diff --git a/.github/workflows/python-app.yml b/.github/workflows/python-app.yml
index f35db00..608beda 100644
--- a/.github/workflows/python-app.yml
+++ b/.github/workflows/python-app.yml
@@ -38,7 +38,7 @@ jobs:
         with:
           path: |
             /usr/share/miniconda3/envs/deepicedrain
-          key: cache-venv-${{ github.ref }}-${{ hashFiles('**/environment.yml') }}-${{ hashFiles('**/poetry.lock') }}
+          key: cache-venv-${{ github.ref }}-${{ hashFiles('**/environment.yml') }}-${{ hashFiles('**/poetry.lock') }}-${{ hashFiles('**/deepicedrain/*.py') }}
           restore-keys: |
             cache-venv-refs/heads/master-
 
@@ -66,4 +66,4 @@ jobs:
 
       - name: Test with pytest
         shell: bash -l {0}
-        run: poetry run pytest --verbose tests/
+        run: poetry run pytest --verbose deepicedrain/
diff --git a/.gitignore b/.gitignore
index a6afd9b..fc6100b 100644
--- a/.gitignore
+++ b/.gitignore
@@ -26,3 +26,6 @@ MANIFEST
 
 # Jupyter Notebook
 .ipynb_checkpoints
+
+# Data files
+**/*.h5
diff --git a/README.md b/README.md
index 550b42d..5cf8cec 100644
--- a/README.md
+++ b/README.md
@@ -11,6 +11,8 @@ in Antarctica using remote sensing and machine learning.
 
 ![ATL11 Cycle 6 minus Cycle 5 height change over Antarctica](https://user-images.githubusercontent.com/23487320/83100017-ffb0ba00-a102-11ea-9603-ac469f09e58b.png)
 
+![DeepIceDrain Pipeline](https://yuml.me/diagram/scruffy;dir:LR/class/[Land-Ice-Elevation|atl06_play.ipynb]->[Convert|atl06_to_atl11.ipynb],[Convert]->[Ice-Sheet-H(t)-Series|atl11_play.ipynb])
+
 # Getting started
 
 ## Quickstart
@@ -66,3 +68,36 @@ Finally, double-check that the libraries have been installed.
     python -m ipykernel install --user --name deepicedrain  # to install conda env properly
     jupyter kernelspec list --json                          # see if kernel is installed
     jupyter lab &
+
+## Usage
+
+Once you've installed properly installed the `deepicedrain` package,
+you can use it to do some quick calculations on ICESat-2 datasets.
+The example below shows how to calculate ice surface elevation change
+on a sample ATL11 dataset between ICESat's Cycle 3 and Cycle 4.
+
+    import deepicedrain
+    import xarray as xr
+
+    # Loads a sample ATL11 file from the intake catalog into xarray
+    atl11_dataset: xr.Dataset = deepicedrain.catalog.test_data.atl11_test_case.read()
+
+    # Calculate elevation change in metres from ICESat-2 Cycle 3 to Cycle 4
+    delta_height: xr.DataArray = deepicedrain.calculate_delta(
+          dataset=atl11_dataset, oldcyclenum=3, newcyclenum=4, variable="h_corr"
+    )
+
+    # Quick plot of delta_height along the ICESat-2 track
+    delta_height.plot()
+
+![ATL11 delta_height along ref_pt track](https://user-images.githubusercontent.com/23487320/83319030-bf7e4280-a28e-11ea-9bed-331e35dbc266.png)
+
+## Related Projects
+
+This work would not be possible without inspiration
+from the following cool open source projects!
+Go check them out if you have time.
+
+- [ATL11](https://github.com/suzanne64/ATL11)
+- [ICESAT-2 HackWeek](https://github.com/ICESAT-2HackWeek)
+- [icepyx](https://github.com/icesat2py/icepyx)
diff --git a/atl06_play.ipynb b/atl06_play.ipynb
index 207abc9..0fa589a 100644
--- a/atl06_play.ipynb
+++ b/atl06_play.ipynb
@@ -48,7 +48,7 @@
     "import tqdm\n",
     "import xarray as xr\n",
     "\n",
-    "# %matplotlib inline"
+    "import deepicedrain"
    ]
   },
   {
@@ -109,23 +109,19 @@
     "\n",
     "Use our [intake catalog](https://intake.readthedocs.io/en/latest/catalog.html) to get some sample ATL06 data\n",
     "(while making sure we have our Earthdata credentials set up properly),\n",
-    "and view it using [xarray](https://xarray.pydata.org) and [hvplot](https://hvplot.pyviz.org)."
+    "and view it using [xarray](https://xarray.pydata.org) and [hvplot](https://hvplot.pyviz.org).\n",
+    "\n",
+    "open the local intake data catalog file containing ICESat-2 stuff\n",
+    "catalog = intake.open_catalog(\"deepicedrain/atlas_catalog.yaml\")\n",
+    "or if the deepicedrain python package is installed, you can use either of the below:\n",
+    "catalog = deepicedrain.catalog\n",
+    "catalog = intake.cat.atlas_cat"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "# open the local catalog file containing ICESat-2 stuff\n",
-    "catalog = intake.open_catalog(uri=\"catalog.yaml\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1035,7 +1031,7 @@
        "    data_rate:    Data within this group are sparse.  Data values are provide..."
       ]
      },
-     "execution_count": 4,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1051,7 +1047,7 @@
     "    )\n",
     "    raise\n",
     "\n",
-    "# depends on .netrc file in home folder\n",
+    "# data download will depend on having a .netrc file in home folder\n",
     "dataset = catalog.icesat2atl06.to_dask().unify_chunks()\n",
     "dataset"
    ]
@@ -1752,21 +1748,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "transformer = pyproj.Transformer.from_crs(\n",
-    "    crs_from=pyproj.CRS.from_epsg(4326),\n",
-    "    crs_to=pyproj.CRS.from_epsg(3031),\n",
-    "    always_xy=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dfs[\"x\"], dfs[\"y\"] = transformer.transform(\n",
-    "    xx=dfs.longitude.values, yy=dfs.latitude.values\n",
+    "dfs[\"x\"], dfs[\"y\"] = deepicedrain.lonlat_to_xy(\n",
+    "    longitude=dfs.longitude, latitude=dfs.latitude\n",
     ")"
    ]
   },
diff --git a/atl06_play.py b/atl06_play.py
index 14562e2..4a95868 100644
--- a/atl06_play.py
+++ b/atl06_play.py
@@ -52,7 +52,7 @@
 import tqdm
 import xarray as xr
 
-# %matplotlib inline
+import deepicedrain
 
 # %%
 # Configure intake and set number of compute cores for data download
@@ -73,9 +73,11 @@
 # (while making sure we have our Earthdata credentials set up properly),
 # and view it using [xarray](https://xarray.pydata.org) and [hvplot](https://hvplot.pyviz.org).
 
-# %%
-# open the local catalog file containing ICESat-2 stuff
-catalog = intake.open_catalog(uri="catalog.yaml")
+# open the local intake data catalog file containing ICESat-2 stuff
+catalog = intake.open_catalog("deepicedrain/atlas_catalog.yaml")
+# or if the deepicedrain python package is installed, you can use either of the below:
+# catalog = deepicedrain.catalog
+# catalog = intake.cat.atlas_cat
 
 # %%
 try:
@@ -88,7 +90,7 @@
     )
     raise
 
-# depends on .netrc file in home folder
+# data download will depend on having a .netrc file in home folder
 dataset = catalog.icesat2atl06.to_dask().unify_chunks()
 dataset
 
@@ -345,15 +347,8 @@ def six_laser_beams(filepaths: list) -> dask.dataframe.DataFrame:
 # ### Transform from EPSG:4326 (lat/lon) to EPSG:3031 (Antarctic Polar Stereographic)
 
 # %%
-transformer = pyproj.Transformer.from_crs(
-    crs_from=pyproj.CRS.from_epsg(4326),
-    crs_to=pyproj.CRS.from_epsg(3031),
-    always_xy=True,
-)
-
-# %%
-dfs["x"], dfs["y"] = transformer.transform(
-    xx=dfs.longitude.values, yy=dfs.latitude.values
+dfs["x"], dfs["y"] = deepicedrain.lonlat_to_xy(
+    longitude=dfs.longitude, latitude=dfs.latitude
 )
 
 # %%
diff --git a/atl11_play.ipynb b/atl11_play.ipynb
index 3d0b110..c5b02c5 100644
--- a/atl11_play.ipynb
+++ b/atl11_play.ipynb
@@ -20,7 +20,6 @@
     "import glob\n",
     "\n",
     "import deepicedrain\n",
-    "import pointCollection.is2_calendar\n",
     "\n",
     "import dask\n",
     "import dask.array\n",
@@ -160,24 +159,11 @@
    },
    "outputs": [],
    "source": [
-    "lonlat_to_xy = lambda longitude, latitude: pyproj.Proj(projparams=3031)(\n",
-    "    longitude, latitude\n",
+    "ds[\"x\"], ds[\"y\"] = deepicedrain.lonlat_to_xy(\n",
+    "    longitude=ds.longitude, latitude=ds.latitude\n",
     ")"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [],
-   "source": [
-    "x, y = lonlat_to_xy(ds.longitude.values, ds.latitude.values)\n",
-    "ds[\"x\"] = xr.DataArray(data=x, coords=ds.longitude.coords)\n",
-    "ds[\"y\"] = xr.DataArray(data=y, coords=ds.latitude.coords)"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 7,
@@ -214,18 +200,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "ICESAT2_EPOCH = np.datetime64(pointCollection.is2_calendar.t_0())\n",
-    "# ICESAT2_EPOCH = np.datetime64(datetime.datetime(2018, 1, 1, 0, 0, 0))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "utc_time = dask.array.asarray(ICESAT2_EPOCH) + ds.delta_time.data\n",
-    "ds[\"utc_time\"] = xr.DataArray(data=utc_time, coords=ds.delta_time.coords)"
+    "ds[\"utc_time\"] = deepicedrain.deltatime_to_utctime(dataarray=ds.delta_time)"
    ]
   },
   {
@@ -277,19 +252,21 @@
    "source": [
     "# Dictionary of Antarctic bounding box locations with EPSG:3031 coordinates\n",
     "regions = {\n",
-    "    \"kamb\": deepicedrain.BBox(\n",
+    "    \"kamb\": deepicedrain.Region(\n",
     "        name=\"Kamb Ice Stream\",\n",
     "        xmin=-739741.7702261859,\n",
     "        xmax=-411054.19240523444,\n",
     "        ymin=-699564.516934089,\n",
     "        ymax=-365489.6822096751,\n",
     "    ),\n",
-    "    \"antarctica\": deepicedrain.BBox(\"Antarctica\", -2700000, 2800000, -2200000, 2300000),\n",
-    "    \"siple_coast\": deepicedrain.BBox(\n",
+    "    \"antarctica\": deepicedrain.Region(\n",
+    "        \"Antarctica\", -2700000, 2800000, -2200000, 2300000\n",
+    "    ),\n",
+    "    \"siple_coast\": deepicedrain.Region(\n",
     "        \"Siple Coast\", -1000000, 250000, -1000000, -100000\n",
     "    ),\n",
-    "    \"kamb2\": deepicedrain.BBox(\"Kamb Ice Stream\", -500000, -400000, -600000, -500000),\n",
-    "    \"whillans\": deepicedrain.BBox(\n",
+    "    \"kamb2\": deepicedrain.Region(\"Kamb Ice Stream\", -500000, -400000, -600000, -500000),\n",
+    "    \"whillans\": deepicedrain.Region(\n",
     "        \"Whillans Ice Stream\", -350000, -100000, -700000, -450000\n",
     "    ),\n",
     "}"
@@ -303,7 +280,7 @@
    "source": [
     "# Do the actual computation to find data points within region of interest\n",
     "region = regions[\"kamb\"]  # Select Kamb Ice Stream region\n",
-    "ds_subset = ds.where(cond=region.subset(ds=ds), drop=True)\n",
+    "ds_subset = region.subset(ds=ds)\n",
     "ds_subset = ds_subset.unify_chunks()\n",
     "ds_subset = ds_subset.compute()"
    ]
@@ -671,7 +648,7 @@
    "source": [
     "# Select region here, see dictionary of regions at top\n",
     "placename: str = \"antarctica\"\n",
-    "region: deepicedrain.BBox = regions[placename]"
+    "region: deepicedrain.Region = regions[placename]"
    ]
   },
   {
diff --git a/atl11_play.py b/atl11_play.py
index 944f314..fcb74c8 100644
--- a/atl11_play.py
+++ b/atl11_play.py
@@ -24,7 +24,6 @@
 import glob
 
 import deepicedrain
-import pointCollection.is2_calendar
 
 import dask
 import dask.array
@@ -87,17 +86,10 @@
 # to the Antarctic Polar Stereographic (EPSG:3031) projection.
 
 # %%
-lonlat_to_xy = lambda longitude, latitude: pyproj.Proj(projparams=3031)(
-    longitude, latitude
+ds["x"], ds["y"] = deepicedrain.lonlat_to_xy(
+    longitude=ds.longitude, latitude=ds.latitude
 )
 
-
-# %%
-x, y = lonlat_to_xy(ds.longitude.values, ds.latitude.values)
-ds["x"] = xr.DataArray(data=x, coords=ds.longitude.coords)
-ds["y"] = xr.DataArray(data=y, coords=ds.latitude.coords)
-
-
 # %%
 # Also set x, y as coordinates in xarray.Dataset
 ds = ds.set_coords(names=["x", "y"])
@@ -118,12 +110,7 @@
 # in the future.
 
 # %%
-ICESAT2_EPOCH = np.datetime64(pointCollection.is2_calendar.t_0())
-# ICESAT2_EPOCH = np.datetime64(datetime.datetime(2018, 1, 1, 0, 0, 0))
-
-# %%
-utc_time = dask.array.asarray(ICESAT2_EPOCH) + ds.delta_time.data
-ds["utc_time"] = xr.DataArray(data=utc_time, coords=ds.delta_time.coords)
+ds["utc_time"] = deepicedrain.deltatime_to_utctime(dataarray=ds.delta_time)
 
 # %% [markdown]
 # ## Mask out low quality height data
@@ -148,19 +135,21 @@
 # %%
 # Dictionary of Antarctic bounding box locations with EPSG:3031 coordinates
 regions = {
-    "kamb": deepicedrain.BBox(
+    "kamb": deepicedrain.Region(
         name="Kamb Ice Stream",
         xmin=-739741.7702261859,
         xmax=-411054.19240523444,
         ymin=-699564.516934089,
         ymax=-365489.6822096751,
     ),
-    "antarctica": deepicedrain.BBox("Antarctica", -2700000, 2800000, -2200000, 2300000),
-    "siple_coast": deepicedrain.BBox(
+    "antarctica": deepicedrain.Region(
+        "Antarctica", -2700000, 2800000, -2200000, 2300000
+    ),
+    "siple_coast": deepicedrain.Region(
         "Siple Coast", -1000000, 250000, -1000000, -100000
     ),
-    "kamb2": deepicedrain.BBox("Kamb Ice Stream", -500000, -400000, -600000, -500000),
-    "whillans": deepicedrain.BBox(
+    "kamb2": deepicedrain.Region("Kamb Ice Stream", -500000, -400000, -600000, -500000),
+    "whillans": deepicedrain.Region(
         "Whillans Ice Stream", -350000, -100000, -700000, -450000
     ),
 }
@@ -168,7 +157,7 @@
 # %%
 # Do the actual computation to find data points within region of interest
 region = regions["kamb"]  # Select Kamb Ice Stream region
-ds_subset = ds.where(cond=region.subset(ds=ds), drop=True)
+ds_subset = region.subset(ds=ds)
 ds_subset = ds_subset.unify_chunks()
 ds_subset = ds_subset.compute()
 
@@ -317,7 +306,7 @@
 # %%
 # Select region here, see dictionary of regions at top
 placename: str = "antarctica"
-region: deepicedrain.BBox = regions[placename]
+region: deepicedrain.Region = regions[placename]
 
 # %%
 # Find subglacial lakes (Smith et al., 2009) within region of interest
diff --git a/deepicedrain/__init__.py b/deepicedrain/__init__.py
index bd39aaf..f695f62 100644
--- a/deepicedrain/__init__.py
+++ b/deepicedrain/__init__.py
@@ -1,4 +1,18 @@
-__version__ = "0.1.0"
+import importlib.resources
+import logging
 
-from deepicedrain.geo import BBox
+import intake
+
+import deepicedrain
 from deepicedrain.deltamath import calculate_delta
+from deepicedrain.spatiotemporal import Region, deltatime_to_utctime, lonlat_to_xy
+
+__version__: str = "0.1.0"
+
+# Loads the ICESat-2 ATLAS intake data catalog
+_catalog_path = importlib.resources.path(
+    package=deepicedrain, resource="atlas_catalog.yaml"
+)
+with _catalog_path as uri:
+    logging.info(f"Loading intake catalog from {uri}")
+    catalog: intake.catalog.local.YAMLFileCatalog = intake.open_catalog(uri=str(uri))
diff --git a/catalog.yaml b/deepicedrain/atlas_catalog.yaml
similarity index 88%
rename from catalog.yaml
rename to deepicedrain/atlas_catalog.yaml
index 4c7efa9..ea16697 100644
--- a/catalog.yaml
+++ b/deepicedrain/atlas_catalog.yaml
@@ -1,7 +1,9 @@
 metadata:
+  description: 'An intake catalog for loading ICESat-2 ATLAS datasets'
   version: 1
 sources:
   icesat2atlasdownloader:
+    description: 'A catalog for downloading Antarctic ICESat-2 ATLAS products from NSIDC based on date'
     args:
       urlpath:
         - simplecache::https://n5eil01u.ecs.nsidc.org/ATLAS/ATL{{atlproduct}}.00{{version}}/{{date.strftime("%Y.%m.%d")}}/ATL{{atlproduct}}_*_*10_00{{version}}_0{{revision}}.h5
@@ -42,6 +44,7 @@ sources:
         default: 1
     driver: intake_xarray.netcdf.NetCDFSource
   icesat2atl06:
+    description: 'Reads in ICESat-2 ATL06 data into an xarray.Dataset, one date and one laser pair at a time'
     args:
       chunks:
         delta_time: 50000
@@ -85,7 +88,6 @@ sources:
           type: int
           default: 3
           allowed: [1, 2, 3]
-    description: ''
     driver: intake_xarray.netcdf.NetCDFSource
     metadata:
       plots:
@@ -101,3 +103,9 @@ sources:
           height: 500
           geo: True
           coastline: True
+  test_data:
+    args:
+      path: '{{ CATALOG_DIR }}/tests/test_catalog.yaml'
+    description: 'Sample ICESat-2 datasets for testing purposes'
+    driver: intake.catalog.local.YAMLFileCatalog
+    metadata: {}
diff --git a/deepicedrain/geo.py b/deepicedrain/geo.py
deleted file mode 100644
index 93696ce..0000000
--- a/deepicedrain/geo.py
+++ /dev/null
@@ -1,52 +0,0 @@
-"""
-Geographic class that implements some handy geographic tools.
-"""
-import dataclasses
-
-import numpy as np
-import xarray as xr
-
-
-@dataclasses.dataclass(frozen=True)
-class BBox:
-    """
-    A BoundingBox structure that outputs nice tuples of coordinates,
-    includes xarray subsetting capabilities and has a map scale property.
-    """
-
-    name: str  # name of region
-    xmin: float  # left coordinate
-    xmax: float  # right coordinate
-    ymin: float  # bottom coordinate
-    ymax: float  # top coordinate
-
-    @property
-    def scale(self) -> int:
-        """
-        Automatically set a map scale (1:scale)
-        based on x-coordinate range divided by 0.2
-        """
-        return int((self.xmax - self.xmin) / 0.2)
-
-    def bounds(self, style="lrbt") -> tuple:
-        """
-        Convenience function to get the bounding box coordinates
-        of the region in two different styles, lrbt or lbrt.
-        Defaults to 'lrbt', i.e. left, right, bottom, top.
-        """
-        if style == "lrbt":  # left, right, bottom, top (for PyGMT)
-            return (self.xmin, self.xmax, self.ymin, self.ymax)
-        elif style == "lbrt":  # left, bottom, right, top (for Shapely, etc)
-            return (self.xmin, self.ymin, self.xmax, self.ymax)
-        else:
-            raise NotImplementedError(f"Unknown style type {style}")
-
-    def subset(self, ds: xr.Dataset, x_dim: str = "x", y_dim: str = "y") -> xr.Dataset:
-        """
-        Convenience function to find datapoints in an xarray.Dataset
-        that fit within the bounding boxes of this region
-        """
-        return np.logical_and(
-            np.logical_and(ds[x_dim] > self.xmin, ds[x_dim] < self.xmax),
-            np.logical_and(ds[y_dim] > self.ymin, ds[y_dim] < self.ymax),
-        )
diff --git a/deepicedrain/spatiotemporal.py b/deepicedrain/spatiotemporal.py
new file mode 100644
index 0000000..3d95be4
--- /dev/null
+++ b/deepicedrain/spatiotemporal.py
@@ -0,0 +1,100 @@
+"""
+Geospatial and Temporal class that implements some handy tools.
+Does bounding box region subsets, coordinate/time conversions, and more!
+"""
+import dataclasses
+import datetime
+
+import numpy as np
+import pyproj
+import xarray as xr
+
+
+@dataclasses.dataclass(frozen=True)
+class Region:
+    """
+    A nice region data structure that outputs a tuple of bounding box
+    coordinates, has xarray subsetting capabilities and a map scale property.
+    """
+
+    name: str  # name of region
+    xmin: float  # left coordinate
+    xmax: float  # right coordinate
+    ymin: float  # bottom coordinate
+    ymax: float  # top coordinate
+
+    @property
+    def scale(self) -> int:
+        """
+        Automatically set a map scale (1:scale)
+        based on x-coordinate range divided by 0.2
+        """
+        return int((self.xmax - self.xmin) / 0.2)
+
+    def bounds(self, style="lrbt") -> tuple:
+        """
+        Convenience function to get the bounding box coordinates
+        of the region in two different styles, lrbt or lbrt.
+        Defaults to 'lrbt', i.e. left, right, bottom, top.
+        """
+        if style == "lrbt":  # left, right, bottom, top (for PyGMT)
+            return (self.xmin, self.xmax, self.ymin, self.ymax)
+        elif style == "lbrt":  # left, bottom, right, top (for Shapely, etc)
+            return (self.xmin, self.ymin, self.xmax, self.ymax)
+        else:
+            raise NotImplementedError(f"Unknown style type {style}")
+
+    def subset(
+        self, ds: xr.Dataset, x_dim: str = "x", y_dim: str = "y", drop: bool = True
+    ) -> xr.Dataset:
+        """
+        Convenience function to find datapoints in an xarray.Dataset
+        that fit within the bounding boxes of this region
+        """
+        cond = np.logical_and(
+            np.logical_and(ds[x_dim] > self.xmin, ds[x_dim] < self.xmax),
+            np.logical_and(ds[y_dim] > self.ymin, ds[y_dim] < self.ymax),
+        )
+
+        return ds.where(cond=cond, drop=drop)
+
+
+def deltatime_to_utctime(
+    dataarray: xr.DataArray,
+    start_epoch: np.datetime64 = np.datetime64("2018-01-01T00:00:00.000000"),
+) -> xr.DataArray:
+    """
+    Converts GPS time in nanoseconds from an epoch (default is 2018 Jan 1st)
+    to Coordinated Universal Time (UTC).
+
+    Note, does not account for leap seconds! There are none declared since the
+    last one announced on 31/12/2016, so it should be fine for now as of 2020.
+    """
+    utc_time: xr.DataArray = dataarray.__class__(start_epoch) + dataarray
+
+    return utc_time
+
+
+def lonlat_to_xy(
+    longitude: xr.DataArray, latitude: xr.DataArray, epsg: int = 3031
+) -> (xr.DataArray, xr.DataArray):
+    """
+    Reprojects longitude/latitude EPSG:4326 coordinates to x/y coordinates.
+    Default conversion is to Antarctic Stereographic Projection EPSG:3031.
+    """
+    if hasattr(longitude, "__array__") and callable(longitude.__array__):
+        # TODO upgrade to PyProj 3.0 to remove this workaround for passing in
+        # dask.dataframe.core.Series or xarray.DataArray objects
+        # Based on https://github.com/pyproj4/pyproj/pull/625
+        _longitude = longitude.__array__()
+        _latitude = latitude.__array__()
+
+    x, y = pyproj.Proj(projparams=epsg)(_longitude, _latitude)
+
+    if hasattr(longitude, "coords"):
+        return (
+            xr.DataArray(data=x, coords=longitude.coords),
+            xr.DataArray(data=y, coords=latitude.coords),
+        )
+    else:
+        return x, y
diff --git a/tests/__init__.py b/deepicedrain/tests/__init__.py
similarity index 100%
rename from tests/__init__.py
rename to deepicedrain/tests/__init__.py
diff --git a/tests/test_calculate_delta.py b/deepicedrain/tests/test_calculate_delta.py
similarity index 58%
rename from tests/test_calculate_delta.py
rename to deepicedrain/tests/test_calculate_delta.py
index 7d58363..0fbf4d9 100644
--- a/tests/test_calculate_delta.py
+++ b/deepicedrain/tests/test_calculate_delta.py
@@ -7,26 +7,14 @@
 import pytest
 import xarray as xr
 
-from deepicedrain import calculate_delta
+from deepicedrain import calculate_delta, catalog
 
 
-@pytest.fixture(scope="module")
-def atl11_dataset():
-    """
-    Loads a sample ATL11 test dataset from the intake catalog
-    """
-    catalog: intake.catalog.local.YAMLFileCatalog = intake.open_catalog(
-        uri="tests/test_catalog.yaml"
-    )
-    atl11_dataset: xr.Dataset = catalog.atl11_test_case.read()
-
-    return atl11_dataset
-
-
-def test_calculate_delta_height(atl11_dataset):
+def test_calculate_delta_height():
     """
     Check that calculating change in elevation works.
     """
+    atl11_dataset: xr.Dataset = catalog.test_data.atl11_test_case.to_dask()
     delta_height = calculate_delta(
         dataset=atl11_dataset, oldcyclenum=3, newcyclenum=4, variable="h_corr"
     )
@@ -37,11 +25,14 @@ def test_calculate_delta_height(atl11_dataset):
     npt.assert_allclose(actual=delta_height.mean().data, desired=-0.90124122)
     npt.assert_allclose(actual=delta_height.max().data, desired=9.49908442)
 
+    atl11_dataset.close()
 
-def test_calculate_delta_time(atl11_dataset):
+
+def test_calculate_delta_time():
     """
     Check that calculating change in time works.
     """
+    atl11_dataset: xr.Dataset = catalog.test_data.atl11_test_case.to_dask()
     delta_time = calculate_delta(
         dataset=atl11_dataset, oldcyclenum=3, newcyclenum=4, variable="delta_time"
     )
@@ -49,11 +40,13 @@ def test_calculate_delta_time(atl11_dataset):
     assert isinstance(delta_time, xr.DataArray)
     assert delta_time.shape == (1404,)
     npt.assert_equal(
-        actual=delta_time.min().data, desired=np.timedelta64(7846786703322903)
+        actual=np.asarray(delta_time.min()), desired=np.timedelta64(7846786703322903)
     )
     npt.assert_equal(
-        actual=delta_time.mean().data, desired=np.timedelta64(7846786865357197)
+        actual=np.asarray(delta_time.mean()), desired=np.timedelta64(7846786865357197),
     ),
     npt.assert_equal(
-        actual=delta_time.max().data, desired=np.timedelta64(7846787022726588)
+        actual=np.asarray(delta_time.max()), desired=np.timedelta64(7846787022726588)
     )
+
+    atl11_dataset.close()
diff --git a/tests/test_catalog.yaml b/deepicedrain/tests/test_catalog.yaml
similarity index 91%
rename from tests/test_catalog.yaml
rename to deepicedrain/tests/test_catalog.yaml
index 21e0d7d..14c2238 100644
--- a/tests/test_catalog.yaml
+++ b/deepicedrain/tests/test_catalog.yaml
@@ -2,16 +2,18 @@ metadata:
   version: 1
 sources:
   atl11_test_case:
+    description: 'An example ATL11 hdf5 file for testing various calculations'
     args:
+      chunks:
+        cycle_number: 2
       urlpath: simplecache::https://github.com/suzanne64/ATL11/raw/125ee1a653d78e6b86864b35c9d0fcfd72d64a85/ATL11_test_case/ATL11_078805_0304_02_v002.h5
       xarray_kwargs:
         engine: h5netcdf
         group: /pt2/corrected_h
       storage_options:
         simplecache:
-          cache_storage: tests/test_data
+          cache_storage: '{{ CATALOG_DIR }}/test_data'
           same_names: True
-    description: 'An example ATL11 hdf5 file for testing various calculations'
     driver: intake_xarray.netcdf.NetCDFSource
     metadata:
       coords:
diff --git a/deepicedrain/tests/test_deepicedrain.py b/deepicedrain/tests/test_deepicedrain.py
new file mode 100644
index 0000000..1577f87
--- /dev/null
+++ b/deepicedrain/tests/test_deepicedrain.py
@@ -0,0 +1,17 @@
+import intake
+
+from deepicedrain import __version__, catalog
+
+
+def test_version():
+    assert __version__ == "0.1.0"
+
+
+def test_deepicedrain_catalog():
+    """
+    Test that the intake ATLAS data catalog can be loaded via both
+    `deepicedrain.catalog` and `intake.cat.atlas_cat`
+    """
+    catalog_entries = ["icesat2atlasdownloader", "icesat2atl06", "test_data"]
+    assert list(catalog) == catalog_entries
+    assert list(intake.cat.atlas_cat) == catalog_entries
diff --git a/tests/test_bbox.py b/deepicedrain/tests/test_region.py
similarity index 60%
rename from tests/test_bbox.py
rename to deepicedrain/tests/test_region.py
index a2e3176..440f135 100644
--- a/tests/test_bbox.py
+++ b/deepicedrain/tests/test_region.py
@@ -1,51 +1,51 @@
 """
-Tests behaviour of the BBox class
+Tests behaviour of the Region class
 """
 import numpy as np
 import pytest
 import xarray as xr
 
-from deepicedrain import BBox
+from deepicedrain import Region
 
 
-def test_bbox_scale():
+def test_region_scale():
     """
-    Tests that a map scale is output based on the BBox region.
+    Tests that a map scale is output based on the region.
     """
-    region = BBox("Antarctica", -2700000, 2800000, -2200000, 2300000)
+    region = Region("Antarctica", -2700000, 2800000, -2200000, 2300000)
     assert region.scale == 27500000
 
 
-def test_bbox_bounds_lrbt():
+def test_region_bounds_lrbt():
     """
     Tests that PyGMT style bounds are given (by default).
     """
-    region = BBox("Siple Coast", -1000000, 250000, -1000000, -100000)
+    region = Region("Siple Coast", -1000000, 250000, -1000000, -100000)
     assert region.bounds() == (-1000000, 250000, -1000000, -100000)
 
 
-def test_bbox_bounds_lbrt():
+def test_region_bounds_lbrt():
     """
     Tests that Shapely style bounds are given
     """
-    region = BBox("Whillans Ice Stream", -350000, -100000, -700000, -450000)
+    region = Region("Whillans Ice Stream", -350000, -100000, -700000, -450000)
     assert region.bounds(style="lbrt") == (-350000, -700000, -100000, -450000)
 
 
-def test_bbox_bounds_ltrb():
+def test_region_bounds_ltrb():
     """
     Tests that error is raised when passing in a style that is not implemented.
     """
-    region = BBox("Kamb Ice Stream", -500000, -400000, -600000, -500000)
+    region = Region("Kamb Ice Stream", -500000, -400000, -600000, -500000)
     with pytest.raises(NotImplementedError):
         print(region.bounds(style="ltrb"))
 
 
-def test_bbox_subset():
+def test_region_subset():
     """
     Test that we can subset an xarray.Dataset based on the region's bounds
     """
-    region = BBox("South Pole", -100, 100, -100, 100)
+    region = Region("South Pole", -100, 100, -100, 100)
     dataset = xr.Dataset(
         data_vars={"h_corr": (["x", "y"], np.random.rand(50, 50))},
         coords={
@@ -53,6 +53,6 @@ def test_bbox_subset():
             "y": np.linspace(start=-160, stop=160, num=50),
         },
     )
-    ds_subset = dataset.where(cond=region.subset(ds=dataset), drop=True)
+    ds_subset = region.subset(ds=dataset)
     assert isinstance(ds_subset, xr.Dataset)
     assert ds_subset.h_corr.shape == (24, 30)
diff --git a/deepicedrain/tests/test_spatiotemporal_conversions.py b/deepicedrain/tests/test_spatiotemporal_conversions.py
new file mode 100644
index 0000000..d6f03e4
--- /dev/null
+++ b/deepicedrain/tests/test_spatiotemporal_conversions.py
@@ -0,0 +1,84 @@
+"""
+Tests various conversions between geospatial and temporal units
+"""
+import datetime
+
+import dask
+import numpy as np
+import numpy.testing as npt
+import pandas as pd
+import xarray as xr
+
+from deepicedrain import catalog, deltatime_to_utctime, lonlat_to_xy
+
+
+def test_deltatime_to_utctime():
+    """
+    Test that converting from ICESat-2 delta_time to utc_time works,
+    and that the xarray dimensions are preserved in the process.
+    """
+    atl11_dataset: xr.Dataset = catalog.test_data.atl11_test_case.to_dask()
+
+    utc_time: xr.DataArray = deltatime_to_utctime(dataarray=atl11_dataset.delta_time)
+
+    assert utc_time.shape == (1404, 2)
+    assert utc_time.dims == ("ref_pt", "cycle_number")
+    assert dask.is_dask_collection(utc_time)
+
+    utc_time = utc_time.compute()
+
+    npt.assert_equal(
+        actual=utc_time.data.min(),
+        desired=np.datetime64("2019-05-19T20:53:51.039891534"),
+    )
+    npt.assert_equal(
+        actual=np.datetime64(pd.DataFrame(utc_time.data)[0].mean()),
+        desired=np.datetime64("2019-05-19 20:54:00.925868"),
+    )
+    npt.assert_equal(
+        actual=np.datetime64(pd.DataFrame(utc_time.data)[1].mean()),
+        desired=np.datetime64("2019-08-18 16:33:47.791226"),
+    )
+    npt.assert_equal(
+        actual=utc_time.data.max(),
+        desired=np.datetime64("2019-08-18T16:33:57.834610209"),
+    )
+
+    atl11_dataset.close()
+
+
+def test_lonlat_to_xy_dask_series():
+    """
+    Test that converting from longitude/latitude to x/y in EPSG:3031 works when
+    passing them in as dask.dataframe.core.Series objects.
+    """
+    atl11_dataset: xr.Dataset = catalog.test_data.atl11_test_case.to_dask()
+    atl11_dataframe: dask.dataframe.core.DataFrame = atl11_dataset.to_dask_dataframe()
+
+    x, y = lonlat_to_xy(
+        longitude=atl11_dataframe.longitude, latitude=atl11_dataframe.latitude,
+    )
+    npt.assert_equal(actual=x.mean(), desired=-56900105.00307033)
+    npt.assert_equal(actual=y.mean(), desired=48141607.48486084)
+
+    atl11_dataset.close()
+
+
+def test_lonlat_to_xy_xarray_dataarray():
+    """
+    Test that converting from longitude/latitude to x/y in EPSG:3031 works when
+    passing them in as xarray.DataArray objects. Ensure that the xarray
+    dimensions are preserved in the process.
+    """
+    atl11_dataset: xr.Dataset = catalog.test_data.atl11_test_case.to_dask()
+
+    x, y = lonlat_to_xy(
+        longitude=atl11_dataset.longitude, latitude=atl11_dataset.latitude
+    )
+
+    assert x.dims == y.dims == ("ref_pt",)
+    assert x.shape == y.shape == (1404,)
+    npt.assert_equal(actual=x.mean().data, desired=-56900105.00307034)
+    npt.assert_equal(actual=y.mean().data, desired=48141607.48486084)
+
+    atl11_dataset.close()
diff --git a/poetry.lock b/poetry.lock
index 5fab5db..376f9d8 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -172,7 +172,7 @@ description = "Python package for providing Mozilla's CA Bundle."
 name = "certifi"
 optional = false
 python-versions = "*"
-version = "2019.11.28"
+version = "2020.4.5.1"
 
 [[package]]
 category = "main"
@@ -180,7 +180,7 @@ description = "Time-handling functionality from netcdf4-python"
 name = "cftime"
 optional = false
 python-versions = "*"
-version = "1.1.1.2"
+version = "1.1.3"
 
 [package.dependencies]
 numpy = "*"
@@ -751,7 +751,10 @@ description = "A Python implementation of the JSON5 data format."
 name = "json5"
 optional = false
 python-versions = "*"
-version = "0.9.4"
+version = "0.9.5"
+
+[package.extras]
+dev = ["hypothesis"]
 
 [[package]]
 category = "main"
@@ -1403,7 +1406,7 @@ description = "Python parsing module"
 name = "pyparsing"
 optional = false
 python-versions = ">=2.6, !=3.0.*, !=3.1.*, !=3.2.*"
-version = "2.4.6"
+version = "2.4.7"
 
 [[package]]
 category = "main"
@@ -1529,7 +1532,7 @@ description = "Python bindings for 0MQ"
 name = "pyzmq"
 optional = false
 python-versions = ">=2.7,!=3.0.*,!=3.1.*,!=3.2.*"
-version = "19.0.0"
+version = "19.0.1"
 
 [[package]]
 category = "dev"
@@ -1898,26 +1901,26 @@ cartopy = [
     {file = "Cartopy-0.18.0.tar.gz", hash = "sha256:7ffa317e8f8011e0d965a3ef1179e57a049f77019867ed677d49dcc5c0744434"},
 ]
 certifi = [
-    {file = "certifi-2019.11.28-py2.py3-none-any.whl", hash = "sha256:017c25db2a153ce562900032d5bc68e9f191e44e9a0f762f373977de9df1fbb3"},
-    {file = "certifi-2019.11.28.tar.gz", hash = "sha256:25b64c7da4cd7479594d035c08c2d809eb4aab3a26e5a990ea98cc450c320f1f"},
+    {file = "certifi-2020.4.5.1-py2.py3-none-any.whl", hash = "sha256:1d987a998c75633c40847cc966fcf5904906c920a7f17ef374f5aa4282abd304"},
+    {file = "certifi-2020.4.5.1.tar.gz", hash = "sha256:51fcb31174be6e6664c5f69e3e1691a2d72a1a12e90f872cbdb1567eb47b6519"},
 ]
 cftime = [
-    {file = "cftime-1.1.1.2-cp36-cp36m-macosx_10_9_x86_64.whl", hash = "sha256:33a78faacac37cdfb68f998400c81ece5acf368cbf803a9fde7cf01e527d0860"},
-    {file = "cftime-1.1.1.2-cp36-cp36m-manylinux1_i686.whl", hash = "sha256:ba44e4ae980db94e999ae91e160d734c7865ab437e48591a96fe98ad46b541cb"},
-    {file = "cftime-1.1.1.2-cp36-cp36m-manylinux1_x86_64.whl", hash = "sha256:9d49569ed1c2e8fc3a4c0f95bdbe14a2e146a794ed7a6970dcb54fea455acb88"},
-    {file = "cftime-1.1.1.2-cp36-none-win32.whl", hash = "sha256:a47357917b1f28af3a8b53e3fa0004fa1c7b4e454d1744a26fda46571991def5"},
-    {file = "cftime-1.1.1.2-cp36-none-win_amd64.whl", hash = "sha256:43644b85c8a9351f5b208dda7442132c78c782ed2ed86e6cdc8580306f2d9afb"},
-    {file = "cftime-1.1.1.2-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:056a3843f3b74789f73770180d4a80416c0558841c55a22c05a99a04a44d0f1c"},
-    {file = "cftime-1.1.1.2-cp37-cp37m-manylinux1_i686.whl", hash = "sha256:a915c640da4baff6221b87a2ab09f1aef1132611072e300cfd6ccc6960693bc9"},
-    {file = "cftime-1.1.1.2-cp37-cp37m-manylinux1_x86_64.whl", hash = "sha256:1a3e9025d315ef7d023a2865f377f105d75907f0c66913fca56158fce8aa6f2e"},
-    {file = "cftime-1.1.1.2-cp37-none-win32.whl", hash = "sha256:10974085d22c8345fbaf7ae5dbf8560ddca5477fc899126a2bcd57f65f1f52b0"},
-    {file = "cftime-1.1.1.2-cp37-none-win_amd64.whl", hash = "sha256:9c3698285c77e24a3250d0366d872526470e5333d66874d0c0733ce99e242114"},
-    {file = "cftime-1.1.1.2-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:d09d0469e617c78115d860c2483fcc6e5e55154738c7ea7146a3591254aa366f"},
-    {file = "cftime-1.1.1.2-cp38-cp38-manylinux1_i686.whl", hash = "sha256:21a28362757b3a7db4db0e8f174843798116b07c93976f6194d0afe3eb6fe1c0"},
-    {file = "cftime-1.1.1.2-cp38-cp38-manylinux1_x86_64.whl", hash = "sha256:298c97970031c2073064249184395cc040b0ec5f6439720bab2ac49354285715"},
-    {file = "cftime-1.1.1.2-cp38-none-win32.whl", hash = "sha256:89ee6f94dbb81c22576f81c939a1b8d985c3bd31f63063babeda7e969339ff3b"},
-    {file = "cftime-1.1.1.2-cp38-none-win_amd64.whl", hash = "sha256:2405ca220a9f90edbf4836f175fd5535455d8cdd1863213a09f41613c03530fb"},
-    {file = "cftime-1.1.1.2.tar.gz", hash = "sha256:35711b5ec3928b9e724817bfa1b7325da205788ee04eae9166cbcd96ea7976a6"},
+    {file = "cftime-1.1.3-cp36-cp36m-macosx_10_9_x86_64.whl", hash = "sha256:106d8bd1c144c83de1288c51225fd6846539074b60bb9e05b5e357d5e1eed6b9"},
+    {file = "cftime-1.1.3-cp36-cp36m-manylinux1_i686.whl", hash = "sha256:ead81301dbfcfef2b452ce6997f4f82cc0c2d968d27a2795251b5cdfc4b17295"},
+    {file = "cftime-1.1.3-cp36-cp36m-manylinux1_x86_64.whl", hash = "sha256:623255e0264de5cde085f0027ab03365965f3f416de407b604b00abe676f20f2"},
+    {file = "cftime-1.1.3-cp36-none-win32.whl", hash = "sha256:bdd3a0b85fda45585529825e0954d6140d6df40b7bc489770e72fa6ce79ab9ea"},
+    {file = "cftime-1.1.3-cp36-none-win_amd64.whl", hash = "sha256:3fb1f637aed7391c9a5d718175014f4cf705970e1d596bbc80bcac078eefefb0"},
+    {file = "cftime-1.1.3-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:9ad8521598be2b354d159538e9572afb4c0d4ac2b0b6240eef7197e1d709a89e"},
+    {file = "cftime-1.1.3-cp37-cp37m-manylinux1_i686.whl", hash = "sha256:fb9a5ed38dee8ac43235ba161bf5fb61274e965b9b396a650ba4d515b9d70a7c"},
+    {file = "cftime-1.1.3-cp37-cp37m-manylinux1_x86_64.whl", hash = "sha256:9e1c863e38d410de0b59c5be7799ef05cd6f241cc254c6d656dfdae7546c1025"},
+    {file = "cftime-1.1.3-cp37-none-win32.whl", hash = "sha256:555c101a0d03f6f6231253d0d84620a5ffb69757680be532086a70b033b9969b"},
+    {file = "cftime-1.1.3-cp37-none-win_amd64.whl", hash = "sha256:ad0b9db793eae28ed1f53157e87044119c8153bf211420414f2c6b6e27fc86db"},
+    {file = "cftime-1.1.3-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:dd44afef96467f2cb3ec9324e9f471653e5daa55b05198f8da389ccb85d38157"},
+    {file = "cftime-1.1.3-cp38-cp38-manylinux1_i686.whl", hash = "sha256:7c4d0de4ab846b76f73a9a45d9085e5c6321610e74548bdeaaf6c588fe841587"},
+    {file = "cftime-1.1.3-cp38-cp38-manylinux1_x86_64.whl", hash = "sha256:8c9b27142cbd41049346128c8a5b78d292b675a3c185e16ed58645c273156e16"},
+    {file = "cftime-1.1.3-cp38-none-win32.whl", hash = "sha256:d65d0fcc7120db05ddd34625310bc56038063fddcf63f3129379949d2ee762e9"},
+    {file = "cftime-1.1.3-cp38-none-win_amd64.whl", hash = "sha256:eab0fb0268fc4743f65eb6efac5998af2b0805d914c2e2f83226531add57fd98"},
+    {file = "cftime-1.1.3.tar.gz", hash = "sha256:fd84b8631dca1db9b40a75e18671b9edafd3515580d8ab33ce1ebafee75451f0"},
 ]
 chardet = [
     {file = "chardet-3.0.4-py2.py3-none-any.whl", hash = "sha256:fc323ffcaeaed0e0a02bf4d117757b98aed530d9ed4531e3e15460124c106691"},
@@ -2126,8 +2129,8 @@ jinja2 = [
     {file = "Jinja2-2.11.2.tar.gz", hash = "sha256:89aab215427ef59c34ad58735269eb58b1a5808103067f7bb9d5836c651b3bb0"},
 ]
 json5 = [
-    {file = "json5-0.9.4-py2.py3-none-any.whl", hash = "sha256:4e0fc461b5508196a3ddb3b981dc677805923b86d6eb603c7f58f2459ab1458f"},
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 ]
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@@ -2610,8 +2613,8 @@ pygmt = [
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 ]
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@@ -2703,34 +2706,34 @@ pyyaml = [
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 ]
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+    {file = "pyzmq-19.0.1.tar.gz", hash = "sha256:13a5638ab24d628a6ade8f794195e1a1acd573496c3b85af2f1183603b7bf5e0"},
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 regex = [
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diff --git a/pyproject.toml b/pyproject.toml
index e7040e9..24bcbf5 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -35,6 +35,9 @@ black = "^19.10b0"
 jupytext = "^1.4.2"
 pytest = "^5.4.2"
 
+[tool.poetry.plugins."intake.catalogs"]
+"atlas_cat" = "deepicedrain:catalog"
+
 [build-system]
 requires = ["poetry>=0.12"]
 build-backend = "poetry.masonry.api"
diff --git a/tests/test_deepicedrain.py b/tests/test_deepicedrain.py
deleted file mode 100644
index c3f9e32..0000000
--- a/tests/test_deepicedrain.py
+++ /dev/null
@@ -1,5 +0,0 @@
-from deepicedrain import __version__
-
-
-def test_version():
-    assert __version__ == "0.1.0"