diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index c1850b4..0cce0ab 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -22,7 +22,7 @@ jobs:
 
       - uses: actions/setup-python@v5
         with:
-          python-version: "3.10"
+          python-version: "3.11"
 
       - name: Install package
         run: python -m pip install .[dev]
diff --git a/.gitignore b/.gitignore
index 5afa67f..639449f 100644
--- a/.gitignore
+++ b/.gitignore
@@ -2,6 +2,10 @@
 docs/_build/
 docs/api/
 
+# Icepyx-related
+.order_restart
+notebooks/.order_restart
+
 # Temporary local data store
 downloaded-data/
 
diff --git a/docs/altimetry-data-overview.md b/docs/altimetry-data-overview.md
new file mode 100644
index 0000000..44e4aea
--- /dev/null
+++ b/docs/altimetry-data-overview.md
@@ -0,0 +1,196 @@
+# Altimetry Data at the NSIDC DAAC: Point Cloud Data Overview
+
+## Altimetry Data Introduction
+
+<div>
+<img align="right" width="50%" height="200px" src='./img/vaex.png'/>
+</div>
+
+This page aims to teach students and researchers interested in cryospheric
+sciences about airborne altimetry and related data sets from NASA’s
+[IceBridge](https://www.nasa.gov/mission_pages/icebridge/index.html) mission,
+and satellite altimetry data from
+[ICESat/GLAS](https://icesat.gsfc.nasa.gov/icesat/) and
+[ICESat-2](https://icesat-2.gsfc.nasa.gov/). Accessing and combining data from
+these different missions can be difficult as file formats and coordinate
+reference systems changed over time.
+
+## Mission Overview
+
+In 2003, NASA launched the Ice, Cloud and Land Elevation Satellite mission with
+the Geoscience Laser Altimeter System (ICESat/GLAS) instrument onboard. Over the
+following six years, ICESat/GLAS collected valuable ice thickness data in the
+Polar Regions. Unfortunately, the ICESat/GLAS mission ended in 2009 before a
+follow-up mission could be launched. An airborne campaign called Operation
+IceBridge was funded to fill the gap and continue ice thickness measurements.
+Between 2009 and 2019, Operation IceBridge flew numerous campaigns over
+Greenland, the Antarctic ice sheets, and sea ice in the Arctic and Southern
+Ocean. In September 2018, ICESat-2 was launched to continue NASA's collection of
+ice, cloud and land elevation data.
+
+### Pre-IceBridge
+
+The Airborne Topographic Mapper (ATM) is a conically-scanning laser altimeter
+that measures the surface topography of a swath of terrain directly beneath the
+path of an aircraft. ATM surveys can be used to detect surface changes.
+Differences of laser swaths surveyed over the same area but a few years apart
+can be used to estimate elevation changes between the first and second survey.
+Comparing the surveys conducted 1993-4 and 1998-9 resulted in the first
+comprehensive assessment of the mass balance change of the Greenland ice sheet
+([Krabill et al., 1999](https://science.sciencemag.org/content/283/5407/1522),
+[2000](https://science.sciencemag.org/content/289/5478/428)). ATM surveys can
+also be used to calibrate/validate satellite altimeter measurements (e.g.
+[Martin et al., 2005](https://atm.wff.nasa.gov/files/browser/ATM_Calibration_Procedures_and_Accuracy_Assessment_2012.pdf)).
+The ATM was deployed on a wide variety of platforms, including the NASA P3, a
+Chilean Navy P3, a US Navy P3, the NASA DC8, the NCAR C-130, and a half-dozen
+Twin Otters to collected high quality topographic data. For a complete list of
+the ATM deployments visit
+[https://atm.wff.nasa.gov/deployments/](https://atm.wff.nasa.gov/deployments/)
+
+### ICESat/GLAS
+
+ICESat/GLAS was the benchmark Earth Observing System mission for measuring ice
+sheet mass balance, cloud and aerosol heights, as well as land topography and
+vegetation characteristics. From 2003 to 2009, the ICESat/GLAS mission provided
+multi-year elevation data for ice sheet mass balance as well as stratospheric
+cloud property information over polar areas. This mission also provided
+topographic and vegetation data from around the globe beyond the polar-specific
+ice height information over the Greenland and Antarctic ice sheets. Launched on
+12 January 2003, after seven years in orbit and 18 laser-operation campaigns,
+the ICESat/GLAS science mission ended due to the failure of its primary
+instrument in 2009.
+
+### IceBridge
+
+The operation IceBridge was with its surveying flights from 2009 to 2019 the
+largest airborne survey of the Earth's polar ice. It has yielded an
+unprecedented three-dimensional view of the Arctic and Antarctic ice sheets, ice
+shelves and sea ice. The flights provide a yearly, multi-instrument look at the
+behavior of the rapidly changing features of the polar ice. Data collected
+during Operation IceBridge helps scientists bridge the gap in polar satellite
+observations between ICESat/GLAS (2003-2009) and ICESat-2 (2018-present).
+Although the IceBridge data are not continuous its mission became critical for
+extending the ice altimetry time series in the Arctic and Antarctic, after
+ICESat/GLAS stopped collecting data in 2009.
+
+IceBridge flights were generally conducted in March-May over Greenland and in
+October-November over Antarctica.
+
+### ICESat-2
+
+The ICESat-2 mission was designed to provide elevation data needed to determine
+ice sheet mass balance as well as vegetation canopy information. It provides
+topographic measurements of cities, lakes and reservoirs, oceans and land
+surfaces around the globe. The sole instrument on ICESat-2 is the Advanced
+Topographic Laser Altimeter System (ATLAS), a space-based Lidar. It was designed
+and built at Goddard Space Flight Center, with the laser generation and
+detection systems provided by Fibertek. ATLAS measures the travel time of laser
+photons from the satellite to Earth and back; travel times from multiple laser
+pulses are used to determine elevation data.
+
+```{note}
+
+We recommend using the [_icepyx_](https://github.com/icesat2py/icepyx)
+library to access and interact with ICESat-2 data. Learn more about using `icepyx` with `icelfow` in the [Using iceflow with icepyx to Generate an Elevation Timeseries](notebooks/iceflow-with-icepyx) Jupyter notebook.
+
+```
+
+<p align="center">
+<img style="align: center;" width="80%" src='./img/iceflow-coverage.jpg'/>
+    <br>
+    <b><center>Fig 2. mission coverages</center></b>
+</p>
+
+## Data Sets and their Spatial and Temporal Coverage
+
+NSIDC provides a list of all available data sets within each mission including
+further mission information and documentation for each data set:
+
+- [ICESat/GLAS data sets at NSIDC](https://nsidc.org/data/icesat/data.html)
+- [Pre-IceBridge and IceBridge data sets at NSIDC](https://nsidc.org/data/icebridge/data_summaries.html)
+- [ICESat-2 data sets at NSIDC](https://nsidc.org/data/icesat-2/data-sets)
+
+---
+
+| Data Set                                                  | Spatial Coverage                                                           | Temporal Coverage                              | Mission       | Sensors                                              |
+| --------------------------------------------------------- | -------------------------------------------------------------------------- | ---------------------------------------------- | ------------- | ---------------------------------------------------- |
+| [BLATM L1B](https://nsidc.org/data/BLATM1B)               | South: N:-53, S: -90, E:180, W:-180 <br> North: N:90, S: 60, E:180, W:-180 | 23 Jun. 1993 - 30 Oct. 2008                    | Pre-IceBridge | ATM                                                  |
+| [ILATM L1B V1](https://nsidc.org/data/ILATM1B/versions/1) | South: N:-53, S: -90, E:180, W:-180 <br> North: N:90, S: 60, E:180, W:-180 | 31 Mar. 2009 - 8 Nov. 2012 <br> (updated 2013) | IceBridge     | ATM                                                  |
+| [ILATM L1B V2](https://nsidc.org/data/ILATM1B/versions/2) | South: N:-53, S: -90, E:180, W:-180 <br> North: N:90, S: 60, E:180, W:-180 | 20 Mar. 2013 - 16 May 2019 <br> (updated 2020) | IceBridge     | ATM                                                  |
+| [ILVIS2](https://nsidc.org/data/ILVIS2)                   | North: N:90, S: 60, E:180, W:-180                                          | 25 Aug. 2017 - 20 Sept. 2017                   | IceBridge     | ALTIMETERS, LASERS, LVIS                             |
+| [GLAH06](https://nsidc.org/data/GLAH06/)                  | Global: N:86, S: -86, E:180, W:-180                                        | 20 Feb. 2003 - 11 Oct. 2009                    | ICESat/GLAS   | ALTIMETERS, CD, GLAS, GPS, <br> GPS Receiver, LA, PC |
+
+---
+
+```{note}
+
+If you have questions about the data sets please refer to the user
+guides or contact NSIDC user services at nsidc@nsidc.org
+
+```
+
+## Challenges
+
+The wealth of data from these missions presents an opportunity to study the
+evolution of ice thickness over several decades. However, combining data from
+these missions presents several challenges:
+
+- Data from the Airborne Topographic Mapper (ATM) flown during the IceBridge
+  campaigns is stored in four different file formats. ICESat/GLAS and ICESat-2
+  data are also in different file formats. Therefore, the data needs to be
+  harmonized, that means placed into similar formats before comparisons can be
+  made.
+- The coordinate reference systems used to locate measurements have changed over
+  the years, as the Earth's surface is not static and changes shape. To account
+  for these changes, terrestrial reference frames that relate latitude and
+  longitude to points on the Earth are updated on a regular basis. Since the
+  launch of ICESat/GLAS, the International Terrestrial Reference Frame
+  [(ITRF)](https://www.iers.org/IERS/EN/DataProducts/ITRF/itrf.html) has been
+  updated three times. The geolocation of a point measured at the beginning of
+  the record and the end of the record is not the same even though the latitude
+  and longitude is the same. These changes in geolocation need to be reconciled
+  to allow meaningful comparisons within the long-term data record.
+
+The `iceflow` Python library addresses these concerns by providing the ability
+to search, download, and access laser altimetry data from (pre-)Operation
+IceBridge and ICESat/GLAS datasets. The library also supports International
+Terrestrial Reference Frame (ITRF) transformations to facilitate comparisons
+across datasets.
+
+Companion Jupyter notebooks give additional information and contain example code
+about `iceflow`.
+
+[NSIDC Iceflow example](./notebooks/iceflow-example) provides an example of how
+to search for, download, and interact with `ILATM1B v1` data for a small area of
+interest. This notebook also illustrates how to perform
+[ITRF](https://itrf.ign.fr/) transformations to facilitate comparisons across
+datasets. To learn more about ITRF transformations, see the
+[Applying Coordinate Transformations to Facilitate Data Comparison](./notebooks/corrections)
+notebook.
+
+[Using iceflow with icepyx to Generate an Elevation Timeseries](./notebooks/iceflow-with-icepyx)
+shows how to search for, download, and interact with a large amount of data
+across many datasets supported by `iceflow`. It also illustrates how to utilize
+[icepyx](https://icepyx.readthedocs.io/en/latest/) to find and access ICESat-2
+data. Finally, the notebook provides a simple time-series analysis for elevation
+change over an area of interest across `iceflow` supported datasets and
+ICESat-2.
+
+## References
+
+- [Airborne Topographic Mapper Calibration Procedures and Accuracy Assessment](https://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/20120008479.pdf)
+
+## Related Tools
+
+- [OpenAltimetry](https://openaltimetry.earthdatacloud.nasa.gov/data/): Advanced
+  discovery, processing, and visualization services for ICESat and ICESat-2
+  altimeter data
+- [ITS_LIVE](https://its-live.jpl.nasa.gov/): A NASA MEaSUREs project to provide
+  automated, low latency, global glacier flow and elevation change data sets.
+
+## Next steps
+
+With the knowledge gained from reading this page, users should be prepared for
+the [NSIDC Iceflow example](./notebooks/iceflow-example) notebook, which
+provides an example of how to search for, download, and interact with
+`ILATM1B v1` data for a small area of interest with the `iceflow` library.
diff --git a/docs/conf.py b/docs/conf.py
index 6b5133a..73aa7cd 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -80,3 +80,26 @@
 always_document_param_types = True
 
 nb_execution_mode = "off"
+
+
+def copy_notebook_images(app, _exception):
+    """Copy notebook static images into place post-build."""
+    import shutil
+    from pathlib import Path
+
+    source_dir = Path(app.srcdir) / "img"
+    dest_dir = Path(app.outdir) / "img"
+
+    if dest_dir.exists():
+        shutil.rmtree(dest_dir)
+
+    shutil.copytree(source_dir, dest_dir)
+
+
+def setup(app):
+    """Setup build-finished action to copy images into place.
+
+    Because the images are only referenced in jupyter notebooks, and not source
+    files parsed directly by sphinx, the images will not be copied on their own.
+    """
+    app.connect("build-finished", copy_notebook_images)
diff --git a/docs/contributing.md b/docs/contributing.md
index 98af320..3ca7def 100644
--- a/docs/contributing.md
+++ b/docs/contributing.md
@@ -44,7 +44,8 @@ Available tasks:
 
 ## Jupyter Notebooks
 
-There are Jupyter Notebooks showing `iceflow` functionality under `notebooks/`.
+There are Jupyter Notebooks showing `iceflow` functionality under
+`docs/notebooks/`.
 
 To get started with developing existing or new notebooks, first install the
 notebook-specific dependencies:
@@ -56,15 +57,13 @@ pip install --editable .[notebooks]
 Then:
 
 ```
-cd notebooks/
+cd docs/notebooks/
 jupyter lab
 ```
 
 Develop notebooks and confirm that they work as expected. Before committing
-changes, clear all outputs.
-
-See below for information about adding rendered versions of notebooks to
-ReadTheDocs.
+changes, ensure all cells are evaluated so that outputs are populated. These
+notebooks are included in the docs and will be displayed on ReadTheDocs.
 
 ## Documentation
 
@@ -90,15 +89,6 @@ Please review this carefully and ensure that any changes are accurately
 reflected. On a merge to main, changes to the documentation will be reflected on
 the main, public-facing documentation site.
 
-> [!NOTE] Most of the documentation is designed to be generated automatically.
-> E.g., changes to the API and markdown documents will be automatically
-> refelcted in the documentation. The one exception are the jupyter notebooks
-> (e.g., `iceflow-example.ipynb`), which are static and must be re-generated
-> manually. Use the `invoke docs.render-notebooks-for-docs` task to re-generate
-> these files, and then commit any changes to the repository. See the
-> `notebooks/generate_rendered_notebooks_for_docs.sh` for how to add new
-> rendered notebooks to the docs.
-
 ## Releasing
 
 To release a new version of the software, first update the CHANGELOG.md to
@@ -111,11 +101,21 @@ $ bump-my-version bump {major|minor|patch}
 
 This will update files containing the software version number.
 
-> [!WARNING]
->
-> Please do not attempt to update version numbers by hand!
+:::{warning}
+
+Please do not attempt to update version numbers by hand!
+
+:::
 
 Commit these changes and, once ready, merge them into `main` (through the use of
 a Pull Request on a feature branch). Tag the commit you want to release on
 `main` to initiate a GitHub Action (GHA) that will release the package to
 anaconda.org.
+
+:::{warning}
+
+Jupyter notebooks found in `./notebooks` may need to be re-rendered, depending
+on the changes introduced. It is a good idea to review the notebooks and
+identify any necessary changes that should accompany a PR.
+
+:::
diff --git a/docs/getting-started.md b/docs/getting-started.md
index be47a41..e4d47a2 100644
--- a/docs/getting-started.md
+++ b/docs/getting-started.md
@@ -1,26 +1,16 @@
 # Getting started with iceflow
 
+## Altimetry Overview
+
+Before working with `iceflow` directly, it may be helpful to understand the
+basics about pre-IceBridge, IceBridge, ICESat/GLAS and ICESat-2 datasets. Learn
+about these `iceflow` supported datasets in
+[Altimetry Data at the NSIDC DAAC: Point Cloud Data Overview](./altimetry-data-overview)
+
 ## Jupyter Notebooks
 
-[Jupyter notebooks](https://docs.jupyter.org/en/latest/) provide executable
-examples of how to use `iceflow`:
-
-- [iceflow-example.ipynb](./iceflow-example.ipynb) provides an example of how to
-  search for, download, and interact with `ILATM1B v1` data for a small area of
-  interest. This notebook also illustrates how to perform
-  [ITRF](https://itrf.ign.fr/) transformations to facilitate comparisons across
-  datasets. To learn more about ITRF transformations, see the
-  [corrections.ipynb](https://github.com/nsidc/NSIDC-Data-Tutorials/blob/main/notebooks/iceflow/corrections.ipynb)
-  notebook in the
-  [NSIDC-Data-Tutorials GitHub repository](https://github.com/nsidc/NSIDC-Data-Tutorials).
-
-- [iceflow-with-icepyx.ipynb](./iceflow-with-icepyx.ipynb) shows how to search
-  for, download, and interact with a large amount of data across many datasets
-  supported by `iceflow`. It also illustrates how to utilize
-  [icepyx](https://icepyx.readthedocs.io/en/latest/) to find and access ICESat-2
-  data. Finally, the notebook provides a simple time-series analysis for
-  elevation change over an area of interest across `iceflow` supported datasets
-  and ICESat-2.
+Executable Jupyter Notebooks provide a great starting point for using `iceflow`.
+See [Jupyter Notebooks](./notebooks/index.md) for more information.
 
 ## API overview
 
diff --git a/docs/img/iceflow-coverage.jpg b/docs/img/iceflow-coverage.jpg
new file mode 100644
index 0000000..6bfea6b
Binary files /dev/null and b/docs/img/iceflow-coverage.jpg differ
diff --git a/docs/img/vaex.png b/docs/img/vaex.png
new file mode 100644
index 0000000..0b4d99d
Binary files /dev/null and b/docs/img/vaex.png differ
diff --git a/docs/index.md b/docs/index.md
index 30a6062..a059ef3 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -6,8 +6,8 @@
 :caption: content
 
 getting-started.md
-iceflow-example.ipynb
-iceflow-with-icepyx.ipynb
+altimetry-data-overview.md
+notebooks/index.md
 contributing.md
 API Docs <api/nsidc>
 LICENSE
diff --git a/docs/notebooks/corrections.ipynb b/docs/notebooks/corrections.ipynb
new file mode 100644
index 0000000..56f79ea
--- /dev/null
+++ b/docs/notebooks/corrections.ipynb
@@ -0,0 +1,470 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Applying Coordinate Transformations to Facilitate Data Comparison\n",
+    "\n",
+    "## Applying ITRF and Plate Movement Models to Lidar Data from Pre-IceBridge, ICESat/GLAS, IceBridge, and ICESat-2 Data Sets\n",
+    "\n",
+    "Comparing measurements across altimetry data sets where latitude, longitude, and surface heights have been collected over long periods of time – a span of 25+ year in the case of ICESat/GLAS, IceBridge and ICESat-2 – requires us to account for evolving coordinate reference systems and tectonic plate movement. To demonstrate why plate movement matters over time, let’s look at the Australian plate. It is moving at a rate of around 7 cm / year, so over 25 years the continent will have moved nearly 1.8 m. All of Earth’s land masses are affected by plate movement to varying degrees, and over a multi-decade time series it’s important to recognize that the position recorded for a location in 1993 is going to be different from what would be recorded at that same location today. Plate movement and updates to coordinate reference systems over time must be accounted for when analyzing data over long time periods to avoid drawing spurious conclusions from the data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Important Terms and Concepts\n",
+    "\n",
+    "* Coordinate system: The 2-dimensional (latitude, longitude) or 3-dimensional (latitude, longitude, height) system, and unit of measure used to define where a point is located in space.\n",
+    "* Datum: A modeled version of the shape of the Earth which defines the origin used to place the coordinate system in space.\n",
+    "* Coordinate Reference System (CRS): A CRS comprises a coordinate system and a datum.\n",
+    "* Epoch (datum epoch): An epoch is used to identify the date at which a given reference frame was realized. They are also used to date when the coordinates for a particular observation (e.g., lat, lon, elevation) were obtained. Epochs are noted in decimal year format (YYYY.DD) where numbers to the right of the decimal are derived from the Julian day-in-year. The decimal year is equal to: year + day-in-year/365 (or 366 for leap years). For example, 1 January 2010 is written as 2010.00; 5 May 1991 is the 128th Julian day-in-year and written as 1991.35. To determine what month and day is represented by, say, 2018.5, multiply the numbers to the right of the decimal by the total number of days in 2018 (.5 * 365) to determine the Julian day-in-year is 183 (after rounding up to a whole number). Referencing a Julian date calendar (e.g., [here](https://www.timeanddate.com/calendar/custom.html?year=2020&country=1&doy=1&df=1) ) we see that 2018.5 corresponds to 2 July 2018.\n",
+    "* International Terrestrial Reference Frame (ITRF): A realization (i.e., a major adjustment) of the International Terrestrial Reference System (ITRS). Its origin is at the center of mass of the whole earth including the oceans and atmosphere. New ITRF solutions are produced every few years as the Earth changes and to employ the latest mathematical and surveying techniques to attempt to realize the ITRS as precisely as possible. Examples include ITRF88, ITRF93, ITRF2000, etc., with ITRF2014 being the latest realization of the ITRS. The ITRS is a formal description of the coordinate reference system, along with the mathematical and experimental procedures used to create reference frames and is maintained by the International Earth Rotation Service (IERS). The IERS issues parameters for transforming to and from older ITRF realizations. This makes it convenient to compare locations at any point on the Earth, but only for the same time, or epoch. Comparing coordinates in a dynamic frame from different epochs presents problems since the geographic features are being moved by plate tectonics, resulting in different coordinates for the same feature."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example Data Set\n",
+    "\n",
+    "For the remainder of the notebook, we will use a fictitious data set comprising latitude, longitude, and elevation coordinates. We will illustrate the use of *PROJ* and *PyProj* libraries to perform transformations between CRSs, and coordinate propagation (the shift in coordinates for a given geographic feature as a result of tectonic plate movement) between epochs in the same CRS using a Plate Motion Model. We construct a set of coordinates roughly on the Greenland ice sheet, with fictitious elevations. The particular values are used to illustrate the effects of coordinate transformation and propagation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from __future__ import annotations\n",
+    "\n",
+    "import matplotlib as mpl\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from pyproj import Transformer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# We're going to illustrate an example of fictitious data from 1993 being transformed to be comparable to data from 2018.\n",
+    "epoch1 = 1993.5\n",
+    "itrf1 = \"ITRF93\"\n",
+    "epoch2 = 2018.5\n",
+    "itrf2 = \"ITRF2014\"\n",
+    "\n",
+    "# Create lats and lons evenly spaced 1x1m grid (1 cm spacing) anchored to a point on the Greenland icesheet.\n",
+    "lat0 = 70.0\n",
+    "lon0 = -50.0\n",
+    "lat_deg_m = 1 / 111000.0  # (~degrees / m)\n",
+    "lon_deg_m = 1 / 34300.0  # (~degrees / m)\n",
+    "delta_lat = lat_deg_m * 0.01  # 1 cm\n",
+    "delta_lon = lon_deg_m * 0.01  # 1 cm\n",
+    "num_points = 11\n",
+    "lats = lat0 + np.arange(num_points) * delta_lat\n",
+    "lons = lon0 + np.arange(num_points) * delta_lon\n",
+    "lon_grid, lat_grid = np.meshgrid(lons, lats)\n",
+    "\n",
+    "# Create elevations for illustration of coordinate transformation and propagation effects. The resulting \"surface\" will just be plane at constant elevation.\n",
+    "elev_grid = np.ones(lon_grid.shape)\n",
+    "\n",
+    "# We set the observation epoch of the points of our sample data set.\n",
+    "epoch_grid = epoch1 * np.ones(lon_grid.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here we show a 10 x 10 grid spaced at 1 cm intervals, and set the elevations of each point at 1 m. The absolute latitude longitude values are at 70 N, 50 W, a spot \"on\" the Greenland ice sheet. We use this grid of points as our fictitious data set that we are going to transform using two methods. In the examples that follow, we're going to assume this data was collected in 1993 and we'd like to compare it to data collected in 2018. The fictitious data set which we will transform will be in the ITRF93 reference frame, and assume the 2018 data we'd like to compare it to is in the ITRF2014 frame.\n",
+    "\n",
+    "Note the scale difference on the z-axis. All axes units are meters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<mpl_toolkits.mplot3d.art3d.Line3DCollection at 0x787feefb32c0>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1600x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize=(16, 10))\n",
+    "ax = fig.add_subplot(111, projection=\"3d\")\n",
+    "ax.view_init(40, 230)\n",
+    "ax.set_xlim(-0.01, 0.11)\n",
+    "ax.set_ylim(-0.01, 0.11)\n",
+    "\n",
+    "ax.plot_wireframe(\n",
+    "    (lon_grid - lon0) / lon_deg_m,\n",
+    "    (lat_grid - lat0) / lat_deg_m,\n",
+    "    elev_grid,\n",
+    "    color=\"blue\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Coordinate Transformations between Reference Systems"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As stated above, we'd like to transform the data collected in 1993 in the ITRF93 reference frame to allow comparison with points in the ITRF2014 frame. To do so, we create a *PROJ* pipeline that consists of a series of steps:\n",
+    "\n",
+    "* Declare that all the ellipsoids we're dealing with are WGS84\n",
+    "* Convert from degrees to radians\n",
+    "* Convert from lat/lon/elevations to Cartesian coordinates\n",
+    "* Convert from the ITRF93 frame to ITRF2014\n",
+    "* Convert back to latitude / longitude coordinates\n",
+    "* Convert from radians to degrees"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def create_transformer(from_crs, to_crs, to_epoch=None):\n",
+    "    plate_model_step = \"\"\n",
+    "    if to_epoch:\n",
+    "        plate_model_step = f\"+step +inv +init={to_crs}:NOAM +t_epoch={to_epoch} \"\n",
+    "\n",
+    "    pipeline = (\n",
+    "        f\"+proj=pipeline +ellps=WGS84 \"\n",
+    "        f\"+step +proj=unitconvert +xy_in=deg +xy_out=rad \"\n",
+    "        f\"+step +proj=latlon \"\n",
+    "        f\"+step +proj=cart \"\n",
+    "        f\"+step +inv +init={to_crs}:{from_crs} \"\n",
+    "        + plate_model_step\n",
+    "        + \"+step +proj=cart +inv \"\n",
+    "        \"+step +proj=unitconvert +xy_in=rad +xy_out=deg\"\n",
+    "    )\n",
+    "    return Transformer.from_pipeline(pipeline)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We now calculate the magnitude of the coordinate shifts from ITRF93 to ITRF2014, and then plot the original grid (blue) and the newly shifted grid (green)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Longitude delta (m): 0.005\n",
+      "Latitude delta (m): 0.028\n",
+      "Elevation delta (m): 0.005\n"
+     ]
+    }
+   ],
+   "source": [
+    "t = create_transformer(itrf1, itrf2)\n",
+    "\n",
+    "(lons_2014, lats_2014, elevs_2014, epochs_2014) = t.transform(\n",
+    "    lon_grid.T.flatten(),\n",
+    "    lat_grid.T.flatten(),\n",
+    "    elev_grid,\n",
+    "    epoch1 * np.ones(lon_grid.shape),\n",
+    ")\n",
+    "\n",
+    "max_lon_delta = np.max(abs(lons_2014 - lon_grid.T.flatten()))\n",
+    "print(f\"Longitude delta (m): {max_lon_delta / lon_deg_m:0.3f}\")\n",
+    "max_lat_delta = np.max(abs(lats_2014 - lat_grid.T.flatten()))\n",
+    "print(f\"Latitude delta (m): {max_lat_delta / lat_deg_m:0.3f}\")\n",
+    "max_elev_delta = np.max(abs(elevs_2014 - elev_grid))\n",
+    "print(f\"Elevation delta (m): {max_elev_delta:0.3f}\")\n",
+    "\n",
+    "lons_2014 = np.reshape(lons_2014, (num_points, num_points))\n",
+    "lats_2014 = np.reshape(lats_2014, (num_points, num_points))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<mpl_toolkits.mplot3d.art3d.Line3DCollection at 0x787fec752c00>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1600x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize=(16, 10))\n",
+    "ax = fig.add_subplot(111, projection=\"3d\")\n",
+    "ax.view_init(40, 255)\n",
+    "ax.set_xlim(-0.01, 0.11)\n",
+    "ax.set_ylim(-0.01, 0.11)\n",
+    "ax.set_zlim(1, 1.01)\n",
+    "\n",
+    "ax.plot_wireframe(\n",
+    "    (lon_grid - lon0) / lon_deg_m,\n",
+    "    (lat_grid - lat0) / lat_deg_m,\n",
+    "    elev_grid,\n",
+    "    color=\"blue\",\n",
+    ")\n",
+    "ax.plot_wireframe(\n",
+    "    (lons_2014 - lon0) / lon_deg_m,\n",
+    "    (lats_2014 - lat0) / lat_deg_m,\n",
+    "    elevs_2014,\n",
+    "    color=\"green\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Coordinate Propagation between Epochs within One Coordinate Reference System\n",
+    "\n",
+    "Now that our example data set is in the same reference frame as the data with which we'd like to compare it, we'd like to account for the shift in coordinates for a given geographic feature as a result of tectonic plate movement. To do so, we can make use of the ITRF2014 Plate Motion Model (PMM) and apply both the ITRF93 => ITRF2014 coordinate transformation _*and*_ the epoch 1993.5 => 2018.5 shift using the ITRFs PMM model parameters. \n",
+    "\n",
+    "Here we show the original data set (blue), the data set with only the ITRF93 => ITRF2014 transformation applied (green), and the data set obtained by applying the PMM coordinate propagation from 1993.5 through 2018.5 in five-year increments (red). We see that the ITRF93 => ITRF2014 shift applied a relatively small vertical and horizontal shift, and the PMM results in a much larger shift roughly to the northwest. We see that the magnitude of this PMM shift is much more significant, and perhaps significant to the comparison of data between epochs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def propagated_data(\n",
+    "    from_crs, to_crs, obs_epoch, to_epoch, lon_grid, lat_grid, elev_grid\n",
+    "):\n",
+    "    t = create_transformer(from_crs, to_crs, to_epoch)\n",
+    "    (lons_new, lats_new, elevs_new, epochs_new) = t.transform(\n",
+    "        lon_grid.T.flatten(),\n",
+    "        lat_grid.T.flatten(),\n",
+    "        elev_grid,\n",
+    "        obs_epoch * np.ones(lon_grid.shape),\n",
+    "    )\n",
+    "    lons_new = np.reshape(lons_new, (num_points, num_points))\n",
+    "    lats_new = np.reshape(lats_new, (num_points, num_points))\n",
+    "\n",
+    "    return (lons_new, lats_new, elevs_new, epochs_new)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dict_keys([np.float64(1993.5), np.float64(1998.5), np.float64(2003.5), np.float64(2008.5), np.float64(2013.5), np.float64(2018.5)])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<mpl_toolkits.mplot3d.art3d.Line3DCollection at 0x787feefb3a70>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1600x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize=(16, 10))\n",
+    "ax = fig.add_subplot(111, projection=\"3d\")\n",
+    "ax.view_init(40, 255)\n",
+    "ax.set_zlim(1, 1.01)\n",
+    "\n",
+    "ax.plot_wireframe(\n",
+    "    (lon_grid - lon0) / lon_deg_m,\n",
+    "    (lat_grid - lat0) / lat_deg_m,\n",
+    "    elev_grid,\n",
+    "    color=\"blue\",\n",
+    ")\n",
+    "\n",
+    "count = 6\n",
+    "propd_data = {\n",
+    "    y: propagated_data(itrf1, itrf2, epoch1, y, lon_grid, lat_grid, elev_grid)\n",
+    "    for y in np.linspace(epoch1, epoch2, count)\n",
+    "}\n",
+    "print(propd_data.keys())\n",
+    "\n",
+    "for _y, (lons, lats, elevs, _) in propd_data.items():\n",
+    "    ax.plot_wireframe(\n",
+    "        (lons - lon0) / lon_deg_m,\n",
+    "        (lats - lat0) / lat_deg_m,\n",
+    "        elevs,\n",
+    "        color=mpl.colors.hsv_to_rgb([1, 0.5, 0.8]),\n",
+    "    )\n",
+    "\n",
+    "ax.plot_wireframe(\n",
+    "    (lons_2014 - lon0) / lon_deg_m,\n",
+    "    (lats_2014 - lat0) / lat_deg_m,\n",
+    "    elevs_2014,\n",
+    "    color=\"green\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Longitude delta (m): 0.37\n",
+      "Latitude delta (m): 0.33\n",
+      "Elevation delta (m): 0.0007\n"
+     ]
+    }
+   ],
+   "source": [
+    "max_lon_delta = np.max(abs(propd_data[2018.5][0] - propd_data[1993.5][0]))\n",
+    "print(f\"Longitude delta (m): {max_lon_delta / lon_deg_m:0.2f}\")\n",
+    "\n",
+    "max_lat_delta = np.max(abs(propd_data[2018.5][1] - propd_data[1993.5][1]))\n",
+    "print(f\"Latitude delta (m): {max_lat_delta / lat_deg_m:0.2f}\")\n",
+    "\n",
+    "max_elev_delta = np.max(abs(propd_data[2018.5][2] - propd_data[1993.5][2]))\n",
+    "print(f\"Elevation delta (m): {max_elev_delta:0.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## References\n",
+    "\n",
+    "### Overview of the Reference Frame and Plate Shift Problem\n",
+    "1. [The many paths to a common ground: A comparison of transformations between GDA94 and ITRF](https://www.spatial.nsw.gov.au/__data/assets/pdf_file/0003/165702/2011_Haasdyk_and_Janssen_IGNSS2011_comparison_of_GDA94-ITRF_transformations.pdf)\n",
+    "1. [GDA94, ITRF & WGS84What’s the difference?](http://www.quickclose.com.au/stanaway07pres.pdf)\n",
+    "1. [Misaligned maps? High-accuracy data must become time-dependent](https://www.opengeospatial.org/blog/3045)\n",
+    "1. [Dynamic Datum Transformations in Australia and New Zealand](http://ceur-ws.org/Vol-1142/paper6.pdf)\n",
+    "1. [Making Sense of Evolving Datums:WGS84 and NAD83](http://www.naref.org/transf/nad83_hydroscan2006.pdf)\n",
+    "1. [Re: Dynamic/static WGS84 datum problem with web-mapping and map misalignment](http://osgeo-org.1560.x6.nabble.com/Re-Dynamic-static-WGS84-datum-problem-with-web-mapping-and-map-misalignment-td5412013.html)\n",
+    "1. [From static to dynamic datums: 150 years of geodetic datums in New Zealand](https://doi.org/10.1080/00288306.2015.1128451)\n",
+    "1. [The new world of Earth-fixed datums](https://www.spatialsource.com.au/company-industry/new-world-earth-fixed-datums)\n",
+    "1. [FIG/IAG/UN-GGIM-AP/UN-ICG/NZISTechnical Seminar Reference Frame in Practice](https://www.fig.net/resources/proceedings/2016/2016_05_reference%20frame/1_Rizos.pdf)\n",
+    "\n",
+    "### ITRF\n",
+    "1. [ITRF](https://www.iers.org/IERS/EN/DataProducts/ITRF/itrf.html;jsessionid=04D3589399F3433E18FD3739F941D96A.live1)\n",
+    "1. [IERS CONVENTIONS (2003) / IERS Technical Note No. 32](https://www.iers.org/IERS/EN/Publications/TechnicalNotes/tn32.html)\n",
+    "1. [IERS CONVENTIONS (2010) / IERS Technical Note No. 36](https://www.iers.org/IERS/EN/Publications/TechnicalNotes/tn36.html)\n",
+    "1. [Analysis and results of ITRF2014 / IERS Technical Note No. 38](https://www.iers.org/IERS/EN/Publications/TechnicalNotes/tn38.html)\n",
+    "1. [ITRF2008: an improved solution of the internationalterrestrial reference frame](https://doi.org/10.1007/s00190-011-0444-4)\n",
+    "1. [ITRF2008 plate motion model](https://doi.org/10.1029/2011JB008930)\n",
+    "1. [ITRF2014: A new release of the International Terrestrial Reference Frame modeling nonlinear station motions](https://doi.org/10.1002/2016JB013098)\n",
+    "1. [ITRF2014 plate motion model](https://doi.org/10.1093/gji/ggx136)\n",
+    "\n",
+    "### GPS\n",
+    "1. [GPS Reference Systems](http://www.nbmg.unr.edu/staff/pdfs/Blewitt_Encyclopedia_of_Geodesy.html)\n",
+    "1. [GPS Geodetic Reference System  WGS 84](https://www.gps.gov/multimedia/presentations/2009/09/ICG/wiley.pdf)\n",
+    "1. [Reference Frames in Practice Manual. FIG Publication No. 64](http://www.fig.net/resources/publications/figpub/pub64/Figpub64.pdf)\n",
+    "1. [Data collection of WGS 84 information — or is it?](https://www.gpsworld.com/data-collection-of-wgs-84-information-or-is-it/)\n",
+    "\n",
+    "### Libraries\n",
+    "1. [PROJ](https://proj.org/index.html)\n",
+    "1. [PyProj](https://pyproj4.github.io/pyproj/stable/index.html)\n",
+    "1. [GitHub: Add plate motion model parameters to ITRF init-files](https://github.com/OSGeo/PROJ/commit/403f930355926aced5caba5bfbcc230ad152cf86)\n",
+    "1. [[PROJ] Static/Dynamic Webmapping Problem version 2.0](https://lists.osgeo.org/pipermail/proj/2019-July/008703.html)\n",
+    "1. [[PROJ] Seeking clarification on PROJ support for temporal transformations](https://lists.osgeo.org/pipermail/proj/2019-August/008789.html)\n",
+    "1. [[PROJ] OGC blog post summarising Web-mapping misalignment problem](https://lists.osgeo.org/pipermail/proj/2019-August/008776.html)\n",
+    "1. [Re: [gdal-dev] Static/Dynamic datum problems](http://osgeo-org.1560.x6.nabble.com/Re-gdal-dev-Static-Dynamic-datum-problems-td5408589.html)\n",
+    "\n",
+    "### Plate Motion\n",
+    "1. [UNAVCO Plate Motion Calculator](https://www.unavco.org/software/geodetic-utilities/plate-motion-calculator/plate-motion-calculator.html)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "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": "3.12.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/docs/iceflow-example.ipynb b/docs/notebooks/iceflow-example.ipynb
similarity index 98%
rename from docs/iceflow-example.ipynb
rename to docs/notebooks/iceflow-example.ipynb
index a2f8762..d84ee66 100644
--- a/docs/iceflow-example.ipynb
+++ b/docs/notebooks/iceflow-example.ipynb
@@ -7,13 +7,15 @@
    "source": [
     "# NSIDC Iceflow example\n",
     "\n",
-    "This notebook shows an example of how to use the `nsidc-iceflow` Python library to do ITRF transformations with real data. We recommend starting with the [corrections.ipynb](https://github.com/nsidc/NSIDC-Data-Tutorials/blob/main/notebooks/iceflow/corrections.ipynb) notebook to learn more about ITRF transformations and why they matter. \n",
+    "This notebook shows an example of how to use the `nsidc-iceflow` Python library to do ITRF transformations with real data. We recommend starting with the [Applying Coordinate Transformations to Facilitate Data Comparison](./corrections) notebook to learn more about ITRF transformations and why they matter.\n",
+    "\n",
+    "For background on airborne altimetry missions supported by `nsidc-iceflow`, see [Altimetry Data at NSIDC: Point Cloud Data Overview](../altimetry-data-overview).\n",
     "\n",
     "Lets assume we want to do an analysis using [IceBridge ATM L1B Qfit Elevation and Return Strength, Version 1 (ILATM1B)](https://nsidc.org/data/ilatm1b/versions/1) data near Pine Island Glacier in Antarctica.\n",
     "\n",
     "Finding, downloading, and reading ILATM1B v1 data with `nsidc-iceflow` is straightforward. ILATM1B data can be very large, so for the purposes of this example we will focus on just a small area near Pine Island Glacier along with a short date range in order to fetch a manageable amount of data.\n",
     "\n",
-    "To learn about how to download and manage larger amounts of data across many datasets with `nsidc-iceflow`, see the [Using iceflow with icepyx to Generate an Elevation Timeseries](./iceflow-with-icepyx.ipynb) notebook."
+    "To learn about how to download and manage larger amounts of data across many datasets with `nsidc-iceflow`, see the [Using iceflow with icepyx to Generate an Elevation Timeseries](./iceflow-with-icepyx) notebook."
    ]
   },
   {
@@ -30,14 +32,7 @@
    "cell_type": "code",
    "execution_count": 1,
    "id": "e29f9cf9",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-05-23T20:44:18.513598Z",
-     "iopub.status.busy": "2025-05-23T20:44:18.513238Z",
-     "iopub.status.idle": "2025-05-23T20:44:19.770438Z",
-     "shell.execute_reply": "2025-05-23T20:44:19.769877Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from __future__ import annotations\n",
@@ -76,18 +71,12 @@
    "cell_type": "code",
    "execution_count": 2,
    "id": "f45d86d9-404a-4720-9ad0-9959114aa629",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-05-23T20:44:19.772146Z",
-     "iopub.status.busy": "2025-05-23T20:44:19.771905Z",
-     "iopub.status.idle": "2025-05-23T20:44:19.774835Z",
-     "shell.execute_reply": "2025-05-23T20:44:19.774375Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Downloaded data will go here.\n",
     "data_path = Path(\"./downloaded-data/\")\n",
+    "data_path.mkdir(exist_ok=True)\n",
     "\n",
     "# Define the dataset that we want to search for.\n",
     "atm1b_v1_dataset = ILATM1BDataset(version=\"1\")\n",
@@ -118,14 +107,7 @@
    "cell_type": "code",
    "execution_count": 3,
    "id": "c440ba2d-0c4a-47ff-b41e-a7486709d3e0",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-05-23T20:44:19.776364Z",
-     "iopub.status.busy": "2025-05-23T20:44:19.776267Z",
-     "iopub.status.idle": "2025-05-23T20:44:23.583689Z",
-     "shell.execute_reply": "2025-05-23T20:44:23.583008Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -161,22 +143,57 @@
    "cell_type": "code",
    "execution_count": 4,
    "id": "e3b3b3bc-2d2e-49d3-bafc-3f9a4269ee57",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-05-23T20:44:23.586032Z",
-     "iopub.status.busy": "2025-05-23T20:44:23.585852Z",
-     "iopub.status.idle": "2025-05-23T20:44:27.277473Z",
-     "shell.execute_reply": "2025-05-23T20:44:27.276998Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\u001b[32m2025-05-23 14:44:23.586\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mnsidc.iceflow.data.fetch\u001b[0m:\u001b[36m_download_iceflow_search_result\u001b[0m:\u001b[36m72\u001b[0m - \u001b[1mDownloading 1 granules to downloaded-data/ILATM1B_1.\u001b[0m\n"
+      "\u001b[32m2025-06-10 11:00:52.469\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mnsidc.iceflow.data.fetch\u001b[0m:\u001b[36m_download_iceflow_search_result\u001b[0m:\u001b[36m72\u001b[0m - \u001b[1mDownloading 1 granules to downloaded-data/ILATM1B_1.\u001b[0m\n"
      ]
     },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "28d39c1f197042658119f2853eb69670",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "QUEUEING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1e4341335843469e8757c8ebe0c3ca3a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "PROCESSING TASKS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b43146c391224e4494a864e6c3f0f364",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "COLLECTING RESULTS | :   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -202,14 +219,7 @@
    "cell_type": "code",
    "execution_count": 5,
    "id": "15c0633c-1812-45d7-a91f-36d627fdb3b5",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-05-23T20:44:27.279598Z",
-     "iopub.status.busy": "2025-05-23T20:44:27.279212Z",
-     "iopub.status.idle": "2025-05-23T20:44:28.387399Z",
-     "shell.execute_reply": "2025-05-23T20:44:28.386896Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -449,14 +459,7 @@
    "cell_type": "code",
    "execution_count": 6,
    "id": "99e03fbd",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-05-23T20:44:28.388580Z",
-     "iopub.status.busy": "2025-05-23T20:44:28.388478Z",
-     "iopub.status.idle": "2025-05-23T20:44:28.395682Z",
-     "shell.execute_reply": "2025-05-23T20:44:28.395282Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -566,14 +569,7 @@
    "cell_type": "code",
    "execution_count": 7,
    "id": "6a0cd1e6",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-05-23T20:44:28.397189Z",
-     "iopub.status.busy": "2025-05-23T20:44:28.397067Z",
-     "iopub.status.idle": "2025-05-23T20:44:30.186712Z",
-     "shell.execute_reply": "2025-05-23T20:44:30.186381Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -683,14 +679,7 @@
    "cell_type": "code",
    "execution_count": 8,
    "id": "b64693c0",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-05-23T20:44:30.187943Z",
-     "iopub.status.busy": "2025-05-23T20:44:30.187793Z",
-     "iopub.status.idle": "2025-05-23T20:44:30.194792Z",
-     "shell.execute_reply": "2025-05-23T20:44:30.194408Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -733,14 +722,7 @@
    "cell_type": "code",
    "execution_count": 9,
    "id": "14e431b0",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-05-23T20:44:30.196039Z",
-     "iopub.status.busy": "2025-05-23T20:44:30.195884Z",
-     "iopub.status.idle": "2025-05-23T20:44:31.933712Z",
-     "shell.execute_reply": "2025-05-23T20:44:31.933309Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -853,14 +835,7 @@
    "cell_type": "code",
    "execution_count": 10,
    "id": "8c562732",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-05-23T20:44:31.935322Z",
-     "iopub.status.busy": "2025-05-23T20:44:31.935190Z",
-     "iopub.status.idle": "2025-05-23T20:44:31.941332Z",
-     "shell.execute_reply": "2025-05-23T20:44:31.940791Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -893,14 +868,7 @@
    "cell_type": "code",
    "execution_count": 11,
    "id": "5bafd254",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-05-23T20:44:31.942702Z",
-     "iopub.status.busy": "2025-05-23T20:44:31.942580Z",
-     "iopub.status.idle": "2025-05-23T20:44:31.951861Z",
-     "shell.execute_reply": "2025-05-23T20:44:31.951373Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "filter_condition = (iceflow_df.reset_index().index > 50) & (\n",
@@ -923,14 +891,7 @@
    "cell_type": "code",
    "execution_count": 12,
    "id": "0ffce6d2",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-05-23T20:44:31.953492Z",
-     "iopub.status.busy": "2025-05-23T20:44:31.953385Z",
-     "iopub.status.idle": "2025-05-23T20:44:32.066983Z",
-     "shell.execute_reply": "2025-05-23T20:44:32.066631Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
diff --git a/docs/iceflow-with-icepyx.ipynb b/docs/notebooks/iceflow-with-icepyx.ipynb
similarity index 76%
rename from docs/iceflow-with-icepyx.ipynb
rename to docs/notebooks/iceflow-with-icepyx.ipynb
index 80cfedd..541b082 100644
--- a/docs/iceflow-with-icepyx.ipynb
+++ b/docs/notebooks/iceflow-with-icepyx.ipynb
@@ -240,7 +240,7 @@
        "    document.body.appendChild(element);\n",
        "  }\n",
        "\n",
-       "  const js_urls = [\"https://cdn.holoviz.org/panel/1.7.0/dist/bundled/reactiveesm/es-module-shims@^1.10.0/dist/es-module-shims.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-3.7.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.7.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.7.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.7.3.min.js\", \"https://cdn.holoviz.org/panel/1.7.0/dist/panel.min.js\"];\n",
+       "  const js_urls = [\"https://cdn.holoviz.org/panel/1.7.1/dist/bundled/reactiveesm/es-module-shims@^1.10.0/dist/es-module-shims.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-3.7.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.7.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.7.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.7.3.min.js\", \"https://cdn.holoviz.org/panel/1.7.1/dist/panel.min.js\"];\n",
        "  const js_modules = [];\n",
        "  const js_exports = {};\n",
        "  const css_urls = [];\n",
@@ -321,7 +321,7 @@
        "  setTimeout(load_or_wait, 100)\n",
        "}(window));"
       ],
-      "application/vnd.holoviews_load.v0+json": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const force = true;\n  const py_version = '3.7.3'.replace('rc', '-rc.').replace('.dev', '-dev.');\n  const reloading = false;\n  const Bokeh = root.Bokeh;\n\n  // Set a timeout for this load but only if we are not already initializing\n  if (typeof (root._bokeh_timeout) === \"undefined\" || (force || !root._bokeh_is_initializing)) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks;\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n    if (js_modules == null) js_modules = [];\n    if (js_exports == null) js_exports = {};\n\n    root._bokeh_onload_callbacks.push(callback);\n\n    if (root._bokeh_is_loading > 0) {\n      // Don't load bokeh if it is still initializing\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    } else if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n      // There is nothing to load\n      run_callbacks();\n      return null;\n    }\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n    window._bokeh_on_load = on_load\n\n    function on_error(e) {\n      const src_el = e.srcElement\n      console.error(\"failed to load \" + (src_el.href || src_el.src));\n    }\n\n    const skip = [];\n    if (window.requirejs) {\n      window.requirejs.config({'packages': {}, 'paths': {}, 'shim': {}});\n      root._bokeh_is_loading = css_urls.length + 0;\n    } else {\n      root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n    }\n\n    const existing_stylesheets = []\n    const links = document.getElementsByTagName('link')\n    for (let i = 0; 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i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const escaped = encodeURI(url)\n      if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n        if (!window.requirejs) {\n          on_load();\n        }\n        continue;\n      }\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n    for (let i = 0; i < js_modules.length; i++) {\n      const url = js_modules[i];\n      const escaped = encodeURI(url)\n      if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n        if (!window.requirejs) {\n          on_load();\n        }\n        continue;\n      }\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      element.type = \"module\";\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n    for (const name in js_exports) {\n      const url = js_exports[name];\n      const escaped = encodeURI(url)\n      if (skip.indexOf(escaped) >= 0 || root[name] != null) {\n        if (!window.requirejs) {\n          on_load();\n        }\n        continue;\n      }\n      var element = document.createElement('script');\n      element.onerror = on_error;\n      element.async = false;\n      element.type = \"module\";\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      element.textContent = `\n      import ${name} from \"${url}\"\n      window.${name} = ${name}\n      window._bokeh_on_load()\n      `\n      document.head.appendChild(element);\n    }\n    if (!js_urls.length && !js_modules.length) {\n      on_load()\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  const js_urls = [\"https://cdn.holoviz.org/panel/1.7.0/dist/bundled/reactiveesm/es-module-shims@^1.10.0/dist/es-module-shims.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-3.7.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.7.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.7.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.7.3.min.js\", \"https://cdn.holoviz.org/panel/1.7.0/dist/panel.min.js\"];\n  const js_modules = [];\n  const js_exports = {};\n  const css_urls = [];\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {} // ensure no trailing comma for IE\n  ];\n\n  function run_inline_js() {\n    if ((root.Bokeh !== undefined) || (force === true)) {\n      for (let i = 0; i < inline_js.length; i++) {\n        try {\n          inline_js[i].call(root, root.Bokeh);\n        } catch(e) {\n          if (!reloading) {\n            throw e;\n          }\n        }\n      }\n      // Cache old bokeh versions\n      if (Bokeh != undefined && !reloading) {\n        var NewBokeh = root.Bokeh;\n        if (Bokeh.versions === undefined) {\n          Bokeh.versions = new Map();\n        }\n        if (NewBokeh.version !== Bokeh.version) {\n          Bokeh.versions.set(NewBokeh.version, NewBokeh)\n        }\n        root.Bokeh = Bokeh;\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    }\n    root._bokeh_is_initializing = false\n  }\n\n  function load_or_wait() {\n    // Implement a backoff loop that tries to ensure we do not load multiple\n    // versions of Bokeh and its dependencies at the same time.\n    // In recent versions we use the root._bokeh_is_initializing flag\n    // to determine whether there is an ongoing attempt to initialize\n    // bokeh, however for backward compatibility we also try to ensure\n    // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n    // before older versions are fully initialized.\n    if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n      // If the timeout and bokeh was not successfully loaded we reset\n      // everything and try loading again\n      root._bokeh_timeout = Date.now() + 5000;\n      root._bokeh_is_initializing = false;\n      root._bokeh_onload_callbacks = undefined;\n      root._bokeh_is_loading = 0\n      console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n      load_or_wait();\n    } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n      setTimeout(load_or_wait, 100);\n    } else {\n      root._bokeh_is_initializing = true\n      root._bokeh_onload_callbacks = []\n      const bokeh_loaded = root.Bokeh != null && (root.Bokeh.version === py_version || (root.Bokeh.versions !== undefined && root.Bokeh.versions.has(py_version)));\n      if (!reloading && !bokeh_loaded) {\n        if (root.Bokeh) {\n          root.Bokeh = undefined;\n        }\n        console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n      }\n      load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n        console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n        run_inline_js();\n      });\n    }\n  }\n  // Give older versions of the autoload script a head-start to ensure\n  // they initialize before we start loading newer version.\n  setTimeout(load_or_wait, 100)\n}(window));"
+      "application/vnd.holoviews_load.v0+json": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const force = true;\n  const py_version = '3.7.3'.replace('rc', '-rc.').replace('.dev', '-dev.');\n  const reloading = false;\n  const Bokeh = root.Bokeh;\n\n  // Set a timeout for this load but only if we are not already initializing\n  if (typeof (root._bokeh_timeout) === \"undefined\" || (force || !root._bokeh_is_initializing)) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks;\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n    if (js_modules == null) js_modules = [];\n    if (js_exports == null) js_exports = {};\n\n    root._bokeh_onload_callbacks.push(callback);\n\n    if (root._bokeh_is_loading > 0) {\n      // Don't load bokeh if it is still initializing\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    } else if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n      // There is nothing to load\n      run_callbacks();\n      return null;\n    }\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n    window._bokeh_on_load = on_load\n\n    function on_error(e) {\n      const src_el = e.srcElement\n      console.error(\"failed to load \" + (src_el.href || src_el.src));\n    }\n\n    const skip = [];\n    if (window.requirejs) {\n      window.requirejs.config({'packages': {}, 'paths': {}, 'shim': {}});\n      root._bokeh_is_loading = css_urls.length + 0;\n    } else {\n      root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n    }\n\n    const existing_stylesheets = []\n    const links = document.getElementsByTagName('link')\n    for (let i = 0; i < links.length; i++) {\n      const link = links[i]\n      if (link.href != null) {\n        existing_stylesheets.push(link.href)\n      }\n    }\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const escaped = encodeURI(url)\n      if (existing_stylesheets.indexOf(escaped) !== -1) {\n        on_load()\n        continue;\n      }\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }    var existing_scripts = []\n    const scripts = document.getElementsByTagName('script')\n    for (let i = 0; i < scripts.length; i++) {\n      var script = scripts[i]\n      if (script.src != null) {\n        existing_scripts.push(script.src)\n      }\n    }\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const escaped = encodeURI(url)\n      if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n        if (!window.requirejs) {\n          on_load();\n        }\n        continue;\n      }\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n    for (let i = 0; i < js_modules.length; i++) {\n      const url = js_modules[i];\n      const escaped = encodeURI(url)\n      if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n        if (!window.requirejs) {\n          on_load();\n        }\n        continue;\n      }\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      element.type = \"module\";\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n    for (const name in js_exports) {\n      const url = js_exports[name];\n      const escaped = encodeURI(url)\n      if (skip.indexOf(escaped) >= 0 || root[name] != null) {\n        if (!window.requirejs) {\n          on_load();\n        }\n        continue;\n      }\n      var element = document.createElement('script');\n      element.onerror = on_error;\n      element.async = false;\n      element.type = \"module\";\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      element.textContent = `\n      import ${name} from \"${url}\"\n      window.${name} = ${name}\n      window._bokeh_on_load()\n      `\n      document.head.appendChild(element);\n    }\n    if (!js_urls.length && !js_modules.length) {\n      on_load()\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  const js_urls = [\"https://cdn.holoviz.org/panel/1.7.1/dist/bundled/reactiveesm/es-module-shims@^1.10.0/dist/es-module-shims.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-3.7.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.7.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.7.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.7.3.min.js\", \"https://cdn.holoviz.org/panel/1.7.1/dist/panel.min.js\"];\n  const js_modules = [];\n  const js_exports = {};\n  const css_urls = [];\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {} // ensure no trailing comma for IE\n  ];\n\n  function run_inline_js() {\n    if ((root.Bokeh !== undefined) || (force === true)) {\n      for (let i = 0; i < inline_js.length; i++) {\n        try {\n          inline_js[i].call(root, root.Bokeh);\n        } catch(e) {\n          if (!reloading) {\n            throw e;\n          }\n        }\n      }\n      // Cache old bokeh versions\n      if (Bokeh != undefined && !reloading) {\n        var NewBokeh = root.Bokeh;\n        if (Bokeh.versions === undefined) {\n          Bokeh.versions = new Map();\n        }\n        if (NewBokeh.version !== Bokeh.version) {\n          Bokeh.versions.set(NewBokeh.version, NewBokeh)\n        }\n        root.Bokeh = Bokeh;\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    }\n    root._bokeh_is_initializing = false\n  }\n\n  function load_or_wait() {\n    // Implement a backoff loop that tries to ensure we do not load multiple\n    // versions of Bokeh and its dependencies at the same time.\n    // In recent versions we use the root._bokeh_is_initializing flag\n    // to determine whether there is an ongoing attempt to initialize\n    // bokeh, however for backward compatibility we also try to ensure\n    // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n    // before older versions are fully initialized.\n    if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n      // If the timeout and bokeh was not successfully loaded we reset\n      // everything and try loading again\n      root._bokeh_timeout = Date.now() + 5000;\n      root._bokeh_is_initializing = false;\n      root._bokeh_onload_callbacks = undefined;\n      root._bokeh_is_loading = 0\n      console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n      load_or_wait();\n    } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n      setTimeout(load_or_wait, 100);\n    } else {\n      root._bokeh_is_initializing = true\n      root._bokeh_onload_callbacks = []\n      const bokeh_loaded = root.Bokeh != null && (root.Bokeh.version === py_version || (root.Bokeh.versions !== undefined && root.Bokeh.versions.has(py_version)));\n      if (!reloading && !bokeh_loaded) {\n        if (root.Bokeh) {\n          root.Bokeh = undefined;\n        }\n        console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n      }\n      load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n        console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n        run_inline_js();\n      });\n    }\n  }\n  // Give older versions of the autoload script a head-start to ensure\n  // they initialize before we start loading newer version.\n  setTimeout(load_or_wait, 100)\n}(window));"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -578,12 +578,12 @@
      "data": {
       "application/vnd.holoviews_exec.v0+json": "",
       "text/html": [
-       "<div id='ed1a6c13-0bc7-4ae2-947c-a0825e67d55c'>\n",
-       "  <div id=\"e0bb8757-ddd7-48fa-84e5-d4871abb47ec\" data-root-id=\"ed1a6c13-0bc7-4ae2-947c-a0825e67d55c\" style=\"display: contents;\"></div>\n",
+       "<div id='753443f4-86c4-4b92-b4da-d5cbacca9fc2'>\n",
+       "  <div id=\"db7f5a15-5cc5-4866-8691-90a14ae56456\" data-root-id=\"753443f4-86c4-4b92-b4da-d5cbacca9fc2\" style=\"display: contents;\"></div>\n",
        "</div>\n",
        "<script type=\"application/javascript\">(function(root) {\n",
-       "  var docs_json = {\"e6e0ef57-e379-4780-bfd3-2e8983755af1\":{\"version\":\"3.7.3\",\"title\":\"Bokeh Application\",\"roots\":[{\"type\":\"object\",\"name\":\"panel.models.browser.BrowserInfo\",\"id\":\"ed1a6c13-0bc7-4ae2-947c-a0825e67d55c\"},{\"type\":\"object\",\"name\":\"panel.models.comm_manager.CommManager\",\"id\":\"4d9cb9fa-8862-4f2d-bf44-180b4e968d5e\",\"attributes\":{\"plot_id\":\"ed1a6c13-0bc7-4ae2-947c-a0825e67d55c\",\"comm_id\":\"d6dbbaea29194d9186b975f81fafc009\",\"client_comm_id\":\"167be4152b674bcdaeb493b9e09c84d4\"}}],\"defs\":[{\"type\":\"model\",\"name\":\"ReactiveHTML1\"},{\"type\":\"model\",\"name\":\"FlexBox1\",\"properties\":[{\"name\":\"align_content\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"align_items\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"flex_direction\",\"kind\":\"Any\",\"default\":\"row\"},{\"name\":\"flex_wrap\",\"kind\":\"Any\",\"default\":\"wrap\"},{\"name\":\"gap\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"justify_content\",\"kind\":\"Any\",\"default\":\"flex-start\"}]},{\"type\":\"model\",\"name\":\"FloatPanel1\",\"properties\":[{\"name\":\"config\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"contained\",\"kind\":\"Any\",\"default\":true},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"right-top\"},{\"name\":\"offsetx\",\"kind\":\"Any\",\"default\":null},{\"name\":\"offsety\",\"kind\":\"Any\",\"default\":null},{\"name\":\"theme\",\"kind\":\"Any\",\"default\":\"primary\"},{\"name\":\"status\",\"kind\":\"Any\",\"default\":\"normalized\"}]},{\"type\":\"model\",\"name\":\"GridStack1\",\"properties\":[{\"name\":\"ncols\",\"kind\":\"Any\",\"default\":null},{\"name\":\"nrows\",\"kind\":\"Any\",\"default\":null},{\"name\":\"allow_resize\",\"kind\":\"Any\",\"default\":true},{\"name\":\"allow_drag\",\"kind\":\"Any\",\"default\":true},{\"name\":\"state\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"drag1\",\"properties\":[{\"name\":\"slider_width\",\"kind\":\"Any\",\"default\":5},{\"name\":\"slider_color\",\"kind\":\"Any\",\"default\":\"black\"},{\"name\":\"value\",\"kind\":\"Any\",\"default\":50}]},{\"type\":\"model\",\"name\":\"click1\",\"properties\":[{\"name\":\"terminal_output\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"debug_name\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"clears\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"FastWrapper1\",\"properties\":[{\"name\":\"object\",\"kind\":\"Any\",\"default\":null},{\"name\":\"style\",\"kind\":\"Any\",\"default\":null}]},{\"type\":\"model\",\"name\":\"NotificationArea1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"max_notifications\",\"kind\":\"Any\",\"default\":5},{\"name\":\"notifications\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0},{\"name\":\"types\",\"kind\":\"Any\",\"default\":[{\"type\":\"map\",\"entries\":[[\"type\",\"warning\"],[\"background\",\"#ffc107\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-exclamation-triangle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]},{\"type\":\"map\",\"entries\":[[\"type\",\"info\"],[\"background\",\"#007bff\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-info-circle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]}]}]},{\"type\":\"model\",\"name\":\"Notification\",\"properties\":[{\"name\":\"background\",\"kind\":\"Any\",\"default\":null},{\"name\":\"duration\",\"kind\":\"Any\",\"default\":3000},{\"name\":\"icon\",\"kind\":\"Any\",\"default\":null},{\"name\":\"message\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"notification_type\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_rendered\",\"kind\":\"Any\",\"default\":false},{\"name\":\"_destroyed\",\"kind\":\"Any\",\"default\":false}]},{\"type\":\"model\",\"name\":\"TemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"BootstrapTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"TemplateEditor1\",\"properties\":[{\"name\":\"layout\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"MaterialTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"ReactiveESM1\",\"properties\":[{\"name\":\"esm_constants\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}}]},{\"type\":\"model\",\"name\":\"JSComponent1\",\"properties\":[{\"name\":\"esm_constants\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}}]},{\"type\":\"model\",\"name\":\"ReactComponent1\",\"properties\":[{\"name\":\"esm_constants\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}}]},{\"type\":\"model\",\"name\":\"AnyWidgetComponent1\",\"properties\":[{\"name\":\"esm_constants\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}}]},{\"type\":\"model\",\"name\":\"request_value1\",\"properties\":[{\"name\":\"fill\",\"kind\":\"Any\",\"default\":\"none\"},{\"name\":\"_synced\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_request_sync\",\"kind\":\"Any\",\"default\":0}]}]}};\n",
-       "  var render_items = [{\"docid\":\"e6e0ef57-e379-4780-bfd3-2e8983755af1\",\"roots\":{\"ed1a6c13-0bc7-4ae2-947c-a0825e67d55c\":\"e0bb8757-ddd7-48fa-84e5-d4871abb47ec\"},\"root_ids\":[\"ed1a6c13-0bc7-4ae2-947c-a0825e67d55c\"]}];\n",
+       "  var docs_json = {\"78c6274f-6be4-4b07-8e16-4321f899ff89\":{\"version\":\"3.7.3\",\"title\":\"Bokeh Application\",\"roots\":[{\"type\":\"object\",\"name\":\"panel.models.browser.BrowserInfo\",\"id\":\"753443f4-86c4-4b92-b4da-d5cbacca9fc2\"},{\"type\":\"object\",\"name\":\"panel.models.comm_manager.CommManager\",\"id\":\"43e89475-8913-469d-afc9-c6a317f0c8bb\",\"attributes\":{\"plot_id\":\"753443f4-86c4-4b92-b4da-d5cbacca9fc2\",\"comm_id\":\"13b3cb424cb44716bbdf6d78d67870e3\",\"client_comm_id\":\"a3f90c20bdd34021bb420bd3c2613d53\"}}],\"defs\":[{\"type\":\"model\",\"name\":\"ReactiveHTML1\"},{\"type\":\"model\",\"name\":\"FlexBox1\",\"properties\":[{\"name\":\"align_content\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"align_items\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"flex_direction\",\"kind\":\"Any\",\"default\":\"row\"},{\"name\":\"flex_wrap\",\"kind\":\"Any\",\"default\":\"wrap\"},{\"name\":\"gap\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"justify_content\",\"kind\":\"Any\",\"default\":\"flex-start\"}]},{\"type\":\"model\",\"name\":\"FloatPanel1\",\"properties\":[{\"name\":\"config\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"contained\",\"kind\":\"Any\",\"default\":true},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"right-top\"},{\"name\":\"offsetx\",\"kind\":\"Any\",\"default\":null},{\"name\":\"offsety\",\"kind\":\"Any\",\"default\":null},{\"name\":\"theme\",\"kind\":\"Any\",\"default\":\"primary\"},{\"name\":\"status\",\"kind\":\"Any\",\"default\":\"normalized\"}]},{\"type\":\"model\",\"name\":\"GridStack1\",\"properties\":[{\"name\":\"ncols\",\"kind\":\"Any\",\"default\":null},{\"name\":\"nrows\",\"kind\":\"Any\",\"default\":null},{\"name\":\"allow_resize\",\"kind\":\"Any\",\"default\":true},{\"name\":\"allow_drag\",\"kind\":\"Any\",\"default\":true},{\"name\":\"state\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"drag1\",\"properties\":[{\"name\":\"slider_width\",\"kind\":\"Any\",\"default\":5},{\"name\":\"slider_color\",\"kind\":\"Any\",\"default\":\"black\"},{\"name\":\"value\",\"kind\":\"Any\",\"default\":50}]},{\"type\":\"model\",\"name\":\"click1\",\"properties\":[{\"name\":\"terminal_output\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"debug_name\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"clears\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"FastWrapper1\",\"properties\":[{\"name\":\"object\",\"kind\":\"Any\",\"default\":null},{\"name\":\"style\",\"kind\":\"Any\",\"default\":null}]},{\"type\":\"model\",\"name\":\"NotificationArea1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"max_notifications\",\"kind\":\"Any\",\"default\":5},{\"name\":\"notifications\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0},{\"name\":\"types\",\"kind\":\"Any\",\"default\":[{\"type\":\"map\",\"entries\":[[\"type\",\"warning\"],[\"background\",\"#ffc107\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-exclamation-triangle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]},{\"type\":\"map\",\"entries\":[[\"type\",\"info\"],[\"background\",\"#007bff\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-info-circle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]}]}]},{\"type\":\"model\",\"name\":\"Notification\",\"properties\":[{\"name\":\"background\",\"kind\":\"Any\",\"default\":null},{\"name\":\"duration\",\"kind\":\"Any\",\"default\":3000},{\"name\":\"icon\",\"kind\":\"Any\",\"default\":null},{\"name\":\"message\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"notification_type\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_rendered\",\"kind\":\"Any\",\"default\":false},{\"name\":\"_destroyed\",\"kind\":\"Any\",\"default\":false}]},{\"type\":\"model\",\"name\":\"TemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"BootstrapTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"TemplateEditor1\",\"properties\":[{\"name\":\"layout\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"MaterialTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"ReactiveESM1\",\"properties\":[{\"name\":\"esm_constants\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}}]},{\"type\":\"model\",\"name\":\"JSComponent1\",\"properties\":[{\"name\":\"esm_constants\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}}]},{\"type\":\"model\",\"name\":\"ReactComponent1\",\"properties\":[{\"name\":\"esm_constants\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}}]},{\"type\":\"model\",\"name\":\"AnyWidgetComponent1\",\"properties\":[{\"name\":\"esm_constants\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}}]},{\"type\":\"model\",\"name\":\"request_value1\",\"properties\":[{\"name\":\"fill\",\"kind\":\"Any\",\"default\":\"none\"},{\"name\":\"_synced\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_request_sync\",\"kind\":\"Any\",\"default\":0}]}]}};\n",
+       "  var render_items = [{\"docid\":\"78c6274f-6be4-4b07-8e16-4321f899ff89\",\"roots\":{\"753443f4-86c4-4b92-b4da-d5cbacca9fc2\":\"db7f5a15-5cc5-4866-8691-90a14ae56456\"},\"root_ids\":[\"753443f4-86c4-4b92-b4da-d5cbacca9fc2\"]}];\n",
        "  var docs = Object.values(docs_json)\n",
        "  if (!docs) {\n",
        "    return\n",
@@ -652,7 +652,7 @@
      },
      "metadata": {
       "application/vnd.holoviews_exec.v0+json": {
-       "id": "ed1a6c13-0bc7-4ae2-947c-a0825e67d55c"
+       "id": "753443f4-86c4-4b92-b4da-d5cbacca9fc2"
       }
      },
      "output_type": "display_data"
@@ -795,6 +795,7 @@
     "\n",
     "# All of our data will be downloaded to this location.\n",
     "OUTPUT_DIR = Path(\"./downloaded-data/\")\n",
+    "OUTPUT_DIR.mkdir(exist_ok=True)\n",
     "\n",
     "# ICESat2 data products use ITRF2014 (e.g., see https://nsidc.org/data/atl06/versions/6):\n",
     "# > WGS 84 ellipsoid, ITRF2014 reference frame\n",
@@ -834,7 +835,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\u001b[32m2025-05-27 15:09:03.034\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mnsidc.iceflow.data.fetch\u001b[0m:\u001b[36m_find_iceflow_data\u001b[0m:\u001b[36m53\u001b[0m - \u001b[33m\u001b[1mFound no results for dataset.short_name='ILVIS2' dataset.version='1' with bounding_box=BoundingBox(lower_left_lon=-49.149, lower_left_lat=69.186, upper_right_lon=-48.949, upper_right_lat=69.238) temporal=(datetime.date(2007, 1, 1), datetime.date(2024, 10, 28))\u001b[0m\n"
+      "\u001b[32m2025-06-04 11:51:06.931\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mnsidc.iceflow.data.fetch\u001b[0m:\u001b[36m_find_iceflow_data\u001b[0m:\u001b[36m53\u001b[0m - \u001b[33m\u001b[1mFound no results for dataset.short_name='ILVIS2' dataset.version='1' with bounding_box=BoundingBox(lower_left_lon=-49.149, lower_left_lat=69.186, upper_right_lon=-48.949, upper_right_lat=69.238) temporal=(datetime.date(2007, 1, 1), datetime.date(2024, 10, 28))\u001b[0m\n"
      ]
     },
     {
@@ -878,12 +879,246 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\u001b[32m2025-05-27 15:09:17.769\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mnsidc.iceflow.data.fetch\u001b[0m:\u001b[36m_download_iceflow_search_result\u001b[0m:\u001b[36m72\u001b[0m - \u001b[1mDownloading 4 granules to downloaded-data/ILATM1B_1.\u001b[0m\n",
-      "\u001b[32m2025-05-27 15:09:17.783\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mnsidc.iceflow.data.fetch\u001b[0m:\u001b[36m_download_iceflow_search_result\u001b[0m:\u001b[36m72\u001b[0m - \u001b[1mDownloading 14 granules to downloaded-data/ILATM1B_2.\u001b[0m\n",
-      "\u001b[32m2025-05-27 15:09:17.787\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mnsidc.iceflow.data.fetch\u001b[0m:\u001b[36m_download_iceflow_search_result\u001b[0m:\u001b[36m72\u001b[0m - \u001b[1mDownloading 13 granules to downloaded-data/BLATM1B_1.\u001b[0m\n",
-      "\u001b[32m2025-05-27 15:09:17.791\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mnsidc.iceflow.data.fetch\u001b[0m:\u001b[36m_download_iceflow_search_result\u001b[0m:\u001b[36m72\u001b[0m - \u001b[1mDownloading 2 granules to downloaded-data/ILVIS2_2.\u001b[0m\n",
-      "\u001b[32m2025-05-27 15:09:17.793\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mnsidc.iceflow.data.fetch\u001b[0m:\u001b[36m_download_iceflow_search_result\u001b[0m:\u001b[36m72\u001b[0m - \u001b[1mDownloading 8 granules to downloaded-data/GLAH06_034.\u001b[0m\n"
+      "\u001b[32m2025-06-04 11:52:14.452\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mnsidc.iceflow.data.fetch\u001b[0m:\u001b[36m_download_iceflow_search_result\u001b[0m:\u001b[36m72\u001b[0m - \u001b[1mDownloading 4 granules to downloaded-data/ILATM1B_1.\u001b[0m\n"
      ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ca63d63e19014633b71967b263484c37",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "QUEUEING TASKS | :   0%|          | 0/4 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d199f54bd55244cda8bbc605f8c1bed2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "PROCESSING TASKS | :   0%|          | 0/4 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ac681ddec1dc4ca59749d568c23a5e95",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "COLLECTING RESULTS | :   0%|          | 0/4 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[32m2025-06-04 11:52:14.477\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mnsidc.iceflow.data.fetch\u001b[0m:\u001b[36m_download_iceflow_search_result\u001b[0m:\u001b[36m72\u001b[0m - \u001b[1mDownloading 14 granules to downloaded-data/ILATM1B_2.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "63f589360d1a43a89cc8a04454781a66",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "QUEUEING TASKS | :   0%|          | 0/14 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2574b24ee5f64f2c82c4b1574bd13676",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "PROCESSING TASKS | :   0%|          | 0/14 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9cfc0242b53b4bf89e9190281fb40aad",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "COLLECTING RESULTS | :   0%|          | 0/14 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[32m2025-06-04 11:52:28.984\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mnsidc.iceflow.data.fetch\u001b[0m:\u001b[36m_download_iceflow_search_result\u001b[0m:\u001b[36m72\u001b[0m - \u001b[1mDownloading 13 granules to downloaded-data/BLATM1B_1.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c295fe0222a14137ac864c2d677745f8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "QUEUEING TASKS | :   0%|          | 0/13 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c46643c331dc4e8490ba1eaa08425bdb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "PROCESSING TASKS | :   0%|          | 0/13 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "38c9bf62b400451b8efd172c9516e6e1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "COLLECTING RESULTS | :   0%|          | 0/13 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[32m2025-06-04 11:53:22.233\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mnsidc.iceflow.data.fetch\u001b[0m:\u001b[36m_download_iceflow_search_result\u001b[0m:\u001b[36m72\u001b[0m - \u001b[1mDownloading 2 granules to downloaded-data/ILVIS2_2.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e51abeb748d643918274ef8b412fd6ac",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "QUEUEING TASKS | :   0%|          | 0/2 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "49cd7733c5bd4429a31d66bd4d430f7c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "PROCESSING TASKS | :   0%|          | 0/2 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1cb8501eb6e04d56bd768cc8989a2b35",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "COLLECTING RESULTS | :   0%|          | 0/2 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[32m2025-06-04 11:53:38.957\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mnsidc.iceflow.data.fetch\u001b[0m:\u001b[36m_download_iceflow_search_result\u001b[0m:\u001b[36m72\u001b[0m - \u001b[1mDownloading 8 granules to downloaded-data/GLAH06_034.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5d980d3811254793bb703907ea244242",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "QUEUEING TASKS | :   0%|          | 0/8 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "df1cddf19ca04c2396da15c99414b4d7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "PROCESSING TASKS | :   0%|          | 0/8 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5b85a97304044c0e8958e0cbef43551d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "COLLECTING RESULTS | :   0%|          | 0/8 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -907,15 +1142,7 @@
    "execution_count": 5,
    "id": "17e03a49-405a-4d00-a08d-74e5890564da",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001b[32m2025-05-27 15:09:17.800\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mnsidc.iceflow.api\u001b[0m:\u001b[36mmake_iceflow_parquet\u001b[0m:\u001b[36m85\u001b[0m - \u001b[1mRemoving existing iceflow.parquet\u001b[0m\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "parquet_path = make_iceflow_parquet(\n",
     "    data_dir=OUTPUT_DIR,\n",
@@ -1059,7 +1286,7 @@
     {
      "data": {
       "text/plain": [
-       "<icepyx.core.query.Query at 0x74ce7066ea50>"
+       "<icepyx.core.query.Query at 0x79d5414a40e0>"
       ]
      },
      "execution_count": 7,
@@ -1102,7 +1329,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Harmony job ID:  54412b95-badb-45f3-91c7-286f6bcd2754\n",
+      "Harmony job ID:  a1907989-c780-4530-8e8e-9d8a0ccea9d7\n",
       "Initial status of your harmony order request: running\n"
      ]
     },
@@ -1121,17 +1348,17 @@
        "            </thead>\n",
        "            <tbody>\n",
        "                <tr>\n",
-       "                    <td>54412b95-badb-45f3-91c7-286f6bcd2754</td>\n",
+       "                    <td>a1907989-c780-4530-8e8e-9d8a0ccea9d7</td>\n",
        "                    <td>subset</td>\n",
        "                    <td>running</td>\n",
-       "                    <td><a target=\"_blank\" href=\"https://harmony.earthdata.nasa.gov/workflow-ui/54412b95-badb-45f3-91c7-286f6bcd2754\">View Details</a></td>\n",
+       "                    <td><a target=\"_blank\" href=\"https://harmony.earthdata.nasa.gov/workflow-ui/<bound method DataOrder.job_id of DataOrder(job_id=a1907989-c780-4530-8e8e-9d8a0ccea9d7, type=subset, granules=EarthdataAuth obj with session initialized)>\">View Details</a></td>\n",
        "                </tr>\n",
        "            </tbody>\n",
        "        </table>\n",
        "        "
       ],
       "text/plain": [
-       "DataOrder(job_id=54412b95-badb-45f3-91c7-286f6bcd2754, type=subset, granules=EarthdataAuth obj with session initialized)"
+       "DataOrder(job_id=a1907989-c780-4530-8e8e-9d8a0ccea9d7, type=subset, granules=EarthdataAuth obj with session initialized)"
       ]
      },
      "execution_count": 8,
@@ -1154,136 +1381,136 @@
      "output_type": "stream",
      "text": [
       "Your harmony job status is still running. Please continue waiting... this may take a few moments.\n",
-      "......................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................Downloading results for harmony job 54412b95-badb-45f3-91c7-286f6bcd2754\n",
-      "downloaded-data/ATL06/101082229_ATL06_20190112025232_02240205_006_02_subsetted.h5\n",
-      "downloaded-data/ATL06/101082227_ATL06_20181111054901_06660105_006_02_subsetted.h5\n",
-      "downloaded-data/ATL06/101082228_ATL06_20181217152316_12220103_006_02_subsetted.h5\n",
-      "downloaded-data/ATL06/101082230_ATL06_20190115135914_02770203_006_02_subsetted.h5\n",
-      "downloaded-data/ATL06/101082235_ATL06_20190617064249_12220303_006_02_subsetted.h5\n",
-      "downloaded-data/ATL06/101082231_ATL06_20190318110313_12220203_006_02_subsetted.h5\n",
-      "downloaded-data/ATL06/101082237_ATL06_20190810164806_06660405_006_02_subsetted.h5\n",
-      "downloaded-data/ATL06/101082239_ATL06_20191011135158_02240505_006_02_subsetted.h5\n",
-      "downloaded-data/ATL06/101082238_ATL06_20190916022239_12220403_006_02_subsetted.h5\n",
-      "downloaded-data/ATL06/101082241_ATL06_20191109122800_06660505_006_01_subsetted.h5\n",
-      "downloaded-data/ATL06/101082242_ATL06_20191215220228_12220503_006_01_subsetted.h5\n",
-      "downloaded-data/ATL06/101082234_ATL06_20190511210825_06660305_006_02_subsetted.h5\n",
-      "downloaded-data/ATL06/101082243_ATL06_20200110093142_02240605_006_01_subsetted.h5\n",
-      "downloaded-data/ATL06/101082245_ATL06_20200208080743_06660605_006_01_subsetted.h5\n",
-      "downloaded-data/ATL06/101082246_ATL06_20200315174217_12220603_006_01_subsetted.h5\n",
-      "downloaded-data/ATL06/101082247_ATL06_20200410051129_02240705_006_02_subsetted.h5\n",
-      "downloaded-data/ATL06/101082250_ATL06_20200614132201_12220703_006_01_subsetted.h5\n",
-      "downloaded-data/ATL06/101082249_ATL06_20200509034734_06660705_006_01_subsetted.h5\n",
-      "downloaded-data/ATL06/101082254_ATL06_20200913090149_12220803_006_02_subsetted.h5\n",
-      "downloaded-data/ATL06/101082255_ATL06_20201008203102_02240905_006_01_subsetted.h5\n",
-      "downloaded-data/ATL06/101082253_ATL06_20200807232719_06660805_006_01_subsetted.h5\n",
-      "downloaded-data/ATL06/101082256_ATL06_20201106190706_06660905_006_01_subsetted.h5\n",
-      "downloaded-data/ATL06/101082257_ATL06_20201213044139_12220903_006_01_subsetted.h5\n",
-      "downloaded-data/ATL06/101082258_ATL06_20210107161057_02241005_006_01_subsetted.h5\n",
-      "downloaded-data/ATL06/101082260_ATL06_20210205144704_06661005_006_01_subsetted.h5\n",
-      "downloaded-data/ATL06/101082261_ATL06_20210314002135_12221003_006_01_subsetted.h5\n",
-      "downloaded-data/ATL06/101082264_ATL06_20210507102653_06661105_006_01_subsetted.h5\n",
-      "downloaded-data/ATL06/101082265_ATL06_20210612200124_12221103_006_01_subsetted.h5\n",
-      "downloaded-data/ATL06/101082262_ATL06_20210408115050_02241105_006_02_subsetted.h5\n",
-      "downloaded-data/ATL06/101082268_ATL06_20210806060645_06661205_006_01_subsetted.h5\n",
-      "downloaded-data/ATL06/101082266_ATL06_20210708073039_02241205_006_01_subsetted.h5\n",
-      "downloaded-data/ATL06/101082269_ATL06_20211007031039_02241305_006_01_subsetted.h5\n",
-      "downloaded-data/ATL06/101082273_ATL06_20220105225028_02241405_006_01_subsetted.h5\n",
-      "downloaded-data/ATL06/101082271_ATL06_20211105014647_06661305_006_01_subsetted.h5\n",
-      "downloaded-data/ATL06/101082272_ATL06_20211211112116_12221303_006_01_subsetted.h5\n",
-      "downloaded-data/ATL06/101082275_ATL06_20220203212633_06661405_006_01_subsetted.h5\n",
-      "downloaded-data/ATL06/101082276_ATL06_20220312070106_12221403_006_01_subsetted.h5\n",
-      "downloaded-data/ATL06/101082277_ATL06_20220505170624_06661505_006_01_subsetted.h5\n",
-      "downloaded-data/ATL06/101082278_ATL06_20220611024058_12221503_006_01_subsetted.h5\n",
-      "downloaded-data/ATL06/101082279_ATL06_20220706141022_02241605_006_02_subsetted.h5\n",
-      "downloaded-data/ATL06/101082281_ATL06_20220804124627_06661605_006_01_subsetted.h5\n",
-      "downloaded-data/ATL06/101082282_ATL06_20220909222056_12221603_006_01_subsetted.h5\n",
-      "downloaded-data/ATL06/101082283_ATL06_20221005095006_02241705_006_01_subsetted.h5\n",
-      "downloaded-data/ATL06/101082285_ATL06_20221103082608_06661705_006_01_subsetted.h5\n",
-      "downloaded-data/ATL06/101082286_ATL06_20221209180033_12221703_006_02_subsetted.h5\n",
-      "downloaded-data/ATL06/101082287_ATL06_20230104052948_02241805_006_02_subsetted.h5\n",
-      "downloaded-data/ATL06/101082290_ATL06_20230405010947_02241905_006_02_subsetted.h5\n",
-      "downloaded-data/ATL06/101082289_ATL06_20230202040603_06661805_006_02_subsetted.h5\n",
-      "downloaded-data/ATL06/101082292_ATL06_20230503234538_06661905_006_03_subsetted.h5\n",
-      "downloaded-data/ATL06/101082293_ATL06_20230609091949_12221903_006_02_subsetted.h5\n",
-      "downloaded-data/ATL06/101082296_ATL06_20230802192451_06662005_006_02_subsetted.h5\n",
-      "downloaded-data/ATL06/101082297_ATL06_20230908045912_12222003_006_02_subsetted.h5\n",
-      "downloaded-data/ATL06/101082301_ATL06_20231208003854_12222103_006_03_subsetted.h5\n",
-      "downloaded-data/ATL06/101082299_ATL06_20231101150428_06662105_006_01_subsetted.h5\n",
-      "downloaded-data/ATL06/101082302_ATL06_20240102120809_02242205_006_01_subsetted.h5\n",
-      "downloaded-data/ATL06/101082304_ATL06_20240131104358_06662205_006_01_subsetted.h5\n",
-      "downloaded-data/ATL06/101082305_ATL06_20240402074729_02242305_006_01_subsetted.h5\n",
-      "downloaded-data/ATL06/101082307_ATL06_20240501062333_06662305_006_01_subsetted.h5\n",
-      "downloaded-data/ATL06/101082308_ATL06_20240702032717_02242405_006_01_subsetted.h5\n",
-      "downloaded-data/ATL06/101082311_ATL06_20240905113742_12222403_006_01_subsetted.h5\n",
-      "downloaded-data/ATL06/101082312_ATL06_20240930230647_02242505_006_02_subsetted.h5\n",
-      "downloaded-data/ATL06/101082310_ATL06_20240731020304_06662405_006_01_subsetted.h5\n"
+      ".............Downloading results for harmony job a1907989-c780-4530-8e8e-9d8a0ccea9d7\n",
+      "downloaded-data/ATL06/101365877_ATL06_20181217152316_12220103_006_02_subsetted.h5\n",
+      "downloaded-data/ATL06/101365876_ATL06_20181111054901_06660105_006_02_subsetted.h5\n",
+      "downloaded-data/ATL06/101365880_ATL06_20190318110313_12220203_006_02_subsetted.h5\n",
+      "downloaded-data/ATL06/101365879_ATL06_20190115135914_02770203_006_02_subsetted.h5\n",
+      "downloaded-data/ATL06/101365878_ATL06_20190112025232_02240205_006_02_subsetted.h5\n",
+      "downloaded-data/ATL06/101365883_ATL06_20190511210825_06660305_006_02_subsetted.h5\n",
+      "downloaded-data/ATL06/101365888_ATL06_20191011135158_02240505_006_02_subsetted.h5\n",
+      "downloaded-data/ATL06/101365892_ATL06_20200110093142_02240605_006_01_subsetted.h5\n",
+      "downloaded-data/ATL06/101365887_ATL06_20190916022239_12220403_006_02_subsetted.h5\n",
+      "downloaded-data/ATL06/101365886_ATL06_20190810164806_06660405_006_02_subsetted.h5\n",
+      "downloaded-data/ATL06/101365894_ATL06_20200208080743_06660605_006_01_subsetted.h5\n",
+      "downloaded-data/ATL06/101365884_ATL06_20190617064249_12220303_006_02_subsetted.h5\n",
+      "downloaded-data/ATL06/101365899_ATL06_20200614132201_12220703_006_01_subsetted.h5\n",
+      "downloaded-data/ATL06/101365895_ATL06_20200315174217_12220603_006_01_subsetted.h5\n",
+      "downloaded-data/ATL06/101365890_ATL06_20191109122800_06660505_006_01_subsetted.h5\n",
+      "downloaded-data/ATL06/101365896_ATL06_20200410051129_02240705_006_02_subsetted.h5\n",
+      "downloaded-data/ATL06/101365898_ATL06_20200509034734_06660705_006_01_subsetted.h5\n",
+      "downloaded-data/ATL06/101365902_ATL06_20200807232719_06660805_006_01_subsetted.h5\n",
+      "downloaded-data/ATL06/101365891_ATL06_20191215220228_12220503_006_01_subsetted.h5\n",
+      "downloaded-data/ATL06/101365906_ATL06_20201213044139_12220903_006_01_subsetted.h5\n",
+      "downloaded-data/ATL06/101365907_ATL06_20210107161057_02241005_006_01_subsetted.h5\n",
+      "downloaded-data/ATL06/101365917_ATL06_20210806060645_06661205_006_01_subsetted.h5\n",
+      "downloaded-data/ATL06/101365903_ATL06_20200913090149_12220803_006_02_subsetted.h5\n",
+      "downloaded-data/ATL06/101365904_ATL06_20201008203102_02240905_006_01_subsetted.h5\n",
+      "downloaded-data/ATL06/101365913_ATL06_20210507102653_06661105_006_01_subsetted.h5\n",
+      "downloaded-data/ATL06/101365911_ATL06_20210408115050_02241105_006_02_subsetted.h5\n",
+      "downloaded-data/ATL06/101365927_ATL06_20220611024058_12221503_006_01_subsetted.h5\n",
+      "downloaded-data/ATL06/101365930_ATL06_20220804124627_06661605_006_01_subsetted.h5\n",
+      "downloaded-data/ATL06/101365905_ATL06_20201106190706_06660905_006_01_subsetted.h5\n",
+      "downloaded-data/ATL06/101365910_ATL06_20210314002135_12221003_006_01_subsetted.h5\n",
+      "downloaded-data/ATL06/101365946_ATL06_20230908045912_12222003_006_02_subsetted.h5\n",
+      "downloaded-data/ATL06/101365931_ATL06_20220909222056_12221603_006_01_subsetted.h5\n",
+      "downloaded-data/ATL06/101365915_ATL06_20210708073039_02241205_006_01_subsetted.h5\n",
+      "downloaded-data/ATL06/101365941_ATL06_20230503234538_06661905_006_03_subsetted.h5\n",
+      "downloaded-data/ATL06/101365914_ATL06_20210612200124_12221103_006_01_subsetted.h5\n",
+      "downloaded-data/ATL06/101365920_ATL06_20211105014647_06661305_006_01_subsetted.h5\n",
+      "downloaded-data/ATL06/101365936_ATL06_20230104052948_02241805_006_02_subsetted.h5\n",
+      "downloaded-data/ATL06/101365928_ATL06_20220706141022_02241605_006_02_subsetted.h5\n",
+      "downloaded-data/ATL06/101365939_ATL06_20230405010947_02241905_006_02_subsetted.h5\n",
+      "downloaded-data/ATL06/101365925_ATL06_20220312070106_12221403_006_01_subsetted.h5\n",
+      "downloaded-data/ATL06/101365932_ATL06_20221005095006_02241705_006_01_subsetted.h5\n",
+      "downloaded-data/ATL06/101365924_ATL06_20220203212633_06661405_006_01_subsetted.h5\n",
+      "downloaded-data/ATL06/101365922_ATL06_20220105225028_02241405_006_01_subsetted.h5\n",
+      "downloaded-data/ATL06/101365935_ATL06_20221209180033_12221703_006_02_subsetted.h5\n",
+      "downloaded-data/ATL06/101365918_ATL06_20211007031039_02241305_006_01_subsetted.h5\n",
+      "downloaded-data/ATL06/101365938_ATL06_20230202040603_06661805_006_02_subsetted.h5\n",
+      "downloaded-data/ATL06/101365921_ATL06_20211211112116_12221303_006_01_subsetted.h5\n",
+      "downloaded-data/ATL06/101365934_ATL06_20221103082608_06661705_006_01_subsetted.h5\n",
+      "downloaded-data/ATL06/101365945_ATL06_20230802192451_06662005_006_02_subsetted.h5\n",
+      "downloaded-data/ATL06/101365909_ATL06_20210205144704_06661005_006_01_subsetted.h5\n",
+      "downloaded-data/ATL06/101365948_ATL06_20231101150428_06662105_006_01_subsetted.h5\n",
+      "downloaded-data/ATL06/101365926_ATL06_20220505170624_06661505_006_01_subsetted.h5\n",
+      "downloaded-data/ATL06/101365957_ATL06_20240702032717_02242405_006_01_subsetted.h5\n",
+      "downloaded-data/ATL06/101365954_ATL06_20240402074729_02242305_006_01_subsetted.h5\n",
+      "downloaded-data/ATL06/101365951_ATL06_20240102120809_02242205_006_01_subsetted.h5\n",
+      "downloaded-data/ATL06/101365956_ATL06_20240501062333_06662305_006_01_subsetted.h5\n",
+      "downloaded-data/ATL06/101365953_ATL06_20240131104358_06662205_006_01_subsetted.h5\n",
+      "downloaded-data/ATL06/101365961_ATL06_20240930230647_02242505_006_02_subsetted.h5\n",
+      "downloaded-data/ATL06/101365942_ATL06_20230609091949_12221903_006_02_subsetted.h5\n",
+      "downloaded-data/ATL06/101365959_ATL06_20240731020304_06662405_006_01_subsetted.h5\n",
+      "downloaded-data/ATL06/101365950_ATL06_20231208003854_12222103_006_03_subsetted.h5\n",
+      "downloaded-data/ATL06/101365960_ATL06_20240905113742_12222403_006_01_subsetted.h5\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "[PosixPath('downloaded-data/ATL06/101082229_ATL06_20190112025232_02240205_006_02_subsetted.h5'),\n",
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-       " PosixPath('downloaded-data/ATL06/101082228_ATL06_20181217152316_12220103_006_02_subsetted.h5'),\n",
-       " PosixPath('downloaded-data/ATL06/101082230_ATL06_20190115135914_02770203_006_02_subsetted.h5'),\n",
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-       " PosixPath('downloaded-data/ATL06/101082239_ATL06_20191011135158_02240505_006_02_subsetted.h5'),\n",
-       " PosixPath('downloaded-data/ATL06/101082238_ATL06_20190916022239_12220403_006_02_subsetted.h5'),\n",
-       " PosixPath('downloaded-data/ATL06/101082241_ATL06_20191109122800_06660505_006_01_subsetted.h5'),\n",
-       " PosixPath('downloaded-data/ATL06/101082242_ATL06_20191215220228_12220503_006_01_subsetted.h5'),\n",
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-       " PosixPath('downloaded-data/ATL06/101082245_ATL06_20200208080743_06660605_006_01_subsetted.h5'),\n",
-       " PosixPath('downloaded-data/ATL06/101082246_ATL06_20200315174217_12220603_006_01_subsetted.h5'),\n",
-       " PosixPath('downloaded-data/ATL06/101082247_ATL06_20200410051129_02240705_006_02_subsetted.h5'),\n",
-       " PosixPath('downloaded-data/ATL06/101082250_ATL06_20200614132201_12220703_006_01_subsetted.h5'),\n",
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-       " PosixPath('downloaded-data/ATL06/101082255_ATL06_20201008203102_02240905_006_01_subsetted.h5'),\n",
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-       " PosixPath('downloaded-data/ATL06/101082256_ATL06_20201106190706_06660905_006_01_subsetted.h5'),\n",
-       " PosixPath('downloaded-data/ATL06/101082257_ATL06_20201213044139_12220903_006_01_subsetted.h5'),\n",
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-       " PosixPath('downloaded-data/ATL06/101082260_ATL06_20210205144704_06661005_006_01_subsetted.h5'),\n",
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-       " PosixPath('downloaded-data/ATL06/101082262_ATL06_20210408115050_02241105_006_02_subsetted.h5'),\n",
-       " PosixPath('downloaded-data/ATL06/101082268_ATL06_20210806060645_06661205_006_01_subsetted.h5'),\n",
-       " PosixPath('downloaded-data/ATL06/101082266_ATL06_20210708073039_02241205_006_01_subsetted.h5'),\n",
-       " PosixPath('downloaded-data/ATL06/101082269_ATL06_20211007031039_02241305_006_01_subsetted.h5'),\n",
-       " PosixPath('downloaded-data/ATL06/101082273_ATL06_20220105225028_02241405_006_01_subsetted.h5'),\n",
-       " PosixPath('downloaded-data/ATL06/101082271_ATL06_20211105014647_06661305_006_01_subsetted.h5'),\n",
-       " PosixPath('downloaded-data/ATL06/101082272_ATL06_20211211112116_12221303_006_01_subsetted.h5'),\n",
-       " PosixPath('downloaded-data/ATL06/101082275_ATL06_20220203212633_06661405_006_01_subsetted.h5'),\n",
-       " PosixPath('downloaded-data/ATL06/101082276_ATL06_20220312070106_12221403_006_01_subsetted.h5'),\n",
-       " PosixPath('downloaded-data/ATL06/101082277_ATL06_20220505170624_06661505_006_01_subsetted.h5'),\n",
-       " PosixPath('downloaded-data/ATL06/101082278_ATL06_20220611024058_12221503_006_01_subsetted.h5'),\n",
-       " PosixPath('downloaded-data/ATL06/101082279_ATL06_20220706141022_02241605_006_02_subsetted.h5'),\n",
-       " PosixPath('downloaded-data/ATL06/101082281_ATL06_20220804124627_06661605_006_01_subsetted.h5'),\n",
-       " PosixPath('downloaded-data/ATL06/101082282_ATL06_20220909222056_12221603_006_01_subsetted.h5'),\n",
-       " PosixPath('downloaded-data/ATL06/101082283_ATL06_20221005095006_02241705_006_01_subsetted.h5'),\n",
-       " PosixPath('downloaded-data/ATL06/101082285_ATL06_20221103082608_06661705_006_01_subsetted.h5'),\n",
-       " PosixPath('downloaded-data/ATL06/101082286_ATL06_20221209180033_12221703_006_02_subsetted.h5'),\n",
-       " PosixPath('downloaded-data/ATL06/101082287_ATL06_20230104052948_02241805_006_02_subsetted.h5'),\n",
-       " PosixPath('downloaded-data/ATL06/101082290_ATL06_20230405010947_02241905_006_02_subsetted.h5'),\n",
-       " PosixPath('downloaded-data/ATL06/101082289_ATL06_20230202040603_06661805_006_02_subsetted.h5'),\n",
-       " PosixPath('downloaded-data/ATL06/101082292_ATL06_20230503234538_06661905_006_03_subsetted.h5'),\n",
-       " PosixPath('downloaded-data/ATL06/101082293_ATL06_20230609091949_12221903_006_02_subsetted.h5'),\n",
-       " PosixPath('downloaded-data/ATL06/101082296_ATL06_20230802192451_06662005_006_02_subsetted.h5'),\n",
-       " PosixPath('downloaded-data/ATL06/101082297_ATL06_20230908045912_12222003_006_02_subsetted.h5'),\n",
-       " PosixPath('downloaded-data/ATL06/101082301_ATL06_20231208003854_12222103_006_03_subsetted.h5'),\n",
-       " PosixPath('downloaded-data/ATL06/101082299_ATL06_20231101150428_06662105_006_01_subsetted.h5'),\n",
-       " PosixPath('downloaded-data/ATL06/101082302_ATL06_20240102120809_02242205_006_01_subsetted.h5'),\n",
-       " PosixPath('downloaded-data/ATL06/101082304_ATL06_20240131104358_06662205_006_01_subsetted.h5'),\n",
-       " PosixPath('downloaded-data/ATL06/101082305_ATL06_20240402074729_02242305_006_01_subsetted.h5'),\n",
-       " PosixPath('downloaded-data/ATL06/101082307_ATL06_20240501062333_06662305_006_01_subsetted.h5'),\n",
-       " PosixPath('downloaded-data/ATL06/101082308_ATL06_20240702032717_02242405_006_01_subsetted.h5'),\n",
-       " PosixPath('downloaded-data/ATL06/101082311_ATL06_20240905113742_12222403_006_01_subsetted.h5'),\n",
-       " PosixPath('downloaded-data/ATL06/101082312_ATL06_20240930230647_02242505_006_02_subsetted.h5'),\n",
-       " PosixPath('downloaded-data/ATL06/101082310_ATL06_20240731020304_06662405_006_01_subsetted.h5')]"
+       "[PosixPath('downloaded-data/ATL06/101365877_ATL06_20181217152316_12220103_006_02_subsetted.h5'),\n",
+       " PosixPath('downloaded-data/ATL06/101365876_ATL06_20181111054901_06660105_006_02_subsetted.h5'),\n",
+       " PosixPath('downloaded-data/ATL06/101365880_ATL06_20190318110313_12220203_006_02_subsetted.h5'),\n",
+       " PosixPath('downloaded-data/ATL06/101365879_ATL06_20190115135914_02770203_006_02_subsetted.h5'),\n",
+       " PosixPath('downloaded-data/ATL06/101365878_ATL06_20190112025232_02240205_006_02_subsetted.h5'),\n",
+       " PosixPath('downloaded-data/ATL06/101365883_ATL06_20190511210825_06660305_006_02_subsetted.h5'),\n",
+       " PosixPath('downloaded-data/ATL06/101365888_ATL06_20191011135158_02240505_006_02_subsetted.h5'),\n",
+       " PosixPath('downloaded-data/ATL06/101365892_ATL06_20200110093142_02240605_006_01_subsetted.h5'),\n",
+       " PosixPath('downloaded-data/ATL06/101365887_ATL06_20190916022239_12220403_006_02_subsetted.h5'),\n",
+       " PosixPath('downloaded-data/ATL06/101365886_ATL06_20190810164806_06660405_006_02_subsetted.h5'),\n",
+       " PosixPath('downloaded-data/ATL06/101365894_ATL06_20200208080743_06660605_006_01_subsetted.h5'),\n",
+       " PosixPath('downloaded-data/ATL06/101365884_ATL06_20190617064249_12220303_006_02_subsetted.h5'),\n",
+       " PosixPath('downloaded-data/ATL06/101365899_ATL06_20200614132201_12220703_006_01_subsetted.h5'),\n",
+       " PosixPath('downloaded-data/ATL06/101365895_ATL06_20200315174217_12220603_006_01_subsetted.h5'),\n",
+       " PosixPath('downloaded-data/ATL06/101365890_ATL06_20191109122800_06660505_006_01_subsetted.h5'),\n",
+       " PosixPath('downloaded-data/ATL06/101365896_ATL06_20200410051129_02240705_006_02_subsetted.h5'),\n",
+       " PosixPath('downloaded-data/ATL06/101365898_ATL06_20200509034734_06660705_006_01_subsetted.h5'),\n",
+       " PosixPath('downloaded-data/ATL06/101365902_ATL06_20200807232719_06660805_006_01_subsetted.h5'),\n",
+       " PosixPath('downloaded-data/ATL06/101365891_ATL06_20191215220228_12220503_006_01_subsetted.h5'),\n",
+       " PosixPath('downloaded-data/ATL06/101365906_ATL06_20201213044139_12220903_006_01_subsetted.h5'),\n",
+       " PosixPath('downloaded-data/ATL06/101365907_ATL06_20210107161057_02241005_006_01_subsetted.h5'),\n",
+       " PosixPath('downloaded-data/ATL06/101365917_ATL06_20210806060645_06661205_006_01_subsetted.h5'),\n",
+       " PosixPath('downloaded-data/ATL06/101365903_ATL06_20200913090149_12220803_006_02_subsetted.h5'),\n",
+       " PosixPath('downloaded-data/ATL06/101365904_ATL06_20201008203102_02240905_006_01_subsetted.h5'),\n",
+       " PosixPath('downloaded-data/ATL06/101365913_ATL06_20210507102653_06661105_006_01_subsetted.h5'),\n",
+       " PosixPath('downloaded-data/ATL06/101365911_ATL06_20210408115050_02241105_006_02_subsetted.h5'),\n",
+       " PosixPath('downloaded-data/ATL06/101365927_ATL06_20220611024058_12221503_006_01_subsetted.h5'),\n",
+       " PosixPath('downloaded-data/ATL06/101365930_ATL06_20220804124627_06661605_006_01_subsetted.h5'),\n",
+       " PosixPath('downloaded-data/ATL06/101365905_ATL06_20201106190706_06660905_006_01_subsetted.h5'),\n",
+       " PosixPath('downloaded-data/ATL06/101365910_ATL06_20210314002135_12221003_006_01_subsetted.h5'),\n",
+       " PosixPath('downloaded-data/ATL06/101365946_ATL06_20230908045912_12222003_006_02_subsetted.h5'),\n",
+       " PosixPath('downloaded-data/ATL06/101365931_ATL06_20220909222056_12221603_006_01_subsetted.h5'),\n",
+       " PosixPath('downloaded-data/ATL06/101365915_ATL06_20210708073039_02241205_006_01_subsetted.h5'),\n",
+       " PosixPath('downloaded-data/ATL06/101365941_ATL06_20230503234538_06661905_006_03_subsetted.h5'),\n",
+       " PosixPath('downloaded-data/ATL06/101365914_ATL06_20210612200124_12221103_006_01_subsetted.h5'),\n",
+       " PosixPath('downloaded-data/ATL06/101365920_ATL06_20211105014647_06661305_006_01_subsetted.h5'),\n",
+       " PosixPath('downloaded-data/ATL06/101365936_ATL06_20230104052948_02241805_006_02_subsetted.h5'),\n",
+       " PosixPath('downloaded-data/ATL06/101365928_ATL06_20220706141022_02241605_006_02_subsetted.h5'),\n",
+       " PosixPath('downloaded-data/ATL06/101365939_ATL06_20230405010947_02241905_006_02_subsetted.h5'),\n",
+       " PosixPath('downloaded-data/ATL06/101365925_ATL06_20220312070106_12221403_006_01_subsetted.h5'),\n",
+       " PosixPath('downloaded-data/ATL06/101365932_ATL06_20221005095006_02241705_006_01_subsetted.h5'),\n",
+       " PosixPath('downloaded-data/ATL06/101365924_ATL06_20220203212633_06661405_006_01_subsetted.h5'),\n",
+       " PosixPath('downloaded-data/ATL06/101365922_ATL06_20220105225028_02241405_006_01_subsetted.h5'),\n",
+       " PosixPath('downloaded-data/ATL06/101365935_ATL06_20221209180033_12221703_006_02_subsetted.h5'),\n",
+       " PosixPath('downloaded-data/ATL06/101365918_ATL06_20211007031039_02241305_006_01_subsetted.h5'),\n",
+       " PosixPath('downloaded-data/ATL06/101365938_ATL06_20230202040603_06661805_006_02_subsetted.h5'),\n",
+       " PosixPath('downloaded-data/ATL06/101365921_ATL06_20211211112116_12221303_006_01_subsetted.h5'),\n",
+       " PosixPath('downloaded-data/ATL06/101365934_ATL06_20221103082608_06661705_006_01_subsetted.h5'),\n",
+       " PosixPath('downloaded-data/ATL06/101365945_ATL06_20230802192451_06662005_006_02_subsetted.h5'),\n",
+       " PosixPath('downloaded-data/ATL06/101365909_ATL06_20210205144704_06661005_006_01_subsetted.h5'),\n",
+       " PosixPath('downloaded-data/ATL06/101365948_ATL06_20231101150428_06662105_006_01_subsetted.h5'),\n",
+       " PosixPath('downloaded-data/ATL06/101365926_ATL06_20220505170624_06661505_006_01_subsetted.h5'),\n",
+       " PosixPath('downloaded-data/ATL06/101365957_ATL06_20240702032717_02242405_006_01_subsetted.h5'),\n",
+       " PosixPath('downloaded-data/ATL06/101365954_ATL06_20240402074729_02242305_006_01_subsetted.h5'),\n",
+       " PosixPath('downloaded-data/ATL06/101365951_ATL06_20240102120809_02242205_006_01_subsetted.h5'),\n",
+       " PosixPath('downloaded-data/ATL06/101365956_ATL06_20240501062333_06662305_006_01_subsetted.h5'),\n",
+       " PosixPath('downloaded-data/ATL06/101365953_ATL06_20240131104358_06662205_006_01_subsetted.h5'),\n",
+       " PosixPath('downloaded-data/ATL06/101365961_ATL06_20240930230647_02242505_006_02_subsetted.h5'),\n",
+       " PosixPath('downloaded-data/ATL06/101365942_ATL06_20230609091949_12221903_006_02_subsetted.h5'),\n",
+       " PosixPath('downloaded-data/ATL06/101365959_ATL06_20240731020304_06662405_006_01_subsetted.h5'),\n",
+       " PosixPath('downloaded-data/ATL06/101365950_ATL06_20231208003854_12222103_006_03_subsetted.h5'),\n",
+       " PosixPath('downloaded-data/ATL06/101365960_ATL06_20240905113742_12222403_006_01_subsetted.h5')]"
       ]
      },
      "execution_count": 9,
@@ -1317,48 +1544,24 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "file=PosixPath('downloaded-data/ATL06/100842369_ATL06_20230503234538_06661905_006_03_subsetted.h5') contains an error and will not be read\n",
-      "file=PosixPath('downloaded-data/ATL06/100842334_ATL06_20201213044139_12220903_006_01_subsetted.h5') contains an error and will not be read\n",
-      "file=PosixPath('downloaded-data/ATL06/100842297_ATL06_20240930230647_02242505_006_02_subsetted.h5') contains an error and will not be read\n",
-      "file=PosixPath('downloaded-data/ATL06/101082282_ATL06_20220909222056_12221603_006_01_subsetted.h5') contains an error and will not be read\n",
-      "file=PosixPath('downloaded-data/ATL06/101082297_ATL06_20230908045912_12222003_006_02_subsetted.h5') contains an error and will not be read\n",
-      "file=PosixPath('downloaded-data/ATL06/101082230_ATL06_20190115135914_02770203_006_02_subsetted.h5') contains an error and will not be read\n",
-      "file=PosixPath('downloaded-data/ATL06/101082268_ATL06_20210806060645_06661205_006_01_subsetted.h5') contains an error and will not be read\n",
-      "file=PosixPath('downloaded-data/ATL06/100842307_ATL06_20190115135914_02770203_006_02_subsetted.h5') contains an error and will not be read\n",
-      "file=PosixPath('downloaded-data/ATL06/100842267_ATL06_20220909222056_12221603_006_01_subsetted.h5') contains an error and will not be read\n",
-      "file=PosixPath('downloaded-data/ATL06/100842293_ATL06_20240702032717_02242405_006_01_subsetted.h5') contains an error and will not be read\n",
-      "file=PosixPath('downloaded-data/ATL06/100842243_ATL06_20210107161057_02241005_006_01_subsetted.h5') contains an error and will not be read\n",
-      "file=PosixPath('downloaded-data/ATL06/100842345_ATL06_20210806060645_06661205_006_01_subsetted.h5') contains an error and will not be read\n",
-      "file=PosixPath('downloaded-data/ATL06/100842230_ATL06_20200208080743_06660605_006_01_subsetted.h5') contains an error and will not be read\n",
-      "file=PosixPath('downloaded-data/ATL06/101082257_ATL06_20201213044139_12220903_006_01_subsetted.h5') contains an error and will not be read\n",
-      "file=PosixPath('downloaded-data/ATL06/100842292_ATL06_20240501062333_06662305_006_01_subsetted.h5') contains an error and will not be read\n",
-      "file=PosixPath('downloaded-data/ATL06/100842384_ATL06_20240501062333_06662305_006_01_subsetted.h5') contains an error and will not be read\n",
-      "file=PosixPath('downloaded-data/ATL06/100842322_ATL06_20200208080743_06660605_006_01_subsetted.h5') contains an error and will not be read\n",
-      "file=PosixPath('downloaded-data/ATL06/100842215_ATL06_20190115135914_02770203_006_02_subsetted.h5') contains an error and will not be read\n",
-      "file=PosixPath('downloaded-data/ATL06/101082292_ATL06_20230503234538_06661905_006_03_subsetted.h5') contains an error and will not be read\n",
-      "file=PosixPath('downloaded-data/ATL06/100842277_ATL06_20230503234538_06661905_006_03_subsetted.h5') contains an error and will not be read\n",
-      "file=PosixPath('downloaded-data/ATL06/101082258_ATL06_20210107161057_02241005_006_01_subsetted.h5') contains an error and will not be read\n",
-      "file=PosixPath('downloaded-data/ATL06/100842355_ATL06_20220611024058_12221503_006_01_subsetted.h5') contains an error and will not be read\n",
-      "file=PosixPath('downloaded-data/ATL06/101082278_ATL06_20220611024058_12221503_006_01_subsetted.h5') contains an error and will not be read\n",
-      "file=PosixPath('downloaded-data/ATL06/101082308_ATL06_20240702032717_02242405_006_01_subsetted.h5') contains an error and will not be read\n",
-      "file=PosixPath('downloaded-data/ATL06/100842385_ATL06_20240702032717_02242405_006_01_subsetted.h5') contains an error and will not be read\n",
-      "file=PosixPath('downloaded-data/ATL06/101082312_ATL06_20240930230647_02242505_006_02_subsetted.h5') contains an error and will not be read\n",
-      "file=PosixPath('downloaded-data/ATL06/100842263_ATL06_20220611024058_12221503_006_01_subsetted.h5') contains an error and will not be read\n",
-      "file=PosixPath('downloaded-data/ATL06/100842359_ATL06_20220909222056_12221603_006_01_subsetted.h5') contains an error and will not be read\n",
-      "file=PosixPath('downloaded-data/ATL06/100842242_ATL06_20201213044139_12220903_006_01_subsetted.h5') contains an error and will not be read\n",
-      "file=PosixPath('downloaded-data/ATL06/100842282_ATL06_20230908045912_12222003_006_02_subsetted.h5') contains an error and will not be read\n",
-      "file=PosixPath('downloaded-data/ATL06/100842253_ATL06_20210806060645_06661205_006_01_subsetted.h5') contains an error and will not be read\n",
-      "file=PosixPath('downloaded-data/ATL06/101082245_ATL06_20200208080743_06660605_006_01_subsetted.h5') contains an error and will not be read\n",
-      "file=PosixPath('downloaded-data/ATL06/101082307_ATL06_20240501062333_06662305_006_01_subsetted.h5') contains an error and will not be read\n",
-      "file=PosixPath('downloaded-data/ATL06/100842335_ATL06_20210107161057_02241005_006_01_subsetted.h5') contains an error and will not be read\n",
-      "file=PosixPath('downloaded-data/ATL06/100842374_ATL06_20230908045912_12222003_006_02_subsetted.h5') contains an error and will not be read\n",
-      "file=PosixPath('downloaded-data/ATL06/100842389_ATL06_20240930230647_02242505_006_02_subsetted.h5') contains an error and will not be read\n"
+      "file=PosixPath('downloaded-data/ATL06/101365956_ATL06_20240501062333_06662305_006_01_subsetted.h5') contains an error and will not be read\n",
+      "file=PosixPath('downloaded-data/ATL06/101365961_ATL06_20240930230647_02242505_006_02_subsetted.h5') contains an error and will not be read\n",
+      "file=PosixPath('downloaded-data/ATL06/101365906_ATL06_20201213044139_12220903_006_01_subsetted.h5') contains an error and will not be read\n",
+      "file=PosixPath('downloaded-data/ATL06/101365931_ATL06_20220909222056_12221603_006_01_subsetted.h5') contains an error and will not be read\n",
+      "file=PosixPath('downloaded-data/ATL06/101365946_ATL06_20230908045912_12222003_006_02_subsetted.h5') contains an error and will not be read\n",
+      "file=PosixPath('downloaded-data/ATL06/101365907_ATL06_20210107161057_02241005_006_01_subsetted.h5') contains an error and will not be read\n",
+      "file=PosixPath('downloaded-data/ATL06/101365879_ATL06_20190115135914_02770203_006_02_subsetted.h5') contains an error and will not be read\n",
+      "file=PosixPath('downloaded-data/ATL06/101365927_ATL06_20220611024058_12221503_006_01_subsetted.h5') contains an error and will not be read\n",
+      "file=PosixPath('downloaded-data/ATL06/101365957_ATL06_20240702032717_02242405_006_01_subsetted.h5') contains an error and will not be read\n",
+      "file=PosixPath('downloaded-data/ATL06/101365917_ATL06_20210806060645_06661205_006_01_subsetted.h5') contains an error and will not be read\n",
+      "file=PosixPath('downloaded-data/ATL06/101365894_ATL06_20200208080743_06660605_006_01_subsetted.h5') contains an error and will not be read\n",
+      "file=PosixPath('downloaded-data/ATL06/101365941_ATL06_20230503234538_06661905_006_03_subsetted.h5') contains an error and will not be read\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "150"
+       "50"
       ]
      },
      "execution_count": 10,
@@ -1765,49 +1968,49 @@
        "Coordinates:\n",
        "  * photon_idx           (photon_idx) int64 40B 0 1 2 3 4\n",
        "  * spot                 (spot) uint8 5B 1 2 3 4 5\n",
-       "  * gran_idx             (gran_idx) uint64 40B 66619 122201 66612 66604 66603\n",
-       "    source_file          (gran_idx) &lt;U81 2kB &#x27;downloaded-data/ATL06/101082296...\n",
-       "    delta_time           (gran_idx, photon_idx) datetime64[ns] 200B 2023-08-0...\n",
+       "  * gran_idx             (gran_idx) uint64 40B 66613 66600 66616 66621 22416\n",
+       "    source_file          (gran_idx) &lt;U81 2kB &#x27;downloaded-data/ATL06/101365924...\n",
+       "    delta_time           (gran_idx, photon_idx) datetime64[ns] 200B 2022-02-0...\n",
        "Data variables:\n",
-       "    sc_orient            (gran_idx) int8 5B 0 0 0 1 0\n",
-       "    cycle_number         (gran_idx) int8 5B 20 2 13 5 4\n",
-       "    rgt                  (gran_idx) int16 10B 666 1222 666 666 666\n",
+       "    sc_orient            (gran_idx) int8 5B 0 1 1 1 1\n",
+       "    cycle_number         (gran_idx) int8 5B 14 1 17 22 17\n",
+       "    rgt                  (gran_idx) int16 10B 666 666 666 666 224\n",
        "    atlas_sdp_gps_epoch  (gran_idx) datetime64[ns] 40B 2018-01-01T00:00:18 .....\n",
-       "    data_start_utc       (gran_idx) datetime64[ns] 40B 2023-08-02T19:24:49.85...\n",
-       "    data_end_utc         (gran_idx) datetime64[ns] 40B 2023-08-02T19:29:30.57...\n",
+       "    data_start_utc       (gran_idx) datetime64[ns] 40B 2022-02-03T21:26:32.51...\n",
+       "    data_end_utc         (gran_idx) datetime64[ns] 40B 2022-02-03T21:30:49.91...\n",
        "    h_li                 (spot, gran_idx, photon_idx) float32 500B nan ... nan\n",
        "    latitude             (spot, gran_idx, photon_idx) float64 1kB nan ... nan\n",
        "    longitude            (spot, gran_idx, photon_idx) float64 1kB nan ... nan\n",
-       "    gt                   (gran_idx, spot) object 200B nan nan ... &#x27;gt2r&#x27; &#x27;gt3l&#x27;\n",
+       "    gt                   (gran_idx, spot) object 200B nan nan ... nan &#x27;gt1r&#x27;\n",
        "Attributes:\n",
        "    data_product:  ATL06\n",
        "    Description:   The land_ice_height group contains the primary set of deri...\n",
-       "    data_rate:     Data within this group are sparse.  Data values are provid...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-1586303a-b12e-4b18-a35b-48db49cee0e2' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1586303a-b12e-4b18-a35b-48db49cee0e2' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>gran_idx</span>: 5</li><li><span class='xr-has-index'>spot</span>: 5</li><li><span class='xr-has-index'>photon_idx</span>: 5</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-5e05337f-9fc6-494d-923f-850de2bb11ae' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5e05337f-9fc6-494d-923f-850de2bb11ae' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>photon_idx</span></div><div class='xr-var-dims'>(photon_idx)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4</div><input id='attrs-3d03daf7-7747-4a53-847d-bcec25f9c42c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3d03daf7-7747-4a53-847d-bcec25f9c42c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-313d47de-a227-47fe-96a7-b853bec90907' class='xr-var-data-in' type='checkbox'><label for='data-313d47de-a227-47fe-96a7-b853bec90907' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3, 4])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>spot</span></div><div class='xr-var-dims'>(spot)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>1 2 3 4 5</div><input id='attrs-0ba1830a-6307-4ee7-bae8-2da6b7bdafa7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0ba1830a-6307-4ee7-bae8-2da6b7bdafa7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0aec6439-d476-42be-927e-ca6042a1ced4' class='xr-var-data-in' type='checkbox'><label for='data-0aec6439-d476-42be-927e-ca6042a1ced4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1, 2, 3, 4, 5], dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>gran_idx</span></div><div class='xr-var-dims'>(gran_idx)</div><div class='xr-var-dtype'>uint64</div><div class='xr-var-preview xr-preview'>66619 122201 66612 66604 66603</div><input id='attrs-3780c9c2-21a1-4fb6-a440-26f505467405' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3780c9c2-21a1-4fb6-a440-26f505467405' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4e235e9c-3e80-48a2-9b64-b5a2db3b3fb5' class='xr-var-data-in' type='checkbox'><label for='data-4e235e9c-3e80-48a2-9b64-b5a2db3b3fb5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 66619, 122201,  66612,  66604,  66603], dtype=uint64)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>source_file</span></div><div class='xr-var-dims'>(gran_idx)</div><div class='xr-var-dtype'>&lt;U81</div><div class='xr-var-preview xr-preview'>&#x27;downloaded-data/ATL06/101082296...</div><input id='attrs-70aeab1a-fbf7-410f-b8b8-78898d077f8a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-70aeab1a-fbf7-410f-b8b8-78898d077f8a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-95ece38b-812d-4f1d-aec1-8c9ede4662a5' class='xr-var-data-in' type='checkbox'><label for='data-95ece38b-812d-4f1d-aec1-8c9ede4662a5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;downloaded-data/ATL06/101082296_ATL06_20230802192451_06662005_006_02_subsetted.h5&#x27;,\n",
-       "       &#x27;downloaded-data/ATL06/100842216_ATL06_20190318110313_12220203_006_02_subsetted.h5&#x27;,\n",
-       "       &#x27;downloaded-data/ATL06/100842348_ATL06_20211105014647_06661305_006_01_subsetted.h5&#x27;,\n",
-       "       &#x27;downloaded-data/ATL06/101082241_ATL06_20191109122800_06660505_006_01_subsetted.h5&#x27;,\n",
-       "       &#x27;downloaded-data/ATL06/100842222_ATL06_20190810164806_06660405_006_02_subsetted.h5&#x27;],\n",
-       "      dtype=&#x27;&lt;U81&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>delta_time</span></div><div class='xr-var-dims'>(gran_idx, photon_idx)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2023-08-02T19:27:41.104134976 .....</div><input id='attrs-b4657462-cd2b-4f5c-b2f0-d9413bbecff4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b4657462-cd2b-4f5c-b2f0-d9413bbecff4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d518ec3d-943e-4859-b9c3-4caf1c2798d1' class='xr-var-data-in' type='checkbox'><label for='data-d518ec3d-943e-4859-b9c3-4caf1c2798d1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>contentType :</span></dt><dd>referenceInformation</dd><dt><span>description :</span></dt><dd>Number of GPS seconds since the ATLAS SDP epoch. The ATLAS Standard Data Products (SDP) epoch offset is defined within /ancillary_data/atlas_sdp_gps_epoch as the number of GPS seconds between the GPS epoch (1980-01-06T00:00:00.000000Z UTC) and the ATLAS SDP epoch. By adding the offset contained within atlas_sdp_gps_epoch to delta time parameters, the time in gps_seconds relative to the GPS epoch can be computed.</dd><dt><span>long_name :</span></dt><dd>Elapsed GPS seconds</dd><dt><span>source :</span></dt><dd>section 4.4</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([[&#x27;2023-08-02T19:27:41.104134976&#x27;, &#x27;2023-08-02T19:27:41.106963104&#x27;,\n",
-       "        &#x27;2023-08-02T19:27:41.109778144&#x27;, &#x27;2023-08-02T19:27:41.112586336&#x27;,\n",
-       "        &#x27;2023-08-02T19:27:41.115391520&#x27;],\n",
-       "       [&#x27;2019-03-18T11:05:46.449270096&#x27;, &#x27;2019-03-18T11:05:46.452087128&#x27;,\n",
-       "        &#x27;2019-03-18T11:05:46.454899792&#x27;, &#x27;2019-03-18T11:05:46.457712952&#x27;,\n",
-       "        &#x27;2019-03-18T11:05:46.460525432&#x27;],\n",
-       "       [&#x27;2021-11-05T01:49:37.825884656&#x27;, &#x27;2021-11-05T01:49:37.828692816&#x27;,\n",
-       "        &#x27;2021-11-05T01:49:37.831504464&#x27;, &#x27;2021-11-05T01:49:37.834317856&#x27;,\n",
-       "        &#x27;2021-11-05T01:49:37.837132576&#x27;],\n",
-       "       [&#x27;2019-11-09T12:30:51.635645192&#x27;, &#x27;2019-11-09T12:30:51.638434720&#x27;,\n",
-       "        &#x27;2019-11-09T12:30:51.641254440&#x27;, &#x27;2019-11-09T12:30:51.644074152&#x27;,\n",
-       "        &#x27;2019-11-09T12:30:51.646898856&#x27;],\n",
-       "       [&#x27;2019-08-10T16:50:56.801623176&#x27;, &#x27;2019-08-10T16:50:56.804422984&#x27;,\n",
-       "        &#x27;2019-08-10T16:50:56.807226696&#x27;, &#x27;2019-08-10T16:50:56.810036016&#x27;,\n",
-       "        &#x27;2019-08-10T16:50:56.812850024&#x27;]], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5140da80-3bcd-47c0-bc22-5a35f3fde3c5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5140da80-3bcd-47c0-bc22-5a35f3fde3c5' class='xr-section-summary' >Data variables: <span>(10)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>sc_orient</span></div><div class='xr-var-dims'>(gran_idx)</div><div class='xr-var-dtype'>int8</div><div class='xr-var-preview xr-preview'>0 0 0 1 0</div><input id='attrs-5b358e58-defd-45bc-ae99-68eb79ca4edb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5b358e58-defd-45bc-ae99-68eb79ca4edb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bb9891e4-a2e1-44a1-af10-c250ae32db6a' class='xr-var-data-in' type='checkbox'><label for='data-bb9891e4-a2e1-44a1-af10-c250ae32db6a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 0, 0, 1, 0], dtype=int8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cycle_number</span></div><div class='xr-var-dims'>(gran_idx)</div><div class='xr-var-dtype'>int8</div><div class='xr-var-preview xr-preview'>20 2 13 5 4</div><input id='attrs-b43f19c9-4c03-480f-a4e6-aecddb9b39d2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b43f19c9-4c03-480f-a4e6-aecddb9b39d2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f696b727-2398-4e51-8f9a-b3b71bc8ba8f' class='xr-var-data-in' type='checkbox'><label for='data-f696b727-2398-4e51-8f9a-b3b71bc8ba8f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([20,  2, 13,  5,  4], dtype=int8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rgt</span></div><div class='xr-var-dims'>(gran_idx)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>666 1222 666 666 666</div><input id='attrs-655e1dc2-068c-4486-aaa7-72b4802e77f6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-655e1dc2-068c-4486-aaa7-72b4802e77f6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7f9c505d-d8df-4630-b236-5a7cfa436eb5' class='xr-var-data-in' type='checkbox'><label for='data-7f9c505d-d8df-4630-b236-5a7cfa436eb5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 666, 1222,  666,  666,  666], dtype=int16)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>atlas_sdp_gps_epoch</span></div><div class='xr-var-dims'>(gran_idx)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2018-01-01T00:00:18 ... 2018-01-...</div><input id='attrs-d0357c91-a275-4a77-a928-671fb8443f71' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d0357c91-a275-4a77-a928-671fb8443f71' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-de5d4caf-4626-45ba-b0ef-37237408707e' class='xr-var-data-in' type='checkbox'><label for='data-de5d4caf-4626-45ba-b0ef-37237408707e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2018-01-01T00:00:18.000000000&#x27;, &#x27;2018-01-01T00:00:18.000000000&#x27;,\n",
+       "    data_rate:     Data within this group are sparse.  Data values are provid...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-78c5bde1-dd51-49c6-ae36-413785da49ea' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-78c5bde1-dd51-49c6-ae36-413785da49ea' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>gran_idx</span>: 5</li><li><span class='xr-has-index'>spot</span>: 5</li><li><span class='xr-has-index'>photon_idx</span>: 5</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-17e237a3-8b4a-4d1d-a4b7-e2d807ebfa1a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-17e237a3-8b4a-4d1d-a4b7-e2d807ebfa1a' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>photon_idx</span></div><div class='xr-var-dims'>(photon_idx)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4</div><input id='attrs-25da2acc-980e-434c-b761-8c4885b67c3b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-25da2acc-980e-434c-b761-8c4885b67c3b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-60a5b8a6-1e41-4db4-98dd-9382442300b1' class='xr-var-data-in' type='checkbox'><label for='data-60a5b8a6-1e41-4db4-98dd-9382442300b1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3, 4])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>spot</span></div><div class='xr-var-dims'>(spot)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>1 2 3 4 5</div><input id='attrs-0c58da5d-7418-400e-9102-64591c261127' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0c58da5d-7418-400e-9102-64591c261127' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f069e56b-5d7f-4e44-8b7e-2ca00156d52c' class='xr-var-data-in' type='checkbox'><label for='data-f069e56b-5d7f-4e44-8b7e-2ca00156d52c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1, 2, 3, 4, 5], dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>gran_idx</span></div><div class='xr-var-dims'>(gran_idx)</div><div class='xr-var-dtype'>uint64</div><div class='xr-var-preview xr-preview'>66613 66600 66616 66621 22416</div><input id='attrs-c8db7f73-3309-470d-8686-a4c895d924cd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c8db7f73-3309-470d-8686-a4c895d924cd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9631d611-779d-4e2d-a86d-ba02c131e5be' class='xr-var-data-in' type='checkbox'><label for='data-9631d611-779d-4e2d-a86d-ba02c131e5be' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([66613, 66600, 66616, 66621, 22416], dtype=uint64)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>source_file</span></div><div class='xr-var-dims'>(gran_idx)</div><div class='xr-var-dtype'>&lt;U81</div><div class='xr-var-preview xr-preview'>&#x27;downloaded-data/ATL06/101365924...</div><input id='attrs-d07b7bbc-01fd-43e6-a9a0-03dde7fd9271' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d07b7bbc-01fd-43e6-a9a0-03dde7fd9271' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b70a2801-006b-4f42-befd-59e51e20e41f' class='xr-var-data-in' type='checkbox'><label for='data-b70a2801-006b-4f42-befd-59e51e20e41f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;downloaded-data/ATL06/101365924_ATL06_20220203212633_06661405_006_01_subsetted.h5&#x27;,\n",
+       "       &#x27;downloaded-data/ATL06/101365876_ATL06_20181111054901_06660105_006_02_subsetted.h5&#x27;,\n",
+       "       &#x27;downloaded-data/ATL06/101365934_ATL06_20221103082608_06661705_006_01_subsetted.h5&#x27;,\n",
+       "       &#x27;downloaded-data/ATL06/101365953_ATL06_20240131104358_06662205_006_01_subsetted.h5&#x27;,\n",
+       "       &#x27;downloaded-data/ATL06/101365932_ATL06_20221005095006_02241705_006_01_subsetted.h5&#x27;],\n",
+       "      dtype=&#x27;&lt;U81&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>delta_time</span></div><div class='xr-var-dims'>(gran_idx, photon_idx)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2022-02-03T21:29:23.789603600 .....</div><input id='attrs-f8aaebeb-8a34-4727-9c2c-e68005c20c0d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f8aaebeb-8a34-4727-9c2c-e68005c20c0d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-de71c25e-4566-401a-8a27-e45c2c104469' class='xr-var-data-in' type='checkbox'><label for='data-de71c25e-4566-401a-8a27-e45c2c104469' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>contentType :</span></dt><dd>referenceInformation</dd><dt><span>description :</span></dt><dd>Number of GPS seconds since the ATLAS SDP epoch. The ATLAS Standard Data Products (SDP) epoch offset is defined within /ancillary_data/atlas_sdp_gps_epoch as the number of GPS seconds between the GPS epoch (1980-01-06T00:00:00.000000Z UTC) and the ATLAS SDP epoch. By adding the offset contained within atlas_sdp_gps_epoch to delta time parameters, the time in gps_seconds relative to the GPS epoch can be computed.</dd><dt><span>long_name :</span></dt><dd>Elapsed GPS seconds</dd><dt><span>source :</span></dt><dd>section 4.4</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([[&#x27;2022-02-03T21:29:23.789603600&#x27;, &#x27;2022-02-03T21:29:23.792457200&#x27;,\n",
+       "        &#x27;2022-02-03T21:29:23.795305152&#x27;, &#x27;2022-02-03T21:29:23.798141072&#x27;,\n",
+       "        &#x27;2022-02-03T21:29:23.800966512&#x27;],\n",
+       "       [&#x27;2018-11-11T05:51:51.899189164&#x27;, &#x27;2018-11-11T05:51:51.901995228&#x27;,\n",
+       "        &#x27;2018-11-11T05:51:51.904801764&#x27;, &#x27;2018-11-11T05:51:51.907609160&#x27;,\n",
+       "        &#x27;2018-11-11T05:51:51.910419076&#x27;],\n",
+       "       [&#x27;2022-11-03T08:28:59.300359456&#x27;, &#x27;2022-11-03T08:28:59.303184416&#x27;,\n",
+       "        &#x27;2022-11-03T08:28:59.306009600&#x27;, &#x27;2022-11-03T08:28:59.308833984&#x27;,\n",
+       "        &#x27;2022-11-03T08:28:59.311656928&#x27;],\n",
+       "       [&#x27;2024-01-31T10:46:52.068006656&#x27;, &#x27;2024-01-31T10:46:52.070818720&#x27;,\n",
+       "        &#x27;2024-01-31T10:46:52.073629824&#x27;, &#x27;2024-01-31T10:46:52.076441184&#x27;,\n",
+       "        &#x27;2024-01-31T10:46:52.079253536&#x27;],\n",
+       "       [&#x27;2022-10-05T09:52:59.377053088&#x27;, &#x27;2022-10-05T09:52:59.379875072&#x27;,\n",
+       "        &#x27;2022-10-05T09:52:59.382695168&#x27;, &#x27;2022-10-05T09:52:59.385513152&#x27;,\n",
+       "        &#x27;2022-10-05T09:52:59.388328448&#x27;]], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2c841f87-ea3a-4f88-9e15-b0bae71627ba' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2c841f87-ea3a-4f88-9e15-b0bae71627ba' class='xr-section-summary' >Data variables: <span>(10)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>sc_orient</span></div><div class='xr-var-dims'>(gran_idx)</div><div class='xr-var-dtype'>int8</div><div class='xr-var-preview xr-preview'>0 1 1 1 1</div><input id='attrs-b623e3fd-6505-4a1a-8d1b-f7d6ff55212b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b623e3fd-6505-4a1a-8d1b-f7d6ff55212b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-570b90c7-8bf6-4037-ac9b-8766ce9387f9' class='xr-var-data-in' type='checkbox'><label for='data-570b90c7-8bf6-4037-ac9b-8766ce9387f9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 1, 1, 1], dtype=int8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cycle_number</span></div><div class='xr-var-dims'>(gran_idx)</div><div class='xr-var-dtype'>int8</div><div class='xr-var-preview xr-preview'>14 1 17 22 17</div><input id='attrs-934aa154-1128-40af-a4f6-a121e4f83e4b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-934aa154-1128-40af-a4f6-a121e4f83e4b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4f848ab8-4602-4473-8097-d3a6f3842ef4' class='xr-var-data-in' type='checkbox'><label for='data-4f848ab8-4602-4473-8097-d3a6f3842ef4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([14,  1, 17, 22, 17], dtype=int8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rgt</span></div><div class='xr-var-dims'>(gran_idx)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>666 666 666 666 224</div><input id='attrs-26b5f00a-c3ed-4d99-9d57-4085369d82a7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-26b5f00a-c3ed-4d99-9d57-4085369d82a7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a7434e3d-d7d7-412f-807c-f5953ea94a74' class='xr-var-data-in' type='checkbox'><label for='data-a7434e3d-d7d7-412f-807c-f5953ea94a74' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([666, 666, 666, 666, 224], dtype=int16)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>atlas_sdp_gps_epoch</span></div><div class='xr-var-dims'>(gran_idx)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2018-01-01T00:00:18 ... 2018-01-...</div><input id='attrs-71e4784e-d770-475a-8f2b-9301c23089b0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-71e4784e-d770-475a-8f2b-9301c23089b0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5fe37147-22f3-44d9-87da-388e70652e9c' class='xr-var-data-in' type='checkbox'><label for='data-5fe37147-22f3-44d9-87da-388e70652e9c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2018-01-01T00:00:18.000000000&#x27;, &#x27;2018-01-01T00:00:18.000000000&#x27;,\n",
        "       &#x27;2018-01-01T00:00:18.000000000&#x27;, &#x27;2018-01-01T00:00:18.000000000&#x27;,\n",
-       "       &#x27;2018-01-01T00:00:18.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>data_start_utc</span></div><div class='xr-var-dims'>(gran_idx)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2023-08-02T19:24:49.851373 ... 2...</div><input id='attrs-89f8100f-3e17-4031-acc8-af2be7e18980' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-89f8100f-3e17-4031-acc8-af2be7e18980' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-876b585f-1a85-43ac-a312-5e9f14751d0c' class='xr-var-data-in' type='checkbox'><label for='data-876b585f-1a85-43ac-a312-5e9f14751d0c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2023-08-02T19:24:49.851373000&#x27;, &#x27;2019-03-18T11:03:27.347613000&#x27;,\n",
-       "       &#x27;2021-11-05T01:46:46.533978000&#x27;, &#x27;2019-11-09T12:28:00.315532000&#x27;,\n",
-       "       &#x27;2019-08-10T16:48:05.512920000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>data_end_utc</span></div><div class='xr-var-dims'>(gran_idx)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2023-08-02T19:29:30.577057 ... 2...</div><input id='attrs-a8d2ed26-09c2-4459-97eb-87304aeb68d1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a8d2ed26-09c2-4459-97eb-87304aeb68d1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3a13b798-c786-4575-8b4a-f3b01c248628' class='xr-var-data-in' type='checkbox'><label for='data-3a13b798-c786-4575-8b4a-f3b01c248628' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2023-08-02T19:29:30.577057000&#x27;, &#x27;2019-03-18T11:08:38.956207000&#x27;,\n",
-       "       &#x27;2021-11-05T01:51:27.365325000&#x27;, &#x27;2019-11-09T12:32:41.144377000&#x27;,\n",
-       "       &#x27;2019-08-10T16:52:46.340060000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>h_li</span></div><div class='xr-var-dims'>(spot, gran_idx, photon_idx)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-1e1eb094-cdaf-4934-a8f6-3aeea92f68d1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1e1eb094-cdaf-4934-a8f6-3aeea92f68d1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f21b8c70-8e6f-4d9e-b7c5-7d2a43db68e9' class='xr-var-data-in' type='checkbox'><label for='data-f21b8c70-8e6f-4d9e-b7c5-7d2a43db68e9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>contentType :</span></dt><dd>physicalMeasurement</dd><dt><span>description :</span></dt><dd>Standard land-ice segment height determined by land ice algorithm, corrected for first-photon bias, representing the median- based height of the selected PEs</dd><dt><span>long_name :</span></dt><dd>Land Ice height</dd><dt><span>source :</span></dt><dd>section 4.4</dd><dt><span>units :</span></dt><dd>meters</dd></dl></div><div class='xr-var-data'><pre>array([[[      nan,       nan,       nan,       nan,       nan],\n",
+       "       &#x27;2018-01-01T00:00:18.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>data_start_utc</span></div><div class='xr-var-dims'>(gran_idx)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2022-02-03T21:26:32.512841 ... 2...</div><input id='attrs-d76e428e-36f5-44f9-9944-b2f929ef7562' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d76e428e-36f5-44f9-9944-b2f929ef7562' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bc537f24-28af-4989-8cc4-95ad2a6e8d8d' class='xr-var-data-in' type='checkbox'><label for='data-bc537f24-28af-4989-8cc4-95ad2a6e8d8d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-02-03T21:26:32.512841000&#x27;, &#x27;2018-11-11T05:49:00.591189000&#x27;,\n",
+       "       &#x27;2022-11-03T08:26:08.046995000&#x27;, &#x27;2024-01-31T10:44:00.667677000&#x27;,\n",
+       "       &#x27;2022-10-05T09:50:07.988328000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>data_end_utc</span></div><div class='xr-var-dims'>(gran_idx)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2022-02-03T21:30:49.910003 ... 2...</div><input id='attrs-dbbc825e-f416-4285-b0ca-464fb7a927b9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dbbc825e-f416-4285-b0ca-464fb7a927b9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6464e0a2-4575-4d46-9fc8-59a6ac663fd6' class='xr-var-data-in' type='checkbox'><label for='data-6464e0a2-4575-4d46-9fc8-59a6ac663fd6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-02-03T21:30:49.910003000&#x27;, &#x27;2018-11-11T05:53:36.362877000&#x27;,\n",
+       "       &#x27;2022-11-03T08:30:42.533714000&#x27;, &#x27;2024-01-31T10:47:47.697799000&#x27;,\n",
+       "       &#x27;2022-10-05T09:54:41.433623000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>h_li</span></div><div class='xr-var-dims'>(spot, gran_idx, photon_idx)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-3a4a4ab4-ddcc-4139-b1dc-51c898350086' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3a4a4ab4-ddcc-4139-b1dc-51c898350086' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-65b1cab6-0031-4a70-ac7f-0afb13a7fb08' class='xr-var-data-in' type='checkbox'><label for='data-65b1cab6-0031-4a70-ac7f-0afb13a7fb08' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>contentType :</span></dt><dd>physicalMeasurement</dd><dt><span>description :</span></dt><dd>Standard land-ice segment height determined by land ice algorithm, corrected for first-photon bias, representing the median- based height of the selected PEs</dd><dt><span>long_name :</span></dt><dd>Land Ice height</dd><dt><span>source :</span></dt><dd>section 4.4</dd><dt><span>units :</span></dt><dd>meters</dd></dl></div><div class='xr-var-data'><pre>array([[[      nan,       nan,       nan,       nan,       nan],\n",
        "        [      nan,       nan,       nan,       nan,       nan],\n",
        "        [      nan,       nan,       nan,       nan,       nan],\n",
        "        [      nan,       nan,       nan,       nan,       nan],\n",
@@ -1819,24 +2022,24 @@
        "        [      nan,       nan,       nan,       nan,       nan],\n",
        "        [      nan,       nan,       nan,       nan,       nan]],\n",
        "\n",
-       "       [[927.8509 , 927.1209 , 926.18414, 925.3821 , 924.555  ],\n",
+       "       [[930.8973 , 930.2617 , 929.6081 , 928.93475, 928.3954 ],\n",
+       "        [      nan,       nan,       nan,       nan,       nan],\n",
        "        [      nan,       nan,       nan,       nan,       nan],\n",
-       "        [931.6751 , 931.0944 , 930.7457 , 930.31055, 929.71906],\n",
        "        [      nan,       nan,       nan,       nan,       nan],\n",
-       "        [935.19025, 935.1171 , 935.2232 , 934.96454, 933.7116 ]],\n",
+       "        [      nan,       nan,       nan,       nan,       nan]],\n",
        "\n",
        "       [[      nan,       nan,       nan,       nan,       nan],\n",
-       "        [      nan,       nan,       nan,       nan,       nan],\n",
-       "        [      nan,       nan,       nan,       nan,       nan],\n",
-       "        [      nan, 927.60345,       nan, 926.2496 , 925.53784],\n",
+       "        [937.6597 , 936.9613 , 936.2806 , 935.87787, 936.11194],\n",
+       "        [927.01556, 926.70105, 926.90283, 927.3765 , 926.99695],\n",
+       "        [926.54614, 925.97144, 925.37213, 924.98376, 924.72174],\n",
        "        [      nan,       nan,       nan,       nan,       nan]],\n",
        "\n",
        "       [[      nan,       nan,       nan,       nan,       nan],\n",
-       "        [815.0689 , 810.5152 , 806.9575 , 806.5851 , 808.393  ],\n",
+       "        [      nan,       nan,       nan,       nan,       nan],\n",
        "        [      nan,       nan,       nan,       nan,       nan],\n",
        "        [      nan,       nan,       nan,       nan,       nan],\n",
        "        [      nan,       nan,       nan,       nan,       nan]]],\n",
-       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(spot, gran_idx, photon_idx)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-3b25d754-ee67-4a90-9fa9-55b2b78e7d27' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3b25d754-ee67-4a90-9fa9-55b2b78e7d27' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-885914af-3a5b-4f8e-a642-b583399c7337' class='xr-var-data-in' type='checkbox'><label for='data-885914af-3a5b-4f8e-a642-b583399c7337' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>contentType :</span></dt><dd>physicalMeasurement</dd><dt><span>description :</span></dt><dd>Latitude of segment center, WGS84, North=+,</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>source :</span></dt><dd>section 3.10</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>valid_max :</span></dt><dd>90.0</dd><dt><span>valid_min :</span></dt><dd>-90.0</dd></dl></div><div class='xr-var-data'><pre>array([[[        nan,         nan,         nan,         nan,\n",
+       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(spot, gran_idx, photon_idx)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-30c41885-f215-4bf5-b837-4a7d087f733b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-30c41885-f215-4bf5-b837-4a7d087f733b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e16a8d2d-680a-46cb-9382-53b8060e8863' class='xr-var-data-in' type='checkbox'><label for='data-e16a8d2d-680a-46cb-9382-53b8060e8863' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>contentType :</span></dt><dd>physicalMeasurement</dd><dt><span>description :</span></dt><dd>Latitude of segment center, WGS84, North=+,</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>source :</span></dt><dd>section 3.10</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>valid_max :</span></dt><dd>90.0</dd><dt><span>valid_min :</span></dt><dd>-90.0</dd></dl></div><div class='xr-var-data'><pre>array([[[        nan,         nan,         nan,         nan,\n",
        "                 nan],\n",
        "        [        nan,         nan,         nan,         nan,\n",
        "                 nan],\n",
@@ -1858,25 +2061,25 @@
        "        [        nan,         nan,         nan,         nan,\n",
        "...\n",
        "                 nan],\n",
-       "        [        nan,         nan,         nan,         nan,\n",
-       "                 nan],\n",
-       "        [        nan,         nan,         nan,         nan,\n",
-       "                 nan],\n",
-       "        [69.2358987 , 69.2357217 , 69.23554264, 69.23536548,\n",
-       "         69.23518736],\n",
+       "        [69.23784402, 69.23766594, 69.23748786, 69.23730979,\n",
+       "         69.2371317 ],\n",
+       "        [69.23785314, 69.23767502, 69.2374969 , 69.23731878,\n",
+       "         69.23714068],\n",
+       "        [69.23785489, 69.23767682, 69.23749875, 69.23732067,\n",
+       "         69.23714258],\n",
        "        [        nan,         nan,         nan,         nan,\n",
        "                 nan]],\n",
        "\n",
        "       [[        nan,         nan,         nan,         nan,\n",
        "                 nan],\n",
-       "        [69.18607514, 69.18625321, 69.18643127, 69.18660933,\n",
-       "         69.18678738],\n",
        "        [        nan,         nan,         nan,         nan,\n",
        "                 nan],\n",
        "        [        nan,         nan,         nan,         nan,\n",
        "                 nan],\n",
        "        [        nan,         nan,         nan,         nan,\n",
-       "                 nan]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(spot, gran_idx, photon_idx)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-21994f29-87f8-4f32-b171-e60a3be762b2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-21994f29-87f8-4f32-b171-e60a3be762b2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c87376ce-603b-407d-a5fd-a6067d934b28' class='xr-var-data-in' type='checkbox'><label for='data-c87376ce-603b-407d-a5fd-a6067d934b28' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>contentType :</span></dt><dd>physicalMeasurement</dd><dt><span>description :</span></dt><dd>Longitude of segment center, , WGS84, East=+</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>source :</span></dt><dd>section 3.10</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>valid_max :</span></dt><dd>180.0</dd><dt><span>valid_min :</span></dt><dd>-180.0</dd></dl></div><div class='xr-var-data'><pre>array([[[         nan,          nan,          nan,          nan,\n",
+       "                 nan],\n",
+       "        [        nan,         nan,         nan,         nan,\n",
+       "                 nan]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(spot, gran_idx, photon_idx)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-2441b530-7e07-4805-a1b1-7542decf663a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2441b530-7e07-4805-a1b1-7542decf663a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8463cdea-f4f7-4dd7-b721-4c0f1ae37bcb' class='xr-var-data-in' type='checkbox'><label for='data-8463cdea-f4f7-4dd7-b721-4c0f1ae37bcb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>contentType :</span></dt><dd>physicalMeasurement</dd><dt><span>description :</span></dt><dd>Longitude of segment center, , WGS84, East=+</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>source :</span></dt><dd>section 3.10</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>valid_max :</span></dt><dd>180.0</dd><dt><span>valid_min :</span></dt><dd>-180.0</dd></dl></div><div class='xr-var-data'><pre>array([[[         nan,          nan,          nan,          nan,\n",
        "                  nan],\n",
        "        [         nan,          nan,          nan,          nan,\n",
        "                  nan],\n",
@@ -1898,29 +2101,29 @@
        "        [         nan,          nan,          nan,          nan,\n",
        "...\n",
        "                  nan],\n",
-       "        [         nan,          nan,          nan,          nan,\n",
-       "                  nan],\n",
-       "        [         nan,          nan,          nan,          nan,\n",
-       "                  nan],\n",
-       "        [-48.97026312, -48.97034582, -48.97038867, -48.97046749,\n",
-       "         -48.97052812],\n",
+       "        [-48.96509661, -48.96515837, -48.96522002, -48.96528169,\n",
+       "         -48.96534311],\n",
+       "        [-48.96947921, -48.96953984, -48.96960052, -48.96966133,\n",
+       "         -48.96972229],\n",
+       "        [-48.96951967, -48.96958158, -48.96964333, -48.96970492,\n",
+       "         -48.96976634],\n",
        "        [         nan,          nan,          nan,          nan,\n",
        "                  nan]],\n",
        "\n",
        "       [[         nan,          nan,          nan,          nan,\n",
        "                  nan],\n",
-       "        [-49.08365797, -49.08371962, -49.08378144, -49.08384343,\n",
-       "         -49.08390542],\n",
        "        [         nan,          nan,          nan,          nan,\n",
        "                  nan],\n",
        "        [         nan,          nan,          nan,          nan,\n",
        "                  nan],\n",
        "        [         nan,          nan,          nan,          nan,\n",
-       "                  nan]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>gt</span></div><div class='xr-var-dims'>(gran_idx, spot)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>nan nan &#x27;gt2l&#x27; ... &#x27;gt2r&#x27; &#x27;gt3l&#x27;</div><input id='attrs-4bdf5cbc-c196-412e-8331-53d921461102' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4bdf5cbc-c196-412e-8331-53d921461102' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f984e83d-b641-4496-964a-ff20107cccfc' class='xr-var-data-in' type='checkbox'><label for='data-f984e83d-b641-4496-964a-ff20107cccfc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[nan, nan, &#x27;gt2l&#x27;, &#x27;gt2r&#x27;, &#x27;gt3l&#x27;],\n",
-       "       [nan, nan, nan, nan, &#x27;gt3l&#x27;],\n",
-       "       [nan, nan, &#x27;gt2l&#x27;, &#x27;gt2r&#x27;, &#x27;gt3l&#x27;],\n",
+       "                  nan],\n",
+       "        [         nan,          nan,          nan,          nan,\n",
+       "                  nan]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>gt</span></div><div class='xr-var-dims'>(gran_idx, spot)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>nan nan &#x27;gt2l&#x27; ... nan nan &#x27;gt1r&#x27;</div><input id='attrs-0f134599-b9ce-4472-8e88-5941dfe5a02e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0f134599-b9ce-4472-8e88-5941dfe5a02e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3fd6f02a-5665-45e1-9d4f-a2a9fdf12ae4' class='xr-var-data-in' type='checkbox'><label for='data-3fd6f02a-5665-45e1-9d4f-a2a9fdf12ae4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[nan, nan, &#x27;gt2l&#x27;, &#x27;gt2r&#x27;, &#x27;gt3l&#x27;],\n",
+       "       [&#x27;gt3r&#x27;, &#x27;gt3l&#x27;, &#x27;gt2r&#x27;, &#x27;gt2l&#x27;, nan],\n",
+       "       [&#x27;gt3r&#x27;, &#x27;gt3l&#x27;, &#x27;gt2r&#x27;, &#x27;gt2l&#x27;, nan],\n",
        "       [&#x27;gt3r&#x27;, &#x27;gt3l&#x27;, &#x27;gt2r&#x27;, &#x27;gt2l&#x27;, nan],\n",
-       "       [nan, nan, &#x27;gt2l&#x27;, &#x27;gt2r&#x27;, &#x27;gt3l&#x27;]], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-fd269297-ca06-4709-b796-e6f92f023857' class='xr-section-summary-in' type='checkbox'  ><label for='section-fd269297-ca06-4709-b796-e6f92f023857' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>photon_idx</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-068ae8aa-71c2-4b90-b49b-6b375daa3f30' class='xr-index-data-in' type='checkbox'/><label for='index-068ae8aa-71c2-4b90-b49b-6b375daa3f30' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3, 4], dtype=&#x27;int64&#x27;, name=&#x27;photon_idx&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>spot</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-c39147eb-37b3-4f6b-b4fa-add9320ec855' class='xr-index-data-in' type='checkbox'/><label for='index-c39147eb-37b3-4f6b-b4fa-add9320ec855' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1, 2, 3, 4, 5], dtype=&#x27;uint8&#x27;, name=&#x27;spot&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>gran_idx</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-9aa11084-bae3-4be8-898a-eaf72f7ba1e1' class='xr-index-data-in' type='checkbox'/><label for='index-9aa11084-bae3-4be8-898a-eaf72f7ba1e1' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([66619, 122201, 66612, 66604, 66603], dtype=&#x27;uint64&#x27;, name=&#x27;gran_idx&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d9bb9c6c-3076-4fb2-9641-d0fd815fc20a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d9bb9c6c-3076-4fb2-9641-d0fd815fc20a' class='xr-section-summary' >Attributes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>data_product :</span></dt><dd>ATL06</dd><dt><span>Description :</span></dt><dd>The land_ice_height group contains the primary set of derived ATL06 products. This includes geolocation, height, and standard error and quality measures for each segment. This group is sparse, meaning that parameters are provided only for pairs of segments for which at least one beam has a valid surface-height measurement.</dd><dt><span>data_rate :</span></dt><dd>Data within this group are sparse.  Data values are provided only for those ICESat-2 20m segments where at least one beam has a valid land ice height measurement.</dd></dl></div></li></ul></div></div>"
+       "       [nan, nan, nan, nan, &#x27;gt1r&#x27;]], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-75fb2729-f2d0-4b26-bf44-90f13c45b693' class='xr-section-summary-in' type='checkbox'  ><label for='section-75fb2729-f2d0-4b26-bf44-90f13c45b693' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>photon_idx</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-1fd19553-6411-4eb2-a566-e919086f4d01' class='xr-index-data-in' type='checkbox'/><label for='index-1fd19553-6411-4eb2-a566-e919086f4d01' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3, 4], dtype=&#x27;int64&#x27;, name=&#x27;photon_idx&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>spot</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-e262b8b1-ca84-4ff7-af94-199ad9dd802b' class='xr-index-data-in' type='checkbox'/><label for='index-e262b8b1-ca84-4ff7-af94-199ad9dd802b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1, 2, 3, 4, 5], dtype=&#x27;uint8&#x27;, name=&#x27;spot&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>gran_idx</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-dc6f33cc-5afd-48b5-b66f-d6d9b856f3bc' class='xr-index-data-in' type='checkbox'/><label for='index-dc6f33cc-5afd-48b5-b66f-d6d9b856f3bc' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([66613, 66600, 66616, 66621, 22416], dtype=&#x27;uint64&#x27;, name=&#x27;gran_idx&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-59216e39-dbc4-4dd1-90eb-d13779835f76' class='xr-section-summary-in' type='checkbox'  checked><label for='section-59216e39-dbc4-4dd1-90eb-d13779835f76' class='xr-section-summary' >Attributes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>data_product :</span></dt><dd>ATL06</dd><dt><span>Description :</span></dt><dd>The land_ice_height group contains the primary set of derived ATL06 products. This includes geolocation, height, and standard error and quality measures for each segment. This group is sparse, meaning that parameters are provided only for pairs of segments for which at least one beam has a valid surface-height measurement.</dd><dt><span>data_rate :</span></dt><dd>Data within this group are sparse.  Data values are provided only for those ICESat-2 20m segments where at least one beam has a valid land ice height measurement.</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
        "<xarray.Dataset> Size: 5kB\n",
@@ -1928,20 +2131,20 @@
        "Coordinates:\n",
        "  * photon_idx           (photon_idx) int64 40B 0 1 2 3 4\n",
        "  * spot                 (spot) uint8 5B 1 2 3 4 5\n",
-       "  * gran_idx             (gran_idx) uint64 40B 66619 122201 66612 66604 66603\n",
-       "    source_file          (gran_idx) <U81 2kB 'downloaded-data/ATL06/101082296...\n",
-       "    delta_time           (gran_idx, photon_idx) datetime64[ns] 200B 2023-08-0...\n",
+       "  * gran_idx             (gran_idx) uint64 40B 66613 66600 66616 66621 22416\n",
+       "    source_file          (gran_idx) <U81 2kB 'downloaded-data/ATL06/101365924...\n",
+       "    delta_time           (gran_idx, photon_idx) datetime64[ns] 200B 2022-02-0...\n",
        "Data variables:\n",
-       "    sc_orient            (gran_idx) int8 5B 0 0 0 1 0\n",
-       "    cycle_number         (gran_idx) int8 5B 20 2 13 5 4\n",
-       "    rgt                  (gran_idx) int16 10B 666 1222 666 666 666\n",
+       "    sc_orient            (gran_idx) int8 5B 0 1 1 1 1\n",
+       "    cycle_number         (gran_idx) int8 5B 14 1 17 22 17\n",
+       "    rgt                  (gran_idx) int16 10B 666 666 666 666 224\n",
        "    atlas_sdp_gps_epoch  (gran_idx) datetime64[ns] 40B 2018-01-01T00:00:18 .....\n",
-       "    data_start_utc       (gran_idx) datetime64[ns] 40B 2023-08-02T19:24:49.85...\n",
-       "    data_end_utc         (gran_idx) datetime64[ns] 40B 2023-08-02T19:29:30.57...\n",
+       "    data_start_utc       (gran_idx) datetime64[ns] 40B 2022-02-03T21:26:32.51...\n",
+       "    data_end_utc         (gran_idx) datetime64[ns] 40B 2022-02-03T21:30:49.91...\n",
        "    h_li                 (spot, gran_idx, photon_idx) float32 500B nan ... nan\n",
        "    latitude             (spot, gran_idx, photon_idx) float64 1kB nan ... nan\n",
        "    longitude            (spot, gran_idx, photon_idx) float64 1kB nan ... nan\n",
-       "    gt                   (gran_idx, spot) object 200B nan nan ... 'gt2r' 'gt3l'\n",
+       "    gt                   (gran_idx, spot) object 200B nan nan ... nan 'gt1r'\n",
        "Attributes:\n",
        "    data_product:  ATL06\n",
        "    Description:   The land_ice_height group contains the primary set of deri...\n",
@@ -2032,43 +2235,43 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>2019-11-09 12:30:52.011367880</th>\n",
+       "      <th>2018-11-11 05:51:52.285210152</th>\n",
        "      <td>ITRF2014</td>\n",
-       "      <td>861.617493</td>\n",
-       "      <td>69.236462</td>\n",
-       "      <td>-49.054704</td>\n",
+       "      <td>869.074219</td>\n",
+       "      <td>69.237864</td>\n",
+       "      <td>-49.049478</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2019-11-09 12:30:52.014178496</th>\n",
+       "      <th>2018-11-11 05:51:52.288058836</th>\n",
        "      <td>ITRF2014</td>\n",
-       "      <td>861.684326</td>\n",
-       "      <td>69.236284</td>\n",
-       "      <td>-49.054766</td>\n",
+       "      <td>868.753601</td>\n",
+       "      <td>69.237686</td>\n",
+       "      <td>-49.049540</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2019-11-09 12:30:52.016990360</th>\n",
+       "      <th>2018-11-11 05:51:52.290899424</th>\n",
        "      <td>ITRF2014</td>\n",
-       "      <td>861.453247</td>\n",
-       "      <td>69.236105</td>\n",
-       "      <td>-49.054828</td>\n",
+       "      <td>868.397583</td>\n",
+       "      <td>69.237508</td>\n",
+       "      <td>-49.049602</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2019-11-09 12:30:52.019803920</th>\n",
+       "      <th>2018-11-11 05:51:52.293726964</th>\n",
        "      <td>ITRF2014</td>\n",
-       "      <td>861.426636</td>\n",
-       "      <td>69.235927</td>\n",
-       "      <td>-49.054891</td>\n",
+       "      <td>868.105896</td>\n",
+       "      <td>69.237330</td>\n",
+       "      <td>-49.049663</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2019-11-09 12:30:52.022619600</th>\n",
+       "      <th>2018-11-11 05:51:52.296544788</th>\n",
        "      <td>ITRF2014</td>\n",
-       "      <td>861.381531</td>\n",
-       "      <td>69.235749</td>\n",
-       "      <td>-49.054954</td>\n",
+       "      <td>867.913452</td>\n",
+       "      <td>69.237152</td>\n",
+       "      <td>-49.049724</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -2078,19 +2281,19 @@
       "text/plain": [
        "                                   ITRF   elevation   latitude  longitude  \\\n",
        "utc_datetime                                                                \n",
-       "2019-11-09 12:30:52.011367880  ITRF2014  861.617493  69.236462 -49.054704   \n",
-       "2019-11-09 12:30:52.014178496  ITRF2014  861.684326  69.236284 -49.054766   \n",
-       "2019-11-09 12:30:52.016990360  ITRF2014  861.453247  69.236105 -49.054828   \n",
-       "2019-11-09 12:30:52.019803920  ITRF2014  861.426636  69.235927 -49.054891   \n",
-       "2019-11-09 12:30:52.022619600  ITRF2014  861.381531  69.235749 -49.054954   \n",
+       "2018-11-11 05:51:52.285210152  ITRF2014  869.074219  69.237864 -49.049478   \n",
+       "2018-11-11 05:51:52.288058836  ITRF2014  868.753601  69.237686 -49.049540   \n",
+       "2018-11-11 05:51:52.290899424  ITRF2014  868.397583  69.237508 -49.049602   \n",
+       "2018-11-11 05:51:52.293726964  ITRF2014  868.105896  69.237330 -49.049663   \n",
+       "2018-11-11 05:51:52.296544788  ITRF2014  867.913452  69.237152 -49.049724   \n",
        "\n",
        "                               spot  \n",
        "utc_datetime                         \n",
-       "2019-11-09 12:30:52.011367880     1  \n",
-       "2019-11-09 12:30:52.014178496     1  \n",
-       "2019-11-09 12:30:52.016990360     1  \n",
-       "2019-11-09 12:30:52.019803920     1  \n",
-       "2019-11-09 12:30:52.022619600     1  "
+       "2018-11-11 05:51:52.285210152     1  \n",
+       "2018-11-11 05:51:52.288058836     1  \n",
+       "2018-11-11 05:51:52.290899424     1  \n",
+       "2018-11-11 05:51:52.293726964     1  \n",
+       "2018-11-11 05:51:52.296544788     1  "
       ]
      },
      "execution_count": 13,
@@ -2371,7 +2574,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x74ce70870c50>"
+       "<matplotlib.legend.Legend at 0x79d541e68920>"
       ]
      },
      "execution_count": 17,
@@ -2380,7 +2583,7 @@
     },
     {
      "data": {
-      "image/png": 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gQYPw9OlT3L9/HwcPHkRISAjq1q0LAPjtt9/QoUMHLFiwAK6urihbtizKli0rHV+6dGkEBQXh5MmTWjWr1Wp4e3tj+vTpOHnyZLqzdzmNlxESEREREWUisxmt7LQzxMuXL3Ho0CGMHTtWK2i9y5CVK9euXYuOHTvC3t4eAwYMgI+PT7bqCg0NRWpqKlq3bi1tq1SpEkqVKoXg4GAAwOHDh6HRaPD48WN4enqiZMmS6N27Nx4+fAgAsLOzQ6dOnaQZqjQbN25Et27dYG1tjeDgYDg4OEhBCwBat24NuVyOc+fOpVvbrVu3cPDgQTRv3lxr+4wZM1CsWDEMHz48W+ecHQxbRERERER51K1btyCEgIeHh9Z2Jycn2NjYwMbGBpMmTdKrL41GAz8/PwwYMAAA0LdvX5w6dUp6sPa7SpYsKfWf9nrw4IG0PyoqCubm5nBwcNA6rnjx4oiKigLw9vLHtEv5lixZgm3btiE6OhqffvopUlJSALy9RHDXrl3SfWdxcXHYv38/vL29pXGKFSumNYZSqUSRIkWkcdI0btwYlpaWqFChApo2bYoZM2ZI+06dOgUfHx+sWbNGr88qpzBsERERERHlM+fPn0dYWBiqVKmC5ORkvY45fPgwXr9+jQ4dOgB4G9g+/fRT+Pr66rQ9efIkwsLCtF6urq4G1ajRaJCamoply5ahbdu2aNiwITZt2oSIiAgcO3YMANChQweYmZlhz549AIDt27fDzs5Oa8ZMXwEBAbh48SL8/f2xf/9+LFiwAMDbGceBAwdizZo1cHJyMrjfD8F7toiIiIiI8qjy5ctDJpPh5s2bWtvT7lGysrLSuy8fHx9ER0drHaPRaHD58mVMnz4dcvn/zcO4u7vrzFoplf8XHZydnZGSkoKYmBitdk+fPoWzszMAwMXFBQBQuXJlaX/RokXh5OQkzZKZm5vjs88+g7+/P/r27Qt/f3/06dNHGsvZ2RnPnj3TqkOlUiE6OloaJ42bm5s0nlqtxsiRI/Htt9/i9u3buHfvHjp37qx13mnndPPmTZQrV06PT9BwnNkiIiIiIsqjHB0d8emnn2L58uV4/fp1tvt5+fIldu/ejc2bN2vNVv3777949eoVDh06ZFB/derUgZmZGY4ePSptu3nzJh48eIBGjRoBAJo0aSJtTxMdHY0XL16gdOnS0jZvb28cPHgQ165dQ2BgoHQJIQA0atQIMTExCA0NlbYFBgZCo9GgQYMGGdaXNqum0WhQqVIlXLlyReu8u3TpAi8vL4SFhUkhzRg4s0VERERElIetWLECTZo0Qd26dTFt2jRUr14dcrkcISEh+O+//1CnTh2p7ePHjxEWFqZ1fOnSpbF+/Xo4Ojqid+/eOgtqdOjQAT4+PmjXrp3eNdnb22P48OH45ptvUKRIEdjZ2eHLL79Eo0aN0LBhQwBAxYoV0bVrV3z11VdYvXo17OzsMHnyZFSqVAleXl5SX82aNYOzszO8vb3h7u6uFaI8PT3Rrl07jBgxAqtWrUJqairGjRuHvn37Spc1bty4EWZmZtKS+BcuXMDkyZPRp08fmJmZwczMDFWrVtWqP2027v3tOY1hi4iIiIgoDytXrhz+/fdfzJ49G5MnT8ajR49gYWGBypUr47vvvsOYMWOktgsWLJDuVUqzfv16+Pr6onv37umuXNizZ08MHDgQL168MKiuxYsXQy6Xo2fPnkhOTkbbtm2xYsUKrTbr1q3DhAkT0LFjR8jlcjRv3hwHDx6EmZmZ1EYmk6Ffv36YN28epk6dqjPOxo0bMW7cOLRq1Uoab9myZdJ+pVKJX3/9FeHh4RBCoHTp0hg3bhwmTJhg0PkYg0wYsjD/RyouLg729vaIjY2FnZ2dqcshIiIiIgMlJSXh7t27cHd3N/ghvhcvXtSaPcpIaGgoateund0SKQ/J7OfFkGzAe7aIiIiIiDJha2ubo+3o48HLCImIiIiIMlGhQgWEh4dn+tBiW1tbVKhQIRerovyAYYuIiIiIKAsMUpQdvIyQiIiIiIjICBi2iIiIiIiIjIBhi4iIiIiIyAgYtoiIiIiIiIyAYYuIiIiIiMgIGLaIiIiIiIiMgGGLiIiIiMgAKpUKKSkppi6D8gGGLSIiIiIiPb18+RK1atVClSpVEBkZafTxhgwZAplMJr0cHR3Rrl07XL58WWojk8mwa9euLPuaM2cOFAoF5s+fL20rU6aMVv/vv4YMGSKNIZPJcPbsWa0+k5OT4ejoCJlMhqCgIGn7rFmz0LhxY1hbW8PBwUGnlnv37mmNY25ujvLly2PmzJkQQuj9+SQlJWHIkCGoVq0alEolunXrpvexuYFhi4iIiIhIDy9fvkTz5s1x48YN3Lt3D02bNs2VwNWuXTtERkYiMjISR48ehVKpRKdOnQzux9fXF99//z18fX2lbSEhIVLf27dvBwDcvHlT2rZ06VKprZubG9auXavV586dO2FjY6MzVkpKCnr16oXRo0dnWtORI0cQGRmJiIgITJ8+HbNmzdKqLytqtRpWVlYYP348WrdurfdxuYVhi4iIiIgoC2lB67///oNarYZKpcL9+/dzJXBZWFjA2dkZzs7OqFmzJn744Qc8fPgQz58/17uP48eP482bN5gxYwbi4uJw5swZAEDRokWlvosUKQIAKFasmLTN3t5e6mPw4MHYvHkz3rx5I23z9fXF4MGDdcabPn06JkyYgGrVqmVal6OjI5ydnVG6dGl4e3ujSZMmuHjxIgDg0KFDsLS0RExMjNYxX331FVq2bAkAKFSoEFauXIkRI0bA2dlZ788jtzBsERERERFl4v2glSY3A1eahIQEbNiwAeXLl4ejo6Pex/n4+KBfv34wMzNDv3794OPjY/DYderUQZkyZaQZsAcPHuDEiRMYOHCgwX2l58KFCwgNDUWDBg0AAK1atYKDg4M0HvB2JisgIADe3t45MqaxMWwREREREWUgo6CVJjcC1759+2BjYwMbGxvY2tpiz549CAgIgFyu31f5uLg4bNu2DQMGDAAADBgwAFu2bEFCQoLBtQwbNky6zM/Pzw8dOnRA0aJFDe4nTePGjWFjYwNzc3PUq1cPvXv3xqBBgwAACoUCffv2hb+/v9T+6NGjiImJQc+ePbM9Zm5i2CIiIiIiSkdWQSuNsQOXl5cXwsLCEBYWhvPnz6Nt27Zo37497t+/r9fxmzZtQrly5VCjRg0AQM2aNVG6dGkEBAQYXMuAAQMQHByMO3fuwM/PD8OGDTO4j3cFBAQgLCwMly5dwpYtW7B792788MMP0n5vb28EBQXhyZMnAICNGzeiY8eO6S66kRcxbBERERERpcPX1xfXrl3LNGilUalUuH37NlasWJHjdRQqVAjly5dH+fLlUa9ePfz55594/fo11qxZo9fxPj4+uHbtGpRKpfS6fv26QQtRpHF0dESnTp0wfPhwJCUloX379gb38S43NzeUL18enp6e6NWrF77++mssXLgQSUlJAIB69eqhXLly0r1iO3fuzDeXEAKA0tQFEBERERHlRaNGjcKOHTsQEhKSZeBSKBSoUqUKJkyYYPS6ZDIZ5HK51kIVGbly5QouXLiAoKAgaQEMAIiOjkaLFi3w33//oVKlSgaNP2zYMHTo0AGTJk2CQqEwuP7MKBQK6TlmlpaWAN7Obm3cuBElS5aEXC5Hx44dc3RMY2LYIiIiIiJKh62tLQ4dOoQ2bdpkGrjSgtaxY8e0Ak1OSU5ORlRUFADg1atXWL58ORISEtC5c2epzd27dxEWFqZ1XIUKFeDj44P69eujWbNmOv3Wq1cPPj4+Ws/d0ke7du3w/Plz2NnZZdjmwYMHiI6OxoMHD6BWq6Xaypcvr7VU/MuXLxEVFQWVSoUrV65g6dKl8PLy0urb29sb06ZNw6xZs/DZZ5/BwsJCa6zr168jJSUF0dHRiI+Pl8aqWbOmQedlDAxbREREREQZyCpwGTtoAcDBgwfh4uIi1VOpUiVs3boVLVq0kNp88803OscdP34cGzZswKRJk9Ltt2fPnli4cCFmz54NMzMzveuRyWRwcnLKtM3UqVPx119/Se9r1aoFADh27JhW3WnPxlIoFHBxcUGHDh0wa9Ysrb7Kly+P+vXr4/z581iyZInOWB06dNC6fy1tLEMejmwsMpEXqsjj4uLiYG9vj9jY2EwTPBERERHlTUlJSbh79y7c3d2ly9MMER8frxO4ciNokWlk9vNiSDbgAhlERERERFlIm+GqV68eFAoFgxbphZcREhERERHpIS1wdejQAYmJiTh8+DCDFmWKYYuIiIiISE+2trY4ceIEhBB6P1SYPl4MW0REREREBpDJZJDJZKYug/IBxnEiIiIiIiIjYNgiIiIiIiIyAoYtIiIiIiIiI2DYIiIiIiIiMgKGLSIiIiIiIiNg2CIiIiIiIjIChi0iIiIiojxqyJAh6Natm86fM/Po0SOYm5ujatWq0rZp06ZJS9Zn9EobQyaTYdSoUTr9jh07FjKZDEOGDJG2nThxAp07d4arqytkMhl27dqlc1yLFi20xilevDh69eqF+/fvG/RZrF69Gi1atICdnR1kMhliYmIMOt4UGLaIiIiIiLKQ2Rf7vPal38/PD71790ZcXBzOnTsHAPjuu+8QGRkpvUqWLIkZM2ZobUvj5uaGzZs3482bN9K2pKQk+Pv7o1SpUlpjvX79GjVq1MDvv/+eaU0jRoxAZGQknjx5gt27d+Phw4cYMGCAQeeVmJiIdu3a4ccffzToOFNi2CIiIiIiysTWrVtRsWJFXL58WWdfeHg4qlSpAl9fXxNUpksIgbVr12LgwIHo378/fHx8AAA2NjZwdnaWXgqFAra2tlrb0tSuXRtubm7YsWOHtG3Hjh0oVaoUatWqpTVe+/btMXPmTHTv3j3TuqytreHs7AwXFxc0bNgQ48aNw8WLFwEAGo0GJUuWxMqVK7WO+ffffyGXy6UZsK+//ho//PADGjZsmP0PKJcxbBERERERZaJ169Zwc3NDy5YttQJXeHg4WrRoAQcHB3Ts2NGEFf6fY8eOITExEa1bt8aAAQOwefNmvH792uB+hg0bhrVr10rvfX19MXTo0BypMTo6Glu2bEGDBg0AAHK5HP369YO/v79Wu40bN6JJkyYoXbp0joxrCgxbRERERESZKFy4MI4cOYLSpUtLgSstaBUuXBiBgYEoXry4qcsEAPj4+KBv375QKBSoWrUqypYti61btxrcz4ABA3Dq1Cncv38f9+/fx+nTpw2+7O9dK1asgI2NDQoVKgRHR0fcvHlTazbQ29sbp0+fxoMHDwC8ne3avHkzvL29sz1mXsCwRURERESUhXcDV40aNeDh4ZHnglZMTAx27NihFYoGDBggXUpoiKJFi6Jjx47w8/PD2rVr0bFjRzg5OWW7Nm9vb4SFheHSpUs4deoUypcvjzZt2iA+Ph4AULNmTXh6ekqzW8ePH8ezZ8/Qq1evbI+ZFyhNXQARERERUX5QuHBhrFixQrpnaMmSJXkmaAGAv78/kpKSpMvzgLf3cGk0GoSHh6NixYoG9Tds2DCMGzcOALJcACMr9vb2KF++PACgfPny8PHxgYuLCwICAvD5558DeBvI/P398cMPP8Df3x/t2rWDo6PjB41rapzZIiIiIiLSQ3h4OLp37w4XFxeULFkS/fr1S3fRDFPx8fHBt99+i7CwMOl16dIlNG3aNFsLeLRr1w4pKSlITU1F27Ztc7RWhUIBAForHvbv3x9Xr15FaGgotm3blu8vIQQ4s0VERERElKX379EyNzdH69at0bJlSwQGBqJ69eq5UkdsbCzCwsK0tjk6OuLly5e4ePEiNm7ciEqVKmnt79evH2bMmIGZM2dCqdT/679CocCNGzekP6cnISEBt27dkt7fvXsXYWFhKFKkiNYy8YmJiYiKigIAPH36FL/88gssLS3Rpk0bqU2ZMmXQuHFjDB8+HGq1Gl26dNEaKyoqClFRUdJ4V65cga2tLUqVKoUiRYrofV65iTNbRERERESZSG8xjPQWzcgNQUFBqFWrltZr+vTp8PHxQeXKlXWCFgB0794dz549w4EDBwwez87ODnZ2dhnuv3DhglQHAHzzzTeoVasWpk6dqtVuzZo1cHFxgYuLC7y8vPDixQscOHAAHh4eWu28vb1x6dIldO/eHVZWVlr7Vq1ahVq1amHEiBEAgGbNmqFWrVrYs2ePweeVW2RCCGHqIvK6uLg42NvbIzY2NtMfNiIiIiLKm5KSknD37l24u7vD0tLSoGODgoIwceJE7Nu3T+cerVevXqFjx46YMmUKOnTokJMlkwll9vNiSDbgZYRERERERJlo0aIFzp07B7lc96KwwoUL49SpU+nuI+JPBRERERFRFjILUwxalBH+ZBARERERERkBwxYREREREZERMGwREREREREZAcMWERERERGRETBsERERERERGQHDFhERERERkREwbBERERERERmBScPWiRMn0LlzZ7i6ukImk2HXrl1a+2UyWbqv+fPnS23KlCmjs3/u3Lla/Vy+fBlNmzaFpaUl3NzcMG/evNw4PSIiIiIi+oiZNGy9fv0aNWrUwO+//57u/sjISK2Xr68vZDIZevbsqdVuxowZWu2+/PJLaV9cXBzatGmD0qVLIzQ0FPPnz8e0adOwevVqo54bEREREdGHGjJkCLp166bz58w8evQI5ubmqFq1qrRt2rRpGU5kpL3SxpDJZBg1apROv2PHjoVMJsOQIUOkbVlNngBAixYttMYpXrw4evXqhfv37+v9OURHR+PLL7+Eh4cHrKysUKpUKYwfPx6xsbF692EKJg1b7du3x8yZM9G9e/d09zs7O2u9du/eDS8vL5QtW1arna2trVa7QoUKSfs2btyIlJQU+Pr6okqVKujbty/Gjx+PRYsWGfXciIiIiKhgEULg7NmzmDNnDmbPno0TJ05ACGHqsnT4+fmhd+/eiIuLw7lz5wAA3333ndbkRMmSJXUmLNK4ublh8+bNePPmjbQtKSkJ/v7+KFWqlNZYWU2epBkxYgQiIyPx5MkT7N69Gw8fPsSAAQP0PqcnT57gyZMnWLBgAa5evQo/Pz8cPHgQw4cP17sPU1CaugB9PX36FPv378dff/2ls2/u3Ln45ZdfUKpUKfTv3x8TJkyAUvn21IKDg9GsWTOYm5tL7du2bYtff/0Vr169QuHChXX6S05ORnJysvQ+Li7OCGdERERERPnFw4cP0aNHD1y4cAEKhQIAoFarUa1aNezcuRPlypUzcYVvCSGwdu1arFixAiVLloSPjw8aNGgAGxsb2NjYSO0UCoU0YfG+2rVr4/bt29ixYwe8vb0BADt27ECpUqXg7u6u1bZ9+/Zo3759lnVZW1tLY7m4uGDcuHH44osvAAAajQalSpXC//73P4wePVo65t9//0WdOnVw9+5dVK1aFdu3b5f2lStXDrNmzcKAAQOgUqmk7/55Tb5ZIOOvv/6Cra0tevToobV9/Pjx2Lx5M44dO4YvvvgCs2fPxvfffy/tj4qKQvHixbWOSXsfFRWV7lhz5syBvb299HJzc8vhsyEiIiKi/CIhIQHNmzdHWFgYgLchS61WAwBu3LiBpk2bIjo62oQV/p9jx44hMTERrVu3xoABA7B582a8fv3a4H6GDRuGtWvXSu99fX0xdOjQHKkxOjoaW7ZsQYMGDQAAcrkc/fr1g7+/v1a7jRs3okmTJihdunS6/cTGxsLOzi7PBi0gH4UtX19feHt7w9LSUmv7N998gxYtWqB69eoYNWoUFi5ciN9++01rZspQkydPRmxsrPR6+PDhh5ZPRERERPnU+vXrce/ePahUKp19KpUKT58+xZo1a0xQmS4fHx/07dsXCoUCVatWRdmyZbF161aD+xkwYABOnTqF+/fv4/79+zh9+rRBl/29b8WKFbCxsUGhQoXg6OiImzdvwtfXV9rv7e2N06dP48GDBwDeznZt3rxZmll734sXL/DLL79g5MiR2a4pN+SLsHXy5EncvHkTn3/+eZZtGzRoAJVKhXv37gF4e9/X06dPtdqkvU9v2hQALCwsYGdnp/UiIiIioo/Txo0bM92v0WiwYcOGXKomYzExMdixY4dWKBowYAB8fHwM7qto0aLo2LEj/Pz8sHbtWnTs2BFOTk7Zrs3b2xthYWG4dOkSTp06hfLly6NNmzaIj48HANSsWROenp7S7Nbx48fx7Nkz9OrVS6evuLg4dOzYEZUrV8a0adOyXVNuyLtzbu/w8fFBnTp1UKNGjSzbhoWFQS6Xo1ixYgCARo0a4X//+x9SU1NhZmYGADh8+DA8PDzSvV+LiIiIiOhdL1++zHIhjLxwGaG/vz+SkpKky/OAt/dwaTQahIeHo2LFigb1N2zYMIwbNw4AslwAIyv29vYoX748AKB8+fLw8fGBi4sLAgICpAkVb29v+Pv744cffoC/vz/atWsHR0dHrX7i4+PRrl072NraYufOndL3+7zKpDNbCQkJCAsLk65/vXv3LsLCwqTpQ+Btct26dWu6s1rBwcFYsmQJLl26hDt37mDjxo2YMGECBgwYIAWp/v37w9zcHMOHD8e1a9cQEBCApUuX4ptvvsmVcyQiIiKi/K1ixYrSohjpkcvlBgcZY/Dx8cG3334rfb9Om0lq2rSp1iV7+mrXrh1SUlKQmpqKtm3b5mitaZ/nuyse9u/fH1evXkVoaCi2bdumcwlh2iOdzM3NsWfPHp3bi/Iik85sXbhwAV5eXtL7tAA0ePBg+Pn5AQA2b94MIQT69eunc7yFhQU2b96MadOmITk5Ge7u7pgwYYJWkLK3t8ehQ4cwduxY1KlTB05OTpg6dWqev76TiIiIiPKGL774Anv27Mlwv0ajkVbWM7bY2FhpoiKNo6MjXr58iYsXL2Ljxo2oVKmS1v5+/fphxowZmDlzpkGLSSgUCty4cUP6c3oSEhJw69Yt6X3a5EmRIkW0lolPTEyUFqd7+vQpfvnlF1haWqJNmzZSmzJlyqBx48YYPnw41Go1unTpIu1LC1qJiYnYsGED4uLipBXDixYtmmkYNiWThq0WLVpkOSU7cuTIDINR7dq1cfbs2SzHqV69Ok6ePJmtGomIiIjo49a+fXv07t0bW7du1fnuKpfL0bZtW3z22We5UktQUBBq1aqltW348OGwsrJC5cqVdYIWAHTv3h3jxo3DgQMHtAKMPrJau0CfyRMAWLNmjbSISOHChVG9enUcOHAAHh4eWv15e3tjzJgxGDRoEKysrKTtFy9elJ4ZlnY5Ypq7d++iTJkyBp1XbpGJvPgktjwmLi4O9vb20vKSRERERJS/JCUl4e7du3B3d8/W5WcqlQpz587FkiVL8PLlSwBvr6AaN24cpk6dqvVMV8r/Mvt5MSQb5IsFMoiIiIiITEmpVGLKlCn4/vvvcePGDQghUKlSpXxx3xCZDsMWEREREZGezM3N9VohmwjIJ8/ZIiIiIiIiym8YtoiIiIiIiIyAYYuIiIiIiMgIGLaIiIiIiIiMgGGLiIiIiIjICBi2iIiIiIiIjIBhi4iIiIiIyAgYtoiIiIiIiIyAYYuIiIiIKI8aMmQIunXrpvPnNAsXLkThwoWRlJSkc2xiYiLs7OywbNkyAECZMmWwZMkSaf+lS5fQpUsXFCtWDJaWlihTpgz69OmDZ8+eSfv79esHNzc3WFlZwdPTE0uXLjWo/pzoIz9j2CIiIiIiMsCZM2dw7NgxU5cBABg4cCBev36NHTt26Ozbtm0bUlJSMGDAAJ19z58/R6tWrVCkSBH8888/uHHjBtauXQtXV1e8fv0aABAaGopixYphw4YNuHbtGv73v/9h8uTJWL58ud715UQf+ZnS1AUQEREREeUXb968QdeuXZGcnIyHDx/C3t7epPUUK1YMnTt3hq+vL/r376+1z9fXF926dUORIkV0jjt9+jRiY2Px559/Qql8Gwnc3d3h5eUltRk2bJjWMWXLlkVwcDB27NiBcePGITw8HB4eHrhx4wYqVaoktVu8eDGWL1+O27dvZ9lHQceZLSIiIiIiPa1evRovX77E69evpcvzTG348OEIDAzE/fv3pW137tzBiRMnMHz48HSPcXZ2hkqlws6dOyGE0Hus2NhYKbxVrFgRdevWxcaNG7XabNy4USf4ZdRHQcewRURERESkhzdv3mDmzJkQQkCj0WD+/PmIjY01dVlo27YtXF1dsXbtWmmbn58f3Nzc0KpVq3SPadiwIX788Uf0798fTk5OaN++PebPn4+nT59mOM6ZM2cQEBCAkSNHStu8vb2xadMm6X14eDhCQ0Ph7e2tdx8FGcMWEREREZEe0ma10uSV2S2FQoHBgwfDz89PCoJ//fUXhg4dCrk846/7s2bNQlRUFFatWoUqVapg1apVqFSpEq5cuaLT9urVq+jatSt+/vlntGnTRtret29f3Lt3D2fPngXwdlardu3aWpcVZtVHQcawRURERESUhXdntdLkpdmtYcOG4cGDBwgMDMTRo0fx8OFDDB06NMvjHB0d0atXLyxYsAA3btyAq6srFixYoNXm+vXraNWqFUaOHIkpU6Zo7XN2dkbLli3h7+8PAPD39093ViuzPgoyhi0iIiIioiy8P6uVJq/MbpUrVw7NmzeHr68v1q5di9atW6N06dIG9WFubo5y5cpJqxECwLVr1+Dl5YXBgwdj1qxZ6R7n7e2NgIAABAcH486dO+jbt6/Wfn36KKi4GiERERERUSbSm9VKkza7NX78+FxZmTA2NhZhYWFa2xwdHeHm5obhw4djxIgRAN7es5WZffv2YfPmzejbty8qVqwIIQT27t2LAwcOSPd+Xb16FS1btkTbtm3xzTffICoqCsDbyxaLFi0q9dWjRw+MHj0ao0ePhpeXF1xdXaV9+vZRUHFmi4iIiIgoE6tXr8aLFy8y3B8fH59rs1tBQUGoVauW1mv69OkAgJ49e8LCwgLW1tY6Dz9+X+XKlWFtbY1vv/0WNWvWRMOGDbFlyxb8+eefGDhwIIC3z+l6/vw5NmzYABcXF+lVr149rb5sbW3RuXNnXLp0SecSQn37KKhkwpC1Hj9ScXFxsLe3R2xsLOzs7ExdDhEREREZKCkpCXfv3oW7uzssLS0NOrZdu3b4559/Mm3TsGFDBAcHf0iJlIdk9vNiSDbgZYRERERERJnYtm0bHj58mGmbEiVK5FI1lJ8wbBERERERZcLGxgaenp6mLoPyId6zRUREREREZAQMW0REREREREbAsEVERERERGQEDFtERERERERGwLBFRERERERkBAxbRERERERERsCwRUREREREZAQMW0REREREREbAsEVERERElMcFBwdDoVCgY8eOAIAhQ4ZAJpNl+CpTpgwAoEWLFvj6668z7Dc6Ohre3t6ws7ODg4MDhg8fjoSEBK02QggsWLAAFStWhIWFBUqUKIFZs2YZfA5+fn6oXr06LC0tUaxYMYwdO9bgPvIbpakLICIiIiLKT/755x8kJSWha9euuTamj48PvvzyS/j4+ODJkydYunQp5s6dK+13cXHB2rVr0a5dOwCAQqHQq19vb29ERkbi8OHDSE1NxdChQzFy5Ej4+/tLbb766iscOnQICxYsQLVq1RAdHY3o6GiD6l+0aBEWLlyI+fPno0GDBnj9+jXu3btnUB/5EcMWEREREZGehBAYO3YsEhIS0LlzZ8jlxr9QLCEhAQEBAbhw4QKioqLg5+eHH3/8Efb29lrtHBwc4OzsrHe/N27cwMGDBxESEoK6desCAH777Td06NABCxYsgKurK27cuIGVK1fi6tWr8PDwAAC4u7tLfYSHh8PDwwM3btxApUqVpO2LFy/G8uXLcfv2bbx69QpTpkzB3r170apVK6lN9erVs/V55Ce8jJCIiIiISE/Xr1/H7du38fTpU5w/fz5XxtyyZQsqVaoEDw8PDBgwAL6+vhBCfHC/wcHBcHBwkIIWALRu3RpyuRznzp0DAOzduxdly5bFvn374O7ujjJlyuDzzz+XZrYqVqyIunXrYuPGjVp9b9y4Ef379wcAHD58GBqNBo8fP4anpydKliyJ3r174+HDhx98DnkdwxYRERERUSZ27twJLy8vtGjRAj179oRCoYBSqcTAgQPRokULtGjRAhs2bDDa+D4+PhgwYAAAoF27doiNjcXx48c/uN+oqCgUK1ZMa5tSqUSRIkUQFRUFALhz5w7u37+PrVu3Yt26dfDz80NoaCg+++wz6Rhvb29s2rRJeh8eHo7Q0FB4e3tLfWg0GsyePRtLlizBtm3bEB0djU8//RQpKSkffB55GcMWEREREVEmzMzMcObMGRw/fhw3b96EWq2GSqXCrVu3cPz4cZw+fRpmZmZGGfvmzZs4f/48+vXrB+BtGOrTpw98fHyMMt77NBoNkpOTsW7dOjRt2hQtWrSAj48Pjh07hps3bwIA+vbti3v37uHs2bMA3s5q1a5dW7qsUKPRIDU1FcuWLUPbtm3RsGFDbNq0CRERETh27FiunIepMGwREREREWWiU6dOuHjxIipVqgSZTCZtl8vlcHd3x7lz59CnTx+jjO3j4wOVSgVXV1colUoolUqsXLkS27dvR2xs7Af17ezsjGfPnmltU6lUiI6Olu79cnFxgVKpRMWKFaU2np6eAIAHDx5I/bRs2VJaVMPf31+a1UrrAwAqV64sbStatCicnJykPgoqhi0iIiIioixUqVIFQ4YMgRBCWl5dCIF+/fqhdu3aRhlTpVJh3bp1WLhwIcLCwqTXpUuX4OrqqnXpXnY0atQIMTExCA0NlbYFBgZCo9GgQYMGAIAmTZpApVLh9u3bUpvw8HAAQOnSpaVt3t7eCAgIQHBwMO7cuYO+fftK+5o0aQIA0kwY8HbJ+RcvXmj1URDJRE7cXVfAxcXFwd7eHrGxsbCzszN1OURERERkoKSkJNy9exfu7u6wtLTMVh+1atVCWFgYrK2toVQqERcXh3LlyiEiIkJrxiun7Nq1C3369MGzZ890Vh6cNGkSAgMDERISAgCQyWTYuXMnunXrptWuRYsWKFGiBCZOnKi13cXFBcWLF0f79u3x9OlTrFq1Slr6vW7dutIslUajQb169WBjY4MlS5ZAo9Fg7NixsLOzw6FDh6T+4uPjUbx4cVSsWBFOTk44cuSI1njdunXDrVu3sHr1atjZ2WHy5Mm4c+cOwsLCjHYJ5ofI7OfFkGzAmS0iIiIioiy8fv0aYWFhqFOnDq5cuYKrV6+iSZMm0sqExuDj44PWrVvrBC0A6NmzJy5cuIDLly9n2Y+/vz9q1aql9VqzZg2At/dXVapUCa1atUKHDh3wySefYPXq1dKxcrkce/fuhZOTE5o1a4aOHTvC09MTmzdv1hrD1tYWnTt3xqVLl7QuIUyzbt06NGjQAB07dkTz5s1hZmaGgwcP5smglZM4s6UHzmwRERER5W85MbN18eJFVKtWTQoIarUaYWFhqF27tlFmtsh0cmpmiw81JiIiIiLSw/v3ZikUCtSpU8dE1VB+wMsIiYiIiIiIjIBhi4iIiIiIyAgYtoiIiIiIiIyAYYuIiIiIiMgIGLaIiIiI6KPBhbhJHzn1c8LVCImIiIiowDMzM4NMJsPz589RtGhRLtVOGRJC4Pnz55DJZB/8HDCGLSIiIiIq8BQKBUqWLIlHjx7h3r17pi6H8jiZTIaSJUtCoVB8UD8MW0RERET0UbCxsUGFChWQmppq6lIojzMzM/vgoAUwbBERERHRR0ShUOTIl2gifXCBDCIiIiIiIiNg2CIiIiIiIjIChi0iIiIiIiIjYNgiIiIiIiIyAoYtIiIiIiIiI2DYIiIiIiIiMgKGLSIiIiIiIiNg2CIiIiIiIjIChi0iIiIiIiIjYNgiIiIiIiIyAoYtIiIiIiIiI2DYIiIiIiIiMgKGLSIiIiIiIiMwadg6ceIEOnfuDFdXV8hkMuzatUtr/5AhQyCTybRe7dq102oTHR0Nb29v2NnZwcHBAcOHD0dCQoJWm8uXL6Np06awtLSEm5sb5s2bZ+xTIyIiIiKij5xJw9br169Ro0YN/P777xm2adeuHSIjI6XXpk2btPZ7e3vj2rVrOHz4MPbt24cTJ05g5MiR0v64uDi0adMGpUuXRmhoKObPn49p06Zh9erVRjsvIiIiIiIipSkHb9++Pdq3b59pGwsLCzg7O6e778aNGzh48CBCQkJQt25dAMBvv/2GDh06YMGCBXB1dcXGjRuRkpICX19fmJubo0qVKggLC8OiRYu0QhkREREREVFOyvP3bAUFBaFYsWLw8PDA6NGj8fLlS2lfcHAwHBwcpKAFAK1bt4ZcLse5c+ekNs2aNYO5ubnUpm3btrh58yZevXqVeydCREREREQfFZPObGWlXbt26NGjB9zd3XH79m38+OOPaN++PYKDg6FQKBAVFYVixYppHaNUKlGkSBFERUUBAKKiouDu7q7Vpnjx4tK+woUL64ybnJyM5ORk6X1cXFxOnxoRERERERVweTps9e3bV/pztWrVUL16dZQrVw5BQUFo1aqV0cadM2cOpk+fbrT+iYiIiIio4MvzlxG+q2zZsnBycsKtW7cAAM7Oznj27JlWG5VKhejoaOk+L2dnZzx9+lSrTdr7jO4Fmzx5MmJjY6XXw4cPc/pUiIiIiIiogMtXYevRo0d4+fIlXFxcAACNGjVCTEwMQkNDpTaBgYHQaDRo0KCB1ObEiRNITU2V2hw+fBgeHh7pXkIIvF2Uw87OTutFRERERERkCJOGrYSEBISFhSEsLAwAcPfuXYSFheHBgwdISEjAxIkTcfbsWdy7dw9Hjx5F165dUb58ebRt2xYA4OnpiXbt2mHEiBE4f/48Tp8+jXHjxqFv375wdXUFAPTv3x/m5uYYPnw4rl27hoCAACxduhTffPONqU6biIiIiIg+AjIhhDDV4EFBQfDy8tLZPnjwYKxcuRLdunXDv//+i5iYGLi6uqJNmzb45ZdfpAUugLcPNR43bhz27t0LuVyOnj17YtmyZbCxsZHaXL58GWPHjkVISAicnJzw5ZdfYtKkSXrXGRcXB3t7e8TGxnKWi4iIiIjoI2ZINjBp2MovGLaIiIiIiAgwLBvkq3u2iIiIiIiI8guGLSIiIiIiIiNg2CIiIiIiIjIChi0iIiIiIiIjYNgiIiIiIiIyAoYtIiIiIiIiI2DYIiIiIiIiMgKGLSIiIiIiIiNg2CIiIiIiIjIChi0iIiIiIiIjYNgiIiIiIiIyAoYtIiIiIiIiI2DYIiIiIiIiMgKGLSIiIiIiIiNg2CIiIiIiIjIChi0iIiIiIiIjYNgiIiIiIiIyAoYtIiIiIiIiI2DYIiIiIiIiMgKGLSIiIiIiIiNg2CIiIiIiIjIChi0iIiIiIiIjYNgiIiIiIiIyAoYtIiIiIiIiI2DYIiIiIiIiMgKGLSIiIiIiIiNg2CIiIiIiIjIChi0iIiIiIiIjYNgiIiIiIiIyAoYtIiIiIiIiI2DYIiIiIiIiMgKGLSIiIiIiIiNg2CIiIiIiIjIChi0iIiIiIiIjYNgiIiIiIiIyAoYtIiIiIiIiI2DYIiIiIiIiMgKGLSIiIiIiIiNg2CIiIiIiIjIChi0iIiIiIiIjYNgiIiIiIiIyAoYtIiIiIiIiI2DYIiIiIiIiMgKGLSIiIiIiIiNg2CIiIiIiIjIChi0iIiIiIiIjYNgiIiIiIiIyAoYtIiIiIiIiI2DYIiIiIiIiMgKGLSIiIiIiIiNg2CIiIiIiIjIChi0iIiIiIiIjYNgiIiIiIiIyAoYtIiIiIiIiI2DYIiIiIiIiMgKGLSIiIiIiIiNg2CIiIiIiIjIChi0iIiIiIiIjYNgiIiIiIiIyAoYtIiIiIiIiI2DYIiIiIiIiMgKGLSIiIiIiIiNg2CIiIiIiIjIChi0iIiIiIiIjYNgiIiIiIiIyAoYtIiIiIiIiI2DYIiIiIiIiMgKThq0TJ06gc+fOcHV1hUwmw65du6R9qampmDRpEqpVq4ZChQrB1dUVgwYNwpMnT7T6KFOmDGQymdZr7ty5Wm0uX76Mpk2bwtLSEm5ubpg3b15unB4REREREX3ETBq2Xr9+jRo1auD333/X2ZeYmIiLFy/ip59+wsWLF7Fjxw7cvHkTXbp00Wk7Y8YMREZGSq8vv/xS2hcXF4c2bdqgdOnSCA0Nxfz58zFt2jSsXr3aqOdGREREREQfN6UpB2/fvj3at2+f7j57e3scPnxYa9vy5ctRv359PHjwAKVKlZK229rawtnZOd1+Nm7ciJSUFPj6+sLc3BxVqlRBWFgYFi1ahJEjR+bcyRAREREREb0jX92zFRsbC5lMBgcHB63tc+fOhaOjI2rVqoX58+dDpVJJ+4KDg9GsWTOYm5tL29q2bYubN2/i1atX6Y6TnJyMuLg4rRcREREREZEhTDqzZYikpCRMmjQJ/fr1g52dnbR9/PjxqF27NooUKYIzZ85g8uTJiIyMxKJFiwAAUVFRcHd31+qrePHi0r7ChQvrjDVnzhxMnz7diGdDREREREQFXb4IW6mpqejduzeEEFi5cqXWvm+++Ub6c/Xq1WFubo4vvvgCc+bMgYWFRbbGmzx5sla/cXFxcHNzy17xRERERET0UcrzYSstaN2/fx+BgYFas1rpadCgAVQqFe7duwcPDw84Ozvj6dOnWm3S3md0n5eFhUW2gxoRERERERGQx+/ZSgtaEREROHLkCBwdHbM8JiwsDHK5HMWKFQMANGrUCCdOnEBqaqrU5vDhw/Dw8Ej3EkIiIiIiIqKcYNKZrYSEBNy6dUt6f/fuXYSFhaFIkSJwcXHBZ599hosXL2Lfvn1Qq9WIiooCABQpUgTm5uYIDg7GuXPn4OXlBVtbWwQHB2PChAkYMGCAFKT69++P6dOnY/jw4Zg0aRKuXr2KpUuXYvHixSY5ZyIiIiIi+jjIhBDCVIMHBQXBy8tLZ/vgwYMxbdo0nYUt0hw7dgwtWrTAxYsXMWbMGPz3339ITk6Gu7s7Bg4ciG+++UbrMsDLly9j7NixCAkJgZOTE7788ktMmjRJ7zrj4uJgb2+P2NjYLC9jJCIiIiKigsuQbGDSsJVfMGwRERERERFgWDbI0/dsERERERER5VcMW0REREREREbAsEVERERERGQEDFtERERERERGwLBFRERERERkBAxbRERERERERsCwRUREREREZAQMW0REREREREbAsEVERERERGQEDFtERERERERGwLBFRERERERkBAxbRERERERERsCwRUREREREZAQMW0REREREREbAsEVERERERGQEDFtERERERERGwLBFRERERERkBMrsHhgREYFjx47h2bNn0Gg0WvumTp36wYURERERERHlZ9kKW2vWrMHo0aPh5OQEZ2dnyGQyaZ9MJmPYIiIiIiKij162wtbMmTMxa9YsTJo0KafrISIiIiLK9/744w8UKlQIAwYMMHUpZEIyIYQw9CA7OzuEhYWhbNmyxqgpz4mLi4O9vT1iY2NhZ2dn6nKIiIiIKA9LTU2Fk5MTbG1t8fDhQ62rwCj/MyQbZGuBjF69euHQoUPZKo6IiIiIqCA7fvw44uLi8PjxY1y8eNHU5ZAJZesywvLly+Onn37C2bNnUa1aNZiZmWntHz9+fI4UR0RERESUHyxevBhLliyBEAJxcXFQKpUQQqBNmzYoVKgQZDIZfvrpJ3z++eemLpVyUbYuI3R3d8+4Q5kMd+7c+aCi8hpeRkhEREREmTly5Ah69OiB+Pj4dPcXLlwY+/fvR6NGjXK5MspphmSDbM1s3b17N1uFEREREREVRK1bt8aVK1fQq1cvXLhwAe/OZzRr1gwBAQFwdnY2YYVkCh/8UGMhBLIxOUZEREREVKCULl1amrlSKBSQy99+1W7RogWD1kcq22Fr3bp1qFatGqysrGBlZYXq1atj/fr1OVkbEREREVG+odFosGXLFgghUKJECRQtWhQAsGXLFhNXRqaSrbC1aNEijB49Gh06dMCWLVuwZcsWtGvXDqNGjcLixYtzukYiIiIiojzv2bNniIqKQv/+/XHlyhVcu3YNXbp0QXh4OBITE01dHplAthfImD59OgYNGqS1/a+//sK0adMK3D1dXCCDiIiIiPTx7NkzFCtWTHovhMDz58+1tlH+ZvTnbEVGRqJx48Y62xs3bozIyMjsdElERERElO+9H6pkMhmD1kcsW2GrfPny6V57GhAQgAoVKnxwUURERERERPldtpZ+nz59Ovr06YMTJ06gSZMmAIDTp0/j6NGjvAGQiIiIiIgI2ZzZ6tmzJ86dOwcnJyfs2rULu3btgpOTE86fP4/u3bvndI1EREQfPT5qhYgo/8nWAhkfGy6QQUREpqBSqeDj44PffvsN169fh7m5Obp27YrvvvsO9erVM3V5REQfJUOygd5hKy4uTuosLi4u07YFLZAwbBERUW5TqVTo2bMn9u7dCwDSrJZSqYQQAgEBAejZs6cpSyQi+igZkg30vmercOHCiIyMRLFixeDg4ACZTKbTRggBmUwGtVpteNVEREQkWblyJfbu3atz6aBKpYJMJoO3tzdatGgBR0dHE1VIRERZ0TtsBQYGokiRIgCAY8eOGa0gIiKij50QAsuWLct0f2pqKvz8/PDtt9/mYmVERGQIvcNW8+bNpT+7u7vDzc1NZ3ZLCIGHDx/mXHVEREQfoaSkJNy6dSvTNjKZDBcvXsylioiIKDuytRqhu7s7nj9/rrM9Ojoa7u7uH1wUERHRx0ypVKZ7uf67ZDIZLCwscqkiIiLKjmyFrbR7s96XkJAAS0vLDy6KiIjoY2ZmZoZWrVpBoVBk2EalUqFTp065WBURERnKoIcaf/PNNwDe/jbtp59+grW1tbRPrVbj3LlzqFmzZo4WSERUEEVERGD16tW4du0abGxs0KNHD/To0QPm5uamLo3yiB9++AFHjx5Nd59SqUTp0qXRpUuXXK6KiIgMYVDY+vfffwG8ndm6cuWK1pcCc3Nz1KhRA999913OVkhEVMDMnz8fkyZNglwuh1qthkKhwNatW1GhQgUcPXoUbm5upi6R8oBWrVrhjz/+wKhRo6SVfhUKBdRqNdzc3HDo0CEolQb9M05ERLksWw81Hjp0KJYuXfrRPHOKz9kiopyyY8eODJ+NpFQqUalSJVy6dAlyebau8qYC6OHDh1izZg0uX74MS0tLdO3aFT179uQsKBGRiRjlocYfM4YtIsop9evXR2hoKDQaTYZt/vnnH7Rp0yYXqyIiIiJ9GeWhxu+7cOECtmzZggcPHiAlJUVr344dO7LbLRFRgfXq1SuEhIRk2sbMzAz79+9n2CIiIioAsnWdyubNm9G4cWPcuHEDO3fuRGpqKq5du4bAwEDY29vndI1ERAXC+7+Y+tB2RERElLdlK2zNnj0bixcvxt69e2Fubo6lS5fiv//+Q+/evVGqVKmcrpGIqEAoWrQoXFxcMm2TmpqKunXr5lJFREREZEzZClu3b99Gx44dAbxdhfD169eQyWSYMGECVq9enaMFEhEVFHK5HF9++WWGi1/I5XLY2dmhX79+uVwZERERGUO2wlbhwoURHx8PAChRogSuXr0KAIiJiUFiYmLOVUdEVMB8++23aN26NWQymdbD4ZVKJczMzLBt2zatZxgSERFR/pWtsNWsWTMcPnwYANCrVy989dVXGDFiBPr164dWrVrlaIFERAWJubk59u3bh99//x2enp5QKBSwsbHBwIEDERoaik8//dTUJRIREVEOydbS79HR0UhKSoKrqys0Gg3mzZuHM2fOoEKFCpgyZQoKFy5sjFpNhku/ExERERERwOds5TiGLSIiIiIiAgzLBtm6jLB169bw8/NDXFxctgokIiIiIiIq6LIVtqpUqYLJkyfD2dkZvXr1wu7du5GamprTtREREREREeVb2QpbS5cuxePHj7Fr1y4UKlQIgwYNQvHixTFy5EgcP348p2skIiIiIiLKd3Lknq2kpCTs3bsXs2bNwpUrV6BWq3OitjyD92wRERERERFgWDZQfuhgUVFR2Lx5MzZs2IDLly+jfv36H9olERERERFRvpetywjj4uKwdu1afPrpp3Bzc8PKlSvRpUsXRERE4OzZszldIxERERERUb6TrZmt4sWLo3DhwujTpw/mzJmDunXr5nRdREREelOr1Xjz5g0KFSoEmUxm6nKIiIgAZHNma8+ePXj06BEWL17MoEVERCYTHh6OoUOHolChQrC1tUWRIkXw/fff48WLF6YujYiIKPsLZKhUKgQFBeH27dvo378/bG1t8eTJE9jZ2cHGxian6zQpLpBBRJT3hIaGokWLFkhKSoJKpZK2KxQKlCxZEsHBwXBxcTFhhUREVBAZ/aHG9+/fR7Vq1dC1a1eMHTsWz58/BwD8+uuv+O6777LTJRERkd6EEOjbty/evHmjFbSAt5cUPn78GF9//bVpiiMiIvr/shW2vvrqK9StWxevXr2ClZWVtL179+44evRojhVHRESUnuPHj+PWrVsZPmpEpVJh+/btePr0aS5XRkRE9H+ytUDGyZMncebMGZibm2ttL1OmDB4/fpwjhREREWXk0qVLkMvl0Gg0GbZRq9W4ceMGihcvnouVERER/Z9szWxpNJp0f5v46NEj2NrafnBRREQfKrOHqxe0B69/jKysrDINWmksLS1zoRoiIqL0ZStstWnTBkuWLJHey2QyJCQk4Oeff0aHDh1yqjYiomwJDQ1F9erVER4errMvISEBrVq1go+Pjwkqo5zSoUMHyOWZ/xNWrFgxrphLREQmla2wtXDhQpw+fRqVK1dGUlIS+vfvL11C+Ouvv+Z0jUREBilRogSEEPDy8tIKXAkJCejYsSMuXrwIT09PE1ZIH6pkyZIYOHBgpoHrxx9/hFKZravljebSpUsYN24cPv30U/Tu3Rvbt2/XWeCDiIgKjmyFrZIlS+LSpUv48ccfMWHCBNSqVQtz587Fv//+i2LFiundz4kTJ9C5c2e4urpCJpNh165dWvuFEJg6dSpcXFxgZWWF1q1bIyIiQqtNdHQ0vL29YWdnBwcHBwwfPhwJCQlabS5fvoymTZvC0tISbm5umDdvXnZOm4jyCWdnZwQGBsLe3l4KXGlB699//8XBgwfRuHFjU5dJH2jlypXo1KkTAECpVEKhUEjh6vvvv8f48eNNWZ4WIQQmTpyImjVr4o8//sCRI0ewY8cOfPbZZ6hTpw6ePXtm6hKJiMgIsv2crZzw999/4/Tp06hTpw569OiBnTt3olu3btL+X3/9FXPmzMFff/0Fd3d3/PTTT7hy5QquX78uXYffvn17REZG4o8//kBqaiqGDh2KevXqwd/fH8DbdfArVqyI1q1bY/Lkybhy5QqGDRuGJUuWYOTIkXrVyedsEeVPUVFRaNmyJR4/foxChQohISGBQcsAMTEx2LRpE8LDw2Fra4tevXqhWrVqpi5LixACISEh2LBhA168eIEyZcpg6NChqFChgqlL0/LHH39g1KhR6e5TKpVo1KgRTpw4kctVERFRdhiSDfQOW3v27NG7gC5duujdVipEJtMKW0IIuLq64ttvv5We3RUbG4vixYvDz88Pffv2xY0bN1C5cmWEhIRI1+UfPHgQHTp0wKNHj+Dq6oqVK1fif//7H6KioqTVE3/44Qfs2rUL//33n161MWwR5V+3b99G+fLlAQB+fn4YPHiwiSvKH/766y+MGjUKycnJUCqVEEJApVKhe/fu2LBhA6ytrU1dYr6h0WhQtmxZ3L9/P9N27/5bRkREeZch2UDvi9nfnXHKjEwmy5GVvu7evYuoqCi0bt1a2mZvb48GDRogODgYffv2RXBwMBwcHLT+cWrdujXkcjnOnTuH7t27Izg4GM2aNdNapr5t27b49ddf8erVKxQuXFhn7OTkZCQnJ0vv4+LiPvh8iAqKN2/eaD1f710ajQYpKSl5ZgW4hIQEDBs2THr/448/olGjRqhYsaIJq8r7Dhw4gCFDhkjvU1NTpT/v2bMHgwYNwrZt20xQWf50+/btLIOWQqHA33//zbBFRFTA6H3Plkaj0euVU0sqR0VFAYDO81GKFy8u7YuKitK5R0ypVKJIkSJabdLr490x3jdnzhzY29tLLzc3tw8/IaICICYmBjVq1MDvv/+us0+j0WDUqFHo2rUrTHh1suTde7ROnz6NyMhIrXu4KGPTpk3LcOEJtVqN7du348aNG7lcVf6VkpKSZRu5XK5XOyIiyl8MWiCjQ4cOiI2Nld7PnTsXMTEx0vuXL1+icuXKOVacqUyePBmxsbHS6+HDh6YuiShPsLe3R5cuXTBu3DitwJUWtP7880/0798fMpnMhFUi3cUw0ls0g3Q9efIEISEhmT7DSqFQYPv27blYVf5Wrly5LC8zSU1NRb169XKpIiIiyi0Gha2DBw9qXV43e/ZsREdHS+9VKhVu3ryZI4U5OzsDAJ4+faq1/enTp9I+Z2dnnRWcVCoVoqOjtdqk18e7Y7zPwsICdnZ2Wi8ienuZ8Pz58/Htt99KgevdoLV27do8cU9UaGgorl27prMYxruBa+fOnSasMO+Kj4/Pso1cLterHb1laWmJkSNHQqFQpLtfoVCgZMmS6NixYy5XRkRExpatpd/TGPNSIXd3dzg7O+Po0aPStri4OJw7dw6NGjUCADRq1AgxMTEIDQ2V2gQGBkKj0aBBgwZSmxMnTmjdc3D48GF4eHike78WEWXu/cClUCjyVNACgObNm+Pu3bvprjro7OyMc+fO4fvvvzdBZXlfyZIls7znLjU1FZUqVcqligqG6dOno379+pDJZFozv0qlEtbW1tixY0eGYYyIiPKvDwpbHyohIQFhYWEICwsD8HZRjLCwMDx48AAymQxff/01Zs6ciT179uDKlSsYNGgQXF1dpcU6PD090a5dO4wYMQLnz5/H6dOnMW7cOPTt2xeurq4AgP79+8Pc3BzDhw/HtWvXEBAQgKVLl+Kbb74x0VkT5X8ymUzreXV169bNM0Erja2tbab7TH2pY15VqFAhDB48OMOHActkMtjY2KB37965XFn+Zm1tjcDAQCxevBgVK1aEmZkZihQpgtGjRyMsLIyXEBIRFVTCAHK5XDx79kx6b2NjI+7cuSO9j4qKEnK5XO/+jh07JgDovAYPHiyEEEKj0YiffvpJFC9eXFhYWIhWrVqJmzdvavXx8uVL0a9fP2FjYyPs7OzE0KFDRXx8vFabS5cuiU8++URYWFiIEiVKiLlz5xpy2iI2NlYAELGxsQYdR1RQqdVqMWLECCGTyYSTk5MAIJYvX27qsiiHPHv2TLi7uwuFQqH1/81yuVzIZDKxZcsWU5dIRERkMoZkA4MeaiyXy9G+fXtYWFgAAPbu3YuWLVuiUKFCAN4umX7w4MEcW5Ewr+Bztoj+z/v3aA0aNAgTJ07EwoULsXz5cowdO9bUJVIOeP78OaZPnw5fX1+8efMGwNvLM3/++Wd4eXmZuDoiIvpQQggIITJcfZYyZpSHGgPA0KFD9Wq3du1afbvMFxi2iN7KaDEMIQQDVwH15s0bREVFwdbWFk5OTqYuh4iIcsjPP/+MAwcOICQkxNSl5DtGC1sfK4YtordiY2PRvHlzTJgwQecerbTAdevWLezcuZP3RBEREeVRQgi4ubnh8ePHuHHjBhc9MpAh2SD9O6CJiNJhb2+PkJAQmJmZ6exLW6VQrVYzaBEREeUxKpUKV65cgRACt2/fxuPHjwEAf/zxBwYOHAjg7WrgXK07Z3FmSw+c2SIiIiKi/GzVqlUYPXq09F6hUOiss9C1a1fs2rUrlyvLfwzJBrwjjiiPe/+h3O969uyZUZ93R0RERAXD4MGD8cUXX0jv3w9a9erVw+LFi3O7rAKPYYs+WhqNJsN9QohM9+eWv//+G+XKlUNgYKDOvlu3bqFOnTr49ddfTVAZERER5SdWVlZYtWoVli5dqrVdLpejS5cuOH36NNzd3U1UXcHFsEUfpdjYWHzyySfYt2+fzj4hBKZOnYoRI0aYfNbIy8sLn3zyCTp16qQVuG7dugUvLy9YW1vnuYcJExERUd718uVLAG9Dlkwmg0ajwePHj9O9H5s+HMMWfZSsra3h7OyMHj16aAWutKA1c+ZMVKpUyeQLPVhaWmLXrl1o1qyZFLjeDVpBQUFwcXExaY1ERESUfwQEBAAA2rZtix9//BEymQyhoaF4+PChiSsrmBi26KNkZmaGgIAAdOrUSQpc7watefPmYeLEiaYuE4B24GrVqhUqVKjAoEVERETZ4unpiWXLlmH//v2YOXMmjh8/jubNm+vcw0U5g6sR6oGrERZcqamp6NOnD/bt24cqVaogLCwsTwWtd129ehXVqlUDAGzYsAHe3t4mroiIiIjo48OHGucwhq2CLTU1Febm5gCAZs2a4fjx4yauSFfapYNyuRwODg6IiIjAvn370LJlS1OXRkRERPRR4dLvRHoSQmDGjBnS++Dg4HQXzTCld+/ROnv2LM6dO6d1DxcRERER5U0MW/TRev8erZSUFK17uPKC9BbDSG/RDCIiIiLKexi26KOU3mIY6S2aYWqJiYkoXbq0zmIYaYGrVatWiI2NNWGFRET/Z/ny5YiIiDB1GUREeQbv2dID79kqeF68eIHq1atjwoQJOothpC2aERcXh8OHD5t8+XchRIY1ZLaPiCg3BQUFwcvLCy1atMCxY8dMXQ4RkdFwgYwcxrBVMMXExMDBwSHdfampqUhOToaNjU3uFkVElE81a9YMp06dghACJ06cQNOmTU1dEhGRUXCBDCI9ZBS0gLfP4WLQIiLST1BQEE6ePAkhBBQKBaZOnZqj/avVauzatQvdu3dHgwYN0KtXL/z999/QaDQ5Og4RUU7jzJYeOLNFREQFnUqlQlxcHGxtbWFmZmbQsc2aNcOZM2e0HoqaU7NbCQkJ6NChA06ePAmFQgG1Wg2lUgmVSoX27dtjx44dsLS0/OBxiIj0xZktIiIi0svDhw8xatQo2NnZwdHREba2thg2bBju3Lmj1/Fps1rvBq2cnN364osvcObMGQCQxlCpVACAf/75J08+hJ6IKA1ntvTAmS0iIiqIbt26hUaNGiEmJkYKMACgVCphY2ODkydPomrVqpn2kd6sVpoPnd16/PgxSpUqlenlghYWFoiMjEThwoWzPQ4RkSE4s0VERERZGjFiBF69eqUVtIC3M0fx8fEYPHhwpsenN6uVJidmt4KCgrK8Lys5ORnBwcEfNA4RkbEwbJFJbd68GVWrVsWlS5dMXQoR0UclPDwcQUFB6QYl4O0lexcvXsS///6bYR9Tp07N8PETarVaCmPZlVFt73s/LBJR/nfq1Cl8+umn+f5/3wxbZDKbN29G//79cf36dTRv3pyBi4goF129evWD2qWmpuLChQvI6m6E06dPG1xbmgYNGmTZRi6Xo27dutkeg4jyplWrVuHIkSM4fvy4qUv5IEpTF0Afp7SglfaPdEJCApo3b47jx4+jRo0aJq6OiKjgs7a21qudlZVVutvNzMwQFRWFhISETI93cXExuLY0Hh4eaNWqFY4fP57ub7cVCgV69OgBV1fXbI+Rnk2bNsHGxgadO3c26LiUlBQEBgbixYsXKF26NJo0aQK5nL/XJjJUcnIydu3aBQDYtm0bWrVqZdqCPgAXyNADF8jIWe8HrTRyuRy2trYMXEREuSAxMRHOzs6Ij4/PsI2lpSWioqJgb2+fi5Vpe/z4MZo2bYr79+9L92+lXbro6emJEydOwNHRMcfGU6vVKFasGAoXLoyIiIgML5N835o1azB58mS8fPlS2la2bFmsXLkSbdq0ybH6iAqqkJAQfPLJJ0hJScmwTa1atXDx4sVcrCp9XCCD8qyMghYAaDQaxMfH85JCIqJcYG1tnemy6TKZDOPHjzdp0AKAEiVK4OLFi5g9ezbKly8POzs7VKpUCQsXLsS5c+dyNGgBb+8TiY6Oxu3bt3H9+nW9jlm1ahVGjhypFbQA4O7du+jQoQOOHTuWozUSFURVqlRBp06dMtxvYWGBr7/+OvcKyiGc2dIDZ7ZyRmZB612c4SIiyh0ajQZfffUVli9fDqXy/+4sUKlUGDZsGP744w+t7QVRfHw8fvzxR5w9exZCCDx9+hRRUVEQQsDNzQ2Ojo6QyWSYOHEievfurXN8VjOEcrkc1atXT3ehkYiICFSoUCHT+pKTk7FixQqMGTMGFhYW2TvJ9xw+fBheXl4F/u+W8h8hBP744w+MHj1a2iaXy1GiRAn8888/8PT0NGF1/4czW5TnREVFwdvbO8ugBbz9xz82Nhbdu3fPhcqIiD5ecrkcv/32G27evInvv/8e3t7e+Pbbb3Ht2jX4+Ph8FF/Gly5diuXLlyMsLAyhoaF49OgRVCoV1Go17t27h9DQUFy/fh1FihRJ9/i9e/dmeimmRqNBWFiYzizZwYMHUbFixSwvidqxYwe++eYb7Ny50/CTS8f58+fRpk0brF+/Pkf6e/36NcqXL49//vkn03ZCCHz++ee4cOFCln1OnDgR586dy5H6DBEaGooHDx7k+rj0f2QyGcqXL6/1XqPRIDU1FR4eHiasLPsYtihXFCtWDP369TPomM8//9xI1RAR0bsqVqyIWbNmwc/PD3PnzkXlypVNXVKuCQgIAACMGzcOFStW1FrQQiaToXbt2rh8+TJat26d7vFRUVF6LYIRGRmZ7rhbt27N9Li0/du2bctyDH2k9ZPVuPo6ePAgbt++DR8fn0zbXbhwAT4+Pvjtt98ybffff/9hwYIFWLBgQY7UB7y9n+78+fOZtlGr1Wjbti2+/PLLLPt7/vw54uLicqo8aDSaLJ8n9zHZvn07AMDBwUH67hgVFYWQkBBTlpVtBf9XVpQnyOVyrF27FmfPnsXt27f1bj906NAPWsmKiIjoXS9fvsT8+fORnJyMlJQUaWn7nTt3wtHRERqNRloUQwiBXr16oVy5chn2Z2FhodcX5aJFi2LHjh3SLNiOHTsAABs2bEClSpUAAE5OTmjdujXGjh2LmJgYAMD+/fsBvJ1B++yzzwC8/RK6YsUKmJub63XOK1eulMJe2ozW4cOH8dNPP0Emk6FQoUL46quvYGlpqVd/8fHxePXqFQDA399fqu/27dswMzODhYUFihcvrnVM2hfonTt3Ys2aNRnWntZu3759ePPmTYarYeorJiYGY8aMQatWrXDw4MEM2506dQovX77EwYMHkZCQABsbmwzbtmrVCp6enlJg/lCDBg1CSkoKtmzZkiP95bRTp07h1KlT+OGHHzJtt2vXLvj6+mL37t16LyyTngcPHqBFixbYsGEDSpQogT59+uDzzz/Hw4cP9XocRF7De7b0wHu2csbOnTvRo0cPg47x9vbGhg0bjFQRERF9bG7evIk6derg9evXkMvlkMlkUKvVUCgU0kOUlUolrKysEB8fj5o1a2b4YOdvv/0WiYmJWLNmTaYPYHZ0dMTVq1dRqlQppKamAng7a/b+VzBbW1vcv38fTZs2xbVr1zLsr0qVKggJCdEriKhUKlSoUAH37t2Tzk2lUkGpVEKtVkMIARsbG9y8eVPvJfTLlCmD+/fvZ9rm3LlzmDlzpnQ54KtXr6Rzd3R0hEKhgIWFBXbu3InffvtNWkTk+fPnePPmDQDA2dkZ5ubmMDc3x7p169CoUSO96nvXhg0bMHDgQCiVSjx//hwODg7SvkOHDiEwMBAAcObMGZw+fRoajQb9+vVDqVKlIJPJMHjwYCkMA28fBu7h4QFLS0u8fPlS70coZCQhIUEK+S9fvsyT3zPbtm2Lw4cPIzIyUidEv8vLywtBQUEICwv7oHvuhRA6YS29baZkUDYQlKXY2FgBQMTGxpq6lHwtLi5ONGjQQCgUCgEg05dCoRA2NjYiJCTE1GUTEVEBEx4eLmrUqCFkMpnOvz+lSpUS9+/fF/fv3xeNGjUSAMSbN290+njz5o2wsrISVlZWYvny5Rn+eyaXy8Xx48eFEEKcP39elCpVSsjlcp02lStXFtevXxdCCJGYmChGjx6dbn9jxowRiYmJBp3vy5cvRZcuXXT6kslkok6dOuL27dsG9ff333+LwoULp1ufTCYTP/zwg1CpVGLx4sU65/ruq379+uLFixdi8eLF6f5dpL0qVaokHj16pHd9c+bMEZ988on45JNPhIuLi1RDtWrVxCeffCKaNWsmTp48KcaPHy+NoVQqpb+Ld7+nBAQEiPDwcLF+/Xqxfv16MWTIEKnWH374QdoeGRmpd32JiYkiNDRUhIaGisWLF0tjzZ8/X9r++vVrg/5OjOXVq1fSZ7Nq1Sqd/ampqSI1NVU8ffpU+pz/97//Sds1Go0JqjY+Q7IBw5YeGLZyjj6BKy1onT9/3tTlEhFRARUREaETEjw9PYVarZbapKamiv/++096r9FoxIsXL8SLFy/E5s2bpWP9/f3FjBkz0g1ahw8f1ho3OjpamJmZabVzdHTUCXRpYe7ddlZWViIpKSlb56vRaESJEiV06nv16pXex//777/S+ydPnohy5crphKn9+/drHXfmzBlRvHhxnX/3J0+eLFJTU6V2p06dEs7OzjrtRo4caXC4XLBgQaa/0HVwcBAXLlwQarVaLFy4UCiVSq1xFQqFKFKkiDh48KAQQoiRI0fq/Ky8X+eCBQv0ru/777/XOvb98QGIr776yqBzzknHjh0TVapUER4eHqJUqVLSOdvb2wsPDw9RqVIl8e2334oDBw5kGpIBiNatW5vsPIyJYSuHMWzlrMwCF4MWERHlhndnFNL+PZLJZJnOUEyePFnnS3Lab/3ffVWoUEH685EjR7T6CAoKSvdL6Y0bN7Ta7dmzR9r37izSvn37snW+169f1woLaX/eunWrXscfPnxYAJBm6VJSUoSdnZ1OnwcOHNA5tmLFilqfMwARGBio065evXo67dLrT996nZycdL5rNGvWTGeWbMyYMTrt/P39pf3vzjS++9kpFAohl8vF7NmzhUql0ru2jGYa017t27cXz58/z9Z554T//vtPJ5i/H9JXrFghXr16Jdq1a5dhOzs7u2z/vOZ1hmQDrkZIuc7W1haHDx9G3bp1oVAopO0KhQJWVlYIDAxEvXr1TFghEREVdAcOHIBcLsecOXPw8OFDtG7dGkKITBdRGDFiBGrVqiW9V6lUUKlU0nulUol169bh5s2b8PHxgZWVFc6fP4+YmBi8ePECwNtFBACgadOmWs+TTNue5ujRo1AoFPjll18QFRWFGTNmQC6X48iRI3qf47u1pS200a5dO9y7dw+jRo0C8HYhCn2kLd6Q9t9jx44hLi4OSqUS//vf/+Dm5gbg/xa4SHPjxg2Eh4cDABo3biw9K+z9do8ePZJWm6tevXqG7fTVunVrtGzZEhqNRmu1yLFjx6JEiRLSeyEE9uzZA7VarXVP0N9//y392crKCitWrMD48eO12qjVaixduhSTJ0/W+j6TlSJFimDXrl2YMGGCzr6xY8di//79cHJy0ru/nObh4YGrV6+iZ8+eWtsVCgVcXV1x6tQpjB49Gg4ODjhw4ABmz56t065KlSq4evUqOnbsmJul501Gj34FAGe2jOPdGS65XM4ZLSIiyjVHjhwRFy5ckN6r1Wrh7+8vHj58mOlxycnJYtCgQTq/xe/du7d4+fKlVtvnz5+LpKQk0blzZ1GnTh0hhBDbt28XixcvlmZCkpOTxcyZM3UuN7x37564fPmy1rbLly+LwMBAMWjQIJGSkpJpnYGBgcLGxkY8fvxYCCHElStXxLp167Tuofn777/TnWES4u1lg+vXrxfLly8Xy5cvFw4ODtIlj8uXLxejRo0S7u7uIjQ0VAjx9rvS4MGDxciRI7X6CQgIEBYWFmL58uVCo9GI69eviypVqoiGDRuK6dOni+joaCHE25k8uVwuZsyYIVQqlbh27Zrw9PQU1atXz/Q8M5KYmCgsLS0FAOHs7CzNQPbq1UurXdqMX5kyZURISIjYuHGjsLa2Fg4ODjp91q9fX2cmb8iQIdmqTwghBg4cKM0UpV2O2adPn2z3l9MCAgK0ZvFkMplo3769mDx5shg6dKjUbv369dJnknYeVatWNWHlxsfLCHMYw5bxpAUuBi0iIsovRowYIX0BTbv87PPPP0+3bWxsrPRF/+7dux889oQJEwQA8c8//2TabsiQIQKA+O2337I1zqtXr4S1tbXWpWPv/heAsLW1FXFxcZn2o9FoRExMjNa21NRUsXXrVgFALF68WGoXFRWl1S45OVm8ePEiW/WfPn1aCsExMTHi4sWLoly5cqJo0aLi3r170v1iCQkJYsWKFVrncfv2beHn5ycePXokhdrIyEgBQFhaWoo///xTzJgxQ7qP6d37/PSVnJwsbGxsBAAxbNgw6b4wa2vrdBdk+RCJiYnZutevV69e0jmnXRqrUCiEnZ2dUCqV0t9r2iWRrq6uok+fPtLPh6ELr+QnDFs5jGHLuFJSUnR+G0hERJQXqVQq6R6q1q1bizZt2gjg7aILaV/gHz58KPz8/ISfn58UjmQymRg2bJi0PSIiwuCx313k4v0ZpLRZo5CQEHH+/Hnpfqr69euLkJAQERISIu7du2fQeGmzUO8vgiGTyUT16tXFzZs3DT6HNEOHDhUARMOGDbNse/ToUZGcnGxQ/2q1Wly8eFFrJi8hIUGcPXtWWFpaiiVLlmR6fEJCgrC1tRWzZ88WQggRExMjxowZo7VgyqlTp8TXX3+drRX3EhMThZeXlwgICJC2bd++XbRo0ULEx8cb3F9mvLy8RN++fQ0+rmTJkqJKlSri+vXr4u7du9LfWdpr1apVIjIyUtSpU0f06NFDmqXctGmTcHJyEidOnMjR84iMjBRnz57N0T6zi2ErhzFsERERkRBvr8goW7asWLBggVCr1UKtVotFixYJd3d36XvC7NmzdRZ/en/VtvHjx+s1Xnh4uChXrpxwcXERxYsX11qcw8XFRbi4uIjmzZuLmzdvZrhQwbszUYYs5BAWFibi4+N1VkW0sbExeKZErVYLb29v0bJlS9GyZUupT5lMJry8vETLli1Fly5ddH75evnyZQFArFy50qDxMuLn5ycAiHr16mXabtu2bQKAqFKlSo6Ma4jRo0cLX19fne3x8fF6rx6ZJjIyUshkMmFhYSESEhIMOvbRo0ciOTlZZ6XN9FZPHDNmjNaxxljyffDgwaJQoUI5PvOXHQxbOYxhi4iI6OMTGBgoOnbsaPBlYiqVSvzyyy86z2xKmyH65ptv9A4r8fHxolmzZpmGqClTpgghhFi9erWwsLBIdznuokWL6qyMmJnw8HDpmVnpjWnoczBVKpX49NNPMz0PT09Pne9aP//8swAgWrRoYdB47zp9+rRYt26dWLdunfTsNADijz/+EOvWrRObN28WiYmJIiQkRGzYsEFs2LBBtGzZUmq3YsUKsWHDBrF582aDA4uhoqKihEwmE5UqVdLZ99lnn4n69etn2cfTp0/FwYMHxcGDB8V3330nnccvv/wibU+bhdLHkydPxCeffJLh31u1atWyNVNriOTkZGFraysAiD179hh1LH0wbOUwhi0iIqKPT48ePQQAcerUqWwdv2jRIp3L7yZOnGhwPyqVSsyaNUunL3t7e+lZUGmuXLmiE7ZcXV3Fs2fPDBpzzpw5Ani7cAQA0apVKxEaGioFv7SAZ+h5zJkzJ91Hv4wdO1aasfjzzz/F1KlTxdSpU0XJkiWloDplyhQxdepUMWfOHINCT9WqVXXGe//SyNOnT4sGDRpk2e7QoUMGn7chVq1aJY317iWL8fHxwtzcXADI8nLQMWPG6JzD+5/5zz//bFBdKpVKjB07Vufz6dOnT7af/ZaVO3fuiMmTJ4tJkyZJ9yDKZDJRv359MWnSJDFp0iSxe/duo4ydFYatHMawRURE9HFJSEiQVrObMGFCtvp4d7GAtFfTpk2z1delS5e0LksEIIoVK6Yz63b8+HHpS+m77fWZydi5c6dYtmyZWLZsmbQgglwuF5999plYsmSJWLNmjUhISBDLly8XR48ezdZ5qFQq4ejoqFWjTCaTwqBGoxEeHh5al6yl/TetvZWVlUGLjTx+/Fg0b95c5+8ibbGHnTt3CiHeziqlN/umUCiEtbW12LhxY7bOOSvDhw/XWpHw3T+nrfDXqVMn6f0XX3whtmzZIrZs2ZJu8IqNjRX9+vXTOY+0z2/06NEGPyhaCCG++uor6fNI+xk05uqJBw4ckD6Hd8d899l2gwcPNtr4mWHYymEMW0RERAXfyZMnhbm5uZDL5TqzQ2lfgJs0aaJXXykpKcLa2looFAoxb9486RI/uVyerRX2fvrpJ+mL5ruXFZ45c0ar3ZdffikAiHLlyonffvtN2NvbCwDCz88v0/41Go0oXbq0zozOuzMiSqXygxbFEOL/wiAAadYKgFizZo3U5tWrV+Kzzz5Ld5apWrVqWjM++lKpVKJ+/fo6MzzvLv8vxNt7y1q0aKHzsOq0hzkbw/Hjx6UAmtkr7VE5724bPXp0un1qNBoxfvx4nT5mzJiRrRrVarVwdnaWZjnTZn2trKyMeg9VUFCQcHZ21vp7S/scfvnlF4PuQcxJfKgxUS6JiIjAxYsXM31FRESYukwiItJDjRo10Lp1a2g0GgghtPZpNBqYm5tjzJgxevWlUCjwxRdfIDg4GBMnTsSIESPw77//YuzYsbC0tDS4trCwMJQpUwbBwcEICgqCr68vrKysEBYWptXOxcUFY8aMwaVLlzBu3Dhcu3YNPXr0gL29fab9y2QyBAcHw8vLSzpf4O2DexUKBdzc3HDq1ClUrFjR4NrfFRQUBIVCgZkzZ+LevXtYu3YtrKysEBQUJLVxcHDAli1bUK5cOa1jNRoNjhw5Ag8PD4PHTU5OxuXLl6FWq7W2h4aGar1Xq9W4ePGi1gOhAeDixYsGj6mvZs2a4fr162jUqJHWQ5MBoE6dOpDJZJDL5VCr1dBoNFKbkSNHYsGCBen2KZPJpIdJy2Qy6Zhbt25lq0aVSoXChQvj119/xaFDh7Bt2zasXr0aJUuWRHx8fLb61Efz5s3h4+Oj8/fWv39/TJkyxaCHSZuM8bNf/seZLUpPeHh4lr+FSnuFh4ebulwiItKDRqMRCxYs0JlRcHd3z9aMSk6Jj4/XWf781atXOf6bfbVaLSpUqKAzs/fo0aMc6f/ly5c6n+PDhw91HiZ9586ddP89Xb9+fbbG3bVrlwAgXFxcxIEDB6RLPNu0aaPV7siRIwKAcHJyErt375YeYK3vjOaH6NChg84M1oIFC3R+HmUymfj6668z7Svt+W4ymUxMnz5dfPvttwJ4uyJlVg/EzmvevVcs7XMpXLiwyWa1hODMFlGuMOQ3Ocb8rQ8REeUcmUwmzZy8O6MA4INndT6EjY0NzM3NtbY5ODjk+G/2o6KiEBERoTOzd/LkyRzpv0iRIjozUyVLlkTJkiW1tu3fvx8A4OXlhbt37+LLL78EAOzbty9b45YrVw4TJkzAtWvX0L59e2zatAnr169Hx44dtdq5ublJM4JdunTBX3/9hU2bNqFLly7ZGldfcXFxOHToEIC351y6dGkAQEBAAC5dugQA0uyUEEKvmbZu3brhxIkTmDp1KhYsWIC///4bHTp00Jk9y+v27t0LCwsLrFmzBmFhYfDw8MCrV68QEhJi6tL0Y/ToVwBwZovSExoaqvfMVmhoqKnLJSIiPaWtfFa4cGGte4cuX75s6tKM7o8//hDA2+W8Q0JCpIUWsvNQ3A9x48YN4ePjo7UAyKFDh8Q///yTq3XkluvXrwsbGxuxaNEioVarRXx8vBg8eLAoUaKEsLW1FTKZTEydOlUsW7ZMmJmZZfvev/zI399f3LhxQ3r/+vVrsXLlShETE2OymgzJBjIh3vvVBemIi4uDvb09YmNjYWdnZ+pyKI+4ePEi6tSpo1fb0NBQ1K5d28gVERFRTvDy8oJcLseGDRvg4uKCLVu24IsvvsC6devQuXNnU5dnVGFhYTh48CAmTJgACwsLCCEQEBAAuVyO3r17m7q8Ak2j0UAu177oTKVSYdy4cejfvz+aNWsGALh8+TKWLVuGxYsXw9bW1hSlfvQMyQYMW3pg2KL0MGwRERVM6X3pTW8bEX2cDMkG/H8NIiIionekF6oYtIgoO/j/HEREREREREbAsEVERERERGQEDFtERERERERGwLBFlE2GrADE1YKIiMhUhBA4d+6czrOz8gohBFatWoXnz5+buhQyEiEENBqNqcswCYYtomyqUKECwsPDERoamukrPDwcFSpUMHW5RERUwFy8eBFFihTBvXv3Mm136NAhNGzYEIGBgTky7tOnT+Hu7o7Q0NAc6e/y5csYPXo0fvvttxzpTwiBIUOGICgoKEf6M8SkSZOwadOmLNvt3bsXwcHBWbbr3bs3xowZk2W7KVOmYPLkyXrVmJNWrVqF/v37Z9lu3rx5qF69ep4N/EZlrId9FSR8qDERERHlNV9//bUAIObNm5dpu+HDhwsA4osvvsiRcVesWCEAiPHjx+dIfz/99JMAICpWrJgj/YWFhQkAomvXrpm202g0omnTpsLX1zdHxn3x4oWQy+WiatWqmbZLTU0VDg4Ool69epm2i46OFgqFQhQqVEgkJSVl2C45OVnY2NgIa2vrTNsZQ8WKFQUA8fDhw0zblS9fXgAQoaGhuVSZcRmSDZQmS3lEREREZJAHDx4gISEBALBlyxYAgL+/Pzp27AgAcHBwgLOzM9auXYvY2FgAwLZt26T2FStWlNoNHToUt27dwqBBg7B37144OTllOG5cXBzi4+MBQJq52bJlCyZOnAiZTAYrKysUKVJE7/OYMmUKbt68CQDSDFR4eDi6desGMzMzWFhYYN68eXB1ddW7zzTbt28HAPz9999ISEiAjY1Nuu3CwsJw8uRJJCUlYejQoQaP8749e/ZAo9Hg6tWruH37NsqVK5duuxMnTiAmJgYhISF49OgRSpYsKe1LSkrC3bt3pfrVajVev36NDRs2oHHjxgCA8uXLQ6lU4s2bNwCAI0eOSD8T+/fvR7t27QAAVlZWkMlkH3xe70tMTIQQAhEREQgPDwcABAQEYNSoUdK4QghcuXIFGo0Gjx8/xq1btwAAa9aswYgRIwAApUuXhqOjY47Xl+cYPfoVAJzZIiIiIlN7/PixAJDpy9zcXDx9+lTY2tpK2xQKhdZ/AQh7e3sRFxcn/ve//wkAYtWqVRmOm5qaqtVfei+ZTCbu3r2r97l06tRJ69j3+ytWrJi4f/++3v116dJF2NraCltbW63ztLa2Fra2tsLR0VEcP35cJCUlicjISBEZGSm+/fZbqd2lS5dEZGSkePbsmd5jCiHEl19+KWxtbYWNjY0wMzMTcrlcyOVyYWlpKWxsbISdnZ3YuHGjWL9+vejSpYvo0qWL8PT0FAqFQshkMlGvXj1p+9GjR8WYMWO0PgeFQiGUSqXWtilTpoipU6dqbVMqlTrtfvzxR4PORR++vr5aY8jlcp2/v759+wo/Pz+ddu//HX/66ac5Xl9uMSQbMGzpgWGLiIiI8oLVq1cLCwuLdAOKra2t2LZtmxBCiFu3bolatWrptJPJZKJOnToiPDxcqFQqUaFCBQFAeHl5CZVKJVQqlVCr1TrjbtiwQVhZWaUbtORyuZg9e7bQaDR6n4darRZz584VCoVC54t4hw4dxPPnzw36XNauXSvMzMwyDIONGjUSz58/Fy1btkw3KL77fuvWrXqPe/DgQWFnZ5fhuB4eHuL27dti+fLl6f6dpb3MzMzEgQMHxKNHj8Qnn3ySYbv27duL58+fi/DwcFG5cuUM21WqVEncvHnToM9QH0+fPhXNmjXLcFwXFxcRHBwskpKSxFdffZVhmK5WrZpR6sstDFs5jGGLiIiI8opr167pBIvChQuLe/fuabV78+aNsLa21mpnbW2dYWhKexUpUiTde38iIiKEk5OTTtA6c+ZMts+lSpUqOuNHRERkq6/Lly+L0qVLa81sARCTJ08WqampQgghzpw5I1xdXTM89379+on4+HiDxn3w4IGoU6eOTqgYPHiweP36tdTuyJEjomjRolr1yeVyUbFiRXH58mWpnUqlEkOHDtWpbcKECVqB9s2bN6J///467Xr37i0SExOz9RnqQ6VSie+//14nsDZt2lRER0drtV2zZo1WO4VCIT799NNcv7cspxmSDbgaIREREVE+kpqaitTUVK1tMTExsLCw0NoWGhqKxMRErW2JiYno1q1bhn3L5XJMmjRJpy8AcHJyQkxMjNY2jUYDS0tLw07g/7tz5w6uXbsG4O09ZGl27NiRrf6qVasGOzs7qNVqKBQKaXvDhg2hVL5dpqBRo0a4evUqGjRooNUGAJYuXYqNGzdmeI9XRtzc3ODm5gYAUCqVkMvffr2uWbMmrK2tpXatWrWCt7c3hBDSvVQajQYTJkxAtWrVpHYKhUK6P04ul0t1JiYmat2DZWlpCXt7e2nctHYODg6wsrIy6BwMoVAopPv70uoTQkCpVKJw4cJabaOjo6V2AKBWq/Ho0aN0f74KKoYtIiIionwkbQEIZ2dnzJ8/H/b29hBCYNeuXVrtdu/eDQBo3749rl69ijZt2gAAypQpgwMHDkgBJI2dnR1Onz6N77//Pt1x9+7dC5VKBUtLS0yZMkVa3CCtHkMdOXIEADB8+HA8evQIfn5+sLKykrYb6s6dO7hy5QoAwMvLS3rG5fv1FS5cGPHx8VCr1ZDJZFIQsLCwyNaCEgkJCThw4ACEEKhTpw6KFy8O4P8WMEkjhMCWLVug0WhQqFAh6fNPW8AkTWJiIvbt2wcAGDx4MLp37w4A2Lp1K9RqtdROo9Fg69atAIAGDRpIC2hs27ZNq50xBAQEAAA8PDyk8H78+HG8fPlSq93mzZsBvP37mDZtGuRyOW7cuIGIiAij1penGHeSrWDgZYRERESUV8ybN0/07dtXvHjxQgghxMOHD0Xnzp3F2rVrtdoFBgYKHx8f6dIztVot1qxZI4KCgkRERITO5WdKpTLT7zq+vr6icePG4r///hNCCBEVFSW6dOkiJk+enK3ziI6OFidPntTadvfuXXHp0qVs9bd//35haWkpVqxYITQajbh//75o1KiRqFOnjla78PBw6ZzHjBkjPD09BQDRqlWrbI179epVoVQqxc8//yxSU1NFdHS06N69u3ByctJql/aZN2rUSNy/f1/8+++/oly5ckKpVGrdJxcdHS3q168vNm/eLIR4u0T9mjVrRMOGDUVycrLULi4uTjg7O4sZM2ZI99vNmjVLODs7i1evXmXrXPTl6ekpxowZIxITE4VGoxG+vr7C2dlZ3LhxQ6tdnz59xLx586TzCw4OFs2bN5d+hvIrQ7KBTIiP8elihomLi4O9vT1iY2NhZ2dn6nKIiIiIPsivv/6KH374AZaWlhgwYADWrl0LtVqNjRs36vWQ2rzqzZs3WpfQCSGQnJysdaljaGgovL29sXjxYrRv3x5v3rzB999/j5s3b+LQoUPZGjcpKUnncsr3t2k0Gpw4cQKffPKJNKv1+vVrXLp0SZqVovzBkGzAsKUHhi0iIiIqSL7++mucOHECmzZtgoeHB86fP48BAwZg5MiR+O6770xdHlGexrCVwxi2iIiIqCAR7yzS8O42AEZ5EC5RQWJINuACGUREREQfmfQClUwmy9NBS61Wo3v37jh16pSpSyHSW54PW2XKlJH+x//ua+zYsQCAFi1a6OwbNWqUVh8PHjxAx44dYW1tjWLFimHixIlQqVSmOB0iIiIiyoYzZ85g165d+P33301dCpHelFk3Ma2QkBCt5SuvXr2KTz/9FL169ZK2jRgxAjNmzJDev/tMA7VajY4dO8LZ2RlnzpxBZGQkBg0aBDMzM8yePTt3ToKIiIiIPkjaEu579uxJd0EKorwoz4etokWLar2fO3cuypUrh+bNm0vbrK2t4ezsnO7xhw4dwvXr13HkyBEUL14cNWvWxC+//IJJkyZh2rRpMDc3N2r9RERERJQ9zZo1w7lz5wBAepBzYmIi7OzsIJPJYG5ujsOHD6Nhw4amLJMoQ3n+MsJ3paSkYMOGDRg2bJjWNcUbN26Ek5MTqlatismTJ2s9LT04OBjVqlWTHjAHAG3btkVcXJz01PL3JScnIy4uTutFRERERLlr6NChAN5+B3x3TbfU1FSkpKTgk08+QZUqVUxVHlGW8lXY2rVrF2JiYjBkyBBpW//+/bFhwwYcO3YMkydPxvr16zFgwABpf1RUlFbQAiC9j4qKSnecOXPmwN7eXnq5ubnl/MkQERERUaaGDh2K0NBQuLm5QaFQaO2bP38+Dhw4AFtbWxNVR5S1PH8Z4bt8fHzQvn17uLq6SttGjhwp/blatWpwcXFBq1atcPv2bZQrVy5b40yePBnffPON9D4uLo6Bi4iIiMgEqlSpArlcDrVaDblcDo1GAwCoXr16nl49kQjIRzNb9+/fx5EjR/D5559n2q5BgwYAgFu3bgEAnJ2d8fTpU602ae8zus/LwsICdnZ2Wi8iIiIiyn2XLl3C/fv3AQBdunSRvpelLZhBlJflm7C1du1aFCtWDB07dsy0XVhYGADAxcUFANCoUSNcuXIFz549k9ocPnwYdnZ2qFy5stHqJSIiIqIPd+fOHdjZ2WHTpk3YuXMnrl69iiZNmiAiIsLUpRFlSSbevdswj9JoNHB3d0e/fv0wd+5cafvt27fh7++PDh06wNHREZcvX8aECRNQsmRJHD9+HMDbpd9r1qwJV1dXzJs3D1FRURg4cCA+//xzvZd+N+Qp0URERESUs1QqFZRKZZbbiHKDIdkgX/yEHjlyBA8ePMCwYcO0tpubm+PIkSNYsmQJXr9+DTc3N/Ts2RNTpkyR2igUCuzbtw+jR49Go0aNUKhQIQwePFjruVxERERElHelF6oYtCg/yBczW6bGmS0iIiIiIgIMywb55p4tIiIiIiKi/IRhi4iIiIiIyAgYtoiIiIiIiIyAYYuIiIiIiMgIGLaIiIiIiIiMgGGLiIiIiIjICBi2iIiIiIiIjIBhi4iIiIiIyAgYtoiIiIiIiIyAYYuIiIiIiMgIGLaIiIiIiIiMgGGLiIiIiIjICBi2iIiIiIiIjIBhi4iIiIiIyAgYtoiIiIiIiIyAYYuIiIiIiMgIlKYugLIWERGB+Pj4TNvY2tqiQoUKuVQRERERERFlhWErj4uIiEDFihX1ahseHs7ARURERESUR/Aywjwuqxmt7LYlIiIiIiLjYtgiIiIiIiIyAoYtIiIiIiIiI2DYIiIiIiIiMgKGLSIiIiIiIiNg2CIiIiIiIjIChi0iIiIiIiIjYNgiIiIiIiIyAoatPM7W1tYobYmIiIiIyLiUpi6AMlehQgWEh4dn+cBiW1tbVKhQIZeqIiIiIiKirDBs5QMMUURERERE+Q8vIyQiIiLKQ1JSUrBnzx4IIUxdChF9IIYtIiIiojzEz88PXbt2xfnz501dChF9IIYtIiIiojxky5YtAIDt27ebuBIi+lC8Z4uIiIjIhF6/fo358+fj9evXEEIgKCgIAPDXX39JlxK6urpiwoQJJqySiLJDJnhBcJbi4uJgb2+P2NhY2NnZmbocIiIiKkCePHmCypUrIzY2FjKZDHK5HGq1GkqlEiqVCgBQsWJFXL9+HQqFwsTVEpEh2YCXERIRERGZkKurKy5fvoxGjRoBANRqNQBIQat///4ICQlh0CLKhzizpQfObBEREZGxvXr1Co6OjlqrELr9v/buPqzq+v7j+As4wAQ5mMadhqSbWORtroxMy1Ko0FnZ5uwGNbNpsKWVOZvXFHcVZXYzN127KailOWm1yswyUVpKWzFJbkzJMnQClhs3ZiE3n98f/TjrlJrK+ZxzkOfjus51cb7fD9/v5/2+Tnx99b058fGqrKz04awAfB1ntgAAADqY9evXu4JW21msvXv3qry83JfTAtAOhC0AAAA/8Morr0iS7rrrLu3bt08TJkyQJK1bt86X0wLQDlxGeAK4jBAAANj297//Xa2trbr00kslScYYPffccxo0aJD69+/v49kBaHMy2YCwdQIIWwAAAAAk7tkCAAAAAJ8jbAEAAACABYQtAAAAALCAsAUAAAAAFhC2AAAAAMACwhYAAAAAWEDYAgAAAAALCFsAAAAAYAFhCwAAAAAsIGwBAAAAgAWELQAAAACwgLAFAAAAABYQtgAAAADAAsIWAAAAAFhA2AIAAAAACwhbAAAAAGABYQsAAAAALCBsAQAAAIAFhC0AAAAAsICwBQAAAAAWELYAAAAAwALCFgAAAABYQNgCAAAAAAsIWwAAAABgAWELAAAAACwgbAEAAACABX4dthYtWqSAgAC31znnnONa/8UXXygjI0M9evRQ165dNXHiRNXU1Lhto7KyUmlpaQoLC1N0dLTmzp2r5uZmb5cCAAAAoJNx+HoC3+a8887TG2+84XrvcPxvynPmzNErr7yivLw8RUZGKjMzU9ddd522bNkiSWppaVFaWppiY2O1detWVVVVKT09XcHBwbr//vu9XgsAAACAzsPvw5bD4VBsbOw3ltfV1emJJ57QqlWrdPnll0uScnJydO655+rtt9/WRRddpNdff13l5eV64403FBMToyFDhuhXv/qV5s2bp0WLFikkJMTb5QAAAADoJPz6MkJJqqioUM+ePdW3b1/deOONqqyslCQVFRWpqalJY8aMcY0955xz1Lt3bxUWFkqSCgsLNXDgQMXExLjGpKamqr6+XmVlZd4tBAAAAECn4tdntoYPH67c3Fz1799fVVVVysrK0siRI1VaWqrq6mqFhISoW7dubr8TExOj6upqSVJ1dbVb0Gpb37buWBobG9XY2Oh6X19f76GKAAAAAHQWfh22rrrqKtfPgwYN0vDhw5WQkKA1a9aoS5cu1vabnZ2trKwsa9sHAAAAcPrz+8sIv6pbt25KTEzUBx98oNjYWB05ckS1tbVuY2pqalz3eMXGxn7j6YRt7492H1ib+fPnq66uzvXau3evZwsBAAAAcNrrUGHr0KFD2r17t+Li4jRs2DAFBwdr48aNrvU7d+5UZWWlkpOTJUnJyckqKSnRgQMHXGM2bNggp9OppKSkY+4nNDRUTqfT7QUAAAAAJ8OvLyO8++67NX78eCUkJGj//v1auHChgoKCNHnyZEVGRmr69Om688471b17dzmdTv30pz9VcnKyLrroIklSSkqKkpKSdPPNN2vJkiWqrq7WggULlJGRodDQUB9XBwAAAOB05tdha9++fZo8ebIOHjyoqKgoXXLJJXr77bcVFRUlSXr00UcVGBioiRMnqrGxUampqVqxYoXr94OCgrR27VrNmjVLycnJCg8P15QpU7R48WJflQQAAACgkwgwxhhfT8Lf1dfXKzIyUnV1dVxSCAAAAHRiJ5MNOtQ9WwAAAADQURC2AAAAAMACwhYAAAAAWEDYAgAAAAALCFsAAAAAYAFhCwAAAAAsIGwBAAAAgAWELQAAAACwwOHrCcC3Kioq1NDQcMz1ERER6tevnxdnBAAAAJweCFudWEVFhRITE7913K5duwhcAAAAwEniMsJO7HhntE5lHAAAAID/IWwBAAAAgAWELQAAAACwgLAFAAAAABYQtgAAAADAAsIWAAAAAFhA2AIAAAAACwhbnVhERIRHxwEAAAD4H77UuBPr16+fdu3addzv0YqIiOALjQEAAIBTQNjq5AhSAAAAgB1cRggAAAAAFhC2AAAAAMACwhYAAAAAWEDYAgAAAAALCFsAAAAAYAFhCwAAAAAsIGwBAAAAgAWELQAAAACwgLAFAAAAABYQtgAAAADAAsIWAAAAAFhA2AIAAAAACwhbAAAAAGABYQsAAAAALCBsAQAAAIAFhC0AAAAAsICwBQAAAAAWELYAAAAAwALCFgAAAABYQNgCAAAAAAsIWwAAAABgAWELAAAAACwgbAEAAACABYQtAAAAALCAsAUAAAAAFhC2AAAAAMACwhYAAAAAWEDYAgAAAAALCFsAAAAAYAFhCwAAAAAsIGwBAAAAgAWELQAAAACwgLAFAAAAABYQtgAAAADAAsIWAAAAAFhA2AIAAAAACwhbAAAAAGABYQsAAAAALCBsAQAAAIAFhC0AAAAAsICwBQAAAAAWELYAAAAAwALCFgAAAABYQNgCAAAAAAsIWwAAAABgAWELAAAAACwgbAEAAACABX4dtrKzs3XBBRcoIiJC0dHRuuaaa7Rz5063MZdddpkCAgLcXjNnznQbU1lZqbS0NIWFhSk6Olpz585Vc3OzN0sBAAAA0Mk4fD2B4ykoKFBGRoYuuOACNTc3695771VKSorKy8sVHh7uGjdjxgwtXrzY9T4sLMz1c0tLi9LS0hQbG6utW7eqqqpK6enpCg4O1v333+/VegAAAAB0HgHGGOPrSZyoTz75RNHR0SooKNCoUaMkfXlma8iQIXrssceO+juvvvqqxo0bp/379ysmJkaS9Pjjj2vevHn65JNPFBIS8q37ra+vV2RkpOrq6uR0Oj1WDwAAAICO5WSygV9fRvh1dXV1kqTu3bu7LV+5cqXOPPNMDRgwQPPnz9fhw4dd6woLCzVw4EBX0JKk1NRU1dfXq6ys7Kj7aWxsVH19vdsLAAAAAE6GX19G+FWtra2aPXu2RowYoQEDBriW33DDDUpISFDPnj21fft2zZs3Tzt37tTzzz8vSaqurnYLWpJc76urq4+6r+zsbGVlZVmqBAAAAEBn0GHCVkZGhkpLS/XWW2+5Lb/ttttcPw8cOFBxcXG64oortHv3bn33u989pX3Nnz9fd955p+t9fX294uPjT23iAAAAADqlDnEZYWZmptauXatNmzbprLPOOu7Y4cOHS5I++OADSVJsbKxqamrcxrS9j42NPeo2QkND5XQ63V4AAAAAcDL8OmwZY5SZmakXXnhB+fn56tOnz7f+TnFxsSQpLi5OkpScnKySkhIdOHDANWbDhg1yOp1KSkqyMm8AAAAA8OvLCDMyMrRq1Sq9+OKLioiIcN1jFRkZqS5dumj37t1atWqVrr76avXo0UPbt2/XnDlzNGrUKA0aNEiSlJKSoqSkJN18881asmSJqqurtWDBAmVkZCg0NNSX5QEAAAA4jfn1o98DAgKOujwnJ0dTp07V3r17ddNNN6m0tFSfffaZ4uPjde2112rBggVul/59/PHHmjVrljZv3qzw8HBNmTJFDzzwgByOE8uaPPodAAAAgHRy2cCvw5a/IGwBAAAAkE7j79kCAAAAgI6CsAUAAAAAFhC2AAAAAMACwhYAAAAAWEDYAgAAAAALCFsAAAAAYAFhCwAAAAAsIGwBAAAAgAWELQAAAACwgLAFAAAAABY4fD0BdFwVFRVqaGg45vqIiAj169fPizMCAAAA/AdhC6ekoqJCiYmJ3zpu165dBC4AAAB0SlxGiFNyvDNapzIOAAAAON0QtgAAAADAAsIWAAAAAFhA2AIAAAAACwhbAAAAAGABYQsAAAAALCBsAQAAAIAFhC2ckoiICI+OAwAAAE43fKkxTkm/fv20a9eu436PVkREBF9oDAAAgE6LsIVTRpACAAAAjo3LCAEAAADAAsIWAAAAAFhA2AIAAAAACwhbAAAAAGABYQsAAAAALCBsAQAAAIAFhC0AAAAAsICwBQAAAAAWELYAAAAAwALCFgAAAABYQNgCAAAAAAsIWwAAAABgAWELAAAAACwgbAEAAACABYQtAAAAALCAsAUAAAAAFhC2AAAAAMACwhYAAAAAWEDYAgAAAAALCFsAAAAAYIHD1xPoCIwxkqT6+nofzwQAAACAL7VlgraMcDyErRPQ0NAgSYqPj/fxTAAAAAD4g4aGBkVGRh53TIA5kUjWybW2tmr//v2KiIhQQECAr6fjc/X19YqPj9fevXvldDp9PZ0Oh/61Hz1sH/rXfvSw/ehh+9C/9qOH7dOZ+2eMUUNDg3r27KnAwOPflcWZrRMQGBios846y9fT8DtOp7PT/cflSfSv/ehh+9C/9qOH7UcP24f+tR89bJ/O2r9vO6PVhgdkAAAAAIAFhC0AAAAAsICwhZMWGhqqhQsXKjQ01NdT6ZDoX/vRw/ahf+1HD9uPHrYP/Ws/etg+9O/E8IAMAAAAALCAM1sAAAAAYAFhCwAAAAAsIGwBAAAAgAWELQAAAACwgLDVCWVnZ+uCCy5QRESEoqOjdc0112jnzp1uY7744gtlZGSoR48e6tq1qyZOnKiamhq3MZWVlUpLS1NYWJiio6M1d+5cNTc3u41ZuXKlBg8erLCwMMXFxemWW27RwYMHrddom6d6+LOf/UzDhg1TaGiohgwZctR9bd++XSNHjtR3vvMdxcfHa8mSJbbK8hpv9W/z5s2aMGGC4uLiFB4eriFDhmjlypU2S/Mab34G23zwwQeKiIhQt27dPFyN93mzf8YYLV26VImJiQoNDVWvXr1033332SrNa7zZw9dee00XXXSRIiIiFBUVpYkTJ2rPnj2WKvMeT/Twvffe0+TJkxUfH68uXbro3HPP1a9//etv7Gvz5s06//zzFRoaqu9973vKzc21XZ513urf888/r7FjxyoqKkpOp1PJycl67bXXvFKjbd78DLbZsmWLHA7Htx5zTheErU6ooKBAGRkZevvtt7VhwwY1NTUpJSVFn332mWvMnDlz9PLLLysvL08FBQXav3+/rrvuOtf6lpYWpaWl6ciRI9q6daueeuop5ebm6pe//KVrzJYtW5Senq7p06errKxMeXl5+uc//6kZM2Z4tV4bPNHDNrfccosmTZp01P3U19crJSVFCQkJKioq0kMPPaRFixbpD3/4g7XavMFb/du6dasGDRqkv/71r9q+fbumTZum9PR0rV271lpt3uKtHrZpamrS5MmTNXLkSI/X4gve7N8dd9yhP/3pT1q6dKnef/99vfTSS7rwwgut1OVN3urhRx99pAkTJujyyy9XcXGxXnvtNX366adH3U5H44keFhUVKTo6Ws8884zKysr0i1/8QvPnz9dvf/tb15iPPvpIaWlpGj16tIqLizV79mzdeuutHT4weKt/b775psaOHat169apqKhIo0eP1vjx47Vt2zav1muDt3rYpra2Vunp6briiiu8Up9fMOj0Dhw4YCSZgoICY4wxtbW1Jjg42OTl5bnG7Nixw0gyhYWFxhhj1q1bZwIDA011dbVrzO9+9zvjdDpNY2OjMcaYhx56yPTt29dtX8uWLTO9evWyXZLXnUoPv2rhwoVm8ODB31i+YsUKc8YZZ7h6aowx8+bNM/379/d8ET5kq39Hc/XVV5tp06Z5ZN7+xHYP77nnHnPTTTeZnJwcExkZ6enp+5yt/pWXlxuHw2Hef/99a3P3F7Z6mJeXZxwOh2lpaXEte+mll0xAQIA5cuSI5wvxofb2sM3tt99uRo8e7Xp/zz33mPPOO89tzKRJk0xqaqqHK/AtW/07mqSkJJOVleWZifsR2z2cNGmSWbBgwUkdtzs6zmxBdXV1kqTu3btL+vL/UDQ1NWnMmDGuMeecc4569+6twsJCSVJhYaEGDhyomJgY15jU1FTV19errKxMkpScnKy9e/dq3bp1MsaopqZGzz33nK6++mpvleY1p9LDE1FYWKhRo0YpJCTEtSw1NVU7d+7Uf//7Xw/N3vds9e9Y+2rbz+nEZg/z8/OVl5en5cuXe27CfsZW/15++WX17dtXa9euVZ8+fXT22Wfr1ltv1X/+8x/PFuAHbPVw2LBhCgwMVE5OjlpaWlRXV6c///nPGjNmjIKDgz1bhI95qodf/ztXWFjotg3py2NJe/+e+htb/fu61tZWNTQ0cCw5yR7m5OToww8/1MKFCy3M3H8Rtjq51tZWzZ49WyNGjNCAAQMkSdXV1QoJCfnGfRkxMTGqrq52jflq0Gpb37ZOkkaMGKGVK1dq0qRJCgkJUWxsrCIjI0+7f7Cdag9PxIn0uaOz2b+vW7Nmjd555x1NmzatPVP2OzZ7ePDgQU2dOlW5ublyOp2enLbfsNm/Dz/8UB9//LHy8vL09NNPKzc3V0VFRbr++us9WYLP2exhnz599Prrr+vee+9VaGiounXrpn379mnNmjWeLMHnPNXDrVu36i9/+Ytuu+0217JjHUvq6+v1+eefe7YQH7HZv69bunSpDh06pB/96Ecem78/sNnDiooK/fznP9czzzwjh8NhrQZ/RNjq5DIyMlRaWqrVq1d7fNvl5eW644479Mtf/lJFRUVav3699uzZo5kzZ3p8X75ks4edgbf6t2nTJk2bNk1//OMfdd5551ndl7fZ7OGMGTN0ww03aNSoUR7ftr+w2b/W1lY1Njbq6aef1siRI3XZZZfpiSee0KZNm75xE3pHZrOH1dXVmjFjhqZMmaJ33nlHBQUFCgkJ0fXXXy9jjMf35yue6GFpaakmTJighQsXKiUlxYOz83/e6t+qVauUlZWlNWvWKDo6+pT35Y9s9bClpUU33HCDsrKylJiY6KnpdhiErU4sMzNTa9eu1aZNm3TWWWe5lsfGxurIkSOqra11G19TU6PY2FjXmK8/UartfduY7OxsjRgxQnPnztWgQYOUmpqqFStW6Mknn1RVVZXFyrynPT08ESfS547Mdv/aFBQUaPz48Xr00UeVnp7e3mn7Fds9zM/P19KlS+VwOORwODR9+nTV1dXJ4XDoySef9FQZPmO7f3FxcXI4HG7/wDj33HMlfflE19OB7R4uX75ckZGRWrJkiYYOHapRo0bpmWee0caNG/WPf/zDU2X4lCd6WF5eriuuuEK33XabFixY4LbuWMcSp9OpLl26eLYYH7DdvzarV6/WrbfeqjVr1nzjssyOzmYPGxoa9O677yozM9N1LFm8eLHee+89ORwO5efnW63N1whbnZAxRpmZmXrhhReUn5+vPn36uK0fNmyYgoODtXHjRteynTt3qrKyUsnJyZK+vB+rpKREBw4ccI3ZsGGDnE6nkpKSJEmHDx9WYKD7RywoKMg1h47MEz08EcnJyXrzzTfV1NTkWrZhwwb1799fZ5xxRvsL8RFv9U/68nHHaWlpevDBB497WUhH460eFhYWqri42PVavHixIiIiVFxcrGuvvdZj9Xibt/o3YsQINTc3a/fu3a5lu3btkiQlJCS0swrf8lYPj3csaW1tbUcFvuepHpaVlWn06NGaMmXKUb9WIDk52W0b0pfHkpP9e+pvvNU/SXr22Wc1bdo0Pfvss0pLS7NTkA94o4dOp1MlJSVux5KZM2eqf//+Ki4u1vDhw+0W6Wu+eS4HfGnWrFkmMjLSbN682VRVVblehw8fdo2ZOXOm6d27t8nPzzfvvvuuSU5ONsnJya71zc3NZsCAASYlJcUUFxeb9evXm6ioKDN//nzXmJycHONwOMyKFSvM7t27zVtvvWW+//3vmwsvvNCr9drgiR4aY0xFRYXZtm2b+clPfmISExPNtm3bzLZt21xPH6ytrTUxMTHm5ptvNqWlpWb16tUmLCzM/P73v/dqvZ7mrf7l5+ebsLAwM3/+fLf9HDx40Kv12uCtHn7d6fI0Qm/1r6WlxZx//vlm1KhR5l//+pd59913zfDhw83YsWO9Wq8N3urhxo0bTUBAgMnKyjK7du0yRUVFJjU11SQkJLjtqyPyRA9LSkpMVFSUuemmm9y2ceDAAdeYDz/80ISFhZm5c+eaHTt2mOXLl5ugoCCzfv16r9brad7q38qVK43D4TDLly93G1NbW+vVem3wVg+/rjM9jZCw1QlJOuorJyfHNebzzz83t99+uznjjDNMWFiYufbaa01VVZXbdvbs2WOuuuoq06VLF3PmmWeau+66yzQ1NbmNWbZsmUlKSjJdunQxcXFx5sYbbzT79u3zRplWeaqHl1566VG389FHH7nGvPfee+aSSy4xoaGhplevXuaBBx7wUpX2eKt/U6ZMOer6Sy+91HvFWuLNz+BXnS5hy5v9+/e//22uu+4607VrVxMTE2OmTp16WgR+b/bw2WefNUOHDjXh4eEmKirK/OAHPzA7duzwUqX2eKKHCxcuPOo2EhIS3Pa1adMmM2TIEBMSEmL69u3rto+Oylv9O9ZndMqUKd4r1hJvfga/qjOFrQBjOvj1XAAAAADgh7hnCwAAAAAsIGwBAAAAgAWELQAAAACwgLAFAAAAABYQtgAAAADAAsIWAAAAAFhA2AIAAAAACwhbAAAAAGABYQsA0OlMnTpVAQEBCggIUHBwsGJiYjR27Fg9+eSTam1tPeHt5Obmqlu3bvYmCgDo0AhbAIBO6corr1RVVZX27NmjV199VaNHj9Ydd9yhcePGqbm52dfTAwCcBghbAIBOKTQ0VLGxserVq5fOP/983XvvvXrxxRf16quvKjc3V5L0yCOPaODAgQoPD1d8fLxuv/12HTp0SJK0efNmTZs2TXV1da6zZIsWLZIkNTY26u6771avXr0UHh6u4cOHa/Pmzb4pFADgM4QtAAD+3+WXX67Bgwfr+eeflyQFBgZq2bJlKisr01NPPaX8/Hzdc889kqSLL75Yjz32mJxOp6qqqlRVVaW7775bkpSZmanCwkKtXr1a27dv1w9/+ENdeeWVqqio8FltAADvCzDGGF9PAgAAb5o6dapqa2v1t7/97RvrfvzjH2v79u0qLy//xrrnnntOM2fO1Keffirpy3u2Zs+erdraWteYyspK9e3bV5WVlerZs6dr+ZgxY3ThhRfq/vvv93g9AAD/5PD1BAAA8CfGGAUEBEiS3njjDWVnZ+v9999XfX29mpub9cUXX+jw4cMKCws76u+XlJSopaVFiYmJbssbGxvVo0cP6/MHAPgPwhYAAF+xY8cO9enTR3v27NG4ceM0a9Ys3XffferevbveeustTZ8+XUeOHDlm2Dp06JCCgoJUVFSkoKAgt3Vdu3b1RgkAAD9B2AIA4P/l5+erpKREc+bMUVFRkVpbW/Xwww8rMPDLW5zXrFnjNj4kJEQtLS1uy4YOHaqWlhYdOHBAI0eO9NrcAQD+h7AFAOiUGhsbVV1drZaWFtXU1Gj9+vXKzs7WuHHjlJ6ertLSUjU1Nek3v/mNxo8fry1btujxxx9328bZZ5+tQ4cOaePGjRo8eLDCwsKUmJioG2+8Uenp6Xr44Yc1dOhQffLJJ9q4caMGDRqktLQ0H1UMAPA2nkYIAOiU1q9fr7i4OJ199tm68sortWnTJi1btkwvvviigoKCNHjwYD3yyCN68MEHNWDAAK1cuVLZ2dlu27j44os1c+ZMTZo0SVFRUVqyZIkkKScnR+np6brrrrvUv39/XXPNNXrnnXfUu3dvX5QKAPARnkYIAAAAABZwZgsAAAAALCBsAQAAAIAFhC0AAAAAsICwBQAAAAAWELYAAAAAwALCFgAAAABYQNgCAAAAAAsIWwAAAABgAWELAAAAACwgbAEAAACABYQtAAAAALCAsAUAAAAAFvwfQ4k9Z8HecOoAAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 1000x800 with 1 Axes>"
       ]
diff --git a/docs/notebooks/index.md b/docs/notebooks/index.md
new file mode 100644
index 0000000..9207ab7
--- /dev/null
+++ b/docs/notebooks/index.md
@@ -0,0 +1,61 @@
+# Jupyter Notebooks
+
+```{toctree}
+:maxdepth: 2
+:hidden:
+:caption: content
+
+./corrections
+./iceflow-example
+./iceflow-with-icepyx
+```
+
+[Jupyter notebooks](https://docs.jupyter.org/en/latest/) provide executable
+examples of how to use `iceflow`.
+
+## Prerequisites
+
+The `iceflow` notebooks are best approached with some familiarity with Python
+and its geoscience stack. If you feel like learning more about geoscience and
+Python, you can find great tutorials by CU Boulder's Earth Lab here:
+[Data Exploration and Analysis Lessons](https://www.earthdatascience.org/tags/data-exploration-and-analysis/)
+or by the Data Carpentry project:
+[Introduction to Geospatial Concepts](https://datacarpentry.org/organization-geospatial/)
+
+Some Python packages/libraries that users may consider investigating include:
+
+- [_icepyx_](https://icepyx.readthedocs.io/en/latest/): Library for ICESat-2
+  data users
+- [_geopandas_](https://geopandas.org/): Library to simplify working with
+  geospatial data in Python (using pandas)
+- [_h5py_](https://github.com/h5py/h5py): Pythonic wrapper around the
+  [\*HDF5 library](https://en.wikipedia.org/wiki/Hierarchical_Data_Format)
+- [_matplotlib_](https://matplotlib.org/): Comprehensive library for creating
+  static, animated, and interactive visualizations in Python
+- [_vaex_](https://github.com/vaexio/vaex): High performance Python library for
+  lazy Out-of-Core dataframes (similar to _pandas_), to visualize and explore
+  big tabular data sets
+
+## Iceflow notebooks
+
+- [NSIDC Iceflow example](./iceflow-example) provides an example of how to
+  search for, download, and interact with `ILATM1B v1` data for a small area of
+  interest. This notebook also illustrates how to perform
+  [ITRF](https://itrf.ign.fr/) transformations to facilitate comparisons across
+  datasets. To learn more about ITRF transformations, see the
+  [Applying Coordinate Transformations to Facilitate Data Comparison](./corrections)
+  notebook.
+
+- [Using iceflow with icepyx to Generate an Elevation Timeseries](./iceflow-with-icepyx)
+  shows how to search for, download, and interact with a large amount of data
+  across many datasets supported by `iceflow`. It also illustrates how to
+  utilize [icepyx](https://icepyx.readthedocs.io/en/latest/) to find and access
+  ICESat-2 data. Finally, the notebook provides a simple time-series analysis
+  for elevation change over an area of interest across `iceflow` supported
+  datasets and ICESat-2.
+
+### Downloading Iceflow notebooks
+
+Users may wish to try executing the Iceflow notebooks themselves. Iceflow
+notebooks can be downloaded
+[from GitHub](https://github.com/nsidc/iceflow/tree/main/docs/notebooks/).
diff --git a/notebooks/generate_rendered_notebooks_for_docs.sh b/notebooks/generate_rendered_notebooks_for_docs.sh
deleted file mode 100755
index f7fef3f..0000000
--- a/notebooks/generate_rendered_notebooks_for_docs.sh
+++ /dev/null
@@ -1,8 +0,0 @@
-#!/bin/bash
-
-set -euxo pipefail
-
-THIS_DIR="$( cd "$(dirname "$0")"; pwd -P )"
-
-jupyter nbconvert --to notebook --execute --output="${THIS_DIR}/../docs/iceflow-example.ipynb" "${THIS_DIR}/iceflow-example.ipynb"
-jupyter nbconvert --to notebook --execute --output="${THIS_DIR}/../docs/iceflow-with-icepyx.ipynb" "${THIS_DIR}/iceflow-with-icepyx.ipynb"
diff --git a/notebooks/iceflow-example.ipynb b/notebooks/iceflow-example.ipynb
deleted file mode 100644
index d023405..0000000
--- a/notebooks/iceflow-example.ipynb
+++ /dev/null
@@ -1,1465 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "7174387c-05aa-4f8f-b3bd-902d4f635d9b",
-   "metadata": {},
-   "source": [
-    "# NSIDC Iceflow example\n",
-    "\n",
-    "This notebook shows an example of how to use the `nsidc-iceflow` Python library to do ITRF transformations with real data. We recommend starting with the [corrections.ipynb](https://github.com/nsidc/NSIDC-Data-Tutorials/blob/main/notebooks/iceflow/corrections.ipynb) notebook to learn more about ITRF transformations and why they matter. \n",
-    "\n",
-    "Lets assume we want to do an analysis using [IceBridge ATM L1B Qfit Elevation and Return Strength, Version 1 (ILATM1B)](https://nsidc.org/data/ilatm1b/versions/1) data near Pine Island Glacier in Antarctica.\n",
-    "\n",
-    "Finding, downloading, and reading ILATM1B v1 data with `nsidc-iceflow` is straightforward. ILATM1B data can be very large, so for the purposes of this example we will focus on just a small area near Pine Island Glacier along with a short date range in order to fetch a manageable amount of data.\n",
-    "\n",
-    "To learn about how to download and manage larger amounts of data across many datasets with `nsidc-iceflow`, see the [Using iceflow with icepyx to Generate an Elevation Timeseries](./iceflow-with-icepyx.ipynb) notebook."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5d3b7c5f-bd78-4d2f-9414-777b41a162cd",
-   "metadata": {},
-   "source": [
-    "## Import required packages\n",
-    "\n",
-    "We will import several functions from the `nsidc-iceflow` library along with `pathlib` to specify a download path, `datetime` to define our date range, and `matplotlib` to plot our results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e29f9cf9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from __future__ import annotations\n",
-    "\n",
-    "import datetime as dt\n",
-    "from pathlib import Path\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "from nsidc.iceflow import (\n",
-    "    BoundingBox,\n",
-    "    DatasetSearchParameters,\n",
-    "    ILATM1BDataset,\n",
-    "    download_iceflow_results,\n",
-    "    find_iceflow_data,\n",
-    "    read_iceflow_datafiles,\n",
-    ")\n",
-    "\n",
-    "# note: use `inline` to save the resulting image as an embedded png (nice for sharing). \\\n",
-    "# Use `widget` to obtain interactive controls to explore the data in depth!\n",
-    "%matplotlib inline\n",
-    "# %matplotlib widget"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c81302b0-b327-4010-b7f9-961653d2b5c8",
-   "metadata": {},
-   "source": [
-    "## Finding, downloading, and reading data\n",
-    "\n",
-    "Next we define our download path, data set of interest, and filter parameters:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f45d86d9-404a-4720-9ad0-9959114aa629",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Downloaded data will go here.\n",
-    "data_path = Path(\"./downloaded-data/\")\n",
-    "\n",
-    "# Define the dataset that we want to search for.\n",
-    "atm1b_v1_dataset = ILATM1BDataset(version=\"1\")\n",
-    "\n",
-    "# Define the dataset that we want to search for.\n",
-    "atm1b_v1_dataset = ILATM1BDataset(version=\"1\")\n",
-    "# Define a bounding box for our area of interest.\n",
-    "BBOX = BoundingBox(\n",
-    "    lower_left_lon=-103.125559,\n",
-    "    lower_left_lat=-75.180563,\n",
-    "    upper_right_lon=-102.677327,\n",
-    "    upper_right_lat=-74.798063,\n",
-    ")\n",
-    "\n",
-    "# We will define a short date range in 2009 to search for data.\n",
-    "date_range = (dt.date(2009, 11, 1), dt.date(2009, 12, 31))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ada043ae-65ed-4f22-ba19-dc9da6b8e0d2",
-   "metadata": {},
-   "source": [
-    "Next, we use the `find_iceflow_data` function to search for data matching our search parameters. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c440ba2d-0c4a-47ff-b41e-a7486709d3e0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "search_results = find_iceflow_data(\n",
-    "    dataset_search_params=DatasetSearchParameters(\n",
-    "        datasets=[atm1b_v1_dataset],\n",
-    "        bounding_box=BBOX,\n",
-    "        temporal=date_range,\n",
-    "    ),\n",
-    ")\n",
-    "len(search_results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "752d5a6a-27ec-42cc-9505-923df8b69549",
-   "metadata": {},
-   "source": [
-    "Once we have found data matching our area of interest, we can download the results with `download_iceflow_results`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e3b3b3bc-2d2e-49d3-bafc-3f9a4269ee57",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "downloaded_files = download_iceflow_results(search_results, output_dir=data_path)\n",
-    "print(downloaded_files)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1526b5bb-c08a-43d1-84b4-a207d9db924c",
-   "metadata": {},
-   "source": [
-    "Finally, we can read the data we just downloaded with `read_iceflow_datafiles`. The output is a `pandas` DataFrame containing the matching data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "15c0633c-1812-45d7-a91f-36d627fdb3b5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "iceflow_df = read_iceflow_datafiles(downloaded_files)\n",
-    "\n",
-    "iceflow_df.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d94a6e32",
-   "metadata": {},
-   "source": [
-    "For the purposes of the rest of this example, we will narrow our focus to just the latitude, longitude, elevation, and ITRF values."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "99e03fbd",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "iceflow_df = iceflow_df[[\"latitude\", \"longitude\", \"elevation\", \"ITRF\"]]\n",
-    "iceflow_df.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ceb7fdc7",
-   "metadata": {},
-   "source": [
-    "## ITRF transformations\n",
-    "\n",
-    "We can see that ILATM1B v1 uses ITRF2005. Lets say we have other data that are in ITRF2014, and we want to compare that to the ILATM1B v1 data we just fetched. `nsidc-iceflow` provides a `transform_itrf` function that allows the user to transform latitude, longitude, and elevation data from an `nsidc-iceflow` dataframe into a target ITRF."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "6a0cd1e6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from nsidc.iceflow import transform_itrf\n",
-    "\n",
-    "itrf2014_df = transform_itrf(data=iceflow_df, target_itrf=\"ITRF2014\")\n",
-    "itrf2014_df.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "27ecc8da",
-   "metadata": {},
-   "source": [
-    "Let's take a look at the differences between the original data (ITRF2005) and the data that has been transformed to ITRF2014."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b64693c0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for variable in [\"latitude\", \"longitude\", \"elevation\"]:\n",
-    "    print(\n",
-    "        f\"Max difference in {variable}: {abs(itrf2014_df[variable] - iceflow_df[variable]).max()}\"\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3bc41f8f",
-   "metadata": {},
-   "source": [
-    "We can see here that the differences are very small! The largest elevation difference is 0.007 - 7mm!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "10c48a2e",
-   "metadata": {},
-   "source": [
-    "## Propagating data to a new epoch\n",
-    "\n",
-    "Now lets assume we need to propagate the data to a target epoch of 2019.7, which corresponds to September 13, 2019. This accounts for continental plate motion.\n",
-    "\n",
-    "The `transform_itrf` function optionally takes a `target_epoch` in order to do this transformation. We'll use the original data as input."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "14e431b0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "itrf2014_epoch_2019_7_df = transform_itrf(\n",
-    "    data=iceflow_df,\n",
-    "    target_itrf=\"ITRF2014\",\n",
-    "    target_epoch=\"2019.7\",\n",
-    ")\n",
-    "\n",
-    "itrf2014_epoch_2019_7_df.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e11a8a1c",
-   "metadata": {},
-   "source": [
-    "The maximum difference is still small:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8c562732",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for variable in [\"latitude\", \"longitude\", \"elevation\"]:\n",
-    "    print(\n",
-    "        f\"Max difference in {variable}: {abs(itrf2014_epoch_2019_7_df[variable] - iceflow_df[variable]).max()}\"\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5395451d",
-   "metadata": {},
-   "source": [
-    "## Visualizing the differences\n",
-    "\n",
-    "To visualize the differences, which are very small, we need to zoom in on just a small subset of points. First we set a filter condition and apply it to each DataFrame."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5bafd254",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "filter_condition = (iceflow_df.reset_index().index > 50) & (\n",
-    "    iceflow_df.reset_index().index < 60\n",
-    ")\n",
-    "sampled_iceflow_df = iceflow_df[filter_condition]\n",
-    "sampled_itrf2014_df = itrf2014_df[filter_condition]\n",
-    "sampled_itrf2014_epoch_2019_7_df = itrf2014_epoch_2019_7_df[filter_condition]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "04cf4f0f-5e5b-4316-8ef4-dc2bdd5f317d",
-   "metadata": {},
-   "source": [
-    "Now we create a 3D plot using `matplotlib` to visualize the differences across latitude, longitude, and elevation. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "0ffce6d2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fig = plt.figure(figsize=(10, 8))\n",
-    "ax = fig.add_subplot(111, projection=\"3d\")\n",
-    "ax.scatter(\n",
-    "    sampled_iceflow_df.longitude.values,\n",
-    "    sampled_iceflow_df.latitude.values,\n",
-    "    sampled_iceflow_df.elevation.values,\n",
-    "    color=\"blue\",\n",
-    "    marker=\"o\",\n",
-    "    label=\"original\",\n",
-    ")\n",
-    "\n",
-    "ax.scatter(\n",
-    "    sampled_itrf2014_df.longitude.values,\n",
-    "    sampled_itrf2014_df.latitude.values,\n",
-    "    sampled_itrf2014_df.elevation.values,\n",
-    "    color=\"green\",\n",
-    "    marker=\"x\",\n",
-    "    label=\"ITRF2014\",\n",
-    ")\n",
-    "\n",
-    "ax.scatter(\n",
-    "    sampled_itrf2014_epoch_2019_7_df.longitude.values,\n",
-    "    sampled_itrf2014_epoch_2019_7_df.latitude.values,\n",
-    "    sampled_itrf2014_epoch_2019_7_df.elevation.values,\n",
-    "    color=\"red\",\n",
-    "    marker=\"v\",\n",
-    "    label=\"ITRF2014 epoch 2019.7\",\n",
-    ")\n",
-    "\n",
-    "ax.set_xlabel(\"longitude (degrees)\", labelpad=10)\n",
-    "ax.set_ylabel(\"latitude (degrees)\", labelpad=10)\n",
-    "ax.set_zlabel(\"elevation (m)\", labelpad=10)\n",
-    "plt.legend(loc=\"upper left\")\n",
-    "plt.show()"
-   ]
-  }
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diff --git a/notebooks/iceflow-with-icepyx.ipynb b/notebooks/iceflow-with-icepyx.ipynb
deleted file mode 100644
index 668dc4e..0000000
--- a/notebooks/iceflow-with-icepyx.ipynb
+++ /dev/null
@@ -1,5965 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "7174387c-05aa-4f8f-b3bd-902d4f635d9b",
-   "metadata": {},
-   "source": [
-    "# Using iceflow with icepyx to Generate an Elevation Timeseries\n",
-    "\n",
-    "This notebook shows how to produce a harmonized elevation timeseries across all iceflow-supported datasets along with ICESat-2 data using [icepyx](https://github.com/icesat2py/icepyx).\n",
-    "\n",
-    "If this is your first time using `iceflow`, we recommend starting with the [NSIDC Iceflow example Jupyter Notebook](https://iceflow.readthedocs.io/en/latest/iceflow-example.html) first.\n",
-    "\n",
-    "Similarly, if you are new to `icepyx`, we suggest reviewing the [icepyx documentation](https://icepyx.readthedocs.io/en/latest/) for more information about how to use `icepyx`."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "64fb8fa5-e304-4607-9bad-33d3832cd70b",
-   "metadata": {},
-   "source": [
-    "## Scenario: assessing ice surface elevation change near Sermeq Kujalleq (Jakobshavn Isbrae)\n",
-    "\n",
-    "In this notebook, we will focus on a small area near Sermeq Kujalleq (Jakobshavn Isbrae).\n",
-    "\n",
-    "This notebook will demonstrate how to:\n",
-    "\n",
-    "* Search for and download all iceflow-supported data for our area of interest and timeframe.\n",
-    "* Search for and download ICESat-2 data using `icepyx`.\n",
-    "* Average elevation data over our area of interest at a weekly resolution.\n",
-    "* Plot the results as a timeseries."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "40ce5145-d640-4f30-9135-29dc58c979ed",
-   "metadata": {},
-   "source": [
-    "### Import required packages\n",
-    "\n",
-    "We will import several packages needed to perform our analysis:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2ad99b0d-4e15-466c-b9aa-7bfa75ef5f26",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Imports\n",
-    "from __future__ import annotations\n",
-    "\n",
-    "import datetime as dt\n",
-    "from pathlib import Path\n",
-    "\n",
-    "import dask.dataframe as dd\n",
-    "import icepyx as ipx\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import xarray as xr\n",
-    "\n",
-    "from nsidc.iceflow import (\n",
-    "    BoundingBox,\n",
-    "    DatasetSearchParameters,\n",
-    "    IceflowDataFrame,\n",
-    "    download_iceflow_results,\n",
-    "    find_iceflow_data,\n",
-    "    make_iceflow_parquet,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "037235e4-598d-4870-bbcc-c29a9f5ee173",
-   "metadata": {},
-   "source": [
-    "### Search for and download iceflow-supported data for our area of interest and timeframe.\n",
-    "\n",
-    "We first need to define some constants, including our download path, ITRF, and filter parameters. These will be used throughout the rest of the notebook.\n",
-    "\n",
-    "**Note that the data supported by `iceflow` and `icepyx` can be very large! This tutorial will download ~2GB worth of data to local disk.**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d7143806-d23d-421a-8b7a-3ca15b05f7cf",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Constants\n",
-    "\n",
-    "# All of our data will be downloaded to this location.\n",
-    "OUTPUT_DIR = Path(\"./downloaded-data/\")\n",
-    "\n",
-    "# ICESat2 data products use ITRF2014 (e.g., see https://nsidc.org/data/atl06/versions/6):\n",
-    "# > WGS 84 ellipsoid, ITRF2014 reference frame\n",
-    "# NOTE/TODO: This is expected to change in the near future! ICESat2 release\n",
-    "# 7, scheduled for spring 2025, is expected to be referenced to ITRF2020.\n",
-    "ICESAT2_ITRF = \"ITRF2014\"\n",
-    "\n",
-    "# This bounding box covers an area near Sermeq Kujalleq (Jakobshavn Isbrae)\n",
-    "BBOX = BoundingBox(\n",
-    "    lower_left_lon=-49.149,\n",
-    "    lower_left_lat=69.186,\n",
-    "    upper_right_lon=-48.949,\n",
-    "    upper_right_lat=69.238,\n",
-    ")\n",
-    "\n",
-    "# Range of dates we want to evaluate\n",
-    "DATE_RANGE = (dt.date(2007, 1, 1), dt.date(2024, 10, 28))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "420bf37c-c5cb-4339-bd26-ebb91ecb3973",
-   "metadata": {},
-   "source": [
-    "Next we will use the `find_iceflow_data` function from the `iceflow` API to find data matching our area of interest.\n",
-    "\n",
-    "By default, `DatasetSearchParameters` will include all `iceflow` supported datasets, unless one or more are specified as a filter with the `datasets` kwarg. There may be warnings raised about there not being search results for specific datasets supported by `iceflow`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ee315618-32ad-4710-9629-0a3d2e2ed26b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "search_results = find_iceflow_data(\n",
-    "    dataset_search_params=DatasetSearchParameters(\n",
-    "        bounding_box=BBOX,\n",
-    "        temporal=DATE_RANGE,\n",
-    "    ),\n",
-    ")\n",
-    "len(search_results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b57c25fc-ebf1-4397-bcb7-076428855b7c",
-   "metadata": {},
-   "source": [
-    "Now download the matching files to disk using `download_iceflow_results`.\n",
-    "\n",
-    "**Note: This next step may take a while, and will download data to your local disk.**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "84e53851-53a8-49e0-bce3-2789253498b6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "downloaded_files = download_iceflow_results(search_results, output_dir=OUTPUT_DIR)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fee85cd6-522f-4352-86ea-9530add68000",
-   "metadata": {},
-   "source": [
-    "Finally, we will create a parquet dataset using `make_iceflow_parquet`.Writing data to a parquet dataset allows `dask` (which we will use later!) to read the chunks of data it needs to do calculations (e.g., `mean`) without needing to read all of the data into memory at once. This is important because `iceflow` can find many millions of data points for even small areas of interest!\n",
-    "\n",
-    "This function transforms all data found in the `data_dir` to the provided `target_itrf`, further facilitating analysis across many datasets. \n",
-    "\n",
-    "**Note: This next step may take a while**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "17e03a49-405a-4d00-a08d-74e5890564da",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "parquet_path = make_iceflow_parquet(\n",
-    "    data_dir=OUTPUT_DIR,\n",
-    "    target_itrf=ICESAT2_ITRF,\n",
-    "    overwrite=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "199e0c36-f3cc-4400-9fb0-0df70fc2f5e6",
-   "metadata": {},
-   "source": [
-    "Now we read the data stored in the parquet dataset using dask:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e29f9cf9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "iceflow_df = dd.read_parquet(parquet_path)\n",
-    "\n",
-    "# Ensure that our index is set as a datetime object\n",
-    "iceflow_df = iceflow_df.reset_index()\n",
-    "iceflow_df[\"utc_datetime\"] = dd.to_datetime(iceflow_df[\"utc_datetime\"])\n",
-    "iceflow_df = iceflow_df.set_index(\"utc_datetime\")\n",
-    "\n",
-    "iceflow_df.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "925eede5-e9be-4793-9b1d-9c5562c73b7c",
-   "metadata": {},
-   "source": [
-    "###  Search for and download ICESat-2 data using `icepyx`\n",
-    "\n",
-    "Next, we will use icepyx to find [ATL06](https://nsidc.org/data/atl06/versions/6) data for the same area of interest and timeframe. \n",
-    "\n",
-    "To learn more about `icepyx.Query`, which is used below, see the [documentation](https://icepyx.readthedocs.io/en/latest/_icepyx/icepyx.Query.html#icepyx.Query)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "bda00a2c-51dd-4d01-8ba4-4dcb0985931d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# We will compare the ILATM1B data to ATL06 data from October 2018.\n",
-    "result = ipx.Query(\n",
-    "    \"ATL06\",\n",
-    "    [\n",
-    "        BBOX.lower_left_lon,\n",
-    "        BBOX.lower_left_lat,\n",
-    "        BBOX.upper_right_lon,\n",
-    "        BBOX.upper_right_lat,\n",
-    "    ],\n",
-    "    DATE_RANGE,\n",
-    ")\n",
-    "result"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "25fbed4b-beb7-41d8-a4fd-788e4821e505",
-   "metadata": {},
-   "source": [
-    "Now we place an order and download the results.  Note that this may take a while to download data to local disk.\n",
-    "\n",
-    "**Note: These next steps may take a while**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "cf36923b-8f75-4783-aa84-0ed6a5248731",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "result.order_granules(subset=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "0c146fa2-fdb9-4774-a6bf-481e2498948a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "result.download_granules(\"downloaded-data/ATL06/\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d12d21ed-0774-4f58-b75e-56f2734d27a6",
-   "metadata": {},
-   "source": [
-    "###  Read ICESat-2 data into `xarray` and `pandas`\n",
-    "\n",
-    "Next, we will use [icepyx.Read](https://icepyx.readthedocs.io/en/latest/_icepyx/icepyx.Read.html#icepyx.Read) to read the data into an [xarray Dataset](https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html).\n",
-    "\n",
-    "Note that the code below wraps the reading of each file in a `try/except` block because of a known issue with subsetting. See https://github.com/icesat2py/icepyx/issues/576 for more information."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "081376b5-308b-43c3-bc6d-b9c4342586d0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "datasets = []\n",
-    "for file in Path(\"downloaded-data/ATL06/\").glob(\"*.h5\"):\n",
-    "    try:\n",
-    "        reader = ipx.Read(str(file))\n",
-    "        reader.variables.append(var_list=[\"h_li\", \"latitude\", \"longitude\"])\n",
-    "        ds = reader.load()\n",
-    "        datasets.append(ds)\n",
-    "    except Exception:\n",
-    "        print(f\"{file=} contains an error and will not be read\")\n",
-    "        continue\n",
-    "\n",
-    "len(datasets)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "dc7fc4b2-37ee-4f44-a21e-d6225eccc0f8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ds = xr.concat(datasets, dim=\"gran_idx\")\n",
-    "ds.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "31d5cf8f-750e-4e9d-9cda-431d0a1a6a61",
-   "metadata": {},
-   "source": [
-    "`icepyx` reads ICESat-2 data as an xarray dataset. `xarray` is a powerful tool and the data is ready to use in this format, but to simplify things for this notebook and make the data more compatible with `iceflow`, the next step will convert the data into an `iceflow`-compatible [pandas DataFrame](https://pandas.pydata.org/pandas-docs/stable/reference/frame.html).\n",
-    "\n",
-    "The first step is to rename the variables to be consistent with how `iceflow` identifies the elevation and time fields:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "09a51188-be09-486d-be10-a7bede9d02ce",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ds = ds.rename({\"h_li\": \"elevation\", \"delta_time\": \"utc_datetime\"})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "abb10f74-183f-486c-979d-04860ec13b98",
-   "metadata": {},
-   "source": [
-    "The following code block converts the `xarray` dataset into an iceflow dataframe:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "422d0a32-b676-4fe2-89ba-14f31bba6e80",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "spot_dfs = []\n",
-    "for spot in ds.spot.data.flatten():\n",
-    "    df = pd.DataFrame.from_dict(\n",
-    "        {\n",
-    "            \"elevation\": ds.sel(spot=spot).elevation.data.flatten(),\n",
-    "            \"latitude\": ds.sel(spot=spot).latitude.data.flatten(),\n",
-    "            \"longitude\": ds.sel(spot=spot).longitude.data.flatten(),\n",
-    "            \"utc_datetime\": ds.sel(spot=spot).utc_datetime.data.flatten(),\n",
-    "            \"spot\": [spot] * len(ds.sel(spot=spot).elevation.data.flatten()),\n",
-    "            \"ITRF\": ICESAT2_ITRF,\n",
-    "        },\n",
-    "    )\n",
-    "    spot_dfs.append(df)\n",
-    "\n",
-    "\n",
-    "df = pd.concat(spot_dfs, sort=True)\n",
-    "# Drop rows where lat, lon, or elev are missing.\n",
-    "df = df.dropna(subset=[\"latitude\", \"longitude\", \"elevation\"], how=\"any\")\n",
-    "df = df.set_index(\"utc_datetime\")\n",
-    "# Cast the df as an IceflowDataFrame.\n",
-    "# The `atl06_df` can now be used with e.g., `iceflow`'s ITRF conversion function\n",
-    "# to perform plate motion model adjustments if desired.\n",
-    "atl06_df = IceflowDataFrame(df)\n",
-    "atl06_df.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "10f36362-4312-4ae9-95d5-fefe2a123171",
-   "metadata": {},
-   "source": [
-    "The ATL06 data contains some negative elevation values, as we see printed below. We will filter these out, as we expect positive elevations."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "826ace9e-0638-4240-8769-5b0a1385b173",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(atl06_df.elevation.min())\n",
-    "\n",
-    "# Filter out negative values. We expect positive elevations.\n",
-    "atl06_df = atl06_df[atl06_df.elevation > 0]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c3c8966f-482c-4ecc-91a5-c6dce34f37de",
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-   "source": [
-    "### Average elevation data over our area of interest at a weekly resolution\n",
-    "\n",
-    "In this next step, we will resample the ATL06 and `iceflow` data to a weekly resolution, taking the mean of elevation over our area of interest. This will provide us with one data point per week where data is available, giving us a general idea of how the elevation of our area of interest changes over time. First we resample ATL06 data to a weekly mean:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "fb60aa6c-4b9d-4592-95be-4687636ccd1b",
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-   "source": [
-    "atl06_avg_df = atl06_df[[\"elevation\"]].resample(\"W\").mean()\n",
-    "atl06_avg_df = atl06_avg_df.dropna(how=\"any\")\n",
-    "atl06_avg_df.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2d8c51f3-1ab3-45ed-854c-e0a681a3590f",
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-   "source": [
-    "And now we resample the iceflow data to a weekly mean:"
-   ]
-  },
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-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c5a76ba1-d2b1-49fd-96f7-bf41264c26a6",
-   "metadata": {},
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-   "source": [
-    "iceflow_df_sampled = iceflow_df.repartition(freq=\"1W\")\n",
-    "iceflow_df_sampled = iceflow_df_sampled.dropna(how=\"any\")\n",
-    "\n",
-    "iceflow_df_sampled = iceflow_df_sampled[iceflow_df_sampled.elevation > 0]\n",
-    "\n",
-    "iceflow_avg = iceflow_df_sampled.resample(\"W\").agg(\n",
-    "    {\n",
-    "        \"elevation\": \"mean\",\n",
-    "        \"dataset\": lambda x: \", \".join(x.astype(\"str\").unique()),\n",
-    "    }\n",
-    ")\n",
-    "iceflow_avg = iceflow_avg.replace([np.inf, -np.inf], np.nan)\n",
-    "iceflow_avg = iceflow_avg.dropna(how=\"any\")\n",
-    "iceflow_avg = iceflow_avg.compute()\n",
-    "\n",
-    "iceflow_avg.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4a530b6e-2474-400e-8f04-a5665587591b",
-   "metadata": {},
-   "source": [
-    "### Plot the results as a timeseries\n",
-    "\n",
-    "Now we will use [matplotlib](https://matplotlib.org/) to plot the results as a timeseries:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5e733fe6-cc28-4696-aadf-272fe755ad2c",
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-   "source": [
-    "%matplotlib inline\n",
-    "\n",
-    "# We will use a unique marker for each dataset\n",
-    "iceflow_marker_map = {\n",
-    "    \"GLAH06v034\": \"s\",\n",
-    "    \"BLATM1Bv1\": \"D\",\n",
-    "    \"ILATM1Bv1\": \"x\",\n",
-    "    \"ILATM1Bv2\": \"o\",\n",
-    "    \"ILVIS2v1\": \"v\",\n",
-    "    \"ILVIS2v2\": \"^\",\n",
-    "}\n",
-    "\n",
-    "fig = plt.figure(figsize=(10, 8))\n",
-    "\n",
-    "for dataset, marker in iceflow_marker_map.items():\n",
-    "    subset = iceflow_avg[iceflow_avg.dataset == dataset]\n",
-    "    if subset.elevation.any():\n",
-    "        plt.scatter(\n",
-    "            subset.index,\n",
-    "            subset.elevation,\n",
-    "            marker=marker,\n",
-    "            label=dataset,\n",
-    "            linestyle=\"\",\n",
-    "            color=\"black\",\n",
-    "        )\n",
-    "\n",
-    "plt.scatter(\n",
-    "    atl06_avg_df.index,\n",
-    "    atl06_avg_df.elevation,\n",
-    "    color=\"black\",\n",
-    "    marker=\"*\",\n",
-    "    label=\"ATL06v6\",\n",
-    "    linestyle=\"\",\n",
-    ")\n",
-    "\n",
-    "plt.xlabel(\"Date\")\n",
-    "plt.ylabel(\"Elevation\")\n",
-    "plt.legend(title=\"Dataset\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "33313a43-93e9-4391-b5c8-db85008f2cae",
-   "metadata": {},
-   "source": [
-    "## Conclusions\n",
-    "\n",
-    "In this notebook, we found and analyzed laser altimetry data from a variety of datasets using `iceflow` and `icepyx`. \n",
-    "\n",
-    "In the timeseries plot above, we can see how the surface elevation of a small area near Sermeq Kujalleq (Jakobshavn Isbrae) changes over time.\n",
-    "\n",
-    "Note that this analysis was relatively simple. Although the data and plot above give us an idea of surface elevation changes, it should be noted that there are still a number of things a researcher should consider when doing an analysis across many datasets over time. To further this analysis, we may want to consider doing one or more of the following:\n",
-    "\n",
-    "* Outlier detection and filtering\n",
-    "* Cross-calibration of data between sensors/datasets\n",
-    "* Plate motion model coordinate propagation (see the [NSIDC Iceflow example Jupyter Notebook](https://iceflow.readthedocs.io/en/latest/iceflow-example.html) for an example of how to do this)\n",
-    "* Account for spatial variability within our region of interest"
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-       "value": 8
-      }
-     }
-    },
-    "version_major": 2,
-    "version_minor": 0
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/pyproject.toml b/pyproject.toml
index 4a6d287..18cd5cc 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -7,7 +7,7 @@ authors = [
 description = "Harmonized access to (pre)OIB/IceSAT/IceSAT2 data"
 readme = "README.md"
 license.file = "LICENSE"
-requires-python = ">=3.10"
+requires-python = ">=3.11"
 classifiers = [
   "Development Status :: 1 - Planning",
   "Intended Audience :: Science/Research",
@@ -17,7 +17,6 @@ classifiers = [
   "Programming Language :: Python",
   "Programming Language :: Python :: 3",
   "Programming Language :: Python :: 3 :: Only",
-  "Programming Language :: Python :: 3.10",
   "Programming Language :: Python :: 3.11",
   "Programming Language :: Python :: 3.12",
   "Topic :: Scientific/Engineering",
@@ -207,4 +206,6 @@ search = 'version = "v{current_version}"'
 replace = 'version = "v{new_version}"'
 
 [tool.codespell]
-skip="docs/*.ipynb"
+# Skip all ipynb notebooks. These contain binary data (rendered images from cell
+# outputs) that the checker will flag as errors.
+skip="docs/notebooks/*.ipynb"
diff --git a/tasks/docs.py b/tasks/docs.py
index e163c56..bb1f7f6 100644
--- a/tasks/docs.py
+++ b/tasks/docs.py
@@ -43,16 +43,12 @@ def watch(_ctx):
         (
             f'sphinx-autobuild --pre-build "{_build_apidocs_cmd()}"'
             f" --watch {PROJECT_DIR}/src"
+            f" --watch {PROJECT_DIR}/docs/notebooks"
+            f" --ignore {PROJECT_DIR}/docs/notebooks/.ipynb_checkpoints"
+            f" --ignore {PROJECT_DIR}/docs/notebooks/_sources"
+            f" --ignore {PROJECT_DIR}/docs/notebooks/_static"
+            f" --ignore {PROJECT_DIR}/docs/notebooks/downloaded-data/"
             f" {PROJECT_DIR}/docs {PROJECT_DIR}/docs/_build/html"
         ),
         pty=True,
     )
-
-
-@task()
-def render_notebooks_for_docs(_ctx):
-    """Render jupyter notebooks for docs."""
-    print_and_run(
-        (f"{PROJECT_DIR}/notebooks/generate_rendered_notebooks_for_docs.sh"),
-        pty=True,
-    )
diff --git a/tests/unit/test_itrf_converter.py b/tests/unit/test_itrf_converter.py
index 8875837..cbe2a61 100644
--- a/tests/unit/test_itrf_converter.py
+++ b/tests/unit/test_itrf_converter.py
@@ -37,7 +37,7 @@ def test_transform_itrf():
 
     # Before and after values were extracted from the synthetic dataset in the
     # ITRF corrections notebook put together by Kevin. See:
-    # https://github.com/nsidc/NSIDC-Data-Tutorials/blob/a35203f18841456d258fac3a11dc04f44f839d9d/notebooks/iceflow/corrections.ipynb
+    # ./docs/notebooks/corrections.ipynb
     assert result.latitude.to_numpy()[0] == 69.99999974953995
     assert result.longitude.to_numpy()[0] == -50.0000001319163
     # Rounding to 8 decimals in a field assumed to be measured in meters is 10nm accuracy!