Fix broken links (#609)

* Fix broken links

* Modernize some more jupyterbook links

* Fix use some cross-references

* Remove errant xref:python

Author: brian-rose

README.md

@@ -7,4 +7,4 @@ This is the source repository for the Pythia Foundations content collection.
 
 The rendered site can be found at https://foundations.projectpythia.org
 
-The book is powered by [Jupyter Book](https://jupyterbook.org/intro.html).
+The book is powered by [MyST](https://mystmd.org) and [Jupyter Book](https://jupyterbook.org/).

appendix/glossary.md

@@ -15,7 +15,7 @@ BinderHub
 
 Conda
 : Conda is an open-source, cross-platform, language-agnostic package manager and environment management system that allows you to quickly install, run, and update packages within your work environment(s). To install `conda`, we recommend {term}`miniconda`.
-: See [Conda documentation](https://docs.conda.io/en/latest/) and the [Conda cheat sheet](https://docs.conda.io/projects/conda/en/4.6.0/_downloads/) and @conda-commands in the context of Project Pythia.
+: See [Conda documentation](https://docs.conda.io/en/latest/), particularly the [Conda cheat sheet](https://docs.conda.io/projects/conda/en/latest/user-guide/cheatsheet.html), and @conda-commands in the context of Project Pythia.
 
 Jupyter Notebooks
 : The Jupyter Notebook software is an open-source web application that allows you to create and share Jupyter Notebooks (`*.ipynb` files). Jupyter Notebooks contain executable code, LaTeX equations, visualizations (e.g., plots, pictures), and narrative text. The code does not have to just be Python, other languages such as Julia or R are supported as well. Jupyter Notebooks are celebrated for their interactive output that allows movement between code, code output, explanations, and more code - similar to how scientists think and solve problems. Jupyter Notebooks can be thought of as a living, runnable publication and make for a great presentation platform. See also {term}`Jupyter Kernels`, {term}`Jupyter Lab`, {term}`Jupyter Hub`, {term}`Binder`, and {term}`BinderHub`.

appendix/template.ipynb

@@ -245,7 +245,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We also suggest checking out Jupyter Book's [brief demonstration](https://jupyterbook.org/v1/content/metadata.html#add-metadata-to-notebooks) on adding cell tags to your cells in Jupyter Notebook, Lab, or manually. Using these cell tags can allow you to [customize](https://jupyterbook.org/v1/interactive/hiding.html) how your code content is displayed and even [demonstrate errors](https://jupyterbook.org/v1/content/execute.html#dealing-with-code-that-raises-errors) without altogether crashing our loyal army of machines!"
+    "We also suggest checking out MyST's [brief demonstration](https://mystmd.org/guide/notebook-configuration#notebook-cell-tags) on adding cell tags to your cells in Jupyter Notebook, Lab, or manually. See [this table](https://mystmd.org/guide/notebook-configuration#tbl-notebook-cell-tags) for a list of supported cell tags, which you can use to customize how your code content is displayed and even [demonstrate errors](https://mystmd.org/guide/execute-notebooks#allow-a-code-cell-to-error-without-failing-the-build) without altogether crashing our loyal army of machines!"
    ]
   },
   {

core/datetime/datetime.ipynb

@@ -232,15 +232,15 @@
     "\n",
     "### Parsing lightning data timestamps with the `datetime.strptime` method\n",
     "\n",
-    "In this example, we are analyzing [US NLDN lightning data](https://ghrc.nsstc.nasa.gov/uso/ds_docs/vaiconus/vaiconus_dataset.html). Here is a sample row of data:\n",
+    "In this example, we are analyzing [US NLDN lightning data](https://www.vaisala.com/en/products/national-lightning-detection-network-nldn). Here is a sample row of data:\n",
     "\n",
     "    06/27/07 16:18:21.898 18.739 -88.184 0.0 kA 0 1.0 0.4 2.5 8 1.2 13 G\n",
     "\n",
-    "Part of the task involves parsing the `06/27/07 16:18:21.898` time string into a `datetime` object. (Although it is outside the scope of this page's tutorial, a full description of this lightning data format can be found [here](https://ghrc.nsstc.nasa.gov/uso/ds_docs/vaiconus/vaiconus_dataset.html#a6).) In order to parse this string or others that follow the same format, you will need to employ the [datetime.strptime()](https://docs.python.org/3/library/datetime.html#datetime.datetime.strptime) method from the `datetime` module. This method takes two arguments: \n",
+    "Part of the task involves parsing the `06/27/07 16:18:21.898` time string into a `datetime` object. In order to parse this string or others that follow the same format, you will need to employ the [datetime.strptime()](https://docs.python.org/3/library/datetime.html#datetime.datetime.strptime) method from the `datetime` module. This method takes two arguments: \n",
     "1. the date/time string you wish to parse\n",
     "2. the format which describes exactly how the date and time are arranged. \n",
     "\n",
-    "[The full range of formatting options for strftime() and strptime() is described in the Python documentation](https://docs.python.org/3/library/datetime.html#strftime-and-strptime-behavior). In most cases, finding the correct formatting options inherently takes some degree of experimentation to get right. This is a situation where Python shines; you can use the IPython interpreter, or a Jupyter notebook, to quickly test numerous formatting options. Beyond the official documentation, Google and Stack Overflow are your friends in this process. \n",
+    "The full range of formatting options for `strftime()` and `strptime()` is described in [the Python documentation](https://docs.python.org/3/library/datetime.html#strftime-and-strptime-behavior). In most cases, finding the correct formatting options inherently takes some degree of experimentation to get right. This is a situation where Python shines; you can use the IPython interpreter, or a Jupyter notebook, to quickly test numerous formatting options. Beyond the official documentation, Google and Stack Overflow are your friends in this process. \n",
     "\n",
     "After some trial and error (as described above), you can find that, in this example, the format string `'%m/%d/%y %H:%M:%S.%f'` will convert the date and time in the data to the correct format."
    ]

core/xarray/xarray-intro.ipynb

@@ -26,7 +26,7 @@
     "\n",
     "The following topics will be covered in this tutorial:\n",
     "\n",
-    "1. Create a <xref:xarray#generated/xarray.DataArray>, one of the core object types in Xarray\n",
+    "1. Create a [DataArray](xref:xarray#xarray.DataArray), one of the core object types in Xarray\n",
     "1. Understand how to use named coordinates and metadata in a `DataArray`\n",
     "1. Combine individual `DataArrays` into a `Dataset`, the other core object type in Xarray\n",
     "1. Subset, slice, and interpolate the data using named coordinates\n",
@@ -297,7 +297,7 @@
    "source": [
     "### The `Dataset`: a container for `DataArray`s with shared coordinates\n",
     "\n",
-    "Along with the `DataArray`, the other main object type in Xarray is the `Dataset`.  `Datasets` are containers similar to Python dictionaries; each `Dataset` can hold one or more `DataArrays`. In addition, the `DataArrays` contained in a `Dataset` can share coordinates, although this behavior is optional.  (For more information, see the [official documentation page](http://xarray.pydata.org/en/stable/user-guide/data-structures.html#dataset).)\n",
+    "Along with the `DataArray`, the other main object type in Xarray is the `Dataset`.  `Datasets` are containers similar to Python dictionaries; each `Dataset` can hold one or more `DataArrays`. In addition, the `DataArrays` contained in a `Dataset` can share coordinates, although this behavior is optional.  (For more information, see the [official documentation page](xref:xarray#xarray.Dataset).)\n",
     "\n",
     "`Dataset` objects are most often created by loading data from a data file. We will cover this functionality in a later example; in this example, we will create a `Dataset` from two `DataArrays`. We will use our existing temperature `DataArray` for one of these `DataArrays`; the other one is created in the next example.\n",
     "\n",
@@ -500,7 +500,7 @@
     "\n",
     "In this example, we are trying to extract a timeseries for Boulder, CO, which is located at 40°N latitude and 105°W longitude. Our `DataArray` does not contain a longitude data value of -105, so in order to retrieve this timeseries, we must interpolate between data points.\n",
     "\n",
-    "The `.interp()` method allows us to retrieve data from any latitude and longitude by means of interpolation. This method uses coordinate-value selection, similarly to `.sel()`.  (For more information on the `.interp()` method, see the official documentation [here](http://xarray.pydata.org/en/stable/interpolation.html).)"
+    "The `.interp()` method allows us to retrieve data from any latitude and longitude by means of interpolation. This method uses coordinate-value selection, similarly to `.sel()`.  (For more information on the `.interp()` method, see the official documentation [here](xref:xarray#interp).)"
    ]
   },
   {
@@ -901,13 +901,12 @@
     "\n",
     "This tutorial contains content adapted from the material in [Unidata's Python Training](https://unidata.github.io/python-training/workshop/XArray/xarray-and-cf/).\n",
     "\n",
-    "Most basic questions and issues with Xarray can be resolved with help from the material in the [Xarray documentation](http://xarray.pydata.org/en/stable/). Some of the most popular sections of this documentation are listed below:\n",
-    "- [Why Xarray](http://xarray.pydata.org/en/stable/getting-started-guide/why-xarray.html)\n",
-    "- [Quick overview](http://xarray.pydata.org/en/stable/getting-started-guide/quick-overview.html#)\n",
-    "- [Example gallery](http://xarray.pydata.org/en/stable/gallery.html)\n",
+    "Most basic questions and issues with Xarray can be resolved with help from the material in the [Xarray documentation](xref:xarray). Some of the most popular sections of this documentation are listed below:\n",
+    "- [Why Xarray](xref:xarray#getting-started-guide/why-xarray)\n",
+    "- [Quick overview](xref:xarray#getting-started-guide/quick-overview)\n",
+    "- [Example gallery](xref:xarray#gallery)\n",
     "\n",
-    "Another resource you may find useful is this [Xarray Tutorial collection](https://xarray-contrib.github.io/xarray-tutorial/), created from content hosted on GitHub.\n",
-    "\n"
+    "Another resource you may find useful is this [Xarray Tutorial collection](https://xarray-contrib.github.io/xarray-tutorial/), created from content hosted on GitHub."
    ]
   }
  ],