👆
Click the Dashboard link above. Or click the \"Search\" button in the dashboard.\n", + "\n", + "Let's test that the dashboard is working..\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5", + "metadata": {}, + "outputs": [], + "source": [ + "import dask.array\n", + "\n", + "dask.array.ones((1000, 4), chunks=(2, 1)).compute() # should see activity in dashboard" + ] + }, + { + "cell_type": "markdown", + "id": "6", + "metadata": {}, + "source": [ + "Let's open a dataset. We specify `chunks` so that we create a dask arrays for the DataArrays" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7", + "metadata": {}, + "outputs": [], + "source": [ + "ds = xr.tutorial.open_dataset(\"air_temperature\", chunks={\"time\": 100})\n", + "ds" + ] + }, + { + "cell_type": "markdown", + "id": "8", + "metadata": { + "tags": [] + }, + "source": [ + "## A simple example\n", + "\n", + "All the concepts from applying numpy functions carry over.\n", + "\n", + "However the handling of dask arrays needs to be explicitly activated.\n", + "\n", + "There are three options for the `dask` kwarg.\n", + "\n", + "```\n", + " dask : {\"forbidden\", \"allowed\", \"parallelized\"}, default: \"forbidden\"\n", + " How to handle applying to objects containing lazy data in the form of\n", + " dask arrays:\n", + "\n", + " - 'forbidden' (default): raise an error if a dask array is encountered.\n", + " - 'allowed': pass dask arrays directly on to ``func``. Prefer this option if\n", + " ``func`` natively supports dask arrays.\n", + " - 'parallelized': automatically parallelize ``func`` if any of the\n", + " inputs are a dask array by using :py:func:`dask.array.apply_gufunc`. Multiple output\n", + " arguments are supported. Only use this option if ``func`` does not natively\n", + " support dask arrays (e.g. converts them to numpy arrays).\n", + "```\n", + "\n", + "We will work through the following two:\n", + "\n", + "1. `dask=\"allowed\"` Dask arrays are passed to the user function. This is a good\n", + " choice if your function can handle dask arrays and won't compute the result unless \n", + " explicitly requested.\n", + "2. `dask=\"parallelized\"`. This applies the user function over blocks of the dask\n", + " array using `dask.array.apply_gufunc`. This is useful when your function cannot\n", + " handle dask arrays natively (e.g. scipy API)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9", + "metadata": { + "tags": [ + "raises-exception" + ] + }, + "outputs": [], + "source": [ + "# Expect an error here\n", + "def squared_error(x, y):\n", + " return (x - y) ** 2\n", + "\n", + "\n", + "xr.apply_ufunc(squared_error, ds.air, 1)" + ] + }, + { + "cell_type": "markdown", + "id": "10", + "metadata": { + "tags": [] + }, + "source": [ + " \n", + "A good thing to check is whether the applied function (here `squared_error`) can handle pure dask arrays. \n", + "To do this call `squared_error(ds.air.data, 1)` and make sure of the following:\n", + "1. That you don't see any activity on the dask dashboard\n", + "2. That the returned result is a dask array." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "11", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "squared_error(ds.air.data, 1)" + ] + }, + { + "cell_type": "markdown", + "id": "12", + "metadata": { + "tags": [] + }, + "source": [ + "Since `squared_error` can handle dask arrays without computing them, we specify\n", + "`dask=\"allowed\"`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "13", + "metadata": {}, + "outputs": [], + "source": [ + "sqer = xr.apply_ufunc(\n", + " squared_error,\n", + " ds.air,\n", + " 1,\n", + " dask=\"allowed\",\n", + ")\n", + "sqer # dask-backed DataArray! with nice metadata!" + ] + }, + { + "cell_type": "markdown", + "id": "14", + "metadata": { + "tags": [] + }, + "source": [ + "### Understanding what's happening\n", + "\n", + "Let's again use the wrapper trick to understand what `squared_error` receives.\n", + "\n", + "We see that it receives a dask array (analogous to the numpy array in the previous example)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "15", + "metadata": {}, + "outputs": [], + "source": [ + "def wrapper(x, y):\n", + " print(f\"received x of type {type(x)}, shape {x.shape}\")\n", + " print(f\"received y of type {type(y)}\")\n", + " return squared_error(x, y)\n", + "\n", + "\n", + "xr.apply_ufunc(wrapper, ds.air, 1, dask=\"allowed\")" + ] + }, + { + "cell_type": "markdown", + "id": "16", + "metadata": { + "tags": [] + }, + "source": [ + "## Core dimensions\n", + "\n", + "`squared_error` operated on a per-element basis. How about a reduction like `np.mean`?\n", + "\n", + "Such functions involve the concept of \"core dimensions\". This concept is independent of the underlying array type, and is a property of the applied function. See the [core dimensions with NumPy](core-dimensions) tutorial for more.\n", + "\n", + "\n", + "::::{admonition} Exercise\n", + ":class: tip\n", + "\n", + "Use `dask.array.mean` as an example of a function that can handle dask\n", + "arrays and uses an `axis` kwarg. \n", + "\n", + ":::{admonition} Solution\n", + ":class: dropdown\n", + "\n", + "```python\n", + "def time_mean(da):\n", + " return xr.apply_ufunc(\n", + " dask.array.mean,\n", + " da,\n", + " input_core_dims=[[\"time\"]],\n", + " dask=\"allowed\",\n", + " kwargs={\"axis\": -1}, # core dimensions are moved to the end\n", + " )\n", + " \n", + "time_mean(ds.air)\n", + "```\n", + ":::\n", + "::::\n" + ] + }, + { + "cell_type": "markdown", + "id": "17", + "metadata": { + "tags": [] + }, + "source": [ + "Again, this is identical to the built-in `mean`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "18", + "metadata": {}, + "outputs": [], + "source": [ + "def time_mean(da):\n", + " return xr.apply_ufunc(\n", + " dask.array.mean,\n", + " da,\n", + " input_core_dims=[[\"time\"]],\n", + " dask=\"allowed\",\n", + " kwargs={\"axis\": -1}, # core dimensions are moved to the end\n", + " )\n", + "\n", + "\n", + "ds.air.mean(\"time\").identical(time_mean(ds.air))" + ] + }, + { + "cell_type": "markdown", + "id": "19", + "metadata": { + "tags": [] + }, + "source": [ + "## Automatically parallelizing dask-unaware functions\n", + "\n", + "### Basics\n", + "\n", + "Not all functions can handle dask arrays appropriately by default.\n", + "\n", + "A very useful `apply_ufunc` feature is the ability to apply arbitrary functions\n", + "in parallel to each block. This ability can be activated using\n", + "`dask=\"parallelized\"`. \n", + "\n", + "We will use `scipy.integrate.trapezoid` as an example of a function that cannot\n", + "handle dask arrays and requires a core dimension. If we call `trapezoid` with a dask\n", + "array, we get a numpy array back that is, the values have been eagerly computed.\n", + "This is undesirable behaviour\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "20", + "metadata": {}, + "outputs": [], + "source": [ + "import scipy as sp\n", + "import scipy.integrate\n", + "\n", + "sp.integrate.trapezoid(\n", + " ds.air.data, axis=ds.air.get_axis_num(\"lon\")\n", + ") # does NOT return a dask array, you should see activity on the dashboard" + ] + }, + { + "cell_type": "markdown", + "id": "21", + "metadata": { + "tags": [] + }, + "source": [ + "Let's activate automatic parallelization by using `apply_ufunc` with `dask=\"parallelized\"`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "22", + "metadata": {}, + "outputs": [], + "source": [ + "integrated = xr.apply_ufunc(\n", + " sp.integrate.trapezoid,\n", + " ds,\n", + " input_core_dims=[[\"lon\"]],\n", + " kwargs={\"axis\": -1},\n", + " dask=\"parallelized\",\n", + ")\n", + "integrated" + ] + }, + { + "cell_type": "markdown", + "id": "23", + "metadata": { + "tags": [] + }, + "source": [ + "And make sure the returned data is a dask array" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "24", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "integrated.air.data" + ] + }, + { + "cell_type": "markdown", + "id": "25", + "metadata": {}, + "source": [ + "Now you have control over executing this parallel computation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "26", + "metadata": {}, + "outputs": [], + "source": [ + "# Dask -> Numpy array of integrated values\n", + "parallelized_results = integrated.compute()\n", + "parallelized_results" + ] + }, + { + "cell_type": "markdown", + "id": "27", + "metadata": { + "tags": [] + }, + "source": [ + "### Understanding `dask=\"parallelized\"`\n", + "\n", + "It is very important to understand what `dask=\"parallelized\"` does. To fully understand it, requires understanding some core concepts.\n", + "\n", + "```{seealso}\n", + "For `dask=\"parallelized\"` `apply_ufunc` will call `dask.array.apply_gufunc`. See the dask documentation on [generalized ufuncs](https://docs.dask.org/en/stable/array-gufunc.html) and [`apply_gufunc`](https://docs.dask.org/en/stable/generated/dask.array.gufunc.apply_gufunc.html) for more.\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "28", + "metadata": { + "tags": [] + }, + "source": [ + "#### Embarrassingly parallel or blockwise operations\n", + "\n", + "`dask=\"parallelized\"` works well for \"blockwise\" or \"embarrassingly parallel\" operations ([Wikipedia](https://en.wikipedia.org/wiki/Embarrassingly_parallel)).\n", + "\n", + "These are operations where one block or chunk of the output array corresponds to one block or chunk of the input array. Specifically, the blocks or chunks of the _core dimension_ is what matters. Importantly, no communication between blocks is necessary to create the output, which makes parallelization quite simple or \"embarrassing\".\n", + "\n", + "Let's look at the dask repr for `ds` and note chunksizes are (100,25,53) for a array with shape (2920, 25, 53). This means that each block or chunk of the array contains all `lat`, `lon` points and a subset of `time` points." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "29", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "ds.air.data" + ] + }, + { + "cell_type": "markdown", + "id": "30", + "metadata": { + "tags": [] + }, + "source": [ + "The core dimension for `trapezoid` is `lon`, and there is only one chunk along `lon`. This means that integrating along `lon` is a \"blockwise\" or \"embarrassingly parallel\" operation and `dask=\"parallelized\"` works quite well. \n", + "\n", + "```{caution} Question\n", + "Do you understand why `integrate(ds)` when `ds` has a single chunk along `lon` is a \"embarrassingly parallel\" operation?\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "31", + "metadata": { + "tags": [] + }, + "source": [ + "::::{admonition} Exercise\n", + ":class: tip\n", + "Apply the integrate function to `ds` after rechunking to have a different chunksize along `lon` using `ds.chunk(lon=4)` (for example). What happens?\n", + "\n", + ":::{admonition} Solution\n", + ":class: dropdown\n", + "\n", + "`apply_ufunc` complains that it cannot automatically parallelize because the dataset `ds` is now chunked along the core dimension `lon`. You should see the following error:\n", + "\n", + " ValueError: dimension lon on 0th function argument to apply_ufunc with dask='parallelized' \n", + " consists of multiple chunks, but is also a core dimension. To fix, either rechunk \n", + " into a single array chunk along this dimension, i.e., \n", + " ``.chunk(dict(lon=-1))``, or pass ``allow_rechunk=True`` in ``dask_gufunc_kwargs`` \n", + " but beware that this may significantly increase memory usage.\n", + "\n", + ":::\n", + "::::" + ] + }, + { + "cell_type": "markdown", + "id": "32", + "metadata": { + "tags": [] + }, + "source": [ + "#### Understanding execution\n", + "\n", + "We are layering many concepts together there so it is important to understand how the function is executed, and what input it will receive. Again we will use our wrapper trick." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "33", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "def integrate_wrapper(array, **kwargs):\n", + " print(f\"received array of type {type(array)}, shape {array.shape}\")\n", + " result = sp.integrate.trapezoid(array, **kwargs)\n", + " print(f\"received array of type {type(result)}, shape {result.shape}\")\n", + " return result\n", + "\n", + "\n", + "integrated = xr.apply_ufunc(\n", + " integrate_wrapper,\n", + " ds,\n", + " input_core_dims=[[\"lon\"]],\n", + " kwargs={\"axis\": -1},\n", + " dask=\"parallelized\",\n", + ")\n", + "integrated" + ] + }, + { + "cell_type": "markdown", + "id": "34", + "metadata": {}, + "source": [ + "Note that we received an Xarray object back (`integrated`) but our wrapper function was called with a numpy array of shape `(1,1,1)`.\n", + "\n", + "```{important}\n", + "the full 3D array has **not yet been** passed to `integrate_wrapper`. Yet dask needs to know the shape and dtype of the result. This is key. \n", + "```\n", + "\n", + "The `integrate_wrapper` function is treated like a black box, and its effect on the inputs has to either be described through additional keyword arguments, or inferred by passing dummy inputs.\n", + "\n", + "To do so, `dask.array.apply_gufunc` calls the user function with dummy inputs (here a numpy array of shape `(1,1,1)`), and inspects the returned value to understand that one dimension was removed (returned a numpy array of shape `(1,1)`.\n", + "\n", + "````{caution}\n", + ":class: dropdown\n", + "\n", + "Some functions can have trouble handling such dummy inputs. Alternatively you can pass `meta = np.ones((1,1))` in `dask_gufunc_kwargs` to prevent dask from providing dummy inputs to the array.\n", + "```python\n", + "xr.apply_ufunc(\n", + " integrate_wrapper,\n", + " ds,\n", + " input_core_dims=[[\"lon\"]],\n", + " kwargs={\"axis\": -1},\n", + " dask=\"parallelized\",\n", + " dask_gufunc_kwargs={\"meta\": np.ones((1,1))},\n", + ")\n", + "```\n", + "````\n", + "\n", + "Since no errors were raised we proceed as-is.\n", + "\n", + "Let's compute the array to get real values." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "35", + "metadata": { + "tags": [ + "output-scroll" + ] + }, + "outputs": [], + "source": [ + "integrated.compute()" + ] + }, + { + "cell_type": "markdown", + "id": "36", + "metadata": {}, + "source": [ + "We see that `integrate_wrapper` is called many times! As many times as there are blocks in the array in fact, which is 30 here (`ds.air.data.numblocks`).\n", + "\n", + "Our function is independently executed on each block of the array, and then the results are concatenated to form the final result.\n", + "\n", + "Conceptually, there is a two-way flow of information between various packages when executing `integrated.compute()`:\n", + "\n", + "`xarray.apply_ufunc` ↔ `dask.array.apply_gufunc` ↔ `integrate_wrapper` ↔ `scipy.integrate.trapezoid` ↔ `ds.air.data`\n", + "\n", + "\n", + "When executed\n", + "\n", + "1. Xarray loops over all data variables.\n", + "1. Xarray unwraps the underlying dask array (e.g. `ds.air`) and passes that to dask's `apply_gufunc`.\n", + "1. `apply_gufunc` calls `integrate_wrapper` on each block of the array.\n", + "1. For each block, `integrate_wrapper` calls `scipy.integrate.trapezoid` and returns one block of the output array.\n", + "1. dask stitches all the output blocks to form the output array.\n", + "1. `xarray.apply_ufunc` wraps the output array with Xarray metadata to give the final result.\n", + "\n", + "Phew!\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "37", + "metadata": { + "tags": [] + }, + "source": [ + "## More complex situations\n", + "\n", + "Here we quickly demonstrate that all the concepts from the numpy material earlier carry over.\n", + "\n", + "Xarray needs a lot of extra metadata, so depending\n", + "on the function, extra arguments such as `output_dtypes` and `output_sizes` may\n", + "be necessary for supporting dask arrays. We demonstrate this below." + ] + }, + { + "cell_type": "markdown", + "id": "38", + "metadata": { + "tags": [] + }, + "source": [ + "### Adding new dimensions\n", + "\n", + "We use the `np.expand_dims` to change the size of the input along a single dimension.\n", + "\n", + "```python\n", + "def add_new_dim(array):\n", + " return np.expand_dims(array, axis=0)\n", + "```\n", + "\n", + "When automatically parallelizing with `dask`, we need to provide some more information about the outputs.\n", + "1. When adding a new dimensions, we need to provide the size in `dask_gufunc_kwargs` using the key `output_sizes`\n", + "2. Usually we need provide the datatype or `dtype` of the returned array. Usually the dtype of the input is a good guess." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "39", + "metadata": { + "tags": [ + "raises-exception" + ] + }, + "outputs": [], + "source": [ + "def add_new_dim(array):\n", + " return np.expand_dims(array, axis=-1)\n", + "\n", + "\n", + "xr.apply_ufunc(\n", + " add_new_dim, # first the function\n", + " ds.air.chunk({\"time\": 2, \"lon\": 2}),\n", + " output_core_dims=[[\"newdim\"]],\n", + " dask=\"parallelized\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "40", + "metadata": {}, + "source": [ + "Provide the size of the newly added dimension `newdim` in `output_sizes` as part of the `dask_gufunc_kwargs` keyword argument:\n", + "\n", + " dask_gufunc_kwargs (dict, optional) – Optional keyword arguments passed to dask.array.apply_gufunc() \n", + " if dask=’parallelized’. Possible keywords are output_sizes, allow_rechunk and meta.\n", + " \n", + "The syntax is \n", + "```python\n", + "dask_gufunc_kwargs={\n", + " \"output_sizes\": {\"newdim\": 1}\n", + "}\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "41", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "xr.apply_ufunc(\n", + " add_new_dim, # first the function\n", + " ds.air.chunk({\"time\": 2, \"lon\": 2}),\n", + " output_core_dims=[[\"newdim\"]],\n", + " dask=\"parallelized\",\n", + " dask_gufunc_kwargs={\"output_sizes\": {\"newdim\": 1}},\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "42", + "metadata": {}, + "source": [ + "### Dimensions that change size" + ] + }, + { + "cell_type": "markdown", + "id": "43", + "metadata": {}, + "source": [ + "We will now repeat the [interpolation example from earlier](interp-add-new-dim) with `\"lat\"` as the output core dimension. See the numpy notebook on [complex output](complex-output) for more.\n", + "\n", + "```python\n", + "newlat = np.linspace(15, 75, 100)\n", + "\n", + "xr.apply_ufunc(\n", + " np.interp,\n", + " newlat,\n", + " ds.air.lat,\n", + " ds.air.chunk({\"time\": 2, \"lon\": 2}),\n", + " input_core_dims=[[\"lat\"], [\"lat\"], [\"lat\"]],\n", + " output_core_dims=[[\"lat\"]],\n", + " exclude_dims={\"lat\"},\n", + ")\n", + "```\n", + "\n", + "We will first add `dask=\"parallelized\"` and provide `output_sizes` in `dask_gufunc_kwargs`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "44", + "metadata": { + "tags": [ + "raises-exception" + ] + }, + "outputs": [], + "source": [ + "newlat = np.linspace(15, 75, 100)\n", + "\n", + "xr.apply_ufunc(\n", + " np.interp, # first the function\n", + " newlat,\n", + " ds.air.lat,\n", + " ds.air.chunk({\"time\": 2, \"lon\": 2}),\n", + " input_core_dims=[[\"lat\"], [\"lat\"], [\"lat\"]],\n", + " output_core_dims=[[\"lat\"]],\n", + " exclude_dims={\"lat\"},\n", + " # The following are dask-specific\n", + " dask=\"parallelized\",\n", + " dask_gufunc_kwargs=dict(output_sizes={\"lat\": len(newlat)}),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "45", + "metadata": {}, + "source": [ + "This error means that we need to provide `output_dtypes`\n", + "\n", + " output_dtypes (list of dtype, optional) – Optional list of output dtypes. \n", + " Only used if dask='parallelized' or vectorize=True." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "46", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "newlat = np.linspace(15, 75, 100)\n", + "\n", + "xr.apply_ufunc(\n", + " np.interp, # first the function\n", + " newlat,\n", + " ds.air.lat,\n", + " ds.air.chunk({\"time\": 100, \"lon\": -1}),\n", + " input_core_dims=[[\"lat\"], [\"lat\"], [\"lat\"]],\n", + " output_core_dims=[[\"lat\"]],\n", + " exclude_dims={\"lat\"},\n", + " # The following are dask-specific\n", + " dask=\"parallelized\",\n", + " dask_gufunc_kwargs=dict(output_sizes={\"lat\": len(newlat)}),\n", + " output_dtypes=[ds.air.dtype],\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "47", + "metadata": {}, + "source": [ + "```{tip}\n", + "Dask can sometimes figure out the output sizes and dtypes. The usual workflow is to read the error messages and iteratively pass more information to `apply_ufunc`.\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "48", + "metadata": {}, + "source": [ + "### Automatic Vectorizing\n", + "\n", + "[Automatic vectorizing](vectorize) with `vectorize=True` also carries over!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "49", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "interped = xr.apply_ufunc(\n", + " np.interp, # first the function\n", + " newlat,\n", + " ds.air.lat,\n", + " ds.chunk({\"time\": 100, \"lon\": -1}),\n", + " input_core_dims=[[\"lat\"], [\"lat\"], [\"lat\"]],\n", + " output_core_dims=[[\"lat\"]],\n", + " exclude_dims={\"lat\"}, # dimensions allowed to change size. Must be set!\n", + " dask=\"parallelized\",\n", + " dask_gufunc_kwargs=dict(output_sizes={\"lat\": len(newlat)}),\n", + " output_dtypes=[ds.air.dtype],\n", + " vectorize=True,\n", + ")\n", + "interped" + ] + }, + { + "cell_type": "markdown", + "id": "50", + "metadata": {}, + "source": [ + "Again, it is important to understand the conceptual flow of information between the variuus packages when executing `interped.compute()` which looks ilke\n", + "\n", + "`xarray.apply_ufunc` ↔ `dask.array.apply_gufunc` ↔ `numpy.vectorize` ↔ `numpy.interp`\n", + "\n", + "\n", + "When executed\n", + "\n", + "1. Xarray loops over all data variables.\n", + "1. Xarray unwraps the underlying dask array (e.g. `ds.air`) and passes that to dask's `apply_gufunc`.\n", + "1. `apply_gufunc` calls the vectorized function on each block of the array.\n", + "1. For each block, `numpy.vectorize` handles looping over the loop dimensions \n", + " and passes 1D vectors along the core dimension to `numpy.interp`\n", + "1. The 1D results for each block are concatenated by `numpy.vectorize` to create one output block.\n", + "1. dask stitches all the output blocks to form the output array.\n", + "1. `xarray.apply_ufunc` wraps the output array with Xarray metadata to give the final result.\n", + "\n", + "Phew!\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "51", + "metadata": { + "tags": [] + }, + "source": [ + "## Clean up the cluster" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "52", + "metadata": { + "tags": [ + "remove-output" + ] + }, + "outputs": [], + "source": [ + "client.close();" + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/advanced/apply_ufunc/example-interp.ipynb b/advanced/apply_ufunc/example-interp.ipynb new file mode 100644 index 00000000..57318fd1 --- /dev/null +++ b/advanced/apply_ufunc/example-interp.ipynb @@ -0,0 +1,731 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "# np.interp : An end-to-end example\n", + "\n", + "**Author** [Deepak Cherian (NCAR)](https://cherian.net)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "This example will illustrate how to conveniently apply an unvectorized function `func` to xarray objects using `apply_ufunc`. `func` expects 1D numpy arrays and returns a 1D numpy array. Our goal is to conveniently apply this function along a dimension of xarray objects that may or may not wrap dask arrays with a signature.\n", + "\n", + "We will illustrate this using [`np.interp`](https://numpy.org/doc/stable/reference/generated/numpy.interp.html): \n", + "\n", + " Signature: np.interp(x, xp, fp, left=None, right=None, period=None)\n", + " Docstring:\n", + " One-dimensional linear interpolation.\n", + "\n", + " Returns the one-dimensional piecewise linear interpolant to a function\n", + " with given discrete data points (`xp`, `fp`), evaluated at `x`.\n", + "\n", + "and write an `xr_interp` function with signature\n", + "\n", + " xr_interp(xarray_object, dimension_name, new_coordinate_to_interpolate_to)\n", + " \n", + " \n", + "## Learning goals \n", + "\n", + "Our goal is to use `apply_ufunc` with a general function so that we can reuse our code to apply to different xarray datasets or along different dimensions. Specifically, this example will illustrate \n", + "1. Specifying core dimensions with `input_core_dims`\n", + "1. Handling core dimensions of the output with `output_core_dims`\n", + "1. Handling core dimensions that change size using `exclude_dims`\n", + "1. Automatic vectorizing or looping over dimensions that are not core dimensions using `vectorize=True`\n", + "1. Automatically parallelization with dask arrays using `dask=\"parallelized\"`\n", + "1. High-performance vectorization with numba and `vectorize=False`.\n", + "\n", + "It puts together all the concepts covered earlier.\n", + "\n", + "\n", + "```{tip}\n", + "We'll reduce the length of error messages using in this tutorial using `%xmode minimal` See the [ipython documentation](https://ipython.readthedocs.io/en/stable/interactive/magics.html#magic-xmode) for details.\n", + "```\n", + "\n", + "## Load data\n", + "\n", + "First lets load an example dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "%xmode minimal\n", + "\n", + "import numpy as np\n", + "import xarray as xr\n", + "\n", + "# limit the amount of information printed to screen\n", + "xr.set_options(display_expand_data=False)\n", + "np.set_printoptions(threshold=10, edgeitems=2)\n", + "\n", + "air = (\n", + " xr.tutorial.load_dataset(\"air_temperature\")\n", + " .air.sortby(\"lat\") # np.interp needs coordinate in ascending order\n", + " .isel(time=slice(4), lon=slice(3))\n", + ") # choose a small subset for convenience\n", + "air" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "The function we will apply is `np.interp` which expects 1D numpy arrays. This functionality is already implemented in xarray so we use that capability to make sure we are not making mistakes." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "newlat = np.linspace(15, 75, 100)\n", + "air.interp(lat=newlat)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's define a function that works with one vector of data along `lat` at a time." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "def interp1d_np(data, x, xi):\n", + " return np.interp(xi, x, data)\n", + "\n", + "\n", + "interped = interp1d_np(air.isel(time=0, lon=0), air.lat, newlat)\n", + "expected = air.interp(lat=newlat)\n", + "\n", + "# no errors are raised if values are equal to within floating point precision\n", + "np.testing.assert_allclose(expected.isel(time=0, lon=0).values, interped)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "No errors are raised so our interpolation is working.\n", + "\n", + "This function consumes and returns numpy arrays, which means we need to do a lot of work to convert the result back to an xarray object with meaningful metadata. This is where `apply_ufunc` is very useful.\n", + "\n", + "## `apply_ufunc`\n", + "\n", + " Apply a vectorized function for unlabeled arrays on xarray objects.\n", + "\n", + " The function will be mapped over the data variable(s) of the input arguments using \n", + " xarray’s standard rules for labeled computation, including alignment, broadcasting, \n", + " looping over GroupBy/Dataset variables, and merging of coordinates.\n", + " \n", + "`apply_ufunc` has many capabilities but for simplicity this example will focus on the common task of vectorizing 1D functions over nD xarray objects. We will iteratively build up the right set of arguments to `apply_ufunc` and read through many error messages in doing so." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "raises-exception" + ] + }, + "outputs": [], + "source": [ + "xr.apply_ufunc(\n", + " interp1d_np, # first the function\n", + " air.isel(time=0, lon=0), # now arguments in the order expected by 'interp1_np'\n", + " air.lat,\n", + " newlat,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`apply_ufunc` needs to know a lot of information about what our function does so that it can reconstruct the outputs. In this case, the size of dimension lat has changed and we need to explicitly specify that this will happen. xarray helpfully tells us that we need to specify the kwarg `exclude_dims`." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "## `exclude_dims`\n", + "\n", + "\n", + "```\n", + "exclude_dims : set, optional\n", + " Core dimensions on the inputs to exclude from alignment and\n", + " broadcasting entirely. Any input coordinates along these dimensions\n", + " will be dropped. Each excluded dimension must also appear in\n", + " ``input_core_dims`` for at least one argument. Only dimensions listed\n", + " here are allowed to change size between input and output objects.\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "raises-exception" + ] + }, + "outputs": [], + "source": [ + "xr.apply_ufunc(\n", + " interp1d_np, # first the function\n", + " air.isel(time=0, lon=0), # now arguments in the order expected by 'interp1_np'\n", + " air.lat,\n", + " newlat,\n", + " exclude_dims=set((\"lat\",)), # dimensions allowed to change size. Must be set!\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Core dimensions\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Core dimensions are central to using `apply_ufunc`. In our case, our function expects to receive a 1D vector along `lat` — this is the dimension that is \"core\" to the function's functionality. Multiple core dimensions are possible. `apply_ufunc` needs to know which dimensions of each variable are core dimensions.\n", + "\n", + " input_core_dims : Sequence[Sequence], optional\n", + " List of the same length as ``args`` giving the list of core dimensions\n", + " on each input argument that should not be broadcast. By default, we\n", + " assume there are no core dimensions on any input arguments.\n", + "\n", + " For example, ``input_core_dims=[[], ['time']]`` indicates that all\n", + " dimensions on the first argument and all dimensions other than 'time'\n", + " on the second argument should be broadcast.\n", + "\n", + " Core dimensions are automatically moved to the last axes of input\n", + " variables before applying ``func``, which facilitates using NumPy style\n", + " generalized ufuncs [2]_.\n", + " \n", + " output_core_dims : List[tuple], optional\n", + " List of the same length as the number of output arguments from\n", + " ``func``, giving the list of core dimensions on each output that were\n", + " not broadcast on the inputs. By default, we assume that ``func``\n", + " outputs exactly one array, with axes corresponding to each broadcast\n", + " dimension.\n", + "\n", + " Core dimensions are assumed to appear as the last dimensions of each\n", + " output in the provided order.\n", + " \n", + "Next we specify `\"lat\"` as `input_core_dims` on both `air` and `air.lat`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "raises-exception" + ] + }, + "outputs": [], + "source": [ + "xr.apply_ufunc(\n", + " interp1d_np, # first the function\n", + " air.isel(time=0, lon=0), # now arguments in the order expected by 'interp1_np'\n", + " air.lat,\n", + " newlat,\n", + " input_core_dims=[[\"lat\"], [\"lat\"], []],\n", + " exclude_dims=set((\"lat\",)), # dimensions allowed to change size. Must be set!\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "xarray is telling us that it expected to receive back a numpy array with 0 dimensions but instead received an array with 1 dimension corresponding to `newlat`. We can fix this by specifying `output_core_dims`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "xr.apply_ufunc(\n", + " interp1d_np, # first the function\n", + " air.isel(time=0, lon=0), # now arguments in the order expected by 'interp1_np'\n", + " air.lat,\n", + " newlat,\n", + " input_core_dims=[[\"lat\"], [\"lat\"], []], # list with one entry per arg\n", + " output_core_dims=[[\"lat\"]],\n", + " exclude_dims=set((\"lat\",)), # dimensions allowed to change size. Must be set!\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally we get some output! Let's check that this is right\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "interped = xr.apply_ufunc(\n", + " interp1d_np, # first the function\n", + " air.isel(time=0, lon=0), # now arguments in the order expected by 'interp1_np'\n", + " air.lat,\n", + " newlat,\n", + " input_core_dims=[[\"lat\"], [\"lat\"], []], # list with one entry per arg\n", + " output_core_dims=[[\"lat\"]],\n", + " exclude_dims=set((\"lat\",)), # dimensions allowed to change size. Must be set!\n", + ")\n", + "interped[\"lat\"] = newlat # need to add this manually\n", + "xr.testing.assert_allclose(expected.isel(time=0, lon=0), interped)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "No errors are raised so it is right!" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "## Automatic vectorization with `np.vectorize`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now our function currently only works on one vector of data which is not so useful given our 3D dataset.\n", + "Let's try passing the whole dataset. We add a `print` statement so we can see what our function receives." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "raises-exception" + ] + }, + "outputs": [], + "source": [ + "def interp1d_np(data, x, xi):\n", + " print(f\"data: {data.shape} | x: {x.shape} | xi: {xi.shape}\")\n", + " return np.interp(xi, x, data)\n", + "\n", + "\n", + "interped = xr.apply_ufunc(\n", + " interp1d_np, # first the function\n", + " air.isel(lon=slice(3), time=slice(4)), # now arguments in the order expected by 'interp1_np'\n", + " air.lat,\n", + " newlat,\n", + " input_core_dims=[[\"lat\"], [\"lat\"], []], # list with one entry per arg\n", + " output_core_dims=[[\"lat\"]],\n", + " exclude_dims=set((\"lat\",)), # dimensions allowed to change size. Must be set!\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "That's a hard-to-interpret error but our `print` call helpfully printed the shapes of the input data: \n", + "\n", + " data: (10, 53, 25) | x: (25,) | xi: (100,)\n", + "\n", + "We see that `air` has been passed as a 3D numpy array which is not what `np.interp` expects. Instead we want loop over all combinations of `lon` and `time`; and apply our function to each corresponding vector of data along `lat`.\n", + "\n", + "\n", + "`apply_ufunc` makes this easy by specifying `vectorize=True`:\n", + "\n", + " vectorize : bool, optional\n", + " If True, then assume ``func`` only takes arrays defined over core\n", + " dimensions as input and vectorize it automatically with\n", + " :py:func:`numpy.vectorize`. This option exists for convenience, but is\n", + " almost always slower than supplying a pre-vectorized function.\n", + " Using this option requires NumPy version 1.12 or newer.\n", + " \n", + "```{caution}\n", + "The documentation for [`np.vectorize`](https://numpy.org/doc/stable/reference/generated/numpy.vectorize.html) points out that\n", + "\"The vectorize function is provided primarily for convenience, not for performance. The implementation is essentially a for loop.\"\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "raises-exception" + ] + }, + "outputs": [], + "source": [ + "def interp1d_np(data, x, xi):\n", + " print(f\"data: {data.shape} | x: {x.shape} | xi: {xi.shape}\")\n", + " return np.interp(xi, x, data)\n", + "\n", + "\n", + "interped = xr.apply_ufunc(\n", + " interp1d_np, # first the function\n", + " air, # now arguments in the order expected by 'interp1_np'\n", + " air.lat, # as above\n", + " newlat, # as above\n", + " input_core_dims=[[\"lat\"], [\"lat\"], []], # list with one entry per arg\n", + " output_core_dims=[[\"lat\"]], # returned data has one dimension\n", + " exclude_dims=set((\"lat\",)), # dimensions allowed to change size. Must be set!\n", + " vectorize=True, # loop over non-core dims\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This unfortunately is another cryptic error from numpy. \n", + "\n", + "Notice that `newlat` is not an xarray object. Let's add a dimension name `new_lat` and modify the call. Note this cannot be `lat` because xarray expects dimensions to be the same size (or broadcastable) among all inputs. `output_core_dims` needs to be modified appropriately. We'll manually rename `new_lat` back to `lat` for easy checking." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "def interp1d_np(data, x, xi):\n", + " print(f\"data: {data.shape} | x: {x.shape} | xi: {xi.shape}\")\n", + " return np.interp(xi, x, data)\n", + "\n", + "\n", + "interped = xr.apply_ufunc(\n", + " interp1d_np, # first the function\n", + " air, # now arguments in the order expected by 'interp1_np'\n", + " air.lat, # as above\n", + " newlat, # as above\n", + " input_core_dims=[[\"lat\"], [\"lat\"], [\"new_lat\"]], # list with one entry per arg\n", + " output_core_dims=[[\"new_lat\"]], # returned data has one dimension\n", + " exclude_dims=set((\"lat\",)), # dimensions allowed to change size. Must be a set!\n", + " vectorize=True, # loop over non-core dims\n", + ")\n", + "interped = interped.rename({\"new_lat\": \"lat\"})\n", + "interped[\"lat\"] = newlat # need to add this manually\n", + "xr.testing.assert_allclose(\n", + " expected.transpose(*interped.dims), interped\n", + ") # order of dims is different\n", + "interped" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that the printed input shapes are all 1D and correspond to one vector along the `lat` dimension.\n", + "\n", + "The result is now an xarray object with coordinate values copied over from `data`. This is why `apply_ufunc` is so convenient; it takes care of a lot of boilerplate necessary to apply functions that consume and produce numpy arrays to xarray objects.\n", + "\n", + "One final point: `lat` is now the *last* dimension in `interped`. This is a \"property\" of core dimensions: they are moved to the end before being sent to `interp1d_np` as was noted in the docstring for `input_core_dims`\n", + "\n", + " Core dimensions are automatically moved to the last axes of input\n", + " variables before applying ``func``, which facilitates using NumPy style\n", + " generalized ufuncs [2]_." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Parallelization with dask\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "So far our function can only handle numpy arrays. A real benefit of `apply_ufunc` is the ability to easily parallelize over dask chunks _when needed_. \n", + "\n", + "We want to apply this function in a vectorized fashion over each chunk of the dask array. This is possible using dask's `blockwise`, `map_blocks`, or `apply_gufunc`. Xarray's `apply_ufunc` wraps dask's `apply_gufunc` and asking it to map the function over chunks using `apply_gufunc` is as simple as specifying `dask=\"parallelized\"`. With this level of flexibility we need to provide dask with some extra information: \n", + " 1. `output_dtypes`: dtypes of all returned objects, and \n", + " 2. `output_sizes`: lengths of any new dimensions. \n", + " \n", + "Here we need to specify `output_dtypes` since `apply_ufunc` can infer the size of the new dimension `new_lat` from the argument corresponding to the third element in `input_core_dims`. \n", + "\n", + "Here I choose the chunk sizes to illustrate that `np.vectorize` is still applied so that our function receives 1D vectors even though the blocks are 3D." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "def interp1d_np(data, x, xi):\n", + " print(f\"data: {data.shape} | x: {x.shape} | xi: {xi.shape}\")\n", + " return np.interp(xi, x, data)\n", + "\n", + "\n", + "interped = xr.apply_ufunc(\n", + " interp1d_np, # first the function\n", + " air.chunk({\"time\": 2, \"lon\": 2}), # now arguments in the order expected by 'interp1_np'\n", + " air.lat, # as above\n", + " newlat, # as above\n", + " input_core_dims=[[\"lat\"], [\"lat\"], [\"new_lat\"]], # list with one entry per arg\n", + " output_core_dims=[[\"new_lat\"]], # returned data has one dimension\n", + " exclude_dims=set((\"lat\",)), # dimensions allowed to change size. Must be a set!\n", + " vectorize=True, # loop over non-core dims\n", + " dask=\"parallelized\",\n", + " output_dtypes=[air.dtype], # one per output\n", + ").rename({\"new_lat\": \"lat\"})\n", + "interped[\"lat\"] = newlat # need to add this manually\n", + "xr.testing.assert_allclose(expected.transpose(*interped.dims), interped)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Yay! our function is receiving 1D vectors, so we've successfully parallelized applying a 1D function over a block. If you have a distributed dashboard up, you should see computes happening as equality is checked.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## High performance vectorization: gufuncs, numba & guvectorize\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "`np.vectorize` is a very convenient function but is unfortunately slow. It is only marginally faster than writing a for loop in Python and looping. A common way to get around this is to write a base interpolation function that can handle nD arrays in a compiled language like Fortran and then pass that to `apply_ufunc`.\n", + "\n", + "Another option is to use the numba package which provides a very [convenient `guvectorize` decorator](https://numba.readthedocs.io/en/stable/user/vectorize.html#the-guvectorize-decorator). Any decorated function gets compiled and will loop over any non-core dimension in parallel when necessary. \n", + "\n", + "We need to specify some extra information:\n", + "\n", + " 1. Our function cannot return a variable any more. Instead it must receive a variable (the last argument) whose contents the function will modify. So we change from `def interp1d_np(data, x, xi)` to `def interp1d_np_gufunc(data, x, xi, out)`. Our computed results must be assigned to `out`. All values of `out` must be assigned explicitly.\n", + " \n", + " 2. `guvectorize` needs to know the dtypes of the input and output. This is specified in string form as the first argument. Each element of the tuple corresponds to each argument of the function. In this case, we specify `float64` for all inputs and outputs: `\"(float64[:], float64[:], float64[:], float64[:])\"` corresponding to `data, x, xi, out`\n", + " \n", + " 3. Now we need to tell numba the size of the dimensions the function takes as inputs and returns as output i.e. _core dimensions_. This is done in symbolic form i.e. `data` and `x` are vectors of the same length, say `n`; `xi` and the output `out` have a different length, say `m`. So the second argument is (again as a string)\n", + " `\"(n), (n), (m) -> (m).\"` corresponding again to `data, x, xi, out`\n", + " \n", + "```{seealso}\n", + "\n", + "Read the [numba documentation](https://numba.readthedocs.io/en/stable/user/vectorize.html#the-guvectorize-decorator) for more details.\n", + "```\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from numba import float64, guvectorize\n", + "\n", + "\n", + "@guvectorize(\"(float64[:], float64[:], float64[:], float64[:])\", \"(n), (n), (m) -> (m)\")\n", + "def interp1d_np_gufunc(data, x, xi, out):\n", + " # numba doesn't really like this.\n", + " print(\"data: \" + str(data.shape) + \" | x:\" + str(x.shape) + \" | xi: \" + str(xi.shape))\n", + " out[:] = np.interp(xi, x, data)\n", + " # gufuncs don't return data\n", + " # instead you assign to a the last arg\n", + " # return np.interp(xi, x, data)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "The warnings are about [object-mode compilation](https://numba.readthedocs.io/en/stable/user/performance-tips.html) relating to the `print` statement. This means we don't get much speed up. We'll keep the `print` statement temporarily to make sure that `guvectorize` acts like we want it to." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "interped = xr.apply_ufunc(\n", + " interp1d_np_gufunc, # first the function\n", + " air.chunk({\"time\": 2, \"lon\": 2}), # now arguments in the order expected by 'interp1_np'\n", + " air.lat, # as above\n", + " newlat, # as above\n", + " input_core_dims=[[\"lat\"], [\"lat\"], [\"new_lat\"]], # list with one entry per arg\n", + " output_core_dims=[[\"new_lat\"]], # returned data has one dimension\n", + " exclude_dims=set((\"lat\",)), # dimensions allowed to change size. Must be a set!\n", + " # vectorize=True, # not needed since numba takes care of vectorizing\n", + " dask=\"parallelized\",\n", + " output_dtypes=[air.dtype], # one per output\n", + ").rename({\"new_lat\": \"lat\"})\n", + "interped[\"lat\"] = newlat # need to add this manually\n", + "xr.testing.assert_allclose(expected.transpose(*interped.dims), interped)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "Yay! Our function is receiving 1D vectors and is working automatically with dask arrays. \n", + "\n", + "Finally let's comment out the print line and wrap everything up in a nice reusable function" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from numba import float64, guvectorize\n", + "\n", + "\n", + "@guvectorize(\n", + " \"(float64[:], float64[:], float64[:], float64[:])\",\n", + " \"(n), (n), (m) -> (m)\",\n", + " nopython=True,\n", + ")\n", + "def interp1d_np_gufunc(data, x, xi, out):\n", + " out[:] = np.interp(xi, x, data)\n", + "\n", + "\n", + "def xr_interp(data, dim, newdim):\n", + " interped = xr.apply_ufunc(\n", + " interp1d_np_gufunc, # first the function\n", + " data, # now arguments in the order expected by 'interp1_np'\n", + " data[dim], # as above\n", + " newdim, # as above\n", + " input_core_dims=[[dim], [dim], [\"__newdim__\"]], # list with one entry per arg\n", + " output_core_dims=[[\"__newdim__\"]], # returned data has one dimension\n", + " exclude_dims=set((dim,)), # dimensions allowed to change size. Must be a set!\n", + " # vectorize=True, # not needed since numba takes care of vectorizing\n", + " dask=\"parallelized\",\n", + " output_dtypes=[data.dtype], # one per output; could also be float or np.dtype(\"float64\")\n", + " ).rename({\"__newdim__\": dim})\n", + " interped[dim] = newdim # need to add this manually\n", + "\n", + " return interped\n", + "\n", + "\n", + "xr.testing.assert_allclose(\n", + " expected.transpose(*interped.dims),\n", + " xr_interp(air.chunk({\"time\": 2, \"lon\": 2}), \"lat\", newlat),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "## Summary\n", + "\n", + "This technique is generalizable to any 1D function that [can be compiled](https://numba.readthedocs.io/en/stable/reference/pysupported.html#pysupported) by Numba." + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3" + }, + "nbsphinx": { + "allow_errors": true + }, + "org": null, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": false, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": true + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/advanced/apply_ufunc/numba-vectorization.ipynb b/advanced/apply_ufunc/numba-vectorization.ipynb new file mode 100644 index 00000000..3da709c5 --- /dev/null +++ b/advanced/apply_ufunc/numba-vectorization.ipynb @@ -0,0 +1,292 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "0", + "metadata": { + "tags": [] + }, + "source": [ + "# Fast vectorization with Numba\n", + "\n", + "![](\"https://numba.pydata.org/_static/numba-blue-horizontal-rgb.svg\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "`np.vectorize` is a very convenient function but is unfortunately slow. It is only marginally faster than writing a for loop in Python and looping. \n",
+ "\n",
+ "A common way to get around this is to write a base interpolation function that can handle nD arrays in a compiled language like C or Fortran and then pass that to `apply_ufunc`.\n",
+ "\n",
+ "Another option is to use the [numba package](https://numba.pydata.org/) which provides two very convenient decorators to build [numpy universal functions or ufuncs](https://numba.readthedocs.io/en/stable/user/vectorize.html):\n",
+ "1. [`vectorize`](https://numba.readthedocs.io/en/stable/user/vectorize.html#the-vectorize-decorator) for functions that act on scalars, and \n",
+ "2. [`guvectorize`](https://numba.readthedocs.io/en/stable/user/vectorize.html#the-guvectorize-decorator) for functions that operates on subsets of the array along core-dimensions. Any decorated function gets compiled and will loop over the loop dimensions in parallel when necessary. \n",
+ "\n",
+ "For `apply_ufunc` the key concept is that we must provide `vectorize=False` (the default) when using Numba vectorized functions. \n",
+ "Numba handles the vectorization (or looping) and `apply_ufunc` handles converting Xarray objects to bare arrays and handling metadata."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2",
+ "metadata": {},
+ "source": [
+ "## Load data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "%xmode minimal\n",
+ "\n",
+ "import numpy as np\n",
+ "import xarray as xr\n",
+ "\n",
+ "da = xr.DataArray(\n",
+ " np.arange(12).reshape(3, 4),\n",
+ " dims=(\"x\", \"y\"),\n",
+ " coords={\"x\": [12, 13, 14]},\n",
+ " attrs={\"foo\": \"bar\"},\n",
+ ")\n",
+ "da"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4",
+ "metadata": {},
+ "source": [
+ "## `vectorize`\n",
+ "\n",
+ "Our `squared_error` example from earlier works element-by-element, and is a great example for `vectorize`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "from numba import float64, vectorize\n",
+ "\n",
+ "\n",
+ "@vectorize([float64(float64, float64)])\n",
+ "def squared_error(x, y):\n",
+ " return (x - y) ** 2"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6",
+ "metadata": {},
+ "source": [
+ "See the numba documentation to understand `@vectorize([float64(float64, float64)])`\n",
+ "\n",
+ "Now use `apply_ufunc` to apply it."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "xr.apply_ufunc(squared_error, da, 1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8",
+ "metadata": {},
+ "source": [
+ "## `guvectorize`\n",
+ "\n",
+ "`guvectorize` is for functions that work on small subsets of the data. Quoting the Numba documentation\n",
+ "> While `vectorize()` allows you to write ufuncs that work on one element at a time, the `guvectorize()` decorator takes the concept one step further and allows you to write ufuncs that will work on an arbitrary number of elements of input arrays, and take and return arrays of differing dimensions. The typical example is a running median or a convolution filter.\n",
+ "\n",
+ "This description should remind you of `apply_ufunc`!\n",
+ "\n",
+ "We will use the example function `g` from the [numba docs](https://numba.readthedocs.io/en/stable/user/vectorize.html#the-guvectorize-decorator), which adds a scalar `y` to a 1D vector `x`. The `res` argument here will contain the output (this is a Numba detail).\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "from numba import guvectorize, int64\n",
+ "\n",
+ "\n",
+ "@guvectorize([(int64[:], int64, int64[:])], \"(n),()->(n)\")\n",
+ "def g(x, y, res):\n",
+ " for i in range(x.shape[0]):\n",
+ " res[i] = x[i] + y\n",
+ "\n",
+ "\n",
+ "a = np.arange(5)\n",
+ "g(a, 2)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "10",
+ "metadata": {},
+ "source": [
+ "Unlike `squared_error` we cannot pass an Xarray object to `g` directly."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "11",
+ "metadata": {
+ "tags": [
+ "raises-exception"
+ ]
+ },
+ "outputs": [],
+ "source": [
+ "g(da, 1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "12",
+ "metadata": {},
+ "source": [
+ "Now use `apply_ufunc`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "13",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "xr.apply_ufunc(\n",
+ " g,\n",
+ " da,\n",
+ " 1,\n",
+ " input_core_dims=[[\"x\"], []],\n",
+ " output_core_dims=[[\"x\"]],\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "14",
+ "metadata": {},
+ "source": [
+ "Notice the following:\n",
+ "1. The `guvectorize` decorator includes the concept of \"core dimensions\": `'(n),()->(n)'`. This string means that the `g` takes a 1D vector of size `n`, a scalar, and returns a 1D vector of size `n`. There is one core dimension for the input, and one core dimension for the output. Both core dimensions have the same size.\n",
+ "2. That string translates to `input_core_dims=[[\"x\"], []], output_core_dims=[[\"x\"]]` in `apply_ufunc`.\n",
+ "3. We don't provide `vectorize=True` to `apply_ufunc` since `numba` will handle the vectorization in compiled code automatically."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "15",
+ "metadata": {},
+ "source": [
+ "## With dask\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "16",
+ "metadata": {},
+ "source": [
+ "Use the chunked DataArray"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "17",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "da_dask = da.chunk({\"y\": 1})\n",
+ "da_dask"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "18",
+ "metadata": {},
+ "source": [
+ "::::{admonition} Exercise\n",
+ ":class: tip\n",
+ "\n",
+ "Apply `g` to `da_dask`\n",
+ "\n",
+ ":::{admonition} Solution\n",
+ ":class: dropdown\n",
+ "\n",
+ "```python\n",
+ "xr.apply_ufunc(\n",
+ " g,\n",
+ " da_dask, \n",
+ " 1, \n",
+ " input_core_dims=[[\"x\"], []], \n",
+ " output_core_dims=[[\"x\"]],\n",
+ " dask=\"parallelized\",\n",
+ ")\n",
+ "```\n",
+ ":::\n",
+ "::::"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "19",
+ "metadata": {},
+ "source": [
+ "## Next\n",
+ "\n",
+ "For more, see the numpy.interp end-to-end example in the left sidebar."
+ ]
+ }
+ ],
+ "metadata": {
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/advanced/apply_ufunc/simple_numpy_apply_ufunc.ipynb b/advanced/apply_ufunc/simple_numpy_apply_ufunc.ipynb
new file mode 100644
index 00000000..4305f936
--- /dev/null
+++ b/advanced/apply_ufunc/simple_numpy_apply_ufunc.ipynb
@@ -0,0 +1,432 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "0",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "(gentle-intro)=\n",
+ "# A gentle introduction\n",
+ "\n",
+ "Many, but not all, useful array methods are wrapped by Xarray and accessible\n",
+ "as methods on Xarray objects. For example `DataArray.mean` calls `numpy.nanmean`.\n",
+ "A very common use-case is to apply functions that expect and return NumPy \n",
+ "(or other array types) on Xarray objects. For example, this would include all of SciPy's API. \n",
+ "Applying many of these functions to Xarray object involves a series of repeated steps.\n",
+ "`apply_ufunc` provides a convenient wrapper function that generalizes the steps\n",
+ "involved in applying such functions to Xarray objects.\n",
+ "\n",
+ "```{tip}\n",
+ "Xarray uses `apply_ufunc` internally to implement much of its API, meaning that it is quite powerful!\n",
+ "```\n",
+ "\n",
+ "Our goals are to learn that `apply_ufunc` automates aspects of applying computation functions that are designed for pure arrays (like numpy arrays) on xarray objects including\n",
+ "- Propagating dimension names, coordinate variables, and (optionally) attributes.\n",
+ "- Handle Dataset input by looping over data variables.\n",
+ "- Allow passing arbitrary positional and keyword arguments\n",
+ "\n",
+ "\n",
+ "```{tip}\n",
+ "We'll reduce the length of error messages using `%xmode minimal` See the [ipython documentation](https://ipython.readthedocs.io/en/stable/interactive/magics.html#magic-xmode) for details.\n",
+ "```\n",
+ "\n",
+ "\n",
+ "## Setup"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%xmode minimal\n",
+ "\n",
+ "import numpy as np\n",
+ "import xarray as xr\n",
+ "\n",
+ "# limit the amount of information printed to screen\n",
+ "xr.set_options(display_expand_data=False)\n",
+ "np.set_printoptions(threshold=10, edgeitems=2)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2",
+ "metadata": {},
+ "source": [
+ "Let's load a dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds = xr.tutorial.load_dataset(\"air_temperature\")\n",
+ "ds"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "## A simple example: pure numpy\n",
+ "\n",
+ "Simple functions that act independently on each value should work without any\n",
+ "additional arguments. \n",
+ "\n",
+ "Consider the following `squared_error` function"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5",
+ "metadata": {
+ "tags": [
+ "raises-exception"
+ ]
+ },
+ "outputs": [],
+ "source": [
+ "def squared_error(x, y):\n",
+ " return (x - y) ** 2"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6",
+ "metadata": {},
+ "source": [
+ "````{tip}\n",
+ "\n",
+ "This function uses only arithmetic operations. For such simple functions, you can pass Xarray objects directly and receive Xarray objects back.\n",
+ "Try\n",
+ "```python\n",
+ "squared_error(ds.air, 1)\n",
+ "```\n",
+ "\n",
+ "We use it here as a very simple example\n",
+ "````"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7",
+ "metadata": {},
+ "source": [
+ "We can apply `squared_error` manually by extracting the underlying numpy array"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "numpy_result = squared_error(ds.air.data, 1)\n",
+ "numpy_result"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9",
+ "metadata": {},
+ "source": [
+ "To convert this result to a DataArray, we could do it manually"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "10",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "xr.DataArray(\n",
+ " data=numpy_result,\n",
+ " # propagate all the Xarray metadata manually\n",
+ " dims=ds.air.dims,\n",
+ " coords=ds.air.coords,\n",
+ " attrs=ds.air.attrs,\n",
+ " name=ds.air.name,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "11",
+ "metadata": {},
+ "source": [
+ "A shorter version uses [DataArray.copy](https://docs.xarray.dev/en/stable/generated/xarray.DataArray.copy.html)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "12",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds.air.copy(data=numpy_result)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "13",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "```{caution}\n",
+ "Using `DataArray.copy` works for such simple cases but doesn't generalize that well. \n",
+ "\n",
+ "For example, consider a function that removed one dimension and added a new dimension.\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "14",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "## apply_ufunc\n",
+ "\n",
+ "`apply_ufunc` can handle more complicated functions. Here's how to use it with `squared_error`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "15",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "xr.apply_ufunc(squared_error, ds.air, 1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "16",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "## How does apply_ufunc work?\n",
+ "\n",
+ "\n",
+ "This line\n",
+ "```python\n",
+ "xr.apply_ufunc(squared_error, ds.air, 1)\n",
+ "```\n",
+ "is equivalent to `squared_error(ds.air.data, 1)` with automatic propagation of xarray metadata like dimension names, coordinate values etc.\n",
+ "\n",
+ "\n",
+ "To illustrate how `apply_ufunc` works, let us write a small wrapper function. This will let us examine what data is received and returned from the applied function. \n",
+ "\n",
+ "```{tip}\n",
+ "This trick is very useful for debugging\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "17",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def wrapper(x, y):\n",
+ " print(f\"received x of type {type(x)}, shape {x.shape}\")\n",
+ " print(f\"received y of type {type(y)}\")\n",
+ " return squared_error(x, y)\n",
+ "\n",
+ "\n",
+ "xr.apply_ufunc(wrapper, ds.air, 1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "18",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "We see that `wrapper` receives the underlying numpy array (`ds.air.data`), and the integer `1`. \n",
+ "\n",
+ "Essentially, `apply_ufunc` does the following:\n",
+ "1. extracts the underlying array data (`.data`), \n",
+ "2. passes it to the user function, \n",
+ "3. receives the returned values, and \n",
+ "4. then wraps that back up as a DataArray\n",
+ "\n",
+ "```{tip}\n",
+ "`apply_ufunc` always takes in at least one DataArray or Dataset and returns one DataArray or Dataset\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "19",
+ "metadata": {},
+ "source": [
+ "## Handling attributes\n",
+ "\n",
+ "By default, attributes are omitted since they may now be inaccurate"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "20",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "result = xr.apply_ufunc(wrapper, ds.air, 1)\n",
+ "result.attrs"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "21",
+ "metadata": {},
+ "source": [
+ "To propagate attributes, pass `keep_attrs=True`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "22",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "result = xr.apply_ufunc(wrapper, ds.air, 1, keep_attrs=True)\n",
+ "result.attrs"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "23",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "## Handling datasets\n",
+ "\n",
+ "`apply_ufunc` easily handles both DataArrays and Datasets. \n",
+ "\n",
+ "When passed a Dataset, `apply_ufunc` will loop over the data variables and sequentially pass those to `squared_error`.\n",
+ "\n",
+ "So `squared_error` always receives a _single_ numpy array.\n",
+ "\n",
+ "To illustrate that lets create a new `Dataset` with two arrays. We'll create a new array `air2` that is 2D `time, lat`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "24",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "ds2 = ds.copy()\n",
+ "ds2[\"air2\"] = ds2.air.isel(lon=0) ** 2"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "25",
+ "metadata": {},
+ "source": [
+ "We see that `wrapper` is called twice"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "26",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "xr.apply_ufunc(wrapper, ds2, 1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "27",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "xr.apply_ufunc(squared_error, ds2, 1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "28",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "## Passing positional and keyword arguments\n",
+ "\n",
+ "```{seealso}\n",
+ "See the Python tutorial on [defining functions](https://docs.python.org/3/tutorial/controlflow.html#defining-functions) for more on positional and keyword arguments.\n",
+ "```\n",
+ "\n",
+ "`squared_error` takes two arguments named `x` and `y`.\n",
+ "\n",
+ "In `xr.apply_ufunc(squared_error, ds.air, 1)`, the value of `1` for `y` was passed positionally. \n",
+ "\n",
+ "to use the keyword argument form, pass it using the `kwargs` keyword argument to `apply_ufunc`\n",
+ "> kwargs (dict, optional) – Optional keyword arguments passed directly on to call func."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "29",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "xr.apply_ufunc(squared_error, ds.air, kwargs={\"y\": 1})"
+ ]
+ }
+ ],
+ "metadata": {
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/advanced/backends/1.Backend_without_Lazy_Loading.ipynb b/advanced/backends/1.Backend_without_Lazy_Loading.ipynb
new file mode 100644
index 00000000..e396d869
--- /dev/null
+++ b/advanced/backends/1.Backend_without_Lazy_Loading.ipynb
@@ -0,0 +1,180 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Binary data without lazy loading\n",
+ "\n",
+ "\n",
+ "**Author**: Aureliana Barghini ([B-Open](https://www.bopen.eu/))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## BackendEntrypoint\n",
+ "Implement a subclass of `BackendEntrypoint` that expose a method `open_dataset`:\n",
+ "\n",
+ "```python\n",
+ "from xarray.backends import BackendEntrypoint\n",
+ "\n",
+ "class MyBackendEntrypoint(BackendEntrypoint):\n",
+ " def open_dataset(\n",
+ " self,\n",
+ " filename_or_obj,\n",
+ " *,\n",
+ " drop_variables=None,\n",
+ " ):\n",
+ "\n",
+ " return my_open_dataset(filename_or_obj, drop_variables=drop_variables)\n",
+ "\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## BackendEntrypoint integration\n",
+ "Declare this class as an external plugin in your `setup.py`:\n",
+ "\n",
+ "```python\n",
+ "setuptools.setup(\n",
+ " entry_points={\n",
+ " 'xarray.backends': ['engine_name=package.module:my_backendentrypoint'],\n",
+ " },\n",
+ ")\n",
+ "```\n",
+ "or pass it in `xr.open_dataset`:\n",
+ "\n",
+ "```python\n",
+ "xr.open_dataset(filename, engine=MyBackendEntrypoint)\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Example backend for binary files"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import xarray as xr"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Create sample files"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "arr = np.arange(30000000, dtype=np.int64)\n",
+ "with open(\"foo.bin\", \"w\") as f:\n",
+ " arr.tofile(f)\n",
+ "\n",
+ "arr = np.arange(30000000, dtype=np.float64)\n",
+ "with open(\"foo_float.bin\", \"w\") as f:\n",
+ " arr.tofile(f)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Define the entrypoint\n",
+ "Example of backend to open binary files"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class BinaryBackend(xr.backends.BackendEntrypoint):\n",
+ " def open_dataset(\n",
+ " self,\n",
+ " filename_or_obj,\n",
+ " *,\n",
+ " drop_variables=None,\n",
+ " # backend specific parameter\n",
+ " dtype=np.int64,\n",
+ " ):\n",
+ " with open(filename_or_obj) as f:\n",
+ " arr = np.fromfile(f, dtype)\n",
+ "\n",
+ " var = xr.Variable(dims=(\"x\"), data=arr)\n",
+ " coords = {\"x\": np.arange(arr.size) * 10}\n",
+ " return xr.Dataset({\"foo\": var}, coords=coords)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### It Works! \n",
+ "But it may be memory demanding"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "arr = xr.open_dataarray(\"foo.bin\", engine=BinaryBackend)\n",
+ "arr"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "arr = xr.open_dataarray(\"foo_float.bin\", engine=BinaryBackend, dtype=np.float64)\n",
+ "arr"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "arr.sel(x=slice(0, 100))"
+ ]
+ }
+ ],
+ "metadata": {
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/advanced/backends/2.Backend_with_Lazy_Loading.ipynb b/advanced/backends/2.Backend_with_Lazy_Loading.ipynb
new file mode 100644
index 00000000..ce2fe9f8
--- /dev/null
+++ b/advanced/backends/2.Backend_with_Lazy_Loading.ipynb
@@ -0,0 +1,235 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Binary data with lazy loading\n",
+ "\n",
+ "**Author**: Aureliana Barghini ([B-Open](https://www.bopen.eu/))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "If you want to make your backend effective with big datasets, then you should\n",
+ "support lazy loading. \n", + "For doing that you need:\n", + "- Implement `_raw_indexing_method` for reading blocks form disk
\n", + "- Implement some glue code to make it work with Xarray:
\n", + " - put your `_raw_indexing_method` in a `BackendArray` subclass
\n", + " - replace the `numpy.ndarray` inside your **dataset** with your subclass of `BackendArray`\n", + " \n", + " \n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create sample files" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "import dask\n", + "import numpy as np\n", + "import xarray as xr" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "arr = np.arange(30000000, dtype=np.int64)\n", + "with open(\"foo.bin\", \"w\") as f:\n", + " arr.tofile(f)\n", + "\n", + "arr = np.arange(30000000, dtype=np.float64)\n", + "with open(\"foo_float.bin\", \"w\") as f:\n", + " arr.tofile(f)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## BinaryBackendArray" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The BackendArray subclass shall implement the following method and attributes:\n", + "\n", + "- `_raw_indexing_method` method, supporting **item selection** and **slicing**\n", + "\n", + "- `__getitem__` that wraps `_raw_indexing_method` with an xarray helper function `explicit_indexing_adapter` (threadsafe)\n", + "\n", + "- `shape` attribute\n", + "\n", + "- `dtype` attribute.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class BinaryBackendArray(xr.backends.BackendArray):\n", + " def __init__(\n", + " self,\n", + " filename_or_obj,\n", + " shape,\n", + " dtype,\n", + " lock,\n", + " ):\n", + " self.filename_or_obj = filename_or_obj\n", + " self.shape = shape\n", + " self.dtype = dtype\n", + " self.lock = lock\n", + "\n", + " def __getitem__(self, key: tuple):\n", + " return xr.core.indexing.explicit_indexing_adapter(\n", + " key,\n", + " self.shape,\n", + " xr.core.indexing.IndexingSupport.BASIC,\n", + " self._raw_indexing_method,\n", + " )\n", + "\n", + " def _raw_indexing_method(self, key: tuple):\n", + " key0 = key[0]\n", + " size = np.dtype(self.dtype).itemsize\n", + "\n", + " if isinstance(key0, slice):\n", + " start = key0.start or 0\n", + " stop = key0.stop or self.shape[0]\n", + " offset = size * start\n", + " count = stop - start\n", + " else:\n", + " offset = size * key0\n", + " count = 1\n", + "\n", + " with self.lock, open(self.filename_or_obj) as f:\n", + " arr = np.fromfile(f, np.int64, offset=offset, count=count)\n", + "\n", + " if isinstance(key, int):\n", + " arr = arr.squeeze()\n", + "\n", + " return arr" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## BinaryBackend Entrypoint" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class BinaryBackend(xr.backends.BackendEntrypoint):\n", + " def open_dataset(self, filename_or_obj, *, drop_variables=None, dtype=np.int64):\n", + " size = np.dtype(dtype).itemsize\n", + " shape = os.stat(filename_or_obj).st_size // size\n", + "\n", + " backend_array = BinaryBackendArray(\n", + " filename_or_obj=filename_or_obj,\n", + " shape=(shape,),\n", + " dtype=dtype,\n", + " lock=dask.utils.SerializableLock(),\n", + " )\n", + " data = xr.core.indexing.LazilyIndexedArray(backend_array)\n", + "\n", + " var = xr.Variable(dims=(\"x\"), data=data)\n", + " return xr.Dataset({\"foo\": var})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## Reduced memory usage with dask" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "arr = xr.open_dataarray(\"foo.bin\", engine=BinaryBackend, chunks=10000)\n", + "arr" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "arr.mean()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "arr.sel(x=slice(0, 10))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "arr.sel(x=slice(0, 10)).compute()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "arr.load()" + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/advanced/backends/backends.md b/advanced/backends/backends.md new file mode 100644 index 00000000..d5b28a14 --- /dev/null +++ b/advanced/backends/backends.md @@ -0,0 +1,50 @@ +# Reading data using backends + +## Introduction + +You can [read different types of files](https://docs.xarray.dev/en/stable/user-guide/io.html) in `xr.open_dataset` by specifying the engine to be used: + +```python +import xarray as xr + +xr.open_dataset("my_file.grib", engine="cfgrib") +``` + +Navigating Xarray backends can be confusing, +so we recommend checking out [this flow chart](https://docs.xarray.dev/en/stable/user-guide/io.html) +to help you figure out which engine you need and how to use it. + +You can see what backends are currently available in your working environment +with `xarray.backends.list_engines()`. + +## Why use the Xarray backend API to write your own backend? + +- Your users don't need to learn a new interface; they can use `xr.open_dataset` with the `engine` kwarg. +- With little extra effort you can have lazy loading with Dask. Simply implement a function for reading blocks and Xarray will manage lazy loading with Dask for you. +- It's easy to implement: using the backend API (introduced in v0.18.0), you don't need to integrate any code in Xarray. + +## More Information + +See the [documentation](https://docs.xarray.dev/en/stable/internals/how-to-add-new-backend.html) for more details on adding and registering a new backend. + +Follow the tutorials on creating a new backend for binary files. + +```{tableofcontents} + +``` + +### Links to internal backends + +- [netcdf4](https://pypi.org/project/netCDF4/) - netCDF4 +- [scipy](https://scipy.org/) - netCDF3 +- [zarr](https://pypi.org/project/zarr/) - Zarr +- [pydap](https://pydap.github.io/pydap/) - Data Access Protocol (DAP/DODS/OPeNDAP) +- [h5netcdf](https://h5netcdf.org/) - hdf5 + +### Links to external backends (not comprehensive) + +- [cfgrib](https://github.com/ecmwf/cfgrib) - GRIB +- [tiledb](https://github.com/TileDB-Inc/TileDB-CF-Py) - TileDB +- [rioxarray](https://corteva.github.io/rioxarray/stable/) - GeoTIFF, JPEG-2000, ESRI-hdr, etc (via GDAL) +- [xarray-sentinel](https://github.com/bopen/xarray-sentinel) - Sentinel-1 SAFE +- ... diff --git a/advanced/backends/create_custom_backend.ipynb b/advanced/backends/create_custom_backend.ipynb new file mode 100644 index 00000000..6cf850ec --- /dev/null +++ b/advanced/backends/create_custom_backend.ipynb @@ -0,0 +1,304 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "0", + "metadata": {}, + "source": [ + "# Create your own Xarray Backend\n", + "In this lesson, we will learn about and how to create your own custom xarray backend\n", + "\n", + ":::{admonition} Learning Goals\n", + "- Learn about xarray's support for custom backends\n", + "- Learn how to use a custom `imageio` backend to open and manipulate GIFs\n", + "- Learn how to extend the `imagegio` backend to write out a GIF\n", + ":::\n", + "\n", + "## Why should you create your own Xarray backend?\n", + "\n", + "- Allows you to use xarray's interface\n", + " - Attribute-like syntax, dict-like syntax and label based indexing\n", + "- You don’t need to integrate any code in Xarray\n", + "- Easy and fast!\n" + ] + }, + { + "cell_type": "markdown", + "id": "1", + "metadata": {}, + "source": [ + "## Setting up the BackendEntrypoint\n", + "\n", + "To set up a `BackendEntrypoint` we can implement a subclass of `BackendEntrypoint` and expose the `open_dataset` method to it. For this tutorial we have the `ImageIOBackend` already defined but we will extend the functionally to write GIFS." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2", + "metadata": {}, + "outputs": [], + "source": [ + "image_path = \"io.gif\"" + ] + }, + { + "cell_type": "markdown", + "id": "3", + "metadata": {}, + "source": [ + "We can write a simple image reader function that we can then plug into our `MyBackendEntrypoint` class. For this example we are going to use `imageio` an image reader and writer library. With `imageio` you can read an image file with `iio.imopen`\n", + "\n", + ":::{note}\n", + "The `ImageIOBackend` also defines a `ImageIOBackendArray` with basic indexing.\n", + ":" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4", + "metadata": {}, + "outputs": [], + "source": [ + "import imageio as iio\n", + "from xarray.backends import BackendEntrypoint\n", + "\n", + "\n", + "def imageio_open(\n", + " filename_or_obj,\n", + "):\n", + " img = iio.imopen(filename_or_obj, io_mode=\"r\")\n", + " return img.read()\n", + "\n", + "\n", + "imageio_open(image_path)\n", + "\n", + "\n", + "class MyImageReader(BackendEntrypoint):\n", + " def open_dataset(\n", + " self,\n", + " filename_or_obj,\n", + " *,\n", + " drop_variables=None,\n", + " ):\n", + " return imageio_open(filename_or_obj)" + ] + }, + { + "cell_type": "markdown", + "id": "5", + "metadata": {}, + "source": [ + "## Reading image data\n", + "\n", + "Lets use our `ImageIOBackend` to open a GIF.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6", + "metadata": {}, + "outputs": [], + "source": [ + "import xarray as xr\n", + "from imageio_ import ImageIOBackend\n", + "\n", + "gif_ds = xr.open_dataset(image_path, engine=ImageIOBackend)\n", + "gif_ds" + ] + }, + { + "cell_type": "markdown", + "id": "7", + "metadata": {}, + "source": [ + "### Examining our image dataset\n", + "\n", + "Since our image is a `Dataset` object we can use xarray's interface for `Dataset` objects.\n", + "\n", + "Let's try listing all of the variables, dimensions and selecting data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8", + "metadata": {}, + "outputs": [], + "source": [ + "gif_ds.variables" + ] + }, + { + "cell_type": "markdown", + "id": "9", + "metadata": {}, + "source": [ + "We can list our dimensions" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "10", + "metadata": {}, + "outputs": [], + "source": [ + "gif_ds.dims" + ] + }, + { + "cell_type": "markdown", + "id": "11", + "metadata": {}, + "source": [ + "Let's try getting our `DataArray`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "12", + "metadata": {}, + "outputs": [], + "source": [ + "gif_ds[\"data\"]" + ] + }, + { + "cell_type": "markdown", + "id": "13", + "metadata": {}, + "source": [ + "## GIF metadata\n", + "We can examine, update and add metadata to our `Dataset` object. \n", + "\n", + "We can examine the attributes in our GIF \"data\" variable with the `.attrs` method" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "14", + "metadata": {}, + "outputs": [], + "source": [ + "gif_ds.data.attrs" + ] + }, + { + "cell_type": "markdown", + "id": "15", + "metadata": {}, + "source": [ + "## Exercise" + ] + }, + { + "cell_type": "markdown", + "id": "16", + "metadata": {}, + "source": [ + "::::{admonition} Exercise\n", + ":class: tip\n", + "\n", + "Can you add a new attribute to our GIF \"data\" variable called \"fps\". This is the frames per second we can write our GIF to. You can set it to any value.\n", + "\n", + ":::{admonition} Solution\n", + ":class: dropdown\n", + "\n", + "```python\n", + "gif_ds.data.attrs['fps'] = 100\n", + "```\n", + ":::\n", + "::::" + ] + }, + { + "cell_type": "markdown", + "id": "17", + "metadata": {}, + "source": [ + "### GIF writer\n", + "We can extend our backend with an GIF writer. Here we use `matplotlib`'s animation functions and `PillowWriter`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "18", + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.animation as animation\n", + "import matplotlib.pyplot as plt\n", + "from imageio_ import ImageIOBackend\n", + "from matplotlib.animation import PillowWriter\n", + "\n", + "\n", + "class SimpleImageWriter(ImageIOBackend):\n", + " def to_giff(\n", + " self,\n", + " dataset,\n", + " variable,\n", + " time_dim,\n", + " out_filename,\n", + " **kwargs,\n", + " ):\n", + " fig, ax = plt.subplots()\n", + "\n", + " frames = []\n", + " variable_da = dataset[variable]\n", + "\n", + " for time in variable_da[time_dim]:\n", + " variable_da = variable_da.transpose(\"time\", \"width\", \"height\", \"color\")\n", + " to_plot = ax.pcolormesh(\n", + " variable_da.height,\n", + " variable_da.width,\n", + " variable_da.sel(time=time),\n", + " animated=True,\n", + " shading=\"auto\",\n", + " **kwargs,\n", + " )\n", + "\n", + " frames.append([to_plot])\n", + "\n", + " try:\n", + " writer = PillowWriter(fps=variable_da.attrs[\"fps\"], **kwargs)\n", + " except KeyError:\n", + " writer = PillowWriter(fps=50, **kwargs)\n", + "\n", + " ani = animation.ArtistAnimation(fig, frames, blit=True, repeat=True)\n", + " ani.save(filename=out_filename, writer=writer)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "19", + "metadata": {}, + "outputs": [], + "source": [ + "img_writer = SimpleImageWriter()\n", + "img_writer.to_giff(gif_ds.compute(), \"data\", \"time\", \"io_writer.gif\")" + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/advanced/backends/imageio_.py b/advanced/backends/imageio_.py new file mode 100644 index 00000000..95c33e1f --- /dev/null +++ b/advanced/backends/imageio_.py @@ -0,0 +1,119 @@ +from __future__ import annotations + +from dataclasses import dataclass +from typing import TYPE_CHECKING + +import numpy as np +import pandas as pd +import xarray as xr + +if TYPE_CHECKING: + import os + from typing import Any, Literal, Protocol + + import numpy.typing as npt + + class FileLike(Protocol): ... + + class LockType(Protocol): + def __enter__(self) -> Any: ... + + def acquire(self, blocking: bool = True, timeout: int = -1) -> bool: ... + def release(self) -> None: ... + + IndexerType = int | slice | npt.NDArray[np.integer] + FilenameOrObjectType = str | os.PathLike | bytes | FileLike + + +@dataclass +class ImageIOBackendArray(xr.backends.BackendArray): + filename_or_obj: FilenameOrObjectType + + shape: tuple[int] + dtype: npt.DtypeLike + + lock: LockType + + def __getitem__(self, key: tuple[IndexerType]): + return xr.core.indexing.explicit_indexing_adapter( + key, + self.shape, + xr.core.indexing.IndexingSupport.BASIC, + self.basic_indexing, + ) + + def basic_indexing(self, key: tuple[IndexerType]) -> npt.NDArray: + import imageio.v3 as iio + + with self.lock, iio.imopen(self.filename_or_obj, io_mode="r") as f: + if key == (slice(None),) * len(self.shape): + return f.read() + + first_indexer = key[0] + if isinstance(first_indexer, int): + data = f.read(index=first_indexer, mode="P", writable=True) + + remaining_indexers = key[1:] + else: + if isinstance(first_indexer, slice): + indices = range(*first_indexer.indices(self.shape[0])) + else: + indices = first_indexer + + data = np.concatenate([f.read(index=index, mode="P") for index in indices], axis=0) + + remaining_indexers = (..., *key[1:]) + + return data[remaining_indexers] + + +class ImageIOBackend(xr.backends.BackendEntrypoint): + def open_dataset( + self, + filename_or_obj: FilenameOrObjectType, + *, + drop_variables: bool | None = None, + mode: Literal["grayscale", "color"] = "color", + ) -> xr.Dataset: + import imageio.v3 as iio + + with iio.imopen(filename_or_obj, io_mode="r") as f: + properties = f.properties() + metadata = f.metadata() + + dims = ["time", "height", "width", "color"] + + background = metadata["background"] + duration = metadata["duration"] + loop = metadata["loop"] + + shape = properties.shape + dtype = properties.dtype + + if isinstance(duration, (int, float)): + time_values = np.timedelta64(duration, "ms") * np.arange(shape[0]) + else: + time_values = np.array(duration, dtype="timedelta64[ms]") + + time = xr.indexes.PandasIndex(pd.Index(time_values), dim="time") + + backend_array = ImageIOBackendArray( + filename_or_obj=filename_or_obj, + shape=shape, + dtype=dtype, + lock=xr.backends.locks.SerializableLock(), + ) + data = xr.core.indexing.LazilyIndexedArray(backend_array) + + var = xr.Variable( + dims=dims, + data=data, + attrs={"loop": loop}, + encoding={ + "preferred_chunks": dict(zip(dims, (1, *shape[1:]))), + "fill_value": background, + }, + ) + coords = xr.Coordinates.from_xindex(time).assign(color=["red", "green", "blue"]) + + return xr.Dataset({"data": var}, coords=coords) diff --git a/advanced/backends/intro-backends.ipynb b/advanced/backends/intro-backends.ipynb new file mode 100644 index 00000000..d7002d09 --- /dev/null +++ b/advanced/backends/intro-backends.ipynb @@ -0,0 +1,182 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "0", + "metadata": {}, + "source": [ + "# Introduction to Xarray Backends\n", + "In this lesson, we will learn about and how to use xarray's backends\n", + "\n", + ":::{admonition} Learning Goals\n", + "- Learn about xarray's internal and external backends\n", + "- Learn how xarray selects backend engines\n", + "- Learn how to specify backend engines\n", + ":::\n", + "\n", + "## What are Xarray Backends\n", + "\n", + "Xarray supports direct serialization and IO to several file formats, through the use of internal and external backends. Xarray's internal backends cover many common data formats such as \"netCDF4\", \"h5netcdf\", and \"zarr\". These backends provide a set of instructions that tells xarray how read data and store it into a `Dataset`, `DataTree` or `DataTree` model and are stored in the underlying “backend”. \n", + "\n", + "Xarray also supports external backends such as \"rioxarray\" and \"cfgrib\" and even creating your own backend.\n", + "\n", + "\n", + ":::{admonition} See also\n", + "You can learn more about xarray's [internal](https://tutorial.xarray.dev/advanced/backends/backends.html#links-to-internal-backends) and [external](https://tutorial.xarray.dev/advanced/backends/backends.html#links-to-external-backends-not-comprehensive) with our [guide](https://docs.xarray.dev/en/latest/user-guide/io.html) to backends.\n", + "\n", + ":::" + ] + }, + { + "cell_type": "markdown", + "id": "1", + "metadata": {}, + "source": [ + "## Reading a dataset with the netCDF4 engine\n", + "\n", + "We can read in a dataset by selecting the \"netcdf4\" engine" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2", + "metadata": {}, + "outputs": [], + "source": [ + "import xarray as xr\n", + "\n", + "xr.tutorial.open_dataset(\"air_temperature\", engine=\"netcdf4\")" + ] + }, + { + "cell_type": "markdown", + "id": "3", + "metadata": {}, + "source": [ + "## Internal Backend Selection\n", + "\n", + "When opening a file or URL without explicitly specifying the ``engine`` parameter,\n", + "xarray automatically selects an appropriate backend based on the file path or URL.\n", + "The backends are tried in order: **netcdf4 → h5netcdf → scipy → pydap → zarr**.\n" + ] + }, + { + "cell_type": "markdown", + "id": "4", + "metadata": {}, + "source": [ + "## Check for available backends\n", + "\n", + "We can get a list of our available engines with `list_engines`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5", + "metadata": {}, + "outputs": [], + "source": [ + "import xarray as xr\n", + "\n", + "xr.backends.list_engines()" + ] + }, + { + "cell_type": "markdown", + "id": "6", + "metadata": {}, + "source": [ + "## Reading without specifying engine\n", + "\n", + "When opening a file or URL without explicitly specifying the ``engine`` parameter,\n", + "xarray automatically selects an appropriate backend based on the file path or URL.\n", + "The backends are tried in order: **netcdf4 → h5netcdf → scipy → pydap → zarr**.\n", + "\n", + "In the following example this dataset is read with the \"netcdf4\" engine without explicitly setting the `\"engine=netcdf4\"`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7", + "metadata": {}, + "outputs": [], + "source": [ + "xr.tutorial.open_dataset(\"air_temperature\")" + ] + }, + { + "cell_type": "markdown", + "id": "8", + "metadata": {}, + "source": [ + "## Setting engine order\n", + "\n", + "You can customize the order in which netcdf4 backends are resolved using `xr.set_options(netcdf_engine_order=)`\n", + "\n", + "Let's update our engine order to have \"h5netcdf\" and the \"netCDF4\" and reopening the dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9", + "metadata": {}, + "outputs": [], + "source": [ + "xr.set_options(netcdf_engine_order=[\"h5netcdf\", \"netcdf4\", \"scipy\"])" + ] + }, + { + "cell_type": "markdown", + "id": "10", + "metadata": {}, + "source": [ + "## Specifying the wrong backend" + ] + }, + { + "cell_type": "markdown", + "id": "11", + "metadata": {}, + "source": [ + "Lets trying specifying a bad engine for opening our dataset\n", + "\n", + ":::{warning}\n", + "This will fail because we cannot use the \"pydap\" engine to read this dataset\n", + ":::" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "12", + "metadata": { + "tags": [ + "raises-exception" + ] + }, + "outputs": [], + "source": [ + "xr.tutorial.open_dataset(\"air_temperature\", engine=\"pydap\")" + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/advanced/backends/io.gif b/advanced/backends/io.gif new file mode 100644 index 00000000..b404e3e5 Binary files /dev/null and b/advanced/backends/io.gif differ diff --git a/advanced/backends/ocean.gif b/advanced/backends/ocean.gif new file mode 100644 index 00000000..bfbe061a Binary files /dev/null and b/advanced/backends/ocean.gif differ diff --git a/advanced/backends/rasterio_backend.ipynb b/advanced/backends/rasterio_backend.ipynb new file mode 100644 index 00000000..2b3b9d49 --- /dev/null +++ b/advanced/backends/rasterio_backend.ipynb @@ -0,0 +1,289 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "0", + "metadata": {}, + "source": [ + "# Xarray's Rasterio backend\n", + "\n", + "In this lesson, we will learn how to use xarray's rasterio backend engine to open GeoTIFF rasters. By the end of the lesson, we will be able to:\n", + "\n", + ":::{admonition} Learning Goals\n", + "- Learn about the GeoTIFF format\n", + "- Lean about xarray's \"rasterio\" backend and the \"rioxarray\" accessor\n", + "- Learn how to read and plot GeoTIFF files with xarray\n", + "- Explore how to perform reprojection operations on rasters\n", + ":::\n", + "\n", + "## What are GeoTIFFs?\n", + "\n", + "The TIFF (Tagged Image File Format) format is a metadata rich image format for raster data. GeoTIFF (Geographic Tagged Image File Format) files are TIFF files that use georeferencing information (such as map projection and coordinate systems) as metadata. A GeoTIFF file with a single band contains 2D raster data for a single characteristic (i.e., variable) and maps to a geographic region. A GeoTIFF file can have multiple bands that all map to the same geographic region.\n", + "\n", + "\n", + "\n", + "## Rasterio and Rioxarray Backends\n", + "\n", + "[Rasterio](https://rasterio.readthedocs.io/en/stable/intro.html) is a geospatial raster library that expresses GDAL ([Geospatial Data Abstraction Library](http://gdal.org)) data model with a Python API and CLI. [Rioxarray](https://corteva.github.io/rioxarray/stable/readme.html) is a wrapper around the rasterio library, that also extends the xarray api with the *rio* accessor. When you open a GeoTIFF file with the \"rasterio\" engine it returns the data as an xarray object with access to methods from the *rio* accessor.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "1", + "metadata": {}, + "source": [ + "## Reading a GeoTIFF\n", + "\n", + "Xarray's \"rasterio\" backend supports reading GeoTIFFs.\n", + "\n", + "Lets read a GeoTIFF file as an `xr.Dataset` by selecting `engine='rasterio'`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2", + "metadata": {}, + "outputs": [], + "source": [ + "import xarray as xr" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3", + "metadata": {}, + "outputs": [], + "source": [ + "rds = xr.tutorial.open_dataset(\"RGB.byte.tif\", engine=\"rasterio\")" + ] + }, + { + "cell_type": "markdown", + "id": "4", + "metadata": {}, + "source": [ + ":::{note} We can also read GeoTIFFs with `rioxarray.open_rasterio`.\n", + "\n", + "The following code snippet opens a GeoTIFF and returns a `DataArray` object\n", + "```python\n", + "import rioxarray\n", + "rioxarray.open_rasterio(\"RGB.byte.tif\")\n", + "```\n", + "\n", + ":::" + ] + }, + { + "cell_type": "markdown", + "id": "5", + "metadata": {}, + "source": [ + "## Raster bands\n", + "\n", + "GeoTIFFS can have multiple bands each representing a range or band in the electromagnetic spectrum. Arrays stored within bands in xarray are data variables and `DataArray`'s objects in the the Xarray Data Model." + ] + }, + { + "cell_type": "markdown", + "id": "6", + "metadata": {}, + "source": [ + "Lets get the `DataArray` object (or variable) from our dataset. The variable name is \"band_data\"." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7", + "metadata": {}, + "outputs": [], + "source": [ + "rda = rds[\"band_data\"]\n", + "rda" + ] + }, + { + "cell_type": "markdown", + "id": "8", + "metadata": {}, + "source": [ + "Lets try getting the total number of bands for this GeoTIFF. Since `rioxarray` extends xarray with the *rio* accessor we can this accessor be able use many of the builtin `rasterio` methods." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9", + "metadata": {}, + "outputs": [], + "source": [ + "rda.rio.count" + ] + }, + { + "cell_type": "markdown", + "id": "10", + "metadata": {}, + "source": [ + "### Selection by bands\n", + "We can also select by bands. Since there are 3 bands in this dataset we can return raster array for the first band of this `xr.DataArray` by with indexing\n", + "\n", + "Lets get the first band of this dataset and try plotting it" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "11", + "metadata": {}, + "outputs": [], + "source": [ + "rda[0].plot(cmap=\"pink\")" + ] + }, + { + "cell_type": "markdown", + "id": "12", + "metadata": {}, + "source": [ + "## Bounds\n", + "With `.rio.bounds()` we can get the spatial bounding box of our `DataArray`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "13", + "metadata": {}, + "outputs": [], + "source": [ + "rda.rio.bounds()" + ] + }, + { + "cell_type": "markdown", + "id": "14", + "metadata": {}, + "source": [ + "## Transformation\n", + "\n", + "With `.rio.transform()` we can get the affine transformation matrix that maps pixel locations in (col, row) coordinates to (x, y) spatial positions.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "15", + "metadata": {}, + "outputs": [], + "source": [ + "rda.rio.transform()" + ] + }, + { + "cell_type": "markdown", + "id": "16", + "metadata": {}, + "source": [ + "### Coordinate Reference System (CRS)\n", + "We `rio.crs` we can get the CRS of our raster and reproject our raster." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "17", + "metadata": {}, + "outputs": [], + "source": [ + "rda.rio.crs" + ] + }, + { + "cell_type": "markdown", + "id": "18", + "metadata": {}, + "source": [ + "### Reprojection\n", + "We can also reproject our raster from one CRS to another.\n", + "\n", + "Lets reproject our raster from \"EPSG:6326\" to \"EPSG:32612\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "19", + "metadata": {}, + "outputs": [], + "source": [ + "rda_reproj = rda.rio.reproject(\"EPSG:32612\")" + ] + }, + { + "cell_type": "markdown", + "id": "20", + "metadata": {}, + "source": [ + ":::{note}\n", + "We have to update our CRS system to the new projection with `rio.write_crs`. We set `inplace=True` to write the CRS to the existing dataset." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "21", + "metadata": {}, + "outputs": [], + "source": [ + "rda_reproj.rio.write_crs(\"EPSG:32612\", inplace=True)" + ] + }, + { + "cell_type": "markdown", + "id": "22", + "metadata": {}, + "source": [ + "## Exercise" + ] + }, + { + "cell_type": "markdown", + "id": "23", + "metadata": {}, + "source": [ + "::::{admonition} Exercise\n", + ":class: tip\n", + "\n", + "Can you reproject and update the CRS of second band of data to 'ESPG:3857' and plot your results?\n", + "\n", + ":::{admonition} Solution\n", + ":class: dropdown\n", + "\n", + "```python\n", + "rda[1].rio.reproject(\"EPSG:3857\").rio.write_crs(\"EPSG:3857\", inplace=True).plot()\n", + "```\n", + ":::\n", + "::::\n" + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/advanced/indexing/indexing.md b/advanced/indexing/indexing.md new file mode 100644 index 00000000..72dae2a4 --- /dev/null +++ b/advanced/indexing/indexing.md @@ -0,0 +1,5 @@ +# Indexing + +```{tableofcontents} + +``` diff --git a/advanced/indexing/why-trees.md b/advanced/indexing/why-trees.md new file mode 100644 index 00000000..18565713 --- /dev/null +++ b/advanced/indexing/why-trees.md @@ -0,0 +1,628 @@ +--- +jupytext: + formats: ipynb,md:myst + text_representation: + extension: .md + format_name: myst + format_version: 0.13 + jupytext_version: 1.19.1 +kernelspec: + display_name: Python 3 (ipykernel) + language: python + name: python3 +--- + +# Tree-Based Indexing + +```{seealso} +[NDPointIndex](https://xarray-indexes.readthedocs.io/blocks/ndpoint.html) — use KD-trees and Ball trees with xarray's indexing system for efficient nearest-neighbor lookups on real datasets. +``` + +Imagine you have measurements at irregular locations and want to find the **nearest** data point to your query location. + +**In this notebook you'll learn:** + +- Why naive nearest-neighbor search is slow (O(n) comparisons) +- How KD-trees speed this up dramatically (O(log n) comparisons) +- Why KD-trees can give wrong answers for geographic lat/lon data +- When to use a Ball tree instead + ++++ + +## The nearest neighbor problem in 1D + +Let's start with a simple 1D example: + +**The problem:** What temperature is it at 4.7 km? We need to find the nearest measurement. + +```{code-cell} ipython3 +--- +tags: [hide-input] +--- +import numpy as np +import matplotlib.pyplot as plt + +# Temperature measurements at 7 locations along a transect +locations = np.array([1, 3, 4, 7, 8, 9, 12]) +temperatures = np.array([15, 18, 17, 22, 24, 23, 19]) + +# Plot the data +fig, ax = plt.subplots(figsize=(10, 3)) +ax.scatter(locations, np.zeros_like(locations), s=100, c='blue', zorder=5) +for loc, temp in zip(locations, temperatures): + ax.annotate(f'{temp}°', (loc, 0.15), ha='center', fontsize=10) +ax.set_xlim(0, 14) +ax.set_ylim(-0.5, 0.8) +ax.set_xlabel('Location (km)') +ax.set_yticks([]) +ax.set_title('Temperature measurements at 7 irregular locations') +plt.tight_layout() +plt.show() +``` + +The naive approach checks the distance to every point: + +```{code-cell} ipython3 +--- +tags: [hide-input] +--- +# === Configuration: change this to explore different queries === +query = 4.7 + +# Naive approach: check distance to EVERY point +fig, ax = plt.subplots(figsize=(10, 4)) + +# Draw the data points on the number line +ax.scatter(locations, np.zeros_like(locations), s=100, c='blue', zorder=5) +ax.scatter(query, 0, s=150, c='red', marker='x', zorder=10, lw=3) +ax.axhline(0, color='black', lw=0.5, zorder=1) + +# Draw horizontal distance lines - stacked vertically for visibility +for i, loc in enumerate(locations): + y_offset = 0.12 * (i + 1) + # Horizontal line showing the distance + ax.plot([query, loc], [y_offset, y_offset], 'gray', alpha=0.7, lw=2) + # Vertical ticks at endpoints + ax.plot([query, query], [y_offset - 0.03, y_offset + 0.03], 'gray', alpha=0.7, lw=1) + ax.plot([loc, loc], [y_offset - 0.03, y_offset + 0.03], 'gray', alpha=0.7, lw=1) + # Label + ax.annotate(f'{abs(loc - query):.1f} km', ((query + loc)/2, y_offset + 0.04), + ha='center', fontsize=8, color='gray') + +ax.set_xlim(0, 14) +ax.set_ylim(-0.2, 1.1) +ax.set_xlabel('Location (km)') +ax.set_yticks([]) +ax.set_title(f'Naive search: compute distance to ALL {len(locations)} points (query={query})') +plt.tight_layout() +plt.show() + +print(f"Query: {query} km") +print(f"Nearest point: {locations[np.argmin(np.abs(locations - query))]} km (distance = {np.min(np.abs(locations - query)):.1f} km)") +print(f"Comparisons needed: {len(locations)}") +``` + +With 7 points this is fine, but with millions of points this becomes slow. + +**The solution:** Pre-compute a tree structure that partitions the space. In 1D, this is essentially a binary search tree - each split divides the remaining points in half: + +```{code-cell} ipython3 +--- +tags: [hide-input] +--- +from scipy.spatial import KDTree +from matplotlib.patches import Rectangle + +# === Configuration === + +# Build the tree (this is the pre-computation step) +tree = KDTree(locations.reshape(-1, 1)) + +# Query the tree first to get the result +dist, idx = tree.query([[query]]) +nearest = locations[idx[0]] + +# Map from value to node name for finding the result node +value_to_node = {1: 'LL', 3: 'L1', 4: 'LR', 7: 'root', 8: 'RL', 9: 'R1', 12: 'RR'} +found_node = value_to_node[nearest] + +# Determine the search path based on query value +if query < 7: + if query < 3: + path_nodes = ['root', 'L1', 'LL'] + regions = [(0, 14), (0, 7), (0, 3)] + else: + path_nodes = ['root', 'L1', 'LR'] + regions = [(0, 14), (0, 7), (3, 7)] +else: + if query < 9: + path_nodes = ['root', 'R1', 'RL'] + regions = [(0, 14), (7, 14), (7, 9)] + else: + path_nodes = ['root', 'R1', 'RR'] + regions = [(0, 14), (7, 14), (9, 14)] + +# Create visualization: tree on left, 3 narrowing steps on right +fig = plt.figure(figsize=(16, 9)) + +# Left side: Tree diagram with spatial ranges +ax_tree = fig.add_subplot(1, 2, 1) +ax_tree.set_xlim(0, 16) +ax_tree.set_ylim(-0.5, 5.5) +ax_tree.axis('off') +ax_tree.set_title('KD-tree structure\n(each node shows the spatial range it covers)', fontsize=12, fontweight='bold') + +# Tree node positions - now includes spatial range for each node +nodes = { + 'root': {'pos': (8, 4.5), 'value': 7, 'color': 'steelblue', 'label': 'split=7', 'range': '[0, 14]'}, + 'L1': {'pos': (4, 2.6), 'value': 3, 'color': 'coral', 'label': 'split=3', 'range': '[0, 7)'}, + 'R1': {'pos': (12, 2.6), 'value': 9, 'color': 'seagreen', 'label': 'split=9', 'range': '[7, 14]'}, + 'LL': {'pos': (2, 0.8), 'value': 1, 'color': 'gray', 'label': '1', 'range': '[0, 3)'}, + 'LR': {'pos': (6, 0.8), 'value': 4, 'color': 'gray', 'label': '4', 'range': '[3, 7)'}, + 'RL': {'pos': (10, 0.8), 'value': 8, 'color': 'gray', 'label': '8', 'range': '[7, 9)'}, + 'RR': {'pos': (14, 0.8), 'value': 12, 'color': 'gray', 'label': '12', 'range': '[9, 14]'}, +} + +# Draw edges +edges = [('root', 'L1'), ('root', 'R1'), ('L1', 'LL'), ('L1', 'LR'), ('R1', 'RL'), ('R1', 'RR')] +for parent, child in edges: + px, py = nodes[parent]['pos'] + cx, cy = nodes[child]['pos'] + ax_tree.plot([px, cx], [py, cy], 'k-', lw=2, zorder=1) + +# Draw nodes with spatial range labels +for name, node in nodes.items(): + x, y = node['pos'] + is_split = 'split' in node['label'] + size = 2200 if is_split else 1500 + ax_tree.scatter(x, y, s=size, c=node['color'], zorder=5, edgecolors='black', linewidths=2) + ax_tree.annotate(node['label'], (x, y), ha='center', va='center', + fontsize=11 if is_split else 10, fontweight='bold', color='white') + # Add range label below each node + ax_tree.annotate(node['range'], (x, y - 0.55), ha='center', va='top', + fontsize=9, color='black', style='italic', + bbox=dict(boxstyle='round,pad=0.2', facecolor='white', edgecolor='gray', alpha=0.8)) + +# Highlight the path taken +for i in range(len(path_nodes) - 1): + px, py = nodes[path_nodes[i]]['pos'] + cx, cy = nodes[path_nodes[i+1]]['pos'] + ax_tree.plot([px, cx], [py, cy], 'r-', lw=5, alpha=0.4, zorder=2) + +# Add query annotation +ax_tree.annotate(f'query={query}', (8, 4.5), xytext=(11, 5.2), + fontsize=11, color='red', fontweight='bold', + arrowprops=dict(arrowstyle='->', color='red', lw=2)) + +# Mark the found node +found_x, found_y = nodes[found_node]['pos'] +ax_tree.annotate(f'found {nearest}!', (found_x + 1.2, found_y + 0.3), fontsize=11, ha='left', color='red', fontweight='bold') + +# Right side: 3 subplots showing narrowing search space +steps = [ + ("Step 1: Start with all points", regions[0], 'steelblue', f'{query} < 7? → go left' if query < 7 else f'{query} > 7? → go right'), + ("Step 2: After first split", regions[1], 'coral', f'{query} < 3? → go left' if query < 3 else f'{query} > 3? → go right' if query < 7 else f'{query} < 9? → go left' if query < 9 else f'{query} > 9? → go right'), + (f"Step 3: Found nearest = {nearest}", regions[2], 'gold', None), +] + +for i, (title, (region_start, region_end), color, annotation) in enumerate(steps): + ax = fig.add_subplot(3, 2, 2*(i+1)) + + # Draw all data points + for loc in locations: + in_region = region_start <= loc <= region_end + ax.scatter(loc, 0, s=100 if in_region else 60, + c='blue' if in_region else 'lightgray', + zorder=5, edgecolors='black' if in_region else 'gray', linewidths=1) + if in_region: + ax.annotate(f'{loc}', (loc, -0.25), ha='center', fontsize=9, fontweight='bold') + + # Draw query point + ax.scatter(query, 0, s=150, c='red', marker='x', zorder=10, lw=3) + + # Highlight the active region + rect = Rectangle((region_start, -0.15), region_end - region_start, 0.3, + fill=True, facecolor=color, alpha=0.2, edgecolor=color, lw=2, zorder=2) + ax.add_patch(rect) + + # Draw split lines + if i == 0: + ax.axvline(7, color='steelblue', lw=2, ls='--', alpha=0.8) + ax.annotate('split=7', (7, 0.25), ha='center', fontsize=9, color='steelblue', fontweight='bold') + elif i == 1: + if query < 7: + ax.axvline(3, color='coral', lw=2, ls='--', alpha=0.8) + ax.annotate('split=3', (3, 0.25), ha='center', fontsize=9, color='coral', fontweight='bold') + else: + ax.axvline(9, color='seagreen', lw=2, ls='--', alpha=0.8) + ax.annotate('split=9', (9, 0.25), ha='center', fontsize=9, color='seagreen', fontweight='bold') + + # Add decision annotation + if annotation: + ax.annotate(annotation, (0.98, 0.95), xycoords='axes fraction', ha='right', va='top', + fontsize=10, color='darkgreen', fontweight='bold', + bbox=dict(boxstyle='round', facecolor='lightyellow', edgecolor='green', alpha=0.8)) + + ax.set_xlim(-0.5, 14.5) + ax.set_ylim(-0.4, 0.45) + ax.set_title(title, fontsize=11, fontweight='bold') + ax.set_yticks([]) + if i == 2: + ax.set_xlabel('Location (km)', fontsize=10) + +plt.tight_layout() +plt.show() + +print(f"Nearest point: {nearest} km") +print(f"Comparisons needed: ~{len(path_nodes)} (log₂({len(locations)}) ≈ 3)") +``` + +## Extending to 2D + +The same idea works in higher dimensions. Now our measurements are scattered across a 2D area: + +```{code-cell} ipython3 +--- +tags: [hide-input] +--- +# 2D example: temperature measurements scattered across an area +from matplotlib.patches import Rectangle + +np.random.seed(42) +points_2d = np.random.rand(20, 2) * 10 # 20 points in a 10x10 area + +# === Configuration === +query_2d = np.array([6.5, 4.0]) # Change this to query a different location + +# Build tree - using leafsize=2 to demonstrate meaningful subdivision +# (default leafsize=10 would barely split with only 20 points!) +LEAFSIZE = 2 +tree_2d = KDTree(points_2d, leafsize=LEAFSIZE) +dist, idx = tree_2d.query([query_2d]) +nearest_2d = points_2d[idx[0]] + +# With leafsize=2, we get ~4 levels of splits (log2(20/2) ≈ 3-4) +# Let's show the first 2 splits conceptually, then the final leaf comparison + +# Approximate the splits (KD-tree alternates x, y, x, y...) +x_split = np.median(points_2d[:, 0]) # ~4.0 + +# Determine which half based on query x +if query_2d[0] >= x_split: + # Right half + half_points = points_2d[points_2d[:, 0] >= x_split] + x_decision = f"x={query_2d[0]} > {x_split:.1f}? → go right" + x_region = (x_split, 0, 10, 10) # (x_min, y_min, x_max, y_max) +else: + # Left half + half_points = points_2d[points_2d[:, 0] < x_split] + x_decision = f"x={query_2d[0]} < {x_split:.1f}? → go left" + x_region = (0, 0, x_split, 10) + +y_split = np.median(half_points[:, 1]) + +# Determine which quadrant based on query y +if query_2d[1] >= y_split: + # Upper region + y_decision = f"y={query_2d[1]} > {y_split:.1f}? → go up" + if query_2d[0] >= x_split: + final_region = (x_split, y_split, 10, 10) # top-right + else: + final_region = (0, y_split, x_split, 10) # top-left +else: + # Lower region + y_decision = f"y={query_2d[1]} < {y_split:.1f}? → go down" + if query_2d[0] >= x_split: + final_region = (x_split, 0, 10, y_split) # bottom-right + else: + final_region = (0, 0, x_split, y_split) # bottom-left + +# Define regions for visualization +regions = [ + (0, 0, 10, 10), # Step 1: all points + x_region, # Step 2: half based on x + final_region, # Step 3: quadrant based on y +] + +# Get actual points in final region (these are the leaf candidates) +x_min, y_min, x_max, y_max = final_region +final_candidates = [pt for pt in points_2d + if x_min <= pt[0] <= x_max and y_min <= pt[1] <= y_max] + +# Create figure +fig, axes = plt.subplots(2, 2, figsize=(12, 10)) +axes = axes.flatten() + +step_titles = [ + "Step 1: Start with all 20 points", + f"Step 2: Split on x ≈ {x_split:.1f}", + f"Step 3: Split on y ≈ {y_split:.1f}", + f"Step 4: Compare {len(final_candidates)} candidates in leaf" +] +step_colors = ['steelblue', 'coral', 'gold', 'limegreen'] +decisions = [x_decision, y_decision, None, None] + +for i, ax in enumerate(axes): + x_min, y_min, x_max, y_max = regions[min(i, 2)] + + # Get points in current region + points_in_region = [(pt, x_min <= pt[0] <= x_max and y_min <= pt[1] <= y_max) for pt in points_2d] + + # Draw all points + for pt, in_region in points_in_region: + ax.scatter(pt[0], pt[1], s=80 if in_region else 40, + c='blue' if in_region else 'lightgray', + edgecolors='black' if in_region else 'gray', + zorder=5, linewidths=1) + + # Draw query point + ax.scatter(*query_2d, s=150, c='red', marker='x', zorder=10, lw=3) + + # Draw the active region + rect = Rectangle((x_min, y_min), x_max - x_min, y_max - y_min, + fill=True, facecolor=step_colors[i], alpha=0.15, + edgecolor=step_colors[i], lw=2, zorder=2) + ax.add_patch(rect) + + # Draw split lines + if i >= 1: + ax.axvline(x_split, color='steelblue', lw=2, ls='--', alpha=0.8) + ax.annotate(f'x={x_split:.1f}', (x_split, 9.7), ha='center', fontsize=9, + color='steelblue', fontweight='bold') + if i >= 2: + # Only draw y split line in the relevant half + if query_2d[0] >= x_split: + ax.axhline(y_split, xmin=x_split/10, xmax=1, color='coral', lw=2, ls='--', alpha=0.8) + else: + ax.axhline(y_split, xmin=0, xmax=x_split/10, color='coral', lw=2, ls='--', alpha=0.8) + ax.annotate(f'y={y_split:.1f}', (9.7, y_split + 0.2), + ha='right', va='bottom', fontsize=9, color='coral', fontweight='bold') + + # Final step: draw lines to ALL candidates + if i == 3: + for pt, in_region in points_in_region: + if in_region: + is_nearest = np.allclose(pt, nearest_2d) + ax.plot([query_2d[0], pt[0]], [query_2d[1], pt[1]], + color='limegreen' if is_nearest else 'gray', + lw=3 if is_nearest else 1.5, + alpha=1.0 if is_nearest else 0.6, + zorder=6 if is_nearest else 4) + + ax.scatter(*nearest_2d, s=200, facecolors='none', edgecolors='limegreen', lw=3, zorder=15) + ax.annotate('nearest!', (nearest_2d[0] + 0.3, nearest_2d[1] + 0.3), + ha='left', fontsize=10, color='green', fontweight='bold') + + # Add decision annotation + if decisions[i]: + ax.annotate(decisions[i], (0.98, 0.98), xycoords='axes fraction', + ha='right', va='top', fontsize=10, color='darkgreen', fontweight='bold', + bbox=dict(boxstyle='round', facecolor='lightyellow', edgecolor='green', alpha=0.9)) + + ax.set_xlim(-0.5, 10.5) + ax.set_ylim(-0.5, 10.5) + ax.set_aspect('equal') + ax.set_xlabel('x') + ax.set_ylabel('y') + ax.set_title(step_titles[i], fontsize=11, fontweight='bold') + +plt.tight_layout() +plt.show() + +print(f"Query: ({query_2d[0]}, {query_2d[1]})") +print(f"KDTree built with leafsize={LEAFSIZE}") +print(f"Started with {len(points_2d)} points") +print(f"After 2 tree splits: narrowed to {len(final_candidates)} candidates") +print(f"Final step: {len(final_candidates)} distance calculations") +print(f"Total: 2 splits + {len(final_candidates)} comparisons = {2 + len(final_candidates)} operations (vs 20 naive)") +``` + +## How it scales + +The same principle extends to 3D, 4D, and beyond. Here's how the number of comparisons scales: + +```{code-cell} ipython3 +--- +tags: [hide-input] +--- +# How comparisons scale with data size +data_sizes = np.array([10, 100, 1000, 10000, 100000, 1000000]) +naive_comparisons = data_sizes # O(n) +kdtree_comparisons = np.log2(data_sizes).astype(int) + 1 # O(log n) + +fig, ax = plt.subplots(figsize=(10, 5)) + +ax.plot(data_sizes, naive_comparisons, 'o-', color='gray', lw=2, markersize=8, label='Naive: O(n)') +ax.plot(data_sizes, kdtree_comparisons, 's-', color='steelblue', lw=2, markersize=8, label='KD-tree: O(log n)') + +ax.set_xscale('log') +ax.set_yscale('log') +ax.set_xlabel('Number of data points') +ax.set_ylabel('Comparisons per query') +ax.set_title('Finding nearest neighbor: Naive vs KD-tree') +ax.legend(fontsize=11) +ax.grid(True, alpha=0.3) + +# Annotate key points +for n, naive, kd in zip(data_sizes[::2], naive_comparisons[::2], kdtree_comparisons[::2]): + ax.annotate(f'{naive:,}', (n, naive), textcoords="offset points", xytext=(0,10), ha='center', fontsize=9, color='gray') + ax.annotate(f'{kd}', (n, kd), textcoords="offset points", xytext=(0,-15), ha='center', fontsize=9, color='steelblue') + +plt.tight_layout() +plt.show() + +print("With 1 million points: naive needs 1,000,000 comparisons, KD-tree needs ~20") +``` + +(the-problem-with-geographic-coordinates)= + +## The problem with geographic coordinates + +KD-trees use **Euclidean distance**—they measure straight-line distance in whatever coordinate system you give them. This works perfectly for x/y coordinates in meters or kilometers. + +But for **latitude/longitude coordinates**, Euclidean distance over degrees is wrong! Here's why: + +- **Latitude degrees are constant**: 1° latitude ≈ 111 km everywhere on Earth +- **Longitude degrees shrink toward the poles**: 1° longitude ≈ 111 km at the equator, but only ~19 km at 80°N + +This means a KD-tree treating lat/lon as flat coordinates will systematically pick the **wrong** nearest neighbor at high latitudes: + +```{code-cell} ipython3 +--- +tags: [hide-input] +--- +# Visualize haversine vs Euclidean - 2D circle diagram +from sklearn.neighbors import BallTree + +# At 80°N (near Arctic): longitude degrees are MUCH shorter! +lat = 80 +km_per_deg_lon = 111 * np.cos(np.radians(lat)) # ~19 km at 80°N! +km_per_deg_lat = 111 # always ~111 km + +# Query point and two candidates (in lat/lon degrees) +query_latlon = np.array([[lat, 0]]) +point_a_latlon = np.array([[lat, 2.0]]) +point_b_latlon = np.array([[lat + 0.5, 0]]) +points_latlon = np.vstack([point_a_latlon, point_b_latlon]) + +# Test both trees +kd_tree = KDTree(points_latlon) +kd_dist, kd_idx = kd_tree.query(query_latlon) +kd_picked = "A" if kd_idx[0] == 0 else "B" + +ball_tree = BallTree(np.radians(points_latlon), metric='haversine') +ball_dist, ball_idx = ball_tree.query(np.radians(query_latlon)) +ball_picked = "A" if ball_idx[0] == 0 else "B" + +km_to_a = 2.0 * km_per_deg_lon +km_to_b = 0.5 * km_per_deg_lat + +# Create figure +fig = plt.figure(figsize=(14, 10), constrained_layout=True) + +# === Top: Circle diagram showing arc vs chord === +ax_circle = fig.add_subplot(211) +ax_circle.set_aspect('equal') +ax_circle.axis('off') + +# Draw circle +radius = 1 +theta_full = np.linspace(0, 2*np.pi, 100) +ax_circle.plot(radius * np.cos(theta_full), radius * np.sin(theta_full), 'k-', lw=2.5) + +# Two points on the circle +theta_p = np.radians(120) # Point P +theta_q = np.radians(60) # Point Q + +p_x, p_y = radius * np.cos(theta_p), radius * np.sin(theta_p) +q_x, q_y = radius * np.cos(theta_q), radius * np.sin(theta_q) + +# Draw the ARC (haversine distance) - along the circle surface +arc_theta = np.linspace(theta_q, theta_p, 50) +arc_x = radius * np.cos(arc_theta) +arc_y = radius * np.sin(arc_theta) +ax_circle.plot(arc_x, arc_y, 'r-', lw=6, solid_capstyle='round', label='Arc length (haversine)', zorder=5) + +# Draw the CHORD (Euclidean distance) - straight line +ax_circle.plot([p_x, q_x], [p_y, q_y], 'b--', lw=4, label='Chord length (Euclidean)', zorder=4) + +# Draw points +ax_circle.scatter([p_x, q_x], [p_y, q_y], s=250, c='dodgerblue', edgecolors='black', lw=2, zorder=10) + +# Labels +ax_circle.annotate('P', (p_x - 0.18, p_y + 0.08), fontsize=24, fontweight='bold') +ax_circle.annotate('Q', (q_x + 0.1, q_y + 0.08), fontsize=24, fontweight='bold') + +# Distance annotations - positioned to avoid overlap +# Arc annotation (above the arc) +arc_mid_theta = (theta_p + theta_q) / 2 +arc_mid_x = 1.22 * np.cos(arc_mid_theta) +arc_mid_y = 1.22 * np.sin(arc_mid_theta) +ax_circle.annotate('arc length\n(along surface)', (arc_mid_x, arc_mid_y + 0.05), + fontsize=13, color='darkred', fontweight='bold', ha='center', + bbox=dict(boxstyle='round,pad=0.3', facecolor='mistyrose', edgecolor='red', alpha=0.9)) + +# Chord annotation (below the chord) +chord_mid_x = (p_x + q_x) / 2 +chord_mid_y = (p_y + q_y) / 2 +ax_circle.annotate('chord\n(straight line)', (chord_mid_x, chord_mid_y - 0.25), + fontsize=13, color='darkblue', fontweight='bold', ha='center', + bbox=dict(boxstyle='round,pad=0.3', facecolor='lightblue', edgecolor='blue', alpha=0.9)) + +# Calculate actual distances for display +arc_length = radius * abs(theta_p - theta_q) # s = r * theta +chord_length = np.sqrt((q_x - p_x)**2 + (q_y - p_y)**2) + +ax_circle.set_xlim(-1.5, 1.5) +ax_circle.set_ylim(-1.0, 1.7) +ax_circle.set_title('Haversine vs Euclidean: arc length ≠ chord length', + fontsize=16, fontweight='bold', pad=15) + +# Legend on the left side +ax_circle.legend(loc='center left', fontsize=11, bbox_to_anchor=(-0.15, 0.3)) + +# Formula box on the right side +formula_text = f"Arc length > Chord length\n(arc = {arc_length:.2f}, chord = {chord_length:.2f})" +ax_circle.text(1.15, 0.3, formula_text, fontsize=12, ha='center', + bbox=dict(boxstyle='round', facecolor='wheat', alpha=0.9)) + +# === Bottom row: Side-by-side comparison === +# Bottom-left: KD-tree view (flat degrees) +ax_kd = fig.add_subplot(223) +ax_kd.scatter(0, 0, s=200, c='red', marker='x', zorder=10, lw=3, label='Query') +ax_kd.scatter(2.0, 0, s=140, c='green', zorder=5, label='Point A (2° east)') +ax_kd.scatter(0, 0.5, s=140, c='orange', zorder=5, label='Point B (0.5° north)') +ax_kd.plot([0, 2.0], [0, 0], 'g-', lw=2.5, alpha=0.7) +ax_kd.plot([0, 0], [0, 0.5], color='orange', lw=2.5, alpha=0.7) + +circle_kd = plt.Circle((0, 0.5), 0.15, fill=False, color='black', lw=3, linestyle='--', zorder=15) +ax_kd.add_patch(circle_kd) +ax_kd.annotate('KD-tree picks B\n(smaller in degrees)', (0.3, 0.75), fontsize=12, fontweight='bold') + +ax_kd.set_xlabel('Longitude offset (°)', fontsize=13) +ax_kd.set_ylabel('Latitude offset (°)', fontsize=13) +ax_kd.set_title(f'KD-tree: Euclidean on degrees\n(at {lat}°N latitude)', fontsize=13, fontweight='bold') +ax_kd.legend(loc='upper right', fontsize=10) +ax_kd.set_xlim(-0.5, 2.5) +ax_kd.set_ylim(-0.5, 1.2) +ax_kd.set_aspect('equal') +ax_kd.grid(True, alpha=0.3) +ax_kd.annotate('2.0°', (1.0, -0.15), ha='center', fontsize=14, color='green', fontweight='bold') +ax_kd.annotate('0.5°', (-0.25, 0.25), ha='center', fontsize=14, color='orange', fontweight='bold', rotation=90) + +# Bottom-right: Reality in kilometers +ax_real = fig.add_subplot(224) +ax_real.scatter(0, 0, s=200, c='red', marker='x', zorder=10, lw=3, label=f'Query ({lat}°N)') +ax_real.scatter(km_to_a, 0, s=140, c='green', zorder=5, label='Point A') +ax_real.scatter(0, km_to_b, s=140, c='orange', zorder=5, label='Point B') +ax_real.plot([0, km_to_a], [0, 0], 'g-', lw=2.5, alpha=0.7) +ax_real.plot([0, 0], [0, km_to_b], color='orange', lw=2.5, alpha=0.7) + +circle_ball = plt.Circle((km_to_a, 0), 6, fill=False, color='black', lw=3, linestyle='--', zorder=15) +ax_real.add_patch(circle_ball) +ax_real.annotate('Ball tree picks A\n(smaller in km)', (5, 55), fontsize=12, fontweight='bold') + +ax_real.set_xlabel('East-West distance (km)', fontsize=13) +ax_real.set_ylabel('North-South distance (km)', fontsize=13) +ax_real.set_title(f'Ball tree: haversine (true distance)\n1° longitude = only {km_per_deg_lon:.0f} km at {lat}°N!', + fontsize=13, fontweight='bold') +ax_real.legend(loc='upper right', fontsize=10) +ax_real.set_xlim(-10, 70) +ax_real.set_ylim(-10, 70) +ax_real.set_aspect('equal') +ax_real.grid(True, alpha=0.3) +ax_real.annotate(f'{km_to_a:.0f} km', (km_to_a/2, -6), ha='center', fontsize=14, color='green', fontweight='bold') +ax_real.annotate(f'{km_to_b:.0f} km', (-7, km_to_b/2), ha='center', fontsize=14, color='orange', fontweight='bold', rotation=90) + +plt.show() + +print(f"At {lat}°N: 1° longitude = {km_per_deg_lon:.0f} km, 1° latitude = {km_per_deg_lat} km") +print(f"\nPoint A: 2° east = {km_to_a:.0f} km (along surface)") +print(f"Point B: 0.5° north = {km_to_b:.0f} km") +print(f"\nKD-tree picked: Point {kd_picked} {'✗ WRONG!' if kd_picked == 'B' else '✓'}") +print(f"Ball tree picked: Point {ball_picked} {'✓ CORRECT!' if ball_picked == 'A' else '✗'}") +``` + +## Next steps + +Ready to use tree-based indexing with xarray? See [NDPointIndex](https://xarray-indexes.readthedocs.io/blocks/ndpoint.html) for how to integrate KD-trees and Ball trees with xarray's indexing system. diff --git a/advanced/map_blocks/map_blocks.md b/advanced/map_blocks/map_blocks.md new file mode 100644 index 00000000..6ad6d333 --- /dev/null +++ b/advanced/map_blocks/map_blocks.md @@ -0,0 +1,5 @@ +# map_blocks + +```{tableofcontents} + +``` diff --git a/advanced/map_blocks/simple_map_blocks.ipynb b/advanced/map_blocks/simple_map_blocks.ipynb new file mode 100644 index 00000000..af46f405 --- /dev/null +++ b/advanced/map_blocks/simple_map_blocks.ipynb @@ -0,0 +1,269 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "0", + "metadata": { + "tags": [] + }, + "source": [ + "# A gentle introduction\n", + "\n", + "`map_blocks` is inspired by the `dask.array` function of the same name and lets\n", + "you map a function on blocks of the xarray object (including Datasets!).\n", + "\n", + "At _compute_ time, your function will receive a chunk of an xarray object with concrete\n", + "(computed) values along with appropriate metadata. This function should return\n", + "an xarray object.\n" + ] + }, + { + "cell_type": "markdown", + "id": "1", + "metadata": {}, + "source": [ + "## Setup" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2", + "metadata": {}, + "outputs": [], + "source": [ + "import dask\n", + "import numpy as np\n", + "import xarray as xr" + ] + }, + { + "cell_type": "markdown", + "id": "3", + "metadata": {}, + "source": [ + "First lets set up a `LocalCluster` using [dask.distributed](https://distributed.dask.org/).\n", + "\n", + "You can use any kind of dask cluster. This step is completely independent of\n", + "xarray. While not strictly necessary, the dashboard provides a nice learning\n", + "tool.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4", + "metadata": {}, + "outputs": [], + "source": [ + "from dask.distributed import Client\n", + "\n", + "client = Client()\n", + "client" + ] + }, + { + "cell_type": "markdown", + "id": "5", + "metadata": {}, + "source": [ + "
👆
Click the Dashboard link above. Or click the \"Search\" button in the dashboard.\n", + "\n", + "Let's test that the dashboard is working..\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6", + "metadata": {}, + "outputs": [], + "source": [ + "import dask.array\n", + "\n", + "dask.array.ones((1000, 4), chunks=(2, 1)).compute() # should see activity in dashboard" + ] + }, + { + "cell_type": "markdown", + "id": "7", + "metadata": {}, + "source": [ + "Let's open a dataset. We specify `chunks` so that we create a dask arrays for the DataArrays.\n", + "\n", + "Depending on the desired function to be applied on the chunks, it is vital to set the chunks correctly. Our goal is to compute the mean along the time dimension. Therefore we do not chunk the time dimension at all (indicated by `\"time\": -1`). We deliberately set `lat` and `lon` chunks to something smaller then the size of their respective dimension (otherwise we would potentially end up with a single big chunk for the entire `ds`)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8", + "metadata": {}, + "outputs": [], + "source": [ + "ds = xr.tutorial.open_dataset(\"air_temperature\", chunks={\"time\": -1, \"lat\": 5, \"lon\": 10})\n", + "ds" + ] + }, + { + "cell_type": "markdown", + "id": "9", + "metadata": {}, + "source": [ + "## Simple example\n", + "\n", + "Here is an example" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "10", + "metadata": {}, + "outputs": [], + "source": [ + "def time_mean(obj: xr.Dataset):\n", + " # use xarray's convenient API here\n", + " # you could convert to a pandas dataframe and use pandas' extensive API\n", + " # or use .plot() and plt.savefig to save visualizations to disk in parallel.\n", + " return obj.mean(\"time\")\n", + "\n", + "\n", + "ds.map_blocks(time_mean) # this is lazy!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "11", + "metadata": {}, + "outputs": [], + "source": [ + "# this triggers the actual computation\n", + "ds.map_blocks(time_mean).compute()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "12", + "metadata": {}, + "outputs": [], + "source": [ + "# this will calculate values and will return True if the computation works as expected\n", + "ds.map_blocks(time_mean).equals(ds.mean(\"time\"))" + ] + }, + { + "cell_type": "markdown", + "id": "13", + "metadata": {}, + "source": [ + "### Exercises\n" + ] + }, + { + "cell_type": "markdown", + "id": "14", + "metadata": {}, + "source": [ + "::::{admonition} Exercise 1\n", + ":class: tip\n", + "\n", + "When opening the dataset, set the chunks for the dimension to anything smaller than the size of the time dimension (< 2920), e.g., `\"time\": 100`, and keep the size of the other chunks the same:\n", + "\n", + "```python\n", + "ds = xr.tutorial.open_dataset(\n", + " \"air_temperature\",\n", + " chunks={\"time\": 100, \"lat\": 5, \"lon\": 10},\n", + ")\n", + "```\n", + "\n", + "Now run the notebook again. The result of `ds.map_blocks(time_mean)` is no more equivalent to `ds.mean(\"time\")`. Why does `ds.map_blocks(time_mean)` return a different result this time?\n", + "\n", + ":::{admonition} Solution\n", + ":class: dropdown\n", + "\n", + "Quoting from the documentation of `map_blocks`: _The function will receive a subset or ‘block’ of obj (see below), corresponding to one chunk along each chunked dimension._\n", + "\n", + "`ds.mean(\"time\")` computes the mean over the entire time dimension. In our example `ds.map_blocks(time_mean)` passes individual chunks of `ds` to `time_mean`. Once the time dimension is chunked, `time_mean` receives more than a single chunk along the dimension, meaning `time_mean` computes the mean along the time dimension for a single chunk rather than along the entire time dimension. Therefore we do not receive an identical result.\n", + "\n", + "You can also modify the function to show the shape of the chunks passed to `time_mean`. Compare the output of the modified function with `ds.chunks` to find out how they relate to each other!\n", + "\n", + "```python\n", + "def time_mean(obj: xr.Dataset):\n", + " print(f\"received obj of type {type(obj)}\")\n", + " print(\"obj contains the following data variables:\")\n", + " for data_var in obj.data_vars:\n", + " print(f\"'{data_var}' with shape {obj[data_var].shape}\")\n", + "\n", + " return obj.mean(\"time\")\n", + "```\n", + "\n", + ":::\n", + "::::\n" + ] + }, + { + "cell_type": "markdown", + "id": "15", + "metadata": {}, + "source": [ + "::::{admonition} Exercise 2\n", + ":class: tip \n", + "\n", + "Try applying the following function with `map_blocks`. Specify `scale` as an\n", + "argument and `offset` as a kwarg.\n", + "\n", + "The docstring should help:\n", + "https://docs.xarray.dev/en/stable/generated/xarray.map_blocks.html\n", + "\n", + "```python\n", + "def time_mean_scaled(obj, scale, offset):\n", + " return obj.mean(\"lat\") * scale + offset\n", + "```\n", + "\n", + "::::" + ] + }, + { + "cell_type": "markdown", + "id": "16", + "metadata": {}, + "source": [ + "### More advanced functions\n", + "\n", + "`map_blocks` needs to know what the returned object looks like _exactly_. It\n", + "does so by passing a 0-shaped xarray object to the function and examining the\n", + "result. This approach cannot work in all cases For such advanced use cases,\n", + "`map_blocks` allows a `template` kwarg. See\n", + "https://docs.xarray.dev/en/stable/user-guide/dask.html#map-blocks for more details\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "17", + "metadata": {}, + "outputs": [], + "source": [ + "client.close()" + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/advanced/parallel-intro.md b/advanced/parallel-intro.md new file mode 100644 index 00000000..7d20b5c9 --- /dev/null +++ b/advanced/parallel-intro.md @@ -0,0 +1,19 @@ +# Parallelizing custom functions + +Almost all of xarray’s built-in operations work on Dask arrays. + +Sometimes analysis calls for functions that aren't in xarray's API (e.g. scipy). +There are three ways to apply these functions in parallel on each block of your +xarray object: + +1. Extract Dask arrays from xarray objects ([.data](https://docs.xarray.dev/en/stable/generated/xarray.DataArray.data.html)) and use Dask directly e.g. + ([apply_gufunc](https://docs.dask.org/en/latest/generated/dask.array.gufunc.apply_gufunc.html), [map_blocks](https://docs.dask.org/en/latest/generated/dask.array.map_blocks.html), [map_overlap](https://docs.dask.org/en/latest/generated/dask.array.map_overlap.html), [blockwise](https://docs.dask.org/en/latest/generated/dask.array.core.blockwise.html), [reduction](https://docs.dask.org/en/latest/generated/dask.array.reduction.html)). Then wrap the result as an Xarray object. + +2. Use [apply_ufunc](https://docs.xarray.dev/en/stable/generated/xarray.apply_ufunc.html) to apply functions that consume and return duck arrays. This automates extracting the data from Xarray objects, applying a function, and then converting the bare array result back to a Xarray object. + +3. Use [map_blocks](https://docs.xarray.dev/en/stable/generated/xarray.map_blocks.html), [Dataset.map_blocks](https://docs.xarray.dev/en/stable/generated/xarray.Dataset.map_blocks.html) or [DataArray.map_blocks](https://docs.xarray.dev/en/stable/generated/xarray.DataArray.map_blocks.html) + to apply functions that consume and return xarray objects. + +Which method you use ultimately depends on the type of input objects expected by +the function you're wrapping, and the level of performance or convenience you +desire. diff --git a/ci/environment.yml b/ci/environment.yml deleted file mode 100644 index fc3b68a7..00000000 --- a/ci/environment.yml +++ /dev/null @@ -1,20 +0,0 @@ -name: xarray -channels: - - conda-forge - - default -dependencies: - - pathlib - - nbconvert - - nbformat - - jupyter_client - - ipykernel - - nbsphinx - - sphinx_rtd_theme - - sphinx - - doctr - - scipy - - zarr - - dask - - distributed - - netcdf4 - - python-graphviz diff --git a/conf.py b/conf.py deleted file mode 100644 index 5fe5a284..00000000 --- a/conf.py +++ /dev/null @@ -1,177 +0,0 @@ -# -*- coding: utf-8 -*- -# -# Configuration file for the Sphinx documentation builder. -# -# This file does only contain a selection of the most common options. For a -# full list see the documentation: -# http://www.sphinx-doc.org/en/master/config - -# -- Path setup -------------------------------------------------------------- - -# If extensions (or modules to document with autodoc) are in another directory, -# add these directories to sys.path here. If the directory is relative to the -# documentation root, use os.path.abspath to make it absolute, like shown here. -# -# import os -# import sys -# sys.path.insert(0, os.path.abspath('.')) - - -# -- Project information ----------------------------------------------------- - -project = "Xarray Tutorial" -copyright = "2020, Xarray Developers" -author = "Xarray Developers" - -# The short X.Y version -version = "" -# The full version, including alpha/beta/rc tags -release = "" - - -# -- General configuration --------------------------------------------------- - -# If your documentation needs a minimal Sphinx version, state it here. -# -# needs_sphinx = '1.0' - -# Add any Sphinx extension module names here, as strings. They can be -# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom -# ones. -extensions = [ - "sphinx.ext.mathjax", - "nbsphinx", -] - -nbsphinx_timeout = 600 -# nbsphinx_execute = "always" -nbsphinx_allow_errors = True - -nbsphinx_prolog = """ -{% set docname = env.doc2path(env.docname, base=None) %} - -You can run this notebook in a `live session![](\"https://www.unidata.ucar.edu/images/logos/netcdf-400x400.png\")
\n",
+ "\n",
+ "## NetCDF\n",
+ "\n",
+ "The recommended way to store xarray data structures is NetCDF, which is a binary\n",
+ "file format for self-described datasets that originated in the geosciences.\n",
+ "Xarray is based on the netCDF data model, so netCDF files on disk directly\n",
+ "correspond to Dataset objects.\n",
+ "\n",
+ "Xarray reads and writes to NetCDF files using the `open_dataset` /\n",
+ "`open_dataarray` functions and the `to_netcdf` method.\n",
+ "\n",
+ "Let's first create some datasets and write them to disk using `to_netcdf`, which\n",
+ "takes the path we want to write to:\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import xarray as xr\n",
+ "\n",
+ "# Ensure random arrays are the same each time\n",
+ "np.random.seed(0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Ensure we start with a clean directory for the tutorial\n",
+ "import pathlib\n",
+ "import shutil\n",
+ "\n",
+ "datadir = pathlib.Path(\"../data/io-tutorial\")\n",
+ "if datadir.exists():\n",
+ " shutil.rmtree(datadir)\n",
+ "else:\n",
+ " datadir.mkdir()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3",
+ "metadata": {},
+ "source": [
+ "The constructor of `Dataset` takes three parameters:\n",
+ "\n",
+ "- `data_vars`: dict-like mapping names to values. Values are either `DataArray` objects\n",
+ " or defined with tuples consisting of of dimension names and arrays.\n",
+ "- `coords`: same as for `DataArray`\n",
+ "- `attrs`: same as for `DataArray`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds1 = xr.Dataset(\n",
+ " data_vars={\n",
+ " \"a\": ((\"x\", \"y\"), np.random.randn(4, 2)),\n",
+ " \"b\": ((\"z\", \"x\"), np.random.randn(6, 4)),\n",
+ " },\n",
+ " coords={\n",
+ " \"x\": np.arange(4),\n",
+ " \"y\": np.arange(-2, 0),\n",
+ " \"z\": np.arange(-3, 3),\n",
+ " },\n",
+ ")\n",
+ "ds2 = xr.Dataset(\n",
+ " data_vars={\n",
+ " \"a\": ((\"x\", \"y\"), np.random.randn(7, 3)),\n",
+ " \"b\": ((\"z\", \"x\"), np.random.randn(2, 7)),\n",
+ " },\n",
+ " coords={\n",
+ " \"x\": np.arange(6, 13),\n",
+ " \"y\": np.arange(3),\n",
+ " \"z\": np.arange(3, 5),\n",
+ " },\n",
+ ")\n",
+ "\n",
+ "# write datasets\n",
+ "ds1.to_netcdf(datadir / \"ds1.nc\")\n",
+ "ds2.to_netcdf(datadir / \"ds2.nc\")\n",
+ "\n",
+ "# write dataarray\n",
+ "ds1.a.to_netcdf(datadir / \"da1.nc\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5",
+ "metadata": {},
+ "source": [
+ "Reading those files is just as simple:\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "xr.open_dataset(datadir / \"ds1.nc\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "xr.open_dataarray(datadir / \"da1.nc\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8",
+ "metadata": {},
+ "source": [
+ "![](\"https://zarr.readthedocs.io/en/stable/_static/logo1.png\"){kind=logo}
\n",
+ "\n",
+ "\n",
+ "## Zarr\n",
+ "\n",
+ "[Zarr](https://zarr.readthedocs.io/en/stable/) is a Python package and data\n",
+ "format providing an implementation of chunked, compressed, N-dimensional arrays.\n",
+ "Zarr has the ability to store arrays in a range of ways, including in memory, in\n",
+ "files, and in cloud-based object storage such as Amazon S3 and Google Cloud\n",
+ "Storage. Xarray’s Zarr backend allows xarray to leverage these capabilities.\n",
+ "\n",
+ "Zarr files can be written with:\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds1.to_zarr(datadir / \"ds1.zarr\", mode=\"w\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "10",
+ "metadata": {},
+ "source": [
+ "We can then read the created file with:\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "11",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "xr.open_zarr(datadir / \"ds1.zarr\", chunks=None)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "12",
+ "metadata": {},
+ "source": [
+ "setting the `chunks` parameter to `None` avoids `dask` (more on that in a later\n",
+ "session)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "13",
+ "metadata": {},
+ "source": [
+ "**tip:** You can write to any dictionary-like (`MutableMapping`) interface:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "14",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "mystore = {}\n",
+ "\n",
+ "ds1.to_zarr(store=mystore)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "15",
+ "metadata": {},
+ "source": [
+ "## Raster files using rioxarray\n",
+ "\n",
+ "[rioxarray](https://corteva.github.io/rioxarray/) is an *Xarray extension* that allows reading and writing a wide variety of geospatial image formats compatible with Geographic Information Systems (GIS), for example GeoTIFF.\n",
+ "\n",
+ "If rioxarray is installed your environment it will be automatically detected and give you access to the `.rio` accessor:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "16",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "da = xr.DataArray(\n",
+ " data=ds1.a.data,\n",
+ " coords={\n",
+ " \"y\": np.linspace(47.5, 47.8, 4),\n",
+ " \"x\": np.linspace(-122.9, -122.7, 2),\n",
+ " },\n",
+ ")\n",
+ "\n",
+ "# Add Geospatial Coordinate Reference https://epsg.io/4326\n",
+ "# this is stored as a 'spatial_ref' coordinate\n",
+ "da.rio.write_crs(\"epsg:4326\", inplace=True)\n",
+ "da"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "17",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "da.rio.to_raster(datadir / \"ds1_a.tiff\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "18",
+ "metadata": {},
+ "source": [
+ "NOTE: you can now load this file into GIS tools like [QGIS](https://www.qgis.org)! Or open back into Xarray:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "19",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "DA = xr.open_dataarray(datadir / \"ds1_a.tiff\", engine=\"rasterio\")\n",
+ "DA.rio.crs"
+ ]
+ }
+ ],
+ "metadata": {
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/fundamentals/01_data_structures.md b/fundamentals/01_data_structures.md
new file mode 100644
index 00000000..389f1f4d
--- /dev/null
+++ b/fundamentals/01_data_structures.md
@@ -0,0 +1,69 @@
+# Data Structures
+
+Multi-dimensional (a.k.a. N-dimensional, ND) arrays (sometimes called “tensors”)
+are an essential part of computational science. They are encountered in a wide
+range of fields, including physics, astronomy, geoscience, bioinformatics,
+engineering, finance, and deep learning. In Python, [NumPy](https://numpy.org/)
+provides the fundamental data structure and API for working with raw ND arrays.
+However, real-world datasets are usually more than just raw numbers; they have
+labels which encode information about how the array values map to locations in
+space, time, etc.
+
+The N-dimensional nature of Xarray’s data structures makes it suitable for
+dealing with multi-dimensional scientific data, and its use of dimension names
+instead of axis labels (`dim='time'` instead of `axis=0`) makes such arrays much
+more manageable than the raw NumPy ndarray: with Xarray, you don’t need to keep
+track of the order of an array’s dimensions or insert dummy dimensions of size 1
+to align arrays (e.g., using np.newaxis).
+
+The immediate payoff of using Xarray is that you’ll write less code. The
+long-term payoff is that you’ll understand what you were thinking when you come
+back to look at it weeks or months later.
+
+## Example: Weather forecast
+
+Here is an example of how we might structure a dataset for a weather forecast:
+
+
+
+You'll notice multiple data variables (temperature, precipitation), coordinate
+variables (latitude, longitude), and dimensions (x, y, t). We'll cover how these
+fit into Xarray's data structures below.
+
+Xarray doesn’t just keep track of labels on arrays – it uses them to provide a
+powerful and concise interface. For example:
+
+- Apply operations over dimensions by name: `x.sum('time')`.
+
+- Select values by label (or logical location) instead of integer location:
+ `x.loc['2014-01-01']` or `x.sel(time='2014-01-01')`.
+
+- Mathematical operations (e.g., `x - y`) vectorize across multiple dimensions
+ (array broadcasting) based on dimension names, not shape.
+
+- Easily use the split-apply-combine paradigm with groupby:
+ `x.groupby('time.dayofyear').mean()`.
+
+- Database-like alignment based on coordinate labels that smoothly handles
+ missing values: `x, y = xr.align(x, y, join='outer')`.
+
+- Keep track of arbitrary metadata in the form of a Python dictionary:
+ `x.attrs`.
+
+## Example: Mosquito genetics
+
+Although the Xarray library was originally developed with Earth Science datasets in mind, the datastructures work well across many other domains! For example, below is a side-by-side view of a data schematic on the left and Xarray Dataset representation on the right taken from a mosquito genetics analysis:
+
+
+
+The data can be stored as a 3-dimensional array, where one dimension of the array corresponds to positions (**variants**) within a reference genome, another dimension corresponds to the individual mosquitoes that were sequenced (**samples**), and a third dimension corresponds to the number of genomes within each individual (**ploidy**)."
+
+You can explore this dataset in detail via the [training course in data analysis for genomic surveillance of African malaria vectors](https://anopheles-genomic-surveillance.github.io/workshop-5/module-1-xarray.html)!
+
+## Explore on your own
+
+The following collection of notebooks provide interactive code examples for working with example datasets and constructing Xarray data structures manually.
+
+```{tableofcontents}
+
+```
diff --git a/fundamentals/01_datastructures.ipynb b/fundamentals/01_datastructures.ipynb
new file mode 100644
index 00000000..f9add737
--- /dev/null
+++ b/fundamentals/01_datastructures.ipynb
@@ -0,0 +1,426 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Xarray's Data structures\n",
+ "\n",
+ "In this lesson, we cover the basics of Xarray data structures. By the end of the lesson, we will be able to:\n",
+ "\n",
+ ":::{admonition} Learning Goals\n",
+ "- Understand the basic Xarray data structures `DataArray` and `Dataset` \n",
+ "- Customize the display of Xarray data structures\n",
+ "- The connection between Pandas and Xarray data structures\n",
+ ":::"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Data structures\n",
+ "\n",
+ "Xarray provides two data structures: the `DataArray` and `Dataset`. The\n",
+ "`DataArray` class attaches dimension names, coordinates and attributes to\n",
+ "multi-dimensional arrays while `Dataset` combines multiple DataArrays.\n",
+ "\n",
+ "Both classes are most commonly created by reading data.\n",
+ "To learn how to create a DataArray or Dataset manually, see the [Creating Data Structures](01.1_creating_data_structures.ipynb) tutorial."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import xarray as xr\n",
+ "\n",
+ "# When working in a Jupyter Notebook you might want to customize Xarray display settings to your liking\n",
+ "# The following settings reduce the amount of data displayed out by default\n",
+ "xr.set_options(display_expand_attrs=False, display_expand_data=False)\n",
+ "np.set_printoptions(threshold=10, edgeitems=2)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Dataset\n",
+ "\n",
+ "`Dataset` objects are dictionary-like containers of DataArrays, mapping a variable name to each DataArray.\n",
+ "\n",
+ "Xarray has a few small real-world tutorial datasets hosted in this GitHub repository https://github.com/pydata/xarray-data.\n",
+ "We'll use the [xarray.tutorial.load_dataset](https://docs.xarray.dev/en/stable/generated/xarray.tutorial.open_dataset.html#xarray.tutorial.open_dataset) convenience function to download and open the `air_temperature` (National Centers for Environmental Prediction) Dataset by name."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds = xr.tutorial.load_dataset(\"air_temperature\")\n",
+ "ds"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can access \"layers\" of the Dataset (individual DataArrays) with dictionary syntax"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds[\"air\"]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can save some typing by using the \"attribute\" or \"dot\" notation. This won't work for variable names that clash with built-in\n",
+ "method names (for example, `mean`)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds.air"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### HTML vs text representations\n",
+ "\n",
+ "Xarray has two representation types: `\"html\"` (which is only available in\n",
+ "notebooks) and `\"text\"`. To choose between them, use the `display_style` option.\n",
+ "\n",
+ "So far, our notebook has automatically displayed the `\"html\"` representation (which we will continue using).\n",
+ "The `\"html\"` representation is interactive, allowing you to collapse sections (▶) and\n",
+ "view attributes and values for each value (📄 and ≡)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "with xr.set_options(display_style=\"html\"):\n",
+ " display(ds)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "☝️ From top to bottom the output consists of:\n",
+ "\n",
+ "- **Dimensions**: summary of all *dimensions* of the `Dataset` `(lat: 25, time: 2920, lon: 53)`: this tells us that the first dimension is named `lat` and has a size of `25`, the second dimension is named `time` and has a size of `2920`, and the third dimension is named `lon` and has a size of `53`. Because we will access the dimensions by name, the order doesn't matter.\n",
+ "- **Coordinates**: an unordered list of *coordinates* or dimensions with coordinates with one item per line. Each item has a name, one or more dimensions in parentheses, a dtype and a preview of the values. Also, if it is a dimension coordinate, it will be printed in **bold** font. *dimensions without coordinates* appear in plain font (there are none in this example, but you might imagine a 'mask' coordinate that has a value assigned at every point).\n",
+ "- **Data variables**: names of each nD *measurement* in the dataset, followed by its dimensions `(time, lat, lon)`, dtype, and a preview of values.\n",
+ "- **Indexes**: Each dimension with coordinates is backed by an \"Index\". In this example, each dimension is backed by a `PandasIndex`\n",
+ "- **Attributes**: an unordered list of metadata (for example, a paragraph describing the dataset)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Compare that with the string representation, which is very similar except the dimensions are given a `*` prefix instead of bold and you cannot collapse or expand the outputs."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "with xr.set_options(display_style=\"text\"):\n",
+ " display(ds)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To understand each of the components better, we'll explore the \"air\" variable of our Dataset."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### DataArray\n",
+ "\n",
+ "The `DataArray` class consists of an array (data) and its associated dimension names, labels, and attributes (metadata).\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "da = ds[\"air\"]\n",
+ "da"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### String representations\n",
+ "\n",
+ "We can use the same two representations (`\"html\"`, which is only available in\n",
+ "notebooks, and `\"text\"`) to display our `DataArray`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "with xr.set_options(display_style=\"html\"):\n",
+ " display(da)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "with xr.set_options(display_style=\"text\"):\n",
+ " display(da)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In the string representation of a `DataArray` (versus a `Dataset`), we also see:\n",
+ "- the `DataArray` name ('air')\n",
+ "- a preview of the array data (collapsible in the `\"html\"` representation)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can also access the data array directly:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds.air.data # (or equivalently, `da.data`)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Named dimensions \n",
+ "\n",
+ "`.dims` are the named axes of your data. They may (dimension coordinates) or may not (dimensions without coordinates) have associated values. Names can be anything that fits into a Python `set` (i.e. calling `hash()` on it doesn't raise an error), but to be\n",
+ "useful they should be strings.\n",
+ "\n",
+ "In this case we have 2 spatial dimensions (`latitude` and `longitude` are stored with shorthand names `lat` and `lon`) and one temporal dimension (`time`)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds.air.dims"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Coordinates\n",
+ "\n",
+ "\n",
+ "`.coords` is a simple [dict-like](https://docs.python.org/3/glossary.html#term-mapping) [data container](https://docs.xarray.dev/en/stable/user-guide/data-structures.html#coordinates)\n",
+ "for mapping coordinate names to values. These values can be:\n",
+ "- another `DataArray` object\n",
+ "- a tuple of the form `(dims, data, attrs)` where `attrs` is optional. This is\n",
+ " roughly equivalent to creating a new `DataArray` object with\n",
+ " `DataArray(dims=dims, data=data, attrs=attrs)`\n",
+ "- a 1-dimensional `numpy` array (or anything that can be coerced to one using [`numpy.array`](https://numpy.org/doc/stable/reference/generated/numpy.array.html), such as a `list`) containing numbers, datetime objects, strings, etc. to label each point.\n",
+ "\n",
+ "Here we see the actual timestamps and spatial positions of our air temperature data:\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds.air.coords"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The difference between the dimension labels (dimension coordinates) and normal\n",
+ "coordinates is that for now it only is possible to use indexing operations\n",
+ "(`sel`, `reindex`, etc.) with dimension coordinates. Also, while coordinates can\n",
+ "have arbitrary dimensions, dimension coordinates have to be one-dimensional."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Attributes \n",
+ "\n",
+ "`.attrs` is a dictionary that can contain arbitrary Python objects (strings, lists, integers, dictionaries, etc.) containing information about your data. Your only\n",
+ "limitation is that some attributes may not be writeable to certain file formats."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds.air.attrs"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## To Pandas and back\n",
+ "\n",
+ "`DataArray` and `Dataset` objects are frequently created by converting from\n",
+ "other libraries such as [pandas](https://pandas.pydata.org/) or by reading from\n",
+ "data storage formats such as\n",
+ "[NetCDF](https://www.unidata.ucar.edu/software/netcdf/) or\n",
+ "[zarr](https://zarr.readthedocs.io/en/stable/).\n",
+ "\n",
+ "To convert from / to `pandas`, we can use the\n",
+ "[to_xarray](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.to_xarray.html)\n",
+ "methods on [pandas](https://zarr.readthedocs.io/en/stable/) objects or the\n",
+ "[to_pandas](https://docs.xarray.dev/en/stable/generated/xarray.DataArray.to_pandas.html)\n",
+ "methods on `xarray` objects:\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "series = pd.Series(np.ones((10,)), index=list(\"abcdefghij\"))\n",
+ "series"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "arr = series.to_xarray()\n",
+ "arr"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "arr.to_pandas()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can also control what `pandas` object is used by calling `to_series` /\n",
+ "`to_dataframe`:\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### to_series\n",
+ "This will always convert `DataArray` objects to `pandas.Series`, using a `MultiIndex` for higher dimensions\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds.air.to_series()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### to_dataframe\n",
+ "\n",
+ "This will always convert `DataArray` or `Dataset` objects to a `pandas.DataFrame`. Note that `DataArray` objects have to be named for this. Since columns in a `DataFrame` need to have the same index, they are\n",
+ "broadcasted."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds.air.to_dataframe()"
+ ]
+ }
+ ],
+ "metadata": {
+ "interpreter": {
+ "hash": "e1fd91e1433c68a24bbc2950cbc84f731f846faaebb12a808b0de0faaa910846"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/fundamentals/01_datatree_hierarchical_data.ipynb b/fundamentals/01_datatree_hierarchical_data.ipynb
new file mode 100644
index 00000000..4c4d4142
--- /dev/null
+++ b/fundamentals/01_datatree_hierarchical_data.ipynb
@@ -0,0 +1,241 @@
+{
+ "cells": [
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# `xarray.DataTree` and hierarchical data\n",
+ "\n",
+ "In this lesson, we will learn how to use `xarray.DataTree` with hierarchical data. By the end of the lesson, we will be able to:\n",
+ "\n",
+ ":::{admonition} Learning Goals\n",
+ "- Understand a basic `DataTree` structure (nodes, parents and children)\n",
+ "- Selecting `DataTree`\n",
+ "- Understand coordinate inheritance :::\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "## What is a DataTree\n",
+ "\n",
+ "### Example DataTree 1\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "### Example DataTree 2\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import xarray as xr"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Opening a dataset with `open_datatree()`\n",
+ "\n",
+ "Let's open up a precipitation dataset. This dataset was derived from \"GPM_3IMERGHH_07\" and \"M2T1NXFLX_5.12.4\" products."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "precipitation = xr.tutorial.open_datatree(\"precipitation.nc4\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Nodes\n",
+ "Groups in a netcdf4 or hdf5 file in the DataTree model are represented as \"nodes\" in the DataTree model.\n",
+ "We can list all of the groups with `.groups`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "precipitation.groups"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Accessing variables in a nested groups\n",
+ "Nested variables and groups can be accessed with either dict-like syntax or method based syntax."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "precipitation[\"observed\"]\n",
+ "\n",
+ "# Returns a DataTree object, containing the variables, dimensions, and coordinates in the \"observed\" node"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "precipitation[\"/observed/precipitation\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "precipitation.reanalysis.precipitation\n",
+ "\n",
+ "# Method based syntax"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Get the parent and child nodes from a group"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "precipitation[\"reanalysis\"].parent"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "precipitation.children"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Inheritance\n",
+ "DataTree implements a simple inheritance mechanism. Coordinates, dimensions and their associated indices are propagated downward from the root node to all descendent nodes.\n",
+ "\n",
+ "Let's take a look at some inherited coordinates with our precipitation dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "precipitation.time"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The `\"time\"` dimension is defined at the root node of our dataset and is propagated downward to the \"observed\" and \"reanalysis\" group"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "precipitation.observed"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "precipitation.reanalysis"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Review\n",
+ "\n",
+ "\n",
+ "### Example DataTree 1\n",
+ "\n",
+ "\n",
+ "\n",
+ "### Example DataTree 2\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Exercises\n",
+ "\n",
+ "\n",
+ "1. Make a plot of plot of the Oberserved and Predicted precipitation\n",
+ "2. Think through if there are datasets from your field"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/fundamentals/02.1_indexing_Basic.ipynb b/fundamentals/02.1_indexing_Basic.ipynb
new file mode 100644
index 00000000..217c21ab
--- /dev/null
+++ b/fundamentals/02.1_indexing_Basic.ipynb
@@ -0,0 +1,787 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Indexing and Selecting Data\n",
+ "\n",
+ "## Learning Objectives\n",
+ "\n",
+ "- Understanding the difference between position and label-based indexing\n",
+ "- Select data by position using `.isel` with values or slices\n",
+ "- Select data by label using `.sel` with values or slices\n",
+ "- Use nearest-neighbor lookups with `.sel`\n",
+ "- Select timeseries data by date/time with values or slices\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Introduction\n",
+ "\n",
+ "Xarray offers extremely flexible indexing routines that combine the best features of NumPy and Pandas for data selection.\n",
+ "\n",
+ "The most basic way to access elements of a `DataArray` object is to use Python’s `[]` syntax, such as `array[i, j]`, where `i` and `j` are both integers.\n",
+ "\n",
+ "As xarray objects can store coordinates corresponding to each dimension of an array, label-based indexing is also possible (e.g. `.sel(latitude=0)`, similar to `pandas.DataFrame.loc`). In label-based indexing, the element position `i` is automatically looked-up from the coordinate values.\n",
+ "\n",
+ "By leveraging the labeled dimensions and coordinates provided by Xarray, users can effortlessly access, subset, and manipulate data along multiple axes, enabling complex operations such as slicing, masking, and aggregating data based on specific criteria. \n",
+ "\n",
+ "This indexing and selection capability of Xarray not only enhances data exploration and analysis workflows but also promotes reproducibility and efficiency by providing a convenient interface for working with multi-dimensional data structures.\n",
+ "\n",
+ "## Quick Overview \n",
+ "\n",
+ "In total, xarray supports four different kinds of indexing, as described below and summarized in this table:\n",
+ "\n",
+ "| Dimension lookup | Index lookup | `DataArray` syntax | `Dataset` syntax |\n",
+ "| ---------------- | ------------ | ---------------------| ---------------------|\n",
+ "| Positional | By integer | `da[:,0]` | *not available* |\n",
+ "| Positional | By label | `da.loc[:,'IA']` | *not available* |\n",
+ "| By name | By integer | `da.isel(space=0)` or `da[dict(space=0)]` | `ds.isel(space=0)` or `ds[dict(space=0)]` |\n",
+ "| By name | By label | `da.sel(space='IA')` or `da.loc[dict(space='IA')]` | `ds.sel(space='IA')` or `ds.loc[dict(space='IA')]` |\n",
+ "\n",
+ "\n",
+ "----------\n",
+ "\n",
+ "In this tutorial, first we cover the positional indexing and label-based indexing, next we will cover more advanced techniques such as nearest neighbor lookups. \n",
+ "\n",
+ "First, let's import packages: "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "import xarray as xr\n",
+ "\n",
+ "xr.set_options(display_expand_attrs=False, display_expand_data=False);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Here we’ll use air temperature tutorial dataset from the [National Center for Environmental Prediction](https://www.weather.gov/ncep/). "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "ds = xr.tutorial.load_dataset(\"air_temperature\")\n",
+ "ds"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "da = ds[\"air\"]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Position-based Indexing\n",
+ "\n",
+ "Indexing a `DataArray` directly works (mostly) just like it does for numpy `ndarrays`, except that the returned object is always another `DataArray`:\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### NumPy Positional Indexing\n",
+ "\n",
+ "When working with numpy, indexing is done by position (slices/ranges/scalars).\n",
+ "\n",
+ "For example:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "np_array = ds[\"air\"].data # numpy array\n",
+ "np_array.shape"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Indexing is 0-based in NumPy:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "np_array[1, 0, 0]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Similarly, we can select a range in NumPy:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# extract a time-series for one spatial location\n",
+ "np_array[:, 20, 40]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "### Positional Indexing with Xarray"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Xarray offers extremely flexible indexing routines that combine the best\n",
+ "features of NumPy and pandas for data selection."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "#### NumPy style indexing with Xarray\n",
+ "\n",
+ "NumPy style indexing works exactly the same with Xarray but it also preserves labels and metadata. \n",
+ "\n",
+ "This approach however does not take advantage of the dimension names and coordinate location information that is present in a Xarray object."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "da[:, 20, 40]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Positional Indexing Using Dimension Names"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Remembering the axis order can be challenging even with 2D arrays:\n",
+ "- is `np_array[0,3]` the first row and third column or first column and third row? \n",
+ "- or did I store these samples by row or by column when I saved the data?!. \n",
+ "\n",
+ "The difficulty is compounded with added dimensions. \n",
+ "\n",
+ "Xarray objects eliminate much of the mental overhead by allowing indexing using dimension names instead of axes numbers:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "da.isel(lat=20, lon=40).plot();"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Slicing is also possible similarly:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "da.isel(time=slice(0, 20), lat=20, lon=40).plot();"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "```{note}\n",
+ "Using the `isel` method, the user can choose/slice the specific elements from a Dataset or DataArray.\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "Indexing a DataArray directly works (mostly) just like it does for numpy arrays, except that the returned object is always another DataArray; however,when indexing with multiple arrays, positional indexing in Xarray behaves differently compared to NumPy.\n",
+ "\n",
+ "```{caution}\n",
+ "Positional indexing deviates from the NumPy behavior when indexing with multiple arrays. \n",
+ "```\n",
+ "We can show this with an example: "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "np_array[:, [0, 1], [0, 1]].shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "da[:, [0, 1], [0, 1]].shape"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "Please note how the dimension of the `DataArray()` object is different from the `numpy.ndarray`.\n",
+ "\n",
+ "```{tip}\n",
+ "However, users can still achieve NumPy-like pointwise indexing across multiple labeled dimensions by using Xarray vectorized indexing techniques. We will delve further into this topic in the advanced indexing notebook.\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "So far, we have explored positional indexing, which relies on knowing the exact indices. But, what if you wanted to select data specifically for a particular latitude? It becomes challenging to determine the corresponding indices in such cases. Xarray reduce this complexity by introducing label-based indexing. \n",
+ "\n",
+ "## Label-based Indexing\n",
+ "\n",
+ "To select data by coordinate labels instead of integer indices we can use the same syntax, using `sel` instead of `isel`:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "For example, let's select all data for Lat 25 °N and Lon 210 °E using `sel` :"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "tags": [
+ "hide-output"
+ ]
+ },
+ "outputs": [],
+ "source": [
+ "da.sel(lat=25, lon=210).plot();"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Similarly we can do slicing or filter a range using the `.slice` function: "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# demonstrate slicing\n",
+ "da.sel(lon=slice(210, 215))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# demonstrate slicing\n",
+ "da.sel(lat=slice(50, 25), lon=slice(210, 215))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Dropping using `drop_sel`\n",
+ "\n",
+ "If instead of selecting data we want to drop it, we can use `drop_sel` method with syntax similar to `sel`:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "da.drop_sel(lat=50.0, lon=200.0)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "So far, all the above will require us to specify exact coordinate values, but what if we don't have the exact values? We can use nearest neighbor lookups to address this issue:\n",
+ "\n",
+ "## Nearest Neighbor Lookups\n",
+ "\n",
+ "The label based selection methods `sel()` support `method` and `tolerance` keyword argument. The `method` parameter allows for enabling nearest neighbor (inexact) lookups by use of the methods `pad`, `backfill` or `nearest`:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "da.sel(lat=52.25, lon=251.8998, method=\"nearest\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "`tolerance` argument limits the maximum distance for valid matches with an inexact lookup:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "da.sel(lat=52.25, lon=251.8998, method=\"nearest\", tolerance=2)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "```{tip}\n",
+ "All of these indexing methods work on the dataset too!\n",
+ "```\n",
+ "\n",
+ "We can also use these methods to index all variables in a dataset simultaneously, returning a new dataset:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "ds.sel(lat=52.25, lon=251.8998, method=\"nearest\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Datetime Indexing\n",
+ "\n",
+ "\n",
+ "Datetime indexing is a critical feature when working with time series data, which is a common occurrence in many fields, including finance, economics, and environmental sciences. Essentially, datetime indexing allows you to select data points or a series of data points that correspond to certain date or time criteria. This becomes essential for time-series analysis where the date or time information associated with each data point can be as critical as the data point itself.\n",
+ "\n",
+ "Let's see some of the techniques to perform datetime indexing in Xarray:\n",
+ "\n",
+ "### Selecting data based on single datetime\n",
+ "\n",
+ "Let's say we have a Dataset ds and we want to select data at a particular date and time, for instance, '2013-01-01' at 6AM. We can do this by using the `sel` (select) method, like so:\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "ds.sel(time=\"2013-01-01 06:00\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "By default, datetime selection will return a range of values that match the provided string. For e.g. `time=\"2013-01-01\"` will return all timestamps for that day (4 of them here):"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "ds.sel(time=\"2013-01-01\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "We can use this feature to select all points in a year:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "ds.sel(time=\"2014\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "or a month:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "ds.sel(time=\"2014-May\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "### Selecting data for a range of dates\n",
+ "\n",
+ "Now, let's say we want to select data between a certain range of dates. We can still use the `sel` method, but this time we will combine it with slice:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# This will return a subset of the dataset corresponding to the entire year of 2013.\n",
+ "ds.sel(time=slice(\"2013-01-01\", \"2013-12-31\"))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "```{note}\n",
+ "\n",
+ "The slice function takes two arguments, start and stop, to make a slice that includes these endpoints. When we use `slice` with the `sel` method, it provides an efficient way to select a range of dates. The above example shows the usage of slice for datetime indexing.\n",
+ "\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Indexing with a DatetimeIndex or date string list\n",
+ "\n",
+ "Another technique is to use a list of datetime objects or date strings for indexing. For example, you could select data for specific, non-contiguous dates like this:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "dates = [\"2013-07-09\", \"2013-10-11\", \"2013-12-24\"]\n",
+ "ds.sel(time=dates)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Fancy indexing based on year, month, day, or other datetime components\n",
+ "\n",
+ "In addition to the basic datetime indexing techniques, Xarray also supports \"fancy\" indexing options, which can provide more flexibility and efficiency in your data analysis tasks. You can directly access datetime components such as year, month, day, hour, etc. using the `.dt` accessor. Here is an example of selecting all data points from July across all years:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "ds.sel(time=ds.time.dt.month == 7)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "Or, if you wanted to select data from a specific day of each month, you could use:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "ds.sel(time=ds.time.dt.day == 15)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Exercises\n",
+ "\n",
+ "Practice the syntax you’ve learned so far:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "::::{admonition} Exercise\n",
+ ":class: tip\n",
+ "\n",
+ "Select the first 30 entries of `latitude` and 30th to 40th entries of `longitude`:\n",
+ "\n",
+ ":::{admonition} Solution\n",
+ ":class: dropdown\n",
+ "```python\n",
+ "ds.isel(lat=slice(None, 30), lon=slice(30, 40))\n",
+ "```\n",
+ "\n",
+ ":::\n",
+ "::::"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "::::{admonition} Exercise\n",
+ ":class: tip\n",
+ "\n",
+ "Select all data at 75 degree north and between Jan 1, 2013 and Oct 15, 2013\n",
+ "\n",
+ ":::{admonition} Solution\n",
+ ":class: dropdown\n",
+ "```python\n",
+ "ds.sel(lat=75, time=slice(\"2013-01-01\", \"2013-10-15\"))\n",
+ "```\n",
+ ":::\n",
+ "::::"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "::::{admonition} Exercise\n",
+ ":class: tip\n",
+ "\n",
+ "Remove all entries at 260 and 270 degrees\n",
+ "\n",
+ ":::{admonition} Solution\n",
+ ":class: dropdown\n",
+ "```python\n",
+ "ds.drop_sel(lon=[260, 270])\n",
+ "```\n",
+ ":::\n",
+ "::::"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Summary\n",
+ "\n",
+ "In total, Xarray supports four different kinds of indexing, as described below and summarized in this table:\n",
+ "\n",
+ "\n",
+ "| Dimension lookup | Index lookup | `DataArray` syntax | `Dataset` syntax |\n",
+ "| ---------------- | ------------ | ---------------------| ---------------------|\n",
+ "| Positional | By integer | `da[:,0]` | *not available* |\n",
+ "| Positional | By label | `da.loc[:,'IA']` | *not available* |\n",
+ "| By name | By integer | `da.isel(space=0)` or `da[dict(space=0)]` | `ds.isel(space=0)` or `ds[dict(space=0)]` |\n",
+ "| By name | By label | `da.sel(space='IA')` or `da.loc[dict(space='IA')]` | `ds.sel(space='IA')` or `ds.loc[dict(space='IA')]` |\n",
+ "\n",
+ "\n",
+ "For enhanced indexing capabilities across all methods, you can utilize DataArray objects as an indexer. For more detailed information, please see the Advanced Indexing notebook.\n",
+ "\n",
+ "\n",
+ "## More Resources\n",
+ "\n",
+ "- [Xarray Docs - Indexing and Selecting Data](https://docs.xarray.dev/en/stable/indexing.html)"
+ ]
+ }
+ ],
+ "metadata": {
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3"
+ },
+ "toc": {
+ "base_numbering": 1,
+ "nav_menu": {},
+ "number_sections": true,
+ "sideBar": true,
+ "skip_h1_title": false,
+ "title_cell": "Table of Contents",
+ "title_sidebar": "Contents",
+ "toc_cell": true,
+ "toc_position": {},
+ "toc_section_display": true,
+ "toc_window_display": true
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/fundamentals/02.2_manipulating_dimensions.ipynb b/fundamentals/02.2_manipulating_dimensions.ipynb
new file mode 100644
index 00000000..f158badd
--- /dev/null
+++ b/fundamentals/02.2_manipulating_dimensions.ipynb
@@ -0,0 +1,117 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Manipulating Dimensions (Data Resolution)\n",
+ "\n",
+ "Sometimes we need to change the resolution of our data. We might need to look at inferred values between dimension (grid) spaces\n",
+ "or change the dimension spacing completely (for instance to add another variable). Learning goals:\n",
+ "\n",
+ "- Interpolate data to new coordinates"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import xarray as xr\n",
+ "\n",
+ "np.random.seed(0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "arr = xr.DataArray(\n",
+ " np.random.randn(4, 6),\n",
+ " dims=(\"x\", \"y\"),\n",
+ " coords={\n",
+ " \"x\": [-3.2, 2.1, 5.3, 6.5],\n",
+ " \"y\": pd.date_range(\"2009-01-05\", periods=6, freq=\"ME\"),\n",
+ " },\n",
+ ")\n",
+ "arr"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Interpolation\n",
+ "\n",
+ "If we want to look at values between the current grid cells (interpolation), we\n",
+ "can do that with `interp` (requires `scipy`):\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "tags": [
+ "hide-output"
+ ]
+ },
+ "outputs": [],
+ "source": [
+ "arr.interp(\n",
+ " x=np.linspace(2, 6, 10),\n",
+ " y=pd.date_range(\"2009-04-01\", \"2009-04-30\", freq=\"D\"),\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "when trying to extrapolate, the resulting values will be `nan`.\n",
+ "\n",
+ "If we already have a object with the desired coordinates, we can use\n",
+ "`interp_like`:\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "tags": [
+ "hide-output"
+ ]
+ },
+ "outputs": [],
+ "source": [
+ "other = xr.DataArray(\n",
+ " dims=(\"x\", \"y\"),\n",
+ " coords={\n",
+ " \"x\": np.linspace(2, 4, 10),\n",
+ " \"y\": pd.date_range(\"2009-04-01\", \"2009-04-30\", freq=\"D\"),\n",
+ " },\n",
+ ")\n",
+ "arr.interp_like(other)"
+ ]
+ }
+ ],
+ "metadata": {
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/fundamentals/02.3_aligning_data_objects.ipynb b/fundamentals/02.3_aligning_data_objects.ipynb
new file mode 100644
index 00000000..490fbc88
--- /dev/null
+++ b/fundamentals/02.3_aligning_data_objects.ipynb
@@ -0,0 +1,570 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Computing with Multiple Objects\n",
+ "\n",
+ "Learning goals:\n",
+ "\n",
+ "- Perform operations across multiple datasets\n",
+ "- Understand two important concepts: broadcasting and alignment."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import xarray as xr\n",
+ "\n",
+ "%config InlineBackend.figure_format='retina'\n",
+ "\n",
+ "plt.style.use(\"bmh\")\n",
+ "\n",
+ "np.random.seed(0)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Here is a motivating calculation where we subtract two DataArrays with data available at different locations in the (space, time) plane."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "arr1 = xr.DataArray(\n",
+ " np.arange(12).reshape(3, 4),\n",
+ " dims=(\"space\", \"time\"),\n",
+ " coords={\"space\": [\"a\", \"b\", \"c\"], \"time\": [0, 1, 2, 3]},\n",
+ ")\n",
+ "arr1"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "arr2 = xr.DataArray(\n",
+ " [0, 1],\n",
+ " dims=\"space\",\n",
+ " coords={\"space\": [\"b\", \"d\"]},\n",
+ ")\n",
+ "arr2"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Note that `arr1` is 2D; while `arr2` is 1D along `space` and has values at two locations only.\n",
+ "\n",
+ "Now subtract the two."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "arr1 - arr2"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To understand this output, we must understand two fundamental concepts underlying computation with Xarray objects\n",
+ "\n",
+ "1. Broadcasting: The objects need to have compatible shapes.\n",
+ "2. Alignment: The objects need to have values at the same coordinate labels\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Broadcasting: adjusting arrays to the same shape\n",
+ "\n",
+ "**Broadcasting** allows an operator or a function to act on two or more arrays\n",
+ "to operate even if these arrays do not have the same shape. That said, not all\n",
+ "the dimensions can be subjected to broadcasting; they must meet certain rules.\n",
+ "The image below illustrates how an operation on arrays with\n",
+ "different coordinates will result in automatic broadcasting\n",
+ "\n",
+ "\n",
+ "\n",
+ "Credit: Stephan Hoyer --\n",
+ "[xarray ECMWF Python workshop](https://docs.google.com/presentation/d/16CMY3g_OYr6fQplUZIDqVtG-SKZqsG8Ckwoj2oOqepU/)\n",
+ "\n",
+ "Numpy's broadcasting rules, based on array shape, can sometimes be\n",
+ "difficult to understand and remember. Xarray does broadcasting by dimension name,\n",
+ "rather than array shape. This is a huge convenience.\n",
+ "\n",
+ "Here are two 1D arrays"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "array1 = xr.DataArray(\n",
+ " np.arange(3),\n",
+ " dims=\"space\",\n",
+ " coords={\"space\": [\"a\", \"b\", \"c\"]},\n",
+ " name=\"array1\",\n",
+ ")\n",
+ "array2 = xr.DataArray(\n",
+ " np.arange(4),\n",
+ " dims=\"time\",\n",
+ " coords={\"time\": [0, 1, 2, 3]},\n",
+ " name=\"array2\",\n",
+ ")\n",
+ "display(array1)\n",
+ "display(array2)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's subtract the two:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "array1 - array2"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We see that the result is a 2D array. \n",
+ "\n",
+ "When subtracting, Xarray first realizes that `array1` is missing the dimension `time` and `array2` is missing the dimension `space`. Xarray then broadcasts or \"expands\" both arrays to 2D with dimensions `space`, `time`. Here is an illustration:\n",
+ "\n",
+ "\n",
+ "\n",
+ "While this detail is hidden, we can explicitly broadcast any number of arrays against each other using [xr.broadcast](https://docs.xarray.dev/en/stable/generated/xarray.broadcast.html)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "array1_broadcasted, array2_broadcasted = xr.broadcast(array1, array2)\n",
+ "display(array1_broadcasted.dims)\n",
+ "display(array2_broadcasted.dims)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To get the final anomaly, Xarray calculates"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# identical to array1 - array2\n",
+ "array1_broadcasted - array2_broadcasted"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Broadcasting in numpy\n",
+ "\n",
+ "For contrast let us examine the pure numpy version of this calculation. We use [.data](https://docs.xarray.dev/en/stable/generated/xarray.DataArray.data.html) to extract the underlying numpy array object.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "tags": [
+ "raises-exception"
+ ]
+ },
+ "outputs": [],
+ "source": [
+ "array1.data - array2.data"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To get this calculation to work, we need to insert new axes manually using [np.newaxis](https://numpy.org/doc/stable/reference/constants.html?highlight=newaxis)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "array1.data[:, np.newaxis] - array2.data[np.newaxis, :]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "Because xarray knows about dimension names we avoid having to create unnecessary\n",
+ "size-1 dimensions using `np.newaxis` or `.reshape`. This is yet another example where the _metadata_ (dimension names) reduces the mental overhead associated with coding a calculation\n",
+ "\n",
+ "For more, see the [Xarray documentation](https://docs.xarray.dev/en/stable/user-guide/computation.html#broadcasting-by-dimension-name) and the [numpy documentation](https://numpy.org/doc/stable/user/basics.broadcasting.html) on broadcasting.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "::::{admonition} Exercise\n",
+ ":class: tip\n",
+ "\n",
+ "Consider the following 2D array. What are the dimensions of `array - array.mean(\"time\")`?\n",
+ "```python\n",
+ "array = xr.DataArray(\n",
+ " np.arange(12).reshape(3, 4),\n",
+ " dims=(\"space\", \"time\"),\n",
+ " coords={\"space\": [\"a\", \"b\", \"c\"], \"time\": [0, 1, 2, 3]},\n",
+ " name=\"array\",\n",
+ ")\n",
+ "```\n",
+ "\n",
+ "\n",
+ ":::{admonition} Solution\n",
+ ":class: dropdown\n",
+ "\n",
+ "```python\n",
+ "(array - array.mean(\"time\")).dims\n",
+ "```\n",
+ ":::\n",
+ "::::"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "## Alignment: putting data on the same grid\n",
+ "\n",
+ "\n",
+ "When combining two input arrays using an arithmetic operation, both arrays must first be converted to the same coordinate system. This is \"alignment\".\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "Here are two 2D DataArrays with different shapes."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "arr1 = xr.DataArray(\n",
+ " np.arange(12).reshape(3, 4),\n",
+ " dims=(\"space\", \"time\"),\n",
+ " coords={\"space\": [\"a\", \"b\", \"c\"], \"time\": [0, 1, 2, 3]},\n",
+ ")\n",
+ "arr1"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "arr2 = xr.DataArray(\n",
+ " np.arange(14).reshape(2, 7),\n",
+ " dims=(\"space\", \"time\"),\n",
+ " coords={\"space\": [\"b\", \"d\"], \"time\": [-2, -1, 0, 1, 2, 3, 4]},\n",
+ ")\n",
+ "arr2"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "`arr1` and `arr2` have the same dimensions (space, time) but have values at different locations in the (space, time) plane with some locations in common.\n",
+ "\n",
+ ":::{note}\n",
+ "xarray assumes coordinate labels are in the same coordinate system such that space='b' in arr1 is the same as space='b' in arr2. For more sophisticated handling of coordinate systems see [rioxarray](https://corteva.github.io/rioxarray/stable/)\n",
+ ":::"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "tags": [
+ "hide-input"
+ ]
+ },
+ "outputs": [],
+ "source": [
+ "def visualize_mesh(array, *args, ax=None, **kwargs):\n",
+ " \"\"\"Visualizes array's mesh ((points at which values are present).\"\"\"\n",
+ "\n",
+ " # Use broadcast to generate 2D x_, y_ arrays from the 1D x,y arrays\n",
+ " space_, time_ = xr.broadcast(array.space, array.time)\n",
+ " if ax is None:\n",
+ " ax = plt.gca()\n",
+ " kwargs.setdefault(\"fillstyle\", \"none\")\n",
+ " kwargs.setdefault(\"markersize\", 10)\n",
+ " ax.plot(space_.data.ravel(), time_.data.ravel(), *args, **kwargs)\n",
+ " ax.set_xlabel(\"space\")\n",
+ " ax.set_ylabel(\"time\")\n",
+ "\n",
+ "\n",
+ "visualize_mesh(arr1, \"<\")\n",
+ "visualize_mesh(arr2, \">\")\n",
+ "plt.ylim([-3, 6])\n",
+ "plt.legend([\"arr1\", \"arr2\"]);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We see that both arrays only have values in common at `x=\"b\"` and `y=[0, 1, 2, 3]`. Before applying an arithmetic operation we must first modify each DataArray so that they have values at the *same* points. This is \"alignment\".\n",
+ "\n",
+ "### Controlling alignment\n",
+ "\n",
+ "We can explicitly align objects using [xr.align](https://docs.xarray.dev/en/stable/generated/xarray.align.html). The key decision to make is how to decide which points must be kept. The other way to think of alignment is that objects must be converted to a common grid prior to any operation combining multiiple objects. This decision is controlled by the `\"join\"` keyword argument. Xarray provides 5 ways to convert the coordinate labels of multiple Datasets to a common grid. This [terminology](https://en.wikipedia.org/wiki/Join_(SQL)) originates in the database community.\n",
+ "\n",
+ "1. `join=\"inner\"` or reindex to the \"intersection set\" of coordinate labels\n",
+ "2. `join=\"outer\"` or reindex to the \"union set\" of coordinate labels\n",
+ "3. `join=\"left\"` or reindex to the coordinate labels of the leftmost object\n",
+ "4. `join=\"right\"` or reindex to the coordinate labels of the rightmost object\n",
+ "5. `join=\"exact\"` checks for exact equality of coordinate labels before the operation.\n",
+ "\n",
+ "First lets try an inner join. This is the default for arithmetic operations in Xarray. We see that the result has values for locations that `arr1` and `arr2` have in common: `x=\"b\"` and `y=[0, 1, 2, 3]`. Here is an illustration\n",
+ "\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "a1_aligned, a2_aligned = xr.align(arr1, arr2, join=\"inner\")\n",
+ "a1_aligned"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "a2_aligned"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Here's a visual depiction of all the `join` options"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "tags": [
+ "hide-input"
+ ]
+ },
+ "outputs": [],
+ "source": [
+ "def visualize_join(a1, a2, join, ax=None):\n",
+ " a1_aligned, a2_aligned = xr.align(arr1, arr2, join=join)\n",
+ "\n",
+ " visualize_mesh(a1, \"<\", ax=ax)\n",
+ " visualize_mesh(a2, \">\", ax=ax)\n",
+ " visualize_mesh(a1_aligned, \".\", markersize=32, color=\"C3\", ax=ax)\n",
+ "\n",
+ " ax.set_ylim([-3, 6])\n",
+ " ax.set_title(f\"join={join!r}\")\n",
+ "\n",
+ "\n",
+ "f, ax = plt.subplots(1, 4, sharex=True, sharey=True)\n",
+ "for axx, join in zip(ax, [\"inner\", \"outer\", \"left\", \"right\"]):\n",
+ " visualize_join(arr1, arr2, join, ax=axx)\n",
+ "ax[-1].legend([\"arr1\", \"arr2\", \"after align\"], bbox_to_anchor=(1, 1))\n",
+ "f.set_size_inches(10, 4);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "tags": [
+ "hide-input"
+ ]
+ },
+ "source": [
+ "::::{admonition} Exercise\n",
+ ":class: tip\n",
+ "\n",
+ "Consider the following two arrays. Write down the `x` and `y` coordinate locations for `da1 - da2`\n",
+ "```python\n",
+ "da1 = xr.DataArray(\n",
+ " np.arange(12).reshape(3, 4),\n",
+ " dims=(\"space\", \"time\"),\n",
+ " coords={\"space\": [\"a\", \"b\", \"c\"], \"time\": [0, 1, 2, 3]},\n",
+ ")\n",
+ "da2 = xr.DataArray(\n",
+ " [0, 1],\n",
+ " dims=\"space\",\n",
+ " coords={\"space\": [\"b\", \"d\"]},\n",
+ ")\n",
+ "```\n",
+ "\n",
+ ":::{admonition} Solution\n",
+ ":class: dropdown\n",
+ "\n",
+ "`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",
+ ":::\n",
+ "::::"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Further control over alignment\n",
+ "\n",
+ "#### Controlling the fill value\n",
+ "\n",
+ "For all join options other than `\"inner\"` Xarray will insert a `fill_value` at locations not present in the original dataset. By default this is NaN"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "arr1_aligned, _ = xr.align(arr1, arr2, join=\"outer\")\n",
+ "arr1_aligned"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Control the \"fill value\" by specifying the `fill_value` keyword argument"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "arr1_aligned, _ = xr.align(arr1, arr2, join=\"outer\", fill_value=0)\n",
+ "arr1_aligned"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Checking that objects are aligned\n",
+ "\n",
+ "`join=\"exact\"` is special in that it checks to make sure that the objects _are_ aligned.\n",
+ "\n",
+ "For `arr1` and `arr2` this will raise an error since `arr1.x` is not identical to `arr2.x` (and similarly for `y`)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "tags": [
+ "raises-exception"
+ ]
+ },
+ "outputs": [],
+ "source": [
+ "xr.align(arr1, arr2, join=\"exact\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Controlling automatic alignment\n",
+ "\n",
+ "Xarray's default for arithmetic operations is `join=\"inner\"`. This is controllable using the [xr.set_options](https://docs.xarray.dev/en/stable/generated/xarray.set_options.html) context manager"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "with xr.set_options(arithmetic_join=\"outer\"):\n",
+ " result = arr1 - arr2\n",
+ "result"
+ ]
+ }
+ ],
+ "metadata": {
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/fundamentals/02_labeled_data.md b/fundamentals/02_labeled_data.md
new file mode 100644
index 00000000..380cf91c
--- /dev/null
+++ b/fundamentals/02_labeled_data.md
@@ -0,0 +1,5 @@
+# Labeled data
+
+```{tableofcontents}
+
+```
diff --git a/fundamentals/03.1_computation_with_xarray.ipynb b/fundamentals/03.1_computation_with_xarray.ipynb
new file mode 100644
index 00000000..8ef77a3e
--- /dev/null
+++ b/fundamentals/03.1_computation_with_xarray.ipynb
@@ -0,0 +1,299 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "(fundamentals/basic-computation)=\n",
+ "# Basic Computation\n",
+ "\n",
+ "In this lesson, we discuss how to do scientific computations with xarray\n",
+ "objects. Our learning goals are as follows. By the end of the lesson, we will be\n",
+ "able to:\n",
+ "\n",
+ "- Apply basic arithmetic and numpy functions to xarray DataArrays / Dataset.\n",
+ "- Use Xarray's label-aware reduction operations (e.g. `mean`, `sum`) weighted\n",
+ " reductions.\n",
+ "- Apply arbitrary functions to Xarray data via `apply_ufunc`.\n",
+ "- Use Xarray's broadcasting to compute on arrays of different dimensionality."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import xarray as xr\n",
+ "\n",
+ "# Ask Xarray to not show data values by default\n",
+ "xr.set_options(display_expand_data=False)\n",
+ "\n",
+ "%config InlineBackend.figure_format='retina'"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Example Dataset\n",
+ "\n",
+ "First we load a dataset. We will use the\n",
+ "[NOAA Extended Reconstructed Sea Surface Temperature (ERSST) v5](https://www.ncei.noaa.gov/products/extended-reconstructed-sst)\n",
+ "product, a widely used and trusted gridded compilation of of historical data going back to 1854."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds = xr.tutorial.load_dataset(\"ersstv5\")\n",
+ "ds"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's do some basic visualizations of the data, just to make sure it looks\n",
+ "reasonable.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds.sst.isel(time=0).plot(vmin=-2, vmax=30);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Arithmetic\n",
+ "\n",
+ "Xarray dataarrays and datasets work seamlessly with arithmetic operators and\n",
+ "numpy array functions.\n",
+ "\n",
+ "For example, imagine we want to convert the temperature (given in Celsius) to\n",
+ "Kelvin:\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "sst_kelvin = ds.sst + 273.15\n",
+ "sst_kelvin"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The dimensions and coordinates were preserved following the operation.\n",
+ "\n",
+ "\n",
+ " Warning: Although many xarray datasets have a
\n",
+ "\n",
+ "\n",
+ "## Applying functions\n",
+ "\n",
+ "We can apply more complex functions to Xarray objects.\n",
+ "Imagine we wanted to compute the following expression as a function of SST\n",
+ "($\\Theta$) in Kelvin:\n",
+ "\n",
+ "$$ f(\\Theta) = 0.5 \\ln(\\Theta^2) $$\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "f = 0.5 * np.log(sst_kelvin**2)\n",
+ "f"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Applying Arbitrary Functions\n",
+ "\n",
+ "It's awesome that we can call `np.log(ds)` and have it \"just work\". However, not\n",
+ "all third party libraries work this way.\n",
+ "\n",
+ "numpy's [nan_to_num](https://numpy.org/devdocs/reference/generated/numpy.nan_to_num.html) for example will return a numpy array"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "tags": [
+ "output_scroll"
+ ]
+ },
+ "outputs": [],
+ "source": [
+ "np.nan_to_num(ds.sst, nan=0)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "It would be nice to keep our dimensions and coordinates. \n",
+ "\n",
+ "We can accomplish this with [xr.apply_ufunc](https://docs.xarray.dev/en/stable/generated/xarray.apply_ufunc.html#xarray.apply_ufunc)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "xr.apply_ufunc(np.nan_to_num, ds.sst, kwargs={\"nan\": 0})"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "```{tip}\n",
+ "`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",
+ "```\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Reductions\n",
+ "\n",
+ "Reductions are functions that reduce the dimensionlity of our dataset. For example taking the mean sea surface temperature along `time` of our 3D data, we \"reduce\" the `time` dimension and are left with a 2D array.\n",
+ "\n",
+ "Just like in numpy, we can reduce xarray DataArrays along any number of axes."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "sst = ds.sst\n",
+ "sst.mean(axis=0)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "However, rather than performing reductions by specifying `axis` (as in numpy), we can instead perform\n",
+ "them using _dimension names_. This turns out to be a huge convenience, particularly in\n",
+ "complex calculations it can be hard to remember which axis corresponds to \n",
+ "which dimension name:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "sst.mean(dim=\"time\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "You can reduce over multiple dimensions"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "sst.mean([\"lat\", \"time\"])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "If no dimension is specified, the reduction is applied across all dimensions."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "sst.mean()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "All of the standard numpy reductions (e.g. `min`, `max`, `sum`, `std`, etc.) are\n",
+ "available on both [Datasets](https://docs.xarray.dev/en/stable/api.html#aggregation) and [DataArrays](https://docs.xarray.dev/en/stable/api.html#id6).\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "::::{admonition} Exercise\n",
+ ":class: tip\n",
+ "Take the mean of `sst` in both longitude and latitude. Make a simple timeseries plot.\n",
+ "\n",
+ ":::{admonition} Solution\n",
+ ":class: dropdown\n",
+ "\n",
+ "```python\n",
+ "sst.mean([\"lat\", \"lon\"]).plot();\n",
+ "```\n",
+ ":::\n",
+ "::::\n"
+ ]
+ }
+ ],
+ "metadata": {
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/fundamentals/03.2_groupby_with_xarray.ipynb b/fundamentals/03.2_groupby_with_xarray.ipynb
new file mode 100644
index 00000000..5e4cc5cc
--- /dev/null
+++ b/fundamentals/03.2_groupby_with_xarray.ipynb
@@ -0,0 +1,674 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Grouped Computations\n",
+ "\n",
+ "In this lesson, we discuss how to do scientific computations with defined \"groups\" of data\n",
+ "within our xarray objects. Our learning goals are as follows:\n",
+ "\n",
+ "- Perform \"split / apply / combine\" workflows in Xarray using `groupby`,\n",
+ " including\n",
+ " - reductions within groups\n",
+ " - transformations on groups\n",
+ "- Use `resample` to change the time frequency of the data\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import xarray as xr\n",
+ "\n",
+ "# don't expand data by default\n",
+ "xr.set_options(display_expand_data=False, display_expand_attrs=False)\n",
+ "\n",
+ "%config InlineBackend.figure_format='retina'"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Example Dataset\n",
+ "\n",
+ "First we load a dataset. We will use the\n",
+ "[NOAA Extended Reconstructed Sea Surface Temperature (ERSST) v5](https://www.ncei.noaa.gov/products/extended-reconstructed-sst)\n",
+ "product, a widely used and trusted gridded compilation of of historical data\n",
+ "going back to 1854."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds = xr.tutorial.load_dataset(\"ersstv5\")\n",
+ "ds"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Groupby\n",
+ "\n",
+ "Xarray copies Pandas' very useful groupby functionality, enabling the \"split /\n",
+ "apply / combine\" workflow on xarray DataArrays and Datasets.\n",
+ "\n",
+ "Let's examine a timeseries of SST at\n",
+ "a single point.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds.sst.sel(lon=300, lat=50).plot();"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "As we can see from the plot, the timeseries at any one point is totally\n",
+ "dominated by the seasonal cycle. We would like to remove this seasonal cycle\n",
+ "(called the \"climatology\") in order to better see the long-term variaitions in\n",
+ "temperature. We can accomplish this using **groupby**.\n",
+ "\n",
+ "Before moving forward, we note that xarray correctly parsed the time index,\n",
+ "resulting in a Pandas datetime index on the time dimension.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds.time"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The syntax of Xarray's groupby is almost identical to Pandas.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "tags": [
+ "output_scroll"
+ ]
+ },
+ "outputs": [],
+ "source": [
+ "?ds.groupby"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Identifying groups"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The most important argument is `group`: this defines the unique values or labels we will\n",
+ "us to \"split\" the data for grouped analysis. We can pass either a DataArray or a\n",
+ "name of a variable in the dataset. Let's first use a DataArray. \n",
+ "\n",
+ "Just like with\n",
+ "Pandas, we can use the time index to extract specific components of dates and\n",
+ "times. Xarray uses a special syntax for this `.dt`, called the\n",
+ "[DatetimeAccessor](https://docs.xarray.dev/en/stable/generated/xarray.core.accessor_dt.DatetimeAccessor.html?highlight=DatetimeAccessor). See the [documentation](https://docs.xarray.dev/en/stable/user-guide/time-series.html#datetime-components) for more\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds.time.dt"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds.time.dt.month"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds.time.dt.year"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Split step"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can use these arrays in a groupby operation:\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "gb = ds.groupby(ds.time.dt.month)\n",
+ "gb"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Xarray also offers a more concise syntax when the variable you're grouping on is\n",
+ "already present in the dataset. This is identical to the previous line:\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "gb = ds.groupby(\"time.month\")\n",
+ "gb"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "`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",
+ "\n",
+ "\n",
+ "```{tip}\n",
+ "\n",
+ "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",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "### Apply & Combine\n",
+ "\n",
+ "Now that we have groups defined, it's time to \"apply\" a calculation to the\n",
+ "group. Like in Pandas, these calculations can either be:\n",
+ "\n",
+ "- _aggregation_ or _reduction_: reduces the size of the group\n",
+ "- _transformation_: preserves the group's full size\n",
+ "\n",
+ "At then end of the apply step, xarray will automatically combine the aggregated\n",
+ "/ transformed groups back into a single object."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Aggregations or Reductions"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Most commonly, we want to perform a reduction operation like `sum` or `mean` on our groups. Xarray conveniently provides these reduction methods on Groupby objects for both [DataArrays and Datasets](https://docs.xarray.dev/en/stable/api.html#groupby-objects).\n",
+ "\n",
+ "Here we calculate the monthly mean."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds_mm = gb.mean()\n",
+ "ds_mm"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "So we did what we wanted to do: calculate the climatology at every point in the\n",
+ "dataset. Let's look at the data a bit.\n",
+ "\n",
+ "_Climatology at a specific point in the North Atlantic_\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds_mm.sst.sel(lon=300, lat=50).plot();"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "_Zonal Mean Climatology_\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds_mm.sst.mean(dim=\"lon\").plot.contourf(x=\"month\", levels=12, vmin=-2, vmax=30);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "_Difference between January and July Climatology_\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "(ds_mm.sst.sel(month=1) - ds_mm.sst.sel(month=7)).plot(vmax=10);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Custom Aggregations\n",
+ "\n",
+ "The most fundamental way to apply a function and combine the results together to use the `.map` method."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "tags": [
+ "output_scroll"
+ ]
+ },
+ "outputs": [],
+ "source": [
+ "?gb.map"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "`.map` accepts as its argument a function that expects and returns xarray\n",
+ "objects. We define a custom function. This function takes a single argument--the\n",
+ "group dataset--and returns a new dataset to be combined:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def time_mean(a):\n",
+ " return a.mean(dim=\"time\")\n",
+ "\n",
+ "\n",
+ "gb.map(time_mean)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This is identical to `gb.mean()`"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Apply by iteration\n",
+ "\n",
+ "We can manually iterate over the group. The\n",
+ "iterator returns the key (group name) and the value (the actual dataset\n",
+ "corresponding to that group) for each group.\n",
+ "\n",
+ "\n",
+ "You could apply any function you want in the loop but you would have to manually [combine](https://docs.xarray.dev/en/stable/user-guide/combining.html) the results together."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "for group_name, group_ds in gb:\n",
+ " # stop iterating after the first loop\n",
+ " break\n",
+ "print(group_name)\n",
+ "group_ds"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Transformations\n",
+ "\n",
+ "Now we want to _remove_ this climatology from the dataset, to examine the\n",
+ "residual, called the _anomaly_, which is the interesting part from a climate\n",
+ "perspective. Removing the seasonal climatology is a perfect example of a\n",
+ "transformation: it operates over a group, but doesn't change the size of the\n",
+ "dataset. Here is one way to code it\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def remove_time_mean(x):\n",
+ " return x - x.mean(dim=\"time\")\n",
+ "\n",
+ "\n",
+ "ds_anom = ds.groupby(\"time.month\").map(remove_time_mean)\n",
+ "ds_anom"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Xarray makes these sorts of transformations easy by supporting _groupby\n",
+ "arithmetic_. This concept is easiest explained with an example:\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "gb = ds.groupby(\"time.month\")\n",
+ "ds_anom = gb - gb.mean()\n",
+ "ds_anom"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now we can view the climate signal without the overwhelming influence of the\n",
+ "seasonal cycle.\n",
+ "\n",
+ "_Timeseries at a single point in the North Atlantic_\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds_anom.sst.sel(lon=300, lat=50).plot();"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "_Difference between Jan. 1 2018 and Jan. 1 1970_\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "(ds_anom.sel(time=\"2018-01-01\") - ds_anom.sel(time=\"1970-01-01\")).sst.plot();"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "::::{admonition} Exercise\n",
+ ":class: tip\n",
+ "\n",
+ "Using `groupby`, plot the annual mean time series of SST at 300°E, 50°N\n",
+ "\n",
+ ":::{admonition} Solution\n",
+ ":class: dropdown\n",
+ "```python\n",
+ "ds.groupby(\"time.year\").mean().sst.sel(lon=300, lat=50).plot();\n",
+ "```\n",
+ ":::\n",
+ "::::"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Resample\n",
+ "\n",
+ "Resampling means changing the time frequency of data, usually reducing to a coarser frequency: e.g. converting daily frequency data to monthly frequency data using `mean` to reduce the values. This operation can be thought of as a groupby operation where each group is a single month of data. Resampling can be applied only to time-index dimensions. \n",
+ "\n",
+ "First note that `ds_anom` has data at monthly frequency (i.e. one point every month)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds_anom.time"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Here we compute the five-year mean along the `time` dimension by passing `time='5Y'`. `'5Y'` is a special frequency string. Xarray uses pandas to convert such a frequency string to a groupby operation. See the [pandas documentation](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases) for how to specify a different frequency."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "resample_obj = ds_anom.resample(time=\"5YE\")\n",
+ "resample_obj"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "```{note}\n",
+ "`resample` only works with proper datetime64 coordinate labels. Note the `dtype` of `time` in the repr above.\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Resampling objects are exactly like groupby objects and allow reductions, iteration, etc."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds_anom_resample = resample_obj.mean()\n",
+ "ds_anom_resample"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "for label, group in resample_obj:\n",
+ " break\n",
+ "print(label, \"\\n\\n\", group)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds_anom.sst.sel(lon=300, lat=50).plot()\n",
+ "ds_anom_resample.sst.sel(lon=300, lat=50).plot(marker=\"o\");"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "::::{admonition} Exercise\n",
+ ":class: tip\n",
+ "\n",
+ "Using `resample`, plot the annual mean time series of SST at 300°E, 50°N.\n",
+ "\n",
+ "Compare this output to the groupby output. What differences do you see?\n",
+ "\n",
+ ":::{admonition} Solution\n",
+ ":class: dropdown\n",
+ "```python\n",
+ "resampled = ds.resample(time='YE').mean().sst.sel(lon=300, lat=50)\n",
+ "resampled.plot();\n",
+ "```\n",
+ ":::\n",
+ "::::"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "tags": [
+ "hide-input"
+ ]
+ },
+ "source": [
+ "## GroupBy vs Resample \n",
+ "\n",
+ "Let's compare the grouped and resampled outputs.\n",
+ "\n",
+ "\n",
+ "1. Note the different dimension names: when grouped, `time` is renamed to `year`. When resampled, the `time` dimension name is preserved\n",
+ "2. The values for `year` are integers, while those for `resampled.time` are timestamps, similar to the input dataset\n",
+ "3. But all values are equal"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "from IPython.display import display_html\n",
+ "\n",
+ "grouped = ds.groupby(\"time.year\").mean().sst.sel(lon=300, lat=50)\n",
+ "resampled = ds.resample(time=\"YE\").mean().sst.sel(lon=300, lat=50)\n",
+ "display_html(grouped)\n",
+ "display_html(resampled)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "np.array_equal(grouped.data, resampled.data)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Going further\n",
+ "\n",
+ "1. See the documentation on [groupby](https://docs.xarray.dev/en/stable/user-guide/groupby.html) and [resample](https://docs.xarray.dev/en/stable/user-guide/time-series.html#resampling-and-grouped-operations)\n",
+ "2. Follow the tutorial on [high-level computation patterns](https://tutorial.xarray.dev/intermediate/01-high-level-computation-patterns.html)"
+ ]
+ }
+ ],
+ "metadata": {
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/fundamentals/03.3_windowed.ipynb b/fundamentals/03.3_windowed.ipynb
new file mode 100644
index 00000000..9bdf9644
--- /dev/null
+++ b/fundamentals/03.3_windowed.ipynb
@@ -0,0 +1,401 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "0",
+ "metadata": {},
+ "source": [
+ "# Windowed Computations\n",
+ "\n",
+ "Xarray has built-in support for windowed operations:\n",
+ "1. [rolling](https://docs.xarray.dev/en/stable/user-guide/computation.html#rolling-window-operations) - Sliding windows of fixed length.\n",
+ "2. [coarsen](https://docs.xarray.dev/en/stable/user-guide/computation.html#coarsen-large-arrays) - block windows of fixed length.\n",
+ "\n",
+ "\n",
+ "In this notebook, we'll learn to \n",
+ "1. Compute rolling, or sliding window, means along one or more dimensions.\n",
+ "2. Compute block averages along a dimension.\n",
+ "3. Use `construct` to reshape arrays so that a new dimension provides windowed views to the data.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import xarray as xr\n",
+ "\n",
+ "np.set_printoptions(threshold=10, edgeitems=2)\n",
+ "xr.set_options(display_expand_data=False)\n",
+ "\n",
+ "%config InlineBackend.figure_format='retina'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds = xr.tutorial.load_dataset(\"ersstv5\")\n",
+ "ds"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3",
+ "metadata": {},
+ "source": [
+ "## Rolling or moving windows"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4",
+ "metadata": {},
+ "source": [
+ "Rolling window operations \n",
+ "1. can be applied along any dimension, or along multiple dimensions.\n",
+ "2. returns object of same shape as input\n",
+ "3. pads with NaNs to make (2) possible\n",
+ "\n",
+ "Again, all common reduction operations are [available](https://docs.xarray.dev/en/stable/api.html#rolling-objects)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "rolling = ds.rolling(time=12, center=True)\n",
+ "rolling"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6",
+ "metadata": {},
+ "source": [
+ "```{tip}\n",
+ "Xarrays' computation methods (`groupby`, `groupby_bins`, `rolling`, `coarsen`, `weighted`) all return special objects that represent the basic underlying computation pattern. For e.g. `rolling` above is a `DatasetRolling` object that represents 12-point rolling windows 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",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds_rolling = rolling.mean()\n",
+ "ds_rolling"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds.sst.sel(lon=300, lat=50).plot(label=\"monthly anom\")\n",
+ "ds_rolling.sst.sel(lon=300, lat=50).plot(label=\"12 month rolling mean\")\n",
+ "plt.legend()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9",
+ "metadata": {},
+ "source": [
+ "We can apply rolling mean along multiple dimensions as a 2D smoother in (lat, lon). Here is an example of a 5-point running mean applied along both the `lat` and `lon` dimensions"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "10",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "extract = ds.sst.isel(time=0)\n",
+ "smoothed = extract.rolling(lon=5, lat=5, center=True).mean()\n",
+ "\n",
+ "f, ax = plt.subplots(2, 1, sharex=True, sharey=True)\n",
+ "extract.plot(ax=ax[0], robust=True)\n",
+ "smoothed.plot(ax=ax[1], robust=True)\n",
+ "f.set_size_inches((10, 7))\n",
+ "plt.tight_layout()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "11",
+ "metadata": {},
+ "source": [
+ "Note the addition of NaNs at the data boundaries and near continental boundaries."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "12",
+ "metadata": {},
+ "source": [
+ "### Custom reductions\n",
+ "\n",
+ "While common reductions are implemented by default, sometimes it is useful to apply our own windowed operations. For these uses, Xarray provides the `construct` methods for [DataArray.rolling](https://docs.xarray.dev/en/stable/generated/xarray.computation.rolling.DataArrayRolling.construct.html#xarray.computation.rolling.DataArrayRolling.construct) and [Dataset.rolling](https://docs.xarray.dev/en/stable/generated/xarray.computation.rolling.DatasetRolling.construct.html#xarray.computation.rolling.DatasetRolling.construct).\n",
+ "\n",
+ "For rolling over a dimension `time` with a window size `N`, `construct` adds a new dimension (with user-provided name) of size `N`. \n",
+ "\n",
+ "We illustrate with a simple example array:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "13",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "simple = xr.DataArray(np.arange(10), dims=\"time\", coords={\"time\": np.arange(10)})\n",
+ "simple"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "14",
+ "metadata": {},
+ "source": [
+ "We call `construct` and provide a name for the new dimension: `window`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "15",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# adds a new dimension \"window\"\n",
+ "simple.rolling(time=5, center=True).construct(\"window\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "16",
+ "metadata": {},
+ "source": [
+ ":::{admonition} Exercise\n",
+ ":class: tip\n",
+ "Illustrate the difference between `center=True` and `center=False` for rolling by looking at the `construct`-ed array.\n",
+ "\n",
+ ":::{admonition} Solution\n",
+ ":class: dropdown\n",
+ "```python\n",
+ "display(\"center=True\")\n",
+ "display(simple.rolling(time=5, center=True).construct(\"window\"))\n",
+ "\n",
+ "display(\"center=False\")\n",
+ "display(simple.rolling(time=5, center=False).construct(\"window\"))\n",
+ "```\n",
+ ":::\n",
+ "::::"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "17",
+ "metadata": {},
+ "source": [
+ "## Coarsening\n",
+ "\n",
+ "`coarsen` does something similar to `rolling`, but allows us to work with discrete *non-overlapping* blocks of data.\n",
+ "\n",
+ "You will need to specify `boundary` if the length of the dimension is not a multiple of the window size (\"block size\"). You can choose to\n",
+ "1. `trim` the excess values\n",
+ "2. `pad` with NaNs\n",
+ "\n",
+ "Again, all standard reductions are [implemented](https://docs.xarray.dev/en/stable/api.html#coarsen-objects)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "18",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "coarse = ds.coarsen(lon=5, lat=5)\n",
+ "coarse"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "19",
+ "metadata": {},
+ "source": [
+ "Xarrays' computation methods (groupby, groupby_bins, rolling, coarsen, weighted) all return special objects that represent the basic underlying computation pattern. For e.g. `coarse` above is a `DatasetCoarsen` object that represents 5-point windows along lat, lon 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)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "20",
+ "metadata": {
+ "tags": [
+ "raises-exception",
+ "output-scroll"
+ ]
+ },
+ "outputs": [],
+ "source": [
+ "# we expect an error here because lat has size 89, which is not divisible by block size 5\n",
+ "coarse.mean()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "21",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "coarse = ds.coarsen(lat=5, lon=5, boundary=\"trim\").mean()\n",
+ "coarse"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "22",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "coarse.sst.isel(time=0).plot();"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "23",
+ "metadata": {},
+ "source": [
+ "### Custom reductions\n",
+ "\n",
+ "Like `rolling`, `coarsen` also provides a `construct` method for custom block operations. \n",
+ "\n",
+ "```{tip} \n",
+ "`coarsen.construct` is a handy way to reshape Xarray objects.\n",
+ "```\n",
+ "\n",
+ "Consider a \"monthly\" 1D timeseries. This simple example has one value per month for 2 years"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "24",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "months = xr.DataArray(\n",
+ " np.tile(np.arange(1, 13), reps=2),\n",
+ " dims=\"time\",\n",
+ " coords={\"time\": np.arange(1, 25)},\n",
+ ")\n",
+ "months"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "25",
+ "metadata": {},
+ "source": [
+ "Now we reshape to get one new dimension `year` of size 12."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "26",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# break \"time\" into two new dimensions: \"year\", \"month\"\n",
+ "months.coarsen(time=12).construct(time=(\"year\", \"month\"))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "27",
+ "metadata": {},
+ "source": [
+ ":::{admonition} Exercise\n",
+ ":class: tip\n",
+ "\n",
+ "Imagine the array `months` was one element shorter. Use `boundary=\"pad\"` and the `side` kwarg to reshape `months.isel(time=slice(1, None))` to a 2D DataArray with the following values:\n",
+ "\n",
+ "```python\n",
+ "array([[nan, 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12.],\n",
+ " [ 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12.]])\n",
+ "```\n",
+ "\n",
+ ":::{admonition} Solution\n",
+ ":class: dropdown\n",
+ "\n",
+ "```python\n",
+ "months.isel(time=slice(1, None)).coarsen({\"time\": 12}, boundary=\"pad\", side=\"right\").construct(\n",
+ " time=(\"year\", \"month\")\n",
+ ")\n",
+ "```\n",
+ ":::\n",
+ "::::"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "28",
+ "metadata": {},
+ "source": [
+ "Note that `coarsen` pads with NaNs. For more control over padding, use\n",
+ "[DataArray.pad](https://docs.xarray.dev/en/stable/generated/xarray.DataArray.pad.html) explicitly."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "29",
+ "metadata": {
+ "jp-MarkdownHeadingCollapsed": true,
+ "tags": []
+ },
+ "source": [
+ "## Going further\n",
+ "\n",
+ "1. See the documentation on [rolling](https://docs.xarray.dev/en/stable/user-guide/computation.html#rolling-window-operations) and [coarsen](https://docs.xarray.dev/en/stable/user-guide/computation.html#coarsen-large-arrays).\n",
+ "2. Follow the tutorial on [high-level computational patterns](https://tutorial.xarray.dev/intermediate/01-high-level-computation-patterns.html#)"
+ ]
+ }
+ ],
+ "metadata": {
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/fundamentals/03.4_weighted.ipynb b/fundamentals/03.4_weighted.ipynb
new file mode 100644
index 00000000..c6d49756
--- /dev/null
+++ b/fundamentals/03.4_weighted.ipynb
@@ -0,0 +1,172 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "0",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "# Weighted Reductions\n",
+ "\n",
+ "Xarray supports [weighted reductions](https://docs.xarray.dev/en/stable/user-guide/computation.html#weighted-array-reductions).\n",
+ "\n",
+ "For demonstration, we will create a “weights” array proportional to cosine of latitude.\n",
+ "Modulo a normalization, this is the correct area-weighting factor for data on a regular lat-lon grid.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import xarray as xr\n",
+ "\n",
+ "%config InlineBackend.figure_format='retina'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds = xr.tutorial.load_dataset(\"ersstv5\")\n",
+ "ds"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "weights = np.cos(np.deg2rad(ds.lat))\n",
+ "display(weights.dims)\n",
+ "\n",
+ "weights.plot()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4",
+ "metadata": {},
+ "source": [
+ "## Manual weighting\n",
+ "\n",
+ "Thanks to the automatic broadcasting and alignment discussed earlier, if we multiply this by SST, it “just works,” and the arrays are broadcasted properly:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "(ds.sst * weights).dims"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6",
+ "metadata": {},
+ "source": [
+ "We could imagine computing the weighted spatial mean of SST manually."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "sst_mean = (ds.sst * weights).sum(dim=(\"lon\", \"lat\")) / weights.sum(dim=\"lat\")\n",
+ "sst_mean.plot()\n",
+ "plt.title(\"This is wrong!\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8",
+ "metadata": {},
+ "source": [
+ "That would be wrong, however, because the denominator (`weights.sum(dim='lat')`)\n",
+ "needs to be expanded to include the `lon` dimension and modified to account for\n",
+ "the missing values (land points).\n",
+ "\n",
+ "\n",
+ "## The `weighted` method\n",
+ "In general, weighted reductions on multidimensional arrays are complicated. To\n",
+ "make it a bit easier, Xarray provides a mechanism for weighted reductions. \n",
+ "\n",
+ "It does this by creating a special intermediate `DataArrayWeighted` object, to\n",
+ "which different reduction operations can applied.\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "sst_weighted = ds.sst.weighted(weights)\n",
+ "sst_weighted"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "10",
+ "metadata": {},
+ "source": [
+ "\n",
+ "units attribute, which is used in plotting,\n",
+ " Xarray does not inherently understand units. However, xarray can integrate with pint, which provides full unit-aware operations. See pint-xarray for more.\n",
+ "\n",
+ " Xarrays' computation methods (groupby, groupby_bins, rolling, coarsen, weighted) all return special objects that represent the basic underlying computation pattern. For e.g. `sst_weighted` above is a `DatasetWeighted` object that represents the weighting by `weights` of the data in `ds.sst`. It is usually helpful to save and reuse these objects for multiple operations (e.g. a mean and standard deviation calculation).\n",
+ "
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "11",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "sst_weighted.mean(dim=(\"lon\", \"lat\")).plot()\n",
+ "plt.title(\"Correct Global Mean SST\");"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "12",
+ "metadata": {},
+ "source": [
+ "A handful of reductions have been implemented: [mean, sum, std, var](https://docs.xarray.dev/en/stable/api.html#weighted-objects)."
+ ]
+ }
+ ],
+ "metadata": {
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/fundamentals/03_computation.md b/fundamentals/03_computation.md
new file mode 100644
index 00000000..c5dcdc3d
--- /dev/null
+++ b/fundamentals/03_computation.md
@@ -0,0 +1,5 @@
+# Computation
+
+```{tableofcontents}
+
+```
diff --git a/fundamentals/04.0_plotting.md b/fundamentals/04.0_plotting.md
new file mode 100644
index 00000000..98a76a5b
--- /dev/null
+++ b/fundamentals/04.0_plotting.md
@@ -0,0 +1,5 @@
+# Plotting and Visualization
+
+```{tableofcontents}
+
+```
diff --git a/fundamentals/04.1_basic_plotting.ipynb b/fundamentals/04.1_basic_plotting.ipynb
new file mode 100644
index 00000000..034a8168
--- /dev/null
+++ b/fundamentals/04.1_basic_plotting.ipynb
@@ -0,0 +1,377 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "toc": true
+ },
+ "source": [
+ "# Basic Visualization\n",
+ "\n",
+ "At the end of this lesson you will learn:\n",
+ "\n",
+ "1. how to use xarray's convenient [matplotlib-backed](https://matplotlib.org/) plotting interface to\n",
+ " visualize your datasets.\n",
+ "2. that `hvplot` provides an equally convenient interface for bokeh-backed plots"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import matplotlib as mpl\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import xarray as xr\n",
+ "\n",
+ "%config InlineBackend.figure_format='retina'"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Load data\n",
+ "\n",
+ "First let's load up a tutorial dataset to visualize.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds = xr.tutorial.open_dataset(\"air_temperature_gradient\")\n",
+ "ds"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This dataset has three \"data variables\", `Tair` is air temperature and `dTdx`\n",
+ "and `dTdy` are horizontal gradients of this temperature field. All three \"data\n",
+ "variables\" are three-dimensional with dimensions `(time, lat, lon)`.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "## Basic plotting: .plot()\n",
+ "\n",
+ "DataArray objects have a [plot](https://docs.xarray.dev/en/stable/generated/xarray.DataArray.plot.html) method. This method creates plots using\n",
+ "`matplotlib` so all of your existing matplotlib knowledge carries over!\n",
+ "\n",
+ "By default `.plot()` makes\n",
+ "\n",
+ "1. a line plot for 1-D arrays using `plt.plot()`\n",
+ "2. a `pcolormesh` plot for 2-D arrays using `plt.pcolormesh()`\n",
+ "3. a histogram for everything else using `plt.hist()`\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "## Histograms\n",
+ "\n",
+ "`Tair` is three-dimensional, so we got a histogram of temperature values. Notice\n",
+ "the label on the x-axis. One of xarray's convenient plotting features is that it\n",
+ "uses the `attrs` of `Tair` to nicely label axes and colorbars.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds.Tair.plot()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "You can pass extra arguments to the underlying `hist()` call. See the [matplotlib\n",
+ "docs](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.hist.html) for\n",
+ "all possible keyword arguments.\n",
+ "\n",
+ "**Tip:** Note that the returned values are exactly what matplotlib would return\n",
+ "\n",
+ "### Exercise\n",
+ "\n",
+ "Update the above plot to show 50 bins with unfilled steps instead of filled\n",
+ "bars.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "## 2D plots\n",
+ "\n",
+ "Now we will explore 2D plots. Let's select a single timestep of `Tair` to\n",
+ "visualize.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds.Tair.isel(time=1).plot()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This is identical to `.plot.pcolormesh` which is more explicit"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds.Tair.isel(time=1).plot.pcolormesh()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Notice how much information is on that plot!\n",
+ "\n",
+ "1. The x- and y-axes are labeled with full names — \"Latitude\", \"Longitude\" — along with units. \n",
+ "2. The colorbar has a nice label, again with units. \n",
+ "3. And the title tells us the timestamp of the data presented.\n",
+ "\n",
+ "`plot.pcolormesh` takes many [keyword arguments](https://docs.xarray.dev/en/stable/generated/xarray.plot.pcolormesh.html) and is quite sophisticated.\n",
+ "\n",
+ "Here is a more complicated figure that explicitly sets `time` as the x-axis,\n",
+ "customizes the colorbar, and overlays two contours at specific levels.\n",
+ "\n",
+ "**Tip:** Other options for 2D plots include [.plot.contour](https://docs.xarray.dev/en/stable/generated/xarray.plot.contour.html), [.plot.contourf](https://docs.xarray.dev/en/stable/generated/xarray.plot.pcolormesh.html),\n",
+ "[.plot.imshow](https://docs.xarray.dev/en/stable/generated/xarray.plot.imshow.html)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds.Tair.isel(lon=1).plot(\n",
+ " x=\"time\", # coordinate to plot on the x-axis of the plot\n",
+ " robust=True, # set colorbar limits to 2nd and 98th percentile of data\n",
+ " cbar_kwargs={ # passed to plt.colorbar\n",
+ " \"orientation\": \"horizontal\",\n",
+ " \"label\": \"custom label\",\n",
+ " \"pad\": 0.3,\n",
+ " },\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Exercise\n",
+ "\n",
+ "Update the above plot to use a different matplotlib colormap.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "tags": [
+ "hide-input"
+ ]
+ },
+ "outputs": [],
+ "source": [
+ "ds.Tair.isel(lon=1).plot(\n",
+ " x=\"time\", # coordinate to plot on the x-axis of the plot\n",
+ " robust=True, # set colorbar limits to 2nd and 98th percentile of data\n",
+ " cmap=mpl.cm.RdYlBu_r,\n",
+ " cbar_kwargs={ # passed to plt.colorbar\n",
+ " \"orientation\": \"horizontal\",\n",
+ " \"label\": \"custom label\",\n",
+ " \"pad\": 0.3,\n",
+ " },\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Exercise\n",
+ "\n",
+ "Now overlay a [contour plot](https://docs.xarray.dev/en/stable/generated/xarray.plot.contour.html) on top of the previous plot\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "tags": [
+ "hide-input"
+ ]
+ },
+ "outputs": [],
+ "source": [
+ "ds.Tair.isel(lon=1).plot.pcolormesh(\n",
+ " x=\"time\", # coordinate to plot on the x-axis of the plot\n",
+ " robust=True, # set colorbar limits to 2nd and 98th percentile of data\n",
+ " cmap=mpl.cm.RdYlBu_r,\n",
+ " cbar_kwargs={ # passed to plt.colorbar\n",
+ " \"orientation\": \"horizontal\",\n",
+ " \"label\": \"custom label\",\n",
+ " \"pad\": 0.3,\n",
+ " },\n",
+ ")\n",
+ "ds.Tair.isel(lon=1).plot.contour(\n",
+ " x=\"time\", # coordinate to plot on the x-axis of the plot\n",
+ " levels=5, # autoselect 5 levels between max, min\n",
+ " # The following are passed to plt.contour\n",
+ " colors=\"k\",\n",
+ " linewidths=0.5,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "## 1D line plots\n",
+ "\n",
+ "xarray is also able to plot lines by wrapping `plt.plot()`. As in the earlier\n",
+ "examples, the axes are labelled and keyword arguments can be passed to the\n",
+ "underlying `matplotlib` call.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds.Tair.isel(time=1, lon=10).plot(marker=\"o\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Again, this is equivalent to the more explicit [plot.line](https://docs.xarray.dev/en/stable/generated/xarray.plot.line.html)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds.Tair.isel(time=1, lon=10).plot.line(marker=\"o\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Multiple lines with `hue` \n",
+ "\n",
+ "Lets say we want to compare line plots of temperature at three different\n",
+ "latitudes. We can use the `hue` kwarg to do this.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds.Tair.isel(time=1).sel(lat=[40, 50, 60], method=\"nearest\").plot(x=\"lon\", hue=\"lat\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Customization\n",
+ "\n",
+ "All of xarray's plotting functions take an large list kwargs that customize\n",
+ "behaviour. A full list can be seen [here](https://docs.xarray.dev/en/stable/generated/xarray.plot.pcolormesh.html). That\n",
+ "said xarray does not wrap all matplotlib functionality.\n",
+ "\n",
+ "The general strategy for making plots that are more complicated that the\n",
+ "examples above is\n",
+ "\n",
+ "1. Create a matplotlib axis `ax`\n",
+ "2. Use xarray to make a close approximation of the final plot specifying\n",
+ " `ax=ax`.\n",
+ "3. Use `ax` methods to fully customize the plot\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "jp-MarkdownHeadingCollapsed": true,
+ "tags": []
+ },
+ "source": [
+ "## More resources\n",
+ "\n",
+ "1. [Xarray's visualization gallery](https://docs.xarray.dev/en/stable/examples/visualization_gallery.html)\n",
+ "2. [Xarray's plotting documentation](https://docs.xarray.dev/en/stable/plotting.html)\n",
+ "\n"
+ ]
+ }
+ ],
+ "metadata": {
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3"
+ },
+ "toc": {
+ "base_numbering": 1,
+ "nav_menu": {},
+ "number_sections": true,
+ "sideBar": true,
+ "skip_h1_title": false,
+ "title_cell": "Table of Contents",
+ "title_sidebar": "Contents",
+ "toc_cell": true,
+ "toc_position": {},
+ "toc_section_display": true,
+ "toc_window_display": true
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/fundamentals/04.2_faceting.ipynb b/fundamentals/04.2_faceting.ipynb
new file mode 100644
index 00000000..9e10f902
--- /dev/null
+++ b/fundamentals/04.2_faceting.ipynb
@@ -0,0 +1,245 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "0",
+ "metadata": {},
+ "source": [
+ "# Faceting\n",
+ "\n",
+ "Faceting is the art of presenting \"small multiples\" of the data. It is an\n",
+ "effective way of visualizing variations of 3D data where 2D slices are\n",
+ "visualized in a panel (subplot) and the third dimensions is varied between\n",
+ "panels (subplots).\n",
+ "\n",
+ "Here is where xarray really augments matplotlib's functionality. We will use\n",
+ "monthly means to illustrate\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import matplotlib as mpl\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import xarray as xr\n",
+ "\n",
+ "%config InlineBackend.figure_format='retina'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds = xr.tutorial.open_dataset(\"air_temperature_gradient\")\n",
+ "monthly_means = ds.groupby(\"time.month\").mean()\n",
+ "# xarray's groupby reductions drop attributes. Let's assign them back so we get nice labels.\n",
+ "monthly_means.Tair.attrs = ds.Tair.attrs"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3",
+ "metadata": {},
+ "source": [
+ "Note that the dimensions are now `lat, lon, month`.\n",
+ "\n",
+ "## Basic faceting\n",
+ "\n",
+ "We want to visualize how the monthly mean air temperature varies with month of\n",
+ "the year.\n",
+ "\n",
+ "The simplest way to facet is to specify the `row` or `col` kwargs which are\n",
+ "expected to be a dimension name. Here we use `month` so that each panel or\n",
+ "\"facet\" of the plot presents the mean temperature field in a given month. Since\n",
+ "a 12 column plot would be too small to interpret, we can \"wrap\" the facets into\n",
+ "multiple rows using `col_wrap`\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "fg = monthly_means.Tair.plot(\n",
+ " col=\"month\",\n",
+ " col_wrap=4, # each row has a maximum of 4 columns\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5",
+ "metadata": {},
+ "source": [
+ "## Customizing\n",
+ "\n",
+ "All the usual customizations are possible\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "fg = monthly_means.Tair.plot(\n",
+ " col=\"month\",\n",
+ " col_wrap=4,\n",
+ " # The remaining kwargs customize the plot just as for not-faceted plots\n",
+ " robust=True,\n",
+ " cmap=mpl.cm.RdYlBu_r,\n",
+ " cbar_kwargs={\n",
+ " \"orientation\": \"horizontal\",\n",
+ " \"shrink\": 0.8,\n",
+ " \"aspect\": 40,\n",
+ " \"pad\": 0.1,\n",
+ " },\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7",
+ "metadata": {},
+ "source": [
+ "The returned FacetGrid object `fg` has many useful properties and methods e.g.\n",
+ "\n",
+ "1. `fg.fig` provides a handle to the figure\n",
+ "2. `fg.axes` is a numpy object array with handles to each individual axes\n",
+ "3. `fg.set_xlabels` and `fg.set_ylabels` can be used to change axes labels.\n",
+ "\n",
+ "See the [documentation](https://docs.xarray.dev/en/stable/api.html#faceting) for a full list."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8",
+ "metadata": {},
+ "source": [
+ "### Exercise\n",
+ "\n",
+ "Use these methods to set a title for the figure using `suptitle`, as well as\n",
+ "change the x- and y-labels."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "fg"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "10",
+ "metadata": {},
+ "source": [
+ "## Modifying all facets\n",
+ "\n",
+ "The FacetGrid object has some more advanced methods that let you customize the\n",
+ "plot further.\n",
+ "\n",
+ "Here we illustrate the use of `map` and `map_dataarray` that let you map custom\n",
+ "plotting functions to an existing `FacetGrid`. The functions passed to `map` and\n",
+ "`map_dataarray` must have a particular signature. See the docstring for more\n",
+ "details.\n",
+ "\n",
+ "Alternatively one can loop over `fg.axes` and modify each individual subplot as\n",
+ "needed\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "11",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "fg = monthly_means.Tair.plot(col=\"month\", col_wrap=4)\n",
+ "\n",
+ "# Use this to plot contours on each panel\n",
+ "# Note that this plotting call uses the original DataArray gradients\n",
+ "fg.map_dataarray(xr.plot.contour, x=\"lon\", y=\"lat\", colors=\"k\", levels=13, add_colorbar=False)\n",
+ "\n",
+ "# Add a point (or anything else!)\n",
+ "fg.map(lambda: plt.plot(250, 40, markersize=20, marker=\".\", color=\"w\"))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "12",
+ "metadata": {},
+ "source": [
+ "## Faceting multiple DataArrays\n",
+ "\n",
+ "Faceting can be used to plot multiple DataArrays in a Dataset. The trick is to\n",
+ "use `to_array()` to convert a Dataset to a DataArray and then facet that.\n",
+ "\n",
+ "This trick only works when it is sensible to use the same colormap and color\n",
+ "scale for all DataArrays like with `dTdx` and `dTdy`\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "13",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "gradients = monthly_means[[\"dTdx\", \"dTdy\"]].to_array(\"gradient\")\n",
+ "gradients"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "14",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "fg = gradients.isel(month=slice(None, None, 3)).plot.contourf(\n",
+ " levels=13,\n",
+ " col=\"month\",\n",
+ " row=\"gradient\",\n",
+ " robust=True,\n",
+ " cmap=mpl.cm.coolwarm,\n",
+ " cbar_kwargs={\n",
+ " \"orientation\": \"horizontal\",\n",
+ " \"shrink\": 0.8,\n",
+ " \"aspect\": 40,\n",
+ " \"label\": \"Gradient [°C/m]\",\n",
+ " },\n",
+ ")"
+ ]
+ }
+ ],
+ "metadata": {
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/fundamentals/04.3_geographic_plotting.ipynb b/fundamentals/04.3_geographic_plotting.ipynb
new file mode 100644
index 00000000..fc3620cd
--- /dev/null
+++ b/fundamentals/04.3_geographic_plotting.ipynb
@@ -0,0 +1,123 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "0",
+ "metadata": {},
+ "source": [
+ "# Geography with Cartopy\n",
+ "\n",
+ "Since xarray's default plotting functionality builds on matplotlib, we can\n",
+ "seamlessly use cartopy to make nice maps:\n",
+ "\n",
+ "1. Specify a `projection` for the plot when creating a new figure `fig` with\n",
+ " axis `axis`.\n",
+ "2. Explicitly ask xarray to plot to axis `axis` by passing the kwarg `ax=axis`.\n",
+ "3. Specify the projection of the data using `transform` (`PlateCarree` here) in\n",
+ " `.plot()`.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import cartopy.crs as ccrs\n",
+ "import matplotlib as mpl\n",
+ "import matplotlib.pyplot as plt\n",
+ "import xarray as xr\n",
+ "\n",
+ "%config InlineBackend.figure_format='retina'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds = xr.tutorial.open_dataset(\"air_temperature_gradient\")\n",
+ "monthly_means = ds.groupby(\"time.month\").mean()\n",
+ "# xarray's groupby reductions drop attributes. Let's assign them back so we get nice labels.\n",
+ "monthly_means.Tair.attrs = ds.Tair.attrs"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3",
+ "metadata": {},
+ "source": [
+ "## Basic plot"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "fig, axis = plt.subplots(1, 1, subplot_kw=dict(projection=ccrs.Orthographic(-90, 30)))\n",
+ "\n",
+ "ds.Tair.isel(time=1).plot(\n",
+ " ax=axis,\n",
+ " transform=ccrs.PlateCarree(), # this is important!\n",
+ " # usual xarray stuff\n",
+ " cbar_kwargs={\"orientation\": \"horizontal\", \"shrink\": 0.7},\n",
+ " robust=True,\n",
+ ")\n",
+ "axis.coastlines() # cartopy function"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5",
+ "metadata": {},
+ "source": [
+ "## Faceting maps\n",
+ "\n",
+ "We can make faceted maps. Since `FacetGrid` creates the axes it plots to, we\n",
+ "need to pass the `projection` kwarg in `subplot_kws`. This makes sure that the\n",
+ "subplots are set up properly for cartopy.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "fg = monthly_means.Tair.isel(month=[1, 2, 3]).plot(\n",
+ " col=\"month\",\n",
+ " transform=ccrs.PlateCarree(), # remember to provide this!\n",
+ " subplot_kws={\"projection\": ccrs.LambertConformal(central_longitude=-95, central_latitude=45)},\n",
+ " cbar_kwargs={\"orientation\": \"horizontal\", \"shrink\": 0.8, \"aspect\": 40},\n",
+ " robust=True,\n",
+ ")\n",
+ "\n",
+ "# lets add a coastline to each axis\n",
+ "# great reason to use FacetGrid.map\n",
+ "fg.map(lambda: plt.gca().coastlines())"
+ ]
+ }
+ ],
+ "metadata": {
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/scipy-tutorial/05_intro_to_dask.ipynb b/fundamentals/05_intro_to_dask.ipynb
similarity index 95%
rename from scipy-tutorial/05_intro_to_dask.ipynb
rename to fundamentals/05_intro_to_dask.ipynb
index c073fe9e..cf7065d5 100644
--- a/scipy-tutorial/05_intro_to_dask.ipynb
+++ b/fundamentals/05_intro_to_dask.ipynb
@@ -4,8 +4,6 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "![](\"http://xarray.pydata.org/en/stable/_static/dataset-diagram-logo.png\"){kind=logo}
\n",
- "\n",
"# Introduction to Dask\n",
"\n",
"In this lesson, we discuss cover the basics of Dask. Our learning goals are as\n",
@@ -13,7 +11,7 @@
"\n",
"- Identify and describe Dask Collections (Array, DataFrame) and Schedulers\n",
"- Work with Dask Array's in much the same way you would work with a NumPy array\n",
- "- Understand some of the tradeoffs surounding chunk size, chunk shape, and\n",
+ "- Understand some of the tradeoffs surrounding chunk size, chunk shape, and\n",
" computational overhead\n",
"- Deploy a local Dask Distributed Cluster and access the diagnostics dashboard\n",
"\n",
@@ -162,12 +160,12 @@
"A crucal difference with Dask is that we must specify the `chunks` argument.\n",
"\"Chunks\" describes how the array is split up over many sub-arrays.\n",
"\n",
- "\n",
+ "\n",
"_source:\n",
- "[Dask Array Documentation](http://dask.pydata.org/en/latest/array-overview.html)_\n",
+ "[Dask Array Documentation](http://docs.dask.org/en/latest/array-overview.html)_\n",
"\n",
"There are\n",
- "[several ways to specify chunks](http://dask.pydata.org/en/latest/array-creation.html#chunks).\n",
+ "[several ways to specify chunks](http://docs.dask.org/en/latest/array-creation.html#chunks).\n",
"In this lecture, we will use a block shape.\n"
]
},
@@ -504,7 +502,7 @@
"\n",
"Dask can be deployed on distributed infrastructure, such as a an HPC system or a\n",
"cloud computing system. There is a growing ecosystem of Dask deployment projects\n",
- "that faciliate easy deployment and scaling of Dask clusters on a wide variety of\n",
+ "that facilitate easy deployment and scaling of Dask clusters on a wide variety of\n",
"computing systems.\n",
"\n",
"### HPC\n",
@@ -552,11 +550,6 @@
}
],
"metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
"language_info": {
"codemirror_mode": {
"name": "ipython",
@@ -566,8 +559,7 @@
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.8.2"
+ "pygments_lexer": "ipython3"
}
},
"nbformat": 4,
diff --git a/scipy-tutorial/README.md b/fundamentals/README.md
similarity index 100%
rename from scipy-tutorial/README.md
rename to fundamentals/README.md
diff --git a/github_deploy_key_xarray_contrib_xarray_tutorial.enc b/github_deploy_key_xarray_contrib_xarray_tutorial.enc
deleted file mode 100644
index 4c411c7c..00000000
--- a/github_deploy_key_xarray_contrib_xarray_tutorial.enc
+++ /dev/null
@@ -1 +0,0 @@
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diff --git a/index.rst b/index.rst
deleted file mode 100644
index 6abd1989..00000000
--- a/index.rst
+++ /dev/null
@@ -1,28 +0,0 @@
-Xarray Tutorial
-===============
-
-You can run this tutorial in a live session here: |Binder|
-
-..
-.. |Binder| image:: https://mybinder.org/badge_logo.svg
- :target: https://mybinder.org/v2/gh/pydata/xarray-tutorial/master?urlpath=lab
-
-
-.. toctree::
- :maxdepth: 1
- :caption: OceanHackWeek 2020
-
- oceanhackweek-2020/xarray-oceanhackweek20.ipynb
-
-
-.. toctree::
- :maxdepth: 1
- :caption: SciPy 2020
-
- scipy-tutorial/00_overview.ipynb
- scipy-tutorial/01_datastructures_and_io.ipynb
- scipy-tutorial/02_working_with_labeled_data.ipynb
- scipy-tutorial/03_computation_with_xarray.ipynb
- scipy-tutorial/04_plotting_and_visualization.ipynb
- scipy-tutorial/05_intro_to_dask.ipynb
- scipy-tutorial/06_xarray_and_dask.ipynb
diff --git a/intermediate/BiologyDataset.ipynb b/intermediate/BiologyDataset.ipynb
new file mode 100644
index 00000000..7d4d6ced
--- /dev/null
+++ b/intermediate/BiologyDataset.ipynb
@@ -0,0 +1,120 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "0",
+ "metadata": {},
+ "source": [
+ "# Datasets in other fields\n",
+ "\n",
+ "\n",
+ "While there are many Xarray examples from geosciences the Xarray data model is not constrained to that field. Our examples so far solve problems that are highly analogous to the problems faced in biology. In this notebook we will load a dataset based on the [cells3d](https://scikit-image.org/docs/0.25.x/api/skimage.data.html#skimage.data.cells3d) dataset from scikit-image."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import xarray as xr"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2",
+ "metadata": {},
+ "source": [
+ "## Loading the Dataset\n",
+ "\n",
+ "This dataset follows a classic pattern in microscopy where we have a stack of images, with two fluoresence channels. In addition to that we have a mask (or labels layer) that identifies individual cells "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds = xr.tutorial.load_dataset(\"cells3d\")\n",
+ "ds"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds[\"images\"].max(\"z\").sel(c=\"nuclei\").plot()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds[\"images\"].sel(z=9, method=\"nearest\").sel(c=\"membrane\").plot()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds[\"images\"].max(\"z\").sel(c=\"nuclei\").plot()\n",
+ "ds[\"mask\"].plot.contour(colors=\"Red\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds[\"mask\"].plot()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8",
+ "metadata": {},
+ "source": [
+ "## Allows high level computations.\n",
+ "\n",
+ "We won't get into here - but this structuring data allows you to leverage Xarray to do powerful operations such as computing per cell properties. See the [Computational Patterns Notebook](./computation/01-high-level-computation-patterns.ipynb) for more."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/intermediate/cataloging.ipynb b/intermediate/cataloging.ipynb
new file mode 100644
index 00000000..60c4a3af
--- /dev/null
+++ b/intermediate/cataloging.ipynb
@@ -0,0 +1,203 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "0",
+ "metadata": {
+ "vscode": {
+ "languageId": "plaintext"
+ }
+ },
+ "source": [
+ "# Data cataloguing for Xarray\n",
+ "\n",
+ "**Goals:** At the end of this tutorial, you'll have an overview of what data cataloging is, why it is done, what tools are available. You'll also know how to open and browse some data catalogues.\n",
+ "\n",
+ "> A data catalog is a detailed inventory of data assets within an organization. It helps users easily discover, understand, manage, curate and access data. -[IBM](https://www.ibm.com/think/topics/data-catalog)\n",
+ "\n",
+ "Within an organisation, you may work with a lot of different datasets. As someone managing this data, you might want the following things:\n",
+ "\n",
+ "- **An organized, easily browsable collection**: By easing discovery and loading of your datasets you can reduce the friction for those analysing the data. Users can eaily get an overview of available datasets, discover datasets of interest, and load them for analysis.\n",
+ "- **Access control and logging**: You can limit who is able to access these datasets, and track when datasets are being accessed - keeping the data private, and providing metrics that can inform data management (e.g., by removing little-used datasets).\n",
+ "- **Combine individual data assets**: Combine individual data assets that are similar into a larger object (e.g., combining individual time snapshots into a larger data cube)\n",
+ "\n",
+ "There are a bunch of solutions available in Xarray that meet some of these needs - we'll broadly call these \"data catalogs\". These solutions differ in functionality, and the correct data cataloguing solution depends on your needs as an institute.\n",
+ "\n",
+ "If you feel there are data cataloguing tools missing from this page, please submit a PR.\n",
+ "\n",
+ "## intake v2\n",
+ "\n",
+ "Intake is a lightweight Python package which allows one to specify data catalogues (or a hierarchy of catalogues) via YAML files. These YAML files describe:\n",
+ "\n",
+ "- how to load the dataset. This is done via \"readers\", which support different file formats, and support lazily defining additional transformations to the dataset using Dask\n",
+ "- additional metadata for the dataset\n",
+ "\n",
+ "Uploading the catalogue file to a Git forge like GitHub allows for versioning of this file.\n",
+ "\n",
+ "This catalogue can be browsed in Python, or via third-party tools which parse the datalog, allowing the user to select and open a dataset. This tool has been adopted in the geosciences community for working with Xarray datasets, but is also flexible to work with other dataset types.\n",
+ "\n",
+ "There is no separate authentication layer in Intake, hence access control and logging depends entirely on the way the data is accessed. If the dataset is loaded from a file location on a mounted drive, then you need to have access to that mounted drive. If the dataset is located in an S3 bucket, you need to have the appropriate access credentials for the resource.\n",
+ "\n",
+ "Intake was originally developed within Anaconda by the team behind Dask.\n",
+ "\n",
+ "### Resources\n",
+ "\n",
+ "Project Pythia has a [cookbook on Intake](https://projectpythia.org/intake-cookbook/notebooks/creating-catalogs/).\n",
+ "\n",
+ "### Example code\n",
+ "\n",
+ "Here we work based off [an Intake tutorial on easy.gems!](https://easy.gems.dkrz.de/Processing/intake.html) from the DKRZ (German Climate Supercomputing Center). Note that the tutorial is based on v1 of Intake (which also uses `intake-xarray`).\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Browse an existing catalogue\n",
+ "\n",
+ "import intake\n",
+ "\n",
+ "cat = intake.open_catalog(\"https://data.nextgems-h2020.eu/online.yaml\")\n",
+ "# TIP: Its just a YAML file - open it in your browser and you'll see!\n",
+ "\n",
+ "# view the available subcatalogues or datasets\n",
+ "list(cat)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Access catalogue elements using `[\"attr\"]` or `.attr` notation\n",
+ "# NOTE: The `.attr` notation only works if the entry is a valid Python attribute name - which the entry might not be\n",
+ "\n",
+ "# Let's see what the `tutorial` item is...\n",
+ "cat[\"tutorial\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# it's a subcatalogue!\n",
+ "\n",
+ "print(list(cat[\"tutorial\"]))\n",
+ "\n",
+ "# Let's look at an entry within...\n",
+ "cat[\"tutorial\"][\"ICON.native.2d_PT6H_inst\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Let's open it as an xarray dataset\n",
+ "\n",
+ "ds = cat[\"tutorial\"][\"ICON.native.2d_PT6H_inst\"].to_dask()\n",
+ "ds"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5",
+ "metadata": {},
+ "source": [
+ "That's it for the example! Feel free to poke around further to see what other datasets are available in this catalogue, or to see which other catalogues are available online."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6",
+ "metadata": {},
+ "source": [
+ "## STAC\n",
+ "\n",
+ "> SpatioTemporal Asset Catalogs\n",
+ "> The STAC specification is a common language to describe geospatial information, so it can more easily be worked with, indexed, and discovered.\n",
+ "> -stacspec.org\n",
+ "\n",
+ "Let's set the scene a bit: During satellite data processing, many image files (predominantly TIFF) get generated. Each of these files can have different spatial and temporal extents, and over time these individual files can number in the millions, containing terrabytes of data in total. When doing analyses on these files, the first thing one needs to know is whether the file is even relevant to the analysis at hand (i.e., at least \"what is the spatial and temporal extent of the file?\"), however downloading the file just to check this metadata within is very wasteful. This is the context from which the STAC project was born.\n",
+ "\n",
+ "The goal of the STAC project is to make geospatial data more accessible by providing a specification to describe assets and their metadata. Adding this metadata alongside the asset (or lifting it out above the asset itself) allows:\n",
+ "\n",
+ "- The efficient querying of assets based on properties of interest without downloading the entire asset.\n",
+ "- The combining of these assets into a datacube object (i.e., Xarray dataset) by reprojecting or interpolating the data to a common grid.\n",
+ "\n",
+ "\n",
+ "Within the STAC project is:\n",
+ "\n",
+ "- **The STAC Specification**: A set of specifications describing (from most atomic to least) STAC Items, STAC Catalogs, and STAC collections. There is also a spec for the STAC API.\n",
+ "- **Tooling for working with STAC**: More on this just now\n",
+ "- **The community**\n",
+ "\n",
+ "There are various tools in the STAC ecosystem. There are online STAC browsers such as the [Radiant Earth STAC browser](https://radiantearth.github.io/stac-browser/#/). There are Python packages for the creating of STAC items and searching of STAC catalogues (e.g., [`rustac-py`](https://stac-utils.github.io/rustac/) and [`pystac`](https://github.com/stac-utils/pystac)). There are also Python packages for combining STAC objects into larger datacubes (e.g., [`odc-stac`](https://github.com/opendatacube/odc-stac), [`stacstac`](https://github.com/gjoseph92/stackstac) and [`xpystac`](https://github.com/stac-utils/xpystac)).\n",
+ "\n",
+ "There are many packages with some overlapping functionality. An example workflow you might follow would be using an online STAC browser to get a better understanding of the STAC assets you're working with, and what properties are available to query on, using `rustac-py` to generate and run the query, and then (once you're happy with the result) using `stackstac` or `odc-stac` to open the result as an Xarray Dataset.\n",
+ "\n",
+ "### Resources\n",
+ "\n",
+ "- https://stacspec.org/en\n",
+ "- https://stacindex.org/\n",
+ "- Example using odc-stac: See example notebook from `odc-stac`docs [\"Access Sentinel 2 Data from AWS\"](https://odc-stac.readthedocs.io/en/latest/notebooks/stac-load-e84-aws.html).\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7",
+ "metadata": {},
+ "source": [
+ "## Commercial offering - Arraylake\n",
+ "\n",
+ "There are also commercial offerings available that have data cataloguing functionality.\n",
+ "\n",
+ "Arraylake is Earthmover's managed cloud platform built on top of the open-source Icechunk data format\n",
+ "(where Icechunk is a project offering versioning of Zarr-like datasets).\n",
+ "\n",
+ "See [here](https://icechunk.io/en/latest/arraylake/) for a full comparison between Icechunk and Arraylake.\n",
+ "\n",
+ "A notable feature is that Arraylake integrates with [Earthmover's data marketplace](https://www.earthmover.io/marketplace/),\n",
+ "powering data discovery and allowing for the dissemination of datasets (or subsets of datasets) to other interested users.\n",
+ "\n",
+ "\n",
+ "\n",
+ "## More resources\n",
+ "\n",
+ "https://guide.cloudnativegeo.org/cookbooks/zarr-stac-report/data-consumers/"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8",
+ "metadata": {},
+ "source": []
+ }
+ ],
+ "metadata": {
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/intermediate/computation/01-high-level-computation-patterns.ipynb b/intermediate/computation/01-high-level-computation-patterns.ipynb
new file mode 100644
index 00000000..517fb604
--- /dev/null
+++ b/intermediate/computation/01-high-level-computation-patterns.ipynb
@@ -0,0 +1,1484 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "0",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ },
+ "tags": []
+ },
+ "source": [
+ "# Computational Patterns\n",
+ "\n",
+ "Often when writing code we repeat certain patterns, whether we realize it or not.\n",
+ "If you have learned to write list comprehensions, you are taking advantage of a \"control pattern\".\n",
+ "Often, these patterns are so common that many packages have built in functions to implement them.\n",
+ "\n",
+ "Quoting the [toolz documentation](https://toolz.readthedocs.io/en/latest/control.html):\n",
+ "\n",
+ "> The Toolz library contains dozens of patterns like map and groupby. Learning a\n",
+ "> core set (maybe a dozen) covers the vast majority of common programming tasks\n",
+ "> often done by hand. A rich vocabulary of core control functions conveys the\n",
+ "> following benefits:\n",
+ ">\n",
+ "> - You identify new patterns\n",
+ "> - You make fewer errors in rote coding\n",
+ "> - You can depend on well tested and benchmarked implementations\n",
+ "\n",
+ "The same is true for xarray.\n",
+ "\n",
+ "\n",
+ "```{seealso}\n",
+ "\n",
+ "The concepts covered here, particularly the emphasis on deleting for loops and focusing on large elements of the computation, are very related to the [array programming style](https://en.wikipedia.org/wiki/Array_programming) or paradigm. See this SciPy 2023 tutorial on [\"Thinking Like Arrays\"](https://github.com/jpivarski-talks/2023-07-11-scipy2023-tutorial-thinking-in-arrays) by Jim Pivarski if you are interested in these ideas.\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ },
+ "tags": []
+ },
+ "source": [
+ "## Motivation / Learning goals\n",
+ "\n",
+ "- Learn what high-level computational patterns are available in Xarray.\n",
+ "- Learn that these patterns replace common uses of the `for` loop.\n",
+ "- Identify when you are re-implementing an existing computational pattern.\n",
+ "- Implement that pattern using built-in Xarray functionality.\n",
+ "- Understand the difference between `map` and `reduce`."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "subslide"
+ },
+ "tags": []
+ },
+ "source": [
+ "## Xarray's high-level patterns\n",
+ "\n",
+ "Xarray allows you to leverage dataset metadata to write more readable analysis\n",
+ "code. The metadata is stored with the data; not in your head.\n",
+ "\n",
+ "1. Dimension names: `dim=\"latitude\"` instead of `axis=0`\n",
+ "2. Coordinate \"labels\": or axis tick labels. `data.sel(latitude=45)` instead of\n",
+ " `data[10]`\n",
+ "\n",
+ "Xarray also provides computational patterns that cover many data\n",
+ "analysis tasks.\n",
+ "\n",
+ "Xarray provides methods for high-level analysis patterns:\n",
+ "\n",
+ "1. [`rolling`](![](\"https://raw.githubusercontent.com/ProjectPythia/pythia-foundations/refs/heads/main/core/xarray/images/xarray-split-apply-combine.jpeg\")
\n",
+ "\n",
+ "But really there is a \"hidden\" first step: *identifying* groups (also called \"factorization\" or sometimes \"binning\"). Usually this is the hard part.\n",
+ "\n",
+ "In reality the workflow is: \"identify groups\" → \"split into groups\" → \"apply function\" → \"combine results\".\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "55",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# recall our earlier DataArray\n",
+ "da"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "56",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# GroupBy returns an iterator that traverses the specified groups, here by month.\n",
+ "# Notice that groupby is clever enough for us to leave out the `.dt` before `.month`\n",
+ "# we would need to specify to access the month data directly, as in `da.time.dt.month`.\n",
+ "da.groupby(\"time.month\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "57",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# for each group (e.g. the air temperature in a given month for all the years),\n",
+ "# compute the mean\n",
+ "da.groupby(\"time.month\").mean()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "58",
+ "metadata": {},
+ "source": [
+ "Notice that since we have averaged over all the years for each month, our resulting DataArray no longer has a \"year\" coordinate.\n",
+ "\n",
+ "If we want to see how Xarray identifies \"groups\" for the monthly climatology computation, we can plot our input to `groupby`. GroupBy is clever enough to figure out how many values there are an thus how many groups to make.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "59",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "da[\"time.month\"].plot();"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "60",
+ "metadata": {},
+ "source": [
+ "Similarly for binning (remember this is useful when the parameter you are binning over is not \"exact\", like a float),\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "61",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "data.groupby_bins(\"lat\", bins=[20, 35, 40, 45, 50])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "62",
+ "metadata": {},
+ "source": [
+ "and resampling...\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "63",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "da.resample(time=\"ME\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "64",
+ "metadata": {},
+ "source": [
+ "```{note}\n",
+ "\n",
+ "Resampling is changing the frequency of our data to monthly (for two years), so we have 24 bins. GroupBy is taking the average across all data in the same month for two years, so we have 12 bins.\n",
+ "\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "65",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "subslide"
+ },
+ "tags": []
+ },
+ "source": [
+ "### Constructing group labels\n",
+ "\n",
+ "If the automatic group detection doesn't work for your problem then these functions are useful for constructing specific \"group labels\" in many cases\n",
+ "\n",
+ "1. [numpy.digitize](![](\"https://docs.xarray.dev/en/stable/_static/Xarray_Logo_RGB_Final.svg\"){kind=logo}
\n",
+ "\n",
+ "\n",
+ "In this lesson, we cover the basics of Xarray data structures. By the\n",
+ "end of the lesson, we will be able to:\n",
+ "\n",
+ "- Understand the basic data structures in Xarray\n",
+ "- Inspect `DataArray` and `Dataset` objects.\n",
+ "- Select Data using Dataset Coordinates\n",
+ "- Understand that these Datastructures generalize across fields\n",
+ "\n",
+ "\n",
+ "We'll start by reviewing the various components of the Xarray data model, represented here visually:\n",
+ "\n",
+ "![](\"https://docs.xarray.dev/en/stable/_images/dataset-diagram.png\")
\n",
+ "\n",
+ "\n",
+ "\n",
+ "## Variable\n",
+ "\n",
+ "At the the core of everything Xarray works with Variables:\n",
+ "\n",
+ "Variables hold arbitrary metadata, dimension names, and an Array of data\n",
+ "\n",
+ "\n",
+ "\n",
+ "## DataArray\n",
+ "\n",
+ "A DataArray is lowest level object you will regularly interact with. It is a collection of 1 variable containing the data, and optionally Variables associated with the dimensions (coords)\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "## Dataset\n",
+ "\n",
+ "A Dataset is also a collection of Variables. However it can contain multiple data variables. All dimensions MUST be compatible. When we access one of the data variables a DataArray will automatically created for us.\n",
+ "\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import xarray as xr"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Xarray has a few small real-world tutorial datasets hosted in the [xarray-data](https://github.com/pydata/xarray-data) GitHub repository.\n",
+ "\n",
+ "[xarray.tutorial.load_dataset](https://docs.xarray.dev/en/stable/generated/xarray.tutorial.open_dataset.html#xarray.tutorial.open_dataset) is a convenience function to download and open DataSets by name (listed at that link).\n",
+ "\n",
+ "Here we'll use `air temperature` from the [National Center for Environmental Prediction](https://www.weather.gov/ncep/). Xarray objects have convenient HTML representations to give an overview of what we're working with:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds = xr.tutorial.load_dataset(\"air_temperature\")\n",
+ "ds"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Note that behind the scenes the `tutorial.open_dataset` downloads a file. It then uses [`xarray.open_dataset`](https://docs.xarray.dev/en/stable/generated/xarray.open_dataset.html#xarray-open-dataset) function to open that file (which for this datasets is a [netCDF](https://www.unidata.ucar.edu/software/netcdf/) file). \n",
+ "\n",
+ "\n",
+ "As a preview of the power of Xarray, using this data structure allows us to make very readable and powerful statements such as:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds.air.mean(dim=\"time\").plot(x=\"lon\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## What's in a Dataset? \n",
+ "\n",
+ "*Many DataArrays!* \n",
+ "\n",
+ "Datasets are dictionary-like containers of \"DataArray\"s. They are a mapping of\n",
+ "variable name to DataArray:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# pull out \"air\" dataarray with dictionary syntax\n",
+ "ds[\"air\"]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "You can save some typing by using the \"attribute\" or \"dot\" notation. This won't\n",
+ "work for variable names that clash with a built-in method name (like `mean` for\n",
+ "example).\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# pull out dataarray using dot notation\n",
+ "ds.air"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## What's in a DataArray? \n",
+ "\n",
+ "*data + (a lot of) metadata*"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Name (optional)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "da = ds.air\n",
+ "\n",
+ "da.name"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Named dimensions \n",
+ "\n",
+ "`.dims` correspond to the axes of your data. \n",
+ "\n",
+ "In this case we have 2 spatial dimensions (`latitude` and `longitude` are stored with shorthand names `lat` and `lon`) and one temporal dimension (`time`)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "da.dims"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Coordinate variables \n",
+ "\n",
+ "`.coords` is a simple [data container](https://docs.xarray.dev/en/stable/user-guide/data-structures.html#coordinates)\n",
+ "for coordinate variables.\n",
+ "\n",
+ "Here we see the actual timestamps and spatial positions of our air temperature data:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "da.coords"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Coordinates objects support similar indexing notation\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# extracting coordinate variables\n",
+ "da.lon"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# extracting coordinate variables from .coords\n",
+ "da.coords[\"lon\"]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "It is useful to think of the values in these coordinate variables as axis\n",
+ "\"labels\" such as \"tick labels\" in a figure. These are coordinate locations on a\n",
+ "grid at which you have data.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Arbitrary attributes \n",
+ "\n",
+ "`.attrs` is a dictionary that can contain arbitrary Python objects (strings, lists, integers, dictionaries, etc.) Your only\n",
+ "limitation is that some attributes may not be writeable to certain file formats."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "da.attrs"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# assign your own attributes!\n",
+ "da.attrs[\"who_is_awesome\"] = \"xarray\"\n",
+ "da.attrs"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "da"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Underlying data \n",
+ "\n",
+ "`.data` contains the [numpy array](https://numpy.org) storing air temperature values.\n",
+ "\n",
+ "![](\"https://raw.githubusercontent.com/numpy/numpy/623bc1fae1d47df24e7f1e29321d0c0ba2771ce0/branding/logo/primary/numpylogo.svg\"){kind=logo}
\n",
+ "\n",
+ "Xarray structures wrap underlying simpler array-like data structures. This part of Xarray is quite extensible allowing for distributed array, GPU arrays, sparse arrays, arrays with units etc. We'll briefly look at this later in this tutorial."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "da.data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# what is the type of the underlying data\n",
+ "type(da.data)"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Review\n",
+ "\n",
+ "Two of Xarray's main data structures are:\n",
+ "\n",
+ "1. [`DataArrays`](https://docs.xarray.dev/en/stable/user-guide/data-structures.html#dataarray) that wrap underlying data containers (e.g. numpy arrays) and contain associated metadata\n",
+ "1. [`Datasets`](https://docs.xarray.dev/en/stable/user-guide/data-structures.html#dataset) that are dictionary-like containers of DataArrays\n",
+ "\n",
+ "DataArrays contain underlying arrays and associated metadata:\n",
+ "1. Name\n",
+ "2. Dimension names\n",
+ "3. Coordinate variables\n",
+ "4. and arbitrary attributes.\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Exercise\n",
+ "\n",
+ "In this exercise the air dataset has been deconstructed into a dictionary of NumPy arrays. Please\n",
+ "\n",
+ "- Build a Dataset from scratch from this dictionary of NumPy arrays.\n",
+ "\n",
+ "\n",
+ "Hint: You will want to start by building DataArrays, then combining them into a Dataset.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def air_as_dictionary():\n",
+ " ds = xr.tutorial.open_dataset(\"air_temperature\")\n",
+ " return {\n",
+ " \"lat\": ds[\"lat\"].values,\n",
+ " \"lon\": ds[\"lon\"].values,\n",
+ " \"attrs\": ds.attrs,\n",
+ " \"time\": ds[\"time\"].values,\n",
+ " \"air-temp\": ds[\"air\"].values,\n",
+ " }\n",
+ "\n",
+ "\n",
+ "data = air_as_dictionary()\n",
+ "\n",
+ "\n",
+ "# Your code here"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Why Xarray? \n",
+ "\n",
+ "Metadata provides context and provides code that is more legible. This reduces the likelihood of errors from typos and makes analysis more intuitive and fun!\n",
+ "\n",
+ "### Analysis without xarray: `X(`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# plot the first timestep\n",
+ "lat = ds.air.lat.data # numpy array\n",
+ "lon = ds.air.lon.data # numpy array\n",
+ "temp = ds.air.data # numpy array"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "plt.figure()\n",
+ "plt.pcolormesh(lon, lat, temp[0, :, :]);"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "temp.mean(axis=1) ## what did I just do? I can't tell by looking at this line."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Analysis with xarray `=)`\n",
+ "\n",
+ "How readable is this code?\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds.air.isel(time=0).plot(x=\"lon\");"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Use dimension names instead of axis numbers\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds.air.mean(dim=\"time\").plot(x=\"lon\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---\n",
+ "\n",
+ "## Extracting data or \"indexing\" \n",
+ "\n",
+ "Xarray supports\n",
+ "\n",
+ "- label-based indexing using `.sel`\n",
+ "- position-based indexing using `.isel`\n",
+ "\n",
+ "See the [user guide](https://docs.xarray.dev/en/stable/indexing.html) for more."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Label-based indexing\n",
+ "\n",
+ "Xarray inherits its label-based indexing rules from pandas; this means great\n",
+ "support for dates and times!\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# here's what ds looks like\n",
+ "ds"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# pull out data for all of 2013-May\n",
+ "ds.sel(time=\"2013-05\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# demonstrate slicing\n",
+ "ds.sel(time=slice(\"2013-05\", \"2013-07\"))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds.sel(time=\"2013\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# demonstrate \"nearest\" indexing\n",
+ "ds.sel(lon=240.2, method=\"nearest\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# \"nearest indexing at multiple points\"\n",
+ "ds.sel(lon=[240.125, 234], lat=[40.3, 50.3], method=\"nearest\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Position-based indexing\n",
+ "\n",
+ "This is similar to your usual numpy `array[0, 2, 3]` but with the power of named\n",
+ "dimensions!\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds.air.data[0, 2, 3]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# pull out time index 0, lat index 2, and lon index 3\n",
+ "ds.air.isel(time=0, lat=2, lon=3) # much better than ds.air[0, 2, 3]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# demonstrate slicing\n",
+ "ds.air.isel(lat=slice(10))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Other field DataStructures\n",
+ "\n",
+ "\n",
+ "Link to next notebook: [Data structures In Biology](./BiologyDataset.ipynb)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Exercise\n",
+ "\n",
+ "Dataset of non-alignable data.\n",
+ "\n",
+ "For this exercise we will be using the `\"precipitation\"` example data. This data has two parts observed rainfall (`observed`), and predicted rainfall, on a different lat-lon grid. The next cell calls a function that will give you two different Datasets. Try to combine them using the tools we've discussed thus far and observe what goes wrong.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "tree = xr.tutorial.open_datatree(\"precipitation.nc4\")\n",
+ "reanalysis = xr.Dataset(tree[\"observed\"])\n",
+ "observed = xr.Dataset(tree[\"reanalysis\"])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "For more see the [user guide](https://docs.xarray.dev/en/stable/plotting.html), the [gallery](https://docs.xarray.dev/en/stable/examples/visualization_gallery.html), and [the tutorial material](https://tutorial.xarray.dev/fundamentals/04.0_plotting.html)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3"
+ },
+ "toc": {
+ "base_numbering": 1,
+ "nav_menu": {},
+ "number_sections": true,
+ "sideBar": true,
+ "skip_h1_title": false,
+ "title_cell": "Table of Contents",
+ "title_sidebar": "Contents",
+ "toc_cell": true,
+ "toc_position": {},
+ "toc_section_display": true,
+ "toc_window_display": true
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/intermediate/hierarchical_zarr_store.ipynb b/intermediate/hierarchical_zarr_store.ipynb
new file mode 100644
index 00000000..6288cc03
--- /dev/null
+++ b/intermediate/hierarchical_zarr_store.ipynb
@@ -0,0 +1,341 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "0",
+ "metadata": {},
+ "source": [
+ "# Zarr Stores with Xarray\n",
+ "\n",
+ "## Learning Objectives:\n",
+ "- Learn about the Zarr data format and xarray's \"zarr\" backend engine\n",
+ "- Learn how to read a Zarr store with a hierarchical structure with `xr.DataTree`\n",
+ "- Learn how to select dask arrays from a Zarr store\n",
+ "- Explore how to use Zarr stores with xarray for computations and visualizations\n",
+ "\n",
+ "## What is Zarr?\n",
+ "\n",
+ "The Zarr data format is an open, community-maintained format designed for efficient, scalable storage of large N-dimensional arrays. It stores data as compressed and chunked arrays in a format well-suited to parallel processing and cloud-native workflows. Xarray’s Zarr backend allows xarray to leverage these capabilities, including the ability to store and analyze datasets far too large fit onto disk (particularly in combination with [dask](https://docs.xarray.dev/en/latest/user-guide/dask.html#dask)).\n",
+ "\n",
+ "### Zarr Data Organization:\n",
+ "- **Arrays**: N-dimensional arrays that can be chunked and compressed.\n",
+ "- **Groups**: A container for organizing multiple arrays and other groups with a hierarchical structure.\n",
+ "- **Metadata**: JSON-like metadata describing the arrays and groups, including information about data types, dimensions, chunking, compression, and user-defined key-value fields. \n",
+ "- **Dimensions and Shape**: Arrays can have any number of dimensions, and their shape is defined by the number of elements in each dimension.\n",
+ "- **Coordinates & Indexing**: Zarr supports coordinate arrays for each dimension, allowing for efficient indexing and slicing.\n",
+ "\n",
+ "The diagram below from [the Zarr v3 specification](https://wiki.earthdata.nasa.gov/display/ESO/Zarr+Format) showing the structure of a Zarr store:\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "NetCDF and Zarr share similar terminology and functionality, but the key difference is that NetCDF is a single file, while Zarr is a directory-based “store” composed of many chunked files, making it better suited for distributed and cloud-based workflows."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1",
+ "metadata": {},
+ "source": [
+ "## Reading a zarr store\n",
+ "With xarray's \"zarr\" backend, we can read data remotely from cloud storage buckets. Since Zarr is a cloud-optimized format, our dataset is lazily-loaded as dask arrays.\n",
+ "The Zarr store in this tutorial is in a public storage bucket and is a precipitation dataset with a group hierarchical structure. The groups: \"observed\" and \"reanalysis\", each contain a \"precipitation\" data variable derived from *[GPM_3IMERGHH_07](https://disc.gsfc.nasa.gov/datasets/GPM_3IMERGHH_07/summary)* and *[M2T1NXFLX_5.12.4](https://disc.gsfc.nasa.gov/datasets/M2T1NXFLX_5.12.4/summary)* products, respectively. The precipitation data variables in each group have different spatial resolutions that cannot be represented in a single group, so we need to read them as `DataTree` objects.\n",
+ "\n",
+ "Let's read in our zarr store with `xr.open_datatree`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import xarray as xr\n",
+ "\n",
+ "precipitation_store = \"https://pub-45a1d62ac8d94c4c89f4dc63681a98ed.r2.dev/precipitation.zarr\"\n",
+ "\n",
+ "precip_dt = xr.open_datatree(precipitation_store, engine=\"zarr\", chunks={}, consolidated=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "precip_dt.observed.precipitation"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4",
+ "metadata": {},
+ "source": [
+ ":::{note} We selected `\"zarr\"` backend engine, which tells xarray to load and decode a dataset from a Zarr store. The `chunks={}` parameter is used to load the data into a dask array. And `consolidated=True` enables zarr’s consolidated metadata capability. This lets us read all of the metadata from a single file which can improve performance.\n",
+ ":::"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5",
+ "metadata": {},
+ "source": [
+ "## Variable selection\n",
+ "With our `DataTree` object, we can select variables from our Zarr store with either dictionary and or attribute like syntax."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6",
+ "metadata": {},
+ "source": [
+ "### Dictionary-like interface"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "precip_dt[\"observed/precipitation\"]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8",
+ "metadata": {},
+ "source": [
+ "### Attribute-like access"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "precip_dt.observed.precipitation"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "10",
+ "metadata": {},
+ "source": [
+ "### Dictionary and Attribute like access"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "11",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "precip_dt.observed[\"precipitation\"]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "12",
+ "metadata": {},
+ "source": [
+ ":::{note} All of these variable selection options return the same \"precipitation\" `xr.DataArray` object, as a chunked `dask.Array`, from the \"observed\" group of our Zarr store.\n",
+ ":::"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "13",
+ "metadata": {},
+ "source": [
+ "## Time slicing\n",
+ "\n",
+ "We can index and subset by time on our `xr.DataTree` object. Each time slice in our Zarr store represents one hour of data with a total of 10 hours of data.\n",
+ "\n",
+ "Let's explore the different ways we can get the first 5 hours of data. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "14",
+ "metadata": {},
+ "source": [
+ "### Label-based indexing"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "15",
+ "metadata": {},
+ "source": [
+ "Let's try getting the first 5 hours of data with `.sel(time=)`. \n",
+ "\n",
+ "Since the time slices are ordered we can get a subset of the array of our time coordinate and pass it to the `.sel` method."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "16",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "time_index = precip_dt.time[0:5]\n",
+ "precip_dt.sel(time=time_index)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "17",
+ "metadata": {},
+ "source": [
+ "### Datetime indexing\n",
+ "We can also subset by time with a `datetime` string."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "18",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "precip_dt.sel(time=slice(\"2021-08-29T07:30:00\", \"2021-08-29T16:30:00\"))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "19",
+ "metadata": {},
+ "source": [
+ "### Positional Indexing\n",
+ "Or by the index of our time dimension `.isel(time=slice())`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "20",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "precip_dt.isel(time=slice(0, 5))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "21",
+ "metadata": {
+ "vscode": {
+ "languageId": "plaintext"
+ }
+ },
+ "source": [
+ "## Chunking\n",
+ "Chunking is the process of dividing arrays into smaller pieces, which allows for parallel processing and efficient storage.\n",
+ "\n",
+ "To examine the chunks in our Zarr store, with `xarray` you can use the `chunks` attribute on the `xr.DataArray` object."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "22",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "precip_dt.observed[\"precipitation\"].data.chunks"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "23",
+ "metadata": {},
+ "source": [
+ "### Selecting by chunks\n",
+ "\n",
+ "Since we loaded our data as a `dask.Array`, we can access data from each chunked array in our Zarr store. \n",
+ "\n",
+ "Let's get the first chunk of the \"observed/precipitation\" variable in our zarr store."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "24",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "precip_dt.observed[\"precipitation\"].data.blocks[0, 0, 0].compute()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "25",
+ "metadata": {},
+ "source": [
+ ":::{note}\n",
+ "We added `.data` to our `xr.DataArray` to access the `dask.Array`. The `.blocks[]` method allows you to index by chunk and `.compute()` returns the `np.ndarray`. \n",
+ ":::"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "26",
+ "metadata": {},
+ "source": [
+ "## Exercise"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "27",
+ "metadata": {},
+ "source": [
+ "::::{admonition} Exercise\n",
+ ":class: tip\n",
+ "\n",
+ "Can you calculate and plot the mean precipitation, starting at 09:55 for the reanalysis group in this zarr store?\n",
+ "\n",
+ ":::{admonition} Hint\n",
+ ":class: dropdown\n",
+ "This is how you could calculate mean \"precipitation\"\n",
+ "\n",
+ "```python\n",
+ "precip_dt.reanalysis['precipitation'].mean(dim='time')\n",
+ "```\n",
+ ":::\n",
+ "\n",
+ ":::{admonition} Solution\n",
+ ":class: dropdown\n",
+ "\n",
+ "```python\n",
+ "precip_dt.reanalysis['precipitation'].sel(time=slice('2021-08-29T09:55:00', '2021-08-29T16:30:00')).mean(dim='time').plot()\n",
+ "```\n",
+ ":::\n",
+ "::::"
+ ]
+ }
+ ],
+ "metadata": {
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/intermediate/hvplot.ipynb b/intermediate/hvplot.ipynb
new file mode 100644
index 00000000..fa458c7c
--- /dev/null
+++ b/intermediate/hvplot.ipynb
@@ -0,0 +1,175 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "0",
+ "metadata": {},
+ "source": [
+ "# Interactive plots using hvplot\n",
+ "\n",
+ "Xarray's builtin plotting functionality wraps matplotlib.\n",
+ "\n",
+ "The `holoviews` [ecosystem](https://hvplot.holoviz.org/) provides the `hvplot` package to allow easy\n",
+ "visualization of xarray (and other) objects. These\n",
+ "plots build on [Bokeh](https://bokeh.org/).\n",
+ "\n",
+ "`hvplot` makes uses of xarray's [accessor interface](https://docs.xarray.dev/en/stable/internals/extending-xarray.html). This means that all xarray\n",
+ "objects gain a `.hvplot` attribute that lets you access `.hvplot` functionality\n",
+ "as easily as you would use `.plot`. All you need to do is `import hvplot.xarray`\n",
+ "\n",
+ "For more, see hvplot's [documentation](https://hvplot.holoviz.org/user_guide/Gridded_Data.html)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import cartopy.crs as ccrs\n",
+ "import hvplot.xarray\n",
+ "import xarray as xr"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds = xr.tutorial.open_dataset(\"air_temperature.nc\").rename({\"air\": \"Tair\"})"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3",
+ "metadata": {},
+ "source": [
+ "## Basics\n",
+ "\n",
+ "`hvplot` makes the same default choices as `DataArray.plot`\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds.Tair.hvplot()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# 2D array yields a quadmesh plot\n",
+ "ds.Tair.isel(time=1).hvplot(cmap=\"fire\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# 1D array yields a line plot\n",
+ "ds.Tair.isel(time=1, lon=1).hvplot()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7",
+ "metadata": {},
+ "source": [
+ "## Interactivity\n",
+ "\n",
+ "But `hvplot` shines when interactivity is used. Here we can give it _all_ the\n",
+ "data and ask it to create a nice slider to control the time slice using the\n",
+ "`groupby` kwarg.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds.Tair.hvplot(\n",
+ " groupby=\"time\",\n",
+ " clim=(250, 295), # adds a widget for time # sets colorbar limits\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9",
+ "metadata": {},
+ "source": [
+ "## Animations\n",
+ "\n",
+ "are easy.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "10",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# set constant colorbar limits\n",
+ "ds.Tair.hvplot(\n",
+ " groupby=\"time\", # adds a widget for time\n",
+ " clim=(250, 295), # sets colormap limits\n",
+ " widget_type=\"scrubber\",\n",
+ " widget_location=\"bottom\",\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "11",
+ "metadata": {},
+ "source": [
+ "## Geography\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "12",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds.Tair.isel(time=1).hvplot(\n",
+ " projection=ccrs.Orthographic(-90, 30),\n",
+ " coastline=True,\n",
+ ")"
+ ]
+ }
+ ],
+ "metadata": {
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/intermediate/indexing/advanced-indexing.ipynb b/intermediate/indexing/advanced-indexing.ipynb
new file mode 100644
index 00000000..b6ea47cb
--- /dev/null
+++ b/intermediate/indexing/advanced-indexing.ipynb
@@ -0,0 +1,432 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Advanced Indexing\n",
+ "\n",
+ "## Learning Objectives\n",
+ "\n",
+ "* Orthogonal vs. Pointwise (Vectorized) Indexing.\n",
+ "* Pointwise indexing in Xarray to extract data at a collection of points.\n",
+ "* Understand the difference between NumPy and Xarray indexing behavior."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Overview\n",
+ "\n",
+ "In the previous notebooks, we learned basic forms of indexing with Xarray, including positional and label-based indexing, datetime indexing, and nearest neighbor lookups. We also learned that indexing an Xarray DataArray directly works (mostly) like it does for NumPy arrays; however, Xarray indexing behavior deviates from NumPy when using multiple arrays for indexing, like `arr[[0, 1], [0, 1]]`.\n",
+ "\n",
+ "To better understand this difference, let's take a look at an example of 2D 5x5 array:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "\n",
+ "# Create a 5x5 array with values from 1 to 25\n",
+ "np_array = np.arange(1, 26).reshape(5, 5)\n",
+ "np_array"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now create a Xarray DataArray from this NumPy array: "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import xarray as xr\n",
+ "\n",
+ "da = xr.DataArray(np_array, dims=[\"x\", \"y\"])\n",
+ "da"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now, let's see how the indexing behavior is different between NumPy array and Xarray DataArray when indexing with multiple arrays:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "np_array[[0, 2, 4], [0, 2, 4]]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "da[[0, 2, 4], [0, 2, 4]]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The image below summarizes the difference between vectorized and orthogonal indexing for a 2D 5x5 NumPy array and Xarray DataArray:\n",
+ "\n",
+ "\n",
+ "\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**Pointwise** or **Vectorized indexing**, shown on the left, selects specific elements at given coordinates, resulting in an array of those individual elements. In the example shown, the indices `[0, 2, 4]`, `[0, 2, 4]` select the elements at positions `(0, 0)`, `(2, 2)`, and `(4, 4)`, resulting in the values `[1, 13, 25]`. This is the default behavior of NumPy arrays.\n",
+ " \n",
+ "In contrast, **orthogonal indexing** uses the same indices to select entire rows and columns, forming the Cartesian product of the specified indices. This method results in sub-arrays that include all combinations of the selected rows and columns. The example demonstrates this by selecting rows 0, 2, and 4 and columns 0, 2, and 4, resulting in a subarray containing `[[1, 3, 5], [11, 13, 15], [21, 23, 25]]`. This is Xarray DataArray's default behavior.\n",
+ " \n",
+ "The output of vectorized indexing is a `1D array`, while the output of orthogonal indexing is a `3x3` array. \n",
+ "\n",
+ "\n",
+ ":::{tip} To Summarize: \n",
+ "\n",
+ "- *Pointwise* or *vectorized* indexing is a more general form of indexing that allows for arbitrary combinations of indexing arrays. This method of indexing is analogous to the broadcasting rules in NumPy, where the dimensions of the indexers are aligned and the result is determined by the shape of the indexers. This is the default behavior in NumPy.\n",
+ "\n",
+ "- *Orthogonal* or *outer* indexing allows for indexing along each dimension independently, treating the indexers as one-dimensional arrays. The principle of outer or orthogonal indexing is that the result mirrors the effect of independently indexing along each dimension with integer or boolean arrays, treating both the indexed and indexing arrays as one-dimensional. This method of indexing is analogous to vector indexing in programming languages like MATLAB, Fortran, and R, where each indexer component independently selects along its corresponding dimension. This is the default behavior in Xarray.\n",
+ "\n",
+ "\n",
+ ":::\n",
+ "\n",
+ ":::{admonition} Orthogonal indexing with NumPy\n",
+ ":class: note dropdown\n",
+ "\n",
+ "While pointwise indexing is the default behavior in NumPy, you can achieve orthogonal indexing by using the [`np.ix_` function](https://numpy.org/doc/stable/reference/generated/numpy.ix_.html). This function constructs an open mesh from multiple arrays, allowing you to index along each dimension independently similar to Xarray indexing behavior. For example: \n",
+ "\n",
+ "```python\n",
+ "ixgrid = np.ix_([0, 2, 4], [0, 2, 4])\n",
+ "np_array[ixgrid]\n",
+ "```\n",
+ "\n",
+ ":::"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Orthogonal Indexing in Xarray\n",
+ "\n",
+ "As explained earlier, when you use only integers, slices, or unlabeled arrays (arrays without dimension names, such as `np.ndarray` or `list`, but not `DataArray`) to index an `Xarray DataArray`, Xarray interprets these indexers orthogonally. This means it indexes along independent axes, rather than using NumPy's broadcasting rules to vectorize the indexers. \n",
+ "\n",
+ "In the example above we saw this behavior, but let's see this behavior in action with a real dataset. Here we’ll use `air temperature` data from the National Center for Environmental Prediction:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import xarray as xr\n",
+ "\n",
+ "xr.set_options(display_expand_attrs=False)\n",
+ "np.set_printoptions(threshold=10, edgeitems=2)\n",
+ "%config InlineBackend.figure_format='retina'\n",
+ "\n",
+ "ds = xr.tutorial.load_dataset(\"air_temperature\")\n",
+ "da_air = ds.air\n",
+ "da_air"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "selected_da = da_air.isel(time=0, lat=[2, 4, 10, 13], lon=[1, 6, 7]) # -- orthogonal indexing\n",
+ "selected_da"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "👆 Please note that the output shape in the example above is `4x3` because the latitude indexer selects 4 rows, and the longitude indexer selects 3 columns."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "For more flexibility, you can supply `DataArray()` objects as indexers. Dimensions on resultant arrays are given by the ordered union of the indexers’ dimensions.\n",
+ "\n",
+ "For example, in the example below we do orthogonal indexing using `DataArray()` objects. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "target_lat = xr.DataArray([31, 41, 42, 42], dims=\"degrees_north\")\n",
+ "target_lon = xr.DataArray([200, 201, 202, 205], dims=\"degrees_east\")\n",
+ "\n",
+ "da_air.sel(lat=target_lat, lon=target_lon, method=\"nearest\") # -- orthogonal indexing"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In the above example, you can see how the output shape is `time` x `lats` x `lons`. Please note that there are no shared dimensions between the indexers, so the output shape is the union of the dimensions of the indexers.\n",
+ "\n",
+ "```{attention}\n",
+ "Please note that slices or sequences/arrays without named-dimensions are treated as if they have the same dimension which is indexed along.\n",
+ "```\n",
+ "\n",
+ "For example:\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "da_air.sel(lat=[20, 30, 40], lon=target_lon, method=\"nearest\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "\n",
+ "But what if we'd like to find the nearest climate model grid cell to a collection of specified points (for example observation sites, or weather stations)?\n",
+ "\n",
+ "## Vectorized or Pointwise Indexing in Xarray\n",
+ "\n",
+ "Like NumPy and pandas, Xarray supports indexing many array elements at once in a *vectorized* manner. \n",
+ "\n",
+ "**Vectorized indexing** or **Pointwise Indexing** using `DataArrays()` can be used to extract information from the nearest grid cells of interest, for example, the nearest climate model grid cells to a collection of specified observation tower data latitudes and longitudes.\n",
+ "\n",
+ "```{hint}\n",
+ "To trigger vectorized indexing behavior, you will need to provide the selection dimensions with a new **shared** output dimension name. This means that the dimensions of both indexers must be the same, and the output will have the same dimension name as the indexers.\n",
+ "```\n",
+ "\n",
+ "Let's see how this works with an example:\n",
+ "\n",
+ "A researcher wants to find the nearest climate model grid cell to a collection of observation sites. They have the latitude and longitude of the observation sites as following:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "obs_lats = [31.81, 41.26, 22.59, 44.47, 28.57]\n",
+ "\n",
+ "obs_lons = [200.16, 201.57, 305.54, 210.56, 226.59]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "If the researcher use the lists to index the DataArray, they will get the orthogonal indexing behavior, which is not what they want."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "da_air.sel(lat=obs_lats, lon=obs_lons, method=\"nearest\") # -- orthogonal indexing"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To trigger the pointwise indexing, they need to create DataArray objects with the same dimension name, and then use them to index the DataArray. \n",
+ "For example, the code below first create DataArray objects for the latitude and longitude of the observation sites using a shared dimension name `points`, and then use them to index the DataArray `air_temperature`:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "## latitudes of weather stations with a dimension of \"points\"\n",
+ "lat_points = xr.DataArray(obs_lats, dims=\"points\")\n",
+ "lat_points"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "## longitudes of weather stations with a dimension of \"points\"\n",
+ "lon_points = xr.DataArray(obs_lons, dims=\"points\")\n",
+ "lon_points"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now, retrieve data at the grid cells nearest to the target latitudes and longitudes (weather stations):"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "da_air.sel(lat=lat_points, lon=lon_points, method=\"nearest\") # -- pointwise indexing"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "👆 Please notice how the shape of our `DataArray` is `time` x `points`, extracting time series for each weather stations. \n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "da_air.sel(lat=lat_points, lon=lon_points, method=\"nearest\").dims"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now, let's plot the data for all stations."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "da_air.sel(lat=lat_points, lon=lon_points, method=\"nearest\").plot(x=\"time\", hue=\"points\");"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Exercises\n",
+ "\n",
+ "::::{admonition} Exercise\n",
+ ":class: tip\n",
+ "\n",
+ "In the simple 2D 5x5 Xarray data array above, select the sub-array containing (0,4),(2,2),(4,0):\n",
+ "\n",
+ ":::{admonition} Solution\n",
+ ":class: dropdown\n",
+ "```python\n",
+ "\n",
+ "x_indices = np.array([0, 2, 4])\n",
+ "y_indices = np.array([4, 2, 0])\n",
+ "\n",
+ "da_x = xr.DataArray(x_indices, dims=\"points\")\n",
+ "da_y = xr.DataArray(y_indices, dims=\"points\")\n",
+ "\n",
+ "subset_da = da.sel(x=da_x, y=da_y)\n",
+ "subset_da\n",
+ "```\n",
+ ":::\n",
+ "::::"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Additional Resources\n",
+ "\n",
+ "- [Xarray Docs - Indexing and Selecting Data](https://docs.xarray.dev/en/stable/indexing.html)\n",
+ "\n",
+ "\n",
+ ":::{seealso}\n",
+ "- [Introductions to Fancy Indexing](https://jakevdp.github.io/PythonDataScienceHandbook/02.07-fancy-indexing.html)\n",
+ "- [NumPy Docs - Advanced Indexing](https://numpy.org/doc/stable/user/basics.indexing.html#advanced-indexing)\n",
+ "\n",
+ ":::\n"
+ ]
+ }
+ ],
+ "metadata": {
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3"
+ },
+ "toc": {
+ "base_numbering": 1,
+ "nav_menu": {},
+ "number_sections": true,
+ "sideBar": true,
+ "skip_h1_title": false,
+ "title_cell": "Table of Contents",
+ "title_sidebar": "Contents",
+ "toc_cell": true,
+ "toc_position": {},
+ "toc_section_display": true,
+ "toc_window_display": true
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/intermediate/indexing/boolean-masking-indexing.ipynb b/intermediate/indexing/boolean-masking-indexing.ipynb
new file mode 100644
index 00000000..6432b86a
--- /dev/null
+++ b/intermediate/indexing/boolean-masking-indexing.ipynb
@@ -0,0 +1,496 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Boolean Indexing & Masking\n",
+ "\n",
+ "## Learning Objectives\n",
+ "\n",
+ "* The concept of boolean masks\n",
+ "* Dropping/Masking data using `where`\n",
+ "* Using `isin` for creating a boolean mask"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Overview\n",
+ "\n",
+ "*Boolean masking*, known as *boolean indexing*, is a functionality in Python that enables the filtering of values based on a specific condition.\n",
+ "\n",
+ "A boolean mask refers to a binary array or a boolean-valued (`True`/`False`) array that is used as a *filter* to select specific elements from another array. The boolean mask acts as a criterion or condition, where each element in the mask corresponds to an element in the target array. An element in the target array is selected when the corresponding `mask` value is `True`. \n",
+ "\n",
+ "Xarray provides different capabilities to allow filtering and boolean indexing. In this notebook, we will learn more about it.\n",
+ "\n",
+ "First, let's import the packages needed for this notebook: "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import cartopy.crs as ccrs\n",
+ "import matplotlib as mpl\n",
+ "import numpy as np\n",
+ "import xarray as xr\n",
+ "from matplotlib import pyplot as plt\n",
+ "\n",
+ "xr.set_options(display_expand_attrs=False)\n",
+ "np.set_printoptions(threshold=10, edgeitems=2)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In this tutorial, we’ll use the Regional Arctic System Mode (RASM) example dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds = xr.tutorial.load_dataset(\"rasm\").isel(time=0)\n",
+ "ds"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In this dataset, the logical coordinates are `x` and `y`, while the physical coordinates are `xc` and `yc`, which represent the latitudes and longitude of the data."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(ds.xc.attrs)\n",
+ "print(ds.yc.attrs)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "da = ds.Tair\n",
+ "da"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Masking with `where()`\n",
+ "\n",
+ "Indexing methods on Xarray objects generally return a subset of the original data. However, it is sometimes useful to select an object with the same shape as the original data, but with some elements masked. \n",
+ "\n",
+ "By applying `.where()`, the original data's shape is maintained, with values masked based on a Boolean condition. Values that satisfy the condition (`True`) are returned unchanged, while values that do not meet the condition (`False`) are replaced with a predefined value.\n",
+ "\n",
+ "In the example below, we replace all `nan` values with `-9999`:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Let's replace the missing values (nan) with some placeholder\n",
+ "ds.Tair.where(ds.Tair.notnull(), -9999)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "As you can see, in the example above `.where()` preserved the **shape** of the original data by masking the values with a boolean condition. \n",
+ "\n",
+ "Most uses of `.where()` check whether or not specific data values are less than or greater than a constant value. \n",
+ "\n",
+ "The data values specified in the boolean condition of `.where()` can be any of the following:\n",
+ "\n",
+ "* a `DataArray`\n",
+ "* a `Dataset`\n",
+ "* a function\n",
+ "\n",
+ "In the following example, we make use of `.where()` to mask all temperature below 0°C.\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "da_masked = da.where(da >= 0)\n",
+ "\n",
+ "# -- making both plots for comparison:\n",
+ "fig, axes = plt.subplots(ncols=2, figsize=(15, 5))\n",
+ "\n",
+ "# -- for reference (without masking):\n",
+ "da.plot(ax=axes[0], vmin=-30, vmax=30, cmap=mpl.cm.RdBu_r)\n",
+ "\n",
+ "# -- masked DataArray\n",
+ "da_masked.plot(ax=axes[1], vmin=-30, vmax=30, cmap=mpl.cm.RdBu_r);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "```{tip}\n",
+ "By default Xarray set the masked values to `nan`. But as we saw in the first example, we can set it to other values too. \n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "::::{admonition} Exercise\n",
+ ":class: tip\n",
+ "\n",
+ "Using the syntax you’ve learned so far, mask all the points with latitudes above 60° N.\n",
+ "\n",
+ ":::{admonition} Solution\n",
+ ":class: dropdown\n",
+ "\n",
+ "```python\n",
+ "da_masked = da.where(da.yc >= 60)\n",
+ "da_masked[:, :].plot();\n",
+ "```\n",
+ ":::\n",
+ "::::"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# write your answer here!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "As mentioned above, by default `where` maintains the original size of the data. You can use the option `drop=True` to clip coordinate elements that are fully masked:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "da_masked = da.where(da.yc > 60, drop=True)\n",
+ "da_masked.plot();"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Please note that in this dataset, the variables `xc` (longitude) and `yc` (latitude) are two-dimensional scalar fields.\n",
+ "\n",
+ "When we plotted the data variable `Tair`, by default we get the logical coordinates (i.e. `x` and `y`) as we show in the example above. \n",
+ "\n",
+ "In order to visualize the data on a conventional latitude-longitude grid, we can take advantage of Xarray’s ability to apply `cartopy` map projections."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "plt.figure(figsize=(14, 6))\n",
+ "ax = plt.axes(projection=ccrs.PlateCarree())\n",
+ "ax.set_global()\n",
+ "ds.Tair.plot.pcolormesh(ax=ax, transform=ccrs.PlateCarree(), x=\"xc\", y=\"yc\", add_colorbar=False)\n",
+ "ax.coastlines()\n",
+ "ax.set_ylim([20, 90]);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Using `where` with Multiple Conditions\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In Xarray's `.where()` function, boolean conditions can be combined using logical operators. The bitwise `and` operator (`&`) and the bitwise `or` operator (`|`) are relevant in this case. This allows for specifying multiple masking conditions within a single `.where()` statement.\n",
+ "\n",
+ "We can select data for one specific region using bound boxes. For example, here we want to access data over a region over Alaska :"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# -- define a region\n",
+ "min_lon = 190\n",
+ "min_lat = 55\n",
+ "max_lon = 230\n",
+ "max_lat = 85"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "First we have to create our boolean masks:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "mask_lon = (ds.xc >= min_lon) & (ds.xc <= max_lon)\n",
+ "mask_lat = (ds.yc >= min_lat) & (ds.yc <= max_lat)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Next, we can use the boolean masks for filtering data for that region: "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "da_masked = da.where(mask_lon & mask_lat, drop=True)\n",
+ "\n",
+ "da_masked.plot();"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "plt.figure(figsize=(5, 5))\n",
+ "ax = plt.axes(projection=ccrs.PlateCarree())\n",
+ "ax.set_global()\n",
+ "da_masked.plot.pcolormesh(ax=ax, transform=ccrs.PlateCarree(), x=\"xc\", y=\"yc\", add_colorbar=False)\n",
+ "ax.coastlines()\n",
+ "ax.set_ylim([50, 80])\n",
+ "ax.set_xlim([-180, -120]);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Exercise\n",
+ "\n",
+ "If we load air temperature dataset from NCEP, we could use `sel` method for selecting a region:\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "::::{admonition} Exercise\n",
+ ":class: tip\n",
+ "\n",
+ "If we load air temperature dataset from NCEP, we could use `sel` method for selecting a region:\n",
+ "\n",
+ "```python\n",
+ "ds = xr.tutorial.open_dataset(\"air_temperature\")\n",
+ "ds_region = ds.sel(lat=slice(75,50), lon=slice(250,300))\n",
+ "\n",
+ "ds_region.air.plot();\n",
+ "```\n",
+ "Can you use a similar method as above using `sel` to crop a region using the RASM dataset? Why?\n",
+ "\n",
+ ":::{admonition} Solution\n",
+ ":class: dropdown\n",
+ "This method will not work here as the dimensions are different from coordinates here. Specifically, the variables xc (longitude) and yc (latitude) are two-dimensional scalar fields, which differ from the logical coordinates represented by x and y.\n",
+ "\n",
+ "So the code below will not give the correct answer!\n",
+ "```python\n",
+ "cropped_ds = ds.sel(x=slice(min_lat,max_lat), y=slice(min_lon,max_lon))\n",
+ "cropped_ds.Tair.plot()\n",
+ "```\n",
+ ":::\n",
+ "::::"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Using `xr.where` with a Function\n",
+ "\n",
+ "We can use `xr.where` with a function as a condition too. For example, here we want to convert temperature to Kelvin and find if temperature is greater than 280 K:\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Define a function to use as a condition\n",
+ "def is_greater_than_threshold(x, threshold=300):\n",
+ " # function to convert temp to K\n",
+ " # and compare with threshold\n",
+ " x = x + 273.15\n",
+ " return x > threshold\n",
+ "\n",
+ "\n",
+ "# Apply the condition using xarray.where()\n",
+ "masked_data = xr.where(is_greater_than_threshold(da, 280), da, 0)\n",
+ "\n",
+ "masked_data.plot()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Selecting Values with `isin`\n",
+ "\n",
+ "To check whether elements of an xarray object contain a single object, you can compare with the equality operator `==` (e.g., `arr == 3`). \n",
+ "\n",
+ "To check multiple values, we use `isin()`:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Here is a simple example: "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "x_da = xr.DataArray([1, 2, 3, 4, 5], dims=[\"x\"])\n",
+ "\n",
+ "# -- select points with values equal to 2 and 4:\n",
+ "x_da.isin([2, 4])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "```{tip}\n",
+ "`isin()` works particularly well with `where()` to support indexing by arrays that are not already labels of an array. \n",
+ "```\n",
+ "\n",
+ "For example, we have another `DataArray` that displays the status flags of the data-collecting device for our data. \n",
+ "\n",
+ "Here, flags with value 0 and -1 signifies the device was functioning correctly, while 0 indicates a malfunction, implying that the resulting data collected may not be accurate."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "flags = xr.DataArray(np.random.randint(-1, 5, da.shape), dims=da.dims, coords=da.coords)\n",
+ "flags"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now, we want to only see the data for points where out measurement device is working correctly: "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "da_masked = da.where(flags.isin([1, 2, 3, 4, 5]), drop=True)\n",
+ "da_masked.plot();"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "```{warning}\n",
+ "Please note that when done repeatedly, this type of indexing is significantly slower than using `sel()`. \n",
+ "\n",
+ "Use `sel` instead of `where` as much as possible.\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Additional Resources\n",
+ "\n",
+ "- [Xarray Docs - Indexing and Selecting Data](https://docs.xarray.dev/en/stable/indexing.html)\n"
+ ]
+ }
+ ],
+ "metadata": {
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3"
+ },
+ "toc": {
+ "base_numbering": 1,
+ "nav_menu": {},
+ "number_sections": true,
+ "sideBar": true,
+ "skip_h1_title": false,
+ "title_cell": "Table of Contents",
+ "title_sidebar": "Contents",
+ "toc_cell": true,
+ "toc_position": {},
+ "toc_section_display": true,
+ "toc_window_display": true
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/intermediate/indexing/indexing.md b/intermediate/indexing/indexing.md
new file mode 100644
index 00000000..72dae2a4
--- /dev/null
+++ b/intermediate/indexing/indexing.md
@@ -0,0 +1,5 @@
+# Indexing
+
+```{tableofcontents}
+
+```
diff --git a/intermediate/indexing/why-an-index.ipynb b/intermediate/indexing/why-an-index.ipynb
new file mode 100644
index 00000000..3123f3d1
--- /dev/null
+++ b/intermediate/indexing/why-an-index.ipynb
@@ -0,0 +1,1009 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "0",
+ "metadata": {},
+ "source": [
+ "# Why does xarray need an *index*? \n",
+ "\n",
+ "and when do you need **custom** one?\n",
+ "\n",
+ "Xarray `coordinates` allow you to `sel`. For simple 1D cases this is equivalent to calling `argmin` on your array. But that is just the start of what indexes allow you to do. In general an index lets you tell xarray about the *structure* in your coordinates. Such as\n",
+ "\n",
+ " - Complex Geographic Coordinate System\n",
+ " - Periodic Boundaries\n",
+ " - Lens Distortion\n",
+ "\n",
+ "And allows you to make ergonomic and efficient data selections by exploiting that knowledge of the structure\n",
+ "\n",
+ "## Learning objectives\n",
+ "\n",
+ "- See what a built-in `Index` lets you do\n",
+ "- Recognize when the *structure* of your coordinates outgrows the default index.\n",
+ "- Understand the basics of writing a custom `Index`\n",
+ "\n",
+ "## Outline\n",
+ "\n",
+ "**Built-in indexes** carry you a long way\n",
+ " - A video's **time axis** (`PandasIndex`)\n",
+ " - A **too-big-to-store** regular axis (`RangeIndex`)\n",
+ "\n",
+ "**A Simple Custom Index**\n",
+ " - Periodic on a ring\n",
+ "\n",
+ "**Non-linear coordinate transformations**\n",
+ "\n",
+ "\n",
+ "*See also: the [Xarray Indexes Gallery](https://xarray-indexes.readthedocs.io/) · [how to create a custom index](https://docs.xarray.dev/en/stable/internals/how-to-create-custom-index.html) · [indexing user guide](https://docs.xarray.dev/en/stable/user-guide/indexing.html).*"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "\n",
+ "os.environ.setdefault(\"KMP_WARNINGS\", \"0\") # silence a benign OpenMP info line in some envs\n",
+ "\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import xarray as xr\n",
+ "from xarray import Coordinates\n",
+ "from xarray.indexes import (\n",
+ " CoordinateTransform,\n",
+ " CoordinateTransformIndex,\n",
+ " NDPointIndex,\n",
+ " RangeIndex,\n",
+ ")\n",
+ "\n",
+ "\n",
+ "# helper reused later, when we get to the metal ring\n",
+ "def draw_circle(ax, r=1, lim=1.4):\n",
+ " circ = plt.Circle((0, 0), r, fill=False, ls=\":\", color=\"0.7\")\n",
+ " ax.add_patch(circ)\n",
+ " ax.set_aspect(\"equal\")\n",
+ " ax.set_xlim(-lim, lim)\n",
+ " ax.set_ylim(-lim, lim)\n",
+ " ax.set_xlabel(\"x\")\n",
+ " ax.set_ylabel(\"y\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2",
+ "metadata": {},
+ "source": [
+ "## 1. Built-in indexes \n",
+ "\n",
+ "For 1-D coordinates built in Indexes are excellent. Here we will \n",
+ "- `PandasIndex`\n",
+ "- `RangeIndex`\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3",
+ "metadata": {},
+ "source": [
+ "## 1.1 A coordinate is just an array of labels\n",
+ "\n",
+ "The most familiar index hides in plain sight. A video is just a **stack of frames**, and each frame happens at a **time**. That `time` axis is a coordinate — literally *an array of labels* — and it has exactly the structure the built-in [`PandasIndex`](https://docs.xarray.dev/en/stable/generated/xarray.indexes.PandasIndex.html) is built for: 1‑D and monotonic.\n",
+ "\n",
+ "We'll open a short clip with a tiny xarray backend that reads image formats (`ocean.gif`), so the whole video arrives as an ordinary `Dataset` with dims `(time, height, width, color)`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# A little xarray backend (from the \"Backends\" tutorial) that reads image formats via imageio.\n",
+ "# It turns a gif/mp4 into a Dataset with dims (time, height, width, color).\n",
+ "import sys\n",
+ "\n",
+ "sys.path.insert(0, \"../../advanced/backends\")\n",
+ "from imageio_ import ImageIOBackend\n",
+ "\n",
+ "# .load() pulls the whole (small) clip into memory up front\n",
+ "ocean = xr.open_dataset(\"../../advanced/backends/ocean.gif\", engine=ImageIOBackend).load()\n",
+ "ocean"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ocean.time"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6",
+ "metadata": {},
+ "source": [
+ "\n",
+ "### Finding a time point the naive way: `argmin`\n",
+ "\n",
+ "If we want the frame closest to 0.825 s we could write it using `np.argmin`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "target = np.timedelta64(825, \"ms\")\n",
+ "pos = np.abs(ocean.time.values - target).argmin() # a full O(n) scan\n",
+ "print(f\"nearest frame is index {pos}, at {ocean.time.values[pos] / np.timedelta64(1, 's'):g} s\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8",
+ "metadata": {},
+ "source": [
+ "### Let the index do it"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# select by timestamp (not by frame number!) — the index finds the nearest frame\n",
+ "frame = ocean.sel(time=\"0.825s\", method=\"nearest\")\n",
+ "\n",
+ "# Or a range of times\n",
+ "clip = ocean.sel(time=slice(\"0.5s\", \"1.2s\"))\n",
+ "\n",
+ "seconds = frame.time.values / np.timedelta64(1, \"s\")\n",
+ "fig, ax = plt.subplots(figsize=(5, 3))\n",
+ "ax.imshow(frame[\"data\"].values)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "10",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# 📝 Play with it: change these timestamps (in seconds) and re-run — .sel picks the nearest frame each time\n",
+ "times = [\"0s\", \"0.4s\", \"0.8s\", \"1.2s\", \"1.6s\"]\n",
+ "\n",
+ "montage = ocean[\"data\"].sel(time=times, method=\"nearest\")\n",
+ "fig, axes = plt.subplots(1, len(times), figsize=(3 * len(times), 2))\n",
+ "for ax, t in zip(axes, montage.time.values):\n",
+ " ax.imshow(montage.sel(time=t).values)\n",
+ " ax.set(title=f\"{t / np.timedelta64(1, 's'):g} s\", xticks=[], yticks=[])\n",
+ "fig.suptitle(\"select frames by time — one .sel, no frame numbers\", y=1.05)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "11",
+ "metadata": {},
+ "source": [
+ "### Recap: the 1‑D index\n",
+ "\n",
+ "On a 1‑D, monotonic coordinate, the default [`PandasIndex`](https://docs.xarray.dev/en/stable/generated/xarray.indexes.PandasIndex.html) gives you:\n",
+ "\n",
+ "- **ergonomics:** `nearest`, `tolerance`, and label `slice` as one-liners\n",
+ "- **alignment:** automatic label joins in `+`, `concat`, `merge`\n",
+ "- **performance:** a hash table for exact matches, binary search over the sorted labels for ranges and `nearest`\n",
+ "\n",
+ "The structure that this relies on is the index being monotonic, so it can create a precomputed look up structure, which lets use beat the performance of `argmin`."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "12",
+ "metadata": {},
+ "source": [
+ "## 1.2 Generated, not stored: `RangeIndex`\n",
+ "\n",
+ "- A regular 1‑D axis can get *huge*:\n",
+ " - At 60 fps hour of video is ~216k frames\n",
+ " - A brain slice stack can be enormous (exabyte scale is the goal)\n",
+ "\n",
+ "Xarray coordinates are eagerly loaded into memory. So if we had an axis with 2 billion entries and we recorded a `float64` entry for each one we would use a ton of memory!\n",
+ "\n",
+ "\n",
+ "If we have regular spacing on the coordinate (structure!) we can encode that in the index and avoid the memory issues.\n",
+ "\n",
+ "[`RangeIndex`](https://docs.xarray.dev/en/stable/generated/xarray.indexes.RangeIndex.html) keeps three numbers (`start`, `stop`, `step`).\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "13",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "n_z = 2_000_000_000\n",
+ "z = Coordinates.from_xindex(RangeIndex.arange(0.0, n_z * 4.0, 4.0, dim=\"z\"))[\"z\"]\n",
+ "z"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "14",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "type(z.variable._data).__name__"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "15",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# select by physical position (nm); only the requested points are ever computed\n",
+ "depth_nm = xr.DataArray([1e6, 5e6], dims=\"probe\") # 1 mm and 5 mm deep\n",
+ "z.sel(z=depth_nm, method=\"nearest\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "16",
+ "metadata": {},
+ "source": [
+ "The labels never materialize unless we request them, so we didn't run out of memory."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "17",
+ "metadata": {},
+ "source": [
+ "## 1.3 Slicing in space *and* time\n",
+ "\n",
+ "- One `.sel` selects across *several* dimensions at once\n",
+ " - a time window\n",
+ " - a spatial crop.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "18",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# give the pixel axes coordinates so BOTH space and time can be sliced by label\n",
+ "frames = ocean.assign_coords(\n",
+ " height=(\"height\", np.linspace(0, 1, ocean.sizes[\"height\"])),\n",
+ " width=(\"width\", np.linspace(0, 1, ocean.sizes[\"width\"])),\n",
+ ")\n",
+ "\n",
+ "# one .sel across time AND space: a temporal window + a spatial box\n",
+ "clip = frames.sel(time=slice(\"0.2s\", \"0.7s\"), height=slice(0.25, 0.75), width=slice(0.3, 0.7))\n",
+ "print(\"full:\", dict(frames.sizes))\n",
+ "print(\"clip:\", dict(clip.sizes))\n",
+ "\n",
+ "fig, axes = plt.subplots(1, 2, figsize=(7, 3))\n",
+ "axes[0].imshow(frames[\"data\"].isel(time=0).values)\n",
+ "axes[0].set(title=\"full frame\", xticks=[], yticks=[])\n",
+ "axes[1].imshow(clip[\"data\"].isel(time=0).values)\n",
+ "axes[1].set(title=\"sel(time, height, width)\", xticks=[], yticks=[])\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "attachments": {
+ "cccdc7e2-3388-44f0-8822-d62a07849acb.png": {
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"
+ }
+ },
+ "cell_type": "markdown",
+ "id": "19",
+ "metadata": {},
+ "source": [
+ "## 2. A metal ring: when the axis wraps around\n",
+ "\n",
+ "Not every 1-D coordinate lies on a straight line. Position around a **metal ring** is a *periodic* coordinate: `345°` and `0°` are neighbours, and `0°` **is** `360°`. Our hot spot sits at `0°`, right where the labels wrap — exactly where a linear index gets confused. The default `PandasIndex` treats `angle` as a straight axis `[0, 345]` and has no idea the ends meet.\n",
+ "\n",
+ "\n",
+ "\n",
+ "```{admonition} This is a real physical problem\n",
+ ":class: note\n",
+ "Heat flow around a ring is the classic *periodic boundary* problem — the one Fourier introduced Fourier series to solve. Temperature at `360°` **is** the temperature at `0°`, so the model has to know the axis wraps.\n",
+ "\n",
+ "- 📺 [Fourier's experiments on the heating of a metal ring (video)](https://www.youtube.com/watch?v=O-zNVW7tFPQ&list=PLTGvK6jMx5QBhFJ27J4n7JGxHCnZ0USMf&index=6)\n",
+ "- 📄 [Reconstructing the early history of the theory of heat through Fourier's experiments (Eur. J. Phys.)](https://iopscience.iop.org/article/10.1088/1361-6404/acdf2e)\n",
+ "```\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "20",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def ring_temperature(angle, hot_at=0.0, sigma=45.0):\n",
+ " d = (angle - hot_at + 180) % 360 - 180 # circular distance to the hot spot\n",
+ " return 15 + 20 * np.exp(-(d**2) / (2 * sigma**2))\n",
+ "\n",
+ "\n",
+ "angle = np.arange(0, 360, 15)\n",
+ "metal_ring = xr.DataArray(\n",
+ " ring_temperature(angle, hot_at=0),\n",
+ " dims=\"angle\",\n",
+ " coords={\n",
+ " \"angle\": angle,\n",
+ " \"x\": (\"angle\", np.cos(np.deg2rad(angle))),\n",
+ " \"y\": (\"angle\", np.sin(np.deg2rad(angle))),\n",
+ " },\n",
+ " name=\"temperature\",\n",
+ ")\n",
+ "metal_ring"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "21",
+ "metadata": {
+ "tags": [
+ "hide-input"
+ ]
+ },
+ "outputs": [],
+ "source": [
+ "fig, ax = plt.subplots(figsize=(5.2, 5.2))\n",
+ "\n",
+ "# the angle is the coordinate, so we drop the cartesian x/y frame\n",
+ "circ = plt.Circle((0, 0), 1, fill=False, ls=\":\", color=\"0.7\")\n",
+ "ax.add_patch(circ)\n",
+ "sc = ax.scatter(metal_ring.x, metal_ring.y, c=metal_ring, cmap=\"coolwarm\", s=140, zorder=3)\n",
+ "\n",
+ "# label the angle around the ring\n",
+ "for a in range(0, 360, 45):\n",
+ " t = np.deg2rad(a)\n",
+ " ax.text(\n",
+ " 1.28 * np.cos(t),\n",
+ " 1.28 * np.sin(t),\n",
+ " f\"{a}°\",\n",
+ " ha=\"center\",\n",
+ " va=\"center\",\n",
+ " color=\"0.4\",\n",
+ " fontsize=9,\n",
+ " )\n",
+ "\n",
+ "ax.set_aspect(\"equal\")\n",
+ "ax.set_xlim(-1.5, 1.5)\n",
+ "ax.set_ylim(-1.5, 1.6)\n",
+ "ax.axis(\"off\")\n",
+ "fig.colorbar(sc, ax=ax, label=\"temperature (°C)\", shrink=0.8)\n",
+ "ax.set_title(\"A metal ring: a hot spot at 0°, where the labels wrap\")\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "22",
+ "metadata": {},
+ "source": [
+ "### The problem! \n",
+ "\n",
+ "What if we try to plot based on another valid way of describing the angles? (-180, 180) instead of (0, 360)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "23",
+ "metadata": {
+ "tags": [
+ "hide-input"
+ ]
+ },
+ "outputs": [],
+ "source": [
+ "q = np.linspace(-180, 180, 721)\n",
+ "default_only = metal_ring.sel(angle=q, method=\"nearest\")\n",
+ "\n",
+ "sensor_fold = ((metal_ring.angle.values + 180) % 360) - 180\n",
+ "\n",
+ "fig, ax = plt.subplots(figsize=(9, 4))\n",
+ "ax.scatter(sensor_fold, metal_ring.values, color=\"0.3\", s=45, zorder=5, label=\"the sensors\")\n",
+ "ax.plot(q, default_only, color=\"tab:red\", lw=2, label=\"default index: .sel(nearest)\")\n",
+ "ax.axvline(0, color=\"0.6\", ls=\":\", lw=1)\n",
+ "ax.set_xlabel(\"query angle (°)\")\n",
+ "ax.set_ylabel(\"temperature (°C)\")\n",
+ "ax.legend(loc=\"lower center\")\n",
+ "ax.set_xticks(np.arange(-180, 181, 45))\n",
+ "plt.tight_layout()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "24",
+ "metadata": {},
+ "source": [
+ "## 2.1 📝 Write your own: a `PeriodicIndex`\n",
+ "\n",
+ "\n",
+ "For this we need an index that knows about the periodicity of the ring. \n",
+ "\n",
+ "A full custom `Index` can be tiny — the only method that carries the idea is **`sel`**. We've filled in the boilerplate: `__init__` wraps a plain `PandasIndex` (which holds the labels) plus the `period`, and `from_variables` is how `set_xindex` builds it.\n",
+ "\n",
+ "::::{admonition} Exercise — implement `sel`\n",
+ ":class: tip\n",
+ "\n",
+ "Selection should **wrap around** the period. \n",
+ "\n",
+ "```python\n",
+ "def sel(self, labels, method=None, tolerance=None):\n",
+ " # labels : {coord_name: query} e.g. .sel(angle=370) -> {\"angle\": 370} (one scalar)\n",
+ " # return : IndexSelResult({dim_name: positions}) positions = list of int rows, e.g. [3]\n",
+ " (name, label), = labels.items()\n",
+ " base = self._index.index.to_numpy() # the stored labels (one period)\n",
+ " q = float(label)\n",
+ " ... # circular distance -> nearest position -> pos\n",
+ " return IndexSelResult({self._index.dim: [pos]})\n",
+ "```\n",
+ "\n",
+ ":::{admonition} Solution\n",
+ ":class: dropdown\n",
+ "```python\n",
+ "def sel(self, labels, method=None, tolerance=None):\n",
+ " # labels : {coord_name: query} e.g. .sel(angle=370) -> {\"angle\": 370} (one scalar)\n",
+ " # return : IndexSelResult({dim_name: positions}) positions = list of int rows, e.g. [3]\n",
+ " (name, label), = labels.items()\n",
+ " base = self._index.index.to_numpy()\n",
+ " q = float(label)\n",
+ " d = np.abs((q - base + self.period / 2) % self.period - self.period / 2)\n",
+ " pos = int(d.argmin())\n",
+ " return IndexSelResult({self._index.dim: [pos]})\n",
+ "```\n",
+ ":::\n",
+ "::::\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "25",
+ "metadata": {
+ "tags": [
+ "hide-input"
+ ]
+ },
+ "outputs": [],
+ "source": [
+ "from xarray import Index\n",
+ "from xarray.core.indexing import IndexSelResult\n",
+ "from xarray.indexes import PandasIndex\n",
+ "\n",
+ "\n",
+ "class PeriodicIndex(Index):\n",
+ " \"\"\"A 1-D index whose axis wraps every ``period`` (e.g. angle, period=360).\"\"\"\n",
+ "\n",
+ " def __init__(self, pandas_index, period):\n",
+ " self._index = pandas_index # a plain PandasIndex holding the labels\n",
+ " self.period = period\n",
+ "\n",
+ " @classmethod\n",
+ " def from_variables(cls, variables, *, options):\n",
+ " # boilerplate: how set_xindex builds us from the coordinate\n",
+ " return cls(PandasIndex.from_variables(variables, options={}), options[\"period\"])\n",
+ "\n",
+ " def sel(self, labels, method=None, tolerance=None):\n",
+ " # labels : {coord_name: query} e.g. .sel(angle=370) -> {\"angle\": 370}\n",
+ " # return : IndexSelResult({dim_name: [position]}) position = int row into the axis\n",
+ " print(\"sel received:\", labels)\n",
+ " # the stored labels (one period) are self._index.index.to_numpy()\n",
+ " pos = 0 # <- replace: circular distance -> nearest position\n",
+ " return IndexSelResult({self._index.dim: [pos]})"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "26",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# attach OUR (unfinished) index to the metal ring, then select by angle\n",
+ "periodic = metal_ring.drop_indexes(\"angle\").set_xindex(\"angle\", PeriodicIndex, period=360.0)\n",
+ "\n",
+ "# .sel routes the query into PeriodicIndex.sel — run it to see what the index receives:\n",
+ "periodic.sel(angle=370)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "27",
+ "metadata": {},
+ "source": [
+ "### Does your index fix the -180, 180 issue?\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "28",
+ "metadata": {
+ "tags": [
+ "hide-input"
+ ]
+ },
+ "outputs": [],
+ "source": [
+ "# the same query, now resolved by BOTH indexes — the periodic one wraps\n",
+ "default_temp = metal_ring.sel(angle=q, method=\"nearest\")\n",
+ "periodic_temp = periodic.sel(angle=q)\n",
+ "\n",
+ "fig, ax = plt.subplots(figsize=(9, 4))\n",
+ "ax.scatter(sensor_fold, metal_ring.values, color=\"0.3\", s=45, zorder=5, label=\"the sensors\")\n",
+ "ax.plot(q, periodic_temp, color=\"tab:green\", lw=2.5, label=\"PeriodicIndex .sel\")\n",
+ "ax.plot(q, default_temp, color=\"tab:red\", lw=2, label=\"default index\")\n",
+ "ax.axvline(0, color=\"0.6\", ls=\":\", lw=1)\n",
+ "ax.set_xlabel(\"query angle (°)\")\n",
+ "ax.set_ylabel(\"temperature (°C)\")\n",
+ "ax.legend(loc=\"lower center\")\n",
+ "ax.set_xticks(np.arange(-180, 181, 45))\n",
+ "plt.tight_layout()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "29",
+ "metadata": {},
+ "source": [
+ "## 3. Build your own: a `CoordinateTransform` (step 1 of 2 — a 1-D ruler)\n",
+ "\n",
+ "We just subclassed `Index` and wrote `sel` by hand. A **`CoordinateTransform`** makes this much easier for many common cases. You just write **`forward`/`reverse`** and xarray does the rest.\n",
+ "\n",
+ "We'll build two, increasing in power:\n",
+ "\n",
+ "1. A **RangeIndex** (here)\n",
+ "2. A nonlinear **warp** — a fisheye lens\n",
+ "\n",
+ "\n",
+ "\n",
+ "A `CoordinateTransform` needs just two methods:\n",
+ "\n",
+ "- `forward` — dimension positions → coordinate labels\n",
+ "- `reverse` — coordinate labels → dimension positions\n",
+ "\n",
+ "::::{admonition} Exercise — implement the ruler\n",
+ ":class: tip\n",
+ "\n",
+ "Fill in `forward` and `reverse` so the transform maps slice index ↔ depth (`depth = index · thickness`). Each is handed a **dict** and returns a **dict** — run the cell after it to see what.\n",
+ "\n",
+ ":::{admonition} Solution\n",
+ ":class: dropdown\n",
+ "```python\n",
+ " def forward(self, dim_positions): # slice index -> depth\n",
+ " return {\"depth\": dim_positions[\"slice\"] * self.thickness}\n",
+ "\n",
+ " def reverse(self, coord_labels): # depth -> slice index\n",
+ " return {\"slice\": coord_labels[\"depth\"] / self.thickness}\n",
+ "```\n",
+ ":::\n",
+ "::::"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "30",
+ "metadata": {
+ "tags": [
+ "hide-input"
+ ]
+ },
+ "outputs": [],
+ "source": [
+ "class DIYRangeIndex(CoordinateTransform):\n",
+ " def __init__(self, n, thickness):\n",
+ " super().__init__((\"depth\",), {\"slice\": n})\n",
+ " self.n, self.thickness = n, thickness\n",
+ "\n",
+ " def forward(self, dim_positions): # index space -> world space\n",
+ " # given : {dim_name: positions} e.g. {\"slice\": array([0, 1, 2, ...])}\n",
+ " # return : {coord_name: labels} e.g. {\"depth\": ![](\"https://raw.githubusercontent.com/dask/dask/main/docs/source/images/dask-array.svg\")
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import dask\n",
+ "import dask.array\n",
+ "\n",
+ "dasky = dask.array.ones((10, 5), chunks=(2, 2))\n",
+ "dasky"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Why dask.array\n",
+ "\n",
+ "1. Use parallel resources to speed up computation\n",
+ "2. Work with datasets bigger than RAM (\"out-of-core\")\n",
+ " > \"dask lets you scale from memory-sized datasets to disk-sized datasets\"\n",
+ "\n",
+ "### dask is lazy\n",
+ "\n",
+ "Operations are not computed until you explicitly request them. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dasky.mean(axis=-1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "So what did dask do when you called `.mean`? It added that operation to the \"graph\" or a blueprint of operations to execute later."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dask.visualize(dasky.mean(axis=-1))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dasky.mean(axis=-1).compute()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### More\n",
+ "\n",
+ "See the [dask.array tutorial](https://tutorial.dask.org/02_array.html)\n",
+ "\n",
+ "\n",
+ "### Dask + Xarray\n",
+ "\n",
+ "Remember that Xarray can wrap many different array types. So Xarray can wrap dask arrays too. \n",
+ "\n",
+ "We use Xarray to enable using our metadata to express our analysis."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "## Creating dask-backed Xarray objects\n",
+ "\n",
+ "The `chunks` argument to both `open_dataset` and `open_mfdataset` allow you to\n",
+ "read datasets as dask arrays. \n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%xmode minimal\n",
+ "\n",
+ "import numpy as np\n",
+ "import xarray as xr\n",
+ "\n",
+ "# limit the amount of information printed to screen\n",
+ "xr.set_options(display_expand_data=False)\n",
+ "np.set_printoptions(threshold=10, edgeitems=2)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds = xr.tutorial.open_dataset(\"air_temperature\")\n",
+ "ds.air"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds = xr.tutorial.open_dataset(\n",
+ " \"air_temperature\",\n",
+ " chunks={ # this tells xarray to open the dataset as a dask array\n",
+ " \"lat\": \"auto\",\n",
+ " \"lon\": 25,\n",
+ " \"time\": -1,\n",
+ " },\n",
+ ")\n",
+ "ds"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The representation (\"repr\" in Python parlance) for the `air` DataArray shows the very nice HTML dask array repr. You can access the underlying chunk sizes using `.chunks`:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds.air.chunks"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "```{tip}\n",
+ "All variables in a `Dataset` need _not_ have the same chunk size along\n",
+ "common dimensions.\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Extracting underlying data\n",
+ "\n",
+ "There are two ways to pull out the underlying array object in an xarray object.\n",
+ "\n",
+ "1. `.to_numpy` or `.values` will always return a NumPy array. For dask-backed xarray objects,\n",
+ " this means that compute will always be called\n",
+ "2. `.data` will return a Dask array\n",
+ "\n",
+ "```{tip}\n",
+ "Use `to_numpy` or `as_numpy` instead of `.values` so that your code generalizes to other array types (like CuPy arrays, sparse arrays)\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds.air.data # dask array, not numpy"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "ds.air.as_numpy().data ## numpy array"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "::::{admonition} Exercise\n",
+ ":class: tip\n",
+ "Try calling `ds.air.values` and `ds.air.data`. Do you understand the difference?\n",
+ "::::"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# use this cell for the exercise"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "ds.air.to_numpy()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "\n",
+ "\n",
+ "## Lazy computation \n",
+ "\n",
+ "Xarray seamlessly wraps dask so all computation is deferred until explicitly\n",
+ "requested."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "mean = ds.air.mean(\"time\")\n",
+ "mean"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Dask actually constructs a graph of the required computation. Here it's pretty simple: The full array is subdivided into 3 arrays. Dask will load each of these subarrays in a separate thread using the default [single-machine scheduling](https://docs.dask.org/en/stable/scheduling.html). You can visualize dask 'task graphs' which represent the requested computation:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "mean.data # dask array"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# visualize the graph for the underlying dask array\n",
+ "# we ask it to visualize the graph from left to right because it looks nicer\n",
+ "dask.visualize(mean.data, rankdir=\"LR\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "tags": [],
+ "toc-hr-collapsed": true
+ },
+ "source": [
+ "## Getting concrete values\n",
+ "\n",
+ "At some point, you will want to actually get concrete values (_usually_ a numpy array) from dask.\n",
+ "\n",
+ "There are two ways to compute values on dask arrays.\n",
+ "\n",
+ "1. `.compute()` returns an xarray object *just like a dask array*\n",
+ "2. `.load()` replaces the dask array in the xarray object with a numpy array.\n",
+ " This is equivalent to `ds = ds.compute()`\n",
+ " \n",
+ "```{tip}\n",
+ "There is a third option : \"persisting\". `.persist()` loads the values into distributed RAM. The values are computed but remain distributed across workers. So `ds.air.persist()` still returns a dask array. This is useful if you will be repeatedly using a dataset for computation but it is too large to load into local memory. You will see a persistent task on the dashboard. See the [dask user guide](https://docs.dask.org/en/latest/api.html#dask.persist) for more on persisting\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "::::{admonition} Exercise\n",
+ ":class: tip\n",
+ "\n",
+ "Try running `mean.compute` and then examine `mean` after that. Is it still a dask array?\n",
+ "\n",
+ ":::{admonition} Solution\n",
+ ":class: dropdown\n",
+ "\n",
+ "Computing returns a numpy array but does not modify in-place. So `mean` still contains a dask array.\n",
+ ":::\n",
+ "::::"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "::::{admonition} Exercise\n",
+ ":class: tip\n",
+ "\n",
+ "Now repeat that exercise with `mean.load`.\n",
+ "\n",
+ ":::{admonition} Solution\n",
+ ":class: dropdown\n",
+ "\n",
+ "`load` modifies an Xarray object in-place so `mean` now contains a numpy array.\n",
+ ":::\n",
+ "::::"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Distributed Clusters"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "As your data volumes grow and algorithms get more complex it can be hard to print out task graph representations and understand what Dask is doing behind the scenes. Luckily, you can use Dask's 'Distributed' scheduler to get very useful diagnotisic information.\n",
+ "\n",
+ "First let's set up a `LocalCluster` using [dask.distributed](https://distributed.dask.org/).\n",
+ "\n",
+ "You can use any kind of Dask cluster. This step is completely independent of\n",
+ "xarray. While not strictly necessary, the dashboard provides a nice learning\n",
+ "tool.\n",
+ "\n",
+ "By default, Dask uses the current working directory for writing temporary files.\n",
+ "We choose to use a temporary scratch folder `local_directory='/tmp'` in the example below instead."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "\n",
+ "# This piece of code is just for a correct dashboard link mybinder.org or other JupyterHub demos\n",
+ "import dask\n",
+ "from dask.distributed import Client\n",
+ "\n",
+ "# if os.environ.get('JUPYTERHUB_USER'):\n",
+ "# dask.config.set(**{\"distributed.dashboard.link\": \"/user/{JUPYTERHUB_USER}/proxy/{port}/status\"})\n",
+ "\n",
+ "client = Client()\n",
+ "client"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "☝️ Click the Dashboard link above. \n",
+ "\n",
+ "👈 Or click the \"Search\" 🔍 button in the [dask-labextension](https://github.com/dask/dask-labextension) dashboard.\n",
+ "\n",
+ "```{note}\n",
+ "if using the dask-labextension, you should disable the 'Simple' JupyterLab interface (`View -> Simple Interface`), so that you can drag and rearrange whichever dashboards you want. The `Workers` and `Task Stream` are good to make sure the dashboard is working!\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "import dask.array\n",
+ "\n",
+ "dask.array.ones((1000, 4), chunks=(2, 1)).compute() # should see activity in dashboard"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Computation"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's go back to our xarray DataSet, in addition to computing the mean, other operations such as indexing will automatically use whichever Dask Cluster we are connected to!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds.air.isel(lon=1, lat=20)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "and more complicated operations...\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "rolling_mean = ds.air.rolling(time=5).mean() # no activity on dashboard\n",
+ "rolling_mean # contains dask array"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "timeseries = rolling_mean.isel(lon=1, lat=20) # no activity on dashboard\n",
+ "timeseries # contains dask array"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "computed = rolling_mean.compute() # activity on dashboard\n",
+ "computed # has real numpy values"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Note that `mean` still contains a dask array\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "rolling_mean"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "```{tip}\n",
+ "While these operations all work, not all of them are necessarily the optimal implementation for parallelism. Usually analysis pipelines need some tinkering and tweaking to get things to work. In particular read the user guidie recommendations for [chunking](https://docs.xarray.dev/en/stable/user-guide/dask.html#chunking-and-performance).\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Xarray data structures are first-class dask collections.\n",
+ "\n",
+ "This means you can do things like `dask.compute(xarray_object)`,\n",
+ "`dask.visualize(xarray_object)`, `dask.persist(xarray_object)`. This works for\n",
+ "both DataArrays and Datasets"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "jp-MarkdownHeadingCollapsed": true,
+ "tags": []
+ },
+ "source": [
+ "::::{admonition} Exercise\n",
+ "Visualize the task graph for a few different computations on `ds.air`!\n",
+ "::::"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Use this cell for the exercise"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Finish up\n",
+ "Gracefully shutdown our connection to the Dask cluster. This becomes more important when you are running on large HPC or Cloud servers rather than a laptop!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "client.close()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Next\n",
+ "\n",
+ "\n",
+ "See the [Xarray user guide on dask](https://docs.xarray.dev/en/stable/user-guide/dask.html). "
+ ]
+ }
+ ],
+ "metadata": {
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/intermediate/xarray_ecosystem.ipynb b/intermediate/xarray_ecosystem.ipynb
new file mode 100644
index 00000000..d455b486
--- /dev/null
+++ b/intermediate/xarray_ecosystem.ipynb
@@ -0,0 +1,684 @@
+{
+ "cells": [
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# A Tour of Xarray Customizations\n",
+ "\n",
+ "Xarray is easily extensible.\n",
+ "This means it is easy to add on to to build custom packages that tackle particular computational problems or supply domain specific functionality.\n",
+ "\n",
+ "These packages can plug in to xarray in various different ways. They may build directly on top of xarray, or they may take advantage of some of xarray's dedicated interfacing features:\n",
+ "- Accessors (extensions)\n",
+ "- Backend (filetype) entrypoint\n",
+ "- Metadata attributes\n",
+ "- Duck-array wrapping interface\n",
+ "- Flexible indexes (coming soon!)"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Here we introduce several popular or interesting extensions that are installable as their own packages (via conda and pip). These packages integrate with xarray using one or more of the features mentioned above.\n",
+ "\n",
+ "- [hvplot](https://hvplot.holoviz.org/), a powerful interactive plotting library\n",
+ "- [rioxarray](https://corteva.github.io/rioxarray/stable/index.html), for working with geospatial raster data using rasterio\n",
+ "- [cf-xarray](https://cf-xarray.readthedocs.io/en/latest/), for interpreting CF-compliant data\n",
+ "- [pint-xarray](https://pint-xarray.readthedocs.io/en/latest/), for unit-aware computations using pint."
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Specific examples for implementing your own xarray customizations using these interfacing features are available in the ADVANCED section of this book."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import xarray as xr"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Quick note on accessors"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "Before we look at the packages we need to briefly introduce a feature they commonly use: [\"xarray accessors\"](https://docs.xarray.dev/en/stable/internals/extending-xarray.html).\n",
+ "\n",
+ "The accessor-style syntax is used heavily by the other libraries we are about to cover.\n",
+ "\n",
+ "For users, accessors just allow us to have a familiar dot (method-like) syntax on xarray objects, for example `da.hvplot()`, `da.pint.quantify()`, or `da.cf.describe()`.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## hvplot via accessors"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The [HoloViews library](https://holoviews.org/) makes great use of accessors to allow seamless plotting of xarray data using a completely different plotting backend (by default, xarray uses [matplotlib](https://matplotlib.org/))."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We first need to import the code that registers the hvplot accessor"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import hvplot.xarray"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "And now we can call the `.hvplot` method to plot using holoviews in the same way that we would have used `.plot` to plot using matplotlib."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds = xr.tutorial.load_dataset(\"air_temperature\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds[\"air\"].isel(time=1).hvplot(cmap=\"fire\")"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "For some more examples of how powerful HoloViews is [see here](https://tutorial.xarray.dev/intermediate/hvplot.html)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Rioxarray via the backend entrypoint\n",
+ "\n",
+ "[Rioxarray](https://corteva.github.io/rioxarray/stable/index.html) is a Python library that enhances Xarray's ability to work with geospatial data and coordinate reference systems. Geographic information systems use GeoTIFF and [many other formats](https://gdal.org/drivers/raster/index.html) to organize and store gridded, or *raster*, datasets. \n",
+ "\n",
+ "The Geospatial Data Abstraction Library ([GDAL](https://gdal.org)) provides foundational drivers and geospatial algorithms, and the [rasterio](https://rasterio.readthedocs.io/en/latest) library provides a Pythonic interface to GDAL. `Rioxarray` brings key features of rasterio to Xarray:\n",
+ "\n",
+ "1. A backend *engine* to read any format recognized by *GDAL*\n",
+ "1. A `.rio` *accessor* for *rasterio's* geospatial algorithms such as reprojection\n",
+ "\n",
+ "Below a couple brief examples to illustrate these features:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "import rioxarray # ensure you have rioxarray installed in your environment"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "You can explicitly use rioxarray's 'rasterio' engine to load myriad geospatial raster formats. Below is a [Cloud-Optimized Geotiff](https://www.cogeo.org) from an AWS [public dataset](https://registry.opendata.aws/sentinel-1-rtc-indigo/) of synthetic aperture radar data over Washington, State, USA. `overview_level=4` is an argument specific to the `rasterio` engine that allows opening a pre-computed lower resolution \"overview\" of the data."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "url = \"https://sentinel-s1-rtc-indigo.s3.us-west-2.amazonaws.com/tiles/RTC/1/IW/10/U/CU/2017/S1A_20170101_10UCU_ASC/Gamma0_VV.tif\"\n",
+ "da = xr.open_dataarray(url, engine=\"rasterio\", open_kwargs={\"overview_level\": 2})\n",
+ "da"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The `spatial_ref` coordinate is added by rioxarray to store standardized geospatial Coordinate Reference System (CRS) information. We can access that information and additional methods via the `.rio` accessor:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "da.rio.crs"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "EPSG refers to 'European Petroleum Survey Group', a database of the many CRS definitions for our Planet used over the years! EPSG=32610 is the [\"UTM 10N\" CRS](https://epsg.io/32610), with coordinate units in meters. Let's say you want longitude,latitude coordinate points in degrees instead. You'd have to *reproject* this data:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "da_lonlat = da.rio.reproject(\"epsg:4326\")\n",
+ "da_lonlat"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Note that that the size of the data has changed as well as the coordinate values. This is typical of reprojection, as your data must be resampled and often interpolated to match the new CRS grid! A quick plot will compare the results of our reprojected data:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import panel as pn\n",
+ "\n",
+ "img1 = da.sel(band=1).hvplot.image(\n",
+ " x=\"x\", y=\"y\", rasterize=True, cmap=\"gray\", clim=(0, 0.5), title=\"UTM\"\n",
+ ")\n",
+ "img2 = da_lonlat.sel(band=1).hvplot.image(\n",
+ " rasterize=True, cmap=\"gray\", clim=(0, 0.5), title=\"LON/LAT\"\n",
+ ")\n",
+ "\n",
+ "pn.Column(img1, img2)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## cf-xarray via metadata attributes"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Xarray objects can store [arbitrary metadata](https://docs.xarray.dev/en/stable/get-help/faq.html#what-is-your-approach-to-metadata) in the form of a `dict` attached to each `DataArray` and `Dataset` object, accessible via the `.attrs` property."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "xr.DataArray(name=\"Hitchhiker\", data=0, attrs={\"life\": 42, \"name\": \"Arthur Dent\"})"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Normally xarray operations ignore this metadata, simply carting it around until you explicitly choose to use it. However sometimes we might want to write custom code which makes use of the metadata."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "[cf_xarray](https://cf-xarray.readthedocs.io/) is a project that tries to\n",
+ "let you make use of other [Climate and Forecast metadata convention attributes](http://cfconventions.org/) (or \"CF attributes\") that xarray ignores. It attaches itself\n",
+ "to all xarray objects under the `.cf` namespace.\n",
+ "\n",
+ "Where xarray allows you to specify dimension names for analysis, `cf_xarray`\n",
+ "lets you specify logical names like `\"latitude\"` or `\"longitude\"` instead as\n",
+ "long as the appropriate CF attributes are set.\n",
+ "\n",
+ "For example, the `\"longitude\"` dimension in different files might be labelled as: (lon, LON, long, x…), but cf_xarray let's you always refer to the logical name `\"longitude\"` in your code:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import cf_xarray"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# describe cf attributes in dataset\n",
+ "ds.air.cf.describe()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The following `mean` operation will work with any dataset that has appropriate\n",
+ "attributes set that allow detection of the \"latitude\" variable (e.g.\n",
+ "`units: \"degress_north\"` or `standard_name: \"latitude\"`)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# demonstrate equivalent of .mean(\"lat\")\n",
+ "ds.air.cf.mean(\"latitude\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# demonstrate indexing\n",
+ "ds.air.cf.sel(longitude=242.5, method=\"nearest\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Pint via duck array wrapping"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Why use pint"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "[Pint](https://pint.readthedocs.io/en/stable/) defines physical units, allowing you to work with numpy-like arrays which track the units of your values through computations."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Pint defines a numpy-like array class called `pint.Quantity`, which is made up of a numpy array and a `pint.Unit` instance."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from pint import Unit\n",
+ "\n",
+ "# you can create a pint.Quantity by multiplying a value by a pint.Unit\n",
+ "d = np.array(10) * Unit(\"metres\")\n",
+ "d"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "These units are automatically propagated through operations"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "t = 1 * Unit(\"seconds\")\n",
+ "v = d / t\n",
+ "v"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Or if the operation involves inconsistent units, a `pint.DimensionalityError` is raised."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "tags": [
+ "raises-exception"
+ ]
+ },
+ "outputs": [],
+ "source": [
+ "d + t"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### pint inside xarray objects"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We have already seen that xarray can wrap numpy arrays or dask arrays, but in fact xarray can wrap any type of array that behaves similarly to a numpy array.\n",
+ "Using this feature we can store a `pint.Quantity` array inside an xarray DataArray"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "da = xr.DataArray(d)\n",
+ "da"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can see that the data type stored within the DataArray is a `Quantity` object, rather than just a `np.ndarray` object, and the units of the data are displayed in the repr."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The reason this works is that a `pint.Quantity` array is what we call a \"duck array\", in that it behaves so similarly to a `numpy.ndarray` that xarray can treat them the same way. (See [python duck typing](https://realpython.com/duck-typing-python/))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### pint-xarray"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The convenience package [pint-xarray](https://pint-xarray.readthedocs.io/en/latest/index.html) makes it easier to get the benefits of pint whilst working with xarray objects.\n",
+ "\n",
+ "It provides utility accessor methods for promoting xarray data to pint quantities (which we call \"quantifying\") in various ways and for manipulating the resulting objects."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# to be able to read unit attributes following the CF conventions\n",
+ "import cf_xarray.units\n",
+ "import pint_xarray\n",
+ "\n",
+ "xr.set_options(display_expand_data=False)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "pint-xarray provides the `.pint` accessor, which firstly allows us to easily extract the units of our data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "da.pint.units"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The `.pint.quantify()` accessor gives us various ways to convert normal xarray data to be unit-aware."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "```{note}\n",
+ "It is preferred to use `.pint.quantify()` to convert xarray data to use pint rather than explicitly creating a `pint.Quantity` array and placing it inside the xarray object, because pint-xarray will handle various subtleties involving dask etc.\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can explicitly specify the units we want"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "da = xr.DataArray([4.5, 6.7, 3.8], dims=\"time\")\n",
+ "da.pint.quantify(\"V\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Or if the data has a \"units\" entry in its `.attrs` metadata dictionary, we can automatically use that to convert each variable.\n",
+ "\n",
+ "For example, the xarray tutorial dataset we opened earlier has units in its attributes"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds.air.attrs[\"units\"]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "which we can automatically read with `.pint.quantify()`:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "quantified_air = ds.pint.quantify()\n",
+ "quantified_air"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Here we have actually gone even further, and used cf-xarray to automatically interpret cf-compliant units in the `.attrs` to valid pint units.\n",
+ "This automatically happened just as a result of importing cf-xarray above."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "When we plot quantified data with xarray the correct units will automatically appear on the plot"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "quantified_air[\"air\"].isel(time=500).plot();"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "If we want to cast the pint arrays back to numpy arrays, we can use `.pint.dequantify()`, which will also write the current units back out to the `.attrs[\"units\"]` field"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "quantified_air.pint.dequantify()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "::::{admonition} Exercise\n",
+ ":class: tip\n",
+ "Write a function which will raise a `pint.DimensionalityError` if supplied with Xarray DataArray with the wrong units.\n",
+ "\n",
+ "\n",
+ ":::{admonition} Solution\n",
+ ":class: dropdown\n",
+ "\n",
+ "```python\n",
+ "from pint import DimensionalityError\n",
+ "\n",
+ "\n",
+ "def special_science_function(distance):\n",
+ " if distance.pint.units != \"miles\":\n",
+ " raise DimensionalityError(\n",
+ " \"this function will only give the correct answer if the input is in units of miles\"\n",
+ " )\n",
+ "```\n",
+ ":::\n",
+ "::::"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "::::{admonition} Exercise\n",
+ "Try this on some of your data!\n",
+ "\n",
+ "After you have imported pint-xarray (and maybe cf-xarray) as above, start with something like\n",
+ "\n",
+ "`ds = xr.open_dataset(my_data).pint.quantify()`\n",
+ "::::"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Take a look at the [pint-xarray documentation](https://pint-xarray.readthedocs.io/en/latest/) or the [pint documentation](https://pint.readthedocs.io/en/stable/) if you get stuck."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## The wider world..."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "There are many other libraries in the wider xarray ecosystem. For a list of a few packages we particularly like for geoscience work [see here](https://tutorial.xarray.dev/overview/xarray-in-45-min.html#other-cool-packages), and for a [more exhaustive list see here](https://docs.xarray.dev/en/stable/user-guide/ecosystem.html)."
+ ]
+ }
+ ],
+ "metadata": {
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/intro.md b/intro.md
new file mode 100644
index 00000000..3ca09db0
--- /dev/null
+++ b/intro.md
@@ -0,0 +1,47 @@
+# Welcome to the Xarray Tutorial!
+
+**[`Xarray`](https://xarray.dev) is an open source project and Python package that makes working with labelled multi-dimensional arrays simple, efficient, and fun!**
+
+📖 On this Jupyter Book website you'll find easy-to-run tutorial notebooks for Xarray. Whether you're new to Xarray or a seasoned user we hope you'll learn something new and get a head start on your own projects by exploring this material!
+
+````{grid}
+:class-container: text-center
+:gutter: 3
+
+```{grid-item-card}
+:link: overview/get-started
+:link-type: doc
+Get started! 🚀
+^^^
+Learn how to navigate the tutorials and run code examples interactively
+```
+
+```{grid-item-card}
+:link: https://www.youtube.com/channel/UCBlxVSA6xQXeb-i4GgTlO7g
+Watch videos 🎬
+^^^
+Live walk-throughs of this material and more on Xarray's YouTube Channel
+```
+
+```{grid-item-card}
+:link: https://docs.xarray.dev/en/stable/get-help/help-diagram.html#help-flowchart
+Get involved 🙋
+^^^
+Ask questions and participate in the Xarray community!
+```
+
+````
+
+## Citation
+
+✏️ To cite this material, you can use the recommended [Xarray citation](https://docs.xarray.dev/en/stable/get-help/faq.html#how-should-i-cite-xarray): {cite:p}`hoyerhamman2017`, {cite:p}`xarray_v202230`.
+
+## Acknowledgements
+
+This website is the result of many contributions from the Xarray community! We're very grateful for everyone's volunteered effort as well as [sponsored development](https://xarray.dev/#sponsors). Funding for SciPy 2022, SciPy 2023 tutorial material development specifically was supported by NASA's Open Source Tools, Frameworks, and Libraries Program (award 80NSSC22K0345).
+
+## Bibliography
+
+```{bibliography}
+:style: plain
+```
diff --git a/overview/fundamental-path/README.md b/overview/fundamental-path/README.md
new file mode 100644
index 00000000..c5435f9e
--- /dev/null
+++ b/overview/fundamental-path/README.md
@@ -0,0 +1,57 @@
+(fundamental-path)=
+
+# Fundamental Path
+
+This syllabus comes from Scipy 2022 tutorial workshop.
+This path covers fundamental Xarray concepts with
+increasingly complex real-world data analysis tasks.
+
+The tutorial was organized by:
+
+- Scott Henderson (Univ. Washington)
+- Deepak Cherian (National Center for Atmospheric Research)
+- Jessica Scheick (Univ. New Hampshire)
+- Emma Marshall (Univ. Utah)
+- Anderson Banihirwe (CarbonPlan)
+- Tom Nicholas (Lamont-Doherty Earth Observatory)
+
+Launch cloud-hosted Jupyter Lab environment:
+[](https://mybinder.org/v2/gh/xarray-contrib/xarray-tutorial/HEAD?labpath=overview/fundamental-path/index.ipynb)
+
+## Topics
+
+_Below are links to sections of this website that are part of this journey_:
+
+```{dropdown} Introduction
+{doc}`../../overview/get-started`
+```
+
+```{dropdown} Introduction to Xarray
+{doc}`../../fundamentals/01_datastructures`
+
+{doc}`../../fundamentals/01.1_io`
+```
+
+```{dropdown} Working with Labeled Data
+{doc}`../../fundamentals/02.1_indexing_Basic`
+```
+
+```{dropdown} Computation
+{doc}`../../fundamentals/03.1_computation_with_xarray`
+
+{doc}`../../fundamentals/02.3_aligning_data_objects`
+
+{doc}`../../fundamentals/03.2_groupby_with_xarray`
+```
+
+```{dropdown} Plotting and Visualization
+{doc}`../../fundamentals/04.1_basic_plotting`
+
+{doc}`../../fundamentals/04.2_faceting`
+
+{doc}`../../fundamentals/04.3_geographic_plotting`
+```
+
+```{dropdown} Xarray Ecosystem
+{doc}`../../intermediate/xarray_ecosystem`
+```
diff --git a/overview/fundamental-path/index.ipynb b/overview/fundamental-path/index.ipynb
new file mode 100644
index 00000000..48c3f5fb
--- /dev/null
+++ b/overview/fundamental-path/index.ipynb
@@ -0,0 +1,62 @@
+{
+ "cells": [
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "0",
+ "metadata": {},
+ "source": [
+ "![](\"../../images/scipylogo.png\")
\n",
+ "\n",
+ "# Welcome to the Xarray SciPy 2022 Tutorial! \n",
+ "\n",
+ "**Xarray**: *Friendly, Interactive, and Scalable Scientific Data Analysis*\n",
+ "\n",
+ "Learning goals:\n",
+ "- understand the Xarray data model\n",
+ "- effectively use Xarray’s multidimensional labeled indexing\n",
+ "- utilize Xarray’s powerful built-in computational methods\n",
+ "- learn about the ecosystem of software extending Xarray\n",
+ "\n",
+ "This *4-hour* workshop will explore content from https://tutorial.xarray.dev, which contains a comprehensive collection of hands-on tutorial Jupyter Notebooks. We won't cover it all today, but instead will review a curated set of examples that will prepare you for increasingly complex real-world data analysis tasks!\n",
+ "\n",
+ "\n",
+ "## Schedule \n",
+ "*Times in Central Daylight (Austin, TX)\n",
+ "\n",
+ "| Topic | Time | Notebook Links | \n",
+ "| :- | - | - | \n",
+ "| Introduction | 1:30 (20 min) | [getting started](../../overview/get-started.md) | \n",
+ "| Data Structures | 1:50 (30 min) | [datastructures](../../fundamentals/01_datastructures.ipynb) [input/output](../../fundamentals/01.1_io.ipynb) | \n", + "| *Break* \n", + "| Labeled Data | 2:30 (20 min) | [working_with_labeled_data](../../fundamentals/02.1_working_with_labeled_data.ipynb) | \n", + "| Visualizations | 2:50 (30 min) | [basic_plotting](../../fundamentals/04.1_basic_plotting.ipynb)
[facet plots](../../fundamentals/04.2_faceting.ipynb) | \n", + "| *Break* | \n", + "| Computation | 3:30 (50 min) | [computation with xarray](../../fundamentals/03.1_computation_with_xarray.ipynb)
[aligning data objects](../../fundamentals/02.3_aligning_data_objects.ipynb)
[groupby with xarray](../../fundamentals/03.2_groupby_with_xarray.ipynb) | \n", + "| *Break* | \n", + "| Xarray ecosystem | 4:30 (30 min) | [xarray ecosystem](../../intermediate/xarray_ecosystem.ipynb) | \n", + "| Scaling Xarray | 5:00 (20 min) | | \n", + "| | **End 5:30** | |\n", + "\n", + "**Thanks for attending!**\n", + "\n", + "Please continue to explore the subfolders in the JupyterLab File Browser for additional tutorial notebooks to run, or read the rendered notebooks at https://tutorial.xarray.dev" + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/overview/get-started.md b/overview/get-started.md new file mode 100644 index 00000000..b4e0192c --- /dev/null +++ b/overview/get-started.md @@ -0,0 +1,75 @@ +
+
+(get-started)=
+
+# Get Started
+
+## Organization
+
+Tutorials are approximately divided into sections with increasing levels of complexity: `Fundamentals`, `Intermediate`, `Advanced`. You'll also find content specific to various `Workshops` hosted over the years, often with accompanying video recordings of instructors going over content and answering questions that come up.
+
+Most of the tutorial content is written as Jupyter Notebooks that mix
+code, text, visualization, and exercises. You can either browse rendered versions of these notebooks on this website, or _execute_ the code examples interactively.
+
+Many notebooks use special formatting ([Myst Markdown](https://mystmd.org/guide/quickstart-jupyter-lab-myst)) that renders best in a JupyterLab web interface. If you are new to JupyterLab, spend some time reviewing the [documentation and videos](https://jupyterlab.readthedocs.io/en/stable/getting_started/overview.html).
+
+## Run code interactively
+
+### On the Cloud
+
+The easiest way to start modifying and experimenting with tutorial content is to launch a pre-configured server on the Cloud. This is easy thanks to several free resources which offer ephemeral computing instances (be aware you may loose your connection or work at any time)
+
+```{warning}
+Be patient, it can take a few minutes for a server to become available on the Cloud!
+```
+
+#### Mybinder.org
+
+Clicking [](https://mybinder.org/v2/gh/xarray-contrib/xarray-tutorial/HEAD) will load a pre-configured Jupyter Lab interface with _all_ tutorial notebooks for you to run. _You have minimal computing resources and any changes you make will not be saved._ Any page with executable content also has a {octicon}`rocket;2em` icon in the upper right that will launch an interactive session for that particular page.
+
+#### GitHub Codespaces
+
+This tutorial is available to run within [GitHub Codespaces](https://github.com/features/codespaces) - a preconfigured development environment running in Microsoft Azure.
+
+[](https://github.com/codespaces/new/xarray-contrib/xarray-tutorial)
+
+☝️ Click the button above to go to options window to launch a GitHub codespace.
+
+You can choose from a selection of virtual machine types: 2 cores - 8 GB RAM should be sufficient for all code examples in this repository.
+Additionally, you are able to chose from various configurations for specific workshops (such as Scipy2024).
+GitHub currently gives every user [120 vCPU hours per month for free](https://docs.github.com/en/billing/managing-billing-for-github-codespaces/about-billing-for-github-codespaces#monthly-included-storage-and-core-hours-for-personal-accounts), beyond that you must pay. **So be sure to explicitly stop your codespace when you are done by going to this page (https://github.com/codespaces).** You can also chose to fully delete your codespace when you're done exploring tutorial content.
+
+```{tip}
+By default Codespaces open VSCode in a browser window. But you can also launch a JupyterLab interface. Once you've launched a codespace, use the auto-generated name in the following URL: `https://github.com/codespaces/CODESPACE-NAME?editor=jupyter`
+```
+
+Using the GitHub CLI is another very convenient way to start a codespace:
+
+```bash
+gh codespace create -R xarray-contrib/xarray-tutorial
+```
+
+```bash
+# Wait a few minutes for `gh codespace create` to finish, then run:
+gh codespace jupyter
+```
+
+### On your computer
+
+Running tutorials on your computer requires some setup:
+
+We recommend using [`pixi`](https://pixi.sh/latest/) to ensure a fully reproducible Python environment
+
+```bash
+git clone https://github.com/xarray-contrib/xarray-tutorial.git
+cd xarray-tutorial
+pixi run tutorial
+```
+
+If you prefer to use conda/mamba:
+
+```
+mamba env create -f .binder/environment.yml -n xarray-tutorial
+conda activate xarray-tutorial
+jupyter lab
+```
diff --git a/overview/intermediate-path/README.md b/overview/intermediate-path/README.md
new file mode 100644
index 00000000..10e50f51
--- /dev/null
+++ b/overview/intermediate-path/README.md
@@ -0,0 +1,42 @@
+# Intermediate Path
+
+This learning path was presented at SciPy 2023. It covers intermediate
+and more advanced topics and is a good follow-on from the [Fundamental Path](fundamental-path)
+
+The tutorial was organized by:
+
+- Deepak Cherian (National Center for Atmospheric Research)
+- Scott Henderson (Univ. Washington)
+- Jessica Scheick (Univ. New Hampshire)
+- Negin Sobhani (National Center for Atmospheric Research)
+- Tom Nicholas (Lamont-Doherty Earth Observatory)
+- Anderson Banihirwe (CarbonPlan)
+- Don Setiawan (Univ. Washington)
+
+## Outline
+
+```{dropdown} Introduction
+{doc}`../../overview/get-started`
+```
+
+```{dropdown} Indexing
+{doc}`../../intermediate/indexing/advanced-indexing`
+```
+
+```{dropdown} Computational Patterns
+{doc}`../../intermediate/computation/01-high-level-computation-patterns`
+```
+
+```{dropdown} Wrapping other arrays: dask
+{doc}`../../intermediate/xarray_and_dask`
+```
+
+```{dropdown} Wrapping custom computation
+{doc}`../../advanced/apply_ufunc/simple_numpy_apply_ufunc`
+
+{doc}`../../advanced/apply_ufunc/core-dimensions`
+
+{doc}`../../advanced/apply_ufunc/complex-output-numpy`
+
+{doc}`Explore the remaining material <../../advanced/apply_ufunc/apply_ufunc>`
+```
diff --git a/overview/intermediate-path/index.ipynb b/overview/intermediate-path/index.ipynb
new file mode 100644
index 00000000..33a6bcd9
--- /dev/null
+++ b/overview/intermediate-path/index.ipynb
@@ -0,0 +1,61 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "0",
+ "metadata": {},
+ "source": [
+ "![](\"../../images/scipylogo2023.png\")
\n",
+ "\n",
+ "\n",
+ "# Welcome to the Xarray SciPy 2023 Tutorial! \n",
+ "\n",
+ "**Xarray**: *Friendly, Interactive, and Scalable Scientific Data Analysis*\n",
+ "\n",
+ "\n",
+ "This *4-hour* workshop will explore content from [the Xarray tutorial](https://tutorial.xarray.dev), which contains a comprehensive collection of hands-on tutorial Jupyter Notebooks. We won't cover it all today, but instead will review a curated set of examples that will prepare you for increasingly complex real-world data analysis tasks!\n",
+ "\n",
+ "\n",
+ "## Schedule \n",
+ "*Times in Central Daylight (Austin, TX)\n",
+ "\n",
+ "Use the links to navigate to the right notebooks.\n",
+ "\n",
+ "**Remember to select `global-global-xarray environment` for the notebooks when prompted.**\n",
+ "\n",
+ "| Topic | Time | Notebook Links | \n",
+ "| :- | - | - | \n",
+ "| Introduction and Setup | 1:30 (20 min) | --- | \n",
+ "| Indexing | 1:50 (30 min) | [Advanced and Vectorized Indexing](../../intermediate/indexing/advanced-indexing.ipynb) | \n",
+ "| *10 minute Break* \n",
+ "| Computational Patterns | 2:30 (50 min) | [Computation Patterns](../../intermediate/01-high-level-computation-patterns.ipynb) | \n",
+ "| *10 minute Break* | \n",
+ "| Wrapping other arrays | 3:30 (50 min) | [Xarray and Dask](../../intermediate/xarray_and_dask.ipynb) | \n",
+ "| *10 minute Break* | \n",
+ "| Wrapping custom computation (apply_ufunc) | 4:30 (30 min) 5:00 (30 min) | [A gentle introduction](../../advanced/apply_ufunc/simple_numpy_apply_ufunc.ipynb)
[Core dimensions](../../advanced/apply_ufunc/core-dimensions.ipynb)
[Handling complex output](../../advanced/apply_ufunc/complex-output-numpy.ipynb)
Explore the rest of the material |\n", + "| | **End 5:30** | |\n", + "\n", + "\n", + "\n", + "## Thanks for attending!\n", + "\n", + "Please continue to explore the subfolders in the JupyterLab File Browser for additional tutorial notebooks to run, or read the rendered notebooks at [https://tutorial.xarray.dev](https://tutorial.xarray.dev)" + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/overview/learning-paths.md b/overview/learning-paths.md new file mode 100644 index 00000000..9e7922e4 --- /dev/null +++ b/overview/learning-paths.md @@ -0,0 +1,11 @@ +# Learning Paths + +Learning Paths are guided tours through the material on the site. +These paths were originally built for in-person tutorials at the SciPy conference. +They are useful if you would like to work through the material in an organized manner. + +Follow the links below to try out each path. + +```{tableofcontents} + +``` diff --git a/oceanhackweek-2020/xarray-oceanhackweek20.ipynb b/overview/xarray-in-45-min.ipynb similarity index 50% rename from oceanhackweek-2020/xarray-oceanhackweek20.ipynb rename to overview/xarray-in-45-min.ipynb index b479e838..2566262d 100644 --- a/oceanhackweek-2020/xarray-oceanhackweek20.ipynb +++ b/overview/xarray-in-45-min.ipynb @@ -4,78 +4,60 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "
\n",
+ "\n",
"\n",
"# Xarray in 45 minutes\n",
"\n",
- "In this lesson, we discuss cover the basics of Xarray data structures. By the\n",
+ "In this lesson, we cover the basics of Xarray data structures. By the\n",
"end of the lesson, we will be able to:\n",
"\n",
"- Understand the basic data structures in Xarray\n",
"- Inspect `DataArray` and `Dataset` objects.\n",
"- Read and write netCDF files using Xarray.\n",
- "- Understand that there are many packages that build on top of xarray\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## A practical example\n"
+ "- Understand that there are many packages that build on top of xarray\n",
+ "\n",
+ "\n",
+ "We'll start by reviewing the various components of the Xarray data model, represented here visually:\n",
+ "\n",
+ ""
]
},
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-08-19T16:07:17.256417Z",
- "start_time": "2020-08-19T16:07:15.713910Z"
- }
- },
+ "metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import xarray as xr\n",
"\n",
- "%matplotlib inline"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-08-19T16:07:18.118460Z",
- "start_time": "2020-08-19T16:07:18.017413Z"
- }
- },
- "outputs": [],
- "source": [
- "# load tutorial dataset\n",
- "ds = xr.tutorial.load_dataset(\"air_temperature\")"
+ "xr.set_options(keep_attrs=True, display_expand_data=False)\n",
+ "np.set_printoptions(threshold=10, edgeitems=2)\n",
+ "\n",
+ "%xmode minimal\n",
+ "%matplotlib inline\n",
+ "%config InlineBackend.figure_format='retina'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "## What's in a dataset? many DataArrays\n"
+ "Xarray has a few small real-world tutorial datasets hosted in the [xarray-data](https://github.com/pydata/xarray-data) GitHub repository.\n",
+ "\n",
+ "[xarray.tutorial.load_dataset](https://docs.xarray.dev/en/stable/generated/xarray.tutorial.open_dataset.html#xarray.tutorial.open_dataset) is a convenience function to download and open DataSets by name (listed at that link).\n",
+ "\n",
+ "Here we'll use `air temperature` from the [National Center for Environmental Prediction](https://www.weather.gov/ncep/). Xarray objects have convenient HTML representations to give an overview of what we're working with:"
]
},
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-08-19T16:07:19.783476Z",
- "start_time": "2020-08-19T16:07:19.719819Z"
- }
- },
+ "metadata": {},
"outputs": [],
"source": [
- "# dataset repr\n",
+ "ds = xr.tutorial.load_dataset(\"air_temperature\")\n",
"ds"
]
},
@@ -83,19 +65,27 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Datasets are dict-like containers of DataArrays i.e. they are a mapping of\n",
- "variable name to DataArray.\n"
+ "Note that behind the scenes the `tutorial.open_dataset` downloads a file. It then uses [`xarray.open_dataset`](https://docs.xarray.dev/en/stable/generated/xarray.open_dataset.html#xarray-open-dataset) function to open that file (which for this datasets is a [netCDF](https://www.unidata.ucar.edu/software/netcdf/) file). \n",
+ "\n",
+ "A few things are done automatically upon opening, but controlled by keyword arguments. For example, try passing the keyword argument `mask_and_scale=False`... what happens?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## What's in a Dataset? \n",
+ "\n",
+ "*Many DataArrays!* \n",
+ "\n",
+ "Datasets are dictionary-like containers of \"DataArray\"s. They are a mapping of\n",
+ "variable name to DataArray:"
]
},
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-08-19T16:07:23.486767Z",
- "start_time": "2020-08-19T16:07:23.452957Z"
- }
- },
+ "metadata": {},
"outputs": [],
"source": [
"# pull out \"air\" dataarray with dictionary syntax\n",
@@ -118,65 +108,80 @@
"outputs": [],
"source": [
"# pull out dataarray using dot notation\n",
- "ds.air ## same as ds[\"air\"]"
+ "ds.air"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "## What's in a DataArray? data + (a lot of) metadata\n",
+ "## What's in a DataArray? \n",
"\n",
- "### Named dimensions `.dims`\n"
+ "*data + (a lot of) metadata*"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Name (optional)"
]
},
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-08-19T16:08:27.758670Z",
- "start_time": "2020-08-19T16:08:27.727504Z"
- }
- },
+ "metadata": {},
"outputs": [],
"source": [
- "ds.air.dims"
+ "da = ds.air\n",
+ "\n",
+ "da.name"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Coordinate variables or \"tick labels\" (`.coords`)\n",
+ "### Named dimensions \n",
"\n",
- "`.coords` is a simple\n",
- "[data container](https://xarray.pydata.org/en/stable/data-structures.html#coordinates)\n",
- "for coordinate variables.\n"
+ "`.dims` correspond to the axes of your data. \n",
+ "\n",
+ "In this case we have 2 spatial dimensions (`latitude` and `longitude` are stored with shorthand names `lat` and `lon`) and one temporal dimension (`time`)."
]
},
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-08-19T16:08:29.769909Z",
- "start_time": "2020-08-19T16:08:29.749571Z"
- }
- },
+ "metadata": {},
"outputs": [],
"source": [
- "ds.air.coords"
+ "da.dims"
]
},
{
"cell_type": "markdown",
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-08-19T16:09:26.528972Z",
- "start_time": "2020-08-19T16:09:26.512791Z"
- }
- },
+ "metadata": {},
+ "source": [
+ "### Coordinate variables \n",
+ "\n",
+ "`.coords` is a simple [data container](https://docs.xarray.dev/en/stable/user-guide/data-structures.html#coordinates)\n",
+ "for coordinate variables.\n",
+ "\n",
+ "Here we see the actual timestamps and spatial positions of our air temperature data:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "da.coords"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
"source": [
"Coordinates objects support similar indexing notation\n"
]
@@ -188,7 +193,7 @@
"outputs": [],
"source": [
"# extracting coordinate variables\n",
- "ds.air.lon"
+ "da.lon"
]
},
{
@@ -197,8 +202,8 @@
"metadata": {},
"outputs": [],
"source": [
- "# extracting coorindate variables from .coords\n",
- "ds.coords[\"lon\"]"
+ "# extracting coordinate variables from .coords\n",
+ "da.coords[\"lon\"]"
]
},
{
@@ -214,67 +219,61 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Arbitrary attributes (`.attrs`)\n",
+ "### Arbitrary attributes \n",
"\n",
- "`.attrs` is a dictionary that can contain arbitrary python objects. Your only\n",
- "limitation is that some attributes may not be writeable to a netCDF file\n"
+ "`.attrs` is a dictionary that can contain arbitrary Python objects (strings, lists, integers, dictionaries, etc.) Your only\n",
+ "limitation is that some attributes may not be writeable to certain file formats."
]
},
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-08-19T16:09:43.977690Z",
- "start_time": "2020-08-19T16:09:43.951688Z"
- }
- },
+ "metadata": {},
"outputs": [],
"source": [
- "ds.air.attrs"
+ "da.attrs"
]
},
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-08-19T16:09:45.395462Z",
- "start_time": "2020-08-19T16:09:45.373786Z"
- }
- },
+ "metadata": {},
"outputs": [],
"source": [
- "# assign your own attribute\n",
- "ds.air.attrs[\"who_is_awesome\"] = \"xarray\"\n",
- "ds.air.attrs"
+ "# assign your own attributes!\n",
+ "da.attrs[\"who_is_awesome\"] = \"xarray\"\n",
+ "da.attrs"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "da"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Underlying data (`.data`)\n",
+ "### Underlying data \n",
+ "\n",
+ "`.data` contains the [numpy array](https://numpy.org) storing air temperature values.\n",
"\n",
- "Xarray structures wrap underlying simpler data structures. In this case, the\n",
- "underlying data is a numpy array which you may be familiar with.\n",
+ "\n",
"\n",
- "This part of xarray is quite extensible allowing for GPU arrays, sparse arrays,\n",
- "arrays with units etc. See the demo at the end.\n"
+ "Xarray structures wrap underlying simpler array-like data structures. This part of Xarray is quite extensible allowing for distributed array, GPU arrays, sparse arrays, arrays with units etc. We'll briefly look at this later in this tutorial."
]
},
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-08-19T16:09:47.536691Z",
- "start_time": "2020-08-19T16:09:47.501968Z"
- }
- },
+ "metadata": {},
"outputs": [],
"source": [
- "ds.air.data"
+ "da.data"
]
},
{
@@ -284,75 +283,71 @@
"outputs": [],
"source": [
"# what is the type of the underlying data\n",
- "type(ds.air.data)"
+ "type(da.data)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "A numpy array!\n",
+ "### Review\n",
"\n",
- "![](\"https://numpy.org/images/logos/numpy.svg\")
\n"
+ "Xarray provides two main data structures:\n",
+ "\n",
+ "1. [`DataArrays`](https://docs.xarray.dev/en/stable/user-guide/data-structures.html#dataarray) that wrap underlying data containers (e.g. numpy arrays) and contain associated metadata\n",
+ "1. [`Datasets`](https://docs.xarray.dev/en/stable/user-guide/data-structures.html#dataset) that are dictionary-like containers of DataArrays\n",
+ "\n",
+ "DataArrays contain underlying arrays and associated metadata:\n",
+ "1. Name\n",
+ "2. Dimension names\n",
+ "3. Coordinate variables\n",
+ "4. and arbitrary attributes."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Review\n",
- "\n",
- "Xarray provides two main data structures\n",
- "\n",
- "- DataArrays that wrap underlying data containers (e.g. numpy arrays) and\n",
- " contain associated metadata\n",
- "- Datasets that are dict-like containers of DataArrays\n",
- "\n",
- "For more see\n",
- "\n",
- "- https://xarray.pydata.org/en/stable/data-structures.html#dataset\n",
- "- https://xarray.pydata.org/en/stable/data-structures.html#dataarray\n"
+ "---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "---\n",
+ "## Why Xarray? \n",
"\n",
- "## Why xarray? Use metadata for fun and ~profit~ papers!\n",
+ "Metadata provides context and provides code that is more legible. This reduces the likelihood of errors from typos and makes analysis more intuitive and fun!\n",
"\n",
- "### Analysis without xarray: `X(`\n"
+ "### Analysis without xarray: `X(`"
]
},
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-08-19T16:10:25.379707Z",
- "start_time": "2020-08-19T16:10:25.192751Z"
- }
- },
+ "metadata": {},
"outputs": [],
"source": [
"# plot the first timestep\n",
"lat = ds.air.lat.data # numpy array\n",
"lon = ds.air.lon.data # numpy array\n",
- "temp = ds.air.data # numpy array\n",
+ "temp = ds.air.data # numpy array"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
"plt.figure()\n",
- "plt.pcolormesh(lon, lat, temp[0, :, :])"
+ "plt.pcolormesh(lon, lat, temp[0, :, :]);"
]
},
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-08-19T16:10:28.631492Z",
- "start_time": "2020-08-19T16:10:28.594725Z"
- }
- },
+ "metadata": {},
"outputs": [],
"source": [
"temp.mean(axis=1) ## what did I just do? I can't tell by looking at this line."
@@ -370,15 +365,10 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-08-19T16:10:53.391181Z",
- "start_time": "2020-08-19T16:10:53.151292Z"
- }
- },
+ "metadata": {},
"outputs": [],
"source": [
- "ds.air.isel(time=1).plot(x=\"lon\")"
+ "ds.air.isel(time=0).plot(x=\"lon\");"
]
},
{
@@ -391,15 +381,10 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-08-19T16:10:49.711769Z",
- "start_time": "2020-08-19T16:10:49.668537Z"
- }
- },
+ "metadata": {},
"outputs": [],
"source": [
- "ds.air.mean(\"time\")"
+ "ds.air.mean(dim=\"time\").plot(x=\"lon\")"
]
},
{
@@ -408,14 +393,14 @@
"source": [
"---\n",
"\n",
- "## Extracting data or \"indexing\" : `.sel`, `.isel`\n",
+ "## Extracting data or \"indexing\" \n",
"\n",
"Xarray supports\n",
"\n",
"- label-based indexing using `.sel`\n",
"- position-based indexing using `.isel`\n",
"\n",
- "For more see https://xarray.pydata.org/en/stable/indexing.html\n"
+ "See the [user guide](https://docs.xarray.dev/en/stable/indexing.html) for more."
]
},
{
@@ -428,6 +413,16 @@
"support for dates and times!\n"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# here's what ds looks like\n",
+ "ds"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -448,6 +443,15 @@
"ds.sel(time=slice(\"2013-05\", \"2013-07\"))"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds.sel(time=\"2013\")"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -484,8 +488,17 @@
"metadata": {},
"outputs": [],
"source": [
- "# pull out time index 0 and lat index 0\n",
- "ds.air.isel(time=0, lat=0) # much better than ds.air[0, 0, :]"
+ "ds.air.data[0, 2, 3]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# pull out time index 0, lat index 2, and lon index 3\n",
+ "ds.air.isel(time=0, lat=2, lon=3) # much better than ds.air[0, 2, 3]"
]
},
{
@@ -504,23 +517,44 @@
"source": [
"---\n",
"\n",
- "## Concepts for computation\n"
+ "## Concepts for computation\n",
+ "\n",
+ "Consider calculating the *mean air temperature per unit surface area* for this dataset. Because latitude and longitude correspond to spherical coordinates for Earth's surface, each 2.5x2.5 degree grid cell actually has a different surface area as you move away from the equator! This is because *latitudinal length* is fixed ($ \\delta Lat = R \\delta \\phi $), but *longitudinal length varies with latitude* ($ \\delta Lon = R \\delta \\lambda \\cos(\\phi) $)\n",
+ "\n",
+ "So the [area element for lat-lon coordinates](https://en.wikipedia.org/wiki/Spherical_coordinate_system#Integration_and_differentiation_in_spherical_coordinates) is\n",
+ "\n",
+ "\n",
+ "$$ \\delta A = R^2 \\delta\\phi \\, \\delta\\lambda \\cos(\\phi) $$\n",
+ "\n",
+ "where $\\phi$ is latitude, $\\delta \\phi$ is the spacing of the points in latitude, $\\delta \\lambda$ is the spacing of the points in longitude, and $R$ is Earth's radius. (In this formula, $\\phi$ and $\\lambda$ are measured in radians)"
]
},
{
- "cell_type": "markdown",
+ "cell_type": "code",
+ "execution_count": null,
"metadata": {},
+ "outputs": [],
"source": [
- "### Broadcasting: expanding data\n",
+ "# Earth's average radius in meters\n",
+ "R = 6.371e6\n",
"\n",
- "Let's try to calculate grid cell area associated with the air temperature data.\n",
- "We may want this to make a proper area-weighted domain-average for example\n",
+ "# Coordinate spacing for this dataset is 2.5 x 2.5 degrees\n",
+ "dϕ = np.deg2rad(2.5)\n",
+ "dλ = np.deg2rad(2.5)\n",
"\n",
- "A very approximate formula is\n",
- "\n",
- "\\begin{equation} Δlat \\times Δlon \\times \\cos(\\text{latitude}) \\end{equation}\n",
- "\n",
- "assuming that $Δlon$ = 111km and $Δlat$ = 111km\n"
+ "dlat = R * dϕ * xr.ones_like(ds.air.lon)\n",
+ "dlon = R * dλ * np.cos(np.deg2rad(ds.air.lat))\n",
+ "dlon.name = \"dlon\"\n",
+ "dlat.name = \"dlat\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "There are two concepts here:\n",
+ "1. you can call functions like `np.cos` and `np.deg2rad` ([\"numpy ufuncs\"](https://numpy.org/doc/stable/reference/ufuncs.html)) on Xarray objects and receive an Xarray object back.\n",
+ "2. We used [ones_like](https://docs.xarray.dev/en/stable/generated/xarray.ones_like.html) to create a DataArray that looks like `ds.air.lon` in all respects, except that the data are all ones"
]
},
{
@@ -529,8 +563,8 @@
"metadata": {},
"outputs": [],
"source": [
- "dlon = np.cos(ds.air.lat * np.pi / 180) * 111e3\n",
- "dlon"
+ "# returns an xarray DataArray!\n",
+ "np.cos(np.deg2rad(ds.lat))"
]
},
{
@@ -539,10 +573,29 @@
"metadata": {},
"outputs": [],
"source": [
- "dlat = 111e3 * xr.ones_like(ds.air.lon)\n",
+ "# cell latitude length is constant with longitude\n",
"dlat"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# cell longitude length changes with latitude\n",
+ "dlon"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Broadcasting: expanding data\n",
+ "\n",
+ "Our longitude and latitude length DataArrays are both 1D with different dimension names. If we multiply these DataArrays together the dimensionality is expanded to 2D by _broadcasting_:"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -561,11 +614,10 @@
"`lat` are different so it automatically \"broadcasts\" to get a 2D result. See the\n",
"last row in this image from _Jake VanderPlas Python Data Science Handbook_\n",
"\n",
- "![](\"https://jakevdp.github.io/PythonDataScienceHandbook/figures/02.05-broadcasting.png\")
\n",
+ "\n",
"\n",
"Because xarray knows about dimension names we avoid having to create unnecessary\n",
- "size-1 dimensions using `np.newaxis` or `.reshape`. For more, see\n",
- "https://xarray.pydata.org/en/stable/computation.html#broadcasting-by-dimension-name\n"
+ "size-1 dimensions using `np.newaxis` or `.reshape`. For more, see the [user guide](https://docs.xarray.dev/en/stable/user-guide/computation.html#broadcasting-by-dimension-name)\n"
]
},
{
@@ -578,7 +630,7 @@
"\n",
"When doing arithmetic operations xarray automatically \"aligns\" i.e. puts the\n",
"data on the same grid. In this case `cell_area` and `ds.air` are at the same\n",
- "lat, lon points so things are multiplied as you would expect\n"
+ "lat, lon points we end up with a result with the same shape (25x53):\n"
]
},
{
@@ -587,7 +639,7 @@
"metadata": {},
"outputs": [],
"source": [
- "(cell_area * ds.air.isel(time=1))"
+ "ds.air.isel(time=1) / cell_area"
]
},
{
@@ -604,9 +656,9 @@
"outputs": [],
"source": [
"# make a copy of cell_area\n",
- "# then add 1e-5 to lat\n",
+ "# then add 1e-5 degrees to latitude\n",
"cell_area_bad = cell_area.copy(deep=True)\n",
- "cell_area_bad[\"lat\"] = cell_area.lat + 1e-5\n",
+ "cell_area_bad[\"lat\"] = cell_area.lat + 1e-5 # latitudes are off by 1e-5 degrees!\n",
"cell_area_bad"
]
},
@@ -623,11 +675,44 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "**Tip:** If you notice extra NaNs or missing points after xarray computation, it\n",
+ "The result is an empty array with no latitude coordinates because none of them were aligned!\n",
+ "\n",
+ "```{tip}\n",
+ "If you notice extra NaNs or missing points after xarray computation, it\n",
"means that your xarray coordinates were not aligned _exactly_.\n",
+ "```\n",
"\n",
+ "To make sure variables are aligned as you think they are, do the following:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "tags": [
+ "raises-exception"
+ ]
+ },
+ "outputs": [],
+ "source": [
+ "xr.align(cell_area_bad, ds.air, join=\"exact\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The above statement raises an error since the two are not aligned."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "```{seealso}\n",
"For more, see\n",
- "https://xarray.pydata.org/en/stable/computation.html#automatic-alignment\n"
+ "[the Xarray documentation](https://docs.xarray.dev/en/stable/user-guide/computation.html#automatic-alignment). [This tutorial notebook](https://tutorial.xarray.dev/fundamentals/02.3_aligning_data_objects.html) also covers alignment and broadcasting (*highly recommended*)\n",
+ "```"
]
},
{
@@ -636,21 +721,26 @@
"source": [
"---\n",
"\n",
- "## High level computation: `groupby`, `resample`, `rolling`, `coarsen`, `weighted`\n",
+ "## High level computation \n",
+ "\n",
+ "(`groupby`, `resample`, `rolling`, `coarsen`, `weighted`)\n",
"\n",
"Xarray has some very useful high level objects that let you do common\n",
"computations:\n",
"\n",
"1. `groupby` :\n",
- " [Bin data in to groups and reduce](https://xarray.pydata.org/en/stable/groupby.html)\n",
+ " [Bin data in to groups and reduce](https://docs.xarray.dev/en/stable/groupby.html)\n",
"1. `resample` :\n",
- " [Groupby specialized for time axes. Either downsample or upsample your data.](https://xarray.pydata.org/en/stable/time-series.html#resampling-and-grouped-operations)\n",
+ " [Groupby specialized for time axes. Either downsample or upsample your data.](https://docs.xarray.dev/en/stable/user-guide/time-series.html#resampling-and-grouped-operations)\n",
"1. `rolling` :\n",
- " [Operate on rolling windows of your data e.g. running mean](https://xarray.pydata.org/en/stable/computation.html#rolling-window-operations)\n",
+ " [Operate on rolling windows of your data e.g. running mean](https://docs.xarray.dev/en/stable/user-guide/computation.html#rolling-window-operations)\n",
"1. `coarsen` :\n",
- " [Downsample your data](https://xarray.pydata.org/en/stable/computation.html#coarsen-large-arrays)\n",
+ " [Downsample your data](https://docs.xarray.dev/en/stable/user-guide/computation.html#coarsen-large-arrays)\n",
"1. `weighted` :\n",
- " [Weight your data before reducing](https://xarray.pydata.org/en/stable/computation.html#weighted-array-reductions)\n"
+ " [Weight your data before reducing](https://docs.xarray.dev/en/stable/user-guide/computation.html#weighted-array-reductions)\n",
+ "\n",
+ "\n",
+ "Below we quickly demonstrate these patterns. See the user guide links above and [the tutorial](https://tutorial.xarray.dev/intermediate/01-high-level-computation-patterns.html) for more."
]
},
{
@@ -660,6 +750,16 @@
"### groupby\n"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# here's ds\n",
+ "ds"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -673,12 +773,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-08-19T16:14:03.590082Z",
- "start_time": "2020-08-19T16:14:03.507391Z"
- }
- },
+ "metadata": {},
"outputs": [],
"source": [
"# make a seasonal mean\n",
@@ -691,24 +786,28 @@
"metadata": {},
"source": [
"The seasons are out of order (they are alphabetically sorted). This is a common\n",
- "annoyance. The solution is to use `.reindex`\n"
+ "annoyance. The solution is to use `.sel` to change the order of labels\n"
]
},
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-08-19T16:15:07.345540Z",
- "start_time": "2020-08-19T16:15:07.304690Z"
- }
- },
+ "metadata": {},
"outputs": [],
"source": [
- "seasonal_mean = seasonal_mean.reindex(season=[\"DJF\", \"MAM\", \"JJA\", \"SON\"])\n",
+ "seasonal_mean = seasonal_mean.sel(season=[\"DJF\", \"MAM\", \"JJA\", \"SON\"])\n",
"seasonal_mean"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "seasonal_mean.air.plot(col=\"season\")"
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
@@ -723,7 +822,7 @@
"outputs": [],
"source": [
"# resample to monthly frequency\n",
- "ds.resample(time=\"M\").mean()"
+ "ds.resample(time=\"ME\").mean()"
]
},
{
@@ -740,7 +839,7 @@
"outputs": [],
"source": [
"# weight by cell_area and take mean over (time, lon)\n",
- "ds.weighted(cell_area).mean([\"lon\", \"time\"]).air.plot()"
+ "ds.weighted(cell_area).mean([\"lon\", \"time\"]).air.plot(y=\"lat\");"
]
},
{
@@ -749,60 +848,51 @@
"source": [
"---\n",
"\n",
- "## Visualization: `.plot`\n",
+ "## Visualization\n",
+ "\n",
+ "(`.plot`)\n",
"\n",
- "For more see https://xarray.pydata.org/en/stable/plotting.html and\n",
- "https://xarray.pydata.org/en/stable/examples/visualization_gallery.html\n",
"\n",
- "We have seen very simple plots earlier. Xarray has some support for visualizing\n",
+ "We have seen very simple plots earlier. Xarray also lets you easily visualize\n",
"3D and 4D datasets by presenting multiple facets (or panels or subplots) showing\n",
- "variations across rows and/or columns.\n"
+ "variations across rows and/or columns."
]
},
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-08-19T16:14:06.090565Z",
- "start_time": "2020-08-19T16:14:05.580534Z"
- }
- },
+ "metadata": {},
"outputs": [],
"source": [
"# facet the seasonal_mean\n",
- "seasonal_mean.air.plot(col=\"season\")"
+ "seasonal_mean.air.plot(col=\"season\", col_wrap=2);"
]
},
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-08-19T16:15:13.777572Z",
- "start_time": "2020-08-19T16:15:13.136044Z"
- },
- "scrolled": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"# contours\n",
- "seasonal_mean.air.plot.contour(col=\"season\", levels=20, add_colorbar=True)"
+ "seasonal_mean.air.plot.contour(col=\"season\", levels=20, add_colorbar=True);"
]
},
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-08-19T16:15:15.998115Z",
- "start_time": "2020-08-19T16:15:15.805569Z"
- }
- },
+ "metadata": {},
"outputs": [],
"source": [
"# line plots too? wut\n",
- "seasonal_mean.air.mean(\"lon\").plot.line(hue=\"season\", y=\"lat\")"
+ "seasonal_mean.air.mean(\"lon\").plot.line(hue=\"season\", y=\"lat\");"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "For more see the [user guide](https://docs.xarray.dev/en/stable/plotting.html), the [gallery](https://docs.xarray.dev/en/stable/examples/visualization_gallery.html), and [the tutorial material](https://tutorial.xarray.dev/fundamentals/04.0_plotting.html)."
]
},
{
@@ -811,10 +901,10 @@
"source": [
"---\n",
"\n",
- "## Reading and writing to disk\n",
+ "## Reading and writing files\n",
"\n",
"Xarray supports many disk formats. Below is a small example using netCDF. For\n",
- "more see https://xarray.pydata.org/en/stable/io.html\n"
+ "more see the [documentation](https://docs.xarray.dev/en/stable/user-guide/io.html)\n"
]
},
{
@@ -823,10 +913,19 @@
"metadata": {},
"outputs": [],
"source": [
- "# write ds to netCDF\n",
+ "# write to netCDF\n",
"ds.to_netcdf(\"my-example-dataset.nc\")"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "```{note}\n",
+ "To avoid the `SerializationWarning` you can assign a _FillValue for any NaNs in 'air' array by adding the keyword argument `encoding=dict(air=dict(_FillValue=-9999))`\n",
+ "```"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -852,30 +951,14 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "**Tip:** A common use case to read datasets that are a collection of many netCDF\n",
- "files. See\n",
- "https://xarray.pydata.org/en/stable/io.html#reading-multi-file-datasets for how\n",
- "to handle that\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "---\n",
+ "```{tip}\n",
+ "A common use case to read datasets that are a collection of many netCDF\n",
+ "files. See the [documentation](https://docs.xarray.dev/en/stable/user-guide/io.html#reading-multi-file-datasets) for how\n",
+ "to handle that.\n",
+ "```\n",
"\n",
- "## More information\n",
- "\n",
- "1. A description of common terms used in the xarray documentation:\n",
- " https://xarray.pydata.org/en/stable/terminology.html\n",
- "1. For information on how to create a DataArray from an existing numpy array:\n",
- " https://xarray.pydata.org/en/stable/data-structures.html#creating-a-dataarray\n",
- "1. Answers to common questions on \"how to do X\" are here:\n",
- " https://xarray.pydata.org/en/stable/howdoi.html\n",
- "1. Our more extensive Scipy 2020 tutorial material:\n",
- " https://xarray-contrib.github.io/xarray-tutorial/\n",
- "1. Ryan Abernathey has a book on data analysis with a chapter on Xarray:\n",
- " https://earth-env-data-science.github.io/lectures/xarray/xarray_intro.html\n"
+ "Finally to read other file formats, you might find yourself reading in the data using a different library and then creating a DataArray([docs](https://docs.xarray.dev/en/stable/user-guide/data-structures.html#creating-a-dataarray), [tutorial](https://tutorial.xarray.dev/fundamentals/01.1_creating_data_structures.html)) from scratch. For example, you might use `h5py` to open an HDF5 file and then create a Dataset from that.\n",
+ "For MATLAB files you might use `scipy.io.loadmat` or `h5py` depending on the version of MATLAB file you're opening and then construct a Dataset."
]
},
{
@@ -884,11 +967,11 @@
"source": [
"---\n",
"\n",
- "## The scientific python / pangeo ecosystem: demo\n",
+ "## The scientific python ecosystem\n",
"\n",
"Xarray ties in to the larger scientific python ecosystem and in turn many\n",
"packages build on top of xarray. A long list of such packages is here:\n",
- "https://xarray.pydata.org/en/stable/related-projects.html.\n",
+ "![](\"https://docs.dask.org/en/latest/_static/images/dask-horizontal-white.svg\")
\n",
+ "### Alternative array types\n",
"\n",
- "**dask** : parallel arrays https://xarray.pydata.org/en/stable/dask.html &\n",
- "https://docs.dask.org/en/latest/array.html\n",
+ "This notebook has focused on Numpy arrays. Xarray can wrap [other array](https://docs.xarray.dev/en/stable/user-guide/duckarrays.html) types! For example:\n",
"\n",
- "![](\"https://sparse.pydata.org/en/stable/_images/logo.png\"){kind=logo}
\n",
+ "![](\"https://docs.dask.org/en/stable/_images/dask_horizontal.svg\")
[distributed parallel arrays](https://docs.dask.org/en/latest/array.html) & [Xarray user guide on Dask](https://docs.xarray.dev/en/stable/user-guide/dask.html)\n",
"\n",
- "**pydata/sparse** : sparse arrays http://sparse.pydata.org\n",
+ "![](\"https://raw.githubusercontent.com/pydata/sparse/master/docs/logo.svg\"){kind=logo}
**pydata/sparse** : [sparse arrays](https://sparse.pydata.org)\n",
"\n",
- "![](\"https://raw.githubusercontent.com/cupy/cupy.dev/master/images/cupy_logo.png\"){kind=logo}
\n",
+ " [GPU arrays](https://cupy.dev) & [cupy-xarray](https://cupy-xarray.readthedocs.io/)\n",
"\n",
- "**cupy** : GPU arrays http://cupy.chainer.org\n",
- "\n",
- "![](\"https://pint.readthedocs.io/en/stable/_images/logo-full.jpg\"){kind=logo}
\n",
- "\n",
- "**pint** : unit-aware computations https://pint.readthedocs.org &\n",
- "https://github.com/xarray-contrib/pint-xarray\n"
+ "![](\"https://pint.readthedocs.io/en/stable/_static/logo-full.jpg\"){kind=logo}
**pint** : [unit-aware arrays](https://pint.readthedocs.io) & [pint-xarray](https://github.com/xarray-contrib/pint-xarray)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Xarray + dask\n",
+ "### Dask\n",
"\n",
"Dask cuts up NumPy arrays into blocks and parallelizes your analysis code across\n",
"these blocks\n",
"\n",
- "![](\"https://dask.org/_images/dask-array-black-text.svg\")
\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-08-19T16:16:28.269631Z",
- "start_time": "2020-08-19T16:16:27.141306Z"
- }
- },
- "outputs": [],
- "source": [
- "# make dask cluster; this is for demo purposes\n",
- "import dask\n",
- "import distributed\n",
- "\n",
- "cluster = distributed.LocalCluster()"
+ "\n"
]
},
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-08-19T16:16:28.912370Z",
- "start_time": "2020-08-19T16:16:28.843239Z"
- }
- },
- "outputs": [],
- "source": [
- "client = distributed.Client(cluster)\n",
- "client"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-08-19T16:16:30.205686Z",
- "start_time": "2020-08-19T16:16:30.140967Z"
- }
- },
+ "metadata": {},
"outputs": [],
"source": [
"# demonstrate dask dataset\n",
"dasky = xr.tutorial.open_dataset(\n",
- " \"air_temperature\", chunks={\"time\": 10}, # 10 time steps in each block\n",
+ " \"air_temperature\",\n",
+ " chunks={\"time\": 10}, # 10 time steps in each block\n",
")\n",
"\n",
"dasky.air"
@@ -1039,12 +1062,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-08-19T16:16:32.606881Z",
- "start_time": "2020-08-19T16:16:32.561338Z"
- }
- },
+ "metadata": {},
"outputs": [],
"source": [
"# demonstrate lazy mean\n",
@@ -1061,12 +1079,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-08-19T16:16:38.409452Z",
- "start_time": "2020-08-19T16:16:36.914676Z"
- }
- },
+ "metadata": {},
"outputs": [],
"source": [
"# \"compute\" the mean\n",
@@ -1077,61 +1090,62 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "### holoviews: javascript interactive plots\n",
+ "### HoloViz\n",
+ "\n",
+ "Quickly generate interactive plots from your data!\n",
"\n",
- "the `hvplot` package is a nice easy way to access\n",
- "[holoviews](http://holoviews.org/) functionality. It attaches itself to all\n",
- "xarray objects under the `.hvplot` namespace. So instead of using `.plot` use\n",
- "`.hvplot`\n"
+ "The [`hvplot` package](https://hvplot.holoviz.org/user_guide/Gridded_Data.html) attaches itself to all\n",
+ "xarray objects under the `.hvplot` namespace. So instead of using `.plot` use `.hvplot`"
]
},
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-08-19T16:17:26.384153Z",
- "start_time": "2020-08-19T16:17:24.365996Z"
- }
- },
+ "metadata": {},
"outputs": [],
"source": [
- "import hvplot.xarray\n",
- "\n",
- "ds.air.hvplot(groupby=\"time\", clim=(270, 300))"
+ "import hvplot.xarray"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds.air.hvplot(groupby=\"time\", clim=(270, 300), widget_location=\"bottom\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "Try the slider!\n"
+ "```{note}\n",
+ "The time slider will only work if you're executing the notebook, rather than viewing the website\n",
+ "```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "### cf_xarray : use even more metadata for even more fun and ~profit~ papers\n",
+ "### cf_xarray \n",
"\n",
- "[cf_xarray](https://cf-xarray.readthedocs.io/) is a new project that tries to\n",
+ "[cf_xarray](https://cf-xarray.readthedocs.io/) is a project that tries to\n",
"let you make use of other CF attributes that xarray ignores. It attaches itself\n",
"to all xarray objects under the `.cf` namespace.\n",
"\n",
"Where xarray allows you to specify dimension names for analysis, `cf_xarray`\n",
"lets you specify logical names like `\"latitude\"` or `\"longitude\"` instead as\n",
- "long as the appropriate CF attributes are set.\n"
+ "long as the appropriate CF attributes are set.\n",
+ "\n",
+ "For example, the `\"longitude\"` dimension in different files might be labelled as: (lon, LON, long, x…), but cf_xarray let's you always refer to the logical name `\"longitude\"` in your code:"
]
},
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-08-19T16:17:43.015284Z",
- "start_time": "2020-08-19T16:17:42.981131Z"
- }
- },
+ "metadata": {},
"outputs": [],
"source": [
"import cf_xarray"
@@ -1140,16 +1154,11 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-08-19T16:18:31.279888Z",
- "start_time": "2020-08-19T16:18:31.245785Z"
- }
- },
+ "metadata": {},
"outputs": [],
"source": [
"# describe cf attributes in dataset\n",
- "ds.air.cf.describe()"
+ "ds.air.cf"
]
},
{
@@ -1164,12 +1173,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-08-19T16:18:33.806279Z",
- "start_time": "2020-08-19T16:18:33.738629Z"
- }
- },
+ "metadata": {},
"outputs": [],
"source": [
"# demonstrate equivalent of .mean(\"lat\")\n",
@@ -1179,12 +1183,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-08-19T16:19:27.628895Z",
- "start_time": "2020-08-19T16:19:27.580971Z"
- }
- },
+ "metadata": {},
"outputs": [],
"source": [
"# demonstrate indexing\n",
@@ -1199,7 +1198,7 @@
"\n",
"- [xgcm](https://xgcm.readthedocs.io/) : grid-aware operations with xarray\n",
" objects\n",
- "- [xrft](https://xgcm.readthedocs.io/) : fourier transforms with xarray\n",
+ "- [xrft](https://xrft.readthedocs.io/) : fourier transforms with xarray\n",
"- [xclim](https://xclim.readthedocs.io/) : calculating climate indices with\n",
" xarray objects\n",
"- [intake-xarray](https://intake-xarray.readthedocs.io/) : forget about file\n",
@@ -1210,16 +1209,31 @@
"- [MetPy](https://unidata.github.io/MetPy/latest/index.html) : tools for working\n",
" with weather data\n",
"\n",
- "More here: https://xarray.pydata.org/en/stable/related-projects.html\n"
+ "Check the Xarray [Ecosystem](https://docs.xarray.dev/en/stable/user-guide/ecosystem.html) page and [this tutorial](https://tutorial.xarray.dev/intermediate/xarray_ecosystem.html) for even more packages and demonstrations."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Next\n",
+ "\n",
+ "1. Read the [tutorial](https://tutorial.xarray.dev) material and [user guide](https://docs.xarray.dev/en/stable/user-guide/index.html)\n",
+ "1. See the description of [common terms](https://docs.xarray.dev/en/stable/terminology.html) used in the xarray documentation: \n",
+ "1. Answers to common questions on \"how to do X\" with Xarray are [here](https://docs.xarray.dev/en/stable/get-help/howdoi.html)\n",
+ "1. Ryan Abernathey has a book on data analysis with a [chapter on Xarray](https://earth-env-data-science.github.io/lectures/xarray/xarray_intro.html)\n",
+ "1. [Project Pythia](https://projectpythia.org/) has [foundational](https://foundations.projectpythia.org) and more [advanced](https://cookbooks.projectpythia.org/) material on Xarray. Pythia also aggregates other [Python learning resources](https://projectpythia.org/resource-gallery).\n",
+ "1. The [Xarray Github Discussions](https://github.com/pydata/xarray/discussions) and [Pangeo Discourse](https://discourse.pangeo.io/) are good places to ask questions.\n",
+ "1. Tell your friends! Tweet!\n",
+ "\n",
+ "\n",
+ "## Welcome!\n",
+ "\n",
+ "Xarray is an open-source project and gladly welcomes all kinds of contributions. This could include reporting bugs, discussing new enhancements, contributing code, helping answer user questions, contributing documentation (even small edits like fixing spelling mistakes or rewording to make the text clearer). Welcome!"
]
}
],
"metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
"language_info": {
"codemirror_mode": {
"name": "ipython",
@@ -1229,8 +1243,7 @@
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.7.8"
+ "pygments_lexer": "ipython3"
},
"toc": {
"base_numbering": 1,
@@ -1244,13 +1257,6 @@
"toc_position": {},
"toc_section_display": true,
"toc_window_display": true
- },
- "widgets": {
- "application/vnd.jupyter.widget-state+json": {
- "state": {},
- "version_major": 2,
- "version_minor": 0
- }
}
},
"nbformat": 4,
diff --git a/pixi.lock b/pixi.lock
new file mode 100644
index 00000000..ce4b217d
--- /dev/null
+++ b/pixi.lock
@@ -0,0 +1,15980 @@
+version: 7
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+- name: linux-64
+- name: osx-arm64
+- name: win-64
+environments:
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+ license: MIT
+ license_family: MIT
+ run_exports:
+ weak:
+ - yaml >=0.2.5,<0.3.0a0
+ size: 63944
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diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 00000000..5e04d23b
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,115 @@
+[project]
+name = "xarray-tutorial"
+description = "Xarray Tutorial Website"
+readme = "README.md"
+license.file = "LICENSE"
+requires-python = ">=3.10"
+classifiers = [
+ "Development Status :: 1 - Planning",
+ "Intended Audience :: Science/Research",
+ "Intended Audience :: Developers",
+ "License :: OSI Approved :: MIT License",
+ "Operating System :: OS Independent",
+ "Programming Language :: Python",
+ "Programming Language :: Python :: 3",
+ "Programming Language :: Python :: 3 :: Only",
+ "Programming Language :: Python :: 3.10",
+ "Programming Language :: Python :: 3.11",
+ "Programming Language :: Python :: 3.12",
+ "Topic :: Scientific/Engineering",
+ "Typing :: Typed",
+]
+
+[tool.black]
+line-length = 100
+target-version = ['py311']
+
+[tool.ruff.lint]
+select = [
+ "I", # isort
+ "W", # pycodestyle warnings
+ "B", # flake8-bugbear
+ "F", # Pyflakes
+ "E", # Pycodestyle
+]
+ignore = [
+ "E203", # whitespace before '{symbol}'
+ "E266", # too many leading '#' before block comment
+ "E501", # line too long
+ "E722", # do not use bare except
+ "E402", # module level import not at top of file
+ "E731", # do not assign a lambda expression, use a def
+ # to remove fix eventually
+ "F401", # unused import
+ "F811", # redefinition of name
+ "B006", # mutable argument defaults
+ "B007", # unused loop control variable
+ "B905", # zip without explicit strict parameter
+]
+fixable = ["I"]
+
+[tool.codespell]
+ignore-words-list = "nd"
+skip = "pixi.lock,.pixi,qaqc.yaml,intermediate/indexing/why-an-index.ipynb"
+
+[tool.pixi.workspace]
+name = "xarray-tutorial"
+channels = ["conda-forge"]
+platforms = ["osx-arm64", "linux-64", "win-64"]
+
+[tool.pixi.pypi-dependencies]
+
+[tool.pixi.tasks]
+tutorial = "jupyter lab --LabApp.default_url='/lab/tree/overview/xarray-in-45-min.ipynb'"
+build = "jupyter-book build ./ --warningiserror --keep-going"
+precommit = "pre-commit run --all"
+checklinks = "jupyter-book build ./ --builder linkcheck"
+clear-cache = "jupyter-book clean ."
+# To clear a specific notebook pass ID number: jcache cache -p _build/.jupyter_cache remove 28
+# To clear all cached notebooks: jupyter-book clean . --all"
+watch = { cmd = "jupyter-book config sphinx ./ && sphinx-autobuild ./ _build/html -b html --re-ignore '_build/.*'", description = "Build and serve with live reload" }
+list-cache = "jcache cache -p _build/.jupyter_cache list"
+
+[tool.pixi.dependencies]
+jupyter-book = ">=1.0.4.post1,<2"
+pre-commit = ">=4.6.0,<5"
+dask-labextension = ">=7.0.0,<8"
+jupyterlab = ">=4.6.1,<5"
+jupyter_bokeh = ">=4.1.0,<5"
+jupyterlab-myst = ">=2.7.0,<3"
+jupyter-resource-usage = ">=1.2.1,<2"
+cartopy = ">=0.25.0,<0.26"
+cf_xarray = ">=0.11.3,<0.12"
+dask = ">=2026.7.0,<2027"
+datashader = ">=0.19.1,<0.20"
+distributed = ">=2026.7.0,<2027"
+gcsfs = ">=2026.7.0,<2027"
+geoviews-core = ">=1.15.1,<2"
+gsw = ">=3.6.23,<4"
+hvplot = ">=0.12.2,<0.13"
+h5netcdf = ">=1.8.1,<2"
+ipykernel = ">=7.3.0,<8"
+matplotlib-base = ">=3.11.0,<4"
+netcdf4 = ">=1.7.4,<2"
+numpy = ">=2.4.6,<3"
+pint-xarray = ">=0.6.1,<0.7"
+pydap = ">=3.5.10,<4"
+python-graphviz = ">=0.21,<0.22"
+pooch = ">=1.9.0,<2"
+rioxarray = ">=0.22.0,<0.23"
+scikit-learn = ">=1.9.0,<2"
+scipy = ">=1.18.0,<2"
+sphinx-codeautolink = ">=0.18.1,<0.19"
+sphinxcontrib-mermaid = ">=2.0.3,<3"
+sphinx-notfound-page = ">=1.1.0,<2"
+sphinx-autobuild = ">=2025.8.25,<2026"
+sphinxext-rediraffe = ">=0.3.0,<0.4"
+s3fs = ">=2026.6.0,<2027"
+xarray = ">=2026.7.0,<2027"
+zarr = ">=3.2.1,<4"
+flox = ">=0.11.2,<0.12"
+numbagg = ">=0.9.4,<0.10"
+rich = ">=15.0.0,<16"
+imageio = ">=2.37.0,<3"
+intake = "<2"
+intake-xarray = "<2"
diff --git a/reference/glossary.md b/reference/glossary.md
new file mode 100644
index 00000000..99ab718d
--- /dev/null
+++ b/reference/glossary.md
@@ -0,0 +1,23 @@
+# Glossary
+
+For Xarray data structure terminology see https://docs.xarray.dev/en/stable/user-guide/terminology.html
+
+```{glossary}
+[Xarray](https://docs.xarray.dev)
+ An open source project and Python package that makes working with labelled multi-dimensional arrays simple, efficient, and fun!
+
+[Zarr](https://zarr.readthedocs.io)
+ A Python package that provides an implementation of chunked, compressed, N-dimensional arrays
+
+[GDAL](https://gdal.org)
+ (Geospatial Data Abstraction Library) a translator library for raster and vector geospatial data formats
+
+[HDF](https://www.hdfgroup.org/solutions/hdf5/)
+ (Hierarchical Data Format) binary file format for heterogeneous N-dimensional datasets
+
+[netCDF](https://www.unidata.ucar.edu/software/netcdf/)
+ (Network Common Data Form) binary file format for N-dimensional self-described datasets that originated in the geosciences
+
+[CF Conventions](https://cfconventions.org)
+ Metadata conventions designed to promote the processing and sharing of netCDF files
+```
diff --git a/reference/references.bib b/reference/references.bib
new file mode 100644
index 00000000..e8bf1925
--- /dev/null
+++ b/reference/references.bib
@@ -0,0 +1,20 @@
+@article{hoyerhamman2017,
+ title = {xarray: {N-D} labeled arrays and datasets in {Python}},
+ author = {Hoyer, S. and J. Hamman},
+ journal = {Journal of Open Research Software},
+ volume = {5},
+ number = {1},
+ year = {2017},
+ publisher = {Ubiquity Press},
+ doi = {10.5334/jors.148},
+ url = {https://doi.org/10.5334/jors.148}
+}
+
+@misc{xarray_v202230,
+ author = {Stephan Hoyer and Clark Fitzgerald and Joe Hamman and others},
+ title = {xarray: v2022.3.0},
+ month = May,
+ year = 2022,
+ doi = {10.5281/zenodo.59499},
+ url = {https://doi.org/10.5281/zenodo.59499}
+ }
diff --git a/reference/resources.md b/reference/resources.md
new file mode 100644
index 00000000..854da382
--- /dev/null
+++ b/reference/resources.md
@@ -0,0 +1,41 @@
+# Keep Exploring!
+
+To help you go deeper, we've also create a list of notebooks that
+demonstrate real-world applications of Xarray in a variety of use cases. These
+need not be explored in any particular sequence, instead they are meant to
+provide a sampling of what Xarray can be used for.
+
+```{seealso}
+1. The [Xarray blog](https://xarray.dev/blog) has a number of user stories showcasing Xarray in
+many scientific domains.
+2. [Project Pythia Foundations](https://foundations.projectpythia.org/) is a great collection of
+material on Xarray and related packages.
+```
+
+## Weather and Climate
+
+1. [Global Mean Surface Temperature from CMIP6](https://gallery.pangeo.io/repos/pangeo-gallery/cmip6/):
+ Start with `global_mean_surface_temp.ipynb` then feel free to explore the
+ rest of the notebooks.
+
+1. [National Water Model Streamflow Analysis](https://gallery.pangeo.io/repos/rsignell-usgs/esip-gallery/):
+ Start with `02_National_Water_Model.ipynb` then feel free to explore the rest
+ of the notebooks.
+1. The [Project Pythia Cookbooks](https://cookbooks.projectpythia.org/) are a great collection of
+ resources showcasing use of Xarray with real world datasets.
+1. [Examples using the Microsoft Planetary Computer](https://github.com/microsoft/PlanetaryComputerExamples)
+
+## Bayesian Statistical Modeling
+
+1. [Xarray and PyMC3](https://mybinder.org/v2/gh/pymc-devs/pymc3/main?filepath=%2Fdocs%2Fsource%2Fnotebooks):
+ Start with `multilevel_modeling.ipynb` then feel free to explore the rest of
+ the notebooks. Also checkout [Arviz](https://arviz-devs.github.io/arviz/)
+ which uses Xarray as its data model.
+
+## Genomics
+
+1. [From "data analysis for genomic surveillance of African malaria vectors" ](https://anopheles-genomic-surveillance.github.io/workshop-5/module-1-xarray.html)
+
+## Electrophysiology
+
+1. ["Analyzing intracranial electrophysiology data with xarray"](https://chrisholdgraf.com/blog/2019/2019-10-22-xarray-neuro)
diff --git a/scipy-tutorial/00_overview.ipynb b/scipy-tutorial/00_overview.ipynb
deleted file mode 100644
index 5ffcbc1a..00000000
--- a/scipy-tutorial/00_overview.ipynb
+++ /dev/null
@@ -1,194 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "# Introduction\n",
- "\n",
- "Welcome to the Xarray Tutorial.\n",
- "\n",
- "Xarray is an open source project and Python package that makes working with\n",
- "labelled multi-dimensional arrays simple, efficient, and fun!\n",
- "\n",
- "Xarray introduces labels in the form of dimensions, coordinates and attributes\n",
- "on top of raw [NumPy](https://numpy.org/)-like arrays, which allows for a more\n",
- "intuitive, more concise, and less error-prone developer experience. The package\n",
- "includes a large and growing library of domain-agnostic functions for advanced\n",
- "analytics and visualization with these data structures.\n",
- "\n",
- "Xarray is inspired by and borrows heavily from\n",
- "[pandas](https://pandas.pydata.org/), the popular data analysis package focused\n",
- "on labelled tabular data. It is particularly tailored to working with\n",
- "[netCDF files](http://www.unidata.ucar.edu/software/netcdf), which were the\n",
- "source of Xarray’s data model, and integrates tightly with\n",
- "[Dask](http://dask.org/) for parallel computing.\n",
- "\n",
- "## Tutorial Setup\n",
- "\n",
- "This tutorial is designed to run on [Binder](https://mybinder.org/). This will\n",
- "allow you to run the turoial in the cloud without any additional setup. To get\n",
- "started, simply click\n",
- "[here](https://mybinder.org/v2/gh/xarray-contrib/xarray-tutorial/master?urlpath=lab):\n",
- "[](https://mybinder.org/v2/gh/xarray-contrib/xarray-tutorial/master)\n",
- "\n",
- "If you choose to install the tutorial locally, follow these steps:\n",
- "\n",
- "1. Clone the repository:\n",
- "\n",
- " ```\n",
- " git clone https://github.com/xarray-contrib/xarray-tutorial.git\n",
- " ```\n",
- "\n",
- "1. Install the environment. The repository includes an `environment.yaml` in the\n",
- " `.binder` subdirectory that contains a list of all the packages needed to run\n",
- " this tutorial. To install them using conda run:\n",
- "\n",
- " ```\n",
- " conda env create -f .binder/environment.yml\n",
- " conda activate xarray\n",
- " ```\n",
- "\n",
- "1. Start a Jupyter session:\n",
- "\n",
- " ```\n",
- " jupyter lab\n",
- " ```\n",
- "\n",
- "## Useful links\n",
- "\n",
- "1. References\n",
- "\n",
- "- [Documentation](http://xarray.pydata.org/en/stable/)\n",
- "- [Code Repository](https://github.com/pydata/xarray)\n",
- "\n",
- "1. Ask for help:\n",
- "\n",
- "- Use the\n",
- " [python-xarray](https://stackoverflow.com/questions/tagged/python-xarray) on\n",
- " StackOverflow\n",
- "- [GitHub Issues](https://github.com/pydata/xarray/issues) for bug reports and\n",
- " feature requests\n",
- "\n",
- "## Tutorial Structure\n",
- "\n",
- "This tutorial is made up of multiple Jupyter Notebooks. These notebooks mix\n",
- "code, text, visualization, and exercises.\n",
- "\n",
- "If you haven't used JupyterLab before, it's similar to the Jupyter Notebook. If\n",
- "you haven't used the Notebook, the quick intro is\n",
- "\n",
- "1. There are two modes: command and edit\n",
- "1. From command mode, press Enter to edit a cell (like this markdown cell)\n",
- "1. From edit mode, press Esc to change to command mode\n",
- "1. Press shift+enter to execute a cell and move to the next cell.\n",
- "1. The toolbar has commands for executing, converting, and creating cells.\n",
- "\n",
- "The layout of the tutorial will be as follows:\n",
- "\n",
- "1. [Introduction + Data structures for multi-dimensional data](./01_datastructures_and_io.ipynb)\n",
- "1. [Working with labeled data](02_working_with_labeled_data.ipynb)\n",
- "1. [Computation with Xarray](03_computation_with_xarray.ipynb)\n",
- "1. [Plotting and Visualization](04_plotting_and_visualization.ipynb)\n",
- "1. [Introduction to Dask](05_intro_to_dask.ipynb)\n",
- "1. [Dask and Xarray](06_xarray_and_dask.ipynb)\n",
- "\n",
- "## Exercise: Print Hello, world!\n",
- "\n",
- "Each notebook will have exercises for you to solve. You'll be given a blank or\n",
- "partially completed cell, followed by a hidden cell with a solution. For\n",
- "example.\n",
- "\n",
- "Print the text \"Hello, world!\".\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Your code here"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "In some cases, the next cell will have the solution. Click the ellipses to\n",
- "expand the solution, and always make sure to run the solution cell, in case\n",
- "later sections of the notebook depend on the output from the solution.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "jupyter": {
- "source_hidden": true
- }
- },
- "outputs": [],
- "source": [
- "print(\"Hello, world!\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Going Deeper\n",
- "\n",
- "We've designed the notebooks above to cover the basics of Xarray from beginning\n",
- "to end. To help you go deeper, we've also create a list of notebooks that\n",
- "demonstrate real-world applications of Xarray in a variety of use cases. These\n",
- "need not be explored in any particular sequence, instead they are meant to\n",
- "provide a sampling of what Xarray can be used for.\n",
- "\n",
- "### Xarray and Weather/Climate Model Data\n",
- "\n",
- "1. [Global Mean Surface Temperature from CMIP6](https://binder.pangeo.io/v2/gh/pangeo-gallery/cmip6/binder?urlpath=git-pull?repo=https://github.com/pangeo-gallery/cmip6%26amp%3Burlpath=lab/tree/cmip6):\n",
- " Start with `global_mean_surface_temp.ipynb` then feel free to explore the\n",
- " rest of the notebooks.\n",
- " \n",
- "1. [National Water Model Streamflow Analysis](https://aws-uswest2-binder.pangeo.io/v2/gh/rsignell-usgs/esip-gallery/binder?urlpath=git-pull?repo=https://github.com/rsignell-usgs/esip-gallery%26amp%3Burlpath=lab/tree/esip-gallery):\n",
- " Start with `02_National_Water_Model.ipynb` then feel free to explore the rest\n",
- " of the notebooks.\n",
- "\n",
- "### Xarray and Satellite Data\n",
- "\n",
- "1. [Landsat-8 on AWS](https://aws-uswest2-binder.pangeo.io/v2/gh/pangeo-data/landsat-8-tutorial-gallery/master/?urlpath=git-pull?repo=https://github.com/pangeo-data/landsat-8-tutorial-gallery%26amp%3Burlpath=lab/tree/landsat-8-tutorial-gallery/landsat8.ipynb%3Fautodecode)\n",
- "\n",
- "### Xarray and Baysian Statistical Modeling\n",
- "\n",
- "1. [Xarray and PyMC3](https://mybinder.org/v2/gh/pymc-devs/pymc3/master?filepath=%2Fdocs%2Fsource%2Fnotebooks):\n",
- " Start with `multilevel_modeling.ipynb` then feel free to explore the rest of\n",
- " the notebooks. Also checkout [Arviz](https://arviz-devs.github.io/arviz/)\n",
- " which uses Xarray as its data model.\n"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.7.6"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/scipy-tutorial/01_datastructures_and_io.ipynb b/scipy-tutorial/01_datastructures_and_io.ipynb
deleted file mode 100644
index 9c109a73..00000000
--- a/scipy-tutorial/01_datastructures_and_io.ipynb
+++ /dev/null
@@ -1,886 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "# Data structures for multi-dimensional data\n",
- "\n",
- "In this lesson, we discuss cover the basics of Xarray data structures. Our\n",
- "learning goals are as follows. By the end of the lesson, we will be able to:\n",
- "\n",
- "- Understand the basic data structures in Xarray\n",
- "- Build and inspect `DataArray` and `Dataset` objects.\n",
- "- Read and write various data formats (e.g. NetCDF, Zarr) using Xarray.\n",
- "\n",
- "---\n",
- "\n",
- "## Introduction\n",
- "\n",
- "Multi-dimensional (a.k.a. N-dimensional, ND) arrays (sometimes called “tensors”)\n",
- "are an essential part of computational science. They are encountered in a wide\n",
- "range of fields, including physics, astronomy, geoscience, bioinformatics,\n",
- "engineering, finance, and deep learning. In Python, [NumPy](https://numpy.org/)\n",
- "provides the fundamental data structure and API for working with raw ND arrays.\n",
- "However, real-world datasets are usually more than just raw numbers; they have\n",
- "labels which encode information about how the array values map to locations in\n",
- "space, time, etc.\n",
- "\n",
- "Here is an example of how we might structure a dataset for a weather forecast:\n",
- "\n",
- "![](\"http://xarray.pydata.org/en/stable/_images/dataset-diagram.png\")
\n",
- "\n",
- "You'll notice multiple data variables (temperature, precipitation), coordinate\n",
- "variables (latitude, longitude), and dimensions (x, y, t). We'll cover how these\n",
- "fit into Xarray's data structures below.\n",
- "\n",
- "Xarray doesn’t just keep track of labels on arrays – it uses them to provide a\n",
- "powerful and concise interface. For example:\n",
- "\n",
- "- Apply operations over dimensions by name: `x.sum('time')`.\n",
- "\n",
- "- Select values by label (or logical location) instead of integer location:\n",
- " `x.loc['2014-01-01']` or `x.sel(time='2014-01-01')`.\n",
- "\n",
- "- Mathematical operations (e.g., `x - y`) vectorize across multiple dimensions\n",
- " (array broadcasting) based on dimension names, not shape.\n",
- "\n",
- "- Easily use the split-apply-combine paradigm with groupby:\n",
- " `x.groupby('time.dayofyear').mean()`.\n",
- "\n",
- "- Database-like alignment based on coordinate labels that smoothly handles\n",
- " missing values: `x, y = xr.align(x, y, join='outer')`.\n",
- "\n",
- "- Keep track of arbitrary metadata in the form of a Python dictionary:\n",
- " `x.attrs`.\n",
- "\n",
- "The N-dimensional nature of xarray’s data structures makes it suitable for\n",
- "dealing with multi-dimensional scientific data, and its use of dimension names\n",
- "instead of axis labels (`dim='time'` instead of `axis=0`) makes such arrays much\n",
- "more manageable than the raw numpy ndarray: with xarray, you don’t need to keep\n",
- "track of the order of an array’s dimensions or insert dummy dimensions of size 1\n",
- "to align arrays (e.g., using np.newaxis).\n",
- "\n",
- "The immediate payoff of using xarray is that you’ll write less code. The\n",
- "long-term payoff is that you’ll understand what you were thinking when you come\n",
- "back to look at it weeks or months later.\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Data structures\n",
- "\n",
- "Xarray provides two data structures: the `DataArray` and `Dataset`. The\n",
- "`DataArray` class attaches dimension names, coordinates and attributes to\n",
- "multi-dimensional arrays while `Dataset` combines multiple arrays.\n",
- "\n",
- "Both classes are most commonly created by reading data, but to understand them\n",
- "let's first look at creating them programmatically.\n",
- "\n",
- "### DataArray\n",
- "\n",
- "The `DataArray` class is used to attach a name, dimension names, labels, and\n",
- "attributes to an array.\n",
- "\n",
- "As an example, let's create a `DataArray` named `a` with three dimensions (named\n",
- "`x`, `y`, `z`) from a `numpy` array:\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import numpy as np\n",
- "import xarray as xr\n",
- "\n",
- "rng = np.random.default_rng(seed=0) # we'll use this later"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "da = xr.DataArray(\n",
- " np.ones((3, 4, 2)),\n",
- " dims=(\"x\", \"y\", \"z\"),\n",
- " name=\"a\",\n",
- " coords={\"z\": [-1, 1], \"u\": (\"x\", [0.1, 1.2, 2.3])},\n",
- " attrs={\"attr\": \"value\"},\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "In this case, we used a 3x4 `numpy` array with all values being equal to `1`,\n",
- "but it can be anything that either behaves like a `numpy` array or can be\n",
- "coerced to a `numpy` array using\n",
- "[`numpy.array`](https://numpy.org/doc/stable/reference/generated/numpy.array.html).\n",
- "\n",
- "We also passed a sequence (a tuple here, but could also be a list) containing\n",
- "the dimension names `x`, `y`, and `z` to `dims`. In case we have only a single\n",
- "dimension we can also pass just the dimension name. For example:\n",
- "\n",
- "```python\n",
- "xr.DataArray([1, 1], dims=\"x\")\n",
- "```\n",
- "\n",
- "The dimension names (and the array's `name`) can be anything that fits into a\n",
- "python `set` (i.e. calling `hash()` on it doesn't raise an error), but to be\n",
- "useful they should be strings.\n",
- "\n",
- "`coords` is a [dict-like](https://docs.python.org/3/glossary.html#term-mapping)\n",
- "container of arrays (coordinates) that label each point (e.g., 1-dimensional\n",
- "arrays of numbers, datetime objects or strings). We will look at its format\n",
- "later.\n",
- "\n",
- "We can also attach arbitrary metadata (attributes) to the `DataArray` by passing\n",
- "a dict-like to the `attrs` parameter.\n",
- "\n",
- "#### String representations\n",
- "\n",
- "Now that we have the `DataArray` we can look at its string representation.\n",
- "\n",
- "xarray has two representation types: `\"html\"` (which is only available in\n",
- "notebooks) and `\"text\"`. To choose between them, use the `display_style` option.\n",
- "\n",
- "Let's first look at the text representation:\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "with xr.set_options(display_style=\"text\"):\n",
- " display(da)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "It consists of:\n",
- "\n",
- "- the name of the `DataArray` (`'a'`). If we didn't provide a name, this will be\n",
- " omitted\n",
- "- the dimensions of the array `(x: 3, y: 4, z: 2)`: this tells us that the first\n",
- " dimension is named `x` and has a size of `3`, the second dimension is named\n",
- " `y` and has a size of `4`, and the third dimension is named `z` and has a size\n",
- " of `2`\n",
- "- a preview of the data\n",
- "- a (unordered) list of coordinates or dimensions with coordinates with one item\n",
- " per line. Each item has a name, one or more dimensions in parentheses, a dtype\n",
- " and a preview of the values. Also, if it is a dimension coordinate, it will be\n",
- " marked with a `*`.\n",
- "- a alphabetically sorted list of dimensions without coordinates\n",
- "- a (unordered) list of attributes\n",
- "\n",
- "The `\"html\"` representation looks similar:\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "with xr.set_options(display_style=\"html\"):\n",
- " display(da)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "except the data preview was collapsed to a single line (we can expand it by\n",
- "clicking on the symbol on the left) and the dimensions are marked by a bold font\n",
- "instead of a `*` prefix.\n",
- "\n",
- "Going forward, we'll use the HTML representation except when explaining the text\n",
- "representation.\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Once we have created the `DataArray`, we can look at its data:\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "da.data"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "da.dims"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "da.coords"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "da.attrs"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### coordinates\n",
- "\n",
- "As mentioned above, `coords` is a dict-like mapping names to values. These\n",
- "values can be either\n",
- "\n",
- "- another `DataArray` object\n",
- "- a tuple of the form `(dims, data, attrs)` where `attrs` is optional. This is\n",
- " roughly equivalent to creating a new `DataArray` object with\n",
- " `DataArray(dims=dims, data=data, attrs=attrs)`\n",
- "- a `numpy` array (or anything that can be coerced to one using `numpy.array`).\n",
- "\n",
- "Let's look at an example:\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "da = xr.DataArray(\n",
- " np.ones((3, 4)),\n",
- " dims=(\"x\", \"y\"),\n",
- " coords={\n",
- " \"x\": [\"a\", \"b\", \"c\"],\n",
- " \"y\": np.arange(4),\n",
- " \"u\": (\"x\", np.arange(3), {\"attr1\": 0}),\n",
- " },\n",
- ")\n",
- "da"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We can see that we assigned labels to the `x` and `y` dimensions and also\n",
- "created a coordinate named `u` along `x` with its own metadata (click on the\n",
- "sheet icon to look at them).\n",
- "\n",
- "The difference between the dimension labels (dimension coordinates) and normal\n",
- "coordinates is that for now it only is possible to use indexing operations\n",
- "(`sel`, `reindex`, etc) with dimension coordinates. Also, while coordinates can\n",
- "have arbitrary dimensions, dimension coordinates have to be one-dimensional.\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Exercises\n",
- "\n",
- "create a `DataArray` named \"height\" from random data\n",
- "\n",
- "1. with dimensions named \"latitude\" and \"longitude\"\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "height = rng.random((180, 360)) * 400\n",
- "xr.DataArray(\n",
- " # your code here\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "2. with dimension coordinates:\n",
- "\n",
- "- \"latitude\": -90 to 90 with step size 1\n",
- "- \"longitude\": -180 to 180 with step size 1\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "xr.DataArray(\n",
- " # your code here\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "3. with metadata for both data and coordinates:\n",
- "\n",
- "- height: \"type\": \"ellipsoid\"\n",
- "- latitude: \"type\": \"geodetic\"\n",
- "- longitude: \"prime_meridian\": \"greenwich\"\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "xr.DataArray(\n",
- " # your code here\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Dataset\n",
- "\n",
- "`Dataset` objects collect multiple data variables, each with possibly different\n",
- "dimensions.\n",
- "\n",
- "The constructor of `Dataset` takes three parameters:\n",
- "\n",
- "- `data_vars`: dict-like mapping names to values. It has the format described in\n",
- " [coordinates](#coordinates) except we need to use either `DataArray` objects\n",
- " or the tuple syntax since we have to provide dimensions\n",
- "- `coords`: same as for `DataArray`\n",
- "- `attrs`: same as for `Dataset`\n",
- "\n",
- "For example, let's create a `Dataset` with two variables:\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "ds = xr.Dataset(\n",
- " data_vars={\n",
- " \"a\": ((\"x\", \"y\"), np.ones((3, 4))),\n",
- " \"b\": (\"t\", np.full((8,), 3), {\"attr\": \"value\"}),\n",
- " },\n",
- " coords={\"x\": [-1, 0, 1],},\n",
- " attrs={\"attr\": \"value\"},\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### String representations\n",
- "\n",
- "Let's again first look at the text representation:\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "with xr.set_options(display_style=\"text\"):\n",
- " display(ds)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "It consists of\n",
- "\n",
- "- a summary of all dimensions in the dataset and their lengths\n",
- "- a unordered list of coordinates (same format as the `DataArray`)\n",
- "- a unordered list of dimensions without coordinates\n",
- "- a unordered list of data variables: each item has the same format as the\n",
- " coordinates with the exception of the dimension mark (`*`)\n",
- "\n",
- "Again, the HTML representation is similar:\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "with xr.set_options(display_style=\"html\"):\n",
- " display(ds)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### coordinates\n",
- "\n",
- "As with `DataArray`, a `Dataset` really becomes useful once we assign\n",
- "coordinates. Here we also introduce using Pandas datetime objects as coordinate\n",
- "values:\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import pandas as pd\n",
- "\n",
- "xr.Dataset(\n",
- " data_vars={\n",
- " \"a\": ((\"x\", \"y\"), np.ones((3, 4))),\n",
- " \"b\": ((\"t\", \"x\"), np.full((8, 3), 3)),\n",
- " },\n",
- " coords={\n",
- " \"x\": [\"a\", \"b\", \"c\"],\n",
- " \"y\": np.arange(4),\n",
- " \"t\": pd.date_range(\"2020-07-05\", periods=8, freq=\"D\"),\n",
- " },\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "If we have variables with different values along the same dimension, we can't\n",
- "use the shortcut syntax anymore. Instead, we need to use `DataArray` objects:\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "x_a = np.arange(1, 4)\n",
- "x_b = np.arange(-1, 3)\n",
- "\n",
- "a = xr.DataArray(np.linspace(0, 1, 3), dims=\"x\", coords={\"x\": x_a})\n",
- "b = xr.DataArray(np.zeros(4), dims=\"x\", coords={\"x\": x_b})\n",
- "\n",
- "xr.Dataset(data_vars={\"a\": a, \"b\": b})"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "which combines the coordinates and fills in floating-point `nan` values for\n",
- "missing data (converting the data type to `float` in the process). For example,\n",
- "`b` doesn't have a value for `x == 3` so `nan` was used.\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Exercises\n",
- "\n",
- "1. create a Dataset with two variables along `latitude` and `longitude`:\n",
- " `height` and `gravity_anomaly`\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "height = rng.random((180, 360)) * 400\n",
- "gravity_anomaly = rng.random((180, 360)) * 400 - 200\n",
- "\n",
- "xr.Dataset(\n",
- " # your code here\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "2. add coordinates to `latitude` and `longitude`:\n",
- "\n",
- "- `latitude`: from -90 to 90 with step size 1\n",
- "- `longitude`: from -180 to 180 with step size 1\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "xr.Dataset(\n",
- " # your code here\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "3. add metadata to coordinates and variables:\n",
- "\n",
- "- `latitude`: \"type\": \"geodetic\"\n",
- "- `longitude`: \"prime_meridian\": \"greenwich\"\n",
- "- `height`: \"ellipsoid\": \"wgs84\"\n",
- "- `gravity_anomaly`: \"ellipsoid\": \"grs80\"\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "xr.Dataset(\n",
- " # your code here\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Roundtripping and I/O\n",
- "\n",
- "`DataArray` and `Dataset` objects are frequently created by converting from\n",
- "other libraries such as [pandas](https://pandas.pydata.org/) or by reading from\n",
- "data storage formats such as\n",
- "[NetCDF](https://www.unidata.ucar.edu/software/netcdf/) or\n",
- "[zarr](https://zarr.readthedocs.io/en/stable/).\n",
- "\n",
- "To convert from / to `pandas`, we can use the\n",
- "[to_xarray](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.to_xarray.html)\n",
- "methods on [pandas](https://zarr.readthedocs.io/en/stable/) objects or the\n",
- "[to_pandas](http://xarray.pydata.org/en/stable/generated/xarray.DataArray.to_pandas.html)\n",
- "methods on `xarray` objects:\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "series = pd.Series(np.ones((10,)), index=list(\"abcdefghij\"))\n",
- "series"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "arr = series.to_xarray()\n",
- "arr"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "arr.to_pandas()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We can also control what `pandas` object is used by calling `to_series` /\n",
- "`to_dataframe`:\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "ds = xr.Dataset(\n",
- " data_vars={\"a\": (\"x\", np.arange(5)), \"b\": ((\"x\", \"y\"), np.ones((5, 4)))}\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "**to_series**: This will always convert `DataArray` objects to\n",
- "`pandas.Series`, using a `MultiIndex` for higher dimensions\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "ds.a.to_series()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "ds.b.to_series()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "**to_dataframe**: This will always convert `DataArray` or `Dataset`\n",
- "objects to a `pandas.DataFrame`. Note that `DataArray` objects have to be named\n",
- "for this.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "ds.a.to_dataframe()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Since columns in a `DataFrame` need to have the same index, they are\n",
- "broadcasted.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "ds.to_dataframe()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### I/O\n",
- "\n",
- "One of Xarray's most widely used features is its ability to read from and write\n",
- "to a variety of data formats. For example, Xarray can read the following\n",
- "formats:\n",
- "\n",
- "- [NetCDF](https://www.unidata.ucar.edu/software/netcdf/) / GRIB (via\n",
- " `open_dataset` / `open_mfdataset`, `to_netcdf` / `save_mfdataset`)\n",
- "- [Zarr](https://zarr.readthedocs.io/en/stable/) (via `open_zarr`, `to_zarr`)\n",
- "- [GeoTIFF](https://gdal.org/drivers/raster/gtiff.html) /\n",
- " [GDAL rasters](https://svn.osgeo.org/gdal/tags/gdal_1_2_5/frmts/formats_list.html)\n",
- " (via `open_rasterio`)\n",
- "\n",
- "#### NetCDF\n",
- "\n",
- "The recommended way to store xarray data structures is NetCDF, which is a binary\n",
- "file format for self-described datasets that originated in the geosciences.\n",
- "Xarray is based on the netCDF data model, so netCDF files on disk directly\n",
- "correspond to Dataset objects.\n",
- "\n",
- "Xarray reads and writes to NetCDF files using the `open_dataset` /\n",
- "`open_dataarray` functions and the `to_netcdf` method.\n",
- "\n",
- "Let's first create some datasets and write them to disk using `to_netcdf`, which\n",
- "takes the path we want to write to:\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "ds1 = xr.Dataset(\n",
- " data_vars={\n",
- " \"a\": ((\"x\", \"y\"), np.random.randn(4, 2)),\n",
- " \"b\": ((\"z\", \"x\"), np.random.randn(6, 4)),\n",
- " },\n",
- " coords={\"x\": np.arange(4), \"y\": np.arange(-2, 0), \"z\": np.arange(-3, 3),},\n",
- ")\n",
- "ds2 = xr.Dataset(\n",
- " data_vars={\n",
- " \"a\": ((\"x\", \"y\"), np.random.randn(7, 3)),\n",
- " \"b\": ((\"z\", \"x\"), np.random.randn(2, 7)),\n",
- " },\n",
- " coords={\"x\": np.arange(6, 13), \"y\": np.arange(3), \"z\": np.arange(3, 5),},\n",
- ")\n",
- "\n",
- "# write datasets\n",
- "ds1.to_netcdf(\"ds1.nc\")\n",
- "ds2.to_netcdf(\"ds2.nc\")\n",
- "\n",
- "# write dataarray\n",
- "ds1.a.to_netcdf(\"da1.nc\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Reading those files is just as simple:\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "xr.open_dataset(\"ds1.nc\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "xr.open_dataarray(\"da1.nc\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Zarr\n",
- "\n",
- "[Zarr](https://zarr.readthedocs.io/en/stable/) is a Python package and data\n",
- "format providing an implementation of chunked, compressed, N-dimensional arrays.\n",
- "Zarr has the ability to store arrays in a range of ways, including in memory, in\n",
- "files, and in cloud-based object storage such as Amazon S3 and Google Cloud\n",
- "Storage. Xarray’s Zarr backend allows xarray to leverage these capabilities.\n",
- "\n",
- "Zarr files can be written with:\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "ds1.to_zarr(\"ds1.zarr\", mode=\"w\")"
- ]
- },
- {
- "cell_type": "raw",
- "metadata": {},
- "source": [
- "or to any `MutableMapping` interface:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "mystore = {}\n",
- "\n",
- "ds1.to_zarr(store=mystore)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We can then read the created file with:\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "xr.open_zarr(\"ds1.zarr\", chunks=None)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "setting the `chunks` parameter to `None` avoids `dask` (more on that in a later\n",
- "session)\n"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.8.2"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/scipy-tutorial/02_working_with_labeled_data.ipynb b/scipy-tutorial/02_working_with_labeled_data.ipynb
deleted file mode 100644
index 159ec1c3..00000000
--- a/scipy-tutorial/02_working_with_labeled_data.ipynb
+++ /dev/null
@@ -1,505 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "# Working with labeled data\n",
- "\n",
- "Learing goals:\n",
- "\n",
- "- Use different forms of indexing to select data based on position and\n",
- " coordinates\n",
- "- Select datatime ranges\n",
- "- Interpolate data to new coordinates\n",
- "\n",
- "## Named dimensions\n",
- "\n",
- "As mentioned in the previous session, labeled dimensions really help to make the\n",
- "code less difficult to understand. Compare pure `numpy` indexing:\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import numpy as np\n",
- "import pandas as pd\n",
- "import xarray as xr\n",
- "\n",
- "np.random.seed(0)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# axis0: x, axis1: y\n",
- "np_array = np.random.randn(3, 4)\n",
- "np_array[1, 3]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "and slicing:\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "np_array[:2, 1:]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "with label based indexing:\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "arr = xr.DataArray(np_array, dims=(\"x\", \"y\"))\n",
- "arr.isel(x=1, y=3)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "This is the same as\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "arr[{\"x\": 1, \"y\": 1}]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Due to the language syntax, slices have to be constructed manually:\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "ds = xr.Dataset(\n",
- " {\n",
- " \"a\": ((\"x\", \"y\"), np.random.randn(3, 4)),\n",
- " \"b\": ((\"x\", \"y\"), np.random.randn(3, 4)),\n",
- " }\n",
- ")\n",
- "ds.isel(x=slice(None, 2), y=slice(1, None))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We can also use these names to peek at the data if the automatic preview is not\n",
- "enough:\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "ds.head(x=2, y=3)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "see also `tail` and `thin`.\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Coordinate labels and label based indexing\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "xarray objects become much more interesting when adding coordinate labels:\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "arr = xr.DataArray(\n",
- " np.random.randn(4, 6),\n",
- " dims=(\"x\", \"y\"),\n",
- " coords={\n",
- " \"x\": [-3.2, 2.1, 5.3, 6.5],\n",
- " \"y\": pd.date_range(\"2009-01-05\", periods=6, freq=\"M\"),\n",
- " },\n",
- ")\n",
- "arr"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "To select data by coordinate labels instead of integer indices we can use the\n",
- "same syntax, using `sel` instead of `isel`:\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "arr.sel(x=5.3, y=\"2009-04-30\") # or a.loc[{\"x\": 5.3, \"y\": \"2009-04-30\"}]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "this will require us to specify exact values. If we don't have those, we can use\n",
- "the `method` parameter (see `Dataset.sel` for documentation):\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "arr.sel(x=4, y=\"2009-04-01\", method=\"nearest\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We can also select multiple values:\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "arr.sel(x=[-3.2, 6.5], y=slice(\"2009-02-28\", \"2009-05-31\"))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "If instead of selecting data we want to drop it, we can use `drop_sel`:\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "arr.drop_sel(x=[-3.2, 5.3])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Exercises\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "ds = xr.tutorial.open_dataset(\"air_temperature\")\n",
- "ds"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "1. Select the first 30 entries of latitude and 20th to 40th entries of longitude\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# your code here"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "2. Select all data at 75 degree north and between Jan 1, 2013 and Oct 15, 2013\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# your code here"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "3. Remove all entries at 260 and 270 degrees\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# your code here"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Interpolation\n",
- "\n",
- "If we want to look at values between the current grid cells (interpolation), we\n",
- "can do that with `interp` (requires `scipy`):\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "arr.interp(\n",
- " x=np.linspace(2, 6, 10),\n",
- " y=pd.date_range(\"2009-04-01\", \"2009-04-30\", freq=\"D\"),\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "when trying to extrapolate, the resulting values will be `nan`.\n",
- "\n",
- "If we already have a object with the desired coordinates, we can use\n",
- "`interp_like`:\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "other = xr.DataArray(\n",
- " dims=(\"x\", \"y\"),\n",
- " coords={\n",
- " \"x\": np.linspace(2, 4, 10),\n",
- " \"y\": pd.date_range(\"2009-04-01\", \"2009-04-30\", freq=\"D\"),\n",
- " },\n",
- ")\n",
- "arr.interp_like(other)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Exercises\n",
- "\n",
- "Increase the step size along latitude and longitude from 2.5 degrees to 1\n",
- "degree.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# your code here"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Broadcasting and automatic alignment\n",
- "\n",
- "Labels help with combining arrays with different coordinates:\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "a = xr.DataArray(\n",
- " np.random.randn(3, 4),\n",
- " dims=(\"x\", \"y\"),\n",
- " coords={\"x\": [\"a\", \"b\", \"c\"], \"y\": np.arange(4)},\n",
- ")\n",
- "b = xr.DataArray(\n",
- " np.random.randn(2, 7),\n",
- " dims=(\"x\", \"y\"),\n",
- " coords={\"x\": [\"b\", \"d\"], \"y\": [-2, -1, 0, 1, 2, 3, 4]},\n",
- ")\n",
- "\n",
- "a + b"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "This will automatically select only common labels from both arrays (a inner\n",
- "join) and then perform the operation.\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Broadcasting works similar:\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "arr1 = xr.DataArray(\n",
- " np.random.randn(3), dims=\"x\", coords={\"x\": [\"a\", \"b\", \"c\"]},\n",
- ")\n",
- "arr2 = xr.DataArray(np.random.randn(4), dims=\"y\", coords={\"y\": np.arange(4)},)\n",
- "\n",
- "arr1 + arr2"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "where both arrays were automatically broadcasted against each other:\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "arr1_, arr2_ = xr.broadcast(arr1, arr2)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "arr1_"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "arr2_"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "and then the operation (a sum) was executed.\n",
- "\n",
- "We can also call `align` speciically with different options.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "a_al, b_al = xr.align(a, b, join=\"inner\")\n",
- "b_al"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.8.1"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/scipy-tutorial/03_computation_with_xarray.ipynb b/scipy-tutorial/03_computation_with_xarray.ipynb
deleted file mode 100644
index 6f870991..00000000
--- a/scipy-tutorial/03_computation_with_xarray.ipynb
+++ /dev/null
@@ -1,969 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "# Computation with Xarray\n",
- "\n",
- "In this lesson, we discuss how to do scientific computations with xarray\n",
- "objects. Our learning goals are as follows. By the end of the lesson, we will be\n",
- "able to:\n",
- "\n",
- "- Apply basic arithmetic and numpy functions to xarray DataArrays / Dataset.\n",
- "- Use Xarray's label-aware reduction operations (e.g. `mean`, `sum`) weighted\n",
- " reductions.\n",
- "- Apply arbitrary functions to Xarray data via `apply_ufunc`.\n",
- "- Use Xarray's broadcasting to compute on arrays of different dimensionality.\n",
- "- Perform \"split / apply / combine\" workflows in Xarray using `groupby`,\n",
- " including\n",
- " - reductions within groups\n",
- " - transformations on groups\n",
- "- Use the `resample`, `rolling` and `coarsen` functions to manipulate data.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import expectexception\n",
- "import numpy as np\n",
- "import xarray as xr\n",
- "from matplotlib import pyplot as plt"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Example Dataset\n",
- "\n",
- "First we load a dataset. We will use the\n",
- "[NOAA Extended Reconstructed Sea Surface Temperature (ERSST) v5](https://www.ncdc.noaa.gov/data-access/marineocean-data/extended-reconstructed-sea-surface-temperature-ersst-v5)\n",
- "product, a widely used and trusted gridded compilation of of historical data\n",
- "going back to 1854.\n",
- "\n",
- "Since the data is provided via an\n",
- "[OPeNDAP](https://en.wikipedia.org/wiki/OPeNDAP) server, we can load it directly\n",
- "without downloading anything:\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "### NOTE: If hundreds of people connect to this server at once and download the same dataset,\n",
- "### things might not go so well! Recommended to use the Google Cloud copy instead.\n",
- "\n",
- "# url = \"http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/noaa.ersst.v5/sst.mnmean.nc\"\n",
- "# # drop an unnecessary variable which complicates some operations\n",
- "# ds = xr.open_dataset(url, drop_variables=[\"time_bnds\"])\n",
- "# # will take a minute or two to complete\n",
- "# ds = ds.sel(time=slice(\"1960\", \"2018\")).load()\n",
- "# ds"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import gcsfs\n",
- "\n",
- "fs = gcsfs.GCSFileSystem(token=\"anon\")\n",
- "ds = xr.open_zarr(\n",
- " fs.get_mapper(\"gs://pangeo-noaa-ncei/noaa.ersst.v5.zarr\"), consolidated=True\n",
- ").load()\n",
- "ds"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Let's do some basic visualizations of the data, just to make sure it looks\n",
- "reasonable.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "ds.sst[0].plot(vmin=-2, vmax=30)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Basic Arithmetic\n",
- "\n",
- "Xarray dataarrays and datasets work seamlessly with arithmetic operators and\n",
- "numpy array functions.\n",
- "\n",
- "For example, imagine we want to convert the temperature (given in Celsius) to\n",
- "Kelvin:\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "sst_kelvin = ds.sst + 273.15\n",
- "sst_kelvin"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The dimensions and coordinates were preseved following the operation.\n",
- "\n",
- "\n",
- " Warning: Although many xarray datasets have a
\n",
- "\n",
- "We can apply more complex functions, including numpy ufuncs, to Xarray objects.\n",
- "Imagine we wanted to compute the following expression as a function of SST\n",
- "($\\Theta$) in Kelvin:\n",
- "\n",
- "$$ f(\\Theta) = 0.5 \\ln(\\Theta^2) $$\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "f = 0.5 * np.log(sst_kelvin ** 2)\n",
- "f"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Applying Aribtrary Functions\n",
- "\n",
- "It's awesome that we can call `np.log(ds)` and have it \"just work\". However, not\n",
- "all third party libraries work this way.\n",
- "\n",
- "In this example, we will use functions from the\n",
- "[Gibbs Seawater Toolkit](https://teos-10.github.io/GSW-Python/), a package for\n",
- "the thermodynamics of seawater. This package provides ufuncs that operate on\n",
- "numpy arrays.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import gsw\n",
- "\n",
- "# an example function\n",
- "# http://www.teos-10.org/pubs/gsw/html/gsw_t90_from_t68.html\n",
- "gsw.t90_from_t68?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "gsw.t90_from_t68(ds.sst) # -> returns a numpy array"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "It would be nice to keep our dimensions and coordinates. We can accomplish this\n",
- "with `xr.apply_ufunc`.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "xr.apply_ufunc(gsw.t90_from_t68, ds.sst)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "units attribute, which is used in plotting,\n",
- " Xarray does not inherently understand units. However, work is underway to integrate xarray\n",
- " with pint, which will provide full unit-aware operations.\n",
- "\n",
- " Note:
\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Reductions\n",
- "\n",
- "Just like in numpy, we can reduce xarray DataArrays along any number of axes:\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "sst = ds.sst\n",
- "sst.mean(axis=0)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "sst.mean(axis=(1, 2))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "sst.mean()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "However, rather than performing reductions on axes (as in numpy), we can perform\n",
- "them on dimensions. This turns out to be a huge convenience, particularly in\n",
- "complex calculations when you can't easily remember which axis corresponds to\n",
- "which dimension:\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "sst.mean(dim=\"time\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "All of the standard numpy reductions (e.g. `min`, `max`, `sum`, `std`, etc.) are\n",
- "available\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Exercise\n",
- "\n",
- "Take the mean of `sst` in both longitude and latitude. Make a simple timeseries\n",
- "plot:\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# your code here"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Broadcasting\n",
- "\n",
- "Broadcasting refers to the alignmed of arrays with different numbers of\n",
- "dimensions. Numpy's broadcasting rules, based on array shape, can sometimes be\n",
- "difficult to understand and remember. Xarray does brodcasting by dimension name,\n",
- "rather than array shape. This is a huge convenience.\n",
- "\n",
- "Let's now create two arrays with some dimensions in common. For this example, we\n",
- "will create a \"weights\" array proportional to cosine of latitude. Modulo a\n",
- "normalization, this is the correct area-weighting factor for data on a regular\n",
- "lat-lon grid.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "weights = np.cos(np.deg2rad(ds.lat))\n",
- "weights.dims"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "If we multiply this by SST, it \"just works,\" and the arrays are broadcasted\n",
- "properly:\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "(ds.sst * weights).dims"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "apply_ufunc is a powerful and mysterious function.\n",
- " It has many options for doing more complicated things.\n",
- " Unfortunately, we don't have time to go into more depth here.\n",
- " Please consult the [Xarray docs](http://xarray.pydata.org/en/latest/generated/xarray.apply_ufunc.html) for more details.\n",
- "\n",
- " Warning: If the arrays being broadcasted share a dimension name, but have different coordinates,\n",
- " they will first be aligned using Xarray's default align settings (including filling missing values with NaNs).\n",
- " If that's not what you want, it's best to call
\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Weighted Reductions\n",
- "\n",
- "We could imagine computing the weighted spatial mean of SST manually, like this:\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "sst_mean = (ds.sst * weights).sum(dim=(\"lon\", \"lat\")) / weights.sum(dim=\"lat\")\n",
- "sst_mean.plot()\n",
- "plt.title(\"This is wrong!\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "That would be wrong, however, because the denominator (`weights.sum(dim='lat')`)\n",
- "needs to be expanded to include the `lon` dimension and modified to account for\n",
- "the missing values (land points).\n",
- "\n",
- "In general, weighted reductions on multidimensional arrays are complicated. To\n",
- "make it a bit easier, Xarray provides a mechanism for weighted reductions. It\n",
- "does this by creating a special intermediate `DataArrayWeighted` object, to\n",
- "which different reduction operations can applied.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "sst_weighted = ds.sst.weighted(weights)\n",
- "sst_weighted"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "sst_weighted.mean(dim=(\"lon\", \"lat\")).plot()\n",
- "plt.title(\"Correct Global Mean SST\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Groupby\n",
- "\n",
- "Xarray copies Pandas' very useful groupby functionality, enabling the \"split /\n",
- "apply / combine\" workflow on xarray DataArrays and Datasets.\n",
- "\n",
- "To provide a physically motivated example, let's examine a timeseries of SST at\n",
- "a single point.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "ds.sst.sel(lon=300, lat=50).plot()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "As we can see from the plot, the timeseries at any one point is totally\n",
- "dominated by the seasonal cycle. We would like to remove this seasonal cycle\n",
- "(called the \"climatology\") in order to better see the long-term variaitions in\n",
- "temperature. We can accomplish this using **groupby**.\n",
- "\n",
- "Before moving forward, w note that xarray correctly parsed the time index,\n",
- "resulting in a Pandas datetime index on the time dimension.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "ds.time"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The syntax of Xarray's groupby is almost identical to Pandas.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "ds.groupby?"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Split Step\n",
- "\n",
- "The most important argument is `group`: this defines the unique values we will\n",
- "us to \"split\" the data for grouped analysis. We can pass either a DataArray or a\n",
- "name of a variable in the dataset. Lets first use a DataArray. Just like with\n",
- "Pandas, we can use the time indexe to extract specific components of dates and\n",
- "times. Xarray uses a special syntax for this `.dt`, called the\n",
- "`DatetimeAccessor`.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "ds.time.dt"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "ds.time.dt.month"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "ds.time.dt.year\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We can use these arrays in a groupby operation:\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "gb = ds.groupby(ds.time.dt.month)\n",
- "gb"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Xarray also offers a more concise syntax when the variable you're grouping on is\n",
- "already present in the dataset. This is identical to the previous line:\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "gb = ds.groupby(\"time.month\")\n",
- "gb"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Now that the data are split, we can manually iterate over the group. The\n",
- "iterator returns the key (group name) and the value (the actual dataset\n",
- "corresponding to that group) for each group.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "for group_name, group_ds in gb:\n",
- " # stop iterating after the first loop\n",
- " break\n",
- "print(group_name)\n",
- "group_ds"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Apply & Combine\n",
- "\n",
- "Now that we have groups defined, it's time to \"apply\" a calculation to the\n",
- "group. Like in Pandas, these calculations can either be:\n",
- "\n",
- "- _aggregation_: reduces the size of the group\n",
- "- _transformation_: preserves the group's full size\n",
- "\n",
- "At then end of the apply step, xarray will automatically combine the aggregated\n",
- "/ transformed groups back into a single object.\n",
- "\n",
- "The most fundamental way to apply is with the `.map` method.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "gb.map?"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Aggregations\n",
- "\n",
- "`.apply` accepts as its argument a function. We can pass an existing function:\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "gb.map(np.mean)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Because we specified no extra arguments (like `axis`) the function was applied\n",
- "over all space and time dimensions. This is not what we wanted. Instead, we\n",
- "could define a custom function. This function takes a single argument--the group\n",
- "dataset--and returns a new dataset to be combined:\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "def time_mean(a):\n",
- " return a.mean(dim=\"time\")\n",
- "\n",
- "\n",
- "gb.map(time_mean)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Like Pandas, xarray's groupby object has many built-in aggregation operations\n",
- "(e.g. `mean`, `min`, `max`, `std`, etc):\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# this does the same thing as the previous cell\n",
- "ds_mm = gb.mean(dim=\"time\")\n",
- "ds_mm"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "So we did what we wanted to do: calculate the climatology at every point in the\n",
- "dataset. Let's look at the data a bit.\n",
- "\n",
- "_Climatlogy at a specific point in the North Atlantic_\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "ds_mm.sst.sel(lon=300, lat=50).plot()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "_Zonal Mean Climatolgoy_\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "ds_mm.sst.mean(dim=\"lon\").plot.contourf(x=\"month\", levels=12, vmin=-2, vmax=30)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "_Difference between January and July Climatology_\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "(ds_mm.sst.sel(month=1) - ds_mm.sst.sel(month=7)).plot(vmax=10)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Transformations\n",
- "\n",
- "Now we want to _remove_ this climatology from the dataset, to examine the\n",
- "residual, called the _anomaly_, which is the interesting part from a climate\n",
- "perspective. Removing the seasonal climatology is a perfect example of a\n",
- "transformation: it operates over a group, but doesn't change the size of the\n",
- "dataset. Here is one way to code it\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "def remove_time_mean(x):\n",
- " return x - x.mean(dim=\"time\")\n",
- "\n",
- "\n",
- "ds_anom = ds.groupby(\"time.month\").map(remove_time_mean)\n",
- "ds_anom"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Xarray makes these sorts of transformations easy by supporting _groupby\n",
- "arithmetic_. This concept is easiest explained with an example:\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "gb = ds.groupby(\"time.month\")\n",
- "ds_anom = gb - gb.mean(dim=\"time\")\n",
- "ds_anom"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Now we can view the climate signal without the overwhelming influence of the\n",
- "seasonal cycle.\n",
- "\n",
- "_Timeseries at a single point in the North Atlantic_\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "ds_anom.sst.sel(lon=300, lat=50).plot()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "_Difference between Jan. 1 2018 and Jan. 1 1960_\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "(ds_anom.sel(time=\"2018-01-01\") - ds_anom.sel(time=\"1960-01-01\")).sst.plot()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Grouby-Related: Resample, Rolling, Coarsen\n",
- "\n",
- "Resample in xarray is nearly identical to Pandas. It is effectively a group-by\n",
- "operation, and uses the same basic syntax. It can be applied only to time-index\n",
- "dimensions. Here we compute the five-year mean.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "resample_obj = ds_anom.resample(time=\"5Y\")\n",
- "resample_obj"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "ds_anom_resample = resample_obj.mean(dim=\"time\")\n",
- "ds_anom_resample"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "ds_anom.sst.sel(lon=300, lat=50).plot()\n",
- "ds_anom_resample.sst.sel(lon=300, lat=50).plot(marker=\"o\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "align explicitly before broadcasting.\n",
- "\n",
- " Note:
\n",
- "\n",
- "Rolling is also similar to pandas, but can be applied along any dimension. It\n",
- "works with logical coordinates.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "ds_anom_rolling = ds_anom.rolling(time=12, center=True).mean()\n",
- "ds_anom_rolling"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "ds_anom.sst.sel(lon=300, lat=50).plot(label=\"monthly anom\")\n",
- "ds_anom_resample.sst.sel(lon=300, lat=50).plot(\n",
- " marker=\"o\", label=\"5 year resample\"\n",
- ")\n",
- "ds_anom_rolling.sst.sel(lon=300, lat=50).plot(label=\"12 month rolling mean\")\n",
- "plt.legend()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "`coarsen` does something similar to `resample`, but without being aware of time.\n",
- "It operates on logical coordinates only but can work on multiple dimensions at a\n",
- "time.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "ds_anom_coarsen_time = ds_anom.coarsen(time=12).mean()\n",
- "\n",
- "ds_anom_rolling.sst.sel(lon=300, lat=50).plot(label=\"12 month rolling mean\")\n",
- "ds_anom_coarsen_time.sst.sel(lon=300, lat=50).plot(\n",
- " marker=\"^\", label=\"12 item coarsen\"\n",
- ")\n",
- "plt.legend()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "%%expect_exception\n",
- "ds_anom_coarsen_space = ds_anom.coarsen(lon=4, lat=4).mean()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "ds_anom_coarsen_space = (\n",
- " ds_anom.isel(lat=slice(0, -1)).coarsen(lon=4, lat=4).mean()\n",
- ")\n",
- "ds_anom_coarsen_space"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "ds_anom_coarsen_space.sst.isel(time=0).plot()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Exercise\n",
- "\n",
- "Load the following \"basin mask\" dataset, and use it to take a weighted average\n",
- "of SST in each ocean basin. Figure out which ocean basins are the warmest and\n",
- "coldest.\n",
- "\n",
- "**Hint:** you will first need to align this dataset with the SST dataset. Use\n",
- "what you learned in the \"indexing and alignment\" lesson.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "basin = xr.open_dataset(\n",
- " \"http://iridl.ldeo.columbia.edu/SOURCES/.NOAA/.NODC/.WOA09/.Masks/.basin/dods\"\n",
- ")\n",
- "basin"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.8.1"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/scipy-tutorial/04_plotting_and_visualization.ipynb b/scipy-tutorial/04_plotting_and_visualization.ipynb
deleted file mode 100644
index 00cba153..00000000
--- a/scipy-tutorial/04_plotting_and_visualization.ipynb
+++ /dev/null
@@ -1,835 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {
- "toc": true
- },
- "source": [
- "resample only works with proper datetime indexes.\n",
- "\n",
- "\n",
- "# Plotting and Visualization\n",
- "\n",
- "At the end of this lesson you will learn:\n",
- "\n",
- "1. how to use xarray's convenient matplotlib-backed plotting interface to\n",
- " visualize your datasets.\n",
- "2. that `hvplot` provides an equally convenient interface for bokeh-backed plots\n",
- "\n",
- "## Table of contents\n",
- "\n",
- "1. [Basic plotting](#basic)\n",
- "1. [Histograms](#hist)\n",
- "1. [2D plots](#2d)\n",
- "1. [1D line plots](#1d)\n",
- "1. [Faceting or multiple subplots](#facet)\n",
- "1. [Geography: matplotlib and cartopy](#geo)\n",
- "1. [Interactive bokeh plots using hvplot](#bokeh)\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-06-11T02:27:32.419178Z",
- "start_time": "2020-06-11T02:27:30.533425Z"
- }
- },
- "outputs": [],
- "source": [
- "import matplotlib as mpl\n",
- "import matplotlib.pyplot as plt\n",
- "import numpy as np\n",
- "import xarray as xr"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-06-11T02:27:32.437582Z",
- "start_time": "2020-06-11T02:27:32.421755Z"
- }
- },
- "outputs": [],
- "source": [
- "xr.set_options(display_style=\"html\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Load data\n",
- "\n",
- "First lets load up a tutorial dataset to visualize.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-06-11T02:27:37.555998Z",
- "start_time": "2020-06-11T02:27:37.357785Z"
- }
- },
- "outputs": [],
- "source": [
- "ds = xr.tutorial.open_dataset(\"air_temperature.nc\").rename({\"air\": \"Tair\"})\n",
- "\n",
- "# we will add a gradient field with appropriate attributes\n",
- "ds[\"dTdx\"] = ds.Tair.differentiate(\"lon\") / 110e3 / np.cos(ds.lat * np.pi / 180)\n",
- "ds[\"dTdy\"] = ds.Tair.differentiate(\"lat\") / 105e3\n",
- "ds.dTdx.attrs = {\"long_name\": \"$∂T/∂x$\", \"units\": \"°C/m\"}\n",
- "ds.dTdy.attrs = {\"long_name\": \"$∂T/∂y$\", \"units\": \"°C/m\"}\n",
- "\n",
- "ds"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "This dataset has three \"data variables\", `Tair` is air temperature and `dTdx`\n",
- "and `dTdy` are horizontal gradients of this temperature field. All three \"data\n",
- "variables\" are three-dimensional with dimensions `(time, lat, lon)`.\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "## Basic plotting: .plot()\n",
- "\n",
- "DataArray objects have a `plot` method. This method creates plots using\n",
- "`matplotlib` so all of your existing matplotlib knowledge carries over!\n",
- "\n",
- "By default `.plot()` makes\n",
- "\n",
- "1. a line plot for 1-D arrays using `plt.plot()`\n",
- "2. a `pcolormesh` plot for 2-D arrays using `plt.pcolormesh()`\n",
- "3. a histogram for everything else using `plt.hist()`\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "## Histograms\n",
- "\n",
- "`Tair` is three-dimensional, so we got a histogram of temperature values. Notice\n",
- "the label on the x-axis. One of xarray's convenient plotting features is that it\n",
- "uses the `attrs` of `Tair` to nicely label axes and colorbars.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-05-30T13:15:04.121767Z",
- "start_time": "2020-05-30T13:15:03.884190Z"
- }
- },
- "outputs": [],
- "source": [
- "ds.Tair.plot()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "You can pass extra arguments to the underlying `hist()` call. See the matplotlib\n",
- "docs (https://matplotlib.org/3.2.1/api/_as_gen/matplotlib.pyplot.hist.html) for\n",
- "all possible keyword arguments.\n",
- "\n",
- "**Tip:** Note that the returned values are exactly what matplotlib would return\n",
- "\n",
- "#### Exercise\n",
- "\n",
- "Update the above plot to show 50 bins with unfilled steps instead of filled\n",
- "bars.\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "## 2D plots\n",
- "\n",
- "Now we will explore 2D plots. Let's select a single timestep of `Tair` to\n",
- "visualize.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-05-30T13:15:04.364426Z",
- "start_time": "2020-05-30T13:15:04.124100Z"
- }
- },
- "outputs": [],
- "source": [
- "ds.Tair.isel(time=1).plot()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Notice how much information is on that plot!\n",
- "\n",
- "The x- and y-axes are labeled with full names — \"Latitude\", \"Longitude\" — along\n",
- "with units. The colorbar has a nice label, again with units. And the title tells\n",
- "us the timestamp of the data presented.\n",
- "\n",
- "`plot` takes many keyword arguments and is quite sophisticated (see\n",
- "https://xarray.pydata.org/en/stable/generated/xarray.plot.pcolormesh.html).\n",
- "\n",
- "Here is a more complicated figure that explicitly sets `time` as the x-axis,\n",
- "customizes the colorbar, and overlays two contours at specific levels.\n",
- "\n",
- "**Tip:** Other options for 2D plots include `.plot.contour`, `.plot.contourf`,\n",
- "`.plot.imshow`\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-06-11T02:52:00.680192Z",
- "start_time": "2020-06-11T02:52:00.365423Z"
- }
- },
- "outputs": [],
- "source": [
- "ds.Tair.isel(lon=1).plot(\n",
- " x=\"time\", # coordinate to plot on the x-axis of the plot\n",
- " robust=True, # set colorbar limits to 2nd and 98th percentile of data\n",
- " cbar_kwargs={\n",
- " \"orientation\": \"horizontal\",\n",
- " \"label\": \"custom label\",\n",
- " \"pad\": 0.2,\n",
- " }, # passed to plt.colorbar\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Exercise\n",
- "\n",
- "Update the above plot to use a different matplotlib colormap.\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Exercise\n",
- "\n",
- "Now overlay a contour plot on top of the previous plot\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "## 1D line plots\n",
- "\n",
- "xarray is also able to plot lines by wrapping `plt.plot()`. As in the earlier\n",
- "examples, the axes are labelled and keyword arguments can be passed to the\n",
- "underlying `matplotlib` call.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-06-11T02:49:58.450746Z",
- "start_time": "2020-06-11T02:49:58.282708Z"
- }
- },
- "outputs": [],
- "source": [
- "ds.Tair.isel(time=1, lon=10).plot(marker=\"o\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Lets say we want to compare line plots of temperature at three different\n",
- "latitudes. We can use the `hue` kwarg to do this.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-06-11T02:55:01.478139Z",
- "start_time": "2020-06-11T02:55:01.290510Z"
- }
- },
- "outputs": [],
- "source": [
- "ds.Tair.isel(time=1).sel(lat=[40, 50, 60], method=\"nearest\").plot(\n",
- " x=\"lon\", hue=\"lat\"\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Customization\n",
- "\n",
- "All of xarray's plotting functions take an large list kwargs that customize\n",
- "behaviour. A full list can be seen here:\n",
- "https://xarray.pydata.org/en/stable/generated/xarray.plot.pcolormesh.html. That\n",
- "said xarray does not wrap all matplotlib functionality.\n",
- "\n",
- "The general strategy for making plots that are more complicated that the\n",
- "examples above is\n",
- "\n",
- "1. Create a matplotlib axis `ax`\n",
- "2. Use xarray to make a close approximation of the final plot specifying\n",
- " `ax=ax`.\n",
- "3. Use `ax` methods to fully customize the plot\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "## Faceting\n",
- "\n",
- "Faceting is the art of presenting \"small multiples\" of the data. It is an\n",
- "effective way of visualizing variations of 3D data where 2D slices are\n",
- "visualized in a panel (subplot) and the third dimensions is varied between\n",
- "panels (subplots).\n",
- "\n",
- "Here is where xarray really augments matplotlib's functionality. We will use\n",
- "monthly means to illustrate\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-06-11T03:00:50.654212Z",
- "start_time": "2020-06-11T03:00:50.534996Z"
- }
- },
- "outputs": [],
- "source": [
- "monthly_means = ds.groupby(\"time.month\").mean()\n",
- "# xarray's groupby reductions drop attributes. Let's assign them back so we get nice labels.\n",
- "monthly_means.Tair.attrs = ds.Tair.attrs\n",
- "monthly_means"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Note that the dimensions are now `lat, lon, month`.\n",
- "\n",
- "We want to visualize how the monthly mean air temperature varies with month of\n",
- "the year.\n",
- "\n",
- "The simplest way to facet is to specify the `row` or `col` kwargs which are\n",
- "expected to be a dimension name. Here we use `month` so that each panel or\n",
- "\"facet\" of the plot presents the mean temperature field in a given month. Since\n",
- "a 12 column plot would be too small to interpret, we can \"wrap\" the facets into\n",
- "multiple rows using `col_wrap`\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-06-11T03:01:53.163195Z",
- "start_time": "2020-06-11T03:01:51.194451Z"
- }
- },
- "outputs": [],
- "source": [
- "fg = monthly_means.Tair.plot(\n",
- " col=\"month\", col_wrap=4, # each row has a maximum of 4 columns\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "All the usual customizations are possible\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-06-11T03:02:02.757064Z",
- "start_time": "2020-06-11T03:02:00.759775Z"
- }
- },
- "outputs": [],
- "source": [
- "fg = monthly_means.Tair.plot(\n",
- " col=\"month\",\n",
- " col_wrap=4,\n",
- " # The remaining kwargs customize the plot just as for not-faceted plots\n",
- " robust=True,\n",
- " cmap=mpl.cm.RdYlBu_r,\n",
- " cbar_kwargs={\n",
- " \"orientation\": \"horizontal\",\n",
- " \"shrink\": 0.8,\n",
- " \"aspect\": 40,\n",
- " \"pad\": 0.1,\n",
- " },\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The returned FacetGrid object `fg` has many useful properties and methods e.g.\n",
- "\n",
- "1. `fg.fig` provides a handle to the figure\n",
- "2. `fg.axes` is a numpy object array with handles to each individual axes\n",
- "3. `fg.set_xlabels` and `fg.set_ylabels` can be used to change axes labels.\n",
- "\n",
- "See https://xarray.pydata.org/en/stable/api.html#faceting for a full list.\n",
- "\n",
- "### Exercise\n",
- "\n",
- "Use these methods to set a title for the figure using `suptitle`, as well as\n",
- "change the x- and y-labels.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "fg"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Modifying all facets of a plot\n",
- "\n",
- "The FacetGrid object has some more advanced methods that let you customize the\n",
- "plot further.\n",
- "\n",
- "Here we illustrate the use of `map` and `map_dataarray` that let you map custom\n",
- "plotting functions to an existing `FacetGrid`. The functions passed to `map` and\n",
- "`map_dataarray` must have a particular signature. See the docstring for more\n",
- "details.\n",
- "\n",
- "Alternatively one can loop over `fg.axes` and modify each individual subplot as\n",
- "needed\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-06-11T03:06:40.368314Z",
- "start_time": "2020-06-11T03:06:38.735106Z"
- }
- },
- "outputs": [],
- "source": [
- "fg = monthly_means.Tair.plot(col=\"month\", col_wrap=4)\n",
- "\n",
- "# Use this to plot contours on each panel\n",
- "# Note that this plotting call uses the original DataArray gradients\n",
- "fg.map_dataarray(\n",
- " xr.plot.contour, x=\"lon\", y=\"lat\", colors=\"k\", levels=13, add_colorbar=False\n",
- ")\n",
- "\n",
- "# Add a point (or anything else!)\n",
- "fg.map(lambda: plt.plot(250, 40, markersize=20, marker=\".\", color=\"w\"))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Faceting tip: Use faceting to plot multiple DataArrays\n",
- "\n",
- "Faceting can be used to plot multiple DataArrays in a Dataset. The trick is to\n",
- "use `to_array()` to convert a Dataset to a DataArray and thne facet that.\n",
- "\n",
- "This trick only works when it is sensible to use the same colormap and color\n",
- "scale for all DataArrays like with `dTdx` and `dTdy`\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-06-11T03:05:00.036216Z",
- "start_time": "2020-06-11T03:04:59.973964Z"
- }
- },
- "outputs": [],
- "source": [
- "gradients = monthly_means[[\"dTdx\", \"dTdy\"]].to_array(\"gradient\")\n",
- "gradients"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-06-11T03:05:02.894528Z",
- "start_time": "2020-06-11T03:05:01.454396Z"
- }
- },
- "outputs": [],
- "source": [
- "fg = gradients.isel(month=slice(None, None, 3)).plot.contourf(\n",
- " levels=13,\n",
- " col=\"month\",\n",
- " row=\"gradient\",\n",
- " robust=True,\n",
- " cmap=mpl.cm.coolwarm,\n",
- " cbar_kwargs={\n",
- " \"orientation\": \"horizontal\",\n",
- " \"shrink\": 0.8,\n",
- " \"aspect\": 40,\n",
- " \"label\": \"Gradient [°C/m]\",\n",
- " },\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "## Geography: Matplotlib and Cartopy\n",
- "\n",
- "Since xarray's default plotting functionality builds on matplotlib, we can\n",
- "seamlessly use cartopy to make nice maps:\n",
- "\n",
- "1. Specify a `projection` for the plot when creating a new figure `fig` with\n",
- " axis `axis`.\n",
- "2. Explicitly ask xarray to plot to axis `axis` by passing the kwarg `ax=axis`.\n",
- "3. Specify the projection of the data using `transform` (`PlateCarree` here) in\n",
- " `.plot()`.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-06-11T03:17:25.685880Z",
- "start_time": "2020-06-11T03:17:25.512169Z"
- }
- },
- "outputs": [],
- "source": [
- "import cartopy.crs as ccrs\n",
- "\n",
- "fig, axis = plt.subplots(\n",
- " 1, 1, subplot_kw=dict(projection=ccrs.Orthographic(-90, 30))\n",
- ")\n",
- "\n",
- "ds.Tair.isel(time=1).plot(\n",
- " ax=axis,\n",
- " transform=ccrs.PlateCarree(), # this is important!\n",
- " # usual xarray stuff\n",
- " cbar_kwargs={\"orientation\": \"horizontal\", \"shrink\": 0.7},\n",
- " robust=True,\n",
- ")\n",
- "axis.coastlines() # cartopy function"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Faceting maps\n",
- "\n",
- "We can make faceted maps. Since `FacetGrid` creates the axes it plots to, we\n",
- "need to pass the `projection` kwarg in `subplot_kws`. This makes sure that the\n",
- "subplots are set up properly for cartopy.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-06-11T03:22:50.356379Z",
- "start_time": "2020-06-11T03:22:50.044591Z"
- }
- },
- "outputs": [],
- "source": [
- "fg = monthly_means.Tair.isel(month=[1, 2, 3]).plot(\n",
- " col=\"month\",\n",
- " transform=ccrs.PlateCarree(), # remember to provide this!\n",
- " subplot_kws={\n",
- " \"projection\": ccrs.LambertConformal(\n",
- " central_longitude=-95, central_latitude=45\n",
- " )\n",
- " },\n",
- " cbar_kwargs={\"orientation\": \"horizontal\", \"shrink\": 0.8, \"aspect\": 40},\n",
- " robust=True,\n",
- ")\n",
- "\n",
- "# lets add a coastline to each axis\n",
- "# great reason to use FacetGrid.map\n",
- "fg.map(lambda: plt.gca().coastlines())"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "## Interactive bokeh plots using hvplot\n",
- "\n",
- "Xarray's builtin plotting functionality wraps matplotlib.\n",
- "\n",
- "The `holoviews` plotting ecosystem provides the `hvplot` package to allow easy\n",
- "visualization of xarray (and other) objects (https://hvplot.holoviz.org/). These\n",
- "plots build on [Bokeh](https://bokeh.org/).\n",
- "\n",
- "`hvplot` makes uses of xarray's accessor interface. This means that all xarray\n",
- "objects gain a `.hvplot` attribute that lets you access `hvplot` functionality\n",
- "as easily as you would use `.plot`\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-06-11T03:12:07.379960Z",
- "start_time": "2020-06-11T03:12:05.559749Z"
- }
- },
- "outputs": [],
- "source": [
- "import hvplot.xarray"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "**`hvplot` makes the same default choices as xarray**\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-06-11T03:12:10.251511Z",
- "start_time": "2020-06-11T03:12:09.004741Z"
- }
- },
- "outputs": [],
- "source": [
- "ds.Tair.hvplot()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-06-11T03:12:13.498644Z",
- "start_time": "2020-06-11T03:12:13.391829Z"
- }
- },
- "outputs": [],
- "source": [
- "# 2D array yields a quadmesh plot\n",
- "ds.Tair.isel(time=1).hvplot(cmap=\"fire\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-06-11T03:12:15.282022Z",
- "start_time": "2020-06-11T03:12:15.180088Z"
- }
- },
- "outputs": [],
- "source": [
- "# 1D array yields a line plot\n",
- "ds.Tair.isel(time=1, lon=1).hvplot()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Interactivity\n",
- "\n",
- "But `hvplot` shines when interactivity is used. Here we can give it _all_ the\n",
- "data and ask it to create a nice slider to control the time slice using the\n",
- "`groupby` kwarg.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-06-11T03:12:41.353540Z",
- "start_time": "2020-06-11T03:12:41.143491Z"
- }
- },
- "outputs": [],
- "source": [
- "ds.Tair.hvplot(\n",
- " groupby=\"time\",\n",
- " clim=(250, 295), # adds a widget for time # sets colorbar limits\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Animations are easy\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-06-11T03:12:47.032547Z",
- "start_time": "2020-06-11T03:12:46.820441Z"
- }
- },
- "outputs": [],
- "source": [
- "# set constant colorbar limits\n",
- "ds.Tair.hvplot(\n",
- " groupby=\"time\", # adds a widget for time\n",
- " clim=(250, 295), # sets colormap limits\n",
- " widget_type=\"scrubber\",\n",
- " widget_location=\"bottom\",\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Geography\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2020-06-11T03:23:34.446468Z",
- "start_time": "2020-06-11T03:23:32.211041Z"
- }
- },
- "outputs": [],
- "source": [
- "ds.Tair.isel(time=1).hvplot(\n",
- " projection=ccrs.Orthographic(-90, 30), coastline=True,\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## More resources\n",
- "\n",
- "1. Xarray's visualization gallery:\n",
- " https://xarray.pydata.org/en/stable/examples/visualization_gallery.html\n",
- "2. Xarray's plotting documentation:\n",
- " https://xarray.pydata.org/en/stable/plotting.html\n",
- "3. hvplot's plotting documentation:\n",
- " https://hvplot.holoviz.org/user_guide/Gridded_Data.html\n"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.7.6"
- },
- "toc": {
- "base_numbering": 1,
- "nav_menu": {},
- "number_sections": true,
- "sideBar": true,
- "skip_h1_title": false,
- "title_cell": "Table of Contents",
- "title_sidebar": "Contents",
- "toc_cell": true,
- "toc_position": {},
- "toc_section_display": true,
- "toc_window_display": true
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/scipy-tutorial/06_xarray_and_dask.ipynb b/scipy-tutorial/06_xarray_and_dask.ipynb
deleted file mode 100644
index 5248da70..00000000
--- a/scipy-tutorial/06_xarray_and_dask.ipynb
+++ /dev/null
@@ -1,661 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "# Xarray and Dask\n",
- "\n",
- "This notebook demonstrates one of xarray's most powerful features: the ability\n",
- "to wrap dask arrays and allow users to seamlessly execute analysis code in\n",
- "parallel.\n",
- "\n",
- "By the end of this notebook, you will:\n",
- "\n",
- "1. Xarray DataArrays and Datasets are \"dask collections\" i.e. you can execute\n",
- " top-level dask functions such as `dask.visualize(xarray_object)`\n",
- "2. Learn that all xarray built-in operations can transparently use dask\n",
- "3. Learn that xarray provides tools to easily parallelize custom functions\n",
- " across blocks of dask-backed xarray objects.\n",
- "\n",
- "## Table of contents\n",
- "\n",
- "1. [Reading data with Dask and Xarray](#readwrite)\n",
- "2. [Parallel/streaming/lazy computation using dask.array with Xarray](#compute)\n",
- "3. [Automatic parallelization with apply_ufunc and map_blocks](#applymap)\n",
- "\n",
- "First lets do the necessary imports, start a dask cluster and test the dashboard\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import expectexception\n",
- "import numpy as np\n",
- "import xarray as xr"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "First lets set up a `LocalCluster` using `dask.distributed`.\n",
- "\n",
- "You can use any kind of dask cluster. This step is completely independent of\n",
- "xarray.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "from dask.distributed import Client\n",
- "\n",
- "client = Client()\n",
- "client"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "👆
Click the Dashboard link above. Or click the \"Search\" button in the dashboard.\n", - "\n", - "Let's test that the dashboard is working..\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import dask.array\n", - "\n", - "dask.array.ones(\n", - " (1000, 4), chunks=(2, 1)\n", - ").compute() # should see activity in dashboard" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "## Reading data with Dask and Xarray\n", - "\n", - "The `chunks` argument to both `open_dataset` and `open_mfdataset` allow you to\n", - "read datasets as dask arrays. See\n", - "https://xarray.pydata.org/en/stable/dask.html#reading-and-writing-data for more\n", - "details\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ds = xr.tutorial.open_dataset(\n", - " \"air_temperature\",\n", - " chunks={\n", - " \"lat\": 25,\n", - " \"lon\": 25,\n", - " \"time\": -1,\n", - " }, # this tells xarray to open the dataset as a dask array\n", - ")\n", - "ds" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The repr for the `air` DataArray shows the dask repr.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ds.air" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ds.air.chunks" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Tip**: All variables in a `Dataset` need _not_ have the same chunk size along\n", - "common dimensions.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "mean = ds.air.mean(\"time\") # no activity on dashboard\n", - "mean # contains a dask array" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This is true for all xarray operations including slicing\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ds.air.isel(lon=1, lat=20)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "and more complicated operations...\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "## Parallel/streaming/lazy computation using dask.array with Xarray\n", - "\n", - "Xarray seamlessly wraps dask so all computation is deferred until explicitly\n", - "requested\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "mean = ds.air.mean(\"time\") # no activity on dashboard\n", - "mean # contains a dask array" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This is true for all xarray operations including slicing\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "timeseries = (\n", - " ds.air.rolling(time=5).mean().isel(lon=1, lat=20)\n", - ") # no activity on dashboard\n", - "timeseries # contains dask array" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "timeseries = ds.air.rolling(time=5).mean() # no activity on dashboard\n", - "timeseries # contains dask array" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Getting concrete values from dask arrays\n", - "\n", - "At some point, you will want to actually get concrete values from dask.\n", - "\n", - "There are two ways to compute values on dask arrays. These concrete values are\n", - "usually numpy arrays but could be a `pydata/sparse` array for example.\n", - "\n", - "1. `.compute()` returns an xarray object\n", - "2. `.load()` replaces the dask array in the xarray object with a numpy array.\n", - " This is equivalent to `ds = ds.compute()`\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "computed = mean.compute() # activity on dashboard\n", - "computed # has real numpy values" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note that `mean` still contains a dask array\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "mean" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "But if we call `.load()`, `mean` will now contain a numpy array\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "mean.load()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's check that again...\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "mean" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Tip:** `.persist()` loads the values into distributed RAM. This is useful if\n", - "you will be repeatedly using a dataset for computation but it is too large to\n", - "load into local memory. You will see a persistent task on the dashboard.\n", - "\n", - "See https://docs.dask.org/en/latest/api.html#dask.persist for more\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Extracting underlying data: `.values` vs `.data`\n", - "\n", - "There are two ways to pull out the underlying data in an xarray object.\n", - "\n", - "1. `.values` will always return a NumPy array. For dask-backed xarray objects,\n", - " this means that compute will always be called\n", - "2. `.data` will return a Dask array\n", - "\n", - "#### Exercise\n", - "\n", - "Try extracting a dask array from `ds.air`\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now extract a NumPy array from `ds.air`. Do you see compute activity on your\n", - "dashboard?\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Xarray data structures are first-class dask collections.\n", - "\n", - "This means you can do things like `dask.compute(xarray_object)`,\n", - "`dask.visualize(xarray_object)`, `dask.persist(xarray_object)`. This works for\n", - "both DataArrays and Datasets\n", - "\n", - "#### Exercise\n", - "\n", - "Visualize the task graph for `mean`\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Visualize the task graph for `mean.data`. Is that the same as the above graph?\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "## Automatic parallelization with apply_ufunc and map_blocks\n", - "\n", - "Almost all of xarray’s built-in operations work on Dask arrays.\n", - "\n", - "Sometimes analysis calls for functions that aren't in xarray's API (e.g. scipy).\n", - "There are three ways to apply these functions in parallel on each block of your\n", - "xarray object:\n", - "\n", - "1. Extract Dask arrays from xarray objects (`.data`) and use Dask directly e.g.\n", - " (`apply_gufunc`, `map_blocks`, `map_overlap`, or `blockwise`)\n", - "\n", - "2. Use `xarray.apply_ufunc()` to apply functions that consume and return NumPy\n", - " arrays.\n", - "\n", - "3. Use `xarray.map_blocks()`, `Dataset.map_blocks()` or `DataArray.map_blocks()`\n", - " to apply functions that consume and return xarray objects.\n", - "\n", - "Which method you use ultimately depends on the type of input objects expected by\n", - "the function you're wrapping, and the level of performance or convenience you\n", - "desire.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### `map_blocks`\n", - "\n", - "`map_blocks` is inspired by the `dask.array` function of the same name and lets\n", - "you map a function on blocks of the xarray object (including Datasets!).\n", - "\n", - "At _compute_ time, your function will receive an xarray object with concrete\n", - "(computed) values along with appropriate metadata. This function should return\n", - "an xarray object.\n", - "\n", - "Here is an example\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def time_mean(obj):\n", - " # use xarray's convenient API here\n", - " # you could convert to a pandas dataframe and use pandas' extensive API\n", - " # or use .plot() and plt.savefig to save visualizations to disk in parallel.\n", - " return obj.mean(\"lat\")\n", - "\n", - "\n", - "ds.map_blocks(time_mean) # this is lazy!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# this will calculate values and will return True if the computation works as expected\n", - "ds.map_blocks(time_mean).identical(ds.mean(\"lat\"))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Exercise\n", - "\n", - "Try applying the following function with `map_blocks`. Specify `scale` as an\n", - "argument and `offset` as a kwarg.\n", - "\n", - "The docstring should help:\n", - "https://xarray.pydata.org/en/stable/generated/xarray.map_blocks.html\n", - "\n", - "```\n", - "def time_mean_scaled(obj, scale, offset):\n", - " return obj.mean(\"lat\") * scale + offset\n", - "```\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### More advanced functions\n", - "\n", - "`map_blocks` needs to know what the returned object looks like _exactly_. It\n", - "does so by passing a 0-shaped xarray object to the function and examining the\n", - "result. This approach cannot work in all cases For such advanced use cases,\n", - "`map_blocks` allows a `template` kwarg. See\n", - "https://xarray.pydata.org/en/latest/dask.html#map-blocks for more details\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### apply_ufunc\n", - "\n", - "`apply_ufunc` is a more advanced wrapper that is designed to apply functions\n", - "that expect and return NumPy (or other arrays). For example, this would include\n", - "all of SciPy's API. Since `apply_ufunc` operates on lower-level NumPy or Dask\n", - "objects, it skips the overhead of using Xarray objects making it a good choice\n", - "for performance-critical functions.\n", - "\n", - "`apply_ufunc` can be a little tricky to get right since it operates at a lower\n", - "level than `map_blocks`. On the other hand, Xarray uses `apply_ufunc` internally\n", - "to implement much of its API, meaning that it is quite powerful!\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### A simple example\n", - "\n", - "Simple functions that act independently on each value should work without any\n", - "additional arguments. However `dask` handling needs to be explictly enabled\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "raises-exception" - ] - }, - "outputs": [], - "source": [ - "%%expect_exception\n", - "\n", - "squared_error = lambda x, y: (x - y) ** 2\n", - "\n", - "xr.apply_ufunc(squared_error, ds.air, 1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "There are two options for the `dask` kwarg.\n", - "\n", - "1. `dask=\"allowed\"` Dask arrays are passed to the user function. This is a good\n", - " choice if your function can handle dask arrays and won't call compute\n", - " explicitly.\n", - "2. `dask=\"parallelized\"`. This applies the user function over blocks of the dask\n", - " array using `dask.array.blockwise`. This is useful when your function cannot\n", - " handle dask arrays natively (e.g. scipy API).\n", - "\n", - "Since `squared_error` can handle dask arrays without computing them, we specify\n", - "`dask=\"allowed\"`.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "sqer = xr.apply_ufunc(squared_error, ds.air, 1, dask=\"allowed\",)\n", - "sqer # dask-backed DataArray! with nice metadata!" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### A more complicated example with a dask-aware function\n", - "\n", - "For using more complex operations that consider some array values collectively,\n", - "it’s important to understand the idea of **core dimensions** from NumPy’s\n", - "generalized ufuncs. Core dimensions are defined as dimensions that should not be\n", - "broadcast over. Usually, they correspond to the fundamental dimensions over\n", - "which an operation is defined, e.g., the summed axis in `np.sum`. A good clue\n", - "that core dimensions are needed is the presence of an `axis` argument on the\n", - "corresponding NumPy function.\n", - "\n", - "With `apply_ufunc`, core dimensions are recognized by name, and then moved to\n", - "the last dimension of any input arguments before applying the given function.\n", - "This means that for functions that accept an `axis` argument, you usually need\n", - "to set `axis=-1`\n", - "\n", - "Let's use `dask.array.mean` as an example of a function that can handle dask\n", - "arrays and uses an `axis` kwarg\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def time_mean(da):\n", - " return xr.apply_ufunc(\n", - " dask.array.mean,\n", - " da,\n", - " input_core_dims=[[\"time\"]],\n", - " dask=\"allowed\",\n", - " kwargs={\"axis\": -1}, # core dimensions are moved to the end\n", - " )\n", - "\n", - "\n", - "time_mean(ds.air)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ds.air.mean(\"time\").identical(time_mean(ds.air))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Automatically parallelizing dask-unaware functions\n", - "\n", - "A very useful `apply_ufunc` feature is the ability to apply arbitrary functions\n", - "in parallel to each block. This ability can be activated using\n", - "`dask=\"parallelized\"`. Again xarray needs a lot of extra metadata, so depending\n", - "on the function, extra arguments such as `output_dtypes` and `output_sizes` may\n", - "be necessary.\n", - "\n", - "We will use `scipy.integrate.trapz` as an example of a function that cannot\n", - "handle dask arrays and requires a core dimension.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import scipy as sp\n", - "import scipy.integrate\n", - "\n", - "sp.integrate.trapz(ds.air.data) # does NOT return a dask array" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Exercise\n", - "\n", - "Use `apply_ufunc` to apply `sp.integrate.trapz` along the `time` axis so that\n", - "you get a dask array returned. You will need to specify `dask=\"parallelized\"`\n", - "and `output_dtypes` (a list of `dtypes` per returned variable).\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## More\n", - "\n", - "1. https://xarray.pydata.org/en/stable/examples/apply_ufunc_vectorize_1d.html#\n", - "2. https://docs.dask.org/en/latest/array-best-practices.html\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.2" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/setup.cfg b/setup.cfg deleted file mode 100644 index 30a5d175..00000000 --- a/setup.cfg +++ /dev/null @@ -1,12 +0,0 @@ -[flake8] -max-line-length = 100 -per-file-ignores = conf.py:E501 - -[isort] -default_section = THIRDPARTY -known_third_party = nbconvert,nbformat -multi_line_output = 3 -include_trailing_comma = True -force_grid_wrap = 0 -use_parentheses = True -line_length = 100 diff --git a/workshops/oceanhackweek2020/README.md b/workshops/oceanhackweek2020/README.md new file mode 100644 index 00000000..9155eae2 --- /dev/null +++ b/workshops/oceanhackweek2020/README.md @@ -0,0 +1,15 @@ +# Oceanhackweek 2020 + +Presented August 2020 at [OceanHackWeek](https://oceanhackweek.github.io) by Deepak Cheerian + +This 45-minute tutorial covers the basics of Xarray data structures. + +Video Recording: +https://www.youtube.com/watch?v=q020HHnGAWo + +By the end of the lesson, we will be able to: + +- Understand the basic data structures in Xarray +- Inspect `DataArray` and `Dataset` objects. +- Read and write netCDF files using Xarray. +- Understand that there are many packages that build on top of xarray diff --git a/workshops/online-tutorial-series/01_xarray_fundamentals.ipynb b/workshops/online-tutorial-series/01_xarray_fundamentals.ipynb new file mode 100644 index 00000000..03eded1c --- /dev/null +++ b/workshops/online-tutorial-series/01_xarray_fundamentals.ipynb @@ -0,0 +1,448 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Xarray Fundamentals\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Learning Objectives\n", + "\n", + "- Provide an overview of xarray\n", + "- Describe the core xarray data structures, the DataArray and the Dataset, and\n", + " the components that make them up\n", + "- Load xarray dataset from a netCDF file\n", + "- View and set attributes\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## What Is Xarray?\n", + "\n", + "- Unlabeled, N-dimensional arrays of numbers (e.g., NumPy’s ndarray) are the\n", + " most widely used data structure in scientific computing. However, they lack a\n", + " meaningful representation of the metadata associated with their data.\n", + " Implementing such functionality is left to individual users and\n", + " domain-specific packages.\n", + "\n", + "- xarray expands on the capabilities of NumPy arrays, providing a lot of\n", + " streamline data manipulation.\n", + "\n", + "- Xarray's interface is based largely on the netCDF data model (variables,\n", + " attributes, and dimensions), but it goes beyond the traditional netCDF\n", + " interfaces to provide functionality similar to netCDF-java's Common Data Model\n", + " (CDM).\n", + "\n", + "- xarray is motivated by weather and climate use cases but is **domain\n", + " agnostic**...\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Core Data Structures\n", + "\n", + "- xarray has 2 fundamental data structures:\n", + "\n", + " - `DataArray`, which holds single multi-dimensional variables and its\n", + " coordinates\n", + " - `Dataset`, which holds multiple variables that potentially share the same\n", + " coordinates\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Loading data from netCDF\n", + "\n", + "- NetCDF (network Common Data Form) is a file format for storing\n", + " multidimensional array data\n", + "- NetCDF is a self-describing, meaning that a netCDF file includes information\n", + " about the data it contains, and the necessary metadata such as coordinate\n", + " system used, attributes describing the data, etc...\n", + "- NetCDF is used extensively in the geoscience communities\n", + "- Xarray's interface is based largely on the netCDF data model\n", + "\n", + "Learn more about netCDF\n", + "[here](https://docs.unidata.ucar.edu/netcdf-c/current/faq.html#What-Is-netCDF).\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import xarray as xr\n", + "\n", + "%config InlineBackend.figure_format='retina'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Load mean sea surface temperature dataset\n", + "ds = xr.open_dataset(\"../../data/sst.mnmean.nc\", engine=\"netcdf4\")\n", + "\n", + "# xarray's HTML representation\n", + "ds" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# If you prefer a text based repr, you can set the display_style='text' by uncommenting the line below\n", + "# xr.set_options(display_style=\"text\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "hide-output", + "output_scroll" + ] + }, + "outputs": [], + "source": [ + "# Look at the netCDF representation\n", + "ds.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `Dataset`\n", + "\n", + "- Xarray's `Dataset` is a dict-like container of labeled arrays (`DataArrays`)\n", + " with aligned dimensions. - It is designed as an in-memory representation of a\n", + " netCDF dataset.\n", + "- In addition to the dict-like interface of the dataset itself, which can be\n", + " used to access any `DataArray` in a `Dataset`. Datasets have the following key\n", + " properties:\n", + "\n", + "| Attribute | Description |\n", + "| ----------- | ---------------------------------------------------------------------------------------------------------------------------------------- |\n", + "| `data_vars` | OrderedDict of `DataArray` objects corresponding to data variables. |\n", + "| `dims` | dictionary mapping from dimension names to the fixed length of each dimension (e.g., {`lat`: 6, `lon`: 6, `time`: 8}). |\n", + "| `coords` | a dict-like container of arrays (coordinates) that label each point (e.g., 1-dimensional arrays of numbers, datetime objects or strings) |\n", + "| `attrs` | OrderedDict to hold arbitrary metadata pertaining to the dataset. |\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# variables are in our dataset\n", + "ds.data_vars" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# select one variable and pick the first entry along the first axis (time)\n", + "ds.sst[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot one timestep\n", + "ds.sst[0].plot();" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# dataset dimensions\n", + "ds.dims" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# dataset coordinates\n", + "ds.coords" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "output_scroll" + ] + }, + "outputs": [], + "source": [ + "# dataset global attributes\n", + "ds.attrs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `DataArray`\n", + "\n", + "The DataArray is xarray's implementation of a labeled, multi-dimensional array.\n", + "It has several key properties:\n", + "\n", + "| Attribute | Description |\n", + "| --------- | ---------------------------------------------------------------------------------------------------------------------------------------- |\n", + "| `data` | `numpy.ndarray` or `dask.array` holding the array's values. |\n", + "| `dims` | dimension names for each axis. For example:(`x`, `y`, `z`) (`lat`, `lon`, `time`). |\n", + "| `coords` | a dict-like container of arrays (coordinates) that label each point (e.g., 1-dimensional arrays of numbers, datetime objects or strings) |\n", + "| `attrs` | an `OrderedDict` to hold arbitrary attributes/metadata (such as units) |\n", + "| `name` | an arbitrary name of the array |\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Extract the sst Variable/DataArray\n", + "ds[\"sst\"] # Equivalent to ds.sst" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "hide-output" + ] + }, + "outputs": [], + "source": [ + "# The actual (numpy) array data\n", + "ds.sst.data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# DataArray/Variable dimensions\n", + "ds.sst.dims" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# DataArray/Variable coordinates\n", + "ds.sst.coords" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# DataArray/Variable attributes\n", + "ds.sst.attrs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Coordinates vs dimensions\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- DataArray objects inside a Dataset may have any number of dimensions but are\n", + " presumed to share a common coordinate system.\n", + "- Coordinates can also have any number of dimensions but denote\n", + " constant/independent quantities, unlike the varying/dependent quantities that\n", + " belong in data.\n", + "- A dimension is just a name of an axis, like \"time\"\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ds.dims" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ds.coords" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "hide-output" + ] + }, + "outputs": [], + "source": [ + "# extracting a coordinate variable\n", + "ds.sst.lon" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "hide-output" + ] + }, + "outputs": [], + "source": [ + "# extracting a coordinate variable from .coords\n", + "ds.coords[\"time\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Attributes\n", + "\n", + "Attributes can be used to store metadata. What metadata should you store? It\n", + "depends on your domain and your needs\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "output_scroll" + ] + }, + "outputs": [], + "source": [ + "# Look at global attributes\n", + "ds.attrs" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Look at variable specific attributes\n", + "ds.sst.attrs" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Set some arbitrary attribute on a data Variable/DataArray\n", + "ds.sst.attrs[\"my_custom_attribute\"] = \"Foo Bar\"\n", + "ds.sst.attrs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Going Further\n", + "\n", + "- Xarray Documentation on Data Structures:\n", + " https://docs.xarray.dev/en/stable/data-structures.html\n", + "- Xarray Documentation on Reading files and writing files:\n", + " https://docs.xarray.dev/en/stable/user-guide/io.html\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n",
+ "
\n"
+ ]
+ }
+ ],
+ "metadata": {
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3"
+ },
+ "toc": {
+ "base_numbering": 1,
+ "nav_menu": {},
+ "number_sections": true,
+ "sideBar": true,
+ "skip_h1_title": false,
+ "title_cell": "Table of Contents",
+ "title_sidebar": "Contents",
+ "toc_cell": true,
+ "toc_position": {},
+ "toc_section_display": true,
+ "toc_window_display": true
+ },
+ "toc-autonumbering": true
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/workshops/online-tutorial-series/02_indexing.ipynb b/workshops/online-tutorial-series/02_indexing.ipynb
new file mode 100644
index 00000000..8e7ffe79
--- /dev/null
+++ b/workshops/online-tutorial-series/02_indexing.ipynb
@@ -0,0 +1,351 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Indexing and Selecting Data\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Learning Objectives\n",
+ "\n",
+ "- Select data by position using `.isel` with values or slices\n",
+ "- Select data by label using `.sel` with values or slices\n",
+ "- Select timeseries data by date/time with values or slices\n",
+ "- Use nearest-neighbor lookups with `.sel`\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Why do we need label-based indexing?\n",
+ "\n",
+ "Scientific data is inherently labeled. For example, time series data includes\n",
+ "timestamps that label individual periods or points in time, spatial data has\n",
+ "coordinates (e.g. longitude, latitude, elevation), and model or laboratory\n",
+ "experiments are often identified by unique identifiers.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import xarray as xr\n",
+ "\n",
+ "%config InlineBackend.figure_format='retina'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds = xr.open_dataset(\"../../data/sst.mnmean.nc\")\n",
+ "ds"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## NumPy Positional Indexing\n",
+ "\n",
+ "When working with numpy, indexing is done by position (slices/ranges/scalars).\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "tags": [
+ "hide-output"
+ ]
+ },
+ "outputs": [],
+ "source": [
+ "t = ds[\"sst\"].data # numpy array\n",
+ "t"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "t.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "tags": [
+ "hide-output"
+ ]
+ },
+ "outputs": [],
+ "source": [
+ "# extract a time-series for one spatial location\n",
+ "t[:, 20, 40]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Next: Indexing
\n", + "\n",
+ "but wait, what labels go with 20 and 40? Was that lat/lon or lon/lat? Where are the timestamps that go along with this time-series?\n",
+ "
\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Indexing with xarray\n",
+ "\n",
+ "xarray offers extremely flexible indexing routines that combine the best\n",
+ "features of NumPy and pandas for data selection.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "tags": [
+ "hide-output"
+ ]
+ },
+ "outputs": [],
+ "source": [
+ "da = ds[\"sst\"] # Extract data array\n",
+ "da"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "- **NumPy style indexing still works (but preserves the labels/metadata)**\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "tags": [
+ "hide-output"
+ ]
+ },
+ "outputs": [],
+ "source": [
+ "da[:, 20, 40]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "- **Positional indexing using dimension names**\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "da.isel(lat=60, lon=40).plot();"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "- **Label-based indexing**\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "da.sel(lat=-32, lon=80).plot();"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "da.sel(lat=50.0, lon=200.0, time=\"2020\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# demonstrate slicing\n",
+ "ds.sel(time=slice(\"2019-05\", \"2020-07\"))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "- **Nearest Neighbor Lookups**\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "tags": [
+ "hide-output"
+ ]
+ },
+ "outputs": [],
+ "source": [
+ "da.sel(lat=52.25, lon=251.8998, method=\"nearest\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "- **All of these indexing methods work on the dataset too:**\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds.sel(lat=52.25, lon=251.8998, method=\"nearest\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Vectorized Indexing\n",
+ "\n",
+ "Like numpy and pandas, xarray supports indexing many array elements at once in a\n",
+ "vectorized manner:\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# generate a coordinates for a transect of points\n",
+ "lat_points = xr.DataArray([60, 80, 90], dims=\"points\")\n",
+ "lon_points = xr.DataArray([250, 250, 250], dims=\"points\")\n",
+ "lat_points"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "lon_points"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# nearest neighbor selection along the transect\n",
+ "da.sel(lat=lat_points, lon=lon_points, method=\"nearest\").plot();"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Indexing with `where()`\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "tags": [
+ "hide-output"
+ ]
+ },
+ "outputs": [],
+ "source": [
+ "# Let's replace the missing values (nan) with some placeholder\n",
+ "ds.sst.where(ds.sst.notnull(), -99)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Going Further\n",
+ "\n",
+ "- [Xarray Docs - Indexing and Selecting Data](https://docs.xarray.dev/en/stable/indexing.html)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "
\n"
+ ]
+ }
+ ],
+ "metadata": {
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3"
+ },
+ "toc": {
+ "base_numbering": 1,
+ "nav_menu": {},
+ "number_sections": true,
+ "sideBar": true,
+ "skip_h1_title": false,
+ "title_cell": "Table of Contents",
+ "title_sidebar": "Contents",
+ "toc_cell": true,
+ "toc_position": {},
+ "toc_section_display": true,
+ "toc_window_display": true
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/workshops/online-tutorial-series/03_computation.ipynb b/workshops/online-tutorial-series/03_computation.ipynb
new file mode 100644
index 00000000..f4ae294e
--- /dev/null
+++ b/workshops/online-tutorial-series/03_computation.ipynb
@@ -0,0 +1,312 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Computation\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Learning Objectives\n",
+ "\n",
+ "- Do basic arithmetic with DataArrays and Datasets\n",
+ "- Perform aggregation (reduction) along one or multiple dimensions of a\n",
+ " DataArray or Dataset\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Arithmetic Operations\n",
+ "\n",
+ "Arithmetic operations with a single DataArray automatically vectorize (like\n",
+ "numpy) over all array values:\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import xarray as xr"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds = xr.open_dataset(\"../../data/sst.mnmean.nc\")\n",
+ "da = ds[\"sst\"]\n",
+ "da"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "da + 273.15"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Aggregation (Reduction) Methods\n",
+ "\n",
+ "Xarray supports many of the aggregations methods that numpy has. A partial list\n",
+ "includes: all, any, argmax, argmin, max, mean, median, min, prod, sum, std, var.\n",
+ "\n",
+ "Whereas the numpy syntax would require scalar axes, xarray can use dimension\n",
+ "names:\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "da_mean = da.mean(dim=\"time\")\n",
+ "da_mean"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "da.std(dim=[\"lat\", \"lon\"]).plot()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Broadcasting:\n",
+ "\n",
+ "**Broadcasting** allows an operator or a function to act on two or more arrays\n",
+ "to operate even if these arrays do not have the same shape. That said, not all\n",
+ "the dimensions can be subjected to broadcasting; they must meet certain rules.\n",
+ "The image below illustrates how performing an operation on arrays with\n",
+ "differently coordinates will result in automatic broadcasting\n",
+ "\n",
+ "\n",
+ "\n",
+ "Credit: Stephan Hoyer --\n",
+ "[xarray ECMWF Python workshop](https://docs.google.com/presentation/d/16CMY3g_OYr6fQplUZIDqVtG-SKZqsG8Ckwoj2oOqepU/)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "da.shape, da.dims"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "da_mean.shape, da_mean.dims"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Subtract the mean (2D array) from the original array (3D array)\n",
+ "x = da - da_mean\n",
+ "x"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## High level computation: groupby, resample, rolling, coarsen, weighted\n",
+ "\n",
+ "Xarray has some very useful high level objects that let you do common\n",
+ "computations:\n",
+ "\n",
+ "- `groupby` :\n",
+ " [Bin data in to groups and reduce](https://docs.xarray.dev/en/stable/groupby.html)\n",
+ "- `resample` :\n",
+ " [Groupby specialized for time axes. Either downsample or upsample your data](https://docs.xarray.dev/en/stable/user-guide/time-series.html#resampling-and-grouped-operations).\n",
+ "- `rolling` :\n",
+ " [Operate on rolling windows of your data e.g. running mean](https://docs.xarray.dev/en/stable/user-guide/computation.html#rolling-window-operations)\n",
+ "- `coarsen` :\n",
+ " [Downsample your data](https://docs.xarray.dev/en/stable/user-guide/computation.html#coarsen-large-arrays)\n",
+ "- `weighted` :\n",
+ " [Weight your data before applying reductions](https://docs.xarray.dev/en/stable/user-guide/computation.html#weighted-array-reductions)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### groupby\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# seasonal groups\n",
+ "ds.groupby(\"time.season\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# day of the week groups\n",
+ "ds.groupby(\"time.dayofweek\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# compute a seasonal mean\n",
+ "seasonal_mean = ds.groupby(\"time.season\").mean()\n",
+ "seasonal_mean"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# The seasons are out of order (they are alphabetically sorted). This is a common annoyance. The solution is to use .reindex\n",
+ "seasonal_mean = seasonal_mean.reindex(season=[\"DJF\", \"MAM\", \"JJA\", \"SON\"])\n",
+ "seasonal_mean"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "seasonal_mean.sst.plot(col=\"season\", robust=True, cmap=\"turbo\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### resample\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# resample to bi-monthly frequency\n",
+ "ds.sst.resample(time=\"2MS\").mean()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### rolling window operations\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# A rolling mean with a window size of 7\n",
+ "ds.sst.rolling(time=7).mean()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Going Further\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Previous: xarray fundamentals
\n", + "Next: Computation
\n", + "\n",
+ "
\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": []
+ }
+ ],
+ "metadata": {
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3"
+ },
+ "toc": {
+ "base_numbering": 1,
+ "nav_menu": {},
+ "number_sections": true,
+ "sideBar": true,
+ "skip_h1_title": false,
+ "title_cell": "Table of Contents",
+ "title_sidebar": "Contents",
+ "toc_cell": true,
+ "toc_position": {},
+ "toc_section_display": true,
+ "toc_window_display": true
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/workshops/online-tutorial-series/README.md b/workshops/online-tutorial-series/README.md
new file mode 100644
index 00000000..6b68c89f
--- /dev/null
+++ b/workshops/online-tutorial-series/README.md
@@ -0,0 +1,23 @@
+# Xarray Online Tutorial 2020
+
+Presented October 6 2020 by:
+
+- Anderson Banihirwe (NCAR)
+- Deepak Cherian (NCAR)
+- Martin Durant (Anaconda)
+
+This 90 minute tutorial covers fundamentals of Xarray, indexing, computation + visualization.
+
+Video Recoding:
+https://www.youtube.com/watch?v=a339Q5F48UQ&list=PLNemzZpJM7lUu_iGP_lA2m7SeSUwKSIvR&index=8
+
+## Schedule
+
+_These are the rough timings for the tutorial_:
+
+| Topic | Time (min) |
+| --------------------------- | ---------- |
+| Xarray fundamentals | 30 |
+| Indexing | 15 |
+| Break | 5 |
+| Computation & visualization | 40 |
diff --git a/workshops/scipy2023/README.md b/workshops/scipy2023/README.md
new file mode 100644
index 00000000..2528e171
--- /dev/null
+++ b/workshops/scipy2023/README.md
@@ -0,0 +1,75 @@
+# SciPy 2023
+
+## Xarray: Friendly, Interactive, and Scalable Scientific Data Analysis
+
+Organized by:
+
+- Deepak Cherian (National Center for Atmospheric Research)
+- Scott Henderson (Univ. Washington)
+- Jessica Scheick (Univ. New Hampshire)
+- Negin Sobhani (National Center for Atmospheric Research)
+- Tom Nicholas (Lamont-Doherty Earth Observatory)
+- Anderson Banihirwe (CarbonPlan)
+- Don Setiawan (Univ. Washington)
+
+## Instructions
+
+:::{note}
+You can access a recording of this tutorial [here](https://www.youtube.com/watch?v=L4FXcIOMlsY)
+:::
+
+### Running Locally
+
+See instructions to set up the environment for running the tutorial material [here](get-started).
+
+### Github Codespaces
+
+This tutorial is available to run within [Github Codespaces](https://github.com/features/codespaces) - "a development environment that's hosted in the cloud"
+
+[](https://github.com/codespaces/new/xarray-contrib/xarray-tutorial/tree/main?devcontainer_path=.devcontainer%2Fscipy2023%2Fdevcontainer.json)
+
+☝️ Click the button above to go to options window to launch a Github codespace.
+
+A codespace is a development environment that's hosted in the cloud.
+You can choose from a selection of virtual machine types: 2 cores - 4 GB RAM - 32 GB storage, and 4 cores - 8 GB RAM - 32GB storage.
+Additionally, you are able to chose from various Dev container configuration, for this specific workshop, please ensure that `Scipy2023` is selected.
+GitHub currently gives every user [120 vCPU hours per month for free](https://docs.github.com/en/billing/managing-billing-for-github-codespaces/about-billing-for-github-codespaces#monthly-included-storage-and-core-hours-for-personal-accounts), beyond that you must pay. **So be sure to explicitly stop or shut down your codespace when you are done by going to this page (https://github.com/codespaces).**
+
+Once your codespace is launched, the following happens:
+
+- [Visual Studio Code](https://code.visualstudio.com/) Interface will open up within your browser.
+- A built in terminal will open and it will execute `jupyter lab` automatically.
+- Once you see a url to click within the terminal, simply `cmd + click` the given url.
+- This will open up another tab in your browser, leading to a [Jupyter Lab](https://jupyterlab.readthedocs.io/en/latest/) Interface.
+
+## Outline
+
+```{dropdown} Introduction
+{doc}`../../overview/get-started`
+```
+
+```{dropdown} Indexing
+{doc}`../../fundamentals/02.1_indexing_Basic`
+
+{doc}`../../intermediate/indexing/advanced-indexing`
+
+{doc}`../../intermediate/indexing/boolean-masking-indexing`
+```
+
+```{dropdown} Computational Patterns
+{doc}`../../intermediate/computation/01-high-level-computation-patterns`
+```
+
+```{dropdown} Wrapping other arrays: dask
+{doc}`../../intermediate/xarray_and_dask`
+```
+
+```{dropdown} Wrapping custom computation
+{doc}`../../advanced/apply_ufunc/simple_numpy_apply_ufunc`
+
+{doc}`../../advanced/apply_ufunc/core-dimensions`
+
+{doc}`../../advanced/apply_ufunc/complex-output-numpy`
+
+{doc}`Explore the remaining material <../../advanced/apply_ufunc/apply_ufunc>`
+```
diff --git a/workshops/scipy2023/index.ipynb b/workshops/scipy2023/index.ipynb
new file mode 100644
index 00000000..3072d80e
--- /dev/null
+++ b/workshops/scipy2023/index.ipynb
@@ -0,0 +1,67 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "0",
+ "metadata": {},
+ "source": [
+ "Computation with xarray (extended version): Computation with xarray notebook
\n", + "Plotting and visualization (extended version): Plotting and Visualization notebook
\n", + "\n",
+ "\n",
+ "\n",
+ "# Welcome to the Xarray SciPy 2023 Tutorial! \n",
+ "\n",
+ "**Xarray**: *Friendly, Interactive, and Scalable Scientific Data Analysis*\n",
+ "\n",
+ "\n",
+ "This *4-hour* workshop will explore content from [the Xarray tutorial](https://tutorial.xarray.dev), which contains a comprehensive collection of hands-on tutorial Jupyter Notebooks. We won't cover it all today, but instead will review a curated set of examples that will prepare you for increasingly complex real-world data analysis tasks!\n",
+ "\n",
+ "## Schedule \n",
+ "*Times in Central Daylight (Austin, TX)\n",
+ "\n",
+ "Use the links to navigate to the right notebooks.\n",
+ "\n",
+ "| Topic | Time | Notebook Links | \n",
+ "| :- | - | - | \n",
+ "| Introduction and Setup | 1:30 (20 min) | --- | \n",
+ "| Indexing | 1:50 (30 min) | [Quick Introduction to Indexing](../../fundamentals/02.1_indexing_Basic.ipynb) [Advanced and Vectorized Indexing](../../intermediate/indexing/advanced-indexing.ipynb)
[Boolean Indexing & Masking](../../intermediate/indexing/boolean-masking-indexing.ipynb) | \n", + "| *10 minute Break* \n", + "| Computational Patterns | 2:30 (50 min) | [Computation Patterns](../../intermediate/01-high-level-computation-patterns.ipynb) | \n", + "| *10 minute Break* | \n", + "| Wrapping other arrays | 3:30 (50 min) | [Xarray and Dask](../../intermediate/xarray_and_dask.ipynb) | \n", + "| *10 minute Break* | \n", + "| Wrapping custom computation (apply_ufunc) | 4:30 (30 min)
5:00 (30 min) | [A gentle introduction](../../advanced/apply_ufunc/simple_numpy_apply_ufunc.ipynb)
[Core dimensions](../../advanced/apply_ufunc/core-dimensions.ipynb)
[Handling complex output](../../advanced/apply_ufunc/complex-output-numpy.ipynb)
Explore the rest of the material |\n", + "| | **End 5:30** | |\n", + "\n", + "\n", + "## Notes\n", + "\n", + "- Remember to select `global-global-xarray environment` for the notebooks when prompted.\n", + "\n", + "- These notebooks render best if you choose the \"Medium instance for Xarray\" option at login.\n", + "\n", + "```{tip}\n", + "Check that you see \"tip\" in a green bar 👆🏾.\n", + "```\n", + "\n", + "## Thanks for attending!\n", + "\n", + "Please continue to explore the subfolders in the JupyterLab File Browser for additional tutorial notebooks to run, or read the rendered notebooks at [https://tutorial.xarray.dev](https://tutorial.xarray.dev)" + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/workshops/scipy2024/index.ipynb b/workshops/scipy2024/index.ipynb new file mode 100644 index 00000000..8d58a17d --- /dev/null +++ b/workshops/scipy2024/index.ipynb @@ -0,0 +1,110 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "0", + "metadata": {}, + "source": [ + "# SciPy 2024\n", + "\n", + "## Welcome to the Xarray SciPy 2024 Tutorial! \n", + "\n", + "
![](\"https://images.squarespace-cdn.com/content/v1/6596dfc539fa52603ef8b8d4/277ecf67-8dd4-401f-a74c-df3574adf1d1/SCIPY-2024-no-textArtboard%2B1%403x.png?format=1500w\")
\n",
+ "\n",
+ "**Xarray**: *Friendly, Interactive, and Scalable Scientific Data Analysis*\n",
+ "\n",
+ "July 8, 13:30–17:30 (US/Pacific), Tacoma Convention Center Ballroom B/C\n",
+ "\n",
+ "This *4-hour* workshop will explore content from [the Xarray tutorial](https://tutorial.xarray.dev), which contains a comprehensive collection of hands-on tutorial Jupyter Notebooks. We will review a curated set of examples that will prepare you for increasingly complex real-world data analysis tasks!\n",
+ "\n",
+ ":::{admonition} Learning Goals\n",
+ "- Orient yourself to Xarray resources to continue on your Xarray journey!\n",
+ "- Effectively use Xarray’s multidimensional indexing and computational patterns\n",
+ "- Understand how Xarray integrates with other libraries in the scientific Python ecosystem\n",
+ "- Learn how to leverage Xarray’s powerful backend and extension capabilities to customize workflows and open a variety of scientific datasets\n",
+ ":::\n",
+ "\n",
+ "## Schedule \n",
+ "\n",
+ "*Times in US/Pacific Timezone (Tacoma, WA)\n",
+ "\n",
+ "Use the links to navigate to the right notebooks.\n",
+ "\n",
+ "| Topic | Time | Notebook Links | \n",
+ "| :- | - | - | \n",
+ "| Introduction and Setup | 1:30 (10 min) | --- | \n",
+ "| The Xarray Data Model | 1:40 (40 min) | [Data structures](../../fundamentals/01_data_structures.md) [Basic Indexing](../../fundamentals/02.1_indexing_Basic.ipynb) | \n", + "| *10 minute Break* \n", + "| Indexing & Computational Patterns | 2:30 (50 min) | [Advanced Indexing](../../intermediate/indexing/indexing.md)
[Computational Patterns](../../intermediate/computation/01-high-level-computation-patterns.ipynb)
| \n", + "| *10 minute Break* | \n", + "| Xarray Integrations and Extensions | 3:30 (50 min) | [The Xarray Ecosystem](../../intermediate/xarray_ecosystem.ipynb) | \n", + "| *10 minute Break* | \n", + "| Backends & Remote data| 4:30 (50 min) | [Remote Data](../../intermediate/remote_data/remote-data.ipynb) |\n", + "| | End 5:30 | |\n", + "\n", + "\n", + "### Tutorial Setup\n", + "\n", + "We recommend using a preconfigured GitHub Codespace for this tutorial. This section describes how to access and manage a GitHub Codespace.\n", + "\n", + ":::{note}\n", + "If you prefer to work on your own computer, refer to instructions in the [Getting Started Section](../../overview/get-started.md)\n", + ":::\n", + "\n", + "This tutorial is available to run within [Github Codespaces](https://github.com/features/codespaces) - \"a development environment that's hosted in the cloud\".\n", + "\n", + "[](https://github.com/codespaces/new/xarray-contrib/xarray-tutorial/tree/main?devcontainer_path=.devcontainer%2Fscipy2024%2Fdevcontainer.json)\n", + "\n", + "☝️ Click the button above to go to options window to launch a Github Codespace.\n", + "\n", + "GitHub currently gives every user [120 vCPU-hours per month for free](https://docs.github.com/en/billing/managing-billing-for-github-codespaces/about-billing-for-github-codespaces#monthly-included-storage-and-core-hours-for-personal-accounts), beyond that you must pay. **So be sure to explicitly stop your Codespace when you are done by going to this page (https://github.com/codespaces).**\n", + "\n", + "Once your Codespace is launched, the following happens:\n", + "\n", + "- [Visual Studio Code](https://code.visualstudio.com/) Interface will open up within your browser.\n", + "- A built in terminal will open and it will execute `jupyter lab` automatically.\n", + "- Once you see a url to click within the terminal, simply `cmd + click` the given url.\n", + "- This will open up another tab in your browser, leading to a [Jupyter Lab](https://jupyterlab.readthedocs.io/en/latest/) Interface.\n", + "\n", + ":::{warning}\n", + "Consider Codespaces as ephemeral environments. You may lose your connection and any edits you make.\n", + ":::\n", + "\n", + "\n", + "## Thanks for attending!\n", + "\n", + "Please continue to explore the subfolders in the JupyterLab File Browser for additional tutorial notebooks to run, or read the rendered notebooks at [https://tutorial.xarray.dev](https://tutorial.xarray.dev)\n", + "\n", + "### SciPy 2024 Organized by:\n", + "\n", + "- Scott Henderson (Univ. Washington)\n", + "- Jessica Scheick (Univ. New Hampshire)\n", + "- Negin Sobhani (National Center for Atmospheric Research)\n", + "- Tom Nicholas [C]worthy\n", + "- Max Jones (CarbonPlan)\n", + "- Wietze Suijker (Space Intelligence)" + ] + }, + { + "cell_type": "markdown", + "id": "1", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/workshops/scipy2025/index.ipynb b/workshops/scipy2025/index.ipynb new file mode 100644 index 00000000..a9670341 --- /dev/null +++ b/workshops/scipy2025/index.ipynb @@ -0,0 +1,80 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "0", + "metadata": {}, + "source": [ + "# SciPy 2025\n", + "\n", + "## Welcome to the Xarray SciPy 2025 Tutorial! \n", + "\n", + "
![](\"https://images.squarespace-cdn.com/content/v1/6743676bb9f2e109f0ddba96/055e9b3f-965f-42c7-b496-2ba3870e216e/Logo_color-black.png?format=1500w\"){kind=logo}
\n",
+ "\n",
+ "**Hierarchical Data Analysis with Xarray DataTree & Zarr**\n",
+ "\n",
+ "July 8, 13:30–17:30 (US/Pacific), Tacoma Convention Center Room 315\n",
+ "\n",
+ "This *4-hour* workshop will explore content from [the Xarray tutorial](https://tutorial.xarray.dev), which contains a comprehensive collection of hands-on tutorial Jupyter Notebooks. We will review a curated set of examples that will prepare you for increasingly complex real-world data analysis tasks!\n",
+ "\n",
+ ":::{admonition} Learning Goals\n",
+ "- Understand Xarray’s core data structures\n",
+ "- Understand how to map typical Xarray computations and workflows over hierarchical data\n",
+ "- Understand how common storage formats correspond to the DataTree model\n",
+ "- Open a public Zarr store in the cloud and manipulate the contents\n",
+ ":::"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1",
+ "metadata": {},
+ "source": [
+ "## Schedule \n",
+ "\n",
+ "*Times in US/Pacific Timezone (Tacoma, WA)\n",
+ "\n",
+ "Use the links to navigate to the right notebooks.\n",
+ "\n",
+ "| Topic | Time | Notebook Links | \n",
+ "| :- | - | - | \n",
+ "| Introduction and Setup | 1:30 (10 min) | | \n",
+ "| The Xarray Data Model | 1:40 (50 min) | [Data structures Part 1](../../intermediate/datastructures-intermediate.ipynb)|\n",
+ "|||[Data structures In Biology](../../intermediate/BiologyDataset.ipynb)| \n",
+ "|||[Data structures Part 2](../../fundamentals/01_data_structures.md)| \n",
+ "| *10 minute Break* \n",
+ "| Computational Patterns | 2:30 (50 min) | [Computational Patterns](../../fundamentals/03.1_computation_with_xarray.ipynb) [Hierarchical computation](../../intermediate/computation/hierarchical_computation.ipynb) | \n", + "| *10 minute Break* | \n", + "| Storage formats | 3:30 (50 min) | [Storage formats](../../fundamentals/01.1_io.ipynb) | \n", + "| *10 minute Break* | \n", + "| Zarr & the cloud | 4:30 (50 min) | [Intro to Zarr](../../intermediate/intro-to-zarr.ipynb) |\n", + "| | End 5:30 | |" + ] + }, + { + "cell_type": "markdown", + "id": "2", + "metadata": {}, + "source": [ + "### Tutorial Setup\n", + "\n", + "Refer to the [Getting Started Section](../../overview/get-started.md) for various options to run tutorial notebooks either on your personal computer or on a free Cloud-hosted server." + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/workshops/scipy2026/index.ipynb b/workshops/scipy2026/index.ipynb new file mode 100644 index 00000000..9ad3538c --- /dev/null +++ b/workshops/scipy2026/index.ipynb @@ -0,0 +1,84 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "0", + "metadata": {}, + "source": [ + "# SciPy 2026\n", + "\n", + "## Welcome to the Xarray SciPy 2026 Tutorial! \n", + "\n", + "> 🚧👷♀️🏗️ This page is under construction. Please check back later.\n", + "\n", + "
![](\"https://images.squarespace-cdn.com/content/v1/69223993a619b0382ba1b905/9ef1aa1c-4fab-404d-818c-17edcb653166/Horizontal.png?format=1500w\")
\n",
+ "\n",
+ "**Everything is an Xarray Dataset**\n",
+ "\n",
+ "July 14, 13:30–17:30 (CDT), University of Minnesota (526 Delaware Street SE), room HSEC 2-138\n",
+ "\n",
+ "This *4-hour* workshop will explore content from [the Xarray tutorial](https://tutorial.xarray.dev), which contains a comprehensive collection of hands-on tutorial Jupyter Notebooks. We will review a curated set of examples that will prepare you for increasingly complex real-world data analysis tasks!\n",
+ "\n",
+ ":::{admonition} Learning Goals\n",
+ "- Understand Xarray’s core data structures\n",
+ "- Understand how to use various backend engines, as well as create your own\n",
+ "- Understand what data cataloguing is, and how it applies to Xarray\n",
+ "- Understand how common storage formats correspond to the DataTree model\n",
+ "- Understand how to use Xarray's flexible indices\n",
+ ":::\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1",
+ "metadata": {},
+ "source": [
+ "## Schedule\n",
+ "\n",
+ "*Times in local time\n",
+ "\n",
+ "Use the links to navigate to the right notebooks.\n",
+ "\n",
+ "**Everything is an Xarray dataset**\n",
+ "\n",
+ "| Topic | Time | Notebook Links | \n",
+ "| :- | - | - | \n",
+ "| Introduction and Setup | 1:30 (10 min) | | \n",
+ "| The Xarray Data Model | 1:40 (50 min) | [Data structures Part 1](../../intermediate/datastructures-intermediate.ipynb) [Data structures Part 2](../../fundamentals/01_data_structures.md)
[Datatrees and Hierarchical data](../../fundamentals/01_datatree_hierarchical_data.ipynb)|\n", + "| *10 minute Break* \n", + "| Xarray backends | 2:40 (60 min) | [Intro to backends](../../advanced/backends/intro-backends.ipynb)
[Zarr stores in the cloud](../../intermediate/hierarchical_zarr_store.ipynb)
[Rioxarray](../../advanced/backends/rasterio_backend.ipynb)
[Creating custom backends](../../advanced/backends/create_custom_backend.ipynb) |\n", + "| *10 minute Break* |\n", + "| Data cataloguing | 3:50 (40 min) | [Data cataloging](../../intermediate/cataloging.ipynb) |\n", + "| *10 minute Break* \n", + "| Flexible indices | 4:40 (50 min) | [Why an index](../../intermediate/indexing/why-an-index.ipynb) |" + ] + }, + { + "cell_type": "markdown", + "id": "2", + "metadata": {}, + "source": [ + "### Tutorial Setup\n", + "\n", + "Refer to the [Getting Started Section](../../overview/get-started.md) for various options to run tutorial notebooks either on your personal computer or on a free Cloud-hosted server." + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/workshops/thinking-like-xarray/README.md b/workshops/thinking-like-xarray/README.md new file mode 100644 index 00000000..3d9fdc70 --- /dev/null +++ b/workshops/thinking-like-xarray/README.md @@ -0,0 +1,8 @@ +# Thinking like Xarray 2022 + +Presented March 2022 for the [NCAR Python Seminar Series](https://ncar.github.io/esds/posts/2022/Thinking-with-Xarray/) by Deepak Cheerian + +This 60-minute, intermediate-level lesson covers common high-level computing patterns. The version of the notebook here includes material inserted during the presentation. + +Video Recording: +https://www.youtube.com/watch?v=TSw3GF_d2y8&list=PLNemzZpJM7lUu_iGP_lA2m7SeSUwKSIvR diff --git a/xarray_and_dask.ipynb b/xarray_and_dask.ipynb deleted file mode 100644 index 69e7bcc1..00000000 --- a/xarray_and_dask.ipynb +++ /dev/null @@ -1,42 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Xarray and Dask\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import xarray as xr\n", - "from dask.distributed import Client" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.2" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/xarray_for_data_analysis.ipynb b/xarray_for_data_analysis.ipynb deleted file mode 100644 index 84c6c347..00000000 --- a/xarray_for_data_analysis.ipynb +++ /dev/null @@ -1,41 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Xarray for Data Analysis\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import xarray as xr" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.2" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/xarray_for_geoscientists.ipynb b/xarray_for_geoscientists.ipynb deleted file mode 100644 index 56428978..00000000 --- a/xarray_for_geoscientists.ipynb +++ /dev/null @@ -1,41 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Xarray for Geoscientists\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import xarray as xr" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.2" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -}