Repository: SlideRuleEarth/h5coro (language: python)

Issue:
Cannot read dataset: 'H5Coro' object has no attribute 'offsetsize'
I built H5Coro from source with python 3.9.18 on Ubuntu 22.04. I am attempting to use it read a publicly accessible file, `ou_process.h5`. 

The program that results in the error:
```
import h5coro
from h5coro import h5coro, s3driver

bucket = "docs.hdfgroup.org"
filepath = "hdf5/h5/ou_process.h5"

h5obj = h5coro.H5Coro(resource=f'{bucket}/{filepath}', driver_class=s3driver.S3Driver)

dsets = [{'dataset': '/dataset', 'startrow':0, 'numrows': h5coro.H5Dataset.ALL_ROWS}]
promise = h5obj.readDatasets(datasets=dsets, block=True)
```

Running this program results in the error `'H5Coro' object has no attribute 'offsetsize'`. Full stack trace output: 
[test_output.txt](https://github.com/ICESat2-SlideRule/h5coro/files/14200125/test_output.txt)


In case this is due to something about the file itself, here is the h5dump output for the `ou_process.h5` file, when read through the ROS3 VFD: 
[h5dump_output.txt](https://github.com/ICESat2-SlideRule/h5coro/files/14200105/h5dump_output.txt)

I thought that `offsetsize` might be a typo'd version of `offsetSize`, but replacing the capitalization and running the program again results in this error:
```
  File "/home/matthewlarson/Downloads/h5coro/h5coro/h5dataset.py", line 1466, in readSymbolTable
    self.readObjHdr(obj_hdr_addr, dlvl + 1)
TypeError: readObjHdr() takes 2 positional arguments but 3 were given
```

Cannot read dataset: 'H5Coro' object has no attribute 'offsetsize'
I built H5Coro from source with python 3.9.18 on Ubuntu 22.04. I am attempting to use it read a publicly accessible file, `ou_process.h5`. 

The program that results in the error:
```
import h5coro
from h5coro import h5coro, s3driver

bucket = "docs.hdfgroup.org"
filepath = "hdf5/h5/ou_process.h5"

h5obj = h5coro.H5Coro(resource=f'{bucket}/{filepath}', driver_class=s3driver.S3Driver)

dsets = [{'dataset': '/dataset', 'startrow':0, 'numrows': h5coro.H5Dataset.ALL_ROWS}]
promise = h5obj.readDatasets(datasets=dsets, block=True)
```

Running this program results in the error `'H5Coro' object has no attribute 'offsetsize'`. Full stack trace output: 
[test_output.txt](https://github.com/ICESat2-SlideRule/h5coro/files/14200125/test_output.txt)


In case this is due to something about the file itself, here is the h5dump output for the `ou_process.h5` file, when read through the ROS3 VFD: 
[h5dump_output.txt](https://github.com/ICESat2-SlideRule/h5coro/files/14200105/h5dump_output.txt)

I thought that `offsetsize` might be a typo'd version of `offsetSize`, but replacing the capitalization and running the program again results in this error:
```
  File "/home/matthewlarson/Downloads/h5coro/h5coro/h5dataset.py", line 1466, in readSymbolTable
    self.readObjHdr(obj_hdr_addr, dlvl + 1)
TypeError: readObjHdr() takes 2 positional arguments but 3 were given
```

Adding access to public S3 buckets
Hi JP, 

This PR adds non-signed requests to S3 when we pass `credentials={"annon": True}` and this makes H5Coro work with data in public buckets, we are doing some testing and wanted to use data staged in the ITS_LIVE public bucket.




Discussion / hints:

Fix patch:
diff --git a/README.md b/README.md
index 11dcb77..06fbde9 100644
--- a/README.md
+++ b/README.md
@@ -17,9 +17,7 @@
 
 For a full list of which parts of the HDF5 specification **h5coro** implements, see the [compatibility](#compatibility) section at the end of this readme.  The major limitations currently present in the package are:
 * The code only implements a subset of the HDF5 specification.  **h5coro** has been shown to work on a number of different datasets, but depending on the version of the HDF5 C library used to write the file, and what options were used during its creation, it is very possible that some part of **h5coro** will need to be updated to support reading it.  Hopefully, over time as more of the spec is implemented, this will become less of a problem.
-* The code is not optimized for local file access.  If you are reading data that is local, you will get much better performance from `h5py`.
 * It is a read-only library and has no functionality to write HDF5 data.
-* It targets one dimensional datasets and is not optimized for high dimensionality data.  In practice this means that if you subset a dataset that is two or more dimensions, only the first dimension will be subsetted with all of the other dimensions collapsed into one serial array of elements.  This limitation will be addressed in future releases.
 
 ## Installation
 
@@ -59,8 +57,8 @@ from h5coro import h5coro, s3driver
 h5obj = h5coro.H5Coro(f'{my_bucket}/{path_to_hdf5_file}', s3driver.S3Driver)
 
 # (3) read
-datasets = [{'dataset': '/path/to/dataset1', 'startrow': 0, 'numrows': h5coro.ALL_ROWS},
-            {'dataset': '/path/to/dataset2', 'startrow': 324, 'numrows': 50}]
+datasets = [{'dataset': '/path/to/dataset1', 'hyperslice': []},
+            {'dataset': '/path/to/dataset2', 'hyperslice': [324, 374]}]
 promise = h5obj.readDatasets(datasets=datasets, block=True)
 
 # (4) display
@@ -154,7 +152,7 @@ We follow a standard Forking Workflow for code changes and additions. Submitted
 | ___Bogus Message___  | <span style="color:red">No</span> | | Unversioned |
 | ___Group Info Message___  | <span style="color:red">No</span> | | Version 0 |
 | ___Filter Pipeline Message___  | <span style="color:green">Yes</span> | Version 1, 2 | |
-| ___Attribute Message___  | <span style="color:blue">Partial</span> | Version 1 | Version 2, 3 |
+| ___Attribute Message___  | <span style="color:blue">Partial</span> | Version 1, 2, 3 | Shared message support for v3 |
 | ___Object Comment Message___  | <span style="color:red">No</span> | | Unversioned |
 | ___Object Modification Time (Old) Message___  | <span style="color:red">No</span> | | Unversioned |
 | ___Shared Message Table Message___  | <span style="color:red">No</span> | | Version 0 |
diff --git a/benchmarks/aos_profiler.py b/benchmarks/aos_profiler.py
new file mode 100644
index 0000000..463678e
--- /dev/null
+++ b/benchmarks/aos_profiler.py
@@ -0,0 +1,134 @@
+# imports
+import time
+import pathlib
+import s3fs
+import h5py
+import boto3
+import h5coro
+from h5coro import s3driver, logger
+
+# configure h5coro
+import logging
+logger.config(logLevel=logging.CRITICAL)
+
+# h5py test
+def eso_h5py_test(hyperslice, with_s3fs):
+
+    # test parameters
+    granule = 'OR_ABI-L1b-RadF-M6C14_G16_s20192950450355_e20192950500042_c20192950500130.nc'
+    bucketname = 'eso-west2-curated'
+    objectname = 'AOS/PoR/geo/GOES-16-ABI-L1B-FULLD/2019/295/04/' + granule
+    objecturl = 's3://' + bucketname + '/' + objectname
+    filename = '/data/AOS/' + granule
+    variable = '/Rad'
+
+    # remove file first
+    try:
+        pathlib.Path.unlink(filename)
+    except:
+        pass
+
+    # read dataset
+    start = time.perf_counter()
+    if with_s3fs:
+        s3 = s3fs.S3FileSystem()
+        fp = h5py.File(s3.open(objecturl, 'rb'), mode='r')
+    else:
+        s3 = boto3.client('s3')
+        with open(filename, 'wb') as f:
+            s3.download_fileobj(bucketname, objectname, f)
+        fp = h5py.File(filename, mode='r')
+    duration1 = time.perf_counter() - start
+    
+    # sum dataset
+    start = time.perf_counter()
+    total = 0
+    if len(hyperslice) > 0:
+        for row in fp[variable][hyperslice[0][0]:hyperslice[0][1]]:
+            total += sum(row[hyperslice[1][0]:hyperslice[1][1]])
+    else:
+        for row in fp[variable]:
+            total += sum(row)
+    duration2 = time.perf_counter() - start
+
+    # return results
+    return duration1, duration2, total
+
+# h5coro test
+def eso_h5coro_test(hyperslice):
+
+    # test parameters
+    granule = 'eso-west2-curated/AOS/PoR/geo/GOES-16-ABI-L1B-FULLD/2019/295/04/OR_ABI-L1b-RadF-M6C14_G16_s20192950450355_e20192950500042_c20192950500130.nc'
+    variable = '/Rad'
+    datasets = [ {"dataset": variable, "hyperslice": hyperslice } ]
+    credentials = {"profile":"default"}
+
+    # read dataset
+    start = time.perf_counter()
+    h5obj = h5coro.H5Coro(granule, s3driver.S3Driver, errorChecking=True, verbose=False, credentials=credentials, multiProcess=False)
+    promise = h5obj.readDatasets(datasets, block=True, enableAttributes=False)
+    duration1 = time.perf_counter() - start
+    
+    # sum dataset
+    start = time.perf_counter()
+    total = 0
+    for row in promise[variable]:
+        total += sum(row)
+    duration2 = time.perf_counter() - start
+
+    # return results
+    return duration1, duration2, total
+
+# h5py - hypersliced - with s3fs
+hyperslice=[(0,10), (0,10)]
+request_time, sum_time, result = eso_h5py_test(hyperslice, True)
+print(f'\ns3fs: {hyperslice}\n======================')
+print(f'Result = {result}')
+print(f'Opening Time = {request_time:.3f} secs')
+print(f'Summing Time = {sum_time:.3f} secs')
+print(f'Total Time = {sum_time + request_time:.3f} secs')
+
+# h5py - full dataset - with s3fs
+hyperslice=[]
+request_time, sum_time, result = eso_h5py_test(hyperslice, True)
+print(f'\ns3fs: {hyperslice}\n======================')
+print(f'Result = {result}')
+print(f'Opening Time = {request_time:.3f} secs')
+print(f'Summing Time = {sum_time:.3f} secs')
+print(f'Total Time = {sum_time + request_time:.3f} secs')
+
+# h5py - hypersliced - download
+hyperslice=[(0,10), (0,10)]
+request_time, sum_time, result = eso_h5py_test(hyperslice, False)
+print(f'\nh5py: {hyperslice}\n======================')
+print(f'Result = {result}')
+print(f'Opening Time = {request_time:.3f} secs')
+print(f'Summing Time = {sum_time:.3f} secs')
+print(f'Total Time = {sum_time + request_time:.3f} secs')
+
+# h5py - full dataset - download
+hyperslice=[]
+request_time, sum_time, result = eso_h5py_test(hyperslice, True)
+print(f'\nh5py: {hyperslice}\n======================')
+print(f'Result = {result}')
+print(f'Opening Time = {request_time:.3f} secs')
+print(f'Summing Time = {sum_time:.3f} secs')
+print(f'Total Time = {sum_time + request_time:.3f} secs')
+
+# h5coro - hypersliced
+hyperslice=[(0,10), (0,10)]
+request_time, sum_time, result = eso_h5coro_test(hyperslice=hyperslice)
+print(f'\nh5coro: {hyperslice}\n======================')
+print(f'Result = {result}')
+print(f'Opening Time = {request_time:.3f} secs')
+print(f'Summing Time = {sum_time:.3f} secs')
+print(f'Total Time = {sum_time + request_time:.3f} secs')
+
+# h5coro - full dataset
+hyperslice=[]
+request_time, sum_time, result = eso_h5coro_test(hyperslice=hyperslice)
+print(f'\nh5coro: {hyperslice}\n======================')
+print(f'Result = {result}')
+print(f'Opening Time = {request_time:.3f} secs')
+print(f'Summing Time = {sum_time:.3f} secs')
+print(f'Total Time = {sum_time + request_time:.3f} secs')
diff --git a/benchmarks/profiler.py b/benchmarks/icesat2_profiler.py
similarity index 54%
rename from benchmarks/profiler.py
rename to benchmarks/icesat2_profiler.py
index 69a9e15..4b232a1 100644
--- a/benchmarks/profiler.py
+++ b/benchmarks/icesat2_profiler.py
@@ -31,13 +31,14 @@
 import time
 import logging
 import os
+import sys
 import configparser
 import warnings
 import s3fs
 import h5py
 import h5coro
-from h5coro import s3driver, filedriver
-from sliderule import sliderule, h5
+from h5coro import s3driver, filedriver, logger
+from sliderule import sliderule, h5, icesat2
 
 ###############################################################################
 # GLOBALS
@@ -77,17 +78,8 @@
     args.desired_nodes = None
     args.time_to_live = None
 
-# Initialize Log Level
-loglevel = logging.CRITICAL
-if args.loglvl == "ERROR":
-    loglevel = logging.ERROR
-elif args.loglvl == "INFO":
-    loglevel = logging.INFO
-elif args.loglvl == "DEBUG":
-    loglevel = logging.DEBUG
-
 # Configure H5Coro
-h5coro.config(logLevel=loglevel)
+logger.config(logLevel=logging.INFO)
 
 # Initialize SlideRule Client
 sliderule.init(args.domain, verbose=args.verbose, organization=args.organization, desired_nodes=args.desired_nodes, time_to_live=args.time_to_live)
@@ -255,3 +247,227 @@ def inpoly (poly, point):
     #  if c == False: number of intersections were even --> point is outside polygon
     #  if c == True: number of intersections were odd --> point is inside polygon
     return c
+
+###############################################################################
+# ATL06 FUNCTIONS
+###############################################################################
+
+#
+# Function: atl06_subsetted_read
+#
+# read ATL06 resource and return variable's data within polygon
+#
+def atl06_subsetted_read(profiler, region, variable):
+
+    # Initialize return variables
+    data = []
+    latitudes = []
+    longitudes = []
+
+    # Create polygon
+    poly = [Point(coord["lon"], coord["lat"]) for coord in region]
+
+    # List of tracks to read
+    tracks = ["1l", "1r", "2l", "2r", "3l", "3r"]
+
+    # Build list of each lat,lon dataset to read
+    geodatasets = []
+    for track in tracks:
+        prefix = "gt"+track+"/land_ice_segments/"
+        geodatasets.append({"dataset": prefix+"latitude", "startrow": 0, "numrows": -1})
+        geodatasets.append({"dataset": prefix+"longitude", "startrow": 0, "numrows": -1})
+
+    # Read lat,lon from resource
+    geocoords = profiler.read(geodatasets)
+
+    # Build list of the subsetted variable datasets to read
+    datasets = []
+    for track in tracks:
+        prefix = "gt"+track+"/land_ice_segments/"
+        lat_dataset = geocoords[prefix+"latitude"]
+        lon_dataset = geocoords[prefix+"longitude"]
+        startrow = -1
+        numrows = -1
+        index = 0
+        while index < len(lat_dataset):
+            point = Point(lon_dataset[index], lat_dataset[index])
+            intersect = inpoly(poly, point)
+            if startrow == -1 and intersect:
+                startrow = index
+            elif startrow != -1 and not intersect:
+                break
+            index += 10 # only sample values for speed increase
+        if startrow >= 0:
+            numrows = index - startrow
+        if numrows > 0:
+            datasets.append({"dataset": prefix+variable, "startrow": startrow, "numrows": numrows, "prefix": prefix})
+
+    # Read variable from resource
+    if len(datasets) > 0:
+        values = profiler.read(datasets)
+
+    # Append results
+    for entry in datasets:
+        latitudes += geocoords[entry["prefix"]+"latitude"][entry["startrow"]:entry["startrow"]+entry["numrows"]].tolist()
+        longitudes += geocoords[entry["prefix"]+"longitude"][entry["startrow"]:entry["startrow"]+entry["numrows"]].tolist()
+        data += values[entry["prefix"]+variable].tolist()
+
+    # Return results
+    return {"latitude":  latitudes,
+            "longitude": longitudes,
+            variable:    data}
+
+#
+# Function: atl06_profile
+#
+def atl06_profile():
+    # Build Profilers
+    profiles = {
+        "s3fs":         Profiler(S3fsReader,        args.granule06),
+    #    "ros3":         Profiler(Ros3Reader,        args.granule06),
+        "sliderule-h5p":Profiler(SlideruleReader,   args.granule06),
+        "h5coro":       Profiler(H5CoroReader,      args.granule06),
+        "h5py-local":   Profiler(H5pyReader,        args.granule06),
+        "h5coro-local": Profiler(LocalH5CoroReader, args.granule06)
+    }
+
+    # Profile Readers
+    for profile in profiles:
+        profiler = profiles[profile]
+        print(f'Profiling {profile}... ', end='')
+        start = time.perf_counter()
+        result = atl06_subsetted_read(profiler, region, variable=args.variable06)
+        print(f'[{len(result[args.variable06])}]: {profiler.duration:.2f} {(time.perf_counter() - start):.2f}')
+
+###############################################################################
+# ATL03 FUNCTIONS
+###############################################################################
+
+#
+# Function: atl03_subsetted_read
+#
+# read ATL03 resource and return variable's data within polygon
+#
+def atl03_subsetted_read(profiler, region, variable, tracks = ["1l", "1r", "2l", "2r", "3l", "3r"]):
+
+    # Initialize return variables
+    data = []
+    latitudes = []
+    longitudes = []
+
+    # Create polygon
+    poly = [Point(coord["lon"], coord["lat"]) for coord in region]
+
+    # Build list of each lat,lon dataset to read
+    geodatasets = []
+    for track in tracks:
+        prefix = "gt"+track+"/geolocation/"
+        geodatasets.append({"dataset": prefix+"reference_photon_lat", "startrow": 0, "numrows": -1})
+        geodatasets.append({"dataset": prefix+"reference_photon_lon", "startrow": 0, "numrows": -1})
+        geodatasets.append({"dataset": prefix+"segment_ph_cnt", "startrow": 0, "numrows": -1})
+
+    # Read lat,lon from resource
+    geocoords = profiler.read(geodatasets)
+
+    # Build list of the subsetted variable datasets to read
+    datasets = []
+    for track in tracks:
+        geoprefix = "gt"+track+"/geolocation/"
+        prefix = "gt"+track+"/heights/"
+        lat_dataset = geocoords[geoprefix+"reference_photon_lat"]
+        lon_dataset = geocoords[geoprefix+"reference_photon_lon"]
+        cnt_dataset = geocoords[geoprefix+"segment_ph_cnt"]
+        startrow = -1
+        numrows = -1
+        index = 0
+        while index < len(lat_dataset):
+            point = Point(lon_dataset[index], lat_dataset[index])
+            intersect = inpoly(poly, point)
+            if startrow == -1 and intersect:
+                startrow = index
+            elif startrow != -1 and not intersect:
+                break
+            index += 10 # only sample values for speed increase
+        if startrow >= 0:
+            numrows = index - startrow
+        if numrows > 0:
+            start_ph = int(sum(cnt_dataset[:startrow]))
+            num_ph = int(sum(cnt_dataset[startrow:startrow+numrows]))
+            datasets.append({"dataset": prefix+variable, "startrow": start_ph, "numrows": num_ph, "col": 0, "startseg": startrow, "numsegs": numrows, "prefix": prefix, "geoprefix": geoprefix})
+
+    # Read variable from resource
+    if len(datasets) > 0:
+        values = profiler.read(datasets)
+
+    # Append results
+    for entry in datasets:
+        segments = geocoords[entry["geoprefix"]+"segment_ph_cnt"][entry["startseg"]:entry["startseg"]+entry["numsegs"]].tolist()
+        k = 0
+        for num_ph in segments:
+            for i in range(num_ph):
+                latitudes += [geocoords[entry["geoprefix"]+"reference_photon_lat"][k]]
+                longitudes += [geocoords[entry["geoprefix"]+"reference_photon_lon"][k]]
+            k += 1
+        data += values[entry["prefix"]+variable].tolist()
+
+    # Return results
+    return {"latitude":  latitudes,
+            "longitude": longitudes,
+            variable:    data}
+
+#
+# Function: atl03_sliderule_request
+#
+# request subsetting from sliderule for ATL03
+#
+def atl03_sliderule_request(region, parquet_file=None, open_on_complete=False, geo_fields=[], ph_fields=[]):
+    parms = {
+        "poly":         region,
+        "srt":          icesat2.SRT_LAND,
+        "len":          40,
+        "res":          40,
+        "pass_invalid": True,
+        "cnf":          -2,
+    }
+
+    if parquet_file != None:
+        parms["output"] = { "path": parquet_file, "format": "parquet", "open_on_complete": open_on_complete }
+
+    if len(geo_fields) > 0:
+        parms["atl03_geo_fields"] = geo_fields
+
+    if len(ph_fields) > 0:
+        parms["atl03_ph_fields"] = ph_fields
+
+    start = time.perf_counter()
+    gdf = icesat2.atl03sp(parms, asset="atlas-s3", resources=[args.granule03.split('/')[-1]])
+    duration = time.perf_counter() - start
+    return len(gdf), duration
+
+#
+# Function: atl03_profile
+#
+def atl03_profile():
+    # Build Profilers
+    profiles = {
+        "s3fs":         Profiler(S3fsReader,        args.granule03),
+    #    "ros3":         Profiler(Ros3Reader,        args.granule03),
+        "sliderule-h5p":Profiler(SlideruleReader,   args.granule03),
+        "h5coro":       Profiler(H5CoroReader,      args.granule03),
+        "h5py-local":   Profiler(H5pyReader,        args.granule03),
+        "h5coro-local": Profiler(LocalH5CoroReader, args.granule03)
+    }
+
+    # Profile Readers
+    for profile in profiles:
+        profiler = profiles[profile]
+        print(f'Profiling {profile}... ', end='')
+        sys.stdout.flush()
+        start = time.perf_counter()
+        result = atl03_subsetted_read(profiler, region, variable=args.variable03)
+        print(f'[{len(result[args.variable03])}]: {profiler.duration:.2f} {(time.perf_counter() - start):.2f}')
+
+###############################################################################
+# MAIN
+###############################################################################
+
diff --git a/benchmarks/subset_atl03.py b/benchmarks/subset_atl03.py
deleted file mode 100644
index 466ce90..0000000
--- a/benchmarks/subset_atl03.py
+++ /dev/null
@@ -1,166 +0,0 @@
-# Copyright (c) 2023, University of Washington
-# All rights reserved.
-#
-# Redistribution and use in source and binary forms, with or without
-# modification, are permitted provided that the following conditions are met:
-#
-# 1. Redistributions of source code must retain the above copyright notice,
-#    this list of conditions and the following disclaimer.
-#
-# 2. Redistributions in binary form must reproduce the above copyright notice,
-#    this list of conditions and the following disclaimer in the documentation
-#    and/or other materials provided with the distribution.
-#
-# 3. Neither the name of the University of Washington nor the names of its
-#    contributors may be used to endorse or promote products derived from this
-#    software without specific prior written permission.
-#
-# THIS SOFTWARE IS PROVIDED BY THE UNIVERSITY OF WASHINGTON AND CONTRIBUTORS
-# “AS IS” AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED
-# TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
-# PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE UNIVERSITY OF WASHINGTON OR
-# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
-# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
-# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
-# OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
-# WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR
-# OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF
-# ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-
-import sys
-import time
-from sliderule import icesat2
-from profiler import args, Point, Profiler, H5CoroReader, SlideruleReader, S3fsReader, Ros3Reader, H5pyReader, LocalH5CoroReader, inpoly, region
-
-###############################################################################
-# LOCAL FUNCTIONS
-###############################################################################
-
-#
-# Function: subsetted_read
-#
-# read ATL06 resource and return variable's data within polygon
-#
-def subsetted_read(profiler, region, variable, tracks = ["1l", "1r", "2l", "2r", "3l", "3r"]):
-
-    # Initialize return variables
-    data = []
-    latitudes = []
-    longitudes = []
-
-    # Create polygon
-    poly = [Point(coord["lon"], coord["lat"]) for coord in region]
-
-    # Build list of each lat,lon dataset to read
-    geodatasets = []
-    for track in tracks:
-        prefix = "gt"+track+"/geolocation/"
-        geodatasets.append({"dataset": prefix+"reference_photon_lat", "startrow": 0, "numrows": -1})
-        geodatasets.append({"dataset": prefix+"reference_photon_lon", "startrow": 0, "numrows": -1})
-        geodatasets.append({"dataset": prefix+"segment_ph_cnt", "startrow": 0, "numrows": -1})
-
-    # Read lat,lon from resource
-    geocoords = profiler.read(geodatasets)
-
-    # Build list of the subsetted variable datasets to read
-    datasets = []
-    for track in tracks:
-        geoprefix = "gt"+track+"/geolocation/"
-        prefix = "gt"+track+"/heights/"
-        lat_dataset = geocoords[geoprefix+"reference_photon_lat"]
-        lon_dataset = geocoords[geoprefix+"reference_photon_lon"]
-        cnt_dataset = geocoords[geoprefix+"segment_ph_cnt"]
-        startrow = -1
-        numrows = -1
-        index = 0
-        while index < len(lat_dataset):
-            point = Point(lon_dataset[index], lat_dataset[index])
-            intersect = inpoly(poly, point)
-            if startrow == -1 and intersect:
-                startrow = index
-            elif startrow != -1 and not intersect:
-                break
-            index += 10 # only sample values for speed increase
-        if startrow >= 0:
-            numrows = index - startrow
-        if numrows > 0:
-            start_ph = int(sum(cnt_dataset[:startrow]))
-            num_ph = int(sum(cnt_dataset[startrow:startrow+numrows]))
-            datasets.append({"dataset": prefix+variable, "startrow": start_ph, "numrows": num_ph, "col": 0, "startseg": startrow, "numsegs": numrows, "prefix": prefix, "geoprefix": geoprefix})
-
-    # Read variable from resource
-    if len(datasets) > 0:
-        values = profiler.read(datasets)
-
-    # Append results
-    for entry in datasets:
-        segments = geocoords[entry["geoprefix"]+"segment_ph_cnt"][entry["startseg"]:entry["startseg"]+entry["numsegs"]].tolist()
-        k = 0
-        for num_ph in segments:
-            for i in range(num_ph):
-                latitudes += [geocoords[entry["geoprefix"]+"reference_photon_lat"][k]]
-                longitudes += [geocoords[entry["geoprefix"]+"reference_photon_lon"][k]]
-            k += 1
-        data += values[entry["prefix"]+variable].tolist()
-
-    # Return results
-    return {"latitude":  latitudes,
-            "longitude": longitudes,
-            variable:    data}
-
-#
-# Function: sliderule_request
-#
-# request subsetting from sliderule for ATL03
-#
-def sliderule_request(region, parquet_file=None, open_on_complete=False, geo_fields=[], ph_fields=[]):
-    parms = {
-        "poly":         region,
-        "srt":          icesat2.SRT_LAND,
-        "len":          40,
-        "res":          40,
-        "pass_invalid": True,
-        "cnf":          -2,
-    }
-
-    if parquet_file != None:
-        parms["output"] = { "path": parquet_file, "format": "parquet", "open_on_complete": open_on_complete }
-
-    if len(geo_fields) > 0:
-        parms["atl03_geo_fields"] = geo_fields
-
-    if len(ph_fields) > 0:
-        parms["atl03_ph_fields"] = ph_fields
-
-    start = time.perf_counter()
-    gdf = icesat2.atl03sp(parms, asset="atlas-s3", resources=[args.granule03.split('/')[-1]])
-    duration = time.perf_counter() - start
-    return len(gdf), duration
-
-###############################################################################
-# MAIN
-###############################################################################
-
-# Build Profilers
-profiles = {
-    "s3fs":         Profiler(S3fsReader,        args.granule03),
-#    "ros3":         Profiler(Ros3Reader,        args.granule03),
-    "sliderule-h5p":Profiler(SlideruleReader,   args.granule03),
-    "h5coro":       Profiler(H5CoroReader,      args.granule03),
-    "h5py-local":   Profiler(H5pyReader,        args.granule03),
-    "h5coro-local": Profiler(LocalH5CoroReader, args.granule03)
-}
-
-# Profile Readers
-for profile in profiles:
-    profiler = profiles[profile]
-    print(f'Profiling {profile}... ', end='')
-    sys.stdout.flush()
-    start = time.perf_counter()
-    result = subsetted_read(profiler, region, variable=args.variable03)
-    print(f'[{len(result[args.variable03])}]: {profiler.duration:.2f} {(time.perf_counter() - start):.2f}')
-
-# Profile SlideRule ATL03 Subsetter - flatrec03 issue
-#print(f'Profiling sliderule-atl03s...', end='')
-#num_photons, duration = sliderule_request(region, "atl03.parquet")
-#print(f'[{num_photons}]: {duration}')
\ No newline at end of file
diff --git a/benchmarks/subset_atl06.py b/benchmarks/subset_atl06.py
deleted file mode 100644
index 1769132..0000000
--- a/benchmarks/subset_atl06.py
+++ /dev/null
@@ -1,122 +0,0 @@
-# Copyright (c) 2023, University of Washington
-# All rights reserved.
-#
-# Redistribution and use in source and binary forms, with or without
-# modification, are permitted provided that the following conditions are met:
-#
-# 1. Redistributions of source code must retain the above copyright notice,
-#    this list of conditions and the following disclaimer.
-#
-# 2. Redistributions in binary form must reproduce the above copyright notice,
-#    this list of conditions and the following disclaimer in the documentation
-#    and/or other materials provided with the distribution.
-#
-# 3. Neither the name of the University of Washington nor the names of its
-#    contributors may be used to endorse or promote products derived from this
-#    software without specific prior written permission.
-#
-# THIS SOFTWARE IS PROVIDED BY THE UNIVERSITY OF WASHINGTON AND CONTRIBUTORS
-# “AS IS” AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED
-# TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
-# PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE UNIVERSITY OF WASHINGTON OR
-# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
-# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
-# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
-# OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
-# WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR
-# OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF
-# ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-
-import time
-from profiler import args, Point, Profiler, H5CoroReader, SlideruleReader, S3fsReader, Ros3Reader, H5pyReader, LocalH5CoroReader, inpoly, region
-
-###############################################################################
-# LOCAL FUNCTIONS
-###############################################################################
-
-#
-# Function: subsetted_read
-#
-# read ATL06 resource and return variable's data within polygon
-#
-def subsetted_read(profiler, region, variable):
-
-    # Initialize return variables
-    data = []
-    latitudes = []
-    longitudes = []
-
-    # Create polygon
-    poly = [Point(coord["lon"], coord["lat"]) for coord in region]
-
-    # List of tracks to read
-    tracks = ["1l", "1r", "2l", "2r", "3l", "3r"]
-
-    # Build list of each lat,lon dataset to read
-    geodatasets = []
-    for track in tracks:
-        prefix = "gt"+track+"/land_ice_segments/"
-        geodatasets.append({"dataset": prefix+"latitude", "startrow": 0, "numrows": -1})
-        geodatasets.append({"dataset": prefix+"longitude", "startrow": 0, "numrows": -1})
-
-    # Read lat,lon from resource
-    geocoords = profiler.read(geodatasets)
-
-    # Build list of the subsetted variable datasets to read
-    datasets = []
-    for track in tracks:
-        prefix = "gt"+track+"/land_ice_segments/"
-        lat_dataset = geocoords[prefix+"latitude"]
-        lon_dataset = geocoords[prefix+"longitude"]
-        startrow = -1
-        numrows = -1
-        index = 0
-        while index < len(lat_dataset):
-            point = Point(lon_dataset[index], lat_dataset[index])
-            intersect = inpoly(poly, point)
-            if startrow == -1 and intersect:
-                startrow = index
-            elif startrow != -1 and not intersect:
-                break
-            index += 10 # only sample values for speed increase
-        if startrow >= 0:
-            numrows = index - startrow
-        if numrows > 0:
-            datasets.append({"dataset": prefix+variable, "startrow": startrow, "numrows": numrows, "prefix": prefix})
-
-    # Read variable from resource
-    if len(datasets) > 0:
-        values = profiler.read(datasets)
-
-    # Append results
-    for entry in datasets:
-        latitudes += geocoords[entry["prefix"]+"latitude"][entry["startrow"]:entry["startrow"]+entry["numrows"]].tolist()
-        longitudes += geocoords[entry["prefix"]+"longitude"][entry["startrow"]:entry["startrow"]+entry["numrows"]].tolist()
-        data += values[entry["prefix"]+variable].tolist()
-
-    # Return results
-    return {"latitude":  latitudes,
-            "longitude": longitudes,
-            variable:    data}
-
-###############################################################################
-# MAIN
-###############################################################################
-
-# Build Profilers
-profiles = {
-    "s3fs":         Profiler(S3fsReader,        args.granule06),
-#    "ros3":         Profiler(Ros3Reader,        args.granule06),
-    "sliderule-h5p":Profiler(SlideruleReader,   args.granule06),
-    "h5coro":       Profiler(H5CoroReader,      args.granule06),
-    "h5py-local":   Profiler(H5pyReader,        args.granule06),
-    "h5coro-local": Profiler(LocalH5CoroReader, args.granule06)
-}
-
-# Profile Readers
-for profile in profiles:
-    profiler = profiles[profile]
-    print(f'Profiling {profile}... ', end='')
-    start = time.perf_counter()
-    result = subsetted_read(profiler, region, variable=args.variable06)
-    print(f'[{len(result[args.variable06])}]: {profiler.duration:.2f} {(time.perf_counter() - start):.2f}')
diff --git a/examples.ipynb b/examples.ipynb
index b4df6d7..87333c1 100644
--- a/examples.ipynb
+++ b/examples.ipynb
@@ -14,6 +14,7 @@
    "cell_type": "markdown",
    "id": "d9d216f9-0a12-47cd-b9a3-80f1cb1ad0fc",
    "metadata": {
+    "tags": [],
     "user_expressions": []
    },
    "source": [
@@ -40,12 +41,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "9f8607a8-2cea-4d68-af68-cdd07e053580",
    "metadata": {
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/jovyan/envs/h5coro-dev/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    }
+   ],
    "source": [
     "import xarray as xr\n",
     "import earthaccess"
@@ -59,7 +69,7 @@
    "outputs": [],
    "source": [
     "from h5coro.datasets import icesat2\n",
-    "col_convs = {\"delta_time\": icesat2.to_datetime}"
+    "col_convs = {} # {\"delta_time\": icesat2.to_datetime}"
    ]
   },
   {
@@ -85,8 +95,8 @@
     "s3url_atl03 = 'nsidc-cumulus-prod-protected/ATLAS/ATL03/006/2019/11/30/' \\\n",
     "                'ATL03_20191130112041_09860505_006_01.h5'\n",
     "auth = earthaccess.login()\n",
-    "\n",
-    "ds = xr.open_dataset(s3url_atl03, engine='h5coro', group='/gt1l/heights', col_convs=col_convs, creds=auth)\n",
+    "creds = auth.get_s3_credentials(daac='NSIDC')\n",
+    "ds = xr.open_dataset(s3url_atl03, engine='h5coro', group='/gt1l/heights', col_convs=col_convs, credentials=creds)\n",
     "ds"
    ]
   },
@@ -117,7 +127,7 @@
    },
    "outputs": [],
    "source": [
-    "local_path = '/home/jovyan/data/ATL03/processed_ATL03_20191130220138_09930502_006_01.h5'\n",
+    "local_path = 'file:///home/jovyan/data/ATL03/processed_ATL03_20191130220138_09930502_006_01.h5'\n",
     "ds = xr.open_dataset(local_path, engine='h5coro', group='/gt1l/heights')\n",
     "ds"
    ]
@@ -190,7 +200,1092 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "9fad7d0e-6e80-49df-ab42-2773cbf4bf8a",
+   "id": "09356670-3413-403e-bc2a-190bd861a3d6",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "125a5c36-6647-4223-bfbc-0a2fb34468a0",
+   "metadata": {
+    "user_expressions": []
+   },
+   "source": [
+    "## Two-dimensional product example (ATL23 & ATL20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "d67608d8-050f-46dc-9c16-fc207175fbd2",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/jovyan/envs/h5coro-dev/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    }
+   ],
+   "source": [
+    "import xarray as xr\n",
+    "import earthaccess"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d74ac3e3-73f0-4dfd-a59e-ac42f69e4dd2",
+   "metadata": {
+    "user_expressions": []
+   },
+   "source": [
+    "### ATL23 \n",
+    "\n",
+    "(~90 second access time)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "612c0a38-08ba-4df8-9b6e-8dddb7b07d1a",
+   "metadata": {
+    "scrolled": true,
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "We are already authenticated with NASA EDL\n",
+      "CPU times: user 1min 11s, sys: 2.47 s, total: 1min 13s\n",
+      "Wall time: 1min 8s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
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+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
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+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
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+       "\n",
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+       "  display: none;\n",
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+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
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+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
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+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
+       "Dimensions:            (lat_dfw_0: 480, lat_dfw_1: 1440, geoid_avg_0: 480,\n",
+       "                        geoid_avg_1: 1440, dot_kurt_dfw_0: 480,\n",
+       "                        dot_kurt_dfw_1: 1440, dot_dfw_0: 480, dot_dfw_1: 1440,\n",
+       "                        length_sum_0: 480, length_sum_1: 1440,\n",
+       "                        length_dfw_0: 480, length_dfw_1: 1440,\n",
+       "                        dot_dfw_uncrtn_0: 480, dot_dfw_uncrtn_1: 1440,\n",
+       "                        ...\n",
+       "                        surf_prcnt_dfw_0: 480, surf_prcnt_dfw_1: 1440,\n",
+       "                        surf_prcnt_dfw_2: 5, lat_avg_0: 480, lat_avg_1: 1440,\n",
+       "                        swh_dfw_0: 480, swh_dfw_1: 1440, ice_conc_0: 480,\n",
+       "                        ice_conc_1: 1440, lon_dfw_0: 480, lon_dfw_1: 1440,\n",
+       "                        ssb_dfw_0: 480, ssb_dfw_1: 1440, dot_sigma_dfw_0: 480,\n",
+       "                        dot_sigma_dfw_1: 1440)\n",
+       "Dimensions without coordinates: lat_dfw_0, lat_dfw_1, geoid_avg_0, geoid_avg_1,\n",
+       "                                dot_kurt_dfw_0, dot_kurt_dfw_1, dot_dfw_0,\n",
+       "                                dot_dfw_1, length_sum_0, length_sum_1,\n",
+       "                                length_dfw_0, length_dfw_1, dot_dfw_uncrtn_0,\n",
+       "                                dot_dfw_uncrtn_1, dof_0, dof_1, dot_skew_dfw_0,\n",
+       "                                dot_skew_dfw_1, ssb_avg_0, ssb_avg_1,\n",
+       "                                ...\n",
+       "                                dot_sigma_avg_0, dot_sigma_avg_1,\n",
+       "                                surf_prcnt_dfw_0, surf_prcnt_dfw_1,\n",
+       "                                surf_prcnt_dfw_2, lat_avg_0, lat_avg_1,\n",
+       "                                swh_dfw_0, swh_dfw_1, ice_conc_0, ice_conc_1,\n",
+       "                                lon_dfw_0, lon_dfw_1, ssb_dfw_0, ssb_dfw_1,\n",
+       "                                dot_sigma_dfw_0, dot_sigma_dfw_1\n",
+       "Data variables: (12/34)\n",
+       "    lat_dfw            (lat_dfw_0, lat_dfw_1) float64 ...\n",
+       "    geoid_avg          (geoid_avg_0, geoid_avg_1) float64 ...\n",
+       "    dot_kurt_dfw       (dot_kurt_dfw_0, dot_kurt_dfw_1) float64 ...\n",
+       "    dot_dfw            (dot_dfw_0, dot_dfw_1) float64 ...\n",
+       "    length_sum         (length_sum_0, length_sum_1) float32 ...\n",
+       "    length_dfw         (length_dfw_0, length_dfw_1) float32 ...\n",
+       "    ...                 ...\n",
+       "    lat_avg            (lat_avg_0, lat_avg_1) float64 ...\n",
+       "    swh_dfw            (swh_dfw_0, swh_dfw_1) float64 ...\n",
+       "    ice_conc           (ice_conc_0, ice_conc_1) float32 ...\n",
+       "    lon_dfw            (lon_dfw_0, lon_dfw_1) float64 ...\n",
+       "    ssb_dfw            (ssb_dfw_0, ssb_dfw_1) float64 ...\n",
+       "    dot_sigma_dfw      (dot_sigma_dfw_0, dot_sigma_dfw_1) float64 ...\n",
+       "Attributes:\n",
+       "    Description:  This group contains data for beams 1, 3, and 5 (strong beam...</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-2a33317d-0f0e-43d7-8c7f-4f53bf026fba' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-2a33317d-0f0e-43d7-8c7f-4f53bf026fba' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>lat_dfw_0</span>: 480</li><li><span>lat_dfw_1</span>: 1440</li><li><span>geoid_avg_0</span>: 480</li><li><span>geoid_avg_1</span>: 1440</li><li><span>dot_kurt_dfw_0</span>: 480</li><li><span>dot_kurt_dfw_1</span>: 1440</li><li><span>dot_dfw_0</span>: 480</li><li><span>dot_dfw_1</span>: 1440</li><li><span>length_sum_0</span>: 480</li><li><span>length_sum_1</span>: 1440</li><li><span>length_dfw_0</span>: 480</li><li><span>length_dfw_1</span>: 1440</li><li><span>dot_dfw_uncrtn_0</span>: 480</li><li><span>dot_dfw_uncrtn_1</span>: 1440</li><li><span>dof_0</span>: 480</li><li><span>dof_1</span>: 1440</li><li><span>dot_skew_dfw_0</span>: 480</li><li><span>dot_skew_dfw_1</span>: 1440</li><li><span>ssb_avg_0</span>: 480</li><li><span>ssb_avg_1</span>: 1440</li><li><span>dot_kurt_avg_0</span>: 480</li><li><span>dot_kurt_avg_1</span>: 1440</li><li><span>podppd_flag_prcnt_0</span>: 480</li><li><span>podppd_flag_prcnt_1</span>: 1440</li><li><span>dot_avg_0</span>: 480</li><li><span>dot_avg_1</span>: 1440</li><li><span>n_ph_srfc_0</span>: 480</li><li><span>n_ph_srfc_1</span>: 1440</li><li><span>geoid_dfw_0</span>: 480</li><li><span>geoid_dfw_1</span>: 1440</li><li><span>depth_avg_0</span>: 480</li><li><span>depth_avg_1</span>: 1440</li><li><span>r_srfc_0</span>: 480</li><li><span>r_srfc_1</span>: 1440</li><li><span>n_segs_0</span>: 480</li><li><span>n_segs_1</span>: 1440</li><li><span>surf_prcnt_avg_0</span>: 480</li><li><span>surf_prcnt_avg_1</span>: 1440</li><li><span>surf_prcnt_avg_2</span>: 5</li><li><span>dot_skew_avg_0</span>: 480</li><li><span>dot_skew_avg_1</span>: 1440</li><li><span>depth_dfw_0</span>: 480</li><li><span>depth_dfw_1</span>: 1440</li><li><span>n_phs_ttl_0</span>: 480</li><li><span>n_phs_ttl_1</span>: 1440</li><li><span>r_noise_0</span>: 480</li><li><span>r_noise_1</span>: 1440</li><li><span>lon_avg_0</span>: 480</li><li><span>lon_avg_1</span>: 1440</li><li><span>swh_avg_0</span>: 480</li><li><span>swh_avg_1</span>: 1440</li><li><span>dot_avg_uncrtn_0</span>: 480</li><li><span>dot_avg_uncrtn_1</span>: 1440</li><li><span>dot_sigma_avg_0</span>: 480</li><li><span>dot_sigma_avg_1</span>: 1440</li><li><span>surf_prcnt_dfw_0</span>: 480</li><li><span>surf_prcnt_dfw_1</span>: 1440</li><li><span>surf_prcnt_dfw_2</span>: 5</li><li><span>lat_avg_0</span>: 480</li><li><span>lat_avg_1</span>: 1440</li><li><span>swh_dfw_0</span>: 480</li><li><span>swh_dfw_1</span>: 1440</li><li><span>ice_conc_0</span>: 480</li><li><span>ice_conc_1</span>: 1440</li><li><span>lon_dfw_0</span>: 480</li><li><span>lon_dfw_1</span>: 1440</li><li><span>ssb_dfw_0</span>: 480</li><li><span>ssb_dfw_1</span>: 1440</li><li><span>dot_sigma_dfw_0</span>: 480</li><li><span>dot_sigma_dfw_1</span>: 1440</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-8c30b71d-c1ea-46ba-b041-b68239aea047' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8c30b71d-c1ea-46ba-b041-b68239aea047' class='xr-section-summary'  title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-02bb215e-d933-42ae-a289-8eda0900f0d7' class='xr-section-summary-in' type='checkbox'  ><label for='section-02bb215e-d933-42ae-a289-8eda0900f0d7' class='xr-section-summary' >Data variables: <span>(34)</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>lat_dfw</span></div><div class='xr-var-dims'>(lat_dfw_0, lat_dfw_1)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-79485c10-10a8-4b73-9300-8a18b764df0f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-79485c10-10a8-4b73-9300-8a18b764df0f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-52d45585-ff39-46c4-8b47-d577b4ded273' class='xr-var-data-in' type='checkbox'><label for='data-52d45585-ff39-46c4-8b47-d577b4ded273' 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>[691200 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>geoid_avg</span></div><div class='xr-var-dims'>(geoid_avg_0, geoid_avg_1)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4237400e-3765-4f84-9b95-3e93b3ee3167' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4237400e-3765-4f84-9b95-3e93b3ee3167' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-68f37197-f406-49f8-b9fe-555b2d014be6' class='xr-var-data-in' type='checkbox'><label for='data-68f37197-f406-49f8-b9fe-555b2d014be6' 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>[691200 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dot_kurt_dfw</span></div><div class='xr-var-dims'>(dot_kurt_dfw_0, dot_kurt_dfw_1)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-77c3cbce-befd-4bf7-8b05-fc3fce036472' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-77c3cbce-befd-4bf7-8b05-fc3fce036472' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fe3d2662-3488-465d-b700-5f9f1e76c387' class='xr-var-data-in' type='checkbox'><label for='data-fe3d2662-3488-465d-b700-5f9f1e76c387' 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>[691200 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dot_dfw</span></div><div class='xr-var-dims'>(dot_dfw_0, dot_dfw_1)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-fee89d12-954b-4430-8755-3ed30c69cd20' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fee89d12-954b-4430-8755-3ed30c69cd20' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ab206f23-4920-482c-8f4b-6ee464bb6037' class='xr-var-data-in' type='checkbox'><label for='data-ab206f23-4920-482c-8f4b-6ee464bb6037' 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>[691200 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>length_sum</span></div><div class='xr-var-dims'>(length_sum_0, length_sum_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1f256aa1-b168-4ede-88f7-6955eaeb0d2f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1f256aa1-b168-4ede-88f7-6955eaeb0d2f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3b04756b-d61f-4c71-90dc-4558fc9b7c20' class='xr-var-data-in' type='checkbox'><label for='data-3b04756b-d61f-4c71-90dc-4558fc9b7c20' 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>[691200 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>length_dfw</span></div><div class='xr-var-dims'>(length_dfw_0, length_dfw_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e89ac59e-b0f6-43ef-93e9-5ffba4a66862' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e89ac59e-b0f6-43ef-93e9-5ffba4a66862' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-45071014-d949-4f08-974e-d37f92348641' class='xr-var-data-in' type='checkbox'><label for='data-45071014-d949-4f08-974e-d37f92348641' 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>[691200 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dot_dfw_uncrtn</span></div><div class='xr-var-dims'>(dot_dfw_uncrtn_0, dot_dfw_uncrtn_1)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d8807990-31b7-4c0c-88bc-248657aa6dad' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d8807990-31b7-4c0c-88bc-248657aa6dad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-15c63a26-1fc8-4b38-934f-ec19e74b17cc' class='xr-var-data-in' type='checkbox'><label for='data-15c63a26-1fc8-4b38-934f-ec19e74b17cc' 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>[691200 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dof</span></div><div class='xr-var-dims'>(dof_0, dof_1)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c4715f35-0293-48b7-93af-1f67c1bf7468' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c4715f35-0293-48b7-93af-1f67c1bf7468' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f8bf593e-ac33-4d4b-93d2-6ac8be854608' class='xr-var-data-in' type='checkbox'><label for='data-f8bf593e-ac33-4d4b-93d2-6ac8be854608' 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>[691200 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dot_skew_dfw</span></div><div class='xr-var-dims'>(dot_skew_dfw_0, dot_skew_dfw_1)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-00de1a7e-9b4b-493c-9425-1c8e8b742950' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-00de1a7e-9b4b-493c-9425-1c8e8b742950' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-862448ca-d982-4e88-b41c-21f4d1d82983' class='xr-var-data-in' type='checkbox'><label for='data-862448ca-d982-4e88-b41c-21f4d1d82983' 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>[691200 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ssb_avg</span></div><div class='xr-var-dims'>(ssb_avg_0, ssb_avg_1)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-dac80f23-7282-4913-a066-2703e2cbda46' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dac80f23-7282-4913-a066-2703e2cbda46' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-559fd74f-c409-4792-94ac-de7f14f19282' class='xr-var-data-in' type='checkbox'><label for='data-559fd74f-c409-4792-94ac-de7f14f19282' 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>[691200 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dot_kurt_avg</span></div><div class='xr-var-dims'>(dot_kurt_avg_0, dot_kurt_avg_1)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9b2f384b-94a8-4984-a8d6-078b4bb36b23' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9b2f384b-94a8-4984-a8d6-078b4bb36b23' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4f0af822-10b9-405f-b7fa-da4aa9b2e0f3' class='xr-var-data-in' type='checkbox'><label for='data-4f0af822-10b9-405f-b7fa-da4aa9b2e0f3' 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>[691200 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>podppd_flag_prcnt</span></div><div class='xr-var-dims'>(podppd_flag_prcnt_0, podppd_flag_prcnt_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3f7ab492-aa3c-490e-92a2-29cb268b4c1c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3f7ab492-aa3c-490e-92a2-29cb268b4c1c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f9ed3d53-0411-429b-875e-4ff7a70ba1bd' class='xr-var-data-in' type='checkbox'><label for='data-f9ed3d53-0411-429b-875e-4ff7a70ba1bd' 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>[691200 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dot_avg</span></div><div class='xr-var-dims'>(dot_avg_0, dot_avg_1)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a28b5b6f-eab3-48b1-8f15-0cc99e12c9fd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a28b5b6f-eab3-48b1-8f15-0cc99e12c9fd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-90e42e12-7324-445c-8e8b-565d2a365376' class='xr-var-data-in' type='checkbox'><label for='data-90e42e12-7324-445c-8e8b-565d2a365376' 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>[691200 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>n_ph_srfc</span></div><div class='xr-var-dims'>(n_ph_srfc_0, n_ph_srfc_1)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9d4c15e2-540f-4f8b-82be-d518a046e681' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9d4c15e2-540f-4f8b-82be-d518a046e681' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a14bf1bb-6473-4026-a366-6c15d1d76dad' class='xr-var-data-in' type='checkbox'><label for='data-a14bf1bb-6473-4026-a366-6c15d1d76dad' 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>[691200 values with dtype=int32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>geoid_dfw</span></div><div class='xr-var-dims'>(geoid_dfw_0, geoid_dfw_1)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-929c2328-ab0d-40f1-b62c-185fb97870bb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-929c2328-ab0d-40f1-b62c-185fb97870bb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fa568efc-3a97-4095-8ea0-ee3694b18da7' class='xr-var-data-in' type='checkbox'><label for='data-fa568efc-3a97-4095-8ea0-ee3694b18da7' 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>[691200 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>depth_avg</span></div><div class='xr-var-dims'>(depth_avg_0, depth_avg_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1992fad4-5c2c-454b-9841-75bda9ffc825' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1992fad4-5c2c-454b-9841-75bda9ffc825' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a2167e58-2620-45de-8c79-00a3cfb4fc8b' class='xr-var-data-in' type='checkbox'><label for='data-a2167e58-2620-45de-8c79-00a3cfb4fc8b' 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>[691200 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>r_srfc</span></div><div class='xr-var-dims'>(r_srfc_0, r_srfc_1)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-056909db-0b4a-420b-8398-99ceded55f90' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-056909db-0b4a-420b-8398-99ceded55f90' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5ef47f57-edf5-4a08-83e1-7141440af744' class='xr-var-data-in' type='checkbox'><label for='data-5ef47f57-edf5-4a08-83e1-7141440af744' 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>[691200 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>n_segs</span></div><div class='xr-var-dims'>(n_segs_0, n_segs_1)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-493fc610-7253-4b5b-9169-2e885434b881' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-493fc610-7253-4b5b-9169-2e885434b881' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d7b59f70-c9a9-40bc-ba74-421a83cac949' class='xr-var-data-in' type='checkbox'><label for='data-d7b59f70-c9a9-40bc-ba74-421a83cac949' 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>[691200 values with dtype=int32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>surf_prcnt_avg</span></div><div class='xr-var-dims'>(surf_prcnt_avg_0, surf_prcnt_avg_1, surf_prcnt_avg_2)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-29420fc0-96b8-41a1-9fd3-561d77501b02' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-29420fc0-96b8-41a1-9fd3-561d77501b02' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ff4946eb-70ff-4153-adff-3ff3a2670335' class='xr-var-data-in' type='checkbox'><label for='data-ff4946eb-70ff-4153-adff-3ff3a2670335' 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>[3456000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dot_skew_avg</span></div><div class='xr-var-dims'>(dot_skew_avg_0, dot_skew_avg_1)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a586db13-1cb9-44f6-9b2f-35a790058e3c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a586db13-1cb9-44f6-9b2f-35a790058e3c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8c5dd4c1-64dc-4e71-aa43-05e8834767a1' class='xr-var-data-in' type='checkbox'><label for='data-8c5dd4c1-64dc-4e71-aa43-05e8834767a1' 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>[691200 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>depth_dfw</span></div><div class='xr-var-dims'>(depth_dfw_0, depth_dfw_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c15496b4-c539-4257-b28f-db5acaeeafb0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c15496b4-c539-4257-b28f-db5acaeeafb0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1e093ba8-3378-43f0-bd58-2833641ac27a' class='xr-var-data-in' type='checkbox'><label for='data-1e093ba8-3378-43f0-bd58-2833641ac27a' 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>[691200 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>n_phs_ttl</span></div><div class='xr-var-dims'>(n_phs_ttl_0, n_phs_ttl_1)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-dae0e628-9a80-4bee-a82c-30b85749066f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dae0e628-9a80-4bee-a82c-30b85749066f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b1127492-9448-4fbb-98dd-5879565d2fe0' class='xr-var-data-in' type='checkbox'><label for='data-b1127492-9448-4fbb-98dd-5879565d2fe0' 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>[691200 values with dtype=int32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>r_noise</span></div><div class='xr-var-dims'>(r_noise_0, r_noise_1)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2d829237-b18a-448a-bc51-757b55e0f28e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2d829237-b18a-448a-bc51-757b55e0f28e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6e93b555-a491-480d-8148-828ff95ab436' class='xr-var-data-in' type='checkbox'><label for='data-6e93b555-a491-480d-8148-828ff95ab436' 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>[691200 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_avg</span></div><div class='xr-var-dims'>(lon_avg_0, lon_avg_1)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d21c54f3-247a-421d-858e-53d8c935e608' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d21c54f3-247a-421d-858e-53d8c935e608' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-65981a4a-f5a3-421e-a7a8-f04773b04967' class='xr-var-data-in' type='checkbox'><label for='data-65981a4a-f5a3-421e-a7a8-f04773b04967' 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>[691200 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>swh_avg</span></div><div class='xr-var-dims'>(swh_avg_0, swh_avg_1)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-fc39f2be-ded5-4b49-a79e-a440bdfb5abc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fc39f2be-ded5-4b49-a79e-a440bdfb5abc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e794e6fe-a6cc-4cdd-bf7b-8c2dfeeee338' class='xr-var-data-in' type='checkbox'><label for='data-e794e6fe-a6cc-4cdd-bf7b-8c2dfeeee338' 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>[691200 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dot_avg_uncrtn</span></div><div class='xr-var-dims'>(dot_avg_uncrtn_0, dot_avg_uncrtn_1)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-37c9b73f-f5dd-4d69-b091-0d500bf3f170' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-37c9b73f-f5dd-4d69-b091-0d500bf3f170' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-25c7bb2b-b3a2-4c2a-9d66-ddaa1753953d' class='xr-var-data-in' type='checkbox'><label for='data-25c7bb2b-b3a2-4c2a-9d66-ddaa1753953d' 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>[691200 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dot_sigma_avg</span></div><div class='xr-var-dims'>(dot_sigma_avg_0, dot_sigma_avg_1)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-aaad3c62-f0dd-4f16-818c-f5c552579b60' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-aaad3c62-f0dd-4f16-818c-f5c552579b60' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-56675ee4-fa79-4ac3-b270-eba747da1f30' class='xr-var-data-in' type='checkbox'><label for='data-56675ee4-fa79-4ac3-b270-eba747da1f30' 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>[691200 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>surf_prcnt_dfw</span></div><div class='xr-var-dims'>(surf_prcnt_dfw_0, surf_prcnt_dfw_1, surf_prcnt_dfw_2)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b493603c-13d5-4dc2-9f37-1ddd5ff7d05f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b493603c-13d5-4dc2-9f37-1ddd5ff7d05f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a7f0328a-5f6f-4960-824b-66909f72ef61' class='xr-var-data-in' type='checkbox'><label for='data-a7f0328a-5f6f-4960-824b-66909f72ef61' 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>[3456000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_avg</span></div><div class='xr-var-dims'>(lat_avg_0, lat_avg_1)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-89318d78-2869-4976-b287-6c3dde8bb446' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-89318d78-2869-4976-b287-6c3dde8bb446' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5246f476-2f7b-44df-bf43-2444a0b2d310' class='xr-var-data-in' type='checkbox'><label for='data-5246f476-2f7b-44df-bf43-2444a0b2d310' 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>[691200 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>swh_dfw</span></div><div class='xr-var-dims'>(swh_dfw_0, swh_dfw_1)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-fdb1191b-59f7-4909-945a-1f69f5ccdac4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fdb1191b-59f7-4909-945a-1f69f5ccdac4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f2b0c1c5-7322-493f-99ef-cf78ed1e14f0' class='xr-var-data-in' type='checkbox'><label for='data-f2b0c1c5-7322-493f-99ef-cf78ed1e14f0' 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>[691200 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ice_conc</span></div><div class='xr-var-dims'>(ice_conc_0, ice_conc_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c4b2969d-b933-45ad-b845-78a2530b74d4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c4b2969d-b933-45ad-b845-78a2530b74d4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9154c672-39a9-49dd-b916-66ba032df066' class='xr-var-data-in' type='checkbox'><label for='data-9154c672-39a9-49dd-b916-66ba032df066' 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>[691200 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_dfw</span></div><div class='xr-var-dims'>(lon_dfw_0, lon_dfw_1)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-170f86a3-c074-4e21-b4e3-69ac313dd844' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-170f86a3-c074-4e21-b4e3-69ac313dd844' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-749c4444-2286-49ae-ba5f-42621945c05e' class='xr-var-data-in' type='checkbox'><label for='data-749c4444-2286-49ae-ba5f-42621945c05e' 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>[691200 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ssb_dfw</span></div><div class='xr-var-dims'>(ssb_dfw_0, ssb_dfw_1)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8105616b-1bae-4b1e-8a55-e2a263d2ee9b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8105616b-1bae-4b1e-8a55-e2a263d2ee9b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-984b9aee-222d-456a-ae99-9fad14544c9f' class='xr-var-data-in' type='checkbox'><label for='data-984b9aee-222d-456a-ae99-9fad14544c9f' 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>[691200 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dot_sigma_dfw</span></div><div class='xr-var-dims'>(dot_sigma_dfw_0, dot_sigma_dfw_1)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-03eddd1c-b0bf-47c8-8e56-161efecd68b9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-03eddd1c-b0bf-47c8-8e56-161efecd68b9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ad63f536-65cc-407e-9587-205235859946' class='xr-var-data-in' type='checkbox'><label for='data-ad63f536-65cc-407e-9587-205235859946' 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>[691200 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a478538c-413f-4204-861f-0fdcd302382c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a478538c-413f-4204-861f-0fdcd302382c' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-b4d25f33-bc88-4a09-9750-448bf9c02f82' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b4d25f33-bc88-4a09-9750-448bf9c02f82' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Description :</span></dt><dd>This group contains data for beams 1, 3, and 5 (strong beams), and beams 2, 4, and 6 (weak beams).</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset>\n",
+       "Dimensions:            (lat_dfw_0: 480, lat_dfw_1: 1440, geoid_avg_0: 480,\n",
+       "                        geoid_avg_1: 1440, dot_kurt_dfw_0: 480,\n",
+       "                        dot_kurt_dfw_1: 1440, dot_dfw_0: 480, dot_dfw_1: 1440,\n",
+       "                        length_sum_0: 480, length_sum_1: 1440,\n",
+       "                        length_dfw_0: 480, length_dfw_1: 1440,\n",
+       "                        dot_dfw_uncrtn_0: 480, dot_dfw_uncrtn_1: 1440,\n",
+       "                        ...\n",
+       "                        surf_prcnt_dfw_0: 480, surf_prcnt_dfw_1: 1440,\n",
+       "                        surf_prcnt_dfw_2: 5, lat_avg_0: 480, lat_avg_1: 1440,\n",
+       "                        swh_dfw_0: 480, swh_dfw_1: 1440, ice_conc_0: 480,\n",
+       "                        ice_conc_1: 1440, lon_dfw_0: 480, lon_dfw_1: 1440,\n",
+       "                        ssb_dfw_0: 480, ssb_dfw_1: 1440, dot_sigma_dfw_0: 480,\n",
+       "                        dot_sigma_dfw_1: 1440)\n",
+       "Dimensions without coordinates: lat_dfw_0, lat_dfw_1, geoid_avg_0, geoid_avg_1,\n",
+       "                                dot_kurt_dfw_0, dot_kurt_dfw_1, dot_dfw_0,\n",
+       "                                dot_dfw_1, length_sum_0, length_sum_1,\n",
+       "                                length_dfw_0, length_dfw_1, dot_dfw_uncrtn_0,\n",
+       "                                dot_dfw_uncrtn_1, dof_0, dof_1, dot_skew_dfw_0,\n",
+       "                                dot_skew_dfw_1, ssb_avg_0, ssb_avg_1,\n",
+       "                                ...\n",
+       "                                dot_sigma_avg_0, dot_sigma_avg_1,\n",
+       "                                surf_prcnt_dfw_0, surf_prcnt_dfw_1,\n",
+       "                                surf_prcnt_dfw_2, lat_avg_0, lat_avg_1,\n",
+       "                                swh_dfw_0, swh_dfw_1, ice_conc_0, ice_conc_1,\n",
+       "                                lon_dfw_0, lon_dfw_1, ssb_dfw_0, ssb_dfw_1,\n",
+       "                                dot_sigma_dfw_0, dot_sigma_dfw_1\n",
+       "Data variables: (12/34)\n",
+       "    lat_dfw            (lat_dfw_0, lat_dfw_1) float64 ...\n",
+       "    geoid_avg          (geoid_avg_0, geoid_avg_1) float64 ...\n",
+       "    dot_kurt_dfw       (dot_kurt_dfw_0, dot_kurt_dfw_1) float64 ...\n",
+       "    dot_dfw            (dot_dfw_0, dot_dfw_1) float64 ...\n",
+       "    length_sum         (length_sum_0, length_sum_1) float32 ...\n",
+       "    length_dfw         (length_dfw_0, length_dfw_1) float32 ...\n",
+       "    ...                 ...\n",
+       "    lat_avg            (lat_avg_0, lat_avg_1) float64 ...\n",
+       "    swh_dfw            (swh_dfw_0, swh_dfw_1) float64 ...\n",
+       "    ice_conc           (ice_conc_0, ice_conc_1) float32 ...\n",
+       "    lon_dfw            (lon_dfw_0, lon_dfw_1) float64 ...\n",
+       "    ssb_dfw            (ssb_dfw_0, ssb_dfw_1) float64 ...\n",
+       "    dot_sigma_dfw      (dot_sigma_dfw_0, dot_sigma_dfw_1) float64 ...\n",
+       "Attributes:\n",
+       "    Description:  This group contains data for beams 1, 3, and 5 (strong beam..."
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "\n",
+    "s3url_atl23 = 'nsidc-cumulus-prod-protected/ATLAS/ATL23/001/2023/03/' \\\n",
+    "                '01/ATL23_20230401000000_10761801_001_01.h5'\n",
+    "auth = earthaccess.login()\n",
+    "creds = auth.get_s3_credentials(daac='NSIDC')\n",
+    "ds = xr.open_dataset(s3url_atl23, engine='h5coro', group='/mid_latitude/beam_1', credentials=creds)\n",
+    "ds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "a896c933-d690-4cb0-a09c-e23908dd7ccc",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# mask out nodata values\n",
+    "depth_dfw = ds.depth_dfw.where(ds.depth_dfw < 3.4*10**38)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "268a8755-ad97-49cd-94dd-35bdeb02e6a5",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x7fd2b77b1f10>"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Must have matplotlib installed in the environment for this example to work\n",
+    "depth_dfw.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "732d36ad-a360-45f4-82a9-0508855e7dc5",
+   "metadata": {
+    "user_expressions": []
+   },
+   "source": [
+    "### ATL20 \n",
+    "\n",
+    "(~5 second access time)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "e7326184-17ad-4035-ac74-30830c2ca852",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "We are already authenticated with NASA EDL\n",
+      "CPU times: user 1.15 s, sys: 117 ms, total: 1.27 s\n",
+      "Wall time: 4.86 s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
+       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
+       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
+       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       "html[theme=dark],\n",
+       "body[data-theme=dark],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
+       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
+       "Dimensions:         (delta_time_end: 1, sigma_0: 332, sigma_1: 316,\n",
+       "                     length_sum_0: 332, length_sum_1: 316, delta_time_beg: 1,\n",
+       "                     mean_fb_0: 332, mean_fb_1: 316, n_segs_0: 332,\n",
+       "                     n_segs_1: 316)\n",
+       "Coordinates:\n",
+       "  * delta_time_end  (delta_time_end) float64 1.655e+08\n",
+       "  * delta_time_beg  (delta_time_beg) float64 1.629e+08\n",
+       "Dimensions without coordinates: sigma_0, sigma_1, length_sum_0, length_sum_1,\n",
+       "                                mean_fb_0, mean_fb_1, n_segs_0, n_segs_1\n",
+       "Data variables:\n",
+       "    sigma           (sigma_0, sigma_1) float32 ...\n",
+       "    length_sum      (length_sum_0, length_sum_1) float32 ...\n",
+       "    mean_fb         (mean_fb_0, mean_fb_1) float32 ...\n",
+       "    n_segs          (n_segs_0, n_segs_1) int32 ...\n",
+       "Attributes:\n",
+       "    Description:  Gridded Monthly averages</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-c8fc418d-65de-4940-8f99-56cab7421b10' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c8fc418d-65de-4940-8f99-56cab7421b10' 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'>delta_time_end</span>: 1</li><li><span>sigma_0</span>: 332</li><li><span>sigma_1</span>: 316</li><li><span>length_sum_0</span>: 332</li><li><span>length_sum_1</span>: 316</li><li><span class='xr-has-index'>delta_time_beg</span>: 1</li><li><span>mean_fb_0</span>: 332</li><li><span>mean_fb_1</span>: 316</li><li><span>n_segs_0</span>: 332</li><li><span>n_segs_1</span>: 316</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-0b00f4e9-d32b-4390-8466-bbc5b23bac8e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0b00f4e9-d32b-4390-8466-bbc5b23bac8e' class='xr-section-summary' >Coordinates: <span>(2)</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'>delta_time_end</span></div><div class='xr-var-dims'>(delta_time_end)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.655e+08</div><input id='attrs-670cca95-36b6-4d35-bced-7291a3711542' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-670cca95-36b6-4d35-bced-7291a3711542' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f0b18aa6-4f9a-42e7-b425-af7293ee2399' class='xr-var-data-in' type='checkbox'><label for='data-f0b18aa6-4f9a-42e7-b425-af7293ee2399' 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([1.655438e+08])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>delta_time_beg</span></div><div class='xr-var-dims'>(delta_time_beg)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.629e+08</div><input id='attrs-298b0560-8dd2-4720-b68c-835e123bbb02' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-298b0560-8dd2-4720-b68c-835e123bbb02' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b6644bad-1437-4efe-9206-f50a449599e0' class='xr-var-data-in' type='checkbox'><label for='data-b6644bad-1437-4efe-9206-f50a449599e0' 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([1.628736e+08])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2001e47f-7cf0-4d8f-841b-b24a62a72e92' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2001e47f-7cf0-4d8f-841b-b24a62a72e92' class='xr-section-summary' >Data variables: <span>(4)</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>sigma</span></div><div class='xr-var-dims'>(sigma_0, sigma_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5b7350f6-6cc2-498f-9f2e-d55b5a5f523e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5b7350f6-6cc2-498f-9f2e-d55b5a5f523e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-734d3ce4-914e-4d4a-a8f9-23179e4cd6d6' class='xr-var-data-in' type='checkbox'><label for='data-734d3ce4-914e-4d4a-a8f9-23179e4cd6d6' 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>[104912 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>length_sum</span></div><div class='xr-var-dims'>(length_sum_0, length_sum_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-021c0790-7fad-4679-bde6-7c913d73d5d6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-021c0790-7fad-4679-bde6-7c913d73d5d6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ccdeea46-2bbc-496e-866b-3d09775fb8e5' class='xr-var-data-in' type='checkbox'><label for='data-ccdeea46-2bbc-496e-866b-3d09775fb8e5' 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>[104912 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mean_fb</span></div><div class='xr-var-dims'>(mean_fb_0, mean_fb_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1e3581a9-caba-440d-a66f-6d4fc5b824bb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1e3581a9-caba-440d-a66f-6d4fc5b824bb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a661c91e-e5f4-4721-b9f1-cb3f3235c026' class='xr-var-data-in' type='checkbox'><label for='data-a661c91e-e5f4-4721-b9f1-cb3f3235c026' 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>[104912 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>n_segs</span></div><div class='xr-var-dims'>(n_segs_0, n_segs_1)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a84c9f03-108b-41c3-b688-9fb3bf67b04e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a84c9f03-108b-41c3-b688-9fb3bf67b04e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b323fe82-d1d2-4276-a877-ca4c1866bd80' class='xr-var-data-in' type='checkbox'><label for='data-b323fe82-d1d2-4276-a877-ca4c1866bd80' 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>[104912 values with dtype=int32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2dec8bbd-107d-476d-b09b-3b88a42c8d57' class='xr-section-summary-in' type='checkbox'  ><label for='section-2dec8bbd-107d-476d-b09b-3b88a42c8d57' class='xr-section-summary' >Indexes: <span>(2)</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>delta_time_end</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ec68ce34-7019-47c7-9a35-5406bdc5aa22' class='xr-index-data-in' type='checkbox'/><label for='index-ec68ce34-7019-47c7-9a35-5406bdc5aa22' 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([165543847.17209634], dtype=&#x27;float64&#x27;, name=&#x27;delta_time_end&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>delta_time_beg</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-db7622f1-88b7-48ae-a4d4-60039bc68c1a' class='xr-index-data-in' type='checkbox'/><label for='index-db7622f1-88b7-48ae-a4d4-60039bc68c1a' 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([162873561.41043884], dtype=&#x27;float64&#x27;, name=&#x27;delta_time_beg&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-40799427-223c-4d54-8dae-9a09d7704175' class='xr-section-summary-in' type='checkbox'  checked><label for='section-40799427-223c-4d54-8dae-9a09d7704175' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Description :</span></dt><dd>Gridded Monthly averages</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset>\n",
+       "Dimensions:         (delta_time_end: 1, sigma_0: 332, sigma_1: 316,\n",
+       "                     length_sum_0: 332, length_sum_1: 316, delta_time_beg: 1,\n",
+       "                     mean_fb_0: 332, mean_fb_1: 316, n_segs_0: 332,\n",
+       "                     n_segs_1: 316)\n",
+       "Coordinates:\n",
+       "  * delta_time_end  (delta_time_end) float64 1.655e+08\n",
+       "  * delta_time_beg  (delta_time_beg) float64 1.629e+08\n",
+       "Dimensions without coordinates: sigma_0, sigma_1, length_sum_0, length_sum_1,\n",
+       "                                mean_fb_0, mean_fb_1, n_segs_0, n_segs_1\n",
+       "Data variables:\n",
+       "    sigma           (sigma_0, sigma_1) float32 ...\n",
+       "    length_sum      (length_sum_0, length_sum_1) float32 ...\n",
+       "    mean_fb         (mean_fb_0, mean_fb_1) float32 ...\n",
+       "    n_segs          (n_segs_0, n_segs_1) int32 ...\n",
+       "Attributes:\n",
+       "    Description:  Gridded Monthly averages"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "\n",
+    "s3url_atl20 = 'nsidc-cumulus-prod-protected/ATLAS/ATL20/004/2023/03/' \\\n",
+    "                '01/ATL20-02_20230301013253_10771801_004_01.h5'\n",
+    "auth = earthaccess.login()\n",
+    "creds = auth.get_s3_credentials(daac='NSIDC')\n",
+    "ds = xr.open_dataset(s3url_atl20, engine='h5coro', group='/monthly', credentials=creds)\n",
+    "ds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "731c6024-b58d-4368-a478-c41c53ad8bc9",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# mask out nodata values\n",
+    "mean_fb = ds.mean_fb.where(ds.mean_fb < 3.4*10**38)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "04be8e43-4491-467a-bf84-caf22a97663d",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x7fd2a7d9fc10>"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Must have matplotlib installed in the environment for this example to work\n",
+    "mean_fb.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "31104994-c6e0-4daa-bde5-33847c2b78e8",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -198,9 +1293,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "h5coro-dev",
    "language": "python",
-   "name": "python3"
+   "name": "h5coro-dev"
   },
   "language_info": {
    "codemirror_mode": {
@@ -212,7 +1307,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.0"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,
diff --git a/h5coro/backends/xarray_h5coro.py b/h5coro/backends/xarray_h5coro.py
index e1f245b..bcc89a2 100644
--- a/h5coro/backends/xarray_h5coro.py
+++ b/h5coro/backends/xarray_h5coro.py
@@ -1,17 +1,11 @@
-import logging
-import re
-import warnings
-
-from xarray.backends import BackendEntrypoint
 from h5coro import h5coro, s3driver, filedriver, logger
 from h5coro.h5view import H5View
+from h5coro.h5promise import massagePath
 import xarray as xr
-import numpy as np
-import earthaccess # removed from requirements.txt
-import os
-
+from xarray.backends import BackendEntrypoint
 from xarray.core.dataset import Dataset
-
+import os
+import re
 
 class H5CoroBackendEntrypoint(BackendEntrypoint):
     '''
@@ -23,112 +17,148 @@ def open_dataset(
         filename_or_obj,
         *,
         group,
-        creds,  # TODO creds is required because there is an indirect error in h5coro if not
+        credentials={},
         log_level='ERROR',
+        verbose=False,
+        multi_process=False,
         col_convs={},
-        drop_variables=None,
+        col_coords={},
+        pick_variables=None,
+        drop_variables=None
     ) -> Dataset:
         '''
-        creds: either: a dict with keys aws_access_key_id, aws_secret_access_key, and aws_session_token,
-        a dict withkeys accessKeyID, secretAccessKey, and sessionToken, an aws boto3 object, or an 
-        earthaccess Auth object
-        log_level: indicates level of debugging output to produce. Passed to h5coro logger.config()
-        parameter logLevel
-        col_convs: the conversion dictionary that is used for the delta_time conversion. conversion code 
-        for delta_time is in the datasets/icesat2.py module
+        Constructor for the H5CoroBackendEntrypoint class which extends the BackendEntrypoint class from xarray.
+
+        Parameters
+        ----------
+        group:          str; REQUIRED
+                        A path indicating which group within the hdf file to read. Root group should be
+                        specified by '/'.
+        credentials:    dict
+                        (1) a dict with keys aws_access_key_id, aws_secret_access_key, and aws_session_token;
+                        (2) a dict with keys accessKeyID, secretAccessKey, and sessionToken;
+                        (3) an aws boto3 object;
+                        (4) an earthaccess credentials object        
+        log_level:      str
+                        Python logging levels supplied as an all uppercase string;
+                        DEBUG, INFO, WARNING, ERROR, CRITICAL
+        verbose:        bool
+                        flag indicating whether h5coro code should print lots of debug messages
+        multi_process:  bool
+                        flag indicating whether h5coro should run in multiprocessing mode
+        col_convs:      dict
+                        dictionary of conversion functions; the key is the column name and the signature
+                        of the conversion function is (raw_value) => converted_value
+        col_coords:     dict
+                        dictionary of coordinate names for the provided columns;
+                        {"<col_name>": ("<dim1_coord>", "<dim2_coord>", ...)}
+        pick_variables: list
+                        list of variables to exclusively include in the final xarray
+        drop_variables: list
+                        list of variables not to include in the final xarray
         '''
-        # set h5coro config to info
+        # sanitize input parameters
+        group = massagePath(group)
+
+        # set h5coro logging level
         logger.config(log_level)
-        
-        # extract the s3 credentials dictionary if creds is an earthaccess Auth object
-        if isinstance(creds, earthaccess.auth.Auth):
-            creds = creds.get_s3_credentials(daac='NSIDC')
-        
+                
+        # determine driver
+        if filename_or_obj.startswith("file://"):
+            filename_or_obj = filename_or_obj[len("file://"):]
+            driver = filedriver.FileDriver
+        else: # elif filename_or_obj.startswith("s3://"):
+            driver = s3driver.S3Driver
+
         # connect to the s3 object
-        h5obj = h5coro.H5Coro(filename_or_obj, s3driver.S3Driver, credentials=creds)
+        h5obj = h5coro.H5Coro(filename_or_obj, driver, credentials=credentials, verbose=verbose, multiProcess=multi_process)
         
         # determine the variables and attributes in the specified group
-        variables, group_attr, groups = h5obj.list(group, w_attr=True)
-        var_paths = [os.path.join(group, name) for name in variables.keys()]
-        
-        # if there were variables in that group, retrieve them
-        variable_dicts = {}
-        coords = {}
-        if var_paths:  
-            # submit data request for variables and attributes and create data view
-            promise = h5obj.readDatasets(var_paths, block=True)
-            view = H5View(promise)
-            for step in group.split('/'):
-                if step != '':  # First group will be '' if there was a leading `/` in the group path
-                    view = view[step]
-        
-            # Format the data variables (and coordinate variables)
-            coordinate_names = []
-            for var in view.keys():  
-                # check dimensionality
-                if variables[var]['__metadata__'].ndims > 1:
-                    # ignore 2d variables
-                    warnings.warn(
-                        ('Variable {} has more than 1 dimension. Reading variables with'
-                         'more than 1 dimension is not currently supported. This variable will be'
-                         'dropped.'.format(var))
-                    )
-                    continue
-                else:
-                    # check for coordinate variables and add any coordinates to the coordinate_names list
-                    try:
-                        coord = re.split(';|,| |\n', variables[var]['coordinates'])
-                        coord = [c for c in coord if c]
-                        for c in coord:
-                            if c not in coordinate_names:
-                                coordinate_names.append(c) 
-                    except KeyError:
-                        # if no coordinates were listed for that variable then set it's coordinate as itself
-                        coord = [var]
+        variables, group_attr, _groups = h5obj.list(group, w_attr=True)
 
-                    # add the variable contents as a tuple to the data variables dictionary
-                    # (use only the first coordinate since xarray doesn't except more coordinates that dimensions)
-                    if var in col_convs:
-                        # convert delta_time column to 
-                        variable_dicts[var] = (coord[0], col_convs[var](view[var]), variables[var])
-                    else:
-                        data = view[var]
-                        if isinstance(data, np.ndarray):
-                            variable_dicts[var] = (coord[0], data, variables[var])
-                        else:
-                            warnings.warn(
-                                'Unable to read variable {} from the file. Skipping this variable'.format(var)
-                            )
+        # override variables requested to be read (remove everything not in list)
+        if type(pick_variables) == list and len(pick_variables) > 0:
+            vars_to_drop = []
+            for var_to_pick in variables:
+                if var_to_pick not in pick_variables:
+                    vars_to_drop.append(var_to_pick)
+            for var_to_drop in vars_to_drop:
+                variables.pop(var_to_drop)
 
+        # remove variables that have been requested to be dropped
+        if type(drop_variables) == list:
+            for var_to_drop in drop_variables:
+                if var_to_drop in variables:
+                    variables.pop(var_to_drop)
 
-            # seperate out the coordinate variables from the data variables
-            for coord_name in coordinate_names:
-                # drop the coordiante variable from variable_dicts
-                coordinate = variable_dicts.pop(coord_name)
-                # add the coordiante variable to the coords dictionary
-                coords[coord_name] = coordinate
+        # submit data request for variables and attributes
+        var_paths = [os.path.join(group, name) for name in variables.keys()]
+        promise = h5obj.readDatasets(var_paths, block=True)
+
+        # create view and move it to the lowest branch node in the tree
+        view = H5View(promise)
+        for step in group.split('/'):
+                view = view[step]
+        
+        # format the data variables (and coordinate variables)
+        data_vars = {}
+        coord_names = set()
+        for var in view.keys():  
+            # get variable coordinates
+            try:
+                # check for coordinate variables
+                var_coords = re.split(';|,| |\n', variables[var]['coordinates'])
+                var_coords = [c for c in var_coords if c]
+                # add any coordinates to the coord_names set
+                for c in var_coords:
+                    coord_names.add(c) 
+            except KeyError:
+                # if no coordinates were listed for that variable then set it's coordinate as itself
+                var_coords = [var]
+            # set variable data
+            if var in col_convs:
+                data = col_convs[var](view[var])
+            else:
+                data = view[var]
+            # get dimensionality of data
+            data_dims = len(data.shape)
+            # set dimension names
+            if var in col_coords:
+                dim_names = col_coords[var]
+            elif data_dims == 1:
+                dim_names = var_coords[0]
+            else:
+                # unable to set multidimensional coordinates automatically
+                dim_names = [f'{var}_{n}' for n in range(data_dims)]
+            # add to data variables (if a coordinate, will later be moved to coordinates)
+            data_vars[var] = (dim_names, data)
 
-            # Ensure consistency of dimension coordinates
-            dimension_coordinates = [val[0] for val in variable_dicts.values()]
-            for coord_name, coordinate in coords.items():
-                # For any of the coordinates that are dimension coordinates, ensure that their own coordinate
-                # is set to itself
-                if coord_name in dimension_coordinates:
-                    coords[coord_name] = (coord_name, coordinate[1], coordinate[2])
+        # seperate out the coordinate variables from the data variables
+        coords = {}
+        for coord_name in coord_names:
+            if coord_name in data_vars:
+                # move coordiante variable from data_vars to coords
+                coords[coord_name] = data_vars.pop(coord_name)
+        
+        # Ensure consistency of dimension coordinates
+        dimension_coordinates = [val[0] for val in data_vars.values()]
+        for coord_name, coordinate in coords.items():
+            # For any of the coordinates that are dimension coordinates, ensure that their own coordinate
+            # is set to itself
+            if coord_name in dimension_coordinates:
+                coords[coord_name] = (coord_name, coordinate[1])
         
         return xr.Dataset(
-                variable_dicts,
+                data_vars,
                 coords = coords,
                 attrs = group_attr,
             )
 
-    open_dataset_parameters = ["filename_or_obj", "drop_variables"]
-
     def guess_can_open(self, filename_or_obj) -> bool:
         try:
             _, ext = os.path.splitext(filename_or_obj)
         except TypeError:
             return False
-        return ext in {".h5", ".h5co"}
+        return ext in {".h5", ".h5co", "nc"}
 
     description = "Support for reading HDF5 files in S3 from H5Coro in xarray"
diff --git a/h5coro/h5coro.py b/h5coro/h5coro.py
index bbc9725..cf0e77d 100644
--- a/h5coro/h5coro.py
+++ b/h5coro/h5coro.py
@@ -33,7 +33,7 @@
 from h5coro.h5promise import H5Promise, massagePath
 from h5coro.h5metadata import H5Metadata
 from h5coro.logger import log
-import concurrent.futures
+from concurrent.futures import as_completed, ThreadPoolExecutor
 
 ###############################################################################
 # CONSTANTS
@@ -58,7 +58,7 @@ def inspectThread(resourceObject, path, w_attr):
         _, attributes, metadata = resourceObject.inspectPath(path, w_attr=w_attr)
         return path, metadata, attributes
     except RuntimeError as e:
-        log.warning(f'H5Coro encountered an error inspecting {path}: {e}')
+        log.warn(f'H5Coro encountered an error inspecting {path}: {e}')
         return path, H5Metadata(), {}
 
 def isolateElement(path, group):
@@ -82,18 +82,19 @@ class H5Coro:
     #######################
     def __init__(self,
         resource,
-        driver_class,
+        driverClass,
         credentials={},
         cacheLineSize = CACHE_LINE_SIZE_DEFAULT,
         errorChecking = ERROR_CHECKING_DEFAULT,
-        verbose = VERBOSE_DEFAULT
-
+        verbose = VERBOSE_DEFAULT,
+        multiProcess = False
     ):
         self.resource = resource
-        self.driver = driver_class(resource, credentials)
+        self.driver = driverClass(resource, credentials)
 
         self.errorChecking = errorChecking
         self.verbose = verbose
+        self.multiProcess = multiProcess
 
         self.cacheLineSize = cacheLineSize
         self.cacheLineMask = (0xFFFFFFFFFFFFFFFF - (cacheLineSize-1))
@@ -116,17 +117,17 @@ def readDatasets(self, datasets, block=True, earlyExit=EARLY_EXIT_DEFAULT, metaO
             return
 
         # make into dictionary
-        datasetTable = {}
+        dataset_table = {}
         for dataset in datasets:
             if type(dataset) == str:
                 dataset = massagePath(dataset)
-                datasetTable[dataset] = {"dataset": dataset, "startrow": 0, "numrows": H5Dataset.ALL_ROWS}
+                dataset_table[dataset] = {"dataset": dataset, "hyperslice": []}
             else:
                 dataset["dataset"] = massagePath(dataset["dataset"])
-                datasetTable[dataset["dataset"]] = dataset
+                dataset_table[dataset["dataset"]] = dataset
 
         # return promise
-        return H5Promise(self, datasetTable, block, earlyExit=earlyExit, metaOnly=metaOnly, enableAttributes=enableAttributes)
+        return H5Promise(self, dataset_table, block, earlyExit=earlyExit, metaOnly=metaOnly, enableAttributes=enableAttributes)
 
     #######################
     # readPath
@@ -177,9 +178,9 @@ def list(self, path, w_attr=True):
         links, attributes, _ = self.inspectPath(path, w_attr)
         # inspect each link to get metadata, attributes, group info, etc
         if len(links) > 0:
-            executor = concurrent.futures.ThreadPoolExecutor(max_workers=(len(links) + len(attributes)))
-            futures = [executor.submit(inspectThread, self, os.path.join(path, link), w_attr) for link in links]
-            for future in concurrent.futures.as_completed(futures):
+            executor = ThreadPoolExecutor(max_workers=(len(links) + len(attributes)))
+            futures = [executor.submit(inspectThread, self, f'{path}/{link}', w_attr) for link in links]
+            for future in as_completed(futures):
                 name, metadata, attrs = future.result() # overwrites attribute set
                 element = isolateElement(name, path)
                 if metadata == None: # group
diff --git a/h5coro/h5dataset.py b/h5coro/h5dataset.py
index ca8ba36..68c5f8d 100644
--- a/h5coro/h5dataset.py
+++ b/h5coro/h5dataset.py
@@ -30,6 +30,7 @@
 from h5coro.h5metadata import H5Metadata
 from h5coro.logger import log
 from datetime import datetime
+from multiprocessing import shared_memory, Process
 import struct
 import zlib
 import ctypes
@@ -61,7 +62,14 @@ class FatalError(RuntimeError):
     pass
 
 ###############################################################################
-# H5Dataset Class
+# LOCAL FUNCTIONS
+###############################################################################
+
+def BTreeReader(dataset, buffer, level):
+    dataset.readBTreeV1(buffer, level)
+
+###############################################################################
+# H5DATASET CLASS
 ###############################################################################
 
 class H5Dataset:
@@ -71,9 +79,6 @@ class H5Dataset:
     #######################
     # local
     CUSTOM_V1_FLAG          = 0x80
-    ALL_ROWS                = -1
-    MAX_NDIMS               = 2
-    FLAT_NDIMS              = 3
     # signatures
     H5_SIGNATURE_LE         = 0x0A1A0A0D46444889
     H5_OHDR_SIGNATURE_LE    = 0x5244484F
@@ -104,22 +109,25 @@ class H5Dataset:
     #######################
     # Constructor
     #######################
-    def __init__(self, resourceObject, dataset, startRow=0, numRows=ALL_ROWS, makeNull=False, *, earlyExit, metaOnly, enableAttributes):
+    def __init__(self, resourceObject, dataset, hyperslice=None, makeNull=False, enableFill=True, *, earlyExit, metaOnly, enableAttributes):
         # initialize object
         self.resourceObject         = resourceObject
         self.earlyExit              = earlyExit
         self.metaOnly               = metaOnly
         self.enableAttributes       = enableAttributes
+        self.enableFill             = enableFill
         self.pos                    = self.resourceObject.rootAddress
         self.currObjHdrPos          = 0
+        self.numElements            = 1 # recalculated below
+        self.shape                  = [] # recalculated below
         self.dataset                = dataset
-        self.datasetStartRow        = startRow
-        self.datasetNumRows         = numRows
+        self.hyperslice             = hyperslice
         self.datasetPath            = list(filter(('').__ne__, self.dataset.split('/')))
         self.datasetPathLevels      = len(self.datasetPath)
         self.datasetFound           = False
         self.dataChunkBufferSize    = 0
         self.meta                   = H5Metadata()
+        self.sharedBuffer           = None
         self.values                 = None
 
         # check for null dataset
@@ -152,141 +160,130 @@ def __init__(self, resourceObject, dataset, startRow=0, numRows=ALL_ROWS, makeNu
             raise FatalError(f'missing data dimension information for {self.dataset}')
         elif self.meta.address == INVALID_VALUE[self.resourceObject.offsetSize]:
             raise FatalError(f'invalid data address for {self.dataset}')
-
-        # calculate size of data row (note dimension starts at 1)
-        row_size = self.meta.typeSize
-        for d in range(1, self.meta.ndims):
-            row_size *= self.meta.dimensions[d]
-
-        # get number of rows
-        first_dimension = (self.meta.ndims > 0) and self.meta.dimensions[0] or 1
-        self.datasetNumRows = (self.datasetNumRows == self.ALL_ROWS) and first_dimension or self.datasetNumRows
-        if (self.datasetStartRow + self.datasetNumRows) > first_dimension:
-            raise FatalError(f'read exceeds number of rows: {self.datasetStartRow} + {self.datasetNumRows} > {first_dimension}')
+        elif len(self.hyperslice) > self.meta.ndims:
+            raise FatalError(f'cannot provide hyperslice with more dimensions that dataset: {len(self.hyperslice)} > {self.meta.ndims}')
+        elif (self.meta.filter[self.meta.DEFLATE_FILTER] or self.meta.filter[self.meta.SHUFFLE_FILTER]) and \
+             ((self.meta.layout == self.COMPACT_LAYOUT) or (self.meta.layout == self.CONTIGUOUS_LAYOUT)):
+            raise FatalError(f'filters unsupported on non-chunked layouts')
+
+        # massage hyperslice
+        for d in range(self.meta.ndims):
+            if d < len(self.hyperslice):
+                if self.hyperslice[d] == None:
+                    self.hyperslice[d] = (0, self.meta.dimensions[d])
+                elif len(self.hyperslice[d]) == 2:
+                    if self.hyperslice[d][0] == None:
+                        self.hyperslice[d][0] = 0
+                    if self.hyperslice[d][1] == None:
+                        self.hyperslice[d][1] = self.meta.dimensions[d]
+                else:
+                    raise FatalError(f'invalid hyperslice, must provide as list of ranges [x,y), got {self.hyperslice}')
+            else:
+                self.hyperslice.append((0, self.meta.dimensions[d]))
+            
+            # check for valid hyperslice
+            if (self.hyperslice[d][1] < self.hyperslice[d][0]) or \
+               (self.hyperslice[d][1] > self.meta.dimensions[d]) or \
+               (self.hyperslice[d][0] < 0) or (self.hyperslice[d][1] < 0):
+                    raise FatalError(f'invalid hyperslice, must provide as list of valid ranges, got {self.hyperslice}')
+
+        # calculate shape and number of elements
+        for d in range(self.meta.ndims):
+            elements_in_dimension = self.hyperslice[d][1] - self.hyperslice[d][0]
+            if elements_in_dimension > 0:
+                self.numElements *= elements_in_dimension
+            self.shape.append(elements_in_dimension)
 
         # calculate size of buffer
-        buffer_size = row_size * self.datasetNumRows
-
-        # calculate buffer start */
-        buffer_offset = row_size * self.datasetStartRow
-
-        # check if data address and data size is valid
-        if self.resourceObject.errorChecking:
-            if (self.meta.size != 0) and (self.meta.size < (buffer_offset + buffer_size)):
-                raise FatalError(f'read exceeds available data: {self.meta.size} < {buffer_offset} + {buffer_size}')
-            if (self.meta.filter[self.meta.DEFLATE_FILTER] or self.meta.filter[self.meta.SHUFFLE_FILTER]) and \
-               ((self.meta.layout == self.COMPACT_LAYOUT) or (self.meta.layout == self.CONTIGUOUS_LAYOUT)):
-                raise FatalError(f'filters unsupported on non-chunked layouts')
-
-        # read dataset
-        if buffer_size > 0:
-            if (self.meta.layout == self.COMPACT_LAYOUT) or (self.meta.layout == self.CONTIGUOUS_LAYOUT):
-                data_addr = self.meta.address + buffer_offset
-                buffer = self.resourceObject.ioRequest(data_addr, buffer_size, caching=False)
-            elif self.meta.layout == self.CHUNKED_LAYOUT:
-                # chunk layout specific error checks
-                if self.resourceObject.errorChecking:
-                    if self.meta.elementSize != self.meta.typeSize:
-                        raise FatalError(f'chunk element size does not match data element size: {self.meta.elementSize} !=  {self.meta.typeSize}')
-                    elif self.meta.chunkElements <= 0:
-                        raise FatalError(f'invalid number of chunk elements: {self.meta.chunkElements}')
-
-                # allocate and initialize buffer
-                buffer = bytearray(buffer_size)
-
-                # fill buffer with fill value (if provided)
-#                if self.meta.fillsize > 0:
-#                    fill_values = struct.pack('Q', self.meta.fillvalue)[:self.meta.fillsize]
-#                    for i in range(0, buffer_size, self.meta.fillsize):
-#                        buffer[i:i+self.meta.fillsize] = fill_values
-
-                # calculate data chunk buffer size
-                self.dataChunkBufferSize = self.meta.chunkElements * self.meta.typeSize
-
-                # read b-tree
-                self.pos = self.meta.address
-                self.readBTreeV1(buffer, buffer_offset, dataset_level)
-
-                # check need to flatten chunks
-                flatten = False
-                for d in range(1, self.meta.ndims):
-                    if self.meta.chunkDimensions[d] != self.meta.dimensions[d]:
-                        flatten = True
-                        break
-
-                # flatten chunks - place dataset in row order
-                if flatten:
-                    # new flattened buffer
-                    fbuf = numpy.empty(buffer_size, dtype=numpy.byte)
-                    bi = 0 # index into source buffer
-
-                    # build number of each chunk per dimension
-                    cdimnum = [0 for _ in range(self.MAX_NDIMS * 2)]
-                    for i in range(self.meta.ndims):
-                        cdimnum[i] = self.meta.dimensions[i] / self.meta.chunkDimensions[i]
-                        cdimnum[i + self.meta.ndims] = self.meta.chunkDimensions[i]
-
-                    # build size of each chunk per flattened dimension
-                    cdimsizes = [0 for _ in range(self.FLAT_NDIMS)]
-                    cdimsizes[0] = self.meta.chunkDimensions[0] * self.meta.typeSize  # number of chunk rows
-                    for i in range(1, self.meta.ndims):
-                        cdimsizes[0] *= cdimnum[i]                          # number of columns of chunks
-                        cdimsizes[0] *= self.meta.chunkDimensions[i]        # number of columns in chunks
-                    cdimsizes[1] = self.meta.typeSize
-                    for i in range(1, self.meta.ndims):
-                        cdimsizes[1] *= self.meta.chunkDimensions[i]        # number of columns in chunks
-                    cdimsizes[2] = self.meta.typeSize
-                    for i in range(1, self.meta.ndims):
-                        cdimsizes[2] *= cdimnum[i]                          # number of columns of chunks
-                        cdimsizes[2] *= self.meta.chunkDimensions[i]        # number of columns in chunks
-
-                    # initialize loop variables
-                    ci = self.FLAT_NDIMS - 1;                               # chunk dimension index
-                    dimi = [0 for _ in range(self.MAX_NDIMS * 2)]           # chunk dimension indices
-
-                    # loop through each chunk
-                    while True:
-                        # calculate start position
-                        start = 0
-                        for i in range(self.FLAT_NDIMS):
-                            start += dimi[i] * cdimsizes[i]
-
-                        # copy into new buffer
-                        for k in range(cdimsizes[1]):
-                            fbuf[int(start + k)] = buffer[bi]
-                            bi += 1
-
-                        # update indices
-                        dimi[ci] += 1
-                        while dimi[ci] == cdimnum[ci]:
-                            dimi[ci] = 0
-                            ci -= 1
-                            if ci < 0:
-                                break
-                            else:
-                                dimi[ci] += 1
+        buffer_size = self.meta.typeSize * self.numElements
+        if buffer_size <= 0:
+            log.warn(f'empty read: typeSize={self.meta.typeSize}, numElements={self.numElements}')
+            return
 
-                        # check exit condition
-                        if ci < 0:
-                            break
-                        else:
-                            ci = self.FLAT_NDIMS - 1
+        # allocate buffer
+        if self.resourceObject.multiProcess:
+            if self.meta.ndims > 0:
+                self.sharedBuffer = shared_memory.SharedMemory(create=True, size=buffer_size)
+                buffer = self.sharedBuffer.buf
+        else:
+            buffer = bytearray(buffer_size)
+
+        # ###################################
+        # read compact and contiguous layouts
+        # ###################################
+        if (self.meta.layout == self.COMPACT_LAYOUT) or (self.meta.layout == self.CONTIGUOUS_LAYOUT):
+            if self.meta.ndims == 0:
+                buffer = self.resourceObject.ioRequest(self.meta.address, buffer_size, caching=False)
+            else:
+                compact_buffer = self.resourceObject.ioRequest(self.meta.address, buffer_size, caching=False)
+                self.readSlice(buffer, self.shape, self.hyperslice, compact_buffer, self.meta.dimensions, self.hyperslice)
 
-                    # replace buffer
-                    buffer = fbuf
+        # ###################################
+        # read chunked layouts
+        # ###################################
+        elif self.meta.layout == self.CHUNKED_LAYOUT:
+            # chunk layout specific error checks
+            if self.resourceObject.errorChecking:
+                if self.meta.elementSize != self.meta.typeSize:
+                    raise FatalError(f'chunk element size does not match data element size: {self.meta.elementSize} !=  {self.meta.typeSize}')
+                elif self.meta.chunkElements <= 0:
+                    raise FatalError(f'invalid number of chunk elements: {self.meta.chunkElements}')
+
+            # fill buffer with fill value (if provided and if enabled)
+            if self.enableFill and self.meta.fillsize > 0:
+                fill_values = struct.pack('Q', self.meta.fillvalue)[:self.meta.fillsize]
+                for i in range(0, buffer_size, self.meta.fillsize):
+                    buffer[i:i+self.meta.fillsize] = fill_values
+
+            # calculate data chunk buffer size
+            self.dataChunkBufferSize = self.meta.chunkElements * self.meta.typeSize
+
+            # calculate step size of each dimension in chunks
+            # ... for example a 12x12x12 cube of unsigned chars
+            # ... with a chunk size of 3x3x3 would be have 4x4x4 chunks
+            # ... the step size in chunks is then 16x4x1
+            self.dimensionsInChunks = [int(self.meta.dimensions[d] / self.meta.chunkDimensions[d]) for d in range(self.meta.ndims)]
+            self.chunkStepSize = [1 for _ in range(self.meta.ndims)]
+            for d in range(self.meta.ndims-1, 0, -1):
+                self.chunkStepSize[d-1] = self.dimensionsInChunks[d] * self.chunkStepSize[d]
+
+            # calculate position of first and last element in hyperslice
+            hyperslice_in_chunks = [(int(self.hyperslice[d][0] / self.meta.chunkDimensions[d]), int(self.hyperslice[d][1] / self.meta.chunkDimensions[d])) for d in range(self.meta.ndims)]
+            self.hypersliceChunkStart = sum([hyperslice_in_chunks[d][0] * self.chunkStepSize[d] for d in range(self.meta.ndims)])
+            self.hypersliceChunkEnd = sum([hyperslice_in_chunks[d][1] * self.chunkStepSize[d] for d in range(self.meta.ndims)])
+
+            # read b-tree
+            self.pos = self.meta.address
+            if self.resourceObject.multiProcess:
+                reader = Process(target=BTreeReader, args=(self, buffer, dataset_level))
+                reader.start()
+                reader.join()
+            else:
+                self.readBTreeV1(buffer, dataset_level)
 
-            elif self.resourceObject.errorChecking:
-                raise FatalError(f'invalid data layout: {self.meta.layout}')
+        elif self.resourceObject.errorChecking:
+            raise FatalError(f'invalid data layout: {self.meta.layout}')
 
         # populate data
         if self.meta.type == H5Metadata.FIXED_POINT_TYPE or self.meta.type == H5Metadata.FLOATING_POINT_TYPE:
             elements = int(buffer_size / self.meta.typeSize)
             datatype = H5Metadata.TO_NUMPY_TYPE[self.meta.type][self.meta.signedval][self.meta.typeSize]
             self.values = numpy.frombuffer(buffer, dtype=datatype, count=elements)
+            if self.meta.ndims > 1:
+                self.values = self.values.reshape(self.shape)
         elif self.meta.type == H5Metadata.STRING_TYPE:
             self.values = ctypes.create_string_buffer(buffer).value.decode('ascii')
         else:
             log.warn(f'{self.dataset} is an unsupported datatype {self.meta.type}: unable to populate values')
+            
+    #######################
+    # Destructor
+    #######################
+    def __del__(self):
+        self.values = None
+        if self.sharedBuffer != None:
+            self.sharedBuffer.close()
+            self.sharedBuffer.unlink()
 
     #######################
     # readField
@@ -365,7 +362,7 @@ def readSuperblock(resourceObject):
             block = resourceObject.ioRequest(12 + (3 * resourceObject.offsetSize), resourceObject.offsetSize)
             root_group_offset = struct.unpack(f'<{SIZE_2_FORMAT[resourceObject.offsetSize]}', block)[0]
 
-        # print file information
+        # display file information
         if resourceObject.verbose:
             log.info(f'File Information @0x{root_group_offset:x}')
             log.info(f'Size of Offsets:      {resourceObject.offsetSize}')
@@ -547,7 +544,7 @@ def readMessagesV1(self, end_of_hdr, obj_hdr_flags, dlvl):
             self.pos += alignment_padding
             bytes_read += alignment_padding
             if self.resourceObject.errorChecking and (bytes_read != msg_size):
-                raise FatalError(f'header v1 message different size than specified: {bytes_read} != {msg_size}')
+                raise FatalError(f'header v1 message of type {msg_type} different size than specified: {bytes_read} != {msg_size}')
 
             # check if dataset found
             if self.earlyExit and self.datasetFound:
@@ -613,14 +610,14 @@ def dataspaceMsgHandler(self, msg_size, obj_hdr_flags, dlvl, meta=None):
         PERM_INDEX_PRESENT = 0x2
         starting_position  = self.pos
         version            = self.readField(1)
-        dimensionality     = self.readField(1)
+        meta.ndims         = self.readField(1)
         flags              = self.readField(1)
         self.pos          += ((version == 1) and 5 or 1) # go past reserved bytes
 
         if self.resourceObject.verbose:
             log.info(f'<<Dataspace Message - {self.dataset}[{dlvl}] @0x{starting_position:x}>>')
             log.info(f'Version:              {version}')
-            log.info(f'Dimensionality:       {dimensionality}')
+            log.info(f'Dimensionality:       {meta.ndims}')
             log.info(f'Flags:                {flags}')
 
         # check version and flags and dimenstionality
@@ -629,12 +626,9 @@ def dataspaceMsgHandler(self, msg_size, obj_hdr_flags, dlvl, meta=None):
                 raise FatalError(f'unsupported dataspace version: {version}')
             if flags & PERM_INDEX_PRESENT:
                 raise FatalError(f'unsupported permutation indexes')
-            if dimensionality > self.MAX_NDIMS:
-                raise FatalError(f'unsupported number of dimensions: {dimensionality}')
 
         # read and populate data dimensions
         meta.dimensions = []
-        meta.ndims = min(dimensionality, self.MAX_NDIMS)
         if meta.ndims > 0:
             for x in range(meta.ndims):
                 dimension = self.readField(self.resourceObject.lengthSize)
@@ -644,7 +638,7 @@ def dataspaceMsgHandler(self, msg_size, obj_hdr_flags, dlvl, meta=None):
 
             # skip over dimension permutations
             if flags & MAX_DIM_PRESENT:
-                skip_bytes = dimensionality * self.resourceObject.lengthSize
+                skip_bytes = meta.ndims * self.resourceObject.lengthSize
                 self.pos += skip_bytes
 
         # return bytes read
@@ -1037,7 +1031,6 @@ def datalayoutMsgHandler(self, msg_size, obj_hdr_flags, dlvl):
         elif self.meta.layout == self.CHUNKED_LAYOUT:
             # read number of dimensions
             chunk_num_dim = self.readField(1) - 1  # dimensionality is plus one over actual number of dimensions
-            chunk_num_dim = min(chunk_num_dim, self.MAX_NDIMS)
             if self.resourceObject.errorChecking and (self.meta.ndims != None) and (chunk_num_dim != self.meta.ndims):
                 raise FatalError(f'number of chunk dimensions does not match dimensionality of data: {chunk_num_dim} != {self.meta.ndims}')
             # read address of B-tree
@@ -1145,7 +1138,7 @@ def attributeMsgHandler(self, msg_size, obj_hdr_flags, dlvl):
         PAD_SIZE            = 8
         starting_position   = self.pos
         version             = self.readField(1)
-        self.pos           += 1
+        v2_3_flags          = self.readField(1)
         name_size           = self.readField(2)
         datatype_size       = self.readField(2)
         dataspace_size      = self.readField(2)
@@ -1156,18 +1149,37 @@ def attributeMsgHandler(self, msg_size, obj_hdr_flags, dlvl):
             log.info(f'Version:              {version}')
 
         # check version
-        if self.resourceObject.errorChecking and (version != 1):
+        if self.resourceObject.errorChecking and (version != 1) and (version != 2) and (version != 3):
             raise FatalError(f'invalid attribute version: {version}')
 
-        # update message sizes
-        datatype_size += ((PAD_SIZE - (datatype_size % PAD_SIZE)) % PAD_SIZE)
-        dataspace_size += ((PAD_SIZE - (dataspace_size % PAD_SIZE)) % PAD_SIZE)
+        # check shared messages
+        if self.resourceObject.errorChecking and (version != 1) and (v2_3_flags != 0):
+            raise FatalError(f'unsupported shared messages for attribute: {v2_3_flags}')
+
+        # character encoding
+        char_encoding = 'utf-8'
+        if version == 3:
+            name_character_set_encoding = self.readField(1)
+            if name_character_set_encoding == 0:
+                char_encoding = 'ascii'
+            elif name_character_set_encoding == 1:
+                char_encoding = 'utf-8'
+            elif self.resourceObject.errorChecking:
+                raise FatalError(f'invalid character set encoding: {name_character_set_encoding}')
+
+        # pad out message sizes (version 1 only)
+        if version == 1:
+            datatype_size += ((PAD_SIZE - (datatype_size % PAD_SIZE)) % PAD_SIZE)
+            dataspace_size += ((PAD_SIZE - (dataspace_size % PAD_SIZE)) % PAD_SIZE)
 
         # read attribute name
-        attr_name = self.readArray(name_size).tobytes().decode('utf-8')[:-1]
-        self.pos += (PAD_SIZE - (name_size % PAD_SIZE)) % PAD_SIZE; # align to next x-byte boundary
+        attr_name = self.readArray(name_size).tobytes().decode(char_encoding).strip('\0')
         attr_path = '/'.join(self.datasetPath[:dlvl] + [attr_name])
 
+        # pad out name size (version 1 only)
+        if version == 1:
+            self.pos += (PAD_SIZE - (name_size % PAD_SIZE)) % PAD_SIZE; # align to next x-byte boundary
+
         # display
         if self.resourceObject.verbose:
             log.info(f'Name:                 {attr_name}')
@@ -1351,10 +1363,9 @@ def symboltableMsgHandler(self, msg_size, obj_hdr_flags, dlvl):
             else:
                 self.pos += 6
 
-        # return bytes read
-        bytes_read = self.resourceObject.offsetSize + self.resourceObject.lengthSize
-        self.pos = return_position + bytes_read
-        return bytes_read
+        # reset position and return bytes read
+        self.pos = return_position 
+        return return_position - starting_position
 
     #######################
     # attributeinfoMsgHandler
@@ -1431,8 +1442,8 @@ def readSymbolTable(self, heap_address, dlvl):
         num_symbols = self.readField(2)
         for _ in range(num_symbols):
             # read symbol entry
-            link_name_offset    = self.readField(self.resourceObject.offsetsize)
-            obj_hdr_addr        = self.readField(self.resourceObject.offsetsize)
+            link_name_offset    = self.readField(self.resourceObject.offsetSize)
+            obj_hdr_addr        = self.readField(self.resourceObject.offsetSize)
             cache_type          = self.readField(4)
             self.pos += 20 # reserved + scratch pad
 
@@ -1459,11 +1470,12 @@ def readSymbolTable(self, heap_address, dlvl):
             self.resourceObject.pathAddresses[group_path] = obj_hdr_addr
 
             # process link
-            return_position = self.pos
             if dlvl < len(self.datasetPath) and link_name == self.datasetPath[dlvl]:
                 if cache_type == 2:
                     raise FatalError(f'symbolic links are unsupported: {link_name}')
-                self.readObjHdr(obj_hdr_addr, dlvl + 1)
+                return_position = self.pos
+                self.pos = obj_hdr_addr
+                self.readObjHdr(dlvl + 1)
                 self.pos = return_position
                 if self.earlyExit:
                     break # datasetFound
@@ -1749,13 +1761,97 @@ def readIndirectBlock(self, heap_info, block_size, obj_hdr_flags, dlvl):
         # return bytes read
         return self.pos - starting_position
 
+    #######################
+    # hypersliceIntersection
+    #######################
+    def hypersliceIntersection(self, node_slice, node_level):
+        if node_level == 0:
+            # check for intersection for all dimensions
+            for d in range(self.meta.ndims):
+                if node_slice[d][1] < self.hyperslice[d][0] or node_slice[d][0] >= self.hyperslice[d][1]:
+                    return False
+            return True
+        else: # node_level > 0
+            # calculate chunk of node slice
+            node_slice_in_chunks = [(int(node_slice[d][0] / self.meta.chunkDimensions[d]), int(node_slice[d][1] / self.meta.chunkDimensions[d])) for d in range(self.meta.ndims)]
+            # calculate element position
+            node_start = sum([node_slice_in_chunks[d][0] * self.chunkStepSize[d] for d in range(self.meta.ndims)])
+            node_end = sum([node_slice_in_chunks[d][1] * self.chunkStepSize[d] for d in range(self.meta.ndims)])
+            # check for intersection of position
+            if node_end < self.hypersliceChunkStart or node_start >= self.hypersliceChunkEnd:
+                return False
+            return True
+
+    #######################
+    # hypersliceSubset
+    #######################
+    def hypersliceSubset(self, chunk_slice):
+        subset = []
+        for d in range(self.meta.ndims):
+            subset.append((max(chunk_slice[d][0], self.hyperslice[d][0]), min(chunk_slice[d][1], self.hyperslice[d][1])))
+        return subset
+
+    #######################
+    # readSlice
+    #######################
+    def readSlice(self, output_buffer, output_dimensions, output_slice, input_buffer, input_dimensions, input_slice):
+
+        # get number of dimensions
+        ndims = len(input_dimensions)
+
+        # build serialized size of each input and output dimension
+        # ... for example a 4x4x4 cube of unsigned chars would be 16,4,1
+        input_dim_step = [self.meta.typeSize for _ in range(ndims)]
+        output_dim_step = [self.meta.typeSize for _ in range(ndims)]
+        for d in range(ndims-1, 0, -1):
+            input_dim_step[d-1] = input_dimensions[d] * input_dim_step[d]
+            output_dim_step[d-1] = output_dimensions[d] * output_dim_step[d]
+
+        # initialize dimension indices
+        input_dim_index = [i[0] for i in input_slice] # initialize to the start index of each input_slice
+        output_dim_index = [i[0] for i in output_slice] # initialize to the start index of each output_slice
+
+        # calculate amount to read each time
+        read_slice = input_slice[-1][1] - input_slice[-1][0]
+        read_size = input_dim_step[-1] * read_slice # size of data to read each time
+
+        # read each input_slice
+        while input_dim_index[0] < input_slice[0][1]: # while the first dimension index has not traversed its range
+
+            # calculate source offset
+            src_offset = 0
+            for d in range(ndims):
+                src_offset += (input_dim_index[d] * input_dim_step[d])
+
+            # calculate destination offset
+            dst_offset = 0
+            for d in range(ndims):
+                dst_offset += (output_dim_index[d] * output_dim_step[d])
+
+            # copy data from input buffer to output buffer
+            output_buffer[dst_offset:dst_offset + read_size] = input_buffer[src_offset:src_offset + read_size]
+
+            # go to next set of input indices
+            input_dim_index[-1] += read_slice
+            i = len(input_dim_index) - 1
+            while i > 0 and input_dim_index[i] == input_slice[i][1]:    # while the level being examined is at the last index
+                input_dim_index[i] = input_slice[i][0]                  # set index back to the beginning of hyperslice
+                input_dim_index[i - 1] += 1                             # bump the previous index to the next element in dimension
+                i -= 1                                                  # go to previous dimension
+
+            # update output indices
+            output_dim_index[-1] += read_slice
+            i = len(output_dim_index) - 1
+            while i > 0 and output_dim_index[i] == output_slice[i][1]:  # while the level being examined is at the last index
+                output_dim_index[i] = output_slice[i][0]                # set index back to the beginning of hyperslice
+                output_dim_index[i - 1] += 1                            # bump the previous index to the next element in dimension
+                i -= 1                                                  # go to previous dimension
+
     #######################
     # readBTreeV1
     #######################
-    def readBTreeV1(self, buffer, buffer_offset, dlvl):
+    def readBTreeV1(self, buffer, dlvl):
         starting_position = self.pos
-        data_key1 = self.datasetStartRow
-        data_key2 = self.datasetStartRow + self.datasetNumRows - 1
 
         # display
         if self.resourceObject.verbose:
@@ -1778,8 +1874,8 @@ def readBTreeV1(self, buffer, buffer_offset, dlvl):
 
         # display
         if self.resourceObject.verbose:
-            log.info(f'Node Level:           {node_level}')
-            log.info(f'Entries Used:         {entries_used}')
+            log.debug(f'Node Level:           {node_level}')
+            log.debug(f'Entries Used:         {entries_used}')
 
         # skip sibling addresses
         self.pos += self.resourceObject.offsetSize * 2
@@ -1791,42 +1887,50 @@ def readBTreeV1(self, buffer, buffer_offset, dlvl):
         for e in range(entries_used):
             child_addr  = self.readField(self.resourceObject.offsetSize)
             next_node   = self.readBTreeNodeV1(self.meta.ndims)
-            child_key1  = curr_node['row_key']
-            child_key2  = next_node['row_key'] # there is always +1 keys
-            if (next_node['chunk_size'] == 0) and (self.meta.ndims > 0):
-                child_key2 = self.meta.dimensions[0]
+
+            # construct node slice
+            if node_level > 0:
+                node_slice = [(start, stop) for start, stop in zip(curr_node['slice'], next_node['slice'])]
+            else:
+                node_slice = [(start, min(start + extent, dimension)) for start, extent, dimension in zip(curr_node['slice'], self.meta.chunkDimensions, self.meta.dimensions)]
 
             # display
             if self.resourceObject.verbose:
+                log.info(f'level={node_level}, entry={e+1} of {entries_used}, node_slice={node_slice}, curr_node={curr_node["slice"]}, next_node={next_node["slice"]}')
                 log.debug(f'Entry <{node_level}>:            {e}')
                 log.debug(f'Chunk Size:           {curr_node["chunk_size"]} | {next_node["chunk_size"]}')
                 log.debug(f'Filter Mask:          {curr_node["filter_mask"]} | {next_node["filter_mask"]}')
-                log.debug(f'Chunk Key:            {child_key1} | {child_key2}')
-                log.debug(f'Data Key:             {data_key1} | {data_key2}')
-                log.debug(f'Slice:                {" ".join([str(d) for d in curr_node["slice"]])}')
+                log.debug(f'Node Slice:           {curr_node["slice"]} | {next_node["slice"]}')
+                log.debug(f'Hyperslice:           {self.hyperslice}')
+                log.debug(f'Chunk Dimensions:     {self.meta.chunkDimensions}')
                 log.debug(f'Child Address:        0x{child_addr:x}')
 
+            # check for short-cutting
+            if self.meta.ndims <= 1 and self.hyperslice[0][1] < node_slice[0][0]:
+                break
+
             # check inclusion
-            if  (data_key1  >= child_key1 and data_key1  <  child_key2) or \
-                (data_key2  >= child_key1 and data_key2  <  child_key2) or \
-                (child_key1 >= data_key1  and child_key1 <= data_key2)  or \
-                (child_key2 >  data_key1  and child_key2 <  data_key2):
+            if self.hypersliceIntersection(node_slice, node_level):
+
+                # display
+                if self.resourceObject.verbose:
+                    log.info(f'entry {node_level}.{e+1} of selected')
+
                 # process child entry
                 if node_level > 0:
                     return_position = self.pos
                     self.pos = child_addr
-                    self.readBTreeV1(buffer, buffer_offset, dlvl)
+                    self.readBTreeV1(buffer, dlvl)
                     self.pos = return_position
-                else:
-                    # calculate chunk location
-                    chunk_offset = 0
-                    for i in range(self.meta.ndims):
-                        slice_size = curr_node['slice'][i] * self.meta.typeSize
-                        for k in range(i):
-                            slice_size *= self.meta.chunkDimensions[k]
-                        for j in range(i + 1, self.meta.ndims):
-                            slice_size *= self.meta.dimensions[j]
-                        chunk_offset += slice_size
+                
+                elif self.meta.ndims == 0:
+                    log.warn(f'Unexpected chunked read of a zero dimensional dataset')
+                    pass # NOT SURE WHAT TO DO HERE, IS THIS POSSIBLE?
+                
+                elif self.meta.ndims == 1:
+                    # calculate offsets
+                    buffer_offset = self.meta.typeSize * self.hyperslice[0][0]
+                    chunk_offset = curr_node['slice'][0] * self.meta.typeSize
 
                     # calculate buffer index - offset into data buffer to put chunked data
                     buffer_index = 0
@@ -1853,29 +1957,29 @@ def readBTreeV1(self, buffer, buffer_offset, dlvl):
                     if self.resourceObject.verbose:
                         log.debug(f'Chunk Offset:         {chunk_offset} ({int(chunk_offset/self.meta.typeSize)})')
                         log.debug(f'Buffer Index:         {buffer_index} ({int(buffer_index/self.meta.typeSize)})')
-                        log.debug(f'Chunk Bytes:          {chunk_bytes} ({int(chunk_bytes/self.meta.typeSize)})')
+                        log.debug(f'Chunk Bytes:          {chunk_bytes} ({int(chunk_bytes/self.meta.typeSize)})\n')
 
                     # read chunk
                     if self.meta.filter[self.meta.DEFLATE_FILTER]:
 
                         # read data into chunk filter buffer (holds the compressed data)
-                        self.dataChunkFilterBuffer = self.resourceObject.ioRequest(child_addr, curr_node['chunk_size'])
+                        chunk_buffer = self.resourceObject.ioRequest(child_addr, curr_node['chunk_size'])
 
                         # inflate directly into data buffer
                         if (chunk_bytes == self.dataChunkBufferSize) and (not self.meta.filter[self.meta.SHUFFLE_FILTER]):
-                            buffer[buffer_index:buffer_index+chunk_bytes] = self.inflateChunk(self.dataChunkFilterBuffer)
+                            buffer[buffer_index:buffer_index+chunk_bytes] = self.inflateChunk(chunk_buffer)
 
                         # inflate into data chunk buffer */
                         else:
-                            dataChunkBuffer = self.inflateChunk(self.dataChunkFilterBuffer)
+                            chunk_buffer = self.inflateChunk(chunk_buffer)
 
                             # shuffle data chunk buffer into data buffer
                             if self.meta.filter[self.meta.SHUFFLE_FILTER]:
-                                buffer[buffer_index:buffer_index+chunk_bytes] = self.shuffleChunk(dataChunkBuffer, chunk_index, chunk_bytes, self.meta.typeSize)
+                                buffer[buffer_index:buffer_index+chunk_bytes] = self.shuffleChunk(chunk_buffer, chunk_index, chunk_bytes, self.meta.typeSize)
 
                             # copy data chunk buffer into data buffer
                             else:
-                                buffer[buffer_index:buffer_index+chunk_bytes] = dataChunkBuffer[chunk_index:chunk_index+chunk_bytes]
+                                buffer[buffer_index:buffer_index+chunk_bytes] = chunk_buffer[chunk_index:chunk_index+chunk_bytes]
 
                     # check filter options
                     elif self.resourceObject.errorChecking and self.meta.filter[self.meta.SHUFFLE_FILTER]:
@@ -1889,6 +1993,36 @@ def readBTreeV1(self, buffer, buffer_offset, dlvl):
                     else:
                         chunk_offset_addr = child_addr + chunk_index
                         buffer[buffer_index:buffer_index+chunk_bytes] = self.resourceObject.ioRequest(chunk_offset_addr, chunk_bytes)
+                
+                elif self.meta.ndims > 1:
+
+                    # read entire chunk
+                    chunk_buffer = self.resourceObject.ioRequest(child_addr, curr_node['chunk_size'])        # read
+                    if self.meta.filter[self.meta.DEFLATE_FILTER]:                                           # if compressed
+                        chunk_buffer = self.inflateChunk(chunk_buffer)                                       #    decompress
+                        if self.meta.filter[self.meta.SHUFFLE_FILTER]:                                       # if shuffled
+                            self.shuffleChunk(chunk_buffer, 0, self.dataChunkBufferSize, self.meta.typeSize) #    reshuffle
+
+                    # get truncated slice to pull out of chunk 
+                    # (intersection of chunk_slice and hyperslice selection)
+                    chunk_slice_to_read = self.hypersliceSubset(node_slice)
+
+                    # build slice that is read
+                    read_slice = []
+                    for d in range(self.meta.ndims):
+                        x0 = abs(chunk_slice_to_read[d][0] - node_slice[d][0])
+                        x1 = x0 + abs(chunk_slice_to_read[d][1] - chunk_slice_to_read[d][0])
+                        read_slice.append((x0, x1))
+
+                    # build slice that is written
+                    write_slice = []
+                    for d in range(self.meta.ndims):
+                        x0 = abs(chunk_slice_to_read[d][0] - self.hyperslice[d][0])
+                        x1 = x0 + abs(chunk_slice_to_read[d][1] - chunk_slice_to_read[d][0])
+                        write_slice.append((x0, x1))
+
+                    # read subset of chunk into return buffer
+                    self.readSlice(buffer, self.shape, write_slice, chunk_buffer, self.meta.chunkDimensions, read_slice)
 
             # goto next key
             curr_node = next_node
@@ -1911,12 +2045,6 @@ def readBTreeNodeV1(self, ndims):
         if self.resourceObject.errorChecking and (trailing_zero % self.meta.typeSize != 0):
             raise FatalError(f'key did not include a trailing zero: {trailing_zero}')
 
-        # set node key
-        if ndims > 0:
-            node['row_key'] = node['slice'][0]
-        else:
-            node['row_key'] = 0
-
         # return copy of node
         return node
 
@@ -1935,9 +2063,9 @@ def shuffleChunk(self, input, output_offset, output_size, type_size):
         output = bytearray(output_size)
         dst_index = 0
         shuffle_block_size = int(len(input) / type_size)
-        num_elements = int(output_size / type_size)
+        elements_to_shuffle = int(output_size / type_size)
         start_element = int(output_offset / type_size)
-        for element_index in range(start_element, start_element + num_elements):
+        for element_index in range(start_element, start_element + elements_to_shuffle):
             for val_index in range(0, type_size):
                 src_index = (val_index * shuffle_block_size) + element_index
                 output[dst_index] = input[src_index]
diff --git a/h5coro/h5promise.py b/h5coro/h5promise.py
index d9a3de2..2fab049 100644
--- a/h5coro/h5promise.py
+++ b/h5coro/h5promise.py
@@ -36,12 +36,12 @@
 # Local Functions
 ###############################################################################
 
-def datasetThread(resourceObject, dataset, startRow=0, numRows=H5Dataset.ALL_ROWS, *, earlyExit, metaOnly, enableAttributes):
+def datasetThread(resourceObject, dataset, hyperslice, *, earlyExit, metaOnly, enableAttributes):
     try:
-        return H5Dataset(resourceObject, dataset, startRow, numRows, earlyExit=earlyExit, metaOnly=metaOnly, enableAttributes=enableAttributes)
+        return H5Dataset(resourceObject, dataset, hyperslice, earlyExit=earlyExit, metaOnly=metaOnly, enableAttributes=enableAttributes)
     except RuntimeError as e:
-        log.warning(f'H5Coro encountered error reading {dataset}: {e}')
-        return H5Dataset(resourceObject, dataset, startRow, numRows, makeNull=True, earlyExit=earlyExit, metaOnly=metaOnly, enableAttributes=enableAttributes)
+        log.warn(f'H5Coro encountered error reading {dataset}: {e}')
+        return H5Dataset(resourceObject, dataset, hyperslice, makeNull=True, earlyExit=earlyExit, metaOnly=metaOnly, enableAttributes=enableAttributes)
 
 def resultThread(promise, futures):
     for future in as_completed(futures):
@@ -77,7 +77,7 @@ def __init__(self, resourceObject, datasetTable, block, *, earlyExit, metaOnly,
 
         # start threads working on each dataset
         executor = ThreadPoolExecutor(max_workers=len(datasetTable))
-        futures = [executor.submit(datasetThread, resourceObject, dataset["dataset"], dataset["startrow"], dataset["numrows"], earlyExit=earlyExit, metaOnly=metaOnly, enableAttributes=enableAttributes) for dataset in datasetTable.values()]
+        futures = [executor.submit(datasetThread, resourceObject, dataset["dataset"], dataset["hyperslice"], earlyExit=earlyExit, metaOnly=metaOnly, enableAttributes=enableAttributes) for dataset in datasetTable.values()]
 
         # wait for datasets to be populated OR populate datasets in the background
         if block:
diff --git a/utils/list_group.py b/utils/list_group.py
index 2cebe48..7aa74f7 100644
--- a/utils/list_group.py
+++ b/utils/list_group.py
@@ -28,27 +28,31 @@
 # ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 
 import h5coro
-from utils import args, credentials
+from utils import args, credentials, execute
 
-h5obj = h5coro.H5Coro(args.granule, args.driver, errorChecking=args.checkErrors, verbose=args.verbose, credentials=credentials)
-variables, attributes, groups= h5obj.list(args.group, w_attr=args.enableAttributes)
+def main():
+    # read object
+    h5obj = h5coro.H5Coro(args.granule, args.driver, errorChecking=args.checkErrors, verbose=args.verbose, credentials=credentials, multiProcess=args.multiProcess)
+    variables, attributes, groups= h5obj.list(args.group, w_attr=args.enableAttributes)
 
-# list groups
-for group, listing in groups.items():
-    print(f'[g] {group}')
-    for key, value in listing.items():
-        print(f'  {key}: {value}')
+    # list groups
+    for group, listing in groups.items():
+        print(f'[g] {group}')
+        for key, value in listing.items():
+            print(f'  {key}: {value}')
 
-# list variables
-for variable, listing in variables.items():
-    metadata = ""
-    if '__metadata__' in listing:
-        metadata = str(listing['__metadata__'])
-        del listing['__metadata__']
-    print(f'[v] {variable}: {metadata}')
-    for key, value in listing.items():
-        print(f'  {key}: {value}')
+    # list variables
+    for variable, listing in variables.items():
+        metadata = ""
+        if '__metadata__' in listing:
+            metadata = str(listing['__metadata__'])
+            del listing['__metadata__']
+        print(f'[v] {variable}: {metadata}')
+        for key, value in listing.items():
+            print(f'  {key}: {value}')
 
-# list attributes
-for attribute, value in attributes.items():
-    print(f'[a] {attribute}: {value}')
+    # list attributes
+    for attribute, value in attributes.items():
+        print(f'[a] {attribute}: {value}')
+
+execute(main)
\ No newline at end of file
diff --git a/utils/open_xarray.py b/utils/open_xarray.py
index 0b617de..6431ae7 100644
--- a/utils/open_xarray.py
+++ b/utils/open_xarray.py
@@ -27,14 +27,35 @@
 # OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF
 # ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 
-import h5coro
 import xarray as xr
 import earthaccess
 from h5coro.datasets import icesat2
-from utils import args, credentials
+from utils import args, execute
 
-col_convs = {"delta_time": icesat2.to_datetime}
-auth = earthaccess.login()
-ds = xr.open_dataset(args.granule, engine='h5coro', group=args.group, col_convs=col_convs, creds=auth)
+def main():
 
-print(ds)
\ No newline at end of file
+    # select conversions for xarray to perform
+    if args.conversions:
+        col_convs = {"delta_time": icesat2.to_datetime}
+    else:
+        col_convs = {}
+
+    # get credentials to access resources in Earthdata Cloud
+    auth = earthaccess.login()
+    creds = auth.get_s3_credentials(daac=args.daac)
+
+    # open up resource as xarray
+    ds = xr.open_dataset( args.granule, 
+                          engine='h5coro', 
+                          group=args.group, 
+                          pick_variables=args.pick, 
+                          col_convs=col_convs, 
+                          credentials=creds, 
+                          log_level=args.loglevel, 
+                          verbose=args.verbose,
+                          multi_process=args.multiProcess )
+
+    # display xarray
+    print(ds)
+
+execute(main)
\ No newline at end of file
diff --git a/utils/read_attribute.py b/utils/read_attribute.py
new file mode 100644
index 0000000..3fadf45
--- /dev/null
+++ b/utils/read_attribute.py
@@ -0,0 +1,39 @@
+# Copyright (c) 2023, University of Washington
+# All rights reserved.
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+#
+# 1. Redistributions of source code must retain the above copyright notice,
+#    this list of conditions and the following disclaimer.
+#
+# 2. Redistributions in binary form must reproduce the above copyright notice,
+#    this list of conditions and the following disclaimer in the documentation
+#    and/or other materials provided with the distribution.
+#
+# 3. Neither the name of the University of Washington nor the names of its
+#    contributors may be used to endorse or promote products derived from this
+#    software without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE UNIVERSITY OF WASHINGTON AND CONTRIBUTORS
+# “AS IS” AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED
+# TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
+# PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE UNIVERSITY OF WASHINGTON OR
+# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
+# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
+# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
+# OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
+# WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR
+# OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF
+# ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+import h5coro
+from utils import args, credentials, execute
+
+def main():
+    h5obj = h5coro.H5Coro(args.granule, args.driver, errorChecking=args.checkErrors, verbose=args.verbose, credentials=credentials, multiProcess=args.multiProcess)
+    promise = h5obj.readDatasets(args.variables, block=True, enableAttributes=True)
+    for attribute in promise:
+        print(f'{attribute}: {promise[attribute]}')
+
+execute(main)
\ No newline at end of file
diff --git a/utils/read_variable.py b/utils/read_variable.py
index dad603e..ab846e7 100644
--- a/utils/read_variable.py
+++ b/utils/read_variable.py
@@ -28,9 +28,12 @@
 # ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 
 import h5coro
-from utils import args, credentials
+from utils import args, credentials, execute
 
-h5obj = h5coro.H5Coro(args.granule, args.driver, errorChecking=args.checkErrors, verbose=args.verbose, credentials=credentials)
-promise = h5obj.readDatasets(args.variables, block=True, enableAttributes=args.enableAttributes)
-for variable in promise:
-    print(f'{variable}: {promise[variable][args.slice[0]:args.slice[1]]}')
+def main():
+    h5obj = h5coro.H5Coro(args.granule, args.driver, errorChecking=args.checkErrors, verbose=args.verbose, credentials=credentials, multiProcess=args.multiProcess)
+    promise = h5obj.readDatasets(args.variables, block=True, enableAttributes=args.enableAttributes)
+    for variable in promise:
+        print(f'{variable}: {promise[variable][args.slice[0]:args.slice[1]]}')
+
+execute(main)
diff --git a/utils/utils.py b/utils/utils.py
index 32e3854..212fca4 100644
--- a/utils/utils.py
+++ b/utils/utils.py
@@ -1,22 +1,27 @@
-import h5coro
 from h5coro import s3driver, filedriver, logger
 import earthaccess
 import argparse
 import logging
+from time import perf_counter
 
 # Command Line Arguments #
 parser = argparse.ArgumentParser(description="""Deep inspection of a subgroup""")
-parser.add_argument('--granule','-f', type=str, default="nsidc-cumulus-prod-protected/ATLAS/ATL03/006/2018/10/17/ATL03_20181017222812_02950102_006_02.h5")
-parser.add_argument('--variables','-x', nargs='+', type=str, default=["/gt2l/heights/h_ph"])
-parser.add_argument('--group','-g', type=str, default="/gt2l/heights")
-parser.add_argument('--slice','-s', nargs=2, type=int, default=[0,10])
-parser.add_argument('--profile','-p', type=str, default="default")
-parser.add_argument('--driver','-d', type=str, default="s3") # s3 or file
-parser.add_argument('--enableAttributes','-a', action='store_true', default=False)
-parser.add_argument('--checkErrors','-e', action='store_true', default=False)
-parser.add_argument('--verbose','-v', action='store_true', default=False)
-parser.add_argument('--loglevel','-l', type=str, default="unset")
-parser.add_argument('--daac','-q', type=str, default="NSIDC")
+parser.add_argument('--granule',            '-f', type=str,             default="nsidc-cumulus-prod-protected/ATLAS/ATL03/006/2018/10/17/ATL03_20181017222812_02950102_006_02.h5")
+parser.add_argument('--variables',          '-x', nargs='+', type=str,  default=["/gt2l/heights/h_ph"])
+parser.add_argument('--pick',               '-k', nargs='*', type=str,  default=[])
+parser.add_argument('--group',              '-g', type=str,             default="/gt2l/heights")
+parser.add_argument('--slice',              '-s', nargs=2, type=int,    default=[0,10])
+parser.add_argument('--conversions',        '-c', action='store_true',  default=False)
+parser.add_argument('--profile',            '-p', type=str,             default="default")
+parser.add_argument('--driver',             '-d', type=str,             default="s3") # s3 or file
+parser.add_argument('--enableAttributes',   '-a', action='store_true',  default=False)
+parser.add_argument('--checkErrors',        '-e', action='store_true',  default=False)
+parser.add_argument('--verbose',            '-v', action='store_true',  default=False)
+parser.add_argument('--multiProcess',       '-m', action='store_true',  default=False)
+parser.add_argument('--loglevel',           '-l', type=str,             default="unset")
+parser.add_argument('--daac',               '-q', type=str,             default="NSIDC")
+parser.add_argument('--perf',               '-r', action='store_true',  default=False)
+parser.add_argument('--perfbin',            '-b', action='store_true',  default=False)
 args,_ = parser.parse_known_args()
 
 # Conifugre I/O Driver #
@@ -42,3 +47,24 @@
     credentials = { "aws_access_key_id": s3_creds["accessKeyId"],
                     "aws_secret_access_key": s3_creds["secretAccessKey"],
                     "aws_session_token": s3_creds["sessionToken"] }
+
+# Execute and Profile #
+def execute(main):
+    if args.perf:
+        from cProfile import Profile
+        from pstats import SortKey, Stats
+        with Profile() as profile:
+            start_time = perf_counter()
+            main()
+            end_time = perf_counter()
+            stats = Stats(profile)
+            print(stats
+                .strip_dirs()
+                .sort_stats(SortKey.CUMULATIVE)
+                .reverse_order()
+                .print_stats())
+            if args.perfbin:
+                stats.dump_stats('perf.bin')
+            print(f'Total time of execution: {end_time - start_time}s')
+    else:
+        main()


Test patch:
diff --git a/tests/test_aos.py b/tests/test_aos.py
new file mode 100644
index 0000000..f0eb038
--- /dev/null
+++ b/tests/test_aos.py
@@ -0,0 +1,36 @@
+import pytest
+import h5coro
+import s3fs
+import h5py
+from h5coro import s3driver
+
+GRANULE = 'OR_ABI-L1b-RadF-M6C14_G16_s20192950450355_e20192950500042_c20192950500130.nc'
+BUCKET_NAME = 'eso-west2-curated'
+OBJECT_NAME = 'AOS/PoR/geo/GOES-16-ABI-L1B-FULLD/2019/295/04/' + GRANULE
+OBJECT_URL = 's3://' + BUCKET_NAME + '/' + OBJECT_NAME
+
+@pytest.mark.region
+class TestAOS:
+
+    def test_rad_read(self):
+        # test parameters
+        variable = '/Rad'
+        datasets = [ {"dataset": variable, "hyperslice": [[17,18], [2400,2600]]} ]
+        credentials = {"profile":"default"}
+
+        # read dataset with h5py
+        s3 = s3fs.S3FileSystem()
+        fp = h5py.File(s3.open(OBJECT_URL, 'rb'), mode='r')
+            
+        # read dataset with h5coro
+        h5obj = h5coro.H5Coro(BUCKET_NAME + '/' + OBJECT_NAME, s3driver.S3Driver, errorChecking=True, verbose=True, credentials=credentials, multiProcess=False)
+        promise = h5obj.readDatasets(datasets, block=True, enableAttributes=False)
+
+        # compare datasets
+        i = 0
+        for row in fp[variable][17:18]:
+            j = 0
+            for value in row[2400:2600]:
+                assert value == promise[variable][i][j]
+                j += 1
+            i += 1
diff --git a/tests/test_hdf.py b/tests/test_hdf.py
new file mode 100644
index 0000000..776b7be
--- /dev/null
+++ b/tests/test_hdf.py
@@ -0,0 +1,18 @@
+import pytest
+import h5coro
+from h5coro import s3driver
+
+HDF_OBJECT = "docs.hdfgroup.org/hdf5/h5/ou_process.h5"
+
+@pytest.mark.region
+class TestHDF:
+
+    def test_dataset_read(self):
+        dataset = "/dataset"
+        hyperslice = [[0,1], [0,5]]
+        datasets = [{'dataset': dataset, 'hyperslice': hyperslice}]
+        h5obj = h5coro.H5Coro(HDF_OBJECT, s3driver.S3Driver, errorChecking=True)
+        promise = h5obj.readDatasets(datasets, block=True)
+        expected = [ 0.0, 0.00370616, -0.00038263, -0.00219702, 0.01771416]
+        for i in range(len(expected)):
+            assert abs(promise[dataset][0][i] - expected[i]) < 0.0001
diff --git a/tests/test_icesat2.py b/tests/test_icesat2.py
index f77db4a..abd6a1e 100644
--- a/tests/test_icesat2.py
+++ b/tests/test_icesat2.py
@@ -1,5 +1,3 @@
-"""Tests for h5 endpoint."""
-
 import earthaccess
 import pytest
 
@@ -17,6 +15,7 @@
 
 ATL03_ATTRIBUTE = "gt2l/heights/h_ph/units"
 ATL03_DATASET = "gt2l/heights/h_ph"
+ATL03_2D_DATASET = "gt2l/heights/signal_conf_ph"
 ATL03_GROUP = "gt2l/heights"
 
 
@@ -93,3 +92,11 @@ def test_list_group(self):
         assert "data_rate" in attributes
         assert type(attributes["data_rate"]) == str
         assert variables["weight_ph"]["valid_max"][0] == 255
+
+    def test_2d_var(self):
+        h5obj = h5coro.H5Coro(ATL03_S3_OBJECT, s3driver.S3Driver, credentials=credentials)
+        promise = h5obj.readDatasets([{"dataset": ATL03_2D_DATASET, "hyperslice": [(0,10)]}], block=True, enableAttributes=False)
+        expected = [[0, -1, -1, -1, -1], [0, -1, -1, -1, -1]]
+        for row in range(len(expected)):
+            for column in range(len(expected[row])):
+                assert promise[ATL03_2D_DATASET][row][column] == expected[row][column]