1zQgghhBC5Ic/atVasWMGxY8c4duxYutsBjdck2dvb88cff9C3b18aN26Mq6srXbp0MfXjBODl5cXKlSvp168fdevWxcfHh0GDBplds5QVZ2dnIiMjLZ6yK+yKat2Lar1B6i51l7oXFUW13qBf3fO1nyYhhBBCiIJKnj0nhBBCCGEFSZqEEEIIIawgSZMQQgghhBUkaRJCCCGEsEKBT5rKlSuHwWBIN/Tr1y/DddatW0edOnVwcXEhNDSUadOm5WPEuSe7dV+7dq3F8ocPH87nyB9OYmIiH374ISEhIbi6uhIaGsqoUaOyfAZRYdjvOal7YdnvoPWAPHDgQIKDg3F1daVRo0Zs374903UKw36H7Ne9oO739evX065dOwIDAzEYDCxZssRsuVKKESNGEBgYiKurK02bNuXAgQNZbnfRokWEhYXh7OxMWFgYv/76ax7VIGfyot4zZ860+Bm4f/9+HtYk+7Kq++LFi2nVqhW+vr4YDAZ2795t1XbzZJ+rAu7y5cvqwoULpmHlypUKUGvWrLFY/vjx48rNzU0NGDBAHTx4UH3//ffK0dFRLVy4MH8DzwXZrfuaNWsUoP7991+z9RITE/M38If0ySefqBIlSqjff/9dnThxQv3yyy/Kw8NDTZo0KcN1Cst+z0ndC8t+V0qpjh07qrCwMLVu3Tp19OhRFRkZqTw9PdXZs2ctli8s+12p7Ne9oO73P//8U33wwQdq0aJFClC//vqr2fKxY8eqYsWKqUWLFql9+/apTp06qYCAABUbG5vhNjdv3qzs7e3Vp59+qg4dOqQ+/fRT5eDgoLZs2ZLHtbFeXtR7xowZytPT02z/X7hwIY9rkn1Z1f3//u//1MiRI9X333+vABUdHZ3lNvNqnxf4pCmtAQMGqPLly6vk5GSLy9977z1VqVIls3m9e/dWDRs2zI/w8lRWdTd+id64cSN/A8tlbdq0Ua+99prZvP/973/q5ZdfznCdwrLfc1L3wrLf7969q+zt7dXvv/9uNr9GjRrqgw8+sLhOYdnvOal7YdjvaX9Ak5OTlb+/vxo7dqxp3v3795WXl5eaNm1ahtvp2LGjat26tdm8Vq1aqc6dO+d6zLkht+o9Y8YM5eXllYeR5j5LSZPRiRMnrE6a8mqfF/jTc6nFx8czZ84cXnvttQx7C4+KiqJly5Zm81q1asWOHTtISEjIjzDzhDV1N6pVqxYBAQE0b96cNWvW5FOEueexxx5j9erVHDlyBIA9e/awceNGnn766QzXKSz7PSd1Nyro+z0xMZGkpCRcXFzM5ru6urJx40aL6xSW/Z6TuhsV9P2e2okTJ7h48aLZPnV2diYiIoLNmzdnuF5Gn4PM1rElOa03wO3btwkODqZ06dK0bduW6OjovA7XJuTVPi9USdOSJUu4efMm3bt3z7DMxYsX0z3o18/Pj8TERK5evZrHEeYda+oeEBDAd999x6JFi1i8eDEVK1akefPmrF+/Pv8CzQXvv/8+L774IpUqVcLR0ZFatWoxcOBAXnzxxQzXKSz7PSd1Lyz7vVixYoSHh/Pxxx9z/vx5kpKSmDNnDlu3buXChQsW1yks+z0ndS8s+z0142O4LO3TtA92T7tedtexJTmtd6VKlZg5cyZLly5l3rx5uLi40LhxY44ePZqn8dqCvNrn+ft44Dw2ffp0nnrqKQIDAzMtl7YlRqV0il6Qn2VnTd0rVqxIxYoVTdPh4eGcOXOG8ePH8/jjj+dHmLliwYIFzJkzh59++okqVaqwe/duBg4cSGBgIN26dctwvcKw33NS98Ky3wFmz57Na6+9RlBQEPb29tSuXZsuXbqwa9euDNcpDPsdsl/3wrTf07K0T7PanzlZx9Zktw4NGzakYcOGpunGjRtTu3ZtJk+ezFdffZVncdqKvNjnhaal6dSpU6xatYqePXtmWs7f3z9dpnn58mUcHBwoUaJEXoaYZ6ytuyUNGzYscP91vPvuuwwZMoTOnTtTrVo1unbtyttvv82YMWMyXKew7Pec1N2SgrjfAcqXL8+6deu4ffs2Z86cYdu2bSQkJBASEmKxfGHZ75D9ultSUPe7kb+/P4DFfZq2VSHtetldx5bktN5p2dnZUa9evQL9GbBWXu3zQpM0zZgxg1KlStGmTZtMy4WHh7Ny5UqzeStWrKBu3bo4OjrmZYh5xtq6WxIdHU1AQEAeRJV37t69i52d+UfX3t4+09vuC8t+z0ndLSmI+z01d3d3AgICuHHjBn///TfPPPOMxXKFZb+nZm3dLSno+z0kJAR/f3+zfRofH8+6deto1KhRhutl9DnIbB1bktN6p6WUYvfu3QX6M2CtPNvnD3UZuY1ISkpSZcuWVe+//366ZUOGDFFdu3Y1TRtvQX777bfVwYMH1fTp0wvsLchKZa/uX3zxhfr111/VkSNH1P79+9WQIUMUoBYtWpSfIT+0bt26qaCgINNt94sXL1a+vr7qvffeM5UprPs9J3UvLPtdKaWWL1+u/vrrL3X8+HG1YsUKVaNGDVW/fn0VHx+vlCq8+12p7Ne9oO73W7duqejoaBUdHa0ANXHiRBUdHa1OnTqllNJuvffy8lKLFy9W+/btUy+++GK6W++7du2qhgwZYpretGmTsre3V2PHjlWHDh1SY8eOtbkuB/Ki3iNGjFDLly9X//33n4qOjlavvvqqcnBwUFu3bs33+mUmq7pfu3ZNRUdHqz/++EMBav78+So6Otqs+4T82ueFImn6+++/Tf2RpNWtWzcVERFhNm/t2rWqVq1aysnJSZUrV05NnTo1nyLNfdmp+2effabKly+vXFxclI+Pj3rsscfUH3/8kY/R5o7Y2Fg1YMAAVbZsWeXi4qJCQ0PVBx98oOLi4kxlCut+z0ndC8t+V0qpBQsWqNDQUOXk5KT8/f1Vv3791M2bN03LC+t+Vyr7dS+o+93YVULaoVu3bkop7fb7yMhI5e/vr5ydndXjjz+u9u3bZ7aNiIgIU3mjX375RVWsWFE5OjqqSpUq2VzymBf1HjhwoCpbtqxycnJSJUuWVC1btlSbN2/Ox1pZJ6u6z5gxw+LyyMhI0zbya58blEq5KlIIIYQQQmSo0FzTJIQQQgiRlyRpEkIIIYSwgiRNQgghhBBWkKRJCCGEEMIKkjQJIYQQQlhBkiYhhBBCCCtI0iSEEEIIYQVJmoQQQgghrCBJkxBCCCGEFSRpEkIIIYSwgiRNQgghhBBWkKRJCCGEEMIK/w98JmMW7icPdgAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 600x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# temperature and salinity\n",
+    "fig,axs = ChartTwoSensors(profiles, [ranges['temperature'], ranges['salinity']], [0], \n",
+    "                          d['temperature'][0], d['temperature'][1], 'Temperature', colors['temperature'],    'ascent',\n",
+    "                          d['salinity'][0],       d['salinity'][1],    'Salinity',  colors['salinity'], 'ascent', 6, 4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "**Note: The above chart shows a temperature excursion to the left at a depth of about 75 meters (red). \n",
+    "There is not a corresponding excursion in the salinity (cyan), this illustrating a *lack* of coincidence.**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Attempting 1 charts\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# temperature: ascent versus descent\n",
+    "fig,axs = ChartTwoSensors(profiles, [ranges['temperature'], ranges['temperature']], [0], \n",
+    "                          d['temperature'][0], d['temperature'][1], 'T-Ascent',   colors['temperature'],    'ascent',\n",
+    "                          d['temperature'][0], d['temperature'][1], 'T-Descent',  'green',                  'descent', 6, 4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Note: The above chart compares profile temperature on ascent (red) with the subsequent descent (green). \n",
+    "The cold excursion noted above is present in both profiles. The temperature structure below 100 meters\n",
+    "is similar but less well-matched. This is an initial view of what we can expect for *stability*.**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
    "metadata": {
     "tags": []
    },
@@ -543,7 +961,6 @@
    ],
    "source": [
     "# this cell connects to the RCA Zarr filesystem on the cloud and lists available data streams\n",
-    "\n",
     "import netCDF4\n",
     "import xarray as xr\n",
     "import s3fs\n",
@@ -599,36 +1016,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 66,
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "OSB CTD stream: ooi-data/RS01SBPS-SF01A-2A-CTDPFA102-streamed-ctdpf_sbe43_sample\n",
-      "\n",
-      "site data is now lazy-loaded\n",
-      "<xarray.DataArray 'time' ()>\n",
-      "array('2024-07-06T11:12:59.607388672', dtype='datetime64[ns]')\n",
-      "Coordinates:\n",
-      "    time     datetime64[ns] 2024-07-06T11:12:59.607388672\n",
-      "Attributes:\n",
-      "    axis:           T\n",
-      "    long_name:      time\n",
-      "    standard_name:  time\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "osb_profiler_streams = [sname for sname in streamlist if 'SF01A' in sname]\n",
-    "for s in osb_profiler_streams: \n",
-    "    if 'ctdpf' in s: osb_sp_ctd = s\n",
-    "\n",
-    "print('OSB CTD stream:', osb_sp_ctd)\n",
-    "print()\n",
+    "from shallowprofiler import *\n",
+    "from charts import *\n",
     "\n",
     "def loadData(stream_name):\n",
     "    fs = s3fs.S3FileSystem(anon=True)\n",
@@ -636,147 +1031,6 @@
     "    ds = xr.open_zarr(zarr_store, consolidated=True)\n",
     "    return ds\n",
     "\n",
-    "siteData = loadData(osb_sp_ctd)\n",
-    "# siteData\n",
-    "print('site data is now lazy-loaded')\n",
-    "\n",
-    "print(siteData.time[-1])\n",
-    "\n",
-    "from shallowprofiler import *\n",
-    "from charts import *\n",
-    "\n",
-    "profiles = ReadProfileMetadata()    # this is an error: Hardcoded for Jan 2022\n",
-    "how_many_charts = 3\n",
-    "profile_list = [n for n in range(how_many_charts)]   # quick check: use d['temperature'].z.plot()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "ename": "FileNotFoundError",
-     "evalue": "[Errno 2] No such file or directory: b'/home/rob/oceanography/book/chapters/rca/sensors/data/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/argo-env2/lib/python3.11/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/argo-env2/lib/python3.11/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/rob/oceanography/book/chapters/rca/sensors/data/osb_ctd_jan22_conductivity.nc',), 'r', (('clobber', True), ('diskless', False), ('format', 'NETCDF4'), ('persist', False)), 'fffbd075-1a5d-4ac9-8644-054921505643']",
-      "\nDuring handling of the above exception, another exception occurred:\n",
-      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[7], line 29\u001b[0m\n\u001b[1;32m     27\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m sensor \u001b[38;5;129;01min\u001b[39;00m sensors:          \u001b[38;5;66;03m# sensor is a list of 2 strings [sensor_str, instrument_str]\u001b[39;00m\n\u001b[1;32m     28\u001b[0m     f \u001b[38;5;241m=\u001b[39m AssembleDataFilename(relative_path, sitestring, sensor[\u001b[38;5;241m1\u001b[39m], monthstring, sensor[\u001b[38;5;241m0\u001b[39m])\n\u001b[0;32m---> 29\u001b[0m     d[sensor[\u001b[38;5;241m0\u001b[39m]] \u001b[38;5;241m=\u001b[39m GetSensorTuple(sensor[\u001b[38;5;241m0\u001b[39m], f)\n",
-      "Cell \u001b[0;32mIn[7], line 19\u001b[0m, in \u001b[0;36mGetSensorTuple\u001b[0;34m(s, f)\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mGetSensorTuple\u001b[39m(s, f):\n\u001b[1;32m     15\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m'''\u001b[39;00m\n\u001b[1;32m     16\u001b[0m \u001b[38;5;124;03m    s is the sensor identifier string like 'temperature'\u001b[39;00m\n\u001b[1;32m     17\u001b[0m \u001b[38;5;124;03m    f is the source filename like './../data/osb_ctd_jan22_temperature.nc' \u001b[39;00m\n\u001b[1;32m     18\u001b[0m \u001b[38;5;124;03m    '''\u001b[39;00m\n\u001b[0;32m---> 19\u001b[0m     df_sensor    \u001b[38;5;241m=\u001b[39m xr\u001b[38;5;241m.\u001b[39mopen_dataset(f)[s]\n\u001b[1;32m     20\u001b[0m     df_z         \u001b[38;5;241m=\u001b[39m xr\u001b[38;5;241m.\u001b[39mopen_dataset(f)[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mz\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m     21\u001b[0m     range_lo     \u001b[38;5;241m=\u001b[39m ranges[s][\u001b[38;5;241m0\u001b[39m]\n",
-      "File \u001b[0;32m~/miniconda3/envs/argo-env2/lib/python3.11/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/argo-env2/lib/python3.11/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/argo-env2/lib/python3.11/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/argo-env2/lib/python3.11/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/argo-env2/lib/python3.11/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/argo-env2/lib/python3.11/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/argo-env2/lib/python3.11/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/argo-env2/lib/python3.11/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/argo-env2/lib/python3.11/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/rob/oceanography/book/chapters/rca/sensors/data/osb_ctd_jan22_conductivity.nc'"
-     ]
-    }
-   ],
-   "source": [
-    "# This cell assembles a data dictionary \"d\" from the old-style datasets (that live within the repo)\n",
-    "\n",
-    "d = {}\n",
-    "\n",
-    "relative_path = 'rca/sensors/data/'\n",
-    "sitestring = 'osb'                    # available: osb = Oregon Slope Base\n",
-    "monthstring = 'jan22'                 # available: apr21, jul21, jan22\n",
-    "\n",
-    "# error: apr21 and jul21 seem to fail on pCO2 despite data files present\n",
-    "\n",
-    "def AssembleDataFilename(relative_path, site, instrument, time, sensor): \n",
-    "    return relative_path + site + '_' + instrument + '_' + time + '_' + sensor + '.nc'\n",
-    "\n",
-    "def GetSensorTuple(s, f):\n",
-    "    '''\n",
-    "    s is the sensor identifier string like 'temperature'\n",
-    "    f is the source filename like './../data/osb_ctd_jan22_temperature.nc' \n",
-    "    '''\n",
-    "    df_sensor    = xr.open_dataset(f)[s]\n",
-    "    df_z         = xr.open_dataset(f)['z']\n",
-    "    range_lo     = ranges[s][0]\n",
-    "    range_hi     = ranges[s][1]\n",
-    "    sensor_color = colors[s]\n",
-    "    return (df_sensor, df_z, range_lo, range_hi, sensor_color)\n",
-    "\n",
-    "\n",
-    "for sensor in sensors:          # sensor is a list of 2 strings [sensor_str, instrument_str]\n",
-    "    f = AssembleDataFilename(relative_path, sitestring, sensor[1], monthstring, sensor[0])\n",
-    "    d[sensor[0]] = GetSensorTuple(sensor[0], f)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# temperature and salinity\n",
-    "fig,axs = ChartTwoSensors(profiles, [ranges['temperature'], ranges['salinity']], profile_list, \n",
-    "                          d['temperature'][0], d['temperature'][1], 'Temperature', colors['temperature'],    'ascent',\n",
-    "                          d['salinity'][0],       d['salinity'][1],    'Salinity',  colors['salinity'], 'ascent', 6, 4)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ChartSensor(profiles, [7., 11.], [0, 1, 2], ds.sea_water_temperature, -ds.sea_water_pressure, 'temperature', 'red', 'ascent', 8, 6)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ChartSensor(profiles, [7., 11.], [0, 1, 2], ds.sea_water_temperature, -ds.sea_water_pressure, 'temperature', 'red', 'descent', 8, 6)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# temperature: ascent versus descent\n",
-    "fig,axs = ChartTwoSensors(profiles, [ranges['temperature'], ranges['temperature']], [0, 1, 2], \n",
-    "                          d['temperature'][0], d['temperature'][1], 'T-Ascent',   colors['temperature'],    'ascent',\n",
-    "                          d['temperature'][0], d['temperature'][1], 'T-Descent',  'green',                  'descent', 6, 4)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t0, t1 = '2022-01-01T00', '2022-12-31T23'\n",
-    "ds = siteData.sel(time=slice(t0, t1))\n",
-    "ds.time[0], ds.time[-1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
     "def SPDataReduce(ds, t0, t1, keepers, rename):\n",
     "    \"\"\"\n",
     "    From a zarr Dataset for a shallow profiler stream ('ds') write a NetCDF file with just \n",
@@ -785,7 +1039,6 @@
     "      - drop or rename data vars\n",
     "      - drop attributes\n",
     "    \"\"\"\n",
-    "    \n",
     "    if not len(ds.dims) == 1: \n",
     "        return False, \"Dataset dims count is not 1 (as assumed)\"\n",
     "    if not list(dict(ds.dims))[0] == 'time': \n",
@@ -804,46 +1057,104 @@
     "    for p in a: ds.attrs.pop(p)\n",
     "    return ds, 'looks ok'\n",
     "\n",
-    "t0, t1 = '2022-01-01T00', '2022-01-31T23'\n",
     "\n",
-    "ds_reduced, reply = SPDataReduce(ds, t0, t1, ['sea_water_temperature', 'sea_water_pressure'], \n",
+    "if False:    \n",
+    "    osb_profiler_streams = [sname for sname in streamlist if 'SF01A' in sname]\n",
+    "    for s in osb_profiler_streams: \n",
+    "        if 'ctdpf' in s: osb_sp_ctd = s\n",
+    "\n",
+    "    print('OSB CTD stream:', osb_sp_ctd)\n",
+    "    print()\n",
+    "\n",
+    "    siteData = loadData(osb_sp_ctd)\n",
+    "\n",
+    "    print('site data is now lazy-loaded\\n')\n",
+    "    print(siteData.time[-1])\n",
+    "\n",
+    "\n",
+    "    profiles = ReadProfileMetadata()    # this is an error: Hardcoded for Jan 2022\n",
+    "    how_many_charts = 1\n",
+    "    profile_list = [n for n in range(how_many_charts)]   # quick check: use d['temperature'].z.plot()\n",
+    "\n",
+    "    t0, t1 = '2022-01-01T00', '2022-12-31T23'\n",
+    "    ds = siteData.sel(time=slice(t0, t1))\n",
+    "    ds.time[0], ds.time[-1]\n",
+    "\n",
+    "    t0, t1 = '2022-01-01T00', '2022-01-31T23'\n",
+    "    ds_reduced, reply = SPDataReduce(ds, t0, t1, ['sea_water_temperature', 'sea_water_pressure'], \n",
     "                                 ['temperature', 'depth'], )\n",
-    "print(reply)\n",
-    "print(ds_reduced)"
+    "    print(reply)\n",
+    "    print(ds_reduced)\n",
+    "    ds_reduced.to_netcdf('osb_temp_jan_2022.nc') "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ds_reduced.to_netcdf('osb_temp_jan_2022.nc') "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 67,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Attempting 1 charts\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
+    "ds_zarr = xr.open_dataset('./data/rca/sensors/osb/zarr/temperature_jan_2022.nc')\n",
+    "\n",
     "# temperature: ascent versus descent\n",
-    "fig,axs = ChartTwoSensors(profiles, [ranges['temperature'], ranges['temperature']], [0, 1, 2], \n",
-    "                          ds_reduced.temperature, -ds_reduced.depth,\n",
+    "fig,axs = ChartTwoSensors(profiles, [ranges['temperature'], ranges['temperature']], [0], \n",
+    "                          ds_zarr.temperature, -ds_zarr.depth,\n",
     "                          'T-Ascent',   colors['temperature'],    'ascent',\n",
-    "                          ds_reduced.temperature, -ds_reduced.depth,\n",
+    "                          ds_zarr.temperature, -ds_zarr.depth,\n",
     "                          'T-Descent',  'green',                  'descent', 6, 4)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 68,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Attempting 1 charts\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# temperature: ascent versus descent\n",
-    "fig,axs = ChartTwoSensors(profiles, [ranges['temperature'], ranges['temperature']], [0, 1, 2], \n",
-    "                          ds_reduced.temperature, -ds_reduced.depth,\n",
+    "fig,axs = ChartTwoSensors(profiles, [ranges['temperature'], ranges['temperature']], [0], \n",
+    "                          ds_zarr.temperature, -ds_zarr.depth,\n",
     "                          'Zarr Data',   colors['temperature'],    'ascent',\n",
     "                          d['temperature'][0], d['temperature'][1], 'OOI Data',   'blue',    'ascent', 6, 4)"
    ]
@@ -859,21 +1170,6 @@
     "```"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ds_read = xr.open_dataset('./data/osb_temp_jan_2022.nc')\n",
-    "\n",
-    "# temperature: ascent versus descent\n",
-    "fig,axs = ChartTwoSensors(profiles, [ranges['temperature'], ranges['temperature']], [0, 1, 2], \n",
-    "                          ds_read.temperature, -ds_read.depth,\n",
-    "                          'Zarr Data',   colors['temperature'],    'ascent',\n",
-    "                          d['temperature'][0], d['temperature'][1], 'OOI Data',   'blue',    'ascent', 6, 4)"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/book/chapters/data/tmp/osb_temp_jan_2022.nc b/book/chapters/data/rca/sensors/osb/zarr/temperature_jan_2022.nc
similarity index 100%
rename from book/chapters/data/tmp/osb_temp_jan_2022.nc
rename to book/chapters/data/rca/sensors/osb/zarr/temperature_jan_2022.nc
diff --git a/book/chapters/data2.ipynb b/book/chapters/data2.ipynb
deleted file mode 100755
index 09dd72c..0000000
--- a/book/chapters/data2.ipynb
+++ /dev/null
@@ -1,160 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "f36780d5-3c04-442d-ac24-3b474266843b",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Attempting 3 charts\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 600x1200 with 6 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d5a0969d-e6f2-4fcd-92c0-917a0934057d",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "59c6f1be-67c8-4e0f-8ebc-b28eaf97feae",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5910c08c-b34d-4772-808c-fdde5e52b72c",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7f47159d-0ac8-4e1b-bdd2-5ab53235ac69",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "6eb876a5-4a96-4dcc-82cb-a78d2094a2fd",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "af8d207e-0f4c-4e14-8a2a-e85be9150ccc",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "cf6b3ace-e719-40a7-8336-5180ee389197",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "879592d0-2abb-4e28-b5d5-c77e83ec0b9a",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "abc613ab-176c-4534-a395-f6e2dc42c565",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "020ecac7-d91e-4d1c-b601-3e87d8f8cfea",
-   "metadata": {},
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "99991068-c65d-476a-8eff-034a5f5b7462",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/book/chapters/shallowprofiler.ipynb b/book/chapters/epipelargosy.ipynb
similarity index 99%
rename from book/chapters/shallowprofiler.ipynb
rename to book/chapters/epipelargosy.ipynb
index 1f01274..9790b57 100644
--- a/book/chapters/shallowprofiler.ipynb
+++ b/book/chapters/epipelargosy.ipynb
@@ -8,7 +8,7 @@
     "[Jupyter Book](https://geo-smart.github.io/oceanography/intro.html) and [GitHub repo](https://github.com/geo-smart/oceanography).\n",
     "\n",
     "\n",
-    "# Shallow Profiler\n",
+    "# Epipelargosy\n",
     "\n",
     "\n",
     "> Wavering between the profit and the loss /\n",
diff --git a/book/chapters/ocean_science.ipynb b/book/chapters/ocean_science.ipynb
index e043148..ab268a6 100644
--- a/book/chapters/ocean_science.ipynb
+++ b/book/chapters/ocean_science.ipynb
@@ -7,6 +7,11 @@
     "[Jupyter Book](https://geo-smart.github.io/oceanography/intro.html) and [GitHub repo](https://github.com/geo-smart/oceanography).\n",
     "\n",
     "\n",
+    "> Author's note: This notebook establishes a science basis that the subsequent chapters will\n",
+    "address (by dint of laborious data wrangling). The chapter concludes with a list of subsequent\n",
+    "grouped chapters to establish a roadmap.\n",
+    "\n",
+    "\n",
     "# Ocean Science\n",
     "\n",
     "\n",
@@ -29,20 +34,16 @@
     "The purpose of this Jupyter Book is to present research ideas in oceanography relative \n",
     "to observational data from sensors. The underlying intent is to provide a reproducible \n",
     "methodology; but this tends to take up all the oxygen in the room. So before diving in\n",
-    "let us frame out the science, starting with an ambitious question:<br><br><br>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "${\\Large \\textrm{How stable is the epipelagic ocean?}}$"
+    "let us frame out the science, starting with an ambitious question:\n",
+    "<br><br><br>"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "${\\Large \\textrm{How stable is the epipelagic ocean?}}$\n",
+    "\n",
     "<br>\n",
     "\n",
     "### The *epipelagic ocean* defined\n",
@@ -121,7 +122,7 @@
     "shape, and second that they occasionally exhibit anomalous structure. Anomalies are interesting\n",
     "for a single sensor type say like temperature. However an anomaly that is present and persistent \n",
     "across multiple sensor types (say temperature, salinity and Chlorophyll-A fluorescence) is\n",
-    "indicating ocean structure on some physical and temporal scale. \n"
+    "indicating ocean structure on some physical and temporal scale. "
    ]
   },
   {
@@ -131,13 +132,12 @@
     "## Agenda\n",
     "\n",
     "\n",
-    "This Geo-SMART Jupyter Book will proceed chapter-by-chapter to cover the following agenda:\n",
     "\n",
     "- Getting our feet wet\n",
     "    - Ocean Science (this chapter): Establish a heirarchy of research questions and terminology\n",
-    "    - Shallow Profiler: Using a particular site and month-duration time frame: A first look at profile data\n",
-    "    - Data: Describe the baseline data access path and an alternative 'zarr' path\n",
-    "    - Anomaly: \n",
+    "    - Data: Structure, necessity of profile metadata, sensors-to-measurements\n",
+    "    - Epipelargosy: A sense of the structure of the epipelagic water column\n",
+    "    - Anomaly and Coincidence: \n",
     "    - Annotation:\n",
     "- Other observation systems\n",
     "    - ARGO:\n",
@@ -629,7 +629,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.3"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,