From a96aaa4ecea17489057fa7db3464608656377a87 Mon Sep 17 00:00:00 2001
From: Mark Piper <mark.piper@colorado.edu>
Date: Fri, 16 Aug 2024 15:31:01 -0600
Subject: [PATCH 1/5] Save notebooks in un-run state

---
 examples/bmi-geotiff.ipynb | 1016 ++++--------------------------------
 examples/geotiff.ipynb     |  901 ++------------------------------
 2 files changed, 136 insertions(+), 1781 deletions(-)

diff --git a/examples/bmi-geotiff.ipynb b/examples/bmi-geotiff.ipynb
index 258da2a..8f8ccae 100644
--- a/examples/bmi-geotiff.ipynb
+++ b/examples/bmi-geotiff.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "eastern-royal",
+   "id": "0",
    "metadata": {},
    "source": [
     "# Read GeoTIFF data through a BMI"
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "boxed-series",
+   "id": "1",
    "metadata": {},
    "source": [
     "This notebook describes how to open and read data from GeoTIFF files\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 set 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": [
     "## Open a file"
@@ -70,7 +70,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "monthly-stereo",
+   "id": "7",
    "metadata": {},
    "source": [
     "Import the `BmiGeoTiff` class from the `bmi-geotiff` 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 BmiGeoTiff in module bmi_geotiff.bmi object:\n",
-      "\n",
-      "class BmiGeoTiff(bmipy.bmi.Bmi)\n",
-      " |  BmiGeoTiff() -> None\n",
-      " |  \n",
-      " |  BMI-mediated access to data and metadata in a GeoTIFF file.\n",
-      " |  \n",
-      " |  Method resolution order:\n",
-      " |      BmiGeoTiff\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",
@@ -749,46 +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-geotiff.ipynb       example-rgb.png\r\n",
-      "\u001b[35mRGB.byte.tif\u001b[m\u001b[m@           config.yaml             example-rgb.py\r\n",
-      "bmi-geotiff-work.ipynb  example-pan.py          geotiff.ipynb\r\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "ls"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "id": "stuck-twins",
+   "execution_count": null,
+   "id": "15",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "bmi-geotiff:\r\n",
-      "  filename: RGB.byte.tif\r\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "cat config.yaml"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "employed-patrick",
+   "id": "16",
    "metadata": {},
    "source": [
     "In this case, the configuration file simply lists the path to a GeoTIFF file\n",
@@ -799,8 +166,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "id": "awful-spirituality",
+   "execution_count": null,
+   "id": "17",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -809,7 +176,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "gentle-italian",
+   "id": "18",
    "metadata": {},
    "source": [
     "The GeoTIFF file listed in the configuration file has now been opened,\n",
@@ -818,7 +185,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "accessory-clinton",
+   "id": "19",
    "metadata": {},
    "source": [
     "## Access data through the BMI"
@@ -826,7 +193,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "short-option",
+   "id": "20",
    "metadata": {},
    "source": [
     "Now that we've opened the GeoTIFF file, let's access the data and metadata it contains through the BMI.\n",
@@ -838,30 +205,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "id": "broadband-stocks",
+   "execution_count": null,
+   "id": "21",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "('gis__raster_data',\n",
-       " 'gis__coordinate_reference_system_well_known_text',\n",
-       " 'gis__affine_transform')"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "m.get_output_var_names()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "whole-stand",
+   "id": "22",
    "metadata": {},
    "source": [
     "The (long) names used for these variables are instances of [CSDMS Standard Names](https://csdms.colorado.edu/wiki/CSDMS_Standard_Names).\n",
@@ -871,7 +225,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "activated-indiana",
+   "id": "23",
    "metadata": {},
    "source": [
     "Find the data type of the raster."
@@ -879,21 +233,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "id": "ideal-neutral",
+   "execution_count": null,
+   "id": "24",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'uint8'"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "dtype = m.get_var_type(\"gis__raster_data\")\n",
     "dtype"
@@ -901,7 +244,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "compatible-hostel",
+   "id": "25",
    "metadata": {},
    "source": [
     "Within the BMI, functions that describe the grids that variables are defined on take an index instead of a variable name.\n",
@@ -911,21 +254,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "id": "endangered-entertainment",
+   "execution_count": null,
+   "id": "26",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "grid = m.get_var_grid(\"gis__raster_data\")\n",
     "grid"
@@ -933,7 +265,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "english-people",
+   "id": "27",
    "metadata": {},
    "source": [
     "Then find the total size of the raster data."
@@ -941,21 +273,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "id": "described-constraint",
+   "execution_count": null,
+   "id": "28",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1703814"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "size = m.get_grid_size(grid)\n",
     "size"
@@ -963,7 +284,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "expensive-harvey",
+   "id": "29",
    "metadata": {},
    "source": [
     "Next, get the raster data values.\n",
@@ -978,21 +299,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "id": "special-aquatic",
+   "execution_count": null,
+   "id": "30",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([0, 0, 0, ..., 0, 0, 0], dtype=uint8)"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "raster = np.ndarray(size, dtype)\n",
     "raster"
@@ -1000,7 +310,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dimensional-assembly",
+   "id": "31",
    "metadata": {},
    "source": [
     "Get the data."
@@ -1008,28 +318,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "id": "cross-tragedy",
+   "execution_count": null,
+   "id": "32",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([0, 0, 0, ..., 0, 0, 0], dtype=uint8)"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "m.get_value(\"gis__raster_data\", raster)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "informed-medium",
+   "id": "33",
    "metadata": {},
    "source": [
     "Note that the array is one-dimensional."
@@ -1037,28 +336,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "id": "small-execution",
+   "execution_count": null,
+   "id": "34",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(1703814,)"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "raster.shape"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "accomplished-parallel",
+   "id": "35",
    "metadata": {},
    "source": [
     "### Reshape the data"
@@ -1066,7 +354,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "norman-beatles",
+   "id": "36",
    "metadata": {},
    "source": [
     "Like all BMI arrays, the raster values returned from the BMI `get_value` function are flattened.\n",
@@ -1075,7 +363,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "eastern-borough",
+   "id": "37",
    "metadata": {},
    "source": [
     "First, determine the dimensionality of the raster variable."
@@ -1083,21 +371,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "id": "productive-black",
+   "execution_count": null,
+   "id": "38",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "3"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "rank = m.get_grid_rank(grid)\n",
     "rank"
@@ -1105,7 +382,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "stunning-jacksonville",
+   "id": "39",
    "metadata": {},
    "source": [
     "Get the dimensions of the raster data, first creating an array to store their values."
@@ -1113,21 +390,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "id": "fabulous-karaoke",
+   "execution_count": null,
+   "id": "40",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([  3, 718, 791])"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "shape = np.empty(rank, dtype=int)\n",
     "m.get_grid_shape(grid, shape)"
@@ -1135,7 +401,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ordered-stretch",
+   "id": "41",
    "metadata": {},
    "source": [
     "Reshape the raster data, creating a new array."
@@ -1143,8 +409,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "id": "pursuant-leisure",
+   "execution_count": null,
+   "id": "42",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1153,28 +419,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "id": "immune-motel",
+   "execution_count": null,
+   "id": "43",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(3, 718, 791)"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "rasterRGB.shape"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "presidential-election",
+   "id": "44",
    "metadata": {},
    "source": [
     "### Get map projection information"
@@ -1182,7 +437,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "exotic-charity",
+   "id": "45",
    "metadata": {},
    "source": [
     "The data in the GeoTIFF file are georeferenced.\n",
@@ -1202,21 +457,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "id": "unexpected-ideal",
+   "execution_count": null,
+   "id": "46",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'float'"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "dtype = m.get_var_type(\"gis__affine_transform\")\n",
     "dtype"
@@ -1224,21 +468,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "id": "governmental-bacteria",
+   "execution_count": null,
+   "id": "47",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "grid = m.get_var_grid(\"gis__affine_transform\")\n",
     "grid"
@@ -1246,21 +479,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
-   "id": "anonymous-valley",
+   "execution_count": null,
+   "id": "48",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "9"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "size = m.get_grid_size(grid)\n",
     "size"
@@ -1268,8 +490,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
-   "id": "immediate-warehouse",
+   "execution_count": null,
+   "id": "49",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1278,23 +500,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
-   "id": "loaded-woman",
+   "execution_count": null,
+   "id": "50",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 3.00037927e+02,  0.00000000e+00,  1.01985000e+05,  0.00000000e+00,\n",
-       "       -3.00041783e+02,  2.82691500e+06,  0.00000000e+00,  0.00000000e+00,\n",
-       "        1.00000000e+00])"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "m.get_value(\"gis__affine_transform\", transform)\n",
     "transform"
@@ -1302,7 +511,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "nominated-annotation",
+   "id": "51",
    "metadata": {},
    "source": [
     "## Visualize"
@@ -1310,7 +519,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "romance-auckland",
+   "id": "52",
    "metadata": {},
    "source": [
     "Let's visualize the raster data as an image, with a little help from rasterio."
@@ -1318,40 +527,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
-   "id": "foster-indicator",
+   "execution_count": null,
+   "id": "53",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:>"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "show(rasterRGB, transform=list(transform[0:6]))"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "warming-berkeley",
+   "id": "54",
    "metadata": {},
    "source": [
     "## Conclusion"
@@ -1359,7 +545,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "located-latter",
+   "id": "55",
    "metadata": {},
    "source": [
     "Last, call the BMI `finalize` function."
@@ -1367,8 +553,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
-   "id": "opening-oklahoma",
+   "execution_count": null,
+   "id": "56",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1377,7 +563,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "matched-invalid",
+   "id": "57",
    "metadata": {},
    "source": [
     "This demonstration of the BMI took a lot of code to reproduce a simple result.\n",
diff --git a/examples/geotiff.ipynb b/examples/geotiff.ipynb
index 5f73ac8..e1bc82f 100644
--- a/examples/geotiff.ipynb
+++ b/examples/geotiff.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "threaded-still",
+   "id": "0",
    "metadata": {},
    "source": [
     "# Read GeoTIFF files with the `GeoTiff` class"
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "medieval-tractor",
+   "id": "1",
    "metadata": {},
    "source": [
     "This notebook describes how to open a GeoTIFF file\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": [
     "## Open a file"
@@ -51,7 +51,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "overall-heater",
+   "id": "5",
    "metadata": {},
    "source": [
     "Import the `GeoTiff` class from the `bmi-geotiff` 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": [
     "`GeoTiff` uses the open source [xarray](https://xarray.pydata.org), [rioxarray](https://corteva.github.io/rioxarray/stable/), and [rasterio](https://rasterio.readthedocs.io) packages to open, read, and store data and metadata from GeoTIFF files."
@@ -77,7 +77,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "abandoned-hamburg",
+   "id": "8",
    "metadata": {},
    "source": [
     "Create an instance of `GeoTiff`:"
@@ -85,8 +85,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "mental-instrumentation",
+   "execution_count": null,
+   "id": "9",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -95,7 +95,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "hungry-robertson",
+   "id": "10",
    "metadata": {},
    "source": [
     "The `GeoTiff` class can open local or remote files.\n",
@@ -108,8 +108,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "agreed-border",
+   "execution_count": null,
+   "id": "11",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -119,7 +119,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "sexual-elite",
+   "id": "12",
    "metadata": {},
    "source": [
     "Note that a file can also be opened on instantiation, eliminating the call to `open`."
@@ -127,7 +127,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "christian-criticism",
+   "id": "13",
    "metadata": {},
    "source": [
     "The data (and metadata) from the file are loaded into an `xarray` DataArray, which can be accessed through the `da` property."
@@ -135,415 +135,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "id": "narrative-moscow",
+   "execution_count": null,
+   "id": "14",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
-       "<defs>\n",
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-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data {\n",
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-       ".xr-var-data,\n",
-       ".xr-attrs {\n",
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-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data {\n",
-       "  grid-column: 1 / -1;\n",
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-       "\n",
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-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt,\n",
-       ".xr-attrs dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
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-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2 {\n",
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-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (band: 3, y: 718, x: 791)&gt;\n",
-       "[1703814 values with dtype=uint8]\n",
-       "Coordinates:\n",
-       "  * band         (band) int64 1 2 3\n",
-       "  * x            (x) float64 1.021e+05 1.024e+05 ... 3.389e+05 3.392e+05\n",
-       "  * y            (y) float64 2.827e+06 2.826e+06 ... 2.612e+06 2.612e+06\n",
-       "    spatial_ref  int64 0\n",
-       "Attributes:\n",
-       "    STATISTICS_MAXIMUM:  255\n",
-       "    STATISTICS_MEAN:     29.947726688477\n",
-       "    STATISTICS_MINIMUM:  0\n",
-       "    STATISTICS_STDDEV:   52.340921626611\n",
-       "    _FillValue:          0.0\n",
-       "    scale_factor:        1.0\n",
-       "    add_offset:          0.0\n",
-       "    units:               metre</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>band</span>: 3</li><li><span class='xr-has-index'>y</span>: 718</li><li><span class='xr-has-index'>x</span>: 791</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-4842bce1-dfa6-4c11-ad4e-62fad7346e9d' class='xr-array-in' type='checkbox' checked><label for='section-4842bce1-dfa6-4c11-ad4e-62fad7346e9d' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>...</span></div><div class='xr-array-data'><pre>[1703814 values with dtype=uint8]</pre></div></div></li><li class='xr-section-item'><input id='section-c2d541b7-2ae5-4481-923b-1bd4f7d7c460' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c2d541b7-2ae5-4481-923b-1bd4f7d7c460' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>band</span></div><div class='xr-var-dims'>(band)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3</div><input id='attrs-edc417ea-bcbe-4d0a-834c-abd985d57ff8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-edc417ea-bcbe-4d0a-834c-abd985d57ff8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-03a63d78-a5d7-4997-9fd0-d2ccdff414d3' class='xr-var-data-in' type='checkbox'><label for='data-03a63d78-a5d7-4997-9fd0-d2ccdff414d3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.021e+05 1.024e+05 ... 3.392e+05</div><input id='attrs-d7d649e8-20cd-4c13-a6b9-87118c8ceab8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d7d649e8-20cd-4c13-a6b9-87118c8ceab8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-98c7ae3d-59f3-4ab1-b9a0-6a01ac9b5ec9' class='xr-var-data-in' type='checkbox'><label for='data-98c7ae3d-59f3-4ab1-b9a0-6a01ac9b5ec9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([102135.018963, 102435.05689 , 102735.094817, ..., 338564.905183,\n",
-       "       338864.94311 , 339164.981037])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.827e+06 2.826e+06 ... 2.612e+06</div><input id='attrs-40af70fd-525d-4b8c-939a-1aab5e7fbf8b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-40af70fd-525d-4b8c-939a-1aab5e7fbf8b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ec36a7a3-71e7-4064-ac13-d0f496d04083' class='xr-var-data-in' type='checkbox'><label for='data-ec36a7a3-71e7-4064-ac13-d0f496d04083' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([2826764.979109, 2826464.937326, 2826164.895543, ..., 2612235.104457,\n",
-       "       2611935.062674, 2611635.020891])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-55b055db-f459-45e3-9d6d-18246ced3ef2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-55b055db-f459-45e3-9d6d-18246ced3ef2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1a247310-c9cb-4f40-80fc-ac970bb8200c' class='xr-var-data-in' type='checkbox'><label for='data-1a247310-c9cb-4f40-80fc-ac970bb8200c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>crs_wkt :</span></dt><dd>PROJCS[&quot;WGS 84 / UTM zone 18N&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,-75],PARAMETER[&quot;scale_factor&quot;,0.9996],PARAMETER[&quot;false_easting&quot;,500000],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;32618&quot;]]</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314245179</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>WGS 84</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>WGS 84</dd><dt><span>horizontal_datum_name :</span></dt><dd>World Geodetic System 1984</dd><dt><span>projected_crs_name :</span></dt><dd>WGS 84 / UTM zone 18N</dd><dt><span>grid_mapping_name :</span></dt><dd>transverse_mercator</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>0.0</dd><dt><span>longitude_of_central_meridian :</span></dt><dd>-75.0</dd><dt><span>false_easting :</span></dt><dd>500000.0</dd><dt><span>false_northing :</span></dt><dd>0.0</dd><dt><span>scale_factor_at_central_meridian :</span></dt><dd>0.9996</dd><dt><span>spatial_ref :</span></dt><dd>PROJCS[&quot;WGS 84 / UTM zone 18N&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,-75],PARAMETER[&quot;scale_factor&quot;,0.9996],PARAMETER[&quot;false_easting&quot;,500000],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;32618&quot;]]</dd><dt><span>GeoTransform :</span></dt><dd>101985.0 300.0379266750948 0.0 2826915.0 0.0 -300.041782729805</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7e6aafea-b792-4b4f-b76d-520341792a1a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7e6aafea-b792-4b4f-b76d-520341792a1a' class='xr-section-summary' >Attributes: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>STATISTICS_MAXIMUM :</span></dt><dd>255</dd><dt><span>STATISTICS_MEAN :</span></dt><dd>29.947726688477</dd><dt><span>STATISTICS_MINIMUM :</span></dt><dd>0</dd><dt><span>STATISTICS_STDDEV :</span></dt><dd>52.340921626611</dd><dt><span>_FillValue :</span></dt><dd>0.0</dd><dt><span>scale_factor :</span></dt><dd>1.0</dd><dt><span>add_offset :</span></dt><dd>0.0</dd><dt><span>units :</span></dt><dd>metre</dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.DataArray (band: 3, y: 718, x: 791)>\n",
-       "[1703814 values with dtype=uint8]\n",
-       "Coordinates:\n",
-       "  * band         (band) int64 1 2 3\n",
-       "  * x            (x) float64 1.021e+05 1.024e+05 ... 3.389e+05 3.392e+05\n",
-       "  * y            (y) float64 2.827e+06 2.826e+06 ... 2.612e+06 2.612e+06\n",
-       "    spatial_ref  int64 0\n",
-       "Attributes:\n",
-       "    STATISTICS_MAXIMUM:  255\n",
-       "    STATISTICS_MEAN:     29.947726688477\n",
-       "    STATISTICS_MINIMUM:  0\n",
-       "    STATISTICS_STDDEV:   52.340921626611\n",
-       "    _FillValue:          0.0\n",
-       "    scale_factor:        1.0\n",
-       "    add_offset:          0.0\n",
-       "    units:               metre"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "tif.da"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "e9605efa",
+   "id": "15",
    "metadata": {},
    "source": [
     "Coordinate reference system information is stored in the `spatial_ref` non-dimensional coordinate:"
@@ -551,427 +153,17 @@
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   {
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-   "id": "1fa8459c",
+   "execution_count": null,
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-       "\n",
-       ".xr-var-list,\n",
-       ".xr-var-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > div,\n",
-       ".xr-var-item label,\n",
-       ".xr-var-item > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-even);\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > .xr-var-name:hover span {\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list > li:nth-child(odd) > div,\n",
-       ".xr-var-list > li:nth-child(odd) > label,\n",
-       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-odd);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name {\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dims {\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dtype {\n",
-       "  grid-column: 3;\n",
-       "  text-align: right;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-preview {\n",
-       "  grid-column: 4;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name,\n",
-       ".xr-var-dims,\n",
-       ".xr-var-dtype,\n",
-       ".xr-preview,\n",
-       ".xr-attrs dt {\n",
-       "  white-space: nowrap;\n",
-       "  overflow: hidden;\n",
-       "  text-overflow: ellipsis;\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name:hover,\n",
-       ".xr-var-dims:hover,\n",
-       ".xr-var-dtype:hover,\n",
-       ".xr-attrs dt:hover {\n",
-       "  overflow: visible;\n",
-       "  width: auto;\n",
-       "  z-index: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data {\n",
-       "  display: none;\n",
-       "  background-color: var(--xr-background-color) !important;\n",
-       "  padding-bottom: 5px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt,\n",
-       ".xr-attrs dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2 {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;spatial_ref&#x27; ()&gt;\n",
-       "array(0)\n",
-       "Coordinates:\n",
-       "    spatial_ref  int64 0\n",
-       "Attributes:\n",
-       "    crs_wkt:                           PROJCS[&quot;WGS 84 / UTM zone 18N&quot;,GEOGCS[...\n",
-       "    semi_major_axis:                   6378137.0\n",
-       "    semi_minor_axis:                   6356752.314245179\n",
-       "    inverse_flattening:                298.257223563\n",
-       "    reference_ellipsoid_name:          WGS 84\n",
-       "    longitude_of_prime_meridian:       0.0\n",
-       "    prime_meridian_name:               Greenwich\n",
-       "    geographic_crs_name:               WGS 84\n",
-       "    horizontal_datum_name:             World Geodetic System 1984\n",
-       "    projected_crs_name:                WGS 84 / UTM zone 18N\n",
-       "    grid_mapping_name:                 transverse_mercator\n",
-       "    latitude_of_projection_origin:     0.0\n",
-       "    longitude_of_central_meridian:     -75.0\n",
-       "    false_easting:                     500000.0\n",
-       "    false_northing:                    0.0\n",
-       "    scale_factor_at_central_meridian:  0.9996\n",
-       "    spatial_ref:                       PROJCS[&quot;WGS 84 / UTM zone 18N&quot;,GEOGCS[...\n",
-       "    GeoTransform:                      101985.0 300.0379266750948 0.0 2826915...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'spatial_ref'</div></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-725740a7-a59e-4113-8847-2df99b49d760' class='xr-array-in' type='checkbox' checked><label for='section-725740a7-a59e-4113-8847-2df99b49d760' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0</span></div><div class='xr-array-data'><pre>array(0)</pre></div></div></li><li class='xr-section-item'><input id='section-95bd7aef-a347-4b3e-845f-901cd1cba04f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-95bd7aef-a347-4b3e-845f-901cd1cba04f' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-4433bbcb-bbe6-41d8-8645-89ac0a764306' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4433bbcb-bbe6-41d8-8645-89ac0a764306' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d73a7297-19b2-45fe-8a64-913416f2ca6f' class='xr-var-data-in' type='checkbox'><label for='data-d73a7297-19b2-45fe-8a64-913416f2ca6f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>crs_wkt :</span></dt><dd>PROJCS[&quot;WGS 84 / UTM zone 18N&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,-75],PARAMETER[&quot;scale_factor&quot;,0.9996],PARAMETER[&quot;false_easting&quot;,500000],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;32618&quot;]]</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314245179</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>WGS 84</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>WGS 84</dd><dt><span>horizontal_datum_name :</span></dt><dd>World Geodetic System 1984</dd><dt><span>projected_crs_name :</span></dt><dd>WGS 84 / UTM zone 18N</dd><dt><span>grid_mapping_name :</span></dt><dd>transverse_mercator</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>0.0</dd><dt><span>longitude_of_central_meridian :</span></dt><dd>-75.0</dd><dt><span>false_easting :</span></dt><dd>500000.0</dd><dt><span>false_northing :</span></dt><dd>0.0</dd><dt><span>scale_factor_at_central_meridian :</span></dt><dd>0.9996</dd><dt><span>spatial_ref :</span></dt><dd>PROJCS[&quot;WGS 84 / UTM zone 18N&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,-75],PARAMETER[&quot;scale_factor&quot;,0.9996],PARAMETER[&quot;false_easting&quot;,500000],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;32618&quot;]]</dd><dt><span>GeoTransform :</span></dt><dd>101985.0 300.0379266750948 0.0 2826915.0 0.0 -300.041782729805</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-14959731-522c-49fa-b5b6-9e4c1756fb81' class='xr-section-summary-in' type='checkbox'  ><label for='section-14959731-522c-49fa-b5b6-9e4c1756fb81' class='xr-section-summary' >Attributes: <span>(18)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>crs_wkt :</span></dt><dd>PROJCS[&quot;WGS 84 / UTM zone 18N&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,-75],PARAMETER[&quot;scale_factor&quot;,0.9996],PARAMETER[&quot;false_easting&quot;,500000],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;32618&quot;]]</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314245179</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>WGS 84</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>WGS 84</dd><dt><span>horizontal_datum_name :</span></dt><dd>World Geodetic System 1984</dd><dt><span>projected_crs_name :</span></dt><dd>WGS 84 / UTM zone 18N</dd><dt><span>grid_mapping_name :</span></dt><dd>transverse_mercator</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>0.0</dd><dt><span>longitude_of_central_meridian :</span></dt><dd>-75.0</dd><dt><span>false_easting :</span></dt><dd>500000.0</dd><dt><span>false_northing :</span></dt><dd>0.0</dd><dt><span>scale_factor_at_central_meridian :</span></dt><dd>0.9996</dd><dt><span>spatial_ref :</span></dt><dd>PROJCS[&quot;WGS 84 / UTM zone 18N&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,-75],PARAMETER[&quot;scale_factor&quot;,0.9996],PARAMETER[&quot;false_easting&quot;,500000],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;32618&quot;]]</dd><dt><span>GeoTransform :</span></dt><dd>101985.0 300.0379266750948 0.0 2826915.0 0.0 -300.041782729805</dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.DataArray 'spatial_ref' ()>\n",
-       "array(0)\n",
-       "Coordinates:\n",
-       "    spatial_ref  int64 0\n",
-       "Attributes:\n",
-       "    crs_wkt:                           PROJCS[\"WGS 84 / UTM zone 18N\",GEOGCS[...\n",
-       "    semi_major_axis:                   6378137.0\n",
-       "    semi_minor_axis:                   6356752.314245179\n",
-       "    inverse_flattening:                298.257223563\n",
-       "    reference_ellipsoid_name:          WGS 84\n",
-       "    longitude_of_prime_meridian:       0.0\n",
-       "    prime_meridian_name:               Greenwich\n",
-       "    geographic_crs_name:               WGS 84\n",
-       "    horizontal_datum_name:             World Geodetic System 1984\n",
-       "    projected_crs_name:                WGS 84 / UTM zone 18N\n",
-       "    grid_mapping_name:                 transverse_mercator\n",
-       "    latitude_of_projection_origin:     0.0\n",
-       "    longitude_of_central_meridian:     -75.0\n",
-       "    false_easting:                     500000.0\n",
-       "    false_northing:                    0.0\n",
-       "    scale_factor_at_central_meridian:  0.9996\n",
-       "    spatial_ref:                       PROJCS[\"WGS 84 / UTM zone 18N\",GEOGCS[...\n",
-       "    GeoTransform:                      101985.0 300.0379266750948 0.0 2826915..."
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "tif.da.spatial_ref"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "psychological-cannon",
+   "id": "17",
    "metadata": {},
    "source": [
     "## Visualize"
@@ -979,7 +171,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "alien-facing",
+   "id": "18",
    "metadata": {},
    "source": [
     "Let's visualize the data read from the file.\n",
@@ -988,8 +180,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "id": "italian-resident",
+   "execution_count": null,
+   "id": "19",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -999,42 +191,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "id": "permanent-consciousness",
+   "execution_count": null,
+   "id": "20",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x149ba8040>"
-      ]
-     },
-     "execution_count": 7,
-     "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": [
-    "crs = ccrs.UTM('18', southern_hemisphere=False)\n",
+    "crs = ccrs.UTM(\"18\", southern_hemisphere=False)\n",
     "ax = plt.subplot(projection=crs)\n",
-    "tif.da.plot.imshow(ax=ax, rgb='band', transform=crs)"
+    "tif.da.plot.imshow(ax=ax, rgb=\"band\", transform=crs)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "unusual-muscle",
+   "id": "21",
    "metadata": {},
    "source": [
     "## Conclusion"
@@ -1042,7 +211,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "appreciated-field",
+   "id": "22",
    "metadata": {},
    "source": [
     "The `GeoTiff` class does little more than wrap the existing functionality of xarray, rioxarray, and rasterio.\n",

From 535ffffee7acf7796131b31ef8b6c5d1f1392f33 Mon Sep 17 00:00:00 2001
From: Mark Piper <mark.piper@colorado.edu>
Date: Fri, 16 Aug 2024 15:31:51 -0600
Subject: [PATCH 2/5] Make pretty

---
 README.md           |  2 +-
 bmi_geotiff/bmi.py  | 15 +++++++--------
 docs/source/conf.py |  2 +-
 3 files changed, 9 insertions(+), 10 deletions(-)

diff --git a/README.md b/README.md
index a209b4a..2a746bd 100644
--- a/README.md
+++ b/README.md
@@ -60,7 +60,7 @@ Start a Python session and import the `GeoTiff` class:
 ```
 
 For convenience,
-let's use a test image from the [rasterio](https://rasterio.readthedocs.io) project: 
+let's use a test image from the [rasterio](https://rasterio.readthedocs.io) project:
 ```python
 >>> url = "https://github.com/rasterio/rasterio/raw/main/tests/data/RGB.byte.tif"
 ```
diff --git a/bmi_geotiff/bmi.py b/bmi_geotiff/bmi.py
index 245fcba..e14fd78 100644
--- a/bmi_geotiff/bmi.py
+++ b/bmi_geotiff/bmi.py
@@ -1,4 +1,3 @@
-# -*- coding: utf-8 -*-
 from collections import namedtuple
 from typing import Tuple
 
@@ -375,7 +374,7 @@ 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
@@ -407,7 +406,7 @@ 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
@@ -484,7 +483,7 @@ def get_value(self, name: str, dest: numpy.ndarray) -> numpy.ndarray:
         elif name == self._output_var_names[2]:
             dest[:] = self._da.rio.transform()
         else:
-            raise ValueError("get_value not available for %s." % (name,))
+            raise ValueError("get_value not available for {}.".format(name))
 
         return dest
 
@@ -511,7 +510,7 @@ def get_value_at_indices(
             dest[:] = self.get_value_ptr(name).reshape(-1)[inds]
             return dest
         else:
-            raise ValueError("get_value_at_indices not available for %s." % (name,))
+            raise ValueError("get_value_at_indices not available for {}.".format(name))
 
     def get_value_ptr(self, name: str) -> numpy.ndarray:
         """Get a reference to values of the given variable.
@@ -533,7 +532,7 @@ def get_value_ptr(self, name: str) -> numpy.ndarray:
         if name == self._output_var_names[0]:
             return self._da.values
         else:
-            raise ValueError("get_value_ptr not available for %s." % (name,))
+            raise ValueError("get_value_ptr not available for {}.".format(name))
 
     def get_var_grid(self, name: str) -> int:
         """Get grid identifier for the given variable.
@@ -682,7 +681,7 @@ 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-geotiff", {})
         else:
             self._config = {"filename": None}
@@ -713,7 +712,7 @@ def initialize(self, config_file: str) -> None:
                 grid=0,
             ),
             self._output_var_names[1]: BmiVar(
-                dtype="U{}".format(len(self._da.spatial_ref.crs_wkt)),
+                dtype=f"U{len(self._da.spatial_ref.crs_wkt)}",
                 itemsize=len(self._da.spatial_ref.crs_wkt) * SIZEOF_FLOAT,
                 nbytes=len(self._da.spatial_ref.crs_wkt) * SIZEOF_FLOAT,
                 location="none",
diff --git a/docs/source/conf.py b/docs/source/conf.py
index 5da759c..32f9434 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -28,7 +28,7 @@
 version = pkg_resources.get_distribution("bmi_geotiff").version
 release = version
 this_year = datetime.date.today().year
-copyright = "%s, %s" % (this_year, author)
+copyright = "{}, {}".format(this_year, author)
 
 
 # -- General configuration ---------------------------------------------------

From 6c1a683fe5d4a2450a11ceb62d599871e1ba00d7 Mon Sep 17 00:00:00 2001
From: Mark Piper <mark.piper@colorado.edu>
Date: Fri, 16 Aug 2024 15:34:38 -0600
Subject: [PATCH 3/5] Remove unused import

---
 bmi_geotiff/bmi.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/bmi_geotiff/bmi.py b/bmi_geotiff/bmi.py
index e14fd78..606b0c9 100644
--- a/bmi_geotiff/bmi.py
+++ b/bmi_geotiff/bmi.py
@@ -1,5 +1,4 @@
 from collections import namedtuple
-from typing import Tuple
 
 import numpy
 import yaml

From a5ba93247d969e9ab1330131b7b65b313e8565ef Mon Sep 17 00:00:00 2001
From: Mark Piper <mark.piper@colorado.edu>
Date: Fri, 16 Aug 2024 15:44:01 -0600
Subject: [PATCH 4/5] Use f-strings instead of format method

---
 bmi_geotiff/bmi.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/bmi_geotiff/bmi.py b/bmi_geotiff/bmi.py
index 606b0c9..ce959c8 100644
--- a/bmi_geotiff/bmi.py
+++ b/bmi_geotiff/bmi.py
@@ -482,7 +482,7 @@ def get_value(self, name: str, dest: numpy.ndarray) -> numpy.ndarray:
         elif name == self._output_var_names[2]:
             dest[:] = self._da.rio.transform()
         else:
-            raise ValueError("get_value not available for {}.".format(name))
+            raise ValueError(f"get_value not available for {name}.")
 
         return dest
 
@@ -509,7 +509,7 @@ def get_value_at_indices(
             dest[:] = self.get_value_ptr(name).reshape(-1)[inds]
             return dest
         else:
-            raise ValueError("get_value_at_indices not available for {}.".format(name))
+            raise ValueError(f"get_value_at_indices not available for {name}.")
 
     def get_value_ptr(self, name: str) -> numpy.ndarray:
         """Get a reference to values of the given variable.
@@ -531,7 +531,7 @@ def get_value_ptr(self, name: str) -> numpy.ndarray:
         if name == self._output_var_names[0]:
             return self._da.values
         else:
-            raise ValueError("get_value_ptr not available for {}.".format(name))
+            raise ValueError(f"get_value_ptr not available for {name}.")
 
     def get_var_grid(self, name: str) -> int:
         """Get grid identifier for the given variable.

From 82d2784b73bdf7f272e9e180ce52a9b20fc10e0c Mon Sep 17 00:00:00 2001
From: Mark Piper <mark.piper@colorado.edu>
Date: Fri, 16 Aug 2024 18:00:40 -0600
Subject: [PATCH 5/5] Fix path to coverage configuration

It was in setup.py, but got moved to pyproject.toml in d1affcf.
---
 noxfile.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/noxfile.py b/noxfile.py
index 21f2d67..45dbd03 100644
--- a/noxfile.py
+++ b/noxfile.py
@@ -26,7 +26,7 @@ def test(session: nox.Session) -> None:
 
     if "CI" in os.environ:
         args.append(f"--cov-report=xml:{ROOT.absolute()!s}/coverage.xml")
-        args.append(f"--cov-config={ROOT.absolute()!s}/setup.cfg")
+        args.append(f"--cov-config={ROOT.absolute()!s}/pyproject.toml")
     session.run("pytest", *args)
 
     if "CI" not in os.environ: