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Migrate to exercise syntax (#196)
* Migrate to exercise syntax * fix? * Add dask to syllabus * Tweaks * more tweaks
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‎fundamentals/02.3_aligning_data_objects.ipynb‎

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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Exercise\n",
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"\n",
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"Consider the following 2D array. What are the dimensions of `array - array.mean(\"time\")`?"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"````{exercise}\n",
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":label: ex1\n",
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"\n",
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"Consider the following 2D array. What are the dimensions of `array - array.mean(\"time\")`?\n",
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"```python\n",
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"array = xr.DataArray(\n",
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" np.arange(12).reshape(3, 4),\n",
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" dims=(\"space\", \"time\"),\n",
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" coords={\"space\": [\"a\", \"b\", \"c\"], \"time\": [0, 1, 2, 3]},\n",
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" name=\"array\",\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": [
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"hide-output"
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]
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},
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"outputs": [],
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"source": [
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"(array - array.mean(\"time\")).dims"
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")\n",
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"```\n",
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"````\n",
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"\n",
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"````{solution} ex1\n",
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":class: dropdown\n",
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"\n",
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"```python\n",
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"(array - array.mean(\"time\")).dims\n",
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"```\n",
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"````"
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]
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},
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{
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**Exercise** Consider the following two arrays. Write down the `x` and `y` coordinate locations for `da1 - da2`"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"metadata": {
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"tags": [
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"hide-input"
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]
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},
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"source": [
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"````{exercise}\n",
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":label: dims\n",
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"\n",
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"Consider the following two arrays. Write down the `x` and `y` coordinate locations for `da1 - da2`\n",
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"```python\n",
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"da1 = xr.DataArray(\n",
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" np.arange(12).reshape(3, 4),\n",
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" dims=(\"space\", \"time\"),\n",
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" [0, 1],\n",
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" dims=\"space\",\n",
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" coords={\"space\": [\"b\", \"d\"]},\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"tags": [
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"hide-input"
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]
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},
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"source": [
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"**Answer** `x = [\"b\"], y=[0, 1, 2, 3]` . `da2` has been broadcasted to 2D (so dimension \"y\" has been inserted) and the two arrays are aligned using `join=\"inner\"` prior to subtraction."
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")\n",
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"```\n",
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"````\n",
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"\n",
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"```{solution} dims\n",
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":class: dropdown\n",
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"\n",
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"`x = [\"b\"], y=[0, 1, 2, 3]` . `da2` has been broadcasted to 2D (so dimension \"y\" has been inserted) and the two arrays are aligned using `join=\"inner\"` prior to subtraction.\n",
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"```"
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]
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},
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{

‎fundamentals/03.1_computation_with_xarray.ipynb‎

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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<img src=\"https://docs.xarray.dev/en/stable/_static/dataset-diagram-logo.png\" align=\"right\" width=\"30%\">\n",
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"\n",
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"# Basic Computation\n",
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"\n",
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"In this lesson, we discuss how to do scientific computations with xarray\n",
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<div class=\"alert alert-info\">\n",
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" <strong>Note:</strong> <code>apply_ufunc</code> is a powerful function.\n",
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" It has many options for doing more complicated things.\n",
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" Unfortunately, we don't have time to go into more depth here.\n",
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" Please consult the <a href=\"https://docs.xarray.dev/en/stable/user-guide/dask.html#apply-ufunc\">documentation</a> for more details.\n",
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"</div>\n"
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"```{tip}\n",
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"`apply_ufunc` is a powerful function. It has many options for doing more complicated things. Unfortunately, we don't have time to go into more depth here. See the [`apply_ufunc` tutorial material](https://tutorial.xarray.dev/advanced/apply_ufunc/apply_ufunc.html) for more.\n",
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"```\n"
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]
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{
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"tags": []
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"source": [
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"### Exercise\n",
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"```{exercise}\n",
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":label: sst-mean\n",
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"Take the mean of `sst` in both longitude and latitude. Make a simple timeseries plot.\n",
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"```\n",
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"````{solution} sst-mean\n",
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":class: dropdown\n",
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"\n",
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"Take the mean of `sst` in both longitude and latitude. Make a simple timeseries\n",
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"plot:\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": [
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"hide-input",
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"hide-output"
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]
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"outputs": [],
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"sst.mean([\"lat\", \"lon\"]).plot();"
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"sst.mean([\"lat\", \"lon\"]).plot();\n",
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"```\n"
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]
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}
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],

‎fundamentals/03.2_groupby_with_xarray.ipynb‎

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"cell_type": "markdown",
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"<img src=\"https://docs.xarray.dev/en/stable/_static/dataset-diagram-logo.png\" align=\"right\" width=\"30%\">\n",
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"\n",
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"# Grouped Computations\n",
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"\n",
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"In this lesson, we discuss how to do scientific computations with defined \"groups\" of data\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"# don't expand data by default\n",
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"xr.set_options(display_expand_data=False)\n",
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"xr.set_options(display_expand_data=False, display_expand_attrs=False)\n",
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"\n",
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"%config InlineBackend.figure_format='retina'"
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"`gb` is a DatasetGroupBy object. It represents a GroupBy operation and helpfully tells us the unique \"groups\" or labels found during the split step.\n",
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"\n",
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"\n",
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"<div class=\"alert alert-info\">\n",
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" Xarrays' computation methods (groupby, groupby_bins, rolling, coarsen, weighted) all return special objects that represent the basic underlying computation pattern. For e.g. `gb` above is a `DatasetGroupBy` object that represents monthly groupings of the data in `ds` . It is usually helpful to save and reuse these objects for multiple operations (e.g. a mean and standard deviation calculation).\n",
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"</div>"
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"```{tip}\n",
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"\n",
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"Xarrays' computation methods (`groupby`, `groupby_bins`, `rolling`, `coarsen`, `weighted`) all return special objects that represent the basic underlying computation pattern. For e.g. `gb` above is a `DatasetGroupBy` object that represents monthly groupings of the data in `ds` . It is usually helpful to save and reuse these objects for multiple operations (e.g. a mean and standard deviation calculation).\n",
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"```"
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]
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Exercise\n",
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"```{exercise} \n",
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":label: annual-mean\n",
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"\n",
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"Using `groupby`, plot the annual mean time series of SST at 300°E, 50°N"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": [
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"hide-output"
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"outputs": [],
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"ds.groupby(\"time.year\").mean().sst.sel(lon=300, lat=50).plot();"
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"Using `groupby`, plot the annual mean time series of SST at 300°E, 50°N\n",
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"```\n",
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"````{solution} annual-mean\n",
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":class: dropdown\n",
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"```python\n",
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"ds.groupby(\"time.year\").mean().sst.sel(lon=300, lat=50).plot();\n",
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"```\n",
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"````"
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<div class=\"alert alert-info\">\n",
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" <strong>Note:</strong> <code>resample</code> only works with proper datetime64 coordinate labels. Note the `dtype` of `time` in the repr above.\n",
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"</div>"
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"```{note}\n",
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"`resample` only works with proper datetime64 coordinate labels. Note the `dtype` of `time` in the repr above.\n",
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"```"
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"tags": []
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"source": [
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"### Exercise\n",
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"```{exercise}\n",
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":label: resample-mean\n",
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"\n",
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"Using `resample`, plot the annual mean time series of SST at 300°E, 50°N.\n",
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"\n",
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"Compare this output to the groupby output. What differences do you see?"
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]
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": [
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"hide-output",
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"hide-input"
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]
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"source": [
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"Compare this output to the groupby output. What differences do you see?\n",
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"```\n",
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"````{solution} resample-mean\n",
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":class: dropdown\n",
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"```python\n",
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"resampled = ds.resample(time='Y').mean().sst.sel(lon=300, lat=50)\n",
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"resampled.plot();"
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"resampled.plot();\n",
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"```\n",
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"````"
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]
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"source": [
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"## GroupBy vs Resample \n",
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"\n",
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"Let's compare the grouped and resampled outputs.\n",
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"cell_type": "code",
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"metadata": {
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"tags": []
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"outputs": [],
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"source": [
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"from IPython.display import display_html\n",
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"\n",
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"grouped = ds.groupby(\"time.year\").mean().sst.sel(lon=300, lat=50)\n",
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"resampled = ds.resample(time='Y').mean().sst.sel(lon=300, lat=50)\n",
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"display_html(grouped)\n",
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"display_html(resampled)"
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"tags": []
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"outputs": [],
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"source": [

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