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update exercise format
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‎advanced/apply_ufunc/automatic-vectorizing-numpy.ipynb

Lines changed: 6 additions & 7 deletions
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@@ -63,18 +63,17 @@
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" out[index, :] = np.interp(..., array[index, :], ...)\n",
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"```\n",
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"\n",
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"\n",
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"```{exercise}\n",
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":label: coreloopdims\n",
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"\n",
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"::::{admonition} Exercise\n",
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":class: tip\n",
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"Consider the example problem of interpolating a 2D array with dimensions `space` and `time` along the `time` dimension.\n",
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"Which dimension is the core dimension, and which is the \"loop dimension\"?\n",
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"```\n",
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"```{solution} coreloopdims\n",
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"\n",
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":::{admonition} Solution\n",
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":class: dropdown\n",
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"\n",
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"`time` is the core dimension, and `space` is the loop dimension.\n",
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"```\n",
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":::\n",
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"::::\n",
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"\n",
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"## Vectorization\n",
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"\n",

‎advanced/apply_ufunc/complex-output-numpy.ipynb

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@@ -138,19 +138,20 @@
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"tags": []
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},
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"source": [
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"```{exercise}\n",
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":label: newdim\n",
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"::::{admonition} Exercise\n",
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":class: tip\n",
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"\n",
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"Apply the following function using `apply_ufunc`. It adds a new dimension to the input array, let's call it `newdim`. Specify the new dimension using `output_core_dims`. Do you need any `input_core_dims`?\n",
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"\n",
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"```python\n",
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"def add_new_dim(array):\n",
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" return np.expand_dims(array, axis=-1)\n",
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"```\n",
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"````{solution} newdim\n",
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"\n",
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":::{admonition} Solution\n",
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":class: dropdown\n",
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"\n",
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"``` python\n",
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"```python\n",
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"def add_new_dim(array):\n",
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" return np.expand_dims(array, axis=-1)\n",
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"\n",
@@ -161,7 +162,8 @@
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" output_core_dims=[[\"newdim\"]],\n",
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")\n",
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"```\n",
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"````"
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":::\n",
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"::::"
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]
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},
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{
@@ -327,8 +329,8 @@
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"tags": []
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},
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"source": [
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"````{exercise}\n",
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":label: generalize\n",
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"::::{admonition} Exercise\n",
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":class: tip\n",
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"\n",
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"We presented the concept of \"core dimensions\" as the \"smallest unit of data the function could handle.\" Do you understand how the above use of `apply_ufunc` generalizes to an array with more than one dimension? \n",
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"\n",
@@ -337,9 +339,8 @@
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"air3d = xr.tutorial.load_dataset(\"air_temperature\").air)\n",
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"``` \n",
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"Your goal is to have a minimum and maximum value of temperature across all latitudes for a given time and longitude.\n",
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"````\n",
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"\n",
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"````{solution} generalize\n",
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":::{admonition} Solution\n",
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":class: dropdown\n",
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"\n",
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"We want to use `minmax` to compute the minimum and maximum along the \"lat\" dimension always, regardless of how many dimensions are on the input. So we specify `input_core_dims=[[\"lat\"]]`. The output does not contain the \"lat\" dimension, but we expect two returned variables. So we pass an empty list `[]` for each returned array, so `output_core_dims=[[], []]` just as before.\n",
@@ -352,8 +353,8 @@
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" input_core_dims=[[\"lat\"]],\n",
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" output_core_dims=[[],[]],\n",
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")\n",
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"```\n",
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"````"
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":::\n",
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"::::"
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]
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}
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],

‎advanced/apply_ufunc/core-dimensions.ipynb

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@@ -335,13 +335,12 @@
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"tags": []
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},
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"source": [
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"```{exercise}\n",
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":label: trapezoid\n",
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"::::{admonition} Exercise\n",
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":class: tip\n",
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"\n",
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"Use `apply_ufunc` to apply `scipy.integrate.trapezoid` along the `time` axis.\n",
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"```\n",
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"\n",
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"````{solution} trapezoid\n",
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":::{admonition} Solution\n",
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":class: dropdown\n",
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"\n",
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"```python\n",
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"\n",
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"xr.apply_ufunc(scipy.integrate.trapezoid, ds, input_core_dims=[[\"time\"]], kwargs={\"axis\": -1})\n",
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"```\n",
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"````"
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":::\n",
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"::::"
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]
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}
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],

‎advanced/apply_ufunc/dask_apply_ufunc.ipynb

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"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",
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"\n",
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"\n",
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"```{exercise}\n",
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":label: daskmean\n",
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"::::{admonition} Exercise\n",
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":class: tip\n",
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"\n",
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"Use `dask.array.mean` as an example of a function that can handle dask\n",
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"arrays and uses an `axis` kwarg. \n",
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"```\n",
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"\n",
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"````{solution} daskmean\n",
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":::{admonition} Solution\n",
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":class: dropdown\n",
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"\n",
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"```python\n",
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"def time_mean(da):\n",
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" return xr.apply_ufunc(\n",
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" dask=\"allowed\",\n",
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" kwargs={\"axis\": -1}, # core dimensions are moved to the end\n",
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" )\n",
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"\n",
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"\n",
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" \n",
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"time_mean(ds.air)\n",
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"````\n"
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"```\n",
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":::\n",
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"::::\n"
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]
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},
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{
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"tags": []
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},
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"source": [
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"```{exercise} \n",
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":label: rechunk\n",
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"::::{admonition} Exercise\n",
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":class: tip\n",
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"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",
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"```\n",
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"\n",
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"```{solution} rechunk\n",
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":::{admonition} Solution\n",
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":class: dropdown\n",
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"\n",
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"`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",
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" ``.chunk(dict(lon=-1))``, or pass ``allow_rechunk=True`` in ``dask_gufunc_kwargs`` \n",
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" but beware that this may significantly increase memory usage.\n",
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"\n",
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"```"
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":::\n",
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"::::"
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]
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},
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{
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"source": [
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"### Adding new dimensions\n",
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"\n",
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"We use the [expand_dims example](newdim) that changes the size of the input along a single dimension.\n",
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"We use the `np.expand_dims` to change the size of the input along a single dimension.\n",
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"\n",
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"```python\n",
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"def add_new_dim(array):\n",

‎advanced/apply_ufunc/numba-vectorization.ipynb

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"tags": []
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},
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"source": [
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"<img src=\"https://numba.pydata.org/_static/numba-blue-horizontal-rgb.svg\" width=\"40%\" align=\"right\">\n",
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"# Fast vectorization with Numba\n",
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"\n",
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"# Fast vectorization with Numba"
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"<img src=\"https://numba.pydata.org/_static/numba-blue-horizontal-rgb.svg\" width=\"40%\" align=\"right\">"
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]
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},
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{
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"id": "18",
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"metadata": {},
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"source": [
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"```{exercise}\n",
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":label: g\n",
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"::::{admonition} Exercise\n",
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":class: tip\n",
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"\n",
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"Apply `g` to `da_dask`\n",
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"```\n",
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"````{solution} g\n",
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"\n",
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":::{admonition} Solution\n",
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":class: dropdown\n",
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"\n",
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"```python\n",
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" dask=\"parallelized\",\n",
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")\n",
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"```\n",
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"````"
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":::\n",
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"::::"
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]
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},
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{

‎fundamentals/02.1_indexing_Basic.ipynb

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"da[:, 20, 40]"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": []
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},
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"cell_type": "markdown",
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"metadata": {},
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"```"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"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:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"ds.sel(time=dates)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"```{exercise}\n",
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":label: indexing-1\n",
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"::::{admonition} Exercise\n",
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":class: tip\n",
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"\n",
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"Select the first 30 entries of `latitude` and 30th to 40th entries of `longitude`:\n",
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"```\n",
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"\n",
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"````{solution} indexing-1\n",
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":::{admonition} Solution\n",
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":class: dropdown\n",
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"```python\n",
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"ds.isel(lat=slice(None, 30), lon=slice(30, 40))\n",
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"```\n",
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"\n",
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"````"
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":::\n",
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"::::"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"```{exercise}\n",
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":label: indexing-2\n",
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"::::{admonition} Exercise\n",
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":class: tip\n",
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"\n",
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"Select all data at 75 degree north and between Jan 1, 2013 and Oct 15, 2013 :\n",
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"```\n",
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"````{solution} indexing-2\n",
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"Select all data at 75 degree north and between Jan 1, 2013 and Oct 15, 2013\n",
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"\n",
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":::{admonition} Solution\n",
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":class: dropdown\n",
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"```python\n",
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"ds.sel(lat=75, time=slice(\"2013-01-01\", \"2013-10-15\"))\n",
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"```\n",
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"````"
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":::\n",
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"::::"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"```{exercise}\n",
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":label: indexing-3\n",
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"::::{admonition} Exercise\n",
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":class: tip\n",
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"\n",
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"Remove all entries at 260 and 270 degrees :\n",
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"Remove all entries at 260 and 270 degrees\n",
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"\n",
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"```\n",
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"````{solution} indexing-3\n",
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":::{admonition} Solution\n",
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":class: dropdown\n",
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"```python\n",
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"ds.drop_sel(lon=[260, 270])\n",
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"```\n",
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"````"
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":::\n",
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"::::"
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]
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},
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{

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