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+++ b/public/2024-03-arctic/search.json
@@ -344,7 +344,7 @@
     "href": "sections/parallel-programming.html#task-parallelization-in-python",
     "title": "4  Pleasingly Parallel Programming",
     "section": "4.7 Task parallelization in Python",
-    "text": "4.7 Task parallelization in Python\nPython also provides a number of easy to use packages for concurrent processing. We will review two of these, concurrent.futures and parsl, to show just how easy it can be to parallelize your programs. concurrent.futures is built right into the python3 release, and is a great starting point for learning concurrency.\nWe’re going to start with a task that is a little expensive to compute, and define it in a function. All this task(x) function does is to use numpy to create a fairly large range of numbers, and then sum them.\n\ndef task(x):\n    import numpy as np\n    result = np.arange(x*10**8).sum()\n    return result\n\nWe can start by executing this task function serially ten times with varying inputs. In this case, we create a function run_serial that takes a list of inputs to be run, and it calls the task function for each of those inputs. The @timethis decorator is a simple way to wrap the function with timing code so that we can see how long it takes to execute.\n\nimport numpy as np\n\n@timethis\ndef run_serial(task_list):\n    return [task(x) for x in task_list]\n\nrun_serial(np.arange(10))\n\nrun_serial: 7401.757478713989 ms\n\n\n[0,\n 4999999950000000,\n 19999999900000000,\n 44999999850000000,\n 79999999800000000,\n 124999999750000000,\n 179999999700000000,\n 244999999650000000,\n 319999999600000000,\n 404999999550000000]\n\n\nIn this case, it takes around 25 seconds to execute 10 tasks, depending on what else is happening on the machine and network.\nSo, can we make this faster using a multi-threaded parallel process? Let’s try with concurrent.futures. The main concept in this package is one of a future, which is a structure which represents the value that will be created in a computation in the future when the function completes execution. With concurrent.futures, tasks are scheduled and do not block while they await their turn to be executed. Instead, threads are created and executed asynchronously, meaning that the function returns it’s future potentially before the thread has actually been executed. Using this approach, the user schedules a series of tasks to be executed asynchronously, and keeps track of the futures for each task. When the future indicates that the execution has been completed, we can then retrieve the result of the computation.\nIn practice this is a simple change from our serial implementation. We will use the ThreadPoolExecutor to create a pool of workers that are available to process tasks. Each worker is set up in its own thread, so it can execute in parallel with other workers. After setting up the pool of workers, we use concurrent.futures map() to schedule each task from our task_list (in this case, an input value from 1 to 10) to run on one of the workers. As for all map() implementations, we are asking for each value in task_list to be executed in the task function we defined above, but in this case it will be executed using one of the workers from the executor that we created.\n\nfrom concurrent.futures import ThreadPoolExecutor\n\n@timethis\ndef run_threaded(task_list):\n    with ThreadPoolExecutor(max_workers=20) as executor:\n        return executor.map(task, task_list)\n\nresults = run_threaded(np.arange(10))\n[x for x in results]\n\nrun_threaded: 4440.109491348267 ms\n[0,\n 4999999950000000,\n 19999999900000000,\n 44999999850000000,\n 79999999800000000,\n 124999999750000000,\n 179999999700000000,\n 244999999650000000,\n 319999999600000000,\n 404999999550000000]\nThis execution took about 🎉 4 seconds 🎉, which is about 6.25x faster than serial. Congratulations, you wrote your a multi-threaded python program!",
+    "text": "4.7 Task parallelization in Python\nPython also provides a number of easy to use packages for concurrent processing. We will review two of these, concurrent.futures and parsl, to show just how easy it can be to parallelize your programs. concurrent.futures is built right into the python3 release, and is a great starting point for learning concurrency.\nWe’re going to start with a task that is a little expensive to compute, and define it in a function. All this task(x) function does is to use numpy to create a fairly large range of numbers, and then sum them.\n\ndef task(x):\n    import numpy as np\n    result = np.arange(x*10**8).sum()\n    return result\n\nWe can start by executing this task function serially ten times with varying inputs. In this case, we create a function run_serial that takes a list of inputs to be run, and it calls the task function for each of those inputs. The @timethis decorator is a simple way to wrap the function with timing code so that we can see how long it takes to execute.\n\nimport numpy as np\n\n@timethis\ndef run_serial(task_list):\n    return [task(x) for x in task_list]\n\nrun_serial(np.arange(10))\n\nrun_serial: 7049.6227741241455 ms\n\n\n[0,\n 4999999950000000,\n 19999999900000000,\n 44999999850000000,\n 79999999800000000,\n 124999999750000000,\n 179999999700000000,\n 244999999650000000,\n 319999999600000000,\n 404999999550000000]\n\n\nIn this case, it takes around 25 seconds to execute 10 tasks, depending on what else is happening on the machine and network.\nSo, can we make this faster using a multi-threaded parallel process? Let’s try with concurrent.futures. The main concept in this package is one of a future, which is a structure which represents the value that will be created in a computation in the future when the function completes execution. With concurrent.futures, tasks are scheduled and do not block while they await their turn to be executed. Instead, threads are created and executed asynchronously, meaning that the function returns it’s future potentially before the thread has actually been executed. Using this approach, the user schedules a series of tasks to be executed asynchronously, and keeps track of the futures for each task. When the future indicates that the execution has been completed, we can then retrieve the result of the computation.\nIn practice this is a simple change from our serial implementation. We will use the ThreadPoolExecutor to create a pool of workers that are available to process tasks. Each worker is set up in its own thread, so it can execute in parallel with other workers. After setting up the pool of workers, we use concurrent.futures map() to schedule each task from our task_list (in this case, an input value from 1 to 10) to run on one of the workers. As for all map() implementations, we are asking for each value in task_list to be executed in the task function we defined above, but in this case it will be executed using one of the workers from the executor that we created.\n\nfrom concurrent.futures import ThreadPoolExecutor\n\n@timethis\ndef run_threaded(task_list):\n    with ThreadPoolExecutor(max_workers=20) as executor:\n        return executor.map(task, task_list)\n\nresults = run_threaded(np.arange(10))\n[x for x in results]\n\nrun_threaded: 4440.109491348267 ms\n[0,\n 4999999950000000,\n 19999999900000000,\n 44999999850000000,\n 79999999800000000,\n 124999999750000000,\n 179999999700000000,\n 244999999650000000,\n 319999999600000000,\n 404999999550000000]\nThis execution took about 🎉 4 seconds 🎉, which is about 6.25x faster than serial. Congratulations, you wrote your a multi-threaded python program!",
     "crumbs": [
       "<span class='chapter-number'>4</span>  <span class='chapter-title'>Pleasingly Parallel Programming</span>"
     ]
@@ -354,7 +354,7 @@
     "href": "sections/parallel-programming.html#exercise-parallel-downloads",
     "title": "4  Pleasingly Parallel Programming",
     "section": "4.8 Exercise: Parallel downloads",
-    "text": "4.8 Exercise: Parallel downloads\nIn this exercise, we’re going to parallelize a simple task that is often very time consuming – downloading data. And we’ll compare performance of simple downloads using first a serial loop, and then using two parallel execution libraries: concurrent.futures and parsl. We’re going to see an example here where parallel execution won’t always speed up this task, as this is likely an I/O bound task if you’re downloading a lot of data. But we still should be able to speed things up a lot until we hit the limits of our disk arrays.\nThe data we are downloading is a pan-Arctic time series of TIF images containing rasterized Arctic surface water indices from:\n\nElizabeth Webb. 2022. Pan-Arctic surface water (yearly and trend over time) 2000-2022. Arctic Data Center doi:10.18739/A2NK3665N.\n\n\n\n\nWebb surface water index data\n\n\nFirst, let’s download the data serially to set a benchmark. The data files are listed in a table with their filename and identifier, and can be downloaded directly from the Arctic Data Center using their identifier. To make things easier, we’ve already provided a data frame with the names and identifiers of each file that could be downloaded.\n\n\n\n\n\n\n\n\n\n\nfilename\nidentifier\n\n\n\n\n0\nSWI_2007.tif\nurn:uuid:5ee72c9c-789d-4a1c-95d8-cb2b24a20662\n\n\n1\nSWI_2019.tif\nurn:uuid:9cd1cdc3-0792-4e61-afff-c11f86d3a9be\n\n\n2\nSWI_2021.tif\nurn:uuid:14e1e509-77c0-4646-9cc3-d05f8d84977c\n\n\n3\nSWI_2020.tif\nurn:uuid:1ba473ff-8f03-470b-90d1-7be667995ea1\n\n\n4\nSWI_2001.tif\nurn:uuid:85150557-05fd-4f52-8bbd-ec5a2c27e23d\n\n\n\n\n\n\n\n\n\n4.8.1 Serial\nWhen you have a list of repetitive tasks, you may be able to speed it up by adding more computing power. If each task is completely independent of the others, then it is pleasingly parallel and a prime candidate for executing those tasks in parallel, each on its own core. For example, let’s build a simple loop that downloads the data files that we need for an analysis. First, we start with the serial implementation.\n\nimport urllib\n\ndef download_file(row):\n    service = \"https://arcticdata.io/metacat/d1/mn/v2/object/\"\n    pid = row[1]['identifier']\n    filename = row[1]['filename']\n    url = service + pid\n    print(\"Downloading: \" + filename)\n    msg = urllib.request.urlretrieve(url, filename)\n    return filename\n\n@timethis\ndef download_serial(table):\n    results = [download_file(row) for row in table.iterrows()]\n    return results\n    \nresults = download_serial(file_table[0:5])\nprint(results)\n\nDownloading: SWI_2007.tif\nDownloading: SWI_2019.tif\nDownloading: SWI_2021.tif\nDownloading: SWI_2020.tif\nDownloading: SWI_2001.tif\ndownload_serial: 92904.20699119568 ms\n['SWI_2007.tif', 'SWI_2019.tif', 'SWI_2021.tif', 'SWI_2020.tif', 'SWI_2001.tif']\n\n\nIn this code, we have one function (download_file) that downloads a single data file and saves it to disk. It is called iteratively from the function download_serial. The serial execution takes about 20-25 seconds, but can vary considerably based on network traffic and other factors.\nThe issue with this loop is that we execute each download task sequentially, which means that only one of our processors on this machine is in use. In order to exploit parallelism, we need to be able to dispatch our tasks and allow each to run at the same time, with one task going to each core. To do that, we can use one of the many parallelization libraries in python to help us out.\n\n\n4.8.2 Multi-threaded with concurrent.futures\nIn this case, we’ll use the same download_file function from before, but let’s switch up and create a download_threaded() function to use concurrent.futures with a ThreadPoolExecutor just as we did earlier.\n\nfrom concurrent.futures import ThreadPoolExecutor\n\n@timethis\ndef download_threaded(table):\n    with ThreadPoolExecutor(max_workers=15) as executor:\n        results = executor.map(download_file, table.iterrows(), timeout=60)\n        return results\n\nresults = download_threaded(file_table[0:5])\nfor result in results:\n    print(result)\n\nDownloading: SWI_2007.tif\nDownloading: SWI_2019.tif\nDownloading: SWI_2021.tif\nDownloading: SWI_2020.tif\nDownloading: SWI_2001.tif\ndownload_threaded: 25324.24020767212 ms\nSWI_2007.tif\nSWI_2019.tif\nSWI_2021.tif\nSWI_2020.tif\nSWI_2001.tif\n\n\nNote how the “Downloading…” messages were printed immediately, but then it still took over 20 seconds to download the 5 files. This could be for several reasons, including that one of the files alone took that long (e.g., due to network congestion), or that there was a bottleneck in writing the files to disk (i.e., we could have been disk I/O limited). Or maybe the multithreaded executor pool didn’t do a good job parallelizing the tasks. The trick is figuring out why you did or didn’t get a speedup when parallelizing. So, let’s try this another way, using a multi-processing approach, rather than multi-threading.\n\n\n4.8.3 Multi-process with concurrent.futures\nYou’ll remember from earlier that you can execute tasks concurrently by creating multiple threads within one process (multi-threaded), or by creating and executing muliple processes. The latter creates more independence, as each of the executing tasks has their own memory and process space, but it also takes longer to set up. With concurrent.futures, we can switch to a multi-process pool by using a ProcessPoolExecutor, analogously to how we used ThreadPoolExecutor previously. So, some simple changes, and we’re running multiple processes.\n\nfrom concurrent.futures import ProcessPoolExecutor\n\n@timethis\ndef download_process(table):\n    with ProcessPoolExecutor(max_workers=15) as executor:\n        results = executor.map(download_file, table.iterrows(), timeout=60)\n        return results\n\nresults = download_process(file_table[0:5])\nfor result in results:\n    print(result)\n\nDownloading: SWI_2019.tifDownloading: SWI_2020.tifDownloading: SWI_2021.tif\nDownloading: SWI_2007.tif\n\n\nDownloading: SWI_2001.tif\ndownload_process: 20482.065200805664 ms\nSWI_2007.tif\nSWI_2019.tif\nSWI_2021.tif\nSWI_2020.tif\nSWI_2001.tif\n\n\nAgain, the output messages print almost immediately, but then later the processes finish and report that it took between 10 to 15 seconds to run. Your mileage may vary. When I increase the number of files being downloaded to 10 or even to 20, I notice it is actually about the same, around 10-15 seconds. So, part of our time now is the overhead of setting up multiple processes. But once we have that infrastructure in place, we can make effective euse of the pool of processes to handle many downloads.",
+    "text": "4.8 Exercise: Parallel downloads\nIn this exercise, we’re going to parallelize a simple task that is often very time consuming – downloading data. And we’ll compare performance of simple downloads using first a serial loop, and then using two parallel execution libraries: concurrent.futures and parsl. We’re going to see an example here where parallel execution won’t always speed up this task, as this is likely an I/O bound task if you’re downloading a lot of data. But we still should be able to speed things up a lot until we hit the limits of our disk arrays.\nThe data we are downloading is a pan-Arctic time series of TIF images containing rasterized Arctic surface water indices from:\n\nElizabeth Webb. 2022. Pan-Arctic surface water (yearly and trend over time) 2000-2022. Arctic Data Center doi:10.18739/A2NK3665N.\n\n\n\n\nWebb surface water index data\n\n\nFirst, let’s download the data serially to set a benchmark. The data files are listed in a table with their filename and identifier, and can be downloaded directly from the Arctic Data Center using their identifier. To make things easier, we’ve already provided a data frame with the names and identifiers of each file that could be downloaded.\n\n\n\n\n\n\n\n\n\n\nfilename\nidentifier\n\n\n\n\n0\nSWI_2007.tif\nurn:uuid:5ee72c9c-789d-4a1c-95d8-cb2b24a20662\n\n\n1\nSWI_2019.tif\nurn:uuid:9cd1cdc3-0792-4e61-afff-c11f86d3a9be\n\n\n2\nSWI_2021.tif\nurn:uuid:14e1e509-77c0-4646-9cc3-d05f8d84977c\n\n\n3\nSWI_2020.tif\nurn:uuid:1ba473ff-8f03-470b-90d1-7be667995ea1\n\n\n4\nSWI_2001.tif\nurn:uuid:85150557-05fd-4f52-8bbd-ec5a2c27e23d\n\n\n\n\n\n\n\n\n\n4.8.1 Serial\nWhen you have a list of repetitive tasks, you may be able to speed it up by adding more computing power. If each task is completely independent of the others, then it is pleasingly parallel and a prime candidate for executing those tasks in parallel, each on its own core. For example, let’s build a simple loop that downloads the data files that we need for an analysis. First, we start with the serial implementation.\n\nimport urllib\n\ndef download_file(row):\n    service = \"https://arcticdata.io/metacat/d1/mn/v2/object/\"\n    pid = row[1]['identifier']\n    filename = row[1]['filename']\n    url = service + pid\n    print(\"Downloading: \" + filename)\n    msg = urllib.request.urlretrieve(url, filename)\n    return filename\n\n@timethis\ndef download_serial(table):\n    results = [download_file(row) for row in table.iterrows()]\n    return results\n    \nresults = download_serial(file_table[0:5])\nprint(results)\n\nDownloading: SWI_2007.tif\nDownloading: SWI_2019.tif\nDownloading: SWI_2021.tif\nDownloading: SWI_2020.tif\nDownloading: SWI_2001.tif\ndownload_serial: 48173.44856262207 ms\n['SWI_2007.tif', 'SWI_2019.tif', 'SWI_2021.tif', 'SWI_2020.tif', 'SWI_2001.tif']\n\n\nIn this code, we have one function (download_file) that downloads a single data file and saves it to disk. It is called iteratively from the function download_serial. The serial execution takes about 20-25 seconds, but can vary considerably based on network traffic and other factors.\nThe issue with this loop is that we execute each download task sequentially, which means that only one of our processors on this machine is in use. In order to exploit parallelism, we need to be able to dispatch our tasks and allow each to run at the same time, with one task going to each core. To do that, we can use one of the many parallelization libraries in python to help us out.\n\n\n4.8.2 Multi-threaded with concurrent.futures\nIn this case, we’ll use the same download_file function from before, but let’s switch up and create a download_threaded() function to use concurrent.futures with a ThreadPoolExecutor just as we did earlier.\n\nfrom concurrent.futures import ThreadPoolExecutor\n\n@timethis\ndef download_threaded(table):\n    with ThreadPoolExecutor(max_workers=15) as executor:\n        results = executor.map(download_file, table.iterrows(), timeout=60)\n        return results\n\nresults = download_threaded(file_table[0:5])\nfor result in results:\n    print(result)\n\nDownloading: SWI_2007.tif\nDownloading: SWI_2019.tif\nDownloading: SWI_2021.tif\nDownloading: SWI_2020.tif\nDownloading: SWI_2001.tif\ndownload_threaded: 19610.64839363098 ms\nSWI_2007.tif\nSWI_2019.tif\nSWI_2021.tif\nSWI_2020.tif\nSWI_2001.tif\n\n\nNote how the “Downloading…” messages were printed immediately, but then it still took over 20 seconds to download the 5 files. This could be for several reasons, including that one of the files alone took that long (e.g., due to network congestion), or that there was a bottleneck in writing the files to disk (i.e., we could have been disk I/O limited). Or maybe the multithreaded executor pool didn’t do a good job parallelizing the tasks. The trick is figuring out why you did or didn’t get a speedup when parallelizing. So, let’s try this another way, using a multi-processing approach, rather than multi-threading.\n\n\n4.8.3 Multi-process with concurrent.futures\nYou’ll remember from earlier that you can execute tasks concurrently by creating multiple threads within one process (multi-threaded), or by creating and executing muliple processes. The latter creates more independence, as each of the executing tasks has their own memory and process space, but it also takes longer to set up. With concurrent.futures, we can switch to a multi-process pool by using a ProcessPoolExecutor, analogously to how we used ThreadPoolExecutor previously. So, some simple changes, and we’re running multiple processes.\n\nfrom concurrent.futures import ProcessPoolExecutor\n\n@timethis\ndef download_process(table):\n    with ProcessPoolExecutor(max_workers=15) as executor:\n        results = executor.map(download_file, table.iterrows(), timeout=60)\n        return results\n\nresults = download_process(file_table[0:5])\nfor result in results:\n    print(result)\n\nDownloading: SWI_2019.tifDownloading: SWI_2021.tifDownloading: SWI_2007.tif\nDownloading: SWI_2020.tif\nDownloading: SWI_2001.tif\n\n\ndownload_process: 23428.152322769165 ms\nSWI_2007.tif\nSWI_2019.tif\nSWI_2021.tif\nSWI_2020.tif\nSWI_2001.tif\n\n\nAgain, the output messages print almost immediately, but then later the processes finish and report that it took between 10 to 15 seconds to run. Your mileage may vary. When I increase the number of files being downloaded to 10 or even to 20, I notice it is actually about the same, around 10-15 seconds. So, part of our time now is the overhead of setting up multiple processes. But once we have that infrastructure in place, we can make effective euse of the pool of processes to handle many downloads.",
     "crumbs": [
       "<span class='chapter-number'>4</span>  <span class='chapter-title'>Pleasingly Parallel Programming</span>"
     ]
@@ -364,7 +364,7 @@
     "href": "sections/parallel-programming.html#parallel-processing-with-parsl",
     "title": "4  Pleasingly Parallel Programming",
     "section": "4.9 Parallel processing with parsl",
-    "text": "4.9 Parallel processing with parsl\nconcurrent.futures is great and powerful, but it has its limits. Particularly as you try to scale up into the thousands of concurrent tasks, other libraries like Parsl (docs), Dask, Ray, and others come into play. They all have their strengths, but Parsl makes it particularly easy to build parallel workflows out of existing python code through it’s use of decorators on existing python functions.\nIn addition, Parsl supports a lot of different kinds of providers, allowing the same python code to be easily run multi-threaded using a ThreadPoolExecutor and via multi-processing on many different cluster computing platforms using the HighThroughputExecutor. For example, Parsl includes providers supporting local execution, and on Slurm, Condor, Kubernetes, AWS, and other platforms. And Parsl handles data staging as well across these varied environments, making sure the data is in the right place when it’s needed for computations.\nSimilarly to before, we start by configuring an executor in parsl, and loading it. We’ll use multiprocessing by configuring the HighThroughputExecutor to use our local resources as a cluster, and we’ll activate our virtual environment to be sure we’re executing in a consistent environment.\n\n# Required packages\nimport parsl\nfrom parsl import python_app\nfrom parsl.config import Config\nfrom parsl.executors import HighThroughputExecutor\nfrom parsl.providers import LocalProvider\n\n# Configure the parsl executor\nactivate_env = 'workon scomp'\nhtex_local = Config(\n    executors=[\n        HighThroughputExecutor(\n            max_workers=15,\n            provider=LocalProvider(\n                worker_init=activate_env\n            )\n        )\n    ],\n)\nparsl.clear()\nparsl.load(htex_local)\n\n&lt;parsl.dataflow.dflow.DataFlowKernel at 0x7f4991800cd0&gt;\n\n\nWe now have a live parsl executor (htex_local) that is waiting to execute processes. We tell it to execute processes by annotating functions with decorators that indicate which tasks should be parallelized. Parsl then handles the scheduling and execution of those tasks based on the dependencies between them. In the simplest case, we’ll decorate our previous function for downloading a file with the @python_app decorator, which tells parsl that any function calls with this function should be run on the default executor (in this case, htex_local).\n\n# Decorators seem to be ignored as the first line of a cell, so print something first\nprint(\"Create decorated function\")\n\n@python_app\ndef download_file_parsl(row):\n    import urllib\n    service = \"https://arcticdata.io/metacat/d1/mn/v2/object/\"\n    pid = row[1]['identifier']\n    filename = row[1]['filename']\n    url = service + pid\n    print(\"Downloading: \" + filename)\n    msg = urllib.request.urlretrieve(url, filename)\n    return filename\n\nCreate decorated function\n\n\nNow we just write regular python code that calls that function, and parsl handles the scheduling. Parsl app executors return an AppFuture, and we can call the AppFuture.done() function to determine when the future result is ready without blocking. Or, we can just block on AppFuture.result() which waits for each of the executions to complete and then returns the result.\n\n#! eval: true\nprint(\"Define our download_futures function\")\n\n@timethis\ndef download_futures(table):\n    results = []\n    for row in table.iterrows():\n        result = download_file_parsl(row)\n        print(result)\n        results.append(result)\n    return(results)\n\n@timethis\ndef wait_for_futures(table):\n    results = download_futures(table)\n    done = [app_future.result() for app_future in results]\n    print(done)\n\nwait_for_futures(file_table[0:5])\n\nDefine our download_futures function\n&lt;AppFuture at 0x7f49902857e0 state=pending&gt;\n&lt;AppFuture at 0x7f4990284040 state=pending&gt;\n&lt;AppFuture at 0x7f49902879a0 state=pending&gt;\n&lt;AppFuture at 0x7f4990286e30 state=pending&gt;\n&lt;AppFuture at 0x7f4989e208e0 state=pending&gt;\ndownload_futures: 10.964393615722656 ms\n['SWI_2007.tif', 'SWI_2019.tif', 'SWI_2021.tif', 'SWI_2020.tif', 'SWI_2001.tif']\nwait_for_futures: 65006.14833831787 ms\n\n\nWhen we’re done, be sure to clean up and shutdown the htex_local executor, or it will continue to persist in your environment and utilize resources. Generally, an executor should be created when setting up your environment, and then it can be used repeatedly for many different tasks.\n\nhtex_local.executors[0].shutdown()\nparsl.clear()",
+    "text": "4.9 Parallel processing with parsl\nconcurrent.futures is great and powerful, but it has its limits. Particularly as you try to scale up into the thousands of concurrent tasks, other libraries like Parsl (docs), Dask, Ray, and others come into play. They all have their strengths, but Parsl makes it particularly easy to build parallel workflows out of existing python code through it’s use of decorators on existing python functions.\nIn addition, Parsl supports a lot of different kinds of providers, allowing the same python code to be easily run multi-threaded using a ThreadPoolExecutor and via multi-processing on many different cluster computing platforms using the HighThroughputExecutor. For example, Parsl includes providers supporting local execution, and on Slurm, Condor, Kubernetes, AWS, and other platforms. And Parsl handles data staging as well across these varied environments, making sure the data is in the right place when it’s needed for computations.\nSimilarly to before, we start by configuring an executor in parsl, and loading it. We’ll use multiprocessing by configuring the HighThroughputExecutor to use our local resources as a cluster, and we’ll activate our virtual environment to be sure we’re executing in a consistent environment.\n\n# Required packages\nimport parsl\nfrom parsl import python_app\nfrom parsl.config import Config\nfrom parsl.executors import HighThroughputExecutor\nfrom parsl.providers import LocalProvider\n\n# Configure the parsl executor\nactivate_env = 'workon scomp'\nhtex_local = Config(\n    executors=[\n        HighThroughputExecutor(\n            max_workers=15,\n            provider=LocalProvider(\n                worker_init=activate_env\n            )\n        )\n    ],\n)\nparsl.clear()\nparsl.load(htex_local)\n\n&lt;parsl.dataflow.dflow.DataFlowKernel at 0x7f0ca1bff550&gt;\n\n\nWe now have a live parsl executor (htex_local) that is waiting to execute processes. We tell it to execute processes by annotating functions with decorators that indicate which tasks should be parallelized. Parsl then handles the scheduling and execution of those tasks based on the dependencies between them. In the simplest case, we’ll decorate our previous function for downloading a file with the @python_app decorator, which tells parsl that any function calls with this function should be run on the default executor (in this case, htex_local).\n\n# Decorators seem to be ignored as the first line of a cell, so print something first\nprint(\"Create decorated function\")\n\n@python_app\ndef download_file_parsl(row):\n    import urllib\n    service = \"https://arcticdata.io/metacat/d1/mn/v2/object/\"\n    pid = row[1]['identifier']\n    filename = row[1]['filename']\n    url = service + pid\n    print(\"Downloading: \" + filename)\n    msg = urllib.request.urlretrieve(url, filename)\n    return filename\n\nCreate decorated function\n\n\nNow we just write regular python code that calls that function, and parsl handles the scheduling. Parsl app executors return an AppFuture, and we can call the AppFuture.done() function to determine when the future result is ready without blocking. Or, we can just block on AppFuture.result() which waits for each of the executions to complete and then returns the result.\n\n#! eval: true\nprint(\"Define our download_futures function\")\n\n@timethis\ndef download_futures(table):\n    results = []\n    for row in table.iterrows():\n        result = download_file_parsl(row)\n        print(result)\n        results.append(result)\n    return(results)\n\n@timethis\ndef wait_for_futures(table):\n    results = download_futures(table)\n    done = [app_future.result() for app_future in results]\n    print(done)\n\nwait_for_futures(file_table[0:5])\n\nDefine our download_futures function\n&lt;AppFuture at 0x7f0c60792920 state=pending&gt;\n&lt;AppFuture at 0x7f0c607920e0 state=pending&gt;\n&lt;AppFuture at 0x7f0c60792b00 state=pending&gt;\n&lt;AppFuture at 0x7f0c9fa9e890 state=pending&gt;\n&lt;AppFuture at 0x7f0c6043e5f0 state=pending&gt;\ndownload_futures: 12.877464294433594 ms\n['SWI_2007.tif', 'SWI_2019.tif', 'SWI_2021.tif', 'SWI_2020.tif', 'SWI_2001.tif']\nwait_for_futures: 29422.613620758057 ms\n\n\nWhen we’re done, be sure to clean up and shutdown the htex_local executor, or it will continue to persist in your environment and utilize resources. Generally, an executor should be created when setting up your environment, and then it can be used repeatedly for many different tasks.\n\nhtex_local.executors[0].shutdown()\nparsl.clear()",
     "crumbs": [
       "<span class='chapter-number'>4</span>  <span class='chapter-title'>Pleasingly Parallel Programming</span>"
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@@ -644,7 +644,7 @@
     "href": "sections/data-ethics.html#intro-to-data-ethics",
     "title": "8  Data Ethics for Scalable Computing",
     "section": "8.2 Intro to Data Ethics",
-    "text": "8.2 Intro to Data Ethics\n\nTo recap, the Arctic Data Center is an openly-accessible data repository and the data published through the repository is open for anyone to reuse, subject to conditions of the license (at the Arctic Data Center, data is released under one of two licenses: CC-0 Public Domain and CC-By Attribution 4.0). In facilitating use of data resources, the data stewardship community has converged on principles surrounding best practices for open data management.\nTwo principles that the Arctic Data Center explicitly adopts are FAIR Principles (Findable, Accessible, Interoperable, and Reproducible) and CARE Principles for Indigenous Governance (Collective Benefit, Authority to Control, Responsibility, Ethics).\nFAIR and CARE principles are relevant in the context of data ethics for multiple reasons. FAIR speaks to how metadata is managed, stored, and shared.\n\nFAIR principles and open science are overlapping concepts, but are distinctive from one another. Open science supports a culture of sharing research outputs and data, and FAIR focuses on how to prepare the data. The FAIR principles place emphasis on machine readability, “distinct from peer initiatives that focus on the human scholar” (Wilkinson et al 2016) and as such, do not fully engage with sensitive data considerations and with Indigenous rights and interests (Research Data Alliance International Indigenous Data Sovereignty Interest Group, 2019). Metadata can be FAIR but not open. For example, sensitive data (data that contains personal information) may not be appropriate to share, however sharing the anonymized metadata that is easily understandable will reduce research redundancy.\n\nResearch has historically perpetuated colonialism and represented extractive practices, meaning that the research results were not mutually beneficial. These issues also related to how data was owned, shared, and used. To address issues like these, the Global Indigenous Data Alliance (GIDA) introduced CARE Principles for Indigenous Data Governance to support Indigenous data sovereignty. CARE Principles speak directly to how the data is stored and shared in the context of Indigenous data sovereignty. CARE Principles stand for:\n\nCollective Benefit - Data ecosystems shall be designed and function in ways that enable Indigenous Peoples to derive benefit from the data\nAuthority to Control - Indigenous Peoples’ rights and interests in Indigenous data must be recognized and their authority to control such data be empowered. Indigenous data governance enables Indigenous Peoples and governing bodies to determine how Indigenous Peoples, as well as Indigenous lands, territories, resources, knowledges and geographical indicators, are represented and identified within data.\nResponsibility - Those working with Indigenous data have a responsibility to share how those data are used to support Indigenous Peoples’ self-determination and collective benefit. Accountability requires meaningful and openly available evidence of these efforts and the benefits accruing to Indigenous Peoples.\nEthics - Indigenous Peoples’ rights and wellbeing should be the primary concern at all stages of the data life cycle and across the data ecosystem. To many, the FAIR and CARE principles are viewed by many as complementary: CARE aligns with FAIR by outlining guidelines for publishing data that contributes to open-science and at the same time, accounts for Indigenous’ Peoples rights and interests.",
+    "text": "8.2 Intro to Data Ethics\n\nThe Arctic Data Center is an openly-accessible data repository. The data published through it is open for anyone to reuse, subject to one of two license: CC-0 Public Domain and CC-By Attribution 4.0. As an open access repository, we prioritize long-term preservation and embrace principles from the data stewardship community, which established a set of best practices for open data management. n adherence, two principles adopted by the Arctic Data Center are FAIR Principles (Findable, Accessible, Interoperable, and Reproducible) and CARE Principles for Indigenous Governance (Collective Benefit, Authority to Control, Responsibility, Ethics). Both of which serve as frameworks in how to consider data ethics.\nThe FAIR Principles\nFAIR speaks to how metadata is managed, stored, and shared.\n\nWhat is the difference between FAIR principles and open science?\nFAIR principles and open science are overlapping concepts, but are distinctive from one another. Open science supports a culture of sharing research outputs and data, and FAIR focuses on how to prepare the data. The FAIR principles place emphasis on machine readability, “distinct from peer initiatives that focus on the human scholar” (Wilkinson et al 2016) and as such, do not fully engage with sensitive data considerations and with Indigenous rights and interests (Research Data Alliance International Indigenous Data Sovereignty Interest Group, 2019). Metadata can be FAIR but not open. For example, sensitive data (data that contains personal information) may not be appropriate to share, however sharing the anonymized metadata that is easily understandable will reduce research redundancy.\n\nResearch has historically perpetuated colonialism and represented extractive practices, meaning that the research results were not mutually beneficial. These issues also related to how data was owned, shared, and used. To address issues like these, the Global Indigenous Data Alliance (GIDA) introduced CARE Principles for Indigenous Data Governance to support Indigenous data sovereignty. CARE Principles speak directly to how the data is stored and shared in the context of Indigenous data sovereignty. CARE Principles (Carroll et al. 2020) stand for:\n\nCollective Benefit - Data ecosystems shall be designed and function in ways that enable Indigenous Peoples to derive benefit from the data\nAuthority to Control - Indigenous Peoples’ rights and interests in Indigenous data must be recognized and their authority to control such data be empowered. Indigenous data governance enables Indigenous Peoples and governing bodies to determine how Indigenous Peoples, as well as Indigenous lands, territories, resources, knowledges and geographical indicators, are represented and identified within data.\nResponsibility - Those working with Indigenous data have a responsibility to share how those data are used to support Indigenous Peoples’ self-determination and collective benefit. Accountability requires meaningful and openly available evidence of these efforts and the benefits accruing to Indigenous Peoples.\nEthics - Indigenous Peoples’ rights and wellbeing should be the primary concern at all stages of the data life cycle and across the data ecosystem.\n\nTo many, the FAIR and CARE principles are viewed by many as complementary: CARE aligns with FAIR by outlining guidelines for publishing data that contributes to open-science and at the same time, accounts for Indigenous’ Peoples rights and interests (Carroll et al. 2020).\nIn Arctic-based research, there is a paradigm shift to include more local Indigenous Peoples, their concerns, and knowledge throughout the research process (Loseto 2020). At the 2019 ArcticNet Annual Scientific Meeting (ASM), a 4-hour workshop was held between Indigenous and non-Indigenous participants to address the challenge of peer-reviewed publications arising when there is a lack of co-production and co-management in the research process between both groups. In the context of peer review, involving Indigenous People and Indigenous Knowledge (IK) not only can increase the validity of research findings, but also ensure the research is meaningful to those most impacted by it. Moreover, it gives power back to the appropriate people to decide who can be knowledge holders of Indigenous knowledge (Loseto et al. 2020). This example underscores the advocacy CARE framework for Indigenous sovereignty, emphasizing the essential integration of people and purpose in the peer review publication stage. Failure to do so perpetuates power imbalances between science institutions and Indigenous communities. Hence, an equitable framework would adhere to the idea ‘Not about us without us’. As an Arctic research community, it is important to reflect on ways we can continue to engage and incorporate Indigenous communities and if there are gaps to address. However, it is important there is no ‘one size fits all’ approach.",
     "crumbs": [
       "<span class='chapter-number'>8</span>  <span class='chapter-title'>Data Ethics for Scalable Computing</span>"
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@@ -654,17 +654,17 @@
     "href": "sections/data-ethics.html#ethics-at-the-arctic-data-center",
     "title": "8  Data Ethics for Scalable Computing",
     "section": "8.3 Ethics at the Arctic Data Center",
-    "text": "8.3 Ethics at the Arctic Data Center\nTransparency in data ethics is a vital part of open science. Regardless of discipline, various ethical concerns are always present, including professional ethics such as plagiarism, false authorship, or falsification of data, to ethics regarding the handling of animals, to concerns relevant to human subjects research. As the primary repository for the Arctic program of the National Science Foundation, the Arctic Data Center accepts Arctic data from all disciplines. Recently, a new submission feature was released which asks researchers to describe the ethical considerations that are apparent in their research. This question is asked to all researchers, regardless of disciplines.\nSharing ethical practices openly, similar in the way that data is shared, enables deeper discussion about data management practices, data reuse, sensitivity, sovereignty and other considerations. Further, such transparency promotes awareness and adoption of ethical practices.\nInspired by CARE Principles for Indigenous Data Governance (Collective Benefit, Authority to Control, Responsibility, Ethics) and FAIR Principles (Findable, Accessible, Interoperable, Reproducible), we include a space in the data submission process for researchers to describe their ethical research practices. These statements are published with each dataset, and the purpose of these statements is to promote greater transparency in data collection and to guide other researchers. For more information about the ethical research practices statement, check out this blog.\nTo help guide researchers as they write their ethical research statements, we have listed the following ethical considerations that are available on our website. The concerns are organized first by concerns that should be addressed by all researchers, and then by discipline.\nConsider the following ethical considerations that are relevant for your field of research.\n\n8.3.1 Ethical Considerations for all Arctic Researchers\n\nResearch Planning\n\nWere any permits required for your research?\nWas there a code of conduct for the research team decided upon prior to beginning data collection?\nWas institutional or local permission required for sampling?\nWhat impact will your research have on local communities or nearby communities (meaning the nearest community within a 100 mile radius)?\n\nData Collection\n\nWere any local community members involved at any point of the research process, including study site identification, sampling, camp setup, consultation or synthesis?\nWere the sample sites near or on Indigenous land or communities?\n\nData Sharing and Publication\n\nHow were the following concerns accounted for: misrepresentation of results, misrepresentation of experience, plagiarism, improper authorship, or the falsification or data?\nIf this data is intended for publication, are authorship expectations clear for everyone involved? Other professional ethics can be found here\n\n\n\n8.3.2 Archaeological and Paleontological Research\n\nResearch Planning\n\nWere there any cultural practices relevant to the study site? If yes, how were these practices accounted for by the research methodologies.\n\nData Collection\n\nDid your research include the removal or artifacts?\nWere there any contingencies made for the excavation and return of samples after cleaning, processing, and analysis?\n\nData Sharing and Publication\n\nWere the samples deposited to a physical repository?\nWere there any steps taken to account for looting threats? Please explain why or why not?\n\n\n\n8.3.3 Human Participation and Sensitive Data\n\nResearch Planning\n\nPlease describe the institutional IRB approval that was required for this research.\nWas any knowledge provided by community members?\n\nData Collection 3. Did participants receive compensation for their participation? 4. Were decolonization methods used?\nData Sharing and Publication\n\nHave you shared this data with the community or participants involved?\n\n\n\n8.3.4 Marines Sciences (e.g. Marine Biology Research)\n\nResearch Planning\n\nWere any of the study sites or species under federal or local protection?\n\nData Collection\n\nWere endangered, threatened, or otherwise special-status species collected?\nHow were samples collected? Please describe any handling practices used to collect data.\nWhat safety measures were in place to keep researchers, research assistants, technicians, etc., out of harms way during research?\nHow were animal care procedures evaluated, and do they follow community norms for organismal care?\n\nData Sharing and Publication 6. Did the species or study area represent any cultural importance to local communities, or include culturally sensitive information? Please explain how you came to this conclusion and how any cultural sensitivity was accounted for.\n\n\n8.3.5 Physical Sciences (e.g. Geology, Glaciology, and Ice Research)\n\nResearch Planning 1. Was any knowledge provided by community members, including information regarding the study site?\nData Collection\n\nWhat safety measures were in place to keep researchers, research assistants, technicians, etc., out of harms way during research?\nWere there any impacts to the environment/habitat before, during or after data collection?\n\nData Sharing and Publication\n\nIs there any sensitive information including information on sensitive sites, valuable samples, or culturally sensitive information?\n\n\n\n8.3.6 Plant and Soil Research\n\nResearch Planning\n\nWere any of the study sites protected under local or federal regulation?\nWas any knowledge provided by nearby community members, including information regarding the study site?\n\nData Collection\n\nDid sample collection result in erosion of soil or other physical damage? If so, how was this addressed?\nWhat safety measures were in place to keep researchers, research assistants, technicians, etc., out of harms way during research?\n\nData Sharing and Publication\n\nDo any of the study sites or specimens represent culturally sensitive areas or species? Please explain how you came to this conclusion, and if yes, how was this accounted for?\n\n\n\n8.3.7 Spatial Data\n\nResearch Planning\n\nWere any land permits required for this research?\n\nData Collection\n\nWere any data collected using citizen science or community participation?\nIf yes, were community members compensated for their time and made aware of their data being used and for what purpose?\n\nData Sharing and Publication\n\nIf data were ground-truthed, was institutional or local permissions required and/or obtained for land/property access?\nHave you shared this data with the community or participants involved?\nIf location sensitive data was obtained (endangered/threatened flora & fauna location, archaeological and historical sites, identifiable ships, sensitive spatial information), how were the data desensitized?\n\n\n\n8.3.8 Wildlife Sciences (e.g. Ecology and Biology Research)\n\nResearch Planning\n\nWere any permits required for data sampling?\n\nData Collection\n\nWere endangered, threatened, or otherwise special-status plants or animal species collected?\nHow were samples collected? Please describe any handling practices used to collect data.\nWhat safety measures were in place to keep researchers, research assistants, technicians, etc., out of harms way during research?\nHow were animal care procedures evaluated, and do they follow community norms for organism care?\n\nData Sharing and Publication\n\nDo any of the study sites or specimens represent culturally sensitive areas or species? Please explain how you came to this conclusion, and if yes, how was this accounted for?\n\nMenti question:\n\nHave you thought about any of the ethical considerations listed above before?\nWere any of the considerations new or surprising?\nAre there any for your relevant discipline that are missing?",
+    "text": "8.3 Ethics at the Arctic Data Center\nTransparency in data ethics is a vital part of open science. Regardless of discipline, various ethical concerns are always present, including professional ethics such as plagiarism, false authorship, or falsification of data, to ethics regarding the handling of animals, to concerns relevant to human subjects research. As the primary repository for the Arctic program of the National Science Foundation, the Arctic Data Center accepts Arctic data from all disciplines. Recently, a new submission feature was released which asks researchers to describe the ethical considerations that are apparent in their research. This question is asked to all researchers, regardless of disciplines.\nSharing ethical practices openly, similar in the way that data is shared, enables deeper discussion about data management practices, data reuse, sensitivity, sovereignty and other considerations. Further, such transparency promotes awareness and adoption of ethical practices.\nInspired by CARE Principles for Indigenous Data Governance (Collective Benefit, Authority to Control, Responsibility, Ethics) and FAIR Principles (Findable, Accessible, Interoperable, Reproducible), we include a space in the data submission process for researchers to describe their ethical research practices. These statements are published with each dataset, and the purpose of these statements is to promote greater transparency in data collection and to guide other researchers. For more information about the ethical research practices statement, check out this blog.\nTo help guide researchers as they write their ethical research statements, we have listed the following ethical considerations that are available on our website. The concerns are organized first by concerns that should be addressed by all researchers, and then by discipline.\nConsider the following ethical considerations that are relevant for your field of research.\n\n8.3.1 Ethical Considerations for all Arctic Researchers\n\nResearch Planning\n\nWere any permits required for your research?\nWas there a code of conduct for the research team decided upon prior to beginning data collection?\nWas institutional or local permission required for sampling?\nWhat impact will your research have on local communities or nearby communities (meaning the nearest community within a 100 mile radius)?\n\nData Collection\n\nWere any local community members involved at any point of the research process, including study site identification, sampling, camp setup, consultation or synthesis?\nWere the sample sites near or on Indigenous land or communities?\n\nData Sharing and Publication\n\nHow were the following concerns accounted for: misrepresentation of results, misrepresentation of experience, plagiarism, improper authorship, or the falsification or data?\nIf this data is intended for publication, are authorship expectations clear for everyone involved? Other professional ethics can be found here\n\n\n\n8.3.2 Archaeological and Paleontological Research\n\nResearch Planning\n\nWere there any cultural practices relevant to the study site? If yes, how were these practices accounted for by the research methodologies.\n\nData Collection\n\nDid your research include the removal or artifacts?\nWere there any contingencies made for the excavation and return of samples after cleaning, processing, and analysis?\n\nData Sharing and Publication\n\nWere the samples deposited to a physical repository?\nWere there any steps taken to account for looting threats? Please explain why or why not?\n\n\n\n8.3.3 Human Participation and Sensitive Data\n\nResearch Planning\n\nPlease describe the institutional IRB approval that was required for this research.\nWas any knowledge provided by community members?\n\nData Collection\n\nDid participants receive compensation for their participation?\nWere decolonization methods used?\n\nData Sharing and Publication\n\nHave you shared this data with the community or participants involved?\n\n\n\n8.3.4 Marines Sciences (e.g. Marine Biology Research)\n\nResearch Planning\n\nWere any of the study sites or species under federal or local protection?\n\nData Collection\n\nWere endangered, threatened, or otherwise special-status species collected?\nHow were samples collected? Please describe any handling practices used to collect data.\nWhat safety measures were in place to keep researchers, research assistants, technicians, etc., out of harms way during research?\nHow were animal care procedures evaluated, and do they follow community norms for organismal care?\n\nData Sharing and Publication\n\nDid the species or study area represent any cultural importance to local communities, or include culturally sensitive information? Please explain how you came to this conclusion and how any cultural sensitivity was accounted for.\n\n\n\n8.3.5 Physical Sciences (e.g. Geology, Glaciology, and Ice Research)\n\nResearch Planning\n\nWas any knowledge provided by community members, including information regarding the study site?\n\nData Collection\n\nWhat safety measures were in place to keep researchers, research assistants, technicians, etc., out of harms way during research?\nWere there any impacts to the environment/habitat before, during or after data collection?\n\nData Sharing and Publication\n\nIs there any sensitive information including information on sensitive sites, valuable samples, or culturally sensitive information?\n\n\n\n8.3.6 Plant and Soil Research\n\nResearch Planning\n\nWere any of the study sites protected under local or federal regulation?\nWas any knowledge provided by nearby community members, including information regarding the study site?\n\nData Collection\n\nDid sample collection result in erosion of soil or other physical damage? If so, how was this addressed?\nWhat safety measures were in place to keep researchers, research assistants, technicians, etc., out of harms way during research?\n\nData Sharing and Publication\n\nDo any of the study sites or specimens represent culturally sensitive areas or species? Please explain how you came to this conclusion, and if yes, how was this accounted for?\n\n\n\n8.3.7 Spatial Data\n\nResearch Planning\n\nWere any land permits required for this research?\n\nData Collection\n\nWere any data collected using citizen science or community participation?\nIf yes, were community members compensated for their time and made aware of their data being used and for what purpose?\n\nData Sharing and Publication\n\nIf data were ground-truthed, was institutional or local permissions required and/or obtained for land/property access?\nHave you shared this data with the community or participants involved?\nIf location sensitive data was obtained (endangered/threatened flora & fauna location, archaeological and historical sites, identifiable ships, sensitive spatial information), how were the data desensitized?\n\n\n\n8.3.8 Wildlife Sciences (e.g. Ecology and Biology Research)\n\nResearch Planning\n\nWere any permits required for data sampling?\n\nData Collection\n\nWere endangered, threatened, or otherwise special-status plants or animal species collected?\nHow were samples collected? Please describe any handling practices used to collect data.\nWhat safety measures were in place to keep researchers, research assistants, technicians, etc., out of harms way during research?\nHow were animal care procedures evaluated, and do they follow community norms for organism care?\n\nData Sharing and Publication\n\nDo any of the study sites or specimens represent culturally sensitive areas or species? Please explain how you came to this conclusion, and if yes, how was this accounted for?\n\nMenti question:\n\nHave you thought about any of the ethical considerations listed above before?\nWere any of the considerations new or surprising?\nAre there any for your relevant discipline that are missing?",
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     "title": "8  Data Ethics for Scalable Computing",
-    "section": "8.4 Ethics in Machine Learning",
-    "text": "8.4 Ethics in Machine Learning\nMenti poll\n\nWhat is your level of familiarity with machine learning\nHave you thought about ethics in machine learning prior to this lesson?\nCan anyone list potential ethical considerations in machine learning?\n\nWhat comes to mind when considering ethics in machine learning?\nThe stories that hit the news are often of privacy breaches or biases seeping into the training data. Bias can enter at any point of the research project, from preparing the training data, designing the algorithms, to collecting and interpreting the data. When working with sensitive data, a question to also consider is how to deanonymize, anonymized data. A unique aspect to machine learning is how personal bias can influence the analysis and outcomes. A great example of this is the case of ImageNet.\n\n8.4.1 ImageNet: A case study of ethics and bias in machine learning\n Image source: Kate Crawford and Trevor Paglen, “Excavating AI: The Politics of Training Sets for Machine Learning” (September 19, 2019).\nImageNet is a great example of how personal bias can enter machine learning through the training data. ImageNet was a training data set of photos that was used to train image classifiers. The data set was initially created as a large collection of pictures, which were mainly used to identify objects, but some included images of people. The creators of the data set created labels to categorize the images, and through crowdsourcing, people from the internet labeled these images. (This example is from Kate Crawford and Trevor Paglen, “Excavating AI: The Politics of Training Sets for Machine Learning”, September 19, 2019).\n\n8.4.1.1 Discussion:\n\nWhere are the two areas bias could enter this scenario?\nAre there any ways that this bias could be avoided?\nWhile this example is specific to images, can you think of any room for bias in your research?",
+    "section": "8.4 Ethics in Artificial Intelligence",
+    "text": "8.4 Ethics in Artificial Intelligence\nMenti poll\n\nWhat is your level of familiarity with machine learning\nHave you thought about ethics in machine learning prior to this lesson?\nCan anyone list potential ethical considerations in machine learning?\n\nWhat comes to mind when considering ethics in machine learning?\nThe stories that hit the news are often of privacy breaches or biases seeping into the training data. Bias can enter at any point of the research project, from preparing the training data, designing the algorithms, to collecting and interpreting the data. When working with sensitive data, a question to also consider is how to deanonymize, anonymized data. A unique aspect to machine learning is how personal bias can influence the analysis and outcomes. A great example of this is the case of ImageNet.\n\n8.4.1 ImageNet: A case study of ethics and bias in machine learning\n Image source: Kate Crawford and Trevor Paglen, “Excavating AI: The Politics of Training Sets for Machine Learning” (September 19, 2019).\nImageNet is a great example of how personal bias can enter machine learning through the training data. ImageNet was a training data set of photos that was used to train image classifiers. The data set was initially created as a large collection of pictures, which were mainly used to identify objects, but some included images of people. The creators of the data set created labels to categorize the images, and through crowdsourcing, people from the internet labeled these images. (This example is from Kate Crawford and Trevor Paglen, “Excavating AI: The Politics of Training Sets for Machine Learning”, September 19, 2019).\n\n8.4.1.1 Discussion:\n\nWhere are the two areas bias could enter this scenario?\nAre there any ways that this bias could be avoided?\nWhile this example is specific to images, can you think of any room for bias in your research?",
     "crumbs": [
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@@ -674,7 +674,7 @@
     "href": "sections/data-ethics.html#references-and-further-reading",
     "title": "8  Data Ethics for Scalable Computing",
     "section": "8.5 References and Further Reading",
-    "text": "8.5 References and Further Reading\nCarroll, S.R., Herczog, E., Hudson, M. et al. (2021) Operationalizing the CARE and FAIR Principles for Indigenous data futures. Sci Data 8, 108 https://doi.org/10.1038/s41597-021-00892-0\nChen, W., & Quan-Haase, A. (2020) Big Data Ethics and Politics: Towards New Understandings. Social Science Computer Review. https://journals.sagepub.com/doi/10.1177/0894439318810734\nCrawford, K., & Paglen, T. (2019) Excavating AI: The Politics of Training Sets for Machine Learning. https://excavating.ai/\nGray, J., & Witt, A. (2021) A feminist data ethics of care framework for machine learning: The what, why, who and how. First Monday, 26(12), Article number: 11833\nPuebla, I., & Lowenberg, D. (2021) Recommendations for the Handling for Ethical Concerns Relating to the Publication of Research Data. FORCE 11. https://force11.org/post/recommendations-for-the-handling-of-ethical-concerns-relating-to-the-publication-of-research-data/\nResearch Data Alliance International Indigenous Data Sovereignty Interest Group. (2019). “CARE Principles for Indigenous Data Governance.” The Global Indigenous Data Alliance. GIDA-global.org\nWilkinson, M., Dumontier, M., Aalbersberg, I. et al. (2016) The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 3, 160018. https://doi.org/10.1038/sdata.2016.18\nZwitter, A., Big Data ethics. (2014) Big Data and Society. DOI: 10.1177/2053951714559253",
+    "text": "8.5 References and Further Reading\nCarroll, S.R., Herczog, E., Hudson, M. et al. (2021) Operationalizing the CARE and FAIR Principles for Indigenous data futures. Sci Data 8, 108 https://doi.org/10.1038/s41597-021-00892-0\nChen, W., & Quan-Haase, A. (2020) Big Data Ethics and Politics: Towards New Understandings. Social Science Computer Review. https://journals.sagepub.com/doi/10.1177/0894439318810734\nCrawford, K., & Paglen, T. (2019) Excavating AI: The Politics of Training Sets for Machine Learning. https://excavating.ai/\nGray, J., & Witt, A. (2021) A feminist data ethics of care framework for machine learning: The what, why, who and how. First Monday, 26(12), Article number: 11833\nL.L.Loseto, K.Breton-Honeyman, D.N.Etiendem, N.Johnson, T.Pearce, J.Allen, A.Amos, J.Arqviq, J.E.Baak, É.Bélanger, M.P.T.Bourdages, J.R.Brammer, D.Fawcett, J.Gérin-Lajoie, G.Gilbert, K.Hansen-Craik, E.Loring, A.Perrin, and M.Slavitch. 2020. Indigenous participation in peer review publications and the editorial process: reflections from a workshop. Arctic Science. 6(3): 352-360. https://doi.org/10.1139/as-2020-0023\nPuebla, I., & Lowenberg, D. (2021) Recommendations for the Handling for Ethical Concerns Relating to the Publication of Research Data. FORCE 11. https://force11.org/post/recommendations-for-the-handling-of-ethical-concerns-relating-to-the-publication-of-research-data/\nResearch Data Alliance International Indigenous Data Sovereignty Interest Group. (2019). “CARE Principles for Indigenous Data Governance.” The Global Indigenous Data Alliance. GIDA-global.org\nWilkinson, M., Dumontier, M., Aalbersberg, I. et al. (2016) The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 3, 160018. https://doi.org/10.1038/sdata.2016.18\nZwitter, A., Big Data ethics. (2014) Big Data and Society. DOI: 10.1177/2053951714559253",
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@@ -884,7 +884,7 @@
     "href": "sections/software-design-1.html#locks",
     "title": "12  Software Design I: Functions and Concurrency",
     "section": "12.7 Locks",
-    "text": "12.7 Locks\nLocks are a mechanism to manage access to a resource so that multiple threads can access the resource. By adding locks to an otherwise parallel process, we introduce a degree of serial execution to the locked portion of the process. Basically, each thread can only access the resource when it has the lock, and only one lock is given out at a time. Take this example of what happens without locking:\n\nfrom concurrent.futures import ProcessPoolExecutor\nimport time\n\ndef hello(i):\n    print(i, 'Hello')\n    print(i, 'world')\n\nexecutor = ProcessPoolExecutor()\nfutures = [executor.submit(hello, i) for i in range(3)]\nfor future in futures:\n    future.result()\n\n012  HelloHello \n\nHello\n102   worldworld\nworld\n\n\n\nYou can see that the results come back in a semi-random order, and the call to sleep creates a delay between printing the two words, which means that the three messages get jumbled when printed. To fix this, we can introduce a lock from the multiprocessing package.\n\nfrom concurrent.futures import ProcessPoolExecutor\nimport time\nimport multiprocessing\n\ndef hello(i, lock):\n    with lock:\n        print(i, 'Hello')\n        print(i, 'world')\n\nlock = multiprocessing.Manager().Lock()\nexecutor = ProcessPoolExecutor()\nfutures = [executor.submit(hello, i, lock) for i in range(3)]\nfor future in futures:\n    future.result()\n\n0 Hello\n0 world\n2 Hello\n2 world\n1 Hello\n1 world\n\n\nThe lock is generated and then passed to each invocation of the hello function. Using with triggers the use of the context manager for the lock, which allows the manager to synchronize use of the lock. This ensures that only one process can be printing at the same time, ensuring that the outputs are properly ordered.\n\n\n\n\n\n\nWarning\n\n\n\nSynchronizing with a Lock turns a parallel process back into a serial process, at least while the lock is in use. So use with care lest you lose all benefits of concurrency.",
+    "text": "12.7 Locks\nLocks are a mechanism to manage access to a resource so that multiple threads can access the resource. By adding locks to an otherwise parallel process, we introduce a degree of serial execution to the locked portion of the process. Basically, each thread can only access the resource when it has the lock, and only one lock is given out at a time. Take this example of what happens without locking:\n\nfrom concurrent.futures import ProcessPoolExecutor\nimport time\n\ndef hello(i):\n    print(i, 'Hello')\n    print(i, 'world')\n\nexecutor = ProcessPoolExecutor()\nfutures = [executor.submit(hello, i) for i in range(3)]\nfor future in futures:\n    future.result()\n\n102  Hello Hello\nHello1\n\n2 0 world\n worldworld\n\n\n\nYou can see that the results come back in a semi-random order, and the call to sleep creates a delay between printing the two words, which means that the three messages get jumbled when printed. To fix this, we can introduce a lock from the multiprocessing package.\n\nfrom concurrent.futures import ProcessPoolExecutor\nimport time\nimport multiprocessing\n\ndef hello(i, lock):\n    with lock:\n        print(i, 'Hello')\n        print(i, 'world')\n\nlock = multiprocessing.Manager().Lock()\nexecutor = ProcessPoolExecutor()\nfutures = [executor.submit(hello, i, lock) for i in range(3)]\nfor future in futures:\n    future.result()\n\n0 Hello\n0 world\n2 Hello\n2 world\n1 Hello\n1 world\n\n\nThe lock is generated and then passed to each invocation of the hello function. Using with triggers the use of the context manager for the lock, which allows the manager to synchronize use of the lock. This ensures that only one process can be printing at the same time, ensuring that the outputs are properly ordered.\n\n\n\n\n\n\nWarning\n\n\n\nSynchronizing with a Lock turns a parallel process back into a serial process, at least while the lock is in use. So use with care lest you lose all benefits of concurrency.",
     "crumbs": [
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diff --git a/public/2024-03-arctic/sections/data-ethics.html b/public/2024-03-arctic/sections/data-ethics.html
index 0463191f..94ebc4be 100644
--- a/public/2024-03-arctic/sections/data-ethics.html
+++ b/public/2024-03-arctic/sections/data-ethics.html
@@ -241,7 +241,7 @@ <h2 id="toc-title">Table of contents</h2>
   <li><a href="#spatial-data" id="toc-spatial-data" class="nav-link" data-scroll-target="#spatial-data"><span class="header-section-number">8.3.7</span> Spatial Data</a></li>
   <li><a href="#wildlife-sciences-e.g.-ecology-and-biology-research" id="toc-wildlife-sciences-e.g.-ecology-and-biology-research" class="nav-link" data-scroll-target="#wildlife-sciences-e.g.-ecology-and-biology-research"><span class="header-section-number">8.3.8</span> Wildlife Sciences (e.g.&nbsp;Ecology and Biology Research)</a></li>
   </ul></li>
-  <li><a href="#ethics-in-machine-learning" id="toc-ethics-in-machine-learning" class="nav-link" data-scroll-target="#ethics-in-machine-learning"><span class="header-section-number">8.4</span> Ethics in Machine Learning</a>
+  <li><a href="#ethics-in-artificial-intelligence" id="toc-ethics-in-artificial-intelligence" class="nav-link" data-scroll-target="#ethics-in-artificial-intelligence"><span class="header-section-number">8.4</span> Ethics in Artificial Intelligence</a>
   <ul class="collapse">
   <li><a href="#imagenet-a-case-study-of-ethics-and-bias-in-machine-learning" id="toc-imagenet-a-case-study-of-ethics-and-bias-in-machine-learning" class="nav-link" data-scroll-target="#imagenet-a-case-study-of-ethics-and-bias-in-machine-learning"><span class="header-section-number">8.4.1</span> ImageNet: A case study of ethics and bias in machine learning</a></li>
   </ul></li>
@@ -282,25 +282,28 @@ <h2 data-number="8.1" class="anchored" data-anchor-id="learning-objectives"><spa
 <section id="intro-to-data-ethics" class="level2" data-number="8.2">
 <h2 data-number="8.2" class="anchored" data-anchor-id="intro-to-data-ethics"><span class="header-section-number">8.2</span> Intro to Data Ethics</h2>
 <p><img src="../images/calvin_hobbes5.png" class="img-fluid"></p>
-<p><strong>To recap, the Arctic Data Center is an openly-accessible data repository and the data published through the repository is open for anyone to reuse</strong>, subject to conditions of the license (at the Arctic Data Center, data is released under one of two licenses: CC-0 Public Domain and CC-By Attribution 4.0). In facilitating use of data resources, the data stewardship community has converged on principles surrounding best practices for open data management.</p>
-<p>Two principles that the Arctic Data Center explicitly adopts are FAIR Principles (Findable, Accessible, Interoperable, and Reproducible) and CARE Principles for Indigenous Governance (Collective Benefit, Authority to Control, Responsibility, Ethics).</p>
-<p>FAIR and CARE principles are relevant in the context of data ethics for multiple reasons. FAIR speaks to how metadata is managed, stored, and shared.</p>
+<p><strong>The Arctic Data Center is an openly-accessible data repository.</strong> The data published through it is open for anyone to reuse, subject to one of two license: <a href="https://creativecommons.org/public-domain/cc0/">CC-0 Public Domain</a> and <a href="https://creativecommons.org/licenses/by/4.0/deed.en">CC-By Attribution 4.0</a>. As an open access repository, we prioritize long-term preservation and embrace principles from the data stewardship community, which established a set of best practices for open data management. n adherence, two principles adopted by the Arctic Data Center are <strong>FAIR Principles</strong> (Findable, Accessible, Interoperable, and Reproducible) and <strong>CARE Principles for Indigenous Governance</strong> (Collective Benefit, Authority to Control, Responsibility, Ethics). Both of which serve as frameworks in how to consider data ethics.</p>
+<p><strong>The FAIR Principles</strong></p>
+<p>FAIR speaks to how metadata is managed, stored, and shared.</p>
 <p><img src="../images/fair.png" class="img-fluid"></p>
+<p>What is the difference between FAIR principles and open science?</p>
 <p>FAIR principles and open science are overlapping concepts, but are distinctive from one another. Open science supports a culture of sharing research outputs and data, and FAIR focuses on how to prepare the data. <strong>The FAIR principles place emphasis on machine readability, “distinct from peer initiatives that focus on the human scholar” (Wilkinson et al 2016) and as such, do not fully engage with sensitive data considerations and with Indigenous rights and interests (Research Data Alliance International Indigenous Data Sovereignty Interest Group, 2019).</strong> Metadata can be FAIR but not open. For example, sensitive data (data that contains personal information) may not be appropriate to share, however sharing the anonymized metadata that is easily understandable will reduce research redundancy.</p>
 <p><img src="../images/fair_care.png" class="img-fluid"></p>
-<p>Research has historically perpetuated colonialism and represented extractive practices, meaning that the research results were not mutually beneficial. These issues also related to how data was owned, shared, and used. <strong>To address issues like these, the Global Indigenous Data Alliance (GIDA) introduced CARE Principles for Indigenous Data Governance to support Indigenous data sovereignty.</strong> CARE Principles speak directly to how the data is stored and shared in the context of Indigenous data sovereignty. CARE Principles stand for:</p>
+<p>Research has historically perpetuated colonialism and represented extractive practices, meaning that the research results were not mutually beneficial. These issues also related to how data was owned, shared, and used. <strong>To address issues like these, the Global Indigenous Data Alliance (GIDA) introduced CARE Principles for Indigenous Data Governance to support Indigenous data sovereignty.</strong> CARE Principles speak directly to how the data is stored and shared in the context of Indigenous data sovereignty. CARE Principles (Carroll et al.&nbsp;2020) stand for:</p>
 <ul>
 <li><p><em>Collective Benefit</em> - Data ecosystems shall be designed and function in ways that enable Indigenous Peoples to derive benefit from the data</p></li>
 <li><p><em>Authority to Control</em> - Indigenous Peoples’ rights and interests in Indigenous data must be recognized and their authority to control such data be empowered. Indigenous data governance enables Indigenous Peoples and governing bodies to determine how Indigenous Peoples, as well as Indigenous lands, territories, resources, knowledges and geographical indicators, are represented and identified within data.</p></li>
 <li><p><em>Responsibility</em> - Those working with Indigenous data have a responsibility to share how those data are used to support Indigenous Peoples’ self-determination and collective benefit. Accountability requires meaningful and openly available evidence of these efforts and the benefits accruing to Indigenous Peoples.</p></li>
-<li><p><em>Ethics</em> - Indigenous Peoples’ rights and wellbeing should be the primary concern at all stages of the data life cycle and across the data ecosystem. To many, the FAIR and CARE principles are viewed by many as complementary: CARE aligns with FAIR by outlining guidelines for publishing data that contributes to open-science and at the same time, accounts for Indigenous’ Peoples rights and interests.</p></li>
+<li><p><em>Ethics</em> - Indigenous Peoples’ rights and wellbeing should be the primary concern at all stages of the data life cycle and across the data ecosystem.</p></li>
 </ul>
+<p>To many, the FAIR and CARE principles are viewed by many as complementary: CARE aligns with FAIR by outlining guidelines for publishing data that contributes to open-science and at the same time, accounts for Indigenous’ Peoples rights and interests (Carroll et al.&nbsp;2020).</p>
+<p>In Arctic-based research, there is a paradigm shift to include more local Indigenous Peoples, their concerns, and knowledge throughout the research process (Loseto 2020). At the 2019 ArcticNet Annual Scientific Meeting (ASM), a 4-hour workshop was held between Indigenous and non-Indigenous participants to address the challenge of peer-reviewed publications arising when there is a lack of co-production and co-management in the research process between both groups. In the context of peer review, involving Indigenous People and Indigenous Knowledge (IK) not only can increase the validity of research findings, but also ensure the research is meaningful to those most impacted by it. Moreover, it gives power back to the appropriate people to decide who can be knowledge holders of Indigenous knowledge (Loseto et al.&nbsp;2020). This example underscores the advocacy CARE framework for Indigenous sovereignty, emphasizing the essential integration of people and purpose in the peer review publication stage. Failure to do so perpetuates power imbalances between science institutions and Indigenous communities. Hence, an equitable framework would adhere to the idea ‘Not about us without us’. As an Arctic research community, it is important to reflect on ways we can continue to engage and incorporate Indigenous communities and if there are gaps to address. However, it is important there is no ‘one size fits all’ approach.</p>
 </section>
 <section id="ethics-at-the-arctic-data-center" class="level2" data-number="8.3">
 <h2 data-number="8.3" class="anchored" data-anchor-id="ethics-at-the-arctic-data-center"><span class="header-section-number">8.3</span> Ethics at the Arctic Data Center</h2>
 <p><strong>Transparency in data ethics is a vital part of open science.</strong> Regardless of discipline, various ethical concerns are always present, including professional ethics such as plagiarism, false authorship, or falsification of data, to ethics regarding the handling of animals, to concerns relevant to human subjects research. As the primary repository for the Arctic program of the National Science Foundation, the Arctic Data Center accepts Arctic data from all disciplines. Recently, a new submission feature was released which asks researchers to describe the ethical considerations that are apparent in their research. This question is asked to all researchers, regardless of disciplines.</p>
 <p>Sharing ethical practices openly, similar in the way that data is shared, enables deeper discussion about data management practices, data reuse, sensitivity, sovereignty and other considerations. Further, such transparency promotes awareness and adoption of ethical practices.</p>
-<p>Inspired by CARE Principles for Indigenous Data Governance (Collective Benefit, Authority to Control, Responsibility, Ethics) and FAIR Principles (Findable, Accessible, Interoperable, Reproducible), we include a space in the data submission process for researchers to describe their ethical research practices. These statements are published with each dataset, and the purpose of these statements is to promote greater transparency in data collection and to guide other researchers. For more information about the ethical research practices statement, check out this blog.</p>
+<p>Inspired by CARE Principles for Indigenous Data Governance (Collective Benefit, Authority to Control, Responsibility, Ethics) and FAIR Principles (Findable, Accessible, Interoperable, Reproducible), we include a space in the data submission process for researchers to describe their ethical research practices. These statements are published with each dataset, and the purpose of these statements is to promote greater transparency in data collection and to guide other researchers. For more information about the ethical research practices statement, <a href="https://arcticdata.io/all/blog/2022/04/applying-care-documenting-ethical-data-procedures-and-sensitive-data-at-the-arctic-data-center/">check out this blog</a>.</p>
 <p>To help guide researchers as they write their ethical research statements, we have listed the following ethical considerations that are available on our website. The concerns are organized first by concerns that should be addressed by all researchers, and then by discipline.</p>
 <p>Consider the following ethical considerations that are relevant for your field of research.</p>
 <section id="ethical-considerations-for-all-arctic-researchers" class="level3" data-number="8.3.1">
@@ -350,7 +353,11 @@ <h3 data-number="8.3.3" class="anchored" data-anchor-id="human-participation-and
 <li>Please describe the institutional IRB approval that was required for this research.</li>
 <li>Was any knowledge provided by community members?</li>
 </ol>
-<p><strong>Data Collection</strong> 3. Did participants receive compensation for their participation? 4. Were decolonization methods used?</p>
+<p><strong>Data Collection</strong></p>
+<ol start="3" type="1">
+<li>Did participants receive compensation for their participation?</li>
+<li>Were decolonization methods used?</li>
+</ol>
 <p><strong>Data Sharing and Publication</strong></p>
 <ol start="5" type="1">
 <li>Have you shared this data with the community or participants involved?</li>
@@ -370,12 +377,18 @@ <h3 data-number="8.3.4" class="anchored" data-anchor-id="marines-sciences-e.g.-m
 <li>What safety measures were in place to keep researchers, research assistants, technicians, etc., out of harms way during research?</li>
 <li>How were animal care procedures evaluated, and do they follow community norms for organismal care?</li>
 </ol>
-<p><strong>Data Sharing and Publication</strong> 6. Did the species or study area represent any cultural importance to local communities, or include culturally sensitive information? Please explain how you came to this conclusion and how any cultural sensitivity was accounted for.</p>
+<p><strong>Data Sharing and Publication</strong></p>
+<ol start="6" type="1">
+<li>Did the species or study area represent any cultural importance to local communities, or include culturally sensitive information? Please explain how you came to this conclusion and how any cultural sensitivity was accounted for.</li>
+</ol>
 </section>
 <section id="physical-sciences-e.g.-geology-glaciology-and-ice-research" class="level3" data-number="8.3.5">
 <h3 data-number="8.3.5" class="anchored" data-anchor-id="physical-sciences-e.g.-geology-glaciology-and-ice-research"><span class="header-section-number">8.3.5</span> Physical Sciences (e.g.&nbsp;Geology, Glaciology, and Ice Research)</h3>
 <p><img src="../images/ice.png" class="img-fluid"></p>
-<p><strong>Research Planning</strong> 1. Was any knowledge provided by community members, including information regarding the study site?</p>
+<p><strong>Research Planning</strong></p>
+<ol type="1">
+<li>Was any knowledge provided by community members, including information regarding the study site?</li>
+</ol>
 <p><strong>Data Collection</strong></p>
 <ol start="2" type="1">
 <li>What safety measures were in place to keep researchers, research assistants, technicians, etc., out of harms way during research?</li>
@@ -449,8 +462,8 @@ <h3 data-number="8.3.8" class="anchored" data-anchor-id="wildlife-sciences-e.g.-
 </ol>
 </section>
 </section>
-<section id="ethics-in-machine-learning" class="level2" data-number="8.4">
-<h2 data-number="8.4" class="anchored" data-anchor-id="ethics-in-machine-learning"><span class="header-section-number">8.4</span> Ethics in Machine Learning</h2>
+<section id="ethics-in-artificial-intelligence" class="level2" data-number="8.4">
+<h2 data-number="8.4" class="anchored" data-anchor-id="ethics-in-artificial-intelligence"><span class="header-section-number">8.4</span> Ethics in Artificial Intelligence</h2>
 <p>Menti poll</p>
 <ol type="1">
 <li>What is your level of familiarity with machine learning</li>
@@ -479,6 +492,7 @@ <h2 data-number="8.5" class="anchored" data-anchor-id="references-and-further-re
 <p>Chen, W., &amp; Quan-Haase, A. (2020) Big Data Ethics and Politics: Towards New Understandings. Social Science Computer Review. https://journals.sagepub.com/doi/10.1177/0894439318810734</p>
 <p>Crawford, K., &amp; Paglen, T. (2019) Excavating AI: The Politics of Training Sets for Machine Learning. https://excavating.ai/</p>
 <p>Gray, J., &amp; Witt, A. (2021) A feminist data ethics of care framework for machine learning: The what, why, who and how. First Monday, 26(12), Article number: 11833</p>
+<p>L.L.Loseto, K.Breton-Honeyman, D.N.Etiendem, N.Johnson, T.Pearce, J.Allen, A.Amos, J.Arqviq, J.E.Baak, É.Bélanger, M.P.T.Bourdages, J.R.Brammer, D.Fawcett, J.Gérin-Lajoie, G.Gilbert, K.Hansen-Craik, E.Loring, A.Perrin, and M.Slavitch. 2020. Indigenous participation in peer review publications and the editorial process: reflections from a workshop. Arctic Science. 6(3): 352-360. https://doi.org/10.1139/as-2020-0023</p>
 <p>Puebla, I., &amp; Lowenberg, D. (2021) Recommendations for the Handling for Ethical Concerns Relating to the Publication of Research Data. FORCE 11. https://force11.org/post/recommendations-for-the-handling-of-ethical-concerns-relating-to-the-publication-of-research-data/</p>
 <p>Research Data Alliance International Indigenous Data Sovereignty Interest Group. (2019). “CARE Principles for Indigenous Data Governance.” The Global Indigenous Data Alliance. GIDA-global.org</p>
 <p>Wilkinson, M., Dumontier, M., Aalbersberg, I. et al.&nbsp;(2016) The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 3, 160018. https://doi.org/10.1038/sdata.2016.18</p>
diff --git a/public/2024-03-arctic/sections/data-structures-netcdf.html b/public/2024-03-arctic/sections/data-structures-netcdf.html
index be33d786..f0b7e4fc 100644
--- a/public/2024-03-arctic/sections/data-structures-netcdf.html
+++ b/public/2024-03-arctic/sections/data-structures-netcdf.html
@@ -527,7 +527,7 @@ <h3 data-number="5.4.1" class="anchored" data-anchor-id="xarray.dataarray"><span
 <section id="create-an-xarray.dataarray" class="level4" data-number="5.4.1.1">
 <h4 data-number="5.4.1.1" class="anchored" data-anchor-id="create-an-xarray.dataarray"><span class="header-section-number">5.4.1.1</span> Create an <code>xarray.DataArray</code></h4>
 <p>Let’s suppose we want to make an <code>xarray.DataArray</code> that includes the information from our previous exercise about measuring temperature across three days. First, we import all the necessary libraries.</p>
-<div id="c9affdcc" class="cell" data-execution_count="1">
+<div id="c6947e5a" class="cell" data-execution_count="1">
 <div class="sourceCode cell-code" id="cb1"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> os              </span>
 <span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> urllib </span>
 <span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> pandas <span class="im">as</span> pd</span>
@@ -537,7 +537,7 @@ <h4 data-number="5.4.1.1" class="anchored" data-anchor-id="create-an-xarray.data
 </div>
 <p><strong>Variable Values</strong></p>
 <p>The underlying data in the <code>xarray.DataArray</code> is a <code>numpy.ndarray</code> that holds the variable values. So we can start by making a <code>numpy.ndarray</code> with our mock temperature data:</p>
-<div id="7a8d22c7" class="cell" data-execution_count="2">
+<div id="5810de13" class="cell" data-execution_count="2">
 <div class="sourceCode cell-code" id="cb2"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="co"># values of a single variable at each point of the coords </span></span>
 <span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a>temp_data <span class="op">=</span> np.array([np.zeros((<span class="dv">5</span>,<span class="dv">5</span>)), </span>
 <span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a>                      np.ones((<span class="dv">5</span>,<span class="dv">5</span>)), </span>
@@ -580,7 +580,7 @@ <h4 data-number="5.4.1.1" class="anchored" data-anchor-id="create-an-xarray.data
 <li>longitude coordinates are 60, 70, 80, 90, 100 (notice increasing order)</li>
 </ul>
 <p>We add the dimensions as a tuple of strings and coordinates as a dictionary:</p>
-<div id="dbfee7d1" class="cell" data-execution_count="3">
+<div id="ab5d8a86" class="cell" data-execution_count="3">
 <div class="sourceCode cell-code" id="cb4"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a><span class="co"># names of the dimensions in the required order</span></span>
 <span id="cb4-2"><a href="#cb4-2" aria-hidden="true" tabindex="-1"></a>dims <span class="op">=</span> (<span class="st">'time'</span>, <span class="st">'lat'</span>, <span class="st">'lon'</span>)</span>
 <span id="cb4-3"><a href="#cb4-3" aria-hidden="true" tabindex="-1"></a></span>
@@ -591,7 +591,7 @@ <h4 data-number="5.4.1.1" class="anchored" data-anchor-id="create-an-xarray.data
 </div>
 <p><strong>Attributes</strong></p>
 <p>Next, we add the attributes (metadata) for our temperature data as a dictionary:</p>
-<div id="5ff613e4" class="cell" data-execution_count="4">
+<div id="e237c90c" class="cell" data-execution_count="4">
 <div class="sourceCode cell-code" id="cb5"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a><span class="co"># attributes (metadata) of the data array </span></span>
 <span id="cb5-2"><a href="#cb5-2" aria-hidden="true" tabindex="-1"></a>attrs <span class="op">=</span> { <span class="st">'title'</span> : <span class="st">'temperature across weather stations'</span>,</span>
 <span id="cb5-3"><a href="#cb5-3" aria-hidden="true" tabindex="-1"></a>          <span class="st">'standard_name'</span> : <span class="st">'air_temperature'</span>,</span>
@@ -599,7 +599,7 @@ <h4 data-number="5.4.1.1" class="anchored" data-anchor-id="create-an-xarray.data
 </div>
 <p><strong>Putting It All Together</strong></p>
 <p>Finally, we put all these pieces together (data, dimensions, coordinates, and attributes) to create an <code>xarray.DataArray</code>:</p>
-<div id="a47aa778" class="cell" data-execution_count="5">
+<div id="e0e0f153" class="cell" data-execution_count="5">
 <div class="sourceCode cell-code" id="cb6"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a><span class="co"># initialize xarray.DataArray</span></span>
 <span id="cb6-2"><a href="#cb6-2" aria-hidden="true" tabindex="-1"></a>temp <span class="op">=</span> xr.DataArray(data <span class="op">=</span> temp_data, </span>
 <span id="cb6-3"><a href="#cb6-3" aria-hidden="true" tabindex="-1"></a>                    dims <span class="op">=</span> dims,</span>
@@ -995,7 +995,7 @@ <h4 data-number="5.4.1.1" class="anchored" data-anchor-id="create-an-xarray.data
 Attributes:
     title:          temperature across weather stations
     standard_name:  air_temperature
-    units:          degree_c</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">time</span>: 3</li><li><span class="xr-has-index">lat</span>: 5</li><li><span class="xr-has-index">lon</span>: 5</li></ul></div><ul class="xr-sections"><li class="xr-section-item"><div class="xr-array-wrap"><input id="section-72ed7b03-7a75-4525-84b0-4225716e9bd2" class="xr-array-in" type="checkbox" checked=""><label for="section-72ed7b03-7a75-4525-84b0-4225716e9bd2" title="Show/hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-array-preview xr-preview"><span>0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ... 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2</span></div><div class="xr-array-data"><pre>array([[[0, 0, 0, 0, 0],
+    units:          degree_c</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">time</span>: 3</li><li><span class="xr-has-index">lat</span>: 5</li><li><span class="xr-has-index">lon</span>: 5</li></ul></div><ul class="xr-sections"><li class="xr-section-item"><div class="xr-array-wrap"><input id="section-f8046874-074a-4e84-ae3f-7a0bc9adb6b4" class="xr-array-in" type="checkbox" checked=""><label for="section-f8046874-074a-4e84-ae3f-7a0bc9adb6b4" title="Show/hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-array-preview xr-preview"><span>0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ... 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2</span></div><div class="xr-array-data"><pre>array([[[0, 0, 0, 0, 0],
         [0, 0, 0, 0, 0],
         [0, 0, 0, 0, 0],
         [0, 0, 0, 0, 0],
@@ -1011,12 +1011,12 @@ <h4 data-number="5.4.1.1" class="anchored" data-anchor-id="create-an-xarray.data
         [2, 2, 2, 2, 2],
         [2, 2, 2, 2, 2],
         [2, 2, 2, 2, 2],
-        [2, 2, 2, 2, 2]]])</pre></div></div></li><li class="xr-section-item"><input id="section-079c74d3-4569-461f-a772-89dd05214e81" class="xr-section-summary-in" type="checkbox" checked=""><label for="section-079c74d3-4569-461f-a772-89dd05214e81" class="xr-section-summary">Coordinates: <span>(3)</span></label><div class="xr-section-inline-details"></div><div class="xr-section-details"><ul class="xr-var-list"><li class="xr-var-item"><div class="xr-var-name"><span class="xr-has-index">time</span></div><div class="xr-var-dims">(time)</div><div class="xr-var-dtype">datetime64[ns]</div><div class="xr-var-preview xr-preview">2022-09-01 2022-09-02 2022-09-03</div><input id="attrs-64ed972a-e97a-4e1a-b2ce-b5c4e7ba3a93" class="xr-var-attrs-in" type="checkbox" disabled=""><label for="attrs-64ed972a-e97a-4e1a-b2ce-b5c4e7ba3a93" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-7e0dfd21-f256-44de-a4af-0d7a0263d00f" class="xr-var-data-in" type="checkbox"><label for="data-7e0dfd21-f256-44de-a4af-0d7a0263d00f" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"></dl></div><div class="xr-var-data"><pre>array(['2022-09-01T00:00:00.000000000', '2022-09-02T00:00:00.000000000',
-       '2022-09-03T00:00:00.000000000'], dtype='datetime64[ns]')</pre></div></li><li class="xr-var-item"><div class="xr-var-name"><span class="xr-has-index">lat</span></div><div class="xr-var-dims">(lat)</div><div class="xr-var-dtype">int64</div><div class="xr-var-preview xr-preview">70 60 50 40 30</div><input id="attrs-9769521c-5f68-4718-97fa-091e3863bc96" class="xr-var-attrs-in" type="checkbox" disabled=""><label for="attrs-9769521c-5f68-4718-97fa-091e3863bc96" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-557200bc-bdb4-4857-bd80-dd94e7847b08" class="xr-var-data-in" type="checkbox"><label for="data-557200bc-bdb4-4857-bd80-dd94e7847b08" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"></dl></div><div class="xr-var-data"><pre>array([70, 60, 50, 40, 30])</pre></div></li><li class="xr-var-item"><div class="xr-var-name"><span class="xr-has-index">lon</span></div><div class="xr-var-dims">(lon)</div><div class="xr-var-dtype">int64</div><div class="xr-var-preview xr-preview">60 70 80 90 100</div><input id="attrs-546c57ea-0a61-4bbb-8229-69b1535d9d34" class="xr-var-attrs-in" type="checkbox" disabled=""><label for="attrs-546c57ea-0a61-4bbb-8229-69b1535d9d34" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-a8fe410e-549a-4907-b6d6-a2a2b24fe1f6" class="xr-var-data-in" type="checkbox"><label for="data-a8fe410e-549a-4907-b6d6-a2a2b24fe1f6" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"></dl></div><div class="xr-var-data"><pre>array([ 60,  70,  80,  90, 100])</pre></div></li></ul></div></li><li class="xr-section-item"><input id="section-bcb0d782-86a2-45ac-b3bf-d874f3dfc667" class="xr-section-summary-in" type="checkbox"><label for="section-bcb0d782-86a2-45ac-b3bf-d874f3dfc667" class="xr-section-summary">Indexes: <span>(3)</span></label><div class="xr-section-inline-details"></div><div class="xr-section-details"><ul class="xr-var-list"><li class="xr-var-item"><div class="xr-index-name"><div>time</div></div><div class="xr-index-preview">PandasIndex</div><div></div><input id="index-c3bb5fae-ed17-4bcb-b625-446b36834110" class="xr-index-data-in" type="checkbox"><label for="index-c3bb5fae-ed17-4bcb-b625-446b36834110" title="Show/Hide index repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-index-data"><pre>PandasIndex(DatetimeIndex(['2022-09-01', '2022-09-02', '2022-09-03'], dtype='datetime64[ns]', name='time', freq='D'))</pre></div></li><li class="xr-var-item"><div class="xr-index-name"><div>lat</div></div><div class="xr-index-preview">PandasIndex</div><div></div><input id="index-3b202676-c294-4e93-854d-eb1fd800c6d3" class="xr-index-data-in" type="checkbox"><label for="index-3b202676-c294-4e93-854d-eb1fd800c6d3" title="Show/Hide index repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-index-data"><pre>PandasIndex(Index([70, 60, 50, 40, 30], dtype='int64', name='lat'))</pre></div></li><li class="xr-var-item"><div class="xr-index-name"><div>lon</div></div><div class="xr-index-preview">PandasIndex</div><div></div><input id="index-88275124-35f4-4663-a01c-8b009d967c09" class="xr-index-data-in" type="checkbox"><label for="index-88275124-35f4-4663-a01c-8b009d967c09" title="Show/Hide index repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-index-data"><pre>PandasIndex(Index([60, 70, 80, 90, 100], dtype='int64', name='lon'))</pre></div></li></ul></div></li><li class="xr-section-item"><input id="section-d203bd89-3b66-4523-b235-f15baacef619" class="xr-section-summary-in" type="checkbox" checked=""><label for="section-d203bd89-3b66-4523-b235-f15baacef619" class="xr-section-summary">Attributes: <span>(3)</span></label><div class="xr-section-inline-details"></div><div class="xr-section-details"><dl class="xr-attrs"><dt><span>title :</span></dt><dd>temperature across weather stations</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>units :</span></dt><dd>degree_c</dd></dl></div></li></ul></div></div>
+        [2, 2, 2, 2, 2]]])</pre></div></div></li><li class="xr-section-item"><input id="section-c89fb1a6-cf36-4257-97b1-fb2150760ea7" class="xr-section-summary-in" type="checkbox" checked=""><label for="section-c89fb1a6-cf36-4257-97b1-fb2150760ea7" class="xr-section-summary">Coordinates: <span>(3)</span></label><div class="xr-section-inline-details"></div><div class="xr-section-details"><ul class="xr-var-list"><li class="xr-var-item"><div class="xr-var-name"><span class="xr-has-index">time</span></div><div class="xr-var-dims">(time)</div><div class="xr-var-dtype">datetime64[ns]</div><div class="xr-var-preview xr-preview">2022-09-01 2022-09-02 2022-09-03</div><input id="attrs-863cc640-6d52-4517-b761-d7be3597bb64" class="xr-var-attrs-in" type="checkbox" disabled=""><label for="attrs-863cc640-6d52-4517-b761-d7be3597bb64" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-929ac761-8728-4e7b-8d51-fe64a33d3d01" class="xr-var-data-in" type="checkbox"><label for="data-929ac761-8728-4e7b-8d51-fe64a33d3d01" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"></dl></div><div class="xr-var-data"><pre>array(['2022-09-01T00:00:00.000000000', '2022-09-02T00:00:00.000000000',
+       '2022-09-03T00:00:00.000000000'], dtype='datetime64[ns]')</pre></div></li><li class="xr-var-item"><div class="xr-var-name"><span class="xr-has-index">lat</span></div><div class="xr-var-dims">(lat)</div><div class="xr-var-dtype">int64</div><div class="xr-var-preview xr-preview">70 60 50 40 30</div><input id="attrs-de42f533-26f8-42c7-909e-be1171657e3a" class="xr-var-attrs-in" type="checkbox" disabled=""><label for="attrs-de42f533-26f8-42c7-909e-be1171657e3a" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-bef238e3-9585-466d-8c48-aee43f214f96" class="xr-var-data-in" type="checkbox"><label for="data-bef238e3-9585-466d-8c48-aee43f214f96" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"></dl></div><div class="xr-var-data"><pre>array([70, 60, 50, 40, 30])</pre></div></li><li class="xr-var-item"><div class="xr-var-name"><span class="xr-has-index">lon</span></div><div class="xr-var-dims">(lon)</div><div class="xr-var-dtype">int64</div><div class="xr-var-preview xr-preview">60 70 80 90 100</div><input id="attrs-7a19ba39-15dc-4f2d-b076-e7508616dec8" class="xr-var-attrs-in" type="checkbox" disabled=""><label for="attrs-7a19ba39-15dc-4f2d-b076-e7508616dec8" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-06ff6af0-abad-4bd3-9a3b-f9fa712e91fa" class="xr-var-data-in" type="checkbox"><label for="data-06ff6af0-abad-4bd3-9a3b-f9fa712e91fa" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"></dl></div><div class="xr-var-data"><pre>array([ 60,  70,  80,  90, 100])</pre></div></li></ul></div></li><li class="xr-section-item"><input id="section-02788633-0423-4191-babf-4ccec37b8b18" class="xr-section-summary-in" type="checkbox"><label for="section-02788633-0423-4191-babf-4ccec37b8b18" class="xr-section-summary">Indexes: <span>(3)</span></label><div class="xr-section-inline-details"></div><div class="xr-section-details"><ul class="xr-var-list"><li class="xr-var-item"><div class="xr-index-name"><div>time</div></div><div class="xr-index-preview">PandasIndex</div><div></div><input id="index-2703b8ab-9c7c-4ca0-807d-ed958674c53d" class="xr-index-data-in" type="checkbox"><label for="index-2703b8ab-9c7c-4ca0-807d-ed958674c53d" title="Show/Hide index repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-index-data"><pre>PandasIndex(DatetimeIndex(['2022-09-01', '2022-09-02', '2022-09-03'], dtype='datetime64[ns]', name='time', freq='D'))</pre></div></li><li class="xr-var-item"><div class="xr-index-name"><div>lat</div></div><div class="xr-index-preview">PandasIndex</div><div></div><input id="index-b6681854-e068-4d43-ac73-0a860035587f" class="xr-index-data-in" type="checkbox"><label for="index-b6681854-e068-4d43-ac73-0a860035587f" title="Show/Hide index repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-index-data"><pre>PandasIndex(Index([70, 60, 50, 40, 30], dtype='int64', name='lat'))</pre></div></li><li class="xr-var-item"><div class="xr-index-name"><div>lon</div></div><div class="xr-index-preview">PandasIndex</div><div></div><input id="index-75a27169-5a29-4fa4-b206-f812bc024442" class="xr-index-data-in" type="checkbox"><label for="index-75a27169-5a29-4fa4-b206-f812bc024442" title="Show/Hide index repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-index-data"><pre>PandasIndex(Index([60, 70, 80, 90, 100], dtype='int64', name='lon'))</pre></div></li></ul></div></li><li class="xr-section-item"><input id="section-ea2d01ab-3ef2-4212-a61e-c088ad0ea194" class="xr-section-summary-in" type="checkbox" checked=""><label for="section-ea2d01ab-3ef2-4212-a61e-c088ad0ea194" class="xr-section-summary">Attributes: <span>(3)</span></label><div class="xr-section-inline-details"></div><div class="xr-section-details"><dl class="xr-attrs"><dt><span>title :</span></dt><dd>temperature across weather stations</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>units :</span></dt><dd>degree_c</dd></dl></div></li></ul></div></div>
 </div>
 </div>
 <p>We can also update the variable’s attributes after creating the object. Notice that each of the coordinates is also an <code>xarray.DataArray</code>, so we can add attributes to them.</p>
-<div id="a0570dcd" class="cell" data-execution_count="6">
+<div id="5621f229" class="cell" data-execution_count="6">
 <div class="sourceCode cell-code" id="cb7"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a><span class="co"># update attributes</span></span>
 <span id="cb7-2"><a href="#cb7-2" aria-hidden="true" tabindex="-1"></a>temp.attrs[<span class="st">'description'</span>] <span class="op">=</span> <span class="st">'simple example of an xarray.DataArray'</span></span>
 <span id="cb7-3"><a href="#cb7-3" aria-hidden="true" tabindex="-1"></a></span>
@@ -1419,7 +1419,7 @@ <h4 data-number="5.4.1.1" class="anchored" data-anchor-id="create-an-xarray.data
     title:          temperature across weather stations
     standard_name:  air_temperature
     units:          degree_c
-    description:    simple example of an xarray.DataArray</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">time</span>: 3</li><li><span class="xr-has-index">lat</span>: 5</li><li><span class="xr-has-index">lon</span>: 5</li></ul></div><ul class="xr-sections"><li class="xr-section-item"><div class="xr-array-wrap"><input id="section-a791aa40-d913-4653-80fb-e793eaea22c4" class="xr-array-in" type="checkbox" checked=""><label for="section-a791aa40-d913-4653-80fb-e793eaea22c4" title="Show/hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-array-preview xr-preview"><span>0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ... 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2</span></div><div class="xr-array-data"><pre>array([[[0, 0, 0, 0, 0],
+    description:    simple example of an xarray.DataArray</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">time</span>: 3</li><li><span class="xr-has-index">lat</span>: 5</li><li><span class="xr-has-index">lon</span>: 5</li></ul></div><ul class="xr-sections"><li class="xr-section-item"><div class="xr-array-wrap"><input id="section-934f83d3-2965-4783-ad07-4fa4341d9b33" class="xr-array-in" type="checkbox" checked=""><label for="section-934f83d3-2965-4783-ad07-4fa4341d9b33" title="Show/hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-array-preview xr-preview"><span>0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ... 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2</span></div><div class="xr-array-data"><pre>array([[[0, 0, 0, 0, 0],
         [0, 0, 0, 0, 0],
         [0, 0, 0, 0, 0],
         [0, 0, 0, 0, 0],
@@ -1435,8 +1435,8 @@ <h4 data-number="5.4.1.1" class="anchored" data-anchor-id="create-an-xarray.data
         [2, 2, 2, 2, 2],
         [2, 2, 2, 2, 2],
         [2, 2, 2, 2, 2],
-        [2, 2, 2, 2, 2]]])</pre></div></div></li><li class="xr-section-item"><input id="section-f353d76a-e18d-4d94-aad1-b1655e54fd2d" class="xr-section-summary-in" type="checkbox" checked=""><label for="section-f353d76a-e18d-4d94-aad1-b1655e54fd2d" class="xr-section-summary">Coordinates: <span>(3)</span></label><div class="xr-section-inline-details"></div><div class="xr-section-details"><ul class="xr-var-list"><li class="xr-var-item"><div class="xr-var-name"><span class="xr-has-index">time</span></div><div class="xr-var-dims">(time)</div><div class="xr-var-dtype">datetime64[ns]</div><div class="xr-var-preview xr-preview">2022-09-01 2022-09-02 2022-09-03</div><input id="attrs-cb383dd9-e6e8-4dd1-b2fa-b8595b7a1512" class="xr-var-attrs-in" type="checkbox"><label for="attrs-cb383dd9-e6e8-4dd1-b2fa-b8595b7a1512" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-78aeb9b2-3747-4b30-94e9-338a7e0daf87" class="xr-var-data-in" type="checkbox"><label for="data-78aeb9b2-3747-4b30-94e9-338a7e0daf87" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>description :</span></dt><dd>date of measurement</dd></dl></div><div class="xr-var-data"><pre>array(['2022-09-01T00:00:00.000000000', '2022-09-02T00:00:00.000000000',
-       '2022-09-03T00:00:00.000000000'], dtype='datetime64[ns]')</pre></div></li><li class="xr-var-item"><div class="xr-var-name"><span class="xr-has-index">lat</span></div><div class="xr-var-dims">(lat)</div><div class="xr-var-dtype">int64</div><div class="xr-var-preview xr-preview">70 60 50 40 30</div><input id="attrs-f12add38-4355-49c2-a34c-8e201684a3ec" class="xr-var-attrs-in" type="checkbox"><label for="attrs-f12add38-4355-49c2-a34c-8e201684a3ec" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-544f6d1a-1504-4416-a9ca-1c656aa9a051" class="xr-var-data-in" type="checkbox"><label for="data-544f6d1a-1504-4416-a9ca-1c656aa9a051" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>standard_name :</span></dt><dd>grid_latitude</dd><dt><span>units :</span></dt><dd>degree_N</dd></dl></div><div class="xr-var-data"><pre>array([70, 60, 50, 40, 30])</pre></div></li><li class="xr-var-item"><div class="xr-var-name"><span class="xr-has-index">lon</span></div><div class="xr-var-dims">(lon)</div><div class="xr-var-dtype">int64</div><div class="xr-var-preview xr-preview">60 70 80 90 100</div><input id="attrs-3c0cd170-345a-4f41-bd74-8b30b7f60247" class="xr-var-attrs-in" type="checkbox"><label for="attrs-3c0cd170-345a-4f41-bd74-8b30b7f60247" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-aff950d4-da7b-4602-a75c-dbfc56521a10" class="xr-var-data-in" type="checkbox"><label for="data-aff950d4-da7b-4602-a75c-dbfc56521a10" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>standard_name :</span></dt><dd>grid_longitude</dd><dt><span>units :</span></dt><dd>degree_E</dd></dl></div><div class="xr-var-data"><pre>array([ 60,  70,  80,  90, 100])</pre></div></li></ul></div></li><li class="xr-section-item"><input id="section-faf8a64e-1bc2-4e18-b0d0-aa74c3662a18" class="xr-section-summary-in" type="checkbox"><label for="section-faf8a64e-1bc2-4e18-b0d0-aa74c3662a18" class="xr-section-summary">Indexes: <span>(3)</span></label><div class="xr-section-inline-details"></div><div class="xr-section-details"><ul class="xr-var-list"><li class="xr-var-item"><div class="xr-index-name"><div>time</div></div><div class="xr-index-preview">PandasIndex</div><div></div><input id="index-f3b23733-5b88-477b-b0d7-1d2ae2a38387" class="xr-index-data-in" type="checkbox"><label for="index-f3b23733-5b88-477b-b0d7-1d2ae2a38387" title="Show/Hide index repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-index-data"><pre>PandasIndex(DatetimeIndex(['2022-09-01', '2022-09-02', '2022-09-03'], dtype='datetime64[ns]', name='time', freq='D'))</pre></div></li><li class="xr-var-item"><div class="xr-index-name"><div>lat</div></div><div class="xr-index-preview">PandasIndex</div><div></div><input id="index-de5aa4ba-3bab-4b3e-81a0-4ab08d9006a7" class="xr-index-data-in" type="checkbox"><label for="index-de5aa4ba-3bab-4b3e-81a0-4ab08d9006a7" title="Show/Hide index repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-index-data"><pre>PandasIndex(Index([70, 60, 50, 40, 30], dtype='int64', name='lat'))</pre></div></li><li class="xr-var-item"><div class="xr-index-name"><div>lon</div></div><div class="xr-index-preview">PandasIndex</div><div></div><input id="index-ca7f1ba8-5343-44ba-84ef-b07158148eb5" class="xr-index-data-in" type="checkbox"><label for="index-ca7f1ba8-5343-44ba-84ef-b07158148eb5" title="Show/Hide index repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-index-data"><pre>PandasIndex(Index([60, 70, 80, 90, 100], dtype='int64', name='lon'))</pre></div></li></ul></div></li><li class="xr-section-item"><input id="section-51dc374a-7ff1-4b85-b021-f7550ac2140b" class="xr-section-summary-in" type="checkbox" checked=""><label for="section-51dc374a-7ff1-4b85-b021-f7550ac2140b" class="xr-section-summary">Attributes: <span>(4)</span></label><div class="xr-section-inline-details"></div><div class="xr-section-details"><dl class="xr-attrs"><dt><span>title :</span></dt><dd>temperature across weather stations</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>units :</span></dt><dd>degree_c</dd><dt><span>description :</span></dt><dd>simple example of an xarray.DataArray</dd></dl></div></li></ul></div></div>
+        [2, 2, 2, 2, 2]]])</pre></div></div></li><li class="xr-section-item"><input id="section-b703ca92-232c-4660-b6ca-bffab8ed9a8f" class="xr-section-summary-in" type="checkbox" checked=""><label for="section-b703ca92-232c-4660-b6ca-bffab8ed9a8f" class="xr-section-summary">Coordinates: <span>(3)</span></label><div class="xr-section-inline-details"></div><div class="xr-section-details"><ul class="xr-var-list"><li class="xr-var-item"><div class="xr-var-name"><span class="xr-has-index">time</span></div><div class="xr-var-dims">(time)</div><div class="xr-var-dtype">datetime64[ns]</div><div class="xr-var-preview xr-preview">2022-09-01 2022-09-02 2022-09-03</div><input id="attrs-aa5c4190-1ab0-41f3-b31a-033809c7b74d" class="xr-var-attrs-in" type="checkbox"><label for="attrs-aa5c4190-1ab0-41f3-b31a-033809c7b74d" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-9a465210-0bfb-4450-9e90-6a61ffa2ac31" class="xr-var-data-in" type="checkbox"><label for="data-9a465210-0bfb-4450-9e90-6a61ffa2ac31" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>description :</span></dt><dd>date of measurement</dd></dl></div><div class="xr-var-data"><pre>array(['2022-09-01T00:00:00.000000000', '2022-09-02T00:00:00.000000000',
+       '2022-09-03T00:00:00.000000000'], dtype='datetime64[ns]')</pre></div></li><li class="xr-var-item"><div class="xr-var-name"><span class="xr-has-index">lat</span></div><div class="xr-var-dims">(lat)</div><div class="xr-var-dtype">int64</div><div class="xr-var-preview xr-preview">70 60 50 40 30</div><input id="attrs-8dc3ae14-50ac-48e3-8c96-c18d7c29fc31" class="xr-var-attrs-in" type="checkbox"><label for="attrs-8dc3ae14-50ac-48e3-8c96-c18d7c29fc31" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-7a3e22b7-1fd7-4764-bb85-f30954502161" class="xr-var-data-in" type="checkbox"><label for="data-7a3e22b7-1fd7-4764-bb85-f30954502161" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>standard_name :</span></dt><dd>grid_latitude</dd><dt><span>units :</span></dt><dd>degree_N</dd></dl></div><div class="xr-var-data"><pre>array([70, 60, 50, 40, 30])</pre></div></li><li class="xr-var-item"><div class="xr-var-name"><span class="xr-has-index">lon</span></div><div class="xr-var-dims">(lon)</div><div class="xr-var-dtype">int64</div><div class="xr-var-preview xr-preview">60 70 80 90 100</div><input id="attrs-766beafc-444b-421e-9b56-6a3b06ce5f0f" class="xr-var-attrs-in" type="checkbox"><label for="attrs-766beafc-444b-421e-9b56-6a3b06ce5f0f" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-977c365e-a722-4d26-8e93-92ce3425eeca" class="xr-var-data-in" type="checkbox"><label for="data-977c365e-a722-4d26-8e93-92ce3425eeca" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>standard_name :</span></dt><dd>grid_longitude</dd><dt><span>units :</span></dt><dd>degree_E</dd></dl></div><div class="xr-var-data"><pre>array([ 60,  70,  80,  90, 100])</pre></div></li></ul></div></li><li class="xr-section-item"><input id="section-883a6176-5963-40f1-807f-f2ad9bb6ab36" class="xr-section-summary-in" type="checkbox"><label for="section-883a6176-5963-40f1-807f-f2ad9bb6ab36" class="xr-section-summary">Indexes: <span>(3)</span></label><div class="xr-section-inline-details"></div><div class="xr-section-details"><ul class="xr-var-list"><li class="xr-var-item"><div class="xr-index-name"><div>time</div></div><div class="xr-index-preview">PandasIndex</div><div></div><input id="index-bb0728df-8808-4bdd-9dd9-170a405a71e2" class="xr-index-data-in" type="checkbox"><label for="index-bb0728df-8808-4bdd-9dd9-170a405a71e2" title="Show/Hide index repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-index-data"><pre>PandasIndex(DatetimeIndex(['2022-09-01', '2022-09-02', '2022-09-03'], dtype='datetime64[ns]', name='time', freq='D'))</pre></div></li><li class="xr-var-item"><div class="xr-index-name"><div>lat</div></div><div class="xr-index-preview">PandasIndex</div><div></div><input id="index-55056863-e35d-42e7-8fba-c4fa624e809e" class="xr-index-data-in" type="checkbox"><label for="index-55056863-e35d-42e7-8fba-c4fa624e809e" title="Show/Hide index repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-index-data"><pre>PandasIndex(Index([70, 60, 50, 40, 30], dtype='int64', name='lat'))</pre></div></li><li class="xr-var-item"><div class="xr-index-name"><div>lon</div></div><div class="xr-index-preview">PandasIndex</div><div></div><input id="index-35374478-1ec6-4a35-a27c-9a24627d3e0a" class="xr-index-data-in" type="checkbox"><label for="index-35374478-1ec6-4a35-a27c-9a24627d3e0a" title="Show/Hide index repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-index-data"><pre>PandasIndex(Index([60, 70, 80, 90, 100], dtype='int64', name='lon'))</pre></div></li></ul></div></li><li class="xr-section-item"><input id="section-14be5cc2-e370-4199-98b4-4287d7d8b6a0" class="xr-section-summary-in" type="checkbox" checked=""><label for="section-14be5cc2-e370-4199-98b4-4287d7d8b6a0" class="xr-section-summary">Attributes: <span>(4)</span></label><div class="xr-section-inline-details"></div><div class="xr-section-details"><dl class="xr-attrs"><dt><span>title :</span></dt><dd>temperature across weather stations</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>units :</span></dt><dd>degree_c</dd><dt><span>description :</span></dt><dd>simple example of an xarray.DataArray</dd></dl></div></li></ul></div></div>
 </div>
 </div>
 <p>At this point, since we have a single variable, the dataset attributes and the variable attributes are the same.</p>
@@ -1445,7 +1445,7 @@ <h4 data-number="5.4.1.1" class="anchored" data-anchor-id="create-an-xarray.data
 <h4 data-number="5.4.1.2" class="anchored" data-anchor-id="indexing"><span class="header-section-number">5.4.1.2</span> Indexing</h4>
 <p>An <code>xarray.DataArray</code> allows both positional indexing (like <code>numpy</code>) and label-based indexing (like <code>pandas</code>). Positional indexing is the most basic, and it’s done using Python’s <code>[]</code> syntax, as in <code>array[i,j]</code> with i and j both integers. <strong>Label-based indexing</strong> takes advantage of dimensions in the array having names and coordinate values that we can use to access data instead of remembering the positional order of each dimension.</p>
 <p>As an example, suppose we want to know what was the temperature recorded by the weather station located at 40°0′N 80°0′E on September 1st, 2022. By recalling all the information about how the array is setup with respect to the dimensions and coordinates, we can access this data positionally:</p>
-<div id="64698bab" class="cell" data-execution_count="7">
+<div id="a396df6c" class="cell" data-execution_count="7">
 <div class="sourceCode cell-code" id="cb8"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a>temp[<span class="dv">0</span>,<span class="dv">1</span>,<span class="dv">2</span>]</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="7">
 <div><svg style="position: absolute; width: 0; height: 0; overflow: hidden">
@@ -1821,11 +1821,11 @@ <h4 data-number="5.4.1.2" class="anchored" data-anchor-id="indexing"><span class
     title:          temperature across weather stations
     standard_name:  air_temperature
     units:          degree_c
-    description:    simple example of an xarray.DataArray</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></div><ul class="xr-sections"><li class="xr-section-item"><div class="xr-array-wrap"><input id="section-a6cffbdb-e7dd-483c-a670-c41927bd5570" class="xr-array-in" type="checkbox" checked=""><label for="section-a6cffbdb-e7dd-483c-a670-c41927bd5570" title="Show/hide data repr"><svg class="icon xr-icon-database"><use 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-0a361e31-21d1-4a18-a6ca-0aaf4d2fca96" class="xr-section-summary-in" type="checkbox" checked=""><label for="section-0a361e31-21d1-4a18-a6ca-0aaf4d2fca96" class="xr-section-summary">Coordinates: <span>(3)</span></label><div class="xr-section-inline-details"></div><div class="xr-section-details"><ul class="xr-var-list"><li class="xr-var-item"><div class="xr-var-name"><span>time</span></div><div class="xr-var-dims">()</div><div class="xr-var-dtype">datetime64[ns]</div><div class="xr-var-preview xr-preview">2022-09-01</div><input id="attrs-1f375569-2bea-43d9-8cf9-e1581e9f1c98" class="xr-var-attrs-in" type="checkbox"><label for="attrs-1f375569-2bea-43d9-8cf9-e1581e9f1c98" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-e80dd8ca-6d02-425a-9d18-d5a90f038361" class="xr-var-data-in" type="checkbox"><label for="data-e80dd8ca-6d02-425a-9d18-d5a90f038361" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>description :</span></dt><dd>date of measurement</dd></dl></div><div class="xr-var-data"><pre>array('2022-09-01T00:00:00.000000000', dtype='datetime64[ns]')</pre></div></li><li class="xr-var-item"><div class="xr-var-name"><span>lat</span></div><div class="xr-var-dims">()</div><div class="xr-var-dtype">int64</div><div class="xr-var-preview xr-preview">60</div><input id="attrs-b0f7a239-9e21-461f-8261-951853f8ce25" class="xr-var-attrs-in" type="checkbox"><label for="attrs-b0f7a239-9e21-461f-8261-951853f8ce25" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-a6b5ca43-88c5-4ed2-b0e2-acec54b9fd02" class="xr-var-data-in" type="checkbox"><label for="data-a6b5ca43-88c5-4ed2-b0e2-acec54b9fd02" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>standard_name :</span></dt><dd>grid_latitude</dd><dt><span>units :</span></dt><dd>degree_N</dd></dl></div><div class="xr-var-data"><pre>array(60)</pre></div></li><li class="xr-var-item"><div class="xr-var-name"><span>lon</span></div><div class="xr-var-dims">()</div><div class="xr-var-dtype">int64</div><div class="xr-var-preview xr-preview">80</div><input id="attrs-4a3b09aa-d0b6-4d38-99ab-2234b02cc7bb" class="xr-var-attrs-in" type="checkbox"><label for="attrs-4a3b09aa-d0b6-4d38-99ab-2234b02cc7bb" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-d9710143-e87c-448f-9974-9b4aa8375506" class="xr-var-data-in" type="checkbox"><label for="data-d9710143-e87c-448f-9974-9b4aa8375506" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>standard_name :</span></dt><dd>grid_longitude</dd><dt><span>units :</span></dt><dd>degree_E</dd></dl></div><div class="xr-var-data"><pre>array(80)</pre></div></li></ul></div></li><li class="xr-section-item"><input id="section-55739d58-57fe-48c0-96b9-5957b9b6a1af" class="xr-section-summary-in" type="checkbox" disabled=""><label for="section-55739d58-57fe-48c0-96b9-5957b9b6a1af" class="xr-section-summary" title="Expand/collapse section">Indexes: <span>(0)</span></label><div class="xr-section-inline-details"></div><div class="xr-section-details"><ul class="xr-var-list"></ul></div></li><li class="xr-section-item"><input id="section-857a6008-83b3-41f2-8d61-8207f3ad6674" class="xr-section-summary-in" type="checkbox" checked=""><label for="section-857a6008-83b3-41f2-8d61-8207f3ad6674" class="xr-section-summary">Attributes: <span>(4)</span></label><div class="xr-section-inline-details"></div><div class="xr-section-details"><dl class="xr-attrs"><dt><span>title :</span></dt><dd>temperature across weather stations</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>units :</span></dt><dd>degree_c</dd><dt><span>description :</span></dt><dd>simple example of an xarray.DataArray</dd></dl></div></li></ul></div></div>
+    description:    simple example of an xarray.DataArray</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></div><ul class="xr-sections"><li class="xr-section-item"><div class="xr-array-wrap"><input id="section-47f4fe9d-c845-44d5-8afe-55dc9f4c555e" class="xr-array-in" type="checkbox" checked=""><label for="section-47f4fe9d-c845-44d5-8afe-55dc9f4c555e" title="Show/hide data repr"><svg class="icon xr-icon-database"><use 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-64c1f718-34f5-4d5a-8e2b-6472557b0862" class="xr-section-summary-in" type="checkbox" checked=""><label for="section-64c1f718-34f5-4d5a-8e2b-6472557b0862" class="xr-section-summary">Coordinates: <span>(3)</span></label><div class="xr-section-inline-details"></div><div class="xr-section-details"><ul class="xr-var-list"><li class="xr-var-item"><div class="xr-var-name"><span>time</span></div><div class="xr-var-dims">()</div><div class="xr-var-dtype">datetime64[ns]</div><div class="xr-var-preview xr-preview">2022-09-01</div><input id="attrs-6acb1c03-b827-434c-9cf7-1d8c2ddeaacd" class="xr-var-attrs-in" type="checkbox"><label for="attrs-6acb1c03-b827-434c-9cf7-1d8c2ddeaacd" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-ca8e5203-386f-440e-861d-75f395766c29" class="xr-var-data-in" type="checkbox"><label for="data-ca8e5203-386f-440e-861d-75f395766c29" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>description :</span></dt><dd>date of measurement</dd></dl></div><div class="xr-var-data"><pre>array('2022-09-01T00:00:00.000000000', dtype='datetime64[ns]')</pre></div></li><li class="xr-var-item"><div class="xr-var-name"><span>lat</span></div><div class="xr-var-dims">()</div><div class="xr-var-dtype">int64</div><div class="xr-var-preview xr-preview">60</div><input id="attrs-7c2606bf-a02f-4fd2-b2a4-fdce5bbe7b69" class="xr-var-attrs-in" type="checkbox"><label for="attrs-7c2606bf-a02f-4fd2-b2a4-fdce5bbe7b69" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-d4d3eb95-dbf8-4db3-bef7-831ffe5f1650" class="xr-var-data-in" type="checkbox"><label for="data-d4d3eb95-dbf8-4db3-bef7-831ffe5f1650" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>standard_name :</span></dt><dd>grid_latitude</dd><dt><span>units :</span></dt><dd>degree_N</dd></dl></div><div class="xr-var-data"><pre>array(60)</pre></div></li><li class="xr-var-item"><div class="xr-var-name"><span>lon</span></div><div class="xr-var-dims">()</div><div class="xr-var-dtype">int64</div><div class="xr-var-preview xr-preview">80</div><input id="attrs-21c2943c-098f-4307-8dc9-a4469e34ec53" class="xr-var-attrs-in" type="checkbox"><label for="attrs-21c2943c-098f-4307-8dc9-a4469e34ec53" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-05200dc0-0c83-4493-a668-28b45bbf977e" class="xr-var-data-in" type="checkbox"><label for="data-05200dc0-0c83-4493-a668-28b45bbf977e" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>standard_name :</span></dt><dd>grid_longitude</dd><dt><span>units :</span></dt><dd>degree_E</dd></dl></div><div class="xr-var-data"><pre>array(80)</pre></div></li></ul></div></li><li class="xr-section-item"><input id="section-e98c7e03-506a-4ec1-b992-8a9d60782fe7" class="xr-section-summary-in" type="checkbox" disabled=""><label for="section-e98c7e03-506a-4ec1-b992-8a9d60782fe7" class="xr-section-summary" title="Expand/collapse section">Indexes: <span>(0)</span></label><div class="xr-section-inline-details"></div><div class="xr-section-details"><ul class="xr-var-list"></ul></div></li><li class="xr-section-item"><input id="section-fa7ea9c2-69da-404d-9d4c-ca63d21c2061" class="xr-section-summary-in" type="checkbox" checked=""><label for="section-fa7ea9c2-69da-404d-9d4c-ca63d21c2061" class="xr-section-summary">Attributes: <span>(4)</span></label><div class="xr-section-inline-details"></div><div class="xr-section-details"><dl class="xr-attrs"><dt><span>title :</span></dt><dd>temperature across weather stations</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>units :</span></dt><dd>degree_c</dd><dt><span>description :</span></dt><dd>simple example of an xarray.DataArray</dd></dl></div></li></ul></div></div>
 </div>
 </div>
 <p>Or, we can use the dimensions names and their coordinates to access the same value:</p>
-<div id="158397be" class="cell" data-execution_count="8">
+<div id="de2e376b" class="cell" data-execution_count="8">
 <div class="sourceCode cell-code" id="cb9"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a>temp.sel(time<span class="op">=</span><span class="st">'2022-09-01'</span>, lat<span class="op">=</span><span class="dv">40</span>, lon<span class="op">=</span><span class="dv">80</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="8">
 <div><svg style="position: absolute; width: 0; height: 0; overflow: hidden">
@@ -2201,7 +2201,7 @@ <h4 data-number="5.4.1.2" class="anchored" data-anchor-id="indexing"><span class
     title:          temperature across weather stations
     standard_name:  air_temperature
     units:          degree_c
-    description:    simple example of an xarray.DataArray</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></div><ul class="xr-sections"><li class="xr-section-item"><div class="xr-array-wrap"><input id="section-11e6b14f-66d0-4a40-babe-f5df0f40bbe6" class="xr-array-in" type="checkbox" checked=""><label for="section-11e6b14f-66d0-4a40-babe-f5df0f40bbe6" title="Show/hide data repr"><svg class="icon xr-icon-database"><use 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-bd53eefd-f875-4934-81ef-2a4beb464cb7" class="xr-section-summary-in" type="checkbox" checked=""><label for="section-bd53eefd-f875-4934-81ef-2a4beb464cb7" class="xr-section-summary">Coordinates: <span>(3)</span></label><div class="xr-section-inline-details"></div><div class="xr-section-details"><ul class="xr-var-list"><li class="xr-var-item"><div class="xr-var-name"><span>time</span></div><div class="xr-var-dims">()</div><div class="xr-var-dtype">datetime64[ns]</div><div class="xr-var-preview xr-preview">2022-09-01</div><input id="attrs-43b86765-add1-4b10-9b1e-0d2a445c511c" class="xr-var-attrs-in" type="checkbox"><label for="attrs-43b86765-add1-4b10-9b1e-0d2a445c511c" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-6f5cd3b2-74c5-40d8-8d82-aaffc2e18892" class="xr-var-data-in" type="checkbox"><label for="data-6f5cd3b2-74c5-40d8-8d82-aaffc2e18892" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>description :</span></dt><dd>date of measurement</dd></dl></div><div class="xr-var-data"><pre>array('2022-09-01T00:00:00.000000000', dtype='datetime64[ns]')</pre></div></li><li class="xr-var-item"><div class="xr-var-name"><span>lat</span></div><div class="xr-var-dims">()</div><div class="xr-var-dtype">int64</div><div class="xr-var-preview xr-preview">40</div><input id="attrs-6392942c-698d-4a16-a9c6-6b04abda7ab3" class="xr-var-attrs-in" type="checkbox"><label for="attrs-6392942c-698d-4a16-a9c6-6b04abda7ab3" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-1f11a4aa-3bd3-4fd8-85f9-3743bb34b6da" class="xr-var-data-in" type="checkbox"><label for="data-1f11a4aa-3bd3-4fd8-85f9-3743bb34b6da" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>standard_name :</span></dt><dd>grid_latitude</dd><dt><span>units :</span></dt><dd>degree_N</dd></dl></div><div class="xr-var-data"><pre>array(40)</pre></div></li><li class="xr-var-item"><div class="xr-var-name"><span>lon</span></div><div class="xr-var-dims">()</div><div class="xr-var-dtype">int64</div><div class="xr-var-preview xr-preview">80</div><input id="attrs-69931cbc-2f86-4714-8093-cf042aefb3fa" class="xr-var-attrs-in" type="checkbox"><label for="attrs-69931cbc-2f86-4714-8093-cf042aefb3fa" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-74febcca-c6a1-4cdd-8ca7-47052f342481" class="xr-var-data-in" type="checkbox"><label for="data-74febcca-c6a1-4cdd-8ca7-47052f342481" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>standard_name :</span></dt><dd>grid_longitude</dd><dt><span>units :</span></dt><dd>degree_E</dd></dl></div><div class="xr-var-data"><pre>array(80)</pre></div></li></ul></div></li><li class="xr-section-item"><input id="section-70c9770b-6108-4934-89dc-de48b65de340" class="xr-section-summary-in" type="checkbox" disabled=""><label for="section-70c9770b-6108-4934-89dc-de48b65de340" class="xr-section-summary" title="Expand/collapse section">Indexes: <span>(0)</span></label><div class="xr-section-inline-details"></div><div class="xr-section-details"><ul class="xr-var-list"></ul></div></li><li class="xr-section-item"><input id="section-309ff780-1597-4908-a5f5-51a1dc71a3fc" class="xr-section-summary-in" type="checkbox" checked=""><label for="section-309ff780-1597-4908-a5f5-51a1dc71a3fc" class="xr-section-summary">Attributes: <span>(4)</span></label><div class="xr-section-inline-details"></div><div class="xr-section-details"><dl class="xr-attrs"><dt><span>title :</span></dt><dd>temperature across weather stations</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>units :</span></dt><dd>degree_c</dd><dt><span>description :</span></dt><dd>simple example of an xarray.DataArray</dd></dl></div></li></ul></div></div>
+    description:    simple example of an xarray.DataArray</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></div><ul class="xr-sections"><li class="xr-section-item"><div class="xr-array-wrap"><input id="section-952c9ca1-8261-4318-b818-bd6385e0c142" class="xr-array-in" type="checkbox" checked=""><label for="section-952c9ca1-8261-4318-b818-bd6385e0c142" title="Show/hide data repr"><svg class="icon xr-icon-database"><use 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-46cedcf5-03fb-4a5d-9949-a9eea33b7299" class="xr-section-summary-in" type="checkbox" checked=""><label for="section-46cedcf5-03fb-4a5d-9949-a9eea33b7299" class="xr-section-summary">Coordinates: <span>(3)</span></label><div class="xr-section-inline-details"></div><div class="xr-section-details"><ul class="xr-var-list"><li class="xr-var-item"><div class="xr-var-name"><span>time</span></div><div class="xr-var-dims">()</div><div class="xr-var-dtype">datetime64[ns]</div><div class="xr-var-preview xr-preview">2022-09-01</div><input id="attrs-fe662708-5c5e-436c-9a09-5aad5ada43c8" class="xr-var-attrs-in" type="checkbox"><label for="attrs-fe662708-5c5e-436c-9a09-5aad5ada43c8" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-5d81cfd5-2863-4fb5-8f34-0fac5516d3c4" class="xr-var-data-in" type="checkbox"><label for="data-5d81cfd5-2863-4fb5-8f34-0fac5516d3c4" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>description :</span></dt><dd>date of measurement</dd></dl></div><div class="xr-var-data"><pre>array('2022-09-01T00:00:00.000000000', dtype='datetime64[ns]')</pre></div></li><li class="xr-var-item"><div class="xr-var-name"><span>lat</span></div><div class="xr-var-dims">()</div><div class="xr-var-dtype">int64</div><div class="xr-var-preview xr-preview">40</div><input id="attrs-07867c79-e782-45b1-a988-da8c2e4ea315" class="xr-var-attrs-in" type="checkbox"><label for="attrs-07867c79-e782-45b1-a988-da8c2e4ea315" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-4ee2d389-4b02-4627-b788-fe3531b99960" class="xr-var-data-in" type="checkbox"><label for="data-4ee2d389-4b02-4627-b788-fe3531b99960" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>standard_name :</span></dt><dd>grid_latitude</dd><dt><span>units :</span></dt><dd>degree_N</dd></dl></div><div class="xr-var-data"><pre>array(40)</pre></div></li><li class="xr-var-item"><div class="xr-var-name"><span>lon</span></div><div class="xr-var-dims">()</div><div class="xr-var-dtype">int64</div><div class="xr-var-preview xr-preview">80</div><input id="attrs-6522d3cd-2d74-44eb-b8d6-6cf7740fc87d" class="xr-var-attrs-in" type="checkbox"><label for="attrs-6522d3cd-2d74-44eb-b8d6-6cf7740fc87d" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-68db4ba4-9b66-43f1-aa9c-245395f41dc4" class="xr-var-data-in" type="checkbox"><label for="data-68db4ba4-9b66-43f1-aa9c-245395f41dc4" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>standard_name :</span></dt><dd>grid_longitude</dd><dt><span>units :</span></dt><dd>degree_E</dd></dl></div><div class="xr-var-data"><pre>array(80)</pre></div></li></ul></div></li><li class="xr-section-item"><input id="section-f1dcdce1-98da-4dff-a2a2-93517216eb95" class="xr-section-summary-in" type="checkbox" disabled=""><label for="section-f1dcdce1-98da-4dff-a2a2-93517216eb95" class="xr-section-summary" title="Expand/collapse section">Indexes: <span>(0)</span></label><div class="xr-section-inline-details"></div><div class="xr-section-details"><ul class="xr-var-list"></ul></div></li><li class="xr-section-item"><input id="section-785db93f-c320-4fcc-82f7-705ab62d3e8d" class="xr-section-summary-in" type="checkbox" checked=""><label for="section-785db93f-c320-4fcc-82f7-705ab62d3e8d" class="xr-section-summary">Attributes: <span>(4)</span></label><div class="xr-section-inline-details"></div><div class="xr-section-details"><dl class="xr-attrs"><dt><span>title :</span></dt><dd>temperature across weather stations</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>units :</span></dt><dd>degree_c</dd><dt><span>description :</span></dt><dd>simple example of an xarray.DataArray</dd></dl></div></li></ul></div></div>
 </div>
 </div>
 <p>Notice that the result of this indexing is a 1x1 <code>xarray.DataArray</code>. This is because operations on an <code>xarray.DataArray</code> (resp. <code>xarray.DataSet</code>) always return another <code>xarray.DataArray</code> (resp. <code>xarray.DataSet</code>). In particular, operations returning scalar values will also produce <code>xarray</code> objects, so we need to cast them as numbers manually. See <a href="https://docs.xarray.dev/en/stable/generated/xarray.DataArray.item.html">xarray.DataArray.item</a>.</p>
@@ -2210,7 +2210,7 @@ <h4 data-number="5.4.1.2" class="anchored" data-anchor-id="indexing"><span class
 <section id="reduction" class="level4" data-number="5.4.1.3">
 <h4 data-number="5.4.1.3" class="anchored" data-anchor-id="reduction"><span class="header-section-number">5.4.1.3</span> Reduction</h4>
 <p><code>xarray</code> has implemented several methods to reduce an <code>xarray.DataArray</code> along any number of dimensions. One of the advantages of <code>xarray.DataArray</code> is that, if we choose to, it can carry over attributes when doing calculations. For example, we can calculate the average temperature at each weather station over time and obtain a new <code>xarray.DataArray</code>.</p>
-<div id="62e66cb5" class="cell" data-execution_count="9">
+<div id="370237bd" class="cell" data-execution_count="9">
 <div class="sourceCode cell-code" id="cb10"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb10-1"><a href="#cb10-1" aria-hidden="true" tabindex="-1"></a>avg_temp <span class="op">=</span> temp.mean(dim <span class="op">=</span> <span class="st">'time'</span>) </span>
 <span id="cb10-2"><a href="#cb10-2" aria-hidden="true" tabindex="-1"></a><span class="co"># to keep attributes add keep_attrs = True</span></span>
 <span id="cb10-3"><a href="#cb10-3" aria-hidden="true" tabindex="-1"></a></span>
@@ -2590,11 +2590,11 @@ <h4 data-number="5.4.1.3" class="anchored" data-anchor-id="reduction"><span clas
   * lat      (lat) int64 40B 70 60 50 40 30
   * lon      (lon) int64 40B 60 70 80 90 100
 Attributes:
-    title:    average temperature over three days</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">lat</span>: 5</li><li><span class="xr-has-index">lon</span>: 5</li></ul></div><ul class="xr-sections"><li class="xr-section-item"><div class="xr-array-wrap"><input id="section-2c6b76ea-3362-4e23-8d2c-9c01eb7bd0b9" class="xr-array-in" type="checkbox" checked=""><label for="section-2c6b76ea-3362-4e23-8d2c-9c01eb7bd0b9" title="Show/hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-array-preview xr-preview"><span>1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0</span></div><div class="xr-array-data"><pre>array([[1., 1., 1., 1., 1.],
+    title:    average temperature over three days</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">lat</span>: 5</li><li><span class="xr-has-index">lon</span>: 5</li></ul></div><ul class="xr-sections"><li class="xr-section-item"><div class="xr-array-wrap"><input id="section-bda03013-b014-408a-a5d4-362869ba530f" class="xr-array-in" type="checkbox" checked=""><label for="section-bda03013-b014-408a-a5d4-362869ba530f" title="Show/hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-array-preview xr-preview"><span>1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0</span></div><div class="xr-array-data"><pre>array([[1., 1., 1., 1., 1.],
        [1., 1., 1., 1., 1.],
        [1., 1., 1., 1., 1.],
        [1., 1., 1., 1., 1.],
-       [1., 1., 1., 1., 1.]])</pre></div></div></li><li class="xr-section-item"><input id="section-34319134-e0a8-492a-9744-34eb236377da" class="xr-section-summary-in" type="checkbox" checked=""><label for="section-34319134-e0a8-492a-9744-34eb236377da" class="xr-section-summary">Coordinates: <span>(2)</span></label><div class="xr-section-inline-details"></div><div class="xr-section-details"><ul class="xr-var-list"><li class="xr-var-item"><div class="xr-var-name"><span class="xr-has-index">lat</span></div><div class="xr-var-dims">(lat)</div><div class="xr-var-dtype">int64</div><div class="xr-var-preview xr-preview">70 60 50 40 30</div><input id="attrs-bd24ee31-f7d2-4427-934a-17116ea77d6e" class="xr-var-attrs-in" type="checkbox"><label for="attrs-bd24ee31-f7d2-4427-934a-17116ea77d6e" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-5f9c7abc-2550-4de4-9202-46e725c71824" class="xr-var-data-in" type="checkbox"><label for="data-5f9c7abc-2550-4de4-9202-46e725c71824" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>standard_name :</span></dt><dd>grid_latitude</dd><dt><span>units :</span></dt><dd>degree_N</dd></dl></div><div class="xr-var-data"><pre>array([70, 60, 50, 40, 30])</pre></div></li><li class="xr-var-item"><div class="xr-var-name"><span class="xr-has-index">lon</span></div><div class="xr-var-dims">(lon)</div><div class="xr-var-dtype">int64</div><div class="xr-var-preview xr-preview">60 70 80 90 100</div><input id="attrs-5c02b117-3a07-41a6-b593-275f39fc104a" class="xr-var-attrs-in" type="checkbox"><label for="attrs-5c02b117-3a07-41a6-b593-275f39fc104a" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-1c67c55b-3226-405d-bb86-b75e8abc0eda" class="xr-var-data-in" type="checkbox"><label for="data-1c67c55b-3226-405d-bb86-b75e8abc0eda" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>standard_name :</span></dt><dd>grid_longitude</dd><dt><span>units :</span></dt><dd>degree_E</dd></dl></div><div class="xr-var-data"><pre>array([ 60,  70,  80,  90, 100])</pre></div></li></ul></div></li><li class="xr-section-item"><input id="section-de1a6fe6-8f58-4966-ba4b-5b95bc4aaae5" class="xr-section-summary-in" type="checkbox"><label for="section-de1a6fe6-8f58-4966-ba4b-5b95bc4aaae5" class="xr-section-summary">Indexes: <span>(2)</span></label><div class="xr-section-inline-details"></div><div class="xr-section-details"><ul class="xr-var-list"><li class="xr-var-item"><div class="xr-index-name"><div>lat</div></div><div class="xr-index-preview">PandasIndex</div><div></div><input id="index-7b25dd59-f6e3-4c37-a5c5-b76b4999c483" class="xr-index-data-in" type="checkbox"><label for="index-7b25dd59-f6e3-4c37-a5c5-b76b4999c483" title="Show/Hide index repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-index-data"><pre>PandasIndex(Index([70, 60, 50, 40, 30], dtype='int64', name='lat'))</pre></div></li><li class="xr-var-item"><div class="xr-index-name"><div>lon</div></div><div class="xr-index-preview">PandasIndex</div><div></div><input id="index-7f9d55c2-5883-41e9-bea6-afa1c47f860f" class="xr-index-data-in" type="checkbox"><label for="index-7f9d55c2-5883-41e9-bea6-afa1c47f860f" title="Show/Hide index repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-index-data"><pre>PandasIndex(Index([60, 70, 80, 90, 100], dtype='int64', name='lon'))</pre></div></li></ul></div></li><li class="xr-section-item"><input id="section-713cb3cc-e3ec-4971-98ef-ccd264335a6f" class="xr-section-summary-in" type="checkbox" checked=""><label for="section-713cb3cc-e3ec-4971-98ef-ccd264335a6f" class="xr-section-summary">Attributes: <span>(1)</span></label><div class="xr-section-inline-details"></div><div class="xr-section-details"><dl class="xr-attrs"><dt><span>title :</span></dt><dd>average temperature over three days</dd></dl></div></li></ul></div></div>
+       [1., 1., 1., 1., 1.]])</pre></div></div></li><li class="xr-section-item"><input id="section-fe2da2ac-fc9c-449d-86bb-2eb5568c75e4" class="xr-section-summary-in" type="checkbox" checked=""><label for="section-fe2da2ac-fc9c-449d-86bb-2eb5568c75e4" class="xr-section-summary">Coordinates: <span>(2)</span></label><div class="xr-section-inline-details"></div><div class="xr-section-details"><ul class="xr-var-list"><li class="xr-var-item"><div class="xr-var-name"><span class="xr-has-index">lat</span></div><div class="xr-var-dims">(lat)</div><div class="xr-var-dtype">int64</div><div class="xr-var-preview xr-preview">70 60 50 40 30</div><input id="attrs-687e0fae-1da3-4c2e-9f3c-2e221f0e7b7d" class="xr-var-attrs-in" type="checkbox"><label for="attrs-687e0fae-1da3-4c2e-9f3c-2e221f0e7b7d" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-523c6972-8cd8-485c-9d8f-4a503ef81a88" class="xr-var-data-in" type="checkbox"><label for="data-523c6972-8cd8-485c-9d8f-4a503ef81a88" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>standard_name :</span></dt><dd>grid_latitude</dd><dt><span>units :</span></dt><dd>degree_N</dd></dl></div><div class="xr-var-data"><pre>array([70, 60, 50, 40, 30])</pre></div></li><li class="xr-var-item"><div class="xr-var-name"><span class="xr-has-index">lon</span></div><div class="xr-var-dims">(lon)</div><div class="xr-var-dtype">int64</div><div class="xr-var-preview xr-preview">60 70 80 90 100</div><input id="attrs-5772cdbb-575e-408e-bb4d-e0af60f9f18a" class="xr-var-attrs-in" type="checkbox"><label for="attrs-5772cdbb-575e-408e-bb4d-e0af60f9f18a" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-e89f711b-a6b6-45f5-907e-4acd85595e22" class="xr-var-data-in" type="checkbox"><label for="data-e89f711b-a6b6-45f5-907e-4acd85595e22" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>standard_name :</span></dt><dd>grid_longitude</dd><dt><span>units :</span></dt><dd>degree_E</dd></dl></div><div class="xr-var-data"><pre>array([ 60,  70,  80,  90, 100])</pre></div></li></ul></div></li><li class="xr-section-item"><input id="section-09d68578-1f68-47d3-9c87-4fdd577789c5" class="xr-section-summary-in" type="checkbox"><label for="section-09d68578-1f68-47d3-9c87-4fdd577789c5" class="xr-section-summary">Indexes: <span>(2)</span></label><div class="xr-section-inline-details"></div><div class="xr-section-details"><ul class="xr-var-list"><li class="xr-var-item"><div class="xr-index-name"><div>lat</div></div><div class="xr-index-preview">PandasIndex</div><div></div><input id="index-636a2be7-5db8-45a7-9e63-a27b6d32cf03" class="xr-index-data-in" type="checkbox"><label for="index-636a2be7-5db8-45a7-9e63-a27b6d32cf03" title="Show/Hide index repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-index-data"><pre>PandasIndex(Index([70, 60, 50, 40, 30], dtype='int64', name='lat'))</pre></div></li><li class="xr-var-item"><div class="xr-index-name"><div>lon</div></div><div class="xr-index-preview">PandasIndex</div><div></div><input id="index-50d0bcf3-76b3-4d97-9db9-d4f246db1dc5" class="xr-index-data-in" type="checkbox"><label for="index-50d0bcf3-76b3-4d97-9db9-d4f246db1dc5" title="Show/Hide index repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-index-data"><pre>PandasIndex(Index([60, 70, 80, 90, 100], dtype='int64', name='lon'))</pre></div></li></ul></div></li><li class="xr-section-item"><input id="section-d3dfb8a2-104d-4e15-bbbd-36cd4528b04b" class="xr-section-summary-in" type="checkbox" checked=""><label for="section-d3dfb8a2-104d-4e15-bbbd-36cd4528b04b" class="xr-section-summary">Attributes: <span>(1)</span></label><div class="xr-section-inline-details"></div><div class="xr-section-details"><dl class="xr-attrs"><dt><span>title :</span></dt><dd>average temperature over three days</dd></dl></div></li></ul></div></div>
 </div>
 </div>
 <p>More about <a href="https://docs.xarray.dev/en/stable/user-guide/computation.html"><code>xarray</code> computations</a>.</p>
@@ -2606,7 +2606,7 @@ <h3 data-number="5.4.2" class="anchored" data-anchor-id="xarray.dataset"><span c
 <section id="create-an-xarray.dataset" class="level4" data-number="5.4.2.1">
 <h4 data-number="5.4.2.1" class="anchored" data-anchor-id="create-an-xarray.dataset"><span class="header-section-number">5.4.2.1</span> Create an <code>xarray.DataSet</code></h4>
 <p>Following our previous example, we can create an <code>xarray.DataSet</code> by combining the temperature data with the average temperature data. We also add some attributes that now describe the whole dataset, not only each variable.</p>
-<div id="8b5f4f65" class="cell" data-execution_count="10">
+<div id="2f2945d1" class="cell" data-execution_count="10">
 <div class="sourceCode cell-code" id="cb11"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb11-1"><a href="#cb11-1" aria-hidden="true" tabindex="-1"></a><span class="co"># make dictionaries with variables and attributes</span></span>
 <span id="cb11-2"><a href="#cb11-2" aria-hidden="true" tabindex="-1"></a>data_vars <span class="op">=</span> {<span class="st">'avg_temp'</span>: avg_temp,</span>
 <span id="cb11-3"><a href="#cb11-3" aria-hidden="true" tabindex="-1"></a>            <span class="st">'temp'</span>: temp}</span>
@@ -2624,7 +2624,7 @@ <h4 data-number="5.4.2.1" class="anchored" data-anchor-id="create-an-xarray.data
 <li><p><code>temp</code> is a variable that uses all three dimensions in the dataset, and</p></li>
 <li><p><code>aveg_temp</code> is a variable that only uses two dimensions (latitude and longitude).</p></li>
 </ul>
-<div id="ebf7c58d" class="cell" data-execution_count="11">
+<div id="cb61588c" class="cell" data-execution_count="11">
 <div class="sourceCode cell-code" id="cb12"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb12-1"><a href="#cb12-1" aria-hidden="true" tabindex="-1"></a>temp_dataset</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="11">
 <div><svg style="position: absolute; width: 0; height: 0; overflow: hidden">
@@ -3001,12 +3001,12 @@ <h4 data-number="5.4.2.1" class="anchored" data-anchor-id="create-an-xarray.data
     temp      (time, lat, lon) int64 600B 0 0 0 0 0 0 0 0 0 ... 2 2 2 2 2 2 2 2
 Attributes:
     title:        temperature data at weather stations: daily and and average
-    description:  simple example of an xarray.Dataset</pre><div class="xr-wrap" style="display:none"><div class="xr-header"><div class="xr-obj-type">xarray.Dataset</div></div><ul class="xr-sections"><li class="xr-section-item"><input id="section-3a7a3192-5c9b-4981-a28c-6c17673f1ac9" class="xr-section-summary-in" type="checkbox" disabled=""><label for="section-3a7a3192-5c9b-4981-a28c-6c17673f1ac9" class="xr-section-summary" title="Expand/collapse section">Dimensions:</label><div class="xr-section-inline-details"><ul class="xr-dim-list"><li><span class="xr-has-index">lat</span>: 5</li><li><span class="xr-has-index">lon</span>: 5</li><li><span class="xr-has-index">time</span>: 3</li></ul></div><div class="xr-section-details"></div></li><li class="xr-section-item"><input id="section-4cb24762-07e4-4a6e-b25b-7b3e62b963aa" class="xr-section-summary-in" type="checkbox" checked=""><label for="section-4cb24762-07e4-4a6e-b25b-7b3e62b963aa" class="xr-section-summary">Coordinates: <span>(3)</span></label><div class="xr-section-inline-details"></div><div class="xr-section-details"><ul class="xr-var-list"><li class="xr-var-item"><div class="xr-var-name"><span class="xr-has-index">lat</span></div><div class="xr-var-dims">(lat)</div><div class="xr-var-dtype">int64</div><div class="xr-var-preview xr-preview">70 60 50 40 30</div><input id="attrs-735a078b-a645-4f21-be17-e6efec5dc67f" class="xr-var-attrs-in" type="checkbox"><label for="attrs-735a078b-a645-4f21-be17-e6efec5dc67f" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-dc82356b-a014-443d-a029-2e7ed9c5b498" class="xr-var-data-in" type="checkbox"><label for="data-dc82356b-a014-443d-a029-2e7ed9c5b498" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>standard_name :</span></dt><dd>grid_latitude</dd><dt><span>units :</span></dt><dd>degree_N</dd></dl></div><div class="xr-var-data"><pre>array([70, 60, 50, 40, 30])</pre></div></li><li class="xr-var-item"><div class="xr-var-name"><span class="xr-has-index">lon</span></div><div class="xr-var-dims">(lon)</div><div class="xr-var-dtype">int64</div><div class="xr-var-preview xr-preview">60 70 80 90 100</div><input id="attrs-63dd5aca-6a29-4d73-a5bb-71b931047560" class="xr-var-attrs-in" type="checkbox"><label for="attrs-63dd5aca-6a29-4d73-a5bb-71b931047560" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-ffc4bf19-53f5-4b2e-bfd7-d1197a016afa" class="xr-var-data-in" type="checkbox"><label for="data-ffc4bf19-53f5-4b2e-bfd7-d1197a016afa" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>standard_name :</span></dt><dd>grid_longitude</dd><dt><span>units :</span></dt><dd>degree_E</dd></dl></div><div class="xr-var-data"><pre>array([ 60,  70,  80,  90, 100])</pre></div></li><li class="xr-var-item"><div class="xr-var-name"><span class="xr-has-index">time</span></div><div class="xr-var-dims">(time)</div><div class="xr-var-dtype">datetime64[ns]</div><div class="xr-var-preview xr-preview">2022-09-01 2022-09-02 2022-09-03</div><input id="attrs-3b5c2152-8515-4af6-b0af-8a28fc9c6710" class="xr-var-attrs-in" type="checkbox"><label for="attrs-3b5c2152-8515-4af6-b0af-8a28fc9c6710" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-4f17b5d1-887d-40a7-9351-3ea69093f79c" class="xr-var-data-in" type="checkbox"><label for="data-4f17b5d1-887d-40a7-9351-3ea69093f79c" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>description :</span></dt><dd>date of measurement</dd></dl></div><div class="xr-var-data"><pre>array(['2022-09-01T00:00:00.000000000', '2022-09-02T00:00:00.000000000',
-       '2022-09-03T00:00:00.000000000'], dtype='datetime64[ns]')</pre></div></li></ul></div></li><li class="xr-section-item"><input id="section-81951457-07fe-4f74-b690-276bbe039a2f" class="xr-section-summary-in" type="checkbox" checked=""><label for="section-81951457-07fe-4f74-b690-276bbe039a2f" class="xr-section-summary">Data variables: <span>(2)</span></label><div class="xr-section-inline-details"></div><div class="xr-section-details"><ul class="xr-var-list"><li class="xr-var-item"><div class="xr-var-name"><span>avg_temp</span></div><div class="xr-var-dims">(lat, lon)</div><div class="xr-var-dtype">float64</div><div class="xr-var-preview xr-preview">1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0</div><input id="attrs-6399962b-cf28-4bd7-8fdb-8453167716fd" class="xr-var-attrs-in" type="checkbox"><label for="attrs-6399962b-cf28-4bd7-8fdb-8453167716fd" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-2ed026d4-2fcb-421f-a0a0-1f054cf4f9b7" class="xr-var-data-in" type="checkbox"><label for="data-2ed026d4-2fcb-421f-a0a0-1f054cf4f9b7" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>title :</span></dt><dd>average temperature over three days</dd></dl></div><div class="xr-var-data"><pre>array([[1., 1., 1., 1., 1.],
+    description:  simple example of an xarray.Dataset</pre><div class="xr-wrap" style="display:none"><div class="xr-header"><div class="xr-obj-type">xarray.Dataset</div></div><ul class="xr-sections"><li class="xr-section-item"><input id="section-6f6b39a8-ccd6-4187-a226-4f0054dd0a0a" class="xr-section-summary-in" type="checkbox" disabled=""><label for="section-6f6b39a8-ccd6-4187-a226-4f0054dd0a0a" class="xr-section-summary" title="Expand/collapse section">Dimensions:</label><div class="xr-section-inline-details"><ul class="xr-dim-list"><li><span class="xr-has-index">lat</span>: 5</li><li><span class="xr-has-index">lon</span>: 5</li><li><span class="xr-has-index">time</span>: 3</li></ul></div><div class="xr-section-details"></div></li><li class="xr-section-item"><input id="section-c482d416-4e02-4a90-a118-807c748a08c4" class="xr-section-summary-in" type="checkbox" checked=""><label for="section-c482d416-4e02-4a90-a118-807c748a08c4" class="xr-section-summary">Coordinates: <span>(3)</span></label><div class="xr-section-inline-details"></div><div class="xr-section-details"><ul class="xr-var-list"><li class="xr-var-item"><div class="xr-var-name"><span class="xr-has-index">lat</span></div><div class="xr-var-dims">(lat)</div><div class="xr-var-dtype">int64</div><div class="xr-var-preview xr-preview">70 60 50 40 30</div><input id="attrs-7c407c53-15ae-4176-b8fc-a4f7b5c1334d" class="xr-var-attrs-in" type="checkbox"><label for="attrs-7c407c53-15ae-4176-b8fc-a4f7b5c1334d" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-0798eb28-d1cf-47c2-acb8-48a219432cb6" class="xr-var-data-in" type="checkbox"><label for="data-0798eb28-d1cf-47c2-acb8-48a219432cb6" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>standard_name :</span></dt><dd>grid_latitude</dd><dt><span>units :</span></dt><dd>degree_N</dd></dl></div><div class="xr-var-data"><pre>array([70, 60, 50, 40, 30])</pre></div></li><li class="xr-var-item"><div class="xr-var-name"><span class="xr-has-index">lon</span></div><div class="xr-var-dims">(lon)</div><div class="xr-var-dtype">int64</div><div class="xr-var-preview xr-preview">60 70 80 90 100</div><input id="attrs-f6e7cbf6-0e5c-4a08-a6e4-93a01e6db92b" class="xr-var-attrs-in" type="checkbox"><label for="attrs-f6e7cbf6-0e5c-4a08-a6e4-93a01e6db92b" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-0017e68f-f398-4d03-9c85-9f5ce3392372" class="xr-var-data-in" type="checkbox"><label for="data-0017e68f-f398-4d03-9c85-9f5ce3392372" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>standard_name :</span></dt><dd>grid_longitude</dd><dt><span>units :</span></dt><dd>degree_E</dd></dl></div><div class="xr-var-data"><pre>array([ 60,  70,  80,  90, 100])</pre></div></li><li class="xr-var-item"><div class="xr-var-name"><span class="xr-has-index">time</span></div><div class="xr-var-dims">(time)</div><div class="xr-var-dtype">datetime64[ns]</div><div class="xr-var-preview xr-preview">2022-09-01 2022-09-02 2022-09-03</div><input id="attrs-90478093-91d9-456b-9f8b-d703cebdf6d3" class="xr-var-attrs-in" type="checkbox"><label for="attrs-90478093-91d9-456b-9f8b-d703cebdf6d3" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-9718d6fb-c79b-46c5-9b38-ed1fcf90e552" class="xr-var-data-in" type="checkbox"><label for="data-9718d6fb-c79b-46c5-9b38-ed1fcf90e552" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>description :</span></dt><dd>date of measurement</dd></dl></div><div class="xr-var-data"><pre>array(['2022-09-01T00:00:00.000000000', '2022-09-02T00:00:00.000000000',
+       '2022-09-03T00:00:00.000000000'], dtype='datetime64[ns]')</pre></div></li></ul></div></li><li class="xr-section-item"><input id="section-9ff0d212-9719-4644-9c35-afd644ace019" class="xr-section-summary-in" type="checkbox" checked=""><label for="section-9ff0d212-9719-4644-9c35-afd644ace019" class="xr-section-summary">Data variables: <span>(2)</span></label><div class="xr-section-inline-details"></div><div class="xr-section-details"><ul class="xr-var-list"><li class="xr-var-item"><div class="xr-var-name"><span>avg_temp</span></div><div class="xr-var-dims">(lat, lon)</div><div class="xr-var-dtype">float64</div><div class="xr-var-preview xr-preview">1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0</div><input id="attrs-a7e9a5d8-90f6-49ef-b173-6ddfb73e396c" class="xr-var-attrs-in" type="checkbox"><label for="attrs-a7e9a5d8-90f6-49ef-b173-6ddfb73e396c" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-59903972-52a4-435a-aa21-fef87f7be90e" class="xr-var-data-in" type="checkbox"><label for="data-59903972-52a4-435a-aa21-fef87f7be90e" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>title :</span></dt><dd>average temperature over three days</dd></dl></div><div class="xr-var-data"><pre>array([[1., 1., 1., 1., 1.],
        [1., 1., 1., 1., 1.],
        [1., 1., 1., 1., 1.],
        [1., 1., 1., 1., 1.],
-       [1., 1., 1., 1., 1.]])</pre></div></li><li class="xr-var-item"><div class="xr-var-name"><span>temp</span></div><div class="xr-var-dims">(time, lat, lon)</div><div class="xr-var-dtype">int64</div><div class="xr-var-preview xr-preview">0 0 0 0 0 0 0 0 ... 2 2 2 2 2 2 2 2</div><input id="attrs-22d21928-31d2-4a90-bde2-15cffbfd2b32" class="xr-var-attrs-in" type="checkbox"><label for="attrs-22d21928-31d2-4a90-bde2-15cffbfd2b32" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-6d70e7c4-7a1e-4d5d-ae58-e6c9e206bf30" class="xr-var-data-in" type="checkbox"><label for="data-6d70e7c4-7a1e-4d5d-ae58-e6c9e206bf30" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>title :</span></dt><dd>temperature across weather stations</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>units :</span></dt><dd>degree_c</dd><dt><span>description :</span></dt><dd>simple example of an xarray.DataArray</dd></dl></div><div class="xr-var-data"><pre>array([[[0, 0, 0, 0, 0],
+       [1., 1., 1., 1., 1.]])</pre></div></li><li class="xr-var-item"><div class="xr-var-name"><span>temp</span></div><div class="xr-var-dims">(time, lat, lon)</div><div class="xr-var-dtype">int64</div><div class="xr-var-preview xr-preview">0 0 0 0 0 0 0 0 ... 2 2 2 2 2 2 2 2</div><input id="attrs-fa6f3300-932f-4526-8c8a-d81c05d5b15c" class="xr-var-attrs-in" type="checkbox"><label for="attrs-fa6f3300-932f-4526-8c8a-d81c05d5b15c" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-31e3866c-bf56-43f1-b543-76404c800da6" class="xr-var-data-in" type="checkbox"><label for="data-31e3866c-bf56-43f1-b543-76404c800da6" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>title :</span></dt><dd>temperature across weather stations</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>units :</span></dt><dd>degree_c</dd><dt><span>description :</span></dt><dd>simple example of an xarray.DataArray</dd></dl></div><div class="xr-var-data"><pre>array([[[0, 0, 0, 0, 0],
         [0, 0, 0, 0, 0],
         [0, 0, 0, 0, 0],
         [0, 0, 0, 0, 0],
@@ -3022,14 +3022,14 @@ <h4 data-number="5.4.2.1" class="anchored" data-anchor-id="create-an-xarray.data
         [2, 2, 2, 2, 2],
         [2, 2, 2, 2, 2],
         [2, 2, 2, 2, 2],
-        [2, 2, 2, 2, 2]]])</pre></div></li></ul></div></li><li class="xr-section-item"><input id="section-37f31df3-4789-4b3a-9bae-e2497d1ec65c" class="xr-section-summary-in" type="checkbox"><label for="section-37f31df3-4789-4b3a-9bae-e2497d1ec65c" class="xr-section-summary">Indexes: <span>(3)</span></label><div class="xr-section-inline-details"></div><div class="xr-section-details"><ul class="xr-var-list"><li class="xr-var-item"><div class="xr-index-name"><div>lat</div></div><div class="xr-index-preview">PandasIndex</div><div></div><input id="index-f20f1f18-136f-4d13-beba-8cff4fb68ef0" class="xr-index-data-in" type="checkbox"><label for="index-f20f1f18-136f-4d13-beba-8cff4fb68ef0" title="Show/Hide index repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-index-data"><pre>PandasIndex(Index([70, 60, 50, 40, 30], dtype='int64', name='lat'))</pre></div></li><li class="xr-var-item"><div class="xr-index-name"><div>lon</div></div><div class="xr-index-preview">PandasIndex</div><div></div><input id="index-c8e96765-a8bf-4ebd-9d4e-eb964c7059c1" class="xr-index-data-in" type="checkbox"><label for="index-c8e96765-a8bf-4ebd-9d4e-eb964c7059c1" title="Show/Hide index repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-index-data"><pre>PandasIndex(Index([60, 70, 80, 90, 100], dtype='int64', name='lon'))</pre></div></li><li class="xr-var-item"><div class="xr-index-name"><div>time</div></div><div class="xr-index-preview">PandasIndex</div><div></div><input id="index-c35d6402-b696-41bc-913a-f5cf508639a8" class="xr-index-data-in" type="checkbox"><label for="index-c35d6402-b696-41bc-913a-f5cf508639a8" title="Show/Hide index repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-index-data"><pre>PandasIndex(DatetimeIndex(['2022-09-01', '2022-09-02', '2022-09-03'], dtype='datetime64[ns]', name='time', freq='D'))</pre></div></li></ul></div></li><li class="xr-section-item"><input id="section-3c395e0a-1730-4558-86c9-bffc4b3d364b" class="xr-section-summary-in" type="checkbox" checked=""><label for="section-3c395e0a-1730-4558-86c9-bffc4b3d364b" class="xr-section-summary">Attributes: <span>(2)</span></label><div class="xr-section-inline-details"></div><div class="xr-section-details"><dl class="xr-attrs"><dt><span>title :</span></dt><dd>temperature data at weather stations: daily and and average</dd><dt><span>description :</span></dt><dd>simple example of an xarray.Dataset</dd></dl></div></li></ul></div></div>
+        [2, 2, 2, 2, 2]]])</pre></div></li></ul></div></li><li class="xr-section-item"><input id="section-af450126-0be2-4208-802c-8880579b8721" class="xr-section-summary-in" type="checkbox"><label for="section-af450126-0be2-4208-802c-8880579b8721" class="xr-section-summary">Indexes: <span>(3)</span></label><div class="xr-section-inline-details"></div><div class="xr-section-details"><ul class="xr-var-list"><li class="xr-var-item"><div class="xr-index-name"><div>lat</div></div><div class="xr-index-preview">PandasIndex</div><div></div><input id="index-61cc18b9-67ae-444c-be3a-49335433a344" class="xr-index-data-in" type="checkbox"><label for="index-61cc18b9-67ae-444c-be3a-49335433a344" title="Show/Hide index repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-index-data"><pre>PandasIndex(Index([70, 60, 50, 40, 30], dtype='int64', name='lat'))</pre></div></li><li class="xr-var-item"><div class="xr-index-name"><div>lon</div></div><div class="xr-index-preview">PandasIndex</div><div></div><input id="index-0e3f8c75-80e7-49f3-9fcf-6fc7c8d3a913" class="xr-index-data-in" type="checkbox"><label for="index-0e3f8c75-80e7-49f3-9fcf-6fc7c8d3a913" title="Show/Hide index repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-index-data"><pre>PandasIndex(Index([60, 70, 80, 90, 100], dtype='int64', name='lon'))</pre></div></li><li class="xr-var-item"><div class="xr-index-name"><div>time</div></div><div class="xr-index-preview">PandasIndex</div><div></div><input id="index-df7a6863-9dcd-4ba3-9c0e-c2a9cfbb97d5" class="xr-index-data-in" type="checkbox"><label for="index-df7a6863-9dcd-4ba3-9c0e-c2a9cfbb97d5" title="Show/Hide index repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-index-data"><pre>PandasIndex(DatetimeIndex(['2022-09-01', '2022-09-02', '2022-09-03'], dtype='datetime64[ns]', name='time', freq='D'))</pre></div></li></ul></div></li><li class="xr-section-item"><input id="section-fca2e249-c526-4e38-af6b-e55cc2734fc9" class="xr-section-summary-in" type="checkbox" checked=""><label for="section-fca2e249-c526-4e38-af6b-e55cc2734fc9" class="xr-section-summary">Attributes: <span>(2)</span></label><div class="xr-section-inline-details"></div><div class="xr-section-details"><dl class="xr-attrs"><dt><span>title :</span></dt><dd>temperature data at weather stations: daily and and average</dd><dt><span>description :</span></dt><dd>simple example of an xarray.Dataset</dd></dl></div></li></ul></div></div>
 </div>
 </div>
 </section>
 <section id="save-and-reopen" class="level4" data-number="5.4.2.2">
 <h4 data-number="5.4.2.2" class="anchored" data-anchor-id="save-and-reopen"><span class="header-section-number">5.4.2.2</span> Save and Reopen</h4>
 <p>Finally, we want to save our dataset as a NetCDF file. To do this, specify the file path and use the <em>.nc</em> extension for the file name. Then save the dataset using the <code>to_netcdf</code> method with your file path. Opening NetCDF is similarly straightforward using <code>xarray.open_dataset()</code>.</p>
-<div id="db3c9f68" class="cell" data-execution_count="12">
+<div id="04bfdf1d" class="cell" data-execution_count="12">
 <div class="sourceCode cell-code" id="cb13"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb13-1"><a href="#cb13-1" aria-hidden="true" tabindex="-1"></a><span class="co"># specify file path: don't forget the .nc extension!</span></span>
 <span id="cb13-2"><a href="#cb13-2" aria-hidden="true" tabindex="-1"></a>fp <span class="op">=</span> os.path.join(os.getcwd(),<span class="st">'temp_dataset.nc'</span>) </span>
 <span id="cb13-3"><a href="#cb13-3" aria-hidden="true" tabindex="-1"></a><span class="co"># save file</span></span>
@@ -3413,8 +3413,8 @@ <h4 data-number="5.4.2.2" class="anchored" data-anchor-id="save-and-reopen"><spa
     temp      (time, lat, lon) int64 600B ...
 Attributes:
     title:        temperature data at weather stations: daily and and average
-    description:  simple example of an xarray.Dataset</pre><div class="xr-wrap" style="display:none"><div class="xr-header"><div class="xr-obj-type">xarray.Dataset</div></div><ul class="xr-sections"><li class="xr-section-item"><input id="section-803bad27-1f6e-41fa-95f5-a48a67e0d383" class="xr-section-summary-in" type="checkbox" disabled=""><label for="section-803bad27-1f6e-41fa-95f5-a48a67e0d383" class="xr-section-summary" title="Expand/collapse section">Dimensions:</label><div class="xr-section-inline-details"><ul class="xr-dim-list"><li><span class="xr-has-index">lat</span>: 5</li><li><span class="xr-has-index">lon</span>: 5</li><li><span class="xr-has-index">time</span>: 3</li></ul></div><div class="xr-section-details"></div></li><li class="xr-section-item"><input id="section-ab6a0489-5176-48cd-9971-c92534adf07a" class="xr-section-summary-in" type="checkbox" checked=""><label for="section-ab6a0489-5176-48cd-9971-c92534adf07a" class="xr-section-summary">Coordinates: <span>(3)</span></label><div class="xr-section-inline-details"></div><div class="xr-section-details"><ul class="xr-var-list"><li class="xr-var-item"><div class="xr-var-name"><span class="xr-has-index">lat</span></div><div class="xr-var-dims">(lat)</div><div class="xr-var-dtype">int64</div><div class="xr-var-preview xr-preview">70 60 50 40 30</div><input id="attrs-08bf5c40-6594-4de5-84ba-bac351ab7008" class="xr-var-attrs-in" type="checkbox"><label for="attrs-08bf5c40-6594-4de5-84ba-bac351ab7008" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-f7e0fa60-90b3-4297-9270-fec5fa2473d3" class="xr-var-data-in" type="checkbox"><label for="data-f7e0fa60-90b3-4297-9270-fec5fa2473d3" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>standard_name :</span></dt><dd>grid_latitude</dd><dt><span>units :</span></dt><dd>degree_N</dd></dl></div><div class="xr-var-data"><pre>array([70, 60, 50, 40, 30])</pre></div></li><li class="xr-var-item"><div class="xr-var-name"><span class="xr-has-index">lon</span></div><div class="xr-var-dims">(lon)</div><div class="xr-var-dtype">int64</div><div class="xr-var-preview xr-preview">60 70 80 90 100</div><input id="attrs-b6348f8c-33ac-483a-a515-6a179754210a" class="xr-var-attrs-in" type="checkbox"><label for="attrs-b6348f8c-33ac-483a-a515-6a179754210a" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-b6a8201b-ac9d-4a71-ad3a-d51a11704a8d" class="xr-var-data-in" type="checkbox"><label for="data-b6a8201b-ac9d-4a71-ad3a-d51a11704a8d" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>standard_name :</span></dt><dd>grid_longitude</dd><dt><span>units :</span></dt><dd>degree_E</dd></dl></div><div class="xr-var-data"><pre>array([ 60,  70,  80,  90, 100])</pre></div></li><li class="xr-var-item"><div class="xr-var-name"><span class="xr-has-index">time</span></div><div class="xr-var-dims">(time)</div><div class="xr-var-dtype">datetime64[ns]</div><div class="xr-var-preview xr-preview">2022-09-01 2022-09-02 2022-09-03</div><input id="attrs-7a75b59a-f97e-4cff-bfba-79c463ce7e03" class="xr-var-attrs-in" type="checkbox"><label for="attrs-7a75b59a-f97e-4cff-bfba-79c463ce7e03" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-4ba3cbf2-0f23-4421-9114-b5670a7c431f" class="xr-var-data-in" type="checkbox"><label for="data-4ba3cbf2-0f23-4421-9114-b5670a7c431f" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>description :</span></dt><dd>date of measurement</dd></dl></div><div class="xr-var-data"><pre>array(['2022-09-01T00:00:00.000000000', '2022-09-02T00:00:00.000000000',
-       '2022-09-03T00:00:00.000000000'], dtype='datetime64[ns]')</pre></div></li></ul></div></li><li class="xr-section-item"><input id="section-44a369fa-5e04-4366-9df9-f905b469c4cc" class="xr-section-summary-in" type="checkbox" checked=""><label for="section-44a369fa-5e04-4366-9df9-f905b469c4cc" class="xr-section-summary">Data variables: <span>(2)</span></label><div class="xr-section-inline-details"></div><div class="xr-section-details"><ul class="xr-var-list"><li class="xr-var-item"><div class="xr-var-name"><span>avg_temp</span></div><div class="xr-var-dims">(lat, lon)</div><div class="xr-var-dtype">float64</div><div class="xr-var-preview xr-preview">...</div><input id="attrs-f050fe27-b33e-4e10-8e89-338efdce62ad" class="xr-var-attrs-in" type="checkbox"><label for="attrs-f050fe27-b33e-4e10-8e89-338efdce62ad" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-ee372e49-af60-4ed0-a315-8405bdf527f5" class="xr-var-data-in" type="checkbox"><label for="data-ee372e49-af60-4ed0-a315-8405bdf527f5" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>title :</span></dt><dd>average temperature over three days</dd></dl></div><div class="xr-var-data"><pre>[25 values with dtype=float64]</pre></div></li><li class="xr-var-item"><div class="xr-var-name"><span>temp</span></div><div class="xr-var-dims">(time, lat, lon)</div><div class="xr-var-dtype">int64</div><div class="xr-var-preview xr-preview">...</div><input id="attrs-96c0dbfa-ad11-4b3d-99e3-16563373d6ef" class="xr-var-attrs-in" type="checkbox"><label for="attrs-96c0dbfa-ad11-4b3d-99e3-16563373d6ef" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-e4475d3a-49b1-4fa3-8224-e266358752de" class="xr-var-data-in" type="checkbox"><label for="data-e4475d3a-49b1-4fa3-8224-e266358752de" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>title :</span></dt><dd>temperature across weather stations</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>units :</span></dt><dd>degree_c</dd><dt><span>description :</span></dt><dd>simple example of an xarray.DataArray</dd></dl></div><div class="xr-var-data"><pre>[75 values with dtype=int64]</pre></div></li></ul></div></li><li class="xr-section-item"><input id="section-2acd2c3f-efab-4014-adf7-baedd0348fa6" class="xr-section-summary-in" type="checkbox"><label for="section-2acd2c3f-efab-4014-adf7-baedd0348fa6" class="xr-section-summary">Indexes: <span>(3)</span></label><div class="xr-section-inline-details"></div><div class="xr-section-details"><ul class="xr-var-list"><li class="xr-var-item"><div class="xr-index-name"><div>lat</div></div><div class="xr-index-preview">PandasIndex</div><div></div><input id="index-b68d2367-42d7-4830-89d9-f63599f8c887" class="xr-index-data-in" type="checkbox"><label for="index-b68d2367-42d7-4830-89d9-f63599f8c887" title="Show/Hide index repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-index-data"><pre>PandasIndex(Index([70, 60, 50, 40, 30], dtype='int64', name='lat'))</pre></div></li><li class="xr-var-item"><div class="xr-index-name"><div>lon</div></div><div class="xr-index-preview">PandasIndex</div><div></div><input id="index-2828728d-4d62-4b06-b92b-c8f9c1b075ef" class="xr-index-data-in" type="checkbox"><label for="index-2828728d-4d62-4b06-b92b-c8f9c1b075ef" title="Show/Hide index repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-index-data"><pre>PandasIndex(Index([60, 70, 80, 90, 100], dtype='int64', name='lon'))</pre></div></li><li class="xr-var-item"><div class="xr-index-name"><div>time</div></div><div class="xr-index-preview">PandasIndex</div><div></div><input id="index-84f582d5-465b-45be-95b0-62d3ae78d70a" class="xr-index-data-in" type="checkbox"><label for="index-84f582d5-465b-45be-95b0-62d3ae78d70a" title="Show/Hide index repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-index-data"><pre>PandasIndex(DatetimeIndex(['2022-09-01', '2022-09-02', '2022-09-03'], dtype='datetime64[ns]', name='time', freq=None))</pre></div></li></ul></div></li><li class="xr-section-item"><input id="section-1c97273a-c42f-4cf7-b8b5-9b45e92d27aa" class="xr-section-summary-in" type="checkbox" checked=""><label for="section-1c97273a-c42f-4cf7-b8b5-9b45e92d27aa" class="xr-section-summary">Attributes: <span>(2)</span></label><div class="xr-section-inline-details"></div><div class="xr-section-details"><dl class="xr-attrs"><dt><span>title :</span></dt><dd>temperature data at weather stations: daily and and average</dd><dt><span>description :</span></dt><dd>simple example of an xarray.Dataset</dd></dl></div></li></ul></div></div>
+    description:  simple example of an xarray.Dataset</pre><div class="xr-wrap" style="display:none"><div class="xr-header"><div class="xr-obj-type">xarray.Dataset</div></div><ul class="xr-sections"><li class="xr-section-item"><input id="section-4c1dc562-1de1-44fd-98fe-c0eedd87472d" class="xr-section-summary-in" type="checkbox" disabled=""><label for="section-4c1dc562-1de1-44fd-98fe-c0eedd87472d" class="xr-section-summary" title="Expand/collapse section">Dimensions:</label><div class="xr-section-inline-details"><ul class="xr-dim-list"><li><span class="xr-has-index">lat</span>: 5</li><li><span class="xr-has-index">lon</span>: 5</li><li><span class="xr-has-index">time</span>: 3</li></ul></div><div class="xr-section-details"></div></li><li class="xr-section-item"><input id="section-229e2966-a3b8-42b2-869d-8c3cb59f9e70" class="xr-section-summary-in" type="checkbox" checked=""><label for="section-229e2966-a3b8-42b2-869d-8c3cb59f9e70" class="xr-section-summary">Coordinates: <span>(3)</span></label><div class="xr-section-inline-details"></div><div class="xr-section-details"><ul class="xr-var-list"><li class="xr-var-item"><div class="xr-var-name"><span class="xr-has-index">lat</span></div><div class="xr-var-dims">(lat)</div><div class="xr-var-dtype">int64</div><div class="xr-var-preview xr-preview">70 60 50 40 30</div><input id="attrs-c68bc725-e17e-4771-b753-2cd704f345f1" class="xr-var-attrs-in" type="checkbox"><label for="attrs-c68bc725-e17e-4771-b753-2cd704f345f1" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-f0e23370-f229-4c41-8b2c-511330c52145" class="xr-var-data-in" type="checkbox"><label for="data-f0e23370-f229-4c41-8b2c-511330c52145" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>standard_name :</span></dt><dd>grid_latitude</dd><dt><span>units :</span></dt><dd>degree_N</dd></dl></div><div class="xr-var-data"><pre>array([70, 60, 50, 40, 30])</pre></div></li><li class="xr-var-item"><div class="xr-var-name"><span class="xr-has-index">lon</span></div><div class="xr-var-dims">(lon)</div><div class="xr-var-dtype">int64</div><div class="xr-var-preview xr-preview">60 70 80 90 100</div><input id="attrs-1af91dbd-9bdd-4627-8346-42503711235b" class="xr-var-attrs-in" type="checkbox"><label for="attrs-1af91dbd-9bdd-4627-8346-42503711235b" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-c2dd4b3a-0f63-453b-8f08-ee93cf0dc3f2" class="xr-var-data-in" type="checkbox"><label for="data-c2dd4b3a-0f63-453b-8f08-ee93cf0dc3f2" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>standard_name :</span></dt><dd>grid_longitude</dd><dt><span>units :</span></dt><dd>degree_E</dd></dl></div><div class="xr-var-data"><pre>array([ 60,  70,  80,  90, 100])</pre></div></li><li class="xr-var-item"><div class="xr-var-name"><span class="xr-has-index">time</span></div><div class="xr-var-dims">(time)</div><div class="xr-var-dtype">datetime64[ns]</div><div class="xr-var-preview xr-preview">2022-09-01 2022-09-02 2022-09-03</div><input id="attrs-ed83d562-0e23-4dd7-ba2e-8a06fda5e03e" class="xr-var-attrs-in" type="checkbox"><label for="attrs-ed83d562-0e23-4dd7-ba2e-8a06fda5e03e" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-68a0d259-bb24-4390-bec6-fd406fe0bb5b" class="xr-var-data-in" type="checkbox"><label for="data-68a0d259-bb24-4390-bec6-fd406fe0bb5b" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>description :</span></dt><dd>date of measurement</dd></dl></div><div class="xr-var-data"><pre>array(['2022-09-01T00:00:00.000000000', '2022-09-02T00:00:00.000000000',
+       '2022-09-03T00:00:00.000000000'], dtype='datetime64[ns]')</pre></div></li></ul></div></li><li class="xr-section-item"><input id="section-b73ac7b5-ee86-40e6-a379-fb00674d78e0" class="xr-section-summary-in" type="checkbox" checked=""><label for="section-b73ac7b5-ee86-40e6-a379-fb00674d78e0" class="xr-section-summary">Data variables: <span>(2)</span></label><div class="xr-section-inline-details"></div><div class="xr-section-details"><ul class="xr-var-list"><li class="xr-var-item"><div class="xr-var-name"><span>avg_temp</span></div><div class="xr-var-dims">(lat, lon)</div><div class="xr-var-dtype">float64</div><div class="xr-var-preview xr-preview">...</div><input id="attrs-0bf40bf8-07c7-4059-bbdf-f072f5afb391" class="xr-var-attrs-in" type="checkbox"><label for="attrs-0bf40bf8-07c7-4059-bbdf-f072f5afb391" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-25c3fc75-22dd-4076-b614-4530011d0364" class="xr-var-data-in" type="checkbox"><label for="data-25c3fc75-22dd-4076-b614-4530011d0364" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>title :</span></dt><dd>average temperature over three days</dd></dl></div><div class="xr-var-data"><pre>[25 values with dtype=float64]</pre></div></li><li class="xr-var-item"><div class="xr-var-name"><span>temp</span></div><div class="xr-var-dims">(time, lat, lon)</div><div class="xr-var-dtype">int64</div><div class="xr-var-preview xr-preview">...</div><input id="attrs-06303646-dc3e-4c77-9545-26ed640b2572" class="xr-var-attrs-in" type="checkbox"><label for="attrs-06303646-dc3e-4c77-9545-26ed640b2572" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-488ca990-197a-4d8b-89ab-50d152ea63b1" class="xr-var-data-in" type="checkbox"><label for="data-488ca990-197a-4d8b-89ab-50d152ea63b1" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>title :</span></dt><dd>temperature across weather stations</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>units :</span></dt><dd>degree_c</dd><dt><span>description :</span></dt><dd>simple example of an xarray.DataArray</dd></dl></div><div class="xr-var-data"><pre>[75 values with dtype=int64]</pre></div></li></ul></div></li><li class="xr-section-item"><input id="section-3be41740-8e11-41eb-9647-a2fbf9c64560" class="xr-section-summary-in" type="checkbox"><label for="section-3be41740-8e11-41eb-9647-a2fbf9c64560" class="xr-section-summary">Indexes: <span>(3)</span></label><div class="xr-section-inline-details"></div><div class="xr-section-details"><ul class="xr-var-list"><li class="xr-var-item"><div class="xr-index-name"><div>lat</div></div><div class="xr-index-preview">PandasIndex</div><div></div><input id="index-7bedbbe4-48fd-4c6b-ad7e-8319e15fcbaa" class="xr-index-data-in" type="checkbox"><label for="index-7bedbbe4-48fd-4c6b-ad7e-8319e15fcbaa" title="Show/Hide index repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-index-data"><pre>PandasIndex(Index([70, 60, 50, 40, 30], dtype='int64', name='lat'))</pre></div></li><li class="xr-var-item"><div class="xr-index-name"><div>lon</div></div><div class="xr-index-preview">PandasIndex</div><div></div><input id="index-674a8c9e-918e-4d74-966d-bd99a02e56b3" class="xr-index-data-in" type="checkbox"><label for="index-674a8c9e-918e-4d74-966d-bd99a02e56b3" title="Show/Hide index repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-index-data"><pre>PandasIndex(Index([60, 70, 80, 90, 100], dtype='int64', name='lon'))</pre></div></li><li class="xr-var-item"><div class="xr-index-name"><div>time</div></div><div class="xr-index-preview">PandasIndex</div><div></div><input id="index-45eaf4a9-27e5-4e40-96a0-9a439941f3bc" class="xr-index-data-in" type="checkbox"><label for="index-45eaf4a9-27e5-4e40-96a0-9a439941f3bc" title="Show/Hide index repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-index-data"><pre>PandasIndex(DatetimeIndex(['2022-09-01', '2022-09-02', '2022-09-03'], dtype='datetime64[ns]', name='time', freq=None))</pre></div></li></ul></div></li><li class="xr-section-item"><input id="section-b798a92f-f849-4b47-9f65-9d931f69f311" class="xr-section-summary-in" type="checkbox" checked=""><label for="section-b798a92f-f849-4b47-9f65-9d931f69f311" class="xr-section-summary">Attributes: <span>(2)</span></label><div class="xr-section-inline-details"></div><div class="xr-section-details"><dl class="xr-attrs"><dt><span>title :</span></dt><dd>temperature data at weather stations: daily and and average</dd><dt><span>description :</span></dt><dd>simple example of an xarray.Dataset</dd></dl></div></li></ul></div></div>
 </div>
 </div>
 </section>
@@ -3422,12 +3422,12 @@ <h4 data-number="5.4.2.2" class="anchored" data-anchor-id="save-and-reopen"><spa
 <section id="exercise-1" class="level3" data-number="5.4.3">
 <h3 data-number="5.4.3" class="anchored" data-anchor-id="exercise-1"><span class="header-section-number">5.4.3</span> Exercise</h3>
 <p>For this exercise, we will use a dataset including time series of annual Arctic freshwater fluxes and storage terms. The data was produced for the publication <a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2020GL088854">Jahn and Laiho, 2020</a> about changes in the Arctic freshwater budget and is archived at the Arctic Data Center <a href="https://arcticdata.io/catalog/view/doi%3A10.18739%2FA2280504J">doi:10.18739/A2280504J</a></p>
-<div id="e7c1cb6d" class="cell" data-execution_count="13">
+<div id="c1426192" class="cell" data-execution_count="13">
 <div class="sourceCode cell-code" id="cb14"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb14-1"><a href="#cb14-1" aria-hidden="true" tabindex="-1"></a>url <span class="op">=</span> <span class="st">'https://arcticdata.io/metacat/d1/mn/v2/object/urn%3Auuid%3A792bfc37-416e-409e-80b1-fdef8ab60033'</span></span>
 <span id="cb14-2"><a href="#cb14-2" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb14-3"><a href="#cb14-3" aria-hidden="true" tabindex="-1"></a>msg <span class="op">=</span> urllib.request.urlretrieve(url, <span class="st">"FW_data_CESM_LW_2006_2100.nc"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
-<div id="f6f39b97" class="cell" data-execution_count="14">
+<div id="c7fa4662" class="cell" data-execution_count="14">
 <div class="sourceCode cell-code" id="cb15"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb15-1"><a href="#cb15-1" aria-hidden="true" tabindex="-1"></a>fp <span class="op">=</span> os.path.join(os.getcwd(),<span class="st">'FW_data_CESM_LW_2006_2100.nc'</span>)</span>
 <span id="cb15-2"><a href="#cb15-2" aria-hidden="true" tabindex="-1"></a>fw_data <span class="op">=</span> xr.open_dataset(fp)</span>
 <span id="cb15-3"><a href="#cb15-3" aria-hidden="true" tabindex="-1"></a>fw_data</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
@@ -3819,7 +3819,7 @@ <h3 data-number="5.4.3" class="anchored" data-anchor-id="exercise-1"><span class
     author:          Alexandra Jahn, CU Boulder, alexandra.jahn@colorado.edu
     title:           Annual timeseries of freshwater data from the CESM Low W...
     description:     Annual mean Freshwater (FW) fluxes and storage relative ...
-    data_structure:  The data structure is |Ensemble member | Time (in years)...</pre><div class="xr-wrap" style="display:none"><div class="xr-header"><div class="xr-obj-type">xarray.Dataset</div></div><ul class="xr-sections"><li class="xr-section-item"><input id="section-96723971-7891-4a27-ad30-056b7ae0aef0" class="xr-section-summary-in" type="checkbox" disabled=""><label for="section-96723971-7891-4a27-ad30-056b7ae0aef0" class="xr-section-summary" title="Expand/collapse section">Dimensions:</label><div class="xr-section-inline-details"><ul class="xr-dim-list"><li><span class="xr-has-index">time</span>: 95</li><li><span class="xr-has-index">member</span>: 11</li></ul></div><div class="xr-section-details"></div></li><li class="xr-section-item"><input id="section-6ef66bbb-4b84-40e4-9596-fa4d9961da0a" class="xr-section-summary-in" type="checkbox" checked=""><label for="section-6ef66bbb-4b84-40e4-9596-fa4d9961da0a" class="xr-section-summary">Coordinates: <span>(2)</span></label><div class="xr-section-inline-details"></div><div class="xr-section-details"><ul class="xr-var-list"><li class="xr-var-item"><div class="xr-var-name"><span class="xr-has-index">time</span></div><div class="xr-var-dims">(time)</div><div class="xr-var-dtype">float64</div><div class="xr-var-preview xr-preview">2.006e+03 2.007e+03 ... 2.1e+03</div><input id="attrs-4149cf65-737e-4cbe-8d5a-b058f4d9d24b" class="xr-var-attrs-in" type="checkbox"><label for="attrs-4149cf65-737e-4cbe-8d5a-b058f4d9d24b" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-7d3893d7-1646-4cd8-b585-4991e63e0b59" class="xr-var-data-in" type="checkbox"><label for="data-7d3893d7-1646-4cd8-b585-4991e63e0b59" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>long_name :</span></dt><dd>time in years, 1920-2100</dd></dl></div><div class="xr-var-data"><pre>array([2006., 2007., 2008., 2009., 2010., 2011., 2012., 2013., 2014., 2015.,
+    data_structure:  The data structure is |Ensemble member | Time (in years)...</pre><div class="xr-wrap" style="display:none"><div class="xr-header"><div class="xr-obj-type">xarray.Dataset</div></div><ul class="xr-sections"><li class="xr-section-item"><input id="section-427605a6-7a9a-4f08-b68e-b83b25cdcccd" class="xr-section-summary-in" type="checkbox" disabled=""><label for="section-427605a6-7a9a-4f08-b68e-b83b25cdcccd" class="xr-section-summary" title="Expand/collapse section">Dimensions:</label><div class="xr-section-inline-details"><ul class="xr-dim-list"><li><span class="xr-has-index">time</span>: 95</li><li><span class="xr-has-index">member</span>: 11</li></ul></div><div class="xr-section-details"></div></li><li class="xr-section-item"><input id="section-03af827c-0ae7-4382-9b38-6fd02d92c354" class="xr-section-summary-in" type="checkbox" checked=""><label for="section-03af827c-0ae7-4382-9b38-6fd02d92c354" class="xr-section-summary">Coordinates: <span>(2)</span></label><div class="xr-section-inline-details"></div><div class="xr-section-details"><ul class="xr-var-list"><li class="xr-var-item"><div class="xr-var-name"><span class="xr-has-index">time</span></div><div class="xr-var-dims">(time)</div><div class="xr-var-dtype">float64</div><div class="xr-var-preview xr-preview">2.006e+03 2.007e+03 ... 2.1e+03</div><input id="attrs-fc0e6f99-751f-4f3c-a6d9-15a1bca74180" class="xr-var-attrs-in" type="checkbox"><label for="attrs-fc0e6f99-751f-4f3c-a6d9-15a1bca74180" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-333b0378-b91b-4b8b-8568-ef283a1e3b0f" class="xr-var-data-in" type="checkbox"><label for="data-333b0378-b91b-4b8b-8568-ef283a1e3b0f" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>long_name :</span></dt><dd>time in years, 1920-2100</dd></dl></div><div class="xr-var-data"><pre>array([2006., 2007., 2008., 2009., 2010., 2011., 2012., 2013., 2014., 2015.,
        2016., 2017., 2018., 2019., 2020., 2021., 2022., 2023., 2024., 2025.,
        2026., 2027., 2028., 2029., 2030., 2031., 2032., 2033., 2034., 2035.,
        2036., 2037., 2038., 2039., 2040., 2041., 2042., 2043., 2044., 2045.,
@@ -3828,7 +3828,7 @@ <h3 data-number="5.4.3" class="anchored" data-anchor-id="exercise-1"><span class
        2066., 2067., 2068., 2069., 2070., 2071., 2072., 2073., 2074., 2075.,
        2076., 2077., 2078., 2079., 2080., 2081., 2082., 2083., 2084., 2085.,
        2086., 2087., 2088., 2089., 2090., 2091., 2092., 2093., 2094., 2095.,
-       2096., 2097., 2098., 2099., 2100.])</pre></div></li><li class="xr-var-item"><div class="xr-var-name"><span class="xr-has-index">member</span></div><div class="xr-var-dims">(member)</div><div class="xr-var-dtype">float64</div><div class="xr-var-preview xr-preview">1.0 2.0 3.0 4.0 ... 9.0 10.0 11.0</div><input id="attrs-c5aa8fe7-18de-472a-aeb4-fea2d4b26515" class="xr-var-attrs-in" type="checkbox"><label for="attrs-c5aa8fe7-18de-472a-aeb4-fea2d4b26515" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-8cd00c13-ae97-4b32-8cdb-ac5d72e25e8e" class="xr-var-data-in" type="checkbox"><label for="data-8cd00c13-ae97-4b32-8cdb-ac5d72e25e8e" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>long_name :</span></dt><dd>Ensemble member, 1-11</dd></dl></div><div class="xr-var-data"><pre>array([ 1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., 11.])</pre></div></li></ul></div></li><li class="xr-section-item"><input id="section-01367bf9-6988-4722-abad-8a3d71f3860c" class="xr-section-summary-in" type="checkbox"><label for="section-01367bf9-6988-4722-abad-8a3d71f3860c" class="xr-section-summary">Data variables: <span>(16)</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>FW_flux_Fram_annual_net</span></div><div class="xr-var-dims">(time, member)</div><div class="xr-var-dtype">float64</div><div class="xr-var-preview xr-preview">...</div><input id="attrs-f51ce042-ac00-449a-b961-ff461ad91d77" class="xr-var-attrs-in" type="checkbox"><label for="attrs-f51ce042-ac00-449a-b961-ff461ad91d77" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-6d320c56-c6e4-4b85-8ad5-fc798704b933" class="xr-var-data-in" type="checkbox"><label for="data-6d320c56-c6e4-4b85-8ad5-fc798704b933" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>units :</span></dt><dd>km3/yr</dd><dt><span>long_name :</span></dt><dd>Net liquid FW flux through Fram Strait, relative to 34.8</dd></dl></div><div class="xr-var-data"><pre>[1045 values with dtype=float64]</pre></div></li><li class="xr-var-item"><div class="xr-var-name"><span>FW_flux_Barrow_annual_net</span></div><div class="xr-var-dims">(time, member)</div><div class="xr-var-dtype">float64</div><div class="xr-var-preview xr-preview">...</div><input id="attrs-398185ab-66b0-4efa-a5d1-99d420b4f423" class="xr-var-attrs-in" type="checkbox"><label for="attrs-398185ab-66b0-4efa-a5d1-99d420b4f423" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-d9b3d376-4991-43ce-845c-c079a1b47a9f" class="xr-var-data-in" type="checkbox"><label for="data-d9b3d376-4991-43ce-845c-c079a1b47a9f" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>units :</span></dt><dd>km3/yr</dd><dt><span>long_name :</span></dt><dd>Net liquid FW flux through Barrow Strait, relative to 34.8</dd></dl></div><div class="xr-var-data"><pre>[1045 values with dtype=float64]</pre></div></li><li class="xr-var-item"><div class="xr-var-name"><span>FW_flux_Nares_annual_net</span></div><div class="xr-var-dims">(time, member)</div><div class="xr-var-dtype">float64</div><div class="xr-var-preview xr-preview">...</div><input id="attrs-c0b26824-c043-4030-b995-d54e15f1136c" class="xr-var-attrs-in" type="checkbox"><label for="attrs-c0b26824-c043-4030-b995-d54e15f1136c" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-17c7e433-224b-4800-a0ec-0fa6f8f1ac28" class="xr-var-data-in" type="checkbox"><label for="data-17c7e433-224b-4800-a0ec-0fa6f8f1ac28" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>units :</span></dt><dd>km3/yr</dd><dt><span>long_name :</span></dt><dd>Net liquid FW flux through Nares Strait, relative to 34.8</dd></dl></div><div class="xr-var-data"><pre>[1045 values with dtype=float64]</pre></div></li><li class="xr-var-item"><div class="xr-var-name"><span>FW_flux_Davis_annual_net</span></div><div class="xr-var-dims">(time, member)</div><div class="xr-var-dtype">float64</div><div class="xr-var-preview xr-preview">...</div><input id="attrs-9fcd664a-7dd8-41ed-8fcb-b61eb2091dfa" class="xr-var-attrs-in" type="checkbox"><label for="attrs-9fcd664a-7dd8-41ed-8fcb-b61eb2091dfa" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-8ee343ba-6e82-4967-ba9f-7e019540a945" class="xr-var-data-in" type="checkbox"><label for="data-8ee343ba-6e82-4967-ba9f-7e019540a945" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>units :</span></dt><dd>km3/yr</dd><dt><span>long_name :</span></dt><dd>Net liquid FW flux through Davis Strait, relative to 34.8</dd></dl></div><div class="xr-var-data"><pre>[1045 values with dtype=float64]</pre></div></li><li class="xr-var-item"><div class="xr-var-name"><span>FW_flux_BSO_annual_net</span></div><div class="xr-var-dims">(time, member)</div><div class="xr-var-dtype">float64</div><div class="xr-var-preview xr-preview">...</div><input id="attrs-2c1a6ed4-17aa-472d-ad4e-123df4b8a3e0" class="xr-var-attrs-in" type="checkbox"><label for="attrs-2c1a6ed4-17aa-472d-ad4e-123df4b8a3e0" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-477bec62-0a05-45c9-946a-96e1a3297b80" class="xr-var-data-in" type="checkbox"><label for="data-477bec62-0a05-45c9-946a-96e1a3297b80" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>units :</span></dt><dd>km3/yr</dd><dt><span>long_name :</span></dt><dd>Net liquid FW flux through the Barents Sea Opening, relative to 34.8</dd></dl></div><div class="xr-var-data"><pre>[1045 values with dtype=float64]</pre></div></li><li class="xr-var-item"><div class="xr-var-name"><span>FW_flux_Bering_annual_net</span></div><div class="xr-var-dims">(time, member)</div><div class="xr-var-dtype">float64</div><div class="xr-var-preview xr-preview">...</div><input id="attrs-419b9423-d71a-4655-898f-c7ea1d473cfd" class="xr-var-attrs-in" type="checkbox"><label for="attrs-419b9423-d71a-4655-898f-c7ea1d473cfd" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-4e241e37-b25d-48cc-b093-d1486bca9686" class="xr-var-data-in" type="checkbox"><label for="data-4e241e37-b25d-48cc-b093-d1486bca9686" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>units :</span></dt><dd>km3/yr</dd><dt><span>long_name :</span></dt><dd>Net liquid FW flux through Bering Strait, relative to 34.8</dd></dl></div><div class="xr-var-data"><pre>[1045 values with dtype=float64]</pre></div></li><li class="xr-var-item"><div class="xr-var-name"><span>Solid_FW_flux_Fram_annual_net</span></div><div class="xr-var-dims">(time, member)</div><div class="xr-var-dtype">float64</div><div class="xr-var-preview xr-preview">...</div><input id="attrs-da7f48b8-9f52-4c5f-9f41-425e15e351dc" class="xr-var-attrs-in" type="checkbox"><label for="attrs-da7f48b8-9f52-4c5f-9f41-425e15e351dc" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-3442d274-cbc2-4d80-8ea3-ac9ac49b093d" class="xr-var-data-in" type="checkbox"><label for="data-3442d274-cbc2-4d80-8ea3-ac9ac49b093d" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>units :</span></dt><dd>km3/yr</dd><dt><span>long_name :</span></dt><dd>Net Solid (ice+snow) FW flux through Fram Strait, relative to 34.8</dd></dl></div><div class="xr-var-data"><pre>[1045 values with dtype=float64]</pre></div></li><li class="xr-var-item"><div class="xr-var-name"><span>Solid_FW_flux_Barrow_annual_net</span></div><div class="xr-var-dims">(time, member)</div><div class="xr-var-dtype">float64</div><div class="xr-var-preview xr-preview">...</div><input id="attrs-0a9e5ad5-80ba-4141-89b5-a78bc8164c66" class="xr-var-attrs-in" type="checkbox"><label for="attrs-0a9e5ad5-80ba-4141-89b5-a78bc8164c66" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-0c321b79-0041-48d2-8d8a-1ba76aaaacbb" class="xr-var-data-in" type="checkbox"><label for="data-0c321b79-0041-48d2-8d8a-1ba76aaaacbb" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>units :</span></dt><dd>km3/yr</dd><dt><span>long_name :</span></dt><dd>Net Solid (ice+snow) FW flux through Barrow Strait, relative to 34.8</dd></dl></div><div class="xr-var-data"><pre>[1045 values with dtype=float64]</pre></div></li><li class="xr-var-item"><div class="xr-var-name"><span>Solid_FW_flux_Nares_annual_net</span></div><div class="xr-var-dims">(time, member)</div><div class="xr-var-dtype">float64</div><div class="xr-var-preview xr-preview">...</div><input id="attrs-77e22263-fd29-4d54-9165-95e8ba6caabf" class="xr-var-attrs-in" type="checkbox"><label for="attrs-77e22263-fd29-4d54-9165-95e8ba6caabf" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-ce8ea840-e656-4c0f-8336-07de8a8d0794" class="xr-var-data-in" type="checkbox"><label for="data-ce8ea840-e656-4c0f-8336-07de8a8d0794" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>units :</span></dt><dd>km3/yr</dd><dt><span>long_name :</span></dt><dd>Net Solid (ice+snow) FW flux through Nares Strait, relative to 34.8</dd></dl></div><div class="xr-var-data"><pre>[1045 values with dtype=float64]</pre></div></li><li class="xr-var-item"><div class="xr-var-name"><span>Solid_FW_flux_Davis_annual_net</span></div><div class="xr-var-dims">(time, member)</div><div class="xr-var-dtype">float64</div><div class="xr-var-preview xr-preview">...</div><input id="attrs-c719bbaa-d25f-45a8-b436-5de40568f8ef" class="xr-var-attrs-in" type="checkbox"><label for="attrs-c719bbaa-d25f-45a8-b436-5de40568f8ef" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-cb07fbdd-3f7c-4e43-b09b-d240b013c45e" class="xr-var-data-in" type="checkbox"><label for="data-cb07fbdd-3f7c-4e43-b09b-d240b013c45e" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>units :</span></dt><dd>km3/yr</dd><dt><span>long_name :</span></dt><dd>Net Solid (ice+snow) FW flux through Davis Strait, relative to 34.8</dd></dl></div><div class="xr-var-data"><pre>[1045 values with dtype=float64]</pre></div></li><li class="xr-var-item"><div class="xr-var-name"><span>Solid_FW_flux_BSO_annual_net</span></div><div class="xr-var-dims">(time, member)</div><div class="xr-var-dtype">float64</div><div class="xr-var-preview xr-preview">...</div><input id="attrs-1adeeb25-875d-4520-bf61-dcb2ca7f52b9" class="xr-var-attrs-in" type="checkbox"><label for="attrs-1adeeb25-875d-4520-bf61-dcb2ca7f52b9" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-c9bf6b9e-597d-4ada-ba7d-78b335be2313" class="xr-var-data-in" type="checkbox"><label for="data-c9bf6b9e-597d-4ada-ba7d-78b335be2313" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>units :</span></dt><dd>km3/yr</dd><dt><span>long_name :</span></dt><dd>Net Solid (ice+snow) FW flux through the Barents Sea Opening, relative to 34.8</dd></dl></div><div class="xr-var-data"><pre>[1045 values with dtype=float64]</pre></div></li><li class="xr-var-item"><div class="xr-var-name"><span>Solid_FW_flux_Bering_annual_net</span></div><div class="xr-var-dims">(time, member)</div><div class="xr-var-dtype">float64</div><div class="xr-var-preview xr-preview">...</div><input id="attrs-f1c20910-35f9-4fd6-9d09-f6169756e1a2" class="xr-var-attrs-in" type="checkbox"><label for="attrs-f1c20910-35f9-4fd6-9d09-f6169756e1a2" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-47a4e765-0e2b-4f67-9b3e-19a996ccf1a5" class="xr-var-data-in" type="checkbox"><label for="data-47a4e765-0e2b-4f67-9b3e-19a996ccf1a5" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>units :</span></dt><dd>km3/yr</dd><dt><span>long_name :</span></dt><dd>Net Solid (ice+snow) FW flux through Bering Strait, relative to 34.8</dd></dl></div><div class="xr-var-data"><pre>[1045 values with dtype=float64]</pre></div></li><li class="xr-var-item"><div class="xr-var-name"><span>runoff_annual</span></div><div class="xr-var-dims">(time, member)</div><div class="xr-var-dtype">float64</div><div class="xr-var-preview xr-preview">...</div><input id="attrs-978d4bfc-29e2-48e7-8c33-67f4b81aa3e7" class="xr-var-attrs-in" type="checkbox"><label for="attrs-978d4bfc-29e2-48e7-8c33-67f4b81aa3e7" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-a38439c6-73f4-4357-8532-4144fd82a942" class="xr-var-data-in" type="checkbox"><label for="data-a38439c6-73f4-4357-8532-4144fd82a942" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>units :</span></dt><dd>km3/yr</dd><dt><span>long_name :</span></dt><dd>FW flux from river runoff into the Arctic Ocean domain, relative to 34.8</dd></dl></div><div class="xr-var-data"><pre>[1045 values with dtype=float64]</pre></div></li><li class="xr-var-item"><div class="xr-var-name"><span>netPrec_annual</span></div><div class="xr-var-dims">(time, member)</div><div class="xr-var-dtype">float64</div><div class="xr-var-preview xr-preview">...</div><input id="attrs-e8f02502-e38f-4915-860a-a1597b775e9e" class="xr-var-attrs-in" type="checkbox"><label for="attrs-e8f02502-e38f-4915-860a-a1597b775e9e" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-f086ca95-930f-4ace-88d2-1391a187c7d8" class="xr-var-data-in" type="checkbox"><label for="data-f086ca95-930f-4ace-88d2-1391a187c7d8" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>units :</span></dt><dd>km3/yr</dd><dt><span>long_name :</span></dt><dd>Net FW flux from precipitation minus evaporation over the Arctic Ocean domain, relative to 34.8</dd></dl></div><div class="xr-var-data"><pre>[1045 values with dtype=float64]</pre></div></li><li class="xr-var-item"><div class="xr-var-name"><span>Liquid_FW_storage_Arctic_annual</span></div><div class="xr-var-dims">(time, member)</div><div class="xr-var-dtype">float64</div><div class="xr-var-preview xr-preview">...</div><input id="attrs-5529e106-8171-4a8f-84e7-b56e01412cf3" class="xr-var-attrs-in" type="checkbox"><label for="attrs-5529e106-8171-4a8f-84e7-b56e01412cf3" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-dff3cf2f-28db-4070-ba46-560bc38b7f95" class="xr-var-data-in" type="checkbox"><label for="data-dff3cf2f-28db-4070-ba46-560bc38b7f95" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>units :</span></dt><dd>km3</dd><dt><span>long_name :</span></dt><dd>FW storage in the Arctic Ocean domain, , relative to 34.8. Ignoring any negative FW (water above reference salinity)</dd></dl></div><div class="xr-var-data"><pre>[1045 values with dtype=float64]</pre></div></li><li class="xr-var-item"><div class="xr-var-name"><span>Solid_FW_storage_Arctic_annual</span></div><div class="xr-var-dims">(time, member)</div><div class="xr-var-dtype">float64</div><div class="xr-var-preview xr-preview">...</div><input id="attrs-bf61e283-b967-4cd6-bb32-39020a66879f" class="xr-var-attrs-in" type="checkbox"><label for="attrs-bf61e283-b967-4cd6-bb32-39020a66879f" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-0954836f-af78-4497-8887-6334214a711f" class="xr-var-data-in" type="checkbox"><label for="data-0954836f-af78-4497-8887-6334214a711f" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>units :</span></dt><dd>km3</dd><dt><span>long_name :</span></dt><dd>FW storage in sea ice and snow in the Arctic Ocean domain, relative to 34.8</dd></dl></div><div class="xr-var-data"><pre>[1045 values with dtype=float64]</pre></div></li></ul></div></li><li class="xr-section-item"><input id="section-6f6102b6-4906-4787-8d55-420cbb760c32" class="xr-section-summary-in" type="checkbox"><label for="section-6f6102b6-4906-4787-8d55-420cbb760c32" class="xr-section-summary">Indexes: <span>(2)</span></label><div class="xr-section-inline-details"></div><div class="xr-section-details"><ul class="xr-var-list"><li class="xr-var-item"><div class="xr-index-name"><div>time</div></div><div class="xr-index-preview">PandasIndex</div><div></div><input id="index-f457988f-e231-4f54-b52f-60a8e23c3486" class="xr-index-data-in" type="checkbox"><label for="index-f457988f-e231-4f54-b52f-60a8e23c3486" title="Show/Hide index repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-index-data"><pre>PandasIndex(Index([2006.0, 2007.0, 2008.0, 2009.0, 2010.0, 2011.0, 2012.0, 2013.0, 2014.0,
+       2096., 2097., 2098., 2099., 2100.])</pre></div></li><li class="xr-var-item"><div class="xr-var-name"><span class="xr-has-index">member</span></div><div class="xr-var-dims">(member)</div><div class="xr-var-dtype">float64</div><div class="xr-var-preview xr-preview">1.0 2.0 3.0 4.0 ... 9.0 10.0 11.0</div><input id="attrs-91aba423-692c-4656-b4c9-bead1e9f38e3" class="xr-var-attrs-in" type="checkbox"><label for="attrs-91aba423-692c-4656-b4c9-bead1e9f38e3" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-8c85cd9d-94c6-49d4-affb-39bfddbb2015" class="xr-var-data-in" type="checkbox"><label for="data-8c85cd9d-94c6-49d4-affb-39bfddbb2015" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>long_name :</span></dt><dd>Ensemble member, 1-11</dd></dl></div><div class="xr-var-data"><pre>array([ 1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., 11.])</pre></div></li></ul></div></li><li class="xr-section-item"><input id="section-f67afbaf-6b24-4707-9161-954e4986aeec" class="xr-section-summary-in" type="checkbox"><label for="section-f67afbaf-6b24-4707-9161-954e4986aeec" class="xr-section-summary">Data variables: <span>(16)</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>FW_flux_Fram_annual_net</span></div><div class="xr-var-dims">(time, member)</div><div class="xr-var-dtype">float64</div><div class="xr-var-preview xr-preview">...</div><input id="attrs-210533c6-18b6-4a5f-b124-be812e5ff569" class="xr-var-attrs-in" type="checkbox"><label for="attrs-210533c6-18b6-4a5f-b124-be812e5ff569" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-08128643-a0e8-488e-84fc-b126c8af7bec" class="xr-var-data-in" type="checkbox"><label for="data-08128643-a0e8-488e-84fc-b126c8af7bec" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>units :</span></dt><dd>km3/yr</dd><dt><span>long_name :</span></dt><dd>Net liquid FW flux through Fram Strait, relative to 34.8</dd></dl></div><div class="xr-var-data"><pre>[1045 values with dtype=float64]</pre></div></li><li class="xr-var-item"><div class="xr-var-name"><span>FW_flux_Barrow_annual_net</span></div><div class="xr-var-dims">(time, member)</div><div class="xr-var-dtype">float64</div><div class="xr-var-preview xr-preview">...</div><input id="attrs-0d6b7e18-2dc1-40dd-a2ba-d42fa2896133" class="xr-var-attrs-in" type="checkbox"><label for="attrs-0d6b7e18-2dc1-40dd-a2ba-d42fa2896133" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-6e96a8fa-b226-4dc7-9aff-d03848a9f51a" class="xr-var-data-in" type="checkbox"><label for="data-6e96a8fa-b226-4dc7-9aff-d03848a9f51a" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>units :</span></dt><dd>km3/yr</dd><dt><span>long_name :</span></dt><dd>Net liquid FW flux through Barrow Strait, relative to 34.8</dd></dl></div><div class="xr-var-data"><pre>[1045 values with dtype=float64]</pre></div></li><li class="xr-var-item"><div class="xr-var-name"><span>FW_flux_Nares_annual_net</span></div><div class="xr-var-dims">(time, member)</div><div class="xr-var-dtype">float64</div><div class="xr-var-preview xr-preview">...</div><input id="attrs-bf34128a-d0ea-4c9f-a0d8-06a5b34315d5" class="xr-var-attrs-in" type="checkbox"><label for="attrs-bf34128a-d0ea-4c9f-a0d8-06a5b34315d5" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-752d1d71-d3ab-4ecc-b552-d9a74ca874aa" class="xr-var-data-in" type="checkbox"><label for="data-752d1d71-d3ab-4ecc-b552-d9a74ca874aa" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>units :</span></dt><dd>km3/yr</dd><dt><span>long_name :</span></dt><dd>Net liquid FW flux through Nares Strait, relative to 34.8</dd></dl></div><div class="xr-var-data"><pre>[1045 values with dtype=float64]</pre></div></li><li class="xr-var-item"><div class="xr-var-name"><span>FW_flux_Davis_annual_net</span></div><div class="xr-var-dims">(time, member)</div><div class="xr-var-dtype">float64</div><div class="xr-var-preview xr-preview">...</div><input id="attrs-fd1f387f-e11a-487f-a158-9cd2fd17f5ab" class="xr-var-attrs-in" type="checkbox"><label for="attrs-fd1f387f-e11a-487f-a158-9cd2fd17f5ab" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-55efd394-1308-4ef8-b9bd-05493ccb6de8" class="xr-var-data-in" type="checkbox"><label for="data-55efd394-1308-4ef8-b9bd-05493ccb6de8" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>units :</span></dt><dd>km3/yr</dd><dt><span>long_name :</span></dt><dd>Net liquid FW flux through Davis Strait, relative to 34.8</dd></dl></div><div class="xr-var-data"><pre>[1045 values with dtype=float64]</pre></div></li><li class="xr-var-item"><div class="xr-var-name"><span>FW_flux_BSO_annual_net</span></div><div class="xr-var-dims">(time, member)</div><div class="xr-var-dtype">float64</div><div class="xr-var-preview xr-preview">...</div><input id="attrs-dd526463-4dd2-493c-bf5b-73fe3652fe6b" class="xr-var-attrs-in" type="checkbox"><label for="attrs-dd526463-4dd2-493c-bf5b-73fe3652fe6b" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-d67ed63a-b7f5-4706-9121-dc95ab0ded95" class="xr-var-data-in" type="checkbox"><label for="data-d67ed63a-b7f5-4706-9121-dc95ab0ded95" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>units :</span></dt><dd>km3/yr</dd><dt><span>long_name :</span></dt><dd>Net liquid FW flux through the Barents Sea Opening, relative to 34.8</dd></dl></div><div class="xr-var-data"><pre>[1045 values with dtype=float64]</pre></div></li><li class="xr-var-item"><div class="xr-var-name"><span>FW_flux_Bering_annual_net</span></div><div class="xr-var-dims">(time, member)</div><div class="xr-var-dtype">float64</div><div class="xr-var-preview xr-preview">...</div><input id="attrs-dd3d596b-a2b0-460b-bd6e-68ca7f54ccff" class="xr-var-attrs-in" type="checkbox"><label for="attrs-dd3d596b-a2b0-460b-bd6e-68ca7f54ccff" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-4fd76fd7-eee3-4b5a-bb9a-08f2802a8748" class="xr-var-data-in" type="checkbox"><label for="data-4fd76fd7-eee3-4b5a-bb9a-08f2802a8748" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>units :</span></dt><dd>km3/yr</dd><dt><span>long_name :</span></dt><dd>Net liquid FW flux through Bering Strait, relative to 34.8</dd></dl></div><div class="xr-var-data"><pre>[1045 values with dtype=float64]</pre></div></li><li class="xr-var-item"><div class="xr-var-name"><span>Solid_FW_flux_Fram_annual_net</span></div><div class="xr-var-dims">(time, member)</div><div class="xr-var-dtype">float64</div><div class="xr-var-preview xr-preview">...</div><input id="attrs-b5693c71-04df-4863-8250-e41ad2eca349" class="xr-var-attrs-in" type="checkbox"><label for="attrs-b5693c71-04df-4863-8250-e41ad2eca349" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-e82fa446-3447-44cd-9983-7aeb337d7cf6" class="xr-var-data-in" type="checkbox"><label for="data-e82fa446-3447-44cd-9983-7aeb337d7cf6" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>units :</span></dt><dd>km3/yr</dd><dt><span>long_name :</span></dt><dd>Net Solid (ice+snow) FW flux through Fram Strait, relative to 34.8</dd></dl></div><div class="xr-var-data"><pre>[1045 values with dtype=float64]</pre></div></li><li class="xr-var-item"><div class="xr-var-name"><span>Solid_FW_flux_Barrow_annual_net</span></div><div class="xr-var-dims">(time, member)</div><div class="xr-var-dtype">float64</div><div class="xr-var-preview xr-preview">...</div><input id="attrs-0be0e374-a82a-4f5c-b477-ca5e59cadc9c" class="xr-var-attrs-in" type="checkbox"><label for="attrs-0be0e374-a82a-4f5c-b477-ca5e59cadc9c" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-88bd29d5-63f3-4a6b-afe6-b970e6f4aa62" class="xr-var-data-in" type="checkbox"><label for="data-88bd29d5-63f3-4a6b-afe6-b970e6f4aa62" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>units :</span></dt><dd>km3/yr</dd><dt><span>long_name :</span></dt><dd>Net Solid (ice+snow) FW flux through Barrow Strait, relative to 34.8</dd></dl></div><div class="xr-var-data"><pre>[1045 values with dtype=float64]</pre></div></li><li class="xr-var-item"><div class="xr-var-name"><span>Solid_FW_flux_Nares_annual_net</span></div><div class="xr-var-dims">(time, member)</div><div class="xr-var-dtype">float64</div><div class="xr-var-preview xr-preview">...</div><input id="attrs-70207b70-6494-498a-afd4-6915d94ddc1e" class="xr-var-attrs-in" type="checkbox"><label for="attrs-70207b70-6494-498a-afd4-6915d94ddc1e" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-82a1a3df-c9f5-443b-b2b5-bbebf15cfb39" class="xr-var-data-in" type="checkbox"><label for="data-82a1a3df-c9f5-443b-b2b5-bbebf15cfb39" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>units :</span></dt><dd>km3/yr</dd><dt><span>long_name :</span></dt><dd>Net Solid (ice+snow) FW flux through Nares Strait, relative to 34.8</dd></dl></div><div class="xr-var-data"><pre>[1045 values with dtype=float64]</pre></div></li><li class="xr-var-item"><div class="xr-var-name"><span>Solid_FW_flux_Davis_annual_net</span></div><div class="xr-var-dims">(time, member)</div><div class="xr-var-dtype">float64</div><div class="xr-var-preview xr-preview">...</div><input id="attrs-6268cbbc-f2ac-46cb-8efd-45601dcc799e" class="xr-var-attrs-in" type="checkbox"><label for="attrs-6268cbbc-f2ac-46cb-8efd-45601dcc799e" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-a5007588-78dc-44e3-bfb3-518a872d951b" class="xr-var-data-in" type="checkbox"><label for="data-a5007588-78dc-44e3-bfb3-518a872d951b" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>units :</span></dt><dd>km3/yr</dd><dt><span>long_name :</span></dt><dd>Net Solid (ice+snow) FW flux through Davis Strait, relative to 34.8</dd></dl></div><div class="xr-var-data"><pre>[1045 values with dtype=float64]</pre></div></li><li class="xr-var-item"><div class="xr-var-name"><span>Solid_FW_flux_BSO_annual_net</span></div><div class="xr-var-dims">(time, member)</div><div class="xr-var-dtype">float64</div><div class="xr-var-preview xr-preview">...</div><input id="attrs-4c9a586b-e530-43e7-979a-0367727fb7e1" class="xr-var-attrs-in" type="checkbox"><label for="attrs-4c9a586b-e530-43e7-979a-0367727fb7e1" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-fe7f6955-a32c-42f0-8270-3cf35a24a6c7" class="xr-var-data-in" type="checkbox"><label for="data-fe7f6955-a32c-42f0-8270-3cf35a24a6c7" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>units :</span></dt><dd>km3/yr</dd><dt><span>long_name :</span></dt><dd>Net Solid (ice+snow) FW flux through the Barents Sea Opening, relative to 34.8</dd></dl></div><div class="xr-var-data"><pre>[1045 values with dtype=float64]</pre></div></li><li class="xr-var-item"><div class="xr-var-name"><span>Solid_FW_flux_Bering_annual_net</span></div><div class="xr-var-dims">(time, member)</div><div class="xr-var-dtype">float64</div><div class="xr-var-preview xr-preview">...</div><input id="attrs-19438bda-2355-40fe-b3b5-f7e84427795d" class="xr-var-attrs-in" type="checkbox"><label for="attrs-19438bda-2355-40fe-b3b5-f7e84427795d" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-dc575561-090b-404f-a15f-ccfec4e3e4ce" class="xr-var-data-in" type="checkbox"><label for="data-dc575561-090b-404f-a15f-ccfec4e3e4ce" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>units :</span></dt><dd>km3/yr</dd><dt><span>long_name :</span></dt><dd>Net Solid (ice+snow) FW flux through Bering Strait, relative to 34.8</dd></dl></div><div class="xr-var-data"><pre>[1045 values with dtype=float64]</pre></div></li><li class="xr-var-item"><div class="xr-var-name"><span>runoff_annual</span></div><div class="xr-var-dims">(time, member)</div><div class="xr-var-dtype">float64</div><div class="xr-var-preview xr-preview">...</div><input id="attrs-88526ba7-7f76-4b8a-8cd3-55825fc22d69" class="xr-var-attrs-in" type="checkbox"><label for="attrs-88526ba7-7f76-4b8a-8cd3-55825fc22d69" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-5ec3cbf2-50be-4bed-bbc3-5f2b10c532de" class="xr-var-data-in" type="checkbox"><label for="data-5ec3cbf2-50be-4bed-bbc3-5f2b10c532de" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>units :</span></dt><dd>km3/yr</dd><dt><span>long_name :</span></dt><dd>FW flux from river runoff into the Arctic Ocean domain, relative to 34.8</dd></dl></div><div class="xr-var-data"><pre>[1045 values with dtype=float64]</pre></div></li><li class="xr-var-item"><div class="xr-var-name"><span>netPrec_annual</span></div><div class="xr-var-dims">(time, member)</div><div class="xr-var-dtype">float64</div><div class="xr-var-preview xr-preview">...</div><input id="attrs-7871de37-e7c6-4200-94af-916d5596ed81" class="xr-var-attrs-in" type="checkbox"><label for="attrs-7871de37-e7c6-4200-94af-916d5596ed81" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-f506ccfb-97f5-42ec-89ff-1248eada688b" class="xr-var-data-in" type="checkbox"><label for="data-f506ccfb-97f5-42ec-89ff-1248eada688b" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>units :</span></dt><dd>km3/yr</dd><dt><span>long_name :</span></dt><dd>Net FW flux from precipitation minus evaporation over the Arctic Ocean domain, relative to 34.8</dd></dl></div><div class="xr-var-data"><pre>[1045 values with dtype=float64]</pre></div></li><li class="xr-var-item"><div class="xr-var-name"><span>Liquid_FW_storage_Arctic_annual</span></div><div class="xr-var-dims">(time, member)</div><div class="xr-var-dtype">float64</div><div class="xr-var-preview xr-preview">...</div><input id="attrs-d3818716-9b86-408e-9d51-7d6e1312f172" class="xr-var-attrs-in" type="checkbox"><label for="attrs-d3818716-9b86-408e-9d51-7d6e1312f172" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-abfbf396-c957-4ffd-9954-be39fb66b404" class="xr-var-data-in" type="checkbox"><label for="data-abfbf396-c957-4ffd-9954-be39fb66b404" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>units :</span></dt><dd>km3</dd><dt><span>long_name :</span></dt><dd>FW storage in the Arctic Ocean domain, , relative to 34.8. Ignoring any negative FW (water above reference salinity)</dd></dl></div><div class="xr-var-data"><pre>[1045 values with dtype=float64]</pre></div></li><li class="xr-var-item"><div class="xr-var-name"><span>Solid_FW_storage_Arctic_annual</span></div><div class="xr-var-dims">(time, member)</div><div class="xr-var-dtype">float64</div><div class="xr-var-preview xr-preview">...</div><input id="attrs-edbe712d-00a1-494f-b250-ddc16fa6ba61" class="xr-var-attrs-in" type="checkbox"><label for="attrs-edbe712d-00a1-494f-b250-ddc16fa6ba61" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-306b30e4-e9ca-4023-bae7-99da51994c99" class="xr-var-data-in" type="checkbox"><label for="data-306b30e4-e9ca-4023-bae7-99da51994c99" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"><dt><span>units :</span></dt><dd>km3</dd><dt><span>long_name :</span></dt><dd>FW storage in sea ice and snow in the Arctic Ocean domain, relative to 34.8</dd></dl></div><div class="xr-var-data"><pre>[1045 values with dtype=float64]</pre></div></li></ul></div></li><li class="xr-section-item"><input id="section-da4f3a21-700b-4be1-80ca-8a5d971f6850" class="xr-section-summary-in" type="checkbox"><label for="section-da4f3a21-700b-4be1-80ca-8a5d971f6850" class="xr-section-summary">Indexes: <span>(2)</span></label><div class="xr-section-inline-details"></div><div class="xr-section-details"><ul class="xr-var-list"><li class="xr-var-item"><div class="xr-index-name"><div>time</div></div><div class="xr-index-preview">PandasIndex</div><div></div><input id="index-a551ea06-b4b2-4c5f-b189-a9df8c3bd99e" class="xr-index-data-in" type="checkbox"><label for="index-a551ea06-b4b2-4c5f-b189-a9df8c3bd99e" title="Show/Hide index repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-index-data"><pre>PandasIndex(Index([2006.0, 2007.0, 2008.0, 2009.0, 2010.0, 2011.0, 2012.0, 2013.0, 2014.0,
        2015.0, 2016.0, 2017.0, 2018.0, 2019.0, 2020.0, 2021.0, 2022.0, 2023.0,
        2024.0, 2025.0, 2026.0, 2027.0, 2028.0, 2029.0, 2030.0, 2031.0, 2032.0,
        2033.0, 2034.0, 2035.0, 2036.0, 2037.0, 2038.0, 2039.0, 2040.0, 2041.0,
@@ -3839,7 +3839,7 @@ <h3 data-number="5.4.3" class="anchored" data-anchor-id="exercise-1"><span class
        2078.0, 2079.0, 2080.0, 2081.0, 2082.0, 2083.0, 2084.0, 2085.0, 2086.0,
        2087.0, 2088.0, 2089.0, 2090.0, 2091.0, 2092.0, 2093.0, 2094.0, 2095.0,
        2096.0, 2097.0, 2098.0, 2099.0, 2100.0],
-      dtype='float64', name='time'))</pre></div></li><li class="xr-var-item"><div class="xr-index-name"><div>member</div></div><div class="xr-index-preview">PandasIndex</div><div></div><input id="index-95b4dc9a-9817-4dbb-9668-cb32381c888c" class="xr-index-data-in" type="checkbox"><label for="index-95b4dc9a-9817-4dbb-9668-cb32381c888c" title="Show/Hide index repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-index-data"><pre>PandasIndex(Index([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0], dtype='float64', name='member'))</pre></div></li></ul></div></li><li class="xr-section-item"><input id="section-68f29b65-1b02-40bd-93ad-5357e1a796d4" class="xr-section-summary-in" type="checkbox" checked=""><label for="section-68f29b65-1b02-40bd-93ad-5357e1a796d4" class="xr-section-summary">Attributes: <span>(5)</span></label><div class="xr-section-inline-details"></div><div class="xr-section-details"><dl class="xr-attrs"><dt><span>creation_date :</span></dt><dd>02-Jun-2020 15:38:31</dd><dt><span>author :</span></dt><dd>Alexandra Jahn, CU Boulder, alexandra.jahn@colorado.edu</dd><dt><span>title :</span></dt><dd>Annual timeseries of freshwater data from the CESM Low Warming Ensemble</dd><dt><span>description :</span></dt><dd>Annual mean Freshwater (FW) fluxes and storage relative to 34.8 shown in Jahn and Laiho, GRL, 2020, calculated from the 11-member Community Earth System Model (CESM) Low Warming Ensemble output (Sanderson et al., 2017, Earth Syst. Dynam., 8, 827-847. doi: 10.5194/esd-8-827-2017). These 11 ensemble members were branched from the first 11 ensemble members of the CESM Large Ensemble (companion data file) at the end of 2005. Convention for the fluxes is that positive fluxes signify a source of FW to the Arctic and negative fluxes are a sink/export of FW for the Arctic. FW fluxes are the net fluxes through a strait over the full ocean depth, adding up any positive and negative fluxes. Liquid FW storage is calculated over the full depth of the ocean but ignoring any negative FW (resulting from salinties over 34.8). Solid FW storage includes FW stored in sea ice and FW stored in snow on sea ice. Surface fluxes and FW storage is calculated over the Arctic domain bounded by Barrow Strait, Nares Strait, Bering Strait, Fram Strait, and the Barents Sea Opeing (BSO). Davis Strait fluxes are included for reference only and are outside of the Arctic domain. A map showing the domain and the location of the straits can be found in Jahn and Laiho, GRL, 2020.</dd><dt><span>data_structure :</span></dt><dd>The data structure is |Ensemble member | Time (in years)|. All data are annual means. The data covers 2006-2100. There are 11 ensemble members.</dd></dl></div></li></ul></div></div>
+      dtype='float64', name='time'))</pre></div></li><li class="xr-var-item"><div class="xr-index-name"><div>member</div></div><div class="xr-index-preview">PandasIndex</div><div></div><input id="index-bb045f8d-b4df-4970-9196-19835ff745e6" class="xr-index-data-in" type="checkbox"><label for="index-bb045f8d-b4df-4970-9196-19835ff745e6" title="Show/Hide index repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-index-data"><pre>PandasIndex(Index([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0], dtype='float64', name='member'))</pre></div></li></ul></div></li><li class="xr-section-item"><input id="section-fc5eb9d6-ebb6-45c1-968b-6a4604c3ad01" class="xr-section-summary-in" type="checkbox" checked=""><label for="section-fc5eb9d6-ebb6-45c1-968b-6a4604c3ad01" class="xr-section-summary">Attributes: <span>(5)</span></label><div class="xr-section-inline-details"></div><div class="xr-section-details"><dl class="xr-attrs"><dt><span>creation_date :</span></dt><dd>02-Jun-2020 15:38:31</dd><dt><span>author :</span></dt><dd>Alexandra Jahn, CU Boulder, alexandra.jahn@colorado.edu</dd><dt><span>title :</span></dt><dd>Annual timeseries of freshwater data from the CESM Low Warming Ensemble</dd><dt><span>description :</span></dt><dd>Annual mean Freshwater (FW) fluxes and storage relative to 34.8 shown in Jahn and Laiho, GRL, 2020, calculated from the 11-member Community Earth System Model (CESM) Low Warming Ensemble output (Sanderson et al., 2017, Earth Syst. Dynam., 8, 827-847. doi: 10.5194/esd-8-827-2017). These 11 ensemble members were branched from the first 11 ensemble members of the CESM Large Ensemble (companion data file) at the end of 2005. Convention for the fluxes is that positive fluxes signify a source of FW to the Arctic and negative fluxes are a sink/export of FW for the Arctic. FW fluxes are the net fluxes through a strait over the full ocean depth, adding up any positive and negative fluxes. Liquid FW storage is calculated over the full depth of the ocean but ignoring any negative FW (resulting from salinties over 34.8). Solid FW storage includes FW stored in sea ice and FW stored in snow on sea ice. Surface fluxes and FW storage is calculated over the Arctic domain bounded by Barrow Strait, Nares Strait, Bering Strait, Fram Strait, and the Barents Sea Opeing (BSO). Davis Strait fluxes are included for reference only and are outside of the Arctic domain. A map showing the domain and the location of the straits can be found in Jahn and Laiho, GRL, 2020.</dd><dt><span>data_structure :</span></dt><dd>The data structure is |Ensemble member | Time (in years)|. All data are annual means. The data covers 2006-2100. There are 11 ensemble members.</dd></dl></div></li></ul></div></div>
 </div>
 </div>
 <ol type="1">
@@ -3858,11 +3858,11 @@ <h3 data-number="5.4.3" class="anchored" data-anchor-id="exercise-1"><span class
 <div id="callout-4" class="callout-4-contents callout-collapse collapse">
 <div class="callout-body-container callout-body">
 <p>We can see in the object viewer that the <code>runoff_annual</code> variable has two dimensions: <code>time</code> and <code>member</code>, in that order. We can also access the dimensions by calling:</p>
-<div id="bcb2de22" class="cell" data-execution_count="15">
+<div id="0c6275d5" class="cell" data-execution_count="15">
 <div class="sourceCode cell-code" id="cb16"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb16-1"><a href="#cb16-1" aria-hidden="true" tabindex="-1"></a>fw_data.runoff_annual.dims</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>The second dimensions is <code>member</code>. Near the top of the object viewer, under coordinates, we can see that that member’s coordinates is an array from 1 to 11. We can directly see this array by calling:</p>
-<div id="0f57ab7d" class="cell" data-execution_count="16">
+<div id="14918327" class="cell" data-execution_count="16">
 <div class="sourceCode cell-code" id="cb17"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb17-1"><a href="#cb17-1" aria-hidden="true" tabindex="-1"></a>fw_data.member</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 </div>
@@ -3883,7 +3883,7 @@ <h3 data-number="5.4.3" class="anchored" data-anchor-id="exercise-1"><span class
 </div>
 <div id="callout-5" class="callout-5-contents callout-collapse collapse">
 <div class="callout-body-container callout-body">
-<div id="85030caa" class="cell" data-execution_count="17">
+<div id="ceb2976c" class="cell" data-execution_count="17">
 <div class="sourceCode cell-code" id="cb18"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb18-1"><a href="#cb18-1" aria-hidden="true" tabindex="-1"></a>member2 <span class="op">=</span> fw_data.netPrec_annual.sel(member<span class="op">=</span><span class="dv">2</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 </div>
@@ -3905,7 +3905,7 @@ <h3 data-number="5.4.3" class="anchored" data-anchor-id="exercise-1"><span class
 <div id="callout-6" class="callout-6-contents callout-collapse collapse">
 <div class="callout-body-container callout-body">
 <p>Based on our previous answer, this maximum is:</p>
-<div id="8e57dc4e" class="cell" data-execution_count="18">
+<div id="b0d042c4" class="cell" data-execution_count="18">
 <div class="sourceCode cell-code" id="cb19"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb19-1"><a href="#cb19-1" aria-hidden="true" tabindex="-1"></a>x_max <span class="op">=</span> member2.loc[<span class="dv">2022</span>:<span class="dv">2100</span>].<span class="bu">max</span>()</span>
 <span id="cb19-2"><a href="#cb19-2" aria-hidden="true" tabindex="-1"></a>x_max.item()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
@@ -3934,10 +3934,10 @@ <h3 data-number="5.5.1" class="anchored" data-anchor-id="tabular-to-netcdf"><spa
 <h3 data-number="5.5.2" class="anchored" data-anchor-id="pandas-to-xarray"><span class="header-section-number">5.5.2</span> <code>pandas</code> to <code>xarray</code></h3>
 <p>What does the previous example look like when working with <code>pandas</code> and <code>xarray</code>?</p>
 <p>Let’s work with a csv file containing the previous temperature measurements. Essentially, we need to read this file as a <code>pandas.DataFrame</code> and then use the <code>pandas.DataFrame.to_xarray()</code> method, taking into account that the dimensions of the resulting <code>xarray.DataSet</code> will be formed using the index column(s) of the <code>pandas.DataFrame</code>. In this case, we know the first three columns will be our dimension columns, so we need to group them as a <a href="https://pandas.pydata.org/docs/user_guide/advanced.html#advanced-hierarchical">multindex</a> for the <code>pandas.DataFrame</code>. We can do this by using the <code>index_col</code> argument directly when we read in the csv file.</p>
-<div id="10ddfccf" class="cell" data-execution_count="20">
+<div id="781d5b8c" class="cell" data-execution_count="20">
 <div class="sourceCode cell-code" id="cb20"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb20-1"><a href="#cb20-1" aria-hidden="true" tabindex="-1"></a>fp <span class="op">=</span> os.path.join(os.getcwd(),<span class="st">'netcdf_temp_data.csv'</span>) </span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
-<div id="3d52d0a3" class="cell" data-execution_count="21">
+<div id="ecb416d9" class="cell" data-execution_count="21">
 <div class="sourceCode cell-code" id="cb21"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb21-1"><a href="#cb21-1" aria-hidden="true" tabindex="-1"></a><span class="co"># specify columns representing dimensions</span></span>
 <span id="cb21-2"><a href="#cb21-2" aria-hidden="true" tabindex="-1"></a>dimension_columns <span class="op">=</span> [<span class="dv">0</span>,<span class="dv">1</span>,<span class="dv">2</span>]</span>
 <span id="cb21-3"><a href="#cb21-3" aria-hidden="true" tabindex="-1"></a></span>
@@ -4011,7 +4011,7 @@ <h3 data-number="5.5.2" class="anchored" data-anchor-id="pandas-to-xarray"><span
 </div>
 </div>
 <p>And this is our resulting <code>xarray.DataSet</code>:</p>
-<div id="d9c067c2" class="cell" data-execution_count="22">
+<div id="e2fc9bf8" class="cell" data-execution_count="22">
 <div class="sourceCode cell-code" id="cb22"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb22-1"><a href="#cb22-1" aria-hidden="true" tabindex="-1"></a>temp.to_xarray()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="17">
 <div><svg style="position: absolute; width: 0; height: 0; overflow: hidden">
@@ -4384,11 +4384,11 @@ <h3 data-number="5.5.2" class="anchored" data-anchor-id="pandas-to-xarray"><span
   * longitude    (longitude) int64 16B 30 40
   * latitude     (latitude) int64 16B 60 70
 Data variables:
-    temperature  (time, longitude, latitude) int64 64B 0 0 0 0 1 1 1 1</pre><div class="xr-wrap" style="display:none"><div class="xr-header"><div class="xr-obj-type">xarray.Dataset</div></div><ul class="xr-sections"><li class="xr-section-item"><input id="section-4f2f6880-ab2a-4498-99cc-fb2c2b94b7b1" class="xr-section-summary-in" type="checkbox" disabled=""><label for="section-4f2f6880-ab2a-4498-99cc-fb2c2b94b7b1" class="xr-section-summary" title="Expand/collapse section">Dimensions:</label><div class="xr-section-inline-details"><ul class="xr-dim-list"><li><span class="xr-has-index">time</span>: 2</li><li><span class="xr-has-index">longitude</span>: 2</li><li><span class="xr-has-index">latitude</span>: 2</li></ul></div><div class="xr-section-details"></div></li><li class="xr-section-item"><input id="section-eb5c71d7-6d58-4a7c-8c31-55c9a9861129" class="xr-section-summary-in" type="checkbox" checked=""><label for="section-eb5c71d7-6d58-4a7c-8c31-55c9a9861129" class="xr-section-summary">Coordinates: <span>(3)</span></label><div class="xr-section-inline-details"></div><div class="xr-section-details"><ul class="xr-var-list"><li class="xr-var-item"><div class="xr-var-name"><span class="xr-has-index">time</span></div><div class="xr-var-dims">(time)</div><div class="xr-var-dtype">object</div><div class="xr-var-preview xr-preview">'2022-09-01' '2022-09-02'</div><input id="attrs-589cfc20-fb47-4f2a-a22d-c9506366fceb" class="xr-var-attrs-in" type="checkbox" disabled=""><label for="attrs-589cfc20-fb47-4f2a-a22d-c9506366fceb" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-0a32688b-5398-4c3d-ad62-185cd4e21daa" class="xr-var-data-in" type="checkbox"><label for="data-0a32688b-5398-4c3d-ad62-185cd4e21daa" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"></dl></div><div class="xr-var-data"><pre>array(['2022-09-01', '2022-09-02'], dtype=object)</pre></div></li><li class="xr-var-item"><div class="xr-var-name"><span class="xr-has-index">longitude</span></div><div class="xr-var-dims">(longitude)</div><div class="xr-var-dtype">int64</div><div class="xr-var-preview xr-preview">30 40</div><input id="attrs-186b05dc-45a9-484b-acb9-6e08cccafa9f" class="xr-var-attrs-in" type="checkbox" disabled=""><label for="attrs-186b05dc-45a9-484b-acb9-6e08cccafa9f" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-e44b0f52-b490-4efd-86a0-7b757b892a9c" class="xr-var-data-in" type="checkbox"><label for="data-e44b0f52-b490-4efd-86a0-7b757b892a9c" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"></dl></div><div class="xr-var-data"><pre>array([30, 40])</pre></div></li><li class="xr-var-item"><div class="xr-var-name"><span class="xr-has-index">latitude</span></div><div class="xr-var-dims">(latitude)</div><div class="xr-var-dtype">int64</div><div class="xr-var-preview xr-preview">60 70</div><input id="attrs-b0fc6dd1-79a2-4956-99aa-be490f3fc651" class="xr-var-attrs-in" type="checkbox" disabled=""><label for="attrs-b0fc6dd1-79a2-4956-99aa-be490f3fc651" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-461be86d-78a6-414a-938a-6a091287f336" class="xr-var-data-in" type="checkbox"><label for="data-461be86d-78a6-414a-938a-6a091287f336" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"></dl></div><div class="xr-var-data"><pre>array([60, 70])</pre></div></li></ul></div></li><li class="xr-section-item"><input id="section-8340eb2f-bbbf-40e3-a4a7-a81b7d8d5ae3" class="xr-section-summary-in" type="checkbox" checked=""><label for="section-8340eb2f-bbbf-40e3-a4a7-a81b7d8d5ae3" class="xr-section-summary">Data variables: <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>temperature</span></div><div class="xr-var-dims">(time, longitude, latitude)</div><div class="xr-var-dtype">int64</div><div class="xr-var-preview xr-preview">0 0 0 0 1 1 1 1</div><input id="attrs-d5f9cc41-5853-4f1c-830a-aa07d3815c64" class="xr-var-attrs-in" type="checkbox" disabled=""><label for="attrs-d5f9cc41-5853-4f1c-830a-aa07d3815c64" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-6bf78ae2-887f-441c-89b6-e44c9b728787" class="xr-var-data-in" type="checkbox"><label for="data-6bf78ae2-887f-441c-89b6-e44c9b728787" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"></dl></div><div class="xr-var-data"><pre>array([[[0, 0],
+    temperature  (time, longitude, latitude) int64 64B 0 0 0 0 1 1 1 1</pre><div class="xr-wrap" style="display:none"><div class="xr-header"><div class="xr-obj-type">xarray.Dataset</div></div><ul class="xr-sections"><li class="xr-section-item"><input id="section-25f8f60a-41e9-4fdd-a18a-55f2ccfb13ef" class="xr-section-summary-in" type="checkbox" disabled=""><label for="section-25f8f60a-41e9-4fdd-a18a-55f2ccfb13ef" class="xr-section-summary" title="Expand/collapse section">Dimensions:</label><div class="xr-section-inline-details"><ul class="xr-dim-list"><li><span class="xr-has-index">time</span>: 2</li><li><span class="xr-has-index">longitude</span>: 2</li><li><span class="xr-has-index">latitude</span>: 2</li></ul></div><div class="xr-section-details"></div></li><li class="xr-section-item"><input id="section-b401fa96-26d5-4090-a6ea-8d8e7e818304" class="xr-section-summary-in" type="checkbox" checked=""><label for="section-b401fa96-26d5-4090-a6ea-8d8e7e818304" class="xr-section-summary">Coordinates: <span>(3)</span></label><div class="xr-section-inline-details"></div><div class="xr-section-details"><ul class="xr-var-list"><li class="xr-var-item"><div class="xr-var-name"><span class="xr-has-index">time</span></div><div class="xr-var-dims">(time)</div><div class="xr-var-dtype">object</div><div class="xr-var-preview xr-preview">'2022-09-01' '2022-09-02'</div><input id="attrs-5f85ef11-582b-4129-82ba-56e766c0af04" class="xr-var-attrs-in" type="checkbox" disabled=""><label for="attrs-5f85ef11-582b-4129-82ba-56e766c0af04" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-ad586660-5413-4484-a4ba-e8b83f4b4f9a" class="xr-var-data-in" type="checkbox"><label for="data-ad586660-5413-4484-a4ba-e8b83f4b4f9a" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"></dl></div><div class="xr-var-data"><pre>array(['2022-09-01', '2022-09-02'], dtype=object)</pre></div></li><li class="xr-var-item"><div class="xr-var-name"><span class="xr-has-index">longitude</span></div><div class="xr-var-dims">(longitude)</div><div class="xr-var-dtype">int64</div><div class="xr-var-preview xr-preview">30 40</div><input id="attrs-8f996904-850f-4645-9679-1fbf7f6199bc" class="xr-var-attrs-in" type="checkbox" disabled=""><label for="attrs-8f996904-850f-4645-9679-1fbf7f6199bc" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-b614b104-7c76-4945-abbd-ee9dc2dbd10a" class="xr-var-data-in" type="checkbox"><label for="data-b614b104-7c76-4945-abbd-ee9dc2dbd10a" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"></dl></div><div class="xr-var-data"><pre>array([30, 40])</pre></div></li><li class="xr-var-item"><div class="xr-var-name"><span class="xr-has-index">latitude</span></div><div class="xr-var-dims">(latitude)</div><div class="xr-var-dtype">int64</div><div class="xr-var-preview xr-preview">60 70</div><input id="attrs-f4251c75-6fbb-4158-ac54-7f490daf5ea0" class="xr-var-attrs-in" type="checkbox" disabled=""><label for="attrs-f4251c75-6fbb-4158-ac54-7f490daf5ea0" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-667aa07e-3453-451a-bf5d-8d8e268895b7" class="xr-var-data-in" type="checkbox"><label for="data-667aa07e-3453-451a-bf5d-8d8e268895b7" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"></dl></div><div class="xr-var-data"><pre>array([60, 70])</pre></div></li></ul></div></li><li class="xr-section-item"><input id="section-df4410f6-bb39-4dc4-9904-c3039246af85" class="xr-section-summary-in" type="checkbox" checked=""><label for="section-df4410f6-bb39-4dc4-9904-c3039246af85" class="xr-section-summary">Data variables: <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>temperature</span></div><div class="xr-var-dims">(time, longitude, latitude)</div><div class="xr-var-dtype">int64</div><div class="xr-var-preview xr-preview">0 0 0 0 1 1 1 1</div><input id="attrs-d8645abd-df18-4da6-8f70-11d28af10338" class="xr-var-attrs-in" type="checkbox" disabled=""><label for="attrs-d8645abd-df18-4da6-8f70-11d28af10338" title="Show/Hide attributes"><svg class="icon xr-icon-file-text2"><use href="#icon-file-text2"></use></svg></label><input id="data-ce7c59a2-ba90-450a-ae73-a5527062c927" class="xr-var-data-in" type="checkbox"><label for="data-ce7c59a2-ba90-450a-ae73-a5527062c927" title="Show/Hide data repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-var-attrs"><dl class="xr-attrs"></dl></div><div class="xr-var-data"><pre>array([[[0, 0],
         [0, 0]],
 
        [[1, 1],
-        [1, 1]]])</pre></div></li></ul></div></li><li class="xr-section-item"><input id="section-d065954b-81d3-471e-8efe-f89d18b7375d" class="xr-section-summary-in" type="checkbox"><label for="section-d065954b-81d3-471e-8efe-f89d18b7375d" class="xr-section-summary">Indexes: <span>(3)</span></label><div class="xr-section-inline-details"></div><div class="xr-section-details"><ul class="xr-var-list"><li class="xr-var-item"><div class="xr-index-name"><div>time</div></div><div class="xr-index-preview">PandasIndex</div><div></div><input id="index-0c296d57-cca2-4ad9-8737-a1268a6cd665" class="xr-index-data-in" type="checkbox"><label for="index-0c296d57-cca2-4ad9-8737-a1268a6cd665" title="Show/Hide index repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-index-data"><pre>PandasIndex(Index(['2022-09-01', '2022-09-02'], dtype='object', name='time'))</pre></div></li><li class="xr-var-item"><div class="xr-index-name"><div>longitude</div></div><div class="xr-index-preview">PandasIndex</div><div></div><input id="index-cd24ef40-e1f9-46f3-86e4-99afa46578ed" class="xr-index-data-in" type="checkbox"><label for="index-cd24ef40-e1f9-46f3-86e4-99afa46578ed" title="Show/Hide index repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-index-data"><pre>PandasIndex(Index([30, 40], dtype='int64', name='longitude'))</pre></div></li><li class="xr-var-item"><div class="xr-index-name"><div>latitude</div></div><div class="xr-index-preview">PandasIndex</div><div></div><input id="index-4aafe24a-1143-43a2-bd35-7af8d8a036fe" class="xr-index-data-in" type="checkbox"><label for="index-4aafe24a-1143-43a2-bd35-7af8d8a036fe" title="Show/Hide index repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-index-data"><pre>PandasIndex(Index([60, 70], dtype='int64', name='latitude'))</pre></div></li></ul></div></li><li class="xr-section-item"><input id="section-a5bf64b9-4fc7-498e-9b17-0dc39345a846" class="xr-section-summary-in" type="checkbox" disabled=""><label for="section-a5bf64b9-4fc7-498e-9b17-0dc39345a846" class="xr-section-summary" title="Expand/collapse section">Attributes: <span>(0)</span></label><div class="xr-section-inline-details"></div><div class="xr-section-details"><dl class="xr-attrs"></dl></div></li></ul></div></div>
+        [1, 1]]])</pre></div></li></ul></div></li><li class="xr-section-item"><input id="section-ff2bad8d-a600-4fc0-a9b8-eb9242de3c2a" class="xr-section-summary-in" type="checkbox"><label for="section-ff2bad8d-a600-4fc0-a9b8-eb9242de3c2a" class="xr-section-summary">Indexes: <span>(3)</span></label><div class="xr-section-inline-details"></div><div class="xr-section-details"><ul class="xr-var-list"><li class="xr-var-item"><div class="xr-index-name"><div>time</div></div><div class="xr-index-preview">PandasIndex</div><div></div><input id="index-77fe8e7c-3215-4d59-8ce9-beaedbc38111" class="xr-index-data-in" type="checkbox"><label for="index-77fe8e7c-3215-4d59-8ce9-beaedbc38111" title="Show/Hide index repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-index-data"><pre>PandasIndex(Index(['2022-09-01', '2022-09-02'], dtype='object', name='time'))</pre></div></li><li class="xr-var-item"><div class="xr-index-name"><div>longitude</div></div><div class="xr-index-preview">PandasIndex</div><div></div><input id="index-29e04416-972a-4bcf-929f-384b1ddaea77" class="xr-index-data-in" type="checkbox"><label for="index-29e04416-972a-4bcf-929f-384b1ddaea77" title="Show/Hide index repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-index-data"><pre>PandasIndex(Index([30, 40], dtype='int64', name='longitude'))</pre></div></li><li class="xr-var-item"><div class="xr-index-name"><div>latitude</div></div><div class="xr-index-preview">PandasIndex</div><div></div><input id="index-dfb1fc69-2c6c-43bf-9f27-626d0e30baa4" class="xr-index-data-in" type="checkbox"><label for="index-dfb1fc69-2c6c-43bf-9f27-626d0e30baa4" title="Show/Hide index repr"><svg class="icon xr-icon-database"><use href="#icon-database"></use></svg></label><div class="xr-index-data"><pre>PandasIndex(Index([60, 70], dtype='int64', name='latitude'))</pre></div></li></ul></div></li><li class="xr-section-item"><input id="section-241b45b9-2076-41d9-b656-fd15861cc592" class="xr-section-summary-in" type="checkbox" disabled=""><label for="section-241b45b9-2076-41d9-b656-fd15861cc592" class="xr-section-summary" title="Expand/collapse section">Attributes: <span>(0)</span></label><div class="xr-section-inline-details"></div><div class="xr-section-details"><dl class="xr-attrs"></dl></div></li></ul></div></div>
 </div>
 </div>
 <p>For further reading and examples about switching between <code>pandas</code> and <code>xarray</code> you can visit the following:</p>
diff --git a/public/2024-03-arctic/sections/geopandas.html b/public/2024-03-arctic/sections/geopandas.html
index 4676ec50..4323fed9 100644
--- a/public/2024-03-arctic/sections/geopandas.html
+++ b/public/2024-03-arctic/sections/geopandas.html
@@ -336,7 +336,7 @@ <h2 data-number="9.2" class="anchored" data-anchor-id="introduction"><span class
 <h2 data-number="9.3" class="anchored" data-anchor-id="pre-processing-raster-data"><span class="header-section-number">9.3</span> Pre-processing raster data</h2>
 <p>First we need to load in our libraries. We’ll use <code>geopandas</code> for vector manipulation, <code>rasterio</code> for raster maniupulation.</p>
 <p>First, we’ll use <code>requests</code> to download the ship traffic raster from <a href="https://doi.org/10.18739/A2SQ8QJ9S">Kapsar et al.</a>. We grab a one month slice from August, 2020 of a coastal subset of data with 1km resolution. To get the URL in the code chunk below, you can right click the download button for the file of interest and select “copy link address.”</p>
-<div id="6501dafe" class="cell" data-execution_count="1">
+<div id="387010a0" class="cell" data-execution_count="1">
 <div class="sourceCode cell-code" id="cb1"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> urllib</span>
 <span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a>url <span class="op">=</span> <span class="st">'https://arcticdata.io/metacat/d1/mn/v2/object/urn%3Auuid%3A6b847ab0-9a3d-4534-bf28-3a96c5fa8d72'</span></span>
@@ -344,7 +344,7 @@ <h2 data-number="9.3" class="anchored" data-anchor-id="pre-processing-raster-dat
 <span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a>msg <span class="op">=</span> urllib.request.urlretrieve(url, <span class="st">"Coastal_2020_08.tif"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>Using <code>rasterio</code>, open the raster file, plot it, and look at the metadata. We use the <code>with</code> here as a context manager. This ensures that the connection to the raster file is closed and cleaned up when we are done with it.</p>
-<div id="a11e8f0a" class="cell" data-execution_count="2">
+<div id="6601de9b" class="cell" data-execution_count="2">
 <div class="sourceCode cell-code" id="cb2"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> rasterio</span>
 <span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> matplotlib.pyplot <span class="im">as</span> plt</span>
 <span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a></span>
@@ -368,13 +368,13 @@ <h2 data-number="9.3" class="anchored" data-anchor-id="pre-processing-raster-dat
 </div>
 </div>
 <p>You’ll notice that we are saving two objects here, <code>ships</code> and <code>ships_meta</code>. Looking at the types of these two objects is useful to understand what <code>rasterio</code> is doing.</p>
-<div id="e7850de1" class="cell" data-execution_count="3">
+<div id="4158a1ef" class="cell" data-execution_count="3">
 <div class="sourceCode cell-code" id="cb4"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a><span class="bu">type</span>(ships)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="3">
 <pre><code>numpy.ndarray</code></pre>
 </div>
 </div>
-<div id="73b30313" class="cell" data-execution_count="4">
+<div id="e4f0d25e" class="cell" data-execution_count="4">
 <div class="sourceCode cell-code" id="cb6"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a><span class="bu">type</span>(ships_meta)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="4">
 <pre><code>rasterio.profiles.Profile</code></pre>
@@ -402,20 +402,20 @@ <h2 data-number="9.3" class="anchored" data-anchor-id="pre-processing-raster-dat
 <section id="pre-processing-vector-data" class="level2" data-number="9.4">
 <h2 data-number="9.4" class="anchored" data-anchor-id="pre-processing-vector-data"><span class="header-section-number">9.4</span> Pre-processing vector data</h2>
 <p>Now download a vector dataset of commercial fishing districts in Alaska.</p>
-<div id="be729942" class="cell" data-execution_count="5">
+<div id="d5f77dca" class="cell" data-execution_count="5">
 <div class="sourceCode cell-code" id="cb9"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a>url <span class="op">=</span> <span class="st">'https://knb.ecoinformatics.org/knb/d1/mn/v2/object/urn%3Auuid%3A7c942c45-1539-4d47-b429-205499f0f3e4'</span></span>
 <span id="cb9-2"><a href="#cb9-2" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb9-3"><a href="#cb9-3" aria-hidden="true" tabindex="-1"></a>msg <span class="op">=</span> urllib.request.urlretrieve(url, <span class="st">"Alaska_Commercial_Salmon_Boundaries.gpkg"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>Read in the data using <code>geopandas</code>.</p>
-<div id="3fcc6bea" class="cell" data-execution_count="6">
+<div id="26740564" class="cell" data-execution_count="6">
 <div class="sourceCode cell-code" id="cb10"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb10-1"><a href="#cb10-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> geopandas <span class="im">as</span> gpd</span>
 <span id="cb10-2"><a href="#cb10-2" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb10-3"><a href="#cb10-3" aria-hidden="true" tabindex="-1"></a>comm <span class="op">=</span> gpd.read_file(<span class="st">"Alaska_Commercial_Salmon_Boundaries.gpkg"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>Note the “pandas” in the library name “geopandas.” Our <code>comm</code> object is really just a special type of pandas data frame called a geodataframe. This means that in addition to any geospatial stuff we need to do, we can also just do regular <code>pandas</code> things on this data frame.</p>
 <p>For example, we can get a list of column names (there are a lot!)</p>
-<div id="2c904ce2" class="cell" data-execution_count="7">
+<div id="cba5f85f" class="cell" data-execution_count="7">
 <div class="sourceCode cell-code" id="cb11"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb11-1"><a href="#cb11-1" aria-hidden="true" tabindex="-1"></a>comm.columns.values</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="7">
 <pre><code>array(['OBJECTID', 'GEOMETRY_START_DATE', 'GEOMETRY_END_DATE',
@@ -433,7 +433,7 @@ <h2 data-number="9.4" class="anchored" data-anchor-id="pre-processing-vector-dat
 </div>
 </div>
 <p>We can also look at the head of the data frame:</p>
-<div id="891fc699" class="cell" data-execution_count="8">
+<div id="3cce170d" class="cell" data-execution_count="8">
 <div class="sourceCode cell-code" id="cb13"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb13-1"><a href="#cb13-1" aria-hidden="true" tabindex="-1"></a>comm.head()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="8">
 <div>
@@ -600,7 +600,7 @@ <h2 data-number="9.4" class="anchored" data-anchor-id="pre-processing-vector-dat
 <p><img src="../images/vector-diagram.png" class="img-fluid"></p>
 <p>Source: <a href="https://www.neonscience.org/resources/learning-hub/tutorials/dc-raster-data-r">Leah A. Wasser, Megan A. Jones, Zack Brym, Kristina Riemer, Jason Williams, Jeff Hollister, Mike Smorul. Raster 00: Intro to Raster Data in R. National Evologial Observatory Network (NEON).</a></p>
 <p>The diagram above shows the three different types of geometries that geospatial vector data can take, points, lines or polygons. Whatever the geometry type, the geometry information (the x,y points) is stored in the column named <code>geometry</code> in the <code>geopandas</code> data frame. In this example, we have a dataset containing polygons of fishing districts. Each row in the dataset corresponds to a district, with unique attributes (the other columns in the dataset), and its own set of points defining the boundaries of the district, contained in the <code>geometry</code> column.</p>
-<div id="d3a1bbd8" class="cell" data-execution_count="9">
+<div id="c1c5ae99" class="cell" data-execution_count="9">
 <div class="sourceCode cell-code" id="cb14"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb14-1"><a href="#cb14-1" aria-hidden="true" tabindex="-1"></a>comm[<span class="st">'geometry'</span>][:<span class="dv">5</span>]</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="9">
 <pre><code>0    MULTIPOLYGON (((-151.32805 64.96913, -151.3150...
@@ -612,7 +612,7 @@ <h2 data-number="9.4" class="anchored" data-anchor-id="pre-processing-vector-dat
 </div>
 </div>
 <p>So, now we know where our x,y points are, where is all of the other information like the <code>crs</code>? With vector data, all of this information is contained within the geodataframe. We can access the <code>crs</code> attribute on the data frame and print it like so:</p>
-<div id="dc2f66d5" class="cell" data-execution_count="10">
+<div id="7881b4f6" class="cell" data-execution_count="10">
 <div class="sourceCode cell-code" id="cb16"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb16-1"><a href="#cb16-1" aria-hidden="true" tabindex="-1"></a>comm.crs</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="10">
 <pre><code>&lt;Geographic 2D CRS: EPSG:4326&gt;
@@ -629,7 +629,7 @@ <h2 data-number="9.4" class="anchored" data-anchor-id="pre-processing-vector-dat
 </div>
 </div>
 <p>We can also plot one geometry cell by simply calling it:</p>
-<div id="08d908e4" class="cell" data-execution_count="11">
+<div id="43558789" class="cell" data-execution_count="11">
 <div class="sourceCode cell-code" id="cb18"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb18-1"><a href="#cb18-1" aria-hidden="true" tabindex="-1"></a>comm[<span class="st">'geometry'</span>][<span class="dv">8</span>]</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="11">
 <div>
@@ -640,7 +640,7 @@ <h2 data-number="9.4" class="anchored" data-anchor-id="pre-processing-vector-dat
 </div>
 </div>
 <p>Now that we know a little about what we are working with, let’s get this data ready to analyze. First, we can make a plot of it just to see what we have.</p>
-<div id="d2c5c511" class="cell" data-execution_count="12">
+<div id="0e2e0638" class="cell" data-execution_count="12">
 <div class="sourceCode cell-code" id="cb19"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb19-1"><a href="#cb19-1" aria-hidden="true" tabindex="-1"></a>comm.plot(figsize<span class="op">=</span>(<span class="dv">9</span>,<span class="dv">9</span>))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display">
 <div>
@@ -651,7 +651,7 @@ <h2 data-number="9.4" class="anchored" data-anchor-id="pre-processing-vector-dat
 </div>
 </div>
 <p>This plot doesn’t look so good. Turns out, these data are in WGS 84 (EPSG 4326), as opposed to Alaska Albers (EPSG 3338), which is what our raster data are in. To make pretty plots, and allow our raster data and vector data to be analyzed together, we’ll need to reproject the vector data into 3338. To to this, we’ll use the <code>to_crs</code> method on our <code>comm</code> object, and specify as an argument the projection we want to transform to.</p>
-<div id="2b9e6238" class="cell" data-execution_count="13">
+<div id="28f90a61" class="cell" data-execution_count="13">
 <div class="sourceCode cell-code" id="cb20"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb20-1"><a href="#cb20-1" aria-hidden="true" tabindex="-1"></a>comm_3338 <span class="op">=</span> comm.to_crs(<span class="st">"EPSG:3338"</span>)</span>
 <span id="cb20-2"><a href="#cb20-2" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb20-3"><a href="#cb20-3" aria-hidden="true" tabindex="-1"></a>comm_3338.plot()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
@@ -669,7 +669,7 @@ <h2 data-number="9.4" class="anchored" data-anchor-id="pre-processing-vector-dat
 <h2 data-number="9.5" class="anchored" data-anchor-id="crop-data-to-area-of-interest"><span class="header-section-number">9.5</span> Crop data to area of interest</h2>
 <p>For this example, we are only interested in south central Alaska, encompassing Prince William Sound, Cook Inlet, and Kodiak. Our raster data is significantly larger than that, and the vector data is statewide. So, as a first step we might want to crop our data to the area of interest.</p>
 <p>First, we’ll need to create a bounding box. We use the <code>box</code> function from <code>shapely</code> to create the bounding box, then create a <code>geoDataFrame</code> from the points, and finally convert the WGS 84 coordinates to the Alaska Albers projection.</p>
-<div id="c56dc0c3" class="cell" data-execution_count="14">
+<div id="63da2c46" class="cell" data-execution_count="14">
 <div class="sourceCode cell-code" id="cb21"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb21-1"><a href="#cb21-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> shapely.geometry <span class="im">import</span> box</span>
 <span id="cb21-2"><a href="#cb21-2" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb21-3"><a href="#cb21-3" aria-hidden="true" tabindex="-1"></a>coord_box <span class="op">=</span> box(<span class="op">-</span><span class="fl">159.5</span>, <span class="dv">55</span>, <span class="op">-</span><span class="fl">144.5</span>, <span class="dv">62</span>)</span>
@@ -679,7 +679,7 @@ <h2 data-number="9.5" class="anchored" data-anchor-id="crop-data-to-area-of-inte
 <span id="cb21-7"><a href="#cb21-7" aria-hidden="true" tabindex="-1"></a>    geometry <span class="op">=</span> [coord_box]).to_crs(<span class="st">"EPSG:3338"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>Now, we can read in raster again cropped to bounding box. We use the <code>mask</code> function from <code>rasterio.mask</code>. Note that we apply this to the connection to the raster file (<code>with rasterio.open(...)</code>), then update the metadata associated with the raster, because the <code>mask</code> function requires as its first <code>dataset</code> argument a dataset object opened in <code>r</code> mode.</p>
-<div id="b5cc6678" class="cell" data-execution_count="15">
+<div id="bea20acb" class="cell" data-execution_count="15">
 <div class="sourceCode cell-code" id="cb22"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb22-1"><a href="#cb22-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> rasterio.mask</span>
 <span id="cb22-2"><a href="#cb22-2" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> numpy <span class="im">as</span> np</span>
 <span id="cb22-3"><a href="#cb22-3" aria-hidden="true" tabindex="-1"></a></span>
@@ -693,7 +693,7 @@ <h2 data-number="9.5" class="anchored" data-anchor-id="crop-data-to-area-of-inte
 <span id="cb22-11"><a href="#cb22-11" aria-hidden="true" tabindex="-1"></a>    <span class="co"># turn the no-data values into NaNs.</span></span>
 <span id="cb22-12"><a href="#cb22-12" aria-hidden="true" tabindex="-1"></a>    shipc_arr[shipc_arr <span class="op">==</span> ship_con.nodata] <span class="op">=</span> np.nan</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
-<div id="4fb69bd5" class="cell" data-execution_count="16">
+<div id="df2e0f5b" class="cell" data-execution_count="16">
 <div class="sourceCode cell-code" id="cb23"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb23-1"><a href="#cb23-1" aria-hidden="true" tabindex="-1"></a>shipc_meta.update({<span class="st">"driver"</span>: <span class="st">"GTiff"</span>,</span>
 <span id="cb23-2"><a href="#cb23-2" aria-hidden="true" tabindex="-1"></a>                 <span class="st">"height"</span>: shipc_arr.shape[<span class="dv">0</span>],</span>
 <span id="cb23-3"><a href="#cb23-3" aria-hidden="true" tabindex="-1"></a>                 <span class="st">"width"</span>: shipc_arr.shape[<span class="dv">1</span>],</span>
@@ -701,7 +701,7 @@ <h2 data-number="9.5" class="anchored" data-anchor-id="crop-data-to-area-of-inte
 <span id="cb23-5"><a href="#cb23-5" aria-hidden="true" tabindex="-1"></a>                 <span class="st">"compress"</span>: <span class="st">"lzw"</span>})</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>Now we’ll do a similar task with the vector data. Tin this case, we use a spatial join. The join will be an inner join, and select only rows from the left side (our fishing districts) that are <strong>within</strong> the right side (our bounding box). I chose this method as opposed to a clipping type operation because it ensures that we don’t end up with any open polygons at the boundaries of our box, which could cause problems for us down the road.</p>
-<div id="cdf01ece" class="cell" data-execution_count="17">
+<div id="41e567ab" class="cell" data-execution_count="17">
 <div class="sourceCode cell-code" id="cb24"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb24-1"><a href="#cb24-1" aria-hidden="true" tabindex="-1"></a>comm_clip <span class="op">=</span> gpd.sjoin(comm_3338,</span>
 <span id="cb24-2"><a href="#cb24-2" aria-hidden="true" tabindex="-1"></a>                      coord_box_df,</span>
 <span id="cb24-3"><a href="#cb24-3" aria-hidden="true" tabindex="-1"></a>                      how<span class="op">=</span><span class="st">'inner'</span>,</span>
@@ -710,7 +710,7 @@ <h2 data-number="9.5" class="anchored" data-anchor-id="crop-data-to-area-of-inte
 <section id="check-extents" class="level3" data-number="9.5.1">
 <h3 data-number="9.5.1" class="anchored" data-anchor-id="check-extents"><span class="header-section-number">9.5.1</span> Check extents</h3>
 <p>Now let’s look at the two cropped datasets overlaid on each other to ensure that the extents look right.</p>
-<div id="4b7662fa" class="cell" data-execution_count="18">
+<div id="61a34b9c" class="cell" data-execution_count="18">
 <div class="sourceCode cell-code" id="cb25"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb25-1"><a href="#cb25-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> rasterio.plot</span>
 <span id="cb25-2"><a href="#cb25-2" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb25-3"><a href="#cb25-3" aria-hidden="true" tabindex="-1"></a><span class="co"># set up plot</span></span>
@@ -741,7 +741,7 @@ <h2 data-number="9.6" class="anchored" data-anchor-id="calculate-total-distance-
 <section id="zonal-statistics-over-one-polygon" class="level4" data-number="9.6.0.1">
 <h4 data-number="9.6.0.1" class="anchored" data-anchor-id="zonal-statistics-over-one-polygon"><span class="header-section-number">9.6.0.1</span> Zonal statistics over one polygon</h4>
 <p>Let’s look at how this works over just one fishing area first. We use the <code>rasterize</code> method from the <code>features</code> module in <code>rasterio</code>. This takes as arguments the data to rasterize (in this case the 40th row of our dataset), and the shape and transform the output raster will take. We also set the <code>all_touched</code> argument to true, which means any pixel that touches a boundary of our vector will be burned into the mask.</p>
-<div id="c24ae3e2" class="cell" data-execution_count="19">
+<div id="de224c65" class="cell" data-execution_count="19">
 <div class="sourceCode cell-code" id="cb26"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb26-1"><a href="#cb26-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> rasterio <span class="im">import</span> features</span>
 <span id="cb26-2"><a href="#cb26-2" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb26-3"><a href="#cb26-3" aria-hidden="true" tabindex="-1"></a>r40 <span class="op">=</span> features.rasterize(comm_clip[<span class="st">'geometry'</span>][<span class="dv">40</span>].geoms,</span>
@@ -750,7 +750,7 @@ <h4 data-number="9.6.0.1" class="anchored" data-anchor-id="zonal-statistics-over
 <span id="cb26-6"><a href="#cb26-6" aria-hidden="true" tabindex="-1"></a>                                    all_touched<span class="op">=</span><span class="va">True</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>If we have a look at a plot of our rasterized version of the single fishing district, we can see that instead of a vector, we now have a raster showing the rasterized district (with pixel values of 1) and any area not in the district has a pixel value of 0.</p>
-<div id="20e921ee" class="cell" data-execution_count="20">
+<div id="12ad2018" class="cell" data-execution_count="20">
 <div class="sourceCode cell-code" id="cb27"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb27-1"><a href="#cb27-1" aria-hidden="true" tabindex="-1"></a><span class="co"># set up plot</span></span>
 <span id="cb27-2"><a href="#cb27-2" aria-hidden="true" tabindex="-1"></a>fig, ax <span class="op">=</span> plt.subplots(figsize<span class="op">=</span>(<span class="dv">7</span>, <span class="dv">7</span>))</span>
 <span id="cb27-3"><a href="#cb27-3" aria-hidden="true" tabindex="-1"></a><span class="co"># plot the raster</span></span>
@@ -770,14 +770,14 @@ <h4 data-number="9.6.0.1" class="anchored" data-anchor-id="zonal-statistics-over
 </div>
 </div>
 <p>A quick call to <code>np.unique</code> shows our unique values are 0 or 1, which is what we expect.</p>
-<div id="6fff0157" class="cell" data-execution_count="21">
+<div id="eed3171b" class="cell" data-execution_count="21">
 <div class="sourceCode cell-code" id="cb28"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb28-1"><a href="#cb28-1" aria-hidden="true" tabindex="-1"></a>np.unique(r40)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="21">
 <pre><code>array([0, 1], dtype=uint8)</code></pre>
 </div>
 </div>
 <p>Finally, we need to know the indices where the fishing district is. We can use <code>np.where</code> to extract this information</p>
-<div id="5a21b0ae" class="cell" data-execution_count="22">
+<div id="569855ab" class="cell" data-execution_count="22">
 <div class="sourceCode cell-code" id="cb30"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb30-1"><a href="#cb30-1" aria-hidden="true" tabindex="-1"></a>r40_index <span class="op">=</span> np.where(r40 <span class="op">==</span> <span class="dv">1</span>)</span>
 <span id="cb30-2"><a href="#cb30-2" aria-hidden="true" tabindex="-1"></a><span class="bu">print</span>(r40_index)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
@@ -797,7 +797,7 @@ <h4 data-number="9.6.0.1" class="anchored" data-anchor-id="zonal-statistics-over
 </div>
 </div>
 <p>In the last step, we’ll use these indices to extract the values of the data from the ship traffic raster, and sum them to get a total distance travelled.</p>
-<div id="68258ca5" class="cell" data-execution_count="23">
+<div id="d96f408d" class="cell" data-execution_count="23">
 <div class="sourceCode cell-code" id="cb32"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb32-1"><a href="#cb32-1" aria-hidden="true" tabindex="-1"></a>np.nansum(shipc_arr[r40_index])</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="23">
 <pre><code>14369028.0</code></pre>
@@ -807,11 +807,11 @@ <h4 data-number="9.6.0.1" class="anchored" data-anchor-id="zonal-statistics-over
 </section>
 <section id="zonal-statistics-over-all-polygons" class="level4" data-number="9.6.0.2">
 <h4 data-number="9.6.0.2" class="anchored" data-anchor-id="zonal-statistics-over-all-polygons"><span class="header-section-number">9.6.0.2</span> Zonal statistics over all polygons</h4>
-<div id="6ce60ecb" class="cell" data-execution_count="24">
+<div id="8a60a578" class="cell" data-execution_count="24">
 <div class="sourceCode cell-code" id="cb34"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb34-1"><a href="#cb34-1" aria-hidden="true" tabindex="-1"></a>comm_clip[<span class="st">'id'</span>] <span class="op">=</span> <span class="bu">range</span>(<span class="dv">0</span>,<span class="bu">len</span>(comm_clip))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>For each district (with <code>geometry</code> and <code>id</code>), we run the <code>features.rasterize</code> function. Then we calculate the sum of the values of the shipping raster based on the indices in the raster where the district is located.</p>
-<div id="f64a349f" class="cell" data-results="false" data-execution_count="25">
+<div id="9e3f5c2d" class="cell" data-results="false" data-execution_count="25">
 <div class="sourceCode cell-code" id="cb35"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb35-1"><a href="#cb35-1" aria-hidden="true" tabindex="-1"></a>distance_dict <span class="op">=</span> {}</span>
 <span id="cb35-2"><a href="#cb35-2" aria-hidden="true" tabindex="-1"></a><span class="cf">for</span> geom, idx <span class="kw">in</span> <span class="bu">zip</span>(comm_clip[<span class="st">'geometry'</span>], comm_clip[<span class="st">'id'</span>]):</span>
 <span id="cb35-3"><a href="#cb35-3" aria-hidden="true" tabindex="-1"></a>    rasterized <span class="op">=</span> features.rasterize(geom.geoms,</span>
@@ -823,7 +823,7 @@ <h4 data-number="9.6.0.2" class="anchored" data-anchor-id="zonal-statistics-over
 <span id="cb35-9"><a href="#cb35-9" aria-hidden="true" tabindex="-1"></a>    distance_dict[idx] <span class="op">=</span> np.nansum(shipc_arr[r_index])</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>Now we just create a data frame from that dictionary, and join it to the vector data using <code>pandas</code> operations.</p>
-<div id="bda451f1" class="cell" data-execution_count="26">
+<div id="779dd7bc" class="cell" data-execution_count="26">
 <div class="sourceCode cell-code" id="cb36"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb36-1"><a href="#cb36-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> pandas <span class="im">as</span> pd</span>
 <span id="cb36-2"><a href="#cb36-2" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb36-3"><a href="#cb36-3" aria-hidden="true" tabindex="-1"></a><span class="co"># create a data frame from the result</span></span>
@@ -836,14 +836,14 @@ <h4 data-number="9.6.0.2" class="anchored" data-anchor-id="zonal-statistics-over
 <span id="cb36-10"><a href="#cb36-10" aria-hidden="true" tabindex="-1"></a>distance_df[<span class="st">'distance'</span>] <span class="op">=</span> distance_df[<span class="st">'distance'</span>]<span class="op">/</span><span class="dv">1000</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>Now we join the result to the original geodataframe.</p>
-<div id="2ed5b44c" class="cell" data-execution_count="27">
+<div id="bb9b0338" class="cell" data-execution_count="27">
 <div class="sourceCode cell-code" id="cb37"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb37-1"><a href="#cb37-1" aria-hidden="true" tabindex="-1"></a><span class="co"># join the sums to the original data frame</span></span>
 <span id="cb37-2"><a href="#cb37-2" aria-hidden="true" tabindex="-1"></a>res_full <span class="op">=</span> comm_clip.merge(distance_df,</span>
 <span id="cb37-3"><a href="#cb37-3" aria-hidden="true" tabindex="-1"></a>                           on <span class="op">=</span> <span class="st">"id"</span>,</span>
 <span id="cb37-4"><a href="#cb37-4" aria-hidden="true" tabindex="-1"></a>                           how <span class="op">=</span> <span class="st">'inner'</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>Finally, we can plot our result!</p>
-<div id="7ab4843d" class="cell" data-execution_count="28">
+<div id="12c451d2" class="cell" data-execution_count="28">
 <div class="sourceCode cell-code" id="cb38"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb38-1"><a href="#cb38-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> matplotlib.ticker</span>
 <span id="cb38-2"><a href="#cb38-2" aria-hidden="true" tabindex="-1"></a>fig, ax <span class="op">=</span> plt.subplots(figsize<span class="op">=</span>(<span class="dv">7</span>, <span class="dv">7</span>))</span>
 <span id="cb38-3"><a href="#cb38-3" aria-hidden="true" tabindex="-1"></a></span>
@@ -866,13 +866,13 @@ <h4 data-number="9.6.0.2" class="anchored" data-anchor-id="zonal-statistics-over
 </div>
 </div>
 <p>From here we can do any additional <code>geopandas</code> operations we might be interested in. For example, what if we want to calculate the total distance by registration area (a superset of fishing district). We can do that using <code>dissolve</code> from <code>geopandas</code>.</p>
-<div id="fcf09da0" class="cell" data-execution_count="29">
+<div id="52a92e9a" class="cell" data-execution_count="29">
 <div class="sourceCode cell-code" id="cb39"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb39-1"><a href="#cb39-1" aria-hidden="true" tabindex="-1"></a>reg_area <span class="op">=</span> res_full.dissolve(by <span class="op">=</span> <span class="st">"REGISTRATION_AREA_NAME"</span>, </span>
 <span id="cb39-2"><a href="#cb39-2" aria-hidden="true" tabindex="-1"></a>                             aggfunc <span class="op">=</span> <span class="st">'sum'</span>,</span>
 <span id="cb39-3"><a href="#cb39-3" aria-hidden="true" tabindex="-1"></a>                             numeric_only <span class="op">=</span> <span class="va">True</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>Let’s have a look at the same plot as before, but this time over our aggregated data.</p>
-<div id="619644fc" class="cell" data-execution_count="30">
+<div id="6f196ccb" class="cell" data-execution_count="30">
 <div class="sourceCode cell-code" id="cb40"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb40-1"><a href="#cb40-1" aria-hidden="true" tabindex="-1"></a>fig, ax <span class="op">=</span> plt.subplots(figsize<span class="op">=</span>(<span class="dv">7</span>, <span class="dv">7</span>))</span>
 <span id="cb40-2"><a href="#cb40-2" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb40-3"><a href="#cb40-3" aria-hidden="true" tabindex="-1"></a>ax <span class="op">=</span> reg_area.plot(column <span class="op">=</span> <span class="st">"distance"</span>, legend <span class="op">=</span> <span class="va">True</span>, ax <span class="op">=</span> ax)</span>
diff --git a/public/2024-03-arctic/sections/parallel-programming.html b/public/2024-03-arctic/sections/parallel-programming.html
index f50d7fdc..44ca4832 100644
--- a/public/2024-03-arctic/sections/parallel-programming.html
+++ b/public/2024-03-arctic/sections/parallel-programming.html
@@ -449,14 +449,14 @@ <h2 data-number="4.6" class="anchored" data-anchor-id="modes-of-parallelization"
 <h2 data-number="4.7" class="anchored" data-anchor-id="task-parallelization-in-python"><span class="header-section-number">4.7</span> Task parallelization in Python</h2>
 <p>Python also provides a number of easy to use packages for concurrent processing. We will review two of these, <code>concurrent.futures</code> and <code>parsl</code>, to show just how easy it can be to parallelize your programs. <a href="https://docs.python.org/3/library/concurrent.futures.html"><code>concurrent.futures</code></a> is built right into the python3 release, and is a great starting point for learning concurrency.</p>
 <p>We’re going to start with a task that is a little expensive to compute, and define it in a function. All this <code>task(x)</code> function does is to use numpy to create a fairly large range of numbers, and then sum them.</p>
-<div id="8b4d7577" class="cell" data-execution_count="2">
+<div id="e047ffe7" class="cell" data-execution_count="2">
 <div class="sourceCode cell-code" id="cb4"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a><span class="kw">def</span> task(x):</span>
 <span id="cb4-2"><a href="#cb4-2" aria-hidden="true" tabindex="-1"></a>    <span class="im">import</span> numpy <span class="im">as</span> np</span>
 <span id="cb4-3"><a href="#cb4-3" aria-hidden="true" tabindex="-1"></a>    result <span class="op">=</span> np.arange(x<span class="op">*</span><span class="dv">10</span><span class="op">**</span><span class="dv">8</span>).<span class="bu">sum</span>()</span>
 <span id="cb4-4"><a href="#cb4-4" aria-hidden="true" tabindex="-1"></a>    <span class="cf">return</span> result</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>We can start by executing this task function serially ten times with varying inputs. In this case, we create a function <code>run_serial</code> that takes a list of inputs to be run, and it calls the <code>task</code> function for each of those inputs. The <code>@timethis</code> decorator is a simple way to wrap the function with timing code so that we can see how long it takes to execute.</p>
-<div id="9b3fd165" class="cell" data-execution_count="3">
+<div id="5c150142" class="cell" data-execution_count="3">
 <div class="sourceCode cell-code" id="cb5"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> numpy <span class="im">as</span> np</span>
 <span id="cb5-2"><a href="#cb5-2" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb5-3"><a href="#cb5-3" aria-hidden="true" tabindex="-1"></a><span class="at">@timethis</span></span>
@@ -465,7 +465,7 @@ <h2 data-number="4.7" class="anchored" data-anchor-id="task-parallelization-in-p
 <span id="cb5-6"><a href="#cb5-6" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb5-7"><a href="#cb5-7" aria-hidden="true" tabindex="-1"></a>run_serial(np.arange(<span class="dv">10</span>))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
-<pre><code>run_serial: 7401.757478713989 ms</code></pre>
+<pre><code>run_serial: 7049.6227741241455 ms</code></pre>
 </div>
 <div class="cell-output cell-output-display" data-execution_count="3">
 <pre><code>[0,
@@ -483,7 +483,7 @@ <h2 data-number="4.7" class="anchored" data-anchor-id="task-parallelization-in-p
 <p>In this case, it takes around <em>25 seconds</em> to execute 10 tasks, depending on what else is happening on the machine and network.</p>
 <p>So, can we make this faster using a multi-threaded parallel process? Let’s try with <code>concurrent.futures</code>. The main concept in this package is one of a <code>future</code>, which is a structure which represents the value that will be created in a computation in the future when the function completes execution. With <code>concurrent.futures</code>, tasks are scheduled and do not block while they await their turn to be executed. Instead, threads are created and executed <em>asynchronously</em>, meaning that the function returns it’s <code>future</code> potentially before the thread has actually been executed. Using this approach, the user schedules a series of tasks to be executed asynchronously, and keeps track of the futures for each task. When the future indicates that the execution has been completed, we can then retrieve the result of the computation.</p>
 <p>In practice this is a simple change from our serial implementation. We will use the <code>ThreadPoolExecutor</code> to create a pool of workers that are available to process tasks. Each worker is set up in its own thread, so it can execute in parallel with other workers. After setting up the pool of workers, we use concurrent.futures <code>map()</code> to schedule each task from our <code>task_list</code> (in this case, an input value from 1 to 10) to run on one of the workers. As for all map() implementations, we are asking for each value in <code>task_list</code> to be executed in the <code>task</code> function we defined above, but in this case it will be executed using one of the workers from the <code>executor</code> that we created.</p>
-<div id="8d442586" class="cell" data-execution_count="4">
+<div id="ab0a8710" class="cell" data-execution_count="4">
 <div class="sourceCode cell-code" id="cb8"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> concurrent.futures <span class="im">import</span> ThreadPoolExecutor</span>
 <span id="cb8-2"><a href="#cb8-2" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb8-3"><a href="#cb8-3" aria-hidden="true" tabindex="-1"></a><span class="at">@timethis</span></span>
@@ -521,7 +521,7 @@ <h2 data-number="4.8" class="anchored" data-anchor-id="exercise-parallel-downloa
 </figure>
 </div>
 <p>First, let’s download the data serially to set a benchmark. The data files are listed in a table with their filename and identifier, and can be downloaded directly from the Arctic Data Center using their identifier. To make things easier, we’ve already provided a data frame with the names and identifiers of each file that could be downloaded.</p>
-<div id="95a73189" class="cell" data-execution_count="5">
+<div id="7709e7a7" class="cell" data-execution_count="5">
 <div class="cell-output cell-output-display" data-execution_count="4">
 <div>
 <div>
@@ -571,7 +571,7 @@ <h2 data-number="4.8" class="anchored" data-anchor-id="exercise-parallel-downloa
 <section id="serial" class="level3" data-number="4.8.1">
 <h3 data-number="4.8.1" class="anchored" data-anchor-id="serial"><span class="header-section-number">4.8.1</span> Serial</h3>
 <p>When you have a list of repetitive tasks, you may be able to speed it up by adding more computing power. If each task is completely independent of the others, then it is pleasingly parallel and a prime candidate for executing those tasks in parallel, each on its own core. For example, let’s build a simple loop that downloads the data files that we need for an analysis. First, we start with the serial implementation.</p>
-<div id="fe37a46e" class="cell" data-execution_count="6">
+<div id="e567d569" class="cell" data-execution_count="6">
 <div class="sourceCode cell-code" id="cb10"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb10-1"><a href="#cb10-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> urllib</span>
 <span id="cb10-2"><a href="#cb10-2" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb10-3"><a href="#cb10-3" aria-hidden="true" tabindex="-1"></a><span class="kw">def</span> download_file(row):</span>
@@ -596,7 +596,7 @@ <h3 data-number="4.8.1" class="anchored" data-anchor-id="serial"><span class="he
 Downloading: SWI_2021.tif
 Downloading: SWI_2020.tif
 Downloading: SWI_2001.tif
-download_serial: 92904.20699119568 ms
+download_serial: 48173.44856262207 ms
 ['SWI_2007.tif', 'SWI_2019.tif', 'SWI_2021.tif', 'SWI_2020.tif', 'SWI_2001.tif']</code></pre>
 </div>
 </div>
@@ -606,7 +606,7 @@ <h3 data-number="4.8.1" class="anchored" data-anchor-id="serial"><span class="he
 <section id="multi-threaded-with-concurrent.futures" class="level3" data-number="4.8.2">
 <h3 data-number="4.8.2" class="anchored" data-anchor-id="multi-threaded-with-concurrent.futures"><span class="header-section-number">4.8.2</span> Multi-threaded with <code>concurrent.futures</code></h3>
 <p>In this case, we’ll use the same <code>download_file</code> function from before, but let’s switch up and create a <code>download_threaded()</code> function to use <code>concurrent.futures</code> with a <code>ThreadPoolExecutor</code> just as we did earlier.</p>
-<div id="5a263e00" class="cell" data-execution_count="7">
+<div id="d6915e2e" class="cell" data-execution_count="7">
 <div class="sourceCode cell-code" id="cb12"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb12-1"><a href="#cb12-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> concurrent.futures <span class="im">import</span> ThreadPoolExecutor</span>
 <span id="cb12-2"><a href="#cb12-2" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb12-3"><a href="#cb12-3" aria-hidden="true" tabindex="-1"></a><span class="at">@timethis</span></span>
@@ -624,7 +624,7 @@ <h3 data-number="4.8.2" class="anchored" data-anchor-id="multi-threaded-with-con
 Downloading: SWI_2021.tif
 Downloading: SWI_2020.tif
 Downloading: SWI_2001.tif
-download_threaded: 25324.24020767212 ms
+download_threaded: 19610.64839363098 ms
 SWI_2007.tif
 SWI_2019.tif
 SWI_2021.tif
@@ -637,7 +637,7 @@ <h3 data-number="4.8.2" class="anchored" data-anchor-id="multi-threaded-with-con
 <section id="multi-process-with-concurrent.futures" class="level3" data-number="4.8.3">
 <h3 data-number="4.8.3" class="anchored" data-anchor-id="multi-process-with-concurrent.futures"><span class="header-section-number">4.8.3</span> Multi-process with <code>concurrent.futures</code></h3>
 <p>You’ll remember from earlier that you can execute tasks concurrently by creating multiple threads within one process (multi-threaded), or by creating and executing muliple processes. The latter creates more independence, as each of the executing tasks has their own memory and process space, but it also takes longer to set up. With <code>concurrent.futures</code>, we can switch to a multi-process pool by using a <code>ProcessPoolExecutor</code>, analogously to how we used <code>ThreadPoolExecutor</code> previously. So, some simple changes, and we’re running multiple processes.</p>
-<div id="e9cc0d81" class="cell" data-execution_count="8">
+<div id="9ba4a6ad" class="cell" data-execution_count="8">
 <div class="sourceCode cell-code" id="cb14"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb14-1"><a href="#cb14-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> concurrent.futures <span class="im">import</span> ProcessPoolExecutor</span>
 <span id="cb14-2"><a href="#cb14-2" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb14-3"><a href="#cb14-3" aria-hidden="true" tabindex="-1"></a><span class="at">@timethis</span></span>
@@ -650,12 +650,12 @@ <h3 data-number="4.8.3" class="anchored" data-anchor-id="multi-process-with-conc
 <span id="cb14-10"><a href="#cb14-10" aria-hidden="true" tabindex="-1"></a><span class="cf">for</span> result <span class="kw">in</span> results:</span>
 <span id="cb14-11"><a href="#cb14-11" aria-hidden="true" tabindex="-1"></a>    <span class="bu">print</span>(result)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
-<pre><code>Downloading: SWI_2019.tifDownloading: SWI_2020.tifDownloading: SWI_2021.tif
-Downloading: SWI_2007.tif
+<pre><code>Downloading: SWI_2019.tifDownloading: SWI_2021.tifDownloading: SWI_2007.tif
+Downloading: SWI_2020.tif
+Downloading: SWI_2001.tif
 
 
-Downloading: SWI_2001.tif
-download_process: 20482.065200805664 ms
+download_process: 23428.152322769165 ms
 SWI_2007.tif
 SWI_2019.tif
 SWI_2021.tif
@@ -671,7 +671,7 @@ <h2 data-number="4.9" class="anchored" data-anchor-id="parallel-processing-with-
 <p><code>concurrent.futures</code> is great and powerful, but it has its limits. Particularly as you try to scale up into the thousands of concurrent tasks, other libraries like <a href="https://parsl-project.org/">Parsl</a> (<a href="https://parsl.readthedocs.io/">docs</a>), <a href="https://www.dask.org/">Dask</a>, <a href="https://www.ray.io/">Ray</a>, and others come into play. They all have their strengths, but Parsl makes it particularly easy to build parallel workflows out of existing python code through it’s use of decorators on existing python functions.</p>
 <p>In addition, Parsl supports a lot of different kinds of <a href="https://parsl.readthedocs.io/en/stable/userguide/execution.html#execution-providers">providers</a>, allowing the same python code to be easily run multi-threaded using a <code>ThreadPoolExecutor</code> and via multi-processing on many different cluster computing platforms using the <code>HighThroughputExecutor</code>. For example, Parsl includes providers supporting local execution, and on Slurm, Condor, Kubernetes, AWS, and other platforms. And Parsl handles data staging as well across these varied environments, making sure the data is in the right place when it’s needed for computations.</p>
 <p>Similarly to before, we start by configuring an executor in parsl, and loading it. We’ll use multiprocessing by configuring the <code>HighThroughputExecutor</code> to use our local resources as a cluster, and we’ll activate our virtual environment to be sure we’re executing in a consistent environment.</p>
-<div id="43711b25" class="cell" data-execution_count="9">
+<div id="9ca4d333" class="cell" data-execution_count="9">
 <div class="sourceCode cell-code" id="cb16"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb16-1"><a href="#cb16-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Required packages</span></span>
 <span id="cb16-2"><a href="#cb16-2" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> parsl</span>
 <span id="cb16-3"><a href="#cb16-3" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> parsl <span class="im">import</span> python_app</span>
@@ -694,11 +694,11 @@ <h2 data-number="4.9" class="anchored" data-anchor-id="parallel-processing-with-
 <span id="cb16-20"><a href="#cb16-20" aria-hidden="true" tabindex="-1"></a>parsl.clear()</span>
 <span id="cb16-21"><a href="#cb16-21" aria-hidden="true" tabindex="-1"></a>parsl.load(htex_local)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="8">
-<pre><code>&lt;parsl.dataflow.dflow.DataFlowKernel at 0x7f4991800cd0&gt;</code></pre>
+<pre><code>&lt;parsl.dataflow.dflow.DataFlowKernel at 0x7f0ca1bff550&gt;</code></pre>
 </div>
 </div>
 <p>We now have a live parsl executor (<code>htex_local</code>) that is waiting to execute processes. We tell it to execute processes by annotating functions with decorators that indicate which tasks should be parallelized. Parsl then handles the scheduling and execution of those tasks based on the dependencies between them. In the simplest case, we’ll decorate our previous function for downloading a file with the <code>@python_app</code> decorator, which tells parsl that any function calls with this function should be run on the default executor (in this case, <code>htex_local</code>).</p>
-<div id="97d0fb3e" class="cell" data-execution_count="10">
+<div id="85ed5821" class="cell" data-execution_count="10">
 <div class="sourceCode cell-code" id="cb18"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb18-1"><a href="#cb18-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Decorators seem to be ignored as the first line of a cell, so print something first</span></span>
 <span id="cb18-2"><a href="#cb18-2" aria-hidden="true" tabindex="-1"></a><span class="bu">print</span>(<span class="st">"Create decorated function"</span>)</span>
 <span id="cb18-3"><a href="#cb18-3" aria-hidden="true" tabindex="-1"></a></span>
@@ -717,7 +717,7 @@ <h2 data-number="4.9" class="anchored" data-anchor-id="parallel-processing-with-
 </div>
 </div>
 <p>Now we just write regular python code that calls that function, and parsl handles the scheduling. Parsl app executors return an <a href="https://parsl.readthedocs.io/en/stable/userguide/futures.html#appfutures"><code>AppFuture</code></a>, and we can call the <code>AppFuture.done()</code> function to determine when the future result is ready without blocking. Or, we can just block on <code>AppFuture.result()</code> which waits for each of the executions to complete and then returns the result.</p>
-<div id="4aad70f3" class="cell" data-execution_count="11">
+<div id="fed5fb75" class="cell" data-execution_count="11">
 <div class="sourceCode cell-code" id="cb20"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb20-1"><a href="#cb20-1" aria-hidden="true" tabindex="-1"></a><span class="co">#! eval: true</span></span>
 <span id="cb20-2"><a href="#cb20-2" aria-hidden="true" tabindex="-1"></a><span class="bu">print</span>(<span class="st">"Define our download_futures function"</span>)</span>
 <span id="cb20-3"><a href="#cb20-3" aria-hidden="true" tabindex="-1"></a></span>
@@ -739,18 +739,18 @@ <h2 data-number="4.9" class="anchored" data-anchor-id="parallel-processing-with-
 <span id="cb20-19"><a href="#cb20-19" aria-hidden="true" tabindex="-1"></a>wait_for_futures(file_table[<span class="dv">0</span>:<span class="dv">5</span>])</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
 <pre><code>Define our download_futures function
-&lt;AppFuture at 0x7f49902857e0 state=pending&gt;
-&lt;AppFuture at 0x7f4990284040 state=pending&gt;
-&lt;AppFuture at 0x7f49902879a0 state=pending&gt;
-&lt;AppFuture at 0x7f4990286e30 state=pending&gt;
-&lt;AppFuture at 0x7f4989e208e0 state=pending&gt;
-download_futures: 10.964393615722656 ms
+&lt;AppFuture at 0x7f0c60792920 state=pending&gt;
+&lt;AppFuture at 0x7f0c607920e0 state=pending&gt;
+&lt;AppFuture at 0x7f0c60792b00 state=pending&gt;
+&lt;AppFuture at 0x7f0c9fa9e890 state=pending&gt;
+&lt;AppFuture at 0x7f0c6043e5f0 state=pending&gt;
+download_futures: 12.877464294433594 ms
 ['SWI_2007.tif', 'SWI_2019.tif', 'SWI_2021.tif', 'SWI_2020.tif', 'SWI_2001.tif']
-wait_for_futures: 65006.14833831787 ms</code></pre>
+wait_for_futures: 29422.613620758057 ms</code></pre>
 </div>
 </div>
 <p>When we’re done, be sure to clean up and shutdown the <code>htex_local</code> executor, or it will continue to persist in your environment and utilize resources. Generally, an executor should be created when setting up your environment, and then it can be used repeatedly for many different tasks.</p>
-<div id="96680c67" class="cell" data-execution_count="12">
+<div id="85922b3c" class="cell" data-execution_count="12">
 <div class="sourceCode cell-code" id="cb22"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb22-1"><a href="#cb22-1" aria-hidden="true" tabindex="-1"></a>htex_local.executors[<span class="dv">0</span>].shutdown()</span>
 <span id="cb22-2"><a href="#cb22-2" aria-hidden="true" tabindex="-1"></a>parsl.clear()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
diff --git a/public/2024-03-arctic/sections/parallel-with-dask.html b/public/2024-03-arctic/sections/parallel-with-dask.html
index a6eae351..bb4a97ed 100644
--- a/public/2024-03-arctic/sections/parallel-with-dask.html
+++ b/public/2024-03-arctic/sections/parallel-with-dask.html
@@ -382,16 +382,16 @@ <h2 data-number="6.3" class="anchored" data-anchor-id="dask-cluster"><span class
 <h2 data-number="6.4" class="anchored" data-anchor-id="connect-to-an-existing-cluster"><span class="header-section-number">6.4</span> Connect to an existing cluster</h2>
 <p>The instructor is going to start a local cluster on the server that everyone can connect to. This scenario is a realistic simulation of what it would look like for you to connect to a shared Dask resource. You can create your own local cluster running on your laptop using code at the end of this lesson.</p>
 <p>First, the instructor (and only the instructor!) will run:</p>
-<div id="2c03f2c4" class="cell" data-execution_count="1">
+<div id="8e480cb8" class="cell" data-execution_count="1">
 <div class="sourceCode cell-code" id="cb1"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> dask.distributed <span class="im">import</span> LocalCluster</span>
 <span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a>cluster <span class="op">=</span> LocalCluster(n_workers<span class="op">=</span><span class="dv">70</span>, memory_limit<span class="op">=</span><span class="st">'auto'</span>, processes<span class="op">=</span><span class="va">True</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>To connect to it, we will use the address that the scheduler is listening on. The port is generated randomly, so first the instructor needs to get address for you to use in your code. In a ‘real world’ scenario, this address would be given to you by the administrator of the Dask cluster.</p>
-<div id="73cbe713" class="cell" data-execution_count="2">
+<div id="62d84b5d" class="cell" data-execution_count="2">
 <div class="sourceCode cell-code" id="cb2"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a>cluster.scheduler_address</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>Now, you can pass the address to the <code>Client</code> function, which sets up your session as a client of the Dask cluster,</p>
-<div id="413ec240" class="cell" data-execution_count="3">
+<div id="5cf3e88e" class="cell" data-execution_count="3">
 <div class="sourceCode cell-code" id="cb3"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> dask.distributed <span class="im">import</span> LocalCluster, Client</span>
 <span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb3-3"><a href="#cb3-3" aria-hidden="true" tabindex="-1"></a><span class="co"># if you are copy pasting this from the book, make sure </span></span>
@@ -433,19 +433,19 @@ <h2 data-number="6.5" class="anchored" data-anchor-id="dask.dataframes"><span cl
 <section id="reading-a-csv" class="level3" data-number="6.5.1">
 <h3 data-number="6.5.1" class="anchored" data-anchor-id="reading-a-csv"><span class="header-section-number">6.5.1</span> Reading a csv</h3>
 <p>To get familiar with <code>dask.dataframes</code>, we will use tabular data of soil moisture measurements at six forest stands in northeastern Siberia. The data has been collected since 2014 and is archived at the Arctic Data Center (<a href="https://arcticdata.io/catalog/view/doi%3A10.18739%2FA24B2X59C">Loranty &amp; Alexander, doi:10.18739/A24B2X59C</a>). Just as we did in the previous lesson, we will download the data using the <code>requests</code> package and the data’s URL obtained from the Arctic Data Center.</p>
-<div id="f0f40b1c" class="cell" data-execution_count="4">
+<div id="01285081" class="cell" data-execution_count="4">
 <div class="sourceCode cell-code" id="cb4"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> os              </span>
 <span id="cb4-2"><a href="#cb4-2" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> urllib </span>
 <span id="cb4-3"><a href="#cb4-3" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb4-4"><a href="#cb4-4" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> dask.dataframe <span class="im">as</span> dd</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
-<div id="8ea40797" class="cell" data-execution_count="5">
+<div id="5dda7573" class="cell" data-execution_count="5">
 <div class="sourceCode cell-code" id="cb5"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a>url <span class="op">=</span> <span class="st">'https://arcticdata.io/metacat/d1/mn/v2/object/urn%3Auuid%3A27e4043d-75eb-4c4f-9427-0d442526c154'</span></span>
 <span id="cb5-2"><a href="#cb5-2" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb5-3"><a href="#cb5-3" aria-hidden="true" tabindex="-1"></a>msg <span class="op">=</span> urllib.request.urlretrieve(url, <span class="st">"dg_soil_moisture.csv"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>In the Arctic Data Center metadata we can see this file is 115 MB. To import this file as a <code>dask.dataframe</code> with more than one partition, we need to specify the size of each partition with the <code>blocksize</code> parameter. In this example, we will split the data frame into six partitions, meaning a block size of approximately 20 MB.</p>
-<div id="ee4ed7c8" class="cell" data-execution_count="6">
+<div id="faa2e98e" class="cell" data-execution_count="6">
 <div class="sourceCode cell-code" id="cb6"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a>fp <span class="op">=</span> os.path.join(os.getcwd(),<span class="st">'dg_soil_moisture.csv'</span>)</span>
 <span id="cb6-2"><a href="#cb6-2" aria-hidden="true" tabindex="-1"></a>df <span class="op">=</span> dd.read_csv(fp, blocksize <span class="op">=</span> <span class="st">'20MB'</span> , encoding<span class="op">=</span><span class="st">'ISO-8859-1'</span>)</span>
 <span id="cb6-3"><a href="#cb6-3" aria-hidden="true" tabindex="-1"></a>df</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
@@ -463,7 +463,7 @@ <h3 data-number="6.5.1" class="anchored" data-anchor-id="reading-a-csv"><span cl
 <div id="callout-1" class="callout-1-contents callout-collapse collapse">
 <div class="callout-body-container callout-body">
 <p><em>About the encoding parameter:</em> If we try to import the file directly, we will receive an <code>UnicodeDecodeError</code>. We can run the following code to find the file’s encoding and add the appropriate encoding to <code>dask.dataframe.read_csv</code>.</p>
-<div id="5a8a67c0" class="cell" data-execution_count="7">
+<div id="eb6a33bf" class="cell" data-execution_count="7">
 <div class="sourceCode cell-code" id="cb7"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> chardet</span>
 <span id="cb7-2"><a href="#cb7-2" aria-hidden="true" tabindex="-1"></a>fp <span class="op">=</span> os.path.join(os.getcwd(),<span class="st">'dg_soil_moisture.csv'</span>)</span>
 <span id="cb7-3"><a href="#cb7-3" aria-hidden="true" tabindex="-1"></a><span class="cf">with</span> <span class="bu">open</span>(fp, <span class="st">'rb'</span>) <span class="im">as</span> rawdata:</span>
@@ -475,20 +475,20 @@ <h3 data-number="6.5.1" class="anchored" data-anchor-id="reading-a-csv"><span cl
 </div>
 <p>Notice that we cannot see any values in the data frame. This is because Dask has not really loaded the data. It will wait until we explicitly ask it to print or compute something to do so.</p>
 <p>However, we can still do <code>df.head()</code>. It’s not costly for memory to access a few data frame rows.</p>
-<div id="c40c0ab0" class="cell" data-execution_count="8">
+<div id="de585d0c" class="cell" data-execution_count="8">
 <div class="sourceCode cell-code" id="cb8"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a>df.head(<span class="dv">3</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 </section>
 <section id="lazy-computations" class="level3" data-number="6.5.2">
 <h3 data-number="6.5.2" class="anchored" data-anchor-id="lazy-computations"><span class="header-section-number">6.5.2</span> Lazy Computations</h3>
 <p>The application programming interface (API) of a <code>dask.dataframe</code> is a subset of the <code>pandas.DataFrame</code> API. So if you are familiar with pandas, many of the core <code>pandas.DataFrame</code> methods directly translate to <code>dask.dataframes</code>. For example:</p>
-<div id="3aed9cf5" class="cell" data-execution_count="9">
+<div id="00d9f474" class="cell" data-execution_count="9">
 <div class="sourceCode cell-code" id="cb9"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a>averages <span class="op">=</span> df.groupby(<span class="st">'year'</span>).mean(numeric_only<span class="op">=</span><span class="va">True</span>)</span>
 <span id="cb9-2"><a href="#cb9-2" aria-hidden="true" tabindex="-1"></a>averages</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>Notice that we cannot see any values in the resulting data frame. A major difference between <code>pandas.DataFrames</code> and <code>dask.dataframes</code> is that <code>dask.dataframes</code> are “lazy”. This means an object will queue transformations and calculations without executing them until we explicitly ask for the result of that chain of computations using the <code>compute</code> method. Once we run <code>compute,</code> the scheduler can allocate memory and workers to execute the computations in parallel. This kind of <strong>lazy evaluation</strong> (or <strong>delayed computation</strong>) is how most Dask workloads work. This varies from <strong>eager evaluation</strong> methods and functions, which start computing results right when they are executed.</p>
 <p>Before calling <code>compute</code> on an object, open the Dask dashboard to see how the parallel computation is happening.</p>
-<div id="b8851835" class="cell" data-execution_count="10">
+<div id="0d103c74" class="cell" data-execution_count="10">
 <div class="sourceCode cell-code" id="cb10"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb10-1"><a href="#cb10-1" aria-hidden="true" tabindex="-1"></a>averages.compute()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 </section>
@@ -503,20 +503,20 @@ <h2 data-number="6.6" class="anchored" data-anchor-id="dask.arrays"><span class=
 </figure>
 </div>
 <p>In this short example we will create a 200x500 <code>dask.array</code> by specifying chunk sizes of 100x100.</p>
-<div id="fa006aeb" class="cell" data-execution_count="11">
+<div id="d56ecd00" class="cell" data-execution_count="11">
 <div class="sourceCode cell-code" id="cb11"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb11-1"><a href="#cb11-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> numpy <span class="im">as</span> np</span>
 <span id="cb11-2"><a href="#cb11-2" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb11-3"><a href="#cb11-3" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> dask.array <span class="im">as</span> da</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
-<div id="b4e8a8e8" class="cell" data-execution_count="12">
+<div id="e91fc34d" class="cell" data-execution_count="12">
 <div class="sourceCode cell-code" id="cb12"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb12-1"><a href="#cb12-1" aria-hidden="true" tabindex="-1"></a>data <span class="op">=</span> np.arange(<span class="dv">100_000</span>).reshape(<span class="dv">200</span>, <span class="dv">500</span>)</span>
 <span id="cb12-2"><a href="#cb12-2" aria-hidden="true" tabindex="-1"></a>a <span class="op">=</span> da.from_array(data, chunks<span class="op">=</span>(<span class="dv">100</span>, <span class="dv">100</span>))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>Computations for <code>dask.arrays</code> also work lazily. We need to call <code>compute</code> to trigger computations and bring the result to memory.</p>
-<div id="3fd036ce" class="cell" data-execution_count="13">
+<div id="4baebb9a" class="cell" data-execution_count="13">
 <div class="sourceCode cell-code" id="cb13"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb13-1"><a href="#cb13-1" aria-hidden="true" tabindex="-1"></a>a.mean()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
-<div id="73e49204" class="cell" data-execution_count="14">
+<div id="00d4c5d7" class="cell" data-execution_count="14">
 <div class="sourceCode cell-code" id="cb14"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb14-1"><a href="#cb14-1" aria-hidden="true" tabindex="-1"></a>a.mean().compute()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 </section>
@@ -530,7 +530,7 @@ <h3 data-number="6.7.1" class="anchored" data-anchor-id="open-.tif-file"><span c
 <p>The NDVI is calculated using the near-infrared and red bands of the satellite image. The formula is</p>
 <p><span class="math display">\[NDVI = \frac{NIR - Red}{NIR + Red}.\]</span></p>
 <p>First, we download the data for the near-infrared (NIR) and red bands from the Arctic Data Center:</p>
-<div id="c04bba6d" class="cell" data-execution_count="15">
+<div id="24c283dd" class="cell" data-execution_count="15">
 <div class="sourceCode cell-code" id="cb15"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb15-1"><a href="#cb15-1" aria-hidden="true" tabindex="-1"></a><span class="co"># download red band</span></span>
 <span id="cb15-2"><a href="#cb15-2" aria-hidden="true" tabindex="-1"></a>url <span class="op">=</span> <span class="st">'https://arcticdata.io/metacat/d1/mn/v2/object/urn%3Auuid%3Aac25a399-b174-41c1-b6d3-09974b161e5a'</span></span>
 <span id="cb15-3"><a href="#cb15-3" aria-hidden="true" tabindex="-1"></a>msg <span class="op">=</span> urllib.request.urlretrieve(url, <span class="st">"RU_ANS_TR2_FL005M_red.tif"</span>)</span>
@@ -543,10 +543,10 @@ <h3 data-number="6.7.1" class="anchored" data-anchor-id="open-.tif-file"><span c
 </div>
 <p>Because these are .tif files and have geospatial metadata, we will use <code>rioxarray</code> to read them. You can find more information about <code>rioxarray</code> <a href="https://corteva.github.io/rioxarray/stable/getting_started/getting_started.html">here</a>.</p>
 <p>To indicate we will open these .tif files with <code>dask.arrays</code> as the underlying object to the <code>xarray.DataArray</code> (instead of a <code>numpy.array</code>), we need to specify either a shape or the size in bytes for each chunk. Both files are 76 MB, so let’s have chunks of 15 MB to have roughly six chunks.</p>
-<div id="59cfa4db" class="cell" data-execution_count="16">
+<div id="7c5330bd" class="cell" data-execution_count="16">
 <div class="sourceCode cell-code" id="cb16"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb16-1"><a href="#cb16-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> rioxarray <span class="im">as</span> rioxr</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
-<div id="28f49628" class="cell" data-execution_count="17">
+<div id="9cf0d009" class="cell" data-execution_count="17">
 <div class="sourceCode cell-code" id="cb17"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb17-1"><a href="#cb17-1" aria-hidden="true" tabindex="-1"></a><span class="co"># read in the file</span></span>
 <span id="cb17-2"><a href="#cb17-2" aria-hidden="true" tabindex="-1"></a>fp_red <span class="op">=</span> os.path.join(os.getcwd(),<span class="st">"RU_ANS_TR2_FL005M_red.tif"</span>)</span>
 <span id="cb17-3"><a href="#cb17-3" aria-hidden="true" tabindex="-1"></a>red <span class="op">=</span> rioxr.open_rasterio(fp_red, chunks <span class="op">=</span> <span class="st">'15MB'</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
@@ -557,12 +557,12 @@ <h3 data-number="6.7.1" class="anchored" data-anchor-id="open-.tif-file"><span c
 <li>There is geospatial information (transformation, CRS, resolution) and no-data values.</li>
 <li>There is an unnecessary dimension: a constant value for the band. So our next step is to squeeze the array to flatten it.</li>
 </ul>
-<div id="2b0ab803" class="cell" data-execution_count="18">
+<div id="bffc0124" class="cell" data-execution_count="18">
 <div class="sourceCode cell-code" id="cb18"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb18-1"><a href="#cb18-1" aria-hidden="true" tabindex="-1"></a><span class="co"># getting rid of unnecessary dimension</span></span>
 <span id="cb18-2"><a href="#cb18-2" aria-hidden="true" tabindex="-1"></a>red <span class="op">=</span> red.squeeze()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>Next, we read in the NIR band and do the same pre-processing:</p>
-<div id="5c662830" class="cell" data-execution_count="19">
+<div id="8df7ad51" class="cell" data-execution_count="19">
 <div class="sourceCode cell-code" id="cb19"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb19-1"><a href="#cb19-1" aria-hidden="true" tabindex="-1"></a><span class="co"># open data</span></span>
 <span id="cb19-2"><a href="#cb19-2" aria-hidden="true" tabindex="-1"></a>fp_nir <span class="op">=</span> os.path.join(os.getcwd(),<span class="st">"RU_ANS_TR2_FL005M_nir.tif"</span>)</span>
 <span id="cb19-3"><a href="#cb19-3" aria-hidden="true" tabindex="-1"></a>nir <span class="op">=</span> rioxr.open_rasterio(fp_nir, chunks <span class="op">=</span> <span class="st">'15MB'</span>)</span>
@@ -574,15 +574,15 @@ <h3 data-number="6.7.1" class="anchored" data-anchor-id="open-.tif-file"><span c
 <section id="calculating-ndvi" class="level3" data-number="6.7.2">
 <h3 data-number="6.7.2" class="anchored" data-anchor-id="calculating-ndvi"><span class="header-section-number">6.7.2</span> Calculating NDVI</h3>
 <p>Now we set up the NDVI calculation. This step is easy because we can handle xarrays and Dask arrays as NumPy arrays for arithmetic operations. Also, both bands have values of type float32, so we won’t have trouble with the division.</p>
-<div id="c1d3aafe" class="cell" data-execution_count="20">
+<div id="0b504154" class="cell" data-execution_count="20">
 <div class="sourceCode cell-code" id="cb20"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb20-1"><a href="#cb20-1" aria-hidden="true" tabindex="-1"></a>ndvi <span class="op">=</span> (nir <span class="op">-</span> red) <span class="op">/</span> (nir <span class="op">+</span> red)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>When we look at the NDVI we can see the result is another <code>dask.array</code>, nothing has been computed yet. Remember, Dask computations are lazy, so we need to call <code>compute()</code> to bring the results to memory.</p>
-<div id="63fef901" class="cell" data-execution_count="21">
+<div id="2876379c" class="cell" data-execution_count="21">
 <div class="sourceCode cell-code" id="cb21"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb21-1"><a href="#cb21-1" aria-hidden="true" tabindex="-1"></a>ndvi_values <span class="op">=</span> ndvi.compute()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>And finally, we can see what these look like. Notice that <code>xarray</code> uses the value of the dimensions as labels along the x and y axes. We use <code>robust=True</code> to ignore the no-data values when plotting.</p>
-<div id="601b6a9c" class="cell" data-execution_count="22">
+<div id="48fdee78" class="cell" data-execution_count="22">
 <div class="sourceCode cell-code" id="cb22"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb22-1"><a href="#cb22-1" aria-hidden="true" tabindex="-1"></a>ndvi_values.plot(robust<span class="op">=</span><span class="va">True</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 </section>
@@ -592,16 +592,16 @@ <h2 data-number="6.8" class="anchored" data-anchor-id="setting-up-your-own-local
 <section id="setting-up-a-local-cluster" class="level3" data-number="6.8.1">
 <h3 data-number="6.8.1" class="anchored" data-anchor-id="setting-up-a-local-cluster"><span class="header-section-number">6.8.1</span> Setting up a Local Cluster</h3>
 <p>We can create a <strong>local cluster</strong> as follows:</p>
-<div id="df3d9de0" class="cell" data-execution_count="23">
+<div id="9217b8c2" class="cell" data-execution_count="23">
 <div class="sourceCode cell-code" id="cb23"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb23-1"><a href="#cb23-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> dask.distributed <span class="im">import</span> LocalCluster, Client</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
-<div id="2caf2628" class="cell" data-execution_count="24">
+<div id="f60d0a14" class="cell" data-execution_count="24">
 <div class="sourceCode cell-code" id="cb24"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb24-1"><a href="#cb24-1" aria-hidden="true" tabindex="-1"></a>cluster <span class="op">=</span> LocalCluster(n_workers<span class="op">=</span><span class="dv">4</span>, memory_limit<span class="op">=</span><span class="fl">0.1</span>, processes<span class="op">=</span><span class="va">True</span>)</span>
 <span id="cb24-2"><a href="#cb24-2" aria-hidden="true" tabindex="-1"></a>cluster</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p><img src="../images/dask_cluster_panel.png" class="img-fluid"></p>
 <p>And then we create a client to connect to our cluster, passing the <code>Client</code> function the cluster object.</p>
-<div id="85c6ea54" class="cell" data-execution_count="25">
+<div id="96ec590b" class="cell" data-execution_count="25">
 <div class="sourceCode cell-code" id="cb25"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb25-1"><a href="#cb25-1" aria-hidden="true" tabindex="-1"></a>client <span class="op">=</span> Client(cluster)</span>
 <span id="cb25-2"><a href="#cb25-2" aria-hidden="true" tabindex="-1"></a>client</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
diff --git a/public/2024-03-arctic/sections/python-intro.html b/public/2024-03-arctic/sections/python-intro.html
index fcfdebc0..98a757af 100644
--- a/public/2024-03-arctic/sections/python-intro.html
+++ b/public/2024-03-arctic/sections/python-intro.html
@@ -379,7 +379,7 @@ <h4 data-number="3.4.0.3" class="anchored" data-anchor-id="installing-locally-op
 <h2 data-number="3.5" class="anchored" data-anchor-id="brief-overview-of-python-syntax"><span class="header-section-number">3.5</span> Brief overview of python syntax</h2>
 <p>We’ll very briefly go over some basic python syntax and the base variable types. First, open a python script. From the File menu, select New File, type “python”, then save it as ‘python-intro.py’ in the top level of your directory.</p>
 <p>In your file, assign a value to a variable using <code>=</code> and print the result.</p>
-<div id="8a5c0f3c" class="cell" data-execution_count="2">
+<div id="48f15afa" class="cell" data-execution_count="2">
 <div class="sourceCode cell-code" id="cb9"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a>x <span class="op">=</span> <span class="dv">4</span></span>
 <span id="cb9-2"><a href="#cb9-2" aria-hidden="true" tabindex="-1"></a><span class="bu">print</span>(x)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
@@ -402,7 +402,7 @@ <h2 data-number="3.5" class="anchored" data-anchor-id="brief-overview-of-python-
 <li>Dictionary</li>
 </ul>
 <p>We already saw a number type, here is a string:</p>
-<div id="255c24f8" class="cell" data-execution_count="3">
+<div id="263cbb97" class="cell" data-execution_count="3">
 <div class="sourceCode cell-code" id="cb11"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb11-1"><a href="#cb11-1" aria-hidden="true" tabindex="-1"></a><span class="bu">str</span> <span class="op">=</span> <span class="st">'Hello World!'</span></span>
 <span id="cb11-2"><a href="#cb11-2" aria-hidden="true" tabindex="-1"></a><span class="bu">print</span>(<span class="bu">str</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
@@ -410,7 +410,7 @@ <h2 data-number="3.5" class="anchored" data-anchor-id="brief-overview-of-python-
 </div>
 </div>
 <p>Lists in python are very versatile, and are created using square brackets <code>[]</code>. Items in a list can be of different data types.</p>
-<div id="99b332c5" class="cell" data-execution_count="4">
+<div id="3363d990" class="cell" data-execution_count="4">
 <div class="sourceCode cell-code" id="cb13"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb13-1"><a href="#cb13-1" aria-hidden="true" tabindex="-1"></a><span class="bu">list</span> <span class="op">=</span> [<span class="dv">100</span>, <span class="dv">50</span>, <span class="op">-</span><span class="dv">20</span>, <span class="st">'text'</span>]</span>
 <span id="cb13-2"><a href="#cb13-2" aria-hidden="true" tabindex="-1"></a><span class="bu">print</span>(<span class="bu">list</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
@@ -418,7 +418,7 @@ <h2 data-number="3.5" class="anchored" data-anchor-id="brief-overview-of-python-
 </div>
 </div>
 <p>You can access items in a list by index using the square brackets. Note indexing starts with 0 in python. The slice operator enables you to easily access a portion of the list without needing to specify every index.</p>
-<div id="74c330e6" class="cell" data-execution_count="5">
+<div id="b78b3241" class="cell" data-execution_count="5">
 <div class="sourceCode cell-code" id="cb15"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb15-1"><a href="#cb15-1" aria-hidden="true" tabindex="-1"></a><span class="bu">list</span>[<span class="dv">0</span>] <span class="co"># print first element</span></span>
 <span id="cb15-2"><a href="#cb15-2" aria-hidden="true" tabindex="-1"></a><span class="bu">list</span>[<span class="dv">1</span>:<span class="dv">3</span>] <span class="co"># print 2nd until 4th elements</span></span>
 <span id="cb15-3"><a href="#cb15-3" aria-hidden="true" tabindex="-1"></a><span class="bu">list</span>[:<span class="dv">2</span>] <span class="co"># print first until the 3rd</span></span>
@@ -437,7 +437,7 @@ <h2 data-number="3.5" class="anchored" data-anchor-id="brief-overview-of-python-
 </div>
 </div>
 <p>The <code>+</code> and <code>*</code> operators work on lists by creating a new list using either concatenation (<code>+</code>) or repetition (<code>*</code>).</p>
-<div id="a18b01f3" class="cell" data-execution_count="6">
+<div id="9c956e1f" class="cell" data-execution_count="6">
 <div class="sourceCode cell-code" id="cb20"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb20-1"><a href="#cb20-1" aria-hidden="true" tabindex="-1"></a>list2 <span class="op">=</span> [<span class="st">'more'</span>, <span class="st">'things'</span>]</span>
 <span id="cb20-2"><a href="#cb20-2" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb20-3"><a href="#cb20-3" aria-hidden="true" tabindex="-1"></a><span class="bu">list</span> <span class="op">+</span> list2</span>
@@ -450,7 +450,7 @@ <h2 data-number="3.5" class="anchored" data-anchor-id="brief-overview-of-python-
 </div>
 </div>
 <p>Tuples are similar to lists, except the values cannot be changed in place. They are constructed with parentheses.</p>
-<div id="47818b4b" class="cell" data-execution_count="7">
+<div id="7eeae28d" class="cell" data-execution_count="7">
 <div class="sourceCode cell-code" id="cb23"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb23-1"><a href="#cb23-1" aria-hidden="true" tabindex="-1"></a><span class="bu">tuple</span> <span class="op">=</span> (<span class="st">'a'</span>, <span class="st">'b'</span>, <span class="st">'c'</span>, <span class="st">'d'</span>)</span>
 <span id="cb23-2"><a href="#cb23-2" aria-hidden="true" tabindex="-1"></a><span class="bu">tuple</span>[<span class="dv">0</span>]</span>
 <span id="cb23-3"><a href="#cb23-3" aria-hidden="true" tabindex="-1"></a><span class="bu">tuple</span> <span class="op">*</span> <span class="dv">3</span></span>
@@ -466,7 +466,7 @@ <h2 data-number="3.5" class="anchored" data-anchor-id="brief-overview-of-python-
 </div>
 </div>
 <p>Observe the difference when we try to change the first value. It works for a list:</p>
-<div id="09237e34" class="cell" data-execution_count="8">
+<div id="24c4e391" class="cell" data-execution_count="8">
 <div class="sourceCode cell-code" id="cb27"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb27-1"><a href="#cb27-1" aria-hidden="true" tabindex="-1"></a><span class="bu">list</span>[<span class="dv">0</span>] <span class="op">=</span> <span class="st">'new value'</span></span>
 <span id="cb27-2"><a href="#cb27-2" aria-hidden="true" tabindex="-1"></a><span class="bu">list</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="8">
@@ -474,12 +474,12 @@ <h2 data-number="3.5" class="anchored" data-anchor-id="brief-overview-of-python-
 </div>
 </div>
 <p>…and errors for a tuple.</p>
-<div id="e97c204b" class="cell" data-execution_count="9">
+<div id="5d9c9cd6" class="cell" data-execution_count="9">
 <div class="sourceCode cell-code" id="cb29"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb29-1"><a href="#cb29-1" aria-hidden="true" tabindex="-1"></a><span class="bu">tuple</span>[<span class="dv">0</span>] <span class="op">=</span> <span class="st">'new value'</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <pre><code>TypeError: 'tuple' object does not support item assignment</code></pre>
 <p>Dictionaries consist of key-value pairs, and are created using the syntax <code>{key: value}</code>. Keys are usually numbers or strings, and values can be any data type.</p>
-<div id="9d87d86a" class="cell" data-execution_count="10">
+<div id="cbdda81b" class="cell" data-execution_count="10">
 <div class="sourceCode cell-code" id="cb31"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb31-1"><a href="#cb31-1" aria-hidden="true" tabindex="-1"></a><span class="bu">dict</span> <span class="op">=</span> {<span class="st">'name'</span>: [<span class="st">'Jeanette'</span>, <span class="st">'Matt'</span>],</span>
 <span id="cb31-2"><a href="#cb31-2" aria-hidden="true" tabindex="-1"></a>        <span class="st">'location'</span>: [<span class="st">'Tucson'</span>, <span class="st">'Juneau'</span>]}</span>
 <span id="cb31-3"><a href="#cb31-3" aria-hidden="true" tabindex="-1"></a></span>
@@ -493,7 +493,7 @@ <h2 data-number="3.5" class="anchored" data-anchor-id="brief-overview-of-python-
 </div>
 </div>
 <p>To determine the type of an object, you can use the <code>type()</code> method.</p>
-<div id="2940e150" class="cell" data-execution_count="11">
+<div id="4f3d8e74" class="cell" data-execution_count="11">
 <div class="sourceCode cell-code" id="cb34"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb34-1"><a href="#cb34-1" aria-hidden="true" tabindex="-1"></a><span class="bu">type</span>(<span class="bu">list</span>)</span>
 <span id="cb34-2"><a href="#cb34-2" aria-hidden="true" tabindex="-1"></a><span class="bu">type</span>(<span class="bu">tuple</span>)</span>
 <span id="cb34-3"><a href="#cb34-3" aria-hidden="true" tabindex="-1"></a><span class="bu">type</span>(<span class="bu">dict</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
@@ -522,7 +522,7 @@ <h2 data-number="3.6" class="anchored" data-anchor-id="jupyter-notebooks"><span
 <section id="load-libraries" class="level3" data-number="3.6.1">
 <h3 data-number="3.6.1" class="anchored" data-anchor-id="load-libraries"><span class="header-section-number">3.6.1</span> Load libraries</h3>
 <p>In your first code chunk, lets load in some modules. We’ll use <code>pandas</code>, <code>numpy</code>, <code>matplotlib.pyplot</code>, <code>requests</code>, <code>skimpy</code>, and <code>exists</code> from <code>os.path</code>.</p>
-<div id="23f31f55" class="cell" data-execution_count="12">
+<div id="97ca3fd8" class="cell" data-execution_count="12">
 <div class="sourceCode cell-code" id="cb38"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb38-1"><a href="#cb38-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> pandas <span class="im">as</span> pd</span>
 <span id="cb38-2"><a href="#cb38-2" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> numpy <span class="im">as</span> np</span>
 <span id="cb38-3"><a href="#cb38-3" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> matplotlib.pyplot <span class="im">as</span> plt</span>
@@ -547,11 +547,11 @@ <h3 data-number="3.6.2" class="anchored" data-anchor-id="read-in-a-csv"><span cl
 <li>Click ‘copy link address’</li>
 </ul>
 <p>Create a variable called URL and assign it the link copied to your clipboard. Then use <code>urllib.request.urlretrieve</code> to download the file, and <code>open</code> to write it to disk, to a directory called <code>data/</code>. We’ll write this bundled in an <code>if</code> statement so that we only download the file if it doesn’t yet exist. First, we create the directory if it doesn’t exist:</p>
-<div id="d42d7ad3" class="cell" data-execution_count="13">
+<div id="443e1a5e" class="cell" data-execution_count="13">
 <div class="sourceCode cell-code" id="cb40"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb40-1"><a href="#cb40-1" aria-hidden="true" tabindex="-1"></a><span class="cf">if</span> <span class="kw">not</span> os.path.exists (<span class="st">'data/'</span>):</span>
 <span id="cb40-2"><a href="#cb40-2" aria-hidden="true" tabindex="-1"></a>        os.mkdir(<span class="st">'data/'</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
-<div id="f40b32c7" class="cell" data-execution_count="14">
+<div id="72d80861" class="cell" data-execution_count="14">
 <div class="sourceCode cell-code" id="cb41"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb41-1"><a href="#cb41-1" aria-hidden="true" tabindex="-1"></a><span class="cf">if</span> <span class="kw">not</span> os.path.exists(<span class="st">'data/discharge_timeseries.csv'</span>):</span>
 <span id="cb41-2"><a href="#cb41-2" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb41-3"><a href="#cb41-3" aria-hidden="true" tabindex="-1"></a>        url <span class="op">=</span> <span class="st">'https://arcticdata.io/metacat/d1/mn/v2/object/urn%3Auuid%3Ae248467d-e1f9-4a32-9e38-a9b4fb17cefb'</span></span>
@@ -559,7 +559,7 @@ <h3 data-number="3.6.2" class="anchored" data-anchor-id="read-in-a-csv"><span cl
 <span id="cb41-5"><a href="#cb41-5" aria-hidden="true" tabindex="-1"></a>        msg <span class="op">=</span> urllib.request.urlretrieve(url, <span class="st">'data/discharge_timeseries.csv'</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>Now we can read in the data from the file.</p>
-<div id="839bb727" class="cell" data-execution_count="15">
+<div id="eb2b8138" class="cell" data-execution_count="15">
 <div class="sourceCode cell-code" id="cb42"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb42-1"><a href="#cb42-1" aria-hidden="true" tabindex="-1"></a>df <span class="op">=</span> pd.read_csv(<span class="st">'data/discharge_timeseries.csv'</span>)</span>
 <span id="cb42-2"><a href="#cb42-2" aria-hidden="true" tabindex="-1"></a>df.head()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="14">
@@ -634,7 +634,7 @@ <h3 data-number="3.6.2" class="anchored" data-anchor-id="read-in-a-csv"><span cl
 </div>
 <p>The column names are a bit messy so we can use <code>clean_columns</code> from <code>skimpy</code> to make them cleaner for programming very quickly. We can also use the <code>skim</code> function to get a quick summary of the data.</p>
 <div class="column-page">
-<div id="5cc939ee" class="cell" data-execution_count="16">
+<div id="8af36a5e" class="cell" data-execution_count="16">
 <div class="sourceCode cell-code" id="cb43"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb43-1"><a href="#cb43-1" aria-hidden="true" tabindex="-1"></a>clean_df <span class="op">=</span> skimpy.clean_columns(df)</span>
 <span id="cb43-2"><a href="#cb43-2" aria-hidden="true" tabindex="-1"></a>skimpy.skim(clean_df)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display">
@@ -669,7 +669,7 @@ <h3 data-number="3.6.2" class="anchored" data-anchor-id="read-in-a-csv"><span cl
 </div>
 <p>We can see that the <code>date</code> column is classed as a string, and not a date, so let’s fix that.</p>
 <div class="column-page">
-<div id="3ec536f3" class="cell" data-execution_count="17">
+<div id="eeacc523" class="cell" data-execution_count="17">
 <div class="sourceCode cell-code" id="cb44"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb44-1"><a href="#cb44-1" aria-hidden="true" tabindex="-1"></a>clean_df[<span class="st">'date'</span>] <span class="op">=</span> pd.to_datetime(clean_df[<span class="st">'date'</span>])</span>
 <span id="cb44-2"><a href="#cb44-2" aria-hidden="true" tabindex="-1"></a>skimpy.skim(clean_df)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display">
@@ -709,21 +709,21 @@ <h3 data-number="3.6.2" class="anchored" data-anchor-id="read-in-a-csv"><span cl
 <li>summarize over it by taking the mean</li>
 </ul>
 <p>First we should probably rename our existing date/time column to prevent from getting confused.</p>
-<div id="5f17a0b8" class="cell" data-execution_count="18">
+<div id="72e430e0" class="cell" data-execution_count="18">
 <div class="sourceCode cell-code" id="cb45"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb45-1"><a href="#cb45-1" aria-hidden="true" tabindex="-1"></a>clean_df <span class="op">=</span> clean_df.rename(columns <span class="op">=</span> {<span class="st">'date'</span>: <span class="st">'datetime'</span>})</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>Now create the new date column</p>
-<div id="6bc53e04" class="cell" data-execution_count="19">
+<div id="da9237cf" class="cell" data-execution_count="19">
 <div class="sourceCode cell-code" id="cb46"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb46-1"><a href="#cb46-1" aria-hidden="true" tabindex="-1"></a>clean_df[<span class="st">'date'</span>] <span class="op">=</span> clean_df[<span class="st">'datetime'</span>].dt.date</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>Finally, we use group by to split the data into groups according to the date. We can then apply the <code>mean</code> method to calculate the mean value across all of the columns. Note that there are other methods you can use to calculate different statistics across different columns (eg: <code>clean_df.groupby('date').agg({'discharge_m_3_s': 'max'})</code>).</p>
-<div id="5be1704a" class="cell" data-execution_count="20">
+<div id="9c9e98cf" class="cell" data-execution_count="20">
 <div class="sourceCode cell-code" id="cb47"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb47-1"><a href="#cb47-1" aria-hidden="true" tabindex="-1"></a>daily_flow <span class="op">=</span> clean_df.groupby(<span class="st">'date'</span>, as_index <span class="op">=</span> <span class="va">False</span>).mean(numeric_only <span class="op">=</span> <span class="va">True</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <ul>
 <li>create a simple plot</li>
 </ul>
-<div id="8e12206e" class="cell" data-execution_count="21">
+<div id="d580f15a" class="cell" data-execution_count="21">
 <div class="sourceCode cell-code" id="cb48"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb48-1"><a href="#cb48-1" aria-hidden="true" tabindex="-1"></a>var <span class="op">=</span> <span class="st">'discharge_m_3_s'</span></span>
 <span id="cb48-2"><a href="#cb48-2" aria-hidden="true" tabindex="-1"></a>var_labs <span class="op">=</span> {<span class="st">'discharge_m_3_s'</span>: <span class="st">'Total Discharge'</span>}</span>
 <span id="cb48-3"><a href="#cb48-3" aria-hidden="true" tabindex="-1"></a></span>
@@ -763,7 +763,7 @@ <h2 data-number="3.7" class="anchored" data-anchor-id="functions"><span class="h
 <ul>
 <li>create <code>myplot.py</code></li>
 </ul>
-<div id="84ffa69a" class="cell" data-execution_count="22">
+<div id="520a767a" class="cell" data-execution_count="22">
 <div class="sourceCode cell-code" id="cb51"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb51-1"><a href="#cb51-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> matplotlib.pyplot <span class="im">as</span> plt</span>
 <span id="cb51-2"><a href="#cb51-2" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb51-3"><a href="#cb51-3" aria-hidden="true" tabindex="-1"></a><span class="kw">def</span> myplot(df, var):</span>
@@ -784,7 +784,7 @@ <h2 data-number="3.7" class="anchored" data-anchor-id="functions"><span class="h
 <li>add libraries</li>
 <li>replace old plot method with new function</li>
 </ul>
-<div id="5b68432f" class="cell" data-execution_count="23">
+<div id="2de0053f" class="cell" data-execution_count="23">
 <div class="sourceCode cell-code" id="cb52"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb52-1"><a href="#cb52-1" aria-hidden="true" tabindex="-1"></a>myplot(daily_flow, <span class="st">'temperature_degrees_c'</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display">
 <div>
diff --git a/public/2024-03-arctic/sections/software-design-1.html b/public/2024-03-arctic/sections/software-design-1.html
index dd70ea0a..701672c3 100644
--- a/public/2024-03-arctic/sections/software-design-1.html
+++ b/public/2024-03-arctic/sections/software-design-1.html
@@ -337,7 +337,7 @@ <h2 data-number="12.2" class="anchored" data-anchor-id="why-functions"><span cla
 <section id="functions-as-objects" class="level2" data-number="12.3">
 <h2 data-number="12.3" class="anchored" data-anchor-id="functions-as-objects"><span class="header-section-number">12.3</span> Functions as objects</h2>
 <p>Functions are first-class objects in Python (and many other languages). This has some real benefits for and implications for parallel programming. Because a function is an object, it means that it can be 1) stored in a variable, and 2) passed as an argument to another function. We saw that in the module on pleasingly parallel codes when we used <code>ThreadPoolExecutor.map()</code>, which takes a function and an iterable object as arguments. Let’s check out how you can use and manipulate functions as objects. First, let’s define a simple function, assign it to another variable, and then use both:</p>
-<div id="3e4868df" class="cell" data-execution_count="1">
+<div id="eb5fc97e" class="cell" data-execution_count="1">
 <div class="sourceCode cell-code" id="cb1"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="kw">def</span> double(x):</span>
 <span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a>    <span class="cf">return</span> <span class="dv">2</span><span class="op">*</span>x</span>
 <span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a></span>
@@ -349,7 +349,7 @@ <h2 data-number="12.3" class="anchored" data-anchor-id="functions-as-objects"><s
 </div>
 </div>
 <p>Note that when we print it to screen, we see that <code>prod</code> is of type <code>function</code>, and when we use the two instances, we get identical results:</p>
-<div id="8fb6709b" class="cell" data-execution_count="2">
+<div id="2a21f2af" class="cell" data-execution_count="2">
 <div class="sourceCode cell-code" id="cb3"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a><span class="bu">print</span>(double(<span class="dv">7</span>))</span>
 <span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a><span class="bu">print</span>(twotimes(<span class="dv">7</span>))</span>
 <span id="cb3-3"><a href="#cb3-3" aria-hidden="true" tabindex="-1"></a><span class="bu">print</span>(double(<span class="dv">5</span>) <span class="op">==</span> twotimes(<span class="dv">5</span>))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
@@ -360,14 +360,14 @@ <h2 data-number="12.3" class="anchored" data-anchor-id="functions-as-objects"><s
 </div>
 </div>
 <p>This representation of a function as an object comes in handy when we want to invoke a function in multiple different contexts, such as in a parallel execution environment via a <code>map()</code> function.</p>
-<div id="da4480a7" class="cell" data-execution_count="3">
+<div id="b7ee18d0" class="cell" data-execution_count="3">
 <div class="sourceCode cell-code" id="cb5"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a><span class="bu">list</span>(<span class="bu">map</span>(twotimes, [<span class="dv">2</span>,<span class="dv">3</span>,<span class="dv">4</span>]))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="3">
 <pre><code>[4, 6, 8]</code></pre>
 </div>
 </div>
 <p>This works because the function <code>twotimes</code> can be passed to <code>map</code> and executed from within <code>map</code>. When you execute a function that is passed in via an argument, it is called <strong>function composition</strong>. We can easily illustrate this by creating some function and passing it to a wrapper function to be executed:</p>
-<div id="e5d90771" class="cell" data-execution_count="4">
+<div id="9be5394f" class="cell" data-execution_count="4">
 <div class="sourceCode cell-code" id="cb7"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a><span class="kw">def</span> some_function():</span>
 <span id="cb7-2"><a href="#cb7-2" aria-hidden="true" tabindex="-1"></a>    <span class="bu">print</span>(<span class="st">"Ran some_function"</span>)</span>
 <span id="cb7-3"><a href="#cb7-3" aria-hidden="true" tabindex="-1"></a></span>
@@ -413,7 +413,7 @@ <h2 data-number="12.3" class="anchored" data-anchor-id="functions-as-objects"><s
 <section id="global-variables" class="level2" data-number="12.4">
 <h2 data-number="12.4" class="anchored" data-anchor-id="global-variables"><span class="header-section-number">12.4</span> Global variables</h2>
 <p>When executing a function, the variables that are in scope to that function are local, meaning that the presence of another variable with the same name in another scope will not affect a calculation. For example:</p>
-<div id="1ec1c87e" class="cell" data-execution_count="5">
+<div id="ab96e365" class="cell" data-execution_count="5">
 <div class="sourceCode cell-code" id="cb9"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a><span class="kw">def</span> do_task():</span>
 <span id="cb9-2"><a href="#cb9-2" aria-hidden="true" tabindex="-1"></a>    x <span class="op">=</span> <span class="dv">10</span></span>
 <span id="cb9-3"><a href="#cb9-3" aria-hidden="true" tabindex="-1"></a></span>
@@ -425,7 +425,7 @@ <h2 data-number="12.4" class="anchored" data-anchor-id="global-variables"><span
 </div>
 </div>
 <p>However, if that same variable is declared as global inside the function, then the assignment will have global impact on the value of the parent scope:</p>
-<div id="853b0196" class="cell" data-execution_count="6">
+<div id="9f42ae81" class="cell" data-execution_count="6">
 <div class="sourceCode cell-code" id="cb11"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb11-1"><a href="#cb11-1" aria-hidden="true" tabindex="-1"></a><span class="kw">def</span> do_task():</span>
 <span id="cb11-2"><a href="#cb11-2" aria-hidden="true" tabindex="-1"></a>    <span class="kw">global</span> x</span>
 <span id="cb11-3"><a href="#cb11-3" aria-hidden="true" tabindex="-1"></a>    x <span class="op">=</span> <span class="dv">10</span></span>
@@ -439,7 +439,7 @@ <h2 data-number="12.4" class="anchored" data-anchor-id="global-variables"><span
 </div>
 <p>So, you can see that writing a function that uses global variables can have effects outside of the scope of the function call. This can have drastic consequences on concurrent code, as the order in which function calls are made when operating concurrently are not deterministic, and so the impact of global variables will also not be deterministic.</p>
 <p>A related issue arises when code in a function depends on its enclosing namespace, such as when a function is defined inside of another function. When resolving a variable, python first looks in the Local namespace, and then in the Enclosing namespace, Global namespace, and Built-in namespace. So, even if a variable is not defined locally, it might still be resolved by one of the other namespaces in surprising ways.</p>
-<div id="7b6b11d3" class="cell" data-execution_count="7">
+<div id="d6b9b242" class="cell" data-execution_count="7">
 <div class="sourceCode cell-code" id="cb13"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb13-1"><a href="#cb13-1" aria-hidden="true" tabindex="-1"></a>a <span class="op">=</span> <span class="dv">3</span></span>
 <span id="cb13-2"><a href="#cb13-2" aria-hidden="true" tabindex="-1"></a><span class="kw">def</span> do_stuff(b):</span>
 <span id="cb13-3"><a href="#cb13-3" aria-hidden="true" tabindex="-1"></a>    <span class="cf">return</span> a<span class="op">*</span>b</span>
@@ -476,7 +476,7 @@ <h2 data-number="12.6" class="anchored" data-anchor-id="task-dependencies"><span
 <section id="locks" class="level2" data-number="12.7">
 <h2 data-number="12.7" class="anchored" data-anchor-id="locks"><span class="header-section-number">12.7</span> Locks</h2>
 <p>Locks are a mechanism to manage access to a resource so that multiple threads can access the resource. By adding locks to an otherwise parallel process, we introduce a degree of serial execution to the locked portion of the process. Basically, each thread can only access the resource when it has the lock, and only one lock is given out at a time. Take this example of what happens without locking:</p>
-<div id="ce520bd4" class="cell" data-execution_count="8">
+<div id="30ee053d" class="cell" data-execution_count="8">
 <div class="sourceCode cell-code" id="cb15"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb15-1"><a href="#cb15-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> concurrent.futures <span class="im">import</span> ProcessPoolExecutor</span>
 <span id="cb15-2"><a href="#cb15-2" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> time</span>
 <span id="cb15-3"><a href="#cb15-3" aria-hidden="true" tabindex="-1"></a></span>
@@ -489,16 +489,16 @@ <h2 data-number="12.7" class="anchored" data-anchor-id="locks"><span class="head
 <span id="cb15-10"><a href="#cb15-10" aria-hidden="true" tabindex="-1"></a><span class="cf">for</span> future <span class="kw">in</span> futures:</span>
 <span id="cb15-11"><a href="#cb15-11" aria-hidden="true" tabindex="-1"></a>    future.result()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
-<pre><code>012  HelloHello 
+<pre><code>102  Hello Hello
+Hello1
 
-Hello
-102   worldworld
-world
+2 0 world
+ worldworld
 </code></pre>
 </div>
 </div>
 <p>You can see that the results come back in a semi-random order, and the call to <code>sleep</code> creates a delay between printing the two words, which means that the three messages get jumbled when printed. To fix this, we can introduce a lock from the <code>multiprocessing</code> package.</p>
-<div id="d1afb348" class="cell" data-execution_count="9">
+<div id="0120712d" class="cell" data-execution_count="9">
 <div class="sourceCode cell-code" id="cb17"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb17-1"><a href="#cb17-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> concurrent.futures <span class="im">import</span> ProcessPoolExecutor</span>
 <span id="cb17-2"><a href="#cb17-2" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> time</span>
 <span id="cb17-3"><a href="#cb17-3" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> multiprocessing</span>
diff --git a/public/2024-03-arctic/sections/software-design-2.html b/public/2024-03-arctic/sections/software-design-2.html
index 18e98a20..2a8ea0c8 100644
--- a/public/2024-03-arctic/sections/software-design-2.html
+++ b/public/2024-03-arctic/sections/software-design-2.html
@@ -320,7 +320,7 @@ <h2 data-number="16.1" class="anchored" data-anchor-id="learning-objectives"><sp
 <section id="python-modules-and-packages" class="level2" data-number="16.2">
 <h2 data-number="16.2" class="anchored" data-anchor-id="python-modules-and-packages"><span class="header-section-number">16.2</span> Python modules and packages</h2>
 <p>The <a href="https://pypi.org">Python Package Index</a> currently houses over 400,000 software projects that you can install, reuse, and extend to accelerate your work. If you’ve worked in python for long at all, you have certainly used the <code>import</code> statement to to load <strong>modules</strong> that you need in your code into a namespace for use.</p>
-<div id="833249c8" class="cell" data-execution_count="1">
+<div id="df49be1a" class="cell" data-execution_count="1">
 <div class="sourceCode cell-code" id="cb1"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> numpy <span class="im">as</span> np</span>
 <span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a>np.pi<span class="op">*</span>np.power(<span class="dv">3</span>, <span class="dv">2</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="1">
@@ -350,7 +350,7 @@ <h2 data-number="16.2" class="anchored" data-anchor-id="python-modules-and-packa
 <section id="anatomy-of-modules-and-packages" class="level2" data-number="16.3">
 <h2 data-number="16.3" class="anchored" data-anchor-id="anatomy-of-modules-and-packages"><span class="header-section-number">16.3</span> Anatomy of modules and packages</h2>
 <p>Modules in python are loaded from the definitions stored in files of the same name with a <code>.py</code> file extension. Within each file are definitions of objects which can be accessed when the module is imported. For example, imagine you have a file named <em>hello.py</em> in your current directory that contains a function called <code>helloworld</code> with the following contents:</p>
-<div id="a74a947e" class="cell" data-execution_count="2">
+<div id="a4af4921" class="cell" data-execution_count="2">
 <div class="sourceCode cell-code" id="cb3"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a><span class="kw">def</span> helloworld():</span>
 <span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a>  <span class="co">"""</span></span>
 <span id="cb3-3"><a href="#cb3-3" aria-hidden="true" tabindex="-1"></a><span class="co">  Print a hello message</span></span>
@@ -358,7 +358,7 @@ <h2 data-number="16.3" class="anchored" data-anchor-id="anatomy-of-modules-and-p
 <span id="cb3-5"><a href="#cb3-5" aria-hidden="true" tabindex="-1"></a>  <span class="bu">print</span>(<span class="st">"Hello world!"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>With that in place, you can then import and use that function in other code you write:</p>
-<div id="f33a236a" class="cell" data-execution_count="3">
+<div id="8ae9379f" class="cell" data-execution_count="3">
 <div class="sourceCode cell-code" id="cb4"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> helloworld</span>
 <span id="cb4-2"><a href="#cb4-2" aria-hidden="true" tabindex="-1"></a>helloworld()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
@@ -541,7 +541,7 @@ <h3 data-number="16.6.1" class="anchored" data-anchor-id="load-software-dependen
 <section id="adding-code-to-modules" class="level3" data-number="16.6.2">
 <h3 data-number="16.6.2" class="anchored" data-anchor-id="adding-code-to-modules"><span class="header-section-number">16.6.2</span> Adding code to modules</h3>
 <p>Let’s write some module code! In this case, we want to implement the <code>adcmodel.data.load_adc_csv(identifier)</code> function that we’ll use for data loading and caching. We do this by creating a new file called <code>data.py</code> in the <code>adcmodel</code> directory, and then implementing the function. Create a new python file called <code>data.py</code> and save it to the <code>adcmodel</code> module directory, with the following function implementation:</p>
-<div id="de4ff0d0" class="cell" data-execution_count="4">
+<div id="ddc8b983" class="cell" data-execution_count="4">
 <div class="sourceCode cell-code" id="cb13"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb13-1"><a href="#cb13-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> pandas <span class="im">as</span> pd</span>
 <span id="cb13-2"><a href="#cb13-2" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> urllib.request</span>
 <span id="cb13-3"><a href="#cb13-3" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> hashlib</span>
@@ -564,7 +564,7 @@ <h3 data-number="16.6.2" class="anchored" data-anchor-id="adding-code-to-modules
 <span id="cb13-20"><a href="#cb13-20" aria-hidden="true" tabindex="-1"></a>    <span class="cf">return</span> df</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>Once you save the file, we can use <code>poetry install</code> to install the package and try running the function in an interactive session. Check it out by opening a new Jupyter notebook and running the following in the first cell (after you install the needed jupyter packages):</p>
-<div id="fcc9f89a" class="cell" data-execution_count="5">
+<div id="58c4761e" class="cell" data-execution_count="5">
 <div class="sourceCode cell-code" id="cb14"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb14-1"><a href="#cb14-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> adcmodel.data <span class="im">import</span> load_adc_csv</span>
 <span id="cb14-2"><a href="#cb14-2" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb14-3"><a href="#cb14-3" aria-hidden="true" tabindex="-1"></a>df <span class="op">=</span> load_adc_csv(<span class="st">"urn:uuid:e248467d-e1f9-4a32-9e38-a9b4fb17cefb"</span>)</span>
@@ -586,7 +586,7 @@ <h2 data-number="16.7" class="anchored" data-anchor-id="testing"><span class="he
 <p>In pytest and other frameworks, the common pattern is to use the python <code>assert</code> function to determine whether a value produced from your code matches expectations. For example, given a function that produces a product of two numbers, we could assert that <code>product(5, 6) == 30</code> and that <code>product(-3, -2) == 6</code>.</p>
 <p>Generally, it is good practice to produce at least one test file for each of your package source files, and their names are prefixed with <code>test_</code>. So, for our <code>data.py</code> source file, we might expect to build tests in a file called <code>test_data.py</code>. Let’s create that file in the <code>tests</code> directory and walk through populating it with some simple tests. The functions in the test file are also named with <code>test_</code> as the prefix on the function name, and generally each test function atomically tests one aspect of the code. That way, when a test function fails, it should be quickly clear what type of an error would produce that kind of failure, and how to locate it in the code.</p>
 <p>To get started, create <code>tests/test_data.py</code> and populate it with our first test function:</p>
-<div id="c7eb1d1f" class="cell" data-execution_count="6">
+<div id="2fd31969" class="cell" data-execution_count="6">
 <div class="sourceCode cell-code" id="cb16"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb16-1"><a href="#cb16-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> adcmodel.data <span class="im">import</span> load_adc_csv</span>
 <span id="cb16-2"><a href="#cb16-2" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb16-3"><a href="#cb16-3" aria-hidden="true" tabindex="-1"></a><span class="kw">def</span> test_load_adc_csv():</span>
@@ -595,7 +595,7 @@ <h2 data-number="16.7" class="anchored" data-anchor-id="testing"><span class="he
 </div>
 <p>You can run the tests after saving the file by running <code>pytest</code> on the commandline, which will produce output indicating which tests passed and failed.</p>
 <p>We can add other tests that test multiple conditions, and we can use any of the features of python to creatively test that our code will produce correct results. Here’s another test to check that the data file we loaded has the right number of rows and columns:</p>
-<div id="d0419a19" class="cell" data-execution_count="7">
+<div id="ef50cf22" class="cell" data-execution_count="7">
 <div class="sourceCode cell-code" id="cb17"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb17-1"><a href="#cb17-1" aria-hidden="true" tabindex="-1"></a><span class="kw">def</span> test_load_adc_csv_shape():</span>
 <span id="cb17-2"><a href="#cb17-2" aria-hidden="true" tabindex="-1"></a>    df <span class="op">=</span> load_adc_csv(<span class="st">"urn:uuid:e248467d-e1f9-4a32-9e38-a9b4fb17cefb"</span>)</span>
 <span id="cb17-3"><a href="#cb17-3" aria-hidden="true" tabindex="-1"></a>    <span class="cf">assert</span> df <span class="kw">is</span> <span class="kw">not</span> <span class="va">None</span></span>
@@ -603,7 +603,7 @@ <h2 data-number="16.7" class="anchored" data-anchor-id="testing"><span class="he
 </div>
 <p><img src="../images/vscode-pytest-results.png" class="img-fluid"></p>
 <p>Finally, what happens when tests are run that fail? Try adding the following test, and determine if the failure is due to the code, the test, or the data? What would you change to make this test reliably pass, or fail more gracefully?</p>
-<div id="7cf59c85" class="cell" data-execution_count="8">
+<div id="c364cb67" class="cell" data-execution_count="8">
 <div class="sourceCode cell-code" id="cb18"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb18-1"><a href="#cb18-1" aria-hidden="true" tabindex="-1"></a><span class="kw">def</span> test_load_adc_csv_inputs():</span>
 <span id="cb18-2"><a href="#cb18-2" aria-hidden="true" tabindex="-1"></a>    df <span class="op">=</span> load_adc_csv()</span>
 <span id="cb18-3"><a href="#cb18-3" aria-hidden="true" tabindex="-1"></a>    <span class="cf">assert</span> df <span class="kw">is</span> <span class="kw">not</span> <span class="va">None</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
diff --git a/public/2024-03-arctic/sections/zarr.html b/public/2024-03-arctic/sections/zarr.html
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@@ -364,7 +364,7 @@ <h2 data-number="14.3" class="anchored" data-anchor-id="using-zarr"><span class=
 <h2 data-number="14.4" class="anchored" data-anchor-id="retrieving-cmip6-data-from-google-cloud"><span class="header-section-number">14.4</span> Retrieving CMIP6 Data from Google Cloud</h2>
 <p>Here, we will show how to uze Zarr and <code>xarray</code> to read in the CMIP6 climate model data from the World Climate Research Program, hosted by <a href="https://console.cloud.google.com/marketplace/details/noaa-public/cmip6?pli=1">Googe Cloud</a>. CMIP6 is a huge set of climate models, with around 100 models produced across 49 different groups. There is an enormous amount of data produced by these models, and in this demonstration we will load a tiny fraction of it. This demonstration is based off of an example put together by <a href="https://github.com/pangeo-data/pangeo-cmip6-examples">Pangeo</a></p>
 <p>First we’ll load some libraries. <code>pandas</code>, <code>numpy</code>, and <code>xarray</code> should all be familiar by now. We’ll also load in <code>zarr</code>, of course, and <code>gcsfs</code>, the Google Cloud Storage File System library, which enables access to Google Cloud datasets, including our CMIP6 example.</p>
-<div id="1db4b0cb" class="cell" data-execution_count="1">
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 <div class="sourceCode cell-code" id="cb1"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> pandas <span class="im">as</span> pd</span>
 <span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> numpy <span class="im">as</span> np</span>
 <span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> zarr</span>
@@ -372,7 +372,7 @@ <h2 data-number="14.4" class="anchored" data-anchor-id="retrieving-cmip6-data-fr
 <span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> gcsfs</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>Next, we’ll read a csv file that contains information about all of the CMIP6 stores available on Google Cloud. You can find this URL to this csv file by navigating to the <a href="https://console.cloud.google.com/marketplace/details/noaa-public/cmip6?pli=1">Google Cloud CMIP6 home page</a>, clicking on “View Dataset,” and pressing the copy link button for the file in the table.</p>
-<div id="ac95cf56" class="cell" data-execution_count="2">
+<div id="cca741dd" class="cell" data-execution_count="2">
 <div class="sourceCode cell-code" id="cb2"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a>df <span class="op">=</span> pd.read_csv(<span class="st">'https://storage.googleapis.com/cmip6/cmip6-zarr-consolidated-stores.csv'</span>)</span>
 <span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a>df.head()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="2">
@@ -477,7 +477,7 @@ <h2 data-number="14.4" class="anchored" data-anchor-id="retrieving-cmip6-data-fr
 </div>
 <p>CMIP6 is an extremely large collection of datasets, with their own terminology. We’ll be making a request based on the experiment id (scenario), table id (tables are organized roughly by themes), and variable id.</p>
 <p>For this example, we’ll select data from a simulation of the recent past (historical) from the ocean daily (Oday) table, and select the sea surface height (tos) variable. We’ll also only select results from NOAA Geophysical Fluid Dynamics Laboratory (NOAA-GFDL) runs. We’ll do this by passing a logical statment with these criteria to the <code>query</code> method of our <code>pandas</code> data.frame.</p>
-<div id="dfeef20d" class="cell" data-execution_count="3">
+<div id="bd5f5f95" class="cell" data-execution_count="3">
 <div class="sourceCode cell-code" id="cb3"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a>df_ta <span class="op">=</span> df.query(<span class="st">"activity_id=='CMIP' &amp; table_id == 'Oday' &amp; variable_id == 'tos' &amp; experiment_id == 'historical' &amp; institution_id == 'NOAA-GFDL'"</span>)</span>
 <span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a>df_ta</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="3">
@@ -568,41 +568,41 @@ <h2 data-number="14.4" class="anchored" data-anchor-id="retrieving-cmip6-data-fr
 </div>
 <p>Note in the table above there is a column <code>zstore</code>. This gives the URI for the store described in each row of the dataset. Note there is also a <code>version</code> column indicating the date of that particular version. We will use both of these columns to select a single store of data.</p>
 <p>First, though, we need to set up a connection to the Google Cloud Storage file system (GCSFS). We do this using the <code>gcsfs</code> library to call the <code>GCSFileSystem</code> method using an anonymous token. Because this dataset is completely open, we do not have to authenticate at all to access it.</p>
-<div id="8e5cea7a" class="cell" data-execution_count="4">
+<div id="5d9af997" class="cell" data-execution_count="4">
 <div class="sourceCode cell-code" id="cb4"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a>gcs <span class="op">=</span> gcsfs.GCSFileSystem(token<span class="op">=</span><span class="st">'anon'</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>Now, we’ll create a variable with the path to the most recent store from the table above (note the table is sorted by version, so we grab the store URL from the last row), and create a mapping interface to the store using the connection to the Google Cloud Storage system. This interface is what allows us to access the Zarr store with all of the data we are interested in.</p>
-<div id="5b8ed57e" class="cell" data-execution_count="5">
+<div id="ff647900" class="cell" data-execution_count="5">
 <div class="sourceCode cell-code" id="cb5"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a>zstore <span class="op">=</span> df_ta.zstore.values[<span class="op">-</span><span class="dv">1</span>]</span>
 <span id="cb5-2"><a href="#cb5-2" aria-hidden="true" tabindex="-1"></a>mapper <span class="op">=</span> gcs.get_mapper(zstore)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>Nex, we open the store using the <code>xarray</code> method <code>open_zarr</code> and examine its metadata. Note that the first argument to <code>open_zarr</code> can be a “MutableMapping where a Zarr Group has been stored” (what we are using here), or a path to a directory in file system (if a local Zarr store is being used).</p>
-<div id="94620331" class="cell" data-execution_count="6">
+<div id="df4425fb" class="cell" data-execution_count="6">
 <div class="sourceCode cell-code" id="cb6"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a>ds <span class="op">=</span> xr.open_zarr(mapper)</span>
 <span id="cb6-2"><a href="#cb6-2" aria-hidden="true" tabindex="-1"></a>ds</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>This piece of data is around 30 GB total, and we made it accessible to our environment nearly instantly. Consider also that the total volume of CMIP6 data available on Google Cloud is in the neighborhood of a few <strong>petabytes</strong>, and it is all available at your fingertips using Zarr.</p>
 <p>From here, using the <code>xarray</code> indexing, we can easily grab a time slice and make a plot of the data.</p>
-<div id="1b1c8b35" class="cell" data-execution_count="7">
+<div id="1800105b" class="cell" data-execution_count="7">
 <div class="sourceCode cell-code" id="cb7"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a>ds.tos.sel(time<span class="op">=</span><span class="st">'1999-01-01'</span>).squeeze().plot()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>We can also get a timeseries slice, here on the equator in the Eastern Pacific.</p>
-<div id="23892315" class="cell" data-execution_count="8">
+<div id="94fd82db" class="cell" data-execution_count="8">
 <div class="sourceCode cell-code" id="cb8"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a>ts <span class="op">=</span> ds.tos.sel(lat <span class="op">=</span> <span class="dv">0</span>, lon <span class="op">=</span> <span class="dv">272</span>, method <span class="op">=</span> <span class="st">"nearest"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p><code>xarray</code> allows us to do some pretty cool things, like calculate a rolling annual mean on our daily data. Again, because none of the data are loaded into memory, it is nearly instant.</p>
-<div id="4998b2d8" class="cell" data-execution_count="9">
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 <div class="sourceCode cell-code" id="cb9"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a>ts_ann <span class="op">=</span> ts.rolling(time <span class="op">=</span> <span class="dv">365</span>).mean()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>Note that if we explicitly want to load the data into memory, we can by using the <code>load</code> method.</p>
-<div id="8bf99140" class="cell" data-execution_count="10">
+<div id="7c12c984" class="cell" data-execution_count="10">
 <div class="sourceCode cell-code" id="cb10"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb10-1"><a href="#cb10-1" aria-hidden="true" tabindex="-1"></a>ts.load()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
-<div id="52ed27a3" class="cell" data-execution_count="11">
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 <div class="sourceCode cell-code" id="cb11"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb11-1"><a href="#cb11-1" aria-hidden="true" tabindex="-1"></a>ts_ann.load()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>Finally, we can make a simple plot of the daily temperatures, along with the rolling annual mean.</p>
-<div id="e492f194" class="cell" data-execution_count="12">
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 <div class="sourceCode cell-code" id="cb12"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb12-1"><a href="#cb12-1" aria-hidden="true" tabindex="-1"></a>ts.plot(label <span class="op">=</span> <span class="st">"daily"</span>)</span>
 <span id="cb12-2"><a href="#cb12-2" aria-hidden="true" tabindex="-1"></a>ts_ann.plot(label <span class="op">=</span> <span class="st">"rolling annual mean"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
@@ -614,7 +614,7 @@ <h2 data-number="14.4" class="anchored" data-anchor-id="retrieving-cmip6-data-fr
 <h2 data-number="14.5" class="anchored" data-anchor-id="additional-reference-creating-a-zarr-dataset"><span class="header-section-number">14.5</span> Additional Reference: Creating a Zarr Dataset</h2>
 <p>This is a simplified version of the official <a href="https://zarr.readthedocs.io/en/stable/tutorial.html">Zarr tutorial</a>.</p>
 <p>First, create an array of 10,000 rows and 10,000 columns, filled with zeros, divided into chunks where each chunk is 1,000 x 1,000.</p>
-<div id="6268f263" class="cell" data-execution_count="13">
+<div id="2b2c329c" class="cell" data-execution_count="13">
 <div class="sourceCode cell-code" id="cb14"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb14-1"><a href="#cb14-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> zarr</span>
 <span id="cb14-2"><a href="#cb14-2" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> numpy <span class="im">as</span> np</span>
 <span id="cb14-3"><a href="#cb14-3" aria-hidden="true" tabindex="-1"></a>z <span class="op">=</span> zarr.zeros((<span class="dv">10000</span>, <span class="dv">10000</span>), chunks<span class="op">=</span>(<span class="dv">1000</span>, <span class="dv">1000</span>), dtype<span class="op">=</span><span class="st">'i4'</span>)</span>
@@ -624,7 +624,7 @@ <h2 data-number="14.5" class="anchored" data-anchor-id="additional-reference-cre
 </div>
 </div>
 <p>The array can be treated just like a numpy array, where you can assign values using indexing.</p>
-<div id="e1a4a905" class="cell" data-execution_count="14">
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 <div class="sourceCode cell-code" id="cb16"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb16-1"><a href="#cb16-1" aria-hidden="true" tabindex="-1"></a>z[<span class="dv">0</span>, :] <span class="op">=</span> np.arange(<span class="dv">10000</span>)</span>
 <span id="cb16-2"><a href="#cb16-2" aria-hidden="true" tabindex="-1"></a>z[:]</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="5">
@@ -638,11 +638,11 @@ <h2 data-number="14.5" class="anchored" data-anchor-id="additional-reference-cre
 </div>
 </div>
 <p>To save the file, just use the <code>save</code> method.</p>
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 <div class="sourceCode cell-code" id="cb18"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb18-1"><a href="#cb18-1" aria-hidden="true" tabindex="-1"></a>zarr.save(<span class="st">'data/example.zarr'</span>, z)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>And <code>open</code> to open it again.</p>
-<div id="72051f6d" class="cell" data-execution_count="16">
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 <div class="sourceCode cell-code" id="cb19"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb19-1"><a href="#cb19-1" aria-hidden="true" tabindex="-1"></a>arr <span class="op">=</span> zarr.<span class="bu">open</span>(<span class="st">'data/example.zarr'</span>)</span>
 <span id="cb19-2"><a href="#cb19-2" aria-hidden="true" tabindex="-1"></a>arr</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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@@ -650,13 +650,13 @@ <h2 data-number="14.5" class="anchored" data-anchor-id="additional-reference-cre
 </div>
 </div>
 <p>To create groups in your store, use the <code>create_group</code> method after creating a root group.</p>
-<div id="a75e8b58" class="cell" data-execution_count="17">
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 <div class="sourceCode cell-code" id="cb21"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb21-1"><a href="#cb21-1" aria-hidden="true" tabindex="-1"></a>root <span class="op">=</span> zarr.group()</span>
 <span id="cb21-2"><a href="#cb21-2" aria-hidden="true" tabindex="-1"></a>temp <span class="op">=</span> root.create_group(<span class="st">'temp'</span>)</span>
 <span id="cb21-3"><a href="#cb21-3" aria-hidden="true" tabindex="-1"></a>precip <span class="op">=</span> root.create_group(<span class="st">'precip'</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>You can then create a dataset (array) within the group, again specifying the shape and chunks.</p>
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 <div class="sourceCode cell-code" id="cb22"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb22-1"><a href="#cb22-1" aria-hidden="true" tabindex="-1"></a>t100 <span class="op">=</span> temp.create_dataset(<span class="st">'t100'</span>, shape<span class="op">=</span>(<span class="dv">10000</span>, <span class="dv">10000</span>), chunks<span class="op">=</span>(<span class="dv">1000</span>, <span class="dv">1000</span>), dtype<span class="op">=</span><span class="st">'i4'</span>)</span>
 <span id="cb22-2"><a href="#cb22-2" aria-hidden="true" tabindex="-1"></a>t100</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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 </div>
 </div>
 <p>Groups can easily be accessed by name and index.</p>
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 <div class="sourceCode cell-code" id="cb24"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb24-1"><a href="#cb24-1" aria-hidden="true" tabindex="-1"></a>root[<span class="st">'temp'</span>]</span>
 <span id="cb24-2"><a href="#cb24-2" aria-hidden="true" tabindex="-1"></a>root[<span class="st">'temp/t100'</span>][:, <span class="dv">3</span>]</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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@@ -672,15 +672,15 @@ <h2 data-number="14.5" class="anchored" data-anchor-id="additional-reference-cre
 </div>
 </div>
 <p>To get a look at your overall dataset, the <code>tree</code> and <code>info</code> methods are helpful.</p>
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 <div class="sourceCode cell-code" id="cb26"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb26-1"><a href="#cb26-1" aria-hidden="true" tabindex="-1"></a>root.tree()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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