diff --git a/CREDITS.md b/CREDITS.md
index 34ca573..afa1b7a 100644
--- a/CREDITS.md
+++ b/CREDITS.md
@@ -16,5 +16,5 @@ Acknowledgments
 ---------------
 
 This work is supported by the National Science Foundation under Award No.
-[2026951](https://www.nsf.gov/awardsearch/showAward?AWD_ID=2026951), 
+[2026951](https://www.nsf.gov/awardsearch/showAward?AWD_ID=2026951),
 *EarthCube Capabilities: Cloud-Based Accessible and Reproducible Modeling for Water and Sediment Research*.
diff --git a/README.md b/README.md
index 1d5c452..3272928 100644
--- a/README.md
+++ b/README.md
@@ -81,7 +81,7 @@ there are three ways to use it with *bmi-topography*:
 3. *dot file*: Put the API key in the file `.opentopography.txt` in the current directory or in your home directory.
 
 If you attempt to use *bmi-topography* to access an OpenTopography dataset without an API key,
-you'll get a error like this: 
+you'll get a error like this:
 ```
 requests.exceptions.HTTPError: 401 Client Error: This dataset requires an API Key for access.
 ```
diff --git a/bmi_topography/api_key.py b/bmi_topography/api_key.py
index 50b1649..a07a3cf 100644
--- a/bmi_topography/api_key.py
+++ b/bmi_topography/api_key.py
@@ -75,7 +75,7 @@ def from_env(cls):
     def from_file(cls):
         """Read the key from a file."""
         if filepath := _find_first_of(ApiKey.API_KEY_FILES):
-            with open(filepath, "r") as fp:
+            with open(filepath) as fp:
                 api_key = fp.read().strip()
         else:
             raise MissingKeyError(
diff --git a/bmi_topography/bbox.py b/bmi_topography/bbox.py
index c5c7a75..9b7b376 100644
--- a/bmi_topography/bbox.py
+++ b/bmi_topography/bbox.py
@@ -1,5 +1,4 @@
 from collections.abc import Iterable
-from typing import Tuple
 
 
 class BoundingBox:
@@ -16,56 +15,40 @@ class BoundingBox:
 
     """
 
-    def __init__(self, lower_left: Tuple[float], upper_right: Tuple[float]) -> None:
+    def __init__(self, lower_left: tuple[float], upper_right: tuple[float]) -> None:
         self._lower_left = lower_left
         self._upper_right = upper_right
 
         if not isinstance(self.lower_left, Iterable) or len(self.lower_left) != 2:
             raise ValueError(
-                "lower left coordinate ({0}) must have two elements".format(
-                    self.lower_left
-                )
+                f"lower left coordinate ({self.lower_left}) must have two elements"
             )
 
         if not isinstance(self.upper_right, Iterable) or len(self.upper_right) != 2:
             raise ValueError(
-                "upper right coordinate ({0}) must have two elements".format(
-                    self.upper_right
-                )
+                f"upper right coordinate ({self.upper_right}) must have two elements"
             )
 
         if self.south > 90 or self.south < -90:
-            raise ValueError(
-                "south coordinate ({0}) must be in [-90,90]".format(self.south)
-            )
+            raise ValueError(f"south coordinate ({self.south}) must be in [-90,90]")
 
         if self.north > 90 or self.north < -90:
-            raise ValueError(
-                "north coordinate ({0}) must be in [-90,90]".format(self.north)
-            )
+            raise ValueError(f"north coordinate ({self.north}) must be in [-90,90]")
 
         if self.south > self.north:
             raise ValueError(
-                "south coordinate ({0}) must be less than north ({1})".format(
-                    self.south, self.north
-                )
+                f"south coordinate ({self.south}) must be less than north ({self.north})"
             )
 
         if self.west > 180 or self.west < -180:
-            raise ValueError(
-                "west coordinate ({0}) must be in [-180,180]".format(self.west)
-            )
+            raise ValueError(f"west coordinate ({self.west}) must be in [-180,180]")
 
         if self.east > 180 or self.east < -180:
-            raise ValueError(
-                "east coordinate ({0}) must be in [-180,180]".format(self.east)
-            )
+            raise ValueError(f"east coordinate ({self.east}) must be in [-180,180]")
 
         if self.west > self.east:
             raise ValueError(
-                "west coordinate ({0}) must be less than east ({1})".format(
-                    self.west, self.east
-                )
+                f"west coordinate ({self.west}) must be less than east ({self.east})"
             )
 
     @property
@@ -95,5 +78,5 @@ def east(self):
         return self.upper_right[1]
 
     def __str__(self):
-        s = "[{0}, {1}]".format(self.lower_left, self.upper_right)
+        s = f"[{self.lower_left}, {self.upper_right}]"
         return s
diff --git a/bmi_topography/bmi.py b/bmi_topography/bmi.py
index fbdf008..e3b24e0 100644
--- a/bmi_topography/bmi.py
+++ b/bmi_topography/bmi.py
@@ -1,6 +1,4 @@
-# -*- coding: utf-8 -*-
 from collections import namedtuple
-from typing import Tuple
 
 import numpy
 import yaml
@@ -357,7 +355,7 @@ def get_input_item_count(self) -> int:
         """
         return len(self._input_var_names)
 
-    def get_input_var_names(self) -> Tuple[str]:
+    def get_input_var_names(self) -> tuple[str]:
         """List of a model's input variables.
 
         Input variable names must be CSDMS Standard Names, also known
@@ -389,7 +387,7 @@ def get_output_item_count(self) -> int:
         """
         return len(self._output_var_names)
 
-    def get_output_var_names(self) -> Tuple[str]:
+    def get_output_var_names(self) -> tuple[str]:
         """List of a model's output variables.
 
         Output variable names must be CSDMS Standard Names, also known
@@ -650,7 +648,7 @@ def initialize(self, config_file: str) -> None:
         with placeholder values is used by the BMI.
         """
         if config_file:
-            with open(config_file, "r") as fp:
+            with open(config_file) as fp:
                 self._config = yaml.safe_load(fp).get("bmi-topography", {})
         else:
             self._config = Topography.DEFAULT.copy()
diff --git a/bmi_topography/cli.py b/bmi_topography/cli.py
index ba23961..f32da4c 100644
--- a/bmi_topography/cli.py
+++ b/bmi_topography/cli.py
@@ -78,7 +78,7 @@ def main(quiet, dem_type, south, north, west, east, output_format, api_key, no_f
         path_to_dem = topo.fetch()
         if not quiet:
             click.secho(
-                "File downloaded to {}".format(getattr(topo, "cache_dir")),
+                f"File downloaded to {getattr(topo, 'cache_dir')}",
                 fg="green",
                 err=True,
             )
diff --git a/bmi_topography/topography.py b/bmi_topography/topography.py
index c3116d3..6106074 100644
--- a/bmi_topography/topography.py
+++ b/bmi_topography/topography.py
@@ -64,9 +64,7 @@ def __init__(
         if dem_type in Topography.VALID_DEM_TYPES:
             self._dem_type = dem_type
         else:
-            raise ValueError(
-                "dem_type must be one of %s." % (Topography.VALID_DEM_TYPES,)
-            )
+            raise ValueError(f"dem_type must be one of {Topography.VALID_DEM_TYPES}.")
 
         if output_format in Topography.VALID_OUTPUT_FORMATS.keys():
             self._output_format = output_format
diff --git a/docs/source/conf.py b/docs/source/conf.py
index 2acecb1..2e96cf3 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -28,7 +28,7 @@
 version = pkg_resources.get_distribution("bmi_topography").version
 release = version
 this_year = datetime.date.today().year
-copyright = "%s, %s" % (this_year, author)
+copyright = f"{this_year}, {author}"
 
 
 # -- General configuration ---------------------------------------------------
diff --git a/docs/source/index.rst b/docs/source/index.rst
index 3f022c8..ea0a208 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -51,5 +51,5 @@ Acknowledgments
 ---------------
 
 This work is supported by the National Science Foundation under Award No.
-`2026951 <https://www.nsf.gov/awardsearch/showAward?AWD_ID=2026951>`_, 
+`2026951 <https://www.nsf.gov/awardsearch/showAward?AWD_ID=2026951>`_,
 *EarthCube Capabilities: Cloud-Based Accessible and Reproducible Modeling for Water and Sediment Research*.
diff --git a/examples/bmi-topography.ipynb b/examples/bmi-topography.ipynb
index 7367eeb..1e5f1d5 100644
--- a/examples/bmi-topography.ipynb
+++ b/examples/bmi-topography.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "eastern-royal",
+   "id": "0",
    "metadata": {},
    "source": [
     "# Get SRTM data through a BMI"
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "boxed-series",
+   "id": "1",
    "metadata": {},
    "source": [
     "This notebook describes how to download Shuttle Radar Topography Mission (SRTM) elevation data\n",
@@ -19,7 +19,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "metropolitan-intake",
+   "id": "2",
    "metadata": {},
    "source": [
     "## Setup"
@@ -27,7 +27,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "applied-partnership",
+   "id": "3",
    "metadata": {},
    "source": [
     "To ensure all dependencies are met, set up a conda environment using the environment file found in the root directory of this repository:\n",
@@ -43,7 +43,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "moving-reliance",
+   "id": "4",
    "metadata": {},
    "source": [
     "Import a pair of libraries for later use:"
@@ -51,8 +51,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "major-porter",
+   "execution_count": null,
+   "id": "5",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -62,7 +62,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "binary-easter",
+   "id": "6",
    "metadata": {},
    "source": [
     "## Fetch and load data"
@@ -70,7 +70,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "monthly-stereo",
+   "id": "7",
    "metadata": {},
    "source": [
     "Import the `BmiTopography` class from the newly installed `bmi-topography` package:"
@@ -78,8 +78,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "universal-module",
+   "execution_count": null,
+   "id": "8",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -88,7 +88,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "related-machinery",
+   "id": "9",
    "metadata": {},
    "source": [
     "Create an instance of this class."
@@ -96,8 +96,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "dynamic-deviation",
+   "execution_count": null,
+   "id": "10",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -106,7 +106,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "prepared-pantyhose",
+   "id": "11",
    "metadata": {},
    "source": [
     "Calling `help` on the instance displays all the BMI methods that are available."
@@ -114,631 +114,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "id": "mental-character",
+   "execution_count": null,
+   "id": "12",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Help on BmiTopography in module bmi_topography.bmi object:\n",
-      "\n",
-      "class BmiTopography(bmipy.bmi.Bmi)\n",
-      " |  BmiTopography() -> None\n",
-      " |  \n",
-      " |  BMI-mediated access to NASA SRTM land elevation data.\n",
-      " |  \n",
-      " |  Method resolution order:\n",
-      " |      BmiTopography\n",
-      " |      bmipy.bmi.Bmi\n",
-      " |      abc.ABC\n",
-      " |      builtins.object\n",
-      " |  \n",
-      " |  Methods defined here:\n",
-      " |  \n",
-      " |  __init__(self) -> None\n",
-      " |      Initialize self.  See help(type(self)) for accurate signature.\n",
-      " |  \n",
-      " |  finalize(self) -> None\n",
-      " |      Perform tear-down tasks for the model.\n",
-      " |      \n",
-      " |      Perform all tasks that take place after exiting the model's time\n",
-      " |      loop. This typically includes deallocating memory, closing files and\n",
-      " |      printing reports.\n",
-      " |  \n",
-      " |  get_component_name(self) -> str\n",
-      " |      Name of the component.\n",
-      " |      \n",
-      " |      Returns\n",
-      " |      -------\n",
-      " |      str\n",
-      " |          The name of the component.\n",
-      " |  \n",
-      " |  get_current_time(self) -> float\n",
-      " |      Current time of the model.\n",
-      " |      \n",
-      " |      Returns\n",
-      " |      -------\n",
-      " |      float\n",
-      " |          The current model time.\n",
-      " |  \n",
-      " |  get_end_time(self) -> float\n",
-      " |      End time of the model.\n",
-      " |      \n",
-      " |      Returns\n",
-      " |      -------\n",
-      " |      float\n",
-      " |          The maximum model time.\n",
-      " |  \n",
-      " |  get_grid_edge_count(self, grid: int) -> int\n",
-      " |      Get the number of edges in the grid.\n",
-      " |      \n",
-      " |      Parameters\n",
-      " |      ----------\n",
-      " |      grid : int\n",
-      " |          A grid identifier.\n",
-      " |      \n",
-      " |      Returns\n",
-      " |      -------\n",
-      " |      int\n",
-      " |          The total number of grid edges.\n",
-      " |  \n",
-      " |  get_grid_edge_nodes(self, grid: int, edge_nodes: numpy.ndarray) -> numpy.ndarray\n",
-      " |      Get the edge-node connectivity.\n",
-      " |      \n",
-      " |      Parameters\n",
-      " |      ----------\n",
-      " |      grid : int\n",
-      " |          A grid identifier.\n",
-      " |      edge_nodes : ndarray of int, shape *(2 x nnodes,)*\n",
-      " |          A numpy array to place the edge-node connectivity. For each edge,\n",
-      " |          connectivity is given as node at edge tail, followed by node at\n",
-      " |          edge head.\n",
-      " |      \n",
-      " |      Returns\n",
-      " |      -------\n",
-      " |      ndarray of int\n",
-      " |          The input numpy array that holds the edge-node connectivity.\n",
-      " |  \n",
-      " |  get_grid_face_count(self, grid: int) -> int\n",
-      " |      Get the number of faces in the grid.\n",
-      " |      \n",
-      " |      Parameters\n",
-      " |      ----------\n",
-      " |      grid : int\n",
-      " |          A grid identifier.\n",
-      " |      \n",
-      " |      Returns\n",
-      " |      -------\n",
-      " |      int\n",
-      " |          The total number of grid faces.\n",
-      " |  \n",
-      " |  get_grid_face_edges(self, grid: int, face_edges: numpy.ndarray) -> numpy.ndarray\n",
-      " |      Get the face-edge connectivity.\n",
-      " |      \n",
-      " |      Parameters\n",
-      " |      ----------\n",
-      " |      grid : int\n",
-      " |          A grid identifier.\n",
-      " |      face_edges : ndarray of int\n",
-      " |          A numpy array to place the face-edge connectivity.\n",
-      " |      \n",
-      " |      Returns\n",
-      " |      -------\n",
-      " |      ndarray of int\n",
-      " |          The input numpy array that holds the face-edge connectivity.\n",
-      " |  \n",
-      " |  get_grid_face_nodes(self, grid: int, face_nodes: numpy.ndarray) -> numpy.ndarray\n",
-      " |      Get the face-node connectivity.\n",
-      " |      \n",
-      " |      Parameters\n",
-      " |      ----------\n",
-      " |      grid : int\n",
-      " |          A grid identifier.\n",
-      " |      face_nodes : ndarray of int\n",
-      " |          A numpy array to place the face-node connectivity. For each face,\n",
-      " |          the nodes (listed in a counter-clockwise direction) that form the\n",
-      " |          boundary of the face.\n",
-      " |      \n",
-      " |      Returns\n",
-      " |      -------\n",
-      " |      ndarray of int\n",
-      " |          The input numpy array that holds the face-node connectivity.\n",
-      " |  \n",
-      " |  get_grid_node_count(self, grid: int) -> int\n",
-      " |      Get the number of nodes in the grid.\n",
-      " |      \n",
-      " |      Parameters\n",
-      " |      ----------\n",
-      " |      grid : int\n",
-      " |          A grid identifier.\n",
-      " |      \n",
-      " |      Returns\n",
-      " |      -------\n",
-      " |      int\n",
-      " |          The total number of grid nodes.\n",
-      " |  \n",
-      " |  get_grid_nodes_per_face(self, grid: int, nodes_per_face: numpy.ndarray) -> numpy.ndarray\n",
-      " |      Get the number of nodes for each face.\n",
-      " |      \n",
-      " |      Parameters\n",
-      " |      ----------\n",
-      " |      grid : int\n",
-      " |          A grid identifier.\n",
-      " |      nodes_per_face : ndarray of int, shape *(nfaces,)*\n",
-      " |          A numpy array to place the number of edges per face.\n",
-      " |      \n",
-      " |      Returns\n",
-      " |      -------\n",
-      " |      ndarray of int\n",
-      " |          The input numpy array that holds the number of nodes per edge.\n",
-      " |  \n",
-      " |  get_grid_origin(self, grid: int, origin: numpy.ndarray) -> numpy.ndarray\n",
-      " |      Get coordinates for the lower-left corner of the computational grid.\n",
-      " |      \n",
-      " |      Parameters\n",
-      " |      ----------\n",
-      " |      grid : int\n",
-      " |          A grid identifier.\n",
-      " |      origin : ndarray of float, shape *(ndim,)*\n",
-      " |          A numpy array to hold the coordinates of the lower-left corner of\n",
-      " |          the grid.\n",
-      " |      \n",
-      " |      Returns\n",
-      " |      -------\n",
-      " |      ndarray of float\n",
-      " |          The input numpy array that holds the coordinates of the grid's\n",
-      " |          lower-left corner.\n",
-      " |  \n",
-      " |  get_grid_rank(self, grid: int) -> int\n",
-      " |      Get number of dimensions of the computational grid.\n",
-      " |      \n",
-      " |      Parameters\n",
-      " |      ----------\n",
-      " |      grid : int\n",
-      " |          A grid identifier.\n",
-      " |      \n",
-      " |      Returns\n",
-      " |      -------\n",
-      " |      int\n",
-      " |          Rank of the grid.\n",
-      " |  \n",
-      " |  get_grid_shape(self, grid: int, shape: numpy.ndarray) -> numpy.ndarray\n",
-      " |      Get dimensions of the computational grid.\n",
-      " |      \n",
-      " |      Parameters\n",
-      " |      ----------\n",
-      " |      grid : int\n",
-      " |          A grid identifier.\n",
-      " |      shape : ndarray of int, shape *(ndim,)*\n",
-      " |          A numpy array into which to place the shape of the grid.\n",
-      " |      \n",
-      " |      Returns\n",
-      " |      -------\n",
-      " |      ndarray of int\n",
-      " |          The input numpy array that holds the grid's shape.\n",
-      " |  \n",
-      " |  get_grid_size(self, grid: int) -> int\n",
-      " |      Get the total number of elements in the computational grid.\n",
-      " |      \n",
-      " |      Parameters\n",
-      " |      ----------\n",
-      " |      grid : int\n",
-      " |          A grid identifier.\n",
-      " |      \n",
-      " |      Returns\n",
-      " |      -------\n",
-      " |      int\n",
-      " |          Size of the grid.\n",
-      " |  \n",
-      " |  get_grid_spacing(self, grid: int, spacing: numpy.ndarray) -> numpy.ndarray\n",
-      " |      Get distance between nodes of the computational grid.\n",
-      " |      \n",
-      " |      Parameters\n",
-      " |      ----------\n",
-      " |      grid : int\n",
-      " |          A grid identifier.\n",
-      " |      spacing : ndarray of float, shape *(ndim,)*\n",
-      " |          A numpy array to hold the spacing between grid rows and columns.\n",
-      " |      \n",
-      " |      Returns\n",
-      " |      -------\n",
-      " |      ndarray of float\n",
-      " |          The input numpy array that holds the grid's spacing.\n",
-      " |  \n",
-      " |  get_grid_type(self, grid: int) -> str\n",
-      " |      Get the grid type as a string.\n",
-      " |      \n",
-      " |      Parameters\n",
-      " |      ----------\n",
-      " |      grid : int\n",
-      " |          A grid identifier.\n",
-      " |      \n",
-      " |      Returns\n",
-      " |      -------\n",
-      " |      str\n",
-      " |          Type of grid as a string.\n",
-      " |  \n",
-      " |  get_grid_x(self, grid: int, x: numpy.ndarray) -> numpy.ndarray\n",
-      " |      Get coordinates of grid nodes in the x direction.\n",
-      " |      \n",
-      " |      Parameters\n",
-      " |      ----------\n",
-      " |      grid : int\n",
-      " |          A grid identifier.\n",
-      " |      x : ndarray of float, shape *(nrows,)*\n",
-      " |          A numpy array to hold the x-coordinates of the grid node columns.\n",
-      " |      \n",
-      " |      Returns\n",
-      " |      -------\n",
-      " |      ndarray of float\n",
-      " |          The input numpy array that holds the grid's column x-coordinates.\n",
-      " |  \n",
-      " |  get_grid_y(self, grid: int, y: numpy.ndarray) -> numpy.ndarray\n",
-      " |      Get coordinates of grid nodes in the y direction.\n",
-      " |      \n",
-      " |      Parameters\n",
-      " |      ----------\n",
-      " |      grid : int\n",
-      " |          A grid identifier.\n",
-      " |      y : ndarray of float, shape *(ncols,)*\n",
-      " |          A numpy array to hold the y-coordinates of the grid node rows.\n",
-      " |      \n",
-      " |      Returns\n",
-      " |      -------\n",
-      " |      ndarray of float\n",
-      " |          The input numpy array that holds the grid's row y-coordinates.\n",
-      " |  \n",
-      " |  get_grid_z(self, grid: int, z: numpy.ndarray) -> numpy.ndarray\n",
-      " |      Get coordinates of grid nodes in the z direction.\n",
-      " |      \n",
-      " |      Parameters\n",
-      " |      ----------\n",
-      " |      grid : int\n",
-      " |          A grid identifier.\n",
-      " |      z : ndarray of float, shape *(nlayers,)*\n",
-      " |          A numpy array to hold the z-coordinates of the grid nodes layers.\n",
-      " |      \n",
-      " |      Returns\n",
-      " |      -------\n",
-      " |      ndarray of float\n",
-      " |          The input numpy array that holds the grid's layer z-coordinates.\n",
-      " |  \n",
-      " |  get_input_item_count(self) -> int\n",
-      " |      Count of a model's input variables.\n",
-      " |      \n",
-      " |      Returns\n",
-      " |      -------\n",
-      " |      int\n",
-      " |        The number of input variables.\n",
-      " |  \n",
-      " |  get_input_var_names(self) -> Tuple[str]\n",
-      " |      List of a model's input variables.\n",
-      " |      \n",
-      " |      Input variable names must be CSDMS Standard Names, also known\n",
-      " |      as *long variable names*.\n",
-      " |      \n",
-      " |      Returns\n",
-      " |      -------\n",
-      " |      list of str\n",
-      " |          The input variables for the model.\n",
-      " |      \n",
-      " |      Notes\n",
-      " |      -----\n",
-      " |      Standard Names enable the CSDMS framework to determine whether\n",
-      " |      an input variable in one model is equivalent to, or compatible\n",
-      " |      with, an output variable in another model. This allows the\n",
-      " |      framework to automatically connect components.\n",
-      " |      \n",
-      " |      Standard Names do not have to be used within the model.\n",
-      " |  \n",
-      " |  get_output_item_count(self) -> int\n",
-      " |      Count of a model's output variables.\n",
-      " |      \n",
-      " |      Returns\n",
-      " |      -------\n",
-      " |      int\n",
-      " |        The number of output variables.\n",
-      " |  \n",
-      " |  get_output_var_names(self) -> Tuple[str]\n",
-      " |      List of a model's output variables.\n",
-      " |      \n",
-      " |      Output variable names must be CSDMS Standard Names, also known\n",
-      " |      as *long variable names*.\n",
-      " |      \n",
-      " |      Returns\n",
-      " |      -------\n",
-      " |      list of str\n",
-      " |          The output variables for the model.\n",
-      " |  \n",
-      " |  get_start_time(self) -> float\n",
-      " |      Start time of the model.\n",
-      " |      \n",
-      " |      Model times should be of type float.\n",
-      " |      \n",
-      " |      Returns\n",
-      " |      -------\n",
-      " |      float\n",
-      " |          The model start time.\n",
-      " |  \n",
-      " |  get_time_step(self) -> float\n",
-      " |      Current time step of the model.\n",
-      " |      \n",
-      " |      The model time step should be of type float.\n",
-      " |      \n",
-      " |      Returns\n",
-      " |      -------\n",
-      " |      float\n",
-      " |          The time step used in model.\n",
-      " |  \n",
-      " |  get_time_units(self) -> str\n",
-      " |      Time units of the model.\n",
-      " |      \n",
-      " |      Returns\n",
-      " |      -------\n",
-      " |      float\n",
-      " |          The model time unit; e.g., `days` or `s`.\n",
-      " |      \n",
-      " |      Notes\n",
-      " |      -----\n",
-      " |      CSDMS uses the UDUNITS standard from Unidata.\n",
-      " |  \n",
-      " |  get_value(self, name: str, dest: numpy.ndarray) -> numpy.ndarray\n",
-      " |      Get a copy of values of the given variable.\n",
-      " |      \n",
-      " |      This is a getter for the model, used to access the model's\n",
-      " |      current state. It returns a *copy* of a model variable, with\n",
-      " |      the return type, size and rank dependent on the variable.\n",
-      " |      \n",
-      " |      Parameters\n",
-      " |      ----------\n",
-      " |      name : str\n",
-      " |          An input or output variable name, a CSDMS Standard Name.\n",
-      " |      dest : ndarray\n",
-      " |          A numpy array into which to place the values.\n",
-      " |      \n",
-      " |      Returns\n",
-      " |      -------\n",
-      " |      ndarray\n",
-      " |          The same numpy array that was passed as an input buffer.\n",
-      " |  \n",
-      " |  get_value_at_indices(self, name: str, dest: numpy.ndarray, inds: numpy.ndarray) -> numpy.ndarray\n",
-      " |      Get values at particular indices.\n",
-      " |      \n",
-      " |      Parameters\n",
-      " |      ----------\n",
-      " |      name : str\n",
-      " |          An input or output variable name, a CSDMS Standard Name.\n",
-      " |      dest : ndarray\n",
-      " |          A numpy array into which to place the values.\n",
-      " |      indices : array_like\n",
-      " |          The indices into the variable array.\n",
-      " |      \n",
-      " |      Returns\n",
-      " |      -------\n",
-      " |      array_like\n",
-      " |          Value of the model variable at the given location.\n",
-      " |  \n",
-      " |  get_value_ptr(self, name: str) -> numpy.ndarray\n",
-      " |      Get a reference to values of the given variable.\n",
-      " |      \n",
-      " |      This is a getter for the model, used to access the model's\n",
-      " |      current state. It returns a reference to a model variable,\n",
-      " |      with the return type, size and rank dependent on the variable.\n",
-      " |      \n",
-      " |      Parameters\n",
-      " |      ----------\n",
-      " |      name : str\n",
-      " |          An input or output variable name, a CSDMS Standard Name.\n",
-      " |      \n",
-      " |      Returns\n",
-      " |      -------\n",
-      " |      array_like\n",
-      " |          A reference to a model variable.\n",
-      " |  \n",
-      " |  get_var_grid(self, name: str) -> int\n",
-      " |      Get grid identifier for the given variable.\n",
-      " |      \n",
-      " |      Parameters\n",
-      " |      ----------\n",
-      " |      name : str\n",
-      " |          An input or output variable name, a CSDMS Standard Name.\n",
-      " |      \n",
-      " |      Returns\n",
-      " |      -------\n",
-      " |      int\n",
-      " |        The grid identifier.\n",
-      " |  \n",
-      " |  get_var_itemsize(self, name: str) -> int\n",
-      " |      Get memory use for each array element in bytes.\n",
-      " |      \n",
-      " |      Parameters\n",
-      " |      ----------\n",
-      " |      name : str\n",
-      " |          An input or output variable name, a CSDMS Standard Name.\n",
-      " |      \n",
-      " |      Returns\n",
-      " |      -------\n",
-      " |      int\n",
-      " |          Item size in bytes.\n",
-      " |  \n",
-      " |  get_var_location(self, name: str) -> str\n",
-      " |      Get the grid element type that the a given variable is defined on.\n",
-      " |      \n",
-      " |      The grid topology can be composed of *nodes*, *edges*, and *faces*.\n",
-      " |      \n",
-      " |      *node*\n",
-      " |          A point that has a coordinate pair or triplet: the most\n",
-      " |          basic element of the topology.\n",
-      " |      \n",
-      " |      *edge*\n",
-      " |          A line or curve bounded by two *nodes*.\n",
-      " |      \n",
-      " |      *face*\n",
-      " |          A plane or surface enclosed by a set of edges. In a 2D\n",
-      " |          horizontal application one may consider the word “polygon”,\n",
-      " |          but in the hierarchy of elements the word “face” is most common.\n",
-      " |      \n",
-      " |      Parameters\n",
-      " |      ----------\n",
-      " |      name : str\n",
-      " |          An input or output variable name, a CSDMS Standard Name.\n",
-      " |      \n",
-      " |      Returns\n",
-      " |      -------\n",
-      " |      str\n",
-      " |          The grid location on which the variable is defined. Must be one of\n",
-      " |          `\"node\"`, `\"edge\"`, or `\"face\"`.\n",
-      " |      \n",
-      " |      Notes\n",
-      " |      -----\n",
-      " |      CSDMS uses the `ugrid conventions`_ to define unstructured grids.\n",
-      " |      \n",
-      " |      .. _ugrid conventions: http://ugrid-conventions.github.io/ugrid-conventions\n",
-      " |  \n",
-      " |  get_var_nbytes(self, name: str) -> int\n",
-      " |      Get size, in bytes, of the given variable.\n",
-      " |      \n",
-      " |      Parameters\n",
-      " |      ----------\n",
-      " |      name : str\n",
-      " |          An input or output variable name, a CSDMS Standard Name.\n",
-      " |      \n",
-      " |      Returns\n",
-      " |      -------\n",
-      " |      int\n",
-      " |          The size of the variable, counted in bytes.\n",
-      " |  \n",
-      " |  get_var_type(self, name: str) -> str\n",
-      " |      Get data type of the given variable.\n",
-      " |      \n",
-      " |      Parameters\n",
-      " |      ----------\n",
-      " |      name : str\n",
-      " |          An input or output variable name, a CSDMS Standard Name.\n",
-      " |      \n",
-      " |      Returns\n",
-      " |      -------\n",
-      " |      str\n",
-      " |          The Python variable type; e.g., ``str``, ``int``, ``float``.\n",
-      " |  \n",
-      " |  get_var_units(self, name: str) -> str\n",
-      " |      Get units of the given variable.\n",
-      " |      \n",
-      " |      Standard unit names, in lower case, should be used, such as\n",
-      " |      ``meters`` or ``seconds``. Standard abbreviations, like ``m`` for\n",
-      " |      meters, are also supported. For variables with compound units,\n",
-      " |      each unit name is separated by a single space, with exponents\n",
-      " |      other than 1 placed immediately after the name, as in ``m s-1``\n",
-      " |      for velocity, ``W m-2`` for an energy flux, or ``km2`` for an\n",
-      " |      area.\n",
-      " |      \n",
-      " |      Parameters\n",
-      " |      ----------\n",
-      " |      name : str\n",
-      " |          An input or output variable name, a CSDMS Standard Name.\n",
-      " |      \n",
-      " |      Returns\n",
-      " |      -------\n",
-      " |      str\n",
-      " |          The variable units.\n",
-      " |      \n",
-      " |      Notes\n",
-      " |      -----\n",
-      " |      CSDMS uses the `UDUNITS`_ standard from Unidata.\n",
-      " |      \n",
-      " |      .. _UDUNITS: http://www.unidata.ucar.edu/software/udunits\n",
-      " |  \n",
-      " |  initialize(self, config_file: str) -> None\n",
-      " |      Perform startup tasks for the model.\n",
-      " |      \n",
-      " |      Perform all tasks that take place before entering the model's time\n",
-      " |      loop, including opening files and initializing the model state. Model\n",
-      " |      inputs are read from a text-based configuration file, specified by\n",
-      " |      `filename`.\n",
-      " |      \n",
-      " |      Parameters\n",
-      " |      ----------\n",
-      " |      config_file : str, optional\n",
-      " |          The path to the model configuration file.\n",
-      " |      \n",
-      " |      Notes\n",
-      " |      -----\n",
-      " |      Models should be refactored, if necessary, to use a\n",
-      " |      configuration file. CSDMS does not impose any constraint on\n",
-      " |      how configuration files are formatted, although YAML is\n",
-      " |      recommended. A template of a model's configuration file\n",
-      " |      with placeholder values is used by the BMI.\n",
-      " |  \n",
-      " |  set_value(self, name: str, values: numpy.ndarray) -> None\n",
-      " |      Specify a new value for a model variable.\n",
-      " |      \n",
-      " |      This is the setter for the model, used to change the model's\n",
-      " |      current state. It accepts, through *src*, a new value for a\n",
-      " |      model variable, with the type, size and rank of *src*\n",
-      " |      dependent on the variable.\n",
-      " |      \n",
-      " |      Parameters\n",
-      " |      ----------\n",
-      " |      var_name : str\n",
-      " |          An input or output variable name, a CSDMS Standard Name.\n",
-      " |      src : array_like\n",
-      " |          The new value for the specified variable.\n",
-      " |  \n",
-      " |  set_value_at_indices(self, name: str, inds: numpy.ndarray, src: numpy.ndarray) -> None\n",
-      " |      Specify a new value for a model variable at particular indices.\n",
-      " |      \n",
-      " |      Parameters\n",
-      " |      ----------\n",
-      " |      var_name : str\n",
-      " |          An input or output variable name, a CSDMS Standard Name.\n",
-      " |      indices : array_like\n",
-      " |          The indices into the variable array.\n",
-      " |      src : array_like\n",
-      " |          The new value for the specified variable.\n",
-      " |  \n",
-      " |  update(self) -> None\n",
-      " |      Advance model state by one time step.\n",
-      " |      \n",
-      " |      Perform all tasks that take place within one pass through the model's\n",
-      " |      time loop. This typically includes incrementing all of the model's\n",
-      " |      state variables. If the model's state variables don't change in time,\n",
-      " |      then they can be computed by the :func:`initialize` method and this\n",
-      " |      method can return with no action.\n",
-      " |  \n",
-      " |  update_until(self, time: float) -> None\n",
-      " |      Advance model state until the given time.\n",
-      " |      \n",
-      " |      Parameters\n",
-      " |      ----------\n",
-      " |      time : float\n",
-      " |          A model time later than the current model time.\n",
-      " |  \n",
-      " |  ----------------------------------------------------------------------\n",
-      " |  Data and other attributes defined here:\n",
-      " |  \n",
-      " |  __abstractmethods__ = frozenset()\n",
-      " |  \n",
-      " |  ----------------------------------------------------------------------\n",
-      " |  Data descriptors inherited from bmipy.bmi.Bmi:\n",
-      " |  \n",
-      " |  __dict__\n",
-      " |      dictionary for instance variables (if defined)\n",
-      " |  \n",
-      " |  __weakref__\n",
-      " |      list of weak references to the object (if defined)\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "help(m)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "limiting-ferry",
+   "id": "13",
    "metadata": {},
    "source": [
     "The first step in using a BMI is calling the `initialize` method.\n",
@@ -749,52 +135,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "id": "mighty-carrier",
+   "execution_count": null,
+   "id": "14",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "README.md              bmi-topography_ex.py   topography.ipynb\r\n",
-      "bmi-topography.ipynb   bmi-topography_ex.sh\r\n",
-      "bmi-topography_ex.png  config.yaml\r\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "ls"
+    "!ls"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "id": "stuck-twins",
+   "execution_count": null,
+   "id": "15",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "bmi-topography:\r\n",
-      "  dem_type: SRTMGL3\r\n",
-      "  south: 36.738884\r\n",
-      "  north: 38.091337\r\n",
-      "  west: -120.168457\r\n",
-      "  east: -118.465576\r\n",
-      "  output_format: GTiff\r\n",
-      "  cache_dir: \"~/.bmi_topography\"\r\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "cat config.yaml"
+    "!cat config.yaml"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "employed-patrick",
+   "id": "16",
    "metadata": {},
    "source": [
     "Call `initialize` with the sample configuration file."
@@ -802,8 +163,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "id": "awful-spirituality",
+   "execution_count": null,
+   "id": "17",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -812,7 +173,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "gentle-italian",
+   "id": "18",
    "metadata": {},
    "source": [
     "This step may take a moment, as the `Topography` library fetches and downloads the data from the internet.\n",
@@ -822,27 +183,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "id": "emotional-fighter",
+   "execution_count": null,
+   "id": "19",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "SRTMGL3_36.738884_-120.168457_38.091337_-118.465576.tif\r\n",
-      "SRTMGL3_39.75_-105.25_40.25_-104.75.tif\r\n",
-      "SRTMGL3_39.93_-105.33_40.0_-105.26.tif\r\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "ls ~/.bmi_topography"
+    "!ls ~/.bmi_topography"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "accessory-clinton",
+   "id": "20",
    "metadata": {},
    "source": [
     "## Access data through the BMI"
@@ -850,7 +201,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "short-option",
+   "id": "21",
    "metadata": {},
    "source": [
     "Now that we've fetched the data, let's access it through the BMI.\n",
@@ -862,28 +213,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "id": "broadband-stocks",
+   "execution_count": null,
+   "id": "22",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "('land_surface__elevation',)"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "m.get_output_var_names()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "whole-stand",
+   "id": "23",
    "metadata": {},
    "source": [
     "The (long) name for the variable representing elevation is an instance of a [CSDMS Standard Name](https://csdms.colorado.edu/wiki/CSDMS_Standard_Names).\n",
@@ -892,7 +232,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "activated-indiana",
+   "id": "24",
    "metadata": {},
    "source": [
     "Find the data type of the elevation data."
@@ -900,21 +240,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "id": "ideal-neutral",
+   "execution_count": null,
+   "id": "25",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'int16'"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "dtype = m.get_var_type(\"land_surface__elevation\")\n",
     "dtype"
@@ -922,7 +251,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "compatible-hostel",
+   "id": "26",
    "metadata": {},
    "source": [
     "Within the BMI, functions that describe the grids that variables are defined on take an index instead of a variable name.\n",
@@ -932,21 +261,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "id": "endangered-entertainment",
+   "execution_count": null,
+   "id": "27",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "grid = m.get_var_grid(\"land_surface__elevation\")\n",
     "grid"
@@ -954,7 +272,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "english-people",
+   "id": "28",
    "metadata": {},
    "source": [
     "Then find the total size of the elevation data."
@@ -962,21 +280,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "id": "described-constraint",
+   "execution_count": null,
+   "id": "29",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "3315789"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "size = m.get_grid_size(grid)\n",
     "size"
@@ -984,7 +291,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "expensive-harvey",
+   "id": "30",
    "metadata": {},
    "source": [
     "Next, get the elevation values.\n",
@@ -999,21 +306,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "id": "special-aquatic",
+   "execution_count": null,
+   "id": "31",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([0, 0, 0, ..., 0, 0, 0], dtype=int16)"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "elevation = np.ndarray(size, dtype)\n",
     "elevation"
@@ -1021,7 +317,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dimensional-assembly",
+   "id": "32",
    "metadata": {},
    "source": [
     "Get the elevation data."
@@ -1029,28 +325,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "id": "cross-tragedy",
+   "execution_count": null,
+   "id": "33",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([1295, 1339, 1380, ..., 3519, 3467, 3423], dtype=int16)"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "m.get_value(\"land_surface__elevation\", elevation)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "informed-medium",
+   "id": "34",
    "metadata": {},
    "source": [
     "Note that the elevation array is one-dimensional."
@@ -1058,28 +343,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "id": "small-execution",
+   "execution_count": null,
+   "id": "35",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(3315789,)"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "elevation.shape"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "accomplished-parallel",
+   "id": "36",
    "metadata": {},
    "source": [
     "### Reshape data"
@@ -1087,7 +361,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "norman-beatles",
+   "id": "37",
    "metadata": {},
    "source": [
     "Like all BMI arrays, the elevations returned from the BMI `get_value` function are flattened.\n",
@@ -1096,7 +370,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "eastern-borough",
+   "id": "38",
    "metadata": {},
    "source": [
     "First, determine the dimensionality of the elevation variable."
@@ -1104,21 +378,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "id": "productive-black",
+   "execution_count": null,
+   "id": "39",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "rank = m.get_grid_rank(grid)\n",
     "rank"
@@ -1126,7 +389,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "stunning-jacksonville",
+   "id": "40",
    "metadata": {},
    "source": [
     "Get the dimensions of the elevation data, first creating an array to store their values."
@@ -1134,21 +397,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "id": "fitted-input",
+   "execution_count": null,
+   "id": "41",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([1623, 2043])"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "shape = np.ndarray(rank, dtype=int)\n",
     "shape"
@@ -1156,28 +408,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "id": "fabulous-karaoke",
+   "execution_count": null,
+   "id": "42",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([1623, 2043])"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "m.get_grid_shape(grid, shape)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "ordered-stretch",
+   "id": "43",
    "metadata": {},
    "source": [
     "Reshape the elevation data, creating a new array."
@@ -1185,8 +426,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "id": "pursuant-leisure",
+   "execution_count": null,
+   "id": "44",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1195,28 +436,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "id": "immune-motel",
+   "execution_count": null,
+   "id": "45",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(1623, 2043)"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "elevation2D.shape"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "nominated-annotation",
+   "id": "46",
    "metadata": {},
    "source": [
     "## Visualize"
@@ -1224,7 +454,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "romance-auckland",
+   "id": "47",
    "metadata": {},
    "source": [
     "Let's visualize the elevation data as an image."
@@ -1232,40 +462,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
-   "id": "disturbed-blend",
+   "execution_count": null,
+   "id": "48",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x14a688460>"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "plt.imshow(elevation2D)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "warming-berkeley",
+   "id": "49",
    "metadata": {},
    "source": [
     "## Conclusion"
@@ -1273,7 +480,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "located-latter",
+   "id": "50",
    "metadata": {},
    "source": [
     "Last, call the BMI `finalize` function."
@@ -1281,8 +488,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
-   "id": "opening-oklahoma",
+   "execution_count": null,
+   "id": "51",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1291,7 +498,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "matched-invalid",
+   "id": "52",
    "metadata": {},
    "source": [
     "This demonstration of the BMI took a lot of code to reproduce a simple result.\n",
diff --git a/examples/topography.ipynb b/examples/topography.ipynb
index cccabef..e5a1fe2 100644
--- a/examples/topography.ipynb
+++ b/examples/topography.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "threaded-still",
+   "id": "0",
    "metadata": {},
    "source": [
     "# Get SRTM data with the Topography class"
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "medieval-tractor",
+   "id": "1",
    "metadata": {},
    "source": [
     "This notebook describes how to download Shuttle Radar Topography Mission (SRTM) elevation data\n",
@@ -19,7 +19,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "pleasant-rugby",
+   "id": "2",
    "metadata": {},
    "source": [
     "## Setup"
@@ -27,7 +27,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "opening-gallery",
+   "id": "3",
    "metadata": {},
    "source": [
     "To ensure all dependencies are met, set up a conda environment using the environment file found in the root directory of this repository:\n",
@@ -43,7 +43,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "upper-offset",
+   "id": "4",
    "metadata": {},
    "source": [
     "## Fetch and load data"
@@ -51,7 +51,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "overall-heater",
+   "id": "5",
    "metadata": {},
    "source": [
     "Import the `Topography` class from the newly installed `bmi-topography` package:"
@@ -59,8 +59,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "based-ukraine",
+   "execution_count": null,
+   "id": "6",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -69,7 +69,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "religious-submission",
+   "id": "7",
    "metadata": {},
    "source": [
     "`Topography` downloads and stores SRTM data through the [OpenTopography](https://opentopography.org/) [REST API](https://portal.opentopography.org/apidocs/#/Public/getGlobalDem). OpenTopography is an NSF-supported project that provides open access to high-resolution topography data and services."
@@ -77,7 +77,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "abandoned-hamburg",
+   "id": "8",
    "metadata": {},
    "source": [
     "Create an instance of `Topography` using parameters to describe\n",
@@ -92,8 +92,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "mental-instrumentation",
+   "execution_count": null,
+   "id": "9",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -104,13 +104,13 @@
     "    west=-120.168457,\n",
     "    east=-118.465576,\n",
     "    output_format=\"GTiff\",\n",
-    "    cache_dir=\".\"\n",
-    "    )"
+    "    cache_dir=\".\",\n",
+    ")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "hungry-robertson",
+   "id": "10",
    "metadata": {},
    "source": [
     "While this step sets up a call to the OpenTopography API, it doesn't download the data. Download the data by calling the `fetch` method:"
@@ -118,18 +118,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "agreed-border",
+   "execution_count": null,
+   "id": "11",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "/Users/mpiper/projects/bmi-topography/examples/SRTMGL3_36.738884_-120.168457_38.091337_-118.465576.tif\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "fname = topo.fetch()\n",
     "print(fname)"
@@ -137,7 +129,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dental-transport",
+   "id": "12",
    "metadata": {},
    "source": [
     "This step may take a few moments to run while the data are fetched from OpenTopography and downloaded."
@@ -145,7 +137,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "christian-criticism",
+   "id": "13",
    "metadata": {},
    "source": [
     "The `fetch` method only downloads data; it doesn't load it into memory. Call the `load` method to open the downloaded  file and load it into an `xarray` DataArray:"
@@ -153,30 +145,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "id": "narrative-moscow",
+   "execution_count": null,
+   "id": "14",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<xarray.DataArray 'SRTMGL3' (band: 1, y: 1623, x: 2043)>\n",
-      "[3315789 values with dtype=int16]\n",
-      "Coordinates:\n",
-      "  * band         (band) int64 1\n",
-      "  * x            (x) float64 -120.2 -120.2 -120.2 ... -118.5 -118.5 -118.5\n",
-      "  * y            (y) float64 38.09 38.09 38.09 38.09 ... 36.74 36.74 36.74 36.74\n",
-      "    spatial_ref  int64 0\n",
-      "Attributes:\n",
-      "    _FillValue:    0.0\n",
-      "    scale_factor:  1.0\n",
-      "    add_offset:    0.0\n",
-      "    units:         meters\n",
-      "    location:      node\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "da = topo.load()\n",
     "print(da)"
@@ -184,7 +156,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "sexual-elite",
+   "id": "15",
    "metadata": {},
    "source": [
     "Note that `load` calls `fetch`, so the latter can be omitted if the goal is the get the data into memory."
@@ -192,7 +164,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "psychological-cannon",
+   "id": "16",
    "metadata": {},
    "source": [
     "## Visualize"
@@ -200,7 +172,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "alien-facing",
+   "id": "17",
    "metadata": {},
    "source": [
     "Finally, let's visualize the downloaded elevation data."
@@ -208,43 +180,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "id": "italian-resident",
+   "execution_count": null,
+   "id": "18",
    "metadata": {},
    "outputs": [],
-   "source": [
-    "import matplotlib"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "permanent-consciousness",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x1359b5870>"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
    "source": [
     "da.plot()"
    ]
diff --git a/noxfile.py b/noxfile.py
index 50e6a17..be60e14 100644
--- a/noxfile.py
+++ b/noxfile.py
@@ -69,7 +69,7 @@ def format(session: nox.Session) -> None:
 
     session.run("black", *black_args, *PATHS)
     session.run("isort", *PATHS)
-    session.run("ruff", *PATHS)
+    session.run("ruff", "check", *PATHS)
 
 
 @nox.session(name="prepare-docs")
diff --git a/tests/test_config.py b/tests/test_config.py
index 5a2145f..328fa82 100644
--- a/tests/test_config.py
+++ b/tests/test_config.py
@@ -10,7 +10,7 @@
 
 
 def test_read_config(shared_datadir):
-    with open(shared_datadir / CONFIG_FILE, "r") as fp:
+    with open(shared_datadir / CONFIG_FILE) as fp:
         conf = yaml.safe_load(fp).get("bmi-topography", {})
     assert conf["dem_type"] == DEM_TYPE
     assert conf["output_format"] == OUTPUT_FORMAT